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

Speaker 1 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.

Speaker 1 Today we talk about the state of AI and 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.

Speaker 1 Saji, thanks so much for being here.

Speaker 2 Thanks for having me, Sarah. Excited to be here.

Speaker 1 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? Sure.

Speaker 2 So 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.

Speaker 1 Oh my God. I know.

Speaker 2 So I'm a software engineer by background, but I worked in a biology lab.

Speaker 2 I was like really interested in medicine and coming from the world of software and software developers have amazing tools for working on code and for collaborating.

Speaker 2 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.

Speaker 2 And I think that was really frustrating for me personally. And, you know, I thought, a little bit naive at the time, I thought, how hard would it be to build good tools for scientists?

Speaker 2 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.

Speaker 2 Today we work with about 1,300 biotech and pharma companies, scientists at over 7,000 academic institutions, universities all around the world.

Speaker 2 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 Zera and companies like that.

Speaker 2 So we get to see the innovation happening across the entire biotech sector and then build software that helps power it.

Speaker 1 I'm super excited to like actually use that vantage point and ask you a bunch of questions about bio and the macro.

Speaker 1 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. Sure.
And like the assay like said yes or no.

Speaker 1 Like what other types of, what is the data that's actually invented?

Speaker 2 I think what's really interesting for everyone to understand is like making a drug, there's like 9,998 steps in making a drug after you come up with a molecule.

Speaker 2 So you have to, to make a medicine, you have to find a biologically meaningful target in the body, something you want to drug. You have to design a molecule to optimize that molecule.

Speaker 2 You have to test that molecule in petri dishes and cell lines and animals, various kinds of animals.

Speaker 2 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.

Speaker 2 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 so that eventually in seven to 10 years, you can have a drug that you give to people commercially.

Speaker 2 And even then, there's 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.

Speaker 2 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 scale-up data coming out of the fermenters when you're figuring out the process to manufacture it.

Speaker 2 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.

Speaker 1 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.

Speaker 1 Can you just characterize like where we are in the biomacro cycle?

Speaker 2 Yeah, yeah. And I'm definitely not a sort of macro specialist.
Otherwise, I'd probably be, you know, an investor or something like that.

Speaker 1 But it's all your customers.

Speaker 2 Yeah.

Speaker 2 It is.

Speaker 2 I would say like biotech has, it is definitely an industry that has gone through cycles.

Speaker 2 We're probably like the last couple of years are probably like the equivalent of like the dot-com bust happening for

Speaker 2 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.

Speaker 2 And there was 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.

Speaker 2 There was 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.

Speaker 2 become commercially successful as fast as people wanted. So a whole confluence of factors there.

Speaker 1 What are you referring to in terms of scientific technology?

Speaker 1 Got people hyped.

Speaker 2 Yeah, like I would say there's a lot of generalist excitement for gene editing, selling gene therapies, RNA, and all of these.

Speaker 1 So new delivery methods. Yeah.

Speaker 2 New, like, I would call them kind of categories or form factors of medicines.

Speaker 2 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

Speaker 2 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.

Speaker 2 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.

Speaker 2 And we're kind of in the trough of disillusionment for some of them now.

Speaker 1 And my understanding is that they have taken longer and been more expensive

Speaker 1 than people expected or than investors expected.

Speaker 2 Absolutely. Maybe the scientists.
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.

Speaker 2 And here's hundreds of millions of dollars and capital is free. And investors can change their strategies a lot faster than companies can.

Speaker 2 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 have been like simpler problems to solve with smaller patient populations.

Speaker 2 And then all of a sudden, you know, investors change their minds. Platforms are no longer valuable.
You're working on a thing that is just supposed to be a proof of concept.

Speaker 2 And all of a sudden, it's like defining you. And so it's a really tough spot for those companies to be in.

Speaker 1 What's the relevance of China and all this?

Speaker 2 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.

Speaker 2 And China is very good at things related to speed and cost.

Speaker 2 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,

Speaker 2 really fast and really cheap, even in some of these new modalities.

Speaker 2 So you've seen this huge uptick in pharma going to China and buying molecules that they typically would have bought from American biotechs.

Speaker 1 And this is like top 30 pharma. Yeah.
Yeah. Yeah.

Speaker 2 These are the biggest companies in the world, the Mercks and Pfizer's and Lillies and so forth.

Speaker 2 Many of them have like gone to China and bought molecules that historically they would have bought from American biotechs.

Speaker 1 Are there medicines that people would recognize like in the market today?

Speaker 2 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.

Speaker 2 They saw the data that Legend presented and, you know, it was a time when people were pretty suspicious. And so they were like, ah, it's like.
The data is probably not going to replicate.

Speaker 2 It might not be real, but like J and J, I think, saw it and realized how promising it was. And they've taken it.
And it's a, it's actually a cancer immunotherapy.

Speaker 2 So it kind of reprograms the human immune system. I think it's for multiple myeloma.

Speaker 2 And like that, that medicine is very commercially successful and widely distributed in the U.S. to those cancer patients.

Speaker 1 What's been the reaction of Western biotechs to this?

Speaker 2 It's a mixed bag. I think there's some folks who are like, it's kind of inspired that American biotech needs to be faster, cheaper, more competitive.

Speaker 2 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?

Speaker 2 So, some skepticism as well. But by and large, I think it's like very much here to stay that China is going to be like a major, major biotech player.

Speaker 1 I do want to get to

Speaker 1 macro. Yeah, I want to get to the meat of our discussion, which I also think is,

Speaker 1 you know, there's some premise that like the answer to faster, cheaper, better might in part be AI in biotech.

Speaker 2 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.

Speaker 2 So it's very good that everyone's excited by that.

Speaker 1 You, I don't think of you, I think there's an amazing CEO, but not really a content marketing guy to date.

Speaker 1 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?

Speaker 1 And then we'll link it in the show. Absolutely.

Speaker 2 Yeah,

Speaker 2 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.

Speaker 2 I think I think like we take for granted how awesome medicines are.

Speaker 2 I think 9% of healthcare spend, prescription drug sales are 9% of healthcare spending in the U.S. Like we have obviously this healthcare cost problem, but drugs are this amazing ROI.

Speaker 2 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.

Speaker 2 And like 20 years ago, it was some expensive medicine.

Speaker 1 And that's like. It's not obvious any other part of the healthcare system gets cheaper over time.
It's not. Yeah.

Speaker 2 The rest of healthcare is very labor dependent and labor generally gets more expensive over time. I am very optimistic for AI to help help there too.
But drugs are this amazing thing.

Speaker 2 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,

Speaker 2 generally about 10 years to bring a medicine to market. And most of those medicines will fail very late in this process.

Speaker 2 You get seven to 10 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.

Speaker 2 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.

Speaker 2 And I know $2 billion probably isn't that, you know, I feel like AI has desensitized us all.

Speaker 2 You know, everything is like, you know, $100 billion data centers and whatever, like $2 billion, like, what's that?

Speaker 2 But when there's that high of a failure rate, it's very difficult for investors to underwrite that. And that was, you know, while we had all these new categories of medicines being

Speaker 2 kind of invented over the last decade, I think that's like, that's important and it's here to stay. But the industry has to change.

Speaker 2 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.

Speaker 2 Like biotech is this place where, if you look, I'll sort of take the digital and physical realms for a second. Uh, they've actually done a good job of systematizing the physical realm.

Speaker 2 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.

Speaker 2 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

Speaker 2 how people collaborate, how data is structured and shared, the the workflows that are used in science, which is all about collecting data.

Speaker 2 All of that's basically bespoke and invented one-off by every company.

Speaker 2 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.

Speaker 2 So you're not really like building for scale and building for durability.

Speaker 1 That seems like it also comes from some of the structure of like where the innovation happens, right?

Speaker 1 Because if 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. Totally.
Yeah.

Speaker 2 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.

Speaker 2 But that's such a, that's like a high capital, long-term, high-risk thing to do. It's very hard.

Speaker 2 And after seven, eight years and you have some good clinical data, like, do I roll the dice again and keep going for another 10 or do I sell?

Speaker 2 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.

Speaker 2 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.

Speaker 2 And they're important, don't get me wrong, but I think it's actually a bit of a red, red herring where

Speaker 2 Yes, there are operational problems, like some studies are designed badly.

Speaker 2 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.

Speaker 2 And so we need better molecules and we need to move them to people faster.

Speaker 1 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.

Speaker 1 Right. And so examples of GLP1s and Ketruda, like some of the amazing breakout successes were not super obvious to the buyers.

Speaker 2 Yeah, I think those two stories are really interesting. And there's a great quote from Dario, the Anthropic CEO, and

Speaker 2 his kind of essay about the returns to intelligence in scientific progress are very high.

Speaker 1 You're talking about machines of love and grace. Yeah.

Speaker 2 Yeah. So the returns to intelligence are very high.
And I think like the stories of GLP1s and Ketruda are like great, great examples of that.

Speaker 2 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.

Speaker 1 Why do you think it was unfundable?

Speaker 2 I think like, again, because we know so little about biology and there's so many failures in that space.

Speaker 2 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.

Speaker 2 Like pharma companies generally aren't willing to underwrite that stuff sometimes. I mean, neurodegenerative diseases are the same way.
Like Alzheimer's is just a graveyard of

Speaker 2 billion dollar failures and like it's getting back in now. But there's a period period of time where everyone left the space.

Speaker 2 And so, but the core science for GLP1s was kind of sitting on the shelf in some sense. Like, it's been known since like the 90s.
Um, and so it took some insights and conviction.

Speaker 2 And then, all of a sudden, like, we have this category-defining medicine that's going to go on to probably be the best-selling drug of all time. And that's happening.

Speaker 2 And then, Ketruda 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 to Ketron.

Speaker 1 People are giving up on it. Yeah.

Speaker 2 And then a competitive thread pops up and someone sees that, hey, this is kind of like Key Trudeau.

Speaker 2 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.

Speaker 2 So it's just another example of like, there's, there's a lot, it's a pretty inefficient system. It's, and people are pretty, they're rational actors.

Speaker 2 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.

Speaker 2 And so if you can get to the clinic faster, 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.

Speaker 2 But like in biotech, you're underwriting, you know, four, five, six years, big team, hundreds of millions of dollars. So like, how do you compress that so you get feedback faster?

Speaker 1 So benchling is a system of record company. It's a data platform.
What is benchling AI?

Speaker 2 Benchling AI has kind of two major components to it. The first is tools for simulation.

Speaker 2 So this is taking open source, proprietary companies' internal models and making making them accessible to scientists directly in their workflow.

Speaker 2 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.

Speaker 2 And then the results are linked to all of their other information in Benchling.

Speaker 2 And then we also see that laddering up to being able to help scientists recommend like help recommend for scientists the next best experiment.

Speaker 2 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 it's like an exciting way to approach the co-scientist problem.

Speaker 2 Then the other facet of benchling AI is agents that automate work for you. And so we've released this deep research agent.

Speaker 2 It works similar to the deep research agents from Anthropic and other foundation labs. But what it does is it works over benchling data with the context of the benchling data model.

Speaker 2 And so it enables scientists to ask these very difficult, and science is fundamentally about like asking and answering questions.

Speaker 2 And so for our customers, it helps them to do a type of question that previously in the past would have taken weeks or months to do and do that in just a couple hours.

Speaker 2 So a great example of this is we had a customer that was getting ready to run some mouse studies.

Speaker 2 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.

Speaker 2 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.

Speaker 2 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.

Speaker 2 And so there's so much of science that lives in like folklore and institutional knowledge and that's kind of lost over time.

Speaker 2 And so we sort of view this as being able to unlock like memory for these organizations and help make scientific data reusable over time.

Speaker 1 And they could just accelerate because they didn't have to do that piece of experimentation anymore.

Speaker 2 Exactly.

Speaker 2 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 voice and vision and things like that.

Speaker 1 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

Speaker 1 scientists like a couple of years out?

Speaker 2 Oh, wow, that's so interesting. So yeah, when I hear sort of AI scientists, I think it definitely evokes this image of a kind of fully AIified, you know, design, make, test, analyze loop.

Speaker 2 And 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.

Speaker 2 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 a little bit more bullish on sort of the augmentation model.

Speaker 2 Like I kind of think of it as like a Waymo versus Tesla approach, where you can do the Waymo approach to autonomy. You just need a lot of money and a lot of patience.
And it's going to take some time.

Speaker 2 I think the Tesla approach has been a little bit more, I would say, taking steps. I don't want to call it incremental because it's not.

Speaker 2 And so I think if you can kind of get those ingredients to take the Waymo approach, which some companies have, that's awesome.

Speaker 2 But I think for the rest of science, there's a huge opportunity to just like

Speaker 2 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.

Speaker 2 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 uh but i think the model that's worked i think like 40 yeah probably yeah 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 i don't know get sued if something goes wrong

Speaker 1 Yeah. I mean, that makes sense to me in clinical practice.
I'm more hopeful that like some of the experimental decisions can be more automated. But one question that I think biology faces,

Speaker 1 that other fields in AI face as well, is the question of like.

Speaker 1 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.

Speaker 1 I didn't really read it. I don't know if that's a good architectural decision.
Like, what's happening? Yeah.

Speaker 1 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. Yeah.

Speaker 2 I, I think right now, when I, when I look at biotech, we are in,

Speaker 2 so that's absolutely like the right point of like, are scientists going to trust this? How do we know if it's accurate?

Speaker 2 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 bio AI companies, from everyone.

Speaker 2 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.

Speaker 2 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 happened.

Speaker 2 We haven't figured out what that is in bio yet.

Speaker 2 We have some ideas, but by and large, and I just got back from a month on the road and I was in Boston, London, a bunch of other places that are sort of scientific capitals outside of SF.

Speaker 2 And like most people aren't really using that much AI and R ⁇ D yet. They all want to.
They're primed to, but there's a lot of concerns about accuracy, IP,

Speaker 2 security, legal. And I think the farther you go from SF, the

Speaker 2 larger those

Speaker 2 concerns get.

Speaker 1 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.

Speaker 2 Yeah, I think that's like, I think legible enough to be useful.

Speaker 2 I think in a vertical, I think 90% of the work is actually like translation. It's taking something and

Speaker 2 making sure scientists trust it. It's the right point in their workflow.
It's easy to use and it's accurate. I think the AI that wins is going to be the one that people actually use.

Speaker 1 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.
Oh, yeah.

Speaker 2 There's excitement

Speaker 2 for sure. There is optimism and belief.

Speaker 2 I think they're pretty pragmatic though. And I think

Speaker 2 they're all looking to transform, but they're being methodical.

Speaker 2 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.

Speaker 2 And they're doing a lot of pilots of different technologies, but I haven't seen their RD orgs transformed yet.

Speaker 2 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.

Speaker 1 Like experimental data generation.

Speaker 2 They can generate data to train their own models at a scale that most biotech startups can't match.

Speaker 2 So I think while it's early on sort of the agent tech 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.

Speaker 1 What do you like? Can you help characterize

Speaker 1 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

Speaker 1 overall cycle time or rate of success, whatever you think is more important here. Yeah.

Speaker 2 It's been really cool to see like the whole ecosystem of these tools grow a ton.

Speaker 2 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.

Speaker 2 It's you're seeing all these interesting models come out, like the bolts, for example, on the structure prediction side.

Speaker 2 And I think like that's like a really interesting thing that's going to change biology. I think you're seeing new approaches to like federated learning as well.

Speaker 2 Eli Lilly put out this announcement about a project they have called Toon Lab, where they're taking their internal models and making them available to the broader scientific ecosystem.

Speaker 2 So, at pharma companies saying, Hey, you can use our models, but it's give to get. So, we get to train and like it's federated or and whatnot.
So, they don't get to see the actual scientific data.

Speaker 2 But I think like those approaches are like, this is the beginning, basically. And so, we've got some really cool stuff.

Speaker 2 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's really good.
But there's, I would say,

Speaker 2 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.

Speaker 2 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 much faster, for example, at

Speaker 2 making molecules and you have a higher success rate, you still have to come up with a process to manufacture that molecule.

Speaker 2 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 a lot of yield, speed, cost out of, out of a molecule?

Speaker 1 Is that like where you would say the highest value missing predictive model opportunity is?

Speaker 1 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

Speaker 1 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.

Speaker 2 Yeah, I feel like the naysayers kind of have this sort of worldview in mind where it's like, oh, I just like type a disease and then then I like get a molecule out and like

Speaker 2 amazing AI discovered drugs. And then this is where I go back to like my mental model is like there are so many steps.
Those steps are all cumbersome and difficult.

Speaker 2 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.

Speaker 2 But like there's still many, many years after that that we can compress and shape off.

Speaker 2 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?

Speaker 2 And I bet that share is like getting higher every day.

Speaker 1 Part of what I think has been really interesting, and there's like good and bad about the investor enthusiasm of,

Speaker 1 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.

Speaker 1 Do you think that's going to happen?

Speaker 2 I would like it to happen. I think right now for companies, I mean, so one, as a, as a toolmaker, I think there should be more tools.
Tools are good.

Speaker 2 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?

Speaker 2 I think it's unlikely. It's possible, but it's unlikely that sort of.

Speaker 2 hey, they're just going to remain pure model companies who just do deals with pharma where pharma pays them $100 million up front or something like that. And they have five customers and whatnot.

Speaker 2 Like, I feel like the

Speaker 2 sort of model building is probably commoditizing too fast for that to be a tractable business model.

Speaker 2 But to take that expertise and to make, to be fundamentally better at doing research and early development to make molecules and sort of morphing into a biopharma company, like that seems like one logical path.

Speaker 2 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 community.

Speaker 2 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 Benchling.

Speaker 2 They're mostly open source, but also we've got, you know, we've got Chai, we've got AlphaFold, things like that.

Speaker 2 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 company is benefiting that, and you can build models and like have a scalable business model on the other end?

Speaker 2 Like, I think that'd be really interesting. And then there's going to be like more data transactions, I think, as well.

Speaker 2 Like data wasn't is not, it's 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.

Speaker 2 That's because no one trusts anyone else's data.

Speaker 2 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.

Speaker 2 You don't know what format it's in. Do you trust the way it was created? Like, it's just not.

Speaker 1 If there's tooling and normalization about it, you might be able to transact on it. Yeah.

Speaker 2 And like, will people be selling like their negative data at some point into some pool that other people can learn from that? It was all kinds of crazy stuff I can think of.

Speaker 1 I'm sure you've heard in the 13 years you've been building benchling, the conventional wisdom is that the only way to create value in pharma is assets, not tools, right? Like, where were they wrong?

Speaker 1 Or maybe the tools just weren't that important before and they weren't as embedded as they need to be.

Speaker 2 Yeah, I don't know if this is a

Speaker 2 Thermo Fisher and Danaher are like sneaky big companies. And I think people don't always realize that.
And they've done it largely by like systematization of tools in the physical realm.

Speaker 2 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.

Speaker 2 I think, just frankly, like looking back, the technology probably hasn't been there.

Speaker 2 Like, when we started benchling in 2012, cloud was like the norm everywhere, but most of life science was like paper, on-premise spreadsheets.

Speaker 2 Okay. So, like, we spent the first couple years like

Speaker 1 wild for such an advanced field in other areas. Yeah.

Speaker 2 I mean, and that is because like you, you could argue, like, hey,

Speaker 2 we're, we're, it's such a like

Speaker 2 high-stakes game of poker for them that the only thing that matters is like, does this drug get to, get to, get to patients and is successful?

Speaker 2 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.

Speaker 2 So we spent the first couple of years basically just evangelizing like. bring science online.
Like it's going to be better.

Speaker 2 And then spent the next like 10 years after that kind of convincing people that structured data mattered. And because because that's, that's sort of like the core premise eventually.

Speaker 2 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.

Speaker 2 And there's a set of people who they got it and they believed.

Speaker 1 And it seems obvious to tech people.

Speaker 2 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.

Speaker 2 But there's a set of people who believed and a set of people who maybe weren't convinced. But now with like AI, one, I think the benefits are much more immediately obvious to everyone.

Speaker 2 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 have been skeptics in the past.

Speaker 1 Yes, I don't come at that from a holier than that view because one might actually claim that in venture investing, the only thing that matters is

Speaker 1 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.
Yeah.

Speaker 1 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.

Speaker 1 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

Speaker 1 roundly good for humanity if we have more medicines, as you said.

Speaker 2 It's like on the 10 years ago when the crypto people were like, ah, it's all international remittances.

Speaker 1 Right.

Speaker 1 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.

Speaker 2 I go back to that sort of returns to intelligence piece where I think science is a

Speaker 2 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 this vast pool of unstructured text.

Speaker 2 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.

Speaker 2 And so the idea that like you could be like truly standing on the shoulders of giants, I think is very appealing. Now I've got a scientist and I'm in an early stage biotech.

Speaker 2 And now I can have access to the world's best like clinical design expert or the world's best toxicologist or the best research assistant who can even read papers better than me to figure things out.

Speaker 2 Like there's a lot about science that, again, is so artisanal and inefficient that it seems like a problem that AI is going to be much better at. I think that's one thing.

Speaker 2 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 in bio? Like, there are big problems to be solved.

Speaker 2 There's an incredible like because the failure rate's so bad. The failure rate's so bad and like the impact is huge.
I think everyone's now seen what like GLP1s can do.

Speaker 2 Everyone saw what COVID vaccines can do. Magic, yeah.
Yeah. It's like when it works, it's magic and like people need this stuff.

Speaker 2 And so like, I don't know, if AGI starts, you know, automating away as software engineers or whatnot, like, what's left? Like, Got to make drugs for people.

Speaker 1 All right, more scientists.

Speaker 2 And what about the partnership? Yeah, so we have a partnership with Anthropic.

Speaker 2 I think we feel very, we work with, we use sort of the, all the foundational model labs capabilities, but like we found that there's like a strong commitment to science from Anthropic.

Speaker 2 I mean, Dario is 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 code.

Speaker 2 And I think for our customers, trust is super, super important. And I think their posture plus their technology is one that really appeals to them.

Speaker 2 And so it's one where just to the start, like Benchling and Claude kind of like natively interoperate very well.

Speaker 2 So if you want to, you know, work through Benchling and Claude, or you want to, it works pretty effectively.

Speaker 2 So scientists can, you know, generate reports, ask questions, and things like that from a very simple

Speaker 2 AI interface that they're used to. And I think it's just like the start with them.

Speaker 1 Can we talk a little bit about company building? Sure. Just 13 years of wisdom and like two minute takes.

Speaker 2 Every mistake made at this point.

Speaker 1 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.

Speaker 1 I called a bunch of friends around this company

Speaker 1 and our mutual friends.

Speaker 1 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.

Speaker 2 It's funny.

Speaker 2 I think we started early, but at the same time, I feel late still.

Speaker 1 Okay, all of us.

Speaker 2 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.

Speaker 2 And I think like, this was a, it was a pretty controversial decision in our company.

Speaker 2 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.

Speaker 2 And like, B, if I look stupid, that's okay. Nice.
Yeah. 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.

Speaker 2 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 oh no no metaphorically metaphorically didn't quit just going going full-time on ai yeah um and it was it was controversial like uh biotech's been obviously like had this kind of dot-com level bust and so a lot of our you know our team is 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 yeah how do you invest like that 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.

Speaker 2 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.

Speaker 2 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?

Speaker 2 Like, again, it goes back to needing to translate some of these amazing things that come out of the Bay Area and Silicon Valley into like useful vertical applications in very complex regulated domains that we felt like we're the right people for.

Speaker 1 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.

Speaker 1 Like what has been hardest? What are some lessons from that?

Speaker 2 My, I think like the... My like most trusted algorithm for this is like go talk to customers.
And all the, I know, super obvious, right?

Speaker 2 But all the times in which like I feel like the company has been like at its lowest or at its worst or like I'm feeling at my lowest, it's it's because I've gotten too far from customers.

Speaker 2 And like product market fit is this moving target. And sometimes your market changes.

Speaker 2 Like our market changed of like, you had a bunch of biotech companies that were going to take over the world and then like, then they weren't.

Speaker 2 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.

Speaker 2 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.

Speaker 2 And like, that's the thing I have to role model. So the whole company does it.
And that's the number one piece of advice I'd give people.

Speaker 1 So that answers part of one question from another friend who used to work for you.

Speaker 1 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.

Speaker 1 But given, you know, Benchling is a complicated company, biology is a complicated field. Like, how do you decide what to focus on and then like focus your team on?

Speaker 2 That's a nice compliment, though. It's like telling someone they have a large context window.

Speaker 1 It is. good model, man.
Yeah.

Speaker 2 Thank you. Thank you to whoever said that kind thing.

Speaker 1 It was Malay. Hi, Malay.
Yeah.

Speaker 2 I, you know, 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.

Speaker 1 Much less great software. Yeah, yeah.

Speaker 2 There are just not many. And so you have a very underserved demographic where they're not used to someone coming and sort of asking what they specifically need.

Speaker 2 They're used to very general purpose horizontal software and trying to like

Speaker 2 do klooji things to make it work.

Speaker 2 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.

Speaker 2 But then you like talk to enough customers and it turns out they almost all want the same thing.

Speaker 2 I find that having five, 10 customers is actually like a pretty representative model for what the entire industry needs.

Speaker 2 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, I thought has been successful.

Speaker 2 It's 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.

Speaker 2 And like, maybe that's like the YC 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.

Speaker 2 Even for our new AI tools,

Speaker 2 we have all of our customer base.

Speaker 2 They're available to all of our customers now, but our team is on like daily, weekly calls with five or 10 customers to the point of like, 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.

Speaker 2 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.

Speaker 1 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.

Speaker 1 Like I work with many more research scientists in different fields than, well, actually, it's some in biome, but than I anticipated, let's say five years ago when I was just like good old engineering.

Speaker 1 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.

Speaker 1 So like, what advice do you have on like recognizing that talent, getting them to be productive, managing it?

Speaker 2 That is a really hard question. I have, I have serious battle scars of that.
We've had to build a very interdisciplinary company to be successful.

Speaker 2 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.

Speaker 2 We have to take the software people, take the science people, make them sit together, learn from each other.

Speaker 2 I actually, and this is going to sound obvious, but I've actually found that like the most conflict has been around

Speaker 2 the like actually like how the mission gets solved.

Speaker 2 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.

Speaker 2 So like there is like very, very, very cheap, right? Grad student labor. And like the currency is publishing a paper.
Yep. And that's how you get more funding to do more things and so forth.

Speaker 2 Whereas in a company, like we have to sell software.

Speaker 2 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.

Speaker 2 And a lot of attention has come from like that, the need to do that.

Speaker 2 And so making sure our scientific teams really understand that the better we do as a business, the more amazing innovation we can bring to our customers.

Speaker 2 And by the way, if we don't do this, who else is going to do it?

Speaker 2 Where are the other next 10 companies building software for science and R D that are going to power the next discoveries of these biotech and pharma companies?

Speaker 2 Like where we're like, I think we're the only independent

Speaker 2 scaled player doing this at this point.

Speaker 1 Do you interview at all for this orientation?

Speaker 2 I think we try try to. I don't know that we've like I have some amazing predictive way to define it.

Speaker 1 I have a friend who's a founder who asks what I don't find to be a controversial question because it's literally in my title, right?

Speaker 1 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, I will try that.

Speaker 1 I'm not recommending it, but I do think it is

Speaker 1 interesting the set of answers you get, actually, because i i have tried it one more question for you on culture you spent 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 great great question i think the biotech and pharma world can learn something from how tech communicates and tells stories.

Speaker 2 Like there's the whole go direct wave in tech right now that I really, really resonates with me as a founder.

Speaker 2 I think like biotech and pharma companies need to tell their stories.

Speaker 2 Like most people.

Speaker 1 This is not what I thought you were going to say.

Speaker 2 I'm curious what you thought I was going to say.

Speaker 2 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.

Speaker 2 Everyone knows who you say Sam and it's like first name basis, right? For a lot of them and or Jensen or something like that.

Speaker 1 There's a lot of heroes journeying. A lot of heroes journey in tech.

Speaker 2 But like,

Speaker 2 and I think talking about the patients and scientists, though, would like change a lot of the public perception of biotech and science.

Speaker 2 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,

Speaker 2 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.

Speaker 2 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.

Speaker 2 And like there was tons of panic and fear about it in the 90s and early 2000s. And like, Gilead's like cured HIV, basically, at this point.
And like, most people have no idea.

Speaker 2 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, I think it's like easy to, easy to hate on them and underappreciate.

Speaker 2 So I think they need to tell their story and go direct. Yeah.
That's one thing.

Speaker 2 I think on what tech can learn from bio is I think once you start, I think sort of tech has sort of become everything in sort of gone from, you know, very

Speaker 2 sort of ambitions of tech have grown a ton.

Speaker 2 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.

Speaker 2 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.

Speaker 2 biopharma, again, for all the things that are difficult with this, has figured out how to be safe. Like the U.S.
is still the gold standard for how to like deliver medicines safely to people.

Speaker 2 I think that stuff matters more and more.

Speaker 1 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.

Speaker 1 It was probably a year and a half ago about like,

Speaker 1 well, you know,

Speaker 1 how can I better use these models in very

Speaker 1 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

Speaker 1 this is an amazing research scientist, but I'd be like, that seems unambitious, right? Because there are many benefits to intelligence in these fields that are really important to all of us.

Speaker 1 And so I do feel like more people should work with them too.

Speaker 2 Yeah. Yeah.
Yeah. I think in Silk Valley, sometimes it's a little bit easy to like oversimplify other people's jobs.

Speaker 2 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 AI.

Speaker 1 Last question for you.

Speaker 1 Something you're excited about in AI outside of bio or outside of like Benchling's immediate purview, I guess.

Speaker 2 On a personal level, uh i haven't written a line of code i had not written a line of code in probably

Speaker 2 i'm very embarrassed to say this uh maybe eight or nine it's been oh wow it's been some time like i remember i stopped coding around the time that react started being a thing like

Speaker 2 i'm dating i'm dating yeah all the all the kids listening are gonna turn off um 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.

Speaker 2 So like that, that's pretty cool.

Speaker 2 That's probably the one I'm most excited about. And then I'm excited for my like parents, actually,

Speaker 2 and for them to have like new technology that's pretty easy for them to access. Like my mom's a ChatGPT user, and I'm sure it's sort of Google search plus plus at this point, but like.

Speaker 1 that's been like pretty cool too of like technology that's so intuitive that like i don't have to like go home and be it support at christmas yeah i think it's actually undervalued as um just because there are audiences that are uh traditionally not as lucrative as like the fast tech adopting audience of your 15 to 40 year old.

Speaker 1 But it is incredibly wild how the UX of like natural language and voice are changing who can use it. Absolutely.

Speaker 1 AI tutoring for my kids and such. And

Speaker 1 same experience.

Speaker 2 Learning is such a joy with AI. Help them learn the right things, but like

Speaker 1 thanks so much for doing this, Saji.

Speaker 2 Thanks, Sarah.

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