On this episode of Coins2Day’s Leadership Next podcast, cohosts Diane Brady, executive editorial director of the Coins2Day CEO Initiative and Coins2Day Live Media, and editorial director Kristin Stoller talk to Daphne Koller, founder and CEO of Insitro. They talk about using AI to combat the antiquity of drug discovery, working with Big Pharma and the Trump Administration, and helping society prepare for a post-AI world.
Listen to the episode or read the transcript below.
Koller: I think that the future is in a partnership between the human and the machine. I think for every technology that we’ve constructed in the past, people are like, “Oh, my God, this is going to take away my job.” And in fact, it did take away a lot of jobs. I mean, electricity certainly took a lot of jobs, right? I mean, the agricultural revolution took away a lot of jobs. This will certainly be the case here as well. But I think human creativity, human innovation, is is still something that is an important partner.
Diane Brady: Hi, everyone. Welcome to Leadership Next. The podcast about the people…
Kristin Stoller: …and trends…
Brady: …that are shaping the future of business. I’m Diane Brady.
Stoller: And I’m Kristin Stoller.
Brady: Kristin, this week, we are speaking with a MacArthur Genius recipient.
Stoller: A genius, she’s in our midst. I’m so excited.
Brady: I’m sounding like a cynical Canadian here. I actually am very fascinated with her. I’m fascinated first of all with the concept of genius writ large. I have a friend who got one of those grants, who’s a jazz musician. Here’s a woman who cofounded Coursera. Now has Insitro, which is drug discovery. I mean, what does it mean to be a genius?
Stoller: It’s, I mean, founding two companies is a great start to being a genius in my book. And Stanford professor too.
Brady: Yes, let’s not forget that. Let’s not forget the Stanford professor. Well, look, I think the most exciting use case for AI is in health care, drug discovery, and she’s right in the heart of some of the most difficult diseases that…
Stoller: She really is. They’re focused on ALS. They’re focused on fatty liver. Those are two really big ones that haven’t seen a lot of innovation.
Brady: No, and heartbreaking ones. My old boss has ALS, and it’s heartbreaking to see the decline people have. And I think with her in particular, she does seem to be somebody who follows her curiosity, from previous interviews I’ve seen. So I’m curious to know what it is about this particular part of the AI ecosystem that drove her to it. She could have gone anywhere.
Stoller: I totally agree. And speaking of AI, I think she’s catching us on a very good week, because we had such an AI-themed week last week.
Brady: I know, you were even interviewing my digital avatar.
Stoller: I was, I was interviewing your digital self to prep for an upcoming conference. I had an AI dinner on Wednesday that I went to, and then Thursday, you and I sat down with this fascinating CEO in the AI space. Sadly, we have to gatekeep his name a little bit, but remember how fascinating the debates we had.
Brady: Yeah, look, I disagree with him on a lot of things.
Stoller: Well, that’s what made it fun.
Brady: Number one, being the idea that AI will reduce drinking because it’ll help us connect more socially. I think that’s open to debate.
Stoller: Let’s unpack that, because he was saying that if there’s, you know, ChatGPT, you could have, like, ChatGPT and AI in a social setting helping tell you, like, conversation starters, questions to ask, and no one needs to take a shot anymore.
Brady: Look, some of us may be neurodivergent. I’m not making any projections here, but maybe that would help some people. Yeah, I don’t think that’s why everybody drinks, A, and B, look, I think EQ and IQ are two different things, so let’s just leave it at that. And I do think AI has a lot of potential. Clearly, this is an area that excites people. And I’m really curious to know what it is and when she hopes to be making some of these big advances. I’m also curious about Coursera, I mean, to me, it’s a fascinating company that did not fully realize the dream of online learning, don’t you think? I mean it, we’ve basically proven the fact, in the same way that AI cannot help you at a party necessarily, to be determined, I don’t think online learning helps you learn as well as being in a classroom.
Stoller: I’m with you there. I think the bigger problem, for sure, is Insitro, because I feel like you know, of course, education is an issue, but with drug discovery, it’s such a field that has so much potential. It’s such a costly field, billions of dollars are invested in every year. It takes so long to bring a drug to the market, 10 to 15 years at best, and it’s rarely successful. They have a 90% failure rate in clinical trials. Not very effective
Brady: Big Pharma.
Stoller: Absolutely, yeah.
Brady: Look, she has a phrase that I’ve heard used in other interviews: Calculus is to physics as AI is to biology. We’re going to have her unpack that. But I do think that we now have an infrastructure to improve drug discovery, no question. Can a startup disrupt Big Pharma, or is it really additive to what they’re doing? To be determined.
Stoller: Well, let’s find out with Daphne after the break.
Brady: Great, we’ll be right back.
Brady: The best business leaders today know the value and importance of empowering those around them, personally and professionally. By encouraging and enabling others to grow, take risks, and fuel innovation, business leaders are not only driving greater engagement and performance, but also future-proofing their organization for years to come. I’m joined by Jason Girzadas, the CEO of Deloitte US, to talk more about this. Welcome, Jason.
Jason Girzadas: Well, thank you, Diane. Great to be here.
Brady: Innovation is about empowering the people around you, and that’s something that a lot of CEOs struggle with. How do they embed it into their leadership style?
Girzadas: Well, I think there’s all types of CEO leadership styles, clearly, and proven that there’s maybe not one recipe for success, but it does require, I do believe, a commitment to inclusive leadership, where all are expected and invited to contribute around innovation. I think there’s also a collaboration and a collaborative culture that’s a requirement that’s also not something that maybe comes as naturally and has to be cultivated and be intentional about. And then also, I think giving leaders some autonomy to actually look at opportunities for innovation, look at opportunities for creative, new ideas to bring forth that requires a degree of trust and a degree of openness by CEOs in particular, to allow for that within an organization.
Brady: So Jason, I want to—on a personal note, I’m talking to a CEO here. What are some of the most effective strategies you think for fostering open dialogue, collaboration? A lot of what you’re talking about is the ingredients to innovation.
Girzadas: Well, for me, it starts with being genuine and authentic as a leader. Being clear that the single leader doesn’t have all the answers to every question, and certainly in my case, it’s inviting a very broad organization to participate in addressing the issues and challenges that we face. So I think that genuineness and that transparency and authentic leadership style is the key ingredient from my experience.
Brady: Good advice. Thanks for joining us, Jason.
Girzadas: Thank you, Diane.
Brady: Daphne, in doing my research, I came across an interesting quote that you said, which was, “Calculus is to physics as AI is to biology.” What did you mean by that?
Koller: So, discipline has become something that we can manage as humans, when they become predictable, when we can have a formalism that allows us to predict what will happen in an experiment and have it be approximately correct. And calculus was that for Newtonian physics, whether it’s a ball rolling down a hill or a pendulum swinging, we were able to make predictions about what a system will do. And we’ve never had that for biology, because biology is complicated and intertwined, and we really can’t predict, for almost any experiment, what will happen. And now, with new measurement technologies on the one hand, and the power of AI on the other, we can start to create models of biological systems where we can start to make predictions that are at least somewhat likely to be correct. And I think that is the path forward to many applications, including, of course, in human health. But I think it’s going to be an incredibly important discipline in the 21st century.
Stoller: You talk in these amazing, you know, philosophical phrases. That wasn’t the only quote that Diane and I came across that I thought was interesting. You have a lot of others. And you’re a Stanford professor. Do you come up with all these yourself? Do you have friends that help you?
Koller: I think much of what all of us do is a distillation of bits and pieces of things that we hear and kind of put together in slightly new ways. I can’t say that I invented these from whole cloth. But rather, you know, you develop a certain way of thinking about things when you get exposed to all these little bits and pieces, and begin to understand how to think about the world.
Brady: I do think, and I want to get back to, of course, AI and what you’re doing now at Insitro, but I do think that having an eclectic background does allow for original thinking. And you are somebody who has had a MacArthur Genius Grant. That impresses me, because I know two other people who’ve had it. One is a novelist, Edward P. Jones. Other is a jazz musician, Mary Halvorson, and here you are, you know, to put you on the spot a bit, how do you define genius?
Koller: You know, I always felt uncomfortable when that word was applied to myself, because I’d always had a very aspirational definition of what genius means. And you know, that was Albert Einstein or Leonardo da Vinci. It wasn’t me. And when I did get that MacArthur Award, I actually felt very humbled and unworthy. And one might even say that, and I’ve even said that to the people who gave me the MacArthur Award, that much of my career journey following the MacArthur Award was an attempt to kind of pay it back, to prove myself as having deserved that.
Brady: No offense, that sounds very female impostor syndrome.
Koller: Well, maybe it is, but that’s certainly how I felt. And I think the Coursera experience was definitely—I felt the need to go into something…
Brady: …which you founded and were co-CEO, for those who don’t remember…
Koller: Yes, and that was an attempt to do something that was sufficiently ambitious and world-changing that it deserved that recognition. And so I don’t know what genius is, but I can tell you that one of the things that I consider to be my superpower, trying to avoid that female impostor syndrome, is that ability to connect the dots across different disciplines and see connections that are oftentimes maybe obvious in retrospect, but weren’t obvious at the time. And I find that ability to transfer ideas from one discipline to another and create a synthesis that did not exist before is where a lot of really interesting ideas often emerge. And you can think about what we did in Coursera as kind of bringing the web in a truly meaningful way to education. And you can think of what we do at Insitro as the blend of biology and computer science. And in fact, it’s even in the name. Insitro is the synthesis, the deep integration of in silico, which means in silicone of the computer, and in vitro, which means in the lab. And that is kind of the ethos of the company.
Stoller: Oh, I had no idea as an in vitro baby. I love that too.
Koller: Oh, that’s lovely.
Stoller: Yes, so I’m wondering, Daphne, what was it that was the spark that made you kind of transition from academia into being a founder, and how did you feel? Were you scared? Because I would have been terrified.
Koller: I was absolutely petrified. Not only had I never founded a company, my career journey was such that I’d never even been at a company. I was an academic, through and through. My parents were academics. I was convinced I would retire as an academic, be a professor emeritus, like my father. And then, as I mentioned, that MacArthur thing happened and really helped put a point on—I wanted to make an impact. I wanted to do something that was directly changing the world, as opposed to publishing papers and hoping that someone will read them and do something with that, or, you know, graduating students that go off and do great things, and that’s amazing.
Brady: Especially Stanford, that’s the hub. You also grew up in Israel, which is also a hub of biotech and entrepreneurship…
Koller: …and tech…
Brady: …and tech, and you did college and high school simultaneously. I think you graduated at 17 from university, correct?
Koller: Yes.
Brady: So talk a little bit about what intrigued you there? Because computer science wasn’t really as much of a discipline per se when you and I were growing up….
Koller: …no….
Stoller: …and for women especially, I might add. I think that’s really impressive.
Koller: For sure. No, I got into computers actually, when we were on sabbatical, when my father was here on sabbatical in the United States, and I was 12 years old, and I was fortunate to go to a high school that had a computer lab, and it’s, you know…
Stoller: …Like Bill Gates…
Koller: …well, yeah, kind of like Bill Gates, a few years later. So we had somewhat better computers than he had access to when he was a preteen. But that ability to kind of tell the computer what to do, and then it does stuff I found fascinating, and it really fit in with my kind of wish to create models of complexity, and that theme has been with me throughout my career journey, of taking complex, sort of gnarly things and create models that lend clarity. And I hesitate to say that computers understand, because computers are not conscious, they don’t understand. So to me, a computer can be said to understand when it’s making predictions that are reasonably accurate, which comes back to the comments that I started with. So creating models that are predictive of real-world systems, to me, was just an incredibly fascinating endeavor.
Stoller: I think that’s so important that you said they don’t understand. Because right now, I think a lot of people are using AI like it’s a real human with emotions and feelings. And my friends tell me they use it for therapy. And I’m like…
Brady: …don’t fall in love with your operating system….
Stoller: …yeah, exactly. How are you advising people?
Koller: I mean, look, if it’s useful for you as as a human to speak out what you’re feeling and have something that prompts you in an intelligent way to bring things out, I think that’s great, and I think it’s fine to use a computer as a therapist, but I think it’s important to realize that there is no emotions on the other side, there’s no sentience, there’s no consciousness. It is effectively a really sophisticated mirror that allows you to work your way through, for example, complicated feelings.
Brady: Thus making it particularly well-suited, I think, to things like drug discovery, right? I mean, well, tell me a little bit about the genesis of Insitro and what it is that you felt you could accomplish, especially in the world of Big Pharma, where, you know, it takes years, decades, in some cases, to get to the kinds of drugs that you’re currently developing for diseases for which there’s currently no cure.
Koller: Yeah, so my sort of journey into drug discovery came out of my actually early academic work at Stanford, where I’d been working at that intersection of biology and computer science and machine learning for quite a number of years prior to my detour at Coursera. And when my time at Coursera was over, I realized because I’d actually been at Coursera when the machine learning revolution in 2012 started. In 2016 I lifted my head over the trenches, and I was like, wow, machine learning is changing the world, but not having as big of an impact in life sciences. And I felt like I could potentially make a difference in that. And so I spent some time at Calico, which is a great company, and learned about drug discovery for the first time. And I was like, wait, that’s how we make medicines? I mean, this is, so, what’s the word, serendipitous.
Brady: I thought you were going to say antiquated.
Stoller: I was thinking the exact same thing.
Koller: Fair enough. It’s both, and this is not, you know, this is not to say anything bad about Calico specifically. The whole process is like—a scientist has an idea and based on some intuition, reading some paper, whatever, does some bespoke experiments to kind of try and prove that out, and then you push that into the clinic, and it fails over 90% of the time. And 90% of the time is from the time that you put this into the clinic. Which is pretty late in the process.
Stoller: Why is that?
Koller: Because we are not able to predict upfront what the experiment…
Brady: …your eureka moment could just be a flight of fancy…
Koller: Yeah, I mean, you are making predictions based on experiments in often animal models that are just not human. You know how many mice we’ve cured of cancer? Many, many mice. If only mice were the target audience, we’d be in great shape. But we’re not able to do experiments on humans until the very end of the process, as one should in the clinical trial. And we’re not able to make predictions upfront of what will or will not have a therapeutic impact, will make people better or not. And so what I wanted to do was to bring the power of machine learning on the one hand, and equally importantly, the power of modern-day data collection from humans and different types of human-derived systems, bring that together to make as accurate a set of predictions as one can around whether a drug will have a benefit to a particular patient or not, and that requires having the right AI and having the right data to feed the AI, and those together are what is allowing us to create an engine that turns and makes more and more predictions about therapeutic hypotheses that we think are both novel and more likely to have a success in the clinic.
Stoller: Tell us about this data, because I’m curious where you’re getting that, where it’s coming from. What’s that like?
Koller: So, data is the fuel for AI. I think everyone understands that now. The thing that has given us that ChatGPT moment is the incredible amount of data that is available on the web in terms of text and images. What has given us alphafold Is the significant amount of data about proteins folding. And so in order to give us that next moment, that next ChatGPT moment, we need the right data to feed the AI. And so at Insitro, we actually put together two different types of data that we think are synergistic and complementary. One type is data from humans, where we measure a human system, and we understand how changes in human genetics, the DNA that makes you and me different, has impact on traits that are relevant to health and disease, and we’re able to use measurement technologies like imaging of whole bodies or different organ systems, omics from blood and tissue to really get a picture of human physiology and understand how it relates to genetics on the one hand, and the clinical outcomes on the other. Which is great, but you can do experiments in humans, and you do need to experiment with your hypotheses. So the other half of what we do is we print massive amounts of data in our lab, and this, of course, is data in microcosm systems and cells. But those cells are allowing us to kind of make interventions and see what a particular change in a gene does, in this case, to cellular systems and those together can triangulate us on hypotheses that are actually going to…
Brady: Let me pause a second on the human data, on two fronts. One, I was very early. Me and my family, I forced them to spit in test tubes and send it off to 23 and…
Stoller: …still too scared to do that…
Brady: …and because the promise of how DNA collectively could unlock drug discovery excited me. I’m curious why those models, without making it too personal, have proven to not be as promising, it seems as—or maybe it’s just the business model of some of them. But then I wonder, secondly, about the trust issue, I trust spitting in a test tube. Kristin may not. But also, there are a lot of populations that have been reluctant to share their data. Do you see any pain points around genetic data privacy, trust—and just because it’s so promising, clearly, if everybody in the world contributed their information how lucky we would be, and they’re not.
Koller: Yeah, so I think that is something that has been done poorly in some cases and very well in others. So let me point to a success story, which is something that we leverage a lot, which is resources like the U.K. Biobank, where I think it was done as well as one can possibly hope for. They got half a million people to agree to have their DNA measured, as well as a whole bunch of other what we call phenotypic measurements, which are various traits. And they took people and imaged them, they collected blood samples, they took all sorts of anthropomorphic measurements. And actually it turns out that those together are way more powerful than either one in isolation. When you have both the genetics and the phenotypic data, is where you really start getting insights about what genetics does to actual clinical outcomes. And they did it in a way where people were consented, they knew what they were doing, and they were excited to contribute to medical research. And I think if this was something that was done more broadly, I think we would be, to your point, in a much better situation, and it is possible do it in a way that is privacy-preserving. You create research environments that are protected, the data are anonymized. You can’t download the data, and all of those things are solvable engineering problems. What one needs is the recognition of just how valuable this is from a medical research perspective. Not just medical, by the way—the U.K. Biobank gave us a tremendous amount of insights about nonmedical situations, like the impact of various other environmental factors, for example, on human health and other human outcomes. And when you create that resource, it’s just a gift that keeps on giving. And I wish other governments were, especially ones to your point, that have access to more diverse populations, because the U.K. Biobank was very Eurocentric, would make that effort and create these resources that would allow us to address human health across different ethnic backgrounds.
Stoller: I think diversity is, was, my next question, because going along with the ethics of it, having a diverse dataset really worries me, especially when it comes to health care. How are you at Insitro ensuring that all the datasets are diverse and inclusive?
Koller: So I think it’s important to distinguish between data that’s used for scientific discovery and data that’s used for the delivery of care. And we’ve seen that there is significant differences in terms of, you know, for instance, diagnostic tests that are developed for one population being less accurate than tests that are developed for another. When you’re doing basic biology discovery, and what you’re coming to is the recognition that this gene is a significant causal driver of a particular disease, the prevalence of the disease might differ in different populations, but typically the underlying biology is consistent, and so that same driver is likely to be relevant across populations, even if the prevalence of the disease might be more relevant in some populations than others. So I think it’s less of a concern when you’re doing drug discovery than when you’re doing the delivery of care. However, what we’ve also seen, which is why diversity is, I think, really important for all of us, is that as you get insights about new populations, new biologies emerge that might be kind of just not there in another population. And so you might uncover even more new things by having access to more and more different, diverse populations. So I wish we had that.
Brady: Can you talk a little bit about how you chose ALS, fatty liver disease? What was it that made you target the diseases that are currently on your radar screen?
Koller: So first and foremost, the lens that we begin by applying is how significant of a disease is this and to what extent is there what we call unmet need, because there’s certainly a lot of diseases that are reasonably well treated right now, and there’s companies out there that make, you know, “me-too,” “me-better” drugs for those diseases. And, you know, there’s value in that. But that is not what we wanted to do. What we wanted to do was to uncover new intervention points that make a really significant difference. So ALS, to take a very concrete example, has absolutely nothing. There are four approved drugs. They extend life-span by two to three months. The patients have a three- to five-year life-span. I mean, it’s worse than most cancers, to be honest. And patients die an excruciating death. They basically, they start losing their motor function to the point that eventually they just are unable to breathe, and that’s just a horrible way to die. And so we believe that in the work that we did in ALS, we have uncovered a novel master regulator, actually, we think maybe even more than one, that modulates a number of key proteins that we know are implicated in the pathophysiology of ALS. And so now it’s early days, we haven’t put this in a human, and unfortunately, ALS is one of those diseases where the animal models are particularly poor, and so our proof of success will only be when we put this in a human, which, by the way, is true in general, but maybe even more true in ALS because of the lack of good animal models. But we are so excited about the experiments that we’ve done across multiple different ways of measuring ALS biology and finding that our targets modulate these different aspects of the disease. So we’re really excited about that, and we keep looking for other examples of that. Now the other element, of course, one is unmet need. And the other is, do you have a unique, differentiated ability to do something about this disease? Because there’s a lot of diseases out there that have unmet need, but what makes us the best people to tackle that? And so what we look for are diseases that manifest in meaningful ways, in the kind of high-content data that we have access to on the one hand, and have a sufficient genetic basis on the other, so that we know that genetics is a sufficient driver of the disease, so we can find those genotype phenotype connections. I mean, if it’s a disease that’s driven by smoking…
Brady: …yeah, it seems random. Stop smoking, you’re good to go…
Stoller: …so I want to go back to Diane’s original point when we started this, which is, you know, drug discovery right now can take decades. How far away are we from testing, you know, the ALS research you’re telling me about in humans, and is it, is it still going to be decades?
Koller: Well, so there are certain parts that one can accelerate and others that are harder to accelerate. So for example, at least as of now, there’s a certain set of requirements that regulators require, for example, on toxicity that just take as long as it takes—takes a year to do these experiments. You know, purity of the drug substance is a toxic in two species and so on. So that takes a year. We are hopeful that our ALS drug, which is very much in drug discovery right now, will be heading towards the clinic in, I don’t want to give timelines here in real time, but, but we’ll be heading towards the clinic in the next couple of years and then we’ll have to see how well we are able to address the disease. Now, the other part that can’t be compressed, but then again maybe can, is the actual clinical development. So clinical development is what happens once the clinical trial begins, and you can say, well, if the disease takes a certain amount of time to develop and progress, then you can’t accelerate that. And so it takes a certain amount of time to see that your drug is making a difference relative to the natural progression of the disease. And that is absolutely true, but there are places where the kind of machine learning and AI capabilities allow you to get early signs that your drug is working by looking at, you know, biomarkers that are much more quantitative, that are not things like, is the patient walking better, or…
Brady: …it’s a space with a lot of innovation right now. I mean, I think of Insilico medicine, where they’re using AI for creating molecules, you know, for clinical trials. So there’s so when you look at the ecosystem right now, let’s start with the fact that, if I’m from Big Pharma and I’m here to meet you, are you my friend? Are you my enemy? Or a bit of both?
Koller: Um, I think enemy is probably a strong word. I think…
Brady: …competitor. How about that?
Koller: I think we have places where we have collaborated very effectively with Big Pharma. We have an, I mean, the work on ALS is a collaboration with BMS. There have been wonderful partners. We have actually an kind of inverse collaboration with Lilly, where they have extra capacity and capabilities in making a particular type of molecules that isn’t within our sweet spot, and they’re making that for us. And so collaboration makes a ton of sense in a bunch of cases, and then in other cases, yes, of course, you end up being a competitor. If we make a drug and someone else is making a drug towards the same disease, then you end up competing. But I will just say that there is so much unmet need in the ecosystem right now, not a single company can own the entire market. And so it requires a lot of collaboration, and takes an ecosystem to help us address. I mean, a real enemy is disease. It’s not a different company.
Stoller: Speaking of enemies or challenges, I want to talk about the current political climate, because…
Brady: Wait, I thought the FDA is our friend. Let’s…
Stoller: Yeah, it’s even more challenging than ever now to get funding for biotech and everything that you’re trying to do, how has what the current administration is doing, especially in terms of funding, been affecting what what you do, and how are you getting through it?
Koller: Yeah, so I think it’s definitely challenging times for our industry. I think the thing that is most challenging as far as our industry is concerned, is the uncertainty. When you’re doing drug discovery, the, these are very long timelines, and investors underwrite a particular proposition, and if they know kind of what the path is, they can do the appropriate, you know, ROI analysis, and say, okay, this is how much this is worth. I’m going to underwrite this proposition. But if things change every day, you don’t know what to underwrite. And so people are like, well, I’m just going to sit back for now and see what’s going on. And I think that’s really one of the biggest problems. The other of course, is I think there needs to be more support for scientific discovery in the United States. Continued support for what has been an engine of innovation that has driven so much value in terms of companies being formed, discoveries that have been world-changing and while certainly there have been inefficiencies and ways that this can be made better, I worry that there’s actions that are throwing out the baby with the bathwater, because there is not sufficient sort of distinction between, okay, these are actions that are making the system better, versus we’re just, you know, going to take a chainsaw to a whole bunch of stuff.
Brady: The word “chainsaw” is, of course, it’s very evocative. No name shall be mentioned. But, I mean, I’m a British-Canadian, I’m an immigrant here. You’re an immigrant here. As an academic in particular, we know how important foreign talent has been to this ecosystem. It must be quite painful right now, on so many levels, as well as an academic, what’s going on.
Koller: I mean, to your point about immigration, immigrants have built this country. Immigrants continue to build this country. If you look at almost every slice of companies, whether it’s Coins2Day 500 companies, whether it’s, you know, startups worth over a billion dollars, even startups as a whole, something on the order of 50 to 60% have been founded by immigrants. And when you look at the talent that is needed to drive forward, especially those companies that are at the frontier of their field—tech companies, biotech companies, AI companies—those disproportionately draw on talented people who come to this country to study and then decide to stay and make a life here, and without that talent, we are not going to be able to build, to continue to build these companies. And so has immigration gone wild? Sure, there were years where there was a flood of immigrants that strained the social network of a lot of communities, and we probably needed to be more thoughtful about, you know, making sure that we had the right flow at the right amount with the right type of talent. But again, I worry that we’re throwing out the baby with the bathwater by cutting off some of those talent pipelines that have been so critical to the success of this country.
Stoller: And the two worlds you live in, too, academia, science, both are heavily under attack right now. How are you thinking about the next couple of years and getting through them? Do you have any optimism? Is there mostly pessimism, or…?
Koller: I wish I had a crystal ball. Mostly what I’m doing is hunkering down and trying to build a company in the best way that we can. I will say that a silver lining here is that there is increasingly greater support for the use of AI. I think this is one thing this administration has done well, is recognize the value that AI can bring to the economy and and different parts of the economy. I mean, even the FDA has moved to embrace AI much more now than it’s had in the, you know, in the past few years. We see that in other places. And so I’m hopeful. So if you’re looking for a reason for optimism, I think that one of the consequences of the push for efficiency, for example, could be and is also aligned with at least the stated ethos of of the administration, is to have a greater use of AI technology across the board.
Brady: And reduced regulation. You only want so much reduced regulation when you’re talking about health care, of course. But you know, one of the things that intrigues me, especially you being in Silicon Valley. You mentioned when you were sitting down, you’d just been in Boston. Typically, the ecosystems for health have been Boston, maybe, you know, Cleveland. What is it about…
Koller: …and the Bay Area…
Brady: …and the Bay Area. But, I’m curious about Silicon Valley. Everybody tries to re-create it in various parts of the country and the world with mixed success. What do you think it is about that ecosystem that has been so successful?
Koller: Yeah, and I think that is a great question, and touches on the same topics that we just discussed. The Bay Area has been the success that it is because it is home to two of the greatest universities in the world, Stanford and Berkeley. Those universities draw a disproportionately large number of super talented people, both from within the U.S. But also from abroad, and create this incredible kind of entrepreneurial feel so people, there’s like a pipeline of people that go from these universities to sound companies…
Brady: …and a lot of money…
Koller: …and there’s a lot of money, and there is indeed a lot of money that supports those companies and allows them to thrive. And I think it’s that mix of things, and we need to make sure we preserve all of those three: the incredible powerhouse of universities, the talent pipeline that flows through these universities into the ecosystem, and enough money to support innovation that happens right here in the U.S. To create new companies that provide incredible amounts of value, both in terms of hiring people as well as, you know, other forms of value to the U.S. And I really hope we don’t lose that.
Stoller: As a two-time founder, was there something that you got wrong the first time with Coursera, that you said, you know what, I’m going to do that totally differently with Insitro? And what was that?
Koller: Yeah, so that’s a great question, and the answer is yes. When we built Coursera, I can tell you one thing that I got right and one thing that I wish I’d gotten right. When we built Coursera, I assumed I didn’t know anything about culture, a company culture, and I remember when I was interviewing for the first time a relatively senior leader, and he asked me, as a Coursera founder, what would you like the culture to be? And I remember sitting there across the desk, thinking to myself, what is culture? Why do you need it? You come to work, you do your work, you go home. I mean, what do you mean, culture?
Stoller: Gen Z will not like that.
Koller: Well, no, and companies are not like that. And one of the things that I discovered is that culture is absolutely critical, and it needs to be nurtured with a lot of deliberation. And at Coursera we were lucky, because we hired amazing people at the beginning, and the organic culture that evolved was actually pretty good, but there were some places where I wish it would have been better. And at Insitro, I have been much more deliberate in nurturing the culture from the very beginning. And we had values that really spoke to, for example, that cross functional collaboration that is so critical to the kind of company that we’re looking to build. Create together is one of our values. Engage with respect is another one. And I think I did that much better the second time around. What I wish I’d done better the second time around, that I didn’t, was, and everyone will tell you that a company is a reflection of the founder. I’m not by nature a particularly process-driven person. In academia, processes are not a thing very much. I mean, you come in, you create, you ideate. It’s very free form. It’s very free-form. And I assumed that we could build a company along the same lines, and that served us well up to about 50 and at that point it became something that we probably should have instituted better processes for…how do we make decisions?
Brady: …hire a CHRO…
Koller: …yeah, yeah. Well, we actually, yeah. So not just people processes, decision processes, governance processes, things like that, even purchasing process. And I will use as an excuse that when we hit 50 was also when the pandemic hit, and so we were busy putting in COVID protocols, which was all about process, and didn’t have enough time to think about the other kind. But that is something that I wish I’d done sooner.
Brady: Well, it gets it takes me back to something you said, I think probably to your dad when you were 13, that you were bored in high school. It sounds like you’re a little bored with processes. So tell me about what your own strengths [are] right now and I’d love to get a sense of how you structure your day as a leader, because obviously, I’m assuming you have people who now take care of the processes so you can focus on…
Koller: …yes, thank God, and they’re very good…
Brady: …what do you focus on?
Koller: So first of all, I’ve been incredibly lucky in the leadership team that I’ve been able to recruit, and they’re remarkable leaders in their own right but also have come in with the clear goal of, you know, reaching an arm out across the chasm and really collaborating with someone with a discipline other than their own. And it’s been really great to kind of bring them together. However, one of the things that I still need to do is to connect the dots, to come back to that phrase, because even though they’re all very inspired to have that reach out to a discipline that’s not their own, I find myself oftentimes kind of still being the synthesizer, the integrator of people with those different disciplines, and kind of figuring out how to put the puzzle together so that the whole is actually truly bigger than the sum of the parts, as opposed to just the sum of the parts, or, as it is in many companies, less than the sum of the parts. And I think we have gotten to better than the sum of the parts. And that is an ongoing journey.
Stoller: We talk a lot on this podcast about, you know, when you’re a CEO or a top company leader, it’s very lonely at the top. And I’m one, wondering if you feel that way; two: You have your foot in so many different worlds, AI, science, academia—who do you consider your trusted advisor, mentors, friends that you go to to talk and get rid of some of that loneliness?
Koller: You know, it is very difficult to be, first of all, I think it’s lonely to be a CEO no matter what. Because while you know I have, as I said, wonderful people on the team, there are certain conversations that you don’t have with people on your team, and we have a wonderful board, and there are certain conversations you don’t have with someone on your board. I think what makes it even more challenging in the situation that I find myself in is that the company that I’m building has never been built before. There’s not like, 50 other role models as there is for some other companies, where it’s like, okay, I’m building a SaaS company. There’s 50 other SaaS companies. Or I’m building a whatever clinical development, clinical-stage biotech company, there’s hundreds of those, right? No one has built the kind of company that we’re looking to build and address the kinds of problems that we need to solve in order to make it successful. And so there isn’t a single person that I can go to to ask, how should I do this? And so I end up gleaning bits and pieces from various mentors, various colleagues, various friends, and then connecting the dots.
Brady: So let’s talk about those dots in terms of what’s on your radar that maybe might not be obvious, that you’d put on ours. Meaning, are there particular developments? Or, you know, what are you seeing out there, Silicon Valley or beyond, that’s intriguing to you, whether it applies to Insitro or not.
Stoller: And what’s next after academia, now health care?
Koller: So I remain very excited by the potential of AI and the potential of foundation models within AI, but specifically expanding the notion of foundation models beyond the sort of ones that people typically think about, which is text and images on the web, and, therefore, that need to reach into and create data that’s fit for purpose, to develop new foundation models that maybe leverage the existing ones of text and images and whatever, but are really the next generation that are special purpose to new tasks, and specifically what I would call deep tech tasks. And that obviously is what we try and do at Insitro for, you know, drug discovery and development. But I think that same, that same idea, applies to other deep test tech tasks, whether it’s in sustainability or in energy or in the environment or whatever. So I think that is a big development for probably the next 20 or 30 years that I remain very excited about. I’m proud to be part of that wave. I don’t think we’re the only one, but I think it’s a really exciting one. As far as my own, uh, career journey, there’s still a lot of work to be done at Insitro, and I’m very excited about what we’ve built, but I think for us, it’s like we’ve built a foundation, and now it’s time to really leverage it to bring benefit to patients, and I’m committed to continuing to do that, so I’m very excited.
Stoller: You mentioned sustainability and energy. So of course, my mind goes to climate. Is that something that excites you, or could be the next thing you work?
Brady: Excites you in California, that’d be hard.
Koller: Well, it’s definitely something that I’m excited about. It’s not something that I personally can take on right now, given the full-time nature of my role. But by proxy, I have two lovely daughters, both of whom are working in different ways in that space, and so by proxy, I am tackling that by sending my next generation to try and solve what I think is a fundamental problem of our generation, and I hope that they, as well as others, can make a difference.
Brady: Professor, what is your advice to the next generation? Since people are concerned that AI, may be if only temporarily, stopping some of those on ramps, you know, for careers, what do you suggest people do that prepares them?
Koller: I think that the future is in a partnership between the human and the machine. I think for every technology that we’ve constructed in the past, people are like, “Oh, my God, this is going to take away my job.” And in fact, did take away a lot of jobs. I mean, electricity certainly took a lot of jobs, right? I mean, the agricultural revolution took away a lot of jobs. This will certainly be the case here as well. But I think human creativity, human innovation, is still something that is an important partner to the machines, and so I would encourage people to develop skills that are ones that help transcend what the machine can do for us today. So things like connecting the dots, things like long arcs of thinking that require different building blocks to be put together in order to address really large, complex problems. So the kind of structured thinking that is so critical in solving hard problems. I mean, even when you think about, you know, as you know, I did education for a lot of a lot of my life, rote memorization was something that Google and such basically made obsolete 20-plus years ago. But it didn’t make people obsolete. It allowed people to move away from memorizing facts and figures and really elevate to the next challenge, which is, how do you put together facts and figures to create good, you know, structured narratives. And I think that will happen here, but one level up, and I think it is critical that we as a society teach our kids the right set of skills that they can leverage technology in the right way.
Stoller: 15-year-old high school me is just clapping internally and wanting to tell my history teacher, “You’re right. I did not have to memorize every single war in order.” Are there any AI fears that you have, or others have, that you think are legitimate, that we should watch out for?
Koller: I would say there’s probably a couple worth highlighting. One is, job loss is disruptive. It’s disruptive to society, at least for a period, as the society adapts. And I think governments should be thinking very deeply about how to mitigate that disruption, so that, so that we avoid some of the social challenges that will emerge when a lot of people become jobless, at least for a while, while we develop hopefully, I hope, the next generation of jobs, and how to educate people to be ready for that next generation. So I would say that is a legitimate fear, and one that we should be investing more in, versus the cataclysm of, you know, Terminator 2–type scenarios. The other one is: These systems are very powerful, and like any tool, they can be deployed for good as well as ill. So you can certainly use these systems to design better medicines, I hope. But you can also use them to design worse toxins, right? You could use them as mental health aids to help people, but you could also use them to twist kids’ minds and get them to do things that you don’t want kids to do, and we’ve even seen that with simpler tools. I mean, social media is not AI, but boy, has it managed to twist the minds of a lot of our kids. And so, thinking about how to avoid the misuse of a tool, it’s not about regulating the tool. It’s about regulating the use case. I don’t think you can stop the advance of technology. I think that is something that has never succeeded for us as a society, but restricting the use cases of a technology and regulating that, I think, is something that I wish more thought would be devoted to, so that we can kind of prevent some of those really nasty use cases where people use technology for ill.
Brady: Give us a sense as to what’s next in terms of—when are we going to see, in your mind, some significant milestones that for you will constitute success in terms of where you want to go?
Koller: So for me personally and Insitro, I think success will be when we put our drugs into patients, and patients actually get better, and I view that as a truly aspirational goal, but elevating beyond that, we are committed to something we call pipeline through platform, and that means that we don’t just make a drug that makes people better. I mean, that’s great and super important, but we also want to do it in a way that is repeatable, that is an engine, that is able to create not just one drug, but a second and a third and a fourth, and where the platform that does that actually gets better and better over time, both because it learns, because it’s a learning platform, and also because, as the technology keeps getting better, we have new ways of collecting data, new ways of leveraging machine learning to interrogate that data, and we’re able to create an accelerated engine for making new drugs, new therapeutics that make people better. And when you go one step beyond that, I think we are finally getting—we’re able to see on our horizon, I would hope, the fulfillment of this dream that has been talked about for probably 30 years, which is this notion of personalized medicine, where we move away from one-size-fits-all. Where you go into a doctor’s office and you are characterized by a disease, and you get the medicine that that disease gets, but rather, you are seen as a full-blown individual. We can measure you in a variety of different ways. Measure you objectively in a data-driven way, not just by your own subjective experience, but by actual measurements, and therefore able to tailor a course of treatment, both at the beginning and throughout the journey to the needs of individual patients. And that requires, I think, a significant technological change, both in measurements as well as in the AI, but it also requires a sociological change of having doctors be willing to trust that the machine is telling them things that they are maybe not able to see for themselves, and that ability to trust the machine and partner with the machine, it’s not making doctors obsolete, but it’s a bit, it’s allowing the insight from the machine to guide course of treatments using subtle signals that a doctor will not be able to perceive probably ever. I think that is a sociological change that we need to see in medicine, but we also need to see in other parts of society. And so coming back to the really big picture, I think the future has to be getting people to partner well and create together with the machines. Because I think here, too, the whole is going to be much bigger than the sum of the parts, but only if we do this the right way.
Brady: Thanks for joining us.
Koller: Thank you.
Brady: Leadership Next is produced and edited by Ceylan Ersoy.
Stoller: Our executive producer is Lydia Randall.
Brady: Our head of video and audio is Adam Banicki.
Stoller: Our theme is by Jason Snell.
Brady: Leadership Next is a production of Coins2Day Media. I’m Diane Brady.
Stoller: And I’m Kristin Stoller.
Brady: See you next time.
Leadership Next episodes are produced by Coins2Day ‘s editorial team. The views and opinions expressed by podcasters and guests are solely their own and do not reflect the opinions of Deloitte or its personnel. Nor does Deloitte advocate or endorse any individuals or entities featured on the episodes.
