Secretly working multiple full-time remote jobs may sound like a nightmare — but Americans looking to make their financial dreams come true willingly hustle for it.Over the past two years, Business Insider has interviewed more than two dozen "overemployed" workers, many of whom work in tech roles. They tend to work long hours but say the extra earnings are worth it to pay off student debt, save for an early retirement, and afford expensive vacations and weight-loss drugs. Many started working multiple jobs during the pandemic, when remote job openings soared.One example is Sarah, who's on track to earn about $300,000 this year by secretly working two remote IT jobs. Over the last few years, Sarah said the extra income from job juggling has helped her save more than $100,000 in her 401(k)s, pay off $17,000 in credit card debt, and furnish her home.Sarah, who's in her 50s and lives in the Southeast, said working 12-hour days is worth it for the job security. This security came in handy when she was laid off from one of her jobs last year. She's since found a new second gig."I want to ride this out until I retire," Sarah previously told BI. Business Insider verified her identity, but she asked to use a pseudonym, citing fears of professional repercussions. BI spoke to one boss who caught an employee secretly working another job and fired him. Job juggling could breach some employment contracts and be a fireable offense.Overemployed workers like Sarah told BI how they've landed extra roles, juggled the workload, and stayed under the radar. Some said they rely on tactics like blocking off calendars, using separate devices, minimizing meetings, and sticking to flexible roles with low oversight. While job juggling could have professional repercussions or lead to burnout, and some readers have questioned the ethics of this working arrangement, many workers have told BI they don't feel guilty about their job juggling — and that the financial benefits generally outweigh the downsides and risks. In recent years, some have struggled to land new remote gigs, due in part to hiring slowdowns and return-to-office mandates. Most said they plan to continue pursuing overemployment as long as they can.Read the stories ahead to learn how some Americans have managed the workload, risks, and stress of working multiple jobs — and transformed their finances.

Greg Touhill, director of the Software Engineering’s Institute’s (SEI’s) Computer Emergency Response Team (CERT) division is an atypical technology leader. For one thing, he’s been in tech and other leadership positions that span the US Air Force, the US government, the private sector and now SEI’s CERT. More importantly, he’s been a major force in the cybersecurity realm, making the world a safer place and even saving lives. Touhill earned a bachelor’s degree from the Pennsylvania State University, a master’s degree from the University of Southern California, a master’s degree from the Air War College, was a senior executive fellow at the Harvard University Kennedy School of Government and completed executive education studies at the University of North Carolina. “I was a student intern at Carnegie Mellon, but I was going to college at Penn State and studying chemical engineering. As an Air Force ROTC scholarship recipient, I knew I was going to become an Air Force officer but soon realized that I didn’t necessarily want to be a chemical engineer in the Air Force,” says Touhill. “Because I passed all the mathematics, physics, and engineering courses, I ended up becoming a communications, electronics, and computer systems officer in the Air Force. I spent 30 years, one month and three days on active duty in the United States Air Force, eventually retiring as a brigadier general and having done many different types of jobs that were available to me within and even beyond my career field.” Related:Specifically, he was an operational commander at the squadron, group, and wing levels. For example, as a colonel, Touhill served as director of command, control, communications and computers (C4) for the United States Central Command Forces, then he was appointed chief information officer and director, communications and information at Air Mobility Command. Later, he served as commander, 81st Training Wing at Kessler Air Force Base where he was promoted to brigadier general and commanded over 12,500 personnel. After that, he served as the senior defense officer and US defense attaché at the US Embassy in Kuwait, before concluding his military career as the chief information officer and director, C4 systems at the US Transportation Command, one of 10 US combatant commands, where he and his team were awarded the NSA Rowlett Award for the best cybersecurity program in the government. While in the Air Force, Touhill received numerous awards and decorations including the Bronze Star medal and the Air Force Science and Engineering Award. He is the only three-time recipient of the USAF C4 Professionalism Award. Related:Greg Touhill“I got to serve at major combatant commands, work with coalition partners from many different countries and represented the US as part of a diplomatic mission to Kuwait for two years as the senior defense official at a time when America was withdrawing forces out of Iraq. I also led the negotiation of a new bilateral defense agreement with the Kuwaitis,” says Touhill. “Then I was recruited to continue my service and was asked to serve as the deputy assistant secretary of cybersecurity and communications at the Department of Homeland Security, where I ran the operations of what is now known as the Cybersecurity and Infrastructure Security Agency. I was there at a pivotal moment because we were building up the capacity of that organization and setting the stage for it to become its own agency.” While at DHS, there were many noteworthy breaches including the infamous US Office of People Management (OPM) breach. Those events led to Obama’s visit to the National Cybersecurity and Communications Integration Center.  “I got to brief the president on the state of cybersecurity, what we had seen with the OPM breach and some other deficiencies,” says Touhill. “I was on the federal CIO council as the cybersecurity advisor to that since I’d been a federal CIO before and I got to conclude my federal career by being the first United States government chief information security officer. From there, I pivoted to industry, but I also got to return to Carnegie Mellon as a faculty member at Carnegie Mellon’s Heinz College, where I've been teaching since January 2017.” Related:Touhill has been involved in three startups, two of which were successfully acquired. He also served on three Fortune 100 advisory boards and on the Information Systems Audit and Control Association board, eventually becoming its chair for a term during the seven years he served there. Touhill just celebrated his fourth year at CERT, which he considers the pinnacle of the cybersecurity profession and everything he’s done to date. “Over my career I've led teams that have done major software builds in the national security space. I've also been the guy who's pulled cables and set up routers, hubs and switches, and I've been a system administrator. I've done everything that I could do from the keyboard up all the way up to the White House,” says Touhill. “For 40 years, the Software Engineering Institute has been leading the world in secure by design, cybersecurity, software engineering, artificial intelligence and engineering, pioneering best practices, and figuring out how to make the world a safer more secure and trustworthy place. I’ve had a hand in the making of today’s modern military and government information technology environment, beginning as a 22-year-old lieutenant, and hope to inspire the next generation to do even better.” What ‘Success’ Means Many people would be satisfied with their careers as a brigadier general, a tech leader, the White House’s first anything, or working at CERT, let alone running it. Touhill has spent his entire career making the world a safer place, so it’s not surprising that he considers his greatest achievement saving lives. “In the Middle East and Iraq, convoys were being attacked with improvised explosive devices. There were also ‘direct fire’ attacks where people are firing weapons at you and indirect fire attacks where you could be in the line of fire,” says Touhill. “The convoys were using SINCGARS line-of-site walkie-talkies for communications that are most effective when the ground is flat, and Iraq is not flat. As a result, our troops were at risk of not having reliable communications while under attack. As my team brainstormed options to remedy the situation, one of my guys found some technology, about the size of an iPhone, that could covert a radio signal, which is basically a waveform, into a digital pulse I could put on a dedicated network to support the convoy missions.” For $11 million, Touhill and his team quickly architected, tested, and fielded the Radio over IP network (aka “Ripper Net”) that had a 99% reliability rate anywhere in Iraq. Better still, convoys could communicate over the network using any radios. That solution saved a minimum of six lives. In one case, the hospital doctor said if the patient had arrived five minutes later, he would have died. Sage Advice Anyone who has ever spent time in the military or in a military family knows that soldiers are very well disciplined, or they wash out. Other traits include being physically fit, mentally fit, and achieving balance in life, though that’s difficult to achieve in combat. Still, it’s a necessity. “I served three and a half years down range in combat operations. My experience taught me you could be doing 20-hour days for a year or two on end. If you haven’t built a good foundation of being disciplined and fit, it impacts your ability to maintain presence in times of stress, and CISOs work in stressful situations,” says Touhill. “Staying fit also fortifies you for the long haul, so you don’t get burned out as fast.” Another necessary skill is the ability to work well with others.  “Cybersecurity is an interdisciplinary practice. One of the great joys I have as CERT director is the wide range of experts in many different fields that include software engineers, computer engineers, computer scientists, data scientists, mathematicians and physicists,” says Touhill. “I have folks who have business degrees and others who have philosophy degrees. It's really a rich community of interests all coming together towards that common goal of making the world a safer, more secure and more trusted place in the cyber domain. We’re are kind of like the cyber neighborhood watch for the whole world.” He also says that money isn’t everything, having taken a pay cut to go from being an Air Force brigadier general to the deputy assistant secretary of the Department of Homeland Security . “You’ll always do well if you pick the job that matters most. That’s what I did, and I’ve been rewarded every step,” says Touhill.  The biggest challenge he sees is the complexity of cyber systems and software, which can have second, third, and fourth order effects.  “Complexity raises the cost of the attack surface, increases the attack surface, raises the number of vulnerabilities and exploits human weaknesses,” says Touhill. “The No. 1 thing we need to be paying attention to is privacy when it comes to AI because AI can unearth and discover knowledge from data we already have. While it gives us greater insights at greater velocities, we need to be careful that we take precautions to better protect our privacy, civil rights and civil liberties.” 

LUBBOCK — Every winter, after the sea of cotton has been harvested in the South Plains and the ground looks barren, technicians with the High Plains Underground Water Conservation District check the water levels in nearly 75,000 wells across 16 counties. For years, their measurements have shown what farmers and water conservationists fear most—the Ogallala Aquifer, an underground water source that’s the lifeblood of the South Plains agriculture industry, is running dry. That’s because of a century-old law called the rule of capture. The rule is simple: If you own the land above an aquifer in Texas, the water underneath is yours. You can use as much as you want, as long as it’s not wasted or taken maliciously. The same applies to your neighbor. If they happen to use more water than you, then that’s just bad luck. To put it another way, landowners can mostly pump as much water as they choose without facing liability to surrounding landowners whose wells might be depleted as a result. Following the Dust Bowl—and to stave off catastrophe—state lawmakers created groundwater conservation districts in 1949 to protect what water is left. But their power to restrict landowners is limited. “The mission is to save as much water possible for as long as possible, with as little impact on private property rights as possible,” said Jason Coleman, manager for the High Plains Underground Water Conservation District. “How do you do that? It’s a difficult task.” A 1953 map of the wells in Lubbock County hangs in the office of the groundwater district. [Photo: Annie Rice for The Texas Tribune] Rapid population growth, climate change, and aging water infrastructure all threaten the state’s water supply. Texas does not have enough water to meet demand if the state is stricken with a historic drought, according to the Texas Water Development Board, the state agency that manages Texas’ water supply. Lawmakers want to invest in every corner to save the state’s water. This week, they reached a historic $20 billion deal on water projects. High Plains Underground Water District General Manager Jason Coleman stands in the district’s meeting room on May 21 in Lubbock. [Photo: Annie Rice for The Texas Tribune] But no one wants to touch the rule of capture. In a state known for rugged individualism, politically speaking, reforming the law is tantamount to stripping away freedoms. “There probably are opportunities to vest groundwater districts with additional authority,” said Amy Hardberger, director for the Texas Tech University Center for Water Law and Policy. “I don’t think the political climate is going to do that.” State Sen. Charles Perry, a Lubbock Republican, and Rep. Cody Harris, a Palestine Republican, led the effort on water in Austin this year. Neither responded to requests for comment. Carlos Rubinstein, a water expert with consulting firm RSAH2O and a former chairman of the water development board, said the rule has been relied upon so long that it would be near impossible to undo the law. “I think it’s better to spend time working within the rules,” Rubinstein said. “And respect the rule of capture, yet also recognize that, in and of itself, it causes problems.” Even though groundwater districts were created to regulate groundwater, the law effectively stops them from doing so, or they risk major lawsuits. The state water plan, which spells out how the state’s water is to be used, acknowledges the shortfall. Groundwater availability is expected to decline by 25% by 2070, mostly due to reduced supply in the Ogallala and Edwards-Trinity aquifers. Together, the aquifers stretch across West Texas and up through the Panhandle. By itself, the Ogallala has an estimated three trillion gallons of water. Though the overwhelming majority in Texas is used by farmers. It’s expected to face a 50% decline by 2070. Groundwater is 54% of the state’s total water supply and is the state’s most vulnerable natural resource. It’s created by rainfall and other precipitation, and seeps into the ground. Like surface water, groundwater is heavily affected by ongoing droughts and prolonged heat waves. However, the state has more say in regulating surface water than it does groundwater. Surface water laws have provisions that cut supply to newer users in a drought and prohibit transferring surface water outside of basins. Historically, groundwater has been used by agriculture in the High Plains. However, as surface water evaporates at a quicker clip, cities and businesses are increasingly interested in tapping the underground resource. As Texas’ population continues to grow and surface water declines, groundwater will be the prize in future fights for water. In many ways, the damage is done in the High Plains, a region that spans from the top of the Panhandle down past Lubbock. The Ogallala Aquifer runs beneath the region, and it’s faced depletion to the point of no return, according to experts. Simply put: The Ogallala is not refilling to keep up with demand. “It’s a creeping disaster,” said Robert Mace, executive director of the Meadows Center for Water and the Environment. “It isn’t like you wake up tomorrow and nobody can pump anymore. It’s just happening slowly, every year.” [Image: Yuriko Schumacher/The Texas Tribune] Groundwater districts and the law The High Plains Water District was the first groundwater district created in Texas. Over a protracted multi-year fight, the Legislature created these new local government bodies in 1949, with voter approval, enshrining the new stewards of groundwater into the state Constitution. If the lawmakers hoped to embolden local officials to manage the troves of water under the soil, they failed. There are areas with groundwater that don’t have conservation districts. Each groundwater districts has different powers. In practice, most water districts permit wells and make decisions on spacing and location to meet the needs of the property owner. The one thing all groundwater districts have in common: They stop short of telling landowners they can’t pump water. In the seven decades since groundwater districts were created, a series of lawsuits have effectively strangled groundwater districts. Even as water levels decline from use and drought, districts still get regular requests for new wells. They won’t say no out of fear of litigation. The field technician coverage area is seen in Nathaniel Bibbs’ office at the High Plains Underground Water District. Bibbs is a permit assistant for the district. [Photo: Annie Rice for The Texas Tribune] “You have a host of different decisions to make as it pertains to management of groundwater,” Coleman said. “That list has grown over the years.” The possibility of lawsuits makes groundwater districts hesitant to regulate usage or put limitations on new well permits. Groundwater districts have to defend themselves in lawsuits, and most lack the resources to do so. A well spacing guide is seen in Nathaniel Bibbs’ office. [Photo: Annie Rice for The Texas Tribune] “The law works against us in that way,” Hardberger, with Texas Tech University, said. “It means one large tool in our toolbox, regulation, is limited.” The most recent example is a lawsuit between the Braggs Farm and the Edwards Aquifer Authority. The farm requested permits for two pecan orchards in Medina County, outside San Antonio. The authority granted only one and limited how much water could be used based on state law. It wasn’t an arbitrary decision. The authority said it followed the statute set by the Legislature to determine the permit. “That’s all they were guaranteed,” said Gregory Ellis, the first general manager of the authority, referring to the water available to the farm. The Braggs family filed a takings lawsuit against the authority. This kind of claim can be filed when any level of government—including groundwater districts—takes private property for public use without paying for the owner’s losses. Braggs won. It is the only successful water-related takings claim in Texas, and it made groundwater laws murkier. It cost the authority $4.5 million. “I think it should have been paid by the state Legislature,” Ellis said. “They’re the ones who designed that permitting system. But that didn’t happen.” An appeals court upheld the ruling in 2013, and the Texas Supreme Court denied petitions to consider appeals. However, the state’s supreme court has previously suggested the Legislature could enhance the powers of the groundwater districts and regulate groundwater like surface water, just as many other states have done. While the laws are complicated, Ellis said the fundamental rule of capture has benefits. It has saved Texas’ legal system from a flurry of lawsuits between well owners. “If they had said ‘Yes, you can sue your neighbor for damaging your well,’ where does it stop?” Ellis asked. “Everybody sues everybody.” Coleman, the High Plains district’s manager, said some people want groundwater districts to have more power, while others think they have too much. Well owners want restrictions for others, but not on them, he said. “You’re charged as a district with trying to apply things uniformly and fairly,” Coleman said. Can’t reverse the past Two tractors were dropping seeds around Walt Hagood’s farm as he turned on his irrigation system for the first time this year. He didn’t plan on using much water. It’s too precious. The cotton farm stretches across 2,350 acres on the outskirts of Wolfforth, a town 12 miles southwest of Lubbock. Hagood irrigates about 80 acres of land, and prays that rain takes care of the rest. Walt Hagood drives across his farm on May 12, in Wolfforth. Hagood utilizes “dry farming,” a technique that relies on natural rainfall. [Photo: Annie Rice for The Texas Tribune] “We used to have a lot of irrigated land with adequate water to make a crop,” Hagood said. “We don’t have that anymore.” The High Plains is home to cotton and cattle, multi-billion-dollar agricultural industries. The success is in large part due to the Ogallala. Since its discovery, the aquifer has helped farms around the region spring up through irrigation, a way for farmers to water their crops instead of waiting for rain that may not come. But as water in the aquifer declines, there are growing concerns that there won’t be enough water to support agriculture in the future. At the peak of irrigation development, more than 8.5 million acres were irrigated in Texas. About 65% of that was in the High Plains. In the decades since the irrigation boom, High Plains farmers have resorted to methods that might save water and keep their livelihoods afloat. They’ve changed their irrigation systems so water is used more efficiently. They grow cover crops so their soil is more likely to soak up rainwater. Some use apps to see where water is needed so it’s not wasted. A furrow irrigation is seen at Walt Hagood’s cotton farm. [Photo: Annie Rice for The Texas Tribune] Farmers who have not changed their irrigation systems might not have a choice in the near future. It can take a week to pump an inch of water in some areas from the aquifer because of how little water is left. As conditions change underground, they are forced to drill deeper for water. That causes additional problems. Calcium can build up, and the water is of poorer quality. And when the water is used to spray crops through a pivot irrigation system, it’s more of a humidifier as water quickly evaporates in the heat. According to the groundwater district’s most recent management plan, 2 million acres in the district use groundwater for irrigation. About 95% of water from the Ogallala is used for irrigated agriculture. The plan states that the irrigated farms “afford economic stability to the area and support a number of other industries.” The state water plan shows groundwater supply is expected to decline, and drought won’t be the only factor causing a shortage. Demand for municipal use outweighs irrigation use, reflecting the state’s future growth. In Region O, which is the South Plains, water for irrigation declines by 2070 while demand for municipal use rises because of population growth in the region. Coleman, with the High Plains groundwater district, often thinks about how the aquifer will hold up with future growth. There are some factors at play with water planning that are nearly impossible to predict and account for, Coleman said. Declining surface water could make groundwater a source for municipalities that didn’t depend on it before. Regions known for having big, open patches of land, like the High Plains, could be attractive to incoming businesses. People could move to the country and want to drill a well, with no understanding of water availability. The state will continue to grow, Coleman said, and all the incoming businesses and industries will undoubtedly need water. “We could say ‘Well, it’s no one’s fault. We didn’t know that factory would need 20,000 acre-feet of water a year,” Coleman said. “It’s not happening right now, but what’s around the corner?” Coleman said this puts agriculture in a tenuous position. The region is full of small towns that depend on agriculture and have supporting businesses, like cotton gins, equipment and feed stores, and pesticide and fertilizer sprayers. This puts pressure on the High Plains water district, along with the two regional water planning groups in the region, to keep agriculture alive. “Districts are not trying to reduce pumping down to a sustainable level,” said Mace with the Meadows Foundation. “And I don’t fault them for that, because doing that is economic devastation in a region with farmers.” Hagood, the cotton farmer, doesn’t think reforming groundwater rights is the way to solve it. What’s done is done, he said. “Our U.S. Constitution protects our private property rights, and that’s what this is all about,” Hagood said. “Any time we have a regulation and people are given more authority, it doesn’t work out right for everybody.” Rapid population growth, climate change, and aging water infrastructure all threaten the state’s water supply. [Photo: Annie Rice for The Texas Tribune] What can be done The state water plan recommends irrigation conservation as a strategy. It’s also the least costly water management method. But that strategy is fraught. Farmers need to irrigate in times of drought, and telling them to stop can draw criticism. In Eastern New Mexico, the Ogallala Land and Water Conservancy, a nonprofit organization, has been retiring irrigation wells. Landowners keep their water rights, and the organization pays them to stop irrigating their farms. Landowners get paid every year as part of the voluntary agreement, and they can end it at any point. Ladona Clayton, executive director of the organization, said they have been criticized, with their efforts being called a “war” and “land grab.” They also get pushback on why the responsibility falls on farmers. She said it’s because of how much water is used for irrigation. They have to be aggressive in their approach, she said. The aquifer supplies water to the Cannon Air Force Base. “We don’t want them to stop agricultural production,” Clayton said. “But for me to say it will be the same level that irrigation can support would be untrue.” There is another possible lifeline that people in the High Plains are eyeing as a solution: the Dockum Aquifer. It’s a minor aquifer that underlies part of the Ogallala, so it would be accessible to farmers and ranchers in the region. The High Plains Water District also oversees this aquifer. If it seems too good to be true—that the most irrigated part of Texas would just so happen to have another abundant supply of water flowing underneath—it’s because there’s a catch. The Dockum is full of extremely salty brackish water. Some counties can use the water for irrigation and drinking water without treatment, but it’s unusable in others. According to the groundwater district, a test well in Lubbock County pulled up water that was as salty as seawater. Rubinstein, the former water development board chairman, said there are pockets of brackish groundwater in Texas that haven’t been tapped yet. It would be enough to meet the needs on the horizon, but it would also be very expensive to obtain and use. A landowner would have to go deeper to get it, then pump the water over a longer distance. “That costs money, and then you have to treat it on top of that,” Rubinstein said. “But, it is water.” Landowners have expressed interest in using desalination, a treatment method to lower dissolved salt levels. Desalination of produced and brackish water is one of the ideas that was being floated around at the Legislature this year, along with building a pipeline to move water across the state. Hagood, the farmer, is skeptical. He thinks whatever water they move could get used up before it makes it all the way to West Texas. There is always brackish groundwater. Another aquifer brings the chance of history repeating—if the Dockum aquifer is treated so its water is usable, will people drain it, too? Hagood said there would have to be limits. Disclosure: Edwards Aquifer Authority and Texas Tech University have been financial supporters of The Texas Tribune. Financial supporters play no role in the Tribune’s journalism. Find a complete list of them here. This article originally appeared in The Texas Tribune, a member-supported, nonpartisan newsroom informing and engaging Texans on state politics and policy. Learn more at texastribune.org.

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

Click to enlarge Housing build-ups in Dhaka. Image: Jeremy Smith 1 of 11 Marina Tabassum Architects’ Bait Ur Rouf Jame Mosque. Image: Jeremy Smith 2 of 11 Marina Tabassum Architects’ Bait Ur Rouf Jame Mosque. Image: Jeremy Smith 3 of 11 Marina Tabassum Architects’ Bait Ur Rouf Jame Mosque. Image: Jeremy Smith 4 of 11 Marina Tabassum Architects’ Bait Ur Rouf Jame Mosque. Image: Jeremy Smith 5 of 11 Old Dhaka and 45,000 people per square kilometre. Image: Jeremy Smith 6 of 11 Buses collage life on the roads. Image: Jeremy Smith 7 of 11 Lattice-work roofing of the informal settlements, viewed from Salauddin Ahmed’s Atelier Robin Architects studio. Image: Jeremy Smith 8 of 11 The colour and vibrancy of Dhaka life. Image: Jeremy Smith 9 of 11 The colour and vibrancy of Dhaka life. Image: Jeremy Smith 10 of 11 The colour and vibrancy of Dhaka life. Image: Jeremy Smith 11 of 11 Architects Jeremy Smith and Murali Bhaskar go looking for water and hard-to-find buildings in what is already one of the world’s most populous mega-cities, Dhaka. Architecture here is rarely properly lost. Even now, as we navigate a way to higher-density living, we tend not to misplace buildings. There’s still the space to eye-spy our most wayward elevations. At worst, we might GPS a tricky driveway or pull out an Andrew Barrie map to pinpoint some retiring architecture. But what happens if you really diamond-up the density. At our country-wide 19-people-per-square-kilometre or even downtown Auckland’s sky-high 2500, you can see what’s coming and cities mostly plan out as planned. Teleport forward though to 45,000-people-per-square-kilometre and cities accelerate lives of their own. Here, anything and everything can be lost in the crowd, even buildings. So, on a 2024 invitation from the Bengal Institute for Architecture, Landscape and Settlements to share some Unfinished & Far Far Away adventures in “the toughest city in the world”,1 I pack some extra compassing in architect buddy Murali Bhaskar and go architectural orienteering in Dhaka. It’s hot hot; architecture can wait. We start by looking for water. This, after all, is the land of rivers. Following on from Aotearoa in 2017 being the first to give a specific river, Te Awa Tupua, legal rights, Bangladesh in 2019 became the first country to grant all of its some 700 rivers the same legal status that humans have.2 But the count varies. Protections readily miss smaller tributaries and, with all that water pouring out of the Himalayas and delta-ing into the Bay of Bengal, the land is accretional. The colour and vibrancy of Dhaka life. Image:  Jeremy Smith From the million or so starters in a newly independent 1971 Dhaka, today, it is the fourth-most-populated city in the world with somewhere near 25 million people. Whether for disaster relief, economics or just the bright lights, urbanisation draws more than 400,000 new residents annually to the city. Throw in some family time and, with Tokyo and Shanghai shrinking, Dhaka’s population is predicted to be an eyewatering 35 million by 2050 (and outnumbered only by Delhi and, in some books, Mumbai). When every possible place looks inhabited, it’s not just water that can quickly go to ground. Kazi Khaleed Ashraf, who heads the Bengal Institute, has some learned thinkers in tow in trying to keep pace architecturally. Throw in societal and climatic concerns, and questions about how contextualisation might operate at such speed and the inquiry takes global precedence. Kenneth Frampton, Rounaq Jahan, Suha Özkan, Shamsul Wares and, formerly, BV Doshi, sit on the advisory panel and have drawn other such worldly thinkers as Juhani Pallasmaa, David Leatherbarrow and, even, Peter Stutchbury from down our way to come experience an urban existence “symptomatic of the gravest environmental challenges”.3 It’s serious stuff. Ashraf researches “hydraulic flow in which horizontal and vertical movements of water may direct architectural and landscape formations”.4 This ‘form follows water’ mantra isn’t just free planning Le Corbusier’s ‘form follows function’ with some Charles Correa’s ‘form follows climate’ to connect to life outside, it’s a watery warning to the navigations quickly necessitating within our collective future. Ashraf’s timely prompt that “Embankment is a barrier. How can we deconstruct it”5 can be seen in the way we increasingly plan the separation of wet and dry in our cities. Main streets like Queen Street and Cambridge Terrace already run down streams and our remaining water edges risk becoming increasingly marginalised by infrastructure rising with the water. But the steering is different at density and Dhaka’s rapid growth has meant letting go of the controls with which we still understand cities to flow. As Ashraf puts it, “Dhaka builds furiously”. While we dutifully plan buildings as if crawling a length or two at the aquatic centre, architecture in Dhaka must high-dive into a torrent. Its buildings must learn to surface and really start kicking. Anything trying to hold ground risks being swept away. Dhaka has become a river. As if to university-entrance the swimming lesson to densification, we’ve arrived only a few months after an Indian helicopter plucked Bangladesh’s president from a student-led flood of unrest amongst civil rights and corruption demonstrations. We might think of universities as offering time for trying things on but, sink or swim, the students here now run the country. With the parliament dissolved, there’s no chance of us seeing inside Louis Kahn’s 1982 National Assembly Building, which, like many of Dhaka’s institutional buildings, took on something of a freshening in the coup. Remembering our government’s pre-departure, bold-italic travel advisory, we head out to practise avoiding street demonstrations and are rewarded with a fenced-off view of Kahn’s epic, which brought global architectural discourse to post-independent Bangladesh. No such authoritative access issues back at the university, where, amongst the student political murals, we visit Muzharul Islam’s 1953–1956 Fine Arts Institute. Islam introduced modernism to the then East Pakistan6 and, in testament, the school still functions as a school, with its external verandah circulation and louvred ventilating classrooms. The rallying extends to getting around with cars sporting dodgem bumpers. Travelling 10 kilometres takes an hour, a million beeps and some financial socialising out the windows. Public transport may be working hard to keep pace with the kinetic city but it starts at the back of the grid, as the panelwork to the buses visibly collage. Getting to where we want to go takes some effort. An above-ground subway system has been started but not finished and the folk enticingly riding on top of trains typically aren’t off looking for architecture. There’s the three-wheeled rickshaw option, of course: formerly pedalled but, in recent months, souped-up with the allowance of car batteries to the back axle. Even so, manoeuvring further than nearby takes more than any rider is up for. So, as we head out for lunch with architect Marina Tabassum and then beep beep beep out further to her extraordinary Bait Ur Rouf Jame Mosque in Dhaka’s northern expansion, we learn that having everything close helps. Neighbourhoods remain important in megacities. Marina Tabassum Architects’ Bait Ur Rouf Jame Mosque. Image:  Jeremy Smith The mosque deserves the full medley and gently uplifts as all great architecture does, be it for the community or off-the-street visitors like us. Marina Tabassum Architects is, of course, internationally renowned for its architectural stand against globalised buildings that are out of place and context, notably winning an Aga Khan Award for Architecture in 2016 and being selected to undertake the 2025 Serpentine Pavilion in London. With the site at 13 degrees to the axis of qibla in Mecca, Tabassum sits the mosque on a five-step plinth with a squared, ventilating brick jali and a circular ceilinged prayer space rotated off centre. In a lesson to building only what you need, the spaces between remain unroofed and the perimeter daylight illumination provides a diminished and equalling light to the prayer space. It needs no explanation: look up and there are constellations in the sky; look outwards and find community; look to the mihrab notching the outside wall and orientate to Mecca. Tabassum’s dive is splashless, for the mosque has self-navigated being enveloped by the city. The entry pond may have gone and the mihrab now reveals buildings rather than fields but the light still shines the way. Four hundred people take prayer several times a day within the inner circle, and the weekend Friday crowd spreads outwards to the borders and plinth. We are two days in at this point and our not-getting-lost-practice is going well. We meet architect Salauddin Ahmed whose Atelier Robin Architects studio and gallery in a former tannery building is so hidden away that it feels both lost and right at home. It’s surrounded by the latticing roofs of informal settlements and, remarkably, feels quiet and yet, genuinely, part of the city. No mean feat in a city, “living”, as Ahmed puts it, “as if this is the last day on earth”. Noise is life in Dhaka; Ahmed’s windows are open and the river is flowing. We talk the same language of architecture understanding existing context and needing to accommodate change in shorter and shorter time frames. Where I say “participate”, Ahmed terms “navigate” and without any sense of overseeing for there is just so much life in Dhaka. We mean the same thing and get there from very different landscapes. The next morning, we go where transport can’t. Old Dhaka’s alleyways require some extra eyes, so Ahmed calls in his friend, photographer Khademul Insan, who has lived this labyrinth. This is the densest part of Dhaka and there’s a lot in the air. “Wear this,” says Ahmed, passing a mask. “Otherwise, you’ll cough for four weeks.” It is deep. There’s so much WiFi that it strands like some kind of underworld sun-shading. Our service provider isn’t expecting this kind of roaming and we have no connection. If our collective Kiwi wayfinding skills might have fluked a way in, we certainly need leading out. As the lanes narrow, the industry broadens into some kind of Mad Max circular economy where everything of anything has value and the fires that keep these people afloat run continuously. Mercifully, it’s not raining or there’d be a different type of river afoot. Fifteen kilometres and all day later, we’ve walked to search for culturally significant mosques, houses, courtyards and schools. Some we locate; others, there’s just no finding. Maybe they are there, maybe they aren’t. Occasionally, there are scripts cautioning against graffiti or carving a name into the stonework at the risk of imprisonment, but there are few clues to any architectural history. In the pinch, buildings jostle to just about every possible place a building might go: on top, under, in front, behind. They infill courtyards, hang over laneways, squeeze into gaps, even penalising what’s left of a football field. Every seat is taken, literally. Whenever we find public space off the street, there are couples dating. There’s a lot of romance in 25 million. Eventually, we exit and finally see a river. I remember the swimming lessons are strictly metaphoric and look but don’t touch. You don’t need to get wet to learn how to swim. As Ahmed guides, and he speaks with Ashraf, Tabassum, Insan and experience to what we must remember in densifying our own cities. “I belong to one of the last generations that truly understand what it means to have neighbours.”7 Context counts no matter the size. Our rivers are not yet streams. REFERENCES 1 Kazi Khaleed Ashraf, ‘Note from the Director General: Land, Water and Settlements’. bengal.institute/about Accessed 29.12.2024. 2 Ashley Westerman, 2019, “Should rivers have same legal rights as humans? A growing number of voices say yes”, National Public Radio. npr.org/2019/08/03/740604142 3 August 2019. 3 Kazi Khaleed Ashraf, ‘Note from the Director General: Land, Water and Settlements’. bengal.institute/about Accessed 29.12.2024. 4 Kazi Khaleed Ashraf, ‘Wet Narratives: Architecture Must Recognise that the Future is Fluid’ in The Mother Tongue of Architecture: Selected writings of Kazi Khaleed Ashraf. ORO Editions and Bengal Institute for Architecture, Landscape and Settlements, China: p. 251. 5 Ibid. 6 Adnan Morshed, 2017, ‘Modernism as Postnationalist Politics: Muzharul Islam’s Faculty ofFine Arts (1953–1956)’, Journal of the Society of Architectural Historians, 2017. 7 Salauddin Ahmed, 2024, “Design must not be a superimposed idea, but a logical one”, The Daily Star, Dhaka, 25 December 2024.

An SNL Icon Retires Fans of Saturday Night Live have long wondered: What happens behind the scenes of the sketch-comedy show? In 2024, we got a glimpse with Jason Reitman’s Saturday Night, which showcased just how stressful producing weekly live sketch comedy can be.  After 50 years of constructing Saturday Night Live’s sets for their various sketches, Stephen “Demo” DeMaria is retiring at age 87. And while DeMaria likely felt stressed out at times leading a team of carpenters for such a large-scale production on a time crunch, he says, “I didn’t have a bored day in my life. Never.”  “According to the report, DeMaria’s schedule at the start of each new fall season included starting his Thursdays at 1 a.m., receiving the week’s set design sketches by 2 a.m., and then splitting the work among five teams of approximately 50 total carpenters.”  Read more at Entertainment Weekly American Idol Crowns Its Latest Winner One of the most intriguing aspects of the early 2000s era of reality TV was fan voting. I remember crowding around the TV with my mom and sisters, watching The Voice, and pulling out our phones every chance we could vote for our favorite contestants.  American Idol has stood the test of time, as it has been producing stars since 2002. Season 23 of American Idol declared its new champion, 27-year-old Jamal Roberts, on May 18. The physical education teacher from Mississippi has shined all season, landing him in the final three, alongside John Foster and Breanna Nix.  “The crooner, who excelled across all the genres, is the second Black male artist to win the competition after Ruben Studdard took the title during the show’s second season in 2003.” Read more at CNN Sesame Street Gets a New Home Join our mailing list Get the best of Den of Geek delivered right to your inbox! Elmo’s home is now on Netflix. Following Warner Bros. Discovery’s decision not to renew Sesame Street on HBO Max, Netflix secured a deal with Sesame Workshop to keep the educational children’s show alive.  Notably, when the show was premiering on HBO Max, the streaming service was the only way to watch new episodes. In the wake of budget cuts to public broadcast, which resulted in countless layoffs and furloughs, Netflix is partnering up with PBS to release episodes to public broadcast the same day that they premiere on Netflix.  PBS, where Sesame Street aired originally, has provided accessible educational programming for children in low-income households for over 55 years. Sesame Workshop CEO, Sherri Westin, said in a statement that Netflix will showcase Sesame Street to a global audience, and thanks to this unique public-private deal, new episodes will be accessible in the U.S. for free through public TV.  “The deal with Netflix and PBS not only provides much needed financial stability for the non-profit (it is slated to host its annual fundraiser next week), but also provides expanded access to the program for free, an extremely unusual arrangement for Netflix.”  Read more at The Hollywood Reporter Sebastián Lelio Makes Waves at the Cannes Film Festival Lelio spoke with Deadline at the festival following the premiere of his newest project, The Wave (La Ola)— a movie musical that surrounds the Chilean feminist wave in 2018. 2018 marked a year of mass protests, strikes and civil unrest in Chile, mostly carried out by university and high school students in response to sexism and violence against women in educational institutions. This movie comes at a culturally significant time, as there has been an increase in student-led protests.  You might be thinking: Why is this a musical? After the mixed-to-negative response to Emilia Pérez, a movie musical that is also in Spanish, viewers may be weary to give this new movie musical a chance.  In his interview with Deadline, Lelio makes it clear that the use of music and performance in this movie are intentional. He described the musical element of the movie as “more of depiction of political cacophony.”  “Daniela López stars in the film – which debuted in Cannes Premiere – as a music student who joins the cause, haunted by an incident with her voice teacher’s assistant. She is joined in the cast by a raft of young Chilean acting talents including Paulina Cortés, Lola Bravo and Avril Aurora.”  Read more at Deadline  Latest Kristen Stewart Project Gets a Streaming Release Date Even if you love Kristen Stewart, you might not have appreciated her starring role as a weather buoy in the 2024 post-apocalyptic romance, Love Me. If you left the theater with mixed feelings in 2024, or just overall confusion, you’ll be happy to know that on June 16 the film will be available for streaming on Paramount+ with Showtime.  “The movie, which follows a buoy and a satellite who meet online long after human extinction (yes, you read that right), will be available on Paramount+ with Showtime via Bleecker Street’s continuous partnership with the service. During their journey together, Me/Deja (Stewart) and Iam/Liam (Yeun) discover what life on earth was like for humans and in the process find out who they are, and what it means to love and live (and, presumably, laugh).”  Read more at MovieWeb

Star Wars: Heritage Pack $30 (was $60) See at Amazon See at GameStop If you love Star Wars and own a Nintendo Switch, you'll want to check out Amazon's deal on Star Wars: Heritage Pack. This rarely discounted collection of Star Wars games is on sale for $30 at Amazon and GameStop. Star Wars: Heritage Pack comes with seven classic Star Wars games, including the Jedi Knight and Knights of the Old Republic series. The $30 discount matches the best deal yet, and we don't expect it to remain in stock at Amazon for very long. Star Wars: Heritage Pack $30 (was $60) The physical edition of Star Wars: Heritage Pack typically sells for $60. Oddly enough, the digital version of the Star Wars: Heritage Pack used to sell for $80, but it has actually been removed from sale. In comparison, the physical edition was already a bargain at full price, but now you're getting each game for just over $4 each--which is far lower than the price of buying the games separately from the eShop.Star Wars: Heritage collection pulls together seven classic Star Wars games:Star Wars Jedi Knight: Jedi AcademyStar Wars Jedi Knight II: Jedi OutcastStar Wars Episode I RacerStar Wars Republic CommandoStar Wars: The Force UnleashedKnights of the Old Republic (Downloadable)Knights of the Old Republic 2 (Downloadable) See at Amazon See at GameStop While the first five games are playable directly off the Switch cartridge, both Knights of the Old Republic are digital versions that need to be downloaded from the eShop--so make sure you have a good internet connection and a bit of space on your microSD card. Combined, the two KOTOR games clock in just shy of 30GB.More Gaming, Tech, and Entertainment Deals & Preorders Highly Limited Super Mario Bros. 30th Anniversary 4K Blu-Ray In Stock At Amazon Lego Monkey Palace Board Game Is Over 50% Off At Amazon Right Now 8BitDo Reveals Leverless Arcade Controllers For PC, Switch, And Xbox + Show More More Gaming, Tech, and Entertainment Deals & Preorders Links (5) Locke & Key Keyhouse Comic Compendium Gets Massive Limited-Time Discount Lego's Awesome New Mario Kart Display Set Is Available Now First Book In Joe Abercrombie's New Fantasy Series Is Finally Here Amazon Restocks Lego Transformers Optimus Prime, Offers Big Discount PSA: Lego Bowser's Muscle Car Is Retiring - Save Big On 3 Mario Sets Before They're Gone

TRS-80, Commodore 64. Early PCs have laughable specifications by today’s standards, but they inspired a lot of creativity. Take Jacob Anderson, owner of Beyond Ordinary Software, for example. He started programming a Commodore 64 as a tween by building character management tools for his Dungeons and Dragons game. The Commodore 64 was an 8-bit machine with the Basic programming language built in. “I was 11 years old and very isolated in a small town, so I didn't really have any exposure to the outside world and everything that was happening with the whole personal computer revolution,” says Anderson. “My dad was the janitor at the middle school, so I helped him clean. One evening, he sat me down in a math classroom that had a Commodore 64-style environment, so I started playing Artillery Duel. I noticed a button on the keyboard called, ‘Run Stop,’ and if you hit that key, the program stops executing and becomes a terminal. I hit that key by accident and typed “list” and I saw all the source code. I instinctively understood everything.” His uncle subsequently helped his family buy a Commodore 64 and peripherals for Anderson, including a dot matrix printer. He became obsessed, spending nearly all his time programming. However, in high school, his progress slowed as he discovered girls and did the things high school kids do. When he went to college on a US Navy ROTC program scholarship at Worcester Polytechnic Institute (WPI), he discovered the program actually ran at The College of the Holy Cross in the evenings, which conflicted with his computer science schedule. Anderson chose to give up his three-year Navy scholarship to pursue a dual major in nuclear engineering and computer science. Since he had to figure out a way to pay for school, he got into the Science and Engineering Research Semester (SERS) program at Los Alamos National Laboratory’s Applied Theoretical Physics Division, which develops novel applications of theoretical physics.  Related:At the time, Los Alamos was retiring its punch card mainframes and adopting modern software development practices. That was significant because at the time, the legacy software had been written in very old Monte Carlo N-Particle Transport Code (MCNP).  When Anderson arrived for the SERS program, his advisor was John Hendricks, a Ph.D. nuclear engineer from MIT. Hendricks had Anderson running MCNP test problems to validate the physics that the problems were testing.  “I took the SERS program to complete my major qualifying project (MQP) at WPI, which was required for graduation. However, I felt that running test problems was a waste of time, so I voiced my concerns to my WPI advisor, John Mayer, and later to John Hendricks, who didn't appreciate my attitude,” says Anderson. “As a result, I planned to leave the SERS program and return to WPI to work on a different MQP.” Related:However, before Anderson could leave, Ken Van Riper, a Ph.D. astrophysicist from Cornell, met with him.  “[Ken] appreciated my perspective and offered me a project he was working on. I proposed developing a full GUI for it, and he let me take the lead. I stayed in the SERS program and completed the project, which became MUD—MCNP User Demonstration,” says Anderson. “MUD was a 3D graphics-based problem setup tool that could create MCNP input files, run MCNP and visualize the output as particle tracks. Nobody had previously developed a complete package with a simple ‘click the button’ approach. After I graduated from WPI, [Los Alamos] hired me as staff.” Next, he went to work at Bolt, Beranek and Newman (BBN), where he found himself working for the Department of Defense (DoD) again and PRAJA, a dot-com immersive experience company. It focused on 3D visualization tracking of people in complex environments. While at PRAJA, he was the project lead on FOX NFL GameTracker 2000 and PRAJA Football 99. After that, he founded Beyond Ordinary Consulting alongside corporate roles as President of AccessQuery, a web-based job search engine, and XPLive, a SaaS company. He also served as managing partner and later managing director of Totally Evil Entertainment. Related:Important Lesson CIOs Can Learn Vicariously One thing Anderson has learned along the way is that military personnel can benefit the tech industry. “Military personnel are often highly trained, but they're focused on a very unique niche, and they own that entire niche. Whatever their operational job was, they own it. And that’s somewhat unique, because in the [civilian world], most people take a job for a little while, and then they bounce. They're very scattered when it comes to their career choices,” says Anderson. “When you deal with technical people you want them well versed in their niche job. And that's where the DoD comes in very handy, because the people who get that role are going to know it inside, out and backwards. That’s one of the reasons why I wanted to hire DoD people.” Those who worked for the DoD are very regimented because they must adhere to certain policies and rules. “Military personnel understand the playing field and limitations. They’re good at limiting themselves, and they also understand large-scale systems on a worldwide scale,” says Anderson. “A defense department in any country is enormous, much larger than entities in the private sector. They know how to compartmentalize and manage complex systems. Most people have a really hard time compartmentalizing at a world scale.” However, he says cultural IQ is the most important thing CIOs and other organizational leaders must understand and use to their advantage.“Because the DoD is world scale, you get experience with different cultures, different people from different parts of the world. As a result, you must learn to understand individuals from their cultural point of view. Otherwise, you’re just going to be frustrated all the time,” says Anderson. “The military is the same. It’s important to understand the nuances and respect them so you can engage people more effectively. The military personnel who aren’t good at that wash out early. The ones that are really good at it rise.” 

Tom Cruise hopes to be making movies “into [his] 100s.”The 62-year-old actor has no plans of slowing down, following the release of Mission: Impossible – The Final Reckoning, and Cruise has revealed that he doesn't have any intention of ever retiring from the film business.The actor, who previously said he wanted to keep making films into his 80s, like his inspiration Harrison Ford, told The Hollywood Reporter: “I’m going to make them into my 100s.”"I will never stop. I will never stop doing action, I will never stop doing drama, comedy films — I’m excited.”Mission: Impossible - The Final ReckoningParamountloading...Cruise made his film debut in the 1981 romantic drama Endless Love, before he enjoyed a career breakthrough in 1986’s Top Gun.The actor has starred in the Mission: Impossible film franchise since 1996, and remains passionate about making movies.Recalling his “exceptional” experience of making the films, Cruise said: “There’s been so many levels of reward with the filmmakers that I’ve collaborated with, the crews, the people, the cultures that we’ve worked in. Everything that I’ve learned and continue to learn about storytelling, about life, about leadership, about character and every aspect of filmmaking.”“It’s been exceptional, it really is exceptional. I feel very fortunate to be able to make the films that I make, and I love it, I just making movies.”Cruise previously revealed that he has to be fueled with energy before he does his stunts, explaining that he’ll eat “almost a dozen” eggs with bacon and sausages and down several cups of coffee before the cameras start rolling.Speaking to People magazine, Cruise shared: “I actually eat a massive breakfast ... The amount of energy it takes - I train so hard for that wing-walking.”"I’ll eat, like, sausage and almost a dozen eggs and bacon and toast and coffee and fluids. Oh, I’m eating! Picture: It’s cold up there. We’re at high altitude. My body is burning a lot.”Mission: Impossible — The Final Reckoning opens in theaters this weekend.Get our free mobile app10 Movies That Were Supposed to End Their FranchisesThese films were supposed to be the end of the road for their franchises. Then plans changed.Gallery Credit: Emma StefanskyCategories: Movie News