CitriniPocalypse, Dot Com Lore, Gene-Edited Polo Horses | Alap Shah, Will Brown, Michelle Lee, Mike Annunziata

15:18Alap Shah:Travel booking I thought was funny because most travel agents don't actually take fees from the consumer. They take fees from the side of the hotel, the airline, whatever. And so the idea that you'll just immediately get every AI agent or every individual will just immediately, I didn't fully process that one. And anyways, back to you. I did think that John Loeber, I wanted to go through his piece because I thought he had one of the better rebuttals. Yeah, I thought that was good.

15:55Mike Annunziata:I think the thing that just keeps sticking out to me is like, and I was debating with Sagar and Jetty about this as well, like 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. It's doing a lot for the markets. It's doing a lot for the future. But like in 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, 40 billion dollars. And okay, maybe there's like a 5X multiple on that. And so you're generating$200 billion of GDP on top of those tokens.

16:39Mike Annunziata: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 actual workforce and what Americans do. And so there's this odd disconnect, and I keep coming back to the Tyler Cowen slow takeoff philosophy around what's actually holding up the American economy. It's like healthcare jobs. And there's a lot of jobs that are, they feel very AI resistant. I don't know, maybe something changes, but it just feels like the number of people that are software developers, less than 1 % of America, the number of people that work at tech companies broadly is less than 10%.

17:30Mike Annunziata:And so even if there's some massive reallocation there, and then you go into like, even in like white collar, if everything shifts, like the rest of world is hit. And there's just a lot of other dynamics that feel like you can see crazy gyration in the markets and 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 the real economy. There's always this disconnect.

18:00Alap Shah:I'm 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. And then somebody writes like kind of a cool piece of science fiction, but easily can poke a million holes in it. And then it sends all these mega caps down. Let's go through this piece from John Loeber.

18:29Mike Annunziata: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.

18:38Alap Shah:John Loeber wrote a great piece very quickly after this called Contra Citrini. He says, popular markets commentator Cetrini 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 doom saying, I want to present a critique of Cetrini's work and show a much likelier, more positive view of the future. One, never underestimate institutional momentum. In 2007, people thought the U.S. was geopolitically done under peak oil. In 2008, they thought the US dollar was just shy of collapse.

19:13Alap Shah:In 2014, they thought AMD and NVIDIA were done. Then came ChachiBT and they thought Google was done. 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 prove fragile. Real estate, where buyers had tolerated 5 to 6 percent commissions for decades because of information is asymmetry between agent and consumer. People have been calling for the end of the real estate broker for 20 years.

19:45Alap Shah:You don't need super intelligence for this. 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. Our buyer's agent made about$50 ,000 on the deal for about 10 hours of form filling and party coordination that I could have done myself.

20:18Alap Shah: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. 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.

20:47Alap Shah: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 backend systems is indefensible. Satrini and others talk of AI coding is a spell of the end of jobs at SaaS companies are one, the products become obsolete, zero margin, and two, the jobs themselves disappear. What everyone seems to be missing is this. These products effing S-U-C-K. That's his opinion. I can say this because I've actually spent hundreds of thousands of dollars on these products.

21:19Alap Shah:Sure, maybe AI enables competition to replicate their products, but more importantly, AI enables competition to deliver better products. It's no surprise to see the stocks drop. An uncompetitive, sticky lock-in sector filled with another swear word, incumbents. becoming competitive again. And my own personal call out here is, even until we see a round of layoffs at a company that is 5 ,000 software engineers at once, it's hard to believe that AI is replacing software engineers versus just making them a lot more productive. And if somebody is a lot more productive, you'll pay at least the equivalent amount to maintain them.

22:04Yeah.

22:05Alap Shah: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. I have not been able to send a wire using Citibank's online banking in three years.

22:18Mike Annunziata:This was my pushback against Rune. Rune was like, oh, Codex is so good. You can just vibe code everything. It's amazing. And I was like, why is the United app bad? The United app is good. 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 will die on this hill. But yes, hopefully it's going to get better.

22:42Alap Shah: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 100x before beginning to saturate demand. Three, this was probably the best point from his response, reindustrialization. There will be some labor displacement, of course. Driving stands out. Many types of white-collar work, as Cetrini suggests, will undergo some gyration as some jobs disappear and others change meaningfully.

23:17Alap Shah: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.S., we have virtually limitless capacity, a need for re-industrialization. You may have heard about bringing back manufacturing, but it's more than that. We are large, 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.

23:49Alap Shah:We can barely make fertilizer.

23:52Mike Annunziata: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 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 he's 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 color jobs to AI. We have to be able to provide with a continued high quality of life.

24:26Mike Annunziata:Part of this 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 Cetrini'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. On that front, preventing a market meltdown the way Cetrini imagines is actually pretty easy, and the federal government's response during COVID showed how proactive and aggressive it is willing to be.

25:04Mike Annunziata: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. That's a good response.

25:32Alap Shah: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 It's 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 is like, yeah, I just launched a delivery app and your food will be here in four hours. Yeah, it does feel like if I was Tony, I'd be I'd be flying to find Satrini face to face, He's opening up a can of...

26:10Mike Annunziata:I know where you're going. Yeah, it feels like, okay, so 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 more economically efficient. 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 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 believe that to some extent, but it just feels like still a little bit farther away because of adoption and actual.

27:08Alap Shah:Yeah, DoorDash has modes. That is the big, that is the simple trick that all vibe coders hate. Yes. Yeah.

27:18Mike Annunziata:I 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 onboarding the pool of, in the marketplace, like the actual liquidity.

27:31Alap Shah:Yeah, the only scenario is that I can see the sort of like vibe-coded delivery or sharing economy app working is at a local level, but there's already a bunch of competition there. Like I have a guy that when I want a ride to the airport, I call him, he picks me up. I don't necessarily use Uber because I like having the same. The guy's my buddy now. I like going to the airport with him, right?

27:56Mike Annunziata:So, yeah, 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. Yes. I know the Amazon basics paper towels are cheaper, but I just happen to like this particular brand that's a little bit more tailored for me.

28:28Alap Shah:But Amazon Basics was like, hey, we're, you buy paper towels from this brand normally. We're going to sell you the same product with our logo.

28:39Mike Annunziata:Effectively the same product.

28:40Alap Shah:And I think the AI disruption that is much more real is like you have entirely new paradigms for software, an entirely new relationship with software. 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 thinking about like, oh, I need to be monitoring this dashboard. Yeah, no, no, I agree.

29:07Mike Annunziata:I think the Amazon basics of Salesforce probably is not that big of a business opportunity because the whole value prop is that it's lower margin. And so Amazon basics is not driving Amazon's market cap.

29:21Alap Shah:Well, yeah, Amazon has solved the distribution. They're like, we have the customer, 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 and build out sales and distribution and all that stuff. It's a very different situation. It's not like people are just going to the SaaS supermarket.

29:40Mike Annunziata:I think his pushback would be they will effectively because they will go to an agent that says, 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 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 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 just keep coming back to timelines here before we move on let me tell you about console console builds AI agents that automate 70 % of IT HR and finance support giving employees instant resolution for access requests and password resets.

30:23Mike Annunziata:Let's continue.

30:25Alap Shah:Very funny post from Dimes Square Holdings. 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.

30:35Mike Annunziata:People are really having fun with this. What was this next one? Dimes is on a roll.

30:41Alap Shah:Average 2026 AI macro research.

30:44Mike Annunziata:I am legend.

30:46Alap Shah:This may be the most terrifying novel you will ever read.

30:49Mike Annunziata:Is that just the zombie apocalypse movie? That might be the most market moving piece ever written, says Clouseau Investments. That seems accurate. There are other factors going on, but this does really feel remarkably impactful. I don't know, maybe it's a buying opportunity, maybe it's a warning to everyone else, but it certainly broke through. I mean, these articles are really, really underrated. I've been I noticed like I heard about something big is happening on a car podcast. I got that forwarded to me in an email like like these pieces of like AI is a thing that you need to be paying attention to are breaking through in a way that 20 2027 did not and like the you haven't seen Ilya on Dorkash 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 a hundred million views, which actually means like everyone read it Beyond it went it went it broke containment like it truly truly went big And that and that's and that's certainly market moving in the Citrini post it too We can we can go through his agent of commerce thing, but it's a little wonky Let's see what adventure anthropologist had to say Cetrini 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.

32:20Mike Annunziata:Yeah, people are pushing back. And someone was saying that this is similar to Karl Marx's critique of capitalism. 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 SOSC 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.

32:58Mike Annunziata: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. Citrini's piece describes this exact dynamic, then declares there's no natural break, but it's the most Marxist piece of financial analysis written in years, and it makes the same errors Marx did. Schumpter offered the obvious rebuttal 80 years ago. Creative destruction doesn't just destroy, it creates industries we can't yet conceive of.

33:32Mike Annunziata: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, Satrini says doesn't exist. Who funds$200 billion a quarter in CapEx when equities are down 40 %? Private credit marks are in the 50s, and consumer demand has collapsed. Cost of capital rises, incremental build out becomes uneconomical, capital gets destroyed and reallocated. Cetrini also unknowingly describes Marx's proletarianization of the petit bourgeoisie. The 180k p.m. driving Uber is textbook, but the article claims this collapses consumer demand and that's where it breaks.

34:11Mike Annunziata:The top decile drives 50 plus percent of their spending and their wealth is in equities, not W2 income. From there long, the hyperscaler is 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. In the stable aggregate demand, the petty bourgeoisie finds a way to reinvent itself. I think the Citrini 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, deflation, more leisure and higher quality of life.

34:51Mike Annunziata:Marx's heir wasn't diagnosing the disruption, it was underestimating the system's ability to adapt. Very good. 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. It's Monday. Everyone is reading the latest Cetrini 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 there. I wonder how broadly the article virality, like long form has not been going viral on X or Twitter for a decade.

35:36Mike Annunziata:Even before the link ban and stuff, it was really, really hard for articles and links to really go big. 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...

35:58Alap Shah:Steve asked, does Tyler clap for each ad reader? Is that a soundbite? It is real. He claps. Yeah, I clap for every single one.

36:05Mike Annunziata:We'll give you a chance to do it again. Bin.ai, the number one AI 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. So Tyler, constructive criticism there. Anyway, should we continue with the Satrini back and forth? We do have someone from Satrini coming on the show. Yeah, we can keep going. The thing about the Satrini piece that is internally inconsistent is where does all the surplus go? Okay, we become impoverished and aggregate demand collapses. is what financial asset is spared from that?

36:40Mike Annunziata:A lot of people were asking that. And yeah, I mean, the answer is commodities. You want to 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. John says he claps as well.

36:57Alap Shah:Thank you, Sean. Thank you. Let's switch gears to something that is certainly more important than the Citrini piece, a street-legal modded garbage truck. What? Pratt & Whitney J3 jet engine. Okay, let's see it. Is this real? Let's pull this up. You don't even know anymore.

37:15Mike Annunziata:Wait, I've seen this Pratt & Whitney before. Hermia's, I think, bought one. I have no idea if that's real, but that's remarkable if true. Yeah, no, those are expensive, but I do think that they will sell those to you.

37:30Alap Shah:Feels like a six-foot flame at the end of your vehicle is not street legal. I would agree. I would agree. Anyways, horrors coming out of Mexico yesterday. Really sad situation. Our very own Joe Wiesenthal had been in the Puerto Vallarta area and had just left.

37:52Mike Annunziata:I think he got out of there an hour before the chaos erupted.

37:57Alap Shah:So very grateful for that.

37:58Mike Annunziata:I hope everyone who's down there is safe.

38:00Alap Shah:r slash Marriott on Reddit. somebody said Weston Puerto Vallarta won't honor late checkout with streets closed I am platinum elite over 1 ,000 lifetime Marriott nights I thought that was a joke

38:15Mike Annunziata:I didn't realize somebody actually posted this

38:17Alap Shah:TV is on fire due to the cartels setting 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 2 p.m. and said we would have to use the hospitality suite. We're supposed to be leaving for Buqueros this afternoon, but that isn't looking very good. Worst Bonvoy property I have ever experienced. I don't think anyone will be checking in today, so there's no reason to at least not extend us to 4 p.m. This is so fascinating.

38:53Mike Annunziata: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 was not just fires in a few cars. This was like a military operation, correct?

39:07Alap Shah:Because my wife texted me yesterday afternoon while I'm on X, monitoring all the open source intel. You texted me.

39:17Mike Annunziata:You texted me. And I was like, oh, what's up with Joe?

39:20Alap Shah:Like, wow, I've expected something like this for a long time, given the tensions down there. And then I'm just watching this. my wife texted me, our friends want to go to Mexico in April. Can we go? And a long weekend. And I was like, are you, are you, are you joking? Uh, anyways, the really, really sad situation, basically, uh, the, uh, leader of the cartel CJNG, which is like effectively his paramilitary group at any, like, if you looked at any photo or video of them over the last 10, 20 years, They look like they're special forces. I think the story is that many of them actually did train at some point with U.S.

40:03Alap Shah:special forces and then flipped.

40:05Mike Annunziata:Or probably the Mexican military.

40:07Alap Shah:Yeah, no, but the U.S. special forces have trained the Mexican military. Okay, okay. And so these guys are elite. Like they have their own version of Delta.

40:15Mike Annunziata:It's not a LARP like, oh, they just like picked up something and they watch like a video on YouTube.

40:20Alap Shah:Yeah, I mean, I'm sure some of them are not elite, but in general, this is like a paramilitary organization. It's like one of the largest private armies in the world, probably the largest private army in the world. And so Almencho, their main guy, gets taken out, and then they respond by starting to just blow up roads. They took over an airport, I guess. They just start causing mass chaos. Yeah, because they're leaderless. And so this 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 everywhere.

40:57Alap Shah:It literally looks like a war zone. So for somebody to be hitting Reddit at this moment and being frustrated, it's like, hey, maybe just the State Department put out almost exactly when this person was posting a security alert saying due to ongoing security operations and road blockages and criminal activity, U.S. citizens in the following location should shelter in place until further notice. And like that you're you're getting a shelter in place warning and you're you're mad about your Marriott points. But again, hopefully hopefully things settle down. I agree. I agree. Down south.

41:35Mike Annunziata:Well, I want to move on to some nostalgia. you we are going to get Tyler Cosgrove up to speed on what it was like to live in the 90s and the early 2000s. First, I'm going to tell you about MongoDB. What's the only thing faster than the AI market? Your business on MongoDB. Don't just build AI. 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, and you had to store files and text files. No, MySQL existed. But the 90s and early 2000s were iconic, and we got to get Tyler Cosgrove up to speed on what it was like during the heyday.

42:19Mike Annunziata:These were the vibes. The blockbuster. So much consumer electronics. Like, everything had a... You had a different device for everything. A Walkman, a Game Boy, an Xbox. You can still go see a Monster Truck Rally.

42:38Will Brown:I had an Xbox.

42:40Mike Annunziata:You had an Xbox?

42:40Will Brown:Yeah, we had Mod Retros right here. Okay, okay.

42:42Mike Annunziata:So you're maybe up to speed. Well, there's another one 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. I feel like this is overdue for a comeback. It's pretty close.

43:02Alap Shah:Amorphous fluid forms. Good music, too.

43:03Will Brown:terms floating around were blobism blobism and biomorphic design to see that industrial designers

43:12Mike Annunziata:were trying to push past because everything was really blocky in the 80s for things like sports

43:18Alap Shah:wear watches music play clear clear case this is a prime example of when technology influences form

43:25Will Brown:cad a modeling software introduced what is called nerves non-uniform rational beast blinds what this

43:32Alap Shah:allowed is for industrial designers to create mathematically smooth curves to be calculated

43:38Mike Annunziata:with precision. It enabled people to make. So before you had to just like use blocks basically and like you'd get like a sphere and that was it. And you can do like a sphere and a cube, but you couldn't really do whatever shape.

43:49Alap Shah:And back then they were rocking this kind of hardware when they were saying that all retail stores globally will be wiped out in the next five to 10 years.

44:00Mike Annunziata:Yeah. Yeah. Yeah, I mean, reflecting on the dot-com boom, I think is particularly interesting right now. I mean, I do like the takeaway from the dot-com boom when most people pull up the dot-com boom, 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. So there's an article in the New York Times that's sort of comparing the dot-com boom to the AI boom.

44:37Mike Annunziata:It's a piece by David Streitfeld at the New York Times. He says, people loved the dot-com boom, the AI boom, not so much. I buy that generally. The tenor around the dot-com era, yes, there was a lot of froth, but in general, people were like, oh, this is sort of interesting and cool. well, I gotta play with this like tinker. They just missed it more as a toy than like true doom. Yes, there was Y2K and people were worried about that, but the stats weren't quite the same. So right now, there was just -

45:12Alap Shah:Were you like aware of Y2K?

45:14Mike Annunziata:Extremely aware, extremely aware. Were you, how did you process it? Because my parents were trying to explain to - Yeah.

45:23Alap Shah:Like a five year old. Yeah, yeah. No, I was like - I could generally.

45:29Mike Annunziata:Yeah, yeah. I remember the turn of the millennium, I was overseas on a vacation, and there was a little bit of fear. Yeah. You gotta get out, everything's collapsing. You gotta seek refuge. No, there was a fear that, okay, something crazy might happen, but in general, my parents were dialed in enough that they had been through the boom and bust of something bad will happen to actually understand that like, no, the fixes were in place. And there were a bunch of interesting fixes. So if you're not familiar with Y2K, basically the idea was computers were programmed to store dates as two-digit numbers.

46:09Mike Annunziata: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 00, and all of a sudden, all your interest calculations for your bank account freak out. You have negative money. The whole financial system collapses. anything that's planned, right? All of this was like the fear of what might happen. Tyler?

46:31Will Brown:Yeah, that doesn't make any sense, right? Because we'll like - Easy for you to say, Tyler, you weren't born for - Oh my gosh, the numbers are resetting all of a sudden. Like you can see, it's like a calendar.

46:43Alap Shah:You had to be there, Tyler. Yes. You had to be there.

46:45Will Brown:Like, did no one have foresight?

46:47Mike Annunziata:No. I guess nothing happened, right? No, no, yeah, yeah. People did have foresight and so they started implementing changes and the changes cost a ton of money. I think the total bill for Y2K systems updates, because if you had hard coded like our dates and 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 of days of work. Yeah, they're writing some code and type. 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 Y2K. To prevent that.

47:21Mike Annunziata:Hit the gong for the Y2K paydays. A lot of people made a lot of money.

47:29Mike Annunziata:But there were also a whole bunch of interesting rules. And, you know, Hotshot over here, I'm going to give him a pop quiz. Do you know how to calculate a leap year?

47:39Alap Shah:If you get this wrong, there's going to be consequences. Okay, okay. No, no, no, no, no, stop, stop, stop.

47:44Mike Annunziata:Do you know how to calculate that? Consequences. Oh, no, no. No.

47:50Alap Shah:I told you there'd be consequences, Tyler. All right, okay. That was the most Zoomer thing I've ever seen in my life. Yeah.

47:58Mike Annunziata:He just has to go. He's brain-dewing. Wait, how to calculate leap year? Yes. Like when do leap years happen? I don't know. Like the actual calculation. Isn't it every four years? Every four years. Except every hundred years. Except every thousand years. So they toggle back and forth. And so at 1900 was not a leap year. And so 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. It's a leap year, 2000. But if you knew the 100 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.

48:43Mike Annunziata:And you can fact check this too, cause I'm not sure, I'm just riffing here. It might be wildly wrong. But the thousand cancels out the 100 and you wind up with just a normal year. And so if you did nothing, you win.

48:56Will Brown:Yeah, but okay, so Gregorian calendar goes into place 1582. Yeah. So you have like 400 years to figure this out. Yes, yes. And we did, but it cost us$100 billion,

49:06Mike Annunziata:as is all technological change.

49:13Mike Annunziata:But Y2K was like, it was very millenarian. People were dooming about the apocalypse, but these were like fringe sort of cult types. The same thing happened with 2012. I don't know if you remember 2012 apocalypse stuff. Y2K was the same thing, but it was not widespread. AI doom 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 gonna be the end or 2000 was gonna be the end. Like I would be shocked if either of those dates were single digit percentages.

49:51Mike Annunziata:Most people were like, yeah, okay, I might need to print out my bank statements. A lot of people were doing that, like print out your bank statements before Y2K, because then you'll have a backup and you'll be able to go in and say, no, I actually have$10 ,000 in my bank. I don't have negative 9 million because it's not the year 1 ,000 right now. But 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.

50:25Mike Annunziata: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? Oh, it's probably pretty low. It's extremely low. 20%. It's exactly 20%. Nailed it. 80 % said that AI was having no impact on their productivity or employment. We got to get those numbers up folks. There are clearly good ways to use AI to benefit your company and your people. I mean, it could also be like -

50:56Alap Shah:Those surveys, you never know. You never know. Might be somebody who's just not, who's going to answer the survey quickly and they don't realize it.

51:04Mike Annunziata:But I mean, just think about it. 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. Or like all LLMs are blocked on my local work network because the IT department and still figuring out how we roll it out. These things happen all over, and it affects lots and lots of people. If you're just a cashier at Walmart, is AI helping you? It's just not, right?

51:28Will Brown:Yeah, but I'm sure they use services that behind a bunch of layers, there actually is AI going on, right?

51:35Mike Annunziata:I agree, I agree.

51:36Will Brown:So maybe they're not interfacing with LLMs directly, but at some level, there are LLMs running.

51:41Mike Annunziata:Totally, totally. And even if you zoom out to AI just being machine learning, it's like, okay, so the Walmart person and the checkout counter is not seeing a benefit, but like Walmart definitely has a recommendation system on their website.

51:52Will Brown:Yeah, or like the Walmart software is being updated faster than it usually would be. Isn't that, that's AI, right?

51:59Mike Annunziata:You believe that that's happening?

52:01Will Brown:Could be.

52:03Mike Annunziata:You're willing to bet it all on that. 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 that they're still like, let's figure this thing out? I think they're probably running stuff faster than they used to be. You think they're past the pitch deck face? Maybe, hopefully. Yeah.

52:19Alap Shah:We should talk about Rufus. Do you guys hear about Rufus? No, what happened with Rufus? Rufus is going crazy.

52:25Mike Annunziata: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.

52:35Alap Shah:I'm trying to pull up the...

52:37Mike Annunziata:I have some more. 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. right up until the bombs drop. That's the general tenor around AI. Like the vibes are rough. But if we go back to the dot-com bubble and try and understand what's different, there's some interesting stuff that we can learn. 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.

53:18Mike Annunziata:Before the SaaS-pocalypse, there was what you referred to earlier, the retail apocalypse, the most extreme formulation was total physical retail extinction within 10 years. So within 10 years, they predicted by 2009, there would not be a single retail store anywhere in America. This was the prediction. This was the prediction. This was the prediction.

53:39Will Brown:Directionally accurate.

53:40Mike Annunziata:Directionally accurate, for sure, for sure. So shopping malls would become obsolete. All brands would be commoditized by cheap online alternatives. Some of this happened. Amazon Basics is popular. Tmue flooded America. Shopping malls are struggling. But Walmart's a trillion-dollar company. Nike's worth 90 billion. And Rick Caruso has seemed to sort of figure out a way to make malls work in LA at least. There were also a ton of other crazy dot-com proclamations. Revenue doesn't matter. Only eyeballs matter. or all media will permanently be free because file sharing and products like Napster simply cannot be stopped.

54:16Mike Annunziata:And so every piece of media will be free forever. That obviously didn't happen. And 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. And most importantly, this transformation would happen in five years, not 50 years. And so time compression was the biggest forecasting error here. Not every dot-com prediction, like nearly every dot-com prediction had some directionally correct element to it. But various breaks were applied, either voluntarily or involuntarily, and things slowed down.

54:59Mike Annunziata:Media companies sued file-sharing companies, for example. Financial markets pulled back. companies adjusted their strategies and retreated to internet-proof modes. And so protests and political movements also had another role to play as a break. There was this really interesting anti-tech protest in the late 90s, the Battle of Seattle. So over four days, 40 ,000 protesters rallied against the World Trade Organization to 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.

55:42Mike Annunziata: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 comparison to AI, like it's tiny.

56:19Alap Shah:It's generous to call it a data center.

56:21Mike Annunziata:Yeah, it's more of like a data point. Edge computing. Yeah. So it's 25 ,000 square feet. The current like meta large data center campus is 500 ,000 square feet. So 5 % of the size. And so this data center, we don't know who was actually gonna buy the capacity, where it was gonna go, but you can think of it much more like, you know, delivering you Netflix faster than, you know, 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 policy makers, decision makers, leaders for a long time. And I think that that will affect things.

57:10Mike Annunziata:So, you know, 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, this is helpful. And then mitigate the negative externalities before they turn into real problems for average Americans. The energy issue was foreseeable. Like it was predictable. And maybe that's what we need to be forecasting. 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.

57:52Mike Annunziata: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 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.

58:15Alap Shah:We need a moment of silence for international business machines. What happened? falls over 10%, actually 11 % now after Anthropic announces that Claude can streamline COBOL code. Oh, no. There we go. Wild times. The example that I was talking about earlier about Amazon's Rufus.

58:38Mike Annunziata:I thought you were going to say Anthropic announces they're going to launch an international business machine. We are an international business machine.

58:46Alap Shah: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 13 hours, and it was not the first time it happened. I love it. Sometimes the best course of action is to delete and recreate.

59:09Mike Annunziata:Delete everything. Sometimes that's what you got to 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. Is this a real article?

59:30Alap Shah:1887.

59:31Mike Annunziata:1887

59:32Alap Shah:you can actually find it on the New York Times website October 25th 1887 the Russian peasantry appear to be sunk in ignorance and superstition during the recent eclipse of the sun three famous Russian savants descend I'm trying to read this is in the times machine

59:51Mike Annunziata:it's a scan it's not text incredibly hard to read but the peasants

1:00:00Alap Shah:did destroy the balloon.

1:00:02Mike Annunziata:They destroyed it. Somebody asked, would you live next to a date center? Not a data center, but a center for dates.

1:00:09Alap Shah:Dates are underrated.

1:00:11Mike Annunziata:Yeah, dates are good. Healthy. Delicious. Anyway, 11 Labs. Build intelligent real-time conversational agents. Reimagine human technology interaction with 11 Labs. Let's go over to the horse section of the show. There's some big horse news going on.

1:00:26Alap Shah:The moment you've been waiting for.

1:00:28Mike Annunziata:In the Financial Times. A horse walks into a lab. Let's see.

1:00:34Alap Shah:And it says peasants destroying a balloon in 1887 is setting a Waymo on fire in 2025.

1:00:38Mike Annunziata:Yes. But OK, so the interesting thing about the Waymo fires was that we live in L.A. 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, which was directly like a block away from where the main protest was. And we were like, whoa, man, like this seems pretty dangerous from what we're seeing online. And it was fine, right? You just pulled right in?

1:01:08Will Brown:Yeah, I saw someone holding a flag, and that was it. I didn't even see the fires at all.

1:01:13Mike Annunziata:And so a lot of these, 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 a couple hundred 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 uh it can actually be sort of small i remember that video of the data center protest really he runs outside and he's like we did it we did it like it seems huge but i actually think there were only like a couple hundred people there and you comp that to the world trade center organization battle of Seattle, it was 40 ,000 people, they arrested 600 people.

1:01:50Mike Annunziata:That's pretty significant. And so, I guess I'm not saying we're still early for protests, but it is important to understand the scale of what's happening in the real world and the actual impact of that. And you need to be charting this because if they're getting bigger, they need to be addressed more. 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 you could, you could tell that that new Brunswick debate would go way differently. If the hyperscalers were there saying like, Hey, good news.

1:02:29Mike Annunziata:Like we've done so much forward thinking here that your energy prices are going to go down. Like people would be like, Oh, okay, cool. And we're making it beautiful it's going to be building a park and we're building underground we're putting uh grass on the roof exactly yeah there's there's like five easy tricks to like get you know data centers approved all over the country uh but everyone's been putting them in the on the low priority pile but they're certainly going to be more pride uh more important over the next couple yeah why don't you read us 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.

1:03:11Mike Annunziata:The sun is shining in a sky of clear Argentine blue. The jacarandas, this is too noisy, I'm going to put this down. 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 epaulets and shiny blackjack boots has just marched away. Argentina's president Javier Malay, famous for his Elvis sideburns and economic chainsaw, has taken his seat above the center line. Eight players center on to the pitch, tanned arms, taut muscles, hair curling over the collars of their polo shirts.

1:03:49Mike Annunziata:It's the first semi-final 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 Cambasio, the world's number one player for more than two decades and the sports goat the greatest of all time. Cambasio has changed the way Polo works, not only through his skill and tactical genius, but as a result of a bet he made nearly 20 years ago. He bought into the idea of cloning ponies a decade before his rivals. 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.

1:04:32Mike Annunziata:The players line up four against four. The ponies waiting, ears pricked, poised for action. The whistle blows. The game begins. The players streak up the pitch, sticks 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 ponies' necks. If there's a break, they gallop to one end and leap onto a fresh pony before charging back into the fray.

1:05:02Mike Annunziata:You can tell sitting high in the stands, the ponies get the game. They can anticipate what's going to 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. Horses have charged into battle, dragged plows through rocky fields, and carriages through the slop of medieval cities. The real slop problem. Medieval cities.

1:05:33Alap Shah:Humanity versus slop.

1:05:35Mike Annunziata: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. 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 entrepreneur, are going beyond making copies of an original. They are engineering polo ponies to make them even faster in the hope that in the game of high stakes and slim margins, it will give them the edge to win.

1:06:12Mike Annunziata:For one pony, Polo Piresa, the circle of life began on an estancia near the town of coronel suarez a six and a half hour drive southwest of buenos ares she was born on december 12 1988 a sight 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 legend that's elite. Polo remains a macho sport. The top players are male, though there is an open tournament for women too.

1:06:56Mike Annunziata: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. 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 14 years winning the cup for best pony at the open among many other awards and speaking of ponies the purple llamas Vanta automate compliance and security Vanta is the leading AI trust management platform that mayor was a natural polo player one in a million he said the guy said in a video made to celebrate Polo Puerza's induction into the Polo Pony Hall of Fame she had an impressive heart also she was a mare who never got tired Polo Puerza retired in 2004 before she died samples of her DNA were banked in liquid nitrogen in the laboratories of Kirion a biotechnology company co-founded 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.

1:08:23Mike Annunziata:When I first emailed him, he apologized for not responding sooner. He was competing in the World Indoor Archery Championships. Made sense. 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 Puerza's remaining cells. There are photographs of horses on the wall. Each clone, each is a clone. Vichara 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.

1:09:07Mike Annunziata: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 Pueza that I have watched. What do you think? Would you go to a polo match if all the ponies were genetically modified and cloned or do you want a natty league? Natty ponies only.

1:09:36Alap Shah:I'm in favor. You're in favor of cloning? Yeah, I mean why not? Push the sport to the limit.

1:09:43Mike Annunziata: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 uh well they should they should have the the the

1:09:53Alap Shah:sort of like wild league yeah where you can just go find i think this does a wild horse go go play

1:09:58Mike Annunziata:this definitely creates an opportunity for the wild league yeah sure this is kind of the uh free range league what do they call it the uh the enhanced games yeah this is the enhanced games for polo for sure yeah anyway tyler are you a polo acceleration list sorry your business is ai their business is securing it crowd strike secures ai and stops breaches tyler are you a polo purist i i think i'm in favor of the cloning you're a polo accelerationist yeah p-ac as they say yeah well uh good news for you there's lots of cloned ponies coming your way to a polo field near you What did this post say?

1:10:40Mike Annunziata:If you run every day, you'll be ready for any situation that calls for extreme cowardice. Was this deleted?

1:10:48Alap Shah:I think so. I think it's hilarious. Probably the running community.

1:10:52Mike Annunziata:Yeah.

1:10:53Alap Shah:Zach Pogrob came in and said, you're about to be running for me.

1:10:56Mike Annunziata:Yeah. Let me tell you about app love and profitable advertising made easy with Axon.ai. Get access to over 1 billion daily active users and grow your business today.

1:11:05Alap Shah:There is a game called Data Center on Steam, which lets 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 learn a thing or two about edutainment.

1:11:20Mike Annunziata:Edutainment. This is fantastic. Somebody was saying this.

1:11:23Alap Shah:It's not out yet. It's coming out March 31st.

1:11:25Mike Annunziata:Oh, okay. Mark your calendars. Yeah, I'm going to grind this. Productivity is going to fall.

1:11:30Alap Shah:Somebody was saying it could easily be an Ender's Game scenario where it's just...

1:11:37Mike Annunziata:Those racks weren't simulated. Those were real NVL-72s, Ender. Yeah, I love that. This is all a ploy. The humanoids are already deployed. They just need you to wire everything up. This is amazing. And it feels like 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. You're also deciding tax treatment and what software runs and getting probably Kubernetes installed or something like that. Slurm. Very, very fun. I love these one-off games.

1:12:16Alap Shah:Apparently there's another game just called Insider Trading coming to Steam.

1:12:23Mike Annunziata:If you're good at Insider Trading, you're going to love this game. Steam has a game called Insider Trade and 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. But I think it's, I don't know, I'll give it a try. I wonder if it will have microtransactions. That's the big question. or if it's pure for the love of the sport, love of the game. But these roguelike deck builders are fantastic. Bellatro went mega viral a couple years ago.

1:13:04Mike Annunziata: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 insane things and sort of turns it into a completely different game.

1:13:18Alap Shah:Brian says, someone make a TVP an intern simulator.

1:13:21Mike Annunziata:That'd be good. I'm still waiting for show testing. No.

1:13:24Alap Shah:Now that Tyler's promoted and just a real deal employee, which happened after two weeks last year. But that would be kind of fun for all of us to relive the days of intern summer.

1:13:41Mike Annunziata:Yeah, whoever has the highest score gets hired. I do wonder, we've seen this. I heard this story about, everyone talks about the death of AAA games right now. Have you heard about this? So it used to be, you know, GTA 5, Halo 3, 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. Like Fortnite is a game that has endless updates and monetizes forever. 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.

1:14:29Mike Annunziata:Roblox, great example. Versus if you're doing like Bioshock and you make a bunch of money, then you have to do Bioshock 2 if you want more money from those customers. And then you have to do Bioshock 3. And at each point, people are like, well, I didn't actually finish Bioshock 1, so I'm sort of out of the market for Bioshock 2. 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, we've seen what Counter-Strike has done.

1:15:04Mike Annunziata:We've seen what League of Legends has done. We want that for our company. So let's go. Yeah, free to play is the model, but there's something around the word service that's the gaming lingo. but there's been a lot of flops recently. A lot of companies have spent a ton of money on these online service games that they hope will become the next League of Legends or the next Counter-Strike 2, and then they just flop, and they're shut down in a couple 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.

1:15:40Mike Annunziata:Live service games. Thank you, Bobby Cosmic. They're called live service games. Nailed it. 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 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 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 click on it.

1:16:27Mike Annunziata:But like the mechanic actually has to be good too.

1:16:29Alap Shah:A million people will think it's funny. Yes. 10 ,000 will try it. Yes. How many people actually play for more than 10 minutes.

1:16:38Mike Annunziata:And the key to playing for more than 10 minutes is not, like the 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 to be buggy. And you'll be able to generate assets and actually make the thing look like it. 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. Now I'm going to determine his again. Yeah, you never want to launch a product called Peak.

1:17:12Mike Annunziata:Anyway, really quickly, let me tell you about Lambda. Lambda is the super intelligence cloud building AI super computers for training and inference that scale from one GPU to hundreds of thousands.

1:17:21Alap Shah:Signal says, serious question. How do you make someone with absolutely zero gaming experience CEO of a very prominent and important gaming platform? Asha was announced as the new EVP and CEO of Microsoft Gaming after a multi-year run over on the enterprise AI side of the business. A lot of people had opinions on this.

1:17:47Mike Annunziata:Well, I have an opinion. 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, you have no meetings. Microsoft Teams is shut down for you.

1:18:09Alap Shah:Clippy's got it.

1:18:10Mike Annunziata:Yeah, we're handling everything. Your job is to speed run every major Xbox game, Fable, all the Call of Duty series. You're going to play all the Halo games. You're going to get good. you're going to 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 38 years and his and his profile picture is just the Xbox X because he's a legend and Palmer we're lucky, quoted it and put an F in the chat because the world you grow up in no longer exists, apparently.

1:18:58Mike Annunziata:What else is going on here? Okay, so someone asked Asha, what's your favorite game? And SmashJT says, okay, I'll play your game, you rogue. Chrono Trigger, forever goaded, Final Fantasy VII, GoldenEye 007. Chrono Trigger will forever be number one. I never played Chrono Trigger. I did play Final Fantasy VII and GoldenEye. and Asha said, great list, I did my top three in another reply, Halo, Valheim, which I believe is newer and has had much less sticking power in 007. 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.

1:19:40Mike Annunziata:And is the question that Chrono Trigger doesn't have multiple endings? 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. You could play it later, which would be a very young age to play it, but maybe she loves JRPG's and picked 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? He says, Chrono Trigger is my favorite game of all time, and I was only three when it came out.

1:20:20Mike Annunziata:True, yeah, good point. Also, I don't know, Chrono Trigger, like, I played Final Fantasy VII. I don't actually, I think there are multiple endings. Like, I would not remember. I don't know. Anyway, what else is in the timeline? Well, we should tell everyone about the linear lineup for today because we have four guests joining us. We have Alop from Citrini. We have Will Brown from Prime Intellect and Michelle's coming.

1:20:44Alap Shah:And Alop is not at Citrini. He just co-authored the piece.

1:20:48Mike Annunziata:He co-authored the piece. And 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$150. So Linear, of course, is the system for modern software development. 70 % of enterprise workspaces on Linear are using agents, and you should be too.

1:21:11Alap Shah:Hot take? Doesn't matter if it's... Yeah. Nick says hot take doesn't matter if CEO is 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. New Xbox CEO, Asha, does 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 thing I think they maybe could have done better with the announcement is talk about what the rest of the management team looks like, because if you position Shanasha is this like elite operator who's going to like, like really like there's a way like if she's like managing a team of 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.

1:22:06Alap Shah:That's more compelling than, you know, bringing somebody in then that. Yeah.

1:22:11Mike Annunziata:I'm trying to think of other industries where the CEO doesn't use the product. Part of this is like industries where the CEO doesn't use a product.

1:22:24Alap Shah:I mean, like, think about almost every category of enterprise software.

1:22:29Mike Annunziata:I think they all dog food the product.

1:22:32Alap Shah:But not on a personal level. Like, their teams might.

1:22:35Mike Annunziata:Yeah, I guess that's right. Yeah, I was trying to think of, like, are there, 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 uh hollywood agents or power players like maybe they didn't know how to use a film camera but they watched movies i believe like 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 greenlit it because like i knew it was Yeah, one thing's for sure.

1:23:09Alap Shah:Xbox is not in founder mode. Yep. And will never be. Should it be, though?

1:23:17Mike Annunziata:Should it be? I don't know.

1:23:19Alap Shah:Well, the guy who started Xbox, what is the guy that's Seamus Blackley is credited with creating and designing the original Xbox? I think he's now in the chocolate business.

1:23:37Mike Annunziata:Yeah, I mean, I definitely think 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 where you just have studio heads that go and green light projects. It's not all directly related.

1:24:01Alap Shah:Handel says, Larry Ellison using Oracle on a nice Sunday morning. let's go good job that's correct i don't know he probably does store a lot of data in uh in in oracle who knows um yeah aaron says the ceo necessarily is not in the product daily yeah who knows we'll wait to see it's uh it already happened yeah well yeah i mean we'll see yeah we're working on we're we we got in touch with her on friday we're gonna find a time for her to jump on the show.

1:24:31Mike Annunziata:Yeah, PS6 might be delayed because of the memory stuff. There's also, I don't know.

1:24:36Alap Shah:Yeah, they should just delay the next Xbox and let Asha just game for three months, like you said, six months.

1:24:45Mike Annunziata:Yeah, the really interesting thing on the hardware side is a lot of people were freaking out over the weekend playing with chatjimmy.ai from Talos. We had the founder on the show. he has baked Llama 3 8B onto silicon and so it runs at 16 ,000 tokens per second 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 but it's clearly like incredible and this is coming and we've experienced with codex 5.3 cerebris or spark is that what they call it um and there's a few others and rock um and so

1:25:31Alap Shah:and it doesn't have web search yeah yeah so so there's so if you ask it what is tbpn it says when butchers pizza network we gave you a different answer this time wow it's really

1:25:43Mike Annunziata:hallucinating anyway uh that i i think that the uh the the system on a chip cerebris the the the wafer scale, super fast inference is gonna be very amazing for all labs.

1:25:55Alap Shah:Yeah, it really is just a next token predictor. It says, TVPN could also stand for the Black Pine Network. This is not a well-known term or organization, but it could be a fictional or made up name.

1:26:09Mike Annunziata:It's having fun.

1:26:10Alap Shah:It's having fun.

1:26:10Mike Annunziata: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 GTA g-force like 4090 3090 there's a section of the chip that's trained to take a 1080p video game and up res it in real time to 4k and so you can run if your hardware can only run the game at 720p 60 frames a second it will it will up res all of those frames it's not perfect but it gives you a sharper image 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 photo real like make it like a movie that prompt that we've seen happen and you're like wow that's 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 photo real because the actual game engine graphics have totally plateaued and there doesn't really feel like they're just going to jump to cinema quality anytime soon but if you use ai to do the last step i feel like that could

1:27:40Will Brown:really good what do you think tyler uh yeah you could also do um like a genie 3 type model baked

1:27:44Mike Annunziata:down right so like interactive video oh yeah yeah yeah that's like it's really slow and limited right now but if you bake that down you could play that yeah i still think there's a lot of work to be done on genie 3. yeah maybe it's like one or two more models yeah like those like clearly those are like llama 2 level right now but yes yes yes i completely agree um anyway Let me tell you about Restream. One live stream, 30 plus destinations. If you want to multi-stream, go to Restream.com. Dan's Gaming says, My theory is that Phil and Sarah did not want to shove AI into everything at Xbox. They were forced to retire and resign.

1:28:20Mike Annunziata: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'm completely white-pilled on AI and gaming, as I just said. 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 the NPC dialogue that they don't have to sit there and write, okay, this townsperson's going to offer you five coins for your sword. It's like, no, just like be an NPC, be, you know, you have coins, act agentically. And then you go up and you're exchanging with the townsperson your sword for your coins or whatever, and you have like a much more natural interaction.

1:28:57Mike Annunziata:That feels really great. I don't know. There's a million bull cases for AI in gaming, in my opinion. it seems like the hardest it seems like one of the easiest things to sort of justify well we have mr shah so let's tell you about figma ship the best version not the first one with figma including introducing claude code to figma explore more options and push ideas further and without further ado we'll bring in our first guest of the show elah how are you doing what's going on

1:29:25Alap Shah:Oh, great. How are you guys? Doing great. Is this your first time triggering a global sell-off?

1:29:34Michelle Lee:The first time so far, but I'm just the messenger is the way I look at it. We've got a lot of opportunities and a lot of scary things coming down the pipe.

1:29:42Mike Annunziata:Okay. So, yeah, take us through 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? Then maybe we can go into some of the reactions and your reactions to those reactions.

1:29:58Michelle Lee:Absolutely. The process, ultimately, is that I've been building in AI for 15 years, and I've been an investor for 20. 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. Just thinking through, hey, how is this going to—we're early. We're a startup. you know we're going to be at the leading edge of how people are adopting things you know assume the corporate world is a year or two years away uh it's going to 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 i hadn't actually seen the extent of that until i kind of looked at you know specifically what we call like the information sector so different parts of kind of technology those jobs are down eight percent from the peak in 2022 already and so those are the places where people are adopting the most aggressively already.

1:30:50Michelle Lee:And we know, you know, every week there's firings out of like big tech. And so in that world, what happens when the technology that big tech's been using for a while, it's gotten a lot better. And now, you know, your average corporate starts using it as well. It can get quite scary. And so, you know, we wanted to kind of think through the implications of that and, you know, the piece. But how much of those,

1:31:10Alap Shah:how much of those layoffs do you think 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 reprioritizing resources and not actually because they just launched some new agent and suddenly everything's changed. Hey, we don't need these thousand engineers anymore.

1:31:37Michelle Lee:So I think, you know, those are all great corporate euphemisms. And of course, that's how they're going to say it. But I think the way I would think about this is it's not necessarily like agentic powers happened and now everyone's going to get fired. You know, agents and LLMs broadly are just sort of on the tech tree as a continuum from software. 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. 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.

1:32:10Michelle Lee:and we think about the age starting now in 26 as just something that's going to accelerate that.

1:32:16Mike Annunziata:Okay. So, yeah, what else was key in the thesis or maybe potentially overlooked that you think people should be really focusing on?

1:32:28Michelle Lee:I think the problem, A, the first thing, the most important thing is just the labor market dynamics. We've just been in a really weak labor market for a while and that's before these things roll out. But then you put that together with the fact that we just have a very structural environment where what is the thing that drives our entire economy? It's wages. Most of those wages that are ultimately driving all the discretionary spending is coming from the white-collar worker. And the problem with that is that we're now entering this place where you made all these assumptions on loaning money to all these companies, to mortgages, and everything else.

1:33:07Michelle Lee:like where 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 people were making these loans, no one ever consumed in a world in which, wow, okay, 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 4 % or 5 % a year, then we need action a lot more quickly.

1:33:33Mike Annunziata: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, you know, like there's a lot of health in more blue collar sectors, health care is growing. and then you also see dynamics like just, you know, 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 just continuing, continuing, continue 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.

1:34:19Michelle Lee:It's a great question. I think the issue here is that it's all just one labor market. And right now, blue collar is doing better because there are not firings there. I think robots are probably 24 to 36 months behind other forms of LLMs that are just diffusing through society. But the problem is, let's just say that it's one labor market ultimately. And if the white collar jobs are going away, let's say in our scenario, we talk about 5 % of folks might get fired 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 gig economy and the blue collar labor force and so that puts pressure yeah on the higher labor market not just the white-collar one and to answer your other question health care is growing education is growing the reason those things are growing ultimately and 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 that's why health care is growing they're the biggest pair in in health care they're they're guaranteeing all the loans in the uh the education industry 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 uh and primarily payroll taxes because the average worker pays a lot more in taxes you know per dollar than the average corporate does uh 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 uh on saturday john and i were going

1:35:44Alap Shah:back and forth about uh some of the really wild predictions around the impact of the internet that were being made in the 90s there was clicks replace bricks people were predicting total die off did well i mean to be fair i mean to be like i'll just finish they were expecting a total die off of all brick and mortar stores in five to 10 years, which was like, yeah, why 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? Yeah, and I think a couple of a couple others. So like, not as relevant to your piece, but people were predicting like permanent high growth, the end of business cycles.

1:36:29Alap Shah:uh there was the like media disintermediation narrative which was like the the napster era everyone was going to get all media for free forever newspapers would would die off record

1:36:41Michelle Lee:labels aren't you guys the aren't you guys the media disintermediation narrative yeah we are but

1:36:47Alap Shah:yeah it's all about timelines 20 years later and cnbc is still a much much bigger business than than all business media, at least in our world.

1:36:58Michelle Lee:But newspapers, magazines, completely gone, right? All of that has moved to the internet.

1:37:04Mike Annunziata:Totally, totally. It's just like 5 % employment shock in a quarter is way different. 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 different things. So the other thing, the last thing I would say

1:37:21Alap Shah: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 you know this we we believe we will stake our entire reputation on on this sort of uh narrative but what do you think what what how much did you pay attention to like the 90s early 2000s internet predictions? What do you think they got wrong? Why is this time different in terms of how a new technology will diffuse the economy?

1:38:02Michelle Lee:I think the difference is if you just plot what's happening to technology, it's all just going exponential. 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. And so I think most of the predictions that 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. Because like, if you have the ability to just freely, you know, have commerce the way you do today, it doesn't work if you still have to do all the work.

1:38:38Michelle Lee:Ultimately, like you have to go and you have to log and 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, 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 that's why this is the year that I think it really begins, because now it is completely seamless. And no one's really doing this yet, but it's going to happen, I think, in the next six months, is just tell Gemini, tell ChatGPT, go buy these things. It has your credit card.

1:39:07Michelle Lee:And now that world that they were describing is truly going to come to pass.

1:39:10Mike Annunziata: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?

1:39:39Michelle Lee:It's a tricky question. And 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. But certainly I think we called it out. The consulting sectors in India are certainly going to be challenged in other places as well. But the reality is like the 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, chat, chat, chat, chat, and open AI will send you a forward deployed engineer if you have billion dollars in budget.

1:40:16Michelle Lee:Right. If you have a 10 million dollar budget, they're not going to. 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 likely going to 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. And so it's a bit of a tricky timeline there.

1:40:39Mike Annunziata: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 moats do exist? Like do network effects stay? Do complex coordination, intellectual property? Like what doesn't break down?

1:41:13Michelle Lee:Real brand value, where people are choosing you over other things because of the brand and the status signaling across brands matters a ton. Network effects are more powerful than ever, I think, in this world. So things like meta really have a lot to sort of gain in that sense. But I think things that look like they're network effect businesses, but in fact are just the ones that are doing the hard work of aggregating demand and supply, I think will be more challenging. So DoorDash is a good example there. It's not necessarily the biggest risk versus some of the other things. I just was in a thread with Gavin Baker talking about this.

1:41:45Michelle Lee: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. They're both hard jobs, but the demand side is the harder side. And we think the world of the future, there are lots of folks in, let's say, food delivery. Instacart wants to get a bunch of market share. And Grubhub wants to get a bunch of market share. And so let's say the agents are the ones doing the buying. It's 2028 and 40 % of the sales are through agents. You just tell Gemini, hey, order me some noodles. In that world, instead of, it's going to go to each and every provider.

1:42:22Michelle Lee:And right now there are four providers that do that. But now it's very easy as if I'm building a startup in the 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 supply side going. So it makes it much easier for new entrants to come in. And for existing third, fourth tier players can really sort of say, I'm going to relax my margins, try to get more top line. And so you would think that whatever the 15 % VIG is that DoorDash gets today, maybe it's more than that.

1:42:55Michelle Lee:Some of that, I would think Gemini and ChatGPT are going to ask for themselves. Wherever I send the traffic, I'm going to get a piece of that. And then some of that's going to go back to the consumer.

1:43:03Mike Annunziata:hmm yeah it feels like it was this the most like stretched or controversial prediction

1:43:13Michelle Lee: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 through because you know it's an amazing business and they're gaining a bunch of market share yeah but the fundamental idea that you're because what did the lock-in right like the drivers have lock-in on DoorDash or on Uber? Not really, right? Most drivers are doing Lyft and Uber, so they're not locked in. The real lock-in, the real business value, the franchise value of an Uber or a DoorDash is the customer lock-in because the customer gets comfortable.

1:43:43Michelle Lee:They've got everything saved. They want to hit a couple of buttons. 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 are ultimately built on customer lock-in.

1:43:55Mike Annunziata:Yeah.

1:43:55Alap Shah:Yeah. I don't know. I think the interviews that we've had with the lifts i mean you know again take take it with a grain of salt they have a narrative that uh is important to their business 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 just hard pressed to imagine a world in which uh 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 chat gpt or with another agent i say like i want food it's like the agent wants to get the best possible service 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 going to 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 so let's let's consider

1:45:32Michelle Lee:what actually happens here right you make the the order on door dash door dash sends it to the restaurant uh the restaurant essentially you know sometimes they use their own driver sometimes they send the drivers from door dash but now imagine the agent can take you directly to the restaurant site and place the order directly with the restaurant uh and you can keep half the savings and uh the agent can keep half the savings right but where's the driver where's the driver coming

1:46:02Mike Annunziata:because I feel like I understand 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. I understand getting all the restaurants on board because you email them and say, hey, it's 5 % instead of 15 % there. Sure, we'll turn it on. But for the drivers, How do you actually reach out to them and get them on the platform? And how does AI lower that cost? Because right now, I think about what was the driver marketing budget over the last decade at Uber or at DoorDash?

1:46:45Mike Annunziata:And it's probably in the billions of dollars. And so I feel like to generate that 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, I can make more money here. but that feels hard.

1:47:01Michelle Lee:I think it's going to take time, but I think there are a bunch of smaller sort of driver aggregation networks that exist today that are 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. We split it between half of them are our own employee drivers and the other half, you know, I think we have 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. All those businesses now will also just have huge opportunities to kind of take market share. Ultimately, what we're saying is the friction in doing commerce is going way down.

1:47:34Michelle Lee:Places where there are rents, the prices can go down. But ultimately, this is just an opportunity for more entrepreneurs to kind of build businesses for the new world.

1:47:41Mike Annunziata:Yeah, I think it's interesting because we're here debating this somewhat temporary thing because self-driving cars, robotics, changes all of that in a huge way. But we use the term sloppable for companies that can be vibe-coded away 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 shocked to see.

1:48:10Alap Shah:What would you have spent more time on if you knew you were going to get 50 million views and the markets would react in the way that they have?

1:48:22Michelle Lee:I would have finished writing the third piece where I talk about solutions, which I have not gotten to.

1:48:27Mike Annunziata:A lot of people are demanding solutions. 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 XAI through SpaceX are not public. there's sort of like this massive, you know, multiple hundred billion dollar 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. 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.

1:49:05Mike Annunziata:It's a lot of bull case for AI labs. And but 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. But do you think that the world would change when the big labs get out in the public markets?

1:49:22Michelle Lee:I think it's absolutely going to change. I have a strong suspicion that Anthropic is going to go in the next three to six months. They just have so much momentum, and there's a lot of value being first. P &L also just looks a lot better than anyone else. So I would think that gets public, and it's going to be pretty interesting if it happens. Certainly labs are ultimately, they seem like they're very well positioned to win. I would wonder over the medium term, like, you know, what happens with some of the Chinese models and whatnot, if people actually want just something that's more local and something that they own.

1:49:51Michelle Lee:But it does seem like the most likely outcome is going to be that the existing incumbents are going to get the most share. And I think Google is particularly well positioned since they already own all 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. There's also a world in which you end up with just a lot more competition and people trade and change. But the thing that seems very clear to me that the absolute, there's no way they won't be the hugest winners here, is going to be the underlying tech, meaning the semiconductors.

1:50:24Michelle Lee:So everything in semiconductors.

1:50:25Mike Annunziata:You could go even deeper. You could go into commodities and copper and energy and oil and natural gas and stuff. And people have.

1:50:33Alap Shah:Did you see some of the criticism was that the essay was very Marxist? Oh, yeah.

1:51:10Alap Shah:break. It's the most Marxist piece of financial analysis. Not my word. I don't think you were expecting that critique. It makes the same errors Marx did. Creative destruction doesn't just destroy. It creates industries we can't yet conceive of.

1:51:27Mike Annunziata:Maybe that's going in the solutions.

1:51:28Michelle Lee:Is that going in the solutions? Let me address it a few ways. Marx was a really smart dude. He got a lot of things right very early. Marxist can mean communist. Marxist can also mean just understanding how capital and labor interact. And in that sense, yes, it was Marxist. He was very insightful. But I think the thing that we're missing here is that there's the economic layer, but ultimately it's the political layer that matters. And we're in a world where we've had two parties, and both parties economically have a little bit of difference, but not a huge amount of difference. And so we kind of bicker.

1:52:05Michelle Lee:But in a world in which jobs are going away really fast, I think there's going to be a much stronger alignment for just the laboring class overall to say, hey, we need to fix this problem. It's a very fixable problem. What we're actually expounding here is that GDP, if done properly, will absolutely explode. We're getting way more efficient. We've built a machine dot. We build machine intelligence. But we have to structure our society such that as those things happen, hopefully very slowly, we do the right thing from a taxation perspective to say the winners should win. But if that's what's causing the displacement, let's sort of make the pie a little bit bigger for everyone.

1:52:44Michelle Lee:And that, I think, ultimately should be something that appeals to a lot of folks in the AI complex. Because if we don't, then something like this is likely to happen. And AI progress will slow down because we'll have an economic crisis and we're not going to be able to finance nearly as much of it as we otherwise would.

1:52:59Alap Shah:So do you think the future is what Anthropik's head of sales position in France, the company will be spending 530 ,000 euros per year. The government will get 340 ,000 euros and the employee will get 190 ,000. Is that the level of taxation do you think we're headed for?

1:53:20Michelle Lee:I think when we're at France's level of government spending, then the math probably means roughly that. I would say that government spending would be at France's level, I'm guessing, like you know five seven years from now if this if this scenario kind of comes to pass uh 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 so the bet here is that the pie if done properly can inc just increase multiples of what it is today and and thus you know there's it's just a win-win one question i mean it sounds like

1:53:55Mike Annunziata: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 potentially negative scenarios. We have the white collar work number. 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 save us, or unemployment insurance, or UBI, like all of those like solutions that I think people, it's funny because people are quoting your post being like, this is easily solved with this solution.

1:54:43Mike Annunziata:It's like, okay, well, that's great if we all agree. And I think you might with some of the quotes, people are all over the place. But I'm wondering about your reflection on like the the like the messaging from the labs around solutions versus pure focus on problems

1:54:58Michelle Lee: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 kind of sounded the alarm bells uh people aren't really he's like saying people are not listening obviously a lot has changed 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 going to 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.

1:55:31Michelle Lee:And so I think it's other folks like ourselves that kind of have that duty to go and really start thinking that through. It seems like Anthropic is pretty engaged, you know, should that conversation really start happening. And I think this is the year it needs to really start happening. and so I think they all kind of get it and so it's just a question of like how do we as a society start moving in that direction

1:55:50Mike Annunziata:yeah I think you know obviously 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 like how useful this process of writing an article for a particular audience is like I disagreed with a lot of you know something big is happening but it hit with a very different audience than machines of loving grace or the adolescence of AI or a 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 not amazing results but maybe it's maybe it's worthwhile because we we will get really great solutions and a better conversation around it.

1:56:46Mike Annunziata:So I think in due time, this discussion needed to be had. So thank you.

1:56:55Alap Shah:What's an industry or job of the future that you could see emerging?

1:57:02Michelle Lee:I think again, if we solve this, everything related to leisure is going to absolutely Zoom, and those are going to be the biggest growth industries of the future. right like what do humans want to do total shesky victory watch polo

1:57:16Mike Annunziata:yes exactly cloned horse play polo for sure yeah so you know imagine humans have like the entire

1:57:23Michelle Lee:day to just enjoy themselves uh instead of having to work now that is something i've been promised

1:57:28Mike Annunziata:for a hundred years so i'm deeply skeptical but this time is different i want to be different Let's bring on the leisure. Boom. I'm here for it. I'm here.

1:57:38Alap Shah:On anything in your solutions, Doc, around reindustrialization, I mean, the frustration that so many people in tech that have that have been building in in kind of hardware in the real world or trying to recruit people that that are getting offers from social media companies or now labs or SaaS companies. You know, one of the problems for America in the last 20 years was that if you just wanted to make$100 million, you probably were much more likely to do that building enterprise software than building critical infrastructure or anything in the real world. So is that is kind of new new infrastructure and reindustrialization like a potential landing point for people that had the 180 K a year PM job that might be going away?

1:58:32Michelle Lee:It's a great question. I think there's certainly going to be a lot more opportunity in those sectors. And I think 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 reindustrialization side, just like it does for writing code. And so that's where I think it will get trickier. I think as a country, we're going to spend an awful lot more on that. And I think we're going to catch up. but it's not clear that's going to be through just creating a bunch of additional jobs versus the ultimate thing we're seeing with AI period is just high agency people who really know how to reuse the tools can just do the work of many, many people.

1:59:13Michelle Lee:And I think that trend applies in every industry to some extent.

1:59:17Mike Annunziata:Yeah. What an exciting time. Thank you so much for taking the time. When's the next piece dropping?

1:59:24Michelle Lee:Hopefully by the end of the week, but don't hold me to that.

1:59:27Alap Shah:Now when you know that it could be hard for the follow-up to get as much reach as this one, that's kind of the way these things go. But now the pressure's on to really pay attention to every single word. Don't have any SQL anxiety.

1:59:41Mike Annunziata:You'll be fine. We're excited to read it. And we'll talk to you soon.

1:59:44Alap Shah:Yeah, great to meet you. Have a great rest of your day.

1:59:46Mike Annunziata:Thanks so much. Let me tell you about Turbo Puffer, serverless vector and full-text search, built from first principles on object storage, fast, 10x cheaper, and extremely scalable. And I'm also going to tell you about Gusto, the unified platform for payroll benefits and HR built to evolve with small and medium-sized businesses. And without further ado, we have Will Brown from Prime Intellect in the TVP. How's it going? Welcome to the show, Will. How are you doing? It's been too long. I'm doing great.

2:00:14Will Brown:I'm doing great. It's great to be back. I think this is the fourth. Something like that. I looked at, there was a list at some point of the record and some people are like, have been on 25.

2:00:23Mike Annunziata:Some people have been on something like 18. I'm looking forward to the 400th. It's great. It's been a lot of fun.

2:00:30Alap Shah:What are your old buddies at Morgan Stanley thinking about the current thing in tech, the 2028 intelligence crisis? Have you gotten any messages? That's a great question. I have not had the full deep tech. They're too busy hitting the sell button.

2:00:47Mike Annunziata:No, everyone at Morgan Stanley is too busy setting up Mac minis to run Open Claw. That's what's happening. Because they all just read something big is happening. Right.

2:00:57Will Brown:And so like, I think there's definitely a lot of opinions on all sides. And I feel like that, to me, the piece was pretty cool. 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 conversation ended up getting like into the weeds of monetary policy and like how people are going to react and like how hard is it to put a DoorDash clone and like these sorts of things I think are actually like the sorts of conversations that are good for more people to be having like whether or not a certain prediction is right I think 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.

2:01:47Mike Annunziata:Yeah. Well, give us an update from Prime Intellect. What's going on in your world?

2:01:52Will Brown:Yeah, yeah. So there's a few things that I think are interesting as well as like I want to talk about just today given some other stuff that's happening on the timeline. But so a couple weeks ago, we 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 it an agent native experience where you're kind of like, there was a lot of people have been kind of tweeting out their experiences that the term people have been using is vibe rl which is uh 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 And now we're like, this is pretty accessible for people to kind of go train models.

2:02:42Will Brown:And it's been pretty cool kind of seeing people have fun with it.

2:02:47Mike Annunziata:Yeah, concretize some of the actual applications. I imagine this works best if everything flows through text, flows through CLI tools. Because when I think like, okay, great, I'm going to 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. I'm just thinking about like editing a short video that we have, right?

2:03:12Will Brown:Of course, yes. Some of that stuff definitely, like there's definitely a range of like simple to complicated. But I think there is a lot of sweet spots where it's like the, it's coding tool use and interacting with kind of app simulators, 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 the Ramp guys from Ramp Labs showing off how they've been using it for some stuff. It was 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 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 backend, but it's enough to be able to capture the task.

2:03:57Will Brown:Yeah. And the coding agents are good enough that with human in the loop using, like it's all CLI data. So you go into your terminal, we have our CLI, the prime CLI, and you use that to kind of like set up your skills and get your coding agent configured and your agents MD. And so we've tried to like make that really smooth, but then you just are kind of like talking to your agent about, hey, my data is here, my app codes over here, let's put it all in the right place and kick off some runs. Yeah.

2:04:24Mike Annunziata:Where are we on the path to personalized RL? I'm thinking back to the RL that went into RLHF around chat GPT, GPT-4. And I remember it was like they had maybe tens of thousands of contractors grading responses, giving thumbs up, thumbs down, giving feedback, varying levels, a really large scale generalized process. 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 rling and it's getting better and all of a sudden it can just do what i do with just like

2:05:12Will Brown:one prompt yeah it's pretty close uh it's definitely it's that's not it's not that sci-fi Like if you have stuff, especially if we focus on like text or image input, full screen recording gets a little tricky. 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 trickiest part is refining criteria about like what counts as good for like rescoring another tribe. but a lot of times the criteria are either pretty general across tasks, or you can infer a lot of them from a user's response, or just from the initial prompt.

2:05:51Will Brown: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. But for things where it's like there's a right answer, and it's not too hard to see if the model got the right answer from doing an agent trace, either from the human's response or let's say that if companies want to 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 kind of like it doesn't always work but it works reliably enough that it i think the barrier to entry and the cost are just like at a point where it it's cool to see that this is now a thing people can go do yeah and we're seeing a lot of people like have success with it yeah how

2:06:32Mike Annunziata:are you thinking about the debate between mcp and uh cli uh peter was going back and forth and it 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 remember going to the front end and inspect element and see what's coming across. And, oh, there's an HTML request right there. Let's reverse engineer that.

2:06:58Will Brown:Yeah, it's all kind of the same thing. It's sending requests. And so I think people realize models were good enough at coding that the skills are essentially 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, like, auth stuff going on, where there's, like, kind of credentials, and you want to be able to kind of notice that it's being done and have the user, like, approve certain requests or not approve others. That's where, like, the formalism of, like, the tool call is really useful as opposed to it just being code that has an API token.

2:07:30Mike Annunziata:Yeah.

2:07:31Will Brown:But from the production of capabilities, skills are nice. MCP has its areas where it makes sense. But it's really just models using computers, whether it's MCP or code or skill files and reading docs. It's like models are pretty good at reading stuff. And if it has instructions on how to do a thing, it can kind of just do the thing. And they can do that for a while enough that it's useful. Yeah.

2:07:56Mike Annunziata:How intermediated do you think this product will be? And what I mean is that, like, let's just use some toy example, like, you know, 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 generated RL, or 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. You just give us the data and we'll act as your customer. How do you think that plays out in the market?

2:08:38Will Brown: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 like AI native and the ones who are going to work with kind of, because I think we're really like building for developers as our kind of target audience, 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 the agent frameworks, that's really like our target audience. People who like think about evals and prompting versus people who like don't think about that.

2:09:07Will Brown: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. But you can see a lot of these companies want to like know that their environments are good. And so like using RL as part of this process is the way that you evaluate the quality and be able to prove, hey, we've got the good stuff because it actually improves capabilities. And so there is this whole economy of companies that really specialize on building environments and working with data.

2:09:41Will Brown:And I imagine this does become a big part of the way that this stuff is consumed by end companies is through people with that kind of expertise at the data level.

2:09:50Alap Shah:Talk about what the Chinese labs are up to. I was going to ask the exact same thing. in terms of distilling American models. Talk about kind of the scale. I've seen some rave reviews.

2:10:03Mike Annunziata:I've genuinely 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's real and what are you experiencing?

2:10:17Will Brown:Yeah, so I think they're definitely a couple months behind. Like they're not at the 4.6 or the Kodak's 5.3 level. But they're pretty close to what we had before that. 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 get much excited is like they're good enough that going the extra mile with customization is a different caterer 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. And so I think that is even if you have to do this every three months, like there's it's always a capabilities race.

2:10:49Will Brown:But I think if this pipeline, if this process of like 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 the open source models generally, I think there was some interesting debate on the timeline today that I dove into for a little bit around Anthropic and DeepSeq and doing distillation. And I think it feels like 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, like that's not allowed.

2:11:27Will Brown:And there's also like the idea of like distillation more broadly of like, is it and the two I totally get the first two, but I think the 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 going to review the PR, then they're going to merge it. And this is like perfect training data and so the internet is just getting flooded with perfect claw distillation training data interesting yeah and and there's not much you can do about that and so it's like is distillation really the hill we want to die on um okay yeah um i i i guess the the secondary

2:12:02Mike Annunziata:question is like uh 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 it's just a less effective 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 through 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 right so it depends

2:12:40Will Brown:a lot on your uh application so they think there's certain things for like the models are already like more than good enough and these are like kind of more commodity like extraction or summarization or labeling use cases where like you kind of just want to optimize for cost in some cases you want to 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-wide 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 and you can have a lot more fine-grained control of like saying hey this is my eval.

2:13:13Will Brown:This is how I'm measuring performance. We are just going to hill climb this. And then it's up to you as a 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 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.

2:13:46Will Brown:I think that's kind of what we see currently as the most dominant paradigm for mix and matching between the proprietary models and the fine-tuned open models.

2:13:57Mike Annunziata:If you had told someone a year ago that there were going to be probably millions of people running agents locally with custom setups and.md files for various skills, they'd probably be like wow that's pretty aggressive uh do you think that we'll be in a world in like a year or two where uh at least you know people on x will be talking about like my fine tune i got the i i did but i did rl on my pacific problem my personalized agent is like even better now because i did the rl

2:14:33Will Brown: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 you can these you can get these models to beat any of the closed source models on sufficiently well scoped tasks yeah pretty quickly um it's not rocket science you can you can basically vibe code it um 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

2:14:59Mike Annunziata:sort of prompting that everyone's doing for coding yeah then yeah you can just kind of plug it in and

2:15:03Will Brown:to get trained to work. But I think 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. 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 a year ago we saw like Cloud Code's about a year old. It came out I think February last year. And at the time it was like, it wasn't actually that useful yet. But I remember playing with it and feeling like, oh, this isn't actually something I want to use that heavily today because it's kind of slop.

2:15:36Will Brown:It's very chaotic. It just makes a mess. And 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. 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, open claw kind of works, but there's also a lot of trouble it gets into. Same with like, if you saw like the Gastown thing or these like crazy multi-agent systems where it's like, they aren't actually excellent yet for shipping quality production code. But the thing we had a year ago now is the level where like cloud code is used for like most production code, but by the heavy adapters or codecs.

2:16:15Will Brown:And so like it feels like it is a matter of time until these things stabilize. And like the goals of having that system kind of end back up in the models and people training for it, but like the recipes of how to train these models uh 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 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 yeah uh what

2:16:49Alap Shah:are you are you expecting real tangible breakthroughs in the next uh in the first half of this year i I mean, our intern keeps saying that he's close to cracking continual learning. Oh, yeah, continual learning is going to fall pretty quickly, I think.

2:17:04Will Brown:Do you think our internal? It'll be less of a big thing than... No. I mean, I think it's more of an engineering problem. Okay. I think it's like... Explain. No one's actually trying.

2:17:11Mike Annunziata:No one's actually trying. Why not?

2:17:12Will Brown:Like, no one... Like, OpenAI and Anthropic don't want to continuously train their models for each user. Like, it's expensive and annoying and hard to serve at scale. But, like, from a research perspective, like, we do continual learning where the model learns new they just could keep training the model more and it knows more stuff because they put more

2:17:28Mike Annunziata:internet in it sure and uh yeah like yeah yeah yeah yeah uneconomical right now but uh but yeah

2:17:36Alap Shah:that's very interesting product like frontier i could imagine that that would be a selling point if you're mckinsey and you're going to a big right yeah institution so so yeah if you if you

2:17:47Mike Annunziata:hypothetically like i don't know 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, it makes a ton of sense.

2:18:01Will Brown:Yeah, and there's enough kind of tricks. I think there's a lot of experimentation around like exactly the recipe that's going to be the most reliable. But we kind of have a grab bag of like six or seven tricks that kind of work or they work in different ways and you can mix and match them. And it's just going to be like, whatever's the best combination of these tricks, people are going to 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

2:18:26Alap Shah:being practical that's cool uh what are you tracking on the silicon side we were playing around with chat jimmy.ai oh yeah that was sick crazy right jimmy's quick yeah but it's like

2:18:39Mike Annunziata:too fast you have to like scroll up once you get the answer yeah i was trying to see how many tokens

2:18:46Will Brown: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 but like llama just won't do that no matter how you prompt it it'll always stop after like a few thousand tokens um so you can't actually get to feel it like blitzing

2:19:00Mike Annunziata:past whoa interesting yeah yeah yeah yeah it was like sort of a throwback experiencing llama 3 8b because i remember when that model came out and there was a lot of hype because open source developers just love open source stuff and it was exciting it was cool it was like wow they really did train a big model and they just put it out there uh and i remember some people being like yeah like if you actually go talk to it like it hallucinates a fair amount like i don't know that this is like actually at the frontier might have done okay on some benchmarks but it's not quite there uh and it was a little bit of a throwback but you can just imagine baking any of the current frontier back there giving it access to tools giving it a reasoning loop like yeah it's going to be even if it's only 10 times as fast like that's still so much faster than like okay gotta close the app and come back after 20 minutes because my thing is running now it's going to be completely different and i think it'll be a big like step change for like people that are like oh yeah ai like hallucinates and like i need to check that out it'll be like no like it's like totally you can just have it right there and 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

2:20:09Will Brown:an ai i want the one that goes out of your bed and folds your clothes oh okay have you seen that It looks like a Pixar thing.

2:20:15Mike Annunziata: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.

2:20:25Alap Shah:I don't care if there's a one in 10 ,000 chance that it goes crazy.

2:20:30Mike Annunziata:I don't care if there's like a one in 10 chance of me just being dismembered in the middle of my night because it gets mad at me. I was trying to prompt injected or something. No, I am excited for hardware. It feels like even the first gen hardware, like the Humane AI pin, the Rabbit R1, all that stuff with like frontier models starts to get interesting. 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.

2:21:05Will Brown:Do you want local on device for something? Yeah. Yeah, you can, I think, especially for like these narrow things, like if the R, the rabbit, whatever, and this is also Apple's strategy, it seems like, because Apple's like, they like keeping stuff on device. 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 there rolling out of features, but it means that like the stuff like summarization and the image search, like they can do this locally now because the hardware is good enough and the models are good enough at that scale.

2:21:39Mike Annunziata:Yeah, yeah. You have to imagine that the same talus 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 of years and then you need another couple of 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 i'm excited yeah future is bright i think the yeah definitely exciting i think the

2:22:13Will Brown:people always said the internet was gonna like run on the 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 oh interesting so you can kind of like you kind of get this flywheel of like there's just more data to learn from and it's all getting better as the models get better and you do more on top of that 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 well thank

2:22:41Mike Annunziata:you for coming on the show and producing a bunch more data that's helpful it goes on to youtube

2:22:45Alap Shah:it's an honor to produce data with you it's it's an honor to join the training set with you

2:22:50Mike Annunziata:yeah that's the goal that's the goal and thank you to everyone in the chat who's also providing data for the internet it's god's work thanks for having us we'll talk to you soon have a good fun 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 label box is the data factory behind the world's leading ai teams and i believe we have our next guest already in the Restream waiting room.

2:23:26Mike Annunziata:Five minutes ahead of schedule. Michelle Lee from Medra is in. What's going on? The TVP at Elgin. Welcome to the show. Hey, guys. Excited to be here. Thank you so much. Great to have you and your robots. Punctuality. Oh, yes. What is behind you? Wow. There's a robot that's actually working. Explain. Introduce yourself, please.

2:23:47Will Brown:Absolutely. So I'm Michelle. I'm the founder and CEO of Medra. and a little bit about me. I studied chemical engineering in undergrad. I was a typical chemistry, life science nerd. And then I did an internship at SpaceX and was just really excited about, like, what if we can build in the physical world? I wanted to build in the physical world. I want to build real things with real impact. I ended up doing my PhD at Stanford, at the Stanford AI lab in robotics, building robotics foundation models. I worked with Jeanette Bogue and also with Fefe Li. Shout out World Labs. And I ended up, when I finished my PhD, decided I wanted to combine life sciences, robotics, AI.

2:24:35Will Brown:And I started Medra. And we are building physical AI scientists, which we think that is necessary to eradicate disease.

2:24:44Mike Annunziata:How narrow do you want to go to start? I mean, 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. You can have monkeys in there. There's a million things that you could do. But I feel like you probably want to pick a beachhead, but you tell me what the strategy is.

2:25:08Will Brown:Look, like one day we will have Medra robots doing animal studies, like mark my words, right? But you're right. We have to start somewhere. And we're starting with early discovery and development. We have physical AI robots at Medra that can do experiments at scale. We can work with instruments that humans already can use. And most importantly, we truly have intelligent robotics. This is not just lab automation where you program things and they do it exactly like 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 an AI scientist that can actually reason about the science itself.

2:25:52Mike Annunziata: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 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. 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. But where does the variability come in?

2:26:26Will Brown:Definitely. I think like if you think about the best scientists, right? The best scientists are the ones who are reading all the papers. They have all the scientific knowledge, but they're also the ones going into lab and running the experiments. They can sense what's happening. They can smell it. They can visualize what's going on. And they can make changes as they see things start happening inside the experiments. That's what we're trying to capture, right? The ability to be really flexible, to actually reason about the science 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've run.

2:27:08Will Brown:That's actually what enables good science.

2:27:12Mike Annunziata: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?

2:27:31Will Brown:Yeah, we are building the infrastructure layer. We want to be the TSMC for drug discovery. So we are partnering closely with pharma companies, biotech, such as Genentech, where 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 labs in the united states uh in 2026.

2:28:00Mike Annunziata:talk to us about the fundraising i think we missed you on the day you announced your series a but i still want to ring the gong

2:28:06Will Brown:what happened bring it in how much did you raise yeah we raised uh 52 million dollars for a series

2:28:13Alap Shah:thank you amazing who did you raise it from yeah uh human capital uh lead uh they came in and

2:28:23Will Brown:pre-seed and seed and they tripled down on us for series a really preempted the race uh we also have lux who is also a repeat investor um also mental ventures catalio uh great investors uh joining in for a very ambitious mission and very ambitious journey of eradicating disease and is 52 million

2:28:45Mike Annunziata:series a that's feels like a lot of money congratulations but is is 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

2:28:59Will Brown:well actually the hardware that we use at medra is all off the shelf um 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 uh be able to use our uh what we have trained ourselves, which is the vision language lab action model to be able to autonomously run experiments. 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 for life sciences, to be like a more core search, but for biological and life science and chemical chemistry data so that our partners can train foundation

2:29:48Mike Annunziata:models in biology 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

2:29:58Will Brown:correct 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 on three orders of magnitude less data than like you know even like oh one yeah yeah yeah i think google launched one

2:30:14Mike Annunziata:that 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. Jordi, anything else?

2:30:24Alap Shah:No, this is super exciting. Congratulations. And in the future.

2:30:28Mike Annunziata:And I'm sure we'll have you back on the show soon. Have a great rest of your day.

2:30:31Alap Shah:Great to meet you.

2:30:31Mike Annunziata: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.

2:30:43Alap Shah:and we got a little bit of time i think we do that's great next one well then i'll tell people

2:30:48Mike Annunziata:about the new york stock exchange want to change the world raise capital at the new york stock

2:30:54Alap Shah:exchange and let's pull up this post from gucci gucci what did gucci not the kind of account we pull up every day they say primavera february 27 2 p.m cet and this picture is created with ai They hit this, they dropped this on main and the photo looks -

2:31:17Mike Annunziata:I've not seen any hallucinations. It looks, it looks complete.

2:31:20Alap Shah:Quite tiled. It looks like it could have been out of a catalog, any catalog over the last 20 years. I'm sure it's really peaceful over there at the Gucci offices now. Yeah, I'm sure, I'm sure this wasn't controversial at all.

2:31:32Mike Annunziata: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 Svedka ad 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 and it takes place in like Greek mythology that's like notoriously terrible there's one with the rock in Scorpion King where he comes out and like it's very clear they just didn't give the 3D artist long enough to make it look good and so it just like looks really awkward like just 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 uh you got to know when when to actually go in dip your toe in there's been another turn of uh 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 Netflix takes over, or that Paramount takes over Warner Brothers.

2:32:44Mike Annunziata:Netflix is at 36%, and none is at 10%.

2:32:47Alap Shah:And I believe the final offers need to be submitted by tonight or tomorrow night. I forget exactly, but Paramount's revised offer for Warner Brothers will likely come in at$32 per share. Let's pull up this video. From Ted Sarandos? Ted Sarandos having a chat.

2:33:08Mike Annunziata: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 150 ,000 organizations use it to keep their apps working. And let's go over to Deadline.

2:33:21Will Brown:I like how you asked that question. We've been working hard on this transaction to acquire Warner Brothers and HBO and we're deep in that deal every day. Will he prevail? Yes, absolutely. Absolutely. There's no reason for it not to. Right now our deal is the best deal. It was determined by the Warner Brothers board. It was reiterated to suggest that to their shareholders we're going to vote on March 20th. And that there's no rational reason to block the deal. We're 9 % market share growing to 10. So there's really no concentration risk in our deal. And what's exciting I think is we'll be able to have this 100 year legacy of great storytelling.

2:34:02Will Brown:He's kind of talking about Trump?

2:34:11Mike Annunziata:The Trump language kind of 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 got to do it every once in a while.

2:34:27Will Brown:It's going to look like that next year and the year after and the year after and the year after.

2:34:31Mike Annunziata:acquisition that the world has ever seen.

2:34:33Michelle Lee:Traditional 45 day windows, theatrical exclusivity.

2:34:38Mike Annunziata:You should be like, didn't you read the Cetrini piece? Everything's going to zero. Does it matter? Does it matter if two companies that are zero combined? No, just let it happen.

2:34:49Will Brown:So we're excited to be in there. We want to help them win. He's lapel maxing. Pam and Mike, who run Warner Brothers, they've opened nine number one films in a row. That's amazing. That's the kind of track record we're excited about.

2:34:59Alap Shah:All right, we can pause it. I love it. Over the weekend, there was some new reporting from Bloomberg. The Justice Department's investigation of Netflix's proposed takeover. Warner Brothers includes a scrutiny of whether the streaming giant's behavior wields anti-competitive leverage over creators. He talked about it going from 9 % to 10 % market share in streaming. But the issue is it's taking buyers from a handful of buyers down to one. There's more real.

2:35:25Mike Annunziata:And I hadn't considered that.

2:35:26Alap Shah:Well, and that's what we were talking about with Ashley Vance. Hey, you sell documentaries. You excited to have -

2:35:33Mike Annunziata:One less buyer.

2:35:34Alap Shah:Literally no one to play offers against each other, just kind of, here's the price, take it or leave it.

2:35:40Mike Annunziata: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 million. He's like, he didn't say that. I was just texting with him. What do you wanna say, Tyler?

2:35:51Will Brown: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.

2:35:58Mike Annunziata:Sure, sure. Yeah, that makes sense. Yeah. I mean, honestly, like a Sora deal wouldn't be out of the question for Warner Brothers. I wonder if 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. Like, we're not going to 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 effectively.

2:36:46Alap Shah:We don't want anyone to out-slop us.

2:36:49Mike Annunziata:Yes. Let me tell you about Railway. Railway is the all-in-one intelligent cloud provider. or use your favorite agent to deploy web apps, servers, databases, and more, while Railway automatically takes care of scaling, monitoring, and security. Sorry, I'll cut you off.

2:37:02Alap Shah:I think it's going to be a big moment when Disney IP.

2:37:05Mike Annunziata:Oh, yes. But also when this concludes. I mean, it's going to be, it is neck and neck, as we've seen in the Kalshi chart. And then also, there's a lot at stake, like the breakup fees and the billions, I think. It's a political story. There's so many different things going on.

2:37:23Alap Shah:Well, anyway, I think we have our next guest.

2:37:26Mike Annunziata: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 true. Oh Mike Good to meet you. How you doing? Well, he's sitting down. Let me tell you about vibe.co We're DTC brands B2B startups and AI companies advertise on streaming TV pick channels target audiences and measure sales just like what's going on How you doing? What's up gentlemen? What's up? Please introduce yourself. First time on the show.

2:37:58Will Brown:Sure. First time on the show. Happy to be here. Long time watching. Nice hat. Thank you. We were actually talking earlier. How long have you been watching the show? I was like, I can't remember the hotel room, but I remember the printed tweets. Oh yeah. The printed tweets for that long.

2:38:12Mike Annunziata: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. And then we print some other stuff. We got to bring back the printed tweet for the best tweet of the day. But honestly, the printer was a major rate block, rate limiter for us. Because we would be like, we're going live. And we used to just start the show around 11. We'd be like, we're 30 minutes late, because we would just do RSS. Now that we're live at 11, it's like, the printer's got to work. And we would print hundreds of pages. Yeah. Because we'd be printing whole articles.

2:38:44Mike Annunziata:Anyway, sorry, I interrupted your introduction.

2:38:47Will Brown:No worries. So Mike Enunciata, founder and managing partner at Also Capital,

2:38:52Mike Annunziata:and we're an early stage hard tech fund, investing in inception, pre-seed.

2:38:57Will Brown:How did you get into VC? How did I get into VC? Had a bit of an interesting path, a little bit nontraditional. Stanford? Stanford. Did you actually go to Stanford? No. Okay. I started at Harker, and then now it's good. There you go. No, no, no. So I've been doing venture for a little bit more than a decade, but actually... Overnight success. Overnight success. So a little bit more than a decade. Went to Cornell for undergrad. Still Ivy League.

2:39:26Mike Annunziata:Still Ivy League. You got it. We got it. You got it. You got it. Exactly. Have you ever heard of it, though? Yeah, I have. Yeah, I have.

2:39:33Will Brown:Yeah, Cornell undergrad. Did the family office thing for a few years. Actually worked at the Cornell Endowment, so I've been an LP. So I've been on that side of the table. And then business school and then started a hard tech company back in 2016. Okay, okay.

2:39:47Mike Annunziata:So founder, yeah.

2:39:48Will Brown:Founder, yeah. Same year, Andrel started. Cool. We're worth a fraction less than$6 billion right now. But, you know. What were you doing? We were doing food technology development. Okay. Oh, cool. Around the same time you're doing Soylent, John. Yeah, that's fun. So I'm sure we were doing food manufacturing technology. Very cool. Kind of from scratch, me and a co-founder in a lab through Series B. Company is still going, built out of a big facility. So I've been doing hard tech manufacturing for quite a bit.

2:40:13Alap Shah:Give us the history of also, then.

2:40:15Will Brown:History of also. So, you know, it's funny, he also started as Will Brewey, Mike Enziata, and Colin Smith's Backyard Angel Investing Adventure in 2019. He was on the show on Friday. Yeah, he was on the show on Friday. I saw it. So I did dorm room fund when I was in business school. So I've been a DRF partner for a number of years, non-alum. Started writing angel checks in 2019, kind of scaled up through SPVs, and then raised our first fund in 2023. So that was a$22 million fund. And through that journey, we wrote the first check into Radiant Nuclear. Varda followed shortly after that. And I've been on the board of Varda since inception.

2:40:51Will Brown:Yeah, we did K2 at Seed as well. AnySignal and Software Time Radios. We raised our first fund in 23 that I mentioned. And then wrote the first check into Northwood. So it's been a bunch of...

2:41:01Mike Annunziata:Was that post-ZERP ending the crash?

2:41:05Will Brown:Yeah, so it was post-ZERP. Kind of hard time to raise a fund one as a new manager.

2:41:10Mike Annunziata:But you're not saying I'm just going to go into crypto and like the frothy stuff. You're in the stuff that's on the next boom.

2:41:18Will Brown:Well, I think we should talk a lot more about this. But I think 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 of that. It grew from that.

2:41:31Alap Shah:How many checks had you written into Gundo companies or Gundo adjacent companies before John went and put it on the map with that video? Yeah, that's a small part in that.

2:41:42Will Brown:Yeah, I don't know, we did like six or seven before the Gundo bus and your thing.

2:41:46Mike Annunziata:So the pre-bus and bus bus. Yeah, the bus is really the defining line.

2:41:51Will Brown:Exactly, you know, BC, pre-bus. Yes, I mean, we did a bunch of it, VARTA, Radiant, K2, AnySignal, that kind of crew of folks, and then since then did Northwood, first check there. You guys had Mesh on the show, we did that one recently as well. No way, basically every one.

2:42:10Alap Shah:Every company. Yeah.

2:42:11Will Brown: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.

2:42:24Alap Shah:What is your non-technical, but so what is your process for underwriting some of these companies where at Pre-Seed they can often actually seem like a science project And that's like the, I probably made that mistake once or twice across 60-some bets where I invest, you know, I'm not 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 opportunities.

2:43:01Will Brown:Yeah, look, I think the unique lens that I kind of bring to this fact that I ran a company doing hard tech stuff for seven years You know engineering built the whole team kind of 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 people that have done these kinds of things before and if you look by example You know the Varda team a lot of those guys are doing dragon at SpaceX, 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.

2:43:32Will Brown:If you look at any signal, they're doing radios. A lot of the Northwood team, you know, doing ground stations, right? 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 you can kind of see that once you've lived it for seven years. 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. I think we need that. Yeah, exactly.

2:43:56Alap Shah:I'm really excited about it. Yeah. The broader. Yeah, exactly. Silicon Valley community coming in and marketing up five times, ten times. Jordy, I got an on-cap note for you if you're really excited about one of these things.

2:44:07Mike Annunziata: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. Yes.

2:44:22Will Brown:So heavy assets, low obsolescence, which is a term that I just learned a week or so ago. So thank you, JP Morgan. Are they a sponsor yet? Not yet. Do we want them to be?

2:44:33Mike Annunziata:Depends on what the product is.

2:44:36Will Brown:We love JP Morgan. We love JP Morgan. Yeah, I think this heavy assets, low obsolescence is these things that are very difficult to replicate. You build a chemical plant or you build an aerospace space production capacity, these kinds of things are much more resilient than your traditional B2B SaaS product that may be able to be replicated by Cloud Code, for example, or OpenClaw, something like that.

2:44:58Mike Annunziata:This was Dalian's bit when we had him on for the slop versus steel debate with Randall, debating of 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? Because there still is a lot of R &D that's happening. And then low obsolescence. I want to know more about the curve of what can be obsolesced. Because there are some companies, when I think about a lot of people have been saying, oh, buy raw materials, go long gold. And it's like, that is fungible. So there's no real moat there. If you just own some gold, you just, it's a commodity.

2:45:45Mike Annunziata:So how do you think about non-commodity products?

2:45:49Will Brown:So two things. First, any great venture business at the core is a company that has the potential to generate high return on capital in the long term. 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. 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. We're not going to remake all those chemical plants. Or it could come from something like a radianuclear who is building very complex nuclear reactors in a shipping container equivalent, and they're going to scale up not just the design of that, but the manufacturing, the supply chain, the regulatory.

2:46:28Will Brown:And that gets to the second piece that we always talk about, which is we love companies that are novel in the aggregate. We're not looking to, to your point, how do you underwrite a science or engineering, Jordi? It's not one specific thing we're trying to understand. 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 not impossible for somebody else to get a re-entry capsule like a Varda Yeah, right It's not impossible for somebody to build ground stations like a Northwood But the dynamism of these companies is in the ability to execute and move with speed and integrate these complex systems That become low ass absolutes, but they are heavy ass.

2:47:05Mike Annunziata:Yeah, it's like the factory is the product I've always heard that thrown around as like, and I always read it as like, the real challenge is manufacturing at scale. But 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.

2:47:31Will Brown:Yeah, I think it really depends, right? Like I love the theory behind the factories, 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? And if you look, SpaceX is a good canonical example of this. Why should they build a factory? It 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. So they had to do it themselves. So they couldn't meet a spec.

2:48:01Will Brown:So they had to do it themselves. And I think that's the different dynamic where you should not, and I post about this a lot because it definitely is a thing I believe pretty strongly, you should not vertically integrate just for vertical integration's sake. Yeah, no card at all. 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, for example. But if you look at like a Varda, for example, cadence is everything for that business. So if cadence is everything, you've got to be able to turn fast. So they have a lot of capabilities they've built in-house to be able to do that.

2:48:31Will Brown: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. Speed's really the advantage, but it is built on a foundation of the ability to deliver at rate. And that's kind of the core. When you say factory's the product, I agree with that. 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, pre-seed. We're thinking like, if you're gonna build a factory in three years, you need to be thinking about it now.

2:48:59Will Brown:So why are you building that factory? Why are you doing that? Like what competitive advantage does it give you? I think that's how the people that we backed have really had a good sound like head on their shoulders and how to think about those tradeoffs. Why build decisions?

2:49:11Alap Shah:How did you react to the general intelligence crisis of 2028, the viral essay that nuked the markets? I saw this guy, John Loeber had a pretty kind of a response to it, breaking it down. he was in some part saying like institutions have a lot of momentum they can carry that momentum and adapt to these new market forces he also suggested that re-industrialization 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 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 kind of push paper around.

2:50:00And so are you optimistic that

2:50:04Alap Shah:this could shift talent from, you know, making the 50th like vertical CRM to making stuff? Do you think this could be a...

2:50:13Will Brown:Look, like I'll say this, I don't think we need to send our boys back to the coal mines.

2:50:20Will Brown:Let's the children. You're in for the mines. The children. No, I'm just kidding. You know, my kids, if you're watching, which I think you are. Yeah.

2:50:28Alap Shah:Get off my aircraft.

2:50:30Mike Annunziata:It's light blue collars.

2:50:31Will Brown:Yeah. Well, I think it's, you know, what are we doing next? Right? So if you go back to horse and carriage, right, it's, hey, Ford has a job for you on the production line. Like, go learn how to do that. And I think the opportunity is in the companies that develop recurring education, rework as processes or capabilities as part of their normal operating cadence. So a lot of our best companies, they will get seed funded and they will immediately start an 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 right I think we could bring back that retraining but inside the corporate entity as it's the responsibility of the corporation is really our company's problem there's a talent he had someone who was working basically a blue-collar job and just

2:51:28Mike Annunziata:wanted to get like a forklift certification and he brought him on and And then pretty quickly he was writing CAM, CAD automation software, computer aided manufacturing. And sort of became a CAM programmer, almost. And that was a lot longer path a long time ago. And so the upskilling thing is definitely real at these companies.

2:51:49Will Brown:Look, I think we could get into this whole different thing around the student debt crisis is definitely a big bottleneck to people being able to do this upskilling. Oh, sure, sure, sure. they may be constrained financially that they just have to kind of keep working to be able to make their student debt payments, for example. So I think that's a big thing that people...

2:52:06Alap Shah:Yeah, I was having a conversation with Tyler. We've had a number 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, but then 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. And going back, it feels like the, uh, the, the sort of like unpaid intern is like completely like no go now.

2:52:46Alap Shah:Like it's a net, it's a negative signal if a company is doing it. Uh, but it might have to come back. And it actually, if people really want it, it's like, yeah, you can work a job that is very low skill and then spend all the rest of your time re-skilling yourself. I don't know. I think it might have to come back, although I'm sure there's a million reasons why somebody would hate that idea.

2:53:14Will Brown: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 spacex's of the world that are now continue to take on really big challenges they're doing two things they're doing things that are really meaningful that when you get at the root of them and there's an authentic drive behind a lot of what they're doing but they're also willing to tell the stories and inspire other people to be better if you look at jared isaacman coming as nasa administrator like he is an inspiring human being right that i think we we need more people like a jared who's 30 40 years old whatever it is like young person He's been to space.

2:53:50Will Brown:He's been to space. He's processed payments.

2:53:53Alap Shah:Every time I get a shift payment checkout, I just go. Yes.

2:54:01Mike Annunziata:Talk about debt. What is a reasonable debt load for a series A hard tech company? In software, venture debt is seen as cancer. Like you do not want to go near it with a 10 foot pole. Paul Graham said it blows up companies left and right. It's very scary. 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. How are you talking to founders in your portfolio about debt?

2:54:39Will Brown:Yeah. Like I could get hyper technical about this, but I won't for this audience.

2:54:44Alap Shah:No, get technical. Get technical. Get hyper-technical.

2:54:49Will Brown:The right amount of debt at Series A is zero. Okay. The right way to use debt in companies is in two places.

2:54:59Mike Annunziata:Says the guy who sells equity.

2:55:00Will Brown:Says the guy who sells equity.

2:55:01Mike Annunziata:He's like, no one should ever raise debt. Just come to me for another check. Come to pop up.

2:55:07Will Brown:At the right time in the right place, it makes a lot of sense. There's really - When does it make sense?

2:55:11Mike Annunziata:When does it start? you could take me out to series D, I don't care. But where - For sure. 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?

2:55:28Will Brown:Yeah. So, you know, in a past life, when I mentioned I worked at the Cornell Endowment, I also got my CFA while I was there and then went and got my MBA. So probably - Such a non-traditional background.

2:55:37Mike Annunziata:Yeah, such a non-traditional background. How did you wind up in finance with just a CFA and an MBA?

2:55:41Will Brown:Yeah. But, you know, revenge of the balance sheets is kind of what I think about what we're investing in now. But look, I think you're using debt for a couple of situations. Number one, to buy long-lived assets. Yep. But the caveat is they need to be tied to actual contracts that will be pushing out real revenue and cash flow. Yeah. If you are using debt to speculatively invest in capacity, you are taking a lot of existential risk into the business until you have contracted revenue that has significant bookings and backlog. All right. So if you've got two years of backlog and you want to take on an interest rate of 7 % while your equity is 30 % and you think you can get to a revenue milestone that blows out your equity multiple, that's actually a really good use, right?

2:56:26Will Brown:Or if you have, there's a canonical example I always think of when I was working at the endowment, we looked at a venture debt fund and it was super interesting, like 2015, this was 2015 and they had a very good use case of using venture debt and it was they were lending to Pandora mmm and Pinto first Pandora cheers to Pandora so they were like service not they were letting up place yeah yes exactly not the jeweler the jeweler the streaming service they were lending to Pandora and Pandora basically had like a cash flow timing issue where they needed to buy servers to be able to store the music and stream it and there would be revenue that would come off of the stream.

2:57:07Will Brown:So the revenue would lag the cash investments in the servers. But as they were growing streams, their multiple on the revenue was growing. Oh, interesting. So you actually could use the venture debt to bridge to a higher revenue multiple when you went to raise more equity and reduce the cost of equity. So even if you were paying 18 % implied cost of equity IRR on the venture debt, including the warrants, it was still cheaper because the cost of equity was declining as you hit milestones. Those episodic use where you knew you could raise equity as you hit a specific milestone to pay it down. Or it was part of the permanent capital structure that was tied specifically to cash flow.

2:57:45Will Brown:The trap is when you use debt to try to extend runway as a crutch for not being able to raise

2:57:49Mike Annunziata:Yeah, so even in that Pandora example, like 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.

2:57:59Will Brown:And you know the revenue is going to come. Because you know streams are going to go up and then you'll be able to refinance it because equity investors say, you know, you're at 10 million streams. I need you to be at 15 million 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, like those inflection points or permanent capital. That's very cool. Jordy, anything else?

2:58:24Alap Shah:No. What founders in what categories do you want to meet?

2:58:28Will Brown:What founders in what categories?

2:58:29Alap Shah:Like what kinds of founders, not specific.

2:58:33Will Brown:What kinds of founders? Good question. Look, our big thing is founders that have fun. Have fun, but they play to win. We talk about that a lot. Like have fun, play to win. There's like plenty of research that if you're having fun, that's when you do your best work.

2:58:48Mike Annunziata:You see that with the Varda team all the time. See that with the Varda team all the time. I feel like they named the thing Winnebago's like a funny thing. Like clearly they're having a good time. They did the LK99 thing and it was like. Totally, totally. Midnight side project, very fun.

2:58:58Will Brown:Totally, yeah. It's like, are you building these little cultural things that people have a good time? I think that's really, really important. But also the playing to win side too, because I think you can over index on let's have too much fun and realize like, hey, you're competing out here. So that's really what we're looking for is you know it, like you guys have a ton of fun here, right? Like I think this cultural vibe is definitely what we look for in founders is are they having fun? It's how you kind of get to the hard stuff. So we're looking for -

2:59:22Mike Annunziata:Let's put the hard tech to work. Tell us about - Why don't you hit the gong? Yeah, you hit the gong. You hit the gong, all right. You can tell us the announcement. 50 million? All right.

2:59:30Will Brown:Our second fund, 50 million. Awesome capital.

2:59:33Mike Annunziata:Go smash that gong.

2:59:41Mike Annunziata:Thank you so much for coming on down to the TBP at Ultradale. This is great. 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

3:00:02Alap Shah:at least 50 of the breakout hard tech companies of the last five years at the earliest stage

3:00:10Mike Annunziata:really wild uh i like this this deep dive from uh 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. In 2005.

3:00:30Alap Shah:I think that it seems obvious at this point that And PG's blog and Sam's blog are both like... Deep in the training. Yeah, core to the... Anyways, we got a bunch of timeline here.

3:00:47Mike Annunziata:Let's rip through it. Take me through it.

3:00:52Alap Shah:Chairlift Capital says, Killing application software to then try to rebuild application software is the funniest timeline. Still, opportunities in the enterprise. guys. We've got to have somebody on from McKinsey or one of these big consulting firms to hear how they're positioning it to customers.

3:01:12Mike Annunziata:I think it's a deck. I see a deck.

3:01:15Alap Shah:Moving on. Where are we? Jim Kramer, underrated poster. What did he say? He, this morning at 3.33 a.m., was outside. He doesn't saying cold early dot dot dot i am not worried anthropic has a solution i read it in a report what vague post incredible i mean yeah of course sent sent ibm down 11 going to work uh dolly bolly says investors simultaneously think ai is over invested and ai will take over industries

3:01:48Mike Annunziata:yep it's a funny moment right now this last one sas is dead open claw replaced all my subscriptions i went from 480 a month on tools to 1245 a month on api costs and 15 hours a week fixing yaml files

3:02:06Alap Shah:adapter be left behind hope hopes hopes revenge says woke up to claude bot running its claw through my wife's hair. I had explicitly asked it not to do that.

3:02:17Mike Annunziata:Stop, stop, stop. There was a post about some meta researcher, alignment researcher, got all of her emails deleted by OpenClaw or something.

3:02:28Alap Shah:Yeah, that was...

3:02:30Mike Annunziata:That feels like a crazy thing to post. I think she

3:02:32Alap Shah: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 open claw on her main meta email but I think people read it yeah on her main account which uh seems crazy people were saying like oh it's bearish like

3:02:55Mike Annunziata:she's literally an alignment researcher it's like that actually might make her better at her job though well she was at scale too before she wasn't like in you know in the less wrong like grinding through like ea stuff yeah like an ea would never be caught getting their emails deleted no way just all of less wrong gets deleted by open call by accident palantir is launching valley

3:03:15Alap Shah:forge grants ten thousand dollar awards for high schoolers to solve the problem that most inspires them using our software ditch the coffee runs and fake intern projects whoa shots shots fire Tyler. Claude with ads was incredibly real. We'll pay you up front to, we'll pay you to confront the challenge you care most about. At Valley Forge, Washington soldiers endured a brutal winter, turned the tide and won the revolution. Their fortitude has carried America to its 250th birthday. We're looking for pioneers to lead America to its 500th. I like the sound of that. Very, Very, very cool opportunity.

3:03:51Alap Shah:Based on our audience data, we have very, very few high school students in the audience. But if you're listening to this, go apply. Feel free to DM us if you do apply, and we will try to nudge.

3:04:08Mike Annunziata:What happened with this F1 race? Someone smashed? Someone crashed?

3:04:12Alap Shah:Yeah, who in the chat was actually at this demo? Because San Francisco, I've never seen San Francisco go this extreme let's pull up this video first of the truck actually jumping uh over one of the hills

3:04:26Mike Annunziata:john i want you to see this okay let's play this wow this is san francisco this is in san francisco they still got it 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 there's a million other events that happen throughout honestly i used to do something like that with my clutch

3:04:47Alap Shah:when I would be driving my manual.

3:04:49Mike Annunziata:Jumping over an F1 car, wow. That is amazing.

3:04:53Alap Shah:Driving manual as a high schooler in San Francisco 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. People were thinking this was Yuki Sonoda, but apparently it was one of the other drivers.

3:05:09Mike Annunziata:This is the red one.

3:05:10Alap Shah:Wait, why is it on fire? At one point, it caught on fire.

3:05:13Mike Annunziata:I thought it just had some minor bumper damage.

3:05:16Alap Shah:Yeah, this was a wild demo. I think Oracle and Red Bull really wanted to make a statement.

3:05:23Mike Annunziata:They're spinning around here, okay, and then they go forward and smash into the wall. That feels like the easiest crash to prevent. When I was watching this, I was like, while you're spinning around, that's the point where you're going to go crazy, but then it's just smoothly going forward and smashing. Front wing decimated. No brakes, I guess.

3:05:41Alap Shah:No brakes. And then at another point, the car caught on fire. 10 out of 10. 10 out of 10 demo.

3:05:47Mike Annunziata:Red Bull knows how to entertain. They know how to entertain. Yeah, this was on purpose. Actually, in the Apple Vision Pro update, I watched a Red Bull video, an immersive video. It was a skier video, about 15 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 going to go heli skiing, backcountry alone. But this like just you feel the scale and everything. It's so good.

3:06:23Alap Shah:I called you at like 10 or 1030 on Friday night. Yeah, no, I didn't even call you. I just said, you up? And John sends me back a picture of him in a selfie in the Apple Vision Pro. In the Apple Vision Pro. You know it. You were probably the only active user at that moment.

3:06:39Mike Annunziata:Well, you know what? I got a Mac Mini hooked up to my Apple Vision Pro now because I got a capture card that can take in an HDMI input. So I hooked the PS5 up to the Mac Mini and then mirrored that to the Apple Vision Pro so I can play PS5 in Apple Vision Pro with way too much latency and it actually doesn't work well at all. But it was a fun 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 want to play all sorts of stuff that's HDMI compatible.

3:07:23Mike Annunziata:Anyway, very, very annoying.

3:07:24Alap Shah:If you are in the market for a Western sort of vintage Gulfstream, Greg over on Axe has got you covered. It's a 1994 Gulfstream GIVSP. the latest ask was only 3.75 million so this is beautiful the carpet we don't know how to make jets like this anymore everything about this is amazing it's so it's so good so opinionated look at the look at the bathroom i love it the bathroom they went bathroom is crazy steampunk

3:07:58Mike Annunziata:yeah yeah star wars moat cantina yeah uh should we watch this latest uh video from c dance let's do

3:08:07Alap Shah:it.

3:08:07Mike Annunziata:It starts by saying Hollywood is cooked. Hollywood is cooked based on the new C-Dance AI video model. It's showing Transformers, but let's actually track what's going on here. Starts out as a jet, a combat aircraft. He gets out of the jet, turns in around. Now it's Transformer. Okay. Then he gets in another cockpit. Now it's a helicopter and it has a gun on it. Okay. He's shooting it. That's useful. You needed to be in a helicopter mode, but now you got to to get back in the transformer again. So he gets back in the transformer. Oh, turns out the transformer can run like a human and walk, but it turns back into a plane.

3:08:45Mike Annunziata:Back into a plane. Then what do you want to do when you're a plane? You want to 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 the front cockpit.

3:08:58Alap Shah:This is entirely realistic.

3:08:58Mike Annunziata:And now you're back in human mode, humanoid mode. And then you blast off.

3:09:03Alap Shah:You would not be criticizing this if this was an actual...

3:09:05Mike Annunziata:And then you turn into a plane and you fly backwards.

3:09:08Alap Shah:John, if this was an actual scene in Transformers, you would just be watching it being like, that's tight. Okay, so I agree.

3:09:17Mike Annunziata:I would be saying that's tight. And it is incredible visual fidelity. Incredible visual fidelity. And just an amazing video. And entertaining to watch. And that's why 4 million people enjoyed it. And it only has one community note. And the community note is just AI Slop Engagement Farm. Yeah, obviously this is not cooking Hollywood today. 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 and they don't, and they tuck inside.

3:09:59Mike Annunziata:It's so difficult. It's the Mount Everest of motion graphics and CGI. and Tyler could do it every game 10 minutes, but I see your point. And the final step in any of these, you can actually go and animate the rigid bodies in Cinema 4D 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 reflections on the glass all of that's like another step because you you create the jet and then it just looks like you know a 3d render of a jet so you have to blend it in 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 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 it makes sense because a lot of the CGI that happens in Transformers, I know some of the Transformers clips are silly, but a lot of it's very motivated.

3:11:02Mike Annunziata: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 weight, and there's some weight and timing, and there's art direction that sits on top of the actual CGI. Anyway.

3:11:23Alap Shah:apparently open claw fueled ordering frenzy creates apple mac shortage delivery for high unified memory units now ranges from six days to six weeks i called it it's happening i did call it you did we did the math a couple weeks ago and it seemed obvious that if if the frenzy kept up yeah there would eventually be yeah some shortages i mean demand for ai is continuing unabated like

3:11:48Mike Annunziata:People are using this stuff. Martin Shkreli shared the NVIDIA demand check on Lambda. Lots of things are out of capacity right now. People are using stuff. If you need to hop on Lambda, hit us up, and we'll introduce you.

3:12:03Alap Shah:People were pretty triggered by Gary Tan and the YC crew jumping on a podcast dressed as lobsters. And I was triggered by people's reaction to it because we have done similar gesture maxing many, many times. I like a costume. I think they were just having a little fun.

3:12:24Mike Annunziata:Let them have fun.

3:12:25Alap Shah:But apparently fun is illegal.

3:12:29Mike Annunziata:Fun is illegal.

3:12:31Alap Shah:Hunter Weiss.

3:12:32Mike Annunziata:You have to pack it up. We got to talk about our secret plan. Oh, yeah. 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 million. And we're going to move everyone there. I'm hearing some claps. I think people are in. They're down. We're going to move the whole crew. We're going to build a hut. It's a 40-acre village. And it was first listed for$5.5 million. You get the whole town.

3:13:02Alap Shah:What actually qualifies as a village, though?

3:13:05Mike Annunziata:Well, it has 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. We're a small team. We got like 10 people. That's enough. There's not 21 houses, Jordy. There's 21 structures. So it's entirely likely that Tyler over there will have to live in a barn or shed or whatever at the church.

3:13:26Alap Shah:Fitting. In the stable.

3:13:28Mike Annunziata:But other properties found on the unique compound include a 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 GT3RS. You can also sleep in a multi-bay garage.

3:13:43Will Brown:If you get some cloned horses.

3:13:45Mike Annunziata: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 have a need for 21 structures. Head over to Maine and pick this up.

3:14:00Alap Shah:Hunter Weiss shares one of the best product ads ever. I agree. It's the iRun, the iPod Shuffle. Really good. We don't know how to make ads like this anymore.

3:14:08Mike Annunziata:Really good.

3:14:08Alap Shah:Simply. Really, really good.

3:14:09Mike Annunziata:Tells you exactly what it's going to do. Ride around.

3:14:11Alap Shah:Really, really, really good. Bring it back. Will DePue says, whoever builds Gmail app search should be burned at the stake. Every time I use this app, I want a KMS.

3:14:25Mike Annunziata:75 likes.

3:14:27Alap Shah:Proof of insurance and is just pulling up a bunch of Delta Airlines receipts.

3:14:33Mike Annunziata: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 got to do something here. I think it's a big app, so there's probably some overhead to fully rewriting this. But search is tough. Search is hard, even in the AI apps. I find that they generate so much text now that if I search for one keyword and I have in my mind it's like well that word came up the last 50 chats like it comes up all the time um and so it's been it's been very hard uh but i do think i do think it'll get better but

3:15:15Alap Shah:certainly an opportunity dylan field uh 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. We got Congresswoman vague posting now. I love it. I love it. Let's keep it up.

3:15:47Mike Annunziata:You got to vague post every once in a while. Bone. Any other vague posts to go through? Last one says, my culture is not your costume,

3:15:56Alap Shah:brian johnson because brian said he decided to live life on friday he was spotted just playing some video games having some taco bell some pizza some dr pepper having a lot of fun looking uh not his not his usual self but locked in on on on the big game or something like that plant the bomb i

3:16:16Mike Annunziata:have some words of inspiration for the listeners wait no we have to we have to we have to say this last one that's my words of inspiration after you play no and then i actually have a few more Okay, read this one off.

3:16:28Alap Shah:Let's see here.

3:16:30Mike Annunziata:Excuse me. Neil Rennick 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.

3:16:42Alap Shah:Last thing we'll cover today. Anthropic posted earlier, we've identified industrial scale distillation attacks on our models by DeepSeek, Moonshot, and Minimax. Wow. These labs created over 24 ,000 fraudulent accounts and generated over 16 million 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?

3:17:20Mike Annunziata:Oh, yeah. Pot cuddling, middle black potential.

3:17:24Alap Shah:Another person says, I can't believe someone would just steal from Anthropic like this. The millions of man hours Anthropic spent handwriting code, text, art, books, et cetera, to generate enough data for training must be taken into consideration here. Where is the respect for IP?

3:17:39Mike Annunziata:Wild. Fun times.

3:17:42Alap Shah:And 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 to follow our LinkedIn.

3:17:55Mike Annunziata:Follow our LinkedIn. We're on LinkedIn. We're posting regularly now. We've 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.

3:18:08Alap Shah:Computer, make sure everyone in the audience has the best evening of their life.

3:18:13Mike Annunziata:and sign up for the tbpn newsletter at tbpn.com. Goodbye.

3:18:21Alap Shah:Nice work, brothers. I'll see you on the next one.