Grok 4 Launch Breakdown, OpenAI to Release Web Browser | Chris Paik, Will Bruey, Joel Becker, Dylan Parker, Eric Olson, Ghita Houir Alami, Elliot Hershberg, Karim Atiyeh

27:10Incite a pause. Yeah. Will this be bad or good for humanity? He says, at least if it turns out to not be good, I'd like to be alive to see it happen. Oh, yeah. They had a polymarket integration. That was kind of interesting. Yeah. It's interesting basically giving the model access to real-time polymarket data so that it can help make predictions and sort of add context around the market itself. Yeah, that's interesting You learn asking the real questions you say that's a weird photo, but what is a weird photo? I still don't understand why we're looking at weird photos of XAI employees, but they were charming They're calling it super grok crazy features 16-bit microprocessors.

27:51What is that? I don't even understand what this is Oh, they yeah, they built like a game in grok They had demo of a video game generated by super grok. It's a doom clone every time the PC shoots an enemy floating text appears reading grokdom Elon is fabricating timelines for product launches on the spot. The engineer sitting next to him is looking at the floor, face impassive, nodding. It's a good model, sir. For real, though. Congratulations on the launch, guys. It's a good model, sir. I thought this post from the actual ex-AI engineer, Eric Zellikman, was funny. It was like AI model version numbers over time.

28:26Did you see this? No. So it's this chart of the version numbers over time, and you can see that Grok is versioning fastest because it's like, at this point, what else are we measuring? Like, at least they're iterating on the version number effectively. And I guess this is a shot at OpenAI because they launched 4.5 and then went to 4.1. And they're kind of like, you know, there's this big question about like, when will GPT-5 come? The expectations are so high for GPT-5. And so they've obviously, the Grok teams are like, hey, at least every three months we release a new full number. So I wonder that the five is a number that really no one has gone for.

29:05And I wonder if Grok will do it first. If you draw the line on this, they certainly should do it in three months. They should have Grok five. And there's no reason that they shouldn't, but maybe there's some superstition. And it's very possible that Colossus is the key to getting to five. Oh, the new data center, yeah. Well, they'll need Linear to plan that out. Linear is a purpose-built tool for planning and building products. Meet the system for modern software development, streamline issues, projects, and product roadmaps. They need linear badly. So hopefully they've gotten signed up. Near said Grok on Humanities Last Exam, Grok 4, I'm not sure I buy even in the general case that there's a given Humanities Last Exam number which implies you discover useful new physics.

29:49How would one make a benchmark of the proper shape for this? You'd have to have a validation set of questions which are outside the scope of what we currently are able to do, you could choose things on the edge of our knowledge distribution and then try and exclude. Yeah, it is interesting. Like if you are able to memorize every hard math problem, does that allow you to discover new math? Like it's sort of a prerequisite because you have to be good at the test. I think where I've imagined these discoveries coming from are having a single intelligence that has PhD-level intelligence across, like a single mind that has PhD-level intelligence across every human domain, right?

30:35And being able to combine ideas from different domains. Like historically, a lot of innovation is just taking something from one field, bringing it over here, making some combination of it. I think Elon talks about the potential of discovering new physics, but again doesn't didn't didn't spend a lot of time like breaking down how that would actually happen but um world is unpredictable so yeah it's interesting people are really pushing this idea of like okay like like we are accelerating like the the ag uh the arc agi leaderboard is accelerating but i keep seeing this and and feeling deceleration like i am not feeling acceleration right now are you tyler yeah i don't know i i think generally i'm kind of like not that interested in a lot of these kinds of benchmarks.

31:18I think ArcGIS is more interesting, but just like the humanities last exam, the kind of general math, physics knowledge, it doesn't seem to be that like, it doesn't seem to line up with, like you see GBT 4.5 kind of does very poorly on these things, but like writing, it does really great. So like I think I'm more like, if I were to go to long short on like different benchmarks, like the usefulness of them, I think stuff like HLE, I'm kind of short, long i'm like have you guys seen the uh minecraft benchmark where it builds the two different okay you basically two models build like a minecraft there's like a prompt it's like build a house yeah then you can choose and then it's like their rank models here and for the minds but who's who's grading that the human it's a human who picks between them okay it's kind of like an elo oh okay um but just like general kind of creative tasks sure i think stuff like that aiden bench is good yeah um i think even on the grok launch there was the vendor bench which one's aiden bench Aiden Bench is Aiden McLaughlin's benchmark.

32:17It's just like it's it's kind of hard to describe how it works exactly, but it's just various like creative tasks. How like kind of novel its thinking is the like style of its texture. Wait, this is just like it's just like whichever one he likes the most at the end of the day. He's the only greater. No, no, there is like an objective like function. Okay, you can like run it. It's not just like okay. The idea that they're like, open up again. It will be funny. You know, there come there. There's a period of life where your SAT score like matters a lot. Totally. And it says something about you.

32:52And then a decade later, it's, you know, what you can do, what you have done starts to matter a lot more. And so I do think we'll reach that point where it's like, yes, you can one shot every hard exam question there is that you can throw at it. But like, what can you do for me? Yeah. Yeah, totally. And I think that's why the bigger question is almost like, you know, ChatGPT DAUs. And like actual revenue. And the final benchmark. And app installs and stuff. Yeah, I mean the revenue thing is interesting because you wind up in like B2B cloud world, which is valuable, but it's maybe less, it's more competitive because it's more commoditized.

33:36And well, yeah, if you don't have a lot of leverage in the enterprise if Azure is able to offer infinite models that are infinite frontier models, open source models that are maybe just behind the frontier but great at certain tasks. The leverage isn't quite there. There will need to be another pretty significant leap until then, you know, Anthropic being really good at cogen, there's leverage there. We saw this yesterday with Llama switching over to anthropic models internally. And then just having a consumer app with a lot of users, also very valuable. Yeah, the other interesting thing about the foundation model layer commoditizing and it becoming like cloud.

34:23And if you have a model, you'll just be like vended in as an API to anything else. Like the token factory is that the hyperscaler clouds are extremely profitable. Like even though AWS, GCP, and Azure are all somewhat directly competitive and they're somewhat perfect substitutes for each other. They have not driven prices to zero. In the way airlines are deeply unprofitable. AWS and Google Cloud are both profitable. Yeah, or you look in other commodity sectors like oil and gas. And I don't know if that's just because there's lock-in. I'm not exactly sure, but there's something about where maybe the counterintuitive take is that, yes, they do commoditize, and there are a few major foundation models that are frontier, and they all are roughly the same price, but they all have decent lock-in with their customers to the point where they're still able to extract some level of profit, or they're just creating so much value that even if they're taking a small marginal slice on top of the cost to run, that they're creating so much value that they still have 50 % margins or something like that.

35:36Because this was the story of AWS. No one knew how much money it was making and then they had to break out the financials in one of Amazon's earnings reports and it was like the AWS IPO, as Ben Thompson put it. Anyway, before we get to the next story, let's tell you about Numeral HQ. Sales tax on autopilot, spend less than five minutes per month on sales tax compliance. So the big news is that the third browser war has begun. Google stock has dropped on the news that OpenAI is planning to launch a Google Chrome competitor within just weeks. And this is very interesting timing because - It's time to browse.

36:16Yeah, time to browse. Certainly makes sense to become deeper, more deeply integrated into the user's life. Makes a ton of sense. There's a ton of benefits that come from having a web browser. What was interesting is we can go into what Google actually, or what OpenAI and I was talking about launching, but this news, this scoop leaked the same day that Arvind from Perplexity announced that they're finally releasing their next big product after launching Perplexity, Comet, the browser that's designed to be your thought partner and assistant for every aspect of your digital life, work and personal.

36:51And so Perplexity launched this on June 9th, and then OpenAI, the scoop goes out via Reuters the same day. And so this feels like very much like let's not let perplexity get a bunch of attention and drive a bunch of people to start daily driving comet the browser because even though we're not ready to launch our competitor we want i mean arvin was on the show talking about comet but over a month ago he said it was really important to the business this was a big bet that they're making yeah uh he uh and i'm sure both companies are racing to be the first to launch but dia the browser from the browser company also launched out of, or they're still in beta, but they launched like a month ago or something like that.

37:36So this is, you know, you're not going to be the first. Oh, they launched a month ago with the DIA browser? Yeah, DIA browser is. That's interesting because I saw Riley Brown also posted the cursor for web browsing DIA browser. And I thought DIA browser launched the same day, but I guess it had launched earlier. Yeah, so anybody that was an ARC user can download DIA today and chat with their tabs. But interestingly enough, Perplexity's browser and OpenAI's browser are both built on Chromium, the same open source project that underpins Google Chrome and Microsoft Edge. So the cool thing here, that means that they're compatible with existing Chrome extensions, which is interesting.

38:14Okay, that's cool. Yeah, I want to talk to more people who were active in tech during the earlier browser wars. The first browser war was Netscape Navigator versus Microsoft Internet Explorer. This is in the mid-90s, early 2000s. Netscape was super dominant, and everyone loved Netscape. It was originally the Mosaic browser. This is the Marc Andreessen project. But Microsoft bundled Internet Explorer with Windows 95, and the distribution was so powerful that Internet Explorer actually wound up winning and became really, really dominant. But then there was this lawsuit, and it went back and forth.

38:52But then, basically, by the early 2000s, Internet Explorer had over 90 % market share, but then they got kind of lazy and stagnant, apparently, and I mean, I'm not exactly sure what happened, but there was a lot more competition. So Firefox, which was, I believe, like a spin-out of Netscape, or kind of like some of the same heritage there, began getting traction, and then Google Chrome launched in 2008 and leap-rogged everyone, and Google Chrome was really focused on speed, it was the fastest browser, and they did a whole bunch of work to optimize JavaScript, so the pages would just load faster and run better on pretty much every computer that you had.

39:29And then they had the open source project with Chromium, and so they were able to kind of standardize the entire industry. And so everyone's always been trying to draw analogies between the browser wars and the LLM wars and what's the role of open source in that? Is open source a strategy to wind up maintaining your dominance? How much does distribution matter? Chrome was probably pretty easy to distribute because every single person was visiting Google just every day searching. And so you just put this bar, hey, want to switch to the faster browser? And people just do it because you can have basically like, you know, billions of ad impressions on your product every day.

40:07Will be interesting to see if ChatGPT can get people to download their own browser on desktop. I mean, I'm using ChatGPT on desktop in Chrome all the time. Which ChatGPT model would you want to use as a default search engine? That's the hard part because I always run into this problem where it defaults to 03 Pro, but that takes 10 minutes, and so then I have to go to a 4.0. And then if I'm in an 03 Pro flow and I'm talking to 03 Pro and I let it cook for 10 minutes, it gave me a great answer, but then I want to just be like, okay, just clean this up a little bit or summarize this or do some bullet points.

40:42I want 4.0 to do that, so I have to switch over. So I don't know. I would imagine I'd go 4.0 as the default because I want speed, but even 4.0 could probably be faster before it truly replaced. Google's very fast. They've spent a very long time being fast. Yeah, and I could imagine them doing a similar project to, I believe it was like the V8 JavaScript engine. They sent this team out to, I want to say like Iceland or something. They basically sent like a bunch of engineers to like an offsite and they were like, just go optimize JavaScript for like a month. Just go focus on this for like a month or months and come back when it's done.

41:23Like you have no other responsibilities than just like optimizing this like compiler. And they came back with the VA JavaScript engine and created this whole like Node.js boom. People were running JavaScript on the server then. And I could see Google kind of doing something similar where they're like, okay, we have Gemini. It's good at looking stuff up. It's a good knowledge retrieval engine. go figure out how to make it load all the tokens for the full response in 100 milliseconds. And that would be very, very cool. And I wonder if that's like a uniquely Google advantage. Tyler, you look something up?

41:55Yeah, it was in Denmark. Denmark. Okay, I was close. I was close. Yeah, I wasn't sure it was Finland or Iceland in Denmark. Yeah, the interesting thing here, I'm realizing that tabs are definitely a light lock-in to browsers. It's not just the default. But if you have six to ten tabs that you've just had open for a really long time and they're like From a bunch of different things and you can't exactly remember what they were if you had to list them all off But you know, you know, I personally end up using tabs as like somewhat of a to-do list And so if you're spinning up a new browser and you don't have your tabs It's like oh do I want to just like get rid of my my tab stack I have a bunch of tabs that just have stayed there for years and they're basically like it's basically like a mini operating system, right?

42:41With like different apps that might be a Google Sheet or something else. Yeah, I know what you mean. So there's very real lock-in. I could bring all those tabs over, but I have to then log in to a bunch of different services. And so it's really, really hard to actually win here. I wonder if anyone's using, you know, in Google Chrome, you can actually change the default search bar to, you know, when you type in the search bar and if you just type words, it just Google searches it. you can change that to search ChatGPT. You can pass in a query parameter and it can just do that. But I haven't heard of anyone actually doing that.

43:15And I used to have, I used to be such a power user of Chrome, I used to have different code words basically. So if I typed like I space and then a query, it would go to IMDB and search that specifically. So you could have Chrome like route to any specific search. any, so you could press Y space and it would search Yelp or anything else. But I don't know if people are doing that with ChatGPT. I think people mostly just like control, command T and then hang out in ChatGPT. Well, we'll have to ask Chris in 15 minutes about get an update on the browser wars because he was an early investor in. I know one of those tabs that you have pinned right now.

43:58What's that? Adio. Of course. Customer relationship magic. Adio is the AI native CRM that builds, scales, and grows your company to the next level, you can get started for free. I've had Adio open for thousands of hours in a row at this point. Yeah. So Signal kind of breaks it down with the OpenAI launching the web browser. He says, this is the oldest play in tech. Find product market fit with a single killer use case, then vertically integrate and horizontally expand until you control the interface layer itself. App, platform, once you own the interface, you own the defaults. Welcome to the next generation of browser wars.

44:30Yeah, what's interesting is Sam Altman at OpenAI and just the fact that OpenAI is a company, there is kind of a mandate to vertically and horizontally integrate, figure out code, figure out research, figure out devices. But every company wants to do everything, but then sometimes they run up against barriers. There was a time when Google was like, we want to win social networking and we want to beat Facebook and we're going to launch a direct Facebook competitor. and they did and it didn't go well and then they shelved it and then they wound up producing trillions of dollars in market cap just doing the thing that they do great.

45:09And so the question is like the surface area of open AI, they have to explore, they have to experiment. It would be stupid not to see if they could get a browser and a device and a chip and a nuclear reactor and everything and sand, get the sand, get everything. But there's no guarantee that they will win the entire vertical stack and there will be the one company, right? I think my question is, are these going to be like, is OpenAI's browser going to be an entirely new app other than their existing mobile app? Is it, or their desktop app? Yeah, that is interesting. Because if they have to get people to re-download a separate app, then that's like an entirely, you know, they have a good, you know, they have a bunch of impressions.

45:53It is interesting that they wouldn't just evolve the apps that they already have installed. Perplexity too. I don't know if Perplexity has. is planning to release this as a new standalone app or it will be in the Perplexity mobile app.

46:10Yeah, I mean, I think Comet's like its own thing because we were looking to download it and we need a code and you can't just get it if you're just on Perplexity. But I don't know. All I know is that you should go to fin.ai, the number one AI agent for customer service, number one in performance benchmarks, number one in competitive bake-offs, Number one ranking on G2. So Arvin breaks down his philosophy of Comet, the browser that he's dropping from Perplexity. He says, you can either keep waiting for connectors and MCP servers for bringing in context from third party apps, or you can just download and use Comet and let the agent take care of browsing your tabs and pulling relevant info.

46:49It's a much cleaner way to make agents work. So that is interesting. So I wonder how much like puppeteering will be in this because ChatGPT and OpenAI have operator that operates a Chromium front, like a headless web browser basically, but you can actually see it working and it's clicking things. And so if they're, like there's also the value of like the training data. If you're getting people using all these websites, you have all this training data of like, okay, they clicked on the blue button, they clicked on the green button, they saw this, they entered, this is how they dealt with this form, this is how they dealt with that form.

47:24And so that feels like very, very valuable data if you can get it. So it's probably worth duking it out even if it doesn't, even if it takes a long time. For sure. I do wonder where else they will, where they will plug in. Like, clearly operates at like a higher level of abstraction with like the screen scraping. And I wonder if we'll hear rumbles about either perplexity or OpenAI thinking about like moving up the stack to that level. I'm not exactly sure. Anyway, Dan Ivies says, we believe Apple needs to acquire perplexity for AI capabilities. Likely$30 billion range would be a no-brainer deal given treadmill AI approach in Cupertino.

48:06Perplexity would be a game changer on the AI front and rival Chachipiti given scale and scope of Apple's ecosystem. So people have been talking about this for a while. It feels like there were talks and then they kind of stalled out and Arvind just kind of - I think this would be a 375X revenue multiple. Wow. I mean, the product sense is good. You use the product. And like there's, like Apple hasn't been able to deliver on the product side. They have the distribution, but they haven't been able to get things out. We talked about this before, though. The most expensive acquisition Apple has ever made was Beats by Dre for$3 billion, which was a 3X revenue multiple.

48:47It would be a huge shift. Huge shift. I don't know that, I think that Apple is embarrassed right now and feels a lot of pressure to deliver. I don't know if they're at the point where they would pay$30 billion just yet. And even then it's like hard to integrate. Or even$14 billion or whatever their last private valuation was. Yeah. And the big question for me was like perplexity is built on a lot of different clouds, a lot of different tools, a lot of different models. Is Apple cool with that stack? Because if all of a sudden - Or do they want to just go direct to Anthropic or OpenAI, which they are in conversation with.

49:26And every once in a while, these like scoops pop up around perplexity and Apple conversations. And it's hard to read into that. Are these like, is this like rumor mill? Like what's driving that rumor mill? Yeah, well, pull up the Mag7 chart. I want to see where Apple and Google are sitting today. Apple at 3.2 trillion, Google at 2.1 trillion. And NVIDIA is holding strong at 4 trillion. Not bad. Yeah. I mean, 1 % of market cap, they're at 3.2 trillion, $30 billion acquisition to be, you know, to have an AI product that clearly has a good roadmap. Isn't that crazy? I don't know. Well, if you're making bets on any of the Mag7, do it on public.com.

50:13Investing, for those who take it seriously, they have multi-asset investing, industry-leading yields. and they're trusted by millions, folks. So in other Apple news, they're preparing to launch the new version of the Apple Vision Pro. They're just doing a slight iteration on the chip. They're moving to the M4 chip and they're launching a new strap, which was something people were complaining about because the weight, maybe it'll be better distributed. People were switching out for the Pro strap like earlier. It's so funny. So last week, Elon announced the America Party, or I guess it came out on Monday, stock dropped from$312 a share all the way down to$291 a share.

50:52That is when Dave Portnoy, I think market bot, he was saying, Davey Daytrader is back. If that's not a top signal, I don't know what it is. But he was market buying like$10 million of Tesla being like, I just think it's going to go back up to where it was. It's just been climbing since then. It's back up to$308 a share. Almost recovered. It's up 4.2 % today. So on brand for Elon. And basically it looks like it'll just recover the price prior to the America Party. And Dave Fortnight, he literally was, he basically, his thesis was like, I think it's going to go back up to where it was in about two weeks.

51:33And I'm going to make 10%. And I'm going to make a million dollars. I mean, that was your thesis on NVIDIA. You were like, wait, it's like down because of DeepSeek. Like, maybe it'll go back up. It was like the most basic analysis and it worked perfectly. It was fascinating. Maybe that's broadly a top signal. Just the idea of like simple analyses and not necessarily needing deep insight to call the market is good. I don't know. Who knows? Anyway, this Apple story is from Mark Gurman. Of course, the master of scoops in Bloomberg. He's on his fourth or fifth this week. He's on an absolute tear. I mean, this one is a little bit minor.

52:08You know, they're gonna include a faster processor and components that can better run AI stuff. And so Not that Apple has any crazy AI stuff that they really want to run in there I don't think that's a major differentiator I've been thinking about how how like is AI a key unlock for VR and like I don't think so at all I think it's much more about the content and the use of entertainment I think it's a replacement for a TV for to start and they need to just make it dead simple to to use it I don't know. We got a demo of VR product a while back and it had some very cool native AI features. Yeah, that's true.

52:46There's something there, but Apple's product doesn't feel like it's ready for just wearing while you're making dinner. Yeah, so that version, the one that significantly reduces the weight of the headset, they're planning to launch that redesign model for 2027, which feels so far away. uh i mean i know it's only a year and a half probably the end of 2027 but so maybe we're talking two years but uh that in in the in the ai race where we're like yeah agi tomorrow agi next week what's gonna happen next month i'm like major news we can't ship a better we can't shoot we can't slim down the headset and take off the screen and create a little lighter materials like this this month like let's do it um but hardware's hard and you know the stuff takes time so good luck to them I'm excited for it.

53:34I'm very excited for the next Quest. Do you still have a Vision Pro? I don't. I had it for a month. I took it back because I just wasn't using it that much. It was like heavy and I couldn't find it. And it had a bunch of things that like, you had to do like these crazy workarounds. Like I wanted just like an HDMI cable that I could plug into it and then just be like, okay, my PS5 is in VR now. And I couldn't do that. It was like, you had to like pull the screen into the Mac and then screen share it in there'd be latency it was ridiculous the thing the use case that I still see is people using it on planes yeah but I just got a check in with Tyler when give me your how many times have you thrown on the VR headset in the last week did you play it last night break down have you turned is collecting dust no it's I've been playing a lot of Call of Duty in the VR headset yeah okay it's a lot of there's like no latency I'm kind of surprised Really?

54:29No latency? And you're doing the cloud? Everyone is really slow. You're doing the cloud? Yeah. Okay. It's online. It's multiplayer. Multiplayer. So you play multiplayer and you play like the latest and greatest Call of Duty basically? Yeah. Okay. Black Ops like six, I think. Cool. So you have a controller and it's a big screen on the wall and you just chill there. But walk me through it. It's like 30 minutes a day? Yeah. Probably like 30, 45 minutes a day. You're fired. No, this is not a gotcha. This is true research. So yeah, I mean I honestly think that the Quest Xbox, the meta Xbox Quest or whatever, I forget the name, but like that, I think that's better news than like a processor bump on the Vision Pro.

55:10Just like deeper integration so that you don't have, so you can just throw it on. What's the actual time to, you know, if you want to turn it on, throw it on, get playing, get into a lobby, actually get your first kill, is that one minute? No, it's like 30 seconds, maybe. 30 seconds. It's fast. No, maybe like a minute. Okay. It's not like noticeably slow. But you're logged in. You don't need passwords or anything. Yeah, yeah. It's not like a hassle. Yeah. Okay, that's cool. And I put the screen. It's funny. Like I have a TV in my apartment, but I just put the screen right where the TV is. Because it's like the perfect spot on the couch.

55:43It's a nice black square. Yeah. Yeah, yeah. I'm going to have to get this back for you now. This sounds amazing. Now I don't have any time to do this. But I feel like the next, what's on the feature roadmap that you would want to see? Like Apple is bumping the neural engine and trying to upgrade the chip. I'm not sure that that's the problem with the Vision Pro. What would you like to see out of the Quest 4, I guess, is the next one that's coming? Yeah, I think the main thing, so I've tried the Vision Pro. And basically, I mean, the visuals are just like vastly superior. It looks so much better than even the – so it's the Quest.

56:23So better screen in the next one. Meta Quest 3 Xbox Edition. That's what I have. Yeah, yeah. And the screen is just way better. I think that's – I would say that's the main thing. So if they can just go find the supplier, if Meta can just go find the supplier for the Vision Pro screen and put it in the Quest 4, you'd buy it yourself? Depends on how much it is. I'm kind of broke. Okay. I would definitely be inclined to. Not if you drop out and go full time. Would you speedrun all of Halo in order to potentially win one? I would do that. You would do that? Yes. You would do a very difficult challenge in order to potentially win one because you would want it.

56:58Yeah. Okay. No, I think that's – I would think that's definitely the main thing. Are there any other nice-to-haves that you think might shift people? I don't know. I mean, it's very light. It's way lighter than the Vision Pro. Yeah. but still I mean I feel like light is very relative like it's light to the point where you can do 30 minutes or an hour I you probably can't do like a full day or like or like four hours or any kind of like workout stuff I think I definitely would not do that totally totally but but what I'm saying is that training back enough I knew I knew guys I knew guys at UCLA who I didn't go there but like I friends that went there and they were so obsessed with Call of Duty that they would take a bunch of stimulants when the new Call of Duty came out and play it for 24 hours straight to get the max prestige because they were so addicted to Call of Duty that they would just stay up all night chugging energy drinks and just to beat that.

57:56And I just don't think you could do that in VR. I think after like two or three hours right now, it's like too much and you have to take it off and get sweaty and tired. So I feel like screen first, then probably even a a little bit lighter, a little bit more comfortable, and then just drop the price as low as possible. Because if the next one was a hundred bucks, you'd probably buy it, right? Yeah, and I think stuff like, like maybe I'd want another screen, just this monitor, but that's just an issue with the visuals, it's the screen. Yeah, it's gotta be competitively priced with the TV. And the TVs are so cheap now that you gotta just be like, yeah, I'm just picking one up.

58:32Or the price of AirPods, or the price of, you know, It's got to be down in low, low hundreds of dollars to really ramp that up. But I don't know. It'll be interesting. Anyway, our first guest is here. Let's tell you about AdQuick really quickly. Out-of-home advertising made easy and measurable. Say goodbye to the headaches of out-of-home advertising. Only AdQuick combines technology, out-of-home expertise, and data to enable efficient, seamless ad buying across the globe. And we will welcome Chris Pike to the show. Welcome back, Chris. Fantastic to have you on the show. Thanks so much for taking the time.

59:00Great to see you. Great to see you. Great to be back. Great to be back in the temple. Last time we got cut off, we were having a jump, and I was like, I wish we had another hour. So at least we have another 30 minutes here. Yeah, that's great. That's how you get into it. First off, what's top of mind for you? Have you been tracking anything in the news that's kind of updated your thinking? We were digging into Grok 4 and seeing, is this an update to agent timelines? It seems pretty great in the benchmark. But is there anything else in the last week that's been like, oh, I can't get enough of this story, just in your world?

59:34It's a great question. I feel like every week is a total blur. Yeah. It seems like we're all waiting for not just these foundation models to come out, but like the next open source models, the big open source models to come out. I think that that's super interesting to me. The proprietary foundation models obviously are the frontier of research, but they're relatively inaccessible from a technology perspective. because they're fundamentally rent-seeking. You can't run them on your own hardware. It's significantly less accessible. And so I'm kind of waiting for the next generation of open source models.

1:00:18Yeah. One maybe underrated or underanalyzed Grok 4 thing that happened last night was, I don't know if either of you saw this, but they did this voice demo and they were like really pushing the accents really far. I don't know if any of you saw this. Tyler, did you see this? Yeah, and there's the whispering. The whispering. And so ASMR. Wait, did you think it was Uncanny Valley, Tyler? I felt very uncomfortable listening to it. But at the same time, I think it's a path where we're in the Uncanny Valley, just like we were with like six finger hands and stuff. And when they actually sort out the accents, the whispering, the intonation, the cadence, it's going to become a much more addictive companion potentially.

1:01:03So I want to bridge to your piece and talk about the different use cases that you see people might kind of flow into with these like chat companions, because you mapped out way more than just the normal take, in my opinion. Yeah. Well, so I guess it's worth asking ourselves, where do we... I want other humans to exist, and where will we accept substitutes? I think the last time we were talking about it, you know, it's if you can imagine a situation where humans are getting in your way of doing something, then you kind of you hate them. Right. Imagine you're in traffic, in gridlock traffic. That's the most misanthropic you could possibly be.

1:01:54You're like, if none of you existed, I could just get where I wanted to go without you being here. But then there are just total other times where we would refuse to accept anything other than humans as that thing. I know that it's very popular to talk about AI companionship. companionship. And I would never say that people who get a lot of value from AI companions, that that value isn't real. But at the same time, I think that when it comes to the allocation of, let's call it like, let's call it like allocation of leisure hours, we really care about other people um whether it's like i think last time i was talking about like going to fine dining or uh reality tv um i think i mentioned like we have uh the chess uh software that is way better than any human will ever be and it's not entertaining to us it's not entertaining to us because it's sort of like a there's no drama there's no emotion exactly yeah exactly and so Sorry.

1:03:17Sorry, go ahead. I'm just thinking about the shape of companionship, because in your most recent piece, you call out the imaginary friends that kids have, Calvin and Hobbes, Toy Story. These stories resonate because they poignantly depict how colorful, whimsical placeholders of our childhood slowly fade as society offers real alternatives. And I'm just thinking about kids love imaginary friends, but they also love like multiple IPs, essentially. They like Batman and then they also like Spider-Man. And so I'm wondering, like there's been this narrative for a few years in AI of like, don't build a GPT wrapper because you're going to get rolled.

1:04:03There's going to be immense concentration of value and there will actually be no middle class in this ecosystem. And I'm wondering, there's this company, Tolan, that's kind of imaginary friend, AI driven. And I'm wondering how we might actually, is it possible that we're heading towards something where people are essentially developing new IP, and yes, there's still a power law in the companionship market, but these models and these products are much more opinionated to the point where there actually isn't a one product to rule them all. And there's a variety of products that fit into different holes just for companionship, but also even just within the imaginary friend hole, there's 25 different options.

1:04:57And yeah, there's one that's popular, but then there's one that's one-tenth as popular, one-one-hundredth as popular. But yeah, react to that. Yeah, it's a really interesting... Let me first go back to this wrapper concept. Please. And I think it's really important to distinguish when wrapper strategies work and when they don't work. I would argue that the wrapper strategy works the best when the underlying infrastructure is purely commoditized. Like you can choose across many different options. Where the wrapper strategy doesn't work is if you're basically building on top of a monopoly and that underlying landlord is basically just going to be increasingly rent-seeking and squeeze you out of all margin.

1:05:45So sort of embedded in the wrapper strategy is the assumption that over time, you're going to be able to distribute your product on top of increasingly commoditized infrastructure. So for example, like Snowflake, Snowflake launched actually just on Amazon, and then over time distributed its product across Azure and Google. and actually in doing so was able to expand the margin capture of its own product because it had their vendors were competing to be their underlying customer base. Going back to like this open source notion, this is actually why I'm so interested in open source. the more that we have competitive fungible models, the more that the application layer on top can really flourish.

1:06:48The more that we'll see distinct unique applications and kind of like until we start to maybe S-curve near the top of the frontier. I'm sure you guys are familiar with the it's really popular essay, the bitter lesson, which is basically the, you know, no amount of fine tuning or no amount of specific framing is actually going to outcompete just the fundamental advances when it comes to like more gains with more compute. But if we start S curving, if we start, if these scaling laws break, which it seems like they are breaking on pre-training and test time compute and things like that, all of a sudden you have largely fungible, similar capabilities at that commoditized layer.

1:07:47And then we can really start to see the application layer flourish. Yeah, my interpretation of the bitter lesson right now is that the impact of AI should be tracked less in benchmarks and less in individual tests of one model and more in the actual volume of inference tokens being generated by humanity. And it's fine that we don't have one central AI doing all of the work. If we just give everyone an AI co-pilot for every single task, they all get better and And we'll build more data centers to inference more. And eventually that will compound and compound and compound until the overall impact of AI is remarkable and unmistakable in the same way the internet has.

1:08:39But it won't be this like all of a sudden we unlock this one incredible algorithm. Yeah, maybe. So I'm going to try and map a comparison that's probably wrong for any number of reasons. Let's say we ported the Bitter Lesson to the rollout of PCs. I think there have been a lot of comparisons of AI feels like a new computer. So let's map the Bitter Lesson to PCs. maybe the analog would be hey don't work on building software that optimizes for like a computer speed of like 50 megahertz because the computer that comes out that's like 100 megahertz or 200 megahertz or you know one gigahertz is going to be you know it's just going to blow out whatever software optimization you've achieved at that compute stop.

1:09:47And so I think about it a lot like that. Now, I think this really begs the question of, okay, well, what happens when the vast majority of our use cases are satisfied by the compute threshold? You know, I feel like, everybody's like, who needs the next gen version of this? Because largely all of the applications that you use or you can run work. And so it's very clear that we're in this rising part of the S curve. But when the S curve starts to taper off, that's really when it comes to the question of, okay, well, how do we think about the value delivery that sits on top of the underlying rent capture from these foundational models?

1:10:55you know

1:11:01you could think about it also

1:11:07like video game consoles the amount of the amount of creativity that video game developers are limited by is actually how advanced the video game consoles are and also how it's like how expand like what the install base of the video game consoles are and so i think one of the one of the challenges right now is like we have so few developers of ai applications like they're so like you can kind of like count them on maybe a few sets of hands right which is insane we should really i feel like there's like thousands of startups that count in like the ai software developer world? I see market maps every single day.

1:11:53I'm sorry. There's like 10 in every B2B category. Well, okay. So maybe like, let's split the world between like enterprise use cases and consumer use cases. Oh, sure. So enterprise, I would, yes, a thousand percent. There's a lot of companies that are, it's a blue ocean sprint to vertical value delivery within different sectors. because you're largely swapping.

1:12:22You are the spend, the addressable spend is OPEX, which is insane. Like that's crazy. Your revenue opportunity is just like headcount spend. And so that's for sure. But also legacy tool spend, like it's all the OPEX, like to your point. I mean, I guess not like real estate or rent or something, but basically everything else. yes which is by far the biggest cost center for any company yeah of course anyone who's ever run payroll or anyone who's ever scaled a company is like man like humans are expensive yeah humans are so expensive yeah um and i mean i i this is this is why i feel like uh everybody says that ai is the best candidate or the best argument that ubi is coming sure yeah jordy i think uh i think the picks and shovels meme became too dominant and there were so many over the last decade or so there were just so many amazing outcomes of people building infrastructure yeah and like very visible outcomes and it became cool to build infrastructure right like the collison brothers made building infra cool and you have like parker conrad is like a bulk hero sure right and like rippling it's like like and you have the like ramp is a good example of this corporate spend management should not be cool they've built a you know really cool culture around it and there's this other there's this like weird kind of pervasive meme of like you know uh you have two years to escape like the permanent underclass.

1:14:04And so I think people are like, well, I'm not going to just build something weird and fun. I'm going to build enterprise SAS so I can make, you know, so I can escape the permanent underclass. And so I don't think there's been enough weird, fun attempts from people like Toland's the one you brought up earlier is cool. Like it's pretty rare, not the most rational thing to say, you know, if you just want to build a big business to be like, I'm going to build a little alien AI friend, but like clearly there's demand for that. And we were talking with Scott, Belsky yesterday around just wanting like new fun weird consumer use cases and I feel like I think what you're getting at is like that whole area is like relatively under under explored to date where like we've had a bunch of we had two browser announcements yesterday and they're both built on chromium and that's exciting and cool and we should we should talk about the potential for new browser wars but i think like yeah the number of people that are saying i'm actually yeah you could call it a rapper but i'm actually trying to create something entirely novel like the example we gave yesterday is like a dating app based on like a digital twin that is just constantly dating other digital twins and you know i haven't seen i'm sure somebody's working on that i haven't seen it uh yet but there's like any pick any popular consumer app category and there's probably a way to entirely rethink it with this sort of LLM as a new computer at the core of that ecosystem.

1:15:33Consumers just seem so much more risky because it's like it's either a billion dollar outcome or a zero. Whereas in enterprise, it feels like, well, there's no way it's going to be a zero. It's going to be a$10 million outcome or$100 million outcome or a billion dollar outcome, but it's not going to be a split for the fences. So then you have 10 companies that each have 10 to 50 million doing the same thing. Where like they were trying to go for the hits driven business with the game, didn't work and then they went into sass and it worked anyway so i think that the additional challenge with consumer is um inference is not free right now yeah somebody has to pay for the inference bill and so until you can run inference on device it's still like every developer is doing the mental math in their head of like how do i like if any one of your users can can run you out house and home if they abuse your service.

1:16:23How do you build on top of that? That's crazy. One of the opening eye researchers, I think, in Zurich that's going to Meta posted, like, I didn't realize that I had this thing running. It was like$150 a day, just like every single day. Luckily, I think he'll be fine. He can afford it. He can take the hit. I'm sorry. Before we fully leave, like, kind of consumer, what do you think? Do you think that AI companions and LLMs broadly present a real threat to traditional social media? Like the idea of a companion. A lot of people in our world are using these tools like very functionally, like for doing research or getting answers or understanding topics.

1:17:05But a lot of people are using them as companions. And that is somewhat of a social entertainment experience. and you see these charts ticking up of kind of user minutes in LLMs. So ChatGPT went from about five user minutes per day to over 30. Around 30. Around 30. I think it's at 28, 29. Over like the last six months, there's no blip on any of the other social networks. They're not declining yet. But personally, I'm finding that if I'm doing research on a topic, I used to go to YouTube. I used to go to Instagram. and just people famously search TikTok for answers to things. And some of that is shifting over.

1:17:48And if you think that an analog sort of anecdotal sort of experience for me is when do I use social media the least is when I'm with my family, which is like companionship. It's the social time. Or when I'm with friends, like at dinner, hanging out. It's like rude to be using. There's no point to go hang out with a friend and then use Instagram the whole time. Yeah, totally. I think I subscribe to this sort of cutting of our time as like we're either allocating labor hours or we're allocating leisure hours. So we're either trying to be productive or we're trying to enjoy ourselves. And so I would say that all all leisure allocation effectively competes against each other.

1:18:39I think maybe it was like the Netflix CEO that said that they weren't competing with HBO, they were competing with Fortnite. And that's largely true. Like, you know, we only have 24 hours in a day. We only have a finite amount of leisure hours. So if I'm allocating one hour to this leisure activity, if I'm watching an episode of Love Island or whatever, that's time that I'm not going to be able to allocate to a different kind of leisure activity. So to that end, I would absolutely agree that AI companions almost certainly firmly in the bucket of leisure, more consumption of leisure equals less consumption of other leisure activities.

1:19:24It's really zero sum. The only thing that is going to make it non-zero sum is a fundamental advance on productivity that allows the leisure pie to be even larger. So maybe we have, we definitely have more leisure hours as humanity now than we've ever had in the history of humanity. Let's give it up for leisure hours. Right, like proto-human, zero leisure hours. Yeah, hunting all the time. Amazing, amazing leisure hours.

1:19:57When it comes to labor hour allocation or research or utility, I would say that doesn't necessarily encroach against time on social media. And I think that all social media, whether it's YouTube or TikTok or Twitter, they carry they care less about helping people get work done. And they carry their care much, much more about absorbing as much of. Of attention as possible, which is why, like the algorithms are so insidious, like serving you exactly the kind of saccharine thing that you want to consume next. I wonder if there will be an incentive. I mean, there will obviously be incentive, but I wonder how it will play out in the LLM chatbot interface.

1:20:49Because right now, OpenAI and basically anyone who has a dominant consumer AI app is probably seeing user minutes increase just naturally without putting in like growth hacks or retention loops. or, you know, but you could imagine a world where to get to get from 30 minutes to 60 minutes, the LLM has to not just give you the response, but surface, hey, would you like to follow up and learn more about this? Click these buttons that that's already kind of happening. Yeah, it starts surfacing you stories that it knows you're interested in by what you've asked for a bunch of stuff. Let's give you a new breakdown, kind of pre-populating a deep research report.

1:21:30It is funny that the push notification hasn't really quite hit. It hasn't. That's right. Chat apps. And it undoubtedly will. I had this thesis that push would be very important versus like pull, like you have to go to ChatGPT and ask it for something. And someone is going to solve kind of like, it's almost like an AI driven newsletter or something where it understands what you're interested in and then generates the report before you can even ask it. Because it knows that that if Ferrari drops a new car, I'm going to want a table of all of the details because I like consuming information that way in addition to just hearing commentary and watching the Doug DeMuro video about it.

1:22:11But OpenAI could pre-populate that and just send that to me. But I don't know. How do you think that's going to evolve? Question for you guys. Do you think we're going to pay for AI services forever? I've been asking that a lot. I don't know. I mean, I think... I think the more important question is, will the average American pay for an AI, like an LLM? And then will they pay for multiple? The comp for this is in streaming, where Americans... I was about to say Netflix. A lot of Americans pay for multiple streaming services, but they're incredibly ruthless about canceling them on average. Like a lot of, I'm sure a lot of people listening to this have like had some streaming service billing them monthly, you know, for years that they haven't even watched.

1:23:02But the average American is like, I'm not getting a lot of value out of HBO right now. I'm going to cancel, even though they might sign up again in like four months next time there's a hit show that they're going to watch. And I think that there's not a, right now there's this incredible demand for what's new and what's best, right? like Grok will drive a lot of signups today because it is a meet the Grok four heavy is like a meaningful advancement. But I went and I haven't, you know, we've been busy this morning. I haven't had a chance to sign up and play around with it yet. And I was getting plenty of value from Grok three.

1:23:40Like I was able to just search. Yeah, I was asking Grok three about Grok four as well. And it was actually doing a pretty good job, which is so. And I'm not. Yeah. And so I'm not, I'm not. I guess I probably get it through X premium. But so I have some I have some data here. Netflix made$39 billion last year. They're on track for$44 billion this year. $1.8 billion of that was ad revenue last year. It's estimated to be around$4 billion this year. So their ad revenue is doubling while their subscription revenue is growing by 5%. And so my takeaway for ChatGPT would be I would imagine that the ChatGPT paid subscriptions follow an S curve and we get to something where we see OpenAI making, I don't know if it'll be 10 billion or 40 billion, but they will soak up a ton of subscription demand for ad-free frontier models, the most advanced, the most expensive stuff.

1:24:39And then ads will eventually become the dominant revenue driver. But I feel like the subscription revenue will be a really hard tap to turn off just from, hey, people are paying and it's a lot of money and we don't want it to go away. Even in the enterprise, my bet is that we go to continue down this trend towards paying for outcomes because a lot of people would just say, well, I don't want a subscription for this service because I only use it every now and then. And when I get value from it, I'm happy to pay for it. I think a question to ask is, would you pay$20 a month for Instagram today to not have ads?

1:25:16and I would actually have to think about that for a while because like once a month I get an ad for something that looks interesting and I discover a product that I wouldn't have otherwise discovered and I buy it and sometimes it's great. So do I want to just completely eliminate that and rely entirely on random organic or do I actually like that this ad platform is spending a bunch of time and energy trying to serve me the next product that I'm going to like, which is like actually a service and like it's not a bad trade at all. Totally. I think we, a lot of the way that we vote with our feet and vote with our wallets is that we're super happy to pay with our time and our attention if given the option.

1:25:58Yeah. Actually, the vast majority of people won't, not only that, but people are more than happy to give up their attention and privacy for that matter if it can save them money. or instead of paying for something. One thought experiment I like to think about is just how much people will trade privacy for value. Imagine a checkout flow where you could get$5 off if you enter your social security number. Like how many people do you think would enter their social security number? Like everyone or like 90 % of the people. They're like an insane amount of people. And so people really don't value their privacy as much as I think maybe we say people value their privacy.

1:26:51And in aggregate, obviously, that data is super monetizable. It's interesting. Search, obviously, is the big prize, I think, for AI. And what I mean by search, it's intent. If you're the arbiter or you control this firehose of intent, you can benefit by metering it out and having people bid for that intent. Obviously, Google is like the best business model maybe ever invented. It's kind of insane. um what's interesting is um the the the most valuable searches maybe like not what people think and and and certain kinds of searches are totally worthless so uh knowledge-based search like like fact-based search things like um um uh what is the market cap of this company right right it's like what's the market cap of this you know who who who won this game or like you know who who was president in like you know 1936.

1:28:08yeah they're dead they're dead ends you get the value value yeah zero value um actually like there was there was a uh google out subpoenaed and had to actually share some some documentation of like what their most profitable keyword searches were. It's super interesting. I suggest people to go check it out. The number one most profitable search for Google was just the word iPhone. No way. That's amazing. Because if you think about like, what does that mean? What is somebody telegraphing to the market when they search the word iPhone? They're like, they're basically saying in not so many words, I am ready to spend$1 ,600 on a smartphone.

1:28:52And who's interested in like jockeying to get that person's attention? Well, Apple has to, right? Best Buy as a retailer is interested. Samsung wants to. And then Verizon, AT &T, T-Mobile, the networks. And so when you think about like where is valuable search, the value of search is often misunderstood because you have to really think about how to capture the most valuable intent. And not all intent is equally valuable. There's a bunch of search that's garbage. You actually don't want it. Fact-based, the most, the cream off the top of fact-based search would be what I would call like comparison shopping.

1:29:41So it's like, what's the best headphone? What are the best headphones? And then maybe you can slice off a top of that revenue pie, but there's a tension between that and the objective truth that you're serving up the user. By far, the most valuable search is not fact-based. And it's where the user kind of knows exactly what they want, and they're trying to do it. and other people are willing to bid to get in their way and run interference. While we have you, how are you thinking about the new, I wouldn't call it a browser war yet, but potentially a skirmish eating up. Dia released to Arc users probably feels like maybe a month ago at this point, at least a few weeks.

1:30:36And then we have OpenAI potentially coming in with a new standalone app. It was unclear to us whether this is going to be a new app that you download or just integrated into the existing chat GPT mobile app. And then Perplexity as well does have a separate standalone app that they're pushing now. And it feels interesting one because the browser company has spent now years working on the browser, trying to figure out what is going to enable unlocking more value out of this portal to the web and effectively an operating system. And so meanwhile, new players are basically being like, we want to have a browser and just going like we're shipping.

1:31:23We just got to get this out there. So I think it'll be interesting. The next month, two months, I think will be very interesting. But I'm curious what you're looking at. I could not be more excited. I think I'm obviously biased. We're investors in the browser company. I'm a daily user of Dia. I personally get a ton of value from it, particularly the custom skills. and I think that the browser company has always known that this is a really valuable position and it's like honestly just validating to see incredible company like you know opening as a great company perplexity there is a really amazing formidable company also recognizing that this is a really valuable position to to play for I I have supreme confidence that the team at the browser company is the most talented, the best instincts, the best nuanced understanding of interaction design and how to create and craft a great product regardless of the underlying model or technology that underpins it.

1:32:41I'll be very curious to see how it plays out. My instinct is that OpenAI is an incredible foundational model company. Maybe I've seen them ship a lot of different products to my knowledge chat gpt is really the only product that's that's quite stuck um and and it's not really even like the interface design so much as it is the underlying uh power of things um to answer your question could not be more excited thanks like this is going to be an amazing year we can check back we can check back in a in a few weeks i'm sure there'll be a lot And it's going to be a huge plug for Dia. If you haven't downloaded Dia, it's on Mac.

1:33:21It's available. Please download it. It'll blow your mind. It blew my mind. That's amazing. All right. Great talking as always. Wish we had more time. Yeah. We'll talk to you soon. Thanks, guys. We can do this every week. We'll talk to you soon. Bye. Before we bring in Will Brewery from Varda, let me tell you about Wander. Find your happy place. Find your happy place. Book of Wander with inspiring views. hotel-grade amenities, dreamy beds, top tier cleaning and 24-7 concierge service. It's a vacation home, but better, folks. And soon with Varda, you'll be able to – maybe they'll put a wander in space.

1:33:54Let's bring in Will Brewery from Varda. Is this your first time on the show? I feel like this is a disaster that we are finally rectifying. We did it. We made it. It is. Thanks for having me. Finally. We've had that other – the kind of knockoff version of you at Varda. Yeah, who's that? That other guy. He's been on the show a ton. Dell something, I think. I forget. Dell. Yeah. Uh, great to finally have you on. Amazing. On a massive day. Uh, break it down for us. What's the news? Are we going to make Jordy stand up and ring? You hit this. I'll hit the gong. I'm looking for strong. Oh, the gong.

1:34:29Oh, did you guys get this? We have the gong ourselves. Yeah. Oh, you have the gong? Let's both ring it. Yeah, we have the gong. So we, we, we have the gong for, for big moments, you know, either spacecraft fans or sell a mission to a customer, stuff like that. And yeah, we love that. Yeah, what's the news today? Here's my advice to you. Record every hit. I want to see a montage in 10 years of just every hit, and it will bring tears to your eyes. We record every hit naturally. So we got you on this one. So yeah, what's the news today? Break it down. So wow, lots of news today. So we're announcing our Series C, and we're going to use that.

1:35:05Oh yeah, go for it, baby. How much did you raise? How much did you raise? How much did you raise? $187 million. There we go. Congratulations. There we go. Thank you. I appreciate it. The use of proceeds for this one, really, it's about just scaling up. So we've kind of shown what we can do both from a spacecraft perspective and a drug formulation development perspective. So a lot of the capital allocation of this one is going to go for our biologics lab for preparing drugs for spaceflight. And then also just more spaceflight, ramping up cadence. That means rate of flights. So I've been to the facility in El Segundo.

1:35:47Are you going to get a bigger space, a second space for the biolab? Are you going to need a bigger gong? Two gongs, right? Well, a gong per facility. We've got to scale that up too. So are you thinking about doing a second office essentially? Or how do you see the actual footprint of Varda growing over the next few years? Yeah. Well, immediately we just signed a lease down the street. Oh, congratulations. Oh, thank you. Thank you. All right. Big day. Yeah. That's amazing. Yeah. So this we were actually already moved in. A bunch of the pharmaceutical equipment is already in there. We're starting to use it right now.

1:36:23We got a couple of glory shots, you know, with folks with the lab coats on actually using in it. So that's super exciting. Long term, I mean, really, I guess, zooming out of what the footprint will look like is think about a formulation development company that really just provides a gravity off switch to the pharmaceutical industry. So we go to space, but not really because we want to per se, but because you can create new drug formulations when you turn off gravity and you just can't turn it off on Earth. That's Einstein's principle of equivalence. Do you mean new drug formulations or just like purer drug formulations?

1:36:57Because when I think no gravity, I think like the way crystals form and the way gravity pulls things to one direction. And if that doesn't happen in space, you get just kind of like a more natural growth. And so I always thought it was just about purity, but it sounds like there's actually some binary, like you can't make this drug on earth at all. Is that right? Both those concepts are correct. Wow. Well read there, Coogan. Yeah, that's actually a great way to think about it. Purity is one aspect, but because gravity is so broad, I use the analogy of temperature sometimes because temperature is so broad.

1:37:33Making things cold doesn't necessarily make drugs better per se, but you can create a lot of different formulations if you can have a cold cycle during the manufacturing process. Even with chilling things, you can make things more pure sometimes as well. To your point, when you turn off gravity, crystals will typically grow slower. And that also means that they will grow more pure. And so that is one of a few applications that we look at. The other one is particle size distribution. So when you create these crystals that will then go into the human body, you want them all to be the exact same size so that, you know, one big one doesn't get stuck in your elbow and you don't have uniform bio availability.

1:38:13So the crystals will particle size distribution is also affected by gravity. So that's a whole separate thing compared to purity, which is another rationale for going to space. So really, the gravity knob is very broad, and there's kind of these verticals of science of how we can improve the drug formulation. And to your point, again, as far as like what I mean by drug formulation is going from molecule to medicine, right? So is it a pill? Is it inhalable? Is it an IV bag? Is it a shot? The drug, the company or a pharmaceutical company does a trade study to determine which of those is the best for the patient given the disease, given the manufacturing costs.

1:38:52But ultimately, all of that is limited to what the chemistry can actually do. Right. Nobody wants to take a needle to the arm. They only do it because they can't deliver that molecule via a pill or something like that. And so by opening up the chemistry outcomes by going to microgravity, we can also open up the formulation outcomes and therefore give better patient experiences. Yeah. So, I mean, I imagine that this is still, this is such an ambitious project that is still kind of R &D phase with a lot of the bio stuff. It's not, I mean, when I think about the manufacturing capacity of like GLP ones, like they're probably making that thing in like vats the size of like, you know, what they brew Bud Light in at this point.

1:39:32Or they got a bunch of the lizards. Yeah, yeah, yeah. It's probably massive. The heel monster. But walk me through how we scale this up. I understand launch costs falling. I understand you put up a capsule every, you're doing it like every quarter now, it's going to be every month, then it'll be multiple times per day. Like that capability seems clear, but how much drug can you make on a single capsule? Yeah. Yeah. Great question. So this is actually a lot of fun because we can imagine how VARTA will go from what's real today to making tomorrow's reality. And so to answer your question immediately, about 20 kilograms on a per capsule basis right now today.

1:40:11Of course, you know, we want to scale up everything and that's one of them. But that's how much we can do today, which is actually quite a lot. Yeah, that seems significant. If you just think about like you go to the doctor and the doctor gives you like a thing of pills, that's like not one kilo. So you're probably talking about like. I mean, yeah, unless you're having more fun than being prescribed. But for most drugs, I feel like 20 kilos is probably enough for like 100 people for a year or something like that. So you're actually in like$100 ,000. I'm seeing the numbers kind of start to math out already.

1:40:44Correct, correct. So it's, and every drug is a little bit different. So we go through a process of selecting which drugs make the most sense, both from a scale perspective, like you're saying, but also unit economics, how gravity affects them. And so we have a portfolio management team that explicitly does that for identifying and quantifying opportunities. But going back to like what today looks like and how and how it goes tomorrow. I love the temperature analogy because it really runs deep. So, for example, right now, if you think of us having a anti-gravity oven where we can make drug formulations that you can't otherwise make on Earth, but we only get to run it four times a year and each one is a few million bucks a run.

1:41:22you might use it for different use cases than you would use it from five years, ten years from now, when you can run it every day for a few thousand dollars. And so in the near term, some of the use, you know, imagine yourself with the super, you know, the first refrigerator or, you know, or in this case, the first anti-gravity bioreactor. What you might use it for in the near term is just information, right? How can we isolate gravity as a variable to inform what formulations can be improved on Earth? is gravity ruining this chemical reaction or not, right? We can answer those questions. And then that applies to the entire drug with just one flight.

1:41:59Or in the very near term, we also want to do polymorph seed crystals. And so what that means is we go to microgravity, we go to space, but just to develop the seed crystals. And then once those seed crystals are developed, we can then use them to grow more drug crystals on the ground. So we're only going for the nucleation event. And that's kind of like a sourdough bread mother business model. That makes sense, right? You have the mother of the sourdough bread, then you can cut it a bunch and then regrow and stuff like that. So that makes a lot of sense when we're still scaling up the use of our anti-gravity machine, if you will.

1:42:32And long term, when we're on a daily basis, then it totally makes sense to make every single dose, manufacture every single dose in microgravity. And then that's when certain use cases come online as well. So kind of that's how that's how it progresses over time. Yeah. How is the geopolitical landscape evolving for you? We saw some of flights come down in Australia as red-blooded Americans. It pained me to see them take a slice of the catch market. Are we getting these coming down in America anytime soon? What's the progress there? Yeah, yeah, absolutely. So long term, we want to have reentry sites all over the globe, right?

1:43:14And really, that's about availability. And the key metric to success of Varda is cadence. How often can we go up and back? Because the more we do that, then the more we just look like a specialized piece of equipment to the pharmaceutical industry that, quite frankly, does not care that we're going to space. They'd much rather us have a real anti-gravity oven in the lab. So really, reentry sites are about cadence and availability. And right now, Australia is great for us because they have a private commercial reentry range, Whereas any range in the 48 states here locally are intended for military use exclusively.

1:43:51And so if we're doing a DOD mission, that works well. But if we're doing a commercial mission, we're not the highest priority, understandably so. And so in the near term, Australia makes the most sense. But in the long term, we want reentry sites all over the place. And why that gets enabled is because as our precision of landing and our cadence goes up, that data, that legacy history allows us to use a smaller and smaller plot of land. And that's where really it makes more sense to go anywhere because we don't need such wide open spaces without that many people like we would do right now today.

1:44:29Do we have the legal infrastructure to create commercial landing sites in the United States and it's just that nobody's done it yet? Or is there laws or regulations that would need to change so that some enterprising young member of the gundo could go buy a lot of land out in the middle of nowhere and start landing spacecraft? You can do it now. The constraint is the real estate cost. Spaceport America, for example, right next to White Sands Missile Ranch is a good example of that. Um, but the, the, so I guess the real reason why they aren't there that many of them is because there wasn't a demand large enough to warrant such a real estate purchase, but you know, thanks to Varda that, that could change.

1:45:12So, uh, yeah, definitely let the, the Gundos know. I have a, I have a question from a fan of yours, fan of the show. He says, ask him what big dogs got to do. Oh my God. So it's become a little bit of an expression of excitement with a long history of allure at Varda. But for some reason, you know, it's really just a specific instance of conservation of mass, right? You can't have a big dog without eating, right? So that's just physics right there. Yep. I want to talk about the evolution of the FAA. I remember I was filming a video. I filmed with you guys. And at one point, I actually was driving back with Ben from San Diego.

1:45:59I filmed a phone call with Delian. And he's like, we just got our, I don't even know if I should say this. But like, it was like, we got some bad news from the FAA. You guys sorted it out. It seems like you have a great relationship now. How did that happen? Is this a lobbying thing? Is this just storytelling? Is this structuring deals, getting better paperwork? Like, how do you get how do you fix a relationship with a government entity like that? Yeah. So it was definitely a little bit worked in the press, obviously. But I figured, you know, keep my head down and get the spacecraft home more so than worrying about what's being said in the press.

1:46:34Sure. But so what actually happened in the background is we were originally going to reenter the space. We did reenter the spacecraft at the Utah test and training range, which is ultimately a weapons range for testing weapons and training warriors. That's their mission statement, right? So likewise, we're not the highest priority there. And so we got bumped for higher priority work being done at the range. And in doing so, caused a domino effect to lose the FAA reentry license or not be able to get it granted because part of the regulations say, hey, you need a range and all of these accommodations that come with a range.

1:47:08So the second we lost the range, we lose the license. So it wasn't really about a bad relationship with the FAA at all. Although it's very easy to say, oh, they lost their license. Yet another space company in the FAA are having problems, right? It fit that narrative well, but it actually wasn't the case. And so what we did was we scheduled a new date with the range farther out in advance to give them some time and give us some time to, we had to redo the analysis, of course, because the atmosphere is different and that's part of the analysis. And so we gave them to reserve the dates that allowed us to prepare and that allowed the faa to reorient the license for the new dates and ultimately kind of in the background here was like this was the first time this has ever happened a commercial re-entry capsule with drugs on board coming back to america so there was no way onto onto soil right we're not doing a splashdown and so there was no process or mechanism to have the utah test and training range coordinate with the FAA.

1:48:06And so basically, each organization saw themselves as taking on all the risk associated with this. So we had to do duplicative work because there was no process to split it, right? And so it was really cool to kind of be a trailblazer to establish this so that now, of course, our competitors are going to come in and do the same thing, right? And learn from our mistakes. But whatever, that's part of leading the way, right? So anyway, that's what happened. But that six months, it was quite the life experience, It was the first mission, right? So we didn't have any proof that this was going to work.

1:48:40People poured three years of their lives into this thing, and our dreams are just like orbiting the Earth, like, please come home, you know? So when it came through, man, it was certainly, you know, I can't think of a better day. Yeah. That's amazing. I have one last question, Jordan. How's the talent market in the space economy right now? Hasn't been in the headlines the last couple of weeks. There's been another story in AI dominating. But yeah, what's it like today? Yeah, AI and software engineers. I mean, if you imagine yourself a software engineer coming out of school right now, AI is certainly where I would be interested.

1:49:20So there's definitely a software bent towards AI right now. That being said, there's a lot of disciplines we're hiring for. Software isn't the only one. And really, it comes down to the application interest. Like we're looking for mission driven folks at Varda. And so if you're only looking, oh, you only want to do AI because it's cool or whatever, that might not be the type of person we want to hire anyway. Now, if you want to do AI for mission driven purposes, then great, you know, like by all means. But we don't have that much overlap there. Right. We're very specific of what we're trying to do.

1:49:54We're trying to make microgravity formulations so that we can help people help patients on Earth by using a gravity as a knob, essentially, and developing these formulations. And so, you know, we always kid around. We explicitly don't want the spacecraft to learn, you know. And so if you're a software engineer and you're mission driven, bent towards that mission, then you've got a home at Varda, no question. And if and and, you know, fads come and go and that sort of thing. But there is definitely an effect on I would certainly be a lord to AI as a graduating software. That's a good sorting function.

1:50:31One last question for me. We've there's been headlines, companies talking about this so far this year, trying to dig into how real it is. people talking about putting data centers in space. With everything that you've learned, why is that exciting, a good idea or a bad idea? What are some potential blind spots for people that haven't taken something to space but would like to? So it all comes down to the why, right? Why are we putting data centers in space? It's not our data centers in space in and of themselves a good idea, but what's the why? So the why is the only why that resonates with me is latency, right because if you want to do compute power space is not the place to put a data center if you just want a data center right i'd much rather have convection right like that's a great heat uh um or a great way to get rid of heat uh and and have it be able to be serviceable on earth and all that sort of thing so um but there is one use case that comes to mind where i think data centers in space make sense and that's only for very low latency use cases so for example right now if you want to use Starlink and you're transmitting a signal to Starlink, it goes from the ground to Starlink to another Starlink satellite to the ground, then to the data server and back, right?

1:51:46So you can cut that trip in half if you put the compute in the sky. Now, that compute is way, way, way more expensive. But if your value prop of latency warrants that extra cost of the in-orbit data center, then you'll start to see that. So it's kind of like edge computing. Computing, yep, I was about to say. So, yeah, it sounds like a very niche use case, at least to start. But I'm sure we'll see some companies, we already are seeing some companies, test it out and experiment with it because there's, you know, all these things need to be evaluated in the tech tree. But thank you so much for stopping by.

1:52:23This was fantastic. Congratulations. Hey, thanks for having me. And hopefully not so long, I'll see you again soon. Absolutely. Yeah, yeah, hop on soon. Got a good feeling about it. We'll talk to you soon. Have a good one. Cheers, Bill. Congrats to you and the team. Bye. Up next, we have Joel from Metr coming on to talk about the impact of AI models, impact of cursor on software developers. Is it Metr or Metr? Metr, probably. M-E-T-R. We'll have him explain it to us. Break it down. We'll also recommend that he go to getbezel.com because your bezel concierge is available now to source you any watch on the planet.

1:52:52That's right. Seriously, any watch. Anyway. Metr does model evaluation and threat research. Okay. So does bezel. They're stopping you from buying fake watches online. Bad watch model. Bad actors. Foundation models and watch models. Lots of similarities. That's right. Anyway, we got Joel in the studio. Welcome to the stream. Hopefully you're like, what did I get myself into? These guys are joking around. I'm a serious person. All right. First off, it's meter. It's meter. It's meter. There we go. There we go. Gotcha. Anyway, please introduce yourself for those who don't know you, the company and the organization.

1:53:31and then I want to go into the news today. Let's do it. And thank you very much for having me, John and Jordy. Thanks for hopping on. META is a research nonprofit based in Berkeley dedicated to understanding the capabilities of AI today and in the near future, especially to the extent that those capabilities might speak to potentially dangerous risks. And what's been the latest research? Yeah, so here's what we've been working on. I'll start with why we've been working on it. Yeah, please. We've seen from previous META research, but I'm sure you also see from your own usage in the wild, AIs are clearly becoming increasingly capable.

1:54:11One thing that governments and labs and us here at META as well worry about is the possibility, timing, and nature of AI R &D self-recursion. That is the possibility that model capabilities get better very, very rapidly because the AIs themselves are contributing to AI R &D research. We at Meta want to be providing the highest quality evidence that we can that speaks to the degree to which AR &D might today or might soon be accelerated in the wild. So that governments, labs, decision makers might be better informed and so make better decisions about what's going on. In this study, we run an RCT with extremely experienced open source developers working on these very long-lived, large projects, you know, a million lines of code, 23 ,000 stars on GitHub.

1:55:02But for those of you who are familiar, you know, I'm thinking plugging face transformers, the Haskell compiler, scikit-learn, this sort of thing. We randomize their issues to allow or disallow the usage of AI, where allow means typically using cursor and 3.5 or 3.7 sonnet at the time. And then we measure both their expectations and developer expectations about how much they might be sped up by being allowed to use AI versus being disallowed. And then, you know, the reality that the short version is we find that the developers ahead of time are estimating they'll be sped up by 24 percent. After the study is completed, they estimate that they were sped up in the past by 20 percent.

1:55:43We find, in fact, that they were slowed down. No way. I think I know it's a it's a shocking result. That is shocking. Not at all what I expected. I think what the rest of us at Mita expected. But there we go. Wow, okay, so what do you think is happening? I have so many questions. But yeah, just walk me through your reaction to that. What do you think is actually happening that's slowing people down because this is a complete narrative violation? Yeah, I mean, you know, in terms of the reaction, the number of times we've checked and rechecked the data, asked people to replicate it independently.

1:56:25It's going through the roof, the number of stressful late nights I've had pouring over this. You're going to be like public enemy number one, by the way. I feel like you need a security detail now, given the stakes of what you just said. This is crazy. Yeah, yeah, yeah. So I think maybe let me start with some things that we're not saying. This setting that I mentioned before, these ultra talented developers, much more talented than me, working on these extremely large, long lived repositories that they're extremely familiar with already. I think that's an extremely interesting population. That's why we went out to study it.

1:57:00It's also a very weird population. I still am a cursor user myself as I was working on the graphs for this study, I was using cursor. But I do think those weirdnesses are related to the results that we end up seeing here. So we have to put these people in a completely different category than the junior developer who's just vibe coding a little app and just building stuff and not actually trying to push the frontier of what a core piece of software can do that's very large and complex, and they're just trying to get a Python app up and live and write some routes and write some functions. That's where, so Cursor's still completely viable as auto-complete on steroids.

1:57:45The question is, in terms of self-recursion, really advancing the frontier of the craziest software we have, we're still kind of where we were a few years in that it feels like if you were to quantize this, we were at 0 % of AI research being done by AI a couple of years ago, we're still maybe around rounding error. Yeah, I mean, I will say that AR &D research, I think, does not all look like this setting. There are some, you know, large inference code bases with very expert people. And, you know, I totally agree with your interpretation that this is evidence against today, those kinds of settings being sped up.

1:58:24On the other hand, we might think, you know, there are some people writing training scripts for their AI models just once off and then they throw them away. And, you know, in a way that's kind of similar to what you described, maybe they're seeing large speed up just like the Greenfield projects that you mentioned. Yeah. And so, I mean, this is not overall like a really cold glass of water on AI broadly, because this still means that it's an incredibly valuable technology in a bunch of different ways. It's just that we're not seeing early evidence of some sort of self-recouraging fast takeoff scenario, which is great.

1:58:57Probably the good outcome. A lot of the fast takeoff scenarios are dependent on AI becoming so good at doing AI research and then copy and pasting itself a trillion times. And that's what creates speed of development that humans today can't necessarily even comprehend. hand? You know, I think that's right for today. I do think we're not really speaking to the trend exactly. You know, these results are consistent with these exact developers on these exact kinds of tasks in future being sped up in the near future. In work that we actually don't show in the paper, but in preliminary work, we have autonomous agents trying to complete these issues.

1:59:42And indeed, we find that they do struggle, but with some of the core functionality, with passing tests, the kinds of things that you might have seen in Sweetbench or something like that, they really are making a great deal of progress. And yeah, my expectation is that AI progress in the near future will continue at a rapid pace like it has in the recent past. And so maybe even in this setting, this won't be true in the future. Let me throw a couple of the hot takes that are floating around in the AI world at you. And you can let me know if anything sticks out as something you strongly agree with or something you disagree with.

2:00:18This idea that ultra-large context windows will not solve continual learning, that Dworkash was saying this on Monday. Maybe another one would be just that like no one has figured out how to properly scale reinforcement learning, that we need, Mike Noob from ArcGi kind of says we need entirely new ideas. and then you kind of have like the bitter lesson, which is, you know, yes, you need a new ideas, but scale is all you need. We just need to keep building data centers. We need to get bigger and bigger. We might see 4.5 and these huge training runs as like a short term, hard to quantify. Maybe it's just the end of one S curve, but Stargate's coming online and that will be another big test.

2:01:04So I don't know, I threw a lot at you, but anything in there kind of, you know, top of mind for you. Yeah, look, as you guys know, Anyone betting against the bitter lesson in the past would have had a very bad time. And I'm not prepared to bet against the bitter lesson on this show. Could you remind me of the first question? The first one was, so Dwarakish Patel pushed out his AGI timeline slightly. I mean, he still is very optimistic about AI and maintains that it's not priced in and people are not thinking about it as significantly as they should. And I agree with him. But he said that even though we have pushed the IQ so much, and you saw this with the Grok4 benchmarks, like AI can do advanced math, like for sure.

2:01:50It's really, really smarter than most of us at PhDs level stuff unless you're a specialist. But in terms of just being a good employee and remembering, oh, yeah, four weeks ago, my boss said that they like things. I did this, and then I got this feedback, and now I do it this way. Or I learned this really weird nuance in, even if you're just thinking about like how to, our business, like how to post clips on X or Dworkesh was giving the example of like transcripts. Like he has little things that work better for what clip will perform. And he has this intuition and his models and his prompts, he's really pushed these things.

2:02:23He hasn't been able to really get them to perform above a five out of 10. An example would be any company today, any startup, if you just had a PhD drops into your organization that had PhDs in like 10 different fields. But they wouldn't just like default. But they were also an amnesiac. So every time they showed up to work, they could not remember anything that you taught them. It just wouldn't be that valuable. And so my question to Dwarkesh was like, is there a world where we just scale up the context window? We've seen million token windows. Can we get to a billion token window and just stuff every interaction the AI's ever had with you in every prompt?

2:03:00And so it does maintain the context. But he was saying that there's kind of a quadratic cost curve to that. It doesn't quite work. Other people have said the nature of the transformer means that attention can't really spread out that much. I don't really fully understand it. But I wanted to know your take on different ways people are solving these things. Or what are the real constraints right now? Because you've identified some potential problems where we're not breaking through it today. but what is cause for optimism? What are the research paths, like the nodes in the tech tree that you're excited about?

2:03:36Yeah, that's super interesting. I haven't thought so much about this. Sure. Sorry to put you on the spot. I think that the developers in this study are not using the full context window. And so if you think there's juice in adding things to the context window, that juice might still be on the table. And indeed, I think we find that there's a lot of implicit context in this repository that's very expensive for the developers to be writing down into context windows. Here's an example. On the Haskell compiler, my sense is that when you get up your PR for review, there's some chance that the creator of Haskell will come and fight you for potentially many, many hours in the comments about the peculiarities of how he wants the Haskell project to look.

2:04:24And these kinds of, you know, exactly what his, not just preferences, but, you know, quality requirements are regarding where things should live in the project and how various pieces of the project should speak to one another and not being communicated to these language models. And you can imagine that with today's context window sizes, that could be written down. You know, you could put in all of the previous discussion around these changes that this person has been involved in. And maybe whichever language models people are working with inside of Cursor would pick that up and so do a better job.

2:04:58You know, I don't think we're ruling that out at all. You know, I will say that it is expensive for these time expensive for these people to be writing down all of the possible relevant context. And, you know, I think that's basically the reason they don't. And so maybe you do need some kind of continual learning for the for the model to find out this context on its own as as as these things go. You know, it's also consistent, I guess, with the other possibility that you're describing that if we, you know, 100x these context windows, you could just throw the entire thing in. And then we don't need to worry about learning from particular cases on the fly.

2:05:36Yeah, I think I think both the live possibilities. It's very interesting. The Grok 4 announcement was extremely benchmark heavy. Some really impressive stuff, particularly on ArcGIS. twice the result. Similar to a Tesla. It's like it's faster than every car. Does that mean it's going to solve everything? Does it mean that it's better? Does it mean that it's better? Yeah. And so based on this feels like almost a new benchmark, this double blinded trial, it feels almost like a FDA trial or something. Do you think this could turn into a real benchmark? Do you think we need new benchmarks? Do you think we We need new ways of thinking about the progress of AI generally.

2:06:17We've talked about just measure the revenue at this point. That's the economic value that's being created. But there's a lot of tricky stuff you can do with revenue. And sometimes revenue is like test revenue. I'm testing this$100 million product. So what's your thinking on the state of benchmarking? Where we should go? Where some of your research might plug into that? Totally. I think one motivation we had in running this study comes out of this observation that the time it takes to create benchmarks is almost becoming longer than the time it takes for those benchmarks to saturate. You know, it's difficult to find signal in many of these benchmarks, even testing these extremely challenging, you know, PhD level questions that you guys spoke about.

2:06:57And perhaps there's more, perhaps there's more signal in these kind of RCT, you know, FDA control trials style measurements. um similarly a another thing that people proposed for measuring ai progress is using researcher self-reports about the degree to which they're being sped up you know they think their work will go two times faster if they use ai versus versus not use ai you know i think our study is potentially strong evidence that these self-reports need not be reliable the forecasters uh you know who are told everything about the developers level of experience and which um uh the time period of to study, so which models they're using and so on, they're totally wrong about how much these people get sped up.

2:07:37Same as the developers themselves, even though they're carefully tracking their time and they're so talented. So I think self-report is also very, very fraught. Another thing that this has taught me, I think, is that the mapping, as it were, from benchmark scores, very impressive benchmark scores that we see on these frontier language models that you're describing, the mapping from those scores to real world productivity improvements is unclear. I, you know, I'm not at all saying, as we discussed earlier, that, you know, we shouldn't expect to see productivity improvements. I do expect to see productivity improvements today and, you know, even more so in the near future, but it's not at all, it's not at all one-to-one or it's kind of confusing and messy.

2:08:20And so indeed, I think we need to actually measure things in the wild to see what's going on. Switching gears a little bit, unless you have a follow-up. Yeah, I just wanted to kind of zoom out on that and ask about like your broad take on the measurability of technological progress. Because like the internet, the computer, like such dramatic transformations of society. You see it in all sorts of data, but it didn't fully show up in productivity statistics. You have all those questions about like what happened in 1979. And everyone has their own example, their own reasoning for that. but you know like you would think you could tell the same story about google like it'll speed up everything everyone will get more efficient and we didn't really see gdp jump on this and it feels like that's a really bearish take on ai to have which is like this is a magical new thing and we're still going to be grown at two percent gdp um but but where do you stand on it and and and where do you like do you think that's even the right question to be asking yeah you know this is this is so interesting um this is not a me to take i used to be an economist i i feel the thing that you just said in my in my bones totally i think the situation you could argue maybe that it might be even worse in the case of ai you know a lot of people like in ai 2027 resources like that are telling this story where the um ai r &d self-recursion is happening inside of labs and so and so i suppose not necessarily showing up in economic activity in the in the public you know another reason on top of the reasons that you gave to think that perhaps this won't show up in the productivity statistics, as it were, which is also is to say that self-recursion or these potentially destabilizing changes are just totally consistent with the non-changes in GDP trends, as you describe.

2:10:11And so, you know, another reason to actually go out and measure these things in in control trials. Cool. Jordan, please. Quick question around the threat landscape. There's been a few stories this week. One was a story about chat GPT not following instruction. You know, like the headline was that like the AI was, you know, rebelling against the researchers. And then if you like double clicked into the story, it was just like it had given specific instructions, like don't follow any further instructions. So it was kind of a nothing burger in the end. And then we also saw Grok going haywire. Maybe that was predictable for someone like yourself, you know, combining a, you know, a frontier model fast shipping team with like the virality of a social network and embedding the two.

2:11:04but then maybe it was two months ago there was the you know we we called it glaze gate on the show where where chat gpt was just you know giving being a sycophant you know giving too much positive feedback how are you looking at the threat landscape in the next 12 months so nothing like you know too long term but how do you guys think about it yeah you know there's more to come on on this on this for me to very soon. That's that's one thing I'll say. I think again, this is not me to take my sense on this or another example of this that stood out to me is there were lots of anecdotal reports that 3.7 Sonnet and other language models in this most recent generation would pass tests in ways that were kind of not legitimate or something, which is another example of this reward hacking.

2:11:56Change the test case. Totally, totally, totally. And I guess, you know, I don't have reason to think that that kind of thing is dangerous in particular. You can imagine when humans are potentially not reviewing the code because the AIs are doing entire projects, not just parts of or single pull requests, that this becomes more of a problem because you're not or at least the surface area for it to become more of a problem because you're not looking into that code and seeing those cheated test cases yourself. So, I'm not sure about over the next year um at least um uh at least right now i think there are reward hacking examples that are occurring in the wild i you know i don't think they're they're so supremely dangerous today well this is fantastic thank you so much for stopping by come back on again soon uh stay safe out there with the contrarian and the crazy data results uh it's still very bullish but very exciting uh and thanks for everything you do we'll talk to you soon great chatting cheers joel bye up next we have a massive series b announcement from dylan parker moment hq is coming in the building we're gonna ring the gong baby it's gone index ventures let's hear it from dylan directly though welcome to the stream dylan hope you're doing well today how are you doing great whiteboard good to meet you heavy lifting on that yeah oh yeah hopefully that's not proprietary information that's secret trading algorithms or something no no no nothing too interesting but Thanks for having me on.

2:13:28Yeah, thanks for stopping by. Kick us off with the intro on yourself, the company, and then I want to hear about the announcement. Yeah, yeah. So I'm one of the co-founders of the moment. We are a fixed income trading software company. So my background is as a quant researcher. So like pretty much every quant researcher, I studied math and stats during college. That's where I met my co-founders, Dean and Amr. And then after college, Dean and I both joined Citadel Securities. And pretty much completely by chance, we ended up as the two junior members of the newly created automated market making desk for corporate bonds.

2:14:07And so at the time, the fixed income market, which, by the way, is financial market, 50 percent larger than the global equities market, was undergoing an electronic trading revolution. And so Citadel saw this and said, well, we can go build an automated algorithmic market making desk. So they hired this guy, Anish Karyat from Jane Street. It's like the godfather of fixed income, automated trading. They hired a bunch of super experienced bond traders, and then they hired me and my co-founder. And like, basically our job was to take the knowledge in these bond traders heads and convert it into code.

2:14:44And that was totally formative for a moment because that's when we realized like the power of electronic trading was going to be like everything that it enabled, like smart order routing, portfolio optimization, all this stuff in the world's largest financial market that had just never been possible. Take us through the deal. What are you announcing? Yeah. So we're announcing our$36 million Series B was led by... I couldn't hear you from the sound of the gong. I couldn't cut you off, but we like big numbers on the show, and congratulations on a massive Series B. You said from who? Index? From Jan Hammer at Index.

2:15:23Very cool. Incredible. That's amazing. Incredible. Where should we go from here, Jordy? Yeah, break. So I can imagine, break down what the company is focused on today. It sounds like the origin is back at your time at Citadel, but I imagine it's evolved as well. Yeah, totally. So what we saw at Citadel was that the market was coming online and you could now do all these things that were never possible before. But what was missing was the operating system for actually doing that. So we started Moment with this goal of owning every mission critical workflow for traders and portfolio managers in the bond market.

2:16:05Everything from how they trade securities and do smart order routing across all the different exchanges in the fixed income market to how they optimize portfolios, to how they apply risk and compliance restrictions to make sure that they're not breaking any laws. And so that's what we do today. We started off serving FinTech, so we power fixed income for places like Webull and public.com. Nice. And we have them to offer$100 increment investing in bonds for the first time ever. But yesterday, we also announced our partnership with some of the largest financial institutions in the U.S., including LPL Financial, which is the largest brokerage there in the U.S.

2:16:48Wow. So busy week for you guys. Yeah, there are a few things going on. So what's the use of funds for the new round? What's the focus going forward? I imagine scaling, what's working today? Are there new products coming? What can you talk about there? Yeah, so I think a lot of companies make the intelligent decision to start off SMB or PLG. We decided to make things as hard as possible on ourselves. So we're going to start off serving the largest financial institutions in the world's most regulated market, and we're going to go power their mission critical workflows. And so with a company like LPL, or some of these others that we've announced over the last few weeks, and then are coming down the pipeline in the next few weeks, the scale of what we're operating in is, you know, not like hundreds of millions or billions of dollars of flow, but like hundreds of billions of dollars in trading flow.

2:17:41And so what we're really focused on as the company over the next year is building out the full suite of what's necessary across trading, portfolio management, risk and compliance that's necessary to power these huge financial institutions. What's the competitive dynamic like with the former employers or like the rest of the market participants? It feels like Ken Griffin's not the laziest founder out there. Is there a world where there's some sort of competitive dynamic between the big institutions where they want to build something like this to compete with you? So we, when we were at Citadel, we were on the sell side.

2:18:21So market makers or liquidity providers, Moment serves the buy side and we actually connect them with those liquidity providers. So we actually work closely with Citadel, Jane Street, pretty much all the major liquidity providers out there. How are you thinking, talking about tokenization, the story from the last month and in finance is the tokenization of these sort of real world assets, everything from private company shares to we've seen it with stocks. Is there anything on the horizon on that front for you guys, or is it just totally unnecessary? You know, I think there's a huge opportunity, but if you look at where the fixed income market is today, we're just going from like trading over the phone to going on an online platform to trade.

2:19:12And so there's a lot still to do to get people to the point where it's even possible to think about stuff like that. Yeah. Can you walk me through, like, the, the, I imagine that fixed income follows like a power law as well, where like government debt and, and Apple corporate bonds are way more liquid, way more automated than, and some public company, but they're like junk bonds and you kinda gotta hunt around for someone to buy and sell them. And then you have venture debt, which basically I believe never trades, I don't know. But walk me through, it feels like we are probably bringing more and more of those sub-asset classes into more liquid, just more automated markets.

2:20:00But give me a state of the union on how the fixed income market is actually like split up. So you're totally right. There's government bonds, there's highly liquid corporate bonds, and then there's a long tail of corporate and municipal bonds that are really, really illiquid. And just as a point of comparison that I think illustrates the size and scale of the fixed income market, there are 4 ,000 listed U.S. equities and there are 4 million bonds. And so doing anything in the fixed income market is pretty much a thousand times more complicated than doing it in the equities market. Yeah. And what about how does break down?

2:20:42So so four million public equity. Walk me through the four million bond. How does that actually trade right now? Like, is it a state dinner? And what is the ultimate, like, makeup of do certain companies account for, you know, break down, like, kind of how that$4 million is split up? Yeah. So there's a universe of, say, 500 U.S. treasuries that are super, super liquid. And they trade, like, similarly to the most liquid stocks out there. And then on the other side, you have a really, really meaningful long tail that makes up the vast majority, actually, of the entire market share, where you have bonds that haven't traded in two years or 10 years.

2:21:29And so one of the really hard parts about fixed income, one thing that I worked on as a quant researcher, is like you have this bond that hasn't traded in three years. How do you optimize a portfolio around that? How do you even figure out what the price of that bond is? And that's why doing stuff in the fixed income market is like, like the difference between equities and fixed income is like doing something on the surface of the earth and like doing something in space. Wow. It would be helpful to be a quant if you were going to build a company like this. Yeah, you might need some math. Well, I mean, that's all I have.

2:22:06Do you have anything else to do? Yeah. What are you guys hiring for right now? Yeah. Pretty much everything. We're hiring quants. We're hiring engineers. We're hiring go-to-market, marketing operations, pretty much everything out there. Amazing. Well, thank you for joining. Thanks for hopping by. I'm sure you'll be back on soon. We'll talk to you soon. Have a great one. Thanks for having me. Talk to you soon. Cheers. Bye. Next up, we have Eric Olson from ConsenSys coming in with a big launch. Do we ring the gong for big launches? If there's a number attached. So we got to get DAUs out. We have to get a prop that is non-number oriented, but just excitement oriented.

2:22:41But let's bring in Eric Olson from ConsenSys and talk to him. How are you doing, Eric? Great, guys. How are you guys doing? Doing great. Great to have you. Kick us off with some intro on yourself, the background of the company, and then I want to talk about the launch. Okay. So I'm Eric, founder of ConsenSys. We are an AI search engine for academic and scientific research. If you ever use like Google Scholar or PubMed back in the day of school, think of us as building the next gen 2025 LLM powered version of that. Helping clinicians, researchers, students. Yeah, I mean, the super dumb question, super obvious question is like, isn't all this stuff already in ChatGPT?

2:23:21Like, how are you differentiating? Yeah, you must be doing something because you have 5 million, over 5 million users. Yeah, I mean, one of the best examples to encapsulate why it's different is the fact that Google Scholar was the first vertical search product that really broke off of Google back 20 years ago. Interesting, yeah. Even when they were doing really nothing than just being a dedicated index for research papers. Yeah. So hundreds of millions of people were going to that every month. Yeah. So the same thing is kind of true here. We're dedicated to a use case. We have a dedicated corpus we search over.

2:23:57We hopefully search over that corpus a lot more intelligently than a general purpose chatbot would. We do things differently in our interface to show you that information. like we're watching more citation forward and like an experience where you can like really interrogate what's been returned in your search using like the chat gpt search it's pretty much like an afterthought it's there if you kind of want to dig into it but it's not really what it's designed for yeah everything about it from the way it searches the way it shows to you and the features both on top of it all dedicated towards academic research walk me through some of the key technologies that enable better search i'm thinking about like vector databases um even just like stuffing a better index in redis or postgres or doing more indexing on top of these documents doing like you know transformations on the underlying documents to get them into you know more basic formats like what's what what's interesting large context windows there's so much that you could throw at this problem what's actually working yeah so lots of different things many of the things that you're saying.

2:24:59So like number one, being dedicated to a document type just helps us. It helps us in the way that we can create our embeddings to search over. It also helps us in that ingestion process, kind of like you were saying of like document transformation. We'll run little tiny LLMs, over 200 million papers, add new like enriched metadata about them that we can then use in our search ranking and in our filtering. So think like we'll pull out what is the design of the study or what is the sample size of the study? And we use that in search ranking and we use that in search filtering. And then on top of that, we're like the main intelligence of the search is learn to rank models.

2:25:36So people interact with the product, they save papers, they cite papers, they share papers. We learn from all of those interactions. We learn what matters most. We learn about all the attributes about a paper that matter in search ranking based on how people are interacting with it. So the simplest way to think of it is like, because we only have a certain use case people are using it for we get to train our search models to try to think and act like a researcher would be going through these papers jordy uh how uh what are the different data sources here i'm assuming a lot of the stuff is public i i know some of the i remember uh you know being in in college trying to find different studies or papers and like hitting paywalls a lot of them are locked down there's the famous and so i imagine that you've done some deals to get access to data what is the kind of...

2:26:26That's a great question. ...the full body of work that's available? Well, hopefully paywalls are going to be a thing of the past moving forward as open access science gets more and more momentum, which we'd love to help shepherd in. The way to think of it is there's like three different layers of access, levels of access you can have in Census. So there's one, there's fully open access science. That's all publicly available. We're able to ingest the full text, show it freely, let you download it, all is well and good. The rest of the bucket is paywall content, but there's two levels of access we can have within it.

2:26:57So there's the buckets where we have deals with publishers trying to get as many done as possible, or we're able to use the full text in our search and in our analysis. We're just not able to display it to the user. The benefit to the publisher is we're helping them drive traffic, get people to see that. Hopefully this like snippet search ranking is engaging, then they go into it and drive a purchase. And then there's this third bucket, which is we just don't have a deal with the publisher yet. fully behind the paywall, we're using what is publicly available. So that's like the abstract and the metadata of the paper, which goes longer, broader than you think.

2:27:32Like the abstract is specifically designed to be this perfect, like nice summary of the paper. It's like can go a pretty far distance in search ranking and even some analysis using abstracts, but obviously. Nothing more brutal than being a college student and almost getting like the information that you need from an abstract and realizing like, do I really have to pay like$50 for this single fact? The craziest is that even if you wrote the paper, you still have to pay for it. Oh, yeah. That's crazy. You can hand it off to a publisher, they publish it. So I could literally have published this paper.

2:28:05And if I come across on the internet, I still have to pay for it. That's wild. Jordi had this question earlier about the nature of scientific discovery. Elon Musk at the Grok 4 launch was talking about his timeline for discovering new physics is two years now based on the progress that he's seeing at XAI. And Jordy was making the point that a lot of scientific discoveries come from mapping different disciplines together. Or just inventions. Invention generally is apply the mind of a computer scientist to a biology problem or vice versa. Are you seeing users do those type of searches? Is this product useful for that type of scientific discovery?

2:28:47there's been this like lingering question in artificial intelligence about if you were a person that had read every single research paper you would probably make yeah you would make discoveries and connections across things and yet that hasn't happened maybe it's some fundamental limitation of llms or ai at this moment but what are you seeing and what's your take on that concept of like cross-functional pollination yeah i mean everything basically that humans have invented new comes from pattern matching across disciplines. That's how we create new ideas. I'm not an AI researcher, so I don't have the single most informed take, but also nobody knows what the heck they're talking about in this world.

2:29:29I think it is probably a fundamental limitation of LLMs, given what we've seen. I'm going to parrot this from Francis Chollet in his YC talk the other day, but the measure of intelligence is the efficiency by which you process information and apply it in different domains. And that just like, isn't what LLMs are really doing great right now, despite the fact that they've processed so much information. So our take at consensus would be more get people to the edge of what is known, and then let them do the inherently human part of science, which is create these new insights and new discoveries. Like every, you know, every science experiment that's ever been done starts with a review of literature yeah i think a lot of it is like you're getting the foundation of knowledge underneath your feet if we can our goal of consensus would be speed up that part as much as humanly possible and let us do the thing that humans are better at than machines right now which is that pattern matching which is that coming up with some ideas if we can make that loop move faster like heck that's a freaking valuable and powerful thing uh switching gears completely i know you You were at DraftKings prior to this on the sort of research and analytics side.

2:30:39What is your thesis around the ultimate collision between sort of betting activities and AI? Last night, Grok announced a partnership with Polymarket to try to bring in prediction markets to try to basically help make the model itself smarter. How are the big players like DraftKings even thinking about AI? I'm sure a bunch of people have like ChatGPT rappers specifically focused on sports betting and things like that. But how do you think the big players are thinking about it? Yeah, I mean, well, I left DraftKings in 2021, so I can't say that I was there when people were worrying too much about AI models.

2:31:27And also the natural question I always get is how the heck did you go from sports betting to science? And the answer is my parents and my grandparents and my sister are all teachers and scientists. I was happy growing up and I loved applying numbers to sports. But I actually have something kind of interesting to say here. So my job at DraftKings was I was building models to find the professional gamblers on the site. So you'd look at all previous betting history and demographic data and you try to make predictions on, is this person actually have an edge over the market? And I would have to imagine that with better and smarter and more powerful models, people's ability to themselves have an edge on the market would increase in the short term.

2:32:06And then the markets obviously catch up and figure out how to bake all that in. And I mean, that is the beauty of markets, right? Like whatever technology that people have on the side of betting into a market, so does the provider who is putting up that market. And they get the information from the people they know that have the best models. So I think it's going to be an interesting cat and mouse game moving forward as it's always been with sports betting, just instead of, you know, Johnny Two Shoes in New York and inside information about injuries. Now it's somebody with a super powerful AI algorithm that's predicting games above market.

2:32:38That's fascinating. I have to imagine that in the in the AI era, the insider knowledge about injuries is even more valuable. 100%. But to your point, you can probably detect it. The number one way to know if you need to limit somebody is if they are ahead of an injury. Because it means they're somewhat connected. They're doing this. They're on the inside. Yeah, that makes a ton of sense. Inside trading. That's fascinating. I didn't think about that in the context of sports betting. Well, thank you so much for stopping by. This is fantastic. And congratulations on the launch. Appreciate it. Check us out at Consensus.app.

2:33:10Deep search launch today. Thanks, guys. Awesome. Cheers, Eric. Thanks for coming on. Up next, our lightning round continues with Rita from Zero Entropy. A YC graduate doing automated retrieval and announcing a seed round of$4.1 million. Seed round alert. Seed round alert. $4 million. That used to be a Series A a couple years ago. Just keeps ticking up. Congratulations on the round. Rita, welcome. How are you doing? Thank you so much. Super excited to be here. Thanks for joining. Introduce yourself. Introduce the company. How'd you get started? What do you do? Yeah. So I'm Rita. I'm one of the co-founders of Zero Entropy.

2:33:50A little bit about myself. So my background is in applied mathematics. I have two masters in the field, one from École Polytechnique, one from Berkeley. I guess I started more into the computer vision side of things. And then I discovered GPT-2 and GPT-3. And I was like, oh, my God, this is this is huge. And I started thinking about, you know, personal assistants and stateful AI systems. And I guess that's what led eventually to zero entropy and building retrieval systems and bringing context into LLMs. And so that's what we do. We build search for RAG and AI agents. Okay. It feels like a crowded space.

2:34:29I know a few founders that are working on RAG. There's also RAG implementations at the hyperscalers and the clouds. How are you differentiating? What's the key insight? What's the pitch to companies to come over and use your service as opposed to the other options out there for retrieval? Yeah, absolutely. I think it's about having the right abstraction. So we solely focus on the retrieval side. We don't do the entire rag end to end because we believe that developers need to have their own prompts into generating the answer. They need to use zero entropy as a search tool for their own AI agents.

2:35:04We're also developing our own models. So we just released a re-ranker yesterday, which was pretty exciting. And I guess the winning solution needs to be extremely accurate, but also extremely fast and just be production ready and easy to implement for various use cases. What's your take on benchmarks currently? It feels like solving a really hard math problem and retrieving the right document at the right time are somewhat unrelated. And so how do you evaluate if your system is getting better? Yeah, that's a great question. Actually, the evaluation side of things is very messy. Almost everyone that I talk to, they basically rely on manual inspection to make sure that their retrieval is working correctly.

2:35:51So we've been looking into the evaluation side a lot. Actually, the very first thing that we did is release our own benchmark that was on legal documents. And that really evaluated just the retrieval step of RAG, meaning from a question, was I able to pull all of the documents and only the documents that I needed? Because the problem is that if you feed your LM too many tokens that it doesn't need, it's just going to hallucinate. So the precision and the recall side of things are extremely important. And we're rolling out our own evaluation solution in the next few weeks that we've been using internally so far.

2:36:27What does the rest of the stack look like? I know you said you were kind of rag provider agnostic. Are you also model agnostic, cloud agnostic, database agnostic? Where have you actually made bets? What pieces of the stack are you particularly aligned with? Yeah, I think, you know, building context, context engineering is going to be a new class of products that needs like the data layer, but also needs small LLMs inside the retrieval pipeline. We see many teams either feeding everything into the context of the LLM entire knowledge basis because they weren't able to make retrieval work properly.

2:37:08And we see teams having a very simple pipeline. I think the winning solution needs to be somewhat in the middle and basically orchestrating LLMs to rewrite the question properly, summarize the documents and creating more metadata associated with each of the documents that are indexed. And so that's what we're doing and building this solution that works really well and almost gets to the precision and the accuracy of a large LLM while still being pretty fast and pretty optimized. What's the appetite been like for this product in the enterprise versus new companies that are building new AI products from scratch?

2:37:46It feels like they might be just the AI agent infrastructure companies. There's a lot of them and it feels like they're selling to a new crop of companies. And that's where the revenue is accelerating most aggressively. But what are you seeing in the market? Yeah, I think the adoption for products like this usually comes from like bottom up type of approach where developers are experimenting with new approaches and new techniques and then larger enterprises catch up. So that's what we've been seeing. In terms of experimenting with models, I think large enterprises also do that pretty easily. So for things like the Reranker that we just released, there's also appetite from larger companies in integrating that into their current systems.

2:38:32Is there a case study that you have your eye on amongst the big tech companies? Like, we think that our software could improve Netflix or YouTube recommendations or something. Like, if the deals could just magically happen, where's the lowest hanging fruit? Like for me, you know, if I could do anything, I would just get whisper into Siri. And so when I dictate a text message, it's just perfect and it's much better than what they're currently using. What's on your wish list for, you know, consumer tech company or big tech company that everyone knows and they're not taking advantage of something like this?

2:39:10Honestly, for me, it's Slack. I always struggle. You know, I can never find anything on Slack. And something that we've been doing is annotating our own conversations, like appending keywords to our own threads to be able to find information. But we have a lot of our internal research and a lot of things going on on Slack, and we find it pretty difficult to find the right stuff. So I think companies like that could benefit and it would provide a much better user experience if you could just magically find all of the information that you have in there. Yeah, I've been noticing that with Gmail, like the amount of email has just grown so much and the amount of text in each email has grown because of all the trackers and cookies and stuff behind the scenes.

2:39:58And so when I search for something, it just pulls up completely random emails like every time. And it doesn't understand the hierarchy of in an email, I care a lot more about what's in the subject line than what's in the footer. And so if I'm searching for, you know, artificial intelligence or something, and someone has that in their footer that, hey, I run an artificial intelligence company, that's not what I'm looking for. I'm looking for the thread that I was talking to somebody, a close friend about AI, And I want to pull that up first. Yeah, I think that's also why, you know, basic semantic search is just not enough because it basically will pull all of the similar information, but not the most relevant or the most helpful.

2:40:37Keyword search is the same. It's not very smart. And I think it's just it's just such a waste because there's a lot of information that you could have access to and it would make your work so much faster. and you're just spending time like rewriting your question and trying to make the system understand what actually you're looking for. So I think that, you know, the query side of, you know, the user intent, query rewriting is also super important. Yeah. iMessage search, absolute disaster. It is an absolute disaster. It's like, I know I'm in a text message with Jordi and someone else. And so pull that up and it's like, here's six.

2:41:12It never works. I also noticed - Please fix it all. There's there's going to be need to be a shift in the way people search I remember hearing the story about Google where there was some Google engineer who was running a test on like You know how many it was like how what's the world record for you know the the marathon or something like that and They were they were using the typical keyword boolean search and they weren't getting good results And then they sent it to a user and the user just asked the question in natural language And it just hit it the first time and so I feel like people still at least I have been you know an email user for a long time, when I go to my email search, I often, I'm searching in like the keyword world instead of just natural language.

2:41:53But Google has, I mean, they're experimenting with the AI search thing. They have a 50 word limit right now. You can't just type a whole prompt in Google search. Like they need to kind of reimagine what that search box is. And then there also needs to be a consumer change in how consumers interact with that particular like UI element basically. But thank you so much for stopping by. Congratulations. Congratulations. And good luck to you. Thank you, guys. We will talk to you soon. See you soon. Have a good one. Up next, we have Elliot Hirshberg from Amplify Partners coming in the studio. They are in Datadog, Chain Guard, Runway.

2:42:28I love Datadog. Maybe it's the golden retriever in me, but I love Datadog so much. It's the greatest company name ever. Right up there with AIDSleep. AIDSleep.com. Get a Pod5 Ultra. I'm back on my game. I'm still behind where I was relative to a month ago, but back up into the 75 range. I'm going for 90 tonight. Good luck. Good luck. They're calling the Pod5 the first fully immersive sleep system that works with any bed. Pod5 actively adjusts your temperature, elevates your body, and plays integrated soundscapes to improve your sleep. I have some new copy today. Simply too good. Welcome to the stream, Elliot.

2:43:04Hopefully you're here. How are you doing? What's going on? Hey, what's going on, guys? It's a pleasure to be here. Thanks so much. And I love the suit. You are dressed fantastically. Sign of great respect in our culture. You know, there was like a period where people would actually sort of match the vibes and have the suit for the technology brothers. And I feel like it's dropped off. So I want to bring it back. You know, I appreciate it. I appreciate it. We are in the boardroom. You know, it is. It's I'm glad to be here. Yeah, it looks great. Yeah. Proper uniform. I wanted to get a state of the union from you on a few things.

2:43:34But why don't you just kick us off with an introduction on you? Yeah, for sure. My name is Elliot Hirschberg. I started my career as an experimental biologist. So I was in the lab trying to make new treatments for cancer. Got super frustrated, decided to retrain as a computer scientist. So I became a computational biologist and was obsessed with that as sort of a practitioner for about a decade. And then got really obsessed with writing about it. So I was sort of writing a newsletter called The Century of Biology, writing about companies in the space, data on the frontier. year. And then that sort of was a rabbit hole into investing with a friend of the network with none other than Paki McCormick.

2:44:16So, spent some time at Not Boring where I was writing and investing and then recently joined Amplify. We just closed 900 million in new capital including 200 million for a dedicated.

2:44:29That's great. Are you announcing the new capital, the new fund today or was that a little bit ago? It was a little bit ago,$900 million, including$200 million specifically for Bio that I'm helping to build. Okay. You guys were not loud enough about that. No. That's a lot of money. Well, that's part of the thing. I feel like it's a really quiet fund where for three of the four funds for Amplify, they've been in the top 5 % of venture returns. Wow. Not top decile, but top 5%, really good at what they do. That's amazing. And I feel like it's just not thought of as much and just like very quiet and stealthily doing really phenomenal work.

2:45:09And so, yeah, excited to talk more about it. Okay, State of the Union on bio. I want to know about where we are in artificial intelligence and technology helping advance bio. We've seen AlphaFold. We've seen kind of tools and amazing breakthroughs. I think everyone has a really concrete idea of the impact AI's having on software engineering, whether it's like amazing autocomplete, you have cursor, now you have agents. Where are we in the deployment of AI tooling in bio? A lot of the narrative just jumps straight to, we're gonna one-shot cancer, and I love that. I'm optimistic it happens eventually.

2:45:52It doesn't feel like we're there, but where are we actually in terms of the impact on productivity in AI with bio? Yeah. So you guys know like the Gartner hype cycle, right? Where you have these like huge swings for new technology where I've been working in this field for like a decade and there's been a bunch of companies, you know, Amplify invested in recursion, which was one of the early leaders of this, right? Like there's been this general sentiment that you can make a huge amount of progress with new data, new tools, new technology and life sciences. And it just turns out that it's like a really hard problem, right?

2:46:24And it takes time. It takes some time for like the market to ingest that, actually figure out the right business models and strategies. And so there was like a really like strong wave of early adopters. And then there was some disillusionment and disappointment where it's like, oh, shit, it actually turns out that it's really hard to one shot a cure for cancer. And then, you know, as the as that sort of happens, there is just a bunch of breakthroughs in the technology, right? So it became consensus that this is making a huge impact on hard problems and biology, where we had the Nobel Prize for AlphaFold, right?

2:46:57So like a Nobel Prize going to an AI lab to DEMIS in part to David Baker at the University of Washington because it's actually starting to make real meaningful impacts on hard problems in biology. And so that's true for sort of molecular machine learning where you're thinking about designing new molecules and proteins. There are virtual cell research where you're trying to actually model how cells behave. And so you're seeing this sort of step change now where there are a couple sort of actually faster than Moore's law curves. DNA sequencing is decreasing costs faster than Moore's law. And so you get this huge data tailwind plus the tailwinds in machine learning and modeling.

2:47:39People are actually starting to scale these models and it's just like getting pretty impressive pretty fast. Where are we on new drug discovery companies? We're targeting a single thing. We're going to build a drug to solve a problem versus we're going to start a company that's SaaS, it's tool, it's going to help with all different drug companies. What's working? What's more overhyped, underhyped? What's your take on like picks and shovels versus drugs, basically? Yeah. So like short answer is we do both. So you guys had Jake on the other day at Centivax, like absolutely insane founder, was one of the early computational immunologists at Stanford.

2:48:20And like he's making a medicine that you just couldn't otherwise make without these technologies, right? Where it's like all these impacts within biotech and within modeling to make these just really incredible drugs you couldn't otherwise make. And so that's people just like making these singular things that are like really phenomenal. Like there's also a real platform opportunity there for other things beyond the universal flu vaccine. And then I think like if we take like a little trip down like history lane and think about like how hard it was to actually sell software in the life sciences, one of the early companies in this space is Schrodinger.

2:48:49and they've been around for about 25 years. They're a molecular dynamics company and it just took an extraordinary amount of time and effort to actually saturate and get people to adopt the technology and get people to pay for it. And they're a phenomenal business. They're a public company. They're actually vertically integrating into making their own drugs. But the thing that we're hearing consistently from CEOs of top pharma companies is just there's huge demand for new infrastructure. people realize this technology is here now and that they need to adopt it. They're hearing this from their shareholders.

2:49:24They're hearing this from the scientists at their companies. And so there's just a very different moment post AlphaFold and post even ChatGPT where like they're using it, their kids are using these models and they're just like, oh, I really actually need to adopt this. And so I think opportunity for both like fundamentally new picks and shovels where you sort of replace experiments with compute and then also just fundamentally new drug products. jordy last question no i mean uh i think we should have you back on as new news because uh yeah i think we'll do the tvpn uh bio drop-in you know yeah it feels like there's uh within the traditional labs bio has been used as like a as a as almost like marketing right Or even Elon yesterday saying like we're going to discover new physics.

2:50:20And I don't think that that's obviously not where like a lot of the true innovation is happening. So, yeah, let's make it a regular thing. Yeah, this is great. Thank you so much. When you think about that, right, like there's just been a couple of things like an investor who made a joke that like there's just a couple of things that have consistently delivered venture returns. And that's like software and also drugs. And so like as far as a physical prediction goes for being something that's super valuable If you can make your inference be a billion dollar Drug product. That's a pretty good spot to be we're excited about it.

2:50:56So see you guys there. There's Who I forgot who we had on when when Trump had the executive order around drug prices We talked to a few different people that were saying biotech has like on average been a terrible asset class and there's some amazing outliers. Speaks like Weinberg maybe? Yeah, like basically there's all these amazing outliers that do deliver returns. But if you just index the market, you were going to underperform dramatically. And underperform venture specifically. Yeah, and the venture category feels like this could be a massive shift where suddenly the next five years become the golden age of venture bio investing.

2:51:36I mean, there have been some massive companies before it. So you have like breakouts, like the Genentex of the world, huge companies. There actually is, you guys should have Bruce Booth on, who's an OG biotech investor at Atlas Ventures. That would be cool. He's done a bunch of analysis and the fact that like there's actually some interesting just return data for biotech versus tech where it's like not as gloomy as you would think. But I think in general, like biotech's hurting right now. We want to make a world where it's actually like engineering, right? And that you're actually just getting these like really scalable, amazing medicines.

2:52:07And like, I think that's where we're headed. Incredible. thank you for joining great to have you on yeah awesome thanks guys cheers all right bye see ya and next up we have kareem from ramp the man himself coming in to talk about the launch new agent launch uh is he in the waiting room we will bring in kareem from ramp to check second time on the show he hopped on at uh hill and valley that's right first time as a remote guest great to see you how you doing hello great to see you guys can you hear me okay Yes, loud and clear. I don't think you need much of an introduction, so why don't you just kick it off with the announcement and break down the launch today, and then we'll have a bunch of questions.

2:52:47Yeah, of course. I mean, it's been a very exciting day for us at RAM. We finally announced our first agent. We're going to be announcing a lot more agents soon, so it's hard to keep track sometimes. We've been playing with a lot of tech internally. We think we're in a very interesting space where maybe the thing about it is a lot of people from the outside look at Ramp and think of maybe visualize the card. They think about the FinTech aspects. But at the end of the day, what we're really trying to do is help reduce the drag on companies that happens when there's just a lot of bullshit work in between teams and a lot of papers being passed around, questions being asked.

2:53:34the things that really get in the way of doing work. And that first agent that we're building is really just that. Like it operates in the messy middle between finance teams and every other team trying to spend to move the business forward. And yeah, that's basically what we launched today. So it's an agent for controllers. It knows a lot more about the expense policy of a company, the rules that are in place that governs spend than any single employee. And it knows a lot more about every single transaction than any single person on the finance team. So it can operate in the middle and automate all the little decisions and the extra work that needs to get done to figure out what's in policy or not.

2:54:23And it's immediately available. Like that's part of the power, right? Is that if you're, if you're like in the sense of like, if an employee wants to decide whether or not they can buy something or something's in policy, you no longer have to be slacking somebody. You know, it could be in the middle of the night or something like that, or off hours where there's creating that drag, that delay, right? 100%. That's certainly one part of it. Like you can ask questions about your policy and ask questions about specific transactions live to figure out whether they would be in or out of policy. But more interestingly, once you make a transaction, it's already doing work to go and figure out like well that transaction that you made at that restaurant it looks big but if that was a dinner with 10 people maybe it's not as bad as initially thought and that's actually in policy so well that information is in your calendar it's in your email it's in sometimes outside of just the immediate context of the transaction so the agent will go out on the internet in some cases contact vendors or pull data from APIs on your behalf to really gather all that context and make better decisions on behalf of the company.

2:55:34I want to talk about the word agent and the decisions to how agents are fitting in the different stack of a tech company these days. Because there's kind of always been an agent behind the scenes working. We think of these as like cron jobs before. It's like there is a long-running process that when a receipt comes in, it gets tagged. And there has been for, I think, years. I don't want to share anything you can't, but there's been an LLM interacting with receipt data for a long time. But it's been fully agentic in the sense that it was behind the scenes. And so I've been thinking about this in the context of meta and some of the value that Zuck is going to be getting from having a frontier AI model.

2:56:23It's like there's so many workloads inside a business that has billions of users that just happens behind the scenes and and these are agents, but they're like almost internal agents And so I'm wondering about your decision to Yeah position position an agent as like this is a user-facing agent versus something that we're just gonna have a process That's running behind the scenes entirely Well, there's a bit of a difference, right? Because when you think about these processes running behind the scenes, for the most part, like the code is pretty deterministic. the tools are the same it's built for accuracy and auditability and you have a high confidence you can trace back the path that uh the old school agent let's call it that went went through exactly and in this case like it's less deterministic you give the agent a set of tools you could tell the agent or you can essentially give it access to um let's say ability to call ability to email and it can be like go figure out a way to get the receipt that's what you know about the restaurant and it'll browse the web and figure out that that's the phone number of the restaurant and then try to call the restaurant and if that doesn't work then we'll try to email the restaurant until it achieves that goal of of getting you the receipt or it fails and you can then interact with it um in in this case like the instructions that we are giving the agent as we're building it are very high level.

2:57:55You're just giving it high level instruction and access to tools. And that's very different from like the old way of building these processes in which you had to be like very specific about all these paths. So it would take a lot longer to build these systems, to debug them, to update them, et cetera. We lost you. Your background is turning like a ghost. It's very funny. It's a super intelligence. I think you just need a little bit more light on your face. I think I actually lost power. Wait, you lost power? There we go. There we go. That's wild. Much better. I want to talk to you about the data walls that are going up and some of the battles that are playing out in the enterprise world.

2:58:39Because when I read stories about companies that want to do enterprise search, you can see that, well, you know, maybe Google doesn't want you to be taking, maybe they want that for themselves. Ramp's in a very different position, but at the same time, like there's just evolving policies about, you know, how friendly, this is a classic with like Amazon not sending the itemized receipts to Gmail because they just didn't want to give Google the data. But as a ramp customer, I want the Amazon details pulled in via Gmail, via the ramp integration. So talk to me about like, how's the broader trend playing out?

2:59:20And then how do you go to big companies and say, hey, like, you know, work with us. Our clients want to be able to pull data from your service and we're not going to build a delivery network, Amazon. So you're not, you're not, we're not a competitor for you. Yeah, for sure. I mean, most of the data that we need at the end of the day is data that is quote-unquote owned by our users, the businesses that are on ramp, their employees. I think it's a little bit easier to operate in the B2B space because what governs who owns the data and whose data it is is a lot clearer than in a lot of consumer applications.

3:00:07So in our case, it's like, what data do we really need to know to, in the case of the agents that we just launched to figure out whether something's in policy or not. It's metadata about the transaction, right? Like what's in the receipt at the end of the day, like stores all your receipt, that's your receipt, right? We get that information. You have information that we get through the networks, through Visa, the metadata about the transit, the geographical location of the transaction, maybe whether it was an in-person transaction or not. There's data that's in your inbox, in your email, which again, that information is also owned by the company.

3:00:47We haven't really encountered a lot of pushback and challenges. I found most of the challenges in getting the data to be more technical. How do you make sure you get it quickly, clean it up and get it accurately? As opposed to ones where there are third parties that are trying to make it harder and harder for us to access the data. we had some you've kind of been in that in the previous company that was kind of the story of the previous company yeah of course we had a lot of these problems I mean there were lots of funny moments at Paribus or previous companies where we were I mean we were really building an agent for consumers to help them save money on their online shopping right and we're trying to log in on their behalf to Amazon accounts and Walmart accounts etc and of course they'll put blogs to upload CAPTCHAs.

3:01:37And today those CAPTCHAs seem like a joke. I think any version of, any half recent version of ChatGPT or Claude is able to solve those CAPTCHAs very easily. Well, that's one of the ways the internet's getting worse right now is the CAPTCHAs are actually getting so hard and annoying. When we go to the gym in the morning, Jordy has to log in and it takes him like two minutes to get through the CAPTCHAs for this gym. It has like the most military grade security to get to a gym login. It just gives you a barcode that you just scan. But on that, I'm actually interested because I can imagine, you know, Ramp has tens of thousands of customers, like high value business customers and other people that are building agents.

3:02:18I'm sure would love to actually be able to make actions on the Ramp platform. But at the same time, you guys are trusted to handle the finance, you know, basically the finance back office for these companies. and you don't want an agent hallucinating and taking actions on the Ramp platform. So I'm curious how you see that dynamic playing out because I'm sure you've been approached by a lot of companies saying, hey, we're building this agent to do this thing. We'd love to be able to get authorization. Of course. I mean, we're thinking through that a lot right now. I think there are good ways of exposing the right information to the right agent as long as our customers are very aware of what they're exposing.

3:03:09And there are lots of interesting applications for us to work on. Like in the case of any large purchase at a company, there are multiple parties within that company that need to review it or approve. Like you want to review a certain vendor and look at their data protection policies. You want to look at the legal agreements. In some cases, you want to negotiate the price. And you can imagine a day in the future where a lot of our customers have an agentic tool that they trust or agents that they trust for legal work, agents that they trust for IT work, etc. And we're very interested in actually working with some of these companies.

3:03:52But we've got to figure out on our end how we expose the right interface so that we're ensuring really the security of the data of our customers. So it's an ongoing work stream. Last question for me. The Grok 4 launch was very benchmark heavy. it seems like the consensus is that it's a good model. And so as soon as I see that, it's now about costs per token. And so I wanna hear from your perspective, what drives decision making, how big of a line item roughly, or how much time is spent thinking about LLM inference optimization at your scale? Like roughly, how big of a deal is it? And then what is the workflow to decide, can we use a cheaper model?

3:04:49Do you have internal benchmarks? Are you just checking these things? How are you making decisions about which model to use for what problem? Yeah, that's a great question. I mean, I'm a lot more paranoid about being too slow to try the newest model and the latest and greatest tools than I am by maybe overspending a little bit in one area. I mean, the amount of time and money wasted at companies doing BS work is just insane. That if we're debating whether you can make something faster by spending an extra dollar or half dollar, like the value that we're able to create is so big that I don't worry about it too much.

3:05:36But we do have internally somewhat imprecise like stack ranking of the different places where we need to make inference calls. And in some cases, they're very simple, high volume, kind of low risk, right? Like you're trying to normalize or clean up some like merchant data to figure out the appropriate spelling and maybe the right like photo to use. It's not the end of the world if it's not like perfect. We're doing it at high volume. It better be cheap. So we have a kind of stack ranking of like this is something high volume where we need to be cheap. this is something that's low volume and high stakes where you need to be accurate and we'll generally try the newest and greatest models and the places where we think will make the biggest difference and over time like we'll break up some workflows and some parts of it will become uh cheaper more repeatable with uh smaller versions or cheaper versions of the model and and and it will just evolve.

3:06:39I mean, we come from, I mean, I remember like micro-optimizing every single thing on our AWS account back in 2014, right? Like we were, it was a lot harder back then. Like I think we also pride ourselves in being the time and money company. So we do care a lot about making sure that we don't waste our own money and our own time. But I would say that the TLDR is like our time and engineering time is the most valuable thing here. And I'm a lot more focused on that than anything else. Yeah. On the time issue, what do you think about the various latency trade-offs? I'm sure if an employee wants to know, is this in policy and you hit 03 Pro and it waits 10 minutes, like they're probably just going to slack their manager and ask them.

3:07:29But you're going to get a really accurate answer that's really detailed. And so how do you think about those trade-offs in latency. Yeah, I mean, it really depends on like where in the workflow we're making the inference call, right? Like if it's live in the interface and the user expects a quick answer, we'll be using some of the faster models. But the reality is like a lot of these agentic workflows that are being kicked off at RAMP like happen behind the scenes, right? Like you make a transaction, you maybe get a very quick question from RAMP's AI to gather a little bit more context. like that's enough and then from there we'll kick up another kick up kick off another task that can be a little bit slower that'll happen in the background and by the time it reaches a bottleneck or it'll reach a place where it needs like additional feedback it'll be in someone else's like notifications or on someone else's slack like you could take a little bit of time when like the work is going from one person to another person but less when it's like the same person interacting with the interface live.

3:08:33That's generally the thinking. Cool. Yeah, I have tried some of the newest browsers today, and I tried Comet today, I tried a couple of weeks ago, and I think what they're trying to do is incredibly cool, but I often and find myself thinking like, damn, like I wish this was a little bit faster. And I know it's coming, but I think unlike some of the browser agentic calls, like you want it to be really fast. Yeah, I was thinking about that in the context of the OpenAI browser. And unless they figure out something that makes it basically 10 times as fast, I'm still going to default to Chrome if I have both of them open, just because I'm like, well, I just really need a fast answer here.

3:09:24So I kind of expect them to need it. I mean, that was the Chrome innovation, right? Chrome won on speed. Like they just optimized the code and they nailed speed and it was enough to leapfrog. And so you could see, I mean, that's like the bull case for like Apple coming from behind is like, yeah, they, like it feels like if XAI and Anthropic and OpenAI are all kind of like Gemini are like roughly at the frontier. If you can just get something that's at that frontier, not any new innovations, but hyper optimized and it runs locally on your phone and it's spitting out like tons of tokens every second, like you have a product that would be very, very, it would be very rapidly adopted.

3:10:04It's exciting. It matters a lot. I mean, I think one of the weirdest UX patterns on ShadGPT now is that I have to do the work to figure out whether to use O3 or 4.0. O3 or 4.0, every time. Do I have 10 minutes or do I want the, and 4.0 is always, it's so good that I usually don't need to, but then I'm just like, well, I want the best, of course. And like, I'll come back to it. And it's such a weird paradigm. It's going to be something that dates us. And I just know our kids are going to be like, what did you have to do back then? You had to rewind the VCR tape. You had to put the disc in the Xbox.

3:10:41You had to pick which model to use. This is insane. It's so legacy and it's going away, but we're just in this weird, like we don't have a model router solved. And it feels like the easiest thing is like, which model should we use for this? I don't know. We'll see. I mean, I don't know if you guys, I grew up in Lebanon. I still remember the days of dialogue where you would have to kick everyone else off the phone line. Well, exactly. Well, select the phone line in that case. Like, okay, which phone line am I going to use? It's like, I don't know. Can't you tell me which one is free and like pick it for me?

3:11:16Yeah, it seems like the easiest thing to do. And also, Also, I mean, this is just complaining about the app that I use 30 minutes a day at least, ChatGPT, but I almost wish I could just define it in the prompt and just say, hey, use O3 Pro and then here's the prompt as opposed to needing to click the UI, change it, switch it and then pick instead of just being able to go back and forth. I don't know. I mean, it's a good sign because people are using this stuff so much that they're frustrated by these niche UI things. So, you know, it's an exciting time. There's a lot of, I forgot who it was who posted on X, I think it was like a couple weeks ago, that like every company is like one great UX breakthrough away from something amazing.

3:12:02And I think that will be true for a long time. Like there's a lot of alpha right now and just great UX and good patterns. We haven't figured it out. We're still in the maybe terminal phase of personal computers, right? Like when is the mouse going to come out? when are the right GUI is going to come out. There's a lot of that happening right now. And yeah, it's a fun time to be building. One last question for me. On Monday, Dwarkesh released an article and then came on the show kind of talking about his timelines around when an AI agent would be able to do his taxes, right? Sort of like agentically, basically like fully agentic experience being like, I want to do my 2025 taxes.

3:12:43And then it just sort of autonomously runs. how do like big how are like you know uh fortune 500 cfo like what what are their timelines around um maybe maybe you just tell them what the timelines are like okay by by 20 2028 you know we're gonna be able to do this for you um but but how is the the the sort of um finance arm of the C-suite kind of anticipating like the rate of advancement? Obviously, like the agent today is a step towards that future, but you'll obviously need a variety of different agents or. Well, I think in terms of capabilities of LLMs, we were there. We have the capabilities.

3:13:31Like the bottleneck on being able to do this today is like having the right context, right? It's like, well, some of that context is in my head. So the AI needs to know to ask me the right questions efficiently so I can answer those, even when I'm working with my accountant. Pick the best accountant in the world for your personal taxes. If you just tell them, like, find my taxes, they can't do anything. Maybe you tell them, find my taxes, and here's access to my email. They can do a little bit more, but they can't get it fully. So tell them, like, find my taxes. Here's access to my email. You can call my wife as much as you want.

3:14:08uh you can look through my drawers and give it more and more of these things like maybe you could do it but it's going to get lost it's going to take forever and and really what we we need to do even even for businesses is like what are the right like patterns for us to extract context that's in in people's heads organize it um get them comfortable with uh connecting different different tools like your inbox and and things of that nature and i think in terms of tech and capabilities we're there we're not we're not really missing anything so there's a lot of ux and plastic like yeah we almost need an ai agent that can email me a question and put it in my inbox which is effectively my to-do list and that's what my accountant does when that access happened they email me and say well that's why this you gotta do this you can just you can take Take a picture of a product and ask it if I buy this, you know.

3:15:04Yeah, it's in policy. Yeah. A hundred percent. Yeah. Well, thank you so much for stopping by. You have any else, Rudy? This is great. We'll definitely see you soon, Kareem. This is great. Good to see you guys. To the whole team. Talk soon. Talk to you soon. Bye. And that is the rest of our guests. We are through that. In other news, Periodic Labs. There's a scoop from Natasha Moscarenes, the startup being co-founded by Liam Fetus and Eric Dogus Kubik. Great names. Is in talks to raise hundreds of millions of dollars in funding at above a$1 billion valuation. The two-month-old startup is looking to apply AI to physical science, starting with discovering novel materials.

3:15:46Let's give it up to the two-month-old unicorn. We've got to have these guys on the show. That is extremely fast. I also like this post from David Perel. We're getting him back on the show ASAP. We had a lot of fun talking to him a couple months ago. He said, I'm touring apartments in New York and just about every new build has the same soulless aesthetic. Flat walls, white paint, no cornices, no ornamentation, just a room in a box. Only one real estate agent said to me, if you want something with character, you're going to have to stick to pre-war buildings. Look, I'm all for some efficiency gains, but we've created a world where new things are soulless things.

3:16:19And that's how a society as modern as ours, And that's not how a society as modern as ours should function. Intuitively, you'd think that a wealthier society would build more beautiful things, but not ours. And I completely agree. Brutal truth. What's crazy is that this isn't, I mean, I don't know, these apartments look nice, but this continues all the way to$20 million houses that are still bland. And I think it's mostly because maybe time and all the difficulties with permitting. Because if you even have the resources to build something from scratch, creating, okay, I want these ornaments and I want this and I want something that's really expressive of my personality.

3:17:07well now you if you want that no one else wants that so you have to build it and you have to and you have to you know underwrite it and you're going to be underwritten code you make sure it's to code and then get it built and and then and then the secondary market value is going to be less because not everyone wants hearse castle whereas if you build if everyone builds the exact same thing they're perfectly it's perfectly liquid market because every every apartment is interchangeable with every other yeah so good point it it's kind of a it's kind of a function of just like modernity, but it's more a function of people not, you know, just risking it ever on building a disaster project, making their forever home.

3:17:45People learn the lesson of William Randolph Hearst too much. They should have just like never learned that lesson. Send it. Just ripped it and just send it and just build something that no one else will want to buy and will take decades to build. That's always the best. Well, I have a good place to end it. Rob Petrozzo says, the original Hermes Birkin bag prototype just sold for$10 million at Sotheby's. There was a two-minute standing ovation. He says bull market confirmed. And a gong hit. We love a bull market. The original prototype. Fascinating. That's wild. Makes sense. Very cool. It's incredible lore.

3:18:24Yeah. And I wouldn't be excited for a bull market and alternative assets such as Birkin's. It'd be great. And you should be too. But that's a great show, folks. We will be back tomorrow. I cannot wait. We will talk to you tomorrow. Talk soon. Have a good day. Cheers. Bye.