Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more When DeepSeek released its R1 model this January, it wasn’t just another AI announcement. It was a watershed moment that sent shockwaves through the tech industry, forcing industry leaders to reconsider their fundamental approaches to AI development. What makes DeepSeek’s accomplishment remarkable isn’t that the company developed novel capabilities; rather, it was how it achieved comparable results to those delivered by tech heavyweights at a fraction of the cost. In reality, DeepSeek didn’t do anything that hadn’t been done before; its innovation stemmed from pursuing different priorities. As a result, we are now experiencing rapid-fire development along two parallel tracks: efficiency and compute.  As DeepSeek prepares to release its R2 model, and as it concurrently faces the potential of even greater chip restrictions from the U.S., it’s important to look at how it captured so much attention. Engineering around constraints DeepSeek’s arrival, as sudden and dramatic as it was, captivated us all because it showcased the capacity for innovation to thrive even under significant constraints. Faced with U.S. export controls limiting access to cutting-edge AI chips, DeepSeek was forced to find alternative pathways to AI advancement. While U.S. companies pursued performance gains through more powerful hardware, bigger models and better data, DeepSeek focused on optimizing what was available. It implemented known ideas with remarkable execution — and there is novelty in executing what’s known and doing it well. This efficiency-first mindset yielded incredibly impressive results. DeepSeek’s R1 model reportedly matches OpenAI’s capabilities at just 5 to 10% of the operating cost. According to reports, the final training run for DeepSeek’s V3 predecessor cost a mere $6 million — which was described by former Tesla AI scientist Andrej Karpathy as “a joke of a budget” compared to the tens or hundreds of millions spent by U.S. competitors. More strikingly, while OpenAI reportedly spent $500 million training its recent “Orion” model, DeepSeek achieved superior benchmark results for just $5.6 million — less than 1.2% of OpenAI’s investment. If you get starry eyed believing these incredible results were achieved even as DeepSeek was at a severe disadvantage based on its inability to access advanced AI chips, I hate to tell you, but that narrative isn’t entirely accurate (even though it makes a good story). Initial U.S. export controls focused primarily on compute capabilities, not on memory and networking — two crucial components for AI development. That means that the chips DeepSeek had access to were not poor quality chips; their networking and memory capabilities allowed DeepSeek to parallelize operations across many units, a key strategy for running their large model efficiently. This, combined with China’s national push toward controlling the entire vertical stack of AI infrastructure, resulted in accelerated innovation that many Western observers didn’t anticipate. DeepSeek’s advancements were an inevitable part of AI development, but they brought known advancements forward a few years earlier than would have been possible otherwise, and that’s pretty amazing. Pragmatism over process Beyond hardware optimization, DeepSeek’s approach to training data represents another departure from conventional Western practices. Rather than relying solely on web-scraped content, DeepSeek reportedly leveraged significant amounts of synthetic data and outputs from other proprietary models. This is a classic example of model distillation, or the ability to learn from really powerful models. Such an approach, however, raises questions about data privacy and governance that might concern Western enterprise customers. Still, it underscores DeepSeek’s overall pragmatic focus on results over process. The effective use of synthetic data is a key differentiator. Synthetic data can be very effective when it comes to training large models, but you have to be careful; some model architectures handle synthetic data better than others. For instance, transformer-based models with mixture of experts (MoE) architectures like DeepSeek’s tend to be more robust when incorporating synthetic data, while more traditional dense architectures like those used in early Llama models can experience performance degradation or even “model collapse” when trained on too much synthetic content. This architectural sensitivity matters because synthetic data introduces different patterns and distributions compared to real-world data. When a model architecture doesn’t handle synthetic data well, it may learn shortcuts or biases present in the synthetic data generation process rather than generalizable knowledge. This can lead to reduced performance on real-world tasks, increased hallucinations or brittleness when facing novel situations.  Still, DeepSeek’s engineering teams reportedly designed their model architecture specifically with synthetic data integration in mind from the earliest planning stages. This allowed the company to leverage the cost benefits of synthetic data without sacrificing performance. Market reverberations Why does all of this matter? Stock market aside, DeepSeek’s emergence has triggered substantive strategic shifts among industry leaders. Case in point: OpenAI. Sam Altman recently announced plans to release the company’s first “open-weight” language model since 2019. This is a pretty notable pivot for a company that built its business on proprietary systems. It seems DeepSeek’s rise, on top of Llama’s success, has hit OpenAI’s leader hard. Just a month after DeepSeek arrived on the scene, Altman admitted that OpenAI had been “on the wrong side of history” regarding open-source AI.  With OpenAI reportedly spending $7 to 8 billion annually on operations, the economic pressure from efficient alternatives like DeepSeek has become impossible to ignore. As AI scholar Kai-Fu Lee bluntly put it: “You’re spending $7 billion or $8 billion a year, making a massive loss, and here you have a competitor coming in with an open-source model that’s for free.” This necessitates change. This economic reality prompted OpenAI to pursue a massive $40 billion funding round that valued the company at an unprecedented $300 billion. But even with a war chest of funds at its disposal, the fundamental challenge remains: OpenAI’s approach is dramatically more resource-intensive than DeepSeek’s. Beyond model training Another significant trend accelerated by DeepSeek is the shift toward “test-time compute” (TTC). As major AI labs have now trained their models on much of the available public data on the internet, data scarcity is slowing further improvements in pre-training. To get around this, DeepSeek announced a collaboration with Tsinghua University to enable “self-principled critique tuning” (SPCT). This approach trains AI to develop its own rules for judging content and then uses those rules to provide detailed critiques. The system includes a built-in “judge” that evaluates the AI’s answers in real-time, comparing responses against core rules and quality standards. The development is part of a movement towards autonomous self-evaluation and improvement in AI systems in which models use inference time to improve results, rather than simply making models larger during training. DeepSeek calls its system “DeepSeek-GRM” (generalist reward modeling). But, as with its model distillation approach, this could be considered a mix of promise and risk. For example, if the AI develops its own judging criteria, there’s a risk those principles diverge from human values, ethics or context. The rules could end up being overly rigid or biased, optimizing for style over substance, and/or reinforce incorrect assumptions or hallucinations. Additionally, without a human in the loop, issues could arise if the “judge” is flawed or misaligned. It’s a kind of AI talking to itself, without robust external grounding. On top of this, users and developers may not understand why the AI reached a certain conclusion — which feeds into a bigger concern: Should an AI be allowed to decide what is “good” or “correct” based solely on its own logic? These risks shouldn’t be discounted. At the same time, this approach is gaining traction, as again DeepSeek builds on the body of work of others (think OpenAI’s “critique and revise” methods, Anthropic’s constitutional AI or research on self-rewarding agents) to create what is likely the first full-stack application of SPCT in a commercial effort. This could mark a powerful shift in AI autonomy, but there still is a need for rigorous auditing, transparency and safeguards. It’s not just about models getting smarter, but that they remain aligned, interpretable, and trustworthy as they begin critiquing themselves without human guardrails. Moving into the future So, taking all of this into account, the rise of DeepSeek signals a broader shift in the AI industry toward parallel innovation tracks. While companies continue building more powerful compute clusters for next-generation capabilities, there will also be intense focus on finding efficiency gains through software engineering and model architecture improvements to offset the challenges of AI energy consumption, which far outpaces power generation capacity.  Companies are taking note. Microsoft, for example, has halted data center development in multiple regions globally, recalibrating toward a more distributed, efficient infrastructure approach. While still planning to invest approximately $80 billion in AI infrastructure this fiscal year, the company is reallocating resources in response to the efficiency gains DeepSeek introduced to the market. Meta has also responded, With so much movement in such a short time, it becomes somewhat ironic that the U.S. sanctions designed to maintain American AI dominance may have instead accelerated the very innovation they sought to contain. By constraining access to materials, DeepSeek was forced to blaze a new trail. Moving forward, as the industry continues to evolve globally, adaptability for all players will be key. Policies, people and market reactions will continue to shift the ground rules — whether it’s eliminating the AI diffusion rule, a new ban on technology purchases or something else entirely. It’s what we learn from one another and how we respond that will be worth watching. Jae Lee is CEO and co-founder of TwelveLabs. Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here. An error occured.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. The Trump administration has shut down more than 100 climate studies The Trump administration has terminated National Science Foundation grants for more than 100 research projects related to climate change, according to an MIT Technology Review analysis of a database that tracks such cuts. The move will cut off what’s likely to amount to tens of millions of dollars for studies that were previously approved and, in most cases, already in the works. Many believe the administration’s broader motivation is to undermine the power of the university system and prevent research findings that cut against its politics. Read the full story. —James Temple This architect wants to build cities out of lava Arnhildur Pálmadóttir is an architect with an extraordinary mission: to harness molten lava and build cities out of it.Pálmadóttir believes the lava that flows from a single eruption could yield enough building material to lay the foundations of an entire city. She has been researching this possibility for more than five years as part of a project she calls Lavaforming. Together with her son and colleague Arnar Skarphéðinsson, she has identified three potential techniques that could change how future homes are designed and built from repurposed lava. Read the full story.—Elissaveta M. Brandon This story is from the most recent edition of our print magazine, which is all about how technology is changing creativity. Subscribe now to read it and to receive future print copies once they land. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 America is failing to win the tech race against ChinaIn fields as diverse as drones and energy. (WSJ $)+ Humanoid robots is an area of particular interest. (Bloomberg $)+ China has accused the US of violating the pair’s trade truce. (FT $) 2 Who is really in charge of DOGE?According to a fired staffer, it wasn’t Elon Musk. (Wired $)+ DOGE’s tech takeover threatens the safety and stability of our critical data. (MIT Technology Review) 3 Brazilians will soon be able to sell their digital dataIt’s the first time citizens will be able to monetize their digital footprint. (Rest of World) 4 The Trump administration’s anti-vaccine stance is stoking fear among scientistsIt’s slashing funding for mRNA trials, and experts are afraid to speak out. (The Atlantic $)+ This annual shot might protect against HIV infections. (MIT Technology Review) 5 Tech companies want us to spend longer talking to chatbotsThose conversations can easily veer into dangerous territory. (WP $)+ How we use AI in the future is up to us. (New Yorker $)+ This benchmark used Reddit’s AITA to test how much AI models suck up to us. (MIT Technology Review) 6 Tiktok’s mental health videos are rife with misinformationA lot of the advice is useless at best, and harmful at worst. (The Guardian) 7 Lawyers are hooked on ChatGPTEven though it’s inherently unreliable. (The Verge)+ Yet another lawyer has been found referencing nonexistent citations. (The Guardian)+ How AI is introducing errors into courtrooms. (MIT Technology Review) 8 How chefs are using generative AI They’re starting to experiment with using it to create innovative new dishes. (NYT $)+ Watch this robot cook shrimp and clean autonomously. (MIT Technology Review) 9 The influencer suing her rival has dropped her lawsuitThe legal fight over ownership of a basic aesthetic has come to an end. (NBC News) 10 Roblox’s new game has sparked a digital fruit underground marketAnd players are already spending millions of dollars every week. (Bloomberg $) Quote of the day “We can’t substitute complex thinking with machines. AI can’t replace our curiosity, creativity or emotional intelligence.” —Mateusz Demski, a journalist in Poland, tells the Guardian about how his radio station employer laid him off, only to later launch shows fronted by AI-generated presenters. One more thing ​​Adventures in the genetic time machineAn ancient-DNA revolution is turning the high-speed equipment used to study the DNA of living things on to specimens from the past.The technology is being used to create genetic maps of saber-toothed cats, cave bears, and thousands of ancient humans, including Vikings, Polynesian navigators, and numerous Neanderthals. The total number of ancient humans studied is more than 10,000 and rising fast.The old genes have already revealed remarkable stories of human migrations around the globe.But researchers are hoping ancient DNA will be more than a telescope on the past—they hope it will have concrete practical use in the present. Read the full story.  —Antonio Regalado We can still have nice things A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.) + The ancient Persians managed to keep cool using an innovative breeze-catching technique that could still be useful today.+ Knowledge is power—here’s a helpful list of hoaxes to be aware of.+ How said it: Homer Simpson or Pete Hegseth?+ I had no idea London has so many cat statues.

Many don’t like it, but (almost) everybody agrees it’s the future.“Vibe Coding” is everywhere. Tools and game engines are implementing AI-assisted coding, vibe coding interest skyrocketed on Google search, on social media, everybody claims to build apps and games in minutes, while the comment section gets flooded with angry developers calling out the pile of garbage code that will never be shipped.A screenshot from Andrej Karpathy with the original “definition” of Vibe CodingBUT, how do professionals feel about it?This is what I will cover in this article. We will look at:How people react to the term vibe coding,How their attitude differs based on who they are and their professional experienceThe reason for their stance towards “vibe coding” (with direct quotes)How they feel about the impact “vibe coding” will have in the next 5 yearsIt all started with this survey on LinkedIn. I have always been curious about how technology can support creatives and I believe that the only way to get a deeper understanding is to go beyond buzzwords and ask the hard questions. That’s why for over a year, I’ve been conducting weekly interviews with both the founders developing these tools and the creatives utilising them. If you want to learn their journeys, I’ve gathered their insights and experiences on my blog called XR AI Spotlight.Driven by the same motives and curious about people’s feelings about “vibe coding”, I asked a simple question: How does the term “Vibe Coding” make you feel?Original LinkedIn poll by Gabriele RomagnoliIn just three days, the poll collected 139 votes and it was clear that most responders didn’t have a good “vibe” about it. The remaining half was equally split between excitement and no specific feeling.But who are these people? What is their professional background? Why did they respond the way they did?Curious, I created a more comprehensive survey and sent it to everyone who voted on the LinkedIn poll.The survey had four questions:Select what describes you best: developers, creative, non-creative professionalHow many years of experience do you have? 1–5, 6–10, 11–15 or 16+Explain why the term “vibe coding” makes you feel excited/neutral/dismissive?Do you think “vibe coding” will become more relevant in the next 5 years?: It’s the future, only in niche use cases, unlikely, no idea)In a few days, I collected 62 replies and started digging into the findings, and that’s when I finally started understanding who took part in the initial poll.The audienceWhen characterising the audience, I refrained from adding too many options because I just wanted to understand:If the people responding were the ones making stuffWhat percentage of makers were creatives and what developersI was happy to see that only 8% of respondents were non-creative professionals and the remaining 92% were actual makers who have more “skin in the game“ with almost a 50/50 split between creatives and developers. There was also a good spread in the degree of professional experience of the respondents, but that’s where things started to get surprising.Respondents are mostly “makers” and show a good variety in professional experienceWhen creating 2 groups with people who have more or less than 10 years of experience, it is clear that less experienced professionals skew more towards a neutral or negative stance than the more experienced group.Experienced professionals are more positive and open to vibe codingThis might be because senior professionals see AI as a tool to accelerate their workflows, while more juniors perceive it as a competitor or threat.I then took out the non-professional creatives and looked at the attitude of these 2 groups. Not surprisingly, fewer creatives than developers have a negative attitude towards “vibe coding” (47% for developers Vs 37% for creatives), but the percentage of creatives and developers who have a positive attitude stays almost constant. This means that creatives have a more indecisive or neutral stance than developers.Creatives have a more positive attitude to vibe coding than developersWhat are people saying about “vibe coding”?As part of the survey, everybody had the chance to add a few sentences explaining their stance. This was not a compulsory field, but to my surprise, only 3 of the 62 left it empty (thanks everybody). Before getting into the sentiment analysis, I noticed something quite interesting while filtering the data. People with a negative attitude had much more to say, and their responses were significantly longer than the other group. They wrote an average of 59 words while the others barely 37 and I think is a good indication of the emotional investment of people who want to articulate and explain their point. Let’s now look at what the different groups of people replied.😍 Patterns in Positive Responses to “Vibe Coding”Positive responders often embraced vibe coding as a way to break free from rigid programming structures and instead explore, improvise, and experiment creatively.“It puts no pressure on it being perfect or thorough.”“Pursuing the vibe, trying what works and then adapt.”“Coding can be geeky and laborious… ‘vibing’ is quite nice.”This perspective repositions code not as rigid infrastructure, but something that favors creativity and playfulness over precision.Several answers point to vibe coding as a democratizing force opening up coding to a broader audience, who want to build without going through the traditional gatekeeping of engineering culture.“For every person complaining… there are ten who are dabbling in code and programming, building stuff without permission.”“Bridges creative with technical perfectly, thus creating potential for independence.”This group often used words like “freedom,” “reframing,” and “revolution.”.😑 Patterns in Neutral Responses to “Vibe Coding”As shown in the initial LinkedIn poll, 27% of respondents expressed mixed feelings. When going through their responses, they recognised potential and were open to experimentation but they also had lingering doubts about the name, seriousness, and future usefulness.“It’s still a hype or buzzword.”“I have mixed feelings of fascination and scepticism.”“Unsure about further developments.”They were on the fence and were often enthusiastic about the capability, but wary of the framing.Neutral responders also acknowledged that complex, polished, or production-level work still requires traditional approaches and framed vibe coding as an early-stage assistant, not a full solution.“Nice tool, but not more than autocomplete on steroids.”“Helps get setup quickly… but critical thinking is still a human job.”“Great for prototyping, not enough to finalize product.”Some respondents were indifferent to the term itself, viewing it more as a label or meme than a paradigm shift. For them, it doesn’t change the substance of what’s happening.“At the end of the day they are just words. Are you able to accomplish what’s needed?”“I think it’s been around forever, just now with a new name.”These voices grounded the discussion in the terminology and I think they bring up a very important point that leads to the polarisation of a lot of the conversations around “vibe coding”.🤮 Patterns in Negative Responses to “Vibe Coding”Many respondents expressed concern that vibe coding implies a casual, unstructured approach to coding. This was often linked to fears about poor code quality, bugs, and security issues.“Feels like building a house without knowing how electricity and water systems work.”“Without fundamental knowledge… you quickly lose control over the output.”The term was also seen as dismissive or diminishing the value of skilled developers. It really rubbed people the wrong way, especially those with professional experience.“It downplays the skill and intention behind writing a functional, efficient program.”“Vibe coding implies not understanding what the AI does but still micromanaging it.”Like for “neutral” respondents, there’s a strong mistrust around how the term is used (especially on social media) where it’s seen as fueling unrealistic expectations or being pushed by non-experts.“Used to promote coding without knowledge.”“Just another overhyped term like NFTs or memecoins.”“It feels like a joke that went too far.”Ultimately, I decided to compare attitudes that are excited (positive) and accepting (neutral) of vibe coding vs. those that reject or criticise it. After all, even among people who were neutral, there was a general acceptance that vibe coding has its place. Many saw it as a useful tool for things like prototyping, creative exploration, or simply making it easier to get started. What really stood out, though, was the absence of fear that was very prominent in the “negative” group and saw vibe coding as a threat to software quality or professional identity.People in the neutral and positive groups generally see potential. They view it as useful for prototyping, creative exploration, or making coding more accessible, but they still recognise the need for structure in complex systems. In contrast, the negative group rejects the concept outright, and not just the name, but what it stands for: a more casual, less rigorous approach to coding. Their opinion is often rooted in defending software engineering as a disciplined craft… and probably their job.😍 “As long as you understand the result and the process, AI can write and fix scripts much faster than humans can.”🤮 “It’s a joke. It started as a joke… but to me doesn’t encapsulate actual AI co-engineering.”On the topic of skill and control, the neutral and positive group sees AI as a helpful assistant, assuming that a human is still guiding the process. They mention refining and reviewing as normal parts of the workflow. The negative group sees more danger, fearing that vibe coding gives a false sense of competence. They describe it as producing buggy or shallow results, often in the hands of inexperienced users.😑 “Critical thinking is still a human job… but vibe coding helps with fast results.”🤮“Vibe-Coding takes away the very features of a good developer… logical thinking and orchestration are crucial.”Culturally, the divide is clear. The positive and neutral voices often embrace vibe coding as part of a broader shift, welcoming new types of creators and perspectives. They tend to come from design or interdisciplinary backgrounds and are more comfortable with playful language. On the other hand, the negative group associates the term with hype and cringe, criticising it as disrespectful to those who’ve spent years honing their technical skills.😍“It’s about playful, relaxed creation — for the love of making something.”🤮Creating a lot of unsafe bloatware with no proper planning.”What’s the future of “Vibe Coding”?The responses to the last question were probably the most surprising to me. I was expecting that the big scepticism towards vibe coding would align with the scepticism on its future, but that was not the case. 90% of people still see “vibe coding” becoming more relevant overall or in niche use cases.Vibe coding is here to stayOut of curiosity, I also went back to see if there was any difference based on professional experience, and that’s where we see the more experienced audience being more conservative. Only 30% of more senior Vs 50% of less experienced professionals see vibe coding playing a role in niche use cases and 13 % Vs only 3% of more experienced users don’t see vibe coding becoming more relevant at all.More experienced professionals are less likely to think Vibe Coding is the futureThere are still many open questions. What is “vibe coding” really? For whom is it? What can you do with it?To answer these questions, I decided to start a new survey you can find here. If you would like to further contribute to this research, I encourage you to participate and in case you are interested, I will share the results with you as well.The more I read or learn about this, I feel “Vibe Coding” is like the “Metaverse”:Some people hate it, some people love it.Everybody means something differentIn one form or another, it is here to stay.What professionals really think about “Vibe Coding” was originally published in UX Collective on Medium, where people are continuing the conversation by highlighting and responding to this story.

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]

Attorneys Karen Dunn (left) and Jeannie Rhee (right), along with their fellow partners, Bill Isaacson and Jessica Phillips, have resigned from Paul Weiss to start their own firm. Kevin Lamarque/REUTERS 2025-05-24T01:27:10Z Save Saved Read in app This story is available exclusively to Business Insider subscribers. Become an Insider and start reading now. Have an account? Four top Paul Weiss partners announced Friday that they've resigned to start their own firm. Paul Weiss is one of the firms that made a deal with Trump to reverse an EO against the firm. The Big Law firms that have negotiated with Trump have faced criticism from others in the profession. Four partners at Paul Weiss announced Friday that they are leaving the white-shoe firm, which two months ago struck a deal with the Trump administration.Karen Dunn, a star litigator who has helped Democratic candidates prepare for presidential debates, her longtime partners Bill Isaacson and Jessica Phillips, and the former prosecutor Jeannie Rhee said in an email addressed to "partners and friends" that they are starting their own firm.The high-profile departures underscore the ongoing turmoil at Big Law firms surrounding the firms' handling of punitive executive actions from President Donald Trump's administration. The departing lawyers did not give a reason for leaving in their statement.Several major firms — including Perkins Coie and Jenner & Block — chose to challenge the legality of the orders in court, and have so far been successful after two judges declared two different orders unconstitutional. Other firms, including Paul Weiss, chose to make deals with the administration, prompting concern among associates and partners over their willingness to cooperate rather than fight.The new firm's name isn't clear. Since April, several domain names containing Dunn's name and those of other lawyers have been registered anonymously. None of the websites contains any details, and it's not clear who registered them.The lawyers have represented prominent clients like Google, Amazon, and Apple over the years. Isaacson is one of the country's top antitrust litigators. Antitrust issues have been a focus for both former President Joe Biden and Trump, who have criticized the power of large tech companies. Rhee managed the firm's Washington, DC, office, and Dunn co-chaired its litigation department."It has been an honor to work alongside such talented lawyers and to call so many of you our friends," their departing email said. "We hope to continue to collaborate with all of you in the years to come and are incredibly grateful for your warm and generous partnership."Paul Weiss's chair, Brad Karp, said in a statement, "We are grateful to Bill, Jeannie, Jessica, and Karen for their many contributions to the firm. We wish them well in their future endeavors."The departures come several months after the Trump administration began targeting Big Law firms with punitive executive actions. Among them was Paul Weiss, which faced an executive order that revoked the security clearances of the firm's attorneys and ordered a review of its government contracts.On March 20, Trump announced on Truth Social that he would drop the executive order against Paul Weiss after negotiating a deal that would require the firm to end any diversity, equity, and inclusion initiatives in its hiring practices and contribute $40 million of pro bono legal services to causes aligned with the administration's priorities, such as veterans affairs issues and the administration's antisemitism task force.Business Insider previously reported that the copy of the deal shared internally among Paul Weiss partners omitted language regarding DEI that was present in the president's announcement.Other firms that chose to negotiate with Trump also saw high-profile departures from partners and associates concerned with their firms' decisions not to challenge the administration.Wilkie Farr lost its longest-serving lawyer in April after Joseph Baio, a partner who'd worked there for 47 years, resigned over the firm's preemptive deal with Trump, The New York Times reported.Another firm, Skadden, Arps, Slate, Meagher & Flom, made a preemptive deal with the Trump administration in late March to avoid a similar executive order against it. The decision led to a series of public resignations from several Skadden associates, including Rachel Cohen and Brenna Frey.Cohen told Business Insider she had not been in touch with the attorneys who had resigned from Paul Weiss on Friday.

Chief Technology Officer Shyam Sankar, co-founder and president Stephen Cohen and other executives also dumped millions worth of shares.

Start SlideshowStart SlideshowImage: The Pokémon Company / KotakuRefreshing the Pokémon Center to see what new items have been added each day has become something of an obsession for me. The site adds new stock so incredibly frequently as to be constantly astonishing, and today is no different. The latest arrivals on the store are a new collection of plushies that feature Ditto in 22 new disguises. And they are adorable.Ditto is a Pokémon who is capable of taking on the size, shape and color of any other creature, and even mimicking its attacks. However, its weakness is faces, where it gives itself away with its two little round eyes and a curved-line smile. The result is the derpiest version of every Pokémon, and that gets even cuter when created in a cuddly form.Before I started working with Kotaku, I knew almost nothing about Pokémon. Five years ago, I’d started playing Pokémon GO with my then-five-year-old, and was beginning to learn the names of a few of the monsters. Now, I sit in front of a shelving unit filled with Pokémon plushies, tins, collection boxes, and more plushies. Which is to say nothing of the far larger cuddly pocket monsters around the bedroom, and the Squishmallow Gengar and Snorlax that now live in our living room. Last week I couldn’t stop myself from buying the sleeping Snorlax with a sleeping Pikachu on its belly. Today a finger-puppet Munchlax arrived in the mail. I’m 47 years old. I’m now staring at this wall of Ditto cuddlies, between 6 and 9 inches tall, and doing all in my power not to buy the lot. There’s a Snorlax one! There’s a Leafeon! Oh my goodness, there’s a Mimikyu. This isn’t fair. Let alone a Gengar and a Magikarp Ditto! And Dragonite! And Mew!This isn’t the first time the site has sold Ditto plushies. Mimikyu and Mareep Ditto previously appeared in 2020, along with other forms that aren’t included in the new collection: Misdreavus, Furret, Poipole, Lapras and Morelull. All have been sold out for years, and sadly show no sign of being restocked. Which is to say, if one of these is something you really want, it’s generally a good idea not to wait—the Pokémon Center is as peculiar and mercurial as the whole company, and there’s a good chance it’ll sell this lot and then never make them again. These Ditto plushies are undoubtedly going to be on sale at future card shows for three times the price, and you’ll kick yourself.Click on to have a look at all 22 of the new Ditto cuddlies, and be prepared to use up your entire week’s supply of “Awwww!”s.Previous SlideNext Slide2 / 23List slidesLucario DittoList slidesLucario DittoPhoto: The Pokémon Company / KotakuPrevious SlideNext Slide3 / 23List slidesDragonite DittoList slidesDragonite DittoPhoto: The Pokémon Company / KotakuPrevious SlideNext Slide4 / 23List slidesEevee DittoList slidesEevee DittoPhoto: The Pokémon Company / KotakuPrevious SlideNext Slide5 / 23List slidesPikachu DittoList slidesPikachu DittoPhoto: The Pokémon Company / KotakuPrevious SlideNext Slide6 / 23List slidesMareep DittoList slidesMareep DittoPhoto: The Pokémon Company / KotakuPrevious SlideNext Slide7 / 23List slidesFlareon DittoList slidesFlareon DittoPhoto: The Pokémon Company / KotakuPrevious SlideNext Slide8 / 23List slidesJolteon DittoList slidesJolteon DittoPhoto: The Pokémon Company / KotakuPrevious SlideNext Slide9 / 23List slidesVaporeon DittoList slidesVaporeon DittoPhoto: The Pokémon Company / KotakuPrevious SlideNext Slide10 / 23List slidesGengar DittoList slidesGengar DittoPhoto: The Pokémon Company / KotakuPrevious SlideNext Slide11 / 23List slidesJigglypuff DittoList slidesJigglypuff DittoPhoto: The Pokémon Company / KotakuPrevious SlideNext Slide12 / 23List slidesLapras DittoList slidesLapras DittoPhoto: The Pokémon Company / KotakuPrevious SlideNext Slide13 / 23List slidesEspeon DittoList slidesEspeon DittoPhoto: The Pokémon Company / KotakuPrevious SlideNext Slide14 / 23List slidesUmbreon DittoList slidesUmbreon DittoPhoto: The Pokémon Company / KotakuPrevious SlideNext Slide15 / 23List slidesMimikyu DittoList slidesMimikyu DittoPhoto: The Pokémon Company / KotakuPrevious SlideNext Slide16 / 23List slidesVulpix DittoList slidesVulpix DittoPhoto: The Pokémon Company / KotakuPrevious SlideNext Slide17 / 23List slidesLeafeon DittoList slidesLeafeon DittoPhoto: The Pokémon Company / KotakuPrevious SlideNext Slide18 / 23List slidesSylveon DittoList slidesSylveon DittoPhoto: The Pokémon Company / KotakuPrevious SlideNext Slide19 / 23List slidesGlaceon DittoList slidesGlaceon DittoPhoto: The Pokémon Company / KotakuPrevious SlideNext Slide20 / 23List slidesMew DittoList slidesMew DittoPhoto: The Pokémon Company / KotakuPrevious SlideNext Slide21 / 23List slidesSnorlax DittoList slidesSnorlax DittoPhoto: The Pokémon Company / KotakuPrevious SlideNext Slide22 / 23List slidesMagikarp DittoList slidesMagikarp DittoPhoto: The Pokémon Company / Kotaku.

This is the second in a multi-part series on creating web applications with generative AI integration. Part 1 focused on explaining the AI stack and why the application layer is the best place in the stack to be. Check it out here. Table of Contents Introduction I thought spas were supposed to be relaxing? Microservices are for Macrocompanies Ruby and Python: Two Sides of the Same Coin Recent AI Based Gems Summary Introduction It’s not often that you hear the Ruby language mentioned when discussing AI. Python, of course, is the king in this world, and for good reason. The community has coalesced around the language. Most model training is done in PyTorch or TensorFlow these days. Scikit-learn and Keras are also very popular. RAG frameworks such as LangChain and LlamaIndex cater primarily to Python. However, when it comes to building web applications with AI integration, I believe Ruby is the better language. As the co-founder of an agency dedicated to building MVPs with generative AI integration, I frequently hear potential clients complaining about two things: Applications take too long to build Developers are quoting insane prices to build custom web apps These complaints have a common source: complexity. Modern web apps have a lot more complexity in them than in the good ol’ days. But why is this? Are the benefits brought by complexity worth the cost? I thought spas were supposed to be relaxing? One big piece of the puzzle is the recent rise of single-page applications (SPAs). The most popular stack used today in building modern SPAs is MERN (MongoDB, Express.js, React.js, Node.js). The stack is popular for a few reasons: It is a JavaScript-only stack, across both front-end and back-end. Having to only code in only one language is pretty nice! SPAs can offer dynamic designs and a “smooth” user experience. Smooth here means that when some piece of data changes, only a part of the site is updated, as opposed to having to reload the whole page. Of course, if you don’t have a modern smartphone, SPAs won’t feel so smooth, as they tend to be pretty heavy. All that JavaScript starts to drag down the performance. There is a large ecosystem of libraries and developers with experience in this stack. This is pretty circular logic: is the stack popular because of the ecosystem, or is there an ecosystem because of the popularity? Either way, this point stands.React was created by Meta. Lots of money and effort has been thrown at the library, helping to polish and promote the product. Unfortunately, there are some downsides of working in the MERN stack, the most critical being the sheer complexity. Traditional web development was done using the Model-View-Controller (MVC) paradigm. In MVC, all of the logic managing a user’s session is handled in the backend, on the server. Something like fetching a user’s data was done via function calls and SQL statements in the backend. The backend then serves fully built HTML and CSS to the browser, which just has to display it. Hence the name “server”. In a SPA, this logic is handled on the user’s browser, in the frontend. SPAs have to handle UI state, application state, and sometimes even server state all in the browser. API calls have to be made to the backend to fetch user data. There is still quite a bit of logic on the backend, mainly exposing data and functionality through APIs. To illustrate the difference, let me use the analogy of a commercial kitchen. The customer will be the frontend and the kitchen will be the backend. MVCs vs. SPAs. Image generated by ChatGPT. Traditional MVC apps are like dining at a full-service restaurant. Yes, there is a lot of complexity (and yelling, if The Bear is to be believed) in the backend. But the frontend experience is simple and satisfying: all the customer has to do is pick up a fork and eat their food. SPAs are like eating at a buffet-style dining restaurant. There is still quite a bit of complexity in the kitchen. But now the customer also has to decide what food to grab, how to combine them, how to arrange them on the plate, where to put the plate when finished, etc. Andrej Karpathy had a tweet recently discussing his frustration with attempting to build web apps in 2025. It can be overwhelming for those new to the space. The reality of building web apps in 2025 is that it's a bit like assembling IKEA furniture. There's no "full-stack" product with batteries included, you have to piece together and configure many individual services:– frontend / backend (e.g. React, Next.js, APIs)– hosting…— Andrej Karpathy (@karpathy) March 27, 2025 In order to build MVPs with AI integration rapidly, our agency has decided to forgo the SPA and instead go with the traditional MVC approach. In particular, we have found Ruby on Rails (often denoted as Rails) to be the framework best suited to quickly developing and deploying quality apps with AI integration. Ruby on Rails was developed by David Heinemeier Hansson in 2004 and has long been known as a great web framework, but I would argue it has recently made leaps in its ability to incorporate AI into apps, as we will see. Django is the most popular Python web framework, and also has a more traditional pattern of development. Unfortunately, in our testing we found Django was simply not as full-featured or “batteries included” as Rails is. As a simple example, Django has no built-in background job system. Nearly all of our apps incorporate background jobs, so to not include this was disappointing. We also prefer how Rails emphasizes simplicity, with Rails 8 encouraging developers to easily self-host their apps instead of going through a provider like Heroku. They also recently released a stack of tools meant to replace external services like Redis. “But what about the smooth user experience?” you might ask. The truth is that modern Rails includes several ways of crafting SPA-like experiences without all of the heavy JavaScript. The primary tool is Hotwire, which bundles tools like Turbo and Stimulus. Turbo lets you dynamically change pieces of HTML on your webpage without writing custom JavaScript. For the times where you do need to include custom JavaScript, Stimulus is a minimal JavaScript framework that lets you do just that. Even if you want to use React, you can do so with the react-rails gem. So you can have your cake, and eat it too! SPAs are not the only reason for the increase in complexity, however. Another has to do with the advent of the microservices architecture. Microservices are for Macrocompanies Once again, we find ourselves comparing the simple past with the complexity of today. In the past, software was primarily developed as monoliths. A monolithic application means that all the different parts of your app — such as the user interface, business logic, and data handling — are developed, tested, and deployed as one single unit. The code is all typically housed in a single repo. Working with a monolith is simple and satisfying. Running a development setup for testing purposes is easy. You are working with a single database schema containing all of your tables, making queries and joins straightforward. Deployment is simple, since you just have one container to look at and modify. However, once your company scales to the size of a Google or Amazon, real problems begin to emerge. With hundreds or thousands of developers contributing simultaneously to a single codebase, coordinating changes and managing merge conflicts becomes increasingly difficult. Deployments also become more complex and risky, since even minor changes can blow up the entire application! To manage these issues, large companies began to coalesce around the microservices architecture. This is a style of programming where you design your codebase as a set of small, autonomous services. Each service owns its own codebase, data storage, and deployment pipelines. As a simple example, instead of stuffing all of your logic regarding an OpenAI client into your main app, you can move that logic into its own service. To call that service, you would then typically make REST calls, as opposed to function calls. This ups the complexity, but resolves the merge conflict and deployment issues, since each team in the organization gets to work on their own island of code. Another benefit to using microservices is that they allow for a polyglot tech stack. This means that each team can code up their service using whatever language they prefer. If one team prefers JavaScript while another likes Python, this is no issue. When we first began our agency, this idea of a polyglot stack pushed us to use a microservices architecture. Not because we had a large team, but because we each wanted to use the “best” language for each functionality. This meant: Using Ruby on Rails for web development. It’s been battle-tested in this area for decades. Using Python for the AI integration, perhaps deployed with something like FastAPI. Serious AI work requires Python, I was led to believe. Two different languages, each focused on its area of specialty. What could go wrong? Unfortunately, we found the process of development frustrating. Just setting up our dev environment was time-consuming. Having to wrangle Docker compose files and manage inter-service communication made us wish we could go back to the beauty and simplicity of the monolith. Having to make a REST call and set up the appropriate routing in FastAPI instead of making a simple function call sucked. “Surely we can’t develop AI apps in pure Ruby,” I thought. And then I gave it a try. And I’m glad I did. I found the process of developing an MVP with AI integration in Ruby very satisfying. We were able to sprint where before we were jogging. I loved the emphasis on beauty, simplicity, and developer happiness in the Ruby community. And I found the state of the AI ecosystem in Ruby to be surprisingly mature and getting better every day. If you are a Python programmer and are scared off by learning a new language like I was, let me comfort you by discussing the similarities between the Ruby and Python languages. Ruby and Python: Two Sides of the Same Coin I consider Python and Ruby to be like cousins. Both languages incorporate: High-level Interpretation: This means they abstract away a lot of the complexity of low-level programming details, such as memory management. Dynamic Typing: Neither language requires you to specify if a variable is an int, float, string, etc. The types are checked at runtime. Object-Oriented Programming: Both languages are object-oriented. Both support classes, inheritance, polymorphism, etc. Ruby is more “pure”, in the sense that literally everything is an object, whereas in Python a few things (such as if and for statements) are not objects. Readable and Concise Syntax: Both are considered easy to learn. Either is great for a first-time learner. Wide Ecosystem of Packages: Packages to do all sorts of cool things are available in both languages. In Python they are called libraries, and in Ruby they are called gems. The primary difference between the two languages lies in their philosophy and design principles. Python’s core philosophy can be described as: There should be one — and preferably only one — obvious way to do something. In theory, this should emphasize simplicity, readability, and clarity. Ruby’s philosophy can be described as: There’s always more than one way to do something. Maximize developer happiness. This was a shock to me when I switched over from Python. Check out this simple example emphasizing this philosophical difference: # A fight over philosophy: iterating over an array # Pythonic way for i in range(1, 6): print(i) # Ruby way, option 1 (1..5).each do |i| puts i end # Ruby way, option 2 for i in 1..5 puts i end # Ruby way, option 3 5.times do |i| puts i + 1 end # Ruby way, option 4 (1..5).each { |i| puts i } Another difference between the two is syntax style. Python primarily uses indentation to denote code blocks, while Ruby uses do…end or {…} blocks. Most include indentation inside Ruby blocks, but this is entirely optional. Examples of these syntactic differences can be seen in the code shown above. There are a lot of other little differences to learn. For example, in Python string interpolation is done using f-strings: f"Hello, {name}!", while in Ruby they are done using hashtags: "Hello, #{name}!". Within a few months, I think any competent Python programmer can transfer their proficiency over to Ruby. Recent AI-based Gems Despite not being in the conversation when discussing AI, Ruby has had some recent advancements in the world of gems. I will highlight some of the most impressive recent releases that we have been using in our agency to build AI apps: RubyLLM (link) — Any GitHub repo that gets more than 2k stars within a few weeks of release deserves a mention, and RubyLLM is definitely worthy. I have used many clunky implementations of LLM providers from libraries like LangChain and LlamaIndex, so using RubyLLM was like a breath of fresh air. As a simple example, let’s take a look at a tutorial demonstrating multi-turn conversations: require 'ruby_llm' # Create a model and give it instructions chat = RubyLLM.chat chat.with_instructions "You are a friendly Ruby expert who loves to help beginners." # Multi-turn conversation chat.ask "Hi! What does attr_reader do in Ruby?" # => "Ruby creates a getter method for each symbol... # Stream responses in real time chat.ask "Could you give me a short example?" do |chunk| print chunk.content end # => "Sure! # ```ruby # class Person # attr... Simply amazing. Multi-turn conversations are handled automatically for you. Streaming is a breeze. Compare this to a similar implementation in LangChain: from langchain_openai import ChatOpenAI from langchain_core.schema import SystemMessage, HumanMessage, AIMessage from langchain_core.callbacks.streaming_stdout import StreamingStdOutCallbackHandler SYSTEM_PROMPT = "You are a friendly Ruby expert who loves to help beginners." chat = ChatOpenAI(streaming=True, callbacks=[StreamingStdOutCallbackHandler()]) history = [SystemMessage(content=SYSTEM_PROMPT)] def ask(user_text: str) -> None: """Stream the answer token-by-token and keep the context in memory.""" history.append(HumanMessage(content=user_text)) # .stream yields message chunks as they arrive for chunk in chat.stream(history): print(chunk.content, end="", flush=True) print() # newline after the answer # the final chunk has the full message content history.append(AIMessage(content=chunk.content)) ask("Hi! What does attr_reader do in Ruby?") ask("Great - could you show a short example with attr_accessor?") Yikes. And it’s important to note that this is a grug implementation. Want to know how LangChain really expects you to manage memory? Check out these links, but grab a bucket first; you may get sick. Neighbors (link) — This is an excellent library to use for nearest-neighbors search in a Rails application. Very useful in a RAG setup. It integrates with Postgres, SQLite, MySQL, MariaDB, and more. It was written by Andrew Kane, the same guy who wrote the pgvector extension that allows Postgres to behave as a vector database. Async (link) — This gem had its first official release back in December 2024, and it has been making waves in the Ruby community. Async is a fiber-based framework for Ruby that runs non-blocking I/O tasks concurrently while letting you write simple, sequential code. Fibers are like mini-threads that each have their own mini call stack. While not strictly a gem for AI, it has helped us create features like web scrapers that run blazingly fast across thousands of pages. We have also used it to handle streaming of chunks from LLMs. Torch.rb (link) — If you are interested in training deep learning models, then surely you have heard of PyTorch. Well, PyTorch is built on LibTorch, which essentially has a lot of C/C++ code under the hood to perform ML operations quickly. Andrew Kane took LibTorch and made a Ruby adapter over it to create Torch.rb, essentially a Ruby version of PyTorch. Andrew Kane has been a hero in the Ruby AI world, authoring dozens of ML gems for Ruby. Summary In short: building a web application with AI integration quickly and cheaply requires a monolithic architecture. A monolith demands a monolingual application, which is necessary if your end goal is quality apps delivered with speed. Your main options are either Python or Ruby. If you go with Python, you will probably use Django for your web framework. If you go with Ruby, you will be using Ruby on Rails. At our agency, we found Django’s lack of features disappointing. Rails has impressed us with its feature set and emphasis on simplicity. We were thrilled to find almost no issues on the AI side. Of course, there are times where you will not want to use Ruby. If you are conducting research in AI or training machine learning models from scratch, then you will likely want to stick with Python. Research almost never involves building Web Applications. At most you’ll build a simple interface or dashboard in a notebook, but nothing production-ready. You’ll likely want the latest PyTorch updates to ensure your training runs quickly. You may even dive into low-level C/C++ programming to squeeze as much performance as you can out of your hardware. Maybe you’ll even try your hand at Mojo. But if your goal is to integrate the latest LLMs — either open or closed source — into web applications, then we believe Ruby to be the far superior option. Give it a shot yourselves! In part three of this series, I will dive into a fun experiment: just how simple can we make a web application with AI integration? Stay tuned.  If you’d like a custom web application with generative AI integration, visit losangelesaiapps.com The post Building AI Applications in Ruby appeared first on Towards Data Science.

Karper Building Renovation / hé! architectuur + buro kiss + EA+Save this picture!© Tim Van de Velde•Molenbeek-Saint-Jean, Belgium Architects: Year Completion year of this architecture project Year:  2024 Photographs Photographs:Tim Van de VeldeMore SpecsLess Specs Save this picture! Text description provided by the architects. The renovation involves the conversion of an industrial building into housing with a coworking space and studio on the ground floor. It is an upside-down home with the living spaces all the way upstairs to provide maximum light and privacy.Save this picture!Save this picture!Save this picture!Save this picture!Urban dense site - The plot is located in a dense urban network of workshops and industrial sites. Expanding in height is a logical step to respond to a current problem: how to densify the city, create enough housing for a growing population, and still maintain as many green areas in the city as possible. Since the eaves were much lower than those of the neighbors, we had the opportunity to use our roof as a low-cost building site and put a new building on an old building. The new facade retakes the detailing of the historical masonry of the existing facade in a more rational way, in white brick.Save this picture!3 key principles within circular building are central to this project: 1. Flexibility - The flexible and open plan allows spaces to be used in multiple ways and change function in the future. The coworking space was designed so that in a later phase it can easily be transformed into a store or showroom for the Tenue de Ville design studio. The small studios are also designed in such a way that they can be transformed into an extension of the family home at a later stage. Save this picture!Save this picture!2. Regenerative - Regenerative materials are used. These are materials with a longer cycle. *Wood, hemp, straw,...(bio-based, biodegradable, and renewable. They absorb CO2 as they grow). *Earth and loam (geo-based, they are abundant and can be reused infinitely). *Reused materials (2nd, 3rd, 4th,... hands materials). The outer shell of the new roof volume will be realized in prefabricated wooden cassettes and filled in with straw bales, taken from a nearby farm. The existing facades are insulated with lime hemp blocks. A mixture of sand and clay, from the Brussels earth-moving industry, is used as plaster. Existing elements such as floors, joinery, stairs, tiles,... are revalued and supplemented with reclaimed materials. With this project, we want to show that building in back-to-basics materials such as straw, loam, and wood is not only for a rural context but can also be applied in an urban environment.Save this picture!3. Dismountable, simple & repetitive - The open and legible inner structure is perfectly demountable due to its specific assembly (composite columns and beams with loose bolted connections). If broken down, they are easily reused. The number of materials are kept to a minimum and low-tech details or principles are repeated. It is an architecture that is understandable to all and therefore easily adaptable.Save this picture! Project gallerySee allShow less Project locationAddress:Molenbeek-Saint-Jean, BelgiumLocation to be used only as a reference. It could indicate city/country but not exact address.About this officehé! architectuurOffice••• MaterialsWoodBrickMaterials and TagsPublished on May 21, 2025Cite: "Karper Building Renovation / hé! architectuur + buro kiss + EA+" 21 May 2025. ArchDaily. Accessed . <https://www.archdaily.com/1030330/karper-building-renovation-he-architectuur-plus-buro-kiss-plus-ea-plus&gt ISSN 0719-8884Save世界上最受欢迎的建筑网站现已推出你的母语版本!想浏览ArchDaily中国吗?是否 You've started following your first account!Did you know?You'll now receive updates based on what you follow! Personalize your stream and start following your favorite authors, offices and users.Go to my stream