The AI race to save American science and reshape medicine forever

AI is full of possibilities. Enterprises run on realities. That's why Pega combines the world's most powerful AI with the governance, transparency, and control enterprises demand. So you can move faster, adapt faster, and scale AI with confidence. All the power of AI, none of the uncertainty. Learn more at pega.com slash AI. Nobody who's practicing science today has practiced science in the United States in a time where we weren't, quote unquote, winning. From a competitiveness perspective, we weren't at the top. And I genuinely believe that we may lose that competitiveness, that edge, that incumbency. And I'm not sure that I see us fighting for it in the way that we should be. That's Renee Wigerson, founding director of ARPA-H, a federal agency created to speed up health science breakthroughs. Renee is now chief innovation officer of Transfer, which she co-founded after leaving government. I wanted to talk with Renee to better understand how U.S. federal research cuts are impacting America's scientific progress at this moment and whether the private sector is filling the gap. We also talk about her company's efforts to accelerate the next big breakthroughs, how AI and physical lab work combine, and her optimism about what she calls "the next Sputnik moment." I'll admit lab science isn't my natural habitat, but Renee has a gift for making complex things accessible. So let's get to it. I'm Bob Safian, and this is Rapid Response. I'm Bob Safian. I'm here with Dr. Renee Wigerson, co-founder of Transfer. Renee, welcome to the show. Hey, Bob, thanks for having me. You and I first connected when you were at ARPA-H, the inaugural director of a White House effort to accelerate health research. You were amped up about the possibilities, but with the Trump administration, you had to pivot. What did that feel like? Was it hard not to take it personally that they wanted different leadership? Oh, well, I'll tell you, I'm still amped up about it. It's still an amazing idea. Obviously, you know, DARPA's been around for almost 80 years. I hope ARPA-H will have the same. It continues to live on today. I'm a builder. I love to take things forward and really show what's possible. So was it hard when I left? Of course it was hard. I had a really unusual appointment, which was I was a presidential appointee, but I was term limited. So I served through the transition and had a few weeks in the new administration. I served in Trump one, by the way, as well. And so it was a great opportunity to make sure that they really understood why we built the agency. We needed an ARPA-H because the way that we were doing science, broadly, there was something missing, which was how do you move really quickly? How do you get focused on a problem versus building a general knowledge set? And so I was really able, had the opportunity to show them what we were building and convince them that it was worth keeping around. And so I really couldn't be more proud of the agency and their perseverance. I mean, your optimism about that is sort of in contrast in some ways to some of the stories about what's happened during Trump II. I mean, we've seen, you know, the New York Times did a story about the CDC being hobbled. They were hobbled by cuts and controversies over scientific advisory boards dismissed and federally funded research, skepticism. Like how different are things really? I mean, most things in government go through cycles and you've obviously experienced a few of those cycles. Yeah, absolutely. There's a lot of tragedy and chaos that happened. That is no doubt about it. I don't want to minimize that. But where I find optimism and inspiration is sometimes through hardship. You know, one of the, realizations that I hope that we've all had over the last two years is the way that we fund science is not sustainable, right? My own retirement account is not 90% based on one stock or fund, right? It is really a diversification. And a lot of the scientific enterprise in the United States right now is really focused on federal funding. And that's a challenge. You know, how do we really have better connections to the private sector? How do we capitalize on that investment to incentivize it in new and different ways? And that has been a forcing function that I'm really optimistic about, having universities think about all of the great IP that comes out of those universities. How can that be more revenue generating to return to their universities so that you can become less dependent on some of those external sources of funding? You know, how does this look differently to make sure that it is robust and resilient going forward? Well, I mean, and there is more pressure on the private sector to sort of step up and fill that research gap, right? I mean, you worked in the private sector, right? You worked in the private sector even before your current thing at Ginkgo Bioworks where Transfer co-founder, Amory Wagner, was head of AI. Are companies up to the task of spurring science in this way? I mean, we've certainly seen spurring of technology with AI labs. I think so. The intersection of AI and bio in particular, which is my domain, it's hard to move at that pace of that industry if you're not in the private sector, right? It seems like the smaller your organization is, the better because you can make quick decisions you can fail fast and move quickly. And that is hard for governments to do. In fact, that was one of the mandates of ARPA-H was to be able to identify failures and to not pursue work streams that were no longer really delivering on the science. So your new business, Transfer, I saw the New York Times wrote about you and the article kept referring to magic hands. I'm curious what magic hands are. Like, what problem is Transfer trying to solve or address? Have you spent time? Have you spent time in a research lab before? Well, not a ton of time. I'm not that kind of researcher. OK. Yeah. But certainly you've spent time in the kitchen. And you probably have your favorite dish or your parents' or your grandparents' favorite dish. And sometimes they just have the magic hands. Nobody can cook it like they do. Nobody can make it taste the special way that they can. And that happens in science as well. There are just some scientists that have the knack that can deliver over and over really great science. And the challenge is a lot of those scientists, they're such an expert. They can hardly describe what they're doing. It's an art in some ways. And so the magic hands was really a testament to what the scientists can do in the labs. And so the pipeline that we're creating at Transfer is can we create computer vision and perception tools to capture the work of science as it's being done so that we understand if the protocol says mix the cells, we can capture, oh, he's inverting the tube in a certain way that's very gentle, where another researcher may be rigorously shaking or vortexing it, which could destroy the sample. And those little things that are compounded over the many decisions a scientist may make over the course of several hours of experimentation lead to what is magic hands. So it's all this kind of tacit knowledge that that expert isn't even really thinking about. Was this an issue you saw at ARPA-H, the sort of breakthroughs that were trapped in a single lab in some ways? I think tacit knowledge is part of it. I would call it more like embodied knowledge. It's what a scientist does. It's what a researcher does that is not written down. It's not even the way they're holding something in a special way. It's the fact that they pre-labeled all of their tubes before they started the experiment. So they saved 10 minutes between steps. So that embodied knowledge and all of those little decisions is really what we're capturing. And where that manifested at DARPA and at ARPA-H is just a realization through my career of how fragile breakthroughs are. So usually the way a breakthrough is first communicated to the world is a research publication, a paper. That paper is not a product. It's not commercializable. It's not ready for a patient that day. It's not the period at the end of the sentence. That's like the starting gun for what is going to happen. And so the ARPAs of this world are really fantastic at getting to that initial breakthrough and demonstrating what is possible. But following through and delivering is not 100% guaranteed activity. And so when I started ARPA-H, we wanted customers who would be the final users of that technology to give us feedback on what we were developing so that when we got to the last day of experiment, they were demanding that product versus having that be the first contact with a customer, which unfortunately is pretty common around, I would say, traditional grant-making organizations. And so it's rethinking, like, how do you make sure that you can transfer that knowledge off the back end of a program? I mean, because the folks at the lab aren't necessarily worrying or prioritizing about how to make it scalable. They're just trying to see if it works. But of course, eventually, we want it to be scalable. Sometimes the folks in the lab don't even have that expertise or understanding of who they need to talk to next to do that, right? And so, like, we think about science and those transfers as handoffs. We deploy our technology to an originating lab to understand the science and what was important about that discovery. But we can also deploy it in a receiving lab so that we can see how they're deploying it, and instead of the way we transfer science today, which is looking over each other's shoulders to say, oh, you're doing it wrong a little bit, we have this, like, video-connectedness that we can look and help accelerate those iterations of transferring the science. The procedural parts of transfer could sound less sexy than claims about how AI can design new drugs, but you're doing some cool stuff. You're putting cameras on scientists' headbands in the lab, and you're capturing video and audio and equipment settings in real time. And then you're using AI to sort of-- to mine that data, Find out what makes it, what needs to be consistent. Our system of record for science today is a lab notebook or a LIMS system, which again is sort of an outline of what happened. It's not the actual, you know, lossless representation of the work. And so for a scientist at the bench, that's really important because you may spend six hours preparing a sample. You run your gel to do the analysis. You look at the results and you think you might have skipped a well, but having the confidence to look back at the tape and say, yep, there it is. I skipped the well. I know exactly what went wrong. I can move forward with the next step of my experiment versus I don't know what went wrong. Maybe that sample didn't work. I have to repeat the whole experiment, which is the loss of resources across sciences is vast for needed to repeat things just because of just the lack of understanding of what happened. Sometimes you don't ever find out what went wrong. It's disappointing the amount of discoveries that we probably just lose to some sort of procedural variation. That we haven't picked up on. So some of this is about saving costs and some of it is about accelerating the discovery process by by pointing you in the right directions. You talked about how AI is kind of transforming drug design. We think of that as top of the funnel. Now you can test billions of designs in silico, some subset of those you can actually test in the lab. But we haven't seen billions new clinical trials. It's because the rest of that pipeline is still bottlenecked. We're not. Transferring the assays between labs faster than we were before. We're not scaling up manufacturing faster than we did before or better in with more choices than we did before. And that's really this this physical AI infrastructure that we're creating is intended to just kind of break open some of those bottlenecks so that we can do one shot tech transfer of manufacturing to scale up quickly, which is important to get, you know, cures to patients faster. Candidly. Now, I could see some people. Looking at your data capture process as a kind of surveillance within labs. Do you get that pushback that scientists who don't want to be watched? Yes, we get that a lot. And I think it's something that my co-founder and I care really deeply about is that we're doing this for the scientists. We want scientists to be top of license. If a scientist is successful, like we all win, right? Humanity wins if science is successful. And so how can we empower that scientist to make better decisions based on what they're doing, based on real evidence? We're not just engaging with the chief operating officer who sees this opportunity to make sure everything's more reproducible and that we don't lose a batch. We're working with the scientists to understand how is this frictionless for you? You know, a GoPro is heavy to wear on your head. So our headsets are much lighter and longer recording based on direct feedback from those scientists. But it's important that they have ways to do their work. We capture voice as well. And so it's a way to capture their lab. Notebook. They're going to take their gloves off. So that is really our goal is to to empower the scientists. The interfaces that we have are also controlled by the scientists to say they turn on the recording, they turn off the recording. So they are in control of that. The bottleneck that you refer to, it makes me wonder. Think about like science being less rigorous than maybe we've always been led to believe. I mean, I know there's been rising skepticism in certain places, including within the Trump White House, about assertions of expertize. And even published research. Is that skepticism well founded? Science is just incredibly hard. And I think because we're not capturing the real work of what's happening, there's a misunderstanding of like why science goes wrong. And a lot of it is we're just not providing a high enough fidelity account of the work that's being done. You know, if you asked an agent today, how do humans do science? It would say, oh, well, it comes up with a hypothesis and then they publish the materials and methods. Section. And then there's the data of how it works, of course. And, you know, my postdoc was three years long and I had a three page paper summary at the end. It was it was the highlights reel. It was not all the hard work that failed a lot of successes, too, that were probably different paths that I decided not to pursue because they were less interesting. But that could be incredibly useful for the next set of scientists. What's different now, I think, is AI allows us to in the same way it allows us to read a journal article cover to cover, which I couldn't possibly do. For all the journals in my lifetime, we can kind of watch an experiment from beginning to end in many, many contexts to start to understand emergent patterns, different types of contexts like temperature, humidity. You even see tribal behaviors in labs, which is kind of interesting. Tribal behavior. Yeah. So so what I mean by that is like, well, sometimes deploy to a lab and when a step will say something like mix the tube, they'll flick the tube like everybody in that lab flicks the tube. And you're like. Somebody trained the people in that lab to flick the tube, because if you see that same protocol from a vendor kit implemented anywhere else, everybody does it differently than that. Like, why? Why do you do it that way? Something I tried to teach my team at Arpa H was just to ask why constantly and don't assume what's presented in front of you. Let's take a beat here to absorb what Renee is saying. Each lab, each company, each of us may have our own way of doing something, and that variation is exactly why we can come up with creative game changing results. So can any business experiment at the speed of AI frontier models? And is America doing enough to protect its scientific edge? We'll talk about that and more after the break. Stay with us. Every business hits the same wall. The moment your business outgrows the systems that got you here. Spreadsheets everywhere. Five different logins. Data scattered across entities that don't even talk to each other. If your finance team spends more time finding data than using it, that's when you need the Intuit ERP. Intuit Enterprise Suite is the AI-native ERP solution that's powerful, painless, and proven, built for businesses scaling fast without the complexity that usually comes with it. From the makers of QuickBooks, learn more at Intuit.com/ERP. Hey, listeners, Bob here. If you listen to Rapid Response on Masters of Scale, you may be missing half the show, because every Friday we release a second Rapid Response exclusively in the Rapid Response feed. The guests and topics are just as compelling and timely, from Ford's CEO to NASA's administrator to the lessons from the Devil Wears Prada. It takes about 10 seconds to find. Just search Rapid Response. Wherever you listen to podcasts and hit follow to make sure you never miss an episode. I hope to see you there. Humans will never be more intelligent than AI. There's going to be two types of companies. Those are great at AI and those that went out of business because they weren't. How do we build a future that is human-centered? I'm Rana El-Khayoubi, and on my podcast, Pioneers of AI, we answer that question and so many more. As an AI scientist, entrepreneur, and investor, I know what it takes to build AI that works for everyone. Every week, I sit down with the pioneers shaping our future, and we take you behind the scenes of the AI that's transforming our lives. Find Pioneers of AI wherever you tune in. Before the break, ARPA-H Founding Director Renée Wargerson talked about why magic hands inside a lab are so valuable and so hard to pass on. Now, she talks about autonomous labs, how to adjust at the speed of AI frontier models, and searching for the next Sputnik moment. Let's jump back in. The very idea of experimentation, it is by its definition, you know, messy because you're not really sure where you're going. And I guess sometimes you might find things that you didn't realize, and sometimes you don't really know how you got there. We were talking about this yesterday, actually, when there's moments in a scientist's life where they're tinkering over the course of several weeks trying to get something to work. And the moment it works, you're like, "What did I do? What did I do to get it to work?" And so you're not following a protocol. But this is where I think, like, robotics and humans collaborating can become really interesting, where I might recognize that I have a hunch that it's the magnesium concentration of this buffer that really matters. It's not a best use of my time. I'm not top of mind. I'm not top of license if I test 50 magnesium concentrations. But I can outsource that to my robotic partner arm that's here at the lab with me. That is where I see automation, at least in the near term, really empowering the scientist before, you know, we get to a world, I don't even know when it would be, where it's full closed-loop automation. I mean, you hear about these autonomous labs, but there are, I'll tell you, from during my time, even during the pandemic, supporting testing nationwide through the work we did at Ginkgo, the number of labs across the country where the operators were able to get things done, the number of labs where the operator loads the 96-well plate in a flipped direction is like double-digit percentages. The robot didn't do anything wrong. It's the human in the loop that is still part of autonomous science that we're not observing. In those labs, you have many cameras pointing at the robots, because we all assume the robot's going to do something silly, but it's like just regular human error is not captured. Well, I can see as you give that example why some people resist it, because they don't want to be called out for having done something the wrong way. I mean, sometimes the stakes are really high. really high though, right? You know, some of our diagnostics customers are delivering a genetic result to a patient. And so most scientists, I think, want to do the right thing and also want to have the information that they can be coached by understanding where they made that error. You and your co-founder, Amory, you both worked at Ginkgo Bioworks, which focuses on genetics. Like, why not work on applying AI in that area? I mean, biotech has so much runway ahead of it. There's so many different areas. I mean, I think for us, our technology is agnostic to the science that is being performed. And so in some ways, Bob, our narrowing down is just focusing on life sciences to start, you know, chemistry, materials, science are other areas we get a lot of incoming. So maybe that's a future path for us. But life sciences writ large is where we're focusing on. But if you think about it, it makes sense because when you break down molecular biology, if we, in genetics broadly, at the atomic action level, it's like transferring liquids between vessels. You sound like I'm just a bartender making drinks. But it's like the models, like they can understand a lot at that level. They understand what it looks like when there's a bubble in your pipette tip, and that is a deviation. It was a choice for us to focus on that, like, deep foundational level because it is general, you know, purpose for the rest of life sciences. That was the bet that we took rather than say biologics, manufacturing, we're going to crush that. That is not, you know, generalizable to a broad area of life sciences. And the modeling that you're doing, like, is it a different model for each lab? Is there one model that you're training across all of them? Is there an underlying LLM that you use or do you create your own? Honestly, I don't know. Honestly, it depends on the day because we are hardware and model agnostic, right? We know that hardware will commoditize and we use whatever is best in class today. And there's new things that are lighter, cheaper, longer lasting, better batteries. We will integrate that. It's the same with the models. And so there's a new model published every week from all of the frontier AI companies. We evaluate and test those and whatever is performing best on the task at hand, we will adopt that model. And so it allows us to keep pace with the development of that entire ecosystem. It's also how we can work really closely with the frontier AI companies to say, hey, we really appreciate this new model drop. But did you know that when it comes to recognizing X, you're not as good as this other model? It's a helpful loop because we want those models to get better. And so it's a chance to provide some of that feedback. And moving to a potentially a different underlying model, whatever, every two weeks, of course, that's got to keep you on your toes. But then does it mean you have to retrain? Yes, we retrain your model with the new engine, essentially. So we're constantly evaluating the models. We retrain as we get new data. We also use to perturb experiments and then see if the model can detect deviations. So we tell the model we're doing protocol X, but then we have operators come through and we'll change the protocol slightly, intentionally skip a step or add a step or give the wrong reagent. To test? To test the models. So we have the answer key in that case. And so it gives us a good sense of how they're performing. Yeah, because I mean, as we know, these generative models do hallucinate or do make things up and you don't want that happening in the lab. Yeah. And yes, absolutely. They do, Bob. And so we also have human adjudication for our models. Sometimes it hallucinates, sometimes it gets things a little bit wrong. It recognizes that it's a pipette, but it's actually 100 microliter pipette, not a 200 microliter pipette. So we make those corrections along the way. And that data can be used to determine if it's a pipette or not. Yeah. And so we have a lot of data that can be used to train and improve the models iteratively. It's so much detail work, isn't it? And each lab and each experiment and each question is different. And yet if the model is going to work the right way, it needs all that detail. Yeah. And determining what level of detail it needs is really important. And most detailed for a lot of scientific work is just too much information. And it's just going to waste compute if you're always labeling it that effort. So it's important for us internally to understand how can we make our models and training more efficient based on what is the level of information that is needed. And so that's where we experiment quite a bit in the lab. And there's cost for you every time you're running compute. Absolutely. Yes. And then storing data. That's very sensitive. Do you ever get frustrated about how resources are allocated in our world? I mean, you've put your life towards this work and yet so much money is devoted to, I don't know, space exploration or like, you know, the next hot social app, the Metamuse craze. Like, how do you deal? I mean, even government prioritizes things. Like, we have to use these incredible resources that are taxpayer dollars in like a really purposeful way to solve real problems for Americans. And full stop, that is what we're doing. And at pace. I mean, my program managers, they had three-year terms based on statute from Congress. And so I would remind them, I would say to them, how are things going? And they're like, well, maybe next week I'm going to do X. And I'm like, next week? Every two weeks that goes by, 1% of your time is up. Come on, let's, what about this week? But nobody who's practicing science today has practiced science in the United States in a time where we weren't, quote unquote, winning from a competitiveness perspective. We weren't at the top. And I'm not sure that I see us fighting for it in the way that we should be when somebody is facing the potential of a loss every two weeks. You know, like we have to deliver on what we promised to do. And then that's a commitment, you know, not to my, just my country, but like my kids and my family and to do that. So anybody wants to talk about that, tell me your lab problems. We'll try to fix those. And then we can get onto the bigger problems of competitiveness and making sure that we stay on top. Well, Renee, this was great. I was like this. Is there anything that I haven't asked you about that I should have? What I think a lot about is, you know, Sputnik moments, just given like my ARPA. And we're going to look back at this moment in time, like there's something happening that's going to impact whatever's happening 20 to 50 years from now. Like it's, you can feel it in the moment. And like where that happened before, of course, Sputnik, the United States didn't know the Soviet Union was developing that capability. And we didn't have a counter capability. Eisenhower launched ARPA, which became DARPA to create and prevent technological surprise. And so when you started ARPA-H, the timing wise was around COVID. But what I realized is that each individual. Person has a Sputnik moment in their life that is usually health related, right? So my Sputnik moment was when my mother was diagnosed with cancer. That was like, that just stopped everything, you know, in her life, in my life, in my dad's life, in my sister's life. And everybody like has a story, whether it happened to them or their, or their family, or if you talk to a member of Congress, something's happening in their state that they can't deal with. And so I'm like, what is the Sputnik moment now? If ARPA-H was bringing people from like an unhealthy state to a healthy state. What's happening now is taking us beyond that. If we can figure out and solve health and wellness, a lot of people talk about abundance. What is the next trajectory? What is the Sputnik that we're working towards? We should talk again in a year, Bob, and maybe I figured it out. So I don't know that you have the answer to this. It's like augmenting on like going from like protecting a nation to enabling and healing an individual to now allowing that individual to like rise and elevate, be top of license, be their best self. That's why I'm optimistic. Like I can taste it. I don't know what it is yet, but it's here. Thanks so much for doing it. Thanks, Bob. Renee feels a Sputnik moment coming. Certainly big shifts are underway in nearly every part of our lives, our work. What strikes me about Renee is that on the one hand, she focuses on optimism despite the challenges faced by the scientific community. But she's not Pollyanna. Just waiting for good things to fall from the sky. She's also urgent, demanding even about using our resources and our time to maximum impact. We can't expect any magic hands to suddenly appear and fix things for us. But as Renee stresses, each of us has our own hands and it's time to get to work. I'm Bob Safian. Thanks for listening. 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