TechBio Talks Panel with Katie Couric, Renee Gala, Reid Hoffman, Eric Topol, and Eric Lefkofsky

Please welcome all these brainiacs to the stage, please. Thank you.

So, obviously there's enormous interest in healthcare and AI right now to state the obvious, but in an industry with long timelines and high stakes, hype isn't enough as we were talking about earlier. So, let's talk about first and foremost what's driving investment. Renee, over the course of your career at multiple companies, you've managed capital and transactions totaling over $100 billion. So, what kind of opportunities, Renee, are you most excited about focused on right now and how are you looking at things differently because of the role AI is playing in healthcare?

Yeah, well, thank you for the question and just before I get started, thank you to Nat and Recursion for this amazing event. Thrilled to be a part of it. But in terms of how we're focused, we're a biopharma company and so we're focused on R&D, commercial, we're focused on corporate development, but at our core, we are focused on investing in innovation for patients. And we also acknowledge we're a relatively small company. We're less than 3,000 employees. And so that means we can't do it all ourselves. We invest in internal innovation as well as external innovation collaborations. We talked earlier about a collaboration with Stand Up To Cancer and those are really important for our work. With the emergence of AI, it has changed how we think about where we're investing and what we're investing in. So internally, several years ago, we made the decision to start investing in our data environment. It was more to be able to bring all of our data together, invest in data tools, and we're now realizing that having made these investments several years ago, it's putting us in an excellent position today. And then for external innovation, we're continuing to invest in collaborations, but also investing in tools with other companies that are more AI forward, AI focused. So tools that for example we can get access to in drug discovery to be able to move faster whether that's better understanding toxicology or pharmacology just getting drug candidates to the clinic faster and also investing in our people because if we invest in our people capabilities then they'll understand how to use the tools that we have. So every single Jazz employee by the end of this year will be trained at a foundational level of AI and then we have other groups that are much more advanced. So it's pretty much across the board.

Are there specific diseases that you're specific issues that you all are focused on as a company?

So we focus on primarily rare disease and then life-threatening diseases. So rare sleep, rare epilepsy, rare cancers. We just had a drug approved last year which is the first drug approved for this indication at all which is H3K27M mutant DMG which is a devastating and fatal brain cancer that there's been no meaningful innovation for in 60 years. No drug approved. I mean this is back in the time of rotary phones and there's been nothing until now. So those are the things we focus on. Reed, meanwhile, you have built and backed companies that have scaled incredibly quickly and health care, as everyone knows, is a very complicated, fragmented, slow to adapt industry at times and slow to change. So, what convinced you that this is a place where transformational change is possible and the time is now?

So as probably you and a number of people know I am by default a software technologist but it goes from LinkedIn, you know, Airbnb, a whole bunch of other things and it's entirely because of AI. So what happened is I was sitting with my partners at Greylock and I was saying look there's going to be a bunch of coding agents there going to be a bunch of productivity but there's a bunch of areas that I actually think that the classic Silicon Valley software technology are massively transformational that is extremely important to do that won't be the usual oh just CS will solve the whole problem but it can be a really strong amplifier and you know I had the fortune of being you know kind of leading the first commercial investment in OpenAI and being on the board and other things. And so I was seeing what was coming and so I flew out to New York, had dinner with Siddhartha Mukherjee who's here in the audience and said, 'Hey, I've got this idea.' And he said, 'Oh, well that idea is better than you think, right? I've done a bunch of stuff in cardio and all the rest. And here's kind of the way to think about it.' And we said, 'Okay, let's sit down.' And we looked at kind of thin slicing the entire process by which you start with an idea. You kind of have a target. You have kind of what how the whole drug discovery process works all the way to clinical trials and FDA. And it was like okay which places could AI be at minimum a 10x preferably 100 plus x if you kind of reinvent the process. And to kind of give a taste of that also in software like we've announced the deal with Schrödinger because part of what my theory is these large language models are great. They have a large compression of a bunch of different information but actually in fact when you begin to put in the kind of interesting constraints about how you look at it. So for example, Schrödinger brings you know physics and chemistry knowledge and that should be part of the model you're training the space you're exploring as part of what you're doing in order to create an AI generative factory. So fundamentally I'm a software guy and I'm here as an AI guy, right? And the rest of it all just strikes me as kind of a little bit of a horror show from the tech side because it's kind of like the how does that FDA process work? Oh, right. But you know, Sid, Sid covers all that stuff very well.

And Sid is here and he's a friend of mine and he wrote The Emperor of All Maladies, won a Pulitzer Prize and is a brilliant scientist. So, yay, Sid. But are there any particular So, you're kind of combining your brain power. Are there any specific cancers that you're focused on? Are you doing these models to address anything specific or are you kind of throwing out a wide net?

So part of it is if you actually start with a software as a platform and you think about platforms as a way of doing it, we actually already and even our early prototyping have got some interesting INDs and other things just coming out of hey let's try to build the model this way. Let's factor in the data this way. So we're already seeing some pretty interesting early promising signals. Now and all this path is you know everyone else knows better than I do it's a long path but the basic idea is say when you have a target not say ah Eureka I have one idea that I'm going to go spend 10 years on it works it's how do you have AI help you actually force rank targets vis-a-vis feasibility give you lots of variation of answers that you then cycle on very quickly you use the data process by which you're going into them all the way into the clinical trials to refactor your models to make the generativity a lot better for your specific therapeutic cases. So yes, we have some specifics, but it's actually more of a massive acceleration factory is what we're targeting.

And this is Menashe, your company. How much of your time are you spending on this?

Well, you know, I spend call it a day or two a week. And you know, Sid, who's the CEO, does the usual startup CEO thing, which is eight days a week, you know.

Meanwhile, Eric Lefkofsky. I suddenly feel like I'm on Severance, by the way. Tempus was the outgrowth of a very personal experience for you. I think that's how a lot of people really get interested in this field. Your wife was diagnosed with breast cancer in 2014 and I understand you grew extremely frustrated at the time with the lack of data that was being leveraged for her care. Can you talk about what you observed, what your reaction was and how that led you to start Tempus?

Especially when they don't have a ton of time, right, to deal with every patient and to be able to synthesize that quickly and effectively for people is so critically important. And you describe what you do as a flywheel, a powerful data flywheel. Can you explain that to me?

How come only half? Why not everyone?

When you think about all the information we don't know and that doctors don't have access to other than like helping them with longitudinal studies that will kind of guide how they give care or whatever. Are there other areas of technology that you feel are ripe for developing that will help a patient population?

It's error and?

Meanwhile, Eric Topol. I love Dr. Topol.

I mean, let's talk about I mean, you've been practicing medicine for how many years, Dr. Topol?

Sorry to say 40. Yeah.

Yeah. And you've seen this evolution of medicine just unfold before your eyes. And as somebody who's been in the biz, as they say, for a very long time, I just would love to hear your perspective on how the practice of medicine and healthcare at large has changed.

Well, Eric did set up the problem we have that the medical community doesn't like to admit, which is rife with medical errors, diagnostic errors especially, but also treatment errors. But the way things have evolved, unfortunately, Katie is that we've seen this erosion of the patient doctor relationship. And you alluded to it, not enough time. We don't have this gift of time. Physicians are squeezed. All clinicians are heading towards a burnout and disenchantment why they went into the profession and that's been a steady erosion that was exacerbated with electronic health records and various reimbursement tactics. But we're starting to see this flip and that's what's so exciting right now because we're seeing for the first time this is where AI the near term is really kicking in these ambient conversations that are not just made into better notes than what exist in electronic records today but also they drive all the other things like pre-authorization, prescriptions, next visits, labs and even nudge the patient subsequent to the visit about things that were discussed. So this is giving the gift of time getting away from data clerk function which has become such a burden and that's the first step of AI starting to change the way medicine's practiced and starting to restore this critical essential patient doctor relationship.

What when you think about AI and medicine, you know, you're saying we're sort of turning a corner. What are you most worried about?

Yeah. Well, there's plenty. There's a long list that AI has.

Well, we have a fair amount of time, so go for it.

No, no, no. But it's a net benefit, but the worries include not just things like privacy, security, security of the data. The biggest worry to change the medical community, you have to have compelling data. And there hasn't been enough diligent effort to get that data. And sometimes that's randomized trials. Sometimes that's prospective cohorts, but that's really important. But we have issues with regulatory, not knowing how to handle this stuff, reimbursement, transparency of the companies that are doing this work, because they tend to keep things in a very opaque, proprietary locked up way. So there's a long list, but I think the hallucination confabulation story is one that there will be a net benefit. People held clinicians accountable for their errors. They don't realize that that rate is quite high and even though AI may make some mistakes along the way, it's probably got a better chance of bringing that net mistake rate way down from where it is today. So, I think we have to look at that overriding benefit. And what we're seeing now, which is so exciting, is this big jump in accuracy. And that comes with medical images. It could be any type of medical scan. And interpretation of virtually any diagnostic image has soared with particularly mammography and colonoscopy. They've been the subject of massive clinical trials, randomized trials. So we are starting to see that kind of compelling evidence that's going to be requisite to change medicine to make it a whole lot better. And if we get to the point where there's not a lot of data burden, data clerk burden for clinicians, those two things are kind of in the immediate phase that should make a big difference.

Do you all have any thoughts on what Dr. Topol is talking about about some of the challenges and how they're going to be addressed for clinicians and for even scientists and researchers?

Anybody?

There's two things I'd add. So, one is I think it's a nearly certain future that since we already have frontier models, I suspect everybody on this phone has a frontier model on their phone. And already today, if you're not using your frontier model of choice or frontier models of choice as a second opinion or anything serious, you're making a mistake either as a patient or as a physician. It's a quick easy cross check, right? Then part of it is say for example you're a person who doesn't have access to elite medical care doesn't have a concierge doctor they can call at any hour of the day and get that they say I've got some concern myself my spouse my child my parent you can talk through it part of the future that I see in this is it'll say oh I think you might have something serious maybe you want to consider going to the emergency room or a medical clinic and then do you mind if I share a precis of the conversation we just had with the physician with the nurse to say okay these are the questions I asked these are the answers I got here's what I think is possibly going on think about the acceleration of the aid that enables and it's 24 by 7 we cannot afford 24 by 7 medical care for other than a very small number of people that's the kind of acceleration that we are capable of already today we just need the human systems to function the right way and regulatory is part you know weird ass medical liability is another one etc etc.

I'm curious about the role of the government in officiating the so-called shotgun wedding or I mean what is the government doing or what should the government be doing to sort of accelerate and elevate artificial intelligence in drug development and medical settings and a whole host of arenas. Are they doing enough?

I mean, Marty Makary and FDA have made it very strong, known that they're going to be trying to accelerate approvals, whether that's 510K or actual approvals for algorithms that are used in medicine. And that's also involving patient, as Eric alluded to. I mean, just this past week, we saw ChatGPT Health and Claude Health come out as well as Doctronic Utah getting refills with AI with no doctor. So, we're seeing things that without the government that is the companies know that this convergence is not only inevitable, but it's probably the most important application of AI across all the different domains.

What about you, Renee? Do you have any thoughts on that?

Yeah, I would say I think the FDA is reacting to strong data in terms of different accelerated approval paths that in particular in oncology leaning into things like real-time oncology review where you can send them your data right away and they can start processing it so that the timelines can accelerate because ultimately every moment matters for these patients. We have to accelerate, but we also need to see more data innovation at the FDA because we can all take advantage of all of these tools, but if the FDA can't keep up and they need to ensure that they are updating all of their systems and they can take advantage of these tools as well, then they will end up being somewhat of a bottleneck.

I'm curious about access. You know, the medical system in this country, as you were mentioning, Reed, you know, only a handful of people can afford like the super charged, you know, AI-driven medical care. And I'm curious to hear your thoughts on how this is going to make health care more accessible to so many people who either are underinsured or lack insurance or can't go to the fanciest doctors and the most expensive hospitals or most Americans in other words. How do you see it impacting what I find is really upsetting inequities in healthcare in this country? Really it's basically a caste system.

Well, one of the benefits when you're building truly broad-based technology is that you're building it fundamentally for everyone and if you can get the cost ecosystem and also legal liabilities and regulation down then you can actually make that happen. And so part of it is like for example you think of smartphones right your Uber driver has the same iPhone that Tim Cook has right or very similar in terms of thing because it's a mass market device. Same thing in terms of AI and AI accessibility. Part of the reason why I think one of the things we want to move to as robustly as we can is how do we get like anyone who has a smartphone has access to a medical assistant that can be helpful to them in any circumstance. Obviously, it'll still be better for the people who can say, well, I can cross check that with my concierge doctor in the next 15 minutes and I'm part of a medical system that I can check into right away. That's great. And look, part of what happens is this is part of how we make progress. That's I think a fundamental way that when you look at what's coming with AI, it is an equity provider across the entire thing if we allow it to be so.

Do you agree, Eric?

Do you agree?

Well, the US is poorly positioned for this for some of the reasons just mentioned. Outside of the US, we're seeing conscious I mean deliberate efforts to reduce inequities such as a big mental health program with AI in the UK, such as diagnosing eye disease in India and other places in the hinterlands even in grocery stores for diabetics who otherwise don't even get screened for retinopathy and could go blind and lots of other programs like that. So it takes intent otherwise Katie your concern is really legitimate and this country has a long history of inequities and it could be made worse. That's one of the issues.

But do you see ways it could be?

Yeah. If we went after it. The difference between the US and other high-income countries is the other ones all look at they want to get their population maximize extend their health span whereas here we don't have that that's not the objective and that's a real difference and that framework if it doesn't change this is what the runaway costs and the payers and Medicare and everything else it just doesn't the equation doesn't work out.

I want to stay with you, Dr. Topol, for a second because an area I'm particularly interested in having just turned 69 years old. I know I don't look it, but I'm interested in obviously you wrote Superagers and I don't I hope I'm one. I don't think I'm going to be one, Dr. Topol, but we'll discuss that later. But how do you see AI? You know, it was interesting. We were talking about health care for older people and we were talking about Parkinson's earlier. How do you see AI fitting into longevity and health care as baby boomers age? And we're going to have just this incredibly huge swath of older Americans needing care and who want who even if they live longer want to live well.

Right. Yeah. This is I think the most important use and promise of AI of all the other things in the future for healthcare. In fact, Reed and I were talking about this before we came in. The point is right now the average American ends their health span at age 64 because they have one of the age-related chronic diseases whereas their lifespan is 79. That's a 15-year loss of health span. We've got to get that down to the minimum. And the way we do that relies on multimodal AI. We couldn't do it without it. So you have to have all the layers of data electronic records you have to have whether it's genomics, proteomics, biomarkers and you have all these layers with AI to determine who is at high risk and we have to say well Katie are you at high risk for neurodegenerative, cardiovascular or cancer those are the ones that account for 80 plus percent of this diminished health span once we find out which one if one of that you're at high risk, then we get you into prevent mode. And my point about drugs, everyone talks about treatment with drugs. We're going to talk about in the future prevention with drugs on top of lifestyle factors. And we're actually starting in a couple of weeks the first prevention of Alzheimer's trial doing exactly that in the very high-risk people with Alzheimer's. First testing aggressive lifestyle factor intervention and then testing drugs. So I think we're on a path now which is so exciting. It overrides the importance of the other AI use cases because we can now predict and prevent.

And do you think the way to do that is that I know people think I'm obsessed with this but I am. Is that going to be accessible to everybody?

It has to be and it has to be local.

But will it?

Well, to Reed's point software should be cheap right? Should be really inexpensive and scalable. And these things that we're talking about the electronic records already exist. These biomarkers, genes, proteins, they can be done for $100, a couple hundred dollars. This whole package is not costly. And if it saves people from any of those three diseases that are the big age-related diseases, it pays for itself many many fold.

You know, you're looking skeptical, Renee.

Well, I love the thought and we were just talking about your book and all of the mindset shift that's needed because I do think the problem with the health care system when you look at the insurers I mean take the Galleri test for example you can go to your doctor you can get a blood draw and you can find out is there evidence in your blood of cancer and we know if you can catch cancer early you have a much greater shot at being completely cured, putting it in remission. But the reality is that most of our insurers say, 'Well, why do I really care about patient X?' Because they're probably not going to be in my health care system and in my insurance plan two to three years from now when their cancer is actually discovered. And that's the part that I think is broken. And that's why I think adoption of these theories that you're talking about are absolutely critical because in the UK it is cradle to grave and therefore there is a high incentive to focus on prevention to focus on detecting cancer early but how is it that that is not the mindset of the commercial insurance companies it's not.

Which is a bigger conversation for another time but I'm curious You mentioned you do rare diseases and drugs for rare diseases are not necessarily as profitable as those that affect a huge portion of the population. How do you see AI changing sort of how therapies are developed for patient populations who have diseases that are not necessarily that widespread?

Yeah. Well, when you think about patient identification, there are so many advances now coming through AI that allow us to better identify patients, better identifying what the biomarkers are. You look at, for example, epilepsies. There are hundreds of underlying epilepsy syndromes. And so the more and more that we can better understand the underlying nature of these diseases, then the better off we'll be when we're trying to come up with new solutions and whether it's drug development or other services that go around it. I do think though, we talked about patients, physicians having greater access to information. I also think that we as companies, we also get more information now about our customers and our patients. And so we can also better target what are their needs, how is it that they want to be interacted with. We built a tool internally that would allow us to understand what was the probability of a patient coming off therapy and was that due to not having reimbursement, having some reimbursement challenges. Is that due to not fully understanding side effect profiles? What sort of support do they need? And so the more we can also take the initiative of developing more tools to understand what patients need, then the better off we will be at addressing them.

So I want to end with just two questions. I'll just go down the row. What should this audience be paying attention to this year from an investment perspective? So Reed, let's start with you. Eric, why are you looking at me like that?

How about you, Eric?

When is that going to do you think where suddenly it's going to be like wow everything has changed?

Renee, what's your answer to what should this audience be paying attention to from an investment perspective?

Yeah. Well, I know what I really want to see more of, which is more investments in accelerating drug discovery and development. Being able, you talked about it earlier, being able to shorten timelines, being able to see how some of the companies that are using AI just for drug discovery, Amgen for example, how these drugs start to come forward and demonstrate proof of concepts.

And also the ones that don't work too, Renee. Right. Seems to me people invest years and years and years into these drugs and then at the end of the road it's like screw me this is not working right and can't they shrink the time frame for that?

Exactly. I mean the more investment that can go into that the better.

And Dr. Topol.

Yeah, I think we're going to see this pivot to prediction and prevention. We've never done this in medicine. Everything is reactive. Treat, treat, treat. And it's already too late. Even when you have cancer on a scan is billions of cells.

So, what should you be investing in that's going to work toward that end?

Clocks, pace of aging clocks that are going to be available for every organ in the body and the immune system this year. And they are one of the best ways to know if there's a trouble spot in any individual.

Whoa. What do you mean clocks?

So, these are clocks of proteins, plasma proteins. You can get out of 11,000 proteins from Olink, SomaLogic, and other companies now. And that's inexpensive. And you can every organ of the body, heart, artery, brain, immune, and on and on. You'll see if your pace of aging within an individual is off track, that's going to help pave the way for prevention along with specific biomarkers that are being discovered at a pace that we've never seen before and validated. So when you add those to the other layers of data that affords prediction. We've already seen an incredible report recently that you could predict the next 20 years of a person's health arc like a GPS of health accurately. And that was with GPT-2. All right. In 400,000 UK Biobank and 1.9 million people from Denmark. So we're going to just keep building on that. Just last week we saw predicting that was with 1,200 diseases predicted. Last week there's 130 diseases predicted from one night of sleep in a sleep study along with the rest of the layers of data. So this is the biggest change in medicine from a data standpoint I've seen. It's got legs. It's going to keep building. But the immune system by the way is the one that is the common thread of age-related diseases. And so the drugs like GLP-1s and semaglutide and NLRP3, these other potent anti-inflammatories, they're in the pipeline. They're in early clinical trials. Watch this space. GLP-1 family of drugs, which has a lot more to it. That's just the beginning of a potent anti-inflammatory revolution that helps age-related diseases to prevent them.

I definitely have to come and see you when I'm in California next month. Do people want to know like the next 20 years or some people were like I don't really want to know.

It used to be that people with APOE4 which is 1/4 of the population, you know, Cassandra, I don't want to know because there's nothing you can do about it. There's a lot you can do about it now. So, it's changing.

Well, I know that this is the only thing keeping you all from having an alcoholic beverage of some kind. Are we still allowed to do that? Now, everybody says you can't drink at all. What the hell?

Well, Meir says you could only you shouldn't have fruit drinks for breakfast. I think that was.

It's a great place to end. Yeah. Anyway, you all thank you so much. Reid Hoffman and Eric Lefkofsky, Renee Gala, and Eric Topol. Thank you.