Ground Truths

• Sir John Bell: Transforming Life Science and Medicine's Future

  Audio FileGround Truths can also be found on Apple Podcasts, Spotify and YouTube.The UK is the world leader in human genomics, and laid the foundation for advancing medicine with the UK Biobank, Genomes England and now Our Future Health (w/ 5 million participants). Sir John Bell is a major force in driving and advising these and many other initiatives. After 22 years as the Regius Professor of Medicine at the University of Oxford he left in 2024 to be President of the Ellison Institute of Technology. Professor Bell has been duly recognized in the UK: knighted in 2015 and appointed Companion of Honor in 2023. In our conversation, you will get a sense for how EIT will be transformational for using A.I. and life science for promoting human health.Transcript with audio links Eric Topol (00:06):Hello, this is Eric Topol from Ground Truths. And I'm really delighted to welcome today, Sir John Bell who had an extraordinary career as a geneticist, immunologist, we'll talk about several initiatives he's been involved with during his long tenure at University of Oxford, recently became head of the Ellison Institute of Technology (EIT) in the UK. So welcome, John.Sir John Bell (00:30):Thanks, Eric. Thanks very much for having me.Eric Topol (00:34):Well, I think it's just extraordinary the contributions that you have made and continue to make to advance medicine, and I thought what we could do is get into that. I mean, what's interesting, you have had some notable migrations over your career, I think starting in Canada, at Stanford, then over as Rhodes Scholar in Oxford. And then you of course had a couple of decades in a very prestigious position, which as I understand was started in 1546 by King Henry VII, and served as the Regius Professor of Medicine at the University of Oxford. Do I have that right?Sir John Bell (01:11):It was actually Henry VIII, but you were close.Eric Topol (01:14):Henry VIII, that's great. Yeah. Okay, good. Well, that's a pretty notable professorship. And then of course in recent times you left to head up this pretty formidable new institute, which is something that's a big trend going on around the world, particularly in the US and we'll talk about. So maybe we can start with the new thing. Tell us more about the Ellison Institute of Technology (EIT), if you will.Sir John Bell (01:47):Yeah. So as you know, Larry Ellison has been one of the great tech entrepreneurs focused really on developing terrific databases over his career and through Oracle, which is the company that he founded. And Larry is really keen to try and give back something substantial to the world, which is based on science and technology. So he and I did quite a bit together over the Covid pandemic. He and I talked a lot about what we're doing and so on. He came to visit afterwards and he had, I think he decided that the right way to make his contributions would be to set up an institute that would be using the state-of-the-art science and technology with a lot of AI and machine learning, but also some of the other modern tools to address the major problems in healthcare, in food security, in green energy and climate change and in global governance.Sir John Bell (02:49):So anyway, he launched this about 18 months ago. He approached me to ask whether I would run it. He wanted to set it up outside Oxford, and he wanted to do something which is a bit different than others. And that is his view was that we needed to try and create solutions to these problems which are commercially viable and not all the solutions are going to be commercially viable, but where you can create those, you make them sustainable. So the idea is to make sure that we create solutions that people want to buy, and then if they buy them, you can create a sustainable solution to those issues. So we are actually a company, but we are addressing many of the same problems that the big foundations are addressing. And the big issues that you and I talk about in health, for example, are all on our list. So we're kind of optimistic as to where this will go and Larry's supporting the project and we're going to build out an institute here which will have about 5,000 people in it, and we'll be, I think a pretty exciting new addition to the science and technology ecosystem globally.Eric Topol (04:02):Well, I know the reverberations and the excitement is palpable and some of the colleagues I've spoken to, not just in England, but of course all over the world. So congratulations on that. It was a big move for you to leave the hardcore academics. And the other thing I wanted to ask you, John, is you had distinguished your career in immunology, in genetics, type 1 diabetes and other conditions, autoimmune conditions, and now you've really diversified, as you described with these different areas of emphasis at the new institute. Is that more fun to do it or do you have deputies that you can assign to things like climate change in other areas?Sir John Bell (04:50):Trust me, Eric, I'm not making any definitive decisions about areas I know nothing about, but part of this is about how do you set up leadership, run a team, get the right people in. And I have to say one of the really interesting things about the institute is we've been able to recruit some outstanding people across all those domains. And as you know, success is almost all dependent on people. So we're really pretty optimistic we're going to have a significant impact. And of course, we also want to take risks because not a lot of point in us doing stuff that everybody else is doing. So we're going to be doing some things that are pretty way out there and some of them will fail, so we are just going to get used to trying to make sure we get a few of them across the finish line. But the other thing is that, and you've experienced this too, you never get too old to learn. I mean, I'm sucking up stuff that I never thought I would ever learn about, which is fun actually, and really marvel.Eric Topol (05:55):It's fantastic. I mean, you've really broadened and it's great that you have the runway to get these people on board and I think you're having a big building that's under construction?Sir John Bell (06:07):Yeah, we've got the original building that Larry committed to is about 330,000 square feet of space. I mean, this is completely amazing, but we are of course to accommodate up to 5,000 people, we're going to need more than that. So we are looking at a much wider campus here that'll involve more than just that building. I think we'll end up with several million square feet of space by the time we're finished. So mean, it's a really big project, but we've already made progress in some domains to try and get projects and the beginnings of companies on the road to try and solve some of the big problems. So we're quite excited about it.Eric Topol (06:49):Now you, I assume it's pretty close to Oxford, and will you have some kind of inter interactions that are substantial?Sir John Bell (06:58):Yeah, so the university's been terrific about this actually, because of course most universities would say, well, why don't you do it inside the university and just give us the money and it'll all be fine. So of course Larry. Larry wasn't born yesterday, so I said, well, thank you very much, but I think we'll probably do this nearby. But the university also realized this is a really exciting opportunity for them and we've got a really good relationship with them. We've signed an agreement with them as to who will work where. We've agreed not to steal a lot of their staff. We're going to be bringing new people into the ecosystem. Some of the university people will spend some time with us and sometime in the university, so that will help. But we're also bringing quite a few new people into the setting. So the university has been really positive. And I think one of the things that's attractive to the university, and you'll be familiar with this problem in the UK, is that we're quite good. The discovery science here is pretty good.Sir John Bell (08:06):And we do startups now at scale. So Oxford does lots of little startup companies in the biotech space and all the rest of it, but we never scale any of these companies because there isn't the depth of capital for scaling capital to get these things scaled. And so, in a way what we're trying to do here at Ellison actually avoids that problem because Larry knows how to scale companies, and we've got the financial support now. If we have things that are really successful, we can build the full stack solution to some of these problems. So I think the university is really intrigued as to how we might do that. We're going to have to bring some people in that know how to do that and build billion dollar companies, but it's sufficiently attractive. We've already started to recruit some really outstanding people. So as a way to change the UK system broadly, it's actually quite a good disruptive influence on the way the thing works to try and fix some of the fundamental problems.Eric Topol (09:07):I love that model and the ability that you can go from small startups to really transformative companies have any impact. It fits in well with the overall objectives, I can see that. The thing that also is intriguing regarding this whole effort is that in parallel we've learned your influence. The UK is a genomics world leader without any question and no coincidence that that's been your area of emphasis in your career. So we've watched these three initiatives that I think you were involved in the UK Biobank, which has had more impact than any cohort ever assembled. Every day there's another paper using that data that's coming out. There's Genomes England, and then now Our Future Health, which a lot of people don't know about here, which is well into the 5 million people enrollment. Can you tell us about, this is now 15 years ago plus when these were started, and of course now with a new one that's the biggest ever. What was your thinking and involvement and how you built the UK to be a world leader in this space?Sir John Bell (10:26):So if you turn the clock back 20 years, or actually slightly more than 25 years ago, it was clear that genomics was going to have a play. And I think many of us believed that there was going to be a genetic element to most of the major common disease turn out to be true. But at the time, there were a few skeptics, but it seemed to us that there was going to be a genetic story that underpinned an awful lot of human disease and medicine. And we were fortunate because in Oxford as you know, one of my predecessors in the Regius job was Richard Doll, and he built up this fantastic epidemiology capability in Oxford around Richard Peto, Rory Collins, and those folks, and they really knew how to do large scale epidemiology. And one of the things that they'd observed, which is it turns out to be true with genetics as well, is a lot of the effects are relatively small, but they're still quite significant. So you do need large scale cohorts to understand what you're doing. And it was really Richard that pioneered the whole thinking behind that. So when we had another element in the formula, which was the ability to detect genetic variation and put that into the formula, it seemed to me that we could move into an era where you could set up, again, large cohorts, but build into the ability to have DNA, interrogate the DNA, and also ultimately interrogate things like proteomics and metabolomics, which were just in their infancy at that stage.Sir John Bell (12:04):Very early on I got together because I was at that stage at the Nuffield Chair of Medicine, and I got together, Rory and Richard and a couple of others, and we talked a little bit about what it would look like, and we agreed that a half a million people late to middle age, 45 and above would probably over time when you did the power calculations, give you a pretty good insight in most of the major diseases. And then it was really a question of collecting them and storing the samples. So in order to get it funded at the time I was on the council of the MRC and George Radda, who you may remember, was quite a distinguished NMR physiologist here. He was the chief executive of the MRC. So I approached him and I said, look, George, this would be a great thing for us to do in the UK because we have all the clinical records of these people going back for a decade, and will continue to do that.Sir John Bell (13:01):Of course, we immediately sent it out to a peer review committee in the MRC who completely trashed the idea and said, you got to be joking. So I thought, okay, that’s how that lasted. And I did say to George, I said, that must mean this is a really good idea because if it had gone straight through peer review, you would've known you were toast. So anyway, I think we had one more swing at peer review and decided in the end that wasn't going to work. In the end, George to his credit, took it to MRC council and we pitched it and everybody thought, what a great idea, let's just get on and do it. And then the Wellcome came in. Mark Walport was at the Wellcome at the time, great guy, and did a really good job at bringing the Wellcome on board.Sir John Bell (13:45):And people forget the quantum of money we had to do this at the time was about 60 million pounds. I mean, it wasn’t astonishly small. And then of course we had a couple of wise people who came in to give us advice, and the first thing they said, well, if you ever thought you were really going to be able to do genetics on 500,000 people, forget it. That'll never work. So I thought, okay, I'll just mark that one out. And then they said, and by the way, you shouldn't assume you can get any data from the health service because you'll never be able to collect clinical data on any of these people. So I said, yeah, yeah, okay, I get it. Just give us the money and let us get on. So anyway, it's quite an interesting story. It does show how conservative the community actually is for new ideas.Sir John Bell (14:39):Then I chaired the first science committee, and we decided about a year into it that we really needed the chief executive. So we got Rory Collins to lead it and done it. I sat on the board then for the next 10 years, but well look, it was a great success. And as you say, it is kind of the paradigm for now, large genetic epidemiology cohorts. So then, as you know, I advise government for many years, and David Cameron had just been elected as Prime Minister. This was in about 2010. And at the time I'd been tracking because we had quite a strong genomics program in the Wellcome Trust center, which I'd set up in the university, and we were really interested in the genetics of common disease. It became clear that the price of sequencing and Illumina was now the clear leader in the sequencing space.Sir John Bell (15:39):But it was also clear that Illumina was making significant advances in the price of sequencing because as you remember, the days when it cost $5,000 to do a genome. Anyway, it became clear that they actually had technology that gets you down to a much more sensible price, something like $500 a genome. So I approached David and I said, we are now pretty sure that for many of the rare diseases that you see in clinical practice, there is a genetic answer that can be detected if you sequenced a whole genome. So why don't we set something up in the NHS to provide what was essentially the beginnings of a clinical service to help the parents of kids with various disabilities work out what's going on, what's wrong with their children. And David had had a child with Ohtahara syndrome, which as you know is again, and so David was very, he said, oh God, I'll tell you the story about how awful it was for me and for my wife Samantha.Sir John Bell (16:41):And nobody could tell us anything about what was going on, and we weren't looking for a cure, but it would've really helped if somebody said, we know what it is, we know what the cause is, we'll chip away and maybe there will be something we can do, but at least you know the answer. So anyway, he gave us very strong support and said to the NHS, can you please get on and do it? Again massive resistance, Eric as you can imagine, all the clinical geneticists said, oh my God, what are they doing? It's complete disaster, dah, dah, dah. So anyway, we put on our tin hats and went out and got the thing going. And again, they did a really good job. They got to, their idea was to get a hundred thousand genomes done in a reasonable timeframe. I think five years we set ourselves and the technology advance, people often underestimate the parallel development of technology, which is always going on. And so, that really enabled us to get that done, and it still continues. They're doing a big neonatal program at the moment, which is really exciting. And then I was asked by Theresa May to build a life science strategy because the UK, we do this stuff not as big and broad as America, but for a small country we do life sciences pretty well.Eric Topol (18:02):That’s an understatement, by the way. A big understatement.Sir John Bell (18:04):Anyway, so I wrote the strategies in 2017 for Theresa about what we would do as a nation to support life sciences. And it was interesting because I brought a group of pharma companies together to say, look, this is for you guys, so tell us what you want done. We had a series of meetings and what became clear is that they were really interested in where healthcare was going to end up in the next 20 years. And they said, you guys should try and get ahead of that wave. And so, we agreed that one of the domains that really hadn't been explored properly, it was the whole concept of prevention.Sir John Bell (18:45):Early diagnosis and prevention, which they were smart enough to realize that the kind of current paradigm of treating everybody in the last six months of life, you can make money doing that, there's no doubt, but it doesn't really fix the problem. And so, they said, look, we would love it if you created a cohort from the age of 18 that was big enough that we could actually track the trajectories of people with these diseases, identify them at a presymptomatic stage, intervene with preventative therapies, diagnose diseases earlier, and see if we could fundamentally change the whole approach to public health. So we anyway, went back and did the numbers because of course at much wider age group, a lot of people don't get at all sick, but we thought if we collected 5 million people, we would probably have enough. That's 10% of the UK adult population.Sir John Bell (19:37):So anyway, amazingly the government said, off you go. We then had Covid, which as you know, kept you and I busy for a few years before we could get back to it. But then we got at it, and we hired a great guy who had done a bit of this in the UAE, and he came across and we set up a population health recruitment structure, which was community-based. And we rapidly started to recruit people. So we've now got 2.9 million people registered, 2.3 million people consented, and we've got blood in the bank and all the necessary data including questionnaire data for 1.5 million people growing up. So we will get to 5 million and it's amazing.Eric Topol (20:29):It is. It really is, and I’m just blown away by the progress you've made. And what was interesting too, besides you all weren't complacent about, oh, we got this UK Biobank and you just kept forging ahead. And by the way, I really share this importance of finally what has been a fantasy of primary prevention, which never really achieved. It's always, oh, after a heart attack. But that's what I wrote about in the Super Agers book, and I'll get you a copy.Sir John Bell (21:02):No, I know you're a passionate believer in this and we need to do a lot of things. So we need to work out what's the trial protocol for primary prevention. We need to get the regulators on board. We've got to get them to understand that we need diagnostics that define risk, not disease, because that's going to be a key bit of what we're going to try and do. And we need to understand that for a lot of these diseases, you have to intervene quite early to flatten that morbidity curve.Eric Topol (21:32):Yeah, absolutely. What we've learned, for example, from the UK Biobank is not just, of course the genomics that you touched on, but the proteomics, the organ clocks and all these other layers of data. So that gets me to my next topic, which I know you're all over it, which is AI.Eric Topol (21:51):So when I did the NHS review back in 2018, 2019, the group of people which were amazing that I had to work with no doubt why the UK punches well beyond its weight. I had about 50 people, and they just said, you know what? Yeah, we are the world leaders in genomics. We want to be the world leader in AI. Now these days you only hear about US and China, which is ridiculous. And you have perhaps one of the, I would say most formidable groups there with Demis and Google DeepMind, it’s just extraordinary. So all the things that the main foci of the Ellison Institute intersect with AI.Sir John Bell (22:36):They do. And we, we've got two underpinning platforms, well actually three underpinning platforms that go across all those domains. Larry was really keen that we became a real leader in AI. So he's funded that with a massive compute capacity. And remember, most universities these days have a hard time competing on compute because it's expensive.Eric Topol (22:57):Oh yeah.Sir John Bell (22:58):So that is a real advantage to us. He's also funded a great team. We've recruited some people from Demis's shop who are obviously outstanding, but also others from around Europe. So we really, we've recruited now about 15 really outstanding machine learning and AI people. And of course, we're now thinking about the other asset that the UK has got, and particularly in the healthcare space is data. So we do have some really unique data sets because those are the three bits of this that you need if you're going to make this work. So we're pretty excited about that as an underpinning bit of the whole Ellison Institute strategy is to fundamentally underpin it with very strong AI. Then the second platform is generative biology or synthetic biology, because this is a field which is sort of, I hesitate to say limped along, but it's lacked a real focus.Sir John Bell (23:59):But we've been able to recruit Jason Chin from the LMB in Cambridge, and he is one of the real dramatic innovators in that space. And we see there's a real opportunity now to synthesize large bits of DNA, introduce them into cells, microbes, use it for a whole variety of different purposes, try and transform plants at a level that people haven't done before. So with AI and synthetic biology, we think we can feed all the main domains above us, and that's another exciting concept to what we're trying to do. But your report on AI was a bit of a turning point for the UK because you did point out to us that we did have a massive opportunity if we got our skates, and we do have talent, but you can't just do it with talent these days, you need compute, and you need data. So we're trying to assemble those things. So we think we'll be a big addition to that globally, hopefully.Eric Topol (25:00):Yeah. Well that's another reason why I am so excited to talk to you and know more about this Ellison Institute just because it's unique. I mean, there are other institutes as like Chan Zuckerberg, the Arc Institute. This is kind of a worldwide trend that we're seeing where great philanthropy investments are being seen outside of government, but none have the computing resources that are being made available nor the ability to recruit the AI scientists that'll help drive this forward. Now, the last topic I want to get into with you today is one that is where you're really grounded in, and that's the immune response.Eric Topol (25:43):So it's pretty darn clear now that, well, in medicine we have nothing. We have the white cell neutrophil to lymphocyte ratio, what a joke. And then on the other hand, we can do T and B cell sequencing repertoires, and we can do all this stuff, autoantibody screens, and the list goes on and on. How are we ever going to make a big dent in health where we know the immune system is such a vital part of this without the ability to check one's immune status at any point in time in a comprehensive way? What are your thoughts about that?Sir John Bell (26:21):Yeah, so you seem to be reading my mind there. We need to recruit you over here because I mean, this is exactly, this is one of our big projects that we've got that we're leaning into, and that is that, and we all experienced in Covid the ins and outs of vaccines, what works, what doesn't work. But what very clear is that we don't really know anything about vaccines. We basically, you put something together and you hope the trial works, you've got no intermediate steps. So we're building a really substantial immunophenotyping capability that will start to interrogate the different arms of the immune response at a molecular level so that we can use a combination of human challenge models. So we've got a big human challenge model facility here, use human challenge models with pathogens and with associated vaccines to try and interrogate which bits of the immune response are responsible for protection or therapy of particular immunologically mediated diseases or infectious diseases.Sir John Bell (27:30):And a crucial bit to that. And one of the reasons people have tried this before, but first of all, the depth at which you can interrogate the immune system has changed a lot recently, you can get a lot more data. But secondly, this is again, where the AI becomes important because it isn't going to be a simple, oh, it's the T-cell, it's going to be, well, it's a bit of the T cells, but it's also a bit of the innate immune response and don't forget mate cells and don't forget a bit of this and that. So we think that if we can assemble the right data set from these structured environments, we can start to predict and anticipate which type of immune response you need to stimulate both for therapy and for protection against disease. And hopefully that will actually create a whole scientific foundation for vaccine development, but also other kinds of immune therapy and things like cancer and potentially autoimmune disease as well. So that's a big push for us. We're just busy. The lab isn't set up. We've got somebody to run the lab now. We've got the human challenge model set up with Andy Pollard and colleagues. So we're building that out. And within six months, I think we'll be starting to collect data. So I'm just kind of hoping we can get the immune system in a bit more structured, because you’re absolutely right. It's a bit pin the tail on the donkey at the moment. You have no idea what's actually causing what.Eric Topol (29:02):Yeah. Well, I didn't know about your efforts there, and I applaud that because it seems to me the big miss, the hole and the whole story about how we're going to advanced human health and with the recent breakthroughs in lupus and these various autoimmune diseases by just targeting CD19 B cells and resetting like a Ctrl-Alt-Delete of their immune system.Sir John Bell (29:27):No, it's amazing. And you wouldn't have predicted a lot of this stuff. I think that means that we haven't really got under the skin of the mechanistic events here, and we need to do more to try and get there, but there's steady advance in this field. So I'm pretty optimistic we'll make some headway in this space over the course of the next few years. So we're really excited about that. It's an important piece of the puzzle.Eric Topol (29:53):Yeah. Well, I am really impressed that you got all the bases covered here, and what a really exhilarating chance to kind of peek at what you're doing there. And we're going to be following it. I know I'm going to be following it very closely because I know all the other things that you've been involved with in your colleagues, big impact stuff. You don't take the little swings here. The last thing, maybe to get your comment, we're in a state of profound disruption here where science is getting gutted by a madman and his henchmen, whatever you want to call it, which is really obviously a very serious state. I'm hoping this is a short term hit, but worried that this will have a long, perhaps profound. Any words of encouragement that we're going to get through this from the other side of the pond?Sir John Bell (30:52):Well, I think regardless of the tariffs, the scientific community are a global community. And I think we need to remember that because our mission is a global mission, and we need to lean into that together. First of all, America is such a powerhouse of everything that's been done scientifically in the human health domain. But not only that, but across all the other domains that we work in, we can't really make the kind of progress that we need to without America being part of the agenda. So first of all, a lot of sympathy for you and your colleagues. I know it must be massively destabilizing for you, not be confident that the things that work are there to help you. But I'm pretty confident that this will settle down. Most of the science is for, well, all the science is really for public good, and I think the public recognizes it and they'll notice if it's not being prosecuted in the way that it has to be. And the global science community cannot survive without you. So we're all leaning in behind you, and I hope it will settle. One of my worries is that these things take years to set up and literally hours or minutes to destroy. So we can't afford to take years to set them back up again. So we do need to be a bit careful about that, but I still have huge confidence in what you guys can achieve and we're all behind you.Eric Topol (32:37):Well, that's really helpful getting some words of wisdom from you there, John. So this has been terrific. Thanks so much for joining, getting your perspective on what you're doing, what's important is so essential. And we’ll stay tuned for sure.Sir John Bell (32:59):And come and visit us at the EIT, Eric. We'd be glad to see you.*******************************Some of the topics that John and I discussed—immunology, A.I., genomics, and prevention—are emphasized in my new book SUPER AGERS. A quick update: It will have a new cover after making the New York Times Bestseller list and is currently ranked #25 for all books on Amazon. Thanks to so many of you for supporting the book!Here are a few recent podcasts:Dax Shepard: Dr. Mike Sanjay Gupta ***********************Thanks for reading and subscribing to Ground Truths.If you found this interesting please share it!That makes the work involved in putting these together especially worthwhile.All content on Ground Truths— newsletters, analyses, and podcasts—is free, open-access.Paid subscriptions are voluntary and all proceeds from them go to support Scripps Research. They do allow for posting comments and questions, which I do my best to respond to. Please don't hesitate to post comments and give me feedback. Many thanks to those who have contributed—they have greatly helped fund our summer internship programs for the past two years. Get full access to Ground Truths at erictopol.substack.com/subscribe

  --------  

  33:15
• Tyler Cowen: The Prototypic Polymath

  Audio file, also on Apple and SpotifyTyler Cowen, Ph.D, is the Holbert L. Harris Professor of Economics at George Mason University. He is the author of 17 books, most recently Talent.: How to Identify Energizers, Creatives, and Winners Around the World. Tyler has been recognized as one of the most influential economists of the past decade. He initiated and directs the philanthropic project Emergent Ventures, writes a blog Marginal Revolution, and a podcast Conversations With Tyler, and also writes columns for The Free Press." He is writing a new book (and perhaps his last) on Mentors. “Maybe AGI [Artificial General Intelligence] is like porn — I know it when I see it. And I’ve seen it.”—Tyler CowenOur conversation on acquiring information, A.I., A.G.I., the NIH, the assault on science, the role of doctors in the A.I. era,, the meaning of life, books of the future, and much more.Transcript with linksEric Topol (00:06):Well, hello. This is Eric Topol with Ground Truths, and I am really thrilled today to have the chance to have a conversation with Tyler Cowen, who is, when you look up polymath in the dictionary, you might see a picture of him. He is into everything. And recently in the Economist magazine 1843, John Phipps wrote a great piece profile, the man who wants to know everything. And actually, I think there's a lot to that.Tyler Cowen (00:36):That's why we need longevity work, right?Eric Topol (00:39):Right. So he's written a number of books. How many books now, Tyler?Tyler Cowen:17, I'm not sure.Eric Topol:Only 17? And he also has a blog that's been going on for over 20 years, Marginal Revolution that he does with Alex Tabarrok.Tyler Cowen (00:57):Correct.Eric Topol (00:57):And yeah, and then Conversations with Tyler, a podcast, which I think an awful lot of people are tuned into that. So with that, I'm just thrilled to get a chance to talk with you because I used to think I read a lot, but then I learned about you.“Cowen calls himself “hyperlexic”. On a good day, he claims to read four or fivebooks. Secretly, I timed him at 30 seconds per page reading a dense tract byMartin Luther. “—John Phipps, The Economist’s 1843I've been reading more from the AIs lately and less from books. So I'll get one good book and ask the AI a lot of questions.Eric Topol (01:24):Yeah. Well, do you use NotebookLM for that?Tyler Cowen (01:28):No, just o3 from OpenAI at the moment, but a lot of the models are very good. Claude, there's others.Eric Topol (01:35):Yeah, yeah. No, I see how that's a whole different way to interrogate a book and it's great. And in fact, that gets me to a topic I was going to get to later, but I'll do it now. You're soon or you have already started writing for the Free Press with Barri Weiss.Tyler Cowen (01:54):That’s right, yes. I have a piece coming out later today. It's been about two weeks. It's been great so far.“Tyler Cowen has a mind unlike any I've ever encountered. In a single conversation, it’s not at all unusual for him to toggle between DeepSeek, GLP-1s, Haitian art, sacred Tibetan music, his favorite Thai spot in L.A., and LeBron James”—Bari WeissYeah, so that's interesting. I hadn't heard of it until I saw the announcement from Barri and I thought what was great about it is she introduced it. She said, “Tyler Cowen has a mind unlike any I've ever encountered. In a single conversation, it’s not at all unusual for him to toggle between DeepSeek, GLP-1s, Haitian art, sacred Tibetan music, his favorite Thai spot in L.A., and LeBron James. Now who could do that, right. So I thought, well, you know what? I need independent confirmation of that, that is as being a polymath. And then I saw Patrick Collison, who I know at Stripe and Arc Institute, “you can have a specific and detailed discussion with him about 17th-century Irish economic thinkers, or trends in African music or the history of nominal GDP targeting. I don't know anyone who can engage in so many domains at the depth he does.” So you're an information acquirer and one of the books you wrote, I love the title Infovore.Tyler Cowen (03:09):The Age of the Infovore, that’s right.Eric Topol (03:11):I mean, have people been using that term because you are emblematic of it?“You can have a specific and detailed discussion with him about 17th-century Irish economic thinkers, or trends in African music or the history of nominal GDP targeting. I don't know anyone who can engage in so many domains at the depth he does.”—Patrick CollisonIt was used on the internet at some obscure site, and I saw it and I fell in love with that word, and I thought I should try to popularize it, but it doesn't come from me, but I think I am the popularizer of it.Yeah, well, if anybody was ingesting more information and being able to work with it. That's what I didn't realize about you, Tyler, is restaurants and basketball and all these other fine arts, very impressive. Now, one of the topics I wanted to get into you is I guess related to a topic you've written about fair amount, which is the great stagnation, and right now we're seeing issues like an attack on science. And in the past, you've written about how you want to raise the social status of scientists. So how do you see this current, I would even characterize as a frontal assault on science?Tyler Cowen (04:16):Well, I'm very worried about current Trump administration policies. They change so frequently and so unpredictably, it's a little hard to even describe what they always are. So in that sense, it's a little hard to criticize them, but I think they're scaring away talent. They might scare away funding and especially the biomedical sciences, the fixed costs behind a lot of lab work, clinical trials, they're so high that if you scare money away, it does not come back very readily or very quickly. So I think the problem is biggest perhaps for a lot of the biomedical sciences. I do think a lot of reform there has been needed, and I hope somehow the Trump policies evolve to that sort of reform. So I think the NIH has become too high bound and far too conservative, and they take too long to give grants, and I don't like how the overhead system has been done. So there's plenty of room for improvement, but I don't see so far at least that the efforts have been constructive. They've been mostly destructive.Eric Topol (05:18):Yeah, I totally agree. Rather than creative destruction it’s just destruction and it's unfortunate because it seems to be haphazard and reckless to me at least. We of course, like so many institutions rely on NIH funding for the work, but I agree that reform is fine as long as it's done in a very thought out, careful way, so we can eke out the most productivity for the best investment. Now along with that, you started Emergent Ventures where you're funding young talent.Tyler Cowen (05:57):That's right. That's a philanthropic fund. And we now have slightly over 1000 winners. They're not all young, I'd say they're mostly young and a great number of them want to go into the biomedical sciences or have done so. And this is part of what made me realize what an incredible influx of talent we're seeing into those areas. I'm not sure this is widely appreciated by the world. I'm sure you see it. I also see how much of that talent actually is coming from Canada, from Ontario in particular, and I've just become far more optimistic about computational biology and progress in biology and medical cures, fixes, whatever you want to call it, extending lives. 10 years ago, I was like, yeah, who knows? A lot of things looked pretty stuck. Then we had a number of years where life expectancy was falling, and now I think we're on the verge of a true golden age.Eric Topol (06:52):I couldn't agree with you more on that. And I know some of the people that you funded like Anne Wylie who developed a saliva test for Covid out of Yale. But as you say, there's so many great young and maybe not so young scientists all over, Canada being one great reservoir. And now of course I'm worried that we're seeing emigration rather than more immigration of this talent. Any thoughts about that?Tyler Cowen (07:21):Well, the good news is this, I'm in contact with young people almost every day, often from other countries. They still want to come to the United States. I would say I sign an O-1 letter for someone about once a week, and at least not yet has the magic been dissipated. So I'm less pessimistic than some people are, but I absolutely do see the dangers. We’re just the biggest market, the freest place we have by far the most ambitious people. I think that's actually the most significant factor. And young people sense that, and they just want to come here and there's not really another place they can go that will fit them.Eric Topol (08:04):Yeah, I mean one of the things as you've probably noted is there's these new forces that are taking on big shouldering. In fact, Patrick Collison with Arc Institute and Chan Zuckerberg for their institute and others like that, where the work you're doing with Emergent Ventures, you're supporting important projects, talents, and if this whole freefall in NIH funding and other agency funding continues, it looks like we may have to rely more on that, especially if we're going to attract some talent from outside. I don't know how else we're going to make. You're absolutely right about how we are such a great destination and great collaborations and mentors and all that history, but I'm worried that it could be in kind of a threatened mode, if you will.Tyler Cowen (08:59):I hope AI lowers costs. As you probably know at Arc, they had Greg Brockman come in for some number of months and he's one of the people, well, he helped build up Stripe, but he also was highly significant in OpenAI behind the GPT-4 model. And to have Greg Brockman at your institute doing AI for what, six months, that's a massive acceleration that actually no university had the wisdom to do, and Arc did. So I think we're seeing just more entrepreneurial thinking in the area. There's still this problem of bottlenecks. So let's say AI is great for drug discovery as it may be. Well, clinical trials then become a bigger bottleneck. The FDA becomes a bigger bottleneck. So rapid improvement in only one area while great is actually not good enough.Eric Topol (09:46):Yeah, I'm glad you brought up that effect in Arc Institute because we both know Patrick Hsu, who's a brilliant young guy who works there and has published some incredible large language models applied to life science in recent months, and it is impressive how they used AI in almost a singular way as compared to as you said, many other leading institutions. So that is I think, a really important thing to emphasize.Tyler Cowen (10:18):Arc can move very quickly. I think that's not really appreciated. So if Patrick Hsu decides Silvana Konermann, Patrick Collison, if they decide something ought to be bought or purchased or set in motion, it can happen in less than a day. And it does happen basically immediately. And it's not only that it's quicker, I think when you have quicker decisions, they're better and it's infectious to the people you're working with. And there's an understanding that the core environment is not a bureaucratic one. So it has a kind of multiplier effect through the institution.Eric Topol (10:54):Yeah, I totally agree with you. It's always been a philosophy in your mind to get stuff done, get s**t done, whatever you want to call it. They're getting it done. And that's what's so impressive. And not just that they've got some new funds available, but rather they're executing in a way that's parallel to the way the world's evolving in the AI front, which is I think faster than most people would ever have expected, anticipated. Now that gets me to a post you had on Marginal Revolution just last week, which one of the things I love about Marginal Revolution is you don't have to read a whole lot of stuff. You just give the bullets, the juice, if you will. Here you wrote o3 and AGI, is April 16th AGI day? And everybody's talking about artificial general intelligence is here. It's going to be here five years, it's going to be seven years.Eric Topol (11:50):It certainly seems to be getting closer. And in this you wrote, “I think it is AGI, seriously. Try asking it lots of questions, and then ask yourself: just how much smarter was I expecting AGI to be? As I’ve argued in the past, AGI, however you define it, is not much of a social event per se. It still will take us a long time to use it properly. Benchmarks, benchmarks, blah blah blah. Maybe AGI is like porn — I know it when I see it. And I’ve seen it.” I thought that was really well done, Tyler. Anything you want to amplify on that?Tyler Cowen (12:29):Look, if I ask at economics questions and I'm trained as an economist, it beats me. So I don't care if other people don't call it AGI, but one of the original definitions of AGI was that it would beat most experts most of the time on most matters, say 90% or above, and we're there. So people keep on shifting the goalposts. They'll say, well, sometimes it hallucinates or it's not very good at playing tic-tac toe, or there's always another complaint. Those are not irrelevant, but I'll just say, sit down, have someone write at a test of 20 questions, you're a PhD, you take the test, let o3 take the test, then have someone grade, see how you've done, then form your opinion. That's my suggestion.Eric Topol (13:16):I think it's pretty practical. I mean, enough with the Turing test, I mean, we've had that Turing test for decades, and I think the way you described it is a little more practical and meaningful these days. But its capabilities to me at least, are still beyond belief eke out of current, not just the large language models, but large reasoning models. And so, it's just gotten to a point where and it's accelerating, every week there's so many other, the competition is good for taking it to the next level.Tyler Cowen (13:50):It can do tasks and it self improves. So o3-pro will be out in a few weeks. It may be out by the time you're hearing this. I think that's obviously going to be better than just pure o3. And then GPT-5 people have said it will be this summer. So every few months there are major advances and there's no sign of those stopping.Eric Topol (14:12):Absolutely. Now, of course, you've been likened to “Treat Tyler like a really good GPT” that is because you're this information meister. What do you ask the man who you can ask anything? That's kind of what we have when we can go to any one of these sites and start our prompts, whatever. So it's kind of funny in some ways you might've annotated this with your quest for knowledge.Tyler Cowen (14:44):Well, I feel I understand the thing better than most people do for that reason, but it's not entirely encouraging to me personally, selfishly to be described that way, whether or not it's accurate. It just means I have a lot more new competition.Eric Topol (14:59):Well, I love this one. “I'm not very interested in the meaning of life, but I'm very interested in collecting information on what other people think is the meaning of life. And it's not entirely a joke” and that's also what you wrote about in the Free Press thing, that most of the things that are going to be written are going to be better AI in the media and that we should be writing books for the AI that's going to ingest them. How do you see this human AI interface growing or moving?Tyler Cowen (15:30):The AI is your smartest reader. It's your most sympathetic reader. It will remember what you tell it. So I think humans should sit down and ask, what does the AI need to know? And also, what is it that I know that's not on the historical record anywhere? That's not just repetition if I put it down, say on the internet. So there's no point in writing repetitions anymore because the AI already knows those things. So the value of what you'd call broadly, memoir, biography, anecdote, you could say secrets. It's now much higher. And the value of repeating basic truths, which by the way, I love as an economist, to be clear, like free trade, tariffs are usually bad, those are basic truths. But just repeating that people will be going to the AI and saying it again won't make the AI any better. So everything you write or podcast, you should have this point in mind.Eric Topol (16:26):So you obviously have all throughout your life in reading lots of books. Will your practice still be to do the primary reading of the book, or will you then go to o3 or whatever or the other way around?Tyler Cowen (16:42):I've become fussier about my reading. So I'll pick up a book and start and then start asking o3 or other models questions about the book. So it's like I get a customized version of the book I want, but I'm also reading somewhat more fiction. Now, AI might in time become very good at fiction, but we're not there now. So fiction is more special. It's becoming more human, and I should read more of it, and I'm doing that.Eric Topol (17:10):Yeah, no, that's great. Now, over the weekend, there was a lot of hubbub about Bill Gates saying that we won't need doctors in the next 10 years because of AI. What are your thoughts about that?Tyler Cowen (17:22):Well, that's wrong as stated, but he may have put it in a more complex way. He's a very smart guy of course. AI already does better diagnosis on humans than medical doctors. Not by a lot, but by somewhat. And that's free and that's great, but if you need brain surgery for some while, you still need the human doctor. So human doctors will need to adjust. And if someone imagines that at some point robots do the brain surgery better, well fine. But I'm not convinced that's within the next 10 years. That would surprise me.Eric Topol (17:55):So to that point, recently, a colleague of mine wrote an op-ed in the New York Times about six studies comparing AI alone versus doctors with AI. And in all six studies, the AI did better than the doctors who had access to AI. Now, you could interpret that as, well they don't know how to use AI. They have automation bias or that is true. What do you think?Tyler Cowen (18:27):It's probably true, but I would add as an interpretation, the value of meta rationality has gone up. So to date, we have not selected doctors for their ability to work with AI, obviously, but some doctors have the personal quality, it's quite distinct from intelligence, but if just knowing when they should defer to someone or something else, and those doctors and researchers will become much more valuable. They're sufficiently modest to defer to the AI and have some judgment as to when they should do that. That's now a super important quality. Over time, I hope our doctors have much more of that. They are selected on that basis, and then that result won't be true anymore.Eric Topol (19:07):So obviously you would qualify. There's a spectrum here. The AI enthusiasts, you and I are both in that group, and then there's the doomsayers and there's somewhere middle ground, of course, where people are trying to see the right balance. Are there concerns about AI, I mean anything about that, how it's moving forward that you're worried about?Tyler Cowen (19:39):Well, any change that big one should have very real concerns. Maybe our biggest concern is that we're not sure what our biggest concern should be. One simple effect that I see coming soon is it will devalue the status of a lot of our intellectuals and what's called our chattering class. A lot of its people like us, we won't seem so impressive anymore. Now, that's not the end of the world for everyone as a whole, but if you ask, what does it mean for society to have the status of its elites so punctured? At a time when we have some, I would say very negative forces attacking those elites in other ways, that to me is very concerning.Eric Topol (20:25):Do you think that although we've seen what's happening with the current administration with respect to the tariffs, and we've already talked about the effects on science funding, do you see this as a short-term hit that will eventually prevail? Do you see them selectively supporting AI efforts and finding the right balance with the tech companies to support them and the competition that exists globally with China and whatnot? How are we going to get forward and what some people consider pretty dark times, which is of course, so seemingly at odds with the most extraordinary times of human support with AI?Tyler Cowen (21:16):Well, the Trump people are very pro AI. I think that's one of the good things about the administration, much pro AI and more interested than were the Biden people. The Biden people, you could say they were interested, but they feared it would destroy the whole world, and they wanted to choke and throttle it in a variety of ways. So I think there's a great number of issues where the Trump people have gone very badly wrong, but at least so far AI's not one of them. I'd give them there like an A or A+ so far. We'll see, right?Eric Topol (21:44):Yeah. As you've seen, we still have some of these companies in some kind of a hot seat like Meta and Google regarding their monopolies, and we saw how some of the tech leaders, not all of them, became very supportive, potentially you could interpret that for their own interests. They wanted to give money to the inauguration and also get favor curry some political favor. But I haven't yet seen the commitment to support AI, talk about a golden age for the United States because so much of this is really centered here and some of the great minds that are helping to drive the AI and these models. But I wonder if there's more that can be done so that we continue to lead in this space.Tyler Cowen (22:45):There's a number of issues here. The first is Trump administration policy toward the FTC, I think has not been wonderful. They appointed someone who seems like would be more appropriate for a democratic or more left-leaning administration. But if you look at the people in the Office of Science and Technology Policy in the White House, they're excellent, and there's always different forces in any administration. But again, so far so good. I don't think they should continue the antitrust suit against Google that is looking like it's going against Google, but that's not really the Trump administration, that's the judiciary, and that's been underway for quite some while. So with Trump, it's always very hard to predict. The lack of predictability, I would say, is itself a big problem. But again, if you're looking for one area where it's good, that would be my pick.Eric Topol (23:35):Yeah, well, I would agree with that for sure. I just want to see more evidence that we capitalize on the opportunities here and don't let down. I mean, do you think outlawing selling the Nvidia chips to China is the way to do this? It seems like that hurts Nvidia and isn't China going to get whatever they want anyway?Tyler Cowen (24:02):That restriction, I favored when it was put in. I'm now of the view that it has not proved useful. And if you look at how many of those chips get sold, say to Malaysia, which is not a top AI performer, one strongly suspects, they end up going to China. China is incentivized to develop its own high-quality chips and be fully independent of Western supply lines. So I think it's not worked out well.Eric Topol (24:29):Yeah, no, I see that since you've written so much about this, it's good to get your views because I share those views and you know a lot more about this than I would, but it seems like whether it's Malaysia or other channels, they're going to get the Blackwell chips that they want. And it seems like this is almost like during Covid, how you would close down foreign travel. It's like it doesn't really work that well. There's a big world out there, right?Tyler Cowen (25:01):It’s an interesting question. What kind of timing do you want for when both America and China get super powerful AI? And I don't think you actually want only America to have it. It's a bit like nuclear weapons, but you don't want China to have it first. So you want some kind of staggered sequence where we're always a bit ahead of them, but they also maybe are constraining us a bit. I hope we're on track to get that, but I really, really don't want China to have it first.Eric Topol (25:31):Yeah, I mean I think there's, as you're pointing out aptly is a healthy managed competition and that if we can keep that lead there, it is good for both and it's good for the world ideally. But getting back, is there anything you're worried about in AI? I mean because I know you're upbeat about its net effective, and we've already talked about amazing potential for efficiency, productivity. It basically upends a lot of economic models of the past, right?Tyler Cowen (26:04):Yes. I think it changes or will change so many parts of life. Again, it's a bit difficult to specify worries, but how we think of ourselves as humans, how we think of our gods, our religions, I feel all that will be different. If you imagine trying to predict the effects of the printing press after Gutenberg, that would've been nearly impossible to do. I think we're all very glad we got the printing press, but you would not say all of it went well. It's not that you would blame the printing press for those subsequent wars, but it was disruptive to the earlier political equilibrium. I think we need to take great care to do it better this time. AI in different forms will be weaponized. There's great potential for destruction there and evil people will use it. So of course, we need to be very much concerned.Eric Topol (26:54):And there's obviously many of these companies have ways to try to have efforts to anticipate that. That is alignments and various safety type parallel efforts like Ilya did when he moved out of OpenAI and others. Is that an important part of each of these big efforts, whether it's OpenAI, Google, or the rest of them anthropic that they put in resources to keep things from going off the tracks?Tyler Cowen (27:34):That's good and it's important, but I think it's also of limited value because the more we learn how to control AI systems directly, the bad guys will have similar lessons, and they will use alignment possibly to make their AIs bad and worse and that it obeys them. So yeah, I'd rather the good guys make progress on what they're trying to do, but don't think it's going to solve the problem. It creates new problems as well.Eric Topol (28:04):So because of AI, do you think you'll write any more books in the future?Tyler Cowen (28:11):I'm writing a book right now. I suspect it will be my last. That book, its title is Mentors. It's about how to mentor individuals and what do the social sciences know about mentoring. My view is that even if the AI could write the book better than I can, that people actually want to read a book like that from a human. I could be wrong, but I think we should in the future, restrict ourselves to books that are better by a human. I will write every day for the rest of my life, but I'm not sure that books make sense at the current moment.Eric Topol (28:41):Yeah, that's a really important point, and I understand that completely. Now, when you write for the Free Press, which will be besides the Conversations with Tyler podcast and the Marginal Revolution, what kind of things will you be writing about in the Free Press?Tyler Cowen (28:56):Well, I just submitted a piece. It's a defense of elitism. So the problem with our elites is that they have not been elitist enough and have not adhered strictly enough to the scientific method. So it's a very simple point. I think to you it would be pretty obvious, but it needs to be said. It's not out there enough in the debate that yes, sometimes the elites have truly and badly let us down, but the answer is not to reject elitism per se, but to impose higher elitist standards on our sometimes supposed elites. So that's the piece I just sent in. It's coming out soon and should be out by the time anyone hears this.Eric Topol (29:33):Well, I look forward to reading that. So besides a polymath, you might be my favorite polymath, Tyler you didn't know that. Also, you're a futurist because when you have that much information ingested, and now of course with a super performance of AI to help, it really does help to try to predict where we're headed. Have I missed anything in this short conversation that you think we should touch on?Tyler Cowen (30:07):Well, I'll touch on a great interest of yours. I like your new book very much. I think over the course of the next 40 years working with AI, we will beat back essentially every malady that kills people. It doesn't mean you live forever. Many, many more people will simply die of what we now call old age. There's different theories as to what that means. I don't have a lot of expertise in that, but the actual things people are dying from will be greatly postponed. And if you have a kid today to think that kid might expect to live to be 97 or even older, that to me is extremely plausible.Tyler Cowen (30:45):I won't be around to see it, but that's a phenomenal development for human beings.Eric Topol (30:50):I share that with you. I'm sad that I won't be around to see it, but exactly as you've outlined, the fact that we're going to be able to have a huge impact on particularly the age-related diseases, but also as you touched on the genetic diseases with genome editing and many other, I think, abilities that we have now controlling the immune system, I mean a central part of how we get into trouble with diseases. So I couldn't agree with you more, and that's a really good note to finish on because so many of the things that we have discussed today, we share similar views and we come at it from totally different worlds. The economist that has a very wide-angle lens, and I guess you'd say the physician who has a more narrow lens aperture. But thank you so much, Tyler for joining me today.Tyler Cowen (31:48):My pleasure. Let me close by telling you some good news. I have AI friends who think you and I, I’m 63 will be around to see that, I don't agree with them they don't convince me, but there are smart people who think the benefits from this will come quite soon.Eric Topol (32:03):I sure hope they're right.Tyler Cowen (32:05):Yes.*******************************************SUPER AGERS, my new book, was released on May 6th. It’s about extending our healthspan, and I introduce 2 of my patients (one below, Mrs. L.R.) as exemplars to learn from. This potential to prevent the 3 major age-related diseases would not be possible without the jumps in the science of aging and multimodal A.I. My op-ed preview of the book was published in The NY Times last week. Here’s a gift link. I did a podcast with Mel Robbins on the book here. Here’s my publisher ‘s (Simon and Schuster) site for the book. If you’re interested in the audio book, I am the reader (first time I have done this, quite an experience!)The book was reviewed in WSJ. Here’s a gift linkThere have been many pieces written about it. Here’s a gift link to the one in the Wall Street Journal and here for the one in the New York Times .**********************Thanks for reading and subscribing to Ground Truths.If you found this interesting please share it!That makes the work involved in putting these together especially worthwhile.All content on Ground Truths— newsletters, analyses, and podcasts—is free, open-access.Paid subscriptions are voluntary and all proceeds from them go to support Scripps Research. They do allow for posting comments and questions, which I do my best to respond to. Please don't hesitate to post comments and give me feedback. Many thanks to those who have contributed—they have greatly helped fund our summer internship programs for the past two years. Get full access to Ground Truths at erictopol.substack.com/subscribe

  --------  

  32:18
• Bob Bordone and Joel Salinas: How to Deal With Conflict

  In our divided world we face or avoid conflicts on a frequent basis. I turned to Bob Bordone and Joel Salinas to find out the best strategies to deal with these, including having them take on a mock conflict between each other on the merits of Covid research.Audio fileYou can also find this on Spotify and Apple podcasts with Ground Truths.The video is also posted on YouTubeTranscript with Audio LinksEric Topol (00:06):Well, hello. It's Eric Topol with Ground Truths, and we're going to get into a new book called Conflict Resilience: Negotiating Disagreement Without Giving Up or Giving In, and we're lucky to have its two authors, Bob Bordone, who is a Senior Fellow at Harvard Law School, and Joel Salinas, who is a physician, neurologist, a clinician scientist at NYU. So welcome both Bob and Joel.Bob Bordone and Joel Salinas (00:34):Thank you for having us. Yeah, looking forward to the conversation.Eric Topol (00:37):Yeah. So first, how did you guys get together? This is a pretty diverse, you got law and medicine, usually they don't talk to each other very much.Bob Bordone (00:46):Well, we were very fortunate. I mean, we basically were friends, but part of that friendship, I think emerged from work that I do around conflict issues in the Mass General system and then just the larger, bigger Mass General, Harvard community. Yeah, so this began really as a friendship where we were each swimming in very different waters, but then as we would start to talk, we realized there was a lot of connection and maybe the possibility to bring two different disciplines together in a way that might be practically useful and make an impact. And even when we started writing this, which was during Covid, what seemed to be some pretty polarizing times that were unlikely to resolve by the time the book would come out.Eric Topol (01:44):Yeah, well you sure hit it with the divisiveness and the polarized world that we live in is perhaps worse than ever, certainly in all my years, and probably long before then as well. So this topic of resilience, it's a very interesting concept because some people might think of resilience as just being tough. So go into a conflict and just go heavy tough. That obviously is not what you're writing about. And I guess maybe we can start off, what was the goal here? Obviously, there's other books that have addressed this topic, I'm sure, but yours is somewhat unique in many respects because it brings in the science of it and many strategies perhaps that have never been developed. But when you got together, what was the mission that you set out to do?Joel Salinas (02:38):Yeah, well maybe I can start out and then you can add on. So my research has been all around understanding how social relationships influenced brain health, and one of the things that I was seeing was social isolation and loneliness had been steadily increasing. Want to figure out what kind of interventions or what are the factors that are involved here? And I think one of the things that has stood out is just the difficulty with being able to navigate conflict in different contexts. And so, the idea around conflict resilience is really, even though there's been lots of books on what to say and what specific tactics to use, I think that there was this skillset around just being able to sit with the discomfort of that disagreement, which will ultimately help make it much more useful to take on those tactics. One way to think about it, if it's like all these tactics are like learning how to cook with a set of recipes in the kitchen, what we're really proposing here is that you also need to be able to stand the heat of the kitchen to even be able to cook.Eric Topol (03:47):Okay. Go ahead, Bob.Bob Bordone (03:49):Yeah, and I would say I was starting to write about my first kind of piece on this topic where I use the word conflict resilience was in 2018, and it really came from an observed dynamic that I was seeing in my teaching of Harvard Law School students. I was on the admissions committee, I'd been on the admissions committee for many years. I knew that we worked very hard and were quite successful in fact, at bringing together a very diverse student body, including politically. And people sometimes maybe think of elite law schools as being very progressive. But Harvard Law School, the biggest student organization is actually the Federalists, which is the conservative students. And despite that effort, what I noticed in the classroom was a reduction in conversation, diversity of viewpoint across the board, interesting classrooms became boring. And even though I was teaching around conflict and negotiation and difficult conversations, I would read in students' journals things like, I want to avoid conflict or I don't want to get into it.Bob Bordone (04:59):And so, it occurred to me that quite a part, as Joel said, from any skills, if we don't develop this capacity to sit with disagreement, then we will never get to problem solving. I'm in favor of problem solving. But this paper on conflict resilience, its original title was called Against Problem Solving. Mostly because I thought that if we had opened the possibility of problem solving as a precondition for entering the room, then we might never enter the room, particularly if we've told the demonized and dehumanized story about them. And so, that somehow we had to make the case that sitting with the discomfort of the disagreement, even if it didn't mean problem solving, although we hope for that, even if we didn't mean that it was worthwhile and it was important. And so, part of what was really attractive to me about joining up with Joel is that he just brought all of this brain science aspect to it that I had this kind of teaching and kind of academic in the negotiation and dispute resolution research experience, but couldn't bring to bear the kind of brain science parts of, well, what is going on in our brain when we do want to run or when we get into that really unproductive battle.Eric Topol (06:27):Yeah, I agree that the unique part here is that whole scaffolding with the neuroscience, the behavioral science, and those five Fs that you mentioned. You alluded to fight, flight, freeze, fawn, or fester. Yeah, so avoidance of conflict has kind of been the default for many people now because we have political divides, we have anti-science versus pro-science divides and on and on. There's a quote in the book that I thought we'd start off with because it really lays the groundwork from you both. “The biggest hidden barrier to being conflict resilient stems from the inability or unwillingness to face and sit with our own internal conflicts - the negotiations between our divided and sometimes contradictory “selves.” Even more surprising is that although there are dozens of self-help books on negotiation and conflict resolution, almost none of them spend any meaningful time on this critical intrapersonal barrier to handing conflict.” So maybe Joel, maybe start you off here. I guess you were bullied as a kid, and maybe that gives you a little background here. Joel, tell us about that if you would.Bob Bordone (07:46):Hey, Eric. On our bad days sometimes I probably inadvertently bully Joel still today, but he's pretty resilient now.Joel Salinas (07:53):Yeah, I'm a Teflon. So I think I am generally conflict of what an individual, and I think a lot of listeners and viewers can relate with that experience. And I think that also kind of speaks to some of the neuroscience that comes into this, which is that our brain has really evolved to be a fortune telling machine. It takes all of our past experiences, turns them into memories, and then makes projections about what's going to happen. And this projection or prediction of what's going to happen might as well be reality for our brain's sake. And so, if we had really negative experiences with conflict in the past growing up, whether through our families or the schoolyard or others, there'll be likely a very negative charge of negative emotional charge that comes with that. And what that does is that it increases the chances that you'll trigger this system for salience and arousal, which then sets off the alarms essentially in your body that then creates these fight or flight type responses where you're more likely to fall back on these really reflexive behaviors to make the bad thing less bad.Joel Salinas (09:08):And when you do that, whether it's through avoiding or to blowing through conflict like a battering ram that then trains your brain to assign some kind of a reward using the orbital frontal cortex, a system that kind of keeps tabs over how much reward you get for a behavior, it makes it much more likely that you'll do it again. And so, we from a very young age, develop a propensity to either avoid conflict or tackle it. And it varies depending on the context and how you're feeling, but it just makes it much, much harder to be able to bring on a much more thoughtful and deliberative approach to conflict.Eric Topol (09:49):Yeah, I mean, I think one of the salient points is that avoiding the conflict can make things worse. And as you described that it's not, I would've thought that there are some people who are just innately gifted to being diplomatic and artful about having to deal with the conflict issue and others, there's just no hope. But in fact, it can be acquired. And you alluded to this kind of neuroplasticity, the brain and you advocate for chair work. Can you tell us about chair work, because that's something I wouldn't have thought would help in this manner.Bob Bordone (10:30):Sure. I mean, I'll say a little bit work about that. A big part of this chair work idea, frankly, is influenced by work in internal family systems. And I was very fortunate early in my career, even though I was at teaching at law school to start partnering with some folks who did IFS work, they call it peace work often. But the chair work is really identifying some of these conflicted sides of ourself, right? The side of ourself that maybe feels like it's important and okay to raise this issue because it's something that matters to me and maybe the side of ourself that feels like it's pointless and it will hurt the relationship and maybe the side of ourself that's fearful and to name each of them. And then to actually give each in preparation a physical chair where we sit in that chair and give voice to each of those sides.Bob Bordone (11:32):And I'm imagining that at least some people listening to this will say, this sounds very hokey, and does he really mean going to the chairs? And the answer is, yes, I do mean that because there is something about the physicality of it that forces you to give voice to something that is true and real in you. And the chair work is very helpful to set up what an opening might be into a hard conversation, meaning that all of the chairs are real and authentic and okay, they're worthy of getting some voice. So as someone who teaches in a law school, it's all about advocacy. And you would find students who would be very good at advocating on behalf of a client would be incredibly poor at advocating on behalf of themself. And so, separating out the side that maybe has a little bit of feeling, it's selfish, but actually giving it a legitimate voice, help them when they get to the table to be able to say, I'm worried about this, or I realize I may be wrong about this, or it might be upsetting. And also, it's important and deserves to be heard because one of the things around avoidance is we often do avoidance in service of preserving the relationship or not disrupting. And we do maybe preserve the relationship for the time being of the person across the table, except we go home and there's still the side of us that is not feeling good about it, and the person we're not preserving the relationship with is that side, then we just get to have a sleepless night. And so, that's really the kind of idea behind the chair work.Eric Topol (13:22):That's helpful, Bob. I guess managing conflict, of course, I think we know you don't get emotional. Okay, sure. But yeah, there's three parts of that, three components, self-awareness. We've been talking about that deep listening, which of course when you're engaging in a discussion that's potentially leading to escalation of a conflict or the amplification that is really important. And then effective assertion. Now, that's where it seems to me things fall apart. If you're making effective assertion, then everything kind of blows up. So tell us about how you can be assertive and still, you're not trying to win the argument. I get that, but how can you be assertive and still come out in a positive way?Joel Salinas (14:16):Maybe I can start, Bob.Joel Salinas (14:19):I think one of the things that really is a good predictor of how effective you'll be at effective assertion is how good you were at the deep listening part. So the more genuine you are and curious you are about the perspective of the other person, really understanding what are the set of facts, experiences, beliefs that eventually lead up to that headline of what their position is or what their interests are. The better you'll be able articulate your own perspective while still engaging in the conversation. And the other thing that's really important here is that in that listening piece, it's really essential to be able to bring in tenets of really great listening that includes eliminating distractions, both external and internal. It involves having a nonjudgmental position toward the other person and being able to reflect an understanding of what the other person is saying. But all of that does not mean that you are endorsing their point of view. And I think that's really essential. It's really about getting as clear as you can about where the other person is coming from. So that way when you have an opportunity to share your perspective, you're able to really speak to the concerns of the other person and your own.Eric Topol (15:46):Yeah. Well, in reading the book, it took me, interestingly to an evening discussion I had with a very close friend.Eric Topol (15:56):And he was saying, we do need a randomized trial of the measles vaccine, MMR for autism. And I said, what? And I started thinking about, well, I'm going to hear him out because there's so much evidence now that you would think this has been totally debunked. And his view is, well, it can't hurt. And I'm thinking, well, so in that discussion, a lot of these points that you've been raising help me to come not to a point where basically I was trying to put a bow on it, as you said, or trying to externalize or abstract it. But to have a happy ending with him about this saying, okay, well it's never going to get done, but if you want to get it, I'm supportive of that. We don't do enough of this. I had to listen to what he had to say. I had to deal with my own confirmation biases and not get emotional and all that stuff, right. Now, I'd like the two of you to role play on something like that if you would. And let me just give you an example. Maybe you can run with it. Let's go to Covid, okay?Eric Topol (17:14):So one of you will take the side that we shouldn't do any more Covid research because the pandemic is over and we need to be efficient and not use these funds for other things. Covid is over, Long Covid is a hoax, and the other person will take the side that, no, this is a really big deal because Covid has not gone away and there's still a endemic of the virus, Long Covid in millions of people. Who wants to take away the funds? Would that be you, Bob?Bob Bordone (17:52):As a lawyer, I am happy to take any side.Eric Topol (17:55):Okay. You are the one to be on that side. Okay. And Joel, you are going to be the pro science side, if you will. Can you start that argument?Bob Bordone (18:05):Eric, can I make a suggestion? Yeah, but I'm happy to. It might be fun if one of us tries to be a person who hasn't read the book and the other person maybe tries to actually model the skills. What do you think about that?Eric Topol (18:18):Sure. Yeah, that’s fine.Joel Salinas (18:19):Bob, I'll take on the unskilled position.Bob Bordone (18:22):Okay, fine.Joel Salinas (18:25):All right. So Bob, you know what? I keep hearing about people wanting to cut Covid funding and just really, I just can't believe it. It just makes me want to throw up because there's such an important need to do this research. It's just critical to understand the long-term effects of it, and Covid even gone yet. So I just can't believe that people would even want to cut this research at all.Bob Bordone (18:50):Well, first of all, it sounds like you're stunned and surprised by this. Am I right about that?Joel Salinas (18:56):Yeah, I'm beyond stunned. I'm revolted by it.Bob Bordone (19:01):So you're pretty angry about it. And I'm curious if I can ask you, you said that the disease is still going on, and of course Covid still exists. I am curious from your perspective, what do you think the benefits of spending lots and lots of money on the diseases at this point, since it's not at that level where it's killing a lot of people?Joel Salinas (19:30):Well, I think that it is killing a lot of people. Still, the disease hasn't gone away and it has a huge impact on health. I think we're still feeling the impacts on that. So I think that being able to understand what the impact does require funding to be able to do the research. And if we don't do that research, then we don't understand what interventions there can be.Bob Bordone (19:51):And what are the impacts? I mean, clearly there's impacts of the pandemic broadly in our society, but what are the kinds of health impacts from your perspective that research would be helpful to from a medical perspective?Joel Salinas (20:05):Well, for sure it impacts cognition. We have people talking about brain fog and Long Covid, and that has a real societal impact on productivity and people's ability to engage in life. It affects people's mood. And then you've got the people who have respiratory symptoms from Covid that have continued to gone on, and that decreases their ability to do their day-to-day things. It's a real societal impact.Bob Bordone (20:28):And how would you think about balancing whatever impact Covid has from all of the other funding choices that need to be made given a shrinking research pool for funds?Joel Salinas (20:44):I don't know. I mean, I think it's an important priority, and I know that there's a lot of other priorities. I think it needs to be weighed against a lot of other big programs that are out there. I just want to make sure that it doesn't go away because it needs to happen.Bob Bordone (20:56):Yeah. No, it's helpful to hear that. And if we had more time, I'd ask you some more questions. I mean, one thing that, as I think about this is given just the number of priorities out there, I worry that because Covid was in the press so much and is so politicized that we overweight the importance of money in that direction. And I would say that there's probably other things if we have a fixed set of money that kills a lot more people and has a lot more health impact. And so, I'd rather see the funds get placed there than just satisfy some kind of highly salient political issue.Joel Salinas (21:40):And I just want to make sure that the funding happens. I mean, it should be to a level that it makes sense to continue the funding so that we get good results from it, that it can be applied. But yeah, I guess you're right that it needs to be weighed against other research priorities. I mean, that's a whole other topic that gets me upset, but I think I just want to make sure that this funding doesn't go away.Bob Bordone (22:03):Yeah. So it sounds like for you, the concern is less about reduction and more about moving it to zero?Joel Salinas (22:12):I think so, yeah.Bob Bordone (22:13):And if it did move to zero, what is the thing you'd be most worried about?Joel Salinas (22:18):I think we would lose out on this really unique opportunity after all these people had been affected by this condition to understand the long-term effects. So that way, if there's another resurgence, we'll understand what can we do about it to mitigate those effects. I mean, we're still trying to figure out what the effects of a lockdown were on people. I think that's something that needs to be better understood.Bob Bordone (22:40):So for you, the research is very forward looking about future pandemics that might come up.Joel Salinas (22:46):Absolutely.Bob Bordone (22:47):And that might be something that I'd be more interested in than how can we prevent future pandemics than I would worrying about. I mean, it's very regrettable what has happened to this set of people who have Long Covid, of course. I just think that that has happened, and I would almost rather see the funds move in the direction of how do we prevent another pandemic than how do we worry about a relatively small set of people, although it's tragic on them, a relatively small set of people who may still suffer those benefits.Joel Salinas (23:26):Yeah, I think we do want to focus on the prevention, definitely. I still just don't want to lose sight of making sure that we're getting the research done that needs to happen.Bob Bordone (23:38):Should we cut?Eric Topol (23:39):That's helpful. These are two experts in conflict resilience here. I mean, the only thing I'd add is that Long Covid is affecting millions of Americans, perhaps as many as 60 million people around the world, and we have no treatment for it. So it's a big deal.Bob Bordone (23:56):I just want to say for the record, I was just being an actor there.Eric Topol (24:03):Yeah, that’s okay.Bob Bordone (24:04):I don't even know if my arguments on the other side were making sense, but I was trying.Eric Topol (24:08):I think you did a good job. I think both of you did a good job. I think the point here is that you were able to have a civil discussion, make your points, I forced you into it. You couldn't avoid it. You're in touch, obviously with your own innate issues. You kind of really emphasize that throughout the book, which is you got to be in touch with yourself, not just about your priors, but also your current, what you're feeling, your posture, your heart rate, all these other physical things. So you really got us queued into what's important when you're having a discussion that could lead to, it could exacerbate the conflict rather than help come to a happy mid stance or where both people feel that they've expressed themselves adequately. I really love the Frederick Douglass quote in your book, “if there is no struggle, there is no progress. Those who profess to favor freedom and yet depreciate agitation…want crops without plowing up the ground. They want rain without thunder and lightning. They want the ocean without the awful roar of its many waters.” I think that is so rich. And before we wrap up, I just want to get your overall thoughts. What haven't we touched on in our brief conversation about the topic, about the book that we should before we close today? Maybe start with you, Bob.Bob Bordone (25:53):Yeah, I mean, in some sense, I think it connects to exactly that quote, which is that without conflict, we are not going to get the kind of changes and dynamism we would want in our organizations, whether it's a medical center, a country, a family, but also without the conflict, we don't get the deeper connection that is possible because it's not until the first, no, that all of the yeses actually have the meaning that they should. And so, even though it seems scary to go into conflict, what I would say is it offers opportunities maybe for agreement, but if not for agreement, for a deeper kind of more authentic and real relationship. And I would just say for me, part of this is inviting people to reframe the way they think about what conflict can do in their lives.Joel Salinas (26:58):Yeah. I think if there's one thing that listeners or viewers take from this is awareness is more than half the battle. So just really taking the time to become more aware of how you react to different disagreements with different conflicts, how you're responding to it physically and mentally, and what specific patterns might emerge in terms of whether it's with colleagues, with people with authority, with family members. And I think that alone begins to get you to pay more attention about how you can be more deliberate in your responses. And ideally, you can try out some of the skills from the book with those disagreements that are a little less stressful for you. Just like when you go to the gym, you don't start out by lifting the heaviest weights. You start out by getting the reps down with the good form, and then you build that muscle. And similar with building the brain programming wiring around it is to start low and build up from there.Eric Topol (27:57):Yeah. Well, I think what you have put forth in the book will go down anchoring such an important problem. It's magnified now than more than ever. People are socially isolated, not just in the pandemic, but post pandemic and the divisiveness is profound. So hopefully the tips that you've provided, the science behind it, the practical ways to navigate and deal with this will help people as we go forward. So thank you both for the work you did in putting together the book, and hopefully some of our listeners or viewers will use these tools in the future and will have much better exchanges with others who have different views, different what might be considered adversarial perspective, whatever. So thank you very much for joining today.Joel Salinas (28:58):Well, thank you.Bob Bordone (28:59):Thank you for having us. It's been a delight.********************************As you can imagine, I’m excited to get my new book out on May 6th. It’s about extended our healthspan, and I introduce 2 of my patients (one below, Mrs. L.R.) as exemplars to learn from. My op-ed preview of the book was published in The NY Times last week. Here’s a gift link. I did a podcast with Mel Robbins on the book here. Here’s my publisher ‘s (Simon and Schuster) site for the book. If you’re interested in the audio book, I am the reader (first time I have done this, quite an experience!)Here’s the back cover to give you an idea of what some people had to say about it.Thanks for reading and subscribing to Ground Truths.If you found this interesting please share it!That makes the work involved in putting these together especially worthwhile.All content on Ground Truths—its newsletters, analyses, and podcasts, are free, open-access.Paid subscriptions are voluntary and all proceeds from them go to support Scripps Research. They do allow for posting comments and questions, which I do my best to respond to. Please don't hesitate to post comments and give me feedback. Many thanks to those who have contributed—they have greatly helped fund our summer internship programs for the past two years. Get full access to Ground Truths at erictopol.substack.com/subscribe

  --------  

  29:13
• Steve Quake and Charlotte Bunne: The Holy Grail of Biology

  “Eventually, my dream would be to simulate a virtual cell.”—Demis HassabisThe aspiration to build the virtual cell is considered to be equivalent to a moonshot for digital biology. Recently, 42 leading life scientists published a paper in Cell on why this is so vital, and how it may ultimately be accomplished. This conversation is with 2 of the authors, Charlotte Bunne, now at EPFL and Steve Quake, a Professor at Stanford University, who heads up science at the Chan-Zuckerberg Initiative The audio (above) is available on iTunes and Spotify. The full video is linked here, at the top, and also can be found on YouTube.TRANSCRIPT WITH LINKS TO AUDIO Eric Topol (00:06):Hello, it's Eric Topol with Ground Truths and we've got a really hot topic today, the virtual cell. And what I think is extraordinarily important futuristic paper that recently appeared in the journal Cell and the first author, Charlotte Bunne from EPFL, previously at Stanford’s Computer Science. And Steve Quake, a young friend of mine for many years who heads up the Chan Zuckerberg Initiative (CZI) as well as a professor at Stanford. So welcome, Charlotte and Steve.Steve Quake (00:42):Thanks, Eric. It's great to be here.Charlotte Bunne:Thanks for having me.Eric Topol (00:45):Yeah. So you wrote this article that Charlotte, the first author, and Steve, one of the senior authors, appeared in Cell in December and it just grabbed me, “How to build the virtual cell with artificial intelligence: Priorities and opportunities.” It's the holy grail of biology. We're in this era of digital biology and as you point out in the paper, it's a convergence of what's happening in AI, which is just moving at a velocity that's just so extraordinary and what's happening in biology. So maybe we can start off by, you had some 42 authors that I assume they congregated for a conference or something or how did you get 42 people to agree to the words in this paper?Steve Quake (01:33):We did. We had a meeting at CZI to bring community members together from many different parts of the community, from computer science to bioinformatics, AI experts, biologists who don't trust any of this. We wanted to have some real contrarians in the mix as well and have them have a conversation together about is there an opportunity here? What's the shape of it? What's realistic to expect? And that was sort of the genesis of the article.Eric Topol (02:02):And Charlotte, how did you get to be drafting the paper?Charlotte Bunne (02:09):So I did my postdoc with Aviv Regev at Genentech and Jure Leskovec at CZI and Jure was part of the residency program of CZI. And so, this is how we got involved and you had also prior work with Steve on the universal cell embedding. So this is how everything got started.Eric Topol (02:29):And it's actually amazing because it's a who's who of people who work in life science, AI and digital biology and omics. I mean it's pretty darn impressive. So I thought I'd start off with a quote in the article because it kind of tells a story of where this could go. So the quote was in the paper, “AIVC (artificial intelligence virtual cell) has the potential to revolutionize the scientific process, leading to future breakthroughs in biomedical research, personalized medicine, drug discovery, cell engineering, and programmable biology.” That's a pretty big statement. So maybe we can just kind of toss that around a bit and maybe give it a little more thoughts and color as to what you were positing there.Steve Quake (03:19):Yeah, Charlotte, you want me to take the first shot at that? Okay. So Eric, it is a bold claim and we have a really bold ambition here. We view that over the course of a decade, AI is going to provide the ability to make a transformative computational tool for biology. Right now, cell biology is 90% experimental and 10% computational, roughly speaking. And you've got to do just all kinds of tedious, expensive, challenging lab work to get to the answer. And I don't think AI is going to replace that, but it can invert the ratio. So within 10 years I think we can get to biology being 90% computational and 10% experimental. And the goal of the virtual cell is to build a tool that'll do that.Eric Topol (04:09):And I think a lot of people may not understand why it is considered the holy grail because it is the fundamental unit of life and it's incredibly complex. It's not just all the things happening in the cell with atoms and molecules and organelles and everything inside, but then there's also the interactions the cell to other cells in the outside tissue and world. So I mean it's really quite extraordinary challenge that you've taken on here. And I guess there's some debate, do we have the right foundation? We're going to get into foundation models in a second. A good friend of mine and part of this whole I think process that you got together, Eran Segal from Israel, he said, “We're at this tipping point…All the stars are aligned, and we have all the different components: the data, the compute, the modeling.” And in the paper you describe how we have over the last couple of decades have so many different data sets that are rich that are global initiatives. But then there's also questions. Do we really have the data? I think Bo Wang especially asked about that. Maybe Charlotte, what are your thoughts about data deficiency? There's a lot of data, but do you really have what we need before we bring them all together for this kind of single model that will get us some to the virtual cell?Charlotte Bunne (05:41):So I think, I mean one core idea of building this AIVC is that we basically can leverage all experimental data that is overall collected. So this also goes back to the point Steve just made. So meaning that we basically can integrate across many different studies data because we have AI algorithms or the architectures that power such an AIVC are able to integrate basically data sets on many different scales. So we are going a bit away from this dogma. I'm designing one algorithm from one dataset to this idea of I have an architecture that can take in multiple dataset on multiple scales. So this will help us a bit in being somewhat efficient with the type of experiments that we need to make and the type of experiments we need to conduct. And again, what Steve just said, ultimately, we can very much steer which data sets we need to collect.Charlotte Bunne (06:34):Currently, of course we don't have all the data that is sufficient. I mean in particular, I think most of the tissues we have, they are healthy tissues. We don't have all the disease phenotypes that we would like to measure, having patient data is always a very tricky case. We have mostly non-interventional data, meaning we have very limited understanding of somehow the effect of different perturbations. Perturbations that happen on many different scales in many different environments. So we need to collect a lot here. I think the overall journey that we are going with is that we take the data that we have, we make clever decisions on the data that we will collect in the future, and we have this also self-improving entity that is aware of what it doesn't know. So we need to be able to understand how well can I predict something on this somewhat regime. If I cannot, then we should focus our data collection effort into this. So I think that's not a present state, but this will basically also guide the future collection.Eric Topol (07:41):Speaking of data, one of the things I think that's fascinating is we saw how AlphaFold2 really revolutionized predicting proteins. But remember that was based on this extraordinary resource that had been built, the Protein Data Bank that enabled that. And for the virtual cell there's no such thing as a protein data bank. It's so much more as you emphasize Charlotte, it's so much dynamic and these perturbations that are just all across the board as you emphasize. Now the human cell atlas, which currently some tens of millions, but going into a billion cells, we learned that it used to be 200 cell types. Now I guess it's well over 5,000 and that we have 37 trillion cells approximately in the average person adult's body is a formidable map that's being made now. And I guess the idea that you're advancing is that we used to, and this goes back to a statement you made earlier, Steve, everything we did in science was hypothesis driven. But if we could get computational model of the virtual cell, then we can have AI exploration of the whole field. Is that really the nuts of this?Steve Quake (09:06):Yes. A couple thoughts on that, maybe Theo Karaletsos, our lead AI person at CZI says machine learning is the formalism through which we understand high dimensional data and I think that's a very deep statement. And biological systems are intrinsically very high dimensional. You've got 20,000 genes in the human genome in these cell atlases. You're measuring all of them at the same time in each single cell. And there's a lot of structure in the relationships of their gene expression there that is just not evident to the human eye. And for example, CELL by GENE, our database that collects all the aggregates, all of the single cell transcriptomic data is now over a hundred million cells. And as you mentioned, we're seeing ways to increase that by an order of magnitude in the near future. The project that Jure Leskovec and I worked on together that Charlotte referenced earlier was like a first attempt to build a foundational model on that data to discover some of the correlations and structure that was there.Steve Quake (10:14):And so, with a subset, I think it was the 20 or 30 million cells, we built a large language model and began asking it, what do you understand about the structure of this data? And it kind of discovered lineage relationships without us teaching it. We trained on a matrix of numbers, no biological information there, and it learned a lot about the relationships between cell type and lineage. And that emerged from that high dimensional structure, which was super pleasing to us and really, I mean for me personally gave me the confidence to say this stuff is going to work out. There is a future for the virtual cell. It's not some made up thing. There is real substance there and this is worth investing an enormous amount of CZIs resources in going forward and trying to rally the community around as a project.Eric Topol (11:04):Well yeah, the premise here is that there is a language of life, and you just made a good case that there is if you can predict, if you can query, if you can generate like that. It is reminiscent of the famous Go game of Lee Sedol, that world champion and how the machine came up with a move (Move 37) many, many years ago that no human would've anticipated and I think that's what you're getting at. And the ability for inference and reason now to add to this. So Charlotte, one of the things of course is about, well there's two terms in here that are unfamiliar to many of the listeners or viewers of this podcast, universal representations (UR) and virtual instrument (VIs) that you make a pretty significant part of how you are going about this virtual cell model. So could you describe that and also the embeddings as part of the universal representation (UR) because I think embeddings, or these meaningful relationships are key to what Steve was just talking about.Charlotte Bunne (12:25):Yes. So in order to somewhat leverage very different modalities in order to leverage basically modalities that will take measurements across different scales, like the idea is that we have large, may it be transformer models that might be very different. If I have imaging data, I have a vision transformer, if I have a text data, I have large language models that are designed of course for DNA then they have a very wide context and so on and so forth. But the idea is somewhat that we have models that are connected through the scales of biology because those scales we know. We know which components are somewhat involved or in measurements that are happening upstream. So we have the somewhat interconnection or very large model that will be trained on many different data and we have this internal model representation that somewhat capture everything they've seen. And so, this is what we call those universal representation (UR) that will exist across the scales of biology.Charlotte Bunne (13:22):And what is great about AI, and so I think this is a bit like a history of AI in short is the ability to predict the last years, the ability to generate, we can generate new hypothesis, we can generate modalities that we are missing. We can potentially generate certain cellular state, molecular state have a certain property, but I think what's really coming is this ability to reason. So we see this in those very large language models, the ability to reason about a hypothesis, how we can test it. So this is what those instruments ultimately need to do. So we need to be able to simulate the change of a perturbation on a cellular phenotype. So on the internal representation, the universal representation of a cell state, we need to simulate the fact the mutation has downstream and how this would propagate in our representations upstream. And we need to build many different type of virtual instruments that allow us to basically design and build all those capabilities that ultimately the AI virtual cell needs to possess that will then allow us to reason, to generate hypothesis, to basically predict the next experiment to conduct to predict the outcome of a perturbation experiment to in silico design, cellular states, molecular states, things like that. And this is why we make the separation between internal representation as well as those instruments that operate on those representations.Eric Topol (14:47):Yeah, that's what I really liked is that you basically described the architecture, how you're going to do this. By putting these URs into the VIs, having a decoder and a manipulator and you basically got the idea if you can bring all these different integrations about which of course is pending. Now there are obviously many naysayers here that this is impossible. One of them is this guy, Philip Ball. I don't know if you read the language, How Life Works. Now he's a science journalist and he's a prolific writer. He says, “Comparing life to a machine, a robot, a computer, sells it short. Life is a cascade of processes, each with a distinct integrity and autonomy, the logic of which has no parallel outside the living world.” Is he right? There's no way to model this. It's silly, it's too complex.Steve Quake (15:50):We don't know, alright. And it's great that there's naysayers. If everyone agreed this was doable, would it be worth doing? I mean the whole point is to take risks and get out and do something really challenging in the frontier where you don't know the answer. If we knew that it was doable, I wouldn't be interested in doing it. So I personally am happy that there's not a consensus.Eric Topol (16:16):Well, I mean to capture people's imagination here, if you're successful and you marshal a global effort, I don't know who's going to pay for it because it's a lot of work coming here going forward. But if you can do it, the question here is right today we talk about, oh let's make an organoid so we can figure out how to treat this person's cancer or understand this person's rare disease or whatever. And instead of having to wait weeks for this culture and all the expense and whatnot, you could just do it in a computer and in silico and you have this virtual twin of a person's cells and their tissue and whatnot. So the opportunity here is, I don't know if people get, this is just extraordinary and quick and cheap if you can get there. And it's such a bold initiative idea, who will pay for this do you think?Steve Quake (17:08):Well, CZI is putting an enormous amount of resources into it and it's a major project for us. We have been laying the groundwork for it. We recently put together what I think is if not the largest, one of the largest GPU supercomputer clusters for nonprofit basic science research that came online at the end of last year. And in fact in December we put out an RFA for the scientific community to propose using it to build models. And so we're sharing that resource within the scientific community as I think you appreciate, one of the real challenges in the field has been access to compute resources and industry has it academia at a much lower level. We are able to be somewhere in between, not quite at the level of a private company but the tech company but at a level beyond what most universities are being able to do and we're trying to use that to drive the field forward. We're also planning on launching RFAs we this year to help drive this project forward and funding people globally on that. And we are building a substantial internal effort within CZI to help drive this project forward.Eric Topol (18:17):I think it has the looks of the human genome project, which at time as you know when it was originally launched that people thought, oh, this is impossible. And then look what happened. It got done. And now the sequence of genome is just a commodity, very relatively, very inexpensive compared to what it used to be.Steve Quake (18:36):I think a lot about those parallels. And I will say one thing, Philip Ball, I will concede him the point, the cells are very complicated. The genome project, I mean the sort of genius there was to turn it from a biology problem to a chemistry problem, there is a test tube with a chemical and it work out the structure of that chemical. And if you can do that, the problem is solved. I think what it means to have the virtual cell is much more complex and ambiguous in terms of defining what it's going to do and when you're done. And so, we have our work cut out for us there to try to do that. And that's why a little bit, I established our North Star and CZI for the next decade as understanding the mysteries of the cell and that word mystery is very important to me. I think the molecules, as you pointed out earlier are understood, genome sequenced, protein structure solved or predicted, we know a lot about the molecules. Those are if not solved problems, pretty close to being solved. And the real mystery is how do they work together to create life in the cell? And that's what we're trying to answer with this virtual cell project.Eric Topol (19:43):Yeah, I think another thing that of course is happening concurrently to add the likelihood that you'll be successful is we've never seen the foundation models coming out in life science as they have in recent weeks and months. Never. I mean, I have a paper in Science tomorrow coming out summarizing the progress about not just RNA, DNA, ligands. I mean the whole idea, AlphaFold3, but now Boltz and so many others. It's just amazing how fast the torrent of new foundation models. So Charlotte, what do you think accounts for this? This is unprecedented in life science to see foundation models coming out at this clip on evolution on, I mean you name it, design of every different molecule of life or of course in cells included in that. What do you think is going on here?Charlotte Bunne (20:47):So on the one hand, of course we benefit profits and inherit from all the tremendous efforts that have been made in the last decades on assembling those data sets that are very, very standardized. CELLxGENE is very somehow AI friendly, as you can say, it is somewhat a platform that is easy to feed into algorithms, but at the same time we actually also see really new building mechanisms, design principles of AI algorithms in itself. So I think we have understood that in order to really make progress, build those systems that work well, we need to build AI tools that are designed for biological data. So to give you an easy example, if I use a large language model on text, it's not going to work out of the box for DNA because we have different reading directions, different context lens and many, many, many, many more.Charlotte Bunne (21:40):And if I look at standard computer vision where we can say AI really excels and I'm applying standard computer vision, vision transformers on multiplex images, they're not going to work because normal computer vision architectures, they always expect the same three inputs, RGB, right? In multiplex images, I'm measuring up to 150 proteins potentially in a single experiment, but every study will measure different proteins. So I deal with many different scales like larger scales and I used to attention mechanisms that we have in usual computer vision. Transformers are not going to work anymore, they're not going to scale. And at the same time, I need to be completely flexible in whatever input combination of channel I'm just going to face in this experiment. So this is what we right now did for example, in our very first work, inheriting the design principle that we laid out in the paper AI virtual cell and then come up with new AI architectures that are dealing with these very special requirements that biological data have.Charlotte Bunne (22:46):So we have now a lot of computer scientists that work very, very closely have a very good understanding of biologists. Biologists that are getting much and much more into the computer science. So people who are fluent in both languages somewhat, that are able to now build models that are adopted and designed for biological data. And we don't just take basically computer vision architectures that work well on street scenes and try to apply them on biological data. So it's just a very different way of thinking about it, starting constructing basically specialized architectures, besides of course the tremendous data efforts that have happened in the past.Eric Topol (23:24):Yeah, and we're not even talking about just sequence because we've also got imaging which has gone through a revolution, be able to image subcellular without having to use any types of stains that would disrupt cells. That's another part of the deep learning era that came along. One thing I thought was fascinating in the paper in Cell you wrote, “For instance, the Short Read Archive of biological sequence data holds over 14 petabytes of information, which is 1,000 times larger than the dataset used to train ChatGPT.” I mean that's a lot of tokens, that's a lot of stuff, compute resources. It's almost like you're going to need a DeepSeek type of way to get this. I mean not that DeepSeek as its claim to be so much more economical, but there's a data challenge here in terms of working with that massive amount that is different than the human language. That is our language, wouldn't you say?Steve Quake (24:35):So Eric, that brings to mind one of my favorite quotes from Sydney Brenner who is such a wit. And in 2000 at the sort of early first flush of success in genomics, he said, biology is drowning in a sea of data and starving for knowledge. A very deep statement, right? And that's a little bit what the motivation was for putting the Short Read Archive statistic into the paper there. And again, for me, part of the value of this endeavor of creating a virtual cell is it's a tool to help us translate data into knowledge.Eric Topol (25:14):Yeah, well there's two, I think phenomenal figures in your Cell paper. The first one that kicks across the capabilities of the virtual cell and the second that compares the virtual cell to the real or the physical cell. And we'll link that with this in the transcript. And the other thing we'll link is there's a nice Atlantic article, “A Virtual Cell Is a ‘Holy Grail’ of Science. It's Getting Closer.” That might not be quite close as next week or year, but it's getting close and that's good for people who are not well grounded in this because it's much more taken out of the technical realm. This is really exciting. I mean what you're onto here and what's interesting, Steve, since I've known you for so many years earlier in your career you really worked on omics that is being DNA and RNA and in recent times you've made this switch to cells. Is that just because you're trying to anticipate the field or tell us a little bit about your migration.Steve Quake (26:23):Yeah, so a big part of my career has been trying to develop new measurement technologies that'll provide insight into biology. And decades ago that was understanding molecules. Now it's understanding more complex biological things like cells and it was like a natural progression. I mean we built the sequencers, sequenced the genomes, done. And it was clear that people were just going to do that at scale then and create lots of data. Hopefully knowledge would get out of that. But for me as an academic, I never thought I'd be in the position I'm in now was put it that way. I just wanted to keep running a small research group. So I realized I would have to get out of the genome thing and find the next frontier and it became this intersection of microfluidics and genomics, which as you know, I spent a lot of time developing microfluidic tools to analyze cells and try to do single cell biology to understand their heterogeneity. And that through a winding path led me to all these cell atlases and to where we are now.Eric Topol (27:26):Well, we're fortunate for that and also with your work with CZI to help propel that forward and I think it sounds like we're going to need a lot of help to get this thing done. Now Charlotte, as a computer scientist now at EPFL, what are you going to do to keep working on this and what's your career advice for people in computer science who have an interest in digital biology?Charlotte Bunne (27:51):So I work in particular on the prospect of using this to build diagnostic tools and to make diagnostics in the clinic easier because ultimately we have somewhat limited capabilities in the hospital to run deep omics, but the idea of being able to somewhat map with a cheaper and lighter modality or somewhat diagnostic test into something much richer because a model has been seeing all those different data and can basically contextualize it. It's very interesting. We've seen all those pathology foundation models. If I can always run an H&E, but then decide when to run deeper diagnostics to have a better or more accurate prediction, that is very powerful and it's ultimately reducing the costs, but the precision that we have in hospitals. So my faculty position right now is co-located between the School of Life Sciences, School of Computer Science. So I have a dual affiliation and I'm affiliated to the hospitals to actually make this possible and as a career advice, I think don't be shy and stick to your discipline.Charlotte Bunne (28:56):I have a bachelor's in biology, but I never only did biology. I have a PhD in computer science, which you would think a bachelor in biology not necessarily qualifies you through. So I think this interdisciplinarity also requires you to be very fluent, very comfortable in reading many different styles of papers and publications because a publication in a computer science venue will be very, very different from the way we write in biology. So don't stick to your study program, but just be free in selecting whatever course gets you closer to the knowledge you need in order to do the research or whatever task you are building and working on.Eric Topol (29:39):Well, Charlotte, the way you're set up there with this coalescence of life science and computer science is so ideal and so unusual here in the US, so that's fantastic. That's what we need and that's really the underpinning of how you're going to get to the virtual cells, getting these two communities together. And Steve, likewise, you were an engineer and somehow you became one of the pioneers of digital biology way back before it had that term, this interdisciplinary, transdisciplinary. We need so much of that in order for you all to be successful, right?Steve Quake (30:20):Absolutely. I mean there's so much great discovery to be done on the boundary between fields. I trained as a physicist and kind of made my career this boundary between physics and biology and technology development and it's just sort of been a gift that keeps on giving. You've got a new way to measure something, you discover something new scientifically and it just all suggests new things to measure. It's very self-reinforcing.Eric Topol (30:50):Now, a couple of people who you know well have made some pretty big statements about this whole era of digital biology and I think the virtual cell is perhaps the biggest initiative of all the digital biology ongoing efforts, but Jensen Huang wrote, “for the first time in human history, biology has the opportunity to be engineering, not science.” And Demis Hassabis wrote or said, ‘we're seeing engineering science, you have to build the artifact of interest first, and then once you have it, you can use the scientific method to reduce it down and understand its components.’ Well here there's a lot to do to understand its components and if we can do that, for example, right now as both of AI drug discoveries and high gear and there's umpteen numbers of companies working on it, but it doesn't account for the cell. I mean it basically is protein, protein ligand interactions. What if we had drug discovery that was cell based? Could you comment about that? Because that doesn't even exist right now.Steve Quake (32:02):Yeah, I mean I can say something first, Charlotte, if you've got thoughts, I'm curious to hear them. So I do think AI approaches are going to be very useful designing molecules. And so, from the perspective of designing new therapeutics, whether they're small molecules or antibodies, yeah, I mean there's a ton of investment in that area that is a near term fruit, perfect thing for venture people to invest in and there's opportunity there. There's been enough proof of principle. However, I do agree with you that if you want to really understand what happens when you drug a target, you're going to want to have some model of the cell and maybe not just the cell, but all the different cell types of the body to understand where toxicity will come from if you have on-target toxicity and whether you get efficacy on the thing you're trying to do.Steve Quake (32:55):And so, we really hope that people will use the virtual cell models we're going to build as part of the drug discovery development process, I agree with you in a little of a blind spot and we think if we make something useful, people will be using it. The other thing I'll say on that point is I'm very enthusiastic about the future of cellular therapies and one of our big bets at CZI has been starting the New York Biohub, which is aimed at really being very ambitious about establishing the engineering and scientific foundations of how to engineer completely, radically more powerful cellular therapies. And the virtual cell is going to help them do that, right? It's going to be essential for them to achieve that mission.Eric Topol (33:39):I think you're pointing out one of the most important things going on in medicine today is how we didn't anticipate that live cell therapy, engineered cells and ideally off the shelf or in vivo, not just having to take them out and work on them outside the body, is a revolution ongoing, and it's not just in cancer, it's in autoimmune diseases and many others. So it's part of the virtual cell need. We need this. One of the things that's a misnomer, I want you both to comment on, we keep talking about single cell, single cell. And there's a paper spatial multi-omics this week, five different single cell scales all integrated. It's great, but we don't get to single cell. We're basically looking at 50 cells, 100 cells. We're not doing single cell because we're not going deep enough. Is that just a matter of time when we actually are doing, and of course the more we do get down to the single or a few cells, the more insights we're going to get. Would you comment about that? Because we have all this literature on single cell comes out every day, but we're not really there yet.Steve Quake (34:53):Charlotte, do you want to take a first pass at that and then I can say something?Charlotte Bunne (34:56):Yes. So it depends. So I think if we look at certain spatial proteomics, we still have subcellular resolutions. So of course, we always measure many different cells, but we are able to somewhat get down to resolution where we can look at certain colocalization of proteins. This also goes back to the point just made before having this very good environment to study drugs. If I want to build a new drug, if I want to build a new protein, the idea of building this multiscale model allows us to actually simulate different, somehow binding changes and binding because we simulate the effect of a drug. Ultimately, the redouts we have they are subcellular. So of course, we often in the spatial biology, we often have a bit like methods that are rather coarse they have a spot that averages over certain some cells like hundreds of cells or few cells.Charlotte Bunne (35:50):But I think we also have more and more technologies that are zooming in that are subcellular where we can actually tag or have those probe-based methods that allow us to zoom in. There's microscopy of individual cells to really capture them in 3D. They are of course not very high throughput yet, but it gives us also an idea of the morphology and how ultimately morphology determine certain somehow cellular properties or cellular phenotype. So I think there's lots of progress also on the experimental and that ultimately will back feed into the AI virtual cell, those models that will be fed by those data. Similarly, looking at dynamics, right, looking at live imaging of individual cells of their morphological changes. Also, this ultimately is data that we'll need to get a better understanding of disease mechanisms, cellular phenotypes functions, perturbation responses.Eric Topol (36:47):Right. Yes, Steve, you can comment on that and the amazing progress that we have made with space and time, spatial temporal resolution, spatial omics over these years, but that we still could go deeper in terms of getting to individual cells, right?Steve Quake (37:06):So, what can we do with a single cell? I'd say we are very mature in our ability to amplify and sequence the genome of a single cell, amplify and sequence the transcriptome of a single cell. You can ask is one cell enough to make a biological conclusion? And maybe I think what you're referring to is people want to see replicates and so you can ask how many cells do you need to see to have confidence in any given biological conclusion, which is a reasonable thing. It's a statistical question in good science. I think I've been very impressed with how the mass spec people have been doing recently. I think they've finally cracked the ability to look at proteins from single cells and they can look at a couple thousand proteins. That was I think one of these Nature method of the year things at the end of last year and deep visual proteomics.Eric Topol (37:59):Deep visual proteomics, yes.Steve Quake (38:00):Yeah, they are over the hump. Yeah, they are over the hump with single cell measurements. Part of what's missing right now I think is the ability to reliably do all of that on the same cell. So this is what Charlotte was referring to be able to do sort of multi-modal measurements on single cells. That's kind of in its infancy and there's a few examples, but there's a lot more work to be done on that. And I think also the fact that these measurements are all destructive right now, and so you're losing the ability to look how the cells evolve over time. You've got to say this time point, I'm going to dissect this thing and look at a state and I don't get to see what happens further down the road. So that's another future I think measurement challenge to be addressed.Eric Topol (38:42):And I think I'm just trying to identify some of the multitude of challenges in this extraordinarily bold initiative because there are no shortage and that's good about it. It is given people lots of work to do to overcome, override some of these challenges. Now before we wrap up, besides the fact that you point out that all the work has to be done and be validated in real experiments, not just live in a virtual AI world, but you also comment about the safety and ethics of this work and assuming you're going to gradually get there and be successful. So could either or both of you comment about that because it's very thoughtful that you're thinking already about that.Steve Quake (41:10):As scientists and members of the larger community, we want to be careful and ensure that we're interacting with people who said policy in a way that ensures that these tools are being used to advance the cause of science and not do things that are detrimental to human health and are used in a way that respects patient privacy. And so, the ethics around how you use all this with respect to individuals is going to be important to be thoughtful about from the beginning. And I also think there's an ethical question around what it means to be publishing papers and you don't want people to be forging papers using data from the virtual cell without being clear about where that came from and pretending that it was a real experiment. So there's issues around those sorts of ethics as well that need to be considered.Eric Topol (42:07):And of those 40 some authors, do you around the world, do you have the sense that you all work together to achieve this goal? Is there kind of a global bonding here that's going to collaborate?Steve Quake (42:23):I think this effort is going to go way beyond those 40 authors. It's going to include a much larger set of people and I'm really excited to see that evolve with time.Eric Topol (42:31):Yeah, no, it's really quite extraordinary how you kick this thing off and the paper is the blueprint for something that we are all going to anticipate that could change a lot of science and medicine. I mean we saw, as you mentioned, Steve, how that deep visual proteomics (DVP) saved lives. It was what I wrote a spatial medicine, no longer spatial biology. And so, the way that this can change the future of medicine, I think a lot of people just have to have a little bit of imagination that once we get there with this AIVC, that there's a lot in store that's really quite exciting. Well, I think this has been an invigorating review of that paper and some of the issues surrounding it. I couldn't be more enthusiastic for your success and ultimately where this could take us. Did I miss anything during the discussion that we should touch on before we wrap up?Steve Quake (43:31):Not from my perspective. It was a pleasure as always Eric, and a fun discussion.Charlotte Bunne (43:38):Thanks so much.Eric Topol (43:39):Well thank you both and all the co-authors of this paper. We're going to be following this with the great interest, and I think for most people listening, they may not know that this is in store for the future. Someday we will get there. I think one of the things to point out right now is the models we have today that large language models based on transformer architecture, they're going to continue to evolve. We're already seeing so much in inference and ability for reasoning to be exploited and not asking for prompts with immediate answers, but waiting for days to get back. A lot more work from a lot more computing resources. But we're going to get models in the future to fold this together. I think that's one of the things that you've touched on the paper so that whatever we have today in concert with what you've laid out, AI is just going to keep getting better.Eric Topol (44:39):The biology that these foundation models are going to get broader and more compelling as to their use cases. So that's why I believe in this. I don't see this as a static situation right now. I just think that you're anticipating the future, and we will have better models to be able to integrate this massive amount of what some people would consider disparate data sources. So thank you both and all your colleagues for writing this paper. I don't know how you got the 42 authors to agree to it all, which is great, and it's just a beginning of something that's a new frontier. So thanks very much.Steve Quake (45:19):Thank you, Eric.**********************************************Thanks for listening, watching or reading Ground Truths. Your subscription is greatly appreciated.If you found this podcast interesting please share it!That makes the work involved in putting these together especially worthwhile.All content on Ground Truths—newsletters, analyses, and podcasts—is free, open-access, with no ads..Paid subscriptions are voluntary and all proceeds from them go to support Scripps Research. They do allow for posting comments and questions, which I do my best to respond to. Many thanks to those who have contributed—they have greatly helped fund our summer internship programs for the past two years. And such support is becoming more vital In light of current changes of funding by US biomedical research at NIH and other governmental agencies.Thanks to my producer Jessica Nguyen and to Sinjun Balabanoff for audio and video support at Scripps Research. Get full access to Ground Truths at erictopol.substack.com/subscribe

  --------  

  43:43
• Katelyn Jetelina: Public health in the US

  Thank you Katelyn Jetelina, Andy Meyers, Tracy Paeschke, MD, FACC, Bruce Lanphear, Tay MacIntyre, and many others for tuning into my live video with Katelyn Jetelina! Join me for my next live video in the app. Get full access to Ground Truths at erictopol.substack.com/subscribe

  --------  

  26:46

More Science podcasts

Trending Science podcasts

About Ground Truths