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

**Sarah Guo** (0:05)
Hi, listeners. Welcome back to No Priors. Today, I'm here with Sajith, the co-founder and CEO of Benchling, the system of record for biotech R&D. 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. Sajith, thanks so much for being here.

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

**Sarah Guo** (0:37)
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 Wickramasekara** (0:44)
Sure. I'm one of the co-founders of Benchling. We make modern software for scientific progress. I started the company about 13 years ago. It's been a long time.

**Sarah Guo** (0:54)
Oh my God.

**Sajith Wickramasekara** (0:54)
I know. So I'm a software engineer by background, but I worked in a biology lab. I was really interested in medicine and coming from the world of software. In software, developers have amazing tools for working on code and for collaborating. When I got to the biology lab, I found that scientists had paper notebooks and spreadsheets that would sit on their desktops.
It was terrible. It was really hard to work together. I think that was really frustrating for me personally. I thought, a little bit naive at the time, I thought how hard would it be to build good tools for scientists. So I started working on Benchling which helped 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 1300 biotech and pharma companies, scientists at over 7000 academic institutions, universities all around the world. And our software powers household names like Moderna and Sanofi and UiLily and Regeneron, but also like cutting edge biotech startups, the future AI biotechs like Isomorphic Labs and Xera and companies like that. So we get to see the innovation happening across the entire biotech sector and build software that helps power it.

**Sarah Guo** (2:08)
I'm super excited to actually use that vantage point, ask you a bunch of questions about bio in the macro. But just so people who don't come from the domain can picture it a little bit better. I think I can picture gene sequences.

**Sajith Wickramasekara** (2:21)
Sure.

**Sarah Guo** (2:21)
And the assay said yes or no. What other types of data is actually in Benchling?

**Sajith Wickramasekara** (2:28)
I think what's really interesting for everyone to understand is making a drug. There's like 9,990 steps in making a drug after you come up with a molecule. So you have 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. You have to test that molecule in petri dishes and cell lines and animals, various kinds of animals. Then eventually you get to the point where you can take it to a clinical trial and you're testing it in subsequently larger groups of humans. All the while you're figuring out how do I manufacture this thing and develop a process to make it scale economically, safety with quality, all while navigating regulatory bodies. So that eventually in seven to ten years, you can have a drug that you give to people commercially, and even then there's still more work there. So it's just incredibly long and complex process. 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 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 for people just looking at biotech from the outside, it seems a very macrosensitive industry, right? And we are perhaps coming out of kind of an ugly period. Can you just characterize where we are in the biomacro cycle?

**Sajith Wickramasekara** (4:04)
Yeah, yeah. And I'm definitely not a sort of macro specialist, otherwise I'd probably be an investor or something like that.