The Future of GLP-1 Drugs and AI Medicine, With Eli Lilly CEO David Ricks

Summary

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Podcast Summary

Podcast: Plain English with Derek Thompson
Episode: The Future of GLP-1 Drugs and AI Medicine, With Eli Lilly CEO David Ricks
Date: February 24, 2026
Guest: David Ricks, CEO of Eli Lilly

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Overview

This episode delves into the GLP-1 drug revolution, its origins, far-reaching implications, and future directions. Derek Thompson interviews Eli Lilly CEO David Ricks about the science, business, and societal impact of GLP-1 drugs (like Mounjaro/Zepbound), the pharmaceutical innovation landscape, and how artificial intelligence might speed up medical breakthroughs. They discuss how these drugs emerged, their surprising effects beyond weight loss, challenges in adherence and cost, the public’s distrust of “Big Pharma,” and the long-term promise and limits of AI in drug discovery.

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Key Discussion Points and Insights

1. Origins & Development of GLP-1 Drugs

[06:16 – 13:15]

• Historical Context:
  • The story of GLP-1 drugs starts in the 1970s with the discovery of the incretin effect—where hormones from the gut (incretins) regulate how the body processes food.
  • Early breakthroughs identified gut hormones like GIP and GLP-1 that signal satiety and glucose uptake.
  • The Gila monster’s peptide (exendin) paved the way for the first human drugs by providing longer-lasting effects—a foundational discovery for drugs like exenatide (Byetta).
  • Initially, these drugs were used to treat type 2 diabetes, with weight loss observed as a secondary effect.

"The effect is really sort of the insight into the natural system by which we regulate calories, which is kind of core to our existence." — David Ricks (08:24)

• Improvement Cycle:
  • Incremental advances in peptide chemistry led to weekly drugs (Trulicity/Ozempic), making dosing easier and side effects less severe.
  • Dual and now triple agonists (e.g., tirzepatide and retatrutide) aim to increase efficacy and convenience.

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2. GLP-1: From Diabetes to Weight Loss and Beyond

[13:52 – 24:29]

• Metabolic and Broader Benefits:
  • GLP-1 drugs’ core effect is enhancing satiety and facilitating weight loss.
  • Expanded benefits include lowering the risk of heart disease, improving insulin sensitivity, reducing inflammation, and even decreasing the prevalence of some cancers.
  • Early studies show potential in treating addictions (smoking, alcohol), joint pain (osteoarthritis), and possibly neurological disorders.

“We have a study where we watch people on tirzepatide for three years and they had 92% less diabetes at the end of the study.” — David Ricks (15:54)

• Suppressing Addictive Behaviors:
  • Drugs show secondary effects on dopamine-driven behaviors, decreasing impulses for not just food but also substances and possibly habits like shopping or gambling.

“We also report less online shopping and less gambling while on these drugs, which I think wasn’t the initial — no one would have hypothesized that at the beginning.” — David Ricks (19:26)

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3. Adherence Issues and the Challenge of Discontinuation

[24:29 – 29:12]

• Adherence Rates:
  • Over half of patients discontinue GLP-1 drugs within two years—a pattern similar to other chronic medications.
  • Discontinuation is mainly due to life events, cost, supply issues, or side effects (more acutely at higher doses).
• Weight Loss Reversal:
  • Stopping the medication results in weight regain and loss of associated benefits.

“Most of the health gains will reverse pretty quickly for most people…we’re not curing anything here.” — David Ricks (27:30)

• Public Service Note:
  • Ricks emphasizes consulting healthcare professionals before starting these drugs given side effect profiles and the risks of “bypassed” online access.

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4. Pipeline & Future Directions for GLP-1 Drugs

[29:12 – 34:33]

• Key Axes of Progress:

  1. Boosting Weight Loss:
     • Triple agonists (like retatrutide) show promise for 28-30% weight loss—comparable to surgical interventions.
  2. Reducing Side Effects:
     • Development of new molecules (like alurolentide, an amylin-based drug) that offer substantial weight loss with minimal GI side effects.
  3. Alternative Indications:
     • Potential to treat inflammation (e.g., asthma), addiction (alcohol, tobacco), or even neuropsychiatric disorders.

• Convenience Innovations:

  • Oral versions, longer-acting injectables (quarterly, maybe even yearly), and drugs targeting specific comorbidities are all in development.

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5. The Pharmaceutical Business Model Explained

[36:51 – 47:21]

• R&D Cycle:
  • The business hinges on government-granted patents (about 10 years after R&D), after which profit “evaporates.”
  • Around 20% of revenue is reinvested into R&D—the majority funding clinical trials (i.e., “we have 365,000 people in a Lilly clinical trial”).
  • Failed trials are factored into drug pricing—R&D costs include both successful and unsuccessful bets.

“You have this melting iceberg problem…eventually it’ll become nothing and the company needs to recoup its investment.” — David Ricks (42:15)

• Role Relative to NIH:
  • NIH supports frontier basic research (e.g., studying Gila monster saliva); pharma companies focus on turning scientific insights into scalable, patent-protected products.

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6. How to Optimize R&D and the Role of Taste, Efficiency, and Franchising

[47:21 – 55:24]

• Secrets to Picking Winners:
  1. Stability & Domain Expertise:
     • Stick to core disease areas and keep key scientific talent.
  2. Shorten Cycle Times:
     • Invest in R&D and production in parallel ("pre-invest"), accepting some waste for speed gains.
  3. Leverage Hits:
     • Successfully mine a biological insight (like GLP-1s): use “franchise” thinking to create a series of related, improving drugs.
• Platform Analogy:
  • Like an entertainment franchise, breakthroughs can spawn “sequels” with iterative improvements (but each must beat generics to succeed).

“Once something’s hot, you’re almost too late to jump into that space. The cycle time’s too slow in our industry.” — David Ricks (51:02)

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7. Why Does America Distrust Pharma?

[55:24 – 63:53]

• Gallup Data:
  • The pharmaceutical industry consistently ranks lowest in public esteem, even beneath the federal government.
• Ricks’ Diagnosis:
  1. Complexity:
     • R&D timelines are long and hard to communicate.
  2. Necessity & Dependency:
     • Nobody resents buying a phone, but they resent being forced to buy lifesaving drugs.
  3. Out-of-Pocket Costs:
     • Insurance shields patients from most health services’ costs, but not drug costs—drugs are 9% of healthcare but “feel” much more expensive.

“We make products that people feel like they don’t have a choice to purchase. And I think industries that have that dimension are almost universally disliked.” — David Ricks (57:30)

• Cost Structure Challenge:
  • Patent medicine economics create inherent tension: high prices are set precisely when drugs are in highest demand, before generics arrive.

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8. Why Drug Development is So Expensive in the U.S.

[61:01 – 63:53]

• Regulatory Load:
  • Regulation is additive—each new requirement never disappears.
• Scientific Hurdles:
  • Every new drug must outperform cheap, effective generics.
• Technology Escalation:
  • Each improvement layer (e.g., personalized therapies like CAR-T) increases cost, though may drop as tech matures.

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9. The Reality and Limitations of AI in Medicine

[63:53 – 68:24]

• Promise and Pitfalls:
  • General AI (e.g., LLMs like ChatGPT) aren’t equipped for deep biology; they lack specialized training data and understanding.
  • Narrow AI tools (e.g., AlphaFold for protein folding) work well for specific problems but don’t generalize across biological systems.
  • Progress needs a vast new “internet” of biological knowledge—a corpus of high-fidelity, well-tagged biological data to create useful models.
  • Real progress may be decades away, though ongoing investments (partnerships with Nvidia, dedicated supercomputing) aim to accelerate timelines.

“The language models don’t translate to biology. We need a new language, and we need to train it on that. Unfortunately, we don’t speak the language of biology that well. We’re sort of like a toddler in the language of biology.” — David Ricks (68:24)

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Notable Quotes

• “We’re boosting [hormones] to a super normal level and it suppresses appetite, kicks in the metabolism, et cetera…to do that, you need to get to higher levels than we were originally to see the weight loss effects.” — David Ricks (10:53)

• “What seems to be magical here, really quite unique in this pathway, is we do tend to suppress these kind of urges without inducing depression or other things that change brain chemistry in a way that’s quite negative.” — David Ricks (22:12)

• “It wasn’t too long ago, Derek, in 2017, if you said, hey, we’re spending a lot of money on an obesity drug, our investors would have looked at us like we were crazy…Once something’s hot, you’re almost too late to jump into that space.” — David Ricks (51:00)

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Timestamps by Segment

• GLP-1 Drug Origins & Science — [06:16 – 13:15]
• GLP-1 Expanding Benefits & Mechanism Mysteries — [13:52 – 24:29]
• Adherence Problem and Access Issues — [24:29 – 29:12]
• Future Directions: Triple Agonists and New APIs — [29:12 – 34:33]
• Pharma Business 101: Patents, R&D, and the Melting Iceberg — [36:51 – 47:21]
• Picking Winners, Speed, and Franchise Innovation — [47:21 – 55:24]
• Distrust & Cost: Why America Hates Pharma — [55:24 – 63:53]
• Regulatory & Scientific Barriers to Cheaper Drugs — [61:01 – 63:53]
• AI in Drug Development: Hype vs. Reality — [63:53 – 68:24]

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Memorable Moments

• GLP-1 drugs as “a system-wide mechanism for increasing moderation”:
  Thompson’s philosophical musing on how these increasingly affect not just food intake but broad behavioral impulses.

“Spookiest about this…these drugs almost act as a system wide mechanism for increasing moderation…” (20:53)

• Ricks’ candor about public perception:

“We make products that people feel like they don’t have a choice to purchase…industries that have that dimension are almost universally disliked…” (57:30)

• AI’s slow revolution in drug discovery:

“We don’t speak the language of biology that well. We’re sort of like a toddler in the language of biology.” (68:24)

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Summary Takeaway

GLP-1 drugs’ emergence embodies both serendipity and scientific perseverance, offering unexpected benefits for obesity and an array of chronic problems. While the pharmaceutical engine is slow, expensive, and often distrusted, Ricks argues that platforms like Lilly accelerate progress by both strategic focus and serial innovation—with AI promising but not yet realizing its transformative potential. The episode provides rare, plain-English clarity about how drug revolutions happen (and why they’re so costly and slow), why pharma is “hated,” and how medical breakthroughs might come faster in the future—if we can solve the fundamental “language” of biology.

Transcript

[A]

Foreign.

[B]

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[A]

Thanks for having me. Good to be here today.

[B]

So today you are the largest pharmaceutical company in the world in significant part because of your GLP1 drug. 20 years ago, Lilly helped launch the first approved GLP1 drug. I'm fascinated by the history here, and I'd like you to start wherever you think this story should start. 2005, 1970s dawn of Man. Truly, it's up to you. How did this drug category get kicked off through the lens of Eli Lilly?

[A]

Yeah, well, I think this is like Great cocktail party intro, because I get this question all the time and everyone knows the story. And I think you've even told the story of the Gila monsters and, and we'll get to that in a second. But the real first scientific breakthrough that happened was in the early 70s. There were actually two papers that looked at something that was called at that point the incretin effect. And now incretin is what we call this class of hormones or proteins that your gut excretes, like GLP1. And what was noticed was that if you ingested food or calories of any kind orally through your GI tract versus intravenously, there were very different amounts of that nutrient that would spike in your blood and would be absorbed or not absorbed effectively by the GI tract more effectively. So blood glucose was less where if you took it intravenously it would spike and stay up. So it was hypothesized there were some suite of mechanisms by which your gut actually signaled the rest of your body that you had eaten something. And then that work led to identification of what those things were. The first one identified was actually called gip, which is actually the backbone of Tirzepatide or Zeppbound, our bestselling drug. But of course, GLP was another one early identified as well. And that's like the point of origin for this. The companies that worked on this then in the 70s, the 80s, included Lilly and Novo, actually. But it wasn't until the Gila monster story kicked in in the late 80s, I think this guy named John Eng in the Bronx was interested in why certain animals didn't need to eat very often and identified this mimic of the human peptide, but used for this lizard's own purposes, extendin, which mimics GLP1. And this was useful because you could ask, well, why in the 70s didn't you just make this drug then if you knew about this? And the answer is that peptide chemistry wasn't very mature. And if you give humans a native sequence of GLP1 or GIP, they both have like a half life in your body of seven or eight minutes. So it wouldn't be a very convenient drug. You'd have to walk around having like an IV bag infusing it all day long to mimic what we have with the drugs we have today. The Gila monster one was interesting because you could get it to go about eight to 10 hours. So it was a twice a day shot, which barely cleared the hurdle of good enough to be a drug. But we made it into a drug along with a partner Amelin and launched that in 2006. So it's been going on for a while. The effect is really sort of the insight into the natural system by which we regulate calories, which is kind of core to our existence. And drugging this target has proven hard. It took a lot of advancements in peptide chemistry and drug making to get to where we are today with pretty convenient long acting and now dual acting versions of this pathway, the Incretin.

[B]

Your story stopped off with Axanatide, which is the drug that was derived from the Gila monster. Incretin, that was a type 2 diabetes drug. How long did it take Lilly to recognize that what had begun as a type 2 diabetes drug was in fact an incredibly powerful weight loss drug? Because now here we are 21 years later, which is not a short amount of time. So how long did it take for you to realize this underlying mechanism which is now what the drug is more famous for?

[A]

Yeah, you know, it's interesting because when we go back and look on the COVID of our annual report from that year, there's a patient, Maria, and she's talking about her experience with Bayata, the brand name for extendin, and she says it's controlling her blood sugar and there's a quote and that her friends noticed she's losing some weight. So there it was like sitting there. In 2007, in April, when we put out that annual report, we were doing studies, but we had a problem. The problem was the side effects associated particularly with GLP1 are what we call peak to trough. So the difference between the highest amount of drug and the lowest and then going back up again to the highest, caused by this twice a day dosing, which you can imagine someone sort of bouncing around maybe two to three times the drug at peak than trough causes a lot of GI distress. So we've read about those feelings of nausea, sometimes diarrhea, sometimes vomiting. So the more we push the dose up, the more we ran into these things with that particular drug. And so we stopped because it was very unpleasant to take. And, and the weight loss effects were there, but more modest. What happened that started to the weight loss part was to be able to get the dosing levels up. So here we're taking a system, the Incretin system, GLP1 or GIP or coming soon glucagon, and we're kind of boosting it in people who have regular levels. So it's not that people are deficient necessarily in these hormones, it's that we're boosting them to a Super normal level and it suppresses appetite, kicks in the metabolism, et cetera. So to do that you need to get to higher levels than we were originally to see the weight loss effects. Whereas it did have some blood sugar control levels. We produced a once weekly GLP, one launched in 2014 called Trulicity. Anyone who was in the diabetes world would know that very few people in the lay media would remember that drug. Although it was our best selling drug at one time and it was a weekly, we did a trick of protein engineering, we made it last a week and people lost more weight. Our competitor Novo then did a similar trick and made a weekly. They launched that in 2017, it was called Ozempic. Oh, we know that word. And that really only got so famous when they took the chance to study that at an even higher dose in people who didn't have diabetes but had dysmetabolism and obesity. And that came out in 21, I think. And then this whole thing started running. But even while they were doing that, we had switched away from the single acting GLP. We invented this drug called tirzepatide, which happened in 2014 and we started doing studies and by I would say 2018, 2019, we knew this was going to be the most effective obesity drug we've ever seen. And of course since then we've been improving upon that. So it's a case of sort of leapfrogging and iterative improvement. And the story interesting here, people think about the long acting as convenience, but actually the long acting gave us a flatter, steadier drug profile so that the side effects were less bothersome. You could dose up without having that sort of peak trough kind of saw too thing which causes GI distress and made the drugs intolerable without a very flat profile.

[B]

Just a pit stop on the business here. Before I get into the science, what share of Lilly's revenue and profit at the moment is related to there's single agonist Ozempic, there's dual agonist GLP plus gip, which is Tirzepatide, and we'll get to the triple agonist in a second. But what share of your revenue, what share of your profit depends on this class of drugs right now?

[A]

Well, last year it was a little less than half the total company revenue. This year we don't give out like product level guidance, but it's projected to exceed half. It's the fastest growing part of the company by far.

[B]

And getting to the science, I mean, to me the most mysterious, the most tantalizing the most even confounding aspect of these drugs is that they seem to do so many things at once. They increase feelings of fullness, which is probably the most fundamental thing that they do in terms of helping people lose weight. They help people control non food addictions as well, it seems. They help people stop smoking or stop gambling. In some cases they reduce knee pain, you've studied this directly. They seem to reduce inflammation. They seem in some cases to protect against heart disease among people who aren't even losing weight, which suggests some mechanism that is maybe slightly unrelated to the satiety obesity mechanism. They might even have brain benefits. They're being studied for dementia and all this stuff just at the highest level. You are as close to the best scientists studying GLP1s as anybody in the world. What the hell is going on here? What is their theory for how one thing can do so many different things?

[A]

Yeah, I mean it is a surprising thing that most of what you said there has either been proven or will be proven. I'd add one more category which is cancer reduction. So there's a lot of interesting studies about population wide cancer risk reduction, maybe more directly associated with losing weight or maybe the inflammation story. But okay, so just to take it all the way back, I mean a core behavior to keep organisms alive is feeding. Right? We can all understand that. And humans and other animals have grown up in an environment of scarcity. So most of the feeding cycles in our body are, are focused on acquiring more calories. There are very few counter regulatory processes that we've evolved with. Because our ancestors evolved in scarcity, we spent most our day looking for food. As you can imagine a wild animal today, grazing or hunting or whatever, there wasn't really a biologic need. It was unnecessary to put in our genome something that says okay, you're full because it was so rare. But this system was put in I guess as sort of the. So as a pathway it's a little bit unique. I think the signal from the gut to the brain and the rest of your body for metabolism has evolved in a way that we don't really know of many other ways to actually reduce saet effectively. So it's core for that. But also feeding drives a number of other processes in our body and overfeeding turns out is not good for us. Although our ancestors didn't experience that because they were starving most of the time. So in a modern world, like a lot of chronic conditions we have, the human beings have evolved into an environment we weren't built for. And this is an Environment of excess of abundance to maybe hit that note for you here. Food abundance. Right. And so what does that do? I think, of course, the core benefits, which are pretty well documented, are these metabolic effects. And that is about, of course, weight diabetes, which is type 2 diabetes we're talking about here. There's another type of diabetes which is really contracted when we're young and it's an autoimmune disease. But most people with diabetes have this adult onset diabetes, which is a disease of what we call insulin resistance. So you're too heavy and your insulin, which helps you process sugar, no longer works effectively because essentially there's resistance from your body from having too much sugar for too long. So these drugs can break that cycle. You reduce weight, you actually increase insulin sensitivity. We can reduce type 2 diabetes. We have a study where we watch people on tirzepatide for three years and they had 92% less diabetes at the end of the study. So essentially, I mean, nearly staving off diabetes completely. That's a remarkable finding. We know if you add this to people with diabetes or not diabetes and they have heart risk, it reduces their heart risk. That's a story, as you're saying, that may not just be about weight loss. These drugs also reduce inflammatory factors that may be related to over calorie consumption or maybe separate. I think our scientists debate that still. But there's a key signal called hscrp, which is C reactive protein, that sort of measures general inflammation. And on these drugs, that drops 50 to 70% and it drops a lot faster than your weight. So pretty quickly in the course of treatment, your inflammatory factors go down. And when you're having a heart attack, we know that's not just about cholesterol building up in your arteries and the clogging of the arteries that that causes. It's actually the response to that clogging that can cause the blockage, which is an inflammatory event. So you're sort of reducing the risk in two ways, improving cholesterol and metabolism, but then also in an acute phase with the inflammation. Speaking of inflammation, you raise joints. And we did a study with retatrutide, the next generation product we're working on, that reduced knee pain. Well, half the people in the study had no knee pain at the end of the study. I mean, it was the most effective osteoarthritis knee pain drug I think ever studied, which is also interesting here. You have probably two effects, the inflammatory factors, like I just mentioned, but also the offloading of weight. So there's a mechanical Wear problem. And if you talk to orthopedic surgeons, they'd say more than half their knee replacement patients have been obese or overweight most of their life. And so there is a mechanical loading challenge people face with their joints that causes a lot of this pain. But it's not just that, because we also recently read out a couple studies on top of a drug called Tulce, which is for psoriasis and psoriatic arthritis. Psoriasis, a skin inflammation disease. Psoriatic arthritis, a version of that that appears in your joints. And here again, Zeppelin boosted that performance by 50%. Almost as good as adding an entirely new drug class to that category. So pretty miraculous. In addition to like suppressing our appetite for sugary foods, it does also seem to suppress our appetite for vices that are maybe sort of driven by anxiety or driven by kind of a cycle in our brain of like trying to scratch an itch or, you know, have a craving. So tobacco use and alcohol use are the most noted in the literature. We're actually doing prospective studies to prove both those things. But people also, you know, report like less online shopping and less gambling while on these drugs, which I think wasn't the initial. No one would have hypothesized that at the beginning. Yeah.

[B]

What's spookiest about this to me almost at like a philosophical level is the degree to which these drugs almost act as a system wide mechanism for increasing moderation, which is a weird thing for a drug to be able to do that. It moderates our impulse to eat, it moderates our impulse to gamble. In some ways it even seems to like give patients a kind of agency to align their behavior with what they would report wanting to do in the day before their dopamine takes over. I mean, have you talked to scientists? Are you studying, do you have theories about how a drug might even at the dopaminergic level, like how a drug might allow patients to essentially align their behavior with what they would describe as moderate behavior. That's a spooky, strange thing for certainly a type 2 diabetes drug to accidentally cause.

[A]

Cause. Yeah. Let alone a lizard saliva derived protein. Yeah. Yes. So I guess one thing to point out and then the difficulty of actually divining the exact mechanisms of this in the human brain. There have been drugs that reduce satiety and reduce these impulses before we have them marketed as neuropsych drugs. The problem is they're very dose sensitive and they are two way doors. Meaning if you push dopamine down too much, you can reach really Unhealthy levels that cause other brain disorders. What seems to be magical here, really quite unique in this pathway, is we do tend to suppress these kind of urges without inducing depression or other things that change brain chemistry in a way that's quite negative. And there are lots of neuropsych drugs that help people with extreme and clinical neuropsychiatric conditions. But they almost all have this notion that if you pick the wrong one or you dose too far, there's untoward effects that can be worse than the disease itself. We really don't observe that here. And that makes for a very useful primary care drug because you don't have to worry about carefully titrating doses or about flipping people into very negative cycles of depression or anxiety or other things. So that's a unique property here to actually, when we study in rats and rodents, for instance, in the lab, our scientists are doing that. Now, of course, if you have a sugar stimulus, we can reduce that. Well, what about nicotine? We have simulation for nicotine. It does directly reduce the desire for nicotine. It's harder to see if rats want to gamble less, but they do do these studies and they measure the neurotransmitters. It's not just dopamine. There seems to be some other signaling going on. And of course, the human brain is very complicated and pretty poorly understood relative to other organ systems. So we're still trying to figure out exactly the mechanisms of these. What we can say is empirically people report it and we will do and are doing prospective studies to take people without obesity. But with these problems you mentioned. Yeah, too much gambling, too much. We're even doing opioid use disorder just to see, well, can we help? These are such common and challenging conditions to treat. If something like a type 2 diabetes drug that became good for obesity can do it, we should try to prove that and help those people.

[B]

Yeah, I think there's this theory of science that we develop a perfect theory of how the world works, and then we develop technology that applies that theory to the physical world. But it always, almost always, seems to be the opposite way around. Like the classic example, I think, from tech history is theoretically, you would want to understand the principles of thermodynamics to invent the steam engine. But we invented the steam engine and then from it derived the principles of thermodynamics. And so maybe in a similar way, it would be lovely if we accidentally fell into a synthesized Gila monster molecule that taught us the secrets of the gut brain axis, rather than having some perfect theory of how our brains worked and then said, oh, what we should be doing is targeting glucagon and GIP in the gut. So it would be lovely if this technology opened up some big secrets of the brain. But because you use the word magical, I have to interject now as a journalist and say that for all the good that these drugs can do. I believe according to the most recent data, more than half the folks who start on GLP1 drugs discontinue them before two years is up. So there's still a pretty significant adherence problem, made even more significant by the fact that it's not like GLP1 is a drug where you take it once and you extend the weight loss benefits over the course of your life. People who discontinue these drugs often gain the weight back and theoretically inflammation and everything else can come back as well. So let's take some time to talk about this phenomenon here. Why do you think the discontinuation rate, the fact that people aren't adhering to these drugs year after year, why is it so high and what would bring it down?

[A]

Yeah, no thanks. It's important to flag that and before we even get to discontinuation. All prescription drugs have untoward effects for some people. I think these drugs have been used for 20 years in people and we have a lot of data to profile that. But people really should talk to their doctor before initiating these. And I say that because there's lots of kind of bypasses to that available online. And I think it's really best to, if you're interested in this, to go to your primary care doctor who knows your health history and consult there just as a public service announcement here. It's very important. And some people do have untoward effects. That's not the most common cause for discontinuation. In our studies, somewhere between 5 and 10% of people across most our drugs do discontinue due to side effects or other untoward thing. That's not the most common thing. The most common reason people discontinue any chronic medication is. Is actually sort of life getting in the way. And the data so far we see for a product like Manjaro or Zepbound, otherwise known as Tirzepatide, is it's not you mentioned it's so high. It's actually pretty similar to other chronic medications that we sell. There was a period of time where it was higher, but that's when we had shortages going on. And so people were actually having to discontinue because they couldn't find the medicine. That's, I think, quite a different thing than. Than what we see, for instance, all of last year. So that's worrisome, I think, for two reasons. One, most of the health gains will reverse pretty quickly for most people. Not unlike taking antihypertensive or a statin for your cholesterol. If you remove the drug, the condition just returns. It's not curing anything here. We're working on those ideas or permanent changes that would allow people to live without obesity and without drugs. But right now, you need to take the drug to get the benefits. Some people do report being able to go off them or titrate down into very infrequent or different regimens. That's not in our label, but they seem to make a lot of changes to their lifestyle. And once they become lighter weight, they're able to do that. That's a good kind of discontinuation. If that's you, most people, that's not the case. I think what's getting in the way is either some other medical problem where they discontinue the drug to manage that. That's quite common, especially in the elderly. Financial costs. Right. And we can talk about this if you'd like. But a lot of people, more than any other drug class in America, are paying out of pocket, even though they've bought insurance. So they're paying their health premium, but they're not getting covered for this drug. And that is difficult because these drugs are not cheap. And then I think people have sometimes experienced so much success that they want to try going off and then they bounce back on and go off and back on, or there's episodic things like seasons that they're more interested in losing weight in. So that's, I think, explaining the data. But the major point is it's not so different than the rest of the chronic drugs we see being used. We wish it was better because the profound positive effects we just discussed.

[B]

Yeah, you mentioned cost, and I definitely want to get back to cost, not only for tirzepatide and this category of drugs, but also for the pharmaceutical category writ large. One more beat on the frontier of GLP1 drugs. There's a lot of different directions I can imagine you trying to push this class of medication. On the one hand, you can push on the weight loss effects. And in fact, the most recent version of these drugs, retatrutide, which is a triple agonist, as I said, had even greater weight loss effects in the readout that you guys had in December than tirzepatide so you can push on weight loss effects. Another direction I can imagine you pushing on is holding the weight loss effects but reducing the side effects. So how can we continue with say 20, 25% weight loss but we reduce that peak to trough effect so there's less nausea, there's less GI distress And maybe a third direction that you can push in is trying to accentuate a certain non weight loss benefit of this drug category. Like Is there a GLP1 drug? Is there a zepbound for inflammation specifically? Is there a Mounjaro for reducing Alzheimer's plaque in the long run specifically? And so you're not, you're engineering the drug specifically to essentially accentuate one of what we're now calling side effects but in some future category would be the main effect. So I guess I gave you three pathways here. Accentuate weight loss, de emphasize side effects and door number three, some other third thing, make one of the side effects the main effect. What are you investing in the most here right now?

[A]

Well, I mean you've hit on three key ones that's occurred to us and we've been developing drugs for for some time. In fact, all three of those ideas are in the latest phase of testing for us. So in the next three years or so we'll have some answers. We have to do studies. They're controlled and blinded. We don't know the results. But those are all some good ideas. The only one I would add and I'll loop back on the alternate uses because I think that's perhaps again back to this. Why does this work for so many things is convenience. I think people enjoy being able to work it into their daily routine a little easier. A weekly shot has proven not to be so difficult. It's sub Q you administrate yourself. Ours come in a pretty convenient auto injector. And even if you're afraid of needles, you never really see the needle. But people are interested in oral, we have that coming very soon. People are interested in much longer acting. Oh, could I just do this once every three months or maybe on the future horizon even once a year type of therapy. So it's more like getting your annual flu shot. That's obviously very appealing to consumers. The technology is harder and harder to do that. But that's another vector that we're working on looping back to what's in the pipeline. Retatrutide is our triple acting. So if you think ozempic GLP1 is single acting tirzepatide improved on that Zepbound with dual acting, so why not three? So we have a triple acting single protein that's in late phase development. We'll get most of the data this year and submit it in end of the year and probably launch it in 27. This promises to get between maybe 28, maybe up to 30% weight loss. So that's the equivalent to gastric bypass surgery and I think for people who have a lot of overweight and obesity. And unfortunately there's, I think 8% of Americans have a BMI over 40. So even if you lose 25% of your body weight and your BMI is 40, you're still classified as obese. So having more matters, particularly for people who really struggle with their weight. The second thing you mentioned is lose the side effects. That's bothersome to many. And as I said, maybe 5 to 10% of people discontinue just because of that. And most people actually don't go to the highest doses available of our drug or our competitors because of side effects. So we have a drug in our pipeline called Alurolentide. This is actually not a GLP1 or a GIP. It's actually a separate, a different gut secreted hormone called amylin that this is based on that appears to suppress appetite without really much GI side effect at all. And that would be super promising. Get about 18 to 20% weight loss with almost no GI side effects. That's of course under development and needs to be evaluated further. But we've started phase three testing there and then I think this alternate use one is worth exploring. So we put a drug into phase three that in many ways is like Tirzepatide, but maybe has a few different properties that might make it better for some of these other chronic conditions. And we're studying it. That's the one. We're in phase three for alcohol use disorder, but also we're studying it in asthma. Okay, so there you look at asthma. Well, asthma is an inflammatory condition. It is very comorbid with overweight and obesity and it's a major problem in the US and so that's another indication along with tobacco use disorder and bipolar disorder. So here we're taking a drug that appears to have a little more brain properties that might be ideally suited for these, not necessarily just weight loss, but other conditions. It also appears a little more tolerable and longer acting than tirzepatide. So we've selected that one to further explore that pathway.

[B]

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Edward Jones Member, SIPC I'm going to close the door on this chapter of the conversation about GLP1s. We can return at the end to sort of some of the moonshots that Eli Lilly is looking at. But there's a second chapter of this conversation that I think is really important. I would love for you to explain at a high level, and I apologize if this sounds like a weird question, but how the pharmaceutical industry works. I think it's a very unique Industry for a lot of reasons, some of which I won't get into before asking this question, in that you spend so much money on each individual product and there seems to be this limited patent enforced window where you can make the largest profit on that product. And so you're in this cycle of R and D and profit window that is very, very distinct from, I think, most other industries that people are familiar with in the US So maybe before I get into some of my critiques and suggestions, can you just tell me, explain to our audience how you like to explain how the pharmaceutical industry actually works?

[A]

Yeah, well, I could answer this on many levels, but I think you're pointing out kind of from inside the industry the key challenge and maybe from outside the key misunderstanding, which is that once we have a medicine that's successful selling a lot, and that means it's widely useful, otherwise no one would be buying. Seems like this is a great business to be in. But that period is fixed. So why is it fixed? Because we rely on patent protection to have a government enforced monopoly for our invention. When that patent protection ends, we tend to lose almost all that business. It's not unusual within one month of our patent expiry for a major product to have 99% of our volume go to competitors. So really you have quite a fixed and almost predetermined period to recoup investments. That period can be long and I think to the general public seems long. We get 20 years of patent life. About half of that we consume in the drug development process. So you end up with like 10 years. And that's a long time of someone paying full price during those 10 years. To think, boy, isn't this company had a good return on the investment for my medicine. But the answer is actually rarely. So our industry, although profitable in terms of operating profit, et cetera, trades at one of the lowest multiples of any sector in the S&P 500. What does that mean? That means the expectations for growth are amongst the lowest of any industry. That's not a great thing because I think if it was a better performing financial industry, we'd attract more capital to invent more drugs. But that's a tough exercise, in part because of the return cycle we just discussed. Here's an example. We invented Prozac in the 1980s and sold it and became a mega blockbuster. Kind of made the modern company went generic in 2001. The next year we sold 10% of the prior year and the year after that, 1%. So it literally just evaporated. Yet many People are on Prozac today. And so that public benefit goes on and on. It just takes place over kind of a long horizon for people to really appreciate.

[B]

And before we get to a lot of Americans frustration with that business model, what percent of your sales are going back into R and D?

[A]

Yeah, great question. Yeah. So then the R and D cycle is the other part of this that's hard to grasp. We spend today about 20%. So 20 cents of every dollar people pay for our medicines goes back to research to find another drug. Maybe not for the disease you have and probably not for you. And so that's kind of an interesting way to fund future cures and breakthroughs, but that is how it's worked. That tends to be a lot of research dollars. So we have 12,000 people at Lilly who work in research. We have 4,000 PhD scientists, which is the combined number at Harvard and MIT. So this is a major scientific apparatus we run. And what we have to do is both look for new novel, like biologic systems that have been unconquered by previous medicines, because there's no sense in making another medicine that's already generic and cheap. Right. Just a new one that doesn't work, it never sells, and it's not scientifically interesting. What we have to do is find something for that's unconquered, a disease that yet has good solutions or has mediocre ones that can really be improved on. So we have to discover the biology and then we have to find things that impact that biology in a way that's safe for people. And that can take a long time. Obviously consumes a lot of resources, human testing also. People don't understand this, but two thirds of the money we spend on R and D is actually clinical trials. And you'd think, okay, well, they're in clinical trials. It must be working. First of all, not necessarily. A lot of things that we test in animals and now computer systems and other things don't translate because human disease and human biology is actually less well understood than one might think. And we have to prove it in the real world. And we don't just prove it by observation. We do blinded, controlled studies. So it's sort of absolutely fact when you read a study that one thing is better than the other, one thing is better than nothing in a placebo, and it can help your disease. And that process has to be done very carefully because we're essentially running experiments with people. And it also has to be done in healthcare. And so when people enroll in a clinical trial, we're actually and Today we have 365,000 people in a Lilly clinical trial. And when they're in our trials, we're basically supplying their health care for them. So we basically become their primary care physician, we run all their tests, we support their care through the trial. And some of these trials are, well, we have like an Alzheimer's prevention trial going that's on its fifth year. So these are long and complicated endeavors that are often come up with nothing. So that's the last concept is we also have to do enough experiments to produce an outcome, but each time we fail, we have to cover that with returns somewhere. So when people buy our medicine and say, isn't that expensive? You have this melting iceberg problem of like, eventually it'll become nothing and the company needs to recoup its investment. But that investment isn't just the cost to develop that drug. It's the cost to develop some drugs that didn't actually work. And that's a challenging thing to explain to the public.

[B]

Right? Yeah. There's two doors open here that I want to make sure that I remember open. The first is about the size of your RD budget, which I think I've read is about $14 billion. Is that roughly accurate?

[A]

14 billion, yeah.

[B]

NIH is what, 35, $40 billion. So you're 30, 40% of the NIH right there. I do want to be clear. You are not doing the same kind of R, the same kind of research as the nih, Right. Like the NIH is paying for crazy scientists, God bless them, to look inside the mouths of Gila monsters to see if there's some hormone that could theoretically, 25 years later help people lose weight and reduce their cancer burden. You are more spending money on the D part using some basic science that has been publicly funded and maybe some basic science that you've done and putting people, or maybe in the very early stages, putting animals, computer models through the clinical trial pipeline. That is most of what we're calling R and D for Lilly. Is that an accurate distinction or have I gotten something wrong there?

[A]

Yeah, I think in the main, I mean, NIH are government funded. Programs in most developed countries do much more focus on the frontier of research. So sort of going from the zero to one kind of knowledge about a system and kind of uncovering nature. And that's critical not just for our industry, but others too. And the NIH focuses on human health. It's important to note also that most of the NIH funding that's been a separate topic, you know, controversial thing in the last year because the current administration's proposed to cut that funding. But about 80% of it is extramural. They're sending it out to university laboratories under grant agreements, et cetera, to explore a theme or another. It's not actually government researchers. There's very few people in white lab coats in government buildings spending that money. It's mostly in your local R1 university, whether it be in the Ivy League or Indiana University. Here in our city that's important. But they do focus on that new knowledge part. The 0 to 1, I think industry mostly focuses on the 1 to 10 kind of things, which is, okay, here's a biologic system that has some interest. It's not known if we interfere with it in humans that'll help a disease. Let's discover that number one. Number two, how best to interfere with it. What new substance can we create that can change that system in a way that's helpful for human health? Actually, most of what we do in our discovery laboratories before human testing is that second part. So if you think of like a lock and key example, you know, if biology has all these locks we're trying to turn and they are causing malfunction at a cellular or organ level, what is this substance, the key that interferes with that lock in a way that's helpful, minimizes harm and increases benefit. And we're engineering literally new matter. So these can take the form of peptides or gene edits or small molecule chemistry that doesn't exist in nature before. That's what we actually patent and that's what can provide the return. That's what's in the pill or the bottle you buy. But we're actually engineering that from scratch. And drug companies are mostly good at that part. Sometimes we're good at an insight into the biologic pathway as well. Like Lilly and diabetes and metabolism. We've been doing this for 100 years. So we have a lot of deep science there, but I think you've characterized it correctly. And there's sort of this ecosystem between the government funded labs, the universities that do other research that's more exploratory then picked up by industry, biotechs or big pharma that finds new matter that interferes with that insight, tests that whole thing together and then that produces a breakthrough drug with a new indication for something that didn't exist before. And that whole drill takes a lot of money in a lot of people and usually between 10 to 14 years.

[B]

This question was not in my notes, but it just occurred to me, you Mentioned that the pharmaceutical industry writ large trades at a pretty low multiple. And it seems like one fair way to put in plain language what that means is that the market doesn't think that just because Lilly, Pfizer, et cetera, your typical pharmaceutical company, just because you discovered Prozac the 1980s or just because you discovered Drug Z in the 1990s has no predictive value for whether you'll discover drug A in the next decade or the decade after that. It seems like there's a kind of revealed assumption among investors that drug discovery is almost random, that it's a bunch of people throwing tens of billions of dollars at the wall hoping that they'll get GLP1 drugs. How do you think about making your R and D money go further? How do you think about sort of tweaking the odds of getting the next hit? I feel like maybe there's a range between pure taste. Just I know it when I see it. I got a feeling about gut hormone drugs right now versus maybe some statistical analysis that says we have some proprietary model that suggests that the new target zone has to be this new theory of Alzheimer's drugs, for example. How do you think about, at a big picture level, how to make your R and D budget go further? Because it almost seems to me like that's that and maybe picking the right biotech company to fund once they start to have a promising candidate. That seems like almost the whole game here, like picking the right winners. So what is your theory of picking the right molecules and the right winners?

[A]

Yeah, I think there's like three we've had and employed with some success. And then there's a fourth one coming which relates to AI. But of course, if you're in an industry like this, you're trying to figure out, okay, kind of like what's the difference between batting.280 and 300 the hall of Fame. That's sort of how our business works as well, is like marginal differences can actually mean the difference between perpetuating your company because you've invented something useful inside the patent life of your other useful drugs and can launch then to drive growth, or you're simply just replacing revenue forever and never really growing at all, which is the baseline assumption built into the multiple of the industry, which is depressed future growth. That all, by the way, despite, and we can talk about this if you want, that probably the most productive thing in healthcare is drug development. I mean, I think of all the money we spend on healthcare, solving problems with a medicine turns out to be super, super efficient for society. So we should find a way to do more of it. Yet investors think basically that those bets on doing more of it aren't worth it. So that's a challenge we all face.

[B]

We're going to get to society making different bets on the future of medicine in a second. So you can put a pin in that one.

[A]

Okay, let's come back to it. Well, so three things we've tried and I think have worked. One, you talked about taste, and I think that's a general problem in any industry, perhaps whether you're in real estate or some other thing like how do you pick assets to invest in, whether our own or external. But one way to do that is to have consistency, both in terms of the themes, which you kind of build that nuanced judgment, but also in the people themselves. And I think here Lilly's been basically in the same diseases we've been trying to study for a pretty long time. And we've been able to keep the scientists and leaders, scientific leaders, around those problems for a very long time. So we have by far the lowest turnover in the industry in our science group, but also have been pretty consistent strategically, not tacking and retacking back to whatever our competitor at Merck or JJ is selling a lot of, but just trying to stick with what we know. And I think that's a strategy choice that's paid out for us. It wasn't too long ago, Derek, in 2017, if you said, hey, we're spending a lot of money on an obesity drug, our investors would have looked at us like we were crazy because that had been such a dry well for the industry. But we sort of evolved into it. And one thing we like to remind our investors of is that once something's hot, you're almost too late to jump into that space. The cycle time's too slow in our industry. You need to actually go where the kind of the open space is and then be successful enough in having insights, discovering new molecules, and developing them. The second thing we did, which I think has really worked for us, is work on the cycle time. I mentioned it's 10 to 14 years to run from kind of an idea to an approved drug. Well, we set out a while ago to cut that in half. And it's empirically true that we haven't quite gotten to half, but we've cut into about 60%. So we can arrive at the same idea as a competitor, say, on the east coast, and work it and arrive in the market years ahead of them. How do we do that? We did that by a lot of just basic blocking and tackling of how we do processes in our industry, we did that by how we basically pre invest in things. So we made serial investment, which is sort of how the industry works historically parallel and only at scale can you do some of that. If you're a biotech star for cash, you're not going to say figure out the manufacturing process at scale before you have a clinical result. But we do them at the same time. And if we're wrong on the clinical result, we throw away that work. But it's relatively cheap relative to the time saved. So that's been, I think, a big unlock for us. And then the third thing which everybody wants to find, and there have been patterns of this in the industry, we're experiencing one right now with the Incretin medicines is it's sometimes true, although not always, that when you have a breakthrough on an insight, you can sort of mine that biology for two or three iterations more meaning there are improvements to be found, either alternate mechanisms related to it, or even the drugs themselves improved in a way that can make that fixed patent life longer, not for the individual drug, but by launching another drug that's even better and better, but a close cousin of the original, but with improved properties that.

[B]

So are you like the Bob Iger of pharma, like the franchise, the man who understood how to turn franchises into the biggest business in entertainment? That there's. I mean, I'm being 40% glib here, but like it sounds like what you're saying is that there is franchise sequelizing possibility within certain classes of medicines and you have used that to certain effect within.

[A]

Except it's not like for enjoyment or marketing. It's actually because the drugs work better. And so here's an example not in GLP1, when we come back to Tirzepatide as an example of this. But I think there's a whole breakthrough in cancer happening with These things called ADCs or antibody drug conjugates. So we all know people who've had cancer and received chemotherapy and that it may kill cells. It does it indiscriminately and people have lots of side effects. We all know about that. So here what people did is attach the chemotherapy to like a homing device that only seeks cancer cells or primarily seeks them. And this is creating a revolution in cancer care. Well, that's a technology that if you build a capability now that that's sort of been unlocked, you can make a lot of these ADCs and use the same platform to produce more drugs more efficiently. We see it in GLP1 with protein engineering. That's how we got the weekly, and then that's how we combine two and now three and then others. We're sort of using the technology base we've developed to create advantage and repeated hits. Although once those hits, it's not because the brand name or some fabulous affinity our customers have for our product or our company. Once they go off patent, they'll be very cheap because it can be easily copied. And the next hit needs to be that much better than the original one. But that's the third way. Finally, people are working on AI.

[B]

So, like, can we. I don't want you to go into AI yet. There's maybe two or three questions that I want to hit before we end with AI. Sure. So I really wanted to ask you this. Every year, Gallup asks Americans for their overall view on a couple dozen business sectors. How do you feel about the computing industry? How do you feel about restaurants? How do you feel about banks? And it reports the net positive for each industry as of the last survey, which came out last summer, 2025. Computers are plus 45 as an industry. Restaurants are plus 30. Banks are plus 5. Media. My industry is negative 9. And that seems pretty bad until you go to the very, very, very, very, very, very bottom of the list, and that is the pharmaceutical industry, which is at negative 30, the lowest of any rating for any private sector industry. The only thing below, it's the federal government. The polite way to ask this question would be something like, why does the industry most devoted to saving people's lives have the lowest approval rating? The ruder way to ask the question is, why do people say they hate you? And because I've been interested and polite about the GLP1 stuff, I'm just gonna go ahead and ask this in the rude way. Why do you think people say they hate your industry?

[A]

Yeah, I don't mind the rude way, Derek. I get it all the time. I mean, I think we've touched on a few things here that drive that. And believe me, that's no point of pride. I wish people liked more and understood what we do better because I come to work every day because I think it makes a difference in the long term. And there's a lot of ways to make a business work. We employ people, we do good. But I think the output of what we do is uniquely good. And I wish we could articulate that better. I think there's three things we touched on. One, the Mechanics of the industry are complicated and slow, and that is frustrating to explain. And in today's world in particular, the idea that you're going to work on something today, we'll start a project and if we're lucky, we'll produce something that will be useful to you or your family member in 2036 feels pretty unsatisfying. It's not something you can get behind. It's too slow and complicated. Secondly, we make products that people feel like they don't have a choice to purchase. And I think industries that have that dimension are almost universally disliked. Even though the thing you're purchasing is keeping you from having a negative event or even in some cases keeping you alive. And that dependency thing is hard. Now you could say, well, what about insurance and government programs? Aren't they supposed to shield people from that? And do they really pay very much here? A third dimension is within healthcare uniquely, because people don't despise their doctors or their local hospitals like. Like they report to do for their drug companies. Actually, our system financially is built to primarily shield people from services, but not from product costs. And that's a strange thing that's developed through the way we've started to do insurance and federal benefits and healthcare. But it's true so that people pay basically about the same out of pocket for medicines as they do for all other healthcare, even though medicines are 9% of total healthcare. So that gives the impression that this is all very expensive. And I'm being ripped off on top of that with GLP1. We have another problem which is that we just don't have coverage to begin with for a category drugs that could have amazing impacts on human health and longevity in our country and everything else, because it's the last thing that arrived and there is a lot of spending on it and we're fighting through that. And people are paying actually the full price out of pocket, which makes the problem even more challenging. I think those are some of the contributing factors.

[B]

Yeah, I mean, to me, I think the Occam's Razor answer to my own question is cost. Americans care about cost and they care about the cost of medicine even more than they care about the cost of computers, an Apple MacBook, a restaurant, banks, substacks, media, my industry. They care about the cost of the molecules that can save their life, their child's life, their husband's life, their brother's life. And the law. The legal structure that we have, by which we maintain a private sector pharmaceutical industry requires that the inventor of the drug prize and jealously defends a high price for the drug at the very moment that the most desperate patients for that drug in that patent window are saying, please give this to me at a price I can afford. And so there's this. I mean, before you get to greed and big corporations necessarily caring about the bottom line more than they care about patients lives, I think you have this structural issue where the industry is incentivized to cling to a profit margin at the very moment the patients are clinging to the very opposite, which is save my life in a way that I can afford, that my family can afford. And I think that opens up two kinds of questions. One is what we can do to reduce the cost of developing drugs. And two is what we could do to reduce the price of drugs in a way that didn't destroy the R and D principle so that you're not bottoming out in terms of your revenues. You can't invest in the next GLP1. Let's start maybe with the first. I know this is a huge question, but why do you think it has become so much more expensive in the last 20, 30 years to develop a drug in the United States?

[A]

Yeah, well, I think there's two basic reasons. One is that the nature of regulation, which is a huge part of the total cost structure we deal with running experiments, collecting data, doing things that are to satisfy a regulator. But if you really do an eye test on those activities, you can really criticize whether that produces any value in the real world for physicians or patients. And that may be not so different from other regulatory structures. But it is definitely true that we tend to put in rules to solve a problem in one area and then those rules tend to spread and never go away. So we have sort of this de thicketing that gets discussed frequently with the fda, with other global regulators, but in my experience in the industry has never actually occurred. There are faster, better ways to develop drugs that would be cheaper. We just don't do them. And we don't do them because regulators tend to be very risk averse and no one wants to be that regulator that pulled a rule that later ended up, you know, would have saved someone from an untoward effect. That's understandable, but it has a cost that's also, you know, apparent. And you're pointing out here the second is that unlike other products and services, we buy again, where you have a choice and you can discern an incremental benefit. We have this weird thing where actually the legacy products, which sometimes were very good, become very, very cheap. Most of the value in a medicine is in the information it took, the discovery process and the clinical trial process. Once that is no longer protected, the substance itself can be very cheap to manufacture and make, but you still get all those benefits. Everything we make has to beat that standard. And so we're constantly doing comparisons to jump over that hurdle. And that requires, I think, pretty fancy technologies. You take an example, like in blood cancers, right? So the state of the art in blood cancers these days are these things called making car T's, which are extracting people's bone marrow, reprogramming it genetically in a laboratory, and then putting it back inside people. That's how we got over a standard of care of a small molecule pill that became very cheap. So that's inherently expensive, and I think solving that is difficult. Although over time we do see cost benefits at scale and some of these technologies getting cheaper and cheaper. I think it's true both we have incumbent regulatory burden and we always have to improve, which I think is the right incentive, frankly, but causes cost to be high, especially onset.

[B]

Yeah, I want to make sure I get this question in because it's one that I definitely wanted to ask you. When I talk to folks at the Frontier Labs about what they're doing. Automating, coding, creating more advanced chatbots, sometimes when they start to get too doomy or too apocalyptic, they'll say, well, it's okay because these devices will be smart enough to cure cancer. And I always want to know exactly how does that work? I see you automating software development. I see you making it easier for me to essentially Google information in the broad scope of the Internet by using the portal of ChatGPT. I don't actually see you developing new druggable targets for pancreatic cancer. So let's say, and I'm sure this already happened, the Frontier Labs come to you. Anthropic OpenAI, Google, they're coming to you. They're saying, dave, how can we help you develop drugs faster and cheaper? What do you see as the plausible promise of artificial intelligence in your industry?

[A]

Yeah, it's a great question. And I can just say categorically, because we've tried the LLMs, so large language models, if you just ask them to solve biology or chemistry questions, they're not particularly good at it. And that's because they're trained on the human language, not on the language of chemistry, physics and biology. So what is good at it? Well, models built from scratch on those things. I think we see in the case of Alphafold, which is the Google sponsor DeepMind product, which solved a pretty specific problem very well. Kind of the answer to the question, which is if we make models, don't think of them like LLMs, but models that predict things and we train it on an exquisite set of data where we have enough information to say, okay, we know most of the possible permutations of something, train it. Now we can get a machine to predict things pretty well, like predicting the structure of a protein, but that is one, maybe one thousandth of the kind of problems we face in drug discovery. So we use Alphafold and we have a partnership with them. Does that make the next cancer drug cheaper, faster, better? Maybe marginally so. I think the future here is actually to build more and more models of those narrow prediction problems. Because biology, unlike humans language, doesn't follow all the same rules in the same way and string them together in kind of a master model that can help scientists either not do experiments that don't make sense or get to new conclusions faster. And then here, chemistry, physics easier. Biology actually much, much harder. Why? Because we know most of the rules of chemistry and physics. Chemistry is interesting because we know most of the rules. And yet there's billions and billions of permutations of answers. So the machines are actually pretty reasonable at that kind of thing. To help humans accelerate progress. Biology, we might know, I don't know, 15%, maybe less, of the functioning of human biology. So until we know a lot more about that, even the DNA to RNA coding, RNA to protein synthesis, protein to cell structure, cell structure to organ structure, and you have this hierarchical problem too. How do all these things connect on the ladder up? We're going to have trouble using machines to crack that problem. I think it's tractable. We're investing in this. We announced a big deal with Nvidia. We built the biggest biology focused supercomputer in the world here in Indianapolis. We're going to build these frontier models just for this. But we have to produce a lot more data, sometimes just without a reason to produce it other than to train the machine about certain biologic systems to get predictions right. It's coming. But I don't think this is a 20, 26 or 28 solution. I think it's a 2000s type activity

[B]

is a framing that you would expect, that you would accept here. Something like LLMs work so well because they're pre trained on the Internet of human language. But we do not have an Internet of human anatomy. And so we essentially have to almost build a kind of Internet of human anatomy. An enormous corpus of information from which we can train large language models to map meaning on that second corpus, the same way that they are relatively or maybe even extremely skilled at mapping meaning, essentially, and producing language based on that first corpus. The Internet of human language is something like that the way to sort of frame the challenge here?

[A]

Yeah, perfectly stated. I think the language models don't translate to biology. We need a new language and we need to train it on that. And unfortunately, we don't speak the language of biology that well. We're sort of like a toddler in the language of biology.

[B]

Dave Rex, thank you very much.