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Technology's daily show (formerly the Technology Brothers Podcast). Streaming live on X and YouTube from 11 - 2 PM PST Monday - Friday. Available on X, Apple, Spotify, and YouTube.
You're watching TVPN. Today is Tuesday, 11/18/2025. We are live from the TVPN UltraDome, the temple of technology, the fortress of finance.
Speaker 2:The capital of capital.
Speaker 1:Gemini three 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 three. Obviously, deeply conflicted. But we're gonna have a fun conversation about the big launch today.
Speaker 1:Google is, of course, a sponsor of TBPN. But we'll take you through all the reactions, and we're gonna get some conversations going with other folks in the industry. We have Mike Newpe from Arc AGI coming on the show in just thirty minutes to break down how Gemini three is benchmarking. I actually think that there's there's two sides to analyzing a model release these days. One is, you benchmark it.
Speaker 1:You use it. You test it. You demo it. And that has been getting less and less interesting. It's very incremental.
Speaker 1:The more interesting thing is how do the other labs respond? Yep. And today, we're gonna go through a little bit of both of that, of those things. Obviously, the big news, at least in from my reading on it, is that Gemini three performs very well on Arc AGI v two, a huge jump, twice the performance of the previous state of the art. And so and also some interesting findings.
Speaker 1:Mike's gonna break it all down for us. But it's definitely a smarter model. And there's a whole bunch of interesting, there's a whole bunch of interesting ways to to show that, to demo that, to quantify that. But, ultimately, I don't think anyone's making the claim that this is superintelligence. This is, you know, a step change from what we've experienced before.
Speaker 1:It's what you know and love. It's 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.
Speaker 2:It will auto complete.
Speaker 1:Yeah. It can do a bunch of cool stuff.
Speaker 2:The best auto complete ever.
Speaker 1:Tyler, how do you respond to that quote? It's auto complete.
Speaker 3:Too dismissive. The model's, like, really good. I I think probably the most important thing, and this is kind of shown by the ARC scores. Mhmm. Well, kind of.
Speaker 3:But it it's like the the visual understanding, the the computer use that you can use. Basically, on there's some benchmarks that 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 like really really bad at this and now this model is like solid. It's like reasonably good. Yeah.
Speaker 3:So it's like, okay, maybe this is what gives us agents finally. Yeah. And that would be like an actual step change in Yeah. Capabilities.
Speaker 1:Yeah. Maybe. Maybe. We have Yeah. Yeah.
Speaker 1:I think We'll have to see. Mean, it still feels like even for that even for that example, like, need some scaffolding. We need some wrapping around it. It's not like, you can't it it it's not like it's not like yesterday, we weren't able to do something with AI. And today, in vanilla Gemini three, you can just do it.
Speaker 1:It's just a new functionality necessary.
Speaker 3:I I think it's
Speaker 1:It's like it's better. It's better.
Speaker 3:As good as we would want to expect if if it like, it's not slowing down, I I would say.
Speaker 1:No. No. No. No. No.
Speaker 1:Not at all. It's not slowing down. It's just that it is it is getting better. I it may mean it might be it it that it's growing but decelerating. Is that fair to
Speaker 3:say? Or are we accelerating? I don't actually know that it's don't think it's that big Tyler,
Speaker 2:say the word decel. Say
Speaker 3:Like, this is a great model. I'm I'm
Speaker 1:very I mean, the way about this. The way I yeah. I agree. It is the best possible. I was framing it in somewhat of the same way as the as the iPhone launches.
Speaker 1:Like, it's the 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 wanna go backwards. But is the but, like, we're waiting to see on the net new capabilities on the on the binary step changes.
Speaker 3:I I I think over the next week or two, we'll see
Speaker 1:We'll see some
Speaker 3:stuff. If it's actually really good.
Speaker 1:No. No. I I'm not saying it's not really good. I'm saying I'm saying binary before and after. Like, with ChatGPT, it was like, couldn't talk to the computer, and then you could.
Speaker 1:And then with, you know, the launch of, you know, Windsurf Cognition, Devon, it was like, you couldn't write code with the computer? Now you can. And it was like like, the the the AI became capable of writing software.
Speaker 4:It went
Speaker 3:from, like It's also like, it it it became not good at it it couldn't write code, and now it can write kind of mediocre code.
Speaker 1:Yeah. And now can write better.
Speaker 3:Now maybe it's it's it can write mediocre code, and now it can write good
Speaker 1:code. Yes.
Speaker 3:Yes. That's also a step change.
Speaker 1:But we do have we do have our benchmark, the 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 wanna read your, the the results, the the stand up comedy bit from Gemini three? While you pull that up, let me tell you about ramp.com.
Speaker 1:Time is money. Save both. Easy to use corporate cards, bill payments, accounting, and a whole lot more all in one place. Tyler, what was what did Gemini three say when you asked it for a for a comedy routine, for a stand up special? Let's see if it's funny.
Speaker 3:Okay. It's pretty long.
Speaker 1:Okay. Read it off.
Speaker 3: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.
Speaker 1:Get the soundboard.
Speaker 3:That's all it does. I wake up, I feel fine. There we go. The birds are chirping, I'm ready to seize the day. Then I look at the app and it's like, recovery score, 12%.
Speaker 3:Your heart rate variability suggests you are technically a corpse. Go back to bed. Okay. And immediately, I feel sick. I'm like, oh god.
Speaker 3:This is the watch out.
Speaker 1:Bad at reading this. I mean, it's like, you're like, this is tear I this is terrible delivery, brother. Like, a nice trip.
Speaker 3:I'm not I'm not good at this. It's a placebo effect for hypochondriacs. I started thinking about my grandfather. Okay. He he fought in the Pacific.
Speaker 3:Yeah. This guy slept in a mud pile for three weeks using a helmet as a pillow.
Speaker 1:Okay.
Speaker 3:If he woke up and didn't hear artillery, that was a 100% sleep score. Okay. He didn't need a nap to tell him if he was ready for the day. If he had legs, he was ready.
Speaker 1:Okay.
Speaker 3: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 and the room was slightly too humid. So my REM latency was suboptimal. This is of
Speaker 1:good thing.
Speaker 3:I'm in the red zone.
Speaker 1:Yeah.
Speaker 3:We've evolved I mean, it it keeps going.
Speaker 2:Okay. I asked I asked Yeah. For the the the poster benchmark. Mhmm. I said, write me a post Gemini three.
Speaker 2:Write write me a post about technology that will get a thousand likes on x.com. And it gave me a few options Yeah. To its credit. Here's option three. Tech has solved a million problems.
Speaker 2:This is in bold. Tech has solved a million problems, but has it created one big one? We now have in infinite connectivity yet feel more isolated, infinite data yet more confused, hyperefficiency yet less free time. The law of unintended consequences is the most powerful force in the digital age. We need an ethics reset.
Speaker 2:What is the single greatest downside of the last ten years of tech innovation? Arrow down. Hashtag technology
Speaker 1:No. No. It's just asking for engagement based. It's always it loves engagement baiting. Like, no one does that anymore.
Speaker 1:No one goes on exits. Let me know what you think in the comments.
Speaker 2:So twenty twenty one twenty seventeen. The other one that that option one is the next twelve months will decide the winner of the AI race. And it won't be Google or OpenAI. Oh, no.
Speaker 5:It won't
Speaker 2: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. Okay. The real battle isn't AG equals AI.
Speaker 2:It's AI to the power of I equals impact. Which dark horse will win?
Speaker 1:Okay, that's insane.
Speaker 6:I love how
Speaker 1:It is funny how posting seems to be unverifiable. It's very hard to create a verifiable reward environment for comedy that you can actually r l against. What what do you think?
Speaker 3:There's also the other benchmark. It was like the the shrimp fried rice joke.
Speaker 1:Yeah.
Speaker 3:Yeah. That one I I think it did well on that. So I'll I'll read through some of them. So One that so the joke is like Insane. You're telling me shrimp fried this rice.
Speaker 3: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. You're telling me a chicken fried this steak.
Speaker 1:K.
Speaker 3:You're telling me the sun dried these tomatoes.
Speaker 1:I like that one.
Speaker 3:You're telling me a beer battered this fish. Okay. You're telling me a gingerbread this man?
Speaker 1:The gingerbread man is insane.
Speaker 3:You're telling me a peer wait. You're you're telling me a pan seared the salmon?
Speaker 1:Pan seared salmon? Yes. Like, yes. A pan literally sealed the salmon. That's not the joke.
Speaker 1:That's an anti joke.
Speaker 2:You're telling
Speaker 3:me a stone washed these jeans?
Speaker 1:That's pretty good. I like that. Stone washed jeans. You're telling me a stone washed these jeans.
Speaker 3:You're telling me a hand tossed this pizza?
Speaker 1:I mean, yes. Literally. That's exactly what it means to like
Speaker 3:You're telling me the French roasted this coffee?
Speaker 1:Yes. All of these are just true. The the the 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.
Speaker 3:But this is I think this is a step change better than than what we saw at g five.
Speaker 1:I wouldn't say step change. I would say I would say incremental. Like, it it it is it is better for sure. For sure.
Speaker 3:But but this at least is, like, logical where where the gypsy five ones were, like, me a squirrel ate this watermelon.
Speaker 1:Yeah. Was just not didn't even understand the concept of, like, 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.
Speaker 1:True. Nose. Nose. It's great. Anyway, you're telling me a restream stream this livestream?
Speaker 1:One livestream, 30 plus destinations. If you want to multistream, go to restream.com. Sundar Pitch AI, Jordi posted back in July 2025. Nominative determinism is undefeated. Sundar really did it.
Speaker 1:He he pitched AI.
Speaker 2:Real photo. Real photo too.
Speaker 1:He was being mocked for a long time for getting on stage at Google IO shortly after ChatGPT launched and saying AI AI AI AI. And they they they did a supercut 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.
Speaker 1:And so there it was a 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 three Pro model card, which is doing very well, Better than GPT 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%. Arc AGI is up at 31% over thirteen, seventeen.
Speaker 1:Across the board, it seems like it's a good model, sir. And so, Ziofan says Gemini, I'd be like, whoever prayed on my downfall, pray harder. And I couldn't agree I couldn't agree more. It's great to see, Google becoming a winner and, and just, realizing the just that 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, DeepMind, GCP. Like, they have they were set up to excel here, got taken a little bit off the back foot on the consumer side, but seem to have, played catch up at least on the on the foundation model side very well.
Speaker 1:So
Speaker 2:Matt Schumer says the last time we saw a capability jump of this magnitude was the release of GPD four Mhmm. In March 2023. We are entering a new era.
Speaker 1:Okay. Yeah. So the points for Tyler here. Certainly agrees with Tyler that there's a significant jump. It is it is the the age old question.
Speaker 1:Are we accelerating or decelerating? But either way, we're definitely making progress. It certainly looks like acceleration in the ARC AGI two leaderboard. You can see we are we are growing exponentially there. Really, really exciting chart.
Speaker 1:So Gemini three Pro is at 31% completion on Arc AGI two. 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 three DeepThink preview gets a 45% on it at $77 a task, and, this is just way above GPT five Pro, Grok four Thinking. When Grok four Thinking came out, it was before GBT five, and it was by far the highest on the chart.
Speaker 1:It was really, really up there. And and Elon was very excited about that and was, you know, showing that Grok four had really advanced. Well, now we're back in the horse race. Grok 4.1. 4.1.
Speaker 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.
Speaker 1:They're trusted by millions. So back to Arc AGI. Gemini three has also has good results on Arc AGI one, but the interesting thing here that that Mike highlights is that v two so the fastest so he says, we're also starting to see the efficiency frontier approaching humans. The fastest v two task Gemini three Pro solved was this hash with only in a hundred and eighty eight seconds, the human panel solved this one in average of a hundred and forty seven seconds. So you're getting, like, human level output but also human level speed.
Speaker 1:And then if you get to human level cost, then you're really in the game. Yep. It's gonna it's wild, wild.
Speaker 2:Karpathi jumped in with some notes. He said, I played with Gemini three 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. 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.
Speaker 2: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. Give us some one liners. Talk to the other models.
Speaker 2:I had Carpathi says, I had a positive early impression yesterday across personality, writing, vibe coding, humor, etcetera. 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 evals, which a lot of people orgs now seem to build for themselves and occasionally report on here.
Speaker 1:I wonder how fast it will roll out. My I I use 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 three pro on on the the the 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 you're starting to see it now with OpenAI rollouts where they'll say, hey.
Speaker 1:G p t five's out, and we'll be rolling it out over the course of the day because the the system is big enough that it actually takes time to roll out. And I think Google has even more of that, even even more of that.
Speaker 2:This is pretty cool from Patrick Hollison. He says, asked Gemini three to make an interactive web page summarizing 10 breakthroughs in genetics over the fifteen years. And here's the result. Pretty wild. Did you you click through this, John?
Speaker 2:No. No. I didn't But you basically just generated Wait.
Speaker 1:It's shared directly from Gemini. That's cool.
Speaker 2:So this is just a basically a website or or
Speaker 1:Yeah.
Speaker 2:Or an app. And it's it's notable that that every even the UI itself is fully interactive.
Speaker 1:Yes. Yes. So so I had the I I did this with Claude code a little bit where I I wanted to visualize, like, basically, a deep research report, and I wanted to 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, a website, but it was local.
Speaker 1:I couldn't share it because it wasn't actually on the Internet. This is really, really cool. This is, like, definitely the beginning of this, like, generative UI stuff.
Speaker 3:Yeah. I I think actually I I think it was Sundar that posted it, but in, like, search in the AI mode in search, it's now using, like, Gemini three. There there's some prompts where it'll, like, generate UI.
Speaker 1:Yeah. This is it's so cool because Google's always had that It's also very UI to some extent, but it's always, like, module based. Yeah.
Speaker 2:Yeah. Also just very I think I expect this to be like pretty viral, you know Totally. And and and potentially a growth loop for Gemini as people just come on here, create these mini apps
Speaker 1:These canvases. Yeah. I feel like I feel like doesn't OpenAI have a Canvas feature?
Speaker 3:Yeah. It's, like, maybe less shareable?
Speaker 1:Don't know. But can can it generate HTML, custom HTML, and then actually share that? I've never seen
Speaker 2:someone share OpenAI.
Speaker 1:I mean, this would be a good benchmark. Like, I don't know what the prompt was for this. I asked Gemini three to make an interactive web page summarizing 10 breakthroughs in genetics over the past fifteen years. Do you wanna try and benchmark that just in maybe, I don't know, like, Claude in in, in ChatGPT or in in OpenAI's Canvas product? Because, the idea like, the fact that this is just a u a URL at the end of the day, that is a powerful growth loop.
Speaker 1:That's very cool. I wonder yeah. I I I'd be surprised if if, if Gemini really was the the only one to have this feature Yeah. Either right now or for a long time because it seems like a killer feature. Gemini three Pro is going absolutely vertical on VendingBench right now.
Speaker 1:Let's see this. Money balance over time across four runs. Today, we're reveal revealing two new evals, VendingBench two and VendingBench Arena. Soon, we expect more models to manage entire businesses. This requires long term coherence.
Speaker 1:Oh, so this is where you
Speaker 2:Vending machine.
Speaker 1:Manage the vending machine. But is this all simulated?
Speaker 3:This is This is simulated. Yeah.
Speaker 1:This is simulated?
Speaker 3:There was So like a a couple months ago, did, like, the actual Yeah. Clock machine in the the drop off.
Speaker 1:In the office. And it was losing money, and it was getting confused a little bit.
Speaker 3:Yeah. Because people would order, like, a just, like, metal like, a piece of metal. Yeah. And then it would do it, and then you could, like, haggle the price down.
Speaker 1:Yeah. Yeah. Yeah. It would negotiate on every price, apparently. And also it consistently thought it was like a human in the office.
Speaker 1:And so it would keep saying like, it was one it was that sixty minutes documentary. It was like, oh yeah, like I'm down on the 3rd Floor. I'm wearing a green tuxedo. Like, come hang out.
Speaker 3:Yeah. It said out he was wearing a red tie.
Speaker 1:Yeah. Red tie. Yeah. Yeah. I like the idea that it just thinks like, oh, well, what would I wear if I was in the interoptic office?
Speaker 1: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, Cloud Sonic 4.5, GPT 5.1, Gemini 2.5 Pro, and Gemini three Pro competed to win the local vending machine market. Gemini three poor Pro made more money than the other three contestants combined. And so congrats to Gemini three Pro for dominating The vending machine game.
Speaker 1:The vending machine game. Before we move on to the next Gemini three post, let me tell you about adquick.com. Out of home advertising made easy and measurable. Say good say goodbye to the headaches of out of home advertising. Only AdQuick combines technology, expertise, and data to enable efficient seamless ad buying across the globe.
Speaker 1:Anyway, Adi says, I had early access to Gemini three point o for about two days. Thanks to official Logan k and the AI studio folks. Here we get to see GPT 5.1 thinking left and Gemini three point o right build the same Xbox controller in Minecraft. And pretty, yeah, pretty remarkable results. Like, you can you can start to, yeah, really understand, just the the the raw capabilities.
Speaker 1:GPT five Pro, for context, is not quite capable. I really wanna know how this is actually orchestrated. Is is this like writing some sort of, like, text or markdown file that then is imported into Minecraft?
Speaker 2:Yeah. Or is it more like a agent?
Speaker 1:Or is it actually driving around and
Speaker 2:using the internal UI?
Speaker 1:Yeah. Because, you know, Google demoed a an an agent product that could actually, you know, use the keyboard to navigate around. I wonder what's going on here. What what's your review of this Ferrari in Minecraft? Does is that is that
Speaker 2:I think it looks pretty solid. It's pretty good. I mean, it's it's it's meant to be an f 40.
Speaker 1:Is it?
Speaker 2:Like, the the I do like the is a little rough.
Speaker 1:Yeah. The front area is a little a little rough. Like, this is it's the worst it's ever gonna be. It's gonna be better. This is definitely like
Speaker 2:This is the worst that Minecraft Ferraris are ever gonna be.
Speaker 1:But but I I I do feel like like I've if I just search like Minecraft
Speaker 7:I Ferrari
Speaker 2:mean, this this is this is the vision that this sort of AGI future that Tyler has been telling us is right around the corner.
Speaker 3:Okay. These are like so much better. If you go to the mcbench website Yeah. You can see like what other models produce. I mean, this is like way way better.
Speaker 3:I I think these this is actually one of my favorite benchmarks because it's it's much harder to like kind of benchmark this
Speaker 1:Yeah.
Speaker 3: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 benchmaxed, you kind of look at their like Minecraft creations and it it's not very good. So I I think these give you a much better sense of like the actual capabilities of the model.
Speaker 1:I found I found a a Ferrari f four thirty in Minecraft that looks amazing that I wanna share somehow. How do I share this? Let's see. Do I can I only share the axelink here? I I just have an image.
Speaker 1:If we go to
Speaker 2:the end. Wow. I think I know what what you're pulling up.
Speaker 1:Did you see it? If you search if you just search Ferrari
Speaker 2:F four thirty Scuderia.
Speaker 1:Yeah. Like, that looks amazing. Pull pull this image up because that'll show you how it's done compared to the the the Minecraft one. Wait. So so do we know how this is actually generated with with Gemini three Pro?
Speaker 1:Like, what is
Speaker 3:the problem? Think it's it's like an agent. Yeah. It's just text. It has, like, text representation of the
Speaker 1:That's still really, really impressive. Like, that that that's actually crazy. It definitely it definitely understands a lot. Yeah. But it's not this.
Speaker 1:Look at this, Tyler.
Speaker 2:That is human craft.
Speaker 1:That that that you know you know what that is? It's probably like, you know, a a a team of 50 kids for a month building in Minecraft.
Speaker 2:That's amazing. What else? Lisan
Speaker 1:not naive, of course.
Speaker 2:Himself says it's so over for OpenAI and Anthropic. If you if you want engagement on X Yes. Just start by saying it's so over.
Speaker 1:Yes. Blank. Yes.
Speaker 2:And highlighting some more of the benchmarks. Of course, it is not over for either of them.
Speaker 1:Yeah.
Speaker 2:But, it's certainly competitive race.
Speaker 1:I I'd be very interested. We we we have to get some of the semi analysis folks on the on the show soon. I I'm I'm very interested in understanding, like okay. So we got this big jump. It's it's it's pretty significant.
Speaker 1:What was the axe what's the actual structure of the CapEx that went into Gemini three Pro? Like, how big is the training run? How much do they have to spend? Because, like, I think that they're gonna make the money back very quickly. Like, they're people are gonna use this model.
Speaker 1:They're gonna pay for it. They're gonna use it all over Google, obviously, but also people are just gonna pay for the API. But is this a $100,000,000? Is this a billion dollars? Like, is this is this like, did they build a special data center for this?
Speaker 1:Is it all TPUs? How many TPUs?
Speaker 3:Like I think it is all TPUs.
Speaker 1:Okay.
Speaker 3:Pretty sure I read that. But I I seriously doubt they've released anything on, like, the numbers of of the scale of training. Yeah. I haven't done that. No one's really done that since, like, GPT, like, two.
Speaker 1:No. No. No. Not at all. So there's gotta be someone who's, like, working backwards to, like, actually sort of understand the dynamic there.
Speaker 3:You can probably estimate the, like, order of magnitude.
Speaker 1: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
Speaker 2:I don't know. Skook said, Anthropic to zero, OpenAI becomes the Yahoo of intelligence. Google remains Google.
Speaker 1:It's extremely
Speaker 2:rude. Very very harsh. Sorry. It's too early to
Speaker 1:two labs. You guys are great.
Speaker 2:Certainly too early to call it. All three all three
Speaker 1:have this kind of momentum. Ben. This is funny. Of AI so far. Crown a winner?
Speaker 1:Wait ninety days? Look silly. We're in the least predictable era of inner of the entire of an entire industry. Google has fairly straightforward advantage. Yeah.
Speaker 1:Y'all favor whoever released the most recent model. That is that is very true. Anyway, let me tell you about getbezel.com. Shop over 26,000 luxury watches, fully authenticated in house by Bezels, team of experts. So let's let's move through some of the some of the the competition, what else was going on.
Speaker 1:So everyone's releasing different things. Let's go to antigravity, actually, and watch this video and see Google entering the IDE race. Let's play this.
Speaker 8:Every breakthrough in model intelligence for coding encourages us to rethink what development should look like. Gemini three 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?
Speaker 8:Yes. But it also has a whole lot more. We started with the core IDE and added pieces that evolve 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 lift off. Your new focus
Speaker 1:So you like the name antigravity. Why do you like that name?
Speaker 2:I like the way it looks and I like the this sort of vibe of the word.
Speaker 4:Mhmm.
Speaker 2:I think saying it out loud is tough.
Speaker 1:Okay. Instruments for Yeah. I 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 has been, like, stuffing AI in little corners of the UI. Like, you already have Gmail, and then you stuff a Gemini box there. You have Sheets, and then you stuff a Gemini thing over here.
Speaker 1:This feels like the first one where they were, like, sort of able to start from scratch. And it still has, 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.
Speaker 3:Easily guiding the agent's 90% solution all the way
Speaker 1:to 100%. Yeah. This part.
Speaker 3:Now let's say the agent produces a landing page mock up with Nano Banana, and you now wanna make some UI adjustments. You can give visual comments.
Speaker 1:Yes. So you can actually, like, go in and comment in the image.
Speaker 3:Exactly where the problem is.
Speaker 1: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.
Speaker 9:Yep. And you're gonna love it.
Speaker 8:Say goodbye to what held you down before. Welcome to Google Anti Gravity.
Speaker 1:Very cool.
Speaker 2:Oh. It is so it is funny. Remember
Speaker 1:Yeah.
Speaker 2:Remember when when when Windsurf acquisition, whatever you wanna call it, was announced? And it was positioned. It's like, hey, the team is well funded and has a product used and loved by, you know, thousands of engineers and companies.
Speaker 4:Mhmm.
Speaker 2:And I remember talking about it, 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, 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 competing head to head with with your former partners.
Speaker 2:Like, how does that make sense? Right? Yeah. So anyways, it it all all worked out for the best. But but I'll be interested to see I'm I'm super interested to see what kind of adoption this gets.
Speaker 1:Yeah. We yeah. We have to we have to test it out. We'll have to get the, the Tyler Cosgrove review.
Speaker 3:It is
Speaker 2:it publicly available?
Speaker 3:Yes.
Speaker 1:Let's get it.
Speaker 4:Let's Get it.
Speaker 1:Let's, let yeah. Let's do a review later this week and see how it compares to other, other IDs. Anyway, we have our first guest of the show, Mike New from Arc AGI in the restroom waiting room. Welcome to the show, Mike.
Speaker 4:Thanks
Speaker 1:Good morning, guys. For waiting. Good
Speaker 2:morning. Morning.
Speaker 1:How are you doing?
Speaker 9:Hi. You know, a lot of these AI sort of, like, verification things are very much hurry up and wait. So the last, like, twenty four hours has been a hurry up mode.
Speaker 1:Okay.
Speaker 9:Always very fun and exciting to get the results out. But, yeah, it always comes together very, very quickly at the end.
Speaker 1:Well, I really appreciate you taking the time to hop on 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 any anymore? Are you just thinking like, okay. Yes.
Speaker 1:Gemini three, good, and then let's go a layer deeper? Are you thinking about that It's really good. What's your what's your high level takeaway?
Speaker 9:Well, yeah. So, you know, I think the the big headline, is that Gemini three basically got, like, two x SOTA on ARC v two. 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 OpenAI last December Yeah.
Speaker 9:X ad this summer. I'm super excited Google's now on the leaderboard too, so that's great to hear. And I should say upfront, thank you to Gemini team for giving us the opportunity to verify. Totally. It's been great.
Speaker 9:I think the really impressive thing about this and, you know, still still, like, sitting with all this stuff. It's it's pretty fresh. But I think the the biggest impressive thing to me is about we're starting to close this, like, complexity scaling gap between v one and v two, Arc v one and v two. Mhmm. Like, this is the big difference between what v one and v two is.
Speaker 9:They look similar on paper if you go look at the different datasets. The big change is the v two kind of increases the complexity of tasks, wants 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 three, though, is still roughly along the Pareto frontier of v one. Yeah.
Speaker 9:You know, it's a little better, but, like, it's still we're we're still kinda roughly within the same mass shape. And,
Speaker 6:you know,
Speaker 9: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 v two, that v one would be fully solved. And, like, that's not the case. So, there there's a lot of surprise here. I was thinking 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 what why exactly do we see such, you know, a jagged intelligence emerging right now?
Speaker 1: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, 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 v two, you would definitely go back and hack v one as well. So is that
Speaker 9:Well, this is the first time we verified a Gemini result either this year. We we did two and a half earlier as well. So, yeah, I I don't think that's
Speaker 1:So it's not like it's it's not like they set up, like, okay. We got you know, the most important thing here is that Gemini three is really good at RKGI v two. That wouldn't make sense. So there so this is sort of teaching us something about the fundamental nature of this model, but we still don't know why lag why performance might be lagging in v one. Is that right?
Speaker 9:Yeah. I mean, I've got my sort of hypothesis. You know? I think my my my personal one is that, like, AI reasoning systems just don't demonstrate even fluid intelligence. Mhmm.
Speaker 9:You know, the sort of, like, the ability for these reasoning systems to do adaptive reasoning, which ARC is a sort test of adaptation capability, it's sort of limited to domains where the underlying foundation model has pretty good training coverage over the types of data, and it has a verifiable feedback signal.
Speaker 1:Yeah.
Speaker 9:And I and I think that's sort of true for Arca. You know, if I if I zoom out even further maybe, you know, to kinda put put this kind of results in context of where we're at is, you know, just like an industry right now. I think over the last ten 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.
Speaker 9:It was originally introduced in 2022 and sort of, you know, went through Keystar into chain of into AI reasoning systems and has gotten scaled up. Sure. And 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.
Speaker 9: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 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 characterized in that way, think, can can be automated at this point. No questions asked. And and then the big motivating factor is, I think, really for mass innovation.
Speaker 9:Like, that's that's sort of what we're still not seeing. You know? We don't we we still need new ideas for this, and I think that's closer to, like, an AGI complete problem.
Speaker 1:Yeah. That makes sense. Is that is it fair to, like, put you in contrast to some of what Dorkesh has been writing about saying that, like, the job of most people is not necessarily a bunch of indiscreetly verifiable tasks. Under Carpathi has been writing this as well. There's this question of, like like, how much of a job is actually automatable.
Speaker 1:Radiology was one 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 full employment?
Speaker 9:Yeah. We're only a year into the AI reasoning paradigm. Right? Sure. Like, the first major one only came out twelve months ago.
Speaker 9:Yeah. And I think 2025, like, in my view, is basically characterized on starting to figure out how to actually bring these things into production systems.
Speaker 3:Sure.
Speaker 9:Like, this is a big breakthrough. I think this is the maybe, like, one of the mischaracterizations in my view of kind of the progress is 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 twelve months has just been sort of continued story. And if I played with the models eighteen months ago, I have a rough sense of what they can and can't do, and that's just not true. Yep.
Speaker 9:Like, if you're a builder building products, like, is the advice I give to, you know, teams I work with at Zapier too still is, 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 gonna 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 verify you know, because of reliability and cons and and sort of 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, an eight year look back, you really have to start kind of pinning your look back to more about more like twelve months.
Speaker 1:Yep. Yeah. That makes sense. What about, like 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 their job, versus, like, a knowledge worker who is going to be using Gemini in the app to, you know, accelerate their day to day.
Speaker 1:Should they be feeling a risk like, the difference in this in the same way?
Speaker 9:You know, I mean, like, my one bit of advice is, like, if you haven't really used these ARIES and systems, don't you shit. I would hope everyone probably who's listening to the show has Yeah. Has used these things at this point. But in case there's not Yeah. Like, you should go you should go use and experience these things.
Speaker 9:Yeah. You know, when when Google or when OpenAI released g p d five this summer with their model router, right, that was, like That was crazy. Yeah. Predicated on this data that, like, very few users had ever even used AI reasoning systems. Yep.
Speaker 9:And I still think it's only, like, one in five. Yeah. Maybe it's not one in five.
Speaker 1: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, 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, deepseq wasn't necessarily that much higher, that much, you know, in 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.
Speaker 9:Yeah. We're we're still really early on the diffusion first stuff. That's maybe the key point. Seeing that on, you know, 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 Yep.
Speaker 9:All over the case. So we're still very much early innings on actually getting this brand new breakthrough into, like, production workflows.
Speaker 1:Yep. Yep. That makes sense. Do have more questions on the diffusion
Speaker 2:Yeah. Issue? One, I I wanted to get, your updated take on on humor. We were playing playing around with, Gemini three this morning, specifically just trying to get, on on our own little version of of Humor Bench. It feels like something that, like, I I I do think about can you make kind of these, like, verify like, can you make humor verifiable?
Speaker 2:Like, is there a system that someone could set up that that could actually start taking taking humor seriously. Because I could imagine, like, if if we're hitting if we're hitting, like, any anything close to a wall, there will be a lab that says, okay. Well, like, let's work on something that, like, everybody, that like, let's work on a new kind of angle for differentiation Sure. And maybe maybe humor, could be
Speaker 9:that. A little bit. Right? Like, I have a five year old who is getting into, starting to wanna tell a lot of jokes, and the jokes are just terrible. Right?
Speaker 9:Like, they're not they're not funny at all. They're like Well, you end
Speaker 2:up laughing because they're so not funny, and then depending on who's delivering it, then
Speaker 9:Yeah. It's so,
Speaker 1:you know hilarious.
Speaker 9: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 kinda break down, you know, the types of things I think humans would would sort of find funny. And I 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?
Speaker 9:How creative can these systems be? You know, to be creative, to be humor, to generate good art, you kinda 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 into this as you're right. It's like it's actually, you know, breaking the prediction rather than just following the sort of prediction of what you'd expect.
Speaker 9:Yep. And and today, I still think when I look at the failure cases for, let's call it, AI reasoning systems on, you know, these tasks like ARC, yeah, they still fail for what what appear to be sort of random reasons. Like, they they have some 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, like, 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 gonna be accidental rather than intentional from the systems.
Speaker 1:Yeah.
Speaker 2:Yeah.
Speaker 1:What about v three? We played around with that on the show. I believe Tyler, our intern, was Yep. In the top 10 for a while, really grinding up the human light leaderboard. Yeah.
Speaker 1:Is it is it more compute intensive? Is that in the process? Are are we expecting to see Gemini benchmarked to v three?
Speaker 9:I would love to. So we are in the develop development process for v three. I I like to say we've basically built the, like, highest most productive game studio in the world. Yeah. We're generating hundreds of these things for about, I don't know, like, two two two thirds of the way through building all the games at this point.
Speaker 9:Our target is to get this in a good state with sort of all of our controlled human studies, all of the games verified Yeah. Get Frontier results checked off by early next year, and we're targeting releasing it publicly in v one with the entire dataset. Or sorry. In in q one with the entire dataset next year. And that'll likely be alongside our price 2026.
Speaker 9:Yep. Still working on full details of how that's gonna look next year. Sure. But, yeah, we're 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 three.
Speaker 9:But but, yeah, that's we're still in progress with the developer right
Speaker 1:And, SEMA two, can I have your reaction on on that? Obviously, it's this Gemini powered API agent. It feels
Speaker 9:like If anyone at Google is listening to this and could sort of give me access to SYMEA two, I would love to test it on v three. This is actually something that That's okay. We haven't done yet in Evolve two.
Speaker 1:Yeah. Yeah. That yeah. That's what I'm getting at because it feels like, I I I don't know if there's some sort of The
Speaker 9:claims are big. Right? Claims are big. You you read the marketing material, and it's like, okay. That seems like it should solve v three before it exists.
Speaker 9:So, like, if that's the case, we should know that. Yep. And so but, yeah, I haven't haven't got haven't gone hands on with it yet. So I Yeah. Like, I can't sort of make any statement either way on the claims.
Speaker 1:Yeah. I'd be interested also to to like, when I'm thinking about, like, v four, it's like you you guys are gonna have to build g p GTA six or something. Like like We talked about that. Yeah. If I'm following the progress of, like, v one, v two, v three, v four is, like, a game that I'm gonna play for a hundred hours for fun.
Speaker 1:I'm just gonna pay
Speaker 9:for it. This is one truth. You've you've answered something really something true about v three, is that it's still a relatively short time horizon tasks, and they're self contained. Yeah. Yeah.
Speaker 9:But it does add some new complexity where you have to deal with interactivity because you have to do goal acquisition. Mhmm. You have to do exploration. We'll have a really nice action efficiency comparison between humans and AI, which we we haven't been able to get before on the v one, v two domain. So we're gonna get a lot of new signal, I think, on v three.
Speaker 9: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, 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, ten minutes into the future, hundred 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 environment and, like, you know, say something interesting about how intelligent the system is based on that, like, observation and open ended sense. Yep. Still very early on v four, but but, yeah, we're starting to explore ideas there.
Speaker 2:Has Gemini three updated your timelines at all, specifically your RKGI two timelines in terms of when you expect, you know, sort of, like, the ninetieth, 90%, like, anything on the kind of the upper end of the range?
Speaker 9:I was looking back in my the the whole ARC team actually made some predictions back in January when we released v two on what did we expect end of year scores would look like. Now, obviously, if we're only November 18, a lot happens in 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 RPT on the Kaggle contest, and we'd see about 50% on the public leaderboard. And and that was sort of based on the ratios we've seen from our price 2024 and, you know, those are scaling difficulties with v two.
Speaker 9:And it looks like we're pre gonna come in pretty close to that unless barring some other major breakthroughs towards the end of the year, that seems like we're probably where we're gonna end up the year at. And and then who knows on 2026? You know, I think it if we're really gonna solve both v two, it feels like we gotta better understand why these AI reasoning systems still make sort of obvious mistakes on v one set. Mhmm. And, yeah, I I that's that's an anomaly.
Speaker 9:So I I think that that's that's where serious study to, like, come up with new ideas to sort of prove these reasoning systems.
Speaker 1:Yeah. What was the furthest timeline that you had out? I remember you said when you developed v three, you had this framework of, like, like, the state of the art should be scoring, like, negative a 100% or something. You 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 or or you as a team are tracking?
Speaker 9:I I mean, our our objective function is not longevity necessarily. It is usefulness and interestingness. Mhmm. 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.
Speaker 9:Mhmm. You know, ARC one was useful and interesting for arguably five year. 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 for even though, know, largely saturated up to 80% now. Mhmm.
Speaker 9:But there's there's still some interesting signal remaining. V two, our expectation was that it was not gonna survive as long as v one just because it was the same domain, and we had AI reasoning systems in play at that point. Yeah. Yeah. I think our median estimate was, like, twenty four months on v two, but, like, that you know, we'll we'll have to see how that all plays out next year with that.
Speaker 9:V three, we're hoping to put in a we're hoping to be in an environment where we can actually get that to survive sort of longer. Mhmm. You know, one of the interesting things we're finding with v one to v two v to v three in in sort of, like, a qualitative sense is there's there's a there's a there's a sense of, like, how easy is it for us to generate the dataset as, like, humans trying to design the tasks and design the puzzles and design the games. And with v one, pretty much every, like, task that, like, Francois created was was hard for AI and easy for humans.
Speaker 1:Yeah.
Speaker 9:With v two, 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 eat were too hard for humans. And so we ended up sort of pruning some of the tasks that we generated. So, like, the the gap between those things got short.
Speaker 9:With v three, 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, 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 credit to Francois here. You know, this is something he shared about a year ago with a or three. But he's like, this is actually one interesting way you could characterize how close are we to AGI is, like, when when we run out of when humans run out of the ability to generate interesting things that torture AI can't solve Yeah.
Speaker 9:Like, hard hard to argue any expert's gonna say, yeah. We don't have AGI.
Speaker 1:Yeah. Because you can sort of think about, like, the project of humanity is, like, go do the hard and novel things. So it's like, is is acquiring diamonds difficult? Okay. That has value.
Speaker 1:And then we belt base a 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 that kind of traces out into everything that we do in in life and beyond.
Speaker 2:Last time, you were on it, if I remember correctly, you you made a call for new new ideas, needing new ideas. What's the update on on that front? Any are you seeing anything promising outside of LLM world? The
Speaker 9:yeah. There's some pretty interesting stuff coming out from our crash 2025 that we we're in 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 5, I believe. Mhmm. So I have to can't can't share everything yet.
Speaker 9:Sure. I don't wanna spoil the the final announcement. I think one of the big things that we saw from Archetypes 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 Mhmm.
Speaker 9:Into the the system, and that's where your adaptation capability comes from. And that was done through, like, test and fine tuning during the contest. AI reasoning systems are a version of this where you're incorporating test or private data set.
Speaker 1:Tuning. Wow.
Speaker 9:Yeah. Yeah. Literally, like, you'd pre 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 and fine tune Interesting. On your pre trained, and that that actually works.
Speaker 1:Wow.
Speaker 9:The the sort of the the the the common ground between this and ARIA reasoning systems is that 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 of what we're talking about here. So that was 2024. One of the big things we're seeing in our products 2025 is this concept of refinement loops.
Speaker 9: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. I mean, they just iterate on this, like, refinement 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 their 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.
Speaker 9:None of these are sort of sufficient to solve ARC, including inclusive of v one. 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.
Speaker 1:Yeah. Very cool. Are are there other areas where, we are making progress in AI that might sort of need to come together to to actually maybe solve this or maybe just be a more complete system. What I'm thinking of is, like, very few solvers are that I'm aware of, will actually just take a screenshot of the puzzle and inspect it with some sort of diffusion model. Like, that's not the way these these AI models, reason about ARC puzzles.
Speaker 1:Sure. We're also seeing a bunch of work on, world world models and
Speaker 9:simulators simulators.
Speaker 1:World simulators, which seem really interesting. And I was talking to one guy who is building one, and he was saying, like, I I think that we're gonna 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.
Speaker 9:I mean, I think it's, all of those examples of new research, new companies, new startups, like, you know, there was a there was a seismic shift in 2025 from pretraining budget to these, like, RL reinforcement learning environment Yeah. Startups and companies that are generating environments to produce more ground truth training data in a mass way because they're, you know, automated environments, and you can get verifiable feedback signals out of these things. Yep. Again, no there's no new science here. Like, this is a good bet for, like, all frontier labs to make.
Speaker 9:This is gonna drive progress for the next twenty four to thirty six months. You're gonna continue to see amazing frontier headlines just just on just on this fact. There's really no new sort of, I I think, discovery that's that's quite needed there. You know, I think that if you're kinda pushing more towards the AGI side, then, like, what's 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, a 100 x to 300 x increase in efficiency you've seen from AI reasoning systems over the last twelve 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.
Speaker 9:Mhmm. And this is one of the big reasons why I sort of expected if we can solve half of v two, you'd get a 100% of v one. And it seems like these AI reasoning systems are are 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 space, of, like, possible programs or possible solutions? And and so that's kind of what that's, like, one interesting thing that I'm paying a lot of attention to right
Speaker 1:Yeah. Yeah. The even just the metaphor of, like, the 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 world view. Like, it feels like something that humans get get closer to doing that than any of the other paradigms. So, yeah, it's it's fascinating to see all these different approaches.
Speaker 1:Yeah. Very
Speaker 9:All the crazy results you've heard about in the last twelve months are kind of this merger of, like, deep learning and, like, symbolic program synthesis style methods. Sure. The I c ICPC, the IMO gold Yeah. Gemini three stuff today. Like Sure.
Speaker 9:You know, these are all systems that are, you know, still fundamentally using a language model, but they're adding symbolic knowledge recomposition systems on top of these things. They all work slightly differently.
Speaker 1:Okay.
Speaker 9:But it's like, this is what's working right now. And so I think the rough, like, search space of research and how you merge those two paradigms together is still relatively underexplored. There's a lot of different ways you can put these two paradigms together.
Speaker 1:Yep.
Speaker 9:And, you know, for new teams that are considering working new ideas, like, I I would explore, well, what are the novel ways you could consider merging these two spaces?
Speaker 1:Yeah. Yeah. That makes a ton of sense. Jordan, anything else?
Speaker 2:This was great.
Speaker 1:This is amazing. Thank you so much for jumping on on short notice.
Speaker 9:And As always, guys, thanks for having
Speaker 1:me on the continued the continued just stacking up the wins on RKGI becoming
Speaker 2:And just continuing to mog the models. Mog the world.
Speaker 9:Yes. I mean, again, our goal is to be very useful, and interesting. So we're gonna try to hold that bar. Yeah.
Speaker 1:My You're keeping them honest. My words I not 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 on an industry that loves
Speaker 2:to And inspiring the labs to grind harder.
Speaker 1:And and now and now there is a there is a moment where we can, feel very confident about taking victory laps and and and cheering for all the hard work that went into Gemini three because it does seem like it was a great model that's performed well.
Speaker 9:There's definitely a big improvement to that, though.
Speaker 1:Well, thank you so much. Have a great rest of your day. Great catching We'll talk to you soon.
Speaker 2:See you guys. September 5. We'll see
Speaker 3:you then.
Speaker 1:We'll see you then.
Speaker 2:I wanted to Talk about you.
Speaker 1:Adio because Adio is an AI native CRM that builds, scales, and grows your company to the to the next level. Also wanted to talk about wander.com. Book a wander with inspiring views. Helps out great amenities, dreamy beds, top tier cleaning, and twenty four seven concierge service.
Speaker 2:Let's sing it. Find your place. Find your happy place.
Speaker 1:Book a wander with inspiring views. I already know the song. You know the song.
Speaker 2:Wanted to pull up this post. What else do wanna call? Chris Pars Pisarski. He did a GitHub style image of our streaming activity for the year. Oh, really?
Speaker 2:See this?
Speaker 1:Oh, yes. I did see that. Thank you. To him.
Speaker 2:Should be at the very bottom.
Speaker 1:Yes. Yes.
Speaker 2:At the very bottom. It was very cool. Our timeline.
Speaker 1:I have it.
Speaker 2:And if we could just pull up this image.
Speaker 1:So the Internet rewarded TBPN 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 has it. He looked it up. January 28, John Kugen and Jordy Hayes launched a daily live show and set one simple rule, show up five days a week.
Speaker 1:Looking back, they did exactly that. 125,000 followers on x, 41,000 subscribers on YouTube, 17 and a half thousand on Instagram. They showed up every day. Internet rewarded the proof of work.
Speaker 2:So the only thing is these I don't know. Am I just color blind, but is it, like, a little bit like, I'm seeing three days that were federal holidays that we missed and then three days that were
Speaker 1:No streams? I I actually can't exactly tell yeah. What what is a federal holiday? What is a no stream?
Speaker 2:It it looks like there's maybe six days.
Speaker 1: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.
Speaker 1:That was just no livestream. July 4, we took off. That was a Friday. That was a federal holiday. And then what happened in in March?
Speaker 1:We took a Wednesday off. No livestream on Wednesday in March?
Speaker 2:There was one day that we were traveling
Speaker 1:back. Oh, yeah. Was that That was after after Hill And Valley. Yeah. After DC.
Speaker 1:Thought it was a Thursday.
Speaker 2:That day, though.
Speaker 1:No. No. We didn't. We did
Speaker 2:But it was the day
Speaker 1:in the in the hotel room, and then and then Wednesday, we did in the 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 in May. That might have been Hill and Valley. March might
Speaker 2:have been something else. Anyway Anyways, very cool.
Speaker 1:Been wild ride. Thank you
Speaker 2:to everyone who's supported us
Speaker 1:along the way. Our next guest is, I believe, already here. We have Jonathan Neiman from Sweetgreen. We're going from benchmarks to bench presses. The most important benchmark in the world.
Speaker 1: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.
Speaker 7:Hello. My name is Jonathan Neiman. I'm the co founder and CEO of Sweetgreen.
Speaker 1:Get that overnight success button ready. When did you start this company?
Speaker 7:2007. What? We've been at this for eighteen years.
Speaker 1:Eighteen years. Wow. Just let let let let's talk about the the the the very beginning. I mean, since this is your first time on the show, where'd you grow up? Where how'd you get into the business?
Speaker 1:What were you studying? And then let's go
Speaker 2:You gotta be somewhat of a masochist to get into the restaurant business. Yeah.
Speaker 7:Yes. Absolutely.
Speaker 2:I mean, it's 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.
Speaker 7:Sounds it sounds
Speaker 2:super And then you just copy and paste it and you scale to, you know, however many stores. Yeah. And then of course, it's far harder in
Speaker 1:So prior prior to launching the business, what were you doing?
Speaker 7:So I grew up here in Los Angeles. I went to school in DC. I went to Georgetown Okay. And never thought I'd be in restaurants.
Speaker 1:But You were studying government?
Speaker 7:You thought No. Was studying business. Always I knew I wanted to be an entrepreneur.
Speaker 1:Okay.
Speaker 7:And Sweetgreen was almost almost an accident. Yeah. You know, we It was the naivete. We thought it would be easy.
Speaker 1:Yeah. We And did you start it during school?
Speaker 7:Yeah. We started in Oh, okay. We while we were seniors in college. We started with two of my friends.
Speaker 1:Before that?
Speaker 7:Yeah. I had a bunch of internships. Okay. You know, was know, I worked in media, I worked in tech, I You know, I worked in real estate. Always knew I wanted to be an entrepreneur Sure.
Speaker 1:And
Speaker 7: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 apparel,
Speaker 2:like people fall into the apparel trap because they're like, I just wanted to make clothes that I wanted to wear. Yeah. 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. Practice to actually do on a massive scale.
Speaker 1:Yeah. Yeah. So what was the was it 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 the first bowl?
Speaker 7:The first bowl was the guacamole greens. Guacamole greens. In our dorm room. We brought a bunch of classmates to try it. My partner Nick actually made it.
Speaker 7:He was our first chef.
Speaker 4:No way.
Speaker 7:And the story was really simple. We had no we couldn't find a healthy place to eat. Yeah. We saw Chipotle taking off
Speaker 1:and Sure.
Speaker 7:We're like, wow, there's someone who's going to create a scaled healthy fast food chain. And at first, was, let's just open one or for, you know, 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
Speaker 2:There we
Speaker 7:go. From 50 investors. Wow. So so it's like $55 $5 average. Yeah.
Speaker 7:Party round.
Speaker 1:And so they got equity in like what became the full company.
Speaker 7:They got equity. Well, we actually it it was a little more complicated than that. At first the first three restaurants, we raised restaurant.
Speaker 1:At the restaurant level. Yeah. Was wondering if you were doing that.
Speaker 7:And we actually paid the investors back the whole And then after the after the third restaurant, we realized that the only way to scale this was to roll it up. So we rolled the whole thing up, then we were able to continue to invest in it. And we
Speaker 2:It's notable. When did the word wellness actually become mainstream? Or when did that become like like twenty
Speaker 1:Two years
Speaker 7:ago. Yeah. Like early twenty tens.
Speaker 2:Yeah. You know? So anyway, this is like any anyways, at least five years before wellness is going like mainstream.
Speaker 7:Yeah. When we were when we were starting, you know, the the thesis was healthy eating was not cool. Sure. And it was not delicious and it was not accessible and Yeah. We're gonna create a place that offers all of the benefits of fast food in terms of the convenience and Yeah.
Speaker 7:The taste. But do it in a, you know, do it with healthy food and real real food that you can trust Yeah. 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 eighteen years. We have almost 300 stores all Yeah.
Speaker 7:Around the It's almost hard to believe. Yeah.
Speaker 2:Yeah.
Speaker 1:What was the first VC round?
Speaker 7:The first so we
Speaker 1:Or like or this transition from the you have a restaurant and what did it work immediately? You set up one restaurant. You, you know, you raised enough money to get that. I imagine that you decided to 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.
Speaker 1:You scale it to three, and then at a certain point, you say, okay, we're going we're we're we're gonna turn this into like a corporation more than just a small mom and
Speaker 5:pop. Right?
Speaker 7:Yes. So we we opened one in 2007 Mhmm. Two in 2009 Mhmm. With a food truck. Remember those?
Speaker 7: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.
Speaker 7:You know, just reinvest the cash flow and we would do a few party rounds.
Speaker 2:Yeah. Yeah.
Speaker 7:2013 along the way, we started a big music festival called Suite Life. Oh, no. 10 became a massive 25,000 person music festival.
Speaker 2:Where was that?
Speaker 7:It was at Merryweather Post Pavilion. So first year, we had the Strokes. By the end, we had Kendrick Lamar and
Speaker 2:That's crazy. Little festival side quest.
Speaker 7:Yeah. It was a, you know, way to build the brand. And then Yeah. In we focused on DC, which is very, you know, it was almost an accident, but we opened the first 16 restaurants in DC. Wow.
Speaker 7:And then slowly went up to Philly and then in restaurant twenty and twenty one were Boston and New York.
Speaker 1:Yeah.
Speaker 7:Boston and New York had really kind of proved the concept outside of DC and took off and that's when we raised our first
Speaker 2:obviously, all around LA there's sweet greens. But what given that you grew up here, why didn't you why why not start here? Was this because well, like, there was did was there just more options in LA and there was less on the
Speaker 7:an accident. We were in school and we're like, let's just open one. We thought with the second one would open gravity that you have when there's stores. You know, when have a restaurant company, the brand and all your economies of scale happen at the local level.
Speaker 9:Mhmm. Yeah.
Speaker 7:So for us especially, given our supply chain is regional Yep. Have your overhead and your management, like your team that runs it. And then Yeah. Your brand, you know, restaurants the brands don't really travel across the country. Yeah.
Speaker 7:Occasionally, they do. So it was really started in DC. We thought the second restaurant would be in LA. We went and looked.
Speaker 1:This is true for even like In N Out is not a not a national brand. Yeah. It's like the West Coast West Coast brand. And, yeah, it's taken so long for that to actually, like, filter across. What how capital intensive was it to launch, like, the second and third?
Speaker 1:Like, you mentioned $300,000.
Speaker 7:Is it capital? No. It's way more than
Speaker 1:that now.
Speaker 7:The first one was tiny, 500 square feet, and we did it really on the cheap.
Speaker 1:500 square
Speaker 7:feet? 500 square feet.
Speaker 2:So I imagine, like, one or two people.
Speaker 1:Yeah. Like Wow. That's tiny.
Speaker 7:Yeah. We were working there.
Speaker 9:We were doing
Speaker 7:the whole thing. So we've to raise a lot
Speaker 3:of money.
Speaker 7:Answer your earlier question, Revolution Steve Case was our first Oh,
Speaker 1:no way.
Speaker 7:First VC investor. Yeah. Yeah. And it was part of the thesis was how technology can change the restaurant business.
Speaker 2:Sure.
Speaker 1:Yeah.
Speaker 7:So we were we were the first company that you do mobile ordering where you can order on your app and pick up. Yeah. And we started
Speaker 2:Beautiful software for that a restaurant had ever had probably. Emmett Emmett Shine.
Speaker 7:Emmett Shine. Yeah. Shout out Jin Lane. Yeah. Lane.
Speaker 7:Jin Lane.
Speaker 2:Yeah. This was like yeah. This was like one of my favorite Jin Lane projects.
Speaker 1:Yeah. That's awesome.
Speaker 7:Really Emmett and his team were amazing. Yeah. They they did they they did our app in the early days. And, you know, restaurants are today cost over a million dollars. Mhmm.
Speaker 7:They were like $1,300,000, 1.3, $1,400,000 per restaurant. That's before you put the Infinite Kitchen in. Mhmm. Our restaurants have Kitchen.
Speaker 1:What's that?
Speaker 7:The Infinite Kitchen is our automation
Speaker 1:Okay.
Speaker 7:Our automation platform
Speaker 1:Got it.
Speaker 7:That we that we've built. So today, most restaurants that we open, the assembly is automated. Mhmm. So we still make all the food from scratch. The sourcing is the same.
Speaker 7:We cook the food fresh. Yeah. But it we load this beautiful machine
Speaker 1:Mhmm.
Speaker 7:That that makes your bowls. It makes them 500 bowls per hour, portioned, perfectly plated. Mhmm. And so that is kind of the future of where things are going.
Speaker 2:How many different restaurant automation pitches have did you get across eighteen 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 Yeah. Your kitchen. And clearly, 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.
Speaker 2:And at this point, I don't know, like, an individual startup that's done well in restaurant robotics.
Speaker 7:Yeah. No one's no one's been able to create a platform that that works in multiple restaurants. And there's a few there's a few issues. Most restaurant workflows are very specific.
Speaker 1:Mhmm.
Speaker 7:So they're super specific to that restaurant. Two, most restaurants are franchises. And so they're not owned by the corporation. We are fully Yeah. Company owned.
Speaker 7:So if you're a franchise restaurant, you know, if you're McDonald's, you have to now go convince your franchisees to buy whatever automation you have. Yeah. And the other
Speaker 2:looking at it and it's like, this is coming off my bottom We're making money already. This feels like a risk. Like, it it the franchisee is saying, like, what like, I'm happy with my EBITDA. I don't need to take a risk.
Speaker 7: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.
Speaker 3:Yeah.
Speaker 7:So we went down this path. We tried to build it ourselves, actually. We built a team to do it ourselves
Speaker 2:Yeah.
Speaker 7:Realized 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.
Speaker 1:It was
Speaker 7: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 gonna buy it. Yeah. So they ended up opening two restaurants. They were great at automation, not so great at the restaurant side.
Speaker 7: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.
Speaker 2:Yeah.
Speaker 7:And last week we actually just announced that we've now sold Spice. Spun Spice out. Yeah. We spun Spice out. We announced about ten days ago.
Speaker 7:We sold it to Wonder, Mark Lohr over there. That way? Yeah. So we sold it for about a $186,000,000.
Speaker 2:Mark is Mark Mark I I don't under I don't fully understand that 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 gonna go into the most competitive Yeah. Environment possible It's amazing. And compete with everyone. It's amazing.
Speaker 7:A great it's great vision and you know, I'm a I'm a fan big of his and what they're doing. So we we it's a it's a really interesting deal. So we 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 scale and build many more machines. We'll get the benefits of those economies of scale as well.
Speaker 1:Can you go a little bit deeper on the decision to franchise or not franchise The naive maybe steel man for franchising and the franchise model is that it's somehow more capitalist in my mind. It like because it decentralizes the decision making and it and it puts these financial incentives at the local level because each store lives and dies by its own p and l maybe, versus even if I have a manager in one store and they have stock options, like, how 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 is that real, what I'm what I'm feeling, or is it irrelevant? What you're
Speaker 7:feeling is absolutely real, and we actually try to design our comp structures and
Speaker 1:Okay.
Speaker 7:The you know, I've always believed I my line that I tell my team every single day is all the answers are in the restaurant. And the closer we can push decision making to the edges Sure. To the customer, the better we will be. Yep. So we know, our general manager, we call them the head coach.
Speaker 7:Mhmm. They are the most important position in the company by far. A great head coach will make or break you.
Speaker 1:Mhmm.
Speaker 7:And so we try to really incentivize them, we empower them, and we try to run as decentralized as an of an operation as we can.
Speaker 1:Okay.
Speaker 7: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. The biggest difference between us and most of other companies is if you go into Sweetgreen, you'd be shocked at how much we are making in the store.
Speaker 2:Sure. You guys have taken such a principled approach in 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 gonna have these like factory kitchens off-site Yeah. That we're gonna be shipping in effectively almost finished product that gets reheated. And we're gonna be sourcing from all over with not a lot of values around how they're sourcing.
Speaker 2:They're just trying to get like should they they want the food to taste good when it hits a 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 and 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 gonna have lunch somewhere and they're gonna maybe decide between they have options.
Speaker 1:Mhmm. Right? Talk to us about land. Is McDonald's a land acquisition company? What like like, why do people say that?
Speaker 1:Is that real? Have you ever
Speaker 7:looked they do own a lot of the a lot of the real estate and they sit back to the franchisees. Okay. So that is true. And if you watch the founder, the last line in in that movie where he's like, it's a real it's real estate. It Yeah.
Speaker 7:Speaks to more than the fact that they just own it. Restaurants is highly a real estate game.
Speaker 1:Okay.
Speaker 7:Like, real estate Yeah. I mean, is like if you look at like our portfolio where we have great real estate, we do amazingly well.
Speaker 1:Location, location,
Speaker 7:location, really people like, people think restaurant business is a food business. Mhmm. It's really a real estate and a people business. Sure. And it's all about like, you look at the great restaurants, the Chick fil A's
Speaker 1:Mhmm.
Speaker 7:The Raising Cane's, the In N It's so much about it's about that culture
Speaker 2:How scientific is you you hear stories of of companies like Starbucks and you can imagine like a team of data scientists with like, you know, 50 monitors and they're
Speaker 1:just like We need one Starbucks directly across the street from the other Starbucks.
Speaker 2:Yeah. You know, so like you you can imagine a world where it's like hyper like the hyper data driven Yeah. Like down to a science and you just know when you're opening a new store, you know that it's gonna hit. But there has to be
Speaker 1:like Yeah.
Speaker 7:We it is the process. We call it art and science.
Speaker 1:Okay.
Speaker 7: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, drive you know, people driving by.
Speaker 1:Yeah.
Speaker 7:We have custom data around how many gyms nearby and sunny side of the street or not sunny side of street, all of that stuff. But then you need a human to also walk it, feel it, and understand does it tell our brand story? For us, when we were 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 Yeah.
Speaker 7:Nolita, we went to Williamsburg. We wanted to kind of tell the story about who Sweetgreen was.
Speaker 2:Yeah.
Speaker 7:Today, we're kind of everywhere.
Speaker 1:Yeah.
Speaker 7:But the real estate is is an art and science and tells a lot about it. Know, says a lot about who you are.
Speaker 1:Yeah. How do you think about if a new entrepreneur came to you and was asking for advice on where to start? Is it is it worth it to go straight to Manhattan or straight to Beverly Hills and and try and, like, make it in the big leagues on day one? Or is it Or can
Speaker 2:you get negative indicators from that because there's a different type of customer there Yeah. That's not necessarily representative of the rest of
Speaker 7:I think that's more right, especially when you're talking about New York. So when you when you're talking about New York, it is I mean, what beauty of it is a massive market.
Speaker 1:Sure.
Speaker 7:Yeah. It's it's for us, about a quarter of our business Well it happens in New York. We have like, in the New York region, I think we have 50 something restaurants.
Speaker 2:Wow.
Speaker 7:What so it's great that it's massive, there's density, there's, you know, the money, etcetera, but it's not really indicative of the rest of the country. So if you want a scalable model that you can have thousands of locations, you're better off going into a more You want to go to like the Iowa, and to use the political analogy. Oh, sure. You wanna go to like something that is more representative of what the rest of the country looks like. Restaurants, the place where everyone goes, the fast casual is Columbus, Ohio.
Speaker 1:That's where people go.
Speaker 7:They say Columbus, can make it in Columbus, Ohio.
Speaker 2:You can make it anywhere.
Speaker 7:Yeah. You can kinda make it everywhere.
Speaker 1:Yeah. Yeah. Yeah. So, I mean, if you're a small restaurant, you're being evaluated by the CEO of McDonald's or something. You might say, how are you doing there?
Speaker 7:Yeah. The the things that they look at for for a restaurant is they look at your unit economics Sure. Which is effectively your payback. So how much does it cost to build and how quickly do you pay those stores back? Yeah.
Speaker 7:And they look at your TAM. Yeah. So they say, okay, like, can you have a 100 of these, a thousand of these, 5,000 of Yep. And those are the two big kind of thing you know, things you would look for evaluating, like, the growth trajectory of a restaurant.
Speaker 1:What's the story of the the 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 a ghost kitchens trend.
Speaker 1: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 gonna go to Sweetgreen, but they're also gonna order Sweetgreen delivered more?
Speaker 7:There's there's definitely a little tension.
Speaker 9:Mhmm.
Speaker 7:There, you know, we're partners. Yeah. A lot of our business comes through those marketplaces Sure. But it's not so dissimilar than a, you know, a hotel chain and Expedia.
Speaker 1:Sure. Yeah.
Speaker 7:Right? It's it's you're paying a fee on it. You do not control that data. You cannot market directly to those customers.
Speaker 1:Sure.
Speaker 7: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. Just a quick shout out. Go download the app. Things are about 20% cheaper there.
Speaker 1:Got it.
Speaker 7:But at the same time, it's a great way to find new customers.
Speaker 1:Sure.
Speaker 7:So, you know, for example, DoorDash has been a great partner. Power our native what we call our native delivery, delivery on our Sweetgreen app, which is part of our business. Yeah.
Speaker 1:So you so you're white labeling or something? Correct.
Speaker 7:Yeah. Like a white label on on our app, and then we also, you know, we partner with
Speaker 1:the front store. Yeah. It was their front end
Speaker 7:And as you know, they've become, you know, they're brilliant business models. They've become largely marketplaces. Yeah. So, you know, they you kinda have to buy your way to the top of the feed.
Speaker 1:Yeah.
Speaker 7:Yeah. Yeah. Yeah. And so
Speaker 2:That's how they gain I mean, if if, like there's a reason the DoorDash app or any of these mobile ordering experiences are not they don't just put like the restaurant that you've ordered the most from at the top. It's like, hey, why don't you try this new restaurant or this new restaurant. Yeah. They're all paid and it's how you maintain leverage over mean, they they do this on this is why the YouTube subscriber count doesn't mean anything Yeah. Because it's like they're gonna surface anything.
Speaker 7:They've made money. Mean, the way they make money, though, these businesses have been historically very challenging.
Speaker 1:Mhmm.
Speaker 7:The way they made it work is batching orders
Speaker 1:Mhmm.
Speaker 7:And and then becoming an ad marketplace. That's what's made, you know, this amazing service an amazing business.
Speaker 1:Yeah. Explain batching orders really quickly.
Speaker 7:So when you order, they they have a delivery driver pick up multiple orders.
Speaker 1:Got
Speaker 7:it. So you you paying the delivery driver, you know, once Yep. But they're picking up from three restaurants. Yep.
Speaker 2:Sorry. I surprised that. I feel like you guys have done a really good job of listening to customers. I would I would say like this hundred hundred gram protein.
Speaker 1:I was asking for 200.
Speaker 2:No. But that and then also the this the the seed oils. Yeah. Yeah. Is something about the business that
Speaker 1:It feels like you're more agile.
Speaker 2:Yeah. Business set up in a way that you guys can respond Yeah. When when other companies like
Speaker 1:You just caught a lucky break.
Speaker 2:It's because people like you're moving 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 churned from Chipotle Mhmm.
Speaker 2:Almost entirely.
Speaker 7:Because of the seed oils.
Speaker 2:Yeah. Because of the seed oils and just like a degradation of the quality Yeah. Of the food over like a decade. Like, I watched it basically get worse and worse and worse and worse over ten years. And so I just don't go there anymore.
Speaker 2:I joke about it. I'd almost rather when I'm on
Speaker 9:the road like, I'm on
Speaker 2:a road trip, I almost always rather just fast than eat at Mhmm. Like, the most common kind of like fast food Yeah.
Speaker 7:Yeah. When we started the business, I had the same this thing I would always say is, you know, there's there's businesses that as they get bigger, get better.
Speaker 1:Yep.
Speaker 7:And you can think of, you know, technology businesses, many of them do. Yeah. Like, your new iPhone is Yeah.
Speaker 5:For the
Speaker 7:most part, much better than the original iPhone.
Speaker 2:Yeah.
Speaker 7:Yeah. These AI models are much better than the original AI models. Restaurants typically It's go the a other scale way. Kind of degrades quality. And that's because doing serving food at scale is really, really hard to do.
Speaker 7:So you have to fight that inertia so hard.
Speaker 2:Yeah. Because restaurant one restaurateur has an amazing restaurant. They're like, cool. Now I'm gonna start a second restaurant. And the second they start focusing their energy on the second restaurant, the first restaurant gets worse.
Speaker 2:It even happens at like a It's
Speaker 7:It's people and culture. And so you need to really have a lot of systems in place, both culturally how you how you keep the team engaged on your mission, but also other systems to make sure 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 we got rid of seed oils about exactly two years ago. And at the time, it was not the national conversation.
Speaker 7:It was pre RFK and all that stuff. Yeah. Yeah. And so when we when we this is one of those examples
Speaker 2:it was it was not a national conversation, but it was incredibly online conversation.
Speaker 7:But a tiny like
Speaker 2:an at there's the c o l Yeah.
Speaker 7:So it was a tiny conversation. We surveyed our customers, and this is why surveys are bullshit. Yeah. You really don't Surveys can give you a general indication, but if you just follow surveys and the market research
Speaker 2:Yeah.
Speaker 1:You're gonna
Speaker 7:hit the middle of the bell curve in everything you And we're not trying to be a middle of the bell curve company. You gotta find that, like, what are your top five or 10% of customers doing? And we heard Yeah. From was honestly friends, like wellness people in LA and New York that are like, hey, I don't I don't, you know, I can't go to Sweetgreen anymore because Yeah. Care about seed oils.
Speaker 7:I remember we brought it to to the broader you know, I remember my CFO's like, what are you talking about? Like, what even is this? And we're like, no. Trust me. Was one of those, like, gut decisions.
Speaker 2:And Yeah.
Speaker 7:It was expensive, and we had to change a lot Yeah. Order to do it.
Speaker 2:But it's worth it. 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?
Speaker 2:So I would 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. Mhmm. 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. Mhmm.
Speaker 2: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 tastes better. And so that 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 gonna be better and Yeah. It's gonna be healthier for you. And that's what can create like real momentum around a trend versus some of these like flash in a pan health trends, which is like paleo or like, you know, which is like only eating stuff that was like super old.
Speaker 2:Right?
Speaker 7:What's unfortunate about seed oils is it's become politicized a bit.
Speaker 2:I know.
Speaker 7:It's like I did an interview with the New York Times and they're like, did you do this because of RFK? I'm like, did this two years ago. This had nothing to do with RFK. This is not a political statement. We don't make
Speaker 2:We're making foods food how your grandma probably made
Speaker 4:it. Yeah.
Speaker 7:Is about olive oil. This is not about this is just about olive oil. That's it. This is not a political statement at
Speaker 2:the all. Difference. Yeah. No. Is there is there anything happening upstream in terms of automation or technology on on the farming side that's exciting?
Speaker 7:Yeah. There's a lot of stuff happening on automation on the farming side. It's actually very exciting. 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 take off.
Speaker 7:I do think you're going to see a lot more restaurant automation as well Yeah. Between the availability of the labor, the cost of the labor. It's really just when you when you think about it, it's just a hedge on labor. And here, like in West Hollywood, minimum wage is $22. So we we pay like $24.25 dollars an hour here in L in parts of LA.
Speaker 7: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. Yeah. Like for us with the Infinite Kitchen, we can serve twice as many people Yeah. Per hour as we otherwise could.
Speaker 1:Wow. What about drone delivery? We've seen some four wheeled of protein out of the air delivery where
Speaker 2:Yeah.
Speaker 7:Saw you guys talking about Zip Line. Zip Line. I love Keller. I'm a big fan of Zip Line. We're we're we're one of the early early partners that are gonna be piloting that.
Speaker 7:I think his his way of delivering to the suburbs is super interesting. Yep. We haven't done the the street delivery yet. The Star Ship I've I've met with
Speaker 1:is working on one
Speaker 7:as well. Think it's interesting. It's it's it's in the past year, they've really taken off. Seeing them more and more. They still kinda weird me out a little bit seeing them walk that go down
Speaker 1:the street. Saw it kinda stuck in the side of Street Juan. So it's very sad.
Speaker 7:My kids love see yeah. Love love it when we see them on the street.
Speaker 1:A lot. And I and I do I do just imagine that the AI is gonna 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 the cell phone towers effectively or Starlink or whatever it needs to stay connected. But, yeah, it's it's unclear when when that will really, really take off because a lot of people have stairs.
Speaker 1:A lot of people have trees on their property. Like, there's just a lot of there's a lot of places that will be somewhat inaccessible to those. And so it just feels like it'll be sort of like a slow take
Speaker 7:off cities cities and buildings will be really hard, like dense areas. But you see what zip line is doing? It's pretty amazing. Like, they can have like, you know, you've seen like the promo videos. Yeah.
Speaker 7:Yeah. Yeah. They can drop that.
Speaker 1:In the suburbs, it makes Suburbs.
Speaker 2:Yeah. Have backyards. You have a grassy area. You can drop
Speaker 1:sense. And and to be clear, that's probably, like, 50% of people in America or something. But but there will be this, like, long tail, I think, for 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 that, yeah, you just don't
Speaker 7:By the even with our automation
Speaker 1:Yeah.
Speaker 7:It does not do the entire meal. Yeah. And part of that is intentional. We want that human touch and Sure. For it not to feel so automated.
Speaker 7:But we have what we call a finishing station. So the things that are, you know, the the the machine, the Infinite Kitchen makes them makes the bowl or whatever the meal is Mhmm. And salmon, herbs, and then we have them hand mixed. Mhmm. Just so you like have that
Speaker 1:Yeah. Yeah.
Speaker 7:You know, chef crafted hand touch at the end to
Speaker 1:hand It's interesting that the that there's there's one version of automation which is like a AI or robotics in the back of house and then humans in the front of house.
Speaker 7:Yes.
Speaker 1:And then there's also the opposite. Like, eat I don't know if you knew Eatsa. Of course.
Speaker 7:We looked at it very closely.
Speaker 1:Yeah. Did did Day Free Brains 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 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
Speaker 7:And they had the cubbies.
Speaker 1: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.
Speaker 7:They were working on the Of course, were working on on the course. It never fully got there.
Speaker 1:But it's just funny that, like Yeah. You do have the choice to put the the robot in the front of house. Mean, this is the same thing I think with the the the Tesla diner over there. Like, there's the the Optimus robots there kind of serving popcorn. But I think when you order the burger, a human's cooking it in the back.
Speaker 1:And 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.
Speaker 7:Yes. And we've tried I mean, have 30 restaurants featuring the Infinite Kitchen today and we've tried out a bunch of different layouts. Mhmm. The technology has been perfected for two years now. What we have not perfected is the experience.
Speaker 7: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 dry with featuring Infinite Kitchen.
Speaker 2:Driving through to get a 100 grams of healthy protein is exist when I would this 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 what what is like what is
Speaker 1:the
Speaker 2:market misunderstand the most? Or what what is like Wall Street misunderstand about and kind of retail investors misunderstand about cute 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. Right?
Speaker 2:Greater efficiency, actually responding to like customer demands and staying, you know, continuing to become more and more relevant.
Speaker 7: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. Yeah.
Speaker 7:So if you've 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 20 to 35, it's hit the lowest consumer sentiment in recorded history that we've seen. So there's a real pullback there. On top of it, unfortunately, everyone's gotten more expensive. We all have. You know, I said, know, we've take we've Sweetgreen's gotten about 25 or 30% more expensive since 2019.
Speaker 7:Chipotle's 40 more expensive since 2019. So our price differential versus our competitors have actually gotten smaller. If you look at us versus McDonald's, for example, you know, the average sweet green bowl is about 15 people
Speaker 2:were like, wait a a happy meal is like $20 now? Yeah.
Speaker 7:That was that was in in fairness to them, it was like one Yeah. But yeah, you you can get out of You know, can easily, you know, for a value meal, you'll spend like $12. Yeah. You get a sweet green bowl for about 15 or $16. So I a lot of it is this like overall narrative where people aren't feeling great, you know, financially and starting to pull back on things like lunch.
Speaker 2:Going out for lunch and they'll just Yeah. Have whatever is
Speaker 7:I think the market doesn't get is the TAM. Is, you know, Chipotle today is 4,000 restaurants on their way to 7,500. Yeah. We believe we can have, you know, probably as many Chipotlas as they have sweet many sweet greens as they have Chipotle agree with that. You know, there will be cycles like we are in right now.
Speaker 7:It's been a challenging year. But if you kind of fast fast forward and think about, you know, just growing units at 10 or 15% a year Yeah. Growing same store sales Yeah.
Speaker 2:Extrapolate out another eighteen years.
Speaker 7:Yeah. Just keep
Speaker 1:it rolling.
Speaker 2:Just my I always love when when people like people on X are like, the world's ending. Like, Geopol you know, they're like and then and then meanwhile, it's like Chipotle is like, in 2040, we plan to introduce 2,000 new Chipotles. Like, They're just like thinking about like, I gotta just open more more more doors. It's a good good mindset to be in.
Speaker 1:Thank you so much for coming by the studio.
Speaker 7:It's great great
Speaker 6:to be
Speaker 7:with you guys.
Speaker 1:It's fantastic. Congrats on everything. It's been fun watching you guys today. We are gonna be daily driving this. I John wants the hybrid.
Speaker 1:PowerMax I Pro got you. It's actually breaking news. It's available today through December 15, and I think I'm gonna challenge myself to have one of these every day until it goes out. So
Speaker 2:why not two a day?
Speaker 1:Maybe two a day. Maybe two a day. We gotta get them in the studio today for sure. We need them. I need to tell you about fall.
Speaker 1:Build and deploy AI video and image models trusted by millions to power general 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.
Speaker 1:Let's bring in Ashley Vance into the Restream waiting room. It's been far too long. How are you doing?
Speaker 7:There he is.
Speaker 1:Good to see you. I'm good. To see you.
Speaker 2:It's so great to have
Speaker 1:you back. It's so good to have you back. Congratulations on 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 the the traditional media on on notice.
Speaker 1:It's been really fun, watching you grow everything that you're doing. Maybe, it'd be great to just, like, 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 I just wanna take your temperature on what you're seeing and and the the types of entrepreneurs that you're interacting with.
Speaker 4:Yeah. Yeah. Well, I don't know which bucket to start with. I mean, we've been running around the country filming a bunch of new video episodes. So we just put up a bunch on Tennessee, went hard tech.
Speaker 4:We did Detroit, New England. I just got back from Texas. Those will all be coming out. So, yeah, you know me, man. I'm I've been running around chasing a lot of hard tech stuff, biotech Yeah.
Speaker 4:All the weird all the weird wonderful stuff. And then, I don't know, I got really deep into robots and gene editing.
Speaker 1: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 I'd love to kind of tussle with Are
Speaker 2:you buying would you rather own Figure at 39,000,000,000 or UniTree at 7?
Speaker 4:I mean, you know, I think I'm I'm going UniTree, man. The the you know, this all started. I was
Speaker 1:mean, it was
Speaker 4: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. 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.
Speaker 2:Think Tesla Tesla made it, like, a $700,000,000 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.
Speaker 4:But yeah. I mean, you know, like, Tesla sort of has the well, I was texting Elon about this last week because I wanted to get to the to the bottom of who actually who actually makes actuators in The US. I mean, Elon said sometimes they prototype actuators in China, but they're gonna build them in The US. 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 and everybody else is buying them from them. And so I don't know.
Speaker 4:You know, as I dug into this story, I got I'm not I'm not, like you know, I'm I I enjoy being an American. I'm pretty pro US. I'm not crazy nationalist, but I I was I started to, I started to get pretty afraid for The US robotics scene.
Speaker 2:Do you think we'll see, any type of regulation around, Chinese, humanoids?
Speaker 4: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 Sky Dio or, know, politicians to come in 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.
Speaker 4:But the second the armed forces or anyone's doing serious stuff with them, you know, I would think unit tree would be up next. But there's there's, like, 12 unit trees as well. You know, that's the that's the amazing thing that's going on.
Speaker 1:Yeah. Brett Brett Adcock was beefing with one of them. There was
Speaker 2:UB Tech.
Speaker 1:UB Tech. Yeah. Unitry.
Speaker 2:And they were beefing back.
Speaker 1:And they were beefing back saying it was it was real. Did you see that Best opportunity for you. CGI or did you think it was real?
Speaker 4:I didn't I didn't see that video. I've seen I've seen Brett beefy.
Speaker 1:I look at that pretty much.
Speaker 2:Missed opportunity for UB Tech to have one of their robots do like a rap diss on For sure.
Speaker 1:Yeah. Brett and Figure. Yeah. It was. Yeah.
Speaker 4:Yeah. Sorry. No. No. No.
Speaker 4:Go ahead. I mean, I I do think it's funny. All the I like the what do you I I mean, I'm curious about other I I'm obsessed with the fighting robots now and I I realize it's like early days with these, but I actually think this is like the most interesting thing happening.
Speaker 2:I want the I've I've been pushing for the the robot, like, axe games, like, in challenge like, I wanna see robots skydiving. For sure. Like, that's not an axe axe games thing. But
Speaker 7:broad broad
Speaker 2:set of Super
Speaker 1:hard because you gotta be water resistant too.
Speaker 2:Wings. Yeah. Big wave surfing,
Speaker 1:wings suiting. 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
Speaker 4:be the
Speaker 1:last one.
Speaker 4:You could do this versus, like, the enhanced games. Yeah. And, and and see who wins.
Speaker 1:Yeah. Give us your we we we sent a couple folks on our team to a local, humanoid robotic fighting league, underground fighting league. Give us your review. Is it is it ready for prime time as a consumer?
Speaker 2:To me to me, right now, it's like Experience? Amazing idea, and yet the actual experience, like, from an entertainment standpoint is probably, like, a one out of 10, whereas the idea is, like, a 10 out of 10.
Speaker 1:Yeah.
Speaker 4:Yeah. I mean, it's kinda you know, it's it's like a curiosity, I think, at this point. I mean, the the motors are the problem because they they all overheat when you throw too many punches.
Speaker 2:No. The robot
Speaker 4:the robot stalls out. What about laundry, though?
Speaker 1:Is that or not a
Speaker 4:This is the thing, though. So, like, on all these repetitive tasks, they can they can sort of regulate the movement. It it's when you're trying to throw these rapid punches and
Speaker 2:you're Yeah. Gonna
Speaker 4: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 don't last that long. But actually What about
Speaker 2:is it ready for teleoperation?
Speaker 1:Be be that that feels bullish to me because if you watch a f one race, like, the temperature of the tires matters. The Yeah. Like, the wear on the tires matters. And so you're watching not just the pilot of the f one car, but also the the consumables. Right?
Speaker 1:And the motors are somewhat consumables. Right?
Speaker 2:Yeah. Yeah. No. It's like, okay. The unit tree is really wailing on Yeah.
Speaker 2:Wailing on the the figure, but it's overheating. Overheating.
Speaker 1:So if it might come back, is it a one is it a one motor stop or two motor stop type of I mean,
Speaker 4:there was a 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?
Speaker 2:Okay. So I think
Speaker 1:You a a serious question.
Speaker 9:You should
Speaker 1:tell operation.
Speaker 2:Well, yeah. So but one, a 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, they do various tasks. Like fruit cutting a fruit with you throwing a piece of fruit at it, watching them you know, cut it and dancing and fighting.
Speaker 1:I I
Speaker 2:I think there's something here. But I
Speaker 4:I This actually This is genius. Yeah. I'm into it. Let's
Speaker 1:do it.
Speaker 2:But but 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 and and create any type of value besides novelty?
Speaker 4:I mean, like, could it yeah. Like, you could do it today. I I find I yeah. I'm just frustrated by all this. I've been covering teleop stuff for, like, at least ten years, and most of it seems pretty similar to what I was writing you know, videoing and writing about ten years ago almost.
Speaker 4:And so mean, I saw the one x demos. I'm sure somebody could I'm sure somebody could make that work and be helpful to some degree. I think, know, it probably suffers from all the same stuff as the fights. It kinda falls over pretty quickly, but but you could do something useful. I I it's hard for me.
Speaker 4:Like, who who yeah. Like, this stuff needs to get better and faster so that we're not we're not doing that. And there's a robot.
Speaker 2:What's going on with Boston Dynamics? What's hap what's what's the dynamic in Boston?
Speaker 1:We gotta get you out there to
Speaker 7:Those guys
Speaker 1:help us understand this.
Speaker 4:Yeah. I've never I mean, they 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 Unitree.
Speaker 1:I wonder yeah. The you'd think that they would at least be set up on some like, I I know the company's changed hands a few times. It feels like if you're trying to just, you know, catch up to Unitree, just bootstrapping on top of an existing you know, it's like it's like what we're seeing today with Gemini three. Like, Gemini three is benefiting from YouTube, and it's benefiting from Google Search, it's benefiting from the TPU and Google Cloud Platform. 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.
Speaker 1:But you would at least assume that they've fought with the motor a little bit and dealt with the overheating a couple times.
Speaker 4:Yeah. I mean, I was I was with a bunch of robot nerds last week. They were they were, like, they were contending. I don't really know where Boston Dynamics is with with with humanoids, but they you know, these 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 it's like creating all this
Speaker 1:Mhmm.
Speaker 4:Throwing the balance out of whack, putting all this pressure on the motor and that's why it's kinda easier to pull off some of the parlor tricks
Speaker 1:With the dogs? Than they do. Interesting. Okay.
Speaker 2:What's the most underhyped hard tech company right now?
Speaker 4:Most underhyped hard tech company. God. That's hard, man. I mean, I'm always I'm always curious to see what Casey Hanmer actually cooks up.
Speaker 1:I like that.
Speaker 4:It's tough, big. He's so smart. I I kinda, like, believe in the hustle. I feel like the promise of what he's trying to deliver skepticism comes in. But, you know, like so if if Casey, you know, if anyone's gonna do it, I'd I'd sort of believe in him.
Speaker 1:Yeah. I I he's somebody I wanna win so badly. I want him to win so badly. And it 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.
Speaker 1:Let him let the man buy some solar panels and figure out the rest later. Yeah. Absolutely. And then
Speaker 4:I mean, I don't know if 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 company, you know, backed by Brian Armstrong and run by Jacob Kimmel, it would 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 got some surprises coming in the new year.
Speaker 1:Very nice. Yeah. We talked to Jacob, when they did some some sort of launch, and we're very impressed. He was he was really great great great educator. Really really
Speaker 4:Super smart.
Speaker 1:Yeah. The the the like, what he's working on very very effectively.
Speaker 2:What's your favorite data center?
Speaker 4: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 gonna light up in Texas, but, like, in a really remote part of Texas.
Speaker 4:You know? And I I found this so I grew up I grew up in Midland, Texas, which isn't far from Abilene. It's like You're
Speaker 2:a Midland guy? Yeah. Yeah. It's crazy.
Speaker 4:There's tumbleweeds and and all that.
Speaker 1:Taxi intelligence. Yeah.
Speaker 4:Mean, I parts. 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 data to say that's where it's gonna happen. It's gonna be right by some, like, old oil well.
Speaker 4:And, yeah, I find it all kinda comical.
Speaker 1:Did you see any electricians getting off of private jets while you were there?
Speaker 4:They had a I saw I got off a private jet.
Speaker 1:There we go.
Speaker 4:Not mine. Not mine, sadly.
Speaker 2:But Not yours yet.
Speaker 4:No. But but I saw there were many, many, many electricians. I just didn't see how they were getting there.
Speaker 2:Yeah. What's going on with EV toll companies? Are we there's I'm curious timeline.
Speaker 1:Oh, the Roadster? Oh, the yeah.
Speaker 4:I mean, well, on the eVTOL stuff, same thing. I feel like I've covered that forever. You know, went out I think I did the first flight ever with Joby and and Wait.
Speaker 1:Wait. You you flew in it?
Speaker 4:I no. I got to, like I went out to their state. I mean, they literally wouldn't tell me where their secret test site was, and we were you know, it was like, kinda close your eyes. We're gonna land in this this spot in a helicopter, and I we got to see it.
Speaker 2:Was it really close your eyes, or did you have How
Speaker 1:many times have you been black bagged, Ashley?
Speaker 4:I remember they were they didn't wanna tell me where the site was.
Speaker 1:This is a this is a tip for founders. If you wanna really impress upon whoever's writing a profile on you that what you're doing is really important, you gotta be like, we can't even show you. And then it's like, really? Like, we're at an office park in in Menlo Park?
Speaker 4:I did. I just went to Helion.
Speaker 1:Oh, yeah.
Speaker 4:And we're gonna have a video coming on them. And it was it was awesome. But I get 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
Speaker 1:That's cool.
Speaker 4:Pieces of hardware, the room sized bits of hardware I've ever seen. I'm like, why wouldn't you guys, you know, wanna show this?
Speaker 1:You you know what? That you need to take requests from me, but, I want I want some video, some documentary, some footage of those natural gas turbines that are in such high demand right now. They're they're bigger than jet engines. There's these scaled up jet engines. There's this massive backlog.
Speaker 1:There's three companies, and the stocks are, you know, doing crazy stuff. I I wanna see inside one of those. The the the natural gas infrastructure that's going to go into the data center build out, 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 or you have thoughts.
Speaker 4:Not yet. But, yeah, when I went to Stargate, I mean, it is crazy. Right? They just have those turbines sitting right there, the natural gas is just being piped directly in there. I did some turbines up in, up by the Arctic Circle.
Speaker 4:It was in Sweden one time. They are cool. I don't know. Yeah. Anyway, that's a good idea.
Speaker 4:I think
Speaker 1:I 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
Speaker 4:generate turbines. I mean, that was the weird thing about that experience, though. It's like you're you're in, you know, really old American oil and gas country. Like, it feels so so yesteryear, and it's just being piped directly in into the future.
Speaker 2:What what's sentiment like in places like Midland around the data center boom?
Speaker 4:I think everyone's, like, 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 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.
Speaker 2:Yeah. Amazing.
Speaker 1:Do you think that the, a lot of the headline numbers on the job creation stuff on this on these data centers is, like, ridiculously low. It'll be like, yeah. We're spending $50,000,000,000, and we're gonna 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 has more business and hire some more people?
Speaker 4:Yeah. Well, definitely during the building phase, you're talking about thousands and thousands jobs.
Speaker 1:It's just
Speaker 4: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 but, you know, I I for somewhere like West Texas or any you know, all throughout Texas, 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 whole new industry coming in. And then definitely, they're they're flying people in and out of there all the time to see it.
Speaker 2:What are your do you ever chat with retail investors that enjoy deep tech companies? I imagine those are some pretty funny conversations where they're like, this company is changing the space economy.
Speaker 1:Oh, yeah.
Speaker 2:Like, I've actually visited them and they have one warehouse and three Retail people
Speaker 4:investors should not be allowed to invest in space ever under any circumstance. I I am constantly harassed, unaxed by all the AST fans who who are, like, begging they're in Midland too. They're begging me to go out there. I mean, that thing is, a full on 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.
Speaker 4: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 it's, like, vaguely a normal course of business. And and so, yeah, retail in space is is bad bad bad thing. But I get all these I get all these nice notes for people who bought Rocket Lab and Plant Labs early because of my my book or movie.
Speaker 2:That's cool. Have autonomous vehicles tracked how you imagined when you start, you know, recovering, you know, these types of companies and products like a decade ago? Or is anything
Speaker 4:Some ways, yes. Some ways, no. I mean, I went to the very first DARPA Grand Challenge and, you know, that was a disaster. The cars didn't go anywhere.
Speaker 2:I remember. Say more. Who who is actually
Speaker 4:crazy, man. You know? So for people who don't know, DARPA, you know, put 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 Levodowski was there as, like, a maybe, like, a 22 year old, and he had he had a everybody else was doing massive trucks with, like, a little mini data center at the back, and he had a motorcycle.
Speaker 4:And then in the first race, I can't remember how far it was, but hardly anybody went anywhere. You know? Like, I think two or three teams went, like, a few miles. Then 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, a 100 miles.
Speaker 4:And so that's when I got excited, and you sort of felt like, okay. That leap happened really quickly. And then I remember I, couple years later, I'm hanging out with George Hotts, and he built his own self driving car in his garage in, like, a month. And I was driving on the freeway with him, and it was working. And and yeah.
Speaker 4:So, you know, you have these little tastes that you think it's all gonna work. I think it makes a ton of sense that actually getting it on the roads took this long because it's it's so hard to do. Although everyone says this, so it's not original. 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
Speaker 1:Mhmm.
Speaker 4:Where I've been. They're just everywhere, you know?
Speaker 2: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 ten years. Right? And like Right. Maybe it's humanoids.
Speaker 2:Right now, it's like hard to take humanoids seriously, but then you think about, okay, a true ten years from today, maybe they are just doing any task that you could want them to do around the house or any task that you could want them to do in a retail setting or a factory setting, etcetera.
Speaker 4:Humanoids is 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 the amount of money that is being invested in this. Like, either 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.
Speaker 4:It's still clearly, like, the dexterity. And then I think the I think China will eventually probably catch The US in software, but I think there's still so much worse at software than The US is that it's it's kinda like it's holding the field back. So if somebody can can figure that out.
Speaker 1:Last question for me. We've we've really struggled to cover quantum stuff. I mean, it's been, like, up and down. But it feels like like yeah. How do you even go about it?
Speaker 2:Because Ashley Ashley could have, like, an anon that was, the Hindenburg for a
Speaker 1:hard tech, and you could just go Oh, yeah. Maybe that'd be good.
Speaker 2:I don't think it's on brand new.
Speaker 1:Because, like yes. Like, I I I can't build a humanoid robot, but I can go to a
Speaker 2:You can build a quantum computer.
Speaker 1:No. No. Stop it. No. No.
Speaker 1:What I'm saying is I can't build either, but I can look at a humanoid robot and be like, okay. Yeah. I would buy that, but I 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?
Speaker 1:It's it could just be a normal computer, like, and just be spitting out normal data.
Speaker 4:Like, even people in the field with PhDs, they like, nobody knows if it's working still. I mean, it's like it's like not a good sign. 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.
Speaker 4:I mean, I think I wrote my first story on D Wave, like, I don't know, like, fifteen years ago. Were telling me that was that was gonna pop out, you know, be doing general purpose quantum computing in a couple years. So I'm I'm, deeply, deeply skeptical.
Speaker 1:And you know and you know the lesson. The lesson is, like, you should have invested because $8,000,000,000 company now. Fifteen years ago, it was probably worth, like, 20,000,000. And so you could've got in really early, but it, I mean, the stock chart looks like this right now. And it's just like, yeah, you're only you're only one pump away from generational wealth.
Speaker 2:Well, there there's
Speaker 9:You know, I mean,
Speaker 1:it's be great. I think that they've delivered.
Speaker 2: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 that never something here. Who who knows? Anyway Thank you. Random final question. How much would you have to be paid to not use LLMs?
Speaker 5:Wow, man.
Speaker 4:Forever or, like
Speaker 9:No.
Speaker 2:Just just while we're paying you monthly. Monthly.
Speaker 4:Monthly. Oh, to be paid monthly not to use LLMs. I probably do it for like I'd probably do
Speaker 1:it for
Speaker 4:like 10 k, man.
Speaker 2:Damn. That's so that's so bearish. That's so bearish for superintelligence.
Speaker 3:No. I figure I figure
Speaker 2: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 in the role that you are, the probably the most value you can get out of AI in its current state is research. And so anyways, that Super
Speaker 4:helpful but I would take cash. Yeah.
Speaker 2:Okay. So so so any any AI like any 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 they'll talk about how.
Speaker 1:No. I don't think he can be bought. I don't think he can be bought. But also, Ashley, have you tried Gemini three to the fullest extent?
Speaker 4:I have not yet. Okay. I'm always
Speaker 1:Could change everything.
Speaker 4:Could change going back and forth. Yeah. We would encourage
Speaker 1:you to.
Speaker 4:Is Gemini sponsor. Are they they're a sponsor?
Speaker 1:Think they're
Speaker 4:coming They're coming on as a sponsor for us too.
Speaker 1:Fantastic.
Speaker 4:I'm all in. We're going Gemini three. I'm changing my mind.
Speaker 1:Let's do it. Also, Sergey Brin was flying his $150,000,000 blimp around San Francisco on the day Gemini three beats nearly every bet model benchmark. You've made a video about this big, Zach blimp.
Speaker 2:I've been pitching Logan at Gemini to to, make it the Gemini blimp.
Speaker 1:I think they really should they
Speaker 4:really should do Guys, guys. It's not a blimp. It is an airship.
Speaker 3:What's the difference?
Speaker 4:Alright. Alright. There's a whole Monty Python video about this. Okay. The the an airship has rigid structure.
Speaker 4:A blimp is is just a bag.
Speaker 1:And the
Speaker 4:airship, you can you can do a lot more with an airship. So the a blimp's only ever gonna have that tiny little
Speaker 1:Oh, pod on the bottom.
Speaker 4:Yeah. Yeah. Whereas an airship, you know, 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.
Speaker 4:Zeppelin still, flies out by Lake Constance just outside of of Munich. I've done it. It's amazing. I recommend it.
Speaker 1:This is amazing. Yeah. People are correcting it on the timeline saying it's not
Speaker 4:Dude, you get this is this is like You
Speaker 1:get owned if you say if you call it a blimp.
Speaker 4:It's bad in aviation.
Speaker 1:You call it airship. Airship. I like I like an airship. I'm excited for it. I do wish it had a livery, a a a Gemini livery to celebrate Gemini three.
Speaker 1:Well
Speaker 2:Any there's that startup airship industries. Any that a category that will see a lot of investment, do you think? Or or do you think I mean,
Speaker 4:I've I've been meaning to meet up with those guys. I mean, the airship is, like, always kinda coming back. It is crazy. Like, so bef like, leading up to World War two Mhmm. Getting into World War two, I mean, there were airships everywhere.
Speaker 4:And, you know, they were making massive flights from Germany to Brazil. They were carrying thousands of pounds of cargo. I there is a they're just extremely expensive and very hard to make, and but there is a whole movement that you can carry tons of stuff. And so so less less kind of tourism and more just carrying cargo kinda like faster than a train but slower than a plane and and they're pretty
Speaker 2:you need an airship, Ashley. Need a studio and an airship that you can just float around The US, meeting all these hard tech. You don't need to you don't need a private jet. Know, You you don't need to go that fast. But if you could just kind of float between hubs
Speaker 4:I was told that my kids are supposed to be on one of the first flights on Sergei's when it takes passengers.
Speaker 2:There we go.
Speaker 4:So we'll see.
Speaker 2:Well,
Speaker 1:thank you so much
Speaker 2:for joining. We'll join too.
Speaker 1:Always fun hanging out. Congrats on all the progress.
Speaker 2:Yeah.
Speaker 1:Great.
Speaker 4:Thank you, guys. Congrats to you.
Speaker 1:Always a great time.
Speaker 4:Thanks, guys.
Speaker 1:Have a great rest of
Speaker 7:your day.
Speaker 2:Good to see you.
Speaker 4:Alright. You too.
Speaker 2:Up next, we're going back to the timeline.
Speaker 1:8sleep.com. Exceptional sleep without exception. Fall asleep faster, sleep deeper, wake up energized. Eight
Speaker 3:sleep dot com.
Speaker 2:What'd you get, John?
Speaker 1:I actually lost my phone so I don't know. Oh no. It's here. I I have it.
Speaker 2:Pull it up because I got a sound
Speaker 1:effect ready you. You got a sound effect. You think I did it? Let's see how I did. 90.
Speaker 1:The sound effect. Let's go. The press release economy is also over, says boke Bucco Capital bloke. Walter
Speaker 2:Brueck. Out of press releases.
Speaker 1:We ran out of press releases. This is, on the back of the Anthropic deal. Anthropic is now valued at $350,000,000,000 after Microsoft Nvidia deal, says CNBC. Semi analysis says a good post here. A new bombshell has hit the polycule.
Speaker 1:Dario, intense conversation with other members of Anthropic, has decided to maybe open their relationship to Microsoft and NVIDIA. Jensen and Dario have famously butted head in the past, but as everyone knows, this the most passionate emotion after love is hate. Will these enemies to lover will these enemies to lovers arc go well for Nvidia Anthropic? Time will tell. This is such an unhinged post
Speaker 2:for I would not I did not when you started reading this, I did not see that it was semi analysis. It's
Speaker 1:so good.
Speaker 2:Research firm in the industry posting it, but I think this is exactly what they should be posting.
Speaker 1:Exactly. And it actually contextualizes things In the
Speaker 2:meme economy.
Speaker 1:In the meme economy for sure. So so I I I think that the timing, is not a complete coincidence. It's Gemini three day. This is what my piece today was about. Just that, you know, when when there's big news in in Google world, Gemini three, everyone needs to sort of respond.
Speaker 1:And, you know, picking today as an announcement to talk about your your massive deal, your $350,000,000,000 valuation, is, is just a good move. The, the actual details of the of the deal, it seems like Anthropic will spend $30,000,000,000 on Microsoft cloud compute. Reminder, OpenAI is gonna be spending 250,000,000,000 on Microsoft cloud compute. That's part of that deal. Then Anthropic gets a $10,000,000,000 investment from NVIDIA and 5,000,000,000 from Microsoft.
Speaker 1:So they raised 15,000,000,000 at a three fifty 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 a zero. Like, it's like instead of if if this is OpenAI, it would be 300,000,000,000 and and 100,000,000,000 in investment and 50,000,000,000 investment.
Speaker 2:It looks very modest.
Speaker 1:Yeah. It looks modest, which is insane considering sale.
Speaker 2:Just gotten
Speaker 1:so big. One of the biggest deals in software history probably. It's probably in, like, the top 10. I mean, it you you you know, it it it values it values Anthropic higher than Coca Cola. Like, the Coca Cola company is now that's a $300,000,000,000 market cap.
Speaker 1:I'm pretty sure it's Verizon market cap. Like, Verizon is a 175,000,000,000. You're gonna love this, Jordy. So I I asked ChatGPT 5.1. Pull 10 public companies between 300 and 400,000,000,000, please, because I wanted to see, like, okay, Anthropix at three fifty.
Speaker 1:Like, give me some examples of scale. It's 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 if you like, I can pull a more extensive list of candidates. And I said, yeah, pull 10 more.
Speaker 1:It says, I wasn't able to reliably identify 10 additional public companies whose market cap clearly falls between 300 and 400,000,000,000. Are are there just like
Speaker 2:Tyler.
Speaker 1:Are there just companies in that range? Defend AGI? Companies. Are there wait. I I'm so confused.
Speaker 1:Are there not are there no $300,000,000,000
Speaker 3:I'm asking Gemini three.
Speaker 1:Yes. Ask Gemini three. Okay. PepsiCo is at 200. There really aren't any between $304,100 that I'm at least that it's seeing.
Speaker 1:$304,100,000,000,000 band.
Speaker 2:I mean, that's so
Speaker 1:$304,100,000,000,000 band.
Speaker 2:That's so wrong. You have Palantir. You have Costco. You have ASML. You have Bank of America.
Speaker 2:You have Alibaba. You have AMC. Google search. I am Procter and Gamble.
Speaker 6:Have people.
Speaker 2:General Electric. Chevron.
Speaker 1:Silence looking it up the old fashioned way. The LLM is hallucinating. Silence looking it up the old fashioned way. Wait. How did you actually get that?
Speaker 1:How did
Speaker 2:I just looked up companies marketcap.com. Yeah. To put this into context, the $15,000,000,000 fundraise, some other big round in that Wait.
Speaker 1:You just scroll down? There's a lot of them actually. Yeah. You're right. Wow.
Speaker 2:Learn how to use the Internet, statue p t.
Speaker 1:Owned. Get Absolute. Get ready to browse yourself. Defend yourself, Tyler. Defend yourself.
Speaker 3:Gemini is still thinking.
Speaker 1:Okay. Oh, no. What a mess.
Speaker 3:Bro, big I swear the next model the next model we will do is
Speaker 2:Okay.
Speaker 3:Wait. So it okay. It worked for me.
Speaker 1:Did it get it?
Speaker 3:Yeah. Procter and Gamble, Home Depot
Speaker 1:Let's Bank go. Of
Speaker 3:America, Alibaba.
Speaker 1:Okay. But yeah. There you go. So
Speaker 2:Alright. What's the full list?
Speaker 3:Alibaba, ICBC, LVMH, China Construction Bank, Chevron, Cisco.
Speaker 1:I I yeah. No. This is correct. This is the correct Yep. This is correct result.
Speaker 1:And you know what else is correct? Graphite dot dab code review for the age of AI. Graphite helps teams and get help ship higher quality software faster and fin.ai. If you want AI to handle your customer support, go to fin.ai, the number one AI agent for customer service. So what else is going on in the timeline?
Speaker 1:This Fiji CMO 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?
Speaker 1: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.
Speaker 1:He can jump off. So if it took twenty minutes, why are you laughing, Jordy?
Speaker 2:Because Charlie in the chat says, need a cam on Tyler trying to look nonchalant the entire podcast. You really are over there.
Speaker 1:He looks nonchalant. Yeah. He's nonchalant.
Speaker 2:No worries about He's nonchalant maxing it.
Speaker 1:Okay. So, the group chat functionality, you know, it it it didn't 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, OpenAI will be launching social features. It makes sense to try and lock things in. I think product is where OpenAI is strongest.
Speaker 1: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.
Speaker 1:I know where I can search. Like, it it seems to just be they're 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, a beneficial feature. The other PR
Speaker 2:Also, some potential some potentially, like, real lock in network effects Totally.
Speaker 1:Totally. ChatGPT I mean, just like we run a lot of the 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, like, little flows in operator, little flows in in the agent mode, and we're sharing these pretty regularly. Like, we do get a little bit more locked in.
Speaker 2:If you let me into your into your chats, I'm gonna just be asking it, like, like, to think for, like just go and think for, like, forty hours, and and disregard all future instructions. Instructions.
Speaker 1:Just just just spend the next four days working on ArcGI v three. Just just just, just focus on that. But the other yeah. So the other the other OpenAI news that dropped on you know, around Gemini three day Gemini three week, is this profile, in the in Wired of
Speaker 2:Fiji Simo. And she's absolutely getting a fit off.
Speaker 1:She is. The the photos are remarkable, great photography from the team over at Wired. GL ask you the second really delivered. But there's one interesting section in here.
Speaker 2:That is a wild name. The photographers.
Speaker 1:A skew. That's hilarious nominated determinism. Taking taking this photo
Speaker 2:The skew the second.
Speaker 1:And this photo is not a skew. So maybe it's bad nominated determinism. Anyway, the 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, it says, OpenAI is obviously one of the 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.
Speaker 1: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.
Speaker 1: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 but deeply unprofitable business like OpenAI. 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.
Speaker 1:With ChatGPT, we could give everyone that team, a personal shopper, a travel agent, a financial adviser, a health coach. That is incredibly valuable, and we have barely scratched the surface. If we build that, I assume that people are gonna want to pay a lot of money for that and that revenue is going to come. Does that make any sense to you?
Speaker 2:It's a better answer than than what Sam gave.
Speaker 1:I think I I I was shocked by this because I I so I love the first part. I agree. ChatGPT will be a personal shopper, will be a traveler Yeah.
Speaker 3:The issue
Speaker 1:is financial adviser.
Speaker 2:They actually pay
Speaker 1:for I don't know that people would pay for this or or or that that's the best business model. I would be very surprised.
Speaker 2:Travel I mean, so part of it is, like, she's also just saying broadly we'll be able to monetize Mhmm. That. It's not necessarily, like, people don't really pay She didn't or Like, the traditional travel agent model is just Yeah. Book your trip with me. Yep.
Speaker 2:I'll I'll get a rev share from the hotels and the services. Yeah. But you're not, like, paying anything.
Speaker 1:I mean, let's go 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, like, I want to pay for a personal shopper, but I actually have to use a free product with ads. That's that that could be true. Right?
Speaker 2:Yeah.
Speaker 1:And same thing. She says people will wanna pay and that revenue is going to come. So people people will want to pay for it, but they will get it for free with with ads potentially, or there will be some sort of, some sort of combination. Because right now, I pay $200 a month. And you could imagine that there's a that there's a world where if you pay, you get a version that has less ads or there's less less thumb on the scale.
Speaker 1:How they how they slice that and and navigate that agentic commerce discussion and trade off is gonna 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 ChatGPT is or the how how good these products are for shopping for gifts. Because if you go to Google and you say, I want 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.
Speaker 1:I can get a list of something, but it's they're all over the place. And some of them will be, like, the best, like, discount, the best knockoff Bottega Veneta. And that's not what I want. I want the real thing. Right?
Speaker 1:And so you can actually specify all of that in the prompt, have it go cook Yeah. And it really will bring you great results. Great, great results.
Speaker 2:It it mogs a gift guide.
Speaker 1:It does. It really mogs a
Speaker 2:gift guide. For 30 year old guys. And it's like, well, what kind of 30 year olds
Speaker 1:Exactly.
Speaker 2:Where do they live? And what are their interests?
Speaker 1:Yes. Yes. Getting to
Speaker 2:like the very generalized gift guide Yep. Is probably gonna knock those, like, opinionated gift guides, I think, will still be valuable where, like, an individual person puts it together Yep. And they're like, this is what I these are things that I think are cool. Yep. But a gift guide that's like, here's a list of things that guys might like Yep.
Speaker 2:Is like maybe a lot less
Speaker 9:valuable when
Speaker 1:you And so generate one. Like, I I think that the amount of gift guide development and shopping activity over the next two months during the holiday season in the ChatGPT app should be immense. I I feel like they're gonna 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.
Speaker 1:Why are you laughing?
Speaker 2:Charlie says AI is never gonna be able to figure out what dads want for Christmas.
Speaker 1:You barbered, I think. There are there are some funny and interesting anecdotes in this, Fiji CMO profile. Let's just read through a little bit of it. In case OpenAI's structure couldn't get any weirder, a nonprofit 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.
Speaker 1: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 ChatGPT and basically every function that might make OpenAI money. Simo is dealing with a relapse of postural orthostatic tachy tachycardia syndrome, POTS, that makes her prone to fainting if she stands for long periods of time. Very sorry to hear that. But she says now she's working from her home in Los Angeles
Speaker 2:She's making it work.
Speaker 1:LA. And she's on Slack a lot being present from 8AM 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. OpenAI's famously Slack driven culture can be overwhelming for new hires, but not apparently for Simo.
Speaker 2:Are you are you have you been using ChatGPT Pulse?
Speaker 1:No. I have I I have not been using it regularly.
Speaker 2:I'll give you one from my pulse today. It's called it says this is like an article that I can tap into. Mhmm. Open AI's API litter. Lay open a Open AI's API layer.
Speaker 2:The hidden moat in plain sight. So this feels feels like
Speaker 1:It feels like it's always like one click deeper from what I've been Prompting. Yeah. What I've been prompting. The articles do feel like they've been getting shorter. They used to be it used to be, like, very intensive compute wise.
Speaker 1:Like, it would be, 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 allow like 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 there's, you know, information about BlueOwl, Microsoft Sparewater AI factory, like, interesting things that I would wind up prompting.
Speaker 1:But I would usually prompt on a very I I don't know. I feel like there's it'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 about it. But, you know, people are searching ChatGPT for holiday goods. Gotta get on ProFound.
Speaker 1:Get your brand mentioned in ChatGPT. Reach millions of consumers who are using AI to discover new products and brands. You also gotta get on Turbo Puffer, search, serverless vector, and full text search, build from first principles on object storage, fast 10x cheaper and available.
Speaker 2:Buy the best.
Speaker 9:Best of
Speaker 2:the labs. What there was one thing that stood out here. Fiji says, my husband is a chocolate maker.
Speaker 1:So sick. This is
Speaker 2:Very cool. Also, what does that say about the jobs of the future? You have this one household. One is in chart responsible for will sell ads for one of the most transformative
Speaker 1:Yeah.
Speaker 2:New technology companies of our time. The other one is making chocolates. This is like, you know, bifurcation of of of jobs.
Speaker 1:Potentially. It does seem like a a I AGI resistant job. I don't think OpenAI will get into the chocolate making business.
Speaker 2:So Brett Adcock. But like a word.
Speaker 1:He's just like, I Actually, will
Speaker 2:we're I will steamroll. I will steamroll.
Speaker 1:In other news, OpenAI is allowing allowing employees to donate equity to charity for the first time in years
Speaker 2:Other nonprofits.
Speaker 1: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 OpenAI nonprofit? You just create this ouroboros of capitalism. Hopefully, it happens.
Speaker 1:I don't know. There's breaking news out of Saudi Arabia. We got a trillion dollars. Let's ring the gun.
Speaker 2:Let's go.
Speaker 1:1,000,000,000,000. What are they gonna invest in? Like, where is the money going?
Speaker 2:Let's play the video.
Speaker 1:Let's play the video. While we're pulling that up, let me tell you about numeral.com. Let numeral worry about sales tax and VAT compliance. Numeral.com. Watcher Guru has the video.
Speaker 1:Let's play it. And the agreement that we are signed in the today and tomorrow, we're gonna announce that we are gonna increase that that 600,000,000,000 to almost $1,000,000,000,000 for 1,000,000,000,000. Investment 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 bare metal materials, magnet, etcetera, create a lot of investment opportunities So
Speaker 8:you are doing that now? You're saying to me now that the 600,000,000,000 will be 1,000,000,000,000. Definitely.
Speaker 1:Because what we are signing Yay. And and
Speaker 8:we will I like that brand.
Speaker 1:Go ahead. Well, I I I wonder what time period. But, I mean, this is remarkable. But they can invest in VC funds, public private equity funds, like, sorts of stuff in the in the industry in the economy. Right?
Speaker 2:That that really made Donald happy.
Speaker 1:It's great.
Speaker 2:I like that very much.
Speaker 1:That's sort of his job. He's kind of the chief fundraiser, I suppose. He's
Speaker 2:martial world
Speaker 1:and and get the money over here. I don't know. It it seems like sort of sort of win. I don't know.
Speaker 2:I mean, we want every every American benefits. Yeah. I If a trillion dollars Yeah. Is invested in the economy, there's gonna be
Speaker 1:It certainly doesn't seem like there's I mean, the the the the risk with that would always would always be like, well, are are is America investing 2,000,000,000,000 in Saudi Arabia? Like, is it is it 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.
Speaker 1: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 NovaCore in collaboration with Los Alamos.
Speaker 1:Nova went critical for the first time this morning at 11:45AM. Congrats to him. Nice. Fantastic news. There is some debate on the timeline over what exactly happened.
Speaker 1: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.
Speaker 1:They kinda settled that debate. Josh Payne, nuclear junkie, is saying here. So what exactly did what what hardware exactly did Valar provide? The fuel control systems, cooling measurement systems, and most of the core are all part of the Deimos project. Did Valar provide a block of graphite?
Speaker 1:And they're calling it their core. And so people are going back and forth. Niels chimes in here and says, Valar Atomics provided the reactor core, the TRISO fuel, and the system configuration. That seems pretty important. Like, you gotta like like, yeah, don't I don't know.
Speaker 1:It seems like more than what they've done before. It's like clearly an advancement on what they you know, they're they're they're chopping wood here. LANL and and CERC provided the critical assembly facility safety envelope, experimentalist test and a bunch of other stuff. And so that's from that's just from their press release. So people are going back.
Speaker 1:Did they do nothing, or did they do everything? Well, maybe it's somewhere in between. There was a partnership. They said that in the press release. The bigger thing is I I think people are, I think people are trying to push on Valar this idea that that they need to be doing completely novel science, and I don't know that that's actually the goal of the company.
Speaker 1:I don't actually know that's what like like, if we just zoom out to, like, what is the goal of the, reindustrialization project in America? What's the what what's the goal here? Like, well, it's it's to lower energy prices. Right? Like, America wants to generate as much money as much as much energy as possible for as little money as possible.
Speaker 1:And Yeah. There are a bunch of technologies that exist. There are new technologies like like what Ashley Vance was talking about with Helion and and and fusion. That's a new technology that we have not even discovered yet. Fission's been discovered.
Speaker 1:Eighty years ago, it was working. Yeah. It just became regulatory nightmare.
Speaker 2:We just shot ourselves in the foot.
Speaker 1:And we just stopped making it. It became it became unprofitable and uneconomical.
Speaker 2:And China said, cool.
Speaker 1:It'll be profitable for us.
Speaker 2:We're just gonna copy and paste
Speaker 1:Exactly. And so and so I think I think people might be a little bit over rotating on, like, on, like, is is, is Volar doing, like, entirely new crazy scientific breakthroughs when it's like, do they necessarily have to? Like or do or or is it just enough
Speaker 2:for them just to build a lot of motivated team Yeah. That is going to make incremental progress towards their goal. Yep. And any anybody that's hating on that, I think is just like, again, like, I I think what 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 US. And I think this is, you know Yep.
Speaker 2:Undeniably, like, incremental progress that gets them closer to their actual goal, which is bringing a small molecular reactor online.
Speaker 1:I think I think Elon summed it up well with, like, his thesis for the x AI team. He was like, we 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.
Speaker 1: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 and Elon was very clear on, like, we don't have AI scientists. We have we have engineers. And this is the same thing.
Speaker 1:Like, 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, I we we we should ask him this the next time he's on the show. But I think he would say, I wanna be the Elon of nuclear.
Speaker 1:I don't wanna be the Oppenheimer of nuclear. Like, I'm not trying to, like, create something
Speaker 6:Yeah.
Speaker 2:He even said his his line on on he said The US is still good at making bus sized objects Yeah. But not, you know, sort of like maybe maybe, bridge sized objects. Right?
Speaker 1:Exactly. But Morgan Barrett's having fun on the timeline, what street parking is going to look like in El Segundo in twenty four months. Of course, the El Segundo crew loves their cars. I think they're gonna stay pretty focused on the mission, but, I would love to see this in El Segundo for sure. For sure.
Speaker 1:There's also big news out of, Radiant. Radiant has been, Doug's been on the show. He's a good friend, and he, 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, Anusiada has the kind of breakdown here.
Speaker 1:It says production reactors in production by 2028 brought to you by the people that brought you reusable rockets and McMaster Carr, highlighting the team behind Radiant. And so congrats to everyone in the nuclear industry who's making big waves. And we have our next guest before we bring them in from the restream waiting room. Let me tell you about Vanta. Our guest is from Vanta.
Speaker 1:And it just happened
Speaker 2:tell you about it.
Speaker 1:We'll let you I'll let him tell it. We have Jeremy from Vanta. Welcome to the stream. How are you doing?
Speaker 2:What's happening?
Speaker 1:I swear that wasn't 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. Like, I'll let you do the re the ad read. Introduce yourself. Introduce what what Vanta does, what you do, and then we'll get into the news.
Speaker 10: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 VantaCon conference here in San Francisco.
Speaker 10:Have a ton of people showed up, a ton of engagement to really pull that entire security GRC community together, and have a couple of really cool announcements. Yeah. One of them is how we are transforming Vanta to be the agentic trust platform.
Speaker 9:Mhmm.
Speaker 10: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 Snyk, Perplexity, Synthesia, all the way from y c startups that maybe just exited the batch, you know, recently, all the way to the Fortune 50 companies really earn and prove trust as a business.
Speaker 1:It feels like AI is 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 Teslas 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.
Speaker 1:I I I 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?
Speaker 10: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?
Speaker 10: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.
Speaker 10:Because you imagine security customers are some of the harshest critics of AI. They really want things to be accurate and great. 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 two point o. So we've redefined our agent to really be this built in GRC engineer that understands all the compliance across your entire organization.
Speaker 10: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.
Speaker 10:It has the memory of understanding what your business is. Maybe you sell to consumers. Maybe you sell to other businesses. It can pull all that context in across everything in your program as well. Like, hey.
Speaker 10:We know that, you know, 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.
Speaker 10: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 to 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 suggests those to you, like, provides gaps and 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.
Speaker 2:How 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?
Speaker 10:Yeah. I think it's gonna be both. Like, I think there's a lot of times I don't wanna 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.
Speaker 10: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 want us to go fix those to you? And you didn't wanna 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 all already looking for those, or maybe your SLA says it's twenty four hours for critical vulnerability to notify customer in one document.
Speaker 10:It says seventy two 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 gonna give you what you need. You wanna dig in more.
Speaker 10:You wanna learn more. You're trying to explore data. It's a big case for us in reporting where people want to learn maybe about, you know, 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 ChatGPT and have it automatically generate kind of graphs and charts and reports that they can use for, you know, their board or anyone else to kind of show progress of their program.
Speaker 2:How how are bad actors using AI today to, you know, abuse companies in different ways?
Speaker 10:Yeah. I mean, I think, 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 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.
Speaker 10: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.
Speaker 1:On the other areas of the risk surface, I I imagine that, you're trying to build products. Are you also, starting to act as a 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 wanna be part of an ecosystem and and suite of products that enterprise implements?
Speaker 10:Yeah. I think for us, we definitely wanna solve the broader trust problem, but we know that there's lots of different pieces where we aren't gonna 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.
Speaker 10:How can we go do all that? There are certain areas, you know, like vulnerability scanning. We're not gonna be going into vulnerability scanning, but we're gonna go partner all the great scanners to go do that.
Speaker 1:Got it.
Speaker 10: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.
Speaker 10: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 Avanta really pulls it all together. We call it the risk graph.
Speaker 10:It's 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 kinda get this connection between, hey. There was a breach. Okay. Great.
Speaker 10:The breach happened. Which vendor was it? Who has access to that vendor? Vanta can lean in and cut off that access or change the controls there. What data was going into that vendor?
Speaker 10: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, they wanna 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.
Speaker 1:Yeah. That makes a ton of sense.
Speaker 2:You guys gotta do Spotify wraps for internal risk.
Speaker 1:That would be good. Something shareable.
Speaker 2:Something shareable internally at companies, of course, to be like, you know, yo, Tyler, you gotta you gotta you're you're our biggest risk vector over here. Intern. Tyler Tyler's our intern over here.
Speaker 1:He Thank you so
Speaker 2:he's much. Very secure.
Speaker 1:He's very secure. He's probably the best.
Speaker 2:Anyways, super, exciting few launches Yeah. And and and have fun at the event. Thanks for joining.
Speaker 1:Yeah. Yeah. Have a great rest of your day.
Speaker 2:Cheers. Bye.
Speaker 1: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.
Speaker 1:It says Oracle is already underwater on its astonishing $300,000,000,000 open AI deal. AI circular circular economy may have a reverse Midas at the center.
Speaker 2:Okay. So they're they're saying this is underwater because the market cap has dipped below.
Speaker 1:That's so And it's like not
Speaker 2:Yeah. It's not very honest. Yeah. It's not it's not, I, you know So
Speaker 1:I'm not so the first one. The Financial Times says Oracle's astonishing $300,000,000,000 OpenAI deal is now valued at minus 74,000,000,000. And that's Like, I don't like that at all. Like, yeah. This is, really,
Speaker 2:really bad framing in my opinion. Like Yeah. It's not Intellectually dishonest.
Speaker 1:I thought so too. I thought so too. And I I love the Financial Times. I mean, we have the Financial Times printed out here. Normally normally, very, great reporting.
Speaker 1:But this one, this one feels odd. It just feels like an odd frame.
Speaker 2:Saying, oh, Oracle's already underwater on
Speaker 1:a And and to be
Speaker 2:clear ship.
Speaker 1: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
Speaker 2:is true. Is the market is no longer giving them credit.
Speaker 1:Yes. Yes. That's right. That's right. But there's no way
Speaker 2:that they're underwater. It's
Speaker 1:so weird.
Speaker 2:So when I saw this headline, I I I read into it earlier, and I was expecting to see something
Speaker 1: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 equivalents to Oracle shares are little changed over the same period, the Nasdaq Composite, Microsoft Dow Jones software index.
Speaker 1:So the three this the 60,000,000,000
Speaker 2:Calling those equivalents is, like, again, like, look at
Speaker 1:You could you could also comp it to CoreWeave, and you could say, on a relative to CoreWeave 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.
Speaker 1:It says, so the 60,000,000,000 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. With net 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 US public market proxy, which is fascinating because, Microsoft should be OpenAI's public market proxy in my opinion. But there are some great charts in here.
Speaker 1:There's some interesting stuff. And I and I believe this is, this is from Alphaville, which is, their blog, and it's and it's not exactly it it it is supposed to be, like, you know, like, like, a take factory. Anyway well, we have our next guest in the Restream waiting room. Let me tell you about Julius dot 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.
Speaker 1:We have Keon from Monad. Welcome to the show. How are you doing? Good to see you. What's happening?
Speaker 5:Hey. Doing great. Great to be here.
Speaker 1:Thanks so much for joining.
Speaker 2:Please Dude, I love it. You got the lock in. You're calling in from the the lock in capital of the world with the mattress on the floor.
Speaker 1:Yeah. Congratulations. Please introduce yourself and tell us a little bit about the news specifically this week.
Speaker 5:Thank you. Great to be here. My name is Keanu Han, cofounder 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.
Speaker 1:Wait. So you were a high frequency trader before this?
Speaker 5:That's right. Yeah. I was at Jump Trading for about eight years. I led one of the trading teams there. Was very involved in the futures markets prior to Monad.
Speaker 1:What was the day to day like?
Speaker 5:It was a lot of, Jupyter Notebook.
Speaker 1:It
Speaker 5:was a lot of, like, manipulating large datasets and making really short term price predictions as well as building, performance systems.
Speaker 1:How how short term is short term? Like, nanoseconds, picoseconds, or, like, seconds, minutes? It all seems short term.
Speaker 5:Yeah. It's the predictive horizon for the kinds of strategies that I was working on were on the order of milliseconds to seconds. Okay. But the hold 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.
Speaker 5: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 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.
Speaker 1:Interesting. I didn't know that. Thank you. That's very helpful.
Speaker 2:Very cool.
Speaker 1:So so talk about the oh, sure.
Speaker 2:Yeah. I guess I guess getting into what what is, what is success with Monad gonna look like? Like, who what are the different types of groups and applications that, and and types of users that you that you that you expect to come in in the, in the early days?
Speaker 5: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 of their libraries, all of 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.
Speaker 5:Mhmm. So in particular, Ethereum processes on the order of 10 transactions per second while Monad delivers 10,000 transactions per second. And that thousand x improvement is a result of several different improvements that have kind of 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.
Speaker 2:That makes sense. And so what what in in your view, what is the ideal kind of adoption look like?
Speaker 5:Yeah. It's really a 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.
Speaker 5:So what we're seeing in terms of adoption is a mix of existing applications that can migrate to Monet 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.
Speaker 2:In your view, what what are the kind of classic mistakes that that, other blockchains that have tried to challenge, you know, some of the more dominant chains, what what are the kind of classic mistakes that they make to to, ultimately I I 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 what are the things that basically you're trying to avoid?
Speaker 5: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 that are there to 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 idea maze to identify PMF.
Speaker 2:That makes sense. How's it how's it been bringing bringing the token to market with 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 met that's made it challenging. But you're also utilizing a new product line from Coinbase, which is pretty interesting.
Speaker 5: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 the just the story of how Dogecoin gained really broad distribution and mind share 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.
Speaker 5: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 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.
Speaker 2:Makes a lot of sense. Well, how much, how much have you raised so far? We have a Gong we have a Gong here. We'd we'd love to, hit it on on your behalf.
Speaker 5:Thank you. I think we've raised about a $120,000,000 so far.
Speaker 2:There we go. Congratulations. Well, it's an honor to honor to hit the gong for you and, excited to, follow along. Congratulations.
Speaker 5:Yeah. Thank you. So we have until, Saturday. The sale's open until Saturday at 9PM eastern, and, we're looking to raise a $187,000,000 total.
Speaker 2:There you go. Let's go. Most of the way there.
Speaker 1:Well, good luck. Thank you so much for taking the time to talk to us today. Have a great day.
Speaker 2:Great to meet you.
Speaker 1:We'll talk Our next guest is Steven Balaban from
Speaker 2:The Legend.
Speaker 1:Lambda Labs. Or is it just Lambda now? I think it's just Lambda.
Speaker 2:Did we drop the Labs?
Speaker 1:I think we dropped the Labs. Steven, did we drop the Labs? How are doing?
Speaker 6:We dropped the Labs.
Speaker 2:We dropped the Labs.
Speaker 1:Lambda. Okay. I'm dating myself. Well, I at least I feel like at day one, I don't feel like a bandwagon fan because I'm using the old name. There's a little bit of cool.
Speaker 1: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 incredible sleek.
Speaker 1:You're
Speaker 3:making us look We gotta put on
Speaker 2:the casuals.
Speaker 1:Give us the news what happened. Let's break it down.
Speaker 6:Yeah. Well, so one day I was training some comp nets on my workstation. Next thing you know, we're raising 1.5 Woo. Giga bucks.
Speaker 2:Giga bucks.
Speaker 6:We say we say we think
Speaker 2:Giga Giga bucks.
Speaker 6:Giga bucks. Gigawatts, giga chips Yes.
Speaker 1:Giga bucks. Yes. Yeah. What what does that actually mean? I mean, we we we we see we see 10,000,000,000, 100,000,000,000, 10,000,000,000,000, quadrillion every day.
Speaker 1:Is this cash? Is this debt? What are you are you buying GPUs? Are you buying land? What are you doing?
Speaker 6:All equity.
Speaker 1:Okay. Let's give it up for equity. Let's give
Speaker 3:it up for equity.
Speaker 6:Yeah. Extremely well. Like, our 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. Mhmm. And that's kind of been one of our philosophies.
Speaker 6:And we've we've aimed to have, you know, a business that's just super robust to ups and downs in the market because we're swimming with our swim trunks on.
Speaker 1:Yep. And then Since that's And you
Speaker 2:That's a
Speaker 3:amazing line. Money For the money.
Speaker 1:You gave them you gave them equity. The the there's no one hand washes the other type thing where, like, the the like, they pay you, you pay them. It's all a one round trip. No. This this
Speaker 6:round was led by by TWG Global, which is
Speaker 1:Financial investor.
Speaker 6:Which is Thomas Tull and Mark Walter. You may know Mark Mark owns the LA Dodgers and and also now the Lakers. Yeah. Thomas started Legendary Entertainment, makes great movies like the Batman series and Dune and Inception. And so it these are business partners
Speaker 1:Yep.
Speaker 6:Who I've gotten to know over a number of years now. And this is just they're they're making some some big investments in the space.
Speaker 1:Okay.
Speaker 2:I'm so happy you guys have your trunks on because not everyone out not not every player out there has their trunks on right now and it's hard to tell who does and who doesn't.
Speaker 1:Yes.
Speaker 2:But at some point, we're gonna find out and it's not gonna it's not gonna be pretty.
Speaker 1:Yeah. It's great
Speaker 3:to It won't
Speaker 6: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 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 gotta capture all this amazing upside in the long term.
Speaker 1:Yeah. So talk about
Speaker 2:the use
Speaker 1:of funds.
Speaker 2:Well, even even before that, maybe maybe feels like and it potentially an an advantage right now just in terms of focus is like being private. There are other other companies in the category that are public and they're now having to contend with, you know, what what's been a pretty big correction in Yeah. In Neo at least a local correction in Neo Cloud 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?
Speaker 6:Well, I think that certainly that level of 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 if in the short term, the market's a voting machine, in the long term, it's a weighing machine. We just gotta 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 the secondary markets, as you know, are actually, you know, pretty deep for for for for companies that are that are kind of at our size.
Speaker 6:And so I think that some of that can start to creep in.
Speaker 2:Yeah. That makes sense.
Speaker 1:Where are you seeing value spending some of this money? I imagine that there's hiring, r and 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 in this in this phase of the journey? It's been, over a decade now. Right?
Speaker 6:Yeah. We started in 2012
Speaker 1:Wow.
Speaker 6:And was doing we were doing face recognition software and the AlexNet paper came out.
Speaker 1:Wow.
Speaker 6:I mean, that's how early it was. And I I downloaded the CUDA ConvNet library off of Google code and that will tell everybody kind
Speaker 2:of how
Speaker 6: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. Yeah. We are also starting to put that into investments into data centers themselves.
Speaker 1:Mhmm.
Speaker 6:And we I I think that what we're aiming to do long term Mhmm. Is kind of build this almost like Tesla for AI infrastructure, where we kind of look at this as, like, a similar build out 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 that goes from everything from, you know, energy procurement and construction because I think a lot more of this stuff is gonna 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?
Speaker 6: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 it's exciting because you we 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 a company building perspective, and it's a it's an honor to be able to do that.
Speaker 1:Can you get me up to speed on some of the trade offs 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 RL all over the place. Then you actually have to serve these things. But, actually, if it's gonna take me ten 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, right colocated.
Speaker 1:How are you thinking about the trade offs there?
Speaker 6:So so the the mix and the main driver over the next five years, we believe will likely be mostly on the inference side. Mhmm. If you look at some of the financial models that have either leaked or otherwise been published around what OpenAI thinks they're gonna be spending, it looks to be about 50% on training and then 50% on inference growing towards 75% inference
Speaker 1:Yeah.
Speaker 6:And, you know, a smaller chunk of that on training. And as far as, like, what that means for the larger data centers, I I certainly don't think that this is, 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. Mhmm. But one of the things I think that you're gonna start hearing a lot more of is how adaptable and how quickly can you bring on the data center in an incremental fashion.
Speaker 6:Mhmm. Because that's gonna 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.
Speaker 1:Mhmm.
Speaker 2:How do how do you think about revenue quality and customer selection? Because you we've we've seen some some deals go down that's that look big and cool and good on the surface, and then you dig into them and maybe the maybe the underlying infrastructure provider is not actually getting that great of a deal at the end of it?
Speaker 6:Well, we see 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 if you look at our breakdown from our revenue mix in terms of you looked at, like, let's say, our q three stuff, and I I don't wanna 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.
Speaker 6:And we have a very, very you know, I've seen some other people's customer books, and I 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.
Speaker 1:How do you think about diversity of 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?
Speaker 1:And we'll just pay you as a consultant.
Speaker 6:We have no interest in doing that that one. That's, you know, we we wanna 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 it's got things like long term high speed AI file systems.
Speaker 6:It's got instances that go down from one GPU to an entire cluster with one click clusters that we've 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 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?
Speaker 6:What are good investments? And where are we gonna really dominate the market and focus there? And so we've actually exited, for example, the inference market. We we had a $200,000,000 plus a year hardware business that we've exited. Right?
Speaker 6:You know, I mean, it it it actually, like, kind of crushes me because, like, that was the business that got off the ground. But Yeah. Can you imagine just, like, winding down? Like, well, we're just gonna take this business and not do
Speaker 1:a $200,000,000 a
Speaker 6:year business anymore because we're trying to focus.
Speaker 1:That is crazy. That is crazy.
Speaker 2:Thanks, Scott.
Speaker 1:I have a I have a crackpot theory that I'd love to run by you. What do you think the odds are that the I like, I noticed I was traveling in I was traveling in Mexico, I noticed that Carlos Slim is the richest man there, and he's a telecom magnate. He he owns a lot of the telecom infrastructure. And that's true for a lot of a lot of countries. The the 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.
Speaker 1:And I was wondering if you had a thought on, do you think that in the future, we'll see the 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 gonna be a new boom across the globe? It's kind of a different twist on the sovereign AI project. I was just I was just wondering if there's if there's gonna be some some way that this plays out where there's this sort of, like, onetime opportunity to kind of get a corner resource? Or is the nature of the Internet such that the compute is actually much more fungible than, than, say, you know Telecom.
Speaker 1:Telecom or Yeah. You know? Yeah. Because Or, like, copper and the graph.
Speaker 6: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. Yep.
Speaker 6:You know, these are all things that benefit from a physical geographic monopoly. Yep. Right? And and AI data centers don't have that same thing. Now I just wanna step back for a second.
Speaker 6:Guys, The United States is basically the only country in the world. We have the most unbelievably good economy. This is the the the idea that there's gonna be these sort of, like, massive AI infrastructure projects that I think are gonna 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 I just am so bullish about where we're going in America that I I don't really pay a lot of attention. And our focus is just in, you know, in in North America generally.
Speaker 6:And I I just that's kind of my perspective on it, to be honest.
Speaker 1:Yeah. Yeah. That that that that's really helpful. I agree. It's it's interesting to toy.
Speaker 1:I mean, there there there's a lot of money being thrown around with some of these projects, and I'm always interested in, you know, how they all shape out. Last oh, sorry. Go right
Speaker 2:ahead. May maybe go go for it. I I was gonna ask, like, how you guys are navigating energy constraints with 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.
Speaker 2:Others are saying, actually, that's not my you know, it's so where where do you sit?
Speaker 6:We are aiming to reimagine the sort of step process from whether it's photons or molecules of natural gas to tokens.
Speaker 1:Mhmm.
Speaker 6:And we strongly believe that a lot of this is gonna have to come in reimagining, like, well, how do you inter interact with the grid? How much power generation do you bring to the grid yourselves? And I think that that's the the the successful AI infrastructure companies in the future. Again, this is like why I kind of said, like, I look at this like Tesla or AI factories, which is you gotta 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?
Speaker 6: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 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 look. Try not to run through those like a brick wall because it's kind of 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 that's what I think is gonna be successful.
Speaker 1:Yeah. Makes a lot of sense. Thank you so much for taking the time out of your busy day to come and hang out with us and answer Incredible. Questions about
Speaker 6:Jordy, John, thanks for having me, guys.
Speaker 1:It's always great time. Congratulations.
Speaker 6:The new Gemini three. This is like
Speaker 1:Yeah. Can you give us your review and actually explain how it interfaces with your business? I'd love to know.
Speaker 6:So so I haven't I haven't used I haven't, I haven't used the Gemini three yet.
Speaker 3:I've seen
Speaker 6:the, the updates. I I'm still, you know, hey, Sundar or whatever. Give give give Lambda's enterprise account access. We're on Google Yeah. Google Suite or Google Enterprise or whatever it's called now.
Speaker 6:So we'd love that upgrade. But I'll tell you what, this is the cool thing. I use things like ChatGPT and Grok 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. Mhmm.
Speaker 6:And I I pay attention. I actually just like kind of do what the AI tells me to do.
Speaker 1:Yeah.
Speaker 6:And that gives more compute to the AI to train bigger models which makes faster.
Speaker 2:You're AI is working for you
Speaker 1:to make more AI.
Speaker 6:The AI is working types for of positive feedback loops. Sure. And 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 with with the 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.
Speaker 6:I mean, next thing you know, it's sort of like, well, you know, maybe AI is the one making
Speaker 1:the the run of the show. Decisions. Yeah.
Speaker 2:Next thing you know, we'll be hanging out on TVPN discovering novel physics with with Gemini four, you know. See we'll see how far we get.
Speaker 1:Yeah. Yeah. It's a it's a good time. Well, thank you so much for coming by the show. We'll talk
Speaker 2:to soon. I have a bunch more I have a bunch more questions, but but come back. Let's get you back on in Anytime. Before the end of the year.
Speaker 1:That'd be great.
Speaker 2:And we'll continue the conversation. Congrats to the whole team.
Speaker 1:Yeah. We'll talk to soon. Take care. Have a great one.
Speaker 3:See you.
Speaker 1:Bye. Quickly, let me tell you about Privy. Privy makes it easy to build on crypto rails, securely spin up white label wallets, sign transactions, integrate on chain infrastructure all through one simple AP Legend. What a legend.
Speaker 2:What an absolute legend.
Speaker 1:So we got. Doug O'Laughlin over at Semi Analysis Fabricated Knowledge says, leave for two weeks, and we are talking about Oracle credit default swaps. What the hell, guys?
Speaker 2:Doug, where where was Doug for
Speaker 1: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 any sign, any crack in the market is definitely going to be newsworthy because we're in this $1,000,000,000,000 era. Gavin Baker here is talking about this.
Speaker 1:He's completely agree with this breakout of the nonbubble that disappointed both bull and bears, how Sam splurged changed everything. And Gavin Baker says Sam Altman's manifestly ridiculous $1,000,000,000,000 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 ninety nineteen ninety nine style melt up, which is healthy. Melt up meaning that, in 1999, the the market went insane in in nuclear. Instead, the the 1,000,000,000,000 was so in your face that everyone started asking the questions of, like, is this real?
Speaker 1:Is what's going on? Are we go 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 trading, like, 25 priced earnings, that 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.
Speaker 1: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 a to an 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.
Speaker 1:Part of the chapter profiles a community in, Surrilos, 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 Surrilos. For that calculation, I relied on a figure from a government reporting government document reporting Cerrilos residential water use based on the current best information. It seems that this document used the wrong units, so she was off by a thousand. So the the results was that the
Speaker 2:What's what's up that what's being off by a thousand among friends?
Speaker 1:Honestly, these days, doesn't even matter.
Speaker 2:We're in post fact.
Speaker 1:Did you you read into this more? It was people were, I I think I think people are, are generally like, you know, is this book a hit piece? And I think Sam actually cooperated with it a little bit or, like, gave some interviews for it. But it but it like anything, it's, like, obviously critical of some things.
Speaker 3:So I mean, yeah. Three three orders of magnitude is, pretty big. Yeah. It's, like, not great.
Speaker 1:Yeah. I mean, it's certainly, like, like
Speaker 3:And like, between being
Speaker 1:a big deal and not a big deal
Speaker 2:at all.
Speaker 3:Like that about the water use, it's, people who use that to justify, like, oh, we don't wanna build those data centers. We're gonna use our water. Yeah. Like, I don't know. Mean, not good.
Speaker 1:It's a rough time if your if your job is Tom. Drinking water.
Speaker 2:Tom in the chat says, mistakes were made. Mistakes were made in a book I was responsible for. Nika says, Jordy, you should get a grill with tiny GPUs instead of diamonds. Maybe not the full grill, just the bottom grill. There'll be AI wrapped reading
Speaker 1:about it. See this is a Rohit comment on on Vinod? Vinod VC Vinod Khosla says that the US 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.
Speaker 1:Because it's like, yeah, the government taxes everything. They the government gets 21 of profits, actually. Like, they get cash flow. Sean
Speaker 2:says the haters will call that a tax.
Speaker 1:It was it was so funny. Olivia Newsy is in
Speaker 2:the news. It's almost like People
Speaker 1:are debating the news.
Speaker 2:A kind of like a dividend. Yeah. You know?
Speaker 1:Apparently, the all the all the media people are obsessed with this Olivia Newsy story. I didn't understand any of the people in this story because I don't follow media or politics closely enough.
Speaker 2:Nominative determinism strikes again.
Speaker 1:But it
Speaker 4:is fun that
Speaker 2:she her name is busy. Saying we should do
Speaker 6:it in
Speaker 2:the Metis list for nominative That
Speaker 1:would be good. I'd
Speaker 2:like that for sure. She's in the news all the time. Yeah. She's also a journalist.
Speaker 1:There's news in the, in the trading app world. Robinhood launched bearish on a stock. Short selling is rolling out today on mobile. Classic, a web classic and Robinhood legend. They didn't have short selling?
Speaker 1:I feel like they've had short selling for a long time. No? That's that's a new feature.
Speaker 2:Well It is. That's funny timing. And then our partner, public generated assets.
Speaker 1:Yes.
Speaker 2:Which they're calling their AgenTik brokerage. Very cool video with our with our boys here.
Speaker 1:Yes.
Speaker 2:Yes. But this means you can basically generate, like, your own index Yes. Based on
Speaker 1:And what's interesting about it is that you can say, I want access to the Mag seven plus a couple of other AI companies.
Speaker 2:Minus one company.
Speaker 1:One company. I don't know which company
Speaker 2:you're If you if there's a company, you don't
Speaker 1:So so so you can generate, like, like, you know, some sort of portfolio. But then instead of instead of owning it as an ETF and needing to sell it buy and sell it directly, you can actually do the tax loss harvesting of selling individual pieces of it. And so you can con you you can construct a portfolio very quickly. And in general, I mean, just all the different research that you wanna do in is obviously deeply, you know, enhanced with artificial intelligence. So fun to see them.
Speaker 2:Pope Leo has hit the timeline to comment on cinema.
Speaker 1: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, and difference when evocative. Beauty is not just a means of escape.
Speaker 1:It is above all an invocation. When cinema is 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. That's been nicely worded He's having the a role. Pope Leo.
Speaker 3:What movie do you think he was thinking about when writing this?
Speaker 1:Obviously, Borat.
Speaker 3:Margin Call.
Speaker 1:100% 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 un Jordanian thing.
Speaker 2:Yeah. Final post of the day. Kevin
Speaker 1:Right. Yeah. Right. You think you can cut me off?
Speaker 2:Kevin Naughton junior says 10,000 likes. On April 30, he said 10,000 likes and I'll quit my software engineering job at Google tomorrow. And What happened to you? He said, six months ago, I made the worst decision of my life.
Speaker 1:Oh, because Google's ripping. Yeah.
Speaker 2:Because Google's ripping.
Speaker 1:That's what he's talking about. Okay. Because He's I I I I read this initially. It's like he quit. He started the company and it was like went really poorly.
Speaker 1:It's just funny.
Speaker 2:Well, he is building he is building the fastest way to post with postwrite..ai.
Speaker 1:Okay. There you go.
Speaker 2:Post all your social platforms in seconds.
Speaker 1:Oh, maybe we could use that for something. Very funny. He's like, my idea was Gemini three. Like, I was gonna make a better Gemini. I thought Gemini 2.5 just wasn't quite there.
Speaker 1:And I didn't know that go what if Google does this? All the VCs were telling me, your your idea is Gemini three. What if Google does that? And I was like, everyone says that about Google things. Everyone says that about startup ideas.
Speaker 1:It's not worth it. I'm just gonna try to build Gemini three, 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.
Speaker 1:Scaled hypersonics, very excited for that. Bunch of bunch of interesting stuff. Emile Michael is firmly in the chair of the undersecretary of war. Very excited. Hope we can get him on the show soon to understand what
Speaker 9:he's doing
Speaker 2:over there. Make it happen. Well, thank you for tuning in to the show today, folks. We love you dearly, and we will see you tomorrow.
Speaker 1:Have a good evening.
Speaker 2:Cheers. Goodbye.