Zavain Dar on Hugging Face, NVIDIA & Why Silicon Valley Should Pay Attention to China

22:28Eric Newcomer:Quasi, quasi-naval gazing. I know you had the TBPN guys and I really like what they're doing, but I got a text from our friend yesterday showing me a screenshot of like a paparazzi style picture of like some entrepreneur or investor and um and it's just become a little bit navel-gazing and and don't get me wrong we should celebrate um uh entrepreneurs and investors and people taking risk and people creating and inventing um but you also need to give space and time for like long form thinking and focus and space to actually do work china can often make america feel like europe in the same way that when we sit here and we kind of think about like the the work ethic or the drive where the pace of business kind of velocity in Europe, it's a step slower.

23:08Eric Newcomer:I don't know. Having an 11th month old, I am now decidedly anti-996. Or it's like, I understand that there are different phases of life, but it just feels that a company, you know, would have that expectation. It's just like, I mean, this is how Europeans feel about Americans. It's like, we achieved all this to not have to do that. You know, there are some things that matter more. And so, you know, I guess being America somewhere between Europe and China, you know you get to feel a little good about like oh maybe it's the sort of happy medium yeah and some like look i um i wonder if um a normative assessment of 996 is like is the question that we should be asking um or if it's just like observational oh okay this is 996 and and one can and should of course ask ask the question do we as a society have enough to take care of everybody and or have we become slaves to like a perpetual you know uh you know uh hamster real.

24:04And is there any way, can we imagine a world where you find a little bit more balance?

24:07Eric Newcomer:But if you're trying to find the best investments, the ones doing 996 might... Yeah, a little bit of both. I think the best entrepreneurs also tend to be creative and spunky and I think oftentimes are spiky, to use maybe the word of the year in the kind of venture entrepreneurial community, in all sorts of ways that aren't just work-related. I mean, one of the observations that really resonated with me is that, you know, I feel like every work of fiction, you sort of like the villain is created by the good intentions of the heroes or whatever. Or this is, you know, it's opposition. And it's like the U.S.

24:45Eric Newcomer:was like, OK, we want to stop China. We're going to crack down on these NVIDIA chips. And then through the deprivation of not having access to the best chips, China's sort of been able to do things in sort of more humble ways, which has allowed those models to be cheaper, which means that they're more sustainable, more usable, better for these vertical application companies we're talking about. Yeah, I mean, it's a nice story, but I mean, you also want to have like the best chips in the world. Do you think the U.S. made a mistake by restricting the access or this would happen anyway? I think the jury's out.

25:22Certainly, with no doubt, what has happened is we cut off a supply of cutting edge chipsets, namely through NVIDIA. And that forced the frontier labs and engineers in Beijing and Shanghai. And a lot of the AI work specifically happens in Beijing or in and around Beijing, because that's where Xinhua and Peking University, that kind of academic talent is. It forced the, you know, the compilers, engineers, and the systems engineers, and the low-level assembly line and assembly code engineers to really innovate all the way down to the bare metal in a way where the American labs aren't. And in some ways, that circumvented the export controls that we'd put on.

26:03In other ways, the Chinese labs are still struggling. And so what are the two worlds where we'll look back and say, was this a right decision or the wrong decision? If right or wrongness is depending on whether or not we win the AI race against China. If Chinese engineers and computer scientists are able to innovate such that they make irrelevant the US's lead on silicon, then yeah, for sure. We'll look back and say, man, that was the wrong decision. That's not the case right now. Our frontier labs are meaningfully ahead of the Chinese frontier labs in no small part because we have better silicon.

26:43We don't have as much power. And at some point, we'll kind of run up against that wall. But right now, we have a meaningful lead. China, even if it has enough power, it doesn't have enough chips. It doesn't have enough compute. And so right now, they've done an amazing job optimizing at a very low level. It still isn't enough to catch up to our frontier labs. But it's certainly something that we should be paying attention to. And the question is, are we creating an enemy in the future? Or somebody who wants to catch us up on the silicon level, then they'll both have power and better low-level software engineering than us.

27:13That's kind of a scary world. And so we should be tracking those two. I think probably what's the most interesting is to watch the velocity and the cadence of innovation from the Chinese semiconductor industry. And right now that's, I think, moving faster than what most industry kind of viewers would have predicted 24 months ago. and so um you know i think that's probably where we want to be paying the most attention to just do you think they will catch up or in the fullness of time when you have to think that they will catch up nothing is fully defensible forever and if you have um again a large extremely intelligent extremely motivated and capitalized population to go and pursue these things they will china graduates more stem engines more stem graduates uh than the world globally every year and so there's just like hundreds of thousands of extremely intelligent people who will go and pursue these, these questions.

28:05Eric Newcomer:How, to what extent do you feel free to be a global investor? You know, I'm, I'm sort of a globalist. Like I want a world where the U S and China can share technology. Obviously I want Chinese to follow agreed upon rules and compete on a level playing field. And there's some protectionism issues I have, but generally I'd like to see a world of cooperation and fair competition. But just a few years ago, Sequoia Capital had to spin out of China, literally GGV, which was known for being this cross-border investor, split the firm. But now it does seem like there's some comfort. I mean, the information, which obviously journalism is different, but Jessica Lesson just sort of talked about hiring a new reporter to really focus in on China.

28:50And there's just like a mood that people feel a little bit more comfortable

28:54Eric Newcomer:saying you know that they spent time there and you've invested around those companies like what's your read on how free you are to invest in chinese companies and what the what the mood is right now um maybe i'll start with saying like you know we we invest in companies um largely globally um for the vast majority in the west um and um largely avoid you know regimes and areas that we kind of view as unethical or um kind of uh adversarial to western values and so like we don't have investments in russia for example uh or iran for example right now um i i think it's a shame that people are um fearful such that they shunt their curiosity uh in these situations i think that um being open and honest with your curiosity and learning from how others do things is really, really valuable.

29:45We don't have a China strategy per se. And again, we have one investment that we made in a company that's a Cayman Topco, happens to be, you know, footprinted in Shanghai. But I do think that there's a lot that we can learn. And especially right now, if you read the letter, I think one of the points that I really tried to imbue was biotech and tech today are more interwoven between the US and China than arguably they've ever been before. And so to have your eyes shut off to what's happening there i think it's positioning you even if you only focus on the u.s you are disadvantaged to be ignorant or naive about what's happening in china

30:23Eric Newcomer:have have this is gonna this is not my area of expertise but have americans really benefited from like novel chinese drugs to date and is that a world we're about to enter yeah uh We are certainly entering that world. If anyone has been following Summit, which is a U.S. biotech company that's got a drug that's going after PD-1 VEGF, that molecule was out licensed from Acaso, which is a Chinese biotech. B1, which was formerly known as BiGene, now European domiciled, founded and really kind of brought to maturity in China. now has kind of commercial global ambitions with their kind of commercial drug pipeline as well.

31:10If you look at like where the, at this point, the majority of pharma out licensing and BD dollars are going, it's going into China. And so, you know, all you need to do is look at the large billions of, you know, upfront and biobucks dollars that maybe, for example, GSK spending on Chinese assets to see, okay, if those assets are now entering GSK's pipeline, in the next three, four, five years at some point, assuming that they're approved, they will be commercially available. And we'll all be at that point taking an antibody that was maybe invented in a lab in Suzhou, China. So it's coming. It's coming.

31:46Yeah, it's coming.

31:48Eric Newcomer:And so this sort of transitions us to this, you know, field that's, you know, just hard to understand for many of us or like, you know, AI and biology. It's like, you know, I think some of the largest pronouncements from people like Dario at Anthropic or like, oh, you know, I mean, he literally said something the other day that's like, oh, we want to win people over by curing cancer or something, you know, then we'll really earn goodwill. And that's the kind of stuff that will move the needle. And I think some people reacted to that, like, even if that's true, it's going to take so long. And obviously you have to run experiments.

32:23Eric Newcomer:You might think you have the answer, but it could take years and years until people really believe it or, you know, not believe it's even the wrong word until it's proven and goes through sort of the human processes that it has to. But where, you know, what makes you most optimistic about sort of drug discovery and disease, attacking diseases with AI? And where are you sort of like, this is going to take a long time? Maybe I'll preface it in that I've lost in my investment career, which really started in 2013, lost far more money underestimating the velocity of technology and science progress than overestimating the velocity.

33:02Eric Newcomer:Like you missed deals you should have done. I missed deals. I thought this would take too long. I didn't think it was tractable, so on and so forth. And so I think I have learned through a negative Pavlovian response to really attempt to understand why someone who's really intelligent, even if they're saying something that I think is far-fetched to try to like understand where they're coming from um I think what Daria said which is um really inspiring is you know it's it's not it's one thing to talk about it it's another thing to actually go do it and we can talk about coefficient and the team and you know kind of our views on on what they're doing um but I think in our lifetime I don't think I it's it's hard for me to have you know the fidelity to say 7 or 10 years or 15 or 20 years But certainly in our lifetime, assuming we live to ripe, healthy ages, the vast majority of human disease should be solved, should be treatable.

33:56And I think we're on that path. And we can talk about a company like Chai that essentially has developed generative technologies to perturb biology in exactly kind of the way one would want to. So a biologist can say, hey, I want to perturb this protein at this site by upregulating it or downregulating it. Sorry, perturb.

34:18Eric Newcomer:Is this a model or is this in a lab or? In a human or an animal setting. So I want to get a protein. I want to get this protein into this cell type or into this organ type. And then at that organ type, I want it to hit another protein and the protein that I want to hit it on the target in kind of, you know, biotech pharma speak. I want to attach my protein that I generated onto this part of that. It's called an epitope. um and so uh that's kind of amazing that's like a that's a toolkit that we just never had before and you're seeing it in terms of the pharma dollars and the revenue and the traction that they're getting um but that's kind of like one step of many that's all simultaneously kind of happening all at once so in our lifetime we have the ability to sort of treat all these diseases yeah does that mean we're living forever it means we're living to 200 i mean yes and and And maybe before we get to that question, I think the long pole is and will continue to be testing in humans.

35:21We don't have in silico systems to replace, nor do we have animal models to replace in totality. Like the necessary requirement to test in a step-wise function, first safety, then efficacy, then really testing safety and efficacy in a phase one, two, and three kind of trial for most drugs. I actually saw Jacob Kimmel, who's the founder, president, and CEO of New Limit, kind of comment, I think, on Twitter recently that this kind of long pull will continue to exist. We don't really have line of sight to something that's going to meaningfully step function, change it, or decrease kind of the timing of it.

36:00We might be able to multiplex it. So when you're kind of enrolling in a clinical trial, can we be creative or intelligent about testing multiple drugs in one person at the same time to get kind of a -

36:10Eric Newcomer:And to say it in sort of the dumbest way possible, if a drug professes to keep you alive forever, you have to run that experiment. You know, it's like, oh, this is going to help you, you know, 30 years from now. How do you really, you know, it's not hurting you in the near term. Maybe it shows some immediate benefit, but you don't necessarily know. No, you might have some theory of the case why AI believes it'll do X, Y, and Z. Novo just had a failed phase three antibody that was going after an interligin. So kind of an extracellular protein called IL-6. And we had every biological and theoretical and kind of in vivo and even in human reason to believe that if we kind of shut down or downregulated IL-6, it would reduce the rate of like heart disease, essentially ASCVD, various forms of like heart attack and heart disease.

37:02We took this kind of drug through the preclinical setting. We took it into phase one. It proved safe. We took it into a phase two setting, which is a little bit shorter. So, you know, you didn't have the full three or four years required to see, hey, on average, are people having less heart attacks? But we had all of the biomarkers that we thought were highly correlated to heart health. And we were taking down IL-6 and it was taking down all of the biomarkers that we thought were highly correlated to heart disease. We then ran it in a phase three setting. It cost hundreds of millions, I think over$500 million was the aggregate cost of this phase three.

37:37It took about four years to run, and there was zero correlation. There was 1 % correlation. It was a 0.99 hazard ratio, which is the equivalent of 1 % correlation between that drug and decreasing heart disease. And so what does it mean? It means that biology is really hard. We thought we had all of the biomarkers. Those ended up being correlative, not causal. The drug did what we expected it to do. It was taking down IL-6. It was taking down kind of that pathogenic protein, but it wasn't actually reducing the disease. And so until we figure out better biomarkers, until we better understand human disease biology, it's still going to take time and there still will be failure.

38:15Eric Newcomer:But if we cure all disease or just cancer, does that just mean there's still a clock or this is an attempt to sort of we will not die of natural causes? it uh that's somewhat of a philosophical uh question do you think aging is a disease or is it you know part of the human condition i think there there are many smart people who would i answer on both sides oh because your claim might be accept aging or like are you sort of leaving aging out of there there there are reasons to believe that aging might be um uh helpful to some extent uh you know in the for an evolutionary process for the first 20 or 30 years of our lives but then we haven't really evolved.

38:59There's very little in human evolution that's based on the years 30 plus, simply because the vast majority of our ancestors never made it past those years. And so, yeah, could we imagine a world where we maybe freeze the aging function at around 27, 33 in that range? Perhaps, if that's possible. Companies like New Limit, by the way, like not in this fantastical, let's go do it all at once, freeze at 27, but like let's be organ or cell specific and figure out a way to perturb the age of particular cell types or organ types with exquisite precision and control, like that's what they're going after.

39:33Eric Newcomer:And how much is it sort of generative models, like the LLMs we all know, like is it, I go into open AI, you know, some private version of open AI and I ask like, what should we do? And like it spits out some answer and then scientists run around and run experiments or what, how should we imagine what these like sort of AI drug discovery companies are doing or like what's, what are their key approaches? So there are, I think, you know, a handful, like maybe to name a few of them. There's companies like Chai, again, for example, who are developing technology to help you design what an industry is called composition of matter.

40:14So I want to design a small molecule or an antibody or a macrocycle or, you know, a radio ligand therapy. Help me generative algorithm design the actual thing.

40:23Eric Newcomer:um there are so that's i've built a specific model that is sort of thinking through it exactly around these problems trained on it and then it's coming up with answers itself exactly and instead of saying hey write me an essay or write me a poem as one might do to clod or anthropic it's saying design for me the antibody sequence of a drug that binds to that epitope of that protein or design for me a small molecule that hits into that allostery pocket of you know that um intracellular protein so on and so forth um there are companies that are trying to map biology so uh it's not just about designing the medicine it's about knowing what the medicine medicine should do and so that's um often reading literature it's understanding you know um large uh multimodal biological and human health data sets so multi-omeg human health ehr emr kind of coalescing all of that and making sense of it.

41:15We started actually a company internally at Dimension called Coefficient. And one of the ways we used it internally before kind of spinning it out into a new co was to make sense of new papers, new ideas, new pathways, new targets, new potential remedies in ways that we could very quickly make sense and get smart in these areas, even if we'd never kind of seen them.

41:40Eric Newcomer:It could have helped you just invest in great companies. Exactly. And And that's frankly how we used it. And every day across the 10s. This Coefficient Bio, which sold to Anthropik for like$400 million. Exactly. And every day across. It was like, am I making this up like a 30 ,000 IRR or something? I probably shouldn't say the specific financials of it. But although, again, here to my Dimension co-founder, Adam Goldburn, gets a bunch of credit. he was kind of the entrepreneur, investor, co-founder of the business. It was his idea to spin out what we were developing internally into a company. He was single-handedly responsible for recruiting the founding team, galvanizing them, financing them, and then working with them through kind of the early innings of the coefficient build.

42:29We were extremely lucky to partner with Nathan and Aris and Sam and Jesse and the team there. and to give you just maybe like a quick sense of like where at least we were kind of thinking about building it every day there's you know 10 ,000 plus tens of thousands of universities that are all pursuing different parts of science every day they're putting online new results new data if I am let's say Lilly and I'm running a trial against IL-6 maybe and there's a small research lab in Germany that publishes a result what are the odds that i as the you know that the trial investigator will happen to see that result zero to none right but if i have uh an agent quote unquote online scouring the world's kind of corpus of knowledge in real time so being able to incorporate in real time against your world model but you know what do you think the probability distribution of different biological phenomena to be true is if you see a result from a small you know research university you know in the suburbs of berlin come out come online you should be able to the next day update what you're thinking is update the gantt chart no actually you know what we should look at that patient criteria not that not what we had before so on and so forth and so um i think that's where we're trending it makes me really hopeful that we will get more accurate with our kind of scientific uh

43:52Eric Newcomer:i hear this stuff and it's just like the average american is so underestimating how revolutionary what's happening with generative AI is. I mean, Americans are pretty hostile to it. And partially the AI industry gets some of the blame for how they talk about it. But just on science alone, the amount that people see opportunity, it's sad that the American public sort of hasn't gotten that message. It's so fucking cool. And what's happening in math right now? We're potentially nearing the asymptote of the full closure closure of you know a priori human knowledge and um man like as as as a nerd and as a geek growing up like that's like the coolest fucking thing translate that you just you just think ai is about to sort of solve all these any anything that you can deduce with a pen and paper so like mathematics for example um you we should have the full closure of it we can in theory, I think, sooner rather than later, be on a path towards essentially like having a pamphlet of here's all of mathematics and here's kind of like the proofs.

45:01To the extent that, I mean, there's no complete representation of it, but to the extent that for any statement, we should be able to kind of give a true or false proof for the most part, we're on that path. And that's done. Where it's sort of you could solve it with binary is sort of the claim? You can solve, yeah. In philosophy, it's called a priori. So it's things that you can do deduce without kind of empirical testing um physics chemistry astrology space on and so astronomy space uh those are more empirical sciences so it's a posterior a posteriori so you need to be able to kind of then query the universe get more data and then help guide your knowledge back um and that's where uh there are two major uh yeah kind of branches of ai research right now one is in and around kind of mathematics which is like a priori knowledge and the other is around kind of empirical knowledge.

45:50So you're seeing so many kind of labs being spun up to essentially have closed loop interfaces between kind of the AI model and some sort of set of experiments that they can run and then have it come back and update the priors of the AI model.

46:03Eric Newcomer:I feel like, you know, what do you make of Silicon Valley's sort of, I guess, silence on some of the US turn against immigrants? It's so weird. It's so weird. It's sort of dark, right? it's so dark um the fact that we can um i like i'm all for opening the overton window um and uh uh but it uh but i find it deeply troubling that there's not more pushback against um you know essentially a a view that we should have some sort of reverse dei for uh americans like we are i believe we are as a country and as an economy certainly who we are because we've been the beneficiaries of global brain drain and so we've you know for every role in every company in theory all things equal we should be able to you know find the best person source the best person globally um and that's why our companies by and large have been successful and it's good for america i mean palmer lucky you know has been fighting with paul graham probably fighting as we speak for days.

47:08Eric Newcomer:And it's like successful American companies will either hire everybody or they'll open up offices somewhere else. So like a global multinational is still going to hire people outside of the US. And so I don't even understand. It's better for Americans that the talented people are here paying our taxes, like starting our companies here, being aligned with our governments, hiring with and building with our values. But the defense, I mean, Gary Tan, I understand you have like the, you know, the White House space or like the FTC or whatever it was like zeroed in on you. But just like not even immigrants have given the Silicon Valley so much and to not even like defend them at this moment just feels.

47:53yeah like like i was um deeply disenchanted by uh the large um you know sound of a pin dropping from many of my silicon valley venture peers um in the face of this debate right and um i don't think that would have happened like 10 years ago or 15 years ago frankly it's for the most part it's illegal to uh to hire um uh on the base of basis of citizenship so it's like and there are times when like you legally need to find an american citizen because cia or government related it's classified so on and so forth uh but for the most part like again all things equal it is actually illegal to like to make a hiring decision based on citizenship and so um yeah we're just in in a weird moment in time i i hope we're i hope we swing out of it um uh and like i would like i i yeah i hope we swing out of it well uh great to have you on the

48:52Eric Newcomer:show a lot a lot to be hopeful for and so so much change in the world so really appreciate you coming on awesome thanks eric that's our episode this is the newcomer podcast thanks for listening please like, comment, subscribe. Go find us on our sub stack at newcomer.co. If you're hungry for more podcasting, I also have a talk show called the Cerebral Valley Show. We love your comments. Thanks for all your support and see you next week.