EP 06: Building a Time Machine for Science with Chris Fussell (Lila Sciences/McChrystal Group)

Zhane Yamin: My guest today isn’t a scientist, though I think you could call him an engineer of sorts. Chris Fussell is president of business operations at Lila Sciences and vice chairman of McChrystal Group. Before that, he spent 15 years as an officer on Navy SEAL teams. And suffice it to say, Chris has got some knowledge in trying to get large siloed organizations to move like fast connected networks.

Luckily, or perhaps unluckily for us, the same thing could be said about science. That’s why I think it’s such an amazing opportunity to talk to him right now, because the question of whether America meets the AI for science moment might not be entirely a question of science. It might be a question of whether a sprawling enterprise, whether the agencies, labs, universities, and companies can act like they’re on the same team. And that’s the one problem Chris has a lot of experience solving. Chris, welcome to the show.

Chris Fussell: Thanks, Zayn. Yeah, and I think the scientists that I work with at Lila would definitely agree that I’m not a scientist. But I appreciate it. Yeah.

Zhane Yamin: No problem. So I guess I just wanted to start with an honest assessment because I think when we talk about scientific competitiveness, there are a lot of alarm bells that are rung, and they’re rung pretty frequently. And I don’t want to just ring them like everyone else. So I want to start off with one question: when you look at where the U.S. actually stands scientifically right now, how competitive are we?

Chris Fussell: The entire field is at a transition point, right? So there’s a lot of unknowns in that question, right? Go back two years, where are we competitively? We’ve got, you know, but a 60-year advantage, post-World War II, right? But now as AI starts to intersect with science, which is a totally unique space inside of the AI zip code, you know, AI and science is a very specific neighborhood inside of that much broader world. Where are we competitively in that neighborhood? I feel very good about it.

But it’s not one—it needs a special type of attention, both from the U.S. government and allies and from companies like Lila. And there are not a lot of companies in our space. I would say we are uniquely positioned globally. But where are we in that competition globally? I think we have an advantage and I think we are ahead of others, but I think it requires a certain level of attention and understanding from government authorities, private industry, et cetera. And if we don’t give it the proper attention, there’s a world where you get passed. I’m confident we won’t, and I’m confident we have the right pole position right now, but we can’t take our eye off of it.

Zhane Yamin: So one thing I kind of want to track with that is, honestly, this idea of watching, and trying to know what we need to watch. Because I think when we’re talking about scientific competitiveness, there’s always a lot of metrics that are thrown around, like China’s taking us in this, but we’ve got this in the U.S. And so I want to get your thoughts on what sort of things we should be watching and metrics we should be taking account of when we’re talking about AI for science and AI competitiveness globally.

Chris Fussell: Yeah, on the AI for science question, and again, caveating with your opening, I am not a scientist, but I spend my days with them. And so there’s, again, in this neighborhood inside of the AI discussion, once you’ve seen this, you can’t unsee it, which is: the winners of AI and science, I believe, will have to do what we are doing. And this gets to your point of measurability and how do we know how far along we are?

Lila’s thesis and mission and what we are building is an AI model that’s able to connect with physical labs. We’re building many tens of thousands of square feet of labs that will be controlled by AI models, with autonomous systems, robotics, humans very much involved in the labs, which allows incredible throughput, right? And so I like to think of it as a time machine for science. We’ll be able to reach years, decades into the future and pull knowledge into today. So what will be measurable there, rather than just the speed of your model or a lot of stuff that the average person just can’t really get their heads around?

We’re right on the cusp of being able to look at a lab and say—sort of like what happened in manufacturing, if you go back to, I don’t know, the post-industrial revolution all the way through the early 20th century—we changed the way we looked at output. Like, how much can this factory produce per square foot of X, and how much energy does it need? It wasn’t just a human question or a knowledge question or an artisan question. You could look at the size of a factory and say it’s going to be able to produce this much X.

We’re going to be able to do that with science. Like, this lab, with this configuration and the power of this model, can produce this much new scientific knowledge. And we’ll eventually be able to measure that, I believe, down to sort of the square foot. And that’s where the measurement question, I think, becomes really interesting and different than it’s ever been in science. We’ve seen that evolution in other industries, but we’re on the brink of doing that in the scientific space.

Zhane Yamin: So on that point, I want to backtrack into your background and the perspective it gives you. Because again, you’re saying you’re not a scientist, but I think that actually offers you a very unique perspective on this entire landscape. How does that inform your work with Lila Sciences and this idea of autonomous science and building out these labs of the future? What does that strategic perspective offer you in that sense?

Chris Fussell: Yeah, just quickly on my background. My first career, for about 15 years from the late nineties until 2012, I guess, I was an officer in the SEAL teams. And so for your listeners, the U.S. special operations community is now, I don’t know, twice as big as it was 25 years ago, but say 70,000 people. Each branch has its own component of special operations. The SEAL teams sit inside the Navy. And there’s a long history to that that goes back to 1980 when those communities were really created. For the first 20 years or so of their history—and this is the force that I joined in the late ’90s—they were built for siloed excellence. The SEALs had to be really good at X. Army units had to be really good at Y, et cetera. And there wasn’t a world where we’d have to integrate together.

Well, post-9/11, now 25 years ago, we were put into a little bit of a time machine ourselves, because we jumped onto a battlefield that was networked and digitally enabled, and our much less capable, much less well-funded adversaries were connected globally through a digital network, right? That’s obvious now, right? It’s at our fingertips every day. Twenty-five years ago that was new and interesting, and it broke the way that we had to operate. And we had to rebuild and say, okay, how do we create a truly networked set of units that weren’t built for that culturally, operationally—none of our systems integrated, from the human level to the technical level? But we have to be able to do that to defeat this adversary.

And so we went through this whole rebuilding process, and then I spent years post-military thinking about that, writing about that. I teach on it, and then obviously built a consulting firm based on it with retired General Stan McChrystal, who was sort of the father of this inside of special operations. So that’s a quick background on what I think is hyper-applicable here, because if you go back just a few minutes, what we were talking about is building these highly integrated lab systems.

AI doesn’t care if you’re a chemist or a life scientist, et cetera. Eventually it will just understand, like we see in large language models, except there are way more combinations in science. It’s just going to see atoms and molecules and the way that life works and the universe works. But you have to teach it through all those different verticals that we as humans have come to understand. And so the chemist needs to work with these labs and develop an understanding of the AI model of how chemistry works. Same with life sciences, same with material sciences.

All the different expertise that goes into building these types of labs, from AI specialists to software engineers that can connect the AI model down into the physical labs, the labs that need to be built in a very special way that don’t exist yet, the robotics that have to go in and operate in a way that doesn’t yet exist—all of these are very cutting-edge, interdisciplinary requirements. You don’t have to squint too hard to see the parallels with bringing together a global task force of highly siloed, highly competitive, uniquely special people. Right?

So all big complex problems come down to very strong-willed and thoughtful people living in their lane, and you have to connect them across if you want to truly function as a network. So there’s an evolution story there, and that’s how I ended up in this very fun, fortunate, interesting position with Lila.

Zhane Yamin: So do you think, in terms of people currently working on meeting the moment in AI for science, that what you see is a lot of people trying to brute force it—like, we need more budget or we need more force, we just need to try really hard—instead of trying to rework these things? Or do you see that a balance is necessary, in terms of brute forcing it, or maybe the problem is more funding, and then maybe half of our attention span should go to rewiring, I guess? What is that balance that you see, and what do you think it should be? What’s the difference between those two, I guess?

Chris Fussell: I mean, we’re a young company, so I’m always going to say more money, right? Especially now, more compute. You want all the talent that you can find, right? You need the best people in the world. So, yeah, some of those statements are obvious, but I do think one of the big bifurcations in this world right now is: give me more compute, more power, more and more, and I can do all of this in silico. Eventually the model will just understand chemistry. Well, I think that will get us to a certain point where we’re seeing strides there, and that’s all important. But eventually you have to be creating and finding new data that you’re verifying in the real world. If you think you can build that entire world in silico, I think that’s going to hit a wall, right? So we are building labs, which is hard, and that’s a lot of labor, it’s a lot of money, it’s a lot of space. But we’re doing that because we believe that the world, the universe, is the ultimate verifier. Science has been done in the world, not just in people’s brains, since we’ve had science for a reason. You have to do something in the real world to learn how does it actually work out here in this madness we call the universe. And you can get a lot done in silico. Obviously, even our system will bounce back and forth between those two, because you can move incredibly fast when you’re doing it just on compute. But there are going to be times, clearly, where you need to jump out into the physical world and say, let me check that. Let me make sure that works. And so I think that’s the big separation that is already happening, but it’s going to become more and more of the conversation as we move forward.

Zhane Yamin: So one thing I want to take a second to expand on for our audience—I think you talked about it a little bit before—when you’re talking about this work you’re doing at Lila, bringing this rewiring approach to how we approach science and meeting the moment for AI for science, what does that actually look like? Or what are the things that need to get rewired, or what are the nodes in the network, I guess, would be a question that I have.

Chris Fussell: You mean organizationally?

Zhane Yamin: Yeah, organizationally.

Chris Fussell: Yeah, I mean, I’ll compare this to the world I grew up in. This isn’t unlike a lot of governments, a lot of big companies, et cetera. The way we described this back then was: the whole U.S. national security apparatus had been built for generations to do specific things in purpose-built silos, especially inside of special operations. And the language I’ll describe here—there are versions of it that you can see in letters that Caesar would have written to his legions, or Lincoln wrote to his generals in the Civil War. Our language was find, fix, finish, exploit, and analyze. So in the counterterrorism world, there are groups or individuals behind these things, right? It’s not huge numbers.

You’re not looking for the German army. So you have to find where this group is, because they tend to want to hide, right? So there are certain agencies who specialize in finding things, right? To go too deep—that’s probably pretty obvious if you’ve watched any movies about spies. Like, I’m going to go find this person or this group. And then there are other ways that you fix them in time and space. And again, like President Lincoln, he has letters to his generals in the Civil War: I need you to send a reconnaissance force into the Shenandoah Valley and find out exactly where so-and-so’s army is right now. So they are fixing that army. They are in this part of the Shenandoah. And then they can do the math and say, well, they’re going to be here, here, here within a week. We have agencies that go fix things in time and space. It’s a little more technical now, but it’s the same idea. And then I was part of a finish force. Let’s go finish that problem. Send the army out, send the unit out.

And then for years we were trained to grab what we learned from that finish exercise—raw intelligence, insights, et cetera—bring it back and give it to units who specialized in exploitation. I need to download that hard drive, I need to look at those pictures, whatever it is, and I need to clean up my intelligence picture. Then even other groups will analyze that compared to the broad global understanding of what’s going on with this terrorist element. And then you feed it back into the cycle, right? So you find, fix, finish, exploit, analyze, repeat. Those were built in silos so that senior leadership, presidents, secretaries of X, whatever, could control the pace that it moved. And in the 1980s or ’90s, you didn’t have to do that very fast, right? That process I just described could take months, and that’s totally fine. We got into a world where that—

And we used to call the gaps between those blinks, because you were sort of closing your eyes for a second as you hand off to the next organization. And we realized we’re good at all of these, we’re losing on the blinks. Because when you’re fighting in a truly complex, fast-moving battlefield, nobody else is blinking, right? So we have to close the gap between those. And so eventually that became—we described it as a constant circle, which was networked, so you were never blinking. There was always information flowing between all of those different elements. That’s a long-winded way to say I think the exact same approach is necessary inside of science.

And if you think of the scientific method, where you observe—you see an apple fall out of a tree, you make a hypothesis about how gravity might work, you design experiments, you conduct those experiments, you write down all the math that you’ve learned, you refine your hypothesis, you feed it back in—that’s a similar sort of cycle. We put humans into a lot of those components right now, and I could map that over to Lila, but it’s probably obvious. Now, in today’s world, what we’re doing, when you can apply both autonomous labs that can run that nonstop, humans that can plug in where and when they want to, and an AI that can network across all of that and is in a constant state of learning—it doesn’t see the difference between what you’re doing over here and what I’m doing over here.

It can be everywhere at once if it’s allowed to. All of that accelerates, obviously, in a massively exponential version compared to what I was describing in the world I grew up in. But the same sort of theory applies. You have to look at yourself as building organizations that are in a continuous loop of learning and then networked on top of that, so that the cross-functionality is happening in the background without ever thinking about it.

Zhane Yamin: So I guess just an added question on that. I’m getting this idea that we have these fast loops, like on the battlefield. That’s what we want, right? And the analogy to science would be this loop of the autonomous lab or the scientific method and scientific process. There are no blinks in between them; we’re running them as fast as possible. Humans are plugging in where they need to be.

A question that I have is: I think a lot of people have an understanding of scientific institutions. So when you’re thinking about funding institutions or other sorts of scientific institutions that researchers may liaison with, it’s kind of cliché at this point, but maybe that they’re slow or they’re bureaucratic. So I wonder how much you’re thinking, at Lila, when you’re creating these platforms—you’re creating this super fast loop, but is the institution around it, or these bigger agencies?

I’m not sure how much you at Lila specifically might liaison with them, but how do you think of—to maybe make a poor analogy to the defense world—you have this fast-moving battlefield, but when you’re talking about the broader Department of Defense or something, or people not on the battlefield, are they moving at a slow speed, and how does that impact what you’re able to do on the battlefield?

Chris Fussell: It’s a great point, and not to sound cliché, but I think it is actually a proper use of the concept of a paradigm shift from Kuhn’s work, you know, forty years ago. Which is to say, there’s an entire ecosystem of thinking, of organizations, a bureaucratic process that’s going to have to change as a result of this shift that’s happening inside of science. And you named a few. There are many, many more than that, right? Because we built a very specific paradigm based on—one of the key components is how fast can a human learn something?

You know, let’s make it very simple. I’m a researcher, I have to get through school, then I’m going to get into a lab and I have to put stuff in beakers and da-da-da-da. It’s a very methodical process. So you build all these systems around it, and the system can predict what the throughput will look like, how much time I need to assure that that new finding is safe and releasable, and can I go through my testing phases, et cetera. Everything we know. I pass laws about it, I give funding to certain things on this end, whether it’s a university or an individual lab, et cetera.

All that works—sure, it’s problematic, but it’s better than any other system we’ve ever come up with, right? But when one of those things gets exponentially faster, everything else is going to have to change over time, right? The way we think about releasing drugs to the market in, pick a decade, fifty years from now will be archaic. It just will have to be, right? We’re not the ones that are going to change that, right? But we are, again, in the zip code we live in, a wonderful upward pressure that will start to influence all of those other systems.

And when there is—I’m, of course, making this up—at some point there will be a backlog of new drugs in a certain category, and we’re just going to realize the system doesn’t work anymore. We have to figure out how to move these things faster because there are real lives on the line right now. Or new discoveries in material science—you could just pick your category and say, wow, these systems now, we’re feeling this pressure. A lot of other things have to change around it. And I think we wouldn’t have to think too long to find historic analogies of having to massively increase the speed with which we allow certain things to happen as technology advances.

So, yeah, we are one of many in the broad discussion around AI that will end up influencing that. I do think Lila will have a specific voice in that, because we’re going to be doing amazing things in this autonomous lab space before many, many even are aware that it’s happening. So that’s exciting. I think on the government side, there are a lot of folks in that world that are thinking forward on this topic. But it’s going to take more legislators understanding what’s happening in this space. It’s going to take more government focus, government dollars, government influence to try to get ahead of that problem set as well.

Zhane Yamin: So then, not to lean too hard on the historical analogies, but I always wonder about this idea. I think it’s recent that people think about the Sputnik moment, right, when they think about technological competitiveness—and this idea that, instead of a gradual change that slowly shifts paradigms, something happens and everybody flips into a different mode of operation.

Not to make any predictions here, but what do you see? Do you see it being a moment where something happens, either on the international or the domestic stage, and that paradigm shift happens? Or do you think it’s more likely what you’re saying, where it’s a backlog of maybe drugs and at some point people are like, all right, we’ve just got to switch this up, and this has been mounting for a while? Maybe not any specific predictions, but what do you think about that sort of dichotomy?

Chris Fussell: Yeah, I mean, I was a consultant for years, right? So the answer is it’s a little bit of both, right? There will be a series of wow moments, and there will be a series of, you know, now we’ve got to change things to catch up with this. I think the wow moments will—again, it’s fascinating, exciting, sometimes mind-boggling to spend all day every day with brilliant PhDs. They have these moments regularly. But for the average duck, you’re like, I guess that’s really interesting, right? But that’s true in any deeply technical space.

There will be moments in the very near future, I’m quite confident, where the public will be able to hear the story and say, wow, that material used to weigh this much and now it weighs this much, or at least we’re on the path to that. That means my vehicle is going to be lighter. Or that means this offshore drill now has a lubricant, and the parts under salt water and high pressure conditions will last ten times longer, right? And so now we’re reducing all sorts of environmental impacts. There are going to be these wonderful, very translatable wins.

I think the material science place is the one that will shock and surprise the world the most, because we’re surrounded by materials all the time. And when those things start to change, I think that’s going to be one of the first door openers for the public to see, okay, science is really changing the world around me at this accelerated pace. Because most of us have never—those things happen slowly, right? I was kidding around with somebody today, like WD-40: we all have a bottle of it somewhere in our house. It’s the same thing my grandfather used, I think, right?

But all of those little micro changes—we usually don’t see any of those in our world. We’re going to see a lot of them in our lifetimes, and they’re going to have tangible impact that people will feel. And then, of course, the same thing in life sciences, but I think materials is where people are going to really start to connect with this change that’s happening in science.

Zhane Yamin: Wow, yeah, that’s great. I was going to ask about what’s on the table here, and I think you just covered it perfectly. I think for somebody listening at home, and what they can take home and be like, why does this matter, even those materials examples are a very clear example of it.

So now that we have what’s on the table, if you were talking to the policymaker or the policy analyst that’s listening to this podcast, what’s one thing that you would want them to take away from this? What’s one thing where you’re like, this is something that we need everybody to be on the same page about, that I see as a big problem, where people are often either butting heads or they’re not sure, they’re not informed? What is that one issue, and what would you say to that?

Chris Fussell: Well, I guess for the D.C. audience—and I do say we’re down there a lot—there are real strategic issues at play here. Probably stating the obvious based on our conversation so far. But part of the reason I was passionate about coming to Lila from the very start was, like I said at the beginning, once you see this, you can’t unsee it. I grew up in a world in the military where technology changes, material science changes, they have real impacts very quickly, right?

Pick your example from the special operations world: improvement to lasers, improvement to night vision devices, improvement to thermal imagery, all these things. Those happened relatively quickly when an advanced military is at war. I saw many of those cycles take place over a decade. If you turn up the speed on that—not just for military application, obviously, but broadly—there are significant strategic implications as you look at how you then interact with your adversaries. How do allies move up this together and stay connected as technology moves so quickly?

So I think for policy folks, one, be very aware that this is going to be a fast-moving shift with global implications for how we stay connected with our allies, and aware of the balance with adversaries. Because I do believe in geopolitics, national security, if you get too disproportionate, even unintentionally, you can create second-order problems you didn’t want to, right? Because you can force a reaction, like, now I’m really nervous, so I better do something before you’re too far down that path, right? So we want to avoid those.

But then thinking downstream, what are the implications of these sorts of advancements? Look at things like our industrial base. How many things have been happening the same way for generations? And how quickly can that shift? We talked about some examples a moment ago, but how will this impact agriculture? How will this impact the materials we produce every day with a very high energy cost? Look at supply chains globally.

What materials are we dependent upon from nations we wish we weren’t, right? Are there alternatives we’ll be able to create and produce at home or in allied nations? The answer is yes, right? So huge implications there. When there are fast-moving problems—I mean, COVID’s an obvious big example, but you can imagine others—how much faster can our response get to problems like that with this sort of time machine for science, so to speak?

Again, massive implication, and one that you want to make sure you’re keeping the right people informed on as you move up that speed chain, right? So it’s one of those arenas where you want to think, okay, what are the possible, likely outcomes over the next five to ten years? And then let’s assume we get there, what are the downstream effects that that’s going to create? And how do we start mapping that into how we’re thinking about policy, funding, et cetera, in the here and now?

Zhane Yamin: Good advice to heed. I think that’s a perfect ending note. Chris, thank you for joining me on the show.

Chris Fussell: Thanks, Zayn. This was great.