In the centre of the building, a ceremonial room is a calming space that invites community meetings and healing.   TEXT Elsa Lam PHOTOS doublespace photography Fifty years ago, four grandmothers founded the organization Anduhyaun—Ojibwe for “Our Home”—to respond to the needs of the city’s Indigenous women. The organization used a city-owned heritage house in the Annex to offer shelter to women and children suffering from the traumas of violence and homelessness. Last year, they moved to a building designed by LGA Architectural Partners.  The architects took the organization’s name to heart, aiming to provide not just basic shelter, but a place of nurture and grounding. The organization’s long-time executive director, Blanche Meawassige, told the designers that the building needed to feel like it was alive. “From Blanche, we understood that a shelter is a place where rehabilitation—growth and healing—begins,” says architect Brock James, partner at LGA. “Yes, it’s a roof over your head and safety, but it’s also where that spiritual part starts; it has to be about growth and life.” A curved ceiling-to-wall transition brings a unique quality to the bedrooms This thinking shows in the bedrooms, 16 of which are compact, single-occupancy rooms and two of which are designed for families with up to three kids. (Some rooms can also be interconnected, to accommodate larger families or to provide a physical link between friends.) Each room has its own bathroom, a wooden desk, adjustable lighting, and a curved ceiling-to-wall transition that reflects daylight through the space. The curve, says James, “makes the room very ‘here’. It’s only here. It’s not generic.” Curved, tiled walls define the circulation areas on the ground floor Curves recur on the main floor, where seafoam-coloured tiles sweep along flowing walls, adding to the building’s sense of aliveness and alluding to Indigenous teachings about water. The curves lead to a large communal kitchen and a central ceremonial space, currently known as Nookomis, or “grandmother.” The interior of Nookomis, the building’s main ceremonial space Many details illustrate LGA’s commitment to creating specialness with economical means. Nookomis, for instance, is clad with cedar shingles that came in pre-cut profiles. (“It’s just a matter of coming up with a pattern, which I did in my living room during the pandemic,” says James.) The burgundy walls of Nookomis’s interior are made, in part, with a simple foam in a custom colour. (“It’s a straightforward product that’s not good if you were touching it—but up high, controlling the sound, it works.”) A skylight caps the space, like a full moon casting a soft glow from above. Meawassige originally asked for the outside of the building to be anonymous, but later decided that the Indigeneity of the interior should be expressed outside, too. The architects created a design with a similarly elevated attention to detail. They achieved a high level of airtightness in the envelope, and composed a façade made out of standard Vicwest metal, applied in a pattern that ensures that the 300-mm width of the panels would stay intact, with no cuts.   “Good design, an elevated section, beautiful tiles: it’s things like that that makes it feel like somebody cares,” says James. “When I think about aesthetics, we can have lots of stories about them, but I know that it’s communicating that Anduhyaun cares. This is their building, and it’s emoting that they care.”  As appeared in the June 2025 issue of Canadian Architect magazine  The post Our Home, Toronto, Ontario appeared first on Canadian Architect.

The housing crisis in Canada’s North, which has particularly affected the majority Indigenous population in northern communities, has been of ongoing concern to firms such as Taylor Architecture Group (TAG). Formerly known as Pin/Taylor, the firm was established in Yellowknife in 1983. TAG’s Principal, Simon Taylor, says that despite recent political gains for First Nations, “by and large, life is not improving up here.” Taylor and his colleagues have designed many different types of housing across the North. But the problems exceed the normal scope of architectural practice. TAG’s Manager of Research and Development, Kristel Derkowski, says, “We can design the units well, but it doesn’t solve many of the underlying problems.” To respond, she says, “we’ve backed up the process to look at the root causes more.” As a result, “the design challenges are informed by much broader systemic research.”  We spoke to Derkowski about her research, and the work that Taylor Architecture Group is doing to act on it. Here’s what she has to say. Inadequate housing from the start The Northwest Territories is about 51% Indigenous. Most non-Indigenous people are concentrated in the capital city of Yellowknife. Outside of Yellowknife, the territory is very much majority Indigenous.  The federal government got involved in delivering housing to the far North in 1959. There were problems with this program right from the beginning. One issue was that when the houses were first delivered, they were designed and fabricated down south, and they were completely inadequate for the climate. The houses from that initial program were called “Matchbox houses” because they were so small. These early stages of housing delivery helped establish the precedent that a lower standard of housing was acceptable for northern Indigenous residents compared to Euro-Canadian residents elsewhere. In many cases, that double-standard persists to this day. The houses were also inappropriately designed for northern cultures. It’s been said in the research that the way that these houses were delivered to northern settlements was a significant factor in people being divorced from their traditional lifestyles, their traditional hierarchies, the way that they understood home. It was imposing a Euro-Canadian model on Indigenous communities and their ways of life.  Part of what the federal government was trying to do was to impose a cash economy and stimulate a market. They were delivering houses and asking for rent. But there weren’t a lot of opportunities to earn cash. This housing was delivered around the sites of former fur trading posts—but the fur trade had collapsed by 1930. There weren’t a lot of jobs. There wasn’t a lot of wage-based employment. And yet, rental payments were being collected in cash, and the rental payments increased significantly over the span of a couple decades.  The imposition of a cash economy created problems culturally. It’s been said that public housing delivery, in combination with other social policies, served to introduce the concept of poverty in the far North, where it hadn’t existed before. These policies created a situation where Indigenous northerners couldn’t afford to be adequately housed, because housing demanded cash, and cash wasn’t always available. That’s a big theme that continues to persist today. Most of the territory’s communities remain “non-market”: there is no housing market. There are different kinds of economies in the North—and not all of them revolve wholly around cash. And yet government policies do. The governments’ ideas about housing do, too. So there’s a conflict there.  The federal exit from social housing After 1969, the federal government devolved housing to the territorial government. The Government of Northwest Territories created the Northwest Territories Housing Corporation. By 1974, the housing corporation took over all the stock of federal housing and started to administer it, in addition to building their own. The housing corporation was rapidly building new housing stock from 1975 up until the mid-1990s. But beginning in the early 1990s, the federal government terminated federal spending on new social housing across the whole country. A couple of years after that, they also decided to allow operational agreements with social housing providers to expire. It didn’t happen that quickly—and maybe not everybody noticed, because it wasn’t a drastic change where all operational funding disappeared immediately. But at that time, the federal government was in 25- to 50-year operational agreements with various housing providers across the country. After 1995, these long-term operating agreements were no longer being renewed—not just in the North, but everywhere in Canada.  With the housing corporation up here, that change started in 1996, and we have until 2038 before the federal contribution of operational funding reaches zero. As a result, beginning in 1996, the number of units owned by the NWT Housing Corporation plateaued. There was a little bump in housing stock after that—another 200 units or so in the early 2000s. But basically, the Northwest Territories was stuck for 25 years, from 1996 to 2021, with the same number of public housing units. In 1990, there was a report on housing in the NWT that was funded by the Canada Mortgage and Housing Corporation (CMHC). That report noted that housing was already in a crisis state. At that time, in 1990, researchers said it would take 30 more years to meet existing housing need, if housing production continued at the current rate. The other problem is that houses were so inadequately constructed to begin with, that they generally needed replacement after 15 years. So housing in the Northwest Territories already had serious problems in 1990. Then in 1996, the housing corporation stopped building more. So if you compare the total number of social housing units with the total need for subsidized housing in the territory, you can see a severely widening gap in recent decades. We’ve seen a serious escalation in housing need. The Northwest Territories has a very, very small tax base, and it’s extremely expensive to provide services here. Most of our funding for public services comes from the federal government. The NWT on its own does not have a lot of buying power. So ever since the federal government stopped providing operational funding for housing, the territorial government has been hard-pressed to replace that funding with its own internal resources. I should probably note that this wasn’t only a problem for the Northwest Territories. Across Canada, we have seen mass homelessness visibly emerge since the ’90s. This is related, at least in part, to the federal government’s decisions to terminate funding for social housing at that time. Today’s housing crisis Getting to present-day conditions in the NWT, we now have some “market” communities and some “non-market” communities. There are 33 communities total in the NWT, and at least 27 of these don’t have a housing market: there’s no private rental market and there’s no resale market. This relates back to the conflict I mentioned before: the cash economy did not entirely take root. In simple terms, there isn’t enough local employment or income opportunity for a housing market—in conventional terms—to work.  Yellowknife is an outlier in the territory. Economic opportunity is concentrated in the capital city. We also have five other “market” communities that are regional centres for the territorial government, where more employment and economic activity take place. Across the non-market communities, on average, the rate of unsuitable or inadequate housing is about five times what it is elsewhere in Canada. Rates of unemployment are about five times what they are in Yellowknife. On top of this, the communities with the highest concentration of Indigenous residents also have the highest rates of unsuitable or inadequate housing, and also have the lowest income opportunity. These statistics clearly show that the inequalities in the territory are highly racialized.  Given the situation in non-market communities, there is a severe affordability crisis in terms of the cost to deliver housing. It’s very, very expensive to build housing here. A single detached home costs over a million dollars to build in a place like Fort Good Hope (Rádeyı̨lı̨kóé). We’re talking about a very modest three-bedroom house, smaller than what you’d typically build in the South. The million-dollar price tag on each house is a serious issue. Meanwhile, in a non-market community, the potential resale value is extremely low. So there’s a massive gap between the cost of construction and the value of the home once built—and that’s why you have no housing market. It means that private development is impossible. That’s why, until recently, only the federal and territorial governments have been building new homes in non-market communities. It’s so expensive to do, and as soon as the house is built, its value plummets.  The costs of living are also very high. According to the NWT Bureau of Statistics, the estimated living costs for an individual in Fort Good Hope are about 1.8 times what it costs to live in Edmonton. Then when it comes to housing specifically, there are further issues with operations and maintenance. The NWT is not tied into the North American hydro grid, and in most communities, electricity is produced by a diesel generator. This is extremely expensive. Everything needs to be shipped in, including fuel. So costs for heating fuel are high as well, as are the heating loads. Then, maintenance and repairs can be very difficult, and of course, very costly. If you need any specialized parts or specialized labour, you are flying those parts and those people in from down South. So to take on the costs of homeownership, on top of the costs of living—in a place where income opportunity is limited to begin with—this is extremely challenging. And from a statistical or systemic perspective, this is simply not in reach for most community members. In 2021, the NWT Housing Corporation underwent a strategic renewal and became Housing Northwest Territories. Their mandate went into a kind of flux. They started to pivot from being the primary landlord in the territory towards being a partner to other third-party housing providers, which might be Indigenous governments, community housing providers, nonprofits, municipalities. But those other organisations, in most cases, aren’t equipped or haven’t stepped forward to take on social housing. Even though the federal government is releasing capital funding for affordable housing again, northern communities can’t always capitalize on that, because the source of funding for operations remains in question. Housing in non-market communities essentially needs to be subsidized—not just in terms of construction, but also in terms of operations. But that operational funding is no longer available. I can’t stress enough how critical this issue is for the North. Fort Good Hope and “one thing that (kind of) worked” I’ll talk a bit about Fort Good Hope. I don’t want to be speaking on behalf of the community here, but I will share a bit about the realities on the ground, as a way of putting things into context.  Fort Good Hope, or Rádeyı̨lı̨kóé, is on the Mackenzie River, close to the Arctic Circle. There’s a winter road that’s open at best from January until March—the window is getting narrower because of climate change. There were also barges running each summer for material transportation, but those have been cancelled for the past two years because of droughts linked to climate change. Aside from that, it’s a fly-in community. It’s very remote. It has about 500-600 people. According to census data, less than half of those people live in what’s considered acceptable housing.  The biggest problem is housing adequacy. That’s CMHC’s term for housing in need of major repairs. This applies to about 36% of households in Fort Good Hope. In terms of ownership, almost 40% of the community’s housing stock is managed by Housing NWT. That’s a combination of public housing units and market housing units—which are for professionals like teachers and nurses. There’s also a pretty high percentage of owner-occupied units—about 46%.  The story told by the community is that when public housing arrived in the 1960s, the people were living in owner-built log homes. Federal agents arrived and they considered some of those homes to be inadequate or unacceptable, and they bulldozed those homes, then replaced some of them—but maybe not all—with public housing units. Then residents had no choice but to rent from the people who took their homes away. This was not a good way to start up a public housing system. The state of housing in Fort Good Hope Then there was an issue with the rental rates, which drastically increased over time. During a presentation to a government committee in the ’80s, a community member explained that they had initially accepted a place in public housing for a rental fee of $2 a month in 1971. By 1984, the same community member was expected to pay $267 a month. That might not sound like much in today’s terms, but it was roughly a 13,000% increase for that same tenant—and it’s not like they had any other housing options to choose from. So by that point, they’re stuck with paying whatever is asked.  On top of that, the housing units were poorly built and rapidly deteriorated. One description from that era said the walls were four inches thick, with windows oriented north, and water tanks that froze in the winter and fell through the floor. The single heating source was right next to the only door—residents were concerned about the fire hazard that obviously created. Ultimately the community said: “We don’t actually want any more public housing units. We want to go back to homeownership, which was what we had before.”  So Fort Good Hope was a leader in housing at that time and continues to be to this day. The community approached the territorial government and made a proposal: “Give us the block funding for home construction, we’ll administer it ourselves, we’ll help people build houses, and they can keep them.” That actually worked really well. That was the start of the Homeownership Assistance Program (HAP) that ran for about ten years, beginning in 1982. The program expanded across the whole territory after it was piloted in Fort Good Hope. The HAP is still spoken about and written about as the one thing that kind of worked.  Self-built log cabins remain from Fort Good Hope’s 1980s Homeownership Program (HAP). Funding was cost-shared between the federal and territorial governments. Through the program, material packages were purchased for clients who were deemed eligible. The client would then contribute their own sweat equity in the form of hauling logs and putting in time on site. They had two years to finish building the house. Then, as long as they lived in that home for five more years, the loan would be forgiven, and they would continue owning the house with no ongoing loan payments. In some cases, there were no mechanical systems provided as part of this package, but the residents would add to the house over the years. A lot of these units are still standing and still lived in today. Many of them are comparatively well-maintained in contrast with other types of housing—for example, public housing units. It’s also worth noting that the one-time cost of the materials package was—from the government’s perspective—only a fraction of the cost to build and maintain a public housing unit over its lifespan. At the time, it cost about $50,000 to $80,000 to build a HAP home, whereas the lifetime cost of a public housing unit is in the order of $2,000,000. This program was considered very successful in many places, especially in Fort Good Hope. It created about 40% of their local housing stock at that time, which went from about 100 units to about 140. It’s a small community, so that’s quite significant.  What were the successful principles? The community-based decision-making power to allocate the funding. The sweat equity component, which brought homeownership within the range of being attainable for people—because there wasn’t cash needing to be transferred, when the cash wasn’t available. Local materials—they harvested the logs from the land, and the fact that residents could maintain the homes themselves. The Fort Good Hope Construction Centre. Rendering by Taylor Architecture Group The Fort Good Hope Construction Centre The HAP ended the same year that the federal government terminated new spending on social housing. By the late 1990s, the creation of new public housing stock or new homeownership units had gone down to negligible levels. But more recently, things started to change. The federal government started to release money to build affordable housing. Simultaneously, Indigenous governments are working towards Self-Government and settling their Land Claims. Federal funds have started to flow directly to Indigenous groups. Given these changes, the landscape of Northern housing has started to evolve. In 2016, Fort Good Hope created the K’asho Got’ine Housing Society, based on the precedent of the 1980s Fort Good Hope Housing Society. They said: “We did this before, maybe we can do it again.” The community incorporated a non-profit and came up with a five-year plan to meet housing need in their community. One thing the community did right away was start up a crew to deliver housing maintenance and repairs. This is being run by Ne’Rahten Developments Ltd., which is the business arm of Yamoga Land Corporation (the local Indigenous Government). Over the span of a few years, they built up a crew of skilled workers. Then Ne’Rahten started thinking, “Why can’t we do more? Why can’t we build our own housing?” They identified a need for a space where people could work year-round, and first get training, then employment, in a stable all-season environment. This was the initial vision for the Fort Good Hope Construction Centre, and this is where TAG got involved. We had some seed funding through the CMHC Housing Supply Challenge when we partnered with Fort Good Hope. We worked with the community for over a year to get the capital funding lined up for the project. This process required us to take on a different role than the one you typically would as an architect. It wasn’t just schematic-design-to-construction-administration. One thing we did pretty early on was a housing design workshop that was open to the whole community, to start understanding what type of housing people would really want to see. Another piece was a lot of outreach and advocacy to build up support for the project and partnerships—for example, with Housing Northwest Territories and Aurora College. We also reached out to our federal MP, the NWT Legislative Assembly and different MLAs, and we talked to a lot of different people about the link between employment and housing. The idea was that the Fort Good Hope Construction Centre would be a demonstration project. Ultimately, funding did come through for the project—from both CMHC and National Indigenous Housing Collaborative Inc. The facility itself will not be architecturally spectacular. It’s basically a big shed where you could build a modular house. But the idea is that the construction of those houses is combined with training, and it creates year-round indoor jobs. It intends to combat the short construction seasons, and the fact that people would otherwise be laid off between projects—which makes it very hard to progress with your training or your career. At the same time, the Construction Centre will build up a skilled labour force that otherwise wouldn’t exist—because when there’s no work, skilled people tend to leave the community. And, importantly, the idea is to keep capital funding in the community. So when there’s a new arena that needs to get built, when there’s a new school that needs to get built, you have a crew of people who are ready to take that on. Rather than flying in skilled labourers, you actually have the community doing it themselves. It’s working towards self-determination in housing too, because if those modular housing units are being built in the community, by community members, then eventually they’re taking over design decisions and decisions about maintenance—in a way that hasn’t really happened for decades. Transitional homeownership My research also looked at a transitional homeownership model that adapts some of the successful principles of the 1980s HAP. Right now, in non-market communities, there are serious gaps in the housing continuum—that is, the different types of housing options available to people. For the most part, you have public housing, and you have homelessness—mostly in the form of hidden homelessness, where people are sleeping on the couches of relatives. Then, in some cases, you have inherited homeownership—where people got homes through the HAP or some other government program. But for the most part, not a lot of people in non-market communities are actually moving into homeownership anymore. I asked the local housing manager in Fort Good Hope: “When’s the last time someone built a house in the community?” She said, “I can only think of one person. It was probably about 20 years ago, and that person actually went to the bank and got a mortgage. If people have a home, it’s usually inherited from their parents or from relatives.” And that situation is a bit of a problem in itself, because it means that people can’t move out of public housing. Public housing traps you in a lot of ways. For example, it punishes employment, because rent is geared to income. It’s been said many times that this model disincentivizes employment. I was in a workshop last year where an Indigenous person spoke up and said, “Actually, it’s not disincentivizing, it punishes employment. It takes things away from you.” Somebody at the territorial housing corporation in Yellowknife told me, “We have clients who are over the income threshold for public housing, but there’s nowhere else they can go.” Theoretically, they would go to the private housing market, they would go to market housing, or they would go to homeownership, but those options don’t exist or they aren’t within reach.  So the idea with the transitional homeownership model is to create an option that could allow the highest income earners in a non-market community to move towards homeownership. This could take some pressure off the public housing system. And it would almost be like a wealth distribution measure: people who are able to afford the cost of operating and maintaining a home then have that option, instead of remaining in government-subsidized housing. For those who cannot, the public housing system is still an option—and maybe a few more public housing units are freed up.  I’ve developed about 36 recommendations for a transitional homeownership model in northern non-market communities. The recommendations are meant to be actioned at various scales: at the scale of the individual household, the scale of the housing provider, and the scale of the whole community. The idea is that if you look at housing as part of a whole system, then there are certain moves that might make sense here—in a non-market context especially—that wouldn’t make sense elsewhere. So for example, we’re in a situation where a house doesn’t appreciate in value. It’s not a financial asset, it’s actually a financial liability, and it’s something that costs a lot to maintain over the years. Giving someone a house in a non-market community is actually giving them a burden, but some residents would be quite willing to take this on, just to have an option of getting out of public housing. It just takes a shift in mindset to start considering solutions for that kind of context. One particularly interesting feature of non-market communities is that they’re still functioning with a mixed economy: partially a subsistence-based or traditional economy, and partially a cash economy. I think that’s actually a strength that hasn’t been tapped into by territorial and federal policies. In the far North, in-kind and traditional economies are still very much a way of life. People subsidize their groceries with “country food,” which means food that was harvested from the land. And instead of paying for fuel tank refills in cash, many households in non-market communities are burning wood as their primary heat source. In communities south of the treeline, like Fort Good Hope, that wood is also harvested from the land. Despite there being no exchange of cash involved, these are critical economic activities—and they are also part of a sustainable, resilient economy grounded in local resources and traditional skills. This concept of the mixed economy could be tapped into as part of a housing model, by bringing back the idea of a ‘sweat equity’ contribution instead of a down payment—just like in the HAP. Contributing time and labour is still an economic exchange, but it bypasses the ‘cash’ part—the part that’s still hard to come by in a non-market community. Labour doesn’t have to be manual labour, either. There are all kinds of work that need to take place in a community: maybe taking training courses and working on projects at the Construction Centre, maybe helping out at the Band Office, or providing childcare services for other working parents—and so on. So it could be more inclusive than a model that focuses on manual labour. Another thing to highlight is a rent-to-own trial period. Not every client will be equipped to take on the burdens of homeownership. So you can give people a trial period. If it doesn’t work out and they can’t pay for operations and maintenance, they could continue renting without losing their home. Then it’s worth touching on some basic design principles for the homeownership units. In the North, the solutions that work are often the simplest—not the most technologically innovative. When you’re in a remote location, specialized replacement parts and specialized labour are both difficult to come by. And new technologies aren’t always designed for extreme climates—especially as we trend towards the digital. So rather than installing technologically complex, high-efficiency systems, it actually makes more sense to build something that people are comfortable with, familiar with, and willing to maintain. In a southern context, people suggest solutions like solar panels to manage energy loads. But in the North, the best thing you can do for energy is put a woodstove in the house. That’s something we’ve heard loud and clear in many communities. Even if people can’t afford to fill their fuel tank, they’re still able to keep chopping wood—or their neighbour is, or their brother, or their kid, and so on. It’s just a different way of looking at things and a way of bringing things back down to earth, back within reach of community members.  Regulatory barriers to housing access: Revisiting the National Building Code On that note, there’s one more project I’ll touch on briefly. TAG is working on a research study, funded by Housing, Infrastructure and Communities Canada, which looks at regulatory barriers to housing access in the North. The National Building Code (NBC) has evolved largely to serve the southern market context, where constraints and resources are both very different than they are up here. Technical solutions in the NBC are based on assumptions that, in some cases, simply don’t apply in northern communities. Here’s a very simple example: minimum distance to a fire hydrant. Most of our communities don’t have fire hydrants at all. We don’t have municipal services. The closest hydrant might be thousands of kilometres away. So what do we do instead? We just have different constraints to consider. That’s just one example but there are many more. We are looking closely at the NBC, and we are also working with a couple of different communities in different situations. The idea is to identify where there are conflicts between what’s regulated and what’s actually feasible, viable, and practical when it comes to on-the-ground realities. Then we’ll look at some alternative solutions for housing. The idea is to meet the intent of the NBC, but arrive at some technical solutions that are more practical to build, easier to maintain, and more appropriate for northern communities.  All of the projects I’ve just described are fairly recent, and very much still ongoing. We’ll see how it all plays out. I’m sure we’re going to run into a lot of new barriers and learn a lot more on the way, but it’s an incremental trial-and-error process. Even with the Construction Centre, we’re saying that this is a demonstration project, but how—or if—it rolls out in other communities would be totally community-dependent, and it could look very, very different from place to place.  In doing any research on Northern housing, one of the consistent findings is that there is no one-size-fits-all solution. Northern communities are not all the same. There are all kinds of different governance structures, different climates, ground conditions, transportation routes, different population sizes, different people, different cultures. Communities are Dene, Métis, Inuvialuit, as well as non-Indigenous, all with different ways of being. One-size-fits-all solutions don’t work—they never have. And the housing crisis is complex, and it’s difficult to unravel. So we’re trying to move forward with a few different approaches, maybe in a few different places, and we’re hoping that some communities, some organizations, or even some individual people, will see some positive impacts.  As appeared in the June 2025 issue of Canadian Architect magazine  The post Insites: Addressing the Northern housing crisis appeared first on Canadian Architect.

In “The Optimist: Sam Altman, OpenAI, and the Race to Invent the Future,” Wall Street Journal reporter Keach Hagey examines our AI-obsessed moment through one of its key figures — Sam Altman, co-founder and CEO of OpenAI. Hagey begins with Altman’s Midwest childhood, then takes readers through his career at startup Loopt, accelerator Y Combinator, and now at OpenAI. She also sheds new light on the dramatic few days when Altman was fired, then quickly reinstated, as OpenAI’s CEO. Looking back at what OpenAI employees now call “the Blip,” Hagey said the failed attempt to oust Altman revealed that OpenAI’s complex structure — with a for-profit company controlled by a nonprofit board — is “not stable.” And with OpenAI largely backing down from plans to let the for-profit side take control, Hagey predicted that this “fundamentally unstable arrangement” will “continue to give investors pause.” Does that mean OpenAI could struggle to raise the funds it needs to keep going? Hagey replied that it could “absolutely” be an issue. “My research into Sam suggests that he might well be up to that challenge,” she said. “But success is not guaranteed.” In addition, Hagey’s biography (also available as an audiobook on Spotify) examines Altman’s politics, which she described as “pretty traditionally progressive” — making it a bit surprising that he’s struck massive infrastructure deals with the backing of the Trump administration. “But this is one area where, in some ways, I feel like Sam Altman has been born for this moment, because he is a deal maker and Trump is a deal maker,” Hagey said. “Trump respects nothing so much as a big deal with a big price tag on it, and that is what Sam Altman is really great at.” Techcrunch event Save now through June 4 for TechCrunch Sessions: AI Save $300 on your ticket to TC Sessions: AI—and get 50% off a second. Hear from leaders at OpenAI, Anthropic, Khosla Ventures, and more during a full day of expert insights, hands-on workshops, and high-impact networking. These low-rate deals disappear when the doors open on June 5. Exhibit at TechCrunch Sessions: AI Secure your spot at TC Sessions: AI and show 1,200+ decision-makers what you’ve built — without the big spend. Available through May 9 or while tables last. Berkeley, CA | June 5 REGISTER NOW In an interview with TechCrunch, Hagey also discussed Altman’s response to the book, his trustworthiness, and the AI “hype universe.” This interview has been edited for length and clarity.  You open the book by acknowledging some of the reservations that Sam Altman had about the project —  this idea that we tend to focus too much on individuals rather than organizations or broad movements, and also that it’s way too early to assess the impact of OpenAI. Did you share those concerns? Well, I don’t really share them, because this was a biography. This project was to look at a person, not an organization. And I also think that Sam Altman has set himself up in a way where it does matter what kind of moral choices he has made and what his moral formation has been, because the broad project of AI is really a moral project. That is the basis of OpenAI’s existence. So I think these are fair questions to ask about a person, not just an organization. As far as whether it’s too soon, I mean, sure, it’s definitely [early to] assess the entire impact of AI. But it’s been an extraordinary story for OpenAI — just so far, it’s already changed the stock market, it has changed the entire narrative of business. I’m a business journalist. We do nothing but talk about AI, all day long, every day. So in that way, I don’t think it’s too early. And despite those reservations, Altman did cooperate with you. Can you say more about what your relationship with him was like during the process of researching the book? Well, he was definitely not happy when he was informed about the book’s existence. And there was a long period of negotiation, frankly. In the beginning, I figured I was going to write this book without his help — what we call, in the business, a write-around profile. I’ve done plenty of those over my career, and I figured this would just be one more. Over time, as I made more and more calls, he opened up a little bit. And [eventually,] he was generous to sit down with me several times for long interviews and share his thoughts with me. Has he responded to the finished book at all? No. He did tweet about the project, about his decision to participate with it, but he was very clear that he was never going to read it. It’s the same way that I don’t like to watch my TV appearances or podcasts that I’m on. In the book, he’s described as this emblematic Silicon Valley figure. What do you think are the key characteristics that make him representative of the Valley and the tech industry? In the beginning, I think it was that he was young. The Valley really glorifies youth, and he was 19 years old when he started his first startup. You see him going into these meetings with people twice his age, doing deals with telecom operators for his first startup, and no one could get over that this kid was so smart. The other is that he is a once-in-a-generation fundraising talent, and that’s really about being a storyteller. I don’t think it’s an accident that you have essentially a salesman and a fundraiser at the top of the most important AI company today, That ties into one of the questions that runs through the book — this question about Altman’s trustworthiness. Can you say more about the concerns people seem to have about that? To what extent is he a trustworthy figure?  Well, he’s a salesman, so he’s really excellent at getting in a room and convincing people that he can see the future and that he has something in common with them. He gets people to share his vision, which is a rare talent. There are people who’ve watched that happen a bunch of times, who think, “Okay, what he says does not always map to reality,” and have, over time, lost trust in him. This happened both at his first startup and very famously at OpenAI, as well as at Y Combinator. So it is a pattern, but I think it’s a typical critique of people who have the salesman skill set. So it’s not necessarily that he’s particularly untrustworthy, but it’s part-and-parcel of being a salesman leading these important companies. I mean, there also are management issues that are detailed in the book, where he is not great at dealing with conflict, so he’ll basically tell people what they want to hear. That causes a lot of sturm-und-drang in the management ranks, and it’s a pattern. Something like that happened at Loopt, where the executives asked the board to replace him as CEO. And you saw it happen at OpenAI as well. You’ve touched on Altman’s firing, which was also covered in a book excerpt that was published in the Wall Street Journal. One of the striking things to me, looking back at it, was just how complicated everything was — all the different factions within the company, all the people who seemed pro-Altman one day and then anti-Altman the next. When you pull back from the details, what do you think is the bigger significance of that incident? The very big picture is that the nonprofit governance structure is not stable. You can’t really take investment from the likes of Microsoft and a bunch of other investors and then give them absolutely no say whatsoever in the governance of the company. That’s what they have tried to do, but I think what we saw in that firing is how power actually works in the world. When you have stakeholders, even if there’s a piece of paper that says they have no rights, they still have power. And when it became clear that everyone in the company was going to go to Microsoft if they didn’t reinstate Sam Altman, they reinstated Sam Altman. In the book, you take the story up to maybe the end of 2024. There have been all these developments since then, which you’ve continued to report on, including this announcement that actually, they’re not fully converting to a for-profit. How do you think that’s going to affect OpenAI going forward?  It’s going to make it harder for them to raise money, because they basically had to do an about-face. I know that the new structure going forward of the public benefit corporation is not exactly the same as the current structure of the for-profit — it is a little bit more investor friendly, it does clarify some of those things. But overall, what you have is a nonprofit board that controls a for-profit company, and that fundamentally unstable arrangement is what led to the so-called Blip. And I think you would continue to give investors pause, going forward, if they are going to have so little control over their investment. Obviously, OpenAI is still such a capital intensive business. If they have challenges raising more money, is that an existential question for the company? It absolutely could be. My research into Sam suggests that he might well be up to that challenge. But success is not guaranteed. Like you said, there’s a dual perspective in the book that’s partly about who Sam is, and partly about what that says about where AI is going from here. How did that research into his particular story shape the way you now look at these broader debates about AI and society? I went down a rabbit hole in the beginning of the book, [looking] into Sam’s father, Jerry Altman, in part because I thought it was striking how he’d been written out of basically every other thing that had ever been written about Sam Altman. What I found in this research was a very idealistic man who was, from youth, very interested in these public-private partnerships and the power of the government to set policy. He ended up having an impact on the way that affordable housing is still financed to this day. And when I traced Sam’s development, I saw that he has long believed that the government should really be the one that is funding and guiding AI research. In the early days of OpenAI, they went and tried to get the government to invest, as he’s publicly said, and it didn’t work out. But he looks back to these great mid-20th century labs like Xerox PARC and Bell Labs, which are private, but there was a ton of government money running through and supporting that ecosystem. And he says, “That’s the right way to do it.” Now I am watching daily as it seems like the United States is summoning the forces of state capitalism to get behind Sam Altman’s project to build these data centers, both in the United States and now there was just one last week announced in Abu Dhabi. This is a vision he has had for a very, very long time. My sense of the vision, as he presented it earlier, was one where, on the one hand, the government is funding these things and building this infrastructure, and on the other hand, the government is also regulating and guiding AI development for safety purposes. And it now seems like the path being pursued is one where they’re backing away from the safety side and doubling down on the government investment side. Absolutely. Isn’t it fascinating?  You talk about Sam as a political figure, as someone who’s had political ambitions at different times, but also somebody who has what are in many ways traditionally liberal political views while being friends with folks like — at least early on — Elon Musk and Peter Thiel. And he’s done a very good job of navigating the Trump administration. What do you think his politics are right now? I’m not sure his actual politics have changed, they are pretty traditionally progressive politics. Not completely — he’s been critical about things like cancel culture, but in general, he thinks the government is there to take tax revenue and solve problems. His success in the Trump administration has been fascinating because he has been able to find their one area of overlap, which is the desire to build a lot of data centers, and just double down on that and not talk about any other stuff. But this is one area where, in some ways, I feel like Sam Altman has been born for this moment, because he is a deal maker and Trump is a deal maker. Trump respects nothing so much as a big deal with a big price tag on it, and that is what Sam Altman is really great at. You open and close the book not just with Sam’s father, but with his family as a whole. What else is worth highlighting in terms of how his upbringing and family shapes who he is now? Well, you see both the idealism from his father and also the incredible ambition from his mother, who was a doctor, and had four kids and worked as a dermatologist. I think both of these things work together to shape him. They also had a more troubled marriage than I realized going into the book. So I do think that there’s some anxiety there that Sam himself is very upfront about, that he was a pretty anxious person for much of his life, until he did some meditation and had some experiences. And there’s his current family — he just had a baby and got married not too long ago. As a young gay man, growing up in the Midwest, he had to overcome some challenges, and I think those challenges both forged him in high school as a brave person who could stand up and take on a room as a public speaker, but also shaped his optimistic view of the world. Because, on that issue, I paint the scene of his wedding: That’s an unimaginable thing from the early ‘90s, or from the ‘80s when he was born. He’s watched society develop and progress in very tangible ways, and I do think that that has helped solidify his faith in progress. Something that I’ve found writing about AI is that the different visions being presented by people in the field can be so diametrically opposed. You have these wildly utopian visions, but also these warnings that AI could end the world. It gets so hyperbolic that it feels like people are not living in the same reality. Was that a challenge for you in writing the book? Well, I see those two visions — which feel very far apart — actually being part of the same vision, which is that AI is super important, and it’s going to completely transform everything. No one ever talks about the true opposite of that, which is, “Maybe this is going to be a cool enterprise tool, another way to waste time on the internet, and not quite change everything as much as everyone thinks.” So I see the doomers and the boomers feeding off each other and being part of the same sort of hype universe. As a journalist and as a biographer, you don’t necessarily come down on one side or the other — but actually, can you say where you come down on that? Well, I will say that I find myself using it a lot more recently, because it’s gotten a lot better. In the early stages, when I was researching the book, I was definitely a lot more skeptical of its transformative economic power. I’m less skeptical now, because I just use it a lot more.

Ivanka Trump has made it clear that she's done with politics. That hasn't stopped her and husband Jared Kushner from remaining an influential political couple.They have not formally reprised their roles as White House advisors in President Donald Trump's second administration, but they've remained present in Donald Trump's political orbit.While Ivanka Trump opted out of the 2024 campaign trail, she and Kushner still appeared at the Republican National Convention, Donald Trump's victory party on election night, and the inauguration. Kushner also reportedly served as an informal advisor ahead of Donald Trump's trip to the Middle East in May, CNN reported.Ivanka Trump, who is Donald Trump's eldest daughter, converted to Judaism before marrying Kushner in 2009. They have three children: Arabella, Joseph, and Theodore.Here's a timeline of Ivanka Trump and Kushner's relationship. 2007: Ivanka Trump and Jared Kushner met at a networking lunch arranged by one of her longtime business partners. Ivanka Trump and Jared Kushner in 2007. PAUL LAURIE/Patrick McMullan via Getty Images Ivanka Trump and Kushner were both 25 at the time."They very innocently set us up thinking that our only interest in one another would be transactional," Ivanka Trump told Vogue in 2015. "Whenever we see them we're like, 'The best deal we ever made!'" 2008: Ivanka Trump and Kushner broke up because of religious differences. Jared Kushner and Ivanka Trump in 2008. Patrick McMullan/Patrick McMullan via Getty Images Kushner was raised in the modern Orthodox Jewish tradition, and it was important to his family for him to marry someone Jewish. Ivanka Trump's family is Presbyterian. 2008: Three months later, the couple rekindled their romance on Rupert Murdoch's yacht. Ivanka Trump and Jared Kushner in 2008. David X Prutting/Patrick McMullan/Patrick McMullan via Getty Images In his memoir, "Breaking History," Kushner wrote that Murdoch's then-wife, Wendi Murdoch, was a mutual friend who invited them both on the yacht. May 2009: They attended the Met Gala together for the first time. Jared Kushner and Ivanka Trump at the Met Gala. BILLY FARRELL/Patrick McMullan via Getty Images The theme of the Met Gala that year was "The Model As Muse." Ivanka Trump wore a gown by designer Brian Reyes. July 2009: Ivanka Trump completed her conversion to Judaism, and she and Kushner got engaged. Jared Kushner and Ivanka Trump in 2009. Billy Farrell/Patrick McMullan/Patrick McMullan via Getty Images Kushner proposed with a 5.22-carat cushion-cut diamond engagement ring.Ivanka Trump told New York Magazine that she and her fiancé were "very mellow.""We go to the park. We go biking together. We go to the 2nd Avenue Deli," she said. "We both live in this fancy world. But on a personal level, I don't think I could be with somebody — I know he couldn't be with somebody — who needed to be 'on' all the time." October 2009: Ivanka Trump and Kushner married at the Trump National Golf Club in New Jersey. Jared Kushner and Ivanka Trump on their wedding day. Brian Marcus/Fred Marcus Photography via Getty Images The couple invited 500 guests, including celebrities like Barbara Walters, Regis Philbin, and Anna Wintour, as well as politicians such as Rudy Giuliani and Andrew Cuomo. July 2011: The couple welcomed their first child, Arabella. Ivanka Trump and Jared Kushner with Arabella Kushner. Robin Marchant/Getty Images "This morning @jaredkushner and I welcomed a beautiful and healthy little baby girl into the world," Ivanka announced on X, then Twitter. "We feel incredibly grateful and blessed. Thank you all for your support and well wishes!" October 2013: Ivanka Trump gave birth to their second child, Joseph. Ivanka Trump with Arabella Rose Kushner and Joseph Frederick Kushner in 2017. Alo Ceballos/GC Images He was named for Kushner's paternal grandfather Joseph and given the middle name Frederick after Donald Trump's father. March 2016: Kushner and Ivanka Trump welcomed their third child, Theodore, in the midst of Donald Trump's presidential campaign. Ivanka Trump carried her son Theodore as she held hands with Joseph alongside Jared Kushner and daughter Arabella on the White House lawn. SAUL LOEB/AFP via Getty Images "I said, 'Ivanka, it would be great if you had your baby in Iowa.' I really want that to happen. I really want that to happen," Donald Trump told supporters in Iowa in January 2016.All three of the couple's children were born in New York City. May 2016: They attended the Met Gala two months after Ivanka Trump gave birth. Jared Kushner and Ivanka Trump attend the Met Gala. Kevin Mazur/WireImage Ivanka Trump wore a red Ralph Lauren Collection halter jumpsuit.On a 2017 episode of "The Late Late Show with James Corden," Anna Wintour said that she would never invite Donald Trump to another Met Gala. January 2017: Ivanka Trump and Kushner attended Donald Trump's inauguration and danced together at the Liberty Ball. Ivanka Trump and Jared Kushner on Inauguration Day. Photo by Rob Carr/Getty Images The Liberty Ball was the first of three inaugural balls that Donald Trump attended. January 2017: After the inauguration, Ivanka and Kushner relocated to a $5.5 million home in the Kalorama section of Washington, DC. Jared Kushner and Ivanka Trump's house in Washington, DC. PAUL J. RICHARDS/AFP via Getty Images Ivanka Trump and Kushner rented the 7,000-square-foot home from billionaire Andrónico Luksic for $15,000 a month, The Wall Street Journal reported. May 2017: They accompanied Donald Trump on his first overseas trip in office. Jared Kushner and Ivanka Trump with Pope Francis. Vatican Pool - Corbis/Corbis via Getty Images Kushner and Ivanka Trump both served as advisors to the president. For the first overseas trip of Donald Trump's presidency, they accompanied him to Saudi Arabia, Israel, the Vatican, and summits in Brussels and Sicily. October 2019: The couple celebrated their 10th wedding anniversary with a lavish party at Camp David. Ivanka Trump and Jared Kushner at a state dinner. MANDEL NGAN/AFP via Getty Images All of the Trump and Kushner siblings were in attendance. A White House official told CNN that the couple was covering the cost of the party, but Donald Trump tweeted that the cost would be "totally paid for by me!" August 2020: Ivanka Trump spoke about moving their family to Washington, DC, at the Republican National Convention. Jared Kushner and Ivanka Trump at the Republican National Convention. SAUL LOEB/AFP via Getty Images "When Jared and I moved with our three children to Washington, we didn't exactly know what we were in for," she said in her speech. "But our kids loved it from the start." December 2020: Ivanka Trump and Kushner reportedly bought a $32 million empty lot in Miami's "Billionaire Bunker." Jared Kushner and Ivanka Trump's plot of land in Indian Creek Village. The Jills Zeder Group; Samir Hussein/WireImage/Getty Images After Donald Trump lost the 2020 election, Page Six reported that the couple purchased a 1.8-acre waterfront lot owned by singer Julio Iglesias, Enrique Iglesias' father, in Indian Creek Village, Florida.The island where it sits has the nickname "Billionaire Bunker" thanks to its multitude of ultra-wealthy residents over the years, including billionaire investor Carl Icahn, supermodel Adriana Lima, and former Miami Dolphins coach Don Shula. January 2021: They skipped Joe Biden's inauguration, flying with Donald Trump to his Mar-a-Lago residence in Palm Beach, Florida, instead. Ivanka Trump, Jared Kushner, and their children prepared for Donald Trump's departure on Inauguration Day. ALEX EDELMAN/AFP via Getty Images Donald Trump did not attend Biden's inauguration, breaking a long-standing norm in US democracy. While initial reports said that Ivanka Trump was planning to attend the inauguration, a White House official told People magazine that "Ivanka is not expected to attend the inauguration nor was she ever expected to." January 2021: The couple signed a lease for a luxury Miami Beach condo near their Indian Creek Village property. Arte Surfside. Antonio Citterio Patricia Viel Ivanka Trump and Kushner signed a lease for a "large, unfurnished unit" in the amenities-packed Arte Surfside condominium building in Surfside, Florida.Surfside, a beachside town just north of Miami Beach that's home to fewer than 6,000 people, is only a five-minute drive from Indian Creek Island, where they bought their $32 million empty lot. April 2021: Ivanka Trump and Kushner reportedly added a $24 million mansion in Indian Creek Village to their Florida real-estate profile. Ivanka Trump and Jared Kushner on a walk in Florida. MEGA/GC Images The Real Deal reported that Ivanka and Kushner purchased another Indian Creek property — this time, a 8,510-square-foot mansion situated on a 1.3-acre estate. June 2021: Several outlets reported that the couple began to distance themselves from Donald Trump due to his fixation on conspiracy theories about the 2020 election. Ivanka Trump and Jared Kushner behind Donald Trump. Kevin Lamarque/Reuters CNN reported that Trump was prone to complain about the 2020 election and falsely claim it was "stolen" from him to anyone listening and that his "frustrations emerge in fits and starts — more likely when he is discussing his hopeful return to national politics."While Ivanka and Kushner had been living in their Miami Beach condo, not far from Trump's Mar-a-Lago club in Palm Beach, Florida, they'd visited Trump less and less frequently and were absent from big events at Mar-a-Lago, CNN said.The New York Times also reported that Kushner wanted "to focus on writing his book and establishing a simpler relationship" with the former president. October 2021: Ivanka Trump and Kushner visited Israel's parliament for the inaugural event of the Abraham Accords Caucus. Jared Kushner and Ivanka Trump in Israel. AHMAD GHARABLI/AFP via Getty Images The Abraham Accords, which Kushner helped broker in August 2020, normalized relations between Israel and the United Arab Emirates, Bahrain, Sudan, and Morocco.During their visit, Ivanka Trump and Kushner met with then-former Prime Minister Benjamin Netanyahu and attended an event at the Museum of Tolerance Jerusalem with former US Secretary of State Mike Pompeo. August 2022: Kushner released his memoir, "Breaking History," in which he wrote about their courtship. Jared Kushner. John Lamparski/Getty Images for Concordia Summit "In addition to being arrestingly beautiful, which I knew before we met, she was warm, funny, and brilliant," he wrote of getting to know Ivanka Trump. "She has a big heart and a tremendous zest for exploring new things."He also wrote that when he told Donald Trump that he was planning a surprise engagement, Trump "picked up the intercom and alerted Ivanka that she should expect an imminent proposal." November 2022: Kushner attended Donald Trump's 2024 campaign announcement without Ivanka Trump. Kimberly Guilfoyle, Jared Kushner, Eric Trump, and Lara Trump at Donald Trump's presidential campaign announcement. Jonathan Ernst/Reuters Ivanka Trump released a statement explaining her absence from the event."I love my father very much," her statement read. "This time around, I am choosing to prioritize my children and the private life we are creating as a family. I do not plan to be involved in politics. While I will always love and support my father, going forward I will do so outside the political arena." July 2024: Ivanka Trump and Kushner made a rare political appearance at the Republican National Convention. Donald Trump and Melania Trump onstage with Ivanka Trump and Jared Kushner. Jason Armond/Los Angeles Times via Getty Images Ivanka Trump did not campaign for her father or give a speech as she had at past Republican National Conventions, but she and Jared Kushner joined Trump family members onstage after Donald Trump's remarks. November 2024: They joined members of the Trump family in Palm Beach, Florida, to celebrate Donald Trump's election victory.

HBO original The Gilded Age returns for a third season on June 22. This series tells a fictionalized story set during America’s Gilded Age. A time of rapidly increasing prosperity and industry, for those lucky enough to capitalize on it. New York City’s social scene is forced to adapt as people with old money (inherited wealth) and those with new money (wealth from rising industries) clash. Carrie Coon, Morgan Spector, Taissa Farmiga, Cynthia Nixon, and more star in this compelling drama. Fans of The Hunger Games series will be happy to find all four movies in the series on Max from the first of the month. If you need a break from Sunrise on the Reaping theories or simply want to revisit the story that started it all, Max is the place to be. A Minecraft Movie will also be available to stream on Max this month, though the date has yet to be revealed by Warner Bros. Discovery. Here’s everything coming to HBO and Max in June. HBO and Max New Releases – June 2025 June 1 A Hologram for the King (2016)A Nightmare on Elm Street (2010)A Perfect Getaway (2009)Backtrack (2016)Batman and Superman: Battle of the Super Sons (2022)Black Patch (1957)Blues in the Night (1941)Casino (1995)Fight Club (1999)Gentleman Jim (1942)Hellboy (2004)I Am Not Your Negro (2017)Igor (2008)Illegal (1955)In the Good Old Summertime (1949)Invasion of the Body Snatchers (1978)Kid Glove Killer (1942)Meet Me in St. Louis (1944)My Scientology Movie (2017)Numbered Men (1930)One Foot in Heaven (1941)Parasite (2019)Presenting Lily Mars (1943)Pride & Prejudice (2005)Public Enemies (2009)Reign of the Supermen (2019)Serenade (1956)Silver River (1948)Spaceballs (1987)Split (2017)Strike Up the Band (1940)Summer Stock (1950)Superman: Man of Tomorrow (2020)Superman: Red Son (2020)Superman: Unbound (2013)Superman/Batman: Public Enemies (2009)Thank Your Lucky Stars (1943)The Death of Superman (2018)The Fighting 69th (1940)The Harvey Girls (1946)The Hunger Games (2012)The Hunger Games: Catching Fire (2013)The Hunger Games: Mockingjay Part 1 (2014)The Hunger Games: Mockingjay Part 2 (2015)The Man Who Invented Christmas (2017)The Match King (1932)The Mayor of Hell (1933)The Mortician (HBO Original)The Nitwits (1935)The Prince and the Pauper (1937)The Sea Chase (1955)The Sea Hawk (1940)The Sunlit Night (2019)The Verdict (1946)They Made Me a Criminal (1939)This Side of the Law (1950)Three Faces East (1930)Three Strangers (1946)Total Drama Island, Season 2 (Cartoon Network)Wagons West (1952)Words and Music (1948)You’ll Find Out (1940)Ziegfeld Follies (1946)June 2 BBQ Brawl, Season 6 (Food Network)June 3 Bullet Train (2022)Ugliest House in America, Season 6 (HGTV)June 4 1000-lb Roomies, Season 1 (TLC)Fatal Destination, Season 1 (ID)June 5 Bea’s Block, Season 1C (Max Original)Chespirito: Not Really on Purpose, Season 1 (Max Original)June 6 House Hunters International: Volume 9, Season 201 (HGTV)Parthenope (A24) June 10 Virgins, Season 1 (TLC) Join our mailing list Get the best of Den of Geek delivered right to your inbox! June 11 Guy’s Grocery Games, Season 38 (Food Network)June 12 Bitchin’ Rides, Season 11Mini Beat Power Rockers: A Superheroic Night (Discovery International)June 13 Cleaner (2025)House Hunters: Volume 10, Season 240 (HGTV)Maine Cabin Masters, Season 10 (Magnolia Network)Super Sara (Max Original)Toad & Friends, Season 1BJune 16 Hero Ball, Season 3BJune 17 Dr. Sanjay Gupta Reports: Animal Pharm (CNN Originals, 2025)Super Mega Cakes, Season 1 (Food Network) June 19 Expedition Unknown, Season 15 (Discovery)Mystery At Blind Frog Ranch, Season 5 (Discovery)June 20 House Hunters: Volume 10, Season 241 (HGTV)Lu & The Bally Bunch, Season 1C (Cartoon Network)Now or Never: FC Montfermeil (Max Original)Teen Titans Go!, Season 9B (Cartoon Network)June 21 The Kitchen, Season 38 (Food Network)The Never Ever Mets, Season 2 (OWN)June 22The Gilded Age, Season 3 (HBO Original) June 23 Match Me Abroad, Season 2 (TLC)June 24 Enigma (HBO Original)Mean Girl Murders, Season 3 (ID)The Invitation (2022) June 25 Rehab Addict, Season 10 (HGTV)June 27 House Hunters: Volume 10, Season 242 (HGTV)My Mom Jayne (HBO Original)Pati, Seasons 1&2 (Max Original)The Day the Earth Blew Up: A Looney Tunes Movie (2025)June 29 #Somebody’s Son, Season 1 (OWN)Family or Fiancé, Season 4 (OWN)June 30 90 Day Fiancé: Pillow Talk, Season 11 (TLC)Truck U, Season 21

There are very few philosophers who become part of popular culture, and often, if their ideas become influential, people don’t know where they came from.Niccolò Machiavelli, the great 16th-century diplomat and writer, is an exception.I don’t know how many people have actually read Machiavelli, but almost everyone knows the name, and almost everyone thinks they know what the word “Machiavellian” means. It’s someone who’s cunning and shrewd and manipulative. Or as one famous philosopher called him, “the teacher of evil.”But is this fair to Machiavelli, or has he been misunderstood? And if he has been, what are we missing in his work?Erica Benner is a political philosopher and the author of numerous books about Machiavelli including my favorite, Be Like the Fox, which offers a different interpretation of Machiavelli’s most famous work, The Prince.For centuries, The Prince has been popularly viewed as a how-to manual for tyrants. But Benner disagrees. She says it’s actually a veiled, almost satirical critique of authoritarian power. And she argues that Machiavelli is more timely than you might imagine. He wrote about why democracies get sick and die, about the dangers of inequality and partisanship, and even about why appearance and perception matter far more than truth and facts.In another of his seminal works, Discourses on Livy, Machiavelli is also distinctly not authoritarian. In fact, he espouses a deep belief in republicanism (the lowercase-r kind, which affirms representative government).I invited Benner onto The Gray Area to talk about what Machiavelli was up to and why he’s very much a philosopher for our times. As always, there’s much more in the full podcast, so listen and follow The Gray Area on Apple Podcasts, Spotify, Pandora, or wherever you find podcasts. New episodes drop every Monday.This interview has been edited for length and clarity. The popular view of Machiavelli is that he wanted to draw this neat line between morality and politics and that he celebrated ruthless pragmatism. What’s incomplete or wrong about that view?What is true is that he often criticizes the hyper-Christian morality that puts moral judgments into the hands of priests and popes and some abstract kind of God that he may or may not believe in, but in any case doesn’t think is something we can access as humans. If we want to think about morality both on a personal level and in politics, we’ve got to go back to basics. What is the behavior of human beings? What is human nature? What are the drives that propel human beings to do the stuff that we call good or bad? He wants to say that we should see human beings not as fundamentally good or evil. We shouldn’t think that human beings can ever be angels, and we shouldn’t see them as devils when they behave badly.But the basic point is if you want to develop a human morality, you study yourself, you study other humans, you don’t put yourself above other humans because you’re one, too. And then you ask, What kind of politics is going to make such people coexist?I take it you think his most famous book, The Prince, is not well understood?I used to have to teach Machiavelli and I would just say, It’s a handbook for tyrants. But he wrote the Discourses, which is a very, very republican book. So that’s the first thing that sets people off and makes you think, How could he have switched so quickly from writing The Prince to being a super-republican writing the Discourses? So that’s a warning sign. When I started seeing some of the earliest readers of Machiavelli and the earliest comments you get from republican authors, they all see Machiavelli as an ally and they say it. They say he’s a moral writer. Rousseau says, “He has only had superficial and corrupt readers until now.” If you ever pick up The Prince and you read the first four chapters, and most people don’t read them that carefully because they’re kind of boring, the exciting ones are the ones in the middle about morality and immorality and then you come to chapter five, which is about freedom.And up to chapter four, it sounds like a pretty cruel, cold analysis of what you should do. Then you get to chapter five and it’s like, Wow! It’s about how republics fight back, and the whole tone changes. Suddenly republics are fighting back and the prince has to be on his toes because he’s probably not going to survive the wrath of these fiery republics that do not give up.So who is he talking to in the book? Is he counseling future princes or warning future citizens?It’s complicated. You have to remember that he was kicked out of his job and had a big family to support. He had a lot of kids. And he loved his job and was passionate about the republic. He was tortured. He doesn’t know what’s going to happen next. He’s absolutely gutted that Florence’s republican experiment has failed and he can’t speak freely. So what does a guy with a history of writing dramas and satire do to make himself feel better? It’s taking the piss out of the people who have made you and a lot of your friends very miserable, in a low-key way because you can’t be too brutally satirical about it. But I think he’s really writing to expose the ways of tyrants.Would you say that Machiavelli has something like an ideology or is he just a clear-eyed pragmatist?He’s a republican. And again, this is something that, if you just read The Prince, you’re not going to get. But if you read the Discourses, which was written around the same time as The Prince, it’s very, very similar in almost every way except that it praises republics and criticizes tyrants very openly. Whereas The Prince never once uses the words “tyrant” or “tyranny.” So if there’s a guiding political view, whether you call it “ideology” or not, it’s republicanism. And that’s an ideology of shared power. It’s all the people in a city, all the male people in this case. Machiavelli was quite egalitarian. He clearly wanted as broad of a section of the male population to be citizens as possible. He says very clearly, The key to stabilizing your power is to change the constitution and to give everyone their share. Everyone has to have their share. You might want to speak a little bit more for yourself and the rich guys, but in the end, everyone’s got to have a share.Should we treat Machiavelli like a democratic theorist? Do you think of him as someone who would defend what we call democracy today?If you think the main principle of democracy is that power should be shared equally, which is how I understand democracy, then yes. He’d totally agree with that. What kind of institutions would he say a democracy has to have? He’s pretty clear in the Discourses. He says you don’t want a long-term executive. You need to always check power. I realize we exist in a very different world than Machiavelli, but is he a useful guide to understanding contemporary politics, particularly American politics?This is a really Machiavellian moment. If you read The Prince and look not just for those provocative quotes but for the criticisms, and sometimes they’re very subtle, you start to see that he’s exposing a lot of the stuff that we’re seeing today. Chapter nine of The Prince is where he talks about how you can rise to be the ruler of a republic and how much resistance you might face, and he says that people might be quite passive at first and not do very much. But at some point, when they see you start to attack the courts and the magistrates, that’s when you’re going to clash. And he says, That’s when you as a leader — and he’s playing like he’s on the leader’s side — that’s when you’ve got to decide if you’re going to get really, really tough, or are you going to have to find other ways to soften things up a bit?What would he make of Trump?He would put Trump in two categories. He’s got different classifications of princes. He’s got the prince of fortune, somebody who relies on wealth and money and big impressions to get ahead. He would say that Trump has a lot of those qualities, but he’d also call him this word “astutia” — astuteness, which doesn’t really translate in English because we think of that as a good quality, but he means calculating shrewdness. Somebody whose great talent is being able to shrewdly manipulate and find little holes where he can exploit people’s weaknesses and dissatisfactions.This is what he thought the Medici were good at. And his analysis of that is that it can cover you for a long time. People will see the good appearances and hope that you can deliver, but in the long run, people who do that don’t know how to build a solid state. That’s what he would say on a domestic front. I think there’s an unsophisticated way to look at Trump as Machiavellian. There are these lines in The Prince about knowing how to deploy cruelty and knowing when to be ruthless. But to your deeper point, I don’t think Machiavelli ever endorses cruelty for cruelty’s sake, and with Trump — and this is my personal opinion — cruelty is often the point, and that’s not really Machiavellian.Exactly. I wouldn’t say Trump is Machiavellian. Quite honestly, since the beginning of the Trump administration, I’ve often felt like he’s getting advice from people who haven’t really read Machiavelli or put Machiavelli into ChatGPT and got all the wrong pointers, because the ones that they’re picking out are just so crude. But they sound Machiavellian. You’re absolutely right, though. Machiavelli is very, very clear in The Prince that cruelty is not going to get you anywhere in the long term. You’re going to get pure hate. So if you think it’s ever instrumentally useful to be super cruel, think again.This obviously isn’t an endorsement of Trump, but I will say that something I hear often from people is that the system is so broken that we need someone to smash it up in order to save it. We need political dynamite. I bring that up because Machiavelli says repeatedly that politics requires flexibility and maybe even a little practical ruthlessness in order to preserve the republic. Do you think he would say that there’s real danger in clinging to procedural purity if you reach a point where the system seems to have failed?This is a great question. And again, this is one he does address in the Discourses quite a lot. He talks about how the Romans, when their republic started slipping, had “great men” coming up and saying, “I’ll save you,” and there were a lot before Julius Caesar finally “saved” them and then it all went to hell. And Machiavelli says that there are procedures that have to sometimes be wiped out — you have to reform institutions and add new ones. The Romans added new ones, they subtracted some, they changed the terms. He was very, very keen on shortening the terms of various excessively long offices. He also wanted to create emergency institutions where, if you really faced an emergency, that institution gives somebody more power to take executive action to solve the problem. But that institution, the dictatorship as it was called in Rome, it wasn’t as though a random person could come along and do whatever he wanted. The idea was that this dictator would have special executive powers, but he is under strict oversight, very strict oversight, by the Senate and the plebians, so that if he takes one wrong step, there would be serious punishment. So he was very adamant about punishing leaders who took these responsibilities and then abused them.Listen to the rest of the conversation and be sure to follow The Gray Area on Apple Podcasts, Spotify, Pandora, or wherever you listen to podcasts.See More:

Jesse Armstrong loves to pull fictional stories out of reality. His universally acclaimed TV show Succession, for instance, was inspired by real-life media dynasties like the Murdochs and the Hearsts. Similarly, his newest film Mountainhead centers upon characters that share key traits with the tech world’s most powerful leaders: Elon Musk, Mark Zuckerberg, Sam Altman, and others.Mountainhead, which releases on HBO on May 31 at 8 p.m. ET, portrays four top tech executives who retreat to a Utah hideaway as the AI deepfake tools newly released by one of their companies wreak havoc across the world. As the believable deepfakes inflame hatred on social media and real-world violence, the comfortably-appointed quartet mulls a global governmental takeover, intergalactic conquest and immortality, before interpersonal conflict derails their plans.Armstrong tells TIME in a Zoom interview that he first became interested in writing a story about tech titans after reading books like Michael Lewis’ Going Infinite (about Sam Bankman-Fried) and Ashlee Vance’s Elon Musk: Tesla, SpaceX, and the Quest for a Fantastic Future, as well as journalistic profiles of Peter Thiel, Marc Andreessen, and others. He then built the story around the interplay between four character archetypes—the father, the dynamo, the usurper, and the hanger-on—and conducted extensive research so that his fictional executives reflected real ones. His characters, he says, aren’t one-to-one matches, but “Frankenstein monsters with limbs sewn together.” These characters are deeply flawed and destructive, to say the least. Armstrong says he did not intend for the film to be a wholly negative depiction of tech leaders and AI development. “I do try to take myself out of it, but obviously my sense of what this tech does and could do infuses the piece. Maybe I do have some anxieties,” he says. Armstrong contends that the film is more so channeling fears that AI leaders themselves have warned about. “If somebody who knows the technology better than anyone in the world thinks there's a 1/5th chance that it's going to wipe out humanity—and they're some of the optimists—I think that's legitimately quite unnerving,” he says. Here’s how each of the characters in Mountainhead resembles real-world tech leaders. This article contains spoilers. Venis (Cory Michael Smith) is the dynamo.Cory Michael Smith in Mountainhead Macall Polay—HBOVenis is Armstrong’s “dynamo”: the richest man in the world, who has gained his wealth from his social media platform Traam and its 4 billion users. Venis is ambitious, juvenile, and self-centered, even questioning whether other people are as real as him and his friends. Venis’ first obvious comp is Elon Musk, the richest man in the real world. Like Musk, Venis is obsessed with going to outer space and with using his enormous war chest to build hyperscale data centers to create powerful anti-woke AI systems. Venis also has a strange relationship with his child, essentially using it as a prop to help him through his own emotional turmoil. Throughout the movie, others caution Venis to shut down his deepfake AI tools which have led to military conflict and the desecration of holy sites across the world. Venis rebuffs them and says that people just need to adapt to technological changes and focus on the cool art being made. This argument is similar to those made by Sam Altman, who has argued that OpenAI needs to unveil ChatGPT and other cutting-edge tools as fast as possible in order to show the public the power of the technology. Like Mark Zuckerberg, Venis presides over a massively popular social media platform that some have accused of ignoring harms in favor of growth. Just as Amnesty International accused Meta of having “substantially contributed” to human rights violations perpetrated against Myanmar’s Rohingya ethnic group, Venis complains of the UN being “up his ass for starting a race war.”Randall (Steve Carell) is the father.Steve Carell in Mountainhead Macall Polay—HBOThe group’s eldest member is Randall, an investor and technologist who resembles Marc Andreessen and Peter Thiel in his lofty philosophizing and quest for immortality. Like Andreessen, Randall is a staunch accelerationist who believes that U.S. companies need to develop AI as fast as possible in order to both prevent the Chinese from controlling the technology, and to ostensibly ignite a new American utopia in which productivity, happiness, and health flourish. Randall’s power comes from the fact that he was Venis’ first investor, just as Thiel was an early investor in Facebook. While Andreessen pens manifestos about technological advancement, Randall paints his mission in grandiose, historical terms, using anti-democratic, sci-fi-inflected language that resembles that of the philosopher Curtis Yarvin, who has been funded and promoted by Thiel over his career. Randall’s justification of murder through utilitarian and Kantian lenses calls to mind Sam Bankman-Fried’s extensive philosophizing, which included a declaration that he would roll the dice on killing everyone on earth if there was a 51% chance he would create a second earth. Bankman-Fried’s approach—in embracing risk and harm in order to reap massive rewards—led him to be convicted of massive financial fraud. Randall is also obsessed with longevity just like Thiel, who has railed for years against the “inevitability of death” and yearns for “super-duper medical treatments” that would render him immortal. Jeff (Ramy Youssef) is the usurper.Ramy Youssef in Mountainhead Macall Polay—HBOJeff is a technologist who often serves as the movie’s conscience, slinging criticisms about the other characters. But he’s also deeply embedded within their world, and he needs their resources, particularly Venis’ access to computing power, to thrive. In the end, Jeff sells out his values for his own survival and well-being. AI skeptics have lobbed similar criticisms at the leaders of the main AI labs, including Altman—who started OpenAI as a nonprofit before attempting to restructure the company—as well as Demis Hassabis and Dario Amodei. Hassabis is the CEO of Google Deepmind and a winner of the 2024 Nobel Prize in Chemistry; a rare scientist surrounded by businessmen and technologists. In order to try to achieve his AI dreams of curing disease and halting global warning, Hassabis enlisted with Google, inking a contract in 2014 in which he prohibited Google from using his technology for military applications. But that clause has since disappeared, and the AI systems developed under Hassabis are being sold, via Google, to militaries like Israel’s. Another parallel can be drawn between Jeff and Amodei, an AI researcher who defected from OpenAI after becoming worried that the company was cutting back its safety measures, and then formed his own company, Anthropic. Amodei has urged governments to create AI guardrails and has warned about the potentially catastrophic effects of the AI industry’s race dynamics. But some have criticized Anthropic for operating similarly to OpenAI, prioritizing scale in a way that exacerbates competitive pressures. Souper (Jason Schwartzman) is the hanger-on. Jason Schwartzman in Mountainhead Macall Polay—HBOEvery quartet needs its Turtle or its Ringo; a clear fourth wheel to serve as a punching bag for the rest of the group’s alpha males. Mountainhead’s hanger-on is Souper, thus named because he has soup kitchen money compared to the rest (hundreds of millions as opposed to billions of dollars). In order to prove his worth, he’s fixated on getting funding for a meditation startup that he hopes will eventually become an “everything app.” No tech exec would want to be compared to Souper, who has a clear inferiority complex. But plenty of tech leaders have emphasized the importance of meditation and mindfulness—including Twitter co-founder and Square CEO Jack Dorsey, who often goes on meditation retreats. Armstrong, in his interview, declined to answer specific questions about his characters’ inspirations, but conceded that some of the speculations were in the right ballpark. “For people who know the area well, it's a little bit of a fun house mirror in that you see something and are convinced that it's them,” he says. “I think all of those people featured in my research. There's bits of Andreessen and David Sacks and some of those philosopher types. It’s a good parlor game to choose your Frankenstein limbs.”

The new ES90 electric car is the flagship of Volvo's latest digital safety tech.TT News Agency/AFP via Getty Images For decades, the Volvo brand has been synonymous with safety. But keeping passengers secure is no longer just about a strong cabin or cleverly designed crumple zones. Increasingly, safety is about semi-autonomous driving technology that can mitigate collisions or even avoid them entirely. Volvo intends to be ahead of the game in this era too. Its secret weapon? Something called “Gaussian Splatting”. I asked Volvo’s Head of Software Engineering Alwin Bakkenes and subsidiary Zenseact’s VP Product Erik Coelingh exactly what this is and why it’s so important. Volvo: Early Application Of Safety Data “We have a long history of innovations based on data,” says Bakkenes. “The accident research team from the 70s started with measuring tapes. Now in the digital world we’re collecting millions of real-life events. That data has helped us over the years to develop a three-point safety belt and the whiplash protection system. Now, we can see from the data we collect from fleets that a very large portion of serious accidents happen in the dark on country roads where vulnerable road users are involved. That’s why, with the ES90 that we just launched, we are also introducing a function called lighter AES where we have enabled the car to steer away from pedestrians walking on the side of the road or cyclists, which in the dark you can’t see even if you have your high beam on. This technology picks that up earlier than a human driver.” The Volvo EX90 SUV will also benefit from this technology. Volvo Cars uses AI and virtual worlds with the aim to create safer carsVolvo “If you want to lead in collision avoidance and self-driving, you need to have the best possible data from the real world,” adds Bakkenes. “But everyone is looking also at augmenting that with simulated data. The next step is fast automation, so we’re using state-of-the-art end-to-end models to achieve speed in iterations. But sometimes these models hallucinate. To avoid that, we use our 98 years of safety experience and these millions of data points as guardrails to make sure that the car behaves well because we believe that when you start to automate it needs to be trusted. For us every kilometer driven with Pilot Assist or Pilot Assist Plus needs to be safer than when you've driven it yourself. In the world of AI data is king. We use Gaussian Splatting to enhance our data set.” What Is Volvo’s Gaussian Splatting? “Cars are driven all around the world in different weather and traffic conditions by different people,” says Coelingh. “The variation is huge. We collect millions of data points, but it’s still a limited amount compared to reality. Gaussian Splatting is a new technology that some of our PhD students have been developing the last few years into a system where you can take a single data point from the real world where you have all the sensor, camera, radar and LIDAR sequences and then blow it up into thousands or tens of thousands of different scenarios. In that way, you can get a much better representation of the real world because we can test our software against this huge variation. If you do it in software, you can test much faster, so then you can iterate your software much more quickly and improve our product.” “Gaussian Splatting is used in different areas of AI,” continues Coelingh. “It comes from the neural radiance fields (NeRFs).” The original version worked with static images. “The first academic paper was about a drum kit where somebody took still pictures from different angles and then the neural net was trained on those pictures to create a 3D model. It looked perfect from any angle even though there was only a limited set of pictures available. Later that technology was expanded from 3D to 4D space-time, so you could also do it on the video set. We now do this not just with video data, but also with LiDAR and radar data.” A real-world event can be recreated from every angle. “We can start to manipulate other road users in this scenario. We can manipulate real world scenarios and do different simulations around this to make sure that our system is robust to variations.”Gaussian Splatting allows multiple scenario variations to be created from one real event.Volvo Volvo uses this system particularly to explore how small adjustments could prevent accidents. “Most of the work that we do is not about the crash itself,” says Coelingh. “It’s much more about what's happening 4-5 seconds before the crash or potential crash. The data we probe is from crashes, but it's also from events where our systems already did an intervention and in many cases those interventions come in time to prevent an accident and in some cases they come late and we only mitigated it. But all these scenarios are relevant because they happen in the real world, and they are types of edge case. These are rare, but through this technology of Gaussian Splatting, we can go from a few edge cases to suddenly many different edge cases and thereby test our system against those in a way that we previously could not.” Volvo’s Global Safety Focus This is increasingly important for addressing the huge variation in global driving habits and conditions a safety system will be expected to encounter. “Neural Nets are good at learning these types of patterns,” says Coelingh. “Humans can see that because of the behavior of a car the driver is talking into their phone, either slowing down or wiggling in the lane. If you have an end-to-end neural network using representations from camera images, LiDAR and radar, it will anticipate those kinds of things. We are probing data from cars all around the world where Volvo Cars are being driven.” The system acts preemptively, so it can perform a safety maneuver for example when a pedestrian appears suddenly in the path of the vehicle. “You have no time to react,” says Coelingh. Volvo’s safety system will be ready, however. “Even before that, the car already detects free space. It can do an auto steer and it’s a very small correction. It doesn't steer you out of lane. It doesn't jerk you around. It slows down a little bit and it does the correction. It's undramatic, but the impact is massive. Oncoming collisions are incredibly severe. Small adjustments can have big benefits.”Volvo's safety tech can detect pedestrians the human driver may not have seen.Volvo Volvo has developed one software platform to cover both safety and autonomy. “The software stack that we develop is being used in different ways,” says Coelingh. “We want the driver to drive manually undisturbed unless there’s a critical situation. Then we try to assist in the best possible way to avoid collision, either by warning, steering, auto braking or a combination of those. Then we also do cruising or L2 automation.” Volvo demonstrated how it has been using Gaussian Splatting at NVIDIA’s GTC in April. “We went deeply into the safe automation concept,” says Bakkenes. “Neural nets are good at picking up things that you can’t do in a rule-based system. We're developing one stack based on good fleet data which has end-to-end algorithms to achieve massive performance, and it has guard rails to make sure we manage hallucinations. It's not like we have a collision avoidance stack and then we have self-driving stack.” “There was a conscious decision that if we improve performance, then we want the benefits of that to be both for collision avoidance in manual driving and for self-driving,” says Coelingh. “We build everything from the same stack, but the stack itself is scalable. It’s one big neural network that we can train. But then there are parts that we can deploy separately to go from our core premium ADAS system all the way to a system that can do unsupervised automation. Volvo’s purpose is to get to zero collisions, saving lives. We use AI and all our energy to get there.”

Warning: contains finale spoilers for Dept. Q See it? Blink and you won’t have, but it was there for a good half second: a smile on the face of DCI Carl Morck. It’s a rare enough occurrence across nine episodes of this Netflix crime drama to make it worthy of note. Matthew Goode’s irascible detective frowned, scowled and spitted out sarcasm through his new department’s search for missing prosecutor Merritt Lingard, until the show’s very last moment, when he smiled. He’d earned it. Three months earlier, Morck’s cold case unit had rescued Lingard and used dirt dug up during the investigation to blackmail the Lord Advocate (the most senior law officer in Scotland) into a funding hike, a new work car, and fast-tracked DI status for Syrian copper and calmly proficient muscle Akram Salim. And now, Morck’s partner Hardy, formerly thought to have permanently lost the use of his legs after they were both shot in the line of duty, was walking again. It was a happy ending, or at least as happy as things get in a dank Edinburgh basement filled with unsolved case files detailing terrible crimes. With major Dept. Q finale spoilers, let’s dig into the revelations about Merritt’s kidnappers, what we learned about the Leith Park shooting, and more. Who Kidnapped Merritt Lingard and Why? Merritt’s kidnappers were Lyle and Ailsa Jennings, respectively the younger brother and mother of Harry Jennings, Merritt’s boyfriend as a teenager. She was being held on their property on her childhood island home of Mhòr, in a pressurised hyperbaric chamber inside a condemned building that was formerly part of Ailsa’s husband’s oceanography business Shorebird Ocean Systems – the logo for which was the cormorant symbol identified by Merritt’s brother William as on the hat of her stalker and kidnapper. Ailsa was a twisted and abusive mother to Harry and Lyle, as well as a murderer who killed her husband by burning the house down by flicking lighted matches at him while he slept. As a result, Lyle grew up to be a violent psychopath whose teenage crimes were covered up by the family and by the island’s police officer John Cunningham. John knew that Lyle was responsible for Merritt’s disappearance, but didn’t know she was still alive, having believed Lyle when he lied that Merritt had died by falling off the ferry from the mainland, just like Lyle’s brother Harry had done years earlier. Lyle and Ailsa blamed Merritt for Harry’s death because he jumped from the ferry while being pursued by John, who was chasing him because he was suspected of having severely beaten Merritt’s brother William into a coma during an interrupted robbery of the Lingard home. The robbery plan had been seeded by Merritt to steal her mother’s jewellery from her alcoholic father in order to sell for money to run away from home. In fact, Lyle was the one who had attacked William, being unhinged and thinking that he was protecting his brother. Harry and Ailsa covered up Lyle’s guilt and before his death, Harry took the blame. Lyle Jennings, Godhaven, and Sam Haig Lyle, who had a history of stalking, breaking-and-entering, and animal murder, spent time in youth psychiatric facility Godhaven, where he stalked another patient named Sam Haig. The pair fought, and Sam injured Lyle’s eye so severely that it was left permanently discoloured. Years later as an adult who had become an investigative journalist with no online presence, Sam sought Lyle out to apologise for the injury and invite him to come climbing at his favourite spot. Lyle used the information he had on Sam to assume his identity and stalk Merritt Lingard, who didn’t recognise him as an adult. He pretended to be investigating corruption at Merritt’s workplace, and targeted her, eventually starting a sexual relationship still pretending to be Sam. When Merritt told Lyle-as-Sam that she was leaving to go to Mhòr on the 10am ferry the next day, Lyle used that information to kidnap her during the crossing. Merritt’s brother William, who had been unable to speak since his teenage coma, had spotted Lyle on the ferry and because of Lyle’s distinctive hat, recognised him as the man who’d been stalking their house. He tried to communicate this to Merritt by throwing his own hat, and then struck her when he became afraid. It was when Merritt went to retrieve William’s hat that she was taken. Join our mailing list Get the best of Den of Geek delivered right to your inbox! Two days after Lyle kidnapped Merritt, he returned to the mainland to tie up loose ends and kill the real Sam Haig. He lured Sam to a remote spot, beat him to death, staged his corpse to look as though he had been climbing, and threw it off a cliff. Eventually, Morck and co. worked out that somebody else was pretending to be Sam Haig when they had evidence that Merritt had been with “Sam” at a hotel at the same time that the real Sam was having an affair with his friend’s wife at a different hotel. What Happened to Lyle and Ailsa Jennings? Merritt’s kidnappers both died – Ailsa by her own hand as she shot herself in her car before being taken into custody by the police, and Lyle by Akram’s hand, or rather, his trigger finger. After Lyle shot buckshot into Morck’s shoulder, Akram threw a knife at him, disarmed and shot him. Previously, Lyle had brutally killed Constable John Cunningham with a hammer when he learned that Merritt was still alive. Mark Bonnar’s character Stephen Burns had disallowed the appearance of a key witness in the Graham Finch murder trial after Burns’ daughter was run off the road by one of Finch’s goons as an intimidation tactic. Prisoner Kirsty Atkins was willing to testify that she had previously met Finch’s now-dead wife at a shelter for survivors of domestic violence, and that Finch routinely hit her. Because Burns wouldn’t allow her testimony, Finch was acquitted of his wife’s murder despite clearly having been guilty of it. Perhaps he also passed on the information about Kirsty Atkins to Finch’s lawyer, which led to Kirsty being viciously attacked and partially blinded in prison on Finch’s orders to keep her quiet. Who Is the Leith Park Shooter? We still don’t know who shot PC Anderson dead, and attempted to shoot Morck and Hardy dead at the Leith Park flat. If we can take the contents of Morck’s dream as gospel, then the shooting was done on the orders of Leith Park criminal kingpin Eugene Errington, whom we’ve yet to meet. According to Morck’s dream, in which he shot Errington’s muscle Charlie Bell in the head, Errington has a baby with Caroline Kerr – the witness who was intimidated into withdrawing her statement about the shooting. We also know, or at least have a pretty good idea, that PC Anderson was a corrupt officer working for Errington, and that he may even have been the one to stab the victim – thought to have been a police informant – in the head. Anderson was a top recruit pretending to be a klutz, Morck realised, in order to cover up the fact that his fingerprints were all over the victim’s flat. He also lied about being at the flat for a routine welfare check after being alerted by the victim’s daughter – that was a quick-thinking ruse to explain his presence when Morck and Hardy showed up there unexpectedly. The last we saw of the Leith Park case, Moira looked to be taking it away from its lead investigator and possibly closing it, unsolved, which may point to her also having been compromised by Errington. As Hardy was one of the shooting’s victims, surely that can’t be the case file that she gave to Hardy to investigate without Morck in the season’s closing moments? Dept. Q is streaming now on Netflix.

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. 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