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

Sajith Wickramasekara: People get very focused on clinical trials because they're like the biggest line item. And they're important, don't get me wrong, but I think it's actually a bit of a red herring where, yes, there are operational problems, like some studies are designed badly, it's hard to recruit patients. The sticker price is really big, but at the end of the day, a lot of molecules are just not good. And so we need better molecules and we need to move them to people faster. ... 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. ... the core science for GLP-1s was kind of sitting on the shelf in some sense. Like it's been known since like the 90s. And so it took some insights and conviction. And then all of a sudden, like we have this category defining medicine that's going to go on to probably be the best selling drug of all time. And that's happening. And then Keytruda is a similar story where there's a molecule that's gone through a couple different acquisitions. And it's almost like it's at the bottom of some list to be like out licensed and sold off. ... It's a pretty inefficient system. And people are pretty, they're rational actors. It's just that we don't know a lot about biology and our ability to predict what's going to happen in the clinic is so poor.

Sajith Wickramasekara: Benchling AI has kind of two major components to it. The first is tools for simulation. So this is taking open source, proprietary companies' internal models and making them accessible to scientists directly in their workflow. So the right model at the right moment in the scientific workflow, already set up so that a wet lab scientist without computational skills can use it effectively. And then the results are linked to all of their other information in Benchling. ... Then the other facet of benchling AI is agents that automate work for you. And so we've released this deep research agent. It works similar to the deep research agents from Anthropic and other foundation labs. But what it does is it works over benchling data, with the context of the benchling data model. And so it enables scientists to ask these very difficult, and science is fundamentally about like asking and answering questions. And so for our customers, it helps them to do a type of question that previous in the past would have taken weeks or months to do and do that in just a couple hours. So a great example of this is with a customer that was getting ready to run some mouse studies and they were looking at 20 different mouse models and they used a deep research, our deep research capability to look at all the historical of mouse studies that they had run. And it turned out that a bunch of the mouse models that they were about to investigate, which would have taken eight months, huge cost, big experiment to run, someone had already done before. And it was trapped in some lab notebook from 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 folklore and institutional knowledge, and that's just kind of lost over time. And so we sort of view this as being able to unlock memory for these organizations and help make scientific data reusable over time.

Sajith Wickramasekara: So yeah, when I hear from AI scientists, I think it definitely evokes this image of a kind of fully AI-ified design, make, test, analyze, loop. And we'll sit back and let the robots give us drugs. And while I would love for that to happen, and I'm maybe more optimistic on a longer time scale, we will get there. I think in the short term, I'm next one to two years, which already feels like an eternity in AI time. I'm a little bit more bullish on sort of the augmentation model. Like I kind of think of it as like a Waymo versus Tesla approach, where you can 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. 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. And so I think if you can kind of get those ingredients, take the Waymo approach, which some companies have, that's awesome. But I think for the rest of science, there's a huge opportunity to just like make things better one experiment at a time and pick off a lot of low-hanging fruit and see if we can get 7 to 10 years down to two to three years and a lot fewer specialized roles and a lot cheaper to bring a drug to market.

Sajith Wickramasekara: 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. It's really awesome. But I think we're like, we've got GPT, but there's no chat. That's kind of how I think about it. ... I just got back from a month on the road and I was in Boston, London, bunch of other places that are sort of scientific capitals outside of SF. And like most people aren't really using that much AI and R&D yet. They all want to, they're primed to, but there's a lot of concerns about accuracy, IP, security, legal. And I think the farther you go from SF, the like larger those concerns get. ... I think in a vertical, I think 90% of the work is actually like transition. It's taking something and making sure scientists trust it. It's the right point in their workflow. It's easy to use, and it's accurate. I think the AI that wins is going to be the one that people actually use.