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TBPN is a live tech talk show hosted by John Coogan and Jordi Hays, streaming weekdays from 11–2 PT on X and YouTube, with full episodes posted to Spotify immediately after airing.
Described by The New York Times as “Silicon Valley’s newest obsession,” TBPN has interviewed Mark Zuckerberg, Sam Altman, Mark Cuban, and Satya Nadella. Diet TBPN delivers the best moments from each episode in under 30 minutes.
You're Washington TVPN. Today is Monday, 02/23/2026.
Speaker 2:We are live from TVPN, Amsterdam. Temple Of Technology, the fortress of finance, the capital of capital. Let me tell you about ramp.com. Time is money. Say both.
Speaker 2:He's used corporate cards,
Speaker 1:bill pay, accounting,
Speaker 2:and a whole lot more all in one place. Let's go. Massive sell off in the markets. Thanks to friend of the show, Suttrini. We have someone from Suttrini coming on the show in just a little bit.
Speaker 2:Lots of crazy reaction. Really broke through with something that is framed as sort of fan fiction, low probability. And it's always interesting when someone posts something and they're like, I think there's a 10% chance that this happens so it's worth talking about. And I'm like, well, like, what's the 90% scenario? No one's getting any clicks for the 90 it gets completely
Speaker 3:It's not that exciting.
Speaker 2:Yeah. And throwing out something like, oh, yeah, like 10% chance that this crazy thing happens that doesn't that people don't react to it like it's a 10% scenario or or whatever percentage you put on it. 20% chance, 30% PDUM. Only take It's away what you over. Yeah.
Speaker 2:No. Immediately. And it's the same thing with with all of the all of the AI lab CEOs. When pressed, they'll be like, well, I do think that there's a ten percent chance that humanity dies or something like that. And and that own the headline is predicting if humanity
Speaker 1:dies. Yeah.
Speaker 2:Like, you only like, as soon as you say, like, something crazy happens with no matter how low the percentage is, like, that's what you're gonna be known for forever. So be careful out there with those predictions. But
Speaker 3:Okay. So Sunday yesterday. Did this say Sunday? A lot of
Speaker 2:Okay.
Speaker 3:Yeah. Yeah. This was yesterday.
Speaker 2:Oh,
Speaker 3:wow. Around eleven.
Speaker 2:Really took it off.
Speaker 3:Yeah. Doing family stuff. Mhmm. Don't really have a bunch of time to like sit down and
Speaker 2:Yeah.
Speaker 3:Read something. And the entire day, I'm just seeing people quoting it being like, this is the best essay that I have ever read. So many people that I think are generally pretty smart. And then by the time I actually after the kids' bedtime last night, by the time I actually sat down reading it, it was every almost every paragraph, I was experiencing some element of Gaullman's amnesia Mhmm. Where, like, you have, like, three sentences that are like maybe somewhat coherent and then like a statement that feels like so wrong.
Speaker 2:Sure. Sure. Sure.
Speaker 3:Specifically, think a lot of people obviously called out the
Speaker 2:The DoorDash thing.
Speaker 3:The DoorDash comment which there's so many businesses that you could have chosen in in DoorDash's place. It didn't matter. DoorDash is down 6.8% today.
Speaker 2:Yeah. Sattrini sell off is here. Sattrini possible.
Speaker 3:Despite tracking to close to a billion. Isn't it a billion monthly orders?
Speaker 2:Yes.
Speaker 3:Insane.
Speaker 2:My experience with it was you had mentioned to me that I'm like, oh, it's the current thing. And I was pretty offline. And then by the time I I actually started refreshing the timeline, I was like, oh, I'm, like, clearly, like, stuck on some search feature because I'm only seeing Suttrini posts and, like, posts reacting to it and reacting to the reaction. And it had done a full news cycle, both a backlash and then a backlash to the backlash, and Tay Kim's getting in there fighting, and people are posting rebuttals. And someone posted a fully AI generated, just like turn it up another notch version, which is hilarious.
Speaker 2:He's like, if that wasn't extreme enough, I got something even more extreme for you. People were having a lot of fun with it. Sunday is for the posters, I guess. People are having fun. What what what should we read through of the actual Suttrini article?
Speaker 2:Because it is sort of long, and we do have someone from Suttrini coming on the show. So so we can maybe go through some of the reactions. I mean, the the futures were red last night. The market is down broad.
Speaker 3:Yeah. And it's interesting. A lot of people were saying, guys, there's no way that futures are red just because of this Suttrini essay that is obviously science fiction. And then it turns out Bloomberg this morning came out and actually stated that Reported it. That it's the Citrini sell off.
Speaker 2:Yeah. Yeah. But I mean, there were also the tariff things that the the tariff news was sort of digested on Friday, Saturday. So there was totally like, that could be possible. There's a lot of other stuff going on.
Speaker 2:Joe Wiesenthal has opposed to the Sattrini sell off. The the quote from the terminal, software payment stocks slide after Sattrini post on AI risk. DoorDash and American Express led declines in software and payment stocks on Monday. And we were debating, like, you know, PayPal had gotten beat up, and there was a question about, like, what is the what is the strategy going forward? PayPal's always been an interesting situation because, like, absolutely goaded founding team, but they're all disinterested in jumping back in and turning it around.
Speaker 3:Yeah. You have Max with the firm Yeah. Who over a long enough time horizon will probably compete Yeah. In every
Speaker 1:Yeah.
Speaker 2:And it's hard because even even if you
Speaker 1:went back, how do you
Speaker 2:build a position? How do you get control of that company? But it does seem like there's some value there. There's some stickiness, and it'll be interesting to see. Does it go private?
Speaker 2:Does the new management come in? I mean, they already have some new management. So I'm not exactly sure where it goes, but it feels like the the payment rail thing will be sticky for a while. Young Macro had a take, quote tweeting Citrini. Good and interesting piece, but a necessary caveat is that it's essentially a hypothetical condition on severe institutional failure rather than some sort of macro inevitability.
Speaker 2:As the piece itself notes, one of its two failure modes, liquidity stress and capital impairment, includes a liquidity component that the Fed can address quickly with liquidity facilities and asset purchases, repo lines, quantitative easing, as seen in recent episodes of banking stress. Losses from impaired assets won't disappear but can, in principle, be moved where the broadest shoulders are. It's an odd situation because you're seeing all of this capital evaporate from the public tech markets, but the labs aren't public yet. So they can't fully absorb it. Like, they sort of can, but it's much more opaque.
Speaker 2:And it's not like most the average investor can't just read this piece and be like, okay. I I believe it. I am worried about software stocks, so I'm gonna rotate into a basket of Anthropic, OpenAI, and SpaceX. They don't have that option yet. They wait by the end of the year.
Speaker 3:You can buy NVIDIA. You can buy Google. Mhmm. There's plenty of ways.
Speaker 2:Yeah. Totally. Totally. Just look at Leopold Aschenberger's allocations and copy trade that, I suppose. Plenty in this hypothetical economy much more than before, he says.
Speaker 2:There will be plenty of broad shoulders. Couldn't they just tax anthropic into paying all your pensions? In this hypothetical scenario, this would be done through regulatory or fiscal mechanism. The second failure mode, an aggregate demand shortfall for massive unemployment, can be addressed through fiscal policy, transfers, wage subsidies, etcetera. The piece argues that this may be constrained by falling tax revenues.
Speaker 2:But in a deflationary low inflation environment, the Treasury can run large deficits and the Fed can buy as much of that debt as it needs to, leaving aside that we'd expect the tax structure to change. The political process is unlikely to be as meaningful as a bottleneck as the peace claims, as the hypothetical fiscal hawks pointing to unsustainable deficits would not have much of a point, given the economy in this hypothetical will have much higher potential output. Also, that obviously wouldn't pull well. The first principle is intuition obviously tells you something is awry when we're told that people will want at least as many real things as before, and the economy will have the means to produce more real things than before, but the people won't be getting the amount of real things that they want or need. This is because that typically requires major policy slash institutional frictions or delay in translating capacity into purchasing power.
Speaker 2:If you suddenly have two loaves of bread in your house instead of one in your house, and you weren't starving with one, you probably shouldn't starve when there's two, but if by some hypothetical, through the complexity of the novel two bread loaf production process, you suddenly get tangled and can no longer access the cupboard, then it's quite possible you will starve. I thought that was a good take. It is interesting how this moved the markets way more than AI 2027. Like, it's sort of the same piece, and it has a lot of the same sort of extrapolations based on AI progress, lot of same
Speaker 4:But like
Speaker 3:AI 2027, probably, like, the the the market reaction was, like, invest more in the labs
Speaker 2:Yeah.
Speaker 3:At the time Yeah. Because it was kind of aimed for more more of a West Coast audience in general.
Speaker 2:Yeah. I just mean Let me let me If you had taken AI twenty twenty seven and you had just asked, like, I own this basket of public company stocks, what should I be doing? And AI twenty twenty seven is your backbone, you would probably sell a lot like you're selling right now. Like, the AI twenty twenty seven situational awareness PDFs, like, are the Leopold Aschenbrenner philosophy that is now being reflected in the market, but, you know, he didn't go long any of these stocks anyway. Sorry.
Speaker 2:Really quickly.
Speaker 1:I wonder how much that is just because of, like, who is actually writing it. Like, the AI Totally. People are, like, you know, very San Francisco coded, like, Yeah. Vaguely EA maybe safety as people, where this is like, okay, they're like a financial Yeah.
Speaker 2:Research firm.
Speaker 1:Like, they're known for
Speaker 2:And it's written with a financial audience in mind, and it speaks in that language. So it's been somewhat translated. Interesting that it took almost a year for it to be translated in this way. But then, yes, it's interesting for a couple reasons because it didn't this was not the takeaway. And I I remember in the Vanity Fair piece, I I had some funny quote in there being like, like, the market should be moving off of what happens on the DoorDash podcast.
Speaker 2:And it seems sort of silly, but I think that we're now seeing the downstream ramifications of that.
Speaker 3:Jordi, why I did think it'd be helpful to provide a summary of the essay. I think the original essay it feels like they use quite a lot of AI to write it. I'm gonna use AI to summarize it Okay. Before we go. So anyway so this scenario you should have got to this at the beginning but by late twenty twenty five, AgenTeq AI tools become vastly better at coding and complex tasks.
Speaker 3:Obviously, that was, you know, it's historical. Confirmed. Firms found they could use AI to replicate work normally done by humans, radically cutting labor costs. Mhmm. Productivity looks great on paper.
Speaker 3:GDP and productivity metrics soared because AI output counted in the official numbers, but most of that value didn't translate into real consumer spending. So, like, businesses are spending money, but they're spending it on data centers, and that is not as opposed to labor where if you give somebody money, they'll buy a house, they'll do home improvement, they'll buy cars, they'll put their kids in school. Consumption. Consumption. Right?
Speaker 3:So they're calling this ghost GDP economic output that doesn't actually circulate in the real economy. And then they identify an emerging negative feedback loop, which is companies lay off white collar workers and reinvest savings into more AI. Displaced workers have less spending power. Consumer demand weakens, especially for discretionary goods. Companies facing weaker demand invested even more in AI to maintain margins and this creates a negative feedback loop with no natural break.
Speaker 3:The next step is market and credit stress. So they talk about private credit having lent to a bunch of these different SaaS companies that are now being threatened, defaults climbing as this sort of like perceived recurring revenue ends up not being fully recurring. And next, intermediation and friction collapses. There there's a whole segment talking about how like a lot of value capture in the world is actually just humans like not wanting to deal with frictions. Mhmm.
Speaker 3:It's like not switching car insurance even though you know you're paying more than you should, it's just kind of a hassle. But if you could have an AI agent go and and do that, then then maybe you do that more often and that that pulls out some potential earnings from the system. The other thing they talk about is like all these different tech hubs and how many like prime mortgages there are that might not be so prime if there's a layoff and somebody ends up having to switch career paths or something like that. So generally talking about unemployment surging, consumer spending collapsing, severe drawdown in the stock market, and then even our normal economic indicators hiding the overall weakness. And anyways, takeaway, AI being great and powerful may not equal all the markets ripping.
Speaker 3:But
Speaker 2:Well, two different things. Market ripping
Speaker 3:Individual Individual company. Individual companies.
Speaker 2:Yeah. And median income. Yes. Like, they're two they're they're three wildly different things. You can see asset prices rise massively based on future promise of GDP growth.
Speaker 2:If it's guaranteed that GDP growth is gonna happen ten years from now, the market will price that in today. And then if all of that GDP growth goes to one person, you're not gonna see median incomes rise. You're not gonna see, like, broad prosperity in America. Yeah. I have more on that, but let me tell you about Okta.
Speaker 2:Okta helps you assign every AI agent a trusted identity so you get the power of AI without the risk. Secure every agent. Secure any agent. Okay. It's worth Really quickly.
Speaker 2:Yeah. No. I'm sorry.
Speaker 3:Worth maybe noting like the kind of companies they call out in this in the piece by name. So poor all these companies, let's check-in with ServiceNow which is which is one of the companies that was most heavily yep. Down for almost four and a half percent today. So they talk about a bunch of the SaaS tools. They call out monday.com, Asana, Zapier.
Speaker 3:Zapier is kind of funny because in some ways, like, it was just like work automation before work automation was even that cool. Yeah. Like, I was using using Zapier Yeah. Ten years ago. It does feel like they're kind of in a in a in a decent position Mhmm.
Speaker 3:Given that, like, they're already their core businesses like help people automate
Speaker 2:Mhmm.
Speaker 3:Different workflows. But and then DoorDash, we already talked about that. Mastercard and Visa, they talk about suffering revenue pressure because an AI agent would just opt for stable coins, which feels like, again, something that many people on the show have come on and and made the case for why agents will leverage stablecoins or prefer stablecoins. Seems seems like a possibility, but unlikely that that will just, all payment volume will shift over there overnight. And then Amex, they called out specifically because of their consumer base being just generally weakened by labor displacement.
Speaker 3:And then a bunch of others, travel booking, insurance, real estate, tax, etcetera. Travel booking, thought was funny because most travel agents, like, don't actually take fees from the consumer. They take fees from the from the side of the, like, hotel
Speaker 1:Mhmm.
Speaker 3:The airline Mhmm. Whatever. And so the idea that you'll just immediately get like, every AI agent or every individual will just immediately like, I I didn't fully process that one. Mhmm. And, anyways, back to you.
Speaker 3:I I did think that John Lober, I wanted to go through his piece
Speaker 2:Yeah.
Speaker 3:Because I thought he had one of the better rebuttals to
Speaker 2:piece of thought that was good. I I I think the the the thing that just keeps sticking out to me is like and I was debating with Sagar and Jedi about this as well. Like, he he he was telling me, like, AI is the only thing holding up the economy. I was like, no. AI is actually doing very little for the economy right now.
Speaker 2:It's doing a lot for the markets. It's doing a lot for the future. But, like, terms of the actual economic impact of AI, it's very low. And we just know that because you add up the actual AI revenues from the AI labs, and you're talking about like 30,000,000,040 billion dollars And okay, maybe there's like a 5x multiple on that. And so you're generating $200,000,000,000 of GDP on top of those tokens.
Speaker 2:But like, that's just not that much in the grand scheme of the actual America's GDP. And so there's this disconnect between like the market, which is pricing future GDP, future cash flows, future value creation. Then you have what is actually driving GDP today, and then you have, like, the the the actual workforce and what what Americans do. And so there's this there's this odd disconnect, and I keep coming back to the Tyler Cowen, like, slow takeoff philosophy around, like, what's actually holding up the American economy? It's like health care jobs.
Speaker 2:And there's a lot of jobs that are they feel very AI resistant. I don't know. Maybe something changes, but, like, it just feels like like the, like, the number of people that are software developers less than 1% of America. The number of people that, like, work at tech companies broadly is less than 10. And so even if there's some massive, like, reallocation there, and then you go into, like, even in, like, white collar, if everything shifts, like, the rest of world is hit.
Speaker 2:And there's just a lot of other dynamics that feel like like you can see crazy gyration in the markets, you can see really quick reallocation of 10% of capital, billions of dollars flowing around, but that doesn't immediately translate to what is happening in There's the real always this disconnect. I'm
Speaker 3:laughing a little bit because we've seen so many short seller reports over the last few years where somebody accuses a company of like really really bad potentially illegal behavior and the stock will like move down like half a percent. Yep. And then somebody writes like kind of a cool piece of science fiction, but, like, easily can poke a million holes in it. And then it every you know, it sends, like, all these mega caps That's really funny. Down.
Speaker 3:Let's go through this piece from Okay. John.
Speaker 2:Before we do, let me tell you about Cisco. Critical infrastructure for the AI era. Unlock seamless real time experiences and new value with Cisco.
Speaker 3:John Lover wrote a great piece very quickly after this. It's called Contra Citrini. And he says popular markets commentator Citrini recently published a compelling and popular piece of AI doomer fiction, admittedly with some small probability of occurring. But I'm old enough to have seen many cycles of economic doomsaying. I want to present a critique of Cetrini's work and show a much likelier, more positive view of the future.
Speaker 3:One, never underestimate institutional momentum. In 2007, people thought The US was geopolitically done under peak oil. In 2008, they thought the US dollar was just shy of collapse. In 2014, they thought AMD and Nvidia were done. Then came ChatGPT and they thought Google was done.
Speaker 3:Every time, existing institutions with momentum have proven themselves far more durable than onlookers thought. When worried about institutional turnover and rapid labor displacement, it's very funny that Satrini writes, Even places we thought insulated by the value of human relationships proved fragile. Real estate where buyers had tolerated 5% to 6% commissions for decades because of information asymmetry between agent and consumer. People have been calling for the end of the real estate broker for twenty years. You don't need super intelligence for this.
Speaker 3:All you need is Zillow or Redfin or Opendoor. That's exactly This example actually shows the very opposite of Satrini's point. We have the type of labor that most people consider obsolete and yet market inertia and regulatory capture have made the real estate broker far more resilient than anyone would have bet a decade ago. My wife and I bought a house a few months back. The transaction required us to have an agent ostensibly for the above reasons.
Speaker 3:Our buyer's agent made about 50,000 on the deal for about ten hours of form filling and party coordination that I could have done myself. This market will eventually be efficient and price this labor fairly, but it takes a long time to get there. I know a lot about inertia and change management. I built and sold a company that focused on moving insurance brokerages from service to software. And the main thing I learned is the iron rule of dealing with human reality.
Speaker 3:Everything is always more complicated and takes much longer than you think it will, even if you already know about the iron rule. That doesn't mean that a meaningful change in the world won't happen, but that the change will be more gradual, giving us the time to respond and adjust. Second point, software has infinite demand for labor. The software sector has been struggling in recent months as investors fear that companies like Monday, Salesforce, Asana can now be easily replicated and that the value of their back end systems is indefensible. Sutrini and others talk of AI coding as a spell of the end of jobs at SAS companies are, one, the products become obsolete, zero margin, and two, the jobs themselves disappear.
Speaker 3:What everyone seems to be missing is this. These products, effing SUCK. That's his opinion. I can say this because I've actually spent hundreds of thousands of dollars on these products. Sure, maybe AI enables competition to replicate their products.
Speaker 3:But more importantly, AI enables competition to deliver better products. It's no surprise to see the stocks drop in uncompetitive sticky lock in sector filled with another swear word, incumbents becoming competitive again. And and my my own personal call out here is even like until we see a round of layoffs at a company that is 5,000 and software engineers at once Mhmm. It's hard to believe that that AI is replacing software engineers versus just making them a lot more productive. Mhmm.
Speaker 3:If somebody's a lot more productive, you'll pay you'll pay at least the equivalent amount to maintain them.
Speaker 2:Yeah.
Speaker 3:Yeah. Interesting. More generally, it is uncontroversial that virtually all current software is garbage. Everything I use and pay for is littered with bugs. Some software is so broken that I can't even pay for it.
Speaker 3:I have not been able to send a wire using Citibank's online banking in three years.
Speaker 2:This was my pushback against Rune. Rune was like was like, oh, like, Codex is so good. Like, you know, you could just vibe code everything. It's amazing. And I was like, why is the United app bad?
Speaker 1:The United app is good.
Speaker 2:And then and then he was like, it actually is good. And then I used it, and it's like not that bad. But the point holds there is some bad software out there. I I will die on this hill. But yes, hopefully it's gonna get better.
Speaker 3:Anyways, there's a deep and important truth. Even if we get something like the software singularity, the level of demand for labor here is practically infinite. Famously, it is the last few percent of completion that take the most work and by that token, virtually every software product could probably scale up its complexity and features by something like a 100 x before beginning to saturate demand.
Speaker 2:Three,
Speaker 3:this was probably the best point from his response. Re industrialization, there will be some labor displacement of course, driving stands out. Many types of white collar work as Sattrini suggests will undergo some gyration as some jobs disappear and others change meaningfully. AI may be the straw that breaks the camel's backs for jobs like the real estate broker where the job had actually already disappeared a long time ago but the pay was still there. The saving grace here is that in The U.
Speaker 3:S. We have virtually limitless capacity and need for re industrialization. May have heard about bringing back manufacturing, but it's more than that. We largely no longer know how to create and don't have the facilities for making the core building blocks of modern life, batteries, motors, small semis. The whole electric stack is something we are almost entirely dependent on China and other countries for.
Speaker 2:Can So barely make fertilizer. Once you start looking at the physical world, you see a virtually endless scope for work on job creating nation benefiting fundamental infrastructure work that is politically bipartisan. I like that. He where does he close? He says, and beyond, the outcome of industrial mega projects is, of course, that we move toward abundance.
Speaker 2:His abundance build. America will once again be more independent and make things at large scale and low cost. Transcending material scarcity is the key in the long run. If we do lose almost all white collar jobs to AI, we have to be able to provide with a continued high quality of life. Part of this will get automatic.
Speaker 2:We get automatically just because AI taking margins to zero means that those consumer products will become equivalently cheap. This is a deflationary effect. My view is that different parts of the economy will take off at varying speeds, and virtually all the areas are slower than a piece like Satrini's might suggest. To be clear, I'm extremely bullish on AI and expect that one day my labor too will be obsolete, but it's going to take a while to get there. And that time gives us the opportunity to make good policy.
Speaker 2:On that front, preventing a market meltdown the way Saturni imagines is actually pretty easy, and the federal government's response during COVID showed how proactive and aggressive it is willing to be. I'd expect large scale stimulus to kick in quickly once needed. It slightly irks me to say that it won't be efficient, but that's also not the point. The point is material prosperity for people in the course of their lives, broad consumer well-being that legitimizes the state and carries forth the social contract, not satisfying the accounting metrics or economic norms of the past. If we are nimble and responsive to the slow but sure technical revolution, then we will be fine.
Speaker 2:It's a good good good response.
Speaker 3:Rise calls out, out of every example they could have chosen, they went with DoorDash. The barrier to entry for launching a delivery app is not and has never been software. It's distribution restaurant adoption, user adoption, and of course driver adoption. It would be really funny to be using like the vibe coded version where somebody's like, yeah, just launched a delivery app and your food will be here in four hours.
Speaker 2:Yeah. It it does feel like
Speaker 3:If I was Tony, I'd be I'd be flying to find Satrini
Speaker 2:and having to work face
Speaker 3:to face. Opening up a can of I know where
Speaker 2:you're going. Yeah. It it it feels like okay. So you built you like like, you you vibe code a profitless, like, open source delivery app that anyone can use. And you assume that it would get adoption just because it's it's more economically efficient.
Speaker 2:Like, it's cheaper for all parties, so they will join. But DoorDash is actually like a three party transaction, so you need to market to all three. And it's really, really hard to break through right now. And maybe it would just go viral and everyone would onboard. It just feels tough.
Speaker 2:And then he was saying that, like, well, in this future, it's like all three parties are using agents that are perfectly rational and hunting around for the best opportunity, so things can shift faster. And I I believe that to some extent, but it just feels like still a little bit farther away because of adoption and actual
Speaker 3:Yeah. DoorDash has moats. That is the big that that is the the simple trick Yeah. That all vibe coders hate.
Speaker 2:Yes. Yeah. Mean, it's like there's a lot of capital that went into building the network and the app, but also a lot of capital that went into marketing and and onboarding the pool of of in the marketplace, like Yeah. The actual liquidity.
Speaker 3:Yeah. The only the only scenarios that I can see the the sort of like vibe coded delivery or sharing economy Mhmm. App working is at a local level. Yeah. But there's already a bunch of competition there.
Speaker 3:Like I have a guy that when I want a ride to the airport Yeah. I call him, he picks me up. I don't necessarily use Uber because I like having the same Yeah. Guy's my buddy now. I like I like going to the airport with him.
Speaker 2:Yeah.
Speaker 3:Right? So
Speaker 2:Yeah. I I was thinking about Amazon basics and like that hasn't destroyed every company, every brand like and why is that? Like, will that is that a good analogy that, like, okay, the big AI labs will have Amazon basics for SaaS, but people will probably still want certain brands. There will be certain people that are locked in. Okay.
Speaker 2:Yes. I know the Amazon basics paper towels are cheaper, but I just happen to
Speaker 3:like this particular brand that's a little bit more tailored for me. But Amazon Basics was like, hey, we're you buy paper towels from this brand normally. Yeah. We're gonna sell you the same product
Speaker 1:Something else.
Speaker 3:With our with our logo. Yes. Same product. Yeah. And I think the AI disruption that is much more real is like you have entirely new paradigms for software
Speaker 2:Mhmm.
Speaker 3:An entirely new relationship with software. Yep. And it's not just like, oh, you know, somebody built the exact same version of Salesforce. It's like somebody built an app that automatically sets your schedule every day and you're not even Yeah. You're not even thinking about, like, oh, I need to be monitoring this dashboard or
Speaker 2:Yeah. No. No. I I agree. I think the But think the Amazon basics of Salesforce, it probably is not that big of a business opportunity because the whole value prop is that it's lower margin.
Speaker 2:And so Amazon Basics is not driving Amazon's market cap.
Speaker 3:Well, yeah. Amazon has solved the distribution. They're like, we have the customer. Yes. But when you have a lower margin profile and you don't have the customer yet, naturally means you can't spend as much money to acquire customers Okay.
Speaker 3:And build out sales and distribution and all the Yes. The It's a very different situation. It's not like people are just going to like the SaaS, you know, supermarket.
Speaker 2:I I think his I think his pushback would be like they will effectively because they will go to an agent that says like, I need to accomplish this job. And it will say, okay. Well, for that job, I need a tool. There's a legacy SaaS provider, and there's a and there's a neo SaaS provider that's vibe coded. And I could actually build my own version too, and it will pick the most economically efficient across that frontier.
Speaker 2:And so that's where he's that's where he's coming away with like some downward pressure, which I think is like reasonable. It's just again, I I just keep coming back to timelines here. Before we move on, let me tell you about Consul. Console builds AI agents that automate 70% of IT, HR, and finance support giving employees instant resolution for access requests and password resets. Let's continue.
Speaker 3:Very funny post from Dime to Square Holdings. You you think this is crazy but just wait until next weekend when I publish my Substack article. You should freak out and kill yourself right now.
Speaker 2:People are really having fun with this.
Speaker 3:What was this next one? Well, Dimes is on a roll. Average twenty twenty six AI macro research now.
Speaker 2:I am a
Speaker 3:legend. This may be the most terrifying novel you will ever read.
Speaker 2:Isn't that just the zombie apocalypse movie? This that might be the most market moving piece ever written, says Clouseau Investments. That seems accurate. I mean, there are other factors going on, but this does really feel like remarkably impactful. And I don't know, maybe it's a buying opportunity, maybe it's a warning to everyone else, but it certainly certainly broke through.
Speaker 2:I mean, these articles is really, really underrated. I've I've been I noticed, like, I heard about something big is happening on a car podcast. I I I I got that forwarded to me in an email. Like, like, these pieces of, like, AI is a thing that you need to be be paying attention to are breaking through in a way that '20 AI 2027 did not. And, like, the you haven't seen Ilya on DoorDash?
Speaker 2:Like, that actually, that's a joke because it really, that did not break through. But something big is happening did break through to the tune of 100,000,000 views, which actually means, like, everyone read it beyond. It it went it went it broke containment. Like, truly, truly went big. And that's certainly market moving, the Sattrini post did, too.
Speaker 2:Can go through his agentic commerce thing, but it's a little wonky. Let's see what venture anthropologist had to say. Sattrini is completely wrong about the impact of AI on the economy, but his article does correctly show that various forms of AI doomerism will become incredibly popular in 2026. Yeah. People are people are pushing back.
Speaker 2:And someone was saying that this is similar to Karl Marx's critique of capitalism. One of the this is from Mohit. One of the often slept on benefits of attending the University of Chicago is that they make you read Marx as part of the core curriculum, which is why this article gave me flashbacks of taking SOC 114 as a freshman. Marx, writing during the Industrial Revolution, predicted capitalism would periodically devour itself. Firms replace labor with machinery to boost profits, but competition diffuses the technology, drives prices to marginal cost, and the gains get competed away.
Speaker 2:This was the collapse of profits. Meanwhile, displaced workers lose purchasing power, hollowing out the demand for the whole system depends on production rises, but no one can afford to buy what's produced, the contradiction between production and realization. Sattrini's piece describes this exact dynamic, then declares there's no natural break, but it's the most Marxist it's the most Marxist piece of financial analysis written in years, and it makes the same errors Marx did. Schumpter Schumpter offered the obvious rebuttal eighty years ago. Creative destruction doesn't just destroy.
Speaker 2:It creates industries we can't yet conceive of. Everyone in the replies is already making this point, and I think they're right. But the sharper rebuttal is Hayek's. Prices are the break, Sattrini says doesn't exist. Who funds $200,000,000,000 in a quarter in CapEx when equities are down 40%?
Speaker 2:Private credit marks are in the fifties, and consumer demand has collapsed. Cost of capital rises. Incremental build out becomes uneconomical. Capital gets destroyed and reallocated. Sattrini also unknowingly describes Marx's proletarianization of the petit bourgeoisie.
Speaker 2:The one hundred and eighty k p. M. Driving Uber is textbook, but the article claims this collapses consumer demand, and that's where it breaks. The top decile drives 50 plus percent of their spending, and their wealth is in equities, not w two income. They're long.
Speaker 2:The hyperscalers posting records in Citrini's own model. Blue collar is insulated because AI replaces cognitive labor, not physical. The professional middle class gets crushed, but aggregate demand doesn't. The spending class is the capital owning class. The K shaped recovery, they fear, actually stabilizes the demand base, they say is collapsing.
Speaker 2:In the stable aggregate demand, the petty bourgeoisie finds a way to reinvent itself. I think the Sartini piece is excellent and worth reading, but history has repeatedly shown that periods of transformative productivity gains ultimately accrue to the consumer through lower prices, deflection, more leisure and higher quality of life. Marx's AIR wasn't diagnosing the disruption. It was underestimating the system's ability to adapt. Very good.
Speaker 2:Let me tell you about Graphite. Code review for the age of AI. Graphite helps teams on GitHub ship higher quality software faster. Really going heavy on the goat emojis today. I like it.
Speaker 3:It's Monday. Everyone is reading
Speaker 2:the latest Katrina piece thinking it's an institutional research piece when in fact what they are reading is a marketing piece of fiction meant to go viral. And viral it did go. Lesson, lesson there. I wonder how broadly the article the article virality, like, long form has not been going viral on X or Twitter for a decade. Like, even before the link ban and stuff, like, it was really, really hard for articles and links to really go big.
Speaker 2:They did go big when Twitter started because there wasn't that much content. So people would write and then they'd bring that and then they'd discuss that. But I can't remember a really big debate erupting around an article. Maybe a little bit, but
Speaker 3:Steve asked, does Tyler clap for each ad reader? Is that a sound bite? It is real. He claps. Yeah.
Speaker 1:I clap for every single one.
Speaker 2:We'll give you a chance to do it again. Fin dot a I, the number one a I agent for customer service. If you want AI to handle your customer support, go to fin.ai. We did get some feedback that the clapping can be a little loud during the ad reads. You know, Tyler, constructive criticism there.
Speaker 2:Anyway, should we continue with the Strini back and forth? We do have someone from Strini coming on the call.
Speaker 3:Yeah. Can keep going.
Speaker 2:Thing about the Strini piece is that that is internally inconsistent is where does all the surplus go? Okay. We become impoverished and aggregate demand collapses. What financial asset is spared from that? A lot of people were asking that.
Speaker 2:And, yeah, I mean, the answer is commodities. You want be in, like, gold and silicon and whatever is at the bottom of the stack and then whatever has the deepest moat, and then you want to be in the AI companies as well.
Speaker 3:Sean says he's collapsed as well. Thank you, Sean.
Speaker 2:Thank you.
Speaker 3:Let's switch gears to something that is certainly more important than Please. Mr. Trini piece, a street legal modded garbage truck What? Pratt and Whitney j three jet engine.
Speaker 2:Okay. Let's see it. This real?
Speaker 3:Let's For AI. Let's pull this up. You don't even know anymore.
Speaker 2:Wait. I've seen this Pratt and Whitney before. Hermes, I think, bought one. I have no idea if that's real, but that's remarkable if true. Yeah.
Speaker 2:No. You can you can those are expensive, but I do think that they will sell those to you.
Speaker 3:Feels like a six foot flame at the end of your vehicle is not street legal.
Speaker 2:I would agree. I would agree.
Speaker 3:Anyways, horrors coming out of Mexico Yeah. Yesterday. Crazy. Really sad situation. Our very own Joe Wiesenthal had been in the Puerto Vallarta area Yeah.
Speaker 3:Had just left. I think he got out of there an hour Yeah. An hour before the chaos erupted. So very grateful.
Speaker 2:I hope everyone
Speaker 1:is down
Speaker 2:there safe.
Speaker 3:R slash Marriott on Reddit. Somebody said Weston Puerto Vallarta won't honor late checkout with streets closed. I am platinum elite. Way. 1,000 lifetime Marriott nights.
Speaker 1:Wait, I
Speaker 2:thought that was a joke. I didn't realize somebody actually posted this.
Speaker 3:TV is on fire due to the cartels setting fires and bus cars and buses on fire all over the city. The airport is closed and Ubers and taxis are not running. I asked for a 4PM checkout which I'm entitled to based on availability. They won't extend past 2PM and said we would have to use the hospitality suite. We are supposed to be leaving for Buqueros this afternoon but that that isn't looking very good.
Speaker 3:Worst bonvoy property I have ever experienced. I don't think anyone will be checking in today so there's no reason to not to at least not extend us to 4PM. This is so fascinating.
Speaker 2:Does this person just not understand the scale of what's happening? Maybe you could break it down for anyone who's, living under a data center. Like, what actually happened in Mexico? Because it it it was not just cars. This was like a military operation.
Speaker 2:Correct?
Speaker 3:Because, my wife Yeah. Texted me yesterday afternoon while I'm on X moderating
Speaker 2:You're monitoring the situation.
Speaker 3:All the open source intel
Speaker 2:You texted me. You texted me and I was like,
Speaker 3:wow. I've expected something like this
Speaker 2:Yeah.
Speaker 3:For a long time given the tensions down there. And then I'm just watching this and my wife texts me, our friends wanna go to Mexico in April. Can we go? A long weekend. And I was like, are you are you are you joking?
Speaker 3:Anyways, the a really really sad situation basically, the leader of the cartel CJNG which is like effectively his paramilitary group. Any like if you looked at any photo or video of them over the last ten twenty years
Speaker 5:Mhmm.
Speaker 3:They look like they're special forces. Yeah. I think the story is that many of them actually did train at some point with US special forces and then flipped.
Speaker 2:Or or the probably the Mexican military.
Speaker 3:Yeah. No. They were but the but the US special forces have trained Woah. Mexican military. Okay.
Speaker 3:Okay. And so these guys are like like they have their own version.
Speaker 2:Yeah. It's not a LARP like, oh, they just like picked up something they watch like a video on YouTube.
Speaker 3:Yeah. Mean, I'm sure I'm sure some of them are not elite. But in general, this is like a a paramilitary organization. Okay. And it's like one of the largest Yeah.
Speaker 3:Like private armies in the world. Probably the largest private army in the world. Mhmm. And so, El Medco, their main guy gets taken out and then they respond by starting to just like blow up like roads. They they took over an airport, Yeah.
Speaker 3:They just start causing mass chaos.
Speaker 1:Yeah. Because
Speaker 3:person in Puerto Vallarta, if you look at any video of Puerto Vallarta, if you just went outside yesterday and looked around there's like fires rising up Yeah. Everywhere. Looks like it literally looks like a war zone. So for somebody to be hitting Reddit at this moment and being frustrated, it's like Deeply Hey, maybe just, know, the the State Department put out like almost exactly when this person was posting a security alert saying due to ongoing security operations and road blockages and criminal activity, US citizens in the following location should shelter in place until further notice. And like, you're getting a shelter in place warning and you're mad about your Marriott points.
Speaker 3:But again, hopefully hopefully things settle down
Speaker 2:I agree. I agree.
Speaker 3:Down south. Well
Speaker 2:But I wanna move on to some nostalgia. We are gonna get Tyler Cosgrove up to speed on what it was like to live in the nineties and the early two thousands. First, I'm gonna tell you about MongoDB. What's the only thing faster than the AI market? Your business on MongoDB, don't just build AI.
Speaker 2:Own the data platform that powers it. I grew up in an era before MongoDB. I think of my life as pre MongoDB. It's crazy. Back then, you had to store files and text files now.
Speaker 2:MySQL existed. But the nineties and early two thousands were iconic, and we gotta get Tyler Cosgrove up to speed on what it was like during the heyday. These were the vibes, the blockbuster. So much consumer electronics. Like everything had a you had a different device for everything.
Speaker 2:A Walkman, a Game Boy, an Xbox. You can still you can still go see a Monster Shark Rally.
Speaker 1:I had an Xbox.
Speaker 2:You had an Xbox?
Speaker 1:Yeah. We had mod retros right here.
Speaker 2:Okay. Okay. So you're maybe up to speed. Well, there's another one that that talks about the liquid metal object design, and I found this real very informative to show how technology in the digital world actually shaped the physical world. So we can play this, liquid metal object design.
Speaker 2:I feel like this is overdue for a comeback. Amorphous fluid forms. Good music too.
Speaker 4:Other terms floating around were blobism
Speaker 2:or Blobism.
Speaker 4:Minimalism, and biomorphic design. We even just things see industrial designers were trying to push past
Speaker 2:this because everything was really blocky in the eighties.
Speaker 4:Futuristic vision for things like sportswear, watches, music players.
Speaker 2:A clear clear case.
Speaker 4:This is a prime example of when technology influences form. CAD, the modeling software, introduced what is called NURBS. NURBS, non uniform rational b splines. What this allowed is for industrial designers to create mathematically smooth curves to be calculated with precision. It enabled people to make
Speaker 2:So before you had to just, like, use blocks, basically. And, like, you'd get, like, a sphere, and that was it. You could do, like, a sphere and a cube, but you couldn't really do whatever shape
Speaker 3:And back then, they were rocking this kind of hardware Yeah. When they were saying that all retail stores Yes. Globally will be wiped out within the next five to ten years.
Speaker 1:Yeah. You had to
Speaker 3:be there.
Speaker 2:Yeah. I mean, reflecting on the .com boom, I think it's particularly interesting right now. I I mean, I do like, the the the takeaway from the .com boom, when most people when most people pull up the .com boom, they're they're just like, oh, it's a bubble and everything's going to zero. And, like, that's not quite the lesson because the Internet was still, like, actually the most powerful force for economic growth and change, and it did radically change society. It just did so over two decades instead of, like, one year, as was predicted.
Speaker 2:So so there's an article in The New York Times that's sort of comparing the .com boom to the AI boom. And and it's a piece by David Streitfeld at the New York Times. He says people loved the .com boom. The AI the AI boom, not so much. And I buy that generally.
Speaker 2:Like like the the the tenor around the .com era, yes, there was a lot of froth, but in general, were like, oh, this is, like, sort of interesting and cool. I gotta play with this, like, tinker. They just missed it more as a toy than, like, true doom. Yes. There was y two k, and people were worried about that, but the stats weren't quite the same.
Speaker 2:So right now, Were there was just you,
Speaker 3:like, aware of Y2K?
Speaker 2:Extremely aware. Extremely aware. How
Speaker 3:did you process it? Because my parents were trying to explain to Yeah. Like a five year old.
Speaker 1:Yeah. Yeah.
Speaker 2:No. I was I
Speaker 3:was like I could I could generally Yeah.
Speaker 2:Yeah. I I I remember the turn of the millennium, I was overseas on on a vacation and there was a little Yeah. Bit of You you well, you gotta get out. Everything's collapsing. You know, you gotta you gotta seek refuge.
Speaker 2:No. No. There there there was, like, a fear that, like, okay, like, something crazy might happen. But in general, my parents were, like, dialed in enough that they had been through the boom and bust of, like, something bad will happen to actually understand that, like, no, the fixes were in place. And there were a bunch of interesting fixes.
Speaker 2:So if you're not familiar with Y2K, basically, the idea was computers were programmed to store dates as two digit numbers. So you would just say it's 86, then it's 95, 96, 97, 99. What happens when you get to 2000? It just says 2000, and all of a sudden, your interest calculations for your bank account freak out. You have negative money.
Speaker 2:The whole financial system collapses. Anything that's planned. Right? All of this was, like, the fear of what might happen. Tyler?
Speaker 1:Yeah. I mean, that doesn't make any sense. Right? Because Why?
Speaker 3:We'll, like didn't Easy for you to say, Tyler. You weren't born
Speaker 1:for for Oh, gosh. The numbers are resetting all of a sudden. Like, can see it's like a calendar. You can see You
Speaker 3:had to be there, Tyler. Yes. You had to be But
Speaker 1:did no one have foresight?
Speaker 6:No. Nothing happened.
Speaker 2:Right? No. No. Yeah. Yeah.
Speaker 2:People did have foresight, and so they started implementing changes. And the changes cost a ton of money. I think the total bill for y two k systems updates because if you had hard coded, like, our dates in our systems are we're a bank, and we store dates in two digits, You got to go and change that. And that's a couple days of work.
Speaker 3:They didn't
Speaker 2:have They didn't have cloud code back then. And so, yes, it it wound up being something like hundreds of billions of dollars were spent in the lead up to to y two k
Speaker 3:Hit the Gong.
Speaker 2:To prevent that Gong through the y two k paydays. A lot of people made a lot of money. But there were but there were also a whole bunch of interesting rules. And and, you know, Hotshot over here, I'm gonna give him a pop quiz. Do you know how to calculate leap
Speaker 3:year? The If you if you get this wrong, there's gonna be consequences.
Speaker 1:Okay. Okay. No. No. No.
Speaker 1:No. Stop. Stop. Do you know how to calculate Consequences. Do do do do oh, no.
Speaker 1:Flashback, no. I told you there'd be consequences Tyler.
Speaker 2:Alright. Okay. Was the most
Speaker 1:zoomer thing I've ever seen in my life.
Speaker 2:He just has to go. He's he's brain dead.
Speaker 1:Wait. How to calculate leap year?
Speaker 2:Yes. Like like like what like, when do leap years happen?
Speaker 1:I don't know, like, the actual calculation. Isn't that every four years?
Speaker 2:Every four years, except every hundred years, except every thousand years. So they toggle back and forth. And so in in at 1900 was not a leap year. And so if you if you didn't know any of the rules, you would just think, oh, every four any of the special rules. You would just be like, every four years is a leap year.
Speaker 2:It's a leap year, 2000. But if you knew the the hundred year rule, you would be like, oh, actually, it's not a leap year, so I need to hard code the system that is not a leap year, and you can factor this too, because I'm not sure. I'm just riffing here. It might be wildly wrong. But but the thousand count cancels out the 100, and you wind up with just a normal year.
Speaker 2:And so if you did nothing, you win.
Speaker 1:Yeah. But okay. So Gregorian calendar Yeah. Goes into place 1582. Yeah.
Speaker 1:So you have like four hundred years to figure this out.
Speaker 2:Yes. Right? Yes. So I And we did, but it cost us a $100,000,000,000 as is all all technological change. But y two k was, like, it was very millenarian.
Speaker 2:People were dooming about the apocalypse, but these were like fringe sort of cult types. Same thing happened with 2012. I don't know if you remember 2012 apocalypse stuff. Y two k was the same thing. But it was not widespread.
Speaker 2:AI doom is is truly widespread. There's a study in YouGov. More than 30% of Americans are concerned that AI could end human life on Earth. Like, that is a wildly high number compared to how many people believed 2012 was going be the end or 2000 was going be the end. Like, I would be shocked if either of those dates were single digit percentages.
Speaker 2:Most people were like, yeah. Okay. Like, I might need to, like, print out my bank statements. A lot of people are doing that. Like, print out your bank statements before y February because, like, then you'll have a backup, and you'll be able to go in and say, like, no.
Speaker 2:I actually have $10,000 in my bank. I don't have $9,000,000 because it's not the year $1,000 right now. But, you know, everyone got through that. The other interesting thing is that there's this disconnect between the doom AI is going to kill everyone and then what is the impact of AI? There's this new research paper from the National Bureau of Economic Research, NBER.
Speaker 2:They polled a whole bunch of firms in America, a whole bunch of companies, and they said, are you getting a benefit from AI? Tyler, what percent do you think said, yeah, AI is helping out?
Speaker 1:It's probably pretty low.
Speaker 2:It's extremely low.
Speaker 1:20%?
Speaker 2:It's exactly 20%.
Speaker 3:Nailed
Speaker 2:it. 80% said that AI was having no impact on their productivity or employment. We gotta get those numbers up, folks. There are clearly good ways to use AI for to benefit your company and your people.
Speaker 1:I mean, it could
Speaker 3:also Those surveys you never know who
Speaker 2:You never know.
Speaker 3:Might be somebody who's just not Yeah. Who's gonna answer the survey quickly, and they don't realize that
Speaker 2:they But, I mean, just think about it. Like like, there are lots of companies where you have to be HIPAA compliant. Maybe they just don't have a HIPAA compliant LLM available to them. And so they're just like, yeah. I literally can't use it.
Speaker 2:Or, like, like, all LLMs are blocked on my local work network because the IT department's still figuring out how we roll it out. Like, these things happen all over, and it affects, like, lots and lots of people. I mean, if you're just a cashier at Walmart, is AI helping you? Like, it's just not. Right?
Speaker 1:Yeah. But I I'm I'm sure there's still, like they they use services that under you know, like, behind a bunch of layers, there actually is AI going on. Right?
Speaker 2:I agree. I agree. And I mean
Speaker 1:So, like, maybe they're not interfacing with LMs directly, but at some level, like, there are LMs running.
Speaker 2:Totally. Totally. And and even and even if you zoom out to AI just being, machine learning, it's like, okay. So the Walmart person at the checkout counter is not seeing a benefit, but, like, Walmart definitely has a recommendation system on their website.
Speaker 1:Yeah. Or or, like, the Walmart software is being updated faster than it usually would be. Isn't that l m that's AI? Right?
Speaker 2:You you you believe that that's happening?
Speaker 3:Could be.
Speaker 1:I don't
Speaker 2:You're willing to bet it all on you're willing to bet it all on Walmart going through a dramatic digital transformation right now. You don't think it's slow? You don't think they're still like, let's figure this thing out?
Speaker 1:I think they're probably used to be.
Speaker 2:You think they're past the pitch deck phase? Maybe. Hopefully. Yeah. And and
Speaker 3:and We gotta talk about we should we should talk about Rufus. Do you guys hear about Rufus? No. What happened with Rufus? Rufus is going crazy.
Speaker 2:Okay. While you pull that up, let me tell you about public.com investing for those that take it seriously. Stocks, options, bonds, crypto, treasuries, and more with great customer service.
Speaker 3:I'm trying to pull up the
Speaker 2:I have some more. My my takeaway was that the average American believes that they are in Terminator judgment day, but they still have to go to Cyberdyne Systems and do their fake email job and to just right up until the bombs drop. That that's the general tenor around AI. Like, the the vibes are are are rough. But if we go back to the .com bubble and try and understand what's different, there's some interesting stuff that we can learn.
Speaker 2:So first, there was definitely a vibe around permanent high growth and a new economy. There was this economist, analysts, executives, they were arguing that productivity would permanently accelerate and recessions would largely disappear and the business cycle would be broken by information networks that moved at the speed of light. Before the SaaS apocalypse, there was what you referred to earlier, the retail apocalypse. The most extreme formulation was total physical retail extinction within ten years. So within ten years, they predicted by 2009, there would not be a single retail store anywhere in America.
Speaker 2:This was the prediction. This was the prediction. This was the prediction.
Speaker 1:Directionally accurate.
Speaker 2:Directionally accurate. For sure. For sure. So shopping malls would become obsolete. All brands would be commoditized by cheap online alternatives.
Speaker 2:Some of this happened. Amazon Basics is popular. Tmu flooded America. Shopping malls are struggling. But Walmart's a trillion dollar company.
Speaker 2:Nike Nike is worth 90,000,000,000, and Rick Caruso has seemed, you know, to sort of figure out a way to make wall malls work in LA in LA at least. There were also a ton of other crazy.com proclamations. Revenue doesn't matter. Only eyeballs matter. All media will permanently be free because file sharing and products like Napster simply cannot be stopped.
Speaker 2:And so every piece of media will be free forever. That obviously didn't happen. Offices will disappear entirely. Digital currencies will replace fiat money. At its core, the most extreme claim was the Internet was a civilizational phase change equivalent to the printing press or electricity.
Speaker 2:And most importantly, this transformation would happen in five years, not fifty years. And so time compression was the biggest forecasting error here. Not every .com prediction like nearly every .com prediction had some directionally correct element to it, but various breaks were applied either voluntarily or involuntarily, and things slowed down. Media companies sued file sharing companies, for example. Financial markets pulled back.
Speaker 2:Companies adjusted their strategies and retreated to Internet proof moats. And so protests and political movements also had another another role to play as a break. There was this really interesting anti tech protest in the late nineties, the Battle of Seattle. So over four days, 40,000 protesters rallied against the World Trade Organization to put push back against Internet driven capitalism. There were 600 protesters who were arrested at the Battle of Seattle, and they were arguing that the Internet was linked to corporate consolidation, outsourcing, and labor displacement.
Speaker 2:Like, all relatively true things hard to disprove, but the timelines are what matter, of course. And so the battle of Seattle didn't result in any specific dramatic curtailing of Internet adoption, but it did raise the political salience of international trade relations and was clearly in the back of policymakers' minds when they set sectors targeted tariffs and domestic preference procurement rules over the next decade. And so I was thinking about this in the context of the New Brunswick data center protest. So the actual this data center that got canceled in New Jersey, by comparisons to AI, like, it's tiny.
Speaker 3:It's Generous to call it a data center.
Speaker 2:Yeah. It's more of like a data point than a data computing. Yeah. So it's 25,000 square feet. The the the the current, like, meta large data center campus is 500,000 square feet.
Speaker 2:So 5% of the size. And so this data center, we don't know who is actually gonna buy the capacity, where it was gonna go, but you can think of it much more like delivering you Netflix faster than training the next AI model. But like it worked. They got the data center canceled. And so this is going to be like a data point in the minds of AI policymakers, decision makers, leaders for a long time.
Speaker 2:And I think that that will affect things. So the Internet rollout continued even during the bubble and the bubble popping and pushback and all sorts of different things. AI will continue as well. But I think it's important to, like, refocus the conversation on actual impact. Like, the 20% needs to go up, and people need to say, yes.
Speaker 2:This is helpful. And then mitigate the negative externalities before they turn into problem, like real problems for average Americans. The energy issue was foreseeable. Like it was predictable. And maybe that's what we need to be forecasting.
Speaker 2:Like, the next turn of AI 2027 or AI 2028 should be like, here are all the problems that we're going to bump into along the way. Like, let's go mitigate those now because all the hyperscalers could have been subsidizing electrical buildouts like years ago, for sure. So, anyway, let me tell you about Gemini 3.1 Pro. Gemini 3.1 Pro is here with a full with a more capable baseline. It's great for super complex tasks like visualizing difficult concepts, synthesizing data into a single view, or bringing creative projects to life.
Speaker 3:We need a moment of silence for international business machines.
Speaker 2:What happened?
Speaker 3:Falls over 10%, actually 11% now after Anthropic, announces that Claude can streamline COBOL code.
Speaker 2:Oh, no. There we go.
Speaker 3:Wild wild times. The example that I was talking about earlier about Amazon's Rufus
Speaker 2:was gonna say Anthropic announces they're they're going to launch an international business machine. We are an international business.
Speaker 3:Mike Isaac was reporting no. Financial Times, he was just commenting, Amazon's internal AI coding assistant decided the engineer's existing code was inadequate, so the bot deleted it to start from scratch. That resulted in taking down a part of AWS for thirteen hours. And it was not the first time it happened.
Speaker 2:I love it.
Speaker 3:Sometimes the best course of action is to delete and recreate.
Speaker 2:Delete everything. Sometimes that's what you gotta do. Lots of people are having fun with the data center protest. I think it should be taken seriously. But apparently, the New York Times ran an article in 1887 that says peasants destroy a balloon?
Speaker 2:Is this a real is this a real article?
Speaker 3:Peasants 1887.
Speaker 2:1887. What what
Speaker 3:You can actually find it on the New York Times website, 10/25/2018 the Russian peasantry appeared to be sunk in ignorance and superstition During the recent eclipse of the sun, three famous Russian savants descended I'm trying to read. This is in the the times machine. It's very, very
Speaker 2:Oh, it's actually like it's it's a scan. Right? It's not text.
Speaker 3:Yeah. Yeah. Incredibly hard to read. But they the peasants did did destroy the balloon.
Speaker 2:They destroyed it.
Speaker 3:They got it.
Speaker 2:Somebody asked, would you live next to a date center? Not a data center, but a center for dates.
Speaker 3:Dates are underrated.
Speaker 2:Yeah. Dates are good, healthy, delicious. Anyway, Eleven Labs. Build intelligent real time conversational agents. Reimagine human technology interaction with Eleven Labs.
Speaker 2:Go over to the horse section of the show. There's some big horse news Yeah.
Speaker 3:Going on. The moment you've been
Speaker 2:waiting The Financial Times. A horse walks into a lab. Let's see.
Speaker 3:It's says peasants destroying a balloon in 1887 is setting a Waymo on fire in 2025.
Speaker 2:Yes. But oh, okay. So the interesting thing about the Waymo fires was that we we live in LA where the Waymo fires happened. And if you were on the Internet, it looked like Los Angeles was burning to the ground. And Tyler went to the Philharmonic.
Speaker 2:The Philharmonic, which was directly like a block away from where the main protest was. And we were like, woah, man. Like, this seems pretty dangerous from what we're seeing online. And it was fine. Right?
Speaker 1:You just saw someone holding a flag Yeah. And that was it. I didn't see I didn't even see the fires at all.
Speaker 2:And so a lot of these pro the scale of these protests is hard to pick up on because things can go really viral. And you can have a protest that's that's a couple 100 people, and it's if it's in one block and the photographer is good about lining it up and you're not seeing, like, a helicopter shot of, like, tons of people in the street, it can actually be sort of small. I remember that video of the data center protest where he runs outside and he's like, we did it. We did it. Like, it seems huge, but I actually think there were only, a couple 100 people there and you comp that to the World Trade Center Organization battle of Seattle.
Speaker 2:It was 40,000 people. They arrested 600 people. Like, that's pretty significant. And so I guess I'm not saying, like, we're, you know, we're still early for protests, but but it is it is important to understand, like, the scale of, like, what's happening in the real world and and and the actual impact of that. And, you know, you need to be charting this because if they're getting bigger, like, they need to be addressed more.
Speaker 2:And even if they're small, like, people have good points, so they should be they should be listened to and and and, you know, and the solution should be brought to the populace before it gets to a vote. Like, if the you you could you could tell that that New Brunswick debate would go way differently if the hyperscalers were there saying like, hey. Good news. Like, we've done so much forward thinking here that your energy prices are gonna go down. Like, people are going, oh, okay.
Speaker 2:Cool. And we're making it beautiful. It's gonna be
Speaker 3:a beautiful place.
Speaker 2:And we're building it up underground. We're putting
Speaker 3:And it's EMF ships. Grass on the roof.
Speaker 2:Exactly. Yeah. There's there's, like, five easy tricks to, like, get, you know, data centers approved all over the country. But everyone's been putting them in the on the low priority pile, but they're certainly gonna be more more important over the next
Speaker 3:couple years. Why don't you read us
Speaker 2:this? Yes. A horse walks into a lab. It's a December afternoon at the Campo Argento De Polo at Palermo in the northern suburbs of Buenos Aires. The sun is shining in a sky of clear Argentine blue.
Speaker 2:The Jacaranda trees are in bloom. You're sitting in the stands overlooking an immaculate green lawn, six times the size of a football pitch. A military band with brass trumpets and drums, red epaulettes and shiny black jackboots has just marched away. Argentina's president Javier Mele, famous for his eldest sideburns and economic chainsaw, has taken his seat above the center line. Eight players center onto the pitch, hand arms, taut muscles, hair curling over the collars of their polo shirts.
Speaker 2:It's the first semi semifinal of the Argentine Open, the most prestigious tournament in the polo world, the one the players really want to win. This year, the stakes are higher than usual. It may be the last open for Adolfo Cambassio, the world's number one player for more than two decades and the sports GOAT, the greatest of all time. Cambassio has changed the way Polo works, not only through his skill and tactical genius, but as a result of a bet he made nearly twenty years ago. He bought into an the idea of cloning ponies a decade before his rivals.
Speaker 2:This year, many of the ponies he will ride in the open will be clones, identical twins of his favorite horses from years gone by. The players line up four against four. The ponies waiting, ears pricked, poised for action. The whistle blows. The game begins.
Speaker 2:The players streak up the pitch, stick swinging, using their ponies to ride off their opponents to block them from getting to the ball. They gallop, turn, stop, turn on a sixpence, and start speeding in the opposite direction. They bounce on the ball of a small head of the stick. They bounce the ball on the small head of a stick, hit backhands under the pony's neck. If there's a break, they gallop to one end and leap onto a fresh pony before charging back into the fray.
Speaker 2:You can tell sitting on the high sitting high in the stands, the ponies get the game. They can anticipate what's gonna happen. They're enjoying themselves. That in and of itself is remarkable. In the millennia since humans thought to tame the wild horses that roam the steeps and plains, we have bent them to our will for our species.
Speaker 2:Horses have charged into battle, dragged ploughs through rocky fields and carriages through the slop of medieval cities. The real slop problem, medieval cities. Now versus slop. Man has taught them that a hard white ball needs to get to the end of a large field and through the gap between two poles. In return, we feed them, tend to their shoes and teeth.
Speaker 2:We give them massages and march them up and down hills to make sure they are fit enough to play. We polish their coats, plate their tails and bandage their legs and to the best, give them a chance at life through cloning and another and another. But it seems that isn't enough. Now two men, a scientist and an entrepreneur, are going beyond making copies of an original. They are engineering polo ponies to make them even faster in the hope that in a game of high stakes and slim margins, it will give them the edge to win.
Speaker 2:For one pony, Polo Piresa, the circle of life began on an Estancia near the town of Coranel Suarez, a six and a half hour drive Southwest of Buenos Aires. She was born on 12/12/1988, a slight mare raised on a diet of weeping love grass, the silvery fronds that grow in the red soil of the pompous. It was apparent from an early age that she had what it took to make a great polo pony. She played in her first open final aged only five, written by Pepe Higai, one of four brothers who competed at the highest level. Four brothers, all polo legends.
Speaker 3:That's elite.
Speaker 2:Polo remains a macho sport. The top players are male, though there is an open tournament for women too. The grooms who canter the spare ponies up the side of the pitch wearing the Gauchos traditional floppy beret, the bonia, are typically male. The Open has only had multiple has only ever had male umpires, but the ponies are usually female. The players favor mares, citing their intelligence and grit.
Speaker 2:Polo Puerza was one of the great polo mares of her generation. She was a bright bay, the color of autumn conquers with black legs, a white star between her eyes, and another splash of white on her nose. She played at the top level for fourteen years, winning the cup for best pony at the Open, among many other awards. And speaking of ponies, the the purple llamas, Vanta, automate compliance and security. Vanta is the leading AI trust management platform.
Speaker 2:That mayor was a natural polo player. One in a million, he said, the guy said, in a video made to celebrate Polo Poers' induction into the Polo Pony Hall of Fame. She had an impressive heart also. She was a mayor who never got tired. Polo Poers retired in 2004.
Speaker 2:Before she died, samples of her DNA were banked in liquid nitrogen in the laboratories of Kyrion, a biotechnology company cofounded by Gabriel Vachera and based in a science park in the town of Pilar, 35 miles northwest of Palermo's Polo Fields, with his neatly trimmed beard and white lab coat Vachera, 46, would not be mistaken for a polo player. When I first emailed him, he apologized for not responding sooner. He was competing in the World Indoor Archery Championships. Woah. Made sense.
Speaker 2:His company is named after Chiron, the mythical archer, half man, half horse. It's the morning of the open semifinal and we're sitting around a large table in the cool of the conference room at Kirin, just a few meters away from the vats of nitrogen housing Polo Puerta's cells. There are photographs of horses on the wall. Each clone each is a clone at Vishera was responsible for creating, each a genetic replica of a famous Polo pony. One photograph, the newest is of five bright bay foals covered in baby fluff, their legs still too long for their bodies.
Speaker 2:These are the gene edited polo puerzas, he says, the ones you are going to meet. I stare at their photos. There is no clue in the faces of their unusual conception. No way to differentiate them apart from their similarities to each other and to the videos of Polo Puerza that I have watched. What do you think?
Speaker 2:Would you go to a polo match if all the ponies were genetically modified and cloned? Or are you do you want do you want a natty league? Natty ponies only.
Speaker 3:I'm in favor. You're in favor cloning? Yeah. I mean, why not? Push the sport to the limit.
Speaker 2:I feel like it's kind of like not in the spirit of just like a couple guys getting on horses that they just like found. Round them up. You know? It's
Speaker 3:Well, they should they should have the the the sort of like wild league Yeah. Where you can just go find a wild horse, go go play.
Speaker 2:This definitely creates an opportunity for the wild league.
Speaker 6:Yeah.
Speaker 2:For sure. This is kind of the
Speaker 3:Free range league.
Speaker 2:What do they call it? The the enhanced games? Yeah. This is the enhanced games for polo for sure.
Speaker 3:Yeah. Anyway. Tyler, are you a polo accelerationist? Sorry.
Speaker 2:Your business is AI. Their business is securing it. CrowdStrike secures AI and stops breaches. Tyler, are you a polo purist?
Speaker 1:I I think I'm in favor of the cloning.
Speaker 2:You're a polo accelerationist?
Speaker 3:Yeah.
Speaker 2:Piak, as they say.
Speaker 1:Yeah.
Speaker 2:Well, good news for you. There's lots of cloned ponies coming your way to a polo field near you. What what did this post say? If you run every day, you'll be ready for any situation that calls for extreme cowardice. Was this deleted?
Speaker 3:I think so. Think it's Probably the the running community.
Speaker 1:Yeah.
Speaker 3:Zach Pograb came in and said, you're gonna be you're about to be running for me.
Speaker 2:Yeah. Let me tell you about app love and profitable advertising made easy with axon.ai. Get access to over 1,000,000,000 daily active users and grow your business today.
Speaker 3:There is a game called data center on Steam, lets which you build and manage your own data center. This is low key genius, the best way to educate people on a new trait. Hyperscalers should lean should learn a thing or two about edutainment.
Speaker 2:Edutainment. This is fantastic. Tyler
Speaker 3:Somebody was saying this
Speaker 1:It's not out yet. It's coming out March 31.
Speaker 3:Oh, okay.
Speaker 1:But very smart.
Speaker 2:Calendars. Yeah.
Speaker 1:I'm gonna grind this.
Speaker 2:Productivity is gonna fall.
Speaker 3:Everybody Somebody was saying it's like a it could easily be an an ender's game scenario where it's just it's just
Speaker 2:Those racks weren't simulated. Those were real NVL 70 twos, ender. Yeah. I love that. This is this is all a ploy.
Speaker 2:The the humanoids are already deployed. They just need you to wire everything up. This is this is amazing. And it feels like it's it's the the the game mechanics feel, just from this video, remarkably deep. Like, you're not just walking around a data center doing cabling the entire time.
Speaker 2:You're also deciding tax treatment and what software runs and getting probably Kubernetes installed and or something like that, Slurm. Very, very fun. I love these I love these one off games.
Speaker 3:Apparently, there's another game called just called insider trading coming to Steam.
Speaker 7:If you're
Speaker 2:good at insider trading, you're gonna love this game. Steam has a game called insider trading, get ready. It's a roguelike deck builder that lets you literally pump and then crash the market. This is going to be wildly, wildly popular. No, depends a lot on the actual mechanics of the game, but hilarious and says a lot about the society.
Speaker 2:But I think it's I don't know. I'll I'll I'll give it a try. I wonder if it will have micro transactions. That's the big question. Or if it's or if it's pure for the love of the sport, love of the game.
Speaker 2:But these these roguelike deck deck builders are fantastic. Bellatro went mega viral a couple years ago. Really, really fun game. Just crazy poker, basically. It's like poker rules, but with a whole bunch of crazy modifications that allow you to just do like insane things and sort of turns it into a completely different game.
Speaker 3:Ryan says, someone make a TVP an intern simulator.
Speaker 2:That'd be good.
Speaker 3:I'm still actually yeah. No. Now now that Tyler's promoted and just a real deal employee, happened after two weeks last year. But that would be fun for all of us to relive the days of intern intern summer.
Speaker 2:Yeah. Whoever has the highest score gets hired. Yeah. I do I do wonder we've seen this I heard the story about like everyone talks about the death of triple A games right now. Have you heard about this?
Speaker 2:So it used to be, you know, GTA five, Halo three, Bioshock. Like, there were these big games that would sell for $50.60 bucks. They would sell a lot of copies, but then they weren't what do they call them? Permanent service games, online service games or something perpetual. Fortnite is a game that has endless updates and monetizes forever.
Speaker 2:And same thing with Counter Strike, League of Legends. There's a few others that have wound up generating a ton of money for these companies because once they get them up, they're like ecosystems. Roblox, great example. Versus if you're doing like BioShock and you make a bunch of money, you have to do BioShock two if you want more money from those customers. And then you have to do BioShock three.
Speaker 2:And at each point, people are like, Well, I didn't actually finish BioShock one, so I'm sort of out of the market for Bioshock two. And your TAM just gets smaller and smaller while your development costs get higher and higher. And there's been a whole spate of AAA perpetual service. Why am I blanking on the term? There's online service games where they've come out and they said, like, okay.
Speaker 2:We've seen what Counter Strike has done. We've seen what, you know, League of Legends has done. We want that for our company. So let's go. Free to play is the model, but there's there's something around the word service that's the that's the gaming lingo.
Speaker 2:But there's been a lot of flops recently. Like, lot of companies have spent a ton of money on these these online service games that they hope will become the next League of Legends or the next Counter Strike two, and then they just flop and they're shut down in like a couple of months, and it's a huge loss. At the same time, there's been a whole bunch of developers that have gone sort of the indie route and done really, really well. Live service games. Thank you, Bobby Cosmic.
Speaker 2:They're called live service games.
Speaker 3:Nailed it.
Speaker 2:And and the and and and there was this interesting story of this developer that spent, like, three years working on this live service game, and it, like, completely flopped. And then like in the free in his free time made like this game called Peak for in like three months and it went like super viral and did really well. And I'm excited to see like when do we see the actual acceleration in vibe coding? Do we get more of these like meme type games that have, like, like, really interesting mechanics? Does it actually free up the developers to come up with not just interesting viral hooks, like data center simulator is funny enough to get us to, like Yeah.
Speaker 2:Click on it. But, like, the mechanic actually has to be good too.
Speaker 3:A million people will think it's funny. Yes. 10,000 will try it. Yes. How many people actually play for more than ten minutes?
Speaker 2:And the key to playing for more than ten minutes is not like, graphics will be taken care of. We already have Unreal Engine. The engine will work like you're not going to be falling through the floor. It's not going be buggy. But and you'll be able to generate assets and actually make the thing look like it.
Speaker 2:But if you can come up with some sort of novel reward mechanism progression system that's interesting, that shows, Okay, I'm learning and I'm having fun and I'm reengaged, I think that will Dan says, Peak was that developer's peak. Nominal determinism is again. Yeah. You never wanna launch a product called Peak. Anyway, really quickly, let me tell you about Lambda.
Speaker 2:Lambda is the super intelligence cloud, building AI supercomputers for training and inference that scale from one GPU to hundreds of thousands.
Speaker 3:Signal says, serious question. How do you make someone with absolutely zero gaming experience CEO of a very prominent and important gaming platform? Asha action. Was announced as the new EVP and CEO of Microsoft Gaming after a multi year run over on the enterprise AI Mhmm. Side of the business.
Speaker 3:A lot of people had had opinions on this.
Speaker 2:Well, I have an opinion. How do you Signal's question is, how do you make someone with absolutely zero gaming experience CEO of a very prominent and important gaming platform? You make them lock in and spend three months gaming. And so that should be the first task. It should say, okay.
Speaker 2:You have no meetings. Microsoft Teams is shut down for you.
Speaker 3:Clippy's got it.
Speaker 2:Yeah. We're we're handling everything. Your job is to speedrun every major Xbox game, Fable, all the Call of Duty series. You're gonna play all the Halo all the Halo games. You're gonna get good.
Speaker 2:You're gonna rank, and you're going to learn to speed run, and you're going to really, really lock in and establish true credibility that can't be faked, and then we will announce you. That's the that's the hack. People are people are pouring pouring one out for Phil Spencer, who was at Microsoft for thirty eight years. And his profile picture is just the Xbox X, because he's a legend. And Palmer Luckey quoted it and put an F in the chat, because the world you grow up in no longer exists apparently.
Speaker 2:What else is going on here? Okay. So someone asked Asha, what's your favorite game? And Smash JT says, okay, I'll play your game, you rogue Chrono Trigger, forever goaded, Final Fantasy seven, Golden Eye double o seven. Chrono Trigger will forever be number one.
Speaker 2:I never played Chrono Trigger. I did play Final Fantasy VII and GoldenEye. And Asha said, great list. I did my top three. And another reply, Halo, Valheim, which I believe is newer and has had much less sticking power in seven.
Speaker 2:It's been a long time since I played Chrono Trigger. Have you done every ending? Thanks for all the detail. I appreciate it a ton. And is the question that Chrono Trigger doesn't have multiple endings?
Speaker 2:I actually don't know. Okay. So I don't know what's going on here. But Xbox CEO accused of using AI for replies, saying she played Chrono Trigger in her reply to Smash. She would have been six years old.
Speaker 2:You could play it later, which would be a very young age to play it, but maybe she'll have JRPG cheese and pick it up later. It is a curious thought she must be a huge gamer or this is AI. This doesn't read like AI. I don't know. What does Palmer Luckey say?
Speaker 2:Says, Crown Trigger is my favorite game of all time, and I was only three when it came out. True. Yeah. Good point. Also, I don't know.
Speaker 2:Crown Trigger, like, I I played Final Fantasy seven. I don't actually don't I think there are multiple endings. Like, I would not remember. I don't know. Anyway, what else is in the timeline?
Speaker 2:We should tell everyone about the linear lineup for today because we have four guests joining us. We have Alab from Citrini. We have Will Brown from Prime Intellect. And Michelle's coming
Speaker 3:And Alab is not at citrini. He just coauthored the piece.
Speaker 2:He coauthored the piece. He also wrote a part one that's a very good read that was released before the mega viral essay. And then Mike's coming on from also Capital at one fifty. Linear,
Speaker 3:is of
Speaker 2:system for modern software development. 70% of enterprise workspaces on Linear are using agents, and you should be, too.
Speaker 3:Hot So. Take
Speaker 2:doesn't matter if it's
Speaker 3:a Yeah. Nick says hot take doesn't matter if a CEO is a gamer. As Strauss Zelnick has said it's perfectly a CEO's job is to attract, retain and motivate the best talent in the business and then get out of their way.
Speaker 2:Yep.
Speaker 3:That's a good New Xbox CEO, Asha, doesn't need to be a gamer to run a company. She simply needs to do what the CEO's job of running a gaming company is supposed to do, which is to hire talent and allow game studios to make their creative vision come to reality. The the thing I think they maybe could have done better with the announcement is like talk about what the rest of the management team looks like. Yeah. Because if you position Asha as this like elite operator who's going to like like really like there there's a way like if she's like managing a team of Yeah.
Speaker 3:People that are gamers and love gaming and she's like working with them to figure out how to make the platforms better and better, that's more compelling than bringing somebody in then that
Speaker 2:Yeah. I'm trying to think of other industries where the CEO doesn't use the product.
Speaker 3:Part of this is like industries where the CEO doesn't use a product. I mean, like, think about almost every category of enterprise
Speaker 2:I think they all dog food the product.
Speaker 3:But not not not on a personal level, like their teams might.
Speaker 2:Yeah. I guess that's right. Yeah. I was I was trying to think of like, are there like like, I mean, it's like Rick Rubin doesn't know how to play instruments, but he does listen to the music. And I feel like most of the the big, like, Hollywood agents or power players, like, maybe they didn't know how to use a film camera, but they watched movies, I believe.
Speaker 2:Like, I I I don't know if there's, like, someone out there who's just like, yeah, like I've never seen Saving Private Ryan, but it made me a lot of money because I green lit it because like I knew it
Speaker 3:was Yeah. One thing's for sure, Xbox is not in founder mode.
Speaker 2:Yep.
Speaker 3:And will never be.
Speaker 2:But Who would it be though? Should it be? I don't know.
Speaker 3:How do you the the guy the guy who started Xbox, what is the guy that's it's Seamus Blackley is like credited with
Speaker 2:Yeah.
Speaker 3:Creating and designing the original Xbox. I think he's now in the in the chocolate business.
Speaker 2:Yeah. I mean, I I I definitely think the the the CEO of a video game company can just provide a fantastic environment for creative individuals. And also, I mean, Xbox is a hardware company. It's also a live streaming company. It's also a studio that just where you just have studio heads that go and green light projects.
Speaker 2:It's not all directly related.
Speaker 3:Handle says, Larry Ellison using Oracle on a nice Sunday morning.
Speaker 2:Let's go. Good job. That's correct. I don't know. He probably does store a lot of data in in in Oracle.
Speaker 2:Who knows?
Speaker 3:Yeah. Aaron says the CEO necessarily is not in the product daily. Yeah. Knows? We'll wait It's to it already happened.
Speaker 2:Yeah. Well, we'll we'll yeah. I mean, we'll see.
Speaker 3:Yeah. We're working on we're we're we we got in touch with her on Friday. We're gonna find a time for her to jump on the show.
Speaker 2:Yeah. PS six might be delayed because of the memory stuff. There's also I don't know.
Speaker 3:Yeah. They should just delay the next Xbox and let let Asha just game for three months, like you said, six months.
Speaker 2:Yeah. Real the really interesting thing on the hardware side is a lot of people were freaking out over the weekend playing with chatjimmy.ai from Thales. We have the founder on the show. He has baked Llama three eight b onto silicon. And so it runs at 16,000 tokens per second.
Speaker 2:So you ask it your typical LLM query and it just, boom, loads the page. It's all done. There's no token streaming in. You're just at the bottom of the page. It's actually sort of jarring because then you have to scroll back up.
Speaker 2:But it's clearly, like, incredible and this is coming and we've experienced it with Codex 5.3, Cerebras or Spark. Is that what they call it? And there's a few others, Brock. And so
Speaker 3:And it doesn't have web search?
Speaker 2:Yeah. Yeah. So so there there's a lot
Speaker 3:of What is TBPN? It says Yeah. Win Butchers Pizza Network.
Speaker 2:We gave you a different answer this time. Wow. It's really hallucinating. Anyway, that I think that the system on a chip, Cerebras, the wafer scale, superfast inference is going be very amazing for
Speaker 3:it really all is just a next token predictor. Yeah. It says TVPN could also stand for the Black Pine Network. This is not a well known term or organization, but it it could be a fictional or made up name.
Speaker 2:It's having fun.
Speaker 3:It's having fun.
Speaker 2:But I think that there's a very interesting play where the gaming systems basically bake a style transfer diffusion module onto silicon and put it on the chip. This is what NVIDIA did with DLSS, dynamic something super sourcing, super sampling, deep learning super sampling, DLSS. So if you have a NVIDIA, what is it, GeForce like 4,090, 3,090, there's a section of the chip that's trained to take a ten eighty p video game and up res it in real time to four k. And so you can run if your hardware can only run the game at seven twenty p 60 frames a second, it will up res all of those frames. It's not perfect, but it gives you a sharper image.
Speaker 2:It's basically just AI sharpening that's happening. You could imagine a model that is trained to turn the images that are generated from a video game from Unreal Engine into something that's actually photoreal, like make it like a movie, that prompt that we've seen happen, and you're like, wow, that actually looks like a movie, you could run that in real time at 60 frames a second and be playing a video game that looks truly photoreal. Because the actual game engine graphics have totally plateaued, and there doesn't really feel like they're just gonna jump to cinema quality anytime soon. But if you use AI to do the last step, I feel like that could be really good. What do think, Tyler?
Speaker 1:Yeah. You could also do, like, a Genie three type model breakdown. Right? So, like, interactive video
Speaker 2:Oh, yeah. Yeah. Yeah. Yeah. Because that's, actually really slow and limited right now.
Speaker 3:But if you
Speaker 2:bake that down, you could play that. Yeah. I I still think there's a lot of work to be done on Genie three.
Speaker 1:Yeah. Maybe it's like one or two more models.
Speaker 2:Yeah. Like, though Like, clearly, that would great. Llama two level right now, but yes. Yes. Yes.
Speaker 2:I completely agree. Anyway, let me tell you about Restream. One livestream, 30 plus destinations. If you want a multistream, go to restream.com. Dan's Gaming says, my theory is that Phil and Sarah did not wanna shove AI into everything at Xbox.
Speaker 2:They were forced to retire and resign. Microsoft is replacing them with someone with a strong background in AI and no experience in gaming. This is just getting insane. I don't know. I have a very I'm completely white billed on AI and gaming, as I just said.
Speaker 2:Like, I think AI in gaming can be really, really great. I mean, there's a ton of games where the developer would love to have, like, NPC dialogue that they don't have to sit there and write, okay. This townsperson's gonna offer you 5 coins for your sword. It's like no. Just like be an NPC.
Speaker 2:Be a be be you know, you have coins, act act agentically. And then you go up and you're exchanging with the townsperson your sword for your coins or whatever, you have, like, a much more natural interaction. That feels really great.
Speaker 1:I I don't know.
Speaker 2:I I there's a million bull cases for AI and gaming in my opinion. It seems like the hardest it seems like one of the easiest things to sort of justify.
Speaker 5:Well, we
Speaker 3:have mister Shah.
Speaker 2:So let's tell you about Figma. Ship the best version, not the first one with Figma, including introducing clogged code to Figma, explore more options, push ideas further. And without further ado, we'll bring in our first guest of the
Speaker 3:show, Eilat. How are you doing? What's going on?
Speaker 8:Doing great. How are you guys?
Speaker 3:Doing great. Is this your first time
Speaker 2:Is it over?
Speaker 1:Furthering a
Speaker 3:a global sell off?
Speaker 8:The first time so far, but, you know, I'm I'm just the messenger is the way I look at Yes. We've got a lot of opportunities and a lot of scary things coming down the pipe.
Speaker 2:Okay. So, yeah, take us through the the the thought process. Like, how long had this been simmering? What was the actual process of putting together this report? And then what do you want people to take away from it?
Speaker 2:And then maybe we can go into some of the reactions and your reactions to those reactions.
Speaker 8:Absolutely. The the process ultimately is that, you know, I've been building an AI for fifteen years, and I've been an investor for twenty. And so especially the last six months as I've just been using AgenTic coding myself and my teams have adopted it, it's just been a step change function in how much we can get done. Mhmm. And just thinking through, hey.
Speaker 8:How is this gonna know, we're we're early. We're a startup. You know, we're gonna be at the leading edge of how people are adopting things. You know, assuming the corporate world is a year or two years away, it's it's gonna be pretty profound. And I think the underlying thing, you know, as, you know, sort of an amateur macroeconomist is we're just not producing white collar jobs to begin with.
Speaker 8:I hadn't actually seen the extent of that until I kinda looked at, you know, specifically what we call, like, the information sector, different parts of kinda technology. Those jobs are down 8% from the peak in 2022 already. And so those those are the places where people are adopting the most aggressively already, and we know, you know, every week there's firings out of, like, big tech. Yep. And so in that world, what happens when the technology that big tech's been using for a while has gotten a lot better, and now, you know, your average corporate starts using it as well, it can get quite scary.
Speaker 8:And so, you know, we wanted to kinda think through the implications of that and, know, the the the piece.
Speaker 3:But how much of those how much of those layoffs do you think are are you know, we've talked about a bunch of those layoffs on the show. They're usually attributed to AI. But if you dig under the hood, it's like they just wanted to kind of resize or get more efficient or they're re reprioritizing Mhmm. Resources
Speaker 1:Mhmm.
Speaker 3:And not actually because they just launched some new agent and suddenly everything's changed. Hey. We don't need these thousand engineers anymore.
Speaker 8:So I think, you know, those are all great corporate euphemisms, and of course, that's how they're gonna say it. Mhmm. But I think the the way I would think about this is it's not necessarily like agentic powers happened, now everyone's gonna get fired. Mhmm. You know, agents and LLMs broadly are are just sort of on the tech tree as a continuum from software.
Speaker 3:Mhmm.
Speaker 8:And so software has been making companies more efficient for decades, and, you know, that has caused a lot of downstream effects. And now that software has just become much more intelligent. Mhmm. And so in that sense, I think, you know, companies that are efficient have been doing a form of this for a really long time, and we think about, you know, the the the age starting now in '26 is just something that's gonna accelerate that.
Speaker 2:Okay. So, yeah, what what else was, like, key in the thesis or maybe potentially overlooked that you think people should be really focusing on?
Speaker 8:I think the problem, a, the first thing, the most important thing is just the labor market dynamics.
Speaker 2:Mhmm.
Speaker 8:We've just been in a really weak labor market for a while. Mhmm. And that's before these things roll out. But then you put that together with the fact that, you know, we just have a a very structural environment where what what is the thing that drives our entire economy? It's wages.
Speaker 8:Most of those wages that are ultimately driving all the discretionary spending is coming from the white collar worker.
Speaker 4:Mhmm.
Speaker 8:And the problem with that is that we're now entering this place where you made all these assumptions on, like, loaning money to all these companies, you know, to mortgages, and everything else. Like, white collar economy is our economy. If you all of a sudden just take a leg out of that economy, it has a contagion effect into basically every asset in the world. And so that, I think, is the part that people haven't thought about because when, you know, people were making these loans, no one ever consumed the world in which, wow. Okay.
Speaker 8:Now, like, white collar jobs are in sort of permanent decline. Right? If that's at 2% a year, then I think we can skate through. But if it's at four or 5% a year, then, you know, we have to we need action action a lot more quickly.
Speaker 2:Is the is the white collar economy actually the full economy, or is it more just like the stock market? Because it feels like white collar workers are disproportionately allocated to assets versus consumption. And you see things like there's a lot of health in more blue collar sectors. Health care is growing. And then you also see dynamics like just like we've seen like jitters in the consumer market for a long time, and then we just see the health of the American consumer continuing, continuing, continue.
Speaker 2:And it feels like it's maybe driven by something, like, lower level. And there's always this disconnect in my mind between, like, the economy and the market.
Speaker 8:It's a great question. I think the issue here is that it's all just one labor market. Mhmm. And right now, blue collar is doing better because there are not firings there.
Speaker 3:Yeah.
Speaker 8:I don't think you know, I think robots are probably 18 like, twenty four to thirty six months behind. Yeah. Other forms of LMs that are, you know, just diffusing through society. Mhmm. But the problem is, let's just say that it's one labor market ultimately.
Speaker 8:If there's no more white if the white collar jobs are going away, let's say, you know, in our scenario, we talk about five percent of folks might get fired in in a couple years. Those five percent, if there aren't white collar jobs for them to relocate into, then they're gonna have to move into the the gig economy and the blue collar labor force. And so that puts pressure Yeah. On the entire labor market, not just the white collar one. And to answer your other question, health care is growing.
Speaker 8:Education is growing. Yep. The reason those things are growing ultimately and we we did some work in our piece to try and isolate white collar that is not government driven. And so the government continues to spend more.
Speaker 2:Yeah.
Speaker 8:Yeah. That's why health care is growing. They're the biggest payer in in health care. Yeah. They're they're guaranteeing all the loans in the the education industry.
Speaker 2:Mhmm.
Speaker 8:And so those those sectors continue to grow because government spending grows, but that's again, it gets very circular if government spending is coming primarily from taxes Mhmm. And primarily payroll taxes because the average worker pays a lot more in taxes, you know, per dollar than the average corporate does. And so some corporates make a lot more money, workers' payroll taxes go down more, then there is a bit of a contagion effect into bonds as well there, too.
Speaker 3:On Saturday, John and I were going back and forth about some of the really wild predictions around the impact of the Internet that were being made in the nineties. There was clicks replace bricks. People were predicting total die off.
Speaker 8:Licks did? Well, I mean, to be fair
Speaker 3:to be like I'll just finish. They were expecting a total die off of all brick and mortar stores in five to ten years, which was like Yeah. Wide widely widely discussed prediction. It was like, why would you ever go to a store to buy something if you could just get it online sent to you directly?
Speaker 8:Yeah. And I think a
Speaker 3:couple a a couple others. So like not as relevant to your piece but people are predicting like permanent high growth, the end of business cycles. There was the like media disintermediation narrative, was like the the Napster era. Everyone was gonna get all media for free forever. Newspapers would would die off.
Speaker 3:Record labels would die Aren't
Speaker 8:you guys the media disintermediation narrative?
Speaker 3:Yeah. Are, but Yeah.
Speaker 2:It's all about timelines for
Speaker 3:years later. And CNBC is still a much much bigger business than all all business
Speaker 1:Yeah.
Speaker 3:Media, at least in our world. But, like, newspapers Yeah.
Speaker 8:Like, magazines, completely gone. Right? All of that has moved to the Internet.
Speaker 2:Totally. Totally. It's it's just like like Sure.
Speaker 3:But, like
Speaker 2:percent unemployment shock in a quarter is way different. I I mean, like, a 5% unemployment shock is completely different if it happens over a quarter than if it happens over two decades. Right? Like these are just fundamentally way Yeah.
Speaker 3:Yeah. So the other thing Yeah. The last thing I would say is like there was like this concept of like frictionless capitalism, meaning that like middlemen would be eliminated because you could just go directly to the source and that would push pricing pressure down. My question and I know you guys are not writing your piece saying like this we believe we will stake our entire reputation this sort of narrative. But what do you think what what how much did you pay attention to, like, the nineties, early two thousands Internet predictions?
Speaker 3:What do you think they got wrong? Why is this time different in terms of how a new technology will diffuse the economy?
Speaker 8:I think the difference is if you just plot what's happening to technology, it's all just going exponential. So these are all just continuous timelines of, like, we have microcomputers, we have the Internet, we have mobile phones, and today, you know, we have very, very powerful AI. Mhmm. And so I think most of the predictions that you you ticked off there, it's kind of interesting. I would you know, just looking at them today, you know, I would say they couldn't really happen until you had proper AI.
Speaker 8:Because, like, if you have the ability to just freely, you know, like have commerce the way you do today, it doesn't work if you still have to do all the work. Ultimately, like, you have to go in, you have to log in. Think about the amount of friction there is in buying a product for most people today. Right? You still have to go to the website.
Speaker 8:You have to put your credit card in. It's all work. We only have gotten to kind of the tech required for those predictions, I think, this year. And and that's why this is the year that I think it really begins because now it is completely seamless. You just and no one's really doing this yet, but it's gonna happen, I think, you know, in the next six months, is just tell your agent you know, tell Gemini, tell ChatGPT, go buy these things.
Speaker 8:It has your credit card.
Speaker 2:Yep.
Speaker 8:And now that world that they were describing is is truly gonna come to pass.
Speaker 2:Yeah. What about, the canary in the coal mine analogy? I was looking at, unemployment statistics in India and The Philippines, and it doesn't seem to be doom and gloom over there. I don't know. I didn't dig in super far, but would you at least expect that the unemployment rate would spike overseas before it spikes in America, or do you think this all happens simultaneously?
Speaker 8:It's a tricky question. I think, ultimately, white collar work is a lot more of our economy than it is the economy of India and The Philippines. And they are much sort of, like, more immature economies that are growing through investment and things like that. Yeah. But certainly, think we called it out.
Speaker 8:The consulting sectors in India are certainly going to be challenged in other places as well. But the reality is, like, timing is everything in the markets, clearly. But the trick here is if you're a corporate and you are hard pressed to get AI into your organization today, you know, ChatGB ChatGBT and OpenAI will send you a forward deployed engineer if you have billion dollars in budget. Right? Yeah.
Speaker 8:But if you have a $10,000,000 budget, they're not going to. Mhmm. And so who are those folks turning to? They can't usually do it themselves, and so they are going to the outsource providers, the centers of the world. And so I think those businesses are are are likely gonna be in a lot of trouble over the medium term, but they probably will have a big bump from people really putting that AI into their organizations first.
Speaker 8:And so it's a it's a bit of a tricky timeline there.
Speaker 2:What moats do you think hold beyond this? Because I think a lot of people latched on to, like, the DoorDash example as something that they thought had a moat and that in the post, you sort of underline, like, how that could maybe not be as durable as a moat as people thought. But in the long case, like, what what moats do exist? Like, do network effects stay? Do complex coordination, intellectual property?
Speaker 2:Like, what what doesn't break down?
Speaker 8:You know, real brand value, where people are choosing you over other things because of the brand and the status signaling across brands matters a ton. Sure. Network effects are are more powerful than ever, I think, in this world. Yeah. So things like Meta really have a lot to to sort of gain in that sense.
Speaker 8:But I think things that look like their network effect businesses Mhmm. But in fact are just the ones that are doing the hard work of aggregating demand and supply Mhmm. I think will be more challenged. And so DoorDash is a good example there. It's not necessarily the the biggest risk versus some of the other things, but I just was in a thread with Gavin Baker talking about this.
Speaker 8:But the problem for DoorDash and Uber and folks like that is right now, they're doing two jobs. They're doing the job of aggregating demand and the job of aggregating supply. Mhmm. They're both hard jobs, but the demand side is the harder side. And in we think the world of the future, there are lots of folks, like, in, let's say, food delivery, you know, Instacart wants to get a bunch of market share, and, you know, Grubhub wants to get a bunch of market share.
Speaker 8:Mhmm. And so let's say the agents are the ones doing the buying. It's twenty twenty eight and 40% of the the sales are through agents. You just tell Gemini, hey, order me some noodles. In that world, instead of it's gonna go to each and every provider.
Speaker 8:And right now there are four providers that do that. But now it's very easy as if I'm building a startup in this space, previously, I had to get all the drivers on board, get all the restaurants on board, and acquire customers. Now Gemini and ChatGPT are acquiring the customers for me, and all I have to do is get the get the supply side going. So it makes it much easier for new entrants to come in. And for existing, you know, second, third, fourth tier players can really sort of say, I'm gonna, like, relax my margins, try to get more top line.
Speaker 8:And so you think that, you know, whatever the 15% vig is that DoorDash gets today, maybe it's more than that. Mhmm. You know, some of that, I would think Gemini and Chatty BTS are gonna ask for themselves. Wherever I send the traffic, I'm gonna get a piece of that. And then some of that's gonna go back to the consumer.
Speaker 2:Yeah. It feels like was this the most, like, stretched or controversial prediction?
Speaker 8:It seems like it was certainly the one that got, you know, that's getting the most chatter. And I think we did it for a reason. We wanted to be a little provocative in thinking thinking it through because, know, it's an amazing business, and they're gaining a bunch of market share. But the fundamental idea that you're because what did the lock in? Right?
Speaker 8:Like, do the drivers have lock in on DoorDash or on Uber? Not really. Right? They're you know, most most drivers are doing Lyft and Uber, so they're they're not locked in. The real lock in, the real business value, the franchise value of an Uber or DoorDash is the customer lock in because the customer gets comfortable.
Speaker 8:They've got everything saved. They wanna hit a couple buttons. They don't they don't price shop. Agents are happy to price shop as much as possible, and so if you take that away, then it's a real problem for businesses that, know, are ultimately built on customer lock in.
Speaker 3:Yeah. Yeah. I don't know. I think the interviews that we've had with the Lyft, I mean, you know, again, take take it with a grain of salt. They have a narrative that is important to their business.
Speaker 3:But like, if you ask these people what is the greatest challenge, it is managing managing the supply side. It is not the demand side is not where they're saying, like, hey, like, this is really what we need to solve. It's like, hey, as we get more drivers on the platform, revenue naturally naturally goes up. And so I just I'm I'm just hard pressed to imagine a world in which, you know, somebody think think about if somebody in my town which is like 15,000 people, like, vibe codes a delivery a delivery app, and I go into ChatGPT or with another agent, and I say like, I want food. It's like, the agent wants to get the best possible service.
Speaker 3:I would imagine the agent to route to the platform with the supply that is going to be able to deliver in the shortest possible time horizon. And imagining a world where there's like this, you know, vibe coded small team operating that just happens to aggregate as much supply, which is just as increases the likelihood that my order will be delivered on the best possible timeline, which is gonna be the number one factor for customer satisfaction. I just don't see how solving the front end kind of demand piece actually makes a better consumer experience, which I assume the agent would optimize for on behalf of the user.
Speaker 8:So let's let's consider what actually happens here. Right? You make the the the order on DoorDash.
Speaker 1:Yeah.
Speaker 8:DoorDash sends it to the restaurant. Yep. The restaurant essentially, you know, sometimes they use their own drivers, sometimes they send the drivers from DoorDash. But now imagine the agent can take you directly to the restaurant site and place the order directly with the restaurant. And you can keep half the the savings, and the agent can keep half the savings.
Speaker 8:Right?
Speaker 3:The But where's the driver where's the driver never?
Speaker 2:Coming from? Because I I feel like I understand I understand the the the customer demand side. Like, you start with an LLM or an agent who shops around for you. So maybe that's solved. Maybe it'll find you just via SEO, and you can just put out, like, we only take a 5% cut instead of 15%, and the agent picks you.
Speaker 2:I understand getting all the restaurants on board because you email them and say, hey. It's 5% instead of 15%. They're sure. We'll turn it on. But for the drivers, how do you actually reach out to them and get them on the platform?
Speaker 2:And how how does AI, like, lower that cost? Because right now, I I think about, like, what was the driver marketing budget over the last decade at Uber or at DoorDash? And it's probably, like, in the billions of dollars. And so I feel like just to to generate that much much liquidity, I have to invest that much to onboard all those drivers, build awareness. Maybe it just goes viral because they're like, hey.
Speaker 2:I can make more money here, but that feels hard.
Speaker 8:I think it's gonna take time, but I think there are a bunch of smaller sort of driver aggregation networks that exist today
Speaker 1:Mhmm.
Speaker 5:That are
Speaker 8:not the ones that we know about. For instance, I started a business called Thistle, and we do delivery of healthy foods to your door. Mhmm. We we split it between half of them our own employee drivers and the other half, you know, I think we have, like, about, like, 500 or 700 drivers that we just use a third party service to provide. So I think there are a lot more of these businesses.
Speaker 8:All of those businesses now will also just have huge opportunities to kinda take market share. Ultimately, what we're saying is the friction in doing commerce is going way down. Places where there are rents, the prices can go down. But ultimately, this is just an opportunity for more and more entrepreneurs to kinda build businesses for the new world.
Speaker 2:Yeah. I think the the the it it's interesting because we're here, like, debating, like, this this, like, somewhat temporary thing because, like, self driving cars, robotics, like, changes all of that,
Speaker 8:like Yeah.
Speaker 2:In a huge way. Exactly. But but but we we we use the term sloppable for companies that are that can be vibe coded away and and clankable for companies that can be disrupted by robotics. And I've always put the delivery services more in the clankable category than the sloppable category. So I was I was shocked to see
Speaker 3:What what are the
Speaker 2:But it's it's what would you
Speaker 3:spend more time on if you knew you were gonna get 50,000,000 views and the markets would react the way that they have.
Speaker 8:I would have finished writing the third piece where I talk about solutions, which I have not gotten A
Speaker 2:lot of people are demanding solutions. You just gotta you just hit me with a ton of problems. That's funny. Do you think that there's any, there's this question about, like, in my mind, like, yes, Google and NVIDIA are public, but Anthropic, OpenAI, and x AI through SpaceX are not public. They're sort of like this massive, you know, multiple $100,000,000,000 sell off in the public markets that sort of should if you believe your thesis, that should sort of funnel to the labs, I would imagine.
Speaker 2:If if when I read it, like, there's a lot of doom and gloom about companies that are out there, but it's a lot of bull it's a lot of bull case for AI labs. And Yeah. That can't happen in one day because, like, rounds happen every once in a while. They're private. There's all these different things.
Speaker 2:But, do you think that the world would change when the big labs get out in the public markets?
Speaker 8:I think it's absolutely gonna change. I have a strong suspicion that Anthropic is gonna go, you know, in the next three to six months. They just have so much momentum, and there's a lot of value being first. Yeah. Their p and l also just looks a lot better than anyone else.
Speaker 8:So I would think that gets public, and it's gonna be pretty interesting if it happens. Certainly, labs are are ultimately they seem like they're they're we're very well positioned to win. I would wonder over the medium term, like, you know, what happens with some of the the Chinese models and whatnot if people actually want just something that's more local and something that they own, but it does seem like the most likely outcome is gonna be that the existing incumbents are gonna get the most share. And, you I think Google is particularly well positioned since they already own all of those customers today, and they can finance losses from inference a lot longer than everyone else. But I think ultimately, like, there's a world in which the labs are the biggest winners here.
Speaker 8:There's also a world in which, like, you end up with just a lot more competition and people trade and and change. But the thing that seems very clear to me that the absolute, like, there's no way they won't be the hugest winners here, is gonna be the underlying tech, meaning the semiconductors. So, you know, everything is
Speaker 2:go even deeper. You could go into, like, you know, commodities and, like, copper and energy and oil and natural gas and stuff. And people have.
Speaker 8:Yes.
Speaker 6:Did you see the
Speaker 8:I have to
Speaker 3:Yeah. Did you see the the some of the criticism was that the the essay was very Marxist. Oh, yeah. Heat said, Mark Marx writing during the industrial revolution predicted capitalism would periodically devour itself. Firms replace labor machinery to boost profits but competition diffuses the technology, drives prices to marginal costs and the gains get competed away.
Speaker 3:Meanwhile, displaced workers lose purchasing power, hollowing out the demand the whole system depends on. Production rises but no one can afford to buy what's produced. The contradiction between production and realization. Zetrini's piece describes this exact dynamic then declares there's no natural break. It's the most Marxist piece of financial analysis.
Speaker 3:Not my word.
Speaker 1:Don't think you were expecting
Speaker 2:that critique.
Speaker 3:And makes the same errors Marx did. Yeah. Creative destruction doesn't just destroy, it creates industries we can't yet conceive of.
Speaker 2:That's interesting. I mean, maybe that's going into
Speaker 3:solutions. Is that going into solutions?
Speaker 8:So let let me address it a few ways. Marx is a really smart dude. Yeah. He got a lot of things right very early.
Speaker 2:Mhmm.
Speaker 8:Marxist can mean communist. Marxist can also mean just understanding how capital and labor interact. In that sense, yes, it was Marxist. He had he he was very insightful. Mhmm.
Speaker 1:But
Speaker 8:I think the thing that we're missing here is that it's there's the economic layer, but ultimately it's the political layer And, that you know, we're in a world where we've we've had two parties, and both parties, you know, economically have a little bit of difference, but not a huge amount of difference, and so we kind of bicker. But in a world in which jobs are going away really fast, I think there's gonna be a much stronger alignment for, know, just the laboring class overall to say, hey. We need to fix this problem. Yep. It's a very fixable problem.
Speaker 8:What I'm what we're actually expounding here is that GDP, if done properly, will absolutely explode. Right? We're getting way more efficient. We have you know, we've built a machine dot. We've built machine intelligence.
Speaker 8:But we have to structure our society such that as those things happen very you know, hopefully very slowly, you know, we we do the right thing from a taxation perspective to say the winners should win, but, you know, if that's what's causing the displacement, let's sort of make the pie a little bit bigger for everyone. And that, I think, ultimately should be something that appeals to a lot of folks in the AI complex. Yep. Because if we don't, then something like this is likely to happen, and, you know, AI progress will slow down because we'll have an economic crisis, and we're not gonna do to finance nearly as much of it as we otherwise would.
Speaker 3:So do you think the future is, what, Anthropic head of sales position in France? The company will be spending €530,000 per year. The government will get €340,000 and the employee will get a 190,000. Is that is that the level of taxation do you think we're we're headed for?
Speaker 8:I think when we're at, you know, France's level of government spending, then, you know, the math probably means roughly that. I I would say that, you know, government spending would be at France's level, I'm guessing, like, you know, five or seven years from now if this if this scenario kinda comes to pass. And so I think we'll head there over time, but I think it's less a question of the percent of spending and how much goes to the employee versus goes to the the government, and ultimately, what is the size of the total pie. Mhmm. So the bet here is that the pie, if done properly, can just increase multiples of what it is today, and and thus, you know, there's it's just a win win.
Speaker 2:One question. I mean, it sounds like you're working on potential solutions post, which I'm very excited to read. Thank you. I'm interested to know your reflection on the messaging that's coming from the leaders of the AI labs because they've outlined many sort of low probability but, you know, potentially negative scenarios. You know, we have the white collar work number.
Speaker 2:We've had many of these comments from lab leaders. But I rarely hear them follow it up with, and the answer is print, print, print, or interest rates will be will save us or unemployment insurance or UBI. Like, all of those, like, solutions that I think people it's funny because people are quote quoting your post being like, this is easily solved with this solution. It's like, okay. Well, that's great if we all agree.
Speaker 2:And and I think you might with some of the some of the quotes. People are all over the place. But I'm wondering about your reflection on, like, the the the, like, messaging from the labs around solutions versus pure focus on problems?
Speaker 8:I think it's a really interesting question and very interesting setup in that the labs are, on the one hand, you know, want to get the word out there. And so, you know, Dario especially has been the loudest here. There's a really good Axios article from last May where he's he kinda saw the sound of the alarm bells.
Speaker 3:Mhmm.
Speaker 8:People aren't really he's like saying people are not listening. Obviously, a lot's changed
Speaker 2:Yeah.
Speaker 8:Since then. But they can't go so far as to say, like, hey, if you put the pieces together, then this is how it's gonna play out. I think it's too sort of damaging to sort of their reputations and, like, you know, their ability to fundraise and things like that. And so I think it's other folks like ourselves that kinda have that duty to go and really start thinking that through. I I I sort it seems like Anthropic is pretty engaged Yeah.
Speaker 8:You know, should that conversation really start happening. And I think this is the year it needs to really start happening. Yeah. And so I think they all kinda get it. And so it's just a question of, like, how do we as a society start moving in that direction?
Speaker 2:Yeah. I I I think, you know, obviously, there's there's I'm still processing part of the piece. I agree with some of it. I disagree with some of it. But what's really underrated is just, how useful this process of writing an article for a particular audience is.
Speaker 2:Like, I I I disagreed with a lot of, you know, something big is happening, but it hit with a very different audience than machines of love and grace or the adolescents of AI or of machine intelligence. And and there's there's pieces that are written for, like, you know, AI insiders, leaders, researchers. Then there's, like, the broader tech community. Then there's, like, everyday people. And you clearly hit the nail on the head with, like, speaking to the financial community, and we see that in the markets.
Speaker 2:Not amazing results, but maybe it's maybe it's worthwhile because we will get really great solutions and a better conversation around it. So I I I think I think in in due time, this discussion needed to be had. Thank you.
Speaker 3:What's an industry or job of the future that you could see emerging?
Speaker 8:I think, again, if we solve this, like, everything related to sort of leisure is gonna absolutely zoom and that those are gonna be the biggest growth industries of the future. Right? Like, what do humans want to do?
Speaker 3:Total trusty victory.
Speaker 2:Watch polo.
Speaker 8:I just watch a cloned
Speaker 2:horse play polo for sure.
Speaker 8:Yeah. So, you know, imagine humans have, like, the entire day Yeah. To just enjoy themselves, instead of having to work.
Speaker 2:Now that is something I've been promised for a hundred years. So I'm I'm deeply skeptical, but this time is different. I want it to be different. Let's bring on the leisure. Boom.
Speaker 2:I'm I'm I'm here for I'm here
Speaker 5:for it.
Speaker 3:On anything in your solutions doc around re industrialization? I mean, the frustration that so many people in tech that have been building in hardware in the real world, or trying to recruit people that are getting offers from social media companies, or now labs, or SaaS companies. You know, one of the the problems of Ameri you know, for America in the last twenty years was that if you just wanted to make a $100,000,000, you probably were much more likely to do that building enterprise software than building critical infrastructure or or anything in the real world. So is that is is kind of new new infrastructure and reindustrialization like a a a potential landing point for people that had the 180 k a year PM job that might be going away?
Speaker 8:It's a great question. I think there's there's certainly gonna be a lot more opportunity in those sectors, and I think we've we've done some pretty smart policy things that are moving us in that direction. But we're also, you know, just in a lot of ways so far behind China there, and doesn't AI affect kind of those jobs both for, you know, on the industrialization side just like it does for for writing code. Mhmm. And so that's where I think it will get trickier.
Speaker 8:I think over as a as a country, we're gonna spend an awful lot more on that. I think we're gonna we're gonna catch up, but we're not it's not clear that's gonna be through just creating a bunch of additional jobs versus you know, the the ultimate thing we're seeing with AI, period, is just high agency people who really know how to use the tools can just do the work of many, many people. Yeah. And and I think that trend applies in every industry to some extent.
Speaker 2:Yeah. What an exciting time. Thank you so much for taking the time to
Speaker 3:come What's up next piece dropping?
Speaker 8:Hopefully by the end of the week, but don't don't don't hold me to that.
Speaker 2:Well, go ahead. When We'll
Speaker 3:you when you know that
Speaker 1:it then it could be hard for the follow-up to get
Speaker 3:as much reach as as this one. That's kind of the way these things go. But now now the pressure's on to really pay attention
Speaker 2:to that. Don't don't have any sequel anxiety anxiety. You'll be fine. We're excited to read it, and we'll talk to you soon.
Speaker 3:Yeah. Great to meet you.
Speaker 2:Have a great rest of your day. Thanks so much. Let me tell you about Turbo Puffer serverless vector and full text search built from first principles on object storage. Fast, 10 x cheaper, and extremely scalable. And I'm also gonna tell you about Gusto, the unified platform for payroll benefits and HR built to evolve with small and medium sized businesses.
Speaker 2:And without further ado, we have Will Brown from Prime Intellect in the TVP.
Speaker 6:How's it going?
Speaker 2:Welcome to the show, Will. How are you doing? It's been too long.
Speaker 6:I'm doing great. I'm doing great. It's it's great to be back. I think this is the fourth. Something like that.
Speaker 3:I I looked at there
Speaker 6:was a list at some point of the record, and some people are like they've been on
Speaker 2:Some people have been on on something like
Speaker 3:I'm looking forward to the four hundredth.
Speaker 2:It's great.
Speaker 5:It's been
Speaker 2:it's been a
Speaker 3:lot of fun. What are your your old buddies at Morgan Stanley thinking about, the current thing in tech, the, the 2028, intelligence crisis? Have you gotten any messages?
Speaker 6:That's a great question. I have not had the full deep dive too busy they're
Speaker 3:too busy hitting the sell button.
Speaker 2:No. Everyone in Morgan Stanley is too quickly too busy setting up Mac minis to run Open Claw. That's what's happening. Because they all just read something big is happening.
Speaker 6:Right. And so, like, I think there's definitely a lot of opinions on on all sides, and I feel like that to me, the piece was pretty cool.
Speaker 2:Yeah.
Speaker 6:I don't necessarily, like, agree with it, but I think it was effective at getting people to have more interesting conversations than, for example, recent other maybe viral pieces about how everything's going crazy. And it seems like the the conversation ended up getting, like, into the weeds of monetary policy and,
Speaker 3:like Yeah.
Speaker 6:How people are going to react and, like, how hard is it to buy put a DoorDash clone. And, like Yeah. These sorts of things I think are actually, like, the sorts of conversations that are good for more people to be having. Like Sure. Whether or not a certain prediction is right.
Speaker 6:Think like like, it's just generally like as stuff is getting crazier, I feel like this is the sort of stuff that allows the rest of the world to kind of like hear about from their friends, like, a little more grounded discussion about what could happen.
Speaker 1:Yeah.
Speaker 2:Well, give us an update from prime intellect. What's going on in your world?
Speaker 6:Yeah. Yeah. So there's a few things that I think are interesting as well as, like, I wanna talk about just today given some other stuff that's happening on the timeline. But so a couple weeks ago, released a training platform to make it really easy for people to do RL on top of leading open source models with their own environments, and we've tried to really make the make it an agent native experience where you're kind of like there's a some
Speaker 1:a lot of people have been kind
Speaker 6:of tweeting out their experiences with it. The term people have been using is Vibe RL, which is Oh. We're kind of now at the point where the infra to manage the training is kind of in place, and you can do it without thinking about the hardware and the GPUs where the models still kind of struggle. But you can you can really focus on like the environment and designing your tasks and specifying what you want and having turning existing data that you already have into kind of training recipes. And so we're kind of at the point now where like, this is pretty accessible for people to kind of go train models, and it's been pretty cool kind of seeing people have fun with
Speaker 3:it.
Speaker 2:Yeah. Concretize some of the, like, actual applications. I imagine this works best if everything flows through text, flows through CLI tools, like because when when when I think, like, okay. Great. I'm gonna set up an RL environment and automate one of my workflows, and I'm like, well, I'll need to open Adobe Premiere, which has a license, and then I'll need to go to YouTube and download some videos.
Speaker 2:Yeah. I'm just thinking about, like, editing a short video that we have.
Speaker 6:Right? Of course. Yes. So some of that stuff definitely, like there's definitely a range of, simple to complicated. Yeah.
Speaker 6:But I think there is a lot of sweet spots where it's like the you're it's coding tool use and interacting with kind of app simulators
Speaker 2:Yep.
Speaker 6:Which are the sweet spots of a lot of these like focuses for people doing training in the labs anyways rather than like full fledged Photoshop. There's a great tweet actually from I figured the ramp guys Yeah. From ramp labs showing off how they've been using it for some stuff. Oh, cool. It was the the team behind ramp sheets, and so you could kind of imagine like the sorts of things there where like you don't actually have to build the whole thing, but you can like and and this is where I think the coding agent stuff is really useful is there's a lot of ways you can kind of have the right simulation of the application that is like it isn't necessarily the full back end, but it's enough to be able to capture the task.
Speaker 6:Yeah. And the coding agents are good enough that with human loop using like, it's all CLI data. So you go to your terminal, you we have our CLI, the prime CLI. Mhmm. And you use that to kind of like set up your skills and get your coding agent configured and your agents MD.
Speaker 6:And so we've tried to, like, make that really smooth, but then you just are kinda, like, talking to your agent about, hey. My data's here. My app code's over here. Let's put it all in the right place and kick off some runs.
Speaker 2:Yeah. Where are we on the path to personalized RL? I'm I'm I'm thinking back to, like, the RL that went into RLHF around
Speaker 3:Right. Right.
Speaker 2:ChatGPT, GPT four. And I remember it was, like, they had maybe tens of thousands of contractors sort of grading responses, giving thumbs up, thumbs down, giving feedback, varying levels, like a really large scale generalized process.
Speaker 3:Yeah.
Speaker 2:If I'm running a medium sized company, is this something that I can pull from logs of what's happening in the business? Should I be firing up a data labeling company to help me generate more data? Because in the long term, I would love just a screen recorder, watch me what I do, and then it's RL ing, and then it's getting better. Then all of a sudden, it can just do what I do with just, like, a one prompt.
Speaker 6:Yeah. It's pretty close.
Speaker 3:It's pretty
Speaker 1:close. It's
Speaker 6:definitely it's that's not it's not that sci fi. Like, the if you have stuff especially let's if we focus on, like, text or image input, full screen recording gets a little tricky. Sure. But if it's, like, text or image input that kind of comes from, like, agent logs and it's, like, human inputs text and images to an agent log and you have these logs and you're trying to synthesize these logs. I think the the trickiest part is refining criteria about like what counts as good for like rescoring another try.
Speaker 6:But a lot of times the criteria are like either pretty general across tasks or you can infer a lot of them from a user's response or just from the initial prompt. And from what we've seen, it's especially for a lot of very concrete problem solving use cases more so than, let's say, like, creative writing. Yeah. But for things where it's like there's a right answer, and it's not too hard to see if the model, like, got the right answer from doing an agent trace, either from the human's response or let's say that if companies wanna have their humans, like, label as part of using the product by default, yeah, this is doable. And the the RL recipes are kind of stable and scalable enough that, like, it kinda like, it doesn't always work, but it works reliably enough that it I think the barrier to entry and the cost are just, at a point where it it's cool to see that this is now a thing people can go do.
Speaker 6:Yeah. And we're seeing a lot of people, like, have success with it.
Speaker 2:Yeah. How how are you thinking about the debate between MCP and CLI? Peter was going back and forth, and it it I I it was something that I was wondering even when MCB came out. It seemed really cool, but at the same time, it felt like, well, the front end and I I I remember going to the front end and inspect element and see what's coming across. And, oh, there's a HTML request right there.
Speaker 2:Let's reverse engineer that.
Speaker 6:Yeah. It's it's all kind of the same thing. Okay. Like, it's it's sending requests. And so I think people realize models were good enough for coding that
Speaker 2:Yeah.
Speaker 6:The skills are essentially it's doing the same thing, but it's just you have more flexibility to like, I think the area where MCP makes the most sense is when you really want fine grained off stuff going on where there's credentials and you want to be able to notice that it's being done and have the user approve certain requests or not approve others. That's where the formalism of the tool call is really useful as opposed to it just being code that has an API token. Yeah. But from the production of capabilities, skills are nice. MCP has its areas where it makes sense.
Speaker 6:But it's really just models using computers. Yeah. Whether it's MCP or code or skill files and reading docs, it's like models are pretty good at, like, reading stuff. And if it has instructions on how to do a thing, they can kind of just do the thing. Yeah.
Speaker 6:And they can do that for a while enough that it's useful.
Speaker 2:Yeah. How how intermediated do you think this product will be? And what I what I mean is that, like, let's just use some toy example, like, you know, the the widgets company would benefit from a custom fine tuned model or RL model, but they don't go to you. There's actually a company that's an intermediary that is providing, like, a SaaS product that then is fine tuned on like anonymized industry data or they went and or generated they'll even come to the company and say, hey, we'll prime CLI. You're not going to have to know what that is.
Speaker 2:You just give us the data and we'll act as like your customer. How do you think that plays out in the market?
Speaker 6:I mean, it's definitely going to happen across the spectrum. I think the people who we work the most directly with are the ones who are a little more AI native and the ones who are going to work with because I think we're really building for developers
Speaker 1:Okay.
Speaker 6:As our kind of target audience Yeah. But not necessarily researchers. So I think like the people who are like following the benchmarks and reading about the new model releases and building with Cloud Code and Yep. The agent frameworks, that's really like our target audience. People who like think about evals and prompting Mhmm.
Speaker 6:Versus people who like don't think about that. So we do actually work with a lot of like the big data companies where there's like I think maybe the one interesting story is like there's a lot of market demand for because everyone's like building environments and selling them to the labs. Yep. But you can see a lot of these companies want to like know that their environments are good. So like using RL as part of this process Okay.
Speaker 6:Is the way that you value evaluate the quality and be able to prove like, hey, we got we got the good stuff. Yeah. Because it actually like improves capabilities. And so there is this whole economy of companies that really specialize on building environments and working with data, and I imagine this does become a big part of like the way that this stuff is consumed by end companies is through people with that kind of expertise at the data level.
Speaker 2:Mhmm. Jordan?
Speaker 3:Talk about, what the Chinese lab
Speaker 1:I was gonna ask the exact same thing.
Speaker 3:In terms of Yeah. Distilling American models. How how talk about kind of the scale
Speaker 2:I've seen some rave reviews. I I I've genuinely seen seen some rave reviews of Kimmy K2. And then at the same time, I've also seen, like, hey. It kind of fell flat on its face when I pushed it beyond a toy example. So, yeah, what what what's real and what are you experiencing?
Speaker 6:Yeah. So I think the they're definitely a couple months behind. Like, they're not at the 4.6 or the Codex 5.3 level. Mhmm. They're pretty close to what we had before that.
Speaker 6:Mhmm. And I think that's kind of where it's been and it feels like this is tightening. But I think at least where I where I get much excited is like, they're good enough that going the extra mile with customization Mhmm. Is a differentiator where you can take a model that's already almost frontier and make it the best model in the world at your thing pretty easily and pretty quickly. So I think that is even if you have to do this every three months, it's always a capabilities race.
Speaker 6:But I think if this pipeline, if this process of taking your data and improving the latest model becomes really easy and repeatable, then it's like you can get a lot of value out of doing that. And I think that's the sort of thing that's going to be in a lot of people's toolkits. In terms of, like, the open source models generally, I think, like, there is some interesting debate on the timeline today that I dove into for a little bit around Anthropic and DeepSeek and
Speaker 2:Oh, yeah.
Speaker 6:Doing distillation. And I think, like, it feels like there's there's kind of two things. There's the kind of geopolitical element. There's the kind of, like, terms of service of, like, oh, they're doing bot farms. They're scraping.
Speaker 6:Well, that's not allowed. And there's also, like, the idea of, like, distillation more broadly of, like, is it and the two I I totally get, the first two.
Speaker 1:But I
Speaker 3:think the
Speaker 6:thing where I was kind of like trying to push back a bit was like I mean, everything on GitHub is someone typing a prompt to Claude and submitting it to Claude code, and then they're gonna review the PR, and then they're gonna merge it. And this is like perfect training data. Yeah. And so the Internet is just getting flooded with perfect Claude distillation training data.
Speaker 2:Interesting.
Speaker 1:Yeah. And and there's not much you
Speaker 6:can do about that. And so it's like, is distillation really the hill we wanna die on?
Speaker 2:Okay. Yeah. I I I guess the the secondary question is, like, put all of the the that aside and then just ask the question of, like, of, like, is there some, you know, ticking time bomb with using a distilled model where you run into some wall or you have some problem in performance down the road? And so, yeah, you're doing well in benchmarks, but then Right. Which is a less effective.
Speaker 2:And is that, like, actually problematic from a business perspective? Or is it just like, okay. Yeah. Like, I'm getting three months behind, but it's three three, you know, three times cheaper, so I'm fine with that trade off versus, like, I thought I was using something great and then it it blew up on me.
Speaker 6:Right. So it depends a lot on your app so they think there's certain things that, like, the models are already, more than good enough, and these are, like, kind of more commodity, like, extraction or summarization or labeling use cases Mhmm. Where, like, you kinda just wanna optimize for cost. In some cases, you wanna optimize for speed. If you want to optimize for performance, then if like cost isn't a concern and you really just care about top line performance, then customization is really where the open source models become interesting, which is that like you can do more to the open source models than you can do to Claude.
Speaker 6:And you can have a lot more fine grained control of, saying, hey. This is my eval. This is how I'm measuring performance. We are just gonna hill climb this.
Speaker 2:Mhmm.
Speaker 6:And then it's up to you
Speaker 2:as a
Speaker 6:business to define your business logic, say, hey. This is what I actually care about. This is what performance means. And I think we'll see a lot of companies realizing that, like, that is a useful knob to be able to turn Mhmm. To be able to, like and I think concretely what it'll look like for a lot of cases is there'll be these multi agent products that have their main orchestrator agent that's like one of the frontier models with lots of specialized sub agents for things that are related to the business and specific workflows, which are then fine tuned models.
Speaker 6:I think that's kind of what we see currently as like the most like dominant paradigm for mix and matching between the the proprietary models and the the fine tuned open models.
Speaker 2:If you had told someone a year ago that there were gonna be, like, probably millions of people running agents locally with custom setups and dot MD files for various skills. They'd probably be like, wow. That's pretty aggressive. Do you think that we'll be in a world in, a year or two where, at least, you know, people on X will be talking about, like, my fine tune. I got the I I I did but I did RL on my Pacific problem.
Speaker 2:My personalized agent is, like, even better now because I did the RL.
Speaker 6:I mean, so we see it today already with this a little bit where it's like I mean, there's people who are showing
Speaker 1:you can these you can
Speaker 6:get these models to beat any of the closed source models on sufficiently well scoped tasks Yeah. Pretty quickly. Interesting. It's not rocket science. You can you can basically vibe code it.
Speaker 6:Wow. You have to know you have to, like Yeah. Be clear that you have a goal in mind, but if you can define the goal and you can spell this out in English and you can do the same sort of prompting that everyone's doing for coding Yeah. Then, yeah, you can just kind of plug it in and get training to work. But I think
Speaker 2:That's crazy.
Speaker 6:It'll become more like a lot of it is still very much like these kind of more proof of concept or narrow research cases.
Speaker 1:Totally.
Speaker 6:But it does seem like it's quickly especially, like, code becomes cheap. And the more the cheaper that code gets, the more complex you can make your environments. And I think, like, year ago, we saw, like, Cloud Code is about a year old. Yeah. Came out, I think, February.
Speaker 6:And at the time, it was like wasn't actually that useful yet, but I remember playing with it and feeling like, oh, this isn't actually something I wanna use that heavily today because it's kind of slob. It's very chaotic. It just makes a mess. And I was I went back to cursor for a while because it was much more controlled. But it was like, oh, this form factor feels like it could eventually work.
Speaker 6:And I think there are other form factors today that don't actually work yet. In some ways, like, the open claw thing where it's like OpenClaw like kind of works, but there's also a lot of trouble it gets into.
Speaker 1:Yeah.
Speaker 6:Same with like, if you saw like the Gastown thing
Speaker 1:Yep.
Speaker 6:Or these like crazy multi agent systems where it's like, they aren't actually excellent yet for shipping Volley production code. Yeah. But the thing we had a year ago now is the level where, like, Claude code is used for, like, most production code, but by the heavy adapters or codecs.
Speaker 1:Yeah.
Speaker 6:Yeah. And so, like, it it feels like it is a matter of time until the these things stabilize, and like the goals of having that system had to end back up in the models, the people training for it. Mhmm. But like the recipes of how to train these models, it's they've become, like, robust enough over the past year that it does seem to be, like, a good idea in a lot of these cases to to optimize your models for the structure you want them to be in. And if that structure is this crazy multi agent system thing, it's like, yeah, why not?
Speaker 1:Yeah.
Speaker 3:What are you are you expecting real tangible breakthroughs in the next in the first half of this year? I mean, our our intern keeps saying that he's close to cracking continual learning.
Speaker 6:Oh, yeah. Continual learning is gonna fall pretty quickly,
Speaker 8:I think.
Speaker 6:Do you think it'll be less of a big thing than No. I mean, think it's more of an engineering problem. I think it's like Explain. No one's actually trying.
Speaker 2:No one's actually trying. Why not?
Speaker 6:Like, no one like no one like, OpenAI and Anthropic don't want to continuously train their models for each user. Like, that's Yeah. It's expensive and annoying and hard to serve at scale. But like, from a research perspective, like, we're we do continue learning where the model learns new, they just could keep training the model more. It knows more stuff because they put more Internet in it.
Speaker 6:Sure. And
Speaker 1:Yeah. Like Yeah. Yeah. Yeah. Yeah.
Speaker 2:It's all uneconomical right now. But but Yeah. Yeah. I That's very interesting.
Speaker 3:Like Frontier, I could imagine that that would be Right. Right. A selling point if you're McKinsey and you're going to a big Right.
Speaker 2:Yeah. Institution. So so so, yeah, if you if you hypothetically, like, I don't know, you're a law you're a law firm and there's some crazy case update, like, yeah, the model retrains on that, like, the day that the Supreme Court completely changes the way the law works, and then everything else is, like, interpreted from that. Yeah. Makes a ton of sense.
Speaker 6:Yeah. And there's enough kind of tricks. I think there's a lot of experimentation around, like, exactly the recipe that's gonna be the most reliable. Mhmm. But we kinda have a grab bag of, like, six or seven tricks that kinda work Yeah.
Speaker 6:Or they work in different ways, and you can mix and match them.
Speaker 2:Mhmm.
Speaker 6:And it's just gonna be like, whatever's the best combination of these tricks, people are gonna experiment with it and find the versions that work the best. And there doesn't seem to be any, like, big wall inside that prevents that from, like, being practical.
Speaker 2:That's cool.
Speaker 3:What are you tracking on the silicon side? Oh, yeah. We were playing around with chat jimmy dot a I. Oh, yeah.
Speaker 6:That was sick.
Speaker 3:Crazy. Right? Jimmy's quick Yeah. But it'd be smart.
Speaker 2:Yeah. Too fast. You have to, like, scroll up once you get the answer.
Speaker 6:Yeah. I was trying to see how many tokens I could get it to print so that I could actually see it go, and I was like, give me every number between one and, like, 10,000. Yeah. But, like, llama just won't do that. No matter how you prompt it, it'll always stop after, like, a few thousand tokens.
Speaker 2:Oh,
Speaker 6:interesting. So you can't actually get to feel it, like, blitzing past.
Speaker 2:Woah. Interesting. Yeah. Yeah. Yeah.
Speaker 2:Yeah. It was, like, sort of a throwback experiencing llama three a b because I remember when that model came out, and there was a lot of hype because open source just love open source stuff, and it was exciting, and it was cool. It was like, wow. They really did train a big model, and they just put it out there. And I remember some people being like, yeah.
Speaker 2:Like, if you actually go talk to it, like, it it hallucinates a fair amount. Like, I don't know that this is, like, actually at the frontier. It might have done okay on some benchmarks, but it's not quite there. And it was a little bit of a throwback. But you can
Speaker 1:just
Speaker 2:imagine baking any of the current frontier back there, giving it access to tools, giving it a reasoning loop. Like, yeah, it's gonna be even if it's only 10 times as fast, like, that's still so much faster than, like, okay. Got to close the app and come back after twenty minutes because my thing is running now. It's gonna be a completely different and I think it'll be a big, like, step change for, like, people that are like, oh, yeah. AI, like, hallucinates.
Speaker 2:And, like, I need to check that out. It'll be, no. Like, it's, like, totally you can just have it right there, it's perfect, and it works a ton very fast. It's gonna be a really cool moment. Will you be buying an AI lamp?
Speaker 6:An AI I I want the one that goes out of your bed and folds your clothes.
Speaker 2:Oh, okay.
Speaker 6:Have you seen that one? It's
Speaker 2:Yeah. Yeah.
Speaker 6:Yeah. It looks like a Pixar. Do we have the the
Speaker 2:It also looks like it might dismember you if it doesn't like you. It's a little bit horrific, but I do agree if it folds your laundry, like, that's pretty pretty amazing. Right.
Speaker 3:I don't care if there's a one in ten thousand chance that it goes crazy and it's just
Speaker 2:I don't care if there's, like, a one in ten chance of me just being dismembered in the middle of my night because it gets mad at me. Because I was trying to prompt inject it or something. No. I I am excited for hardware. It feels like, it feels like even even the first gen hardware, like the Humane AI pin, the the Rabbit r one, all that stuff with, like, frontier models starts to get interesting.
Speaker 2:I really hope we get a solid next iteration there, even though it's obviously very much outside of your core competency. But maybe some hardware developers will be coming to you looking to fine tune a model RL model.
Speaker 6:Do you want local on device for something? That's Yeah. Way to like, because yeah. You can I think especially for, like, these narrow things, like, the r the rabbit, whatever and this is also Apple's strategy? Seems like because Apple's like, they like keeping stuff on device.
Speaker 6:Yeah. The whole privacy thing is part of their whole pitch. And so I think part of the reason why Apple's been slow on the AI stuff is they're shipping a feature once they can do it on device with a sufficient reliability. Yeah. And so that means they're slower in their rolling out of features, but it means that, like, the stuff like summarization and the image search, like, can do this locally now because the hardware is good enough and the models are good enough at that scale.
Speaker 2:Yeah. Yeah. You have to imagine that that that the same Talos principle of, like, baking the model down to silicon, well, it feels like they're doing something maybe like wafer scale, like not iPhone scale. So, like, maybe that's another couple And then you need another couple years to get it to, okay, it's now frontier on a chip that's the size of your phone, fits in your phone, doesn't suck your battery down. But you play that out and you get to something like really, really fun and interesting.
Speaker 2:I'm excited. Yeah. Future is bright.
Speaker 6:And I think the, yeah. Definitely exciting. I think the people always said the Internet was gonna, like, run out of data. But I think what we're like, we're getting more data, but it's and it's better data because it's just from the last generation of models.
Speaker 2:Oh, interesting.
Speaker 6:And so you can kinda, like you kinda get this flywheel of, there's just more data to learn from and it's all getting better as the models get better. And then you do more on top of that
Speaker 1:Yeah.
Speaker 6:To boost beyond where you were from the old data. And that's where the RL and the filtering comes in and the human data. Yeah. But like, it seems like you just have a pretty clear path of models getting better as you put more data into them and we have the data.
Speaker 2:Well, thank you for coming on the show and producing a bunch more data. Thank you. That's helpful. It goes on to YouTube.
Speaker 3:It's an honor to produce data with you.
Speaker 2:It's it's an honor to join the training set with you.
Speaker 6:Yeah. That's the goal.
Speaker 2:That's the That is the goal. And thank you to everyone in the chat who's also providing data for the Internet. It's God's work.
Speaker 6:Thanks for having us.
Speaker 2:We'll talk to you soon, Will.
Speaker 3:Talk soon.
Speaker 2:Have a good one. Let me tell you about Plaid. Plaid powers the apps you use to spend, save, borrow, and invest securely connecting bank accounts to move money, fight fraud, and improve lending now with AI. And speaking of data, let me tell you about Labelbox, RL environments, voice, robotics, evals, and expert human data. Labelbox is the data factory behind the world's leading AI teams.
Speaker 2:And I believe we have our next guest already in the Restream waiting room. Five minutes ahead of schedule, Michelle Liu from Medra is in
Speaker 1:What's going on?
Speaker 2:The TV at Aldrin. Welcome to the show.
Speaker 7:Hey, guys. Hey. Excited to be here.
Speaker 2:Thank you so much for
Speaker 3:talking about and your robot.
Speaker 2:Punctuality. Oh, yes. What is behind you? Wow. There's a robot that's actually working.
Speaker 2:Explain. Introduce yourself, please.
Speaker 7:Absolutely. So I'm Michelle. I'm the founder and CEO of Medra. And a little bit about me.
Speaker 1:Yeah.
Speaker 7:I come I studied chemical engineering in undergrad. I was your typical chemistry, life science nerd.
Speaker 2:Yeah.
Speaker 7:And then I did an internship at SpaceX.
Speaker 2:Cool.
Speaker 7:And was just really excited about, like, what if we can build in the physical world? Yeah. Right? Like, I wanted to build in the physical world. I wanna build real things with real impact.
Speaker 7:Yeah. Ended up doing my PhD at Stanford at the Stanford AI Lab in robotics, building robotics foundation models. I worked with Jeanette Vogue and also with Faiza Li. Shout out World Labs.
Speaker 2:No way. Faiza.
Speaker 7:And I ended up, when I finished my PhD, decided I wanted to combine life sciences, robotics, AI. Mhmm. And I started Medra, and we are building physical AI scientists, which we think that is necessary to eradicate disease.
Speaker 2:How narrow do you wanna go to start? I mean, it it feels like there's pipetting. There's centrifuging. There's different stuff going on behind you. But, like, medicine, bio, these are massive terms, can be animal studies, mice models.
Speaker 2:You can have monkeys in there. There's a million things that you could do. But I feel like you you you probably wanna pick a beachhead, but you tell me what the strategy is.
Speaker 7:Definitely. Look. Like, one day, we will have medro robots doing animal studies, like, mark my words. Right? But you're right.
Speaker 7:Yeah. We have to start somewhere. Yeah. And we're starting with early discovery and development. We have physical AI robots at Medra that can do experiments at scale.
Speaker 7:We can work with instruments that humans already can use. And most importantly, we truly have intelligent robotics.
Speaker 5:Mhmm.
Speaker 7:This is not just lab automation where you program things and they do it exactly like Mhmm. You tell it to do. This is actually physical AI autonomy that is intelligent, that's constantly sensing, making corrections. And more importantly, we also have AI scientists that can actually reason about the science itself.
Speaker 2:Mhmm. So what is an example in the lab where you actually do want some probabilistic reasoning or some stochastic result as opposed to something deterministic? Because if I'm if I'm vibe coding a website, like, I don't want it to guess what an HTML tag is. I want it to just use a div every time. It does a great job at that.
Speaker 2:But so I imagine there's some things where, you know, the pipette always needs to go in the same place. So it's okay to stand on the shoulders of giants and puppeteer that. Definitely. Where does the variability come in?
Speaker 7:Definitely. I I I think, like, if you think about the best scientists. Right? The best scientists are the ones who are reading all the papers.
Speaker 2:They have
Speaker 7:all the scientific knowledge, but they're also the ones going into lab and running the experiments. They can, like, sense what's happening. They can smell it. They can, like, visualize what's going on, and they can make changes as they see things start happening inside the experiments.
Speaker 2:Yeah.
Speaker 7:That's what we're trying to capture. Right? The ability to be really flexible, to actually reason about the signs at as it is happening and also taking into account all of the knowledge that's come before us, all the scientific papers, all the different results, all the past experiments you run.
Speaker 3:Mhmm.
Speaker 7:That's actually what enables good science.
Speaker 2:So tell me about the distribution business model. I could imagine a world where you're basically doing drug discovery, going through the FDA process. At the same time, you could sort of sell a lab in a box to a pharmaceutical company. There's a lot of different ways I could see this taking shape. Where do you think this goes?
Speaker 7:Yeah. We are building the infrastructure layer. We want to be the TSMC for drug discovery. Mhmm. So we are partnering closely with pharma companies, biotechs, such as Genentech Mhmm.
Speaker 7:Where we are they can either work with us by using our system, our physical AI scientists in their own lab, or we're actually about to open our own lab, our own fully autonomous lab, one of the largest autonomous lab in The United States in 2026.
Speaker 2:Talk to us about the fundraising. I think we missed you on the day you announced your series a, but I still wanna ring the gong. What happened? Who's in? How much did you raise?
Speaker 7:Yeah. We raised, $52,000,000 for series a game. Yeah. Scott. Thank you.
Speaker 3:Amazing. Who who'd you raise it from?
Speaker 7:Yeah. Human Capital led. They came in and pre seed and seed, and they tripled down on us for series a, really preempted the race. We also have Lux, who is also a repeat investor. Also, Menlo Ventures, Cataglio, great investors joining in for a very ambitious mission and very ambitious journey of eradicating disease.
Speaker 2:And 52,000,000 series a, that's feels like a lot of money. Congratulations. But is is I I could imagine spending it on a training run for a foundation model or buying a bunch of robots like that stuff behind you doesn't look too cheap. Where do you see the money going? What does it unlock?
Speaker 7:Well, actually, the hardware that we use at Medra is all off the shelf. Robots right now, especially their hardware, is fairly commoditized. And we use this off the shelf hardware so we can build AI on top of it, so we can actually reason about the science and actually be able to use our what we have trained ourselves, which is the vision language lab action model
Speaker 2:Okay.
Speaker 7:To be able to autonomously run experiments.
Speaker 8:Mhmm.
Speaker 7:And a lot of what we have raised our series a for is actually to open our own lab right in San Francisco to be able to scale up data generation. Because ultimately, what we want to do is to be a data foundry
Speaker 2:Sure.
Speaker 7:For life sciences, to be like a Merkur or Surge, but for biological and life science and chemical chemistry data Sure. So that our partners can train foundation models in biology.
Speaker 2:Yeah. Because there's probably not a lot of really clean data out there on GitHub or out on the open Internet, and so you have to sort of generate it yourself. Is that generally correct?
Speaker 7:That's right. That's right. I mean, if you think about in biology, like, the largest biology foundation models are still about three orders of magnitude, trained off three orders of magnitude less data than, like, you know, even, like, o one, which
Speaker 2:is Yeah. Yeah. Think Google launched one that was showed really impressive results, and the scaling laws were there, but it was much smaller than what you see elsewhere. So, yeah, very interesting. Very exciting.
Speaker 2:Jordy, anything else?
Speaker 3:No. This is super exciting.
Speaker 2:Congratulations. And in the future. And I'm sure we'll have you back on the show soon. Have a great rest of your day.
Speaker 3:Great to meet you.
Speaker 2:We'll talk to you soon. Let me tell you about Shopify. Shopify is the commerce platform that grows with your business and lets you sell in seconds online, in store, on mobile, on social, on marketplaces, and now with AI agents.
Speaker 3:And We got a little bit of time, I think.
Speaker 2:We do. That's great.
Speaker 3:Our next one.
Speaker 2:Well, then I'll tell people about the New York Stock Exchange. Wanna change the world, raise capital at the New York Stock Exchange.
Speaker 3:And let's pull up this post from Gucci. Gucci?
Speaker 2:What did Gucci do?
Speaker 3:Not the kind of account we pull up every day.
Speaker 1:Okay.
Speaker 3:They say primavera, February 27, 2PM CET, and this picture is created with AI. They hit this they dropped this on main and the photo looks
Speaker 2:seen any hallucinations. It looks looks complete.
Speaker 3:Tiled. It looks like it could have been out of a cattle any catalog over the last
Speaker 2:Yeah. Twenty years. I'm sure it's really peaceful over there at the Gucci offices now.
Speaker 3:Yeah. I'm sure I'm sure this wasn't controversial at all.
Speaker 2:No. But they're they're they're going this is this does feel like a more tasteful dipping your toe in the AI boom than, say, the Svetaq ad where where everyone was kind of like, this just it's not polished enough. It's still in the uncanny valley. And I feel like we're gonna go through the same thing as CGI where, like, there are some terrible movies out there that are CGI based. There's this there's this one in it takes place in, like, Greek mythology that's, like, notoriously terrible.
Speaker 2:There's one with the rock in Scorpion King where he comes out and, like, it's very clear that they just didn't give the three d artist long enough to make it look good, so it just, like, looks really awkward.
Speaker 3:Like, just shit.
Speaker 2:Didn't age well. But then some CGI from, like, the original Star Wars in 1979, you see the green screens, and you're like, wow. That still looks amazing. It holds up. And so you gotta know when when to actually go in, dip your toe in.
Speaker 2:There's been another turn of events in the Warner Brothers takeover. Have you seen this on Kalshi? It's been going back and forth neck and neck. Now Paramount is in the lead. 54% chance that that Netflix takes over or that Paramount takes over Warner Brothers.
Speaker 2:Netflix is at 36%.
Speaker 3:And And I believe the final offers need to be submitted by tonight or tomorrow night. I forget exactly. But Warner, Paramount's revised offer
Speaker 2:Yes.
Speaker 3:For Warner Brothers will likely come in at $32 per share. Let's pull up this video. Ted Sarandos. Ted Sarandos having a chat.
Speaker 2:Absolute goat. While we pull that up, let me tell you about Sentry. Sentry shows developers what's broken and helps them fix it fast. That's why a 150,000 organizations use to keep their apps working. And let's go over to Deadline.
Speaker 9:I like you you asked that question. We've been working hard on this transaction to to acquire Warner Brothers and HBO, and we're deep in that deal every day. Would you prefer? Yes. Absolutely.
Speaker 9:There's there's no reason for not to. Right now, our our deal is the best deal. It was determined by the Warner Brothers board. It was reiterated, to suggest it to their shareholders who were gonna vote on March 20, and that there's no rational reason to block the deal. It is you know, we're nine percent market share growing to 10, so there's really no concentration risk in our deal.
Speaker 9:And what's exciting, I think, is we were able to have this hundred year legacy of great storytelling finally in the hands
Speaker 3:of He's kinda talking about Trump?
Speaker 9:Celebrated and to make sure and invest in it and grow it.
Speaker 2:Think the people surrender. The thing
Speaker 1:that people want to know, you know this.
Speaker 2:The Trump language kinda comes it works its way in because you're hanging out with your friends, you start doing some Trump impressions, and then it just comes out. It just comes out sometimes. It's just one of the greatest impressions ever. So you just gotta do it every once in a while.
Speaker 9:Year, the year before, the year before. It's gonna look like that next year and the year after and the year after.
Speaker 1:Greatest acquisition that the world has ever seen.
Speaker 9:Traditional forty five day windows. It's theatrical exclusivity.
Speaker 2:You should be like, didn't you read the Sattrini piece? Everything's going to zero. Does it matter does it matter if two companies that are zero combined? No. Just let it happen.
Speaker 9:So we're excited to be in there. We wanna help them win.
Speaker 2:He's lapel maxing.
Speaker 9:Pam and Mike Googan, the Warner Brothers, they've opened opened nine number one films in a row. That's amazing. That's the kind of track record we're excited about.
Speaker 3:Alright. We can pause it. I love it. Over the weekend, there is some new
Speaker 1:Yeah.
Speaker 3:Reporting from Bloomberg. The Justice Department's investigation of Netflix's proposed takeover. Warner Brothers includes the scrutiny of whether the streaming giant's behavior wields anti competitive leverage over creators. He talked about it going from nine to 10% market share Yep. Streaming.
Speaker 3:But the issue is it's taking your buyers from like Yep. A handful of buyers down to one. This is more real. I
Speaker 2:hadn't considered that than
Speaker 3:it is. Well, that's that's that's what we were talking about with Ashley Vance. Hey, you sell documentaries. Yeah. You excited to have
Speaker 2:One less buyer.
Speaker 3:Literally no one to play offers against each other, just kind of you're surprised, take it or leave it.
Speaker 2:Yeah. Well, Netflix for a while has had co CEOs. Maybe you can mommy daddy them. Go to one. Oh, the other one said he was gonna buy it for 500,000,000.
Speaker 2:He's like, he didn't say that. I was just texting with him. What do wanna say, Tyler?
Speaker 1:At some point, the big labs are gonna be buying these documentaries. Right? If you have good enough if you have very high quality training data, you can sell straight to OpenAI.
Speaker 2:Sure. Sure. Yeah. That makes sense. Yeah.
Speaker 2:I mean, honestly, like a Sora deal wouldn't be out of the question for for Warner Brothers. You know? I wonder if the Disney we the Disney deal is exclusive in that they will not be on another AI generation app. But is it exclusive the other way in the sense that Sora will not add Superman or Batman and they will only have Spider Man? Because it does feel like never the two shall meet.
Speaker 2:Like, we're not gonna see Captain America and Superman fighting in anything other than a Chinese model that's getting a cease and desist. But in theory, Sora could go and do a deal with Warner Brothers in addition to Disney, but that might have been stipulated as like, no, we want to be the exclusive provider of superheroes.
Speaker 3:We don't want anyone to out out slap us.
Speaker 2:Yes. Let me tell you about Railway. Railway is the only one intelligent cloud provider. Use your favorite agent to deploy web apps, servers, databases, and more while Railway to automatically care of scaling monitoring and security. So cut you off.
Speaker 3:I think it's gonna be a big moment when Disney IP Oh, yes. That sore.
Speaker 2:But also when this when this concludes, I mean, it's gonna be it is neck and neck as we've seen in the call sheet chart. And then also, it's it's there's a lot at stake, like the breakup fees and the billions, I think. Like, there there's it's a, you know, political story. There's so many different things going on.
Speaker 3:Well
Speaker 2:Anyway
Speaker 3:I think
Speaker 2:it's We have our next guest. Well, while we bring him in, let me tell you about Cognition. They're the makers of Devon, the AI software engineer. Crush your backlog with your personal AI engineering team. Come on to the TV panel.
Speaker 2:Mike, good to meet you. How are doing? While he's sitting down, let me tell you about vibe.co. We're d to c brands, b to b startups and AI companies advertise on streaming TV, pick channels target audiences and measure sales just like
Speaker 1:going on?
Speaker 2:How are doing?
Speaker 5:What's up, gentlemen?
Speaker 2:What's up? Please introduce yourself. First time on the show.
Speaker 5:Sure. First time on the show. Happy to be here. Long time watcher.
Speaker 2:Nice talking.
Speaker 5:Thank you.
Speaker 4:Thank you.
Speaker 5:Were actually talking earlier
Speaker 1:Oh, saying how long have you
Speaker 5:been watching the show? You're back to the hotel room. Was like, can't remember the hotel room, but I remember the printed tweets.
Speaker 2:Oh, yeah.
Speaker 5:The printed tweets day are
Speaker 2:We have a lot of paper today. We always stack up a ton of papers on Monday because we get the weekend edition and the Monday edition. Yeah. And then we print some other stuff. Yeah.
Speaker 2:We gotta bring back the printed tweet for, like, the best tweet of the day. But honestly, the printer was, like, a major rate block, rate limiter for us. Like, because we'd be like So much friction. We're going live, and and we used to just, like, start the show around eleven. We'd be like, we're thirty minutes late, you know, because we would just do RSS.
Speaker 2:Now that we're live at eleven, it's like, the printer's gotta work and we print like hundreds of pages.
Speaker 5:Yeah.
Speaker 2:Because we'd be printing whole article. Anyway, sorry. I introduced I I interrupted your introduction.
Speaker 5:No worries. So Mike Anunziata, founder and managing partner at Also Capital. We're early stage heart tech fund.
Speaker 2:Yeah.
Speaker 5:Investigate inception, pre seed seed.
Speaker 2:Yeah. How did you get into VC?
Speaker 5:How did I get into VC? Had a bit of an interesting path, a little bit nontraditional. So Stanford? Stanford. Stanford.
Speaker 5:Yeah. Stanford.
Speaker 2:Did you actually go to Stanford?
Speaker 5:No. Okay.
Speaker 3:No. I
Speaker 5:started at Harker and then when I was good.
Speaker 3:Okay. There you go.
Speaker 5:There you go. No. No. No. So I've I've been doing venture for a little bit more than a decade, but actually Overnight success.
Speaker 5:Overnight success. So a little bit more than a decade. Yeah. Started my went to Cornell for undergrad Okay. Which is,
Speaker 3:you know
Speaker 1:Still Ivy League. So I mean, you got it. Oh, you
Speaker 5:got it.
Speaker 2:You got
Speaker 1:got it. Wow.
Speaker 3:Have you ever
Speaker 5:heard of it, though?
Speaker 2:Yeah. Have.
Speaker 5:Yeah. Cornell undergrad. Okay. Did the family office thing for a few years. Okay.
Speaker 5:I actually worked at the Cornell Endowment, so I've been an LP. So Okay. I've been
Speaker 1:on that side
Speaker 5:of the table. Then business school and then started a a hard tech company back in 2016.
Speaker 2:Okay. Okay. So founder yeah.
Speaker 5:Founder yeah. Yeah. Same same year Andriel started. Cool. We're worth a fraction less than 6,000,000,000 right now, but,
Speaker 2:you know. What what were you doing?
Speaker 5:We're doing food technology development. So Okay.
Speaker 2:Oh, cool.
Speaker 5:Around the same time you're doing Soilent, John. So I'm sure we're doing food manufacturing technology. Kind of from scratch, me and a co founder in a lab through series b companies still going, built out a big facility. So, I've been doing art tech manufacturing
Speaker 2:Yeah.
Speaker 5:For quite a quite a bit. Yeah.
Speaker 3:Give us the history of All then.
Speaker 5:History of also. So, you know, it's funny. Also started as Will Brewery, Mike Enensiada, and Colin Smith's Backyard Angel investing adventure
Speaker 2:Oh, no way.
Speaker 5:In 2019. He
Speaker 3:was the
Speaker 2:show on Friday.
Speaker 3:Yeah. He was
Speaker 5:on the show on Friday, I saw that. So I did dormant fund when I was in business school. Cool. So I've been a DRF partner for for a number of years, now an alum. Started writing angel checks in 2019, kinda scaled up through SPVs, and then raised our first fund in 2023.
Speaker 5:So that was a $22,000,000 fund. And through that journey, you know, we wrote the first check into Radiant Nuclear, you know, Varda followed shortly after that, and I've been on the the board of Varda since inception. These are good companies. Yeah. We did k two at Seed as well.
Speaker 1:This is killer too.
Speaker 5:Any signal software time radios. We raised our first fund in in '23 that I mentioned. Yeah. And then wrote the first check-in to Northwood. So it's been a bunch of Was
Speaker 2:that post Zerp ending the the crash?
Speaker 5:Yeah. So it was post Zerp, you know, kinda hard time to raise a fun as a as a new manager.
Speaker 2:But you're not saying I'm just gonna go into crypto and like the the frothy stuff, you're in the stuff that's on the next boom.
Speaker 5:Well, you
Speaker 4:know, I think you could let's just talk a
Speaker 5:lot more about this, but I think, you know, our thing from the beginning is who are your smartest friends and how do you believe in them before others do? And then I think the hard tick thing candidly grew outside Yeah. Of that. It grew from that.
Speaker 3:When how many checks had you written into Gundo companies or Gundo adjacent companies before John went Oh, yeah. Put it on the map with that video.
Speaker 5:There's a small part
Speaker 3:in that.
Speaker 4:Yeah. I I don't know.
Speaker 5:We did like six or seven before the Gundo bus and your things. So Pre bus and bus.
Speaker 2:Yeah. Bus is really the defining line.
Speaker 5:Exactly. You know, PC
Speaker 2:Yeah. Pretty
Speaker 5:bus. Yeah. So, I mean, we did a bunch of it. Did Varda, Radiant, K2, Any Signal, you know, that kind of crew of of folks. And then since then did Northwood Yeah.
Speaker 5:First check Their. We you guys had Mesh on the show. Yeah. Did that one recently as well. So Yeah.
Speaker 3:Basically, every every company. Yeah.
Speaker 5:Yeah. So we've been very fortunate. They let, like, the non engineer guy somehow cosplay as an engineer VC, which is a lot of fun sometimes. But I'm not afraid to make myself look silly every once in a while to try to learn something new.
Speaker 3:Is how what is your you you you're so you're non technical, but so what is your process for underwriting Some of these companies were at pre seed, they can often actually seem like a science project and and that's like the I probably made that mistake once or twice across 60 some bets where I invest, you know, I'm I'm not a a professional investor, but accidentally invest in a science project that was being positioned as, like, a commercial opportunity. But the ones you listed off, you know, started as kind of, like, far out ideas and now have very real commercial Yeah. Opportunities.
Speaker 5:Yeah. Look, I think the the unique lens that I kind of bring to the spec that I ran a company doing hard tech stuff for seven years Yeah. Across, you know, engineering, built the whole team, kinda know what a good engineer sounds like and how they execute, you know, product, go to market operations, all that vertically integrated. We, for the most part, are investing in people that have done these kinds of things before. And if you look by example, you know, the VARTA team, a lot of those guys are doing Dragon at SpaceX.
Speaker 5:Right? They miniaturized it and turned it into Winnebago. If you look at the mesh optical team, they're doing lasers at SpaceX, they're doing lasers now. If you look at any signal, right, doing radios, a lot of the Northwood team, you know, doing ground stations. Right?
Speaker 5:They're doing the thing they were doing before, but with a different market opportunity. So the common thread is like these are serious people building serious companies and that's you can kinda see that once you've lived it Mhmm. For seven years is, you know, I didn't you know, it's great that more people are coming in and wanting to be excited about investing in this category, putting more capital to work in the category.
Speaker 3:Think Marking you
Speaker 5:out. We need that. Yeah. Exactly.
Speaker 3:I'm really excited about it. Yeah. That's a broader Yeah. Exactly. Valley Exactly.
Speaker 3:Community coming in and marking you up five times, 10 times.
Speaker 4:Jordy, I got an uncapped note for you if you're
Speaker 5:if you're really excited about one of these things. It
Speaker 2:it it was in the cover of the Wall Street Journal business and finance section today. Investors go heavy on AI immune assets. Explain to us what Halo is, what it stands for, what it means.
Speaker 5:Yes. So heavy assets, low obsolescence, which is a term that I just learned Yes. A week or so ago. Mhmm. So thank you, JPMorgan.
Speaker 2:Yes.
Speaker 5:Are they a sponsor yet? Not yet. Do we want them to be?
Speaker 2:Depends on what the product is.
Speaker 1:We love JPMorgan.
Speaker 5:We love JPMorgan. Yeah. I think, you know, this heavy assets, low obsolescence is, you know, these things that are very difficult to replicate. Mhmm. You build a chemical plant Mhmm.
Speaker 5:Or you build an aerospace production capacity. Mhmm. These kinds of things are much more resilient than your traditional b to b SaaS product Sure. That may be able to be replicated by Cloud Code Yeah. For example, or OpenClaw, something like that.
Speaker 2:This was Dalian's bid when we had him on for the slop versus steal debate Randall debating of of, you know The pay per view. Would be yeah. It was a pay per view. What would be most resilient? But unpack that a little bit because heavy assets, high assets, what does that actually mean in the defense tech context?
Speaker 2:Because there still is a lot of r and d that's happening. Yeah. And then low obsolescence. I wanna know more about, like, the curve of, like, what can be obsolesced because Yeah. There are some companies when I think about, like, you know, a lot of people have been saying, like, oh, like, buy raw materials, like, go long gold.
Speaker 2:And it's like, is fungible. So Yeah. It's there's no real moat there. If you just own some gold, you just commodities. So how do you think about non commodity For non commodity products?
Speaker 5:So so two things. First, look, any great venture business at the core is some is an is a company that has the potential to generate high return on capital in the long term. Yep. Like, you have to account for the assets that it takes to generate the revenue, and then how durable is the revenue over the long term.
Speaker 2:Yep.
Speaker 5:That can come from Dow Chemical, which has just such massive scale that it's very difficult to replicate that scale, and they have a very durable business, low obsolescence.
Speaker 2:Yeah. Not
Speaker 5:gonna remake all those chemical plants. Yeah. Or it could come from something like a radiant nuclear Mhmm. Who is building very complex nuclear reactors in a shipping container equivalent. And they're gonna scale up not just the design of that, but the manufacturing, the supply chain, the regulatory.
Speaker 5:And that gets to the second piece that we always talk about, which is we love companies that are novel in the aggregate. Mhmm. Right? We're not looking to, to your point, how do you underwrite a science or engineering, Jordy? It's like, it's not one specific thing we're trying to understand.
Speaker 5:It's what are the 32 things that have to come together. Why is this the right moment right now for this thing to happen? And if it does, now all of a sudden we've got a moat. Because it's it's not impossible for somebody else to get a reentry capsule like a VARTA.
Speaker 2:Yeah.
Speaker 5:Right? It's not impossible for somebody to build ground stations a But the dynamism of these companies is in the ability to execute and move with speed Yeah. And integrate these complex systems
Speaker 2:Yeah.
Speaker 5:That become low ass absolutions. But they are heavy assets.
Speaker 2:Yeah. It's like the factory is the product. I've always heard that thrown around. It's like and I always read it as like the real challenge is manufacturing at scale. But I I think there's another cut on the factory as the product, which is probably something like like you actually would be very reticent to invest in a hard tech company like Varda if they were like, yeah, we have a third party manufacturer that produces the capsules, we just send them a CAD file.
Speaker 5:Yeah. I think it really depends. Right? Like, I I I love the I love the theory behind the factories of the product, but I also think there's some pretty strong, you know, strategy that that needs to be built on. You know, why should we build a factory?
Speaker 5:And if you look, SpaceX is a good canonical example of Why should they build a factory? Was because they were creating a lot of the demand at the same time as they were trying to deliver it at a given unit price to unlock a bigger economic opportunity and there was no third party even available. Yep. That they so they had to do it themselves. So they couldn't meet a spec, so they had to do it themselves.
Speaker 5:Mhmm. And I think that's the different dynamic where you should not, and I post about this a lot because it like definitely is a thing I believe pretty strongly. You should not vertically integrate just for vertical integration's sake.
Speaker 2:Yeah. No carbon.
Speaker 5:It's a poor use of capital to take a bunch of equity and shove it into a commodity machine which you could not do Mhmm. For example. But if you look at like a VARTA for example, cadence is everything for that business. Mhmm. So if cadence is everything, you gotta be able to turn fast.
Speaker 3:Mhmm.
Speaker 5:So they have a lot of capabilities they built in house to be able to do that. Same kind of thing with the Northwood. Right? In house as much as they possibly can because they have to turn fast to move with speed. Yeah.
Speaker 5:Speed's really the advantage, but it is built on a foundation of the ability to deliver at rate. Yeah. And that's kind of the core. When you say factory is the product, I think I agree with that. Yeah.
Speaker 5:But where are you pointing that advantage? Where are you pointing that? Because it is an investment that you're making as a company. And it's something that even in day zero investments, like all of our stuff is pretty much inception precede. Yeah.
Speaker 5:We're thinking like, if you're gonna build a factory in three years, you need to be thinking about it now.
Speaker 2:Yeah.
Speaker 5:So why are you building that factory? Why are you doing that? Like, what competitive advantages does it give you? Think that's how, you know, the people that we backed have really had a good sound, like, head on their shoulders of how to think about those trade offs of why build decisions.
Speaker 3:How did you react to the general intelligence crisis of twenty twenty eight, the the viral essay that nuked the markets? I saw this guy, John Lober had a had a pretty, kind of a response to it breaking it down. He was in some parts saying like institutions have a lot of momentum. They can carry that momentum and adapt to, these new market forces. He also suggested that, reindustrialization could, it it seems obvious that, if a lot of our jobs can be just automated with a computer, maybe they weren't that real in the first place.
Speaker 3:But there's a lot of work that needs to be done in the physical world. There's America needs to figure out how to make stuff again, not just Yeah. Kinda push paper around. Yeah. And so, are you optimistic that this could shift talent from, you know, making the the fiftieth like vertical CRM to Yeah.
Speaker 3:Making stuff? Do you think this could be a
Speaker 5:Look, like I I I'll say this. I don't think we need to send our boys back to the coal mines.
Speaker 2:Let's The children
Speaker 3:are eating. You're eating for the mines.
Speaker 2:The children you're eating.
Speaker 3:The children.
Speaker 1:You're not.
Speaker 5:The children. No, was kidding. Yeah. You know, my kids, if you're watching, which I think you are.
Speaker 3:Yeah. Get off my
Speaker 2:Get off my It's light blue collar.
Speaker 5:Yeah. It's well, I think it's, you know, what are we doing next?
Speaker 2:Mhmm.
Speaker 5:Right? So if you go back to horse and carriage
Speaker 2:Mhmm.
Speaker 5:Right? It's, hey, Ford has a job for you on the production line. Mhmm. Like, go learn how to do that. Right?
Speaker 5:And I think the opportunity is in the companies that develop recurring education rework as, you know, processes or or capabilities as part of their normal operating cadence. Right? So a lot of our best companies, they will get seed funded and they will immediately start internship program. And that internship program will become a funnel for new talent to come in after they graduate, for example. And I think institutionalizing that around these companies that are moving fast in new area in new areas, whether it's lasers or nuclear or space.
Speaker 5:Right? I think we could bring back that retraining, but inside the corporate entity
Speaker 3:Totally.
Speaker 5:As it's the responsibility of the corporation is is really our company's problem, that there's a talent shortage.
Speaker 2:We should take that on. He had someone who was working basically a blue collar job and just wanted to get like a fork forklift certification, and he brought him on. And then pretty quickly, he was, like, writing CAM Mhmm. CAD automation software Computer aided Yeah. Manufacturing.
Speaker 2:Yeah. And like sort of became a CAM programmer, like Yeah. Almost. And that was like a lot longer path a long time ago. And so the upscaling thing is definitely real at these companies.
Speaker 5:Look, I think the the we could get into like this whole different thing around like Yeah. The student debt crisis is definitely a big bottleneck to people being able to do this upscaling
Speaker 2:Oh,
Speaker 5:sure. Where they may be constrained financially that they just have to kinda keep keep working to be able to make their student debt payments, for So I think that's a big thing that people
Speaker 3:Yeah. I was I was was having a conversation with Tyler. We've we've had an an a number of of interns, all of which have been always paid. But I was thinking if I didn't have the opportunity to just work for free for people in college, where at the time I was like working a job at a hotel like grinding. Then Yeah.
Speaker 3:In my free time, I didn't have a lot of skills. And so I would just tell entrepreneurs like, hey, let me just pick up little things to do and ultimately learned a number of things that allowed me to start my first business and all that stuff. Going back, it it it feels like the the sort of like unpaid intern is like completely like no go now. Like it's a net it's Yep. A negative signal if a company is doing it.
Speaker 3:But it might have to come back and it actually if, if people really want it, it's like, yeah, you can work a job that is very low skill so that and then spend all the rest of your time, like, reskilling yourself. Yeah. So, I don't know. I think it might have to might have to come back. Although, I'm sure the there's a million reasons why somebody would hate that idea.
Speaker 5:Yeah. Look, I think it's just really important. So one of the things that's most inspiring to me about this moment in time is it feels like not only are there people spinning off of like the SpaceXs of the world that are now continuing to take on really big challenges Mhmm. They're doing two things. They're doing things that are really meaningful, that when you get at the root of them and the the there's an authentic drive behind a lot of what they're doing, but they're also willing to tell the stories that inspire other people to be better.
Speaker 1:If you
Speaker 5:look at Jared Isaacman coming as an NASA administrator, like, he is an inspiring human being. Yeah. Right? That I think we we need more people like a Jared who's 30, 40 years old, whatever it is, like, young person
Speaker 2:He's been to space.
Speaker 5:Future. He's been to space.
Speaker 3:He's processed payments. Every time I get a shift shift payment checkout, I just go. Yes.
Speaker 2:Talk about debt. What is a reasonable debt load for a series a hard tech company?
Speaker 5:Yeah.
Speaker 2:In software, venture debt is seen as cancer. Like, you do not wanna go near it with a 10 foot pole. Yeah. Paul Graham said it blows up companies left and right. Yeah.
Speaker 2:It's very scary. Yeah. I think a lot of entrepreneurs over time, they get comfortable, they learn how to use it effectively, but then it's always just like, well, I have this money sloshing around anyway. It's all one big pool. Like, it's not like the dollars get allocated one place or another.
Speaker 2:Yeah. How are you talking to founders in your portfolio about debt? Yeah.
Speaker 5:Like, could get hyper technical about this, but I won't for this audience. No.
Speaker 3:Get technical.
Speaker 5:Get technical. Get hyper technical. The right amount of debt at series a is zero.
Speaker 2:Okay.
Speaker 5:The the right way to use debt in companies is in in two places.
Speaker 2:Says the guy who sells equity.
Speaker 5:Says the guy who
Speaker 3:sells equity.
Speaker 2:He's like, no one should
Speaker 3:ever raise debt.
Speaker 2:Just come to me for another check.
Speaker 1:I'm gonna
Speaker 3:pop this with you.
Speaker 5:At the right time and the right place, it makes a lot of sense. There's really
Speaker 2:When does it make sense? When does it start to you could take me out to series d. I don't care. But where
Speaker 3:For sure.
Speaker 2:Because there are a lot of companies that we've talked to where it's hard tech and they wind up buying a ton of machines in a big warehouse, and it just feels like the liability side of the business is very different than a couple programmers in a bedroom or in a garage. Right?
Speaker 5:Yeah. So, you know, in a past life, when I mentioned I worked at the the Cornell Endowment. Yeah. I also got my CFA while I was there.
Speaker 4:The way. And then went and got
Speaker 5:my MBA. So a traditional Yeah. Such a nontraditional
Speaker 2:How do you wind up in finance just a and an MBA?
Speaker 5:Yeah. You know, revenge of the balance sheets is kind of what I how we think about what we're investing in now. Okay. Yeah. But, look, I think you're using debt for a couple situations.
Speaker 5:Number one, to buy long lived assets.
Speaker 2:Yep.
Speaker 5:But the caveat is they need to be tied to actual contracts
Speaker 1:Okay.
Speaker 5:That will be pushing out real revenue Okay.
Speaker 6:And cash flow.
Speaker 5:Yeah. If you are buying if you are using debt in to speculatively invest invest in capacity
Speaker 2:Risky. You
Speaker 5:are taking a lot of existential risk risk into the business until you have contracted revenue that has significant bookings and backlog. Sure. Alright. So if you've got two years of backlog and you wanna take on, an interest rate of 7%
Speaker 2:Yep.
Speaker 5:While your equity is 30%, and you think you can get to a revenue milestone that blows out your equity multiple
Speaker 3:Mhmm.
Speaker 5:That's actually a really good use. Right? Or if you have, you know, there's a canonical example I always think of when I was working at the endowment, we looked at a venture debt fund. Mhmm. And it was super interesting, like, '15.
Speaker 5:This was 2015. And they had a very good use case
Speaker 2:Mhmm.
Speaker 5:Of using venture debt. And it was they were lending to Pandora. And for sharing. Here's Pandora. Here's to Pandora.
Speaker 5:They're lending
Speaker 2:service, not the makeup place.
Speaker 5:Yeah. Yes. Exactly. Not the jeweler.
Speaker 2:The jeweler.
Speaker 5:Jewelers. The streaming service.
Speaker 2:Streaming service. Yeah.
Speaker 5:They were lending to Pandora. And Pandora, they said, like, a cash flow timing issue where they needed to buy servers to be able to store the music and And stream there would be revenue that would come off of the stream. So the revenue would lag the cash investments in the servers. Sure. But as they were growing streams, their multiple on the the revenue was growing.
Speaker 2:Oh, interesting.
Speaker 5:So you actually could use the venture debt to bridge to a higher revenue multiple Yeah. When you went to raise more equity Yeah. And reduce the cost of equity. Yeah. So even if you were paying 18% implied cost of equity IRR Yep.
Speaker 5:On the venture debt, including the warrants Yep. It was still cheaper Yep. Because the cost of equity was declining as you hit milestones. So it was episodic use
Speaker 2:Yeah.
Speaker 5:Where you knew you could raise equity as you hit a specific milestone to pay it down Yeah. Or it was part of the permanent capital structure that was tied specifically to cash flow. The trap is when you use debt to try to extend runway
Speaker 2:Yep.
Speaker 5:As a Yeah.
Speaker 2:Yeah. Even in that Pandora example, like, it it maybe it's not an asset backed loan, but it's, like, almost asset backed because you are just taking the money and buying assets Correct. That probably have some resale values.
Speaker 4:And you know the revenue is gonna come.
Speaker 2:And you
Speaker 5:know the Because you know streams are gonna go up Yep. And then you'll be able to refinance it because equity investors say, you're at 10,000,000 streams, I need you to be at 15,000,000 streams and like how do you get there? Well, at the time, like this was one way you could get there and buy the physical asset. So I think understanding where did the slope change of your multiple effectively Yep. Like those inflection points or permanent capital.
Speaker 2:That's very cool. Jordan, anything else?
Speaker 3:No. What what founders and in what categories do you wanna meet?
Speaker 5:What founders and what categories
Speaker 3:Like what kinds of founders, not specific.
Speaker 5:What kinds of founders? Good question. Look, our big thing is founders that have fun.
Speaker 1:Have
Speaker 5:fun, but they play to win.
Speaker 2:Okay.
Speaker 5:We talk about that a Like, have fun, play to win. There's like plenty of research that if you're having fun
Speaker 1:Yeah. That's when you
Speaker 5:do your best work.
Speaker 2:You see that with the Varda team all the time.
Speaker 5:See that with the Varda team
Speaker 1:all the time. Thing Winnebago is
Speaker 2:like a funny Yeah. Like clearly they're having a good time. We did the l k 99 thing and Yeah.
Speaker 4:It's like Totally.
Speaker 2:Totally. Midnight side project. Very fun.
Speaker 5:Totally. Yeah. It's like are you building these little cultural things that people have a good time? Yeah. I think that's really, really important.
Speaker 5:Yeah. But also the playing to win side too, because I think you can over index on let's have too much fun Yeah. And realize like, hey, you're competing out here. So that's really what we're looking for is you you know it. Like, you guys have a ton of fun here.
Speaker 5:Right? Like, I think this cultural vibe is definitely what we look for
Speaker 4:in founders is that they're having fun, that's how you kinda get to parts of it.
Speaker 2:We're Let's put the hard tech to work. Tell us about
Speaker 3:Why don't you hit the gong?
Speaker 2:Yeah. You hit the gong.
Speaker 5:You hit the gong. Alright.
Speaker 3:You can
Speaker 2:tell us the announcement. At 50,000,000?
Speaker 5:Alright. Fund two? Second fund, 50,000,000.
Speaker 3:Woo. Elsa Capital.
Speaker 2:Go smash that gong. Great hit. Thank you so much for coming on down to the TV again, Ultradell. Congrats. This is great.
Speaker 2:Have a good rest of your day, and we will talk to you soon. And lastly, but not least, I will tell you about Phantom Cash. Fund your wallet without exchanges or middlemen and spend with the Phantom card. Mike really backed at least 50%
Speaker 3:of the breakout hard tech companies of the last five years at
Speaker 2:It's crazy.
Speaker 3:At the earliest stage. Really wild.
Speaker 2:I like this this deep dive from, Teo Burga. Who says, reading a 2005 Paul Graham essay, gasp, he used the forbidden sentence structure. And it says, writing doesn't just communicate ideas, it generates them
Speaker 3:in two I thousand I think that, it seems obvious at this point that PG's blog and Sam's blog are both like
Speaker 2:Deep in the training.
Speaker 3:Yeah. Core core to the to the anyways, we got a bunch of timeline here. Let's let's rip.
Speaker 2:You wanna
Speaker 3:rip through some? Let's rip through it.
Speaker 2:Take me through it. What
Speaker 3:you got? Chairlift capital says opening eye killing application software to then try to rebuild application software is the funniest timeline. Still opportunities in the enterprise. We gotta have somebody on from McKinsey or one of these big consulting firms to to hear how they're positioning it to customers.
Speaker 2:I think it's a deck. See a deck.
Speaker 3:Moving on. Where are we? Jim Kramer, underrated poster.
Speaker 2:What did he say?
Speaker 3:He This morning at 03:33AM was outside He
Speaker 2:doesn't sleep.
Speaker 3:Saying cold early dot dot dot. I am not worried. Anthropic has a solution. I read it in a report. What?
Speaker 3:Vague post. Incredible. I mean, yeah. Vague post. Sent IBM down 11%.
Speaker 2:Going to work.
Speaker 3:Dolly Bali says, investors simultaneously think AI is overinvested and AI will take over industries. Yep. It's a funny moment right now.
Speaker 2:This last one. SaaS is dead. Open Claw replaced all my subscriptions. I went from $480 a month on tools to $1,245 a month on API costs and fifteen hours a week fixing YAML files. Adapter be left behind.
Speaker 3:Hope hopes hopes revenge says, woke up to Claude Bot running its claw through my wife's hair. Had explicitly asked it not to do that.
Speaker 2:Stop. Stop. Stop. There was a there was a post about some meta researcher, alignment researcher got all of her emails deleted by OpenClaw or something.
Speaker 3:Yeah. That that was That
Speaker 2:feels like a crazy thing to
Speaker 3:I think she needed to to stress that that I my my sense is that she like set up a new device with a new email. Yeah. It was not just like running OpenClaw on her main meta email, but I think people read it. Yeah. On her main account, which, seems crazy.
Speaker 2:People were saying like, oh, it's it's bearish. Like, she's literally an alignment researcher. It's like, that actually might make her better at her job, though.
Speaker 1:Well, she she was upscale too before. She wasn't like in, you know, in the less wrong like
Speaker 2:Oh, okay.
Speaker 1:Grinding through like EA stuff.
Speaker 2:Yeah. Like And EA would never be caught getting their emails deleted. No way. Just all of less wrong gets deleted by open call by accident.
Speaker 3:Palantir is launching Valley Forge grants 10,000 awards for high schoolers solve the problem that most inspires them using our software ditch, the coffee runs and fake intern projects. Woah. Shots. Shots fired at Tyler. Claude with ads was incredibly real.
Speaker 3:We'll pay you upfront to we'll pay you to confront the challenge you care most about. At Valley Forge, Washington soldiers endured a brutal winner, turned the tide, and won the revolution. Their fortitude has carried America to its 200 birthday. We're looking for pioneers to lead America to its five hundredth. Mhmm.
Speaker 3:I like the sound of that. Very very cool opportunity. Based on our audience data, we we have very very few high school students in the audience. Really? But to
Speaker 2:get them
Speaker 3:listening to this, go apply. Feel free to DM us if you do apply and we will, try to, nudge.
Speaker 2:What happened with this f one race? Someone smashed? Someone crashed?
Speaker 3:Yeah. Who in the chat was actually at the San Francisco San Francisco I've never seen San Francisco go this This is in San Francisco? Let's pull up this video first of the truck actually jumping over one of the hills. John, I want you to see this. Okay.
Speaker 3:Let's play this. Wow. This is San Francisco.
Speaker 1:This is in San Francisco. I still
Speaker 2:got it. Can just this is the best part about San Francisco. They close down roads for crazy stuff all the time. There's beta breakers. Halloween is a whole scene.
Speaker 2:There's a million other events that happen throughout
Speaker 3:that with my clutch when I would be driving my manual.
Speaker 2:Jumping over an f one car. Wow.
Speaker 3:I used to Amazing. Driving manual as a high schooler in San Francisco
Speaker 1:Yeah.
Speaker 3:Made me into a man. Yeah. You're really facing death on every hill. And then let's pull up this. One of the cars did a burnout.
Speaker 3:Mhmm. People were thinking this was Yuki Sonoda, but apparently, it was one of the other drivers. This is
Speaker 2:the Red Bull. Wait. What why
Speaker 1:is it on fire?
Speaker 3:Caught on at one point, it caught on fire.
Speaker 2:I thought it just, like, had some minor bumper damage.
Speaker 3:Yeah. This was a wild this was a wild demo. I think I think
Speaker 2:They really pushed
Speaker 3:the limit. Red Bull really wanted to make a statement.
Speaker 2:They're spinning around here. Okay. And then they go forward and smashing the wall. Like, that feels like the easiest crash to prevent. When I was watching this, was like, while you're spinning around, that's the point where you're gonna go crazy, but then it's just smoothly going forward and smashing.
Speaker 2:Front wing
Speaker 3:No brakes.
Speaker 2:Decimated. No brakes, I guess.
Speaker 3:No brakes. And then at another point, the car caught on fire. 10 out of 10. 10 out of 10 demo.
Speaker 2:Red Bull knows how to entertain. They know how to entertain. Like, they Yeah.
Speaker 3:I see something. Purpose.
Speaker 2:Actually, in the Apple Vision Pro update, I watched a Red Bull video, an immersive video. It was a skier video, about fifteen minutes, and they take you backcountry skiing, heli skiing with these skiers, and they have the Apple Vision Pro thing. And it is amazing. It's like one of the greatest experiences. Because realistically, I'm never actually gonna go heli skiing backcountry alone.
Speaker 2:But this, like, just you you you feel the scale and everything. It's so good.
Speaker 3:I I called you at, like, ten or 10:30 on Friday. You up? Yeah. No. I didn't even call you.
Speaker 3:I just said, are you up? Question mark. And John sends me back a
Speaker 1:picture of him in a selfie in the Apple Vision You're not. You're you're probably the only active user at
Speaker 2:that moment. Well, you know what? I use I I got a Mac mini hooked up to my Apple Vision Pro now because I got an I got a I got a capture card that can take in an HDMI input. So I hooked the PS five up to the Mac mini and then mirror that to the Apple Vision Pro so I can play PS five in Apple Vision Pro with way too much latency and actually doesn't work well at all. But it was a
Speaker 8:fun
Speaker 2:experiment. And an obvious feature that they should have launched two years ago, but they didn't figure out how to do it. Just put the HDMI cable on the actual power brick and just let you plug in anything, and you'd have a massive audience of people that just wanna play all sorts of stuff that's a that's HDMI compatible. Anyway, very, very nice.
Speaker 3:If you are in the market for a western sort of vintage Gulfstream, Greg Greg's got you covered. Axe has got you covered. It's a 1994 Gulfstream GIVSP. The latest ask was only 3,750,000.00. So
Speaker 2:This is beautiful.
Speaker 3:The carpets don't know
Speaker 2:how to
Speaker 3:make jets like this anymore.
Speaker 2:Everything about this is amazing. It's so it's so good, so opinionated.
Speaker 3:Look at the look at the bathroom. I love it. The bathroom, they went The
Speaker 2:bathroom is crazy steampunk.
Speaker 3:Yeah. Yeah. Star Wars mode.
Speaker 2:Yeah. Star Wars cantina. Yeah. Should we watch this latest video from C Dance?
Speaker 3:Let's do it.
Speaker 2:So it starts by saying Hollywood is cooked. Hollywood is cooked based on the new Cdance AI video model, and it's showing transformers. But let's actually track what's going on here. Starts out as a as a jet, a combat aircraft. He gets out of the jet, turns in around.
Speaker 2:Now it's transformer. Okay. Then he gets in another cockpit. Now it's a helicopter, and it has a gun on it. Okay.
Speaker 2:He's shooting it. That's useful. You needed to be in helicopter mode, but now you gotta get in the transformer again. So he gets back in the transformer. Oh, the turns out the transformer can run like a human and walk, but it turns back into a plane.
Speaker 2:Back into a plane. Then what do you wanna do when you're a plane? You wanna land on the freeway, on the highway, on the street. You land. You get back out, then you get back into your transformer to get in
Speaker 3:the front cockpit. Realistic.
Speaker 2:And now you're back in human mode, humanoid mode, and then you blast off.
Speaker 3:You would not be criticizing this if
Speaker 2:you just turn into a plane and you fly backwards.
Speaker 3:John, if this was an actual scene in transformers, you would just be walk watching it being like, that's tight.
Speaker 2:Okay. So I agree. I would be saying that's tight. And it is incredible visual fidelity. Incredible visual fidelity.
Speaker 2:And just an amazing video. And entertaining to watch and that's why 4,000,000 people enjoyed it. And it only has one commune one community note, and the community note is just AI Slop Engagement Farm. Yeah. Obviously, this is not cooking Hollywood today.
Speaker 2:It's a tool. It's cool. But really impressive considering that this truly is the most expensive shot you can do in Hollywood. Like, it is so, so complicated to animate all of those different rigid bodies as they interact with each other
Speaker 3:and Or
Speaker 2:they don't and they tuck inside. It's so difficult. It's it's the Mount Everest of motion graphics and CGI.
Speaker 3:And Tyler could do it if we get ten minutes, but And I see your
Speaker 2:and the and the final step in any of these, like, you can actually go and animate the rigid bodies in Cinema four d or Houdini or something, rig all this up. But the final texturing, the color grading, blending everything in, that's another major step. And it just nails all of this. The lens flares, the the reflections on the glass, all of that's like another step because you you you create the jet, and then it just looks like, you know, a three d render of a jet. So you have to blend it in.
Speaker 2:You have to make sure the colors match, make sure it matches the background. This stuff is so time consuming. And having a tool that'll at least allows you to do some previs, animate, interpolate, do a bunch of different things, Obviously, it would require a lot more art direction to get that to a place where it's amazing and Yeah. It it makes sense. Because a lot of the a lot of the the CGI that happens in transformers I know a lot of some of the transformers clips are silly, but a lot of it's very motivated.
Speaker 2:Like, it shows you how things move and there's a decision driving one transformation to another. It's not just randomly switching from a plane to a car to a plane and back and forth. And it's usually meant for dramatic wait, and there's some wait and timing, and there's art direction that sits on top of the actual CGI. Anyway.
Speaker 3:Apparently, open claw fueled ordering frenzy creates Apple Mac shortage. A for high unified memory units now ranges from six days to six weeks. I called it.
Speaker 1:It's happening.
Speaker 3:I did call it.
Speaker 2:You did.
Speaker 3:We did the math a couple weeks ago and it seemed obvious that if if the frenzy kept up
Speaker 1:Yeah.
Speaker 3:There would eventually be
Speaker 2:Yeah.
Speaker 3:Some shortages.
Speaker 2:I mean, demand for AI is continuing unabated. Like, people are using this stuff. Martin Screlly shared the NVIDIA demand check on Lambda. Lots of things are out of capacity right now. People are using stuff.
Speaker 2:If you need to hop on Lambda, hit us up and we'll introduce you. But,
Speaker 3:people were pretty
Speaker 2:People are
Speaker 3:triggered by Gary Tan and the YC crew jumping on a podcast Yeah. Dressed as lobsters. And I was triggered by people's reaction to it because we have done similar gesture maxing
Speaker 2:Yeah.
Speaker 3:Many many times.
Speaker 2:I thought
Speaker 1:you were I
Speaker 3:think they were just having they they were just having a little fun.
Speaker 2:Let them have fun.
Speaker 3:But but apparently fun fun is illegal. Fun is illegal. Hunter Weiss
Speaker 2:You have to pack it up. Wait. We gotta we gotta talk about our secret plan.
Speaker 3:Oh, yeah.
Speaker 2:We're leaving how we're leaving California, not because of any taxes. Those don't apply to us. But because there is an entire main village with a church and multiple homes that's on the market for $6,000,000, and we're gonna move everyone there. Yeah. I'm hearing some claps.
Speaker 2:I think people are in. They're down.
Speaker 3:We're gonna build that whole crew. We're gonna build that
Speaker 2:40 acre village, and it's it was a first listed for 5,500,000.0. You get the whole town.
Speaker 3:What actually qualifies as a village, though?
Speaker 2:Well, it has There's 21 structures. 21 structures. And this is where it gets funny. So we were saying, let's move the whole team there. There's 21 structures.
Speaker 2:We're a small team. We got, 10 people. That's enough. There's not 21 houses, Jordy. There's 21 structures.
Speaker 2:So it's entirely likely that Tyler over there will have to live in a barn or shed or perhaps the church.
Speaker 3:Fitting. In the stable.
Speaker 2:But other properties found on on the unique compound included Greek revival style dwelling, antique barns, and multi bay garages. Would you go if you had just had to stay in a multi bay garage? You can sleep in a GT three RS. You can also sleep in a multi bay garage.
Speaker 1:If you get some cloned horses.
Speaker 2:Cloned cloned ponies. Let's do it. Everything a homeowner needs to build their own thriving community or set up one of a kind rental venue. Very fun option if you if you have a need for 21 structures. Head over to Maine and pick this up.
Speaker 3:Hunter Weiss shares one of the best product ads ever.
Speaker 2:I agree.
Speaker 3:It's the iRun, the iPod Shuffle.
Speaker 2:Really good.
Speaker 3:We don't know how to make ads like this anymore.
Speaker 2:Really good. Simple.
Speaker 3:Really, really good.
Speaker 2:Tells you exactly what it's gonna do. Ride around.
Speaker 5:Really. Park.
Speaker 3:Really, really good.
Speaker 2:Beautiful.
Speaker 3:Bring it back. Will DePueh says whoever builds Gmail app shirt search should be burned at the stake. Every time I use this app, I want a KMS.
Speaker 2:He's having Five likes. Proof of insurance
Speaker 3:and it's just pulling up a bunch of Delta Airlines receipts.
Speaker 2:This is actually extremely annoying. I don't know how the search got so fuzzy, but you can search for exactly the term and it'll just be like, there's this one cookie that's buried in white text that sort of matches it, and it just shows it to you. They they gotta do something here. I think it's a big app, so there's probably some overhead to, like, fully rewriting this. But search is tough.
Speaker 2:Search is hard. Even in the even in the AI apps, I find that they generate so much text now that if I search for one keyword and I have it in my mind, it's like, well, that word came up the last 50 chats. Like, it comes up all the time. Yeah. And so it's been very hard.
Speaker 2:But I do think it'll get better. But certainly an opportunity.
Speaker 3:Dylan Field was having a little fun. He says, Nothing to see here. Sometimes the chairwoman of the task force on the declassification of federal secrets just likes posting pretty pictures. Please continue talking about the Olympics. And Anna Luna sharing an image of what looks like a wormhole and just vague posting now.
Speaker 3:We got we got congress congresswoman vague posting now. I love it. I love it. Let's keep it up.
Speaker 2:You got a you got
Speaker 1:a vague post every once in while.
Speaker 3:Bone. Vague post to go through. Last one Mhmm. Says, my culture is not your costume, Brian Johnson. Because Brian said he decided to live life on Friday.
Speaker 3:He was spotted just playing some video games, having some Taco Bell.
Speaker 2:He's having fun.
Speaker 3:Some pizza, some Doctor Pepper, having a lot of fun looking, not his not his usual self Mhmm. But locked in on on on the big game or something like that. Plant
Speaker 2:the bomb. I have some words of inspiration for the listeners.
Speaker 3:Wait. No. We have to we have to we have to say this last one.
Speaker 2:That's my words of inspiration after you plant
Speaker 3:the And then I actually have a few more
Speaker 2:last ones. Okay. Read this one off.
Speaker 3:Let's see Excuse
Speaker 2:me. Neil Renick says, everyone you meet is fighting a battle you know nothing about. Send them a Teams meeting link and finish them off. I love it.
Speaker 3:Last thing we'll cover today, Anthropic posted earlier, we've identified industrial scale distillation attacks on our models by DeepSeek, Moonshot and MiniMax.
Speaker 2:Wow.
Speaker 3:These created over 24,000 fraudulent accounts and generated over 16,000,000 exchanges with Claude extracting its capabilities to train and improve their own models. People were having a lot of fun with this. They said, No crying in the copyright casino, in all caps. Or Daniel Luke brought up a vintage Growing Daniel post, Aw. Did someone take your hard work and use it to train a model to mimic your expertise without compensation?
Speaker 3:Oh, yeah. Pot coloring, yellow black potentially. Another person says, can't believe someone would just steal from Anthropic like this. The millions of man hours Anthropic spent handwriting code, text, art, books, etcetera to generate enough data for training must be taken into consideration here. Where is the respect for IP?
Speaker 2:Wild. Fun times.
Speaker 3:And we'll here's where we'll here's where we'll end. Neat says, reading one LinkedIn post is equivalent to unreading five books. And that is a good time to remind you
Speaker 2:To leave us five stars. Yeah. Follow our LinkedIn. We're on LinkedIn. We're posting regularly now.
Speaker 2:We got a bunch of fun stuff. Little clips from the show, rewritten, little takeaways, little things that are on our mind. We really would appreciate a follow over on LinkedIn. Computer. Leave us five stars.
Speaker 3:Make sure everyone in the audience has the best evening of their life.
Speaker 2:And sign up for the TBPN newsletter at tbpn.com. Goodbye.
Speaker 1:Nice work, brothers. I'll see you on the next one.