TBPN - 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

Episode Date: June 19, 2025

(04:10) - Meta Eyes Nat Friedman & Daniel Gross for AI Push (37:47) - Lakers Sold to Mark Walter (43:39) - Surge Hits $1B+ in Capital Without Outside Revenue (56:20) - Mike Knoop, co-founder of Zapier and CEO of Ndea, discusses the evolving landscape of AI reasoning models, highlighting the trade-offs between accuracy and efficiency among leading systems like OpenAI's o3 and DeepSeek's R1. He emphasizes the importance of considering both cost and performance when evaluating AI models, noting that no single model currently dominates across all metrics. Knoop also underscores the need for innovative approaches, such as program synthesis, to advance toward artificial general intelligence. (01:20:23) - David Cahn, a Partner at Sequoia Capital, previously served as General Partner and COO of Venture at Coatue Management, where he led investments in companies like Hugging Face and Runway. In the transcript, he discusses the substantial investments in AI talent by major tech companies, emphasizing the human dynamics and strategic decisions driving these developments. He also explores the competitive landscape, the significance of data centers, and the economic implications of AI advancements. (01:37:55) - Walden Yan, Chief Product Officer and co-founder, discusses the integration of AI models in product development, emphasizing the importance of balancing model intelligence with user experience. He highlights the challenges of managing trade-offs between model responsiveness and accuracy, advocating for systems that abstract model complexity to enhance usability. Yan also addresses the evolving role of AI in software engineering, noting the potential for AI to autonomously perform tasks like coding and debugging, thereby transforming traditional workflows. (01:57:41) - Eoghan McCabe, co-founder and CEO of Intercom, discusses revitalizing his 15-year-old SaaS business by returning to core fundamentals like customer-centric pricing and simplifying the sales process, leading to a tenfold increase in growth over eight quarters. He emphasizes the importance of maintaining high energy and passion from leadership to prevent stagnation, noting that AI innovations have reinvigorated both him and the company. McCabe also highlights the challenges of scaling a company, stressing the need for agility and the willingness to embrace chaos by empowering young, dynamic individuals in leadership roles. (02:16:00) - Jeff Weinstein, a product lead at Stripe, discusses the company's initiatives to integrate AI into commerce, emphasizing the development of agentic commerce where AI agents facilitate seamless transactions. He highlights collaborations with companies like Perplexity and IP Camp to enhance e-commerce experiences and mentions the introduction of Stripe's order intents API, enabling agents to execute purchases on behalf of users. Weinstein also addresses the evolving role of payment methods, including stablecoins, in agentic commerce and underscores the importance of permissioned, secure transactions in this new landscape. (02:32:00) - Garrett Lord, co-founder and CEO of Handshake, discusses the company's evolution from addressing his personal challenges in securing internships at Michigan Tech to becoming the leading early-career network in the U.S., connecting 18 million students and young professionals with a million employers. He highlights Handshake's role in supplying experts to frontier AI labs, emphasizing the demand for specialized data from PhDs and master's students to enhance AI models. Lord also outlines plans to leverage AI in automating recruiting processes and envisions a future where participants can showcase their skills through contributions to AI development, thereby enhancing their professional profiles. (02:44:44) - Tanay Tandon, CEO of Commure, discusses the merger of Athelas and Commure, highlighting their combined efforts to transform healthcare through AI-powered solutions. He emphasizes the importance of automating administrative tasks to improve efficiency and patient care, and outlines the company's strategy to expand its product offerings and customer base. Tandon also shares his vision for the future of healthcare, aiming to eliminate inefficiencies and enhance the overall patient experience. TBPN.com is made possible by: Ramp - https://ramp.comFigma - https://figma.comVanta - https://vanta.comLinear - https://linear.appEight Sleep - https://eightsleep.com/tbpnWander - https://wander.com/tbpnPublic - https://public.comAdQuick - https://adquick.comBezel - https://getbezel.com Numeral - https://www.numeralhq.comPolymarket - https://polymarket.comAttio - https://attio.comFollow TBPN: https://TBPN.comhttps://x.com/tbpnhttps://open.spotify.com/show/2L6WMqY3GUPCGBD0dX6p00?si=674252d53acf4231https://podcasts.apple.com/us/podcast/technology-brothers/id1772360235https://youtube.com/@technologybrotherspod?si=lpk53xTE9WBEcIjV

You're watching TBPN. Today is Thursday, June 19th, 2025. We are live from the TVPN Ultradome, the Temple of Technology. The Fortress of Finance. The Capital of Capital. We have a great show for you today, folks. There's some breaking news that's dropping right now. I think we got to go to the printer cam. Really? Because we have an update from friend of the show. Let's see if this works. Do I need this? No. We need a moment of silence gone. but because it's about the Elon Musk news out of SpaceX but we got an update from friend of the show Ashley Vance coming in here hot he says I happen to be at Neurlink last night when Starship went boom and so is Elon Musk until well past midnight Pacific time he was in three hour plus he was in a three hour plus long meeting when the explosion happened meeting ended I assume that's when he learned about it and then he went back to to work. Wow. Absolutely dog. What a grinder. I mean, video was absolutely insane.

It was. We will cover it in a little bit. Yep. You had shared a transcript from it. Oh yeah. This is crazy. Earlier this morning, I had seen the video of it going boom. Yep. You shared this transcript where one of the engineers is saying, hey, really quick, Sawyer, we just observed a couple of events coming from the common dome in between a locks tank and a methane tank. And from this angle, it almost looks like the methane tank is gone. Then he actually says, question mark. Is that normal?

Jack, is that normal? I'm seeing some venting. Is that unusual? Is that normal? And the guy goes, yeah, it's probably normal. And they literally say, I've just, you have the video?

I just said the video to the team. Let's play it. I have no idea if this is actually related. We'll have to get someone on the show to dig into exactly what happened. I'm sure there'll be a post, like a post-mortem on the explosion. They did say famous last words and then shortly afterwards.

It exploded the entire. It is a crazy, crazy video. And out really quick, Sawyer. We, let's see if we can pull this up. In the meantime, let's tell you about ramp. ramp.com. Time is money saved both. Easy to use corporate cards, bill payments, accounting, and a whole lot more, all in one place.

Go to ramp.com to get started, of course. The other two major news stories we want to cover today. $10 billion, the price to buy the Los Angeles Lakers. 15 billion. The price to buy meta and AI leadership team. Fantastic post by Alex Conrad. They've just observed a couple of vents coming from the common dome in between the locks tank and the methane tank. And from this angle, it almost looks like the methane tank is gone?

Question mark? Is that normal? Jack, is that normal? And then I actually edited it out. I'm seeing some venting coming from in between the methane tank and a locks tank. Is that usual? Is that normal? Yeah, that's probably normal.

Keyword probably. Famous last words. Yeah, weasel words. Famous last weasel words. These guys are just broing out in a live stream watching like a static firing test. This is not a launch. They're not trying to launch the rocket.

They're just putting on the test stand, firing it up to make sure that everything works and it just completely exploded. We'll go deeper into that and some of the reaction in the news in a little bit. But in the meantime, let's talk about the other piece of breaking news that came out of the printer. Just after we got off the stream yesterday. Oh, right.

This is news from the information. Meta is in talks to hire former GitHub CEO Nat Friedman and Daniel Gross to join AI efforts. And partially buy out their venture fund. Yes. So there's a ton of details here. So let's read through the information article and then we'll go to some of the reactions. So Meta Platforms is in advanced talks, not just. Talks, advanced talks.

Has that been defined, quantified? What does that mean? Like are we past coffee meeting? Is this a 30 minute? Is this a two hour conversation? How long are the talks until they become advanced? Yeah, I must know. You know, exact numbers are being thrown around.

It's very possible. Yep. So they're thinking about bringing in Nat Friedman, Daniel Gross, to help lead AI efforts. Part of those talks, meta is in discussion about partially buying out Friedman and Gross's venture capital firm, NFDG, which holds stakes in top AI startups and is worth billions of dollars on paper.

If the talks are successful, Gross would leave safe superintelligence, which he co-founded with former OpenAI chief scientist, Ilya Sutskiver, last year. At Meta, Gross is expected to work mostly on AI products, while Friedman's remit is expected to be broader. Both Gross and Friedman are expected to work closely with Meta CEO, Mark Zuckerberg, in scale AI CEO, Alexander Wang, or Wong, whose hiring by meta was finalized last week in a $14.3 billion deal. Big numbers.

Big numbers being thrown around. I think this is gong worthy, even though we're just in advanced talks. We got to hit the gong. We try not to hit the gong for advanced talks, but scoops or scoops. It's such a big number. Fantastic. We hit, strong hit. As part of the talks.

Yeah. Yeah, I guess some, you know, immediately, a couple things I was thinking about when I saw the headline. One, I don't know if this is. common knowledge, but my understanding was that some of the money from NFDG was Zucks, right? So they were already investing on behalf of Zuck to some degree. So this shouldn't be a huge surprise. And then I think the bigger thing is, what does this say about SSI, right? If DG is willing to leave SSI, despite it being, you know, such a young company and already valued,

I imagine DG steak is in the billions of dollars there. So I have to leave that and go to META says something. I don't know exactly what it says. I think it could potentially say a few things. One is that maybe artificial intelligence is more of a sustaining innovation than disruptive innovation. And so that just by training a fantastic model, you're not immediately going to be able to overcome the network effect at META. And so META is maybe potentially a better place to go, you know, you know, really reap the rewards of artificial intelligence.

That's kind of a signal because, you know, no one at Google was really thinking about joining Yahoo, right? There wasn't a lot of flow that direction. Yeah. It was like, we're on to something. We are going to disrupt, you know. Same thing with Amazon. I'm sure Bezos wasn't losing people to Barnes & Noble.

Right? I think this is the analogy. I'm sure Barnes & Noble threw out a couple of max contracts. Maybe, maybe. But yeah, I mean, if you're, If this was truly disruptive, you would think that you would say, well, I don't, I definitely don't want to be with the incumbent. I don't want to be in the legacy player because there's nothing that they can do to capitalize on the new wave of technology. And so there's been this question about AI, clearly an incredible technology, clearly, you know, like the greatest inventions.

It's up there with electricity and fire. Like, it's really, really cool. The computers talk now. It's incredible. At the same time, what is the market dynamic that drives how this technology will, you know, accrue value in various places. Who will the winners be? A whole bunch of startups. Will there be a monopoly player around, it comes from a startup? Will there be a monopoly player and then sustaining innovation in every other mag seven? And this is the question that everyone has been

talking about for years, for a couple of years now. This is what Ben Thompson writes about with what we talk about all the time. And I think people are gradually waking up to the idea that like it's possible that a lot of the value creation at the foundation model layer will happen at open AI because of their consumer products. Yes. Right. And this aligns with Sam's piece from last week, the gentle singularity. It's basically saying, like, we created intelligence.

And it's less weird than we thought. Yeah. Which is a step back from how things were talked about and is a stark difference than maybe AI 2027, which is, you know, super extremely AGI-I-pilled and just saying, you know, we're going to continue accelerating. And I don't know. I think it's, I think it's right to kind of read into this. And two things can be possible.

It's possible that Ilya will create, you know, very important lab with SSI, but it's also possible that they might never grow into a $30 billion valuation. Is that where they are now? They're currently priced at $30 billion. So for, for DG to leave as a COVID, founder of that company. Sure, I'm sure he's getting, you know, he'll get a 10-figure package if he goes to Meta. Yeah. If this goes through. But he's also leaving, I would imagine, billions of dollars of,

you know, shares. Yeah. This seems like very rumor mill at this point. Like this could go a bunch of different ways. It could just be talks and maybe they just come on as like advisors or something or they join the board. Like meta has a board that includes people that don't work at that company. And, And they add a lot of value there. So that could happen. It could also be that meta winds up acquiring SSI. That would be a wildly different take on this. Or it could be that they leave.

The information seems like somewhat confident about this. But it's only a couple sources. SSI, multiple co-founders. It's very possible that as even since the company was started, certain members of the co-founding team like Ilya, you know, want to build what they see as super intelligence. And it's possible other members of the team are like, yep, this is like,

more software. We're going to vend this out in a bunch of different places. And ultimately, you know, it just, I don't know. You don't typically see people

get off rocket ships. I agree. Especially co-founders unless there was an extreme, you know, Rift. There's another side to this, which is there's a question about

what structure, even if the goal still is super intelligence, What is the corporate structure and the capital formation structure that delivers super intelligence? Because we saw this with Open AI when it was a nonprofit. There was simply no way to marshal a $10 billion donation to a nonprofit for a large GPT 4.5, GPT5 level training run. There was no way to marshal that type of capital. Like the biggest, the richest people in the world had already donated $100 million.

And there was not really a lot of appetite for, yeah, next year I'm 10xing my donation. Yeah. And so they had to become a for-profit. Yeah. Then you look at the flywheel of what it takes to continue to develop and continue to do these training rounds and continue to invest in reinforcement learning. And it feels like you need a data feedback loop. and you also need a financial feedback loop

to be able to justify more and more investments. And so if we're, and we're going to talk to Mike at ARC AGI, and there's this interesting thing that we heard, which was that the reason that the foundation models are not able to one-shot ARC AGI right now is because they're all just like doing a nice thing and not reinforcement learning on it.

But if they actually were, we did like some fine tuning around it and they were like, hey, we want to knock this model off. They could. And what that tells me is that for any really well-defined problem, like chess or Dota 2 or Go or League of Legends, you can go and say,

hey, we're doing a specific training run for this one problem, and it's going to get really good at it. The weird thing is that the economy and the global value creation chain from humanity is potentially extremely long tail. There's potentially not just like five skills. Like, oh, yes, you know IMO level math and you're good and you generalize. You might need to go and dig into all these different pieces of value. Yeah.

And having a feedback loop or an economic model like what OpenAI has with their app that generates a ton of revenue or like what meta has where they can deploy these products in all sorts of different ways and get billions of people using them very quickly, that actually might be a more like, it might be the only. way forward. You might not just be able to go into monk mode, come up with the perfect algorithm, and then train it on some medium-sized cluster. You might actually need to just scale energy, scale data center capacity and scale users smoothly for decades to get there. So I don't know that this is updating my like probability of super intelligence ever happening. The question is how many different labs that are losing billions of dollars a year can the capital markets support and for how long. Exactly.

Right? You have XAI, thinking machines, safe super intelligence. Yep. You know, Anthropic is kind of an open AI or in their own categories and that they are generating a lot of revenue. It's also hard because it's not like biotech where if you come up with a machine learning algorithm or you come up with, you know, the transformer,

you patent it and then you just make money off of it forever. Like that's not the way these innovations. Until you forget to renew. Yeah, yeah. Yeah, but that's not, like, if that was the case, I would actually be maybe more bullish on SSI because I would say, well, Ilya is clearly like an incredible researcher. If he goes into, you know, his team and comes up with the next great training paradigm. Yeah. And then patents that and is able to license that to Google and Open AI.

That could be extremely valuable, but that's just not the way the structure of the market is. Yeah. It's not like drug development. Exactly. Exactly. Anyway, it's a fascinating story. There's been a ton of reaction to this. Nick says, this is somewhat related. Carpathie literally said Meta's Lama ecosystem is becoming the Linux of AI and you're blackpilling. And so this is kind of like a narrative violation. A lot of people would be anti. A little bit more into the article because it does does give some important color. So Friedman has been involved in meta AI's efforts for at least the past year in May 2024. He joined an advisory group to consult with Meta's leaders about. the company's AI technology and products. Earlier this year, Zuckerberg asked Friedman to lead meta's AI,

meta's AI efforts altogether. The person familiar with the discussion said, Friedman declined but helped brainstorm other candidates, including Wang. While Zuckerberg was skeptical Wang would leave scale, Friedman convinced him a deal was possible, said a second person with knowledge of the discussions. As the Wang hiring came together,

Zuckerberg approached Friedman again. This time, Friedman agreed to a deal of his own. He is currently expected to report to Wang, who is roughly 20 years his junior. Both men will be a part of a small group of meta leaders that Zuckerberg refers to as his management team or M team. For Gross, the talks with meta put him in an awkward position with SSI, a startup form with the goal of building a leading AI company insulated from short-term commercial pressures.

So again, SSI strategy from the beginning is saying, we're not going to release anything until we create super intelligence. I just think it might be the nature of the economy and the nature of artificial intelligence and the structure of the market that might mean it is impossible to insulate yourself from short-term commercial. Yeah, the question is you have billions of dollars on your balance sheet, and you hypothetically could just do AI research forever just off of the interest yield alone, except for the fact that if you want to compete from a scaling standpoint, you have to spend billions of dollars on GPUs and data centers and training runs and things like that.

So it'll see there's a very real tension there that will have to be resolved somehow. The startup SSI hasn't yet launched a product or described in detail what it planned to build. Gross's departure for META would damage an important investment for some top venture capital firms. In April, SSI raised $2 billion at a $32 billion valuation from investors such as Green Oaks and Drieson Horwitz and Lightspeed Venture Partners. It has also raised money from Sequoia Capital. Basically got everybody. Everybody got the whole crew together. Together, Friedman and Gross have invested in some of the busiest AI startups,

including search startup perplexity and robotics startup, the bot company. That's Kyle Votes company. The firm had more than $2 billion of assets under management as of the last year, though that figure is likely higher now with the increase in value of some of their startups. It's wild. Anyways, this feels crazy, but Nat Friedman independently going to work at meta is not that crazy. I feel like the craziest part is, you know, is someone like DG going from SSI to meta, but at the same time, you know, it's very possible that SSI and meta could work out

some type of relationship and maybe that's not getting reported yet. What's interesting is that both of these guys, Daniel Gross and Nat Friedman, were both at one time thought to be like future really, really significant leaders in Mag 7 companies. So Daniel Gross, he started an artificial intelligence company, I believe, went through YC and then, or maybe he went to YC after, but he sold it to Apple. And at Apple, everyone was kind of like, wow, now that he's in there leading AI at Apple, he's going to be kind of like this young, incredible talent. Maybe he'll be like the next Steve Jobs. Maybe he'll like take over the company one day. People were kind of like waiting for that.

But I didn't seem like Apple was really set up for this. Accepted into YC in 2010. He was the youngest founder ever accepted. Yeah, yeah, yeah. And then he went back as a partner shortly after because he left Apple. But there is a different like fork in the road where Daniel Gross is like next in line to run Apple after Tim Cook if they were set up to empower someone young, which I don't think any of these big companies really are necessarily, maybe except for meta. And then Nat Friedman has the same thing where he's he's CEO of GitHub. He goes into Microsoft.

You know, it was always a possibility that, you know, GitHub's really important. This was $500 million business. It's growing. It's code gen. Like he's set up in the 10. industry like he could have potentially taken over for sasha at some point yeah yeah yeah it's just co-pilot and so so there was a world where you could see them at the ranks but we don't we don't think about it this way because most of the succession plans in in manager mode

big tech companies are more managers we don't tend to acquire founders and let them take the helm but zuck it's not like he's stepping aside by any means but he's very much leaning into this idea of like there is something special about these founders, these people who have built companies, these people who are at the heart of the technology really, really in the midst of things, get them on my side at any cost. And I love it. I think it's amazing. Net and Daniel both want to make a dent in the world, especially in the context of AI, right? So they're not going to want to go to meta and just cruise and make ads 10% better, you know, that kind of thing. Make it easier to generate.

You do this deal and then you just go and rest and rest. I don't think that's going to happen. No, I can't, I can't see it. Anyway. It would be interesting too. I wonder, you know, would they continue to be able to invest, you know, independently or would they just, you know, or would there be kind of structure that says, like, you know,

you have to actually have to just go all in on this. If I was Zach, I would, I would hope and expect for that. But who knows? Whatever they're working on, I'm sure they'll be using linear over there. Linear is a purpose-built tool for planning and building products. meet the system for modern software development, streamline issues, projects, and product roadmaps. And they got linear for agents, folks.

Dylan Patel is doing a little meme on this. Zuck Founder Mode Master Plan. Don't pay the pie torch in LLM people enough. Lose 20% of the torch people to thinky, thinking machines. Hire Alex Wang, Nat Friedman, for $10 plus billion to help you recruit talent, inflection back the torch people at 10X their previous total comp. And so Dylan's obviously saying like you should just, bet on the same people earlier and kept them,

I unclear how much of it was, you know, really about pay. But clearly that is not a gating issue anymore. The floodgates have opened. The, the, this was something that, that was identified earlier. We covered a timeline post about this where it was like, like, what, how will Apple compete in a world where like they can't, they can't justify paying anyone $10 million a year? Like, if that's the new normal or that that's like the value of some of these people that are going to, do some of this research, you're going to be kind of hamstrung.

And it's not because you're not spending $10 million on an organization. It's because you're not spending $10 million on a person. Yeah, it's a crazy new thing. Yeah, Sam Altman was taking shots at Meta earlier this week. Cover of the Financial Times today. You got it right here. And basically, so, yeah, Sam came out and said, Meta started making these giant offers to a lot of people on our team,

like $100 million signing bonuses and more than that, POMP per year. I'm really happy that, at least so far, none of our best people have decided to take them up on that. Yeah, the meta game in here is like wild. It's so good. 3D chess. Yeah. None of our best people.

He's just getting into getting into his ex-head. Yeah, yeah, yeah. Somebody had a good breakdown of that. He says the strategy of a ton of upfront guaranteed comp and that being the reason you tell someone to join, really the degree to which they're focusing on that and not the work and not the mission. I don't think that's going to set up a great culture. Altman added.

I mean, the only thing here is, like, tell that to the world of, like, Wall Street and, like, hedge funds where, like, if somebody's just really good at making money, you'll just offer them, like, a maxed out contract to come over to your team. And it's entirely, you know, like, motivated by the value that they're creating. But it's a lot harder in tech. But still, you know, it's clearly up there. if you're moving the market cap, like you can kind of tell. Spor says, is Ilya SSI already DOA? If its co-founder is potentially about to be poached, good question. Swick says these guys are already centi-millionaires,

so we're not talking about $100 million signing bonus anymore. It's the first $1 billion signing bonus in history. This is going to cost. Zuck clearly is in spend mode if you think about $100 million bonuses are high. This is a guy who lost $14 billion in 2022, $16 billion in $2023, 18 billion in 2024 and 20 billion in 2025 to invest in VR. All he has to do is cut VR spend for 2025. And he has more money than Anthropic has raised in its entire lifetime.

Wow, I didn't put it in that terms. Never bet against Zuck long term. But I think we're in for another costly period of investment. And we know what happened last time he went so hard on a thing. We do not have the balls or imaginations to do what he is about to do. Yeah. Can you imagine being Tim Cook running Apple, three trillion dollar company, making a paltry 704.6 million in 2024.

Just looking after just going through the most brutal year of his time at Apple, you know, like, it's brutal. Pulling the company back from, you know, back from the brink of this like trade war. Yeah, yeah, yeah. He's sitting there.

He's checking his pay stuff. He's like, like, he's like, co-founder of scale who doesn't even work there. He just got paid out bigger than me. When you put it into context that, that some 24-year-old AI researcher who's cracked and like deserves a great role at a great

company with great pay making more than the CEO of Apple it's rough it's just it's just absolutely brutal so anyway we still need to organize this protest hit the streets for Tim Cook we do we do head over to Cooper Tino yeah we do we should design some posters and Figma for it. Go to figma.com. Think bigger, build faster. Figma helps design and development teams build great products together.

Nathan. While all these companies are duking it out, Figma is powering the design teams of all of them. Yes. So it's kind of like... One hand washes the other scenario. We'd like to see it. Nathan says Zuckpan out Wong Gross

and Friedman to lead AI for tens of billions of dollars was not on my bingo card. Yeah, I don't think many people predicted anything like this happening. I think everyone was kind of saying, like, there's probably going to be like some sort of V2 of the Lama strategy, but being so talent focused, I think was not on the table. It was more like, okay, maybe they'd do an acquisition of a foundation model lab, or maybe they would just build an even bigger data center since they have abilities there. I think. And it's been a very different strategy.

We don't know much about sports, but there's probably, I was trying to think, is like, Luca, Donnson. or whatever going from Dallas to the Lakers. That was a big surprise. SSI co-founder going to Meta. Yeah, this is the link of tech. Yeah. For people who know what reinforcement learning is. Exactly.

Luke Metro chimes in, Dog, how much is suck paying? He's over at Anderol right now. The meme has been, do you want to just sell ads or do you want to build something important at Anderol? Well, with these pay packages, I think you're going to get some Anderol engineers being like, I'm willing to sell ads. I'm willing to optimize ads. Actually, I see a lot of ads throughout my day.

I've always been kind of fascinated by that. You know, protecting the world and ensuring like, you know, Western led peace. Creating world peace is like noble, but like at a certain dollar value ads are cool too. Yeah. Absolutely insane. I'm excited to see this unfold. I mean, some of, it's interesting the way that this reporting is written, it feels at times like it's already happened, but it's clearly not confirmed. Yeah, it could kind of go either way.

But, I mean, we heard the leaks about scale AI like a few days. And there was some speculation about what was going on there. And it became very real. And so, you know, who knows? Maybe it does become real. But we'll be tracking it here. Near Cyan says, ladies and gentlemen, Mid Journey has done it. It's a new AI image to video model.

Justine Moore from Andrews and Horowitz mentioned this yesterday, but the posts have been going out on the timeline. We have our intern Tyler Cosgrove in the studio today, playing with Mid Journey video. How's it going so far? Can you give us a little review? Good. It's been a lot of fun so far.

So you're in the Mid Journey Discord right now. Yes. Fantastic. So I've actually made, I've made four videos so far. Okay. We can pull those up. Yeah, let's see. I'm excited to see these.

He's in the Discord. Live from the Discord. He's in the trenches. How is the, how is the interface been? You just upload an image. Does it do the same thing that you get with a Mid Journey image where you type of prompt and then you get four images and you get to pick one? Yes.

Okay. So you get four video results. And then you upres the one that you like basically? Yeah. Okay. I think when you export it basically does that. Got it. Oh, okay.

Here you kicked it off with a. with an image of us reading the paper. All right, let's see. So this was, you know, kind of bare domestication, right? Okay. So did you include a prompt alongside the image? Yeah, yeah. Okay.

You add an image and then you prompt. Oh, and then you add a prompt. Okay, cool, cool. It knows us do well. Okay, that was the first one. Okay, let's see the second one. Love it. This is great.

Bear domestication is in our future. Okay, this is us on our phones. And what is this? We have kind of an angel fly. Oh, very bizarre. I was thinking Pegasus. Yeah, it's kind of a bit of a demon vibe. Yeah.

Who's the angel? In the back that, uh... What was the prompt for this? Let's see, that one. Angel wings or something. Angel flies up behind two men as they look back and smile. Okay, yeah. The actual video on us.

Okay, what's this one? What's this one here? Uh-oh. This is us in the studio. A little meta. Oh, that's extremely denomonic. Aliens come and steal. This is super. Creeper creepy I don't like this boom bring the air horn back oh that's weird they steal the gong wow

You know, 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 V-03 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. Oh, okay, this one's cool. Okay, we're back. A lighting on that's incredible.

Okay, finish strong, 10-10 for Mid-Jurney. 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.

Yeah. I did not like that. Angel. That was weird. That was very weird. A 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? How much is it cost? Give us like the breakdown of like, you know, the 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, 50 seconds. It's really fast. V-O-3 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 known in 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. But yeah, really great. Awesome. Well, very fun. We'll be tracking it more asking people how it's benchmarking, how it's working. We'll have to get, have some fun. with those. I had a lot of fun with the VO3 ones. We were doing the, the crashing through the

Hollywood sign, a couple, few too many bottles of Dom pairing on the back of Ferrari. Yeah, I didn't like how there weren't guardware, there seemingly weren't guardrails. Yeah, that's an AI safety issue. Yeah, that's an AI safety. You shouldn't be able to visualize yourself drunk driving. Yeah, bottles of champagne flying out of a car. But the quality was remarkable. Anyway, Mid Journey is having fun on X. They say introducing our V1 video model. It'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 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. Okay. It just takes longer. Takes longer.

Yeah. But I'll verify that. That's insane when you put it into the context of those outputs are in many ways better. Yeah. Yeah. At least like 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. Yeah.

Speed of iteration is really, really key. I mean, that's the whole Discord model is like, 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 F-35 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.

But Mid-Journey was having some fun. Devin Fan from X-A-I says, I know what I'll be doing this weekend, and Mid-Journey says, what weekend? And Will DePue says LMAO. Blake Robbins says, Mid-Journey video is breaking my brain, and everyone's having a good reaction to this.

So it's always fun to have a new AI tool. 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 Pippo, Pepe or something. It's the green frog. He's smoking a cigarette. He's not happy.

Probably because RIP to ship 36. Brutal. We actually Vance we should I I think a picture of Elon you know smoking a heater after one of his rockets blow up Blue 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 Ashley 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. It was a flesh wound. It was intense watching it. I mean, the ball of fire here is immense. So the starship exploded during a test in Texas, a setback for

Mars'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 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 what you just pilot steer it around like it's a DT3RS around the Nureberg ring no problem so 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 Fetch's 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. business lines, but this one's very, very important too, and we hope that they can get back on track. Spaceics 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's built a rocket that's roughly on par. We've gotten to the moon before.

The 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, frequency. Engineering systems. So good luck to Elon rebuilding in the entire SpaceX team. I'm sure it's a huge challenge right now. But if,

Let's do some ads to tell you about Adio. Customer relationship magic. Adio is the AI Native CRM that builds scales. It grows your company to the next level. Get started for free. Adio.com. I like that. What is that?

What does that sound at the end? Is that attached to that soundboard? Guys, I think you botched the action movie sound effect. Oh, no. It has the work on that. Okay. In other news, the Los Angeles Lakers has been sold for $10 billion in richest deal sports history.

Guggenheim Partners, CEO Mark Walter, who also owns MLB's The Dodgers, is acquiring the story to NBA team in a move that makes it the world's most valuable sports franchise. And it's so funny because the Walter Dails framing this is like, this is the biggest deal ever. No one's ever done that 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. Congratulations to the Lakers. Mark Walter and the whole team. It's fantastic.

Major premium to the Boston Celtics, who sold for $6.1 billion. And now the lake. Lakers is the most valuable sports franchise. 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. There's always entertainment. 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.

You've got to always have five players on the court. Just constantly. Running up. It's the only option. 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 story team to Mark Walter, the sports investor. And 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 this time again. Just diversification bros. DCA bros, undefeated again. Well, if you're trying to DCA, do it on public.com,

investing for those who take it seriously, multi-asset investing, industry leading yields to trust about 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. He'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 here for Cadillac. Let's go. Yes, congratulations. John front run the Cadillac F1 team. He's got a Cadillac for himself over there. It's great to have an American F1 team in the business now.

Yeah. Yeah, we've fallen off, but we're coming back. You're not going to be able to get one of these in the whole country. I don't think so. It'll be too popular. After the F1 team gets out on. 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 with 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.

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 sports properties. I had no idea that they were founded in Minnesota. That's where the lake name comes from. Interesting. 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, they just brought them to L.A. and kept the name. But that's what Lakers means. Wow. The bus family oversaw the creation of Showtime and presided over the NBA's last three-peat. A-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. Humeral H.Q.com. Sales tax on autopilot. Spend less than five minutes per month on sales tax compliance.

You know all the athletes also 11 championships since 1980 Their rosters have boasted many Of basketball's brightest stars Magic Johnson, Karim Abdul Jabbar Kobe Bryant, Shaquille O'Neal and LeBron James

And LeBron James's 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 like national news story It's such a cool thing

I think it's like not like If they were like 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. Just because you could have earned a better return by DCA 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.

Get the owners. Great run. Yeah. All the perks, you have to add those in. Do you get perks from DCA into the S&P? I like how Lakers are. legend magic johnson hit the timeline said just like i thought when the celtics sold for six b i knew the lakers were worth 10 let's go the confidence of magic johnson great investor too he's got a bunch

of a bunch of good stuff in the portfolio um anyway uh more news on the scale a i transaction so it's closed i believe that alex wong has a badge at meta and shows up to work at at in paloelto and in clocks in at MetaHQ now. Scale AI is still an ongoing concern, is still the 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. Unclear if they can separate out the businesses, if they can separate about fully over time and

sell the position to other investors create like a diversified I mean even they could even take the company public at which point I imagine that it would be a lot less a lot less of a conflict of interest or like a fear but there's been news that that open AI said hey we're not training we're not using scale AI for data anymore because it's too aligned with our competitor Lama maybe but everyone's trying to yeah a lot of this is very predictable yeah right I don't think meta and scales 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? Like, people aren't just going

immediately churn off. And no, they were smart enough to know what would happen. And there was an article, I think, yesterday about Open AI, 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, 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 others there's so many other services having like a single point of failure never makes sense for a business of that size but we'll see so the the information has an article here

about a little known startup that has surged hint hint past scale a I without any investors this is interesting after meta platform scale a idea deal data labeling is looking like Silicon Valley's hottest new interest that's 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.

But 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, Open AI 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 of your bootstrap. because you're not having, 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. And 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. Other 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 like the margins since this is somewhat of a marketplace business. This could be a situation where, you know, 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, like, you know, net revenue, that's still a huge business. It's hard to imagine Serge 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 foundations of model labs. So it seems like a fantastic business. But if surge could earn a valuation from investors similar to the one scale receipt for 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. And so, you know, if you're, 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 know, $20 or $50 or $100 million 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. Yeah.

I 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 tax 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.

Serge justifies the prices with its reputation for industry-leading work. Indeed, one former scale employee said Serge often performed better than scaling 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. So I'm excited to talk to Garrett Lord today about this exact topic. It 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. Wong had made a 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 Scale wasn't profitable I was not profitable which but wasn't burning a ton of money

like I think they had like they were efficient they raised 1.5 billion and they still had like almost a billion in cash yeah so they weren't they weren't in like trouble or anything but at the same time it was like like not not a wildly profitable not a wildly lean business but I don't know what what what what it's absolutely fascinating it's a it's a wide Wild industry. Yeah. 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 something that 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 struggled with data. starting surge Chen worked as 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. To do so, they needed a dataset containing 50,000 accurately labeled businesses, which he found out 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 vendor Facebook had used. In 2020, he left Twitter to found source. surge and picked up some of his first customers, executives from Airbnb's and Niva, 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.

To 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 we talked about. Just as those types of models began to grow in importance. Less than a year later, Open AI had hired Surge to fine-tune its models

by teaching them how to avoid producing harmful responses like a racially biased language, biased based on research paper the company published together by 2022 Anthropic had become a surge customer. They're putting out research papers with Open AI 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. It's interesting. Turing, too, initially was like, I'd be able to. marketplace to just hire developers and I think they pivoted into data labeling data labeling interesting 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. But wow, what what a beast of a business? I had no idea how big this thing is. Amazing. How did you sleep last night? I'm on a comeback. I got an 89. Go to 8Sleep.com.

No. I know what I got. Five year warranty, 30 night risk retrial, free returns, free shipping. What did you get? You got 100? 88 let's go soundboard I demand a soundboard ashton hall let's go john did it i did it beach of my life never i had a terrible night i also took a nap after i got home it's fantastic we have our next guest coming in the studio uh mike from arc a g i breaking down how all the different

models are doing last time yep he was supposed to come on Elon yeah and so annoying 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. a day and later. But if you want to take out an attack out on us, buy a billboard and ad quick. Out of home advertising, made easy and measurable. Say goodbye to the headaches of out of home advertising.

Only ad quick combines technology, out of home expertise, and data to enable efficient, seamless ad buying across the globe. We should do some timeline. I want to do this blank street story, but we can do that later. Let's dig through some what's in the timeline while we wait for Mike to join. Oh, we have Mike in the studio now. Welcome to the show. Good to see you. How you doing?

He's back. Thank you so much for... I'm glad there wasn't a major breaking drama story today. He was actually able to show up. Yes, yes. 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 was having so much fun. Like, no, we like actually have to come. That was a terrible day to launch anything new. And we like launched something. I saw several startups launch stuff. Like regrets to everyone you tried to get anything out that day. Actually, I remember them.

It was Rahul. Yeah, that's right. Julius had a launch. And what's the voice cloning company? 11 labs, I think they launched something. 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. Oh, yeah, this dust up with the government,

right, where the government was like, you have to give us your all chats ever, even if they're private. We don't want to do this. Yeah, I mean, that was like serious. Like, I mean, that got, we're still looking at actually, you know, and results of that. But that went really deep into the world, I feel like, much more than, 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? Yeah. I don't think what was that unique to chat GPT? 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 cellular connection and had all of his data stored in 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, 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 of the different companies. But two, two things to be true. One is that it can be a massive overreach by that court to say, you know, basically you need to eliminate privacy on your platform.

Yeah, yeah. and you can simultaneously have questions around maybe I should use this product in a different way. Totally. And it's the inflammatory nature of it is that people use chat chabitia 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 having those conversations.

Yeah, I think that's why it struck such a court because like that's true. I just saw some reporting from Cotoo this week that like Chachapin minutes per day are like 30 minutes. today now on usage. 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 beyond part 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

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 is 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 karydashians 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 something be born around the stuff today yeah yeah i mean i I find myself all the time, like, instead of scrolling YouTube looking for 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. What 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 you all can pull up. Yeah, yeah. 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?

But 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 compute, test time adaptation regime. People call these data reasoning models, right? We're getting these models to think out loud, additional data. Every major lab now pretty much at this point, I guess, except for meta, has one of these systems that we've been able to test and report results on. And I think there's some really, really interesting stuff we're starting to see. I think the most notable thing is that, like, there's not an absolute clear winner across the sort of, like, landscape right now. There's basically a sort of preto 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, anybody who gives you a benchmark score on an AI system that is a single number is just marketing.

you because the reality is now with these AI reasoning systems, you have to report score on like a two-dimensional action. You have to consider cost and efficiency alongside the accuracy. And all these different lab providers have come out with different A reasoning systems that sort of score differently. They're trading off cost per accuracy at a different point. So like if you want just like the absolute highest horse, you know, highest Ross horsepower sort of cost in time is no option.

O3 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 A. 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 cost you might look at something like Brock or Gemini 2.54 thinking there's not like a single like best best answer which which I think is pretty interesting and 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 foster as low as we can down the human efficiency realms yeah I've noticed

that more recently with kind of my default usage in chat chp t uh 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 and i'm making that kind of like economic calculus there even though yeah because i'm on the pro on the plan it's not an economic cost not a direct i'm hitting a five dollars for you right it's time yeah yeah exactly and so i'm kind of like doing a four-o thing over here and then switching back and forth. It's very odd paradigm that 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-Rey versus the SD. Of course we never did purely hypothetically. But if you're on a Torrance site,

there was a time tradeoff between watching a screener. I think this is actually one of the reasons these AR reasoning systems, I would assert, and I don't have inside baseball on the data. But like from the outside looking, I think there are some interesting suspicions that would suggest that these like AI reasoning systems, at least today in our current form, have like relatively weaker product market fit compared to the like non-a reasoning based systems, right? The pure language model based things. Interesting. That's a huge violation of 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. Everyone 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? Or I wanted to talk about 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 like hey all the foundation labs kind of have like a truce that we won't

reinforcement learn specifically against ARC I 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 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 for poetry or comedy writing and 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.

Like 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 benchmark scores to verge because of how all the labs are starting to do the next abolition of training which is they're using our all environments

to generate synthetic cote traces doing their sort of model trainings on that data and they're trying to go get it on a lot of just different domains um you know the o3 you're original O3 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, relatively they had scores on math encoding, but the gap, 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 encoding, like,

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 and that wasn't the case. I think that's I suspect that's what we'll see there you know that there's obviously the big scale news. The thing that I'm seeing now is there's probably like I don't know a handful that I know these new 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 and like selling them to to sort of the major labs, which are the major frontier folks building these next-gen systems. I think we're going to see more of that. I expect that's kind of what's going to

drive a lot of areas. What do you explain that more? What does the data labeling market look like? Today, we are 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 label labeling market looks like in five years? Do you think that scale was getting out of at the perfect time, you know, I'm curious. I think the timing was pretty good. I mean, look, the macro change here is from a regime where like we're scaling a pre-training wheel one.

As much tax, as much high quality label taxes, we can get our hands on to scale these foundational models into one where we're trying to trend process models or, you know, make the foundation models really good for process thinking in COT generation. That is a complete shift in how you want to generate that data. You want an oral environment that you can create lots and lots of COT traces. really very long traces of or 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 on how they how they work and how the performance increases is you can get more examples of the data there so you know my like i guess like macro about would be you know sort of the trend is heading down towards like the pre-training scale like this yeah so significantly higher on rl environments yeah so so when 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 that the reinforcement learning can happen like autonomously within the system, right?

or effectively like 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 no like example companies here like mechanized work as one that got started recently that's doing this stuff uh morph i think is doing this stuff happen without another world environment that's sort of doing similar stuff sure um there's just a lot and it's like very merchant and all these many of they got founded in the last like couple months. Yeah, yeah, yeah. And I think that's a function of the demand and the pull

from a lot of the frontier research groups that are wanting this data to sort of do their all-trainting stuff. So do you imagine companies like Surge and other players would try to pivot into this if they're expected. I would expect that founder-led companies like that would recognize this is growing part of this and have bets in it if they don't already. Yeah, I was always wondering like the whole story of scale was kind of a series of like various booms and training data.

Like the first one was data labeling for Autonis vehicles. And it seemed like that grew very, very quickly. And then the training paradigm around Waymo's 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. It'd be a very different like training data process.

hooked, but at the same time, like, 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 you could, one definition of intelligence is an 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 close to AI, the limiting factor then becomes like the ability to acquire new information, new data.

And 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 Hatz on the show a few days ago and he was talking about the sufficiency problem where like if you take 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.

And I'm able to generate some level of intelligence, you know, based on that. A golden retrievers level. Yeah, the level of intelligence of a golden retriever. Yet an LLM needs, you know, effectively like, you know, terabytes of data. Sure. The sample efficiency is very low. I mean, this is a true statement about just the paradigm deep learning compared to program synthesis is the bet that we're making at India.

And yet, this is a regime that's much more sample-efficient or generic models that can just, that can generalize out of distribution. But I think it's a completely statement. 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 last like 5,000 years. It's all on the internet. And like what have, what new ideas 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 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 could kind of know about and can define and spec out for these systems. Whereas 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.

You're like, oh, hey, this kind of looks like this. What? Here's a new thought. Right. Yeah, totally. That still feels like something that I think. I think, I mean, it's very exciting to go towards it. I think that's what we all want, right? That is, it is like EGI, it's capable of invention, invention discovery that that will actually increase

the rate of, like, scientific frontier innovation, but we don't have that yet. Switching gears a little bit, five years from now, do you think the average American will pay for an LLM subscription? I 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 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, I'm like, 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 chatchik all the time but she's on the free plan yeah that's like enough to get a lot of value for what for what she does i so i sort suspect that you know yes they're going to be they're going to be fire users and those are going to be the folks who really are doing like amazing powerful things and stuff i suspect the base rate's 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 we're paying a lot of like one off cash for yeah yeah now you might be like products or you know things where intelligence built into i think you're going to be product categories that emerge and like people go by 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 $10 a month for effectively unlimited prompts and you comp that to google's v03 which is $500 a month and you're still heavily gated yeah and it just seems obvious that that five years may not be the right timeline for that prediction by the way it might be longer than that.

Sure. Yeah. 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 an exponential growth path for the AI usage and AI ZAPs. I've looked at this and I've been wondering, is this a function of the technology getting better or the use case diffusion?

And I've looked at the usage and most of the majority of the usage is still in 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 a Gen. Tech Automation is actually not being driven as a result of like technology progress from AI, 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 and not good at. And it's very similar to the option 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. And so you like, we accrete use cases over time. I think we're so very, very early. Yeah. You know, but there's, I, I suspect that a lot of the usage increase, 30 minutes a day, even on chat, and Ptie is a function, just use case diffusion, less tech progress. Yeah.

Where do you think XAI will eventually need to generate a lot of revenue? Where do you think it'll come from? I mean, if they make progress towards AGI, it's probably going to be enabling any other services they have around like the Alliance ecosystem. Yeah. That would be my guess. Less selling it as a direct product itself. and going head to head, 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. You're not bound by just like, you know, the fastest consumer experience you could deliver. I suspect that might be actually what most of the, at least in the near term. Yeah. Who knows over long term?

I think the shape ball is proxam. I mean, yeah, that was in many ways my, my long-term thesis around the Lama project and the superintendents, 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. And 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 AI. This is what ARCS data shows. It's what V2's data shows. V2 is completely unsaturate. We're not even talking about efficiency, just nothing can do it. And V2 looks very similar to be one. Even on like hyper-specific solutions on Kaggle, the ArcR Christ 2020-25 contest,

progress has been slower this year than it was last year. We 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 have not we have not figured out found yet. Does it worry you that that could take years and years and years and what happens to one of the reasons I found of the prize last year. I 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 on alum stuff and make a quick buck before AGI gets here. Interesting. 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 you know kind of what like in the 2010 to 2020 era right very open approach is how we got the transformers to gpt1 and gpt2 and so on for today um you know i'm optimistic i think the last six months have have looked a lot better than the previous two three years i think the AI industry is maturing actually quite quite a bit on on this front of this topic as well. We're being more tolerant and kind of recognizing, okay, we haven't all figured out.

There's, there's more ideas we need. That's been encouraging. And I think it's seeping down with the low levels too. But yeah, my sort of broad view is any capable of human has new ideas to work on AG. I should like,

that's the most important thing you could be doing at this point in. That's amazing. Thank you so much for stopping back. This is always fantastic, fascinating. Yeah, great catch up, guys. Thanks for having again. We'll talk to you soon.

Cheers. Yeah. 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 Khan. 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. And 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 ten people at the top of these big tech companies. They control 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 human dynamics. It's sort of this game of three chess that's being played by these really fascinating individuals. And so as, you know, 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 CFO and CFO-A Anthropic in exchange for their CMO, a cracked PM,

and a couple of Waterloo class of 2026 new grads. Well, there's kind of 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. Is what is driving this? Is it true AGI pilling at the top of these organizations where they think that, you know, it's going to be win or 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. Like, what do you think is driving this? And I want to get into the different cultures of the different MAG 7 because some of them don't seem to be doing this yet. Meta 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, what is your take on like 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. And 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-I-pilled. Like 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's sort of must win. And I think for meta with these decisions, it's almost all in, we have to make, we have to win. Then I think there's a second dynamic, which is you can believe these things, but, you know, we're all humans.

I'm again, fascinated by these kind of human dynamics. And you can get caught up in an arms race, right? And as it is humans, we sort of, we look at evidence and we see evidence through a lens that we already have. And oftentimes we, 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 chat GBT, 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 question on the compute arms race dynamic. And 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 rates because in any arms race, when I up the ante, you have to respond. And I think it would be a fiction to assume that nobody's going to respond to this. Who can respond, at least on a, from a dollar standpoint? I 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, you should become an AI researcher and go to meta. You really should. It's funny that you say that because I, you know, I think people, these numbers are so big that they're kind of hard to grapple with. And so I was actually, after publishing the piece, I was like, hmm, I wonder how much like Fortune 100 CEOs make. Yeah. 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 economy. It's just 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 he's like the like the organization is like flipped from like a per middle standpoint, like you're still at the top. There's always a weird, there's always a weird dynamic with, you know, a founder, CEO who's taking a low salary and wants to hire a big shot and like, can you really have a 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? Maybe they really care about the city. Maybe 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. 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? What does it 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 impact. 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.

There's a lot of criticism of CEO pay historically, right? But CEO pay is functionally, what is the replacement cost of this individual? What 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-furgency trading or on Wall Street? Because 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, like 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 deep see can clone it. Like it 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 Arc Prize, and he was saying, we need new ideas. The issue is if you pay somebody a hundred million dollar signing bonus, they come into your organization and generate a new idea that gets us, you know, one step closer to super intelligence or whatever, you know, you want to define as like what people are aiming for. And then immediately it's like, it's actually not really IP and it just 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

i i i think 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 it's just a life-changing amount of money for my family of course i'm going to do it um And that 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 emotes there. it's unclear actually how that evolves and what you can keep in-house. I do think maybe one dynamic at play here is, 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. And 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's 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 $5 billion. Sure.

You spend $5 billion on talent. Yes. If you believe that you're going to get to AGI. So I also think that the artificial constraint matters. And there's some rationality to that artificial constraint. What we've seen as these research organizations get bigger and bigger is you're not producing more results as you get as you get more headcount. There's a sort of a Pareto, the top 20% of people produce 80% 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 Khan. People call it Kahn's law. Oh, yes. Okay. Yeah, a con size team, one con team. A con.

It's just a con. It's just a con. Yeah, yeah. That's fascinating. Do 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 Open AI, 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. 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 how many unprofitable you know multi-billion dollar AI labs can the capital markets support over the long run over a five-year period if if if we if we stall out for for a few years in terms of you know really meaningful progress which uh you know mike has said people aren't making at least against the arc prize there's not a lot of progress happening right now

opening eye is actually in a great position they have a subscription business business they have a consumer tech company that has a lot of revenue. Anthropic is in 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 training runs and data centers etc so I just have this question around kind of like the next three years 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. What does that actually mean? And I think that we, you know, there were all these essays being published last year, right? Like, AGI's 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 100 people who knows, I really am resistant to these type of arguments. I find that to be frustrating.

But, you know, I'm one of the 100 people who's in the social circle where all my friends building AGI and AGI is coming next year and you guys are all crazy if you don't see it and 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 question. It was initially a $200 billion 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. We 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, you know, total revenue in the AI ecosystem at the time opening I 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, right, in terms of actually generating the revenue of the ecosystem needs. And now, Now, 12 months later, Open AI is at 10 billion, the coding AI ecosystems at 3 billion.

But we're still dramatically under monetizing this technology. And 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 a momentum that's a momentum that's a 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. But 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 to now I was listening to the podcast that with a hundred million dollar signing bonuses and it's like, well, you know, 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. Well, there are big labs that directly benefit from the narrative that AGI is a year away. And then there are labs that will benefit greatly from a gentle singularity in that their competitors will struggle to raise additional capital in the long run, struggle to compete, struggle to retain talent. Yeah, I know exactly.

I'm 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. Yeah. 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, that's not changing. Like we're upping the entity. Right now it's like millions of dollars 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. 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 midstage markets because I'm sure you have a lot of visibility there. But we'll let you go and get back to the rest of your day. But thank you so much for stuff.

I'm glad we coined a new term. A con is a talented group of 50 people. 50 people. No more than 50. No more than 50. Yeah. One con. Get your con.

Get yourself a con. Get yourself a con. And make it happen. Thank you so much. This is 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. Thanks 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 Devin 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. When 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, you know, 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 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 like to, you know, spend some week, 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 know, going and talking to customers, working on the

UI, things like that. Cool. I want to dive right into that question about tradeoffs 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 chat GPT and I have to decide between 4-0 and 03 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 Open AI 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.

Talk to me about in the context of Devin, 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. Yep. There was really this time, I think it was probably around two years ago. I was taking 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 and like the international math Olympiad in just a year I 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 how to respond to high and then there are you know there are models that you know 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.

You 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. People want systems that are just going to work. And, you know, we can put in the months to, you know, in 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. It's going to be one of these things that I think the models are coming on so fast that it only becomes harder and harder to keep up with all of this. And so eventually, I think people are just going to get to the. point where they just want things to work. And that's kind of where we're starting off. Talk to me more about AI winning an IMO gold medal in 2025. Polymarket has it down at like an 11% chance. It was up at 70%. 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. But the polymarket's been down. Might be that what's actually with the dynamic. Go through the effort of trying to do it or too busy working on coding agents. I think, so yeah, when I basically said, I think I lost that bad. It's because we were only one point away from like a gold medal last year. Oh, okay. And that was already much farther than we expected. Yeah, when you look at the problem,

that's a very interesting way to put it. I think part of it is people have considered that already completed. And so perhaps researchers aren't putting it up much effort into it. Like they'll actually come out with a new release because 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. I think it would be the biggest news of the day. I think we've got to get Google Coms on this.

They've got to do this. I think it's an easy thousand-like banger on X. You are absolutely right. Yeah. It seems like top of mind for everyone, the labs, product developers, is really getting coding agents. And part of that is because there's this belief that if you get these coding agents to work really well,

then that will just solve the rest of the research problem for you. We have this joke internally that the only could 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? I'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 a 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 would be great to just not have any of that code, the legacy code sitting around. But is there enough training data around those older programming languages or less use programming languages? Or are you optimistic about new training runs? Maybe we don't get something that's like, oh, it feels way better, the vibes 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, you know, someone who has a 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 all-reled in. And 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, there's like Sonnet 3.7 model. One of the top complaints of people was, hey, like, it seems like this model is like super great now, like finding all the files that needs to change, coming up with the 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 Anthropic was training the model, they told it, hey, we're going to give you points on how many of like the correct things did you do and maybe they forgot to dock points for doing things that were kind of outside of that zone. They fixed these 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 you, when we talk about, hey, can, can these things not do distributed programming yet? Actually, one, 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 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. And so you still find the models are bad today. The good news is these aren't like fundamental limits. I think these are all engineering challenges, then 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 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, we'll give you a point for every time that you do X, Y, Z. You'll find that, hey, that model will just keep on doing X, Y, Z. Keep on doing X, Y, Z. I think the classic example of this is the paperclip generating machine. So, like, you know, if you give it points for generating paperclips, but don't, I count 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 get all the tests to pass, you might find that the system will just learn to delete the tests or make the test just like say, okay, I pass rather than actually fixing the code. So a lot of times you just have to be- Which no software, no real software engineer would ever do that, right? Yeah, 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 there are also like, it reminds me of these. systems that were trained on Slack responses. And when you would ask the system, hey, can you do this for me?

It would say, oh, like, I'll get back to you on Monday. Yeah, what you try to get the model better at really matters. You have to be very thoughtful about it. Yeah, yeah, I've noticed that with some of the whisper transcriptions. If you don't feed it enough text, it'll just say, please like and subscribe. And it's like, okay, I know exactly where you're training data came from. Like that's its default phrase because it's just like what it's what it's hearing. Joy, do you have?

What, what, how are you guys approaching talent acquisition as a firm? You know, 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, you know, nine, nine figure offers or even trying to compete there. But what's been the approach? Is it mean you're, you know, keeping team sizes smaller or, you know, 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 and 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 an internal tools team to maintain all the different services that engineers internally use. We found that internal tools are one of these things that AIs are just really good at. And we can just staff that team with Devens

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 then when they're done to be like, hey, what's my next task? And then 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 well good generalists. And as we build up this company, make sure that we're building it in a way that works in a world where AI can do so many these different roles for you. And I think there will be kind of like a moment for larger companies

as well when they realize, oh, shoot, all of 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. But 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 the 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. Interesting. 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 upfront 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, we in the product started sending him warnings that, hey, your session is going on for really long, or are 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. I do think that this kind of, you know, I do think that this kind 10 minute a.g.i, 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 sense. George, do you have another question? Last question from my side. I'm curious if we know, what kind of learnings you're having around agentic, interface design. It feels like this sort of the default when you think about agentic software is just 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 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 messaging going to be the like, you know, the dominant interface? Is there something else? Like, what are you, what are you kind of seeing or experimenting with on that side? You know, it's funny. 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 I think this fundamentally comes down to as these things become more like coworkers, you know, some people just have certain working style that they like. Some kind of co-workers here work well together and others don't.

And it's funny as you build a product, we find that some people just love the way Devin interacts and then other people are like, Devin's too needy in these ways. Other people are saying like Devin doesn't ask me enough questions. And so there are toggles and controls that you need to have here. Kopathi 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. And 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 better at working with people. Yeah. Yeah, that makes sense. That's an interesting. I mean, the good thing is you can have some type of quick, uh,

conversation, you know, 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, and, and understand a lot more about, that you got that right. Roughly how big is the team now.

Oh, on, on the engineering side where we're probably just a, over 20 or so engineers and 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 how the 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 seems like it's a fantastic time for the business where you have a special product but

Special size. Yeah. So you have those like two pizza teams here, but everyone kind of knows each other's name, basically. You're still a tight-knit group. Anyway, anything else, Jordan? Awesome stuff. Thank you so much for stopping by,

this was fantastic. We'll talk to you soon. Have a great day. See ya. Bye. Really quickly, let me tell you about Bezell. Your Bezell concierge is available now to source you any watch on the planet.

Seriously, any watch. Go to getbezzled.com. And we have our next guest, own McCabe coming into the studio to tell us the story of Intercom. How are you doing? There he is. Doing good. I did just sprint three and a half blocks, summer blocks.

Oh, I'm sorry. You can always just text us. I mean, if you're running late, it's all good. We'll just do more ads. The fans love it. If you do more ads, does that mean ads for Intercom? Are we officially? Pretty soon, pretty soon.

I think you're breaking 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.

What favors do I get? Can you do a hit piece on Brett Taylor? Yeah, shots fired. I'm just, no, he's a great guy. We just like some hit pieces on our competitors, please. Yeah, we're lucky to not be in the hit piece business. We'll review a hit piece. We're sponsoring the wrong show.

Yeah, yeah, that's rough. Yeah, I think just buy like 100,000 subscriptions to the information and then start putting pressure on them. Say, hey, you might want to look into this company. I would be down to do a hit priest about technological stagnation. Yeah. I hate stagnation. And so I would,

I would want to take down that as a concept. Really slur that whole concept. Or closed IPO windows. Be prepared for a terrible hit piece on closed. Or hit pieces 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. I think that's possible. It's a better response. book. Yeah, walk us through the story 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 of 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 gloomy. And the two things we changed were we went to Back to good old fashioned SaaS fundamentals pricing that people liked, selling the product

in the way that people liked, 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. In 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, you know, 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. And that just propel the business even further.

Finn, our customer agent is now the best performing in that category in our benchmarks. We win every bakeoff against our chief, our primary competitors, we have the most customers, most AOR. 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. 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. Talk to me about like the different breakpoints for growing a company. I feel like mentally I think about it as like just the founders, maybe the first 10. Then we were talking to previous guests about this breakpoint at like 50 people.

Like there's something about, there's a magic of a 50-person team. Everyone knows their, and everyone knows each other's name. Then maybe there's other break points. The Intercom AI group has 47 senior engineers and researchers, so right in that 50-person sweet spot. But,

but I feel like, I feel like there are, like, in the story of startups, we often map them to funding rounds, seed round, series A, series B. And sometimes the head count grows in line with those.

But some, but I feel like head count growth might be, more of a factor in like cultural drift. And I want to go through some of the key moments where you feel like, like, you know, it was only one foot on the gas or the, 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 cult 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's some very, they're 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. And what, tell 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. Look, there's a kind of an 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.

And the early days, the founder typically certainly founders that, you know, have a 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 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 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 SaaS, 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 churn 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 has one of those buzzcuts in the curly hair on top.

It's like life just gets more complicated. And 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. in SaaS. SASS 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 like tips and tricks and advice

here, but really just comes down to energy. And so 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. They kind of haven't heard from them for years. Their stock prices 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. 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 that comes from, I'm CEO of whatever. So it's like this deep, be human, squishy, spiritual challenge rather than an NBA 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 to bring to that organization is potentially worth a lot, you know? Yeah, but at the same time, Zuck is super high energy. Yeah. Right. But, but there's another world where you surround yourself with. Higher low energy people. Yeah, but 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, bring in the gray hairs, the people who will keep the, you know, steady hand on the tiller. And that can lead to a less dynamic, less 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-man. SEP, super sloppy. They get in fights. They get upset. They hung over late. They don't know how to do

larger company professional things. Sure. 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, that they like introduce a lot of like regularity and they like 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. Like we've definitely got roles in Intercom where you can have to collaborate across two time zones, I'm 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. them and you'll crush it. So part of it is knowing how to like work with these people. But also like it is this 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. People 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 $100 million plus of ARR, and then the sort of growth starts slowing. Maybe the CEO gets bored or whatever. They just write it off.

They start partying or they start going to Europe. And the VC's kind of write it off. and they're like, I made my return, or at least I'll get my money back. But 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 you know, you can make quite a lot of money taking a business from 100 million to hundreds of millions of revenue. And that can sometimes be easier than taking it from zero to 10. Totally. So what do you think? I think theoretically, I think, you know, VCs are best, are pattern matches. 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 start adventure back companies. It's straight up difficult. And to my point previously, the idea that, you know, talent isn't that fungible. You know, it's like any given company. If you're replaced 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, you know, so much more accomplished than I am, but I'm pretty accomplished. I know how to run and build and re-accelerate businesses, but I'd be probably a shitty CEO for 99% of other companies just because 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. So I think people should be derrish on turnarounds. You know, turnarounds don't really work. Yeah. They're generally like a failed thing. Yeah. Somebody will figure out. Maybe it's Jeremy Giffon.

Maybe he'll do it. Yeah, well, that's even a different strategy. But yeah, I think this idea of like you need to, I like the idea of bringing in a, like a cracked founder into a company. That's a, it's a similar. Yeah, the crack founder wants to do their own thing. They want to start from scratch. They want all the equity themselves.

Yeah. Like the recap alone that it would take just be palatable to existing investors. Fail 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. Yeah. And in your case, it's like a little bit of luck, the timing of like you going back in,

GPT35, you know, seen the opportunity for a new product, all this stuff. 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. 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 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 I think part of the secret is if people can separate themselves from their egos a little or work on their egos or learn to love their egos and not be ruined 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 on afraid 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 gets, the more freedom you have. It's fantastic. Well, thank you so much for stopping by. Always a pleasure. We could yap like this. Yeah, I feel like, I feel like people are going to listen to this as like a little founder

therapy. A hundred percent. I was like, we can do a little therapy corner. It's amazing. Yeah. Once a month, you come on.

Pump up speech. It's great. This is great. Have a great. Have a great. Diet of meditation if you're interested. That'll be the next one. Thank you,

Jets, hey, this is cheesy. I want to give shout out to my friend Stuart. That's it. I promise I do it. Amazing. Shout out to Stuart. Airhorn for Stewart. Do we need to ring the gong for Stewart?

Yeah. What's doing? We got to ring the gong for Stewart? Okay, ring the gong. He's had a big year. He's had a big year. He's had a big year. Congratulations to Stewart.

Stewart. 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 in the Irish We're going over to Stripe. Stripe. Luck of the Irish hit 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 you doing?

The 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. That was domestic politics. That was South Africa, South African, 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. This great audience is really great to see what you're up to. It's a bummer. We've never met in person. I've had so many zooms with you in this exact room. I have a theory that like you never leave this room. But we're busy. Yeah, you're busy. What is the major update?

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 conversation. Jeff, Jeff's like evolves his role. Yeah, yeah. The last year was running point on Atlas. Oh, yeah. Made it made it a platform that a meaningful percentage of C-Corps

think are started on Atlas today. Yeah, about one and six now. Wow. Our C-CORs are on Atlas. 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. I mean, I'm really quite impressed to see the rate at which discovery has changed, and it feels like around the corner, commerce and AI is going to be very closely mediated. Talk about maybe some early product experiment, what you guys are experimenting on, what you guys have already rolled out, all that stuff. Yeah, we've been trying to work with the fastest-growing

companies as they push the frontier of agenda 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. And then when you go to buy, you're not going to the merchants tab and dealing with the merchant's 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, you know, the 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 because 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 like wake up at 5 a.m. and just be refreshing this like terrible site when like the campsite.

So ready for the age of agentic commerce. And then it was like very unreliable like payments. So it was like it was the equivalent of like a street wear drop. But like the, you know, like some state park with like managing it. I think we're to see this more and more where the inventory of the world is getting closer and closer to intent and agents are a way to bring them together. And 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. to expose their inventory to agents in a safe and permission 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. You 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, okay great, it, you know, 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. Like there's like as soon as I get the current thing, I want the next thing. So 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, we, you know, the borders of the internet are starting to blur. And so you will soon be able to experience, if you check, if you, if you search for something on chat, T, 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 Superbase 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 a 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. It's like, I 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 trussed place. And Striped Link 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 hand it off to a good robot to buying on your behalf.

And 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, you know, whether they're stables or other tokens. They usually have crypto, you know, funds or crypto companies, right? So I, I, I, I've had maybe a more middle of the road view where I can imagine agentic commerce experiences leveraging stable coins. I can also imagine them leveraging cards and ACH and a bunch of other sort of forms of payments. So I'm assuming you spent a lot of time thinking about this and you guys have obviously been acquisitive recently with bridge and privy as well. Yeah.

This is one of the areas in which Stripe is very problem 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. Stablecoins 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 points 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. And then in some purchases, stablecoins might make sense between two parties that natively know how to interact in stablecoin. But often it might be the case that Jority has an amex part 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 have to have. 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. And we're going to need some type of so babblefish 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, you know, change things. The internet today is heavily reliant on, you know, advertising. And if you have a bot, you know, just crawling a website, you know, or even when you look at other services. And so we've talked, Ben Thompson had some good writing around 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, it's hard to see like the obvious path forward here. Yeah, 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 SaaS software provider and you said, hi, you know, I sort of two choices for you. You can have this 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? Or would you like them in situ as they're working to have agents mediate the purchase, recommend it, and be able to like integrate an accomplice 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'll be able to directly attribute where it came from and be able to have a great CAQ for that. And the LTV should even hire because the robot even integrated it directly. 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.

And then I think it'll be a new way for businesses to make sure that their agents can read their docs. can read their product skews, can have access to that information in a new permissioned way. I really like the Carpathie topic I 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. and it can do it on your behalf rather than just reading or reading something from a three-year-old corpus.

Interesting. 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, you know, you know, 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. Talk to me about Stripes culture. Stripes, you know, 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 sort of the email culture you've mentioned where we just, it's very common place at Stripe where if you write, you spoke to a customer or even after going on TBPN, hi, went on TVN, do you just see see a notes list? And now it's a for anyone who wants to subscribe to notes list. But one of the major subscribers to notes list now is agents.

Interesting. 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 with permission to it. And 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. And it is, it takes the first line.

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 Commerce soon quickly too, they're not just read only. They're going to start taking right actions and purchase actions. And for Stripe, internally right actions might be to roll back that deploy or to, you know, auto communicate to that customer. because of an MPF 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 you 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, you know, 10 minutes of time. A new version of shadow band where you first get your question answered. Yeah. Yeah. Do you really want to ask this question because it was answered here, here, here, here's our recommended action. It's like pro auto complete. It's amazing. It'd be interesting. Yeah, Slack's 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 over years that it is somewhat of them waiting for this moment it was worth it yeah that's amazing 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 is fantastic always welcome yeah we'll have you back soon you can talk more great to see all it's great talk to you soon bye let's give it up for jeff next up we have garrit 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 to sauning. I'm a big, big sauna guy. No way, there we go. 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. The cool plunge can fit nearby, though.

I mean, there's still opportunities. Good, yeah. Maybe we've got to get in the cold pledge game. Anyway. It's coming 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 and find my first internship and first job. I 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. And 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? And for us, That was incredible. I mean, we have 500,000 PhDs in the network. We have 3 million master 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.

It'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 tool use or trajectory. So they started reaching out to us and saying, do you have access to these PhDs and master 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. They 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 on 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 their companies? recruiting practices. Recruiting 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 Peter. It's a lot of them. I didn't even know they were six.

I thought they're only five. The big six. You got 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 like what the how are the frontier labs thinking about human data annotation and answer generation it 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 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 go like from the latter part of the question of the first. Like where we say going up the next couple of years? It's definitely going to evolve into audio. It's definitely going to 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. And expert data is in basically every esoteric area of human knowledge. they want to, you know, the models have already kind of sucked up the entirety of books and YouTube and, you know, 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 PhD is 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. And so this kind of perpetually a 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 an hour being a teacher assistant. You know, you could drive door dash. We're paying these students like 60, 70, 80, 100 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? And so we believe that we can help you get more jobs with the 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 good 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, you know, say, hey, scales out of the game. You want a hundred 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, is like, you know, if you don't have an audience, there's no moat. What our competitors are doing is there at some of these companies, they'll have hundreds of people who are recruiters sitting on top of platform, sending messages on companies like Handshake or spending tens of millions at 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 never heard of before, claiming they could pay you $100 an hour. It'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. And that's what we've been seeing. I mean, in the last, you know, month we've grown by over 3x 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. That's for 3X. That's incredible. Are there any, last question, we'll let you go. Are there any, like, weird areas that you think we'll see.

this type of human data generation pop up. I'm imagining like AI seems to be at like 150 IQ. It can write code and yet it can't like book me a flight. Do we need to take like flight like travel, 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 insurance on this flight so that we have a whole bunch of data specifically about that task. I'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 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 a hip-munk, 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. All 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. What you just described is like a trajectory, called a browser trajectory. Sure. And that's basically like you have a goal in mind. Yeah. And you, you know, 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. When 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 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? Yeah, totally. You don't even need to log in, right? So you're going to need audio data.

You're going to need trajectory data. You're going to interact with APIs. Humans' experts will be needed for the next several years to be able to make that data happen. Interesting. Nor to be able to power the frontier of where you want to 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 is fantastic. Be sure your time. We'll talk to you. Great to meet you. Cheers.

Coming in next, we have Tone it coming into the studio to the TVPN Ultradome. A massive round. Oh, oh, we're going to hit the gong again. Yeah, I'm going to let you hit it. The 10th time of the show. Always a good time. There he is. Welcome.

Welcome 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. Tone. 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. Right there. I'm going to send him an email. Okay. You are live.

You are live on TVPN. Okay. We'll pull them 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 Great amenities, dreamy beds, top tier cleaning, and 24-7 concierge service. It's a vacation home but better, folks. We told you about it. We don't realize you live. 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. Vantas 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. Give it up for Vanta. And if we got an extra minute, is here? Are we back? Welcome to the stream.

We made it. How you doing? Sorry for the 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. You're making my day. What's going on? Quick intro. You've had a big day. What's, what's happening? Yeah, thanks for having you. We, uh, we announced a $200 million around.

Whoa. With general. That's fantastic. Bairied the lead journey. You guys got to start selling those. I feel like when you've won in our office. But, uh, yeah, no, we're super excited. Congratulations. obviously works in health care, you know, we power AI workflows, everything from ambient to revenue cycle payments in large hospitals.

Oh, interesting. Give us a quick history of the company because 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. And so I'd love to hear your quick story, kind of how you got here. History of the company. I want to hear about the first customer, too.

For sure. So, I mean, Josh is great. I've known him since. We were at Stanford together, same year. And the story behind Camar is interesting because Camer started as an incubation inside general catalyst. The best analogy I have is it is Hamant's Palantir, very focused on health care. I started a company while I was at Stanford called Ethelis, 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 SMBOS 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 a 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 private practices here in California. That was our first, first customer. It was someone that my co-founder, Deepco literally walked up to and, you know, cold-knocked

on their door and then got them to use one of our first devices and remote monitoring solutions. Amazing. And, yeah, that's the quick story. I got a bunch of questions. Hit us, yeah. 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 SUMA health, a hospital system in Akron with over 20 facilities. And 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 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 Camer 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 SUMA IT teams. We'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. Many people have been calling you the LeBron James of Healthcare AI. Yeah, we got to put that out there. Yeah, I mean, I maybe have been the first. person to say it. You might have coined it here, but many people. I'll say it right now.

You're the Leperon James of Healthcare. Yeah. Now many people are saying. Not just one. Two is many in our book. We're just going to make that a thing now. Fantastic. It's remarkable because running a health system is super hard.

It is a 1 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 P.E. you know, cut and in juice play. It's it really is an investment. That's awesome. Talk about Comer's overall product strategy. You guys have a number of different products. It seems like a very different, you know, we've talked with founders and covered companies that come on and just want to own one, you know, one key area. But

healthcare feels like somewhere where if you can get embedded with the set of customers, you can, you know, more, you know, rapidly kind of add products to, uh, to the platform. So I'd love to understand that the product strategy. We really look up to businesses like Rippling and RAMs. Um, where, you know, there's this concept of you, 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 then the back office, which is all, I mean, 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, you know, little part of the, part of the solution, you might get some initial usage. But 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 system's 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. And 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 home. Let's hear for Porsches. We'd love to hear that. Let's get back to Porsches. We need to return. We need to return. And, you know, all in all, patients got great experiences, too, because of that personal relationship. And 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 really, EMR is proliferated. And Jonathan Bush and Judy and, you know, all these people are legends in the industry because they built Athena, a $20 billion company, Epic, probably $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 health care. We're like where the majority of administrators sitting behind a computer, clicking on forms, it's in health care. And we believe that the EMR will be transformed. We also believe that the labor stack of health care will be transformed. And it'll create more operating cash flow for hospital owners.

Is that narrative of the like the administrative ratio, the administrative load increasing? Is that similar to what happened in academia? Because I remember seeing these charts of like 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? Is that the same thing that's going on in healthcare? And kind of what was the underlying driver of that?

Was it just regulation or lack of tools? Where to come from? I think it's very similar. What I will say is I think in healthcare, 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.

And 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 United Health's market cap, I mean, what is it, like a 12x since Obama. care of a past, 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 a lot of admin staff. Interesting. There was no other option. Yeah. Yeah. Talk about your personal ambition and the team's ambition. You're a $6 billion company now seems.

like, you know, it'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 it now. It's not finished. Look, I think when you walk into a healthcare practice, the inefficiency is shocking. And it's, 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.

This is a $4 trillion industry. You can build for a very long time. But what Don 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, you know, waiting 30, 45 days.

But it's going to require a system overhaul and like new payment rails to go do that. And that's really what's at the heart of what commier is building. Awesome. Well, this is super exciting. I'm glad you're doing what you're doing. And you're our new healthcare expert in correspondent. So expect to call. And the LeBron James.

I'm a LeBron James specifically. The LeBron James of EMRs. Yes. All right. Quoting Kobe, of course. Congratulations on the milestone. I hope to have you on again soon. We'll talk to you soon.

Cheers. Have a good one. Should we do some timeline? Fun show. Fun show. Yeah, we definitely should. We got to talk about Sam Lessons, Oracle versus Salesforce. He's getting in hot water.

You got it in hot water. The timelines and turmoil. We love Sam Lesson of this show. He posted a screenshot. He says, Oracle. Big Tech. I will defend Sam. Sam lesson.

Oracle is 2X sales. but Ellison is worth 25x Benioff what 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's snapshot, basically the market cap of Oracle is 2x Salesforce, 500 billion versus 250 billion. Meanwhile, according to previously directional at best data, Benioff's net worth is 125th, that of Larry Ellison's 10 billion versus 250 billion.

What do you learn from that? do you draw? I like this, the revealed preference. For founders and companies, the old licensing model is better than SaaS. That's interesting. Imagine having 10 billion and just getting Lil Brod by Larry. He has a sort of little browing effect on most people. There's an amazing story about a famous little browing where Phil Knight of Nike was worth something like 10, on the order of like $10 billion.

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 at 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 yeah uh he in his book a shoe dog it's healthy to get low brood shoe dog is a great book is a great book uh and he talks about like all the weird effects of like having immense wealth how like his wife would like hoard immense amounts of like paper towels just because they were like money is no object like what should we do with this and I get

got a lot of paper towels and they'd like figure out okay this is like some weird psychological thing that's going on my brain like I don't actually need all these paper towels the fact that money's no issue doesn't really matter people like to talk about you know you're the you're the you're the whatever the average of your five friends yeah it's like yeah well if you want there there should be some similar law of like your growth rate is like should be is like tied to how often you're little brod yeah yeah never get little brod no no you want to be oh yeah yeah that's true yes you can be on the upward swing that's right if you're not getting little brod enough you're not an upward upward trajectory this is good yeah yeah this is good

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 going to have him back on the show we're gonna have him back on the show he's a regular we're gonna skip to miles he says wrong take ellison is much richer because he didn't sell shares and is steadily buying back two percent of the company every year for 30 years he's increased his ownership from 17 percent to 40 percent is 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 um if ellison you know keeps doing this he could very well

I'll own 150% of his company at some point. That's the future for Open AI. Open AI just becomes the agentic organization. It just buys back so many shares that it eventually owns itself. Yeah. That's the real goal. Anyways, Miles says, meanwhile, CRM, aka Salesforce, made a lot of dilutive acquisitions, and Benioff said, Elise sells his shares yearly.

He doesn't sell them yearly. Daily, two million, daily. Bucco Capital. 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 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 sale strategies. That is at best the nube answer. If 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 Blow quote tweets that and says, it's a timeline and 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 hub spot. 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. He doesn't care. It's not about the business model.

Well, you'll love to see some timeline and turmoil. Very, very fun. In other news, Shiel Monot has the story about Telegram's founder, Pavel Dorov, consistent feature on the TechBro drip account. Everybody says they're pro natalists until they until you ask how many how many children you know have you fathered their 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 versus via compared to the hundred via sperm donation. So every one of them is going to get $140 million. Just to kick off fundraising, just start investing that.

You got your family office on day one if you're one of Pavlov's kids. Pretty remarkable. Yeah, single LP. It's kind of a good dynamic. Yeah. I wonder how he's going to get liquidity for telegram at some point because you get a bunch of telegram shares. It's kind of like this difficult beast to wrangle. Yeah.

But I mean, I guess you take it public at some point and get liquidity out of that. I don't know. I mean, it also just prints money. So even if it's like $14 billion, like you could just. 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 by uh you know having having this type of dynamic you know not just with his many uh children that that he helped conceive directly yeah versus the hundred other so um had to one up

Elon had the little bro Elon he did a little bit and Elon's comments on this too he was like I got rookie numbers gangus ganges con over there is really taking over the world the ganges con of encrypted messaging yep it's very very odd only CFO says the finance department out drink sales feels like sales is inviting finance for the party so they can stick them with the bill and this is this is the data you can only get from ramp dot com slash data apparently 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.

20% 20, 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, $81 of food, $19 of drinks. That's the idea. Alcohol share. Finance, they're getting, you know, $84 of food, $16 of lose. Marketing is drinking sales under the table. Yes, yes. Narrative violations. IT, in the tail end there, 9.7%. Many Huberman devotees in the IT department, apparently. Yeah, not a teetotelers. Not a power lunch, you know, category. No, but the three martini lunch will make it back for the, 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 says just the beginning. There's going to be more stuff like this. This is 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 in 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 a vibe coding tool. Trusted by over 250,000 builders worldwide.

And nice quick flip. Amazing. He's basically getting a similar similar outcome to, you know, a founder that sells their company for a billion dollars, but, you know, goes through a bunch of different financing. Or kind of like a mid-tier AI researcher these days. Yeah, starting, you know, like,

starting out. Yeah, starting out. In other news, John Carmack is absolutely jacked. This is fantastic news. Yeah, Xene 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. Let'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 the level of serious athletes that most offices tend to have a few of and now he's looking built. Palmer Lucky chimed in. It's a great, great day on the timeline. Let's check in with Tyler close out the show. I was going to check in with this polymarket.

Okay. Yeah. Will Chimoth launch a SPAC. Oh yeah. I was supposed to like this. It is up to 70%. It'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 for 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 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

in every SEC filing possible. He'll make an excellent disclosure about the risk and is not short of irony. So what kind of company do you guys want? No crying in the casino. Let's go. Chumma. People are absolutely fuming at him.

The comments, I'm sure. But, honestly, get after it. Pretty fair. Everyone knows what's up. He's just going to play by the rules, you know? And I think at the end of the day, it's very like I look at the next

Jamath's back is like it totally like it probably will pop. It'll probably get a lot of attention. It might turn into a meme stock, right? I will be interested to see what kind of target he. I'm 100% excited to follow the story. It'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, 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. Chad GPD, Claude, Perplexity, meta, they all guess 27 for some reason. We got to get a World Coin Orb in here to be able to prove that Tyler's not, in fact. Yes, we do.

He might just be a deepak. Final review, what did you get done this show? Did you keep playing with Mid Journey? Were you doing something else? What's been going on in 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, he just took my spot? Oh, he took your spot. Wow. Wow.

Oh, he comes in with a paper, breaking news. That's the breaking news, guerrilla. The breaking news girl. We need to get you a breaking news gorilla outfit. And if he has breaking news, you can print it out, come sit down, take our seat. That would be amazing. That's good. That's good.

Are there any others or we're closing it out? Yeah, I think that's it. Okay. That's it. Well, good work. Good work today, Tyler. I feel like the production team laughing like they have some other ones back there. Another productive day.

They're too scary for our audience. I saw one get sent in the chat and it just was really scary, bad looking. Well, we will be back. tomorrow. We have a great show for you folks. Leave us five stars in Apple Podcasts and Spotify. And thank you for watching. Thank you for being here with us. Fantastic show. Have a great evening. Goodbye. Love you.