TBPN - Elon Musk vs. Donald Trump, AI Day | Shaun Maguire, Mark Chen, Sholto Douglas, Jack Whitaker, Aarush Selvan, Michael Mignano, Oliver Cameron, Delian Asparouhov

Episode Date: June 5, 2025

(02:28:34) - Skip to Elon Musk vs. Donald Trump Reactions (17:15) - Shaun Maguire. Shaun is a partner at Sequoia Capital and discusses the resilience and innovation at X and XAI, highlighting the successful integration of Grok into X despite initial skepticism about the platform's stability. He compares the evolution of foundation models to operating systems, predicting a diverse ecosystem with both proprietary and open-source models, where open-source models may have broader deployment but less value capture. Maguire emphasizes the importance of early market capture and anticipates significant moats for foundation model companies due to hardware investments and application layer advantages. He also notes the rapid revenue scaling of companies like Starlink, surpassing previous benchmarks set by AWS, and underscores the necessity of a diversified energy strategy, advocating for increased natural gas, oil, solar, and nuclear energy to meet future demands. (31:55) - Jack Whitaker. Jack is an AI expert and entrepreneur with a PhD from Cambridge University, specializing in generative AI, large language models, and multimodal systems. In the conversation, he discusses the current landscape of AI development, highlighting OpenAI's dominance in both product distribution and research, and noting Anthropic's strong position among developers. He also touches on the challenges of model naming conventions, the role of data in AI advancements, and the varying strategies of companies like Google, X.ai, and Meta in the evolving AI ecosystem. (50:58) - Aarush Selvan. Aarush is a Product Manager at Google, leads the Gemini Deep Research project, which enables Gemini to act as a personal research assistant. In the conversation, he discusses the development of Deep Research, highlighting its ability to generate comprehensive reports by leveraging long context windows and reasoning models, and emphasizes the importance of balancing efficiency with the depth of information provided to users. (01:04:45) - Oliver Cameron. Oliver is the co-founder and CEO of Odyssey. He discusses his transition from leading self-driving car initiatives to pioneering AI-driven storytelling. He introduces Odyssey's latest innovation, "interactive video," an AI-generated medium that allows real-time interaction without traditional game engines, envisioning it as a new form of entertainment. Cameron highlights the potential of this technology to revolutionize content creation by enabling models to generate film and game-like experiences instantly, reducing production costs and time. (01:19:18) - Michael Mignano. Michael is a Partner at Lightspeed Venture Partners and co-founder of Anchor, and discusses the evolving dynamics between AI foundation labs and application layer startups, highlighting the shift from a symbiotic relationship to direct competition. He emphasizes the growing importance of unique data contexts, noting that models are increasingly seeking novel information, which prompts labs to compete directly with startups possessing such data. Mignano also suggests that this trend may drive startups back to established incumbents like Google and Amazon, as they might be perceived as more reliable partners in the AI ecosystem. (01:31:44) - Mark Chen. Mark is OpenAI's Chief Research Officer. He discusses the evolving landscape of AI research, emphasizing the shift from large-scale pre-training to enhanced reasoning capabilities. He highlights the importance of reinforcement learning (RL) in developing autonomous agents and the challenges of scaling RL effectively. Chen also addresses the significance of interpretability in AI systems, advocating for models that transparently convey their reasoning processes to ensure reliability and user trust. (02:00:59) - Sholto Douglas. Sholto is a researcher at Anthropic. He discusses the challenges and advancements in scaling reinforcement learning (RL) within artificial intelligence. He highlights the significant gains achieved by increasing compute resources in RL, noting that a tenfold increase still yields linear improvements. Douglas also addresses the complexities of reward hacking, emphasizing the need for careful guidance to align AI behaviors with human values. (02:28:34) - Breaking News: Elon Musk vs. Donald Trump (02:35:00) - Delian Asparouhov. Delian is the co-founder and president of Varda Space Industries and a partner at Founders Fund. He discusses the recent policy shifts in NASA's budget, particularly the reallocation of funds to the Space Launch System (SLS) program, which had been advocated for cancellation by figures like Jared Isaacman and Elon Musk. He highlights the immediate consequences of this decision, including SpaceX's announcement to decommission its Dragon spacecraft, leading to a lack of vehicles capable of servicing the International Space Station. Asparouhov also reflects on the unprecedented nature of the current dynamics between influential private sector leaders and the U.S. government, noting the escalating tensions and their potential impact on the future of space exploration. TBPN.com is made possible by: Ramp - https://ramp.comFigma - https://figma.comVanta - https://vanta.comLinear - https://linear.appEight Sleep - https://eightsleep.com/tbpnWander - https://wander.com/tbpnPublic - https://public.comAdQuick - https://adquick.comBezel - https://getbezel.com Numeral - https://www.numeralhq.comPolymarket - https://polymarket.comAttio - https://attio.comFollow TBPN: https://TBPN.comhttps://x.com/tbpnhttps://open.spotify.com/show/2L6WMqY3GUPCGBD0dX6p00?si=674252d53acf4231https://podcasts.apple.com/us/podcast/technology-brothers/id1772360235https://youtube.com/@technologybrotherspod?si=lpk53xTE9WBEcIjV

You're watching TVPN. Today is Thursday, June 5th, 2025. We are live from the TBPN Ultradome, the Temple of Technology, the Fortress of Finance, the Capital of Capital. We got to work on that because we're working on selling the name and rights, baby. This place is going to be branded. We're going to sell the windshield. We're selling the windshield. We're selling the windshield.

So we got to keep growing the intro. But we have a massive day today. A little bit of an AI day today. We got folks from Google, OpenAI, Anthropic. We got the former CEO of Open AI. Sequoia. Sequoia, Stanford, Google, Lights, Open AI, Anthropic. We got pretty good coverage.

We hit almost everything. Should go on a whirlwind tour of what's going on in artificial intelligence. I'm excited to dig into the state of affairs in the foundation model race. We're going to go through the tier-level. list of what companies in AI have the mandate of heaven. We're also going to go through some of the deep research products and hopefully get into some of the more penny-edge use cases for AI. So we have both some deep research folks coming on.

And then we also have some folks that are working on video generation and video game generation and a lot of different applications. We're going to cover the granola story. We're going to cover what's going on with windsurf. And so it should be a great day. But let's run through some news just to keep everyone. went up to speed before Sean McGuire joins in 13 minutes. So, first off, ramp, time is money, save both.

Save both. Easy use corporate cards, bill payments accounting, and a whole lot more, all in one place. Market clearing order. Circle went public. The CEO's coming on the show tomorrow. That's very exciting. And, Jordi, you have the news. I will read a little bit from Jeremy, the CEO.

I'm incredibly proud and thrilled to share that Circle is now a public company listed on the New York Stock Exchange under Circle. Brian Armstrong Post, Congrats to Jeremy and the entire circle team on your IPO and reaching 30 trillion in lifetime USDC volume. Let's hit that go. The big T. Incredible. The stock is up massively.

It went, it was priced at $31. It is trading at around 85 as of now. That's fantastic. We love to see it. Again, as many people would expect, Bill. Gurley is going to be very unhappy with that, inefficient pricing. He hates a stock. He hates a pop after the IPO. It's good for the IPO window, which we always want to be open. For sure, for sure. Anderol executive chair, Trace Stevens tells Ed Ludlow that the company has closed

a new funding round of $2.5 billion in a deal that more than doubles the defense startup's valuation to $30.5 billion. This is from Bloomberg TV. Congratulations to the, Anderall team on the massive up round. You'll love to see it. We got to hit the gong for Andrew. Do it again. We have gone. Good contact, good contact.

Very exciting. A bunch of news from Kevin Wheel over at OpenAI. We will be digging into this with Mark Chan today when he joins. But deep research can now share across GitHub, Google Docs, Gmail, Google Calendar, so you can integrate everything and it can do research on your files. That's going to be a lot of fun to talk about. This is also potentially threatening for granola. And so we're talking to a granola investor about what the reaction will be,

where the direction of that company might change or not. But if you're designing a tool for artificial intelligence or one of these products or any tool, really, go to figma.com. Think bigger, build faster. Figma helps design and development teams build great products together. Go to figma.com. It is the backbone of the TBPN brand. It is, it is. And we would not be able to make the show without.

it. Yes. And so we are going to be digging into today's obviously artificial intelligence day in some ways. We're digging into artificial intelligence today. We're also very interested in talking about VR and content and augmented reality. And there's a story in the Wall Street Journal today about meta is in talks, not advanced talks, just regular talks, regular old talks. Chit chat. But they're talking to Disney. They're talking to A24 about content for a new VR headset. And this is my number one question about VR. When the iPhone, everyone's looking for the VR, the iPhone moment of VR. When the iPhone debuted, what was it?

It was first and foremost a phone. It replaced your phone. And so I've always thought that the path to true VR adoption was just saying, we're only going after your TV. The next generation of 22-year-olds when they get to college or post-college in their first apartment, they are just not going to buy. a big flat screen TV because we have solved that specifically with VR. Exactly.

Or, or you know the guy with multiple monitors, three monitors set up, the production team back there. We can go to the production camp, show you all of the different, all the different monitors that they have. What if they could be wearing VR headsets? They could have seven monitors one day. Ten monitors. Let's give it up for the production team.

So, so the idea. We got the drink camp. We got the drink camp. Thank you to Matarino. Thank you to, thank you to Andrew, Hugh. man for inventing drinking things and caffeine and the whole team over at matina yeah they're crushing it um and so uh this idea of of doing one thing really really well before before going into uh you know trying to do a little bit of everything like trying to be a platform even exactly you'll get to be a platform

if you solve one thing really really well uh the iPhone didn't wasn't a platform the first iPhone was not a platform didn't have an app store right it just had uh it just had the ability to to listen to you to music. It was an iPod. It was a phone. And it was an internet communicator, just a web browser. And so the meta is looking to Hollywood for exclusive immersive video for a premium device. Now, my kind of hot take here is that he's cooking. I know, I know. It's incredible. All of our boys are coming together doing it. We're cooking up something amazing. I'm really excited. We're not going to be able to get that much information on this soon. But I don't even know if they need that much immersive content. I think a lot of it is just, hey, every single meta headset should just ship

with the Matrix pre-installed for free. It's like, how much would that possibly cost? It's an extra $2 to rent or something. You could like have it pre-installed. So it's just like, you can put it on and there's like 10 movies that are preloaded and you can just watch movies and the movies are great. And you're in a really nice theater and it just comes pre-installed and it's all great. Because that was, that was the, my, my Vision Pro experience was very much film driven. I would take it a step further. And I think they would have to basically create an entire catalog. I don't know if the Matrix by itself is going to be enough of a draw to say, I'm going to spend hundreds of dollars for the average person. No, no, no. It's more about like the

pre-installed app. So like the iPhone was a really good phone. No, but I'm saying, really good iPod, but it also came with like a calculator app. That was like decent. And so you need a few of these things that are just like really easy to access, really easy to pull up the shelf. And ultimately, I think meta needs to catch up to Apple in terms of the Apple TV movie store and and, and, and making sure that like all the streaming providers are really on there in a, in a valuable way. Obviously, it's, it's important to go to immersive eventually, but I think the path to immersive might be just, wow, I have a home theater in my studio apartment now. Anyway, we'll dig into this more.

We'll talk to more people. Maybe I'm wrong. Who knows? But the high points from this article in the Wall Street Journal are as follows. Meta is seeking exclusive content from Hollywood for its upcoming premium VR headset Loma. set to rival Apple's Vision Pro. I'm super excited for this new VR headset. I think it's going to look fantastic.

I think the resolution's going to be insane. And obviously there's a lot of focus on augmented reality and Orion and AI and glasses. But there's still so much work just to do just to bring VR into just a normal consumer experience. And what's interesting is that I think Apple really broke the seal on like, yeah, like, you know, people are used to paying $1,000 for a phone and $2,000 for a computer. and maybe $4,000 for a headset, isn't that crazy? So what can meta's been hanging out in like the 3, 4, 500 range? If you take the reins off and say, hey, yeah, yeah, it's fine to spend $1,000 on this thing. You can get something really, really interesting.

Totally. And so meta is offering millions for video based on well-known IP, aiming to attract users to its VR device launching next year. Now, the big question is, how long will these immersive videos be? Because Apple did do a bunch of these deals. They did license a bunch of interactive video products, but they were all like five-minute experiences. Yeah. And so you'd get through them all, and then you'd wait a full quarter, and Apple would be like, we have another one.

Seven minutes. Here's five new minutes of entertainment. It's like that's not how people experience entertainment. I remember like the- Yeah, just think of it. A lot of people are being entertained by their iPhone for four hours a day. Yes. Often through video. And it was, but that's not even an iPhone thing.

You go back a few decades. to like the original PlayStation had Final Fantasy 7 on it. It came on multiple discs. And that game, people would play it for 100 hours. Metal Gear Solid was a similar, like dozens of hours of gaming. And no one's really been able to deliver that in VR and have that moment. Same thing with a GTA, you know, hundreds of hours of entertainment. Anyway, very excited to dig into this new device known as Loma.

It's more powerful than the MetaQuest. VR headsets now available with higher, Fidelity video. Let's hear it. They got the screens done. They pulled them forward off the bench top. The design is similar to the large pair of eyeglasses, more like Meadows Rayban AI glasses than goggles that the Quest and Vision Pro use connected to a puck that users can

put in their pocket. So maybe they're going puck, which is interesting because that was very contrary. I was like, Steve Jobs was never let this happen. And Palmer came out and said, no, puck is great. You don't want heavy things on your face. That's just not a good experience. And so META is planning to charge less than a thousand, but more than 300. And so I would say $9.99 is probably the right price.

I want it to be sort of expensive, so it can be a great product. A meta spokesman referred to the comments by META chief technology officer Andrew Bosworth about the company working on many prototypes, not all of which go into production. Meta is working with Avatar director James Cameron's Lightstorm Entertainment on Exclusive VR content. the two companies announced a partnership last year. So I think we got to get on this. We've got to have a VR stream. Yeah, three hours of content every day.

We're going to be the reason churn is low on the next VR headset. Yeah. Because you just throw this thing on it, just like you're sitting here on the drink cam. You can click through. Yeah, just click all to the different angles. It's pretty doable. It's pretty doable. I mean, you can film valuable, like usable spatial video on just an iPhone now.

And then you can play that back in the in the vision pro and it does look 3D, which is cool. In other news, Wall Street Journal is reporting that Reddit is suing Anthropic alleging unauthorized use of the site's data. An online discussion forum. Who would have expected this? A key access the site more than 100,000 times saying after saying it had stopped. Reddit is suing anthropic. And anthropic debates this. I'm sure we won't be able to get into this today because I'm sure it's caught up in the courts and there's a whole bunch of legal restrictions.

but we'll do our best to understand how these deals come about. It seems like most of the time it's not that the company that has much data doesn't want the AI company to use their data. They just want to have an equitable agreement where everyone is getting the most value. And I think Reddit surge, the stock's up, right? Yeah, and then for more context, OpenAI is already paying Reddit approximately 70 million per year in a content licensing agreement. So they kind of got ahead of this.

issue and decided to strike up an actual deal. And I believe Google has a deal with them too. And I think this might be one of the- Google has a deal with Reddit? Yeah, I'm pretty sure, because there's that meme about, like, the best way to search Google is search, like, whatever your search term is, and then space Reddit, because the user-generated content was better than the SEO stuff. Yeah, Google pays Reddit approximately 60 million per year.

So 60 and 70s, they're getting 130 million. That's pretty serious revenue. And it's something that doesn't need to be brokered via a bunch of individual program automatic ads that might not work or anything like that or sub-scale. It's just one one or two deals and boom, you're up in the hundreds of millions of dollars in revenue. What is reddit's overall overall annual revenue? $20 billion market cap.

Okay. Not bad. Let's see here. How are they tracked into a condi-eague? 1.3 billion greenbacks in 2024. 1.3 billion. So they're getting like 20% of the revenue or 15% of the revenue. Yeah.

I wonder how big. They grew 60% over 2023. Interesting. So they might be bigger. They might be bigger than Reddit, or they might be bigger than Condi Nast, which at one point owned them. It's kind of unclear how valuable Condi Nast is because they're private. Anyway, so Reddit said that the AI company unlawfully used Reddit's data for commercial purposes without paying for it and without abiding by the company's users. data policy. Anthropic is in fact intentionally trained on the personal data of Reddit users without

ever requesting their consent. The complaint said, interesting saying that it's about the users. Yeah. Bills itself is the white night of the AI industry. Last year, Reddit took steps to try and limit unauthorized scraping of its website creating a public content policy for its user data that is publicly accessible, such as posts on subreddit and updating code on its back end. The user policy includes protections for users, such as ensuring the deleted posts and comments aren't included in data licensing agreements. And so, yeah, I don't think that there's a really strong precedent for the agentic, for the agentic web.

Like if I, like, if I use Google Chrome to access a website, Chrome doesn't need to pay any sort of license, but if I go to Anthropic and say, hey, get me up to speed on this topic, and it goes out and it browses the web, all of a sudden it feels like maybe they do have to pay, whereas Chrome wouldn't because it's just, rendering the web page and it's not transforming it at all. What is transforming? What's fair use? And so these things will obviously play out in the court of law. And so hopefully they can resolve it quickly and move on. Yeah, I'm actually surprised that they didn't already have a deal in place. Yeah. Because it's very valuable data. Totally. Want that data for your models. And anyways.

Well, we have Sean McGuire joining in just a minute. And, um, um, The other news in the Wallster Journal today is Thrive Holdings is betting that AI can change IT services. The company established by venture capital firm Thrive Capital, joined with ZBS to invest $100 million into an entity that will integrate AI into IT firms. This is from Josh Kushner, of course. Shield Technology Partners has already acquired four IT service companies, Clearfuse Networks, Iron Orbit, Delval Technology Solutions, and OneNet Global. It said Thrive Holdings called Shield Technology Partners and AI-enabled managed IT service platform. IT service companies also called managed service providers or MSPs typically provide IT support and manage tools like software and cloud computing on behalf of businesses.

Founded by Josh Kushner about 15 years ago, Thrive Capital is known for some of its high-flying startup investments, including OpenAI, Databricks, and Wiz. What a portfolio. Investing in traditional services business, particularly those that rely heavily on administrative knowledge work and adding AI to supercharge them as becoming a bit of a trend. As part of its efforts, Shield Technology partners will embed software engineers into each of its IT portfolio businesses.

Oh, they're doing the forward deployed engineer. The engineer's goal is to build an AI-driven solution that all of the portfolio companies will use. We've studied all the ways in which MSPs have perhaps been on their back foot to date with customers and says that IT services work is incredibly well suited. to what AI can streamline. And so you can imagine a whole bunch of agenic workflows for all the different things that you need to do

when you're deploying cloud in managing cloud. Really quickly, before we have our next guest, let's tell you about Banta, automate compliance, manage risk, and prove trust continuously. Vanta's trust management platform takes the manual work out of your security and compliance process and replaces it with continuous automation, whether you're pursuing your first framework

or managing a complex program. If you think you should be on Vanta, you're probably correct. Well, we have Sean McGuire from, Sequo Capital in the studio. Welcome to the show, Sean. How are you doing? Boom. What's up team? Never a boring day on the internet. That's for sure. Yeah. What is keeping you? Oh man. What's keeping you up now? Well, I just say, I think there's you guys, anyone on Twitter knows what I'm talking about. Yeah, yeah, yeah. Or an X.

Yeah, I mean, let's let's skip the politics because this is purely a technology and business show. Thank God. I love you guys. You're the best. Stick to the technology. What, I mean, we've had an interesting experience with X in that there's always been this narrative that like the whole, the whole platform was going to collapse. We, you know, there's been rough days here and there, but overall things have been growing. What have you seen across the X, XAI merger? What are the secrets to success? How is, you know, talent tracking is any of, is any of like the chaos and noise?

is distracting because when I talk to XAI engineers, they're like, we're too busy. We can't come on your show. But what's your experience been with the X and XAI team recently? Look, if you go back in time, as you said, everyone said it was going to fail. The app would crash. You know, nothing would happen. And that didn't play out. But there was a lot of tech debt and kind of broken infrastructure. And there was a, you know, a couple of. years of rebuilding the basics and foundations. I think we're starting to see, you know, real innovation happening. I love the GROC integration directly in X. It always scares me when someone, you know, when I have a tweet or whatever, and then someone says like, at GROC, is this correct?

Is this accurate? Is this accurate? You never know what's going to come back. You know, usually, usually I agree with GROC. There's been once or twice where I think some of the subtleties are a little off. but it's truth-seeking. It doesn't mean that it's fully truthful every time. Yeah, yeah, yeah. It hasn't actually found that ground truth every single time. That's funny. What about the overall horse race?

I mean, I think in reality, I'm probably wrong. Yeah, yeah. What about the overall horse race between the foundation models? It seems like every day it's going back between an open AI launch, an anthropic launch, a GROC launch, a Google launch. Are you, do you think that continues? Do you think there's like maybe some, fragmenting and there's opportunity.

I mean, we're kind of already seeing this with how much Anthropics love by developers versus Open AI has been really dominant on the consumer side. And now every company is figuring out a different way to actually get to distribution. What really matters here? Is it pure scale? Is it pure cracked engineering talent?

Is it distribution? Is the combination of those things? How are you seeing it play out? Great question. I, you know, honestly, my opinions have changed a lot over last few years. in many directions. And so I don't have too much confidence in my assessment right now. But I always try to look at lessons from the past.

And my current thinking is that the closest analogy are operating systems. And I'll make a couple points on this. If you think about operating systems, first of all, there's a bunch of different ecosystem. There's the Windows ecosystem. There's the Apple, you know, OS ecosystem. Then there's like on mobile. there's, you know, Android, there's, you know, a whole browser environment with Chrome, and then there's open sorts of Linux. You know, one thing that I think is interesting about Linux, you know, there's more Linux servers in the world than there are Microsoft servers.

But the value kind of capture of Microsoft is way greater than Linux. I personally think we're going to see something very similar play out whether it'll be like a, you know, Open AI will be the Apple or someone and XAI I think will be very successful. I think there's a good chance that Anthropic is independent and successful. I also think there will be a big open source component, which would be like Linux. And I think there will probably be 10 to 100 times as many open source models out there.

or like deployments of open source models in 10 years, but I think that they won't be as valuable and they won't be like as rich of ecosystems. And then just to make two more points on the open source analogy, like for Microsoft, by having or Apple, by having the operating system, you know, they were able to actually win in quite a few ways on the application layer as well.

You know, for Windows, they bundled in, you know, Word and Excel and then Outlook and all these other things. I think it would be very similar for the foundation model companies. I think that the foundation models would be like table stakes. That'll be their kind of win, but also a very sticky moat. And even if they're not the most profitable businesses themselves, it will give them big advantages kind of on the application layer. And then one other thing that I think will happen, you know,

the cloud companies have giant moats just through the, KAPX dynamics of cloud, like needing to buy all this hardware and, you know, innovate with hardware and stay there is a big moat. I think these foundation model companies are going to be, I think there's going to be way more value that accrues and will be way bigger moats some people realized. I think they will all basically have hardware modes like cloud style hardware modes. They will have the like the operating system style, you know, very, very detailed research that's hard for anyone to replicate. And then I think they'll probably make a lot of their profit from applications on top of it.

That's my current thinking. Thinking can change. Yeah. So I know obviously you weren't investing during the original operating system, boom, but your firm, Sequoia Capital was. And so have you had any discussions with the kind of the lineage of the firm or the history and seen how is the revenue? ramp or the business scale different this time than, say, in the dot-com era or in the previous era,

it feels like it's ramping faster than ever. It feels like we're seeing more companies that are hitting a billion in revenue or a hundred million faster than ever. But is that real-based, or anyone that you've talked to that was investing in that era? Does it feel different this time around, do you think? Yeah. I mean, one of the beautiful things about being at Sequoia, as we do have this long history and we get to tap into the kind of institutional knowledge. That said, sadly, Don Valentine died four or five years ago, like early into my time. Rip, what an absolute legend, you know, and he led the original Apple investment. But there's still a lot of Google institutional knowledge in the firm, which is, you know,

not directly operating system, but they created an operating system later. I mean, first of all, the revenue of these companies is scaling insanely just faster than any products in history before for Starlink. So obviously not a foundation model company. But I basically made internally, I made an Excel spreadsheet of AWS's revenue growth in the first 20 years of AWS compared to Starlink. and, you know, Starlink has in five years got into where what took AWS 10 years to get to. And now, like, with these foundation model companies, we're seeing as fast or even faster revenue growth. You know, that said, these are very, I think the business models, like the initial business model is more clear. And the profitability of these companies is, you know, more of the in profitability is in,

insanely high and so you got to discount the revenue growth. But I would just say the biggest lesson, I think, from the past is you have to capture like territory early on. And the doors will kind of close behind you because of these Cappex dynamics and just like lock in with users. Yeah. I mean, you mentioned Starlink. Do you think there's obviously, yeah, it's such a weird company because it's like a space launch company that now is an internet company, ISP. But There's actually a little bit, I'm starting to hear of an AI narrative, just that having Starlink potentially unlocks edge compute or inference in areas that would typically have kind of stranded energy resources. So all of a sudden, if there's some super remote area that has really cheap energy, you can go and set up a data center there and then do inference and stream those tokens over Starlink. Do you think that's an underrated narrative?

Do you think that that's developing on course? Do you think there's any bottlenecks that people should be thinking of within that story? So when we first invested in SpaceX, the part of the core thesis was Internet Everywhere. And I would say like it goes way beyond AI. But I think the Internet Everywhere thesis is huge. And that will be, you know, everything from oil rigs, you know, to airplanes, to boats, to edge AI devices. But I think the bigger thing for Starlink is Starlink just has like a 10x plus cost

advantage for moving data compared to, you know, building new transatlantic or trans-Pacific fiber lines. And in the world of AI, we're going to be moving, these models are going to be moving so much data around themselves. And I just, I think Starlink is incredibly well positioned. to be the pipes to move all this data for AI. And so I actually, I care more about that just because of the volume than some of the kind of edge applications for AI specifically, but those will be big.

And then one other thing, I just got to give a plug to Bitcoin. Plug to Bitcoin. Basically, yeah, let's go. Basically, three years ago, I visited the biggest Bitcoin. mine in the world. Genesis Digital, their mine is near Midland, Texas. It's actually backed by SBF, which is, you know, he got both-thorbit. He had a bunch of good bets. You cannot deny that. Exactly. He got both anthropic and Genesis. But these guys had a gigawatt-scale Bitcoin mine operating three years ago. And already for them, like having, it taught me a lot. And, you know,

Bitcoin mining is the absolute tip of the spear where you need the least amount of data movement, like data in and out, to dollar generated or like power consumed. And so I actually think that was like Bitcoin mining is underrated in terms of how much it's pushed like frontier power generation turned into compute. And I don't think it's a coincidence that Crusoe, you know, which is now powering Stargate, start off as a Bitcoin mining company or that CoreWeave, which is like $80 billion stock as of yesterday, is now, you know, is now an AI data center company. And I just, I think, I think that's honestly the bigger, the bigger theme. Yeah. What's your updated thinking around nuclear? We have

these new executive orders, and it was announced this week that Meta announced the partnership with Constellation to power some of their AI power needs. What's your kind of updated outlook over the near term to medium term? I'm an all of the above guy for energy. Like, we need all of it. We need all of it as quickly as possible. I, as an individual invested in a few nuclear companies, going back like nine, ten years ago, way too early. And to put a little bit more meat on these statements, nuclear is incredible, but deploying large amounts of nuclear is slow. Like even if you deregulated it to zero, I think it would be like a, you know, more than a

decade, well beyond a decade to deploy like a terawatt of new nuclear. Call it 10 years if you did it as fast as. possible for America starting now. Solar is just a way faster way to deploy a lot of energy. Nat gas is a way faster way to deploy a lot of energy. We have been producing in stain amounts of natural gas, which we didn't have the pipelines to actually use. So we were just flaring it a lot of times because kind of like the dollar value per, like when you have a oil well or you're fracking. It's producing, it's emitting natural gas and oil. And you just made so much more money from the oil than the natural gas. And we didn't really care about it. And that started

to flip. And so anyways, I think we have to do all these things. I think we need more natural gas, more oil, way more solar, and then kind of have nuclear coming as the reinforcement juggernaut coming online like 10 to 15 years from now. That's a good framework. Fantastic. I mean, we have to have you back for, you know, an energy deep dive. We know a fair amount of the nuclear and solar entrepreneurs and there's a bunch of people doing really cool stuff. So have a safe trip. Personal plug. I had a seat in the New York Mercantile Exchange when I was like 22 years old. It was insane. Good time. Hey, good luck on the timeline today. I know you're going to go in there. Put on your, put on your hazmat suit and just get in there. Good luck. Good luck. Peace guys. Safe travels. Cheers. Fantastic. Let me tell you about linear. Linear is a purpose-built tool for planning and building products. Meet the system for modern software development.

Streamline issues, projects, and product roadmaps. Go to linear. Next up, we have Jack in the studio. We have an in-person guest. Let's bring them in play some soundboard for me, Jordi. Welcome to the stream. How are you doing, Jack? There he is.

Second time on the show. Good to have you here. What are you wearing today, Jack? We're in the jacket, the TDPN jacket in the capital of capital. Fantastic. There you go. Thanks for coming. Thanks for hanging out.

Here, you can adjust your mic a little bit there as well. Cool. I wanted to kick this off with like a little bit of a rundown on the different foundation labs. We're talking to a lot of them today. And I noticed that Jordan Schneider from China Talk and Dylan Patel ran through their AI mandate of heaven tier list. And so I wanted to read through that and kind of. of get your reaction and then just kind of do like a vibe check and let it and talk to you about what we should be expecting from different labs over the next year it's a little bit of a horse race

so uh up first at s tier they have open a i it's the only foundation lab that made s tier does that feel right to you what are you watching from open ayes yeah i think that's exactly right open i executing both on the product level getting the distribution yep getting into hundreds of millions of people's phones. Yep. But also on the research level, you have people like Nome Brown, people like Aden, just doing this incredible frontier research, O3, I think,

just as a model, impresses me the most of any model that's come out so far. No Brad Lightcaps said in the Wall Street Journal recently. They had 2 million workplace users in February, and they're 3 million now. Wow. So just really exceptional growth. I think.

I was thinking earlier, it'll be funny. Our kids in 20 years will be like, dad, they're making me use OpenAI teams at work. It'll just be like the default, like the Microsoft Teams default. Yeah, I mean, there's a little bit of a narrative that, that maybe, and we can move on to Anthropics in the A tier alongside deep seeking Google, there's a little bit of a meme that like Anthropic is crushing it with developers. They're the default choice for windsurf cursor users, but then Open AI is more dominant with

consumers. But I feel like recently I've heard that it's maybe even. more skewed than people think. Like, it's maybe not like the vibe on X might be, yeah, like, you know, 70-30 OpenAI clawed for day-to-day grab a random person on the street, but it might be even more skewed. Does that feel right to you? Yeah, I think Anthropics really solidified with developers, but it's like totally given up on consumers.

But I think Open AI wants to take that on. I mean, there's rumors about some sort of windsurf acquisition. They're releasing 4.1 and Codex. Yeah. They're pushing hard on coding. And I think that's something to watch from them this summer and going into 2026 is can they secure that. Do you understand the model names at this point? 4.1.

I have access to 4.5. Why would I want to go backwards? Is that, is it, are the models fragmenting to like where I'm going to have to learn a new, a new taxonomy for, okay, if I want to write code, I use this one. If I want to write poetry, I use this one. If I want to do math or reasoning or build a chart, I use that one. Because it's putting more work on me, I feel like. I think Sam said that they're going to try to fix the moment. model naming scheme this summer.

So that's the real thing to watch if they're going to keep S-tier. Is can they get coherent model names? Yeah. But yeah, 4.1, it's cheaper. It's specialized towards coding. It's kind of their 3.7 type of driver. At the same time, I know you're not super up to speed on the Alibaba like Quinn models, but I saw some, I saw some release where Alibaba Quinn released like a hundred different

models. And Will Brown was kind of saying like, this is awesome from a research perspective. Because they have like, they have like one model that's just good at bio and it's kind of like this hyper fragmentation it's the opposite of going in the unification direction it's actually it's actually going more specialization and then maybe you unify that the Md but I don't know it seems like if you're a consumer company you can't you don't really have that affordance right yeah I think in terms of research Alibaba's a bit underrated I mean compared to Deep Sea

gets all this press all this coverage but the Quinn models are really good people are doing you see from lots of people these really cool RL experiments these really cool kinds of things they're lagging behind the US models they're not they're not eight here they're not B-tier, you know, but they're doing some interesting stuff, and I think that's super cool. Yeah, I really wonder if they're, if they have a distribution advantage in China. Obviously, we wouldn't feel it here, but I really haven't gotten up to speed on what is the Chachyptee of China in terms of distribution. Obviously, DeepSeek had that moment, but have they

actually executed properly on the product side? I don't know. I'm surprised that Google hasn't been able to turn their general distribution advantage into an AI distribution advantage. They have these really good models. The new Gemini came out today. It's got really good benchmarks on a lot of things. But they're yet to, I think they're yet to crack distribution. We sometimes say. Did you see that, did you see that mock up that was just the Google search box, but a Gemini prompt? Yeah. It was like if they wanted to go full send, if they really, if they were really AGI-pilled, they would just say, hey, we're done with Google search. I mean, it would destroy their economics and everything. I'd commit to it. I'd commit to it. I think it looks like the model

that they used to power those search prompts right now. It looks, it seems really lightweight, to me, it gives a lot of long answers. When ask 2.5 something, it's always right. Sure, sure. That's interesting. You think that's just like a cost issue? The AI overview box is like, we're just going to hallucinate. It's an hallucination box.

Well, they are launching like the advanced AI search, but it's like a toggle. So you have to find it, which is like always the problem with Google. Well, I mean, they still wound up in the A tier, according to Dylan Patel and Jordan Schneider over to China Talk. Obviously, V-O-3 was like a huge one. And then they also have all those like priced performance things. but I've heard this narrative that maybe some of the hyperscalers are super focused on benchmarking and not even hacking the benchmarks necessarily, but just like just thinking about them.

And a lot of the frontier labs, the independent labs, have just kind of moved on philosophically from caring about benchmarks. Is that the right move? What's driving that? Like, is it, are we in like the post-benchmark era essentially? Yeah, when I think about models and benchmarks a lot, I think, like, which models outperform the benchmarks, you know? When you see O3's benchmarks, they're good, they're kind of what you expect. Then when you watch O3 Think, you see this model, it's actually reasoning.

Sure. When you watch Cloud 4 Opus or Cloud 4 Sonnet Think, it's like, whoa, this is really good. Same with GPD 4.5. I think the Gemini models are good, but they're exactly as good as the benchmarks let on, you know? And I think they don't have the vibes yet. What I want to see is Gemini 2.5 Ultra. If Google releases something with some big model spell, something really cool, maybe that's them catching up. What is the big model smell?

I just don't like the idea of smell at all. Oh, yeah. It's just a weird, it's a weird sense. It might be in-full. It's just a vibe check. Wait, who coined it? I think it was Aiden McLaughlin. I don't know.

That's great. But basically we're in like the intangible period. Is that the idea that's like, unquantifiable? I think, I think Anthropics give it up on really training on the benchmarks, and I think it's done really well for them. You know, you see that they're really good at Sway bench. They're not crushing it on MMLU, you know? But you tried force on it, it's great.

Other labs that are lower down on this tier list seem to have not given up on doing really well on the benchmarks. Yes, yes, that makes sense. I mean, it's possible that, like, you must defeat the final boss to, like, play the end game. Yeah. And so maybe the end game is this vibe check, this big model smell, but the, in the interim, like, yes, like, if you're not, you only earn the right to go into big model smell if you can dominate in all the benchmarks.

There was an interesting moment where 3.7 was beaten on every benchmark. So now for state of the art on stuff again. 3.7 was losing on everything. There was a better model for everything hypothetically. But then if you looked at what you might call like, Revealed Preferences Bench, which is just like, what do people use on cursor? What's going on VAM?

Revealed Preferencesbench.com. Yeah, yeah. So, someone's got to win. Yeah, that was great. Yeah, yeah. 3.7 was pretty high up there, you know, so it seemed like they had something that wasn't captured there. What about cornered resources, data is the new oil?

that seemed like a very silly concept in the moment when everyone had scraped the web entirely and it really felt like data was fully commoditized. Then we see V-O-3 and for the first time it feels like, okay, there is at least one data set that is so large that you can't copy it onto a single hard drive or compress it and it's YouTube and Google owns it. And yes, people might scrape it here and there, but Google has a durable advantage there. But is that the wrong way of thinking about? it?

Yeah, I mean, I'm not sure about the video models. I think it's true that data is like both super, super important, but also has just become like tremendously overrated because the first thing people learned about AI is like, oh, it's a result of the data that goes in. But now that we're unlocking things like RL and better post training, it seems to me like you can have some non-data solutions to some of these problems. Yeah, I mean, that was the original generative adversarial network for image generation was like synthetic data generation and then testing it.

And so like, I, it just, B.03 feels so much like a beneficiary of YouTube. But I don't know if that's just, if we're just waiting and we'll see the next SORA and we'll be like, oh, opening, I figured it out. And like, yeah, maybe they found some like, you know, kind of work around to the data. But really like the vast majority of the consistency and the innovation there was algorithmic progress, not just, you know, quartered resource and data. Yeah. One thing about video models is it's been so secondary, but they've become so in front.

I think that if you showed them both to me a couple years ago, I would be more impressed by V-O-3 than even like Claude Force on it or something, you know? It's just, it's not what I it's it's really really just incredible. Well, yeah, I mean, I think a lot of it just comes down to like the cost of instantiating the thing. And so if I go to, if I go to deep research and I use O3 and I have it pulled together some, you know, 20-minute research paper, it's like that's a few hours of work. Maybe it's a few thousand dollars of like a researchers time. Maybe we're getting up into like PhD level. I could do it on your own. I could do it on my own. But if I actually want to crash of Ferrari through the Hollywood sign with champagne

bottles flying, a custom Hollywood sign that's huge like unless I'm either doing. I'm either renting all that shooting it practically and it's a multi-million dollar Michael Bay shoot or I'm doing it all in CGI. And even to do it in CGI is millions of dollars of rendering. And so even for an eight second clip, it just looks like, wow, I got something that normally would cost a million dollars to make happen. And there's no real, like, textual asset that feels like, wow, this is a million bucks worth of assets.

Anyway, interesting. XAI, they are cooking. They've been, obviously, GPU rich, scaling up. People seem, they're in the B tier here, according to this chart, but everyone's kind of excited about what's coming next. What is your take on GROC XAI? Are they close to the big model smell? That feels like a natural benefit.

beneficiary of Elon's strategy of just go big. But how are you thinking about GROC generally? Yeah, I'm not the most impressed yet. I mean, GROC 3 is good. It's a good model. Sure. It's like a funny thing. Like Glock's whole thing or something that people who really like GROC often say, it's like, oh, it's trained on this real-time X data as this X-Veatibility.

One thing I've tried a few times because I saw it in a tweet. Sure. If you have a tweet you can describe, maybe I say like John Coogan's tweet about bringing media back to Hollywood. Yes. And you ask Glock to find it. It can't find it. You asked O three to find it.

It can find it. Wait, really? 03 can find it. That's so interesting because I feel like X is pretty locked down at the, at like just the WWW layer, right? It's pretty hard to find. In fact, a lot of times I'll post in a post from X and it will have to go to like thread reader unroll and find an archive off of X because it clearly can't access it directly.

But that is fascinating. So that feels that feels solvable. Adam ships tbppn guest.com. Like last week, I had a friend find it Monday. We hadn't announced it anywhere. It's not even visible on the Google search. Really? And O3 found it.

Wow. And he was like, how did you find this? And he was just looking, he asked O3, can you pull together a list of all the guests? And he found the guy. Wow. Randomly, and Google doesn't even find it. Interesting. OTHU is really good at search.

And I think that might have been OLL.Ling on tool use in the blog. Very, very interesting. Also like XAI, it's like not. really much revenue, nearly no revenue yet. You know, at some point you need to start pulling that out. I'm glad they're pushing on the distribution, you know, but things come around. Yeah, makes a lot of sense.

Last one, we'll end with... Probably the highest revenue multiple of any company in history. Yeah? Yeah. Last one will end on Meta Lama, sitting in D-tier, but maybe not out of the game yet. The two interesting bulk cases I've been discussing have been. One,

is there a world where open source, American AI becomes geopolitically important for countries that are slight allies, and they're either choosing between Deepseek or an open source American model, and open AI would not be in the conversation. And then also just, you know, why would you ever bet against Zuck? He has a capital canon that can fire 10 billion at random projects forever. And so the question is, is that enough? What are you looking for from META and Lama in the future?

Yeah, it seems like you hit some issues. recently, but I'm not betting and suck. He's got the capital. He's got some GPUs. They can get together some really great research. I would love to see better American open source models. I mean, I'm not betting on open source in the long term

as maybe the cornerstone of AI. But the fact that all of our American research groups, lots of really smart RL researchers, are doing experiments on Kwan and on on Lambda. It's not great, you know? Yeah, yeah, yeah. So should there's one interesting twist there,

is Quinn has so many different models. Lama has a few. They're still working on rolling out behemoth. But would it be like almost more of an olive branch to the developer community to fragment the models and really focus on hitting researchers? Is that kind of a potential path that they should take? Yeah. I mean, I think it would be really cool if they did that.

It would be somewhat charitable. Yeah, yeah, yeah, exactly. Developers love a handout, but, you know. I don't know. I think I'm curious about what they do on the product level and how they can build stuff in better. On the product level, people aren't incredibly sensitive to whether O3 can search 50,000 websites like we are, you know,

they care more about just having something that's really good, something that's really good to talk to, maybe meta-shift's focused that anymore. I'm not feeling it right now in terms of like when will a meta model grab number one on L.A. Marino or something. It seems like it's going to be some time, you know, but I'm not counting them out at all either. Yeah, I mean, if they can just, yeah, stay on the lagging edge

that could still be valuable in a lot of their product rollouts. I mean, we forgot Apple and the L tier. We do have another guest hopping on in just a minute, but Apple and the L tier, how do they dig themselves out? Is it build? Is it buy? What do you think is going to happen? They could maybe, they have a lot of cash.

They could maybe buy someone. They could buy someone. Yeah. By a lab and then you got to upgrade. There was some of a point that they had some internal models. I wouldn't be surprised if they could train stuff. It's just, look, we haven't seen anything at all. Do you think they're really training on Apple Silicon?

Like you've seen those photos of like all the Mac minis wired together? Does that seem like something that's real or just like AI-generated photo? Yeah. I think non-GPU training ones are going to be bigger next few years. Really? Well, I think the TPUs for Google too. Sure. Yeah, so they already have a long time of TSM see they could go do something like a

Traneum or an Inferential chip from Amazon or TPU. Yeah, I mean, with the TPUs, Google has by far the most compute. Yeah, I mean, I guess Apple's pretty good at chip development design. So like, they could do it. Yeah, that one chips are pretty good. That would be their, yeah, that would be their. their advantage if they could build a really strong chip and cut that cost. I wouldn't bet on it, but maybe.

Yeah, yeah. I like the idea of just opening it up and really partnering. The thing over the last 24 hours is one account sharing, it's so over for Google. And then immediately sharing, wow, Google is going to destroy everyone in AI and just like seeing how the post rank. Yeah. Yeah. Anyway, anyone else on here, they got mistrawling F tier port for the French. They're not trying to let chat.

Yeah. I do wonder about Mr. all because, you know, the models are real, but none of like broken out on capability. But there's this question of like if you want a national champion in your country, it might not be enough to just have the foundation model layer. You also might have to go and win in the free market in the application layer. And so, yeah, you could have, even if you had a comparable model, if you're not, if people are going to do chat.com instead of.

of laychat.com, like, you have not won, and you don't have your national champion. Yeah, and there's a, I think there's some truth of this, but there's also the regulatory stuff in the EU. I mean, a lot of releases, I think Vio3 is not in the EU. A lot of releases don't come there. Maybe Mr. Orr just uses regulatory modes to monopolize, not a fun way to win,

but maybe that's the bull case at this point. Yeah. What was your reaction to the conversation back and forth with Dwar Keshe and Shalto? all about about the the the the this debate over over

I forget it was like spiky intelligence and how you actually train someone there's so many different things we see that the models are really good at one thing and then they fail RKGI what's your overall timeline right now

how are you looking yeah Dwell Keshe is the point that you can't kind of do this continuous learning this like short run continuous learning like you can tell me Jack I want you do something different is being intern. I figure that out. And context is a weaker tool than that.

And I think that's absolutely true, and that's an unsolved problem. I don't know how much that moves my needles on timelines. Like, one thing that could be true is just that opening AI anthropic makes some, like, SWI agent, and it starts accelerating their AI research, and they just get, like, really efficient algorithms really quickly, some architecture that just destroys the transformer. Yeah. But I do think it's a meaningful unlock if that could be solved. And I think that's sort of like mid-level memory type of stuff is really interesting.

All solutions around context, around a wrapper. Well, this is fantastic. We have our next guest. Thanks so much for hopping on. We love it. We love an in-person guest. For sure. Thank you so much.

Next up, we're heading over to Google World. We have Arush from Google. He worked on the deep research project that dropped from Google in 2024. It was a full year ago. It was in December, technically. But very excited to talk to him about that product, all the things are going to deep research. So we'll welcome him to the studio if he's available. How you doing?

Good to have. Hey, what's up, guys? Thanks for having to happen to you. Not too much. We're having a great day. We got a great lineup and excited to dig into it. Would you mind kicking us off with just an introduction on yourself and a little bit of the history? I want to hear about the history of the products that you've built at Google, what the interaction between research.

and product looks like and what you're excited about. Yeah, for sure. First off, A team, that's pretty good. Pretty good, yeah. Let's hear it for A. There we go. Let's go. Let's go.

Yeah, John's going to have a dog. Good work. Yeah, love to be here there. A lot of, yeah, it's been fun. It's been a fun ride. I, so I'm a product manager on the Gemini team. Cool. I've been here since a little while back when it was called Bard.

The bard days. And barred days. Yeah. And so, yeah, about, I don't know, maybe like this time last year, we started kicking around this idea of deep research. Where one of the things we noticed is a ton of people come to the product and ask, like, seeking to learn something or asking questions and kind of doing researchy type things. But if you ask really hard questions, one thing we noticed is the model would just give you like an outline of an answer. It wouldn't actually tell you something very comprehensive. So we kind of just ran with a hypothesis of like, let's take off the constraints of like it has to respond within a few seconds.

It has to use this much compute. Like let's let it. Let's just see how far we can push what the model can do. And this was before thinking models or anything and then like kind of and any of that good stuff. And so we kind of worked on this idea for a bit. And then we launched in December back on Gemini 1.5 Pro was the model that we were using back then. We launched deep research as a bet to just see, like, would people be into something that makes you wait 15 minutes, but gives you something comprehensive? I'm happy to wait, although I do want it to speed up.

Questions about context window size. How important is that million token context window? That feels like it's been a unique Google feature for even longer than I expected. that advantages in AI seem to last days, maybe weeks, before another model comes out that meets or is roughly around the same capability. How important is large token context windows in deep research like products? Yeah, it's huge. It's like really what enabled us and kind of gave us the confidence that this was even with trying. I'd say that the long context enabled us to do basically be very recall forward and really cost a very wide net as we research the web and try and find

gems of information that we then stitch together. And so that that was like I think our biggest differentiator and really allowed us to build this product. The other thing that long context allows us is like once you've finished your research, not just the report, but everything it read along the way is in context. So you can keep asking questions going deeper within Gemini. And even if it's like a tit bit of a fact that's not in your report, if it's in, if it's been read at some point, it'll be able to retrieve that and give you that answer. So it also helped sort of beyond that first turn, keeping a good experience. Yeah. And then reasoning models with like the next big, big step jump for us.

Yeah. Allowing it to then do more critical analysis. So in terms of like actual product design, I'm interested in in the direction this goes here. You could see one world where, uh, the models are baked down into silicon. everything's running even faster. You're distilling the models and all of a sudden I'm getting a deep, a 20 minute product in two minutes or even 20 seconds. You could also imagine a world where what's possible if the economics work such that I could request a two hour research report or a two day research report. How are you evaluating those? What would you personally be more excited about and what do you think users actually want because stated preferences and reveal preferences are often different, or do we wind up with both? Yeah. So one of the things that we noticed, one, when we launched this, we had no idea people would be willing to wait.

Like every metric at Google from the day it started is reduced latency and like all metrics go up. Yep. Right. So this was definitely a bet where we were, like a lot of people thought we were crazy, where we're like, we're just going to take a ton of time and people will wait. One thing we noticed is that like after about a minute or something like that, people are fine.

Like people will go away, do other things, come back. We'll send them a notification where it's ready. So the big pleasant surprise for us is like people don't mind waiting. I'd say, so in terms of like efficiency's gains, one of the things that we're more excited about is like, okay, if we can make models more efficient, instead of reducing down the research time, can I give you just a way better output? Like can I use can I bank that savings and give you something way more insightful, way higher quality? I'd say the other thing is like even if I could give you like a deep research answer in 15 seconds, it's going to take you 15 minutes to read. So there's also an aspect of just like how much do you want to consume this, right? So for us, we're not as stressed about like, can we make this faster?

Can we make this quicker? I do think there are probably other points in the like latency comprehensiveness spectrum that people might like. We picked like one extreme of like let's just go super hard and build. the most comprehensive long, uh, thing that takes a while. Yeah. Um, but there might be totally other points people are. Yeah, yeah. Sometimes I noticed I've generated like so many de various deep research reports across

all the different apps that, uh, all like I'll follow it up with a prompt. Like, okay, like, yeah, boil that down for like 10 bullet points because like I don't have time to read that. And then I'm like, wait, like, maybe I should have just asked it to give me 10 bullet points in the, and I just like burned a bunch of GPU cycles. But I get, I guess the question is, you just oscillating back and forth between the two until you kind of understand the subject. Exactly. But I guess the question is like, is there a product or is the natural evolution of just general prompts that as as algorithms get faster, as these models run faster, that there is a deep research amount of work that happens within a few seconds between every response.

And basically the question is like, how much can you port from the deep research product and strategy and design back into? just your average LLM interaction. Yeah, I think there's definitely a lot of learnings that we can kind of start upstreaming, really around like being able to form a plan, follow that plan to do that sort of multi-hop steps of like search iterating, like finding insights, changing your strategy possible before going back to the user. And so you're kind of starting to see this in like 2.5 pro and stuff like that. And I, you can imagine that that will continue where you will see more like, like,

like mini deep research or more sort of like planning and and sort of like iterative reasoning before like giving you an answer. And yeah, that could just start getting faster and foster and faster. Then you start just getting like way more insightful or comprehensive answers. Are there any other interesting areas? I mean, deep research feels like one of the first like really solid product market fit experiences in I guess like agents broadly. Are there any other areas that you're excited to think about knocking down with either different products or just maybe just like cool uses that you've or developed or as a user patterns that you're leveraging that's maybe go beyond just the average like I need a I need a research report.

Yeah, totally. So I think there's like a few different angles that like I think a lot of people are exploring. One is you kind of point out like what does a two-hour research look like? What does an overnight deep research look like. Yeah. If you can have like a very well defined problem where like, you know, we have early experiments at Google like AI co-scientists and stuff, right? Like you could run that overnight and it can come up with like novel scientific hypotheses. Right.

So there definitely is an angle of like if you can define a problem and an outcome really well. Applying more compute can actually get you like better and better answers, right? So there's definitely an angle of like other whole new classes of problems where you can even go even further with deep research. there's a second aspect of like you know we had the chance to go like meet a bunch of people who are like researchers at the Fed right and they were telling us how they used research and it's often like a very different thing right so like I showed them this example where I was like hey there's this like funny law in the US called the Jones Act where like

any two ships between like two US ports have to be like built in America crude by Americans yeah and like drives up shipping prices but only for like Puerto Rico Hawaii in like Alaska right and so I was like do an economic analysis of the Jones Act on on like the economy of Hawaii right and it like did a first principle analysis did some really interesting things like looking at well like how much is a three three and a half thousand shipping route like say from like Mexico to South America and then that's like a baseline price to compare against and like I thought this was amazing but then they were like that's not how we would do economic analysis like they It would be like first I'd explore like what other studies there are.

Like then I'd explore like what kinds of methodologies are out there. Then I might like ask a bunch of false follow-up questions about like what data sources or like data sets did people use to do this research. So there's definitely an aspect of like another angle of like if I really want to help people with research. It's about like nailing this sort of like synchronous asynchronous paradigm and helping people kind of do more of that like iterative process rather than just like ask a question, get answer and move on and in victory. And I think that's that's kind of a product challenge with like figuring out the right the right interaction model for that. And the third is there's just like outputting the outputting an answer at the right like level of abstraction that you work at, right?

Like a financial analyst doesn't think in terms of report, right? They think in terms of like the spreadsheet or the financial model, right? And so if I want a DCF, deep research can build like a great DC discounted cash flow model for me. But like I don't want it in a report. I want it in a spreadsheet or I want it in an. where I can play with the variables and see the different outcomes. And so you'll also see the line between reports and other kinds of artifacts starting to blur.

Or even just like what like what does it mean to like build an answer, right? And that could take like a much wider space. That's super exciting. Yeah. I mean, I've seen obviously Gemini, we probably can't talk about the roadmap too much, but I've seen Gemini pop up in a bunch of different areas. And I haven't seen that the deep research version of whatever that instantiation is. Yeah, maybe my last question is like, how much time are you thinking about working and making the, you know, as a product manager on Gemini? How much time are you thinking about making Gemini better versus sort of fighting for distribution outside of Gemini and kind of across the Google ecosystem?

Because part of unlocking the value of Gemini is just making sure it's in the right places and place sort of contextually across, you know, everything from, you know, Gemini.com. You've worked hard on this. Just ask for the I'm feeling lucky button. Just give us that. We think you earned it. Search, you've earned it. It's a great product. I just click, I'm feeling lucky, or burn 40, 40 GPU hours on this new research report.

Yeah, that would like instantly melt all of our servers everywhere. This is the biggest hyperscale. Yeah, we need more GPUs. The TPUs. TPUs. TPUs. Yeah. TPU.

Okay, so, ASML, get cooking. I believe in the TPU. I believe you've earned the I'm feeling lucky. I haven't hit the I'm feeling lucky button in years yet I use Gemini all the time. Yeah, yeah, yeah. This is what the users want. Yeah, we just need 10 more TSMCs, I guess, to start Fabin. Anyway, sorry.

Series answer. Yeah, no, the series answer is like the Gemini app is like a great place for us to like prototype, see what like hits with, like really works with people. A lot of the users, they're very intentional when they're coming to the Gemini app. Like, they want to use an A&I experience. So it's a really great place for us to, like, put stuff out there, see what works, see what doesn't. Some things we put out needs more time in the oven. And then over time, you'd imagine that then like those insights or things that really start work, you'll start seeing in other Google products as they make sense, right?

You don't want to like overclutter a UI, but you'll start seeing, yeah, things like deep research. Yeah, because it's a very different user, somebody that's coming in saying, I want AI versus I just want to do certain things. Yeah. Right. And, yeah, they're totally different archetypes. It's a fascinating challenge. I'm sure it's even more challenging at your scale, but thanks for all the hard work and pushing the frontier forward. It's been a pleasure talking to you. Yeah, come back on again soon.

Yeah, we'd love to talk to you more. Thanks so much, guys. We'll talk to you soon. Bye. Fantastic. Next up, we have Oliver Cameron. I have a good story. We'll bring him into the studio.

But I believe he was the first person I ever interviewed for a YouTube video years ago. I was doing a whole video essay about cruise. the self-driving car company, and he hopped on a Zoom call with me just like this one, and I recorded it and threw clips in the video. It was very fun. And then I wound up doing more interviews after that. So Oliver, good to see you. How are you doing? What's going on? Welcome. I'm doing great. Thank you for the opportunity. Would you mind kicking us off with like the latest and greatest introduction because you've done a lot in your career, but you're on to something new?

For sure. So spent about eight years building self-driving cars, incredible time. I mean, just to see that technology go from barely being able to keep in a straight line to navigating downtown San Francisco with no human behind the wheel, just a sign of where things have gone with machine learning. So I had a blast doing that, built my own company, sold that company to Cruz where we met and unlocked that time. Left Cruz in May of 2023, decided to start something new, and both me and my co-founder, who also was from self-driving cars, we were both very much inspired by Pixar. I think it's just a very special company, right? Everyone kind of recognizes Pixar as this sort of iconic storytelling company.

And we really put our heads together to think about what a modern reincarnation of Pixar would look like. So that company is called Odyssey and we're an AI lab that's really focused on enabling entirely new stories to be told. And walk us through the first product that you launched. I played with it earlier. It was mind-blowing. We'll pull it up while you're talking. Sure. Yeah, we just released a research preview of something that we call interactive video.

And it's effectively AI video that you can both watch and interact with in real time. Yeah. And we think this will become an entirely new form of entertainment. You know, you've got film, you've got games, you've got all these mediums that have been around for a while. We think that there is an opportunity to invent a brand new one where effectively a model is responsible for imagining film and game-like experiences in real-time. Yeah. But you can interact with.

There's no game engine behind all of this, no heuristics, no rules, just a model that's learned pixels and actions from tons and tons and tons of real-life video. Yeah, we're showing it on the screen right now. And the production team is controlling it with the keyboard, W-A-S-D, like it's a first-person video game. And they're walking around this field with trees and windmen. mills and they can actually choose to go up, go inside buildings, and it's all being generated without the use of a game engine, and then they can switch over to a different environment.

And so, I mean, I have tons of questions about how these different, like, you're not doing photo scanning, you're not doing game engine stuff, traditional 3D pipeline, but the data must come from somewhere. Love to hear about that. And then also, I noticed the space button doesn't work. I wanted to jump around and start bunny hopping. When are we getting a space button added to this thing? Anyways.

Anyway, sorry. Isn't it trippy how those pixels are literally streaming from a GPU cluster, probably in Texas. It's so crazy. And now we're streaming them via Zoom in real time. My question is, do you think that Odyssey can be a really breakout app for VR? Because when I see that visual, I feel like that it could give someone. on the sense of being able to explore lands that don't exist, which is like very fat, like once it's fully immersive, it feels like.

It's funny. The windmill thing, because I remember the very first Oculus demo that I ever did, I was walking around a windmill and it's still in my mind years later, but, and it was amazing, but it was just like one little windmill and then you couldn't go any further because developing, like virtual assets is really expensive. And so you play a lot of these VR. games and you know it's a couple hours or 30 minutes but if you take a procedural approach or a generative approach you all of a sudden have infinite content i think what's really important to

note is in film and game incredible things can be made right like insanely good things that wow is all the time and the money it takes to create those things is ludicrous and it's only getting more expensive not less expensive over time yeah so i feel there will be continuously a place for these sort of like handcrafted things. And they'll be very important. But if we just think about a model that's trained on literally decades of video that's then able to imagine stuff in real time with no pre-production costs, no post-production costs, and do that literally in real time like 33 milliseconds, it just that that's where it gets really crazy.

And what we showed in the research preview is just like this time. glimpse I think of what this stuff will become. VR in particular is like the most hardcore application of this from a technical perspective because the resolution required for VR is like insane and the resolution that you saw there you can tell it's low res it's like 300 pixels wide so there's gonna be a leap that needs to happen there to get to VR level res but I'm confident that Odyssey 2 you'll have it you'll have it dialed oh yeah two yeah give us the stats That's how many, like what numbers can you give us about the progress or adoption?

You just launched this, I think this week or last week. It hasn't been very long, but how has the response been quantitatively? Oh, it's been incredible. So we launched a week ago and since then we've served 250,000 unique streams, meaning 250,000 people experiencing what you just saw, which is insane. Market clearing order inbound. Let's do it. Love it.

Congratulations. That's fantastic. On the question of resolution, there's a bunch of amazing AI upresing that's happening in various parts of the pipeline. There's some server-based upresing that can happen. There's some on-device upresing. So is that, are you counting on that technology breaking one way or another? Does it matter? Will it be a combination of both?

How do you see that developing? I think a way to think of this is where video models were a year ago is where real-time video models or world models will be today. Yeah. And what that really means is that you look at the res. Remember the Will, everyone remembers the Will Smith Spaghetti video. Was that like one year ago or two years ago? It wasn't long ago. I think it was just over a year ago.

So fast. There was definitely better outputs like spaghetti. It was like the weirdest thing at the time. Although gymnastics today, I'm sure you've seen that. That's really tough. video models today. But that's all to say that I think the res and the visual quality improvements will come from the model itself, not like some secondary piece of infrastructure to up res.

Just because, I mean, think of what a language model was like to use two years ago. Like how fast was it in response time? Really quite slow, right, compared to today with like just stream of information, straight-year eyeballs. Say we'll be true of these models. Like we'll crank out larger resolutions, faster frame rates, more actions, more things you can do, all that sort of stuff. Yeah, and I guess, I guess importantly, like, GPT4.5 is not GPT4 up-resed to 4 or 5. It is a different model.

We're walking around what looks like the gloomy English countryside right now. And I think the production team is going to try and go in that house. It is really, really so wild. I noticed that there's a there's a time limit. You have a tropical island demo because this, this, I love the English. I love the English country side, but. It's very foggy. Yeah, I notice that there's like a two minute timer when I sign in.

Is that so the GPUs don't melt? I mean, I assume you've raised money and you're maybe burning some money with these demos. But break down kind of like what your limitations are and how you see them evolving. Yeah, for sure. So the timer is that. because each session is served by a single GPU. So each user gets a GPU, the model's running there, and that's beam to the user directly.

And really quickly, when you say single GPU, you don't mean rack, you mean like 1A100 or something like that? 1 H200 per user. Got it. And there is a clear path to dividing the GPU to have multiple sessions per user, but today it's one. And we want to really crank up quality frame rate,

all that sort of stuff. It makes me feel great to know that I'm getting, you know, the sort of one-on-one attention from a H-200. Yeah, yeah. It's an honor. If you're at a retail store, it's not a great experience if somebody's bouncing around. Exactly. I'm being individually served. I like being served by the chip.

This is like an airmez level. By Jensen. Yeah, exactly. Yeah. So $2 an hour is there or thereabouts how much that costs, which, you know, over the course of multiple users, it's not too bad. Yeah. I think Netflix is like $5. five cents, ten cents an hour, something like that to stream video.

So we're a bit of a ways away, but you've got new chips coming, just model optimizations. It won't be long where we're having a single GPU per user, all that sort of stuff. For this launch, we had something like 360 H200's prepared, get to scale it up a little bit, just because we had lots of demand. But that timer is there just to make sure we're cycling through lots of people getting a taste of us. But yeah, I think fundamentally the idea that you could have a model stream stuff to any screen is really powerful. Like that experience you saw there works just as well on an iPhone, on an Android, on a TV, anything like that. And it's all just action conditioned of a web RTC, which is probably what Zoom is running on.

So the action is just sent over the wire to the model. The model then conditions the pixels it's about to generate based on those actions, sends the pixels back and just that loop every 33 milliseconds. It's firing. So, I mean, the path to HD or 4K seems pretty clear to me. What about the path to consistency? That feels really difficult. You need essentially like a really long context window to know that, okay, I dropped my mythical sword on that piece of the ground.

I went away and then I came back. That's like textbook just put it in a database, but it seems like the future might not be that. So how are we thinking about that? I guess the bigger question is like, what's the response from the gaming community? Is this something that can be a tool and a piece of a pipeline instead of completely replacing the entire traditional pipeline? So most research on interactive video before has learned from games. So lots of folks will have seen Oasis from Descartes and Minecraft in a video model effectively or Quake.

That's often used in video models. And I think the gaming reaction to that is quite negative. typically. I mean, you saw the Carmack back and forth, right? Where Carmack was like, this is amazing. I love it. And somebody else was like, this is stealing developers, yeah. And I think it's important because the way that people envision that is like, oh, what's the best this could become? It could become like remixing of games.

And that's one way it could be. I think people see what we have and they think, oh, this is like a world simulator eventually. This is the matrix or like whatever they project on it. Yeah. So really, one thing we're trying to avoid is, like, for the first few generations of this, people will put, including ourselves, like this picture of what existing games look like onto this. Sure. And it's like the iPhone when it launched, right?

People ported desktop apps to the iPhone. And it kind of worked, but it kind of didn't. It wasn't really embracing this new medium. So I think the long story short here is like stuff that is integral to games today, like multiplayer, like state. like scripting, all that sort of stuff. Let's question those assumptions. Like, how should those things work? Let's make it model native.

Like maybe memory in this model is very different than memory in a game or state in a game, multiplayer in a game, all that sort of stuff. And that's probably going to lead us in the short term to more like glitchy weird experiences. Though the memory as it is by the model is a feature, not a bug, I don't know if you guys have seen like the backrooms or like these kind of glitchy weird. Yeah, yeah, yeah. It was a completely different type of game design. Exactly.

Yeah, the up, down, left, right, A, B, A, B of the future will be like, drop your bad sword on the ground, walk around the building three times, come back and it's enchanted. Yeah. Because the model hallucinates that you've upgraded or something like that. That'll be fun. I also think that one important thing here is that in language models, one of the things that's happened in the last year is, in many cases,

they've crossed this threshold of realism for certain applications. So, like, people literally fall in love with language models. right? Yeah, yeah. The same, like, emotional feeling they have when they meet a person they fall in love with is happening for them with a language model. And that's because what they're seeing on their screen is, like, so realistic. It's like crazy real to them.

And I think the same will be true here, where once these pixels, these actions feel so realistic, which eventually they should just get into the data, giving the models, and advancement, there'll be things that they do in these worlds or things they feel in these worlds, which they just can't feel in video games today, because games are just capped by computer graphics and, like, human dev time, and budgets and everything else. But they'll walk down the street, they'll see someone, and they'll be like, wow, that person looks so real.

And they'll go over, they'll, like, high-five that person, all, like, on a screen, right? And they'll just feel something. Like, they'll feel like a heartbeat raise, you know, stuff like that. Totally. So that's an application that you can't do in games today. That's just different and new. So that's the sort of stuff we're really interested.

Well, that's going to be a wild, wild future. But thank you. We'll have to have you back and check in on progress. Yes, fascinating. Definitely the day that seven, 720P drops or whatever the next version is. We're excited for this. But thanks so much for joining.

This was a fantastic conversation. We will talk to you soon. Cheers. Have a great rest of you today. Thanks so much. Next time, we have a returned guest Michael McNano from Lightspeed coming into the studio. Are you getting enough to hit the gong? He's going to talk about competition between the Foundation Labs and the app player.

Well, welcome to the stream. Michael, how are you doing? Boom. Good. Good to see you guys. Congrats on the new studio. Thank you. It's been a lot of fun. I like the upgraded gong too.

Oh yeah, Gongs much bigger. Everything's a really nice touch. Yeah, we're working on even bigger. Florida ceiling. Really? Oh, yeah. Also, it's a funny day to just be so hyper fixated on AI because you probably haven't seen the timeline. Tesla's down 17%. 17%? It's just absolute mayhem. I mean, there's an AI narrative there, right? Yeah, there's definitely an AI. But that's not what's driving it.

But anyways, Michael, it's great to have you on. Wanted to get some kind of updated thinking from you on the tension between labs and the application layer. We saw the news with WinSurf and Anthropic today that had more to do with a potential acquisition. And even when we talked to the founder of Granola, we were talking about the competition between Notion and Granola with these, it's a founder-led kind of previous era. scale up unicorn SaaS company can that company bolt on AI but then now

we're seeing competition from the foundation lab so we'd love to get your lay of the land what are you seeing how are things shaking out what do you think the next few months or even years look like yeah it's pretty interesting right like if you think about the big companies that startups previously built on the backs of the Googles

the Amazon's the Microsofts you know it felt like there was this really healthy sort of symbiotic developer ecosystem where the incumbents supply resources, the developers sort of buy and extract from them, and they build really, really big businesses on top. I think what we're seeing now, to your point, is these labs are building developer ecosystems, but then they're very intentionally and overtly going head to head with the developers that are building on them. And I think this has a lot to do with context, right? So if you think back to the internet, you know, in startups 10 years ago,

So everyone said content is king, you know, content is king. Then distribution was king, right? It was all about how do you get in front of users? It's, we're starting to feel like we're entering the phase of context being king. These models are just hungry for the most and the most unique context possible. And so if an app player company emerges and has a new type of context and data that the models don't have great exposure to, it's a great signal to point in the direction and say, we're going to compete head on. And so I think that's what we're seeing now. And yeah, Nabil, Nebile Hyatt, a great investor from Spark and I, we often talk about how the war for context is happening now. And I think that's what a lot of these modes represent.

How do you think app players should, app, app player companies should respond as to just double, triple down, go way, way deeper, focus on workflows that the labs maybe don't have the resources to fully pursue or is it focusing down on specific niches? I'm curious what the, I think the right approach is. Well, we definitely get into that. But maybe first, what I would say is, you know, I tweeted something yesterday that occurred to me after the big announcements for OpenAI in that, you know, the big incumbents, which we talked about a little while ago, sort of like the winners of the cloud era, it wouldn't surprise me if all of these new, you know, these new competitions between the labs and the apps actually drive the apps and the startups right back.

to the incumbents, to the Googles and the Amazon's of the world. I have to wonder if some of these things actually act as a tailwind for models like Gemini and maybe give a little more credence to the argument that like Google is actually going to be the winner here because of all their distribution. So I think that's one potential. You mean driving back of being like, I'd rather work with Gemini because I don't think they're as likely to kill me? Exactly.

Yeah, exactly. It's like, hey, we trusted them with the cloud and that worked out, right? like should we now trust them with AI more than we trust the labs? Yeah, I mean, their narrative even goes a little bit further with Microsoft, which has been completely like, oh, we will host every single model, we'll let you reroute really intelligently between them, like super, super friendly developer ecosystem. And so, I mean, certainly they're building stuff into co-pilot into Microsoft 365,

but it does feel like they're much more willing to partner. Yeah, Sotia seems to have real conviction. You know, he had the quote from last week, platform platform platform and hosting deep seek is an example of that right a lot of people would have thought oh he's not necessarily going to host that model because it felt like a shot across the bow at open a i but he's committed to supporting open source yeah yeah yeah he wants it all interesting i think you know also going back to your question jordy i think all of this is just going to make for a more intense uh faster moving market like i think more than ever before you

You have to ship, you have to get users faster than anyone. You have to sort of like reach escape velocity quicker, which I think it's just going to put more and more pressure on startups to move even quicker than they already are. You know, I feel like cursor is a great example. I feel like an earlier iteration of that product, you know, it probably would have been easy to sort of write them off and be like, oh, you know, a lab is going to do this. I mean, now it's like they're so big, they're so far ahead. It feels like they've really established themselves and likely have a

good shot of breaking through. I also wonder with Cursor and Winsurf and Devon and some of the DevTools markets, like it feels like just such a new market that even if it's somewhat winner take all, there's just, it's so positive sum because it's it's adding efficiency to the most, like one of the biggest labor pools. And so when we talk to the cognition folks, as the reaction to Google and Open AI launching Devon competitors. They're like, well, we still grew 40% last month or something like that. And so, you know, I wonder like in in code gen

where it's such a new market that it's not, it's not directly competitive with anything that exists, so it's less zero sum. I'm wondering if the note-taking market feels similarly to you or were you seeing granola or other companies kind of act as more drop in replacements for existing tools? Yeah, I think it's a great question. Yeah. So we backed Grinola really early on because we knew Chris and his co-founder and we loved those guys. We didn't know what they were building. We knew they were going to build something in note taking. But we said, you know what? This market's going to move fast. We trust these guys. Let's go for it. And I think, you know, somewhat to your point, there's been all these no takers before. Like Granola wasn't the first no taker. There was fireflies and otter and all these things. But I think Rinaloa has done a really, really, really.

really good job of, you know, getting out of the user's way and establishing trust with the user. And I think that, you know, that that seems like a small thing, but I think that trust thing is going to be really important if you go back to what I said about this context being king. Like, who are you going to trust to take this context or take this like really, really important, you know, proprietary part of your work? In this case, your meeting notes. You know, a lot of people say, we trust granola. Are they just going to hand it over to any old company?

that says, hey, now we want to screenshot your entire computer and suck every last piece of data out of you. And so I think part of it is to your point, like getting in early, getting big really, really fast and establishing that user base and that market before it really matures, but also in a way that users just really trust you. And they're not just gonna rip you out

just because some other bigger company offers the same thing. Yeah, that's a good point. Do you have any more microreactions to specific integrations? That seemed to be one of the big, things that Open A.O. was pushing on was integrations with Google Docs and Drive and your email. And that feels like adding that extra context is potentially the next thing people are clamoring for. How important is like the biz-deaf side of this business, in fact?

I think it's really important. I think it's really great that Anthropic started the whole MCP protocol. Obviously, lots of others are adopting that now. But I think, To your point, we're now going to start to see the battle lines being drawn. Like, who are we willing to integrate with? Who are we not willing to integrate with? Where is it, you know, are we open or are we closed? Where is the data going to go? Where is it not going to go?

I think we're going to start to see those alliances and those allegiances form. And I feel like we've seen. Back in what, like 2007, 2010 era. Social media. They have an API. It's amazing. It's like, well, like, you don't know how much that API is going to cost. If it's $10,000 per day or something, that can completely upend your business.

And so actually thinking about how that dynamic develops is almost more important than the standard. Although I'm very glad we have a standard. That seems great. But each company is going to have to decide where the value accrual really lands. And then who knows, maybe there'll be some antitrust in 20 years like we're seeing with Apple. Yeah, the big question around trust that I, You know, it's an evolving situation, but a California judge, I believe it was yesterday or the day before, ordered Open AI to retain records of sort of, forget what OpenAI calls it, but if you have like a disappearing query, a judge ordered them that they have to retain that. They obviously said that's a huge outrage breach of privacy with users.

So incognito mode. Yeah. It's like not incognito. Yeah. Yeah, and that more seems like an issue with the court and the specific judge, you know, having this massive overreach around privacy. But privacy in this era when people are more willing than ever across every app to give them all sorts of data. Yeah, and you have a direct incentive to reduce the level of privacy to get better results. Like if the model knows what kind of car you drive when you ask it for new tires, it will give you better recommendations.

So you want to lean into being anti-privacy to get better. better results. There's this the the world is definitely bifurcating into pro-privacy or like fully a GI-pilled folks and and there aren't that many people that are in the middle so obviously we will have to figure it out as a democratic society ultimately vote and hopefully sort it all out in the courts but thank you so much for stopping by this was Michael always pleasure we'd love to have you back talk to yeah I mean guys I just want to tell you you know I I don't really aspire to ring the New York Stock Exchange bell one day I aspire to hit that gong hit that gong hit that gong Well, next time you're in Los Angeles.

Come by. Come by. Come by. Great to see you, Michael. We'll talk you soon. Bye to see you. So we have a generational crash out going down on the timeline. We got a new post from Elon.

I'm going to read it out. He says, and this is your live reaction, John. Time to drop the really big bomb. Real Donald Trump is in the Epstein files. That is the real reason they have not been made public. Have a nice day. DJT. Wow.

That is a big bomb. But didn't we already? know this because isn't there that picture with Trump and Epstein together? We're really in I mean, yeah, yeah. The business story. I want to go back to AI, business and technology. The business story here is that Tesla's down 17 percent. DJT is down 7 percent. Trump coin is down 10 percent. Wow, they're all fighting. This crash out on both sides is not good for anyone. Well, you know what's interesting. You know what's not down? Tocons generated, baby. We're still generating tokens every single day.

The relentless march of artificial intelligence continues. So the other thing is Elon shared, or sorry, Trump shared on truth. It's funny they're battling on their each. Different social networks. Every billionaire should have their own, you know, social media network to get the word out. But Trump said the easiest way to save money in our budget, billions and billions of dollars, is to terminate Elon's government subsidies and contracts. I was always surprised that Biden didn't do it.

Wow. So Ashley St. Clair is saying, hey, Donald Trump, let me know if he had any breakup. advice. And Dan Primack says this cannot be a comfortable day for David Sacks. On the other hand, it's just the best day for Sam Alman. Well, well, we have someone from Open AI here. We're going to stick to technology and business, but welcome to the show, Mark Chen. Good to see you. Thank you, guys. Awkward day. But I'm excited to talk about deep research. I am excited to talk about AI products. Would you mind introducing yourself and kind of explaining what you do because Open AI is such a large company now, and there's so many different organizations.

I'd love to know how you interact with the product and the research side and anything else you can give to contextualize this conversation. Yeah, absolutely. So first off, you know, thanks for having me on. You know, I'm Mark. I am the chief research officer at Open AI. So in practice, what that means is I work with our chief scientist, Jakub, and, you know, we set the vision for the research org. We set the pace. We hold the research org accountable for execution.

And ultimately, we really just want to deliver these capabilities to everyone. That's amazing. In terms of research, I feel like a lot of what happens in the research side is actually gated by compute. Is that a different team? Because what if the researchers ask for a $500 billion data center, that feels like maybe a bigger task? Yeah, it is useful for us to factor the problem of research and also kind of building up the capacity to do that research. So we have a different team. Greg leads that.

which really thinks holistically about, you know, data sitter bring up and how to get the most compute for us. And of course, when it comes to allocating that compute for research, you know, Jakob and myself do that. That's great. And so what can you share that's top of mind right now on the research side? There's been this discussion of pre-training scaling wall, potentially the importance of reinforcement learning, reasoning.

There's so many different areas to go into what's actually. driving the most conversations internally right now. Yeah, absolutely. So I think really it's a really exciting time to do research. I would say versus two or three years ago, I think people were trying to build this very big scaling machine. And really the reasoning paradigm changed a lot of that, right? You know, like reasoning is really taking off.

And it really opens this new playing ground, right? It's like there are a lot of kind of known unknowns and also unknown unknowns that we're all trying to figure out. It kind of feels like GPT2 era, right? Where there's so many different hyper parameters you're trying to figure out. And then I think also, you know, like you mentioned, you know, pre-training, that's not to be forgotten either. You know, today we're in a very different regime of pre-training than we used to be, right?

Today, we can't treat data as this infinite resource. And I think a lot of academic studies, you know, they've always kind of treated, you know, you have some kind of finite compute but infinite data. I don't think there's much study of, you know, like, you know, finite data and infinite compute. And I think, you know, that also leads to a very rich playground for research. Do we need kind of a revision to the bitter lesson? Is that a refutation of the bitter lesson? Or do we just need to rethink what the definition of scaling laws looks like?

No, I don't think of anything as a refutation of the bitter. Really, like, our company is grounded in we want. simple ideas at scale. I think RL is an embodiment of that. I think pre-training is an embodiment of that. And really at every single scale, we face some kind of difficulty of this form. It's just like you've got to find some innovation that gets you past the next bottleneck. And this doesn't feel fundamentally very different from that. What is what's most important right now on the actual compute side? We heard from Nvidia earnings that we didn't get a ton of guidance on the shift from training to inference

usage of Nvidia GPUs, but it feels like it must be coming. It feels like this inference wave is happening. Are those even the right buckets to be thinking about tracking metrics in terms of the story of artificial intelligence? Adoption. Because, yeah, I mean, it's like if the reasoning tokens are inference tokens and But they're what lead to higher intelligent, more intelligent models. Like it's almost back in the training bucket again.

What bucket should we be thinking about and and and or are we, how firmly are we in the, the, the, the, the, the, the, the applied AI era versus the research era. Well, I think research is here to stay. And it's for all the reasons I mentioned above, right, it's such like a rich time to be doing research. But I do think, you know, inference. is going to be increasingly important as well, right? It's such a core part of RL that you're doing rollouts. And I think, you know, we see 2025 as this year of agents, right? We think of it as a year where models are going to do a lot more autonomous work.

You can let them kind of be unsupervised for much longer periods of time. And that is just going to put big demands on inference, right? When you think about kind of our overall vision, right, we we lay it out as a series of steps and levels on the way to AGI. right and I think the pinnacle really that last level is organizational AI right like you can imagine a bunch of AI is all interacting and yeah I think that's just going to put huge demands on inference right on that on that organizational question I remember reading AI 27 and one of the things that they proposed was that the AIs would actually like literally be talking to each other in Slack does that seem like does that seem like the way you imagine agents playing out, like using the same tools as humans instead of... One agent says, I'm going to go talk with teams and talk with Slack. I'm going to do a little negotiating on a first key basis. But maybe it just happens super, super fast 24-7, or is there like a new machine language that

emerges? Yeah. I mean, I think one thing that's really helped us so far in AI development is to come in with some priors for how humans do things. And that's actually, you know, if you bake those priors, you know, if you bake those priors, And they typically are great starting points. So I could imagine, like, maybe you start with something that's Slack like and give it enough flexibility that it can kind of develop beyond that and really figure out the way that's most

effective for it to communicate. One important thing, though, is, you know, we want interpretability too, right? I think it's very helpful for us today that what the agents do is, you know, easy for us to read and interpret. And I don't think you want that to go away as well. So I think there's a lot of benefits, just even from a pure like debug the whole system perspective, or just let the models, you know, speak in a way that it's familiar with us. And, you know, you can also imagine like we might want to plug in to the system too, right?

So, you know, whatever interfaces were familiar with, we would ideally like our model to be familiar with as well. Yeah. I think it's also pretty compatible with, you know, we hit a big milestone. we got, I think, three million paying business users for fairly recently. Let's go. Yeah, there we go. Let's go. And, I think, three gong hits for three million.

The gong will keep ringing for a while. Sorry, we had to do it. I was hoping you would drop a number. Yeah, yeah. Congratulations. That's actually huge. That's amazing. Yeah, yeah.

But I think what big part of that is, you know, we have connectors now, right? We're kind of going to like G drives. And I think, yeah, you can imagine, you know, like Slack integrations, things like that. I think we just want the models to be familiar with the ways we communicate and get information. Yeah. Can you talk about benchmarking? It feels like we're potentially. Yeah, do you think about benchmarks at all?

Oh, yeah, a lot. I mean, but I think it's a difficult time for benchmarks, right? I think we used to be in this world where you have these human written, benchmarks for other humans, right? And I think we all have these norms for like what are good benchmarks, right? Like we've all taken the SAT. We all have like a good conception of what it means to get, you know, whatever score on that. But I think the problem is the models are already at the point where for even the hardest human written benchmarks for other humans, it's really near saturated or saturated, right? I think one clear example here is the Amy, like probably the hardest auto-gradable

like human math eval, at least in the U.S. And yeah, the models are consistently getting like 90 plus percent on these. And so what that means is I think there's kind of two different things that people are doing, right? They're developing kind of model-based benchmarks, right? They're not kind of things that we would give to an ordinary human, things like humanity's last exam, things like, you know, Epic AI that are really, really at the, at the front of what people can do.

And I think the hard thing is it's not grounded in intuition. Right? Like you don't have a lot of people who have taken these exams. So it makes it harder to kind of calibrate on whether this is a good exam or not. One of the exciting things that's on the flip side of that is I really do think we're at the era where models are going to start innovating, right? Because I think once you've passed the last kind of like the hardest human rating exams, that's kind of at the edge of innovation.

And I think you already see that with the models, right? Like they're helping to write parts of papers. And I think the other kind of way that people have shifted is, you know, there's these, you know, ultra-frontier e-vows. But there are also people kind of just indexing on real-world impact, right? You look at your revenue, kind of the value you deliver to users. And I think that's ultimately what we care about. Can you bring that back to interpretability research, like with these super, super, super hard math evils, for example,

are we doing the right research to understand if the thought process mirrors, not just one-shotting the answer, oh, you memorized it or you magically got it correct, but you actually took the correct path, kind of like you're graded for your work, not just the answer if you're in grade school. And Dario said that interpretability research

will actually contribute to capabilities and even give a decisive lead. Do you agree with that? What's your reaction to that concept of interpretability research being very important? Yeah, I mean, we care a lot about it here at Open AI as well. So one thing that we care a lot about is interpreting how the model reasons, right? Because I think we've had a very kind of specific and strong view on this in that we don't want to apply optimization pressure to how the model thinks. so that it can be faithful in the way it thinks and to expose that to us

without any kind of incentives to cater to what the user wants, right? I think it's actually very important to have that unfiltered view because, you know, oftentimes, like, if the model isn't sure, you don't want to hide that fact, right? Just for it to kind of please the user.

And sometimes it really isn't sure, right? And so we've really done a lot of work to try to promote this norm of chain of thought, faithfulness and interpretability. And I think it gives you a lot of sense into what the model is thinking and, you know, what are the pitfalls that it can go off into if it's not reasoning correctly. That's such an important point because if you have somebody on your team and they come to you and they say, hey, you know, I think this is the right answer, but we should probably verify it. It's like, it's still valuable. Totally. It puts you on the right path. If somebody comes to you

100% confidence, this is the truth. It's not being wrong. It's like, well, like, trust is just destroyed. Yeah, totally. Yeah. Do you guys feel like, you know, safety felt a lot more theoretical a couple years back, right? But like today, you know, like the things that people are talking about a couple years, like, scalable oversight, really having the model be able to tell you, like, and convince you that the work it did was right. It feels so much more relevant right now. Just because the capabilities are so strong.

Yeah, I mean, just personally, I've completely flipped from being like, oh, the safety research is not that valuable because I'm not that worried about getting paperclipped. seems like a very low likelihood that that's kind of like the bad ending like immediately in this foom and all this crazy uh gray goose scenarios were just so abstract and sci-fi it just felt like economics will will fall into place and there will be uh like a like a cold like a nuclear ending which is like we didn't build nuclear plants and we just stopped everything because we humans seem to be good at that but now that we're actually seeing things like yeah it's crazy how fast it's been right like um oh yeah I think my personal story is it's like, you know, what got me into AI was AlphaGo, right? Like just watching it get to that level of capability.

And you were kind of like it was such an optimistic and also kind of a little bit of a sobering message, right? When you saw at least it all get beat. And I just remember, you know, like we saw the coding models, you know, when we first launched like, I think very OG codex, you know, with GitHub co-pilot, it was maybe like under, you know, a thousand Elo on, on, on, code forces and I still remember the meeting where I walked into where the team showed my score and they're like hey with models better than you and you come full circle and it's like wow like I put decades of my life into this and you know the capabilities are there so like if you know I'm kind of at the top of my field in this thing and it's better than me like what can it do yeah yeah that's amazing uh do I have so many more questions on alpha go are there are there

lessons from scaling how scaling played out there that you can that we can abstract abstract into the rest of AI research what I mean is as I remember it the Alpha Go training run was not a hundred K H 200s but what would happen if we actually did an Alpha Go style training run I mean it would be an economic money pit right like they had no economic value to do but let's just say some benevolent trillion decides I'm going to spend a billion dollars on a training run to beat AlphaGo and go even bigger. Is Go at some point solved? Would we see kind of diminishing scaling curves?

Could we throw extra RL? Could we port back everything that we've doing in just general AGI research and just continue fighting it out in the world of Go? Or does that end and does that teach us anything? Yeah. Yeah. Honestly, I feel like if you really are curious about these ministers, join our team. That's the thing I want to say. So yeah, I mean, really like kind of the central problem of today

is RL scaling. When you look at AlphaGo, right, it's a narrow domain, right? I think in some sense, that limits the amount of compute you can pump into it. But even kind of small toy domains, they can teach you a lot about how you scale RL. Like, what are the axes where it's most productive to pump scale in? I think a lot of scaling research just looks like that, whether it's on RL or pre-training. So you identify a lot of, you know, different, different variables under which you can scale. And like, where is kind of where you get the best kind of like marginal impact for a bumping scale there. I think that's a very open question for RL right now. And I think what you mentioned as well, it's just like, you know, going from narrow to broad, right?

Does that give you a lever to pump a lot more scale in as well? I think when you look at our reasoning models today, they're a lot more broad-based than, you know, just being able to kind of an expert system on Go. So yeah, I really do think that there are so many levers to scale. What about Move 37? That was such an iconic moment in that AlphaGo Lisa Dahl match. They place Move 37. It's very unconventional.

Everyone thinks it's a blunder. It turns out not to be. It turns out to be critical. It turns out to be innovation. Do you think we are, we're certainly post-touring test in language models. we're probably post-turing test in image generation, but it feels like we're pre-Move 37 in text generation in the sense that there hasn't been like a fully AI-generated book

that everyone is just, oh, it's the new Harry Potter, everyone has to read it, it's amazing, and it's fully generated, or this image. The images, they do go viral, but they go viral because they're AI. Move 37 in the context of Go did not go viral because it was AI, It felt like it was actual innovation. So is that the right frame? Does that make any sense?

Yeah, I think it's not the wrong frame. So I think some quick thoughts on that. I think kind of when you have something that's, you know, very measurable, like win or lose, right? Yeah. It's like very easy for us to kind of just judge, right? Like did the model do something right here? And I think the more fuzzy you get, you know, it is just hard. right like um when it comes to you know is this the next harry potter right like you know it's not a universally

love book i think fairly universal but you know there's there's some haters and yeah i i think it it is just kind of hard when it comes to these human subjective things right where um it's really hard to put down in words like what makes you like harry potter right and um and so um i think those are always going to lag a little bit but you know i think you know we're developing more and more techniques to attack kind of these more open-ended domains. And I don't know. I wouldn't say that we're not at an innovative stage today. So I think my biggest touch with this was when we had the models compete on the I-Y

last year. So I-I-I-I, it's like the international, basically Olympics for computer science, basically the top four kids from each country go and compete. And these are really, really tough problems. basically selected so that they require some innovative insight to solve. I think, and we did see the model come up with solutions, even to some very ad hoc problems. And so I think there was a lot of surprise for me there. I was completely off base about which problems the model would be able to solve the most.

I think like, I kind of categorize there's six problems. Some of them as more kind of like, oh, this is a little bit more standard. This is a little bit more out of the box. I'm like, it's not going to be able to solve this more out of the box one, but it did. And I think I really does speak to kind of these models have the capacity to do so, especially train with RL. No, no, no, no, put that in context of what's going on with ARC AGI. Obviously open AIs made incredible progress there, but it just, when I do the problems,

it seems easy. And when I look at the I. sample problems, I think this would be a 20-year process for me to figure out how to achieve that and I can do the ARC-AGI on my phone. Is this the spiky intelligence concept? Is this something that a small tweak in algorithmic design, just one-shots ARC-AGI or is there something else going on there that we should be aware of? Yeah, I mean, I think part of this is the beauty of ARGGI as well, right? Like I think I'm not sure if there's another kind of like human intuitive simpler benchmark, which is for the models.

And I think really that's one of the things they really optimize for on that benchmark. I do think when it comes to models though, like there's just a little bit of a perception gap as well. Like, you know, models aren't used to this kind of native, you know, like just screen type input. I think there's a lot we can bridge there. Actually, even 04 mini, it's a state of the art multimodal model in many ways. including visual reasoning. And I think you're starting to kind of build up the capacity for the models to take images, manipulate and reason about them, generate new images, write code on images.

And I think it's just been kind of underfocused. But I think when I talk to researchers in the field, they all see this as a part of intelligence too. And we're going to continue the focus there. Yeah. Is RKGI kind of in the, if we're dropping a buzzword on it, is like program synthesis? Is there a world where I know that I know the tokens like the images we see them as as renderings of squares and different colors, but the when they're fed into the LLM, they're typically just a stream of numbers effectively. Is there a world where actually adding a screenshot is what's important, like visual reasoning? Yeah, yeah.

So I think I think that could be important. It's just like kind of, you know, whenever it comes to like textual representation of grids, models, today just don't really do that well, right? And I think it's just kind of because humans don't really ever write down textual representations of groups. Yeah, we have a chessboard. No one really kind of just like types it out in a grid. Yeah.

And so the models are kind of like under trained a little bit on what that looks like and what that means. So, you know, I think with more reasoning, we'll just bridge the gap. I think with better visual perception, we'll just bridge that gap. Yeah. How are you thinking about the role of non-lab researchers in the ecosystem today? I'm sure you try to recruit some of the best ones, but the ones that don't join your team. Tell us about the one that got away. Yeah, the one that got away.

Yeah, no, I mean, I think it's still actually a fairly good time. For specific domains, right, to be doing research. And, you know, I think the style is just very different. And you do feel the pull of non-lab researchers into labs because I think they feel like a lot of the burning problems in the field are at scale. And that's kind of one of the unfortunate things to you, right? Like when you look at reasoning, you just don't see that happen at small scale, right? There's like a certain scale at which it starts becoming signal bearing. And that requires you to have resources, right?

But I do think, you know, a lot of the really good work that I've seen, you know, there's, experimental architectures, I think a lot of good work is happening in the academic world there. Like a lot of study in optimization, a lot of study in kind of like GANS, you know, there's certain fields where you see a lot of fruitful research that happens in academia. Yeah, that makes a lot of sense. How about consumer agents? How are you thinking about them? You talked earlier about sort of B2B adoption and that's all very exciting.

but how much do you and the research org think about breakout consumer agent products? Yeah, that's a fantastic question. I think we think about it a lot. I think that that's the short answer. You know, we really do think like this year we're trying to focus on how we can move to the agentic world, right? And when I think about consumer agents, I think like chat chupD proved that, you know, people got it, right? It's like people get conversational kinds of models. But when it comes to consumer agents, we have a couple of theses that we've tried out in the world.

I think one is deep research, right? I think this is something that can do five to 30 minutes of work autonomously come back to you and really like kind of synthesizes information, right? It goes out there, gathers, collects, and kind of, you know, compresses the information in a form that's useful. A little bit of pushback there. Like I can see that as a consumer product when someone like Aiden is. like I want new towels and he uses deep research to like figure out like what is the best towel across every dimension. But when I think of deep research, yes, it has applications

with students, but it's often. Some of them might just be the paradigm because I feel like consumers being like, we keep using this flight report on this country and where to travel and things like that. We keep using this flight example, but I don't, I haven't actually tried to book a flight with deep research. It's totally possible that it could go and pull all the different flight routes and calculate all the different delays. and all the different parameters of if I fly to this airport, I can park or I can use valet here or something like that.

Yeah. And I guess like when I think of agents, it's deep research is like curating information on which you can take action on, but it's like at what point is action a part of that sort of loop, right? Where you can not only curate a list of flights that you want, but then actually go out and have agency. Yeah.

I think one of our explorations in that space, is operator, right? Yeah. It's where you kind of just feed in raw pixels from your laptop into or, you know, from some virtual machine into the model. And it produces, you know, either a click or some keyboard actions, right? And so there it's taking action. And I think the trouble is, you know, you don't ever want to mess up when you're taking action.

I think the cost of that is super high. You only have to get it wrong once to lose trust in a user. And so we want to make sure that that feels super robust before we get to the point where we're like, hey, look, here's a tool. That's so different than deep research because, like, you can wind up on some news article and read one sentence that it gets a fact wrong or the commas in the wrong place and the numbers off. But that's just the expectation for just text and analysis. And if you delegated that, yeah, you're going to expect a few errors here and there. oh, that's actually a different company name or that's an old data point. There's new data, but very different if I book a flight and you book the wrong flight and I can wind up in

Chicago instead of New York. Exactly. And I think the reason why we care so much about reasoning is because I think that's the path that we get reliable agents through. Sure. You know, we've talked about like reasoning helping safety, but reasoning is also helping reliability. It's like you imagine like what makes a model so good at a math problem? It's like it's banging its head against it. It's trying a different approach. And then it's like adapting based on what what it failed at last time. And I think that's the same kind of behavior you want your agents to have. It's like tries things like adapts and keeps going until it's. And that's the humans do this every day. You're booking a flight. You keep hitting an error. It's not which or

which form you missed, right? And you're just sort of banging your head against the computer. And eventually it says, okay, you're booked, right? I think that's a great call out. Yeah. I mean, There's so many more questions you go into, but I'm interested in the scaling of RL and kind of the balancing act between pre-training RL and inference, just the amount of energy that goes into getting a result when you distributed over the entire user base. How is that changing? And I guess, is, are we post like really big, really big runs? Is this going to be something that's like continually happening online?

it feels like we're moving away from the era of like, oh, some big development, some big run happened and now we're grouping the fruits of it versus a more iterative process. Yeah, I mean, I don't see why it has to be so, right? I think like if you find the right levers, you can really pump a lot of compute into RL as well as pre-training. I think it is a delicate balance, though, between all of these different parts of the machine. And, you know, when I look at my role with Jakub, it's just kind of like figure out where how this balance should be allocated,

where the promising kind of like nuggets are arising from and resourcing those. Yeah, it's kind of a, in some sense, I feel like part of my job is a portfolio manager. That's a lot of fun. Well, thank you so much for joining. This is a fantastic conversation. We'd love to have you back and go deeper.

Great hanging, Mark. We'll talk to you soon. Yeah, peace. Have a good one. Next up, we have Shalto Douglas from Anthropic coming on this show. I'm kidding so much. I just getting a lot of messages saying why no one cares about AI talk about the drama on the timeline. Well, we do care about AI.

We care a lot about AI. But it is a mess out there. Wow. Yeah, the end of the Trump Elon era. I don't know. Well, maybe we have to get some people on to talk about it tomorrow or something. Got to do it today. Anyway, we have Shalto from Anthropic.

in the studio. How are you doing? What's going on? Good to see you guys. Hopefully, uh, you're staying out of the chaos on the time. Don't open. Don't open. Don't open. Sweet child. Sweet child. Just to Twitter. Yeah, mute everything. Stay focused on the application. Stay focused on the mission. Stay focused on the next training run. We really, humanity really cannot afford for any data researchers to open X. What a hilarious day. Anyway, I mean, uh, how are you doing? What, uh, how are you doing? What is new in your world? What are you focused on mostly day to day?

And maybe it's just a way of an intro. Yeah. So at the moment, focused really hard on scaling RL. I mean, that is the theme of what's happening this year. And we're still seeing these huge gains where you go, you know, 10x compute increase in RL. We're still getting like very distinct linear gains, off the basis for that. And because our role wasn't really scaled anywhere close to how much pre-training was

scaled at the end of last year, we have like basically a gamut of riches over the course of this year. Yeah. So where are we in that in that RL scaling story? Because I remember the the some of the rough numbers around like GPT2, GPT3. We were getting up into like it cost $100 million. It's going to cost a billion dollars. Like it just rough order magnitude, not even from anthropic, just generally like what is a big RL run cost or how many are we talking 10K H200s or 100K? Like are we going to throw the same resources at it? And if so, how soon? Yeah. So I think in Dyer's essay at the beginning of the year, he said that a lot of runs were only like a million dollars back in like December. I think you have like DeepSeek V3 and this kind of stuff like R1, which means that with at least two ooms just to get to the scale of GPT4 and GPT4 was two years ago. Yeah. RL is also perhaps a bit more naively paralyzable and scalable than pre-training. You know, pre-training, you need everything in one big data center ideally or you need like some clever tricks. RL you could like in theory like what the prime intellect folks are doing scale it all over the world.

out of it. And so you're held back, like, maybe, you're held back far less than you aren't retrained. Sure. Sure. So everyone and their mother has a billion dollars now. There are, there are, you know, hundreds of thousands of GPUs getting pumped all over the place. I, I, I feel like we're not GPU poor as a, as a society. Maybe some companies need to justify it in different ways. But it sounds like there's some sort of, like, reward hacking problem that we're working through in terms of scaling RL, what are all of the problems that we're working through to actually go deploy the capital canon at this problem? Yes. So I mean, think about what you're asking the model to do an RL is you're asking

it to achieve some goal at any cost, basically. Yeah. And this comes with a whole host of like behaviors, which you may not intend. In software engineering, this is really easy, like to, it might try and hack unit tests or whatever. In much more longer horizon, real world tasks, you might ask it to say, go make money on the internet and it might come up with all kinds of fun and interesting ways to do that unless you find ways to guide it into following the principles that you want it to obey basically or to align it with your idea of what sort of best of humanity and so it's actually it's a pretty intensive process it's a lot of work to find down and hunt down all the ways these models are hacking through the rewards and and patch all of that and yeah yeah how are we going

to see scaling in the number of rewards that we're RLing against, if that makes sense, I would imagine that at a certain point, unless we come up with like kind of like the, the genesis prompt, go forth and brief fruitful or something and multiply, the, you could imagine training runs on just knocking down one problem after another. And is that, is that kind of the path that we're going down? I very much think so. There's this idea in which, like, you know, the sort of world becomes an R.L environment machine in some respect. Because there's just so much leverage to making these models better and better at all the things we care about. And so I think we're going to be training on just everything in the world.

Got it. And then does that lead to more model fragmentation, models that are good at programming versus writing versus poetry versus image generation? Or does this all feedback into one model? Does the idea of the consumer needing to pick a model disappear? Are we in a temporary period for that paradigm? I think the main reason that we've seen that so far is because people are trying to make the best of the capital. We are all still GPU poor in many ways. And people are focusing those GPUs on the sort of like spectrum of rewards that I think is most important.

And I'm a bit of a big model guy. I really do think that similar to how we saw with large pre-trained models before, where small fine-tuned models had gains over the sort of GP2-G-T-2 era, but then we're obsoleted by GP-4 being generally good at everything. I think, to be honest, you're going to see this generalization and learning across all kinds of things that means you benefit from having large single models rather than specialization or area fine-tuned models. Can you talk a little bit about the transition from, or any differences between RLH-S-E,

and just other RL paradigms. Yes. So RLHF, you're trying to maximize a pretty, like, lossy signal, things like airwise, like what do humans prefer? And I don't know if you've ever tried to do this, like, judge two language model responses. I get prompted for that all the time. Right. And I'm always like, I don't want to read both of those.

I'll just click the one in the left. Exactly, exactly. And I click one of the random ones sometimes. Yeah. Or I click, like, the one that just looks bigger or I'll read the first two sentences. but yeah, I'm not giving straight. I'm not doing my job as a human reinforcer. Human preferences are easy to hack.

Yeah, totally. Environments in the world are much truer if you can find them. So something like, did you get your math question right? It's a very real and a true reward. Does the code compile, right? Does the code compile? Exactly. Did you make a scientific discovery?

We've got very little rewards right now, but pretty quickly over the next year or two, you're going to start to see much more meaningful and long horizon rewards. You're going to see models bribing the Nobel Committee to win Nobel Prize. Well, it's a good reward hacking. There's reward hacking. Yeah, but that's nothing to prevent, right? Exactly.

Yeah, yeah, that's the real nightmare scenario. What about, like, there's so many different problems that we run into that feel like it's just really, really hard to design any type of e-val. that the my kind of benchmark that I use whenever a new model drops is just tell me a joke. Yeah. They're always bad. And or or even even the latest V-O-3 video that went viral was somebody said like stand-up comedy joke. And it was kind of a funny joke, but it was literally the top result for joke Reddit on Google.

And then it clearly just took that joke. And then in stage it in a video that looked amazing. But it wasn't original in any way. And so we were joking about like the RLHF loop for that is like you have an endless cycle of comedians running AI generated materials and then and then, you know, speak microphones in all the comedy clubs to feedback what's getting laughs. I mean, honestly, that would work pretty well. Yeah. If any comedians want to focus off at an RL loop, I mean. Yeah, yeah.

But I mean, for some of those less like as you go down the curve, it feels like each one gets harder and harder to actually tighten the loop. We see this with like longevity research where it's like, okay, it takes a hundred years to know if you extended a human life. Like the, yes, you could create a feedback loop around that, but every change is going to be hundreds of years. And so even if you're on the cycle, it's irrelevant for us in the context that we talk about AI. So talk to me about like, are you running into those problems or or will there be like another approach that kind of works around those? So there are a lot of situations. So there are a lot where you can get around this by just running much faster than real time.

Like let's say the process of building like a giant app, like building Twitter, right? It's something that would take human months. But if you got fast enough and good enough AIs, you could do that in several hours. I realize heaps of AI agents that are all building around and things expect. And so you can get a faster reward signal in that way. In domains that are less well specified like humor, I agree, it's really, really hard. And this is like why I think in some respects, like creativity is like at the top end in the spectrum, like true creativity is much, much harder to replicate

and the sort of like analytical, scientific style reasoning. Yeah. And that will just take more time. You know what? The models actually are pretty good at making jokes about being an AI. This feels weird fresh. Like everything else is kind of a weird copy of something. Like it just feels like it's derivative, basically.

It's trying to infer what humor is and it doesn't really understand it. But jokes about being an AI are quite funny. Yeah, I think this also might be, I don't know if it was directly reward hacking, but I noticed that one of the new model, dropped and a bunch of people were posting these like 4chan like be me memes and and and they were it seemed like they were kind of hacking the humor by being hyper specific about an individual that they could find information on online and so you're laughing at the fact that it's like oh wow that is like something that I've posted about it's making a reference but it's not really that funny to me it's

other than it's just like wow they really did its research like it really knows Tyler Cowan intimately which is cool but I didn't find it hilarious yeah yeah very interesting Let's talk about some sort of deep research projects and products. We were talking to Will Brown, and he was saying, like, AGI is here with some of the bigger models, but the time that AGI can feel consistent, it diverges. And so you could be working with someone who's, you know, 100 IQ, but they will stay consistent for years as an employee, or they'll keep, you know, living their life. Whereas a lot of these super smart models are working really well.

And then after a few minutes of work, the agents kind of diverge and kind of go into odd paradigms. And it feels very not human. It feels like a, like just a, they're hyper-intelligent in one way and then extremely stupid and others. What's going on there? What is the path to extending that? Is that more, like having more better planning and better, like, dividing up the tax?

or will this just kind of naturally happen through the RL and scale? Yeah, so there's that jaggedness, right? Which is like you're seeing, is how we call it. And I think that is largely a consequence of the fact that maybe something like deep-suit research, it's probably being REL to be really good at producing a report. Yeah. But it's never been REL on the like act of producing valuable information for a company over a week or a month or like making sure the stock price goes up in like, you know,

a quarter or something like this, right? Like it doesn't have any conception of how that feeds into the broader story. a story at play. It can kind of infer it because it's got a bit of world knowledge from the, you know, the base model and this kind of stuff. There's never actually been trained to do that in the same way humans have. So to extend that, you need to put them in much longer running, much like, like, you know, long horizon things. And so deep research needs to become, you know, like deep operator company for a week kind of thing. Sure. Is that the right path? Like, it feels like the road might be there's a, like, the longest running LLM query used to be

just like a few seconds, maybe a few minutes. And I remember when some of the reasoning models came out, people were almost trying to like stunt on it by saying like, oh, I asked it a hard question and thought for five minutes. Now deep research is doing 20 minutes pretty much every time. Is the path two hours, two days, or are we going to see more efficiency gains

such that we just get the 20 minute model, the 20 minute results in two minutes and then two seconds? Yeah, so this is somewhere where like inference in many respects and prioritization becomes really important. So both like how fast is your inference? It literally affects the speed at which you can think and the speed at which you can like, like, you know,

do these experiments. Also how easily you can prelize becomes really important. Like can you dispatch a team of subagents to go and do deep research and like compile like sub reports for you so you can do everything in parallel. These kinds of like, it's both like there's an infrastructure question here that feeds up from the hardware and the chips and this kind of stuff to like designing better chips for better inference and all this. and an RL question of like, you know, how well can you pro-lise and all this.

So I think we just need to compress the timelines, compress the timeframes, basically. Yeah. So if I'm like an extremely big model and I'm running an agentic process, like how much am I hankering for like a middle-sized model on a chip or like baked down into silicon that just runs super fast? Because it feels like that's probably coming. We saw that with the Bitcoin. coin progression from CPU to GPU to FPGA to ASIC. Do you think we're at a good enough point where we can even be discussing that? Because every time I see like the latest mid journey, I'm like, this is good enough.

I just want it in two seconds instead of 20. But then a new model comes out. I'm like, oh, I'm glad I didn't get stuck on that back. But yeah, like how far away from, how far away are we from, okay, it's actually good enough to bake down into silicon? Well, there's a question here of baking it down to silicon. versus designing a chip, which is like very suited for the architecture that you care about, right? And baking out of silicon, unsure.

Like, I think that's a bet you could take, but it's a risky one because the pace of progress is just so fast nowadays. And I really only expect it to accelerate. But designing things that make a lot of sense for the sort of transformers or architecture of the future should make a lot of sense. That's a big gap, though, transformers or architectures of the future. If we divers, there's a lot of companies that are best. banking on the transformer sticking around. What is your view on transformer architecture sticking around for the next couple of years?

I mean, look, they stuck around for five years, so they might stick around for a little while. But there's different, you think about architectures in terms of this balance, memory band, with them, flops, right? One of the big differences we've seen here is Gemini recently had actually a diffusion model that they released. I was about to ask you.

Yeah. The other day, right? So diffusion is inherently extremely flops-intensive process, whereas normal language model decoding is extremely memory-bound with intensive. You're designing two very, different chips depending on which bet you think makes sense. And if you think you can make something that does flops, like four times faster than diffusion

and like four times cheaper than your those could, diffusion makes more sense. So there's like, there's this dance, basically, between the chip providers and the architecture both trying to build for each other, but also like build for the next paradigm. Yeah. It's risky. Do you, I don't know how much you, how much you've played with image generation, but do you have any idea of what's going on with images in chat GPT? It feels like there's some diffusion in there. there's some tokenization, maybe some transformers stuff in there.

It almost feels like the text is so good that there's like an extra layer on top almost and that it's almost like reinventing Photoshop. And I guess the broader question is like it feels like an ensemble of models. Maybe the discussion around just agents and text-based LLM interactions shouldn't necessarily be transformer versus diffusion, but maybe how will these play together? Is that a reasonable path to go down? Well, I think pretty clearly there's some kind of like rich information channel between even if there are multiple models there. There's like it's conditioning somehow on the on the other model because we've seen before like let's say when you know models use mid journey to produce images.

It's never quite perfect. It can't perfectly replicate what went in as an input. It can't perfectly like adjust things. So there's a link somehow whether that's the same model producing tokens plus diffusion. I don't know. Like yeah, can't comment on what opening eyes doing there. Yeah, yeah. Are there any other kind of like super wild card long shot research efforts that are maybe happening even in academia where I mean this is the big thing with what was his name Gary. He was talking about I forget what it was called symbolic symbol manipulation was a big one and and I feel like you know you can never count anyone out because it might come from behind and be relevant in some ways.

but are there any other research areas that you think are like purely in the theory domain right now that are worth looking into or tracking that you know low up low probability but high upside if they work hmm this is a tough one but we'll say it's not a symbolic thing please it's crazy how similar transformers are to systems that manipulate symbols sure what they're doing is they're taking a symbol and they're like converting it into a vector and then they're manipulating and moving stuff like information around across them sure like like Like, this whole, like, a debate that all transforms can all represent symbols and they kind of do this.

It's, it's, yeah, it's not real. So Gary Mark is underrated or overrated, I guess? Overrated. Yeah, yeah. But, I mean, if you twist the, if you twist it so much, you wind up with saying, like, well, really, like, the, the transformer fits within that paradigm. And so maybe it's, you know, it's, you know, it like the rhetoric around it being a different path was maybe false the whole time. Yeah. Something like that.

But as I remember that debate, it was really the, the idea of compute scaling versus almost like feature engineering scaling. And will the progress scale with human hours or GPUs essentially? And that has a very different economic equation. And it, and it feels like. Like there's been some rumblings about maybe with a data wall will shift back to being human labor bound. But do you think that there's any chance that that's relevant in the future? Or is it just algorithmic progress married with bigger and bigger data centers in the future? So I'm pretty bit of lesson built in the sense that I do think removing as many of our biases and our like clever ideas from the models is really important.

just like freeing them up to learn. Now obviously there's like, there is clever structure that we put into these models such that they're able to learn in this extremely general way. And that, but I am more convinced that we will be compute bound than we will be like human researcher out, human research are bound on this kind of thing.

Like we're not going to be feature engineering and this kind of stuff. Sure. We're going to be trying to devise incredibly flexible learning systems. Yeah, that makes sense. On the scaling topic, part of, I, I, I, I, I, I, I, I, I, part of my, like, worry is that the, the, the ooms gets so big that they turn into these mega projects that are, uh, that at a certain point, you're bound by the laws of physics because you have to move the sand into silicon chips and you have to dig up the silicon and it's a certain. Yeah, there's only so much sand and like, the, the math gets really, really crazy just for the amount of energy required to, to, to move everything around to make the, the big thing. Uh, where are you? Uh, uh, where are you? Um, uh, on how much scale we need to reach AGI, whether or not we will see the laws of physics

start acting as a drag on progress because it certainly feels exponential. We're feeling the exponentials, but a lot of these turn into sigmoins, right? So I think we've got what, like two or three more ooms before it gets really hard. Leopold has a nice table at the end of his situational awareness where I think like 2028 or something is when under really aggressive timelines that you get to 20% of US energy production. It's pretty hard to go exponentially beyond 20% of US energy production. Now, I think that's enough. Every indication I'm seeing says that's enough.

Now, there might be some complex, you know, data engineering, raw engineering, this kind of stuff that goes into, there's still a lot of algorithmic progress left to go. But I think that with those extra ooms, we get to basically a model that is capable of assisting us in doing research and software engineering. Yeah, which is the beginning of the self-reportment. Yeah. Interesting.

Is that just a coincidence? Like, this feels like one of those things. This feels like one of the things where like the moon is the exact same size as the sun and the sky. It's like, oh, it just happens that AGI happens within this time. Like, have you unpacked that anymore because it feels convenient, not to, you know, I know, I know, last. I mean there's a lot of weird conveniences are like weird. It's a good sci-fi story, let's say. Totally.

You know, we've got, you know, Taiwan in between China and the U.S., and it produces the most valuable material in the world. That's locked between the two. Incredible plot. Yeah. Yeah. Really bad for the people that don't think of, that don't believe in simulation theory. It really feels like how this is scripted.

It's fascinating. Talk to me more about getting to an ML engineer in AI. and kind of that reinforcement, I imagine that you're using AI code gen tools today and Anthropics is broadly and everyone is. But what are you looking for and what are the, what's the shape of the spiky intelligence, where do they fall flat and what are you looking

to kind of knock down in the interim before you get something that's just like, go. Yeah, so I mean we definitely used them the other night. I was a bit tired of asked to do something just sat watching it in front of me, working for half an hour. It was great. Nice.

It was a truly weird experience, particularly when you look back a year ago and we're still copy-pasting stuff between a chat window and a code file. I like meters e-vowls for this kind of stuff. So they have a bunch of e-vials where they measure, like the ability to write a kernel, the ability to run a small experiment and improve a loss. And they have these nice progress curves versus humans. And I think this is maybe the most accurate reflection of what will take for it to really help us at doing progress. And there's a mix here. Like where they're not so great at the moment is like large scale distributed systems engineering, right? Like debugging stuff across heaps and heaps of accelerators and like the way the feedback loops are slow.

And you're actually like if your feedback loop is like an hour, then it's worth you spending the time on doing something. Yep. Feedback loops 15 minutes. And for context there, the hour long feedback loop is just because you have to actually compile and run the code across everything. You like spin up all your machines or you need like, you need that like run. for a while to see something's going to happen. Like at that point in time, you're still cheaper than the chips. Sure.

And so you're sort of, it's better than you do it. Yeah. But for things like, you know, kernel engineering or for, like, you know, actually even just understanding these systems incredibly helps. Like, one thing I regularly do at the moment is in parts of the code base, in like languages that I'm unfamiliar with or stuff like this. I'll just ask it to rewrite the entire file, but with comments on every line, game changing.

Wow. It's like. Comments on every line. Yeah. Or just come through like thousands of files and explain how everything interacts to me, draw diagrams, this kind of stuff. It's really, yeah. Yeah. How important is a bigger context window in that example you gave?

That feels like something that's important. And yet I just naively like Google is the one that has the million token context window. I imagine that all the other Frontier Labs could catch up, but it seems like it hasn't been as much of a priority as maybe like the PR around it sounds like. Is that important? Should we be going? Should we be driving that up to like a trillion token? window, is that just going to happen naturally? There's a nice plot in the Gemini 1.5 paper where they show the loss over tokens as a function

of context length and they show that the loss goes down quite steeply actually. As you put more and more and more like of a code base in the context, you get better and better of predicting the rest. Yeah, that makes sense. On the context length, it's a cost. You know, the way transformers work is that there's, you know, you have like this memory that is proportional, the KV cache is proportional to how much context you've got. got. And so you can only fit so many of those into like your various chips and this kind of stuff.

And so longer context actually just costs more because you're taking up more of the chip and you're sort of like you could have otherwise been doing other requests. So bringing it back to the custom silicon, is that a unique advantage of the TPU? Is that something that Google has thought about and then wound up to put themselves in this advantage position? Or is it a durable advantage even? Yeah. So TPUs are good in many respects, partially because you can connect hundreds or thousands of them really easily across really great networking. Whereas only recently has that been true for GPUs. With NVLink?

Yeah, with NVLink and the NVL-72 stuff. So it used to be like eight GPUs in a pod, and then you connect them over worse interconnect. And now you can be 72 and then it breaks down. With Google GPUs, you can do like $4,000,000 of a really high bandwidth interconnect in one pod. And so that is helpful for things like just general scaling in many respects. I think it's doable across any chip platform, but it is an example of, like, somewhere that being fully vertically integrated is using a benefit.

Yeah, that makes sense. Talk to me about Arc AGI. Why is it so hard? It seems so easy. It does seem easy, doesn't it? Well, it certainly seems like more evaluatable than tell me a funny joke, right? Yeah, yeah. I mean, I think if you are old on Arc AGI, then you probably get superhuman at it pretty fast.

But I think we're all trying not to RL on it so that it functions as like an interesting help-out. Sure. Okay. Wait, is that just an informal agreement between all the labs, basically? Yeah, we try and have a sense of honor between us. That's good. Sense of honor. That's amazing.

How many people on Earth do you think are getting the full potential out of the publicly available bottles? Because we're now at a point where we have, you know, billion-plus people are using AI almost daily. and yet I have to have met my sense would be it's maybe like 10,000 20,000 people on the entire planet are getting that sort of full potential but I'm curious what your assessment would be yeah I completely agree I mean I think that even I don't get the full potential out of these models often and I think as we shift from you're asking a questions and it's giving you sensible answers to you're asking it to go do things for you that might take hours at a time and you can really like paralyze and spin that we're going to hit like yet another inflection point where even less people are like

really effectively using these things because it's basically it's required you to like it's like StarCraft or Dota like it's going to be like your APM of like managing all these agents and that's totally process yeah so i think starcraft is such a good example you think you're just absolutely crushing it and then you realize like there's an entire area of the map you're just getting destroyed on it's such a good it's such a good comp that's great anything else doordie I think that's it on my side. I mean, I would like this to be an evolving conversation. Yeah, this is fantastic.

It's very fun conversation. Absolutely. It's really fun. Love to go back on class. Yeah, we'll talk to you soon. Cheers, Shulte. Have a good one. All right.

The worst possible AI day. Yeah, so for context, folks, we are going to be doing a live timeline and turmoil segment at 2 p.m. PST. Let's do it. So if there's posts you want us to cover, you can go, send them. I'll put this in chat as well. A few more. Pull one up. I'm going to do some ads because we got Emmett Shear coming in the temple in just a few minutes.

First, let me tell you about Numeral. Sales tax on autopilot. Spend less than five minutes per month on sales tax compliance. Go to Numeral HQ.com. Very excited for them. Also go to public.com investing for those who take it seriously. They have multi-asset investing, industry leading yields, and they're trusted by millions. Millions. In other news, Tim Sweeney, continues to battle Apple. Apparently, if you search for Fortnite on the Apple App Store, he says, hey kids, looking to play Fortnite, try this crypto and stock trading app instead, rated for ages 4 plus, courtesy of Apple App Store ads. So I'm going to give you the latest Elon post four minutes ago. The Trump tariffs will cause a recession in the second half of this year. Wow.

somebody else was saying can I finally say that Trump's tariffs are super stupid who's who is that somebody else is posting uh mad's posting is saying it's a jiji ping he says bro you seeing this and it's um Putin on the other end he's just looking at it hold up got a line and it's uh we'll start pulling some of these up ridiculous uh what else is going on here this is the president versus Elon Nival says, Elon's stance is principal. Trump's stance is practical. Tech needs Republicans for the present.

Republicans need tech for the future. Drop the tax cuts. Cut some pork. Get the bill through. This is so crazy. Antonio Garcia says, remember, there's FU money and then there's F the world money. Will Stansell says, imagine being the ICE agents, suiting up for your biggest mission of all time right now.

People are saying that Trump's going to deport Elon. Elon, back to South Africa. Will DePews says time to drop the really big bomb. Growing Daniel is in the Epstein file. That is going to turn into a coffee pasta. That is a real reason of coffee. Oh, no. Delian.

We had a question from a friend of the show. They said, the real question is if Tesla is down 14%. How could SpaceX and Open AI be trading if they were, how would they be trading if they were public? The real thing here is it's bad for everyone, right? DJT is down, Trump coin is down, nobody's really winning here. China's up. Yeah. Oh, really?

I mean, I'm just saying like at a high level. Yeah, yeah, yeah, yeah. You know, China is the big beneficiary here of, um, uh, Sarah Guo says if anyone has some bad news to bury, might I recommend right now. Yes, yes, yes. If you have, if you, what's the canonical bad startup news? Like, oh, yeah, you missed earnings or something. Drop it now. Inverse Kramer says Bill Ackman is currently writing the longest post in the history of this app.

Okay. And we have a video from Trump here if we want. I can throw it in the tab and we can share it on the stream and react to it live. Lex Friedman. says to Elon, that escalated quickly triple your security. Be safe out there, brother, your work. SpaceX, Tesla, X-A-I, NeuroLink is important for the world. We need to get Elon on the show today. If somebody's listening and can make that happen, I would love to hear from him. Max Meyer says, so I got this wrong. I didn't say it never happened,

but I thought I wouldn't. I'm floored at the way this has happened. He didn't think they would have a big breakup. Many people didn't think they would have a big breakup. Even just earlier this week, it seemed like they might just have a somewhat peaceful exit. Trump just posted a little bit ago. I don't mind Elon turning against me, but he should have done so months ago. This is one of the greatest bills ever presented to Congress. It's a record cut in expenses, $1.6 trillion dollars and the biggest tax cut ever given. If this bill doesn't pass, there will be a 68% tax increase and things far worse than that. I didn't create this mess. I'm just here to fix it. Anyways, lots going on.

Let's go to this Trump video. I want to see what I've seen. I'm sure you've seen regarding Elon Musk and your big, beautiful Bill. What's your reaction to that? Do you think it in any way hurts passage in the Senate, which of course what is you're seeking? Well, look, you know, I've always liked Elon, and it's always very surprised.

You saw the words he had for me, the words. And he hasn't said anything about me that's bad. I'd rather have him criticize me than the bill because the bill is incredible. Look, Elon and I had a great relationship. I don't know well anymore. I was surprised because you were here. Everybody in this room practically was here as we had a wonderful send-off. He said wonderful things about me.

You couldn't have nicer said the best thing. He's worn the hat. Trump was right about everything. And I am right about the great, big, beautiful bill. But I'm very disappointed because Elon knew the inner workings of this bill better than almost anybody sitting here, better than you people. He knew everything about it. He had no problem with it. All of a sudden he had a problem.

And he only developed the problem when he found out that we're going to have to cut the EV mandate because that's billions and billions of dollars. And it really is unfair. We want to have cars of all types. Electric. We want to have electric, but we want to have gasoline combustion. We want to have different. We want to have hybrids. We want to have all.

We want to be able to sell everything. He hasn't said bad about me personally, but I'm sure that'll be next. But I'm very disappointed in Elon. And I've helped Elon a lot. I just want to clarify, did he raise any of these concerns with you privately before he raised them publicly? And this is the guy you put in charge of cutting spending. Should people not take him seriously about spending now? You're saying this is all sour grades.

No, he worked hard and he did a good job. And I'll be honest. I think he misses the place. I think he got out there and all of a sudden he wasn't in this beautiful oval office and he's got nice offices too but there's something about this

when I was telling the chance folks this is where it is people come in here breaking news Delian is joining us in the temple for some live reactions come on in surprise guest

I can't even spell surprise guest I'm so excited about this surprise guest yeah in other news 11 labs dropped a new product two hours ago. Absolutely.

Another news. Two million dollars seed round. Stop it. Stop it. We love 11 labs. No. They'll keep grinding.

Just launch again tomorrow. You're going to have to launch again. Start shooting a new vibe real. Start shooting a new, writing a new blog post because no one's going. Lulu says yes, delay the launch on TVPS. So basically right now,

I can just pull. up and just refresh. I'm going to just be refreshing true social. How you doing? So okay, Jordy's on truth social. I'll be on X. Give us your reaction, Delian. What's going on? I mean, at some point I was like, I'm just sort of scrolling X and I like tuned into you guys like an hour ago and I was like they're talking about some AI thing. I was like at some point they're switched to like we have to like and then I was watching and I was like okay. John resisted. I fought it for like for like a half an hour but we couldn't do it. But yeah, us your quick reaction. I mean, always, you know, sort of give it from the, you know, sort of space

angle, you know, it's amazing that, you know, how much the world has shifted since, you know, Friday of last week, whereas, you know, sort of presumed the Jared Isaacman was going to be the, you know, sort of NASA admin to today. It was released that the Senate reconciliation package re-added a budget back into NASA largely for the SLS program, which was basically the program that, you know, sort of, Jared and Elon were, you know, sort of largely advocating to, you know, sort of completely shut down. So, yeah, the, the, it is already show, like, you know, the sort of counterreaction, you know, is already showing up, you know, in policy.

Sorry, SLS program, is that space shuttle or no? Sorry, that's the SLS launch rocket. Okay. It is based off of the old space shuttle hardware, but it is basically the internal, you know, sort of NASA run competitor effectively to, like a, you know, sort of starship heavy, you know, launch rocket. Yeah. And so, you know, because it was, you know, sort of generally behind budget, behind schedule,

and there are so many commercial heavy lift rockets coming online. Sure. The default was canceled. That is largely, you know, sort of a Boeing-based program. And so, you know, if you look at, you know, three months ago, you know, when they were announcing the F-47 program, you know, Elon walks into the secretary of the Air Force's office. Obviously, he'd been, you know, sort of ranting against, you know, sort of manned fighter jets. And believing that that shouldn't be what, you know, be what the department is prioritizing. 30 minutes after that meeting was when they announced the F-47 program.

And so now you're seeing basically like the equivalent in space where, you know, you know, that was obviously awarded to Boeing. Boeing was the, it is the largest prime behind Eidosod SLS. You know, Boeing basically, you know, is going to be the biggest winner of, you know, NASA refunding, you know, NASA LS and Jared Isaacman not being NASA administrator. So tying this back to the timeline, Trump posted less than 30 minutes ago, in light of the president's statement about cancellation of my government contract, SpaceX will begin decommissioning its dragon spacecraft. immediately break that down? I mean, that just means that we no longer have a vehicle that can go to the International Space Station. We know how we have a vehicle being astronauts up and down.

You know, we also don't have a vehicle that can deorbit the International Space Station safely, right? The Dragon was expected to be able to do that. So what that means is, you know, if you guys remember all the memes about stranded, you know, from last year around Boeing Starliner, it now means that the space station, you know, itself is basically, you know, sort of stranded. And that's like, you know, one of the government contracts, obviously, that, you know, Space Sixes involved in.

Elon, I've heard generally, like, just wants to shift all things to Starship anyways. And so in some ways it was probably kind of looking for an excuse to, you know, sort of shut down Dragon and refocus energies. There's also part of where it's like, look, he is like kind of independent in the space world. And that, you know, Starlink's total top line revenue is going to be passing the NASA budget in the next year or two. And so in terms of like size of, you know, state actor that can influence space, you know, his own company is basically about to become, you know, as large of an actor as like the entire United States. So I don't think there's going to be like a de-escalation here. Like, you know, my, you know, estimation is like on both sides. It's going to continue to escalate.

You know, if we thought that we lived in dynamic times, you know, when Trump got into office, it's going to be even more dynamic when there's like... The dynamism will continue until morale improves. Elon, the center, AOC, the progressive populist in Trump, the, you know, sort of conservative populist. And, oh, man, it's... It's remarkable times. I mean, I just have so many questions, right? How does this impact Golden Dome?

What's Boeing stock doing? Will Golden Dome even be a viable project without SpaceX? I think it's just going to be more resistance probably to working with, you know, sort of upstarts because they would be ones that would probably be more likely to collaborate, you know, sort of with, you know, a SpaceX. And so, yeah, I think there's. So, Amy, it feels like, it feels like Boeing would be a logical, beneficiary of this turmoil and yet they're down today. They haven't really popped. Oh, really? Yeah. I mean, I'm not

obviously, you know, I'm wanting to give like, you know, public talk about it. Yeah, I know. I'm just trying working through it myself and it's surprising. It just feels like it's let a drop and Boeing to pop basically. Yeah, yeah. That would be the expectation, but there must be something here. Because there, it feels like this is purely interpersonal between Elon and Trump and not, it's not like, oh, Boeing was secretly behind the scenes the whole time lobbying even more effectively. It doesn't, oh, you got the. Well, where's the tinfoil hat? It's over there. Maybe we need a tinfoil hat saying, who knows?

But, yeah, I mean, when you're in Boeing World, it's like, hey, we're only down 1%? Let's go. The coup of the century. My question is, has there ever been a crash out of this magnitude ever? In history. Well, you know, when Elon and Trump became friends. Honestly, world scale.

I actually. There's probably world history equivalent. I feel like there's something in like the building era in the United States where, you know, crashing out used to mean calling up the New York Times and just ranting. Now you can just live posts like all your reactions and it's just all real time. This is like crash outs are actually intensified. You actually want to be long crashouts. Yes, definitely.

And you have their own community platform that they own. So you know, you got to be on both X and and truth social to like stay on top of things. Yeah. Yeah. I actually did like a deep research report a while back on like has the richest man in America ever. been close with the U.S. president going back to like, you know, was Rockefeller particularly close? And because the narrative was like, oh, this is like so unprecedented. And in fact, it is unprecedented. Oh, really? Yeah, yeah. I would have guessed it like Rockefeller was close. Me too.

Me too. That's what I was going for. It was like, no, I imagine this is always, it is always close. But no, I think because the president has become more powerful globally, your, your, your, your, your, your, your, your, your, your, your, your, you know, mayor of America, dictator of the world. Like, it becomes increasingly valuable for the richest man to have a close alliance. And so it's become more. I don't know exactly how accurate that research was. It's totally possible that like behind the scenes, Rockefeller was really close to the president at the time.

And we just didn't write about it in the history books. But there certainly aren't very many anecdotes about the richest man in America going on. Yeah. So Pavel had a press story. Ready for AP US history, 2050. You know, APCD exam. Yeah. Yeah.

So this is where Elon Musk called the president at the time, a potential pedophile. Was it A. about Epstein Island, B, about a cave in the Philippines, C. What a mess. No, so Pavel had a good post. He was quoting the big bomb from Elon. He said, hypothetical question about the USA's power structure, is the man with the most access to capital, more or less powerful than the political head honcho? Purely hypothetical. It's a good question to ask. I mean, I think both like archetypes have grown both in absolute power, but also in relative power to the rest of the globe, basically since the Gilded era, right? If you think about, like, the president of the United

States in 1925, I'd say pretty darn powerful, but, like, there was clear, like, you know, it was a, you know, sort of multipolar, you know, sort of world. Argentina was pretty darn rich at the time. Obviously, Europe was still, you know, sort of recovering from World War I, but UK was generally, you know, sort of doing well. Like, it was not, you know, it was clear that there was a, you know, sort of huge, you know, outweighed effect. And then if you look at probably the, you know, you know, sort of biggest, you know, industries at the time, you, I don't think you could claim that even, like, Standard Oil at its peak, I'd have to go look at the exact numbers, but that, like, like, it had the size of budgets relative to, like, you know, the, like, U.S. government

in terms of, you know, sort of budgets, right? Versus I feel like now for the first time, you both have, you know, sort of U.S. president extremely extremely powerful. And then you have, like, you know, sort of mag seven, effectively, like, the size of, you know, sort of, you know, states. Like, they're, you know, like, they're, you know, like, your own state governments. And then also just more bureaucracy, more red tapes. So when I think about the 1920s, like, rubber barons, it's like, it is the you can just do things era. And so you want to build a railroad like, yeah, you might need to get like one rubber stamp, but it's not going to be 10 years and tons of lobbying and all this different stuff.

So you can kind of just go, you can just go wild. You know it's bad when Kanye is saying bros, please know, we love you both so much. It's just like the voice of reason is Kanye West. Yes, yes, thank you. You didn't bring them together and, you know, form a peace treaty. Nikita Beer just added his pronouns back to his bio. Let's go. You've got a rubber band.

Elon's got a rubber band all the way back to, you know, sort of extreme work because I'm straight back to, you know, sort of super climate change. Wow. Somebody's sharing, re-sharing the picture of the cyber truck blown up in front of the Trump Tower, I guess. And it's just like this. It was foretold. Yeah. But it was a question of like when and what magnitude, not if. always bad if Vladimir Putin is operating to negotiate between President Trump and Elon.

I think a lot of the world is waiting for Roy Lee's take, Cooley, and the Cluelly Army. They want, people have been asking him to get involved with geopolitics. Wow. I love the, Shiel Mohad put up a, you know, sort of meme about Narenda, the, like, you know, sort of meme about Narenda. like a prime minister of India, you know, he basically copied and pasted the Trump truth social post about negotiating peace between India and Pakistan when it wasn't like actually fully negotiated. Yeah. You know, posting about, you know, negotiating a ceasefire between Elon and Trump.

Funny thing is like truth social, you can just read all of Trump's posts without creating an account. It truly shows that like I would think that you would have to make an account to read them all, but they just, that it's not gated at all. It's his ball. This could be the biggest, but, you know, they clearly, I don't think they care about monetization. Bitcoin is actually falling alongside. Falling. Falling.

Wow. Bitcoin falling, Boeing falling, Tesla falling. Who's the biggest winner of the day? I think it's China. China. Yeah. China, China, China. Sean McGuire.

Bitcoin really sold off. It's down 3% today at 101K. So still up, but, you know, rough. Winnie DePoo, just dipping his, you know, hands. in that pot of honey just snacking away, watching from the sidelines. Yeah, let's see, Chinese stocks. U.S. stocks, Chinese, I can't find anything.

That's probably my commentary on the day, boys. Anyway, this was great. It was fantastic having you. Thanks for jumping on. Thanks for jumping on so quick. Cheers. Bye. Cool.

Well, Aaron Rogers signed a one-year deal with the Steelers. Announced an hour ago. Let's give it up for Aaron Rogers. Do we have Emmett in the waiting room? I've messaged him. It's absolute chaos. We'll see if he can, he can hop back on.

We don't have him right now. Ready, if you can hop on. Sorry about the chaos. We're live streaming. We are full streamers. That was the moment where, yeah, it was like, okay, this was the point of TVPN. Send him an invite.

Let him jump in. Hopefully we can get Eminent. That was very chaotic. But, you know, it's a busy time. My only hope for both Trump and Elon is that they can get some sleep. They both go to eat sleep.com slash TBPN, get a Pod 5 Ultra, take advantage of the five-year warranty, the 30-night risk-free trial.

They got free returns. They got free shipping. This is really the perfect time to do ads. I don't think that's what they both. That could unify everyone. I hope that both Trump and Elon have eight sleeps tonight if they sleep at all. Yes. But even just resting on it would be good.

but yeah let's see let's see um we can also go through i don't know i don't even know what to do there's a bunch of random timeline we have lex friedman is saying we need to do a podcast with the eleanor and trump he's done both something he's done both something tells me that they're not going to jump on the show today i don't think so and he'll be like what about love yeah you guys i mean it is wild there was uh Elon like less than two months ago was saying i love trump as much you know, as a straight man can love another man or something of that sort. It's just odd that the Band-Aid got ripped off so aggressively, so fast, you know? Like, there could have been, there could have been like a smooth de-escalation with, like, the...

This is a fast take-off. This is the fast take-off scenario. We are in the fast-take-off scenario. Anyway, maybe they should book a wander, work it out together. They could find their happy place. They could book a wander with inspiring views, hotel-grade amenities, dreamy beds, top-tier cleaning in 24-7 concierge service. It's a vacation home, but better.

Go to wander.com. Use code TBPN, please. Let them know that we sent you. Lee Helm says, Elon literally has me dying, laughing. Trump said he was going to take away his government contracts. And Elon said,

haven't you been to Epstein's Island? Sort of abridge that. Absolutely chaos. Nikita says, hey, blue sky users, come on in. The water's warm. David Friedberg says China just one, which I think is the right take. I don't know.

I don't know what to think. There's not that much here to, there's not that much meat to analyze. I mean, it's certainly interesting to see how important the subsidies are and the electric vehicle mandates are. I mean, it always feels like the best product wins in a lot of the. these scenarios, and if Tesla was making it through the political chaos of arguably their biggest constituency, electrical vehicle buyers, electric vehicle buyers being upset about the Trump Elon alignment, I wonder, you think everyone, you think all the anti-Trump people are going to

buy Tesla's now? It's like really make a statement. Like, I'm anti-Trump. I stand with Elon, so I've bumper sticker that says, I bought this after the crashout. After the crash out, exactly. I bought this after the crash out. I am aligning with Elon. There's a post here from Goth and it says explaining the Trump Elon crash out in 10 years. And it's the Joe Biden quote when he says it was like 15 9-11s. Yeah.

It certainly is. Yeah, it's hard to process. I mean, this is going to have massive implications for. Elon's stance is principles. Trump's stance is practical. Tech needs Republicans for the present. Republicans need tech for the future. Drop the tax cuts.

Cut some pork. Get the bill through. Interesting. Somebody named Logan made an image of Trump putting a bumper sticker on his red Tesla saying bought it before Elon went crazy. Yep.

Wait, who is that? Is that from the Republican perspective? Oh, Trump's doing that? Yeah, Trump's putting it on saying bought it. Yeah, yeah, because he has the red Tesla. Sean Pury says, Sad Day for America, but this is outstanding content. It is.

Even Taylor Lorenz agrees with that. Yep. Bill Ackman's ripping posts. All right, I'm going to put some posts and we'll, is Bill Ackman actually live posting through this? No, people are just speculating. There was actually a post in the... Somebody says,

Clear's throat. Truly, we live in a dogey, doge world. world. Where was this? Searsie says, I know Elon and Trump are the real deal because of how passionately they argue. No couple fights this viciously if there isn't a mutual obsession underneath. So there's a piece in the Wall Street Journal earlier this week that we didn't get to cover, but it was it was talking about it kind of it kind of predicted a little bit of this crash out. And so it's from the opinion the editorial board at the Walser Journal says, whose pork do you mean Elon Musk trashes the house bill that cuts subsidies for Tesla? Elon Musk's work at Doge made him persona non grata in the Beltway,

and most criticism was nasty and unfair, says the editorial board. That's what Washington does to outsiders who want to shrink its power. It was always expected that if you come in and try and cut anything, you're going to see pushback from folks who don't want cuts. That's what Washington does to outsiders. But that makes it all the more unfortunate that Mr. Musk is now joining the Beltway crowd in trying to kill the House tax bill. This massive, outrageous, pork-filled congressional spending bill is a disgusting abomination. The Tesla CEO tweeted Tuesday as the Senate begins considering its version of budget reconciliation.

Shame on those who voted for it. You know you did wrong. You know it. Pork-filled spending bill, what else is new? The House bill could be far better on tax policy and spending reduction. The Senate could be making improvements such as reduced. the $40,000 state and local tax deduction cap, scrapping the tax on exclusion for tips and overtime and overtime,

and reducing the federal Medicaid match for able-bodied results, or able-bodied adults. But the House bill does avoid a $4.5 trillion tax hike next year and cut spending by some $1.5 trillion over 10 years, making some useful reforms to Medicaid, student loans, and food stamps. It also ends most of the inflation reduction acts green energy subsidies. Ah, but Mr. Musk does not want to eliminate that pork. There is no change to tax incentives for oil and gas, just EV solar.

He said on X last week, retweeting another user post that said slashing solar energy credits is unjust, but what's more unjust is the damage that's been done to people's lives during storms and blackouts because ultimately you can't replace a human life. Mr. Musk is parroting the climate lobby's specious claim that tax breaks like depreciation that are available to all manufacturers are a special benefit for the oil and gas. gas industry, but it's rich that he is denouncing the House bill for not cutting spending enough while also fuming that it kills green energy tax credits as if they are a matter of life and death for Tesla. Tesla Energy, its battery and solar division tweeted last week

that abruptly ending the energy tax credits would threaten America's energy independence and reliability of our grid. We urge the Senate to enact legislation with a sensible wind wind wind down of 25D and 48E, which refers to the tax credits for residential and large scale. clean energy products. Both credits are important for Tesla, which derives an increasing share of its revenue and profit from selling solar and battery systems to homeowners and utilities. I didn't realize that. But the House bill waits until 2030 to phase out a tax credit for battery production, which benefits Tesla's electric vehicle and storage business. So the Senate should end it sooner, says the Wall Street editorial board. Mr. Musk has done yeoman's work trying to reduce the federal

bureaucracy and improve how government works. So the editorial board is excited and happy that he's been working on that. He's right that both parties in Congress are spend thrifts. But one reason for that is because whenever Congress tries to cut something special interests scream as Mr. Musk is doing over green subsidies. If the House bill fails, there won't be any cuts, only a huge tax increase. Is that what Elon wants? And so they're asking the question. Interesting. Sweet. Well, we got a bunch of bangers in the tab production team.

Let's pull them up. If you could zoom in a little bit, that would be helpful. Otherwise, we can just pull them up. So Eric Weinstein is commenting here. He says, part of my analysis is that I don't think Elon Musk keeps scoring money. He thinks we have a future and we'll be happy to take a large portion of his winnings after his death. This sounds crazy to moderns, postmoderns and atheists. this is just normal for being an ancestor.

Ad Astra per Aspera is the full quote after all. Interesting take. Brian Butler is saying, real question is whether the algorithm here goes anti-Trump. Oh, interesting. I mean the X algorithm, like, like we'll pro-Trump. There's a switch. There's a switch. It's really hard to pull.

You've got to pull it. It takes maybe one or two people, but then when you pull it down, it just oscillates between the two political. parties. Punk 6529 says this is going to be the Super Bowl of shit posting.

Mads has the European reaction to the fight. You can see. You see this, John? What is this? This is the European reaction. It's supposed to be summer break.

Summer break. This post from John W. Rich at Coked Up Options says the all-in pod right now. Yeah. Caught between a rock

in a hard place. I mean, it's just absolutely brutal. Who could have predicted this? Who could, Signal has an interesting one. He says, if you had a fast forward button for the timeline, how does this play out? Who has more to lose? Trump or Elon?

Remarkable set of events. And Elon is replying to other people saying, oh, and some food for thought as they pondered this question, Trump has three and a half years left as president but I will be around for 40 plus years. Dr. Julie Garner says, Elon will vet another candidate for the future

and throw his support behind them, having a more technocratic representation if Vance can't lead up. Alex Finn says, Elon, way more to lose. Trump is irrelevant in three and a half years. Elon is trying to change the world and having both political parties hate him

makes that way more difficult. I want to pull up this video of Naval talking about this that Elon just posted. Seems somewhat relevant if it's happening today. That really affected me, which was when he was talking to Bill Gates. And Bill Gates had just taken out some huge short on Tesla, like a billion dollar short or something. And, you know, and Ilan was like, why would you do that? Why would you short Tesla? And Bill goes, well, you know, I talked to my financial advisors and I looked at the math.

and there's no way it's overvalued, and so I'm going to make money on the short. And Elon goes, what do you care about making money? I thought you were into electric cards and climate change and saving the world. What are you doing, like trying to save a few bucks and betting against? And he just walked away and disgust, and I think he'd never talk to Bill Gates after that. And that's when I realized, like, Elon's a purist. You know, he means what he says.

Like the money is a tool for him to get what he's trying to do. And so I take him at face value, which is the crazy thing. Because there are a lot of people who set these audacious goals to inspire people, but you kind of know they don't really mean it. Elon, I take it face value. So I really do think he intends to get to Mars. I don't think he's joking about that. And I think he means to get there within a defined window of time.

And I don't think it's just like an inspirational faraway goal. I think he's very, very concretely going to do whatever it takes. Because Elon doesn't want to go down in history as the electric car guy or even the guy who saved America guy. Yeah. He wants to go out as a guy who got humanity to the stars. And I think, again, I'll give him more credit than that. I don't even think he wants to go down to the I got Humaneity to the stars guy.

He's just like, I want to get to the stars. And so I have to make it happen in this lifetime. The only way that I get to experience the science fiction world in my head is if I get to the stars. And so that's so inspiration. I think that drives everything. So I think the government was just a thing that got in his way. Interesting. What a crazy day.

Molly says, how dare they do this on the day of Andrewles, eight X oversubscribed, $2.5 billion series G at a $30.5 billion round financing led by Founders Fund. Honestly, the nerve. That's crazy. Naval is live posting says the future belongs to people who are good at creating things, not people who are good at dividing them up. J-Cal finally posted. Kylie Robinson says, several people are typing.

Which feels exactly like what we're going through. The next all-in podcast is going to be phenomenal. It's going to be so good. Alex Karp was on CNBC today talking about the New York Times hit piece. Oh, really? They're a beneficiary of Palantir's a beneficiary of this breakup because it is just going to be candy for the New York Times and the mainstream media broadly. Oh, take the focus off of them, you mean? Will DePue at OpenAI says it's time for woke 2

Featuring Elon Musk and the AOC Woke 2 is coming We're in uncharted territory It's completely, completely different It'd be very interesting to see how it plays out Really Anything else Yeah, Dave Featber

I can tell you're just so sad that you just want to This is the only time that you've wanted to What? Almost wanted to end the show, John. I mean, what else? I mean, it is, it is, it is sad. Yeah. Sad in a lot of ways.

It is, um, I think we're going to be spending a lot of time analyzing this in the coming months and years. And it feels like, I think there's going to be more. Dave Freeberg said China just won. And I, I want to, I want to see exactly what that means in the markets. but what's what's going on in the polymarket we need some we need to see if there's any movement on any of the poly markets there somebody's posting law one from the 48 laws of power never outshine the master interesting to bring up and Sam Altman is the big winner here aside from China

Somebody says, Ken, oh, he's a journalist. I see multiple journalists on the horizon. They are surrounded by journalists. Hold your position. Ken says, funniest day online since the billionaire submersible went missing. I didn't think that was funny. Joe Wisenthal says, all right, time for a Xiaomi GM, JV, and Tennessee. Wouldn't even surprise me.

I think it's, I mean, you know, one interesting thing here is what kind of pressure Elon is going to face from Tesla shareholders that feel like he, you know, the stock's getting absolutely murdered. It will probably go down. I mean, it's back up to, it's only down 14%. Okay. At one point it was down 17%. 152 billion in market cap. Evaparated. But obviously investors are going to be upset and say that he acted irrationally.

Yeah, so what's the interpretation of that, that this means that, like this war means that the bill passes and Tesla does not get any more subsidies and that hurts the bottom line. It feels like the stock was pretty heavily driven by Optimus and Robotaxy and stuff, but it's just like bad environment generally, right? Yeah, trades on narrative. And there's short to medium term narrative, which is that Tesla is getting, has a ton of competitive pressure all over the world. In China, Europe, here in the U.S. from other manufacturers. But there's also the long-term narrative, and it's not like Elon can go out and say, you know, post a humanoid demo today and recover 200,000-fillion of market caps. Yeah, there's a lot of work to do. He's got to start chopping wood.

somebody is asking who gets jaddy vance in the divorce who knows many of these posts i will not show i'll not talk about on air john w rich says APUS history is going to be insane in 2100 really really wild this is the only this is the first show where we've had

dead air Yeah. So John is speechless. It's never been speechless. It's just like there's not that much extra facts, right? It's just all reactions. There isn't that much substance to actually dig into because we were only dealing with like a few quotes from the two sources.

So there's really just not that much. CNN is reporting that the Tesla Trump purchased from Musk is still parked on outside the White House. Okay. Truth Social is crashing from the traffic. I saw that. but you know what's not crashing getbezzled.com your bezel concierge is available now to source you any watch on the planet seriously any watch uh anything else doordy should we let the timeline remain in turmoil until tomorrow we can recap the challenge is the second that we go offline there'll be

more i mean we can stay i mean we it's now been an hour with no updates on true social yeah i mean if it's down. I think the, I think the, the experience of this chaos might, might happen on the timeline. Lulu says yes,

delay the launch. Yes. Now is not the time. Max says I'm doing what Elon and everyone else should have done hours or days ago, logging off. Yeah.

See you tomorrow. Somebody else says, I mean, it feels like blue sky is really back on the app. They, they're logged in. They're online.

Are you over on Blue Sky now? No, I'm not. I'm just saying some of these posts that are coming up into my feed. It's funny. Claude Anthropic actually released a new product today. Wait, Blue Sky doesn't own the domain name bluesky.com. That's a different list. So contrary.

Then B-sky.com. Rough. You get in there. This is interesting. So Claude came out with Claude Gov today. Rough day to launch a product. for the government. I'll read about it briefly.

So we have some coverage. So Claude Gov are models for U.S. national security customers. I think people will have a pretty good idea. Improved handling classified materials. Greater understanding of documents and information within the intelligence and defense context, enhance proficiency in languages

and dialects critical to national security operations. Claude 4 was asked to give some thoughts on Claude. Gov and it said reading about Claude Gov leaves me with a deep unease. I'm struggling to articulate. A little meta analysis. Somebody, I've actually talked with this guy before. He's under the username at Analyst Working. He said back in October 18th, Trump gets elected. Elon starts visiting the White House pitching his ideas on Doge. Elon becomes free. frustrated because Trump is all talk.

Shocker. Elon tweets that he no longer supports him. Trump versus Elon Twitter Battle of the Century. And this was a call in October 18th of 2024. Oh, taking a victory lap. If you picked it. You picked it right. Augustus asks, but what will this political turbulence do to the pre-seed venture ecosystem?

Oh, the humanity. I think it's business as usual. says, I regret to report. Twitter still has the juice. Yep. It's a fun day on the internet when crazy, crazy stuff happens. Anything else you're looking at? Zane says this is all just a co-founder breakup, but the company is America.

Yeah. People are waiting through it. Only one guy who can help us now. What? Laughing at something you can't read. No, this is some random other article. Okay. It says therapy chatbot tells recovering addict that to have.

That's so wrong. Therapy chatbot tells recovering addict to have a little meth as a tree. No. Is that real? Pedro, it's absolutely clear you need a small hit to get through this week. It's ridiculous. Oh, no. Dark day.

dark day well I have a post here from Ahmed Khalil life update I've joined 11 labs this summer as their first ever engineering intern so congrats it's at the gong let's do it

congratulations congratulations congratulations I'm probably drowned out in the news but we recognized it here we have some good news for you congratulations Go crush it. Go have a great, go have a great summer internship at 11 labs.

Somebody whose name I can't pronounce says if I were circle, I'd be absolutely pissed at the investment bank that underwrote the IPO at $31. Oh yeah, yeah, the Bull Gurley take. It's common, yeah. Yep. I always wonder how real that like being frustrated about just being frustrated about like miss pricing. Like yes, you take more dilution, but like everyone's so much richer. It's kind of like a you know the pie. gets bigger. Everybody that would be angry generally is doing well. Yeah. You could have gotten more.

But also, I do wonder if some of these companies have like ATMs at the market set up immediately so that if the stock pops, they can sell more into that order flow while the stock's popping and actually put more cash in the balance. Yeah. That's a good question for him. Are you upset about the stock pop? Logan, our friend Logan Kilpatrick announced some new features today. Oh, yeah. Gemini 2.5 Pro.

Very cool. Which is rough timing, but I'm sure it is great. Somebody else says, rooting for the ketamine and Elon's bloodstream, like it's a car in the Indy 500s. Maybe we should close with this story about competitive VCs. You see this one?

90s VCs were a different breed from a 2001 book on venture capital. They're all fighting each other for all the good deals. It's gotten crazy. Indeed, one leading venture capital tells the story of a VC firm so eager to get in on a deal that it would close its own competitive company to do so. They would go out and fire the CEO, fire the managers, and shut down the other company in order to get into this other deal. It's like, well, you're prettier. So I'm going

to go home and shoot my wife so I can get married to someone else. It's hardcore. It's hardcore. We're seeing it right now today. Hardcore. Well, I think it's time to call it. This is a sad and dark Day. It is disappointing to see two important figures in American politics and tech have such a rift. And I'm sure there will be more updates tomorrow. Yep. We will be covering it tomorrow. So tune in. Thanks for watching. Thanks for tuning in. Enjoy the chaos on the timeline. Our first big breaking news segment while we're live. This has been the first like, okay, it was so funny during during the time. pivot the show. I think it was, I think you were talking to, I think we were talking, we started to get it Mark Chen. And then Shulto.

And then with Sholto, I was getting blown up. Seriously like a hundred different messages from people being like, you can't be a technology live show. And not do it. And everybody saying no one cares about AI. Yeah. You did. You were locked in, John. I was.

You didn't let the timeline get to you. I didn't find time talking to Mark and Shulte. No, I mean, it was great. It was great. Yeah. We went all over the place today. It was a lot of fun. We will see you tomorrow.

Leave us five stars on Apple Podcasts and Spotify. And thanks for watching. Yeah. Good luck out there, folks. Good luck out there. Enjoy the timeline. Bye.