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

What a massive week last week. Alex Karp going back to back with Travis Kalanick. The reactions to the Travis Kalanick interview were phenomenal. I was reading them all week.

I was still emotional the next day.

Yeah. And there's something— I posted this on one of those clips that someone just shared. I was like, this is a great clip. And I was there because, like, you know, you're in the moment and you don't really realize it.

Do I reflect too much on different interviews? Yeah, there's always the next day of interviews. But, you know, watching some of the clips back, Guillermo from Vercel put together that hour-long—

so good—

kind of motivational video. Yeah, it was so good. I think that the Travis Kalanick mindset has been missing.

Totally.

When he kind of left. Yeah, yeah. There was— 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, Yeah, it is just another level.

Yeah. Like the right message at the right time.

Yeah. Especially the thing, the thing that I was, 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 $1 billion at times. And, 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.

Yeah, yeah, that was the best line.

And that was like the best line.

Like, if money matters, as, as we all agree—

So you raised a billion? Wait, why didn't you raise $2 billion? If money matters, why didn't you raise $3 billion?

Yep.

Like, oh, it was easy? Yeah, that means you didn't go hard enough.

Yeah, I mean, that's, that's somewhat the subject of what Dylan Patel was talking to Dwarkesh about on Dhorkesh 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? 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 Gorrell'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 Coyngham reducing the size of his dog Rosie's cancerous tumor by designing a custom mRNA vaccine with the help of ChatGPT. And it produced a substantial amount of discourse over the weekend. Separating facts from the hype cycle around the story, Coyngham 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.

After his dog Rosie had been diagnosed with a deadly mast cell cancer in 2024, Cunningham used 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. 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 tested by humans.

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 mutation is 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. You can compare them and see where there's damage. So once the University of New South Wales produced the DNA sequencing, Mr. Cunningham ran it through a whole bunch of different data pipelines. So there's a— this is something that we're going to go into, you know, 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 like an incentive to amplify this into like the hype, like this crazy story. And then there was also, you know, an incentive to like de-hype this all the way. And the truth, of course, is in the middle. So that's where we're gonna get today. 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 treat 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, again, from the story, "Coeningham 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 Thorntonsen. After several months of navigating red tape, Coeningham 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? 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 Heizer posted that, quote, "It is trivially easy to make a single mRNA vaccine. It's not hard." And Hank Green, 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 LLMs 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, Coiningham 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 Hawking.

So we got to move the goalposts?

I'm ready to move them. I think moving the goalposts.

I mean, 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. Is that what it is? —Yeah, exactly.

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.

That's the secret. —Yeah, and we've talked about this a bunch. I think it would be helpful. Helpful for the industry to refocus some messaging on not AI is going to cure cancer, but human humanity is gonna use AI to cure cancer and do a number of other things, right? And so the bar is not just like one-shotting it with a prompt and it sends it to a lab and you get a, you know, some, some type of treatment in the mail. Maybe we, we, I can imagine that in the future, right? Something, something to that effect. But It is an enabler, it's a tool. Yeah. 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. And I think it's incredibly inspiring. Yeah. Excited to have him on the show later.

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 scale.

The reality of cancer treatment from my understanding is, and this was based on a, a late family member that, uh, 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, there was a decent chance that it could have had a positive effect. 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, there's a treatment out there that is somewhat trivial to actually make. Yeah. You just don't qualify for it. Yep. That level of frustration will eventually drive more individuals, I think, to do this. Right. And so there's definitely, Definitely some like safety. There's huge safety concerns. There's ethical concerns. Yeah, there's— these are things that we have to work through. But ultimately there's going to 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.

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 biotech becomes more democratized. But yeah, hopefully there's like some good symbiotic relationship there with the, 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 5 people or 10 people in my entire career that have this thing. 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 you're— 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. Yeah, it was good. Yeah. Did you go to any data centers or— No data centers this week. No, I was in SF.

Didn't you go to a pig roast?

Yeah, that was on Friday. That was in El Segundo. How was SF? Is something big happening there? Does it feel like being in Wuhan in February of 2020. Something, something big was happening. Yeah, there was— I went to a debate. Oh, you went to the debate? Okay, cool. How was that? It was good. Yeah, yeah, it was about the, uh, billionaire tax. Yeah, yeah, yeah, yeah.

Jensen, uh, is doing his keynote at GDC. Should we pull up the live stream? We can.

Yeah, institutional investor.

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.

All-star team. The leather jacket really has just aged so well.

I also want to thank all the companies that are here. Nvidia, as you know, is a platform company. Mic drop.

We have technology. We have our platform. 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. $1,000 billion of industry.

1,000. I love it.

Technical sessions, 2,000 speakers. This is—

2,000 speakers. Wow.

Every single layer. In one— they're going to do more interviews than we've done all year.

Infrastructure, to chips, to the platforms, the models, and of course the most important and ultimately what's going to take— get this industry taken off is all of the applications. This is the 20th anniversary of CUDA. We've been working on CUDA for 20 years. For 20 years we've been dedicated to this architecture. This revolutionary invention, SIMT, single instruction, multi-threading.

All right, very, very cool. Let's get back to the timeline.

Uh, my take on the whole AI cures dog cancer in, uh, 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. 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, you know, 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. This is very interesting in the age of AI because there's been this narrative of like 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. I'm like, John, like, you gotta tell us your anabolic steroid 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. I can't really share my stack with you, but it's a personal thing. So go, go and kind of figure out your own stack.

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. 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. Normally, this type of workflow spans multiple domain, 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. That is fascinating.

Lee says, ChatGPT, 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. That's ridiculous.

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 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?

We 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, and 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. Oh no. We—

but this is sort of like— they should make it on that other laptop that we saw. 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 like carry over your shoulder like this, or surfboard. You know, people throw it on the top of your car like that. Three fingers. Why? You don't put a surfboard on the top of your car?

Uh, 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 surfer.

In the LA area, it's really, you can clock if somebody's actually a surfer or not just by the way they go to the beach with their board.

Okay. Dylan Patel said on DoorCash, the TAM for GPT-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. The experts would be on there. You got to trust them at all times. And Neil Vaughn, maybe. You know, the funniest thing about that joke is that, like, I actually would like to know that panel of experts, whether— where they deem AGI. Because I feel like between all of them, they could, they could chat with the chatbots and be like, yeah, it's like not that good yet.

Should we go over Ben Thompson's post from this morning? Ben Thompson published this this morning. This is 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, you never dabbled.

What was the one? That we— I'm more of the gorilla mind one. That's the one that I do.

Many people have said you have the mind of a gorilla.

Yes, yes, yes. For more plays, more days.

So you got pumped up. I got pumped up. Ben writes, there is a weird paradox in terms of AI prognostication. 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've very, I've 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. I don't think we're in a bubble. 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 contexts of oracles. I've talked to you about 3 LLM inflection points. I'm not going to go through all of these.

We've talked about this a few times. 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 Claude Code and Codex, basically getting to the point where tasks are being accomplished over hours and and getting to great outcomes. And this is the interesting point, the decreased need for agency. The reason Ben has been writing about these three inflection points over the last couple weeks has been to explain why it is that the industry is so compute constrained and why the massive investment in the CapEx by the hyperscalers is justified. The first paradigm required a lot of compute for training, but inference, actually answering a question, was relatively efficient. You simply sent the user whatever the model spit out. The second paradigm dramatically increased the amount of computing needed for inference for two reasons. First, generating an answer required a lot more tokens because all of the reasoning required tokens in addition to the answer itself. Second, the fact that reasoning made the model so much more useful meant that they were used more, which drove increased token usage in its own right. It's a third paradigm, however, that has truly tipped the scales in favor of CapEx expenditure not being speculative investment but rather badly needed investment in meeting demand that far exceeds supply. First, generating an answer will often entail multiple calls to a reasoning model. Second, the agent itself needs compute, and that compute and the tools the agent uses is better done by CPUs and GPUs. Third, agents are another step function increase in usefulness, which means they are going to be used even more than even reasoning models in a chatbot. It's how this third point will be manifested that I think is underappreciated. After all, far more people use chatbots than agents, but I would make the case that most people are not using chatbots as much as they should. It's been a question of agency. To get the most from AI requires actually taking the initiative to use AI.

There was a very, very interesting take in here where he's talking about the Apple MacBook Neo launch, which is $599, I think $499 for education, potentially very disruptive to other laptop makers.

You said you still get, you still get, uh, discounts, Tyler, or just— I think I—

yeah, I'm still— oh yeah, you're still— I'm on leave because you're on leave.

Yeah, that's great. There you go.

Uh, there are some legendary, uh, leave of absences where people have been away for like 10 years and then they go and do so many See, the goal is to defer for so long, but then also have such a meteoric rise that they have to give you the honorary degree before, while you're still eligible. That's a good one. The point about the MacBook Neo is that at $599, a lot of PC makers should be sort of quaking in their boots because you're selling at that price point. And for a customer who's just like, I want a $600 laptop, Normally it was like, am I going with like ASUS or another brand? I'm not, I'm not in the Apple category. Like it's not an option because that, that store over there, those, those laptops start over $1,000. That's not my budget. So I'm not even going in that store. Well, now you can and you can spend $600 and get a pretty good computer. And the CFO, Nick Wu of ASUS, was on their recent earnings call and he said, actually, Don't worry about it, it's not a threat. We found out about the MacBook Neo shipments in the second half of last year. We made some internal prep, but now that it's out, like, we don't think it's that big of a deal. Like, it has some limitations. Specifically, it only has 8 gigs of RAM. This is more focused on content consumption. It's not a mainstream notebook for notebook usage, for creation, for working. It's not a work device, it's a consumption device. It's more like an iPad. And Ben Thompson's point is that's what people use these laptops for now. It is a lot of consumption. There aren't as many people who are in that $600 price target that are wanting to run powerful applications at that price point. As soon as you're running powerful applications locally, you're probably more of a business buyer and you can spend more. And then he goes on to apply that to, to AI, talking about enterprise and the value of companies have a demonstrated willingness to pay for software that makes their employees more productive. And AI certainly fits that bill in this regard. What makes enterprise executives truly salivate, however, is not the prospect of AI eliminating jobs, doing so precisely because it makes the company as a whole more productive. So increasing production.

My interpretation, he's making the case that there are companies that could cut headcount and actually just grow faster if they're implementing AI properly, not just replacing like the routine workloads. So he says agents, however, will tilt much more heavily towards pure acceleration, making those drivers of value. Okay, actually I'm going to start One paragraph. Yeah, please. It's always been the case, even in large companies, that a relatively small number of people actually move the needle and drive the company forward in meaningful ways. That drive, however, has been filtered through a huge apparatus filled with humans who accelerate the effort in some vectors and retard it in others. That apparatus makes broad impact possible, but it carries massive coordination costs. Agents, however, will tilt much more heavily towards pure acceleration, making those drivers of value much more impactful. I'm sympathetic to the argument that the best companies will want to use AI to do more, not simply save money. The reality of large organizations, however, is that the net positive impact of AI will not be in eliminating jobs, but rather replacing hard-to-manage and motivate human cogs.

It's such a funny ending where he has this point about like, you only need to be worried about a bubble when— like, you don't need to be worried about a bubble if everyone's saying a bubble, because then everyone's like risk-off because everyone agrees that we're, oh, we're in a bubble. Let's not do bubble behavior. And so capitulation is is the sign of a bubble. And he's like, I understand that, and still this is my take. It's a bold, it's a bold take, but I think it's a good one. So Nebbius and Meta have agreed to a $27 billion AI infrastructure pact deal. The talks are advanced to pact stage, a 5-year deal, $27 billion to supply AI infrastructure capacity to Meta. Nebbius has really been on a tear. Fascinating company, formerly part of Yandex, spun out independent now, publicly traded, and, and just one of the neo clouds that's figured out that Microsoft deal and now seems to be doing good work with Meta. Nebbius said it will provide $12 billion of dedicated capacity across multiple locations. Meta will also purchase up to $15 billion in additional capacity over the 5-year, over the 5-year period. Nebbius added that it will use large-scale deployments of NVIDIA's next-generation Vera Rubin AI infrastructure, which Jensen is surely talking about at GTC right now. Why do you have the paper in front of your face?

The team earlier said I look like a third base coach, so I'm covering up. I'm covering up.

Oh, yeah. Because you don't want to— you don't want to let everyone know what play you're calling. There you go.

There was news Friday, late, a rumor Meta is planning sweeping layoffs that could affect 20% or more of the company. 3 sources familiar with the matter told Reuters as Meta seeks to offset AI infrastructure bets and prepare for greater efficiency brought by AI-assisted workers. Again, not super surprising. Stock's up around 2% today. I would expect this to pop even harder once these layoffs are actually announced. Yeah.

Timothée Chalamet is getting taken to task in the Financial Times over his views on opera and ballet, of all things. It's quite sweet, really. So desperate are some people to get their knickers in a twist on the internet that in the face of a lull in the culture wars, we we have real wars now. The only thing they have found to get outraged about recently relates to a man saying nobody cares about ballet and opera anymore. The man I refer to is Timothée Chalamet, a talented young actor who stars in the multi-Oscar-nominated "Martyrs Supreme." He said, "I don't want to be working in ballet or opera or things where it's like, hey, keep this thing alive even though no one cares about this anymore." So his apparent instant regret at his slight flip felt a bit disingenuous. There's a world where the film and movie industry, like, does become like opera and ballet.

I'll tell you why I think, yeah, this whole kerfuffle's happening. Yeah, happened. And as someone who doesn't really follow Hollywood, doesn't follow, uh, film, what is happening is he came out with, like, this new, like, it's okay to pursue greatness. Yeah, on the path to greatness. Sure, sure. I'm, I'm trying to be the GOAT. I'm trying to, you know, like, coming out with kind of like bravado. Yeah. And if you do that and it's like, me, me, me, me, me, me. Sure. I'm trying to be the greatest. Yeah. And then you start just randomly taking shots at another art form where other people are pursuing greatness. Sure. You just invite a lot of criticism. Yeah. Everyone's okay, I think, with somebody like being on their own personal pursuit of greatness. But if you're doing that while trying to tear down other art forms. Yeah. You're just going to invite massive criticism.

Yeah, it does feel like he's sort of— it's sort of collapsing like market cap and like TAM of like, yes, the opera TAM and the ballet TAM is smaller than film.

I'm really right in the middle, Matthew, because I admire people and I've done it myself to go on a talk show and go, hey, we got to keep movie theaters alive. You know, we got to keep this genre alive. And another part of me feels like if people want to see it, like Barbie, like Oppenheimer, they're going to go see it and go out of their way to be loud and proud about it. And I don't want to be working in ballet. Or opera or, you know, things where it's like, hey, keep this thing alive even though no one cares about this anymore. All respect to the ballet and opera people out there. I just lost $0.14 in viewership.

But, um, crazy shot. That's not a shot.

I hear what you're saying. Yeah. Yeah.

So, but all— if, if like the creator of like GTA 5 like stood on stage and was just like, we are 10 times the size of the— Baseball. Baseball, but also like the movie industry. The video gaming industry has been basically 10 times the size of the movie industry for— You mean the movie theater business? No, like Hollywood. Like movie production. Gross revenue. Yeah, totally.

Ragav in the Twitch chat from Deep says, Nvidia CEO just said he sees $1 trillion in revenue through 2027.

That's a bomb. Bring down the gong.

Bring down the mallet. We made the new outro last week, and unfortunately we used a song they didn't want us to play. They didn't want us to play. Yeah. And so they took down—

we're going to work on a new outro. We already got a bunch of ideas cooking. But leave us 5 stars on Apple Podcasts and Spotify. Subscribe to the newsletter at TVPN. Goodbye.