How AI Will Accelerate Breakthroughs in Biotechnology with Benchling CEO Sajith Wickramasekara

Bringing new drugs to market is a costly, time-consuming endeavor. On top of that, most medicines fail at some point in the research and development phase. Sarah Guo is joined by Sajith Wickramasekara, co-founder and CEO of Benchling, a company that has not only become the central system of record for biotech R&D, but uses AI agents to assist scientists to help fix this broken system. Sajith details the roadblocks that impede drug development and approval, the “dot com” bust occurring in biotech, and how AI agents and simulation can help scientists experiment faster. Plus, they talk about China’s competitive rise in the pharma space, and the unique challenges of building an interdisciplinary culture that merges the worlds of science and software. Rebuild biotech for the AI era - Sajith Wickramasekara Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @sajithw | @benchling Chapters: 00:00 – Sajith Wickramasekara Introduction 00:38 – Origin and Mission of Benchling 02:08 – The Drug Development Process 03:49 – Current State of the Biotech industry 08:46 – AI’s Role in Biotech 16:14 – Benchling AI and Its Impact 18:36 – The Future of AI in Biotech 26:28 – Debunking AI Drug Discovery Myths 28:50 – Data’s Role in Biotech 29:35 – The Importance of Tools in Pharma 31:28 – AI’s Impact on Scientific Research 34:55 – Building a Biotech Company 40:18 – Interdisciplinary Collaboration in Biotech 43:06 – Tech and Biotech: Learning from Each Other 48:16 – Conclusion

• 0:05Speaker 00

  Hi listeners, welcome back to No Priors. Today, I'm here with Saji, the co-founder and CEO of Benchling, the system of record for biotech R&D. Today, we talk about the state of AI in bio, Benchling's bet on AI agents to help scientists make better decisions, experiment faster, and deliver drugs more effectively. Why drug programs are so expensive and fail so often? And how to build a culture of science and software together. Saji, thanks so much for being here.

• 0:36Speaker 01

  Thanks for having me, Sarah. Excited to be here.

• 0:37Speaker 00

  Okay, so for our general listener base, can you just give us an overview of what Benchling is and sort of the scale of the business today?

• 0:44Speaker 01

  Sure. I'm one of the co-founders of Benchling. We make modern software for scientific progress. So I started the company about 13 years ago. It's been a long time.

• 0:54Speaker 00

  Oh, my God.

• 0:54Speaker 01

  I know. So I'm a software engineer by background, but I worked in a biology lab. I was really interested in medicine and coming from the world of software. And software developers have amazing tools for working on code and for collaborating. And when I got to the biology lab, I found that scientists had paper notebooks and spreadsheets that would sit on their desktops. And like, it was terrible. And so it was really hard to work together. And I think that was really frustrating for me personally. And I thought, a little bit naive at the time, I thought, how hard would it be to build good tools for scientists? And so I started working on Benchling, which helps scientists design molecules, plan their experiments out, run those experiments in the lab, get the data, organize it, analyze it, and then share it with their colleagues.

• 1:36Speaker 01

  Today, we work with about 1,300 biotech and pharma companies, scientists at over 7,000 academic institutions, universities all around the world. And our software powers, you know, household names like Moderna and Sanofi and Eli Lilly and Regeneron, but also like cutting-edge biotech startups, you know, the future AI biotechs like Isomorphic Labs and Zara and companies like that. So we get to see the innovation happening across the entire biotech sector and then build software that helps power it.

• 2:08Speaker 00

  I'm super excited to actually use that vantage point, ask you a bunch of questions about bio in the macro. But just so people who don't come from the domain can picture it a little bit better, I think like, you know, I can picture like gene sequences.

• 2:21Speaker 01

  Sure.

• 2:23Speaker 00

  The assay like said yes or no, like what other types, what is the data that's actually in benchling?

• 2:27Speaker 01

  I think what's really interesting for everyone to understand is like making a drug, there's like 9,999 steps in making a drug after you come up with a molecule. So you have to, to make a medicine, you have to find a biologically meaningful target in the body, something you want to drug. Design a molecule, to optimize that molecule, you have to test that molecule in petri dishes and cell lines and animals, various kinds of animals. Then eventually you get to the point where you can take it to a clinical trial and you're testing it in subsequently larger groups of humans. All the while you're figuring out, how do I manufacture this thing and develop a process to make it scale economically, safety with the high, with quality, all while navigating regulatory bodies.

• 3:10Speaker 01

  So that eventually in seven to 10 years, you can have a drug that you give to people commercially. And even then, there's still, still more work there. So it's just incredibly long and complex process. And where benchling focuses is all of the scientific data that comes out of the lab. So everything from all the different types of molecules that are being created, to how they're related, to the work that went into creating them, to the different types of tests that you're running on them, to the data coming back from the animals, to the, you know, scale up data coming out of the fermenters when you're figuring out the process to manufacture it. All of that incredibly rich and heterogeneous scientific data has to be brought together in one place, organized, made searchable so that scientists can make decisions based on it.

• 3:49Speaker 00

  If we go zoom out for people just like looking at biotech from the outside, it seems a very macro sensitive industry, right? And we are perhaps coming out of like kind of an ugly period. Can you just characterize like where we are in the biomacro cycle?

• 4:04Speaker 01

  Yeah, yeah. And I'm definitely not a sort of macro specialist, otherwise I'd probably be, you know, investor or something like that.

• 4:10Speaker 00

  But it's all your customers.

• 4:11Speaker 01

  Yeah, it is. I would say like biotech has, it is definitely an industry that has gone through cycles. We're probably like the last couple of years are probably like the equivalent of like the dot com bust happening for

• 4:23Speaker 00

  Okay, that's pretty bad.

• 4:24Speaker 01

  For biotech. Yeah, it's been, it's been a tough time. Covid was sort of the peak when mRNA was this thing that kind of like reopened the world and you know, there's a lot of Generalist money that came in and a lot of exuberance and excitement. And it's not, you know, the sort of dot com bust equivalent wasn't just because of that. There was, you know, changes in interest rates, tariffs, regulatory uncertainty, China, a bunch of different factors and some including like scientific technologies that we got really, really excited about that are still very important and promising, but maybe haven't become commercially successful as fast as people wanted. So a whole confluence of factors there, but a bubble.

• 4:58Speaker 00

  What are you referring to in terms of scientific technologies that got people hyped?

• 5:02Speaker 01

  Yeah, like I would say there's a lot of generalist excitement for gene editing, cell and gene therapies, RNA, and all of these.

• 5:09Speaker 00

  So new delivery methods.

• 5:10Speaker 01

  Yeah. New, like, I would call them kind of categories or form factors of medicines, modalities is the word, but like, you know, the last, the last decade, actually, maybe even longer of biotech has really been this story of new categories of medicines being sort of invented and taken to patients. And some of these, like, there are approved gene editing medicines, there are approved cell therapies where you're reprogramming the patient's immune system, there are approved gene therapies, there's approved mRNA medicines. So these are real categories, but I think investors and companies got very excited and put a lot of money into these categories and we're kind of in the trough of disillusionment for some of them now.

• 5:49Speaker 00

  Uh huh. And my understanding is that they have taken longer and been more expensive.

• 5:53Speaker 01

  Yes.

• 5:54Speaker 00

  Than people expected.

• 5:55Speaker 01

  Absolutely.

• 5:55Speaker 00

  Than investors expected.

• 5:56Speaker 01

  Absolutely.

• 5:57Speaker 00

  Maybe the scientists.

• 5:58Speaker 01

  In 2021, every biotech was getting told by investors, like, you need to build a platform company that's going to cure a bunch of different diseases and here's hundreds of millions of dollars and capital is free. And investors can change their strategies a lot faster than companies can. And, you know, a lot of those companies, because they were taking such a big risk on like a new form of technology, we're going to be the next RNA company, next cell therapy company, they picked diseases that might've been like simpler problems to solve with smaller patient populations. And then all of a sudden, you know, investors changed their minds, platforms are no longer valuable, you're working on a thing that like is just supposed to be a proof of concept and all of a sudden it's like defining you. And so it was a really tough spot for those companies to be in.

• 6:37Speaker 00

  What's the relevance of China in all of this?

• 6:41Speaker 01

  I think so if the last decade was about sort of biologics and these new modalities, I think the next is going to be about speeding costs. Like, people want more drugs and they want them cheaper. And China is very good at things related to speeding cost. And so, all of a sudden, in the last couple of years, you've seen this rise of Chinese biotech companies that are able to create molecules and bring them to patients in clinical trials in China, just early phases of clinical development, really fast and really cheap, even in some of these new modalities. And so, you've seen this huge uptick in pharma going to China and buying molecules that they typically would have bought from American biotechs.

• 7:16Speaker 00

  And this is like, top 30 pharma?

• 7:18Speaker 01

  Yeah, yeah, yeah. These are the biggest companies in the world, the Merck's and Pfizer's and Lilly's and so forth. Many of them have like, gone to China and bought molecules that historically they would have bought from American biotechs.

• 7:29Speaker 00

  Are there medicines that people would recognize? Like, in market today?

• 7:35Speaker 01

  One of the most notable medicines that people might recognize is called Carvicti, and it's a Johnson & Johnson medicine. So, Johnson & Johnson partnered with a Chinese biotech called Legend Biotech. They saw the data that Legend presented, and you know, there was a time when people were pretty suspicious, and so they were like, ah, it's like, the data's probably not going to replicate, it might not be real, but like, J&J, I think, saw it and realized how promising it was, and they've taken it, and it's actually a cancer immunotherapy. So, it kind of reprograms the human immune system. I think it's for multiple myeloma, and like, that medicine is very commercially successful and widely distributed in the US to those cancer patients.

• 8:13Speaker 00

  What's been the reaction of, like, Western biotechs to this?

• 8:17Speaker 01

  Um, it's a mixed bag. I think there's some folks who are like, it's, you know, kind of inspired that American biotech needs to be faster, cheaper, more competitive. There's some more nationalistic reactions, I think, of like, hey, like, well, there's different regulatory or ethical standards over there, are the data, are they all going to replicate? So, some skepticism as well, but by and large, I think it's like, very much here to stay that China's going to be like, a major biotech player.

• 8:42Speaker 00

  I do want to get to

• 8:44Speaker 01

  Yeah, we can spend this whole time talking about macro.

• 8:46Speaker 00

  Yeah, I want to get to the meat of our discussion, which I also think is, you know, there's some premise that like the answer to faster, cheaper, better, might in part be AI in biotech.

• 8:58Speaker 01

  Yeah, I think I believe that now and it's really interesting to see like the general public, you know, big tech startups, the model labs, everyone is saying like AI is going to cure a disease. So it's very good that everyone's excited by that.

• 9:11Speaker 00

  You, I don't think of you, I think you're an amazing CEO, but not really a content marketing guy today. And you wrote an essay very recently that I thought was amazing about how we can possibly change like the scientific field in biotech with AI. Can you give us the Cliff Notes on it and then we'll link it in the show?

• 9:29Speaker 01

  Absolutely. Yeah, I, I think like maybe to step back, like one thing I just like wish people would appreciate more is like medicines are, medicines are magic. I think, I think like we take for granted how awesome medicines are. I think nine percent of healthcare spend, prescription drug sales are nine percent of healthcare spending in the US. Like we have obviously this healthcare cost problem, but drugs are this amazing ROI. And the best part about drugs is they go generic. So a drug today is only going to get cheaper over time and it works just as effectively. I take a statin today that probably costs like nothing. And like 20 years ago, it was some expensive medicine.

• 10:09Speaker 00

  It's not obvious any other part of the healthcare system gets cheaper over time.

• 10:13Speaker 01

  It's not, yeah. The rest of healthcare is very labor dependent and labor generally gets more expensive over time. I am, I'm very optimistic for AI to help out there too, but you know, drugs are this amazing thing. We should want more of them and then we get to like stockpile more and more of these amazing medicines. But it takes over $2 billion, generally about 10 years to bring a medicine to market. And most of those medicines will fail very late in this process. You get seven to 10 years in, you've spent hundreds of millions of dollars and clinical trial fails. Medicine is not safe or not effective. And so it's unbelievably like difficult pursuit. It is probably easier at this point to send things to space or to put people on the moon than it is to get a new medicine approved. And I know $2 billion probably isn't that, you know, I feel like AI has desensitized us

• 10:57Speaker 01

  all, you know, everything is like $100 billion data centers and whatever, like $2 billion, like what's that? But when there's that high of a failure rate, it's very difficult for investors to to underwrite that. And that was, you know, while we had all these new categories of medicines being kind of invented over the last decade, I think that's like, that's important and it's here to stay. But like, the industry has to change, like, the pressure on biotech to be faster and cheaper is just higher than it's ever been before. I think a lot of that cost comes from how artisanal the industry is. Like, biotech is this place where, if you look, I'll sort of take the digital and physical realms for a second, they've actually done a good job of systematizing the physical realm. You brought up sequencing earlier, like Illumina has put sequencers on every

• 11:41Speaker 01

  single bench and every single lab, and now sequencing is this accessible tool to all of science. You could say the same thing has happened with different, like, reagents and lab consumables and things like that. But if you look at, like, the digital realm, where it's like, how people collaborate, how data is structured and shared, the workflows that are used in science, which is all about collecting data, all of that's basically bespoke and invented one-off by every, every company. It's because those companies are playing kind of a, sort of a one-time game, because the process is so long, that you're sort of just trying to survive until you get six, seven years in, you show some clinical success, and a pharma company comes and buys you. So you're not really, like, building for scale and building for durability.

• 12:25Speaker 00

  That seems like it also comes from some of the structure of like where the innovation happens, right? Because if you were doing it across a whole portfolio and actually starting at zero and you owned the innovation, then you would invest in the system.

• 12:37Speaker 01

  Totally. Yeah. If you, if you were setting out to build a company that was going to, you wanted to build the next great pharma company and have a whole portfolio of medicines, you'd probably care a lot about that. But that's such a, that's like a high capital, long-term, high-risk thing to do. It's very hard. And after seven, eight years, and you have some good clinical data, like, you know, like, do I roll the dice again and keep going for another 10, or do I sell? So I think, like, because it's so artisanal, there's this huge opportunity now with AI to get more shots on goal, faster, cheaper, make better molecules, and then bring them to the clinic safely and faster. And I think that's, that's the, that's the big opportunity. People get very focused on clinical trials, because they're like the biggest, the biggest line item. And they're important, don't get me wrong, but I think it's actually a bit of a red

• 13:20Speaker 01

  herring where, yes, there are operational problems, like some studies are designed badly. It's hard to recruit patients. Sticker price is really big, but at the end of the day, like, a lot of molecules are just not good. And so we need better molecules and we need to move them to people faster.

• 13:36Speaker 00

  One other criticism that you kind of imply in your essay as well of like, why the industry isn't more efficient is that even the large pharma companies are not as good at buying innovation and finding it as they could be, right? And so examples of GLP-1s and KEYTRUDA, like some of the amazing breakout successes were not super obvious to the buyers.

• 14:01Speaker 01

  Yeah, I think those two stories are really interesting. And there's a great quote from Dario, the Anthropic CEO, and his kind of essay about the returns to intelligence in scientific progress are very high.

• 14:14Speaker 00

  You're talking about machines of love and grace, yeah.

• 14:16Speaker 01

  Yeah, so the returns to intelligence are very high. And I think like the stories of GLP-1s and KEYTRUDA are like great, great examples of that. So GLP-1s obviously have just transformed obesity as like a treatable disease when, by the way, it was like a totally unfundable category of things like five years ago.

• 14:31Speaker 00

  Why do you think it was unfundable?

• 14:32Speaker 01

  I think like, again, because we know so little about biology and there are so many failures in that space. And again, running a clinical trial for obesity where you need huge populations of people that you monitor for very long periods of time, like, super, super expensive and everything has failed before. Like, pharma companies generally aren't willing to underwrite that stuff sometimes. I mean, neurodegenerative diseases are the same way. Like Alzheimer's is like graveyard of billion-dollar failures and like, it's getting back in now, but there's a period of time where everyone left the space. And so, but the core science for GLP-1s was kind of sitting on the shelf in some sense, like, it's been known since like, the 90s. And so it took some insights and conviction and then all of a sudden, like, we have this category-defining medicine that's going

• 15:16Speaker 01

  to go on to probably be the best-selling drug of all time. And that's happening. And KEYTRUDA is a similar story where there's a molecule that's gone through a couple different acquisitions and it's almost like, it's at the bottom of some list to be Like out licensed and sold off.

• 15:28Speaker 00

  People are giving up on it.

• 15:29Speaker 01

  Yeah. And then a competitive thread pops up and someone sees that, hey, this is kind of like Ketruda. And so, and you know, you know, credit to Merck, they had the courage to go all in after they realized what it, what it could be. So it's just another example of like, there's, there's a lot, uh, it's, it's a pretty inefficient system. And people are pretty, they're rational actors. It's just that we don't know a lot about biology and our ability to predict what's going to happen in the clinic is so poor. And the cost to get there and to make those decisions is so high. And so if you can get to the clinic faster, cheaper, like failure in the software world is you work on a product for a year or two, you spend a couple million bucks and like, it doesn't work, but like in biotech, you're underwriting, you know, four, five, six years, big team, hundreds of millions of dollars. Like, how do you compress that so you get feedback faster?

• 16:13Speaker 00

  So Benchling is a system of record company. It's a data platform. What is Benchling AI?

• 16:19Speaker 01

  Benchling AI has kind of two, two major components to it. The first is tools for simulation. So this is taking open source, proprietary companies, internal models and making them accessible to scientists directly in their workflow. So the right model at the right moment in the scientific workflow, already set up so that a wet lab scientist without computational skills can use it effectively. And then the results are linked to all of their other information in Benchling. And then we also see that laddering up to being able to help scientists recommend, like help recommend for scientists, the next best experiment to run, based on all the work they've done in the past, plus all the public literature available. And so we think it's like an exciting way to approach the, the coscientist problem.

• 17:03Speaker 01

  Then the other facet of Benchling AI is agents that automate work for you. And so we've released this deep research agent. It works similar to the deep research agents from, from Anthropic and, and other foundation labs. But what it does is it works over Benchling data with. The context of the benchling data model. And so it enables scientists to ask these very difficult. And science is fundamentally about like asking and answering questions. And so for our customers, it helps them to do a type of question that previous in the past would have taken weeks or months to do and do that in just, you know, a couple hours. So a great example of this is with a customer that was getting ready to run some mouse studies. And they were looking at 20 different mouse models. And they used a deep research, our deep research capability to look at all the historical mouse studies that they had run.

• 17:48Speaker 01

  And it turned out that a bunch of the mouse models that they were about to like investigate, which would have taken eight months, huge cost, big experiment to run, someone had already done before and it was trapped in some lab notebook from, you know, many years ago from a company that had been bought and all the people were long gone. And so there's so much of science that lives in like folklore and institutional knowledge and that's kind of lost over time. And so we sort of view this as being able to unlock like memory for these organizations and help make scientific data reusable over, over time.

• 18:16Speaker 00

  And they could just accelerate because they didn't have to do that piece of experimentation anymore.

• 18:21Speaker 01

  Exactly. And so we're working towards a world where like there are AI agents that can do all sorts of different tasks in the scientific process, whether it's generating reports and asking questions, or it's even like composing experiments from while you're in the lab with, with voice and vision and things like that.

• 18:35Speaker 00

  If you project out a few years, like everybody loves to talk about this idea of like the AI scientist, a lot of autonomy, AI co-scientist. What do you think is the role of scientists like a couple of years out?

• 18:48Speaker 01

  Oh, wow, that's so interesting. So, yeah, when I hear from AI scientists, I think it definitely evokes this image of a kind of fully AI-ified, you know, design, make, test, analyze, loop. And we'll, we'll sit back and let the robots give us drugs. And like, while I would, I would love for that to happen and I'm, I'm maybe more optimistic on a longer time scale we will get there. I think in the short term, I'm next one to two years, which, you know, already feels like an eternity in AI time. I'm, I'm a little bit more bullish on sort of the augmentation model. Like I kind of think of it as like a Waymo versus Tesla approach where you can, you can do the Waymo approach to autonomy. You just need a lot of money and a lot of patience. And it's gonna take, it's gonna take some time. I think the Tesla approach has been a little

• 19:31Speaker 01

  bit more, I would say, taking steps. I don't want to call it incremental because it's not. And so I think if you can kind of get those ingredients, take the Waymo approach, which some companies have, that's awesome. But I think for the rest of science, there's a huge opportunity to just like, make things better one experiment at a time and pick off a lot of low hanging fruit and see if we can get seven to 10 years down to two to three years and a lot fewer specialized roles and a lot cheaper to bring, bring a drug to market. I think actually like radiology is like an interesting parallel where I feel like, and all people have been saying radiologists are going to go away for, for 10 years. But I think the model that's

• 20:09Speaker 00

  I think like 40, yeah.

• 20:11Speaker 01

  Probably. I think the model that's worked there though, is like, kind of the co-pilot model. And truthfully, like at the, at the end of the day, you probably like, you know, with a radiologist, you probably need a human to be accountable for those decisions. It's not just about the technology. Like someone, someone's got to be there to like, get sued if something goes wrong.

• 20:30Speaker 00

  Yeah. I mean, that makes sense to me in clinical practice. I'm more hopeful that like, some experimental decisions can be more automated. But one question that I think biology faces, that other fields and AI face as well, is the question of like, how do you make these agents like useful, transparent to specialists outside of the domain, right? So if you think about engineers generating a ton of code, like, there's a lot of looks good to me. I didn't really read it. I don't know if that's a good architectural decision, like what's happening? How do you, how do you think about that for like, for example, wet lab scientists and computational analysis, they don't necessarily like deeply grok?

• 21:13Speaker 01

  Yeah. I, I think right now, when I, when I look at biotech, we are in, so it's absolutely like the right point of like, are scientists going to trust this? And how do we know if it's accurate? Right now, I would say, like, there's been amazing advances in capabilities that scientists could use in the life sciences, from the foundation model labs, from bioai companies, from, from everyone. It's really awesome. But I think we're like, we've got GPT, but there's no chat. Like that's kind of how I think about it. Like, I think the chat, and I mean chat metaphorically, like that was the interface that made things really take off in in software. And I don't think it's like really hap- we haven't figured out what that is in in bio yet.

• 21:53Speaker 01

  There's some ideas, but by and large, and I just got back from a month on the road and I was in Boston and London, a bunch of other places that are sort of scientific capitals outside of SF. And like, most people aren't really using that much AI and R and D yet. They all want to, they're primed to, but there's a lot of concerns about accuracy, IP, security, legal. And I think the farther you go from SF, the like, larger those concerns concerns get.

• 22:20Speaker 00

  And so you're optimistic that you can make a lot of the like, context, value, whatever is important for scientists in different domains to understand about an output, like legible through the product itself.

• 22:33Speaker 01

  Yeah, I think that's like, I think-

• 22:35Speaker 00

  Legible enough to be useful.

• 22:36Speaker 01

  Yeah, I think in a vertical, I think 90% of the work is actually like translation. It's taking something and making sure scientists trust it. It's the right point in their workflow. It's easy to use and it's and it's accurate. I think the AI that wins is going to be the one that people actually use.

• 22:52Speaker 00

  Give us the temperature check of like, what large pharma and your customer base thinks about AI right now. They've got these AI officers.

• 22:59Speaker 01

  Oh, yeah, there there is there is excitement for sure. There is optimism and belief.

• 23:05Speaker 00

  Yeah.

• 23:05Speaker 01

  I think they're pretty pragmatic, though. And I think they're all looking to transform, but they're they're being methodical. Like, I would say most of the large pharma at this point that I've worked with, like, you know, they've got co-pilot and things like that. And they're doing a lot of pilots of different technologies, but I haven't seen their R and D orgs transformed yet. Now, the one place I would say that pharma has really leaned in and has an advantage is they have incredible data generation capabilities. And so many of them can and should be training models.

• 23:38Speaker 00

  Like experimental data generation.

• 23:40Speaker 01

  They can generate data to train their own models at a scale that most biotech startups can't can't match. So I think while it's early on sort of the agentic how we work side, I think you're going to see very unique models come out of pharma where their computational scientists are building interesting predictive models that, you know, similar to what's happening in the open source world.

• 23:58Speaker 00

  What do you like, can you help characterize what useful models we already have on the discovery side? And then like where you think we are in the sort of cycle of having, you know, enough to make a real change in the overall cycle time or rate of success, whatever you think is more important here?

• 24:20Speaker 01

  Yeah. It's been really cool to see like the whole ecosystem of these tools grow a ton. Like, open source wasn't really a thing in biology before or in science before. And like, I think in the last two years, it feels like a thing that's here to stay. And you're seeing all these interesting models come out, like the bolts, for example, on the structure prediction side. And I think like, that's like a really interesting thing that's going to change, change biology. I think you're seeing new approaches to like federated learning as well. Eli Lilly put out this announcement about a project they have called ToonLab, where they're taking their internal models and making them available to the broader scientific ecosystem. So a pharma company is saying, hey, you can use our models, but it's give to get.

• 25:04Speaker 01

  So we get to train and like, it's federated or whatnot, so they don't get to see the actual scientific data. But I think like, those approaches are like, we're, this is the beginning, basically. And so we've got some really cool stuff. There are, there are problems that I think are fairly tractable in terms of structure prediction or antibody developability and so forth. So like, that, that's really good, but there's, I would say, a lot of work in front of us in terms of models that are sort of more predictive of what happens when you get into patients, for example. And don't get me wrong, like, discovery is really important, but there's so many other steps that have to happen after you have a concept molecule. Even if you're, if you're much faster, for example, at making molecules and you have

• 25:47Speaker 01

  a higher success rate, you still have to come up with a process to manufacture that molecule. And now you have even less time to do so, if it's like a byproduct of success. And so, how do you optimize manufacturing processes to get, you know, a lot of yield, speed, cost out of, out of a molecule?

• 26:01Speaker 00

  Is that like where you would say the highest value missing predictive model? Opportunity is because I think a bunch of naysayers would be like, okay, yes, I've heard about AlphaFold and like people are working on antibody prediction and like creation platforms, but we are still, you know, many years into this premise, no drugs out the other end of the pipeline that are AI discovered.

• 26:24Speaker 01

  Yeah, I feel like the naysayers kind of have this sort of worldview in mind where it's like, oh, I just like type of disease and then I like get a molecule out and like amazing AI discovered drugs. And this is where I go back to like my mental model is like there are so many steps, the steps are all cumbersome and difficult. And this is a game of like making each single thing better. And like some of the steps matter more than others, like having the right target or having like a great molecule generated, fine, but like there's still many, many years after that that we can compress and shape off. And so right now I would sort of almost argue that we should be thinking about like, what's the share of experiments that have been touched by some kind of predictive capability, some kind of simulation or some kind of AI? And I bet that share is like getting higher every day.

• 27:05Speaker 00

  Part of what I think has been really interesting and there's like good and bad about the investor enthusiasm of, you know, both, let's say, AI's potential impact on biotech and then the potential for platform companies is this theory that we're going to have like very different business models in biotech. Do you think that's going to happen?

• 27:24Speaker 01

  I would like it to happen. I think right now for companies, I mean, so one, as a toolmaker, I think there should be more tools. Tools are good. I do think with some of these model companies in the bio world, there's going to be an interesting question of do they morph into, in the fullness of time, morph into their own therapeutics companies with their own pipelines? I think it's unlikely, it's possible, but it's unlikely that sort of, hey, they're just going to remain pure model companies who just do deals with pharma where pharma, you know, pays them a hundred million dollars upfront or something like that and they have five customers and whatnot. Like, I feel like the sort of model building is probably commoditizing too fast for that to be a attractive business model. But To take that expertise and to be fundamentally better at doing research in early development

• 28:09Speaker 01

  to make molecules and sort of morphing into a biopharma company, like that seems like one logical path. I think there's a world where like, and we're experimenting in this space where sort of models can be more effectively distributed to the larger biopharma communities. So rather than going and doing BD deals with five companies, it's actually a little bit more like kind of a traditional software sale where we've actually got a bunch of models in bench selling. They're mostly open source, but also we've got, you've got chat, but alpha fold, things like that. Is there a model where like some of these are like pay per use or like fee for service, almost like SaaS and the entire biotech companies benefiting that and you can build models and like have a scalable business model on the other end? Like, I think that'd be really interesting. And then there's going to be like more data transactions, I think, as well.

• 28:51Speaker 01

  Like, data wasn't, is not, is interesting for a field that really depends on data as its currency. Like, everything is about data on the molecule. You see very, very few transactions of data.

• 29:01Speaker 00

  Uh huh.

• 29:02Speaker 01

  That's because no one trusts anyone else's data. You wait till there's a clinical trial and the data is positive and you buy the molecule. But you'd think that you'd see a lot more selling of data before that, but you don't because the data like, it's very hard, you don't know what format it's in, do you trust the way it was created? Like, it's just not-

• 29:17Speaker 00

  If there was tooling and normalization about it, you might be able to transact on it.

• 29:21Speaker 01

  Yeah. And like, will people be selling like their negative data at some point into some pool that other people can learn from that? There's all kinds of crazy stuff I can think of.

• 29:27Speaker 00

  I'm sure you've heard in the 13 years you've been building bench selling, the conventional wisdom is that the only way to create value in pharma is assets, not tools, right? Like, where were they wrong? Or maybe the tools just like weren't that important before and they weren't as embedded as they needed to be.

• 29:46Speaker 01

  Yeah, I don't know if this is a, Thermo Fisher and Danaher are like sneaky big companies. And I think people don't, don't always realize that. And they've done it largely by like systematization of tools in the physical realm. So like instruments, reagents, services around them and so forth. So I think there's some kind of at least thing that rhymes with building great tools on the digital side. I think just frankly, like, looking back, the, the technology probably hasn't, hasn't been there. Like, when we started bench selling in 2012, cloud was like the norm everywhere, but most of life science was like paper, on-premise, spreadsheets.

• 30:19Speaker 00

  Okay.

• 30:20Speaker 01

  So like, we spent the first couple of years like-

• 30:21Speaker 00

  It's wild for such an advanced field in other areas.

• 30:24Speaker 01

  Yeah. I mean, and that is because like, you, you could argue like, hey, we're, it's such a like- High stakes game of poker for them that the only thing that matters is like, does this drug get to patients and is successful? And they can, you know, pharma has pretty healthy margins. And so like the operational efficiency isn't always going to like improve the odds of success. So we spent the first couple of years basically just evangelizing, like bring science online, like it's going to be better. And then spent the next like 10 years after that, kind of convincing people that structured data mattered. And because that's, that's sort of like the core premise of venture. It's a system of record will help you have like a data model. And every time you do experiments, that data model is getting populated with information. You can ask questions.

• 31:09Speaker 01

  And there's a set of people who they got it and they believed.

• 31:12Speaker 00

  Seems obvious to tech people.

• 31:14Speaker 01

  It seems obvious, but there's like, it's not for free. Like a piece of paper is much easier and an Excel spreadsheet is much easier. But there's a set of people who believe in a set of people who maybe weren't convinced. But now with like AI, one, I think the benefits are a much more immediately obvious to everyone. And so that's going to be this amazing tailwind to try to like do better here. And I think it will convince a lot of people who might've been skeptics in the past.

• 31:38Speaker 00

  Yes, I don't come at that from a holier than thou view because one might actually claim that in venture investing, the only thing that matters is the quality of the next decision and whether or not you found the winner. And so it's a lot of tech people with a lot of pen and paper, actually.

• 31:56Speaker 01

  Yeah.

• 31:57Speaker 00

  And so, but I think that's likely to change. Two things. One is like all the foundation model companies, DeepMind, Anthropic, OpenAI, they love to talk about AI for drug discovery. Like, and you know, I think that there's fundamentally like a mission orientation there. I also think I'm a bit of a cynic because it's hard to be like, that's a bad idea. Like that seems like just, just roundly good for humanity if we have more medicines, as you said.

• 32:25Speaker 01

  It's like in the 10 years ago when the crypto people were like, ah, it's all international remittances.

• 32:29Speaker 00

  Right. Like, why do you think it is like both so popular with the labs and then even more popular over the last few months? And then like, tell us about your partnership with Anthropic.

• 32:39Speaker 01

  I go back to that sort of returns to intelligence piece where I think science is a problem that has some shake to it that really benefits from the LLM architecture. Like you just think about the corpus of scientific literature as like this vast pool of unstructured text and like, these are kind of, these are like roles where, these are pursuits where like, that's a ton of domain knowledge to hold in your head and there's so much specialization. And so the idea that like, you could be like truly standing on the shoulders of giants, I think is very appealing of, I've got a scientist and I'm in early stage biotech and some, now I can have access to the world's best like clinical design expert or the world's best toxicologist or, or the best research assistant who can even like read papers better than me to figure things out. Like, there's a lot about

• 33:23Speaker 01

  science that again is so artisanal and inefficient that it seems like a problem that AI is going to be, be much better at. I think that's one thing. I think the other is like, I mean, it is like, I think bio is, I don't know, like, I sort of wonder like, why don't more people work, work in bio? Like, there are big problems to be solved. There's an incredible like-

• 33:42Speaker 00

  Because the failure rate's so bad.

• 33:43Speaker 01

  The failure rate's so bad and like, the, the impact is huge. I think everyone's now seen what like GLP-1s can do. Everyone saw what COVID vaccines can do.

• 33:50Speaker 00

  Magic, yeah.

• 33:51Speaker 01

  Yeah. It's like, when it works, it's magic and like, people need this stuff. And so like, I don't know, if AGI starts, you know, automating ways, software engineers or whatnot, like, what's left? Like, gotta make drugs for people.

• 34:01Speaker 00

  All right, more, more scientists. And what about the partnership?

• 34:04Speaker 01

  Yeah, so we have a partnership with Anthropic. I think we feel very, we work with, we use sort of the, all the foundation of Model Labs capabilities, but like, we found that there's like a strong commitment to science from Anthropic. I mean, Dario's a scientist and so it's been really, really good mission alignment with them. And I think sort of they've expressed publicly that science is sort of the next frontier after, after code. And I think for our customers, trust is super, super important. And I think their posture plus their technologies and when that's really appeals to them. And so it's one where, just to the start, like Benchling and Claude kind of like natively interoperate very well. So if you want to, you know, work through Benchling and Claude, or you want to, it works pretty effectively. So scientists can, you know, generate reports, ask questions and things like that from a very simple-

• 34:47Speaker 01

  AI interface that they're they're used to. And I think it's just like the start with with them.

• 34:52Speaker 00

  Can we talk a little bit about company building?

• 34:54Speaker 01

  Sure.

• 34:55Speaker 00

  Just, you know, 13 years of wisdom and like two minute takes.

• 34:58Speaker 01

  Every mistake made at this point.

• 34:59Speaker 00

  Maybe we'll start with the most recent, like, hard decisions, not mistakes. But your co-founder Ashu gave up all his direct reports at some point and went all in on AI. I called I called a bunch of friends around this company and our mutual friends. That is a that's a big decision. Like, when that happened? How did you make the decision? Because you guys started way before AI was working at scale.

• 35:22Speaker 01

  Yeah, it's funny. We I think we started early, but at the same time, I feel late still.

• 35:27Speaker 00

  OK, all of us, yeah.

• 35:28Speaker 01

  I, yeah, you know, one of the interesting things is like, I feel like the power of being a co-founder is actually just in moral authority. And I think like this was a it was a pretty controversial decision in our company. And we needed someone who had the right, I guess, willingness to like, A, like, I don't need any of my Legos anymore. Anyone else can have them. And like, B, if I look stupid, that's OK.

• 35:52Speaker 00

  Nice, yeah.

• 35:53Speaker 01

  And so like, it's funny, he wrote this wrote this post of like, I'm quitting my job to like, do this other thing. And I had a bunch of customers call me and they were like, oh, my God, I'm I'm so sorry. Your co-founder quit. Is everything OK? I was like, oh, no, no.

• 36:03Speaker 00

  Metaphorically.

• 36:04Speaker 01

  Metaphorically, didn't quit, just going going full time on AI.

• 36:07Speaker 00

  Yeah.

• 36:07Speaker 01

  And it was it was controversial, like, biotech's been, obviously, like, had this kind of dot com level bust. And so a lot of our, you know, our team is feeling like, hey, we got to focus on like the basics with our customers. The market's tough right now. You have some companies that are laying people off, shutting down, like, isn't AI?

• 36:23Speaker 00

  Yeah, how do you invest like that?

• 36:24Speaker 01

  Yeah, isn't AI like a distraction? But like, we were actually really fortunate. I think we had good sort of outside the building perspective. Asha was very hands on keyboard himself, and that's how he got convinced. I think he was like, playing with one of the models like during Christmas or something like that, building for himself. And I think that really, really inspired us. And also, like, we realized, like, If we don't do this for our customers, like who is going to do it? Like, again, it goes back to needing to translate some of these amazing things that come out of the area in Silicon Valley into like useful vertical applications in very complex, regulated domains that we felt like we're the right people for.

• 37:00Speaker 00

  Maybe because there are a bunch of entrepreneurs listening to this podcast as well, who are looking at industries that are complex and regulated and want to try to bring, I don't know, the cloud and then AI to them. Like, what has been hardest? What are some lessons from that?

• 37:15Speaker 01

  My, I think like the, my like most trusted algorithm for this is like, go talk to customers and all that. I know, super obvious, right? But all the times in which like I felt like the company has been like at its lowest or at its worst, or like I'm feeling at my lowest, it's because I've gotten too far from customers. And like product market fit is this moving target. And sometimes your market changes, like our market changed of like, you know, a bunch of biotech companies that were going to take over the world and then like, then they weren't. And so like, I still spend, and maybe this is a vertical thing, I probably spend 30, 40, 50% of my time talking to customers still. And I think of myself as like the, I need to go really deeply understand their problems and how they're changing. And I need to go bring it back to the company. And like, that's the thing I have to role model so the whole company does it.

• 37:59Speaker 01

  And that's the number one piece of advice I'd give people.

• 38:02Speaker 00

  So that answers a part of one question from another friend who used to work for you. He said that Saji is amazing at like, understanding the macro of the company and then like being deep in the detail on everything, especially with customers. But given, you know, benchings are complicated company, biologists are complicated fields, like, how do you decide what to focus on and then like focus your team on?

• 38:25Speaker 01

  I, that's a nice compliment, though. It's like telling someone they have a large context window.

• 38:29Speaker 00

  It is. Good model, man.

• 38:31Speaker 01

  Thank you. Thank you to whoever said that kind of thing.

• 38:33Speaker 00

  It was Malay. Hi, Malay.

• 38:36Speaker 01

  Um, I, you know, if one of the most interesting things about being in a vertical is you talk to all your customers and they all, because no one, they're underserved. Like they're just, you know, the world of life science software, there aren't, it's not like go to market tools where there's like 10,000 companies.

• 38:51Speaker 00

  Much less great software companies.

• 38:53Speaker 01

  Yeah, there are, there are just not many. And so you have a very underserved demographic where they're not used to someone coming in and sort of asking what they specifically need. They're used to very general purpose horizontal software and trying to like.

• 39:03Speaker 00

  Yeah, you want some productivity.

• 39:04Speaker 01

  Do cluey things to make it work, you know? Uh, and so when you go talk to them, the first reaction is like, oh my God, what we do is so unique and we're such a snowflake and there's no way like, uh. But then you like talk to enough customers and it turns out they almost all want the same thing. I find that having five, 10 customers is actually like a pretty representative model for what the entire industry needs. You, you don't want to get like too overweighted in like one category of medicine or one size or something, but everything we've built over time that has been successful is because we found a couple of customers. And we got very, very deep with them and we built and built until they were like super happy and then it took off. Uh, and like, maybe that's like the Y C part that's been like programmed into me that I've never gotten out of it of like do things that don't scale. I feel like in a vertical that works 10x, 10x better.

• 39:49Speaker 01

  Even for our new AI tools, we, we have all of our customers, they're, they're available to all of our customers now, but we're, our team is on like daily, weekly calls with five or 10 customers to the point of like, we, we had a customer yesterday, you know, text us about like a meeting they had with the FDA and how they like ran a report inside of our deep research tool. Instead of taking them a day, it took them like five minutes and it was like perfect for them. And like, that's the level of closeness we get with our customers.

• 40:15Speaker 00

  One other unique thing that I think would be useful for a lot of people today, including me, is Benchling knows how to get scientists to work in a software company and work around a software company. Like I work with many more research scientists, so in different fields than, well, actually it's some in bio, but then I anticipated, let's say five years ago when I was just like. Good old engineering, like, and it is philosophically different, right? You have to run programs differently. You're like, well, you know, it's not like this is done by the next sprint. It is, we do not know. Like, what advice do you have on like recognizing that talent, getting them to be productive, managing it?

• 40:54Speaker 01

  That is a really hard question. I have, I have serious battle scars of that. We've had to build a very introductory company to be successful. If I was only hiring like software people who knew bio, I would have like exhausted the pool 10 years ago. It just doesn't exist. We have to take the software people, take the science people, make them sit together, learn from each other. I actually, and this is gonna sound obvious, but I, I've actually found that like, the most conflict has been around the like, actually like how the mission gets solved. Where actually a lot of like, coming from the world of science, especially academia is just a very different incentive structure. And in the world of academia, like your labor is basically free. And so like, there was like very, very, very cheap, right?

• 41:37Speaker 01

  Grad student labor and like the currency is publishing a paper.

• 41:40Speaker 00

  Yep.

• 41:41Speaker 01

  And that's how you get more funding to do more things and so forth. Whereas in a company like we have to sell software. And so like, actually, the most impactful thing has been like, really a lot of repetition that in order for us to achieve our mission and to keep delivering great things to our customers, like we have to make money. And a lot of the tension has come from like that, the need to do that. And so making sure our scientific teams like really understand that the better we do as a business, the more amazing innovation we can bring to our customers. And by the way, if we don't do this, who else is gonna do it? Where are the other like next 10 companies building software for science and R and D? They're gonna power the next discoveries of these biotech and pharma companies like where we're like, I think we're the only independent like scaled player doing this at this point.

• 42:26Speaker 00

  Do you interview at all for this orientation?

• 42:29Speaker 01

  I think we try to. I, I don't know that we've, like, I have some amazing predictive way to, to find it.

• 42:35Speaker 00

  I have a friend who's a founder who asked what I, I don't find to be a controversial question because it's literally in my title, right? I'm a venture capitalist, but they ask people, including research scientists, how do you feel about capitalism? And they think it's a pretty interesting sorting function, actually.

• 42:51Speaker 01

  I, I will try that.

• 42:53Speaker 00

  I'm not recommending it, but I do think it is interesting, the set of answers you get, actually, because I have tried it. One more question for you on culture. You spend a lot of time with biotech and large pharma. You're at your core a software person. Like, what can these two worlds learn from each other?

• 43:10Speaker 01

  Great, great question. I think the biotech and pharma world can learn something from how tech communicates and tells stories. Like, there's the whole go-direct wave in tech right now that I really, really resonates with me as a founder. I think like biotech and pharma companies need to tell their stories. Like, most people-

• 43:30Speaker 00

  This is not what I thought you were gonna say.

• 43:31Speaker 01

  Yeah, I'm curious what you thought I was gonna say. Most, most people, I don't think, could name five scientists or five CEOs of pharma companies, but like, we could name every single like tech CEO. Everyone knows who, you say Sam and it's like first name basis, right? And, or Jensen or something like that.

• 43:47Speaker 00

  There's a lot of heroes journey in tech.

• 43:48Speaker 01

  A lot of heroes journey in tech. But like, and I think talking about the patients and scientists, though, would like, change a lot of the public perception of biotech and science. And only people know how hard it is to like, make a medicine. I mean, I was, you know, I was just thinking recently, like, you look at COVID even, like, Moderna and Pfizer helped the world, whatever you feel about vaccines, like, they played an important part in like, helping the world like reopen. And yet, like, Zoom gets more credit in COVID. Or you have Gilead, who's like, you know, HIV used to be a death sentence in the 1980s, and like, there was tons of panic and fear about it in the 90s and early 2000s. And like, good, it's like cured HIV, basically, at this point. And like, most people have no idea. Like, and so I think like, because sort of the, the way they communicate

• 44:32Speaker 01

  is much more about, almost these like, faceless companies rather than the people, I think it's like, easy to, easy to hate on them and underappreciate. So I think they need to tell their story and go direct.

• 44:41Speaker 00

  Yeah.

• 44:42Speaker 01

  That's one thing. I think on what tech can learn from, from bio is I think, once you start, I think, sort of, tech has sort of become everything, and, and sort of gone from, you know, very, so the ambitions of tech have grown a ton. And I do think, like, some of these other industries have figured something out when it comes to rigor and validity and accuracy and sort of move fast and break things does work for certain domains. But again, once you get to the point where you want to have credibility with regulators to put things in patients, you know, or make a medicine, like, that stuff does come to matter. And like- Biopharma, again, for all the things that are difficult with this is figure out how to be safe. Like, the US is still the gold standard for how to, like, deliver medicines safely to people. I think that stuff matters more and more.

• 45:25Speaker 00

  Yeah, it's really interesting. I was talking to a friend who is a top research scientist at one of the top labs. And I asked him a very basic question, it's probably a year and a half ago, about like, well, you know, how can I, how can I better use these models in very quality-sensitive fields? And he said, just wait, right? Like, there's like, you know, like, why don't you just focus on the use cases that are not as rigorous, essentially? And this is an amazing research scientist, but I'd be like, that seems unambitious, right? Because we, there are many benefits to intelligence in these fields that are really important to all of us. And so I do feel like more people should work on that, too.

• 46:06Speaker 01

  Yeah, yeah, yeah. I think it's still a value sometimes. It's a little bit easy to, like, oversimplify other people's jobs. There's a lot of complexity out there in the world of biotech and pharma, but I'm very optimistic that, like, the sort of tech companies will figure out how to make it work, because I think everyone's very excited about, about AI.

• 46:20Speaker 00

  Last question for you, something you're excited about in AI outside of bio, or outside of, like, Benchling's immediate purview, I guess?

• 46:28Speaker 01

  On a personal level, I haven't written a line of code, I had not written a line of code in probably, I'm very embarrassed to say this, maybe eight or nine, it's been, it's been some time. Like, I remember I stopped coding around the time that React started being a thing.

• 46:43Speaker 00

  Okay, you're dating us.

• 46:44Speaker 01

  I'm dating, I'm dating, yeah, all the, all the kids listening are gonna turn off. And like, I've tried some of the new agentic coding tools lately, and it's just like, on a personal level, fun to just feel like the whimsy of being able to build something very quickly again. It's like, that, that's pretty cool. That's probably the one I'm most excited about. And then I'm excited for my, like, parents, actually, and for them to have, like, new technology that's pretty easy for them to, to access. Like, my mom's a ChatGPT user, and I'm sure it's sort of Google Search++, at this point, but like, that's been, like, pretty cool, too, of like, technology that's so intuitive that, like, I don't have to go, go home and be IT-supported Christmas.

• 47:22Speaker 00

  Yeah, I think it's actually undervalued as, just because there are audiences that are traditionally not as lucrative as, like, the fast-tech adopting audience of your, you know, 15 to 40-year-old.

• 47:33Speaker 01

  Yeah.

• 47:33Speaker 00

  But it is incredibly wild how the UX of, like, natural language and voice are

• 47:40Speaker 00

  And changing who can use it.

• 47:41Speaker 01

  Absolutely.

• 47:42Speaker 00

  AI tutoring for my kids and such.

• 47:44Speaker 01

  Oh, my God.

• 47:45Speaker 00

  Same experience.

• 47:45Speaker 01

  Oh, learning is such a joy with AI. I hope I'm learning the right things, but like.

• 47:50Speaker 00

  Thanks so much for doing this, Saji.

• 47:52Speaker 01

  Thanks, Sarah.

• 47:55Speaker 00

  Find us on Twitter at NoPriorsPod. Subscribe to our YouTube channel if you want to see our faces. Follow the show on Apple Podcasts, Spotify, or wherever you listen. That way you get a new episode every week. And sign up for emails or find transcripts for every episode at nodashpriers.com.