Well, hi everyone. Welcome to this conversation. I'm really thrilled to have Patrick Collison join us. He's founder and CEO of Stripe. And this is a company that I got to know during COVID actually. You might say how did I find out because it wasn't that we were using Stripe as a service but we were introduced and we spent some time talking about biotechnology. He has a keen interest in getting involved in supporting and advancing science in the biomedical fields and we exchanged ideas. He really made me feel old because I actually had to sound wise in responding to his questions and I'm really looking forward to having this discussion because he really is quite an unusual talent both in entrepreneurship but also caring a lot about the scientific enterprise and he and his foundation have done a lot to support this and I really want to get into that. So, welcome Patrick.
Thank you for having me. It's great to be here with a legend of biotech.
Well, legend in my own mind anyway. So, let's dive in. I want the audience to hear from you a little bit about how you got interested in this area, in this topic. Obviously, it has impact, but lots of things have impact. And then, you know, segue into the Arc Institute, what it does, how it functions, and how it's become a platform over time, and how it's changed.
Sure. In really short terms, my brother and I started this technology company called Stripe 15 years ago. Stripe builds what we call economic infrastructure for the internet. Programmable tools that help businesses move money globally, turn a product into a business, and scale to hundreds of countries. Stripe started out serving entrepreneurs and developers. There are lots of big enterprise platforms but we thought there were lots of interesting things happening at the edges. They sometimes want to do things in some novel way that the status quo was not necessarily adequate for them. They care about different things like the barriers to entry and complexity and how easy something is to set up. Started in 2010, businesses like DoorDash, Instacart, Shopify, these giants that at the time were nascent little efforts, were our first customers and now Stripe serves several million customers. That initial bias in Stripe towards entrepreneurs and developers is very much at the heart of the company. We think a lot about what determines whether somebody pursues an idea, which idea they pursue, what makes an idea succeed or not. When I was in college, I thought I wanted to be a scientist. I studied math and physics. I learned that I wasn't good enough to be a world-leading mathematician or a physicist, so that was very valuable, and I decided to pursue software and entrepreneurship instead. But I was still very interested in science. I actually told my girlfriend of the time, now wife, that if Stripe really worked, I'd like to figure out some way to start a new kind of biology or science research organization. Fast forward to 2021, 11 years into Stripe, we decided to start an organization called the Arc Institute. The Arc Institute, we can go into any detail you want, but the very short summary is we fund scientists on renewable eight-year terms. Rather than being on the grant treadmill and having to pursue something that you can make a high confidence case for, instead to be able to pursue something somewhat more exploratory. Secondly, at ARC we build infrastructure platforms. Half the scientists work on these core platforms for things like computation, AI, cellular models, better imaging technology, things beyond the scale of what any individual academic lab can easily establish for itself. Those are the two premises: these platforms and these eight-year grants that enable curiosity-oriented research. Across both Stripe and ARC, the central essence is this question of the dynamics of creation and new ideas and how people make them successful. In both cases, we want to build platforms that maximize the probabilities there.
Great introduction. Maybe give us just a little better sense because the audience may not know as much about ARC as it exists today. How many grantees are there? And roughly what's the size of the ecosystem that's growing around it?
Sure. ARC is about 300 people today. There are 12 core labs. ARC operates in partnership with UCSF, Stanford, and Berkeley. All of the core investigators at ARC also have an appointment at one of those three partner universities. We think that's very important because to be a vital, vibrant research organization, you want to have grad students, you want to be embedded inside the research ecosystem of these other places. Around this time last year, we released EVO 2, which was at the time and still is the largest biology ML model ever trained. It's a DNA language model. We trained that in partnership with Nvidia. I was pretty excited to see that because I think it's a validation of this idea that there are some ideas that are valuable to pursue that are hard to do within the narrow boundaries of what's possible on a single R01. With everything that's happening today, there's some bigger things possible.
And is it kind of like some of the other research institutes collocated? Is there a lot that people do together or are they all individuals?
It's very much... everyone's very excited these days about remote work and distributed collaboration. In the ARC context, we really believe in having people together, so everyone's in a single building. It's in Palo Alto next to Stanford. The other thing I should mention in giving this high-level description, and I'm curious to get your thoughts on this, is one of the motivations for ARC is you can coarsely divide disease into infectious, monogenic, and complex. As a species, we've made great progress against infectious diseases, at least the most problematic of them. We have strategies for dealing with monogenic diseases and screening. For complex diseases, you can debate the definition of a cure, but you can at least make the case that we've never cured a complex disease. The big killers today are neurodegenerative diseases, CVD, cancer, autoimmune, whatever. I'm curious to get your take on this. If the last 70 years have been one of amazing progress in infectious diseases and strong progress in ameliorating some of these complex conditions, but nothing that we can readily say is a cure flat out, what do you think the next 50 years look like? Do you agree that's a reasonable way to characterize the landscape, and what do you think the next 20 years look like?
Super. As a warning to the audience, unlike many of our other discussions, this is going to be a back and forth of questions. If you're wondering if Patrick is going off script asking me a question and worse yet, I'm actually answering it off script, that's what we're doing. Look, I've been involved in this kind of innovation in biotech and human health for now 39 years. One thing I could take away from it if I stopped doing it today would be it's remarkable how much we've done by knowing so little. We now know a lot more than we did before, and we also know that we know even less of what there is to know than we thought in the beginning. It's a little bit like a mountain the peak of which you can't see, and once you go up the mountain you realize it's a much bigger mountain. It's remarkable how much practical things we've done despite that. That's human ingenuity. We've found ways to create micro understanding on little things at the biochemical level or at the cell level, and we've done amazing things with lots of unintended consequences and side effects. For example, we'd like to believe somebody came up with the lock and key analogy, but the reality is our keys open lots of locks and doors, and many other keys can open the same doors. It's a bit of a mess. Despite that, rather than waiting for complete understanding the way physicists would have preferred, people have just said, 'You know what, people are dying, people need to be treated, let's just do better and better.' This whole enterprise with hundreds of billions of dollars of research has been built on that compromised way of advancing with relatively little knowledge. To your point, what is likely to change? Good news is I won't be doing this in 20 years. I'm not very much of a longevity aficionado, so I can make lots of predictions that I don't have to be accountable for. The prediction I would make is that the biggest impact we'll have is in creating models of human biology, physiology, the complex systems that we are, bigger and bigger ones. I don't mean just number of parameters, but bigger and bigger at scale, and begin to use that to make more predictable and more precise interventions. I think that is clearly the promise we have. What began 25 years ago with human genome sequencing that was supposed to cure all immediately is now beginning to be the first layer of the stack of things you need. I think this EVO2 model is a great thing. We were with Jensen Huang yesterday last night when they had this event with Lily, and he talked about EVO 2 among the things they felt very proud of. I think the AI-driven revolution in science is going to be interesting.
You think it's real. There's lots of naysayers and skeptics.
I think there are lots of skeptics and naysayers about the role AI will have in lots of other fields. Fortunately, I don't know anything about those fields. But if there are actually skeptics about what role AI can have in the scientific enterprise, I don't think they have enough data to support the argument, and we do. At Flagship, we have worked in this area for about seven years, long before there were AlphaFold and lots of the more recent generative AI. By trial and error, we've kind of seen what a learning system can do. I'll tell you at a high level because we haven't had a chance to talk about this, my kind of conclusion and premise that we put out last year is this notion of poly-intelligence. If you look out 50 years, I think what's going to happen is that humans will coexist with two other forms of intelligence that we will not understand. One is artificial intelligence, and the other is nature's intelligence. All the forms of nature's intelligences, whether it's a virus that can take down humanity or a tree that every 50 years blooms, these are things that we have nowhere near the sophistication to engineer today, but we will begin to create models for them. Humans will not need to understand to emulate using artificial intelligence, machine intelligence, and nature's intelligence. Those two as they reveal each other will be the new challenge for humans. How do you conduct that process? That's what I think will have a profound effect on better medicines, better cures, better vaccines, prophylactics. It really is going to be a very interesting time.
I don't want to totally usurp your interview and your stage here, but if I've asked so that's a very optimistic take on what's to come. If I was to then jump to the kind of countervailing pessimistic sentiment and mood, the latest figures are something like 40% of new drug candidates are being developed in China, and there are many prognosticators with apocalyptic tones talking about the biotech sector in the US and whether it is viable today, whether it will remain viable. You could also say that the rise of China is in fact a very good thing that will grow the sector as a whole. What are your views?
I put out this letter this week in which I mentioned this as part of the good times and the bad times we're living in at the same time. China competition from China is both a good thing and a threat. It's a good thing that it's coming at a time when we seem to be losing our way in relying on the scientific method and arguing based on facts instead of opinions. All of this science-based policymaking is how we got here in a positive sense, and there seems to be an erosion in that. Whether it's the same 40% could be applied to the proposed funding cuts at NIH, whether it's cutting visas for people like you and for me to come to this country to join universities, to join private enterprise, all of that suggests that there's somehow an undoing of the reliance on the scientific method and its outputs as a way to create a more prosperous, more secure society. In a funny way, the rise of Chinese investment in the very same method and output is a good reminder. Imagine if we stopped doing it and nobody was doing it, we'd kind of go, 'Well, we'll just take our time and we might change our mind later.' We don't have that luxury, and that's the alarming part. The not-so-good part is that it is not a level playing field. It hasn't been in many industries, and it isn't in our industry. I do think there is something to be said about looking carefully at the precondition for collaboration, which is fair competition. I wouldn't collaborate with people that are then going to not compete fairly. Where there are opportunities to improve the competitive landscape, whether it's IP related, whether it's how clinical trials are done, whether it's access to data that is available only to local companies, all that stuff should be factored in. But overall, we can't as a country but welcome competition because this country made it based on its ability to compete. I think we need to ensure that we're not changing some of our principles at the same time as we're seeing this rise. Let's<｜image｜>Noubar Afeyan
Even before the sun rises on the East Coast, it rises in Ireland. So maybe that's the logical place for expansion.
