Arc Institute's Patrick Hsu on Building an App Store for Biology with AI

Patrick Hsu, co-founder of Arc Institute, discusses the opportunities for AI in biology beyond just drug development, and how Evo 2, their new biology foundation model, is enabling a broad ecosystem of applications. Evo 2 was trained on a vast dataset of genomic data to learn evolutionary patterns that would have taken years to find; as a result, the model can be used for applications from identifying mutations that cause disease to designing new molecular and even genome scale biological systems. Hosted by Josephine Chen and Pat Grady, Sequoia Capital Mentioned in this episode: Sequence modeling and design from molecular to genome scale with Evo: Public pre-print of original Evo paper Genome modeling and design across all domains of life with Evo 2: Public pre-print of Evo 2 paper ClinVar: NIH database of the genes that are known to cause disease, and mutations in those genes causally associated with disease state Sequence Read Archive: Massive NIH database of gene sequencing data Machines of Loving Grace: Daria Amodei essay that Patrick cites on how AI could transform the world for the better Arc Virtual Cell Atlas: Arc’s first step toward assembling, curating and generating large-scale cellular data from AI-driven biological discovery (among many other tools) Protein Data Bank (PDB): a global archive of 3D structural information of biomolecules used by DeepMind to train AlphaFold OpenAI Deep Research: The one AI app Patrick uses daily

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  One of the things that we, you know, that the field of computational biology is often asking is, you know, if you have a genetic mutation in your genome, if I sequenced you, whether that's via, you know, 23andMe or, you know, or some other genetic test, right, you'll find mutations in your genome. How do we actually interpret those and understand, you know, what the, what the functional consequences are, right? Sometimes you'll get a rare genetic disease, those are causal genetic mutations that are known to cause a devastating disorder. That might be muscular dystrophy, or cystic fibrosis, or breast cancer, right? But, you know, most of the mutations that you have, they're sort of this, you know, we call them variants of unknown significance, which is fancy kind of scientist speak. We don't really know what they do.

• 0:45Speaker 00

  Yeah, we don't know what the hell is going on, right? And, you know, it turns out the model has an opinion about those mutations and what the hell is going on with them. And it turns out it's sort of state of the art in doing that.

• 1:12Speaker 01

  Today, we're joined by Patrick Xu, a pioneer in genome editing, CRISPR technologies, and the emerging field of genitive biology. He's the co-founder of the ARK Institute, where cutting-edge AI and biology converge to reimagine scientific discovery. Patrick and his collaborators created EVO-2, a revolutionary biological foundation model that can interpret and generate genomic sequences across all domains of life. By training on the fundamental information layer of life, DNA itself, EVO can identify patterns in genetic code at scale and predict effects of both coding and non-coding mutations that could mean the difference between health and disease. In this episode, we'll hear how Patrick's vision goes beyond creating better drugs

• 1:56Speaker 01

  to building a comprehensive understanding of biology at all scales.

• 2:02Speaker 01

  Patrick, welcome to the show. Thank you for coming.

• 2:05Speaker 00

  Thanks for having me on. Excited to spend some time with you today.

• 2:08Speaker 01

  I think maybe the most obvious thing to start with is, you know, people have heard about CS and bio for the longest time. Now it's all about AI and bio. Where are the results? Like, what should we actually be expecting to see? And

• 2:22Speaker 00

  Where are the drugs?

• 2:23Speaker 01

  Why are we not seeing the drugs yet?

• 2:26Speaker 00

  It takes time, right? Well, here's the thing. Even if we had perfect drug design molecules, you know, coming out of these pipelines and fancy models, it was still, you know, you can design a trillion molecules, right? 10 trillion, right? But you still have to actually test them, right? Initially in animals and then in people, right? And so that's the real bottleneck. And even if you pack top of funnel with all the things, it just takes years to actually go through the regulatory apparatus, right? And so I think there are a few intermediate checkpoints along the way in order to kind of realize this potential. But it might be worth taking a step back and just saying, you know, this is a bit of a soapbox of mine that ML for bio is not just drug design, right?

• 3:08Speaker 00

  This is actually ultimately, I think, a very important but narrow part of the potential of biology. And not just as a field of STEM and in the way that affects human lives, right?

• 3:21Speaker 01

  And do you mean basic, just like understand the human body? Or where else do you think the applications are beyond drugs that treat all of us?

• 3:28Speaker 00

  Yeah, one of the things that motivates me academically is the idea that we actually have a unifying theory for biology, right? So unlike the physicists who have been kind of scrimping and, you know, kind of poking for one for a century, right? We have this in biology and it's so obvious that we find it, you know, sort of just an obvious force, right? This is of course evolution, right? And so it acts on biology across all of its different length scales from entire planets, right? Biology can, for example, terraform planets, right? All the way down to, you know, ecosystems and populations, to individuals, to our tissues, to individual cells, to individual molecules, right?

• 4:10Speaker 00

  And so that's this unifying force that is actually very deep and rich and actually, you know, you can learn a lot from, right?

• 4:17Pat Grady?

  How do we activate that unifying theory? How do we put it to work?

• 4:21Speaker 00

  So we've been thinking about this in the lab and recently have been training a series of models that we call EVO, inspired by these forces of evolution that tries to connect biological sequences using this sort of, you know, modern sequence modeling paradigm directly to biological function with the idea that evolution passes down its effects of natural selection throughout generations of life via DNA mutations. And so, you know, last year, of course, you know, multiple Nobel prizes were awarded for AI and for AI in biology, in particular for protein design and for predicting the structure proteins to David Baker and to Demis Hassabis and John Jumper, right?

• 5:05Speaker 00

  But if you read those citations, they both explicitly state for proteins, right? And, you know, we love proteins, right? These are, of course, some of the most important and fundamental molecular machines. But our realization, as for me, as a genome biologist, if you will, right? The idea is that proteins are encoded in DNA along with RNA and with regulatory DNA and all of the things that you need to make life, right? And so we asked, could we train a model on genomes with a long context model so that it could reason over all the different bases and molecules that are embedded inside of genomes to learn about the molecular interactions and how they lead to biological function?

• 5:48Speaker 00

  Now, that was very scientific or academic, right? But we can talk through specific examples of what we were able to actually do with this in a way that grandma can understand, you know, like predicting the effects of breast cancer-causing mutations, right? It actually is best in class at doing this, right? Or being able to design new CRISPR gene editing systems or, you know, I think Uh, you know, in addition to its zero-shot capabilities, I think people are building really an app store for biology on top of all of these kind of foundational layers. One of the kind of funny things in modeling today is how everyone's model has to be more foundational

• 6:30Speaker 00

  than someone else's model. There's a bit of a pissing contest that's happening, right? But maybe our model's more foundational than the other models, but you know.

• 6:39Speaker 01

  Cause your DNA versus protein.

• 6:41Speaker 00

  I, I, I mean, or you know, and then below that, there are these all atom diffusion models. So maybe those are even more fundamental. I don't really know. I think what matters are the capabilities and doing something that actually feels useful. And I think, you know, those are some examples of what we thought was cool and useful from the model.

• 6:58Speaker 01

  Can you actually walk through a couple more of those use cases? Like which are some of the most exciting ones? And why is it possible today with Evo, but wasn't possible before?

• 7:06Pat Grady?

  And the model's open source. And so where has it been picked up? Like where are people running with it with some of those use cases that Joseph even mentioned?

• 7:14Speaker 00

  Yeah. Yeah. So the model, I can just maybe talk about what the model is, right? So, you know, it's, it's an autoregressive sort of multi-convolutional hybrid model, right? But you can think of it like, you know, just a really efficient, long context model that's trained at least in this version autoregressively, right? And basically it does this next token or next base prediction. And it turns out, just like in natural language or in vision or in robotics and embodied intelligence, this general machine learning paradigm is able to find higher order patterns, right? And so just like, you know, if you're doing next word prediction, you can learn about grammar or color and world navigation, right?

• 7:58Speaker 00

  You seem to learn some rich set of representations about biology by predicting the next base or the next amino acid residue or the next gene, right? And the model learns something about the molecular logic that gives rise to itself, right? And so one of the things that we, you know, that the field of computational biology is often asking is, you know, if you have a genetic mutation in your genome, if I sequenced you, whether that's via, you know, 23andMe or, you know, or some other genetic test, right? You'll find mutations in your genome. How do we actually interpret those and understand, you know, what the what the functional consequences are, right? Sometimes you'll get a rare genetic disease.

• 8:40Speaker 00

  Those are causal genetic mutations that are known to cause a devastating disorder. That might be muscular dystrophy, or cystic fibrosis, or breast cancer, right? But, you know, most of the mutations that you have, they're sort of this, you know, we call them variants of unknown significance, which is fancy kind of scientist

• 8:59Speaker 01

  We don't really know what they do.

• 9:00Speaker 00

  Yeah, we don't know what the hell is going on, right? And, you know, it turns out the model has an opinion about those mutations and what the hell is going on with them. And it turns out it's sort of state of the art in doing that.

• 9:11Speaker 01

  Interesting. Wait, what's an example of one of these mutations? And what what did the model discover? And how did you verify that what it discovered was accurate?

• 9:18Speaker 00

  Yeah, yeah. So, so, you know, one, one example that we showcased in the paper is a gene called BRCA1, right? It's sort of a famous gene that's known to cause breast and ovarian cancer. And, you know, if you have the sort of specific causal mutations in BRCA1, many women elect to get double mastectomies, right? And this is obviously, you know, a serious and major life decision and medical decision for you and for your family, right? And, you know, the question is, you know, if you don't have one of the known to be benign mutations, so you're fine, and you just go ahead and get an annual mammogram and just check and monitor, right? There's this entire middle distribution of these VUS, or variants of unknown significance, right?

• 10:00Speaker 00

  And, you know, there is this kind of gold standard database from, you know, the scientific literature. It's known as ClinVar. And it basically has a list of all of the different genes that are known to cause disease, and, you know, which of those mutations in those genes can cause disease state or not, right? And we can basically use this as a ground truth database to assess the predictions of the model for, you know, new mutations that you introduce into the gene, and whether or not those would be pathogenic, right? And so When you develop a new type of model, you have to create a lot of evals as well. And so that was actually something that we put tremendous effort into. And obviously,

• 10:41Speaker 00

  you guys see this horizontally across AI in many different domains is, you know, folks will build things to the benchmarks. No one really, I think, likes building benchmarks. It's really gory. It requires a lot of taste. It takes a lot of time. You have to continually update them as the models get better. And, you know, we dealt with a very similar sort of challenge here, which was making evals that would be similar to the AGI or intelligence evals, where it actually feels meaningful when you're actually able to do it, like Amy or Putnam problems, or, you know, things like that. What would be the equivalent of demonstrating true biological understanding that a cell biologist would

• 11:22Speaker 00

  feel emotion if you were actually able to solve that, right? You know, what would it look like to make all molecular biologists feel what the NLP people felt a few years ago, right? That's sort of the core of, you know, what we're kind of noodling through.

• 11:39Speaker 01

  And I think you mentioned this briefly, but, you know, there's, you guys are working on the DNA layer and you guys didn't actually do anything in the lab. You didn't have a lab in the loop, like there was no RL. Talk us through the decision to do that. And then kind of the decision for people who are doing protein models or affinity models, a lot of them have a lot more lab in the loop. Talk us through the differences between some of those models, too.

• 12:00Speaker 00

  Yeah. So we started with DNA because we think it's the fundamental information layer of life, right? The second is, it's also just pragmatically where we have the most data, right? Where does the data come from? It comes from the entire scientific community, right? And so there are these kind of open source, government funded and maintained databases, you know, known as the sequence read archive, right? Where basically, you know, when you publish a paper, you have to submit your data and all of the sequencing data that's been created over the last 25 plus years, right, goes into these databases, right? And that has all of the genomes that the community has ever sequenced for bacteria,

• 12:42Speaker 00

  for bacteriophage, for viruses, for humans, monkeys, fish, flies, you know, the entire Noah's Ark or menagerie, whatever, right? We've got all those genomes and, you know, so, so the experiment that's already been done, if you will, is the experiment of evolution, right? That, you know, there are different mutations that are what make us different from each other. That's human genomic variation, but also the mutations that make us different from chimpanzees or from worms or from bacteria. And the model can just look across this, you know, you know, trillions of tokens, large data set and learn those patterns.

• 13:23Speaker 00

  And that was sort of the, you know, insight of this sort of Evo series of models that, that we've been training at ARC is to kind of ask if you could predict that next base, right? And then that might be the difference between being healthy or having a sickle cell nemia mutation, right? Or it could predict the next amino acid residue. And that could be the difference between having a catalytically active binding pocket for a key enzyme, right, in your, you know, body physiology or that being a null mutation where that thing doesn't work anymore. Or it could also be the sort of the next gene, right? So these are different levels of abstraction, right?

• 14:07Speaker 00

  That completes some biosynthetic pathway. Or it's a, you know, a different gene that's, you know, you know, that's been removed by some transposon or jumping gene mobile genetic element that's excised. It's some sort of viral interference, if you will, right? So there's just, you know, across large databases, you find new patterns. And it turns out those seem to be biologically meaningful.

• 14:31Speaker 01

  So if you're going from DNA and you know the function, in many ways, you, you mentioned even the DNA model can actually predict binding affinities. Do you even need the structure, the protein structure models at all as intermediate step? Or can you just go straight from sequence to function?

• 14:46Speaker 00

  So so structure is a a a another way to have an abstraction of function. Right? So you have concepts of convergent evolution, for example, right? Where something has similar function, but they have different sequences and slightly different structures that act out the activity or, you know, function of that protein. Right? And so this sort of, you know, sequence to structure to function, token or mapping of protein language modeling, think is very beautiful.

• 15:16Speaker 01

  Yeah.

• 15:16Speaker 00

  Right? It takes advantage of, you know, the central dogma of molecular biology.

• 15:21Speaker 01

  The alpha fold series.

• 15:22Speaker 00

  Right, right. Well, it it takes advantage of just our supervised textbook understanding of how biology works.

• 15:29Pat Grady?

  Hmm.

• 15:29Speaker 00

  Right? And now the interesting oxymoron with these models for me is the way that we use them is, you know, just like you use ChatGPT, it's text in, text out. You know, Evo is DNA in, DNA out.

• 15:42Pat Grady?

  Yeah.

• 15:42Speaker 00

  And it turns out, we don't speak DNA very well. Just sort of imagine if you were using a model like ChatGPT in Russian, right? But one percent of the words were in English, right? That's kind of what it feels like. That's the vibe of using Evo is actually you don't really know what's going on.

• 16:01Speaker 01

  Interesting.

• 16:02Speaker 00

  So you're you have to build lots of annotators and.

• 16:06Speaker 01

  Interpretability.

• 16:07Speaker 00

  Yeah, like techniques to try to interpret and read what's happening. And so the way that we even use and prompt the models is really primitive, right? And so the way that we do fancy prompt engineering workflows to, you know, get more utility out of these models is something that we're just in the very early innings of exploring how that works with these biological language models, because we speak DNA with an extremely heavy accent.

• 16:35Speaker 01

  Who do you think will do some of that work? Because the model is currently open source. Do you envision? A company formed around this will be individuals who are at these pharma companies who learn how to prompt this, like, how does that ecosystem evolve?

• 16:47Speaker 00

  Yeah, I hope everybody, right? Uh, first of all, right? I think the, you know, tools become useful when they meaningfully lower the energy barrier of ad- adoption, right? It's like a, it's a catalysis type of activity where, you know, everyone uses blast for sequence alignment, right? Everyone uses AlphaFold to look at protein structure, right? Uh, you know, everyone uses CRISPR to do gene editing. Everyone does NGS to read DNA or RNA, right? So I think there will be a zoo of different models for not just modeling molecules or mapping sequence to function, but I think every step of the scientific method, right?

• 17:29Speaker 00

  And so I think, you know, you know, i- if 2025 is the year of AI agents, right? I think, you know, there's lots of interest in agents for science and not just agents for interpreting molecules, but also for doing the meta aspect of how scientists work and operate.

• 17:46Speaker 01

  Yeah.

• 17:47Speaker 00

  And we're also very excited about that at ARC and recently released some of our first AI agent work.

• 17:51Pat Grady?

  Well, yeah, what is the most important role for ARC to play in all of this?

• 17:55Speaker 00

  Um, we started the institute to be able to have this mothership that is able to attack long-term research capability breakthroughs and to be able to have the long-term thinking and the multi-disciplinary expertise in order to actually execute on those goals, right? I think if you look in biology, one of the interesting things is that a lot of the biggest mechanistic or basic science breakthroughs do happen in a university context, right? Um, and I would say that's interestingly kind of in contrast to what happens in AI.

• 18:31Speaker 01

  Yeah.

• 18:32Pat Grady?

  Yeah.

• 18:32Speaker 01

  OCS.

• 18:32Speaker 00

  Yeah.

• 18:33Speaker 01

  Yeah.

• 18:33Speaker 00

  Yeah.

• 18:34Speaker 01

  Why do you think that's the case?

• 18:35Speaker 00

  Um, that, that might be a 10-hour epic podcast, right? I, I.

• 18:42Speaker 01

  The one-minute explanation.

• 18:44Speaker 00

  I have a lot to say about this, but, um, I think the, in, in short, it does happen in universities. I think, you know, 30 years ago, the questions that basic science, uh, was interested in and, uh, industry was interested in were quite different. Actually, and I would say today, they seem to heavily overlap, right? And there are some things that, you know, folks don't typically do in university lab, like, you know, people don't tend to study PK or tox or CMC, right? Or, you know, hardcore drug manufacturing type of things. But folks are interested in molecular glues, induced proximity and degraders and new drug concepts. Right?

• 19:25Speaker 00

  Folks are also interested in new machine learning models, um, you know, and, and new delivery mechanisms and, you know, inflammation and stress and, you know, all, all, all kinds of things like this. And so there's much more heavy overlap. But I think the way that the type of product that selects what you do upstream is very different. They're structurally different. You know, end of the day, you have to optimize for grant funding and first or co first off through papers. And, you know, you know, that type of stuff in the academic setting, whereas, you know, you do have to make a drug and have, you know, uh, dozens to hundreds of people lying behind a molecule or a program to reach the holy land, right?

• 20:08Speaker 00

  And I think that does lead to differences in strategy. And I don't know personally, by the way, I think people really kind of over emphasize like what's academia and what's industry. And the reality is these are like heavily overlapping distributions today and how people operate. And so I think the differences are a little overblown, but you know, they do have fundamentally different incentives that drive different behaviors. And a lot of what we tried to do in blending our academic side of the house with our technical staff side of the house, which is built much more like you'd see in industry. Um, it has been, you know, kind of part of what we hope will make ARCA a model that others want to copy or replicate or propagate.

• 20:53Speaker 00

  Yeah.

• 20:54Speaker 01

  Makes sense. And you've had some fun people come through on the technical side of the house, including people like Greg Bachman.

• 21:01Speaker 00

  Yeah, no, it's been, uh, it was a joy. Yeah. So Greg joined us during his sabbatical, uh, from, uh, OpenAI. Uh, we're really the kind of, you know, the first vacation that he had ever taken since starting OpenAI.

• 21:13Speaker 01

  Of course, is to work more.

• 21:15Speaker 00

  So actually, I have a funny story about this. You know, hopefully, hopefully Greg will allow me to tell it. But you know, when he, when he first came by ARC and we were talking to him about, you know, you know, biological language modeling and how, you know, the sort of machine learning breakthroughs of, you know, all these other domains might just port over directly to understanding, you know, molecules. He was, you know, really kind of, you know, jazzed by this and also by the idea that his very specific capability and expertise would be kind of really meaningful and actually making this happen. And, but you know, in, in, in the, you know, initially, he was, you know, he was, you know, saying, you know, this is really the first vacation I've ever taken.

• 21:57Speaker 00

  I can't promise too much, you know, I might not be able to spend so much time on this. You know, you know, I, I, I need to take Anna on vacation. And then, and then Anna gets up and goes to the bathroom, you know, in the middle of this very long meeting. And then he's like, all right, here's my email. Get me on the repo.

• 22:19Speaker 00

  Of course, of course. Yeah. So, so yeah, just built different. Yeah, it was, it was such a joy to, to learn from him.

• 22:26Pat Grady?

  What have you found works well in getting the different disciplines to work together as one unified team?

• 22:32Speaker 00

  Yeah, I know, it's an interesting question and we've thought about this deeply at ARC as a convening center, you know, not just between the three flagship research universities here in the Bay Area and Stanford and Berkeley and UCSF, but also between basic science and the biotech industry, but also biology and the technology sector. For example, you know, our CTO, Dave Burke, just started a few months ago and has really kind of been leading the computational modeling of virtual cells where we're trying to simulate human biology with these AI foundation models. And, you know, Dave used to, he actually has a PhD in biomedical engineering from, you know, many moons ago.

• 23:15Speaker 00

  But, you know, most recently ran engineering at Android and Pixel. And so, you know, I think we have also built an entire operational side of the house with, from finance to legal to lab ops to facilities to university relations and academic affairs. And, you know, we run our own space, our own administration, our own ops. And you, we, we try to do, you know, much of that like a tech company, right? And, you know, we've also, I think, recruited in on the ops side of the house, many people who Maybe ordinarily wouldn't work in a basic science or discovery setting, but are kind of motivated by the mission to be able to take fundamental breakthroughs

• 23:58Speaker 00

  and have a product sensibility where we can get these out into the real world. And we're not optimizing at Arc for nature and science papers, right? If you give Berkeley or Stanford professors millions of dollars to do more science, that's almost the default expectation in output, right?

• 24:13Speaker 01

  Yeah.

• 24:14Speaker 00

  And what we really care about are things that could be tangible, right?

• 24:18Speaker 01

  So what does success look like? Just more people using the products you create? Or yeah, what is success for Arc Institute?

• 24:24Speaker 00

  I think there are lots of ways that you can parse scientific productivity, right? You know, journal publications is of course a important and fundamental part of sharing work with the community and peer reviewing it and all of that good stuff. But technical blogs, you know, code repos, protocols are just platforms that people can use, right? And I think we want to make technologies and platform capabilities that are broadly useful to be able to create new mechanistic insights, but also actually try to cure some diseases, right? And, you know, I think over time, if we can be an Edison shop that's inventing or finding lots of cool things, right? There, you know, hopefully will be, you know, real world valley in those things.

• 25:10Speaker 00

  And there are lots of, you know, partners who specialize in this.

• 25:13Pat Grady?

  Speaking of curing diseases, earlier you were talking about even if you had perfect drug design or infinitely accessible, infinitely intelligent drug design, there's still a long process that has to happen after that before you can make an impact on humans. Can you talk a bit about where you see opportunities in that value chain? And if you had a magic wand and you could just accelerate progress by 10 years, which of those bottlenecks might be alleviated by things you see coming down the pipeline?

• 25:40Speaker 00

  Yeah, so happy to talk about this in the pharma context, which are, you know, large, decentralized, sprawling bureaucracies.

• 25:47Pat Grady?

  Yeah.

• 25:47Speaker 00

  Much like. Universities, or, you know, the, you know, Congress, or the SFDC government, right? And, you know, I think, you know, some parts are, you know, you know, incredibly, you know, functional, and then I think everyone would agree in these different organizations, some parts are, you know, less efficient, right? And so I think the first thing that hopefully we can see is that we can have models that can improve efficiency in discrete individual steps, right? So can we have a more efficient process for a target ID, right, in particular? Right? Can we have a more efficient process for data analysis, right? Can we have a more efficient process for, you know, like, information and literature

• 26:31Speaker 00

  review and summarization, right? For molecule design, absolutely, making a better binder, right? And then figuring out the drug properties of, you know, those different molecules. That could be selectivity, that could be, you know, pharmacokinetics, right? Half-life, expression, manufacturability, what have you, right? And, you know, there will be models or model-guided approaches for each of those steps, right? And then there's, you know, I mean, I think if you look at how AI is actually kind of being used today in pharma companies, a lot of them are actually just taking massive regulatory documents, summarizing them, and then using AI to help them write more of these.

• 27:16Speaker 00

  So there's this compression and decompression of information in a structured fashion that has actually been leading to enterprise adoption in this setting, right? And, I don't know, I think that, I don't know, that says something about the process of where people find things useful. So, you know, one example is if you talk to some pharma execs, right? Not the drug discovery organizational leaders, but the, you know, the, the, the budget people, right? You know, who kind of hold the enterprise purse strings, right? They'll say, "Well, you know, I don't spend, actually, that much money on drug discovery. I actually spend most of my money on drug development. So tell me something about drug development, which is really where most of my dollars go.

• 28:00Speaker 00

  How can AI help me with that?" Right? And I think that is actually like a very deep comment, right? Because it says something about where money is spent and where value can be found.

• 28:15Speaker 00

  You know, the first thing to realize is, you know, our industry probability of success is like 10%, right? And so, you know, I think a lot of the things that people talk about or complain about or comment on in drug discovery and development falls out of that fundamental statistic.

• 28:34Pat Grady?

  Yeah.

• 28:34Speaker 00

  Right? Like, why does the FDA heavily regulate? Why is it so focused on safety? Well, if 90% of the time it doesn't work, they're going to care a lot about safety. Right? And I think the promise of AI is if we can go from 10% POS to 20? Or 30 or 50, right? As you move through those steps, can we?

• 28:55Pat Grady?

  Do you think we can?

• 28:55Speaker 00

  Um, over time, I think here's the thing that I find kind of interesting is we have done a lot of biology with what is not that far from guess and check.

• 29:11Pat Grady?

  Hmm.

• 29:11Speaker 00

  Right? If you look at what happens in the wet lab, like the actual experiments that are happening.

• 29:16Speaker 01

  Yeah.

• 29:16Speaker 00

  Right? You, you, you're just kind of in the arena, trying stuff. Right? And

• 29:23Speaker 01

  Yeah, you're trying out random hypotheses and seeing what happens.

• 29:26Speaker 00

  Yeah, and this is like the missing reasoning trace in the scientific literature is you don't know what didn't work. Everything is narrativized and written in a story of, you know, inexorable logic and vision leading to scientific breakthrough, right? But everyone who makes the sausage knows that's not what actually happens most of the time. Right? And, you know, if you actually work with the, you know, research, um, you know, kind of folks at the bench, right? You know, and this is actually the case across all technical industries is that high fidelity reasoning trace of the true process is kind of not written down anywhere. And that would actually be very useful for these reasoning models and for closing the loop and doing, you know,

• 30:10Speaker 00

  you know, multi-agent frameworks, blah, blah, blah, blah, blah. But that's kind of what we'll need. But if you actually look at what happens, it's guess and check. Right? And so a model with even a modicum of predictive value would be transformative. When with even moderate predictive value, which by the way, we don't have, right? Like, I think biology is a very Pragmatic, salt of the earth, experimental discipline, right? You see this in the culture peer review. Show me the data. You can't, you know, pontificate in your discussion section because, you know, you haven't shown any of this stuff, right? And if you read old papers, right? They were so clear and visionary and, um, you know,

• 30:53Speaker 00

  high fluting in a way that I think, uh, you know, papers today are, you know, we, we have this culture that is very pragmatic. And I think having models that have predictive power will, you know, I think A, obviously be useful for accelerating the efficiency of science. It also changed the culture, which I think hopefully, well, hopefully will change the culture, which I think will be really interesting.

• 31:14Speaker 01

  Why do you think it'll change the culture? To have the models?

• 31:17Speaker 00

  Because you'll believe people's predictions or pontifications, depending on how you.

• 31:23Speaker 01

  It's just another kind of evidence point, basically.

• 31:25Speaker 00

  Just like how model hallucinations could be, you know, predictions, or they could be nonsense and garbage, right? And that depends on how much you trust the model, right?

• 31:36Speaker 01

  Got it. Why do you think it's the case? Um, we've collected a lot of data to, to your point. There's obviously, we basically see what works and we oftentimes don't see what doesn't, although hopefully lab notebooks are recording that in some way, should perform.

• 31:49Speaker 00

  Maybe.

• 31:50Speaker 01

  Maybe, hopefully. Um, but somehow still very, very regularly, even when things work in cells, things work in mice, they fail in humans oftentimes. Like, why is that still the case? Is it, we just don't understand biology deeply enough? Like, why is there still that drop off? And that drop off hasn't really changed over time.

• 32:08Speaker 00

  Well, these are imperfect models, right? And, um, you know, we, we set up this set of filters, um, in the drug discovery process where, you know, first show it works in cell lines, then it show it works in primary cells or in an organoid, then it show it works in a mouse, then it show it works in a monkey, then tested in people, right? And, you know, You know, by the time you've gotten there, like five years and a hundred million dollars has gone by.

• 32:34Speaker 01

  Yep.

• 32:34Speaker 00

  Right. And I think that's, that's very challenging, right? And that's where I think predictive models will really help, right? Because the reason why we do all of these steps in linear series is because we don't have predictive power. And so we have to do things in the arena. And it just, you know, you have to do it in real life, right? And growing cells and growing animals takes months to years to actually do those experiments. And so the promise of having predictive models and just predictive power, it's that you could actually simulate things in a multi-paralysed fashion, right? That's the whole idea behind parts of machines of loving grace. Right? That I thought, you know, Dario really did get right.

• 33:20Speaker 00

  And is the idea that if you had something that could be a trusted oracle, right?

• 33:27Speaker 01

  Yeah.

• 33:27Speaker 00

  That you could just run, you know, 10,000 agents, right? At the same time, right?

• 33:34Speaker 01

  Do we have enough data for that full closed loop to create a trusted oracle?

• 33:39Speaker 00

  Um, I think we will see more examples of this.

• 33:43Speaker 01

  Yeah.

• 33:43Speaker 00

  Coming out over time, right? Today, folks building AI agents for things are doing, you're basically trying to close the gap between,

• 33:50Speaker 01

  Small portion.

• 33:51Speaker 00

  You know, yes, step X and step X plus one.

• 33:54Speaker 01

  Yeah.

• 33:54Speaker 00

  Right? Or X plus four or whatever, right? And the businesses are trying to find the most commercially valuable set of, you know, steps that is a set size that's as small as possible in step number in order to, you know, make a company. And I think we will have agents or co-pilots at each step in the scientific method from hypothesis generation to experimentation to data analysis. And the ability to close the loop is, and write the paper or make the discovery and then decide what to do next, I think is quite far away. But I think something that's very efficient at traversing the steps, I think, will really take off and.

• 34:38Speaker 00

  So as a concrete example, right, one of the things that we recently released at ARC is our virtual cell atlas, right, which is the world's largest data set of single cells, right, that we're using for training these cellular foundation models, right? And the way that it happened was we created an agent that was essentially, it's like a crawler, kind of like, you know, kind of a search crawler, but it's able to crawl the kind of, you know, sequence read archive and then process all of the highly unstructured and messy metadata and, you know, kind of reanalyze and systematically reprocess all single cell data. And this is something that is just running on a cloud bucket instance, just cranking

• 35:21Speaker 00

  away, right, you know, in a tireless fashion, right? And it's the kind of stuff that a talented computational biologist wouldn't want to do because it's so grindset, but actually the scale at which we're able to reach is community-wide. And that was that the leverage and efficiency that our team of, you know, just, you know, really two lead researchers could achieve with one agent, I think was, was a huge mental unlock for me. And so we want to be at the frontier of actually deploying these and making breakthroughs. And I think the meta aspect that folks are going after right now will shake out over

• 36:02Speaker 00

  time, but I care about using these to actually make breakthroughs as opposed to chart the end-to-end closed-loop path.

• 36:09Pat Grady?

  Yeah. You mentioned earlier the, sort of the pragmatism that a lot of research papers have now versus grandiosity or something a bit more visionary back in the day. I wonder if some of that is related to the specificity of the work that people are doing now, meaning it feels like we've gotten more and more specialized over time.

• 36:29Speaker 00

  Yeah.

• 36:29Pat Grady?

  And I wonder if some of that is taking us away from breakthroughs, because in a lot of cases, you need the knowledge from different domains or different disciplines to achieve those breakthroughs. And I guess maybe the question is, One of the nice things about LLMs is that they can incorporate an enormous amount of information. And they're sort of inherently generalized, even when you apply them to a specific domain. How much of the efficacy that we might get out of some of these models is simply related to their ability to go across all these different specialties?

• 37:03Speaker 00

  Yeah, if you look at, at least to me, the best scientists that I've had the pleasure to collaborate with or learn from.

• 37:11Pat Grady?

  Yeah.

• 37:13Speaker 00

  They really do two things. They they they're able to come up with really creative ideas and they're able to execute on them.

• 37:23Pat Grady?

  Yeah.

• 37:23Speaker 00

  The reason why they're able to come up with really creative ideas is because they're able to make connections between things that other people wouldn't make. And in fact, if you got a room of 10 really smart people together to chat science, like that's, you know, a weekly lab meeting in any group, right? If you actually analyze the anthropology of what happens, there's usually a small subset of people who are hearing all the things that are being discussed and then actually saying, this is a connection, or the conceptual bridge between these things, right?

• 37:57Pat Grady?

  Yes.

• 37:57Speaker 00

  And so there's, you know, that's sort of like an out of distribution generalization, right? It's like, this thing that I heard was really novel and let's recognize that and then try to see what can generalize out of that observation, right? And that comes from people who tend to either read a lot or reason a lot, right? And so there's some aspect of pre-training, right? You need to just read a lot of papers, right? Read a lot of chemical biology papers, read a lot of molecular biology papers, read a lot of AI papers, we can like physics papers, right? And do so across domains so that you can traverse those boundaries, right?

• 38:39Speaker 00

  I think, you know, there's this design problem of how do you build a multidisciplinary team, right? And the reality is there, well, for example, you want to work at the interface of bio and ML. There are way more ML people and way more bio people than truly bilingual ML and bio people, right? I have the extreme fortune of working with some of those at Arc, right? And you know, they're they're just rare, right? But those those translators, right, can actually help you power, you know, the the, you know, the the the rest of the population.

• 39:14Pat Grady?

  Yeah. What do you look for in people at ARC?

• 39:17Speaker 00

  It depends on the role, right? I think, you know, depending on how you're trying to to to match specific project needs. But in a way, I I could tell you all the ways that we try to intellectualize our recruiting process. But it actually comes down to very simple things, right? I mean, it's the same thing that I look for in a research technician or an executive on, at least on the science side, right? It's really like, are you thinking about science outside of the lab?

• 39:48Pat Grady?

  Hmm.

• 39:49Speaker 00

  Right? And have you done something end to end before, right? And then the third is, you know, do you have the the the grit to actually kind of walk the path and get it done?

• 40:03Pat Grady?

  Yeah.

• 40:03Speaker 00

  Right?

• 40:04Speaker 01

  What do you mean by the end to end part?

• 40:06Speaker 00

  Um, I think it's very easy to go from step one to two or three to five or, you know, twelve to fifteen. But, you know, going from one through fifteen winnows down the population significantly. And so, you know, you know, I often say, you know, the the last twenty percent of a project is actually eighty percent of the work.

• 40:28Speaker 01

  Yeah.

• 40:28Speaker 00

  Right, and it's because finishing something and then honing your killer instinct from finishing things multiple times really matters.

• 40:37Speaker 01

  What should we expect to be coming out of ARC Institute in the next six months and then over the next few years?

• 40:43Speaker 00

  Well, I'm I'm I'm tremendously excited about lots of things. And I think the thing that maybe many people don't know is the degree to which we have, you know, really been trying to build biology out at ARC. I think people have maybe heard about our our gene editing work or our machine learning work, right? But A lot of what we're actually trying to build is this general concept of applying high-throughput, scalable technologies in the context of multi-systems interactions, right? Really working at the neuro and immune interface. And so, you know, we hired, you know, two incredible scientists out of Penn last year

• 41:27Speaker 00

  who study the process of interoception, right? Well, so proprioception is, you know, when you kind of close your eyes, where your limbs, right? And interoception is the idea of, you know, you know, I feel the weather in my knee, or my tummy feels funny, right? You know, kind of midwives tales type stuff that actually has really deep science. And of course, it's totally unknown and how does your body talk to your brain and vice versa, right? And it turns out there's a deep mechanistic basis for this. And as, you know, I think when people think about programming biology, they think about in the drug paradigm of how do I get a binder that binds to this protein?

• 42:04Speaker 01

  Yep.

• 42:05Speaker 00

  Or how do I get a CRISPR to edit this gene? But if you think about what happens with hormones or with Ozempic, right? Right? You're able to program the way that you think and feel and behave in really powerful ways that controls not just satiety, but energy, mood, you know, you know, you know, muscle synthesis, you know, all focus, all kinds of things. And I think how do we actually program physiology is something that I've been spending a lot of time thinking about in, in our lab, yeah.

• 42:40Speaker 01

  What's one unexpected connection that you think people don't, don't think about?

• 42:47Speaker 00

  One example is the, you know, so, so exercise, right? And so Kristoff, one of our PIs, you know, had a beautiful paper where he showed that there's a specific species of gut bacteria that produce a certain type of molecule that connects via your enteric nervous system, which is the nervous system that lines your gut, that goes to your brain in order to release dopamine. And it is this functional circuit that creates runner's high or exercise reward.

• 43:18Speaker 00

  And when you delete this bacteria, you cut off this E N S to brain circuit, or you you cut off the ability of the brain to release the dopamine. At each of these steps individually, you can block the runner's high. Hmm. Right? And so it really traces in an intact animal. Well, this is a mouse study, right? This, you know, full body circuit. But that also goes in reverse. So when you have deep psychological stress, right? Um, that can lead to signaling from the brain to astrocytes that innervate your gut that releases pro-inflammatory cytokines that leads to gut inflammation. And then, yeah, can give you ulcers. Hmm. Right? So stress causes ulcers.

• 44:03Speaker 00

  We've actually kind of known this. Yes.

• 44:05Speaker 01

  But this is the mechanism for it.

• 44:07Speaker 00

  And how and how can you treat it?

• 44:09Speaker 01

  Yeah.

• 44:09Speaker 00

  Right? Because there are folks who get recurring ulcers.

• 44:11Speaker 01

  Yeah.

• 44:11Speaker 00

  Right? And there's a brain to body axis by which this signals, right? And I think this happens all the time.

• 44:21Speaker 01

  Yeah.

• 44:21Speaker 00

  Some of which is conscious, most of which is unconscious. Hmm. And you can actually start to figure out the dials and knobs. And that's actually, in a way, like a new paradigm for drugs.

• 44:33Speaker 01

  Yeah.

• 44:34Speaker 00

  Or how to think about using drugs, right? It's not just this highly reductive pharmaceutical, you know, kind of binder to biomarker type of thing. But, you know, the, the, that's giving you more of the holistic, kind of almost eastern medicine flavor of just how do I feel healthier?

• 44:55Speaker 01

  Yeah.

• 44:55Speaker 00

  Right? That I think is in vogue in the longevity community today, right? Yeah. What is health span? How do I improve it? How do I improve my diet and my nutrition? Uh, so I just feel better, right? You know, those things are also can have deep scientific grounding and needs to have that.

• 45:11Speaker 01

  Interesting. So how do you think a How do you think people will be treated in the future? It's you will have a full panel. You'll know exactly what's going on in your body and then you'll decide different inputs to influence the whole thing. Like, how do you? How do you tie in functional medicine? Things are going on longevity with the drug industry as it is today. Like, where does that interplay?

• 45:31Speaker 00

  Yeah. I mean, I think we'll want AI doctors, right? That are able to integrate information multimodally, right? And so, just like you have your CGM that monitors your glucose with high temporal resolution. You have your O ring or your whoop that talks about your, you know, various biomarkers. Or you can go to Quest or, you know, Function Health or whatever and get blood tests, right? That, you know, measure what's going on with your liver function or your cholesterol or, you know, your testosterone or estrogen or, you know, other types of hormones, right? Right now, all you really know is that these things are going up or down and whether or not they're in standard or reference range, right?

• 46:13Speaker 00

  It doesn't tell you very much about what you're supposed to do, right? And I think, you know, one thing that has been really missing in personalized genetics or, you know, consumer genetics is the ability to take information content from your genome sequence. Yeah, and meaningfully integrate it with your health biomarkers in a way that gives you the genotype and the environment that can be more predictive of phenotype, right? That G X equals P equation you learn in high school biology, right? But none of that is actually kind of accessible to mom and dad or to even us, right? In the, in the, in the setting of how do I actually live my life?

• 46:56Speaker 00

  And I think we need to go from measuring people with, you know, higher content approaches to connecting that to, you know, genetic signatures and make more accurate predictions, right? So that's sort of like been the theme of our conversation today.

• 47:11Pat Grady?

  And what do you think that'll look like? Do you think that'll look like any of the existing longevity efforts? Or do you think there's some new beast entirely that's going to be created to serve that purpose?

• 47:20Speaker 00

  Yeah. I mean, so I think if you look at, you know, 23andMe was, you know, recently, you know, filed Chapter 11, right? And it's, I think, one of, you know, I think it's an amazing pioneer and visionary kind of pioneering effort in, you know, how to take genetics and, you know, put it in the hands of millions of people, right? I think the Thing that I think I would really love to see in the world is something that can take all of that information with all of your different, you know, kind of body measurements. And then actually, you know, connect that to diet and sleep and give you personalized recommendations about your health in a longitudinal way, right?

• 48:02Speaker 00

  We have very fragmented data sets for being able to do this today. And I think being able to collect this data at scale across populations and over time with temporal resolution will, I don't, I don't, I don't want to be one of these unhinged big data will solve everything people.

• 48:20Speaker 01

  Yeah.

• 48:21Pat Grady?

  But it will.

• 48:22Speaker 00

  But it will.

• 48:25Speaker 01

  Yeah. I do wonder if there's more stuff on the cross-functional side to your point, like, if you know you have a gambling addiction, I don't think anybody's thinking about maybe Manjaro or Zempic could help with that, but it does help with some, some of these things, right? Like, I do think there's something about the cross-functional nature that.

• 48:41Speaker 00

  Is inter-ception.

• 48:43Speaker 01

  That's fair. There needs to be some, some organization that actually makes accessible. Yeah.

• 48:49Speaker 00

  Yeah. Yeah, I don't think that obviously exists today.

• 48:53Speaker 01

  Yeah.

• 48:53Speaker 00

  Think folks are building different hands on the elephant for this, but you know, you guys should start this company.

• 49:00Pat Grady?

  We should.

• 49:01Speaker 01

  Honestly, it's a pretty good idea.

• 49:02Pat Grady?

  It is a good idea.

• 49:04Pat Grady?

  All right, let's ask, let's get a couple of predictions on a couple of different time scales. So we'll start with 2025 and then maybe 2030 and then maybe 2050. What is the most interesting thing we're going to see in the world of AI meets bio in 2025 by 2030 and by 2050?

• 49:23Speaker 00

  My hope is by the end of the year, I mean, and this is already happening, right? We can, you know, just, we can design full IgG antibodies, right? Not single chain binders like nanobodies, but just the real antibody medicines that, you know, we kind of know and love today, that we can just design their CDRE gens, they're going to bind really well. You can one-shot it and you can kind of do point and click on that, you know, that surface of your enzyme, I can just bind it, right? You know, one shot. I think the thing that will mature over the next couple of years is that we can actually design enzymes de novo. I think that will be, that will be really interesting and you know, also lots of efforts. But, and again, this is all in the world of proteins.

• 50:05Speaker 00

  And I think one of the things that mo- most people who think about this stuff are very protein coded. And so a lot of our, our, our work is to sort of zoom out from proteins and think about cells, right? And so I think building the, the sort of the, the PDB of virtual cells, right, is something that we've been focusing a lot on at Arc. That will take some years from today to mature. Uh, so PDB is a protein database, right? And it's the sort of gold standard database of atomic resolution solved, experimentally solved protein structures that was used by, uh, you know, DeepMind to train AlphaFold, right? And so it's the pre-training data that allows the model to reach some sort of capability like

• 50:50Speaker 00

  protein structure prediction, um, at, uh, Angstrom resolution, right? So, you know, what is that for, you know, virtual cells, which we think would help us design better drug targets, increase therapeutic probability of success, right? Um, I think, you know, that will, that's sort of my 2030, uh, sort of prediction is that we have, you know, accurate and useful virtual cell models that make a cell biologist feel emotion. Right? Um, the, the 2050 idea, and hopefully this happens much, much sooner than that, you know, I think, you know, there's lots of chat about scientific superintelligence or, you know, the, the end-to-end kind of, uh, recursion of the scientific method. Um,

• 51:33Speaker 00

  I'd like to see that, right? With, you know, lab in the loop with a fully automated wet lab that's vertically integrated.

• 51:42Pat Grady?

  Do you think, do you think it's possible by 2050, we can simulate with 99.9% accuracy, you know, the impact that a particular drug is going to have on a particular target? You know, validate that in a wet lab? In a fully automated way, in a matter of hours, not months. Like, what do you think the dream scenario is for going from zero to impact? You know, in the future of drug discovery, if you imagine 25-plus years of technological progress?

• 52:13Speaker 00

  Yeah, I mean, I think we've laid out different aspects of the vision over the course of our conversation today. Where things are really slow, right? Like toxicity, long-term follow-ups, right? These are the, you know, it depends a lot on the disease, right? If you're doing some acute oncology thing, that's very different from some really chronic autoimmune thing, right? And so I think the only way that I can imagine you speeding this up is if you have a model with strong predictive power.

• 52:44Pat Grady?

  Yeah.

• 52:45Speaker 00

  And, you know, so a lot of this hinges on our ability to make models that can actually do that.

• 52:51Pat Grady?

  Yeah.

• 52:52Speaker 00

  And I think you will unlock different step sizes of capability based on how good they are.

• 52:57Pat Grady?

  Is there any reason we wouldn't have that by 2050?

• 53:01Speaker 00

  Yeah, basically, if we make the wrong data, I would say is like one obvious one. You know, you can model the mouse in all of its glory to great perfection. It still will not be the human. That's, you know, one, one sort of, I think, you know, trivial example that is something that we still do though, because that's just what's practical. And so I have this other soapbox about how we actually just need to be doing way more experiments in humans.

• 53:35Pat Grady?

  Hmm.

• 53:36Speaker 01

  And what do you think it will take for us if we will do that? Is that just a regulatory thing? Is that?

• 53:41Speaker 00

  I think there will be some aspect of creativity involved in addition to better regulatory innovation. So, so one example would be, you know, there, there, there are these kind of, you, you can take samples from brain-dead patients, for example. And then now you can just get lungs that you can perfuse and keep alive for a week. And then just do experiments in that lung, right? And, you know, there, there was a paper recently published about this.

• 54:08Pat Grady?

  Should we do lightning round?

• 54:10Speaker 01

  Yeah, let's do it. My first one. Favorite new AI app that you've tried in the last three months?

• 54:15Speaker 00

  So this is maybe cheating, but I'm a DAU of OpenAI Deep Research. Right? I just, I find it just by far the main AI app that I find useful enough to use in my day to day work. Right? And so there are lots of other fun AI apps or things that I pay attention to because I'm interested in AI.

• 54:38Speaker 01

  Yeah.

• 54:38Speaker 00

  But the thing that actually changed how I work is these deep research models. And they have, by the way, so much more room to improve and to run.

• 54:48Speaker 01

  Yeah.

• 54:48Speaker 00

  And, uh, yeah, I think it was the first time I felt real emotion, thinking, oh, wow. Okay, someday, maybe I will be automated.

• 54:58Pat Grady?

  Yeah.

• 54:58Speaker 01

  Oh, wow.

• 54:59Pat Grady?

  Yeah. Yeah, I feel that every day.

• 55:02Pat Grady?

  Who would be on your Mount Rushmore of scientists?

• 55:08Speaker 00

  This is maybe a bit smarmy, but it's the folks I get to work with at ARC. I realize this is incredibly smarmy, but it's really genuine.

• 55:22Speaker 00

  I feel, I feel so lucky to go to lab every day and just be around, you know, just passionate, bright, kind, and incredibly ambitious people. And it levels up my game.

• 55:35Speaker 01

  What do you think is going to be the killer application that scientists will use by the end of this year?

• 55:42Speaker 00

  Deep research.

• 55:43Speaker 01

  Still that. All right. Nothing that you guys are going to create for Mark?

• 55:48Speaker 00

  Well, I think these virtual cell models will be incredibly useful. We, I don't think we or anyone will have working models in the sense that they, I think, I think it'll take some time for them to mature to the point where they're Actually fundamentally useful, right? There are currently research problems that will ripen over, over some time. Yeah, but we'd like to deliver those.

• 56:14Pat Grady?

  What's the most important thing you've learned at ARC Institute?

• 56:17Speaker 00

  So there's a Scottish proverb that I'm going to butcher when I paraphrase. But it's basically, you know, you know, be happy when you're alive for you're a long time dead. And, you know, I think that that really hit for me when I, when I read it. And, you know, it was one of those, you know, you're lying on the couch, the phone is six inches from your face. Beaming lux into your eyeballs right before you're supposed to fall asleep. I don't know, that just, it, it reminded me that despite all the complexity of, you know, trying to work really hard to do useful things, you're supposed to have fun. And I think that should, I think we need more of that in life, not just in research labs,

• 57:02Speaker 00

  where I think it can be so easy to be super critical because that's the training and how you make progress is to hate on everything and everything has a problem and figure out why this can go wrong. But being optimistic and happy is not a path to mediocrity and mistakes, but it's actually how you have the emotional capacity for persistence over time to reach those, those long-term goals.

• 57:30Pat Grady?

  Love it. That feels like a good place to end it.

• 57:31Speaker 01

  I know. Well, hopefully this podcast made you happy too, made us happy.

• 57:36Speaker 00

  I'm always happy to see you, bud.

• 57:38Speaker 01

  Thank you again.

• 57:38Pat Grady?

  Thank you for doing this.

• 57:39Speaker 00

  Thanks, guys.