While we’ve had plenty of superhero games come out over the past decade and a half (and I’m always down for more), most have either been open-world adventures or fighting games. I’m as excited as anyone for the upcoming Marvel Tōkon and Invincible VS, but I’m also ready for a little something different. That’s where Dispatch from AdHoc Studio comes in. Dispatch is a game made for people who enjoy watching a rerun of The Office as a palate cleanser after the bloody battles of Invincible. So, me. You’re cast as Robert Robertson, the former superhero known as Mecha Man. He has to step away from frontline superheroics as the mech suit he relied on was destroyed in battle. Needing a job, he starts work at a dispatch center for superheroes, and the demo takes you through a small, 30-minute chunk of his first day. You’ll notice Dispatch’s crude humor early on. The first thing you can do in Dispatch is give a colleague a “bro fist” at a urinal, and the juvenile jokes don’t stop there. Middle school boys are going to love it, though I’d be lying if I said a few of the jokes didn’t get chuckles from me. Another of Robertson’s co-workers, who also used to be a superhero until his powers caused him to rapidly age, introduces Robertson’s team of misfit heroes, though that term should be used loosely. He notes they’re a “motley crew of dangerous fuck-ups” as Robertson examines their files, each with a mugshot and rapsheet. Robertson isn’t in charge of the Avengers — he’s leading a D-List Suicide Squad. The cast, however, is full of A-listers: Laura Bailey, Matthew Mercer, Aaron Paul, and Jeffrey Wright are among those lending their voices to Dispatch. Much like The Boys, Dispatch plays with the idea of the corporatization of superheroes (though without the satire of and parallels to modern-day politics). These heroes aren’t a lone Spider-Man swinging through Manhattan on patrol — they’re employees waiting for an assignment. Gameplay consists of matching the right (or perhaps “good enough”) hero to the job. Some assignments I saw in the demo included breaking up a robbery, catching a 12-year-old thief, and grabbing a kid’s balloon from a tree while also making sure the kid didn’t cry. Seeing as how one of your misfits is a literal bat man and another looks like a tiefling, you have to choose wisely. The real draw of Dispatch for me isn’t the point-and-click assignment gameplay, but rather the choice-based dialogue. It’s developed by AdHoc Studio, which was formed in 2018 by former developers who had worked on Telltale titles like The Wolf Among Us, The Walking Dead, and Tales from the Borderlands, and you can easily see the throughline from those titles to Dispatch. At various points, you have a limited time to select Robertson’s dialogue, and occasionally a pop-up saying a character “will remember that” appears. How much Robertson’s choices actually have consequences or influence his relationships with others remains to be seen, though I have no doubt those choices will be fun to make. After its reveal at The Game Awards six months ago, Dispatch will be coming to Windows PC and unspecified consoles sometime this year. You can check out its demo now on Steam.

Donald Trump’s military parade in Washington this weekend — a show of force in the capital that just happens to take place on the president’s birthday — smacks of authoritarian Dear Leader-style politics (even though Trump actually got the idea after attending the 2017 Bastille Day parade in Paris).Yet as disconcerting as the imagery of tanks rolling down Constitution Avenue will be, it’s not even close to Trump’s most insidious assault on the US military’s historic and democratically essential nonpartisan ethos.In fact, it’s not even the most worrying thing he’s done this week.On Tuesday, the president gave a speech at Fort Bragg, an Army base home to Special Operations Command. While presidential speeches to soldiers are not uncommon — rows of uniformed troops make a great backdrop for a foreign policy speech — they generally avoid overt partisan attacks and campaign-style rhetoric. The soldiers, for their part, are expected to be studiously neutral, laughing at jokes and such, but remaining fully impassive during any policy conversation.That’s not what happened at Fort Bragg. Trump’s speech was a partisan tirade that targeted “radical left” opponents ranging from Joe Biden to Los Angeles Mayor Karen Bass. He celebrated his deployment of Marines to Los Angeles, proposed jailing people for burning the American flag, and called on soldiers to be “aggressive” toward the protesters they encountered.The soldiers, for their part, cheered Trump and booed his enemies — as they were seemingly expected to. Reporters at Military.com, a military news service, uncovered internal communications from 82nd Airborne leadership suggesting that the crowd was screened for their political opinions.“If soldiers have political views that are in opposition to the current administration and they don’t want to be in the audience then they need to speak with their leadership and get swapped out,” one note read.To call this unusual is an understatement. I spoke with four different experts on civil-military relations, two of whom teach at the Naval War College, about the speech and its implications. To a person, they said it was a step towards politicizing the military with no real precedent in modern American history.“That is, I think, a really big red flag because it means the military’s professional ethic is breaking down internally,” says Risa Brooks, a professor at Marquette University. “Its capacity to maintain that firewall against civilian politicization may be faltering.”This may sound alarmist — like an overreading of a one-off incident — but it’s part of a bigger pattern. The totality of Trump administration policies, ranging from the parade in Washington to the LA troop deployment to Secretary of Defense Pete Hegseth’s firing of high-ranking women and officers of color, suggests a concerted effort to erode the military’s professional ethos and turn it into an institution subservient to the Trump administration’s whims. This is a signal policy aim of would-be dictators, who wish to head off the risk of a coup and ensure the armed forces’ political reliability if they are needed to repress dissent in a crisis.Steve Saideman, a professor at Carleton University, put together a list of eight different signs that a military is being politicized in this fashion. The Trump administration has exhibited six out of the eight.“The biggest theme is that we are seeing a number of checks on the executive fail at the same time — and that’s what’s making individual events seem more alarming than they might otherwise,” says Jessica Blankshain, a professor at the Naval War College (speaking not for the military but in a personal capacity).That Trump is trying to politicize the military does not mean he has succeeded. There are several signs, including Trump’s handpicked chair of the Joint Chiefs repudiating the president’s claims of a migrant invasion during congressional testimony, that the US military is resisting Trump’s politicization.But the events in Fort Bragg and Washington suggest that we are in the midst of a quiet crisis in civil-military relations in the United States — one whose implications for American democracy’s future could well be profound.The Trump crisis in civil-military relations, explainedA military is, by sheer fact of its existence, a threat to any civilian government. If you have an institution that controls the overwhelming bulk of weaponry in a society, it always has the physical capacity to seize control of the government at gunpoint. A key question for any government is how to convince the armed forces that they cannot or should not take power for themselves.Democracies typically do this through a process called “professionalization.” Soldiers are rigorously taught to think of themselves as a class of public servants, people trained to perform a specific job within defined parameters. Their ultimate loyalty is not to their generals or even individual presidents, but rather to the people and the constitutional order.Samuel Huntington, the late Harvard political scientist, is the canonical theorist of a professional military. In his book The Soldier and the State, he described optimal professionalization as a system of “objective control”: one in which the military retains autonomy in how they fight and plan for wars while deferring to politicians on whether and why to fight in the first place. In effect, they stay out of the politicians’ affairs while the politicians stay out of theirs.The idea of such a system is to emphasize to the military that they are professionals: Their responsibility isn’t deciding when to use force, but only to conduct operations as effectively as possible once ordered to engage in them. There is thus a strict firewall between military affairs, on the one hand, and policy-political affairs on the other.Typically, the chief worry is that the military breaches this bargain: that, for example, a general starts speaking out against elected officials’ policies in ways that undermine civilian control. This is not a hypothetical fear in the United States, with the most famous such example being Gen. Douglas MacArthur’s insubordination during the Korean War. Thankfully, not even MacArthur attempted the worst-case version of military overstep — a coup.But in backsliding democracies like the modern United States, where the chief executive is attempting an anti-democratic power grab, the military poses a very different kind of threat to democracy — in fact, something akin to the exact opposite of the typical scenario.In such cases, the issue isn’t the military inserting itself into politics but rather the civilians dragging them into it in ways that upset the democratic political order. The worst-case scenario is that the military acts on presidential directives to use force against domestic dissenters, destroying democracy not by ignoring civilian orders, but by following them.There are two ways to arrive at such a worst-case scenario, both of which are in evidence in the early days of Trump 2.0.First is politicization: an intentional attack on the constraints against partisan activity inside the professional ranks.Many of Pete Hegseth’s major moves as secretary of defense fit this bill, including his decisions to fire nonwhite and female generals seen as politically unreliable and his effort to undermine the independence of the military’s lawyers. The breaches in protocol at Fort Bragg are both consequences and causes of politicization: They could only happen in an environment of loosened constraint, and they might encourage more overt political action if gone unpunished.The second pathway to breakdown is the weaponization of professionalism against itself. Here, Trump exploits the military’s deference to politicians by ordering it to engage in undemocratic (and even questionably legal) activities. In practice, this looks a lot like the LA deployments, and, more specifically, the lack of any visible military pushback. While the military readily agreeing to deployments is normally a good sign — that civilian control is holding — these aren’t normal times. And this isn’t a normal deployment, but rather one that comes uncomfortably close to the military being ordered to assist in repressing overwhelmingly peaceful demonstrations against executive abuses of power.“It’s really been pretty uncommon to use the military for law enforcement,” says David Burbach, another Naval War College professor (also speaking personally). “This is really bringing the military into frontline law enforcement when. … these are really not huge disturbances.”This, then, is the crisis: an incremental and slow-rolling effort by the Trump administration to erode the norms and procedures designed to prevent the military from being used as a tool of domestic repression. Is it time to panic?Among the experts I spoke with, there was consensus that the military’s professional and nonpartisan ethos was weakening. This isn’t just because of Trump, but his terms — the first to a degree, and now the second acutely — are major stressors.Yet there was no consensus on just how much military nonpartisanship has eroded — that is, how close we are to a moment when the US military might be willing to follow obviously authoritarian orders.For all its faults, the US military’s professional ethos is a really important part of its identity and self-conception. While few soldiers may actually read Sam Huntington or similar scholars, the general idea that they serve the people and the republic is a bedrock principle among the ranks. There is a reason why the United States has never, in over 250 years of governance, experienced a military coup — or even come particularly close to one.In theory, this ethos should also galvanize resistance to Trump’s efforts at politicization. Soldiers are not unthinking automatons: While they are trained to follow commands, they are explicitly obligated to refuse illegal orders, even coming from the president. The more aggressive Trump’s efforts to use the military as a tool of repression gets, the more likely there is to be resistance.Or, at least theoretically.The truth is that we don’t really know how the US military will respond to a situation like this. Like so many of Trump’s second-term policies, their efforts to bend the military to their will are unprecedented — actions with no real parallel in the modern history of the American military. Experts can only make informed guesses, based on their sense of US military culture as well as comparisons to historical and foreign cases.For this reason, there are probably only two things we can say with confidence.First, what we’ve seen so far is not yet sufficient evidence to declare that the military is in Trump’s thrall. The signs of decay are too limited to ground any conclusions that the longstanding professional norm is entirely gone.“We have seen a few things that are potentially alarming about erosion of the military’s non-partisan norm. But not in a way that’s definitive at this point,” Blankshain says.Second, the stressors on this tradition are going to keep piling on. Trump’s record makes it exceptionally clear that he wants the military to serve him personally — and that he, and Hegseth, will keep working to make it so. This means we really are in the midst of a quiet crisis, and will likely remain so for the foreseeable future.“The fact that he’s getting the troops to cheer for booing Democratic leaders at a time when there’s actually [a deployment to] a blue city and a blue state…he is ordering the troops to take a side,” Saideman says. “There may not be a coherent plan behind this. But there are a lot of things going on that are all in the same direction.”See More: Politics

Additive Manufacturing UK (AMUK)’s first Members Forum of 2025 was held at Siemens’ UK headquarters in South Manchester earlier this year. The event featured presentations from AMUK members and offered attendees a chance to network and share insights.  Ahead of the day-long meetup, 3D Printing Industry caught up with Joshua Dugdale, Head of AMUK, to learn more about the current state of additive manufacturing and the future of 3D printing in Britain.  AMUK is the United Kingdom’s primary 3D printing trade organization. Established in 2014, it operates within the Manufacturing Technologies Association (MTA) cluster. Attendees at this year’s first meetup spanned the UK’s entire 3D printing ecosystem. Highlights included discussion on precious materials from Cookson Industrial, simulation software from Siemens, digital thread solutions from Kaizen PLM, and 3D printing services provided by ARRK.  With a background in mechanical engineering, Dugdale is “responsible for everything and anything AMUK does as an organization.” According to the Loughborough University alumnus, who is also Head of Technology and Skills at the MTA, AMUK’s core mission is to “create an environment in the UK where additive manufacturing can thrive.” He elaborated on how his organization is working to increase the commercial success of its members within the “struggling” global manufacturing environment. Dugdale shared his perspective on the key challenges facing 3D printing in the UK. He pointed to a “tough” operating environment hampered by global financial challenges, which is delaying investments.  Despite this, AMUK’s leader remains optimistic about the sector’s long-term potential, highlighting the UK’s success in R&D and annual 3D printing intellectual property (IP) output. Dugdale emphasized the value of 3D printing for UK defense and supply chain resilience, arguing that “defense will lead the way” in 3D printing innovation.  Looking ahead, Dugdale called on the UK Government to create a unified 3D printing roadmap to replace its “disjointed” approach to policy and funding. He also shared AMUK’s strategy for 2025 and beyond, emphasizing a focus on eductaion, supply chain visibility, and standards. Ultimately, the AMUK figurehead shared a positive outlook on the future of 3D printing in the UK. He envisions a new wave of innovation that will see more British startups and university spinouts emerging over the next five years.          Siemens’ Manchester HQ hosted the first AMUK Members Forum of 2025. Photo by 3D Printing Industry. What is the current state of additive manufacturing in the UK? According to Dugdale, the 3D printing industry is experiencing a challenging period, driven largely by global economic pressures. “I wouldn’t describe it as underperforming, I’d describe it as flat,” Dugdale said. “The manufacturing sector as a whole is facing significant challenges, and additive manufacturing is no exception.” He pointed to increased competition, a cautious investment climate, and the reluctance of businesses to adopt new technologies due to the economic uncertainty.  Dugdale specifically highlighted the increase in the UK’s National Insurance contribution (NIC) rate for employers, which rose from 13.8% to 15% on April 6, 2025. He noted that many British companies postponed investment decisions ahead of the announcement, reflecting growing caution within the UK manufacturing sector. “With additive manufacturing, people need to be willing to take risks,” added Dugdale. “People are holding off at the moment because the current climate doesn’t favor risk.”  Dugdale remains optimistic about the sector’s long-term potential, arguing that the UK continues to excel in academia and R&D. However, for Dugdale, commercializing that research is where the country must improve before it can stand out on the world stage. This becomes especially clear when compared to countries in North America and Asia, which receive significantly greater financial support. “We’re never going to compete with the US and China, because they have so much more money behind them,” he explained. In a European context, Dugdale believes the UK “is doing quite well.” However, Britain remains below Spain in terms of financial backing and technology adoption. “Spain has a much more mature industry,” Dugdale explained. “Their AM association has been going for 10 years, and it’s clear that their industry is more cohesive and further along. It’s a level of professionalism we can learn from.” While the Iberian country faces similar challenges in standards, supply chain, and visibility, it benefits from a level of cohesion that sets it apart from many other European countries. Dugdale pointed to the Formnext trade show as a clear example of this disparity. He expects the Spanish pavilion to span around 200 square meters and feature ten companies at this year’s event, a “massive” difference compared to the UK’s 36 square meters last year. AMUK’s presence could grow to around 70 square meters at Formnext 2025, but this still lags far behind. Dugdale attributes this gap to government support. “They get more funding. This makes it a lot more attractive for companies to come because there’s less risk for them,” he explained.   Josh Dugdale speaking at the AMUK Members Forum in Manchester. Photo by 3D Printing Industry. 3D printing for UK Defense  As global security concerns grow, the UK government has intensified efforts to bolster its defense capabilities. In this context, 3D printing is emerging as a key enabler. Earlier this year, the Ministry of Defence (MoD) released its first Defence Advanced Manufacturing Strategy, outlining a plan to “embrace 3D printing,” with additive manufacturing expected to play a pivotal role in the UK’s future military operations.  Dugdale identified two key advantages of additive manufacturing for defense: supply chain resilience and frontline production. For the former, he stressed the importance of building localized supply chains to reduce lead times and eliminate dependence on overseas shipments. This capability is crucial for ensuring that military platforms, whether on land, at sea, or in the air, remain operational.  3D printing near the front lines offers advantages for conducting quick repairs and maintaining warfighting capabilities in the field. “If a tank needs to get back off the battlefield, you can print a widget or bracket that’ll hold for just five miles,” Dugdale explained. “It’s not about perfect engineering; it’s about getting the vehicle home.”  The British Army has already adopted containerized 3D printers to test additive manufacturing near the front lines. Last year, British troops deployed metal and polymer 3D printers during Exercise Steadfast Defender, NATO’s largest military exercise since the Cold War. Dubbed Project Bokkr, the additive manufacturing capabilities included XSPEE3D cold spray 3D printer from Australian firm SPEE3D.     Elsewhere in 2024, the British Army participated in Additive Manufacturing Village 2024, a military showcase organized by the European Defence Agency. During the event, UK personnel 3D printed 133 functional parts, including 20 made from metal. They also developed technical data packs (TDPs) for 70 different 3D printable spare parts. The aim was to equip Ukrainian troops with the capability to 3D print military equipment directly at the point of need. Dugdale believes success in the UK defense sector will help drive wider adoption of 3D printing. “Defense will lead the way,” he said, suggesting that military users will build the knowledge base necessary for broader civilian adoption. This could also spur innovation in materials science, an area Dugdale expects to see significant advancements in the coming years.     A British Army operator checks a part 3D printed on SPEE3D’s XSPEE3D Cold Spray 3D printer. Photo via the British Army. Advocating for a “unified industrial strategy” Despite promising growth in defence, Dugdale identified major hurdles that still hinder the widespread adoption of additive manufacturing (AM) in the UK.  A key challenge lies in the significant knowledge gap surrounding the various types of AM and their unique advantages. This gap, he noted, discourages professionals familiar with traditional manufacturing methods like milling and turning from embracing 3D printing. “FDM is not the same as WAAM,” added Dugdale. “Trying to explain that in a very nice, coherent story is not always easy.” Dugdale also raised concerns about the industry’s fragmented nature, especially when it comes to software compatibility and the lack of interoperability between 3D printing systems. “The software is often closed, and different machines don’t always communicate well with each other. That can create fear about locking into the wrong ecosystem too early,” he explained.  For Dugdale, these barriers can only be overcome with a clear industrial strategy for additive manufacturing. He believes the UK Government should develop a unified strategy that defines a clear roadmap for development. This, Dugdale argued, would enable industry players to align their efforts and investments.  The UK has invested over £500 million in AM-related projects over the past decade. However, Dugdale explained that fragmented funding has limited its impact. Instead, the AMUK Chief argues that the UK Government’s strategy should recognize AM as one of “several key enabling technologies,” alongside machine tooling, metrology, and other critical manufacturing tools.  He believes this unified approach could significantly boost the UK’s productivity and fully integrate 3D printing into the wider industrial landscape. “Companies will align themselves with the roadmap, allowing them to grow and mature at the same rate,” Dugdale added. “This will help us to make smarter decisions about how we fund and where we fund.”    AMUK’s roadmap and the future of 3D printing in the UK    When forecasting 3D printing market performance, Dugdale and his team track five key industries: automotive, aerospace, medical, metal goods, and chemical processes. According to Dugdale, these industries are the primary users of machine tools, which makes them crucial indicators of market health. AMUK also relies on 3D printing industry surveys to gauge confidence, helping them to spot trends even when granular data is scarce. By comparing sector performance with survey-based confidence indicators, AMUK builds insights into the future market trajectory. The strong performance of sectors like aerospace and healthcare, which depend heavily on 3D printing, reinforces Dugdale’s confidence in the long-term potential of additive manufacturing. Looking ahead to the second half of 2025, AMUK plans to focus on three primary challenges: supply chain visibility, skills development, and standards. Dugdale explains that these issues remain central to the maturation of the UK’s AM ecosystem. Education will play a key role in these efforts.  AMUK is already running several additive manufacturing upskilling initiatives in schools and universities to build the next generation of 3D printing pioneers. These include pilot projects that introduce 3D printing to Key Stage 3 students (aged 11) and AM university courses that are tailored to industry needs.  In the longer term, Dugdale suggests AMUK could evolve to focus more on addressing specific industry challenges, such as net-zero emissions or automotive light-weighting. This would involve creating specialized working groups that focus on how 3D printing can address specific pressing issues.  Interestingly, Dugdale revealed that AMUK’s success in advancing the UK’s 3D printing industry could eventually lead to the organization being dissolved and reabsorbed into the MTA. This outcome, he explained, would signal that “additive manufacturing has really matured” and is now seen as an integral part of the broader manufacturing ecosystem, rather than a niche technology. Ultimately, Dugdale is optimistic for the future of 3D printing in the UK. He acknowledged that AMUK is still “trying to play catch-up for the last 100 years of machine tool technology.” However, additive manufacturing innovations are set to accelerate. “There’s a lot of exciting research happening in universities, and we need to find ways to help these initiatives gain the funding and visibility they need,” Dugdale urged. As the technology continues to grow, Dugdale believes additive manufacturing will gradually lose its niche status and become a standard tool for manufacturers. “In ten years, we could see a generation of workers who grew up with 3D printers at home,” he told me. “For them, it will just be another technology to use in the workplace, not something to be amazed by.”  With this future in mind, Dugdale’s vision for 3D printing is one of broad adoption, supported by clear strategy and policy, as the technology continues to evolve and integrate into UK industry.  Take the 3DPI Reader Survey — shape the future of AM reporting in under 5 minutes. Who won the 2024 3D Printing Industry Awards? Subscribe to the 3D Printing Industry newsletter to keep up with the latest 3D printing news.You can also follow us on LinkedIn, and subscribe to the 3D Printing Industry Youtube channel to access more exclusive content.

The security operations centre (SOC) has served public sector cyber teams well over the years but is fundamentally a reactive tool and now needs to be superseded by something else in order to address not just alerts about in-progress security events but the underlying risks that lead to them, all in the service of ‘doing’ cyber more efficiently and, crucially, cheaper. This is the view of Qualys CEO Sumedh Thakar, who, speaking at an event for federal government IT leaders hosted in the Washington DC suburbs at the end of May, defined the new-generation SOC as a ROC, where the letter R stands for risk. Thakar said that things needed to change in the cyber security world. “Continuing in the way that we have where we would scan every week or two and those scans were dumped somewhere on a hard drive somewhere and then someone goes and triages those manually and then you try to fix everything that comes your way – that approach is not really a success,” he said. “Continuing that approach is just not in the future.” He urged CISOs to stop putting so much effort into attack surface management and refocus on risk surface management, where risk management is defined as the mitigation of risk – or transfer of it to someone else – for the most plausible losses that could affect the organisation. It is not possible to get risk down to zero, so it is important to figure out how to address the most plausible factors and address those instead. For a company the most plausible loss will likely be a dollar revenue or profit figure. However, public sector organisations have it tough because they have a very different perspective on what ‘loss’ looks like beyond the financial cost. For example, they could and should be more worried about the safety of the general public or frontline personnel, national security, critical infrastructure security, economic stability, or public health, said Thakar, referencing attacks such as the infamous Colonial Pipeline incident, which paralysed petrol stations across a swathe of the US in 2022. “For most agencies it is really about aligning factors to what is the potential disruption to the mission, to the programme, that currently is important,” he said. Translating this into action for public sector buyers – wherever they may be located – Jonathan Trull, CISO and senior vice president of security solution architecture, and Mayuresh Ektare, vice president of product management at Qualys, said they wanted to help public sector CISOs make the most of the limited resources they have available to them in the face of a mountain of security data  “Our larger customers are having to deal with not a million findings, but hundreds of millions of findings on a daily basis. It is not humanly possible to go and patch or mitigate them all. This is where the concept of a risk operation centre is absolutely needed,” said Ektare. “You’ve got a limited number of resources at your disposal – how do you point them in the right direction so that you can actually reduce the risk that matters to your agencies the most.” Ektare described running an ROC as being a “peacetime” activity for defenders, comparing it to an SOC which is more akin to a wartime situation room. Trull, who spent 12 years working in cyber roles for the state of Colorado, rising to the post of CISO, said: “If this was a capability I’d have had back in the day … an ability to continuously aggregate [and] normalise, whatever standard they were using, because we didn’t dictate – it was very much you decide what tooling you want  and you use that tooling effectively. But what I needed was an accurate picture to advise the governor and the legislature what risks we’re facing on a monthly basis – that wasn’t available. “If you’re a customer a lot of this is built and in the solution, so in these federated environments in which you’re trying to gain control I can’t think of a better option than looking at this concept of an ROC,” he said. Read more about risk management Data risk management identifies, assesses and mitigates threats to organisational data, safeguarding sensitive information from unauthorised access. Knowing the types of risks businesses commonly face and their applicability to your company is a first step toward effective risk management. Every facet of business operations is exposed to risks, requiring a risk management team that's composed of a diverse mix of corporate executives and managers.

Frontline Protocol – Tactical PvE Strategy Wishlist on Steam and join Playtest for free Hi everyone! I'm an indie developer working on Frontline Protocol, a real-time strategy game with a unique blend of rts, deckbuilding and auto battler mechanics, and a strong focus on cooperative PvE gameplay. This isn't your typical RTS: Instead of controlling units directly, players influence the battlefield by playing cards. Every card you deploy brings instant effects – from spawning units, constructing buildings, placing towers, to launching tactical actions. Positioning, timing, and smart synergy between your three active decks is key. Key Features Card-based RTS Control your army through over 300 cards: infantry, vehicles, towers, buildings, and powerful action cards.1–4 online Multiplayer PvE Co-op Jump into online Multiplayer battles with up to 3 friends. Share spawners, coordinate strategies, and specialize your decks for roles like defense, eco, support, or frontline pressure.Auto Battler Dynamics Once deployed, your units act autonomously – but their effectiveness depends on your deck structure, map control, and team coordination.Meta Progression Earn Booster Credits and unlock card packs containing new units, action cards, and some items. Permanently improve select cards, invest skill points for meta-progression, and shape your long-term strategy. Create new specialized decks like infantry only, anti vehicle, eco buildings or what you want.Mission System Take on dynamic PvE missions with modifiers like time limits or card restrictions. Complete them to earn Mission Coins and unlock exclusive tactical rewards from mission booster packs.Special OrderAdd extra difficulty and earn rare rewards. Special Orders can be activated by the host to increase mission difficulty for all players – by limiting income, increasing enemy spawns or something else. The entire team receives a special loot if they succeed under these conditions. Deployment PlansStrategically boost rewards during missions. Deployment Plans are one-time tactical items drawn from mission booster packs. Use them during missions to gain extra Player EXP, Card EXP, or bonus loot – either per map or at mission completion. Choosing the right plan at the right time can multiply your rewards. Stats: Genre: Real-Time Strategy meets Deckbuilder and Auto Battler Online Multiplayer: Cooperative PvE for 1–4 players Card-Based Tactics: Spawn units, fire missiles, deploy towers, build economy Multi-Deck System: Play with up to three customizable decks simultaneously Booster Packs: Unlock new units, new action cards, and items 300+ Cards: Infantry, vehicles, support units, towers, buildings, and special actions Shared Resources: Teamwide synergy through buildings and spawner sharing Deck Progression: Permanently upgrade cards and build long-term strategies Skill Tree: Earn XP and unlock passive bonuses like faster income or extra hand size Missions & Objectives: Take on randomly generated missions with bonus rewards Built for Co-op: Game design encourages collaboration and tactical communication No Paywalls: All content unlockable through gameplay only Dynamic Enemy Scaling: AI becomes stronger the longer you wait – pressure guaranteed Replay Value: Constant deck experimentation, randomized missions, and tactical depth Get more: Steam Page: Frontline Protocol on Steam Official Website: www.frontlineprotocol.com

Women weren’t allowed to officially serve in combat jobs when Emelie Vanasse started her ROTC program at George Washington University. Instead, she used her biology degree to serve as a medical officer — but it still bothered Vanasse to be shut out of something just because she was a woman. “I always felt like, who really has the audacity to tell me that I can’t be in combat arms? I’m resilient, I am tough, I can make decisions in stressful environments,” Vanasse said.By 2015, the Obama administration opened all combat jobs to women, despite a plea from senior leaders in the Marine Corps to keep certain frontline units male only. Then-Defense Secretary Ash Carter told reporters that, “We cannot afford to cut ourselves off from half the country’s talents and skills.”The policy change meant that women could attend Ranger school, the training ground for the Army Rangers, an elite special operations infantry unit. When Capt. Kristen Griest and 1st Lt. Shaye Haver became the first women to graduate from the school in 2015, Vanasse taped their photos to her desk and swore she would be next, no matter what it took. She went on to become one of the first women to serve as an Army infantry officer and graduated from Ranger school in 2017. After the Pentagon integrated women into combat jobs, the services developed specific fitness standards for jobs like infantry and armor with equal standards for men and women. Special operations and other highly specialized units require additional qualification courses that are also gender-neutral. To continue past the first day of Ranger school, candidates must pass the Ranger Physical Fitness test, for which there is only one standard. Only the semi-annual fitness tests that service members take, which vary by branch, are scaled for age and gender.Despite that, Defense Secretary Pete Hegseth has continued to insist that the standards were lowered for combat roles. In a podcast interview in November, Hegseth said, “We’ve changed the standards in putting [women in combat], which means you’ve changed the capability of that unit.” (Despite Hegseth’s remark, many women worked alongside male infantry units in Iraq and Afghanistan, facing the same dangerous conditions.)In the same interview, Hegseth said that he didn’t believe women should serve in combat roles.In March, Hegseth ordered the military services to make the basic fitness standards for all combat jobs gender-neutral. The Army is the first service to comply: Beginning June 1, most combat specialties will require women to meet the male standard for basic physical fitness, something most women serving in active-duty combat roles are already able to do.Vanasse told Noel King on Today, Explained what it was like to attend Ranger School at a time when some men didn’t want to see a woman in the ranks. What is Ranger School?I went to Ranger School on January 1, 2017. I woke up at 3 am that day in Fort Benning, Georgia, shaved my head, a quarter-inch all the way around, just like the men. Took my last hot shower, choked down some French toast, and then I drove to Camp Rogers, and I remember being very acutely aware of the pain that the school would inflict, both physically and mentally. I was also very aware that there was kind of half of this population of objective graders that just kind of hated my guts for even showing up. They hated you for showing up because you’re a woman?Back in 2016 and 2017, it was so new to have women in Ranger School. I used to think, I don’t have to just be good, I have to be lucky. I have to get a grader who is willing to let a woman pass. I had dark times at that school. I tasted real failure. I sat under a poncho in torrential rain and I shivered so hard my whole body cramped. I put on a ruck that weighed 130 pounds and I crawled up a mountain on my hands and knees. I hallucinated a donut shop in the middle of the Appalachian Mountains and I cried one morning when someone told me I had to get out of my sleeping bag. But I think all of those experiences are quintessential Ranger School experiences. They’re what everyone goes through there. And I think the point of the school is that failure, that suffering, it’s not inherently bad, right? In a way, I like to think Ranger School was the most simplistic form of gender integration that ever could have happened because if I was contributing to the team, there was no individual out there that really had the luxury of disliking or excluding me. When you wanted to give up, what did you tell yourself? What was going through your head? I don’t think I ever considered quitting Ranger School. I just knew that it was something that I could get through and had the confidence to continue. I had a thought going in of What could be so bad that would make me quit? and the answer that I found throughout the school was, Nothing. Did you ever feel like they had lowered the standards for you compared to the men who were alongside you?No. Never. I did the same thing that the men did. I did the same Ranger physical fitness test that all the men took. I ran five miles in 40 minutes. I did 49 pushups, 59 situps, six pullups. I rucked 12 miles in three hours with a 45-pound ruck. I climbed the same mountains. I carried the same stuff. I carried the same exact packing list they did, plus 250 tampons for some reason. At no point were the standards lowered for me. Whose idea was it for you to carry 250 tampons? It was not mine! It was a misguided effort to have everyone very prepared for the first women coming through Ranger School.In Ranger School, there’s only one standard for the fitness test. Everybody has to meet it, and that allows you to get out of Ranger School and say, “Look, fellas, I took the same test as the men and I passed.” Secretary of Defense Pete Hegseth is saying that Army combat jobs should only have one standard of fitness for both men and women. And there’s part of me that thinks: Doesn’t that allow the women who meet the standards to be like, look, We met the same standards as the men. Nothing suspicious here, guys. I think gender-neutral standards for combat arms are very important. It should not be discounted how important physical fitness is for combat arms. I think there’s nuance in determining what is a standard that is useful for combat arms, right? But it’s an important thing. And there have been gender-neutral standards for combat arms. In things like Infantry Basic Officer Leader Course, which is the initial basic training for officers going into the infantry, there are gender-neutral standards that you have to meet: You have to run five miles in 40 minutes, you have to do a 12-mile ruck. All of those standards have remained the same. Pete Hegseth is specifically referring to the Army Combat Physical Fitness test, and to a certain extent I agree, it should be gender-neutral for combat arms. But I think there’s nuance in determining what exactly combat arms entails physically.Secretary Hegseth has a lot to say about women, and sometimes he says it directly and sometimes he alludes to it. What he often does is he talks about lethality as something that is critically important for the military. He says the Army in particular needs more of it, but he never really defines what he means by lethality. What is the definition as you understand it? There’s a component of lethality that is physical fitness and it should not be discounted. But lethality extends far beyond that, right? It’s tactical skills, it’s decision-making, it’s leadership, it’s grit, it’s the ability to build trust and instill purpose and a group of people. It’s how quick a fire team in my platoon can react to contact. How well my SAW [Squad Automatic Weapon] gunner can shoot, how quickly I can employ and integrate combat assets, how fast I can maneuver a squad. All of those things take physical fitness, but they certainly take more than just physical fitness. There’s more to lethality than just how fast you can run and how many pushups you can do.To an average civilian like myself, I hear lethality and I think of the dictionary definition, the ability to kill. Does this definition of lethality involve the ability, physically and emotionally and psychologically, to kill another person? Absolutely. And so when Secretary Hegseth casts doubt on the ability of women to be as lethal as men, do you think there’s some stuff baked in there that maybe gets to his idea of what women are willing and able to do?Yes, possibly. I think the [secretary’s] message is pretty clear. According to him, the women in combat arms achieved success because the standards were lowered for them. We were never accommodated and the standards were never lowered.What’s your response, then, to hearing the Secretary of Defense say women don’t belong in combat? It makes me irate, to be honest. Like, it’s just a complete discounting of all of the accomplishments of the women that came before us. Do you think that if Secretary Hegseth could take a look at what you did in Ranger School, and he could hear from you that there were no second chances, there were no excuses, there was no babying, the men didn’t treat you nicer just because you were a woman, do you think he’d change his mind about women serving in combat? I’d like to think he would, but I’ve met plenty of people whose minds couldn’t be changed by reality. I’d love it if he went to Ranger School. He has a lot of opinions about Ranger School for someone who does not have his Ranger tab.What is a Ranger tab, for civilians? A Ranger tab is what you receive upon graduating Ranger School, which means you have passed all three phases and you are now Ranger-qualified in the military.You have that. And the Secretary of Defense doesn’t. He does not, though he has a lot of opinions about Ranger School.See More:

Welcome to the world of Elden Ring: Nightreign, coming to PlayStation 5 on May 30. This new spin on the beloved dark fantasy classic is a cooperative multiplayer-focused adventure, where you’ll choose a class/character, be matched up with two other adventurers, then go off to try and defeat one of the mythical Nightlords. And if you’re familiar with Elden Ring at all, you know very well that this won’t be an easy task even with a group to help you out. You’ll start with six classes from the outset, with two unlockable afterwards. Each class has a unique Passive Ability, a Character Skill, and an Ultimate Art. Due to Nightreign’s much faster pace and ever-decreasing explorable area, it would be difficult to plan out your builds, so the unique abilities of your currently chosen class and equipped Relics will largely determine your playstyle. To help get you started, we’ll give you a quick look at each class’s strengths and noteworthy attributes. But don’t sweat the choice too much — if one class just isn’t gelling for you, you can swap class anytime between expeditions. Wylder Starting weapons: Wylder’s Greatsword and Wylder’s Small Shield Passive: Sixth Sense Character Skill: Claw Shot  Ultimate Art: Onslaught Stake If you want to just get in, melee some enemies, and do a good chunk of damage, Wylder’s the class for you. Yes, he’s pretty simple, but “simple” certainly doesn’t mean “ineffective.” In fact, Wylder is one of the most versatile classes when it comes to equipment.  Using the Claw Shot, you can bring annoying ranged attackers straight to your face for a good slashing — or, for the big boys, you can fling yourself at them for a slick dashing strike. Plus, you’ve got the ability to nullify one deathblow per revival after taking a break at site of grace with Sixth Sense, making this a good choice for newbies. (Just don’t rely on that ability to get you out of trouble all the time.) Guardian Starting weapon: Guardian’s Halberd Passive: Steel Guard Character Skill: Whirlwind Ultimate Art: Wings of Salvation If you’re looking for a warrior who will stick around, the bird’s the word. Our stalwart avian friend has a huge HP pool and a Steel Guard, a shield stance that consumes less stamina, blocks more effectively, and makes countering a cinch. The Guardian’s ultimate skill, Wings of Salvation, not only looks cool and hurts a large area of foes, it can also help revive near-death teammates within the range of its impact. Build a Guardian wisely, and you’ll have a fearsome tank that can dish it out as well as he takes it. Ironeye Starting weapon: Ironeye’s Bow Passive: Eagle Eye Character Skill: Marking Ultimate Art: Single Shot Not everyone likes to get all up in an enemy’s face, especially when they hit as hard as they do in Nightreign. Ironeye serves as the premiere ranged class, preferring bows and crossbows and specializing in Dexterity above all other stats. He’s also got an eye for treasure, as his Eagle Eye increases the amount of drops you’ll get from felled foes. The Ironeye also specializes in bypassing an enemy’s defenses: Marking a foe with a melee dagger attack will create an opening for extra damage, while Single Shot can ignore defense and obstacles in its path. (And he’s great for distance revival, too.) Raider Starting weapon: Raider’s Greataxe Passive: Fighter’s Resolve Character Skill: Retaliate Ultimate Art: Totem Stela They say that pain makes you stronger, and the Raider epitomizes the phrase: he’s built big to take a beating and hit back hard. He’s focused around the Retaliate skill, which sees him first take a defensive stance, then going into a headbutt. If he’s taken enough damage, his Fighter’s Resolve skill will transform Retaliate into a smashing uppercut that deals massive damage and lets him survive a lethal blow during its execution. But Raider’s also got a softer, supportive side: his Ultimate Art creates a tombstone that serves as a safe perch for long-range attackers and boosts their power. Recluse Starting weapon: Recluse’s Staff Passive: Elemental Defense Character Skill: Magic Cocktails         Ultimate Art: Soulblood Song Compared to other classes, you’re going to need some technical knowledge for the Recluse to really work her magic. She’s a very versatile class, offering both offensive and support capabilities with her spells. However, using her effectively will involve proper collection of elemental affinity residues with her Magic Cocktail ability. Each combination of three elements yields a unique result, so knowing exactly what you’re mixing up is very important. She’s not particularly resilient, either, making her ill-suited for the frontlines. But if you’ve a hankering to cook up trouble with elemental recipes, she’s your go-to gal. Executor Starting weapon: Executor’s Blade Passive: Tenacity                   Character Skill: Cursed Sword            Ultimate Art: Aspects of the Crucible: Beast The Executor’s motto is “Hit fast and cause plenty of pain”–though whether that pain will come from sheer damage output or from status ailments is up to you and your playstyle. Thanks to his passive skill, Tenacity, he gets a nice boost after recovering from status ailments of his own–though we don’t recommend actively seeking out getting afflicted. If you’re skilled at guarding, you can draw out the Cursed Sword for some impressive ripostes. And when things get hairy, you will too, with a big bestial transformation that sees you going feral on enemies for a few precious seconds. Just don’t give him the big weapons — or take too many big-damage hits. You’ll unlock the final two classes — the fleet-footed Duchess and the spirit-calling Revenant — as you progress the game. (Don’t look too hard–you’ll likely stumble upon at least one of the key items required to unlock them during your quests.)  Good luck, and remember: every run will yield a reward, no matter how things go, so keep on questing.

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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