AI vs. Dog Cancer, Timothée Chalamet Under Fire, ‘Agents Over Bubbles' | Diet TBPN

0:01John Coogan:What a massive week last week. Alex Karp going back to back with Travis Kalanick. The reactions to the Travis Kalanick interview was phenomenal. I was reading them all weekend. I was still emotional the next day. Yeah. And there's something I posted this on one of those clips that someone just shared. It was like, this is a great clip. And I was there because, you know, you're in the moment. And you don't realize it. Barely do I reflect too much on different interviews because there's always the next day of interviews. But watching some of the clips back, Guillermo from Vercel put together that hour-long motivational video.

0:41It was so good. I think that the Travis Kalanick mindset has been missing. When he kind of left, there's been a Travis-sized hole in the industry, in the culture and to see him come back and in 45 minutes basically just give the advice that I think everyone that's building in some way can benefit from. Not everyone is going to be Travis, but there isn't anybody out there that's done what Travis has done that is kind of like preaching that. And I don't like listening to founder porn content personally. It's not appealing, but when it comes from Travis, it is just another level.

1:26John Coogan:Yeah, like the right message at the right time. Yeah, especially the thing that I was kind of pulling on is like right now, there's a lot of easy money everywhere, right? There's teams that have built nothing that can raise between$50 to a billion at times. And his feedback on that, his point of view was like, okay, is capital really a constraint in your business? How much does it matter? How much is it going to matter in terms of the competitive dynamics of your market? And if it matters, and in a lot of these AI categories, it does. If it matters and it was easy, that means you didn't go hard enough.

2:01John Coogan:Yeah, that was the best line. And that was like the best line. Like if money matters, as we all agree. So you raised a billion? Why didn't you raise two billion? If money matters, why didn't you raise three billion? Yep. Like, oh, it was easy? Yeah. That means you didn't go hard enough. Yeah, I mean, that's somewhat the subject of what Dylan Patel was talking to Dworkesh about on the Dworkesh Patel podcast. Fantastic show, by the way. Fantastic episode about this, like, you know, being risk on, being aggressive. And Ben Thompson wrote about that today, you know, through a different lens, talking about are we in a bubble?

2:34John Coogan:Maybe. But like all the numbers are penciling out. So go, go, go. Like now is the time to scale. That was that was personal highlight. For sure. Building TVPN. For sure. Friday. Yeah, that was great. Let's read through Brandon Gurel's deep dive on the AI versus dog cancer. What happened? So late Friday, there was a story about an Australian tech entrepreneur named Paul Coiningham, reducing the size of his dog, Rosie's cancerous tumor by designing a custom mRNA vaccine with the help of Chachypte, and it produced a substantial amount of discourse over the weekend separating facts from the hype cycle around the story.

3:13John Coogan:Coiningham is an AGI-pilled tech guy with 17 years of experience in machine learning and data analysis, at one time being a director at a nonprofit called the Data Science and AI Association of Australia. Talk about an incredible association. We don't have enough data science and AI associations globally. It's true. It's great to hear that Australia - After his dog, Rosie, had been diagnosed with a deadly mass cell cancer in 2024, Coiningham use ChatGPT to brainstorm ways he could help. And he did an interview on this, and here's a quote from him. He said, I went to ChatGPT and came up with a plan on how to do this.

3:45John Coogan:The first step was to reach out to the university to get Rosie's DNA sequenced. Is there not a 23andMe for dogs yet, or something like that? Who's doing full genome sequencing these days? I guess dog DNA is probably a separate assay, separate process. Embark. Embark. Dog DNA test. You could do it. Okay, well, anyway. He went to a university, probably for a good reason, probably got good data. He said, the idea is you take the healthy DNA out of her blood, and then you take the DNA out of her tumor, and you sequence both of them to see exactly where the mutations have occurred. It's like having the original engine of your car and then a version of the engine at 300 ,000 kilometers down the road.

4:25John Coogan:You can compare them and see where there's damage. So once the University of New South Wales produced the DNA sequencing, Mr. Clingham ran it through a whole bunch of different data pipelines. So this is something that we're going to go into throughout this story is the question of like, how much was this cure my dog cancer, one shot it, don't make mistakes. I don't think anyone's saying that, but very quickly there was an incentive to amplify this into the hype, this crazy story. And then there was also an incentive to dehype this all the way. And the truth, of course, is in the middle. So that's where we're going to get today.

4:56John Coogan:Once the DNA sequence was produced, he ran it through a whole bunch of custom different data pipelines to find those mutations and then used other algorithms to find drugs to do the cancer. With the help of the University of New South Wales, Coiningham identified a pharmaceutical company that produces an immunotherapy drug that looked like a good candidate for Rosie. So the drug already existed or was available, but the company refused to supply it to him because I don't think it was approved for this particular indication in this particular species. So he was out of luck there. He then turned to, again, the University of New South Wales, their RNA Institute, which used Coiningham's data, crunched down to a half-page formula to create a bespoke mRNA vaccine for Rosie.

5:42John Coogan:Again, from the story, Coiningham ran an algorithm to inform the design of the mRNA and sent it to us, and we made a little nanoparticle. And it's democratizing the whole process, they said. This is the Paul Thor dissent. After several months of navigating red tape, Coiningham and his team administered the vaccine to Rosie, which was effective. One of her tumors shrank by half, though she is not completely cured. And that's just kind of the nature of cancer. Like, cells are dividing all the time. everyone has some sort of low-level baseline of cancer. Most dogs have like a little bit. The question is like, is it runaway?

6:17John Coogan:Is it bad? Is it terrible? And then it's hard to just like snap your fingers and cure it completely, but if you get the amount of cancer down really really far, then you will of course survive. The important thing is that Cunningham says the quality of life of the dog Rosie is much better now. So on X, the news of the story turned into a heated debate on health regulation. Yes. What is that? That was for Rosie. That's for the dog. Air horn for Rosie. Air horn for the dog. That's great. Turning to a heated debate on health regulation after biomedical engineer Patrick Heiser posted that, quote, it is trivially easy to make a single mRNA vaccine.

6:55John Coogan:It's not hard. And Hank Green, a prominent YouTuber, issued something of a rebuttal, which we can go through later. A separate thread in the discourse is focused on the promise of LLM's democratizing access to medical science, with OpenAI President Greg Brockman quote tweeting the story with the caption, a small window into the opportunity of AGI. Well, Coyningham didn't literally cure Rosie's cancer with ChatGPT, as Stripe CEO Patrick Collison pointed out. It acted as a high-powered search tool that ultimately helped his team get to an amazing outcome. Sort of George Hopps. Do we gotta move the goalposts?

7:29John Coogan:I think we do. I'm ready to move them. I think we're moving the goalposts. I mean, we'd be... Where are we moving them to? It has to actually, you have to be able to type cure my cancer. And then from your phone, it just deposits a pill that you just take. Yeah, exactly. Is that what it is? It has to locally end to end. No, ideally, it would be not even a pill that you take. It can just create a video that you watch. The right pattern of light. The right pattern of light coming from, and sound. So the phone has light and sound. And so the light flashes in your eyes at a certain rate. It rewires your brain and your brain decides to go kill the cancer.

8:04Yeah, and we've talked about this a bunch. I think it would be helpful for the industry to refocus some messaging on not AI is going to cure cancer, but humanity is going to use AI to cure cancer and do a number of other things. And so the bar is not just like one-shotting it with a prompt and it sends it to a lab and you get some type of treatment in the mail. Maybe I can imagine that in the future, right? Something to that effect. But it is an enabler. It's a tool. And this has allowed someone to become not an expert in something, but to help somebody understand a process enough to go out and find the right experts to help them solve their problem.

8:49And I think it's incredibly inspiring. Yeah. Excited to have him on the show later.

8:54John Coogan:Freeman Dyson argues that biotechnology will become small and domesticated rather than big and centralized. If AI continues to reduce the cognitive overhead required to navigate biological knowledge and assemble complex pipelines, the boundary between professional research and motivated individuals may begin to blur. That shift will require careful thinking about safety, governance, and responsibility, but it also carries an exciting possibility. Dyson imagined a world in which biological design might eventually become something like a creative craft practiced not only by institutions, but also by curious individuals experimenting at smaller scales.

9:33The reality of cancer treatment, from my understanding, is, and this was based on a late family member that had cancer and ultimately passed away. During his treatment process, which was around a year and a half, he was getting looks at different treatments that were promising, some of which he was able to do, some he didn't qualify for, just based on his personal situation, even though there was a decent chance that it could have had a positive effect.

10:04John Coogan:Yeah. And that sort of the insane frustration that an individual feels or a family feels when they're like, hey, if something's terminal or it's looking really bad, it's progressing in the wrong direction, and there's a treatment out there that is somewhat trivial to actually make, but you just don't qualify for it. That level of frustration will eventually drive more individuals, I think, to do this, right? And so there's definitely some, like, safety. there's huge safety concerns, there's ethical concerns, there's these are things that we have to work through, but ultimately there's gonna be enough like human energy and just overall desire to live that people will take risks that they wouldn't take for a bunch of other more sort of like trivial sort of issues.

10:47John Coogan:It does feel like the FDA's stance might need to change in this case, like they clearly have a role to play currently and in the future where, you know, biotech becomes more democratized, But yeah, hopefully there's like some good symbiotic relationship there with the broader biotech community as it gets bigger. I have a similar story with someone who developed a rare illness and was able to go and read academic research at a very deep level. didn't have a background in biotech or anything like that, but was able, this was pre-AI, was able to read like every published research paper that was at all related to this particular illness and found the world expert in this particular disease, contacted the professor and the professor said, yes, you have the thing that I've been studying and I've only found five people or ten people in my entire career that have this thing.

11:46John Coogan:come down, I will operate on you. The operation happened. It was successful. And it was fundamentally like a high agency person doing a lot of research. And if AI just acts as a search tool that democratizes that, you're going to get better results. So even if we're not in like one-shotting curing cancer, that just feels like making search easier, making research easier, huge benefit. How was your weekend, Tyler? It was good. It was good? Yeah. Did you go to any data centers? No data centers this weekend. I was in SF. Didn't you go to a pig roast? Yeah, that was on Friday. That was in El Segundo.

12:23John Coogan:How was SF? Is something big happening there? Does it feel like being in Wuhan in February of 2020? Something big was happening. I went to a debate. Oh, you went to the debate. Okay, cool. How was that? It was good. Yeah, it was about the billionaire tax. Yeah, yeah, yeah. Jensen is doing his keynote at GDC. Should we pull up the live stream? We can, yeah. These three people are deep in technology, deep in what's going on, and of course, they have just a really broad reach of technology ecosystem. And then of course, all of the VIPs that I hand-selected to join us today, all-star team. I want to thank all of you for that.

13:03John Coogan:The leather jacket really has just aged so well. I also want to thank all the companies that are here. here. NVIDIA, as you know, is a platform company. Mic drop. Oh, by the way, everyone uses that. He's mocking our merch. He is. And today, there are probably 100 % of the$100 trillion of industry here. 450 companies sponsored this event. I want to thank you. $100 trillion of industry. I love it. Technical sessions, 2 ,000 speakers. 2 ,000 speakers. Wow. In one, they're going to do more interviews than we've done all year. In one day. To chips, to the platforms, the models, and of course the most important and ultimately what's going to get this industry taken off is all of the applications.

13:58This is the 20th anniversary of CUDA.

14:02John Coogan:We've been working on CUDA for 20 years.

14:10John Coogan:For 20 years we've been dedicated to this architecture, this revolutionary invention, SIMT, single instruction, multi-threaded. All right, very, very cool. Let's get back to the timeline. My take on the whole AI cures dog cancer in Australia is a very interesting story, but perhaps not for the reasons that are being noted. In 2007, Freeman Dyson published an essay in the New York Review of Books called Our Biotech Future. It contains one of the most memorable predictions about the future of biology that I've ever read. I predict that the domestication of biotechnology will dominate our lives during the next 50 years, at least as much as the domestication of computers has dominated our lives during the previous 50 years.

14:50John Coogan:Dyson believed biology would eventually follow the trajectory of computing. At first, powerful tools live inside large institutions, universities, government labs, major companies. Over time, these tools get cheaper, easier to use, and more widely distributed. Eventually, individuals start doing things that once required entire organizations. You will be the manager of infinite minds. You will have a million agents, and you will also have access to the equivalent of a university lab filled with biotechnology equipment. Biotech will become small and domesticated rather than big and centralized.

15:23John Coogan:This is very interesting in the age of AI because there's been this narrative of AI is a centralizing technology. It is very power law driven. This is sort of counter to that. I don't exactly know how to piece those two things together, but it is interesting that his prediction was actual decentralization in this particular category. He even imagined genome design becoming almost artistic. Designing genomes will be a personal thing, a new art form as creative as painting or sculpture. Dyson's words rang in my mind as I read the AI Cures Dog Cancer story. Much of the coverage framed in this - I gotta say, it's very easy to imagine you in 20 years.

15:58I'm like, John, like, you got to tell us your anabolic steroids stack. And you're like, it's kind of a personal thing. It's kind of a personal thing. It's kind of like an artisanal process that I go through. It's like a sculpture. I'm sort of sculpting myself. I can't really, I'm sorry, I can't really share my stack with you, but it's a personal thing. So go and kind of figure out your own stack.

16:20John Coogan:The scientific pipeline involved here is actually well known. It closely mirrors the workflow used in personalized neoantigen vaccine research that has been under active development for years. The steps are fairly standard. Sequence the tumor, identify somatic mutations, predict which mutated peptides might be recognized by the immune system, encode those sequences into an mRNA construct, and deliver them to stimulate an immune response. The biological targets themselves were almost certainly not new discoveries. I have been unable to find out what they are, but mutations in targets like KIT, which are common, might be involved.

16:55John Coogan:Since the hardest part of drug discovery, whether in humans or dogs, is target validation, the lack of which leads to a lack of efficacy, the number one reason for drug failure. In neoantigen vaccines, the proteins involved are usually ordinary cellular proteins that happen to contain tumor-specific mutations. AlphaFold, which was used to map the mutations onto specific protein structures, is now a standard part of drug discovery pipelines. That's fascinating. The challenge is identifying which mutated peptides might plausibly trigger immunity. What is interesting, though, is how the pipeline was assembled.

17:31John Coogan:Normally, this type of workflow spans multiple domains, genomics, bioinformatics, immunology, and translational medicine. And in institutional settings, those pieces are distributed across specialized teams, document sources, and legal and technical barriers. navigating the literature, selecting computational tools, interpreting sequencing results, and designing a candidate mRNA construct is typically a collaborative process. In this case, AI appears to have helped compress that process, pulling together data and tools from different sources. Instead of requiring multiple experts, a motivated individual was able to assemble the workflow with AI acting as a kind of guide through the technical landscape.

18:07John Coogan:That is fascinating. Lee says, Chad Gbt, cure cancer, make no mistakes. biomedical engineering industry yeah don't do this it's easy and effective but we can't make enough money off of it. It's surprising G Fodor says it's surprising how people are so blatantly talking past each other on this the point is that the system of clinical trials is predicated on an assumption that a given drug will work on a cohort what if there are lots of drugs that will only work on one person. A big a big desire and push for rethinking the system of clinical trials if you're going to have personalized medicine. What does that mean?

18:46We got to go to probably the most important story of the day. Gabe says he had a dream that Apple released a 32 inch MacBook called the MacBook Pro ultra wide. It looked like this. I bought one and unlocked extreme productivity and then it wouldn't fit into my backpack. So I had to leave it behind. This is sort of like a twist on that other laptop that we saw.

19:09John Coogan:They should honestly make this. They should. Walking around looking like maybe you could put skateboard trucks on it. Yeah. That you could use it as transport. Yeah, it's more of like a snowboard build that you carry over your shoulder like this. Or surfboard. You know, people throw it on the top of your car like that. Three-fingered. Why? You don't put a surfboard on the top of your car? Yeah. I mean, real ones don't. Oh, what do they do? They put it inside the car? Truck bed or inside the car. Truck bed, okay. Yeah. I don't pretend to be a surfing expert. In the LA area, you can clock if somebody's actually a surfer or not just by the way they go to reach with their board.

19:46John Coogan:Dylan Patel said on Dorkesh, the TAM for GPC 5.4 is north of$100 billion, but there's adoption lag. That's considered AGI as far as the Microsoft OpenAI contract is concerned. Sam Carter says, the reported$1 billion of profit is no longer the sole trigger for confidential IP research access. It reportedly includes an independent expert review. You were saying Joe Rogan would be on that. Andrew Huberman. Andrew Huberman, the experts would be on there. You got to trust them at all times. Neo Vaughn, maybe. You know, the funniest thing about that joke is that, like, I actually would like to know that panel of experts where they deem AGI.

20:25John Coogan:Because I feel like between all of them, they could chat with the chatbots and be like, ah, it's like not that good yet. Should we go over Ben Thompson's post from this morning? Ben Thompson published this this morning. To me, the second I saw that, I started reading it. It felt like taking a double scoop of C4. Is that a pre-workout? Yeah, you never. I know the can. I didn't know it was a. You never dabbled? What was the one that we. I'm more of the gorilla mind one. That's the one that I. Many people have said you have the mind of a gorilla. Yes, yes, yes. For more plates, more days. So you got pumped up.

21:05I got pumped up. Ben writes, there's a weird paradox in terms of AI prognostization.

21:12John Coogan:Prognostication. Prognostication. That was a good effort, Jordy. On one hand, you don't want to be the one to completely dismiss the most terrifying doomsday scenarios. Who wants to be found out to be foolishly optimistic? At the same time, there's also pressure to give credence to the possibility that we are in a bubble. and all of this hype and spending is going to go belly up. While I have argued against the former, I have very much been on board with the latter, making the case that bubbles can be good. Sitting here in March 2026, however, on the morning of NVIDIA's GTC, I've come to a different conclusion.

21:44I don't think we're in a bubble.

21:45John Coogan:Let's go. Which paradoxically may be the truest evidence we are. Where's the bubble gun? Let's get the bubble gun going. He writes, LLM paradigms over the last couple of weeks, first in the context of NVIDIA earnings, and then last week in the context of Oracle's, I've talked about three LLM inflection points. I'm not going to go through all these. We've talked about this a few times. Basically, LLMs, reasoning models, and then agents, and each one of those increases the demand exponentially for compute. Yeah, so LLM, ChatGPT, O1, and then Opus, as well as Code and Codex, basically getting to the point where tasks are being accomplished over hours and getting to great outcomes.