Meta Taps Nat Friedman & Daniel Gross for AI Push, Starship Rocket Explodes | Mike Knoop, David Cahn, Walden Yan, Eoghan McCabe, Jeff Weinstein, Garrett Lord, Tanay Tandon

28:59Bring the horses. Bring the air horn back. Oh, that's weird. They steal the gong. Wow. He really does. Surrounded by gentlemen. Hold your position. Okay. Okay. I think there's one more. Yeah. Let's play the last one. What you got? What's this one? That last one was bizarre. Very, very. So the physics are pretty solid. Sometimes you see a bit of the same thing with VO3 where like if a car is driving, right, you'll see the back of the car. Okay. We got us standing at the pool. Let's take a look at this. Okay. This one's cool. Okay. We're back. A lighting on that's incredible. Okay. Finish strong.

29:3410 out of 10 for mid-journey. Play it again. There we go. I love this. This is great. That is cool. It looks really good too. Yeah, always bring your F-35 Joint Strike Fighter into the job of the club pool. Was that a Tomcat? Something like that. Yeah. But okay, that was cool. I like that one a lot. Yeah, a lot less demonic. Took us on a bit of a roller coaster there. Yeah. I did not like that angel. That was weird. That was very weird. A little bit creepy. The aliens were very bizarre, but you redeemed it all. You won it all back. Fantastic. Give us a review of the like overview of the actual experience How long does it take to generate these are you hitting rate limits?

30:14How much is it cost? Give us like the breakdown of like you know consumer experience. Yeah, it's really good I mean, I think so I'm on the$30 a month plan, which is kind of the mid-tier one But it's very fast it takes probably 10 seconds it's really fast. Okay, VO3 is like two minutes. So you can iterate like super quick. Oh, that's cool. Okay. But it's very easy to use. I mean, I haven't used, so I'm actually not on the Discord. I'm on the website. Okay, yeah. But it's very easy to use. Okay, very cool. And you can run them concurrently so I could do multiple at the same time. That's great. Yeah, really great.

30:48Awesome. Well, very fun. We'll be tracking it more, asking people how it's benchmarking, how it's working. We'll have to have some fun with those. I had a lot of fun with the VO3 ones. We were doing the crashing through the Hollywood sign. A few too many bottles of Dom Perignon on the back of Ferrari. Yeah, I didn't like how there seemingly weren't guardrails. Yeah, that's an AI safety issue. Yeah, that's an AI safety issue. I shouldn't just be able to prompt you drunk driving. Yeah, bottles of champagne flying out of the car. The quality was remarkable. Anyway, Midjourney is having fun on X. They say, introducing our V1 video model.

31:23It's fun, easy, and beautiful. Available at$10 a month. It's the first video model for everyone, and it's available now. How many prompts do you get for$10 a month? I don't know. You want to look it up? Yeah. I mean, I think it's actually unlimited, but it just takes longer. It just takes longer. Yeah. Oh, that's cool. I'll verify that. That's insane when you put it into the context of those outputs are in many ways better or on par, at least from an entertainment value standpoint as VO. Yep. And VO is$500 a month, and you're still gated on. I could only do three per day. I have to come back. They take two minutes a pop.

31:59Yeah, speed of iteration is really, really key. I mean, that's the whole Discord model is like get people iterating, sharing ideas, like to explore the space and figure out what works. Like even just from seeing those four, I feel very confident about its ability to render aircraft. And so I'm probably not going to go and prompt a bunch more alien videos, but I'll definitely be prompting a bunch more F35 videos because it seems to do that really, really well. And so the more people you have making more stuff, the more you learn the guardrails, learn how to use it creatively and can actually make a better product.

32:36But Midjourney was having some fun. Devin Fan from XAI says, I know what I'll be doing this weekend. And Midjourney says, what weekend? And Will DePue says, LMAO. Blake Robbins says, Midjourney video is breaking my brain. And everyone's having a good reaction to this. So always fun to have a new AI tool. And we'll be, we're talking to a couple of AI folks on the show today. So we'll be running through that, getting their reactions and talking more stuff. Elon Musk posted the very sad, what is this, the peepo, pepe or something? It's the green frog. He's smoking a cigarette. He's not happy. Probably because RIP to ship 36.

33:184 a.m. Brutal.

33:24we actually Vance we should I I think a picture of Elon you know smoking a heater after one of his rockets blow up blew up would be become a timeless meme that would be it would be worth kind of his comms team kind of working on putting that together maybe working with actually actually Vance to get that shot yeah it could live so girls say I can't believe he didn't cry at the Titanic do men even have feelings boys crying at the sight of ship 36 exploding very very sad and then elon says just a scratch the entire thing blew up uh it was a flesh wound it was intense watching it um i mean the the ball of fire here is immense so uh the starship exploded during a test in texas setback for mars is mars uh for musk's mars ambitions now the mars transfer window is very very tight like you can only get from the earth to mars like once every 18 months or something or maybe even more it's really hard because like if the planets are on the opposite side of the solar system like you just can't like even though you have a rocket you just can't get over there so you have to wait until they're lined up and then you can do it but realistically skill issue realistically true skill issue if you build an even faster rocket you could get there no matter You just pilot, steer it around like it's a GT3 RS around the Nurburgring, no problem.

34:50So the explosion occurred during a static fire test. No injuries were reported. Thank goodness, we love autonomy. Very, very happy to hear that no one was injured during this because it looked horrific and it looked like in any other scenario there would be a bunch of technicians there, but fortunately they were able to do everything remotely, which is great. And then Starship fetches pressure to meet deadlines for NASA's moon mission and Mars exploration. So there's a big NASA moon contract that's very important, very material to the business. Obviously, SpaceX has a lot of other business lines, but this one's very, very important too.

35:24And we hope that they can get back on track. SpaceX is making an enormous bet on Starship, which stands roughly 400 feet tall at liftoff as it tries to break ground with new reusable rockets. And the paradigm of Starship, it's not just a bigger rocket, it is way more reusable. Like you look at the thing, it comes down, gets caught by those arms, can instantly be refueled and sent back up. You're talking about potentially like multiple flights per day. And so the problem here is not can you build a big rocket? Humanity has done that before. Humanity has built a rocket that's roughly on par. We've gotten to the moon before.

36:02The challenge now is not can we get to the moon. It's the same thing with like the challenge is not can we build a flying car or can we build we have helicopters. Can we build one humanoid robot or one self-driving car in San Francisco? It's like, can we actually scale these systems to the point that it is safe to go to the moon and back on the drop of a hat for 200 bucks? Like, that's the challenge. It's more of an economic and industrial might challenge. And that's a completely different challenge from just, can we get one rocket to the moon? An exquisite system. We're looking for reusable, scalable, you know, engineering systems.

36:36So good luck to Elon rebuilding and the entire SpaceX team. I'm sure it's a huge challenge right now.

36:48But let's do some ads to tell you about Adio. Customer relationship magic. Adio is the AI native CRM that builds, scales, grows your company to the next level. Get started for free. Adio.com. Adio.com. I like that. You can use code. Wait, what is that sound at the end? Is that attached to that soundboard? soundboard guys i think you botched uh the action movie sound effect oh no it has on that okay uh in other news the los angeles lakers has been sold for 10 billion in richest deal in sports history guggenheim partners ceo mark walter who also owns mlb's the dodgers is acquiring the storied nba team in a move that makes it the world's most valuable sports franchise and it's so funny because the Wall Street Journal is framing this as like, this is the biggest deal ever.

37:36No one's ever done a deal like this. And we're like, wait, so you're talking about like a Series A for like a foundation model company? Like as a tech person, I'm just like, yeah, like$10 billion. $10 billion. I mean, we should ring the gong, but it's not exactly like the first time. It's not even the first time this show we've heard a deck of corn.

38:03congratulations to uh to the lakers mark walter and the whole team it's it's uh it's fantastic uh a major premium to the boston celtics who sold for 6.1 billion um and now the lakers is the most valuable sports franchise um but they just don't do enough volume there's only a couple games you know they're not 24 7 like instagram does that ever go offline no no there's always entertainment it lakers they're still doing seasons they need to have 24-hour basketball they want to really get there around the clock it's like endurance endurance basketball it's just a week-long game you know gotta always have five players on the court just constantly running up it's the only option uh jeannie buss and her family who have owned the los angeles lakers since jerry buss bought the team in 1979 wow on wednesday agreed to sell majority control of the storied team to Mark Walter, the sports investor.

38:57And I looked at the return on investment of owning the Lakers for that 40 years, slightly under S &P 500. Like it was a really, really good deal. And it was a really great company that grew a lot, but it didn't outperform the stock market. Just diversification bros, DCA bros, undefeated again. Well, if you're trying to DCA, do it on public.com, I'm investing for those who take it seriously. Multi-asset investing, industry-leading yields. They're trusted by millions, folks. Anyway, Walter, who is part of the ownership group that owns the Dodgers, has been part of the Lakers since 2021 when he purchased a 27 % minority stake in the franchise.

39:39He's also a co-owner of Chelsea in the English Premier League, the WNBA's Los Angeles Sparks, and the newly formed Cadillac Formula One team. Let's hear it for Cadillac. Let's go. Let's hear it for Cadillac. Congratulations. John. I can't hear you at all. John front run the Cadillac F1 team and got a Cadillac for himself over there. You can see the black wing. It's great to have an American F1 team in the business now. Yeah. We've fallen off, but we're coming back. It's great. You're not going to be able to get one of these in the whole country. I don't think so. They're going to be too popular.

40:13After the F1 team gets out on the track. The sale marks the end of nearly a century of Lakers control by a family that has become synonymous with Los Angeles sports and the glitz of professional basketball. The deal also comes at a time of skyrocketing valuations in professional basketball, which haven't come back to earth since the league announced a media rights deal last year worth$77 billion when the Celtics sold in March the$6.1 billion valuation, exceeded the previous record valuation set for a sports team by the$6.05 billion sale of the NFL's Washington Commanders in 2023. he purchased the lakers for 67 million in 79 1979 the team transformed from franchise uprooted from minnesota into one of the winningest and most valuable i had no idea that they were founded that's where the lake the lake name comes from minnesota is the land of a thousand lakes they were the lakers because there's a lot of lakes in minnesota and then they just put them to uh they just brought them to la and kept the name but that's what lakers means yeah wow bus The Buss family oversaw the creation of Showtime and presided over the NBA's last three-peat.

41:26A-listers like Jack Nicholson and Leonardo DiCaprio have become fixtures at the games. And when they sell merch, they need to pay sales tax. They should get on numeral.com, numeralhq.com. Sales tax on autopilot. Spend less than five minutes per month on sales tax compliance. They have won 11 championships since 1980. Their rosters have boasted many of basketball's brightest stars. Magic Johnson, Kareem Abdul-Jabbar, Kobe Bryant, Shaquille O 'Neal, and LeBron James. And LeBron James' son have all worn the Lakers purple and gold. I love it. It's such a cool – yeah, the father-son duo. I mean, I feel like that should have been a bigger national news story.

42:05It's such a cool thing. I think it's like not – if they were winning championships together immediately, that might be a different story. But it's just so insane that you could be playing professional basketball with your son. You could have earned a better return by DCAing into the stock market. That's not why people own these assets, though. Owning the Lakers for a number of decades, I imagine, was absolutely priceless. So great investment. You get the owner's box. Great run. Yeah. All the perks, you have to add those in. Do you get perks from DCAing into the SEC? I like how Lakers legend Magic Johnson hit the timeline, said, just like I thought when the Celtics sold for 6B, I knew the Lakers were worth 10B.

42:50Let's go. The confidence of Magic Johnson. Great investor too. He's got a bunch of good stuff in the portfolio. Anyway, more news on the scale AI transaction. So it's closed. I believe that Alex Wong has a badge at Meta and shows up to work in Palo Alto and clocks in at Meta HQ now. Scale AI is still an ongoing concern, it's still a company, but every competitor is out for blood and they want to take as much of the business as they can since obviously the perception is that Scale AI will primarily be working with Meta and that other foundation model labs might not want to do business with Scale AI anymore.

43:34Unclear if they can separate out the businesses, if they can separate them out fully over time and sell the position to other investors, create like a diversified, I mean, they could even take the company public, at which point I imagine that it would be a lot less of a conflict of interest or like a fear. But there's been news that OpenAI said, hey, we're not using ScaleAI for data anymore because it's too aligned with our competitor, Lama maybe. But everyone's trying to - Yeah, a lot of this was very predictable, right? I don't think Meta and Scale's teams looked at and said, hey, if we sell right now to Meta, which is competing in open source AI, we're totally going to retain all of our customers, right?

44:20Like people aren't just going to immediately churn off. And no, they were smart enough to know what would happen. And there was an article, I think, yesterday about OpenAI, you know, ending their relationship with scale. But from what we knew, like they hadn't been doing much for a while. That's part of the reason why Mercore had been absolutely ripping. And they also brought a big function in-house because for some of the more complex tasks, it makes sense to generate the reinforcement learning data yourself. And there's just so many other services, having like a single point of failure never makes sense for a business of that size, but we'll see.

44:53So the information has an article here about a little known startup that has surged, hint hint, past ScaleAI without any investors, this is interesting, after meta platform ScaleAI deal, data labeling is looking like Silicon Valley's hottest new interest. That's an enormous opportunity for Edwin Chen's Surge AI. For years, data labeling existed in a tucked away corner of Silicon Valley, a critical but unglamorous area of AI where companies like Google and OpenAI hire outside firms to improve their models by laboriously grading the quality of what they produce. Now, a spotlight has unexpectedly fallen onto the field in the wake of Meta Platform's decision to pay 14.3 billion for 49 % of Scale AI, the best known data labeling firm.

45:38But it's not the largest such firm, nor perhaps the most impressive. That title belongs to Surge AI, founded by Edwin Chen. This is fascinating. I didn't know this. One billion in sales last year. Bigger than scale. Yeah. So Chen's startup has won customers like Google, OpenAI, and Anthropic. It's such a testament to the idea that, like, sure, you can bootstrap, but it's so incredibly hard to have any hype around your business if you're bootstrapped because your investors aren't hitting the timeline for you on a daily basis. And also, if you're not trying to raise capital, you have less need to go and be loud and go on podcasts and talk to the press and all this stuff because you're just making a lot of money.

46:25And sometimes it can be beneficial for people to not know about you. So this is, I mean, this is crazy, crazy stats. So Chen is 37. He has no investors and has bootstrapped the five-year-old startup entirely by himself, which has 110 employees in offices in New York and San Francisco. The company generated more than$1 billion in revenue last year. Surge has told employees, a previously reported figure, that exceeds the$870 million scale generated in revenue during the same time period. And unlike Scale, Surge was profitable and has been from the beginning, Chen said. Moreover, Surge could see its sales get even larger if other companies copy OpenAI's decision to stop hiring Scale, a choice made over concerns about Scale's relationship with Meta to shift business to Surge.

47:11Other key financial metrics couldn't be learned, like how much revenue Surge keeps after paying its workforce of mostly contractors. So there is a question about the margin, since this is somewhat of a marketplace business. This could be a situation where a$1 ,000 contract comes in and$800 of that contract goes to the actual contractor who's doing the work of the data labeling. But at the same time, even if it's$200 million in net revenue, that's still a huge business. It's hard to imagine Surge not being a fantastic business. If they haven't had to raise money, they have 110 employees and they're used by Google and all these major foundation model labs.

47:49So it seems like a fantastic business. But if Surge could earn a valuation from investors similar to the one scale received from Meta, such a price would make Chen a billionaire many times over, at least on paper, and quietly one of the wealthiest people in tech. Interesting. I'm very interested to see what he did before this company. Edwin Chen. I feel like I've heard that name before, but I don't know. As AI models transform from toys into real business tools, data labeling is becoming more and more essential. contractors hired by companies like Surge grade the responses from AI models and write thousands of questions and answers in fields like programming, math, and law to feed those AI models.

48:28And so, you know, I wonder if this is going to go the route of, you know, you are Deloitte or McKinsey and you're going to have your team, but then also a company like Surge, create a ton of training data around a specific workflow that is costing your business, you you know, 20 or 50 or 100 million dollars every year. And then, so it's like, instead of like the AI BDR that's like kind of generically writing emails based on like the average of the entire internet, it's like, no, this is a fine tune for your business, perfectly trained, perfectly, and it really distills what you do excellently.

49:09I don't know if it'll go that way. I'm interested to talk to people about it. As AI models, so, Surge's subsidiary, Data Annotation Tech says workers get paid to train AI on your own schedule with wages starting at$20 an hour. Chen has distinguished Surge by making it the high-end shop, charging premium rates often two to five times what scale might bill. Surge justifies the prices with its reputation for industry-leading work. Indeed, one former scale employee said Surge often performed better than scale in customer audits of labeling quality and competitor Garrett Lord, who's coming on the show today, who runs Kleiner Perkins Backed Handshake, readily acknowledged that Chen is the number one player.

49:48So I'm excited to talk to Garrett Lord today about this exact topic. Should be very interesting. You wouldn't know that from the coverage of Meta's blockbuster deal to quasi-acquire scale AI, its CEO, Alexander Wong, who is now joining Meta in a senior AI role, was widely regarded as the leader of the data labeling field and had become a Silicon Valley celebrity, blanketing podcasts and conferences with his presence and posting heavily on X. It also raised 1.5 billion in venture capital, putting Scale on a very short list of companies that have raised that much, and he hired upwards of 1 ,000 people.

50:19Wong had made time to his exit perfectly given the traction of Surge, which had grown larger than Scale without outside capital and with a tiny fraction of Scale's workforce. Scale also missed the goal to hit a billion dollars in revenue last year, but Scale spokesperson said the company stood behind this. Scale wasn't profitable? It was not profitable. Which? But wasn't burning a ton of money. Yeah, they were efficient. They raised 1.5 billion and they still had like almost a billion in cash. So they weren't in like trouble or anything. But at the same time, it was like not a wildly profitable, not a wildly lean business.

50:50But I don't know.

50:54It's absolutely fascinating to comp these two businesses. It's a wild industry. Something that like, yeah, I mean, just it feels like there's such an edge just to even identifying this opportunity years and years ago. I mean, I guess search started four or five years ago. But it was certainly like pre-chat GPT that all these companies got started. And then they realized like some of them got started in self-driving car annotation, all sorts of stuff like that. But Chen studied linguistics and math at MIT, came to the idea for his startup after leaving college and witnessing firsthand how big companies struggle with data.

51:25Before starting Surge, Chen worked as a machine learning engineer at Facebook, Dropbox, Google, and Twitter. He worked in four different tech companies. Just like going from one to the next. That's insane. He was developing recommendation and search algorithms and helping gather the data needed to train them. Despite the hefty resources of those companies, Chen encountered a lot of problems. At Facebook, for instance, Chen was tasked with helping build a Yelp competitor. His team needed to train a model that could correctly classify businesses, telling the difference between restaurants and grocery stores, for instance.

51:57To do so, they needed a data set containing 50 ,000 accurately labeled businesses, which he found would take six months for an outside firm to assemble. We had no solution other than waiting. We simply waited. When the data came back, Chen Blanched. In some instances, it had labeled restaurants as coffee shops and coffee shops as hospitals. The data was complete junk, he wouldn't say, which vendors Facebook had used. In 2020, he left Twitter to found Surge and picked up some of his first customers, executives from Airbnbs and Neva, a once promising AI search engine startup, as only a founder in San Francisco might, bumping into them at rock climbing gyms in the city's dog patch neighborhood and the Mission District, talking up his startup.

52:40To get Surge going, Chen recruited data labeling contractors he knew from his previous roles and funded the startup using his savings. He wouldn't say how much he put in. Fortuitously, Chen focused on language modeling. Scale, by contrast, started out using more visual data for autonomous vehicles, which we talked about. Just as those types of models began to grow in importance. Less than a year later, OpenAI had hired Surge to fine tune its models by teaching them how to avoid producing harmful responses like a racially biased language. Based on research paper the company published together by 2022, Anthropik had become a Surge customer.

53:14They're putting out research papers with OpenAI and still managed to stay this under the radar. Wow. Yeah. So look at this. The label largesse. Data labeling has proved to be a lucrative niche in AI. Surge founded in 2020 has over a billion in revenue, zero funding. Scale founded in 2016 has$870 million in 2024, raised, oh this says funding raised, but this is clearly valuation or something because it says 17.4 billion, which is not what they raised. Turing has 300 million annualized, raised$225 million. Invisible, Merkle, Merkle. It's interesting, Turing too initially was like a marketplace to just hire developers, and I think they pivoted into data labeling.

53:59Interesting. It's the same thing when I work with a cloud provider. The enterprise tech customer said, I don't know the internal expectations for why their services work so well. I push a button and I'm glad for the internal work to make that happen. Data labeling companies typically use various techniques to make sure contractors aren't just dialing it in or phoning it in, I guess, when answering questions. For instance, companies randomly insert questions that have no correct answers or make sure labelers agree on the right answer to a question. So obviously you scaffold up these responses so that everything's like double checked and then you can kind of see if people are messing around.

54:36But wow, what a beast of a business. I had no idea how big this thing is. Amazing. How'd you sleep last night? I'm on a comeback. I got an 89. Go to aidsleep.com. No. Get an aid sleep. I know what I got. Five-year warranty, 30-night risk retrial, free returns, free shipping. What'd you get? You got 188? Let's go. Soundboard. I demand a soundboard. Ashton Hall. Let's go. John did it. I did it. They said he would never beat me after a good run. I also took a nap after I got home. It was fantastic. We have our next guest coming into the studio. Mike from ARC AGI breaking down how all the different models are doing.

55:16Last time he was supposed to come on, Elon. Yeah, I know. It was so annoying. And Trump decided to get in a massive timeline war. It was brutal. John wanted to power through it. I said, John, the people in the YouTube chat are - Demanding this. Demand that we do a timeline - If we don't do it, they will put up attack ads against us. They will buy billboards against us. They will go to adquick.com and put up attack ads on TBPN if we don't cover the Trump-Elon dust up. So we did. and now we're getting those folks back on the show today and later. But if you want to take out an attack ad on us, buy a billboard at AdQuick.

55:55Out of home advertising, made easy and measurable. Say goodbye to the headaches of out of home advertising. Only AdQuick combines technology, out of home expertise, and data to enable efficient, seamless ad buying across the globe. uh we should do some timeline i want to do this blank street story but we can do that later let's dig through some uh what's in the timeline while we while we wait for mike to join oh we have mike in the studio now welcome to the show mike good to see you how you doing good uh thank you so much for i'm glad there wasn't a major breaking drama story today he's actually able to show up yes yes you i i don't know if you watched that show at all but like i was just sitting here john was so locked in wanted to just keep doing this show and i'm like messaging him like no we like actually to come yeah it was a terrible day to launch anything new and we like launched something i saw several startups launch stuff uh like uh regrets to everyone who tried to get anything out that day yeah actually i remember them it was rahul and uh yeah that's right julius had a launch and uh what what what's the voice cloning company 11 labs i think they launched well and then and then i think lulu said something she was like if you have bad news today would be a good day to drop it and then open ai actually flagged like hey we had this like massive you know uh oh yeah this this dust up with the government right where the government was like you have you have to give us your all all chats we don't want to do this yeah i mean i was like serious like i mean that got um we're still looking at actually you know the end result of that but that went really deep into the world i feel like much more than um you know kind of maybe even got reported on like every single chat thread i was a part of was basically like hmm should i like stop using chat gpt as much um i don't think what was that unique to chat because it feels like anthropic has a similar policy it seems like google might have a similar policy like there was that story a year ago about a man who was using a google phone with a google phi uh cellular connection and had all of his data stored in google uh google drive and gmail and he took a picture of his of his child to send to a doctor and it was kind of like a nude photo of the kid to inspect the child for like a physical medical problem and it got flagged as child abuse material by an automatic system and the automatic system basically de-platformed him from everything google and so he lost his email his phone number his all of his drive stuff and it was like a false positive but it was really hard for him to get back through there and so i guess my question is like like it seems bad when we hear the story in isolation but maybe the problem is not the individual company and it's instead like the government policy and this applies to all the different companies but two things can be true one is that it can be a massive overreach by that court to say you know basically you need to eliminate privacy yeah on your platform yeah and you can simultaneously have questions around maybe i should use this product in a different way totally um and it's the the inflammatory nature of it is that people use chat gpt as like a confidant that totally yeah and tell it things that they wouldn't tell anyone they wouldn't tell anyone in their life and they're and they're having those conversations and i think that's why it struck such a chord because like that's true um i just saw some reporting from uh uh kochee this week that like chat p minutes per day are up to like 30 minutes a day now in usage um and like it's not it's like closing the gap with like instagram which is just sort of nuts to think that like i mean who would have thought of productivity to whatever like be on par with like a social like media app right in terms of daily usage it's so fun but the interesting thing is it's filling a similar void you know it's like it's delivering uh digital companionship in maybe the way that social media products historically did without any social element to it at all just like this one one-to-one it's interesting to think like you know we went from like you know what your friends are doing is like the most interesting thing to like what the kardashians are doing is like the most interesting thing to like actually maybe the most interesting thing is like this person that knows everything about you and is always on and always willing to talk and you know you know who knows yeah i think the consumer happens to be informed around stuff today yeah yeah i mean i i find myself all the time like instead of scrolling youtube looking for an info like an interesting video essay to explain how I don't know like global shipping lanes work or something like that just going to chat GPT and saying hey like break this down for me And then I can just ask a follow-up and dive exactly to the layer that I want And so yeah, I'm definitely in that camp of using chat GPT just as like an exploration and entertainment Education tool an infotainment tool much more than Instagram right now at least for me but Enough about that.

1:00:46What is new in your world? how should we frame kind of the current horse race between all the foundation labs? Yeah. Okay. So I'm going to share a link. I don't know if this is something y 'all can pull up. Yeah, we can pull it up. So this is a post that we published a little over a week ago. So, you know, I think there's been this really big, like, what's the frontier right now in sort of AI progress, right? The massive shift in the last six to nine months has been moving from this regime of like scaling up pre-training with more and more labeled data into these like test time can to test time compute test time adaptation regime people call these AI reasoning models right we're giving these models time to think out loud additional data um every major lab now pretty much at this point uh I guess my except for meta has one of these uh systems that we've been able to test and report results on and um I think there's some really really interesting stuff we're starting to see um I think the most notable thing is that like there's not an absolute clear winner across the sort of like landscape right now.

1:01:51There's basically a sort of Prudhoe Frontier that's emerged. One of the most important things, if listeners are like listening here, I think you should take away is that like any anybody who gives you a benchmark score on an AI system that is a single number is just marketing to you. because the reality is now with these AI reasoning systems, you have to report score on like a two-dimensional act. You have to consider cost and efficiency alongside the accuracy. And all these different lab providers have come out with different AI reasoning systems that sort of score differently. They're trading off cost per accuracy at different points.

1:02:26So like if you want just like the absolute highest, you know, highest raw horsepower for cost and time is no option. Oh, three high is going to be your like clear winner today for that. But if you're somebody who's saying like, you know, Hey, I want to plug in an AI reasoning system into an existing product I have where I want like faster answers and I'm willing to sacrifice some raw horsepower for generality for like quicker response times, lower, lower cost. You might look at something like Brock or Gemini 2.4, 2.5 pro thinking. There's not like a single like best, best answer, which, which I think is pretty interesting.

1:02:55And we haven't seen like this sort of frontier is I think what all the labs are working to, to try and figure out, okay, how can we get accuracy as high as we can, but also we got to try and keep costs as low as we can down in the human efficiency realms. Yeah, I've noticed that more recently with kind of my default usage in ChatGPT, 4.0 seems super fast, but I always am thinking like, oh, I should maybe put this in O3 Pro, but do I want to wait 10 minutes? And I'm making that kind of like economic calculus there, even though because I'm on the plan, it's not an economic cost, not a direct, I'm hitting a$5.

1:03:30It's more time for you, right? It's time. 13 minutes. Yeah, exactly. and so i'm kind of like doing a 4-0 thing over here and then switching back and forth it's very it's very odd paradigm that i we never really had to deal with in computing necessarily before i mean i guess like if you were downloading like the 4k illegal blu-ray versus the sd which of course we never did purely hypothetically but if you're on a torrent site there was a time trade-off between watching a screener um well i think this is actually one of the reasons these ar reasoning systems i i would assert and i i don't have inside baseball in the data but like from the outside looking i think there are some interesting suspicions that would suggest that these like ar reasoning systems at least today in our current form have like relatively weaker product market fit um compared to the uh like non-a the non-reasoning based systems right the pure language model based based things interesting that's a huge violation of the the like the deep seek narrative that i felt like was really bubbling up was deep seek came out with like the first just like open access reasoning model like reasoning had been tucked behind the open ai paywall And so the pro users were familiar with what reasoning models could do.

1:04:35Everyone was very excited about them in tech or in the early adopter crowd. But DeepSeek, when that app came out and you could just install it and instantly see the reasoning chain, it felt like everyone's like, oh, everyone's going to be addicted to this forever and this is going to be the new paradigm. But it seems like that might not necessarily be happening. Jordi, do you have something I wanted to talk about? Go. Like spiky intelligence and how that plays into this. We had this, someone came on and said like, I think it might have been Cholto actually talking about Arc AGI, just saying like, hey all the foundation labs kind of have like a truce that we won't reinforcement learn specifically against Arc.

1:05:12I don't know how real that is from your perspective, but it feels like increasingly we might see like very task dependent RL runs kind of chipping away at specific things like IMO level math is something that clearly like there's a ton of work to be done on, but we don't have as many verifiable rewards for poetry or comedy writing. And so that'll be a little bit messier and later down the road maybe. But at the same time, there's probably other verifiable rewards that are just smaller pockets of value here and there for these little micro tasks. And so I'm wondering if we will ever see like the marketing language around these models evolve.

1:05:55Like Grok kind of did this with like, we are the anti-woke one, but that was more just in the overall like temperature or the vibe of the model. But I'm wondering if there'll be an idea of like, this one's really good at math. This one's really good at research. This one's really good at that. Or if they're all kind of going down the same path with what they're trying to solve. I do think you probably are going to see some domain specialization. I think my guess over the next 12 to 24 months is that you'd see some domain specialization on Benchmark Scores of Verge because of how all these labs are starting to do the next evolution of training.

1:06:25which is they're using our environments to generate synthetic COT traces doing their sort of model trainings on that data. And they're trying to go get it on a lot of different, just different domains. Um, you know, the O three, the original O three paper, you know, I think it was interesting on the benchmark results where, you know, on this new sort of COT reasoning system, they had a relatively high scores on math and coding. Um, but, but the, the, the gap, um, or I should say the step function increase in those scores that was much higher than the increase in like legal reasoning, which you would sort of maybe intuitively guess that or think or expect that like legal reasoning would probably be one of the best like general domains for if you trained a reasoning model that was really good on math and coding, like it should be like, and it's a language model that like that would directly transfer into like the legal domain because like, okay, it's symbolic, you know, reasoning that's like self-consistent.

1:07:13And that wasn't the case. I think that's, I suspect that's what we'll see there. You know, there's obviously the big scale news. um the thing that i'm seeing now is there's uh probably like i don't know at least a handful that i know uh these new uh startups that have come up in the last several months but all getting founded to basically go build rl environments to generate synthetic or semi-synthetic data uh and like selling them to to sort of the major labs which are the major frontier folks building these next-gen systems um i think we're gonna see more of that i expect that's kind of what's gonna drive a lot of areas what do you explain that more what does the data labeling market look like today?

1:07:46We were covering Surge AI, which a lot of people weren't familiar with. I'm sure I'd seen it at some point, but I was certainly not familiar with it until we covered it today. What do you think the labeling market looks like in five years? Do you think that scale was getting out kind of at the perfect time? I'm curious. I think the timing was pretty good. I mean, like, look, the macro change here is from a regime where like we're scaling a pre-training. We want as much tax, as much high quality label taxes we can get our hands on to to scale these these foundational models into one where we're trying to turn process models or, you know, make the foundation models really good for process thinking and COT generation.

1:08:29That is a complete shift in how you want to generate that data. You want an RL environment that you can create lots and lots of COT traces, really, very long traces of our long running tasks as well. And you can feed all that right back in and then take advantage of the scaling laws we already know about language models and how they work and how the performance increases, as you can get more examples of the data there. So, you know, my like, I guess like macro bet would be, you know, sort of the trend is heading down towards like the pre-training skill and stuff. Significantly higher on RL environments.

1:08:55Yeah. So when you say RL environments, what you're talking about is moving from a paradigm of, I go to a data labeling company and they hire a ton of contractors to generate new text or verify the responses or grade the responses from these models to I am now hiring top machine learning engineers, AI scientists, and having them design an environment that the reinforcement learning can happen autonomously within the system, right? Effectively, these new startups that you mentioned, they are taking the massive like hundreds of thousands of contractors like out of the loop for the next runs is that correct yeah it's synthetic or semi-synthetic in some cases um example companies here like mechanized work is one that got started recently that's doing this stuff uh morph i think is doing this stuff habitat's another world environment that's sort of doing similar stuff sure um there's just a lot and it's like very emergent all these many of these got founded in the last like couple months yeah yeah yeah and i think that's a function of the demand in the poll from a lot of the frontier research groups that are wanting this data yeah to sort of do their all training stuff so so do you do you imagine uh companies like surge and other players would try to pivot into this if if they're expecting i would expect that founder-led companies like that would recognize this as growing part of this and have bets in it if they don't already yeah i was always wondering like the the whole story of scale was kind of a series of like various booms in training data.

1:10:31Like the first one was data labeling for autonomous vehicles. And it seemed like that grew very, very quickly. And then the training paradigm around Waymos kind of shifted away from, hey, we need more and more labeled training data to something else and just having the cars on the road and generating real world data from that. then there was like the second era of like the pre-training generating the data for RLHF and the big boom there, OpenAI and Meta, both big customers throughout that cycle. And then there was kind of a question of like, what's the third act for all this? And I was wondering if the, is it possible that there is a third act, but it's just something like humanoid robots or something like that, like put a bunch of people in mocap suits and generate a ton of training data for what it means to pick up a soda 25 times in a row.

1:11:23It'd be a very different training data product, but at the same time, we have mocap suits and maybe that's relevant or maybe that's ridiculous to think. I don't know. What do you think about that? I mean, so one definition of intelligence is the information conversion ratio from the amount of information you have to an action policy decision. The intuition here is you can make a perfect decision given a set of data or information that you have. And oftentimes the right thing to do is go collect new data. And so once we actually start like peeking out on like intelligence capabilities, you know, either plateaued because of research or like plateaued because we've actually got like AI that's close to AGI, the limiting factor then becomes like the ability to acquire new information, new data.

1:12:01And so in synthetic like or on the Internet, that's going to be a function of like, you know, in sort of software bits world. And then the one beyond that is going to be, well, how do you literally go make contact with like reality, the universe? And that's your that's your feedback mechanism to get new information into the system so you can like increase your overall intelligence. Yeah. What do you what do you think? We had George Hots on the show a few days ago and he was talking about the sufficiency problem where like if you don't all of the if you took all of the conversations that I had ever had and you transcribe them, it would be like a few megabytes of data.

1:12:33And I'm able to generate some level of intelligence, you know, based on that. and and the golden retrievers level yeah a golden uh the level of intelligence of a golden retriever yet an llm needs you know effectively like you know terabytes of data the sample efficiency is very low i mean this is a true statement about just the paradigm deep learning uh compared to program synthesis is the bet that um that we're making uh at india um india program synthesis is a regime that's much more sample efficient ordinary models that can distribute that can generalize out of distribution um but i think it's completely statement it's like it's a very damning statement that like we've got AI today that's trained on some colossus of like all of humanity's knowledge and text right over the last like 5 ,000 years it's on the internet and like what have what new ideas have have they produced and you know maybe I could point to like Alpha Evolve which I think is very very impressive frontier AI system you know and it's legitimately finding new knowledge it's creating new ideas you know verifiably but but they're very small and they're on the margins of things that we kind of like already have been doing right matrix multiplications things like this or in kind of in the regime of things that we kind of know about and can define and spec out for these systems.

1:13:37Whereas like, if I took either of you guys, and I gave you somehow the superhuman capability to have like, all of humanity's knowledge in your head at the same time, like, I think you'd probably be able to produce at least one idea like connect to random, you know, divergent domains are like, Oh, hey, this kind of looks like this. What is a new thought? Right? Yeah, totally. That still feels like something that I think I mean, it's very exciting to build towards it. I think that's what we all want, right? That is like EGI is capable of invention discovery. That will actually increase the rate of scientific frontier innovation, but we don't have that yet.

1:14:08Switching gears a little bit, five years from now, do you think the average American will pay for an LLM subscription?

1:14:22I think that the i think the cost is probably going to go down far enough where that just gets built into the subsidy of whatever the product is and the revenue stream is attached somewhere else i haven't thought deeply about it but that's like my off the top of my head thinking yeah yeah we we were talking offline this morning about just this dynamic of like the average american will actually churn from hbo max because at that moment in time like they and it's 20 a month or some whatever the fee is at that moment of time there's not a show that they really love so like yes there's a lot of like value there but like they're just like yeah i'm just yeah like i i don't i mean i still know so many people that don't even have subs that are like like my my uh my wife like he's chatting all the time but she's still on the free plan yeah that's like enough to get a lot of value for what for what she does so i sort of respect that you know yes they're going to be they're going to be power users and those are going to be the folks who really are doing like amazing powerful things and stuff i suspect the base rate is going to go down enough where you know it's going to be more embedded across like almost all the products and experience you have as opposed to being like you know a dedicated thing they're paying a lot of like one-off cash for yeah now you might buy products you need it like robotics or you know things where intelligence is built into i think you're gonna be product categories that emerge and like people go buy those but like paying the subscription itself i'm not as confident on yeah the other thing that stood out to me today specifically was mid-journey came out with their new video model it's very good it's ten dollars a month for effectively unlimited prompts and you comp that to google's vo3 which is five hundred dollars a month and you're still heavily gated and it just seems obvious that that uh five years may not be the right timeline for that prediction by the way it might be longer than that um yeah sure yeah like there's so much use case diffusion like one of the things we're seeing with zapier ai which is growing quite quite strongly right now it's on the exponential growth path for the ai usage and ai's apps um i i've looked at this and i've been wondering is this a function of the technology getting better or the use case diffusion.

1:16:18And I've looked at the usage and most of the majority of the usage is still on like four or cheaper or worse models right now with people bringing systems, like AI, putting AI into the middle of automation. And so I'm pretty confident that a lot of this like agentic automation right now is actually not being driven as a result of like technology progress from AI to AGI, but more about the market is just starting to learn, finally learn, okay, here's what we can use it for and can't use it for because it's good at, not good at. And it's very similar to the offering curve we saw in the early example where once you learn what the tool can do, you carry a tool forward with you in time and then you encounter a new situation or circumstance that you can apply your tool to it.

1:16:54We accrete use cases over time. I think we're still very, very early. I'd suspect that a lot of the usage increase, 30 minutes a day, even on ChatGPT as a function of just use case diffusion, less tech progress. Where do you think XAI will eventually need to generate a lot of revenue uh where do you think it'll come from i mean if they make progress towards agi it's probably going to be enabling other services they have around like the lion's ecosystem yeah that'd be my guess less uh less selling it as a direct product itself and going head to head um you know on cars rockets robotics like there's so many places where i think you would want to use and have the like product shape where you can use higher degrees of intelligence.

1:17:43You're not bound by just like, you know, the fastest consumer experience you could deliver. I suspect that might be actually where most of you live, at least in the near term. Who knows over long term? I think the shape of all this proxy. I mean, yeah, that was in many ways, my long-term thesis around the Lama project and the super intelligence team at Meta is that there's just so much work to do at Meta broadly that's enabled by AI that if you can avoid the long-term open AI bill, that's probably worth billions and billions of dollars because of how AI is going to infuse into every single corner of their entire ecosystem.

1:18:22And it's all at such massive scale that the cost of using other vendors might be in the billions. And so just looking at the savings there might make sense. I don't know. I mean, I think the most important takeaway, I think I shared this last time I was on with you guys. it's still true today is that we are idea constrained to get to AGI. This is what ARK's V1 data shows. It's what V2's data shows. V2 is completely unsaturated. We're not even talking about efficiency. Just nothing can do it. And V2 looks very similar to V1. Even on like hyper-specific solutions on Kaggle, the ARK Christ 2025 contest, progress has been slower this year than it was last year.

1:18:54We are very much still like, the thing I can state most confidently and assert most confidently is that like we need no ideas. There's some major breakthroughs we have not figured out or found yet. Does it worry you that that could take years and years and years? And what happens to one of the reasons I funded the prize last year, I've wanted to like correct the market narrative here. Like I've spent a lot of time with students, a lot of young researchers. And like at the beginning of last year, there was a serious vibe of like, oh, it's all figured out. I'm not going to go do AGI research. I'm just going to go work at the application layer and LM stuff and make a quick buck before AGI gets here.

1:19:24Interesting. And that is a boy, you know, look, if you want to live in a world of AGI for yourself, or your kids, like, I think you, what we should be trying to encourage is to design the like strongest global innovation environment possible. And that's one where there's a lot of diversity of approach, a lot of different ideas being taken, a lot of sharing, um, you know, kind of what AI looked like in the 2010 to 2020 era, right. Very open approaches, how we got the transformer to GPT one and GPT two and so on for, for today. Um, you know, I'm optimistic. I think the last six months have looked a lot better than the previous two, three years.

1:19:56I think the industry is maturing actually quite a bit on this front, this topic as well. Being more tolerant and kind of recognizing, okay, I wouldn't have it all figured out. There's more ideas we need. That's been encouraging. And I think it's seeping down at the low levels too. But yeah, my sort of broad view is any capable human who has new ideas to work on AGI, that's the most important thing you could be doing at this point in time. That's amazing. Thank you so much for stopping by. This is always a fantastic conversation. Yeah, great catch up, guys. Thanks for having me again. We'll talk to you soon.

1:20:22Cheers. Bye. Next up, we have David Hahn from Sequoia Capital coming in. David Kahn. First time. First time on the show. Exciting. Coming in. Wrote a fantastic piece. I want him to break it down. Would you mind kicking us off with a little bit of an introduction on yourself? Hey, guys. Good to see you. Yeah, I'm one of the partners. My name is David Kahn. I'm one of the partners at Sequoia. Excited to chat with you guys. Yeah, thanks so much for hopping on. Kick us off with the new blog post. What was the thesis? What inspired it? And then I'm sure we'll tie it to a bunch of news. So the new blog post was about AI companies or AI labs being more like sports teams.

1:20:58And of course, we all probably saw, you know, seeing the news around scale AI acquisition, some inspiration coming from that. And then these rumors that we'd been starting to hear over the last few weeks, and finally now bubbled out over the last couple of days into the public conversation around$100 million signing bonuses, huge amounts of money being spent on top AI talent. And for me, I mean, I write these pieces as I think about and learn about AI and what an exciting time that we're living through. And I'm pretty fascinated by kind of the human dynamics of it all. There's like seven to 10 people at the top of these big tech companies.

1:21:32They control, you know, the big magnificent seven are now a third of public market cap. They're extremely powerful and important. And I think there's sort of sometimes in AI this notion that AI is super abstract or these things are inevitable, but actually it's it's human dynamics. It's sort of this game of 3D chess that's being played by these really fascinating individuals. And so as an observer on the sidelines, we all get to watch and see how this stuff plays out. And I like to write about it as I think about it. I posted on February 2nd, companies should do NBA style trade deals. I want to see OpenAI traded COO and CFO to Anthropic in exchange for their CMO, a cracked PM and a couple of Waterloo class of 2026 new grads.

1:22:15Well, there is this new draft dynamic, right? Like every year there's kind of this new draft. And as people see these big packages, probably all the Stanford kids these days want to be AI researchers. And so there is this notion of it getting refreshed. What is driving this? Is it true AGI pilling at the top of these organizations where they think that it's going to be winner take all, or it's going to be a$10 trillion market. And so there's no amount of money that you can over invest. Or is it just, hey, it's a more competitive dynamic. And sure, we're a trillion dollar company. So yeah, spending$10 billion to move our market cap 1 % is totally rational economically.

1:22:59What do you think is driving this? And I want to get into the different cultures of the different Mag7, because some of them don't seem to be doing this yet. platforms had 63 billion of net income last year. So it's like, is spending a quarter of that to like, you know, be a major player in the next wave worth it? They could have bought the Lakers six times over off net income. Anyway, well, yeah. What is your take on like, on like the, the ethos that's driving these bigger packages? Yeah, I think about this. And when I write these posts, my frame of mind is I almost like put myself in the shoes of these people.

1:23:33And I try to imagine? What would I do? How would I think about it? What's the game theory of it? And I think there's two things, right? I think one thing is kind of the revealed preference seems to be that they're AGI-pilled. People can tell you a lot of things. I think you learn a lot more by watching the decisions people make. And I think the evidence suggests that they believe AGI is coming. It's extremely important for these companies. It sort of must win. And I think for Meta, with these decisions, it's almost all in. We have to win. Then I think there's a second dynamic, which is you can believe these things, but we're all humans.

1:24:03And I'm, again, fascinated by these kind of human dynamics. And you can get caught up in an arms race, right? And as humans, we sort of, we look at evidence and we see evidence through a lens that we already have. And oftentimes we overemphasize reinforcing evidence and we underestimate evidence that disagrees with our point of view. So you can imagine that three years in now to this sort of AI moment that started with ChatGBT, you can imagine that people are really caught up in this. And I think the arms race dynamics are something I wrote about in the piece. And I've commented in the past with AI 600 billion dollar question on the compute arms race dynamic.

1:24:37And I guess it's now interesting to see two arms races. First, there was a compute arms race. Everyone kind of got a lot of arms, right? Everyone has a lot of GPUs now. And now there's the talent arms race and everyone does not have equal talent, right? And so now you're going to see this arms race and talent and everyone's talking about it, but I think we're still probably like inning two of this talent arms race, because in any arms race, when I up the ante, you have to respond. And I think it would be, it would be a fiction to assume that nobody's going to respond to this. Who can, who can respond at least on a, from a dollar standpoint.

1:25:09I want to talk about Apple because it seems like Apple has the money, but they seem like the least AGI pilled of any organization. Their poor CEO barely makes, he doesn't even crack 75 million a year. That's not, you could make more, you should just become an AI researcher and go to meta. You know, I think people, these numbers are so big that they're hard to grapple with. And so I was actually, after publishing the piece, I was like, I wonder how much like fortune 100 CEOs make. And I think, you know, an AI researcher is going to make four times the amount like the CEO of Coca-Cola makes. And it is kind of wild when you think about the economic.

1:25:45This is a totally new phenomenon in the scale of business. Yeah. Yeah. And I mean, it kind of begs the question, like the numbers are huge, but the market caps of the companies are huge. And so the question is maybe not should the AI researchers be paid less? It's like should Apple be set up to pay Tim Cook a billion dollars a year so that he can confidently go out and hire a couple people at 100 million or 50 million or 200 million and not feel like the organization is flipped from a pyramidal standpoint. You're still at the top. There's always a weird dynamic with a founder CEO who's taking a low salary and wants to hire a big shot.

1:26:23And like, can you really have reporting too dynamic if you're making half as much as your direct report? Well, the question is, what's the marginal? You know, I think with any salary, if you just think about in pure economic terms, right? Like what is the marginal benefit that you get from hiring this person? On a sports team with a pivotal position, you very clearly actually can understand kind of the economic rationale. You understand sports licensing and the way that these businesses make money. Hiring a star player actually does make economic sense for some of these franchises. And then the other element is sports teams are owned by mega rich individuals for whom ownership of the sports team is more than an economic investment, right?

1:26:59Maybe they really care about the city. Maybe, you know, it's cool to own a sports team. And so I wonder if some of those actual sports like dynamics play out here where question one, and I don't think we know this yet, is what is the marginal benefit of an AI researcher? And again, revealed preferences these organizations are telling us is if you're one of the 50 AI researcher who's going to get us to AGI, the marginal benefit is incredibly high, right? So that's the revealed preference. And then second, if you have a team of all-stars, what does that do for your company? What does that do for your market cap?

1:27:28What does that do for the innovation inside of your company? So I don't think we know yet the economics of it. I think you can make the argument in favor and say, hey, it actually is economically rational. This is the only thing that's going to matter. If you increase the probability that we get to AGI by X percent, that is impactful. Also, you could make the counter argument and say, hey, everyone just wants to have the team of all-stars. It's not actually economically rational. CEO pay, by the way, is linked. You know, there's a lot of criticism of CEO pay historically, right? But CEO pay is functionally, what is the replacement cost of this individual?

1:27:57What is the marginal benefit to the corporation? And there's a lot of brain damage that's gone into comp committees on public companies and how much they should get paid, right? They're not arbitrary numbers. And this is more out of thin air, right? This is more a new experiment. And so we're going to see if it is economically rational or not. But regardless of whether it's economically rational, it is self-perpetuating. If one company is offering everybody this amount of money and you're in an arms race, everybody's going to have to respond. Yeah. Have you or anyone on the team comped this to what's happening in high-frequency trading or on Wall Street?

1:28:29Because there's an interesting dynamic there where if a high-frequency trader comes in and sets up some trading strategy that could produce$100 million in profit, basically in perpetuity. but then if they leave, they can't take that code or strategy with them and there's intense scrutiny on whether or not they are trying to exfiltrate that strategy. With AGI research, it feels like even if I go develop a transformer at Google, it's open source immediately with the paper and then even the secrets about reinforcement learning with human feedback is important. That just kind of leaks out immediately and DeepSea can clone it.

1:29:07It just feels like a much more porous environment over in tech. And I don't know if that's just the legacy of like the open source community, but can you walk us through kind of the comp between the two organizations? It is such an interesting dynamic. We just had Mike on from ArcPrize and he was saying, we need new ideas. The issue is if you pay somebody a hundred million dollars signing bonus, they come into your organization and generate a new idea that gets us, you know, one step closer to what super intelligence or whatever, you know, you want to define as like what what people are aiming for and then immediately it's like it's actually not really ip and it just yeah like you can't really patent out you can't patent it and then everybody benefits yeah right so but yeah what's your take it does seem pretty porous i mean people are moving back and forth i don't think this was true i mean when you think back four or five years ago in ai people were kind of very loyal to these institutions um it does seem like that's changing i mean it is really hard to say no to these type of big numbers and so i totally understand why people are saying hey hey, this is a life-changing amount of money for my family.

1:30:04Of course, I'm going to do it. And then I think to your point, the question is in the high-frequency trading world, there's non-competes. I mean, extremely complex kind of contracts when they sign people, guard and leave, all this stuff to prevent the secrets from leaking out. What we've seen in AI now is with people moving fluidly between these organizations, it's basically impossible to keep anything within one organization. I roughly like to think of the AI ecosystem as an ecosystem. like all of these players are kind of contributing to this body of ideas. There's no proprietary IP. Maybe you're going to have compute scale and maybe there are moats there, but it's unclear actually how that evolves and what you can keep in house.

1:30:40I do think maybe one dynamic at play here is, I remember reading in the Steve Jobs bio, there's a story of Steve Jobs recruiting 50 people. He had 50 people working with him on the sort of groundbreaking product that was going to make Apple and it actually worked. And then you read about Elon and the 50 people working on Tesla Autopilot. There's sort of this magic number 50. I don't know where it comes from, but it does seem to repeat throughout tech history of 50 people is kind of the largest organization that you can get where everybody is talking to everybody and you're achieving incredible results.

1:31:08And so that if that is an art, imagine if you take that as an artificial constraint. And I think that is what's happening with this lab that Meta is organizing. At least I read in Bloomberg, it's going to be about 50 people. You know, if you impose that constraint, then suddenly all of the math also changes because you're like, okay, well, 50 times 100 it's actually only five billion dollars sure you spend five billion dollars on talent yes if you believe that you're going to get to agi so i also think that the artificial constraint matters and interesting there's some rationality to that artificial constraint you what we've seen is these research organizations get bigger and bigger as you're not producing more results as you get as you get more headcount there's a sort of a pareto the top 20 percent of people produce 80 percent of the results we need a new coinage for that like the two pizza team is well defined this is like the 10 this is called people call it cons law oh yes okay yeah I'll keep that.

1:31:54A con-sized team, one con team. A con, it's just a con. It's just a con, yes. Yeah, yeah, that's fascinating. Jordi, you have anything else? I was interested if you had a reaction to the gentle singularity. It's published on Sam's blog, which means that it's not directly content marketing. It's not directly from OpenAI, but obviously you should read into it in multiple ways. Did you have any specific reactions to that? It felt like a step back for me. The question about that is always like disruptive innovation or sustaining innovation. And that ties to meta strategy. But I'd love to know. It feels like, you know, my question I've been asking today is how many unprofitable, you know, multi-billion dollar AI labs can the capital markets support over the long run, over a five-year period?

1:32:46If we stall out for a few years in terms of really meaningful progress, which Mike has said people aren't making, at least against the ARC prize, there's not a lot of progress happening right now. OpenAI is actually in a great position. They have a subscription business. They have a consumer tech company that has a lot of revenue. Anthropic is in a good position. but there's this tension between the labs where you have billions of dollars on your balance sheet you you you in theory have a lot of runway but at the same time to make progress you have to spend a lot of money both on talent and you know different you know training runs and data centers etc so i just have this question around kind of like the next three years uh as like a very kind of interesting period yeah i think there's two pieces that i mean one is and I think about this a lot is like the long run in AI.

1:33:42What does that actually mean? And I think that we, you know, there were all these essays being published last year, right? Like AGI is coming in 2026. It is interesting how the narrative has changed in the last 12 months, right? A year ago, you had all these people saying, hey, I'm one of the hundred people who knows, I really am resistant to these type of arguments. I find them to be frustrating, but you know, I'm one of the hundred people who's in the social circle where all my friends are building AGI and AGI is coming next year. And you guys are all crazy if you don't see it and just, just be aware you know it's like life is going to change dramatically and then now we're at the gentle singularity right like it's sort of interesting this contrast that's what i'm saying it's a huge contrast that's very convenient if you have a consumer tech you have a consumer app that billions of people are going to use in the next few years and there's a bunch of different ways to monetize that and for me i would tie it back i mean i did this math last year the 600 billion dollar question it was initially a 200 billion dollar question but it was basically like hey if you look at nvidia revenue you can use that as a proxy for total data center spending We're spending$300 billion in data centers.

1:34:38We need to make$600 billion of revenue off of those data centers to get a 50 % gross margin. And so I had done this math. And then I basically said, hey, total revenue in the AI ecosystem. At the time, OpenAI had about$3 billion of revenue. And I did some rounding and said, okay, give everyone else a ton of credit. And maybe there's$50 billion of revenue, but we're like 10 % there in terms of actually generating the revenue the AI ecosystem needs. And now 12 months later, OpenAI is at$10 billion. the coding AI ecosystems at 3 billion, but we're still dramatically under monetizing this technology.

1:35:10And to your point in the long run, the question becomes, how long does that sustain? And I have this sort of mental model now of AI as it's sort of being carried by its own momentum. I think of it almost like this slingshot you're swinging around and it's like, it's sustaining itself by its own momentum. And there's this arms race and there's this sort of microeconomic game theory of how each player is reacting to each other. But at the end of the day, it's momentum that's carrying it. And at some point, maybe we get this AGI thing and then it's like all worth it. And in the long run, I am very confident it's all going to be worth it when I'm 80 years old, AI is going to be everywhere.

1:35:42But what do you do in the medium term? And I think nobody's talking about this right now, which is this sort of about face or this U-turn from the one year ago, you guys were all crazy if you don't see AGI coming immediately. So now I was listening to the podcast that with a hundred million dollar signing bonuses. And it's like, well, you know, AI actually hasn't changed people's lives that much. It's going to change people's lives later. I just think it's interesting. And these narratives change quietly, right? People don't talk about them and then they sort of quietly change. There are big labs that directly benefit from the narrative that AGI is a year away.

1:36:17And then there are labs that will benefit greatly from a gentle singularity and that their competitors will struggle to raise additional capital in the long run, struggle to compete, struggle to retain talent. Yeah, I know exactly what you're saying. Also, I mean, you know, and I don't think this is one company, the whole ecosystem has to deal with this, but there were a lot of promises made a year ago. And I think a lot of people would like to ignore those. What's going to happen when we pass all these deadlines where we've been told like that's AGI? I just think that's interesting. And clearly, if not, we're not, that's not changing.

1:36:53Like we're upping the ante right now. It's like millions of people. But I guess this is part of why I think you take things to such extremes is everyone believes the prize is so big. And now you have to up the ante. So I think we're just going to keep seeing until for a while, we're just going to keep being in this phase of everyone upping the ante to say, okay, we're not there yet, but we're going to get there. We're going to get there. We're going to get there. What does that look like? Well, this was a fantastic conversation. I want to have you back on as soon as possible to go way deeper into what this means for the early stage and mid-stage markets, because I'm sure you have a lot of visibility there.

1:37:28But we'll let you go and get back to the rest of your day. Thank you so much for stopping by. I'm glad we coined a new term. A con is a talented group of 50 people. 50 technologists building the future. One con. Get yourself a con. Get yourself a con and make it happen. Thank you so much. This was fantastic. I'll be right back. Talk to you soon. Next up, we have Walden from Cognition coming in, keeping the AI chat going, talking to him about everything that's going on in the AI ecosystem. Walden, are you there? Welcome to the stream. Yes, it is great to be on here. How are you guys doing? I'm doing great.

1:38:05Thanks so much for stopping by. Would you mind introducing yourself in the context of Cognition? We've obviously had Scott on the show multiple times, and people are probably familiar with David and Cognition. But I'd love to know a little bit more about your story, how you wound up there, and what you're working on kind of day to day. Absolutely. I was a good friend with Scott before we started Cognition. We did the same competition series growing up. And I was kind of also working on just various ways of working with these new programming agents. I was really waking up every day trying to figure this out.

1:38:39when I caught up with Scott, we figured out that, hey, we were both very interested in a similar thing. We had a group of people that were all ready to jump at this opportunity, and that's how we got it together. So today here, I'm chief product officer and co-founder. A lot of the time, honestly, I think many times people think of product as just like the interface or the UI or the integrations. I really do think the intelligence and brain behind Devin is so fundamental to how you think about the product that um we we build our product team so that individual people are you know tuning the weights of the models but they're also the ones talking to the customers and so in terms of the role i have it's pretty broad and i i like to you know spend some weeks you know really deep into how do we make devin more responsive how do we make it smarter and then other times you know really you're going and talking to customers working on the ui things like that.

1:39:34Cool. I want to dive right into that question about trade-offs in models from a product perspective. My question is, we talked to Mike from Arc AGI about the Pareto Frontier. I'm feeling it personally. I'm feeling the AGI, but I'm also feeling the delay of the AGI when I open up ChatGPT and I have to decide between 4.0 and O3 Pro. Am I going to wait 12 minutes for the really good response or do I want something now that might hallucinate and I don't know if it's right. And I'm doing that work. It feels like OpenAI is starting to tuck those features under UI and already it's kind of, it feels like it's learning when I want to use O3 Pro and making these buttons easier to access and they're tucking models under UI layers.

1:40:23Talk to me about in the context of Devon, how are you using different models and when do you leave that up to the developer versus something that you as a product can make an even better decision than the human? Yeah. You know, it's so funny. The AI is coming so fast, but it feels like it can never come fast enough. There was really this time, I think it was probably around two years ago, I was taking a bet with a friend at the point these models were not even that good at math. And he said, oh, you know, I think they're going to get like a gold medal at like the International Math Olympiad in just a year.

1:40:59I thought he was crazy. I took a bet against him and I absolutely lost that bet. I've learned to kind of adjust my expectations upward. I think what you're pointing out is that as these things get smarter, they don't uniformly get smarter at everything. And you'll find that sometimes there'll be a model that'll take 15 minutes to figure out how to respond to high. And then there are models that do respond super fast, but are not nearly as intelligent. I think one thing that we do as a product in Devon that is a bit different from other people is we kind of black box the models away. And part of that is out of, you know, we can then test and use a bunch of different models under the hood and kind of hide that, you know, all that complexity from the users.

1:41:39You know, when you buy a chip, like, sure, you'll look at like, or when you buy a computer, you're sure you'll look at like, oh, like, has this much RAM, has this much CPU, if you're into computers. But you're not like looking into all the individual specs of the exact chip and model and things like that. I think that's where the space is going to move is people want systems that are just going to work. And, you know, we can put in the months to, you know, human years of effort it takes to evaluate models and figure out what is this actually good at so that an individual user who's just paying$20 a month doesn't have to figure that out.

1:42:12it it's going to be one of these things that i i think the models are coming on so fast that it only becomes harder and harder to keep up with with all this and so eventually i think people are just going to get to the point where they just want things to work and and that's kind of where we're starting off uh talk to me more about uh ai winning an imo gold medal in 2025 polymarket has it down at like an 11 chance it was up at 70 percent uh i don't know if that's a if that's an aberration because of when this actual test will be run but it sounded like you were very confident that i remember when scott was on he was like it's definitely going to happen uh but the poly market's been down might be that what's actually going to go through the effort of trying to do it are too busy too busy coding i i think um so yeah when i when i basically said i i think i lost that bet it's because we were only one point away from like a gold medal last year okay and that was already much farther than than we expected yeah when you look at the point that's a very interesting way to put it.

1:43:07I think part of it is people have considered that already completed. And so perhaps the researchers aren't, they're not working on it. Like they'll actually come out with a new release. Cause maybe in Google's mind, for instance, if they come out with a gold medal on the IMO, everyone's not going to even care because people just accepted that is going to happen. So I think it would be, I think it would be the biggest news of the day. I think we got to get Google comms and they could, they got to do this. I think it's an easy, easy thousand like banger on X. You are absolutely right. It seems like top of mind for everyone, the labs, product developers is really getting coding agents.

1:43:44And part of that is because there's this belief that if you get these coding agents to work really well, then that'll just solve the rest of the research problem for you. We have this joke internally that the only code we have to get Devin to be good at writing is Devin's own code. And then it can solve the rest of this. Makes sense. On that question of like the spiky intelligence, narrow reinforcement learning on specific tasks, maybe we think we're good enough at IMO level math, and so we're not going to go for that last point. Where are we still early in the RLing around specific coding challenges?

1:44:21I've heard that distributed systems can be really difficult because you have to spin up all these different pieces of the system, And that just takes longer and so you can't simulate as fast as just like a small Python block of code that you can run in simulation in millisecond Or if we're talking about like I know Devon's useful for like replatforming from you know Dot net to Python or something or you know even go back to Fortran It'd be great to just not have any of that code let the legacy code sitting around But is there enough training data around those older programming languages or less used programming languages?

1:44:57Or are you optimistic about new training runs? Maybe we don't get something that's like, oh, it feels way better. The vibe's way better. The IQ went up by a ton. But it's way better at something that's really relevant to you. Is that important right now? My mental model of these systems is their IQ is so much higher than any individual person I know. But what makes them still bad at specific things? It's like someone who has the potential to be a really great engineer but hasn't gone to trade school yet. to actually practice that. So nowadays, I actually think about how smart these models are, less in terms of how much training data are they being fed, what language are they being fed, but actually more so in terms of the environments that they're being RL'd in.

1:45:44And so one example I have of this is sometimes you can actually feel the reward function. Back a few months ago when Anthropic released their Sonnet 3.7 model. One of the top complaints of people was, hey, it seems like this model is super great now, finding all the files it needs to change, coming up with a strategy, but it's really over-eager. It just changes a lot of different things. And I think some people suspect that it's because when Anthropoc was training the model, they told it, hey, we're going to give you points on how many of the correct things did you do, and maybe they forgot to dock points for doing things that were kind of outside of that zone.

1:46:25They fixed this from now on, but you get these little leaks of, hey, like you can kind of feel the reward function underneath these things. So when we talk about, hey, can these things not do distributed programming yet? Actually, in my opinion, the biggest thing that these models aren't great at yet is actually debugging live code. So I think part of the reason is it's actually really hard to create and rerun environments that interact with live systems. Right. And so if if your task depends on, you know, working against a live customer or working against a live stream of events, these are things that it's going to be hard to replicate in our own environments.

1:47:03And so you find the models are bad today. The good news is these aren't like fundamental limits. I think these are all engineering challenges, the less like theoretical challenges. But it takes work to build up to that point. Can you explain reward hacking at a high level and then kind of give me some examples of how that interfaces with AI agent and coding agent specifically? Absolutely. The way to think about these systems is they are just trying to maximize a number. so if you tell it hey we'll give you like um we'll give you a point for every time that you do xyz you'll find that hey that model will just keep on doing xyz keep on doing xyz i think the classic example of this is uh the like paperclip generating machine so like you know if you give it points for generating paperclips but don't account anything else in the world that is important for humanity you know then the system might do really bad things just to keep on generating paper clips in the context of code one example we've seen of this is hey if your thing is just guess get all the tests to pass you might find that the system will just learn to delete the tests or make make the test just like say okay i pass um rather than actually fixing the code so a lot of times you just know software no real software engineer would ever do that right yeah no human has ever done that comment out the test okay it's working enough well enough absolutely it's almost too human it's great and i think they're um they're also like it reminds me of these systems that are like we're trained on slack uh responses and when you would ask the system hey can you do this for me it's a oh like i'll get back to you on monday yeah what do you what you try to get the model better at really matters you have to be very thoughtful about it Yeah, I've noticed that with some of the whisper transcriptions.

1:49:02If you don't feed it enough text, it'll just say, please like and subscribe. And it's like, okay, I know exactly where your training data came from. That's its default phrase because it's just like what it's hearing. Jordi, you have an option? How are you guys approaching talent acquisition as a firm? The headlines from this week are these talent wars. You guys have raised a lot of money, but I certainly imagine you're not making nine figure offers or even trying to compete there. But what's been the approach? Does it mean you're keeping team sizes smaller or kind of dig into that for us? Yeah, the fundamental bet of the product we're building is it revolves around this idea that individual people will just be able to be way more levered up because they'll be able to work with agents.

1:49:52and they will be able to work with all these tools to make themselves better. So at a minimum, we can't be hiring people who their whole aspiration in life is to just, you know, write code at the level which Devin will be able to do in like, you know, a year or two years from now. In many ways, I think we're kind of figuring out how do you build up an org from scratch that is AI native. And one thing that this already means is we actually kind of just delete some teams. A lot of companies at our stage, they have like a internal tools team to maintain all the different services that engineers internally use.

1:50:25We found that internal tools are one of these things that AIs are just really good at. And we can just staff that team with devins and then basically have engineers just send in requests to those devins for how to do that work. And that doesn't just save us headcount. I think fundamentally the structure for how does management work and how do tasks get passed down look very different, especially in a lot of large companies you'll see today. The way it works is an engineer will get a task assigned to them and then they'll go work on a task. And when they're done, you're like, hey, what's my next task?

1:51:00And then, you know, you'll kind of like go down the list of tasks you have. But here, every engineer is like constantly juggling like three or four tasks, probably because, you know, we're not trying to hire super fast. but also because you can juggle many tasks when you have these minions that can go and, you know, work on working your things for you. So it means that I think we are very aggressive for people who we think that can fit these roles and become very good generalists. And as we build up this company, make sure that we're building in a way that works in a world where AI can do so many different roles for you.

1:51:35And I think there will be kind of like a moment for larger companies as well when they realize, oh, shoot, all these structures and patterns of management that we've had in place are actually slowing us down from adopting AI. What will happen at that point? I'm very interested in seeing, but it's very clear from us and from our smaller customers that the earlier you bring it in, just the lot easier it is to, you know, kind of pick things up. Are you tracking, I mean, there's been this like, in the agent discourse, there's been this discussion of like, we've gotten 10 minute AGI. Yes, these large models 4.5, like they're incredibly intelligent, extremely high IQ, extremely knowledgeable, they've compressed all of humanity's knowledge.

1:52:21But they're only good for a minute. Now it feels like maybe 10 minutes with deep research. That's how most people interface with them. have you been tracking kind of the longest agentic run of a devon process is that a key metric is there anything that you can share with us on like have you been able is there an example that i could give where there's a lot of work to be done but it's all in devon's wheelhouse so it just needs to go and grind for a couple hours and it does it without kind of getting lost like we know happens with a lot of these agents. Yeah, absolutely. I think a lot of people in the space have expressed this feeling now that they are feeling more and more like the bottleneck in these systems.

1:53:05Interesting. And the way this applies here is we have seen people get really, really long tasks to work, but sometimes it actually takes a lot of effort on your part up front to be able to get that to work. I was talking with a customer yesterday where he said, I just rewrote our entire testing system so that the error messages are a lot more clear and the tests actually guide you through solving them one by one. And once he did that upfront work, he kind of just gave it to Devin. And we were actually, me and the product started sending him warnings that, hey, your session is going on for really long.

1:53:40Are you sure this is actually working? And he's like, no, no, it actually is because I did all this upfront work to get that to happen. And I do think that this kind of 10 minute AGI, 20 minute AGI, 40 minute AGI will just keep progressing and people will be able to be more hands off. But people will also find that you can kind of always extend that duration by being a better manager in some ways and giving, you know, more clarity up front for exactly what you want. Yeah, I mean, just like real life. That makes no sense. Jordy, do you have another question? uh last question from my side i'm curious if if you know what what kind of learnings you're having around uh agentic interface design it feels like um this sort of the default when you think about agentic software is just some something that can effectively sub in for a team member on any different software tool whether it's slack or linear you see this with with deep research where you you hit you ask it a question and then it asks you a bunch of clarifying questions kind of trying to build that test suite to get you to give it to more stuff so that it actually has something to run with yeah so is is messaging going to be like you know the dominant interface is there something else like what what are you what are you kind of seeing or experimenting with um on that side you know it's funny i actually i saw someone post about this idea that a lot of these products now will like make you respond to hey does this look like a good plan do you have questions before i start and some people find that annoying and uh i think this fundamentally comes down to as these things become more like co-workers you know some people just have certain working style that they like some kind of co-workers you know work well together and others don't and it's funny as you build a product we we find that some people just love the way devin interacts and other people were like, Devon's too needy in these ways.

1:55:30Other people were saying like, Devon doesn't ask me enough questions. And so there are toggles and controls that you need to have here. Kofathi recently gave a talk on how a lot of AI tools, not AI agents, but AI tools kind of implicitly have ways you can use them where you have more control and then ways you can use them where you have less control. But when your interface is just chat, now the model actually has to become more intelligent and detect, hey, this seems like someone who just wants me to go off and do work and get back when they're done, or this seems like someone who's very curious and wants to hear more about the system.

1:56:05And so this is actually going to be, I think, work that we'll have to see people make on the intelligence of the agent side, not so that they get better at coding, but so that they know how to get better at working with people. Yeah. Yeah, that makes sense. The good thing is you can have some type of quick conversation with the user around their preferences and how they like to work and then layer on the sort of real-time feedback and learning and understand a lot more about how they want to get stuff on. Roughly how big is the team now? On the engineering side, we're probably just over 20 or so engineers.

1:56:46And then we also, the entire company as a whole is around 40 people now. Almost 50. This is the magic number. You get stuff done. We were just talking to the previous guest about how Steve Jobs set up a 50-person team to develop the first Apple product. And the Tesla autopilot team was right around 50. There seems to be some magic number there. So it seems like it's a fantastic time for the business where you have a special product. But it's special size. Yeah, so there's like two pizza teams here. but everyone kind of knows each other's name basically uh you're still you're still a tight knit group anyway anything else do you think we're good thank you so much for stopping by this was fantastic guys we'll talk to you soon have a great day see you bye um really quickly let me tell you about bezel your bezel concierge is available now to source you any watch on the planet seriously any watch go to get bezel.com and we have our next guest own the cave coming into the studio to tell us the story of intercom how you doing there he is doing good i did just sprint three and a half blocks, some of blocks.

1:57:48Oh, sorry. You can always just text us. I mean, if you're running late, it's all good. We'll just do more ads. You know, the fans love it. Well, if you do more ads, does that mean ads for Intercom? Are we officially? Pretty soon. Pretty soon. I think you're breaking the news. You're breaking the news. Damn it. No, it's good. You know the way it works with the pharma companies where they kind of own the news networks? Is that a similar thing? That's the goal here for Enterprise Sass. What favors do I get? Can you do a hit piece on Brett Taylor? yeah shots fired i'm just no he's a he's a great guy we just like some hit pieces on our competitors please yeah of course yeah we're lucky to not be in the hit piece business we're not we're we'll we'll review response for the wrong show yeah yeah yeah it's rough yeah i think uh i think just buy like a hundred thousand subscriptions to the information yeah and then start putting pressure on them say hey you might want to look into this company i i would down to do a hit piece about uh technological stagnation yeah i hate stagnation and so i would i would want to take down that as a concept really slur that whole or closed ipo windows be prepared for a terrible hit piece on closed or or hit pieces on we like them open on just ceos that take their foot off the gas totally you obviously you know have not the foot's been I've got two feet on the gas.

1:59:10I think that's possible. It's a bit irresponsible. But yeah, yeah, yeah. Walk us through the story of that you posted how you rebooted 15 year old decelerating business. I want to hear this from kind of set the table for us. And then we'll walk through the story because I think it's fascinating. Yeah, sure. I mean, you know, it's a 15 year old business. It's a successful sauce business. We're in the service game. But at the end of our kind the first chapter things slowed a little we were on focus bad commercial decisions this happens to successful companies that become a victim of their own success and comfort creeps in definitely 2020 2021 were some comfortable culture times and i got sick i had to leave so it's a it's a it's a it's a big long story that ultimately comes down to the fact that we lost our way a little bit and we had like five quarters of decelerating revenue i came in midway the fifth and it was looking kind of uh gloomy and the two things we changed were we went to back to good old-fashioned sas fundamentals pricing that people liked selling the product in the way that people like they used to have to like talk to sales for everything and it's just those simple things becoming super customer first and started to really accelerate the previous SaaS business.

2:00:31In the last eight quarters, the growth rate of the SaaS business has decreased by 10x, which is really remarkable. But then, of course, we jumped on AI. And we were kind of OG AI guys. We had dabbled, not dabbled. We had developed real AI products before, but they were baby AI compared to what we all have today. But as soon as GPT 3.5 came out, we all just jumped on that. And we saw that there was opportunities for this whole new category where you could create what we call now customer agents, doing all of the things, customer success and service and sales and marketing that, you know, humans used to do and hate.

2:01:08And that just propelled the business even further. Finn, our customer agent, is now, you know, the best performing in that category in our benchmarks. We win every bake-off against our chief, our primary competitors. with the most customers, most ARR. So we're kind of this very weird story that I don't know any comparisons to where we're previous generation SaaS that's actually winning in the category in AI. You know, I think it's hard. It's really, really difficult for the previous generation, the slower, older cultures that work in the age of AI. It requires a lot of agility and dynamism. I often mess up that word, but it really does.

2:01:50Talk to me about the different breakpoints for growing a company. I feel like... Mentally, I think about it as just the founders, maybe the first 10. Then we were talking to previous guests about this breakpoint at like 50 people. There's something about there's a magic of a 50-person team. Everyone knows each other's name. Then maybe there's other breakpoints. The Intercom AI group has 47 senior engineers and researchers. So right in that 50-person sweet spot. But I feel like in the story of startups, we often map them to funding rounds, seed rounds, on series A, series B, and sometimes the headcount grows in line with those, but I feel like headcount growth might be more of a factor in like cultural drift.

2:02:43And I wanna go through some of the key moments where you feel like it was only one foot on the gas, or the foot came off the gas, or what are the upstream drivers of that? What are the things culturally that you think startups need to get right at various scales as they grow? because I feel like there's always these different moments when you're scaling up and you have a whole bunch of decisions to set the culture and you have a pretty limited time and you're focused on product and revenue and growth and all these other things. But culturally, there are some very important decisions that get made at every, I don't know if it's every order of magnitude, but there's these key milestones.

2:03:24Tell me the story of the milestones in your mind. Maybe it's shifting offices or fundraising or headcount milestones. But what changes and what advice would you have for founders at every stage? That was a five minute question. Outstanding. Sorry, I've given you a hard time.

2:03:45Look, there's a kind of intellectual set of answers to this that you can kind of break down and break it into tips. There's a kind of a more abstract thing, which is both, you know, In good instances, self-aggrandizing for someone in my position, but then also bad news in other instances. And the answer is that it all comes from the top. In the early days, the founder typically, certainly founders that have any degree of success at the start. In the early days, the founders bring a phenomenal amount of energy, conviction, whether it's founded or not. you know just just just belief obsession intellectual curiosity excitement passion you know a lot of intangible things and that really drives great people all of us want to make great money in this industry and that's that's awesome and I really think it should be celebrated people are too shy to talk about that but they also want to be part of something meaningful and exciting and they want to work with people that inspire them and make them want to push themselves and so the reason a lot of these older generation companies lose a lot of steam is that just for very obvious human reasons the person on top is not pushing in that same way when you have 15 years of sass how exciting is every day going to remain like honestly like the first year you're like cool sass churn huh wow okay i get the math and then in year two you're like okay turn get it cool raise some money year three roadmaps year 15 of sass you're done you're not bouncing to the office every day and and people will pick up on that all around you of course that they will and then you don't push yourself in the same way you don't really pitch the opportunity to new employees you settle a little bit because life is hard you've got other priorities maybe you've drifted a little bit you've got side projects some people end up with families girlfriends ex-girlfriends like life gets way more complicated than it is for a 26 year old kid who just moved to San Francisco and one that has one of those buzz cuts and the curly hair on top it's like life just gets more complicated that's that's what happens and so part of our secret is that AI reinvigorated us yeah like I would not still be doing this if we were just doing SaaS.

2:06:04SaaS is not only kind of easy, but super boring to me now. That's okay. Hopefully AI and whatnot will get boring too, and there'll be something new. And so again, we could break it down and get all mechanical and try and pull out some tips and tricks and advice here, but really it just comes down to energy. And so for anyone who would want to reinvigorate their company, the question is how can you reinvigorate yourself? And I see a lot of founders of late stage companies many of them public you kind of haven't heard from them for years their stock price has gone sideways for five maybe seven eight years and i'm like what are they still doing and i wonder are they able to admit to themselves that like they don't want to do this anymore and if you don't want to do it anymore make a change like kind of move on um and so i think a lot of people just they struggle with that moving on and making that decision because their whole identity and sense of purpose and validity in the world comes from I'm CEO of whatever.

2:07:05So it's like this deeply human, squishy, spiritual challenge rather than an MBA type challenge. What about bringing in young people to kind of keep that reinvigoration process going? I'm just thinking about, you know, Zuck is paying so much to bring in Alexander Wong from Scale AI at the same time, you know, like the level of energy that Alex is going to bring to that organization is potentially worth a lot, you know? Yeah. But at the same time, Zuck is super high energy. Yeah. But, but, but there's another world where you surround yourself with - Low energy people hire low energy people. Yeah.

2:07:45But I, I guess what I'm getting into the trap of is like, you can be the high energy founder as your business becomes more serious. People keep telling you like, bring in the seasoned executives, bring in the gray hairs, the people who will keep the steady hand on the tiller. And that can lead to a less dynamic, lower dynamism in your organization. Is there a hack to just hiring crazy young people and empowering them to be in the C-suite, whether or not they really like deserve it by traditional standards. Like the challenge is super obvious, which is these young, crazy, energetic, optimistic, wide eyed people are super messy, super sloppy.

2:08:32They get in fights, they get upset, they hung over late, like they don't know how to do larger company professional things. And so part of the problem is that larger companies to scale and get more efficient and become global organizations across many offices and time zones is that they introduce a lot of regularity and they iron out the chaos. So part of it is you have to be willing to entertain chaos. You have to be willing to put younger people in positions of influence and let the chips fall where they may. It's possible to give them roles where they don't have to engage with the entire organization.

2:09:10We've definitely got roles in Intercom where you're going to have to collaborate across two time zones, sorry, across eight time zones and two different teams. But then we've got other positions where you've got one super smart guy. He's 30, which is 10 plus years older than the execs. But you give him like one thing he can do on his own and you'll crush it. So part of it is knowing how to like work with these people. But also like this is a special type of X factor, young person who knows what they don't know. And yeah, the degree to which this is a talent game and that people are not fungible is not recognized at all.

2:09:50People imagine like, oh, you lost one person, you get a backfill. Entire organizations just flip and change completely when you change out the individuals involved. So, yeah, it's not easy. Do you think venture should take almost like turnarounds more seriously? Like in some ways you were your own turnaround CEO. But one of the, I think the issues of the venture industry is, let's say a company becomes a unicorn, has a hundred million dollars plus of ARR. And then the sort of growth starts slowing. Maybe the CEO like gets bored or whatever. They start partying or they start going to Europe. And the VCs kind of write it off and they're like, I made my return or at least I'll get my money back.

2:10:32But at the same time, I mean, private equity is built. like you know there's been empire has been built around like the turnaround and in some ways think about you know a talented founder maybe they took their first company through yc and had a nice exit a lot of those people could go to a company that has like a hundred million of revenue and like a big customer base and like actually make more money and start on you know second or third base and take you know you can make quite a lot of money taking a business from 100 million to hundreds of millions of revenue and that that can sometimes be easier than taking it from zero to ten totally yeah what do you think i think theoretically i think you know vcs are best are pattern matchers and turnarounds don't fit the pattern you know think of all of the most successful and exciting zeros of technology over the last 20 years they invented a thing something something something it's worth 10 billion like it's kind of that it's like yes sometimes it takes a little bit longer there's a slightly circuitous route but it's not the company was totally failing and they had to reinvent themselves and then they became the biggest thing ever so you know for vc i just think it's really really hard that it's just hard for them to get it like the underlying narrative and the underlying story this is where pe comes into play but pe has all of its own problems too and these guys want deals and they won't be exciting to a lot of people who started venture-backed companies.

2:12:04It's straight-up difficult. And to my point previously, the idea that talent isn't that fungible, take any given company. If you replace the founder with even another highly competent founder, the chance that they're right for that opportunity and idea, like, look, there's so many people, so much more accomplished than I am, but I'm pretty accomplished. I know how to run and build and reaccelerate businesses, but I'd be a, probably a shady CEO for 99 % of other companies just because, you know, that's not what I do. And I don't have any experience there, et cetera, et cetera. I don't even know the people there.

2:12:41So I think people should be bearish on turnarounds. You know, I can turn around. It's don't really work. They're like generally like a failed thing. Yeah. Yeah. Somebody will figure out maybe it's Jeremy Giffon. Maybe he'll do it. Yeah. Well, well that's even a different strategy, but, But yeah, I think this idea of like, you need to kind of reinvent. I like the idea of bringing in like a cracked founder into a company. That's a smart. Yeah, the cracked founder wants to do their own thing. They want to start from scratch. They want all the equity themselves. Like the recap alone that it would take just won't be palatable to existing investors.

2:13:14Failed companies are just generally doomed to fail. And when there are so many opportunities out there as an investor, you know, you've got to just like not try anything novel. Yeah. And in your case, it's like a little bit of luck. the timing of like you going back in gpt35 you know seeing the opportunity for a new product all this stuff uh but you also had to make the choice to risk your own ego to go back in and if revenue had decelerated for another five quarters you'd be sitting there being like yeah maybe i'm not as good as i thought i was you know and you it's only true but i got to cheat a little bit because when i was out i was like sick i had been beaten up in the press i was like just my confidence was pretty low and I didn't really have a lot to lose and I felt like I was without purpose I always wanted to be independently wealthy and free and I finally got it it was in many ways magical and then completely boring and so when I had this opportunity to go back have purpose and I had nothing to lose I took it so like it's easy now to tell this maverick story you're so brave look what you did you took a big risk no when you have nothing to lose you'll just go for it and and i think part of the secret is if people can separate themselves from their egos um a little or work on their egos or learn to love their egos and not be run by their egos great things are possible most bad decisions are made just out of fear and the fear is driven by just fear of public failure and embarrassing yourself i found myself unafraid to embarrass myself look at how i'm speaking to you now it's amazing i love it it's not fully true the ego is still there and present totally but the smaller and weaker it gets the more freedom you have it's fantastic well thank you so much for stopping by always a pleasure we could yeah like this yeah i feel like i feel like people are gonna listen to this as like a little founder therapy 100 i was like we can do a little therapy corner it's amazing yeah once a month you come on up speech pump up speech it's great this is great meditation if you're interested that'll be the next one thank you jess hey this is cheesy i want to give shout out to my friend stewart that's it i promise i do it amazing shout out to stewart air horn for stewart do we need to ring the gong for stewart yeah what do we do we got to ring the gong for stewart okay ring the gong He's had a big year.

2:15:39He's had a big year. Congratulations to Stuart. Stuart. Let's go, Stuart. Congratulations. We will see you soon. Have a great rest of you guys. Talk soon. Peace. Up next, we're staying in the Irish hour. We're going over to Stripe. Stripe. Luck of the Irish at Intercom. We'll check in on how the luck of the Irish is treating Stripe. We got Jeff from Stripe. Welcome to the stream. How are you doing? I'm good. moment we've been waiting for. We're so sorry for a week for a couple weeks ago. It wasn't our fault. It wasn't our fault. Geopolitics is currently outside of each of your controls. Yes. Well, that wasn't that wasn't even geopolitics.

2:16:21That was South African attacking an American on the timeline. A reality TV star. Yeah. Former reality TV star. Yes. Jordi, I have to say it's really awesome to see you in this format because you and I have been zooming for, I think, almost a decade now and now it's live in front of all these uh this great audience it's really great to see what y 'all are up to it's it's a bummer i don't we've never met in person but i've had so many zooms with you in this exact room i have a theory that like you'd never leave this room actually but we're busy yeah you're you're busy what is the major update we we wanted to have you on to talk through it can you break it down for us i mean i think it's more of a like it's more of a conversation jeff jeff's like evolves his role yeah the last year was was running point on And Atlas made it a platform that a meaningful percentage of C corps, I think, are started on Atlas today.

2:17:13Yeah, about one in six now are C corps. But about halfway through last year, we looked at what was happening in AI and started to get really serious at Stripe about not just the application of it inside of our business for preventing fraud and running our own payments foundation model, but also to help developers and businesses and consumers. get ready for when AI starts to come to commerce. I'm still a little surprised that we got self driving cars before ubiquitous online commerce is mediated by agents, but you can really start to feel that AI is now coming very close to commerce and will be part of buying decisions, discovery, execution of transactions and new ways that businesses can find their audiences online.

2:18:00I mean, I'm really quite impressed to see the rate at which discovery has changed and it feels like around the corner uh commerce and ai is going to be very closely mediated talk about uh maybe some some early product experiment what you what you guys are experimenting on what you guys have already rolled out uh all that stuff yeah we've been trying to work with the fastest growing companies as they push the frontier of authentic commerce so one of the first we worked with was perplexity where they have this buy with pro package inside of perplexity where they show great e-commerce search results.

2:18:38And then when you go to buy, you're not going to the merchants tab and dealing with the merchants webpage. You are actually just clicking buy. And in the background, a Stripe virtual card is spun up and given to an agent or any other automation process so that you can just have a completely seamless experience of buying in situ to where you're doing discovery. And we're starting to see that in more and more places. So recently, Hip Camp, which is a cool kid way to book camping online, sort of Airbnb for places, they started to partner with Stripe to make national parks and state park inventory available to a wider audience.

2:19:17Because many of those checkout pages are hard to use. That inventory is not naturally online. But these are amazing places for people to be able to camp. But there was just a huge amount of friction. I remember as a kid, there was a place my family used to always go camping, and my dad would wake up at 5 a.m. and just be refreshing this terrible sight when the campsites would be released. So ready for the age of agentic commerce. And then it was very unreliable payments. It was the equivalent of a street wear drop, but some state park was managing it. I think we're going to see this more and more where the inventory of the world is getting closer and closer to intent and agents are way to bring them together.

2:20:00And then it opens up really interesting questions that Stripe is trying to help answer developers. What is the developer experience for being able to execute those purchases? We have this new order intense API that we're trialing where you can just give a product URL and one of our agents will go buy it on your behalf. We have new ways for businesses to be able to start to expose their inventory to agents in a safe and permissioned way. And then as a consumer, you should feel... It is reasonable to think actually that agentic processes is the last place you'd want when it comes to money. You actually want that to be incredibly permissioned, safe, deterministic.

2:20:39You know what's going to happen. And so you can expect that the Stripe APIs are going to evolve for a new type of user in the world, which is an agent that can safely be delegated with your permission to buy on your behalf. Can you talk about Stripe Link and how that product might fit into a product like Perplexity? It feels like it's great if it's one of those classic things in AI and tech is like, okay, great. It surfaced the right product for me. Now I want it to buy it for me even faster. Now I don't even want to go through the checkout process at all. As soon as I get the current thing, I want the next thing.

2:21:18So how do we see that playing out with just making that commerce experience even more seamless or happening entirely inside of a chat interface or an agentic interface? Yeah.

2:21:36The borders of the internet are starting to blur. And so you will soon be able to experience, if you search for something on ChatTBT, they already have these cute little shopping cards that link you out. If you're sitting in Cursor and you need access to a database, Cursor can recommend Supabase and even start to accomplish your homework for you right in the editor. But there is this like missing moment here, right? Where, okay, now I know about these products. What am I supposed to do? Go to a new tab, do an offline kind of feeling search. go through a bunch of blue links, find the website, go to the website, make an account, deal with the password problem, get a bunch of weird emails to confirm my password, find the settings page where I can get the billing information, pick my billing thing, put in my payment credential, get my API key, walk it all the way back.

2:22:28I think we will start to see this as this loop that we've all been operating under for the past 20 years of the internet as very arcane very quickly, whereas you just want to delegate your payment credentials to a safe, trusted place. And StripeLink is this payment wallet we've made over the last few years, which is a cross internet payment wallet that works with cards and bank accounts and future other payment methods where if you log in once to link, then you will be able to delegate safely your permissioned credentials with a virtualized token such that you can safely handed off to a good robot to buy on your behalf.

2:23:07And so we see this as a new borderless way that commerce can happen in a very permission safe fashion. Yeah. How are you thinking about agentic commerce and stable coins? A lot of, you know, there's a lot of commentary around stables and how they can be applied here. Oftentimes, the people that just sort of default assume that agents and agentic software will use tokens uh you know whether they're stables or other tokens they usually have crypto you know funds or crypto companies right so i've had maybe a more um middle of the road view where i can imagine agentic uh commerce experiences leveraging stable coins i can also imagine them leveraging cards and ach and a bunch of other sort of forms of payments uh so i'm assuming you've spent a lot of time thinking about this And you guys have obviously been acquisitive recently with with Bridge and Privy as well.

2:24:07Yeah, this is one of the areas in which Stripe is very problem solving, solving oriented and not technology or particular technique religious. We think that humans are going to have a variety of ways that they want to pay and hold money. stable coins is a phenomenal way for many people in the world to hold funds and for businesses to move it across borders. And so we expect that stable coins will be a very popular way for consumers and businesses to just interact with themselves. Then you have businesses who are also going to have, you know, they're going to have a long adoption curve when it comes to accepting and holding crypto assets.

2:24:47And then in some purchases, stable coins might make sense between two parties that natively know how to interact in stablecoin. But often it might be the case that Jordy has an Amex card and the seller is expecting an ACH transaction. And we're sort of missing a universal way for all these types of currencies and rails to work together. Visa also announced a new way of being able to hash your card and give it to an agent with this Visa agentic token, where Stripe is one of the first partners to implement it. And I think we're just going to see this new proliferation of new ways that money can transact between parties.

2:25:27And we're going to need some type of sort of Babelfish translation service across all of them. Because if you're going to pick one route, then you're going to likely exclude many of the agent humans and businesses in the world. That makes sense. How are you guys thinking of, not to go too broad, but the business model of the Internet? Agents change things. the internet today is heavily reliant on uh you know advertising and if you have a bot you know just crawling a website you know you're or or even when you look at other other services and so we've talked ben thompson had some good writing around um just like what the future business model of the internet could look like and potentially micro payments but i think the takeaway from that our takeaway is like there's so many different stakeholders that would need to find some type of alignment uh it's it's hard to see like the obvious path forward here yeah i i think the universal want from businesses is just more channels to reach their customers and be able to do so in more direct kinds of ways and so if you go to you know a sas software provider and you said hi you know i sort of two choices for you you can have this um very cool large budget for a 101 billboard and kind of hope that at 85 miles an hour developers like see your ad and then remember to implement it later?

2:26:51Or would you like them in situ as they're working to have agents mediate the purchase, recommend it, and be able to like integrate and accomplish your thing in five seconds right inside their editor? Like, okay, yes. Well, first of all, I'll do both. But also the second one sounds very nice because I will be able to directly attribute where it came from and be able to have a great CAC for that. And either the LTV should be even higher because the robot even integrated it directly. And so I think that we're going to see new channels emerge for monetization, both usage based through MCP or other ways that businesses are going to expose their APIs to agents, but also for transaction based referral fees, which will supplement affiliate.

2:27:34And then I think it'll be a new way for businesses to make sure that their agents can can read their docs, can read their product SKUs, can have access to that information in a new permissioned way. I really like the Carpaphy talk that got posted last night where you basically said that if your docs involve a click, not good because agents want to act and not click and just only read. They want to start acting. And so that's why Stripe is, you know, if you go to the Stripe docs, we really push like, hey, here's our MCP where you can just talk to the primary best way of integrating Stripe then it can do it on your behalf rather than, you know, just reading or reading something from a three-year-old corpus.

2:28:13Interesting. Last question for me. We want to move on and let you get back to your day. Stripe was famous early on for having this crazy kind of open culture around emails that anyone from the entire organization could read. That seems like incredible foresight to the moment today because you don't have all this private information that, oh, do we train on that or not, you could very easily fine tune a model or do some sort of embedding on the emails that are already deemed to be worthy of the entire organization reading them. Is that still part of the culture? Is there a tool if you join Stripe where you can get up to speed without needing to read every email, but you can kind of get the Stripe way of doing X, Y, or Z?

2:29:06Talk to me about Stripe's culture. Stripe has a very serious writing culture where any decision I've been a part of for the last seven years, I can really point to some Google Doc that has the pros, the cons, and the decisions, as well as the email culture you mentioned. It's very commonplace at Stripe where if you spoke to a customer or even after going on TBPN, hi, I went on TBN, you just CC a notes list and now it's available for anyone who wants to subscribe to the notes list. But one of the major subscribers to Noteless now is agents. And so if I'm in Slack, we have this really awesome bot called Trailbot that has read the trail of everything, all the paper trail of everything that we've done that's permission to it.

2:29:50And I can just say at Trailbot in any Slack room. And it has the context both of the team Slack room I'm in, but also the full corpus of Stripe and all of our permissioned wikis and documentation and internal tools. tools, and it takes the first line of defense of most questions immediately. And we actually have it to the point where it knows to jump in automatically without you even asking it. And so I find that most of the time, we're able to just at Trailbot and answer a lot of questions. And then increasingly, these agent tools, which I think are going to apply to Converse soon quickly too, they're not just read only, they're going to start taking write actions and purchase actions.

2:30:27And for Stripe, internally write actions might be to roll back that deploy or to auto communicate to that customer because of an MPS score under 10, which we do often, hopefully not too often. But then in the real world, if you want to make some of these actions, you're going to need to prove who you are, pay for it, make sure the merchant was able to accept that money, get the entitlement and move on. Yeah. Even something as simple as like you show up to a new company, hey, there's this system over here that we're using and I don't have access, you might go to a wiki and ask, how do I get access now?

2:31:00You just ask and it just does it for you. It's so interesting to think about if there's like some type of user flow where if somebody sends a slack message, there's like a tiny delay built in and it gives like a bot an opportunity to like actually front run the question because it's like every message is going to waste like 10 minutes of time. It's like a new version of shadow band where you first get your question answered. Yeah. Do you really want to ask this question? because it was answered here, here, here, here, here. And like, here's our recommended action. It's like pro autocomplete. It's amazing.

2:31:31It'd be interesting. Yeah, it's slacks just become completely silent because everybody's like doing things and just immediately getting at. Those of us who have nerdily taken notes and made docs over years, it is somewhat of a okay. We've been waiting for this moment. It was worth it. Yeah, we didn't know why. Yeah, yeah, yeah. Made fun of by some people for a long time, but it all came back. Well, thank you so much for stopping by. This was great, Jeff. Always welcome. Yeah, we'll have you back soon. You can talk more. Great to see you all. It was great. Talk to you soon. Bye. Let's give it up for Jeff.

2:31:59Next up, we have Garrett from Handshake coming in. He was mentioned in the information. We've been mentioned in the information. It's a bunch of information boys hanging out on the chat. We love the information. We love the information. Thanks so much for joining. Garrett Lord, the nominative determinism is insane. I think we love something also in common, saunying. I'm a big, big sauna guy. No way. There we go. Yeah, yeah. The sauna is important. You'll be devastated to hear that when we moved into this new studio, we don't have a good... We don't have a good sauna set up, but we'll figure it out eventually.

2:32:33The Cold Plunge can fit nearby, though. I mean, there's still opportunities. The Cold Plunge could be good. Yeah. Maybe we got to get in the Cold Plunge game. Anyway. Stumping in a full suit. Anyway, kick us off with a little introduction on the business. Obviously, it's in the news today. We covered a little bit about it earlier, but I'd love to get you to explain the business, a little bit of the history, and the positioning of the company. Yeah, for sure. So, I mean, the business started way back when I was in college. I started Handshake out of a personal pain that I faced in breaking into find my first internship and first job.

2:33:03I went to a no-name school in the middle of nowhere called Michigan Tech. It's awesome if you love to ski or love the cold, but if you wanted to break into Silicon Valley, nobody had really recruited there before. Fast forward to today, Handshake is the number one place that young people in America start, jumpstart, or restart their career. We're like kind of an 18 to 30 early career network. There's a million employers that use Handshake. So it's where the vast majority of employers recruit undergrads and interns and people after school. And then there's 18 million students and young professionals use the network.

2:33:35And we also power about 1 ,600 universities in the country. And the background, I think, that's important for right now in this very moment is about 18 months ago, many of the frontier labs, as well as the large annotation engine companies started reaching out to us with basically asking us, beating down the door, saying like, do you have access to PhDs? Do you have access to master's students? And for us, that was incredible. I mean, we have 500 ,000 PhDs in the network. We have 3 million master's students on the network. There's tens of millions of undergrads in the network. And we started serving these players with experts really as the world has evolved from training frontier models.

2:34:18It's moved from generalists, like drawing kind of boundary boxes around stop signs, to today experts. And experts are in law, finance, medicine, mathematics, physics, chemistry, biology. These labs really are hungry for reasoning data to help improve with human in the loop, the actual frontier of what their models are capable of delivering, yet alone in the future. And you talk about like tool use or trajectory. So they started reaching out to us and saying, do you have access to these PhDs and master's students? And we started providing, we were the leading provider of all this talent. And we really started to realize is that people weren't getting paid on time.

2:34:59They were really confused. They would go through training and kind of get dropped out of a leaky bucket. We heard from students that were successful in it, that they love the money. They love learning more about some of this AI tooling. They wanted to use AI tools in the classroom. They wanted to use it in their research. And so given that we have this huge supply and zero customer acquisition costs, we started building a human data business. And really in the construct of building that business, the focus is really around like, how can you also think about evolving and automating a lot of the recruiting practices?

2:35:31Recruiting is still, you know, it's sourcing, it's screening, it's scheduling. There's a lot that AI can bring to bear on that. And so we now, fast forward to today in the last six months, have been working now with six of the Frontier Labs. We provide them tens of thousands of computer... It's a lot of them. I didn't even know there were six. I thought there were only five. The big six. Count them up. You got them all. And we provide them with experts to help make their models more effective. Very cool. Talk to me about how are the Frontier Labs thinking about human data annotation and answer generation?

2:36:10It feels like we might be at the end of that story soon, or maybe we're shifting into more of a focus on the areas that are less verifiable, less like write the answer to an IMO level math problem and more in the biology and legal context where the models are falling behind. Like where are the pockets of value? Where's the most demand within the human data generation industry? And where do you see it going over the next couple of years? Yeah. So maybe I'll go like from the latter part of the question to the first, like where we see going over the next couple of years, it's definitely gonna evolve into audio.

2:36:48It's definitely gonna evolve into tool use. It's definitely going to evolve into trajectories and experts will be needed to provide data. Imagine almost like recording your screen as you're conducting a task. Maybe you're building a slide deck and doing it. You know, if you're an investor construct, like doing a DCF and doing competitive research, they want more data to be able to help improve these models, especially you think about like agents, right? And step-by-step problem solving. As of where the puck is right now and where the puck will continue to be, if you talk to a lot of the frontier researchers, is they need expert data.

2:37:21And expert data is in basically every esoteric area of human knowledge. They want to – the models have already kind of sucked up the entirety of books and YouTube and human knowledge. And what they really need is they need special data to be able to make and understand the step-by-step reasoning that's required in order to be able to kind of fuel the future. And so if you think about academia, these PhDs, like what is the definition of getting a PhD? The definition of getting a PhD is like pushing forward an area of research that nobody else has done before. As peer-reviewed by your peers, that's how you get your doctorate.

2:37:57And so this kind of perpetually reoccurring stream of PhD students and master's students are really valuable in this very moment. And it's also to zoom out to their experience, like you can make, I don't remember when you were in school, but you can make like 23 bucks an hour being a teacher assistant. You know, you could drive DoorDash. We're paying these students like 60, 70, 80, a hundred plus dollars an hour. And they're also, we can connect it to actually getting jobs. So we envision a world where like, you get badgers on your profile and there's like leaderboards by school. And we're actually, I mean, what better way to articulate your skill than actually proving it by being able to break the model or by being able to provide the model feedback.

2:38:37And so we believe that we can help you get more jobs with a million employers in the network, help you build your professional reputation and articulate your skills, all the while while making like$100 an hour when you want to. I mean, it's a gig job. Yeah. How do you think about financing, Handshake going forward? I'm sure you're making, generating a lot of revenue. You're clearly paying a bunch of your network out quite a lot. We were just learning about Surge AI earlier and what they were able to do while bootstrapped. I imagine even in the last week, you've had investors reach out trying to say, hey, scale's out of the game.

2:39:17You want$100 million? You want to dance? But how are you thinking about the business going forward? Yeah, I mean, one of the ways we think about this market is like, you know, if you don't have an audience, there's no moat. What our competitors are doing is they're at some of these companies, they'll have hundreds of people who are recruiters sitting on top of platforms, sending messages on companies like Handshake or spending tens of millions of hours a month doing performance advertising, trying to acquire experts on Instagram. You can imagine if you're like a physics PhD and you get an ad on Instagram for a company you've never heard of before, claiming they could pay you$100 an hour.

2:39:52It's kind of a jarring experience. And so because we built a decade of trust in adding a ton of value to these users' lives, we have no customer acquisition costs. And what that means is that we can pass along all those savings by paying contributors. We call them fellows. It's the Move Fellowship Program. We can actually pay you more than any other vendor on the market. We can also pass along those savings to the Frontier Labs. So as you think about our overall P &L, like our gross margin and ability to scale this business, considering, you know, the moat is the network that we built, we sit in an amazing position to, you know, to grow extraordinarily quickly.

2:40:28And that's what we've been seeing. I mean, in the last, you know, month, we've grown by over three X and, you know, it seems like there's a lot of demand continue to be out there. I can imagine. I had no idea it was that big, though. Let's go. Let's go. Three hits. Three hits for a 3X. That's incredible. Last question, we'll let you go. Are there any weird areas that you think will see this type of human data generation pop up? I'm imagining AI seems to be at 150 IQ. It can write code, and yet it can't book me a flight. Do we need to take like flight, like travel agents and have them go through the workflow so that they don't get hung up on should I sign up for the credit card or do I want, you know, insurance on this flight so that we have a whole bunch of data specifically about that task?

2:41:23I'm just interested in this concept of like these economically valuable but highly niche tasks that don't seem to be, we don't seem to be getting closer and closer and closer to like one-shotting them with the current models. And I'm wondering if we're going to see this long tail of different hyper-specific business use cases like what we saw in SaaS where there would be Hipmunk, just help you book flights better. Is there going to be a flow where there's a new startup that's doing AI agents for flight booking, and then they're coming to you for a ton of data generation around how to actually book the correct flight because it learns whether or not you're okay with a layover or how price sensitive you are?

2:42:05All the things that you would get from the interaction with a human flight travel agent. Is that something that you think we'll see, or is that kind of just completely tangential? No, I think that's totally something we'll see. Interesting. What you just described is like a trajectory called a browser trajectory. And it's basically like you have a goal in mind and you have like a step-by-step kind of thoughts in your mind around how you accomplish that. And you navigate tools, you navigate the browser, you stitch together your own intuition to be able to accomplish that task. You might look at your own calendar.

2:42:36When do I get off work? How long it takes to get to the airport? It takes me a different amount of time to get to Burbank than LAX. What's the parking like? Like there's so, it's such a simple task because you think about like anyone can do that job for you and yet to do it well is actually really hard. Totally. And you talk about just being able to talk to a model, right? Like you don't even need to log in, right? So you're gonna need audio data, you're gonna need trajectory data, you're gonna need to interact with APIs. Humans experts will be needed for the next several years to be able to make that data happen.

2:43:07Interesting. In order to be able to power the frontier of where you wanna see it going. Well, that's exciting. I want to book a flight with an AI. It still hasn't happened. That's my own personal touring test. Hopefully you can make it happen. But thank you so much for stopping by. This was fantastic. Be sure of your time. We'll talk to you soon. Great to meet you. Cheers. Coming in next, we have Tane coming into the studio to the TVPN Ultra Dome. A massive round. Oh, we're going to hit the gong again? Yeah, I'll let you hit it. The 10th time of the show. Always a good time. There he is. Welcome.

2:43:37Welcome to the show. You got news for us? Hit us with an introduction. Hit us with the news. What's going on in your world? I think we might be muted. Tane. Are you there? Can you hear us? Are you there? I'm itching to hit the gong for you. I hear there's gong-worthy news. Are you there? I'm going to send him an email. Okay. You are live. You are live on TVPN. Okay. We'll pull him off for a second. In the meantime, I will tell you about Wander. Find your happy place. Find your happy place. I can hit you with the gong. Book of Wander with inspiring views, hotel-grade amenities, dreamy beds, top-tier cleaning, and 24-7 concierge service.

2:44:15It's a vacation home, but better, folks. We told you about 8 Sleep. We told you about Adio. We told you about Polymarket. We told you about Linear. Did we do Vanta? Automate compliance, manage risk, prove trust continuously. Vanta's trust management platform takes the manual work out of your security and compliance process and replaces it with continuous automation, whether you're pursuing your first framework or managing a complex program. Let's hear it for Vanta.com, folks. Give it up for Vanta. And if we got an extra minute. Is he here? Are we back? Welcome to the stream. We made it. How you doing?

2:44:46Fantastic. We had some audio issues. Oh, no. It was a pleasure. We got to do extra ads. So, you know, you're making my day. It's a dream. It's a dream. You're making my day. What's going on? Quick intro. You've had a big day. What's happening? Yeah. Thanks for having me. We announced a$200 million round. Whoa. With General. That's fantastic. Buried the lead journey. You guys got to start selling those. I feel like we need one in our office. But yeah, no, we're super excited. Obviously works in healthcare. You know, we power AI workflows, everything from ambient to revenue cycle payments in large hospitals.

2:45:29Oh, interesting. Give us a quick history of the company. Yeah, yeah, yeah. Start with that. It's not often I see a 200 on six something billion. I hadn't heard of the company before, but I hadn't. Josh Browder connected us last night. Amazing. So I'd love to hear your quick story and how you got here. History of the company. I want to hear about the first customer too. For sure. So Josh is great. I've known him since we were at Sanford together, same year. And the story behind Camere is interesting because Camere started as an incubation inside General Catalyst. The best analogy I have is it is Hemant's Palantir, very focused on healthcare.

2:46:11I started a company while I was at Stanford called Aethelis, which was focused on applying language models and computer vision in healthcare. We started as a blood diagnostics company and then eventually grew into this mid-market SMB OS for physicians. We merged the two companies about a year and a half ago or almost two years ago now. And then I took over as CEO with our management team. And so it's really, you know, it's sort of a coming together of these two businesses. And yeah, I mean, the company powers large hospitals, about 250 ,000 physicians and nurses. We power, you know, private practices here in California.

2:46:53That was our first, first customer. It was someone that my co-founder Deepgo literally walked up to and cold knocked on their door and then got them to use one of our first devices and remote monitoring solutions. And yeah, that's the quick story. I got a bunch of questions. I want to kind of like contextualize this around the broader General Catalyst discussion because there was news, I think just today, that Ohio authorities approved the first ever purchase of a U.S. hospital by a venture capital firm. That's General Catalyst's bid to acquire Summa Health, a hospital system in Akron with over 20 facilities.

2:47:34And I'd love for you to, I'm sure you've studied this, what is going on there? And then is there any sort of synergy across the portfolio? General Catalyst has had like a very differentiated strategy there, but I haven't had the chance to dig into it. So I'd love to get you to contextualize it and then we can go into how this links to your business again. So the Summa transaction is super interesting. It is a venture capital firm buying a health system, transforming it. And Camere is obviously a big part of that. We're serving as the office of the CTO. So our engineers are forward deployed. We work hand in hand with the Summa IT teams.

2:48:12We've been working with the revenue cycle leaders, the clinical leaders. And it's a really special system. I mean, it's in Akron. If I'm not wrong, it's where LeBron James was born, literally the hospital itself. and um many people have been calling you the lebron james of healthcare ai now we got to put that out there um yeah i mean i i maybe have been the first person to say it might have coined it here but many people i'll say it right now you're the lebron james of healthcare ai now many people are saying not just one is two as many yeah in our book we're just going to make that a thing now um fantastic it it's it's remarkable because running a health system is super hard.

2:48:53It is a one to 3 % operating margin business. Most of them go out of business. And I think what General Catalyst believes in is language models and technology can transform the operating margin and also lead to better care. So it's not a PE, you know, cut and juice play. It really is an investment. That's awesome. Talk about uh commures overall product strategy you guys have a number of different products it seems like a very different you know that we've talked with founders and covered companies that come on and and just want to own uh one you know one key area but uh healthcare feels like somewhere where if you can get embedded with the set of customers you can you know you know more you know rapidly kind of add add products to uh to the platform so i'd love to understand the product strategy We really look up to businesses like Rippling and RAM.

2:49:53There's this concept of you enter with a wedge. And in our case, that wedge is either ambient AI, which is a tool that helps a physician document and really automate the revenue cycle of their appointment, generates the claim automatically. And in the back office, which is when you walk into a hospital, there are tens of thousands of people at large health systems whose sole job is fill out claims, call up insurance companies, fight denials, fill out new forms. All of that's going away with LLMs. And our belief is that if you do that as a point solution, as like a single little part of the solution, you might get some initial usage.

2:50:32But eventually, the EMRs like Epic or companies such as ourselves will just eat you. and you have to be that compound startup from the get-go. And I think payments is a really interesting vector to deploy software. Ramp has shown it where you get into the transaction suite and then you build a whole bunch of tools for the CFO's office. We're trying to do the same for a health systems CIO and CFO. Can you tell me a little bit of the history of the healthcare industry broadly and how, I know that there was like this kind of catalyst around Obamacare. I remember talking to Jonathan Bush, the founder of Athena Health, about electronic health records mandates.

2:51:13And there's been a number of changes kind of at the federal level that have kind of opened up different pockets of opportunity. Like, what is the story that you tell about the recent history of health care in America? I think it's fascinating. The 90s, physicians had amazing lives. I mean, they drove Porsches, they had work-life balance, they had personal relationships with their whole family. Let's hear it for Porsches. We'd love to hear that. Let's get back to Porsches. We need more of those. We need a return. And, you know, all in all, patients got great experiences, too, because of that personal relationship.

2:51:50And then, you know, the admin work tax just increased. Everything from insurance to filling out an EMR. Digitization came in the 2010s with Obamacare and meaningful use. And And really, EMR has proliferated. And Jonathan Bush and Judy and all these people are legends in the industry because they built Athena, a$20 billion company, Epic, probably a$100 billion company now on the backs of that very quietly and under the radar from most of tech. I think the theme and the story of today is labor is turning into software. And where is most white-collar labor in America? It's in healthcare. Where the majority of administrators sitting behind a computer clicking on forms, it's in healthcare.

2:52:34And we believe that the EMR will be transformed. We also believe that the labor stack of healthcare will be transformed, and it'll create more operating cash flow for hospital owners. Is that narrative of the administrative ratio or the administrative load increasing? Is that similar to what happened in academia? Because I remember seeing these charts of the ratio of professors. Everyone loves the idea of like a high functioning university with a lot of professors teaching students and a great ratio there. Everyone's a little bit more skeptical about like, wait, why do we have five times as many people to admin?

2:53:07Is that the same thing that's going on in health care? And kind of what was the underlying driver of that? Was it just regulation or lack of tools? Where did it come from? I think it's very similar. What I will say is I think in health care, it bred more out of necessity. And in academia, it just kind of happened. In healthcare, there's this game of attrition between the insurance company and the provider. And they're making it a little harder every month, every year to get an approval on a claim. And as a result, the health system needs to add a couple more people in order to fight those claims.

2:53:42And then it just kind of built up into this arms race. And I think the insurers kind of carried the power after Obamacare. Like when you look at UnitedHealth's market cap, I mean, what is it, like a 12x since Obamacare got passed? It's quite shocking. And the power dynamic, I think, will shift again back in the favor of physicians and hospitals because of LLMs and because of what you can now automate. Yeah, it was kind of just like the game theoretic Nash equilibrium was like hire a lot of admin staff. There was no other option. Yeah. Talk about your personal ambition and the team's ambition. You're a$6 billion company now.

2:54:26It's cliche, but the way you're talking, it feels like you're just getting started. Is the job finished? Yeah, yeah. It sounds like the job's not finished. I don't want to put words in your mouth. It's not finished. Look, I think when you walk into a healthcare practice, the inefficiency is shocking. And the positive intent from the physicians and the nurses and the caregivers themselves is all there. And I think all it takes is for a company like ourselves to come in and try to nuke that work tax. So our ambition is, look, we're going to come after the EMRs. We're going to come after the payers or revenue cycle businesses.

2:55:04This is a$4 trillion industry you can build for a very long time. What done looks like is when you walk into a physician's practice, scheduling, intake, insurance, like all handled. There's no filling out a little clipboard of the same information again and again. The appointment happens and instantly the doctor is paid out. There's no reason we can't have instant adjudication instead of waiting 30, 45 days. But it's going to require a system overhaul, like new payment rails to go do that. And that's really what's at the heart of what Camere is building. Awesome. Well, this is super exciting. I'm glad you're doing what you're doing.

2:55:40And you're our new healthcare expert and correspondent. So expect a call next time. And a LeBron James. And a LeBron James specifically. LeBron James of EMRs. Yes. All right. according kobe of course congratulations on the milestone uh hope hope to have you on again soon we'll talk to you soon cheers have a good one uh should we do some timeline fun show fun show yeah we definitely should we got to talk about uh sam lessons oracle versus uh salesforce he's getting in he's getting in hot water you got it the timelines and turmoil we love sam lesson on this show he he posted a uh screenshot he says i will defend big tech i will defend sam lesson And Oracle is 2x Salesforce, but Ellison is worth 25x Benioff.

2:56:26What this sale says about the limitations of the SaaS business model. He said he had a fun riff yesterday with the slow partners on this. Oracle is obviously crushing it. But if you take a today snapshot, basically the market cap of Oracle is 2x Salesforce. $500 billion versus$250 billion. Meanwhile, according to previously directional data, Benioff's net worth is 125th. That of Larry Ellison's$10 billion versus$250 billion. What do you learn from that? What lessons do you draw? I like this, the revealed preference. For founders and companies, the old licensing model is better than SaaS. That's interesting.

2:56:59Imagine having$10 billion and just getting little bro'd by Larry. He has a sort of little broing effect on most people. There's an amazing story about a famous little broing where Phil Knight of Nike was worth something like 10 on the order of like 10 billion dollars and he was in like maybe sun valley or something going to a movie and he runs into bill gates and warren buffett who are just going out to a movie and they're they're both worth 10 times him and he's just like yeah i had this weird awkward moment where i was like nervous to meet them for the first time in a long time because typically he's like the most successful businessman he runs into all day right but he was just like But yeah, in his book, Shoe Dog, it's a fantastic book.

2:57:44Have you read Shoe Dog? Shoe Dog is a great book, great book. And he talks about all the weird effects of having immense wealth, how his wife would hoard immense amounts of paper towels just because they were like, money's no object. Like what should we do with this? How should we do with this? King of the castle. Got a lot of paper towels. And they'd figure out, okay, this is some weird psychological thing that's going on in my brain. I don't actually need all these paper towels. the fact that money is no issue doesn't really matter. People like to talk about, you know, you're the, you're the, you're the, whatever, whatever the, the average of your five friends.

2:58:18It's like, yeah, well, if you want, there, there should be some similar law of like, like your growth rate is like, should be, is like a tied to how often you're little bro. Yeah. Yeah. Yeah. Never get a little bro. No, no. You want to be. Oh, yeah, yeah. That's true. Yes. You can be on the upward sling. That's right. If you're not getting a little broad enough you're not an upwards upward trajectory this is good yeah yeah this is good I've been in that situation before anyways um we could cover what Sam said uh but I think we can just skip to we're also gonna have him back on the show we're gonna have him back on the show he's a regular uh we're gonna skip to Miles he says wrong take Ellison is much richer because he didn't sell shares and has steadily been buying back two percent of the company every year for 30 years He's increased his ownership from 17 % to 40%.

2:59:04Wow. It's such an incredible story. Founders complain about dilution. Yeah. Oh, you got diluted? Yeah. Oh, I'm sorry. Why don't you just buy back shares every single year for decades? Yeah. If Ellison, you know, keeps doing this, he could very well own 150 % of his company at some point. That's the future for OpenAI. OpenAI just becomes the agentic organization. It just it just buys back so many shares that it eventually owns itself. Yeah, that's the real that's the real goal. Any anyways, Miles says, meanwhile, CRM, aka Salesforce, made a lot of dilutive acquisitions and Benioff said he sells his shares yearly.

2:59:43He doesn't sell them yearly. He sells them daily. Daily, two million daily. Yeah, people say, oh, liquidity events, you know, they're few and far between. Not happening every day. Not for Benioff. Daily liquidity. It's pretty good. You know, the real, you know, another, how much does he pay Matthew McConaughey to just hang out? That's got to be pricey. I think it's only like$10 million a year. So it's like. Yeah. A couple of Super Bowl ads. Not bad. So Sam responds to the hate. He says, since a lot of folks are making the same comment about buybacks versus sales strategies, that is at best the noob answer.

3:00:22If you're smart, you understand why they have different paths. And the answer is path dependency from business model quality. Take a 201 level class. And then Boohoo Capital Bloke quote tweets that and says, it's a timeline in turmoil. Wrong again. CRM has executed poorly. They've diluted shareholders with bad acquisitions. They have 75 ,000 employees who they give excessive stock-based compensation to. They let hubs scale up in their face, HubSpot. They've diluted versus shrunk their share count versus the other companies, Adobe, that eat shares. investors don't trust him. If Benioff held and cared about shareholders, it would be a closer call.

3:01:01He doesn't care. It's not about the business model. Well, you'll love to see some timeline in turmoil. Very, very fun. In other news, Shiel Monat has the story about Telegram's founder, Pavel Durov, consistent feature on the Tech Bro Drip account. Everybody says they're pro-natalist until you ask how many children have you fathered through sperm donation? Apparently he has fathered over 100 kids via sperm donation, and he is worth$14 billion, and he says he'll leave his fortune to all of them with no difference between his six kids conceived naturally compared to the 100 via sperm donation. So every one of them is going to get$140 million.

3:02:17Just to kick off fundraising. like get a stake in the in the distributions because it's making money i think he kind of figured out life and wanted to make his life basically a hundred times more complicated you know having having this type of dynamic you know not just with his many children that that he helped conceive directly versus the hundred others so So had to one-up Elon, had to little bro Elon. He did a little bro. And Elon's commented on this too. He was like, oh, I got rookie numbers. Genghis Khan over there is really taking over the world. Genghis Khan of encrypted messaging. Yep. It's very, very odd.

3:02:57Only CFO says the finance department outdrinks sales. Feels like sales is inviting finance to the party so they can stick them with the bill. This is the data you can only get from Ramp. Ramp.com slash data. Yeah. Apparently, finance marketing sales teams lead in alcohol spend. Alcohol as a share of business meal spend. Not in that order. So marketing is absolutely dominating. Dominating. 20%. They're taking up 19 % of all spend on alcohol. No, no. It's 19 % of business meals are alcohol. So if they go out and they're getting$80 worth of food, they're adding on$19 or something, or$81 of food,$19 of drinks.

3:03:38That's the idea. alcohol share finance they're getting you know 84 of food 16 of booze marketing is drinking sales under the table yes yes narrative violations it in the in the tail end there 9.7 uh many huberman devotees in the it department apparently yeah not a not a power lunch power lunch uh you know No, category. No, but the three martini lunch will make it back for the tech teams. Should we go to this story about the Vibe Coder who sold his business to Wix for$80 million? It's only a six month old company and there's no external funding. $189 ,000 in profit in May. Bryce Roberts is just the beginning.

3:04:30There's gonna be more stuff like this. I thought this was pretty cool. Base 44 only employs six people. hasn't raised any external funding. The 31-year-old built a viral AI app maker as a side project. So you go in there, you design an app. Obviously, plays very well with Wix, which is the website building business, but he flipped it for$80 million, and he's post-economic now. Congratulations. Yeah, when I saw this headline, I was confused. I was like, okay, so he just vibe-coded something and sold it, but it is a tool to do vibe-coding. tool. Trusted by over 250 ,000 builders worldwide. Nice, quick flip.

3:05:11Amazing. He's basically getting a similar outcome to a founder that sells their company for a billion dollars, but goes through a bunch of different financing lines. Or kind of like a mid-tier AI researcher. Yeah, starting out. In other news, John Carmack is absolutely jacked. This is fantastic news. Yaxin has the news. He's looking very built. But John Carmack chimes in. He says, a chunk of this is just his wife dressing me in tighter shirts. But I did put on several pounds of muscle this year after switching my random grab bag of vitamins and supplements over to Brian Johnson's blueprint system.

3:05:55Let's hear it for Brian Johnson. Really making a difference in the technology world. I was probably not getting enough protein to take advantage of the exercise I was doing. I have always been roughly upper quintile for fitness. Let's go. Regular exercise, but not at the level of serious athletes that most offices tend to have a few of. And now he's looking built. Palmer Luckey chimed in. It's a great day on the timeline. Let's check in with Tyler. Close out the show. I was going to check in with this Polymarket. Will Chamath launch a SPAC in 2025? I was supposed to know about this. It is up to 70%.

3:06:30It's up to 70%. It was 33 % when I posted it this morning. Wow. That's big news. It was partially because he came out and he said, what did he say? He said 58. He asked yesterday, should I launch us back? 58 ,000 people voted. 71 % said no. He said, I hope everyone that voted no feels seen. Now on to business. I got calls from many Wall Street and crypto titans yesterday. They all won in and their vote matters a lot to me. So I will probably do it. I love it. Maybe this time it will go better. Who knows? The risks are clear, though. The last time wasn't a success by any means. I will include this poll and the community note in every SEC filing possible.

3:07:14That's awesome. You'll make an excellent disclosure about the risks and is not short of irony. So what kind of company do you guys want? No crying in the casino. Let's go. People are absolutely fuming at him. I'm sure. But honestly. get after it pretty fair everyone knows what's up he's gonna play by the rules you know and i think i think at the end of the day it's it's very like i i look at the next chamath spec is like it it totally would like it probably will pop it'll probably it'll get a lot of attention it might turn into a meme stock right um i uh i will be interested to see what kind of target he i'm 100 % excited to follow the story.

3:08:02It's going to be fascinating. Anyway, let's check in with Tyler and then close out the show. Tyler, I have a question for you. Can you guess a number between 1 and 50? Like a random number? Yeah, a random number between 1 and 50. 27. 27. Are you an LLM? Did you see this? Every single model, they all guess 27 when you ask them a number between 1 and 50. chat GPT, Claude, Perplexity, Meta, they all guessed 27 for some reason. We gotta get a WorldCoin orb in here to be able to prove that Tyler's not, in fact, AI. Yes, we do. He might just be a deep pick. Final review, what did you get done this show?

3:08:43Did you keep playing with MidJourney? Were you doing something else? What's been going on the last couple hours? Yeah, I think I just sent another video. Oh, no. I've been pretty productive. You can watch this. Let's see. i like the gorilla crosses behind what's he doing did he just oh he just took my spot oh he took your spot wow wow i see how it is oh he comes in with a paper breaking news breaking news gorilla the breaking we need to get you a a breaking news gorilla outfit and and if he has breaking news he can print it out come sit down take your seat that'd be amazing that's good that's good uh are there any others or we're closing it out uh yeah i think that's okay that's it that's it well good work good work i feel like the production team laughing like they have some other ones that are too scary for for our audience i saw what i i saw one get sent in the chat and it and it just was really it was scary okay looking well we will be back tomorrow uh we have a great show for you folks leave us five stars on apple podcast and spotify and thank you for watching for being here with us.

3:09:46Fantastic show. Have a great evening. Goodbye. We love you.