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TBPN is a live tech talk show hosted by John Coogan and Jordi Hays, streaming weekdays from 11–2 PT on X and YouTube, with full episodes posted to Spotify immediately after airing.
Described by The New York Times as “Silicon Valley’s newest obsession,” TBPN has interviewed Mark Zuckerberg, Sam Altman, Mark Cuban, and Satya Nadella. Diet TBPN delivers the best moments from each episode in under 30 minutes.
You're watching the TBPN.
Speaker 2:Today is Monday, 03/16/2026. We are live from the TBPN, temple of technology, the fortress finance. The capital of capital. Let me tell you about ramp.com. Time is money.
Speaker 2:Save both. Easy use corporate cards, bill pay, accounting, and a whole lot more all in one place. What a massive week last week. Alex Karp going back to back with Travis Kalanick. The reactions to the Travis Kalanick interview was phenomenal.
Speaker 2:I I was reading them all
Speaker 3:week I was
Speaker 4:still emotional the next day.
Speaker 2:Yeah. And there's something I I I posted this on one of those one of those clips that someone just shared. It was like, this is a great clip and I was there. Because like, you know, you're in the moment
Speaker 4:barely do I reflect too much on Yeah. Different interviews Yeah. Because 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. Like, kind of motivational video.
Speaker 4:Yeah. It was so good. I think that the Travis Kalanick mindset Yeah. Has been missing. Totally.
Speaker 4:When he kind of left Yeah. There was there's been a Travis sized hole Yeah. In the industry, in the culture. Yeah. And to see him come back and in, you know, forty five minutes, basically, just give the advice that I think, like, everyone that's building in some way can
Speaker 2:Yeah.
Speaker 4:Can benefit from. Yeah. Not everyone is gonna 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 like founder porn Yeah. Content personally.
Speaker 4:It's not it's not appealing. But when it comes from Travis
Speaker 2:Yeah.
Speaker 4:It is just another level.
Speaker 2:Yeah. Like the right message at the right time. That the thing the
Speaker 4:thing that I was I was kinda pulling on is like right now, like, there's a lot of easy money everywhere. Right? There's teams that have built nothing that can raise between 50 to $1,000,000,000 Yeah. At times. And and his feedback on that, his point of view was like, okay, is capital really a constraint in your business?
Speaker 4:How much does it matter? How much is it gonna matter in in terms of the competitive dynamics of your market? And if it matters, and in a lot of these AI categories, it does. Mhmm. And if it matters and it was easy Yeah.
Speaker 4:That means you didn't go hard enough.
Speaker 2:Yeah. Yeah. That that that was the best line.
Speaker 4:And that was like the best
Speaker 2:Like, money matters, as as we all agree raised a
Speaker 4:billion? You raised 2,000,000,000? Yep. If money matters, why didn't you raise 3,000,000,000?
Speaker 2:Yep.
Speaker 4:Like, oh, it's easy? Yeah. That means you didn't go hard enough.
Speaker 2:Yeah. I mean, that's some that's somewhat the subject of what Dylan Patel was talking to Dorcasch about Dorcasch Patel podcast. Fantastic show, by the way. Fantastic episode. About this, like, you know, being risk on, being aggressive.
Speaker 2:And Ben Thompson wrote about that today, you know, through a different lens talking about, you know, 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. And, yeah.
Speaker 2:It's it was fascinating hearing it from a completely different perspective, at the perfect time. But I really yeah. That was a great great interview.
Speaker 4:Let That was that was personal highlight
Speaker 2:For sure.
Speaker 4:Billing TBPN
Speaker 2:For sure.
Speaker 4:Friday.
Speaker 2:Yeah. No. That was great. It it it was it really was like like the conversation that we set out to have because mean, he mentioned that he's leaving California, but we're not gonna, like, get bogged down in, his political views or whatever like that. It it's it's so much more about the actual craft of scaling a business.
Speaker 2:Yeah. And like I think I think we just nailed that and so that was really fun.
Speaker 4:Yeah. And the good thing is we have plans to do a show like that every single day
Speaker 2:Yeah.
Speaker 4:Of the year because
Speaker 2:Free day.
Speaker 4:No. Unfortunately, it's not possible. Right? It's not very often that someone like Travis Yep. World historic Yeah.
Speaker 4:Founder comes out of media retirement after almost a decade.
Speaker 2:Almost a decade. Yeah. So very special. But thank you for everyone who tuned in. Thanks to everyone who enjoyed any of the clips, saw whatever you saw of it.
Speaker 2:It was a really fun time.
Speaker 4:And if you care if you wanna work in physical AI Yeah. And you don't see yourself at in the Elonverse Yeah. I think that is one of your best Yeah. Possible bets, sort of like an indexed It's
Speaker 2:gonna be like approach to physical insanely hard for Elon. Work extremely hard
Speaker 4:for Travis. Yeah. It's like Not not Or You're gonna have to work hard one way or another.
Speaker 2:Exactly. But that's the nature of
Speaker 4:what you might wanna work hard, there's probably a company out there that's competing with Travis or Elon in physical AI. You could work there. I just wouldn't put much value on your on your RSUs. Yeah.
Speaker 2:It's rough. Anyway, let's pull up the linear lineup. Linear, of course, is the system for modern software development. 70% of enterprise workspaces on linear are using agents. We have Kevin Espiritu from Epic Gardening coming on to tell his story about scaling his YouTube channel.
Speaker 2:I think we have a lot to talk about. We always love create creator economy stories. Paul Coyngham, the dog healer, is coming to to break down how he used AI to augment, delay his dog's cancer. We're gonna be digging into we'll we'll we'll first go through what actually happened. I have some opinions about this, and then we'll talk to him to get his side.
Speaker 2:Then we are pulling our delayed lightning round. We went far longer than we expected with Travis on Friday, so we are, catching up on our lightning round with, Tony from Sunday Robotics.
Speaker 4:And we have Drew from eight VC Yeah. He's a founding partner there. He team backed Quince at Seed. Now a $10,000,000,000 company. The Quince founder is a little under the radar Totally.
Speaker 4:But I wanted to get this story
Speaker 2:Yeah. It's great.
Speaker 4:From the eight VC team, hear how they're thinking about it and then of other folks joining.
Speaker 2:Yeah. Fun. Let's let's read through Brandon Garell'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 Chachi PT, 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 seventeen 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.
Speaker 4:Talk about an incredible association. We don't have enough data science and AI associations globally. It's It's great to hear that.
Speaker 2: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 twenty three and Me for dogs yet or something like that?
Speaker 2:Who's doing who's doing full genome sequencing these days? I guess, I guess dog dog DNA is probably a separate assay, separate process.
Speaker 4:Embark. Embark. DNA test.
Speaker 2:You can do it. Okay. Anyway, he went to the he went to a university, probably for a good reason, probably got good good data. He said the idea you 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.
Speaker 2:You can compare them and see where there's damage. So once the University of New South Wales produced the DNA sequencing, mister Conyngham ran it through a whole bunch of different data pipelines. So there's a this is something that we're gonna 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, dehype this all the way, and the truth, of course, is in the middle.
Speaker 2:So that's where we're gonna get today. So once the, the DNA sequence was produced, they were he ran it through a whole bunch of custom different data pipelines and find the to found 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, Coyngham 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.
Speaker 2:So he then turned to, again, the University of New South Wales, their RNA institute, which used Conyngham's data crunched down to a half page formula to create a bespoke mRNA vaccine for Rosie, again, from the story, Conyngham 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 Thornderson, the director of the RNA Institute at the University of New South Wales. So after several months of navigating red tape, Conyngham and his team administered the vaccine to Rosie, which was affected. One of her tumors shrank by half, though she is not completely cured.
Speaker 2: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?
Speaker 2: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. So the important thing is that Coyngham 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.
Speaker 2:Yes. What is that?
Speaker 4:That was for Rosie.
Speaker 2:That's for the dog.
Speaker 4:Air horn for Rosie.
Speaker 2: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 trivially trivially easy to make a single arm mRNA vaccine. It's not hard. And Hank Green, a prominent YouTuber, issued something of a rebuttal, which we can go through later.
Speaker 2: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, Conyngham 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
Speaker 4:We've to move the goalposts. I'm ready
Speaker 2:to move them. We're moving the goalposts. 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 take.
Speaker 4:Exactly. That what has to locally end to end.
Speaker 2:No, ideally it would be not even a pill that you take. It can just create a video that you
Speaker 4:watch. Pattern of light.
Speaker 2: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. Yeah. And and That's the
Speaker 4:we've we've talked about this a bunch. Yeah. I think I think it would be helpful for the industry to refocus some messaging on not AI is going to cure cancer Yes. But human humanity is gonna use AI
Speaker 2:to Yes. Yes.
Speaker 4:Cure cancer and do a number of other things. Right? And so Yes. The 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.
Speaker 4:Right? Something something Yeah. To that effect. But it is an enabler. It's a tool.
Speaker 4: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. So excited to have him on the show later.
Speaker 2:So there was a chemist who works in AI and biotech by the name of Ash Gilgalacar. And he had a really good summary along those lines with a riff on Freeman Dyson's 2007 New York Review of Books essay, Our Biotech Future, which we should read at some point, in which in this article, Freeman Dyson argues that biotechnology will become small and domesticated rather than big and centralized. The full post is worth reading in full, and we might go through it, but the conclusion is particularly good. 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.
Speaker 2: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.
Speaker 4:Yeah. Think there's there's the reality of cancer treatment from my understanding
Speaker 2:Mhmm.
Speaker 4:Is and this was based on a late family member that
Speaker 2:Yeah.
Speaker 4:Had cancer and ultimately passed away.
Speaker 2:Mhmm.
Speaker 4:During the process during his treatment process, which was around a year and a half Yeah. He was getting looks at different treatments that were promising. Yeah. Some of which he was able to do. Some he didn't qualify for Yeah.
Speaker 4: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 Yeah. Feels or a family feels Yeah.
Speaker 4:When they're like, hey, this you know, if if 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 isn't that is somewhat trivial to actually make Yeah. You just don't qualify for it.
Speaker 2:Yep.
Speaker 4:That level of frustration will eventually drive more individuals, I think, do this. Right? And so there's definite definitely some, like, safety. There's huge safety concerns. There's ethical concerns.
Speaker 4:Yeah. There's these are things that we have to work through. But ultimately, I I just think there's gonna be so much there's there's gonna be enough, like, human energy and just overall desire to live that Yeah. People will take risks that Yeah. They wouldn't take for a bunch of other more sort of like trivial sort of issues.
Speaker 2:There have been initiatives with the FDA, something around right to try in certain scenarios, patients' rights, sort of removing some of the regulation, and allowing people to make decisions like that. 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, hopefully there's 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.
Speaker 2: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 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.
Speaker 2: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 shot in curing cancer, that just feels like making search easier, making research easier, huge benefit.
Speaker 4:Yeah. Conyngham, the guy Yeah. Australia, could have done a lot of this ten years ago. Totally. He just would have needed to spend Yeah.
Speaker 4:I'm sure a bunch of time in Yeah.
Speaker 2:That's the thing.
Speaker 4:Libraries Everything you all do. These things can
Speaker 2:do manually.
Speaker 4:You can do You can just get a guy for
Speaker 2:that. Yeah. You can get a guy or you I mean, you don't even need a spreadsheet. You can do you can do this you can calculate the math by hand, but these things speed things up. So it's it's been a good time.
Speaker 2:Let's read through Ash's post. But first, let me tell you about public.com investing for those that take it seriously, stocks, options, bonds, crypto, treasuries, and more with great customer service. And let me also tell you about Fin, the number one AI agent for customer service. If you want AI to handle your customer support, go to fin.ai. Thank you for clapping, Tyler.
Speaker 2:How was your weekend, Tyler?
Speaker 3:It was good. Yeah.
Speaker 2:It was good? Yeah. Did you go to any data centers or are are you
Speaker 3:No data centers this week. No. I was in SF.
Speaker 4:Yeah. Didn't you go to a pig roast?
Speaker 3:I did Yeah. That was on Friday. Okay. I was in El Segundo.
Speaker 2:How was SF? Is something big happening there? Does it feel like being in Wuhan in February 2020?
Speaker 3:Something something big was happening. Yeah? There was I went to a debate.
Speaker 2:Oh, you went to the debate. Okay. Cool. Yeah. How was that?
Speaker 2:That?
Speaker 3:It was good. Yeah? Yeah. It was about the billionaire tax.
Speaker 2:Yeah. Yeah. Yeah. Yeah. And did you go to the hackathon at all?
Speaker 3:No. I missed that. Okay.
Speaker 2:Yeah. I saw that semi analysis had a hackathon. The winners were crowned. Like a lot of fun. It really does seem like the best time to go to a hackathon just because what you can actually accomplish in in two days is remarkable.
Speaker 2:Yeah.
Speaker 4:Yeah. No. I was
Speaker 2:just Right? Yeah. It was just like I like, people used to do hackathons and it'd be like after two days, they'd be like, we have a landing page. And now it's like And
Speaker 4:a and a cool idea.
Speaker 2:We we created a hackathon simulator with mini games for everything, and it's also making money. We need to give an update on TBPN simulator at some point, but it is coming along. The development has continued at breakneck pace.
Speaker 4:Yeah. We gotta work on the rollout of this.
Speaker 2:Yes. It might be it might be GTA six level by the time GTA six comes out. I think we can get there. We need a new graphics package. What do you think the actual path to triple a graphics is?
Speaker 2:Do you think we should rewrite it in Unreal Engine with ray tracing and insist that people only run it on gaming PCs? Or should we do some sort of style transfer on top of it?
Speaker 3:Yeah. I mean, I I think the the Unreal Engine is probably easier. Right? Because you're just, like, moving the code over. That shouldn't be, like, that difficult.
Speaker 3:You can probably do that in, like,
Speaker 1:a day
Speaker 4:or two.
Speaker 5:It's a
Speaker 2:day or two.
Speaker 6:I think just
Speaker 2:these things used to take, like, years. Like, it took it took Elder Scrolls, like, a decade to get to, like, Nintendo Switch. You know? Like, you know, like, yeah. Takes a day or two to, like
Speaker 3:The real time render thing is interesting, but I I think that's it's just, like, expensive. That that's the problem.
Speaker 2:We had we had someone on the on the show that was doing it on Zoom over in real time.
Speaker 3:That was Descartes? Descartes. Yeah.
Speaker 2:That was a cool demo. Yeah. So you imagine, like, that tech prompted with, like, make take this from from, like, boxy. I I I would say we're at we're above n 64 level graphics, but we're probably more like Xbox three sixty graphics and take us into, you know, modern day PS
Speaker 3:Yeah. I mean, this is why I'm I'm very excited about doing everyone's so up in arms about, like, oh, you the new, like, PSX isn't gonna come out because you hate the memory.
Speaker 2:Yeah.
Speaker 3:Yeah. And peep people are like, oh, I I don't wanna play games in the cloud. Right? But if you're in the cloud, that means you can actually, like, access a ton of compute because Yeah. Like, when you're not playing when not when you're not using the GPU to to run, like, the nice graphics, someone else can be.
Speaker 3:Yeah. You can actually get higher to you can get access to to much better, like, hardware Yeah. When playing video games.
Speaker 2:And then also, yeah, more more iteration on the graphics. Like, it should just be, like, live service model, basically.
Speaker 3:Yeah. If you get the, you know, the Genie three model where it's actually, you know, generating, you know, on spot, like
Speaker 2:Yeah.
Speaker 3:That that's something you can really only do in the cloud.
Speaker 2:I'm excited.
Speaker 4:Jensen is doing his keynote at GTC. Should we pull up the livestream?
Speaker 2:We can.
Speaker 4:Yeah. Let's let's check-in with
Speaker 2:Let me tell you about Okta first. Okta helps you assign every AI agent a trusted identity so you get the power of AI without the risk. Secure every agent. Secure any agent. And let me also tell you about Graphite code review for the age of AI.
Speaker 2:Graphite helps teams on GitHub ship higher quality software faster. Continue. Let's play it. What do we got? We got Jensen?
Speaker 7: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 wanna thank all of you for that.
Speaker 2:All star team. The leather jacket really has just aged so well.
Speaker 7:I also wanna thank all the companies that are here.
Speaker 2:NVIDIA, as you know,
Speaker 7:is a platform company.
Speaker 2:Mic drop. We have technology. We have our platform. By the way, everyone uses We have a
Speaker 3:He's mugging our merch. He
Speaker 2:is. And
Speaker 7:today, there are probably a 100% of the $100,000,000,000,000 of industry here. 450 companies sponsored this event. A 100. I wanna thank you.
Speaker 2:A trillion dollars of industry.
Speaker 7:A thousand
Speaker 2:I love it.
Speaker 7:Technical sessions, 2,000 speakers.
Speaker 4:This is 2,000 speakers?
Speaker 7:Wow. Every single layer.
Speaker 2:Doing one they're gonna do more interviews than we've done all year. The infrastructure In one day.
Speaker 7:Chips
Speaker 2:For two days.
Speaker 7:To the platforms, the models, and, of course, the most important and, ultimately, what's gonna take get this industry taken off is all of the applications.
Speaker 2:This really is the goal for semiconductors.
Speaker 7:It all began here. This is the twentieth anniversary of CUDA. We've been working on CUDA for twenty years. Twenty years. For twenty years, we've been dedicated to this architecture, This revolutionary invention, SIMT, single instruction, multithreading
Speaker 8:Alright.
Speaker 4:Very, very cool. Let's get back the timeline.
Speaker 2:Let's go to Gemini three Pro. Gemini 3.1 Pro is here with a more capable baseline. Great for for super complex tasks like visualizing difficult concepts, synthesizing data into a single view, or bringing creative projects to life. And let me also tell you about Railway. Railway is the all in one intelligent cloud provider.
Speaker 2:Use your favorite agent to deploy web apps, servers, databases, and more while Railway automatically takes care of scaling, monitoring, and security.
Speaker 4:Back on the time Okay.
Speaker 2:In case We gotta go through wait.
Speaker 4:You can just save your dog. That's beautiful. The beautiful picture here.
Speaker 2:You can just save your dog. Remarkable. It it this is a heartwarming story and it also yeah. I I really like how it reveals, like like, current AI capabilities, where things are, the benefits, and and sort of the diffusion narrative. Like, this is this is fundamentally a diffusion story, not a superintelligence story in my opinion.
Speaker 2:But let's go through Ash's post here. My take on the whole AI cures dog cancer in 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 fifty years at least as much as the domestication of computers has dominated our lives during the previous fifty years.
Speaker 2: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.
Speaker 2: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, but this is sort of counter to that. I don't exactly know how to piece those two things together, but it is it is it is interesting that his prediction was actual decentralization in this particular category.
Speaker 2: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.
Speaker 4:I gotta say, it's very easy to imagine you in twenty 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 Yes.
Speaker 4:That I go through. It's like
Speaker 2:a sculpture.
Speaker 4:I'm sort of sculpting myself. I can't really I can't I'm sorry. I can't I can't really share my stack with you, but it's a personal thing. So Yeah. Go go and kind of figure out your own stack.
Speaker 2:Yeah.
Speaker 4:You know?
Speaker 2:Speaking of sculptures, I was walking around my neighborhood and I looked through this like, you know, gap in the trees into this like large lawn. And I saw on this person's like front front lawn behind like, you know, gates and whatnot, Just a a full size statue of a man playing golf who I didn't recognize. It was like it was not Tiger Woods.
Speaker 4:You think it was the owner?
Speaker 2:I think it was the owner. I think the owner was like, I I'm I'm into golf and or, you know, like one of his boys got it for him, which is a hilarious gift.
Speaker 4:Getting someone a life size statue of themselves.
Speaker 2:And just having it delivered and then it's like, well, it's impolite for you to turn it down. You know, what are you gonna do? Anyway, 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.
Speaker 2:The biological targets themselves were almost certainly not new discoveries. I have been able to I haven't not I have been unable to find out what they are, but mutations in targets like KIT are which are common might be involved. Partly therein lies the rub 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.
Speaker 2: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 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.
Speaker 2: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. Anyway, it's a longer post, but you should go read the thing in full.
Speaker 2:Patrick Collison also chimed in. He said, According to the story, the dog's cancer has not been cured. I think it's just 50% smaller, which of course is a win, but not using the term cure is always tricky, but it does go viral. Absent all regulatory and manufacturing constraints, we could not just synthesize magic RNA mRNA cancer cures. The technology is very promising, but it's not any kind of panacea yet.
Speaker 2:The emergent system of regulators and manufacturers is indeed far too conservative and small scale experimentation is much harder than it should be. More people should read the first part of The Rise and Fall of Modern Medicine. 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.
Speaker 2:What if there are lots of drugs that will only work on one person? So definitely a big desire and push for rethinking the system of clinical trials if if you're going to have personalized medicine. What does that mean? There's already a lot of people That's that do all sorts of stuff like this.
Speaker 4:Can't believe he wasted two cups of water to do this. Hashtag ban AI. It is ridiculous. It's a great counterpoint to the doomers.
Speaker 2:What else is going on? Mark Andreessen chimes in. I can't load the post right now.
Speaker 4:We gotta go to probably the most important story of the day. Gabe says Yes. He had a dream Mhmm. 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.
Speaker 4:So I had to leave it
Speaker 2:behind. Oh, no. We but this is this is sort of like They should on that other laptop that we saw.
Speaker 4:They should honestly make this.
Speaker 2:They should.
Speaker 4:This should I mean, walking around So looking like what? Maybe you could put skateboard trucks on it Yeah. So that you could use it as trans transport.
Speaker 2: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 fingered.
Speaker 2:Why? You don't put a surf surfboard on the top of your car?
Speaker 4:Yeah. I mean, real ones don't.
Speaker 2:Oh. What do they do? They put it inside the car?
Speaker 4:Truck battery in inside
Speaker 2:Truck battery. Okay. Yeah. Well, in in I don't
Speaker 4:it's can you can clock if somebody's actually a surfer or not just by the way they go Low. Beach with board. Okay. No. But I think, yeah, throwing it under your arm Yeah.
Speaker 4:Having some trucks on it, skating, being able to get where you need to go. I like the ultra wide.
Speaker 2:What if, what if they're driving a Huracan Stirado? Where would you recommend that they put their surfboard that, Jordy?
Speaker 4:Stirado, I could make exceptions.
Speaker 2:Okay. I like this. Dylan Patel said on Durkash, the TAM for g b c 5.4 is north of a $100,000,000,000, but there's adoption lag. That's considered AGI as far as the Microsoft OpenAI contract is concerned. That's very interesting.
Speaker 2:Sam Carter says the reported 1,000,000,000 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.
Speaker 2:You gotta trust them at all times.
Speaker 4:And Beo Von maybe.
Speaker 2:You. 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
Speaker 6:be like,
Speaker 2:it's, not that good yet. You know? Like, be very realistic about it. Yeah. They're not necessarily just gonna be like, oh, I'm pumping it for whatever reason.
Speaker 2:And they're like, I have this weird bias or whatever. And so it'll be very interesting to see how that how how the the AGI definition plays out because it does feel like we're close. I mean, Dario and Durkash was saying, like, we're near the end of the exponential, which is, like sort of crazy. It feels like, you know, Sequoia declared AGI. They're an investor in OAI.
Speaker 2:And so there's a lot of stuff. What do you think about the AGI time? Do you wanna be on the expert panel?
Speaker 3:I I I think I would say that we already
Speaker 6:reached AGI. Yeah.
Speaker 3:Like, it was maybe earlier
Speaker 4:like thirty seconds before Tyler Cowen did. I think thirty seconds before you came, you tapped me on the shoulder and you said, it's here. And then we went and refreshed X and Tyler Cowen had come out.
Speaker 2:Yeah. Yeah.
Speaker 3:I I think I like, realistically, I'd probably say it was something like when when the, you know, agentic harnesses came out. So stuff like Cloud Code
Speaker 5:Sure.
Speaker 3:Where you can actually just like tell it to build a project and then they would like, it'll there'll be errors. Yeah. It'll see those errors. It'll fix them. It'll, like Yeah.
Speaker 3:Keep working on it.
Speaker 2:Not not not reasoning models?
Speaker 3:I mean, it's so hard. It's, like, on on, like, math or something like this. Right? Like, those basically unlocked,
Speaker 2:like Yeah.
Speaker 3:Yeah. Now they can just do anything.
Speaker 2:Yeah. Yeah. I mean, the agentic thing was talked about for a full year, and then it finally happened, like, in December. And and and and it was pretty broken up until then, and then all
Speaker 3:of stuff. I think, like, you can still just make, like, a very good case that, like, yeah, chat should be tea. Like, that was AGI. Like Yeah. Can just ask question, it'll answer it.
Speaker 3:Yeah. If if you'd never talked to an AI model before and you talked to that, you're like, this okay, this is the person.
Speaker 2:Microsoft Excel. 1985. AGI.
Speaker 4:Jose Macedo says, ultimate narrative violation from the Dylan Patel to our cash pot.
Speaker 2:Oh, yeah.
Speaker 4:Three years later, h one hundreds are actually trading above launch price in secondary markets negative depreciation. Yeah. That's called appreciation. Appreciation. I I just wanna go out and say, I appreciate
Speaker 2:H one hundreds?
Speaker 4:Negative depreciation.
Speaker 2:Yeah. That's funny.
Speaker 4:This completely flips a Michael Burry two year e waste bear thesis on its head. Yeah. I mean, there's Somebody's gotta check on
Speaker 2:I should
Speaker 4:Somebody's gotta check on Michael Burry.
Speaker 2:It's such a different it's such a different dynamic. Because, I mean, they, like, the whole there was a reasonable underpinning for GPU depreciation, which was just look at twenty years of computer equipment history. It's like it all depreciates over, like, maybe five years, maybe ten years. Some stuff sticks around, but, like, they burn out.
Speaker 4:It's just interesting to look at, Jose says, Corey probably benefits most from this. They have 250,000 GPUs in a $66,000,000,000 backlog depending where you think market was pricing depreciation, margins improved by something around 40%, which means 1,000,000,000 a year in additional earnings. Mhmm. Who knows where this stuff actually rerates or Mhmm. Or or how sustainable.
Speaker 4:But great great time to be a NeoLab. Mhmm. One of one of the founding team members at Lambda was posting last week. Basically, congratulations to everybody that booked out like GPUs and on on on an annual basis in 2025. You're looking like absolutely brilliant right now.
Speaker 4:Obviously, Sam is starting to look extremely vindicated on all of the deals that he did Yeah. Last year. So Tomas
Speaker 2:Quickly, let me tell you about MongoDB. What's the only thing faster than the AI market? Your business on MongoDB. Don't just build AI. Own the data platform that powers it.
Speaker 2:And let me also tell you about Turbo Puffer. Serverless vector and full text search built from first principles in object storage. Fast, 10x cheaper, and extremely scalable.
Speaker 4:Tamaz says, we've been growing a lot and are out of GPUs. This is Sam Altman in March 2025. Sasha Katz over at Oracle says, we are still waiving off customers or scheduling them out in the future. This is a situation that we have not seen in our history. Satya says you may actually have a bunch of chips sitting in inventory that I can't plug in.
Speaker 4:I don't have warm shells to plug into. Sundar says what keeps us up at night, the top question is definitely around capacity, all constraints, be it power, land, supply constraints. How do you ramp up to meet this extraordinary demand?
Speaker 2:Sorry, between power, land, supply chain constraints, chips, you were saying that the the the takeaway your takeaway or your read on Dylan Patel on DoorDash was that chips were the main
Speaker 3:Yeah. I mean, it not even like a read. Like, he explicitly said this. He's like between power and chips Chips. Chips is is what's gonna be the big bottleneck.
Speaker 3:Yeah. Because at some point, like
Speaker 2:Yeah.
Speaker 3:You there's always ways that you can actually, like, maybe get, like, 10% of the
Speaker 2:Yeah.
Speaker 3:You know, US energy production to just, go to
Speaker 2:Yeah. You
Speaker 3:know Yeah. Where, like, at at some point, like, okay, we don't have enough, like, EUV tools. Yeah. And, like, they're not billing them right now, which means that they're not gonna have them for at least three, four years. Yeah.
Speaker 2:Yeah. Yeah. This was Ben Thompson's, like, TSMC needs to step up and spend more on CapEx. Their their CapEx guide is, a CapEx guide for ants, like, a mere 45,000,000,000 or something, and it should be probably much much higher based
Speaker 3:on I mean, it even goes down to the, you know, the tool makers
Speaker 2:The tool
Speaker 3:like, really, like, really deep in the supply chain. Yeah. At at least the what what I got from Domitu on that interview was that, like, they still are not really that AGI pilled. They're not expecting
Speaker 2:Yeah.
Speaker 3:That this kind of massive, you know, increase in demand to to stick around.
Speaker 4:Yeah. Trey says a sign of taste is dabbling in the vintage GPU market.
Speaker 3:A a one hundreds.
Speaker 2:Vintage yeah. Ampere. V, you gotta go Volta. You go back to Volta. The yeah.
Speaker 2:I mean, I I remember I was digging into that, like chips versus energy. What's the middle big bottleneck? And I think we're using something like 50% of leading edge capacity. Like, of the of the fab of the fabs that can make AI powered GPUs, like a like GPUs that can run transformer based large language models, We're using, like, 50% of that capacity already, then some of the leading edge nodes go towards, like, you know, Apple silicon chips that are maybe designed system on a chip, something for a phone. And and only, like, 1% of energy right now in America goes towards AI and or less.
Speaker 2:It's like point one or something. So you can reallocate and everyone just turn off your air conditioning.
Speaker 4:One more Close
Speaker 2:the door if the air conditioning's
Speaker 4:Lip Buutan says, there's relief as far as I know. No relief until 2028. Somewhat ominous.
Speaker 2:Keep reading.
Speaker 4:What Thomas says, what happens when your AI doesn't answer? Everything is in short supply. It's no longer just GPUs. It's power, data centers, memory, CPUs. If there's no relief for six more quarters, perhaps it's time to plan for a world where inference isn't freely available on demand.
Speaker 4:Inference prices, have been static, will rise. Subsidies will be harder to justify. Enterprises will need to rationalize workloads deciding which teams receive state of the art models and which don't. Not every CRM update requires a trillion parameter frontier model. Inference rationing normalizes, market marketing receives this much, sales receives that much, software engineers probably receive a lot more.
Speaker 4:Constraint will be the mother of invention. Companies will optimize what they have, adopt open source where they can, and likely move to smaller models for many.
Speaker 2:This is really cool take. I like this. It's also interesting to me is that that not every CRM update requires a trillion parameter frontier model. That's another bull case for Hopper price stability going forward and few and less depreciation. Because if you can leave your CRM update workload where you're just going through and spell checking names and cross referencing data sources and pulling from email and dumping some notes, summarizing.
Speaker 2:And you can do that on a GPT four class model instead of using five four. You can probably distill that model, boil it down, run it on an h 100 fleet really efficiently. And so that's still economically valuable, and so you're able to continue that.
Speaker 4:Should we should we go over Ben Thompson's post from this morning?
Speaker 2:Yeah. We should.
Speaker 4:Now would be a good time. Yeah. It is.
Speaker 2:First, let me tell you about Labelbox, RL environments, voice, robotics, evals, and expert human data. Labelbox is the data factory behind the world's leading AI teams. And let me tell you about vybe.co. We're d to c brands, b to b startups, and AI companies advertise on streaming TV. Pick channels target audiences measure sales just like on meta.
Speaker 4:Over bubbles. Published this this morning. This is To me, the second I saw that, I started reading it. Felt like taking a double scoop of c four. I was
Speaker 2:Is that a pre workout?
Speaker 4:Yeah. You never
Speaker 2:I know the can. I didn't know it was
Speaker 4:a You never you never dabbled?
Speaker 2:What was the one that we I I'm more of the gorilla mind one. That's the one that I
Speaker 4:Many people have said you have the mind of a gorilla.
Speaker 2:Yes. Yes. Yes. For more plates, more dates.
Speaker 4:You're a gorilla in sheep's clothing.
Speaker 2:I think that's literally the pre workout that I have a little bit of use that often. Anyway, so you got pumped up.
Speaker 4:I got pumped up. Ben writes, there's a weird paradox in terms of AI Prognostication. That was a good good effort, Jordy. On one hand
Speaker 2:what are the requirements for having a podcast? Like knowing how to say words? No. Taste.
Speaker 4:I mean, yeah. Ultimately, there's a lot of words that you when you read them Yep. You're just like, oh, yeah. You can just do it and then you try to rip it. On one hand, don't want to be the one to completely dismiss the most terrifying doomsday scenarios.
Speaker 4: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 and all of this hype and spending is going to go belly up. While I've argued against the former, I've very I've 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.
Speaker 2:Let's go.
Speaker 4:Which paradoxically Let's go. Maybe the truest evidence we are.
Speaker 2:Where's the bubble gun? Let's get the bubble gun going.
Speaker 4: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 Oracles. I've talked to you about three LLM inflection I've talked about three LM inflection points. Yeah. I'm not gonna go through all these. Yeah.
Speaker 4:You guys chat talked
Speaker 2:about this a few times. Oh, one. LM LLMs, reasoning models, and then agents, and each one of those increases the demand exponentially for compute.
Speaker 4:Yeah. It's great. LM, ChatGPT o one Yep. And then Opus Yep. As well as Claude Code and Codex.
Speaker 4:Codex. Yeah. Basically getting to the point where tasks are being accomplished over hours Yep. And getting to great outcomes. And this is the interesting point.
Speaker 2:Okay.
Speaker 4: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.
Speaker 4: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 but 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 more compute and that compute and the tools the agent uses is better done by CPUs and GPUs.
Speaker 4:Third, agents are another step function increase in usefulness which means they're gonna 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.
Speaker 4:And he goes into a little bit talking about local local compute, talking about how Apple's opportunity to run LLMs locally.
Speaker 2:There there was a very, very interesting take in here where he's talking about the Apple MacBook Neo launch, is 599. I think $4.99 for education. Potentially very disruptive to other laptop makers. You said
Speaker 4:still get discounts, Tyler? Or does it I think
Speaker 3:I yeah. Can block them. You're on yeah.
Speaker 2:You're still because you're
Speaker 4:on lead.
Speaker 2:That's great.
Speaker 4:There you go.
Speaker 2:I think I'm outside, but There there are some legendary leave of absences where people have been away for like ten years. And then they go and do so many see, the the goal is to defer for so long that 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. Because they're like, oh, well, we gotta I think Mark Zuckerberg got a honorary degree from Harvard, but he was on he was on delay for like a year. And I think they gave him the honorary degree a couple years later.
Speaker 2:So, you know, that's the that's the speed run to beat. But the point about the MacBook Neo is that at May, 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, 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 Yeah.
Speaker 2:The 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 c CFO, Nick Wu of Asus, was on their recent earnings call, he said, actually, don't worry about it. It's not a threat.
Speaker 2: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 eight gigs of RAM.
Speaker 2:So, like, you know, 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.
Speaker 2:And and Ben Thompson's point is that, well, like, that's what people use these laptops for now. They they it is a lot of consumption. It it 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 so and and 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.
Speaker 2:What makes enterprise executives truly salivate, however, is not the prospect of AI eliminating jobs, but doing so precisely because it makes the company as a whole more productive. So increasing production.
Speaker 4:Yeah. And basically, interpretation, he's making a case that there are companies that could cut head count and actually just grow faster
Speaker 2:Yeah.
Speaker 4:If if they're implementing AI properly, not just replacing Yeah. Like the routine workloads.
Speaker 2:Yep.
Speaker 4:So he says agents, however, will tell much more heavily toward pure acceleration, making those drivers of value Okay. Actually, I'm gonna start one paragraph.
Speaker 2:Yeah. Please.
Speaker 4: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 wanna use AI to do more, not simply save money.
Speaker 4: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 in the organizational machine with agents that not only do what they are told but do so tirelessly and continuously until the job is done. Only makes the argument that we are not in a bubble much more compelling
Speaker 2:unless a compute constraint and then the models get lazy. And they're like, I don't know about tirelessly or continuously all I'll get around
Speaker 4:to it when I feel like it.
Speaker 2:I'll give it a crack.
Speaker 4:I'll get around. Yeah. So This only makes the argument that we are not in a bubble that much more compelling. First, all of the weaknesses of LMs are being addressed by exponential increases in compute. Yeah.
Speaker 4:Second, the number of people who need to wield AI effectively for demand to skyrocket is decreasing. Right? You have one Tyler just, you know, he's gonna Tyler's gonna set up to be able to do sign language with his agents to just just be not even speaking. Just Yeah. Just send
Speaker 2:actually ever use any of the voice models? Remember Carpathi was talking about that? How he
Speaker 3:do this because you can just like talk much faster Yeah. I guess. I I haven't done this really. I've used it sometimes use the voice mode
Speaker 2:Yeah.
Speaker 3:But I don't actually use it in like coding agents
Speaker 2:yet. I was using the ChatGPT voice mode, like, the true, like, back and forth voice mode.
Speaker 3:Yeah. Like, real time voice.
Speaker 2:Real time voice mode in the car this morning, and they improved that thing dramatically.
Speaker 3:It's good.
Speaker 2:Yeah. It's so much better. So first off, it doesn't do that, like, that's a great question or anything like that. Or it that that whole pause that was in the Super Bowl ad, like, that just doesn't exist anymore. It just answers.
Speaker 2:And it answers in these, like, really short punchy things. I was asking it about, like, how many jobs are actually in America? And it just says, like, a 164,000,000. And it just, like, gets me the answer. And I'm like, how many jobs are there in China?
Speaker 2:It's, 730,000,000. And it and I'm just able to go back and forth with it and ask more and more detailed back and forth without needing to, like, dictate a whole prompt and then let pro cook on it for ten minutes, come back, have it read it to me. It was, a much better experience. I was, I I I was very pleasantly surprised by how it how the back and forth worked. And they also changed it so that you see the floating bubble of, like, the little animation, but the text populates in real time with the with your question and then the answer and then your question and the answer, so you can just scroll and read as well.
Speaker 2:It's very cool. Anyway.
Speaker 4:Third, the last argument that we are not in the bubble, the economic returns from using agents aren't just impactful on the bottom line Mhmm. I e saving on cost, but the top line as well.
Speaker 2:Let's go.
Speaker 4:In this context, it is any wonder that every single hyperscaler says the demand for compute exceeds supply and that every single hyperscaler is in the face of stock market skepticism announcing CapEx plans that blow away expectations. So I encourage you to go subscribe to Stridecari, max out your plan, pay annual, but, this was extremely notable.
Speaker 2:It's it's such a funny ending where he he 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 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 I 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.
Speaker 2:Really quickly, let me tell you about the New York Stock Exchange. Wanna change the world, raise capital at the New York Stock Exchange. We talked to John Zito at the New York Stock Exchange a couple months ago. Now, he's in The Wall Street Journal talking about arrogance in private markets. Take us through it,
Speaker 4:Jordan. He was going hard.
Speaker 2:He was going hard?
Speaker 4:Yeah. We'll click into this. Top Apollo executive sounds off on arrogance in private markets.
Speaker 2:You always wanna be sound off.
Speaker 4:He says, I literally think all the marks are wrong. Apollo's John Zito set of private equity and previously unreported comments. Apollo says comment was about software companies. Let's go through it. Mhmm.
Speaker 4:Executives at the biggest private credit lenders have sought to play down an exodus of investor money from their funds making carefully worded television appearances to calm jitters about the sector.
Speaker 9:Mhmm.
Speaker 4:Apollo's John Zito, former guest of the show, co president of the firm's asset management arm that is one of private credit's largest players, spoke more bluntly in a previously unreported discussion that UBS arranged for some of its clients late last month. Okay. Zito called out arrogance in private markets, predicted that a private credit loan made to a generic smaller mid sized Joe software company might recover 20 to 40¢ on the dollar and said Federal Reserve chairman Jerome Powell is needling president Trump with his inflation commentary according to audio recordings of the comments reviewed by the Wall Street Journal. It sounds like this wasn't meant to be public.
Speaker 2:I don't know.
Speaker 4:So you know, also detailed
Speaker 2:Calling, you know, people in private markets arrogant is crazy. I I feel like I feel like I know a ton of people in private markets. I don't know anyone who's arrogant at all. It's like remarkable. So a bold bold call by him, but we'll see what he what evidence he has to back up that extraordinary claim.
Speaker 4:He blamed the media for creating a frenzy around private credit. Obviously, we're in the middle of a private
Speaker 3:credit Apparently,
Speaker 4:if you do credit well, it's honestly, I would say we don't understand private credit well enough to like really put everyone up into a frenzy. He says if you do credit well it's supposed to be pretty boring. Yeah. If you do stupid things and you do concentrated things and you do things that you're not supposed to do in your vehicle, you probably will have a bad ending. Sito talked about the sell off in shares of large software companies which was largely sparked by fears about AI.
Speaker 4:He cautioned that smaller software companies bought by private equity, many with private credit loans could face even more challenging conditions. Those dismissing concerns by pointing to strong results from public companies are missing the point. I'm not as rosy and I'm not as confident in what will happen with the technology. Anyone who tells you that the earnings last quarter were really good, so all is good. Anyone who says that clearly doesn't understand most of the businesses that were bought from 2018 to 2022 are lower quality than those companies
Speaker 2:because they're not public yet.
Speaker 4:Smaller than those companies and we're trading at a much higher valuation than those companies. Yeah. And so I am concerned about many of those take privates.
Speaker 2:Yeah. I remember a lot of, like, Logan Bartley was doing a ton of analysis in, the end of the ZERP era at how high the multiples were in the public markets and that's what was driving the 100 x transactions.
Speaker 4:Yeah.
Speaker 2:And you have to imagine even even if we we were like, oh, yeah. That that, you know, VC backed company was sort of over over hyped at a 100 x ARR. Well, that still has a trickle down effect to, you know, the private equity buyout. That's just like Yeah.
Speaker 4:Remember last year when Figma out Yeah. And they priced it Yeah. Very reasonably. Yep. Right?
Speaker 4:They were very intelligent how they priced it. But then obviously there was so much excitement because Yep. It's such a great company. Round trip. It ran up.
Speaker 4:When it in the first couple days
Speaker 2:Yeah.
Speaker 4:There was there were some late stage private task companies that I remember were were posting like, maybe yeah. I think it was the the Parker Yeah. Rippling where like, oh, if I can get
Speaker 2:That's a crazy multiple.
Speaker 4:Yeah. If I can get Yeah. You know, some insane Revenue. Revenue multiple, maybe this appealing. Obviously, lot of those names still could get out this year.
Speaker 2:Yeah. Strong companies.
Speaker 4:They're not not as as eager to get out. Zito pointed to Tomo Bravo's $20.21 6,400,000,000.0 take private of the software firm Medallia, in particular. Several lenders to Medallia, including Apollo, have already written down its debt. He says, there will be an issue with respect to that credit, which I think will be worse than people expect. Asked what kind of recovery rates he anticipates on private credit loan to generic small or mid sized Joe software companies.
Speaker 4:Zito said, Joe software company, if he's in the wrong place, think he's gonna recover somewhere between 20 and 40¢. So 60 to 80% markdown. A lot of the private credit firms have been they'll markdown a loan but like market down to like 95.
Speaker 2:Sure.
Speaker 4:You know? Sure. Like, you know, nothing very significant. Zito noted that he expects private credit loans originated in the next twelve to eighteen months to be a much better vintage as it relates to quality of company amount of leverage, documentation and spread. Mhmm.
Speaker 4:He also weighed in on redemptions and whether private credit managers should enforce limits, typically 5% of a fund's shares each quarter or allow more investors to cash out when they are flooded with requests. It is a topic he and others on Wall Street have recently been asked about as funds take different approaches. You're gonna see elevated redemptions for a handful of quarters. I don't know how long it lasts. Making a decision in one quarter may be the right like decision for fundraising in the near term and then a quarter later, you'll realize it was a really bad decision.
Speaker 4:So my overall bias is to stick to the 5% to protect all of my existing investors.
Speaker 8:Mhmm.
Speaker 4:On vulnerabilities in private equity, Zito sought to shift the focus to private equity where Apollo has less exposure than most of his peers. He suggested investors voracious demand for buying stakes in existing private equity investments, but wariness of the private debt underpinning those deals doesn't add up since the equity would be junior to the debt if there were major problems with these assets. There's unlimited demand for secondary private equity, but they're worried about private credit, which finances 80% of those portfolios. I can't compute but I'm the dumb guy. I don't understand.
Speaker 4:I start saying this and I get these blank stares back at me like, okay. I don't know. He said, I literally think all the marks are wrong. Is that what you're asking me? I think private equity marks are wrong.
Speaker 4:Wow. And again, I I read into this. He's talking so candidly. Yeah. At least in private equity land, he doesn't feel exposed enough to be freaking out.
Speaker 2:Yeah. Let's get a couple
Speaker 4:more quotes. He says, this next cycle is gonna be a big moment in time for the private markets because people are way smarter than I think private market participants, particularly people in the wealth channel. Like, I kind of sense an arrogance of the people who grew up in the private markets business. If you don't mark your book, I think you actually lose trust with the clients. We're going to be the market leader in actually marking our book.
Speaker 4:Let's give it up for being the market leader. On the economy and markets, he said, think it's more likely than not that we go into a recession, a consumer confidence led recession. Most of the companies we lend to are getting a lot of pressure to show clear AI execution. It's forcing people to do stuff before the actual technology works. That's gonna be the first step of just a slowdown in the broader economy.
Speaker 2:Interesting.
Speaker 4:He said, literally think Powell, he's so upset at how it's ending that he's just saying there's inflation every day to piss off the president. Like I literally think that's what's going on and it's so hard for me to see inflation. I don't see it anywhere. I see it much more deflationary. I think that technology is attacking every profit pool.
Speaker 2:What do you say? Asked why a popular high yield high yield corporate bond ETF seen as a benchmark for such debt that is typically under pressure in an economic crunch was relatively flat for the year. Says, don't have any idea. The amount of dispersion going on beneath the surface is kind of crazy. I literally at home, I told my wife last night, I feel like the market should be down at least 10% and it's flat or up.
Speaker 2:Can't make heads or tails of it. On Apollo's credit business, he says, on our balance sheet, we are 95% investment grade, private and public investment grade. I have a view that bigger companies are gonna do better than smaller companies and so I've tried to position my
Speaker 4:gets me every time.
Speaker 2:Yeah. I know.
Speaker 4:Because because they're like, you asked me. I I run a private credit fund. We mostly back We mostly invest in non investment grade opportunities. Yeah. It's like, brother.
Speaker 2:Don't you wanna be investing?
Speaker 4:What were you doing? It's in the name. Now, course,
Speaker 2:high Very very rates fun. End of this journal piece, they have a form. We wanna hear from you. Are you currently an investor in private credit funds or are you planning to become one? We'd like to hear from you.
Speaker 2:Share your thoughts or experiences in the form below. They're looking for snitches. Yeah. I'm I'm I'm I'm I'm I'm going gig along.
Speaker 4:Back to data center land.
Speaker 2:Amazon. Quickly, let me tell everyone about Console. Console builds AI agents that automate 70% of IT, HR, and finance support, giving employees instant resolution for access requests and password resets. And let me also tell you about CrowdStrike. Your business is AI, their business is securing it.
Speaker 2:CrowdStrike secures AI and stops breaches.
Speaker 4:I gotta give another shout out to George Kurtz Yeah. Who went one and two again at the Chinese GP over the weekend. Mercedes on an absolute tear. It's the Kurtz effect.
Speaker 2:I saw a I saw some sort of promotional post for a vintage Le Mans racing series. So twenty four hours, but there's some date where, like, all the cars have to be from early before 1990 or something like that. I don't I don't exactly know how old. I didn't dig into it. Yeah.
Speaker 2:It looked very, very cool.
Speaker 4:Anyway Cerebras just landed AWS. Amazon announced chips deal with Cerebras, which is big. They are proving proving the doubters wrong. Elon is saying that the TeraFab project launches in seven days. Mhmm.
Speaker 4:Beth Jasos says, what? Very, very fast timeline. Obviously, when people, heard about his plans
Speaker 2:Six on days. It's so fast.
Speaker 4:On Dork Cash. Yeah. A lot of people kind of questioned it. Yeah. But Elon's used to being questioned.
Speaker 2:Yeah. Cerebras is is such a cool company. Like, I I I just the first time I mean, we've seen it with, the Chat Jimmy AI and and, you know, just going to Codex desktop, which is, of course, like a coding harness. But you can just ask it questions, and you can experience Codex five point I think 5.3 is on.
Speaker 3:Yeah. 5.3
Speaker 2:Spark. Spark? Yeah. Spark. And it just gives you the answer immediately, and it's actually very, very magical.
Speaker 2:And I think that's gonna be really good for retention, basically. Everyone's gonna be in the smiling curve will smile more as people come back and
Speaker 4:Matt, you
Speaker 2:wait anymore.
Speaker 4:Zaitlin says data center capacity growth is slowing. He's pulling data here that says newly added US data center capacity slows down considerably in q four twenty twenty five as market struggles to keep up with explosive demand. Yeah. 25 gigawatts of data center capacity added to the funnel in q four, fifty percent less in q three. We'll see if that ramps up again or
Speaker 2:seems like
Speaker 7:a lot.
Speaker 2:Like, I the the number that I was hearing was for this year, the target for Anthropic is like five gigawatts, which is like an insane amount of compute. But at the same time, like, in the context of 25 gigawatts in one quarter, like, it feels like like the the the the there is like still significant growth, but of course, you know, risks to all of this.
Speaker 4:One dozen over on x says, they were right to take cigarette ads off TV. I would have smoked a pack a day if I saw this when I was 14.
Speaker 2:What is this video?
Speaker 4:Let's pull it up.
Speaker 2:Is this a real ad? This cannot be a real ad. I
Speaker 4:Get think it's the sound.
Speaker 2:Some sort of vibe I'm
Speaker 8:smashed down here right now.
Speaker 2:I don't know if we're allowed to play this anymore. I think cigarette ads are banned. Is that actually I'm so confused by this ad. I think that's Charlie Sheen. Right?
Speaker 2:Are they is that the Arc De Triomphe Paris? Good music, though.
Speaker 4:This should be the new launch video meta.
Speaker 2:Oh, it was an international ad. The the Parliament. The message there is that they're taking New York to France? But then it was Japanese text on screen? I don't know.
Speaker 2:It seems like some sort of some sort of mash up. I
Speaker 4:don't know. Let's go over to Tyler Cowen. Yes. With some How to whistle
Speaker 2:to in.
Speaker 4:Oh, no. I was gonna go to his why you should work much harder right Okay. Over on Marginal Revolution
Speaker 6:Okay.
Speaker 4:Before we get into the next piece. He says, if strong AI will lower the value of your human capital, your current wage is relatively high compared to your future wage. That is an argument for working harder now at least if your current impending pay can rise with greater effort.
Speaker 8:Mhmm.
Speaker 4:Not true for all jobs. If strong AI can at least potentially boost the value of your human capital, you should be investing in learning AI skills right now. No need to fall behind on something so important. You also might have the chance to use that money and buy into the proper into the proper capital and land assets. So work harder.
Speaker 4:He should have put this into a course. I would I would follow this advice if it it cost me $999 in six installments. Yeah. But because he's given away it for free, it can't can't actually be that valuable. Kidding, of course.
Speaker 4:From Ricardo on the comments, suppose you are the best maker of horse carriages in Belgium around the time the automobile is invented. You might want to take on as many orders as possible for new carriages because you know your future is precarious. Mhmm. Or maybe you get your hands on one of these new fangled automobiles as soon as possible and learn how to fix them. Both options require you to work harder.
Speaker 2:Mhmm.
Speaker 4:But these seem to be the two best options available paradoxical but true.
Speaker 2:That's a good take. I like that. A little bit of a white pill.
Speaker 4:Never never a bad idea to work harder.
Speaker 2:Never a bad idea.
Speaker 4:Should we go through this?
Speaker 2:Yes, we should. First, let me tell you about Cisco. Critical infrastructure for the AI era, unlock seamless real time experiences and new value with Cisco. And let me also tell you about Cognition. They are the makers of the AI software engineer, Devin.
Speaker 2:Crush your backlog with your personal AI engineering team. Where do you wanna go next, Jordy?
Speaker 4:How to lose the AI arms race?
Speaker 2:Let's do it. So investor Leopold Aschenbrenner is now famous for situational awareness. His essay, predicted that major AI companies would end up functionally as part of the government led national security project, possibly even nationalized. Along related lines, economist Noah Smith recently asked a critical question, if AI is a weapon, why don't we regulate it like one? We already know this is Tyler writing in the free press, the fp.com.
Speaker 2:We already know that the Pentagon has been using Anthropics' Claude to interpret collected intelligence data and help plan the attacks in Iran. Advanced AI can also be used for cyberattacks, enemy surveillance, and identification followed by missile or drone attacks Under most extreme scenarios, which may or may not be realistic, advanced AIs might design bioweapons, disable the nuclear weapons of an adversary by disrupting chains of command, or perhaps design and build a scheme to knock missiles out of the sky. Washington DC is starting to ask very basic questions about where we are what we are doing here. Anthropic and the Department of Defense are at loggerheads over whether Anthropic's AI should be banned from government work. Senator Birnbaum Sanders recently raised a broader set of concerns calling for a moratorium on AI data centers with the intention of slowing down progress in artificial intelligence models.
Speaker 2:But circa 2026, neither nationalization nor an AI slowdown are feasible strategies for The United States. We need to keep our lead both in military and civilian uses of the tech, and that requires a dynamic private sector building our artificial intelligence models. Our federal government, working through the Manhattan Project, developed and built the first atomic bomb, but the strongest AI models are creatures of the private sector, whether we like that fact or not. Even China, which is far more statist than The United States, has seen its cutting edge models built by companies, not the government. The top AI models are far too complex and require too much high paid talent, including international talent, to be done well by governments.
Speaker 2:Governments sometimes can succeed in building out massive hardware projects with the space program being another example. They are very but there are very few cases of governments succeeding with advanced software on a large scale. For that, you need private sector dominance. There is no easy way to switch from that mode of organization, which includes salaries of tens of millions of dollars for top researchers, to a more bureaucratic approach. An attempt to do so would destroy or take down those companies, thus thwarting our standing in this new arms race.
Speaker 2:The general reality is this. We all benefit from living in an advanced civilization rather than eking out subsistence as our ancestors did. But there is part of this bargain we have tended to ignore or take for granted. Now that human beings have developed advanced technologies, we, the freer and better societies, must commit to keeping the technological lead. We have not stayed ahead in every area of tech, but we need to be able to protect ourselves and our allies.
Speaker 2:It's a good thing that America built an atomic bomb before either Hitler or Stalin did. To the extent you believe AI is important is important for weaponry and national security mean that means we need to keep up the pace of progress. You might find that a slightly you might find that a slightly unpleasant thought because even under positive visions of an AI future, it will change our world a good deal. Nevertheless, it is a part of technology of a technological bargain we have been living with for a long time. Arguably, since the widespread deployment of firearms or explosives, we seem to have been lulled into a state of stupor by the longstanding technological dominance of The United States after World War II.
Speaker 2:In essence, we have to fight and win yet another arms race. You can't blame AI for that. You can blame AI for that reality if you want, but the reemergence of competitive arms races was inevitable with or without AI. You should redirect your ire toward modern history itself. AI might may have accelerated the world's new arms race, but there are many other technologies that could play and yet and may yet play a comparable role.
Speaker 2:Space weapons, anyone? How about lasers or new types of hypersonic missiles? At least with AI, The U. S. Currently holds the lead.
Speaker 2:The creativity behind top AI models plays into our national strengths. And he closes by saying, so today, we need an odd complex an odd and complex mix of not entirely consistent ideologies for the current arms race to go well. How about some tech accelerationism mixed with capitalism and then a prudent technocratic approach to military procurement to make sure those advances serve national security ends. On the precautionary side, we need a dash of 1960s and '70s new left and libertarian anti war ideologies skeptical of Uncle Sam himself. We do not want to become the bad guys.
Speaker 2:Do you think we can pull that off? The new American challenge is underway. Inspiring. I like this. There's a there was a lot of back and forth around the anthropic d Department of War debate, and Dorkash had a great piece on it and lots of people have chimed in now that, like, the dust has settled a little bit.
Speaker 2:And I think this is a is a good sort of nuanced take. It doesn't it doesn't boil itself down to a to a tweet just yet, but I think we are getting somewhere with the different trade offs that are at stake. What what what do you think, Tyler?
Speaker 3:Yeah. This is good. I mean, think the the whole thing that I basically got to when I wrote, like, the nationalization thing Yeah. Was that, like, there's just there's pretty big scale. Right?
Speaker 3:Yeah. Of like what actually means to nationalize something? Mhmm. There's like the Manhattan Project Mhmm. Which is like, okay, this is like full scale Mhmm.
Speaker 3:Top down. Everything is decided by by one person. It goes down the pyramid. And then there's like the very, you know, kind of distributed like, oh, like Intel is that nationalization. Yeah.
Speaker 2:So I
Speaker 3:I think I broadly agree like, I I don't think really I don't think the the Manhattan Project is is really the best way to do this. Right?
Speaker 2:Yeah.
Speaker 3:Because if you take like, you know, Tata Council thing is like, you know, state capacity libertarianism like, is the government like fully capable of of continuing, you know, this this AI progress that we have right now? Like, would US stay in the lead if if the whole thing is like, you know, set by the government?
Speaker 2:Yeah.
Speaker 3:It's unclear.
Speaker 2:This is this is sort of what I was going back and forth with Karp on was like people have framed this as like a battle between Dario Amade and Pete Hegseth. And I feel like we are a democracy and so like, I would like more more authority to be assigned to the individual American voter for a lot of these things. You know, you have that joke about, like, we gotta talk about it. We gotta just talk about this. Like, what are we gonna do about AI?
Speaker 2:It's like, well, like, we can we can actually vote on it. Like, you can you you can have a plan and then people can vote for it. And and there are a bunch of different ways to exercise political, will. And it feels like there there is a trade off, but we got to a good place with the nuclear weapons one. And I do feel like I, as a voter, I have a very small stake, one of 300,000, you know, I guess, I I I have 300,000,000.
Speaker 2:I guess there's, a 160,000,000 people that vote in the national election. But part of the national election is, you know, do you trust this particular person to have the nuclear football? To have their finger they're gonna have their finger on the button. Like, well, let that sit with you before you cast your ballot. And it will be a continuation of that.
Speaker 2:Like, this this is the person that will decide AI policy, so vote according to that. Right? And and I hope that there's more more of a of a understanding that the American voter, the American citizen does have a huge stake in the AI future and it's not just the the like, you know, the high flying personalities that give, you know, speeches and and and podcast appearances. There there is a lot more to the American project than that. Well, there is a lot of news around Taiwan and what might happen over there.
Speaker 2:We found an interesting call sheet market that sort of tracks just general unrest in Taiwan. So the the question is, will The United States issue a level four travel advisory for Taiwan? That, of course, would be a very, very bad news if that did happen. It's sitting at 46% before 2028, January January 1, 51 before 2029, and 57% above for 2030. And so this is sort of a a way to understand geopolitical risk.
Speaker 2:Obviously, hope this calms down and this market goes to zero because
Speaker 4:You me that headline about increased activity around Taiwan.
Speaker 2:Yeah. Some of it was part of it.
Speaker 4:Yeah. Some of it Yeah. I think the reason that it that it triggered Really calling me
Speaker 2:out here. Sending you fake news. You're like, you you actually fell for a viral hoax recently.
Speaker 4:No. I mean, I I looked at it and it was factually true. It was just Yeah. The activity had dropped enough Okay. That the increased activity looked like a really sharp growth, but it was just kind of normalizing.
Speaker 2:Oh, okay. Okay. Interesting. Well, are certainly hoping for smooth sailing in the Taiwan Strait. Let me tell you about Figma.
Speaker 2:No matter where your idea starts, Figma make, Claude code, codex, or a sketch, the Figma canvas is where ideas connect and products take shape, build in the right direction with Figma. ASML?
Speaker 4:Bernie Hobart. Funny post here. Yeah. He says ASML can't figure out how to make money from EUV machines, so they sell them to TSMC. But TSMC can't figure out how to make money from chips, so they sell them to Apple.
Speaker 4:Apple can't figure out a profitable way to use iPhones, so they sell them. And there you go, the profit. And anyways, doctor Kareem Carr is is
Speaker 2:Someone saying
Speaker 4:bear posting.
Speaker 2:Yes. Bear posting that they don't know how to make money from AI directly. This is really
Speaker 4:This is such a funny criticism.
Speaker 2:It's such a funny criticism.
Speaker 4:Because if they were if they if they actually were the criticism would be insane. It'd be like they created super intelligence themselves.
Speaker 2:Yeah. Exactly. Exactly.
Speaker 4:Whole point is that every single person on earth whether you pay for a plan or not Yeah. Can benefit from today's models.
Speaker 2:Indeed. Well, let's head over to Meta. But first, let me tell you about eleven Labs. Build intelligent real time conversational agents, reimagine human technology interaction with 11. So Nebius and Meta have agreed to a $27,000,000,000 AI infrastructure pact deal.
Speaker 2:The talks are advanced to pact stage. Five year deal, dollars 27,000,000,000 to supply AI infrastructure capacity to Meta. Nebius has really been an unfair 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. So, Nebia said it will provide $12,000,000,000 of dedicated capacity across multiple locations. Meta will also purchase up to 15,000,000,000 in additional capacity over the five year over the five year period.
Speaker 2:These deals are sort of squishy, but it doesn't matter because the people who actually need to know can underwrite them accordingly. Nebius 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. That's expected to be available in the second half of the year, and Nebius will begin delivery of that capacity beginning early next year, which feels like a decade in AI time lines. Why do you have the paper in front of your face?
Speaker 4:The team earlier said I look like a third base coach. So I'm covering up I'm covering up
Speaker 2:like Yeah. Because you don't wanna you don't wanna let everyone know what play you're calling. There you
Speaker 4:go. Exactly.
Speaker 3:That's fine.
Speaker 4:There was news Friday late. A rumor or or some reporting from Reuters. Meta is planning sweeping layoffs that could affect 20% or more of the company. Three 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. How many employees does Meta have?
Speaker 4:I think it's like 60,000?
Speaker 2:Something like that.
Speaker 4:Let's figure it.
Speaker 3:Seventy five?
Speaker 2:Seventy five thousand?
Speaker 3:06/30/2025.
Speaker 4:It will be the same number. Yes. 78,000 Mhmm. As of December 31, somewhere in the same range as Salesforce. And again The company.
Speaker 4:Not super surprising. Stock's up around 2% today. Okay. I would expect this to pop even harder once these layoffs are actually announced. Yeah.
Speaker 2:I mean, the advice is is, you know, become the become aligned with the AI effort at Meta. Like, they're they're you know, if if if these layoffs happen, they're clearly cutting part of the workforce, but then they're also acquiring and hiring all over the place, just more around AI. I mean, saw that today with the Manus announcement.
Speaker 4:They're taking New naming meta. Just call your product a computer. We got Manus Computer. We got No, no, no.
Speaker 2:It's called My Computer.
Speaker 4:Manus My Computer by Manus.
Speaker 2:My Computer by Manus. It works on mobile, works on your computer, Manus Desktop. But wait.
Speaker 4:Again, this was this
Speaker 2:was My computer is the core feature of the new Manus desktop app.
Speaker 6:It's your
Speaker 4:AI agent. Okay.
Speaker 2:So
Speaker 4:It's still called Manus.
Speaker 2:Direct competitor to Codex, Clog Code, co co work, and Microsoft co work. At this point, everyone's doing co work, so maybe you just rip that.
Speaker 4:But Yeah. So so the reason I thought the Manus acquisition
Speaker 2:Mhmm.
Speaker 4:Was interesting Yeah. At the time
Speaker 2:Yeah.
Speaker 4:Is people were positioning it at as more of a talent acquisition. Yeah. Like, these are great product builders that that figured out how to grow products super quickly. I think at the time they sold, they were somewhere in the range of a 100 to 200,000,000 of of run rate. Yeah.
Speaker 4:I was I was interested in it specifically because it seemed like Zuck was trying to take what they had built and actually just scale it, not just roll them into, you know, working on ads or whatever other products. So Yeah. Tyler, please download My Computer by Manus. Yeah. And play around with it and come back with a review.
Speaker 2:So the top recommended action that they showcase here is organizing thousands of unsorted photos. I'm not super into, like, organization for the sake of organization, but that does seem pretty useful. I was taking photos on on a, you know, an actual camera this weekend and had to transfer them from the camera to an iPad, then sort of scroll through them, favorite them, then share them over AirDrop. And there is a cool, like, agentic workflow, which is basically actually download the raws. Some of them were a little bit overexposed.
Speaker 2:Some of them need a little bit of color grading. And if I could have a workflow where Manus or, you know, some desktop agent opens every photo individually in Photoshop and tweaks it and does it intelligently and crops it ever so slightly and is and is, like, thoughtful about it, like, that would that would definitely speed up my life.
Speaker 4:So Dodd says, would trust OpenGlo more than Manus after the Meta acquisition with private data.
Speaker 2:Yeah. Well, Manus branding, the Meta branding on this is so limited. I would be surprised if people sort of you know, if this go if this goes broad, people wouldn't necessarily know that much. I wonder if they'll do the Oculus thing and you'll have to, like, log in with Facebook at some point.
Speaker 3:You can log in with Facebook.
Speaker 2:You can.
Speaker 3:Alright. But you can also log in with normal, like, email people. Yeah.
Speaker 2:I mean, that that Vanas before was wasn't it Chinese company? It was based in Singapore, but it it was, you know, like like rumored to be aligned with China. And so
Speaker 4:I mean, not rumored. They they were building it in China Okay.
Speaker 2:And they bounced Okay.
Speaker 4:To Singapore because the optics were not good.
Speaker 2:So, you know, as far as as far as private data, security goes, I think this is an upgrade. Right? It certainly feels like it. Anyway, let me tell you about Phantom Cash. Fund your wallet without exchanges or middlemen and spend with the Phantom card.
Speaker 2:So the Oscars happened last night. Jordy, just to get you up to speed, the Oscars are an award show that are put on by the Motion Picture and Academy of Arts and Sciences. It it'll Yeah.
Speaker 4:Saw someone at someone on the ramp cap table got an award.
Speaker 2:Yeah. Yeah. Yeah. Michael B Jordan won best actor and he won
Speaker 4:Best ambassador.
Speaker 2:For that. Best ambassador. Yeah. They should have a category for that. But Timothy Chalamet is getting taken to task in the Financial Times over his views on opera and ballet of all things.
Speaker 2:The Financial Times writes, it's quite 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 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 Timothee Chalamet, a talented young actor who stars in the multi Oscar nominated Marty Supreme, which had a very unfortunate showing at Oscars. I think they were nominated for nine awards and they didn't win anything. And so And little bit upset.
Speaker 4:Do you Is your belief that it had to do with his comments disrespecting? No. Or it was just No. The people, the the critics actually just said, hey, like, you know Yeah.
Speaker 2:Fun movie. I I think in every category, he was Marty Supreme was up against, like, a Goliath. Like, was a every fight was sort of a David and Goliath, there were just no upsets because he was going up against sinners and one battle after another, which were heavy favorites, I think, from the very beginning, before these comments were made. So Timothy Shalomay was talking with a fellow actor, Matthew McConaughey, at a town hall event organized by CNN and Variety in February, but the comments actually just got clipped and went viral recently. Two it it was two week delay.
Speaker 2:The slicer's over there. Gotta step it up. He said, I don't wanna be working in ballet or opera or things where it's like, hey, keep this thing alive even though, like, no one cares about this anymore. All respect to the ballet and opera people out there. And then he said, distinctly disrespectfully, I just lost 14¢ in viewership.
Speaker 2:Damn. I just took shots for no reason. There is evidence of Chalamet showing having made similar comments before, such as on the Graham Norton show in 2019 when he called opera a quote outdated art form and at an event the same year where he was worried that cinema would become like opera or ballet or something, kind of a dying art form or something. He also, as many people, as many of those who claim to feel so offended have pointed out, has close family connections to the world of classical dance. His mother, grandmother, and sister all danced with the New York City Ballet.
Speaker 2:Wow. And he has spoken out about growing up dreaming big backstage, at, the Koch Theater in New York where the ballet performs. As someone who tried to pursue a career in pop music while my older sister, this is the writer in the Financial Times, my older sister pursued one in classical piano, I would wager that he has been honing this particular attack or perhaps defense line since adolescence. So his apparent instant regret, his slip felt felt a bit disingenuous. Are are you a are you an opera fan, ballet fan?
Speaker 3:I like the opera.
Speaker 2:Me too.
Speaker 3:And Although I actually have not been to the opera yet. So Yeah. It's hard for me to.
Speaker 9:And I
Speaker 2:I I just think, like, there's a world where where, you know, the film and movie industry, like, does become like opera and ballet, but that's still, like, a beautiful thing with an amazing culture.
Speaker 3:Seen this in LA where I think a lot of movies are now releasing only at these like kind of fancy theaters or Yeah. Chinese theater. These kind things where it's like much more like kind of upstage and like a a real event that you go to.
Speaker 2:Yeah. And of course, it is it is like a, you know, just technological disruption with social media and there's a lot of other like gyrations in the transition there. But
Speaker 4:I I I I'll tell you why I think Yeah. This whole
Speaker 2:Kerfuffles happened. Kerfuffle
Speaker 4:Yeah. Happened. And as someone who doesn't really follow
Speaker 9:Mhmm.
Speaker 4:Hollywood, doesn't follow film, doesn't follow Timothy Chalamet, etcetera, etcetera, I think what is happening is he came out with the like this new, it's okay to pursue greatness Yeah. On the path to greatness.
Speaker 2:Sure. Sure. Sure.
Speaker 4:I'm I'm trying to be the goat. I'm trying to, you know, like Yeah. Coming out with this kind of like Bravado. Bravado.
Speaker 2:Yeah.
Speaker 4:And if you do that and it's like, me me me me me me Sure. I'm I'm trying to be the greatest. Yeah. And then you start just randomly taking shots at another art form where Yeah. Other people are pursuing greatness.
Speaker 4:Sure. You just invite a lot of criticism. Yeah. Because I think like everyone's okay. I think with somebody like, you know, being on their own personal pursuit of greatness.
Speaker 4:Yeah. But if you're doing that while trying to tear down other art forms Yeah. You're just gonna invite massive criticism.
Speaker 2:Yeah. It it it does feel like he's sort of he's sort of collapsing like market cap and like tam of like yes. The the opera tam and the ballet tam is smaller than film. But it would be odd
Speaker 4:the actual
Speaker 2:Yeah. Let's play let's play the first.
Speaker 10:People that are here that are younger than me, where people desire, are desiring things that are more patient and that pull you in. Just saw another article that says Gen Z is a bigger movie going audience than a millennial audience, know? I feel like a fucking grandpa saying that. No, but point being, I think Even like Frankenstein, is like a hugely popular movie this year, I didn't think that pacing was extraordinarily fast or anything, but it pulled people in, you know. But it does take you having to wave a flag of, hey, this is a serious movie or something.
Speaker 10:And some people wanna be entertained and quickly. I'm really right in the middle, Matthew, because I admire people, I've done it myself to go on a talk show and go, hey, we've to keep movie theaters alive. You know, we've 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 go see it and go out of their way to be loud and proud about it. And I don't wanna be working in ballet or opera or, you know, things where it's
Speaker 2:like, hey, keep this thing alive even though cares about this anymore.
Speaker 10:All respect to the ballet
Speaker 2:and opera people out there.
Speaker 10:I just lost 14¢ in viewership.
Speaker 2:Crazy shots. That's not a shot.
Speaker 8:I hear
Speaker 2:what you're saying. Yeah. Yeah. Yeah. I don't know.
Speaker 2:It's it's it's interesting. I was thinking about like if if like the creator of like GTA five like stood on stage and was just like we are 10 times the size of the Baseball. Of baseball. But I mean, also like the movie industry. Like, the gay the video gaming industry has been basically 10 times the size of the of the movie industry for
Speaker 4:You mean the movie theater business?
Speaker 2:No. Like like Hollywood. Like Gross. Production. Yeah.
Speaker 2:Totally. I'm almost positive.
Speaker 4:Not not 10 times the size of your account streaming platform.
Speaker 2:Yeah. Maybe streaming that includes TV shows. And then do you include mobile games or not? That's a big question. But the video game industry is definitely bigger.
Speaker 4:Raghav in the Twitch chat from deeps says, NVIDIA CEO just said he sees 1,000,000,000,000 in revenue through 2020.
Speaker 2:That's That's a gong. Bring down the gong.
Speaker 4:Bring down the mallet.
Speaker 2:Let's go. Congratulations.
Speaker 4:It's amazing. Thank you, Rob.
Speaker 2:And we have our next guest in the restream waiting room. First, let me tell you about Sentry. Sentry shows developers what's broken and helps them fix it fast. That's why a 150,000 organizations use it to keep their apps working. And we are joined by Kevin from Epic Gardening in the Restream Radio.
Speaker 2:Let's bring him to TBPN Health Show. Kevin, how are you doing?
Speaker 4:What's going on?
Speaker 6:What's up, brothers? How you doing?
Speaker 4:Good to see you, brother.
Speaker 2:Thanks so much for taking the time to join the show.
Speaker 4:First up, we gotta talk about that tank.
Speaker 2:Yeah. What's the tank?
Speaker 4:What's in the tank? We've been talking about
Speaker 6:Breeding rare Costa Rican tree frogs in this tank.
Speaker 4:No way. Hey.
Speaker 6:They're endangered.
Speaker 2:Okay. Oh, they're endangered. Okay. What else is what else is special about a And you're planning to release
Speaker 4:them in in all 50 states once you have enough?
Speaker 6:Yeah. This is the goal. This is the goal. We're always scaling over here. Yeah.
Speaker 2:Of course. I love it. I love it. Is it challenging? Like how much of your time is devoted to that particular tank?
Speaker 6:Almost none. Almost none. I just need to make sure that they're fed. Yeah.
Speaker 8:That's cool.
Speaker 2:What do they eat?
Speaker 6:Yeah. They eat crickets. Crickets. Which I'm breeding in that little
Speaker 2:tank right over there. You can see that.
Speaker 4:Crickets too. Yeah.
Speaker 6:We're breeding the different trophic levels over here for sure.
Speaker 4:Okay. And then and then do these frogs have use in your garden? Is it purely just for fun? No.
Speaker 6:I'm just branching out to to to flora and or to fauna now, I guess. Yeah. Here at Epic, you know?
Speaker 2:Okay. Well, first time in the show. So I I wanna kick off with your backstory. I I wanna know about the decision to start making content. I feel like that's always an interesting origin story.
Speaker 2:Like, what what when when did you think, okay, I need to make content?
Speaker 6:Dude, I mean, I I'm an Internet OG, so I was on GeoCities. I was on Angel Fire back in the day. I was on
Speaker 2:Angel Fire too.
Speaker 6:Yeah. Anime tutorials, you know? No way. Don't know what it is. I think genetically, I'm I'm designed to make content.
Speaker 6:But Yeah. For for Epic, it was really a calling card for remember when you used to design WordPress websites back in the day? Like, when people actually paid for that service? Yeah. I I used the blog as like a calling card or a digital business card for like designing websites for Sure.
Speaker 6:For local businesses and then just kinda kept plugging along with it and adding different platforms and and here we are today.
Speaker 2:Yeah. Well, what about the first YouTube video? Like, what was the backstory behind choosing to go to YouTube, choosing to go to video? It's a big lift for people if they're on Substack or they're a writer and they don't know how they're gonna do in front of camera.
Speaker 6:First YouTube video was 2013, so it was a long time ago.
Speaker 2:Wow.
Speaker 6:And ironically, back then, I mean, SEO and blogs were kind of the thing.
Speaker 2:Yeah.
Speaker 6:And so, for me, the first YouTube video, maybe it's the second YouTube video, you can see me using a screen recording app reading a blog article. Yeah. Just literally reading the article. Yeah. And with the hopes that people would watch that video and click the blog link and I would make money off of the advertising on
Speaker 2:the blog. On the blog. So, it's
Speaker 6:a completely backward to today, of course.
Speaker 2:Yeah. Yeah.
Speaker 6:Yeah. Then, obviously, Discover YouTube is is a far better platform, especially these days.
Speaker 2:So what was the what was the flow of traffic? Like, over time, were you able to reroute blog viewers to YouTube, or did the algorithm eventually kick in? Because you're pre algo feed. Right?
Speaker 6:Yeah. Yeah. I think so. Right? I mean, think it was back then if you subscribe to a channel on YouTube, that subscription would just show up Yeah.
Speaker 6:Which was a beautiful time. But, no, I think every platform as you expand every platform, you think like, okay, well, I can get someone from this one to that one. Mhmm. It tends not to work. You tend to have to just play each platform for what it is.
Speaker 6:Yeah. And so, like YouTube became its own thing. Insta, all the other social media platforms have become their own thing now.
Speaker 8:Mhmm.
Speaker 2:How how do you think about, like, serializing content, creating through lines, like the initial formats? Like, what was the actual development of the playbook that you ran on YouTube?
Speaker 6:On YouTube, I think in the early days, because remember, like, I'm 13 years old as a YouTuber, which is like two YouTuber lifespans. I think YouTubers last about six years or so. That's amazing. And so back in the early days, it was just pure SEO, especially for our gardening channels like, hey, how do I how do I grow basil? How do I grow tomatoes?
Speaker 6:How do I prune tomatoes? Sure. These days, those videos have all been made either by me or someone else. Sure. And so, we've had to come up with formats that that work repeatedly over time.
Speaker 6:So for us, it's great. I mean, it's very seasonal business. So Yeah. In March, what to plant in March? In April, what to plant in April?
Speaker 6:Or, you know, in June, how to take care of your garden in June? That kind And of then also coming up with like formats that are a little bit more high effort but tend to do better like garden makeovers or garden tours where you actually have to go somewhere.
Speaker 2:Sure.
Speaker 6:But it's it's easy to kind of like bulk those into a week and produce them.
Speaker 4:What what did the journey look like of transitioning from media into actually making products yourself. Because that is an idea that that is at least in the venture world, people talk about just a very obvious transition. You
Speaker 2:just Content content to
Speaker 4:to commerce and yet there's actually so like few creators who have like made that transition well, actually products that go on to have equity value. I mean, we we we've people bring up all the time, oh, you guys have this audience in in tech. You should create, you know, software, various products for for the audience. And our answer has always been, look, if we do that, we're competing against someone in our audience who is spending a 100% of their time on that on that business.
Speaker 6:Yeah.
Speaker 4:And They could
Speaker 2:be a sponsor.
Speaker 4:That they they could be a sponsor, but but more so, like, I don't wanna compete with someone in our audience that is gets to spend a 100% of their time on something when we can only spend, like, 10% of their time on it. Yeah. Like, they're gonna smoke us. But I think in in what you're doing like very very niched very very niched down and maybe the companies that you're competing with are not like they can't go out and get a $100,000,000 of of funding necessarily right away. But talk about that transition and and how it's evolved.
Speaker 6:Yeah. Yeah. I mean, I think up until 2019, Epic was just media business and that's it. And it would be Google ads, it'd be YouTube ads, and maybe some brand stuff here and there. And I think in 2019, did out of just that pool, a quarter million in revenue.
Speaker 6:Wow. And then that was the year I decided to do product. And so the whole logic being, I can't really control any of those three streams of income. Like, traffic goes down for one reason or another. All of those go down commensurately.
Speaker 6:Yeah. And so I thought, okay. Well, what what can I sell? And the beauty of having content is that you kind of get like a pre validation engine for for what you might wanna put out there. And so there was this raised bed that I had.
Speaker 6:It's just like a guard a metal garden bed that had been sent to me. And I was like, this is the thing I get asked the most about, so I'll figure out how to sell it. I didn't even know who gave it to me initially. So I tracked down the manufacturers, Australian company, and I just kept emailing them every quarter. Was like, can I sell this?
Speaker 6:Can I sell this? They said, no. No. No. No.
Speaker 6:No. They eventually said yes. I think I had $70 in the business bank account. I spent 40 on a shipping container. I knew nothing about e comm.
Speaker 6:So what I thought I would do is this is the most crazy stupid e comm logic of all time. But what I thought I would do is bring it into the Port Of San Diego, which does not take containers. So that that was already a no go. It goes into the Port Of Long Beach. I thought I was gonna go up and get it, like me at the port driving driving the container down.
Speaker 9:I have
Speaker 4:a container here. I'm just picking up.
Speaker 6:Yeah. Just like hauling it down. And then I was looking into Costco self storage to like rent that, unload the container, and like get like some some sort of satellite Internet to print the orders. And I talked to a couple friends and they were like, yeah. Have you heard of a third party logistics company?
Speaker 6:Just ship it there. You know, it's just so stupid. But that's how little I knew at the time. And so what happened is made the order, got it on the water, made an Instagram story and said, hey, all these beds you guys keep asking about, they're here now. I have 550 of them.
Speaker 6:They sold out in two days, used that cash to buy another container.
Speaker 2:No way.
Speaker 6:Sold that that out in two days. So, like, by the end of the year, I think we did quarter mill in just that. Yeah. So the business doubled. And then, of course, setting that up before the global pandemic was insane.
Speaker 6:So we went from Yeah. 500 to, like, 2,800,000.0 to 7,100,000.0 the next year and then raise raise a series a. But, yeah, I mean, immediately, I was like, oh, this is obviously the actual revenue driver behind this business at least, which I agree. Like, a lot of media businesses don't have that easy plug in.
Speaker 2:Yep. Totally. Yeah. What was the team like before and after this transition? Did you have to hire business people?
Speaker 2:How did you Yeah. Like, how do you feel your role was changing? I mean, we've had Doug DeMiro on the show a few times, and he was, like, very happy to hire a CEO to sort of run Cars and Bids and go back into content mode, do podcasts, which grew a ton. But every creator has sort of has different journey as they as they evolve the business.
Speaker 6:Yeah. It's so weird because I run into Doug all the time at the coffee shop down the street. So and we share the same investors. But yeah. So up until 2021 at tail end is when I raised the series a.
Speaker 6:It was me or contractors. So it was me, a creditor, a writer, and an assistant, and that was it. And we had did we did about 7,500,000.0 that year, mostly product sales at that point.
Speaker 2:Yeah. So it
Speaker 6:was like way
Speaker 2:Wait. Wait. Wait. So so so you have four contractors, but all those contractors are on the content side, but mostly it was Yeah.
Speaker 6:So I was doing all the commerce stuff.
Speaker 2:So so you have like
Speaker 4:But you're a single you're a single product at this point? You've just made the best had single product. I'm just gonna sell. I made and you didn't have to develop the I'm sure you've made changes to the product.
Speaker 2:I mean,
Speaker 6:I guess the biggest thing here is I did not make dream.
Speaker 4:Yeah. Like, literally, it was like thing that everybody gets sold and then it doesn't actually work.
Speaker 6:Yeah. Yeah. Was crazy level drop shipping. I guess you could say. Except for, I mean, I I owned the inventory.
Speaker 6:Brought it in. Wasn't dry. I had a three p l. Like, so it wasn't true drop shipping. Just that I didn't invent the product.
Speaker 6:It was a distributor relationship. Eventually, of course, we've started inventing products. And, you know, we scaled, I think, from December 21 to December 22 from four people to about 90 because we used some of the funding to buy a seed seed company that had 60 people. So Sure. Yeah.
Speaker 6:That was a pretty crazy transition.
Speaker 4:That's And talk about that the buy versus build decision on the on the seed side because I'm sure you had opportunities to do both.
Speaker 6:Right. So so with seed, it's almost always gonna be a buy because the infrastructure to actually, like, acquire seed we we sell almost 800 varieties of seed, vegetables, flowers, herbs. It's it's nearly impossible to scale that really quickly if you have, like, buyers' relationships. The buy orders are out a couple years. Really?
Speaker 6:You need, like, pretty specific infrastructure to to actually, like, germinate and test those seeds to pack them appropriately. I think there's, like, three or four companies maybe that sell the packing machines, and they're all in, like, Germany. So some German guy will fly over and, like, fix a machine for you. Crazy. Yeah.
Speaker 6:I mean and plus, let alone, like, we bought the brand of this of seed that I actually started gardening with back in the day. So there's, a heritage sort of story angle there that worked out really well.
Speaker 2:Yep. What about the like, your role shifting as you bring in those 60 new people? I imagine that they had a leadership team at a company of that scale. How are you interfacing with them? What what what does your role look like then?
Speaker 6:Yeah. I mean, the first the first year or two was, like, all out madness. It was, like, whatever I could do at any point in time. So, like, still be the face of the content and architect that, but, you know, hiring, scaling, all sorts of ops types of decisions. Now we have a president similar to Doug's setup
Speaker 5:Yeah.
Speaker 6:Which is extremely, extremely helpful. He's ex chief growth officer at GameStop back in those crazy days.
Speaker 2:Oh, wow.
Speaker 6:So he's got some pretty pretty wild Yeah. And with the seed brand, the founders wanted to leave. And so we had, like, this little holdover position for them, and she kind of coached our leader in. Cool. And they were just ready to go.
Speaker 6:And we can always call on them if we need them, but Yeah. We don't we don't really anymore. Yeah.
Speaker 2:That's great. Talk to me about seeds as a particularly good e commerce business. I imagine, like, when I when I think about the worst e commerce brand, it would be like, I sell a gallon of water. You know?
Speaker 3:Yeah. Yeah.
Speaker 2:It costs $20 to ship and then people buy it for a dollar.
Speaker 4:And it's low margin.
Speaker 2:Low margin. Seeds, it feels like great ecommerce product that maybe people just needed to be educated about. But was that your experience, and what was it like actually scaling up
Speaker 6:the Yeah. I mean, I think, like, those original products, raised beds, like, didn't have to invent them, right, which is great. But every by every other metric, they're not a good ecommerce product.
Speaker 2:Totally.
Speaker 6:The the lightest one is 20 pounds. The heaviest one is 60 pounds. And then you're also you're charged on dimensional weight of the shipping as well. Yeah. And at the time, my my three p l was out of, like, Thousand Oaks.
Speaker 6:So I'm shipping from SoCal to the whole country.
Speaker 2:Yep.
Speaker 6:A 60 pound box, which is just terrible. Yeah. The beauty of that time is that I was charging shipping, is kind of unheard of these days Yeah. And I had no customer acquisition costs. Yeah.
Speaker 6:My customer acquisition cost was actually negative because I was getting paid to make my YouTube videos. You know what I mean? Yeah. And that's what was selling it. Yeah.
Speaker 6:And so I remember back in those times pre pre funding, let's say, kind of like laughing at all the dtocecom bros because I was like, you're running paid
Speaker 2:ads like such a cloud.
Speaker 6:And now I'm like, okay, I understand the the model a little better. Yeah. Yeah. But, yeah, I mean, once we got the seed brand, that's a primarily wholesale business. Mhmm.
Speaker 6:And so when we looked at it, I would say about 20% of the revenue was direct to consumer and they had not focused on it. And so we've tripled d to c Oh. Just by saying we own the business, basically. We haven't done, like, a crazy amount of improvements as far as, like, d to c goes. We just we just actually paid attention to it and plugged it into content.
Speaker 6:But you're right. Yeah. The gross margin on seeds is is quite good relative to everything else in the gardening space.
Speaker 2:Yeah. How do you think about the transition from I mean, it it sounds like you're actually doing the the backwards transition. Most like the d to c bros start online, and then eventually, realize that, okay. Well, I found the efficient frontier of CAC to LTV on Meta and Google. Now it's time to go into retail, and then the whole company needs to pivot.
Speaker 2:They need to hire retail salespeople. Are you going are you going in one direction or both directions? I I've always wondered about the the the retail side of the business.
Speaker 6:Yeah. Yeah. I mean, I think the logic of the the seed brand logic to me, I think there needs to be like a first order logic of buying something and that needs to be true. Mhmm. And then the second orders can be like very beneficial and may or may not play out.
Speaker 6:For me, this the the logic was like what we just talked about. The seed margins are very good and it's it's actually the only item in gardening you literally need every year. Every other thing you technically could get away with not buying again, like a raised bed or something like that. Sure. And so there's a repeatable addition to our business that we now have.
Speaker 6:That's great. But yeah. I mean, the the sort of, like, second order thoughts of of buying the seed brand was, can I introduce the raised beds, the seed trays that we developed to the wholesale network
Speaker 2:Yeah?
Speaker 6:Because that is very, very hard to build out. We're in 75% of all independent nurseries in the country. Mhmm. Which it would be different if like we had Home Depot or Target or something and we could just say, take this line. Yeah.
Speaker 6:Instead, we have reps that can go out to like 5,000 stores and say, do you want this line? Which if we can get penetration on like some of those harder goods Yeah. Then that's a huge benefit that that that could play out for us.
Speaker 2:I I have this thesis about creators that do that launch products is that they they typically underrate the number of b to b buyers in their audiences. Be and and you might not you might think, okay. I'm selling a protein shake. I'll sell it to the consumer. But you might have, like, literally someone whose job is to buy the next protein shake who works at Target or Walmart, and they might be familiar with you.
Speaker 2:Have you had any of those experiences? Has that been advantageous?
Speaker 5:Or Yeah.
Speaker 2:You know, is this a unique unique industry?
Speaker 6:It's it's it's actually really weird because, like, the advantages you get, let's say, in, like, pre validating a new product you might launch by teasing it in content and and sort of seeing early demand, you actually get that to some degree with the wholesale relationships. Like, we're in we're in about 1,300 Petcos now. Oh, cool. And I would say the sole reason is because the the major buyer at Petco has just been an epic fan for a long time. So we were warmed up.
Speaker 6:You know? I don't have to go chase that down and prove it out. We're talking to Walmart for some stuff. Hopefully, that comes to be, but it's a similar sort of way that relationship started too. Mhmm.
Speaker 6:So I think like the content angle, if you can convert it, you have some interesting doors like kind of automatically open.
Speaker 11:Mhmm.
Speaker 4:Do you think you have the most AI proof business in the world? Because we talk about like, one, you're obviously not like trying to sell like, you know, vertical software. But but two, even, you know, the the Suttrini piece pointed out that there's a lot of like AI proof businesses where if demand gets destroyed because your buyer is no longer making 250 a year to do some email job, Like, your business might be fine, but maybe maybe there's less demand. But I feel like even in even in these, like, AI doomsday scenarios, like, I probably you know, if I, you know, lose my job, I still probably want some seeds Yeah. And put them in the ground.
Speaker 6:Oh, I saw that Anthropic piece that came out saying like which which industries are the most vulnerable. Yeah. And I saw groundskeeping at a near zero.
Speaker 2:Near zero.
Speaker 6:Yeah. Which is, you know, gardening is just a, you know, a recreational version of groundskeeping. So I think we're fine.
Speaker 4:Yeah. Yeah. No. No. How are you how are you using AI?
Speaker 4:How are people using AI in gardens? Like, can imagine taking a picture of something happening in your garden and just being like, how do I fix this? Like, lot of a lot of Yeah. That that it could be very useful and
Speaker 6:We have that. Yeah. We have that. So what we did is we launched this membership program that come comes with commercial benefits. You get like 10% off the store, free shipping, free returns, which is great if you wanna buy like a couple seeds here and there.
Speaker 6:Yeah. And then we paired that with an educational sort of side because we have more or less the biggest gardening audience on any of the platforms. So we train the model just on our own internal content and then, like, licensed databases of, let's say, plant facts or weather or something like that. Sure. And so if you ask it a question or send it a picture, it'll give you the answer that the closest answer you could get to what we would actually say, not just, like, what GPT or Claude might say.
Speaker 6:And then it'll kind of funnel you to live support if you want it so you can get actual humans too. So it's kind of like a two tier thing. And then we're just using it, like, along with anyone else that how how you'd use it inside of a company for operations and stuff like that.
Speaker 2:What about on the content side? Are you finding it useful for scripting or thumbnail development or prototyping or sort of, like, layout? Anything there?
Speaker 6:I think it's good. John.
Speaker 2:It's game changer.
Speaker 6:It's game changer. Yeah. I mean, I think it's, like, the way we try to use it for content is, like, you're you're really good first draft that you would normally have to spend how however long to to script out. The beauty of gardening, I think, is it's so bespoke to, like, a particular individual's approach or a particular geography that AI is not really crushing that right now nor nor do I really want it to be. But it's really good for first drafting a lot of different things in content.
Speaker 6:Yeah.
Speaker 4:Yep. What how do you recommend I fall in love with gardening? I grew up my parents basically forced me to do a lot of weeding, a lot of mowing, a lot of just random stuff around our yard. I had bad allergies at the time, so I would come out of that Yeah. Be like destroyed.
Speaker 4:And so I I have not I've had zero desire to to get into gardening as an adult. But I feel like I just gotta find the right wedge products. Yeah. So is it is it, you know, raspberries, tomatoes Look,
Speaker 6:mean, if you pick you pick the crop that you are the most excited to eat and cook with and you grow that, So you if it's tomatoes I mean, I'll send the technology brothers a bed and some seeds, no problem if if it gets you in
Speaker 2:the Fantastic. I love it.
Speaker 4:Yeah. No. Happy to support you. Just just tell us tell us what to get.
Speaker 2:Yeah. How how are you thinking about the interaction between the creator economy, YouTube content, and Hollywood. We've seen, like, you know, Mr. Beast is all over Amazon Prime now. I could imagine you doing content for with more legacy institutions.
Speaker 2:What's your philosophy around the those distribution channels?
Speaker 6:You know, so the two things we've done that are kinda tasting that world is we have a Samsung fast channel now that we're we've licensed 200, hopefully hopefully more soon. And then we just launched last week a eight episode series on Home Depot's YouTube channel. Cool. So kind of like a co produced series. Not not a show, like, on streaming, but that's coming around too.
Speaker 6:I think I don't know. I mean, I think that if you're Jimmy and you can get a massive check to do something on Prime, like, why would you not? Right? But a lot of us on the smaller scale or, like, maybe industry wide big, but not like global big
Speaker 2:Yep.
Speaker 6:The fat the fast channel deals are looking really good right now. Cool. The the sort of shows, if you can brand a even if it's just a YouTube series as a show Yeah. Versus just like a a video or a series of videos, it seems to be pretty palatable to advertisers these days. That's good.
Speaker 6:Which is kinda interesting because like fundamentally, it's just a a list of videos. There's nothing really different about it. But if you call it a show, like Michelle Carre Challenge Accepted. Don't know if Oh, you know
Speaker 2:yeah. Yeah.
Speaker 6:She's great. That that that is very much like a show That's YouTube. A
Speaker 2:For
Speaker 6:sure. And it plays really well for for those types of networks.
Speaker 4:Yeah. Yeah. And eventually, you you have the seasonal element that you were saying, like, eventually, you can be like, here's a hundred hours of just April focused gardening Yeah. Content. And that's like, that's super powerful because the content is evergreen.
Speaker 4:The the Yeah. Plants and the earth and all these things aren't changing
Speaker 2:Yeah.
Speaker 4:Really in any meaningful Yeah. Way year over year.
Speaker 2:That's such a funny mind shift because I don't I don't know if you've had this experience, but it feels like playlists on YouTube like never really got what they deserved. Did you feel that way too? Right? Like
Speaker 6:Yeah. I mean, playlists like maybe back in the early days, you'd like crank through a a let's play video game series
Speaker 5:or something like that. Yeah.
Speaker 2:You just
Speaker 6:let the playlist run.
Speaker 4:Exactly.
Speaker 6:These days, the that I told the team actually, was like, look at every playlist we have, prune them down and then like bucket them into more conceptual shows rather than like, this is my grow tomatoes playlist. It's more like you know? So so that's that's what we're trying to do right now.
Speaker 2:Yeah. Like with Doug, you'll see, like, you know, car reviews or, like, listicles and, like, it's more the structure. But, yeah, that like, you could imagine there's also the question of, like, how set up is Hollywood to work with someone like you? Because if even if they're like, yes. Like, we want you to do a full season on HGTV, but we're gonna need to pull you away from everything else for Yeah.
Speaker 2:And I think the corridor crew guys went through this a little bit where, like, the the numbers just never matched up. Like, they would get bigger, and then Hollywood would get more interested, but then the opportunity cost of taking six months off to do a real Hollywood
Speaker 4:movie just never
Speaker 6:happened to me. Yeah. Happened to me in the pandemic. So 2020, it was June 2020. I did a deal with Chip and Joanna Gaines' then burgeoning network Magnolia.
Speaker 6:Mhmm. I think they were taking over DIY at the time. Yeah. And it was supposed to be this transformation show. You go back, you have this beautiful sort of thing.
Speaker 6:And, obviously, pandemic kinda hampered that. I had just bought a house, so I pitched this idea of I'll just build out this house and we'll show you and we'll go through the so it was like 45 days straight of hardcore filming, like ten ten plus hours a day trying to get this done because there's a skeleton crew. And 2020, of course, was the year I think we started that year at a 180 k on YouTube. I ended that year at over 1,000,000 plus another channel almost at a 100. And so if I take those forty five days and just calculate, let's say I was making call even just 15 more videos, it would have been not only more money straight up, but more sort of brand value to the business to just make the YouTube videos.
Speaker 6:And I think that's what all the creators are running into.
Speaker 4:Yep. Yeah. Have you have you ever gotten tempted to do any of the like selling the actual end product? There's been a number of venture backed companies that are like, you know, trying to make the perfect strawberry or or any of these kind of vertical farming things. I always wanted somebody to do one of those like
Speaker 2:Blueberry company.
Speaker 4:One of those like like butcher's box style thing. Sure. But give me a live video feed from the ranch so you can actually, you know, if you if you have this like real time twenty four seven idyllic ranch, and then and then you're you're able to, like No.
Speaker 2:I have a low TAM there. But I mean, look.
Speaker 6:Like, it's hard enough shipping seeds around the world and shipping hard goods that don't expire. I can't imagine how hard it would be doing. I don't I would I would never wanna do it, honestly.
Speaker 2:Yeah. Yeah. How are you thinking about product expansion? You know, if you go to the nursery, there's so much there. There's certain things that you're equipped for, you're operationally set up for, and there's other stuff that's maybe better content, you know, could could, you know, be marketed, but might be an operational challenge.
Speaker 2:How do you assess, like, new
Speaker 4:Epic Gardening wheelbarrows? I would
Speaker 6:do it. I would do it, honestly. Yeah. Sure. Why not?
Speaker 2:Know, that'd be fun.
Speaker 10:That's awesome.
Speaker 6:I mean, look, like for us, there's a lot of room to run and seed. Yeah. There's there's like tens of thousands of stores we're not in just on our botanical interest line. Yeah. We launched an Epic Gardening line, which is like a guaranteed to work sort of beginners line.
Speaker 6:Mhmm. Maybe that goes through to big box because two thirds of gardeners spend their first dollar in a big box.
Speaker 4:We're not
Speaker 6:in any of them.
Speaker 2:Yeah.
Speaker 6:And the mission of company is to help people grow anywhere they are. So if that's where they walk in, we wanna be there in some way. So I think you can you can run this business quite quite a bit further just on seed alone.
Speaker 3:Sure.
Speaker 6:And then we're architecting the rest of the product strategy around that. So our second best selling line of products is seed starting trays and equipment and lighting. And then from there, it's raised beds. I think we probably do something in soil or fertilizers next. But again, like, do we own a fertilizer and soil mixing facility?
Speaker 6:No. And like No. Do we just want a white label?
Speaker 4:How? Or
Speaker 6:not. Right?
Speaker 4:Yeah. Yeah. Yeah. Yeah. On on fertilizer, is the fertilizer broader global fertilizer crisis because of the straight?
Speaker 4:Is that gonna trickle down to everyday gardeners or they're not it doesn't really matter for them if they're if the price even were to go two x, they still don't need enough product that
Speaker 6:Probably is fine for for us. I don't know. I mean, think it's way more a problem for like industrial agriculture. I think for us, we're we're probably fine.
Speaker 4:Yeah. Yeah. That's great. Very cool. Well
Speaker 2:What about tools? Linus Tech Tips has a screwdriver.
Speaker 6:I know. Yeah. I was just hanging with their CEO and No way. I don't know. We you might even see an LTT Epic collab at some point.
Speaker 2:That'd be great. Yeah. Yeah. He's the king of collabs. Well, thank you so much for taking the time.
Speaker 4:Great to finally meet you. Absolutely love
Speaker 2:Congrats on all the progress.
Speaker 4:Everything that you're doing and
Speaker 2:Say hello to Doug Jamiro.
Speaker 4:This year, I'll give I'll give gardening another shot.
Speaker 2:Wanna see it. We're we're we're at
Speaker 4:least getting Now that now that we have Kevin Kevin AI.
Speaker 6:Yeah. I'll hook you guys up. Don't worry about it. I got you.
Speaker 2:Fantastic. You're the man. Talk to soon.
Speaker 4:Great to hang out.
Speaker 2:Have a good one.
Speaker 1:Take care.
Speaker 2:Let me tell you about Restream. One livestream, 30 plus destinations. If you wanna multistream, go to restream.com. And I believe we have our next guest already in the Restream Room. Paul Cunningham is the dog healer.
Speaker 2:Dog healer. And now he's in the TBPN UltraGround. Paul, how are you doing?
Speaker 8:How's it going, Jess? What's happening?
Speaker 2:It's going fantastic.
Speaker 4:It's great. I don't know
Speaker 2:if it's early or late late, but thank you.
Speaker 4:Or is it is it shockingly early?
Speaker 8:The sun is just rising.
Speaker 2:Okay. There we go. It's rising. Well, we appreciate you getting up early to come chat with us. Why why don't you take us through some of the some of your backstory, your history?
Speaker 2:I feel like you have a very interesting career that led up to this moment, and then we'll go into, the actual, story and process of of of what what what went viral over the weekend.
Speaker 8:Sure. I've been doing machine learning since about 2009. Mhmm. Went full time about 2015.
Speaker 2:Mhmm.
Speaker 8:I ran the Sydney Machine Learning Meetup Group here for six ish years.
Speaker 2:I
Speaker 8:worked with, Sholto and Tristan on a robotic arm project.
Speaker 2:Oh, yeah.
Speaker 8:And, yeah. Consulting.
Speaker 2:Oh, yeah. Correct. The Sydney mafia. Forgot. Yeah.
Speaker 2:That makes sense that you did it over there. Not not not you you weren't in The States for that project. That's fun.
Speaker 8:Yeah. Correct. Yeah.
Speaker 3:That's great. Very cool.
Speaker 2:And so, yeah. Take us through the story of, I I I actually lost this in the story. Like, when did you when did you find out that your dog was suffering from cancer? What was the initial process? Was that what at what point did you leave the traditional veterinarian system?
Speaker 8:Sure. So what what actually happened, the pre story was, Roy's some, like, skin rashes appear on on his skin Mhmm. That took to the vet, and he misdiagnosed it for for three times for about eleven months. So over a period of eleven months, took it to the vet, was misdiagnosed, and on the third time, it started bleeding. So I decided to have the the the tumors removed, and that's when it came back as cancer.
Speaker 8:Unfortunately, tried really hard to have additional surgery just to remove as much cancer as possible
Speaker 2:Mhmm.
Speaker 8:And to, like, essentially try to look at the stem. And because it had been misdiagnosed for so long, one of the tumors that got so large that it wrapped around her leg and we weren't on there's just not enough skin to, like, close it. Oh. So that's when I kind of realized we needed to do try different options.
Speaker 2:Sure.
Speaker 8:And and then tried put on chemotherapy. Yeah. But none of the traditional stuff was essentially, like, like stopping it. It was continuing to grow.
Speaker 2:Okay. Okay. And then so so when do you actually first go to AI tooling? Do you start at a very high level just sort of asking about dog cancer broadly? Like, I at what level did you come into the conversation with AI just understanding the capabilities?
Speaker 8:Well, I I knew about AlphaFold from the AlphaGo days. So it was the progress the the progression technology.
Speaker 2:Mhmm.
Speaker 8:And, I just decided to check TBT one day in November 2024. Yeah. Like, come up with a come up with a plan how we can, like, potentially make a drug to block this cancer. I I didn't really know anything about cancer at this stage. I was just
Speaker 2:Yeah.
Speaker 8:Going through the process of trying figure it all out. Yeah.
Speaker 2:Yeah. And so what what happens next? Who do you actually call to because at some point, you know, it is just text in a box in an app or a website. At what point do you need to go back into the real world to advance the next step? I imagine that ChatGPT at one point tells you, like, okay.
Speaker 2:Well, we'll need the DNA sequence, and we can't get that just from a text box. So where do you go next?
Speaker 8:Yes. Yes. Correct. So the the the first actual piece of of data we needed was the DNA sequencing. Yeah.
Speaker 8:And, yeah, recommended to reach out to professor Martin
Speaker 2:Really?
Speaker 8:At, you know, Stelby. They provided three other people that it was like they gave all the reason that this is the reason why you should reach out to professor Martin.
Speaker 2:That's remarkable.
Speaker 8:And through a mutual friend here in Sydney, I was connected to to professor Martin, and he was very receptive to to just taking it on. Mhmm. Extremely receptive. Yeah.
Speaker 2:And so at some point, you walk me through I've you know, for those who are familiar with twenty three and Me, it's a saliva swab. What's the actual process for getting a dog's DNA sequenced, and then what's the file type that comes back? Do you just get a text file?
Speaker 8:So this is, considerably more advanced than 23andMe. It's is like we have I I have Roy's entire genome on my on a hot on an external hard drive I bought. Wow. So the process to submit the RNA DNA sequencing was quite cumbersome. So it was filling out spreadsheets and stuff to submit.
Speaker 8:But what came back two weeks later was 300 gigabytes of data.
Speaker 5:Wow. Yeah.
Speaker 8:And I had to push through that. Yeah.
Speaker 2:And so and so at at this point, you're not just dragging that file into a consumer chat bot. Right? That's at this point. You're you're starting to build custom pipelines. Correct?
Speaker 8:Yeah. Correct. So, again, I use I use Chateapity. I use Gemini, and I use Grok. Yeah.
Speaker 8:Possibly switching between the two. Mhmm. And, yeah, built out the pipeline to essentially go through the steps of computational pipeline to get to the mutation we need to sort of see what's causing the the cancer, the the root cause.
Speaker 2:Mhmm. And did you actually use AlphaFold? Is there, like, an open source package that you can download and and run?
Speaker 8:Yeah. Yeah. We we use AlphaFold too.
Speaker 2:Okay.
Speaker 8:So from the literature and from also additional LLM sessions, I I find out that there's a gene called c kett
Speaker 2:Mhmm.
Speaker 8:That is one of the primary drivers for Rosie's cancer.
Speaker 2:Mhmm.
Speaker 8:And what we what we essentially did was take the her healthy DNA. So we we sequenced her healthy DNA Mhmm. And we sequenced her cancer DNA Mhmm. Compared them side by side, like a genetic diff between the two
Speaker 2:Yeah.
Speaker 8:And and then focusing on, like, the secret gene, pulled it out, modeled it in alpha fold. Okay. And and I used two different techniques to essentially look for drugs to to try block the cancer. One was genetic algorithms. So we ran genetic algorithms, and we actually came up with a unique chemical compound that could block it.
Speaker 8:Mhmm. But the reason I didn't pursue that is because I actually talked to a chemist about having it made. But the the problem with that is you have to, like, go through the the steps of, you know, first doing it in, like, in in a test tube, then moving to mouse models and moving on Mhmm. Further. So that's too complicated.
Speaker 8:Yeah. And yeah. The the other technique was docking. We docks a whole bunch of these chemical compounds called ligands through the AlphaFold three d three d structure of c kip and mutant ready to be 10 to c kip and essentially discovered a a drug that was very, very strong at blocking it. But, unfortunately, the drug was is owned by a major US international company.
Speaker 8:I I I reached out to them for for compassionate use, and Sure. They slightly declined, which is fair enough.
Speaker 2:Okay.
Speaker 8:But that was kind of the there there's a second part of alpha four we used late in the pipeline, but that is kind of the start of the journey. And around this around this time was about June 2025, and I through went through all of that. And, but it really took the wind out of my sails because I'd like I tried everything. I tried, like, to, like, see if I could synthesize it. I tried to see if I could, like, get hold of a preexisting chemical.
Speaker 8:And, yeah, how one day I was walking rosy down down the street, and I realized maybe I'm actually close to making a vaccine myself and got back on chat GPT and, like, typed away, and it said, yeah. You know, you're halfway there. You've already done the DNA sequencing. These are the next steps you
Speaker 1:need to do.
Speaker 2:That's amazing. So so so back to the lab. You did mention we at this point. So I imagine you've looped in friends, colleagues. Like, who is around you on this project at this point?
Speaker 8:It's myself, and I run a small AI consulting firm here in Sydney.
Speaker 2:Yeah.
Speaker 8:Yeah. So we just like just I kinda worked at an in part time for about two hours every day.
Speaker 2:Wow. Yeah. It's remarkable. Wow. So so so back to the lab and they wind up finally making the drug?
Speaker 8:Yeah. So that was a process in of itself. Mhmm. Went through and did the design of the vaccine construct Mhmm. Pushed I I literally emailed it over to the mRNA institute at the at the UNSW.
Speaker 8:Yeah. It was like half a page of text. Mhmm. And and the the the major blocker was actually getting an ethics approval. Because you can't just, like, go and make a mRNA vaccine in your garage.
Speaker 8:Like, they don't let you do that in Australia. No. So I've been notified that I had to create an ethics approval. And, again, I spent, I don't know, that was three months of my life creating that. And it got to a point where we were actually gonna have to modify the university's license with the government because the vaccine was gonna be administered off-site.
Speaker 8:Mhmm. So the ethics approval would have only been approved in June. Mhmm. So
Speaker 4:Oh, wow.
Speaker 8:Through a connection in in in America in in Seattle, I was connected to professor Mary I don't know how to pronounce her surname. Mhmm. And she she is, like, the preeminent canine cancer person on planet Earth. She connected me to someone in in in a professor in Queensland, which is a state that's about a thousand kilometers. I'm not sure that is in miles.
Speaker 8:And also here, I was talking to chatting to her and then was just saying, like, I'm having trouble with ethics ethics approvals. She said, oh, I actually have a I have an ethics approval with the government for that specific type of Oh. Novel immunotherapies. And, you know, I'm happy to take you under my wing. I just played it completely cool.
Speaker 8:Was like, oh, yeah. Cool. But I actually died. You're jumping
Speaker 2:up and down. That's amazing.
Speaker 8:Yeah. Inside my head. That's remarkable.
Speaker 2:Oh, that's so cool.
Speaker 8:So Yeah. Yeah. So once you got the green light to do that, it all sort of, like, lined up in in in parallel. I I drove Rose up to Queensland. We did the induction phase of the vaccine.
Speaker 8:Mhmm. And then just sort of waited to see the results.
Speaker 7:Mhmm.
Speaker 2:And so yeah. Cancer is like a long fight, but it seems like there's at least some really positive signals, something like a 50% decrease in the size of the tumors. Is that roughly correct? Like, how are you measuring progress these days?
Speaker 8:Okay. There's been a lot of talk about that. So, obviously, the the visual the the the the best, trait is the the reduction in the cancer size. Yeah. We also took blood work, which is gonna be published in a in a paper later this year.
Speaker 8:Mhmm. And just continuing to visually monitor her her tumors essentially.
Speaker 2:Yeah. That's great. So where do you think this goes next? Obviously, there's a lot of attention. Some people are saying, oh, maybe you'll launch a startup around this concept or try and democratize biotech further.
Speaker 2:Do you wanna just continue the story in some way, or is it back to work as usual?
Speaker 8:I think, like, the the process itself was way too hard, and I think there's room to make it much, much, much easier for for not just people like me, but everyone.
Speaker 2:Yeah.
Speaker 8:Yeah. So I think there's definitely I I even know I could probably do the pipeline now in maybe four to six weeks.
Speaker 2:Okay.
Speaker 8:And and that's important because the faster you can do the pipeline, cancer's constantly mutating. So if you can Sure. Run the pipeline fast if you can uprun the speed of the mutation, you can, like, essentially clamp it down.
Speaker 2:Yeah. So talk about the long pole in the tent. It it I I imagine that just sequencing DNA takes time. Actually, producing a chemical or or or synthesizing the actual vaccine, producing the product takes time. It sounded like ethics waivers and the and the approvals also took time, but some of those can be shortened.
Speaker 2:Some of those are gonna be harder to shorten. Where's the biggest opportunity that you see to speed up that cycle time?
Speaker 8:The computational pipeline itself could be sped up. Mhmm. The the sequencing could be sped up. Sequencing is getting better and better every six months. It's, like, on a double exponential.
Speaker 2:Woah. I had no idea.
Speaker 8:And then this we could probably do the the fact the the vaccine itself manufacture faster. Mhmm. And and then ethics as well. I I think there's different that's probably the biggest room for improvement right there, to be honest. Like, yeah, I think Yeah.
Speaker 8:Sure.
Speaker 4:And I think that's gonna be a story that we'll see in a bunch of other use cases and categories where, like, the technology is advancing faster than the society and our legal system can can even adapt. But what I what I just love about this story is as amazing as as it is the role that ChatGPT and these other LMs played in this process, it really is a story of your just like insane determination and Yeah. Agency and high effort over such a long period of time to to save your dog and it's just incredibly admirable.
Speaker 2:I love it.
Speaker 4:And I hope that many many more people hear about this story. I'm sure you've been I'm sure some like documentary crews and things like that have have Of reached out. Yeah. But it's really really special.
Speaker 2:Well, thank you so much for taking the time.
Speaker 4:Yeah. It's great to meet you. And keep keep us posted. Yeah. Send us your progress when you when you put out the paper later this year.
Speaker 4:Come back on, and we're sending our prayers to to Rosie. Yes. It's Tito.
Speaker 8:I hope you can see it right there.
Speaker 4:Oh, there we go. Amazing. Little air horn. Air horn for Rose.
Speaker 2:Well, thank you so much. Incredible stuff. To come chat with us.
Speaker 4:Yeah. Great to meet you, Paul.
Speaker 2:Have a great rest of your
Speaker 8:day. Cheers.
Speaker 2:We'll talk you soon.
Speaker 8:Bye bye.
Speaker 2:Goodbye. Let me tell you about Shopify. Shopify is a commerce platform that grows with your business and lets you sell in seconds online, in store, on mobile, on social, on market marketplaces, and now with AI agents. The US Army awards Andoril Industries a contract with a total value of as much as $20,000,000,000 to buy the defense tech startups software, hardware, and services.
Speaker 4:Is that good? Just put this into context.
Speaker 2:It's really good. Really good. I I actually I don't know where the the the revenues are, but I feel like this is a significant jump up. And folks in the comments are asking, when IPO? It is still private, but people are getting excited.
Speaker 2:And, of course, the team over there, most of which, almost all of them have been on the show, are doing a fantastic job. So congratulations to them. There's never been a better time in history to be a shrimp. Are you familiar with this, Tyler?
Speaker 3:Of course.
Speaker 2:Yeah. So Anthropic's founders and employees are about to get a lot of cash. Anthropic is at over $330,000,000,000 in valuation. I think I saw rumors that secondaries were trading at almost twice that. Obviously, the company's on an absolute tear.
Speaker 2:Revenue's up into the right. They're doing very well. Many of the employees and founders have pledged to give away huge amounts of that cash, but where is it gonna go? And people are wondering about what what you know, if it goes to a nonprofit, well, what is the nonprofit gonna do? And, of course, the joke is is shrimp.
Speaker 3:Yeah. I believe that there was some where if you say that you're gonna, like, send a like, give a lot of charity Yeah. You can't afford much increase.
Speaker 2:Yeah. Yeah.
Speaker 3:Yeah. Like, 1.5 x or something.
Speaker 2:Yeah. You can get a multiplier, which is cool. I do I do wonder, you know, the shrimp thing obviously is a joke, but it will be interesting to see where the nonprofits go. There's been a lot of talk about mosquito nets for a long time. There's been talks about, you know, previous tech booms have created nonprofit funding booms, a lot focused on health and wellness and development and all sorts of different stuff.
Speaker 2:I would like to see a nonprofit that builds data centers. I think that would be sort of if I had excess money, that's what I would put it towards. But
Speaker 3:The Dyson sphere.
Speaker 2:Dyson sphere. Yes. An anthropic maybe an anthropic that's structured like a like a hedge fund, and it can, like, trade stat because the the the public good that it would be delivering is, like, stronger price signals to the market, and it would be creating more efficient markets.
Speaker 3:More efficient markets is, like, mean, I everyone benefits. Good. Everyone benefits. Seriously.
Speaker 2:Yes. And so maybe on, like, a microsecond or millisecond basis, that could be the job of the nonprofit, would be just to create more aligned price incentives. That could be good. Also, like buying buying companies out, like private equity style, loading them up with that, that could be another option for for a nonprofit.
Speaker 4:LBOs?
Speaker 2:I I think LBOs, I mean, there there there's an option there. I actually have no idea what you can do about
Speaker 4:that problem. Of the software LBOs from that 2018 to 2022 are already effectively gonna be nonprofit endeavors.
Speaker 2:I mean, there is a question about, like, you know, a lot of the a lot of the the, you know, the ruthless business people, they'll say, like, yeah, I make a lot of money, but I do it for the love of the game. I'm like, we're about to find out because you do do you go work for the nonprofit then that does LBOs? Do you go work for the nonprofit high frequency trading firm? Put your money where your mouth is. Actually, put the no money where your mouth is if you've been saying that you do it for the money.
Speaker 2:Put the lack of money where your mouth is.
Speaker 4:Anthropic is hiring a national security policy lead.
Speaker 2:Okay.
Speaker 4:And Alexander McCoy says, LOL. No kidding.
Speaker 2:It it is time for that. Although, why are they headquartered in the San Francisco Bay Area? You gotta set up the DC office, folks. If you're if you're going
Speaker 4:They're like, DC comes to us.
Speaker 2:Yeah. Or or or do the Jensen thing and fly around. Go go all over the world doing talking to world leaders. That is the way to
Speaker 4:We'll do 45,000,000,000 in global sales this
Speaker 2:year
Speaker 4:That
Speaker 2:is so much higher than
Speaker 4:I thought. Global iPhone sales will likely be 230,000,000,000 this year for perspective. Yes. Peptides are popular, especially the rigorously tested FDA approved kind. Yeah.
Speaker 4:Huge, huge numbers. Feel like pharma needed a win.
Speaker 2:So semi big
Speaker 4:Big pharma. Pharma. Lucky, it'd getting kinda hoes.
Speaker 2:It had been.
Speaker 4:Biotech broadly. I mean,
Speaker 2:you Yeah. Yeah. Boston.
Speaker 4:You're right. Biotech investors come on here and be like, I don't know why you'd invest in biotech. This is this is absolutely garbage. Yeah. But I'm doing it they were doing it just for the love of the
Speaker 2:game, I mean, with biotech, like, there's a very, very, very straight line to helping people live healthier lives. Semaglutide sales, which is Ozempic and Wagoovy, which you might be more familiar with than tirzepatide, which is sort of the next gen semaglutide. And then there is Reta, which is the popular one in San Francisco. That is the third gen peptide for a bunch of things, but weight loss is the one that people know. Semaglutide is projected to remain high but potentially decline in 2026 with estimates for revenue hovering between 36,000,000,000 and $39,000,000,000 So that is a huge, huge market.
Speaker 2:Those are AI lab numbers for revenue. And the margins are great, too. These things I mean, huge r and d budget, huge r and d cost, but once the manufacturing plant is up and running and the demand is there, of course, you have to, you know, move through insurance. There's a bunch of other other dynamics. But what remarkable business and it's
Speaker 4:There's someone over on Reddit
Speaker 2:Yes.
Speaker 4:That said, funny story about Retta. Basically, he injected himself injected himself with Retta TruTide but he didn't take the cap off? Yes. I understand that.
Speaker 2:So these peptides, they come in a plastic shell device that has a needle inside of it. And then you you press the pen against your skin, I believe. And when you click it, it shoots the needle out very small and then does the injection and then and then retreats into the device so that it can be thrown away. And it's sterile and it's single use. And so you're not, like, doing the bodybuilder thing with the with the needles and the bottles.
Speaker 2:Right? I I I think that's generally how it works. And so you have to prime the device properly. You have to remove the cap. I guess the guy messed up.
Speaker 2:He thought he gave himself peptides. He did not.
Speaker 5:But But
Speaker 4:didn't stop him from losing 10 pounds.
Speaker 2:He lost 10 pounds. He says, he says over the over the week since he took the fake peptides, he lost 10 pounds, got amazing sleep, woke up happy, zero pain in his feet and Achilles in the mornings for the first time in nine months. Food appetite felt suppressed, but I was still able to eat anything. Life was good. Last night on the sixth night, I figured since I had zero side effects and life was great, I'd take an extra click or two.
Speaker 2:Nothing crazy. Just a tad more. This morning when doing so, I realized that I never took the cap off the needle of mine upon my upon injection. I have literally placebo affected my way into feeling absolutely amazing. Who knows how real this story is, but the placebo effect has been studied a ton and it is very real.
Speaker 2:So Tyler, do you have something?
Speaker 3:I was gonna say, like, 10 pounds in a week seems like super fast.
Speaker 2:That seems higher than Yeah. Than what Reta should do to you.
Speaker 3:I mean, that's crazy. I fasted for a week one time and I didn't lose. I I think it was just under 10 pounds.
Speaker 2:You ate nothing for a full week?
Speaker 3:Yeah. Yeah. Like water and salt. Yeah.
Speaker 2:Just water and
Speaker 4:salt. She should do
Speaker 2:it again. Took zero calories.
Speaker 4:She should do it again and we can do a time lapse of you talking across the five days.
Speaker 2:Yeah. See see how it goes. Let me tell you about Vanta. Automate compliance and security. Vanta is the leading AI trust management platform.
Speaker 2:We have our next guest in the Re Street Waiting Room. Tony from Sunday Robotics is here. We had to delay. Oh, let's start the Lambda Lightning round. Play that, Cube.
Speaker 2:We're overdue for Lightning round. We went long on Friday, but we have a Lightning round today. And we will start with Tony from Sunday Robotics. Welcome back to the show.
Speaker 4:Tony, how are you doing? There he is.
Speaker 1:Very good. It's awesome to be back.
Speaker 2:Thank you so much for coming
Speaker 4:back. Congratulations. Busy.
Speaker 2:Extremely busy. Incredible progress. Take us through the progress. How how you framing, like, this the most recent era of Sunday Robotics?
Speaker 1:Yeah. I think the the biggest announcement or commitment that we made is that, hey. We're ending the era of demos.
Speaker 4:Yeah.
Speaker 1:We're focusing on deployments now. That's amazing. And I think, really, the what's behind it is that there are there are so many robotic projects that start as a demo and end as a demo. Mhmm. And that was, like, unfortunate.
Speaker 2:No. No. They start as a demo and end as a YouTube video that goes viral. Yeah.
Speaker 1:So and I think that, like, we just from the how much program we've made through the beginning of this year and all the accumulation of infrastructure and
Speaker 2:Mhmm.
Speaker 1:And systems that we feel like we can deploy it to real homes this year. Yeah. And that's the premise of the whole beta program that we talked about. And yeah. And we're just like, hey.
Speaker 1:That's our sole focus, and we're just going to do it really, really well.
Speaker 4:Okay. What what types of tasks do you have line of sight to everyday consumers benefiting from I'm assuming, some level of supervision, but enough autonomy that they can be, I'm assuming, valuable? Are are you gonna sell them initially? Or are you just gonna place them into homes? Like, how are you how are you thinking about
Speaker 2:for a robot going a horse and pull me in a chariot behind it.
Speaker 4:Yes. That's what I'm into. Yes. But what are you working on?
Speaker 1:So yeah. So on the demo Yeah. On the on the beta program Yeah. We are actually going to document it. We're going to be very transparent.
Speaker 1:Yeah. We're going to be autonomous as well. Mhmm. And the reason is that we have so much data, and the robot will be generalizable. Okay.
Speaker 1:But I think at the same time, when it comes to tasks that we'll address, think I the fun part is it will not be surprising that if you look at all the things that you spend most amount of time on, like the thing that you hate the most. We're talking about laundry. We're talking about dishes. We're talking about, like like, organization, cleaning, these type of things. And we're just going to pick a focus and do it very well and be able to provide value.
Speaker 1:Okay. So that is how we think about it. They will not be like this, like, super surprising pick of tasks.
Speaker 2:Yeah.
Speaker 4:What do you think about the opportunity in offices versus the home? Everyone's focused on the home, but I feel like a office is potentially like a less it less chaotic environment. People are generally more Yeah. Like not not leaving, you know, a trail of clothing around or whatever. Like, maybe there's more straightforward tasks.
Speaker 4:Where where do you where do you where do you see the divide?
Speaker 1:Yeah. I think home to us is such a good, like, a long term goal
Speaker 2:Mhmm.
Speaker 1:That's to drive the AGI moment for physical intelligence, right, to get there because it's so diverse, so many tasks, so much, objects, things are moving.
Speaker 2:Yeah.
Speaker 1:But I think as we approach it, there will be lots of as we build up the capability of the robot, it starts to unlock other use cases. Maybe it's in offices. Maybe it's in hotels. Maybe it's somewhere else that we're actually very open minded to that, and that's something that we are going to think a lot about this year.
Speaker 2:Mhmm.
Speaker 4:What's going on on the data side? You met it sounds like you said you have a lot of data now. Where is that where is that coming from?
Speaker 1:Yeah. So I think for folks who haven't read our website yet, we have this new way of doing data collection
Speaker 2:Mhmm.
Speaker 1:Which is building gloves that are mirroring the robot's hand. So instead of needing to deploy, like, thousands and thousands of robots, we just need to make all these gloves. Mhmm. And people can wear them and collect data in their own homes. So this gives us really high quality data, but also really high diversity and quantity of data.
Speaker 1:So I think this year, we're going to scale to, like, a few thousands of these people to be collecting data for us every day. Mhmm. And we're going to build a high quality and diverse dataset that will be kind of the powering the foundational model that we're going to train.
Speaker 2:Is there a value in having a less transferable, less precise dataset with higher volume Mhmm. Maybe recorded through, like, a face camera, like a Metairie bands? I've seen some examples. I think it was in the LA Times today about people doing chores with basically a GoPro on their head, and they're just recording what they're doing while they're doing chores. And it feels like maybe that's not the perfect data, but if you can transfer that data over to the gloves and transfer that to the robot, maybe you get extra data.
Speaker 2:But what's your thought on, like, the continuum of data quality?
Speaker 1:Yeah. Like, I think the you're talking about, like, egocentric cameras. Right? Yeah. People strap a camera here to record their movements.
Speaker 2:Yep.
Speaker 1:I think if you think about the quality side, we're definitely compromising. For example, we do not have precise movements of how people use their hands. Yep. We do not have force information, technical information Sure. These type of things.
Speaker 1:So just that data will not bring us all the way through. Mhmm. But at the same time, EU centric data and all the data we already have in in a public domain and on the Internet is going to help the robots. Right? Because you can learn the more general physics.
Speaker 1:You can learn some, like, intuition around how, like, rooms are arranged, like, all those common sense. So I think the eventual recipe will be a combination of those video public data with our proprietary datasets. And the way we think about it is that, like, we're going to use our data to bridge this bulk of knowledge that we can extract from the Internet to be a deployable product that is actually useful. Kinda bridge the gap from the gap from, like, demos to something real that's creating value.
Speaker 4:Yeah. How do you how do you incentivize people to to wear the gloves?
Speaker 1:We pay them.
Speaker 4:No. I I know. But, like, is it is it is are you paying them? Like question. Good.
Speaker 4:Good. Good answer. No. But I'm but I'm curious, like, are you you're giving somebody gloves and saying, like, hey, I want you to do, like, at least an hour of activity a day, like a like Is it per task?
Speaker 2:Yeah. Like, what's the structure?
Speaker 4:Yeah. If they just if they just put them on and then watch Netflix, I can't imagine this. That's valuable.
Speaker 1:100%. Like, I think we we both need to give requirements on, like, the the quantity of data and the quality of data and everything else. But I think it's actually a really good part time job to have Mhmm. That you can collect the data anywhere, like like, in your phone
Speaker 2:Yeah.
Speaker 1:And you can do it anytime. You can do it right, like, super early in the morning. You can do it super late at night, like, between your your shifts, whatever it is. We're going to be really happy about that. Mhmm.
Speaker 1:And you don't need to, like, even leave your homes.
Speaker 4:Yeah. It's cool.
Speaker 2:How are you feeling about simulated data? We've talked about the sim to real gap before. And there's always this, you know, there's not enough variation in some unreal engine environment that you build a kinematic model in. But it feels like with generative AI, you should be able to sort of stochastically generate different variations, create better synthetic data. It feels like the LLM companies are doing very well with synthetic data generation in certain cases, various rollouts.
Speaker 2:Like, how are you feeling about it? Do you think it's gonna be in the playbook this year, maybe for a few years and then not anymore? Or it's something that would be valuable farther out and and and maintain from there? How are you thinking about synthetic data?
Speaker 1:Definitely. I think we talk about, like, world models a lot these days. Right? Yeah. And they're like, hey.
Speaker 1:Can we generate synthetic data out of the world models? Like, how good is that? And I think there are there are two sides of it. One is training a world model can allow us to leverage even more compute and even more data, like
Speaker 2:Yeah.
Speaker 1:All the Internet. Right? Yeah. They can bring us a lot of knowledge, like like, without collecting any additional data.
Speaker 5:Mhmm.
Speaker 1:But at the same time, I think it is neither going to bridge the deployment gap, which is means, like, getting from 95% to 99.99% Mhmm. Or it's going to bridge the last millimeter for a certain manipulation task. Mhmm. Because you're just certain like, the fidelity of the data is slightly worse. Sure.
Speaker 1:But what I see that is, like, being a layer that lifts, like, everyone.
Speaker 2:Yeah.
Speaker 1:Like, everyone will be become better.
Speaker 2:Oh, sure.
Speaker 1:Because they're now pre trained on all these synthetic data.
Speaker 2:Yeah. That makes a lot of sense.
Speaker 4:You raised any money lately? How much money?
Speaker 1:Yeah. We actually did it mostly last December, but
Speaker 2:Oh, yeah.
Speaker 1:We raised a $165,000,000 series b led by
Speaker 4:What was the valuation just out
Speaker 2:of curiosity?
Speaker 1:1,150,000,000.00.
Speaker 2:Wow. Unicorn already. I love it. Well, great seven investors. Congratulations.
Speaker 2:We love Kotu over here and they know what they're doing. So good luck.
Speaker 4:Send us a pair of gloves too. Yeah. You don't even need to pay us. Yeah. We'll just we'll continue.
Speaker 2:How else will you know how to adjust a podcast mic 75 times over the course of three hours? Can't solve that. You can't
Speaker 4:Exactly. Well, thanks automated so by Sunday. I invite I invite it.
Speaker 2:Challenge accepted. Have a great rest of your day. We'll talk to you soon, Tony.
Speaker 4:Good good to see you, Tony. Here. Goodbye. Cheers.
Speaker 2:Alright. Let me tell you about Lambda. We are, of course, in the Lambda lightning round, and Lambda is the super intelligence cloud, building AI supercomputers for training and inference that scale from one GPU to hundreds of thousands. And without further ado, we have Drew from eight BC. He's a founding partner there.
Speaker 2:He's in the vision right now. What's going on? TBPN Ultra Home. How are doing, Drew?
Speaker 5:Hey. Doing well. How are you guys doing?
Speaker 4:Doing great. Good to finally have you on the show.
Speaker 5:Yeah. Thanks for having me.
Speaker 4:Talk about Quince. Let's get right into it. I feel like somehow this company came out of nowhere for me. I I haven't I haven't purchased anything there yet, but it didn't come out of nowhere for you guys given that you backed it at Seed. So I would love to, yeah, understand how how you initially found the company, some somewhat of a contrarian move to back a company like this given it's kinda not necessarily right in the sweet spot for eight if you look at the rest of the portfolio.
Speaker 4:And then there's a kind of a graveyard of companies in Silicon Valley that have, like, made different attempts at this opportunity generally.
Speaker 5:Yeah. I mean, we've been involved in one of them is actually instrumental with this. So but we I met Sid a long time ago, the founder, and kind of a basically, like, he was just incredible. He was running a business called Lolly and Pops, which I'd never thought about before. It was basically a a luxury candy company.
Speaker 5:And the way he talked about the about the business and the stuff you wanted to do there, I was just super impressed because I I kinda maybe didn't think there was all that much when it comes to
Speaker 4:Candy.
Speaker 5:You know, luck candy. And so he he kinda told me he was gonna think about his doing his next thing. And so we spent months kinda just ripping on ideas, and it was this was 2018 and wish.com, which we'd invested in from one of our actually, our first fund was was kind of a high flyer at the time. And he was very intrigued by it, interested in in the business model and and their use of technology, I think, frankly, and an ever expanding sort of category of things to to sell. And so he was studying it, and and, you know, I was I'd be totally honest with you.
Speaker 5:I was trying to convince him to, you know, work on some sort of defense tech or buy a manufacturer or some some other business. I just
Speaker 4:Something more. Yes. Something more, in the typical line of sight for you guys.
Speaker 5:Exactly. Which shows, you know, how stupid it is to try to be thematic about things. And so
Speaker 4:Well, you guys were smart. You didn't let you didn't let the you just bet you you you backed the the jockey.
Speaker 5:Exactly. So we I'd already agreed on whatever he wanted to do, and he came, and he pitched us what was called last brand at the time. And now the rest is history.
Speaker 4:And then and then how how quickly did you realize he wasn't too too crazy?
Speaker 5:I mean, I think pretty early on, the business was working. I think what's, you know, it's kinda like it's always tough to see, like, when an exponential curve kinda early on, and it was and it really started to become obvious maybe just after COVID about how fast he was growing and really how unbelievable the operations were. I mean, the the amount of products they were bringing in with really high quality and the amount of, like, retention and repeat purchasing, it's just it's just totally nuts.
Speaker 2:Mhmm.
Speaker 5:I mean, I think that at this point, Quince is now a top 10 retailer in The United States in terms of repeat ordering, and the other top 10 all have grocery or pharma that drives people in, you know, every month or whatever, which has been massive. And the company grew a 100% year over year, last year, cash generative at, like, a multibillion dollar scale. So it's just, it's just really a testament to the way that Sid and the team he's built have been running the business.
Speaker 2:Zooming out, how do you think about d to c retail changing in the AI era? Like, we've been tracking the agent to commerce stuff pretty closely. It feels like it could go exponential this year and go from, like, point 1% to 1%, and it wouldn't necessarily move the needle. But, like, how are you thinking about it as, like, the next decade?
Speaker 5:Yeah. I mean, it's it's something that's that's sort of investing in a huge amount. And, really, I think the way you described it is just like, wants to build the world's most efficient supply chain. Mhmm. And so AI is used in, like, every like, literally every single aspect of the business.
Speaker 2:Sure.
Speaker 5:Obviously, there's some places you can have, I think, a much sort of maybe more there's a lot more, like, of a call option of something being totally transformative, maybe more on, like, some on the front end side. But the the reality is, like, one of the ways that he's been able to just be so unbelievably capital efficient, and cash generative is because their operating expenses are just incredibly low.
Speaker 2:Mhmm.
Speaker 5:And then their sales and marketing is super, super dialed in and and and just basically every dollar they spend in is cash generative. So I think that AI will probably have the biggest impact just in terms of taking a business, which maybe you thought, okay. The max ceiling here and what this thing could generate would be 20 or 30%, you know, operating margins and just drive that even higher. And there's obviously interesting stuff on the on the consumer front end too. But to me, that's what's most impressive at least right now.
Speaker 5:Mhmm.
Speaker 2:Just just thinking about the broad start up landscape, are you more bullish than in previous years around consumer broadly? There's a lot of like, oh, b to b SaaS vertical stuff. You know? Oh, it's gonna get steamrolled, blah blah blah. It feels like d to c commerce, it sort of went through a wave, then there's winners and losers.
Speaker 2:You're clearly in the winner. But how are you thinking about just start up opportunities these days?
Speaker 5:I mean, I think it's it's actually interesting being based here in Austin because Austin maybe is like the maybe probably the most sort of having the highest density of successful CPG founders. And I think the the interesting thing about them is that the vast majority have run super, super capital efficient. Maybe they've raised a little bit of money. Yep. The I think the sort of traditional Silicon
Speaker 4:plan forced to they're they're they're really forced to be capital efficient. I'm sure a lot of them would would love to just be, like, you know, growing, paying whatever it it it took to grow. But you look at the rounds that even great brands with great economics put together, it's like they're pulling in 500 k from over here, a few million over there. And that's with like being an 8 figure revenue business when you look at their any other counterparty even in in defense or AI. If they had that level of revenue, they'd be raising it 50 to a 100 times revenue potentially.
Speaker 2:You know?
Speaker 5:Yes. So I think it's I think that I think it's I think AI offers the potential that and we're talking more on, like, the actual physical, you know, consumer product companies side. I think it offers the potential for higher margin structure. So if you find the right entrepreneur, I think they're gonna be more businesses built even if it doesn't if there's not some super obvious, like, big why now just because people who deploy this the best are going to be they're gonna have a superior cost structure, which means they can spend more marketing, grow faster, invest more in their product quality. And then I think, obviously, on the software side, I think anytime you see one of these big tectonic shifts, there's gonna be tons of businesses built, you know, on the consumer side.
Speaker 5:I I think some of them are gonna be tiny but insanely profitable. I already you know, as we all know, there's people who have one or two people that have thrown together something making, a million or 2,000,000 or $3,000,000, you know, a year, and they're running it basically for cash. Yeah. But we I you know, I think it we're always looking for super high quality founders that match well to the, you know, to the business they wanna build. And at the earliest stage, I think, can't be overly thematic.
Speaker 5:So if another, you know, Sid walks in and wants to build something, other than other than Quince, I think I would back him into it at the early stage. Yeah. But but I I think there will be we will see more founders that are able to run these businesses at pretty pretty insane margins because of AI. What
Speaker 4:what categories in physical AI or or industrials Mhmm. Or even defense do you feel like is still underinvested today? Feels like, obviously, there's great company. Sometimes you wake up and it feels like there's great companies in every category. But from from your vantage point, where do you wanna see more new company formation?
Speaker 5:I mean, I I think that we're kinda just still the first innings of just the I mean, basically, every huge technology wave right now is bottlenecked by the physical world. Mhmm. And I think there's just gonna be an insane amount of companies that are built. Some of them will be incumbents that figure out how to use AI to lower their cost structure, change their incentive structures they have. I mean, and and maybe those will be you know, those won't be done by startups.
Speaker 5:But I just think that pretty much anyone looking to build a big company that is enabling the amount of energy, cement, steel, copper wiring, you know, etcetera, right now is is super super well positioned.
Speaker 8:Mhmm.
Speaker 5:If they can figure out how to run those businesses. They're very different than running a software business.
Speaker 2:Mhmm.
Speaker 5:And so, you know, I think, you know, kinda to the point of what we saw with Quince, I think a lot of people who were great, you know, software engineers and maybe would have built great SaaS companies wouldn't have necessarily been the right founder. I think that, like, when it comes to the reindustrialization that's happening in The United States, like, capital allocation is is just this unbelievable. Like, it's probably the most important sort of lever. And so if you're building billions of dollars worth of facilities a year and you don't, you know, have a relatively sophisticated finance function or a CEO who who has, you know, studied this and comes from that world, I think you could be in trouble, which is kind of an interesting thing to see for the first time. You know, I've started seeing, like, cofounding CFOs of companies.
Speaker 2:That's crazy.
Speaker 5:Or maybe they have a different title, but that's based their background is, they, you know, partner in investment bank or, you know, whatever. And
Speaker 4:they're It's so funny because, like, a decade ago, if you if you if somebody comes in to pitch you and they've got this CFO as, like, a cofounder, you're like, yeah. Yeah. I think you have, like, much more important problems to deal with than, like, the finance function. You should probably get some some, revenue and happy customers first. But now I can see that flipping.
Speaker 5:I mean, totally. It used to be like the I would my advice would be like, I don't wanna see, like, a CFO person, like, around, like, until you have at least, like, 30,000,000 of revenue. I want just, like, hire the smartest investment banking, also you can find it will work, like, a hundred and twenty hours a week and, like, HP don't run out of cash. Right? But now it's like, okay.
Speaker 5:Well, we're gonna go raise a $500,000,000, like, you know, project financing
Speaker 2:Yeah.
Speaker 5:Deal for this facility. It's a little different.
Speaker 2:Yeah. That makes a ton of sense.
Speaker 4:Awesome. Well Thank you so much for taking the for coming on. Always good
Speaker 2:to have. Austin. How's the weather?
Speaker 5:Weather's great. Amazing. It's probably great at least for another few weeks, then it'll get really hot. It's good. Yeah.
Speaker 5:Come visit now.
Speaker 2:Dennis time to go. Water skiing.
Speaker 4:Hill And Valley next week.
Speaker 5:Hill And Valley, we'll have a bunch of folks out there.
Speaker 2:Fantastic. Yeah. Looking forward to it.
Speaker 4:Looking forward to it.
Speaker 2:Well, have a great rest of your day. We'll talk to you soon, sir.
Speaker 5:You too.
Speaker 3:Great to meet
Speaker 5:you guys some Quinn stuff.
Speaker 2:Let's go.
Speaker 4:Let's do it.
Speaker 2:That sounds amazing. Yeah. Thank you.
Speaker 4:Yeah. Well, that's it. Yeah. That's all
Speaker 2:I agree. Let me tell you about AppLovin. Profitable advertising made easy with axon.ai. Get act get access to over 1,000,000,000 daily active users and grow your business today. And without further ado, we have Carina Hong from Axiom in the team.
Speaker 2:What's going on? Carina, welcome to the show. How are
Speaker 4:you doing?
Speaker 12:Hi. Great to meet you.
Speaker 4:Great to
Speaker 2:meet you. You. It's your first time in the show, please introduce yourself and the company.
Speaker 12:Hi. I'm Carina Hong, founder and CEO of Axiom Math. We are building mathematical superintelligence that will be a critical path to verify the superintelligence.
Speaker 2:Amazing. What's your background? How did you start this company?
Speaker 12:Yeah. So I did my, undergrad at MIT, math and physics. Kind of did math Olympiad since I was a kid. And Oh, boy.
Speaker 4:We are
Speaker 8:success.
Speaker 12:Seven months seven months old company. So we're downtown Palo Alto.
Speaker 2:Very cool. And, I mean, it feels like so many of the math benchmarks have been saturated. I don't know. What is the goal? How do you know that you're making progress when the just the frontier model seem really, really good at math?
Speaker 12:Yeah. So we combined the very interesting techniques in post training reasoning with formal verification. We used this language Kotlin, which is program for proofs. And at the Punnam competition last December, which is the hardest undergraduate math test, we competed in real time. And we got a perfect score, one twenty out of one twenty Wow.
Speaker 12:Where the best scoring LLM is 103. Okay. So by PEEPSEK. So there's a lot to be done, you know, if you combine informal and formal approach, and that you will have a really strong superhuman mathematician.
Speaker 4:Okay. Talk about, why why you think math is the pathway to general superintelligence.
Speaker 12:Yeah. So we think that math is the sandbox for reality. You will be very quickly seeing verifiable rewards because in math, there's, like, absolute right or wrong. Sure. And especially when you have Lean, you can check the proof, the solution step by step.
Speaker 12:You will be able to, you know, apply reinforcement learning in a much more sort of efficient way. Mhmm. And we have currently scaled from winning Puntham perfect score to solving a batch of research problems that professional mathematicians find really challenging.
Speaker 2:Wow.
Speaker 12:And we also see this transfer to code verification.
Speaker 2:Okay. On the last IMO, I believe everyone struggled with question six. I believe OpenAI and Google both were unable to answer question six. It was this sort of like tessellation of triangles question. I don't understand it.
Speaker 2:I didn't get it right. But, do you feel like you're making progress?
Speaker 4:You came close.
Speaker 2:I didn't even I didn't even try.
Speaker 4:You came close.
Speaker 2:But, but but do but do you feel like you're the the read on that particular IMO question was that it required a lot of outside the box thinking, and that's why both, many of the students who took the IMO struggled with it, and that's why also the model struggled with it. Do you feel like you're the progress that you're making will transfer to that type of, that type of math question?
Speaker 12:Yeah. So there's this going joke that no no AI could solve it because they were not at the Australian airport because Yep. Actual solution is the the tiling of the floor. So no AI was able to look down to the floor. They can't solve problem six.
Speaker 12:And have seen that consistently since 2024. There were two common attacks problem. No AI was able to solve that. Mhmm. Stayed the same in 2025.
Speaker 12:You know? Still haven't seen an IMO perfect score. But in this year's Putnam, we have seen some really difficult questions by the Mass Olympic hardness scale by Evan Chen. There is a question much harder than any of the five questions on the IMO that Axiom Prover solved perfectly. Okay.
Speaker 12:And we don't believe any other AI has done so.
Speaker 2:Mhmm. How are you thinking about the impact of advanced AI that can solve math? Is there is there a direct benefit for for just advancing the basic research that is done at a high level in the mathematics profession? Is it just once you're good at math, you can also go and, you know, write software that generates economic value? Like, what are you most excited about?
Speaker 12:Yeah. So let's just, like, kind of take the time machine back to 2024.
Speaker 2:Sure.
Speaker 12:I think everyone was kind of aware that Anthropic was working on coding.
Speaker 2:Mhmm.
Speaker 12:And people didn't really think much of it and thought of that as just one vertical in the enterprise AI applications. Mhmm. Turns out coding is a much more horizontal bet, and we believe the same thing with math too. We believe informal math give us a pathway to verified AI to be able to revolutionize how verification has been historically done in, say, hardware and software. Yeah.
Speaker 12:We think the term is all AI code, and we think that in a way, we are looking at the rover, right of first refusal to verify or not all the AI code that will ever be produced. And so this is a bet that's really relevant even if you assume AGI. Yeah. I think to a lot of people, verification is about sort of correcting mistakes. Right?
Speaker 12:Like, of erasing hallucination. To us, we see the upside. We think of verification as a way to have AI agents work with each other, human AI work with each other to compound and scale the brilliant, right, the the intel the superintelligence. In a way, like Ramanujan after he learned proof writing from Hardy and Littlewood, came out to be a much more powerful mathematician, turned his intuition into into theorems, and theorems have proofs. We think that we are seeing a similar thing happening here, and math reasoning will transfer to other parts similar to code and logic.
Speaker 2:Talk about the impact of verifiability on mechanistic interpretability, just the idea of, like, you know, a lot of people have fear around AI. They think it's a black box. They don't understand it. Is this going to make it more of a black box because it's doing math at such a high level that no mathematician can understand it? Or does it make it more interpretable because you can verify what's happening once the AI generates its output?
Speaker 12:Yeah. I think instead of being sort of, like, you know, going to the machine interpretability, which I know some other great companies are doing, we also work with them. Mhmm. That's about, like, the black box of what's going on inside. Verified AI is about being able to trust the output once it is generated.
Speaker 12:And so mathematicians will be able to function at a much higher abstraction than they ever ever have been. So, I mean, if you ask me what is the purpose of, like, you know, proof checkers, like, formal language lean in the mathematical community, if they already have a pretty rigorous, like, peer review process, right, like, the mathematicians will just peer review each other's work, I would say that it's because, you know, the lean and all the tactics within, such as Grind, is able to cover all the low level deduction and for mathematicians to focus on the high level navigation and then do math at a much more compressed timeline. So I think we're quite excited about the future where mathematicians, because of this sort of increased supply of reasoning, can, like, produce way more breakthroughs than before. And that, you know, can then flow to other applied scientific domains. That should be quite interesting.
Speaker 2:You raised a lot of money. $200,000,000. Congratulations. Are you
Speaker 4:Thank you.
Speaker 2:Is is there a large compute budget within your organization because of that?
Speaker 12:Yeah. We are going to spend the new capital in compute and hiring. We are also very excited to continue the amazing team building progress we have been. I think I feel fortunate every day to work with this world class team, and we want to, you know, have people who are interested in program verification to also join us.
Speaker 2:Amazing. Well, Thank you so much. We gotta hit the gong for the $200,000,000.
Speaker 11:Awesome.
Speaker 4:For Acxiom. Oh. Wow. That was a that was a big hit.
Speaker 2:Thank you.
Speaker 4:That's a that's a
Speaker 2:big round.
Speaker 4:A good sign.
Speaker 2:Idea. And this has been a great interview. Thank you so much for taking the time
Speaker 4:to chat with us.
Speaker 2:I'm sure we'll be back on scene. You soon.
Speaker 12:See you.
Speaker 2:Have a good rest of Goodbye. Your Let me tell you about Gusto, the unified platform for payroll benefits and HR built to evolve with modern, small, and medium sized businesses. I nailed it. I didn't say smarter. I didn't say smarter.
Speaker 2:Smarter. I said modern small and medium sized businesses. Our next guest is in the Restream waiting room. We have Cam Fink, the cofounder and CEO of Aaroo. Welcome to the show.
Speaker 2:What's going on? How are you doing?
Speaker 9:Hey. Thanks. Thank you guys so much for having me on. You know, it's been a dream since the day I was born to be a TBPN. Incredible.
Speaker 2:I know you're young. You're you're
Speaker 4:the youngest ever guest. You're 11 years old.
Speaker 2:Yes. Yes. No. Coming soon. But since it is your first time on the show, introduce yourself and the company.
Speaker 9:Yeah. I'm Cam. I'm the cofounder and CEO of Aru. We're a business that predicts human behavior for almost every type of business on the globe. So we tell people who's gonna win elections, what products you're gonna purchase.
Speaker 9:We help people predict the outcome of marketing campaigns, no matter who you are or what type of business you run.
Speaker 2:Okay. Predicting elections is interesting because we just went through a huge, prediction market boom. That financial instrument was one way to harness the wisdom of the crowd. You're sort of doing that through AI and data that you collect, or is it all simulated? Walk me through, like, how do you actually get to a better prediction than what what the state of the art is?
Speaker 9:Yeah. I mean, it all starts with our idea of rather than training off of what humans say they they do or who they say they are. Right? Like, we all know polls, focus groups, surveys are fundamentally wrong. There's survey bias, sampling bias, incentive bias, let alone the fact that people lie.
Speaker 9:Right? We train on ground truth behavioral data only. So we're looking at things like credit card purchase history. We're looking at real marketing campaign click through rates. We're looking at, you know, health insurance information.
Speaker 9:We're looking at real election results. That is all way more indicative of the actual decisions that people make and thus, you know, far likelier to predict elections better than anything else.
Speaker 4:And so so is that, like, if somebody buys an REI, they are more likely to vote a certain way and that factors it? Like like, how how many different ways are you trying to, like, triangulate? And then and then help me out in understanding, how the actual platform works. Yep. Is this like effectively you have your own set of data and then you're spinning up a bunch of agents and it's you're basically prompting it to say like, pretend you're this person and then this event happens, like what is your response?
Speaker 4:Like how does it how does it work? Explain it to me like I'm a podcaster.
Speaker 9:I mean, a 100%. When you start by asking, right, how how is it working in terms of, like, what sort of different data are we including? Your REI example, it's like that but at massive scale. Right? We can understand how the differences in the price of eggs in someone's ZIP code is gonna change their likelihood to vote for one candidate or another or to care about some different marketing campaign or another.
Speaker 6:Right?
Speaker 9:Then as far as it comes to an individual simulation, one simulation has been, you know, composed of tens of thousands of agents for any audience on the globe. Right? So remember, because we're not constricted by what you can reach in a traditional survey and then going and trying to train a model on top of survey responses, we can generate any audience we want. Right? So we can generate maybe an audience of social media influencers.
Speaker 9:We can even generate an audience of podcasters. We have a client in the podcasting business. Probably simulated you, John and Jordy, somewhere in there. That's wild. But because of that and then each agent gets given to a model that we build in house.
Speaker 9:Right? So it's our core model, our foundation. And then that model is able to take on that profile for all ten, twenty, fifty, hundred thousand members of an audience and simulate their behavior more accurately than anything else in the globe. Mhmm.
Speaker 4:How do you how are you working to build sort of confidence within your customer base? Like, this feels like the kind of thing that I'd like, a lot of businesses would be down to try. And then but how do you how do you actually prove accuracy over time?
Speaker 2:The person that they
Speaker 9:predicted gets elected. Great question.
Speaker 2:I mean, that
Speaker 9:You it that's a lot
Speaker 2:of what happened in prediction markets. It's like they got it right, and then everyone was like, okay. I guess it works.
Speaker 9:Well, then well, they get it right most of the time, not all the time. You know, take the Virginia attorney general's election Yeah. Is a good example. But as you look at us, right, like, we've been around for seven hundred eighteen days. Right?
Speaker 9:It's almost two years of Aru. If we didn't work, right, then we wouldn't exist as a business anymore if we weren't able to, like, predict behavior accurately. But let alone that we do have tons of external validation. We did a really good study with on a really tough to reach audience, like, 3,500 individuals, who are ultra high net worth. Right?
Speaker 9:Can't imagine anyone with a $30,000,000 net worth or more taking a survey. Yeah. And we were able to recreate their behavior even more accurately than the survey, which was pretty cool.
Speaker 2:So Okay.
Speaker 9:Great example there.
Speaker 2:How concentrated is the customer base? Because I feel like with prediction markets, was just like, you know, people had the page bookmarked and they were refreshing and going in the election. Very, like, general consumer. It feels like there's a lot of customers maybe. Of course, there's, like, whales.
Speaker 2:But with you, I imagine that you can walk into, like, the CEO of Coca Cola's office and say, like, I can move the needle for you, and that's, a big ticket client. What's the shape of the customer base right now, and where do you want it to evolve?
Speaker 9:Yeah. I mean, behavior is everything. Right? So when we talk about predicting behavior, we can sell to, like, every type of business on the globe. Have film studio clients.
Speaker 9:We have podcast company clients. We have CPG clients. We have utilities businesses, and we sell to the governments. Right? So it's super widespread.
Speaker 9:But I would say the three biggest areas today are consumer, you know, whether that be retail technology or CPG. Yeah. Then And we do a lot of work as well for financial services businesses. And then I would add on top of that kind of, like, the government policy use case. We do a lot of work, like, stimulating the impact of new tax changes.
Speaker 2:What about, like, a self serve, like, you know, for small business that might wanna put down, like, a $100 a month for a service on a credit card. Is that an option? Or do you think that will be an option? Or or do you wanna stay in, like, enterprise y, like, let's let's actually
Speaker 4:They wanna help big business get even bigger.
Speaker 2:I mean, I I'm not gonna be upset about that.
Speaker 4:I'm sorry, little guy.
Speaker 2:Asking. Curious.
Speaker 9:I'm not I'm not I'm not saying we reject small business forever. Right? We we we we work with plenty of really cool businesses across the full size. Just like we work with the massive CPGs, we work with Spindrift
Speaker 2:Cool. Yeah.
Speaker 9:As well. But, you know, in terms of what our core customer base is, look, I think every business on the globe is gonna wanna use ours someday. Sure. Right? Like, there in five years from now, there shouldn't be a decision you're making without using our software.
Speaker 9:Sure. And I would like that to be as accessible as possible. I think it's just a question of making sure that people are are primed to use technology as powerful as this.
Speaker 2:That's been such a fun Is
Speaker 4:this is this company somewhat and and I'm gonna give you ample time to push back, but somewhat short AGI, like I assume a sufficiently advanced model from a Frontier lab in the future. I could talk to it and say, hey, I need to make a decision on this product launch. This is my customer base. I can feed it some data. Predict the outcome for me based on all the data that you have access to.
Speaker 4:I imagine you guys, if you just work harder on collecting the right kind of data, could always have a differentiated data source. But how do you imagine kind of competing with other you're an intelligent I I view you as like an intelligence provider. Right? Like, you guys are Yeah. Predict you're but but more narrow than some of the more general
Speaker 2:I've been running every life decision through GPT two, and people say that my behavior is really chaotic, but it's been working out so far.
Speaker 9:Well, it's actually funny you mentioned that because what we've noticed is the foundation models over time, they actually get worse and worse at predicting behavior. Right? Like, this is something we've seen. We used to be a business where we just give, like, you know, survey data to an LLM and then tell an LLM, try and predict the behavior based off of this past survey data. But what you notice is it just doesn't reach the edges.
Speaker 9:Right? Like, really consistently, it is not going to be able to predict things at the margin. And that's a big issue because it's the predictions at the margin that are the most valuable. Right? Those are the predictions of, like, what are Fortune 500 CEOs gonna do that we can nail than an LLM can't.
Speaker 9:And so, sure, you know, there is a future where, like, Claude is gonna be able to tell you, yeah, I suspect that American household purchase decision makers might buy this product. But for the biggest decisions on the globe, why would you risk it? And that's why people trust us for the toughest decisions they have.
Speaker 2:Makes sense. Very cool. Well, thank you so much.
Speaker 4:We have a I think we have there's a gong in order.
Speaker 2:Is there an official round announcement?
Speaker 9:I'll say we're very well capitalized. Very
Speaker 2:well capitalized.
Speaker 4:Very well capitalized.
Speaker 2:We we need a soundboard queue for that well capitalized. That'd be fantastic.
Speaker 9:Thank you guys so much for having me on.
Speaker 4:Thanks for having I'm sure he'll be back on and congrats to the whole team
Speaker 2:Yeah.
Speaker 4:For the progress. I'm looking forward to
Speaker 9:Congrats to you guys as well.
Speaker 4:Don't really
Speaker 9:When you want a sim for TBPN, we're here anytime.
Speaker 2:We'll figure something out. Yep. We'll talk to you soon.
Speaker 4:Love it. Good to see you. Have a good one. Cheers.
Speaker 2:Let me tell you about Plaid. Plaid powers the app to use to spend, save, borrow, and invest securely connecting bank accounts to move money fight fraud and improve lending now with AI. We have our next guest already in the restream waiting room. So we will bring in Debra from
Speaker 4:What's going on? Great to meet you, Debra. Welcome to the show.
Speaker 2:Hi. On the show. Thank you so much for taking the time to join us. First, since this is your first time on the show, I'd love for a brief introduction on yourself, and then I wanna get into the Oscars. But, tell us a little bit about yourself.
Speaker 11:Oh, thank you for asking. I'm a longtime journalist. I've been covering entertainment for a long time. Yeah. Let's just say a lot of years.
Speaker 11:Great. You cut me open. Can count the rings. Let's put it that way.
Speaker 2:Fantastic. Amazing. And just give me your your overall reaction to the Oscars this year. What took you by surprise? What impressed you?
Speaker 2:What was your personal highlight? Walk me through what you thought the the story of the night was.
Speaker 11:The story of the night has to be one battle after another. I mean, I think it was definitely the film to beat going into the night. I don't think it definitely you know, I don't think it counts as a surprise. -But I think it was great to see that film definitely take away all the wins that it did. I think the winner of the night was definitely Warner Brothers.
Speaker 11:-Mm -You know, given all the coverage that that studio has been getting, it's sort of ironic that it was
Speaker 3:the studio that walked away with
Speaker 11:the biggest wins of the night. -Mm -I think Conyn did a great job as the host. It was exciting to see that. So I think there was a great story. But to me, the I I love Michael B.
Speaker 11:Jordan's win. I think it was historic. It was wonderful. I think he was so emotional about it. And that's kind of what you won under the Oscars.
Speaker 2:Yeah. How are you thinking about I mean, one of Conyngham's, like, funniest bits, I I thought, was his his jokes about the Oscars moving to YouTube and that you'd be inter interrupted by some, you know, sort of sloppy ad. Very tongue in cheek, but what do you think might change about the Oscars as they move to a more Internet native model?
Speaker 11:I know. I thought he did a great job. Was really so self aware about I thought you know, look, I think one of the most awkward moments of the night were the speeches getting cut off. It's always uncomfortable. It's really hard to watch.
Speaker 11:You feel so bad for people. It's the moment of their lives. -And suddenly, they're just sort of jumping up and down down on the stage going, Why don't I get to say thank you?
Speaker 2:That's a really great point. I mean, the one of the amazing things about the Internet, we've been tracking Apple's work with f one. And, with the new f one broadcast, you can say, I want to watch just this car on just this feed, or I wanna watch these three teams, and I want this announcer. And I could imagine in the future a feed where you say, you know what? I I actually don't care about the intros.
Speaker 2:I wanna hear the the the acceptance speeches. Give those to me in full. I don't care if I'm here for five hours. And you could just Exactly. Let the person continue and then cut away on the other feed.
Speaker 2:And maybe that comes it feels like the first version might just be the standard Oscar program on YouTube, but certainly some silver lining there if if that happens. What what what what how would you characterize AI this year at the Oscars? It it feels like there was a lot of demand for statements and and people to share their opinions about
Speaker 4:where things
Speaker 2:are going.
Speaker 4:Some comments.
Speaker 2:Yeah. At the same time, I mean, I artificial intelligence in the machine learning context has been recommending people what to watch on Netflix for two decades. And so, how are how how is Hollywood grappling with the AI issue at such a such a big event like the Oscars?
Speaker 11:The answer is they're
Speaker 2:not. Okay. I think it's
Speaker 11:a raw nerve. I mean, I think no one is willing to admit to your point Yeah. Just how much it factors into it. Yeah. I don't know that I can talk about what this season of the comeback is about, I think everyone is definitely addressing it.
Speaker 11:Let's put it that way. I think, I think it's definitely something that's on the forefront in everyone's mind. -Yeah. -We all use it, and to your point, we use it in ways that we're not even aware that we're using it. We're using it unconsciously.
Speaker 11:And I think everyone is very sensitive about it, and we're seeing the guilds, God forbid there's another strike. Please God, I hope there is no strike.
Speaker 2:I want to settle it.
Speaker 11:But I think we have to get ahead of it and come to terms with the real ways that it's helping us, but also getting ahead of no one wants to see AI writing a script. No one wants to see AI making movies or making creative decisions. But we can also recognize there are a lot of ways that it can help us and make our lives better. So how do we find that happy medium?
Speaker 2:I remember when Avengers I I it it was probably Infinity War, maybe Endgame, won best visual effects. And in the the CGI that went into Thanos' chin, they used AI to transfer the data from the the the facial capture of Josh Brolin. They had a camera pointed in with all the dots, but they needed to be high resolution. They used AI to actually upres that data to make a more compelling character, which was Thanos. And it was a beautiful synergy between the VFX shop that needed to do more and better graphics and Josh Brolin, who still delivered a great performance.
Speaker 2:So, hopefully, there can be more storytelling there, but it is it's such an ambiguous time.
Speaker 4:What
Speaker 11:For sure. And it really like, it came up last season with The Brutalist, you know, and I you know, and it's a really good question of how much it really hurt the Brutalist campaign. But it's like, suddenly, you had Adrian Brody talking Hungarian. Uh-huh. And there was a controversy about how much ADR came into impact all of that and how much it ultimately hurt his campaign and all of that.
Speaker 11:But it's sort of like, let's all be aware of how much it really has to do and how much it actually helps the making of the films. And if it's gonna help films get made and it's not really impacting the acting and the performance, Is it really that difficult? And is it really that painful?
Speaker 2:Yeah. And harmful.
Speaker 4:The other thing is even you mentioned on the script side how many movies have been created that in hindsight, you're like, oh, yeah. AI could have made that exact
Speaker 2:Could have launched. Punched that up or or found that plot hole, maybe.
Speaker 11:Look, I'm not defending AI writing. I'm not saying that. I'm just saying if it can Sure. If it can help to your point, the VFX, if it can help and if it helped the film get into theaters earlier in the technical aspects of it, maybe there's some sort of happy medium to be found there.
Speaker 2:What do you think formatting a script? I mean, it's such a hassle sometimes and there's little things about
Speaker 4:What do you think the the big, like, goals from for the guilds will be around AI? Do you have any sense of, like, what their asks are, what they're pushing for?
Speaker 11:I think it's about protecting the writers' rights, for sure. I mean, think it's really making sure that AI does not come in and write scripts, and that the, you know, the writers' rights, which is really hard to say. But you know what I mean, that the writer's rights are protected. It's probably smart of them to have done as painful as it is to talk about, you know, another negotiation so soon after we just had one of those, because it's all of this is changing so quickly and that it's just happened Mhmm. That we're making sure that we're staying ahead of it.
Speaker 11:So I think knowing how quickly all of this is evolving and how quickly these conversations are happening, making sure that this is a thing that they're ahead of and that they're not gonna, that suddenly some new technology isn't gonna emerge that they haven't thought about.
Speaker 2:Yeah. Yeah. Have you been tracking the, the debate over dialogue legibility or or how hard it is to hear dialogue in Hollywood movies these days. I heard that there's a there's a there's an interesting, like, loop from as TVs got cheaper, a lot of the speakers went out the back towards the wall, And so the sound quality got worse, so Hollywood sort of had to adjust. And I'm wondering about your your your thoughts on how Hollywood is changing as we move to a a culture that consumes movies on their phones at home and less in the theater.
Speaker 2:And what's
Speaker 4:Or in more your place on Apple Vision Pro.
Speaker 2:I did watch two movies last weekend in VR fully.
Speaker 11:Probably the I only
Speaker 2:think it's amazing. I actually I we we we talked to we we talked to James Cameron about this on the show, and he was sort of it it was very clear that he had gotten access to the next VR headset but wasn't able to And talk about it and I think from his perspective, you know, his movies, the Avatar franchise, it's so visually rich that being able to deliver something that instead of a 55 inch TV that maybe is from Costco and is tuned wrong, he can have more control over the actual visual experience. It was something that he was cautiously optimistic about, I think. And, at least in terms of the the really odd silver lining for me in VR, it sounds very, like, anti movie theater, but it puts you in a virtual theater where you actually can't use your phone. And so that whole Netflix thing about they have to restate the plot seven times, that's not an issue.
Speaker 2:And so I watched Susan Kain from start to finish. No breaks. I didn't you know, that's a that's a movie that would challenge the most brain rotted of the younger generation. And and I and I enjoyed it, it was great. And it felt like a movie that I should have gone to the theater to see, and I was able to do that in the Apple Vision Pro.
Speaker 2:So I I've been having a good experience, but I don't know. VR is probably not in the conversation at all in Hollywood right now, is it?
Speaker 11:Not so much, but I do think things that enhance the theatrical experience, anything that can get butts in seats in theaters is definitely gonna move the needle It's gonna be top of mind because I think that really is what is very much on top of mind for people and is really concerning the studios and writers and guilds and actors and all of that. Because I think that's what's really the biggest concern right now. Because there's nothing compared to the theatrical experience. There's nothing compared to, you know Yeah. Seeing one battle after another on a big screen.
Speaker 11:And I know I keep going that, you know, that chasing Yeah.
Speaker 2:Yeah. That chasing Yeah. Is I wonder I wonder how the you know, obviously, the Netflix Warner Brothers deal didn't pan out. But one of the interesting case studies that I heard was about how k pop demon hunters sort of got a second run-in the theater. Once it had gone basically viral online, then there was a sing along version.
Speaker 2:And then it became this experience where even, like, probably 90% of the audience that saw K pop Demon Hunters in theaters had seen it before, but the kids loved it and the parents had seen it. Everyone agreed, this is a great movie. Let's go and see this experience. I wonder if that could be something that the theaters lean into in sort of bringing back the movies that have already been derisked. There's already this audience, but it's the spectacle, and you know that the peep the the the tickets will sell.
Speaker 2:But who knows?
Speaker 11:Nobody thinks it's that communal experience. There's nothing like sitting in a theater with an audience and experiencing it together. Yeah. And on the flip side, you know, I I'm I'm a ride or die, have that fan. And sitting in the audience and and riding that emotional wave of that movie Yes.
Speaker 11:With people in the audience and not just sitting on my couch crying alone, but crying with people next to me and someone turning to me and going, are you okay? Yeah. That was really it was a visceral experience, and there's nothing that can compare to it.
Speaker 1:Yeah.
Speaker 11:So, you know, I think to your part about k pop, Damien Hunter, concert films, Taylor Swift saw that. Yeah. Putting butts in seats where people could experience something like that together, that's what theaters are all about.
Speaker 2:Yeah. I had a I had a similar but much dumber experience with the sequel to the Planet of the Apes movie. I saw it in what's called four d x, which is a, it's a three d movie, but then your seat moves left and right, and there's water that sprays you when something happens on the screen that has water. There's smells that are piped in. And at the end of the movie, there's this crazy avalanche, they all survive and stuff.
Speaker 2:And we were, like, high fiving with the people next to us, and it just created this, like, wild, hilarious experience that I still remember to this day.
Speaker 11:I'm there for it. I'm all there for it.
Speaker 2:Bring it on. Think whatever whatever fits the right experience. You know, for certain things, it'll be just a group of friends. For others, it'll be the the the full tilt 40 x experience or maybe VR. Who knows?
Speaker 2:But I I
Speaker 11:mean And then I can't wait to see what Oscar category they come up with for that.
Speaker 2:Yeah. I I don't know if
Speaker 4:Yeah. What any any predictions on AI specific categories Yeah. In the next few years? Do you think do you think we could see that?
Speaker 11:Oh, that's a third round?
Speaker 2:Yeah. I know. Yeah. I I Think about it.
Speaker 11:It it took some twenty five years to add the the casting category.
Speaker 2:You know,
Speaker 11:we just saw that one last night. Yep. And next year, we're getting stunts. So the wheels of change move slowly. They come, but they're they move slowly.
Speaker 4:Yeah. That's interesting. Stunts being added at the time where I feel like AI will be most dis as it rolls out, potentially more disruptive to stunts than anything else because it just why risk human life?
Speaker 2:Yeah. Insurance and stuff. So yeah. Interesting. Well, I mean, it'll certainly I I I think just like, you know, cinematography, costume design, set design, like, there's so many things where at at when you're at the level of the Oscars, like, you are going to see the people like, you think you think Tom Cruise is gonna stop doing his stunts?
Speaker 2:No way. He's not he's not gonna let AI jump across a building.
Speaker 11:No. He's not. He's gonna be jumping off lanes.
Speaker 2:He's going to do it. Exactly. And it's about, like, the lore that he brings to that performance that part of when you sit down to a Tom Cruise movie is you've been hearing about the process of making that film for months and seen Right. You know, behind the scenes stuff of him jumping the motorcycle off the cliff, and it's real, but you can see some of the camera equipment. And so you go into it, and it's so much easier to, like, suspend you're almost not suspending reality.
Speaker 2:You're suspect you're you know it's real. So Right. It just makes the just makes the excitement so much more thrilling. Well, thank you so much for taking the time to come chat with us.
Speaker 4:Great to meet you. Great to meet you. Of course. Thank you. Come back on anytime anytime you're writing something you think
Speaker 2:We'd love to have
Speaker 4:you our world would be interested in.
Speaker 2:Yeah. We'll talk to soon. I'd love to
Speaker 11:come back. Take care, guys. Have a great rest of
Speaker 4:your day. Well Back to the timeline. What did we miss?
Speaker 2:Well, John Collison asked on the Cheeky Pint podcast if Sierra is a short AGI company. That was a very funny one. I I I think everyone was wondering about this, and, you know, we'll have to watch the full episode to to to get the full answer. But there is this interesting dynamic right now. VC investments usually take five to eight years to exit.
Speaker 2:This is from Ethan Mollick. That means almost every AI VC investment right now is essentially a bet against the vision of Anthropic OpenAI and Gemini have laid out. And so it's got to be a great time to be a VC if you're in those names, a little bit stressful, and it just requires, like, an extra an extra layer of attention because you're not going up against, legacy incumbents that have been rolled up and sold and gone public and gone private. That was so much of the early software boom was, okay. This company has been doing things in on in paper, and we're gonna do it on a website.
Speaker 2:And now, you're going up against Sam Oetting and a lot of people that are in founder mode and very well capitalized and have a very broad vision, and the technology shows a lot of promise.
Speaker 4:So We will end here. Marketing's recent says, it is a 100% true that great men and women of the past were not sitting around moaning about their feelings. I regret nothing. So we are gonna leave it there for today. I hope you spend the rest of your day
Speaker 2:Not feelings.
Speaker 4:Living with regret.
Speaker 2:Yes. Never introspect. Always outrospect. Always respect yourself. Here's our outrospection.
Speaker 4:Right. We made the new outro last week and unfortunately, we used a song.
Speaker 2:They didn't want us to pop.
Speaker 4:They didn't want us to play. Yeah. And so they took down
Speaker 2:We're gonna work on a new outro. We're we're we we already got a bunch of ideas cooking. But leave us five stars in Apple Podcast and Spotify. Thank Subscribe to the newsletter at TBPN. Goodbye.