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TBPN is a live tech talk show hosted by John Coogan and Jordi Hays, streaming weekdays from 11โ2 PT on X and YouTube, with full episodes posted to Spotify immediately after airing.
Described by The New York Times as โSilicon Valleyโs newest obsession,โ TBPN has interviewed Mark Zuckerberg, Sam Altman, Mark Cuban, and Satya Nadella. Diet TBPN delivers the best moments from each episode in under 30 minutes.
You're watching TBPN. Today is Friday, 09/11/2026. Wall to wall coverage in The Wall Street Journal, remembering twenty five years ago, the cover of The Wall Street Journal, every single article, except for one, was about, of course, the World Trade Center. The the the headline that day was terrorists destroy World Trade Center, hit Pentagon in raid with hijacked jets. Bin Laden is on here.
Speaker 1:The only story that, you know, attacks raise fear of a recession, very interesting time capsule. Highly recommend picking up a copy of the journal today and taking a trip down, you know, in in memory of the tragedy. The one piece of news that broke through this day that was not related to the terrorist attack was Xerox reached an equipment financing agreement with GE Capital that will let Xerox erase about 5,000,000,000 of debt. Very, very odd. Every other story I mean, the market was closed.
Speaker 1:Actually, the Internet got a very interesting shout out here. It says telecom systems were strained as terrorist attacks in New York and Washington knocked out telephone wireless services across the Northeast. The Internet proved most the most reliable way to communicate following the attacks as the phone system sagged from severed lines and an extraordinary volume of calls. Corporate executives used email to find employees across town or across the country. So interesting to see.
Speaker 1:But there's so much to go into and lots of interesting retrospectives across all the different media organizations. I don't know that I have a particular anything to dive into there, but it's interesting for you to go and dig into. Anyway, moving on. Let me tell you about ramp.com. Time is money.
Speaker 1:Save both. Easy use corporate cards, bill pay and accounting and a whole lot more all in one place. Now with musicals. Anyway, what are the AI doomers actually proposing? That's the question I was trying to answer this morning for a while.
Speaker 2:Ten year sentences.
Speaker 1:That's one. So, yeah, there will be this weird translation layer between the thought leaders and the people that are writing policy papers effectively, and then what actually gets implemented. What gets, you know, votes basically can be wildly different because to get the populace to actually support something, you need to wrap it in a different different structure potentially. AI twenty twenty seven predicted that this month, you know, late in 2026, Congress would wake up, and that is in the journal. Congress is suddenly waking up to the AI doomsday threat.
Speaker 1:And so this is happening all over the place. Was it Matt Damon who was caught on TMZ being investigated about his thoughts on AI risk? There are now protests. I believe AI 2027 predicts a 10,000 person anti AI protest by the end of the year. I was trying to figure out how big this protest in Chapel Hill, North Carolina earlier this month was.
Speaker 1:I think it came in sub 10 k, but certainly tracking to it. It was about a 150 people at this at this protest in Chapel Hill, but just two oooms away from the prediction from AI 2027. And AI 2027 in the sequel AI 2040, frustratingly vague about impacts outside of the AI industry. So there's a lot of really, really amazing predictions about agentic capabilities and the amount of compute that will be marshaled and and like even lab revenue. But there's not as much predictive stats and calls around like what will it do to GDP?
Speaker 1:What will it do to employment? What will, you know, how how how often will people actually be using this? What will it actually be good at? The diffusion question is still sort of left unanswered. But they're clearly taking it very seriously.
Speaker 1:There's a huge press cycle around this. And the journal says that congress is suddenly waking up. So I wanted to dig into, like, what the actual proposal is because the joke for a while has been just like everyone when they're pressed on this, they just say, we gotta talk about it. We gotta talk about this. AI 2040, Daniel
Speaker 2:One thing that one thing that stands out, it's it's interesting to me that, there's been so much more seemingly grassroots mobilization around the anti flock movement, deflock, when you compare that. And and you don't see
Speaker 3:Totally.
Speaker 2:You don't see videos of of people with, you know, a 100,000 likes saying like, here's how to cut power to your local data center.
Speaker 1:Not for not for x risk reasons, but you will see a post that's just like, I don't like AI image generation because I'm an artist. That will So get a there is a lot of anti x risk.
Speaker 2:The the anti flock sentiment converted into actual physical actions by a bunch of just otherwise normal people.
Speaker 1:Yeah.
Speaker 2:I'm saying we haven't seen that yet. Yeah. With data centers, maybe we don't. Yeah. But it's notable.
Speaker 1:Yeah. Much harder. Data centers are in remote locations, highly fortified. There's fences. Totally.
Speaker 1:There's walking Yeah.
Speaker 2:Flat cameras are in your, you know, on your street maybe.
Speaker 1:Exactly. Yeah. So, yeah. There's there's degrees there. But AI in in the what's weird is the yeah.
Speaker 1:I mean, you're you're calling out grassroots taking down the data centers. The AI twenty forty proposal is effectively the opposite. It's like harden the data centers even more. And the actual proposal is super concrete in AI twenty forty, it's it's very interesting just to hear about how they want to slow things down. So the the the thing that I think a lot of people online who are like, yay, an anti AI sentiment that's going viral are gonna be depressed about is that, like, this is not stop AI at all.
Speaker 1:AI twenty forty is like, keep the inference flowing. The current models are great. Also, let's keep doing capabilities research, but we're just we just wanna reach superintelligence by 2040 instead of 2028 where we're not necessarily prepared. So they want it to be highly controlled by governments, nations, states, highly secured. And there's a whole bunch of very, very tactical recommendations that they make around that.
Speaker 1:So the first mechanism is an AI pause. They want to pause training. They don't want to do any more new frontier training runs or R and D experiments. And to enforce this, they're calling for, to apply inference only verification to essentially all major AI data centers. So anyone who has more than 10,000 h 100 equivalents, roughly a $100,000,000 of equipment, If and and that is, like, pretty easy to figure out.
Speaker 1:You're just like, big building over there. Let's send the inspector inside. Oh, says NVIDIA on all these chips. Count them up. There's over 10,000 of them.
Speaker 1:You got to apply for this permit. You got to do you got to tell us what you're doing. Right? Very easy to enforce at least in The United States. And with an international body, you could kind of do the same thing internationally.
Speaker 1:So the whole goal, you can only inference the current models and you gotta verify your workloads with an independent auditor, probably the government. Maybe there's some sort of nongovernmental organization that's doing this. You know, there's a whole bunch of different solutions that you can pull from across nuclear nonproliferation work that's happened in the past. Major countries, they want them to declare AI compute inventories. Tell everyone, not just your local population, but also the international community, how many how many warheads you got?
Speaker 1:How many h 100 equivalents do you have? Where are they? Everyone shares this. That's gonna be a really tough sell because international agreements are really, really tough sells. It's much easier to have a groundswell of support for something that happens in America.
Speaker 1:America changes. We have a system that we don't really have the international rules to to to quickly implement that in a way that doesn't doesn't allow for a lot of defection. But they want to know who has compute. So major data center owners and semiconductor supply chain companies would be required to turn over sales records. Who do you sell those chips to?
Speaker 1:Where do they go after that? Foreign inspectors would do routine chip counts physically at site, on-site at facilities. Large transfers of chips would only be allowed to go to registered audible counterparties. There's some interesting networking specific components to this proposal too. They want to physically remove high bandwidth East West networking inside of data centers.
Speaker 1:So you can't do large distributed training runs, but you can still do inference. So again, anyone, all the anti AI people who are like, oh, yeah. I I don't want LLMs around anymore. Like, they these are not your guys. They're not fighting for that.
Speaker 1:They are fighting for stopping the next training run, which is probably a line. Those are those are overlapping circles. But it is not moving backwards in time. It is merely slowing down at this current moment. They also want to install passive optical network taps on anything leaving the data center to independently verify traffic.
Speaker 1:For any new AI, R and D data centers, want entirely new facilities built from scratch with nation state level physical security and and verification. So they're saying, like, okay. We're we're like, the the data centers that are built, they can inference the current models, your your astras, your fables, like your your grocs, like those can run because we we we can deal with those. We can harness those. We have control over them.
Speaker 1:We're going to continue to align them. And and that's a solvable problem. But for the next run and the one after that and the one after that as it gets crazier, We want it in a new building built inside a Faraday cage, so you can't communicate it from the outside. We want a bunch of physical controls. It's like going to a nuclear facility, highly verified who gets in the building and when air gap communications.
Speaker 1:This is one interesting, like, proposal that they have that really shows how deep they thought this through. They want the r and d data center to be connected externally. If you want to communicate with it and and you want to tell it what to do, okay, train the next running or do whatever, they will have a bandwidth capped connection at one meg per second. So you can send little instructions, but if you say send me the weights cause I'm taking them somewhere else, it would take you, like, five years to actually trade it. So interesting, like, hardware solution to this.
Speaker 1:I I I you know, how how do you actually go and implement that? There's gonna be a whole bunch of other things, but interesting that they're thinking about, like, the width of the pipe. So it'd be very obvious if you're stealing the model weights because it's like, wait, this one meg pipe has been at full tilt for months. What's going on here? Someone's taken the stuff out of the data center.
Speaker 1:They want a front when frontier model weights move from an r and d facility to an inference facility, they want it to be placed on physical storage devices encrypted independently by both The US and China. So both countries have to sign off and physically escorted by representatives of both countries to It's the a tall order. That one's a tall order for sure. Yeah. And they actually want frontier models to be made deliberately larger than compute optimal.
Speaker 1:So they want the weights to be a 100 terabytes instead of honing them down to something that's just one terabyte that could actually be moved around a little bit easier. There's a bunch of public disclosure proposals in there, restrictions on various trade offs. So labs would have to share the model specification, the fraction of compute devoted to internal AI use, so you don't get a lab that's just internally using way better models than what's available externally. They want qualitative descriptions on how how how powerful models are being used internally, restrictions on how big the gap can be between the best internally deployed model and customer facing products. This has been a common discussion point with, like, the rollout of Mythos and Fable and Astra and Astra Next and all these different models where people have said, like, oh, it's really unfair that, like, this lab gets a better thing than I do.
Speaker 1:We should be on even footing if we're both gonna be competing in web design or we're both gonna be competing in legal. Like, what what why can't I buy this product from you? And so there's some overlap there with, like, the broader business community, which I thought was interesting. And
Speaker 2:Yeah. I mean, first of all, this I mean, a lot of this tracks with the kind of regulation that, we had pushed for beginning about two years ago around podcasts. Yes. Know, wanting podcast studios to be air gapped. Yes.
Speaker 2:Wanting, you know, Faraday Cages Yeah. Around podcast studios.
Speaker 1:A locked briefcase with an s m b seven, SMB seven b in it. And in order to unlock it, Patrick Oseary and David Senra both need to give you codes to independently verify that this podcast is worthy of being recorded. Yeah. I like that one. Yeah.
Speaker 1:Yeah. Just makes sense. So high level value valve, they see as being like the most effective in controlling the speed of capability improvements is compute caps. So there is a world and we're going to the Bernie Sanders thing because it's already getting like sort of twisted. But the the the the the big hammer is just chip controls and data center build out slowdown.
Speaker 1:Yeah. That's the easiest thing. And I think that's, like, the biggest valve that they're going to be that we're gonna see twisted around to actually slow down capabilities. And so the goal is to allow models to get better mainly by adding hardware rather than inventing better algorithms, which can leak to secret projects. So the goal is like, okay, well we know that this model is capable of this so we want this much compute over here.
Speaker 1:Okay. You've done well. We're allocating more compute as opposed to this one weird trick that AI doomers hate. Yeah. So the goal here is Yeah.
Speaker 1:Not to go
Speaker 2:back It's in interesting because when you look at, I mean, anytime you have, you know, really really hardcore government regulation and international coordination around issues like this, you're gonna have a bunch of unintended consequences. Yep. And one thing that feels obvious around this Yeah. If if these policies were to be rolled out, is that you would effectively create an incentive for millions of individuals or groups globally to be in secret, like trying to find entirely new breakthroughs that are
Speaker 3:Yeah.
Speaker 2:And and and again, this incentive already exists Yeah. But it kind of pushes a lot of the idea that humans are just gonna be like, oh, I'm not I'm no longer gonna try to create the God model because Yeah. Like, there's this, you know, big global organization that's that's sort of policing it. Yeah. I just I mean, that's the same thing we
Speaker 1:see with nuclear non proliferation. There's always discussion about what countries are getting the bomb and when and how far along are they and wars break out over this. And it yeah. Like, the game's not over just because you create a framework. But there is at least a I mean, we've avoided World War three.
Speaker 1:So you could say that a lot of like, the vast majority of nuclear nonproliferation work has been successful, even though there's been a ton of examples of people trying to divert around it. In fact, it's like been the backbone of geopolitics for like sixty years, has been like who gets the bomb and what what Yeah. What chips are on the table But ultimately,
Speaker 2:GPUs and computers are much more wide, you know, infinitely more widespread than nuclear materials.
Speaker 1:Yeah. But you still gotta marshal them altogether. Yes. There's some weird scenario where there's a Python script that's AGI that can run on your laptop. But I think most people are convinced, at least in this crowd, that
Speaker 2:Scale.
Speaker 1:That scale is a prerequisite. And, I mean, we were we were joking about, like, how it would be so like, we SSI, Ilya Setskiver's new NeoLab is recently got a big cluster from NVIDIA. And we were like, the most bullish thing you could do if you're this secretive NeoLab would be like, we're actually selling our compute because we've discovered a more compute optimal way to reach AGI and we don't need a lot of compute. But of course, even Ilya is like, it's time to scale up. I need more compute because it seems like even if he's taking a completely orthogonal approach to, you know, innovation and research, he still needs a lot of compute.
Speaker 1:And so it does it does feel like everyone is sort of with the consensus that it's going to be a big building with a lot of energy, big heat signature, definitely visible from space and and pretty simple to to track, at least in the short term until people start building crazy underground facilities and and then you're back to, you know, nuclear non proliferation. But the their goal is at least to, like, you know, try. You know, try.
Speaker 2:Yeah. And then the the other side of this is Not a pain. Does it does AI development actually become something closer to the Manhattan Project Yeah. Where, you know That's what a lot people researchers are working with the government in secret Yeah. Because you can't just assume that Yeah.
Speaker 2:Other countries are gonna slow down or Totally. Do any
Speaker 1:of these things. Yeah. And but in in general, I think the proposal is don't go back in time. It's definitely not stop everything in its tracks. It's a slowdown with the goal of scaling gradually.
Speaker 1:They do actually want to reach super intelligence. They just want to do it by 2040, hence the name of the project. So the goal is gradually scale into top human expert capability around 2035. Now a lot of people are saying, oh, we might get this by 2029, 2028, 2027. And they see that as too fast.
Speaker 1:They wanna push that out to 2035, then wait five years with AGI, and then unlock super intelligence in 2040. This is their initial proposal. Of course, there's a lot that could change over the next decade. And and I think I think like to zoom out overall, you're if worried about x risk, the AI 2040 plan does feel like a concrete path towards slowing down. The conversation definitely gets dragged down into estimates and trying to narrow down exactly how a human extinction scenario plays out.
Speaker 1:And that can be that can I feel like that's almost a sideshow because in a democratic society, in amongst humanity, like, it doesn't really matter the mechanics of getting to 10% PDOM or any of those? It's just like, if everyone feels that way, something will happen. This is a concrete plan of what that might look like. And that's valuable to understand in this case. Yeah.
Speaker 1:So for the safety skeptics, it's easy to see how this level of control over what you can do with computers is authoritarian or anti libertarian. Even if we're talking about $100,000,000 computers, there's a lot of people that say like I should be able to do math on my computer. I can do whatever I want. Let me do cool things. I'm excited about this.
Speaker 1:That limits your freedom. It might create regulatory capture for a few major players. It might crash the stock market or delay economic gains that come in the good ending where alignment is solved and x risk plummets. You can imagine a situation where in a few years if x risk fades into the background, you're like, yes, there's still a risk, but it's the same risk that we face every day with like an asteroid hitting Earth. Like no one it doesn't really change anyone's behavior.
Speaker 1:That would be sort of the good ending in my opinion. So it's a balancing act. And for most of these slowdown proposals, I personally have a hard time black pilling about them in the sense of, like, if the if all of this gets implemented, how frustrated will I be? Like, the models are good. I would like better models.
Speaker 1:I want safe models. But at the same time, like, is this massive capability overhang. The current models can do a lot of interesting work. We're finding new uses even for, like non leading edge models. There's a lot that can be done.
Speaker 1:So I I I don't believe the the doom doomers who are dooming about what the doomers are planning. I find that unconvincing right now. But people are starting to lay it out more. Brad Gerstner, Jensen Huang, David Sachs are talking about the other side of this equation. But but I haven't just like it's hard for some people to concretize the Terminator scenario, I also have a hard time concretizing the the we didn't we didn't race and we're unhappy about that.
Speaker 1:I guess you could say, you know, the housing scenario. There's been other times when we brought in too much regulation, slowed things down too much and been like, this was really not the
Speaker 4:right
Speaker 1:move. But at the same time, I think we we have a lot that we can do with the current technology that there's still cause for optimism even if something like this gets, you know, universally voted on. It wouldn't I I don't think it would be the worst thing for for for companies and consumers and businesses and all sorts of different folks.
Speaker 2:Yeah. But There's also a big question around what does does do the twenty forty people have a point of view on robotics and and physical AGI? Because it seems like if you even if you pause, like, you know, efforts towards RSI. Well, if we add billions of robots into the world that are just running on today's model, that also presents today's, like, you know
Speaker 1:I don't think they're worried about
Speaker 2:that. Yeah. I don't think they're worried about me to me that
Speaker 1:billion robots with GPT six level intelligence and years of alignment work that is currently happening, that's fine. It's the next next next thing, the super intelligence, the thing that might have its own goals. I think I mean, we're talking to someone from OpenAI's robotics team, hooking up Astra to a robot, a paintbrush, and a camera. We talked about it earlier painting. I don't think we're at a point where that poses a risk.
Speaker 1:It's the next model. It's the model with its own volition, basically. Yeah. Which a lot of people still aren't seeing. They're just like, yeah, like the models keep getting better, but they seem to follow your instructions sometimes too much and you need to worry about the paperclip scenario.
Speaker 1:But but it's not that they they want to do their own thing necessarily. I don't know. But people are going back and forth on this. Clem over at Hugging Face is sorry, but asking Jacob about AI extinction risk is like asking your AC guy about climate change. Not saying it's necessarily uninteresting or wrong per se, but let's keep things in perspective and he'll and hear from the full range of expertise across the ecosystem.
Speaker 1:And Nathan Lambert says, banger. What was it? What was our take? AC guy might be right about my
Speaker 2:AC guy would be like, I don't really know about that, but I just wanna make sure when you're hot that we can run this AC cool.
Speaker 1:Yeah. I guess it's yeah. The AC ice seems seems
Speaker 2:don't
Speaker 1:about
Speaker 2:all that mumbo jumbo, but, you know, when it's a hot summer day, I don't want you to be worried about the heat, brother.
Speaker 1:I like that. I yeah. This is an this is an unnecessary shot at AC guys. You know, AC guys are important. I don't know.
Speaker 1:It's funny. Anyway, let me tell you about Figma. Agents meet the canvas. Your AI agents can now create and modify your Figma files with design systems. What did Bernie Sanders have to say?
Speaker 1:Is this is this real? Entities will shall shall be subject to the corporate death penalty and persons shall be shall be subject to more than twenty year not more than twenty years in prison if they don't pause AI development. That seems pretty easy to comply with. I don't know. I guess how do you define AI development?
Speaker 1:Is prompt engineering AI development, then you get caught because your model sort of did a little prompt engineering in the final stage, and then you're guilty of this. Like, yeah. That could be a negative knock on effect, I guess. It does seem aggressive. But his overall proposal is banning artificial super intelligence so no person or entity may develop or deploy super intelligent AI systems.
Speaker 1:He defines artificial super intelligence as an artificial intelligence system that exhibits or can easily be modified to exhibit capabilities that match or exceed human cognitive performance and capabilities across a broad range of domains or tasks. Because it sounds like the mission statement. Sounds like the explicit goal of of like 17 different companies right now. AI AI system or AI systems that have sufficient capabilities to plan and execute the disempowerment of humanity. Okay.
Speaker 1:That's a good one. I like that. I don't like overthrowing or undermining the US government. So strongly in favor of banning that. Pausing advanced AI development until a new federal AI regulatory body is up and running.
Speaker 1:And then the new cab cabinet level federal agency will monitor frontier AI systems at all stages of the life cycle, supervise the removal of dangerous capabilities, and supervise the destruction of artificial superintelligence. We're coming forward, which is similar to the
Speaker 2:Corporate death penalty is a line that you don't hear a lot. Right? Usually, usually these companies just, you know, go bankrupt Yeah. And wind down. But corporate death penalty goes pretty hard.
Speaker 1:It's metal.
Speaker 2:It's kinda metal.
Speaker 1:Yeah. You kinda got me with that one. Yeah. Rough rough rough situation. We'll see we'll see where it goes.
Speaker 1:Jamie Cox over at over at Fluid Stack, the co founder of Fluid Stack, a compute provider, shared his convictions sort of pushing back on a lot of this saying that he thinks America should build more. They're pro freedom, pro democracy. They believe AI will bolster human flourishing. We support simple, clear, enforceable regulation frameworks that set simple requirements proportion to capabilities and risk with clear responsibilities and no unnecessary barriers to competition. Yeah.
Speaker 1:The the real you're going to see a lot of pushback from people who are like the, like, the regulatory stuff is going be like these 10 companies and I'm to be number 11 and I'm basically getting the corporate death penalty then because I didn't make the cut to be one of the regulated, one of the approved companies. I'm still early in my stage. So there's a lot of nervousness, I'm sure. But Will said, we believe AI will make everyone rich, healthy, and free is novel and interesting comms from the frontier. He's he's endorsing this.
Speaker 1:And I I agree. I like I like this I like these convictions. I think it's generally like a positive direction to move in, not a direct response to the the proposals that are going out. But we're gonna get a whole lot more of them. Where do you wanna go to next?
Speaker 2:Over on TikTok, they're sharing photo
Speaker 3:Mhmm.
Speaker 2:Of the whistleblower and saying in every worldwide disaster movie, there's a dude that looks just like this that nobody listened to.
Speaker 4:And
Speaker 2:he really does look like an he does look like an actor here.
Speaker 1:Yeah. He looks good. But people are people are all
Speaker 2:And please stop calling him Scary Potter. I've been seeing people. I've been seeing people over on x calling him Scary Potter.
Speaker 1:That's the goal. The goal is to is to wake up China, wake up congress, wake up everyone.
Speaker 2:DoorDash has entered the conversation. Indeed. Said two years at DoorDash. I do not say this lightly. We are extremely close to the burrito arriving before you decide you want it.
Speaker 2:We are not asking for a ban. We are asking for a pause. I don't know why they would ask for a pause. Yeah. That seems like very very aligned to humanity Yeah.
Speaker 2:And to their business Yeah. Which I think is fantastic.
Speaker 1:The doom is very much contained to the frontier lab work. Everyone in the application layer who's applying the models, diffusing them, they're like, I just I can't get this thing to work right. I gotta get I gotta get forward deployed engineers to teach people how to use this thing. Everyone
Speaker 2:deeper Jim Rainbow over Jim O'Reilly. Sure. Or Jim Riley.
Speaker 1:Yeah. Jim Riley. Jim O'Reilly Auto Parts.
Speaker 2:Oh. Jim Riley over at says, it's a whole lot of mumbo jumbo.
Speaker 1:That was your words.
Speaker 2:He just he's just happy to get, you know, eight hours back.
Speaker 1:Yeah. Yeah. Yeah. Yeah. And then, yeah, everyone deeper in the supply chain, like Jensen and all the different semiconductor manufacturers are are not particularly on this side.
Speaker 1:And then you also have Wall Street who's just like, what's the enterprise acceleration? So lots of different groups around the table that need to be brought on board to this movement. The discourse truly is fascinating. Let me tell you about Console. Console builds AI agents that automate 70% of IT, HR, finance support, giving employees instant resolution for access requests and password resets.
Speaker 2:Wow. That's a new
Speaker 1:Too much tycoking.
Speaker 2:New new approach.
Speaker 1:New. Okay. So what is Tyler Cowen calling for?
Speaker 2:He's banging the table saying bet on this. Tyler Cowen.
Speaker 1:You named after Tyler Cowen? Is that is that your namesake? Tyler Cowen says if you have very pessimistic fears or predictions about AI, name the market prices that will support or confirm them. This is what taking this seriously means. And I love Tyler Cowen.
Speaker 1:I'm not sure this matters because if you if you you're not gonna be around to collect it.
Speaker 2:Right? Yeah. That's what that's all What deep dish says, Tyler, why would short term existential risk affect market prices in any meaningful way? Spell out the exact mechanism, contracts that pay out if everyone dies aren't worth anything to me.
Speaker 3:Yeah. I don't know.
Speaker 5:Yeah. Calling for like, you know, you want to like make a falsifiable claim. Like, is this am I able to tell if if your claim is like true or false? And so it's like very hard with these scenarios where like
Speaker 1:Isn't it an unfalsifiable claim though? Just by definition and like you just have to like accept that and move on?
Speaker 5:Yeah. But then it's like so hard to have any like real discussion.
Speaker 1:Was it what what was nuclear any any different? Like the threat of nuclear apocalypse, the threat of World War three, this was a very motivating factor for decades, most of the twentieth century. People made real decisions based on it, based on where to do business and where where where the where the conflicts were gonna be and the motivation for nuclear treaties and non proliferation and how we treat You
Speaker 5:can make financial decisions like think about nuclear war. Right? You have a you have a bunker. That's like that is what you make. Bunky?
Speaker 5:Or is anyone making like AI bunkers? No. Because they think it's gonna be so totalizing that the bunker actually doesn't do anything.
Speaker 1:Exactly. Yeah. So if you think it's so totalizing, then you don't make the bunker. And so saying, hey, you don't have a bunker is not is not proof that the person doesn't believe what they're saying.
Speaker 5:Yeah. I I like, I don't know the answer to this question either. Yeah.
Speaker 1:It's odd.
Speaker 5:It seems like I don't know. Maybe there's some question you can ask that is falsifiable. I
Speaker 1:don't know. I don't know. I think you just gotta you just gotta believe these the, like, the this crew that, like, that's what they believe and, you know, like, they they believe it. There there is the other side of this, which is Paul Cristiano, who is has been worried about risk. He recently just joined the board of OpenAI.
Speaker 1:And and Tyler Cowen, you know, has this quote, if you're a doomer, why aren't you short the market? And Paul Cristiano is two x levered long, and he's short the isn't he short the the the the bond market or US treasuries? Right? So he has 5% of his net worth in Tesla, 90% of his net worth in AI bets, and a 100% of his net worth in normal investments. No Tesla options.
Speaker 1:That sounds like a scary place with lottery ticket biases and the crazy Tesla investors. And then you LEAs or Yudakowsky says, am I correctly understanding you're two x levered? And Paul Christiano says, yeah. And so he says he's personally short The US the The US thirty year debt. I think that just means you have a mortgage.
Speaker 1:I I I'm pretty sure that's like, if you have a mortgage, you are effectively short The US thirty year because you have you have sold that debt and you've got the cash effectively. That's how that works. But still, it makes sense because you if if you have enough money, you could pay off your mortgage, go long that debt and short the market around a relative basis. So but but again, that's not this doesn't seem like a doom based bet. This seems like this bet also pays out in just like the good ending and like AI is real and delivers value.
Speaker 1:So your AI bets perform well, the market performs well, and money slides from US debt to data centers and AI build out debt or something like that. So he is putting his money where his mouth is, but it doesn't feel like a representation of like doom by any means. Anyway, let me tell you about Shopify. Shopify is a commerce platform that grows your business, lets you sell in seconds online, in store, on mobile, on social
Speaker 6:It sure is.
Speaker 1:On marketplaces, and now with AI agents. Speaking of AI agents, people have been creating AI agents of Fruitflies. Have you seen this?
Speaker 2:Yes.
Speaker 1:Okay. Yes. Yes.
Speaker 4:So
Speaker 1:where should we start? Because I wanna set the table on what what is actually going on. Yeah. Tyler, do you understand this?
Speaker 2:Break down what people are cooking.
Speaker 1:We've seen this before. We've talked about this before. But what's actually going on with the with the Fruitfly? Because now people are taking the Fruitfly all over the place.
Speaker 5:Yes. So my understanding is is Google basically mapped out all of like the neurons in a fruit
Speaker 1:fly. So they probably took a deceased fruit fly and put it in like a mass spectrometer or something that can investigate the brain at a very, very like a deep high high powered microscope effectively.
Speaker 5:Yeah. Like the entire
Speaker 1:Three d. They got the entire structure and now people have been able to recreate that in software in a simulation.
Speaker 5:Yes. I I think in theory you can like replicate all of the the fly's like decisions or whatever. It's like movements.
Speaker 2:Yes. And this is notable because many people have said I have the mind of a of a fruit fly. This is I have the in they've been they've said I have the intellect of a
Speaker 3:Yeah.
Speaker 1:A a now they're gonna put it to the test Yeah. To to see which performs better, the simulated fruit fly or just Jordy Hayes who we got right
Speaker 2:organic farm to table Jordy Hayes.
Speaker 1:So so now that this is out and the code is out and you can run this Fruitfly in simulation however you want, people are having they've been they're doing all sorts of experimentation. So Kevin said he trapped the Fruitfly in his rabbit r one. When he shakes it, he can see its brain brain's escape circuit light up. By the way, our consciousness is physically defined, which is why our physical things like drugs, neurotransmitters, or events alter or initiate or end our consciousness consciousness and things like sleep, hallucination, waking, or death. Yeah.
Speaker 1:Computer simulations are also basically defined representations.
Speaker 2:Yeah. I think what what what ends up being unsettling and weird about this is is if if it just just purely you being human and doing this is is probably not good for your own soul. And, you know, imagine imagine you have a fruit fly in a box shaking it and you're like, look, it it wants to escape. Right? Yeah.
Speaker 1:Totally.
Speaker 2:I'm not a huge fan of small insects. I don't really want them around that much. Okay. But when they're in my house, I try to, you know, if a spider's in my house, even if I know it might wanna take a nice bite of me
Speaker 4:Yeah.
Speaker 2:I'm still gonna, you know, try to transport it out of my Sure. House and and and put it back into the world. I think it's I think it's not good for your for your soul to be, you know, a merchant of death. Sure. In Yeah.
Speaker 2:Situation.
Speaker 1:And yet, if you're playing a real time strategy game and you highlight a bunch of soldiers in this simulation and you send them on a charge that will result in their virtual death, you might not feel
Speaker 2:But those soldiers opted in to riding and dying with
Speaker 3:them.
Speaker 1:Okay. Okay.
Speaker 2:Right. Okay. Just sitting there, John.
Speaker 1:So they made the Fruitfly play doom. What about if you play Doom, you are killing a demon who simulated? Is that immoral? Like
Speaker 2:Well, that's Well, is that demon trying to kill you?
Speaker 1:A lot of it comes down to, like, the fact that it's simulating a the actual representation of the fly makes it a lot more concrete than just, oh, yeah. It's a it's a three d model in a python script. It just says, if you see a if you see a character shoot at them in the simulation. But we're we're clearly starting to grapple with, like, these odd moral questions of if you're simulating something, then the next step is a couple order of magnitude. But you get there and you can simulate a human, and you could talk to that human, and it would do everything the human does.
Speaker 1:Does that human have rights and agency as an ethical moral agent? Or is it merely just a simulated just a really good computer simulation? It's just existing on transistors, so you don't need to feel any moral weight about anything that you do to it. I agree with the just the vibes based analysis that like torturing a real fly, torturing a virtual fly, probably just don't be in the business torturing anything. You don't need to overthink it.
Speaker 1:But people are the the real debate here is like the question of, you know, are LLMs sentient? Are they moral? Do they is there a moral weight to the to to to synthetic intelligence, to artificial intelligence? That's what people are debating here. And I'm sure the debate will continue.
Speaker 1:Who knows if it will ever be ended? But they did teach it how to parallel park, which I think is cool. Interestingly, last night, I had Astra use computer use to play a video game that I very much enjoy playing called Blotro. It's sort of like a modified poker game. And I don't feel like I was torturing the LLM by that.
Speaker 1:I feel like I was giving it a treat. And I was like, hey, instead of doing my taxes, you get to just chill and play a video game. It did very well. It won. Soul was not able to win.
Speaker 1:And it was really fun because I would like pop in while I was using the computer inside kinda armchair quarterback and be like, isn't making the right decision right now? Felt like a felt like a coach coaching like a a kid on the
Speaker 7:soccer
Speaker 1:pitch
Speaker 7:or something.
Speaker 2:I had a funny moment last night. I was on my racing simulator comparing asking JatGPT for for to to compare my times Yeah. Laguna Yeah. To to just other other, you know, what what would best in class be like, what's beginner like, etcetera. And it said, if you want, I can help you, you know, cut cut some seconds off of this time.
Speaker 2:Yeah. And it and it was like, why don't why don't you take a video of a full lap Yeah. So that and I'll analyze them for you. And I was like, yeah, dude. I bet
Speaker 1:I bet you'd love to just watch back and watch track
Speaker 2:hang out and and watch track footage.
Speaker 1:Pretty soon it's gonna be like, you want me to just get in the seat? You might just take over because like I'd
Speaker 2:really Yeah. No. Computer use in iRacing is something I'm I'm gonna experiment with this
Speaker 1:I'm down. I'm down. Take take half my quota, my monthly quota. Just play games, chill, do whatever. It's a nice treat.
Speaker 2:I've got some bank resets
Speaker 1:Yeah.
Speaker 2:That I'll put to work.
Speaker 1:Yeah. Anyway, we have our first guest. I don't wanna mispronounce. Tyler, how do we pronounce his name? Thijs.
Speaker 1:Thijs? Let's bring him in. How do you pronounce your first name?
Speaker 6:It's Thijs. Thijs.
Speaker 1:Thijs. Nice. That's right.
Speaker 6:Like nice.
Speaker 1:Yeah. Well, welcome to the show. Thank you so much for taking
Speaker 2:the
Speaker 1:time.
Speaker 2:Great to have you.
Speaker 1:Tell us about your your role in robotics and also some of your recent work, hooking these models up to robotic tools and and infrastructure. It feels like we're going to be entering a boom of like people they ordered the Mac Mini recently. People are gonna be ordering three d printers and robotic arms and doing hack projects. Super excited for like the DIY world to explode over the next couple months. But let's start with just like your most interesting projects recently.
Speaker 6:Yeah. I'm incredibly excited for that. And and that world's definitely, like, just around the corner. So I'm I'm super excited about that. Yeah.
Speaker 6:I work here on the robotics team at OpenAI. I'm an intern and have been doing lots of exploratory projects. And this is just one of the projects I I I basically noticed that our models are really good at painting and doing various, like, computer use tasks in software like you were just talking about and learned a lot that you could actually basically connect these into physical robots in the real world and wanted to see how they would do that sort of drawing and tasks like that as well.
Speaker 2:Yeah. If you can if you can paint in Google Calendar with calendar invites,
Speaker 1:it
Speaker 4:would
Speaker 2:probably translate that to to the real world.
Speaker 1:Yeah. It's really like everyone is doing everything. Like, I've seen people paint the Mona Lisa in Microsoft Excel or Google Sheets, then you take take it back and you can do Excel and MS Paint now if you want with computers. And it's like everything becomes everything. What what what is actually the the important, like, precursors to a good experience?
Speaker 1:We were talking to a a YC founder who built a humanoid robot with some basic claws for, like, under $2. And it feels like there's there's an importance of some on device, you know, API or some on device models in some case doing some slam on device. But then there's also just tools that give you a very primitive interface that might be kinda clunky, but it doesn't really matter because you can just vibe code your correct interface. But what what what have you liked? What are you excited to to put in the repertoire of tools?
Speaker 6:Like, within robotics?
Speaker 1:Yeah. Within robotics.
Speaker 6:Yeah. I think that, of course, like, lot of these demos and stuff is very early. I think, like, the whole space and exact modeling and methods are still definitely being figured out. But I did really like the fact that you're sort of able to hook in the intelligence of, like, what the models are able to do, as you can see with all that painting stuff, and plug it into something physical in the real world and get it to do things. There's probably gonna need to be, like, some combo right now, like, running basically, my experiment here is is plugged right into Codex, and that's, of course, probably quite expensive.
Speaker 6:Yep. Definitely pretty slow. I think these paintings took between one hour and, like, two hours depending on the methods exactly that that model, like, Toast to use here. But, yeah, there's definitely a lot of improvements to make, but it's quite cool to see what you're able to do already.
Speaker 1:Yeah. How are you balancing trade offs between speed and reasoning effort? I was I was I've been testing computer use on a bunch of video games that I just mentioned and there's like the, you know, like part of what the feel the AGI moment is is when the cursor's moving at like at least near human speed And but then also you don't want to be making a bunch of mistakes and just so that's this, like, balancing act is really key. Did you try multiple reasoning effort levels across different paintings and see, like, noticeable results? Can you see qualitative differences in speed and quality based on models that you pick?
Speaker 6:Yeah. There's, like, a lot of different ways to sort of go about doing this. Yeah. And I initially started out these experiments by just telling the model, like, here I I as you can see, I actually have the bot behind me. Okay.
Speaker 6:Over here, it's currently painting a TBPN logo.
Speaker 5:Oh, cool. Amazing. I
Speaker 7:love it.
Speaker 6:But it has it's it's doing its best.
Speaker 2:There you go.
Speaker 6:Good start. We got basically this camera up here
Speaker 1:Yeah.
Speaker 6:That the model, which is connected right into the codecs over here, is connected to. Yeah. And the biggest challenge is that images are quite large Yeah. To process, especially, like, for the model, there there are a lot of tokens. Yeah.
Speaker 6:And the loop of, like, taking an image, doing taking an action, taking another image, taking an action, that's the thing that has taken a lot of time. So what I ended up doing like, it's not directly doing, like, image, like, one small movement. Image, one small movement. And I think, like, that would probably result in the best performance for,
Speaker 2:like Yeah.
Speaker 6:General robotic tasks and stuff. But, like, at the moment, it's basically taking an image, writing a plan in code of where, like, what it should do and where it should go next after it has done, like, a lot of the calibrations, maybe a slower sort of methods at first. Sure. And then after it has that plan, it executes it and then monitors it in the background, like, taking images every second or few seconds and watching, like, to make sure things are going well and, like, making it small adjustments to the plan throughout. And I I found that has been, like, a a good balance of speed here.
Speaker 1:Have you been thinking about, like, compression on the input? You mentioned that the images are lots of tokens. I I I have this monitor that renders at like five k resolution and I was like Mhmm. This is probably gonna be really slow for computer use. I should like run this game in a window and give it like a seven twenty p input because that's probably enough information.
Speaker 1:But I'm wondering like how important the the the resolution to speed and quality is that you've seen.
Speaker 6:Yeah. I think it's very important. In this case, for, like, a lot of the early demos, I was using, like, five twelve p, just compressing
Speaker 1:Oh, it yeah.
Speaker 6:Just to make, yeah, make sure. Because I think the model is just pretty much it's pretty much solved, like, a lot of the perception challenges here. So it's, like, really good at understanding what's going on even with, like, a lower quality photo here. But in this case, like, with the longer planning and and sort of, like, a plan sort of a minute of action out, it's less important, the, like, specific resolution probably. But, yeah, as we go to more, like, faster, much more smaller loops of, like, control, it's definitely gonna be important.
Speaker 6:Jordan?
Speaker 2:I I'm super excited about about this project mainly because it feels like we're right on the precipice of of sort of, like what what I what I think is gonna be a big breakthrough is, like, a sort of a deep research moment for robotics, like simple robotics use cases where deep research for so many people was their first time using agents. The idea that you could type out a prompt and then get back what would have been maybe, you know, at least hours of human work. Right? Somebody reading all these different sources and combining that information into a document that's that that has a consistent narrative and and understands the the right information. That was just such a big moment because a lot of people were saying, wow, can't believe that that that the AI was able to do something that would have otherwise I would have hired had to hire somebody to do or just taken a bunch of time.
Speaker 2:But there's like very simple tasks that I feel like you could probably start working on sooner than later which is like an example, at least for me, would be like if I could just take all the mail I get and dump it in front of a robot like that and have the robot sort it.
Speaker 1:Interesting. Yeah.
Speaker 2:You know, take all the, you know, 50% of everything I get is probably some sort of advertisement. So like, figure out what's an ad and shred that. And then actually, you know, basically like photograph and respond to if I have like a utility bill or or or any any number of things that I actually need to respond to. Theoretically, you could close that sort of like IRL to digital loop where the agent would like actually get a task from the real world and then close that loop online with with just normal computer use. Like that's the kind of thing that you don't need to you don't need like a $50,000 humanoid robot.
Speaker 2:Sure. You theoretically could have a actual desktop robot that was able to do this thing that otherwise takes me I dread going and like, okay, I have to sort through all this mail and figure out what's important and make sure I don't miss things. But I feel like there's a bunch of other use cases like that where people are like, okay, I didn't just generate a pretty picture or or Yeah. Or answer some question that I had. I like actually
Speaker 1:wanna save iRL spam filter.
Speaker 2:Yeah. An IRL spam filter. The spam Yeah. Spam filter robot. Yeah.
Speaker 6:Yeah. I think there's like two really really cool things about this project which sort of shows that direction that things are going. One is that this arm I don't know if you know about like too much about the prices of, like, classic robotics equipment. It's, like, thousands and thousands of dollars Yeah. At the moment.
Speaker 6:And, like, this arm that I'm using here, like, right behind me, this is a Hugging Face s o 100 robot, which is, like, open source, fully three d printable. You just need to get the actuators, which are, like, much cheaper. I think it at the moment, which is, I think, more expensive just because of supply chain issues. It's, like, around $200 or so, but, like, that's, like, incredibly cheap for robotic equipment and what you're able to do with it. So, like, I can see a world, like, quite soon where similar to and someone put this really well on Twitter.
Speaker 6:Similar to how there's this whole three d printing craze where everyone went and bought three d printers and ran software. Like, everyone's gonna buy these sort of cheap before we get, like, really industrial equipment for, like, personal use and personal product. Buy these plastic cheaper robot arms that you can just, like, sort of clip onto a table and just put stuff in front of and plug them into, like, agents that are already you can already do things with that are, like, already out there. Like, Astra is something that people can just pull up codecs and start controlling robots with right now, which is super sick. And we've also been seeing, like, at least on Twitter, I've noticed a lot of, like, sort of academic researchers at at different institutions start to, like, realize the that you can do this with these models and start doing, like, initial explorations and things with it, is super sick to see.
Speaker 6:And there's so much more Yeah.
Speaker 7:Was space. There was a
Speaker 2:YC company on the show yesterday that has managed to build a a humanoid robot for under $2,000. Oh, my gosh. Yeah. That feels like a price point that people would experiment with. So I if I if I know I can get a robot for like $2 and connect it to Codex and then tell it, like it basically lowers the stakes a lot where I could be like, hey, go every weed like this that you can find in my yard, go like pluck it out and and try to put it in a in a in a bag or whatever.
Speaker 2:Mhmm. And like that that's basically like the gardening robot. Yeah. And it could like flounder and fail, but I have like pretty high confidence that even right now, Astra would be able to identify like hundreds of like a specific type of of weed and probably and there's of course like safety The Nat Friedman. Safety concerns with that.
Speaker 2:But yeah. Yeah. The Nat Friedman like leaf robot like that feels like we're we're here. The models It can do it it's not it's not cheap but we're
Speaker 1:What is what is the next medium that you want to explore? Are you gonna get into whittling? Ceramics? I want to see you whittle a bench as a benchmark. Whittle bench.
Speaker 6:That's really good.
Speaker 1:But whittling a spoon or something. I don't know. It just feels like three d is the next thing.
Speaker 2:You really want to give the robot John wants to give the robot a knife. I don't think that's don't think read room, John. Read the room. Okay. Okay.
Speaker 1:Yeah. Maybe we'll stick
Speaker 2:to to Inter OpenAI intern gives robot a pocket knife. We'll do
Speaker 1:some ceramics, maybe get it on the pottery wheel, make a nice vase, something like that. But, mean, is there anything where you're like, oh, yeah. Okay. This is where this goes next.
Speaker 6:I think I was having the exact same thought, which is like, giving a robot a knife is like probably a very bad idea. But I I do wanna like sort of see if it can help do like cooking tasks or do various tasks that are like things that you do in your life that would be really sick if you could have a robot help you out here and there.
Speaker 1:Yeah. Yeah. That's interesting. Yeah. I I I'm so I'm so interested to see the because there's a whole class of tasks where you can't wait a full minute.
Speaker 1:I was testing on like a a real time strategy game and you can pause the game, but if you're not
Speaker 3:Mhmm.
Speaker 1:Moving at a certain APM, even on easy mode, like you will just get smoked. And so but but it feels like with new chips and Cerebras and Spark models and stuff like the the speed up is gonna come but it's just that's gonna unlock a whole new, you know, whole new host of of capabilities. What what what advice do you have for for young people that wanna get into DIY, you know, like this this type of work? I mean, you mentioned that one Hugging Face device that you have behind you. Are there any other devices or tools, tool kits that you recommend as places to get started?
Speaker 6:Yeah. I think that the Hugging Face robot is, like, an incredible tool. You can also three d print, like, a completely new embodiments and stuff. There's lots of open source projects online where people, like, change it around to get better grippers and things you can do with that. Just, like, also just don't give up after the first attempt.
Speaker 6:Like this was the very first
Speaker 3:Woah.
Speaker 6:Painting that the Woah. That's upside down even. I couldn't even tell. You can see the Golden Gate Bridge here. Can kind of see the ground it was trying to do.
Speaker 6:Yeah. And just like if you keep going and and you can you can sort of see the progression as it as it improves.
Speaker 1:Wow. That's amazing. Yeah. You need to frame those next That's to each that that's incredible.
Speaker 6:I'm thinking putting them all all all five of the progression in like a frame and calling it self improvement.
Speaker 2:Because That's Yeah.
Speaker 6:The model, this was this was a thread. The model just was able to
Speaker 3:Wow.
Speaker 6:Figure things out with a few pointers here and there, how to make get better and better at painting.
Speaker 1:Who's who's gonna sign it? Is it you? Do you have Codex sign it? Astra sign it? Who
Speaker 6:what's I've the also given the robot I've given the robot a a pen and it's gonna try. I don't know how well it's gonna do, but we'll see.
Speaker 1:Some axe or something. Well, thank you so much for coming on the show.
Speaker 2:So cool. Very cool. Come back on soon. Yeah. Yeah.
Speaker 2:Thank you for feeling the physical AGI.
Speaker 1:For sure. For sure.
Speaker 2:Very cool.
Speaker 1:Have a great rest of your day. Goodbye. Cheers. Let me tell you about public.com investing for those who take it seriously. They got stocks, options, bonds, crypto, treasuries, and more with great customer service.
Speaker 1:And let me also tell you about Cisco. Critical infrastructure for the AI era. Unlock seamless real time experiences and new value with Cisco. We have two guests with us in the TBPN UltraDome. How you guys doing?
Speaker 1:We're good. Welcome to the show. Partners. Yes. We're partners.
Speaker 7:Thanks for having us.
Speaker 1:Yeah. Welcome. Introduce yourselves. Introduce yourselves.
Speaker 7:My name is Guy Oseary. Yeah. From Maverick and also Sound Ventures.
Speaker 3:Yeah. Welcome. And I'm Alex. Yeah. Now with Sound.
Speaker 3:Yeah. Also running sources still. Okay. Very excited. Yeah.
Speaker 3:It's been a big week. No no no no on your head. No hat at all. No hat right now.
Speaker 1:We we were debating. Yeah.
Speaker 2:Every time. For the last year, every time I've seen you have the capital
Speaker 1:capital j journalism hat on.
Speaker 3:It's off. But
Speaker 1:it's off now.
Speaker 3:It's off. Okay. Yeah. But How long were you
Speaker 1:a capital j journalist?
Speaker 3:Ten years?
Speaker 1:Ten years?
Speaker 3:Fifteen. Okay. Yeah. Started around in high school.
Speaker 1:Yeah. So
Speaker 3:yeah. And
Speaker 1:then now, capital v venture capitals.
Speaker 3:Capital v. Great.
Speaker 1:Yeah. Capital I, investor.
Speaker 7:Yeah. It it feels we're we're aligned on the vision. Yeah. And really excited to get started. He's this guy's a force.
Speaker 7:But also a talent. Yeah. You know, like you guys. I mean, there's you guys you guys understand how to work with people and talk to people and help them tell their story. And I think it's so aligned with what I've been doing my whole life as well, which is helping people tell their story.
Speaker 7:Yeah. And so when we got together, it was it was just magical.
Speaker 1:Yeah. Yeah. It's awesome. What are you interested in investing in? We're spending a lot
Speaker 3:of time on on different things. Personal agent space Okay. Very interested in. Yeah. It's very hot, obviously.
Speaker 1:Are you a daily driver of anything yet?
Speaker 3:I'm using everything.
Speaker 1:Everything? Yeah. What's the last agentic thing you did? Did you book a flight? Did you email the CEO of Walmart for a refund on $5 of raspberries?
Speaker 1:Did hear about this? No. Oh, yeah. Some of these agents are
Speaker 2:very persistent. There's very persistent personal agents where, you know, a lot of like one one of the seemingly now very obvious use cases of agents is just like, hey, like do a bunch of things that would take me a lot of time Yeah. That could maybe save me some money. And when you're using a free agent, you don't care if it's spinning its wheels for for twenty four hours to get you a $10 refund if the refund if it's not costing you anything. Yeah.
Speaker 2:So apparently, somebody was trying to get a refund on a $5 pack of blueberries they got at Walmart.
Speaker 1:We use raspberries.
Speaker 2:Raspberries. Raspberries. The agent actually reached out to the EA of the CEO of Walmart. So that was the last place to escalate it. It was like, I gotta take this right to the top.
Speaker 3:So these agents are getting crazy. Yeah. They're getting crazy. They're swarming But
Speaker 2:but I guess like re yeah, rewinding a little bit. Guy, you invested in OpenAI and Anthropic like years ago. Hugging Face. Wow. And Hugging Face.
Speaker 2:I didn't know that.
Speaker 3:Yeah.
Speaker 2:Nice. And so I in some ways, like, you probably, the last few years have been just sitting back in awe. Just basically just getting to like getting to experience your own conviction and and and watching the space evolve. But I think like everyone has got to the point in the last or at least has consistently been feeling like nothing is like settled yet. We have these new kinds of businesses, labs.
Speaker 2:Some of the labs, you know, are making products, but there's still tons of room for other players to come in and make things. So are you feeling like renewed excitement around Yeah. Early stage?
Speaker 7:When when we did Anthropic and OpenAI, I think we were the only fund that that went in so deep back then.
Speaker 3:Yeah.
Speaker 2:Yeah.
Speaker 7:And it was confusing to some people, but to to us it felt like this was it. This was the time. These were the companies. These were gonna be long standing foundational platforms. And today it's a lot more confusing.
Speaker 7:I I you know, there's so much going on. And every single week, every single day, you guys are announcing
Speaker 1:Yeah.
Speaker 7:People's raises. They're raising now. They're raising now. They're raising now. It's hard to tell, you know It's not as easy as it was for us to to really decipher these are going to be the things people use in ten years.
Speaker 3:Mhmm.
Speaker 7:And and now, it feels like there's so much going on, but I'm also as excited. I'm also as inspired. I I don't I wanna be part of of these exciting companies. We just have to pick right. Yeah.
Speaker 7:I'm I'm meeting with some really incredible founders and visionaries. It feels really exciting. And so it's like when I'm starting in the music business.
Speaker 1:Yeah.
Speaker 7:It's like the early days where I got going and you're just getting demos everywhere, you know. You're walking out of a club and you're like, hey, yeah, you're the guy at the here's the demo. Here's my demo. Here's my demo. Here's my demo.
Speaker 2:You're like,
Speaker 7:how can I tell which one of these artists? But if you just if you put enough work in, enough time in and you're diligent, they it starts to, like they start to become a little more obvious. Yeah. And and there wasn't. And initially, had a 100 demos.
Speaker 7:I literally was, like, 17 years old with a 100 demos. But as you listen to a 100 my first three demos?
Speaker 2:At 17, they were you were People
Speaker 1:I would people that I would
Speaker 7:go out and go, seven let me hear your music. Me hear your music. And, you know, the first three demos I had, I had my favorite one, my favorite two, my favorite, like, in order. But a 100 in, those three are not even in the top 20. Right?
Speaker 7:Yeah. So you have to just you have to, you know, pattern recognition, you have to do a lot of work, you have to listen, you have to meet a lot of people. And then, through this crazy time, there's it's it's pretty freaking crazy. I think the right things appear. And then Yeah.
Speaker 7:And then you just have to be there to be part of that. And so I'm still as excited. Yeah. I have we have been able to sit back a little bit and watch our our you know, we we've also not we didn't just invest in those companies, we also invested on the way up. Yeah.
Speaker 7:So that also keeps you busy. We put a lot of money into Anthropic on the way up. We put a lot of money into OpenAI on the way up. And I think we have close to $1,000,000,000 worth of of money invested into those two companies. Oh.
Speaker 7:And so so we're not just like laying back, you know?
Speaker 4:Yeah.
Speaker 7:But but it is a lot harder today to decipher between what is real, When what is not
Speaker 2:when you make, you know, when you have like two effectively recent investments that are now two of the most important companies in the world. I do feel like the bar goes up on other on other investments because it's like, it suddenly is like, is it is it as thrilling to invest in a company that that can only be a $10,000,000,000 company. Right? When when in when a lot of VCs were very excited to underwrite a company to 10,000,000,000 Yeah. You know, six years ago, even Yeah.
Speaker 2:Even even during the period that that you were making those two investments.
Speaker 7:Yeah. I I think about it differently. I mean, I have heard some people say, hey, zero to a 100, that's like, it's not a big deal anymore. Yeah. You know?
Speaker 7:I just like I I I've always been attracted to talent and and visionaries. So I don't start with, okay, this you know, we're we're fortunate to be in these two incredible companies. But there's a lot in between and there's a lot that's to come. And I I just love sitting with a founder and problem solving and figuring out how we're gonna get from a to b. And sometimes it's sometimes it's where you get in.
Speaker 7:I saw a lot of people, you know, John from, you know, BetaWorks did really well on Hugging Face. Know, came in. We did well. But he came in where, you know, at seed. Yeah.
Speaker 7:So he did really well. And but sometimes it depends where you get in Yeah. As as well. But for me, what excites me is the same thing. It's been constant my whole life, which is surround yourself with really really incredible brilliant people who are trying to change the world.
Speaker 2:How does identifying creative or musical talent differ from start up entrepreneurial talent? Because I'm sure there's some some common threads, but whereas in music, you might back a musician that's that that has like a you maybe know they have a drug problem and and and that's part of part of the music. But in in in startups, you know, if if founder that's like has like some crazy crazy crazy stuff going on in their personal life, maybe it's like, hey, you should figure that out before you build a, you know, massive team and you're managing people and stuff like that. But there has to be like a bunch of common common ground between the two.
Speaker 7:Well, was able to transition seamlessly because of music. My job was to identify artists before anyone had heard of them and to sign them very quickly and to then help them reach an audience. So when I meet a founder, I I I it actually feels the same. I I always say founders are the rock are the rock stars too. Because when they walk in and they also have their music that they want to share with the world.
Speaker 7:So I have to identify that founder, same way I used to identify music artists.
Speaker 2:Yeah.
Speaker 7:And then go, this guy has or she or whoever have music that is so good. Oh, I love that chorus. That's a great idea. You mean a car shows up and it picks you up and it takes you or you mean an apartment people could share and you're like, oh, wow. That's a hit song.
Speaker 2:Yeah.
Speaker 7:So I I I always listen to every pitch like it's like it's a song or or an album or a music artist. And I just go, that guy's got the talent. He's a rock star. We just need to make sure the world knows it. We need to make sure that people are aware of what he's building.
Speaker 7:Let's go get a base. Let's go let's go create find that audience first and tell the story. So for me, it's I I feel like I've been doing the same job since I was a teenager, which is, you know, identifying talent and helping them helping them reach an audience. But music is the constant. I'm always listening for the chorus.
Speaker 7:Yeah. You know?
Speaker 1:And if
Speaker 7:I don't hear the chorus, I'm like, I'm not sure about the song. This is know, or the performance of the song or don't think we can I don't think this one works? You know, so so it all it always comes from that. The DNA is is Yeah. Is being around music artists.
Speaker 2:Is that in in in tech world, you see entrepreneurs that are truly visionaries and they're seeing opportunities before they're obvious and pursuing those and they have an idea of the way that the world should be and they're trying to sort of mold the world into that state. And then you have actually the majority of entrepreneurs which are just like, they don't know something's an opportunity until they see someone else pursuing it. Right. They're like, that seems Right. Like a good idea.
Speaker 2:I'm gonna do that. Is the same thing in music where, you know, you have somebody that has like truly a new sound and they have a life experience Yeah. That they're trying to like, they need they feel like they need to create art out of. And then there's the the follow on of like, oh, yeah. Wanna back cover band entrepreneur?
Speaker 2:Yeah. Cover band entrepreneurs. That might be a
Speaker 7:good When we started the record label you had Jimmy Iving here the other When we started the record label, it was just like four of us.
Speaker 3:Yeah.
Speaker 7:Small company. Was Madonna's company. Yeah. So that's cool. But we're still there's just no one knew what to make of it.
Speaker 3:Yeah.
Speaker 7:And Jimmy was on fire. Interscope Records was on fire. And and it I was always thinking, if I don't act quickly, he's just gonna pay them more and get them. Yeah. So and we were competing with big labels.
Speaker 7:Jimmy was the the guy who I always looked at, like, hey, he's he's he's he he can he can just come in here and just Yeah. Wow them and get them. So not only do I I have to I have to hear your song and decide right then and there, I want to do it. So Yeah. I don't have any background.
Speaker 7:Yeah. I don't have, oh, they oh, you know, biggest successes were always the things no one else wanted.
Speaker 5:Yeah.
Speaker 7:But, you know, Alanis Morissette, I mean, she tells the story where every single label passed. I didn't have any of that history. She came in she was in my office with her producer, Glenn Ballard. They played me one song, which is called Perfect, and within thirty seconds or forty seconds, I think I was like, I'm in. And and and so that Muse, you know, the band from England, came to to LA.
Speaker 7:Yeah. I flew them in because I like their demo. They flew they they did one after their they they performed a few songs. After the first song, I stopped them. Said, we're ready to go.
Speaker 7:And they're like, we flew all the way from London. Can we just play out the next few songs? I'm like, of course. But I just want you to know. Mhmm.
Speaker 7:So I developed that that act quick intuition Yeah. And it really just came from I had to, or else someone else would just Yeah. Figure it out and Yeah. Overpay and then I couldn't do the Well,
Speaker 2:so many of the the market dynamics that you see in music. Friend of mine, Zach Bia was telling me about like some of the process of signing the artists that he works with, where these artists are like, you know, the same thing that happens on on at like with in in tech where like some x account pops up and maybe there's like a team attached to it. There's no launch video yet, but you see a bunch of people following this person and then you hear that they're meeting with this firm and this firm and that firm and and the whispers start going around. Same thing in music where like an artist might one day have no followers on Instagram, be totally totally under the radar, living like in their parents' basement, but they have some little bit of magic.
Speaker 1:Mhmm.
Speaker 2:And then soon enough, they're like doing a roadshow basically with different labels and then you as a label need to be like, well, how much can we how much can we invest in this person? How big do we want to bet? We need to get to them we need to get to them first, but and then you're also sometimes competing on price, but other times you're just competing on like, well, how how great a partner can I be to this artist? So like, I can see how music translates just so well into venture because the exact same thing. Venture is not a game.
Speaker 2:Once somebody is talented and they're known, it's like very obvious. Obviously, you want to be
Speaker 7:It's harder to get in then.
Speaker 2:Yeah.
Speaker 7:And it's the same with I have competed when things are big. I remember when Prodigy, everyone wanted them and I I flew to London like in twice in four days
Speaker 2:Yeah.
Speaker 7:To try to get that and I got it.
Speaker 2:Yeah. Every VC wanted a story like that where they're like Yeah. I flew to this place. We
Speaker 7:have those. We we we we have all of those. But, you know, you know, you look at when we did Anthropic when we did we did SPPs in Anthropic, we couldn't a lot of people were not you guys to fill? Yeah. Tough to fill.
Speaker 7:A few times people didn't get it. Of course, now, you know, we're we're begging to get more of
Speaker 1:it. Yeah.
Speaker 7:So I just again, I always go back to don't listen to anybody. You know, you know, you talked about I think the other day also you talked about blinders. Did you talk about blinders or something on the show?
Speaker 1:Yeah. Jimmy Ivein has that concept.
Speaker 7:Yeah. So that that
Speaker 1:Horse blinders.
Speaker 7:So our I We have a mutual friend and Yeah. He's my mentor. His name is David Geffen. Yeah. And David said to me when I was like 21 Yeah.
Speaker 7:He told me that story. Yeah. I didn't know this. I he said, you know, Guy, you need to be a racehorse.
Speaker 3:Mhmm.
Speaker 7:And I was like he goes, you know what racehorses do? And I go, yeah, they race. I I I had no idea. Yeah. And he goes, no, no.
Speaker 7:They wear blinders.
Speaker 1:Mhmm.
Speaker 7:And so, just race your own race. Because if you don't wear your blinders if horses don't wear blinders, they could literally they could kill they could die. They could trip over their they'd look over and they could trip over. They could break their legs. And and so it really stuck with me that you guys talked about it.
Speaker 7:That really that that concept stuck with me and I I really try to just not pay attention, you know. When we did Anthropic and OpenAI, a lot of people doubted it. And and we we, you know, we we didn't have any doubt. Mhmm. We were we were determined to do it and and I I wanna stick with we're we're we're I'm really trying to continually connect to that of not listening to all the noise.
Speaker 7:Of course, data is important and we wanna get more details and more information and we're structured.
Speaker 1:Mhmm.
Speaker 7:But that gut that has gotten me here, I need to continually
Speaker 2:respect. Alex, on your side, you've spent how is it fifteen years? Decade? Yeah. Fifteen years ish?
Speaker 2:Did it take Do you feel like it took a few sort of cycles to hone your your intuition around around companies? Because in in our first conversations, I was always impressed with your ability to just see directly through the marketing on so many different companies. Like, some people like marketing just works on them. Marketing works on all of us. Advertising just works period.
Speaker 2:But like marketing works marketing and good comms work like too well on us Yeah. There's some people
Speaker 1:that are just like, this is a good story, so I'm chasing it
Speaker 2:from the capital journalism. For you, you'd be like, you would I would I would We would be talking about something and you would be aware of like a dynamic around a company that no other journalist And had talked at times, like you would be like, yeah, the story's not for me. But you were like clued in on a story and you knew exactly what was going on with the company. And then in the example I'm thinking of, won't name the company, only six months later did it did it has it even started percolating up that that that dynamic is going on. Mhmm.
Speaker 2:Yeah. And so I feel like for me and and at least personally, like I had to see the cycle of like company like starts, gets hot, attracts a bunch of capital, but sometimes you have this intuition around the company where like
Speaker 3:Yeah.
Speaker 2:Something doesn't really like feel right Mhmm. About this company even though it has a lot of momentum.
Speaker 3:Yeah. That's it. Yeah. I don't I don't know where that comes from except that I've I've been fortunate to spend time with a lot of like the best founders in the world. Sure.
Speaker 3:I mean, just had Zuck on the podcast. Sam on them before that. Yep. Incredible lineup coming up. Yeah.
Speaker 3:And I've gotten to know these people over years and years and years. So when you see like the people at the apex crushing it and who are at high integrity or beasts at the game on the field, you you can quickly see when someone is pretending.
Speaker 1:Yeah.
Speaker 3:And I just try to stay really close to like what's actually happening Mhmm. And ask around, do my diligence, leave no stone unturned. And that's got me well so far. But like, the thing you said about like six months later you saw it, I have that a lot where I'm like, oh, this seems really interesting. This seems like everyone's gonna be talking about this and then it happens.
Speaker 3:And it's happened enough in times to where I'm
Speaker 2:like figure out a way to make some money
Speaker 3:on it. Well, And like what Guy was saying about his gut and this is where I think we really hit it off is, yeah, you have to trust your gut. Like if you can get enough pattern matching recognition in, it's just instinctual.
Speaker 1:How do you think the podcast will evolve in this new role?
Speaker 3:It's going full tilt. I mean, first two episodes again were were Sam and and Mark. I can't share the names, but it's it's going.
Speaker 1:It's I just mean like like there's there's interesting ways when you have position in a company. The the the critiques always give me like Yeah. They're only having them on the show because they have a bag or whatever. Yeah. But when I look at like what Dorkash has done with Maddox and Raynor like explaining his expertise Mhmm.
Speaker 1:It doesn't feel like a sales pitch for that company at all. It's actually just tapping the network at a deeper level. Yeah. And at the same time, if I'm a founder and you're the place that I go to hear Mark Zuckerberg talk about his vision, that adds value and attracts even if it's not a company that you're actively investing in because you're not doing publics. Mhmm.
Speaker 1:So I'm I'm wondering if there will be more like less like this person's in the funding track or
Speaker 3:Mhmm.
Speaker 1:More like three sixty views? Do you want to climb the mountain and do all the max seven CEOs? Is that the goal? Or is it more like go deeper with certain experts, build this community of people with particular philosophy? There's like a whole bunch of different ways I could see it evolving.
Speaker 1:I'm wondering if you have any particular The max seven, I feel pretty good about.
Speaker 3:Yeah. You're on that
Speaker 1:track for sure. Yeah.
Speaker 3:I feel great about it. Yeah. I always love the like I love I love putting the people at the top with the people who are up and comers.
Speaker 1:Okay. Yeah.
Speaker 3:Context. I've got to and from base 10 on next week. Amazing.
Speaker 4:Like Legend.
Speaker 3:Legend. Yep. Incredible company. Yep. He's incredible.
Speaker 3:And, you know, he's he's obviously crushing. He's he's huge.
Speaker 1:Yeah. Yeah.
Speaker 3:But like he's not he's not Zuck yet. Yeah. So but I I wanna bridge that world because like this is all one world we're in. Like, everyone talks like the Zucks wanna know what the Tuans think, vice versa. Yeah.
Speaker 3:Wanna learn from each other. So I like I like building that cinematic universe. Sure. And it's like my taste. It's like I wanted to have that combo with Tuan which is coming out next week because like inference is just so important right now.
Speaker 3:Yeah. And it's like, everyone's trying to figure it out. Yeah. And so, you're also getting my POV of what I think is interesting with my guests, and I'm booking everything myself. Sure.
Speaker 3:And I think that's only gonna get better because, again, like sources is separate. I mean, obviously, I'm with the item with sound. But, you know, we'll have I'll have people on the pod that, you know, aren't we're we're not investors in. I'll have Competitors. I'll have other VCs on.
Speaker 1:Of course.
Speaker 3:It's about the ecosystem. Sure. And I I think the brand is important. Like, I wanna
Speaker 1:invest in the brand. What's the future of the writing newsletter? I imagine that you're not gonna be able to put this pen down forever.
Speaker 3:Like Well, yeah. It's interesting.
Speaker 1:There's I gonna be times when you just want to get something out.
Speaker 3:Yeah. I think I think, you know, I I wanna use the newsletter which has just an incredible audience Yeah. To to share what I'm seeing. And it's really like you're getting like an even deeper Mhmm. Sense of what I'm seeing because now I'm like in the room in a way that I was kind of in.
Speaker 3:But I was always like, when you're a journalist and you're in the room, you like get brought into the room and then escorted right back out. And now it's like I get to hang out in the room. That's so you're I'm like, I'm sitting with it and I'm marinating on it. And so when I write like a a piece, like, I'm thinking on something on personal agents actually right now. Yeah.
Speaker 3:It's informed by a lot of conversations. I'm not Sure. Gonna share all those. Right? Like, obviously, confidentiality is Yeah.
Speaker 3:Very important. But I think it's gonna make the perspectives I'm sharing a lot better. But look, I you you started this like hanging up the the capital j journalism hat. Right? There is a sense of like journalism in the traditional sense Yeah.
Speaker 1:Breaking screws.
Speaker 3:Of the and we we were talking about this when I was on the show before Yeah. Like the leaks and all the things that I've been going for over the years. Obviously, not gonna do that.
Speaker 1:Yeah. Yeah.
Speaker 3:But you know what? Like, I've done that
Speaker 1:for ten years. Yeah. You have like, I'm very excited to read this take on personal agents. Yeah. I think a lot of people have been thinking about the the way this category evolves.
Speaker 1:Do you have any interest in turning that into a video essay direct to camera, talking to the camera, putting it on the same feeds like what Dwarkash does when he writes an essay. He also has a video version. Yeah. Could just be a good product but also Maybe. Reach more people.
Speaker 1:People have
Speaker 3:asked me to do that. Yeah. Have I another job.
Speaker 1:I don't know if that makes sense for like other reports where it's like here's the here's some facts. Yeah. It's more of a quick hit. Yeah. But if I'm like getting like a quarterly thesis from you like
Speaker 3:Okay.
Speaker 1:Maybe it's a twenty minute video. I I would watch that.
Speaker 3:Okay. Well
Speaker 1:And it just gives me more optionality to, like, I can browse it in the email. I can Yeah. So like with Dorkhesh, I get his emails. I also see him on YouTube, and then I get in the podcast And sometimes I'll be in the video mood. Sometimes and I'm I'm I'm multi platform with a lot of these creators and
Speaker 3:so I need AI to help me with this, guys. I'm gonna be honest.
Speaker 1:Yeah. I don't
Speaker 3:But but you want the rawness of the person, which of course, but like, I think the pod is conversations for now. Yeah. Branch it out. Maybe it's more
Speaker 1:things a lot of the platforms are very receptive to multi product feeds. Yeah. Like having multiple media products within a feed. I've thought a
Speaker 3:lot about this.
Speaker 1:I've surprised that we've been able to do it with a twenty minute version of the show and a three hour version of the show dropping in the same feed every single day. Yeah. And it hasn't been
Speaker 3:You don't care.
Speaker 1:Bad. No. People just pick whatever they want. Yeah. And then if there's a hero interview, we get Yeah.
Speaker 1:You know, big interview with someone, that goes out as another one. Yeah. And that's its own thing. This is maybe
Speaker 3:a phase two thing. I mean, I have Yeah. More work. Eight eight incredible guests lined up
Speaker 1:Yeah. Over the next
Speaker 3:few weeks. Yeah. So it's
Speaker 1:like I gotta
Speaker 3:get those out.
Speaker 1:That's great.
Speaker 3:I'm gonna mess up with Guy next week doing some. I'm doing Yeah. Four next week. Yeah. I wanna get all those out.
Speaker 3:Yeah. And then, yeah, maybe like the personal agents Yeah.
Speaker 1:That's like a has the lens changed when if you think about a mag seven CEO, there's the the getting the scoop in the interview, the capital j journalist interview in that conversation. Mhmm. And I think that you can there's a bunch of different ways to do that. But then there's also the, you know, what is it what what will the next generation of great founders get out of this particular conversation with this Mag seven CEO? Are you starting to put on that hat of like I don't really think of
Speaker 3:it that way. I think about like what do I wanna know. Mhmm. And Yeah. And I really care about strategy.
Speaker 3:I really care about connecting the dots. Like Mhmm. Getting them to say something they've never said, which you saw with, you know, the last two pods. Yeah. Yeah.
Speaker 3:And that's still gonna be the thing. Sure. And that's that is journalism. Yeah. Like that that I mean, you're getting interesting and facts Yeah.
Speaker 3:Just a little
Speaker 1:bit of content. You can shake some
Speaker 3:off the top points Yeah. Get some And like, you know, these people are are doing a lot. And so it's like they're they're out there, but like, I I don't know. I think I get a lot out of my conversations with them. Yeah.
Speaker 3:No, they're great. I don't I don't think I'm gonna change much. Cool. I mean, I think the only thing is like, yeah, I'm not gonna be like leaking memos anymore. I used to do leaks about one.
Speaker 3:Companies worrying about leaks. Yeah. And that was one of my favorite don't kinds of stories. Think you're gonna have that anymore. I'm not gonna do do that anymore.
Speaker 1:More leak in 2027, No. You get a good
Speaker 3:A decade of that and I'm I'm good.
Speaker 1:You're hanging it up.
Speaker 2:Is the Is now a good time to become the next Alex Heath? Somebody's like twenty twenty twenty two.
Speaker 3:I think that's great. Yeah. I mean, think it's really hard right now if you're early because it's just that the media the traditional media environment is so challenged structurally and I feel like places aren't feel
Speaker 1:like the ads product, the sponsorship product He's saying traditional.
Speaker 3:No. I'm saying traditional.
Speaker 1:Yeah. Oh, like Oh, okay. So like, he would start in the newsroom. Would you
Speaker 3:how would you how would you, you know I've been fortunate people care because Yeah. I've broken a lot of big stories. Yeah. I've gotten a lot
Speaker 1:of big interviews. You left And
Speaker 3:I would came up in an environment where like Yeah. I was in a newsroom learning from incredible people. Gone on to do incredible things and run now in many these newsrooms. So I don't know how you do that now. Like, a lot of places aren't hiring.
Speaker 3:Mhmm. Their traffic's declining. They haven't made the pivot to like what we're doing, this direct thing, like Yeah. Subscriptions, like streaming. I it's really tough.
Speaker 3:I've thought about it. I don't know how you would break out right now unless you just kind of are
Speaker 1:You like a
Speaker 3:start? You maniacally focus on one thing and become the best in the world at that, which is how I started. Yeah. Which is like, I'm gonna be the best in the world at social media, covering Snap back in the day during And the then I was breaking a ton of news on Snap. Yeah.
Speaker 3:And then I got noticed and then I was like, I can shift this into other companies and keep shifting it and shifting it. Yeah. So I would still say that's it. You have to maniacally focus on one thing.
Speaker 1:Yeah. Mean, definitely It niche and beat reporting.
Speaker 3:But it's intersection of niche and matters. It can't be Yeah. You gotta catch What do you
Speaker 2:think about the possibility of instinct having a a bigger valuation than Snap?
Speaker 3:I think I mean, I wouldn't be surprised. I it's crazy out there, guys.
Speaker 1:It's also a new category.
Speaker 3:And What do you guys think about Instinct?
Speaker 1:I think that they're in a unique position because they're a startup so they can like, if there's, like, rough edges, like, those can get those can get ironed out and there's there's more forgiveness, I think, as opposed to Yeah. If you're a startup. Yeah. As opposed to, like, the muse agent is gonna be, like, congressional hearing if it something goes poorly. Whereas Instinct is gonna be like, look, it's a start up.
Speaker 1:You knew you were you were an early adopter. Let's give them the benefit of it out here. Yeah. Other Yeah.
Speaker 2:Yeah. To me, the the most interesting dynamic right now is because of the people's fear around AI, there's like way greater willingness to try new products because you don't wanna be left behind. Mhmm. Right? And so you may somebody may have been trying and being a daily active user of a variety of AI products for years now.
Speaker 2:And still, they're like, they wanna try the new thing because one, the space is progressing. There's so much room for new products and new categories. But then there's also this fear in the back of your mind of like, if I don't try the new thing, then like, I'll Yeah. You know, the the permanent underclass meme. And that's just consistently created this sort of second mover advantage, third mover advantage.
Speaker 2:Sure. And then AI brands, once they're big, they accumulate they've been accumulating baggage. Right? And so people are like, you know, maybe they have some maybe they're just like they're excited to share and talk about the new thing in a way that that they wouldn't be even products that they're using day to day. So I think it creates a big opportunity for for startups.
Speaker 2:But I think that we're I'm very interested to see how the personal agents market ends up comparing to just like the frontier model Mhmm. Inference market because it seems like every company is gonna build a personal agent. Many of them already have, especially if you count LLMs which do have agentic or or just like chat apps which do have agentic capabilities. But it's gonna be an absolute it's gonna be an absolute network
Speaker 1:effect and you get to some sort of take rate on agentic commerce, like you buy your car through it and they make $500. Yeah. Like that's a very, very
Speaker 3:Which Zach told me will be the business for me as a take rate do and
Speaker 1:he has a network effect Yeah. Thing to it.
Speaker 3:So Yeah.
Speaker 1:Instinct certainly challenger.
Speaker 3:Are I'm really interested in the idea of network effects with agents. Yeah. And and since Instinct's doing it, Town is doing it Yep. Meta's gonna do it. Yep.
Speaker 3:And maybe that is the next
Speaker 1:At the same time Network effect. Tricky it's if you can point an agent and say, like, get me off of this thing. Right.
Speaker 3:Well, they're not people, so it's like, do you care? Yeah.
Speaker 1:Do care if your
Speaker 3:agents are in network if it's
Speaker 1:But if you're like this one is the one that's never had a leak or never had a crash or never had a hack Yeah. Then you do stick around. Yeah. In theory, like, the time to build a new social network would be today because you could say, like, go through open up my Snap account or LinkedIn and scrape out every single person I have. They don't get a say in it and go add them on this new network.
Speaker 1:Right? Because like that that was like export the contact book was like Yeah. An arbitrage that closed. Yeah. And it's kinda opening back up.
Speaker 1:You think? I think so.
Speaker 2:I'm I'm waiting for, you know, the the criticism of social media was always like, we created social media to be social and it's made us less social than ever. And with personal agents, it's like It's made us less. It's like less, well, less like personal. Like, we don't have We're not gonna have personal relationships with like service providers and variety of things because it's like even even people with some of these new functionalities which is like, sorry grandma, I don't wanna talk about the road trip that we're going on, just talk to my agent. Yeah.
Speaker 2:You know. And so the new criticism will be like, we're no we're no one's talking to each other. It's only agents You know, we're communicating through like a Intermediate. Can on a string or whatever.
Speaker 3:Yeah.
Speaker 1:We we we gotta hop on with Mitesh from Positron.
Speaker 2:We should This was great. I'm super excited for you guys. Thank you. Big big of both of you.
Speaker 1:We'll let hop on.
Speaker 3:Yeah. Sounds good.
Speaker 2:You guys are gonna absolutely together.
Speaker 3:Thank you. Thank you.
Speaker 1:Thanks so much. Let me tell you about MongoDB. What's the only faster than the AI market? Your business on MongoDB. Don't just build AI, own the data platform that powers it.
Speaker 1:And let me also tell you about CrowdStrike. Your business is AI. Their business is securing it. CrowdStrike secures AI and stops breaches. We are joined by Mitesh Agrawal from Positron AI building a new chip for the AI era.
Speaker 1:Welcome
Speaker 6:to What's going
Speaker 1:on? How are you doing?
Speaker 4:Hey, John Jordy. Pretty good. How are you guys?
Speaker 1:Thanks so much for hopping on the Pretty good. Great to have you here.
Speaker 4:I I just want to start by saying I've been in the background of Steven doing this multiple times now.
Speaker 1:Oh, yeah.
Speaker 4:And Steven Steven Valivan from
Speaker 1:That's right.
Speaker 4:You guys. So this is my first time on, so thanks thanks for having me.
Speaker 1:How how direct is the lineage from from Lambda? You're working at effectively Neo Cloud. You see the problem. You go solve the problem externally with a new startup. Is is the story that simple?
Speaker 4:Yeah. Fairly. I mean, for me, I mean, look, I didn't found Positron. Right? Positron was co founded by Thomas Summers and Edward Kemet.
Speaker 1:Sure.
Speaker 4:Another lineage, Grok lineage from from from before. And, you know, they they designed the actual silicon and and and the system setup. And part of it is just, like, great luck. You know, I've known both Steven and Thomas for over decade. I've been close friends with both of them.
Speaker 4:I've worked with them, been roommates, everything, all of all of those things. And Thomas has been wanting me to join Positron since day one, since he started the company in 2023. Yeah. But Lambda was just starting on its hockey stick growth then, I was like, look. I'm not leaving Lambda.
Speaker 4:Started the company with Steven there in Lambda Cloud. But early twenty twenty five, like late twenty four, you know, reasoning models had come out. O one just started to to to get into zeitgeist, and then video generation. You know, Sora, first Sora came out. Not a lot of people saw it, but I I got to see a little bit behind the scenes on on the video generation models, the amount of memory.
Speaker 4:I remember looking at the Google video model back then, you know, it needed four h one hundreds to run, like, a ten second clip.
Speaker 1:Yeah.
Speaker 4:It's completely memory bound on bandwidth and capacity. Mhmm. And knew what Thomas was building. I was like, this is actually very interesting. Memory is gonna get a big part of the story for inference.
Speaker 4:Somebody's building something about it. Let me go get and and and work at the at actually the fundamental technology there. Like, Lambda builds technology on the cloud and and services, you know, and I'm chemical engineering, studied fabrication, never used it ever. So I was like, alright, I'm gonna go back a little bit to my roots and then come back to it.
Speaker 2:Amazing.
Speaker 1:How much is AI actually accelerating semiconductor design, semiconductor fabrication? We saw one of your investors, Dylan Patel, at semi analysis talking about the the open air jalapeno chips seemed like ahead of schedule or very, very quick. For a long time, we've been hearing, oh, new chip. That's three years. That's five years.
Speaker 1:Feels like it's eighteen months now. What are you actually feeling? What are you seeing?
Speaker 4:Yeah. To start from reverse, like, to answer your last part about it, like Yeah. Man, new chip every twelve months. NVIDIA is the absolute king. And if they're coming out with a new silicon every twelve months, you better get in that game or or, you know, like, you like, don't even be part of the conversation kind of thing.
Speaker 4:Right? So so that's that's for sure. In terms of utilization of AI, mean, look, I'll just, like, focus on Positron itself, but, you
Speaker 1:know Yeah.
Speaker 4:We are a small team. I mean, we we got to we we are right just now. I mean, just yesterday or today, crossed a 100 people, but over 50 of that is over the last three months. So we got our first gen product out with Mhmm. Less than 20 people, and that's built on FPGA, so it's already pre taped out silicon, and we we are deploying and implementing our architecture.
Speaker 4:And our second gen
Speaker 1:Those are the out end of this year. Are the 50 Atlas racks you have at Oracle?
Speaker 4:Oracle? Yeah. Okay.
Speaker 1:So those are FPGAs. Interesting.
Speaker 4:Yeah. Those those are FPGAs. Cool. And it's kind of like harking back to a little bit of of previous times when you used to design and and build silicon. You would actually test on FPGA before going into the
Speaker 1:Yeah.
Speaker 4:The tape out kind of thing. So we just wanted to get a product out as quickly as possible. Like, that was the whole thing. It's like, you know, from the start of the company, we got our first shipment to a customer in fifteen months, and it was, you know, it was built on FPGAs, obviously. But to get the full, you know, big file ready, getting it deployed, getting models running on it, it was all done in the first fifteen months, and then over the last ten, three months killed it out.
Speaker 4:But, yeah, I mean, like, look, we have to use a lot of the AI toolkits, especially on verification. Design, less so, I would say. I mean, look, obviously, we use a lot to kind of interact with, like, now Astra, for example, is phenomenal, right, you know, to interact with it, but you're still not going there and saying, hey. Like, come up with this, like, new design yet. Although, like, you know, Ana's computer cursive and others, are they're obviously built out, and, you know, they raised a big round for for that as well.
Speaker 4:Right? So it's gonna come. You know, you're gonna see very soon. Mhmm. It's like one person an Astra or one person an Astra's Sure.
Speaker 4:Taped out a chip kind of thing. But we have to use it a lot. I mean, if you think about a 100 people or, you know, very recent until very recently, 50 people. Mhmm. For a company that is targeting tape out end of this year to do with, like, fifty, sixty people, it's it's very tiny amount in a in a Silicon world.
Speaker 1:What does the demand side of the equation work? You're already working with jump trading, i3d.net. Is this something where you like, if you can get capacity, if you can get performance, some solid benchmarks, you think that sales isn't going Or to be a are you going to have to find and work very closely with a customer to sort of co design a solution for a particular problem within the AI stack.
Speaker 4:Yeah. I I don't want to trivialize or make it sound simple like like like that.
Speaker 1:Your sales guys might be listening and they're like, we work very hard. Okay? Shut up.
Speaker 4:But, well, we we we only have one sales person. We only have one sales person, you know, like in that way. Right? But the point I will I will make is really around the way we think about the demand curve is you're kind of hitting the nail on the head in saying that, like, look, if you can make your silicon work, show the performance is comparable, especially in the current ecosystem, even within the niche of doing this ag or something like that, but especially if you can make the entire inference kind of workflow have a good TCO and or, and generally people always assume it's an or that, hey, you have a good TCO or you have a great interactivity curve. Mhmm.
Speaker 4:But if you can do and or, you know, you're you're gonna bound to have get demand. More importantly, you know, when we when you step into the rooms of, like, not only just jump trading or, you know, hedge funds or or kind of influencer service providers, but, like, really the big labs, the hyperscalers, Kind of two questions that it boils down to is, like, hey. Like, look, guys. Can you fabricate this in enough quantities? Like, you know, is your supply chain and the way that you are using the technology components, is it robust enough that you can fabricate it that we can be interested in it?
Speaker 4:And then the second thing they're asking is, can we deploy it? You know, is is your power source, like, you know, do you need this kind of liquid pool setup? And and if so, then, you know, we don't we might not have a data center because we've already allocated to GPUs or TPUs. Mhmm. Or can you do something else?
Speaker 4:So so the questions you can see there they are asking is not that, hey. Like, you know, you know, we we we will see. We don't we we're not showing up at the demand curve. So so from that angle, you're kind of spot on that, look, if you can make the frontier models run, you know, you're you're bound to find kind of adoption in in today's market, And that is a really great like, I mean, I'm so like, you know, as positive from we are so lucky to be building silicon in this environment. And that's kind of what you're seeing for with silicon companies is raising rounds right now.
Speaker 1:Yeah. I think $9,000,000,000 has flowed into silicon companies over just the last twelve months and Yeah. Which we were we were talking yesterday
Speaker 2:feels feels incredibly low relative to the spend like Yeah. Spending category.
Speaker 1:Gavin Baker, one of another one of your investors has this quote. Where he says, I see it as a 1% market share is a $100,000,000,000 opportunity. And that sounds like a crazy bold take and you then you realize like, wait, no. NVIDIA is a $5,000,000,000,000 company. Like, it's gonna be a $10,000,000,000,000 market like any day now.
Speaker 1:And so, yeah. Actually, 1% should equal a 100,000,000,000. But yeah, anything else? Sorry.
Speaker 4:No. You you like he said that in a board meeting to me like, I don't even know, like a year ago or something. Yeah. There is is basically like, yeah, he's he's like like, look look look guys, like Mitesh Thomas, just just 1% of the market, you know, a 100,000,000,000 enterprise value. Just focus on your architecture where you can do well.
Speaker 4:You know, like, one of the things that, you know, people always like, whenever a new new chip company raises around the headline, and luckily you guys don't have that, which is like, to rival NVIDIA. It's like, guys. Like, no no one is rivaling NVIDIA. Like, get to at least 10% of their revenue before before putting the tagline on. Right?
Speaker 4:But but but, like, the point there is just like, look, NVIDIA is everywhere. You gotta work in that to both work with them, but also like having a product that is differentiated enough. Like, you have to have technical innovation, obviously, to stand out, and then show your performance TCOs and interactivity. But then you also got to prove that like, look, in the world of HP and CoWoS constraint, like for us, our big stories are know, like, look, HP and then CoWoS bottleneck. You have NVIDIA, TPUs, AMD's ahead of you in the blind.
Speaker 4:You know, how do you get around that? Well Mhmm. Again, you know, you you say, okay. We are using commodity memory. Well, pro and con.
Speaker 4:No no free lunch in Silicon Land. Like, you know, commodity memory is slow. How do you solve that? That's where the technical innovation comes in. And then second thing is, like, okay.
Speaker 4:It's still not trivial to get commodity memory. It's not like I can just show up to Samsung and be like or Micron, be like, hey. Can you give me LPDDR5x? You know? Yeah.
Speaker 4:You have to still figure out how to how to get that and plan it. But it is more feasible to to get it, and that becomes a story of that the company can then scale out and saying, not only we're gonna have a product, but we're gonna have a product that will scale with the requirements of hyperscalers and and and kind of the frontier Yeah.
Speaker 2:How do these how do these customers think about, like, the minimum scale when when they're when they're working with you and they're looking at ordering order making orders that will be delivered in, let's say, 2028, 2029. Right? You need to be able to to really be worth a company at that scale's time, you need to be thinking about like it's almost like, hey, the orders we want delivered in 2029 are like a proof of concept for like the 2,032 order which will be, you know Yeah. At some scale to actually impact and and be able to scale the fleet in a meaningful way. But but how are you thinking about like that feels like the biggest challenge is like minimum viable sort of like 100 deployment.
Speaker 4:100%. I mean, like, literally you kind of circle back on, like, as I said, when we walk in these meetings and like, scale depends on, like, if you're going into hyperscalers and frontier labs, and I said the first question they ask is like, guys, can you fabricate like this? Like, in in enough? And the the question there is, like, in enough quantities that it's, like, worthwhile to us. And and that answer for hyperscalers and and Frontier Labs, like, honestly, they will literally say gigawatt plus.
Speaker 4:Like, come up to us with a proposal of a gigawatt plus, which is kind of insane. Right? Like, in a gigawatt, like, even at NVIDIA scale, you're talking about $75,000,000,000 worth of of of revenue for them. Right? Even, you know, assume ASIC cheaper, blah blah blah, all those things, you're still talking about tens of billions of dollars.
Speaker 4:Right? But, like, at least you have to show a plan of, like, how do we get to hundreds of megawatts in you know, to to use your, like, year specific year 2028. You know, for us, we're taping out this year production kinda ramp up in second half of twenty twenty seven. In 2028, we better have a plan of how do we get to, like, hundreds. And I I don't want to just say cop out by saying hundreds as in just a 100 megawatt.
Speaker 4:Hundreds means truly, like, you know, three, four, five, and above for for those. But
Speaker 2:Yeah. So that's by the next scale up, you're in that gigawatt range.
Speaker 4:Yeah. Exactly. And but also, like, I also don't wanna discount the fact that you have other customers, like, you know, have Inference as service providers, obviously, Sovereign AI Clouds, you know, quantitative finance, quant finance kind of spectrum. And so they have, like, different magnitudes of kind of requirements that that come through with it. So, you know, we although we do internally use kind of go big or go home as as a thing, like, we have to attract one of these large customers to to really be a long term viable company.
Speaker 4:You know, I I don't wanna just, like, discount the fact that, like, look, you can grow the company through the ranks as well. Like, you can grow the company, you know, get 200,000,000 revenue for $250,000,000, a billion, 2,000,000,000 through through this other kind of channels as well. Right? I think that that that becomes a big part of it. But, yeah, like, if you really wanna get to, like, the frontier labs and hyperscalers, you're really talking about hundreds of megawatts.
Speaker 4:And that's why, like, you know, like, look, when we, you know, we have Venture Tech Alliance on our kind of cap table, and and when we speak with TSMC for fab capacity, they are also wanting to know kind of like, can you scale, like, know, do you have the balance sheet to do that? Like, one of the reasons we raised, you know, $875,000,000 is not like we need $875,000,000 to spend tomorrow or even in the next six months. I mean, look, we raised $230,000,000 in series b in February of this year. Untouched. Right?
Speaker 4:We still have all that capital. Part of it is because we have been making revenue this year, but we do have plan to spend that very quickly. Thank you, Joey. That that
Speaker 1:was actually That was one year when you were here.
Speaker 4:Untouched.
Speaker 2:Untouched. Untouched. Untouched. Untouched. Because we're making revenue.
Speaker 2:Yes.
Speaker 4:The the the main point that is there though is like, look, they they want to know, like, if if you actually get a customer, you have the capital. Yeah. And and even that capital is is that is not enough equity capital to scale out to even, you know, 200 megawatt. Right? And then you have to go to the black stones of the world and figure out how do you how do finance that deal, kind of what Lambda has done.
Speaker 4:Right? So
Speaker 1:What's the software side of the equation? You're coming for NVIDIA. You're challenging them. You're gonna drive their market cap to zero. Have to drive that end.
Speaker 1:No. Obviously, this is a market that can sustain multiple players, and there's different tools for the job. But interoperability is important. I'm interested in terms of software development. Are you going to lean more open source with the software side of the business or more integration with just a few buyers and co design on the software side to make sure the integration is really seamless?
Speaker 1:Is is there even do we even need to be having a software conversation in an era where AI agents can write code?
Speaker 4:Yeah. I mean, you you definitely need the software conversation because, like, you you you have to plan around how people wanna use it, and people wanna use it how they're currently using it and gonna continue to use it, which is based on NVIDIA stack, but also primarily based on PyTorch and then, know, VLLMSG lang as the and and I'm I'm specifically focused on on inference items. Like, look, training, you know, that's such a harder challenge, like, you know, what Jensen says, like, true mode around scale out and everything. Right? Like, that's just that's the only reason you have probably only TPU as a potential, kind of only other silicon that can be used for training.
Speaker 4:Right?
Speaker 1:Sure. Sure. Sure.
Speaker 4:But but on the inference side of things, for sure, you have to have the conversation. I will say this, in the era of agentic kind of software development, the worries around like, hey, you know, model drops, if you don't have access to it, it takes you days, weeks, months to bring it up. It's it's going away. Like, you know, know, we we had news glimmer drop and within our team, you know, on our Atlas, first gen could get it up and running within hours. Right?
Speaker 4:And You and that that that yeah. Exactly. Like, I have the same reaction, by the way, when when we had that, and and people are are are making it even faster and more automated too. Like, you don't even have to interact. Model drops, comes in, can can can probably do it in in and that's a very near future of it.
Speaker 4:But to that point, it doesn't give you the right away the efficiency, the optimization. Sure. Like, know, you you wanna extract every dollar off of it. So to your question around, you know, when when the customer is large enough, you wanna work closely with them to, like, literally extract every single dollar. And also, like, I'll I'll be very frank, like Anthropic, OpenAI, this Frontier Labs, hyperscalers, they are so sophisticated.
Speaker 4:They kinda wanna come in and be like, look, guys, even if you don't want it, we are working with you to make sure that this is gonna, like, you know, this is optimized to fullest, right? So the answer, as always, in in this scenario is all of the above, you know, even though it might sound like it's like, oh, it's a very cliched answer, but it really is that way.
Speaker 1:Sounds like the mafia coming in, oh, your software stack isn't open source, so you're about to open it for me. I'm gonna make some changes if I The need
Speaker 4:software stack is, like, gonna be built around open source, like, the sense of, like, if if you wanna make, like, every company to to use us for inference Yeah. You know,
Speaker 1:you have
Speaker 4:to build it on SGLAN and and VLM kind of setup. Right? That's okay. But, you know, it's like when you're talking to SpaceX or Anthropic or OpenAI, they're not using the generic SGLAN or VLM, they have all their optimizations built in, they're going to help you do that. Then obviously there's Disag, then within Disag there's all the different domains that they do, and they're going to figure out, it's like, oh, jalapeno is good for this, All the tronia, you're good for this.
Speaker 4:You And then they're gonna they're gonna say, okay. That's how we're gonna use you guys.
Speaker 1:Amazing. Well, exciting times. I wanna hit the phone for you. You raised 875,000,000. Oh, man.
Speaker 4:That was a solid run.
Speaker 1:Thank you. Job, James. Thank you, John. And thank you
Speaker 2:so Great stuff. Great to meet you.
Speaker 1:Keep an eye out on Instagram. Where you're building. Definitely dropping a NVIDIA challenger slide later today.
Speaker 4:Yeah. Sure. Please do not associate my photo with that.
Speaker 3:Better yes. Well,
Speaker 1:have a great rest of your day.
Speaker 2:Looking forward to the next appearance. Have a good one. Great to hang.
Speaker 1:Goodbye. Cheers. Let me tell you about Railway. Railway is the all in one intelligent cloud provider. Use your favorite agent to deploy web app servers, databases, and more while Railway automatically takes care of scaling, monitoring, and security.
Speaker 1:Lastly, the New York Stock Exchange. Wanna change the world? Raise capital at the New York Stock Exchange. That's can
Speaker 2:see positron over there pretty soon. Yeah. Pretty soon.
Speaker 1:Picking up some
Speaker 3:fresh ones.
Speaker 2:Wonderful week.
Speaker 3:Wonderful week.
Speaker 2:Short week.
Speaker 1:Monday. Yeah. Monday, 11AM.
Speaker 2:Do us a favor and go ahead and have the best weekend
Speaker 1:Best weekend. Of your
Speaker 2:entire life.
Speaker 1:Have the best weekend of your entire Let's
Speaker 2:do it. Put the pieces together. Make it happen.
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