The Exchanges

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

A 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 recommend, 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. It works similar to the deep research agents from, 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, you know, a …

AI assessment note: “Benchling AI has kind of two, two major components to it.”

Q What's the relevance of China in all of this?

A 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 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 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. Uh, really fast and really cheap, even in some of these new modalities. We've seen this huge uptick in pharma going to China and buying molecules that they typically would have bought from American biotechs.

AI assessment note: “China is very good at things related to speed and cost.”

Q And like, Like, the assay, like, said yes or no? Like, what other types of, what is the data that's actually in Benchling?

A Uh, I think what's really interesting for everyone to understand is, like, making a drug, there's like, 9909 steps in making a drug after you come up with a molecule. So you have to, to make a medicine, you have to find a biologically meaningful target in, in the body, something you want a drug, 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 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 BenchLink 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 ferment…

AI assessment note: “where BenchLink focuses is all of the scientific data that comes out of the lab”

Q 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 is this theory that we're going to have like very different business models in biotech. Do you think that's going to happen?

A Uh, 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. Uh, tools are good. I do think with some of these model companies 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? 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 a hundred million dollars upfront or something like that, and they have five customers and whatnot. Like, I feel like the, uh, sort of, Model building is probably commoditizing too fast for that to, to be 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 communities. 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, you know, we've actually got a bunch of models in BenchLink.…

AI assessment note: “I would like it to happen. I think right now for companies”

Q 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.

A 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, 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 really awesome. But I think we're like, we've got GPT, but there's no chat. Like, that, that's kind of how I think about it. 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 happy. We haven't figured out what that is in bio yet with 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. 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, the like, Larger those concerns, concerns get.

AI assessment note: “are scientists going to trust this, and how do we know if it's accurate?”

Q I, uh, anticipated, let's say five years ago, when I was just like, good old engineering, um, 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. Like what advice do you have on, um, like recognizing that talent, getting them to be productive, managing it?

A 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. If, 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. We have to take the software people, take the science people, make them 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, is just a very different incentive structure, and in the world of academia, like, your labor is basically free, and so, like, it was, like, very, very, very cheap, right, grad student labor, and, like, the currency is publishing a paper, 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. 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 the tension has come from like that, the need to do that. And so making sure our scientific teams like really understand that the better we do as a busines…

AI assessment note: “We have to take the software people, take the science people, make them sit together.”