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  • The Architectural Review een koppeling hebt gedeeld
    2025-06-15 06:49:50 - Translate -
    A short history of the roadblock

    Barricades, as we know them today, are thought to date back to the European wars of religion. According to most historians, the first barricade went up in Paris in 1588; the word derives from the French barriques, or barrels, spontaneously put together. They have been assembled from the most diverse materials, from cobblestones, tyres, newspapers, dead horses and bags of ice, to omnibuses and e‑scooters. Their tactical logic is close to that of guerrilla warfare: the authorities have to take the barricades in order to claim victory; all that those manning them have to do to prevail is to hold them. 
    The 19th century was the golden age for blocking narrow, labyrinthine streets. Paris had seen barricades go up nine times in the period before the Second Empire; during the July 1830 Revolution alone, 4,000 barricades had been erected. These barricades would not only stop, but also trap troops; people would then throw stones from windows or pour boiling water onto the streets. Georges‑Eugène Haussmann, Napoleon III’s prefect of Paris, famously created wide boulevards to make blocking by barricade more difficult and moving the military easier, and replaced cobblestones with macadam – a surface of crushed stone. As Flaubert observed in his Dictionary of Accepted Ideas: ‘Macadam: has cancelled revolutions. No more means to make barricades. Nevertheless rather inconvenient.’  
    Lead image: Barricades, as we know them today, are thought to have originated in early modern France. A colour engraving attributed to Achille‑Louis Martinet depicts the defence of a barricade during the 1830 July Revolution. Credit: Paris Musées / Musée Carnavalet – Histoire de Paris. Above: the socialist political thinker and activist Louis Auguste Blanqui – who was imprisoned by every regime that ruled France between 1815 and 1880 – drew instructions for how to build an effective barricade

    Under Napoleon III, Baron Haussmann widened Paris’s streets in his 1853–70 renovation of the city, making barricading more difficult
    Credit: Old Books Images / Alamy
    ‘On one hand,wanted to favour the circulation of ideas,’ reactionary intellectual Louis Veuillot observed apropos the ambiguous liberalism of the latter period of Napoleon III’s Second Empire. ‘On the other, to ensure the circulation of regiments.’ But ‘anti‑insurgency hardware’, as Justinien Tribillon has called it, also served to chase the working class out of the city centre: Haussmann’s projects amounted to a gigantic form of real-estate speculation, and the 1871 Paris Commune that followed constituted not just a short‑lived anarchist experiment featuring enormous barricades; it also signalled the return of the workers to the centre and, arguably, revenge for their dispossession.   
    By the mid‑19th century, observers questioned whether barricades still had practical meaning. Gottfried Semper’s barricade, constructed for the 1849 Dresden uprising, had proved unconquerable, but Friedrich Engels, one‑time ‘inspector of barricades’ in the Elberfeld insurrection of the same year, already suggested that the barricades’ primary meaning was now moral rather than military – a point to be echoed by Leon Trotsky in the subsequent century. Barricades symbolised bravery and the will to hold out among insurrectionists, and, not least, determination rather to destroy one’s possessions – and one’s neighbourhood – than put up with further oppression.  
    Not only self‑declared revolutionaries viewed things this way: the reformist Social Democrat leader Eduard Bernstein observed that ‘the barricade fight as a political weapon of the people has been completely eliminated due to changes in weapon technology and cities’ structures’. Bernstein was also picking up on the fact that, in the era of industrialisation, contention happened at least as much on the factory floor as on the streets. The strike, not the food riot or the defence of workers’ quartiers, became the paradigmatic form of conflict. Joshua Clover has pointed out in his 2016 book Riot. Strike. Riot: The New Era of Uprisings, that the price of labour, rather than the price of goods, caused people to confront the powerful. Blocking production grew more important than blocking the street.
    ‘The only weapons we have are our bodies, and we need to tuck them in places so wheels don’t turn’
    Today, it is again blocking – not just people streaming along the streets in large marches – that is prominently associated with protests. Disrupting circulation is not only an important gesture in the face of climate emergency; blocking transport is a powerful form of protest in an economic system focused on logistics and just‑in‑time distribution. Members of Insulate Britain and Germany’s Last Generation super‑glue themselves to streets to stop car traffic to draw attention to the climate emergency; they have also attached themselves to airport runways. They form a human barricade of sorts, immobilising traffic by making themselves immovable.  
    Today’s protesters have made themselves consciously vulnerable. They in fact follow the advice of US civil rights’ Bayard Rustin who explained: ‘The only weapons we have are our bodies, and we need to tuck them in places so wheels don’t turn.’ Making oneself vulnerable might increase the chances of a majority of citizens seeing the importance of the cause which those engaged in civil disobedience are pursuing. Demonstrations – even large, unpredictable ones – are no longer sufficient. They draw too little attention and do not compel a reaction. Naomi Klein proposed the term ‘blockadia’ as ‘a roving transnational conflict zone’ in which people block extraction – be it open‑pit mines, fracking sites or tar sands pipelines – with their bodies. More often than not, these blockades are organised by local people opposing the fossil fuel industry, not environmental activists per se. Blockadia came to denote resistance to the Keystone XL pipeline as well as Canada’s First Nations‑led movement Idle No More.
    In cities, blocking can be accomplished with highly mobile structures. Like the barricade of the 19th century, they can be quickly assembled, yet are difficult to move; unlike old‑style barricades, they can also be quickly disassembled, removed and hidden. Think of super tripods, intricate ‘protest beacons’ based on tensegrity principles, as well as inflatable cobblestones, pioneered by the artist‑activists of Tools for Action.  
    As recently as 1991, newly independent Latvia defended itself against Soviet tanks with the popular construction of barricades, in a series of confrontations that became known as the Barikādes
    Credit: Associated Press / Alamy
    Inversely, roadblocks can be used by police authorities to stop demonstrations and gatherings from taking place – protesters are seen removing such infrastructure in Dhaka during a general strike in 1999
    Credit: REUTERS / Rafiqur Rahman / Bridgeman
    These inflatable objects are highly flexible, but can also be protective against police batons. They pose an awkward challenge to the authorities, who often end up looking ridiculous when dealing with them, and, as one of the inventors pointed out, they are guaranteed to create a media spectacle. This was also true of the 19th‑century barricade: people posed for pictures in front of them. As Wolfgang Scheppe, a curator of Architecture of the Barricade, explains, these images helped the police to find Communards and mete out punishments after the end of the anarchist experiment.
    Much simpler structures can also be highly effective. In 2019, protesters in Hong Kong filled streets with little archways made from just three ordinary bricks: two standing upright, one resting on top. When touched, the falling top one would buttress the other two, and effectively block traffic. In line with their imperative of ‘be water’, protesters would retreat when the police appeared, but the ‘mini‑Stonehenges’ would remain and slow down the authorities.
    Today, elaborate architectures of protest, such as Extinction Rebellion’s ‘tensegrity towers’, are used to blockade roads and distribution networks – in this instance, Rupert Murdoch’s News UK printworks in Broxbourne, for the media group’s failure to report the climate emergency accurately
    Credit: Extinction Rebellion
    In June 2025, protests erupted in Los Angeles against the Trump administration’s deportation policies. Demonstrators barricaded downtown streets using various objects, including the pink public furniture designed by design firm Rios for Gloria Molina Grand Park. LAPD are seen advancing through tear gas
    Credit: Gina Ferazzi / Los Angeles Times via Getty Images
    Roads which radicals might want to target are not just ones in major metropoles and fancy post‑industrial downtowns. Rather, they might block the arteries leading to ‘fulfilment centres’ and harbours with container shipping. The model is not only Occupy Wall Street, which had initially called for the erection of ‘peaceful barricades’, but also the Occupy that led to the Oakland port shutdown in 2011. In short, such roadblocks disrupt what Phil Neel has called a ‘hinterland’ that is often invisible, yet crucial for contemporary capitalism. More recently, Extinction Rebellion targeted Amazon distribution centres in three European countries in November 2021; in the UK, they aimed to disrupt half of all deliveries on a Black Friday.  
    Will such blockades just anger consumers who, after all, are not present but are impatiently waiting for packages at home? One of the hopes associated with the traditional barricade was always that they might create spaces where protesters, police and previously indifferent citizens get talking; French theorists even expected them to become ‘a machine to produce the people’. That could be why military technology has evolved so that the authorities do not have to get close to the barricade: tear gas was first deployed against those on barricades before it was used in the First World War; so‑called riot control vehicles can ever more easily crush barricades. The challenge, then, for anyone who wishes to block is also how to get in other people’s faces – in order to have a chance to convince them of their cause.       

    2025-06-11
    Kristina Rapacki

    Share
    #short #history #roadblock
    A short history of the roadblock
    Barricades, as we know them today, are thought to date back to the European wars of religion. According to most historians, the first barricade went up in Paris in 1588; the word derives from the French barriques, or barrels, spontaneously put together. They have been assembled from the most diverse materials, from cobblestones, tyres, newspapers, dead horses and bags of ice, to omnibuses and e‑scooters. Their tactical logic is close to that of guerrilla warfare: the authorities have to take the barricades in order to claim victory; all that those manning them have to do to prevail is to hold them.  The 19th century was the golden age for blocking narrow, labyrinthine streets. Paris had seen barricades go up nine times in the period before the Second Empire; during the July 1830 Revolution alone, 4,000 barricades had been erected. These barricades would not only stop, but also trap troops; people would then throw stones from windows or pour boiling water onto the streets. Georges‑Eugène Haussmann, Napoleon III’s prefect of Paris, famously created wide boulevards to make blocking by barricade more difficult and moving the military easier, and replaced cobblestones with macadam – a surface of crushed stone. As Flaubert observed in his Dictionary of Accepted Ideas: ‘Macadam: has cancelled revolutions. No more means to make barricades. Nevertheless rather inconvenient.’   Lead image: Barricades, as we know them today, are thought to have originated in early modern France. A colour engraving attributed to Achille‑Louis Martinet depicts the defence of a barricade during the 1830 July Revolution. Credit: Paris Musées / Musée Carnavalet – Histoire de Paris. Above: the socialist political thinker and activist Louis Auguste Blanqui – who was imprisoned by every regime that ruled France between 1815 and 1880 – drew instructions for how to build an effective barricade Under Napoleon III, Baron Haussmann widened Paris’s streets in his 1853–70 renovation of the city, making barricading more difficult Credit: Old Books Images / Alamy ‘On one hand,wanted to favour the circulation of ideas,’ reactionary intellectual Louis Veuillot observed apropos the ambiguous liberalism of the latter period of Napoleon III’s Second Empire. ‘On the other, to ensure the circulation of regiments.’ But ‘anti‑insurgency hardware’, as Justinien Tribillon has called it, also served to chase the working class out of the city centre: Haussmann’s projects amounted to a gigantic form of real-estate speculation, and the 1871 Paris Commune that followed constituted not just a short‑lived anarchist experiment featuring enormous barricades; it also signalled the return of the workers to the centre and, arguably, revenge for their dispossession.    By the mid‑19th century, observers questioned whether barricades still had practical meaning. Gottfried Semper’s barricade, constructed for the 1849 Dresden uprising, had proved unconquerable, but Friedrich Engels, one‑time ‘inspector of barricades’ in the Elberfeld insurrection of the same year, already suggested that the barricades’ primary meaning was now moral rather than military – a point to be echoed by Leon Trotsky in the subsequent century. Barricades symbolised bravery and the will to hold out among insurrectionists, and, not least, determination rather to destroy one’s possessions – and one’s neighbourhood – than put up with further oppression.   Not only self‑declared revolutionaries viewed things this way: the reformist Social Democrat leader Eduard Bernstein observed that ‘the barricade fight as a political weapon of the people has been completely eliminated due to changes in weapon technology and cities’ structures’. Bernstein was also picking up on the fact that, in the era of industrialisation, contention happened at least as much on the factory floor as on the streets. The strike, not the food riot or the defence of workers’ quartiers, became the paradigmatic form of conflict. Joshua Clover has pointed out in his 2016 book Riot. Strike. Riot: The New Era of Uprisings, that the price of labour, rather than the price of goods, caused people to confront the powerful. Blocking production grew more important than blocking the street. ‘The only weapons we have are our bodies, and we need to tuck them in places so wheels don’t turn’ Today, it is again blocking – not just people streaming along the streets in large marches – that is prominently associated with protests. Disrupting circulation is not only an important gesture in the face of climate emergency; blocking transport is a powerful form of protest in an economic system focused on logistics and just‑in‑time distribution. Members of Insulate Britain and Germany’s Last Generation super‑glue themselves to streets to stop car traffic to draw attention to the climate emergency; they have also attached themselves to airport runways. They form a human barricade of sorts, immobilising traffic by making themselves immovable.   Today’s protesters have made themselves consciously vulnerable. They in fact follow the advice of US civil rights’ Bayard Rustin who explained: ‘The only weapons we have are our bodies, and we need to tuck them in places so wheels don’t turn.’ Making oneself vulnerable might increase the chances of a majority of citizens seeing the importance of the cause which those engaged in civil disobedience are pursuing. Demonstrations – even large, unpredictable ones – are no longer sufficient. They draw too little attention and do not compel a reaction. Naomi Klein proposed the term ‘blockadia’ as ‘a roving transnational conflict zone’ in which people block extraction – be it open‑pit mines, fracking sites or tar sands pipelines – with their bodies. More often than not, these blockades are organised by local people opposing the fossil fuel industry, not environmental activists per se. Blockadia came to denote resistance to the Keystone XL pipeline as well as Canada’s First Nations‑led movement Idle No More. In cities, blocking can be accomplished with highly mobile structures. Like the barricade of the 19th century, they can be quickly assembled, yet are difficult to move; unlike old‑style barricades, they can also be quickly disassembled, removed and hidden. Think of super tripods, intricate ‘protest beacons’ based on tensegrity principles, as well as inflatable cobblestones, pioneered by the artist‑activists of Tools for Action.   As recently as 1991, newly independent Latvia defended itself against Soviet tanks with the popular construction of barricades, in a series of confrontations that became known as the Barikādes Credit: Associated Press / Alamy Inversely, roadblocks can be used by police authorities to stop demonstrations and gatherings from taking place – protesters are seen removing such infrastructure in Dhaka during a general strike in 1999 Credit: REUTERS / Rafiqur Rahman / Bridgeman These inflatable objects are highly flexible, but can also be protective against police batons. They pose an awkward challenge to the authorities, who often end up looking ridiculous when dealing with them, and, as one of the inventors pointed out, they are guaranteed to create a media spectacle. This was also true of the 19th‑century barricade: people posed for pictures in front of them. As Wolfgang Scheppe, a curator of Architecture of the Barricade, explains, these images helped the police to find Communards and mete out punishments after the end of the anarchist experiment. Much simpler structures can also be highly effective. In 2019, protesters in Hong Kong filled streets with little archways made from just three ordinary bricks: two standing upright, one resting on top. When touched, the falling top one would buttress the other two, and effectively block traffic. In line with their imperative of ‘be water’, protesters would retreat when the police appeared, but the ‘mini‑Stonehenges’ would remain and slow down the authorities. Today, elaborate architectures of protest, such as Extinction Rebellion’s ‘tensegrity towers’, are used to blockade roads and distribution networks – in this instance, Rupert Murdoch’s News UK printworks in Broxbourne, for the media group’s failure to report the climate emergency accurately Credit: Extinction Rebellion In June 2025, protests erupted in Los Angeles against the Trump administration’s deportation policies. Demonstrators barricaded downtown streets using various objects, including the pink public furniture designed by design firm Rios for Gloria Molina Grand Park. LAPD are seen advancing through tear gas Credit: Gina Ferazzi / Los Angeles Times via Getty Images Roads which radicals might want to target are not just ones in major metropoles and fancy post‑industrial downtowns. Rather, they might block the arteries leading to ‘fulfilment centres’ and harbours with container shipping. The model is not only Occupy Wall Street, which had initially called for the erection of ‘peaceful barricades’, but also the Occupy that led to the Oakland port shutdown in 2011. In short, such roadblocks disrupt what Phil Neel has called a ‘hinterland’ that is often invisible, yet crucial for contemporary capitalism. More recently, Extinction Rebellion targeted Amazon distribution centres in three European countries in November 2021; in the UK, they aimed to disrupt half of all deliveries on a Black Friday.   Will such blockades just anger consumers who, after all, are not present but are impatiently waiting for packages at home? One of the hopes associated with the traditional barricade was always that they might create spaces where protesters, police and previously indifferent citizens get talking; French theorists even expected them to become ‘a machine to produce the people’. That could be why military technology has evolved so that the authorities do not have to get close to the barricade: tear gas was first deployed against those on barricades before it was used in the First World War; so‑called riot control vehicles can ever more easily crush barricades. The challenge, then, for anyone who wishes to block is also how to get in other people’s faces – in order to have a chance to convince them of their cause.        2025-06-11 Kristina Rapacki Share #short #history #roadblock
    WWW.ARCHITECTURAL-REVIEW.COM
    A short history of the roadblock
    Barricades, as we know them today, are thought to date back to the European wars of religion. According to most historians, the first barricade went up in Paris in 1588; the word derives from the French barriques, or barrels, spontaneously put together. They have been assembled from the most diverse materials, from cobblestones, tyres, newspapers, dead horses and bags of ice (during Kyiv’s Euromaidan in 2013–14), to omnibuses and e‑scooters. Their tactical logic is close to that of guerrilla warfare: the authorities have to take the barricades in order to claim victory; all that those manning them have to do to prevail is to hold them.  The 19th century was the golden age for blocking narrow, labyrinthine streets. Paris had seen barricades go up nine times in the period before the Second Empire; during the July 1830 Revolution alone, 4,000 barricades had been erected (roughly one for every 200 Parisians). These barricades would not only stop, but also trap troops; people would then throw stones from windows or pour boiling water onto the streets. Georges‑Eugène Haussmann, Napoleon III’s prefect of Paris, famously created wide boulevards to make blocking by barricade more difficult and moving the military easier, and replaced cobblestones with macadam – a surface of crushed stone. As Flaubert observed in his Dictionary of Accepted Ideas: ‘Macadam: has cancelled revolutions. No more means to make barricades. Nevertheless rather inconvenient.’   Lead image: Barricades, as we know them today, are thought to have originated in early modern France. A colour engraving attributed to Achille‑Louis Martinet depicts the defence of a barricade during the 1830 July Revolution. Credit: Paris Musées / Musée Carnavalet – Histoire de Paris. Above: the socialist political thinker and activist Louis Auguste Blanqui – who was imprisoned by every regime that ruled France between 1815 and 1880 – drew instructions for how to build an effective barricade Under Napoleon III, Baron Haussmann widened Paris’s streets in his 1853–70 renovation of the city, making barricading more difficult Credit: Old Books Images / Alamy ‘On one hand, [the authorities] wanted to favour the circulation of ideas,’ reactionary intellectual Louis Veuillot observed apropos the ambiguous liberalism of the latter period of Napoleon III’s Second Empire. ‘On the other, to ensure the circulation of regiments.’ But ‘anti‑insurgency hardware’, as Justinien Tribillon has called it, also served to chase the working class out of the city centre: Haussmann’s projects amounted to a gigantic form of real-estate speculation, and the 1871 Paris Commune that followed constituted not just a short‑lived anarchist experiment featuring enormous barricades; it also signalled the return of the workers to the centre and, arguably, revenge for their dispossession.    By the mid‑19th century, observers questioned whether barricades still had practical meaning. Gottfried Semper’s barricade, constructed for the 1849 Dresden uprising, had proved unconquerable, but Friedrich Engels, one‑time ‘inspector of barricades’ in the Elberfeld insurrection of the same year, already suggested that the barricades’ primary meaning was now moral rather than military – a point to be echoed by Leon Trotsky in the subsequent century. Barricades symbolised bravery and the will to hold out among insurrectionists, and, not least, determination rather to destroy one’s possessions – and one’s neighbourhood – than put up with further oppression.   Not only self‑declared revolutionaries viewed things this way: the reformist Social Democrat leader Eduard Bernstein observed that ‘the barricade fight as a political weapon of the people has been completely eliminated due to changes in weapon technology and cities’ structures’. Bernstein was also picking up on the fact that, in the era of industrialisation, contention happened at least as much on the factory floor as on the streets. The strike, not the food riot or the defence of workers’ quartiers, became the paradigmatic form of conflict. Joshua Clover has pointed out in his 2016 book Riot. Strike. Riot: The New Era of Uprisings, that the price of labour, rather than the price of goods, caused people to confront the powerful. Blocking production grew more important than blocking the street. ‘The only weapons we have are our bodies, and we need to tuck them in places so wheels don’t turn’ Today, it is again blocking – not just people streaming along the streets in large marches – that is prominently associated with protests. Disrupting circulation is not only an important gesture in the face of climate emergency; blocking transport is a powerful form of protest in an economic system focused on logistics and just‑in‑time distribution. Members of Insulate Britain and Germany’s Last Generation super‑glue themselves to streets to stop car traffic to draw attention to the climate emergency; they have also attached themselves to airport runways. They form a human barricade of sorts, immobilising traffic by making themselves immovable.   Today’s protesters have made themselves consciously vulnerable. They in fact follow the advice of US civil rights’ Bayard Rustin who explained: ‘The only weapons we have are our bodies, and we need to tuck them in places so wheels don’t turn.’ Making oneself vulnerable might increase the chances of a majority of citizens seeing the importance of the cause which those engaged in civil disobedience are pursuing. Demonstrations – even large, unpredictable ones – are no longer sufficient. They draw too little attention and do not compel a reaction. Naomi Klein proposed the term ‘blockadia’ as ‘a roving transnational conflict zone’ in which people block extraction – be it open‑pit mines, fracking sites or tar sands pipelines – with their bodies. More often than not, these blockades are organised by local people opposing the fossil fuel industry, not environmental activists per se. Blockadia came to denote resistance to the Keystone XL pipeline as well as Canada’s First Nations‑led movement Idle No More. In cities, blocking can be accomplished with highly mobile structures. Like the barricade of the 19th century, they can be quickly assembled, yet are difficult to move; unlike old‑style barricades, they can also be quickly disassembled, removed and hidden (by those who have the engineering and architectural know‑how). Think of super tripods, intricate ‘protest beacons’ based on tensegrity principles, as well as inflatable cobblestones, pioneered by the artist‑activists of Tools for Action (and as analysed in Nick Newman’s recent volume Protest Architecture).   As recently as 1991, newly independent Latvia defended itself against Soviet tanks with the popular construction of barricades, in a series of confrontations that became known as the Barikādes Credit: Associated Press / Alamy Inversely, roadblocks can be used by police authorities to stop demonstrations and gatherings from taking place – protesters are seen removing such infrastructure in Dhaka during a general strike in 1999 Credit: REUTERS / Rafiqur Rahman / Bridgeman These inflatable objects are highly flexible, but can also be protective against police batons. They pose an awkward challenge to the authorities, who often end up looking ridiculous when dealing with them, and, as one of the inventors pointed out, they are guaranteed to create a media spectacle. This was also true of the 19th‑century barricade: people posed for pictures in front of them. As Wolfgang Scheppe, a curator of Architecture of the Barricade (currently on display at the Arsenale Institute for Politics of Representation in Venice), explains, these images helped the police to find Communards and mete out punishments after the end of the anarchist experiment. Much simpler structures can also be highly effective. In 2019, protesters in Hong Kong filled streets with little archways made from just three ordinary bricks: two standing upright, one resting on top. When touched, the falling top one would buttress the other two, and effectively block traffic. In line with their imperative of ‘be water’, protesters would retreat when the police appeared, but the ‘mini‑Stonehenges’ would remain and slow down the authorities. Today, elaborate architectures of protest, such as Extinction Rebellion’s ‘tensegrity towers’, are used to blockade roads and distribution networks – in this instance, Rupert Murdoch’s News UK printworks in Broxbourne, for the media group’s failure to report the climate emergency accurately Credit: Extinction Rebellion In June 2025, protests erupted in Los Angeles against the Trump administration’s deportation policies. Demonstrators barricaded downtown streets using various objects, including the pink public furniture designed by design firm Rios for Gloria Molina Grand Park. LAPD are seen advancing through tear gas Credit: Gina Ferazzi / Los Angeles Times via Getty Images Roads which radicals might want to target are not just ones in major metropoles and fancy post‑industrial downtowns. Rather, they might block the arteries leading to ‘fulfilment centres’ and harbours with container shipping. The model is not only Occupy Wall Street, which had initially called for the erection of ‘peaceful barricades’, but also the Occupy that led to the Oakland port shutdown in 2011. In short, such roadblocks disrupt what Phil Neel has called a ‘hinterland’ that is often invisible, yet crucial for contemporary capitalism. More recently, Extinction Rebellion targeted Amazon distribution centres in three European countries in November 2021; in the UK, they aimed to disrupt half of all deliveries on a Black Friday.   Will such blockades just anger consumers who, after all, are not present but are impatiently waiting for packages at home? One of the hopes associated with the traditional barricade was always that they might create spaces where protesters, police and previously indifferent citizens get talking; French theorists even expected them to become ‘a machine to produce the people’. That could be why military technology has evolved so that the authorities do not have to get close to the barricade: tear gas was first deployed against those on barricades before it was used in the First World War; so‑called riot control vehicles can ever more easily crush barricades. The challenge, then, for anyone who wishes to block is also how to get in other people’s faces – in order to have a chance to convince them of their cause.        2025-06-11 Kristina Rapacki Share
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  • Gaming – Polygon een koppeling hebt gedeeld
    2025-05-31 10:50:54 - Translate -
    Elden Ring Nightreign is hard for completely different reasons than Elden Ring

    OK, I know you just read that headline, but let me admit first off that I don’t actually think Elden Ring is that hard — not if you take it slow and steady, which the game’s design not only allows but encourages. At every step of the way in Elden Ring, you can decide exactly how you want to play it. It’s very customizable and it rewards patience. Elden Ring Nightreign is the complete opposite, and that’s why I don’t think it’s for me. And it might not be for most other FromSoftware game fans, either, which is pretty shocking.It wouldn’t be a FromSoftware game launch without at least a few debates about difficulty and certain players hurling “git gud” at each other like it was ever even remotely cool to say that and not just performatively tryhard at best and antisocial at worst. I try to exist instead in the sector of the FromSoftware fandom that is prosocial rather than antisocial — think Let Me Solo Her, for example, or even consider the real-life story that inspired longtime FromSoftware game director Hidetaka Miyazaki to design Demon’s Souls’ multiplayer elements with prosocial thinking in mind:“The origin of that idea is actually due to a personal experience where a car suddenly stopped on a hillside after some heavy snow and started to slip,” says Miyazaki. “The car following me also got stuck, and then the one behind it spontaneously bumped into it and started pushing it up the hill... That’s it! That’s how everyone can get home! Then it was my turn and everyone started pushing my car up the hill, and I managed to get home safely.”“But I couldn’t stop the car to say thanks to the people who gave me a shove. I’d have just got stuck again if I’d stopped. On the way back home I wondered whether the last person in the line had made it home, and thought that I would probably never meet the people who had helped me. I thought that maybe if we’d met in another place we’d become friends, or maybe we’d just fight...””You could probably call it a connection of mutual assistance between transient people. Oddly, that incident will probably linger in my heart for a long time.”The multiplayer experiences that I’ve had in Dark Souls and Elden Ring definitely do linger in my heart. I’ve also absolutely loved the moments in FromSoftware games in which I’ve personally conquered a difficult section all by myself. But I look back with equal appreciation on the times when I summoned a complete stranger to help me with something — “a connection of mutual assistance between transient people,” as Miyazaki put it. It is how these games are meant to be played, not as brutal solo journeys but as shared experiences.Here’s a screenshot I took of my Elden Ring character at the beginning of the game, before I knew I was going to spend 360 hours playing it Image: FromSoftware via PolygonThis brings us back to Elden Ring Nightreign, a game not directed by Miyazaki but by Junya Ishizaki. The difference in its multiplayer ethos is stark. This is a game designed with three-player squads in mind; it’s currently very punishing for solo players, and the designers are still working on a duos mode. Because it’s three-player by default, I assumed that the game would be designed around teamwork and would actively reward prosocial behaviors, like base Elden Ring. I would argue that it’s not, and that’s why it’s very hard to have a good time in the game — especially if you’re playing with complete strangers.Problem number one: There’s no in-game communication system besides pinging certain locations on the map. Lack of chat options is a FromSoftware classic, and in most of these games, you don’t really need communication to understand what to do. Usually, you’re just summoned to help with a boss battle, and after it’s over, you’re done and you go back to your game. But in Nightreign, it’s three-player for the entire game, obviously, and it’s a match-based game, not a hundreds-of-hours RPG. Matches last 45 minutes and every second counts, which means you and your teammates need to be extremely organized throughout. The lack of communication hurts. But that’s not the only problem. Far from it.Problem number two: The ring of fire. This game is a combination of Elden Ring’s open world areasand a Fortnite-esque ring of fire that closes in on you constantly. There’s also a Diablo-esque loot system, but you better read those loot descriptions fast, because the fire is coming for you. There are randomized boss fights all over the map, but oops, you might not be able to complete them in time to collect runes from them, because that fire is closing in. There are also special upgrades that you can only get if you defeat these mid-game bosses all over the map, but you might barely even have time to read those descriptions of the special abilities and select one in time for… you guessed it… the fire rushing towards you.This second problem becomes even more stressful when you have two other people on your team alongside you. This game has not one but two different sprint buttons in it — a regular sprint, and a super-fast sprint that uses up stamina faster. That’s because, of course, you need to be running from that fire. But that means your teammates, and you, need to constantly be doing the equivalent of screaming “move, move, move” like a drill sergeant in an army movie. You will be unwittingly getting annoyed at your teammate who is spending too damn long looking at loot on the ground or at an upgrade tree. The fire is coming! Hurry the fuck up! Again, this is not a game design choice that rewards prosocial behaviors and instead makes you feel dragged down by the two teammates that you also desperately need to survive the bosses in this game. Even the “revive” process involves you inflicting damage on your teammate to bring them back to life, which is darkly hilarious, because you might also grow to desire hitting them due to how annoyed you might feel that they died during a super difficult fight. Which brings us to the third and final problem.Image: FromSoftwareThird problem: The randomization of the bosses and of the items. The thing about base Elden Ring is that you can figure out a boss and how it worksand then patiently build up a character who can deal with that problem. You can memorize that boss’ attack patterns. You can find a save point nearest to that boss and run it back over and over again until you get past it. These are all of the wonderful and rewarding parts of playing FromSoftware video games; these are also the moments when you might do all of those preparations and then think, “Actually, I want to also summon a complete stranger to help me with this boss because it’s still too freaking hard.” And then you can do that, too. None of that is the case in Nightreign, because everything is completely fucking random.The bosses, except for the very last boss in each area, are random. The loot is random. Do you have the right loot to fight the boss you’re facing right this second? You may very well not. Do your teammates have it? You might not even know; you don’t have a way to communicate with them, after all. Is the boss in this area way overleveled for you and your team? It won’t be obvious until you start hitting it, and once you do that, good luck escaping. And if your team does a complete wipe and everyone dies to that boss together, you don’t get to run back together from the nearest save point, having seen its attack patterns, ready to try again with teamwork in mind. Nope, instead you get to start all over again, except now with new randomized bosses and new randomized loot.In other games with randomized loot, like Diablo, or other roguelikes with random elements like Hades, the game is designed with down time in mind. When you’ve completed a fight in Diablo or Hades, you have infinite time to stand around and make decisions. There is no encroaching circle of fire forcing you to read item descriptions and ability trees quickly. There’s a reason for that; the decision-making is the most fun part of a game with randomized elements. Why would Nightreign take that away?All of these aspects of the game do feel less bad if you’re playing with two good friends on voice chat. But even in that scenario, the game is still really punishing, and again, not in a way that other FromSoftware games are punishing. It’s punishing because you need to spend the entire game running, looking at randomized loot as fast as you possibly can before making a snapdecision, running more, desperately encouraging your teammates to keep on running to keep up, warning your teammates about the encroaching flames about to kill them, and did I mention running? Is this a fun way to spend your weekly gamer night with two other adults who just worked a full-time job all day and maybe just wanted to have a nice time playing a video game together?Image: FromSoftware/Bandai NamcoI’ve had a review code for Nightreign for a while now, so I already was worried about these problems before the game launched, but now that it’s launched and I’m seeing early mixed reviews on Steam, I’m ready to commiserate and validate: Yes, this game really doesn’t feel like Elden Ring, and even after some of this stuff gets patched, it’s still fundamentally super different. And that’s not only because it’s multiplayer, but because the multiplayer just doesn’t feel like other multiplayer FromSoftware experiences. It feels like it’s designed not only for people who have two best friends with whom they play competitive games on a regular basis, but also specifically for people who live for thrills and speed — not the methodical, calculated experiences of other FromSoftware games.For all of those reasons, I’m really not sure how this is going to go for FromSoftware over time. Is this game going to eventually encourage some prosocial behaviors amongst players, against all odds? Will people slowly learn the best ways to get through different areas? Will there be a “meta” for working together that emerges over time?It seems possible, and since it’s only been one day, it’s way too early to tell. Various social norms will emerge in the player community, and hopefully they won’t be toxic ones. But I can tell from having already played the game that this is going to be an uphill climb for FromSoftware fans. It’s a very different game — and its specific form of difficulty is going to be a whole new variety for those fans to get used to. And like me, they might just decide they don’t really care for it.See More:
    #elden #ring #nightreign #hard #completely
    Elden Ring Nightreign is hard for completely different reasons than Elden Ring
    OK, I know you just read that headline, but let me admit first off that I don’t actually think Elden Ring is that hard — not if you take it slow and steady, which the game’s design not only allows but encourages. At every step of the way in Elden Ring, you can decide exactly how you want to play it. It’s very customizable and it rewards patience. Elden Ring Nightreign is the complete opposite, and that’s why I don’t think it’s for me. And it might not be for most other FromSoftware game fans, either, which is pretty shocking.It wouldn’t be a FromSoftware game launch without at least a few debates about difficulty and certain players hurling “git gud” at each other like it was ever even remotely cool to say that and not just performatively tryhard at best and antisocial at worst. I try to exist instead in the sector of the FromSoftware fandom that is prosocial rather than antisocial — think Let Me Solo Her, for example, or even consider the real-life story that inspired longtime FromSoftware game director Hidetaka Miyazaki to design Demon’s Souls’ multiplayer elements with prosocial thinking in mind:“The origin of that idea is actually due to a personal experience where a car suddenly stopped on a hillside after some heavy snow and started to slip,” says Miyazaki. “The car following me also got stuck, and then the one behind it spontaneously bumped into it and started pushing it up the hill... That’s it! That’s how everyone can get home! Then it was my turn and everyone started pushing my car up the hill, and I managed to get home safely.”“But I couldn’t stop the car to say thanks to the people who gave me a shove. I’d have just got stuck again if I’d stopped. On the way back home I wondered whether the last person in the line had made it home, and thought that I would probably never meet the people who had helped me. I thought that maybe if we’d met in another place we’d become friends, or maybe we’d just fight...””You could probably call it a connection of mutual assistance between transient people. Oddly, that incident will probably linger in my heart for a long time.”The multiplayer experiences that I’ve had in Dark Souls and Elden Ring definitely do linger in my heart. I’ve also absolutely loved the moments in FromSoftware games in which I’ve personally conquered a difficult section all by myself. But I look back with equal appreciation on the times when I summoned a complete stranger to help me with something — “a connection of mutual assistance between transient people,” as Miyazaki put it. It is how these games are meant to be played, not as brutal solo journeys but as shared experiences.Here’s a screenshot I took of my Elden Ring character at the beginning of the game, before I knew I was going to spend 360 hours playing it Image: FromSoftware via PolygonThis brings us back to Elden Ring Nightreign, a game not directed by Miyazaki but by Junya Ishizaki. The difference in its multiplayer ethos is stark. This is a game designed with three-player squads in mind; it’s currently very punishing for solo players, and the designers are still working on a duos mode. Because it’s three-player by default, I assumed that the game would be designed around teamwork and would actively reward prosocial behaviors, like base Elden Ring. I would argue that it’s not, and that’s why it’s very hard to have a good time in the game — especially if you’re playing with complete strangers.Problem number one: There’s no in-game communication system besides pinging certain locations on the map. Lack of chat options is a FromSoftware classic, and in most of these games, you don’t really need communication to understand what to do. Usually, you’re just summoned to help with a boss battle, and after it’s over, you’re done and you go back to your game. But in Nightreign, it’s three-player for the entire game, obviously, and it’s a match-based game, not a hundreds-of-hours RPG. Matches last 45 minutes and every second counts, which means you and your teammates need to be extremely organized throughout. The lack of communication hurts. But that’s not the only problem. Far from it.Problem number two: The ring of fire. This game is a combination of Elden Ring’s open world areasand a Fortnite-esque ring of fire that closes in on you constantly. There’s also a Diablo-esque loot system, but you better read those loot descriptions fast, because the fire is coming for you. There are randomized boss fights all over the map, but oops, you might not be able to complete them in time to collect runes from them, because that fire is closing in. There are also special upgrades that you can only get if you defeat these mid-game bosses all over the map, but you might barely even have time to read those descriptions of the special abilities and select one in time for… you guessed it… the fire rushing towards you.This second problem becomes even more stressful when you have two other people on your team alongside you. This game has not one but two different sprint buttons in it — a regular sprint, and a super-fast sprint that uses up stamina faster. That’s because, of course, you need to be running from that fire. But that means your teammates, and you, need to constantly be doing the equivalent of screaming “move, move, move” like a drill sergeant in an army movie. You will be unwittingly getting annoyed at your teammate who is spending too damn long looking at loot on the ground or at an upgrade tree. The fire is coming! Hurry the fuck up! Again, this is not a game design choice that rewards prosocial behaviors and instead makes you feel dragged down by the two teammates that you also desperately need to survive the bosses in this game. Even the “revive” process involves you inflicting damage on your teammate to bring them back to life, which is darkly hilarious, because you might also grow to desire hitting them due to how annoyed you might feel that they died during a super difficult fight. Which brings us to the third and final problem.Image: FromSoftwareThird problem: The randomization of the bosses and of the items. The thing about base Elden Ring is that you can figure out a boss and how it worksand then patiently build up a character who can deal with that problem. You can memorize that boss’ attack patterns. You can find a save point nearest to that boss and run it back over and over again until you get past it. These are all of the wonderful and rewarding parts of playing FromSoftware video games; these are also the moments when you might do all of those preparations and then think, “Actually, I want to also summon a complete stranger to help me with this boss because it’s still too freaking hard.” And then you can do that, too. None of that is the case in Nightreign, because everything is completely fucking random.The bosses, except for the very last boss in each area, are random. The loot is random. Do you have the right loot to fight the boss you’re facing right this second? You may very well not. Do your teammates have it? You might not even know; you don’t have a way to communicate with them, after all. Is the boss in this area way overleveled for you and your team? It won’t be obvious until you start hitting it, and once you do that, good luck escaping. And if your team does a complete wipe and everyone dies to that boss together, you don’t get to run back together from the nearest save point, having seen its attack patterns, ready to try again with teamwork in mind. Nope, instead you get to start all over again, except now with new randomized bosses and new randomized loot.In other games with randomized loot, like Diablo, or other roguelikes with random elements like Hades, the game is designed with down time in mind. When you’ve completed a fight in Diablo or Hades, you have infinite time to stand around and make decisions. There is no encroaching circle of fire forcing you to read item descriptions and ability trees quickly. There’s a reason for that; the decision-making is the most fun part of a game with randomized elements. Why would Nightreign take that away?All of these aspects of the game do feel less bad if you’re playing with two good friends on voice chat. But even in that scenario, the game is still really punishing, and again, not in a way that other FromSoftware games are punishing. It’s punishing because you need to spend the entire game running, looking at randomized loot as fast as you possibly can before making a snapdecision, running more, desperately encouraging your teammates to keep on running to keep up, warning your teammates about the encroaching flames about to kill them, and did I mention running? Is this a fun way to spend your weekly gamer night with two other adults who just worked a full-time job all day and maybe just wanted to have a nice time playing a video game together?Image: FromSoftware/Bandai NamcoI’ve had a review code for Nightreign for a while now, so I already was worried about these problems before the game launched, but now that it’s launched and I’m seeing early mixed reviews on Steam, I’m ready to commiserate and validate: Yes, this game really doesn’t feel like Elden Ring, and even after some of this stuff gets patched, it’s still fundamentally super different. And that’s not only because it’s multiplayer, but because the multiplayer just doesn’t feel like other multiplayer FromSoftware experiences. It feels like it’s designed not only for people who have two best friends with whom they play competitive games on a regular basis, but also specifically for people who live for thrills and speed — not the methodical, calculated experiences of other FromSoftware games.For all of those reasons, I’m really not sure how this is going to go for FromSoftware over time. Is this game going to eventually encourage some prosocial behaviors amongst players, against all odds? Will people slowly learn the best ways to get through different areas? Will there be a “meta” for working together that emerges over time?It seems possible, and since it’s only been one day, it’s way too early to tell. Various social norms will emerge in the player community, and hopefully they won’t be toxic ones. But I can tell from having already played the game that this is going to be an uphill climb for FromSoftware fans. It’s a very different game — and its specific form of difficulty is going to be a whole new variety for those fans to get used to. And like me, they might just decide they don’t really care for it.See More: #elden #ring #nightreign #hard #completely
    WWW.POLYGON.COM
    Elden Ring Nightreign is hard for completely different reasons than Elden Ring
    OK, I know you just read that headline, but let me admit first off that I don’t actually think Elden Ring is that hard — not if you take it slow and steady, which the game’s design not only allows but encourages. At every step of the way in Elden Ring, you can decide exactly how you want to play it. It’s very customizable and it rewards patience. Elden Ring Nightreign is the complete opposite, and that’s why I don’t think it’s for me. And it might not be for most other FromSoftware game fans, either, which is pretty shocking.It wouldn’t be a FromSoftware game launch without at least a few debates about difficulty and certain players hurling “git gud” at each other like it was ever even remotely cool to say that and not just performatively tryhard at best and antisocial at worst. I try to exist instead in the sector of the FromSoftware fandom that is prosocial rather than antisocial — think Let Me Solo Her, for example, or even consider the real-life story that inspired longtime FromSoftware game director Hidetaka Miyazaki to design Demon’s Souls’ multiplayer elements with prosocial thinking in mind (via an old 2010 Eurogamer interview):“The origin of that idea is actually due to a personal experience where a car suddenly stopped on a hillside after some heavy snow and started to slip,” says Miyazaki. “The car following me also got stuck, and then the one behind it spontaneously bumped into it and started pushing it up the hill... That’s it! That’s how everyone can get home! Then it was my turn and everyone started pushing my car up the hill, and I managed to get home safely.”“But I couldn’t stop the car to say thanks to the people who gave me a shove. I’d have just got stuck again if I’d stopped. On the way back home I wondered whether the last person in the line had made it home, and thought that I would probably never meet the people who had helped me. I thought that maybe if we’d met in another place we’d become friends, or maybe we’d just fight...””You could probably call it a connection of mutual assistance between transient people. Oddly, that incident will probably linger in my heart for a long time.”The multiplayer experiences that I’ve had in Dark Souls and Elden Ring definitely do linger in my heart. I’ve also absolutely loved the moments in FromSoftware games in which I’ve personally conquered a difficult section all by myself. But I look back with equal appreciation on the times when I summoned a complete stranger to help me with something — “a connection of mutual assistance between transient people,” as Miyazaki put it. It is how these games are meant to be played, not as brutal solo journeys but as shared experiences.Here’s a screenshot I took of my Elden Ring character at the beginning of the game, before I knew I was going to spend 360 hours playing it Image: FromSoftware via PolygonThis brings us back to Elden Ring Nightreign, a game not directed by Miyazaki but by Junya Ishizaki. The difference in its multiplayer ethos is stark. This is a game designed with three-player squads in mind; it’s currently very punishing for solo players (although an upcoming patch aims to fix some of that), and the designers are still working on a duos mode. Because it’s three-player by default, I assumed that the game would be designed around teamwork and would actively reward prosocial behaviors, like base Elden Ring. I would argue that it’s not, and that’s why it’s very hard to have a good time in the game — especially if you’re playing with complete strangers.Problem number one: There’s no in-game communication system besides pinging certain locations on the map. Lack of chat options is a FromSoftware classic, and in most of these games, you don’t really need communication to understand what to do. Usually, you’re just summoned to help with a boss battle, and after it’s over, you’re done and you go back to your game. But in Nightreign, it’s three-player for the entire game, obviously, and it’s a match-based game, not a hundreds-of-hours RPG. Matches last 45 minutes and every second counts, which means you and your teammates need to be extremely organized throughout. The lack of communication hurts. But that’s not the only problem. Far from it.Problem number two: The ring of fire. This game is a combination of Elden Ring’s open world areas (which encourage slow, methodical exploration) and a Fortnite-esque ring of fire that closes in on you constantly (which means you absolutely shouldn’t be doing any slow, methodical exploration). There’s also a Diablo-esque loot system, but you better read those loot descriptions fast, because the fire is coming for you. There are randomized boss fights all over the map, but oops, you might not be able to complete them in time to collect runes from them, because that fire is closing in. There are also special upgrades that you can only get if you defeat these mid-game bosses all over the map, but you might barely even have time to read those descriptions of the special abilities and select one in time for… you guessed it… the fire rushing towards you.This second problem becomes even more stressful when you have two other people on your team alongside you. This game has not one but two different sprint buttons in it — a regular sprint, and a super-fast sprint that uses up stamina faster. That’s because, of course, you need to be running from that fire. But that means your teammates, and you, need to constantly be doing the equivalent of screaming “move, move, move” like a drill sergeant in an army movie. You will be unwittingly getting annoyed at your teammate who is spending too damn long looking at loot on the ground or at an upgrade tree. The fire is coming! Hurry the fuck up! Again, this is not a game design choice that rewards prosocial behaviors and instead makes you feel dragged down by the two teammates that you also desperately need to survive the bosses in this game. Even the “revive” process involves you inflicting damage on your teammate to bring them back to life (rather than a revive button or item), which is darkly hilarious, because you might also grow to desire hitting them due to how annoyed you might feel that they died during a super difficult fight. Which brings us to the third and final problem.Image: FromSoftwareThird problem: The randomization of the bosses and of the items. The thing about base Elden Ring is that you can figure out a boss and how it works (is it weak to fire? Holy damage? And so on) and then patiently build up a character who can deal with that problem. You can memorize that boss’ attack patterns. You can find a save point nearest to that boss and run it back over and over again until you get past it. These are all of the wonderful and rewarding parts of playing FromSoftware video games; these are also the moments when you might do all of those preparations and then think, “Actually, I want to also summon a complete stranger to help me with this boss because it’s still too freaking hard.” And then you can do that, too. None of that is the case in Nightreign, because everything is completely fucking random.The bosses, except for the very last boss in each area, are random. The loot is random. Do you have the right loot to fight the boss you’re facing right this second? You may very well not. Do your teammates have it? You might not even know; you don’t have a way to communicate with them, after all. Is the boss in this area way overleveled for you and your team? It won’t be obvious until you start hitting it, and once you do that, good luck escaping. And if your team does a complete wipe and everyone dies to that boss together, you don’t get to run back together from the nearest save point, having seen its attack patterns, ready to try again with teamwork in mind. Nope, instead you get to start all over again, except now with new randomized bosses and new randomized loot.In other games with randomized loot, like Diablo, or other roguelikes with random elements like Hades, the game is designed with down time in mind. When you’ve completed a fight in Diablo or Hades, you have infinite time to stand around and make decisions. There is no encroaching circle of fire forcing you to read item descriptions and ability trees quickly. There’s a reason for that; the decision-making is the most fun part of a game with randomized elements. Why would Nightreign take that away?All of these aspects of the game do feel less bad if you’re playing with two good friends on voice chat. But even in that scenario, the game is still really punishing, and again, not in a way that other FromSoftware games are punishing. It’s punishing because you need to spend the entire game running, looking at randomized loot as fast as you possibly can before making a snap (possibly bad) decision, running more, desperately encouraging your teammates to keep on running to keep up, warning your teammates about the encroaching flames about to kill them, and did I mention running? Is this a fun way to spend your weekly gamer night with two other adults who just worked a full-time job all day and maybe just wanted to have a nice time playing a video game together?Image: FromSoftware/Bandai NamcoI’ve had a review code for Nightreign for a while now, so I already was worried about these problems before the game launched, but now that it’s launched and I’m seeing early mixed reviews on Steam, I’m ready to commiserate and validate: Yes, this game really doesn’t feel like Elden Ring, and even after some of this stuff gets patched, it’s still fundamentally super different. And that’s not only because it’s multiplayer, but because the multiplayer just doesn’t feel like other multiplayer FromSoftware experiences. It feels like it’s designed not only for people who have two best friends with whom they play competitive games on a regular basis, but also specifically for people who live for thrills and speed — not the methodical, calculated experiences of other FromSoftware games.For all of those reasons, I’m really not sure how this is going to go for FromSoftware over time. Is this game going to eventually encourage some prosocial behaviors amongst players, against all odds? Will people slowly learn the best ways to get through different areas? Will there be a “meta” for working together that emerges over time?It seems possible, and since it’s only been one day, it’s way too early to tell. Various social norms will emerge in the player community, and hopefully they won’t be toxic ones. But I can tell from having already played the game that this is going to be an uphill climb for FromSoftware fans. It’s a very different game — and its specific form of difficulty is going to be a whole new variety for those fans to get used to. And like me, they might just decide they don’t really care for it.See More:
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  • Microsoft Academic een koppeling hebt gedeeld
    2025-05-29 16:03:36 - Translate -
    What AI’s impact on individuals means for the health workforce and industry

    Transcript    
    PETER LEE: “In American primary care, the missing workforce is stunning in magnitude, the shortfall estimated to reach up to 48,000 doctors within the next dozen years. China and other countries with aging populations can expect drastic shortfalls, as well. Just last month, I asked a respected colleague retiring from primary care who he would recommend as a replacement; he told me bluntly that, other than expensive concierge care practices, he could not think of anyone, even for himself. This mismatch between need and supply will only grow, and the US is far from alone among developed countries in facing it.”      
    This is The AI Revolution in Medicine, Revisited. I’m your host, Peter Lee.   
    Shortly after OpenAI’s GPT-4 was publicly released, Carey Goldberg, Dr. Zak Kohane, and I published The AI Revolution in Medicine to help educate the world of healthcare and medical research about the transformative impact this new generative AI technology could have. But because we wrote the book when GPT-4 was still a secret, we had to speculate. Now, two years later, what did we get right, and what did we get wrong?    
    In this series, we’ll talk to clinicians, patients, hospital administrators, and others to understand the reality of AI in the field and where we go from here.     The book passage I read at the top is from “Chapter 4: Trust but Verify,” which was written by Zak.
    You know, it’s no secret that in the US and elsewhere shortages in medical staff and the rise of clinician burnout are affecting the quality of patient care for the worse. In our book, we predicted that generative AI would be something that might help address these issues.
    So in this episode, we’ll delve into how individual performance gains that our previous guests have described might affect the healthcare workforce as a whole, and on the patient side, we’ll look into the influence of generative AI on the consumerization of healthcare. Now, since all of this consumes such a huge fraction of the overall economy, we’ll also get into what a general-purpose technology as disruptive as generative AI might mean in the context of labor markets and beyond.  
    To help us do that, I’m pleased to welcome Ethan Mollick and Azeem Azhar.
    Ethan Mollick is the Ralph J. Roberts Distinguished Faculty Scholar, a Rowan Fellow, and an associate professor at the Wharton School of the University of Pennsylvania. His research into the effects of AI on work, entrepreneurship, and education is applied by organizations around the world, leading him to be named one of Time magazine’s most influential people in AI for 2024. He’s also the author of the New York Times best-selling book Co-Intelligence.
    Azeem Azhar is an author, founder, investor, and one of the most thoughtful and influential voices on the interplay between disruptive emerging technologies and business and society. In his best-selling book, The Exponential Age, and in his highly regarded newsletter and podcast, Exponential View, he explores how technologies like AI are reshaping everything from healthcare to geopolitics.
    Ethan and Azeem are two leading thinkers on the ways that disruptive technologies—and especially AI—affect our work, our jobs, our business enterprises, and whole industries. As economists, they are trying to work out whether we are in the midst of an economic revolution as profound as the shift from an agrarian to an industrial society.Here is my interview with Ethan Mollick:
    LEE: Ethan, welcome.
    ETHAN MOLLICK: So happy to be here, thank you.
    LEE: I described you as a professor at Wharton, which I think most of the people who listen to this podcast series know of as an elite business school. So it might surprise some people that you study AI. And beyond that, you know, that I would seek you out to talk about AI in medicine.So to get started, how and why did it happen that you’ve become one of the leading experts on AI?
    MOLLICK: It’s actually an interesting story. I’ve been AI-adjacent my whole career. When I wasmy PhD at MIT, I worked with Marvin Minskyand the MITMedia Labs AI group. But I was never the technical AI guy. I was the person who was trying to explain AI to everybody else who didn’t understand it.
    And then I became very interested in, how do you train and teach? And AI was always a part of that. I was building games for teaching, teaching tools that were used in hospitals and elsewhere, simulations. So when LLMs burst into the scene, I had already been using them and had a good sense of what they could do. And between that and, kind of, being practically oriented and getting some of the first research projects underway, especially under education and AI and performance, I became sort of a go-to person in the field.
    And once you’re in a field where nobody knows what’s going on and we’re all making it up as we go along—I thought it’s funny that you led with the idea that you have a couple of months head start for GPT-4, right. Like that’s all we have at this point, is a few months’ head start.So being a few months ahead is good enough to be an expert at this point. Whether it should be or not is a different question.
    LEE: Well, if I understand correctly, leading AI companies like OpenAI, Anthropic, and others have now sought you out as someone who should get early access to really start to do early assessments and gauge early reactions. How has that been?
    MOLLICK: So, I mean, I think the bigger picture is less about me than about two things that tells us about the state of AI right now.
    One, nobody really knows what’s going on, right. So in a lot of ways, if it wasn’t for your work, Peter, like, I don’t think people would be thinking about medicine as much because these systems weren’t built for medicine. They weren’t built to change education. They weren’t built to write memos. They, like, they weren’t built to do any of these things. They weren’t really built to do anything in particular. It turns out they’re just good at many things.
    And to the extent that the labs work on them, they care about their coding ability above everything else and maybe math and science secondarily. They don’t think about the fact that it expresses high empathy. They don’t think about its accuracy and diagnosis or where it’s inaccurate. They don’t think about how it’s changing education forever.
    So one part of this is the fact that they go to my Twitter feed or ask me for advice is an indicator of where they are, too, which is they’re not thinking about this. And the fact that a few months’ head start continues to give you a lead tells you that we are at the very cutting edge. These labs aren’t sitting on projects for two years and then releasing them. Months after a project is complete or sooner, it’s out the door. Like, there’s very little delay. So we’re kind of all in the same boat here, which is a very unusual space for a new technology.
    LEE: And I, you know, explained that you’re at Wharton. Are you an odd fit as a faculty member at Wharton, or is this a trend now even in business schools that AI experts are becoming key members of the faculty?
    MOLLICK: I mean, it’s a little of both, right. It’s faculty, so everybody does everything. I’m a professor of innovation-entrepreneurship. I’ve launched startups before and working on that and education means I think about, how do organizations redesign themselves? How do they take advantage of these kinds of problems? So medicine’s always been very central to that, right. A lot of people in my MBA class have been MDs either switching, you know, careers or else looking to advance from being sort of individual contributors to running teams. So I don’t think that’s that bad a fit. But I also think this is general-purpose technology; it’s going to touch everything. The focus on this is medicine, but Microsoft does far more than medicine, right. It’s … there’s transformation happening in literally every field, in every country. This is a widespread effect.
    So I don’t think we should be surprised that business schools matter on this because we care about management. There’s a long tradition of management and medicine going together. There’s actually a great academic paper that shows that teaching hospitals that also have MBA programs associated with them have higher management scores and perform better. So I think that these are not as foreign concepts, especially as medicine continues to get more complicated.
    LEE: Yeah. Well, in fact, I want to dive a little deeper on these issues of management, of entrepreneurship, um, education. But before doing that, if I could just stay focused on you. There is always something interesting to hear from people about their first encounters with AI. And throughout this entire series, I’ve been doing that both pre-generative AI and post-generative AI. So you, sort of, hinted at the pre-generative AI. You were in Minsky’s lab. Can you say a little bit more about that early encounter? And then tell us about your first encounters with generative AI.
    MOLLICK: Yeah. Those are great questions. So first of all, when I was at the media lab, that was pre-the current boom in sort of, you know, even in the old-school machine learning kind of space. So there was a lot of potential directions to head in. While I was there, there were projects underway, for example, to record every interaction small children had. One of the professors was recording everything their baby interacted with in the hope that maybe that would give them a hint about how to build an AI system.
    There was a bunch of projects underway that were about labeling every concept and how they relate to other concepts. So, like, it was very much Wild West of, like, how do we make an AI work—which has been this repeated problem in AI, which is, what is this thing?
    The fact that it was just like brute force over the corpus of all human knowledge turns out to be a little bit of like a, you know, it’s a miracle and a little bit of a disappointment in some wayscompared to how elaborate some of this was. So, you know, I think that, that was sort of my first encounters in sort of the intellectual way.
    The generative AI encounters actually started with the original, sort of, GPT-3, or, you know, earlier versions. And it was actually game-based. So I played games like AI Dungeon. And as an educator, I realized, oh my gosh, this stuff could write essays at a fourth-grade level. That’s really going to change the way, like, middle school works, was my thinking at the time. And I was posting about that back in, you know, 2021 that this is a big deal. But I think everybody was taken surprise, including the AI companies themselves, by, you know, ChatGPT, by GPT-3.5. The difference in degree turned out to be a difference in kind.
    LEE: Yeah, you know, if I think back, even with GPT-3, and certainly this was the case with GPT-2, it was, at least, you know, from where I was sitting, it was hard to get people to really take this seriously and pay attention.
    MOLLICK: Yes.
    LEE: You know, it’s remarkable. Within Microsoft, I think a turning point was the use of GPT-3 to do code completions. And that was actually productized as GitHub Copilot, the very first version. That, I think, is where there was widespread belief. But, you know, in a way, I think there is, even for me early on, a sense of denial and skepticism. Did you have those initially at any point?
    MOLLICK: Yeah, I mean, it still happens today, right. Like, this is a weird technology. You know, the original denial and skepticism was, I couldn’t see where this was going. It didn’t seem like a miracle because, you know, of course computers can complete code for you. Like, what else are they supposed to do? Of course, computers can give you answers to questions and write fun things. So there’s difference of moving into a world of generative AI. I think a lot of people just thought that’s what computers could do. So it made the conversations a little weird. But even today, faced with these, you know, with very strong reasoner models that operate at the level of PhD students, I think a lot of people have issues with it, right.
    I mean, first of all, they seem intuitive to use, but they’re not always intuitive to use because the first use case that everyone puts AI to, it fails at because they use it like Google or some other use case. And then it’s genuinely upsetting in a lot of ways. I think, you know, I write in my book about the idea of three sleepless nights. That hasn’t changed. Like, you have to have an intellectual crisis to some extent, you know, and I think people do a lot to avoid having that existential angst of like, “Oh my god, what does it mean that a machine could think—apparently think—like a person?”
    So, I mean, I see resistance now. I saw resistance then. And then on top of all of that, there’s the fact that the curve of the technology is quite great. I mean, the price of GPT-4 level intelligence from, you know, when it was released has dropped 99.97% at this point, right.
    LEE: Yes. Mm-hmm.
    MOLLICK: I mean, I could run a GPT-4 class system basically on my phone. Microsoft’s releasing things that can almost run on like, you know, like it fits in almost no space, that are almost as good as the original GPT-4 models. I mean, I don’t think people have a sense of how fast the trajectory is moving either.
    LEE: Yeah, you know, there’s something that I think about often. There is this existential dread, or will this technology replace me? But I think the first people to feel that are researchers—people encountering this for the first time. You know, if you were working, let’s say, in Bayesian reasoning or in traditional, let’s say, Gaussian mixture model based, you know, speech recognition, you do get this feeling, Oh, my god, this technology has just solved the problem that I’ve dedicated my life to. And there is this really difficult period where you have to cope with that. And I think this is going to be spreading, you know, in more and more walks of life. And so this … at what point does that sort of sense of dread hit you, if ever?
    MOLLICK: I mean, you know, it’s not even dread as much as like, you know, Tyler Cowen wrote that it’s impossible to not feel a little bit of sadness as you use these AI systems, too. Because, like, I was talking to a friend, just as the most minor example, and his talent that he was very proud of was he was very good at writing limericks for birthday cards. He’d write these limericks. Everyone was always amused by them.And now, you know, GPT-4 and GPT-4.5, they made limericks obsolete. Like, anyone can write a good limerick, right. So this was a talent, and it was a little sad. Like, this thing that you cared about mattered.
    You know, as academics, we’re a little used to dead ends, right, and like, you know, some getting the lap. But the idea that entire fields are hitting that way. Like in medicine, there’s a lot of support systems that are now obsolete. And the question is how quickly you change that. In education, a lot of our techniques are obsolete.
    What do you do to change that? You know, it’s like the fact that this brute force technology is good enough to solve so many problems is weird, right. And it’s not just the end of, you know, of our research angles that matter, too. Like, for example, I ran this, you know, 14-person-plus, multimillion-dollar effort at Wharton to build these teaching simulations, and we’re very proud of them. It took years of work to build one.
    Now we’ve built a system that can build teaching simulations on demand by you talking to it with one team member. And, you know, you literally can create any simulation by having a discussion with the AI. I mean, you know, there’s a switch to a new form of excitement, but there is a little bit of like, this mattered to me, and, you know, now I have to change how I do things. I mean, adjustment happens. But if you haven’t had that displacement, I think that’s a good indicator that you haven’t really faced AI yet.
    LEE: Yeah, what’s so interesting just listening to you is you use words like sadness, and yet I can see the—and hear the—excitement in your voice and your body language. So, you know, that’s also kind of an interesting aspect of all of this. 
    MOLLICK: Yeah, I mean, I think there’s something on the other side, right. But, like, I can’t say that I haven’t had moments where like, ughhhh, but then there’s joy and basically like also, you know, freeing stuff up. I mean, I think about doctors or professors, right. These are jobs that bundle together lots of different tasks that you would never have put together, right. If you’re a doctor, you would never have expected the same person to be good at keeping up with the research and being a good diagnostician and being a good manager and being good with people and being good with hand skills.
    Like, who would ever want that kind of bundle? That’s not something you’re all good at, right. And a lot of our stress of our job comes from the fact that we suck at some of it. And so to the extent that AI steps in for that, you kind of feel bad about some of the stuff that it’s doing that you wanted to do. But it’s much more uplifting to be like, I don’t have to do this stuff I’m bad anymore, or I get the support to make myself good at it. And the stuff that I really care about, I can focus on more. Well, because we are at kind of a unique moment where whatever you’re best at, you’re still better than AI. And I think it’s an ongoing question about how long that lasts. But for right now, like you’re not going to say, OK, AI replaces me entirely in my job in medicine. It’s very unlikely.
    But you will say it replaces these 17 things I’m bad at, but I never liked that anyway. So it’s a period of both excitement and a little anxiety.
    LEE: Yeah, I’m going to want to get back to this question about in what ways AI may or may not replace doctors or some of what doctors and nurses and other clinicians do. But before that, let’s get into, I think, the real meat of this conversation. In previous episodes of this podcast, we talked to clinicians and healthcare administrators and technology developers that are very rapidly injecting AI today to do various forms of workforce automation, you know, automatically writing a clinical encounter note, automatically filling out a referral letter or request for prior authorization for some reimbursement to an insurance company.
    And so these sorts of things are intended not only to make things more efficient and lower costs but also to reduce various forms of drudgery, cognitive burden on frontline health workers. So how do you think about the impact of AI on that aspect of workforce, and, you know, what would you expect will happen over the next few years in terms of impact on efficiency and costs?
    MOLLICK: So I mean, this is a case where I think we’re facing the big bright problem in AI in a lot of ways, which is that this is … at the individual level, there’s lots of performance gains to be gained, right. The problem, though, is that we as individuals fit into systems, in medicine as much as anywhere else or more so, right. Which is that you could individually boost your performance, but it’s also about systems that fit along with this, right.
    So, you know, if you could automatically, you know, record an encounter, if you could automatically make notes, does that change what you should be expecting for notes or the value of those notes or what they’re for? How do we take what one person does and validate it across the organization and roll it out for everybody without making it a 10-year process that it feels like IT in medicine often is? Like, so we’re in this really interesting period where there’s incredible amounts of individual innovation in productivity and performance improvements in this field, like very high levels of it, but not necessarily seeing that same thing translate to organizational efficiency or gains.
    And one of my big concerns is seeing that happen. We’re seeing that in nonmedical problems, the same kind of thing, which is, you know, we’ve got research showing 20 and 40% performance improvements, like not uncommon to see those things. But then the organization doesn’t capture it; the system doesn’t capture it. Because the individuals are doing their own work and the systems don’t have the ability to, kind of, learn or adapt as a result.
    LEE: You know, where are those productivity gains going, then, when you get to the organizational level?
    MOLLICK: Well, they’re dying for a few reasons. One is, there’s a tendency for individual contributors to underestimate the power of management, right.
    Practices associated with good management increase happiness, decrease, you know, issues, increase success rates. In the same way, about 40%, as far as we can tell, of the US advantage over other companies, of US firms, has to do with management ability. Like, management is a big deal. Organizing is a big deal. Thinking about how you coordinate is a big deal.
    At the individual level, when things get stuck there, right, you can’t start bringing them up to how systems work together. It becomes, How do I deal with a doctor that has a 60% performance improvement? We really only have one thing in our playbook for doing that right now, which is, OK, we could fire 40% of the other doctors and still have a performance gain, which is not the answer you want to see happen.
    So because of that, people are hiding their use. They’re actually hiding their use for lots of reasons.
    And it’s a weird case because the people who are able to figure out best how to use these systems, for a lot of use cases, they’re actually clinicians themselves because they’re experimenting all the time. Like, they have to take those encounter notes. And if they figure out a better way to do it, they figure that out. You don’t want to wait for, you know, a med tech company to figure that out and then sell that back to you when it can be done by the physicians themselves.
    So we’re just not used to a period where everybody’s innovating and where the management structure isn’t in place to take advantage of that. And so we’re seeing things stalled at the individual level, and people are often, especially in risk-averse organizations or organizations where there’s lots of regulatory hurdles, people are so afraid of the regulatory piece that they don’t even bother trying to make change.
    LEE: If you are, you know, the leader of a hospital or a clinic or a whole health system, how should you approach this? You know, how should you be trying to extract positive success out of AI?
    MOLLICK: So I think that you need to embrace the right kind of risk, right. We don’t want to put risk on our patients … like, we don’t want to put uninformed risk. But innovation involves risk to how organizations operate. They involve change. So I think part of this is embracing the idea that R&D has to happen in organizations again.
    What’s happened over the last 20 years or so has been organizations giving that up. Partially, that’s a trend to focus on what you’re good at and not try and do this other stuff. Partially, it’s because it’s outsourced now to software companies that, like, Salesforce tells you how to organize your sales team. Workforce tells you how to organize your organization. Consultants come in and will tell you how to make change based on the average of what other people are doing in your field.
    So companies and organizations and hospital systems have all started to give up their ability to create their own organizational change. And when I talk to organizations, I often say they have to have two approaches. They have to think about the crowd and the lab.
    So the crowd is the idea of how to empower clinicians and administrators and supporter networks to start using AI and experimenting in ethical, legal ways and then sharing that information with each other. And the lab is, how are we doing R&D about the approach of how toAI to work, not just in direct patient care, right. But also fundamentally, like, what paperwork can you cut out? How can we better explain procedures? Like, what management role can this fill?
    And we need to be doing active experimentation on that. We can’t just wait for, you know, Microsoft to solve the problems. It has to be at the level of the organizations themselves.
    LEE: So let’s shift a little bit to the patient. You know, one of the things that we see, and I think everyone is seeing, is that people are turning to chatbots, like ChatGPT, actually to seek healthcare information for, you know, their own health or the health of their loved ones.
    And there was already, prior to all of this, a trend towards, let’s call it, consumerization of healthcare. So just in the business of healthcare delivery, do you think AI is going to hasten these kinds of trends, or from the consumer’s perspective, what … ?
    MOLLICK: I mean, absolutely, right. Like, all the early data that we have suggests that for most common medical problems, you should just consult AI, too, right. In fact, there is a real question to ask: at what point does it become unethical for doctors themselves to not ask for a second opinion from the AI because it’s cheap, right? You could overrule it or whatever you want, but like not asking seems foolish.
    I think the two places where there’s a burning almost, you know, moral imperative is … let’s say, you know, I’m in Philadelphia, I’m a professor, I have access to really good healthcare through the Hospital University of Pennsylvania system. I know doctors. You know, I’m lucky. I’m well connected. If, you know, something goes wrong, I have friends who I can talk to. I have specialists. I’m, you know, pretty well educated in this space.
    But for most people on the planet, they don’t have access to good medical care, they don’t have good health. It feels like it’s absolutely imperative to say when should you use AI and when not. Are there blind spots? What are those things?
    And I worry that, like, to me, that would be the crash project I’d be invoking because I’m doing the same thing in education, which is this system is not as good as being in a room with a great teacher who also uses AI to help you, but it’s better than not getting an, you know, to the level of education people get in many cases. Where should we be using it? How do we guide usage in the right way? Because the AI labs aren’t thinking about this. We have to.
    So, to me, there is a burning need here to understand this. And I worry that people will say, you know, everything that’s true—AI can hallucinate, AI can be biased. All of these things are absolutely true, but people are going to use it. The early indications are that it is quite useful. And unless we take the active role of saying, here’s when to use it, here’s when not to use it, we don’t have a right to say, don’t use this system. And I think, you know, we have to be exploring that.
    LEE: What do people need to understand about AI? And what should schools, universities, and so on be teaching?
    MOLLICK: Those are, kind of, two separate questions in lot of ways. I think a lot of people want to teach AI skills, and I will tell you, as somebody who works in this space a lot, there isn’t like an easy, sort of, AI skill, right. I could teach you prompt engineering in two to three classes, but every indication we have is that for most people under most circumstances, the value of prompting, you know, any one case is probably not that useful.
    A lot of the tricks are disappearing because the AI systems are just starting to use them themselves. So asking good questions, being a good manager, being a good thinker tend to be important, but like magic tricks around making, you know, the AI do something because you use the right phrase used to be something that was real but is rapidly disappearing.
    So I worry when people say teach AI skills. No one’s been able to articulate to me as somebody who knows AI very well and teaches classes on AI, what those AI skills that everyone should learn are, right.
    I mean, there’s value in learning a little bit how the models work. There’s a value in working with these systems. A lot of it’s just hands on keyboard kind of work. But, like, we don’t have an easy slam dunk “this is what you learn in the world of AI” because the systems are getting better, and as they get better, they get less sensitive to these prompting techniques. They get better prompting themselves. They solve problems spontaneously and start being agentic. So it’s a hard problem to ask about, like, what do you train someone on? I think getting people experience in hands-on-keyboards, getting them to … there’s like four things I could teach you about AI, and two of them are already starting to disappear.
    But, like, one is be direct. Like, tell the AI exactly what you want. That’s very helpful. Second, provide as much context as possible. That can include things like acting as a doctor, but also all the information you have. The third is give it step-by-step directions—that’s becoming less important. And the fourth is good and bad examples of the kind of output you want. Those four, that’s like, that’s it as far as the research telling you what to do, and the rest is building intuition.
    LEE: I’m really impressed that you didn’t give the answer, “Well, everyone should be teaching my book, Co-Intelligence.”MOLLICK: Oh, no, sorry! Everybody should be teaching my book Co-Intelligence. I apologize.LEE: It’s good to chuckle about that, but actually, I can’t think of a better book, like, if you were to assign a textbook in any professional education space, I think Co-Intelligence would be number one on my list. Are there other things that you think are essential reading?
    MOLLICK: That’s a really good question. I think that a lot of things are evolving very quickly. I happen to, kind of, hit a sweet spot with Co-Intelligence to some degree because I talk about how I used it, and I was, sort of, an advanced user of these systems.
    So, like, it’s, sort of, like my Twitter feed, my online newsletter. I’m just trying to, kind of, in some ways, it’s about trying to make people aware of what these systems can do by just showing a lot, right. Rather than picking one thing, and, like, this is a general-purpose technology. Let’s use it for this. And, like, everybody gets a light bulb for a different reason. So more than reading, it is using, you know, and that can be Copilot or whatever your favorite tool is.
    But using it. Voice modes help a lot. In terms of readings, I mean, I think that there is a couple of good guides to understanding AI that were originally blog posts. I think Tim Lee has one called Understanding AI, and it had a good overview …
    LEE: Yeah, that’s a great one.
    MOLLICK: … of that topic that I think explains how transformers work, which can give you some mental sense. I thinkKarpathyhas some really nice videos of use that I would recommend.
    Like on the medical side, I think the book that you did, if you’re in medicine, you should read that. I think that that’s very valuable. But like all we can offer are hints in some ways. Like there isn’t … if you’re looking for the instruction manual, I think it can be very frustrating because it’s like you want the best practices and procedures laid out, and we cannot do that, right. That’s not how a system like this works.
    LEE: Yeah.
    MOLLICK: It’s not a person, but thinking about it like a person can be helpful, right.
    LEE: One of the things that has been sort of a fun project for me for the last few years is I have been a founding board member of a new medical school at Kaiser Permanente. And, you know, that medical school curriculum is being formed in this era. But it’s been perplexing to understand, you know, what this means for a medical school curriculum. And maybe even more perplexing for me, at least, is the accrediting bodies, which are extremely important in US medical schools; how accreditors should think about what’s necessary here.
    Besides the things that you’ve … the, kind of, four key ideas you mentioned, if you were talking to the board of directors of the LCMEaccrediting body, what’s the one thing you would want them to really internalize?
    MOLLICK: This is both a fast-moving and vital area. This can’t be viewed like a usual change, which, “Let’s see how this works.” Because it’s, like, the things that make medical technologies hard to do, which is like unclear results, limited, you know, expensive use cases where it rolls out slowly. So one or two, you know, advanced medical facilities get access to, you know, proton beams or something else at multi-billion dollars of cost, and that takes a while to diffuse out. That’s not happening here. This is all happening at the same time, all at once. This is now … AI is part of medicine.
    I mean, there’s a minor point that I’d make that actually is a really important one, which is large language models, generative AI overall, work incredibly differently than other forms of AI. So the other worry I have with some of these accreditors is they blend together algorithmic forms of AI, which medicine has been trying for long time—decision support, algorithmic methods, like, medicine more so than other places has been thinking about those issues. Generative AI, even though it uses the same underlying techniques, is a completely different beast.
    So, like, even just take the most simple thing of algorithmic aversion, which is a well-understood problem in medicine, right. Which is, so you have a tool that could tell you as a radiologist, you know, the chance of this being cancer; you don’t like it, you overrule it, right.
    We don’t find algorithmic aversion happening with LLMs in the same way. People actually enjoy using them because it’s more like working with a person. The flaws are different. The approach is different. So you need to both view this as universal applicable today, which makes it urgent, but also as something that is not the same as your other form of AI, and your AI working group that is thinking about how to solve this problem is not the right people here.
    LEE: You know, I think the world has been trained because of the magic of web search to view computers as question-answering machines. Ask a question, get an answer.
    MOLLICK: Yes. Yes.
    LEE: Write a query, get results. And as I have interacted with medical professionals, you can see that medical professionals have that model of a machine in mind. And I think that’s partly, I think psychologically, why hallucination is so alarming. Because you have a mental model of a computer as a machine that has absolutely rock-solid perfect memory recall.
    But the thing that was so powerful in Co-Intelligence, and we tried to get at this in our book also, is that’s not the sweet spot. It’s this sort of deeper interaction, more of a collaboration. And I thought your use of the term Co-Intelligence really just even in the title of the book tried to capture this. When I think about education, it seems like that’s the first step, to get past this concept of a machine being just a question-answering machine. Do you have a reaction to that idea?
    MOLLICK: I think that’s very powerful. You know, we’ve been trained over so many years at both using computers but also in science fiction, right. Computers are about cold logic, right. They will give you the right answer, but if you ask it what love is, they explode, right. Like that’s the classic way you defeat the evil robot in Star Trek, right. “Love does not compute.”Instead, we have a system that makes mistakes, is warm, beats doctors in empathy in almost every controlled study on the subject, right. Like, absolutely can outwrite you in a sonnet but will absolutely struggle with giving you the right answer every time. And I think our mental models are just broken for this. And I think you’re absolutely right. And that’s part of what I thought your book does get at really well is, like, this is a different thing. It’s also generally applicable. Again, the model in your head should be kind of like a person even though it isn’t, right.
    There’s a lot of warnings and caveats to it, but if you start from person, smart person you’re talking to, your mental model will be more accurate than smart machine, even though both are flawed examples, right. So it will make mistakes; it will make errors. The question is, what do you trust it on? What do you not trust it? As you get to know a model, you’ll get to understand, like, I totally don’t trust it for this, but I absolutely trust it for that, right.
    LEE: All right. So we’re getting to the end of the time we have together. And so I’d just like to get now into something a little bit more provocative. And I get the question all the time. You know, will AI replace doctors? In medicine and other advanced knowledge work, project out five to 10 years. What do think happens?
    MOLLICK: OK, so first of all, let’s acknowledge systems change much more slowly than individual use. You know, doctors are not individual actors; they’re part of systems, right. So not just the system of a patient who like may or may not want to talk to a machine instead of a person but also legal systems and administrative systems and systems that allocate labor and systems that train people.
    So, like, it’s hard to imagine that in five to 10 years medicine being so upended that even if AI was better than doctors at every single thing doctors do, that we’d actually see as radical a change in medicine as you might in other fields. I think you will see faster changes happen in consulting and law and, you know, coding, other spaces than medicine.
    But I do think that there is good reason to suspect that AI will outperform people while still having flaws, right. That’s the difference. We’re already seeing that for common medical questions in enough randomized controlled trials that, you know, best doctors beat AI, but the AI beats the mean doctor, right. Like, that’s just something we should acknowledge is happening at this point.
    Now, will that work in your specialty? No. Will that work with all the contingent social knowledge that you have in your space? Probably not.
    Like, these are vignettes, right. But, like, that’s kind of where things are. So let’s assume, right … you’re asking two questions. One is, how good will AI get?
    LEE: Yeah.
    MOLLICK: And we don’t know the answer to that question. I will tell you that your colleagues at Microsoft and increasingly the labs, the AI labs themselves, are all saying they think they’ll have a machine smarter than a human at every intellectual task in the next two to three years. If that doesn’t happen, that makes it easier to assume the future, but let’s just assume that that’s the case. I think medicine starts to change with the idea that people feel obligated to use this to help for everything.
    Your patients will be using it, and it will be your advisor and helper at the beginning phases, right. And I think that I expect people to be better at empathy. I expect better bedside manner. I expect management tasks to become easier. I think administrative burden might lighten if we handle this right way or much worse if we handle it badly. Diagnostic accuracy will increase, right.
    And then there’s a set of discovery pieces happening, too, right. One of the core goals of all the AI companies is to accelerate medical research. How does that happen and how does that affect us is a, kind of, unknown question. So I think clinicians are in both the eye of the storm and surrounded by it, right. Like, they can resist AI use for longer than most other fields, but everything around them is going to be affected by it.
    LEE: Well, Ethan, this has been really a fantastic conversation. And, you know, I think in contrast to all the other conversations we’ve had, this one gives especially the leaders in healthcare, you know, people actually trying to lead their organizations into the future, whether it’s in education or in delivery, a lot to think about. So I really appreciate you joining.
    MOLLICK: Thank you.  
    I’m a computing researcher who works with people who are right in the middle of today’s bleeding-edge developments in AI. And because of that, I often lose sight of how to talk to a broader audience about what it’s all about. And so I think one of Ethan’s superpowers is that he has this knack for explaining complex topics in AI in a really accessible way, getting right to the most important points without making it so simple as to be useless. That’s why I rarely miss an opportunity to read up on his latest work.
    One of the first things I learned from Ethan is the intuition that you can, sort of, think of AI as a very knowledgeable intern. In other words, think of it as a persona that you can interact with, but you also need to be a manager for it and to always assess the work that it does.
    In our discussion, Ethan went further to stress that there is, because of that, a serious education gap. You know, over the last decade or two, we’ve all been trained, mainly by search engines, to think of computers as question-answering machines. In medicine, in fact, there’s a question-answering application that is really popular called UpToDate. Doctors use it all the time. But generative AI systems like ChatGPT are different. There’s therefore a challenge in how to break out of the old-fashioned mindset of search to get the full value out of generative AI.
    The other big takeaway for me was that Ethan pointed out while it’s easy to see productivity gains from AI at the individual level, those same gains, at least today, don’t often translate automatically to organization-wide or system-wide gains. And one, of course, has to conclude that it takes more than just making individuals more productive; the whole system also has to adjust to the realities of AI.
    Here’s now my interview with Azeem Azhar:
    LEE: Azeem, welcome.
    AZEEM AZHAR: Peter, thank you so much for having me. 
    LEE: You know, I think you’re extremely well known in the world. But still, some of the listeners of this podcast series might not have encountered you before.
    And so one of the ways I like to ask people to introduce themselves is, how do you explain to your parents what you do every day?
    AZHAR: Well, I’m very lucky in that way because my mother was the person who got me into computers more than 40 years ago. And I still have that first computer, a ZX81 with a Z80 chip …
    LEE: Oh wow.
    AZHAR: … to this day. It sits in my study, all seven and a half thousand transistors and Bakelite plastic that it is. And my parents were both economists, and economics is deeply connected with technology in some sense. And I grew up in the late ’70s and the early ’80s. And that was a time of tremendous optimism around technology. It was space opera, science fiction, robots, and of course, the personal computer and, you know, Bill Gates and Steve Jobs. So that’s where I started.
    And so, in a way, my mother and my dad, who passed away a few years ago, had always known me as someone who was fiddling with computers but also thinking about economics and society. And so, in a way, it’s easier to explain to them because they’re the ones who nurtured the environment that allowed me to research technology and AI and think about what it means to firms and to the economy at large.
    LEE: I always like to understand the origin story. And what I mean by that is, you know, what was your first encounter with generative AI? And what was that like? What did you go through?
    AZHAR: The first real moment was when Midjourney and Stable Diffusion emerged in that summer of 2022. I’d been away on vacation, and I came back—and I’d been off grid, in fact—and the world had really changed.
    Now, I’d been aware of GPT-3 and GPT-2, which I played around with and with BERT, the original transformer paper about seven or eight years ago, but it was the moment where I could talk to my computer, and it could produce these images, and it could be refined in natural language that really made me think we’ve crossed into a new domain. We’ve gone from AI being highly discriminative to AI that’s able to explore the world in particular ways. And then it was a few months later that ChatGPT came out—November, the 30th.
    And I think it was the next day or the day after that I said to my team, everyone has to use this, and we have to meet every morning and discuss how we experimented the day before. And we did that for three or four months. And, you know, it was really clear to me in that interface at that point that, you know, we’d absolutely pass some kind of threshold.
    LEE: And who’s the we that you were experimenting with?
    AZHAR: So I have a team of four who support me. They’re mostly researchers of different types. I mean, it’s almost like one of those jokes. You know, I have a sociologist, an economist, and an astrophysicist. And, you know, they walk into the bar,or they walk into our virtual team room, and we try to solve problems.
    LEE: Well, so let’s get now into brass tacks here. And I think I want to start maybe just with an exploration of the economics of all this and economic realities. Because I think in a lot of your work—for example, in your book—you look pretty deeply at how automation generally and AI specifically are transforming certain sectors like finance, manufacturing, and you have a really, kind of, insightful focus on what this means for productivity and which ways, you know, efficiencies are found.  
    And then you, sort of, balance that with risks, things that can and do go wrong. And so as you take that background and looking at all those other sectors, in what ways are the same patterns playing out or likely to play out in healthcare and medicine?
    AZHAR: I’m sure we will see really remarkable parallels but also new things going on. I mean, medicine has a particular quality compared to other sectors in the sense that it’s highly regulated, market structure is very different country to country, and it’s an incredibly broad field. I mean, just think about taking a Tylenol and going through laparoscopic surgery. Having an MRI and seeing a physio. I mean, this is all medicine. I mean, it’s hard to imagine a sector that ismore broad than that.
    So I think we can start to break it down, and, you know, where we’re seeing things with generative AI will be that the, sort of, softest entry point, which is the medical scribing. And I’m sure many of us have been with clinicians who have a medical scribe running alongside—they’re all on Surface Pros I noticed, right?They’re on the tablet computers, and they’re scribing away.
    And what that’s doing is, in the words of my friend Eric Topol, it’s giving the clinician time back, right. They have time back from days that are extremely busy and, you know, full of administrative overload. So I think you can obviously do a great deal with reducing that overload.
    And within my team, we have a view, which is if you do something five times in a week, you should be writing an automation for it. And if you’re a doctor, you’re probably reviewing your notes, writing the prescriptions, and so on several times a day. So those are things that can clearly be automated, and the human can be in the loop. But I think there are so many other ways just within the clinic that things can help.
    So, one of my friends, my friend from my junior school—I’ve known him since I was 9—is an oncologist who’s also deeply into machine learning, and he’s in Cambridge in the UK. And he built with Microsoft Research a suite of imaging AI tools from his own discipline, which they then open sourced.
    So that’s another way that you have an impact, which is that you actually enable the, you know, generalist, specialist, polymath, whatever they are in health systems to be able to get this technology, to tune it to their requirements, to use it, to encourage some grassroots adoption in a system that’s often been very, very heavily centralized.
    LEE: Yeah.
    AZHAR: And then I think there are some other things that are going on that I find really, really exciting. So one is the consumerization of healthcare. So I have one of those sleep tracking rings, the Oura.
    LEE: Yup.
    AZHAR: That is building a data stream that we’ll be able to apply more and more AI to. I mean, right now, it’s applying traditional, I suspect, machine learning, but you can imagine that as we start to get more data, we start to get more used to measuring ourselves, we create this sort of pot, a personal asset that we can turn AI to.
    And there’s still another category. And that other category is one of the completely novel ways in which we can enable patient care and patient pathway. And there’s a fantastic startup in the UK called Neko Health, which, I mean, does physicals, MRI scans, and blood tests, and so on.
    It’s hard to imagine Neko existing without the sort of advanced data, machine learning, AI that we’ve seen emerge over the last decade. So, I mean, I think that there are so many ways in which the temperature is slowly being turned up to encourage a phase change within the healthcare sector.
    And last but not least, I do think that these tools can also be very, very supportive of a clinician’s life cycle. I think we, as patients, we’re a bit …  I don’t know if we’re as grateful as we should be for our clinicians who are putting in 90-hour weeks.But you can imagine a world where AI is able to support not just the clinicians’ workload but also their sense of stress, their sense of burnout.
    So just in those five areas, Peter, I sort of imagine we could start to fundamentally transform over the course of many years, of course, the way in which people think about their health and their interactions with healthcare systems
    LEE: I love how you break that down. And I want to press on a couple of things.
    You also touched on the fact that medicine is, at least in most of the world, is a highly regulated industry. I guess finance is the same way, but they also feel different because the, like, finance sector has to be very responsive to consumers, and consumers are sensitive to, you know, an abundance of choice; they are sensitive to price. Is there something unique about medicine besides being regulated?
    AZHAR: I mean, there absolutely is. And in finance, as well, you have much clearer end states. So if you’re not in the consumer space, but you’re in the, you know, asset management space, you have to essentially deliver returns against the volatility or risk boundary, right. That’s what you have to go out and do. And I think if you’re in the consumer industry, you can come back to very, very clear measures, net promoter score being a very good example.
    In the case of medicine and healthcare, it is much more complicated because as far as the clinician is concerned, people are individuals, and we have our own parts and our own responses. If we didn’t, there would never be a need for a differential diagnosis. There’d never be a need for, you know, Let’s try azithromycin first, and then if that doesn’t work, we’ll go to vancomycin, or, you know, whatever it happens to be. You would just know. But ultimately, you know, people are quite different. The symptoms that they’re showing are quite different, and also their compliance is really, really different.
    I had a back problem that had to be dealt with by, you know, a physio and extremely boring exercises four times a week, but I was ruthless in complying, and my physio was incredibly surprised. He’d say well no one ever does this, and I said, well you know the thing is that I kind of just want to get this thing to go away.
    LEE: Yeah.
    AZHAR: And I think that that’s why medicine is and healthcare is so different and more complex. But I also think that’s why AI can be really, really helpful. I mean, we didn’t talk about, you know, AI in its ability to potentially do this, which is to extend the clinician’s presence throughout the week.
    LEE: Right. Yeah.
    AZHAR: The idea that maybe some part of what the clinician would do if you could talk to them on Wednesday, Thursday, and Friday could be delivered through an app or a chatbot just as a way of encouraging the compliance, which is often, especially with older patients, one reason why conditions, you know, linger on for longer.
    LEE: You know, just staying on the regulatory thing, as I’ve thought about this, the one regulated sector that I think seems to have some parallels to healthcare is energy delivery, energy distribution.
    Because like healthcare, as a consumer, I don’t have choice in who delivers electricity to my house. And even though I care about it being cheap or at least not being overcharged, I don’t have an abundance of choice. I can’t do price comparisons.
    And there’s something about that, just speaking as a consumer of both energy and a consumer of healthcare, that feels similar. Whereas other regulated industries, you know, somehow, as a consumer, I feel like I have a lot more direct influence and power. Does that make any sense to someone, you know, like you, who’s really much more expert in how economic systems work?
    AZHAR: I mean, in a sense, one part of that is very, very true. You have a limited panel of energy providers you can go to, and in the US, there may be places where you have no choice.
    I think the area where it’s slightly different is that as a consumer or a patient, you can actually make meaningful choices and changes yourself using these technologies, and people used to joke about you know asking Dr. Google. But Dr. Google is not terrible, particularly if you go to WebMD. And, you know, when I look at long-range change, many of the regulations that exist around healthcare delivery were formed at a point before people had access to good quality information at the touch of their fingertips or when educational levels in general were much, much lower. And many regulations existed because of the incumbent power of particular professional sectors.
    I’ll give you an example from the United Kingdom. So I have had asthma all of my life. That means I’ve been taking my inhaler, Ventolin, and maybe a steroid inhaler for nearly 50 years. That means that I know … actually, I’ve got more experience, and I—in some sense—know more about it than a general practitioner.
    LEE: Yeah.
    AZHAR: And until a few years ago, I would have to go to a general practitioner to get this drug that I’ve been taking for five decades, and there they are, age 30 or whatever it is. And a few years ago, the regulations changed. And now pharmacies can … or pharmacists can prescribe those types of drugs under certain conditions directly.
    LEE: Right.
    AZHAR: That was not to do with technology. That was to do with incumbent lock-in. So when we look at the medical industry, the healthcare space, there are some parallels with energy, but there are a few little things that the ability that the consumer has to put in some effort to learn about their condition, but also the fact that some of the regulations that exist just exist because certain professions are powerful.
    LEE: Yeah, one last question while we’re still on economics. There seems to be a conundrum about productivity and efficiency in healthcare delivery because I’ve never encountered a doctor or a nurse that wants to be able to handle even more patients than they’re doing on a daily basis.
    And so, you know, if productivity means simply, well, your rounds can now handle 16 patients instead of eight patients, that doesn’t seem necessarily to be a desirable thing. So how can we or should we be thinking about efficiency and productivity since obviously costs are, in most of the developed world, are a huge, huge problem?
    AZHAR: Yes, and when you described doubling the number of patients on the round, I imagined you buying them all roller skates so they could just whizz aroundthe hospital faster and faster than ever before.
    We can learn from what happened with the introduction of electricity. Electricity emerged at the end of the 19th century, around the same time that cars were emerging as a product, and car makers were very small and very artisanal. And in the early 1900s, some really smart car makers figured out that electricity was going to be important. And they bought into this technology by putting pendant lights in their workshops so they could “visit more patients.” Right?
    LEE: Yeah, yeah.
    AZHAR: They could effectively spend more hours working, and that was a productivity enhancement, and it was noticeable. But, of course, electricity fundamentally changed the productivity by orders of magnitude of people who made cars starting with Henry Ford because he was able to reorganize his factories around the electrical delivery of power and to therefore have the moving assembly line, which 10xed the productivity of that system.
    So when we think about how AI will affect the clinician, the nurse, the doctor, it’s much easier for us to imagine it as the pendant light that just has them working later …
    LEE: Right.
    AZHAR: … than it is to imagine a reconceptualization of the relationship between the clinician and the people they care for.
    And I’m not sure. I don’t think anybody knows what that looks like. But, you know, I do think that there will be a way that this changes, and you can see that scale out factor. And it may be, Peter, that what we end up doing is we end up saying, OK, because we have these brilliant AIs, there’s a lower level of training and cost and expense that’s required for a broader range of conditions that need treating. And that expands the market, right. That expands the market hugely. It’s what has happened in the market for taxis or ride sharing. The introduction of Uber and the GPS system …
    LEE: Yup.
    AZHAR: … has meant many more people now earn their living driving people around in their cars. And at least in London, you had to be reasonably highly trained to do that.
    So I can see a reorganization is possible. Of course, entrenched interests, the economic flow … and there are many entrenched interests, particularly in the US between the health systems and the, you know, professional bodies that might slow things down. But I think a reimagining is possible.
    And if I may, I’ll give you one example of that, which is, if you go to countries outside of the US where there are many more sick people per doctor, they have incentives to change the way they deliver their healthcare. And well before there was AI of this quality around, there was a few cases of health systems in India—Aravind Eye Carewas one, and Narayana Hrudayalayawas another. And in the latter, they were a cardiac care unit where you couldn’t get enough heart surgeons.
    LEE: Yeah, yep.
    AZHAR: So specially trained nurses would operate under the supervision of a single surgeon who would supervise many in parallel. So there are ways of increasing the quality of care, reducing the cost, but it does require a systems change. And we can’t expect a single bright algorithm to do it on its own.
    LEE: Yeah, really, really interesting. So now let’s get into regulation. And let me start with this question. You know, there are several startup companies I’m aware of that are pushing on, I think, a near-term future possibility that a medical AI for consumer might be allowed, say, to prescribe a medication for you, something that would normally require a doctor or a pharmacist, you know, that is certified in some way, licensed to do. Do you think we’ll get to a point where for certain regulated activities, humans are more or less cut out of the loop?
    AZHAR: Well, humans would have been in the loop because they would have provided the training data, they would have done the oversight, the quality control. But to your question in general, would we delegate an important decision entirely to a tested set of algorithms? I’m sure we will. We already do that. I delegate less important decisions like, What time should I leave for the airport to Waze. I delegate more important decisions to the automated braking in my car. We will do this at certain levels of risk and threshold.
    If I come back to my example of prescribing Ventolin. It’s really unclear to me that the prescription of Ventolin, this incredibly benign bronchodilator that is only used by people who’ve been through the asthma process, needs to be prescribed by someone who’s gone through 10 years or 12 years of medical training. And why that couldn’t be prescribed by an algorithm or an AI system.
    LEE: Right. Yep. Yep.
    AZHAR: So, you know, I absolutely think that that will be the case and could be the case. I can’t really see what the objections are. And the real issue is where do you draw the line of where you say, “Listen, this is too important,” or “The cost is too great,” or “The side effects are too high,” and therefore this is a point at which we want to have some, you know, human taking personal responsibility, having a liability framework in place, having a sense that there is a person with legal agency who signed off on this decision. And that line I suspect will start fairly low, and what we’d expect to see would be that that would rise progressively over time.
    LEE: What you just said, that scenario of your personal asthma medication, is really interesting because your personal AI might have the benefit of 50 years of your own experience with that medication. So, in a way, there is at least the data potential for, let’s say, the next prescription to be more personalized and more tailored specifically for you.
    AZHAR: Yes. Well, let’s dig into this because I think this is super interesting, and we can look at how things have changed. So 15 years ago, if I had a bad asthma attack, which I might have once a year, I would have needed to go and see my general physician.
    In the UK, it’s very difficult to get an appointment. I would have had to see someone privately who didn’t know me at all because I’ve just walked in off the street, and I would explain my situation. It would take me half a day. Productivity lost. I’ve been miserable for a couple of days with severe wheezing. Then a few years ago the system changed, a protocol changed, and now I have a thing called a rescue pack, which includes prednisolone steroids. It includes something else I’ve just forgotten, and an antibiotic in case I get an upper respiratory tract infection, and I have an “algorithm.” It’s called a protocol. It’s printed out. It’s a flowchart
    I answer various questions, and then I say, “I’m going to prescribe this to myself.” You know, UK doctors don’t prescribe prednisolone, or prednisone as you may call it in the US, at the drop of a hat, right. It’s a powerful steroid. I can self-administer, and I can now get that repeat prescription without seeing a physician a couple of times a year. And the algorithm, the “AI” is, it’s obviously been done in PowerPoint naturally, and it’s a bunch of arrows.Surely, surely, an AI system is going to be more sophisticated, more nuanced, and give me more assurance that I’m making the right decision around something like that.
    LEE: Yeah. Well, at a minimum, the AI should be able to make that PowerPoint the next time.AZHAR: Yeah, yeah. Thank god for Clippy. Yes.
    LEE: So, you know, I think in our book, we had a lot of certainty about most of the things we’ve discussed here, but one chapter where I felt we really sort of ran out of ideas, frankly, was on regulation. And, you know, what we ended up doing for that chapter is … I can’t remember if it was Carey’s or Zak’s idea, but we asked GPT-4 to have a conversation, a debate with itself, about regulation. And we made some minor commentary on that.
    And really, I think we took that approach because we just didn’t have much to offer. By the way, in our defense, I don’t think anyone else had any better ideas anyway.
    AZHAR: Right.
    LEE: And so now two years later, do we have better ideas about the need for regulation, the frameworks around which those regulations should be developed, and, you know, what should this look like?
    AZHAR: So regulation is going to be in some cases very helpful because it provides certainty for the clinician that they’re doing the right thing, that they are still insured for what they’re doing, and it provides some degree of confidence for the patient. And we need to make sure that the claims that are made stand up to quite rigorous levels, where ideally there are RCTs, and there are the classic set of processes you go through.
    You do also want to be able to experiment, and so the question is: as a regulator, how can you enable conditions for there to be experimentation? And what is experimentation? Experimentation is learning so that every element of the system can learn from this experience.
    So finding that space where there can be bit of experimentation, I think, becomes very, very important. And a lot of this is about experience, so I think the first digital therapeutics have received FDA approval, which means there are now people within the FDA who understand how you go about running an approvals process for that, and what that ends up looking like—and of course what we’re very good at doing in this sort of modern hyper-connected world—is we can share that expertise, that knowledge, that experience very, very quickly.
    So you go from one approval a year to a hundred approvals a year to a thousand approvals a year. So we will then actually, I suspect, need to think about what is it to approve digital therapeutics because, unlike big biological molecules, we can generate these digital therapeutics at the rate of knots.
    LEE: Yes.
    AZHAR: Every road in Hayes Valley in San Francisco, right, is churning out new startups who will want to do things like this. So then, I think about, what does it mean to get approved if indeed it gets approved? But we can also go really far with things that don’t require approval.
    I come back to my sleep tracking ring. So I’ve been wearing this for a few years, and when I go and see my doctor or I have my annual checkup, one of the first things that he asks is how have I been sleeping. And in fact, I even sync my sleep tracking data to their medical record system, so he’s saying … hearing what I’m saying, but he’s actually pulling up the real data going, This patient’s lying to me again. Of course, I’m very truthful with my doctor, as we should all be.LEE: You know, actually, that brings up a point that consumer-facing health AI has to deal with pop science, bad science, you know, weird stuff that you hear on Reddit. And because one of the things that consumers want to know always is, you know, what’s the truth?
    AZHAR: Right.
    LEE: What can I rely on? And I think that somehow feels different than an AI that you actually put in the hands of, let’s say, a licensed practitioner. And so the regulatory issues seem very, very different for these two cases somehow.
    AZHAR: I agree, they’re very different. And I think for a lot of areas, you will want to build AI systems that are first and foremost for the clinician, even if they have patient extensions, that idea that the clinician can still be with a patient during the week.
    And you’ll do that anyway because you need the data, and you also need a little bit of a liability shield to have like a sensible person who’s been trained around that. And I think that’s going to be a very important pathway for many AI medical crossovers. We’re going to go through the clinician.
    LEE: Yeah.
    AZHAR: But I also do recognize what you say about the, kind of, kooky quackery that exists on Reddit. Although on Creatine, Reddit may yet prove to have been right.LEE: Yeah, that’s right. Yes, yeah, absolutely. Yeah.
    AZHAR: Sometimes it’s right. And I think that it serves a really good role as a field of extreme experimentation. So if you’re somebody who makes a continuous glucose monitor traditionally given to diabetics but now lots of people will wear them—and sports people will wear them—you probably gathered a lot of extreme tail distribution data by reading the Reddit/biohackers …
    LEE: Yes.
    AZHAR: … for the last few years, where people were doing things that you would never want them to really do with the CGM. And so I think we shouldn’t understate how important that petri dish can be for helping us learn what could happen next.
    LEE: Oh, I think it’s absolutely going to be essential and a bigger thing in the future. So I think I just want to close here then with one last question. And I always try to be a little bit provocative with this.
    And so as you look ahead to what doctors and nurses and patients might be doing two years from now, five years from now, 10 years from now, do you have any kind of firm predictions?
    AZHAR: I’m going to push the boat out, and I’m going to go further out than closer in.
    LEE: OK.AZHAR: As patients, we will have many, many more touch points and interaction with our biomarkers and our health. We’ll be reading how well we feel through an array of things. And some of them we’ll be wearing directly, like sleep trackers and watches.
    And so we’ll have a better sense of what’s happening in our lives. It’s like the moment you go from paper bank statements that arrive every month to being able to see your account in real time.
    LEE: Yes.
    AZHAR: And I suspect we’ll have … we’ll still have interactions with clinicians because societies that get richer see doctors more, societies that get older see doctors more, and we’re going to be doing both of those over the coming 10 years. But there will be a sense, I think, of continuous health engagement, not in an overbearing way, but just in a sense that we know it’s there, we can check in with it, it’s likely to be data that is compiled on our behalf somewhere centrally and delivered through a user experience that reinforces agency rather than anxiety.
    And we’re learning how to do that slowly. I don’t think the health apps on our phones and devices have yet quite got that right. And that could help us personalize problems before they arise, and again, I use my experience for things that I’ve tracked really, really well. And I know from my data and from how I’m feeling when I’m on the verge of one of those severe asthma attacks that hits me once a year, and I can take a little bit of preemptive measure, so I think that that will become progressively more common and that sense that we will know our baselines.
    I mean, when you think about being an athlete, which is something I think about, but I could never ever do,but what happens is you start with your detailed baselines, and that’s what your health coach looks at every three or four months. For most of us, we have no idea of our baselines. You we get our blood pressure measured once a year. We will have baselines, and that will help us on an ongoing basis to better understand and be in control of our health. And then if the product designers get it right, it will be done in a way that doesn’t feel invasive, but it’ll be done in a way that feels enabling. We’ll still be engaging with clinicians augmented by AI systems more and more because they will also have gone up the stack. They won’t be spending their time on just “take two Tylenol and have a lie down” type of engagements because that will be dealt with earlier on in the system. And so we will be there in a very, very different set of relationships. And they will feel that they have different ways of looking after our health.
    LEE: Azeem, it’s so comforting to hear such a wonderfully optimistic picture of the future of healthcare. And I actually agree with everything you’ve said.
    Let me just thank you again for joining this conversation. I think it’s been really fascinating. And I think somehow the systemic issues, the systemic issues that you tend to just see with such clarity, I think are going to be the most, kind of, profound drivers of change in the future. So thank you so much.
    AZHAR: Well, thank you, it’s been my pleasure, Peter, thank you.  
    I always think of Azeem as a systems thinker. He’s always able to take the experiences of new technologies at an individual level and then project out to what this could mean for whole organizations and whole societies.
    In our conversation, I felt that Azeem really connected some of what we learned in a previous episode—for example, from Chrissy Farr—on the evolving consumerization of healthcare to the broader workforce and economic impacts that we’ve heard about from Ethan Mollick.  
    Azeem’s personal story about managing his asthma was also a great example. You know, he imagines a future, as do I, where personal AI might assist and remember decades of personal experience with a condition like asthma and thereby know more than any human being could possibly know in a deeply personalized and effective way, leading to better care. Azeem’s relentless optimism about our AI future was also so heartening to hear.
    Both of these conversations leave me really optimistic about the future of AI in medicine. At the same time, it is pretty sobering to realize just how much we’ll all need to change in pretty fundamental and maybe even in radical ways. I think a big insight I got from these conversations is how we interact with machines is going to have to be altered not only at the individual level, but at the company level and maybe even at the societal level.
    Since my conversation with Ethan and Azeem, there have been some pretty important developments that speak directly to this. Just last week at Build, which is Microsoft’s yearly developer conference, we announced a slew of AI agent technologies. Our CEO, Satya Nadella, in fact, started his keynote by going online in a GitHub developer environment and then assigning a coding task to an AI agent, basically treating that AI as a full-fledged member of a development team. Other agents, for example, a meeting facilitator, a data analyst, a business researcher, travel agent, and more were also shown during the conference.
    But pertinent to healthcare specifically, what really blew me away was the demonstration of a healthcare orchestrator agent. And the specific thing here was in Stanford’s cancer treatment center, when they are trying to decide on potentially experimental treatments for cancer patients, they convene a meeting of experts. That is typically called a tumor board. And so this AI healthcare orchestrator agent actually participated as a full-fledged member of a tumor board meeting to help bring data together, make sure that the latest medical knowledge was brought to bear, and to assist in the decision-making around a patient’s cancer treatment. It was pretty amazing.A big thank-you again to Ethan and Azeem for sharing their knowledge and understanding of the dynamics between AI and society more broadly. And to our listeners, thank you for joining us. I’m really excited for the upcoming episodes, including discussions on medical students’ experiences with AI and AI’s influence on the operation of health systems and public health departments. We hope you’ll continue to tune in.
    Until next time.
    #what #ais #impact #individuals #means
    What AI’s impact on individuals means for the health workforce and industry
    Transcript     PETER LEE: “In American primary care, the missing workforce is stunning in magnitude, the shortfall estimated to reach up to 48,000 doctors within the next dozen years. China and other countries with aging populations can expect drastic shortfalls, as well. Just last month, I asked a respected colleague retiring from primary care who he would recommend as a replacement; he told me bluntly that, other than expensive concierge care practices, he could not think of anyone, even for himself. This mismatch between need and supply will only grow, and the US is far from alone among developed countries in facing it.”       This is The AI Revolution in Medicine, Revisited. I’m your host, Peter Lee.    Shortly after OpenAI’s GPT-4 was publicly released, Carey Goldberg, Dr. Zak Kohane, and I published The AI Revolution in Medicine to help educate the world of healthcare and medical research about the transformative impact this new generative AI technology could have. But because we wrote the book when GPT-4 was still a secret, we had to speculate. Now, two years later, what did we get right, and what did we get wrong?     In this series, we’ll talk to clinicians, patients, hospital administrators, and others to understand the reality of AI in the field and where we go from here.     The book passage I read at the top is from “Chapter 4: Trust but Verify,” which was written by Zak. You know, it’s no secret that in the US and elsewhere shortages in medical staff and the rise of clinician burnout are affecting the quality of patient care for the worse. In our book, we predicted that generative AI would be something that might help address these issues. So in this episode, we’ll delve into how individual performance gains that our previous guests have described might affect the healthcare workforce as a whole, and on the patient side, we’ll look into the influence of generative AI on the consumerization of healthcare. Now, since all of this consumes such a huge fraction of the overall economy, we’ll also get into what a general-purpose technology as disruptive as generative AI might mean in the context of labor markets and beyond.   To help us do that, I’m pleased to welcome Ethan Mollick and Azeem Azhar. Ethan Mollick is the Ralph J. Roberts Distinguished Faculty Scholar, a Rowan Fellow, and an associate professor at the Wharton School of the University of Pennsylvania. His research into the effects of AI on work, entrepreneurship, and education is applied by organizations around the world, leading him to be named one of Time magazine’s most influential people in AI for 2024. He’s also the author of the New York Times best-selling book Co-Intelligence. Azeem Azhar is an author, founder, investor, and one of the most thoughtful and influential voices on the interplay between disruptive emerging technologies and business and society. In his best-selling book, The Exponential Age, and in his highly regarded newsletter and podcast, Exponential View, he explores how technologies like AI are reshaping everything from healthcare to geopolitics. Ethan and Azeem are two leading thinkers on the ways that disruptive technologies—and especially AI—affect our work, our jobs, our business enterprises, and whole industries. As economists, they are trying to work out whether we are in the midst of an economic revolution as profound as the shift from an agrarian to an industrial society.Here is my interview with Ethan Mollick: LEE: Ethan, welcome. ETHAN MOLLICK: So happy to be here, thank you. LEE: I described you as a professor at Wharton, which I think most of the people who listen to this podcast series know of as an elite business school. So it might surprise some people that you study AI. And beyond that, you know, that I would seek you out to talk about AI in medicine.So to get started, how and why did it happen that you’ve become one of the leading experts on AI? MOLLICK: It’s actually an interesting story. I’ve been AI-adjacent my whole career. When I wasmy PhD at MIT, I worked with Marvin Minskyand the MITMedia Labs AI group. But I was never the technical AI guy. I was the person who was trying to explain AI to everybody else who didn’t understand it. And then I became very interested in, how do you train and teach? And AI was always a part of that. I was building games for teaching, teaching tools that were used in hospitals and elsewhere, simulations. So when LLMs burst into the scene, I had already been using them and had a good sense of what they could do. And between that and, kind of, being practically oriented and getting some of the first research projects underway, especially under education and AI and performance, I became sort of a go-to person in the field. And once you’re in a field where nobody knows what’s going on and we’re all making it up as we go along—I thought it’s funny that you led with the idea that you have a couple of months head start for GPT-4, right. Like that’s all we have at this point, is a few months’ head start.So being a few months ahead is good enough to be an expert at this point. Whether it should be or not is a different question. LEE: Well, if I understand correctly, leading AI companies like OpenAI, Anthropic, and others have now sought you out as someone who should get early access to really start to do early assessments and gauge early reactions. How has that been? MOLLICK: So, I mean, I think the bigger picture is less about me than about two things that tells us about the state of AI right now. One, nobody really knows what’s going on, right. So in a lot of ways, if it wasn’t for your work, Peter, like, I don’t think people would be thinking about medicine as much because these systems weren’t built for medicine. They weren’t built to change education. They weren’t built to write memos. They, like, they weren’t built to do any of these things. They weren’t really built to do anything in particular. It turns out they’re just good at many things. And to the extent that the labs work on them, they care about their coding ability above everything else and maybe math and science secondarily. They don’t think about the fact that it expresses high empathy. They don’t think about its accuracy and diagnosis or where it’s inaccurate. They don’t think about how it’s changing education forever. So one part of this is the fact that they go to my Twitter feed or ask me for advice is an indicator of where they are, too, which is they’re not thinking about this. And the fact that a few months’ head start continues to give you a lead tells you that we are at the very cutting edge. These labs aren’t sitting on projects for two years and then releasing them. Months after a project is complete or sooner, it’s out the door. Like, there’s very little delay. So we’re kind of all in the same boat here, which is a very unusual space for a new technology. LEE: And I, you know, explained that you’re at Wharton. Are you an odd fit as a faculty member at Wharton, or is this a trend now even in business schools that AI experts are becoming key members of the faculty? MOLLICK: I mean, it’s a little of both, right. It’s faculty, so everybody does everything. I’m a professor of innovation-entrepreneurship. I’ve launched startups before and working on that and education means I think about, how do organizations redesign themselves? How do they take advantage of these kinds of problems? So medicine’s always been very central to that, right. A lot of people in my MBA class have been MDs either switching, you know, careers or else looking to advance from being sort of individual contributors to running teams. So I don’t think that’s that bad a fit. But I also think this is general-purpose technology; it’s going to touch everything. The focus on this is medicine, but Microsoft does far more than medicine, right. It’s … there’s transformation happening in literally every field, in every country. This is a widespread effect. So I don’t think we should be surprised that business schools matter on this because we care about management. There’s a long tradition of management and medicine going together. There’s actually a great academic paper that shows that teaching hospitals that also have MBA programs associated with them have higher management scores and perform better. So I think that these are not as foreign concepts, especially as medicine continues to get more complicated. LEE: Yeah. Well, in fact, I want to dive a little deeper on these issues of management, of entrepreneurship, um, education. But before doing that, if I could just stay focused on you. There is always something interesting to hear from people about their first encounters with AI. And throughout this entire series, I’ve been doing that both pre-generative AI and post-generative AI. So you, sort of, hinted at the pre-generative AI. You were in Minsky’s lab. Can you say a little bit more about that early encounter? And then tell us about your first encounters with generative AI. MOLLICK: Yeah. Those are great questions. So first of all, when I was at the media lab, that was pre-the current boom in sort of, you know, even in the old-school machine learning kind of space. So there was a lot of potential directions to head in. While I was there, there were projects underway, for example, to record every interaction small children had. One of the professors was recording everything their baby interacted with in the hope that maybe that would give them a hint about how to build an AI system. There was a bunch of projects underway that were about labeling every concept and how they relate to other concepts. So, like, it was very much Wild West of, like, how do we make an AI work—which has been this repeated problem in AI, which is, what is this thing? The fact that it was just like brute force over the corpus of all human knowledge turns out to be a little bit of like a, you know, it’s a miracle and a little bit of a disappointment in some wayscompared to how elaborate some of this was. So, you know, I think that, that was sort of my first encounters in sort of the intellectual way. The generative AI encounters actually started with the original, sort of, GPT-3, or, you know, earlier versions. And it was actually game-based. So I played games like AI Dungeon. And as an educator, I realized, oh my gosh, this stuff could write essays at a fourth-grade level. That’s really going to change the way, like, middle school works, was my thinking at the time. And I was posting about that back in, you know, 2021 that this is a big deal. But I think everybody was taken surprise, including the AI companies themselves, by, you know, ChatGPT, by GPT-3.5. The difference in degree turned out to be a difference in kind. LEE: Yeah, you know, if I think back, even with GPT-3, and certainly this was the case with GPT-2, it was, at least, you know, from where I was sitting, it was hard to get people to really take this seriously and pay attention. MOLLICK: Yes. LEE: You know, it’s remarkable. Within Microsoft, I think a turning point was the use of GPT-3 to do code completions. And that was actually productized as GitHub Copilot, the very first version. That, I think, is where there was widespread belief. But, you know, in a way, I think there is, even for me early on, a sense of denial and skepticism. Did you have those initially at any point? MOLLICK: Yeah, I mean, it still happens today, right. Like, this is a weird technology. You know, the original denial and skepticism was, I couldn’t see where this was going. It didn’t seem like a miracle because, you know, of course computers can complete code for you. Like, what else are they supposed to do? Of course, computers can give you answers to questions and write fun things. So there’s difference of moving into a world of generative AI. I think a lot of people just thought that’s what computers could do. So it made the conversations a little weird. But even today, faced with these, you know, with very strong reasoner models that operate at the level of PhD students, I think a lot of people have issues with it, right. I mean, first of all, they seem intuitive to use, but they’re not always intuitive to use because the first use case that everyone puts AI to, it fails at because they use it like Google or some other use case. And then it’s genuinely upsetting in a lot of ways. I think, you know, I write in my book about the idea of three sleepless nights. That hasn’t changed. Like, you have to have an intellectual crisis to some extent, you know, and I think people do a lot to avoid having that existential angst of like, “Oh my god, what does it mean that a machine could think—apparently think—like a person?” So, I mean, I see resistance now. I saw resistance then. And then on top of all of that, there’s the fact that the curve of the technology is quite great. I mean, the price of GPT-4 level intelligence from, you know, when it was released has dropped 99.97% at this point, right. LEE: Yes. Mm-hmm. MOLLICK: I mean, I could run a GPT-4 class system basically on my phone. Microsoft’s releasing things that can almost run on like, you know, like it fits in almost no space, that are almost as good as the original GPT-4 models. I mean, I don’t think people have a sense of how fast the trajectory is moving either. LEE: Yeah, you know, there’s something that I think about often. There is this existential dread, or will this technology replace me? But I think the first people to feel that are researchers—people encountering this for the first time. You know, if you were working, let’s say, in Bayesian reasoning or in traditional, let’s say, Gaussian mixture model based, you know, speech recognition, you do get this feeling, Oh, my god, this technology has just solved the problem that I’ve dedicated my life to. And there is this really difficult period where you have to cope with that. And I think this is going to be spreading, you know, in more and more walks of life. And so this … at what point does that sort of sense of dread hit you, if ever? MOLLICK: I mean, you know, it’s not even dread as much as like, you know, Tyler Cowen wrote that it’s impossible to not feel a little bit of sadness as you use these AI systems, too. Because, like, I was talking to a friend, just as the most minor example, and his talent that he was very proud of was he was very good at writing limericks for birthday cards. He’d write these limericks. Everyone was always amused by them.And now, you know, GPT-4 and GPT-4.5, they made limericks obsolete. Like, anyone can write a good limerick, right. So this was a talent, and it was a little sad. Like, this thing that you cared about mattered. You know, as academics, we’re a little used to dead ends, right, and like, you know, some getting the lap. But the idea that entire fields are hitting that way. Like in medicine, there’s a lot of support systems that are now obsolete. And the question is how quickly you change that. In education, a lot of our techniques are obsolete. What do you do to change that? You know, it’s like the fact that this brute force technology is good enough to solve so many problems is weird, right. And it’s not just the end of, you know, of our research angles that matter, too. Like, for example, I ran this, you know, 14-person-plus, multimillion-dollar effort at Wharton to build these teaching simulations, and we’re very proud of them. It took years of work to build one. Now we’ve built a system that can build teaching simulations on demand by you talking to it with one team member. And, you know, you literally can create any simulation by having a discussion with the AI. I mean, you know, there’s a switch to a new form of excitement, but there is a little bit of like, this mattered to me, and, you know, now I have to change how I do things. I mean, adjustment happens. But if you haven’t had that displacement, I think that’s a good indicator that you haven’t really faced AI yet. LEE: Yeah, what’s so interesting just listening to you is you use words like sadness, and yet I can see the—and hear the—excitement in your voice and your body language. So, you know, that’s also kind of an interesting aspect of all of this.  MOLLICK: Yeah, I mean, I think there’s something on the other side, right. But, like, I can’t say that I haven’t had moments where like, ughhhh, but then there’s joy and basically like also, you know, freeing stuff up. I mean, I think about doctors or professors, right. These are jobs that bundle together lots of different tasks that you would never have put together, right. If you’re a doctor, you would never have expected the same person to be good at keeping up with the research and being a good diagnostician and being a good manager and being good with people and being good with hand skills. Like, who would ever want that kind of bundle? That’s not something you’re all good at, right. And a lot of our stress of our job comes from the fact that we suck at some of it. And so to the extent that AI steps in for that, you kind of feel bad about some of the stuff that it’s doing that you wanted to do. But it’s much more uplifting to be like, I don’t have to do this stuff I’m bad anymore, or I get the support to make myself good at it. And the stuff that I really care about, I can focus on more. Well, because we are at kind of a unique moment where whatever you’re best at, you’re still better than AI. And I think it’s an ongoing question about how long that lasts. But for right now, like you’re not going to say, OK, AI replaces me entirely in my job in medicine. It’s very unlikely. But you will say it replaces these 17 things I’m bad at, but I never liked that anyway. So it’s a period of both excitement and a little anxiety. LEE: Yeah, I’m going to want to get back to this question about in what ways AI may or may not replace doctors or some of what doctors and nurses and other clinicians do. But before that, let’s get into, I think, the real meat of this conversation. In previous episodes of this podcast, we talked to clinicians and healthcare administrators and technology developers that are very rapidly injecting AI today to do various forms of workforce automation, you know, automatically writing a clinical encounter note, automatically filling out a referral letter or request for prior authorization for some reimbursement to an insurance company. And so these sorts of things are intended not only to make things more efficient and lower costs but also to reduce various forms of drudgery, cognitive burden on frontline health workers. So how do you think about the impact of AI on that aspect of workforce, and, you know, what would you expect will happen over the next few years in terms of impact on efficiency and costs? MOLLICK: So I mean, this is a case where I think we’re facing the big bright problem in AI in a lot of ways, which is that this is … at the individual level, there’s lots of performance gains to be gained, right. The problem, though, is that we as individuals fit into systems, in medicine as much as anywhere else or more so, right. Which is that you could individually boost your performance, but it’s also about systems that fit along with this, right. So, you know, if you could automatically, you know, record an encounter, if you could automatically make notes, does that change what you should be expecting for notes or the value of those notes or what they’re for? How do we take what one person does and validate it across the organization and roll it out for everybody without making it a 10-year process that it feels like IT in medicine often is? Like, so we’re in this really interesting period where there’s incredible amounts of individual innovation in productivity and performance improvements in this field, like very high levels of it, but not necessarily seeing that same thing translate to organizational efficiency or gains. And one of my big concerns is seeing that happen. We’re seeing that in nonmedical problems, the same kind of thing, which is, you know, we’ve got research showing 20 and 40% performance improvements, like not uncommon to see those things. But then the organization doesn’t capture it; the system doesn’t capture it. Because the individuals are doing their own work and the systems don’t have the ability to, kind of, learn or adapt as a result. LEE: You know, where are those productivity gains going, then, when you get to the organizational level? MOLLICK: Well, they’re dying for a few reasons. One is, there’s a tendency for individual contributors to underestimate the power of management, right. Practices associated with good management increase happiness, decrease, you know, issues, increase success rates. In the same way, about 40%, as far as we can tell, of the US advantage over other companies, of US firms, has to do with management ability. Like, management is a big deal. Organizing is a big deal. Thinking about how you coordinate is a big deal. At the individual level, when things get stuck there, right, you can’t start bringing them up to how systems work together. It becomes, How do I deal with a doctor that has a 60% performance improvement? We really only have one thing in our playbook for doing that right now, which is, OK, we could fire 40% of the other doctors and still have a performance gain, which is not the answer you want to see happen. So because of that, people are hiding their use. They’re actually hiding their use for lots of reasons. And it’s a weird case because the people who are able to figure out best how to use these systems, for a lot of use cases, they’re actually clinicians themselves because they’re experimenting all the time. Like, they have to take those encounter notes. And if they figure out a better way to do it, they figure that out. You don’t want to wait for, you know, a med tech company to figure that out and then sell that back to you when it can be done by the physicians themselves. So we’re just not used to a period where everybody’s innovating and where the management structure isn’t in place to take advantage of that. And so we’re seeing things stalled at the individual level, and people are often, especially in risk-averse organizations or organizations where there’s lots of regulatory hurdles, people are so afraid of the regulatory piece that they don’t even bother trying to make change. LEE: If you are, you know, the leader of a hospital or a clinic or a whole health system, how should you approach this? You know, how should you be trying to extract positive success out of AI? MOLLICK: So I think that you need to embrace the right kind of risk, right. We don’t want to put risk on our patients … like, we don’t want to put uninformed risk. But innovation involves risk to how organizations operate. They involve change. So I think part of this is embracing the idea that R&D has to happen in organizations again. What’s happened over the last 20 years or so has been organizations giving that up. Partially, that’s a trend to focus on what you’re good at and not try and do this other stuff. Partially, it’s because it’s outsourced now to software companies that, like, Salesforce tells you how to organize your sales team. Workforce tells you how to organize your organization. Consultants come in and will tell you how to make change based on the average of what other people are doing in your field. So companies and organizations and hospital systems have all started to give up their ability to create their own organizational change. And when I talk to organizations, I often say they have to have two approaches. They have to think about the crowd and the lab. So the crowd is the idea of how to empower clinicians and administrators and supporter networks to start using AI and experimenting in ethical, legal ways and then sharing that information with each other. And the lab is, how are we doing R&D about the approach of how toAI to work, not just in direct patient care, right. But also fundamentally, like, what paperwork can you cut out? How can we better explain procedures? Like, what management role can this fill? And we need to be doing active experimentation on that. We can’t just wait for, you know, Microsoft to solve the problems. It has to be at the level of the organizations themselves. LEE: So let’s shift a little bit to the patient. You know, one of the things that we see, and I think everyone is seeing, is that people are turning to chatbots, like ChatGPT, actually to seek healthcare information for, you know, their own health or the health of their loved ones. And there was already, prior to all of this, a trend towards, let’s call it, consumerization of healthcare. So just in the business of healthcare delivery, do you think AI is going to hasten these kinds of trends, or from the consumer’s perspective, what … ? MOLLICK: I mean, absolutely, right. Like, all the early data that we have suggests that for most common medical problems, you should just consult AI, too, right. In fact, there is a real question to ask: at what point does it become unethical for doctors themselves to not ask for a second opinion from the AI because it’s cheap, right? You could overrule it or whatever you want, but like not asking seems foolish. I think the two places where there’s a burning almost, you know, moral imperative is … let’s say, you know, I’m in Philadelphia, I’m a professor, I have access to really good healthcare through the Hospital University of Pennsylvania system. I know doctors. You know, I’m lucky. I’m well connected. If, you know, something goes wrong, I have friends who I can talk to. I have specialists. I’m, you know, pretty well educated in this space. But for most people on the planet, they don’t have access to good medical care, they don’t have good health. It feels like it’s absolutely imperative to say when should you use AI and when not. Are there blind spots? What are those things? And I worry that, like, to me, that would be the crash project I’d be invoking because I’m doing the same thing in education, which is this system is not as good as being in a room with a great teacher who also uses AI to help you, but it’s better than not getting an, you know, to the level of education people get in many cases. Where should we be using it? How do we guide usage in the right way? Because the AI labs aren’t thinking about this. We have to. So, to me, there is a burning need here to understand this. And I worry that people will say, you know, everything that’s true—AI can hallucinate, AI can be biased. All of these things are absolutely true, but people are going to use it. The early indications are that it is quite useful. And unless we take the active role of saying, here’s when to use it, here’s when not to use it, we don’t have a right to say, don’t use this system. And I think, you know, we have to be exploring that. LEE: What do people need to understand about AI? And what should schools, universities, and so on be teaching? MOLLICK: Those are, kind of, two separate questions in lot of ways. I think a lot of people want to teach AI skills, and I will tell you, as somebody who works in this space a lot, there isn’t like an easy, sort of, AI skill, right. I could teach you prompt engineering in two to three classes, but every indication we have is that for most people under most circumstances, the value of prompting, you know, any one case is probably not that useful. A lot of the tricks are disappearing because the AI systems are just starting to use them themselves. So asking good questions, being a good manager, being a good thinker tend to be important, but like magic tricks around making, you know, the AI do something because you use the right phrase used to be something that was real but is rapidly disappearing. So I worry when people say teach AI skills. No one’s been able to articulate to me as somebody who knows AI very well and teaches classes on AI, what those AI skills that everyone should learn are, right. I mean, there’s value in learning a little bit how the models work. There’s a value in working with these systems. A lot of it’s just hands on keyboard kind of work. But, like, we don’t have an easy slam dunk “this is what you learn in the world of AI” because the systems are getting better, and as they get better, they get less sensitive to these prompting techniques. They get better prompting themselves. They solve problems spontaneously and start being agentic. So it’s a hard problem to ask about, like, what do you train someone on? I think getting people experience in hands-on-keyboards, getting them to … there’s like four things I could teach you about AI, and two of them are already starting to disappear. But, like, one is be direct. Like, tell the AI exactly what you want. That’s very helpful. Second, provide as much context as possible. That can include things like acting as a doctor, but also all the information you have. The third is give it step-by-step directions—that’s becoming less important. And the fourth is good and bad examples of the kind of output you want. Those four, that’s like, that’s it as far as the research telling you what to do, and the rest is building intuition. LEE: I’m really impressed that you didn’t give the answer, “Well, everyone should be teaching my book, Co-Intelligence.”MOLLICK: Oh, no, sorry! Everybody should be teaching my book Co-Intelligence. I apologize.LEE: It’s good to chuckle about that, but actually, I can’t think of a better book, like, if you were to assign a textbook in any professional education space, I think Co-Intelligence would be number one on my list. Are there other things that you think are essential reading? MOLLICK: That’s a really good question. I think that a lot of things are evolving very quickly. I happen to, kind of, hit a sweet spot with Co-Intelligence to some degree because I talk about how I used it, and I was, sort of, an advanced user of these systems. So, like, it’s, sort of, like my Twitter feed, my online newsletter. I’m just trying to, kind of, in some ways, it’s about trying to make people aware of what these systems can do by just showing a lot, right. Rather than picking one thing, and, like, this is a general-purpose technology. Let’s use it for this. And, like, everybody gets a light bulb for a different reason. So more than reading, it is using, you know, and that can be Copilot or whatever your favorite tool is. But using it. Voice modes help a lot. In terms of readings, I mean, I think that there is a couple of good guides to understanding AI that were originally blog posts. I think Tim Lee has one called Understanding AI, and it had a good overview … LEE: Yeah, that’s a great one. MOLLICK: … of that topic that I think explains how transformers work, which can give you some mental sense. I thinkKarpathyhas some really nice videos of use that I would recommend. Like on the medical side, I think the book that you did, if you’re in medicine, you should read that. I think that that’s very valuable. But like all we can offer are hints in some ways. Like there isn’t … if you’re looking for the instruction manual, I think it can be very frustrating because it’s like you want the best practices and procedures laid out, and we cannot do that, right. That’s not how a system like this works. LEE: Yeah. MOLLICK: It’s not a person, but thinking about it like a person can be helpful, right. LEE: One of the things that has been sort of a fun project for me for the last few years is I have been a founding board member of a new medical school at Kaiser Permanente. And, you know, that medical school curriculum is being formed in this era. But it’s been perplexing to understand, you know, what this means for a medical school curriculum. And maybe even more perplexing for me, at least, is the accrediting bodies, which are extremely important in US medical schools; how accreditors should think about what’s necessary here. Besides the things that you’ve … the, kind of, four key ideas you mentioned, if you were talking to the board of directors of the LCMEaccrediting body, what’s the one thing you would want them to really internalize? MOLLICK: This is both a fast-moving and vital area. This can’t be viewed like a usual change, which, “Let’s see how this works.” Because it’s, like, the things that make medical technologies hard to do, which is like unclear results, limited, you know, expensive use cases where it rolls out slowly. So one or two, you know, advanced medical facilities get access to, you know, proton beams or something else at multi-billion dollars of cost, and that takes a while to diffuse out. That’s not happening here. This is all happening at the same time, all at once. This is now … AI is part of medicine. I mean, there’s a minor point that I’d make that actually is a really important one, which is large language models, generative AI overall, work incredibly differently than other forms of AI. So the other worry I have with some of these accreditors is they blend together algorithmic forms of AI, which medicine has been trying for long time—decision support, algorithmic methods, like, medicine more so than other places has been thinking about those issues. Generative AI, even though it uses the same underlying techniques, is a completely different beast. So, like, even just take the most simple thing of algorithmic aversion, which is a well-understood problem in medicine, right. Which is, so you have a tool that could tell you as a radiologist, you know, the chance of this being cancer; you don’t like it, you overrule it, right. We don’t find algorithmic aversion happening with LLMs in the same way. People actually enjoy using them because it’s more like working with a person. The flaws are different. The approach is different. So you need to both view this as universal applicable today, which makes it urgent, but also as something that is not the same as your other form of AI, and your AI working group that is thinking about how to solve this problem is not the right people here. LEE: You know, I think the world has been trained because of the magic of web search to view computers as question-answering machines. Ask a question, get an answer. MOLLICK: Yes. Yes. LEE: Write a query, get results. And as I have interacted with medical professionals, you can see that medical professionals have that model of a machine in mind. And I think that’s partly, I think psychologically, why hallucination is so alarming. Because you have a mental model of a computer as a machine that has absolutely rock-solid perfect memory recall. But the thing that was so powerful in Co-Intelligence, and we tried to get at this in our book also, is that’s not the sweet spot. It’s this sort of deeper interaction, more of a collaboration. And I thought your use of the term Co-Intelligence really just even in the title of the book tried to capture this. When I think about education, it seems like that’s the first step, to get past this concept of a machine being just a question-answering machine. Do you have a reaction to that idea? MOLLICK: I think that’s very powerful. You know, we’ve been trained over so many years at both using computers but also in science fiction, right. Computers are about cold logic, right. They will give you the right answer, but if you ask it what love is, they explode, right. Like that’s the classic way you defeat the evil robot in Star Trek, right. “Love does not compute.”Instead, we have a system that makes mistakes, is warm, beats doctors in empathy in almost every controlled study on the subject, right. Like, absolutely can outwrite you in a sonnet but will absolutely struggle with giving you the right answer every time. And I think our mental models are just broken for this. And I think you’re absolutely right. And that’s part of what I thought your book does get at really well is, like, this is a different thing. It’s also generally applicable. Again, the model in your head should be kind of like a person even though it isn’t, right. There’s a lot of warnings and caveats to it, but if you start from person, smart person you’re talking to, your mental model will be more accurate than smart machine, even though both are flawed examples, right. So it will make mistakes; it will make errors. The question is, what do you trust it on? What do you not trust it? As you get to know a model, you’ll get to understand, like, I totally don’t trust it for this, but I absolutely trust it for that, right. LEE: All right. So we’re getting to the end of the time we have together. And so I’d just like to get now into something a little bit more provocative. And I get the question all the time. You know, will AI replace doctors? In medicine and other advanced knowledge work, project out five to 10 years. What do think happens? MOLLICK: OK, so first of all, let’s acknowledge systems change much more slowly than individual use. You know, doctors are not individual actors; they’re part of systems, right. So not just the system of a patient who like may or may not want to talk to a machine instead of a person but also legal systems and administrative systems and systems that allocate labor and systems that train people. So, like, it’s hard to imagine that in five to 10 years medicine being so upended that even if AI was better than doctors at every single thing doctors do, that we’d actually see as radical a change in medicine as you might in other fields. I think you will see faster changes happen in consulting and law and, you know, coding, other spaces than medicine. But I do think that there is good reason to suspect that AI will outperform people while still having flaws, right. That’s the difference. We’re already seeing that for common medical questions in enough randomized controlled trials that, you know, best doctors beat AI, but the AI beats the mean doctor, right. Like, that’s just something we should acknowledge is happening at this point. Now, will that work in your specialty? No. Will that work with all the contingent social knowledge that you have in your space? Probably not. Like, these are vignettes, right. But, like, that’s kind of where things are. So let’s assume, right … you’re asking two questions. One is, how good will AI get? LEE: Yeah. MOLLICK: And we don’t know the answer to that question. I will tell you that your colleagues at Microsoft and increasingly the labs, the AI labs themselves, are all saying they think they’ll have a machine smarter than a human at every intellectual task in the next two to three years. If that doesn’t happen, that makes it easier to assume the future, but let’s just assume that that’s the case. I think medicine starts to change with the idea that people feel obligated to use this to help for everything. Your patients will be using it, and it will be your advisor and helper at the beginning phases, right. And I think that I expect people to be better at empathy. I expect better bedside manner. I expect management tasks to become easier. I think administrative burden might lighten if we handle this right way or much worse if we handle it badly. Diagnostic accuracy will increase, right. And then there’s a set of discovery pieces happening, too, right. One of the core goals of all the AI companies is to accelerate medical research. How does that happen and how does that affect us is a, kind of, unknown question. So I think clinicians are in both the eye of the storm and surrounded by it, right. Like, they can resist AI use for longer than most other fields, but everything around them is going to be affected by it. LEE: Well, Ethan, this has been really a fantastic conversation. And, you know, I think in contrast to all the other conversations we’ve had, this one gives especially the leaders in healthcare, you know, people actually trying to lead their organizations into the future, whether it’s in education or in delivery, a lot to think about. So I really appreciate you joining. MOLLICK: Thank you.   I’m a computing researcher who works with people who are right in the middle of today’s bleeding-edge developments in AI. And because of that, I often lose sight of how to talk to a broader audience about what it’s all about. And so I think one of Ethan’s superpowers is that he has this knack for explaining complex topics in AI in a really accessible way, getting right to the most important points without making it so simple as to be useless. That’s why I rarely miss an opportunity to read up on his latest work. One of the first things I learned from Ethan is the intuition that you can, sort of, think of AI as a very knowledgeable intern. In other words, think of it as a persona that you can interact with, but you also need to be a manager for it and to always assess the work that it does. In our discussion, Ethan went further to stress that there is, because of that, a serious education gap. You know, over the last decade or two, we’ve all been trained, mainly by search engines, to think of computers as question-answering machines. In medicine, in fact, there’s a question-answering application that is really popular called UpToDate. Doctors use it all the time. But generative AI systems like ChatGPT are different. There’s therefore a challenge in how to break out of the old-fashioned mindset of search to get the full value out of generative AI. The other big takeaway for me was that Ethan pointed out while it’s easy to see productivity gains from AI at the individual level, those same gains, at least today, don’t often translate automatically to organization-wide or system-wide gains. And one, of course, has to conclude that it takes more than just making individuals more productive; the whole system also has to adjust to the realities of AI. Here’s now my interview with Azeem Azhar: LEE: Azeem, welcome. AZEEM AZHAR: Peter, thank you so much for having me.  LEE: You know, I think you’re extremely well known in the world. But still, some of the listeners of this podcast series might not have encountered you before. And so one of the ways I like to ask people to introduce themselves is, how do you explain to your parents what you do every day? AZHAR: Well, I’m very lucky in that way because my mother was the person who got me into computers more than 40 years ago. And I still have that first computer, a ZX81 with a Z80 chip … LEE: Oh wow. AZHAR: … to this day. It sits in my study, all seven and a half thousand transistors and Bakelite plastic that it is. And my parents were both economists, and economics is deeply connected with technology in some sense. And I grew up in the late ’70s and the early ’80s. And that was a time of tremendous optimism around technology. It was space opera, science fiction, robots, and of course, the personal computer and, you know, Bill Gates and Steve Jobs. So that’s where I started. And so, in a way, my mother and my dad, who passed away a few years ago, had always known me as someone who was fiddling with computers but also thinking about economics and society. And so, in a way, it’s easier to explain to them because they’re the ones who nurtured the environment that allowed me to research technology and AI and think about what it means to firms and to the economy at large. LEE: I always like to understand the origin story. And what I mean by that is, you know, what was your first encounter with generative AI? And what was that like? What did you go through? AZHAR: The first real moment was when Midjourney and Stable Diffusion emerged in that summer of 2022. I’d been away on vacation, and I came back—and I’d been off grid, in fact—and the world had really changed. Now, I’d been aware of GPT-3 and GPT-2, which I played around with and with BERT, the original transformer paper about seven or eight years ago, but it was the moment where I could talk to my computer, and it could produce these images, and it could be refined in natural language that really made me think we’ve crossed into a new domain. We’ve gone from AI being highly discriminative to AI that’s able to explore the world in particular ways. And then it was a few months later that ChatGPT came out—November, the 30th. And I think it was the next day or the day after that I said to my team, everyone has to use this, and we have to meet every morning and discuss how we experimented the day before. And we did that for three or four months. And, you know, it was really clear to me in that interface at that point that, you know, we’d absolutely pass some kind of threshold. LEE: And who’s the we that you were experimenting with? AZHAR: So I have a team of four who support me. They’re mostly researchers of different types. I mean, it’s almost like one of those jokes. You know, I have a sociologist, an economist, and an astrophysicist. And, you know, they walk into the bar,or they walk into our virtual team room, and we try to solve problems. LEE: Well, so let’s get now into brass tacks here. And I think I want to start maybe just with an exploration of the economics of all this and economic realities. Because I think in a lot of your work—for example, in your book—you look pretty deeply at how automation generally and AI specifically are transforming certain sectors like finance, manufacturing, and you have a really, kind of, insightful focus on what this means for productivity and which ways, you know, efficiencies are found.   And then you, sort of, balance that with risks, things that can and do go wrong. And so as you take that background and looking at all those other sectors, in what ways are the same patterns playing out or likely to play out in healthcare and medicine? AZHAR: I’m sure we will see really remarkable parallels but also new things going on. I mean, medicine has a particular quality compared to other sectors in the sense that it’s highly regulated, market structure is very different country to country, and it’s an incredibly broad field. I mean, just think about taking a Tylenol and going through laparoscopic surgery. Having an MRI and seeing a physio. I mean, this is all medicine. I mean, it’s hard to imagine a sector that ismore broad than that. So I think we can start to break it down, and, you know, where we’re seeing things with generative AI will be that the, sort of, softest entry point, which is the medical scribing. And I’m sure many of us have been with clinicians who have a medical scribe running alongside—they’re all on Surface Pros I noticed, right?They’re on the tablet computers, and they’re scribing away. And what that’s doing is, in the words of my friend Eric Topol, it’s giving the clinician time back, right. They have time back from days that are extremely busy and, you know, full of administrative overload. So I think you can obviously do a great deal with reducing that overload. And within my team, we have a view, which is if you do something five times in a week, you should be writing an automation for it. And if you’re a doctor, you’re probably reviewing your notes, writing the prescriptions, and so on several times a day. So those are things that can clearly be automated, and the human can be in the loop. But I think there are so many other ways just within the clinic that things can help. So, one of my friends, my friend from my junior school—I’ve known him since I was 9—is an oncologist who’s also deeply into machine learning, and he’s in Cambridge in the UK. And he built with Microsoft Research a suite of imaging AI tools from his own discipline, which they then open sourced. So that’s another way that you have an impact, which is that you actually enable the, you know, generalist, specialist, polymath, whatever they are in health systems to be able to get this technology, to tune it to their requirements, to use it, to encourage some grassroots adoption in a system that’s often been very, very heavily centralized. LEE: Yeah. AZHAR: And then I think there are some other things that are going on that I find really, really exciting. So one is the consumerization of healthcare. So I have one of those sleep tracking rings, the Oura. LEE: Yup. AZHAR: That is building a data stream that we’ll be able to apply more and more AI to. I mean, right now, it’s applying traditional, I suspect, machine learning, but you can imagine that as we start to get more data, we start to get more used to measuring ourselves, we create this sort of pot, a personal asset that we can turn AI to. And there’s still another category. And that other category is one of the completely novel ways in which we can enable patient care and patient pathway. And there’s a fantastic startup in the UK called Neko Health, which, I mean, does physicals, MRI scans, and blood tests, and so on. It’s hard to imagine Neko existing without the sort of advanced data, machine learning, AI that we’ve seen emerge over the last decade. So, I mean, I think that there are so many ways in which the temperature is slowly being turned up to encourage a phase change within the healthcare sector. And last but not least, I do think that these tools can also be very, very supportive of a clinician’s life cycle. I think we, as patients, we’re a bit …  I don’t know if we’re as grateful as we should be for our clinicians who are putting in 90-hour weeks.But you can imagine a world where AI is able to support not just the clinicians’ workload but also their sense of stress, their sense of burnout. So just in those five areas, Peter, I sort of imagine we could start to fundamentally transform over the course of many years, of course, the way in which people think about their health and their interactions with healthcare systems LEE: I love how you break that down. And I want to press on a couple of things. You also touched on the fact that medicine is, at least in most of the world, is a highly regulated industry. I guess finance is the same way, but they also feel different because the, like, finance sector has to be very responsive to consumers, and consumers are sensitive to, you know, an abundance of choice; they are sensitive to price. Is there something unique about medicine besides being regulated? AZHAR: I mean, there absolutely is. And in finance, as well, you have much clearer end states. So if you’re not in the consumer space, but you’re in the, you know, asset management space, you have to essentially deliver returns against the volatility or risk boundary, right. That’s what you have to go out and do. And I think if you’re in the consumer industry, you can come back to very, very clear measures, net promoter score being a very good example. In the case of medicine and healthcare, it is much more complicated because as far as the clinician is concerned, people are individuals, and we have our own parts and our own responses. If we didn’t, there would never be a need for a differential diagnosis. There’d never be a need for, you know, Let’s try azithromycin first, and then if that doesn’t work, we’ll go to vancomycin, or, you know, whatever it happens to be. You would just know. But ultimately, you know, people are quite different. The symptoms that they’re showing are quite different, and also their compliance is really, really different. I had a back problem that had to be dealt with by, you know, a physio and extremely boring exercises four times a week, but I was ruthless in complying, and my physio was incredibly surprised. He’d say well no one ever does this, and I said, well you know the thing is that I kind of just want to get this thing to go away. LEE: Yeah. AZHAR: And I think that that’s why medicine is and healthcare is so different and more complex. But I also think that’s why AI can be really, really helpful. I mean, we didn’t talk about, you know, AI in its ability to potentially do this, which is to extend the clinician’s presence throughout the week. LEE: Right. Yeah. AZHAR: The idea that maybe some part of what the clinician would do if you could talk to them on Wednesday, Thursday, and Friday could be delivered through an app or a chatbot just as a way of encouraging the compliance, which is often, especially with older patients, one reason why conditions, you know, linger on for longer. LEE: You know, just staying on the regulatory thing, as I’ve thought about this, the one regulated sector that I think seems to have some parallels to healthcare is energy delivery, energy distribution. Because like healthcare, as a consumer, I don’t have choice in who delivers electricity to my house. And even though I care about it being cheap or at least not being overcharged, I don’t have an abundance of choice. I can’t do price comparisons. And there’s something about that, just speaking as a consumer of both energy and a consumer of healthcare, that feels similar. Whereas other regulated industries, you know, somehow, as a consumer, I feel like I have a lot more direct influence and power. Does that make any sense to someone, you know, like you, who’s really much more expert in how economic systems work? AZHAR: I mean, in a sense, one part of that is very, very true. You have a limited panel of energy providers you can go to, and in the US, there may be places where you have no choice. I think the area where it’s slightly different is that as a consumer or a patient, you can actually make meaningful choices and changes yourself using these technologies, and people used to joke about you know asking Dr. Google. But Dr. Google is not terrible, particularly if you go to WebMD. And, you know, when I look at long-range change, many of the regulations that exist around healthcare delivery were formed at a point before people had access to good quality information at the touch of their fingertips or when educational levels in general were much, much lower. And many regulations existed because of the incumbent power of particular professional sectors. I’ll give you an example from the United Kingdom. So I have had asthma all of my life. That means I’ve been taking my inhaler, Ventolin, and maybe a steroid inhaler for nearly 50 years. That means that I know … actually, I’ve got more experience, and I—in some sense—know more about it than a general practitioner. LEE: Yeah. AZHAR: And until a few years ago, I would have to go to a general practitioner to get this drug that I’ve been taking for five decades, and there they are, age 30 or whatever it is. And a few years ago, the regulations changed. And now pharmacies can … or pharmacists can prescribe those types of drugs under certain conditions directly. LEE: Right. AZHAR: That was not to do with technology. That was to do with incumbent lock-in. So when we look at the medical industry, the healthcare space, there are some parallels with energy, but there are a few little things that the ability that the consumer has to put in some effort to learn about their condition, but also the fact that some of the regulations that exist just exist because certain professions are powerful. LEE: Yeah, one last question while we’re still on economics. There seems to be a conundrum about productivity and efficiency in healthcare delivery because I’ve never encountered a doctor or a nurse that wants to be able to handle even more patients than they’re doing on a daily basis. And so, you know, if productivity means simply, well, your rounds can now handle 16 patients instead of eight patients, that doesn’t seem necessarily to be a desirable thing. So how can we or should we be thinking about efficiency and productivity since obviously costs are, in most of the developed world, are a huge, huge problem? AZHAR: Yes, and when you described doubling the number of patients on the round, I imagined you buying them all roller skates so they could just whizz aroundthe hospital faster and faster than ever before. We can learn from what happened with the introduction of electricity. Electricity emerged at the end of the 19th century, around the same time that cars were emerging as a product, and car makers were very small and very artisanal. And in the early 1900s, some really smart car makers figured out that electricity was going to be important. And they bought into this technology by putting pendant lights in their workshops so they could “visit more patients.” Right? LEE: Yeah, yeah. AZHAR: They could effectively spend more hours working, and that was a productivity enhancement, and it was noticeable. But, of course, electricity fundamentally changed the productivity by orders of magnitude of people who made cars starting with Henry Ford because he was able to reorganize his factories around the electrical delivery of power and to therefore have the moving assembly line, which 10xed the productivity of that system. So when we think about how AI will affect the clinician, the nurse, the doctor, it’s much easier for us to imagine it as the pendant light that just has them working later … LEE: Right. AZHAR: … than it is to imagine a reconceptualization of the relationship between the clinician and the people they care for. And I’m not sure. I don’t think anybody knows what that looks like. But, you know, I do think that there will be a way that this changes, and you can see that scale out factor. And it may be, Peter, that what we end up doing is we end up saying, OK, because we have these brilliant AIs, there’s a lower level of training and cost and expense that’s required for a broader range of conditions that need treating. And that expands the market, right. That expands the market hugely. It’s what has happened in the market for taxis or ride sharing. The introduction of Uber and the GPS system … LEE: Yup. AZHAR: … has meant many more people now earn their living driving people around in their cars. And at least in London, you had to be reasonably highly trained to do that. So I can see a reorganization is possible. Of course, entrenched interests, the economic flow … and there are many entrenched interests, particularly in the US between the health systems and the, you know, professional bodies that might slow things down. But I think a reimagining is possible. And if I may, I’ll give you one example of that, which is, if you go to countries outside of the US where there are many more sick people per doctor, they have incentives to change the way they deliver their healthcare. And well before there was AI of this quality around, there was a few cases of health systems in India—Aravind Eye Carewas one, and Narayana Hrudayalayawas another. And in the latter, they were a cardiac care unit where you couldn’t get enough heart surgeons. LEE: Yeah, yep. AZHAR: So specially trained nurses would operate under the supervision of a single surgeon who would supervise many in parallel. So there are ways of increasing the quality of care, reducing the cost, but it does require a systems change. And we can’t expect a single bright algorithm to do it on its own. LEE: Yeah, really, really interesting. So now let’s get into regulation. And let me start with this question. You know, there are several startup companies I’m aware of that are pushing on, I think, a near-term future possibility that a medical AI for consumer might be allowed, say, to prescribe a medication for you, something that would normally require a doctor or a pharmacist, you know, that is certified in some way, licensed to do. Do you think we’ll get to a point where for certain regulated activities, humans are more or less cut out of the loop? AZHAR: Well, humans would have been in the loop because they would have provided the training data, they would have done the oversight, the quality control. But to your question in general, would we delegate an important decision entirely to a tested set of algorithms? I’m sure we will. We already do that. I delegate less important decisions like, What time should I leave for the airport to Waze. I delegate more important decisions to the automated braking in my car. We will do this at certain levels of risk and threshold. If I come back to my example of prescribing Ventolin. It’s really unclear to me that the prescription of Ventolin, this incredibly benign bronchodilator that is only used by people who’ve been through the asthma process, needs to be prescribed by someone who’s gone through 10 years or 12 years of medical training. And why that couldn’t be prescribed by an algorithm or an AI system. LEE: Right. Yep. Yep. AZHAR: So, you know, I absolutely think that that will be the case and could be the case. I can’t really see what the objections are. And the real issue is where do you draw the line of where you say, “Listen, this is too important,” or “The cost is too great,” or “The side effects are too high,” and therefore this is a point at which we want to have some, you know, human taking personal responsibility, having a liability framework in place, having a sense that there is a person with legal agency who signed off on this decision. And that line I suspect will start fairly low, and what we’d expect to see would be that that would rise progressively over time. LEE: What you just said, that scenario of your personal asthma medication, is really interesting because your personal AI might have the benefit of 50 years of your own experience with that medication. So, in a way, there is at least the data potential for, let’s say, the next prescription to be more personalized and more tailored specifically for you. AZHAR: Yes. Well, let’s dig into this because I think this is super interesting, and we can look at how things have changed. So 15 years ago, if I had a bad asthma attack, which I might have once a year, I would have needed to go and see my general physician. In the UK, it’s very difficult to get an appointment. I would have had to see someone privately who didn’t know me at all because I’ve just walked in off the street, and I would explain my situation. It would take me half a day. Productivity lost. I’ve been miserable for a couple of days with severe wheezing. Then a few years ago the system changed, a protocol changed, and now I have a thing called a rescue pack, which includes prednisolone steroids. It includes something else I’ve just forgotten, and an antibiotic in case I get an upper respiratory tract infection, and I have an “algorithm.” It’s called a protocol. It’s printed out. It’s a flowchart I answer various questions, and then I say, “I’m going to prescribe this to myself.” You know, UK doctors don’t prescribe prednisolone, or prednisone as you may call it in the US, at the drop of a hat, right. It’s a powerful steroid. I can self-administer, and I can now get that repeat prescription without seeing a physician a couple of times a year. And the algorithm, the “AI” is, it’s obviously been done in PowerPoint naturally, and it’s a bunch of arrows.Surely, surely, an AI system is going to be more sophisticated, more nuanced, and give me more assurance that I’m making the right decision around something like that. LEE: Yeah. Well, at a minimum, the AI should be able to make that PowerPoint the next time.AZHAR: Yeah, yeah. Thank god for Clippy. Yes. LEE: So, you know, I think in our book, we had a lot of certainty about most of the things we’ve discussed here, but one chapter where I felt we really sort of ran out of ideas, frankly, was on regulation. And, you know, what we ended up doing for that chapter is … I can’t remember if it was Carey’s or Zak’s idea, but we asked GPT-4 to have a conversation, a debate with itself, about regulation. And we made some minor commentary on that. And really, I think we took that approach because we just didn’t have much to offer. By the way, in our defense, I don’t think anyone else had any better ideas anyway. AZHAR: Right. LEE: And so now two years later, do we have better ideas about the need for regulation, the frameworks around which those regulations should be developed, and, you know, what should this look like? AZHAR: So regulation is going to be in some cases very helpful because it provides certainty for the clinician that they’re doing the right thing, that they are still insured for what they’re doing, and it provides some degree of confidence for the patient. And we need to make sure that the claims that are made stand up to quite rigorous levels, where ideally there are RCTs, and there are the classic set of processes you go through. You do also want to be able to experiment, and so the question is: as a regulator, how can you enable conditions for there to be experimentation? And what is experimentation? Experimentation is learning so that every element of the system can learn from this experience. So finding that space where there can be bit of experimentation, I think, becomes very, very important. And a lot of this is about experience, so I think the first digital therapeutics have received FDA approval, which means there are now people within the FDA who understand how you go about running an approvals process for that, and what that ends up looking like—and of course what we’re very good at doing in this sort of modern hyper-connected world—is we can share that expertise, that knowledge, that experience very, very quickly. So you go from one approval a year to a hundred approvals a year to a thousand approvals a year. So we will then actually, I suspect, need to think about what is it to approve digital therapeutics because, unlike big biological molecules, we can generate these digital therapeutics at the rate of knots. LEE: Yes. AZHAR: Every road in Hayes Valley in San Francisco, right, is churning out new startups who will want to do things like this. So then, I think about, what does it mean to get approved if indeed it gets approved? But we can also go really far with things that don’t require approval. I come back to my sleep tracking ring. So I’ve been wearing this for a few years, and when I go and see my doctor or I have my annual checkup, one of the first things that he asks is how have I been sleeping. And in fact, I even sync my sleep tracking data to their medical record system, so he’s saying … hearing what I’m saying, but he’s actually pulling up the real data going, This patient’s lying to me again. Of course, I’m very truthful with my doctor, as we should all be.LEE: You know, actually, that brings up a point that consumer-facing health AI has to deal with pop science, bad science, you know, weird stuff that you hear on Reddit. And because one of the things that consumers want to know always is, you know, what’s the truth? AZHAR: Right. LEE: What can I rely on? And I think that somehow feels different than an AI that you actually put in the hands of, let’s say, a licensed practitioner. And so the regulatory issues seem very, very different for these two cases somehow. AZHAR: I agree, they’re very different. And I think for a lot of areas, you will want to build AI systems that are first and foremost for the clinician, even if they have patient extensions, that idea that the clinician can still be with a patient during the week. And you’ll do that anyway because you need the data, and you also need a little bit of a liability shield to have like a sensible person who’s been trained around that. And I think that’s going to be a very important pathway for many AI medical crossovers. We’re going to go through the clinician. LEE: Yeah. AZHAR: But I also do recognize what you say about the, kind of, kooky quackery that exists on Reddit. Although on Creatine, Reddit may yet prove to have been right.LEE: Yeah, that’s right. Yes, yeah, absolutely. Yeah. AZHAR: Sometimes it’s right. And I think that it serves a really good role as a field of extreme experimentation. So if you’re somebody who makes a continuous glucose monitor traditionally given to diabetics but now lots of people will wear them—and sports people will wear them—you probably gathered a lot of extreme tail distribution data by reading the Reddit/biohackers … LEE: Yes. AZHAR: … for the last few years, where people were doing things that you would never want them to really do with the CGM. And so I think we shouldn’t understate how important that petri dish can be for helping us learn what could happen next. LEE: Oh, I think it’s absolutely going to be essential and a bigger thing in the future. So I think I just want to close here then with one last question. And I always try to be a little bit provocative with this. And so as you look ahead to what doctors and nurses and patients might be doing two years from now, five years from now, 10 years from now, do you have any kind of firm predictions? AZHAR: I’m going to push the boat out, and I’m going to go further out than closer in. LEE: OK.AZHAR: As patients, we will have many, many more touch points and interaction with our biomarkers and our health. We’ll be reading how well we feel through an array of things. And some of them we’ll be wearing directly, like sleep trackers and watches. And so we’ll have a better sense of what’s happening in our lives. It’s like the moment you go from paper bank statements that arrive every month to being able to see your account in real time. LEE: Yes. AZHAR: And I suspect we’ll have … we’ll still have interactions with clinicians because societies that get richer see doctors more, societies that get older see doctors more, and we’re going to be doing both of those over the coming 10 years. But there will be a sense, I think, of continuous health engagement, not in an overbearing way, but just in a sense that we know it’s there, we can check in with it, it’s likely to be data that is compiled on our behalf somewhere centrally and delivered through a user experience that reinforces agency rather than anxiety. And we’re learning how to do that slowly. I don’t think the health apps on our phones and devices have yet quite got that right. And that could help us personalize problems before they arise, and again, I use my experience for things that I’ve tracked really, really well. And I know from my data and from how I’m feeling when I’m on the verge of one of those severe asthma attacks that hits me once a year, and I can take a little bit of preemptive measure, so I think that that will become progressively more common and that sense that we will know our baselines. I mean, when you think about being an athlete, which is something I think about, but I could never ever do,but what happens is you start with your detailed baselines, and that’s what your health coach looks at every three or four months. For most of us, we have no idea of our baselines. You we get our blood pressure measured once a year. We will have baselines, and that will help us on an ongoing basis to better understand and be in control of our health. And then if the product designers get it right, it will be done in a way that doesn’t feel invasive, but it’ll be done in a way that feels enabling. We’ll still be engaging with clinicians augmented by AI systems more and more because they will also have gone up the stack. They won’t be spending their time on just “take two Tylenol and have a lie down” type of engagements because that will be dealt with earlier on in the system. And so we will be there in a very, very different set of relationships. And they will feel that they have different ways of looking after our health. LEE: Azeem, it’s so comforting to hear such a wonderfully optimistic picture of the future of healthcare. And I actually agree with everything you’ve said. Let me just thank you again for joining this conversation. I think it’s been really fascinating. And I think somehow the systemic issues, the systemic issues that you tend to just see with such clarity, I think are going to be the most, kind of, profound drivers of change in the future. So thank you so much. AZHAR: Well, thank you, it’s been my pleasure, Peter, thank you.   I always think of Azeem as a systems thinker. He’s always able to take the experiences of new technologies at an individual level and then project out to what this could mean for whole organizations and whole societies. In our conversation, I felt that Azeem really connected some of what we learned in a previous episode—for example, from Chrissy Farr—on the evolving consumerization of healthcare to the broader workforce and economic impacts that we’ve heard about from Ethan Mollick.   Azeem’s personal story about managing his asthma was also a great example. You know, he imagines a future, as do I, where personal AI might assist and remember decades of personal experience with a condition like asthma and thereby know more than any human being could possibly know in a deeply personalized and effective way, leading to better care. Azeem’s relentless optimism about our AI future was also so heartening to hear. Both of these conversations leave me really optimistic about the future of AI in medicine. At the same time, it is pretty sobering to realize just how much we’ll all need to change in pretty fundamental and maybe even in radical ways. I think a big insight I got from these conversations is how we interact with machines is going to have to be altered not only at the individual level, but at the company level and maybe even at the societal level. Since my conversation with Ethan and Azeem, there have been some pretty important developments that speak directly to this. Just last week at Build, which is Microsoft’s yearly developer conference, we announced a slew of AI agent technologies. Our CEO, Satya Nadella, in fact, started his keynote by going online in a GitHub developer environment and then assigning a coding task to an AI agent, basically treating that AI as a full-fledged member of a development team. Other agents, for example, a meeting facilitator, a data analyst, a business researcher, travel agent, and more were also shown during the conference. But pertinent to healthcare specifically, what really blew me away was the demonstration of a healthcare orchestrator agent. And the specific thing here was in Stanford’s cancer treatment center, when they are trying to decide on potentially experimental treatments for cancer patients, they convene a meeting of experts. That is typically called a tumor board. And so this AI healthcare orchestrator agent actually participated as a full-fledged member of a tumor board meeting to help bring data together, make sure that the latest medical knowledge was brought to bear, and to assist in the decision-making around a patient’s cancer treatment. It was pretty amazing.A big thank-you again to Ethan and Azeem for sharing their knowledge and understanding of the dynamics between AI and society more broadly. And to our listeners, thank you for joining us. I’m really excited for the upcoming episodes, including discussions on medical students’ experiences with AI and AI’s influence on the operation of health systems and public health departments. We hope you’ll continue to tune in. Until next time. #what #ais #impact #individuals #means
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    What AI’s impact on individuals means for the health workforce and industry
    Transcript [MUSIC]    [BOOK PASSAGE]  PETER LEE: “In American primary care, the missing workforce is stunning in magnitude, the shortfall estimated to reach up to 48,000 doctors within the next dozen years. China and other countries with aging populations can expect drastic shortfalls, as well. Just last month, I asked a respected colleague retiring from primary care who he would recommend as a replacement; he told me bluntly that, other than expensive concierge care practices, he could not think of anyone, even for himself. This mismatch between need and supply will only grow, and the US is far from alone among developed countries in facing it.” [END OF BOOK PASSAGE]    [THEME MUSIC]    This is The AI Revolution in Medicine, Revisited. I’m your host, Peter Lee.    Shortly after OpenAI’s GPT-4 was publicly released, Carey Goldberg, Dr. Zak Kohane, and I published The AI Revolution in Medicine to help educate the world of healthcare and medical research about the transformative impact this new generative AI technology could have. But because we wrote the book when GPT-4 was still a secret, we had to speculate. Now, two years later, what did we get right, and what did we get wrong?     In this series, we’ll talk to clinicians, patients, hospital administrators, and others to understand the reality of AI in the field and where we go from here.      [THEME MUSIC FADES] The book passage I read at the top is from “Chapter 4: Trust but Verify,” which was written by Zak. You know, it’s no secret that in the US and elsewhere shortages in medical staff and the rise of clinician burnout are affecting the quality of patient care for the worse. In our book, we predicted that generative AI would be something that might help address these issues. So in this episode, we’ll delve into how individual performance gains that our previous guests have described might affect the healthcare workforce as a whole, and on the patient side, we’ll look into the influence of generative AI on the consumerization of healthcare. Now, since all of this consumes such a huge fraction of the overall economy, we’ll also get into what a general-purpose technology as disruptive as generative AI might mean in the context of labor markets and beyond.   To help us do that, I’m pleased to welcome Ethan Mollick and Azeem Azhar. Ethan Mollick is the Ralph J. Roberts Distinguished Faculty Scholar, a Rowan Fellow, and an associate professor at the Wharton School of the University of Pennsylvania. His research into the effects of AI on work, entrepreneurship, and education is applied by organizations around the world, leading him to be named one of Time magazine’s most influential people in AI for 2024. He’s also the author of the New York Times best-selling book Co-Intelligence. Azeem Azhar is an author, founder, investor, and one of the most thoughtful and influential voices on the interplay between disruptive emerging technologies and business and society. In his best-selling book, The Exponential Age, and in his highly regarded newsletter and podcast, Exponential View, he explores how technologies like AI are reshaping everything from healthcare to geopolitics. Ethan and Azeem are two leading thinkers on the ways that disruptive technologies—and especially AI—affect our work, our jobs, our business enterprises, and whole industries. As economists, they are trying to work out whether we are in the midst of an economic revolution as profound as the shift from an agrarian to an industrial society. [TRANSITION MUSIC] Here is my interview with Ethan Mollick: LEE: Ethan, welcome. ETHAN MOLLICK: So happy to be here, thank you. LEE: I described you as a professor at Wharton, which I think most of the people who listen to this podcast series know of as an elite business school. So it might surprise some people that you study AI. And beyond that, you know, that I would seek you out to talk about AI in medicine. [LAUGHTER] So to get started, how and why did it happen that you’ve become one of the leading experts on AI? MOLLICK: It’s actually an interesting story. I’ve been AI-adjacent my whole career. When I was [getting] my PhD at MIT, I worked with Marvin Minsky (opens in new tab) and the MIT [Massachusetts Institute of Technology] Media Labs AI group. But I was never the technical AI guy. I was the person who was trying to explain AI to everybody else who didn’t understand it. And then I became very interested in, how do you train and teach? And AI was always a part of that. I was building games for teaching, teaching tools that were used in hospitals and elsewhere, simulations. So when LLMs burst into the scene, I had already been using them and had a good sense of what they could do. And between that and, kind of, being practically oriented and getting some of the first research projects underway, especially under education and AI and performance, I became sort of a go-to person in the field. And once you’re in a field where nobody knows what’s going on and we’re all making it up as we go along—I thought it’s funny that you led with the idea that you have a couple of months head start for GPT-4, right. Like that’s all we have at this point, is a few months’ head start. [LAUGHTER] So being a few months ahead is good enough to be an expert at this point. Whether it should be or not is a different question. LEE: Well, if I understand correctly, leading AI companies like OpenAI, Anthropic, and others have now sought you out as someone who should get early access to really start to do early assessments and gauge early reactions. How has that been? MOLLICK: So, I mean, I think the bigger picture is less about me than about two things that tells us about the state of AI right now. One, nobody really knows what’s going on, right. So in a lot of ways, if it wasn’t for your work, Peter, like, I don’t think people would be thinking about medicine as much because these systems weren’t built for medicine. They weren’t built to change education. They weren’t built to write memos. They, like, they weren’t built to do any of these things. They weren’t really built to do anything in particular. It turns out they’re just good at many things. And to the extent that the labs work on them, they care about their coding ability above everything else and maybe math and science secondarily. They don’t think about the fact that it expresses high empathy. They don’t think about its accuracy and diagnosis or where it’s inaccurate. They don’t think about how it’s changing education forever. So one part of this is the fact that they go to my Twitter feed or ask me for advice is an indicator of where they are, too, which is they’re not thinking about this. And the fact that a few months’ head start continues to give you a lead tells you that we are at the very cutting edge. These labs aren’t sitting on projects for two years and then releasing them. Months after a project is complete or sooner, it’s out the door. Like, there’s very little delay. So we’re kind of all in the same boat here, which is a very unusual space for a new technology. LEE: And I, you know, explained that you’re at Wharton. Are you an odd fit as a faculty member at Wharton, or is this a trend now even in business schools that AI experts are becoming key members of the faculty? MOLLICK: I mean, it’s a little of both, right. It’s faculty, so everybody does everything. I’m a professor of innovation-entrepreneurship. I’ve launched startups before and working on that and education means I think about, how do organizations redesign themselves? How do they take advantage of these kinds of problems? So medicine’s always been very central to that, right. A lot of people in my MBA class have been MDs either switching, you know, careers or else looking to advance from being sort of individual contributors to running teams. So I don’t think that’s that bad a fit. But I also think this is general-purpose technology; it’s going to touch everything. The focus on this is medicine, but Microsoft does far more than medicine, right. It’s … there’s transformation happening in literally every field, in every country. This is a widespread effect. So I don’t think we should be surprised that business schools matter on this because we care about management. There’s a long tradition of management and medicine going together. There’s actually a great academic paper that shows that teaching hospitals that also have MBA programs associated with them have higher management scores and perform better (opens in new tab). So I think that these are not as foreign concepts, especially as medicine continues to get more complicated. LEE: Yeah. Well, in fact, I want to dive a little deeper on these issues of management, of entrepreneurship, um, education. But before doing that, if I could just stay focused on you. There is always something interesting to hear from people about their first encounters with AI. And throughout this entire series, I’ve been doing that both pre-generative AI and post-generative AI. So you, sort of, hinted at the pre-generative AI. You were in Minsky’s lab. Can you say a little bit more about that early encounter? And then tell us about your first encounters with generative AI. MOLLICK: Yeah. Those are great questions. So first of all, when I was at the media lab, that was pre-the current boom in sort of, you know, even in the old-school machine learning kind of space. So there was a lot of potential directions to head in. While I was there, there were projects underway, for example, to record every interaction small children had. One of the professors was recording everything their baby interacted with in the hope that maybe that would give them a hint about how to build an AI system. There was a bunch of projects underway that were about labeling every concept and how they relate to other concepts. So, like, it was very much Wild West of, like, how do we make an AI work—which has been this repeated problem in AI, which is, what is this thing? The fact that it was just like brute force over the corpus of all human knowledge turns out to be a little bit of like a, you know, it’s a miracle and a little bit of a disappointment in some ways [LAUGHTER] compared to how elaborate some of this was. So, you know, I think that, that was sort of my first encounters in sort of the intellectual way. The generative AI encounters actually started with the original, sort of, GPT-3, or, you know, earlier versions. And it was actually game-based. So I played games like AI Dungeon. And as an educator, I realized, oh my gosh, this stuff could write essays at a fourth-grade level. That’s really going to change the way, like, middle school works, was my thinking at the time. And I was posting about that back in, you know, 2021 that this is a big deal. But I think everybody was taken surprise, including the AI companies themselves, by, you know, ChatGPT, by GPT-3.5. The difference in degree turned out to be a difference in kind. LEE: Yeah, you know, if I think back, even with GPT-3, and certainly this was the case with GPT-2, it was, at least, you know, from where I was sitting, it was hard to get people to really take this seriously and pay attention. MOLLICK: Yes. LEE: You know, it’s remarkable. Within Microsoft, I think a turning point was the use of GPT-3 to do code completions. And that was actually productized as GitHub Copilot (opens in new tab), the very first version. That, I think, is where there was widespread belief. But, you know, in a way, I think there is, even for me early on, a sense of denial and skepticism. Did you have those initially at any point? MOLLICK: Yeah, I mean, it still happens today, right. Like, this is a weird technology. You know, the original denial and skepticism was, I couldn’t see where this was going. It didn’t seem like a miracle because, you know, of course computers can complete code for you. Like, what else are they supposed to do? Of course, computers can give you answers to questions and write fun things. So there’s difference of moving into a world of generative AI. I think a lot of people just thought that’s what computers could do. So it made the conversations a little weird. But even today, faced with these, you know, with very strong reasoner models that operate at the level of PhD students, I think a lot of people have issues with it, right. I mean, first of all, they seem intuitive to use, but they’re not always intuitive to use because the first use case that everyone puts AI to, it fails at because they use it like Google or some other use case. And then it’s genuinely upsetting in a lot of ways. I think, you know, I write in my book about the idea of three sleepless nights. That hasn’t changed. Like, you have to have an intellectual crisis to some extent, you know, and I think people do a lot to avoid having that existential angst of like, “Oh my god, what does it mean that a machine could think—apparently think—like a person?” So, I mean, I see resistance now. I saw resistance then. And then on top of all of that, there’s the fact that the curve of the technology is quite great. I mean, the price of GPT-4 level intelligence from, you know, when it was released has dropped 99.97% at this point, right. LEE: Yes. Mm-hmm. MOLLICK: I mean, I could run a GPT-4 class system basically on my phone. Microsoft’s releasing things that can almost run on like, you know, like it fits in almost no space, that are almost as good as the original GPT-4 models. I mean, I don’t think people have a sense of how fast the trajectory is moving either. LEE: Yeah, you know, there’s something that I think about often. There is this existential dread, or will this technology replace me? But I think the first people to feel that are researchers—people encountering this for the first time. You know, if you were working, let’s say, in Bayesian reasoning or in traditional, let’s say, Gaussian mixture model based, you know, speech recognition, you do get this feeling, Oh, my god, this technology has just solved the problem that I’ve dedicated my life to. And there is this really difficult period where you have to cope with that. And I think this is going to be spreading, you know, in more and more walks of life. And so this … at what point does that sort of sense of dread hit you, if ever? MOLLICK: I mean, you know, it’s not even dread as much as like, you know, Tyler Cowen wrote that it’s impossible to not feel a little bit of sadness as you use these AI systems, too. Because, like, I was talking to a friend, just as the most minor example, and his talent that he was very proud of was he was very good at writing limericks for birthday cards. He’d write these limericks. Everyone was always amused by them. [LAUGHTER] And now, you know, GPT-4 and GPT-4.5, they made limericks obsolete. Like, anyone can write a good limerick, right. So this was a talent, and it was a little sad. Like, this thing that you cared about mattered. You know, as academics, we’re a little used to dead ends, right, and like, you know, some getting the lap. But the idea that entire fields are hitting that way. Like in medicine, there’s a lot of support systems that are now obsolete. And the question is how quickly you change that. In education, a lot of our techniques are obsolete. What do you do to change that? You know, it’s like the fact that this brute force technology is good enough to solve so many problems is weird, right. And it’s not just the end of, you know, of our research angles that matter, too. Like, for example, I ran this, you know, 14-person-plus, multimillion-dollar effort at Wharton to build these teaching simulations, and we’re very proud of them. It took years of work to build one. Now we’ve built a system that can build teaching simulations on demand by you talking to it with one team member. And, you know, you literally can create any simulation by having a discussion with the AI. I mean, you know, there’s a switch to a new form of excitement, but there is a little bit of like, this mattered to me, and, you know, now I have to change how I do things. I mean, adjustment happens. But if you haven’t had that displacement, I think that’s a good indicator that you haven’t really faced AI yet. LEE: Yeah, what’s so interesting just listening to you is you use words like sadness, and yet I can see the—and hear the—excitement in your voice and your body language. So, you know, that’s also kind of an interesting aspect of all of this.  MOLLICK: Yeah, I mean, I think there’s something on the other side, right. But, like, I can’t say that I haven’t had moments where like, ughhhh, but then there’s joy and basically like also, you know, freeing stuff up. I mean, I think about doctors or professors, right. These are jobs that bundle together lots of different tasks that you would never have put together, right. If you’re a doctor, you would never have expected the same person to be good at keeping up with the research and being a good diagnostician and being a good manager and being good with people and being good with hand skills. Like, who would ever want that kind of bundle? That’s not something you’re all good at, right. And a lot of our stress of our job comes from the fact that we suck at some of it. And so to the extent that AI steps in for that, you kind of feel bad about some of the stuff that it’s doing that you wanted to do. But it’s much more uplifting to be like, I don’t have to do this stuff I’m bad anymore, or I get the support to make myself good at it. And the stuff that I really care about, I can focus on more. Well, because we are at kind of a unique moment where whatever you’re best at, you’re still better than AI. And I think it’s an ongoing question about how long that lasts. But for right now, like you’re not going to say, OK, AI replaces me entirely in my job in medicine. It’s very unlikely. But you will say it replaces these 17 things I’m bad at, but I never liked that anyway. So it’s a period of both excitement and a little anxiety. LEE: Yeah, I’m going to want to get back to this question about in what ways AI may or may not replace doctors or some of what doctors and nurses and other clinicians do. But before that, let’s get into, I think, the real meat of this conversation. In previous episodes of this podcast, we talked to clinicians and healthcare administrators and technology developers that are very rapidly injecting AI today to do various forms of workforce automation, you know, automatically writing a clinical encounter note, automatically filling out a referral letter or request for prior authorization for some reimbursement to an insurance company. And so these sorts of things are intended not only to make things more efficient and lower costs but also to reduce various forms of drudgery, cognitive burden on frontline health workers. So how do you think about the impact of AI on that aspect of workforce, and, you know, what would you expect will happen over the next few years in terms of impact on efficiency and costs? MOLLICK: So I mean, this is a case where I think we’re facing the big bright problem in AI in a lot of ways, which is that this is … at the individual level, there’s lots of performance gains to be gained, right. The problem, though, is that we as individuals fit into systems, in medicine as much as anywhere else or more so, right. Which is that you could individually boost your performance, but it’s also about systems that fit along with this, right. So, you know, if you could automatically, you know, record an encounter, if you could automatically make notes, does that change what you should be expecting for notes or the value of those notes or what they’re for? How do we take what one person does and validate it across the organization and roll it out for everybody without making it a 10-year process that it feels like IT in medicine often is? Like, so we’re in this really interesting period where there’s incredible amounts of individual innovation in productivity and performance improvements in this field, like very high levels of it, but not necessarily seeing that same thing translate to organizational efficiency or gains. And one of my big concerns is seeing that happen. We’re seeing that in nonmedical problems, the same kind of thing, which is, you know, we’ve got research showing 20 and 40% performance improvements, like not uncommon to see those things. But then the organization doesn’t capture it; the system doesn’t capture it. Because the individuals are doing their own work and the systems don’t have the ability to, kind of, learn or adapt as a result. LEE: You know, where are those productivity gains going, then, when you get to the organizational level? MOLLICK: Well, they’re dying for a few reasons. One is, there’s a tendency for individual contributors to underestimate the power of management, right. Practices associated with good management increase happiness, decrease, you know, issues, increase success rates. In the same way, about 40%, as far as we can tell, of the US advantage over other companies, of US firms, has to do with management ability. Like, management is a big deal. Organizing is a big deal. Thinking about how you coordinate is a big deal. At the individual level, when things get stuck there, right, you can’t start bringing them up to how systems work together. It becomes, How do I deal with a doctor that has a 60% performance improvement? We really only have one thing in our playbook for doing that right now, which is, OK, we could fire 40% of the other doctors and still have a performance gain, which is not the answer you want to see happen. So because of that, people are hiding their use. They’re actually hiding their use for lots of reasons. And it’s a weird case because the people who are able to figure out best how to use these systems, for a lot of use cases, they’re actually clinicians themselves because they’re experimenting all the time. Like, they have to take those encounter notes. And if they figure out a better way to do it, they figure that out. You don’t want to wait for, you know, a med tech company to figure that out and then sell that back to you when it can be done by the physicians themselves. So we’re just not used to a period where everybody’s innovating and where the management structure isn’t in place to take advantage of that. And so we’re seeing things stalled at the individual level, and people are often, especially in risk-averse organizations or organizations where there’s lots of regulatory hurdles, people are so afraid of the regulatory piece that they don’t even bother trying to make change. LEE: If you are, you know, the leader of a hospital or a clinic or a whole health system, how should you approach this? You know, how should you be trying to extract positive success out of AI? MOLLICK: So I think that you need to embrace the right kind of risk, right. We don’t want to put risk on our patients … like, we don’t want to put uninformed risk. But innovation involves risk to how organizations operate. They involve change. So I think part of this is embracing the idea that R&D has to happen in organizations again. What’s happened over the last 20 years or so has been organizations giving that up. Partially, that’s a trend to focus on what you’re good at and not try and do this other stuff. Partially, it’s because it’s outsourced now to software companies that, like, Salesforce tells you how to organize your sales team. Workforce tells you how to organize your organization. Consultants come in and will tell you how to make change based on the average of what other people are doing in your field. So companies and organizations and hospital systems have all started to give up their ability to create their own organizational change. And when I talk to organizations, I often say they have to have two approaches. They have to think about the crowd and the lab. So the crowd is the idea of how to empower clinicians and administrators and supporter networks to start using AI and experimenting in ethical, legal ways and then sharing that information with each other. And the lab is, how are we doing R&D about the approach of how to [get] AI to work, not just in direct patient care, right. But also fundamentally, like, what paperwork can you cut out? How can we better explain procedures? Like, what management role can this fill? And we need to be doing active experimentation on that. We can’t just wait for, you know, Microsoft to solve the problems. It has to be at the level of the organizations themselves. LEE: So let’s shift a little bit to the patient. You know, one of the things that we see, and I think everyone is seeing, is that people are turning to chatbots, like ChatGPT, actually to seek healthcare information for, you know, their own health or the health of their loved ones. And there was already, prior to all of this, a trend towards, let’s call it, consumerization of healthcare. So just in the business of healthcare delivery, do you think AI is going to hasten these kinds of trends, or from the consumer’s perspective, what … ? MOLLICK: I mean, absolutely, right. Like, all the early data that we have suggests that for most common medical problems, you should just consult AI, too, right. In fact, there is a real question to ask: at what point does it become unethical for doctors themselves to not ask for a second opinion from the AI because it’s cheap, right? You could overrule it or whatever you want, but like not asking seems foolish. I think the two places where there’s a burning almost, you know, moral imperative is … let’s say, you know, I’m in Philadelphia, I’m a professor, I have access to really good healthcare through the Hospital University of Pennsylvania system. I know doctors. You know, I’m lucky. I’m well connected. If, you know, something goes wrong, I have friends who I can talk to. I have specialists. I’m, you know, pretty well educated in this space. But for most people on the planet, they don’t have access to good medical care, they don’t have good health. It feels like it’s absolutely imperative to say when should you use AI and when not. Are there blind spots? What are those things? And I worry that, like, to me, that would be the crash project I’d be invoking because I’m doing the same thing in education, which is this system is not as good as being in a room with a great teacher who also uses AI to help you, but it’s better than not getting an, you know, to the level of education people get in many cases. Where should we be using it? How do we guide usage in the right way? Because the AI labs aren’t thinking about this. We have to. So, to me, there is a burning need here to understand this. And I worry that people will say, you know, everything that’s true—AI can hallucinate, AI can be biased. All of these things are absolutely true, but people are going to use it. The early indications are that it is quite useful. And unless we take the active role of saying, here’s when to use it, here’s when not to use it, we don’t have a right to say, don’t use this system. And I think, you know, we have to be exploring that. LEE: What do people need to understand about AI? And what should schools, universities, and so on be teaching? MOLLICK: Those are, kind of, two separate questions in lot of ways. I think a lot of people want to teach AI skills, and I will tell you, as somebody who works in this space a lot, there isn’t like an easy, sort of, AI skill, right. I could teach you prompt engineering in two to three classes, but every indication we have is that for most people under most circumstances, the value of prompting, you know, any one case is probably not that useful. A lot of the tricks are disappearing because the AI systems are just starting to use them themselves. So asking good questions, being a good manager, being a good thinker tend to be important, but like magic tricks around making, you know, the AI do something because you use the right phrase used to be something that was real but is rapidly disappearing. So I worry when people say teach AI skills. No one’s been able to articulate to me as somebody who knows AI very well and teaches classes on AI, what those AI skills that everyone should learn are, right. I mean, there’s value in learning a little bit how the models work. There’s a value in working with these systems. A lot of it’s just hands on keyboard kind of work. But, like, we don’t have an easy slam dunk “this is what you learn in the world of AI” because the systems are getting better, and as they get better, they get less sensitive to these prompting techniques. They get better prompting themselves. They solve problems spontaneously and start being agentic. So it’s a hard problem to ask about, like, what do you train someone on? I think getting people experience in hands-on-keyboards, getting them to … there’s like four things I could teach you about AI, and two of them are already starting to disappear. But, like, one is be direct. Like, tell the AI exactly what you want. That’s very helpful. Second, provide as much context as possible. That can include things like acting as a doctor, but also all the information you have. The third is give it step-by-step directions—that’s becoming less important. And the fourth is good and bad examples of the kind of output you want. Those four, that’s like, that’s it as far as the research telling you what to do, and the rest is building intuition. LEE: I’m really impressed that you didn’t give the answer, “Well, everyone should be teaching my book, Co-Intelligence.” [LAUGHS] MOLLICK: Oh, no, sorry! Everybody should be teaching my book Co-Intelligence. I apologize. [LAUGHTER] LEE: It’s good to chuckle about that, but actually, I can’t think of a better book, like, if you were to assign a textbook in any professional education space, I think Co-Intelligence would be number one on my list. Are there other things that you think are essential reading? MOLLICK: That’s a really good question. I think that a lot of things are evolving very quickly. I happen to, kind of, hit a sweet spot with Co-Intelligence to some degree because I talk about how I used it, and I was, sort of, an advanced user of these systems. So, like, it’s, sort of, like my Twitter feed, my online newsletter. I’m just trying to, kind of, in some ways, it’s about trying to make people aware of what these systems can do by just showing a lot, right. Rather than picking one thing, and, like, this is a general-purpose technology. Let’s use it for this. And, like, everybody gets a light bulb for a different reason. So more than reading, it is using, you know, and that can be Copilot or whatever your favorite tool is. But using it. Voice modes help a lot. In terms of readings, I mean, I think that there is a couple of good guides to understanding AI that were originally blog posts. I think Tim Lee has one called Understanding AI (opens in new tab), and it had a good overview … LEE: Yeah, that’s a great one. MOLLICK: … of that topic that I think explains how transformers work, which can give you some mental sense. I think [Andrej] Karpathy (opens in new tab) has some really nice videos of use that I would recommend. Like on the medical side, I think the book that you did, if you’re in medicine, you should read that. I think that that’s very valuable. But like all we can offer are hints in some ways. Like there isn’t … if you’re looking for the instruction manual, I think it can be very frustrating because it’s like you want the best practices and procedures laid out, and we cannot do that, right. That’s not how a system like this works. LEE: Yeah. MOLLICK: It’s not a person, but thinking about it like a person can be helpful, right. LEE: One of the things that has been sort of a fun project for me for the last few years is I have been a founding board member of a new medical school at Kaiser Permanente. And, you know, that medical school curriculum is being formed in this era. But it’s been perplexing to understand, you know, what this means for a medical school curriculum. And maybe even more perplexing for me, at least, is the accrediting bodies, which are extremely important in US medical schools; how accreditors should think about what’s necessary here. Besides the things that you’ve … the, kind of, four key ideas you mentioned, if you were talking to the board of directors of the LCME [Liaison Committee on Medical Education] accrediting body, what’s the one thing you would want them to really internalize? MOLLICK: This is both a fast-moving and vital area. This can’t be viewed like a usual change, which [is], “Let’s see how this works.” Because it’s, like, the things that make medical technologies hard to do, which is like unclear results, limited, you know, expensive use cases where it rolls out slowly. So one or two, you know, advanced medical facilities get access to, you know, proton beams or something else at multi-billion dollars of cost, and that takes a while to diffuse out. That’s not happening here. This is all happening at the same time, all at once. This is now … AI is part of medicine. I mean, there’s a minor point that I’d make that actually is a really important one, which is large language models, generative AI overall, work incredibly differently than other forms of AI. So the other worry I have with some of these accreditors is they blend together algorithmic forms of AI, which medicine has been trying for long time—decision support, algorithmic methods, like, medicine more so than other places has been thinking about those issues. Generative AI, even though it uses the same underlying techniques, is a completely different beast. So, like, even just take the most simple thing of algorithmic aversion, which is a well-understood problem in medicine, right. Which is, so you have a tool that could tell you as a radiologist, you know, the chance of this being cancer; you don’t like it, you overrule it, right. We don’t find algorithmic aversion happening with LLMs in the same way. People actually enjoy using them because it’s more like working with a person. The flaws are different. The approach is different. So you need to both view this as universal applicable today, which makes it urgent, but also as something that is not the same as your other form of AI, and your AI working group that is thinking about how to solve this problem is not the right people here. LEE: You know, I think the world has been trained because of the magic of web search to view computers as question-answering machines. Ask a question, get an answer. MOLLICK: Yes. Yes. LEE: Write a query, get results. And as I have interacted with medical professionals, you can see that medical professionals have that model of a machine in mind. And I think that’s partly, I think psychologically, why hallucination is so alarming. Because you have a mental model of a computer as a machine that has absolutely rock-solid perfect memory recall. But the thing that was so powerful in Co-Intelligence, and we tried to get at this in our book also, is that’s not the sweet spot. It’s this sort of deeper interaction, more of a collaboration. And I thought your use of the term Co-Intelligence really just even in the title of the book tried to capture this. When I think about education, it seems like that’s the first step, to get past this concept of a machine being just a question-answering machine. Do you have a reaction to that idea? MOLLICK: I think that’s very powerful. You know, we’ve been trained over so many years at both using computers but also in science fiction, right. Computers are about cold logic, right. They will give you the right answer, but if you ask it what love is, they explode, right. Like that’s the classic way you defeat the evil robot in Star Trek, right. “Love does not compute.” [LAUGHTER] Instead, we have a system that makes mistakes, is warm, beats doctors in empathy in almost every controlled study on the subject, right. Like, absolutely can outwrite you in a sonnet but will absolutely struggle with giving you the right answer every time. And I think our mental models are just broken for this. And I think you’re absolutely right. And that’s part of what I thought your book does get at really well is, like, this is a different thing. It’s also generally applicable. Again, the model in your head should be kind of like a person even though it isn’t, right. There’s a lot of warnings and caveats to it, but if you start from person, smart person you’re talking to, your mental model will be more accurate than smart machine, even though both are flawed examples, right. So it will make mistakes; it will make errors. The question is, what do you trust it on? What do you not trust it? As you get to know a model, you’ll get to understand, like, I totally don’t trust it for this, but I absolutely trust it for that, right. LEE: All right. So we’re getting to the end of the time we have together. And so I’d just like to get now into something a little bit more provocative. And I get the question all the time. You know, will AI replace doctors? In medicine and other advanced knowledge work, project out five to 10 years. What do think happens? MOLLICK: OK, so first of all, let’s acknowledge systems change much more slowly than individual use. You know, doctors are not individual actors; they’re part of systems, right. So not just the system of a patient who like may or may not want to talk to a machine instead of a person but also legal systems and administrative systems and systems that allocate labor and systems that train people. So, like, it’s hard to imagine that in five to 10 years medicine being so upended that even if AI was better than doctors at every single thing doctors do, that we’d actually see as radical a change in medicine as you might in other fields. I think you will see faster changes happen in consulting and law and, you know, coding, other spaces than medicine. But I do think that there is good reason to suspect that AI will outperform people while still having flaws, right. That’s the difference. We’re already seeing that for common medical questions in enough randomized controlled trials that, you know, best doctors beat AI, but the AI beats the mean doctor, right. Like, that’s just something we should acknowledge is happening at this point. Now, will that work in your specialty? No. Will that work with all the contingent social knowledge that you have in your space? Probably not. Like, these are vignettes, right. But, like, that’s kind of where things are. So let’s assume, right … you’re asking two questions. One is, how good will AI get? LEE: Yeah. MOLLICK: And we don’t know the answer to that question. I will tell you that your colleagues at Microsoft and increasingly the labs, the AI labs themselves, are all saying they think they’ll have a machine smarter than a human at every intellectual task in the next two to three years. If that doesn’t happen, that makes it easier to assume the future, but let’s just assume that that’s the case. I think medicine starts to change with the idea that people feel obligated to use this to help for everything. Your patients will be using it, and it will be your advisor and helper at the beginning phases, right. And I think that I expect people to be better at empathy. I expect better bedside manner. I expect management tasks to become easier. I think administrative burden might lighten if we handle this right way or much worse if we handle it badly. Diagnostic accuracy will increase, right. And then there’s a set of discovery pieces happening, too, right. One of the core goals of all the AI companies is to accelerate medical research. How does that happen and how does that affect us is a, kind of, unknown question. So I think clinicians are in both the eye of the storm and surrounded by it, right. Like, they can resist AI use for longer than most other fields, but everything around them is going to be affected by it. LEE: Well, Ethan, this has been really a fantastic conversation. And, you know, I think in contrast to all the other conversations we’ve had, this one gives especially the leaders in healthcare, you know, people actually trying to lead their organizations into the future, whether it’s in education or in delivery, a lot to think about. So I really appreciate you joining. MOLLICK: Thank you. [TRANSITION MUSIC]   I’m a computing researcher who works with people who are right in the middle of today’s bleeding-edge developments in AI. And because of that, I often lose sight of how to talk to a broader audience about what it’s all about. And so I think one of Ethan’s superpowers is that he has this knack for explaining complex topics in AI in a really accessible way, getting right to the most important points without making it so simple as to be useless. That’s why I rarely miss an opportunity to read up on his latest work. One of the first things I learned from Ethan is the intuition that you can, sort of, think of AI as a very knowledgeable intern. In other words, think of it as a persona that you can interact with, but you also need to be a manager for it and to always assess the work that it does. In our discussion, Ethan went further to stress that there is, because of that, a serious education gap. You know, over the last decade or two, we’ve all been trained, mainly by search engines, to think of computers as question-answering machines. In medicine, in fact, there’s a question-answering application that is really popular called UpToDate (opens in new tab). Doctors use it all the time. But generative AI systems like ChatGPT are different. There’s therefore a challenge in how to break out of the old-fashioned mindset of search to get the full value out of generative AI. The other big takeaway for me was that Ethan pointed out while it’s easy to see productivity gains from AI at the individual level, those same gains, at least today, don’t often translate automatically to organization-wide or system-wide gains. And one, of course, has to conclude that it takes more than just making individuals more productive; the whole system also has to adjust to the realities of AI. Here’s now my interview with Azeem Azhar: LEE: Azeem, welcome. AZEEM AZHAR: Peter, thank you so much for having me.  LEE: You know, I think you’re extremely well known in the world. But still, some of the listeners of this podcast series might not have encountered you before. And so one of the ways I like to ask people to introduce themselves is, how do you explain to your parents what you do every day? AZHAR: Well, I’m very lucky in that way because my mother was the person who got me into computers more than 40 years ago. And I still have that first computer, a ZX81 with a Z80 chip … LEE: Oh wow. AZHAR: … to this day. It sits in my study, all seven and a half thousand transistors and Bakelite plastic that it is. And my parents were both economists, and economics is deeply connected with technology in some sense. And I grew up in the late ’70s and the early ’80s. And that was a time of tremendous optimism around technology. It was space opera, science fiction, robots, and of course, the personal computer and, you know, Bill Gates and Steve Jobs. So that’s where I started. And so, in a way, my mother and my dad, who passed away a few years ago, had always known me as someone who was fiddling with computers but also thinking about economics and society. And so, in a way, it’s easier to explain to them because they’re the ones who nurtured the environment that allowed me to research technology and AI and think about what it means to firms and to the economy at large. LEE: I always like to understand the origin story. And what I mean by that is, you know, what was your first encounter with generative AI? And what was that like? What did you go through? AZHAR: The first real moment was when Midjourney and Stable Diffusion emerged in that summer of 2022. I’d been away on vacation, and I came back—and I’d been off grid, in fact—and the world had really changed. Now, I’d been aware of GPT-3 and GPT-2, which I played around with and with BERT, the original transformer paper about seven or eight years ago, but it was the moment where I could talk to my computer, and it could produce these images, and it could be refined in natural language that really made me think we’ve crossed into a new domain. We’ve gone from AI being highly discriminative to AI that’s able to explore the world in particular ways. And then it was a few months later that ChatGPT came out—November, the 30th. And I think it was the next day or the day after that I said to my team, everyone has to use this, and we have to meet every morning and discuss how we experimented the day before. And we did that for three or four months. And, you know, it was really clear to me in that interface at that point that, you know, we’d absolutely pass some kind of threshold. LEE: And who’s the we that you were experimenting with? AZHAR: So I have a team of four who support me. They’re mostly researchers of different types. I mean, it’s almost like one of those jokes. You know, I have a sociologist, an economist, and an astrophysicist. And, you know, they walk into the bar, [LAUGHTER] or they walk into our virtual team room, and we try to solve problems. LEE: Well, so let’s get now into brass tacks here. And I think I want to start maybe just with an exploration of the economics of all this and economic realities. Because I think in a lot of your work—for example, in your book—you look pretty deeply at how automation generally and AI specifically are transforming certain sectors like finance, manufacturing, and you have a really, kind of, insightful focus on what this means for productivity and which ways, you know, efficiencies are found.   And then you, sort of, balance that with risks, things that can and do go wrong. And so as you take that background and looking at all those other sectors, in what ways are the same patterns playing out or likely to play out in healthcare and medicine? AZHAR: I’m sure we will see really remarkable parallels but also new things going on. I mean, medicine has a particular quality compared to other sectors in the sense that it’s highly regulated, market structure is very different country to country, and it’s an incredibly broad field. I mean, just think about taking a Tylenol and going through laparoscopic surgery. Having an MRI and seeing a physio. I mean, this is all medicine. I mean, it’s hard to imagine a sector that is [LAUGHS] more broad than that. So I think we can start to break it down, and, you know, where we’re seeing things with generative AI will be that the, sort of, softest entry point, which is the medical scribing. And I’m sure many of us have been with clinicians who have a medical scribe running alongside—they’re all on Surface Pros I noticed, right? [LAUGHTER] They’re on the tablet computers, and they’re scribing away. And what that’s doing is, in the words of my friend Eric Topol, it’s giving the clinician time back (opens in new tab), right. They have time back from days that are extremely busy and, you know, full of administrative overload. So I think you can obviously do a great deal with reducing that overload. And within my team, we have a view, which is if you do something five times in a week, you should be writing an automation for it. And if you’re a doctor, you’re probably reviewing your notes, writing the prescriptions, and so on several times a day. So those are things that can clearly be automated, and the human can be in the loop. But I think there are so many other ways just within the clinic that things can help. So, one of my friends, my friend from my junior school—I’ve known him since I was 9—is an oncologist who’s also deeply into machine learning, and he’s in Cambridge in the UK. And he built with Microsoft Research a suite of imaging AI tools from his own discipline, which they then open sourced. So that’s another way that you have an impact, which is that you actually enable the, you know, generalist, specialist, polymath, whatever they are in health systems to be able to get this technology, to tune it to their requirements, to use it, to encourage some grassroots adoption in a system that’s often been very, very heavily centralized. LEE: Yeah. AZHAR: And then I think there are some other things that are going on that I find really, really exciting. So one is the consumerization of healthcare. So I have one of those sleep tracking rings, the Oura (opens in new tab). LEE: Yup. AZHAR: That is building a data stream that we’ll be able to apply more and more AI to. I mean, right now, it’s applying traditional, I suspect, machine learning, but you can imagine that as we start to get more data, we start to get more used to measuring ourselves, we create this sort of pot, a personal asset that we can turn AI to. And there’s still another category. And that other category is one of the completely novel ways in which we can enable patient care and patient pathway. And there’s a fantastic startup in the UK called Neko Health (opens in new tab), which, I mean, does physicals, MRI scans, and blood tests, and so on. It’s hard to imagine Neko existing without the sort of advanced data, machine learning, AI that we’ve seen emerge over the last decade. So, I mean, I think that there are so many ways in which the temperature is slowly being turned up to encourage a phase change within the healthcare sector. And last but not least, I do think that these tools can also be very, very supportive of a clinician’s life cycle. I think we, as patients, we’re a bit …  I don’t know if we’re as grateful as we should be for our clinicians who are putting in 90-hour weeks. [LAUGHTER] But you can imagine a world where AI is able to support not just the clinicians’ workload but also their sense of stress, their sense of burnout. So just in those five areas, Peter, I sort of imagine we could start to fundamentally transform over the course of many years, of course, the way in which people think about their health and their interactions with healthcare systems LEE: I love how you break that down. And I want to press on a couple of things. You also touched on the fact that medicine is, at least in most of the world, is a highly regulated industry. I guess finance is the same way, but they also feel different because the, like, finance sector has to be very responsive to consumers, and consumers are sensitive to, you know, an abundance of choice; they are sensitive to price. Is there something unique about medicine besides being regulated? AZHAR: I mean, there absolutely is. And in finance, as well, you have much clearer end states. So if you’re not in the consumer space, but you’re in the, you know, asset management space, you have to essentially deliver returns against the volatility or risk boundary, right. That’s what you have to go out and do. And I think if you’re in the consumer industry, you can come back to very, very clear measures, net promoter score being a very good example. In the case of medicine and healthcare, it is much more complicated because as far as the clinician is concerned, people are individuals, and we have our own parts and our own responses. If we didn’t, there would never be a need for a differential diagnosis. There’d never be a need for, you know, Let’s try azithromycin first, and then if that doesn’t work, we’ll go to vancomycin, or, you know, whatever it happens to be. You would just know. But ultimately, you know, people are quite different. The symptoms that they’re showing are quite different, and also their compliance is really, really different. I had a back problem that had to be dealt with by, you know, a physio and extremely boring exercises four times a week, but I was ruthless in complying, and my physio was incredibly surprised. He’d say well no one ever does this, and I said, well you know the thing is that I kind of just want to get this thing to go away. LEE: Yeah. AZHAR: And I think that that’s why medicine is and healthcare is so different and more complex. But I also think that’s why AI can be really, really helpful. I mean, we didn’t talk about, you know, AI in its ability to potentially do this, which is to extend the clinician’s presence throughout the week. LEE: Right. Yeah. AZHAR: The idea that maybe some part of what the clinician would do if you could talk to them on Wednesday, Thursday, and Friday could be delivered through an app or a chatbot just as a way of encouraging the compliance, which is often, especially with older patients, one reason why conditions, you know, linger on for longer. LEE: You know, just staying on the regulatory thing, as I’ve thought about this, the one regulated sector that I think seems to have some parallels to healthcare is energy delivery, energy distribution. Because like healthcare, as a consumer, I don’t have choice in who delivers electricity to my house. And even though I care about it being cheap or at least not being overcharged, I don’t have an abundance of choice. I can’t do price comparisons. And there’s something about that, just speaking as a consumer of both energy and a consumer of healthcare, that feels similar. Whereas other regulated industries, you know, somehow, as a consumer, I feel like I have a lot more direct influence and power. Does that make any sense to someone, you know, like you, who’s really much more expert in how economic systems work? AZHAR: I mean, in a sense, one part of that is very, very true. You have a limited panel of energy providers you can go to, and in the US, there may be places where you have no choice. I think the area where it’s slightly different is that as a consumer or a patient, you can actually make meaningful choices and changes yourself using these technologies, and people used to joke about you know asking Dr. Google. But Dr. Google is not terrible, particularly if you go to WebMD. And, you know, when I look at long-range change, many of the regulations that exist around healthcare delivery were formed at a point before people had access to good quality information at the touch of their fingertips or when educational levels in general were much, much lower. And many regulations existed because of the incumbent power of particular professional sectors. I’ll give you an example from the United Kingdom. So I have had asthma all of my life. That means I’ve been taking my inhaler, Ventolin, and maybe a steroid inhaler for nearly 50 years. That means that I know … actually, I’ve got more experience, and I—in some sense—know more about it than a general practitioner. LEE: Yeah. AZHAR: And until a few years ago, I would have to go to a general practitioner to get this drug that I’ve been taking for five decades, and there they are, age 30 or whatever it is. And a few years ago, the regulations changed. And now pharmacies can … or pharmacists can prescribe those types of drugs under certain conditions directly. LEE: Right. AZHAR: That was not to do with technology. That was to do with incumbent lock-in. So when we look at the medical industry, the healthcare space, there are some parallels with energy, but there are a few little things that the ability that the consumer has to put in some effort to learn about their condition, but also the fact that some of the regulations that exist just exist because certain professions are powerful. LEE: Yeah, one last question while we’re still on economics. There seems to be a conundrum about productivity and efficiency in healthcare delivery because I’ve never encountered a doctor or a nurse that wants to be able to handle even more patients than they’re doing on a daily basis. And so, you know, if productivity means simply, well, your rounds can now handle 16 patients instead of eight patients, that doesn’t seem necessarily to be a desirable thing. So how can we or should we be thinking about efficiency and productivity since obviously costs are, in most of the developed world, are a huge, huge problem? AZHAR: Yes, and when you described doubling the number of patients on the round, I imagined you buying them all roller skates so they could just whizz around [LAUGHTER] the hospital faster and faster than ever before. We can learn from what happened with the introduction of electricity. Electricity emerged at the end of the 19th century, around the same time that cars were emerging as a product, and car makers were very small and very artisanal. And in the early 1900s, some really smart car makers figured out that electricity was going to be important. And they bought into this technology by putting pendant lights in their workshops so they could “visit more patients.” Right? LEE: Yeah, yeah. AZHAR: They could effectively spend more hours working, and that was a productivity enhancement, and it was noticeable. But, of course, electricity fundamentally changed the productivity by orders of magnitude of people who made cars starting with Henry Ford because he was able to reorganize his factories around the electrical delivery of power and to therefore have the moving assembly line, which 10xed the productivity of that system. So when we think about how AI will affect the clinician, the nurse, the doctor, it’s much easier for us to imagine it as the pendant light that just has them working later … LEE: Right. AZHAR: … than it is to imagine a reconceptualization of the relationship between the clinician and the people they care for. And I’m not sure. I don’t think anybody knows what that looks like. But, you know, I do think that there will be a way that this changes, and you can see that scale out factor. And it may be, Peter, that what we end up doing is we end up saying, OK, because we have these brilliant AIs, there’s a lower level of training and cost and expense that’s required for a broader range of conditions that need treating. And that expands the market, right. That expands the market hugely. It’s what has happened in the market for taxis or ride sharing. The introduction of Uber and the GPS system … LEE: Yup. AZHAR: … has meant many more people now earn their living driving people around in their cars. And at least in London, you had to be reasonably highly trained to do that. So I can see a reorganization is possible. Of course, entrenched interests, the economic flow … and there are many entrenched interests, particularly in the US between the health systems and the, you know, professional bodies that might slow things down. But I think a reimagining is possible. And if I may, I’ll give you one example of that, which is, if you go to countries outside of the US where there are many more sick people per doctor, they have incentives to change the way they deliver their healthcare. And well before there was AI of this quality around, there was a few cases of health systems in India—Aravind Eye Care (opens in new tab) was one, and Narayana Hrudayalaya [now known as Narayana Health (opens in new tab)] was another. And in the latter, they were a cardiac care unit where you couldn’t get enough heart surgeons. LEE: Yeah, yep. AZHAR: So specially trained nurses would operate under the supervision of a single surgeon who would supervise many in parallel. So there are ways of increasing the quality of care, reducing the cost, but it does require a systems change. And we can’t expect a single bright algorithm to do it on its own. LEE: Yeah, really, really interesting. So now let’s get into regulation. And let me start with this question. You know, there are several startup companies I’m aware of that are pushing on, I think, a near-term future possibility that a medical AI for consumer might be allowed, say, to prescribe a medication for you, something that would normally require a doctor or a pharmacist, you know, that is certified in some way, licensed to do. Do you think we’ll get to a point where for certain regulated activities, humans are more or less cut out of the loop? AZHAR: Well, humans would have been in the loop because they would have provided the training data, they would have done the oversight, the quality control. But to your question in general, would we delegate an important decision entirely to a tested set of algorithms? I’m sure we will. We already do that. I delegate less important decisions like, What time should I leave for the airport to Waze. I delegate more important decisions to the automated braking in my car. We will do this at certain levels of risk and threshold. If I come back to my example of prescribing Ventolin. It’s really unclear to me that the prescription of Ventolin, this incredibly benign bronchodilator that is only used by people who’ve been through the asthma process, needs to be prescribed by someone who’s gone through 10 years or 12 years of medical training. And why that couldn’t be prescribed by an algorithm or an AI system. LEE: Right. Yep. Yep. AZHAR: So, you know, I absolutely think that that will be the case and could be the case. I can’t really see what the objections are. And the real issue is where do you draw the line of where you say, “Listen, this is too important,” or “The cost is too great,” or “The side effects are too high,” and therefore this is a point at which we want to have some, you know, human taking personal responsibility, having a liability framework in place, having a sense that there is a person with legal agency who signed off on this decision. And that line I suspect will start fairly low, and what we’d expect to see would be that that would rise progressively over time. LEE: What you just said, that scenario of your personal asthma medication, is really interesting because your personal AI might have the benefit of 50 years of your own experience with that medication. So, in a way, there is at least the data potential for, let’s say, the next prescription to be more personalized and more tailored specifically for you. AZHAR: Yes. Well, let’s dig into this because I think this is super interesting, and we can look at how things have changed. So 15 years ago, if I had a bad asthma attack, which I might have once a year, I would have needed to go and see my general physician. In the UK, it’s very difficult to get an appointment. I would have had to see someone privately who didn’t know me at all because I’ve just walked in off the street, and I would explain my situation. It would take me half a day. Productivity lost. I’ve been miserable for a couple of days with severe wheezing. Then a few years ago the system changed, a protocol changed, and now I have a thing called a rescue pack, which includes prednisolone steroids. It includes something else I’ve just forgotten, and an antibiotic in case I get an upper respiratory tract infection, and I have an “algorithm.” It’s called a protocol. It’s printed out. It’s a flowchart I answer various questions, and then I say, “I’m going to prescribe this to myself.” You know, UK doctors don’t prescribe prednisolone, or prednisone as you may call it in the US, at the drop of a hat, right. It’s a powerful steroid. I can self-administer, and I can now get that repeat prescription without seeing a physician a couple of times a year. And the algorithm, the “AI” is, it’s obviously been done in PowerPoint naturally, and it’s a bunch of arrows. [LAUGHS] Surely, surely, an AI system is going to be more sophisticated, more nuanced, and give me more assurance that I’m making the right decision around something like that. LEE: Yeah. Well, at a minimum, the AI should be able to make that PowerPoint the next time. [LAUGHS] AZHAR: Yeah, yeah. Thank god for Clippy. Yes. LEE: So, you know, I think in our book, we had a lot of certainty about most of the things we’ve discussed here, but one chapter where I felt we really sort of ran out of ideas, frankly, was on regulation. And, you know, what we ended up doing for that chapter is … I can’t remember if it was Carey’s or Zak’s idea, but we asked GPT-4 to have a conversation, a debate with itself [LAUGHS], about regulation. And we made some minor commentary on that. And really, I think we took that approach because we just didn’t have much to offer. By the way, in our defense, I don’t think anyone else had any better ideas anyway. AZHAR: Right. LEE: And so now two years later, do we have better ideas about the need for regulation, the frameworks around which those regulations should be developed, and, you know, what should this look like? AZHAR: So regulation is going to be in some cases very helpful because it provides certainty for the clinician that they’re doing the right thing, that they are still insured for what they’re doing, and it provides some degree of confidence for the patient. And we need to make sure that the claims that are made stand up to quite rigorous levels, where ideally there are RCTs [randomized control trials], and there are the classic set of processes you go through. You do also want to be able to experiment, and so the question is: as a regulator, how can you enable conditions for there to be experimentation? And what is experimentation? Experimentation is learning so that every element of the system can learn from this experience. So finding that space where there can be bit of experimentation, I think, becomes very, very important. And a lot of this is about experience, so I think the first digital therapeutics have received FDA approval, which means there are now people within the FDA who understand how you go about running an approvals process for that, and what that ends up looking like—and of course what we’re very good at doing in this sort of modern hyper-connected world—is we can share that expertise, that knowledge, that experience very, very quickly. So you go from one approval a year to a hundred approvals a year to a thousand approvals a year. So we will then actually, I suspect, need to think about what is it to approve digital therapeutics because, unlike big biological molecules, we can generate these digital therapeutics at the rate of knots [very rapidly]. LEE: Yes. AZHAR: Every road in Hayes Valley in San Francisco, right, is churning out new startups who will want to do things like this. So then, I think about, what does it mean to get approved if indeed it gets approved? But we can also go really far with things that don’t require approval. I come back to my sleep tracking ring. So I’ve been wearing this for a few years, and when I go and see my doctor or I have my annual checkup, one of the first things that he asks is how have I been sleeping. And in fact, I even sync my sleep tracking data to their medical record system, so he’s saying … hearing what I’m saying, but he’s actually pulling up the real data going, This patient’s lying to me again. Of course, I’m very truthful with my doctor, as we should all be. [LAUGHTER] LEE: You know, actually, that brings up a point that consumer-facing health AI has to deal with pop science, bad science, you know, weird stuff that you hear on Reddit. And because one of the things that consumers want to know always is, you know, what’s the truth? AZHAR: Right. LEE: What can I rely on? And I think that somehow feels different than an AI that you actually put in the hands of, let’s say, a licensed practitioner. And so the regulatory issues seem very, very different for these two cases somehow. AZHAR: I agree, they’re very different. And I think for a lot of areas, you will want to build AI systems that are first and foremost for the clinician, even if they have patient extensions, that idea that the clinician can still be with a patient during the week. And you’ll do that anyway because you need the data, and you also need a little bit of a liability shield to have like a sensible person who’s been trained around that. And I think that’s going to be a very important pathway for many AI medical crossovers. We’re going to go through the clinician. LEE: Yeah. AZHAR: But I also do recognize what you say about the, kind of, kooky quackery that exists on Reddit. Although on Creatine, Reddit may yet prove to have been right. [LAUGHTER] LEE: Yeah, that’s right. Yes, yeah, absolutely. Yeah. AZHAR: Sometimes it’s right. And I think that it serves a really good role as a field of extreme experimentation. So if you’re somebody who makes a continuous glucose monitor traditionally given to diabetics but now lots of people will wear them—and sports people will wear them—you probably gathered a lot of extreme tail distribution data by reading the Reddit/biohackers … LEE: Yes. AZHAR: … for the last few years, where people were doing things that you would never want them to really do with the CGM [continuous glucose monitor]. And so I think we shouldn’t understate how important that petri dish can be for helping us learn what could happen next. LEE: Oh, I think it’s absolutely going to be essential and a bigger thing in the future. So I think I just want to close here then with one last question. And I always try to be a little bit provocative with this. And so as you look ahead to what doctors and nurses and patients might be doing two years from now, five years from now, 10 years from now, do you have any kind of firm predictions? AZHAR: I’m going to push the boat out, and I’m going to go further out than closer in. LEE: OK. [LAUGHS] AZHAR: As patients, we will have many, many more touch points and interaction with our biomarkers and our health. We’ll be reading how well we feel through an array of things. And some of them we’ll be wearing directly, like sleep trackers and watches. And so we’ll have a better sense of what’s happening in our lives. It’s like the moment you go from paper bank statements that arrive every month to being able to see your account in real time. LEE: Yes. AZHAR: And I suspect we’ll have … we’ll still have interactions with clinicians because societies that get richer see doctors more, societies that get older see doctors more, and we’re going to be doing both of those over the coming 10 years. But there will be a sense, I think, of continuous health engagement, not in an overbearing way, but just in a sense that we know it’s there, we can check in with it, it’s likely to be data that is compiled on our behalf somewhere centrally and delivered through a user experience that reinforces agency rather than anxiety. And we’re learning how to do that slowly. I don’t think the health apps on our phones and devices have yet quite got that right. And that could help us personalize problems before they arise, and again, I use my experience for things that I’ve tracked really, really well. And I know from my data and from how I’m feeling when I’m on the verge of one of those severe asthma attacks that hits me once a year, and I can take a little bit of preemptive measure, so I think that that will become progressively more common and that sense that we will know our baselines. I mean, when you think about being an athlete, which is something I think about, but I could never ever do, [LAUGHTER] but what happens is you start with your detailed baselines, and that’s what your health coach looks at every three or four months. For most of us, we have no idea of our baselines. You we get our blood pressure measured once a year. We will have baselines, and that will help us on an ongoing basis to better understand and be in control of our health. And then if the product designers get it right, it will be done in a way that doesn’t feel invasive, but it’ll be done in a way that feels enabling. We’ll still be engaging with clinicians augmented by AI systems more and more because they will also have gone up the stack. They won’t be spending their time on just “take two Tylenol and have a lie down” type of engagements because that will be dealt with earlier on in the system. And so we will be there in a very, very different set of relationships. And they will feel that they have different ways of looking after our health. LEE: Azeem, it’s so comforting to hear such a wonderfully optimistic picture of the future of healthcare. And I actually agree with everything you’ve said. Let me just thank you again for joining this conversation. I think it’s been really fascinating. And I think somehow the systemic issues, the systemic issues that you tend to just see with such clarity, I think are going to be the most, kind of, profound drivers of change in the future. So thank you so much. AZHAR: Well, thank you, it’s been my pleasure, Peter, thank you. [TRANSITION MUSIC]   I always think of Azeem as a systems thinker. He’s always able to take the experiences of new technologies at an individual level and then project out to what this could mean for whole organizations and whole societies. In our conversation, I felt that Azeem really connected some of what we learned in a previous episode—for example, from Chrissy Farr—on the evolving consumerization of healthcare to the broader workforce and economic impacts that we’ve heard about from Ethan Mollick.   Azeem’s personal story about managing his asthma was also a great example. You know, he imagines a future, as do I, where personal AI might assist and remember decades of personal experience with a condition like asthma and thereby know more than any human being could possibly know in a deeply personalized and effective way, leading to better care. Azeem’s relentless optimism about our AI future was also so heartening to hear. Both of these conversations leave me really optimistic about the future of AI in medicine. At the same time, it is pretty sobering to realize just how much we’ll all need to change in pretty fundamental and maybe even in radical ways. I think a big insight I got from these conversations is how we interact with machines is going to have to be altered not only at the individual level, but at the company level and maybe even at the societal level. Since my conversation with Ethan and Azeem, there have been some pretty important developments that speak directly to this. Just last week at Build (opens in new tab), which is Microsoft’s yearly developer conference, we announced a slew of AI agent technologies. Our CEO, Satya Nadella, in fact, started his keynote by going online in a GitHub developer environment and then assigning a coding task to an AI agent, basically treating that AI as a full-fledged member of a development team. Other agents, for example, a meeting facilitator, a data analyst, a business researcher, travel agent, and more were also shown during the conference. But pertinent to healthcare specifically, what really blew me away was the demonstration of a healthcare orchestrator agent. And the specific thing here was in Stanford’s cancer treatment center, when they are trying to decide on potentially experimental treatments for cancer patients, they convene a meeting of experts. That is typically called a tumor board. And so this AI healthcare orchestrator agent actually participated as a full-fledged member of a tumor board meeting to help bring data together, make sure that the latest medical knowledge was brought to bear, and to assist in the decision-making around a patient’s cancer treatment. It was pretty amazing. [THEME MUSIC] A big thank-you again to Ethan and Azeem for sharing their knowledge and understanding of the dynamics between AI and society more broadly. And to our listeners, thank you for joining us. I’m really excited for the upcoming episodes, including discussions on medical students’ experiences with AI and AI’s influence on the operation of health systems and public health departments. We hope you’ll continue to tune in. Until next time. [MUSIC FADES]
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  • Discover Magazine een koppeling hebt gedeeld
    2025-05-22 23:11:28 - Translate -
    Ash-Rich Volcanic Eruptions Change Icy Clouds, Regulating Radiation on Earth

    Although volcanic eruptions are mostly known for their devastating impacts on land, they also influence the atmosphere in surprising ways. Particles that are catapulted into the sky by eruptions can alter the composition of clouds, cooling the surface below. Volcanic ash, as new research suggests, plays an unexpected role in cloud formation by manipulating ice crystals.A new study published in Science Advances found that volcanic ash enables ice to take shape in clouds, a process that hasn’t been studied extensively before. The environmental effects — how volcanic ash contributes to radiation on Earth — are still up for debate, but a better understanding of cloud formation could inform geoengineering efforts that aspire to slow global warming. How Clouds and Volcanoes Impact Radiation Clouds have a complex relationship with radiation. This is seen with their influence on Earth’s radiation budget, the balance between incoming shortwave radiation from the Sun and outgoing longwave radiation from Earth. Depending on the thickness and elevation of a cloud, it can either have an absorbing or reflecting effect. Lower, thicker clouds reflect more radiation coming from the Sun and cool the Earth’s surface. Higher, thinner clouds reflect less incoming solar radiation and even tend to absorb outgoing infrared radiation constantly emitted by Earth itself. Cirrus clouds — wispy, icy clouds that sit high in the atmosphere — generally have a net warming effect on Earth. Volcanoes also affect Earth’s radiation budget through the particles they eject. Scientists have mostly focused on sulfate aerosols, which form after sulfur dioxide is released into the atmosphere by an eruption. Although sulfate aerosols can deplete the ozone layer and produce acid rain, they also help cool Earth by reflecting solar radiation. The Impact of Volcanic Ash Volcanic ash hasn’t garnered as much attention as sulfate aerosols, but the new study proves that it also needs to be considered in climate discussions. In the study, researchers from the Lawrence Livermore National Laboratorygathered information on ice crystals in clouds by examining radar and lidar data from NASA’s Cloudsat and CALIPSO missions. The team found that ash-rich volcanic eruptions caused clouds to host fewer, but larger ice crystals. “At the beginning of the study, we did expect clouds affected by volcanic eruptions to look different from natural clouds, but not in the way we ultimately found,” said Lin Lin, a scientist at LLNL, in a statement. “We anticipated that volcanic aerosols would lead to an increase in the number of ice crystals in clouds. But to our surprise, the data showed the opposite.”Dealing with Cirrus CloudsThe team initially thought that an eruption would cause homogenous nucleation, in which ice forms spontaneously without the need for a surface. They instead found that ash-heavy eruptions prompt an opposite reaction called heterogenous nucleation, in which ice needs an “impurity” like volcanic ash to form. After an eruption, water droplets stick to ash particles before they can get cold enough to freeze. By producing fewer and larger ice crystals, clouds that undergo heterogenous nucleation end up reflecting less solar radiation, but they also allow more radiation from Earth to escape into space. The team also determined that ash-rich eruptions led to a higher frequency of cirrus clouds. The researchers say volcanic ash needs to be implemented in more climate models to understand exactly how all of these factors influence Earth’s surface temperature. Further studies on volcanic ash could also guide plans for cirrus cloud thinning, a proposed idea for mitigating global warming. This process would involve spraying aerosols into the atmosphere to thin or eliminate cirrus clouds, allowing more longwave radiation to leave Earth. Article SourcesOur writers at Discovermagazine.com use peer-reviewed studies and high-quality sources for our articles, and our editors review for scientific accuracy and editorial standards. Review the sources used below for this article:Science Advances. Ice nucleation by volcanic ash greatly alters cirrus cloud propertiesJack Knudson is an assistant editor at Discover with a strong interest in environmental science and history. Before joining Discover in 2023, he studied journalism at the Scripps College of Communication at Ohio University and previously interned at Recycling Today magazine.
    #ashrich #volcanic #eruptions #change #icy
    Ash-Rich Volcanic Eruptions Change Icy Clouds, Regulating Radiation on Earth
    Although volcanic eruptions are mostly known for their devastating impacts on land, they also influence the atmosphere in surprising ways. Particles that are catapulted into the sky by eruptions can alter the composition of clouds, cooling the surface below. Volcanic ash, as new research suggests, plays an unexpected role in cloud formation by manipulating ice crystals.A new study published in Science Advances found that volcanic ash enables ice to take shape in clouds, a process that hasn’t been studied extensively before. The environmental effects — how volcanic ash contributes to radiation on Earth — are still up for debate, but a better understanding of cloud formation could inform geoengineering efforts that aspire to slow global warming. How Clouds and Volcanoes Impact Radiation Clouds have a complex relationship with radiation. This is seen with their influence on Earth’s radiation budget, the balance between incoming shortwave radiation from the Sun and outgoing longwave radiation from Earth. Depending on the thickness and elevation of a cloud, it can either have an absorbing or reflecting effect. Lower, thicker clouds reflect more radiation coming from the Sun and cool the Earth’s surface. Higher, thinner clouds reflect less incoming solar radiation and even tend to absorb outgoing infrared radiation constantly emitted by Earth itself. Cirrus clouds — wispy, icy clouds that sit high in the atmosphere — generally have a net warming effect on Earth. Volcanoes also affect Earth’s radiation budget through the particles they eject. Scientists have mostly focused on sulfate aerosols, which form after sulfur dioxide is released into the atmosphere by an eruption. Although sulfate aerosols can deplete the ozone layer and produce acid rain, they also help cool Earth by reflecting solar radiation. The Impact of Volcanic Ash Volcanic ash hasn’t garnered as much attention as sulfate aerosols, but the new study proves that it also needs to be considered in climate discussions. In the study, researchers from the Lawrence Livermore National Laboratorygathered information on ice crystals in clouds by examining radar and lidar data from NASA’s Cloudsat and CALIPSO missions. The team found that ash-rich volcanic eruptions caused clouds to host fewer, but larger ice crystals. “At the beginning of the study, we did expect clouds affected by volcanic eruptions to look different from natural clouds, but not in the way we ultimately found,” said Lin Lin, a scientist at LLNL, in a statement. “We anticipated that volcanic aerosols would lead to an increase in the number of ice crystals in clouds. But to our surprise, the data showed the opposite.”Dealing with Cirrus CloudsThe team initially thought that an eruption would cause homogenous nucleation, in which ice forms spontaneously without the need for a surface. They instead found that ash-heavy eruptions prompt an opposite reaction called heterogenous nucleation, in which ice needs an “impurity” like volcanic ash to form. After an eruption, water droplets stick to ash particles before they can get cold enough to freeze. By producing fewer and larger ice crystals, clouds that undergo heterogenous nucleation end up reflecting less solar radiation, but they also allow more radiation from Earth to escape into space. The team also determined that ash-rich eruptions led to a higher frequency of cirrus clouds. The researchers say volcanic ash needs to be implemented in more climate models to understand exactly how all of these factors influence Earth’s surface temperature. Further studies on volcanic ash could also guide plans for cirrus cloud thinning, a proposed idea for mitigating global warming. This process would involve spraying aerosols into the atmosphere to thin or eliminate cirrus clouds, allowing more longwave radiation to leave Earth. Article SourcesOur writers at Discovermagazine.com use peer-reviewed studies and high-quality sources for our articles, and our editors review for scientific accuracy and editorial standards. Review the sources used below for this article:Science Advances. Ice nucleation by volcanic ash greatly alters cirrus cloud propertiesJack Knudson is an assistant editor at Discover with a strong interest in environmental science and history. Before joining Discover in 2023, he studied journalism at the Scripps College of Communication at Ohio University and previously interned at Recycling Today magazine. #ashrich #volcanic #eruptions #change #icy
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    Ash-Rich Volcanic Eruptions Change Icy Clouds, Regulating Radiation on Earth
    Although volcanic eruptions are mostly known for their devastating impacts on land, they also influence the atmosphere in surprising ways. Particles that are catapulted into the sky by eruptions can alter the composition of clouds, cooling the surface below. Volcanic ash, as new research suggests, plays an unexpected role in cloud formation by manipulating ice crystals.A new study published in Science Advances found that volcanic ash enables ice to take shape in clouds, a process that hasn’t been studied extensively before. The environmental effects — how volcanic ash contributes to radiation on Earth — are still up for debate, but a better understanding of cloud formation could inform geoengineering efforts that aspire to slow global warming. How Clouds and Volcanoes Impact Radiation Clouds have a complex relationship with radiation. This is seen with their influence on Earth’s radiation budget, the balance between incoming shortwave radiation from the Sun and outgoing longwave radiation from Earth. Depending on the thickness and elevation of a cloud, it can either have an absorbing or reflecting effect. Lower, thicker clouds reflect more radiation coming from the Sun and cool the Earth’s surface. Higher, thinner clouds reflect less incoming solar radiation and even tend to absorb outgoing infrared radiation constantly emitted by Earth itself. Cirrus clouds — wispy, icy clouds that sit high in the atmosphere — generally have a net warming effect on Earth. Volcanoes also affect Earth’s radiation budget through the particles they eject. Scientists have mostly focused on sulfate aerosols, which form after sulfur dioxide is released into the atmosphere by an eruption. Although sulfate aerosols can deplete the ozone layer and produce acid rain, they also help cool Earth by reflecting solar radiation. The Impact of Volcanic Ash Volcanic ash hasn’t garnered as much attention as sulfate aerosols, but the new study proves that it also needs to be considered in climate discussions. In the study, researchers from the Lawrence Livermore National Laboratory (LLNL) gathered information on ice crystals in clouds by examining radar and lidar data from NASA’s Cloudsat and CALIPSO missions. The team found that ash-rich volcanic eruptions caused clouds to host fewer, but larger ice crystals. “At the beginning of the study, we did expect clouds affected by volcanic eruptions to look different from natural clouds, but not in the way we ultimately found,” said Lin Lin, a scientist at LLNL, in a statement. “We anticipated that volcanic aerosols would lead to an increase in the number of ice crystals in clouds. But to our surprise, the data showed the opposite.”Dealing with Cirrus CloudsThe team initially thought that an eruption would cause homogenous nucleation, in which ice forms spontaneously without the need for a surface. They instead found that ash-heavy eruptions prompt an opposite reaction called heterogenous nucleation, in which ice needs an “impurity” like volcanic ash to form. After an eruption, water droplets stick to ash particles before they can get cold enough to freeze. By producing fewer and larger ice crystals, clouds that undergo heterogenous nucleation end up reflecting less solar radiation, but they also allow more radiation from Earth to escape into space. The team also determined that ash-rich eruptions led to a higher frequency of cirrus clouds. The researchers say volcanic ash needs to be implemented in more climate models to understand exactly how all of these factors influence Earth’s surface temperature. Further studies on volcanic ash could also guide plans for cirrus cloud thinning, a proposed idea for mitigating global warming. This process would involve spraying aerosols into the atmosphere to thin or eliminate cirrus clouds, allowing more longwave radiation to leave Earth. Article SourcesOur writers at Discovermagazine.com use peer-reviewed studies and high-quality sources for our articles, and our editors review for scientific accuracy and editorial standards. Review the sources used below for this article:Science Advances. Ice nucleation by volcanic ash greatly alters cirrus cloud propertiesJack Knudson is an assistant editor at Discover with a strong interest in environmental science and history. Before joining Discover in 2023, he studied journalism at the Scripps College of Communication at Ohio University and previously interned at Recycling Today magazine.
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  • ArchitectureNow een koppeling hebt gedeeld
    2025-05-19 22:23:32 - Translate -
    Sharing Hundertwasser’s legacy

    Hundertwasser’s home in the Kaurinui Valley, located just 20 minutes north of Kawakawa and less than a three-hour drive from Auckland, is to be the only one of his homes around the world that is open to the public. I was given a tour by volunteers from Living Hundertwasser, including Richard Smart, who worked closely with Hundertwasser for eight years and now represents the non-profit Hundertwasser Foundation in New Zealand.
    Born Friedrich Stowasser in Austria in 1928, Hundertwasser was a world-famous painter and architect, renowned for his radical views and eccentric approach to design. His childhood, marked by the devastations of World War II, led him to find solace in painting alternative worlds filled with nature, vibrant colours and abstract forms that would later influence the trajectory of his environmentalism and architecture.1

    The Eyeslit, Kaurinui, 2025.© Image: 

    Richard Smart

    In 1976, he settled in New Zealand, purchasing a dairy farm in the Kaurinui Valley with the intention of setting nature free.2 He did just that: over two decades planting 150,000 trees and widening the Kaurinui Stream that flows through the farm. His philosophy is embodied in every aspect of the property and, despite recent health-and-safety upgrades, Hundertwasser’s dwellings remain as he left them, down to his last shopping list and paintbrushes left on the table.
    The tour begins at the Eyeslit, a Hundertwasser design built after his death, replacing the old decaying farmhouse. Aligned with his distinctive style, it features vibrant pink walls, colourful mosaics and columns reminiscent of his iconic Kawakawa toilets. The Eyeslit serves as a communal space for a pre-tour introduction to Hundertwasser and his legacy that lives on in Kaurinui.

    The Bottlehaus, Kaurinui.©  Image: 

    Richard Smart

    The tour continues through four of his six idiosyncratic dwellings scattered throughout the property, each reflecting his ecological philosophies. The next stop is The Boatshed, a gabled timber building, home to his boat, La Giudecca. Across a bridge over the Kaurinui Stream is The Cave, a space dug into the hillside, containing a bench and hundreds of wētā. Returning over the stream, we arrive at The Pigsty, Hundertwasser’s primary dwelling, which, true to its name, is a former pigsty converted into a habitable space. Inside, a hallway stretches the length of the home, with the kitchen, dining and living room, and a combined bedroom and bathroom branching off. It is built from recycled glass bottles and natural materials, such as earth bricks and logs laid on their sides, extending from inside to outside, mortared in place with a lime, cement and sawdust mixture. With its spontaneously vegetated green roof, felled tree trunk columns and uneven interior floors, the dwelling echoes his philosophy that buildings, like human skin, should grow and wrinkle over time, evolving alongside nature.3

    Mountain Hut, Kaurinui, 1994/95.©  Image: 

    Richard Smart

    The Bottlehaus, originally the farm’s milking shed, is Hundertwasser’s other main residence. The interior is filled with natural light from the polycarbonate skylight and bottle walls, providing perfect conditions for painting. Not yet included in the tour because of their distance are the Railway Hut and Mountain Hut. Smart recounts how he and his children would hike up to the Mountain Hut, spending the night in the home, built three-quarters underground. The walls and floor are clay earth and the roof, covered in wild greenery, sits just above the ground’s surface.
    Hundertwasser’s alignment with Māori culture is reflected throughout his homes; adorning the walls are timber-carved tiki and the koru flag he designed for New Zealand, symbolising a unified national identity. Hundertwasser was inherently nomadic, moving between buildings based on their various functions, inadvertently resembling the organisation of customary Māori papakāinga settlements, where buildings serve distinct purposes. Māori would move between kāinga seasonally, leaving structures built from natural materials to decay and return to the earth. At the tour’s final stop, the Exhibition Building, a letter from Hundertwasser’s friend A. D. Fagan in 1974 describes him as a guardian of the land, a sentiment akin to Māori identification as kaitiaki – guardians of the whenua. Before his death, Hundertwasser expressed his desire for Māori artists to have equal opportunities in New Zealand. This wish was realised in the Whangārei Hundertwasser Art Centre and Wairau Māori Art Gallery, completed in 2022.4
    Throughout the property, Hundertwasser’s interventions – from a waterwheel and outdoor bath to timber plank bridges and ladders feeding into ponds – speak to a lifestyle that reinforces his commitment to living in harmony with nature. In contrast to his bold European architecture, Hundertwasser’s New Zealand home is more subdued and organic, blending seamlessly into the forest, indistinguishable from the natural environment. As Living Hundertwasser volunteer Clive Jackson explains, “He wanted to let the colours of nature speak.” He allowed nature to exist in its most wild and natural state, supporting his 1983 Peace Treaty with Nature, where he asserted that humanity must put itself behind ecological barriers so the earth can regenerate.5 As an example, he considered trees to be fellow ‘tenants’ on the property, who ‘paid rent’ through their provision of oxygen, beauty and joy.6
    Hundertwasser died in 2000 and, at his own request, was buried under a tulip tree at Kaurinui, his body returning to the earth to nourish the ‘tree tenant’. This final act encapsulates his lifelong philosophy of humanity in harmony with nature and, as such, he lives on through the property.
    Hundertwasser famously stated, “We are only guests of nature and must behave accordingly. Man is the most dangerous pest ever to devastate the earth.”7 In a world where modern architecture is disrupting the natural environment and climate, Kaurinui offers a blueprint for a return to ‘original nature’ – a more sustainable, symbiotic relationship with the earth, and one that resonates with our country’s indigenous identity and the role we must assume as kaitiaki, guardians, of the natural world.
    REFERENCES
    1 Nir Barak, 2022, ‘Friedensreich Hundertwasser’, The Architectural Review, 18 October 2022.
    2 Andreas J. Hirsch, 2022, ‘Hundertwasser’s “Five Skins” Unfold’, in Hundertwasser in New Zealand: The Art of Creating Paradise. Auckland: Oratia Books, p. 72.
    3 Wieland Schmied, 2007, For a More Human Architecture in Harmony with Nature: Hundertwasser Architecture. Köln: Taschen, p. 259.
    4 Cooperation Agreement 2016, p. 24.
    5 Friedensreich Hundertwasser, 1983, Peace Treaty with Nature, Hundertwasser Foundation. hundertwasser.com/en/texts/friedensvertrag_mit_der_natur
    6 Wieland Schmied, 2007, For a More Human Architecture in Harmony with Nature: Hundertwasser Architecture, p. 86.
    7 Hundertwasser Foundation. 2016. Hundertwasser Architektur & Philosophie. Germany: Wörner Verlag GmbH, p. 30.
    #sharing #hundertwassers #legacy
    Sharing Hundertwasser’s legacy
    Hundertwasser’s home in the Kaurinui Valley, located just 20 minutes north of Kawakawa and less than a three-hour drive from Auckland, is to be the only one of his homes around the world that is open to the public. I was given a tour by volunteers from Living Hundertwasser, including Richard Smart, who worked closely with Hundertwasser for eight years and now represents the non-profit Hundertwasser Foundation in New Zealand. Born Friedrich Stowasser in Austria in 1928, Hundertwasser was a world-famous painter and architect, renowned for his radical views and eccentric approach to design. His childhood, marked by the devastations of World War II, led him to find solace in painting alternative worlds filled with nature, vibrant colours and abstract forms that would later influence the trajectory of his environmentalism and architecture.1 The Eyeslit, Kaurinui, 2025.© Image:  Richard Smart In 1976, he settled in New Zealand, purchasing a dairy farm in the Kaurinui Valley with the intention of setting nature free.2 He did just that: over two decades planting 150,000 trees and widening the Kaurinui Stream that flows through the farm. His philosophy is embodied in every aspect of the property and, despite recent health-and-safety upgrades, Hundertwasser’s dwellings remain as he left them, down to his last shopping list and paintbrushes left on the table. The tour begins at the Eyeslit, a Hundertwasser design built after his death, replacing the old decaying farmhouse. Aligned with his distinctive style, it features vibrant pink walls, colourful mosaics and columns reminiscent of his iconic Kawakawa toilets. The Eyeslit serves as a communal space for a pre-tour introduction to Hundertwasser and his legacy that lives on in Kaurinui. The Bottlehaus, Kaurinui.©  Image:  Richard Smart The tour continues through four of his six idiosyncratic dwellings scattered throughout the property, each reflecting his ecological philosophies. The next stop is The Boatshed, a gabled timber building, home to his boat, La Giudecca. Across a bridge over the Kaurinui Stream is The Cave, a space dug into the hillside, containing a bench and hundreds of wētā. Returning over the stream, we arrive at The Pigsty, Hundertwasser’s primary dwelling, which, true to its name, is a former pigsty converted into a habitable space. Inside, a hallway stretches the length of the home, with the kitchen, dining and living room, and a combined bedroom and bathroom branching off. It is built from recycled glass bottles and natural materials, such as earth bricks and logs laid on their sides, extending from inside to outside, mortared in place with a lime, cement and sawdust mixture. With its spontaneously vegetated green roof, felled tree trunk columns and uneven interior floors, the dwelling echoes his philosophy that buildings, like human skin, should grow and wrinkle over time, evolving alongside nature.3 Mountain Hut, Kaurinui, 1994/95.©  Image:  Richard Smart The Bottlehaus, originally the farm’s milking shed, is Hundertwasser’s other main residence. The interior is filled with natural light from the polycarbonate skylight and bottle walls, providing perfect conditions for painting. Not yet included in the tour because of their distance are the Railway Hut and Mountain Hut. Smart recounts how he and his children would hike up to the Mountain Hut, spending the night in the home, built three-quarters underground. The walls and floor are clay earth and the roof, covered in wild greenery, sits just above the ground’s surface. Hundertwasser’s alignment with Māori culture is reflected throughout his homes; adorning the walls are timber-carved tiki and the koru flag he designed for New Zealand, symbolising a unified national identity. Hundertwasser was inherently nomadic, moving between buildings based on their various functions, inadvertently resembling the organisation of customary Māori papakāinga settlements, where buildings serve distinct purposes. Māori would move between kāinga seasonally, leaving structures built from natural materials to decay and return to the earth. At the tour’s final stop, the Exhibition Building, a letter from Hundertwasser’s friend A. D. Fagan in 1974 describes him as a guardian of the land, a sentiment akin to Māori identification as kaitiaki – guardians of the whenua. Before his death, Hundertwasser expressed his desire for Māori artists to have equal opportunities in New Zealand. This wish was realised in the Whangārei Hundertwasser Art Centre and Wairau Māori Art Gallery, completed in 2022.4 Throughout the property, Hundertwasser’s interventions – from a waterwheel and outdoor bath to timber plank bridges and ladders feeding into ponds – speak to a lifestyle that reinforces his commitment to living in harmony with nature. In contrast to his bold European architecture, Hundertwasser’s New Zealand home is more subdued and organic, blending seamlessly into the forest, indistinguishable from the natural environment. As Living Hundertwasser volunteer Clive Jackson explains, “He wanted to let the colours of nature speak.” He allowed nature to exist in its most wild and natural state, supporting his 1983 Peace Treaty with Nature, where he asserted that humanity must put itself behind ecological barriers so the earth can regenerate.5 As an example, he considered trees to be fellow ‘tenants’ on the property, who ‘paid rent’ through their provision of oxygen, beauty and joy.6 Hundertwasser died in 2000 and, at his own request, was buried under a tulip tree at Kaurinui, his body returning to the earth to nourish the ‘tree tenant’. This final act encapsulates his lifelong philosophy of humanity in harmony with nature and, as such, he lives on through the property. Hundertwasser famously stated, “We are only guests of nature and must behave accordingly. Man is the most dangerous pest ever to devastate the earth.”7 In a world where modern architecture is disrupting the natural environment and climate, Kaurinui offers a blueprint for a return to ‘original nature’ – a more sustainable, symbiotic relationship with the earth, and one that resonates with our country’s indigenous identity and the role we must assume as kaitiaki, guardians, of the natural world. REFERENCES 1 Nir Barak, 2022, ‘Friedensreich Hundertwasser’, The Architectural Review, 18 October 2022. 2 Andreas J. Hirsch, 2022, ‘Hundertwasser’s “Five Skins” Unfold’, in Hundertwasser in New Zealand: The Art of Creating Paradise. Auckland: Oratia Books, p. 72. 3 Wieland Schmied, 2007, For a More Human Architecture in Harmony with Nature: Hundertwasser Architecture. Köln: Taschen, p. 259. 4 Cooperation Agreement 2016, p. 24. 5 Friedensreich Hundertwasser, 1983, Peace Treaty with Nature, Hundertwasser Foundation. hundertwasser.com/en/texts/friedensvertrag_mit_der_natur 6 Wieland Schmied, 2007, For a More Human Architecture in Harmony with Nature: Hundertwasser Architecture, p. 86. 7 Hundertwasser Foundation. 2016. Hundertwasser Architektur & Philosophie. Germany: Wörner Verlag GmbH, p. 30. #sharing #hundertwassers #legacy
    ARCHITECTURENOW.CO.NZ
    Sharing Hundertwasser’s legacy
    Hundertwasser’s home in the Kaurinui Valley, located just 20 minutes north of Kawakawa and less than a three-hour drive from Auckland, is to be the only one of his homes around the world that is open to the public. I was given a tour by volunteers from Living Hundertwasser, including Richard Smart, who worked closely with Hundertwasser for eight years and now represents the non-profit Hundertwasser Foundation in New Zealand. Born Friedrich Stowasser in Austria in 1928, Hundertwasser was a world-famous painter and architect, renowned for his radical views and eccentric approach to design. His childhood, marked by the devastations of World War II, led him to find solace in painting alternative worlds filled with nature, vibrant colours and abstract forms that would later influence the trajectory of his environmentalism and architecture.1 The Eyeslit, Kaurinui, 2025.© Image:  Richard Smart In 1976, he settled in New Zealand, purchasing a dairy farm in the Kaurinui Valley with the intention of setting nature free.2 He did just that: over two decades planting 150,000 trees and widening the Kaurinui Stream that flows through the farm. His philosophy is embodied in every aspect of the property and, despite recent health-and-safety upgrades, Hundertwasser’s dwellings remain as he left them, down to his last shopping list and paintbrushes left on the table. The tour begins at the Eyeslit, a Hundertwasser design built after his death, replacing the old decaying farmhouse. Aligned with his distinctive style, it features vibrant pink walls, colourful mosaics and columns reminiscent of his iconic Kawakawa toilets. The Eyeslit serves as a communal space for a pre-tour introduction to Hundertwasser and his legacy that lives on in Kaurinui. The Bottlehaus, Kaurinui.©  Image:  Richard Smart The tour continues through four of his six idiosyncratic dwellings scattered throughout the property, each reflecting his ecological philosophies. The next stop is The Boatshed, a gabled timber building, home to his boat, La Giudecca. Across a bridge over the Kaurinui Stream is The Cave, a space dug into the hillside, containing a bench and hundreds of wētā. Returning over the stream, we arrive at The Pigsty, Hundertwasser’s primary dwelling, which, true to its name, is a former pigsty converted into a habitable space. Inside, a hallway stretches the length of the home, with the kitchen, dining and living room, and a combined bedroom and bathroom branching off. It is built from recycled glass bottles and natural materials, such as earth bricks and logs laid on their sides, extending from inside to outside, mortared in place with a lime, cement and sawdust mixture. With its spontaneously vegetated green roof, felled tree trunk columns and uneven interior floors, the dwelling echoes his philosophy that buildings, like human skin, should grow and wrinkle over time, evolving alongside nature.3 Mountain Hut, Kaurinui, 1994/95.©  Image:  Richard Smart The Bottlehaus, originally the farm’s milking shed, is Hundertwasser’s other main residence. The interior is filled with natural light from the polycarbonate skylight and bottle walls, providing perfect conditions for painting. Not yet included in the tour because of their distance are the Railway Hut and Mountain Hut. Smart recounts how he and his children would hike up to the Mountain Hut, spending the night in the home, built three-quarters underground. The walls and floor are clay earth and the roof, covered in wild greenery, sits just above the ground’s surface. Hundertwasser’s alignment with Māori culture is reflected throughout his homes; adorning the walls are timber-carved tiki and the koru flag he designed for New Zealand, symbolising a unified national identity. Hundertwasser was inherently nomadic, moving between buildings based on their various functions, inadvertently resembling the organisation of customary Māori papakāinga settlements, where buildings serve distinct purposes. Māori would move between kāinga seasonally, leaving structures built from natural materials to decay and return to the earth. At the tour’s final stop, the Exhibition Building, a letter from Hundertwasser’s friend A. D. Fagan in 1974 describes him as a guardian of the land, a sentiment akin to Māori identification as kaitiaki – guardians of the whenua. Before his death, Hundertwasser expressed his desire for Māori artists to have equal opportunities in New Zealand. This wish was realised in the Whangārei Hundertwasser Art Centre and Wairau Māori Art Gallery, completed in 2022.4 Throughout the property, Hundertwasser’s interventions – from a waterwheel and outdoor bath to timber plank bridges and ladders feeding into ponds – speak to a lifestyle that reinforces his commitment to living in harmony with nature. In contrast to his bold European architecture, Hundertwasser’s New Zealand home is more subdued and organic, blending seamlessly into the forest, indistinguishable from the natural environment. As Living Hundertwasser volunteer Clive Jackson explains, “He wanted to let the colours of nature speak.” He allowed nature to exist in its most wild and natural state, supporting his 1983 Peace Treaty with Nature, where he asserted that humanity must put itself behind ecological barriers so the earth can regenerate.5 As an example, he considered trees to be fellow ‘tenants’ on the property, who ‘paid rent’ through their provision of oxygen, beauty and joy.6 Hundertwasser died in 2000 and, at his own request, was buried under a tulip tree at Kaurinui, his body returning to the earth to nourish the ‘tree tenant’. This final act encapsulates his lifelong philosophy of humanity in harmony with nature and, as such, he lives on through the property. Hundertwasser famously stated, “We are only guests of nature and must behave accordingly. Man is the most dangerous pest ever to devastate the earth.”7 In a world where modern architecture is disrupting the natural environment and climate, Kaurinui offers a blueprint for a return to ‘original nature’ – a more sustainable, symbiotic relationship with the earth, and one that resonates with our country’s indigenous identity and the role we must assume as kaitiaki, guardians, of the natural world. REFERENCES 1 Nir Barak, 2022, ‘Friedensreich Hundertwasser (1928–2000)’, The Architectural Review, 18 October 2022. 2 Andreas J. Hirsch, 2022, ‘Hundertwasser’s “Five Skins” Unfold’, in Hundertwasser in New Zealand: The Art of Creating Paradise. Auckland: Oratia Books, p. 72. 3 Wieland Schmied, 2007, For a More Human Architecture in Harmony with Nature: Hundertwasser Architecture. Köln: Taschen, p. 259. 4 Cooperation Agreement 2016, p. 24. 5 Friedensreich Hundertwasser, 1983, Peace Treaty with Nature, Hundertwasser Foundation. hundertwasser.com/en/texts/friedensvertrag_mit_der_natur 6 Wieland Schmied, 2007, For a More Human Architecture in Harmony with Nature: Hundertwasser Architecture, p. 86. 7 Hundertwasser Foundation. 2016. Hundertwasser Architektur & Philosophie. Germany: Wörner Verlag GmbH, p. 30.
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  • TechCrunch een koppeling hebt gedeeld
    2025-05-17 10:38:05 - Translate -
    Thousands of people have embarked on a virtual road trip via Google Street View

    It’s Friday afternoon and I’m listening to Bowdoin College’s radio station, interspersed with ambient car honking noises. I am not in Maine. I am not in a car. I am at my desk. This is Internet Roadtrip.
    Internet Roadtrip is what I will call an MMORTG. Neal Agarwal, the game’s creator, calls it a “road-trip simulator.” Every 10 seconds, viewers vote on what direction for the “car” to drive on Google Street View — or, you can vote to honk the horn or change the radio station. The direction with the most votes gets clicked, and the car continues on its scenic path to … wherever the chat decides to go.
    Internet Roadtrip is reminiscent of Twitch Plays Pokémon, an iconic stream from over 10 years ago in which viewers voted on what button to press as part of a collective Pokémon Red game. But Internet Roadtrip is far less chaotic — both because only a thousand or so people are playing at a time, and because we have better organizational tools than we did in the Twitch Plays Pokémon era.
    Progress on the virtual roadtrip is slow. The car moves at a pace slower than walking. Discord moderators have had to temper newcomers’ expectations, explaining that it’s pointless to suggest driving to Las Vegas from Maine, since it would likely take almost 10 months of real-world time to get there. The same goes for Alaska, but it’s not just a matter of time that’s the issue.
    “Google Street View works by taking multiple pictures and putting them together. In some areas of the roads leading to Alaska, there are gaps in pictures available and so we would get stuck there, were we to go to these roads,” the Discord FAQ reads. “All potential roads to Alaska have these gaps. We checked.”
    There is no objective on Internet Roadtrip, as opposed other Street View-based games like GeoGuessr. Some Discord members discussed driving to Canada, which is a somewhat realistic goal, given our current position in Maine. But the destination isn’t the goal — it’s the joy of spontaneously listening to a college radio from a liberal arts school with a thousand strangers on the internet, while taking in the scenic backroads of Blue Hill, Maine.

    Topics
    #thousands #people #have #embarked #virtual
    Thousands of people have embarked on a virtual road trip via Google Street View
    It’s Friday afternoon and I’m listening to Bowdoin College’s radio station, interspersed with ambient car honking noises. I am not in Maine. I am not in a car. I am at my desk. This is Internet Roadtrip. Internet Roadtrip is what I will call an MMORTG. Neal Agarwal, the game’s creator, calls it a “road-trip simulator.” Every 10 seconds, viewers vote on what direction for the “car” to drive on Google Street View — or, you can vote to honk the horn or change the radio station. The direction with the most votes gets clicked, and the car continues on its scenic path to … wherever the chat decides to go. Internet Roadtrip is reminiscent of Twitch Plays Pokémon, an iconic stream from over 10 years ago in which viewers voted on what button to press as part of a collective Pokémon Red game. But Internet Roadtrip is far less chaotic — both because only a thousand or so people are playing at a time, and because we have better organizational tools than we did in the Twitch Plays Pokémon era. Progress on the virtual roadtrip is slow. The car moves at a pace slower than walking. Discord moderators have had to temper newcomers’ expectations, explaining that it’s pointless to suggest driving to Las Vegas from Maine, since it would likely take almost 10 months of real-world time to get there. The same goes for Alaska, but it’s not just a matter of time that’s the issue. “Google Street View works by taking multiple pictures and putting them together. In some areas of the roads leading to Alaska, there are gaps in pictures available and so we would get stuck there, were we to go to these roads,” the Discord FAQ reads. “All potential roads to Alaska have these gaps. We checked.” There is no objective on Internet Roadtrip, as opposed other Street View-based games like GeoGuessr. Some Discord members discussed driving to Canada, which is a somewhat realistic goal, given our current position in Maine. But the destination isn’t the goal — it’s the joy of spontaneously listening to a college radio from a liberal arts school with a thousand strangers on the internet, while taking in the scenic backroads of Blue Hill, Maine. Topics #thousands #people #have #embarked #virtual
    TECHCRUNCH.COM
    Thousands of people have embarked on a virtual road trip via Google Street View
    It’s Friday afternoon and I’m listening to Bowdoin College’s radio station, interspersed with ambient car honking noises. I am not in Maine. I am not in a car. I am at my desk. This is Internet Roadtrip. Internet Roadtrip is what I will call an MMORTG (massive multiplayer online road trip game). Neal Agarwal, the game’s creator, calls it a “road-trip simulator.” Every 10 seconds, viewers vote on what direction for the “car” to drive on Google Street View — or, you can vote to honk the horn or change the radio station. The direction with the most votes gets clicked, and the car continues on its scenic path to … wherever the chat decides to go. Internet Roadtrip is reminiscent of Twitch Plays Pokémon, an iconic stream from over 10 years ago in which viewers voted on what button to press as part of a collective Pokémon Red game. But Internet Roadtrip is far less chaotic — both because only a thousand or so people are playing at a time, and because we have better organizational tools than we did in the Twitch Plays Pokémon era (thank you, Discord). Progress on the virtual roadtrip is slow. The car moves at a pace slower than walking. Discord moderators have had to temper newcomers’ expectations, explaining that it’s pointless to suggest driving to Las Vegas from Maine, since it would likely take almost 10 months of real-world time to get there. The same goes for Alaska, but it’s not just a matter of time that’s the issue. “Google Street View works by taking multiple pictures and putting them together. In some areas of the roads leading to Alaska, there are gaps in pictures available and so we would get stuck there, were we to go to these roads,” the Discord FAQ reads. “All potential roads to Alaska have these gaps. We checked.” There is no objective on Internet Roadtrip, as opposed other Street View-based games like GeoGuessr. Some Discord members discussed driving to Canada, which is a somewhat realistic goal, given our current position in Maine. But the destination isn’t the goal — it’s the joy of spontaneously listening to a college radio from a liberal arts school with a thousand strangers on the internet, while taking in the scenic backroads of Blue Hill, Maine. Topics
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  • MIT Technology Review een koppeling hebt gedeeld
    2025-05-16 13:04:15 - Translate -
    The Download: the first personalized gene-editing drug, and Montana’s Right to Try experiment

    This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

    This baby boy was treated with the first personalized gene-editing drug

    Doctors say they constructed a bespoke gene-editing treatment in less than seven months and used it to treat a baby with a deadly metabolic condition. The rapid-fire attempt to rewrite the child’s DNA marks the first time gene editing has been tailored to treat a single individual.The baby who was treated, Kyle “KJ” Muldoon Jr., suffers from a rare metabolic condition caused by a particularly unusual gene misspelling. Researchers say their attempt to correct the error demonstrates the high level of precision new types of gene editors offer.

    The project also highlights what some experts are calling a growing crisis in gene-editing technology. That’s because even though the technology could cure thousands of genetic conditions, most are so rare that companies could never recoup the costs of developing a treatment for them. Read the full story.—Antonio Regalado

    Access to experimental medical treatments is expanding across the US

    —Jessica Hamzelou

    A couple of weeks ago I was in Washington, DC, for a gathering of scientists, policymakers, and longevity enthusiasts. They had come together to discuss ways to speed along the development of drugs and other treatments that might extend the human lifespan.

    One approach that came up was to simply make experimental drugs more easily accessible. Now, the state of Montana has passed a new bill that sets out exactly how clinics can sell experimental, unproven treatments in the state to anyone who wants them.

    The passing of the bill could make Montana something of a US hub for experimental treatments. But it represents a wider trend: the creep of Right to Try across the US. And a potentially dangerous departure from evidence-based medicine. Read the full story.

    This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

    Take a new look at AI’s energy use

    Big Tech’s appetite for energy is growing rapidly as adoption of AI accelerates. But just how much energy does even a single AI query use? And what does it mean for the climate?Join editor in chief Mat Honan, senior climate reporter Casey Crownhart, and AI reporter James O’Donnell at 1.30pm ET on Wednesday May 21 for a subscriber-only Roundtables conversation exploring AI’s energy demands now and in the future. Register here.

    The must-reads

    I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

    1 xAI has blamed Grok’s white genocide fixation on an ‘unauthorized modification’Made by an unnamed employee at 3.15am.+ The topic is one the far-right comes back to again and again.+ Memphis residents are struggling to live alongside xAI’s supercomputer.2 Meta has delayed the launch of its next flagship AI modelIts engineers are struggling to improve its Behemoth LLM enough. 

    3 Elon Musk is tapping up friends and allies for federal jobsIt’s creating an unprecedented web of potential conflicts of interests.+ Musk is posting on X less than he used to.4 The US is slashing funding for scientific researchSuch projects produced GPS, LASIK eye surgery, and CAPTCHAs.+ US tech visa applicants are under seriously heavy scrutiny.+ The foundations of America’s prosperity are being dismantled.5 Big Tech wants its AI agents to remember everything about you They’re focusing on improving chatbots’ memory—but critics are worried.+ AI agents can spontaneously develop human-like behavior.+ Generative AI can turn your most precious memories into photos that never existed.6 People keep making anti-DEI modifications for The Sims 4And the gamemaker EA’s attempts to stamp them out aren’t working.7 This chatbot promises to help you get over your ex Closure creates an AI version of ex-partners for users to vent their frustrations at.+ The AI relationship revolution is already here.8 How this AI song became a viral megahit in JapanYAJU&U is completely inescapable, and totally nonsensical.+ AI is coming for music, too.9 Your future overseas trip could be by zeppelinIf these startups get their way.+ Welcome to the big blimp boom.10 Are you a ‘dry texter’? It’s a conflict-averse teen’s worst nightmare.Quote of the day

    “It’s OK to be Chinese overseas.”

    —Chris Pereira, the CEO of iMpact, a communications firm advising Chinese companies expanding abroad, tells Rest of World that DeepSeek has given Chinese startups the confidence not to hide their origins.

    One more thingWe’ve never understood how hunger works. That might be about to change.

    When you’re starving, hunger is like a demon. It awakens the most ancient and primitive parts of the brain, then commandeers other neural machinery to do its bidding until it gets what it wants.

    Although scientists have had some success in stimulating hunger in mice, we still don’t really understand how the impulse to eat works. Now, some experts are following known parts of the neural hunger circuits into uncharted parts of the brain to try and find out.

    Their work could shed new light on the factors that have caused the number of overweight adults worldwide to skyrocket in recent years. And it could also help solve the mysteries around how and why a new class of weight-loss drugs seems to work so well. Read the full story.

    —Adam Piore

    We can still have nice things

    A place for comfort, fun and distraction to brighten up your day.+ Who knew—Harvard Law School’s Magna Carta may be the real deal after all.+ Early relatives of reptiles might have walked the Earth much earlier than we realised.+ New York University’s MFA Students are a talented bunch.+ The Raines sandwich sounds unspeakably awful
    #download #first #personalized #geneediting #drug
    The Download: the first personalized gene-editing drug, and Montana’s Right to Try experiment
    This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. This baby boy was treated with the first personalized gene-editing drug Doctors say they constructed a bespoke gene-editing treatment in less than seven months and used it to treat a baby with a deadly metabolic condition. The rapid-fire attempt to rewrite the child’s DNA marks the first time gene editing has been tailored to treat a single individual.The baby who was treated, Kyle “KJ” Muldoon Jr., suffers from a rare metabolic condition caused by a particularly unusual gene misspelling. Researchers say their attempt to correct the error demonstrates the high level of precision new types of gene editors offer. The project also highlights what some experts are calling a growing crisis in gene-editing technology. That’s because even though the technology could cure thousands of genetic conditions, most are so rare that companies could never recoup the costs of developing a treatment for them. Read the full story.—Antonio Regalado Access to experimental medical treatments is expanding across the US —Jessica Hamzelou A couple of weeks ago I was in Washington, DC, for a gathering of scientists, policymakers, and longevity enthusiasts. They had come together to discuss ways to speed along the development of drugs and other treatments that might extend the human lifespan. One approach that came up was to simply make experimental drugs more easily accessible. Now, the state of Montana has passed a new bill that sets out exactly how clinics can sell experimental, unproven treatments in the state to anyone who wants them. The passing of the bill could make Montana something of a US hub for experimental treatments. But it represents a wider trend: the creep of Right to Try across the US. And a potentially dangerous departure from evidence-based medicine. Read the full story. This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here. Take a new look at AI’s energy use Big Tech’s appetite for energy is growing rapidly as adoption of AI accelerates. But just how much energy does even a single AI query use? And what does it mean for the climate?Join editor in chief Mat Honan, senior climate reporter Casey Crownhart, and AI reporter James O’Donnell at 1.30pm ET on Wednesday May 21 for a subscriber-only Roundtables conversation exploring AI’s energy demands now and in the future. Register here. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 xAI has blamed Grok’s white genocide fixation on an ‘unauthorized modification’Made by an unnamed employee at 3.15am.+ The topic is one the far-right comes back to again and again.+ Memphis residents are struggling to live alongside xAI’s supercomputer.2 Meta has delayed the launch of its next flagship AI modelIts engineers are struggling to improve its Behemoth LLM enough.  3 Elon Musk is tapping up friends and allies for federal jobsIt’s creating an unprecedented web of potential conflicts of interests.+ Musk is posting on X less than he used to.4 The US is slashing funding for scientific researchSuch projects produced GPS, LASIK eye surgery, and CAPTCHAs.+ US tech visa applicants are under seriously heavy scrutiny.+ The foundations of America’s prosperity are being dismantled.5 Big Tech wants its AI agents to remember everything about you They’re focusing on improving chatbots’ memory—but critics are worried.+ AI agents can spontaneously develop human-like behavior.+ Generative AI can turn your most precious memories into photos that never existed.6 People keep making anti-DEI modifications for The Sims 4And the gamemaker EA’s attempts to stamp them out aren’t working.7 This chatbot promises to help you get over your ex Closure creates an AI version of ex-partners for users to vent their frustrations at.+ The AI relationship revolution is already here.8 How this AI song became a viral megahit in JapanYAJU&U is completely inescapable, and totally nonsensical.+ AI is coming for music, too.9 Your future overseas trip could be by zeppelinIf these startups get their way.+ Welcome to the big blimp boom.10 Are you a ‘dry texter’? It’s a conflict-averse teen’s worst nightmare.Quote of the day “It’s OK to be Chinese overseas.” —Chris Pereira, the CEO of iMpact, a communications firm advising Chinese companies expanding abroad, tells Rest of World that DeepSeek has given Chinese startups the confidence not to hide their origins. One more thingWe’ve never understood how hunger works. That might be about to change. When you’re starving, hunger is like a demon. It awakens the most ancient and primitive parts of the brain, then commandeers other neural machinery to do its bidding until it gets what it wants. Although scientists have had some success in stimulating hunger in mice, we still don’t really understand how the impulse to eat works. Now, some experts are following known parts of the neural hunger circuits into uncharted parts of the brain to try and find out. Their work could shed new light on the factors that have caused the number of overweight adults worldwide to skyrocket in recent years. And it could also help solve the mysteries around how and why a new class of weight-loss drugs seems to work so well. Read the full story. —Adam Piore We can still have nice things A place for comfort, fun and distraction to brighten up your day.+ Who knew—Harvard Law School’s Magna Carta may be the real deal after all.+ Early relatives of reptiles might have walked the Earth much earlier than we realised.+ New York University’s MFA Students are a talented bunch.+ The Raines sandwich sounds unspeakably awful #download #first #personalized #geneediting #drug
    WWW.TECHNOLOGYREVIEW.COM
    The Download: the first personalized gene-editing drug, and Montana’s Right to Try experiment
    This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. This baby boy was treated with the first personalized gene-editing drug Doctors say they constructed a bespoke gene-editing treatment in less than seven months and used it to treat a baby with a deadly metabolic condition. The rapid-fire attempt to rewrite the child’s DNA marks the first time gene editing has been tailored to treat a single individual.The baby who was treated, Kyle “KJ” Muldoon Jr., suffers from a rare metabolic condition caused by a particularly unusual gene misspelling. Researchers say their attempt to correct the error demonstrates the high level of precision new types of gene editors offer. The project also highlights what some experts are calling a growing crisis in gene-editing technology. That’s because even though the technology could cure thousands of genetic conditions, most are so rare that companies could never recoup the costs of developing a treatment for them. Read the full story.—Antonio Regalado Access to experimental medical treatments is expanding across the US —Jessica Hamzelou A couple of weeks ago I was in Washington, DC, for a gathering of scientists, policymakers, and longevity enthusiasts. They had come together to discuss ways to speed along the development of drugs and other treatments that might extend the human lifespan. One approach that came up was to simply make experimental drugs more easily accessible. Now, the state of Montana has passed a new bill that sets out exactly how clinics can sell experimental, unproven treatments in the state to anyone who wants them. The passing of the bill could make Montana something of a US hub for experimental treatments. But it represents a wider trend: the creep of Right to Try across the US. And a potentially dangerous departure from evidence-based medicine. Read the full story. This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here. Take a new look at AI’s energy use Big Tech’s appetite for energy is growing rapidly as adoption of AI accelerates. But just how much energy does even a single AI query use? And what does it mean for the climate?Join editor in chief Mat Honan, senior climate reporter Casey Crownhart, and AI reporter James O’Donnell at 1.30pm ET on Wednesday May 21 for a subscriber-only Roundtables conversation exploring AI’s energy demands now and in the future. Register here. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 xAI has blamed Grok’s white genocide fixation on an ‘unauthorized modification’Made by an unnamed employee at 3.15am. (TechCrunch)+ The topic is one the far-right comes back to again and again. (The Atlantic $)+ Memphis residents are struggling to live alongside xAI’s supercomputer. (CNBC) 2 Meta has delayed the launch of its next flagship AI modelIts engineers are struggling to improve its Behemoth LLM enough. (WSJ $)  3 Elon Musk is tapping up friends and allies for federal jobsIt’s creating an unprecedented web of potential conflicts of interests. (WSJ $)+ Musk is posting on X less than he used to. (Semafor) 4 The US is slashing funding for scientific researchSuch projects produced GPS, LASIK eye surgery, and CAPTCHAs. (NYT $)+ US tech visa applicants are under seriously heavy scrutiny. (Wired $)+ The foundations of America’s prosperity are being dismantled. (MIT Technology Review)5 Big Tech wants its AI agents to remember everything about you They’re focusing on improving chatbots’ memory—but critics are worried. (FT $)+ AI agents can spontaneously develop human-like behavior. (The Guardian)+ Generative AI can turn your most precious memories into photos that never existed. (MIT Technology Review) 6 People keep making anti-DEI modifications for The Sims 4And the gamemaker EA’s attempts to stamp them out aren’t working. (Wired $) 7 This chatbot promises to help you get over your ex Closure creates an AI version of ex-partners for users to vent their frustrations at. (404 Media)+ The AI relationship revolution is already here. (MIT Technology Review) 8 How this AI song became a viral megahit in JapanYAJU&U is completely inescapable, and totally nonsensical. (Pitchfork)+ AI is coming for music, too. (MIT Technology Review) 9 Your future overseas trip could be by zeppelinIf these startups get their way. (WP $)+ Welcome to the big blimp boom. (MIT Technology Review) 10 Are you a ‘dry texter’? It’s a conflict-averse teen’s worst nightmare. (Vox) Quote of the day “It’s OK to be Chinese overseas.” —Chris Pereira, the CEO of iMpact, a communications firm advising Chinese companies expanding abroad, tells Rest of World that DeepSeek has given Chinese startups the confidence not to hide their origins. One more thingWe’ve never understood how hunger works. That might be about to change. When you’re starving, hunger is like a demon. It awakens the most ancient and primitive parts of the brain, then commandeers other neural machinery to do its bidding until it gets what it wants. Although scientists have had some success in stimulating hunger in mice, we still don’t really understand how the impulse to eat works. Now, some experts are following known parts of the neural hunger circuits into uncharted parts of the brain to try and find out. Their work could shed new light on the factors that have caused the number of overweight adults worldwide to skyrocket in recent years. And it could also help solve the mysteries around how and why a new class of weight-loss drugs seems to work so well. Read the full story. —Adam Piore We can still have nice things A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)+ Who knew—Harvard Law School’s Magna Carta may be the real deal after all.+ Early relatives of reptiles might have walked the Earth much earlier than we realised.+ New York University’s MFA Students are a talented bunch.+ The Raines sandwich sounds unspeakably awful
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    Rest as an Experience in a Space Rich with Symbolism: Insights from the Argentinian Pavilion at the 2025 Venice Biennale

    Rest as an Experience in a Space Rich with Symbolism: Insights from the Argentinian Pavilion at the 2025 Venice BiennaleSave this picture!© Federico CairoliThe proposal by Argentinian architects Marco Zampieron and Juan Manuel Pachué for the Argentinian Pavilion at the 19th Venice Architecture Biennale 2025 is clear from the outset: upon entering Siestario, visitors are immersed in a space of dim lighting and evocative sound, and immediately encounter—at the center of the room, stretched across its width, and acting as the undeniable protagonist—a large, inflated pink bag that instinctively invites repose. This is a silobag, an object commonly used in the Argentinian countryside to store grain and a potent symbol of the country’s export-driven economy. In this setting, the silobag becomes more than a spatial intervention; it also introduces a temporal dimension: an invitation to pause and reflect amid the relentless rhythm of the Biennale.
    this picture!Under the motto “Intelligens. Natural. Artificial. Collective”, general curator and Italian architect Carlo Ratti invites participants to explore and debate the many meanings of these terms, setting the stage for a critical rethinking of contemporary architecture. Within this framework, both national pavilions and individual projects and installations engage with the tensions and possibilities that emerge between the natural, the artificial, and the collective—each from their own distinct perspective.this picture!Marco Zampieron and Juan Manuel Pachué — Rosario-based architects and co-founders of Cooperativa — were selected through a public competition organized by the Argentine Foreign Ministry, in partnership with the Chamber of Urban Developers, with their winning project to represent Argentina in this edition of the Biennale. Related Article Bahrain Wins the Golden Lion for Best National Participation at the 2025 Venice Architecture Biennale this picture!With the support of CEDU and Base Proyectos, ArchDaily was present at the inauguration of the Argentine Pavilion on May 9, at the extraordinary Arsenale in Venice. During the first days of the event, we had the opportunity to speak with the authors of Siestario about their experience participating in the Biennale, the creative process of the project, and other aspects related to their practice and understanding of architecture.this picture!AD: What was your motivation to participate in the competition to represent Argentina at the Venice Architecture Biennale 2025?Marco Zampieron & Juan Manuel Pachué: As Cooperativa, our group is deeply engaged with competitions. We participate regularly, advocate for them as a valuable tool, and truly enjoy the process they entail. This one, in particular, stood out because, while it’s a national competition, it offers a strong international platform: it’s about representing the country on one of the most important global stages for architecture. Our studio operates as a collaborative collective that expands or contracts depending on the nature of each project. At its core, our practice revolves around competitions. We see them as a way to access commissions without requiring an extensive portfolio, a strong reputation, or an established body of work. That’s why we especially value competitions that are anonymous and transparent — they open the door to meaningful opportunities, such as building a public-scale pavilion with high visibility, through a more horizontal and equitable framework.this picture!AD: How does Siestario relate to the theme proposed by the Venice Biennale 2025 — Intelligens. Natural. Artificial. Collective — and what possible interpretations did you envision based on that premise?MZ & JP: These mottos or curatorial texts often tend to be quite ambiguous — and we see that as something positive, because it opens the door to multiple interpretations. With so many people participating in the Biennale through diverse projects, it’s valuable for the thematic framework to be broad: it allows for a wide range of perspectives and approaches. From there, the question of how to represent Argentina naturally arises. Where do you anchor that representation? In our proposal, what comes through is our way of thinking about architecture, our understanding of the Biennale, and our perspective on the collective.Not only were the notions of intelligence, nature, and collectivity present, but there was also something that moved away from the idea of one place, one solution—a concept that truly captivated us. We find the idea of artificial intelligence somewhat challenging... perhaps we’re a bit romantic in that sense; there’s a certain resistance on our part. When we look at some international pavilions, we see a strong emphasis on technology and technical prowess. And while the artificial and the natural are in constant dialogue, what interests us is thinking about the artificial through the lens of local production—through cultural construction. Not as a race toward technological advancement, but as a way of drawing from the depth of knowledge that already exists within our own culture.It is also important that these competitions are resolved quickly, and often one starts from previous ideas or works. In our case, the collective already had a background, a sensitive research on the territory linked to art and certain key elements. This allowed us to connect with the Biennale's themes more deeply in a short time. We see this in other pavilions as well: many have a foundation of prior work and research. In our architecture — and also in art and other disciplines with which we engage — each person brings a background, certain interests, preferences, and tastes. From the place one comes from, what one observes, all of that forms a layer of information from which we project. We try to reflect on this to make it as conscious as possible, so that we can create something that feels close and authentic to us.this picture!Representing Argentina also forces us to ask ourselves what we want to say, how we want to speak about who we are. We are interested in a certain metaphor, a certain poetics. We think about the conditions the country is currently going through, both economically and politically, and this image of Argentina resting upon the Argentine countryside emerges, along with the cost of this rest. The proposal of Siestario suggests that the visitor to the Biennale, weary from seeing so much, could climb uphill to a silobag — that iconic symbol of Argentina's agricultural production — and rest, take a nap. But of course, that silobag is the one that stores soybeans, and soybeans, with all their symbolic, productive, and also destructive weight: they contaminate water, devastate forests, and affect local communities. So, Siestario doesn’t just offer a break; it also questions what that rest truly means. There’s a critique, a political stance, a reading on the artificial, on comfort, on the cost of being at ease. All of this emerges when the concepts are linked: the natural, the artificial, the collective. And here, the siesta also appears as a gesture, as a metaphor, as a critique, as a refuge.this picture!AD: What motivated you to use the silobag as the central element of the project? How was the process of re-signifying this object within the exhibition context of the Biennale? MZ & JP: The relationship with the silobag is not new to us; in fact, we had already worked with this element in previous projects, documenting and traveling through different towns. This prior research allowed us to see the silobag not just as a functional element, but also as an object loaded with meaning. The process of re-signifying it here at the Biennale involves decontextualizing it. We wanted to change its scale, give it a new role, and use it as a place for a nap, a space for rest. By doing so, the silobag transforms, ceasing to be just an agricultural object and becoming something more playful, connected to the body, fatigue, and the intimacy of the siesta. It’s a simple gesture, but a profound one: it proposes a space for people to recline and feel comfortable in it.Furthermore, the silobag becomes an object that, although it has strong rural and economic connotations, is reinterpreted within the exhibition context, adding a layer of reflection on the everyday and the collective. The intimacy of the siesta becomes social, as the act of resting in a shared space like this creates a collective moment. In the pavilion, we also worked with the atmosphere: the environment is designed to darken, with sounds and lights that induce a sense of relaxation, contributing to creating the right space for that collective rest.this picture!AD: What were you hoping to convey through this installation, and how did you expect visitors to experience or interpret the act of "pausing" within the exhibit's journey?MZ & JP: The proposal for this installation revolves around the idea of "stopping." Stopping to think, to look at what you’re doing, to know yourself, to decide. Ultimately, it’s an invitation to stop, to rest, to sleep. We aim for visitors to find that pause within themselves, to disconnect from the fast pace of their surroundings. Furthermore, this act of "pausing" becomes something desirable, almost like an object of desire: the urge to try it, to climb onto the silobag. We wanted to create a physical and experiential experience, not just a contemplative one. Walking through the pavilion becomes an invitation to interact with the space. It even allows walking on the silobag, changing heights, lying down, and resting.We know that at the Biennale, visitors tend to walk a lot and want to see as much as possible. However, this space demands that you stop, that you dedicate time to the experience. It’s not a place to just pass through quickly; it’s a space that asks you to stay for at least 15 or 20 minutes, to climb onto the silobag, to experience the physical effort of doing so, because it’s not a comfortable chair — it’s an experience that requires an investment of time and attention.The curation and design of the rest of the space, along with the projections, are intended so that the installation partially disappears as one moves through it, creating a visual and spatial effect that forces visitors to interact with the place, to stay alert. The project takes a risk; we know that, in the frenetic pace of the Biennale, some may not pay attention to this proposal. But we believe that, to truly enjoy it, it’s necessary to take that time, lie down, listen to the sounds, and be present.this picture!AD: How did you approach the relationship between architecture, space, and time within the Argentinian Pavilion?MZ & JP: Our approach has always been to give prominence to the public and collective. In the projects we develop, we try to make the ground floors open and accessible spaces where people can enter spontaneously, without the design being entirely directed toward exclusive use. We are interested in simplicity and clarity, in being radical in the sense of the purity of the idea. This pavilion, for example, uses a single element to express and resolve multiple layers of meaning. The design process seeks to simplify and refine complex ideas, ensuring that the result is direct and clear.In relation to time, we are interested in how architecture, over time, ages and transforms. In Venice, for example, one can see how the city and architecture are not static, how the passage of time causes materials to change, deteriorate, and feel lived in. This concept is present not only in the built architecture but also in the thinking and creation process. For us, time is not just a factor that affects buildings, but a constant in the design process. We value the time spent on physical models, on hand drawing, on processes that endure and allow us to generate something with more depth.this picture!this picture!AD: There is something disruptive about Siestario: by not displaying so much information or explicit context, the space becomes entirely an experience. What place did you give to the sensory aspect compared to the more direct information exhibition?MZ & JP: This approach is carried over to all our projects, not just the pavilion. We are interested in making spaces sensory experiences, where people not only see but also feel. We seek the space to invite reflection, but also for the enjoyment of the body, for people to engage directly with the environment. In the pavilion, in particular, we wanted the images to function as thresholds, leading you to other places, to the world of the dreamlike. This project, in particular, emphasizes the idea of pause, of stopping to reflect and rest, which connects with our broader vision of how we understand architecture: not just as something built, but as a lived experience.What we aim to create are silent spaces, ones that impose nothing, but at the same time offer power in their simplicity. This is something noticeable in other projects we've done, where the intervention is almost invisible, yet has a significant impact on people's experience, providing a space for them to claim it and inhabit it in their own way.this picture!AD: How was the curatorial process in constructing the space through the projections on the sides of the space?MZ & JP: 
    The curatorial proposal was built with the aim of integrating architecture, art, and landscape, bringing together the different actors. From the outset, we decided to organize the curation of images into three groups: one linked to architecture, another to art, and another to the very recording of the silobag in the field. This division not only provided the visitor with information about the origin and context of the project but also sought to establish a dialogue between architecture and art, something that is fundamental for us. Through the arts, we wanted to tell the story of our landscape and our territory, and we feel that artists have a special sensitivity to read and construct that landscape in a different way.The curatorial line was also guided by an idea that particularly interested us: that from naps emerge dreams, desires, and aspirations of a country. We wanted to propose a positive perspective within a critical reading, showcasing an Argentina that also produces art and architecture. The selected images engage in dialogue with the territory, and they do not respond to a specific temporal cut but rather bring together works from different moments, generating intersections across diverse times. We were interested in showing how these works relate to one another and how, through them, the variable of time appears in architectural production, in the territory, and in the contexts in which these works are being built.this picture!Additionally, there was significant archival work: we incorporated original drawings and unpublished material to add historical value. The selection included examples from across the country, even works that are not always associated with formal architecture, such as recreational or leisure spaces. We aimed to represent not only the major cities but also other realities, histories, and geographies. We also included maps and references to historical processes of urbanization and territorial defense, combining them with contemporary works to build the narrative.this picture!this picture!AD: What does it mean for you to represent Argentina at an event like the Venice Biennale, and how do you experience the responsibility and opportunity that this entails?MZ & JP: Representing Argentina at the Venice Biennale carries significant weight in the national architectural sphere. Winning the competition gave us a certain level of acceptance within the architecture community in Argentina. It’s difficult to fully reflect on the impact of this, as we are still in the process, but we do notice there is recognition. Our motivation was, above all, a genuine interest in participating in the competition, but upon taking on this responsibility, we realized the importance of representing our country at such a prominent event. For our careers, our studio, and our aspirations, this opportunity is significant. Being part of the Venice Biennale is something that makes us feel good because we know that what we do is part of a system that, although imperfect, allows us to reach places like this. It’s an important point in our trajectory and an achievement that fills us with satisfaction.this picture!

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    Rest as an Experience in a Space Rich with Symbolism: Insights from the Argentinian Pavilion at the 2025 Venice Biennale
    Rest as an Experience in a Space Rich with Symbolism: Insights from the Argentinian Pavilion at the 2025 Venice BiennaleSave this picture!© Federico CairoliThe proposal by Argentinian architects Marco Zampieron and Juan Manuel Pachué for the Argentinian Pavilion at the 19th Venice Architecture Biennale 2025 is clear from the outset: upon entering Siestario, visitors are immersed in a space of dim lighting and evocative sound, and immediately encounter—at the center of the room, stretched across its width, and acting as the undeniable protagonist—a large, inflated pink bag that instinctively invites repose. This is a silobag, an object commonly used in the Argentinian countryside to store grain and a potent symbol of the country’s export-driven economy. In this setting, the silobag becomes more than a spatial intervention; it also introduces a temporal dimension: an invitation to pause and reflect amid the relentless rhythm of the Biennale. this picture!Under the motto “Intelligens. Natural. Artificial. Collective”, general curator and Italian architect Carlo Ratti invites participants to explore and debate the many meanings of these terms, setting the stage for a critical rethinking of contemporary architecture. Within this framework, both national pavilions and individual projects and installations engage with the tensions and possibilities that emerge between the natural, the artificial, and the collective—each from their own distinct perspective.this picture!Marco Zampieron and Juan Manuel Pachué — Rosario-based architects and co-founders of Cooperativa — were selected through a public competition organized by the Argentine Foreign Ministry, in partnership with the Chamber of Urban Developers, with their winning project to represent Argentina in this edition of the Biennale. Related Article Bahrain Wins the Golden Lion for Best National Participation at the 2025 Venice Architecture Biennale this picture!With the support of CEDU and Base Proyectos, ArchDaily was present at the inauguration of the Argentine Pavilion on May 9, at the extraordinary Arsenale in Venice. During the first days of the event, we had the opportunity to speak with the authors of Siestario about their experience participating in the Biennale, the creative process of the project, and other aspects related to their practice and understanding of architecture.this picture!AD: What was your motivation to participate in the competition to represent Argentina at the Venice Architecture Biennale 2025?Marco Zampieron & Juan Manuel Pachué: As Cooperativa, our group is deeply engaged with competitions. We participate regularly, advocate for them as a valuable tool, and truly enjoy the process they entail. This one, in particular, stood out because, while it’s a national competition, it offers a strong international platform: it’s about representing the country on one of the most important global stages for architecture. Our studio operates as a collaborative collective that expands or contracts depending on the nature of each project. At its core, our practice revolves around competitions. We see them as a way to access commissions without requiring an extensive portfolio, a strong reputation, or an established body of work. That’s why we especially value competitions that are anonymous and transparent — they open the door to meaningful opportunities, such as building a public-scale pavilion with high visibility, through a more horizontal and equitable framework.this picture!AD: How does Siestario relate to the theme proposed by the Venice Biennale 2025 — Intelligens. Natural. Artificial. Collective — and what possible interpretations did you envision based on that premise?MZ & JP: These mottos or curatorial texts often tend to be quite ambiguous — and we see that as something positive, because it opens the door to multiple interpretations. With so many people participating in the Biennale through diverse projects, it’s valuable for the thematic framework to be broad: it allows for a wide range of perspectives and approaches. From there, the question of how to represent Argentina naturally arises. Where do you anchor that representation? In our proposal, what comes through is our way of thinking about architecture, our understanding of the Biennale, and our perspective on the collective.Not only were the notions of intelligence, nature, and collectivity present, but there was also something that moved away from the idea of one place, one solution—a concept that truly captivated us. We find the idea of artificial intelligence somewhat challenging... perhaps we’re a bit romantic in that sense; there’s a certain resistance on our part. When we look at some international pavilions, we see a strong emphasis on technology and technical prowess. And while the artificial and the natural are in constant dialogue, what interests us is thinking about the artificial through the lens of local production—through cultural construction. Not as a race toward technological advancement, but as a way of drawing from the depth of knowledge that already exists within our own culture.It is also important that these competitions are resolved quickly, and often one starts from previous ideas or works. In our case, the collective already had a background, a sensitive research on the territory linked to art and certain key elements. This allowed us to connect with the Biennale's themes more deeply in a short time. We see this in other pavilions as well: many have a foundation of prior work and research. In our architecture — and also in art and other disciplines with which we engage — each person brings a background, certain interests, preferences, and tastes. From the place one comes from, what one observes, all of that forms a layer of information from which we project. We try to reflect on this to make it as conscious as possible, so that we can create something that feels close and authentic to us.this picture!Representing Argentina also forces us to ask ourselves what we want to say, how we want to speak about who we are. We are interested in a certain metaphor, a certain poetics. We think about the conditions the country is currently going through, both economically and politically, and this image of Argentina resting upon the Argentine countryside emerges, along with the cost of this rest. The proposal of Siestario suggests that the visitor to the Biennale, weary from seeing so much, could climb uphill to a silobag — that iconic symbol of Argentina's agricultural production — and rest, take a nap. But of course, that silobag is the one that stores soybeans, and soybeans, with all their symbolic, productive, and also destructive weight: they contaminate water, devastate forests, and affect local communities. So, Siestario doesn’t just offer a break; it also questions what that rest truly means. There’s a critique, a political stance, a reading on the artificial, on comfort, on the cost of being at ease. All of this emerges when the concepts are linked: the natural, the artificial, the collective. And here, the siesta also appears as a gesture, as a metaphor, as a critique, as a refuge.this picture!AD: What motivated you to use the silobag as the central element of the project? How was the process of re-signifying this object within the exhibition context of the Biennale? MZ & JP: The relationship with the silobag is not new to us; in fact, we had already worked with this element in previous projects, documenting and traveling through different towns. This prior research allowed us to see the silobag not just as a functional element, but also as an object loaded with meaning. The process of re-signifying it here at the Biennale involves decontextualizing it. We wanted to change its scale, give it a new role, and use it as a place for a nap, a space for rest. By doing so, the silobag transforms, ceasing to be just an agricultural object and becoming something more playful, connected to the body, fatigue, and the intimacy of the siesta. It’s a simple gesture, but a profound one: it proposes a space for people to recline and feel comfortable in it.Furthermore, the silobag becomes an object that, although it has strong rural and economic connotations, is reinterpreted within the exhibition context, adding a layer of reflection on the everyday and the collective. The intimacy of the siesta becomes social, as the act of resting in a shared space like this creates a collective moment. In the pavilion, we also worked with the atmosphere: the environment is designed to darken, with sounds and lights that induce a sense of relaxation, contributing to creating the right space for that collective rest.this picture!AD: What were you hoping to convey through this installation, and how did you expect visitors to experience or interpret the act of "pausing" within the exhibit's journey?MZ & JP: The proposal for this installation revolves around the idea of "stopping." Stopping to think, to look at what you’re doing, to know yourself, to decide. Ultimately, it’s an invitation to stop, to rest, to sleep. We aim for visitors to find that pause within themselves, to disconnect from the fast pace of their surroundings. Furthermore, this act of "pausing" becomes something desirable, almost like an object of desire: the urge to try it, to climb onto the silobag. We wanted to create a physical and experiential experience, not just a contemplative one. Walking through the pavilion becomes an invitation to interact with the space. It even allows walking on the silobag, changing heights, lying down, and resting.We know that at the Biennale, visitors tend to walk a lot and want to see as much as possible. However, this space demands that you stop, that you dedicate time to the experience. It’s not a place to just pass through quickly; it’s a space that asks you to stay for at least 15 or 20 minutes, to climb onto the silobag, to experience the physical effort of doing so, because it’s not a comfortable chair — it’s an experience that requires an investment of time and attention.The curation and design of the rest of the space, along with the projections, are intended so that the installation partially disappears as one moves through it, creating a visual and spatial effect that forces visitors to interact with the place, to stay alert. The project takes a risk; we know that, in the frenetic pace of the Biennale, some may not pay attention to this proposal. But we believe that, to truly enjoy it, it’s necessary to take that time, lie down, listen to the sounds, and be present.this picture!AD: How did you approach the relationship between architecture, space, and time within the Argentinian Pavilion?MZ & JP: Our approach has always been to give prominence to the public and collective. In the projects we develop, we try to make the ground floors open and accessible spaces where people can enter spontaneously, without the design being entirely directed toward exclusive use. We are interested in simplicity and clarity, in being radical in the sense of the purity of the idea. This pavilion, for example, uses a single element to express and resolve multiple layers of meaning. The design process seeks to simplify and refine complex ideas, ensuring that the result is direct and clear.In relation to time, we are interested in how architecture, over time, ages and transforms. In Venice, for example, one can see how the city and architecture are not static, how the passage of time causes materials to change, deteriorate, and feel lived in. This concept is present not only in the built architecture but also in the thinking and creation process. For us, time is not just a factor that affects buildings, but a constant in the design process. We value the time spent on physical models, on hand drawing, on processes that endure and allow us to generate something with more depth.this picture!this picture!AD: There is something disruptive about Siestario: by not displaying so much information or explicit context, the space becomes entirely an experience. What place did you give to the sensory aspect compared to the more direct information exhibition?MZ & JP: This approach is carried over to all our projects, not just the pavilion. We are interested in making spaces sensory experiences, where people not only see but also feel. We seek the space to invite reflection, but also for the enjoyment of the body, for people to engage directly with the environment. In the pavilion, in particular, we wanted the images to function as thresholds, leading you to other places, to the world of the dreamlike. This project, in particular, emphasizes the idea of pause, of stopping to reflect and rest, which connects with our broader vision of how we understand architecture: not just as something built, but as a lived experience.What we aim to create are silent spaces, ones that impose nothing, but at the same time offer power in their simplicity. This is something noticeable in other projects we've done, where the intervention is almost invisible, yet has a significant impact on people's experience, providing a space for them to claim it and inhabit it in their own way.this picture!AD: How was the curatorial process in constructing the space through the projections on the sides of the space?MZ & JP:  The curatorial proposal was built with the aim of integrating architecture, art, and landscape, bringing together the different actors. From the outset, we decided to organize the curation of images into three groups: one linked to architecture, another to art, and another to the very recording of the silobag in the field. This division not only provided the visitor with information about the origin and context of the project but also sought to establish a dialogue between architecture and art, something that is fundamental for us. Through the arts, we wanted to tell the story of our landscape and our territory, and we feel that artists have a special sensitivity to read and construct that landscape in a different way.The curatorial line was also guided by an idea that particularly interested us: that from naps emerge dreams, desires, and aspirations of a country. We wanted to propose a positive perspective within a critical reading, showcasing an Argentina that also produces art and architecture. The selected images engage in dialogue with the territory, and they do not respond to a specific temporal cut but rather bring together works from different moments, generating intersections across diverse times. We were interested in showing how these works relate to one another and how, through them, the variable of time appears in architectural production, in the territory, and in the contexts in which these works are being built.this picture!Additionally, there was significant archival work: we incorporated original drawings and unpublished material to add historical value. The selection included examples from across the country, even works that are not always associated with formal architecture, such as recreational or leisure spaces. We aimed to represent not only the major cities but also other realities, histories, and geographies. We also included maps and references to historical processes of urbanization and territorial defense, combining them with contemporary works to build the narrative.this picture!this picture!AD: What does it mean for you to represent Argentina at an event like the Venice Biennale, and how do you experience the responsibility and opportunity that this entails?MZ & JP: Representing Argentina at the Venice Biennale carries significant weight in the national architectural sphere. Winning the competition gave us a certain level of acceptance within the architecture community in Argentina. It’s difficult to fully reflect on the impact of this, as we are still in the process, but we do notice there is recognition. Our motivation was, above all, a genuine interest in participating in the competition, but upon taking on this responsibility, we realized the importance of representing our country at such a prominent event. For our careers, our studio, and our aspirations, this opportunity is significant. Being part of the Venice Biennale is something that makes us feel good because we know that what we do is part of a system that, although imperfect, allows us to reach places like this. It’s an important point in our trajectory and an achievement that fills us with satisfaction.this picture! Image gallerySee allShow less About this authorPaula PintosAuthor••• Cite: Pintos, Paula. "Rest as an Experience in a Space Rich with Symbolism: Insights from the Argentinian Pavilion at the 2025 Venice Biennale"15 May 2025. ArchDaily. Accessed . < ISSN 0719-8884Save世界上最受欢迎的建筑网站现已推出你的母语版本!想浏览ArchDaily中国吗?是否 You've started following your first account!Did you know?You'll now receive updates based on what you follow! Personalize your stream and start following your favorite authors, offices and users.Go to my stream #rest #experience #space #rich #with
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    Rest as an Experience in a Space Rich with Symbolism: Insights from the Argentinian Pavilion at the 2025 Venice Biennale
    Rest as an Experience in a Space Rich with Symbolism: Insights from the Argentinian Pavilion at the 2025 Venice BiennaleSave this picture!© Federico CairoliThe proposal by Argentinian architects Marco Zampieron and Juan Manuel Pachué for the Argentinian Pavilion at the 19th Venice Architecture Biennale 2025 is clear from the outset: upon entering Siestario, visitors are immersed in a space of dim lighting and evocative sound, and immediately encounter—at the center of the room, stretched across its width, and acting as the undeniable protagonist—a large, inflated pink bag that instinctively invites repose. This is a silobag, an object commonly used in the Argentinian countryside to store grain and a potent symbol of the country’s export-driven economy. In this setting, the silobag becomes more than a spatial intervention; it also introduces a temporal dimension: an invitation to pause and reflect amid the relentless rhythm of the Biennale. Save this picture!Under the motto “Intelligens. Natural. Artificial. Collective”, general curator and Italian architect Carlo Ratti invites participants to explore and debate the many meanings of these terms, setting the stage for a critical rethinking of contemporary architecture. Within this framework, both national pavilions and individual projects and installations engage with the tensions and possibilities that emerge between the natural, the artificial, and the collective—each from their own distinct perspective.Save this picture!Marco Zampieron and Juan Manuel Pachué — Rosario-based architects and co-founders of Cooperativa — were selected through a public competition organized by the Argentine Foreign Ministry, in partnership with the Chamber of Urban Developers (CEDU), with their winning project to represent Argentina in this edition of the Biennale. Related Article Bahrain Wins the Golden Lion for Best National Participation at the 2025 Venice Architecture Biennale Save this picture!With the support of CEDU and Base Proyectos, ArchDaily was present at the inauguration of the Argentine Pavilion on May 9, at the extraordinary Arsenale in Venice. During the first days of the event, we had the opportunity to speak with the authors of Siestario about their experience participating in the Biennale, the creative process of the project, and other aspects related to their practice and understanding of architecture.Save this picture!AD (Paula Pintos): What was your motivation to participate in the competition to represent Argentina at the Venice Architecture Biennale 2025?Marco Zampieron & Juan Manuel Pachué (MZ & JP): As Cooperativa, our group is deeply engaged with competitions. We participate regularly, advocate for them as a valuable tool, and truly enjoy the process they entail. This one, in particular, stood out because, while it’s a national competition, it offers a strong international platform: it’s about representing the country on one of the most important global stages for architecture. Our studio operates as a collaborative collective that expands or contracts depending on the nature of each project. At its core, our practice revolves around competitions. We see them as a way to access commissions without requiring an extensive portfolio, a strong reputation, or an established body of work. That’s why we especially value competitions that are anonymous and transparent — they open the door to meaningful opportunities, such as building a public-scale pavilion with high visibility, through a more horizontal and equitable framework.Save this picture!AD: How does Siestario relate to the theme proposed by the Venice Biennale 2025 — Intelligens. Natural. Artificial. Collective — and what possible interpretations did you envision based on that premise?MZ & JP: These mottos or curatorial texts often tend to be quite ambiguous — and we see that as something positive, because it opens the door to multiple interpretations. With so many people participating in the Biennale through diverse projects, it’s valuable for the thematic framework to be broad: it allows for a wide range of perspectives and approaches. From there, the question of how to represent Argentina naturally arises. Where do you anchor that representation? In our proposal, what comes through is our way of thinking about architecture, our understanding of the Biennale, and our perspective on the collective.Not only were the notions of intelligence, nature, and collectivity present, but there was also something that moved away from the idea of one place, one solution—a concept that truly captivated us. We find the idea of artificial intelligence somewhat challenging... perhaps we’re a bit romantic in that sense; there’s a certain resistance on our part. When we look at some international pavilions, we see a strong emphasis on technology and technical prowess. And while the artificial and the natural are in constant dialogue, what interests us is thinking about the artificial through the lens of local production—through cultural construction. Not as a race toward technological advancement, but as a way of drawing from the depth of knowledge that already exists within our own culture.It is also important that these competitions are resolved quickly, and often one starts from previous ideas or works. In our case, the collective already had a background, a sensitive research on the territory linked to art and certain key elements. This allowed us to connect with the Biennale's themes more deeply in a short time. We see this in other pavilions as well: many have a foundation of prior work and research. In our architecture — and also in art and other disciplines with which we engage — each person brings a background, certain interests, preferences, and tastes. From the place one comes from, what one observes, all of that forms a layer of information from which we project. We try to reflect on this to make it as conscious as possible, so that we can create something that feels close and authentic to us.Save this picture!Representing Argentina also forces us to ask ourselves what we want to say, how we want to speak about who we are. We are interested in a certain metaphor, a certain poetics. We think about the conditions the country is currently going through, both economically and politically, and this image of Argentina resting upon the Argentine countryside emerges, along with the cost of this rest. The proposal of Siestario suggests that the visitor to the Biennale, weary from seeing so much, could climb uphill to a silobag — that iconic symbol of Argentina's agricultural production — and rest, take a nap. But of course, that silobag is the one that stores soybeans, and soybeans, with all their symbolic, productive, and also destructive weight: they contaminate water, devastate forests, and affect local communities. So, Siestario doesn’t just offer a break; it also questions what that rest truly means. There’s a critique, a political stance, a reading on the artificial, on comfort, on the cost of being at ease. All of this emerges when the concepts are linked: the natural, the artificial, the collective. And here, the siesta also appears as a gesture, as a metaphor, as a critique, as a refuge.Save this picture!AD: What motivated you to use the silobag as the central element of the project? How was the process of re-signifying this object within the exhibition context of the Biennale? MZ & JP: The relationship with the silobag is not new to us; in fact, we had already worked with this element in previous projects, documenting and traveling through different towns. This prior research allowed us to see the silobag not just as a functional element, but also as an object loaded with meaning. The process of re-signifying it here at the Biennale involves decontextualizing it. We wanted to change its scale, give it a new role, and use it as a place for a nap, a space for rest. By doing so, the silobag transforms, ceasing to be just an agricultural object and becoming something more playful, connected to the body, fatigue, and the intimacy of the siesta. It’s a simple gesture, but a profound one: it proposes a space for people to recline and feel comfortable in it.Furthermore, the silobag becomes an object that, although it has strong rural and economic connotations, is reinterpreted within the exhibition context, adding a layer of reflection on the everyday and the collective. The intimacy of the siesta becomes social, as the act of resting in a shared space like this creates a collective moment. In the pavilion, we also worked with the atmosphere: the environment is designed to darken, with sounds and lights that induce a sense of relaxation, contributing to creating the right space for that collective rest.Save this picture!AD: What were you hoping to convey through this installation, and how did you expect visitors to experience or interpret the act of "pausing" within the exhibit's journey?MZ & JP: The proposal for this installation revolves around the idea of "stopping." Stopping to think, to look at what you’re doing, to know yourself, to decide. Ultimately, it’s an invitation to stop, to rest, to sleep. We aim for visitors to find that pause within themselves, to disconnect from the fast pace of their surroundings. Furthermore, this act of "pausing" becomes something desirable, almost like an object of desire: the urge to try it, to climb onto the silobag. We wanted to create a physical and experiential experience, not just a contemplative one. Walking through the pavilion becomes an invitation to interact with the space. It even allows walking on the silobag, changing heights, lying down, and resting.We know that at the Biennale, visitors tend to walk a lot and want to see as much as possible. However, this space demands that you stop, that you dedicate time to the experience. It’s not a place to just pass through quickly; it’s a space that asks you to stay for at least 15 or 20 minutes, to climb onto the silobag, to experience the physical effort of doing so, because it’s not a comfortable chair — it’s an experience that requires an investment of time and attention.The curation and design of the rest of the space, along with the projections, are intended so that the installation partially disappears as one moves through it, creating a visual and spatial effect that forces visitors to interact with the place, to stay alert. The project takes a risk; we know that, in the frenetic pace of the Biennale, some may not pay attention to this proposal. But we believe that, to truly enjoy it, it’s necessary to take that time, lie down, listen to the sounds, and be present.Save this picture!AD: How did you approach the relationship between architecture, space, and time within the Argentinian Pavilion?MZ & JP: Our approach has always been to give prominence to the public and collective. In the projects we develop, we try to make the ground floors open and accessible spaces where people can enter spontaneously, without the design being entirely directed toward exclusive use. We are interested in simplicity and clarity, in being radical in the sense of the purity of the idea. This pavilion, for example, uses a single element to express and resolve multiple layers of meaning. The design process seeks to simplify and refine complex ideas, ensuring that the result is direct and clear.In relation to time, we are interested in how architecture, over time, ages and transforms. In Venice, for example, one can see how the city and architecture are not static, how the passage of time causes materials to change, deteriorate, and feel lived in. This concept is present not only in the built architecture but also in the thinking and creation process. For us, time is not just a factor that affects buildings, but a constant in the design process. We value the time spent on physical models, on hand drawing, on processes that endure and allow us to generate something with more depth.Save this picture!Save this picture!AD: There is something disruptive about Siestario: by not displaying so much information or explicit context, the space becomes entirely an experience. What place did you give to the sensory aspect compared to the more direct information exhibition?MZ & JP: This approach is carried over to all our projects, not just the pavilion. We are interested in making spaces sensory experiences, where people not only see but also feel. We seek the space to invite reflection, but also for the enjoyment of the body, for people to engage directly with the environment. In the pavilion, in particular, we wanted the images to function as thresholds, leading you to other places, to the world of the dreamlike. This project, in particular, emphasizes the idea of pause, of stopping to reflect and rest, which connects with our broader vision of how we understand architecture: not just as something built, but as a lived experience.What we aim to create are silent spaces, ones that impose nothing, but at the same time offer power in their simplicity. This is something noticeable in other projects we've done, where the intervention is almost invisible, yet has a significant impact on people's experience, providing a space for them to claim it and inhabit it in their own way.Save this picture!AD: How was the curatorial process in constructing the space through the projections on the sides of the space?MZ & JP:  The curatorial proposal was built with the aim of integrating architecture, art, and landscape, bringing together the different actors. From the outset, we decided to organize the curation of images into three groups: one linked to architecture, another to art, and another to the very recording of the silobag in the field. This division not only provided the visitor with information about the origin and context of the project but also sought to establish a dialogue between architecture and art, something that is fundamental for us. Through the arts, we wanted to tell the story of our landscape and our territory, and we feel that artists have a special sensitivity to read and construct that landscape in a different way.The curatorial line was also guided by an idea that particularly interested us: that from naps emerge dreams, desires, and aspirations of a country. We wanted to propose a positive perspective within a critical reading, showcasing an Argentina that also produces art and architecture. The selected images engage in dialogue with the territory, and they do not respond to a specific temporal cut but rather bring together works from different moments, generating intersections across diverse times. We were interested in showing how these works relate to one another and how, through them, the variable of time appears in architectural production, in the territory, and in the contexts in which these works are being built.Save this picture!Additionally, there was significant archival work: we incorporated original drawings and unpublished material to add historical value. The selection included examples from across the country, even works that are not always associated with formal architecture, such as recreational or leisure spaces. We aimed to represent not only the major cities but also other realities, histories, and geographies. We also included maps and references to historical processes of urbanization and territorial defense, combining them with contemporary works to build the narrative.Save this picture!Save this picture!AD: What does it mean for you to represent Argentina at an event like the Venice Biennale, and how do you experience the responsibility and opportunity that this entails?MZ & JP: Representing Argentina at the Venice Biennale carries significant weight in the national architectural sphere. Winning the competition gave us a certain level of acceptance within the architecture community in Argentina. It’s difficult to fully reflect on the impact of this, as we are still in the process, but we do notice there is recognition. Our motivation was, above all, a genuine interest in participating in the competition, but upon taking on this responsibility, we realized the importance of representing our country at such a prominent event. For our careers, our studio, and our aspirations, this opportunity is significant. Being part of the Venice Biennale is something that makes us feel good because we know that what we do is part of a system that, although imperfect, allows us to reach places like this. It’s an important point in our trajectory and an achievement that fills us with satisfaction.Save this picture! Image gallerySee allShow less About this authorPaula PintosAuthor••• Cite: Pintos, Paula. "Rest as an Experience in a Space Rich with Symbolism: Insights from the Argentinian Pavilion at the 2025 Venice Biennale" [El Pabellón Argentino en la Bienal de Venecia 2025: La siesta como experiencia en un espacio cargado de simbolismos] 15 May 2025. ArchDaily. Accessed . <https://www.archdaily.com/1030139/rest-as-an-experience-in-a-space-rich-with-symbolism-insights-from-the-argentinian-pavilion-at-the-2025-venice-biennale&gt ISSN 0719-8884Save世界上最受欢迎的建筑网站现已推出你的母语版本!想浏览ArchDaily中国吗?是否 You've started following your first account!Did you know?You'll now receive updates based on what you follow! Personalize your stream and start following your favorite authors, offices and users.Go to my stream
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    2025-05-15 04:34:09 - Translate -
    Straw Stack Space in Houhu Art Park / WCY Regional Studio

    Straw Stack Space in Houhu Art Park / WCY Regional StudioSave this picture!© Jinquan KongCommunity Center, Cultural Center•Chang Sha Shi, China

    Architects:
    WCY Regional Studio
    Area
    Area of this architecture project

    Area: 
    748 m²

    Year
    Completion year of this architecture project

    Year: 

    2023

    Photographs

    Photographs:Jinquan Kong, Haohao Xu

    Lead Architects:

    Chunyu Wei, Sheng Ouyang

    More SpecsLess Specs
    this picture!
    Text description provided by the architects. Straw Stack Space is located in Houhu Art Park in Changsha, Hunan Province, which is a special 'village in the middle of the city' in Changsha that grew up spontaneously, located at the foot of Yuelu Mountain, and in the centre of the university town. Since the 1950s, the Houhu Art Park has become a unique cultural landmark in Changsha through its history of fisheries, art training and art exhibitions. And, Straw Stack Space is a small community cultural complex in Houhu Lake, integrates exhibitions, art salons, music performances and cultural and creative exchanges.this picture!Using pure 'Euclidean Geometry' ---square and circle as the basic graphic language, Straw Stack Space tries to revive the poetic atmosphere of Houhu's past as a field and countryside through the abstraction and metaphor of 'wheathaystacks', round barns, sunbathing yards and other memorable scenes in the rural landscape. The building occupies two residential sites, and we combined one house with six 'cylinders' in juxtaposition, while the other house has one 'cylinder' implanted in a pure 'square'. The cylinder array and the standard square form an isomorphic and inverted base relationship.this picture!this picture!this picture!The public cultural and artistic activities that take place in the Straw Stack Space with a sense of 'autonomy' or 'accident', such as contemporary art exhibitions, university graduation design exhibitions, small-scale music parties, modern dances, lawn music festivals, etc. At this point, the 'space' has the ability of 'functional transformation',and it has already departed from the original design preconception, and has generated other interpretations, but it is also an unexpected praise to us.this picture!The windows of the Straw Stack Space are mainly orientated to spatial experience, especially strengthening the use of natural top light: when the light is thrown down from the upper part of the building's cylindrical space, through the vertical directivity of the cylinders, it draws one's visual focus to the sky, establishing a relationship between the space, the body and the sky and the earth. Together with the geometrical order of the arrayed cylinders, it tries to let the body deeply perceive a certain ritual, quiet and poetic spatial atmosphere.this picture!this picture!this picture!After the completion of the project, whether it is the indigenous villagers, neighbouring citizens, teachers and students of the university, or foreign tourists, when they are relaxing in the Straw Stack Space, they will feel a special kind of 'spatial' information, which tries to evoke the memory of a familiar place or unfamiliar reverie, which belongs to the unique 'sense of place' of the Houhu Art Park or the Straw Stack Space, and it is also the power of the Archetype that we are looking for.this picture!

    Project gallerySee allShow less
    Project locationAddress:Chang Sha Shi, ChinaLocation to be used only as a reference. It could indicate city/country but not exact address.About this officeWCY Regional StudioOffice•••
    MaterialBrickMaterials and TagsPublished on May 15, 2025Cite: "Straw Stack Space in Houhu Art Park / WCY Regional Studio" 15 May 2025. ArchDaily. Accessed . < ISSN 0719-8884Save世界上最受欢迎的建筑网站现已推出你的母语版本!想浏览ArchDaily中国吗?是否
    You've started following your first account!Did you know?You'll now receive updates based on what you follow! Personalize your stream and start following your favorite authors, offices and users.Go to my stream
    #straw #stack #space #houhu #art
    Straw Stack Space in Houhu Art Park / WCY Regional Studio
    Straw Stack Space in Houhu Art Park / WCY Regional StudioSave this picture!© Jinquan KongCommunity Center, Cultural Center•Chang Sha Shi, China Architects: WCY Regional Studio Area Area of this architecture project Area:  748 m² Year Completion year of this architecture project Year:  2023 Photographs Photographs:Jinquan Kong, Haohao Xu Lead Architects: Chunyu Wei, Sheng Ouyang More SpecsLess Specs this picture! Text description provided by the architects. Straw Stack Space is located in Houhu Art Park in Changsha, Hunan Province, which is a special 'village in the middle of the city' in Changsha that grew up spontaneously, located at the foot of Yuelu Mountain, and in the centre of the university town. Since the 1950s, the Houhu Art Park has become a unique cultural landmark in Changsha through its history of fisheries, art training and art exhibitions. And, Straw Stack Space is a small community cultural complex in Houhu Lake, integrates exhibitions, art salons, music performances and cultural and creative exchanges.this picture!Using pure 'Euclidean Geometry' ---square and circle as the basic graphic language, Straw Stack Space tries to revive the poetic atmosphere of Houhu's past as a field and countryside through the abstraction and metaphor of 'wheathaystacks', round barns, sunbathing yards and other memorable scenes in the rural landscape. The building occupies two residential sites, and we combined one house with six 'cylinders' in juxtaposition, while the other house has one 'cylinder' implanted in a pure 'square'. The cylinder array and the standard square form an isomorphic and inverted base relationship.this picture!this picture!this picture!The public cultural and artistic activities that take place in the Straw Stack Space with a sense of 'autonomy' or 'accident', such as contemporary art exhibitions, university graduation design exhibitions, small-scale music parties, modern dances, lawn music festivals, etc. At this point, the 'space' has the ability of 'functional transformation',and it has already departed from the original design preconception, and has generated other interpretations, but it is also an unexpected praise to us.this picture!The windows of the Straw Stack Space are mainly orientated to spatial experience, especially strengthening the use of natural top light: when the light is thrown down from the upper part of the building's cylindrical space, through the vertical directivity of the cylinders, it draws one's visual focus to the sky, establishing a relationship between the space, the body and the sky and the earth. Together with the geometrical order of the arrayed cylinders, it tries to let the body deeply perceive a certain ritual, quiet and poetic spatial atmosphere.this picture!this picture!this picture!After the completion of the project, whether it is the indigenous villagers, neighbouring citizens, teachers and students of the university, or foreign tourists, when they are relaxing in the Straw Stack Space, they will feel a special kind of 'spatial' information, which tries to evoke the memory of a familiar place or unfamiliar reverie, which belongs to the unique 'sense of place' of the Houhu Art Park or the Straw Stack Space, and it is also the power of the Archetype that we are looking for.this picture! Project gallerySee allShow less Project locationAddress:Chang Sha Shi, ChinaLocation to be used only as a reference. It could indicate city/country but not exact address.About this officeWCY Regional StudioOffice••• MaterialBrickMaterials and TagsPublished on May 15, 2025Cite: "Straw Stack Space in Houhu Art Park / WCY Regional Studio" 15 May 2025. ArchDaily. Accessed . < ISSN 0719-8884Save世界上最受欢迎的建筑网站现已推出你的母语版本!想浏览ArchDaily中国吗?是否 You've started following your first account!Did you know?You'll now receive updates based on what you follow! Personalize your stream and start following your favorite authors, offices and users.Go to my stream #straw #stack #space #houhu #art
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    Straw Stack Space in Houhu Art Park / WCY Regional Studio
    Straw Stack Space in Houhu Art Park / WCY Regional StudioSave this picture!© Jinquan KongCommunity Center, Cultural Center•Chang Sha Shi, China Architects: WCY Regional Studio Area Area of this architecture project Area:  748 m² Year Completion year of this architecture project Year:  2023 Photographs Photographs:Jinquan Kong, Haohao Xu Lead Architects: Chunyu Wei, Sheng Ouyang More SpecsLess Specs Save this picture! Text description provided by the architects. Straw Stack Space is located in Houhu Art Park in Changsha, Hunan Province, which is a special 'village in the middle of the city' in Changsha that grew up spontaneously, located at the foot of Yuelu Mountain, and in the centre of the university town. Since the 1950s, the Houhu Art Park has become a unique cultural landmark in Changsha through its history of fisheries, art training and art exhibitions. And, Straw Stack Space is a small community cultural complex in Houhu Lake, integrates exhibitions, art salons, music performances and cultural and creative exchanges.Save this picture!Using pure 'Euclidean Geometry' ---square and circle as the basic graphic language, Straw Stack Space tries to revive the poetic atmosphere of Houhu's past as a field and countryside through the abstraction and metaphor of 'wheat (rice) haystacks', round barns, sunbathing yards and other memorable scenes in the rural landscape. The building occupies two residential sites, and we combined one house with six 'cylinders' in juxtaposition, while the other house has one 'cylinder' implanted in a pure 'square'. The cylinder array and the standard square form an isomorphic and inverted base relationship.Save this picture!Save this picture!Save this picture!The public cultural and artistic activities that take place in the Straw Stack Space with a sense of 'autonomy' or 'accident', such as contemporary art exhibitions, university graduation design exhibitions, small-scale music parties, modern dances, lawn music festivals, etc. At this point, the 'space' has the ability of 'functional transformation',and it has already departed from the original design preconception, and has generated other interpretations, but it is also an unexpected praise to us.Save this picture!The windows of the Straw Stack Space are mainly orientated to spatial experience, especially strengthening the use of natural top light: when the light is thrown down from the upper part of the building's cylindrical space, through the vertical directivity of the cylinders, it draws one's visual focus to the sky, establishing a relationship between the space, the body and the sky and the earth. Together with the geometrical order of the arrayed cylinders, it tries to let the body deeply perceive a certain ritual, quiet and poetic spatial atmosphere.Save this picture!Save this picture!Save this picture!After the completion of the project, whether it is the indigenous villagers, neighbouring citizens, teachers and students of the university, or foreign tourists, when they are relaxing in the Straw Stack Space, they will feel a special kind of 'spatial' information, which tries to evoke the memory of a familiar place or unfamiliar reverie, which belongs to the unique 'sense of place' of the Houhu Art Park or the Straw Stack Space, and it is also the power of the Archetype that we are looking for.Save this picture! Project gallerySee allShow less Project locationAddress:Chang Sha Shi, ChinaLocation to be used only as a reference. It could indicate city/country but not exact address.About this officeWCY Regional StudioOffice••• MaterialBrickMaterials and TagsPublished on May 15, 2025Cite: "Straw Stack Space in Houhu Art Park / WCY Regional Studio" 15 May 2025. ArchDaily. Accessed . <https://www.archdaily.com/1030097/straw-stack-space-in-houhu-art-park-wcy-regional-studio&gt ISSN 0719-8884Save世界上最受欢迎的建筑网站现已推出你的母语版本!想浏览ArchDaily中国吗?是否 You've started following your first account!Did you know?You'll now receive updates based on what you follow! Personalize your stream and start following your favorite authors, offices and users.Go to my stream
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