110. Culture Shock: What's Brewing for the Bioeconomy in 2025

[00:00:03] Erum: Nick Edwards potato AI. Hello. Welcome to the grow everything podcast.

[00:00:09] Nick: Uh, thank you, and Carl, it's really a pleasure to come on. I'm a big fan of what you guys are doing. I listened to a few of the recent episodes and some older ones. and, it's very cool. I was thinking as we were starting, should I answer the first question everyone asks, at the outset,

[00:00:25] Karl: What's the first question that everybody asked at the outset?

[00:00:29] Erum: us. Tell me this is great. You're a professional. I mean, people, Nick has a podcast, so he could probably just do this whole thing. But tell us, tell us the first question.

[00:00:39] Nick: why did you name it potato?

[00:00:40] Erum: Yes.

[00:00:42] Nick: so have kids and I think, kids are just natural scientists. They have this innate curiosity. so it was thinking back to the, like the magic of discovery when you're a kid that's that experiment where, you use a potato as a battery for an electrical circuit.

[00:00:56] Nick: it's fun, it's memorable. nobody forgets the name potato.

[00:00:59] Erum: Yeah. And there's a lot of puns you can do. Your background has an amazing potato. So we'll put that online and you guys can see it. but, and then Mr. Potato head, there's a lot you can do there. And I saw the website. There's a lot of potato puns.

[00:01:12] Nick: Oh, yeah, yeah. Maybe a little too much. Yeah.

[00:01:16] Erum: No, no, it's great.

[00:01:17] Erum: It's great. And you don't have just one kid. You don't have two kids, but you have three. Wow.

[00:01:23] Erum: Yeah.

[00:01:24] Erum: Molarity with Carl. You guys are trifecta, the three kid

[00:01:29] Nick: Three. The three kid trifecta. it's a lot of fun. We just had, Thanksgiving recently and I was just mentioning, stayed in a pretty rustic place. So, but yeah, super excited to be talking to you guys about this. And, my kids are fascinated with, science experiments.

[00:01:44] Nick: sometimes I use, some of these tools. I use potato every once in a while to actually, get ideas for experience. we made root beer, one time,

[00:01:50] Karl: Oh,

[00:01:51] Nick: beer recipe.

[00:01:52] Erum: Oh, wow. It's really cool. gave a little bit of description of what potato is in the intro, but tell us a little bit more about what it is. that you're tackling the reproducibility of science, but tell us, tell us what it is from you.

[00:02:05] Nick: Yeah. So, The basic mission that we're going after is to organize the world's scientific information. and so that's, to be able to enable autonomous science. So, the goal is really can we speed up scientific discovery, make more discoveries faster. So I think there's a couple core.

[00:02:21] Nick: Problems and challenges that we're facing with that. So number one is there's way too much information. literature is expanding exponentially scientific literature. it's hard to keep on top of. It's incredibly overwhelming for researchers themselves. so the exponential growth of literature is the first thing.

[00:02:37] Nick: The second thing is, reproducibility, which It's a big challenge. I don't know if you guys ever seen this paper back in nature in 2016. I think, they showed that something like 70 percent of research couldn't be replicated. more than 50 percent of the scientists they surveyed couldn't reproduce their own work.

[00:02:52] Nick: and so to me, it's fundamentally a question of how do we know what to trust and then how do we deal with this massive deluge of, information? the things I started thinking about, a long time back when I was a graduate student. I did my PhD, at Brown, the strength program at the NIH.

[00:03:08] Nick: I was doing some really complicated experiments where we'd inject, a general associated virus into mouse brain, a record from single neurons and slices. And it was this, incredibly, technically challenging and, most people give up on it after a few months, it's called patch comp, electrophysiology.

[00:03:22] Nick: So I'd spent hours digging through papers, trying to figure out what, pharmacology I needed to use. and then you start to realize there's just not enough information in a lot of the papers. And sometimes it's because it's just difficult to include all those details, but I just experienced it myself I've seen that challenge throughout each of the companies that I worked with in biotech, over the last bunch of years.

[00:03:42] Nick: I think the big on walk for me back when potato was still a dream, I started to realize that basically, a lot of science is really communicated through language through this, very unstructured text.

[00:03:53] Nick: so. Can we use AI and language models, that are designed for language to actually, unlock some of this information, make it easier to digest, be able to compare across, papers, structure, some of the scientific information so we can make it more accessible, easier to use speed up discovery.

[00:04:08] Nick: that's the basic idea.

[00:04:10] Karl: that's pretty awesome. I mean, know that there's people who envision a world where you've got a lot of, AI, agents that are possibly doing, scientific work, but, AI research assistants to help you optimize protocols, reference literature. how far away are you from doing that?

[00:04:29] Karl: And what's the biggest challenges to getting there?

[00:04:32] Nick: Yeah. So, if you think about it, AI tools are constantly getting better, they're the worst they'll ever be. and, you think of it almost like the progression of scientist. You get into the lab and you might start out as a research assistant.

[00:04:44] Nick: and I think that we're already there in some ways, from that perspective, because, we've already got six, 700 now ish people, using potato at of the top institutions, so Harvard, Caltech, MIT, Stanford, they use it to review individual papers and come up with like new ideas for hypotheses.

[00:05:00] Nick: We had somebody that came on recently and, they put it in a nature paper they found for next steps, it pointed out. some things that they had just submitted into a grant that they, were working on. so, it helps come up with some ideas, hypotheses. then you can use it to generate literature based protocols.

[00:05:15] Nick: you can use it to go back and forth and ask questions. we're constantly releasing these new features on a super regular basis. just got incredible engineering, with my co founder, Ryan Kosai. It already functions to that, state where it can get me to a baseline, especially with protocol, for example, it's not going to get you to something that you're going to run immediately in the lab, but basically every single person that we show it to, they're shocked at how detailed and how accurate it is, there's definitely room to improve and that's something that we, aim to continue doing.

[00:05:42] Nick: And so the real goal here is not just An AI research assistant, it's an AI scientist, so for something like that, it becomes much more, you need closed loop feedback, you need experiments that you can do, that can be done in the lab, whether that's through automation or having people, run those experiments.

[00:05:58] Nick: You need to analyze those experiments. you need to be able to, run computational experiments as well then connect to lab automation. So there's lots of those types of exciting things that, we've started to make some early progress and some of those things, but there's a lot of work to be done to, scale it up into a full scale AI scientist.

[00:06:14] Nick: But, I firmly believe it, it already works as an AI research assistant and, people are, finding a lot of, value with it.

[00:06:20] Erum: Wow, that's amazing. And congratulations. That's great that you've gotten this far already, that people are using it, 700 people, top institutions, things, spread, you word of mouth in universities. So I can imagine you'll get more and more users in the near future. very, very exciting.

[00:06:36] Erum: but I want to take a step back and think a little bit more about this relationship between the advancement in technology. With the rate of how science is done, the rate of how biology is done, So, with potato, you're aiming to make science faster, more reliable, which is necessary, but how do you balance the need for speed and research with maintaining the rigorous, scientific standards that's required in doing experiments?

[00:06:59] Nick: Yeah, it's a huge question. like that's the fundamental reason why we're taking the approach that we're taking, which is, Reproducibility and reliability has to be first because you can't make science faster if you can't trust it. That's the first and foremost thing that we're working on is focusing on rigor and making sure that it's accurate.

[00:07:18] Nick: we have a number of technologies. One that we're using is retrieval, augmented generation rag, basically, Giving it the ability to understand language and some basic reasoning, but also only drawing from authoritative content that you're indexing.

[00:07:31] Nick: we heavily indexed on peer reviewed protocols right now, open access, but also just recently, we're working with some of the major publishers, we recently, announced a collaboration with Wiley. you want to be able to access some of this authoritative information, make sure that information from, protocols that you generate, you can cite back to individual papers and, know where you're getting that information from.

[00:07:53] Nick: So that's one thing. another thing that we're working on is. we've built some tools to extract all the detailed information for example, a given paper. It could be any kind of unstructured scientific information, but take, for example, a I had this paper that I published back in in nature neuroscience.

[00:08:11] Nick: And, I can put it into potato and will, pull out all the details and then turn it into machine readable code it puts it into hierarchical structure so, you could be able to, pull out all the methods from a given paper there's a number of things that we can do with this technology.

[00:08:25] Nick: starting to work with large enterprise organizations and, the applications are one, I would say is comparing methods across similar papers to try and get consensus, across them. the second thing is, It's extracting this information using it as context to existing data sets that companies already have to make them easier to use so you can imagine if I have some sort of foundation model for biology, there's a bunch of data there.

[00:08:48] Nick: Can we attach metadata from papers or from experimental conditions? so that information that can then become easier to ask questions about, In plain language, the third thing that we're working on right now is, if we can turn these papers into code, then can we translate them into lab automation code?

[00:09:04] Nick: so that you could actually run liquid handlers and lab automation, with some of these protocols. that's really the focus is. Making it rigorous, and then that enables the speed, both now and in the long term.

[00:09:16] Karl: it's amazing because, we start off by talking about, the amount of scientific data that is generated. And I also think about the amount of data that's generated, in China, for example, and apparently, we don't have, full access or people say that people are at a disadvantage because not everything is in English, but I think probably most of it is, but just being able to, access all of that world of scientific knowledge, whatever is behind, paywall, as you mentioned, Wiley Elsevier, those big publishing companies, they probably have so much that should be accessible and probably is not, but let's switch gears a little bit because you also run a podcast called once a scientist, and you've talked to people all over STEM.

[00:09:53] Karl: Some of which have been on our podcast as well. when you think about building potato and your experience with the once a scientist podcast, what's the overlap, what's the Venn diagram there? What have you learned from interviewing all these scientists and what are some lessons we can take?

[00:10:08] Nick: Yeah, that's interesting. one of my guests, SK Sharma, he compared careers to a random walk, which is a physics based concept. things converge in ways. and That's been a really interesting overlap for me. So I started the podcast, back in 2020 and I've interviewed, maybe 90 or so scientists, really about their careers because, I just felt like when I was, Going through training.

[00:10:31] Nick: didn't have a lot of exposure to other things. And part of it was just because my head was down and I was in the lab doing work. But part of it was, I didn't find centralized sources where you could find out, what does a scientist do if whether you're at the bench or you're running something like messaging lab, for example.

[00:10:45] Nick: so I started interviewing people around that It's really interesting because I would start to get individual, unique perspectives from a lot of scientists around, the strategies, they had taken, some of it is post hoc. And so it's people are putting together stories.

[00:10:58] Nick: but so much of. careers and progression is one stochastic, I think. but two, it's an oral tradition and we have this weird apprenticeship model in science where, information is passed from one person to the next.

[00:11:12] Nick: sometimes it's on a napkin, if it's scientific content, sometimes it's just in the form of a, discussion over lunch. I think there's a lot of value in getting perspectives from lots of different sources, pulling them together and then figuring out what makes most sense for you.

[00:11:26] Nick: there's a lot of, interesting. Parallels with what we're doing a potato because, we're pulling data and scientific information from across a bunch of different, publications, people, it's language based so, There is some interesting inspiration there, but I think one of the big things that I realized through the podcast was communication is really important it's vital to, anything you do within science, whether you're professor or whether you're, starting a company, the language is, really important.

[00:11:54] Nick: And think, to me, that was one of the inspirations for actually starting potato is realizing that there's science is not just numbers and data. It is communicated through language. And through stories,

[00:12:04] Erum: Yeah. Well said. Yeah. Absolutely. I wonder if you could take the data from once a scientist pockets all recordings and connect it to potato AI. Is that easy see what, see what happens.

[00:12:17] Nick: yeah, very interesting. I thought at one point maybe I should do a chat bot based off of all these career conversations I've had with scientists and just make it so that, researchers could go in and ask questions about what are the potential options?

[00:12:28] Nick: what do people do? had time, I would totally do

[00:12:32] Erum: Yeah. Yeah. Yeah.

[00:12:34] Nick: yeah, I wonder, interesting. I'm not sure, what data would be the most relevant, but, there's so many applications, for AI within science, whether that's for, something like basic discovery, like we're doing or whether that's, my career advice, I

[00:12:48] Erum: yeah, career bias and just digging through the data and synthesizing into something that's comprehensible to reader, even if it doesn't make actual sense, but the way that the words are laid out, It triggers the idea or triggers motivation to go down a certain pathway, which you certainly have done.

[00:13:04] Erum: I mean, you were also, a consultant at BCG, you were at startups, you did everything. and then now you're here at Potato and as a podcaster. I think that's a great example of one career trajectory that, scientists can go down.

[00:13:18] Nick: yeah, again, I think that there's two key features of, phenotype of a scientist, one is, curiosity and two is rigor I'm pretty curious person. started out as a basic researcher, I'm just the type of person I really like building things.

[00:13:34] Nick: there's this funny story where, I was in my PhD and I was working at the National Institute of Drug Abuse and I had to prepare these, brain sections that I would use to record from single cells and you had to Make this very specific solution.

[00:13:48] Nick: it's called reamer solution. a lot of people in biology know about reamer solution. and we could keep the brain slices alive long as we oxygenated them. And I had the specific solution and we could keep them alive for 10 hours and record from these cells. I had to do these experiments in another lab that was maybe a quarter mile away.

[00:14:04] Nick: so I had to walk over there I remember one day I was doing it in the other lab and they didn't have all the same reagents and it was inconvenient because I wasn't used to the instrumentation so it's like, okay, well, can I just do this our lab and then transferred across campus?

[00:14:17] Nick: And I was like, okay, well, how would I do that? because I have to oxygenate these things. And so I use these lure locks Jerry rig this thing, right? Connected it to a, balloon that had a stop valve and it had CO2 and oxygen. And I would bubble the solution while I was walking across the campus.

[00:14:32] Nick: and I've always liked to build things whether it was like that or optical fibers. And so I got interested in entrepreneurship, in my postdoc. Cause I was continuing to build things like microscopes but I started to realize, During that time that there were different ways to contribute to science, It doesn't have to be through the discoveries that you make at the bench. started to have this realization that maybe that could happen through the business side of science. I worked really hard to got into BCG and, it was really interesting because I basically got a free MBA.

[00:15:01] Nick: actually I got paid to do an MBA,

[00:15:03] Erum: that's

[00:15:03] Karl: Yeah. Yeah,

[00:15:04] Nick: right. And, I focused mostly on the tech industry when I was there, I did some AI strategy and, I thought that was really interesting, but I, realized pretty quickly, I really liked the science and I just wanted to be closer to the science and building things.

[00:15:15] Nick: so I spent some time building software products, at Illumina for their internal sales teams. and Some startups within the last few years. I don't know. I just always taken this hypothesis testing approach to everything I do.

[00:15:26] Nick: it's what you learn in graduate school. you learn to iterate really quickly. You learn to be observant of the little details and the things that you might be able to capitalize on that's exactly the types of skills that you use as an entrepreneur as well.

[00:15:40] Nick: So I think scientists from my experience, make pretty good entrepreneurs because you learn to go through really hard things. You learn to iterate quickly, you learn to test hypotheses and that's what you're doing in business as well.

[00:15:50] Karl: totally. Yeah. Yeah. So, when we talk about growing everything, of the things we talk about a lot is how complicated biology is. It is like, This mysterious black box that we keep unraveling and getting to know better all the time. So it's, full of potential for innovation.

[00:16:07] Karl: how do you think, AI is going to unlock the magical side of biology for researchers and entrepreneurs? Do you think it, will do that? how long until we have that digital model of the cell?

[00:16:17] Nick: Yeah. Interesting. Yeah. that idea is so cool. there's a lot of people working on it. it's a very, very challenging problem, of course. and a lot of it is a data challenge. part of the way that we think about, This is pull out the monotony the mundane, the tedious things that, we have to do, because he eyes can help with those types of things.

[00:16:36] Nick: So, this massive volume of literature, I have to go through and read a single paper for, I don't know how many hours, and try and extract, like, what did they do? What exactly did they do step by step? and then I have to compare that across multiple papers. And, That's hard work, and it's time consuming, , that's low hanging fruit. Also, the language models are good at being able to bring together those types of, things that a mundane aspect of that. And so I think that it really allows for, people to be more Creative and the idea is to really augment scientists. It's so that you take over some of those things. that's how I think about it. I think that, it certainly doesn't take away the important aspects of coming up with hypotheses and, really figuring out what the critical controls are for a given experiment, as it gets better, it's going to help more with that.

[00:17:23] Nick: But there's going to be this massive explosion of human AI collaboration that I think is honestly just going to propel science. It's going to be rocket fuel to scientific discovery.

[00:17:32] Erum: Yeah,

[00:17:33] Karl: I agree with that.

[00:17:34] Erum: yeah, and it's really exciting. I mean, you're, you're talking about science writ large, but of course there's different flavors. And here at Messaging Lab, we work with Biopharma, which I know you work with them as well, And then the other sciences or, what is it kind of a stepchild is the bioindustrials world?

[00:17:49] Erum: No one really we need more people to talk about we think it's everywhere because this is the world we live in But you know, this is why we have this podcast bioingredients climate biotech Is here and it has been here. It's been silent actually for a really long time But it's been cleaning our clothing.

[00:18:05] Erum: It's been, you giving us cheese and beer and those wonderful things. but let's use it for other things, it'd be interesting to discover how much potato plays in the other sciences too.

[00:18:16] Nick: Yeah. that's a great question. It's an area that I'm trying to explore and understand industrial biotech, specifically we're having a bunch of conversations with industrial biotechs right now, because I think that there is massive potential from, applications like use cases, we're starting to see some real interest from companies like that, whether it's from automation perspective or, the things that we've already, built are, Equally applicable.

[00:18:40] Nick: It's just an area that I think is super exciting and interesting. I'm a neuroscientist. So I think about circuitry and emergent properties of the brain and and that type of stuff, so I'm going to go off in a bit of a tangent here.

[00:18:51] Karl: That's

[00:18:52] Erum: a podcast. We are built for tangents.

[00:18:57] Nick: Yeah. so like, if you think about like neuroscience is an interesting field, because there's all these different layers and levels of discovery, , looking at individual, Receptors and, ion channels and recording from them, then you can look at individual cells and action, potential propagation across dendrites and across, axons, then you can look at synapses, connections between cells.

[00:19:17] Nick: and you can look at, you potentiation. Then you can take a level up and you can start to look at circuitry and these large scale oscillations that are happening in synchrony. that's part of the reason that complexity and that layered aspect of neuroscience is just intellectual, catalyst like thinking about it. started to realize, synthetic biology Has industrial biotech has some of those aspects of complex. I mean, you're thinking more about, organismal and being able to look at It's circuitry there's different levels of analysis.

[00:19:46] Nick: I don't know. So I've just Gotten more curious about it. I am definitely not an expert. it's something that I would like to learn about. So I'm curious, what are the areas that you guys think are most interesting right now from an industrial biotech perspective?

[00:19:59] Karl: well, before I answer that, I just want to make a comment and get your reaction to, this Drosophila connectome, and, really the more recent, news about, E11 bio, showing, a brain map, and being able to do it at a lower cost.

[00:20:13] Karl: I mean, I think AI tools fit right into that, but before I answer where I think AI should be applied to industrial biotech, just curious about your thoughts on those.

[00:20:21] Nick: Oh, that's so cool. Yeah. it's so cool. I talked to, June Axip, Eddie 11 on my podcast, a little while back and, Their vision, they're doing in terms of barcoding is, very exciting. bring down the cost of, looking at connectivity or at least, large scale connectivity.

[00:20:37] Nick: that stuff's cool because then, you can start to figure out, what are some new, architectures it's understanding the human brain and it's understanding the model organisms, but they move up to human brain.

[00:20:45] Nick: but, the applications for that are crazy exciting for AI so that we can start to think of new architectures that can make, these tools more efficient. So I think that's super exciting.

[00:20:54] Erum: Yeah. Oh, and then just because we're on the topic and I had this thought my waking state was about how we think and, our brains are in our heads, but Non model organisms like octopi have decentralized brains,

[00:21:06] Erum: And I was thinking, what would it be like to think from my hands?

[00:21:10] Nick: I know. Right. it's unfathomable. when I was in grad school, went into lab at, Woods Hole, friend that just working with cephalopods and it was cuttlefish. And, and we were like, Into the lab at one point. And you could see they follow your movement and they're tracking you with their eyes.

[00:21:25] Nick: And they, recognize certain people. And so if it's somebody that they recognize, they'll come out and they're friendly and they'll interact with them. But like they're hiding behind the rock and looking for you, looking around rocks and, I don't know, fascinating.

[00:21:36] Nick: but one of the things I was going to say, I should mention is that, Thinking about intersection with industrial biotech. I want to get your opinion on where the interesting areas are. Um, but

[00:21:46] Karl: it. I'm holding it

[00:21:46] Erum: we'll get back to hand brains later.

[00:21:48] Nick: Yeah,

[00:21:48] Karl: I want to talk about the Drosophila connectome.

[00:21:51] Nick: Yeah, yeah, yeah, the exciting part about what we're doing with potato is that we're talking with all these companies around structuring their unstructured data and making it more valuable. So we don't even know all the use cases that. There are for this, but we're seeing tons of excitement from large organizations.

[00:22:07] Nick: And one of the interesting things about me for coming on this podcast is if we can start some conversations and, if anybody that's listening is interested in, ways that we can structure some of your, internal data help with that, feel free to reach out to me.

[00:22:19] Nick: It's Nick at ready, set potato. com or you can reach out on LinkedIn, whatever, but, what are the areas you think are exciting?

[00:22:26] Karl: Yeah. I mean, you mentioned one that we've, done some work in when it comes to like structuring old data and not us personally, but companies we worked with, because there is all this old data that, um, you know, that the, the feeling or the, you know, the approach has been new data is better and it.

[00:22:42] Karl: There's all this old data that can be interrogated. Uh, it just needs to be put into a format that, you know, uh, AI or in the machine can, can read from. So I think that that's one area that is kind of more on the macro science level, but I think anything that has to do with process engineering on the industrial side, and, and you mentioned, you know, um, You know, being able to download protocols like, uh, you know, we're in Brooklyn.

[00:23:10] Karl: One of the success stories of Brooklyn biotech is open trons. Open trons has like a open source liquid handler. They share protocols. And, um, you know, that was just mind blowing to me. This ability to share protocols that you plug directly into a liquid handler. Um, and being able to share that with other people, not just for, you know, time saving, but also for reproducibility.

[00:23:33] Karl: So I think anything that has to do with process engineering, um, is going to be a huge area, uh, you know, for AI improvement. And then, you know, going, you know, thinking biopharma, there's like, just like everything that needs to, you know, be optimized on the, um, clinical trial side of the world. There's just so much there.

[00:23:53] Karl: Um, I know there's a couple of companies that are tackling it. There's probably dozens of companies that should be tackling it because, um, you know, you don't get enough people, uh, participating in clinical trials are often the wrong people. The number of people that drop out is like astounding and the whole drug development process.

[00:24:10] Karl: I mean, we could say on one level is broken. It just takes too long, costs too much money to get a drug to the market. So anything that. We can do with AI to optimize that process to identify patients, I think is going to be huge. And not to mention that the front end of that on the drug discovery side, you know, there's, you know, 60 billion, I think is a number of, of, um, investment that's gone into, um, AI drug discovery, but that's just like one.

[00:24:37] Karl: part of this whole thing. You've got to optimize the molecules. You have to be able to synthesize them. You've got to do digital testing on them. There's so much that can be done before you actually hit, um, you know, animal models and then the clinic. So there's a

[00:24:49] Erum: But Carla, what about industrial biotech? He knows all about drug discovery. He's in the work digital sciences program.

[00:24:56] Nick: Process.

[00:24:57] Karl: I did say bioprocessing.

[00:25:00] Nick: What makes, yeah, that's, that's, that's awesome. Uh, it's, it's, I, I'm excited to explore it. And, and, and yeah, I mean, that's, that's the exciting thing about like, uh, uh, broad kind of scientific platform technologies that we can reach across different areas. And, and so, you know, we, we are definitely biopharma, um, uh, and, uh, as a room.

[00:25:24] Nick: I'm sorry, I should probably, I should probably, uh, uh, erom.

[00:25:28] Nick: Okay. All right. Yeah,

[00:25:30] Erum: Oh,

[00:25:33] Nick: Uh, yeah, is, is, uh, erom knows, uh, as a, you know, an advisor to the Merck digital sciences, uh, studio program, by the way, uh, appreciate all your help with that. You're doing a great job on it.

[00:25:47] Erum: thanks.

[00:25:48] Nick: Uh, you know, but I think that these, many of these areas that, that, uh, um, I just don't know about are super exciting.

[00:25:57] Nick: So industrial processing, processing. Okay.

[00:26:00] Erum: bioprocessing is a big thing. Um, I would say like, you know, what we've been talking about a lot is. Like microbiome dynamics, because on the one hand, yes, you do have the gut microbiome, which is being studied by the farmer world and the nutrition world, but then you have the environmental microbiome side.

[00:26:19] Erum: So the soil microbiomes and what's going on in the oceans and the trees and, um, and how they relate to, um, You know, the health of the planet, right? So, and how they can influence, um, different solutions to help, like, for example, clean up, you know, PFAS or forever chemicals that are toxic chemicals that are, you know, kind of pervasive at this point, um, in soil and in water.

[00:26:42] Erum: So. But then, like, I mean, I think one of the fascinating things, um, you know, that, you know, Carl and I being at this intersection of between biopharma and bioindustrials is to see what's, what's the crossover? What's the overlap? Because biology is biology is very complex. But what can we learn? First, say, like, say, for example, from the variety of different immune cells, And then the variety of different, you know, microbiome cells that exist, and then they all have different types of interaction.

[00:27:11] Erum: So you're talking about connectome. What is that? What are those microbiome dynamics that that cause a process to then create a different function or product, whether it's a drug or some type of molecule that your microbiome can create versus you taking a pill and like it's made in a A process outside of your body, but how do you, um, you know, trigger or influence or reorganize different cells or influence them to produce different molecules for your health or for other things like producing more collagen to look younger.

[00:27:44] Erum: I, I, just before this call, I went down to Brian Johnson rabbit hole. He, he released a new video about making his face look younger and it's better. Like before it was kind of like, well, I don't look like a skeleton, but now he, it's like, Okay. Okay. You're doing it. Good job. And so, but he's using different techniques.

[00:28:03] Erum: He's using that and he shared what those are. And he's like, no, you don't just, you know, use fillers because fillers can end up in different parts of your body. It's not that great, but there's another methodology to like, um, You know, promote, you know, boost your own college of production and, you know, and like he was doing clerk restriction, but then he added a little bit more calories, which maintain his biomarkers, but then helps his face get a little fuller naturally.

[00:28:27] Erum: So he is looking very young. If you guys look at him, you know, recently, but anyway, this is to be said, the, the intersection. So, you know, potato is, um, like science or biology. Biology agnostic. It'd be interesting to see how, you know, potato can influence both sides of the biology spectrum.

[00:28:46] Nick: For sure. For sure. I wonder if he was like crushing up, um, the Doritos, you know, there's like, uh, the, uh, tissue clearing, um, uh, it was a little esoteric, but

[00:28:56] Erum: yeah. But just so our audience. Yeah, there was the Doritos. We talked about this before, but the Doritos. Um, yeah, there's the, what is the molecule that's

[00:29:05] Nick: tart, tartarine or

[00:29:06] Erum: The tartrazine.

[00:29:07] Nick: Torture zine.

[00:29:08] Erum: Yeah.

[00:29:09] Nick: Yeah, it can be used for tissue clearing.

[00:29:11] Erum: Yeah. So you can see through your skin. Yeah, that, oh my gosh, I don't think he was doing that, but that, that's a thought.

[00:29:18] Erum: And you can't unthink seeing through skin.

[00:29:21] Nick: exactly. but yeah, for sure. I think that there's going to be some really interesting applications some of these computational models, like, cell modeling and, looking at interaction networks. I'm really excited for those types of applications.

[00:29:31] Nick: I think that there's a lot of power in, new computational tools as well. we've gone down that path a bit. And, there's David Baker and, Hassabis and, John Jumper just won the Nobel prize for some of these types of tools, we think that there's a lot of. promise they're hard to get started with. It's hard to build these models. And so that's really why we're focused on this, unstructured, data to, help augment and fill in some gaps for some of these types of things.

[00:29:56] Erum: Yeah. Yeah. so you talk about data, which is really important there's this whole, struggle or, there's this need for, validated accurate data. how does potato play into that? you said earlier at the top of this episode that, 70 percent of studies are not reproducible.

[00:30:12] Erum: Some scientists can't reproduce their own studies. What does that mean for the data that has come out? Come from those studies and how do you balance the fact that, maybe some people, there's unstructured hypotheses that aren't very, good and again, like the data not being very good.

[00:30:26] Erum: But how do you balance that? How how do you think about that?

[00:30:28] Nick: yeah, it's a challenging problem is, how do you know what to trust? so one of the ways that we have really, focused on that early on is indexing, mostly just protocols, which are the step by step boring instructions of how to do an experiment, add this, reagent, add that reagent, mix it, centrifuge it.

[00:30:45] Nick: and. Those are the types of things that people publish because they want people to reproduce them. that's one of the ways in which we're really focusing from a data perspective is, focused on those lower level instructions. this is also where you have to have, this, really strong human AI collaboration and human oversight, to, have the expert come in and review it, know what are the things that, I can change, what are the things that, I might not be able to.

[00:31:10] Nick: And so that's part of the way that we're thinking about this with, comparing and doing this consensus across multiple papers. So if you can pull out, All the insights from an individual one that you can do it across them, and you can say, okay, what's the consensus here?

[00:31:21] Nick: what is this paper doing? That might be a little bit different or, that's unique. so you can start to pull those things out and make them visible to people. that's really where we're going next with some of this extraction technology that we've built.

[00:31:32] Karl: Yeah, that's pretty cool. I'm just curious, thinking about your career, podcaster, academic, now entrepreneur, with a stint at BCGs, but what are some of the unique challenges or, even insights that you've been offered to the entrepreneurs that are listening to us, what things do you take from your academic time or your BCG time that are helping you out now as the CEO of, potato?

[00:31:55] Nick: Yeah, say it's a couple things. one is the ability to wander into the unknown and, push forward on something despite a lot of ambiguity. Because in science, that's what it is. you're on the frontier of knowledge. You're grasping around the dark and trying to, figure out which way we're going, and testing things as you go.

[00:32:12] Nick: And, you learn a lot through that process that teaches you to be resilient and, to, have a certain level of grit that I think is really important in every aspect of a career, but particularly as a founder of a company. I think that's something that has.

[00:32:27] Nick: really left an impression on me, and shaped my approach to career. there's that, I think there's, again, that curiosity that, I have some ideas and I'm just going to test them. I'm going to do fast iterations. I think the third thing is, the communication, one of the things that I noticed as a scientist was that many of the labs that had the most funding that all the P. I. S. were, speaking at all the conferences and they had, the, high profile papers, They were all good communicators for the most part, and it's not something that just Comes naturally, you have to put time into it and you have to figure out there's a reason why it takes time to write a really good paper, for example, because it's challenging.

[00:33:04] Nick: So you have to put in the work. that academic training is a really good proving ground, for any type of career, that you go into. But I think particularly for, founding a company, working at a startup, The more I've gotten into the business side of things, the more the communication has been even more and more important.

[00:33:20] Nick: that's been distilled into me at BCG, you had to figure out how to address things at a very high level. So you could speak to an executive and say like, here's your answer. And here's the three supporting bullet points, underneath that.

[00:33:32] Nick: and then go into details as necessary. It's like very different from the bottom up style of communication that we typically have, within science. that was a big learning for me and, it's something that I still have to focus on and, figure out ways to address things to different types of audiences.

[00:33:46] Nick: Because, When you're out in the world talking to customers and, our customers are scientists in some cases, but some of them are not or talking to investors, you have to zoom out, think a little bit meta. Like, how do I position this?

[00:33:56] Nick: And I think that that's really important. the things I learned.

[00:34:00] Erum: yeah, no, that's great. I do have a couple of questions you mentioned your users, so 700 users, there's Harvard, the biggest schools, but you said it's not just for scientists. I mean, Other citizen scientists using it.

[00:34:11] Erum: What are your thoughts about making potato accessible to people that don't have this, very expensive, scientific degree, but want to be a scientist.

[00:34:21] Nick: Yeah, that's an interesting question. I mean, we do have people come on and use it for, different types of use cases. anyone that is interested in curious, you can go to ready set potato. com and sign up and use at least certain aspects of it for free.

[00:34:34] Nick: I think word and interesting. Point in the, history of science, when it's going to be more and more accessible and people are going to be able to, pick up some of the background knowledge using computers, that will

[00:34:45] Nick: Open the door. I'm super excited about those types of things. some of the more advanced tools that we've got for data extraction or more for enterprise types of organizations, because they're the ones that are dealing with large, massive amounts of, scientific information.

[00:34:57] Nick: the free tools we ability to start the conversation. but we do see people using them for growing, fungi, or, different types of citizen science applications. So I'm curious to see, how that evolves.

[00:35:09] Erum: Yeah. That's really cool. and then I'm really curious. So another question, because this is another world that we live in, is the world of Deci, which has been really interesting to us. So, you mentioned, people, adding protocols or using protocols. attribution? To people who upload certain protocols, if a big organization uploads a protocol, can a citizen scientist use it?

[00:35:30] Erum: Will they see the logo of Wiley on there? what does that

[00:35:33] Nick: see. Yeah. That's a great question. so there's a couple of things in there. One is that, All the information that people upload is private to their own individual instance, we don't train on it. And so whether that's an organization that's using it, and they're uploading some protocols or some papers, it can't be viewed or accessed by others.

[00:35:49] Nick: So I think that's a really important component because privacy has to be a critical piece of this. I think the second thing is, a question of attribution. right now With the retrieval augmented generation, rag, it cites the individual papers and where information is coming from within a protocol.

[00:36:05] Nick: the question of how we manage, attribution from publishing perspective, whether it's putting it on some sort of DCI platform or, publishing it in a journal, those are things that, we have not come across yet. And, have to explore and figure that out.

[00:36:19] Erum: Yeah. Yeah. We'll introduce you to our friend Michael Fisher. This is his world decentralized science. So

[00:36:24] Nick: super excited to see where it goes.

[00:36:25] Erum: Yeah, Yeah,

[00:36:27] Erum: he has a platform to, but, for the science it is about, creating the ledger, putting everything on the blockchain, being able to identify core sources and then track the sources across usage.

[00:36:38] Erum: it's interesting. I mean, it's also about challenging. The current scientific establishment, the way that it's being done in academia, the way that the, publishers have stranglehold on, scientists and scientists pay to be published.

[00:36:51] Erum: It's crazy. I know you interviewed see me. she talks a lot about that as well.

[00:36:55] Nick: it's interesting. It's going to be exciting. There's all these new organizations that are popping up different ways to structure science. And I think that's just going to continue to happen. And, D size is exciting because you have to have some sort of version control for science.

[00:37:07] Nick: um, you they're looking at the, the ways institutions are about it. Like there's new types of nonprofit just different ways that people are going about things. And, I think we're going to see massive, shift in how science is done in general writ large.

[00:37:20] Nick: and I personally, feel like we're at the beginning of the next scientific revolution. I don't know exactly how it's going to play out. think this, is a turning point for science.

[00:37:28] Erum: yeah, a lot of science is about observation, and you talked about working on microscopes. I had worked at a company that used, machine vision to be able to look through LIDAR to help people with our fall prevention. So was a very advanced, maybe over an engineered solution to prevent people from falling out of their beds the hospital.

[00:37:46] Erum: But it worked really well. but. Anyway, always think about like, can we just see what happens? there's microscopy, you can see things happening, but even on a, more, nano angstrom level, that's crazy. Okay. Could you be able to see that?

[00:38:00] Erum: just have the machine vision, being able to see real time and, have real time representation of what's going on, which it would happen instantaneously because things happening in a very small scale happen. Like quantum seconds. I don't even know if that's the term, the Pico seconds.

[00:38:14] Nick: multi modality is going to be very interesting. when we start to combine computer vision applications with, language applications and lots of other, modalities as well.

[00:38:22] Erum: Audio, that's the modality. Everyone's getting to enjoy at this current moment, but, that was my world too, could, we can detect, different biomarkers through your voice, you can detect your heartbeat through your voice because your voice and audio is a, waveform.

[00:38:38] Erum: so it was your heartbeat. So you're able to learn a lot just. Through voice, but, yeah, the multimodality, then you got your AI scientist right there. If they can see, listen, taste, smell, digital nose, digital tongue. Those are things

[00:38:52] Nick: Yeah. I mean, I don't want to get too far out there, but

[00:38:54] Erum: that's me.

[00:38:56] Erum: Sorry. Welcome to

[00:38:57] Erum: my universe.

[00:38:58] Nick: no, no, think that, it is going to get really interesting, potentially we have humanoid robots, they can, be doing experiments in the lab. you need to be able to figure out what's being done in the lab right now.

[00:39:08] Nick: so I don't know what that future is going to look like, but, I think it's going to be exciting.

[00:39:13] Erum: Yeah, but it's going to be touching the potato, which is what the humanoid hand is doing behind in your back. Oh my God, yeah, guys, I have to see this potato picture. It's like, what is that piece of art called? that's this teen chapel.

[00:39:29] Karl: Yeah. It's yeah,

[00:39:30] Karl: It's Michelangelo.

[00:39:31] Karl: Yeah,

[00:39:31] Erum: it's hilarious.

[00:39:32] Erum: so instead of a lightning bolt, it's a potato. That's funny. Okay. All right. We're gonna, before the potato puns start, we have a fully baked episode right here. We'll slice it up, dice it, dip it in ketchup. I mean, okay, I lost it,

[00:39:49] Karl: yeah, that is the creation of Adam, by the way.

[00:39:52] Karl: A D A M. Yeah.

[00:39:53] Erum: Yeah, that's my son. Yeah,

[00:39:55] Erum: my son's name's

[00:39:55] Nick: Yeah, that's right. That's right. Uh,

[00:40:00] Erum: But this has been awesome.

[00:40:01] Erum: I mean, it's so incredible to just, Speak to you about, how to do science in new ways, leveraging AI. Everyone's talking about different ways of leveraging it, but to do reproducible science is so important. So we're very excited for you. and we're like minded podcast friends. So, that's incredible.

[00:40:16] Nick: I'm gonna have to have you on

[00:40:17] Nick: my podcast at some point

[00:40:18] Nick: as

[00:40:18] Karl: Oh, that's super kind of

[00:40:19] Erum: we were once a scientist. Yeah, absolutely. Yeah. but yeah, we'll have, everything in the show notes, ready set potato. com.

[00:40:27] Nick: Ready set potato. com. That's right. That's right.

[00:40:29] Erum: Cool.

[00:40:30] Erum: Well, thanks so much for joining us next. This was so much fun. We'll do it again soon.

[00:40:33] Nick: Carl, thanks so much. This is lot of fun, so appreciate you taking the time.

[00:40:38] Erum: Yay. See you later.

[00:40:39] Nick: