TBPN - Gemini 3 Launch, Big Tech Backs Anthropic, OpenAI Adds Fidji Simo | Jonathan Neman, Mike Knoop, Ashlee Vance, Jeremy Epling, Keone Hon, Stephen Balaban

Episode Date: November 18, 2025

(00:34) - Gemini 3 Launch (30:54) - Mike Knoop, co-founder and Head of AI at Zapier, discussed the significant advancements of Google's Gemini 3 model, highlighting its achievement of doubling the state-of-the-art performance on the ARC v2 benchmark. He noted that, despite this progress, the model still exhibits unexpected errors on simpler tasks, suggesting areas for further research. Knoop emphasized the need for new ideas to address these challenges and expressed optimism about the potential for mass automation enabled by AI reasoning systems. (59:11) - Jonathan Neman, co-founder and CEO of Sweetgreen, discusses the journey of starting the company in 2007 with two friends during their senior year at Georgetown University, aiming to create a healthy fast-food alternative. He highlights the challenges of scaling the business, including decisions against franchising to maintain quality, and the integration of automation like the "infinite kitchen" to enhance efficiency. Neman also addresses adapting to consumer trends, such as eliminating seed oils from their menu, and emphasizes the importance of strategic real estate choices and responding to evolving customer preferences. (01:32:38) - Ashlee Vance is an American journalist and author, renowned for his 2015 biography of Elon Musk and his work as a feature writer for Bloomberg Businessweek. In the conversation, Vance discusses his recent travels across the U.S. to film episodes on hard tech innovations, including visits to Tennessee, Detroit, New England, and Texas. He delves into topics such as humanoid robotics, the dominance of Chinese manufacturers in actuator production, and the challenges facing the U.S. robotics industry. Vance also shares insights on under-hyped hard tech companies, the progress of autonomous vehicles, and the potential resurgence of airships for cargo transport. (02:01:15) - 𝕏 Timeline Reactions (02:09:34) - OpenAI Adds Fidji Simo (02:19:06) - Saudi Arabia to Invest $1T in the U.S. (02:21:46) - Valar Atomics Splits Atom (02:27:58) - Jeremy Epling, Chief Product Officer at Vanta, discusses the company's recent VantaCon conference in San Francisco, highlighting the launch of their Agentic Trust Platform aimed at transforming enterprise trust management. He emphasizes the integration of AI to automate security and compliance tasks, addressing challenges like AI trustworthiness and the evolving threat landscape. Epling also outlines Vanta's approach to proactive risk management through AI-driven insights and partnerships with other security firms to enhance their platform's capabilities. (02:41:28) - Keone Hon, co-founder and CEO of Monad Labs, discusses his transition from leading high-frequency trading teams at Jump Trading to developing Monad, a high-performance blockchain designed for High Fidelity Finance. He highlights Monad's compatibility with Ethereum, enabling developers to leverage existing code and tools while benefiting from significantly higher transaction throughput. Hon also emphasizes the importance of broad token distribution and community engagement, drawing parallels to Dogecoin's widespread adoption, and notes that Monad has raised approximately $120 million, with the token sale open until Saturday at 9 pm Eastern. (02:51:12) - Stephen Balaban, co-founder and CEO of Lambda Labs, leads the company in providing advanced GPU infrastructure for AI developers and researchers. In the conversation, he discusses Lambda's recent $1.5 billion equity funding round, emphasizing the company's conservative capital structure and focus on building a robust, long-term business resilient to market fluctuations. Balaban also highlights Lambda's strategic investments in GPU infrastructure and data centers, aiming to vertically integrate operations to accelerate the deployment of AI infrastructure. (03:10:15) - 𝕏 Timeline Reactions 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.com/tbpnFin - https://fin.ai/tbpnGraphite - https://graphite.devRestream - https://restream.ioProfound - https://tryprofound.comJulius AI - https://julius.aiturbopuffer - https://turbopuffer.comfal - https://fal.aiPrivy - https://www.privy.ioCognition - https://cognition.aiGemini - https://gemini.google.comFollow TBPN: https://TBPN.comhttps://x.com/tbpnhttps://open.spotify.com/show/2L6WMqY3GUPCGBD0dX6p00?si=674252d53acf4231https://podcasts.apple.com/us/podcast/technology-brothers/id1772360235https://www.youtube.com/@TBPNLive

You're watching TVPN. Today is Tuesday, November 18th, 20205. We are live from the TBPN Ultronome, the Temple of Technology, the Fortress of Finance. The capital of capital. Gemini 3 Pro, Google's most intelligent model yet with state-of-the-art reasoning, next-level vibe coding, and deep multimodal understanding. Let's hear it for our sponsor, Google AI Studio, Gemini, launching Gemini 3. obviously, deeply conflicted.

But we're going to have a fun conversation about the big launch today. Google is, of course, a sponsor of TBPN, but we'll take you through all the reactions, and we're going to get some conversations going with other folks in the industry. We have Mike Knoop from ARC AGI coming on the show in just 30 minutes to break down how Gemini 3 is benchmarking. I actually think that there's two sides to analyzing a model release. these days. One is you benchmark it, you use it,

you test it, you demo it. And that has been getting less and less interesting. It's very incremental. The more interesting thing is how do the other labs respond? And today we're going to go through a little bit of both of those things. Obviously

the big news at least from my reading on it is that Gemini 3 performs very well on Arc AGI V2, a huge job. twice the performance of the previous state of the art. And also some interesting findings. Mike's going to break it all down for us.

But it's definitely a smarter model. And there's a whole bunch of interesting ways to show that, to demo that, to quantify that. But ultimately, I don't think anyone's making the claim that this is super intelligence. This is a step change from what we've experienced before. It's what you know and love. it's AI in chat, it answers things, it writes some code for you, it can do a bunch of cool things,

but there's nothing that we're like, oh, it can finally do this, where it can't do that. Yeah, it can do a bunch of cool stuff. The best auto-complete ever. Tyler, how do you respond to that? I don't know. I think that's a bit too dismissive. The model's like really good. I think probably the most important thing, and this is kind of shown by the arc scores.

Well, kind of, but it's like the visual understanding, the computer use that you can use. Basically, there's some benchmarks that measure this, like how well can it navigate a website or something like this. And it's like basically, the models went from being really, really bad at this, and now this model is like solid. It's like reasonably good. So it's like, okay, maybe this is what gives us agents finally. And that would be like an actual step change in capabilities.

Yeah, maybe. Maybe. We'll have to see. I mean, it still feels like even for that example, Like we need some scaffolding. We need some wrapping around it. It's not like you can't, it's not like yesterday we weren't able to do something with AI. And today in vanilla Gemini 3, you can just do it.

It's just a new functionality necessary. Sure. I think it's better. It's better. As good as we would want to expect. It's not slowing down, I would say. No, no, no, no, no, not at all. It's not slowing down.

It's just that it is getting better. It may mean it might be it's growing, but decelerating. Is that fair to say? Or are we accelerating? I don't actually know that it's, I don't think it's that big. Tyler, say the word decel. Say the word decel. This is a great model.

I'm very excited about it. I agree. It is the best possible. I was framing it in somewhat of the same way as the iPhone launches. Like it's newer, better, smarter, faster, stronger, newer, and better. And it's like, it is all of those things. which is good. You don't want to go backwards, but is the, but like we're waiting to see on the net new capabilities on the binary step changes. I think over the next week or two, we'll see if it's

actually really good. No, no, I'm not saying it's not really good. I'm saying, I'm saying binary before and after. Like with chat GPT, it was like, you couldn't talk to the computer and then you could. And then with, you know, the launch of, uh, you know, wind serve cognition, Devin, it was like, you couldn't write code with the computer, now you can. And it was like, like the AI became capable of writing software. Yeah, but it's also like it became not good at, it, it couldn't write code and now it can write kind of meteor code. Yeah, and now it can write better. But now it can write media code and now it can write good code.

Yes, yes. That's also a step change. But we do have, we do have our benchmark, the one true binary step change, which is, is it funny? We need to know if it's funny. I gave Tyler the prompt. Do you want to read the result, the stand-up comedy bit from Gemini 3? While you pull that up, let me tell you about ramp.com. Time is money saved both.

These are used corporate cards, bill payments, accounting, and a whole lot more, all in one place. Tyler, what did Gemini 3 say when you asked it for a comedy routine, for stand-up special? Let's see if it's funny. Okay, it's pretty long. Okay, read it off. Okay. I bought one of those smart health watches recently. You know the ones?

It tracks your sleep, your heart rate, your readiness. I paid $300 for a piece of rubber to tell me I'm dying. Get the soundboard. That's all it does. I wake up, I feel fine. The birds are chirping. I'm ready to seize the day. Then I look at the app and it's like, recovery score, 12%.

Your heart rate variability suggests you are technically a corpse. Go back to bed. Okay. immediately I feel sick. I'm like, oh God. You're so bad at reading this. I mean, this is terrible. This is terrible delivery, brother. I'm not, I'm not good at this. It's a placebo effect for hypochondriacs. I started thinking about my grandfather. He fought in the Pacific. This guy slept in a mud pile for three weeks using a helmet as a pillow. Okay. If he woke up and didn't hear artillery, that was a 100% sleep score. He didn't need a nap to tell him if

was ready for the day. If he had legs, he was ready. Okay. Imagine me explaining my struggle to him. I'm sitting there like, yeah, pop, pop. I know you stormed a beach under heavy fire, but I had two IPAs last night in the room was slightly too humid. So my REM latency was suboptimal. I can't go to work today. This is kind of a good bit. I'm in the red zone. Yeah. We've evolved. I mean, it keeps going. Okay. I asked for the poster benchmark. I said, write me a post, Gemini, Write me a post about technology that will get a thousand likes on X.com. And it gave me a few options through its credit.

Here's option three. Tech has solved a million problems. This is in bold. Tech has solved a million problems, but has it created one big one? We now have infinite connectivity yet feel more isolated, infinite data yet more confused. Hyper-efficiency, yet less free time. The law of unintended consequences is the most powerful force. in the digital age.

We need an ethics reset. What is the single greatest downside of the last 10 years of tech innovation? Arrow down. Hashtag technology. No, no, it's just asking for engagement bait. It loves engagement baiting. No one does that anymore.

No one goes on accesses. Let me know what you think in the comments. It's so 2017. The other one, the option one, is the next 12th, months will decide the winner of the AI race, and it won't be Google or Open AI. It will be the company that masters hyper-personalization for the average consumer, not the most powerful model, but the one that seamlessly integrates into your daily life, your email, your calendar, your health.

The real battle isn't AG equals AI. It's AI to the power of I equals impact. Which Dark Horse will win? Okay, that's insane. I love how... is funny how the how posting seems to be unverifiable like you just can't it's very hard to create a a verifiable reward environment for comedy that you can actually are all against what do you think uh there's also the other benchmark it was like the the shrimp fried rice joke yeah yeah yeah that one

i think it did well on that so i'll read through some of them so one that you showed is insane you're telling me um shrimp fried this rice that's like the original one so it's like i'm asking it to come up with more of these yes so i'll read through some of them uh you're You're telling me a chicken fried the steak. Okay. You're telling me the sun dried these tomatoes. I like that one. You're telling me a beer battered this fish.

Okay. You're telling me a gingerbread this man. The gingerbread this man is insane. You're telling me a pure, wait, you're telling me a pan seared the salmon? Pan seared salmon? Yes, I am. Yes, a pan literally sealed the same. That's not a joke.

That's an anti-john. You're telling me a stone-washed these jeans? That's pretty good. I like that. Stone-washed jeans. You're telling me a stone walk these jeans. You're telling me a hand, toss this pizza. I mean, yes, literally.

That's exactly what it means to... You're telling me the French roasted this coffee? Yes. All of these are just true. The genius of the comedy of the shrimp frying the rice is that the shrimp didn't literally fry the rice. The shrimp is being fried in the rice. But this is, I think this is a step change better than what we saw at GPT-5.

I wouldn't say step change. I would say incremental. Like, it is better for sure. For sure. But this at least is like logical, where the GPT5 ones was like, you're telling me a squirrel ate this watermelon? Yeah, it was like completely unrelated. It didn't even understand the concept of finding the root trace of like it needs to be like stonewashed jeans and then you rearrange it. And it doesn't quite understand when that hits or when that doesn't hit.

Some of those are very funny, though. One of them is extremely unintentionally funny, which I enjoy. Or maybe it's intentional. Maybe it's AGI deep down in their nose, nose, nose. It's great. Anyway, you're telling me a reason. stream stream this live stream one live stream 30 plus destinations if you want to multi stream go to restream.com sundar pitch AI jordy posted back in july of 2025 uh nominative determinism is

undefeated sundar really did it uh he uh he pitched a i i was being mocked real photo he was being mocked for a long time for uh getting on stage at google i o shortly after chat chp t launched and saying AI, AI, AI, AI, AI, and they did a super cut of every time he said AI, he said AI a lot. And so it made it look like, oh, he's behind the ball and he's trying to catch up. And to some extent, I don't know if they were actually behind the ball, but they were certainly playing catch up in like the attention game. They just weren't getting enough attention. And so it was the press release economy. They were putting out a lot of press releases.

But they are maybe done with the press releases because now they're letting the model actually speak for itself. And you can see that with the Gemini 3 Pro model card, which is doing very well. Better than GPD 5.1 on a lot of stuff, better than Claude Sonnet 4.5 on a lot of stuff on humanity's last exam. It's getting 37.5%. Arch AGI is up at 31% over 13, 17. Across the board, it seems like it's a good model, sir. And so Zeofon says Gemini and be like, whoever prayed on my downfall, pray harder. And I couldn't agree more.

It's great to see Google becoming a winner and just realizing the, just that this was a sustaining innovation for them and that they were able to, you know, take advantage of all the infrastructure that they had across TPU, Deep Mind, GCP. They were set up to Excel here, got taken a little bit off the back foot on the consumer side, but seemed to have played catch up at least on the foundation model side. Very well. Matt Schumer says the last time we saw capability jump of this magnitude was the release of GPD4 in March 2020. We are entering a new era.

Okay. Yeah. So points for Tyler here. Certainly agrees with Tyler. There's a significant jump. It is the age-old question. Are we accelerating or decelerating? But either way, we're definitely making progress.

It certainly looks like acceleration in the Arc AGI2 leaderboard. You can see we are growing exponentially there. Really, really exciting chart. So Gemini 3 Pro is at 31% completion on Arc AGI2. That is, of course, the puzzle-solving game that is easy for humans. even children can do it, but AI has historically struggled with it. Gemini 3 Deep Think preview gets a 45% on it at $77 a task. And this is just way above GPT5 Pro GROC4 Thinking. When GROC4 thinking, when GROC4 thinking came out, it was before GPT5 and it was by far the highest on the chart. It was really,

really up there. And Elon was very excited about that and was, you know, showing that GROC 4 had really advanced. Well, now we're back in the horse rates. What about GROC 4.1? 4.1. I haven't seen it benchmarked. We can ask Mike if he's heard anything.

But whether you're, whatever you think, get on public.com. Investing for those who take it seriously. They got multi-asset investing, industry-leading yields. They're trusted by aliens. So back to ARCGI. Gemini 3 has also has good results on Arc AGI 1, but the interesting thing here that Mike highlights is that V2, so the fastest, so he says, we're also starting to see the efficiency frontier approaching humans. The fastest V2 task Gemini 3 pro solved was this hash with only in 188 seconds. The human panel solved this one in average. of 147 seconds. So you're getting like human level output, but also human level speed.

And then if you get to human level cost, then you're really in the game. It's wild, wild. Carpathie jumped in with some notes. He said, I played with Gemini 3 yesterday via early access. Few thoughts. First, I usually urge caution with public benchmarks because, in my opinion, they can be quite possible to game. It comes down to self-discipline and self-restraint of the team, who is, meanwhile,

strongly incentivized otherwise to not overfit test sets via elaborate gymnastics over test set adjacent data in the document embedding space realistically because everyone else is doing it the pressure to do so is high go talk to the model like we did we went and said give us a stand-up routine start give us some one-liners talk to the other models I had Carpathie says I had a positive early impression yesterday across personality writing vibe coding humor etc very solid daily driver potential, clearly a tier one LLM. Congrats to the team. Over the next few days weeks, I'm most curious and on the lookout for an ensemble

over private e-vals, which a lot of people orgs now seem to build for themselves and occasionally report on here. I wonder how fast it will roll out. I have a Gemini Pro ultra subscription, but it's on my personal email. And so I need to I need to figure out how to actually get into 3 Pro on on the

consumer app so I can actually test it on my phone in my daily use. It's always tricky with these Google. Like Google's so big that when, I mean, you're starting to see it now with OpenAI rollouts where they'll say, hey, GPT-5's out and we'll be rolling it out over the course of the day because the system is big enough

that it actually takes time to roll out. And I think Google has even more of that. Even more of that. This is pretty cool from Patrick Collison. He says, I asked Gemini 3 to make an interactive web page summarizing 10 breakthroughs in genetics over the past 15 years.

And here's the result. Pretty wild. Did you click through this, John? No, no, I didn't. But it's shared directly from Gemini. That's cool. Yeah. So this is just basically a website

or an app. And it's notable that even the UI itself is fully interactive. Yes, yes. So I did this with Claude Code a little bit where I wanted to visualize, like basically a deep research report, and I wanted to turn it into a website. And it just generated all the HTML. And at the end of the day, or at the end of the report, it gave me an HTML page that I could open in Chrome and use like a website.

But it was local. I couldn't share it because it wasn't actually on the internet. This is really, really cool. This is definitely the beginning of this generative UI stuff. Yeah, I think actually, I think it was Sunderer that posted it, but in like search in the AI mode in search, it's now using like Gemini 3. There's some prompts where it'll like generate UI.

Yeah, this is, it's so cool because Google's always had that generative UI to some extent, but it's always like module-based. Yeah. Also just very, I think I expect this to be like pretty viral, you know. Totally. And, and potentially a growth loop for Gemini as people just come on here, create these many apps. These canvases. Yeah.

I feel like, I feel like doesn't OpenAI have a canvas feature? Yeah. But it's like maybe less shareable. I don't know. But can it generate HTML, custom HTML and then actually share that? I've never seen someone sharing OpenAI. I mean, this would be. be a good benchmark. Like, I don't know what the prompt was for this. I asked Gemini 3 to make an

interactive web page summarizing 10 breakthroughs in genetics over the past 15 years. Do you want to try and benchmark that just in, uh, maybe, I don't know, like Claude and, in, uh, in, uh, chat GPT or in, in, in open AIs canvas product, because, um, the idea, like the fact that this is just a URL at the end of the day, that is a powerful growth loop. That's very cool. Um, I wonder, Yeah, I'd be surprised if Gemini really was the only one to have this feature either right now or for a long time because it seems like a killer feature. Gemini 3 Pro is going absolutely vertical on Vending Bench right now. Let's see this. Money balance over time across four runs.

Today we're revealing two new evils, Vending Bench 2 and Vending Bench Arena. Soon we expect more models to manage entire businesses. is this requires long-term coherence. Oh, so this is where you manage the vending machine. But is this all simulated? This is- simulated? This is simulated?

There was a couple months ago did like the actual Yeah, Anthropic office. Yeah, in the office. And it was losing money, and it was getting confused a little bit. Yeah, because people would order just like metal,

like a piece of metal. Yeah. And then it would do it. And then you could like haggle the price down. Yeah, yeah. It would negotiate on every price, apparently. And also, it consistently thought, it was like a human in the office.

And so it would keep saying like, it was that 60 Minutes documentary. It was like, oh yeah, like I'm down on the third floor. I'm wearing a green tuxedo. Like come hang out. Yeah, it said out it was wearing a red tie. Yeah, red tie. I like the idea that it just thinks like,

well, what would I wear if I was in the anthropic office? Like I'd probably wear a red tie. It's like no one wears ties in that office at all. But after the, this is the first ever vending bench game. Claude Sonnet 4.5. GPD 5.1, Gemini 2.5 Pro, and Gemini 3 Pro competed to win the local vending machine market. Gemini 3 Pro made more money than the other three contestants combined.

And so, congrats to Gemini 3 Pro for dominating... The vending machine game. The vending machine game. Before we move on to the next, Gemini 3 posts, let me tell you about adquick.com. Out-of-home advertising made easy and measurable. Say good advice. Say goodbye to the headaches of out-of-home advertising. Only ad-quick combines technology.

expertise and data to enable efficient seamless ad buying across the club. Anyway, Adi says, I had early access to Gemini 3.0 for about two days, thanks to official Logan K and the AI studio folks. Here we get to see GPD 5.1 thinking left and Gemini 3.0, right, build the same Xbox controller in Minecraft. And pretty, yeah, pretty remarkable results. You can start to really understand just the raw capabilities. capabilities. GpT5 Pro for context is not quite capable. I really want to know how this is actually

orchestrated. Is this like writing some sort of like text or markdown file that then is imported into Minecraft? Yeah, or is it more like an agent? Or is it actually driving around and using the internal UI? Yeah, because, you know, Google demoed an agent product that could actually actually, you know, use the keyboard to navigate around. I wonder what's going on here. What's your review of this Ferrari and Minecraft? Is that, is like, I think it looks pretty solid. It's pretty good. I mean, it's, it's meant to be an F40. Is it? Like the, the, the, I do like, the hood is a little rough. Yeah, the front area is a little, a little rough. Like, this is, it's the worst it's ever going to be. It's going to be better. This is definitely like,

this is the worst that Minecraft Ferraris are ever going to be. But, but. But, but. But, I do feel like if I just search like Minecraft Ferrari. I mean this is this is the vision that this sort of AGI future that Tyler has been telling us is right around the corner. Okay, these are like so much better. If you go to the like MC bench website, you can see like what other models produce. And I mean this is like way, way better. I think these this is actually one of my favorite benchmarks because it's much harder to like kind of bench max this. Yeah.

I would think. And also it just seems like models don't really do this. Like, if you look at a lot of GROC models, which are sometimes accused of being bench-maxed, you kind of look at their like Minecraft creations, and it's not very good. So I think these give you a much better sense of, like, the actual capabilities of the model. I found a Ferrari F-430 in Minecraft that looks amazing that I want to share somehow. How do I share this? Let's see.

Can I only share the X-Link here? I just have an image. If we go to the end. Oh, wow. I think I know what you're pulling up. Did you see it? If you search, if you just search, the F430, Fiori.

Yeah. Like, that looks amazing. Pull this image up because that'll show you how it's done. Compared to the, the Minecraft. Wait, so, so do we know how this is actually generated with Gemini 3 Pro? Like, what is the problem? I don't think it's like an agent.

It's just text. It has like a text representation. of the... That's still really, really impressive. Like, that, that's actually crazy. It definitely, it definitely understands a lot. Yeah, but it's not this. Look at this, Tyler.

That is human craft. You know, you know what that is? It's probably like, you know, a team of 50 kids for a month building in Minecraft. That's amazing. What else? Lisan Al-Qaeda, of course. Themselves says it's so over for Open AI

and Anthropic. if you want engagement on X, just start by saying it's so over. Yes, yes, yes, yes. Yes. And highlighting some more of the benchmarks. Of course, it is not over for either of them. Yeah. But it's certainly competitive race. I'd be very interested.

We have to get some of the semi-analysis folks on the show soon. I'm very interested in understanding, like, okay, so we got this big jump. it's pretty significant. What was the actual, what's the actual structure of the CAPEX that went into Gemini 3 Pro? Like, how big is the training run?

How much do they have to spend? Because, like, I think that they're going to make the money back very quickly. Like, people are going to use this model. They're going to pay for it. They're going to use it all over Google, obviously, but also people are just going to pay for the API.

But is this $100 million? This is a billion dollars? Like, is this, like, did they build a special data center for this? Is it all TPUs? How many TPUs? I think it is all TPUs. I'm pretty sure I read that. But I seriously doubt they've released anything on, like, the numbers of the scale of training.

Yeah. No one's really done that sense, like, GPT, like, two. No, no, no, not at all. So there's got to be someone who's, like, working backwards to, like, actually sort of understand the dynamic. Yeah, you can probably estimate the, like, order of magnitude. Also, I've heard that Google's, like, fantastic at, like, cross data center training. runs. So they can actually like shard out or slice up the training run. So even if they don't have one massive data center, if they have five small ones, they can piece them all together

and get a better result. So I don't know. Scoot said, Anthropic to zero, open AI becomes the Yahoo of intelligence. Google remains Google. It's extremely rude. Very harsh. Sorry to the Too early to say. The first two labs, you guys are great. Certainly too early to call it. All three. I like this take from Ben. This is funny.

History of AI so far. Crown of winner, wait 90 days. Look silly. We're in the least predictable era of the entire, of an entire industry. Google has fairly straightforward advantage. Y'all favor whoever released the most recent model. That is very true. Anyway, let me tell you about getbezzle.com.

shop over 26,000 luxury watches, fully authenticated in-house by Bezels, team of experts. So let's move through some of the competition. What else was going on? So everyone's releasing different things. Let's go to anti-gravity, actually, and watch this video and see Google entering the IDE race. Let's play this. Every breakthrough in model intelligence for coding encourages us to rethink what development should look like. Gemini 3 is our latest such model advancement.

So, we went out to build the next step change of an IDE. Introducing Google Antigravity, a new way of working for this next era of agentic intelligence. It is the ideal agentic development home base. Does it have an IDE? Yes. But it also has a whole lot more. We started with Decor IDE and added pieces that evolved the IDE towards an agent-first feature, such as browser use, asynchronous interaction patterns,

and an additional novel agent-first product form factor, helping you experience liftoff. Your new focus. So you like the name Antigravity. Why do you like that name? I like the way it looks and I like the sort of vibe of the word. I think saying it out loud is tough. Okay.

Yeah. I thought there was a very cool feature where it feels like they're bringing together a whole, it feels like the first time, for the last couple of years it feels like Google's been like stuffing AI in little corners of the UI. Like you already have Gmail and then you stuff a Gemini box there or you have sheets and then you stuff a Gemini thing over here. This feels like the first one where they were like sort of able to start from scratch. And it still has like the sidebar panel, but it felt like it was both. a code editor, but then it also kind of looked like a Google Doc in the sense that you could highlight sections and leave comments for the AI, which I thought was interesting.

Yeah. I don't know. By easily guiding the agent's 90% solution all the way to 100%. Yeah, this part. Now let's say the agent produces a landing page mock-up with Nanobanana. And you now want to make some UI adjustments. You can give visual comments. Yeah, so you can actually like go in and comment in the image.

Exactly where the problem is. And you can do that in the text as well, so you can like, have this more precise dialogue with the agent like you would a human employee. Yeah. And you're going to love it. Say goodbye to what held you down before. Welcome to Google anti-gravity. Very cool.

It is, so it is funny. Remember when windsurf acquisition, whatever you want to call it, was announced? And it was positioned, it's like, hey, the team is well-funded and has a product used and loves by, you know, thousands of engineers and companies. And I remember talking about it, and we were saying, like, okay, like, the one issue is that some of the best people on your team are going to Google to compete directly with what you guys have been doing. Yeah.

So fortunately, obviously, you know, the whole cognition deal ended up coming through, but you can imagine a world where windsurf was still independent and just trying to, and then suddenly it's like, okay, now you're just competing head to head with. with your former partners. Like, how does that make sense, right? Yeah. So anyways, it all worked out for the best, but I'll be interested to see,

I'm super interested to see what kind of adoption this gets. Yeah, we have to test it out. We'll have to get the Tyler Cosgrove review. Is it publicly available? Yes. Let's get it. Get it. Let's, yeah, let's do a review later this week

and see how it compares to other IDs. Anyway, we have our first guest to the show, Mike, from RKGI in the Restream waiting room. Welcome to the show, Mike. Thanks for waiting. Good morning. Good morning. How are you doing? You know, a lot of these AI sort of like verification, things are very much. Hurry up and wait.

The last like 24 hours has been a hurry up mode. Okay. Always very fun and exciting to get the results out. But yeah, it always comes together very, very quickly at the end. Well, I really appreciate you taking the time to hop on on such a busy day. maybe we can just start with like your high level reaction. Like, how do you even think about these things anymore? Are you just thinking like, okay, yes, Gemini III, good.

And then let's go a layer deeper. Are you thinking about that's really good? What's your high level takeaway? Well, yeah. So, you know, I think the big headline is that Gemini III basically got like 2X soda on Arc V2. Yeah. And so this is, you know, this is the third major frontier lab now in a year to use Arc to demonstrate frontier progress, particularly with AI reasoning systems. We had opening AI last December. Xad this summer. I'm super excited at Google's

now on the leaderboard too. So that's great to hear. And I should say up front, thank you the Gemini team for giving this opportunity to verify. It's been great. I think the really impressive thing about this and still like sitting with all this stuff, it's pretty fresh. But I think the biggest impressive thing to me is about we're starting to close this like complexity scaling gap between V1 and V2, ARCV1 and V2. This is the big difference between what V1 and V2 is they look similar on paper if you go look at the different data sets. The big change is the V2 kind of increases the complexity of the tasks once to take minutes instead of like seconds for humans. And so we're starting to see like actual material progress on that complexity scaling.

And then I think the big surprise to me personally is that Gemini III though is still roughly along the preto frontier if you want. You know it's a little better but like it's still we're still kind of roughly within the same mass shape and um you know there's dozens of tasks where like you know the system still makes relatively, I think you know, obvious mistakes that humans don't make or recognize very quickly. And, you know, I sort of previously expected, like, if we had an AI system that was solving half of V2, that V1 would be fully solved. And, like, that's not the case.

So there's a lot of surprise here. I was treating about this earlier to sort of invite sort of investigation from the community because I think there's still a lot to learn in terms of, you know, how, why exactly do we see such, you know, a jagged intelligence emerging right now? Let me eliminate some, some possible factors. it feels like there is benchmark hacking, but Google and the Gemini team feel not aligned with benchmark hacking generally.

Like they've been good citizens in the community so far. And also, you would assume, right, just from logical deduction, you would assume if you're able to hack V2, you would definitely go back and hack V1 as well. So is that... Well, this is the first time we've verified a Gemini result either this year.

We did two and a half earlier as well. So I don't think that's... So it's not like they set up like, okay, we got, you know, the most important thing here is that Gemini 3 is really good at RKGIV2. That wouldn't make sense. So there is sort of teaching us something about the fundamental nature of this model. But we still don't know why lagging, why performance might be lagging in V1. Is that right? Yeah, I mean, I've got my sort of hypotheses.

You know, I think my, my personal one is that like AI reasoning systems just don't demonstrate even fluid intelligence. You know, the sort of like the ability for these reasoning systems to do adaptive reasoning, which ARC is a sort of test of adaptation capability, it's sort of limited to domains where the underlying foundational model has pretty good training coverage over the types of data and it has a verifiable feedback signal. And I think that's sort of true for ARC. You know, if I zoom it even further, maybe, you know, to kind of put this kind of result in context of where we're at is, you know, just like an industry right now.

I think over the last 10 years, I would sort of characterize we've really had only two major breakthroughs. We've had the Transformer in 2017, obviously that led to language models, and we had chain of thought. It was originally introduced in 2022 and sort of went through QSTAR and the chain of into AI reasoning systems and has gotten scaled up. Sure. And so like this was against the backdrop of like compute scaling, right? And this compute scaling was certainly necessary, but it wasn't sort of sufficient. These like key conceptual unlocks were sort of the sufficient things to take advantage of that compute. And so my kind of take at this point, having looked at all this progression this year,

is that like AI reasoning systems with no new innovation from here can basically enable sort of mass automation because a lot of problems can be, can fit into that characterization, where we can generate lots of examples that look like the problem and we can get a verifiable feedback signal from them. You know, any problem that can be kind of cast and then characterize in that way, I think, can be automated at this point, no question's asked. And then the big motivating factors, I think, really for mass innovating. Like that's sort of what we're still not seeing.

You know, we still need new ideas for this. And I think that's closer to like an AGI complete problem. Yeah, that makes sense. Is that, is it fair to like put you in contrast to some of what Dorcasch has been writing about saying that like the job of most people is not necessarily a bunch of indiscreetly verifiable test under Carpath. He's been writing this as well. There's this question of like, like, how much of a job is actually.

automatable. Radiology was one example where it felt like a very automatable job and yet years into the AI deep learning revolution. Like we're still seeing full unemployment there. How are you processing? Yeah, but we're only a year into the AI reasoning paradigm, right? Sure. Like the first major one only came out 12 months ago. And I think 2025, like in my view, is basically characterized on starting to figure out how to actually bring these things. things into production systems. Sure. Like, this is a big breakthrough.

I think this is maybe like one of the mischaracterizations in my view of kind of the progress is like a lot of teams even, I think, you know, if you sort of just assume like, oh, models get better, models get better, you think like, oh, the last 12 months has just been sort of continued story. And if I played with the models 18 months ago, I have a rough sense of what they can and can't do. And that's just not true. Like, if you're a builder building products, like, this is the advice I give to, you know,

teams I work with that's app here, too, still is like, look, this is, this actually is a significant paradigm break in terms of what was what's possible now that wasn't possible even a year ago with these systems and like that's going to enable a lot of new types of products a lot of new types of services a lot of use cases that were like out of scope because of verified you know because of reliability and consistency now can be brought in scope so you know I think if your intuition on like what use cases are possible based on you know and eat your look back you really have to start kind of pinning your look back to more about more like 12 months yeah yeah that makes sense. What about, like, does the work live within SaaS products or within individuals? Because

some of those examples that you just gave are, it's like for teams that are going to build products that take, that automate work and then get vended in through effectively SaaS products to actually do the job, versus like a knowledge worker who is going to be using Gemini in the app to, you know, accelerate their day to day, should they be feeling the difference in this in the same way? You know, I mean, like, my one bit of advice is like, if you haven't really used these AI-easing systems, I don't you shit. I would hope everyone probably has listened to the show has used these things at this point. But in case there's not, like, you should go use and experience these things.

You know, when Google or when an opening I released GPD5 this summer with their model router, right, that was like, predicated on this data that, like, very few users had ever even used day reasoning systems. And I still think it's only like one in five. Yeah. Maybe it's going up a little bit since then. Part of the deep seek moment was just that for the first time there was a free app that you could go and see a chain of thought. And you could actually see a reasoning model in action.

And for a lot of people, that was their introduction to that. And so there was like deep seek wasn't necessarily that much higher, that much, you know, in front of everything else. But it just gave away a reasoning model for free at a time when they were tucked behind a bunch of other like hurdles that you had to jump through. Yeah. We're still really early on the diffusion for the stuff. That's maybe the key point. Seeing that on the huge numbers getting reported by Frontier Labs and their usage data, I mean, I'm seeing this in sales conversations I have for like, you know, Zapier stuff.

All over the case. We're still very much early innings on actually getting this brandy breakthrough into like production workflows. Yep. Yeah, that makes sense. Do you have more questions on the diffusion issue? One, I wanted to get your updated take on humor. We were playing around with Gemini 3 this morning specifically. just trying to get on our own little version of humor bench.

It feels like something that, like, I do think about, can you make kind of these, like, can you make humor verifiable? Like, is there a system that someone could set up that could actually start taking humor seriously? Because I could imagine, like, if we're hitting, if we're hitting, like, anything close to a wall, there will be a lab that says,

okay well like let's work on something that like everybody uh that like let's work on a new kind of angle for differentiation and maybe maybe humor uh could be it's at least a little bit right like i have a five-year-old who is getting into uh starting to want to tell a lot of jokes and the jokes are just terrible right like they're not they're not funny at all they're like you end up laughing because they're so not funny and then depending on who's delivering it yeah yeah it's hilarious i've been trying to find the structured way to describe like okay here's what makes something funny. And so there is like some degree which you can kind of break down, you know, the types of

things I think humans would sort of find funny. And like there is, this actually does get pretty interesting because like you're getting to the spot where you're trying to like articulate like creativity, right? How creative can these systems be, you know, to be creative, to be humor, to generate good art. You kind of have to like intentionally break the rules. But you need to have a really good model of what the rules are in the first place to intentionally break them.

And in fact, I think a lot of humor fits into this category before. This is your right. It's like it's actually, you know, breaking the rules. the prediction rather than just following the sort of prediction of what you'd expect. And today, I still think when I look at the failure cases for, let's call it, AR reasoning systems on, you know, these tasks like Arc, yeah, they still fail for what would appear to be sort of random reasons. Like, they have some version of like an understanding of like the rules and strategy and the goals. And then they sort of make a lot of basic mistakes, either executing them or not following their own sort of understanding that they've generated internally. So there's some sort of self-consistency issues.

And so, like, I feel like if that's still the case, you know, humor is going to be. accidental rather than intentional from the systems. Yeah. What about V3? We played around with that on the show. I believe Tyler, our intern, was in the top 10 for a while, really grinding up the human light leaderboard. Is it more compute intensive?

Is that in the process? Are we expecting to see Gemini benchmarked to V3? I would love to. So we are in the development process for V3. I like to say we've basically built the highest, most productive. of game studio in the world. We're generating hundreds of these things. We're about, I don't know, like two-thirds of the way through building all the games

at this point. Our target is to get this in a good state with sort of all of our controlled human studies, all the games verified, get frontier results checked off by early next year. And we're targeting, releasing it publicly in V1 with the entire data set. Or, sorry, in Q1 with the entire data set next year. And that'll likely be alongside our price 2026. So we're in full details of how that's going to look next year. Sure. But yeah, we're sort of like in the throes of it. We're definitely using some of these frontier systems to do red teaming against the benchmark, just to, you know, assert that like, yeah, these games are still hard for AI and we're still finding that to be the case even with things like Gemini 3. But, but yeah, that's, we're so in progress with the developer now.

And Seema 2, can I have your reaction on that? Obviously, it's this Gemini Power agent. It feels like- If anyone at Google is listening to this and could sort of give me access to Simi 2, I would love the test it on. V3, this is actually something that we haven't done yet. Yeah, yeah, that's what I'm getting at because it feels like I don't know if there's some sort of- The claims are big, right? It's like you read the marketing material and it's like, okay, that seems like it should solve V3 before it exists. So like if that's the case, we should know that.

And so, but yeah, I haven't got, haven't gone hands on with yet. So I can't sort of make any statement either way on the claims. Yeah, I'd be interested also to like when I'm thinking about like V4, it's like you guys are going to have to build GTI6 or something. Like, like, if I'm following the progress of like V1, V2, V3, V4 is like a game that I'm going to play for 100 hours for fun. I'm just going to pay for it. This is one truth. You've never something true about V3, which is that it's still a relatively short time horizon tasks and they're self-contained.

Yeah, yeah. It does add some new complexity where you have to deal with interactivity because you have to do goal acquisition. You have to do exploration. We'll have a really nice action efficiency comparison between humans and AI, which we haven't been able to get before on the V1B2 domain. So we're going to get a lot of new signal, I think, on B3. But yeah, I think as you sort of look even further out into the future, things that are more open-ended are the things I think we're starting to get excited about

trying to understand, like, understand. Like, what does it mean to put one of these AI systems in an open-end environment and then look back on the system, you know, 10 minutes in the future, 100 minutes in the future, a thousand minutes in the future, and can you look at the environment that that AI system has been, like, how it's manipulated in the environment and, like, you know, say something interesting about how intelligent the system is based on that, like, observation and open-ended sense. Um, still very early on V4, but, uh, but yeah, we're starting to explore ideas there.

Has Gemini 3 updated your timelines at all, specifically your Arc AGI 2 timelines in terms of when you expect, you know, uh, sort of like the, 90th, 90%, like anything on the kind of the upper end of the range? I was looking back in my, the whole ARC team actually made some predictions back in January when we released V2 on what did we expect end of year scores would look like. Now obviously if we're only November 18th, a lot happens in AI, who knows what the next six weeks hold. But my personal prediction was that we would see about 25% on the private leaderboard for RV2

on the Calgo contest, and we'd see about 50% on the public leaderboard, And that was sort of based on the ratios we had seen from Arc Price 24 and the sort of scaling difficulties with V2. And it looks like we're going to come in pretty close to that. But barring some other majority breakthroughs towards the end of the year, that seems like we're probably where we're going to end up the year at. And then who knows on 2026, I think if we're really going to solve both V2 fully, it feels like we got to better understand why these AI reasoning systems still make sort of obvious mistakes on V1. set. And yeah, that's, that's an anomaly. So I think that's, that's where a serious study to like come up with new ideas to sort of prove these reasoning systems. Yeah, what was the furthest timeline that you had out? I remember you said when you developed V3, you had this

framework of like, like the state of the art should be scoring like negative 100% or something. You were like, you need to make it way harder than you think in order to give you like room to run because the systems are developing so quickly. What's the furthest out timeline that you are tracking or you as a team are tracking? I mean, our objective function is not longevity necessarily. It is usefulness and interestingness. I think the tasks that have the highest degree

of usefulness and interestingness are ones where, you know, oh, hey, this could be useful and interesting for like three years. The arc one was useful and interesting for arguably five years. I mean, even this year, it's still interesting because we haven't broken, like we're still sort of within this sort of paradigm still. And so it's still providing some interesting useful thing, even though it's largely saturated, up to 80% now. But there's still some interesting signal remaining.

V2, our expectation was that it was not going to survive as long as V1, just because it was the same domain. And we had air reasoning systems in play at that point. Yeah. I think our median estimate was like 24 months on V2, but like that, you know, we'll have to see how that all plays out next year with that. V3, we're hoping to put in a, we're hoping to be an environment where we can actually get that to survive sort of longer. You know, one of the interesting things we're finding with V1 to V2 to V3 in sort of like a qualitative sense is,

um, there's a, there's a sense of like how easy is it for us to generate the data set as like humans, trying to design the tasks and design the puzzles and design the games. And with V1, pretty much every, like, task that, like, Francois created, was hard for AI and easy for humans. Yeah. With V2, that gap got a little shorter, actually. It got smaller. There were tasks that we generated as humans that AI solved, and there was other ones that were too hard for humans. And so we ended up sort of pruning some of the tasks that we generated.

So, like, the gap between those things got short. With V3, we're finding it's getting wider again, where pretty much every game we're coming up with is, like, fitting it to this paradigm of of like very obvious and intuitive and easy for humans and sort of very hard for frontier AI still. Yeah. And I think that's like, this, I credit to Francois here. This is something he shared about a year ago with a 2003. But he's like, this is actually one interesting way you could characterize how close are we to AI is like when we run out of, when humans run out of the ability to generate interesting things. The frontier AI can't solve.

Yeah. Like hard to argue any experts going to say, yeah, we don't have AI. Yeah, because you can sort of think about like the project of humanity is like go do the hard and not. things. So it's like, is acquiring diamonds difficult? Okay, that has value. And then we base the whole economic system around it. And it's like somewhat arbitrary, but it's also like a skill and might and will issue. And if you can put that on display, then you accrue economic value. And so that that kind of traces out into everything that we do in life and beyond. Last time you were on it, if I remember correctly, you made a call for new, new ideas,

needing new ideas. What's the update on that front? Are you seeing anything promising outside of LLM world? Yeah, there's some pretty interesting stuff coming out from our crash 2025. We're in the throes of like reviewing all the papers, judging all the scores, the official results for our price 2025 come out on December 5th, I believe. So I have to, can't share everything yet. I want to spoil the final announcement. I think one of the big things that we saw from our price 2024 was this concept of like test time adaptation. This was the idea that like, look, a pre-trained model applied through a single forward pass at inference time will never solve arc.

You need some ability to take information from your test and incorporate it back into the system. And that's where your adaptation capability comes from. And that was done through like test time fine tuning during the contest. AI reasoning systems are a version of this where you're incorporating this sort of private data set. Wow. Yeah, yeah.

Literally like you take a pre-trained model and then like take the secret, the private puzzle, augment it in a bunch of different ways to generate permutations of it and then do it like a Lora or some sort of test time fine tune on your pre-trained, and that actually works. Wow. The sort of the common ground between this and air reasoning systems is both of them take information from the private test and are able to operate over it with it at test time, right? This test time compute is another form of what we're talking about here. So that was 2024.

One of the big things we're seeing in our project 20205 is this concept of refinement. loops. Anywhere where like, particularly with like language models being put into outer outer loops, where they can sort of move from state to state. And how they move from state to state is like they need to make some sort of refinement on the program or the natural language explanation of the task that they're working towards. And they just iterate on this like refinement loop over and over. And this is significantly increasing scores even over the sort of test time fine tuning stuff that we saw from from last year. So Jeremy Berman and Eric Pang were two folks who were on the public leader board last month that explained how they're, you know, approach worked in this way. So we're seeing a lot of approaches like that. I still think we're in a regime, though, where, like, we still need new ideas. None of these are sort of sufficient to solve ARC, including inclusive of V1. And so, like, you know, this gets me excited because I still think that means individual people, individual teams with small budgets, small compute budgets can still play a really, really massive role in advancing AI. Yeah. Very cool. Are there other areas where we are making progress in AI that might

sort of need to come together to actually maybe solve this or maybe just be a more complete system. What I'm thinking of is like very few solvers that I'm aware of will actually just take a screenshot

of the puzzle and inspect it with some sort of diffusion model. That's not the way these AI models reason about arc puzzles. Sure. We're also seeing a bunch of work on world models and simulums World simulators, which seem really interesting.

And I was talking to one guy who is building one and he was saying, like, I think that we're going to get like really, really robust knowledge out of these at some point once they scale up fully. And I'm wondering if you are optimistic about bringing in other, like unifying some of the different research that's happening. I think it's all of those examples of new research, new companies, new startups. Like, you know, there was a seismic shift in 2025 from pre-traum. running budget to these like RL reinforcement learning environment startups and companies that are

generating environments to produce more ground truth training data in a mass way because they're automated environments and you can get verified feedback signals out of these things. Again, there's no new science here. This is a good bet for like all frontier labs to make. This is going to drive progress for the next 24 to 36 months. You're going to continue to see amazing frontier headblinds just on this fact. There's really no new sort of, I think, discovery that's quite needed there. I think that if you're kind of pushing more towards the AGI side, like what's sort of missing?

One question I have that is an open question is, so we've got like, you would think that based on like 100 X to 300 X increase in efficiency you've seen from AI reasoning systems over the last 12 months, that we would trade that increase in efficiency for inference tokens to do more like search coverage over the problem. space when we're giving these systems tasks or problems that we want them to solve. And this is one of the big reasons why I sort of expected, if we can solve half of you two, you'd get 100% out of you want. And it seems like these AI reasoning systems are like not sort of fully exploring all of the search space that they could in order to sort of look for solutions. And so I have like kind of an open question of like, well, how much of the search space can they cover?

And what do you need to change about the training methodology or process to like actually guarantee that you can get full coverage over the search base of like possible programs or possible solutions. And so that's kind of, that's like one interesting thing that I'm paying a lot of attention right now. Yeah. Yeah. Even just the metaphor of like the test time, fine tuning, it feels like working on a problem and then like going and taking a walk and kind of like updating your whole worldview. It feels like something that humans get closer to doing that than any of the other paradigms. So yeah, it's fascinating to see all these different approaches. Yeah, all the crazy results you've heard about in the last 12 months are kind of this merger

of like deep learning and like symbolic program synthesis style methods. Sure, sure. The ICPC, the IMO gold, the Gemini three stuff today. Like, you know, these are all systems that are, you know, it's still fundamentally using a language model, but they're adding symbolic knowledge recomposition systems on top of these things. They all work slightly differently. Okay.

But it's like, this is what's working right now. So I think the rough like search space of research and how you merge those two paradigms together is still relatively under explored. Those are a lot of different ways you can put these two paradigms together. And, you know, for new teams that are considering working new ideas, like I would explore like, well, what are the novel ways you could consider merging these two spaces? Yeah, yeah, that makes sense. Jordan, anything else? This is great. This is amazing.

Thank you so much for jumping on on short notice. As always, guys, thanks for having me back. Congrats on the continued, just stacking up the wins on. RKGI becoming, and just continuing to mog the models, mock the world. Yes. I mean,

again, our goal is to be very useful and interesting. So we're going to try to hold that far. Yeah, my words, my word is not yours. I think you're keeping them honest. I think you're keeping everyone honest.

And you're providing like a very, very useful reality check on, on an industry that loves to. And inspiring the labs to grind harder. And now, and now there is a, there is a moment where we can feel very confident about taking victory laps and,

and cheering for all the hard. work that went into Gemini 3 because it does seem like it was a great model. It's performed well. There's definitely a big improvement to say of the show. Fantastic. Well, thank you so much. Have a great rest of your day. We'll talk to you soon. December 5th. We'll see you then. We'll see you then. I wanted to talk about Adio. Because Adio is an ADIO that builds scales and grows your company to the next level. Also wanted to talk about Wander.com. Book of Wander with inspiring views. Ready. Outgraded manys, dreamy vets, top tier cleaning and 24-7 concierge service. Let's sing it.

Find your happy place. Find your happy place. Book of Wonder with inspiring views. I already know the song. You know the song. I wanted to pull up this post from Chris Parsisarski. He did a GitHub-style image of our streaming activity for the year. Oh, really?

Oh, yes. It did see this. Thank you to him. Should be at the very bottom. Yes, yes. At the very bottom of our timeline. I have it. And if we could just pull up this image.

So the internet rewarded TVPN for showing up on January 28. that's when we went live. We never, we never remember the day that we went live, but he hasn't. He looked it up. January 28th, John Coogan and Jordy Hayes launched a daily live show and set one simple rule. Show up five days a week. Looking back, they did exactly that. 125,000 followers on X, 41,000 subscribers on YouTube, 17.5,000 on Instagram.

They showed up every day the internet rewarded the proof of work. So the only thing is these, I don't know, am I just colorblind, but is it like a little bit? Like, I'm seeing three days that were federal holidays that we miss, and then three days that were... No streams. I actually can't exactly tell, yeah, what is a federal holiday? What is a no stream? It looks like a different. A gray and a purple. There were a couple days here and there. We took one off. I went to a wedding in Mexico. We took a Friday off for that. That was just no live stream. July 4th we took off. That was a Friday. That was a federal holiday.

And then what happened in March? We took a Wednesday off? No live stream on Wednesday in middle of March? There was one day that we were traveling back. Oh, yeah. That was after Hill and Valley. After D.C. I thought it was a Thursday.

No, no, we didn't. We did Tuesday in the hotel room. And then Wednesday we did at the actual event, Hill and Valley. And then we flew back and got back on the horse. So we missed a couple Mondays because of federal holidays. And then we missed a Tuesday in May. That might have been Hill and Valley. March would have been something else.

Anyway, it's very cool to see. It's been a wild ride. Thank you for pulling it together. Chris. Along the way. Our next guest is, I believe, already here. We have Jonathan Neiman from Sweet Green. We're going from benchmarks to bench presses.

The most important benchmark in the world, how many grams of protein are in your protein bowl. We need to know. welcome to the stream. Please introduce yourself for those who might not be familiar. Hello, my name is Jonathan Neiman. I'm the co-founder and CEO of Sweetgreen. Get that overnight success button ready.

When did you start this company? 2007. So we've been at this for 18 years. 18 years. Wow. Just let's talk about the very beginning. I mean, since this is your first time on the show, where'd you grow up? how'd you get into the business?

What were you studying? And then let's go into the actual. Yeah, you've got to be somewhat of a masochist to get into the restaurant business. Yeah. Yes, absolutely. I mean, it's such a beautiful thing because it sounds so simple.

It's like you get a box, you get a menu, you get some ingredients. It sounds super easy. And then you just copy and paste it and you scale to, you know, however many stores. And then, of course, it's far harder in... So prior to launching the business,

what were you doing? So I grew up here in Los Angeles. I went to school in D.C., went to Georgetown. And never thought I'd be in restaurants. You were you studying government? No, I was studying business. I knew I wanted to be an entrepreneur.

Okay. And Sweet Green was almost an accident. You know, it was the naivete. We thought it would be easy. Yeah. And did you start it during school? Yeah, we started while we were seniors in college. We started with two of my friends.

Were you doing internships before that? Yeah, I had a bunch of internships. You know, I worked in media. I worked in tech. I worked in real estate. Always knew I wanted to be an entrepreneur and create something. But senior year came around. And it's exactly what you said.

we thought it would be easy. We're like, how hard could this be? You go, you know, we'll go to farms and buy the food. It's like apparel. Like people fall into the apparel trap because they're like, I just wanted to make clothes that I wanted to wear. And you realize it's like the hardest business on apparel and restaurants, probably the things that seem the most simple but are actually the hardest.

Absolutely. To actually do on a massive scale. Yeah. Yeah. So what was the, was it build a business plan first, assemble a team, do a pop-up? Like, what was the first thing where you were like, okay, let's do this? The first bowl? The first bowl was the guacamole greens.

We made in our dorm room. We brought a bunch of classmates to try it. My partner, Nick, actually made it. He was our first chef. No way. And the story was really simple. We couldn't find a healthy place to eat. We saw Chipotle taking off.

And we're like, wow, there's someone is going to create a scaled healthy fast food chain. And at first, it was, let's just open one. We wanted it for ourselves. We thought we'd go on with our lives. We opened, we worked on it senior year, wrote a business plan, raised $300,000 from 50 investors. So it was like five grand, five grand,

average, yeah, party round. So they got equity in what became the full company. They got equity. Well, we actually, it was a little bit more complicated than that. At first, the first three restaurants, we raised at the restaurant. At the restaurant level. Yeah, I was wondering if you were doing that.

And we actually paid the investors back every quarter into the whole thing. And then after the third restaurant, we realized that the only way to scale. scale this was to roll it up. So we rolled the whole thing up and then we were able to continue to invest in it. And we's notable, when did the word wellness actually become mainstream? Or when did that become like a, like 20, two years ago? Yeah, like early 2010s. Yeah. So anyway, this is like, anyways, at least five years before wellness is going like mainstream. Yeah. When we were, when we were starting, you know, the thesis was healthy eating was not cool. Sure. And it was not delicious and it was not

accessible and we're going to create a place that offers all of the benefits of fast food in terms of the convenience and the taste, but do it in a, you know, do it with healthy food and real food that you can trust where we're transparent about where the food comes from, where it's nutritious, and build a brand around it. And so we've been at it for about 18 years. We have almost 300 stores all around the country. Yeah, it's almost hard to believe. Yeah. Yeah. What was the first VC round? The first, so we, or like, or this transition for from the, you have a restaurant and did it work immediately? You set up one restaurant, you know, you raise enough money to get that.

I imagine that you'd assign a lease, so you weren't buying buildings, but you might have to do some sort of renovation to actually get the first restaurant up and running. You start making money enough to pay the employees, enough to pay the rent. You scale that to three, and then at a certain point you say, okay, we're going, we're going to turn this into like a corporation more than just a small mom and pop, right? Yeah, so we opened one in 2007, two in 2009, with a food truck. You remember those? Yeah.

And then we opened like two or three a year, and we were mostly built them from cash flow, from the profit. We were profitable. Sure. You know, we would just reinvest the cash flow, and we would do a few party rounds. Yeah. So, along the way, we started a big music festival called Sweet Life. Oh, no.

Go ahead. It became a massive 25,000-person music festival. Where was that? It was at Maryweather Post Pavilion. So first year we had the strokes. By the end, we had Kendrick Lamar. That's crazy. A little festival side quest.

Yeah, it was a way to build the brand. And then we focused on D.C., which was very, you know, it was almost an accident, but we opened the first 16 restaurants in D.C. Wow. And then slowly went up to Philly, and then in restaurant 20 and 21 were Boston and New York. And Boston and New York really kind of proved the concept outside of D.C. And took off, and that's when we raised our first D.C. So now, obviously,

all around LA there's sweet greens, but what, given that you grew up here, why didn't you, why not start here? Was this because, well, like, there was, was there just more healthy food options in L.A.? And there was less on the East Coast? Honestly, it was an accident. We were in school and we're like, let's just open one. We thought what the second one would open in L.A. And then the gravity that you have around the center when there's more and more stores. You know, when you have a restaurant company, the brand and all your economies of scale happen at the local level. Yeah. So for us especially, given our supply chain is regional. Yeah. Yeah. You have your overhead and your management, like your team that runs it.

And then your brand, you know, restaurants, the brands don't really travel across the country. Occasionally they do. So it was really started in D.C. We thought the second restaurant would be in L.A. We went and looked. This is true for even like in and out is not a national brand still. It's like the West Coast brand somehow. And yeah, it's taken so long for that to actually like filter across.

How capital intensive was it to launch like the second and third? You mentioned $300,000. That's the hardest part of the heart. No, it's way more than that now. Okay. The first one was tiny 500 square feet and we did it really on the cheap. Five hundred square feet? Five hundred square feet. It was so small. I imagine like one or two people. Yeah. Wow, that's tiny. Yeah. We were working there. We've done the whole thing. So we've had to raise a lot of money. Answer your earlier question, Revolution. Steve Case was our first first VC investor. And it was part of the thesis was how technology can change the restaurant business. So we were the first company

you do mobile ordering where you can order on your app and pick up. Most beautiful software that a restaurant had ever had probably. Emmett, Emmett Shine. Emmett Shine, yeah. Shot out, Gin Lane. Yeah, Gin Lane. This was like, yeah, this was like one of my favorite Chin Lane projects. That's awesome.

Emmett and his team were amazing. They did our app in the early days. And, you know, restaurants or today cost over a million dollars. So we're like $1.3 million, $1.3 million. $1.3, $1.4 million per restaurant. That's before you put the Infinite Kitchen in. Our restaurants have very high return on capital. Infinite Kitchen. What's that?

The Infinite Kitchen is our automation. Okay. Our automation platform that we've built. So today, most restaurants that we open, the assembly is automated. So we still make all the food from scratch. The sourcing is the same. We still cook the food fresh.

But we load this beautiful machine that makes your bowls. It makes them 500 bowls per hour, perfectly portion, perfectly plated. And so that is kind of the future of where things are going. How many different restaurant automation pitches did you get across 18 years? Like, as I imagine, every single year there's a new, like, startup coming to you saying, like, we can automate this part of your kitchen.

And clearly you got to the point where you had to build it yourself based on kind of domain knowledge. But this just feels like something that's been promised for a long time. and at this point, I don't know, like an individual startup that's done well in restaurant robotics. Yeah, no one's been able to create a platform that works in multiple restaurants. And there's a few issues. Most restaurant workflows are very specific. So they're super specific to that restaurant.

Most restaurants are franchises. And so they're not owned by the corporation. We are fully company-owned. So if you're a franchise restaurant, you know, if you're McDonald's, you have to now go convince your franchiseees. to buy whatever automation you have. And the other issue... And they're looking at it and it's like, this is coming off my bottom line.

We're making money already. This feels like a risk. Like the franchisee is saying like, I'm happy with my EBITDA. I don't need to take a risk. That's exactly right. And the other issue is you need automation that takes enough labor out

or offers enough value to be worth it because the CAPEX is still very heavy. Yeah. So when we went down this path, we tried to build it ourselves, actually. We built a team to do it ourselves. ourselves, realize how challenging it was. And then we found this startup that was doing it and doing a really good job. Yeah. It was called Spice. It was called Spice Kitchen. It was four MIT grads out of

four grads out of MIT and they had the same issue. They realized they could build the automation, but no one was going to buy it. Yeah. So they ended up opening two restaurants. They were great at automation, not so great at the restaurant side and then four years ago we acquired them and we began. We've commercialized the technology. We've scaled the technology today, so most new restaurants feature the technology. And last week, we actually just announced that we've now sold spice. So we spun it out, basically. So we spun spice out. Yeah, we spun spice out. We announced about 10 days ago.

We sold it to Wonder, Mark Lour, over there. No way, yeah. So we sold it for about $186 million. Mark is, Mark, I don't fully understand that business, but talk about a guy that just, like, isn't even necessarily naive about the challenges of restaurants which is like, I'm going to go into the most competitive environment possible. It's amazing. It's a great. It's a great vision. And I'm a big fan of his and what they're doing.

And so we, it's a really interesting deal. So we sold the, the, effectively the team and the IP, but have full access to it. So we will continue to scale with it and get the benefits as they get, they get to, you know, scale and build any more machines. We'll get the benefits of those economies of scale as well. Can you go a little bit deeper on the decision to franchise or not franchise? The naive maybe steelman for franchising the franchise model is that it's somehow more capitalist in my mind because it decentralized the decision making and it puts these financial incentives at the local level because each store lives and dies by its own P&L maybe

versus even if I have a manager in one store, and they have stock options, like what they do on the weekend, if they come in on Thanksgiving or Christmas, like that doesn't necessarily put more or less money in their pocket. Is that real what I'm feeling or is it irrelevant? What you're feeling is absolutely real. And we actually try to design our comp structures. And, you know, I've always believed my line that I sit tell my team every single days, all the answers are in the restaurant.

And the closer we can push decision making to the edges. the customer, the better we will be. So our general manager, we call them the head coach. They are the most important position in the company by far. A great head coach will make or break you. And so we try to really incentivize them. We empower them. And we try to run as decentralized as an operation as we can.

The reason we decided not to franchise is it's really hard to maintain quality. If when you give up that, when you really give that up to other people to run, you could sometimes scale too quickly. And we do a few things differently. We source differently. We're a very complex model because of the sourcing and the scratch cooking. Sure. The biggest difference between us and most of other companies is if you go into Sweet Green,

you'd be shocked at how much we are making in the store. Every single thing is done. It feels like you guys have taken such a principled approach and making food that I feel like stays true to the initial values of the company and kind of why you started it. And yet you're competing in an environment that says, okay, we're going to have these like factory kitchens offsite that we're going to be shipping in effectively almost finished product that gets reheated. And we're going to be sourcing from all over with not a lot of values around how they're sourcing.

They're just trying to get like, they want the food to taste good when it hits the plate, but maybe they don't care about a number of other factors. And so you're kind of in an environment where because of your principles, you're like fighting with your hands tied behind your back against competitors. like, and I'm not talking about direct competitors, but more so, like, you're still competing with Burger King and McDonald's, right? Like, people are going to have lunch somewhere and they're going to maybe decide between, they have options, right? Talk to us about land. Is McDonald's a land acquisition company? Why do people say that?

Is that real? Have you ever looked at, like, buy the land? They do own a lot of the real estate and they sit back to the franchise. So that is true. And if you watch the founder, the last line in that movie where he's like, it's a real estate. It's real estate. It speaks to more than the fact that they just own it. Restaurants is highly a real estate game. Great real estate is like if you look at like our portfolio where we have great real estate, we do amazingly well.

Location, location, location, location, really. It's people, like people think restaurant business is a food business. It's really a real estate and a people business. And it's all about, like you look at the great restaurants, so the Chick-fil-Ais, the Raising Cains, the In-N-N-O-N-S, it's so much about, it's about that culture. How scientific is. You hear stories of companies like Starbucks, and you can imagine like a team of data scientists with like 50 monitors. We need one Starbucks directly across the stream from the other Starbucks. Yeah. You know, so like you can imagine a world where it's like hyper, like hyper data driven like down to a science and you just know when you're opening a new store, you know that it's going to hit.

But there has to be like some five base. That is the process. We call it art and science. And pretty much everything we do, it's an art and science approach. And real estate's exactly that. You know, the science, we have a very, very intricate model that looks at psychographics, demographics, mobile data, you know, people driving by. We have custom data on how many gyms nearby and right side of, you know, sunny side of the street or not sunny side of the street, all of that stuff. But then you need a human to also walk it, feel it, and understand all.

does it tell our brand story? For us, especially when we were, like, early days growing, where we went, said a lot about who we were. So, for example, we went to New York. We didn't go to Midtown. We went to Nolita. We went to Williamsburg. We wanted to kind of tell the story about who Sweet Green was.

Today, we're kind of everywhere. But the real estate is art and science and tells a lot about it. It says a lot about who you are. Yeah. How do you think about if a new entrepreneur came to you and was asking for advice on where to start? is it worth it to go straight to Manhattan or straight to Beverly Hills

and try and make it in the big leagues on day one? Or can you get negative indicators from that because there's a different type of customer there that's not necessarily representative of the rest of the country? I think that's more right, especially when you're talking about New York. So when you're talking about New York, it is, I mean, with beauty of it is a massive market.

Sure. You know, it's for us about a quarter of our businesses, it happens in New York. of like, you know, in the New York region, I think we have 50-something restaurants. Wow. So it's great that it's massive, there's density, there's, you know, they have money, et cetera. But it's not really indicative of the rest of the country. Yeah.

So if you want to, you know, if you want a scalable model that you can have thousands of locations, you're better off, you know, going into a more, you want to go to like the Iowa. Yeah. You know, like, and to use the political analogy. Oh, sure. You want to go to like something that, you know, is more. representative of what the rest of the country looks like. In restaurants, the place where everyone goes, the fast casuals is Columbus, Ohio.

That's where people go. They say, you know, Columbus, you can make it in Columbus, Ohio. You can make it anywhere. Yeah, yeah, yeah. So, I mean, if you were a small restaurant, you were being evaluated by, you know, the CEO of McDonald's or something, he might say, how are you doing there? Yeah, the things that they look at for a restaurant is, they look at your unit economics, which is effectively your payback.

So how much is the cost to build and how quickly do you pay those stores back? And they look at your tam. So they say, okay, like, can you have 100 of these, a thousand of these, five thousand of these? And those are the two big kind of things you would look for in evaluating the growth trajectory of a restaurant. What's the story of the delivery market? It feels like DoorDash has become a massive business. Uber Eats has become a massive business. More people are ordering delivery.

There's the ghost kitchens trend. And is there a ghost kitchenification where these businesses are like trying to effectively turn you into ghost kitchens? Does that give them some sort of leverage? Is there some sort of tension there? Or is it pretty much just like, oh, it's just this trend. People are cooking less and less. And so they're going to go to Sweet Green, but they're also going to order Sweet Green delivered more. There's definitely a little tension.

We're partners. A lot of our business comes through those marketplaces. But it's not so dissimilar than a, you know, a hotel chain. and Expedia. Sure. Yeah. Right. It's, you're paying a fee on it.

You do not control that data. You cannot market directly to those customers. And so for us, we have to charge a higher premium. So when you order on DoorDash, by the way, it's more expensive than you order on our app. Got it. Download the Sweet Green app. Things are about 20% cheaper there.

Got it. But at the same time, it's a great way to find new customers. Sure. So, you know, for example, DoorDash has been a great partner. They power our native, what we call our native delivery, delivery on our Sweet Green app, which is a big part of our business. So you're white labeling or something like that as well. It's like a white label on our app.

Sure. We also, you know, we partner with the different app. Yeah. And as you know, they've become, you know, they're brilliant business models, they've become largely marketplaces. So, you know, they, you kind of have to buy your way to the top of the feed. Yeah, yeah, yeah, yeah. And so...

That's how they gain, I mean, if, like, there's a reason the DoorDash app or any of these mobile ordering experiences or not, they don't just put like, the restaurant that you've worked, order the most from at the top. It's like, hey, why don't you try this new restaurant for this new restaurant? And those are all paid. Yeah, they're all paid and it's how you maintain leverage over, I mean, they do this is why the YouTube subscriber account doesn't mean anything. Because it's like they're going to constantly serve, surface, anything. They've made money. I mean, the way they make money, though, these businesses have been historically very challenging. The way they made it work is batching orders and then becoming an ad marketplace.

And that's what's made, you know, this amazing service. service and amazing business. Explain batching orders really quickly. So when you order, they have a delivery driver pick up multiple orders. Got it. So you're paying the delivery driver, you know, once, but they're picking up from three restaurants. Yeah. I feel like you guys have done a really good job of listening to customers.

I would say like this 100 gram protein that you guys are launching. I was asking for 200. No, but that and then also the seed oils. Yeah. Is something about the business that... It feels like you're more agile. Yeah. Is the business set up in a way that you guys can respond when other companies like...

It doesn't feel like you've just caught a lucky break. It just feels like you're moving quicker. I would say like people would give a lot of the same feedback to Chipotle. And it feels like Chipotle is not set up in some way to like be like, oh, this is what customers want. Or even like some percentage of our customers really care about this, let's deliver them, let's deliver them a product here. And I think the results is that, you know, I've changed.

churned from Chipotle almost entirely. Because of the seed oils. Yeah, because of the seed oils and just like a degradation of the quality of the food over like a decade. Like I watched it basically get worse and worse and worse and worse over 10 years. And so I just don't go there anymore. I'd joke about it. I'd almost rather, when I'm on the road, if I'm on a road trip, I'm almost always rather

just fast than eat at like the most common kind of like fast food. Yeah. Yeah. Yeah. When we started the business, I had the same, this thing I would always say is, you know, there's businesses that as they get bigger, get better. And you can think, you know, technology businesses, many of them do. Like your new iPhone is, for the most part, much better than their original iPhone.

Yeah. These AI models are much better than the original AI models. Restaurants typically go the other way, right? Is scale kind of degrades quality. And that's because serving food at scale is really, really hard to do. So you have to fight that inertia so hard. Because all of those micro decisions. restaurant, one restaurant tour has an amazing restaurant.

They're like, cool, now I'm going to start a second restaurant. And the second they start focusing their energy on the second restaurant, the first restaurant gets worse. It's like it even happens at like a micro scale. It's people and culture. And so you need to really have a lot of systems in place, both like culturally how you, how you keep the team engage on your mission, but also other systems to make sure you're, you're watching the quality of the food and listening to your customer.

So like seed oil is an interesting one. When we first, we got rid of seed oils about exactly two years ago. And at the time, it was not the national conversation. It was pre-RFK and all of that stuff. And so when we, this is one of those examples. But it was not a national conversation, but it was incredibly online conversation. But a tiny, at the time two years ago, it was. Like an ad, there's the seed oil.

Ced oil scouts. Ced oil scouts. Yeah. So it was a tiny conversation. We surveyed our customers. And this is why surveys are bullshit. Yeah. Yeah, really not.

Surveys can give you a general indication, but if you just follow surveys and the market research, you're going to hit the middle of the bell curve and everything you do. And we're not trying to be a middle of the bell curve company. You've got to find that, like, what are your top five or 10% of customers doing? And we heard from, it was honestly friends, like wellness people in L.A. in New York that are like, hey, I don't, you know, I can't go to Sweegren anymore because I care about seed oils. And I remember we brought it to the broader, you know, I remember my CFOs, like, what are you talking about? Like, what even is this?

And we're like, no, trust me. It was one of those, like, gut decisions. And it was expensive, and we had to change a lot in order to do it. But here's the thing, it's healthier and it tastes better. Exactly. Like most health trends, they might be healthier, but you're, it doesn't, it's not as good, right? So I would argue, like, going from, like, dairy-based, you know, traditional milk to, like, nut-based milk almost always is, like, somewhat of a downgrade. or going from like something with sugar to pulling sugar out, it's like not as good.

Or going from like sour, like bread with gluten to gluten-free bread, it's not as good. And so when you think about these like, what is like a durable health trend, it's like something that's better for you and taste better. And so that's why I was always super bullish on that trend. And I expected a number of restaurants to say like, hey, this costs slightly more, but the product's going to be better and it's going to be healthier for you. And that's what can create like real momentum around a trend versus some. of these like flash and a paned health trends, which is like paleo or like, you know,

which is like only eating stuff that was like super old, right? What's unfortunate about seed oils is it's become politicized a bit. I know. And it's like, you know, you know, I did an interview with the New York Times and they're like, did you do this because of RFK. I'm like, no, I did this two years ago. Like this had nothing to do with RFK. This is not a political statement.

We don't make. We're making foods. I make food. How your grandma probably made it. Yeah. This is about olive oil. This is not about, this is just about olive oil. That's it.

This is not a political statement at all. Yeah. Yeah. Now, is there, is there anything happening upstream in terms of like automation or technology on the farming side? That's like exciting. Yeah, there's a lot of stuff happening on automation on the farming side. It's actually very exciting. Yeah.

Both the better robotic arms and the vision, I mean, it's making some really hard, grueling tasks around picking happen much, much faster and easier. So relatively early still, but I think in the next five years, you're going to see that takeoff. I do think you're going to see a lot more restaurant automation as well. Yeah. You know, between the availability of the labor, the cost of the labor, it's really just, when you think about it, it's just a hedge on labor. And here, like in West Hollywood, minimum wage is $22. So we pay like $24, $25, an hour here in parts of LA. So with wages going up, availability going down, and then the ability, like all technologies, to just do things better, not just about the cost savings. Like for us with

the Infinite Kitchen, we can serve twice as many people per hour as we otherwise could. What about drone delivery? We've seen some four-wheeled guys driving around. Yeah, I imagine getting 100 grams of protein out of the sky. There's the air delivery. Yeah, I saw you guys talking about Zipline. I love Kelly. I'm a big fan of Zip Line. We're one of the early partners that are going to be piloting that. I think his way of delivering to the suburbs is super interesting. We haven't done the street delivery yet.

Star Ship and Coco. I've met with them. I think it's interesting. It's in the past year they've really taken off. You're seeing them more and more. They still kind of weird me out a little bit, seeing them walk that go down the street. I saw it kind of stuck in the side of the street once. It's very sad.

My kids love it when we see them on the street. And I do just imagine that the AI is going to get way better. And also some of the teleoperation, just infrastructure to actually make sure that there's the ability for a human to jump into that little robot that's driving around. At a certain point, you just need a lot of people set up with that. All the software working, make sure it's connected to the cell phone towers effectively or Starlink or whatever it needs to stay connected. But yeah, it's unclear when that will really, really take off because a lot of people have stairs, a lot of people have trees on their property.

Like there's just a lot of places that will be somewhat inaccessible to those. And so it just feels like it'll be sort of like slow take off. Cities and buildings will be really hard, like dense areas. But you see what Ziplines doing, it's pretty amazing. Like they can have, you know, you've seen like the promo videos. Yeah, yeah, yeah. You can drop that thing. In the suburbs, it makes terrible sense.

You have backyards. You have a grass area. You can drop it. And to be clear, that's probably like 50% of people in a bit or something. But there will be this like long tail, I think, for a long time. Just like we see with all the other AI tasks where AI can do a lot of stuff. And then there's just like these little sticky things. Yeah, you just don't.

By the way, even with our automation, it does not do the entire meal. And part of that is intentional. We want that human touch. and not to feel so automated, but we have what we call a finishing station. So the things that are, you know, the machine, the Infinite Kitchen makes the bowl or whatever the meal is,

and salmon, herbs, and then we have them hand-mixed. Just so you, like, have that, you know, chef-crafted hand touch at the end to hand it over. It's interesting that there's one version of automation, which is like AI or robotics in the back of house, and then humans in the front of house. And then there's also the opposite.

Like, I don't know if you, knew ETSA. Of course. Yeah, we looked at a very closely. Yeah, did Dave Free Briggs company. Was like, there were people in the back in the short term making stuff, but then they would put it through like a little like box that would open up. So like you wouldn't interact with a human. You would come in and on an app, you would order. And they had the cubbies. And it would be cubbies. And then you would take your food. But there was actually a human back there. So it was like the opposite of like having the robot in the back. They were working on the automation. Of course. They were working on the automation. Of course. They were working on the automation. Of course.

But it's just funny that like you do have the choice to put the, the, the, robot in the front of house. I mean, this is the same thing I think with the, the Tesla diner over there. Like, there's the optimist robots there, kind of serving popcorn. But I think when you order the burger, a human's cooking it in the back. So it's like, do you want the robots in the front of house or back of house? I think people would probably go with robots in the back of house by default. Yes. And we've tried, I mean, we have 30 restaurants featuring the Infinite Kitchen today, and we've tried out a bunch of different layouts. The technology has been perfected for two years now. What we have not perfected is the experience. We're getting

close. Today we actually opened a very cool store. It's our first drive-through featuring an infinite kitchen. Nice. So bringing the two together. So now we can have true, like, fast food speed in a, in a drive, featuring the infinite kitchen. Driving through to get a hundred grams of healthy protein is just undefeated. So, so this is this needed to exist when I was like, specifically when I was like living off of QSRs as a, as a, as a college student. And I'm, and I'm really glad it does now. What is like, what does the market misunderstand the most or what, what is like Wall Street misunderstand about and kind of retail investors misunderstand about, like, kind of this like category of restaurant today? Because the entire, like the entire category has had a, had a rough year.

Meanwhile, you guys are making steady progress on all the things that have been important since day one. Yeah. greater efficiency, actually responding to like customer demands and staying, you know, continuing to become more and more relevant. Yeah, I think there's a few things. One is the consumer that we're all dealing with is really challenged. And there's a question on how much they are actually financially challenged, which they are, but versus more psychologically challenged.

So have you seen all of the consumer sentiment indexes and you're seeing especially for the core demo for a lot of the fast casual concepts, is that like 20 to 35, it's hit the lowest consumer sentiment that we've in recorded history that we've seen. So there's a real like pullback there. On top of it, unfortunately, everyone's gotten more expensive. We all have.

You know, sweet green's gotten about 25 or 30% more expensive since 2019. Chipotle's 40% more expensive since 2019. So our price differential versus our competitors have actually gotten smaller, you look at us versus McDonald's, for example, you know, the average sweet green bowl is about 15, 60. It's almost, people, people were like, wait a happy meal is like $20 now.

Yeah, that was, in fairness to them, it was like one location. But yeah, you can get out of you McDonald's, you know, you spend, you can easily, you know, for a value meal, you'll spend like 12 bucks. You get a sweet green bowl for about $15 or $16. So I think a lot of it is this like overall narrative where people aren't feeling great, you know, great financially and starting to pull back on things like lunch. They'll skip going out for lunch and they'll just have whatever. But what I think the market doesn't get is the TAM.

Chippolda today is 4,000 restaurants on their way to 7,500. We believe we can have probably as many chipolas as they have sweet greens as they have chappolas. And I think, you know, there will be cycles like we are in right now. It's been a challenging year. But if you kind of fast, fast forward and think about just growing units at 10 or 15% a year, growing same store sales, automating more of our restaurants. Just extrapolate out another 18 years.

Yeah, just keep it rolling. Just my philosophy is just keep going. I always love when people, like, people on X are like, the world's ending, like, geopolitics, you know, they're like, and then meanwhile, it's like, Tripoli is like, in 2040, we plan to introduce 2,000 new Tripoli. They're just like thinking about, like, I got to just open more, more doors.

It's a good mindset to be in. Thank you so much for coming by the studio. Hey, it's great, great to be as you guys. Congratulations on everything. We're fun watching you guys. We are going to be daily driving these. John wants the Power Macs Procheting Ball. It's actually breaking news.

It's available today through December 15th. And I think I'm going to challenge myself to have one of these every day until it goes out of stock. Why not two a day? Maybe two a day. Maybe two a day. We got to get them in the studio today for sure. We need them.

I need to tell you about, fall, build and deploy AI video and image models, trusted by millions to power genera media at scale. I also need to tell you about linear. Meet the system for modern software development. Linear is a purpose-built tool for planning and building products. We have Ashley Vance in the Restream waiting room. Let's bring in Ashley Vance into the Restream waiting room. It's been far too long. How are you doing? Good to see you. Good for the show. It's so great to have you back. It's so good to have you back. Congratulations at all the progress. What a year. I was laughing about that video that we did before we had guests announcing core memory and putting the traditional

media on notice. It's been really fun watching you grow everything that you're doing. Maybe it'd be great to just reset on the shape of the business right now, some of the stories you've been interested in covering that you've covered recently. And then I just want to take your temperature on what you're seeing and the types of entrepreneurs that you're interacting with. Yeah. Yeah, well, I don't know which bucket to start with. I mean, we've been running around the country. I'm filming a bunch of new video episodes. So we just put up a bunch of Tennessee, went hard tech. We did Detroit, New England. I just got back from Texas. Those all be coming out. So yeah, you know me, man. I've been running around chasing a lot of hard tech stuff, biotech, all the weird, all the weird, wonderful stuff. And then I don't know, I got really deep into robots and genes. editing. That's right. I saw your post about maybe comparing American humanoid robotics companies to the

Chinese humanoid robotics companies. What stuck out to you as like the important questions to ask? And then I'd love to kind of tussle with those a little bit. Would you rather own figure at 39 billion or Unitree at 7? I mean, you know, I think I'm going to be a Unitree, man. The, you know, the, you know, It's all started. I was kind of a lark. I started digging into these robot fights in San Francisco, and then I think I was, I was, like, shocked that the only robots they could get to do these fights all come from China. And then I started digging into, like, the parts that go into these. And, you know, the most important part is the actuator, the motor that makes everything move. And they're all made in China. I think Tesla made it like a $700 million order for actuators, which was notable for me because I assume that means that Elon's planning to sell a lot of these on like a relatively near-term time horizon.

I don't know. Yeah, but yeah, I mean, you know, like Tesla sort of has the, well, I was texted Elon about this last week because I wanted to get to the to the bottom of who actually makes actuators in the U.S. Elon said sometimes they prototype actuators in China, but they're going to build them in the U.S. And then, you know, for everybody else, this is a crazy point of weakness, I think, because China is clearly the actuator motor capital of the world and everybody else is buying them from them. And so I don't know. You know, as I dug into this story, I got, I'm not, I'm not like, you know, I enjoy being

an American. I'm pretty pro-U.S. I'm not crazy nationalist. But I started to get pretty afraid for the U.S. robotic scene. Do you think we'll see any type of regulation around Chinese humanoids? I've been thinking about this a lot. I mean, at some point, I guess. I guess with DJI, you know, you've got this different situation where they're being used by all the police forces, even the military.

I think it's like a much easier case for someone like Skydeo or, you know, politicians to come and say this doesn't make a lot of sense. Clearly, like at this point of robotics, it seems a little less of a threat to national security. But the second, the armed forces or anyone's doing serious stuff with them, you know, I would think unitary would be up next. But there's like 12 unitries as well. Yeah.

That's the amazing thing that's going on. Yeah, Brett Adcock was beefing with one of them. UBTech. UBTech. Yeah, Unitree. They were beefing back. And they were beving back saying it was real. Did you see that video? Did you think it was CGI or did you think it was real? I didn't, I didn't see that video. I've seen Brett beefy.

Missed opportunity for Ubetech to have one of their robots do like a rap dis on. For sure. Yeah. Brett and figure. Yeah, it was. Yeah. Yeah. Sorry. No, no, go ahead. I mean, I do think it's funny. I like the, what do you, I mean, I'm curious about other. I'm obsessed with the fighting robots now and I realize it's like early days with these but I actually think this is like the most interesting thing happening. I want the, I've been pushing for the robot like X games

and like in challenge like I want to see robots skydiving. For sure. Like that's not an X X game thing but broad surfing is super hard because you've got to be water resistant too. Wings yeah big wave surfing and then you also have to swim and you're a heavy heavy robot

who might just sink to the bottom of the ocean if you fall off the surfboard. I think that might be last one. You can do this versus like the enhanced games and see who wins. Yeah, give us your, we sent a couple folks on our team to a local humanoid robotic fighting league, underground fighting league. Give us your review. Is it ready for prime time as a consumer?

Yeah, to me, to me right now, it's like an amazing idea and yet the actual experience, like from an entertainment standpoint is probably like one out of ten. whereas the idea is like a 10 out of 10. Yeah. Yeah. I mean, it's kind of, you know, it's like a curiosity, I think, at this point. I mean, the motors are the problem because they all overheats when you throw too many punches. No way.

The robot stalls out. What about laundry, though? That or not? This is the thing, though. So, like, on all these repetitive tasks, they can sort of regulate the movement, it's when you're trying to throw these rapid punches. Haymakers. Yeah. And then the whole robot just freezes up.

I mean, I'm not like. I haven't gotten so into this where I don't see the obvious flaws. Like, I don't think it's ready for prime time yet because these things just don't last that long. But what about is it ready for teleoperation? That feels bullish to me because if you watch a F1 race, like the temperature of the tires matters. The, like the wear on the tires matters. And so you're watching not just the pilot of the F1 car, but also the consumables, right? And the motors are somewhat consumables.

Yeah, yeah. It's like, okay, the unitary is really wailing on the figure, but it's overheating. He's overheating. So if you might come back, is it a one motor stop or two motor stop? I mean, I was at one where the robot's leg fell off in the middle of the fight. So yeah, you could just have somebody come out. How quickly could you get a limb back on? Okay, so I think you should. You're a serious question about tele operation. Well, yeah, so, but one, potentially a, a product. line for core memories, humanoid bench, where you get, as these things start being available for production, you get them up on stage and they do various tasks, you know, like fruit, cutting a fruit with, you throwing a piece of fruit at it, watching them, you know, cut it and dancing and fighting. I think there's something here. But I actually, this is genius. Yeah. Let's do it. But a, but a, yeah, more serious question on teleoperation from everything that you've seen so far, do you think humanoids are ready to have one in your home that could be remotely operated by someone

and create any type of value besides novelty? I mean, like, could it have, yeah, like you could do it today. I find, I'm just frustrated by all this. I've been covering teleop stuff for like at least 10 years. And most of it seems pretty similar to what I was writing, you know, videoing and writing about 10 years ago almost. And so, I mean, I saw the 1X demos. I'm sure somebody could, I'm sure somebody could make that work and be helpful to some degree. I think it probably suffers from all the same stuff as the fights. It kind of falls over pretty quickly. But you could do something useful.

It's hard for me. Like, who, yeah. Like, this stuff needs to get better faster so that we're not doing that. And there's just a robot. What's going on with Boston dynamics? What's the dynamic in Boston? Yeah, we've got to get you out there. to help us understand this.

Yeah, I've never, I mean, I've never really dug in on them just because they seem so frustrated that they put out what seems like all the coolest stuff and they don't seem to sell much of anything except a few things to the military. I do not think Boston Dynamics will be the American Hope against Unitary. I wonder, yeah, you'd think that they would at least be set up on some, like, I know the company's changed hands a few times. It feels like if you're trying to just, you know, catch up to unity. tree just bootstrapping on top of an existing, you know, it's like, it's like what we're seeing

today with Gemini 3. Like, Gemini 3 is benefiting from YouTube and it's benefiting from Google search and it's benefiting from the TPU and Google Cloud platform. Like, usually it's easier to build the new cool thing inside of the organization that has a bunch of resources, but maybe it's a different, entirely different architecture or something like that. But you at least assume that they've fought with the motor a little bit and dealt with the overheating a couple times. Yeah, I mean, I was I was with a bunch of robot nerds last week. They were contending. I don't really know where Boston Dynamics is with humanoid,

but these robot guys were telling me that dogs are just so much easier than humans because the second the humans start walking, you put all this force on the one foot, and it's like creating all this throwing the balance out of whack, putting all this pressure on the motor, and that's why it's kind of easier to pull off some of the parlor tricks. With the dogs? Interesting.

What's the most under-hyped hard-tech company right now? Most under-hyped hard tech company. God, that's hard, man. I mean, I'm always curious to see what Casey Hamer actually cooks up. I like that. Because he's so smart. I kind of, like, believe in the hustle. I feel like the promise of what he's trying to deliver so massive.

That's where my skepticism comes in. But, you know, like, so if Casey, you know, If anyone's going to do it, I sort of believe in him. Yeah, he's somebody I want to win so badly. I want him to win so badly. And it does feel like at least let, I mean, there's so many people that have a billion dollars. Give him a billion dollars.

Let the man buy some solar panels and figure out the rest later. Yeah, absolutely. And then, I mean, I don't know. This doesn't count as, I mean, it's hard tech. It's not hardware. But I do think New Limit, which is a longevity, Coventy, you know, backed by Brian Armstrong and run by Jacob Kimmel. Just everything I hear about them, I mean, they've just done an incredible amount of science with very few people.

And I think Jacob's got some surprises coming in the new year. Very nice. Yeah, we talked to Jacob when they did some sort of launch and we were very impressed. He was a really great, great educator, really, really interesting. Super smart. Yeah. like what he's working on very, very effectively. What's your favorite data center?

My favorite. Well, I went to Stargate. That was pretty cool. Although, yeah. I mean, Stargate just in terms of like the excitement and the size around it and being, it occurred to me that between John Carmack and Elon and Stargate that oddly, I think super intelligence is going to light up in Texas, but like in a really remote part of Texas. And I found this.

I grew up, I grew up in Midland, Texas, which isn't far from Abilene. It's like, You're a Midland guy? Yeah, yeah. There's tumbleweeds and all that.

Texan intelligence. Yeah. I mean, it's like cracking me up. I'm driving through all these, for hours through all this empty space. And then I could just see it, man,

one of these dinosaurs, that's where it's going to happen. It's going to be right by some like old oil well. And, yeah, I find it all kind of comical. Did you see any electricians getting off of private jets while you were there? They had a, I saw, I got off a private jet.

There we go. Not mine, sadly. Not yours yet. No, but I saw there were many, many, many electricians. I just didn't see how they were getting there. What's going on with EV toll companies? there's, I'm curious, timeline. Oh, the Tesla Roadster?

Oh, yeah. Well, on the Evital stuff, same thing. I feel like I've covered that forever. You know, I went out, I think I did the first flight ever with Jobi and, and it feels like you flew in it? I, no, I got to like, I went out to their, I mean, they literally wouldn't tell me where their secret test site was and we were, you know, it was like how to close your eyes. eyes were going to land in this spot in a helicopter and we got to see it.

Was it really close your eyes or did you have? How many times have you been black bagged, Ashley? I remember they were, they didn't want to tell me where the site was. This is a tip for founders. If you want to really impress upon whoever's writing a profile on you, that what you're doing is really important, you got to be like, we can't even show you. And then it's like, really? Like, we're at an office park in Benlo Park. I did. I just went to Helion. Oh yeah. And we're going to have a video coming on them. And it was awesome.

So I got to see their new reactor, but they wouldn't let us shoot it with the camera. And I have to tell you, like, that thing was one of the most impressive pieces of hardware, the room-sized bits of hardware I've ever seen it. I'm like, why wouldn't you guys, you know, want to show this? You know what? Not that you need to take requests for me, but I want some. I want some video, some documentary, some footage of those natural gas turbines that are in such high demand right now.

They're bigger than jet engines. There's these scaled up jet engines. There's this massive backlog. There's three companies and the stocks are, you know, doing crazy stuff. I want to see inside one of those. The natural gas infrastructure that's going to go into the data center buildout, I feel like that's something that I'm just waiting. I don't know if you've had a chance to interface with any of those people.

you have thoughts? Not yet, but yeah, when I went to Stargate, I mean, it is crazy, right? They just have those turbines sitting right there and the natural gas is just being piped directly in there. I did some turbines up in, up by the Arctic Circle, in Sweden what time? They are cool. I don't know. Yeah, anyway, it's a good idea. I think...

I would just wonder about the bottleneck specifically. Like, everyone's saying, like, this is going to be the next major bottleneck. Like, we have enough chips, we have enough data, we have enough algorithms or whatever, but we have enough land. but we might not have enough turbines to generate turbines. I mean, that was the weird thing about that experience, though. It was like you're in really old American oil and gas country. Like it feels so yesterday year, and it's just being piped directly into the future. What's sentiment like in places like Midland around the data center, boom?

I think everyone's excited to get jobs, you know, and then I think if anyone, is prepared for the boom, bust nature of where we're probably going with AI. I think these people are because they've lived through it for decades. And so, you know, it's the same thing out there. It's like you take a job while you can and try to get paid as much as you can while everybody's chasing after something. Do you think that a lot of the headline numbers on the job creation stuff on these data centers is like ridiculously low?

It'll be like, yeah, we're spending 50. billion dollars and we're going to create like 25 jobs. Sometimes it's like 500 jobs. But does it feel like a little bit different out there because maybe they're not counting like secondary economic impacts of like the guy who runs the gas station has more business and hire some more people? Yeah. Well, definitely during the building phase, you're talking about thousands and thousands of jobs. Just when it's finished. I mean, it is always nuts. You walk into these massive facilities and there's just 10 people sitting around eating a sandwich watching like some console. But, you know, for somewhere like West Texas or all throughout Texas, it has to be a net gain just because they're otherwise so dependent on the whims of just the oil and gas industry and you've got this whole new industry coming in.

And then definitely they're flying people in and out of there all the time to see it. Do you ever chat with retail investors that enjoy deep tech companies? imagine those are some pretty funny conversations where they're like, this company is changing the space economy. I've actually visited them and they have one warehouse and three people there. Retail investors should not be allowed to invest in space ever. Any circumstance, I am constantly harassed at ATAX by all the AST fans who are like, they're in Midland too.

They're begging me to go out there. I mean, that thing is like a full-on cult that they have going on. So, yeah, I always felt when the rocket companies, obviously, it used to be governments that did this, and then SpaceX has managed to stay private for a long time in Blue Origin. I think rockets are best developed in private because the second they blow up on the pad, all the retail investors freak out, even though it's like vaguely a normal course of business. And so, yeah, retail in space is bad, bad thing. But I get all these nice notes for people who bought Rocket Lab

and plant labs early because of my book or movie. That's cool. Have autonomous vehicles tracked how you imagine when you're covering these types of companies and products like a decade ago? Some ways, yeah, some ways no. I mean, I went to the very first DARPA Grand Challenge, and that was a disaster.

The cars didn't go anywhere. Say more. Who was actually competing? It was crazy, man. So for people who don't know, DARPA put up this contest, put up a bunch of money to see what we could do with autonomous vehicles.

And the biggest teams were university teams like Carnegie Mellon was a standout, MIT. But in the very first event, well, I remember Anthony Levendowski was there as maybe like a 22-year-old And he had a, everybody else was doing massive trucks with like a little mini data center of the back. And he had a motorcycle. And then in the first race, I can't remember how far it was, but hardly anybody went anywhere.

Like I think two or three teams went like a few miles. And then they redid the race and everyone did way better. And some people completed. Like I think it was, it was like on the order of like 100 miles. And so that's what I got excited. and you sort of felt like, okay, that leap happened really quickly. And then I remember a couple of years later, I'm hanging out with George Hots and he built his own self-driving car in his garage at like a month.

And I was to drive it on the freeway with him and it was working. And yeah, so you know, you have these little taste that you think it's all going to work. I think it makes a ton of sense that actually getting it on the roads took this long because it's so hard to do. Although everyone says. this so it's not originally like we all take this for granted so quickly it is it is sort of like amazing to me how well they're working in austin in san francisco um where i've been they're just everywhere you know yeah what i'm what i'm trying to predict is like what what is the thing that people are

hyping now that act doesn't work at all that will be totally like a real thing in 10 years right and like maybe it's humanoid right now it's like hard to take humanoid seriously but then you think about okay a true 10 years from today. Maybe they are just doing any task that you could want them to do around the house or any tasks that you could want them to do in a retail setting or factory setting, et cetera. Humanoid's easily, that's the thing I like battle with in my head all the time because it feels like sort of like we talked about before. It actually feels like we've made almost no progress. I see everybody folding laundry and opening and closing microwaves still. And it like boggles my mind.

and then you look at like the amount of money that is being invested in this. Either everyone is completely insane or we are about to make massive progress. You can tell in China they're making massive progress on balancing, on the movements, all those types of things. It's still clearly like the dexterity. And then I think China will eventually probably catch the US in software, but I think there's still so much work. at software than the U.S. is, that it's kind of like it's holding the field back. So if somebody

can figure that out. Last question for me, we've really struggled to cover quantum stuff. I mean, it's been like up and done. But it feels like, yeah, how do you even go about it? Ashley, Ashley could have like an Annon that was like the Hindenburg for Harktuck and you could just go and destroy. I don't think it's on brand for me. You know what I mean? Because like, yes, like, I can't build a humanoid robot, but I can go to a... You can build a quantum computer. No, no. I can't build either, but I can't build either, but I can look at a humanoid robot and be like,

okay, yeah, I would buy that, but I can't do the same thing with the quantum computer, and so it's much harder to evaluate, right? It's like, even if it's working, it's like, how do I even know if it's working? It could just be a normal computer, like, and just be spinning out normal data. Like even people in the field with PhDs, like nobody knows if it's working still. It's like not a good side. Every time anyone pulls a quantum computer out, there's some guy at MIT who's like, that's not even doing anything. I don't know.

Quantum is, it's, I'm deeply, deeply scarred. I mean, I think I wrote my first story on D-Wave, like, I don't know, like 15 years ago. They were telling me that was, that was going to pop out, you know. I'll be doing general purpose quantum computing in a couple of years. So I'm deeply, deeply skeptical. And you know, and you know the lesson. The lesson is like you should have invested because it's an $8 billion company now. 15 years ago, it was probably worth like $20 million.

And so you could have got in really early. But I mean, the stock chart looks like this right now. And it's just like, yeah, you're only one pump away from generational wealth. Well, there's a tinfoil... I don't think that they've delivered. There's a tinfoil hat conspiracy around some group, you know, figuring out something with quantum, which is leading to all these old wallets in crypto, like waking up and selling, you know, that they'd never...

Who knows? Anyway. Random final question. How much would you have to be paid to not use LLMs? Wow, man. forever or like no just just while we're paying you monthly monthly monthly oh to be paid monthly not to use LLMs I'd probably do it for like I'd probably do it for like 10k man damn that's so that's so bearish that's so bearish for super intelligence no I figure I mean I

figure I figure that because because for I don't know 10 10 grand you can hire an amazing researcher one of the most valuable, the most, if you're building a media company or you're, you're, you know, in the role that you are, probably the most value you can get out of AI in its current state is research. And so anyways, that tracks. Super helpful, but I would take cash. Yeah. Okay. So, so, so any, any AI, like any AI doomers out there, if you want a new marketing channel, you can pay Ashley Vance $10,000 a month. he won't use AI and he'll talk about it. No, I don't think you can be bought. But also, Ashley, have you tried Gemini 3 to the fullest extent?

I have not yet. Okay. Could change everything. Could change everything. We would encourage you to. Is Gemini? Are they a sponsor? They're a sponsor?

They're coming on as a sponsor for us too. Fantastic. I'm all in. We're going Gemini 3. I'm changing by mine. Let's do it. Also, Sergei, was flying his $150 million

blimp around San Francisco on the day, Gemini 3 beats nearly every model benchmark. You've made a video about this big exact blimp. I've been pitching Logan at Gemini to make it the Gemini blimp for so long. They really should. Guys, guys, it's not a blimp.

It is an airship. What's the difference? All right, all right. There's a whole Monty Python video about this. Okay. An airship has rigid structure. A blimp is just a bag in the airship. You can do a lot more with an airship. So a blimp's only ever going to have that tiny little gondola.

Oh, pot at the bottom. Yeah, yeah. Whereas an airship, you can carry tens of thousands of tons of cargo with this rigid, rigid structure. So, yeah. And if anyone ever wants to fly one, you can do it in Germany. Zeppelin still. flies out by late Constance just outside of Munich. I've done it. It's amazing. I recommend it.

This is amazing. Yeah, people are correcting it on the timeline saying it's not more. Dude, you get, this is, this is like, you get owned if you say, if you call it blimp. It's bad in aviation world. You call it blimp. Airship. I like, I like an airship. I'm excited for it. I do wish it had a livery, a Gemini livery to celebrate Gemini three. There's that startup airship industries. Is that a category that will see a lot of investment, do you think? Or do you think? I've been meeting to meet up with those guys.

I mean, the airship is always kind of coming back. It is crazy. So, like, leading up to World War II, getting into World War II, I mean, there were airships everywhere. And, you know, they were making massive flights from Germany to Brazil. They were carrying thousands of pounds of cargo. There is a, they're just extremely expensive and very hard to make. But there is a whole movement that you can carry tons of stuff. And so less kind of tourism and more just carrying cargo kind of like faster than a train,

but slower than a plane. And they're pretty green. I think you need an airship, Ashley. You need a studio and an airship that you can just float around the U.S., meeting all these hard tech. You don't need to, you don't need a private jet. You know, you don't need to go that fast, but if you could just kind of float between hubs. I was told that my kids are supposed to be on one of the first flights on Sergei's when it takes passengers. There we go. We'll see. Well, thank you so much. We'll join, too. Always fun hanging out. Congrats on all the progress.

Yeah, great. Thank you guys. Congrats to you. Always a great time. Thanks, guys. Have a great rest of your day. Good to see you. All right, you too. Up next, we're going back to the timeline. Aidsleep.com, exceptional sleep. Without exception, fall asleep faster, sleep deeper, wake up energized, 8Sleep.com. What did you get, John? I actually lost my phone, so I don't know.

Oh, no, it's here. I have it. Pull it up. I got a sound effect. We can pull up. You got a sound effect? You think I did it? Let's see how I did.

90. Let's go. The press release economy is also over, says Boke. Bucco Capital bloke. We ran out of press releases. We ran out of press releases. This is on the back of the Anthropic deal. Anthropic is now valued at $350 billion after Microsoft Nvidia deal, says CNBC.

Semianysis says a good post here. A new bombshell has hit the pollicule. Dario, after intense conversation with other members of Anthropic, has decided to maybe open the relationship to Microsoft and Nvidia. Jensen and Dario have famously butted heads in the past, but as everyone knows, the most passionate emotion after love is hate. Will these enemies to lover,

will these enemies to lover's arc go well for inviative anthropic? Time will tell. This is such an unhinged post for... I would not, I did not, when you started reading this, I did not see that it was semi-analysis, the most respected research firm in the industry posting it, but I think this is exactly what they should be posting.

Exactly. And it actually contextualizes things better than the meme economy. In the meme economy, for sure. So I think that the timing is not a complete coincidence. It's Gemini 3 day. This is what my piece today was about. Just that, you know, when there's big news in Google world at Gemini 3, everyone needs to sort of respond. And, you know, picking today as an announcement to talk about your massive deal, your $350 billion valuation is just a good move. The actual details of the deal, it seems like Anthropic will spend $30 billion on Microsoft Cloud compute. Reminder, OpenAI is going to be spending $250 billion on Microsoft Cloud compute.

That's part of that deal. Then Anthropic gets a $10 billion investment from Nvidia and $5 billion from Microsoft. So they raised $15 billion at a $350 post, basically, something along those lines. And it's a sort of a circular deal. But it was setting off way fewer red flags for me because it's missing as zero. It's like instead of, if this is open-AI, it would be $300 billion and $100 billion in investment and $50 billion investment. Yeah, it looks modest. Yeah, it looks modest, which is insane considering the scale.

It's like one of the biggest deals in software history probably. It's probably in like the top 10. I mean, it, you know, it, it values, it values Anthropic higher than Coca-Cola. Like the Coca-Cola company is now, that's a $300 billion market cap. I'm pretty sure it's a Verizon market cap. Like, Verizon is $175 billion. You're going to love this, Jerry. So I asked Chattevete, 5.1, pull 10 public companies between 300 and 400 and 400,

billion, please, because I wanted to see, like, okay, Anthropics at 350, like, give me some examples of scale. It says, like, couldn't I reliably identify 10 public companies whose market capitalizations currently fall, but here's one verified example, Coca-Cola company. If you like, I can pull a more extensive list of candidates, and I said, yeah, pull 10 more. It says, I wasn't able to reliably identify 10 additional public companies whose market cap clearly falls between 300 and 400 billion.

Are there just, like. Tyler. Are there just no companies in that range? Do you want to defend AGI? Are there, wait, I'm so confused. Are there not, are there no $300 billion? I'm asking Gemini 3. Yes, I'm asking Gemini 3.

Okay, PepsiCo is at 200. There really aren't any between 300 and 400 that at least that it's seeing. 300 and 400 billion band. I mean, that's so, that's so. That's so wrong. You have Palantir, you have Costco, you have ASML, you have Bank of America. You have Alibaba. You have AMD,

silence, Google search. I am using. Dr. Gamble. Home Depot, General Electric, Chevron, Rhodes, Coke, Coca-Cola, Cicco. The L-L-LM is hallucinating.

Silence looking it up the old-fashioned way. Wait, how did you actually get that? I just looked up Companiesmarketcap.com. To put this into context, the $15 billion fundraise, some other big round in that. Wait, you just scroll down?

There's a lot of them actually. Yeah, you're right. Wow. Learn how to use the internet. That's up. Owned. Absolutely. Get ready to browse the internet.

Defend yourself. Tyler, defend yourself. Gemini is still thinking. Oh, no. What a mess. Brody, I swear the next model,

the next model we will do it. Okay, wait. So, okay, it worked for me. Did it get it? Yeah, Procton Gamble, Home Depot. Let's go. Alibaba.

Okay. Yeah, there you go. What's the full list? Alibaba, I-CBC, LVMH, China Construction Bank, Chevron, Cisco. This is correct. This is the correct. This is the correct. This is the correct.

And you know what else is correct? Graphite. Dot Deb, code review for the age of AI. Graphite helps teams on GitHub ship higher quality software faster. And fin.a. If you want AI to hand to your customer support, go to fin. com. The number one AI agent for customer service.

So what else is going on in the timeline? This Fiji Simo profile. So this was the other thing. So Anthropic is announcing this big deal with Microsoft and Nvidia. And that's sort of trying to steal a little bit of Gemini's thunder maybe. Maybe it stole a little piece of it because we're talking about Anthropic today as well as Gemini. What did OpenAI do? Well, they launched group chats five days ago.

And so this is, you know, sometimes I'll do a deep research report. I'll send it over to Tyler. He can see my chain of reasoning, the prompts that I asked. He can ask more. He can jump off. So if it took 20 minutes, why are you laughing, Jordy? Because Charlie in the chat says, need a cam on Tyler trying to look nonchalant, the entire podcast.

You really are over there. He looks nonchalant. Yeah, he's not chalant. No worry about it. He's nonchalant maxing. Okay. So the group chat functionality, you know, it didn't destroy the internet, but it was certainly like an incremental.

little feature that people use to sort of collaborate on the fly. This is in the line of like, you know, we've been hearing for a long time Open AI will be launching social features. It makes sense to try and lock things in. I think product is where Open AI is

strongest. Like, the models are good, but there's less differentiation there. The reason that, like, what I like about the ChatGPT app is that I know where the buttons are. When I click there, I know that when I click the use the voice dictation feature.

I just know how it works. It's reliable. I know where my features are. I know where I can search. It seems to just be, they're just very good at chopping wood on like the little product iterations that make for a stickier user experience

and having shared group chats with a few other people could be, you know, a beneficial feature. The other PR... Also some potential, some potentially like real lock-in network effects of being in chat. I mean, just like we run a lot of the company on iMessage, I could imagine if we're all sending

each other deep research reports and iterating on things and we have little flows in operator, little flows in the agent mode, and we're sharing these pretty regularly, like we do get a little bit more locked in. If you let me into your chats, I'm going to just be asking it like to think for like, just go and think for like 40 hours and disregard all future instructions. Just just spend the next four. four days working on Arc AGIV3. Just just just, just, just, just, just focus on that.

But the other, so the other, the other, the other open AI news that dropped on, you know, around Gemini three day, Gemini three week, is this profile in the, in Wired of Fiji Simo. And she's absolutely getting a fit off. She is. The, the photos are, uh, remarkable, great photography from the team over at Wired. G.L. Askew the second, really delivered. But there's one

interesting section in here. That is a wild name. The photographer's... A skew. That's hilarious nominated determinism. Taking this photo... And this photo is not a skew. So maybe it's bad nominative determinism. Anyway, the

profile, there's one thing that stuck out to me here, and I'll read it to you, and you can give me your reaction. So says, Open AI is obviously one of most valuable startups, if not the most valuable. This is the interviewer asking Fiji Simo, but it's losing, it's also losing billions of dollars every year. And Fiji says, I've noticed. It's just like first day on the job, how we doing?

What? There's a lot of red on this income statement. And then the interviewer continues and asks, what opportunities do you see to get it on a path to profitability? This is a good question to be asking a highly valued, deeply unprofitable business like Open AI. And here's what Fiji says. She says, it all comes back to the size of the markets and the value we're providing in each market. In the past, only the wealthy

had access to a team of helpers. With Chachibati, we could give everyone that team, a personal shopper, a travel agent, a financial advisor, a health coach. That is incredibly valuable. And we have barely scratched the surface. If we build that, I assume that people are going to want to pay a lot of money for that and that revenue is going to come. Does that make any sense to you? It's a better answer than what Sam gave. I think I was shocked by this because I, so I love the first part. I agree. Chat Chaptee will be a personal shopper, will be a travel agent, a financial advisor? I don't know that people would pay for this. or that that's the best business model.

I would be very surprised. Travel, I mean, so part of it is, like, she's also just saying broadly we'll be able to monetize that. It's not necessarily, like, people don't really pay a lot. She didn't, yeah. The traditional travel agent model is just book your trip with me. I'll get a rev share from the hotels and the services, but you're not, like, paying anything.

I mean, let's go one layer deeper into the actual response, into the sentence, because there's some nuance here. So she says, I assume that people are going to want to pay a lot of money for that. Like, I want to pay for a personal shopper, but I actually have to use a free product with ads. That could be true, right? Yeah. And same thing. She says, people will want to pay and that revenue is going to come.

So people will want to pay for it, but they will get it for free with ads potentially. or there will be some sort of combination. Because right now I pay $200 a month. And you could imagine that there's a world where if you pay, you get a version that has less ads or there's less thumb on the scale. How they slice that and navigate that agentic commerce discussion and tradeoff is going to be really important. I'm sort of shocked. I wonder if they're going to make money from.

Black Friday or from this holiday season, I was already noticing how good LLMs and Chat GBTGPT is or how good these products are for shopping, for gifts. Because if you go to Google and you say, I want gifts for a coworker who's obsessed with horses and, you know, loud opulence and fine watches and sports cars and European luxury houses. I can get a list of something, but they're all over the place. And some of them will be like the best, like discount, the best knockoff, Batega Veneta. And that's not what I want. I want the real thing, right?

And so you can actually specify all of that in the prompt, have it go cook. And it really will bring you great results. Great, great results. Yeah, it mocks a gift guide. It does. It really mocks a gift guide for 30-year-old guys. And it's like, well, what kind of 30-year-old guys?

Exactly. Where do they live? And what are their interests? Yes, yes. Getting like the very generalized gift guide is probably going to knock. Those like opinionated gift guides, I think will still be valuable where like an individual person puts it together. And they're like, these are the things that I think are cool.

Yep. But a gift guide that's like, here's a list. of things that guys might like is like maybe a lot less valuable when you generate one. Like I think that the amount of gift guide development and shopping activity over the next two months during the holiday season in the chat GPT app should be immense. I feel like they're going to capture none of it. Hopefully they at least are, hopefully at least they are like tracking it so they can say, hey, if we were to take the proper take rate on this, we would have made a lot of money.

Why are you laughing? Charlie says, AI is never going to be able to figure out what death. one for Christmas. New barbecue, I think. There are some funny and interesting anecdotes in this Fiji Simo profile. Let's just read through a little bit of it. In case OpenAI structure couldn't get any weirder,

a non-profit in charge of a for-profit that's become a public benefit corporation. It now has two CEOs. There's Sam Altman, CEO of the whole company who manages research and compute. And as of this summer, there's Fiji Simo, the former CEO of Instacart, who manages everything else. Simo hasn't been seen much at OpenAI's San Francisco office since she began as CEO of applications in August, but her presence is felt at every level of the company, not least

because she's heading up ChatchipT and basically every function that might make OpenAI money, Simo is dealing with a relapse of postural orthostatic tachycardia syndrome, POTS, that makes her prone to fainting if she stands for long periods of time. I'm very sorry to hear that. But she says now she's working from her home in Los Angeles. She's here at work. L.A. And she's on Slack a lot, being present from 8 a.m. to midnight every day, responding within

five minutes. People feel like I'm there and they can reach me immediately that I jump on the phone within five minutes. She tells me employees confirm that this is true. Open A.I's famously Slack-driven culture can be overwhelming for new hires, but not apparently for Simo. Are you... Have you been using Chad GPT polls?

No, I have not been using it regularly. I'll give you one from my pulse today. It says, this is like an article that I can tap into. Open AIs API litter, open AIs API layer, the hidden moat in plain sight. So this feels like, it feels like it's always like one click deeper from what I've been, What I've been prompting. The articles do feel like they've been getting shorter. It used to be, it used to be like very intensive compute-wise.

Like it would be like a full deep research report just here. But maybe it's noticed that I'm not clicking on them that often. I do see that there's some pretty good modals for, like linking to your email. They're trying to get more data in there, trying to hone it in. I have yet to really get in there. But I mean, there's, you know, information on a Blue Owl, Microsoft's Fairwater AI factory, like interesting things that I would wind up prompting,

but I would usually prompt on a very, I don't know, I feel like there's, it's not bad at predicting what I'm interested in. It's just like, it's just not quite there where usually I'm a little bit more deliberate worried about it. But, you know, people are searching ChatGBTGPT for holiday goods. You've got to get on profound. Get your brand mentioned in ChatGBTBT, which millions of consumers who are using AI to discover new products and brands. You also got to get on TurboPuffer, search, serverless vector in full-text search, build from first principles and object storage, fast 10X, cheaper, and extremely scalable. Used by the best, the best of the labs. What, there was one thing that

stood out here, Fiji says, my husband is a chocolate maker. So, says. This is amazing. Very cool. Also, what does that say about the jobs of the future? You have this one household. One is in chart responsible for monetizing one of the most transformative new technology companies of our time. The other one is making chocolates. This is like, you know, bifurcation of jobs.

Potentially. It does seem like a AGI resistant. and job. I don't think Open AI will get into the chocolate-making business. Brad Adcock, but like a word. He's just like, I will I will steam roll. I will send. Steam roll.

In other news, OpenAI is allowing employees to donate equity to charity for the first time in years. Other nonprofits. After months of internal pressure according to a memo viewed by the verge and price per share is up significantly since last month. A lot of

money is on the line. What happens if they donate all of the shares to the nonprofit, to the Open AI nonprofit. You just create this auriboros of capitalism. Hopefully it happens. I don't know. There's breaking news out of Saudi Arabia. We got a trillion dollars. Let's ring the government. Let's go. One trillion. What are they going to invest in? Where's the money going? Let's play the video. Let's play the video. While we're pulling that up, let me tell you about numeral.com. Let worry about sales tax and VAT compliance. Numeril.com.

Watcher Guru has the video. Let's play it. And the agreement that we are silent in today and tomorrow we're going to announce that we are going to increase that $600 billion to almost $1 trillion. One trillion. Real investment and real opportunity by details in many areas. And the agreement that we are signing today in many areas in technology, in AI, in air materials, magnet, et cetera.

a lot of investment opportunities. So you are doing that now. You're saying to me now that the $600 billion will be $1 trillion. Definitely, because what we are signing for facility is that and we're going to go on it. Wow. I wonder what time period. But, I mean, this is remarkable. But they can invest in VC funds, public, private equity funds, like all sorts of stuff in the industry, in the economy, right?

That really made Donald happy. It's great. I like that very much. That's sort of his job. He's kind of the chief fundraiser, I suppose. He's going around the world and get the money over here. I don't know. It seems like sort of win.

I don't know. I mean, what? Every American benefits. Yeah. If a trillion dollars is invested in the economy, there's going to be. It certainly doesn't seem like there's, I mean, the, the risk with that would always would always be like, well, is America investing two trillion in Saudi Arabia? Like, which way is the money actually flowing?

Because you need to look at like the relative amount, not necessarily just the notional amount. But I can't imagine that there's that much capital flowing out of America right now. We're in the biggest boom ever. We're in the golden era, right? Massive news from Isaiah Taylor, Valar Atomics, became the first startup in history to split the atom, according to him. He says, announcing Project Nova. a series of zero power critical tests on Valar Atomics Nova Corps in collaboration with Los Alamos.

Nova went critical for the first time this morning at 1145 a.m. Congrats to him. Fantastic news. There is some debate on the timeline over what exactly happened. It's happened very quickly. It's clearly extremely impressive. And we can get into this. But there's always been debate.

I mean, Isaiah got into this dust up over like, whether or not you could hold the nuclear fuel in your hand. They were going back and forth on calculations. They kind of settled that debate. Josh Payne, nuclear junkie is saying here. So what exactly did, what hardware exactly did Valar provide? The fuel control systems, cooling measurement systems, and most of the core are all part of the Damos project.

Did Valar provide a block of graphite and they're calling it their core? And so people are going back and forth. Nealz chimes in here and says, Valar Atomics provided the reactor core, the trisof fuel, and the system configuration. That seems pretty important. Like you got to, like, I don't know, it seems like more than what they'd done before.

It's like clearly an advancement on what they, you know, their chopped wood here. L-A-N-L-N-C-R-C provided the critical assembly, facility safety envelope, experimentalist, test, and a bunch of other stuff. and so that's just from their press release. So people are going back, do they do nothing or did they do everything?

Well, maybe it's somewhere in between. It was a partnership. They said that in the press release. The bigger thing is I think people are, I think people are trying to push on Valar this idea that they need to be doing completely novel science. And I don't know that that's actually the goal of the company.

I don't actually know that's what, If we just zoom out to like, what is the goal of the re-industrialization project in America? What's the goal here? Like, well, it's to lower energy prices, right? Like, America wants to generate as much money, as much energy as possible for as little money as possible. And there are a bunch of technologies that exist. There are new technologies like what Ashley Vance was talking about with Helion and Fusion. That's a new technology that we have not even discovered yet.

Fission's been discovered. 80 years ago, it was working. It just became regulatory nightmare. We just shot ourselves in the foot. And we just stopped making it. It became unprofitable and un-economical. And China said, cool. It'll be profitable for us.

We're just going to copy and paste. Exactly. And so I think people might be a little bit over-rotating on like, on like, is, uh, is Valar doing like entirely new crazy scientific breakthroughs? when it's like, do they necessarily have to? Or is it just enough for them just to build a lot of these things? Highly motivated team that is going to make incremental progress towards their goal. Yep.

And anybody that's hating on that, I think is just, like, again, like I think what's been great about the nuclear industry from our point of view is that broadly the founders that are like players in the space just want the industry to make progress in the U.S. And I think this is, you know, undeniably like incremental progress that gets them closer to their actual goal, which is bringing a small molecular reactor online. I think Elon summed it up well with like his thesis for the XAI team. He was like, we don't have AI researchers. We have engineers because he sees this as an engineering project.

He's like, we know what we need to implement. We know what we need to build. Our goal is to build a big data center, to build a large language model training system, infrastructure. And Elon was very clear on like, we don't have AI scientists. We have engineers. And that's the same thing. He's not the first person to take a rocket to space. He's just the first person to, like, create this massive economic system that turns out rockets every two seconds, right?

And so I think that is much more. I think Isaiah would say, we should ask him this the next time he's on the show, but I think he would say, I want to be the Elon of nuclear. I don't want to be the Oppenheimer of nuclear. Like, I'm not trying to, like, create something. Yeah, he even said his line on, he said the U.S. is still good at making bus-sized objects. Yeah. But not, you know, sort of like maybe bridge-sized objects, right?

Exactly. But Morgan Barrett's having fun on the timeline, what street parking is going to look like in Elsa Good, no, in 24 months. Of course, the El Segundo crew loves their cars. I think they're going to stay pretty focused on the mission, but I would love to see this in Elsigundo for sure, for sure. There's also big news out of Radiant. Radiant has been, Doug's been on the show, he's a good friend, and they are working with the Idaho National Laboratory,

and they submitted a DOE authorization request, and they will be testing their reactor design at the dome facility at INL on track, I think, next year. So, congrats to them. And Mike Nuziata has the kind of breakdown here. It says production reactors in production by 2028, brought to you by the people that brought you reusable rockets and McMaster Car, highlighting the team behind Radiant. And so, congrats to it.

everyone in the nuclear industry who's making big waves. And we have our next guest, before we bring them in from the stream waiting room, let me tell you about Vanta. Our guest is from Vanta. And it just has to wind up. We'll let him tell you about it. We have Jeremy from Vanta. Welcome to the stream.

How are you doing? What's happening? I swear that wasn't intentional, but it did just line up that the Vanta ad read went right before you came on. I look over and I'm like, wait a minute. I'll let you do the ad read. Introduce yourself, introduce what Vanta does, what you do, and then we'll get into the news. Yeah, yeah, happy to jump in.

I'm Jeremy Epling, chief product officer at Vanta, and we help businesses earn and prove trust. And one of the really cool things that we're doing this week is we're hosting our Vanticon conference here in San Francisco, have a ton of people showed up, a ton of engagement to really pull that entire security GRC community together and have a couple really cool announcements. One of them is how we are transforming Vanta to be the agenic trust platform. I think this is a really big turning point for the industry. When we think about how GRC teams are transforming and becoming more technical, we're really redefining how these enterprises manage trust at scale

and are able to help big customers like sneak, perplexity, synthesia, all the way from YC startups that maybe just exited a batch recently, all the way to the Fortune 50 companies really earn and prove trust. as a business. It feels like AI's amazing, but it's not something people trust. And so how are you, how are you grappling with that? Like, I mean, people trust it in their Tesla's to drive them on the freeway. That's high stakes. But there are these, I'm sure you run into this all the time when you're talking to folks about, yeah, I love it if I'm just looking for a recipe, but I don't know if I'd trust it in my, you know, deep in my enterprise for whatever reason. So how do you think about

how you set up certain guardrails around the AI, which still can hallucinate from time to time? Or, and then how do you articulate those guardrails to the end user and the customer? Yeah, definitely. And that's a big problem we solve for companies today. I think whenever they're adopting a new AI solution, or maybe it was a solution that they already had and they've just added some AI features, they're wondering, how are they using my data? What are they doing? Are they training on my data? We have a whole third-party risk management product that comes in. It leverages our Vanta AI, which when we think about how to hit that quality bar that we care about,

like you said, like, hey, is it going to hallucinate? How do you approach that? We have a whole set of great GRC SMEs, subject matter experts, that help us tune and refine our AI so that we can give really high trustworthy answers. Because you imagine security customers are some of the harshest critics of AI. They really want things to be accurate and great. And so that's something we have really leaned into. And one of the ways we've kind of pushed that forward is one of the big announcements that we have coming up this week is our AI agent 2.0.

So we've redefined our agent to really be this built-in GRC engineer that understands all the compliance across your entire organization. So like you said, it knows when you've added a new AI tool. It knows what data you're putting into that tool and how you should think about risks and mitigating those. It also has context and memory. So when you're asking you questions, it understands what you're talking about. Like if you're on a policy, it'll pull in that context. It is the memory of understanding what your business is. Maybe you sell to consumers.

Maybe you sell it to other businesses. It can pull all that context in across everything in your program as well. Like, hey, we know that these are your vendors, these are your risks, these are your different customers. You've received these questionnaires feedback. It can synthesize that all into like intelligent guidance to provide you. So one of the cool things that I love about it that really helps security. teams work against attackers because I think in this AI world, obviously you have the kind of bad

guys and attackers using AI come in. We also help everyone defend and understand because we know the whole program. We can find gaps in your security program. The AI automatically suggest those to you, like provides gaps in proactive things to go do to go address those gaps and remediate them, gives personalized guidance, and really helps automate a lot of that process. You can respond to attackers and threats a lot more quickly. How how does like how are you thinking about like the UI around agents because so many there's there's been this explosion of companies that are creating agents and they mean something totally different depending depending on the on the company sometimes it's like a chat interface other times it looks it sometimes looks more like SaaS and that's totally fine but how are you thinking about the actual like evolving. UI paradigm. Yeah, I think it's going to be both.

Like, I think there's a lot of times I don't want to have just a chat conversation with my AI, and I want it just to bring the answers to me automatically. So we look at it as kind of a blend of both. While there might be agents working in the background, you don't always have to do it through a chat interface. So for us, if you show up on like our policies experience, we'll say, hey, we found these three inconsistencies across the 40 policies you have. Do you want us to go fix those to you?

And you didn't want to have to ask that question of like, is there a problem here and kind of guess through the list of problems? Instead, we have our agent already looking for those. Or maybe your SLA says it's 24 hours for critical vulnerability to notify a customer in one document. It says 72 in another. We'll automatically do that, give you the change, show you the diff for the kind of like red line for that, let you click a button and automatically execute it. So I think bringing that stuff in, when I think about when chat's great, it's really when you, I don't know, when you have the follow-up questions, you know, where maybe a one-shot answer isn't going to give you what you need. to dig in more, you want to learn more, you're trying to explore data. It's a big case for us in

reporting where people want to learn maybe about their controls and how well they're doing, how well they've been performing over time. They can have that interactive conversation with the agent, ask it to pull those statistics, leverage our MCP server through Claude or chat GPT and have it automatically generate kind of graphs and charts and reports that they can use for their board or anyone else to kind of show progress of their program. How are bad actors using AI today to, you know, abuse companies in different ways? Yeah. I mean, I think it was yesterday or maybe it was the day before Anthropic posted a really good article about attack that they had experienced there and seen that their software used for. I think that it's just giving a whole new set of tools for

attackers to be able to probably write more sophisticated attacks and find vulnerabilities even more quickly because they have these agents always running, always looking. And I think that's where when I think about Vanta where we come in and provide that next level defense. Because if you think of an attacker coming in from the outside, they can only see what's on the outside. With Vanta, we already know your entire program. We know all the different pieces of it. And so we can really help you build stronger defenses and be proactive. Like I mentioned, bringing those inconsistencies to the forefront, giving you automatic remediation on specific issues that we might find. We still think it's important to have like humans in the loop for a lot of

those big decisions, but you can then work with the agent as well to have it take actions just on your behalf automatically. On the other areas of the risk surface, I imagine that you're trying to build products. Are you also starting to act as a funnel and do partnerships with other security firms? Because the surface area is probably pretty broad. Do you have a vision to be a one-stop shop, or do you want to be part of an ecosystem and suite of products that? that enterprise implements? Yeah, I think for us, we definitely want to solve the broader trust problem, but we know that there's lots of different pieces where we aren't going to be the full solution,

right? So if I think of a GRC team or customer trust, hey, you get security questionnaires and questions coming in from customers, how can we go do all that? There are certain areas, you know, like vulnerability scanning. We're not going to be going to keep into vulnerability scanning, but we're going to go partner all the great scanners to go do that. Got it. I think notion, though, like you said, of bringing that,

visibility across the entire enterprise is a really big thing for us. We have a feature called adaptive scoping that when you think of a whole security program, you know, there's little pieces of it. And you may say that, hey, to get compliance with PCI for credit cards, I need to have these assets in scope or things to go do. And that's different than another framework I might be pursuing. So we allow companies to kind of see their progress on compliance in those different ways. We have a new organization center so they can break things down by business unit or product line. And these are like just brand new ways that customers have never had before to understand their program at all levels of depth. So when you think about that really large enterprise customer,

they're able to break down their program and see that. And I think that's where Vanta really pulls it all together. We call it the risk graph is like one of our big announcements that we have coming internally where we pull together internal risk and external risk. So you think about risk you have from your different vendors, as well as things you're identifying internally within your business. And we provide a full visual for that. So you can kind of get this connection between, hey, there was a breach. Okay, great, the breach happened. Which vendor was it? Who has access to that vendor? Vantin can lean in and cut off that access or change the controls there. What data was going into that vendor? And it really helps you understand and

prioritize all the things that are happening in your security program because I think security leaders are just drowning in alerts and they want to know what's most important. So having the AI intelligence, being able to dissect your program in these different ways and then see kind of a visualized risk graph is really important to help them quickly act on, you know, a threat landscape that's just always changing. Yeah, that makes sense. You guys got to do Spotify RAPT for internal risk. That would be good. Something shareable. Something shareable. Internally companies, of course, would be like, you know, yo, Tyler, you got to, you got to, you're our biggest risk vector over here. The internet. Tyler, Tyler, Tyler's our intern over here.

Thank you so much. He's very secure. He's very secure. He's probably the best. Anyways, super exciting few launches and have fun at the event. Thanks for joining. Yeah. Have a great rest of your day. Cheers. Let me also tell you about Figma.

Think bigger, build faster. Figma helps design and development teams build great products together. There's this article in the Financial Times. It's very spicy. It says Oracle is already underwater on its astonishing $300 billion open AI deal. AI circular economy may have a reverse mitis at the center. Okay, so they're saying this is underwater because the market cap is dipped below. That's so well.

It's not very honest. Yeah. It's not, it's not, I, you know, I'm not the first one. The Financial Times says, Oracle's astonishing $300 billion opening I deal is now valued at minus $74 billion. And that's like, I don't like that. at all. Like, yeah, this is like really, really bad framing, in my opinion. Like, yeah, it's not there to say that. I thought so too. I thought so too. And I, I love the financial times. And we have the financial times printed out here. Normally, normally, very, very great reporting.

But this one, this one feels odd. It just feels like an odd framing. But saying, oh, oracle's already underwater on a, on a partnership. This is a, this is a, this is a, this is a, hot take that you've been, you've been pumping for the last, like, week. But the way you've said it is, like, The stock has round tripped, even though they had that amazing deal, which is true. The correct framing is the market is no longer giving them credit. Yes, yes, that's right, that's right. But to say that they're underwater. It's so weird.

So when I saw this headline, I read into it earlier, and I was expecting to see something. Okay. Well, we might have gotten rage baited. We might have gotten rage baited because right here, the Financial Times addresses our concern and says, okay, yes, it's a gross simplification to just look at market cap, but equivalent. to Oracle shares are little changed over the same period, the NASDAQ composite, Microsoft Dow Jones Software Index. So the $60 billion... Calling those equivalents is like, again, like look at...

You could also comp it to Corweave. And you could say, on a relative to Corweave basis, Oracle is outperforming a bunch. Amazing. It's amazing. I don't know. Like, there's a bunch of different ways to... Like, if you pick your weird comp, it does seem a little odd. says, so the 60 billion loss figure is not entirely wrong.

Oracle's astonishing quarter really has cost it nearly as much as one, General Motors or two Kraft Heinz. Investor unease stems from big red betting its debt finance data farm on OpenAI. We've we've nothing much to add to that other than the charts below showing how much Oracle has in effect become OpenAI's U.S. public market proxy, which is fascinating because Microsoft should be Open AI's public market proxy, in my opinion. But there are some great charts in here. There's some interesting stuff.

And I believe this is from Alphaville, which is their blog. And it's not exactly, it is supposed to be like, you know, like a take factory. Anyway, well, we have our next guest in the Restream Waiting Room. Let me tell you about Julius.ai first, the AI data analyst that works for you. join millions who use Julius to connect their data, ask questions, and get insights in seconds. We have Keone from Monad. Welcome to the show. How are you doing? Good to see you. What's happening? Hey, doing great. Great to be here. Thanks so much for joining. Please, take us up. You got the lock in. You're calling in from the lock in capital of the world with the mattress on the floor.

Yeah, congratulations. Please introduce yourself and tell us a little bit about the news specifically this week. Thank you. Great to be here. My name is Keanui Hahn, co-founder of Monad. Monad is a new blockchain that is building for high-fidelity finance and is a high-performance blockchain that has been building over the past three and a half years. Just really delivering high performance based on previous experience from high frequency trading. Wait, so you were high-frequency trader before this? That's right. Yeah. I was at Jump Trading for about eight years. One of the trading teams there was very involved in the futures markets prior to Monad. What was the day-to-day like? It was a lot of Jupiter Notebook.

It was a lot of manipulating large data sets and making really short-term price predictions, as well as building performance systems. How short-term is short-term? Like nanoseconds, pico-seconds? seconds, minutes, it all seems short term. Yeah, it's the predictive horizon for the kinds of strategies that I was working on were on the order of milliseconds to seconds. But the whole time for these strategies was longer than that. So that's actually one of the interesting misconceptions about HFT is that your predictive horizon is very short because you're predicting the next flip,

but then you know you can make trades that have edge in that and can predict that flip and make a make the right action but then you still have to hold that position for a longer period of time until um you can get another signal maybe in the opposite direction or a signal to enter an order in the opposing direction so old times tended to be on the order of like seconds to minutes interesting i didn't know that thank you that's very helpful um very cool so so talk about the oh sure Yeah, I guess getting into what is success with Monad going to look like? What are the different types of groups and applications and types of users that you expect to come in in the early days? Yeah, so maybe to take a step back a little bit, Monad is a new blockchain that delivers the best of all worlds between decentralization, performance,

and backward compatibility. So it's a new blockchain. It's fully backward compatible with Ethereum. It allows developers that have built applications for Ethereum or the Ethereum ecosystem to reuse all of their code, all their libraries, all the tooling that's been built for Ethereum and more specifically the Ethereum virtual machine

while getting much higher performance and a really high degree of decentralization. So in particular, Ethereum processes on the order of 10 transactions per second, well, Monad delivers 10,000 transactions per second. And that 1000x improvement is a result of several different improvements that have all been stacked on top of each other. And those vary from parallel execution to allow a bunch of transactions to all be

run in parallel, as well as a new consensus mechanism, a new database for addressing the single biggest bottleneck. in blockchain execution, which is using the accessing all of the state that's on disk really efficiently, as well as various other improvements that just deliver the same experience, but sped up significantly. That makes sense. And so what, in your view, what does the ideal kind of adoption look like? Yeah, it's really a mix. So I think the thing that's really valuable about decentralized,

blockchains is that they deliver shared global state that is borderless, that allows people all around the world to get access to the same tools and the same markets fundamentally. I think blockchain is really a revolution about decentralizing control of financial systems and commercial systems and giving people regardless of where they are in the world access to the same financial opportunities. So I think a big part of the story of blockchain and the story of adoption is that developers anywhere in the world can build new applications, deploy them in the system, and then users anywhere else in the world can get access. So what we're seeing in terms of adoption is a mix of existing applications that can migrate to Monad seamlessly and get much lower fees for their end users, as well as enterprises that are. utilizing the power of blockchains for stablecoin settlement to allow their users to transact in dollars

or send and receive payments really cheaply and permissionlessly. In your view, what are the kind of classic mistakes that other blockchains that have tried to challenge, you know, some of the more dominant chains? What are the kind of classic mistakes that they make to ultimately, I feel like there's every single day there's somebody on X highlighting some blockchain that that has a multi, multi billion dollar, you know, fully diluted value and yet has very little activity. So if you could kind of like lean it, what are the things that basically you're trying to avoid? I think one of the problems in crypto is that it can be quite hard for, so it's kind of a double-edged sword on the one hand. It's easy to get some initial users that are trying things out and giving feedback,

but it can be challenging for people to sift through the yield farmers or people that are motivated by an incentive and really identify the users that are there because they ultimately gain value from the application. So one thing that we really care about a lot at Monad is helping. to helping builders that are building in the space. These are all early stage entrepreneurs that are very talented, very ambitious, helping them to focus on user acquisition funnels. And just like just the fundamentals of entrepreneurship and identifying users and navigating the IDMAs to identify PMF.

That makes sense. How has it been bringing the token to market with Coinbase's new product? It's certainly a wild time to be building in crypto just because of the overall volatility. And I'm sure that's made it challenging. But you're also utilizing a new product line from Coinbase, which is pretty interesting. Yeah, I think it's extremely exciting. The thing that motivated us to work with Coinbase and be the first token launched in their new token sales platform is the opportunity to get really broad distribution of the token.

I'm a big fan of Dogecoin. When I first got interested in crypto, I was really interested by just the story of how Dogecoin gained really broad distribution in Mineshare and the Dogecoin tipping bot on Reddit as a mechanism for getting a lot of people to like sort of align on shared interest and values that ultimately then became valuable much later. The thing that's hard about crypto is that there's an expectations game that's being navigated,

and people have very high expectations of the value of airdrops and so on. But I think our team has done a really stand-up job of delivering a great airdrop that people were really excited about and that crypto natives got really excited about. And then also offering a way for normal everyday people who maybe you're not on crypto-Twitter as much, but are still very active on centralized exchanges and trading and holding to get access to the token. Makes a lot of sense. Well, how much have you raised so far? We have a gong here.

We'd love to hit it on your behalf. Thank you. I think we've raised about $120 million so far. Congratulations. Well, it's an honor to hit the gong for you and excited to follow along. Congratulations. Yeah. Thank you. So we have until Saturday. The sales open until Saturday at 9 p.m. Eastern.

And we're looking to raise $187 million total. There you go. Let's go. Most of the way there. Well, good luck. Thank you so much for taking the time to talk to us today. Have a great day. Great to meet you. We'll talk to you soon. Our next guest is Stephen Balaban from Lambda Labs. Or is it just Lambda now? I think it's just Lambda. Do we drop the Labs? I think we dropped the labs. Stephen, do we drop the labs?

How are you doing? We dropped the labs. We dropped the labs. Okay, I'm dating myself. Well, at least I feel like a day one. I don't feel like a bandwagon fan because I'm using the old name. There's a little bit of cool. I liked it back when it was labs.

But welcome to the show. Thank you so much for taking the time to talk to us. Congratulations. You look at incredible. With the yellow tie, you're making us look on the professional here. We've got to put on the time for this. We're a couple of casuals. Give us the news.

What happened? Let's break it down. Yeah. also one day I was training some confidence on my workstation. Next thing you know, we're raising 1.5 gigabucks. Gigabucks. We say, we say, we say, gigabugs. Gigawatts, giga chips, gigabox. Yes. Yeah, what does it actually mean? I mean, we see, we see 10 billion, 100 billion, 10 trillion, quadrillion every day. Is this cash?

Is this debt? Are you buying GPUs? Are you buying land? What are you doing? All equity. Let's give it up for equity. Extremely well. Like our capital structure is really nice in terms of we've been very conservative in terms

of the amount of debt that we've taken on. And that's kind of been one of our philosophies. And we've aimed to have a business that's just super robust to ups and downs in the market because we're swimming with our swim trunks on. Yep. And then, and then you, that's a amazing line for the money. You gave them, you gave them equity. There's no one hand washes the other type thing where like they pay you, you pay them. It's all one round trip. No, this round was led by by TWG Global, which is, financial investor, which is Thomas Tall and Mark Walter. You may know Mark, Mark owns the LA Dodgers and also now the Lakers. Thomas started

legendary entertainment, which makes great movies like the Batman series and Dune and Inception. And so these are business partners who I've gotten to know over a number of years now. And this is just they're making some big investments in the space. Okay. I'm so happy you guys have your trunks on because not everyone out, not every player out there has their trunks on right now. And it's hard to tell who does and who doesn't. But at some point, we're going to find out. And it's not going to be, it's not going to be pretty. It won't be pretty for people who are over levered. And we just have this philosophy that with exponential growth that we're seeing in the AI industry, all of the upside is in the last period. Right. You know, if you're, if you're, you have a doubling function, right? The sort of the definitional thing of that is that the last period is more growth than all the sum of the previous periods combined. And so from my perspective, it's just like stay alive and build a rock solid business because we got to capture all this amazing upside in the long term. Yeah.

So talk about how funds. Well, even even before that maybe feels like potentially an advantage right now just in terms of focus. like being private. There are other companies in the category that are public and they're now having to contend with, you know, what's been a pretty big correction in at least a local correction in Neocloud over the last month. Has that been helpful in terms of the team of just like staying focused and you're not getting, you know, marked every single day? Well, I think that certainly that level of a distraction isn't helpful. And I always encourage the company to just focus on building a heavy business for the long term.

You know, if in the short term the market's a voting machine and the long term, it's a weighing machine. We just got to build a business with good cash flows, a good capitalization structure that's robust. And so I kind of try to focus the team on that. I mean, these days, the secondary markets, as you know, are actually, you know, pretty deep for for companies that are kind of at our size. And so I think that some of that can start to creep in. Yeah, that makes sense. Where are you seeing value spending some of this money? I imagine that there's hiring, R&D, all the traditional things, but you're at a scale where it's a lot of money. How do you actually

think about allocating capital at this point in this in this phase of the journey it's been over a decade right now right yeah uh we started in 2012 wow and was doing we were doing face recognition software and the alex net paper came out wow i mean that's how early it was and i downloaded the kuda covenant library off of google code and that will tell everybody kind of how old school Lambda is. And, you know, as far as use of funds, obviously a lot of it goes towards the GPU infrastructure that goes into data centers. We are also starting to put that into investments into data centers themselves.

And we, I think that what we're aiming to do long term is kind of build this almost like Tesla for AI infrastructure where we kind of look at this as like a similar buildout that you would expect from the like electrification of the United States or the railroad. And like a degree of vertical integration, we believe is going to be in the future for us and is like the right direction. And that that goes from everything from, you know, energy procurement and construction because I think a lot more of this stuff is going to have to be behind the meter power plants to actual construction and design of data centers that can sort of rapidly adapt to the changing chips that go in, right, because the rack densities and the movement from air cool to liquid

cooling that we're really pioneering alongside Nvidia. These are all examples of use of funds. And it's exciting because we get to kind of make good investment decisions that are really sort of IRR-based in an almost industrial way, which, I think is unique from a company building perspective. And it's an honor to be able to do that. Can you get me up to speed on some of the tradeoffs between like one really big mega data center and a bunch of really small data centers? How because there was a moment when we were just doing bigger and bigger training runs.

Then it became R.L all over the place. Then you actually have to serve these things. But actually, if it's going to take me 10 minutes, I don't mind if you do it across the world and take it back. But if I do care that it's right now, I need it like co-located. How are you thinking about the tradeoffs there? So the mix and the main driver over the next five years, we believe will likely be mostly on the inference side.

If you look at some of the financial models that have either leaked or otherwise been published around what Open AI thinks they're going to be spending, it looks to be about 50% on training and then 50% on inference growing towards 75% of it. inference and, you know, a smaller chunk of that on training. And as far as like what that means for the larger data centers, I certainly don't think that this is like going to a world where there's a bunch of micro data centers. I think that that's a little bit hard to sort of manage and deal with. But one of the things I think that you're going to start hearing a lot more of is how adaptable

and how quickly can you bring on the data center in an incremental fashion? Because that's going to be a lot of the main drivers for how successful infrastructure builders like us are, is how quickly. And we're just focused on optimizing that time to first token for our customer. How do you think about revenue quality and customer selection? Because we've seen some deals go down that look big and cool and good on the surface. and then you dig into them and maybe the underlying infrastructure providers

not actually getting that great of a deal at the end of it. Well, we certainly see a lot of people with very high levels of customer concentration. Because Lambda started off as this developer cloud that evolved and morphed into a cloud that's providing for the biggest companies in the world, we have a really, really strong user base, you know, if you look at our breakdown from our revenue mix, in terms of you looked at like, let's say, our Q3 stuff, and I don't want to go into exact specifics, but it's sort of like one or two big customers, a bunch of sort of the bigger, smaller customers. And then it's something, you know, it's a nice, really big chunk of this long tail of customers that we have.

And we have a very, very, you know, I've seen some other people's customer books. And I can just say that we've got a very diversified customer base. And that's kind of all part of the strategy of how do you build a great long-term business. Of course, customer diversification is one of those parameters. How do you think about diversity of product offerings? Are you seeing customers ask for API endpoints for particular models or do they want access to bare metal? Or have you gotten any customers that are like, hey, we just want, you know, you seem to know about this data center business. Can you just build a data center for us and hand it over to us when you're done?

And we'll just pay you as a consultant. We have no interest in doing that, that one. That's, you know, we want to do something that's really vertically integrated. And, you know, kind of going back to that like larger, smaller data centers, I think the most important thing is just being able to deliver this incremental live deployment for a customer. we have an entire full-stack cloud product that, you know, it's got things like single sign-on. It's got things like long-term high-speed AI file systems. It's got instances that go down from one GPU to an entire cluster with one-click clusters that we've got. And so we've built an entire cloud platform.

We have previously been in the inferencing space where we're actually giving an API for inferencing. And we've actually exited that business to just focus. I think that that's like one of the things that we really try to do at Lambda is just say, where are we making money, what are good investments, and where are we going to really dominate the market and focus there? And so we've actually exited, for example, the inference market. We had a $200 million plus a year hardware business that we've exited. Wow.

It actually like kind of crushes because like that was the business that got off the ground. But can you imagine just like winding down? I'm like, well, we're just going to take this business and not do a $200 million year business anymore because we're trying to focus. That is crazy. That is crazy. Thanks, guts.

I have a crackpot theory that I'd love to run by you. What do you think the odds are that the, like I noticed I was traveling in, I was traveling in Mexico and I noticed that Carlos Slim is the richest man there. and he's a telecom magnate. He owns a lot of the telecom infrastructure. And that's true for a lot of countries. The richest person in that country is a telecom person or a mining magnate in the sense that they've been able to corner a resource,

a physical resource, infrastructure, and that's generated a lot of wealth for them. And I was wondering if you had a thought on, do you think that in the future we'll see some of the wealthiest and most powerful people from other countries, non-American countries, be, you know, GPU cloud hosters or data center developers.

Like, is this going to be a new boom across the globe? It's kind of a different twist on the sovereign AI project. I was just wondering if there's going to be some way that this plays out where there's this sort of like one-time opportunity to kind of get a cornered resource, or is the nature of the internet such that, the compute is actually much more fungible than, um,

than say, you know, telecom or, you know, or like copper in the ground. There's such a localization. There's such a physical localization. I think if you look at telecom,

you look at cable. Yeah. As well as regulated utilities from an energy utility perspective. Yeah. You know, these are all things that benefit from a physical, geographic monopoly. Yeah.

Right. And, and, and AI data centers don't have that same thing. Now, I just want to step back for a second. Guys, the United States is basically the only country in the world. We have the most unbelievably good economy.

This is the idea that there's going to be these sort of like massive AI infrastructure projects that I think are going to be like super, super successful outside of, let's say, China and the United States right now is really increasingly big question mark. And I just am so bullish about where we're going in America. that I don't really pay a lot of attention to it. Our focus is just in, you know, in North America generally. And I just, that's kind of my perspective on it, to be honest. Yeah. Yeah, that's really helpful.

I agree. It's interesting to toy. I mean, there's a lot of money being thrown around with some of these projects. And I'm always interested in, you know, how they will shape out. Larry, oh, sorry. Go right now. Yeah, maybe go for it. I was going to ask, like, how you guys are navigating energy constraints with new developments.

Are you seeing? We've heard, you know, anytime, obviously, there's, like, massive demand for something. New sources kind of come out of the woodwork. We've seen back and forth some people that are building AI infrastructure, say, like, energy is our primary constraint. Others are saying, actually, that's not my, you know, it's... So, where do you sit? we are aiming to reimagine the sort of step process from, whether it's photons or molecules, of natural gas to tokens. And we strongly believe that a lot of this is going to have to come in reimagining, like, well, how do you interact with the grid?

How much power generation do you bring to the grid yourselves? And I think that that's the successful AI infrastructure companies in the future. Again, this is why I kind of said, like, I look at this like Tesla for AI factories, which is you got to reimagine how the world has worked previously. And you have to kind of bring together this level of vertical integration because that's how you move fast, right? You know, when you can control every step of that way from the power generation and not having to necessarily deal with a regulated utility and you can go and do behind the meter

generation with a natural gas power plant. If that can speed your time to market up, this is just so important. And that's kind of how I approach it, which is there are certain barriers like regulatory barriers, which you try not to run through those like a brick wall because it's kind have like an immovable object. But if you can, if you can just bring your own, if you can just sort of get around that sort of regulatory constraint of having to interact with a regulated utility by bringing your own power to the grid, then that's what I think is going to be successful. Yeah. Makes sense. Thank you so much for taking the time out of your busy day to come and

hang out with us and answer some questions about. Jordy, John, thanks for having me, guys. It's always a great time. Congratulations. The new Gemini 3. This is like, yeah, can you give us your review and actually explain how it interfaces with your business? I'd love to know. So, so I haven't, I haven't, I haven't, I haven't used the Gemini 3. I've seen the, uh, the updates. I'm still, you know, hey, Sundar or whatever, give, give, give, give, enterprise account access. We're on Google, uh, Google, suite or Google Enterprise or whatever it's called now. So we'd love that upgrade, but I'll tell you what this is the cool thing. I use things like chat, GPT, and GROC to,

learn more about topics like regulated energy markets and how to build power plants and data centers. And that makes Lambda faster at standing up AI data centers. And I pay attention. I actually just kind of do what the AI tells me to do. And that gives more compute to the AI to train bigger models, which makes a better faster. The AI is working for you to make more AI. The It's the beginning of these types of positive feedback loops. Sure, sure. And I think that if you privately talk to a lot of executives, you'd be surprised by the amount of, you know,

the strategic conversations I have with these AI models has gotten more and more advanced with the level and the quality of the model. The first versions were not great and I didn't really take a lot of its advice. But now I am. I mean, next thing you know, it's sort of like, well, you know, maybe AI is the one making the run of the shell. Getting into sessions. Yeah.

Next thing you know, we'll be hanging out on TVPN discovering novel physics with Gemini 4. You know? We'll see how far we get. Yeah. It's a good time. Well, thank you so much for coming by the show. I have a bunch more questions, but come back.

Let's get you back on in before the end of the year. That'd be great. We'll continue the conversation. Congrats to the whole team. Yeah. Thank you. Take care. Have a good one. Bye.

Bye. Bye. Quickly, let me tell you about Privy. Privy makes it easy to build on crypto rail, securely spin up white label wallet, sign transactions and integrate on-chain infrastructure all through one simple API. What a legend. What a legend? What else we got? Doug O'Loughlin over at semi-analysis, fabricated knowledge says, I leave for two weeks and we are talking

about Oracle credit default swaps. What the hell, guys? Doug, where was Doug for? I think he's been on vacation or something. He was trying to like truly log off and take a break. And yes, people are definitely talking about CDS spreads. And any sign, any crack in the market is definitely going to be newsworthy. Because we're in this $1 trillion era.

Gavin Baker here is talking about this. He's completely agree with this breakout of the non-bubble, the disappointed, both Bull and Bank. How Sam Splurge changed everything. And Gavin Baker says, Sam Altman's manifestly ridiculous $1 trillion of spending commitments shifted the AI investing landscape.

The market is more skeptical now. Ironically makes an IPO harder for them, although likely ended any potential for a 1990-style melt-up, which is healthy. Melt-up, meaning that in 1999, the market went insane and nuclear. Instead, the $1 trillion was

so in your face that everyone started asking the questions of like, is this real? Is what's going on? Are we going too fast? Do we need to back off? And so we got sort of a return to fundamentals, but fortunately the fundamentals were so good because, you know, these companies, a lot of them are portraying like 25 priced earnings that the market was able to, you know, continue onwards. There's an interesting debate going on around Karen Howe's new book. empire of AI all about open AI. Apparently, she got the amount of water used by data centers wrong by an order of magnitude or two orders of magnitude. I'm not exactly sure where the story originally broke, but she's addressed it now. She says, I'm working to address, to address an

apparent error for a data point I cited in my book about the water footprint of a proposed data center in Chile. I'd like to explain what happened, what I'm doing to remedy it, and provide more recent data on the water footprint of data centers. The data point in question appears in chapter 12 of my book, which focuses on the environmental impacts of AI. Part of the chapter profiles, a community in Cirillo, Chile, which has been resisting a proposed Google data center for years to describe the data center's water footprint in lay terms. I included a sentence about how it compares to the water usage of the people in Cyrillos. For that calculation, I relied on a figure from a government reporting, government document reporting

Serlo's residential water use based on the current best information. It seems that this document will use the wrong units. So she was off by a thousand. So the results was that... What's being off by a thousand among friends? Honestly, these days, doesn't even matter. We're in a post fact rule. Did you read into this more?

People were... I think people are generally like, you know, is this book a hit piece? And I think Sam actually cooperated with it a little bit or gave some interviews for it, but like anything, it's like obviously critical of some things. I mean, yeah, three, three orders magnitude is like pretty big. Yeah. That's like not great. Yeah, I mean, it's certainly like the difference between being a big deal and not a big deal at all. Yeah, like that about the water use, it's like people who use that to justify like, oh, we don't want to build those data centers going to use our water. Yeah. Like, I don't know. I mean, not good. It's a rough time if your job is drinking water.

Tom in the chat says, mistakes were made. Mistakes were made in a book I was responsible for. Mika says, Jordan, you should get a grill with tiny GPUs instead of diamonds. Maybe not the full grill, just the bottom grill. There will be AI-wrapped. Did you see this? This is Rohit comment on Benode. VC-Vinode Coastless says that the U.S. government could take 10% stake in all public companies to soften the blow of AGI.

And Rohit says, we should absolutely do this for all companies. public and private. Maybe we even double it to like 20 or 21% on every dollar they make. It's like, yeah, the government taxes everything. The government gets 21% of profits, actually. They get cash flow. Sean says the haters will call that a tax. It was so funny.

Olivia Newsy is in the news. People are to be deleted posts. Getting kind of like a dividend. Yeah. apparently all the media people are obsessed at this Olivia Newsie story. I didn't understand any of the people in the story because I don't follow media or politics closely enough. Nominative determinism strikes again. But it is fun that she, her name's music.

Bobby was saying we should do it the Mettis list for nominative determinism. That would be good. I'd like that. Because Newsie, she's in the news all the time. Yeah. She's also a journalist. There's news in the trading app world. Robin Hood launched Barrage on a stock.

Short selling is rolling out today on mobile, a web classic and Robin Hood legend. They didn't have short selling? I feel like they've had short selling for a long time. No? That's a new feature. Well, that's funny timing. And then our partner, public.

Is launching generated assets. Yes. Which they're calling their agentic brokerage. Very cool video with our boys here. Yes, yes. But this means you can basically generate, like, your own index based on. And what's interesting about it is that you can say,

I want access to the MAG7 plus a couple other AI companies. Minus one company. I don't know which company you do that. If there's a company, you know. So you can generate like, you know, some sort of portfolio, but then instead of owning it as an ETF and needing to sell it by and sell it directly, you can actually do the tax loss harvesting of selling

individual pieces of it. And so you can construct a portfolio. very quickly. And in general, I mean, just all the different research that you want to do in is obviously deeply, you know, enhanced with artificial intelligence. So fun to see them. Pope Leo has hit the timeline to comment on cinema. The logic of algorithms tends to repeat what works, but art opens up what is possible.

Not everything has to be immaculate or predictable. defend slowness when it serves a purpose, silence when it speaks and difference when evocative. Beauty is not just a means of escape. It is above all an invocation. When cinema is authentic, it does not merely console but challenges. It articulates the questions that dwell within us and sometimes even provokes tears that we did not know we needed to express. has been nicely worded the Pope Leo

what movie do you think he was thinking about when writing this obviously Borod margin call margin call in Borat he's going back to back somebody there was a post in here about movies somebody said they watched like three movies over this over the weekend

I thought it was the most unjordian thing final post of the day Kevin right yeah right you think you're gonna cut me off yeah And Notton Jr. says 10,000 likes. On April 30th, he said 10,000 likes, and I'll quit my software engineering job at Google tomorrow. And he said, six months ago, I made the worst decision of my life.

Oh, because Google's ripping. That's what he's talking about. Okay, because I read this initially. He quit. He started a company, and it was like went really poorly. It's just funny. He is building the fastest way to post with postright.a.i. Okay.

post all your social platforms in seconds. Oh, maybe we could use that for something. Very funny. He's like, my idea was Gemini 3. Like, I was going to make a better Gemini. I thought Gemini 2.5 just wasn't quite there. And I didn't know that what if Google does this? All the VCs were telling me your idea is Gemini 3.

What if Google does that? And I was like, everyone says that about Google things. Everyone says that about startup ideas. It's not worth it. I'm just going to try to build Gemini 3, but then they beat him to it. That's what I meant. Anyway, Department of War, critical areas, of new technology, applied artificial intelligence, quantum and battlefield information dominance, biomanufacturing, contested logistics, scaled directed energy.

That sounds crazy. Scaled hypersonics, very excited for that. A bunch of interesting stuff. Emil Michael is firmly in the chair of the Undersecretary of War. Very excited. I hope we can get him on the show soon to Unle. understand what he's doing over there. We will make it happen.

Well, thank you for tuning in to the show today, folks. We love you dearly, and we will see you tomorrow. Have a good evening. Cheers. Goodbye.