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No Priors Ep. 140 | With Benchling Co-Founder and CEO Sajith Wickramasekara

ringing 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. Sign up for new podcasts every week. Email feedback to show@no-priors.com 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

Sarah GuohostSajith (Saji) Wickramasekaraguest
Nov 13, 202548mWatch on YouTube ↗

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  1. 0:00 – 0:38

    Sajith Wickramasekara Introduction

    1. SG

      (music plays) 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.

    2. SW

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

  2. 0:38 – 2:08

    Origin and Mission of Benchling

    1. SW

    2. SG

      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?

    3. SW

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

    4. SG

      Oh my God, yeah.

    5. SW

      I know. Uh, so I, I'm a software engineer by background, but I worked in a biology lab. I was like really interested in medicine, um, and coming from the world of software, uh, and software developers have amazing tools for working on code and for, for collaborating. And when I got to the la- the biology lab, I found that scientists had paper notebooks and spreadsheets that would sit on their desktops, and like, it was terrible. Uh, and so it was really hard to work together, and I think that was really frustrating for me personally and, you know, I thought, a little bit naive at the time, I, I thought like, "How hard would it be to build good tools for, for scientists?" And so I started working on Benchling, uh, 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. Today, we work with about 1,300 biotech and pharma companies, uh, scientists at over 7,000 academic, uh, institutions, universities all, all around the world, and, uh, our software powers, you know, household names like Moderna and Sanofi and Eli Lilly and, and Regeneron but also, like, cutting-edge biotech startups, you know, the, you know, future AI biotechs like Isomorphic Labs and Zera and, and, and companies like that. So we get to, we get to see the innovation happening across the entire biotech sector and then build software that helps power it.

  3. 2:08 – 3:49

    The Drug Development Process

    1. SG

      I'm super excited to, like, actually use that vantage point and 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. SW

      Sure.

    3. SG

      ... and like the assay like said yes or no. Like what other types of... What is the data that's actually in Benchling?

    4. SW

      Uh, I think w- 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, to make a medicine, you have to find a biologically meaningful target in, in the body, something you want to drug.

    5. SG

      Mm-hmm.

    6. SW

      You have to design a molecule. You have to optimize that molecule. You have to test that molecule in Petri dishes and cell lines and animals, uh, 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, with quality, all while navigating regulatory bodies 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. Um, 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.

  4. 3:49 – 8:46

    Current State of the Biotech industry

    1. SW

    2. SG

      If we go zoom out, uh, for people just like looking at biotech from the outside, um, 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 bio macro cycle?

    3. SW

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

    4. SG

      But it's all your customers, yeah.

    5. SW

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

    6. SG

      Okay, that's pretty bad, yeah.

    7. SW

      ... bi- 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 was a lot of generalist money that came in, uh, and a lot of exuberance and excitement, and it's not... You know, the, 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, um, 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, whole confluence of factors there, but a, a bubble-

    8. SG

      What are you referring to in terms of scientific technologies that-

    9. SW

      Uh, like the-

    10. SG

      ... got people hyped?

    11. SW

      Yeah, like, uh, I would say there was a lot of generalist excitement for gene editing, cell and gene therapies, RNA, and all of these are-

    12. SG

      So new delivery methods?

    13. SW

      Yeah.

    14. SG

      Yeah.

    15. SW

      New, new like I would call them kinda categories or form factors of medicines. We... Mo- modalities is the word, but like-

    16. SG

      Mm-hmm.

    17. SW

      ... 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, and, um, and, uh, taken to patients. And some of these, like there are g- 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, we're kind of in the trough of disillusionment for some of them now.

    18. SG

      Mm-hmm. And my understanding is that they have taken longer and been more expensive-

    19. SW

      Yes.

    20. SG

      ... than people expected-

    21. SW

      Absolutely.

    22. SG

      ... or than investors expected.

    23. SW

      Absolutely.

    24. SG

      Yeah.

    25. SW

      In, in, in-

    26. SG

      Maybe the scientists know.

    27. SW

      ... 2021, every biotech was getting told by investors, like, "You need to build a platform company that's gonna cure a bunch of different diseases-

    28. SG

      Mm-hmm.

    29. SW

      "... and here's hundreds of millions of dollars, and capital is free."

    30. SG

      (laughs)

  5. 8:46 – 16:14

    AI’s Role in Biotech

    1. SW

      (laughs)

    2. SG

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

    3. SW

      Yeah. I, I think I, I believe that now, and it's, it's really interesting to see, like, the general public, you know, big tech, startups, the model labs, everyone is saying, like, "AI is gonna cure disease."

    4. SG

      Mm-hmm.

    5. SW

      So it's, it's very good that everyone's excited by that.

    6. SG

      You, I don't think of you, uh, I think of as an amazing CEO but not really a, uh, content marketing guy to date, and you wrote an essay very recently that I thought was amazing about, uh, how we can possibly change, like, the scientific field in biotech with AI. Can you give us the CliffNotes on it?

    7. SW

      Yeah.

    8. SG

      And then we'll link it in the show.

    9. SW

      A- absolutely.

    10. SG

      Yeah. Yeah.

    11. SW

      Um, yeah, I, 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, uh-

    12. SG

      Mm-hmm.

    13. SW

      ... I think. I think, like, we take for granted how awesome medicines are.

    14. SG

      Mm-hmm.

    15. SW

      I think 9% of healthcare spend- prescription drug sales are 9% of healthcare spending in the US, like, we have obviously this healthcare cost problem, but drugs are this amazing ROI.

    16. SG

      Ah.

    17. SW

      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, um, and that's like-

    18. SG

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

    19. SW

      It's not, yeah.

    20. SG

      Yeah. (laughs)

    21. SW

      The rest of healthcare is very labor dependent, and labor generally gets more expensive over time. I'm, I'm very optimistic for AI to help, help 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, uh, it takes over two billion dollars, 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 ten years in, you've spent hundreds of millions of dollars and clinical trial fails, medicine's not safe or not effective. And so it's an unbelievably, like, difficult pursuit. Uh, 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 two billion dollars probably isn't that... You know, I feel like AI has desensitized us all, you know, everything is like, you know, hundred billion dollar data centers and, and whatever, like, "Two billion dollars," like, "What, what's that?" But when there's that high of a failure rate, it's very difficult for investors to, to underwrite that. And, and that was, you know, while we had all these new categories of medicines being, uh, kind of, uh, invented over the last decade. Uh, I think that's, like, that's important and it's here to stay, but, like, the, 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 at, sort of take the digital and physical realms for a second, uh, they've actually done a good job of systematizing the physical realm. You, you, you brought up sequencing earlier, like, Illumina has put sequencers on every single bench in 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, and things like that. But if you look at, like, the, the digital realm where it's like, uh, how people collaborate, how data is structured and shared, the workflows that are used, uh, in, in science, which is all about collecting data, um, all of that's basically bespoke and invented one-off by every, every company. It's 'cause those companies are playing kind of a, sort of a one-time game, because the, 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.

    22. SG

      That seems like it also comes from some of the structure of, like-

    23. SW

      Totally.

    24. SG

      ... 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-

    25. SW

      Yeah.

    26. SG

      ... then you would invest in the systems.

    27. SW

      Totally.

    28. SG

      Yeah.

    29. SW

      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, and you have some good clinical data, like, you're like, "Do I roll the dice again and keep going for another ten, or do I sell?" And 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, um, 'cause 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, red herring, where, uh, 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.

    30. SG

      (laughs)

  6. 16:14 – 18:36

    Benchling AI and Its Impact

    1. SG

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

    2. SW

      Benchling AI has kind of two, two major components to it. The first is tools for simulation, so this is taking open source, proprietary, company's 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 recomme- 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 co-scientist problem. Then the other facet of Benchling AI is agents that automate work for you, and so we've released this deep research agent who 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, 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, and do that in just, you know, a couple hours. So a great example of this is we had a customer that was getting ready to run some mouse studies, and, uh, they were looking at 20 different mouse models, and they used the deep research, uh, our deep research capability to look at all the historical mouse studies that they had run. And it turned out that, uh, a bunch of mouse models that they were about to like investigate, which would have taken eight months, huge cost-... big experiment to run, uh, 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.

    3. SG

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

    4. SW

      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- and things like that.

    5. SG

      If you

  7. 18:36 – 26:28

    The Future of AI in Biotech

    1. SG

      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, um, scientists, like, a couple years out?

    2. SW

      Oh, wow. Tha- that's so interesting. So, yeah, wh- when I hear, you know, from an AI scientist, I think it definitely evokes this image of a kind of fully AI-ified, you know, design, make, test, analyze loop.

    3. SG

      Yep.

    4. SW

      And we'll- we'll sit back and let the robots give us drugs.

    5. SG

      Mm-hmm.

    6. SW

      And like, while I would- I would love for that to happen, and I'm- I'm maybe more optimistic on a longer timescale we will get there, I think in the short term, um, 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- I kinda think of it as like a Waymo versus Tesla approach-

    7. SG

      Mm-hmm.

    8. SW

      ... 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- the Tesla approach has been a little bit more, I would say, taking steps, um, I don't wanna call it incremental 'cause it's- it's not, um, and so, uh, I think if you can kinda get those ingredients to take the Waymo approach, which some companies have, that's awesome, but I think for the- the rest of science, there's a huge opportunity to just, like, uh, 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 ten 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, rad- radiology is, like, an interesting parallel, where I feel like ML people have been saying radiologists are gonna go away for- for ten years, uh, but I think the model that's worked-

    9. SG

      I think like 40, yeah.

    10. SW

      Probably.

    11. SG

      Yeah.

    12. SW

      I think the model that's worked there though is, like, kinda the copilot 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 gotta be there to, like, I don't know, get sued if something goes wrong. (laughs)

    13. SG

      Yeah. I mean, that makes sense to me in clinical practice. I'm- I'm more hopeful that, like, some of the experimental decisions-

    14. SW

      Mm-hmm.

    15. SG

      ... can be more automated, but one question that I think biology, um, faces, uh, that other fields and AI face as well is the question of, like, how do you make these agents, like, useful, transparent to, um, specialists outside of the domain, right? So if you think about engineers generating a ton of code-

    16. SW

      Yeah.

    17. SG

      ... 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-"

    18. SW

      Yeah.

    19. SG

      "... decision. Like, what's happening?"

    20. SW

      Yeah.

    21. SG

      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?

    22. SW

      Yeah. I- I think right now, when I- when I look at biotech, we are in... So I- so it's absolutely, like, the right point of, like, are scientists going to trust this?

    23. SG

      Mm-hmm.

    24. SW

      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, uh, from the foundation model labs, from bio AI companies, from- from everyone. It's- it's really awesome. But I think we're, like, we've got GPT but there's no chat. Like, tha- that's kinda how I think about it.

    25. SG

      Mm-hmm.

    26. SW

      Like, I think the chat, and I mean chat metaphorically, like, that was the interface that made things really take off-

    27. SG

      Mm-hmm.

    28. SW

      ... 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. We have some ideas, but by and large, and I just got back from a- a month on- on the road and, you know, I was in Boston, London, a bunch of other places that are sort of scientific capitals out- outside of SF, and, like, most people aren't really using that much AI in R&D yet.

    29. SG

      Mm-hmm.

    30. SW

      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- the, like, larger those concerns- concerns get.

  8. 26:28 – 28:50

    Debunking AI Drug Discovery Myths

    1. SW

      in mind where it's like, "Oh, I just, like, type a disease and then I, like, get a molecule out and, like-

    2. SG

      Yeah.

    3. SW

      ... ma- amazing AI-discovered drugs." And this is where I go back to, like, my, my, my mental model is, like, there's so many steps, those steps are all cumbersome and difficult, and this is a game of, like, making each single thing better.

    4. SG

      Mm-hmm.

    5. SW

      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, and shave 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.

    6. SG

      Part of what I think has been really interesting, and there's, like, good and bad about, uh, the investor enthusiasm of, uh, you know, both, let's say, AI's potential impact on biotech and then the potential for platform companies-

    7. SW

      Mm-hmm.

    8. SG

      ... is this theory that we're gonna have, like, very different business models-

    9. SW

      Mm.

    10. SG

      ... in biotech. Do you think that's gonna happen?

    11. SW

      Uh, I would like to happen. I think right now for companies, I mean, so one, as a, as a tool maker, I think there should be more tools. Uh, tools are good. Uh, I do think with some of these model companies i- in the bio world, there's gonna be an interesting question of do they morph into, in the fullness of time, morph into their own therapeutics companies with their own pipelines?

    12. SG

      Mm-hmm.

    13. SW

      I think it's, it's unlikely, it's possible, but it's unlikely that sort of, hey, they're just gonna remain pure model companies who just do deals with pharma where pharma, you know, pays them $100 million upfront or something like that and they have five customers and, and whatnot. Like, I feel like the, uh, sort of model building is probably commoditizing too fast for that to-

    14. SG

      Yeah.

    15. SW

      ... to be a, a tractable business model. But to take that expertise and to make, to be fundamentally better at doing research and early development to, to make molecules and sort of morphing into a biopharma company, like, that seems like one logical path. Um, I think there's a world where, like... and, and we're experimenting in this space, where sort of models can be more effectively distributed to the larger biopharma community, so rather than going and doing BD deals with five companies, it's actually a little bit more like kind of traditional software sale where, you know, we- we've actually got a bunch of models in Benchling. They're mostly open source, but also we've got, you know, we've got ChAI, we got AlphaFold, things like that. Uh, is there a model where, like, some of these are, like, pay per use or, like, fee for service, almost like SaaS-

    16. SG

      Mm-hmm.

    17. SW

      ... and the entire biotech company's benefiting, then you can build models and, like, have a scalable business model on the other end. Like, I think that'd be really interesting. Uh, and then there's gonna be, like, more data transactions,

  9. 28:50 – 29:35

    Data’s Role in Biotech

    1. SW

      I think, as well. Like, data wasn't... is not... uh, is interesting. For, 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.

    2. SG

      Mm-hmm.

    3. SW

      That's 'cause no one trusts anyone else's data. You, 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, 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-

    4. SG

      If there was tooling and normalization about it-

    5. SW

      Yes.

    6. SG

      ... you might be able to transact on it.

    7. SW

      Yes. Yeah.

    8. SG

      Okay.

    9. SW

      And, like, will people be selling, like, their negative data at some point into some pool that other people can learn from? I don't know. There's all kinds of crazy stuff I can think of.

    10. SG

      I'm sure you've heard in the 13 years-

    11. SW

      Yeah.

    12. SG

      ... you've been building at Benchling-

    13. SW

      Yeah.

    14. SG

      ... the conventional wisdom is that,

  10. 29:35 – 31:28

    The Importance of Tools in Pharma

    1. SG

      um, 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-

    2. SW

      Yeah.

    3. SG

      ... before and they weren't as embedded as they needed to be.

    4. SW

      Yeah, I, 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, 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, when we started Benchling in 2012, cloud was, like, the norm everywhere, but most of life science was, like, paper.-on premise spreadsheets.

    5. SG

      (laughs) Okay.

    6. SW

      Like, so like, we spent the first couple years, like, like-

    7. SG

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

    8. SW

      Yeah.

    9. SG

      Yeah.

    10. SW

      I mean, and that is because like, you, you could argue like, hey, eh, we're, 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, get to mar- 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-

    11. SG

      Mm-hmm.

    12. SW

      ... odds of, odds of success. So, we used the first couple years basically just evangelizing, like, bring science online. Like, it's going to be better. And then we spent the next, like, ten years after that kind of convincing people that structured data mattered, and, 'cause that's, that's sort of, like, the core premise of Benchling. It's, the system of record will help you have, like, a data model, and every time you do experiments, that data model's getting populated with information, you can ask questions. And there's a set of people who, they got it, and they believed, and there’s-

    13. SG

      Seems obvious to tech people.

    14. SW

      It seems obvious-

    15. SG

      Yeah.

    16. SW

      ... but there's like, it's not for free. Like-

    17. SG

      Yeah.

    18. SW

      ... a piece of paper is much easier, an Excel spreadsheet is much easier. Uh, but there's a set of people who believe and a set of people who maybe weren't convinced. But now, with like, AI, one, I think the benefits are a much

  11. 31:28 – 34:55

    AI’s Impact on Scientific Research

    1. SW

      more immediately obvious to everyone. And so that's gonna be this amazing tailwind to try to, like, do better here, and I think it will convince a lot of people who might have been skeptics in the past.

    2. SG

      Yes, I don't come at that from a holier than thou view, because one might actually claim that, uh, in venture investing, the only thing that matters-

    3. SW

      Is the winner.

    4. SG

      ... is the quality of the next decision-

    5. SW

      Yeah.

    6. SG

      ... and whether or not you found the winner. And so, it's a lot of tech people with a lot of, um, pen and paper actually.

    7. SW

      Yeah, yeah, yeah.

    8. SG

      Um, and so, but, but I, 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-

    9. SW

      Drugs. (laughs)

    10. SG

      ... AI for dru- drug discovery. Like, and y- you know, I, 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, um, just roundly good for humanity if we have more medicines, as you said.

    11. SW

      It's like a, it's like when the, 10 years ago, when the crypto people were like, "Ah, it's all international remittances." (laughs)

    12. SG

      Right. Um, 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.

    13. SW

      I, I go back to that, sort of, returns to intelligence piece, where I think science is a, is a problem that has some shape 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-

    14. SG

      Mm-hmm.

    15. SW

      ... these are kind, 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.

    16. SG

      Mm-hmm.

    17. SW

      And so the idea that, like, you could be, like, truly standing on the shoulders of giants I think is very appealing of, now I've got a scientist and I'm an 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 science that, again, is so artisanal and efficient that it seems like a problem that AI's gonna be, be much better at. I think that's one thing. Uh, I think the other is, like, I mean, it is, like, I think bio is... I don't know, like, I, 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-

    18. SG

      'Cause the failure rate's so bad. (laughs)

    19. SW

      The failure rate's so bad and, like, the impact is huge. I think everyone's-

    20. SG

      Yeah.

    21. SW

      ... now seen what, like, GLP-1s can do. Everyone saw what COVID vaccines can do.

    22. SG

      Magic, yeah.

    23. SW

      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 away software engineers or whatnot, like, what's left? Like, gotta make drugs for people.

    24. SG

      All right. More s- more scientists.

    25. SW

      (laughs) Yeah.

    26. SG

      And what about the partnership?

    27. SW

      Um, yeah, so we, we have a partnership with A- Anthropic. Um, I think we feel very... We, we work with, we use, sort of, the, all the foundation model ops capabilities, but like, uh, we found that there's like a strong commitment to science from Anthropic. I mean, Dario's a, a scientist and so it's been really, uh, really good mission alignment, uh, 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 technology is one that's really appeals to them. And so it's one where, just as a start, like, Benchling and Claude kind of like natively inter-operate very well, so if you wanna, you know, work through Benchling and Claude or you want to, uh, it works pretty effectively so scientists can, you know, generate reports, ask questions and things like that from a very simple, uh, AI interface that they're, they're used to. And I think it's just, like, the start with, with them.

    28. SG

      Can we talk a little bit about company building?

  12. 34:55 – 40:18

    Building a Biotech Company

    1. SG

    2. SW

      Sure.

    3. SG

      Just, you know, 13 years of wisdom-

    4. SW

      Yeah.

    5. SG

      ... in like two minute takes.

    6. SW

      E- every mistake made at this point.

    7. SG

      Maybe we'll start with the most recent, um, like hard decisions, uh, 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-

    8. SW

      (laughs)

    9. SG

      ... um, and our mutual friends. Um, 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-

    10. SW

      Yeah.

    11. SG

      ... at scale. Yeah.

    12. SW

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

    13. SG

      Mm-hmm. Okay, all of us.

    14. SW

      Uh, I, yeah, you know, one of the interesting things i- like, I, 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."

    15. SG

      Mm-hmm.

    16. SW

      And like, B, "If I look stupid, that's okay."

    17. SG

      Nice. (laughs) Yeah.

    18. SW

      Uh, and so, like, it's- 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 okay?" I was like-

    19. SG

      Uh, uh.

    20. SW

      ... "No, no, no." (laughs)

    21. SG

      Metaphorically.

    22. SW

      Metaphorically.

    23. SG

      Yeah, yeah.

    24. SW

      Didn't quit, just going, going full time on AI.

    25. SG

      Yeah.

    26. SW

      Um, and it was, it was controversial. Like, uh, biotech's been obviously, like, had this kind of dotcom level bust, and so a lot of our, you know, our team is, is feeling like, "Hey, we gotta 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-

    27. SG

      Yeah, how do you invest like that? Yeah.

    28. SW

      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. Ashu is 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, and also, like, we, we realized, like, if we don't do this for our customers, like, who is going to do it?

    29. SG

      Mm-hmm.

    30. SW

      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, in very complex regulated domains that we felt like we're, we're the right people for.

  13. 40:18 – 43:06

    Interdisciplinary Collaboration in Biotech

    1. SG

      be useful for a lot of people today, including me, is Benchling knows how to get scientists to work in a software company-

    2. SW

      Hmm.

    3. SG

      ... and work around a software company. Like, I work with many more research scientists, uh, in, in different fields, than... Well, actually, it's some in bio, but than I, uh, anticipated.

    4. SW

      Mm-hmm.

    5. SG

      Let's say five years ago-

    6. SW

      Yeah (laughs) .

    7. SG

      ... when I was just, like, good old engineering.

    8. SW

      Yeah.

    9. SG

      Um, like... And it is philosophically different.

    10. SW

      Mm-hmm.

    11. SG

      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." So how-

    12. SW

      Yeah.

    13. SG

      Like, what advice do you have on, um, like, recognizing that talent, getting them to be productive, managing it?

    14. SW

      That is a really hard question. I have, I have serious battle scars, uh, of that. We've had to build a very interdisciplinary company to be successful, that if, if I was only hiring, like, software people who knew bio, I would've, like, exhausted the pool 10 years ago. It just doesn't exist.

    15. SG

      Mm-hmm.

    16. SW

      We have to take the software people, take the science people, make 'em 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, um, where actually a lot of, like... Coming from the world of science, especially academia, it's just a very different incentive structure. And in the world of academia, like, your labor is basically free.

    17. SG

      (laughs)

    18. SW

      And so, like-

    19. SG

      (laughs)

    20. SW

      Or it's like very, very, very cheap, right? Grad student labor. And, like, the currency is publishing a paper.

    21. SG

      Yeah.

    22. SW

      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.

    23. SG

      (laughs)

    24. SW

      Um, 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 attention has come from, like, that, the need to do that.

    25. SG

      Mm-hmm.

    26. SW

      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, where are the other, like, next 10 companies building software for science and R&D that are gonna power the next discoveries of these biotech and pharma companies? Like, we're, we're, like, I think we're the only independent, like, scaled player doing this at this point.

    27. SG

      Do you interview at all for this orientation?

    28. SW

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

    29. SG

      I have a friend who's a founder, um, who asked what I, I don't find to be a controversial question because it's literally in my title.

    30. SW

      Yeah.

  14. 43:06 – 48:16

    Tech and Biotech: Learning from Each Other

    1. SG

      You're at your core a software person. Like, what can these two worlds learn from each other?

    2. SW

      Hmm. Great, great question. I think the biotech and pharma world can learn something from how tech communicates and tells stories.

    3. SG

      Hmm.

    4. SW

      Uh, like, there's the whole go direct wave in tech right now that I, I really, really resonates with me as a, as a founder.

    5. SG

      Mm-hmm.

    6. SW

      Uh, I think, like, biotech and pharma companies need to tell their stories. Um, like most people-

    7. SG

      This is not what I thought you were gonna say.

    8. SW

      Yeah.

    9. SG

      Okay.

    10. SW

      No. Oh. I'm curious what you thought I was gonna say. Um, 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? For-

    11. SG

      Mm. Yeah.

    12. SW

      ... a lot of them. And, or Jensen or something like that.

    13. SG

      There's a lot of hero's journey in tech.

    14. SW

      Yeah. A, a lot of hero's journey in tech.

    15. SG

      Yeah.

    16. SW

      Um, but like, i- and I think talking about the patients and scientists though would, like, change a lot of the public perception of biotech and science. I don't think people know how hard it is to like make a medicine. I mean, I was, you know, I was just thinking recently like, you, 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-

    17. SG

      Mm-hmm.

    18. SW

      ... 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-

    19. SG

      Mm-hmm.

    20. SW

      ... Gilead's like cured HIV-

    21. SG

      Mm-hmm.

    22. SW

      ... basically at this point, and like most people have no idea.

    23. SG

      Mm-hmm.

    24. SW

      Like, and so I think like because sort of the, the way they communicate is much more about almost these like faceless companies rather than the people-

    25. SG

      Mm-hmm.

    26. SW

      ... I think it's like easy to, easy to hate on them and underappreciate. So I think they, they need to tell their story and go, go direct.

    27. SG

      Yeah.

    28. SW

      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 in, in sort of gone from, you know, very, uh, so the ambitions of tech have grown a ton. Uh, 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-

    29. SG

      Mm-hmm.

    30. SW

      ... 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, uh, for how to like deliver medicines safely to people. I think that stuff matters more and more.

Episode duration: 48:13

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