Recursion’s Post

Transcript

Alright, staying with one of BlackRock, seems Biotech, Recursion and Accentia 2 liters in the AI drug discovery space have officially combined. Chris Gibson, the CEO and cofounder of Recursion said the combination quote makes us the leader of the AI enabled drug discovery and the development space and he joins us now. Hey Chris, you from what I understand it, you guys are really a biotech disruptor. You want to make drugs cheaper and faster. So how does Essentia help you do that? Well, look, today it takes over 10 years and over $2 billion of invested R&D for each new medicine that's approved. And that's because 90% of drugs that go into clinical trials ultimately fail. And we believe that's because biology and chemistry are incredibly complicated. And we believe that tools like AI on top of the right datasets, which we have been building here, recursion and Accentia has been building over the last decade, give us an opportunity to take AI and understand how biology and chemistry are interacting so that we can make better medicines more quickly. For patients, in terms of the macro backdrop, I'm wondering sort of the timing of all of this and I appreciate it takes time to go through, but we are in a lower rate environment. We just talked to the head of Thematic over at Black Rock who said one of the reasons why they like biotech is because lower rates will then help and it will help things like M&A. Chris, does this play a factor for you? Well, look, I think Jay was right. This is definitely the build phase for AI in in biotech. But biotech is a space that takes a long time to mature and Recursion and Accentia are two of the companies who've been at this for over a decade. We saw an opportunity to take the two leaders of the space, put them together and really try and catapult ahead of the rest of the industry where frankly, I see a lot of underinvestment in AI compared to many other industries. And so it will take some time for us to read out programs. We've got ten programs in the clinic or near clinic now. Today, which is it's fantastic, but it's still gonna take us over 18 months to read out the next 10 of those programs, the next 10 milestones. And as we do that, I think people are going to start to see the promise of AI in this space where clinical readouts are really lagging indicator of the promise that we see. Well, I am curious if you just kind of explain in layman's terms, Chris, why the use of these AI tools will lead to a better end product, meaning more success, the idea of getting an actual product to market that is. Viable and safe. Do you have some sense here that this is actually pushing us in that direction? Absolutely. I mean, today everyone of our phase one clinical trials has been successful. We've been able to decrease the amount of time it takes to get a drug to the clinic by 50% and we've been able to decrease the cost by roughly 50% as well. And so we're seeing these leading indicators of success. Ultimately though, what really matters is getting a drug to patient to patients and they don't really care if it's designed by AI or not, but these leading indicators suggest to us that as we keep generating momentum in this flywheel, you know the laboratory behind me. Doing the equivalent of my entire PHD's worth of work every 15 minutes. And all of that data is now substrate for these AI models. And as we know from other industries, from the LLM space, etc, these models are just getting better and better. So we're going to have network effects all of a sudden in the field of biotech, which is something that we really haven't seen before. There are also a lot of competitors in this space, Chris, as I'm sure you know, there was just news today on Invita and some of the progress that they've made. I am curious as you build up your own business at Recursion. Kind of what ends up being the Moat? Are these AI tools proprietary in some way that what you discover ends up I guess being just yours? Remain, I think the real mode is the data we've generated or partnered to bring in over 50 petabytes of data. We're moving more data to the cloud than the X fire hose was at least a couple of years ago. We're really generating an incredible quantity of data and in the space of biotech generating, generating data across biology and chemistry and not focused on one disease at a time is actually a pretty rare thing for companies to be doing. We've been making that investment in this build phase like Jay was talking about for a decade and I think we're going to start to see over the coming years. That's going to really start to pay off and ultimately, no matter how much money you have to put into the AI space, data takes time, sells take time to grow. You can't just brute force it with money. It's just gonna take time to do the experiments. And I think we're well ahead of really anybody else in the space there. Hey Chris, your stock is at a 52 week low today. Why? What? What do you think is not getting across to investors based on what you said? Look, we're playing the long game. And I think the reality is when you're building a new sector, it takes time for people to understand. We're making sure we're doing the work to help them see where we're going with this business. But ultimately, as we take medicines to patients, those medicines work for patients. I think the stock price will take care of itself. Shareholders will be happy, and we certainly will be happy as well. So what's going to be the next sort of big date we should be paying attention to? What's in your pipeline? We've got a readout on our CDK 7 program on December 9th. We've got readouts for multiple programs coming in the first half of next year. All of these, we believe, are gonna be catalysts for people starting to really understand how it is that AI is going to help us bring the next generation of medicines to patients. Chris, are you going to remain a standalone company? I know there's got to be people kicking the tires right now somewhere. Ohh, We definitely are going to remain a standalone company. We've been at this for over 11 years and I feel like we're just getting started. I'm not tired. I don't think anybody on our team is tired. So we intend to go this alone. And this is really interesting, the biotech industry, multi trillion dollar industry with over 20 companies with market caps that exceed $50 billion. This is a space where they're, you know, it's known for M&A consolidation, but really there's not many industries where you have that many big players. Yeah, very few of them are anywhere near as tech enabled. As a company like us, so I would say over the next decade, maybe some new players in that top 20 space. Does that change though? Also this idea, I mean, when we talk about biotech stocks and the invest ability, it's always this idea that investors are basically looking for that one big home run. And you kind of mentioned this idea of using these AI tools to sort of look at the whole sort of human biology, if you will, rather than just one specific thing. Does that maybe provide a little bit more room for base hits rather than just swinging for the fences for that one big blockbuster drug? Well, look, there's lots of companies who are going for base hits. We are going to decode biology. And if you can decode biology, you could potentially disrupt this entire industry, an industry that does, you know, $150 billion a year in R&D, multi trillion dollar market cap for just the top 50 companies in the space. So I think there's going to be an Amazon or an NVIDIA of the biopharma industry. And it's a question of who and when and we intend for it to be us. All right, Chris, great stuff. We'll catch up with you again soon, I'm sure. Chris Gibson, thank you.