OpenAI Podcast - Episode 16 - Building AI for Life Sciences

Episode Date: April 16, 2026

What does it take to build AI systems that can actually help scientists? Research lead Joy Jiao and product lead Yunyun Wang discuss how OpenAI is developing models for life sciences and what responsible deployment means in a field with real biosecurity stakes. They explore how AI is already improving research workflows and where it could lead in drug discovery and more autonomous labs — including why a future with less pipetting sounds pretty good to most scientists.Chapters0:39 Introducing the Life Sciences model series3:47 Joy’s path into life sciences5:00 Autonomous lab with Ginkgo Bioworks7:27 Yunyun’s path into life sciences8:12 OpenAI’s life sciences work9:48 Biorisk, access, and safeguards15:43 What models can do in the lab17:51 Building scientific infrastructure20:14 Why compute matters for science24:54 Where are we in 6-12 months?29:51 Scientific adoption and skepticism33:17 Advice for students and researchers40:27 Where are we in 10 years? Hosted on Acast. See acast.com/privacy for more information.

Hello, I'm Andrew Main and this is the Open AI podcast. On today's episode, we're talking with research lead Joy Zhao and product lead Union Wang about Open AI for life sciences. We'll explore what new models are making possible in biology and medicine and what it takes to deploy the most advanced capabilities responsibly. This allows it to kind of reach new levels of difficulty and discovery that we didn't think was even possible before. Putting like really capable, expert level knowledge in the hands of a greater amount of people. One of the taglines was to scale test time compute to cure all disease. So that is like our team tagline. We started off with just a basic API. And then we had chat GPT, which is more conversational, was really good for text as code

became a capability, went through basically code models and then codex. Now that you're getting more scientists and the life sciences working on these systems, does that mean things have to evolve to help with the way researchers might work with these tools. Yeah, we're really excited to build and deploy the Life Sciences model series. So this is a new biochemistry-focused model series that's really anchored on these very complex life science research workflows. And we're focused on adding new, like, mechanistic understanding, starting with genomics understanding and protein understanding and really focused on early discovery use cases because

we feel like that's like one of the core bottlenecks that greater thinking time, greater compute, and really leveraging more capable AI models can help meaningfully stale some of these research barriers. And I think there's also like a model orchestration piece of actually how to embed this into workflows. And it's been really great. First of, having all these different product surfaces to deploy to. We're seeing a lot of really great, like, literature synthesis, workflows happening on chat,

and these models really push the frontier of like long trajectory agented workflows and we're really able to empower that on codex. And more on the model orchestration piece is that I think for enterprise use cases, there's like this reproducibility and repeatability element. And we are trying to overcome this by working on like some of the life sciences research plugins that were shipping for very specific translational bio-use. So the life sciences research plugin has over 50 skills, which are essentially templatized repeatable workflows that if you need to, whether do some sort of cross-evidence match and search across various different papers or do pathway analysis, something that's like repeatable that you often do, you can have like almost like a one-click deploy option by using our life sciences plugins on top. And that's also how we're trying to see the balance between. scaling for very specialized purposes, something we're hoping to get into is maybe clinical purposes,

but also make it still very general use for all foundational biology. I think the models can get quite far by using tools. So, for example, we can use open source protein structure prediction algorithms inside a research stack. And in this case, the model is acting kind of like a regular computational biologist. You will kind of go run these tools on a computer. You will look at the output. You will tweak the input a little bit. So I think that is something our models can already do.

I do think what will make the models even more powerful is to start to turn them more into kind of a biochemistry expert. And I think with this kind of intuition and expertise, you can use these tools even more intelligently and get at the right answer more quickly. How did you get your interest in life sciences? My, I guess, original background was actually in life sciences. So I've always been interested in biology as a kid. I got my PhD in systems biology around like a decade ago from Harvard. Found academia to be very interesting, but the pace was a little bit more slow moving than I would have liked. And I think just the experience of kind of like having to physically be in the lab and kind of like transferring small amounts of liquid from the one, two, to another.

I think I wanted something a little bit faster pace where I felt like I was more in direct control of like my own velocity. So I went from that to software and I ended up here at Open AI. And so this is kind of like a full circle moment for me where I'm like starting to look at biology again and looking at how to accelerate my previous self with AI. So yeah, really excited to see what progress AI can make in the space. So you're like, yeah, this is too slow. Let me go off an AI and speed it up so I can get back into it. Yeah, except, you know, from this end, I don't really ever want to touch a pipet warranty again. So I would prefer for like robots to do it for me.

Yeah, we joke about that a lot. A lot of our motivation for this is we can automate pipetting and never have to do that again. Well, that's what's interesting. I was looking at what you all did with Ginkgo Biowworks and the idea of taking GPD5 and taking an AI system and then working with a robotic lab and how it was able to speed things up. Could you tell us a little about that? Yeah. The Ginkgo work is interesting because I think when it started, I think it was like July of last year, 2025. And at that point, GPD-5 had just finished training.

We were really not sure if the models could do any kind of biology. We didn't really have that much biology under training data. It was mostly math and computer science, which I think makes sense because these things have verifiable solutions. And this is usually not the case in biology unless you can't go and do the experiment in a lab, right? So when we started a collaboration with Ginko, it was really, can the model do any biology at all? Can it design experiments that actually make reactants, like make the product that we want? So it was actually quite surprising, I think, when GPD-5 designed the first set of experiments with Ginko, and the results came back. We made a non-zero amount of protein, and that was actually quite surprising.

And then I think progressing from that point in time, which is just roughly like six months ago to now, where it actually actually, just feels quite obvious that our models can accelerate science is actually just really surprising. And it really shows that the art of the possible, I think. I think before that experiment led by Joy and the Biottoe team was conducted, I think we really didn't know for ourselves. And I always say, like, we kind of learn that for ourselves when we like engage in these experiments and we have a few more in the works with others. And I think that is like the type of like acceleration that we're looking for. Ingesting high throughput, experimental data is really difficult. It's very compute intensive. And I think for a lot of these scientific workflows, like the true bottleneck for

the speed and progress of scientific acceleration is at like how like almost a human bottlenecks. And I think the future that me and Joyce see is that it's no longer human bottlenecks, but rather maybe compute bottlenecks. And we're really able to like, deploy mini subagents doing parallel orchestration to divide and counter on all these tasks. And the researcher can now spend their time on like really analyzing, interpreting like the most meaningful insights coming out of that. So union, how did you get into this? Yeah, I think reflecting back, I've actually been working on like biology research in some shape or form for a majority of my time here at Open AI. So I first started on working on bio risk medications and a lot of our

bio-defense initiatives. And so I feel like coming to now working on the life sciences research side gives me like just appreciation for how difficult this problem is and tackling it from both sides. And I've, my initial entries point into wet lab research was actually through doing a lot of infectious disease and biology work. So I think I've always done like the interest in biosecurity in that way. So this just feels like a really great moment like right now to work on it, especially when our models are getting more capable with beneficial use and just general life sciences. How long has Open Eye been focused on life sciences? Yeah, I would say it was really the way we design our capability evals that show us that this is possible.

So it's been, I think, for at least like two years now that we have worked on a lot of our early research experiments. And now with the gentle autonomous like Wetlap model in the loop experiments. I think we have a few more research partners in the space that we're really excited about. I think I can't actually name everyone right now, but there's a lot of stuff kind of in the chemical design, protein design, enzyme design space that I think is very AI native and a lot of people are interested in. So understanding how the world works, understanding how chemicals react, understanding how cells interact, how pathways inside cells interact, all the way to can we accelerate drug discovery? So given a disease, kind of model, how science. to understand a mechanism. Can we once given a target actually design a drug against that target? Can we even accelerate the FDA approval process? So I think there's a role for AI to play kind of

at every step of this pipeline. And yeah, I think just I think there's a lot of AI possible in everything. I've been to some of those cutting edge labs. And on the outside, you have this impression of it. Then you walk in there and you literally see somebody with, you know, a row of petri dishes, a row of samples, and just some grad student going click, click, click, and I'm like, oh, this is the pace of science. This is fast as me. Yeah, exactly. You're like, enough of this.

I got to go speed this up. But we forget that's often the pace of science is, just how fast the human hands can move through that. You know, the tool like that, it's kind of exciting. When you start using these tools to maybe think about new pathways for treatments or just evaluate, you also introduce the idea that these could be used for things that maybe are less desirable, you know, bio weapons is something that comes up a lot, the fact that if, you know, an AI can figure out how to do a code exploit, might be able to figure out how to do a gene exploit. How are you addressing that? Yeah, that's a great question. And I think it is just probably one of the most severe risks that we're currently really tracking for rising AI capabilities. Our first approach to that was really thinking about how do we assess for information hazards. At what point does a model now maybe give like the, the final step in like a synthesis of a dangerous pathogen.

And what we found is that like the precursor steps to that really looks very benign. And it's really hard to distinguish between. So another way to put it is like the same steps that a beneficial like legitimate actor might take is looks very similar to the ones at a. Um, dangerous harmful actor. You start with E. coli. You start with something. Yes. Exactly.

So I still think that we, we made the right call for really taking a very thing. very risk-averse approach to that. But now I'm really excited about like differentially access and like responsible deployment as really a core pillar of all of our safeguards work and really understanding that there are different user segments. And I almost feel like the future we're going towards is something like models as like professions similar to how they, models have different personalities. And sometimes you want to invoke the right one depending on the type of like workflow you're looking at. So I think how this translates is similar to how biologists working on like therapeutics and their research, they require access to data sets are often

very tightly controlled or they require access to just expert level. Like they all have PhDs and have like expert level like biology like knowledge. How does that compare to how does that translate over to two models. I think that's why we have to kind of similarly take the same training approach, but also the same security approach and deploying that in like a way where we can have those very heightened enterprise grade controls in place. So you just mentioned safeguards. Can you explain how that applies here, where you would need them, why you would need them? Yeah. So we very thoughtfully design and design new safeguards for pretty much all of our models across very different risk areas. But I think when it came to bio, this was like the first dual use risk that is both also a capability risk.

So it very much correlates with how we as capabilities improve, the risk correlates. And I think that's why our first approach, when we really, there was no precedent for a lot of this work. And we were the first to really activate these high safeguards when we saw that significant reasoning jump in our model capabilities. we really wanted to make sure that we did it right. And I think the best way to get it right is to incrementally deploy. Yeah, I think it's really a fine line between having a very capable model that's capable of accelerating benign science and beneficial science versus a model that could be used by a bad actor. And I think the safest model here would be a model just had no capability, right?

It's not very good. Yeah, it's not very good, but it's very good. but it's very safe. And on the other hand, if you had a model that is basically an oracle of the physical world, it basically knows everything about every experiment, that model could fall into the wrong hands and do potentially very bad things because someone can go and say, hey, design a new novel pandemic potential pathogen, and the model can just go and do that autonomously.

So I think we need to kind of figure out where we draw the line in between the two and kind of think about who gets access to a potentially very capable model and who doesn't. And what we found in kind of what we call general access traffic is that it's very difficult to figure out what a user's actual intentions are just from kind of reading a prompt. And I think as an example of this, let's say someone says, hey, how many clone a gene? The model might not even be given what the gene is, but it can come up with a protocol for it. And so this gene could just be something like green fluorescent protein or it could be a toxin. And there's basically no way to figure that out from the context of the conversation. And so this becomes a very difficult problem in production.

And basically, I think, like you said, we decided to kind of err on the side of safety here. And basically say that, okay, if we think that there is a potential for misuse, we either have the model kind of self-refuse the user in which case it tends to say things like, sorry, I can't really help you with that, but I can give you a high-level overview. of this protocol instead. And this unfortunately very, very much annoys our kind of professional, scientists, understandably. And then we also kind of have multiple layers of mitigation on top of that. But I think really to unlock the full capabilities of our models, what we need is this differentiated

access. And what this means is we know who the user actually is. They are a professional working at a legitimate research institution or a pharma company. And because of the regulations around these institutions, we know that, for example, all the reagents are being tracked. All the cell lines that they're using are being tracked. And so this gives us confidence that this is a legitimate user and not a random person in a basement doing who knows what. And that allows us to give them basically more capabilities than we are able to provide to the general access traffic. What can you do right now if you're working with the models, you're working it with,

in a laboratory, what would you say the capability is at this moment? So I think people use the models for very different things. I've talked to people in the Baker Lab recently on kind of how they've been using our models on like codex. And sometimes it's as simple as, hey, can you write a spreadsheet for me? I don't want to just minimize a number of pipetting stuff that I have to make. And this hits me very hard because I had done the same thing by hand in grad school. So that's like a very simple just mathematical software operation.

And then there's much harder tasks. Can you design an enzyme for me with all these biological design tools? So I think there's a huge range of sophistication. Yeah. And something I'm very excited about is how we can use our models to be a more powerful discriminator and like really testing and assessing like new novel ideas. And I think something that I've been noticing as a trend with a lot of our research partners and also the users of our models, is that models for scientific research and

tasks almost require a different like persona or a different prompting style. So we, I often feel like, you know, like a model that is much more scrutinizing or a steptic at good ideas is it's very similar to how human scientists would go assess like originality and feasibility. It's really like, I think, helping understand like out of all the new papers, and new publications out there that push the frontier of a lot of these hypotheses, what are the ones that are really feasible and valid for testing that's gonna help lead to new breakthroughs?

So, and then translating this to something like disease target screening, selection, like the potentials for these drug targets are endless, but it's really like narrowing down the aperture, and I feel like that's where like the assistance comes. Like this is extremely difficult work to do at scale and having a model that can empower and accelerate that process, I think,

is kind of like one of the immediate impacts we're hoping to see by responsibly deploying this model to those users. It seems like it's a very interesting trajectory. You went from there was, you had GPT3 on the API and GPD3.5. Then you get chat GPT and now we have chatypd apps. And now we have codecs. And it sounds like these things just the number of things you can do with this continues to grow. How would you see this building? You know, do you see this is basically just becoming a complete infrastructure for, kind of every kind of inquiry you might want to pursue? Yeah, I think the dream is to have a lot of the basic foundations of scientific workflows happen on codecs. And I think that the goal is to have codex to pretty much be able to do everything that is possible to do on the computer. Of course, we also want to extend beyond that with kind of hooking it up to robotics and so forth. But I think right now we already do things, for example, if we have a bunch of different deaf boxes on our remote, on our laptop, we can actually say, hey, code, go and run this code on all of these different

deaf boxes that are all remote, and then our codecs can do that. We can say, monitor this for me. And I can kind of like go away and do something else. And the codex is like, they're watching all the locks for you. It can build a lot of just kind of fit for purpose software for analyzing specific pieces of data, for visualizing data. So for example, if we have experimental biology data that we're sending each other on the team. What I've noticed recently is instead of sending the raw data, we've started sending HTML files are just these kind of like beautiful UIs that Codex has built

with kind of like spinning proteins. And it's actually just really, it kind of changes the way that we share with each other and collaborate. Yeah, when we first started mapping out how users and organizations might adopt this, I think we envision that each scientist would get their personal assistant or their coworker, and this is a way that they can't scale up their collective output. And then the next paradigm of that would be scaling up whole research institutions where a whole program team can actually deploy a workforce of various agents,

and they can all do parallel task delegation, kind of mimicking a lot of these existing patterns. And we can figure out the pieces of like how they can all collectively, like work together to solve like larger, larger tasks. It's interesting because open eyes talked about the need for compute. And I think that sometimes we just sort of think like, okay, so I can have more conversations and stuff. But when you're talking about the idea of building these tools to become entire platforms or scientific exploration, it sounds like the compute advantage is really critical.

Yeah, I think there's two different axes. We can think about how we are scaling compute. The one that I think everyone's familiar with is just getting bigger models. And I think as we went from, you know, GPT 2 to 3, there was a huge size increase. And there were just these amazing emergent properties from the model. I mean, thinking about, you know, when GPT2 was released, we were all kind of collectively amazed that it was able to write a coherent article about unicorns. And now we're in a completely different world, right?

And a lot of that is driven by model architecture, yes, but also just the number. of parameters in the model just allows it to achieve this incredible intelligence that we never thought was possible before. And then on the other axis, we have what we call test time compute scaling. And this is when you are inferencing the model, when it's kind of spitting out tokens. And this is a thing that happened fairly recently when we call these sub-reasoning models, is that it can sink for a scalable amount of time. And this is a variable depending on how difficult it thinks the problem is.

But we can have the model think for days where really there's kind of ways to just kind of have a think forever about a problem. And this allows it to kind of reach new levels of difficulty and discovery that we didn't think was even possible before. When we think about data centers, we often just sort of think about it as generating cat pictures or doing text conversations. But I think that's really the helpful framework to look at it. These are going to be systems for doing extremely long-term, big complex processes. of thinking about this. And to me, it just makes a lot more sense when, you know, projects like Stargate saying we're going to be building a lot of compute.

It's not just for what we're doing now, but it's going to be for things like that. When we had first announced the team's formation on Slack, I think one of the taglines was to scale test time compute to cure all disease. So that is like our team tagline. It's our team model. That's ambitious. Yeah. I had a friend whose child was born with one of those orphan diseases and she would do fundraising.

do everything she could to try to support. Some researchers were trying to find a cure for this, but they're just not enough time, not enough people. And I'm hopeful that we're kind of in an age now where these kinds of tools are going to make that maybe a thing in the past. Yeah, I think we're already seeing the model help a lot in these cases. I think from things like drug repurposing. So, for example, a drug that's already been clear by the FDA

for use and one different indication, but for kind of like from mechanistic understandings of how that drug works. The model has suggested in many different cases for maybe you can use this drug to temporarily amillary symptoms. We're also seeing a lot of advances in personalized medicine.

So for example, the design of ASOs or are there RNA-based treatments is very common. And I think, yeah, we are actually very, very close to being able to scale this up in a really vast way with AI. just in the next year or two, I think we'll see very big changes here. Every researcher I know, when you ask them what they could use in their lab, they always say more hands, more people, more people doing this kind of work. And you hear some people talk about, well, is AI going to displace that? And I think, no, it sounds like it's just a big accelerator for all the things that could be done.

Yeah, I completely agree. I feel like when you think about lab automation, for example, a lot of the bottleneck from actually being able to translate a protocol into something that can be run on the platform. And we've had partners tell us about how Codex has been helping them do this. And this is kind of fundamentally, I have coding problem, half understanding how YLab works. And then I think thinking about the data analysis piece, I feel like having our models kind of walks through a user who maybe doesn't have the deepest understanding of statistics, they can still rigorously analyze the data that's coming in. the model can kind of help them probe different hypotheses, or it can suggest different statistical

tests, it can point out potential issues and biases in the data. I think these are all ways of kind of uplifting individual scientists and helping them do better science. But I don't think we can ever fully replace the scientists in the loop. So you've been putting it into the lab. You've figured out how to help with automation. Where do you think we're going to be six months from now, 12 months from now? Well, I would really love to get to the point where we can say,

that AI has designed a new drug or cured a disease. I don't know if that can happen to six months, but I will hope in the next few years that's going to happen. I think we're seeing signs of this happening kind of all over different stages of the pipeline. I think obviously earlier in the drug discovery process where you're kind of looking at literature synthesis or the model is kind of discovering new biology,

for that to become a new drug on the market is going to be a very long process or possibly like a decade. But I think there's ways that we can, really speed up this process by starting at maybe the clinical trial stage, we're starting them a little bit before then in the safety reviews or in the truck design phase. So I think, yeah, basically that's what I'm the most excited about coming up in the next few years.

Yeah, for me, I think I'm most excited about all the possibilities that our users, our scientists can do on our platforms. So for one, I think a huge win would be if a researcher can patent a new finding or a new discovery on our platform and using our models. And that's why we really focus on early discovery and starting with building, like teaching the models like the mechanistic understanding. So this is again like trying to provide the most powerful tools through our life sciences models to these scientists. So they can really accelerate the speed of their research. Do you think we'll get to a point where the model, models are going to be really good at basically predicting the cell or predicting the outcome?

I think definitely yes. I think it depends on the complexity of a system. So, for example, one thing our models are already very good at is predicting the outcome of a chemical reaction. And I think as you increase in biochemical and biological complexity, some of the hardest things to predict is given a drug, will this be toxic to a specific person or to a specific system? And I will, want to slowly work our way up to that, but that is definitely on a roadmap, as something we want to do eventually. When we're looking at models that do things like language or math, it's pretty easy to put together to evals for it.

Did it get the problem right or get it wrong? What do e-vals look like for models that are doing biology? Yeah, we have various different ways of evaluating model performance. A really nice way to do this is kind of with experimental data. So someone has already done the experiment, and then you ask the model, can it kind of predict the outcome of these experiments. So a lot of the kind of virtual cell work, basically, it looks like this, right? So someone has done single cell RNAC on millions of different cells, and then you feed it to a model, and then you try to get it to predict

a unseen perturbation. We can also do a lot with synthetic data, and this means that maybe you have generated a set of data and you put very specific characteristics in this data that could be kind of like footguns for the model. And these are things that may be a typical computational biologist might encounter day to day. So this could be some weird bias in the data. It could be some QC thing that you have to do or statistical correction. And because we generated the data ourselves, then we can actually go and test the model's capability as a computational biologist that does a catch all of these different mistakes. So there's a lot of different ways you can be creative with evaluation. But that being said, I think Wynab is still kind of the final,

like real evaluation of the model, right? And as you like to say, nothing in biology is really real until you can prove it in the real world. And so we do have a lot of research collaborations where we try to do just that. Yeah, e-vals have really become more complex and sophisticated over time. And I think that's especially true for designing evals that can really capture both value creation but solving complex problems for life sciences. So I think we really try to focus on examples that are not like toy problems, but really capture that like, for example, like the messiness of like pre-processed site data. And when we design the, these new evaluations. A starting point is often just trying to recreate an existing experiment.

So something that has already a baseline, so we already know what the either current state of the art looks like or the current ground truth looks like. So a evaluation I'm really excited about is looking at if our models can assess like the antibody binding predictions and looking at how that's been done for an existing virus variant. And then once we have already done that baseline, we can push forward and say, can we do this with something that hasn't been done before? And I think that is like some of the precursor steps to de novo antibody design, maybe expanding the neutralization for new viral variants. And that's also on the path to new treatments and potentially developing new vaccines. What has been the reception in the life sciences,

particularly at conferences in the community, people you know, have you seen a lot of willingness to embrace this or skepticism or people who just don't think it's helpful? I think it probably depends on what part of the country you're in. I feel like kind of being on the West Coast, everyone is pretty AI-pilled, and so they really embrace this AI scientist, the agent-tick workflows,

and they really kind of see the future for AI. when I'm at a conference on the East Coast, this changes a lot. I think people are generally a bit more skeptical. Maybe there's a little bit more doubt around the AI capabilities. And yeah, I think it's just maybe like a cultural difference. I think most of the big AI labs are here. And so we kind of have a firsthand experience of what the models are capable of. And this kind of changes our perspective a little bit.

How do you bridge that gap? How do you get more scientists to understand? Because it sounds like the more people contributing, the better because they're our weaknesses or areas need to be improved upon, and the more you get people who are maybe skeptical about this to sort of figure out how to participate. Yeah, I think there's a few different ways. The easiest way is by launching our models through different platforms like chat or Kodak's, and I think just by kind of showing individual scientists how useful this could be, maybe just making a serial dilution spreadsheet for someone who's pipeting. But that has real value, right?

And I think you can kind of build up from there. I think coming from the other end, we do have these more deep research collaborations with labs. For example, like antibody design or enzyme design. And these sort of things are kind of more, you know, they result in publications. And then people will read and say, okay, you know, an AI system did a lot of work. It has biological novelty.

It's been proven out in the white lab. And so I think that also lends credibility to the system. Yeah, I think the simple answer is you show by doing and you show by publishing and engaging with the scientific community. And I think the stepticism is, is really healthy and should be welcomed. I think it's just really great to see people get really excited and also trying to disprove maybe

because the potential for this technology is so great if we get it right and if we can actually really leverage its full capability. So I feel like the carefulness about how do we actually make this work for real problems is very much warranted. But yeah, I think when we publish, and I think that just also shows a need for more rigorous evaluations that represent like these life science workflows and research problems.

So people can look at an e-vout and say, yes. Like I feel like now I have like 100 different ideas for how I can implement this into my lab and solve some of the current bottlenecks I'm facing. I actually think there's a certain amount of stress I've encountered from people who are worried that, you know, AI is really powerful, but they don't know how to use it the right way. And so there is this general feeling of like, I need more AI in my workflow in my life, but they don't know where AI should come in. And I think part of the product vision is to just make it so simple that it just works.

So you can just go to something like Codex and say, hey, I want to do whatever I'm doing today. And then Codex can figure out all the different pieces, the multi-agent workflows, the tool calling, all of that. And so, yeah, basically you don't have to stress about how to get uplift from AI, and it just happens naturally. We do see those step changes every time these models become smarter and understand users better. You get more utility because when people go, I don't have to spend a lot of time trying to prompt it or figure out all the tricks to it. If you were talking to somebody who was considering getting into the life scientist, maybe a high school student right now, what advice would you give them? I feel like when I was in high school. So I did the USA Biology Olympiad back when I was a high school student.

And I think out of all the different Olympias, I think biology was seeing as kind of the most like memorization heavy one versus like math, right, where it's kind of more, I don't, test time-compue scaling, whereas biology is more kind of memory and retrieval. I think my hope is that with AI having kind of like learned all the relationships between all the different research

pieces is that it can really uplift human creativity and just make the process less memorization and more kind of helping people connect different fields of research together and just kind of, I guess, I furthering the frontiers of what people are able to explore in biology. So, yeah, I feel like my advice to, I guess, a high school student would be that maybe

you don't have to kind of go and memorize all the biology books. You should just do more exploration with AI, I think you can definitely read papers and just ask questions. And I think you can do both deeper dives and broader overviews this way. And I think just the way of learning really changes. I found that when I was in the lab, there was like a real like solo, like individual aspect of doing biology research compared to like, for example, when I went to my first like CS hackathon, there was some excitement about just like the collaborative nature when we first. first build our app together. So I feel like that's really the future I hope to see for early adopters and students

using our models and maybe using it in like the Kodak's runtime. Because there is like a collaborative nature to it too. I think like for example sending your scripts or sending your conversations or maybe one day we have like we all have like our own like co-scientists or agent and we can like deploy our agent to now work with a teammate in that way. I think there's just like new like interactions and new modalities. for us. I would just encourage students to adopt early and just to like also pioneer their own path for how they would like to use it. For me personally, I always actually felt like I got into

the wet lab a little bit too early. And like we mentioned earlier, I did not enjoy pipetting. That's the theme here. Nobody likes pipetting. Yeah, there's a lot of like very intense manual tasks involved. And so I hope that like, you know, when our AI models can connect with physical devices, that, yeah, we can just like make a lot of like the learning curve more fun for students so that they can kind of like learn with the models and then kind of like maximize our time with like the really interesting interactions spaces. So I've been working with a student. I like to help students come with projects and one of them is we've taken codex and he's connected to a greenhouse and basically using it to get photos back and to look at it and to evaluate it. And I think it's been fun to see how he's been taking both, you know, AI technology and then something traditional like a greenhouse and combining them to and basically building up the skill set of learning how to use the two of them. When you talk to your peers, you talk to people who are running labs or running experiments or researchers, what advice do you have for them? Because the problem I see is that a lot of them go, that's great.

I just don't have the time. But ultimately, what we're trying to do is save them time. So do you have any kind of quick advice that you give them or any ways you try to maybe inspire them? Most people that I know, I think in academia, use AI in, I think, two main ways that I've seen. One is to kind of talk to AI about an existing piece of research paper or something and just kind of make sure that you're understanding things the right way or kind of fact-checking. And this is personally what I really like to use AI for, because you can ask a really dumb questions and you don't feel any judgment.

It's actually just really wonderful for learning. And then I think people use it a lot for analyzing experimental results. And I think this comes back to the statistics piece that I learned where I mentioned before, where sometimes you don't know what the right way to analyze your data is, or there's just kind of so many different interdisciplinary fields

that your data might touch on something, in chemistry or something in like a random niche field of like protein biology. And the really nice thing is that a model can kind of like pull those different ways of data analysis and for you and kind of explore all of these different paths. I feel like both of those are pretty low-lived ways to try things out. So you could just kind of like throw a PDF file at AI and just be like, hey, help me understand this paper and just have a natural conversation. Or you can, you know, boot up codex and do some data analysis directly on your

laptop. Yeah, I would say that you'd have to start with making sure it doesn't feel like work right away. So maybe it'll be easier when you're focusing on AI adoption to just like work on like a hobby project or a passion project. For me, for example, I actually started working on like more like literature synthesis tasks when I was doing creative writing projects, which are kind of like just something that was not at all related to like our day to day, even though they're, biology is a very creative space. I was just exploring that through a different, different medium. And I think that's actually when I started unlocking a lot of different ways to either prompt

the model or to actually access different data sources. So I think that just gave me a lot of pattern matching abilities for when I was trying to apply it because we're not going to get it right in the first try. And it is really hard. And I feel like the progress and pace of this field moves so fast that every week or month there is a new, like, pretty exciting development that might change. how we engage with models or AI systems. So I think it's just important to get started somewhere.

And I think another theme is the collaboration element. I feel like it's more powerful when you have a recommendation from either somebody on your direct team who is doing the same day-to-day tasks as you. That happens a lot on our team as well where somebody will say, oh, I got Kodads to like now touch these three different like internal like databases that we weren't able to connect before. And I don't even like the latent space, the latent capabilities are just so vast that there's a lot that we just don't know until again we can do it. So I think just having conversations with your friends, your lab mates, your teammates will I think spark a lot of those conversations,

a lot of those creative juices and then help you help you with your own adoption. What does science look like 10 years from now? I think when we started this team, we do have like really just ambitious targets. And one of those is like I think we do want to make meaningful strides towards or even if like assist with like carrying a disease. And I think there's just so many rare like orphan diseases that doesn't really have the attention and the resources that it warrants because it's just such a difficult field to actually like, for example, like clinical research is so difficult to actually bring that to patients and to tumor. market. So while 10 years, I feel like it's just really a really long timeline. I'm really excited about some of the progress that we can make. And I think it's good to like be carefully optimistic that like we're going to see some of those breakthroughs pretty soon. Yeah, I think maybe

this is a bit of a sci-fi vision that I have of the world. I really hope it becomes reality, which is that you have these autonomous labs. There are just mostly robots and you have them all hooked up to AI. And you just have autonomous research institutes that are constantly running and curing human disease. It's maybe making new materials, making new drugs. It's maybe solving personalized medicine. There's a lot of end of one or just ultra rare diseases where people without vast monetary and research, scientific resources can even begin to think about solving. But we can solve that with AI. And I think we can kind of almost break through the, financial and regulatory and monetary constraints with the system. So I think that that's like kind of the

dream. And I think also even separately thinking kind of more about the biosecurity side of things. These systems can be kind of constantly sampling our environment, right? It can be sampling wastewater. It can be sampling the air and constantly detecting potential threats or even just, you know, better predictions for the flu and getting better flu vaccines. But just generally these different medical countermeasures, I think should be happening autonomously in 10 years. And I think that that's basically something, yeah, I'm really excited about. The AI lab is exciting because I think if people really understand what it means is it's not, there aren't scientists, it's, they're more scientists, but they sit at home and they go into codex and say, can you go run this experiment

for me? Like you have a data center. You have a science center doing that. Right. Exactly. Yeah. And I think I didn't talk about the scientists in this division. I was just describing, but obviously there are people involved in here. And I think it's really kind of high-level direction setting from the humans. We're saying, here's a patient with this disease. Here are some potential solutions or things that maybe you can look at. And I think that AI can then go off and explore different ideas. You can design experiments and then come back to the humans and say, here's what I found. What do you think we should do next? And this can be kind of a academic a discussion, it's a little bit similar to kind of the way that people interact with Codex today

where you say, here, go write a function or go write a piece of code. And I write it and say, here's the code and then the person tells you the next thing to do. So I think it's a little bit similar to that kind of interaction, but on a much grander scale and on a much longer time horizon. I think it's really like the democratizing science aspect and putting like really capable expert level knowledge in the hands of a greater amount of people. And I think what that can mean for personalized medicine, for bolster in our societal defenses. There's just like so many naturally occurring new like variants every year, new like influenza strains. So I think it's really just like securing defenses and feeling like we actually have more agency to counter all that.

And I think I'm really excited about a lot of like the medical countermeasure acceleration work as well. Well, it's very excited. Thank you for sharing this with us. Thank you for having us. Yeah. Thank you so much.