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

Sarah Guo 0:00 (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.

Sajith (Saji) Wickramasekara 0:36 Thanks for having me, Sara. Excited to be here.

Sarah Guo 0:38 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?

Sajith (Saji) Wickramasekara 0:44 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.

Sarah Guo 0:54 Oh my God, yeah.

Sajith (Saji) Wickramasekara 0:54 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.

Sarah Guo 2:08 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-

Sajith (Saji) Wickramasekara 2:21 Sure.

Sarah Guo 2:21 ... and like the assay like said yes or no. Like what other types of... What is the data that's actually in Benchling?

Sajith (Saji) Wickramasekara 2:28 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.

Sarah Guo 2:45 Mm-hmm.

Sajith (Saji) Wickramasekara 2:45 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.

Sarah Guo 3:49 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?

Sajith (Saji) Wickramasekara 4:05 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-

Sarah Guo 4:10 But it's all your customers, yeah.

Sajith (Saji) Wickramasekara 4:11 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-

Sarah Guo 4:23 Okay, that's pretty bad, yeah.

Sajith (Saji) Wickramasekara 4:24 ... 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-

Sarah Guo 4:58 What are you referring to in terms of scientific technologies that-

Sajith (Saji) Wickramasekara 5:01 Uh, like the-

Sarah Guo 5:01 ... got people hyped?

Sajith (Saji) Wickramasekara 5:02 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-

Sarah Guo 5:09 So new delivery methods?

Sajith (Saji) Wickramasekara 5:10 Yeah.

Sarah Guo 5:10 Yeah.

Sajith (Saji) Wickramasekara 5:10 New, new like I would call them kinda categories or form factors of medicines. We... Mo- modalities is the word, but like-

Sarah Guo 5:15 Mm-hmm.

Sajith (Saji) Wickramasekara 5:16 ... 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.

Sarah Guo 5:49 Mm-hmm. And my understanding is that they have taken longer and been more expensive-

Sajith (Saji) Wickramasekara 5:54 Yes.

Sarah Guo 5:54 ... than people expected-

Sajith (Saji) Wickramasekara 5:55 Absolutely.

Sarah Guo 5:55 ... or than investors expected.

Sajith (Saji) Wickramasekara 5:57 Absolutely.

Sarah Guo 5:57 Yeah.

Sajith (Saji) Wickramasekara 5:57 In, in, in-

Sarah Guo 5:57 Maybe the scientists know.

Sajith (Saji) Wickramasekara 5:58 ... 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-

Sarah Guo 6:04 Mm-hmm.

Sajith (Saji) Wickramasekara 6:05 "... and here's hundreds of millions of dollars, and capital is free."

Sarah Guo 6:08 (laughs)

Sajith (Saji) Wickramasekara 6:08 And investors can change their strategies a lot faster than companies can and, you know, a lot of those companies, because they were taking such a big risk on, like, a new form of technology, "We're gonna be the next RNA company, next cell therapy company," they picked, uh, diseases that might have ha- been, like, simpler problems to solve with smaller patient populations. And then all of a sudden, you know, investors changed their minds, platforms are no longer valuable, you're working on a thing that, like, is just supposed to be a proof of concept and all of a sudden it's, like, defining you.

Sarah Guo 6:35 Mm-hmm.

Sajith (Saji) Wickramasekara 6:35 And so it was a really tough spot for those companies to be in.

Sarah Guo 6:37 Mm-hmm. What's the relevance of China in all of this?

Sajith (Saji) Wickramasekara 6:41 I think, so if, if the last decade was about sort of biologics and these new modalities, uh, I think the next is gonna be about speed and cost. Like, people want more drugs and they want them cheaper. And China is very good at things related to speed and cost. And so, all of a sudden within 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 ear- early phases of clinical development, uh, really fast and really cheap, even in some of these new modalities.

Sarah Guo 7:09 Mm-hmm.

Sajith (Saji) Wickramasekara 7:09 So we've seen this huge uptick in pharma going to China and buying molecules that they typically would have bought from American biotechs.

Sarah Guo 7:16 And this is, like, top 30 pharma?

Sajith (Saji) Wickramasekara 7:18 Yeah.

Sarah Guo 7:18 Yeah.

Sajith (Saji) Wickramasekara 7:18 Yeah, yeah. These are the biggest companies in the world, the Mercks and Pfizers and Lillys and, and so forth, many of them have, like, gone to China and bought molecules that historically they would have bought from American biotechs.

Sarah Guo 7:30 Mm. Are there medicines that people would recognize-

Sajith (Saji) Wickramasekara 7:33 Um-

Sarah Guo 7:33 ... like, in market today?

Sajith (Saji) Wickramasekara 7:35 One of the most notable medicines that people might recognize is called Carvykti, and it's a Johnson & Johnson medicine. So Johnson & Johnson partnered with a Chinese biotech called Legend Biotech. They saw the data that Legend presented and, you know, it was a time when people were pretty suspicious-

Sarah Guo 7:50 Mm-hmm.

Sajith (Saji) Wickramasekara 7:50 ... and so they were like, "Ah," it's like, "the data's probably not gonna replicate, it might not be real," but, like, J&J I think saw it and realized how promising it was and they've taken it, and it's a, it's actually a cancer immunotherapy, um, so it kinda reprograms the human immune system. I think it's for, uh, multiple myeloma-

Sarah Guo 8:06 Mm-hmm.

Sajith (Saji) Wickramasekara 8:06 ... um, and, like, that, that medicine is very commercially successful and widely distributed in the US to, to those cancer patients.

Sarah Guo 8:13 What's been the, um, reaction of, like, Western biotechs to this?

Sajith (Saji) Wickramasekara 8:17 Um, it's a mixed bag. Uh, I think there's some folks who are like, it's, you know, kind of inspired that American biotech needs to be faster, cheaper, more competitive. There's some more nationalistic reactions, I think, of like, "Hey," like, "well, there's different regulatory or ethical standards over there. Are the data, are they all going to replicate?" So some skepticism as well. But by and large, I think it's, like, very much here to stay that China's gonna be, like, a major, major biotech player.

Sarah Guo 8:43 Uh, I do want to get to the-

Sajith (Saji) Wickramasekara 8:44 Yeah, we can spend this whole time talking about macro.

Sajith (Saji) Wickramasekara 8:46 (laughs)

Sarah Guo 8:46 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.

Sajith (Saji) Wickramasekara 8:58 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."

Sarah Guo 9:08 Mm-hmm.

Sajith (Saji) Wickramasekara 9:08 So it's, it's very good that everyone's excited by that.

Sarah Guo 9:11 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?

Sajith (Saji) Wickramasekara 9:28 Yeah.

Sarah Guo 9:28 And then we'll link it in the show.

Sajith (Saji) Wickramasekara 9:29 A- absolutely.

Sarah Guo 9:30 Yeah. Yeah.

Sajith (Saji) Wickramasekara 9:30 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-

Sarah Guo 9:40 Mm-hmm.

Sajith (Saji) Wickramasekara 9:40 ... I think. I think, like, we take for granted how awesome medicines are.

Sarah Guo 9:43 Mm-hmm.

Sajith (Saji) Wickramasekara 9:44 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.

Sarah Guo 9:55 Ah.

Sajith (Saji) Wickramasekara 9:55 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-

Sarah Guo 10:09 It's not obvious any other part of the healthcare system gets cheaper over time.

Sajith (Saji) Wickramasekara 10:13 It's not, yeah.

Sarah Guo 10:13 Yeah. (laughs)

Sajith (Saji) Wickramasekara 10:13 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.

Sarah Guo 12:25 That seems like it also comes from some of the structure of, like-

Sajith (Saji) Wickramasekara 12:28 Totally.

Sarah Guo 12:28 ... 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-

Sajith (Saji) Wickramasekara 12:35 Yeah.

Sarah Guo 12:35 ... then you would invest in the systems.

Sajith (Saji) Wickramasekara 12:37 Totally.

Sarah Guo 12:37 Yeah.

Sajith (Saji) Wickramasekara 12:37 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.

Sarah Guo 13:32 (laughs)

Sajith (Saji) Wickramasekara 13:32 And so we need better molecules and we need to move them to people faster.

Sarah Guo 13:37 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-

Sajith (Saji) Wickramasekara 13:51 Mm-hmm.

Sarah Guo 13:51 ... and finding it as they could be.

Sajith (Saji) Wickramasekara 13:53 Mm-hmm.

Sarah Guo 13:53 Right? And so examples of GLP-1s and KEYTRUDA, like some of the amazing breakout successes-

Sajith (Saji) Wickramasekara 13:58 Mm-hmm.

Sarah Guo 13:59 ... were not super obvious to the buyers.

Sajith (Saji) Wickramasekara 14:02 Yeah.

Sarah Guo 14:02 You know?

Sajith (Saji) Wickramasekara 14:02 Yeah, I think, I think those two stories are, are really interesting.

Sarah Guo 14:04 Yeah.

Sajith (Saji) Wickramasekara 14:05 And there's a great quote from Dario, the Anthropic CEO, in his, his, his kind of essay about the returns to intelligence in scientific progress are very high. And I think like-

Sarah Guo 14:14 He's talking about Machines of Love and Grace, yeah.

Sajith (Saji) Wickramasekara 14:16 Yeah, yeah. So the re- returns to intelligence are very high, and I think like the stories of GLP-1s and KEYTRUDA are like great, great examples of that. So GLP-1s obviously have just transformed obesity as like a treatable disease when, by the way, it was like a totally unfundable category of things like five years ago.

Sarah Guo 14:31 Why do you think it was unfundable?

Sajith (Saji) Wickramasekara 14:32 Uh, I think like, again, because we know so little about biology and there were so many failures-

Sarah Guo 14:37 Mm-hmm.

Sajith (Saji) Wickramasekara 14:37 ... in that space, and again, running a clinical trial for obesity where you need huge populations of people that you monitor for very long periods of time, like super, super expensive and everything has failed before.

Sarah Guo 14:47 Mm-hmm.

Sajith (Saji) Wickramasekara 14:48 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 like graveyard of billion-dollar f- billion-dollar failures and like it's getting back in now, but there was a period of time where everyone left the space. And so... But the core science for GLP-1s was kind of sitting on the shelf in some sense. Like, it's been known since like the '90s, um, and so it took some insights and conviction and then all of a sudden, like, we have this category-defining medicine that's gonna go on to probably be the, the best-selling drug of all, all time, and that's happening. And then KEYTRUDA's 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 different-

Sarah Guo 15:28 People are giving up on it, yeah.

Sajith (Saji) Wickramasekara 15:29 Yeah, and then a competitive thread pops up and someone sees that, "Hey, this is kinda like KEYTRUDA," and so, and, you know, you know, credit to Merck, they had the courage to go all in after they realized what it, what it could be. So it's just another example of like there's, there's a lot. Uh, it's, it's a pretty inefficient system. It's, and people are pretty... They're, they're rational actors. It's just that we don't know a lot about biology and our ability to predict what's gonna happen in the clinic is so poor, and the cost to get there and to make those decisions is so high. And so if you can get to the clinic faster, cheaper, like failure in the software world is you work on a product for a year or two, you spend a couple million bucks and like it doesn't work, but like in biotech you're underwriting, you know, four, five, six years, big team, hundreds of millions of dollars. So like how do you compress that so you get feedback faster?

Sarah Guo 16:13 So,

Sarah Guo 16:14 Benchling is a system of record company. It's a data platform. What is Benchling AI?

Sajith (Saji) Wickramasekara 16:20 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.

Sarah Guo 18:17 And they could just accelerate because they didn't have to do that piece of experimentation anymore.

Sajith (Saji) Wickramasekara 18:21 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.

Sarah Guo 18:35 If you

Sarah Guo 18:36 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?

Sajith (Saji) Wickramasekara 18:48 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.

Sarah Guo 19:00 Yep.

Sajith (Saji) Wickramasekara 19:00 And we'll- we'll sit back and let the robots give us drugs.

Sarah Guo 19:02 Mm-hmm.

Sajith (Saji) Wickramasekara 19:02 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-

Sarah Guo 19:22 Mm-hmm.

Sajith (Saji) Wickramasekara 19:22 ... 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-

Sarah Guo 20:09 I think like 40, yeah.

Sajith (Saji) Wickramasekara 20:11 Probably.

Sarah Guo 20:11 Yeah.

Sajith (Saji) Wickramasekara 20:12 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)

Sarah Guo 20:30 Yeah. I mean, that makes sense to me in clinical practice. I'm- I'm more hopeful that, like, some of the experimental decisions-

Sajith (Saji) Wickramasekara 20:37 Mm-hmm.

Sarah Guo 20:38 ... 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-

Sajith (Saji) Wickramasekara 20:59 Yeah.

Sarah Guo 20:59 ... 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-"

Sajith (Saji) Wickramasekara 21:02 Yeah.

Sarah Guo 21:02 "... decision. Like, what's happening?"

Sajith (Saji) Wickramasekara 21:05 Yeah.

Sarah Guo 21:05 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?

Sajith (Saji) Wickramasekara 21:13 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?

Sarah Guo 21:21 Mm-hmm.

Sajith (Saji) Wickramasekara 21:21 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.

Sarah Guo 21:41 Mm-hmm.

Sajith (Saji) Wickramasekara 21:41 Like, I think the chat, and I mean chat metaphorically, like, that was the interface that made things really take off-

Sarah Guo 21:47 Mm-hmm.

Sajith (Saji) Wickramasekara 21:48 ... 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.

Sarah Guo 22:07 Mm-hmm.

Sajith (Saji) Wickramasekara 22:07 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.

Sarah Guo 22:20 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?

Sajith (Saji) Wickramasekara 22:34 Yeah. I think that's, like, I- I think about-

Sarah Guo 22:35 Legible enough to be useful.

Sajith (Saji) Wickramasekara 22:36 Yeah.

Sarah Guo 22:36 Yeah.

Sajith (Saji) Wickramasekara 22:37 I think in a vertical, I think 90% of the work is actually, like, translation.

Sarah Guo 22:41 Mm-hmm.

Sajith (Saji) Wickramasekara 22:41 It's taking something and making sci- making sure scientists trust it, it's the right point in their workflow, it's easy to use, and it's- and it's accurate. I think the AI that wins is gonna be the one that people actually use.

Sarah Guo 22:52 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.

Sajith (Saji) Wickramasekara 22:59 Oh, yeah.

Sarah Guo 23:00 Yeah. Okay.

Sajith (Saji) Wickramasekara 23:00 They're- they're- there's- there's excitement.

Sarah Guo 23:02 Okay.

Sajith (Saji) Wickramasekara 23:02 Uh, for sure. There's optimism and belief.

Sarah Guo 23:05 Yeah.

Sajith (Saji) Wickramasekara 23:05 Uh, I think they're pretty pragmatic though, uh, and I think, uh, they're all looking to transform, but they're- they're being methodical. Like, I would say most of the large pharma at this point that I've worked with, like, you know, they've got Copilot and things like that, and they're doing a lot of pilots of different technologies, but I haven't seen their R&D orgs transformed yet. Now, the one place I would say that pharma has really leaned in and has an advantage is, they have incredible data generation capabilities.

Sarah Guo 23:35 Mm-hmm.

Sajith (Saji) Wickramasekara 23:35 And so, many of them can and should be training models.

Sarah Guo 23:38 Like experimental data generation.

Sajith (Saji) Wickramasekara 23:40 Yes. They can generate data to train their own models at a scale that most biotech startups can't- can't match, so I think while it's early on sort of the agentic how we work side, uh, I think you're gonna 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.

Sarah Guo 23:58 What do you th- like, can you help characterize, um, what useful models we already have on the discovery side?

Sajith (Saji) Wickramasekara 24:06 Mm-hmm.

Sarah Guo 24:07 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, um, overall cycle time or, uh, rate of success, whatever you think is more important here.

Sajith (Saji) Wickramasekara 24:19 Mm-hmm.

Sarah Guo 24:20 Yeah.

Sajith (Saji) Wickramasekara 24:20 It, it's been really cool to see, like, the whole ecosystem of these tools grow a ton. Um, 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, it feels like a thing that's here to stay.

Sarah Guo 24:36 Mm-hmm.

Sajith (Saji) Wickramasekara 24:36 Um, as you're seeing all these interesting models come out, like the bolts, for example, on the structure prediction side, um, and I think, like, that's, like, a really interesting thing that's gonna change, change biology. Uh, I think you're seeing new approaches to, like, federated learning as well. Eli Lilly put out this, uh, announcement about a, a project they have called TuneLab, where they're taking their internal models and making them available to the broader scientific ecosystem. So a pharma company is saying, "Hey, you can use our models, but it's give to get." So we get to train and, like, it's, you know, federated or, and whatnot, so they don't get to see the actual scientific data.

Sarah Guo 25:10 Mm-hmm.

Sajith (Saji) Wickramasekara 25:10 But I think, like, those approaches are, like, we're... this is the beginning basically.

Sarah Guo 25:14 Mm-hmm.

Sajith (Saji) Wickramasekara 25:14 And so we've got some really cool stuff. There are, there are problems that I think are fairly tractable in terms of structure prediction or antibody developability and so forth, so, like, that, that's really good, but there's, I would say, a, uh, a lot of work in front of us in terms of models that are sort of more predictive of what happens when you get into patients, for example. And don't get me wrong, like, discovery is really important, but there's so many other steps that have to happen after you have a concept molecule, even h- i- if you're, if you're much faster, for example, at, um, making molecules and you have a higher success rate, you still have to come up with a process to manufacture that molecule, and now you have even less time to do so if it's, like, a byproduct of success. And so, how do you optimize manufacturing processes to get, you know, a lot of yield, speed, cost out of, out of a molecule?

Sarah Guo 26:02 Is that, like, where you would say the, um, highest value missing predictive model opportunity is? 'Cause I think a bunch of naysayers would be like, "Okay, yes, I've heard about AlphaFold and, like, people working on antibody prediction and, like-

Sajith (Saji) Wickramasekara 26:16 Yeah.

Sarah Guo 26:16 ... creation platforms, but we are still, you know, many years into this premise. No drugs out the other end of the pipeline that are-

Sajith (Saji) Wickramasekara 26:23 Yeah.

Sarah Guo 26:24 ... AI discovered." Yeah.

Sajith (Saji) Wickramasekara 26:25 Yeah, I, I feel like the naysayers kinda have this sort of worldview

Sajith (Saji) Wickramasekara 26:28 in mind where it's like, "Oh, I just, like, type a disease and then I, like, get a molecule out and, like-

Sarah Guo 26:31 Yeah.

Sajith (Saji) Wickramasekara 26:32 ... 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.

Sarah Guo 26:43 Mm-hmm.

Sajith (Saji) Wickramasekara 26:43 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.

Sarah Guo 27:05 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-

Sajith (Saji) Wickramasekara 27:18 Mm-hmm.

Sarah Guo 27:19 ... is this theory that we're gonna have, like, very different business models-

Sajith (Saji) Wickramasekara 27:22 Mm.

Sarah Guo 27:22 ... in biotech. Do you think that's gonna happen?

Sajith (Saji) Wickramasekara 27:24 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?

Sarah Guo 27:43 Mm-hmm.

Sajith (Saji) Wickramasekara 27:43 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-

Sarah Guo 28:02 Yeah.

Sajith (Saji) Wickramasekara 28:02 ... 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-

Sarah Guo 28:41 Mm-hmm.

Sajith (Saji) Wickramasekara 28:41 ... 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,

Sajith (Saji) Wickramasekara 28:50 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.

Sarah Guo 29:02 Mm-hmm.

Sajith (Saji) Wickramasekara 29:02 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-

Sarah Guo 29:17 If there was tooling and normalization about it-

Sajith (Saji) Wickramasekara 29:18 Yes.

Sarah Guo 29:18 ... you might be able to transact on it.

Sajith (Saji) Wickramasekara 29:20 Yes. Yeah.

Sarah Guo 29:20 Okay.

Sajith (Saji) Wickramasekara 29:21 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.

Sarah Guo 29:28 I'm sure you've heard in the 13 years-

Sajith (Saji) Wickramasekara 29:31 Yeah.

Sarah Guo 29:31 ... you've been building at Benchling-

Sajith (Saji) Wickramasekara 29:32 Yeah.

Sarah Guo 29:32 ... the conventional wisdom is that,

Sarah Guo 29:35 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-

Sajith (Saji) Wickramasekara 29:43 Yeah.

Sarah Guo 29:43 ... before and they weren't as embedded as they needed to be.

Sajith (Saji) Wickramasekara 29:46 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.

Sarah Guo 30:19 (laughs) Okay.

Sajith (Saji) Wickramasekara 30:20 Like, so like, we spent the first couple years, like, like-

Sarah Guo 30:21 It's wild for such an advanced field in other areas.

Sajith (Saji) Wickramasekara 30:25 Yeah.

Sarah Guo 30:25 Yeah.

Sajith (Saji) Wickramasekara 30:25 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-

Sarah Guo 30:46 Mm-hmm.

Sajith (Saji) Wickramasekara 30:46 ... 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-

Sarah Guo 31:12 Seems obvious to tech people.

Sajith (Saji) Wickramasekara 31:14 It seems obvious-

Sarah Guo 31:15 Yeah.

Sajith (Saji) Wickramasekara 31:15 ... but there's like, it's not for free. Like-

Sarah Guo 31:16 Yeah.

Sajith (Saji) Wickramasekara 31:16 ... 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

Sajith (Saji) Wickramasekara 31:28 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.

Sarah Guo 31:38 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-

Sajith (Saji) Wickramasekara 31:46 Is the winner.

Sarah Guo 31:46 ... is the quality of the next decision-

Sajith (Saji) Wickramasekara 31:48 Yeah.

Sarah Guo 31:48 ... 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.

Sajith (Saji) Wickramasekara 31:56 Yeah, yeah, yeah.

Sarah Guo 31:57 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-

Sajith (Saji) Wickramasekara 32:07 Drugs. (laughs)

Sarah Guo 32:07 ... 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.

Sajith (Saji) Wickramasekara 32:25 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)

Sarah Guo 32:29 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.

Sajith (Saji) Wickramasekara 32:40 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-

Sarah Guo 32:55 Mm-hmm.

Sajith (Saji) Wickramasekara 32:55 ... 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.

Sarah Guo 33:04 Mm-hmm.

Sajith (Saji) Wickramasekara 33:04 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-

Sarah Guo 33:42 'Cause the failure rate's so bad. (laughs)

Sajith (Saji) Wickramasekara 33:43 The failure rate's so bad and, like, the impact is huge. I think everyone's-

Sarah Guo 33:46 Yeah.

Sajith (Saji) Wickramasekara 33:46 ... now seen what, like, GLP-1s can do. Everyone saw what COVID vaccines can do.

Sarah Guo 33:50 Magic, yeah.

Sajith (Saji) Wickramasekara 33:51 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.

Sarah Guo 34:01 All right. More s- more scientists.

Sajith (Saji) Wickramasekara 34:02 (laughs) Yeah.

Sarah Guo 34:03 And what about the partnership?

Sajith (Saji) Wickramasekara 34:04 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.

Sarah Guo 34:52 Can we talk a little bit about company building?

Sajith (Saji) Wickramasekara 34:55 Sure.

Sarah Guo 34:55 Just, you know, 13 years of wisdom-

Sajith (Saji) Wickramasekara 34:57 Yeah.

Sarah Guo 34:57 ... in like two minute takes.

Sajith (Saji) Wickramasekara 34:57 E- every mistake made at this point.

Sarah Guo 35:00 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-

Sajith (Saji) Wickramasekara 35:12 (laughs)

Sarah Guo 35:12 ... 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-

Sajith (Saji) Wickramasekara 35:22 Yeah.

Sarah Guo 35:22 ... at scale. Yeah.

Sajith (Saji) Wickramasekara 35:23 It's funny, we, we, I think we started early, but at the same time, I feel late still.

Sarah Guo 35:27 Mm-hmm. Okay, all of us.

Sajith (Saji) Wickramasekara 35:28 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."

Sarah Guo 35:50 Mm-hmm.

Sajith (Saji) Wickramasekara 35:50 And like, B, "If I look stupid, that's okay."

Sarah Guo 35:52 Nice. (laughs) Yeah.

Sajith (Saji) Wickramasekara 35:53 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-

Sarah Guo 36:02 Uh, uh.

Sajith (Saji) Wickramasekara 36:02 ... "No, no, no." (laughs)

Sarah Guo 36:03 Metaphorically.

Sajith (Saji) Wickramasekara 36:04 Metaphorically.

Sarah Guo 36:05 Yeah, yeah.

Sajith (Saji) Wickramasekara 36:05 Didn't quit, just going, going full time on AI.

Sarah Guo 36:07 Yeah.

Sajith (Saji) Wickramasekara 36:07 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-

Sarah Guo 36:23 Yeah, how do you invest like that? Yeah.

Sajith (Saji) Wickramasekara 36:24 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?

Sarah Guo 36:46 Mm-hmm.

Sajith (Saji) Wickramasekara 36:46 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.

Sarah Guo 37:00 Maybe because there are a bunch of entrepreneurs, uh, 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.

Sajith (Saji) Wickramasekara 37:12 (laughs)

Sarah Guo 37:12 Like, what has been hardest? What are some lessons from that?

Sajith (Saji) Wickramasekara 37:15 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? Uh, 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 because I've gotten too far from customers. And, like, product market fit is this moving target and sometimes your market changes. Like, our market changed of, like, we had a bunch of biotech companies that were gonna take over the world and then, like, then they weren't. And so, like, I still spend, and maybe this is a Vertical thing, I probably spend 30, 40, 50% of my time talking to customers still.

Sarah Guo 37:50 Mm-hmm.

Sajith (Saji) Wickramasekara 37:50 And I think of myself as like the, I need to go really deeply understand their problems and how they're changing and I need to go bring it back to the company and, like, that's the thing I have to role model so the whole company does it. And that's the number one piece of advice I'd give people.

Sarah Guo 38:02 So that answers, uh, part of one question from another friend who used to work for you. He said that Saji is amazing at, like, understanding the macro of the company and then, like, being deep in the detail on everything, especially with customers. But given, you know, Benchling's a complicated company, biology is a com- complicated field, like, how do you decide what to focus on and then, like, focus your team on?

Sajith (Saji) Wickramasekara 38:25 I pre- that was a nice compliment, though. It's like telling someone they have a large context window, uh-

Sarah Guo 38:29 Y- it is. Good model, man.

Sajith (Saji) Wickramasekara 38:30 Yeah. Thank you, thank you to whoever had said that kind thing.

Sarah Guo 38:33 It was Malay. Hi, Malay. Yeah.

Sajith (Saji) Wickramasekara 38:34 Oh (laughs) . Um, I... You know, if... One of the most interesting things about being in a vertical is you talk to all your customers and they all, because no one... They're underserved.

Sarah Guo 38:46 Mm-hmm.

Sajith (Saji) Wickramasekara 38:46 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, like-

Sarah Guo 38:51 Much less great software companies, yeah.

Sajith (Saji) Wickramasekara 38:53 Yeah, yeah, yeah. Yeah, there are, there are just not many, and so you have a very underserved demographic where they're not used to someone coming in sort of asking what they specifically need. They're used to very general purpose horizontal software and trying to, like, do kludge-

Sarah Guo 39:03 Yeah, you want some productivity. Yeah.

Sajith (Saji) Wickramasekara 39:04 ... do kludgey things to make it work, you know?

Sarah Guo 39:07 Yeah.

Sajith (Saji) Wickramasekara 39:07 Uh, and so when you go talk to them, the first reaction is like, "Oh my God, what we do is so unique and we're such a snowflake and th- there's no way, like, uh..." But then you, like, talk to enough customers and it turns out they almost all want the same thing. I find that having five, 10 customers is actually like a pretty representative model for what the entire industry needs. You, you don't wanna 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 is 'cause we found a couple of customers and we got very, very deep with them and we built and built until they were, like, super happy and then it took off. Uh, and, like, maybe that's, like, the 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. Even for our new AI tools, we, we have all of our customer base, they're, they're available to all of our customers now, but what, we're... Our team is on, like, daily, weekly calls with five or 10 customers to the point of, like, we, we had a customer yesterday, you know, text us about, like, a meeting they had with the FDA and how they, like, ran a report inside of our deep research tool and 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.

Sarah Guo 40:15 One other unique thing, um, that I think would

Sarah Guo 40:18 be useful for a lot of people today, including me, is Benchling knows how to get scientists to work in a software company-

Sajith (Saji) Wickramasekara 40:26 Hmm.

Sarah Guo 40:26 ... 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.

Sajith (Saji) Wickramasekara 40:35 Mm-hmm.

Sarah Guo 40:35 Let's say five years ago-

Sajith (Saji) Wickramasekara 40:36 Yeah (laughs) .

Sarah Guo 40:36 ... when I was just, like, good old engineering.

Sajith (Saji) Wickramasekara 40:39 Yeah.

Sarah Guo 40:39 Um, like... And it is philosophically different.

Sajith (Saji) Wickramasekara 40:41 Mm-hmm.

Sarah Guo 40:41 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-

Sajith (Saji) Wickramasekara 40:48 Yeah.

Sarah Guo 40:49 Like, what advice do you have on, um, like, recognizing that talent, getting them to be productive, managing it?

Sajith (Saji) Wickramasekara 40:54 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.

Sarah Guo 41:07 Mm-hmm.

Sajith (Saji) Wickramasekara 41:07 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.

Sarah Guo 41:34 (laughs)

Sajith (Saji) Wickramasekara 41:34 And so, like-

Sarah Guo 41:35 (laughs)

Sajith (Saji) Wickramasekara 41:35 Or it's like very, very, very cheap, right? Grad student labor. And, like, the currency is publishing a paper.

Sarah Guo 41:41 Yeah.

Sajith (Saji) Wickramasekara 41:41 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.

Sarah Guo 41:46 (laughs)

Sajith (Saji) Wickramasekara 41:46 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.

Sarah Guo 42:02 Mm-hmm.

Sajith (Saji) Wickramasekara 42:02 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.

Sarah Guo 42:26 Do you interview at all for this orientation?

Sajith (Saji) Wickramasekara 42:29 I think we try to. I, I don't know that we've... Like, I have some amazing predictive way to, to find it.

Sarah Guo 42:35 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.

Sajith (Saji) Wickramasekara 42:43 Yeah.

Sarah Guo 42:43 Right? I'm a venture capitalist. But they ask people, including research scientists, "How do you feel about capitalism?" And they think it's a pretty interesting sorting function, actually.

Sajith (Saji) Wickramasekara 42:51 That's... I- I will try that (laughs) .

Sarah Guo 42:53 (laughs) I'm not recommending it, but I, I, I do think it is, uh, uh, interesting the set of answers you get, actually, 'cause I, I have tried it. One more question for you on culture. You spend a lot of time with biotech and large pharma.

Sarah Guo 43:06 You're at your core a software person. Like, what can these two worlds learn from each other?

Sajith (Saji) Wickramasekara 43:10 Hmm. Great, great question. I think the biotech and pharma world can learn something from how tech communicates and tells stories.

Sarah Guo 43:19 Hmm.

Sajith (Saji) Wickramasekara 43:19 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.

Sarah Guo 43:25 Mm-hmm.

Sajith (Saji) Wickramasekara 43:25 Uh, I think, like, biotech and pharma companies need to tell their stories. Um, like most people-

Sarah Guo 43:30 This is not what I thought you were gonna say.

Sajith (Saji) Wickramasekara 43:31 Yeah.

Sarah Guo 43:31 Okay.

Sajith (Saji) Wickramasekara 43:31 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-

Sarah Guo 43:45 Mm. Yeah.

Sajith (Saji) Wickramasekara 43:45 ... a lot of them. And, or Jensen or something like that.

Sarah Guo 43:47 There's a lot of hero's journey in tech.

Sajith (Saji) Wickramasekara 43:49 Yeah. A, a lot of hero's journey in tech.

Sarah Guo 43:49 Yeah.

Sajith (Saji) Wickramasekara 43:50 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-

Sarah Guo 44:15 Mm-hmm.

Sajith (Saji) Wickramasekara 44:15 ... 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-

Sarah Guo 44:24 Mm-hmm.

Sajith (Saji) Wickramasekara 44:24 ... Gilead's like cured HIV-

Sarah Guo 44:26 Mm-hmm.

Sajith (Saji) Wickramasekara 44:26 ... basically at this point, and like most people have no idea.

Sarah Guo 44:29 Mm-hmm.

Sajith (Saji) Wickramasekara 44:29 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-

Sarah Guo 44:36 Mm-hmm.

Sajith (Saji) Wickramasekara 44:37 ... 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.

Sarah Guo 44:41 Yeah.

Sajith (Saji) Wickramasekara 44:42 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-

Sarah Guo 45:11 Mm-hmm.

Sajith (Saji) Wickramasekara 45:11 ... 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.

Sarah Guo 45:25 Yeah. It's really interesting. I was talking to a friend who is a top research scientist at one of the top labs, and I asked him a very basic question, it was probably a year and a half ago, about like, "Well, you know, how can I, how can I better use these models in very, um, quality sensitive fields?"

Sajith (Saji) Wickramasekara 45:45 Mm-hmm.

Sarah Guo 45:45 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-

Sajith (Saji) Wickramasekara 45:52 Yeah.

Sarah Guo 45:52 ... essentially. And, um, uh, this is an amazing research scientist, but I'd be like, "That seems unambitious."

Sajith (Saji) Wickramasekara 45:57 (laughs)

Sarah Guo 45:58 Right? Um, because we, there are many benefits to intelligence, uh, in these fields that are really important to all of us.

Sajith (Saji) Wickramasekara 46:04 Yeah.

Sarah Guo 46:04 And so I do feel like more people should work on that too.

Sajith (Saji) Wickramasekara 46:06 Yeah. Yeah, yeah. I, I think in Silicon Valley sometimes it's, it's a little bit easy to like oversimplify other people's jobs. There's a lot of complexity out there in the world of biotech and pharma, but I'm very optimistic that like the sort of tech companies will figure out how to make it work 'cause I think everyone's very excited about, about AI.

Sarah Guo 46:20 Last question for you, um, something you're excited about in AI, uh, outside of bio, or outside of like Benchling's immediate purview, I guess?

Sajith (Saji) Wickramasekara 46:29 On a personal level, uh, I haven't written a line of code. I had not written a line of code in probably, I'm very embarrassed to say this, uh, maybe eight or nine... It's been-

Sarah Guo 46:38 Oh, wow.

Sajith (Saji) Wickramasekara 46:39 ... it's been some time. Like I remember I stopped coding around the time that React started being a thing.

Sarah Guo 46:43 Okay, you're dating us.

Sajith (Saji) Wickramasekara 46:44 Like I'm just like, I'm dating-

Sarah Guo 46:44 Yes. Yeah.

Sajith (Saji) Wickramasekara 46:44 I'm dating... Yeah.

Sarah Guo 46:45 Yeah.

Sajith (Saji) Wickramasekara 46:45 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, so like that, that's pretty cool. Uh, that's probably the one I'm, I'm most excited about. And then I'm excited for my, like, parents actually.

Sarah Guo 47:04 Mm-hmm. Mm-hmm. Okay.

Sajith (Saji) Wickramasekara 47:04 Uh, and for them to have like new technology that's pretty easy for them to, to access. Like my, my mom's a ChatGPT-

Sarah Guo 47:11 Mm-hmm.

Sajith (Saji) Wickramasekara 47:11 ... user, and I'm sure it's sort of Google Search++ at this point, but like that's been like pretty cool too of like technology that's so intuitive that like I don't have to like go home and be IT support at Christmas.

Sarah Guo 47:22 Yeah. I think it's actually undervalued as, um, just because there are audiences that are, uh, traditionally not as lucrative-

Sajith (Saji) Wickramasekara 47:28 Mm-hmm.

Sarah Guo 47:28 ... as like the fast tech adopting audience of your, you know, 15 to 40 year olds.

Sajith (Saji) Wickramasekara 47:33 Yeah.

Sarah Guo 47:33 But it is incredibly, uh, wild how the UX of like natural language and voice-

Sajith (Saji) Wickramasekara 47:39 Mm-hmm.

Sarah Guo 47:39 ... are changing who can use it, right?

Sajith (Saji) Wickramasekara 47:41 Absolutely.

Sarah Guo 47:42 I mean, it's... You know, AI tutoring for my kids and such and it's-

Sajith (Saji) Wickramasekara 47:44 Oh my God.

Sarah Guo 47:44 ... the same, the same experience. Yeah.

Sajith (Saji) Wickramasekara 47:46 Oh, lear- learning is such a joy with, with AI. Help them learn the right things, but like... (laughs)

Sarah Guo 47:50 Thanks so much for doing this, Saji.

Sajith (Saji) Wickramasekara 47:52 Thanks, Sarah.

Narrator 47:52 (instrumental music)

Sarah Guo 47:55 Find us on Twitter @NoPriorsPod. Subscribe to our YouTube channel if you wanna see our faces. Follow the show on Apple Podcasts, Spotify, or wherever you listen. That way you get a new episode every week. And sign up for emails or find transcripts for every episode at no-priors.com.