Nature, Published online: 13 June 2025; doi:10.1038/d41586-025-01804-7The pull of a third galaxy could yank the Milky Way out of the path of Andromeda.

Transcript [MUSIC]      [BOOK PASSAGE]   PETER LEE: “In ‘The Little Black Bag,’ a classic science fiction story, a high-tech doctor’s kit of the future is accidentally transported back to the 1950s, into the shaky hands of a washed-up, alcoholic doctor. The ultimate medical tool, it redeems the doctor wielding it, allowing him to practice gratifyingly heroic medicine. … The tale ends badly for the doctor and his treacherous assistant, but it offered a picture of how advanced technology could transform medicine—powerful when it was written nearly 75 years ago and still so today. What would be the Al equivalent of that little black bag? At this moment when new capabilities are emerging, how do we imagine them into medicine?”   [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 10: The Big Black Bag.”  In imagining AI in medicine, Carey, Zak, and I included in our book two fictional accounts. In the first, a medical resident consults GPT-4 on her personal phone as the patient in front of her crashes. Within seconds, it offers an alternate response based on recent literature. In the second account, a 90-year-old woman with several chronic conditions is living independently and receiving near-constant medical support from an AI aide.    In our conversations with the guests we’ve spoken to so far, we’ve caught a glimpse of these predicted futures, seeing how clinicians and patients are actually using AI today and how developers are leveraging the technology in the healthcare products and services they’re creating. In fact, that first fictional account isn’t so fictional after all, as most of the doctors in the real world actually appear to be using AI at least occasionally—and sometimes much more than occasionally—to help in their daily clinical work. And as for the second fictional account, which is more of a science fiction account, it seems we are indeed on the verge of a new way of delivering and receiving healthcare, though the future is still very much open.  As we continue to examine the current state of AI in healthcare and its potential to transform the field, I’m pleased to welcome Bill Gates and Sébastien Bubeck.   Bill may be best known as the co-founder of Microsoft, having created the company with his childhood friend Paul Allen in 1975. He’s now the founder of Breakthrough Energy, which aims to advance clean energy innovation, and TerraPower, a company developing groundbreaking nuclear energy and science technologies. He also chairs the world’s largest philanthropic organization, the Gates Foundation, and focuses on solving a variety of health challenges around the globe and here at home.  Sébastien is a research lead at OpenAI. He was previously a distinguished scientist, vice president of AI, and a colleague of mine here at Microsoft, where his work included spearheading the development of the family of small language models known as Phi. While at Microsoft, he also coauthored the discussion-provoking 2023 paper “Sparks of Artificial General Intelligence,” which presented the results of early experiments with GPT-4 conducted by a small team from Microsoft Research.    [TRANSITION MUSIC]   Here’s my conversation with Bill Gates and Sébastien Bubeck.  LEE: Bill, welcome.  BILL GATES: Thank you.  LEE: Seb …  SÉBASTIEN BUBECK: Yeah. Hi, hi, Peter. Nice to be here.  LEE: You know, one of the things that I’ve been doing just to get the conversation warmed up is to talk about origin stories, and what I mean about origin stories is, you know, what was the first contact that you had with large language models or the concept of generative AI that convinced you or made you think that something really important was happening?  And so, Bill, I think I’ve heard the story about, you know, the time when the OpenAI folks—Sam Altman, Greg Brockman, and others—showed you something, but could we hear from you what those early encounters were like and what was going through your mind?   GATES: Well, I’d been visiting OpenAI soon after it was created to see things like GPT-2 and to see the little arm they had that was trying to match human manipulation and, you know, looking at their games like Dota that they were trying to get as good as human play. And honestly, I didn’t think the language model stuff they were doing, even when they got to GPT-3, would show the ability to learn, you know, in the same sense that a human reads a biology book and is able to take that knowledge and access it not only to pass a test but also to create new medicines.  And so my challenge to them was that if their LLM could get a five on the advanced placement biology test, then I would say, OK, it took biologic knowledge and encoded it in an accessible way and that I didn’t expect them to do that very quickly but it would be profound.   And it was only about six months after I challenged them to do that, that an early version of GPT-4 they brought up to a dinner at my house, and in fact, it answered most of the questions that night very well. The one it got totally wrong, we were … because it was so good, we kept thinking, Oh, we must be wrong. It turned out it was a math weakness [LAUGHTER] that, you know, we later understood that that was an area of, weirdly, of incredible weakness of those early models. But, you know, that was when I realized, OK, the age of cheap intelligence was at its beginning.  LEE: Yeah. So I guess it seems like you had something similar to me in that my first encounters, I actually harbored some skepticism. Is it fair to say you were skeptical before that?  GATES: Well, the idea that we’ve figured out how to encode and access knowledge in this very deep sense without even understanding the nature of the encoding, …  LEE: Right.   GATES: … that is a bit weird.   LEE: Yeah.  GATES: We have an algorithm that creates the computation, but even say, OK, where is the president’s birthday stored in there? Where is this fact stored in there? The fact that even now when we’re playing around, getting a little bit more sense of it, it’s opaque to us what the semantic encoding is, it’s, kind of, amazing to me. I thought the invention of knowledge storage would be an explicit way of encoding knowledge, not an implicit statistical training.  LEE: Yeah, yeah. All right. So, Seb, you know, on this same topic, you know, I got—as we say at Microsoft—I got pulled into the tent. [LAUGHS]  BUBECK: Yes.   LEE: Because this was a very secret project. And then, um, I had the opportunity to select a small number of researchers in MSR [Microsoft Research] to join and start investigating this thing seriously. And the first person I pulled in was you.  BUBECK: Yeah.  LEE: And so what were your first encounters? Because I actually don’t remember what happened then.  BUBECK: Oh, I remember it very well. [LAUGHS] My first encounter with GPT-4 was in a meeting with the two of you, actually. But my kind of first contact, the first moment where I realized that something was happening with generative AI, was before that. And I agree with Bill that I also wasn’t too impressed by GPT-3.  I though that it was kind of, you know, very naturally mimicking the web, sort of parroting what was written there in a nice way. Still in a way which seemed very impressive. But it wasn’t really intelligent in any way. But shortly after GPT-3, there was a model before GPT-4 that really shocked me, and this was the first image generation model, DALL-E 1.  So that was in 2021. And I will forever remember the press release of OpenAI where they had this prompt of an avocado chair and then you had this image of the avocado chair. [LAUGHTER] And what really shocked me is that clearly the model kind of “understood” what is a chair, what is an avocado, and was able to merge those concepts.  So this was really, to me, the first moment where I saw some understanding in those models.   LEE: So this was, just to get the timing right, that was before I pulled you into the tent.  BUBECK: That was before. That was like a year before.  LEE: Right.   BUBECK: And now I will tell you how, you know, we went from that moment to the meeting with the two of you and GPT-4.  So once I saw this kind of understanding, I thought, OK, fine. It understands concept, but it’s still not able to reason. It cannot—as, you know, Bill was saying—it cannot learn from your document. It cannot reason.   So I set out to try to prove that. You know, this is what I was in the business of at the time, trying to prove things in mathematics. So I was trying to prove that basically autoregressive transformers could never reason. So I was trying to prove this. And after a year of work, I had something reasonable to show. And so I had the meeting with the two of you, and I had this example where I wanted to say, there is no way that an LLM is going to be able to do x.  And then as soon as I … I don’t know if you remember, Bill. But as soon as I said that, you said, oh, but wait a second. I had, you know, the OpenAI crew at my house recently, and they showed me a new model. Why don’t we ask this new model this question?   LEE: Yeah. BUBECK: And we did, and it solved it on the spot. And that really, honestly, just changed my life. Like, you know, I had been working for a year trying to say that this was impossible. And just right there, it was shown to be possible.   LEE: [LAUGHS] One of the very first things I got interested in—because I was really thinking a lot about healthcare—was healthcare and medicine.  And I don’t know if the two of you remember, but I ended up doing a lot of tests. I ran through, you know, step one and step two of the US Medical Licensing Exam. Did a whole bunch of other things. I wrote this big report. It was, you know, I can’t remember … a couple hundred pages.   And I needed to share this with someone. I didn’t … there weren’t too many people I could share it with. So I sent, I think, a copy to you, Bill. Sent a copy to you, Seb.   I hardly slept for about a week putting that report together. And, yeah, and I kept working on it. But I was far from alone. I think everyone who was in the tent, so to speak, in those early days was going through something pretty similar. All right. So I think … of course, a lot of what I put in the report also ended up being examples that made it into the book.  But the main purpose of this conversation isn’t to reminisce about [LAUGHS] or indulge in those reminiscences but to talk about what’s happening in healthcare and medicine. And, you know, as I said, we wrote this book. We did it very, very quickly. Seb, you helped. Bill, you know, you provided a review and some endorsements.  But, you know, honestly, we didn’t know what we were talking about because no one had access to this thing. And so we just made a bunch of guesses. So really, the whole thing I wanted to probe with the two of you is, now with two years of experience out in the world, what, you know, what do we think is happening today?  You know, is AI actually having an impact, positive or negative, on healthcare and medicine? And what do we now think is going to happen in the next two years, five years, or 10 years? And so I realize it’s a little bit too abstract to just ask it that way. So let me just try to narrow the discussion and guide us a little bit.   Um, the kind of administrative and clerical work, paperwork, around healthcare—and we made a lot of guesses about that—that appears to be going well, but, you know, Bill, I know we’ve discussed that sometimes that you think there ought to be a lot more going on. Do you have a viewpoint on how AI is actually finding its way into reducing paperwork?  GATES: Well, I’m stunned … I don’t think there should be a patient-doctor meeting where the AI is not sitting in and both transcribing, offering to help with the paperwork, and even making suggestions, although the doctor will be the one, you know, who makes the final decision about the diagnosis and whatever prescription gets done.   It’s so helpful. You know, when that patient goes home and their, you know, son who wants to understand what happened has some questions, that AI should be available to continue that conversation. And the way you can improve that experience and streamline things and, you know, involve the people who advise you. I don’t understand why that’s not more adopted, because there you still have the human in the loop making that final decision.  But even for, like, follow-up calls to make sure the patient did things, to understand if they have concerns and knowing when to escalate back to the doctor, the benefit is incredible. And, you know, that thing is ready for prime time. That paradigm is ready for prime time, in my view.  LEE: Yeah, there are some good products, but it seems like the number one use right now—and we kind of got this from some of the previous guests in previous episodes—is the use of AI just to respond to emails from patients. [LAUGHTER] Does that make sense to you?  BUBECK: Yeah. So maybe I want to second what Bill was saying but maybe take a step back first. You know, two years ago, like, the concept of clinical scribes, which is one of the things that we’re talking about right now, it would have sounded, in fact, it sounded two years ago, borderline dangerous. Because everybody was worried about hallucinations. What happened if you have this AI listening in and then it transcribes, you know, something wrong?  Now, two years later, I think it’s mostly working. And in fact, it is not yet, you know, fully adopted. You’re right. But it is in production. It is used, you know, in many, many places. So this rate of progress is astounding because it wasn’t obvious that we would be able to overcome those obstacles of hallucination. It’s not to say that hallucinations are fully solved. In the case of the closed system, they are.   Now, I think more generally what’s going on in the background is that there is something that we, that certainly I, underestimated, which is this management overhead. So I think the reason why this is not adopted everywhere is really a training and teaching aspect. People need to be taught, like, those systems, how to interact with them.  And one example that I really like, a study that recently appeared where they tried to use ChatGPT for diagnosis and they were comparing doctors without and with ChatGPT (opens in new tab). And the amazing thing … so this was a set of cases where the accuracy of the doctors alone was around 75%. ChatGPT alone was 90%. So that’s already kind of mind blowing. But then the kicker is that doctors with ChatGPT was 80%.   Intelligence alone is not enough. It’s also how it’s presented, how you interact with it. And ChatGPT, it’s an amazing tool. Obviously, I absolutely love it. But it’s not … you don’t want a doctor to have to type in, you know, prompts and use it that way.  It should be, as Bill was saying, kind of running continuously in the background, sending you notifications. And you have to be really careful of the rate at which those notifications are being sent. Because if they are too frequent, then the doctor will learn to ignore them. So you have to … all of those things matter, in fact, at least as much as the level of intelligence of the machine.  LEE: One of the things I think about, Bill, in that scenario that you described, doctors do some thinking about the patient when they write the note. So, you know, I’m always a little uncertain whether it’s actually … you know, you wouldn’t necessarily want to fully automate this, I don’t think. Or at least there needs to be some prompt to the doctor to make sure that the doctor puts some thought into what happened in the encounter with the patient. Does that make sense to you at all?  GATES: At this stage, you know, I’d still put the onus on the doctor to write the conclusions and the summary and not delegate that.  The tradeoffs you make a little bit are somewhat dependent on the situation you’re in. If you’re in Africa, So, yes, the doctor’s still going to have to do a lot of work, but just the quality of letting the patient and the people around them interact and ask questions and have things explained, that alone is such a quality improvement. It’s mind blowing.   LEE: So since you mentioned, you know, Africa—and, of course, this touches on the mission and some of the priorities of the Gates Foundation and this idea of democratization of access to expert medical care—what’s the most interesting stuff going on right now? Are there people and organizations or technologies that are impressing you or that you’re tracking?  GATES: Yeah. So the Gates Foundation has given out a lot of grants to people in Africa doing education, agriculture but more healthcare examples than anything. And the way these things start off, they often start out either being patient-centric in a narrow situation, like, OK, I’m a pregnant woman; talk to me. Or, I have infectious disease symptoms; talk to me. Or they’re connected to a health worker where they’re helping that worker get their job done. And we have lots of pilots out, you know, in both of those cases.   The dream would be eventually to have the thing the patient consults be so broad that it’s like having a doctor available who understands the local things.   LEE: Right.   GATES: We’re not there yet. But over the next two or three years, you know, particularly given the worsening financial constraints against African health systems, where the withdrawal of money has been dramatic, you know, figuring out how to take this—what I sometimes call “free intelligence”—and build a quality health system around that, we will have to be more radical in low-income countries than any rich country is ever going to be.   LEE: Also, there’s maybe a different regulatory environment, so some of those things maybe are easier? Because right now, I think the world hasn’t figured out how to and whether to regulate, let’s say, an AI that might give a medical diagnosis or write a prescription for a medication.  BUBECK: Yeah. I think one issue with this, and it’s also slowing down the deployment of AI in healthcare more generally, is a lack of proper benchmark. Because, you know, you were mentioning the USMLE [United States Medical Licensing Examination], for example. That’s a great test to test human beings and their knowledge of healthcare and medicine. But it’s not a great test to give to an AI.  It’s not asking the right questions. So finding what are the right questions to test whether an AI system is ready to give diagnosis in a constrained setting, that’s a very, very important direction, which to my surprise, is not yet accelerating at the rate that I was hoping for.  LEE: OK, so that gives me an excuse to get more now into the core AI tech because something I’ve discussed with both of you is this issue of what are the right tests. And you both know the very first test I give to any new spin of an LLM is I present a patient, the results—a mythical patient—the results of my physical exam, my mythical physical exam. Maybe some results of some initial labs. And then I present or propose a differential diagnosis. And if you’re not in medicine, a differential diagnosis you can just think of as a prioritized list of the possible diagnoses that fit with all that data. And in that proposed differential, I always intentionally make two mistakes.  I make a textbook technical error in one of the possible elements of the differential diagnosis, and I have an error of omission. And, you know, I just want to know, does the LLM understand what I’m talking about? And all the good ones out there do now. But then I want to know, can it spot the errors? And then most importantly, is it willing to tell me I’m wrong, that I’ve made a mistake?   That last piece seems really hard for AI today. And so let me ask you first, Seb, because at the time of this taping, of course, there was a new spin of GPT-4o last week that became overly sycophantic. In other words, it was actually prone in that test of mine not only to not tell me I’m wrong, but it actually praised me for the creativity of my differential. [LAUGHTER] What’s up with that?  BUBECK: Yeah, I guess it’s a testament to the fact that training those models is still more of an art than a science. So it’s a difficult job. Just to be clear with the audience, we have rolled back that [LAUGHS] version of GPT-4o, so now we don’t have the sycophant version out there.  Yeah, no, it’s a really difficult question. It has to do … as you said, it’s very technical. It has to do with the post-training and how, like, where do you nudge the model? So, you know, there is this very classical by now technique called RLHF [reinforcement learning from human feedback], where you push the model in the direction of a certain reward model. So the reward model is just telling the model, you know, what behavior is good, what behavior is bad.  But this reward model is itself an LLM, and, you know, Bill was saying at the very beginning of the conversation that we don’t really understand how those LLMs deal with concepts like, you know, where is the capital of France located? Things like that. It is the same thing for this reward model. We don’t know why it says that it prefers one output to another, and whether this is correlated with some sycophancy is, you know, something that we discovered basically just now. That if you push too hard in optimization on this reward model, you will get a sycophant model.  So it’s kind of … what I’m trying to say is we became too good at what we were doing, and we ended up, in fact, in a trap of the reward model.  LEE: I mean, you do want … it’s a difficult balance because you do want models to follow your desires and …  BUBECK: It’s a very difficult, very difficult balance.  LEE: So this brings up then the following question for me, which is the extent to which we think we’ll need to have specially trained models for things. So let me start with you, Bill. Do you have a point of view on whether we will need to, you know, quote-unquote take AI models to med school? Have them specially trained? Like, if you were going to deploy something to give medical care in underserved parts of the world, do we need to do something special to create those models?  GATES: We certainly need to teach them the African languages and the unique dialects so that the multimedia interactions are very high quality. We certainly need to teach them the disease prevalence and unique disease patterns like, you know, neglected tropical diseases and malaria. So we need to gather a set of facts that somebody trying to go for a US customer base, you know, wouldn’t necessarily have that in there.  Those two things are actually very straightforward because the additional training time is small. I’d say for the next few years, we’ll also need to do reinforcement learning about the context of being a doctor and how important certain behaviors are. Humans learn over the course of their life to some degree that, I’m in a different context and the way I behave in terms of being willing to criticize or be nice, you know, how important is it? Who’s here? What’s my relationship to them?   Right now, these machines don’t have that broad social experience. And so if you know it’s going to be used for health things, a lot of reinforcement learning of the very best humans in that context would still be valuable. Eventually, the models will, having read all the literature of the world about good doctors, bad doctors, it’ll understand as soon as you say, “I want you to be a doctor diagnosing somebody.” All of the implicit reinforcement that fits that situation, you know, will be there. LEE: Yeah. GATES: And so I hope three years from now, we don’t have to do that reinforcement learning. But today, for any medical context, you would want a lot of data to reinforce tone, willingness to say things when, you know, there might be something significant at stake.  LEE: Yeah. So, you know, something Bill said, kind of, reminds me of another thing that I think we missed, which is, the context also … and the specialization also pertains to different, I guess, what we still call “modes,” although I don’t know if the idea of multimodal is the same as it was two years ago. But, you know, what do you make of all of the hubbub around—in fact, within Microsoft Research, this is a big deal, but I think we’re far from alone—you know, medical images and vision, video, proteins and molecules, cell, you know, cellular data and so on.  BUBECK: Yeah. OK. So there is a lot to say to everything … to the last, you know, couple of minutes. Maybe on the specialization aspect, you know, I think there is, hiding behind this, a really fundamental scientific question of whether eventually we have a singular AGI [artificial general intelligence] that kind of knows everything and you can just put, you know, explain your own context and it will just get it and understand everything.  That’s one vision. I have to say, I don’t particularly believe in this vision. In fact, we humans are not like that at all. I think, hopefully, we are general intelligences, yet we have to specialize a lot. And, you know, I did myself a lot of RL, reinforcement learning, on mathematics. Like, that’s what I did, you know, spent a lot of time doing that. And I didn’t improve on other aspects. You know, in fact, I probably degraded in other aspects. [LAUGHTER] So it’s … I think it’s an important example to have in mind.  LEE: I think I might disagree with you on that, though, because, like, doesn’t a model have to see both good science and bad science in order to be able to gain the ability to discern between the two?  BUBECK: Yeah, no, that absolutely. I think there is value in seeing the generality, in having a very broad base. But then you, kind of, specialize on verticals. And this is where also, you know, open-weights model, which we haven’t talked about yet, are really important because they allow you to provide this broad base to everyone. And then you can specialize on top of it.  LEE: So we have about three hours of stuff to talk about, but our time is actually running low. BUBECK: Yes, yes, yes.   LEE: So I think I want … there’s a more provocative question. It’s almost a silly question, but I need to ask it of the two of you, which is, is there a future, you know, where AI replaces doctors or replaces, you know, medical specialties that we have today? So what does the world look like, say, five years from now?  GATES: Well, it’s important to distinguish healthcare discovery activity from healthcare delivery activity. We focused mostly on delivery. I think it’s very much within the realm of possibility that the AI is not only accelerating healthcare discovery but substituting for a lot of the roles of, you know, I’m an organic chemist, or I run various types of assays. I can see those, which are, you know, testable-output-type jobs but with still very high value, I can see, you know, some replacement in those areas before the doctor.   The doctor, still understanding the human condition and long-term dialogues, you know, they’ve had a lifetime of reinforcement of that, particularly when you get into areas like mental health. So I wouldn’t say in five years, either people will choose to adopt it, but it will be profound that there’ll be this nearly free intelligence that can do follow-up, that can help you, you know, make sure you went through different possibilities.  And so I’d say, yes, we’ll have doctors, but I’d say healthcare will be massively transformed in its quality and in efficiency by AI in that time period.  LEE: Is there a comparison, useful comparison, say, between doctors and, say, programmers, computer programmers, or doctors and, I don’t know, lawyers?  GATES: Programming is another one that has, kind of, a mathematical correctness to it, you know, and so the objective function that you’re trying to reinforce to, as soon as you can understand the state machines, you can have something that’s “checkable”; that’s correct. So I think programming, you know, which is weird to say, that the machine will beat us at most programming tasks before we let it take over roles that have deep empathy, you know, physical presence and social understanding in them.  LEE: Yeah. By the way, you know, I fully expect in five years that AI will produce mathematical proofs that are checkable for validity, easily checkable, because they’ll be written in a proof-checking language like Lean or something but will be so complex that no human mathematician can understand them. I expect that to happen.   I can imagine in some fields, like cellular biology, we could have the same situation in the future because the molecular pathways, the chemistry, biochemistry of human cells or living cells is as complex as any mathematics, and so it seems possible that we may be in a state where in wet lab, we see, Oh yeah, this actually works, but no one can understand why.  BUBECK: Yeah, absolutely. I mean, I think I really agree with Bill’s distinction of the discovery and the delivery, and indeed, the discovery’s when you can check things, and at the end, there is an artifact that you can verify. You know, you can run the protocol in the wet lab and see [if you have] produced what you wanted. So I absolutely agree with that.   And in fact, you know, we don’t have to talk five years from now. I don’t know if you know, but just recently, there was a paper that was published on a scientific discovery using o3- mini (opens in new tab). So this is really amazing. And, you know, just very quickly, just so people know, it was about this statistical physics model, the frustrated Potts model, which has to do with coloring, and basically, the case of three colors, like, more than two colors was open for a long time, and o3 was able to reduce the case of three colors to two colors.   LEE: Yeah.  BUBECK: Which is just, like, astounding. And this is not … this is now. This is happening right now. So this is something that I personally didn’t expect it would happen so quickly, and it’s due to those reasoning models.   Now, on the delivery side, I would add something more to it for the reason why doctors and, in fact, lawyers and coders will remain for a long time, and it’s because we still don’t understand how those models generalize. Like, at the end of the day, we are not able to tell you when they are confronted with a really new, novel situation, whether they will work or not.  Nobody is able to give you that guarantee. And I think until we understand this generalization better, we’re not going to be willing to just let the system in the wild without human supervision.  LEE: But don’t human doctors, human specialists … so, for example, a cardiologist sees a patient in a certain way that a nephrologist …  BUBECK: Yeah. LEE: … or an endocrinologist might not. BUBECK: That’s right. But another cardiologist will understand and, kind of, expect a certain level of generalization from their peer. And this, we just don’t have it with AI models. Now, of course, you’re exactly right. That generalization is also hard for humans. Like, if you have a human trained for one task and you put them into another task, then you don’t … you often don’t know. LEE: OK. You know, the podcast is focused on what’s happened over the last two years. But now, I’d like one provocative prediction about what you think the world of AI and medicine is going to be at some point in the future. You pick your timeframe. I don’t care if it’s two years or 20 years from now, but, you know, what do you think will be different about AI in medicine in that future than today?  BUBECK: Yeah, I think the deployment is going to accelerate soon. Like, we’re really not missing very much. There is this enormous capability overhang. Like, even if progress completely stopped, with current systems, we can do a lot more than what we’re doing right now. So I think this will … this has to be realized, you know, sooner rather than later.  And I think it’s probably dependent on these benchmarks and proper evaluation and tying this with regulation. So these are things that take time in human society and for good reason. But now we already are at two years; you know, give it another two years and it should be really …   LEE: Will AI prescribe your medicines? Write your prescriptions?  BUBECK: I think yes. I think yes.  LEE: OK. Bill?  GATES: Well, I think the next two years, we’ll have massive pilots, and so the amount of use of the AI, still in a copilot-type mode, you know, we should get millions of patient visits, you know, both in general medicine and in the mental health side, as well. And I think that’s going to build up both the data and the confidence to give the AI some additional autonomy. You know, are you going to let it talk to you at night when you’re panicked about your mental health with some ability to escalate? And, you know, I’ve gone so far as to tell politicians with national health systems that if they deploy AI appropriately, that the quality of care, the overload of the doctors, the improvement in the economics will be enough that their voters will be stunned because they just don’t expect this, and, you know, they could be reelected [LAUGHTER] just on this one thing of fixing what is a very overloaded and economically challenged health system in these rich countries.  You know, my personal role is going to be to make sure that in the poorer countries, there isn’t some lag; in fact, in many cases, that we’ll be more aggressive because, you know, we’re comparing to having no access to doctors at all. And, you know, so I think whether it’s India or Africa, there’ll be lessons that are globally valuable because we need medical intelligence. And, you know, thank god AI is going to provide a lot of that.  LEE: Well, on that optimistic note, I think that’s a good way to end. Bill, Seb, really appreciate all of this.   I think the most fundamental prediction we made in the book is that AI would actually find its way into the practice of medicine, and I think that that at least has come true, maybe in different ways than we expected, but it’s come true, and I think it’ll only accelerate from here. So thanks again, both of you.  [TRANSITION MUSIC]  GATES: Yeah. Thanks, you guys.  BUBECK: Thank you, Peter. Thanks, Bill.  LEE: I just always feel such a sense of privilege to have a chance to interact and actually work with people like Bill and Sébastien.    With Bill, I’m always amazed at how practically minded he is. He’s really thinking about the nuts and bolts of what AI might be able to do for people, and his thoughts about underserved parts of the world, the idea that we might actually be able to empower people with access to expert medical knowledge, I think is both inspiring and amazing.   And then, Seb, Sébastien Bubeck, he’s just absolutely a brilliant mind. He has a really firm grip on the deep mathematics of artificial intelligence and brings that to bear in his research and development work. And where that mathematics takes him isn’t just into the nuts and bolts of algorithms but into philosophical questions about the nature of intelligence.   One of the things that Sébastien brought up was the state of evaluation of AI systems. And indeed, he was fairly critical in our conversation. But of course, the world of AI research and development is just moving so fast, and indeed, since we recorded our conversation, OpenAI, in fact, released a new evaluation metric that is directly relevant to medical applications, and that is something called HealthBench. And Microsoft Research also released a new evaluation approach or process called ADeLe.   HealthBench and ADeLe are examples of new approaches to evaluating AI models that are less about testing their knowledge and ability to pass multiple-choice exams and instead are evaluation approaches designed to assess how well AI models are able to complete tasks that actually arise every day in typical healthcare or biomedical research settings. These are examples of really important good work that speak to how well AI models work in the real world of healthcare and biomedical research and how well they can collaborate with human beings in those settings.  You know, I asked Bill and Seb to make some predictions about the future. You know, my own answer, I expect that we’re going to be able to use AI to change how we diagnose patients, change how we decide treatment options.   If you’re a doctor or a nurse and you encounter a patient, you’ll ask questions, do a physical exam, you know, call out for labs just like you do today, but then you’ll be able to engage with AI based on all of that data and just ask, you know, based on all the other people who have gone through the same experience, who have similar data, how were they diagnosed? How were they treated? What were their outcomes? And what does that mean for the patient I have right now? Some people call it the “patients like me” paradigm. And I think that’s going to become real because of AI within our lifetimes. That idea of really grounding the delivery in healthcare and medical practice through data and intelligence, I actually now don’t see any barriers to that future becoming real.  [THEME MUSIC]  I’d like to extend another big thank you to Bill and Sébastien for their time. And to our listeners, as always, it’s a pleasure to have you along for the ride. I hope you’ll join us for our remaining conversations, as well as a second coauthor roundtable with Carey and Zak.   Until next time.   [MUSIC FADES]

Artificial intelligence is already rewriting the rules of wireless and telecom — powering predictive maintenance, streamlining network operations, and enabling more innovative services. As AI scales, the disruption will be faster, deeper, and harder to reverse than any prior shift in the industry. Compared to the sweeping changes AI is set to unleash, past telecom innovations look incremental. AI is redefining how networks operate, services are delivered, and data is secured — across every device and digital touchpoint. AI Is Reshaping Wireless Networks Already Artificial intelligence is already transforming wireless through smarter private networks, fixed wireless access (FWA), and intelligent automation across the stack. AI detects and resolves network issues before they impact service, improving uptime and customer satisfaction. It’s also opening the door to entirely new revenue streams and business models. Each wireless generation brought new capabilities. AI, however, marks a more profound shift — networks that think, respond, and evolve in real time. AI Acceleration Will Outpace Past Tech Shifts Many may underestimate the speed and magnitude of AI-driven change. The shift from traditional voice and data systems to AI-driven network intelligence is already underway. Although predictions abound, the true scope remains unclear. It’s tempting to assume we understand AI’s trajectory, but history suggests otherwise. Today, AI is already automating maintenance and optimizing performance without user disruption. The technologies we’ll rely on in the near future may still be on the drawing board. Few predicted that smartphones would emerge from analog beginnings—a reminder of how quickly foundational technologies can be reimagined. History shows that disruptive technologies rarely follow predictable paths — and AI is no exception. It’s already upending business models across industries. Technological shifts bring both new opportunities and complex trade-offs. AI Disruption Will Move Faster Than Ever The same cycle of reinvention is happening now — but with AI, it’s moving at unprecedented speed. Despite all the discussion, many still treat AI as a future concern — yet the shift is already well underway. As with every major technological leap, there will be gains and losses. The AI transition brings clear trade-offs: efficiency and innovation on one side, job displacement, and privacy erosion on the other. Unlike past tech waves that unfolded over decades, the AI shift will reshape industries in just a few years — and that change wave will only continue to move forward. AI Will Reshape All Sectors and Companies This shift will unfold faster than most organizations or individuals are prepared to handle. Today’s industries will likely look very different tomorrow. Entirely new sectors will emerge as legacy models become obsolete — redefining market leadership across industries. Telecom’s past holds a clear warning: market dominance can vanish quickly when companies ignore disruption. Eventually, the Baby Bells moved into long-distance service, while AT&T remained barred from selling local access — undermining its advantage. As the market shifted and competitors gained ground, AT&T lost its dominance and became vulnerable enough that SBC, a former regional Bell, acquired it and took on its name. It’s a case study of how incumbents fall when they fail to adapt — precisely the kind of pressure AI is now exerting across industries. SBC’s acquisition of AT&T flipped the power dynamic — proof that size doesn’t protect against disruption. The once-crowded telecom field has consolidated into just a few dominant players — each facing new threats from AI-native challengers. Legacy telecom models are being steadily displaced by faster, more flexible wireless, broadband, and streaming alternatives. No Industry Is Immune From AI Disruption AI will accelerate the next wave of industrial evolution — bringing innovations and consequences we’re only beginning to grasp. New winners will emerge as past leaders struggle to hang on — a shift that will also reshape the investment landscape. Startups leveraging AI will likely redefine leadership in sectors where incumbents have grown complacent. Nvidia’s rise is part of a broader trend: the next market leaders will emerge wherever AI creates a clear competitive advantage — whether in chips, code, or entirely new markets. The AI-driven future is arriving faster than most organizations are ready for. Adapting to this accelerating wave of change is no longer optional — it’s essential. Companies that act decisively today will define the winners of tomorrow.

On November 4, 2003, a doctor gave Jon Gluck some of the worst news imaginable: He had cancer — one that later tests would reveal as multiple myeloma, a severe blood and bone marrow cancer. Jon was told he might have as little as 18 months to live. He was 38, a thriving magazine editor in New York with a 7-month-old daughter whose third birthday, he suddenly realized, he might never see.“The moment after I was told I had cancer, I just said ‘no, no, no,’” Jon told me in an interview just last week. “This cannot be true.”Living in remissionThe fact that Jon is still here, talking to me in 2025, tells you that things didn’t go the way the medical data would have predicted on that November morning. He has lived with his cancer, through waves of remission and recurrence, for more than 20 years, an experience he chronicles with grace and wit in his new book An Exercise in Uncertainty. That 7-month-old daughter is now in college.RelatedWhy do so many young people suddenly have cancer?You could say Jon has beaten the odds, and he’s well aware that chance played some role in his survival. (“Did you know that ‘Glück’ is German for ‘luck’?” he writes in the book, noting his good fortune that a random spill on the ice is what sent him to the doctor in the first place, enabling them to catch his cancer early.) Cancer is still a terrible health threat, one that is responsible for 1 in 6 deaths around the world, killing nearly 10 million people a year globally and over 600,000 people a year in the US. But Jon’s story and his survival demonstrate something that is too often missed: We’ve turned the tide in the war against cancer. The age-adjusted death rate in the US for cancer has declined by about a third since 1991, meaning people of a given age have about a third lower risk of dying from cancer than people of the same age more than three decades ago. That adds up to over 4 million fewer cancer deaths over that time period. Thanks to breakthroughs in treatments like autologous stem-cell harvesting and CAR-T therapy — breakthroughs Jon himself benefited from, often just in time — cancer isn’t the death sentence it once was.Our World in DataGetting better all the timeThere’s no doubt that just as the rise of smoking in the 20th century led to a major increase in cancer deaths, the equally sharp decline of tobacco use eventually led to a delayed decrease. Smoking is one of the most potent carcinogens in the world, and at the peak in the early 1960s, around 12 cigarettes were being sold per adult per day in the US. Take away the cigarettes and — after a delay of a couple of decades — lung cancer deaths drop in turn along with other non-cancer smoking-related deaths.But as Saloni Dattani wrote in a great piece earlier this year, even before the decline of smoking, death rates from non-lung cancers in the stomach and colon had begun to fall. Just as notably, death rates for childhood cancers — which for obvious reasons are not connected to smoking and tend to be caused by genetic mutations — have fallen significantly as well, declining sixfold since 1950. In the 1960s, for example, only around 10 percent of children diagnosed with acute lymphoblastic leukemia survived more than five years. Today it’s more than 90 percent. And the five-year survival rate for all cancers has risen from 49 percent in the mid-1970s to 69 percent in 2019. We’ve made strikes against the toughest of cancers, like Jon’s multiple myeloma. Around when Jon was diagnosed, the five-year survival rate was just 34 percent. Today it’s as high as 62 percent, and more and more people like Jon are living for decades. “There has been a revolution in cancer survival,” Jon told me. “Some illnesses now have far more successful therapies than others, but the gains are real.”Three cancer revolutions The dramatic bend in the curve of cancer deaths didn’t happen by accident — it’s the compound interest of three revolutions.While anti-smoking policy has been the single biggest lifesaver, other interventions have helped reduce people’s cancer risk. One of the biggest successes is the HPV vaccine. A study last year found that death rates of cervical cancer — which can be caused by HPV infections — in US women ages 20–39 had dropped 62 percent from 2012 to 2021, thanks largely to the spread of the vaccine. Other cancers have been linked to infections, and there is strong research indicating that vaccination can have positive effects on reducing cancer incidence. The next revolution is better and earlier screening. It’s generally true that the earlier cancer is caught, the better the chances of survival, as Jon’s own story shows. According to one study, incidences of late-stage colorectal cancer in Americans over 50 declined by a third between 2000 and 2010 in large part because rates of colonoscopies almost tripled in that same time period. And newer screening methods, often employing AI or using blood-based tests, could make preliminary screening simpler, less invasive and therefore more readily available. If 20th-century screening was about finding physical evidence of something wrong — the lump in the breast — 21st-century screening aims to find cancer before symptoms even arise.Most exciting of all are frontier developments in treating cancer, much of which can be tracked through Jon’s own experience. From drugs like lenalidomide and bortezomib in the 2000s, which helped double median myeloma survival, to the spread of monoclonal antibodies, real breakthroughs in treatments have meaningfully extended people’s lives — not just by months, but years.Perhaps the most promising development is CAR-T therapy, a form of immunotherapy. Rather than attempting to kill the cancer directly, immunotherapies turn a patient’s own T-cells into guided missiles. In a recent study of 97 patients with multiple myeloma, many of whom were facing hospice care, a third of those who received CAR-T therapy had no detectable cancer five years later. It was the kind of result that doctors rarely see. “CAR-T is mind-blowing — very science-fiction futuristic,” Jon told me. He underwent his own course of treatment with it in mid-2023 and writes that the experience, which put his cancer into a remission he’s still in, left him feeling “physically and metaphysically new.”A welcome uncertaintyWhile there are still more battles to be won in the war on cancer, and there are certain areas — like the rising rates of gastrointestinal cancers among younger people — where the story isn’t getting better, the future of cancer treatment is improving. For cancer patients like Jon, that can mean a new challenge — enduring the essential uncertainty that comes with living under a disease that’s controllable but which could always come back. But it sure beats the alternative.“I’ve come to trust so completely in my doctors and in these new developments,” he said. “I try to remain cautiously optimistic that my future will be much like the last 20 years.” And that’s more than he or anyone else could have hoped for nearly 22 years ago. A version of this story originally appeared in the Good News newsletter. Sign up here!See More: Health

When Russian troops invaded Ukraine on 24 February 2022, Russia’s datacentre sector was one of the fastest-growing segments of the country’s IT industry, with annual growth rates in the region of 10-12%. However, with the conflict resulting in the imposition of Western sanctions against Russia and an outflow of US-based tech companies from the country, including Apple and Microsoft, optimism about the sector’s potential for further growth soon disappeared. In early March 2025, it was reported that Google had disconnected from traffic exchange points and datacentres in Russia, leading to concerns about how this could negatively affect the speed of access to some Google services for Russian users. Initially, there was hope that domestic technology and datacentre providers might be able to plug the gaps left by the exodus of the US tech giants, but it seems they could not keep up with the hosting demands of Russia’s increasingly digital economy. Oleg Kim, director of the hardware systems department at Russian IT company Axoft, says the departure of foreign cloud providers and equipment manufacturers has led to a serious shortage of compute capacity in Russia. This is because the situation resulted in a sharp, initial increase in demand for domestic datacentres, but Russian providers simply did not have time to expand their capacities on the required scale, continues Kim. According to the estimates of Key Point, one of Russia’s largest datacentre networks, meeting Russia’s demand for datacentres will require facilities with a total capacity of 30,000 racks to be built each year over the next five years. On top of this, it has also become more costly to build datacentres in Russia. Estimates suggest that prior to 2022, the cost of a datacentre rack totalled 100,000 rubles ($1,200), but now exceeds 150,000 rubles. And analysts at Forbes Russia expect these figures will continue to grow, due to rising logistics costs and the impact the war is having on the availability of skilled labour in the construction sector. The impact of these challenges is being keenly felt by users, with several of the country’s large banks experiencing serious problems when finding suitable locations for their datacentres. Sberbank is among the firms affected, with its chairperson, German Gref, speaking out previously about how the bank is in need of a datacentre with at least 200MW of capacity, but would ideally need 300-400MW to address its compute requirements. Stanislav Bliznyuk, chairperson of T-Bank, says trying to build even two 50MW datacentres to meet its needs is proving problematic. “Finding locations where such capacity and adequate tariffs are available is a difficult task,” he said. Read more about datacentre developments North Lincolnshire Council has received a planning permission application for another large-scale datacentre development, in support of its bid to become an AI Growth Zone A proposal to build one of the biggest datacentres in Europe has been submitted to Hertsmere Borough Council, and already has the support of the technology secretary and local councillors. The UK government has unveiled its 50-point AI action plan, which commits to building sovereign artificial intelligence capabilities and accelerating AI datacentre developments – but questions remain about the viability of the plans. Despite this, T-Bank is establishing its own network of data processing centres – the first of which should open in early 2027, he confirmed in November 2024. Kirill Solyev, head of the engineering infrastructure department of the Softline Group of Companies, who specialise in IT, says many large Russian companies are resorting to building their own datacentres – because compute capacity is in such short supply. The situation is, however, complicated by the lack of suitable locations for datacentres in the largest cities of Russia – Moscow and St Petersburg. “For example, to build a datacentre with a capacity of 60MW, finding a suitable site can take up to three years,” says Solyev. “In Moscow, according to preliminary estimates, there are about 50MW of free capacity left, which is equivalent to 2-4 large commercial datacentres. “The capacity deficit only in the southern part of the Moscow region is predicted at 564MW by 2030, and up to 3.15GW by 2042.” As a result, datacentre operators and investors are now looking for suitable locations outside of Moscow and St Petersburg, and seeking to co-locate new datacentres in close proximity to renewable energy sources. And this will be important as demand for datacentre capacity in Russia is expected to increase, as it is in most of the rest of the world, due to the growing use of artificial intelligence (AI) tools and services. The energy-intensive nature of AI workloads will put further pressure on operators that are already struggling to meet the compute capacity demands of their customers. Speaking at the recent Ural Forum on cyber security in finance, Alexander Kraynov, director of AI technology development at Yandex, says solving the energy consumption issue of AI datacentres will not be easy. “The world is running out of electricity, including for AI, while the same situation is observed in Russia,” he said. “In order to ensure a stable energy supply of a newly built large datacentre, we will need up to one year.” According to a recent report of the Russian Vedomosti business paper, as of April 2024, Russian datacentres have used about 2.6GW, which is equivalent to about 1% of the installed capacity of the Unified Energy System of Russia. Accommodating AI workloads will also mean operators will need to purchase additional equipment, including expensive accelerators based on graphic processing units and higher-performing data storage systems. The implementation of these plans and the viability of these purchases is likely to be seriously complicated by the current sanctions regime against Russia. That said, Russia’s prime minister, Mikhail Mishustin, claims this part of the datacentre supply equation is being partially solved by an uptick in the domestic production of datacentre kit. According to the Mishustin, more than half of the server equipment and industrial storage and information processing systems needed for datacentres are already being produced in Russia – and these figures will continue to grow. The government also plans to provide additional financial support to the industry, as – to date – building datacentres in Russia has been prevented by relatively long payback periods, of up to 10 years in some cases, of such projects. One of the possible support measures on offer could include the subsidisation of at least part of the interest rates on loans to datacentre developers and operators. At the same time, though, the government’s actions in other areas have made it harder for operators to build new facilities. For example, in March 2025, the Russian government significantly tightened the existing norms for the establishment of new datacentres in the form of new rules for the design of data processing centres, which came into force after the approval by the Russian Ministry of Construction. According to Nikita Tsaplin, CEO of Russian hosting provider RUVDS, the rules led to additional bureaucracy in the sector (due to the positioning of datacentres as typical construction objects). And, according to his predictions, that situation can extend the construction cycle of a datacentre from around five years to seven years. The government’s intervention here was to prevent the installation of servers in residential areas, such as garages, but it looks set to complicate an already complex situation – prompting questions about whether Russia’s datacentre market will ever reach its full potential.

Elon Musk’s sudden breakup with President Trump took over social media on Thursday. But the escalating feud won’t be enough to salvage a Starlink contract with Canada’s Ontario province, which canceled the deal to hit back at Trump’s trade war. Ontario Premier Doug Ford today confirmed to reporters that the Starlink deal is still dead, even though Musk is on the outs with Trump. Ford also couldn’t help but take a shot at the very public demise of the Musk-Trump partnership.“Yes, Starlink is done,” he said. “Are we surprised that they’re not seeing eye-to-eye? I predicted that as soon as that marriage happened. I thought that there would be a divorce real quick.”As for why Ontario won’t revive the deal, Ford indicated that Musk’s past support of Trump and his trade war against Canada went too far. This included Musk tweeting “Canada is not a real country" in response to a petition urging the Canadian government to rescind his citizenship.Recommended by Our Editors“I don’t want to deal with someone who’s attacking our country. And he was one of the number one culprits, Elon Musk. And that’s unacceptable. I can’t do business with someone that’s doing that,” Ford said. SpaceX’s Starlink business was originally supposed to receive a $100 million CAD ($73 million) contract to deploy satellite internet dishes that would connect 15,000 underserved homes and businesses. But in March, Ontario killed the deal to retaliate against Trump’s 25% tariff on Canadian goods. The decision caused anti-Starlink sentiment skyrocketed in the country, but it also undermined Ontario's effort to bring high-speed broadband to rural and remote areas. Still, some Canadian consumers continue to subscribe to and sign up for the satellite internet service, which has been pushing Starlink promotions in the country. In the meantime, Musk’s emerging feud with Trump could trigger some harsh consequences for SpaceX, which receives numerous federal contracts. In a Truth Social post on Thursday, the President said: “The easiest way to save money in our Budget, Billions and Billions of Dollars, is to terminate Elon’s Governmental Subsidies and Contracts.”

AGI will be embedded into humanoid robots, which makes white-collar and blue-collar jobs a target ... More for walking/talking automation.getty In today’s column, I examine the highly worrisome qualms expressed that the advent of artificial general intelligence (AGI) is likely to usurp white-collar jobs. The stated concern is that since AGI will be on par with human intellect, any job that relies principally on intellectual pursuits such as typical white-collar work will be taken over via the use of AGI. Employers will realize that rather than dealing with human white-collar workers, they can more readily get the job done via AGI. This, in turn, has led to a rising call that people should aim toward blue-collar jobs, doing so because (presumably) those forms of employment will not be undercut via AGI. Sorry to say, that misses the bigger picture, namely that AGI when combined with humanoid robots is coming not only for white-collar jobs but also blue-collar jobs too. It is a proverbial double-whammy when it comes to the attainment of AGI. Let’s talk about it. This analysis of an innovative AI breakthrough is part of my ongoing Forbes column coverage on the latest in AI, including identifying and explaining various impactful AI complexities (see the link here). Heading Toward AGI And ASI First, some fundamentals are required to set the stage for this weighty discussion. There is a great deal of research going on to further advance AI. The general goal is to either reach artificial general intelligence (AGI) or maybe even the outstretched possibility of achieving artificial superintelligence (ASI). AGI is AI that is considered on par with human intellect and can seemingly match our intelligence. ASI is AI that has gone beyond human intellect and would be superior in many if not all feasible ways. The idea is that ASI would be able to run circles around humans by outthinking us at every turn. For more details on the nature of conventional AI versus AGI and ASI, see my analysis at the link here. We have not yet attained AGI. In fact, it is unknown as to whether we will reach AGI, or that maybe AGI will be achievable in decades or perhaps centuries from now. The AGI attainment dates that are floating around are wildly varying and wildly unsubstantiated by any credible evidence or ironclad logic. ASI is even more beyond the pale when it comes to where we are currently with conventional AI. AGI Problem Only Half Seen Before launching into the primary matter at hand in this discussion, let’s contemplate a famous quote attributed to Charles Kettering, a legendary inventor, who said, “A problem well-stated is a problem half-solved.” I bring this up because those loud clamors right now about the assumption that AGI will replace white-collar workers are only seeing half of the problem. The problem as they see it is that since AGI is intellectually on par with humans, and since white-collar workers mainly use intellect in their work endeavors, AGI is going to be used in place of humans for white-collar work. I will in a moment explain why that’s only half of the problem and there is a demonstrative need to more carefully and fully articulate the nature of the problem. Will AGI Axiomatically Take White-Collar Jobs On a related facet, the belief that AGI will axiomatically replace white-collar labor makes a number of other related key assumptions. I shall briefly explore those and then come back to why the problem itself is only half-baked. The cost of using AGI for doing white-collar work will need to be presumably a better ROI choice over human workers. If not, then an employer would be wiser to stick with humans rather than employing AGI. There seems to often be an unstated belief that AGI is necessarily going to be a less costly route than employing humans. We don’t know yet what the cost of using AGI will be. It could be highly expensive. Indeed, some are worried that the world will divide into the AGI haves and AGI have-nots, partially due to the exorbitant cost that AGI might involve. If AGI is free to use, well, that would seem to be the nail in the coffin related to using human workers for the same capacity. Another angle is that AGI is relatively inexpensive in comparison to human labor. In that case, the use of AGI is likely to win over human labor usage. But if the cost of AGI is nearer to the cost of human labor (all in), or more so, then employers would rationally need to weigh the use of one versus the other. Note that when referring to the cost of human labor, there is more to that calculation than simply the dollar-hour labor rate per se. There are lots of other less apparent costs, such as the cost to manage human labor, the cost of dealing with HR-related issues, and many other factors that come into the weighty matter. Thus, an AGI versus human labor ROI will be more complex than it might seem at an initial glance. In addition, keep in mind that AGI would seemingly be readily switched on and off, and have other capacities that human labor would not equally tend to allow. The Other Half Is Coming Too Assume that by and large the advent of AGI will decimate the need for white-collar human labor. The refrain right now is that people should begin tilting toward blue-collar jobs as an alternative to white-collar jobs. This is a logical form of thinking in the sense that AGI as an intellectual mechanism would be unable to compete in jobs that involve hands-on work. A plumber needs to come to your house and do hands-on work to fix your plumbing. This is a physicality that entails arriving at your physical home, physically bringing and using tools, and physically repairing your faulty home plumbing. A truck driver likewise needs to sit in the cab of a truck and drive the vehicle. These are physically based tasks. There is no getting around the fact that these are hands-on activities. Aha, yes, those are physical tasks, but that doesn’t necessarily mean that only human hands can perform them. The gradual emergence of humanoid robots will provide an alternative to human hands. A humanoid robot is a type of robot that is built to resemble a human in form and function. You’ve undoubtedly seen those types of robots in the many online video recordings showing them walking, jumping, grasping at objects, and so on. A tremendous amount of active research and development is taking place to devise humanoid robots. They look comical right now. You watch those videos and laugh when the robot trips over a mere stick lying on the ground, something that a human would seldom trip over. You scoff when a robot tries to grasp a coffee cup and inadvertently spills most of the coffee. It all seems humorous and a silly pursuit. Keep in mind that we are all observing the development process while it is still taking place. At some point, those guffaws of the humanoid robots will lessen. Humanoid robots will be as smooth and graceful as humans. This will continue to be honed. Eventually, humanoid robots will be less prone to physical errors that humans make. In a sense, the physicality of a humanoid robot will be on par with humans, if not better, due to its mechanical properties. Do not discount the coming era of quite physically capable humanoid robots. AGI And Humanoid Robots Pair Up You might remember that in The Wonderful Wizard of Oz, the fictional character known as The Strawman lacked a brain. Without seeming to anthropomorphize humanoid robots, the current situation is that those robots typically use a form of AI that is below the sophistication level of modern generative AI. That’s fine for now due to the need to first ensure that the physical movements of the robots get refined. I have discussed that a said-to-be realm of Physical AI is going to be a huge breakthrough with incredible ramifications, see my analysis at the link here. The idea underlying Physical AI is that the AI of today is being uplifted by doing data training on the physical world. This also tends to include the use of World Models, consisting of broad constructions about how the physical world works, such as that we are bound to operate under conditions of gravity, and other physical laws of nature, see the link here. The bottom line here is that there will be a close pairing of robust AI with humanoid robots. Imagine what a humanoid robot can accomplish if it is paired with AGI. I’ll break the suspense and point out that AGI paired with humanoid robots means that those robots readily enter the blue-collar worker realm. Suppose your plumbing needs fixing. No worries, a humanoid robot that encompasses AGI will be sent to your home. The AGI is astute enough to carry on conversations with you, and the AGI also fully operates the robot to undertake the plumbing tasks. How did the AGI-paired humanoid robot get to your home? Easy-peasy, it drove a car or truck to get there. I’ve previously predicted that all the work on devising autonomous vehicles and self-driving cars will get shaken up once we have suitable humanoid robots devised. There won’t be a need for a vehicle to contain self-driving capabilities. A humanoid robot will simply sit in the driver’s seat and drive the vehicle. This is a much more open-ended solution than having to craft components that go into and onto a vehicle to enable self-driving. See my coverage at the link here. Timing Is Notable One of the reasons that many do not give much thought to the pairing of AGI with humanoid robots is that today’s humanoid robots seem extraordinarily rudimentary and incapable of performing physical dexterity tasks on par with human capabilities. Meanwhile, there is brazen talk that AGI is just around the corner. AGI is said to be within our grasp. Let’s give the timing considerations a bit of scrutiny. There are three primary timing angles: Option 1: AGI first, then humanoid robots. AGI is attained before humanoid robots are sufficiently devised. Option 2: Humanoid robots first, then AGI. Humanoid robots are physically fluently adept before AGI is attained. Option 3: AGI and humanoid robots arrive about at the same time. AGI is attained and at the same time, it turns out that humanoid robots are fluently adept too, mainly by coincidence and not due to any cross-mixing. A skeptic would insist that there is a fourth possibility, consisting of the possibility that we never achieve AGI and/or we fail to achieve sufficiently physically capable humanoid robots. I am going to reject that possibility. Perhaps I am overly optimistic, but it seems to me that we will eventually attain AGI, and we will eventually attain physically capable humanoid robots. I shall next respectively consider each of the three genuinely reasonable possibilities. Option 1: AGI First, Then Humanoid Robots What if we manage to attain AGI before we manage to achieve physically fluent humanoid robots? That’s just fine. We would indubitably put AGI to work as a partner with humans in figuring out how we can push along the budding humanoid robot development process. It seems nearly obvious that with AGI’s capable assistance, we would overcome any bottlenecks and soon enough arrive at top-notch physically adept humanoid robots. At that juncture, we would then toss AGI into the humanoid robots and have ourselves quite an amazing combination. Option 2: Humanoid Robots First, Then AGI Suppose that we devise very physically adept humanoid robots but have not yet arrived at AGI. Are we in a pickle? Nope. We could use conventional advanced AI inside those humanoid robots. The combination would certainly be good enough for a wide variety of tasks. The odds are that we would need to be cautious about where such robots are utilized. Nonetheless, we would have essentially walking, talking, and productive humanoid robots. If AGI never happens, oh well, we end up with pretty good humanoid robots. On the other hand, once we arrive at AGI, those humanoid robots will be stellar. It’s just a matter of time. Option 3: AGI And Humanoid Robots At The Same Time Let’s consider the potential of AGI and humanoid robots perchance being attained around the same time. Assume that this timing isn’t due to an outright cross-mixing with each other. They just so happen to advance on a similar timeline. I tend to believe that’s the most likely of the three scenarios. Here’s why. First, despite all the hubris about AGI being within earshot, perhaps in the next year or two, which is a popular pronouncement by many AI luminaries, I tend to side with recent surveys of AI developers that put the date around the year 2040 (see my coverage at the link here). Some AI luminaires sneakily play with the definition of AGI in hopes of making their predictions come true sooner, akin to moving the goalposts to easily score points. For my coverage on Sam Altman’s efforts of moving the cheese regarding AGI attainment, see the link here. Second, if you are willing to entertain the year 2040 as a potential date for achieving AGI, that’s about 15 years from now. In my estimation, the advancements being made in humanoid robots will readily progress such that by 2040 they will be very physically adept. Probably be sooner, but let’s go with the year 2040 for ease of contemplation. In my view, we will likely have humanoid robots doing well enough that they will be put into use prior to arriving at AGI. The pinnacle of robust humanoid robots and the attainment of AGI will roughly coincide with each other. Two peas in a pod.Impact Of Enormous Consequences In an upcoming column posting, I will examine the enormous consequences of having AGI paired with fully physically capable humanoid robots. As noted above, this will have a humongous impact on white-collar work and blue-collar work. There will be gargantuan economic impacts, societal impacts, cultural impacts, and so on. Some final thoughts for now. A single whammy is already being hotly debated. The debates currently tend to be preoccupied with the loss of white-collar jobs due to the attainment of AGI. A saving grace seems to be that at least blue-collar jobs are going to be around and thriving, even once AGI is attained. The world doesn’t seem overly gloomy if you can cling to the upbeat posture that blue-collar tasks remain intact. The double whammy is a lot more to take in. But the double whammy is the truth. The truth needs to be faced. If you are having doubts as a human about the future, just remember the famous words of Vince Lombardi: “Winners never quit, and quitters never win.” Humankind can handle the double whammy. Stay tuned for my upcoming coverage of what this entails.

Larry Fabbroni is an architect, strategic advisor, and Chief Innovation Officer for Practice of Architecture. Throughout his career, he has led efforts to reform studio culture and innovate practice. He earned his MBA from the University of Chicago’s Booth School of Business. In 2017, as leaders in the AIA’s Young Architects Forum (YAF), we led the launch of the Practice Innovation Lab (PIL) and hosted a symposium that imagined new architectural practice models. At that time, we already felt that practice innovation was overdue in a profession that has not seen scaled disruption to its business model in over a century. Today, we are confident that there has never been a more critical time for the profession to embrace innovation. Redefining Innovation Henley Hall: Institute for Energy Efficiency by KieranTimberlake, Santa Barbara, California | KieranTimberlake’s research expertise creates value beyond a baseline labor model.  Currently, artificial intelligence dominates strategy conversations, but just as we saw back in 2017, larger patterns prompt calls for innovation. Talent attraction is increasingly challenging, disruptive technology continues to emerge, and actors from outside our industry show growing interest in the space. While incremental innovation has long been a part of the profession, relatively few firms have adopted new practices that create value beyond a baseline labor model. Firms such as KieranTimberlake have shown that research expertise can do this. MASS Design has pioneered a mission-driven approach. BIG has taken on the role of architect-as-developer. Snøhetta houses a product design division. We could continue to list great firms that have pushed the boundaries of practice, but they represent exceptions that have yet to be recognized as new standards. Indeed, the confluence of those factors that led to the original PIL continues to make the case that the time for scaled innovation is now. A Melting Iceberg: Incremental Changes Depleting the Profession Powerhouse Telemark by Snøhetta, Vestfold og Telemark, Norway | Photo by Ivar Kvaal | Snøhetta houses a product design division, innovatively presenting a alternative business model for firms.  One of the dangers of operating in a slow-moving industry is that change is difficult to detect and even more challenging to comprehend. If an iceberg loses 1% of mass per year, it’s tough to take notice, but the end result is catastrophic. This is what is happening to our profession. For newcomers, if it feels like there are increasingly more attractive opportunities elsewhere, that’s because there are. For seasoned professionals, if it feels like it’s become more challenging to maintain the same levels of prosperity, that’s because it has. Less(er) Talent In some ways, the shift towards companies recognizing “talent” as their most excellent resource has bewildered architects: we have always relied on talent. However, the patterns of talent leaving our profession are concerning. We say “feel” because there is no significant data. We spoke to Kendall A. Nicholson, Senior Director of Research at the Association of Collegiate Schools of Architecture (ACSA), who confirmed that aggregated data on graduate placement does not exist. So we inquired about what placement looks like at several programs around the country. Omar Khan, Head of the Carnegie Mellon University School of Architecture, informed us that approximately 90% of students pursue a minor to expand their horizons, and that in 2022, nearly one in three graduates entered the tech sector. Khan stated that these opportunities aren’t just student-driven — large innovative companies increasingly seek the value that graduates of architecture schools will provide. This increasing difficulty in capturing the talent that architecture schools are producing results in a shrinking and diluted talent pool. For a profession so reliant on human resources, this presents extreme risk. Pay Gaps In an increasingly expensive world, we are not able to compete for the best talent with emerging industries. It’s easy to understand why a popular career pivot for architects has become UX design. Designing user experience for websites pays significantly better than designing the same for the built environment. According to Glassdoor, 2023 entry-level UX designers earned an average of $78K, while the AIA salary calculator suggests architecture grads can expect to earn an average of $59 K. The talent we do attract into the profession often loses interest when they experience low pay and long hours, all while most firms lack clear paths and criteria for advancement or compensation increases. A Smaller Piece of the Pie Examining data in isolation, one might conclude that the profession continues to grow; the number of architects has increased substantially over the last century, and this trend has persisted in recent years. The problem with this growth is that the estimated share of the US GDP for Architectural Services has shrunk over time. This is not a manageable number to measure before 1999, when NCARB first aggregated local jurisdictional data. Due to limitations in industry economic data, we’re only showing data since 2011 for the purposes of this article. In that time, the number of architects has grown, the market size for services has grown, but the share those services represent as a portion of the US GDP has declined — by 15% if we use US Census data to almost 30% if we use industry research data (we used IbisWorld.com, however we found data that suggested a worse and others that offered a slightly better picture). To put it another way, architecture is a stagnant industry with a shrinking share of the economy. It’s challenging to examine this data and emerge feeling confident about the profession, but there is a silver lining. The biggest impediment to innovation for architects is not a lack of talent, but rather the business model. Design thinking has been widely adopted throughout the world as a key component of innovation processes; however, the problem is that we operate in the realm of professional services, which inherently is not well-suited to promoting innovation. Reliance on that formula is causing our iceberg to melt. The Tsunami: The AI Tidal Wave is Here The Rwanda Institute for Conservation Agriculture by MASS Design Group, Rwanda | MASS Design has pioneered a mission-driven approach that creates value beyond a baseline labor model.  As we confront the exodus of talent, it is easy for both firm owners and clients to imagine AI bringing efficiencies and replacing “CAD-monkeys” with machines. However, any firm that wants to operate — and win — as anything more than a low-cost provider will need a strategy to increase value, not just cut costs. AI is merely part of the toolbox required to confront a perfect storm of forces. Jobs will Disappear Goldman Sachs predicts that as much as 37% of our industry tasks will be replaced by AI. Many see this as a pathway to lower costs and increased profits. However, that is short-sighted. Markets will adjust quickly and demand lower costs for services; additional new value will need to be articulated and proven, and this will only happen through innovation. New Jobs will Emerge (but fewer of them) AI prophets often emphasize that technological innovation has historically led to net employment gains. Previous World Economic Forum estimates predicted losses of up to 85 million existing jobs worldwide, with parallel gains of as many as 97 million new jobs. However, these estimates were revised in the WEF 2023 Economic Outlook, which now anticipates a net loss of 14 million jobs. This stark outlook signals an even greater need for architects to become more innovative. The 2024 RIBA AI Report indicates that 41% of architecture firms were already utilizing AI, though current tools are indeed just the beginning. Marketing, business development and content creation will be standard areas of AI deployment moving forward. Still, revolutionary changes will come in how we learn, not only to use new tools, but also to collaborate with digital agents. How will this happen? We can theorize, but it is not possible to know for sure until it arrives, so we need to have a plan before we can see the tidal wave from land. The Alien Invasion: Outsiders Are Entering Our Orbit VIA 57 West by BIG – Bjarke Ingels Group, New York City, New York | BIG has pioneered a new model for practice by taking on the role of architect-as-developer. For years, we’ve heard cries that “architects gave away the role of master builder.” But how much did architects actually give, and how much was taken by innovative competition? This distinction is critical because the wagons are circling, and the AEC space has become ever more attractive to investors. Venture Capital and Private Equity Investment The numbers are often difficult to parse because architecture can impact so many verticals and does not operate as its own sector in the investment realm; however, the trends suggest a groundswell is underway. A 2023 McKinsey report shows that construction tech deals nearly doubled from 2019 to 2022, growing by 85%. At the same period, the number of deals increased by 30%, indicating that interest continues to grow. An increasing size of deals also suggests a maturity of the market. As interest in infrastructure investments has declined from its high in 2020, and along with real estate, has been blunted by high interest rates, institutional investors continue to see opportunities in the AEC space. Firm Acquisitions AEC firms that deliver predictable returns have proven to be attractive targets for PE firms. In the second quarter of 2024, private equity firms accounted for over one-third of AEC firm mergers and acquisitions. For M&A deals, the industry has seen an increase in attractiveness with expanded infrastructure spending as a catalyst. However, this interest can also be tied to the lack of innovation that has resulted in an industry ripe for consolidation. M&A orchestrators generate large amounts of profit by streamlining operations, eliminating redundancies, and then stamping out competition. An entire community has been built around this, with AEC Advisors hosting an annual “Private Equity Summit” that brings together CEOs of AEC firms with PE investors. Startups As an extension of the growing interest from venture capital in the space, there is an upward trend in the AEC space being targeted for disruption by entrepreneurs who see an industry that represents a significant portion of the global GDP. AEC Works, a project of e-verse that catalogs AEC startups and investors, lists nearly 800 startups from around the world, with almost 200 identified as “architecture-focused.” The signal is clear: startups are looking to figure out how to do what you do cheaper, better, or perhaps both. Combining this environment with depleted talent pools, a declining share of GDP, and revolutionary technology, it is a correct response to be alarmed. Significant change is inevitable. It is time for architects to see the same opportunities that investors and entrepreneurs see, and learn to navigate within these spaces. The Great Opportunity Throughout history, new actors have enjoyed a “leap-frog” effect and been able to surpass established incumbents to reshape industries, markets and economies. From climate change to pandemic ripple effects, to the housing crisis, to generational shifts in the workforce, there are many forces that directly impact the work of architects and call for innovation. The need for new ways of designing and delivering different components of the built environment is ever-present and will be solved by teams that either include — and might be led by — architects, or those that do not. Most end users will only care if the resulting product is superior. This time of tension is indeed a time of great opportunity. Architects who embrace innovation in pursuing new iterations of our dated business models may actually achieve what many of us have dreamed of from the start: to leave a positive mark on the world. We think the future of the profession depends on it. Top image: Powerhouse Telemark by Snøhetta, Vestfold og Telemark, Norway The post Architects, Your Real Competition Isn’t AI — It’s Business Complacency appeared first on Journal.

Drones Set To Deliver Benefits for Labor-Intensive Industries: Forrester By John P. Mello Jr. June 3, 2025 5:00 AM PT ADVERTISEMENT Quality Leads That Turn Into Deals Full-service marketing programs from TechNewsWorld deliver sales-ready leads. Segment by geography, industry, company size, job title, and more. Get Started Now. Aerial drones are rapidly assuming a key role in the physical automation of business operations, according to a new report by Forrester Research. Aerial drones power airborne physical automation by addressing operational challenges in labor-intensive industries, delivering efficiency, intelligence, and experience, explained the report written by Principal Analyst Charlie Dai with Frederic Giron, Merritt Maxim, Arjun Kalra, and Bill Nagel. Some industries, like the public sector, are already reaping benefits, it continued. The report predicted that drones will deliver benefits within the next two years as technologies and regulations mature. It noted that drones can help organizations grapple with operational challenges that exacerbate risks and inefficiencies, such as overreliance on outdated, manual processes, fragmented data collection, geographic barriers, and insufficient infrastructure. Overreliance on outdated manual processes worsens inefficiencies in resource allocation and amplifies safety risks in dangerous work environments, increasing operational costs and liability, the report maintained. “Drones can do things more safely, at least from the standpoint of human risk, than humans,” said Rob Enderle, president and principal analyst at the Enderle Group, an advisory services firm, in Bend, Ore. “They can enter dangerous, exposed, very high-risk and even toxic environments without putting their operators at risk,” he told TechNewsWorld. “They can be made very small to go into areas where people can’t physically go. And a single operator can operate several AI-driven drones operating autonomously, keeping staffing levels down.” Sensor Magic “The magic of the drone is really in the sensor, while the drone itself is just the vehicle that holds the sensor wherever it needs to be,” explained DaCoda Bartels, senior vice president of operations with FlyGuys, a drone services provider, in Lafayette, La. “In doing so, it removes all human risk exposure because the pilot is somewhere safe on the ground, sending this sensor, which is, in most cases, more high-resolution than even a human eye,” he told TechNewsWorld. “In essence, it’s a better data collection tool than if you used 100 people. Instead, you deploy one drone around in all these different areas, which is safer, faster, and higher resolution.” Akash Kadam, a mechanical engineer with Caterpillar, maker of construction and mining equipment, based in Decatur, Ill., explained that drones have evolved into highly functional tools that directly respond to key inefficiencies and threats to labor-intensive industries. “Within the manufacturing and supply chains, drones are central to optimizing resource allocation and reducing the exposure of humans to high-risk duties,” he told TechNewsWorld. “Drones can be used in factory environments to automatically inspect overhead cranes, rooftops, and tight spaces — spaces previously requiring scaffolding or shutdowns, which carry both safety and cost risks,” he said. “A reduction in downtime, along with no requirement for manual intervention in hazardous areas, is provided through this aerial inspection by drones.” “In terms of resource usage, drones mounted with thermal cameras and tools for acquiring real-time data can spot bottlenecks, equipment failure, or energy leakage on the production floor,” he continued. “This can facilitate predictive maintenance processes and [optimal] usage of energy, which are an integral part of lean manufacturing principles.” Kadam added that drones provide accurate field mapping and multispectral imaging in agriculture, enabling the monitoring of crop health, soil quality, and irrigation distribution. “Besides the reduction in manual scouting, it ensures more effective input management, which leads to more yield while saving resources,” he observed. Better Data Collection The Forrester report also noted that drones can address problems with fragmented data collection and outdated monitoring systems. “Drones use cameras and sensors to get clear, up-to-date info,” said Daniel Kagan, quality manager at Rogers-O’Brien Construction, a general contractor in Dallas. “Some drones even make 3D maps or heat maps,” he told TechNewsWorld. “This helps farmers see where crops need more water, stores check roof damage after a storm, and builders track progress and find delays.” “The drone collects all this data in one flight, and it’s ready to view in minutes and not days,” he added. Dean Bezlov, global head of business development at MYX Robotics, a visualization technology company headquartered in Sofia, Bulgaria, added that drones are the most cost and time-efficient way to collect large amounts of visual data. “We are talking about two to three images per second with precision and speed unmatched by human-held cameras,” he told TechNewsWorld. “As such, drones are an excellent tool for ‘digital twins’ — timestamps of the real world with high accuracy which is useful in industries with physical assets such as roads, rail, oil and gas, telecom, renewables and agriculture, where the drone provides a far superior way of looking at the assets as a whole,” he said. Drone Adoption Faces Regulatory Hurdles While drones have great potential for many organizations, they will need to overcome some challenges and barriers. For example, Forrester pointed out that insurers deploy drones to evaluate asset risks but face evolving privacy regulations and gaps in data standardization. Media firms use drones to take cost-effective, cinematic aerial footage, but face strict regulations, it added, while in urban use cases like drone taxis and cargo transport remain experimental due to certification delays and airspace management complexities. “Regulatory frameworks, particularly in the U.S., remain complex, bureaucratic, and fragmented,” said Mark N. Vena, president and principal analyst with SmartTech Research in Las Vegas. “The FAA’s rules around drone operations — especially for flying beyond visual line of sight [BVLOS] — are evolving but still limit many high-value use cases.” “Privacy concerns also persist, especially in urban areas and sectors handling sensitive data,” he told TechNewsWorld. “For almost 20 years, we’ve been able to fly drones from a shipping container in one country, in a whole other country, halfway across the world,” said FlyGuys’ Bartels. “What’s limiting the technology from being adopted on a large scale is regulatory hurdles over everything.” Enderle added that innovation could also be a hangup for organizations. “This technology is advancing very quickly, making buying something that isn’t instantly obsolete very difficult,” he said. “In addition, there are a lot of drone choices, raising the risk you’ll pick one that isn’t ideal for your use case.” “We are still at the beginning of this trend,” he noted. “Robotic autonomous drones are starting to come to market, which will reduce dramatically the need for drone pilots. I expect that within 10 years, we’ll have drones doing many, if not most, of the dangerous jobs currently being done by humans, as robotics, in general, will displace much of the labor force.” John P. Mello Jr. has been an ECT News Network reporter since 2003. His areas of focus include cybersecurity, IT issues, privacy, e-commerce, social media, artificial intelligence, big data and consumer electronics. He has written and edited for numerous publications, including the Boston Business Journal, the Boston Phoenix, Megapixel.Net and Government Security News. Email John. Leave a Comment Click here to cancel reply. Please sign in to post or reply to a comment. New users create a free account. Related Stories More by John P. Mello Jr. view all More in Emerging Tech