YouSaid · the spoken record

Brian He

lines on the record: 77
first: 2025-09-15
most recent: 2025-09-15
sittings or episodes: 1
sources: podcast

Every line below is reproduced as it was said and linked to the record it came from. Nothing here is summarised or generated. Directory · Search · Corrections

“Alpha folders in many ways came out of a protein folding competition called CASP, right? Critical assessment of the structure proteins. And we created our own virtual cell challenge at VirtualSoulChallenge.org where we have $100,000 prizes sponsored by NVIDIA and 10X Genomics and Ultima and others. And it's an open competition that anyone can enter where you can train perturbation prediction models and we can openly and transparently assess these model capabilities both today and in subsequent years follow them to get to that chat GPT moment, right? And so I'm extremely excited about this. We like more people to train models and apply both BioML experts and engineers in any other domain. And I just, I want this thing to exist in the world. Hopefully we're important parts of making that happen, but I just be happy that someone does it.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Training them at 100 million or 650 million parameters like back then. But if you can scale them up to 1B, 7B, 35B, 70B, right? You start to see whether or not Established foundation model companies are doing today, right? As they kind of, in addition to these research teams, these are in many ways becoming applied AI companies. They need to build product shape and all kinds of different enterprises and do RL for businesses and make money, right? And I think or build coding agents and make API revenue. And that's important. I think a timely race to survive today, but I'm just very bullish on the research of, say, like a Sakana AI, right? Which was founded by one of the authors of attention is all you need, right? Ian Jones. And they're doing incredibly interesting stuff on model merging and how you can have kind of sort of like evolutionary selection of different kind of models in MOE. And I think”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“And we're working with an architecture that dates back to 2017. And if you look at the history of deep learning, it's like every eight years, there's something really different, right? And it feels like in 2025, we really overdue for some net new architecture. And I think there are lots of really interesting research ideas that are bubbling up that could do that thing. And in many ways, there's a set of really interesting academic ideas, especially in the golden age of machine learning research from, I don't know, like 2009 to 2015, right? There's so many interesting ideas, little archive papers that have like 30 citations or less. And as the marginal cost of compute goes down, year on year, I think you're going to be able to take all of these ideas and actually scale them up, right? Where you don't see the scaling laws when”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“I think the cool thing about agents generally is that. They do real work, right? Compared to like SaaS companies that came before, agents replace real productivity. And I think they have a lot of errors today. And I would say the computer use agents will probably trail the coding agents by maybe a year, right? But it's coming and we'll follow the trajectory as these go from doing minutes of work without error to hours to days, right? And I think you're going to get a completely different product shape as we march through that across legal BPO, you know, medicine, healthcare, whatever, right? And we'll kind of follow that as an industry. And that's going to be really exciting. And I think that's where we're going to see real heft because most of the economy services spent. It's not software spent. And the reason why we're all excited about this stuff is that it can attack the service.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“If not too difficult, I want to ask Jorge a question adopted to these additional spaces, robotics, sort of BCIs and longevity, if appropriate, terms of, and through questions, I believe where what's overhyped? Where do you see opportunity or path? And what's got heft already?”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“We didn't get these people together and funded at the right time in the right way, right? And that's really what motivates me. And these are kinds of the things that I've been excited about. Backing longevity companies like Nuleman, right? BCI companies like Nudge, right? Robotics companies like the bot company, right? You know, these are some of the examples of kind of things that I think must happen in the world and therefore should happen. And how do we actually find the right people in the right time to actually kind of go on the fellowship of the ring hunt?”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Us new opportunities to attack them, and there's a set of people and teams that are going to be positioned to be able to do that. They need to have technical innovation. And then an intuition about product and business in a way that you kind of in the RPG dice roll of the skills that you get in these three domains, people start at different base levels, right? And you might have an incredibly technical founder who doesn't know how to think commercially or someone who's just natively a very commercial thinker who, you know, it doesn't have very strong product sense, right? Even though they could sell the crap out of it, right? And so I think these sort of this sort of three broad categories of capabilities, you need to kind of bring together in a way that you can allocate capital to in the right times in order to make these ideas possible in a really differentiated way. Like this thing literally wouldn't happen if”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Their time, and in many ways, the story of technology development is trying to use new technologies to solve old tricks, right? Like most of our tools are for productivity in many ways, whether that's the industrial revolution or the computing revolution or the current AI revolution. We're trying to kind of do the same stuff. And so I think there's a relatively small set of very powerful ideas, new technologies.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Labor in an interesting ways. And you can kind of see how each of these three things, even in the sort of medium cases of success really kind of changed the world. And so I'm very interested in helping make these kinds of things possible, right? And so there's sort of, you know, in the kind of techno-optimist sort of vision of the world, right? There's a few different types of scarcity, right? It's very easy when you do research to come up with important ideas. The hard thing is to tackle them in the right time frame, right? It's like, you know, writing futuristic sci-fi things is not that hard. Being able to actually execute on it in the next five years or eight years much, much harder, right? And I would say, you know, academic discovery is littered with plenty of ideas that are interesting and important, but kind of long before.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Oh, yeah, my goal is to Really try to figure out ways that we can improve the human experience in our lifetime. I kind of think of like if I think about the future that we're going to leave to our children, right? There are a few things that if we get them right in our lifetime will fundamentally change the world and how we live in it. I think synthetic biology is obviously one think GLP ones, right? Things that improve sleep, right? Things that can improve longevity, right? These are all things that are kind of easy to get excited about. I think brain computer interfaces is another area where we're going to see really important breakthroughs over the decades to come. And then I think the third is in robotics, both industrial and consumer robotics, right? That allow us to basically scale physical like.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Shifting gears a little bit. We've been talking about science and biotech, but in addition, you're an elite AI investor more broadly. So I want to talk about where your investment focus is right now just as it relates to AM or Broadley. Where are you excited? Where are you spending time? What are you looking forward to?”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Explain physically or geologically or whatever why and how that happens. But as long as it knows if it's going to rain next Tuesday, you're probably happy, right? And I would say similarly with a virtual cell model, it may not tell me literally why, just like a alpha fold doesn't tell me literally why did the protein fold this way and how, but it just told me the end state and it was reasonably accurate. I think that would already be very important.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Think that's almost certainly true, right? Like it seems almost obvious that we're not measuring many of the most important things in biology, right? And you can, of course, find many important exceptions for any of these measurement technologies. Like in biology, we ultimately have two ways to study it in high throughputs, imaging and sequencing, right? But there are so many other types of things that you would care about that those things aren't necessarily going to do at scale, right? And that's really why I think the stuff that we're talking about of the RNA layer as the mirror for other layers of biology is one that we've spent a lot of time thinking about.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“And so we'll work to, you know, gene expression data or it's DNA data or any number of factors, protein and protein interactions, all the things you describe. Um... What if we're missing the core element? Like, what if we just haven't discovered the quark or whatever? Like, we just don't know what we don't know. And therefore, what we're feeding the model is fundamentally or importantly incomplete.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Help us, you know, we can have molecular design models, we can have docking models, we can then have when you bind to this thing in this cell versus all the other off-target proteins will a cell kind of be corrected in the right way, right? These kind of layers of abstraction and complexity start to get to things that feel very tangible through drug discovery. If you could actually traverse these steps reliably and in sequence, you could start to see how you can get the compression, right? And so I think in the long run of time, this should be possible.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Think the core intuition that Dariu had was the idea that important scientific discoveries are independent or they're largely independent. And if they are statistically independent, then it would stand to reason that we could multiparize. And so we had models that were sufficiently predictive and useful. You could have not just 100 of them, but millions, billions of these discovery agents or processes running at a time, which should compress the timeline to new discoveries and turn it into a computation problem, right? I think that is a very futuristic framing for something that is actually very tangible today, right? And if we can have virtual cell models at work, for example, that can start to do these kinds of things that we've been talking about.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Dario Amade's essay, Machines of Love and Grace, he predicts, among other things, the prevention of many infectious diseases and the doubling of lifespans perhaps as soon as the next decade. What's your reaction to his essay's bullishness and some of his predictions?”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“You can use this to tune binding selectivity and affinity. That might be ways to predict safety and efficacy, right? And, you know, how well will that work? Well, that's a feedback loop that we'll have to actually test in the lab. And that's part of what's slow is the testing takes real hours, days, months, right, years. And that's really why we've picked at Arc, the virtual cell models is our initial wedge, because we think it can integrate a lot of these different pieces.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“The internet, and we use funds, we're going to have AI in all parts of the stack, right? And so it's just going to become a native part of everything that we do. And so, you know, like, why hasn't it worked yet? Is this long multi-factorial process that we've been talking about today? There's designing, there's the making, there's the testing, there's the approvals side of it. you know, I think the I do think safety and efficacy as the kind of two pillars in the industry are the two things that we need to get right, right? We need to be able to figure out faster ways that we can predict whether or not molecule will work and if it's going to be safe or not. There are like ways that AI can operationalize this. If you designed a small molecule, right, you could now computationally dock it to every protein in the proteome and see if it's likely to bind to off-target molecules.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“You know, AI for drugs is one of these weird things where everyone who works in the industry is trying to claim that their drug is like the first AI design molecule, right? I feel like increasingly in just a few years, this will just be a native part of the stack, right? Just like we”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, for sure. Yeah. And there's a lot of stuff where you don't have to train, you know, weird biology foundation models and you can write regulatory filings and reports and things like that. That's impactful and important.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“That's the idea that we will That's right. Okay, right. There's heft and anything to do with proteins, right? Obviously, protein binding, but increasingly in protein design, right? And I think there is real heft there. And then, you know, where there's hype is in multimodal biological models, whatever that means, right? And I think, you know, pick your favorite layers. It could be molecular layers. It could be spatial layers. It could be, you know, I mean, actually, I would say there's also Heft in the pathology AI prediction models, you know, like, you know, automating the work of pathologists and radiologists. That's the powerful.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Me examples where you think AI is hyped Potentially overly hyped, where there's real hope, like the sort of, what do we expect, what's next. Where we already see real heft. So, like, if I asked you in AI, where is their hype? Where is their hope? And where are we seen heft today?”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, maybe another way to ask the question is because I always ask the founders a version of this question, like the AI ones, that are like, oh, we're going to do AI for life for drug discovery. So my question that I always like to ask founders is.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“You know, we joke about it. You have to do is you have to get a molecule that can go first in mice and then in mutts and then in monkeys and then in man. Like there's a long time and it's just so hard to compress that. And so when you do, you should make the journey worth it, right? So when you fail on the other end of that, like that's obviously horrible. And so finding ways to make sure that when you walk that path, that it'll be a successful journey as often as possible is what this industry desperately needs. Alpha fold solved protein folding problem, but when didn't it solve judge discovery or more broadly, what would it take to get AIDS started? What is sort of the bottleneck on the tech side at least?”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“From 1980 to 2020, all democratic presidential candidates, both VP and president, went to law school, right? And so you kind of see the echoes of that in the FDA and the regulatory regime and all the kind of the bottlenecks that people talk about developing drugs stateside. And increasingly, you see folks thinking about how we can run phase ones overseas, right? Build data packages that we can bring back domestically for phase two efficacy trials. I think that's interesting directionally, but it's not enough, right? And, you know, I think we need to kind of figure out these two bottlenecks, the making and the testing, even if we can solve the designing part.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Exactly. That's right. Right. China's an engineering state, right? It's kind of Politburo is folks who have engineering degrees. You need to build bridges and roads and buildings. And these are the ways that we solve our problems. Whereas I think from the first 13 American presidents, 10 of them practiced law.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“If we play this out, right? And let's say these AI models work, right? And you can make a trillion binders in silico that will be exquisite drug matter, right? We still need to make these things physically and test them in animals and hopefully predictive models and then actually in people, right? And I think that will increasingly be the bottleneck in many ways, right? And my friend Dan Wang recently released a book called Breakneck, which talks about, you know, kind of like the US and China and the difference between the two countries and their philosophy, the way they approach markets.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Going to get a lot better at designing some of the more complex modalities like the gene therapies of the world or the gene editors of the world. And when you can do that and combine that with our ability to hopefully use things like virtual cell models to really understand what to go after, we're going to have drugs. I would hope and I would expect that the industry will continue to bring forward drugs that have very large effect size for very difficult diseases that hopefully affect a lot of patients. If that's true, then we'll start to see some of these really, really difficult diseases that affect all of society get tackled. Hopefully, you know, one by one by one by one. And so we have obesity. We have metabolic disorder. We're dealing with cardiometabolic disease. We're starting to see interesting promising things happening like neurodegenerative diseases, if we can tackle cancer or at least several cancers that now have begun to be treated more like a chronic condition than a death sentence that they were in the past, the more we see of that.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, look, I think it's going to go back to sort of where we started this conversation. GLP1s as a drug are four decades in the making or something like that. These are not overnight successes. But I do think what we are going to see more of and our hope is that when you combine the fact that we're getting better at understanding what to target, getting better at designing medicines to hit those targets. By the way, in a whole array of new creative ways. So we have small molecules, the natural products that we got from boiling leaves, as you said earlier. Those have gotten, we're getting really good at designing smarter and better smaller molecule, small molecules that do new things, that function in ways that they didn't before. We've gotten quite good at designing biologics or proteins with a lot of help from things like alpha fold that helps understand how proteins fold.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“15 years from now, we're back in this room, we've barely escaped being part of the permanent underclass. And we're reflecting on sort of the GPT moment or maybe the legacy of GLP1s, sort of beyond where they are now. What do you think it could be? Or your take on what do you think is going to be the technological breakthrough that we're going to point back to and say, oh, this is really what set it all, or do you think it's going to be sort of multi-factor combination?”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“And I agree with you, the prize, the juice needs to be worth the squeeze, right? You're right. A lot of biotech has been around, like, go after the low-hanging fruit because it's low risk and we got to eat today. So you go get it, and you start to push off the big ambitious indication, the large population, or the really tough to crack disease. But I do think we're seeing more and more of that. And by the way, like we can get into some of these genetic medicines, but some of these genetic medicines are going after some of the hardest problems, the things that you quite literally couldn't address but for editing DNA. And I think that's incredibly remarkable and laudable and frankly inspiring.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Yeah, and look, I think the trend on that is positive. I would argue the trend on that is positive. You're absolutely right. The demonstration of the value that has been created with the use, the increasing use of JLP1s and the value transfer that's gone to companies like Lilly and Novo, I would argue, is very merited, right? Because they've cracked an endemic social problem in terms of managing diabetes and eventually helping manage obesity. And so I think that's remarkable. And there's a lot of value that goes to that because they tackled, they cracked a very, very challenging problem for society beyond just science. So that's great.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“I hope it will in the trial, which is really, really expensive and costs a lot more in many ways than the preclinical research, right? The problem with this is you go after very well validated disease mechanisms, but with really small patient populations, right? So then the expected value of this actually is relatively low. One of the kind of things that we've seen with GLP1s is the just the kind of value that you can create when you go after really large patient populations. And I think that has culturally ruling net increased the ambition of the industry, both from the investor and from the drug developer side. And I think, you know, that's something that we should keep our foot on the gas floor.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“A few simple observations. The first is the amount of market cap added to Lily and Novo based on the development of GLP1s. He's like over a trillion dollars is more, you know, I mean, NOAA stock has decreased a lot. So trillion dollars, let's say, is more than the market cap of all biotech companies combined over the last 40 years have been started, right? And I think one of the kind of interesting kind of corollaries of this is that when we have a 10% kind of clinical trial success rate for kind of preclinical drug matter, right, you tend to circle the wagons a bit and try to manage your risk, right? And so the way that do this is you try to go after really well established disease mechanisms where if I developed new drugs that go after well understood biology, it should work the way that I”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Step ups that you see in other parts of the industry. And that's just really, really hard from an investing standpoint. So I think we need to see those various factors addressed for this space to really get.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“And the third thing is if we can make better drugs going after better things, the effect size should be higher. So therefore, the answer should be obvious sooner. If we can get those three things right, reduce capital intensity, compress timelines, and effectively increase effect size in some very tough sort of intractable diseases. That is what I think fixes the industry. And from where we sit at the early stage in terms of being early stage investors, The reason why that helps us is if the capital intensity goes down and the value creation goes up, it becomes easier to invest in these companies in the early days because you get rewarded for coming in early. The problem we have right now is that most companies aren't, you're not seeing rewards happening when there's value inflection. So you come in early, you bear the brunt of the capital intensity. And even if a company is successful, that success isn't reflected in the valuation. So we're not seeing the”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Is massively compressed the amount of time it takes us to do the clinical development, the clinical trials, the enrollment of patients, all those things. We're seeing some interesting things coming. We haven't seen sort of the payoff there yet.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Guess what? You're going to have, it's going to take some time to demonstrate a survival benefit. Or if you want to do a longevity drug, that by definition is a lifetime of a trial in terms of length. So there's a lot of these bottlenecks that are really hard to get through. So what helps the industry? I think there are a couple of things that help the industry. One is capital intensity will hopefully at some point go down over time as technology gets better. Capital intensity is something that our industry faces. In some ways, it looks a little bit like AI now, right, in terms of the cost of training these models, but the capital intensity is very, very high. That has not come down. So we got to get to success rates up to impact capital intensity to get it down. The second thing is where can we compress time? So good models can help us compress early discovery time. We still haven't seen, and I think it's coming, but it hasn't happened yet. We haven't seen artificial intelligence or other technology.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“But are there ways to reduce the cost and time associated with getting through the bottleneck of human clinical trials? And it's interesting because we talk about all of the various stakeholders when you're making a drug. There are the companies. There's, of course, the science that supported the company that's trying to commercialize a product. And they're the regulatory agencies. And everyone is trying to ensure, again, that what's first and foremost is the ability to discover and commercialize drugs that are safe and effective for humans. That middle part of actually getting through that bottleneck is hard to speed up in a very obvious way. Like you can increase the rate the way you enroll clinical trials. You can use better technology to change the way we design these clinical trials so maybe they can be faster or shorter, et cetera. But some of them just have a natural timeline. You have to go through. Like if you want to demonstrate that a cancer drug promotes survival.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“So I'll take the second one first if I could. So I think in terms of where the industry is right now, I think one of the big challenges we have is, as Patrick describes very nicely, like, you know, discovery's hard and it takes time. And the fail modes are exactly as you described. Oftentimes when drugs fail, which they do 90% of the time in clinical trials, it's because we're going after the wrong thing or we made the wrong thing to go after the right thing, right? Like those are the two fail modes and that happens all too often. And so I think a lot of the stuff that Patrick has described is going to basically improve our hit rate or our batting average on figuring out what to go after and making the right thing to go after set thing. The challenge we have, I think, in the industry is that the bottlenecks still are the bottlenecks. And the biggest bottleneck we have, which is a necessary one, is we have to prove that whatever we make, that we have the right thing to go after the right thing, so to speak.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Heyday of developing single cell genomics, human genetics, CRISPR gene editing, you know, and so many other things. And I think the kind of early 2010s papers on single cell sequencing would have like 20 cells or 40 cells, right? And at Ark in the next kind of end, like, I don't know, relatively short amount of time, we're going to generate a billion perturbed single cells, right? I mean, how's that for a Moore's law?”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Accurate virtual cells, you'll probably end up with suggestions this GPCR only in heart, but not in literally any other tissue, right? We don't have the drug matter that can do that today. And so that's also why, again, you probably need research to figure out novel chemical, biology matter that allows you to drug pleatropic targets in a tissue or cell types specific way, right? And so, you know, I think part of why biology is slow is because there's just this Russian nesting doll of complexity in terms of understanding, in terms of perturbation, in terms of safety and the crazy thing is the progress in just the short time that I've been doing this is insane, right? Like I did my, you know, PhD at the Rhode Institute and the...”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Mechanistic insights for discovery, but also because if in the case of success it could be industrially really useful. But we'll have to see over time. If we have 90% of drugs failing clinical trials, that kind of means two things and you're not sure what percent of which, right? One is we're targeting the wrong target in the first place. The second is the composition, the drug matter that we're using doesn't do the job, right? It's not clear for each individual failure which one it is or if it's both or what proportion of each. And we'll have to kind of sort that out over time. Like you can imagine even in the case of success when we had 90%”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Like in early AI, we just started with language translation, just basic NLP tasks, right? This is long before the tremendous ambitious scope that we have today. And I think we hopefully can mirror that type of trajectory if we're lucky.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Computation, if you will, right? And let's kind of start there, just like you kind of have to start with, you know, things like math and code and language modeling, right? And things that are just sort of easier to check. You can build a super intelligence over time.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“These are useful words because they describe the goal and the ambition, right? That no, in the long run, we don't care about predicting the kind of perturbation responses of an individual self at all, actually, right? Obviously, we want to be able to predict drug toxicity. We want to be able to predict aging. We want to be able to predict why a liver cell becomes sorotic when you repeatedly challenge it with ethanol molecules or whatever, right? And these sort of chemical or environmental perturbations should be predictable. I think you just kind of have to layer on the complexity, right? Like, why are we so worried about modeling entire bodies over time when we can't do it for an individual cell, right? Where we sort of, you know, accept or broadly believe that this is a kind of, you know, fundamental unit of biological.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Some people don't like the phrase virtual cells because it sounds too media friendly. It's not rigorous enough, right? But I've always found it funny that, you know, but, you know, many people are okay with like digital twins and digital avatars, which talks about modeling biology at a way higher level of abstraction. I think virtual cells, if anything, is actually way more scoped and rigorous than modeling a digital twin or avatar. But I think”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“Right, right, right. I mean, yes, textbooks are what they are. They represent the corpus of reliable knowledge, but everyone knows that there are incredible number of exceptions. And part of what Discovery is, is to find new exceptions, right?”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source
“I would say textbooks are compressed, right? So, for example, when you look at these kind of classic cell signaling diagrams of A signals to B, which inhibits C, right, that's a very kind of two-dimensional representation of R.”
2025-09-15 · a16z Podcast · Faster Science, Better Drugs · IDENTIFIED FROM THE TRANSCRIPT · source