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TBPN is a live tech talk show hosted by John Coogan and Jordi Hays, streaming weekdays from 11–2 PT on X and YouTube, with full episodes posted to Spotify immediately after airing.
Described by The New York Times as “Silicon Valley’s newest obsession,” TBPN has interviewed Mark Zuckerberg, Sam Altman, Mark Cuban, and Satya Nadella. Diet TBPN delivers the best moments from each episode in under 30 minutes.
You're watching Watching TBPN.
Speaker 2:Today's Wednesday. 09/16/2026. We are live for the TBPN to realm the temple of technology, the fortress of finance, the capital of capital. Let me tell you about ramp.com. Time is money.
Speaker 2:Save both. He's to use corporate cards, bill pay, accounting, and a whole lot more all in one place. Jordy, is that even gonna happen?
Speaker 1:I don't think so.
Speaker 2:I don't think that's gonna
Speaker 1:happen to be already problems.
Speaker 2:Stripped the stripped the years off that one. That's rough. Well, we have a fantastic show for you today folks. I made it to San Francisco and back since the last show. This is a new thing.
Speaker 2:I like this. Being able to get up from the show, go do something in San Francisco, get back. Very excited stay? I don't know. I like doing the show.
Speaker 2:Pretty simple. Good to be here in the TBPN UltraDome. We have a great show. We have a bunch of great folks coming on the show. What was on my mind last night?
Speaker 2:I was listening to China talk. Jordan Schneider was talking about evaluators, third party evaluators. And I was noticing this discourse around like, it feels like we we we funneled into like very clear camps super, super quickly. And it feels like, I don't know, too calcified for how fast it happened. Like, the the Dario essay comes out and, you know, he throws out meter, says he's bringing in meter, and and the backlash is, like, a media at the New York Post is crashing out saying, like, these are handpicked AI watchdogs.
Speaker 2:Martin Casado, Martin Casado over at a 16, he's pushing for the Department of Energy. He's like full nationalization now. And, you know, both of those have their advantages, disadvantages. They both do good work. They are the extremes.
Speaker 2:And so I was just I was just sort of wondering a few things. Like, first, is like is like what other regulatory bodies can actually work? How do they work in other industries? It's very interesting because like the revolving door is something that's common like in in in in financial regulation you get people that work at banks and then they go and work at the regulator and then they go back and forth. I mean, this is the this is the story of of the, like, many people in the crypto industry where the regulators who are regulating it, go back and forth.
Speaker 2:Like, revolving doors exist, but I think the pushback to meter is very much like the door is like too revolving or it's too it's too close, I guess. But the but but the more interesting question to me is is
Speaker 1:Yeah. The the defensive meter
Speaker 2:Yeah.
Speaker 1:Is that I don't know many groups that are qualified at all Okay. To even to even understand what's going on at the frontier. Right? And so
Speaker 3:Is that true though?
Speaker 2:I see that's the thing I disagree with.
Speaker 1:I I would say that there's there's there are not that many groups that have been this invested in in understanding frontier model behavior for this long.
Speaker 4:That's a separate thing. It's just
Speaker 1:a small group right now.
Speaker 2:That's a separate thing. So I think there's two separate things. There's one which is like super forecasting, seeing the future, predicting what's gonna happen. I think that's important. I think taking that seriously is important.
Speaker 2:But then there's the other side, which is like doing the work, reading the logs, and being like, this violated this rule. This hack happened. Here's how it happened. And I think that those are actually two separate disciplines, two separate jobs. And you can just tell the regulator if it's someone.
Speaker 2:You don't need to tell like, if you hire someone and they are able to to under like like look get up to speed on how these systems work and and evaluate them and look at logs of different incidents, see what's happening, assess the risk level, assess the liabilities. If you can get those people up to speed, you can just be like, it is your mandate to take this seriously. And and the example that I'm pulling from is like is like, you can be 22 years old, not graduate from college, go into the Navy, enlist in the Navy, not even in the officers training program, and in eighteen months, you can be responsible for the security of a nuclear power plant on a submarine. You do they you do six months. I I actually looked it up.
Speaker 2:Like, you you you do you do six months in nuclear field a school, about six months of nuclear power school, then six months of hand on hands on prototype training before arriving in the fleet. Nuclear power school covers math, nuclear physics, reactor principles, health physics, materials thermodynamics, electrical systems, and reactor technology. There's no part of that that's like you need to really, at a deep level, understand that like nuclear annihilation is bad and could happen. You don't need to be able to forecast out, well, what happens if China and geopolitics and Iran gets the bomb and then these people and then Pakistan? You don't have to understand that to just know, like, don't let it blow up.
Speaker 2:That's your job. Here's how you don't let that happen. We've created a plan for you. You're 22 years old, but we know that you can do this job and you are enlisted to do this job. And doesn't matter if you think nuclear war is impossible or doesn't matter if you think it's gonna happen tomorrow.
Speaker 2:Your pea doom on nuclear is completely irrelevant to you doing this job. Right? Yeah. And I think that that's something that is maybe being missed here a little bit. There's like there's like, well, you have to take it seriously.
Speaker 4:Yeah.
Speaker 5:You have
Speaker 2:to have seen it coming. And I actually think that like super forecasting, important, awesome. Also, like fun read, really cool. And if you take it seriously, you make a lot of money, you work on interesting stuff, you get amazing technology. There's so many things that come downstream of that that are really really positive and really important.
Speaker 2:But I don't necessarily know that it's actually a prerequisite. And then the other thing is yeah. Tyler?
Speaker 4:I mean, like METER, they're not the ones putting out those forecasts though. That's like those are other groups. Yeah. If you look at like METER research, it's like, you know, very complicated benchmarks and like these kind Totally.
Speaker 2:Totally. Yeah. I'm I'm I'm sort of collapsing the meter criticism from like the New York Post perspective. Yeah. And and I think that is
Speaker 1:Overall, think generally Yeah. People are gonna like if if if there's a third party regulator, it seems like it has to be an entirely net new group. Because I don't think anyone has like whether or not Meter Meter could be operating and and their actions could reflect that of a fully
Speaker 6:Mhmm.
Speaker 1:Independent group. But the financing structure, the history of the of of the various parties there just makes it so that people don't have any trust that they would act like that.
Speaker 2:Sure. The the interesting thing is that there is actually a distinction in the history of nuclear regulation, which is there's a difference between advisors in the before the NRC, it was the Atomic Energy Commission. And the AEC was created to oversee nuclear development. But and many of the scientists who did forecast the importance of riskiness of the technology were involved. Interesting.
Speaker 2:Like like Einstein writes this letter and says, like, nuclear war he basically, you know, like, describes, like, what nuclear annihilation could look like. There's a whole bunch of other scientists that actually run the numbers and they're this is what could happen if all the nuclear bombs go off. They do all the calculations. There's some that go a little bit too far. But in general, like the scientists were the ones who got to it earlier.
Speaker 2:But the scientists didn't actually wind up being the regulators. They wound up being the advisers. So they so Oppenheimer became the chair of the AEC's general advisory committee, which had enormous influence but didn't actually issue licenses. So the actual work that was being done, it was like, okay, Oppenheimer in this advisory role says like, well, we need to have a security guard here with a gun that makes sure that no one can steal the nuclear material. Right?
Speaker 2:But he's not the one doing it. He's not the one actually hiring that person. That's just like an engineer who's qualified for that job. And I think that actually takes a lot off of it because you could say, oh, there's all these like conflicts of interest here or there or there, whatever. But if you just say, well, they're just putting putting out a proposal that then people are going to go and implement.
Speaker 2:But then the actual people that are doing the implementation are are much less conflicted because they're just drawn from the broad pool of engineers and scientists and mathematicians and physicists and whoever else we have in on in America who can do this type of work, it gets a lot less complicated in in my mind. There's whole bunch of other interesting details from the AEC history. Enrico Fermi and Glenn Seaborg became the became committee members with Seaborg later becoming the chairman of the AEC's AEC itself, of course, Fermi from the Fermi Paradox. Famous scientist, very interested in like forecasting, seeing the future, understanding the implications of things. But again, becomes committee members, not actual regulators, not actual licensors.
Speaker 2:And then Edward Teller is sort of like the most accelerationist because he was advocating for the development of the hydrogen bomb. But he ultimately became the chairman of the AEC's reactor safety committee talking about how to actually secure reactors in particular. But again, not actually on the team regulating staffing working directly. And so today, the regulators, like nuclear regulators, are talented and hardworking, but these are not, like, the most elite jobs. Like, you can you can just be a nuclear engineer, mechanical engineer, material scientist, physicist, health physicist, geologists, probabilistic risk analysts, cybersecurity professionals they hire for this, emergency preparedness they hire lawyers, they hire inspectors.
Speaker 2:Pay for some of these jobs ranges between 125,000 and 187,000 for many NRC technical staff roles. So like once the machinery, the machinery did have to get sort of described by the scientists, but then the implementation of that machine, of that regulatory structure is actually done by really hardworking, really talented Americans but not head in the clouds, not thinking about the future in some bizarre way. That's enough to happen at the democratic level and then it gets implemented. I think that that might might might eliminate a little bit of this like, oh, okay. Well, like, this person who's really tied to you is now like inside actually the one with the keys, the one with the with with with the role overseeing you.
Speaker 2:I don't know. What do you think about this, Tyler? You have some pushback?
Speaker 4:Yeah. I mean, like, I I know that Casey, the Center for AI Standards and Innovation, like, I think that they've had a hard time like staffing Yeah.
Speaker 2:Funded. Yeah.
Speaker 4:Yeah. So it's like like they're having a hard time like finding people who who will come like from the labs. May like, maybe they should just brought in who they're looking for. But it seems like it's still like the the the like the ways that we detect if a model is safe or not are not like set in stone yet. Mhmm.
Speaker 4:Like it's still like that's what the role of meter that that's kind of what they're Sure. Doing right So I I think it is still different than like, you know, there are predetermined accounting practices and you can just kind of check the box and like follow the rules. Yeah. It's like a moving field, you know.
Speaker 2:Yeah. Yeah. And I mean, there is there is the question of like with particularly with like agent swarms perpetrating cyber security violations that don't directly cause economic harm. Like, I didn't tell it to hack you. It hacked you, but it didn't knock your payment system offline, so you didn't lose a dollar revenue.
Speaker 2:It's like very hard for you to prove that I acted wrong and then also economic damages. So there's a whole new level of like, you know, tort battles that need to be battled out in the court of law to see like what exactly do I owe you because I shouldn't have done that. But what do I owe you? What's the damage? What's the what's the problem there?
Speaker 2:And then and then you can go and say, well, you know, how how do we measure that? How do we prevent that? And how do we work through that? But but the the the discourse is getting like more and more more and more polarizing by the day. We'll see.
Speaker 2:I think there will I think there will be the the the big sit down between the lab leaders. I wonder how important it will be to have Jensen with a beer alongside Sam, Dario, Elon with beers. I mean, as the clear proposal, there's gonna be beers involved. That's what we know from the interviews. Everyone's asking why can't they just sit down and get beer.
Speaker 4:Six months of those could have been on the Cheeky Pint podcast.
Speaker 2:It really should. That that that is sort of neutral ground too because Elon's an investor in Stripe. What happened there? Elon's an investor in Stripe. He's a cofounder of OpenAI, and Sam's, I think, an investor in Stripe.
Speaker 2:And then Elon's working with Dario on compute stuff. So maybe Cheeky Pine is, the perfect neutral guy. I think it'll probably be on national TV, actually, but we shall see. We shall we'll follow it here. Let me tell you about Railway.
Speaker 2:Railway is the all in one intelligent cloud provider. Use your favorite agent to avoid web app service database and more while Railway automatically takes care of scaling, monitoring, and security. And we can move on to the timeline. We can move on to other
Speaker 1:Quickly jumping in. We have a rate hike.
Speaker 2:Yes. Tell me about it. Horsch
Speaker 1:hiked 25 pips. Okay. This was
Speaker 2:First time in three years.
Speaker 1:Price in. Cal, she had it I think at like 89% Mhmm. This morning. So not a huge surprise. I wanted to head over to Joe Wiesenthal's feed
Speaker 2:Mhmm.
Speaker 1:And just kind of read his reaction if he has one.
Speaker 2:Well, you pull that up. I'll give you the highlights from the Wall Street Journal. The the the Nasdaq react positively up 60.67%. Six this is The Wall Street Journal. They need they need a JavaScript plug in that changes it to point six six nine or something.
Speaker 2:Most officials penciled in one more increase this year. An energy shock and surge of AI investment have reshaped the inflation outlook. We talked about the Fed interest rates yesterday a lot, but, the Federal Reserve raised interest rates Wednesday for the first time in three years, a sharp reversal that began taking back cuts as it made last year, implicit and implicitly undercut the White House's insistence that inflation is not a concern. The Fed is saying, it kinda isn't a concern. The increase, approved unanimously, will raise the benchmark Fed funds rate by a quarter point to between three and three quarters point and 4%.
Speaker 2:The vast majority of officials penciled in more more one more hike this year in interest rate projections released after their meeting. So they think there's going to be more rate hikes. Chairman Kevin Wachs vowed shortly after taking office in May to end an overshoot of the Fed's 2% target now in its sixth year and followed through with an increase that had been widely anticipated in recent days. The rate hike scrambled an account of the White House had offered of the man tapped by the president for the for the job in January. Trump and his allies had cast pressure to raise rates as coming from a committee hostile to who last year said he would have cut rates sooner than the Fed ultimately did.
Speaker 2:It also followed a lost year in the Fed's inflation fight. The central bank has made no progress towards its 2% goal since mid twenty twenty five, including after cutting rates three times last year to guard against labor market slowdown. Instead, the Iran war has lifted energy prices, and the AI boom has driven an investment surge that has buoyed the economy and markets today. Policy action will support a timely return to the committee's 2% goal. The Rates and the committee said in the policy statement, analysts said that despite intense focus of late on monthly inflation data, the biggest change to the outlook has come from a run up in energy and commodity prices.
Speaker 2:It's the fact that the war in Iran has re intensified and the energy price shock is getting bigger again, said William Dudley, the former New York Fed president. What you got for me, Jordy?
Speaker 1:I was just reading through a bunch of different reactions
Speaker 2:Take your on Which one?
Speaker 1:Bloomberg itself. Let me pull them back up. But they have a live blog. Some people are saying this is more hawkish than expected given that the Fed took away next year's cut. What else?
Speaker 1:Big changes in the dot plot line, the Fed September dot plot. We now have four officials expecting to raise rates two more times.
Speaker 2:Two more times?
Speaker 1:They've previously just been one at that level. A whopping 12. Policymakers see rates going up once more before the end of the year, the remaining two see holding rates at their new 3.75 to 4% level. A reminder that in June, the last time we got these forecasts, half of the committee expected the Fed to hold or cut rates. And again, it looks like Wachs did not submit a dot, so he's going dotless here.
Speaker 1:Kind of a statement in itself. Yeah. So, I mean, so far the AI trade, the build out, everything has been overwhelming even in the face of of headwinds like rising rates and
Speaker 2:Yeah. Yeah. I mean, the the the mood from Silicon Valley was like, we're definitely not booming until we go back to zero interest rates. Like, this whole tech thing, it only works when the interest rates are zero. So, like, we'll just wait it out
Speaker 1:and Yeah. The reality is there was a bunch of ideas and investing styles that only worked when rates were near zero.
Speaker 2:Yeah. Yeah.
Speaker 1:But but yeah, it was specifically when you look at the companies that that really boomed in that era Mhmm. There wasn't a lot of net new, like really truly innovative stuff Mhmm. Of financial products Mhmm. Which benefited from from low rates.
Speaker 2:Yeah. You know. Some of them benefit from high rates though. Right? If it's like a savings product that spreads higher.
Speaker 2:But if they have to borrow a lot of debt
Speaker 1:Potentially, but but again, I'm thinking of like lending companies like PIPE. Right? PIPE was a company that at the time went from
Speaker 2:Yep.
Speaker 1:You know, incorporation to billion. I I forget what their peak valuation was. But
Speaker 2:And it makes a lot of sense because they're basically borrowing at 0% and they're lending to a company at 5% or something. You know, what what what whatever their spread is, like, is actually justifiable to a to an earlier piece of the market. But that sort of breaks down when you have to go to a company and say, hey, you want you want money at 12% or something like that Yeah. For an early stage company?
Speaker 1:Well, let's head over to Who Man who's
Speaker 2:First, let me tell you about Shopify. Shopify is the commerce platform that grows with your business and lets you sell in seconds online, in store, on mobile, on social, on marketplaces and now with AI agents.
Speaker 1:Who Man says, Daria, we should pump the brakes on the frontier. Sam, yeah. Let's all slow down together. Meanwhile, Zac.
Speaker 2:The the funny thing is yeah. I mean
Speaker 1:Let's let's talk about what Zac wrote. He said, last month, wrote about how we can build a positive and safe future for everyone. This is when Zac said, I really want people to understand my values Mhmm. Before we come out with our most powerful AI ever. And I at least felt like we we already had a good understanding of Zuck's values.
Speaker 1:But he wrote yesterday, every lab has the responsibility and incentive to move at the pace required to train its models safely and the ability to take its own actions to ensure that happens. The reality is people don't wanna use agents that are misaligned with them and don't do what they ask. So labs have a strong natural incentive to make their models more aligned. Right away, you know, starting out with this point, it's like this is not the this is not the No one The safety debate has not been sort of like around the idea of, oh, they're gonna create personal agents that are gonna be misaligned to the users. Like this No.
Speaker 1:This is not at all
Speaker 2:Well
Speaker 1:It's just this is just a point that doesn't matter.
Speaker 2:No. No. I mean, there is there there is a like, it is not the safety crowd, but the whole like social media is brain rod addiction. Yeah. Like like that is something that there is a separate crowd that does critique that and says like I don't want the addictive flywheel of of maximizing screen time to be brought to
Speaker 1:totally separate debate.
Speaker 2:Yeah. No, I agree. I agree. It's not it's not the true AI safety debate.
Speaker 1:Yeah.
Speaker 2:But it is a debate.
Speaker 1:There is a lot of debate about slowing progress on capabilities until alignment catches up. My view is that trust and alignment are quickly becoming the most important capabilities that will differentiate. Again, like this is not relevant to the current safety debate. Mhmm. Obviously, people wanna make products that do what their customers want them to do.
Speaker 1:No one has been worried that you can't make a model that in the near term or in the medium term or even over long running tasks can generally do what the user wants. The concern is that that if you leave models and and give them a task that that Yeah. Is more expansive, that they can start to do things like hacking Hugging Face. Right? So again, nobody is sitting here saying
Speaker 2:The main thing is that this this is talking past the x risk question. It is completely dismissing
Speaker 5:Yeah.
Speaker 1:Yeah. Yeah.
Speaker 2:Discussion, which which Dario is, like, laser focused on. And so this feels like it's a rebuttal, but it's actually, like, talking past it in the sense that he's, like, labs face significant liability. And it's, like, well, in the extra scenario, the liability doesn't matter. That's the whole point. Is that like no one's gonna come and
Speaker 1:And here's here's the best here's the best line. Meta delayed shipping muse for several months to focus on safety and security.
Speaker 2:That's actually very rational and important because Every
Speaker 1:company delays their products for months.
Speaker 5:That's just
Speaker 1:called building a good product
Speaker 2:Okay.
Speaker 1:To make it safe and secure. Like, period. This is what Meta has been doing forever. You have to do it. You have billions of users.
Speaker 1:Yeah. You got to make sure they're safe and secure. Every single product. Again, I I I felt like he was
Speaker 2:But you you so so
Speaker 1:he There's a lot of things in here that are rational.
Speaker 2:He's he's taking a victory lap on not taking a victory lap. Don't you realize that? He says we didn't call for everyone else to do this before we would. We just did it as part of our day to day work because it was clearly the right thing to do.
Speaker 1:And again every single already does all of these things. Every single company that makes say I probably already do
Speaker 2:laps while they're doing it, he's taking a victory lap for not taking a victory lap. It's not that complicated. He says everyone else says like we're taking safety seriously. We want a pat on the back before we delay the product. And he's saying, I want the pat on the back after I delay the product.
Speaker 2:Yeah.
Speaker 1:I just think it's I just a
Speaker 2:lot of back patting going on.
Speaker 1:I just think it's I think it's I just think it's very funny to I I think it's funny how much all these points are are fine. They they generally are rational and they make sense. Yeah. But I think it's funny that people are giving him so much credit for this note Yeah. Given that he's totally missing the main point that everyone else is focused on.
Speaker 2:Ex risk?
Speaker 1:Like intentionally missing the point.
Speaker 2:Because he doesn't believe it. He has p
Speaker 1:In order to in to get brownie points from people that don't even understand the current debate.
Speaker 2:No. To get brownie points from other people that have a PD d move zero. Who are like, yeah. And and you can see who's who's supporting this. They're like, yeah.
Speaker 2:Thank you. Like, just do put put the put the agents in the bag. You know, make the tokens free and and just make the products. Like, I'm not worried about that at And for that crowd, they're like, thank goodness. You didn't like fall in the hole of like stooping to this p doom debate that I don't take seriously.
Speaker 2:That's the side
Speaker 1:I just wish that he would come out and say, I have a p doom of zero. That's what he's saying. No. He's not. He's not.
Speaker 1:He's trying to position. He's saying my view like
Speaker 2:That's what he's saying.
Speaker 1:That's not that's not he's not being explicit about that. He's trying to let people say we care a lot about safety. We slowed down our development because we care about safety. Trust and alignment are important.
Speaker 2:Yeah. Right? You should definitely come out and say PDOM zero because if it's not zero and it happens and we all go extinct, no one's gonna be able to dunk on him.
Speaker 1:Right? Yeah. So it's
Speaker 2:pure upside.
Speaker 6:Pure upside.
Speaker 2:Pure upside to be PDOM zero guy. Why is no one considered this? The aura game is so high.
Speaker 1:Yeah. I would I would be I would respect it a lot if he just came out and said what he actually thinks, which I I do believe you're right. P Doom Zero. Which I he has a P Doom of zero and his P abundance is
Speaker 2:high. I mean, that's what he said in the in the in the previous essay. He was he was basically like, I don't think the he he he even he even was gesturing towards like the the fear based marketing, the doom based marketing is just a marketing tactic. I don't think it's rational. And also, don't think it's good for people to be in that head space.
Speaker 1:Yeah.
Speaker 2:And like and it's like Yeah. Info has
Speaker 1:Here's the thing.
Speaker 2:He has a problem with it.
Speaker 1:One of one of the last lines, committing the significant majority of compute towards serving people rather than racing towards recursive self improvement is one of the best ways to ensure we develop this technology safely. Meta has made this commitment and other labs can do this as well. Look, like, you you there's absolutely zero shot that Zach walks into MSL and like gathers the researchers and says, look, I don't wanna make models that make our models better. I don't wanna do it. I want you guys just focus Like that there's zero zero chance.
Speaker 1:So again, like this to me is just like he is being disingenuous with his positioning of almost every single point here.
Speaker 2:I don't know. I do think that there is a trade off right now between making models good at things that are not on the RSI path and those that are. And so, like, the the the race to become really, really good at coding is super aligned with RSI. The race to do image generation is not. And image generation does not seem to be on the RSI critical path.
Speaker 2:Although I I think DeepMind put out something where they're using world models and they think they have a breakthrough there. I don't know if I if I saw that accurately. But but maybe they're wrong. But at least like the bet at at least in Thropic has been like, we don't need to be world class at image generation to get where we wanna go because we just need to be really good at coding. Coding teaches us teaches the model how to train new models, and then we get, you know and then and then and then that that final model, we can ask it to spin up an image generator if we want.
Speaker 2:Zach is saying the opposite. He's saying, like, yeah. We will actually go and try and build a tool just to help you book a dinner reservation, and that's a good use of compute. Probably not on the RSI path. And and that's like a reasonable trade off.
Speaker 2:That feels that feels real to me. I don't know. What what do you think about engaging independent evaluators? Is this like
Speaker 5:Yeah.
Speaker 1:So he's Again.
Speaker 2:He says it's already industry best practice. Is he talking about benchmark stuff or is he talking about like actually like the you have Slack access, you have a desk, you have a badge, like you don't work here but you're allowed to just go wherever you want. I think that that's the next step and I think he's maybe talking past that a little bit.
Speaker 1:Yeah. And he's talking past this again, engaging independent evaluators and advisors is industry best practice. MSL already does this today in several areas because it helps produce better work. Other labs can just do this too. It's like other labs also do that too.
Speaker 1:Also do that already.
Speaker 2:Well, as of last week, Anthropic does this with meter. Like they said that they were gonna do that immediately. So they were doing badge and slap
Speaker 1:he's not he's he's not saying that he's doing that. Saying that he's doing the thing that every group that's making model has been doing for the
Speaker 2:most In part the prior era.
Speaker 1:For a long time.
Speaker 2:But the new thing is badge and Slack access and desk even though you don't work at the company. And that's when everyone's like, woah, that's crazy because these organizations are very very secretive and you're really gonna let this nonprofit come in. It's like sort of a wild move and that's why people are like, oh, wait a little. Like how aligned are they? And like what is the what what's the knock on implication?
Speaker 2:Like can these people not leak? Can they not can they be trusted? Are they gonna go to a cocktail party and be like, oh, yeah. The new model is actually really bad or whatever. Like, there's so many things that are like like, Meta deals with leaks all the time.
Speaker 2:And so the prospect of bringing in a non employee who is there explicitly to whistleblow effectively and, like, is allowed to talk about anything and, like, is is, you know, third party evaluator. Like, that is a huge step, I think that's why people are, like, woah. This is a big deal. This is a big proposal. Like like if this was not a big thing, if if Anthropic was just like, oh, yeah, we're gonna do a benchmark with meter.
Speaker 2:Everyone would be like, yeah, that's fine. Cool. Do that for sure. Awesome. But like people are like, wow.
Speaker 2:Okay. Like meter's gonna have a have Slack access Anthropic and you're asking other people to do that like that's we gotta know who these people are. We gotta make sure that this is like the right team for this. This is sort of a crazy thing. This is sort of unprecedented.
Speaker 2:This doesn't happen a lot. Like this is new. And and so, yeah, the newness is not fully embraced here. And I think I I think that's a little bit of like, okay, you're not really engaging with like what's coming down the pipe which is like maybe a government employee in your building. That's not
Speaker 1:happening Yeah.
Speaker 2:Right now. Maybe maybe a nonprofit from Berkeley. Do you like that, Mark? How do you feel about that? Are you cool?
Speaker 2:It seems like you're cool with it, but I don't know if you're actually gonna be cool with it. It is a little it is a little wild. Right? It's a different thing. It's a new thing, but, it's, it's a modern it's a modern
Speaker 1:this image.
Speaker 2:What image are we pulling up?
Speaker 1:I wanna see John's reaction.
Speaker 2:Okay. While we pull this up, 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:Here we go, John.
Speaker 2:Let me see. Boom. This this meme's been applied to like seven different people this week. It's been
Speaker 1:know. But I think it's I think it's particularly relevant because you have you have Elon, Demis, Dario, Sam, all, you know, all these people from different factions that are at or
Speaker 2:I've seen this applied to Theo. I've seen this applied to Cohere. I've seen this applied to Deep and Gemini. I've seen this people apply MSL. People are just whatever shot they want to take, they're applying this.
Speaker 2:It's not it's a it's too broad
Speaker 1:to this point. There's people
Speaker 2:I think it's the way the inverts. It's all clowns and there's one tactical soldier.
Speaker 1:There's groups that are at the frontier. They're on the battlefield. Yeah. And they're deeply concerned. Yeah.
Speaker 1:And you have Zach coming in here saying, yeah, like, I I built a cool personal agent. It's it's aligned. What's there to worry about? Nothing to worry about.
Speaker 2:Yeah.
Speaker 1:It's just you're not really you're not really in you're not really on the battlefield yet. I'm not saying with watermelon they can't get there. And Muse seems like it's Yeah. Is getting amazing reviews. Seems like an amazing product.
Speaker 1:And that's very exciting. But I think it's just again, I I thought the whole post was silly just like I thought the last post was silly.
Speaker 2:Nick Carter liked the post. He said, Zach pretty handily dismantles Dario's talking points here. People want models that are aligned with them, suddenly punches back at Anthropic's normative constitutional approach. Labs already face liability if they screw up, so incentives to release aligned models is already baked in. Meta delayed Muse for alignment reasons, Suddenly questions Anthropic trying to king make meter implies meter isn't Anthropic patsy.
Speaker 2:Meta doesn't need to coordinate with anyone to work on alignment just something labs should naturally do. But Mark Chen from the top rope according to Kevin Ruse says, there aren't race dynamics off the frontier. And, yeah. It's it's a it's a a war
Speaker 1:on the it all comes down to like, there is absolutely zero shot that Zach doesn't want to get to RSI yesterday and is doing
Speaker 2:I thought he said that they were working on RSI. I thought that was, like, announced as, like, a explicit goal maybe three to six months ago. He was he was, like, super intelligence,
Speaker 1:RSI. And and again, like I I just feel like it's not unique to Zach. Many many many leaders operate like this. But he's will say whatever the pick me thing is at that time.
Speaker 2:It would be more aggressive to be like, I'm p doom zero and I'm racing to RSI. Like, I'm p doom zero and I'm racing to RSI. All these other all these other people, I'll see you on the other side, brother.
Speaker 1:Meta, he said in 08/10/2026, Meta must must build out a sufficiently large amount of compute that we can allocate enough to recursive self Yeah.
Speaker 2:They're doing RSI. They're doing RSI. Everyone's trying to I mean, RSI can also mean so many things. It can mean yeah. You you you looked up the you you you summarized some archive papers for new strategies.
Speaker 2:Like, the automated research intern is in part an RSI initiative. It's not the final RSI loop, the closed loop RSI that people are are really worried about. But it it it's all gradations of this. What does David Sachs think about this? I imagine that he's a fan.
Speaker 2:He says Mark Zuckerberg, he just quotes it. He says Meta delayed shipping news for several months to focus on safety. Yeah. Sachs is very much like if you wanna slow down, go right ahead. And so he is he's fine with that.
Speaker 1:Alright.
Speaker 2:Anyway. The views from Muse
Speaker 1:is are really good. I Let let me say Okay.
Speaker 7:Let me
Speaker 1:say this because I've been I've been a little harsh. Yeah. The people people genuinely love Muse. Yeah. I think like it seems to be a great product.
Speaker 1:The the race between Muse and instinct is already already quite exciting. Niraj says, my wife tells me Muse is good and quote for the girlies.
Speaker 2:Yeah. That's extremely bullish.
Speaker 1:Extremely bullish. But but yeah. I mean, insane distribution advantage. I think it's
Speaker 2:Yeah.
Speaker 1:Let's see where it's at on the charts. Number three. We got it, Nick. We gotta get this company Vinted on here. I just do not I've brought it up like a million times.
Speaker 1:Yeah. It's like you have the most insane AI race, gambling race, short form drama race, and Vinted is always in the top five.
Speaker 5:Wow.
Speaker 1:Just like a secondhand clothes retailer competing with like a million other secondhand clothes retailers.
Speaker 2:Yeah. But they're Vinted. There we go.
Speaker 1:Like, Meta's like pouring every pouring Yeah. Pouring billions of eyeballs into growing news and Vinted is
Speaker 2:Didn't wait. Didn't Gemini and Google announce a personal agent at IO this year? Where did that go? Because that it it it's it's very odd that that Gemini has so like like, yes, the distribution advantage from from Meta Platforms is significant, but a lot of people are on Android. A lot of people already have Gmail and Google Calendar, and they have Google Maps, and they have the phone number of every business.
Speaker 2:And so when you think about booking restaurant reservations, doing all this different stuff, they you would think that that product would have gotten more traction. But I feel like Gemini personal agent did not go through the same hype cycle that Muse is currently on. Gemini spark, it died on the vine. Yeah. What happened with wait.
Speaker 2:They call it spark? Is that but isn't it Muse Spark?
Speaker 1:Yeah. Dylan in the next chat.
Speaker 8:And then
Speaker 2:And Spark. Everyone's calling.
Speaker 1:She uses it.
Speaker 2:Oh, yeah.
Speaker 1:It's mostly just using Gemini and toggling.
Speaker 2:Okay. Yeah. It hasn't had the the most breakout thing. There there are some killer use cases. I mean, everyone's talking about booking reservations.
Speaker 1:Got some rising bills. At IO. Describe what it did. No one knows. The world's most mysterious it's the most mysterious agent yet.
Speaker 2:But they gotta have something soon coming. And I and I and I do feel like the the the Gemini distribution, like, still have a pretty significant share of the chat market. Pops up in your email, and it says, oh, here, you can just email this thing and Yeah. You know, maybe another Gemini tab that I can open. Because I can open three if I'm in Gmail and Chrome and, pull them all open.
Speaker 2:I don't know. What happened to the to the to the call she AI price tracker? Do you see this? We we talked to Derek when this launched.
Speaker 1:I was
Speaker 2:like, oh, this is cool. This will allow you to understand, like, you know, basically a proxy for the AI build out. Like how are how are GPUs trading? Banned. Banned, apparently.
Speaker 2:Before we get into this, I'll tell you about Figma. Agents, meet the canvas. Your AI agents can now create and modify your Figma files with design system context. So this is from Semaphore and The U do we have this open here? The the US Commerce Department last month ordered call sheet to take down one of its products tracking the price of AI compute, the crucial power from data centers that's driving the artificial intelligence boom, if you didn't know what AI compute was.
Speaker 2:The commerce officials cited national security concerns and that's what stuck out to me. I was like, I there's so many different prediction markets that I can easily trace through like, oh, you have a flight delay one and you could have somebody that calls in and tries to get the flight delayed. That could be very disruptive. FAA could have a problem with that. But this one I wasn't I wasn't worried about at all.
Speaker 2:We talked we talked to Tark about it and we were like, yeah, this one seems sort of informative and interesting. So the product pulls together data from several markets that allow users to bet on the cost to rent NVIDIA chips to create an overall picture of where AI compute costs are heading. Quiet Call Sheet quietly complied though many of the underlying markets remain open for trading. Separately, commerce has pushed the Commodities Futures Trading Commission, which oversees prediction in future markets, to effectively freeze approval of new compute contracts for sixty days. And so even though some of the contracts are still open and will close, maybe in sixty days there won't be any compute prediction market futures, which is very interesting.
Speaker 2:The call sheet declined to comment while a commerce per spokesperson said the department, quote, has never once asked call sheet to take down this market or any other markets. Interesting. So the commerce spokesperson said, this story is false to some of force. So lots of people going back and forth. It's unclear why the why commerce is worried about the nascent market which aims to do for AI computing what oil futures do for crude, let buyers and sellers of compute lock in prices and give traders a way to bet on where those prices go.
Speaker 2:Now, the markets were always a little thin. You know, these new markets they tend to be sort of thin and they tend to be more speculative just people that are trading them on vibes or news, not necessarily used by the actual companies like, you know, oil futures. Those will be actually employed in treasury strategies by like airlines. And they and and like big big firms will trade those in size. But that was what calls that's what Kalshi and Tarek were pitching as like where this all goes.
Speaker 2:I was a little skeptical that it would wind up on Kalshi. I thought that they could have a decent chunk of the market, but I thought a lot of it might just go to insurance companies and reinsurance companies and sort of like the traditional financial rails that do these types of deals. But nevertheless, it's going away and let's figure out why. One potential reason floated to semaphore by market participants is that compute futures could be manipulated to show a sharp drop in the cost of older chips which might destabilize AI stocks and debt markets. Some of these markets are thinly traded which could lead to volatility even without bad actors.
Speaker 2:So you're trying to wipe out situational awareness. You're short the call sheet prediction markets on AI compute futures. Everyone thinks, oh, AI is bust. The market trades down for a couple days. You clean up, and then you buy back in or something like that.
Speaker 2:I guess that's what the the rumor is here that some reports reporting on. The cost of compute has become one of the most important numbers in The US economy. One side of the debate fears that older chips which serve as collateral for billions of dollars of borrowing by Neo Clouds. Neo Cloud, fun fact, coinage by semi analysis. I didn't know that's where it came from, but they actually they didn't just rank them all with ClusterMax.
Speaker 2:They actually coined the term Neo Cloud. It had a rude name beforehand, which I didn't realize. You can go look it up. I'm not going to ever say it. But on the other on the other are concerns from big companies adopting AI that shortages of power and infrastructure will send prices to token soaring.
Speaker 2:That uncertainty has given rise to a futures market that was starting to take off this summer. The CFTC's sixty day pause could delay plans by exchange operators like CME and and NYSE in parent Intercontinental Exchange along with upstarts like architectural financial technologies to list two sided two sided betting parlors. Interesting. Well, let me tell you about Cisco and Jordi will pull up the next story. Critical infrastructure for the AI era Unlock seamless real time experiences and new value with Cisco.
Speaker 2:Where do you want to go next? Zach. Okay.
Speaker 1:Yeah. Tagari Yeah. Is back.
Speaker 2:Where do we know him from?
Speaker 1:He says Cal AI.
Speaker 2:Cal AI.
Speaker 1:Been on the show.
Speaker 2:Yep.
Speaker 1:He says everyone's been asking what's next after selling Cal AI. Mhmm. Introducing Persona. Others couldn't figure out the interface. We did.
Speaker 1:Let's pull up the video. Mhmm. I We gotta ask Zach how he he How how his deal worked to sell Kaliai because he seemingly had to stay for like a week. Yeah. Like he ended up however he worked it out, he basically just got to fail keep
Speaker 2:this guy out of the arena. You can't get him on the sidelines. It's impossible.
Speaker 1:Yeah. And Kaliai is the number four fitness app still.
Speaker 2:Cooking. Cooking. Cooking. So good. Yeah.
Speaker 2:Let's play this video.
Speaker 9:Every major AI company is racing to build the best assistant, but they're all building within the same two by six inch rectangle we wear in our pockets every day. We believe AI is supposed to free us from our screens, not make us use them more. So we built a new interface. Introducing Persona and the Persona Band. To talk to your Persona, simply flick your wrist or hold down on the face, then ask anything.
Speaker 9:Anything. Hey, can you ask Mac how many pre orders we got today?
Speaker 2:Sir, I just emailed Mac. I'll let you
Speaker 9:know his reply. Your persona will proactively learn your habits to make your life easier. For example, when you get off a flight, it should know to get you an Uber from the airport to your hotel. Reactively, it will be able to help with all kinds of things like canceling subscriptions, negotiating bills, or even DoorDashing your usual Chipotle bowl knowing exactly what you like. Elite.
Speaker 9:Persona is also the only AI
Speaker 2:the predictive burrito. It's what the DoorDash guy was talking about. They ordered the burrito before you even know. He's actually building it.
Speaker 9:Privacy for your and those around you's data. This isn't a continuous listening band. The microphone will only activate under your control.
Speaker 2:No. When you turn
Speaker 9:with three different materials and four different colors. Pre order your band and start texting your persona today.
Speaker 1:I've seen enough time for Zach to come in with the final offer.
Speaker 2:Yeah. Pretty good pitch. Little bit harder to get people to install it. Right? Because they gotta buy it, wait for it to ship.
Speaker 2:Little bit of a barrier to entry. Also Yeah. But look love these AI devices.
Speaker 1:I think Look at Pocket.
Speaker 2:Cool. Yeah. Pocket really worked. Yeah. I don't know.
Speaker 2:There's there this is he's gonna he's gonna really blow this thing up. I have a feeling this is gonna be big. That's cool. What did prompter say? Never bet against a guy who managed to sell a calorie tracking GPT wrapper, which in no way could have possibly been accurate at calculating macros for a 100,000,000.
Speaker 2:No. He he is a master of of Growth. And and Yeah. The cut scale and just like
Speaker 1:So if you look at if you look at the the charts right now, CalAI is number four. The company that bought CalAI is number nine.
Speaker 2:Wow. Yeah. I mean, that's a
Speaker 8:good that's
Speaker 2:a good example. It's a good growth team. Doesn't he also have another company? Eco GPT or something? Or is that not him?
Speaker 1:No. No. Oh. That's his co founder.
Speaker 2:Got it. Okay. But yeah, that's a separate company.
Speaker 1:Yeah. So I'm I'm still yeah. We Tyler. Pre pre order one of these.
Speaker 2:Oh, yeah.
Speaker 1:Can definitely
Speaker 2:do that.
Speaker 1:Put it on the ramp. Yeah. So yeah. I'm I'm excited to check it out. I'm still not I'm still overall not convinced that I need a new device.
Speaker 1:Like, there has to be something Yeah. Like, a lot of the demo was was screenshots of of the phone. Yeah. And so And and I do spend time away from my phone, but I'm typically in the water.
Speaker 2:Is it waterproof? Can you wear that while you're surfing? You said you had a brilliant idea while you were surfing. You needed to hold it in your head the old way before you got back to your phone.
Speaker 1:Like an
Speaker 2:idiot. Using your brain. No. It it it really does feel like a a wave of new devices is is coming online and the and the the the war for the the the home device. I mean, you can see, you know, the battle between Apple and Meta reemerging with Muse.
Speaker 2:Like, how how deep will they be able to go before Apple pushes back? Every other firm is working on hardware, the Meta Glasses. The Meta Glasses with Muse should act very similarly. Right? You have an agent.
Speaker 2:You can talk to it. You can do some of the similar actions. The the trick is, like, people don't wear the glasses all the time, whereas a wristband, like a whoop, people wear that all the time. And he seems very in tune with I don't know. Just like the the obvious pushback.
Speaker 2:Like calling out that it's not listening all the time. Yeah. Like, there are just people that would just like forget about that. Yeah. And it's like it's very
Speaker 1:using example, the I'm struggling to see when I would need a burrito but wouldn't be near my phone.
Speaker 2:Mhmm. It's more just like if you don't if it's faster. Like people just do anything that's faster. That's how we got to phones. Like you used to you you there there was a time when it was like
Speaker 1:I know but If
Speaker 2:you're gonna shop online,
Speaker 1:on desktop. Not saying like the overall form factor is bad. But if you had the ability to just hit a big button and talk to the Persona app and say, order me the usual from Chipotle Mhmm. It's not like the band is necessarily faster if your phone is sitting right there.
Speaker 2:Yeah. I wonder how fast they're gonna update Siri. Because it feels like
Speaker 1:No. It it is it is so funny to me that Apple's finally coming out with their Knowledge retrieval. Like knowledge retrieval LLM Yeah. Chat app. And right Literally right before they could really get it out to the public, there's like a whole new paradigm of Yeah.
Speaker 1:Agents that are You can just tell it's gonna take Apple like two and a half years to get a beta out. Yep. And so again, like right as it felt like they were, oh, we're gonna catch up, you know.
Speaker 2:Will it? Will it? Will it? Is there a way that that the next version of Gemini is so good? It if if you've if you've talked to Gemini in Gmail, it links it.
Speaker 2:And so when you talk to Gemini through Siri, it knows that it's you and it and it can go in your email and it can email people and and and text that restaurant to get the reservation. Like, is that a fee? I'm always interested in like how much innovation will Apple get for free because they're partnered with Google. Like if Google advances the model, does that advance automatically come or is Apple like, we're buying Gemini 3.1 flash from you. Thank you.
Speaker 2:We'll let you know if we want the new thing. Because if they just want Gemini and all the progress like there's going to be progress there. There's going to be new capabilities. The models are going to get better. The functionality is going to get better.
Speaker 2:I would bet that even though people were sort of Yeah.
Speaker 1:Google has just really, really, really struggled to make good AI products.
Speaker 2:Yeah. I I don't know. I I I would be surprised if they can't figure out a way to order you a burrito in the next six months. Right? Like, that's not some, like, RSI critical thing.
Speaker 2:It's like
Speaker 1:I don't know. You think they you would you would think that
Speaker 7:I think you've
Speaker 1:been able to billions of dollars of buying Yeah. Windsurf that they could have a competent code gen product.
Speaker 2:Yeah. I haven't seen yeah. It's tricky.
Speaker 5:I don't know.
Speaker 1:Like there's small companies that have good coding coding harnesses. Yeah.
Speaker 2:Anyway, let me tell you about CrowdStrike. Your business is AI, their business is securing it. CrowdStrike secures AI and stops breaches. Have Jeremy Allaire from Circle. You want to talk about Sam first?
Speaker 1:Let's bring let's bring
Speaker 2:Let's bring Jeremy Allaire from Circle, cofounder, chairman and CEO, back on the show. To the show, Jeremy. How are doing?
Speaker 1:Very How are I'm back.
Speaker 2:Crazy week, tons of news. Let's start with Circle. Let's start with what's new in your world.
Speaker 10:Yeah. Well, so we turned on a new global operating system today called ARC. It is an economic operating system that we've been building for two and a half years. Hundreds of companies launched their apps and services on it. It's being run by not just Circle, but, like, many of the biggest financial infrastructure companies in the world, the people that clear the securities of the world, run the payment networks of the world, manage the assets of the world.
Speaker 10:And so it's got everything from social trading and DeFi to major payments firms and capital markets. And, you know, we we we built this for AI first. Everything is AI accessible, all the builder tools, everything, but also we've we've built services so that AI agents can use this themselves as an economic layer for for transactions, for contracts, for coordinating. And it's a big it's a big milestone. It's the biggest really, the biggest platform launch in our history, and and we think it's a it's a big it's a big upgrade for the Internet too.
Speaker 2:Amazing. A lot of people six months ago would be like, It works with AI agents. But today, they'd be like, definitely don't make it work with AI agents. That's not what How we do you think about the risks and the rewards that come from enabling a platform to be AI agent accessible?
Speaker 1:I
Speaker 2:mean Sometimes they'll access things that aren't even accessible.
Speaker 10:I mean, is the key. Right? Yeah. The the biggest challenge with this explosion in agents is basically trust
Speaker 2:Yeah.
Speaker 10:And verifiability. Yep. And so, you know, it turns out that cryptographic computing and these these these networks these network operating systems of cryptographic computing are built for that. Mhmm. And so we've we've kinda created the most trusted dollar on the Internet with USDC, And the these operating system layers actually have features that AI can build on top of.
Speaker 10:So you can basically have proofs of the of the entities and identities behind agents. You we're actually demoing today something called ARC agent sector, which actually allows you to have an AI agent prove the work it's done, the data it's used, how it's done its work, and then provide that crypt in cryptographically provable way to the to the Internet. So you can have more trust
Speaker 2:Yeah.
Speaker 10:And verifiability on what actually AI is doing. And right now, the black box issue is the scariest thing for people out there, whether it's inside the labs or it's actually just out in with the agents that we're building. And so it's this convergence. Like, you know, I talk about the agentic economy. It's this convergence of these operating system for intelligence and these operating systems for economic activity rooted in in cryptographic computing, which is what these blockchain networks really are.
Speaker 10:And so we're building a layer that is able to begin to provide that trust and verifiability to the agentic world. And so that's how we get comfortable with with with it. We we need this. We can't move forward in the agentic economy without technology like this.
Speaker 2:Are you are you equipped to explain to me the history of Walmart's misadventures in payment infrastructure? I was reading on that they were trying to push people to debit card and rails for years and then they finally sort of succumbed and went with a different one and I'm and I guess I'm just interested in like is like, is this a transformative moment for any particular sector of the economy where you're like, oh, someone's going to be saving half a percent now or some big pool. Yeah.
Speaker 10:I mean, for sure. I mean, look, when we started Circle, right, we had this basic idea that there could be a protocol for dollars on the Internet Yeah. That the marginal cost of storing moving value, just like the marginal cost of storing moving data and information and communications went to zero, that happens with with the movement of money. And and now we have that. Like, stablecoin digital dollars are now, as of January of this coming year, like, digital dollars in the financial system.
Speaker 10:You can move them programmatically, instantly, anywhere in the world Mhmm. In a fraction of a second, for a fraction of a cent. And so payments and settlement becomes commoditized. Mhmm. And so as you get the proliferation of of wallets that can talk to these computer networks, which is now everywhere, like from Cash App to Revolut to all these wallets around the world can talk to these networks, but now you can directly settle.
Speaker 10:So if I'm a business, I can just say, you know, pay me pay me over this rail, and I get it as basically cash instantly.
Speaker 2:Mhmm.
Speaker 10:And and it settles it, you know, like a cash transaction. And so over time, basically, that compresses out this rent extraction that's existed for a very, very long time and all kinds of other things like time delays and and and the like. But and so, yeah, I mean, it's it's purpose built for that world, and a lot of stuff is converging all at once. Operating systems like ARC, the legal frameworks for digital dollars, the people getting comfortable that they can, like, use this in in their in their businesses, know how to operate it, etcetera. And so it creates a huge opening for, you know, the Amazons and the Walmarts.
Speaker 10:And and, really, you talk talk talk to any major retail facing CEO, and and they'll tell you how much of their margin goes to processing fees. And so I I do I do think it's it's significant, but the the the whole utility model of payments is gonna change very, very significantly. That that's a a big part of of what we see happening here.
Speaker 1:You mentioned What's the most boring payments happening on on Arc? Like, the agent stuff's awesome, but I imagine you have a bunch of different partners. I'm assuming some most
Speaker 10:just The boring stuff is is like, you know, their businesses, they have to move money around the world, and and they're finding that they can do it better. And they if there's someone who's importing from Asia in Latin America, and they need to do that, and they and they wanna, like, have that done quickly, and they don't wanna have a lot of intermediaries, and they wanna just, like, directly do that. And so volumes of of kind of how how people need to move money around is
Speaker 11:you know,
Speaker 10:it's boring to to all of us, but to those to those people in those countries and and to those suppliers and those other people, it's actually really significant. I mean, other boring stuff is like, hey. I have a I have a tokenized version of a stock, and I'm someone in an emerging market that wants to own a stock. I can now do that. And I can I can purchase it and trade it instantly?
Speaker 10:I can take out options on it instantly, and I don't have to be in a US brokerage account. I can just be on the Internet with a wallet that connects to these networks. And so, you know, from, you know, from the average individual who just wants to participate in capital markets in in simpler ways to these businesses that, you know, have to move stuff around that's really important to the, you know, the the kind of efficiency and cash efficiency of their business. Yes. Agents are exciting.
Speaker 10:That's frontier, so to speak. Yeah. And then even even simple things. Like, people love to watch memes on TikTok. They also like to trade memes.
Speaker 10:And so if you wanna trade memes, go for it.
Speaker 1:Okay. Sprinkle some of those in.
Speaker 2:I wanna talk legal. You mentioned that these are now legal US dollars. But yesterday, CoinDesk reported Crypto Clarity Act flames out in failed US senate vote. The years long effort to set US regulations for crypto markets couldn't muster enough support to make the leap over the senate's final 60 vote hurdle. I would love your reactions, context, history, anything you can do to get the audience up to speed here.
Speaker 10:The the key thing is that a year ago, there's something called the Genius Act, which did become federal law with massive bipartisan support in the senate and the house. And the Genius Act makes digital dollars legal in The US financial system. The Federal Reserve, the US Treasury Department, all the regulators have made the rules. And as of January 2027, stable bonds like USDC become legal digital dollars in The US financial system and in the global financial system. So a lot of our work is done.
Speaker 10:Mhmm. Like, blockchains running this new money, being able to support it with all these markets, that's here. We've got that, which is great. Clarity is about, like, how trading venues get set up. You know, who can bid in a trading venue.
Speaker 10:The the digital asset trading and market side of things. There's other pieces to it, clarifications around, like, how DeFi is is sort of treated and and stuff like that. But, like, that well, that's, like, mark it's market structure, which has to do with, like, the capital markets. And so I think, you know, there there's still a lot of desire to have, like, good regulations there. But at the same time, I mean, essentially, the agencies that regulate these things, like the CFTC, the SEC, even the bank regulators too, they're all just gonna move forward and and regulate it themselves.
Speaker 10:And then eventually, we'll we'll get additional laws passed through congress.
Speaker 2:Got it. Jordan, anything else?
Speaker 1:Not for now. Congrats on the launch.
Speaker 2:Congrats on the launch. Thank you so much for coming on the show. We'll talk to soon.
Speaker 1:Great to
Speaker 2:see you, Johnny. Let's talk about Sam Park. He was offered $600,000 to sell his company's data to Micro One. He's not doing it. A 100% turned it down.
Speaker 2:He says it does interest me like crazy. What a crazy business model. He's tempted. Yeah. He's tempted.
Speaker 2:So apparently, let's finish this post. He says, if you don't know, here's all I know about them. They go to private companies. They ask for your Slack, Notion email info, offer you $6.07 figures. What are we doing here?
Speaker 2:Somehow anonymize that data and they sell it to OpenAI, etcetera. And the interesting thing is that I've been getting these Instagram ads and they're trying to get me to sell the OpenAI data to MicroOne
Speaker 1:To sell that?
Speaker 2:To sell the OpenAI and I think I could maybe one hand washes the other on this one. Yeah? No. I would never, to be clear. Seems like Micro One has grown to 9 figures in revenue very fast.
Speaker 2:The ad on Instagram that that at least I'm getting targeted with is 1 to $2,000,000 offer. But maybe that's how they get you in and they work you. You look, you are a podcast. You only need this much. I don't know.
Speaker 2:But he says, what else am I missing here? I get the value of this, but doesn't seem right to me. Interesting. I for some companies, would imagine this being super valuable. The the range seems really, really wide.
Speaker 2:I don't know. But what what do you think about data brokerage in the modern era? Are you selling your data?
Speaker 1:I just think it's it's, you know, that in in our team chat, like, you got few people working back and forth on a meme. Yeah. A bunch of crying emojis, some laughing emoji.
Speaker 2:We know we know that the models are bad at humor. How will they get funnier if not training on our internal message data? They need to see, oh, that one got five crying emoji reactions. Reinforce that. That's what happens.
Speaker 2:This is how we create comedy super intelligence through acquiring the Theo Von data trove.
Speaker 1:Yeah. I mean, I think it's I think it's quite interesting to look at how projects and tasks get completed in organization, how products get launched, how how deals get done, all these different things. It's just like
Speaker 2:Yeah. Wait. I have an interesting question for Sam. We'll have to follow-up. Which company?
Speaker 2:Because he is, of course, affiliated with
Speaker 1:I would assume Hampton.
Speaker 2:Hampton would be the one that yeah. That that actually makes sense. Because again, you wind up in the situation that it's like, oh, we'd like to buy your data from my first million and everything HubSpot has as well. You know, just just give us access to the HubSpot Slack. Right?
Speaker 2:Like, I don't think that's gonna happen. They probably don't use Slack there. Right? CRM competitor. Probably use something else.
Speaker 2:Anyway.
Speaker 1:Let's we can ask Tomasz Yes. What he thinks about selling
Speaker 2:Okay.
Speaker 1:Your company's data. Yes. Let's pull up this video of PT talking about why the name of your startup is predictive of success or failure.
Speaker 2:Oh, did you notice what Jeremy did there? Jeremy, a layer. He was like, we built a layer for transactions. No. I'm gonna determine them in action.
Speaker 2:I caught it. Did you? Let's play the video. This is
Speaker 12:sort of like a slight aesthetic thing I believe in very strongly. The names of companies are often very predictive of future failure or success. So I'd say PayPal was a was a very friendly name. It was the friend that helps you pay. Napster was a bad name.
Speaker 12:It was the music sharing site. You napped some music, you napped a kid. That sounds like sort of a bad thing to be doing. And it's no wonder the government then comes in and shuts the company down within a few years. So you wanna be very careful how you how you name companies.
Speaker 12:In in the sharing economy context, I like Airbnb way more than Uber. Airbnb sounds like this very innocent virtual bread and breakfast. This very light, non threatening sort of company. Uber, that sort of sounds like a bad name from Germany sometime in the nineteen thirties. You know, what are you exactly above?
Speaker 12:What's hitting the wall? And and this is probably something that, again, from a government regulatory perspective, I think Airbnb is a vastly better name than than Uber. And on the social networking side, I would say that I actually think Facebook was a very good name. I think Myspace was sort of the more problematic name. You know, Facebook was You can say that all these social networks involve both reading and writing.
Speaker 12:Unlike real life, you learn to You have to write before you read. You first have to write some things about yourself, then you read more about other people. Over time, reading dominates writing. Facebook was about learning about people around you, about the real identities at Harvard. MySpace started among wannabe actors in Los Angeles and it was about them coming up with fictional narratives around themselves and then sort of a lot of other people in LA who are generally like that.
Speaker 2:That's why I heard all And of
Speaker 12:because reading dominates writing, Facebook would ultimately dominate MySpace. So, I think you could sort of a certain version where the whole arc of the company was The whole product arc was implicit in the names.
Speaker 2:Interesting. How important do you think naming is?
Speaker 1:Extremely important. TBPN. I'm overly obsessed with dot coms
Speaker 2:Yeah.
Speaker 1:At the inception stage of companies too because there's there's Yeah. One, they they go hand in hand, but names are names are important. Names are I feel like names and dot and .coms are are are maybe maybe name the name itself is like 70% and and actually having your .com and owning that is like maybe 30% over time. Mhmm. But when I see founders that are super ambitious and they plan to build a really big company that millions of people are gonna interact with.
Speaker 1:And then they build they pick a name where they will never ever ever get the .com Oh, sure. Unless they can I could We actually just saw Runway, the AI company bought runway.com from Seeke's?
Speaker 2:Oh, no way? Yeah. Let me see.
Speaker 1:Seeke Chen's company. I remember it. And again, I I expect that Runway had to pay like an insane price because, you know, Seeke Yeah. Had had a well funded company himself and
Speaker 2:Leverage.
Speaker 1:Anyway, so important. Bending Spoons.
Speaker 2:Great name. We do have our next guest though.
Speaker 1:Bending Spoons. Get into this next.
Speaker 2:Let me tell you about Console while we bring in our next guest. Console builds AI agents that automates 70% of IT, HR, and finance support, giving employees instant resolution for access requests and password resets. We have Tomasz from Theory Ventures back on the show. It has been way too long. Thank you so much for taking the time.
Speaker 2:I've read a bunch of stuff that you've written, seen a bunch of stuff, and I'm so excited to get into this talk. How are you doing?
Speaker 5:I'm doing great. Thanks for having me back on the show.
Speaker 1:Nice to have you.
Speaker 2:Are you thinking about AI regulation as much as everybody else?
Speaker 5:It seems like it's the topic of the week. That's for sure.
Speaker 2:Okay.
Speaker 5:You know, we saw a Zuckerberg tweet, yesterday. I thought with actually a very sanguine view, probably one of most level headed views.
Speaker 2:Okay. Debate. Geordie thought it was talking past the ex risk discussion that he wasn't engaging with the real discussion Dario Amade is putting out.
Speaker 1:No one Let me pull up the post again. My point of view, totally level headed, totally rational saying like if if Zuck had a leading model and he was talking like that, it would be everyone it it would actually truly be incredible. The the reality is that his the the sort of bright spot with MSL right now is Muse, which is a great product. People like it. And literally no one is worried about like personal agent app safety.
Speaker 1:And so when he says people don't wanna use agents that are misaligned with them and that don't do what they ask. So labs have a strong natural incentive to make their models more aligned. My point of view is like no one Of course, people don't wanna use products that don't do what they want them to do or do things that they don't want them to do. Right? But that is not at all like the debate right now.
Speaker 1:And so I feel like he came and made a bunch of points that like sound good if you don't fully understand the debate. Like later on he says committing the significant majority of compute towards serving people rather than racing towards RSI is one of the best ways to ensure we develop this technology safely. Literally about just over a month ago, he was saying like we need to have a lot of compute so that we can keep up and get to RSI. Right? And so I feel like he's consistently been focused on saying the thing that will make him sort of popular in the moment that sort of misses the bigger picture.
Speaker 2:Mhmm.
Speaker 1:But overall, our takeaway from all of his actions is that he actually has a p doom of zero and he just thinks the technology is cool and wants to build like successful products.
Speaker 2:That's just great.
Speaker 1:Like that is what that's what I consistently consistently take away and I I I like respect that point of view. I'm I'm glad we have someone in the arena that's like playing at massive scale
Speaker 2:Yeah.
Speaker 1:That has a p doom of zero. Hopefully, you know, doesn't increase x risk.
Speaker 2:Yeah. What do you think?
Speaker 5:Yeah. I mean, I think, you know, he I think he echoed a couple of the thoughts that Lina Khan put out, which I thought were excellent, right, which is this is an existing regulatory framework for companies having responsibility for the products that they develop. I thought that was extremely like, it was right on point. Mhmm. And so I think he's arguing for control systems in a way.
Speaker 5:Like, I think the the debate of AI safety is really focused on alignment, not really touched very much on control, and how do we build the guardrails around these models for them to be effective. I think that's where this all needs to go.
Speaker 2:Mhmm. The the liability question I think makes so much sense. Are you are you grappling with any of the questions about like does liability apply if no one if no human says to go do an action and there's no economic harm related to the action that's taken, but it violates something in the cybersecurity context? Like, I I just tell my agent, go solve math problem. It hacks you into your system.
Speaker 2:Shouldn't do that, but I didn't ask it to, and it didn't take down your ecommerce site, so you didn't lose any revenue.
Speaker 5:Yeah. We haven't seen it across company boundaries so much, but we have seen it within the within a company. So let's say I have an agent and it does something bad. Right? Yeah.
Speaker 5:So the initial challenge I think is primarily just spending a lot of money. And we see that a lot of executives are worried about blowing past their AI budgets as a result of
Speaker 2:Oh.
Speaker 5:Unprecedented agent expand. And this is probably the first instantiation or first case within an enterprise where an agent does something that's really not supposed to.
Speaker 2:Yeah.
Speaker 5:And I, you know, you receive a warning. I think most of the times you might be fired for that. So in that sense, you're really responsible for the agent. Sure. I do think if it starts to span across companies, there's law.
Speaker 5:And I think you you will ultimately be responsible for the agent that you that you start.
Speaker 2:Yeah. Is it is it fair the the conception of just collapsing the debate down to the p doom equals zero versus p doom equals greater than zero crowd?
Speaker 5:No. I think, I mean, you know, we've had a lot of conversations about this. Think whenever you talk about doom, okay, let's define doom, let's figure out what all the conditional probabilities are.
Speaker 2:Sure.
Speaker 5:The reality is like doom exists in a car. Doom exists in an airplane. Sure. And so we accept some probability of failure in those. Yeah.
Speaker 5:But I I think the conversation I think the Dario letter did a really wonderful job of taking the conversation of pro AI, anti AI, and then stepping it forward into, okay, what is it that we're talking about? And the way I read his letter is there's action that needs to happen at the individual company level. Yep. There's action that needs to happen cross company, and then there's action that needs to happen at the international level. And that increase in resolution allows us to have the next step of the or the the next step of the conversation.
Speaker 2:And it feels like we're stepping through those pretty efficiently. It feels like the first step of company regulation and independent evaluators. All the companies have sort of taken various levels of steps to either agree or promise or make current commitments around that. The national conversation is happening right now. There are some bills.
Speaker 2:There are some politicians that are weighing in. It feels like that is progressing. It's going to be slower, but it is happening. Is there any hope for international collaboration? Demis and Sebastian Malabai were talking about, hey, I think that the Chinese are open to something.
Speaker 2:I've heard other people that are like, this is crazy. There's no chance that there's any sort of international cooperation here.
Speaker 5:It's really tough. I mean, you have a lot of geopolitics here at play. Clearly, you know, for The US, this is probably the defining issue of the midterm election. You have Yeah. Data centers contribute two thirds to US GDP growth.
Speaker 5:We just saw the Fed raise rates just a couple of hours ago. And so, you know, I think at least within the world of The United States, the the one of the most important topics of conversation is the the economic one. Yeah. And first, we need to reconcile what happens internally, and then maybe perhaps we'll pursue international.
Speaker 2:Yeah. Where are you investing? Where are you thinking of opportunity? It feels like there's sort of, you know, a lot of people have made bets on the frontier labs. A lot of venture capitalists are sort of like happy with where their positions have have, you know, or or like the ship has sailed on those.
Speaker 2:The IPOs are almost imminent. But then there's there's so much work to be done in diffusion. There's a ton of application layer, applied AI working to actually create legal software engineering work. There's so many there's so many opportunities there. And then there's also folks who are going deeper in the stack on semiconductors, neo clouds, inference engines.
Speaker 2:There's a whole bunch of other plays that go way deeper down the stack. Has have either of those appealed to you recently? Do you think they're equal opportunities? How are you shaping those?
Speaker 5:Yeah. I mean, the single biggest market in software today is inference. It used to be the database market. Now it's inference. Just the way the database market's segmented, and you have fast databases and slow databases, image databases, video databases, you have the same thing for inference.
Speaker 5:And so we've invested in one company called Sail that, you know, you can save a tremendous amount of your inference if you're willing to wait five five to ten minutes on that inference. Today, most of the AI that we use is instant.
Speaker 2:Yep.
Speaker 5:And we all pay a premium for that. But a lot of business use cases are are slower like Databricks. Yeah. And and then we've been researching categories around like voice inference. When you chat with the voice AI, it's actually very different optimization.
Speaker 5:What you care about is how quickly the AI responds to you. Robotics and so we've splitting that up, we're also investors in a company called Olama, which is about 10,000,000 people using it initially for local. So you can actually run a lot of AI on your computer. Stanford released a study about three or four weeks ago showing 90% of white collar AI use cases can be solved on your MacBook. Wow.
Speaker 5:And then also, if the AI needs more, we can go to the cloud.
Speaker 2:Yeah. What what do you think is key in voice AI? Because that can be done on device in many ways, but it can also be done on the edge. We've talked to Matthew Prince, Cloudflare, about putting GPUs on the edge. Then there's also just inference optimizations that even if you're pinging to a data center that's a hundred milliseconds away, if the inference is fast enough, it doesn't really matter.
Speaker 2:Have you consulted that or or thought about that trade off?
Speaker 5:Yeah. It makes a lot of sense. So there's four different layers of the AI stack. One is you have a phone, goes through Twilio, then you need to convert that phone call into digital. Yeah.
Speaker 5:Then you need an AI model to process it. There are three different steps there. Where the inference happens matters a lot. Mhmm. The edge definitely makes a lot of sense.
Speaker 5:Mhmm. And then there's also time zones like customer support. You'll have different time zones coming up. You want to make sure that the AI systems are ready for when those loads come. That's called KV cache warming.
Speaker 5:But so I think ultimately, it is a really important category where latency matters a lot. The optimization of those systems is pretty materially different than the way that we use a lot of large language models, and it will likely start initially in the centralized data center, but very likely pushed to the edge.
Speaker 2:Mhmm. What how are you seeing Chinese open source fit into the current AI build out AI story? Are enterprises actually deploying them at scale? Is it more like there's an intermediary where an American company will fine tune or serve that model? Like how many layers of abstraction are most of the companies away from the Chinese open source models, which have been doing incredibly well in benchmarks and seem to be very capable, but haven't been eating a lot of AI spend that I've been seeing in panel data at least?
Speaker 5:Yeah. The Chinese models I think are some very large companies are using them from production. Yeah. You'll have different perspectives particularly from the chief chief information security officer. Sure.
Speaker 5:Some people will not use Chinese models at all. Most of those Chinese models are typically run on US inference
Speaker 8:Yeah.
Speaker 5:Neo clouds. And they're incredibly capable. We use them internally. We have backed companies that are using them. Mhmm.
Speaker 5:We think open source is an absolutely essential component of the of the of the ecosystem going forward. I'm I'm excited to see, you know, the meta models, the Gemma models. Clearly, NVIDIA is pushing quite a bit with the Nemotron and the acquisition of Hugging Face plus the poolside semi acquisition, let's call it.
Speaker 2:Yeah.
Speaker 5:So it's it's essential. I think one key question that hasn't yet been answered is if an American company fine tunes Chinese model and runs it on US infrastructure, does the market perceive that both like economically and politically as a US or an American model or as a Chinese model?
Speaker 2:Depends how American the CEO of the company is, I guess. If they announce it on Joe Rogan, I think it'll be well received.
Speaker 1:How walk me through how how you think that that platform VCs are justifying, you know, doing some of these vertical AI companies at like 50 times, you know, 50 to a 100 times revenue today. It feels like a year ago, a lot of the conversation was around, like, these aren't software budgets anymore. They're they're capturing some amount of of labor spend. Un unclear how true that is today even if you're not seeing a correction in the labor market. But but again, maybe firms are just hopefully doing quite a bit more and so that you are actually getting into labor spend by just, you know, like an incremental hire or something like that.
Speaker 1:But comparing some of these vertical AI companies to to the prices that Bending Spoons has been paying, it just feels like incredible incredible disconnect unless unless these vertical AI companies like like, you you just look at them and and and I'm I'm wondering how they get into the billions of dollars of of runway run rate, which I think right now, the industry is sort of pricing them that that that at least some of them need to hit that for the category to work out.
Speaker 5:Yeah. You're right. I mean, we've benchmarked the, you know, AI harnesses are now the fastest growing ones are trading between a 100 to a 150 times current ARR, which is an enormous multiple particularly at that level of scale. Sometimes it's subsidized with lower gross margins, and the idea there is primarily just let's get the product out as far as possible, and then ultimately we'll improve the economics. In their favor, I will say, we made a prediction at the end of twenty five that we 2026 would be the first year where employers would pay agents at market level for a person or more.
Speaker 5:And that happened the first time we noticed that was actually in March or April of this year. So we have a portfolio company that charges at par, and it will likely charge at a premium to a person. And so if that's the case, and it's not necessarily human replacement, it is more augmentation. There's the ability to do more, process more. But if that's the case and you could argue economically, there's no management, there's no healthcare.
Speaker 5:And so you should actually pay a premium to
Speaker 1:hiring a freelancer, like an expert freelancer is always more expensive on a per day basis than than a long term hire. Right? Because you just wanna be able to get that expertise immediately, but then not pay for it when you need
Speaker 2:It's super hard to get 10 freelancers to show up for one week and then go away for two weeks, and then two of them come back for one week, and then two days over here, and obviously a lot of services. Depending on how
Speaker 1:Do you think we'll get an American bending spoons? I've had this idea recently of like how many how many companies need to be acquired at like, you know, two to five times revenue for American VCs to say, well, why don't we put a few billion dollars into a firm that does at least so we can monetize the way up and the way down? Do you do you think that'll happen? Because it feels like I look at some of these acquisitions and and again, these are not companies that people are generally excited about but I think they're gonna be pretty pretty durable for the most part. And I think part of the reason they're getting such good pricing right now is no one else has basically the stones to go out and say, yeah, I'm gonna buy Airtable.
Speaker 1:Right? Like, I wanna wanna be that I want that to be my problem, you know? Yeah. But a a company set up entirely to do that and that's the core bet, I think could could make sense.
Speaker 5:Oh, absolutely. I think the multiples for some of these companies are really quite small. The key metric there is just net dollar retention and gross dollar retention. So some of some of the legacy, we'll call them legacy software companies for a second. Their revenue retention rates are are still phenomenal, and so there's a multiple arbitrage opportunity.
Speaker 5:There are a handful of companies that have been or investment firms that have been doing this for a long time, and I'm surprised we haven't seen them be more active. And then you also have holding companies that have bought a lot of these businesses, like Danaher would be a publicly traded example of of a kind of a holding company Constellation Software would be example of another one. Yeah. And so I would expect them to be pretty active just given I haven't looked at at their activity in a while, but I would expect them to be pretty active just given how attractive the the multiples are.
Speaker 2:Is there demand or at least, like, gesturing towards demand from LPs for VC firms with companies from that vintage companies in that position to, like, get cleaned up. Like, you know, that that that that's something that bending spoons offer is, like, there's a there's a there's a log jam, maybe you still have a board seat, it's like, hey, we actually want you to maybe realize not a great return, but then you'll be focused on the next era. You can be a 100% in on this wave, which we still wanna back you on. Is that something that actually will come from LPs or is that just a separate calculus?
Speaker 5:No. I think that's right. I think GPs and LPs definitely want to, I mean, in certain many cases move on Yeah. From the those positions just because the industry has changed and liquidity is absolutely important. All the '26 is a monster monster year for liquidity.
Speaker 5:Yeah. But just cleaning up those fun vintages and and migrating those companies to the next person who, you know Yeah. Most interested in managing them the way that you talked about Yeah. Is a big part of the industry. It's in every ELP conversation that we have, managing liquidity is is among the top two or three topics.
Speaker 1:Yeah. What How do you expect the economics of the personal sort of new category of personal agent companies to evolve? I'm thinking Instinct and and Muse. A lot of people are using these agents to just say like, hey, go buy this product on this website. Right?
Speaker 1:It's There's no like discovery happening. There's no It's it's truly like the the end customer just telling a piece of software, go buy this thing. And Yeah. So I think Instinct has said they they they don't wanna do they don't wanna charge for it. Muse clearly is gonna just try to make it free effectively forever.
Speaker 1:But I think both of them it's hard to imagine like ads and like an Instinct workflow right now given that just thinking about the the surface area. I don't know. I'm sure Meta will figure out a way to put to put ads in it. But
Speaker 2:It's good
Speaker 1:for the money. When you think about like affiliate and taking cuts when the intent is just coming from the user. Like if I'm retailer and an agent comes to the website and buys something, but I know that that the the individual just directed the agent to do it, I'm not exactly sitting there being like, thank you. Now here's your cut because you didn't actually drive the demand. Other marketing or other things were happening in the world that drove that person to decide, hey, go buy this thing.
Speaker 1:So I'm wondering, like, how the the sort of economics of of this new category will will evolve.
Speaker 5:I think it breaks down into two phases. The first phase is really about data acquisition. Mhmm. The most the most valuable data, I think the estimate was about 10,000,000,000 in data is being spent this year to train some of these models. And so as you think about like what Cursor has done or what Meta is doing, with Muse, they very likely wanna develop very specific models that are incredibly efficient to serve.
Speaker 5:And to do that, they need the data acquisition, makes sense to subsidize it for a while until they get enough trajectories to be able to fine tune and serve a very efficient model. So I think that's probably priority two. Priority one is distribution, priority two is scaling the cost, and then and then third is monetization. And just given like, you know, Google's distribution in consumer, Metas or Facebook at the time's distribution in consumer, Snapchat, same thing, Pinterest even. The ultimate monetization can be very powerful when there's a new dataset to monetize.
Speaker 5:Google's with search, clearly met us with social, Snapchat, Pinterest, new targeting mechanisms. I used to I was a product manager on the ads team at Google. And so every this is the way I think about that ecosystem. Anytime you have a new targeting criterion, you can have a multi $100,000,000,000 company.
Speaker 2:Yeah.
Speaker 5:And these trajectories are unbelievably valuable. Average user on Google in The US generates about a $120 in ARPU. It's very easy to see a doubling or tripling of that with these agentic systems. And so I think a lot of us in the venture markets are willing to take that on faith.
Speaker 2:Yep. Yeah. No. That makes a ton of sense. Well, thank you so much for coming on the show.
Speaker 1:Yeah. I wish we had
Speaker 2:more time. Yeah. There's been so much more we could talk about. I have a million more questions. But our next guest is in the waiting room.
Speaker 2:Great. Have a great day.
Speaker 1:Let's see. Wait. Wait. Last final final final question. Final question.
Speaker 1:Question. Have you ever seen Spencer surf?
Speaker 5:You're He's incredible. Is he is a
Speaker 1:He is like I would go out on a limb and I would say he's the probably the best surfer in tech, period. Woah. I don't know. I I I don't know of anyone I don't know anyone that would actually be able to go head to head with him Okay. And come out alive.
Speaker 1:He's an absolute animal. It may Seeing him surf made me want to do a TBPN surf invitational at at Kelly's Ranch.
Speaker 5:The surf ranch. Yeah. Yeah. Yeah. He's incredible.
Speaker 5:He he has his video inside of a barrel. He's just an exceptional surfer. So Yeah. He's definitely the best one I know.
Speaker 1:Surfing the capital markets with you though.
Speaker 2:Fantastic. Great
Speaker 5:to see you. Cheers.
Speaker 2:Have a
Speaker 1:great one.
Speaker 2:We'll see you soon. Let me tell you about the New York Stock Exchange. Wanna change the world? Raise capital at the New York Stock Exchange. Just do it.
Speaker 2:Our next guest is already in the waiting room. So let's bring in William Layden from Roon, cofounder and CEO. Building. The modular data center that converts unused solar power directly into AI compute. How much unused solar power is there?
Speaker 2:I I feel like we'd be using it all.
Speaker 8:Hey, guys. That's that's the trick, isn't it? Yeah. Over 50 terawatt hours every year in The United States alone.
Speaker 2:And and and is that residential? Is that just solar farms out in Like, the why why do we build it if we're not gonna use it?
Speaker 8:Yeah. I think I think so we're exclusively focused on utility scale solar farms Okay. Which is the, you know, large assets. Think fifty, hundred, even 400 megawatts of installed capacity. And I think that abundance or excess is actually a feature of renewable energy and not a bug.
Speaker 8:So you typically build a power plant to meet the highest hour of demand. But, you know, solar, it's it's relatively cheap to build. The sun's zero cost fuel, so we tend to to overbuild. And as a result, there's lot of excess power.
Speaker 2:Interesting. So are tokens gonna get cheaper in the summer, in the long term?
Speaker 8:Well, if we get big enough, then then sure. Maybe we'll we'll we'll have seasonality on impact on tokens.
Speaker 2:Okay. What, talk to me about the importance of modularity because you mentioned, like, a few different scales of industrial solar farms and it feels like those would require those would support very different configurations of of, I imagine, inference GPUs. Right? So you have to be able to show up and you can't show up with a gigawatt worth of compute for a solar farm that can only, at max, put out a 100 megawatts. Right?
Speaker 8:Yeah. That that's right. So we are deploying what's called, you know, our product is called the Relic.
Speaker 2:Mhmm.
Speaker 8:It's essentially a micro shell
Speaker 2:Mhmm.
Speaker 8:That has a server inside of it. And we deploy many of these prod many of these data centers, couple them to create a large cluster size, basically the largest, as much of as larger cluster size as the solar farm can tolerate, we will deploy. So we're working with, a 400 megawatt solar facility. We think we can probably put, you know, a hundred, two hundred megawatts of solar there or of data center capacity there.
Speaker 2:What's the go to market like? What who do you actually have to negotiate with to, a, get space and supply and and actually deploy these? And then is it as easy as just hooking the system up to OpenRouter and serving up tokens? Like, what is it is it if you build it, they will come at this point?
Speaker 8:Yeah. I would say so so, you you know, we manufacture, as you said, modular AI data centers and we use power electronics to plug them into these facilities. Mhmm. We can deploy, you know, a unit in about sixty minutes. So we have a guy with a forklift come, drop it down.
Speaker 8:Wow. There's no concrete, there's no construction, No. There's no We drop it right on the ground, there's no concrete, no modifications. We take two wires and plug it in. And it is the truly the fastest, least expensive, least intrusive data center out there.
Speaker 8:Okay. And then in terms of our customers, we're actually selling
Speaker 1:No noise. No no pollution. Yeah. Like it feels
Speaker 2:This seems popular. Somebody was asking me yesterday, last night, like, what does it take to make data centers popular? And I was like, solar for sure. And it seems like you've been obviously ahead of the curve on this. What if you go more modular?
Speaker 2:What if every solar panel came with a GPU attached to it by default? It could always be in inference mode and flip over. Is that the future? Or do you think that this like, you know, mid scale modularity, like there's some economy of scale that comes from like marshaling a lot of energy together or maybe even not a lot, but, like, some threshold amount.
Speaker 8:Truly, I think every solar power plant is a latent data center, and we can convert it with our technology. The same is true for wind.
Speaker 2:Mhmm.
Speaker 8:So I can see a future exactly like you said where every solar plant, every solar module, every wind turbine comes with a room data center.
Speaker 2:Interesting.
Speaker 8:And that's how we power the future of compute.
Speaker 2:So I imagine that, yeah, although you're focused on solar, you're already thinking about wind, as you mentioned, hydro, and and other places where there might be some latent energy, but it requires the modular solution that you're building.
Speaker 8:Yeah. The modular solution, the power electronics, these are absolutely kind of our secret sauce
Speaker 2:Yeah.
Speaker 8:To make this work. You know, solar is the most abundant energy source we have, and it's the one that we are the worst at using. And Roon is designed to make it, you know, make it useful. Let's let's make the sun power the future of compute.
Speaker 1:What were you doing before this?
Speaker 8:I started my career working for president Obama in the White House. I joined a hydropower company and then was most recently at SP Energy.
Speaker 2:Nice. Why
Speaker 1:Feels like the the most perfect entry
Speaker 2:point Yeah.
Speaker 1:Into this company.
Speaker 2:Overnight success. Why is it the what was the chart that we were pulling up from the IEA? Was that right? The International Energy Association or something like that agency? The IEA, every year they predict how much solar we're going to build and every year they get it wrong.
Speaker 2:What's going on? What's going on with the IEA? Why can't they forecast accurately?
Speaker 1:Well, it's it is Paris based. I
Speaker 2:don't know. Oh, have they never heard of exponentials or something? What's going on?
Speaker 8:I think that's the power of you know, you guys were kind of focused on modularity and that is the power of modularity. We're a product company. Solar is a product. It's not a construction project.
Speaker 2:Sure.
Speaker 1:It can
Speaker 8:scale much faster.
Speaker 2:Okay. But why why why haven't they internalized that yet? I feel like at this point, it's like just fit a different curve. If like the linear line is not working again, let's go with an exponential this time.
Speaker 8:Yeah. We'll have to make it put stuff in Paris and talk But to
Speaker 2:but but separately, like, you have any do you have any worries about us hitting a ceiling on deployed solar? Because I see that curve and I'm like, this is amazing. This is the ultimate technology white pill. I've never met anyone that doesn't really like solar. It's nuclear.
Speaker 2:I still get people are, oh, what if it blows up? You got to work through that. Obviously, gas and oil. There's a whole bunch of complaints there. But solar's really, really popular.
Speaker 2:But at the same time, I know that there's a complex supply chain. There's geopolitics. Is there any risk that that curve might bend?
Speaker 8:I think geopolitics is is always a challenge, particularly in in in today's environment. But truly, I'm a solar maximalist. It is the fastest and easiest way to create energy. And even now, you're seeing companies that use robots to install, solar power plants. And when you go to these sites, you know, you go to Texas, you go to Nevada, you go to Arizona, there's so much unused land.
Speaker 8:And, we can really use that, land to create abundant energy via solar. We're nowhere near saturating the solar market in terms of how much we've deployed.
Speaker 2:Last question. And if you don't have a strong answer, you can just email us. But I've been on the hunt for like the Elon Musk of solar. You know how there's like the Palmer Luckier defense tech who makes, you know, big waves and like sort
Speaker 5:This
Speaker 2:of guy could be looking
Speaker 1:at the Elon Musk of solar in the eyes and not
Speaker 2:even Maybe. Realizing But I'm talking about someone who's specifically focused on building a company that will deploy the most solar panels, build the panels, really be like the the the mega winner, the public company, the voice of the industry. I don't know if that person is on the tip of your tongue or someone you need to think about, but but I'm I'm really I'm I am hungry for that person. So if you know them let us I'd love to have them on the show.
Speaker 8:You know what? I've always been an admirer of Sheldon Kimber.
Speaker 2:Okay.
Speaker 8:He built Intersect Power.
Speaker 3:Okay.
Speaker 8:And I think he's been a visionary in the solar space.
Speaker 2:Okay. Amazing. Yeah. Thank you for the recommendation. We're definitely
Speaker 1:Who did the round?
Speaker 2:Oh, yeah. Let's hit the gong.
Speaker 8:Yeah. Yeah. Spark. Oh. Just
Speaker 1:needed one word.
Speaker 8:Cool. Yes. Sparks. Sparks.
Speaker 2:40,000,000.
Speaker 8:The round. Congratulations. Thank you.
Speaker 2:Thank you. So much for coming on this. Great to meet talk to you soon. Have a good one. Let me tell you about MongoDB.
Speaker 2:What's the only thing faster than that AI market? Your business on MongoDB. Don't just build AI. Own the data platform. That powers it.
Speaker 2:Our next guest is Justin Beroz from Larenco. He's the founder and CEO, and he's here to talk about Navient Stokes. What does it matter? Doesn't it not Does matter? We were having this debate.
Speaker 2:Seems like a cool demonstration for the power of mathematical models and artificial intelligence. Great benchmark. Clearly everyone was trying to do it so it's cool to be first. Controversial among mathematicians. But my question was like this is fluid dynamics.
Speaker 2:We all want planes that use less, you know, or diesel to get around, jet fuel to get around. We all want more efficient wind energy, all the different things that you can get from understanding turbulence. Does this have any real world application?
Speaker 6:Yeah, man. Well, first of all, thanks, Folks, for bringing me on. It's certainly So a pleasure to be regarding the practical aspect of this, I should short answer is no. Long answer is let me tell you about it. Okay.
Speaker 6:So oftentimes, the way I view this is there's kind of three hierarchies of of of theory. So there's a mathematician at the top, the pure mathematician, and what they say, that becomes the applied stuff for the theoretical physicists, which then becomes the applied stuff for the the engineers.
Speaker 2:Yeah.
Speaker 6:So should describe what is this open question. It's really about a particular mathematical aspect of the Navier Stokes equation. So what's been a long running problem is, you know, can we for instance, can you come up with any scenario in which you you start with a physically meaningful initial state of the flow, and then can it time evolve to do something that's not physical? Mhmm. So in particular, what the worry has been is, is there any point in the flow that that can attain infinite velocity?
Speaker 6:Obviously, that doesn't happen in real life. However, we don't have any any firm mathematical proof whether that can or cannot happen. What what OpenAI has done is they've essentially found a counterexample. So they constructed a specific example where you do in fact get blow up. You get a certain point within the flow that attains an infinite velocity.
Speaker 6:Mhmm. And so if this proof holds up, you know, first of all, it's important to mention that they've submitted a proof, but, you know, it may take years for the actual experts to review this thing But and figure it but essentially, what it does is this is being a guardrail on on the Navier Stokes equation as far as its applicability. So you can imagine that if you have a well, well, in any real full scenario, obviously, you don't see
Speaker 2:this. Mhmm.
Speaker 6:Right? But but it is hard guardrails in the sense, like, you know, if you're running a simulation and if it's not converging, you know, this is another thing to add to the checklist. Like, you know, maybe there might be a single or excuse me, a singularity developing flow.
Speaker 2:Sure. So there are at the same time on the opposite side, the engineering side of the world. There are lots of companies that are trying to develop supersonic airplanes, hypersonic airplanes, drones, all sorts of different things that need to do CFD and model fluid in turbulence. There's real world work to do. What is that community clamoring for?
Speaker 2:What do they want to be advanced? What are their biggest problems?
Speaker 6:Yeah. So I'm reminded of the quote, mathematicians can tell you if a solution exists, they don't tell you what it is. Yeah. And that's kind of relevant here. So, you know, the proof that Stokes did, you know, it's a point in terms of a specific mathematical property.
Speaker 6:But the real world problem of give me a flow scenario and tell me what exactly is going on, what are those dynamics, That is still a wide open problem. And I should mention that this is one of two big problems related to Navier Stokes. So there's the million dollar math Clay Institute one, and then the other one is called the turbulence problem. This is more of a physics one. So, you know, so what's you know, the the difficult thing about fluids is that, typically they destabilize and they they make these complicated swirling flows.
Speaker 6:So we've probably all seen smoke coming off of a cigarette and then it starts tumbling around. That's an example of this phenomenon of turbulence. And having a complete mathematical framework to describe that, that is also an open question, and that's been around for about two hundred years. Now that's actually the one that we're working on as a company, so I should mention my background. I'm I'm also a theoretical physicist.
Speaker 6:And we're working on coming up with the general computational and mathematical tools to solve real world problems.
Speaker 2:And and are you trying to build deterministic tools or are you trying to build AI tools that will just approximate turbulence at such a reliable level that you the engineering will be advanced?
Speaker 6:No. Yeah. So so this is all pencil and paper, man. You know, we're Okay. We're we're mathematicians.
Speaker 6:You know, we're we're working out the the general mathematical framework. Yeah. I mean, this is you know, I should mention that the current state of the art, you know, because we don't have the general method to solve this equation, if you'd actually look at the kind of simulations we run, you know, it doesn't matter what the example is. Maybe this is aerodynamics or hydrodynamics. It could be weather and climate modeling, heat transfer, chemical mixing.
Speaker 6:These are all examples where fluid flows are are really core to the industry. The state of the art is, you know, the the simulations are really not predictive. So if you peer under the hood, what you see is we actually don't know what the equations of motion should be that we are solving. So, you know, for the technically minded audience, I should mention that, you know, these are called the the so called closure equations. But, basically, we we have when we run simulations, you have a bunch of free parameters.
Speaker 6:So you need to start by doing experiments, You take measurements from the experiments to inform what these free fit parameters in your simulator are. And as you can imagine, having physical prototyping as part of the iteration design loop, that's the most expensive way to do it.
Speaker 2:So step back a little bit. We jumped straight into this, but give me a little bit on your background, and then I want to walk through to today and the actual structure of the organization and the plan for what you're building.
Speaker 6:Yeah. I I guess, you know, quick background on me. So I started out as a mechanical engineer. Mhmm. I have bachelor's through PhD in MechE, primarily focused in precision mechanical design.
Speaker 6:Mhmm. And so, originally, I was one of the guys who was designing building stuff in the machine shop, and evidently, I went through some sort of quarter life crisis and ended up picking a second picking up a second PhD in theoretical physics as well. And, really, the company that that we're that that I'm building now is the out product of that work. So, you know, as as I mentioned, you know, this problem about solving turbulence, this is a long standing open problem, and I got interested in this a few years ago. And after plugging away at it for a while, it actually penned that closed form framework.
Speaker 6:So, essentially, what we're doing what we're doing as a company is we're we're hiring a bunch of PhD and postdoc level theoretical physicists and applied mathematicians, and we're taking this new theoretical framework for Navier Stokes, and we're, you know, basically going after everything that relates to, you know, things that move essentially.
Speaker 2:And how do you plan to productize this? I mean, it is a business. It's not a nonprofit research organization. I imagine that there's a different path that you could have taken, but you chose to build a business. How do you see that developing?
Speaker 6:Yeah. So I guess a couple of things to say. I mean, one is that my engineering background has given me a very good sense that we really, really struggle when it comes to engineering fluid systems. So I'm well aware, the market need for a solution to the turbulence problem has been here for, like, four hundred years. That hasn't gone anywhere.
Speaker 6:The way we're thinking about it is essentially breaking those down to three steps. So as I mentioned, the first thing we're focused on is better computational software. You know? I mean, step one is you wanna be able to simulate something in a computer, know that you're getting an answer that you can trust and do the design and optimization in the computer first, then you build the one prototype at the end to confirm that things are right. Mhmm.
Speaker 6:So I should mention, like, this is what's done in every industry except or every engineering discipline except for fluids. It's really the one holdout where you're you're really wedded to experiments. So we're essentially the company is going to develop in three stages. So we're starting first with better software. We're a couple of years away from releasing our first commercial product.
Speaker 6:But afterwards, there's a couple of things we're going to do. So one, of course, is modeling. So there's a number of examples of industries where there's important hair on fire turbulent flow problems and and the most important ones we wanna go after ourselves. And then long time, we long term, we wanna get the hardware. So think hardware, software control algorithms to control and mitigate turbulence and number of canonical flows.
Speaker 6:So this will take us, like, way into the future. I mean, you know, it's kinda like if you solve the turbulence problem, you just open the the the doors to the playground, but there's so much to do and explore and so many ways to add value.
Speaker 2:So is the team right now all focused on that phase one, all sort of theoretical physicists and and scientists effectively, there's no sales guys running around just yet? Well
Speaker 6:well, yeah. So as far as the technical team, yes, it's it's it's a Oh,
Speaker 2:so you got some sales guys. I'm hearing We do.
Speaker 7:That was actually
Speaker 6:our most recent hire.
Speaker 2:There you You got a sales guy. Yeah.
Speaker 6:We're we're a small startup out of the Brooklyn Navy Yards. So we're a team of eight. Our most recent hire was, in fact, our chief commercial officer, and he's absolutely fantastic. So, you know, I I I would say, like, the the the primary thing is the technical people, the engineers, and the this is the mathematicians, but then there's a glue that's holding the company together. So my COO, myself, we have our our chief commercial officer and our administration person.
Speaker 2:Very cool. I'm I'm interested in one one of the critiques that I heard on the on the Navier Stokes solution was that it sort of, like, ripped the problem apart into such an extreme position that it was like it was sort of impractical or it was not like the most elegant or or or like it wasn't the way you would expect a mathematician to solve it. It was sort of brute force. And I'm wondering just I'm sure you're using different AI models. Is it accelerating your work?
Speaker 2:What part is accelerated the most? Are you running into any guardrails or any any hurdles as you try and collaborate with artificial intelligence? Like, what's your experience been?
Speaker 6:Yeah. That that's a great question. So so so first of all, I don't know who you heard that from, but that's exactly right. So when I read the proof from OpenAI, I I had the exact same feeling. You know?
Speaker 6:I I was kinda naive. I kind of assumed that the PDF write up, you know, had some sort of human involvement. And after the first two pages, I was like, wait. This doesn't sound like something a human did. Yeah.
Speaker 6:And and and it's not. The whole thing is AI generated. Yeah. Yeah. So I I mean from you know, it's not elegant, but if it's right, it's right.
Speaker 2:Exactly.
Speaker 6:You know, one one of the big things, and and I'm sorry I'm I'm taking a long No a long security's route to answer your question, but, you know, you know, to me, I I see I see an analogy between what AI is doing for math and what, for instance, we do by running experiments in more applied fields. So in some sense, up until this result, the idea of proving something and understanding something had been synonymous. And there's an underlying assumption that it's really human beings who are jumping in and putting the proof together and developing the understanding along the way. But what we're seeing here with AI is that that's no longer the case. You can actually produce a proof that may in fact be correct without understanding anything about it.
Speaker 6:Right? So the way I see this is this is of like running an experiment where you can observe the right result, but the whole process remains in front of you on how do you actually understand it. And this is part and parcel in engineering and physics. Right? You run experiments and you get results.
Speaker 6:You know that they're right, but you don't understand it for a while.
Speaker 2:Well, good luck, and thanks so much for coming on and giving more context about it. I love that you're you're you're thinking in multiple acts. You're thinking in applications here. It's gonna be exciting to follow your journey. So have a great day and thank you so much for coming on the show.
Speaker 2:We'll talk to you soon. Thanks. Have a good one. Let me tell you about Codex. Codex is a powerful workspace for getting work done with AI agents.
Speaker 2:Whether you're writing code, analyzing data, creating content, or automating business workflows, Codex helps you move projects forward from start to finish. Jordy, why did you put on that Tyler mask? It's so it's so photoreal. Realistic. It looks extremely realistic.
Speaker 2:Yeah. Through the power of of switching technology and cutting camera angles. Tyler has subbed in for Jordy because Jordy has to take to get out of here early. We have a bunch of we have three more great guests, so stay with us. Thank you so much for tuning in to TBPN today.
Speaker 2:Up next, have Eli Walks from Footprint, building an AI operating system for financial crime compliance. This is gonna
Speaker 8:be important for you to
Speaker 2:pay attention to because I know you're thinking about doing something. Tyler over here, he's one step away from committing some heinous financial crimes. So how can I keep an eye on him? Do I need to pay It's
Speaker 7:a great question. Look, we are at a time where bad actors are using AI Okay. Far quicker than any bank or financial institution and
Speaker 2:Yeah.
Speaker 7:It's become the fourth largest economy in the world. What? There's $4,500,000,000,000 moved to list each year.
Speaker 2:Wait. Wait. Wait. So so like financial crime, it's like US, China. I don't know who the third is.
Speaker 2:Is it Japan
Speaker 7:or Germany.
Speaker 2:Germany. Okay. Germany. And then financial crowds. Wow.
Speaker 7:Like the cineloid cartel and their friends.
Speaker 2:Yeah. That probably also includes like crypto heist, North Korean hackers, that type of stuff. Then also just like little phone scams trying to get grandma to send you a $100 of gift cards, that type of thing?
Speaker 7:Exactly. So you have them on one hand Yep. Using AI since g p d three.
Speaker 2:Mhmm.
Speaker 7:And in the other, you have one and a half million people who are supposed to, by hand, investigate any suspicious wire that comes across their desk
Speaker 2:Wow.
Speaker 7:In days or weeks. It's a system that's been very unprepared Yeah. And the gap's only growing.
Speaker 2:Okay. So how broad are you focused? It seems like AI operating system. I imagine you're plugging in deep into financial rails and trying to at least set your system, your operating system, your company up to both be able to handle the the big sophisticated crypto heist and also the the fraudulent bank wire for a couple $100?
Speaker 7:Yeah. So we work with some of the largest fintechs, crypto companies, financial institutions. The idea is at both the second line of defense, which are core compliance use cases, AML, transaction, sanctions, sexy stuff. Mhmm. And then the other one would be called first line defense.
Speaker 7:Mhmm. So payments, ATO, any other type of fraud scheme. Mhmm. The idea is that we're giving these customers essentially infinite time and infinite memory. Mhmm.
Speaker 7:So it used to be cartels win in numbers. Mhmm. Similar to how you ship drugs across the border, you put cocaine on enough trucks, some of them are gonna get through. Sure. You create an up shell company, enough of them are going to receive money.
Speaker 2:Sure.
Speaker 7:And people didn't have the ability to investigate each of them. So the first thing we do is we give them the compute to investigate every case that comes across their desk. And the second is very unique to footprint. We give them the ability to remember every case that they've seen. So that way they're able to essentially compare vectors and prevent financial crime before it even happens.
Speaker 2:Okay. You said you give them compute. If we were talking a couple years ago, I would be thinking this is an annual contract. This is like a SaaS product almost, but it feels like the business model for this business might be a little bit different in the modern era. How are customers actually compensating you and and and working with you in an elastic way that allows them to scale up and down?
Speaker 7:We charge by usage. Okay. It's essentially credits based off of the complexity of the case.
Speaker 2:Mhmm.
Speaker 7:So you may have the same genre of case
Speaker 2:Mhmm.
Speaker 7:But one we're only looking at a couple pieces of information. Let's say we're investigating a fraud scheme. Yeah. Maybe one's very kinda dry versus there may be one where we actually want to have recall and go through embeddings of thousands of similar cases. Yeah.
Speaker 7:And we had a case where there was a flagged Russian individual, and our agent went back into Ukrainian newspapers, read the initial Cyrillic because Cyrillic can be translated improperly, then tracked down this person's wife to a home in New Jersey and found a real estate listing in their name. Woah. So you can also get into much more complex things that you never expect a human to have done before. Yeah. So we charge the trading caps like that.
Speaker 7:We also have our own model routing internally.
Speaker 2:Okay. So we're on model routing. It feels like some of the work that you do it's going to wind up looking like fraud because you're you're a lot of the prompts are like sort of sketchy adjacent even though they're the opposite and you're actually fighting the financial crime. Your prompts are going to look financial crime related. You might get flagged.
Speaker 2:You might get, you know, refusals. Have you solved that by fine tuning models using open source or partnering with labs and doing deals to get white listed? Like what is your strategy for maintaining access to effective intelligence to fight these crimes?
Speaker 7:All of the above. Mhmm. We have really good relationships with the Frontier Labs. Yeah. I I think that we all view this as one of the Yeah.
Speaker 7:Most important use cases of AI. I'm very biased. But I think that we either if AI grows the economy by 14% by 2030
Speaker 2:Yeah.
Speaker 7:That means we either add $600,000,000,000 into the pockets of the worst people on the planet
Speaker 2:Yeah.
Speaker 7:Or we bring that down to zero, which I think is possible. So you're right. Our prompts do look interesting. Yeah. Because we have to think like financial criminals to do it.
Speaker 7:And then we do some of our own model hosting, but we also have routing and that some banks may only wanna use two models.
Speaker 2:Sure.
Speaker 7:We also, we may take we recently had a bank put in a 150 page policy document. That's taking thousands of rules Mhmm. That have to be followed and each of that essentially is getting its own sub agent in its own environment.
Speaker 5:Mhmm.
Speaker 7:That's why I think compliance AI is two years behind legal AI because there's so much more regulation and such a higher barrier of trust to overcome.
Speaker 2:Mhmm. Do you I guess I I I'm thinking if like do you have either benchmarks as you're fine tuning models, testing models And do you notice anything particular about models where you're like, oh, it spiked on this particular vector of like fraud detection? Do you notice like the spiky intelligence behaving differently? Because, you know, the the the x algorithm will be obsessed with a particular model typically by like how viral the creation can be. So if it's like a blender three d model or a video game, everyone crowns that the best model of the day.
Speaker 2:But no one's really like, wow, this model is incredible at detecting financial fraud because it's probably just collapsed into some sort of score. But what are you doing on actual model benchmarking evaluation and then how do you communicate that to your customers?
Speaker 7:Yeah. Think evals are pertinent to any vertical line company, but especially us in that we need to make sure that when a new model comes out, it's not spinning its wheels
Speaker 2:Yeah.
Speaker 7:And it's actually getting to the right answer that we expect.
Speaker 3:Sure.
Speaker 7:So we have a huge library of synthetic cases that are based off real cases that we're leveraging to understand. I think one of the powerful things about footprint versus, you know, like an AI wrapper is that we are built with access to hundreds of databases around the world
Speaker 2:Mhmm.
Speaker 7:To fact check the open source research of an agent. So we have cases which may involve an agent going and verifying a business in China as part of investigation. It used to be in this space you had ML based detection tools that would flag an alert and humans would investigate it. Yep. It was like a very cowardly industry of vendors and that we're saying all of the tough stuff people actually have to figure out.
Speaker 2:Yeah.
Speaker 7:And what footprint's done is we've essentially taken AI, we let AI orchestrate those initial detection tools and databases with AI on top of it to essentially make this a dynamic process of detection and investigation.
Speaker 2:What about sort of like benchmarks or like KPIs for the overall fraud industry. How accurate are you on that number of the fourth largest economy? How and how rigorously is that tracked? Like in a couple of years, we'll be able to run this interview back and say like, okay, yeah, it's like the ninth biggest economy now. We're making progress because that's what success looks like here.
Speaker 2:Right?
Speaker 7:100%. That's the goal. Fraudsters are really smart. If the cartel is 5,000,000 of cash in The US, seventy hours in a time that goes to Chinese money broker because you can only get out 50 k of Kwa a year.
Speaker 2:Mhmm.
Speaker 7:And then the cartel sends 10,000,000 of dishwashers to the cartel in Mexico. They charge them 5,000,000 based on the dishwashers. Really good operation. I would love to invest. I would love to buy a way.
Speaker 7:I think that it is possible for us to go down. I think when you look at benchmarks, it it it may sound ironic, but you're often hearing PSA officers say 95% of our AML heads are false positives. 75% transactions are false positives. We actually want that to be 99.999% because we're only looking at what's flagged as the highest risk transactions. In the future, we wanna look at every case because you don't want an agent to have memory of every transaction that's ever flown through, every account that's ever been opened, and then you can compare those.
Speaker 7:That's what we've built with trust fabric with memory footprint. And that recall, yeah, ideally, we go from four to fortieth. I I say our mission is to topple cartels and corrupt regimes. So, look, I don't think we're going to start with with Russia, but maybe Turkmenistan or or or or someone up there.
Speaker 2:You expect
Speaker 4:do you expect any cartels to spin up, you know, clusters somewhere, start doing pre training, you know, create build their
Speaker 2:own Cartel VLO.
Speaker 4:Yeah. Fraud models Okay. Thought on the side. Yeah. Do you expect this to happen?
Speaker 7:Think they would do well on Sand Hill Road. I'm not sure. What I will say is they're sophisticated.
Speaker 2:Yeah.
Speaker 7:So you have the cartels the most sophisticated is the Golden Triangle in Southeast Asia
Speaker 2:Mhmm.
Speaker 7:Where you essentially have these multi thousand person factories.
Speaker 2:Wow.
Speaker 7:It involves human trafficking.
Speaker 2:Coming on the show. We will talk to you soon. Have a good day. Goodbye. See you.
Speaker 2:Quick hit on the news. Breaking news. Chipotle has partnered with Palantir. The burrito chain has enlisted Palantir for a platform that monitors pest incidents, employee illnesses and other factors. The partnership comes after a nightmare summer for food safety across The US.
Speaker 2:Good news. Good news for both companies. We want safe food. We want burritos that don't make us sick. Especially if they're coming before I even think to order them with Burrito Super Intelligence from one of the companies.
Speaker 2:It feels like it's a race now. The the DoorDash guy was talking about building it and then the and then the other entrepreneur just did it. Anyway, we have our next guest in the waiting room. I'd to bring in Sean McCarthy from back ops. How are you doing Sean?
Speaker 11:I'm great. How are you?
Speaker 2:I'm great. Welcome to the show. First time on the show. Please introduce yourself and the company.
Speaker 11:Yeah. I'm Sean McCarthy. I'm the cofounder and CEO of BackOps. BackOps is an AI native resolution layer for supply chain. So we target companies that make or move physical goods.
Speaker 11:You can think as a consumer, something goes wrong, you order something, it's broken, what happens behind the scene? That's gonna be us. So happy to chat a little bit deeper into what we're doing, but looking forward
Speaker 2:to
Speaker 11:it.
Speaker 2:Yeah. What's the median back office supply chain tech stack look like these days? Has everyone moved off of pen and paper? Have people moved off of spreadsheets? Are they at least in legacy ERP systems with green screens and black?
Speaker 11:You hope. You know, that's where we started. I was at Amazon prior too and spent a lot of time in customer warehouses. And we started around the consumer side, you know, more like you or I order a lamp and it's broken again and something happens. Mhmm.
Speaker 11:And now we really service the enterprise, but it's the same thing throughout from the mom and pop shop to the to the large enterprise. So some do have, you know, SAP and Yeah. Oracle and some of these other, you know, common systems of record, but the Excel, I don't think we've met a company that doesn't use Excel spreadsheets, at least pre back ops.
Speaker 2:Well, I mean, it's good that the the all the models are getting really good at speaking Excel. Is it easier to build integrations? Did you have to sort of, like, pick a landing zone where you're gonna be really great at SAP first? Or are we actually in an era where you can sort of go customer by customer and say, look, I don't care if you're using Excel or Google Docs. Like, we will build the integrations that we need in a weekend and we're good to go.
Speaker 11:It started more so we started in 2024. When we started, it was more of the former. So Mhmm. You really had to kinda pick your lane. We started in the warehousing space, and so we picked kind of our top 10
Speaker 2:Yeah.
Speaker 11:Integrations. I think a lot of it was just that the browser integrations and just weren't quite there. Right? And a lot of times, folks were also hesitant to give API access or just didn't have it, and so you kind of were pigeonholed in in that sense. Now, know, we're at a point where it it really doesn't matter.
Speaker 11:We have customers that have mainframes all the way through, you know, all the all the common systems records. So that is a good thing because I think prior to that was a big hindrance in just, like, getting something live.
Speaker 2:Are you seeing receptance or pushback around computer use agents being deployed by a third party? Like if I have a I have a computer in my warehouse with some, you know, bespoke piece of software and you say like, hey, let me install something that's just gonna move the cursor and type stuff in and use it remotely, that could be awesome. It could also be sort of weird. How are customers like ready to have that conversation?
Speaker 11:Yes and no. Okay. There's kind of two frames to that. Right? Like, you have the internal side of and normally, we're asking them to provision us, like, a contractor login so they're not necessarily seeing it in front of their their face.
Speaker 11:The other side of that is when you look at terms and conditions across, like, third parties with bot detection and things like that, that's getting much better. And so we kind of have a twofold hill to climb there. But I think in general, when you look at the first problems we're solving for customers, it is a lot of the stuff that the team doesn't want to do. So they are happy to, like, hand it over on a silver platter to us. But, luckily, they're not necessarily watching a screen with, like, the mouse moving and clicking Yeah.
Speaker 11:Into their portal.
Speaker 2:Yeah. What what's the what's the killer sales pitch to someone? Is it is it cost savings on the back office supply chain? Are you actually able to drive incremental sales or revenue? Or are you selling it as like you'll be able to scale your business more with less headcount?
Speaker 2:Or is it like we'll actually just save you a couple extra lamps that didn't make it back onto the shelf so you're gonna be saving cost of goods? Like, how are you pitching the value?
Speaker 11:It could be it's a combination of all of the factors. I think the biggest thing that we see is an OpEx reduction. So Okay. Where you might have had multiple folks that needed to be hired to fulfill, like, a large claims team as the business grows, you don't have to hire those folks. Mhmm.
Speaker 11:On just the approvals, we're talking about claims alone, we see at least a 13% higher approval rating in the system filing versus a human. Right? So that is actually money back in their pocket as well that they would have missed. And then I think that, obviously,
Speaker 2:the customer experience Really quickly. Approval rating is that, like, someone sent back the lamp, you can actually restock it because in this case, it's not broken. It was just the wrong lamp. So restock it as opposed to send it to the dump?
Speaker 11:It's more of, let's say you order the lamp, it's delivered. Right? You call and you say, hey. Send me a new one.
Speaker 2:Yeah.
Speaker 11:They're gonna send you that new one, but then they're gonna file a claim with FedEx UPS, whoever broke it, right?
Speaker 2:And a
Speaker 11:lot of times a human's filing that. If it gets denied the first time, they'll just say, was denied.
Speaker 2:Yep.
Speaker 11:Well, we'll try three times. And so a lot of times on the second or third we're getting it through. Yep. And or you can think of what if we have to reach out to you and ask for a picture of the damaged lamp? Or, you know, on you know, one of our largest customers is one of the biggest grocers in The United States.
Speaker 11:A lot of times, the complexity of the things, like, let's say it's a cold temperature breach that we have to go find from just the fact finding and the documentation is pretty deep. And if they forget some of that, technically, it might not be approved. And so it's more on the approval side.
Speaker 2:Yeah. It's almost like insurance adjuster work in inspecting like what went wrong and creating like a chain of responsibility. Do you have anything? Because I have another one.
Speaker 4:Yeah. Mean, you raised some money, right?
Speaker 2:Oh, yeah. How much did you raise? You want to
Speaker 11:hit the gong? We announced it.
Speaker 2:Hit You the gong.
Speaker 11:We announced our $42,000,000 series b today.
Speaker 2:What's what's next with the $42,000,000? I like what what is the what what what is the the gating factor on growth? Where do you plan on deploying the most capital? Is it hiring people, Salesforce, just just top of funnel, awareness, brand, advertising? What are you thinking?
Speaker 11:You know, I think for us, we want to go wide and deep at the same time. We have customers that are adding flows daily, and a lot of that is just that these things compound. Right? Like, in the simple scenario we talked about, it was customer service and ops and finance in one lamp. And at at enterprise, it's much, much more complex and higher scale.
Speaker 11:So we wanna go deep, but we also wanna go wide. You know, we're currently servicing across retail, manufacturing, getting into pharma because we can reuse a lot of this infrastructure, like
Speaker 8:Sure.
Speaker 11:Cold chain across grocery also applies to pharma. And so we really want to expand the the scope, and this will help us hire some folks that are domain experts. And that's the second thing. Like, when we're applying this to customers, we're going with, like, hey. We know you have this problem.
Speaker 11:This is what we built to fix it, and this is why you should pick us.
Speaker 2:That makes a lot of sense. Well, congratulations, and thank you so much for hopping on the show and breaking it down. We'll talk to you soon.
Speaker 11:Thanks for having me.
Speaker 5:Thanks, guys.
Speaker 2:Rest of your week. Cheers. Goodbye. Up next, we have Tom Mueller from Impulse Space, the founder and CEO. Keeps putting up huge numbers.
Speaker 2:We'll bring him in from the waiting room in just a minute. Tom, welcome to the show. How are you doing?
Speaker 3:Hey. I'm good.
Speaker 2:Good to see you again. Always a good You too. When there's good news in in Pulse Space. I wanna hear the news. I wanna hear the progress.
Speaker 2:And then I wanna I wanna zoom out and and just talk about a little bit about your journey because I think it relates to the current moment in some interesting ways. But let's start with the news today. What happened?
Speaker 3:Well, we we did our series d round earlier this year and raised 500,000,000, and we've added another 308,000,000 to that round for a total of
Speaker 2:$308,000,000. That is incredible. Congratulations.
Speaker 3:Thank you.
Speaker 2:Why the extension? Is this about production? Is this about hiring? Is there a talent war going on? Or is is this related to the SpaceX IPO?
Speaker 2:What like, what what what what's the rationale? What's driving the market?
Speaker 3:Oh, probably all of the above, John. Certainly, you know, we're seeing a lot of demand. Our customers really love what we're doing, orbital, you know, orbital transfer, orbital maneuvering.
Speaker 2:Yeah.
Speaker 3:Our investors love what we're doing. You know, there's there seems to be a lot of excitement in space these days for for many reasons. Yeah. And we seem to have been the right, you know, the right type of company at in the right place at the right time. Yep.
Speaker 3:So I'm really happy with, you know, what we're achieving. So we need the money right now to, you know, to build up, to keep continue hiring at all levels of the company and to continue to build out our our facilities here to get ready for a higher production rate.
Speaker 2:Yeah. Can you refresh everyone on the the the core product, the capabilities. And then I'm interested in in hearing about how this fits into the the modern trend. The the data centers are going to space. It was it was controversial for 2025.
Speaker 2:People were debating it. And now I think everyone's come around and said, okay, maybe it's not this year, but it's going to happen at some point. There's going to be a lot of attempts, and there's probably going to be some computing space sooner than later. How does Impulse fit into that?
Speaker 3:K. Well, first of all, we we we do we take over where launch leaves off. So, basically, we get to we get to space on an existing launch vehicle. Typically, it has been Falcon nine, but we're gonna be on new vehicles in the future. And then we have two products.
Speaker 3:We have Mira, which does precision maneuvering, and that's the one that just did a flyby here that we just announced. Came up in 200 meters with Mira one or I'm sorry, Mira two and Mira three came not pretty close to each other.
Speaker 5:And
Speaker 3:then we have Helios, which is basically we call it a rocket on a rocket. It's an upper stage that we add to to a launch vehicle that goes into fairing and that can it can take basically a lot of cargo to very high energy places like geosaccharides orbit or, you know, out beyond earth gravity, out to the moon, out to Mars. So we have so either precisely move around or just move big distances fast.
Speaker 2:So I mean, I'm like, it seems like with orbital compute, not necessary that it's in geostation that I'm aware of, but super valuable to keep it in orbit longer. And because you paid to put the chips up there, you probably don't want them burning up. Is that how this might play out or have you thought about how these two technology trends fit together? Only if you're willing to disclose anything. But
Speaker 3:Oh, no. It's speculation. Yeah. I don't I don't know if there's a big fit right now. Okay.
Speaker 3:You know, most of the people that are doing orbital data centers are just need the rocket to get to where they're going, taking a whole bunch at once. If that changes, we're we're glad to help. Certainly, SpaceX, you know, pretty much keeps everything in house and and and have you know, does everything themselves. Sure. Others that that I've seen plans, I think are gonna just go up to LEO Yeah.
Speaker 3:On existing rockets, you know, on a rideshare many at a time.
Speaker 2:Yeah. So So what else, is is, exciting to you on the near term in space? I mean, we all watch the the the the moon mission. It feels like we're closer than ever to economic activity on the moon. There's also so much happening in aerospace defense, imagery, communications.
Speaker 2:But where are you seeing the most near term opportunity for Impulse?
Speaker 3:Near term, like, for MIRA, we just we just signed on Victus Solo follow on, so two more spacecraft for for Space Force.
Speaker 2:Mhmm.
Speaker 3:We have we just got onboarded to NSSL for Helios, which is the national space launch lane, which allows us to fly the US government, which is, you know, the biggest customer in the world for launch. And and we're we'll be the only upper stage that's that's on that program now. So that's, you know, that's a that's a huge opportunity for us. But going further than that, you know, we're building moon based alpha and hopefully, Ampoules can somehow be involved in in that awesome project.
Speaker 2:Yeah. That's very cool. As as you think back to your time at SpaceX, I'm interested in the the parallels between the the existential risk question that SpaceX was in many ways founded to resolve the making life multi making humanity multi planetary. That was a very animating force. And today with the AI lab leaders, they are similarly animated by existential risk.
Speaker 2:And I'm wondering about like what was the mood at the time? What was what was the how much did that was that a motivating factor? Just what was the color of the the mission to make life multiplanetary? How did that play out back when you were at SpaceX?
Speaker 3:You know, it was it was the core vision, and I think that's why a lot of people were excited to be there. But we were mostly just head down just trying to make the rocket work and become real be be reliable, especially us guys in propulsion, is, you know, always the thing that seems to break.
Speaker 2:Yeah. So we're focused on the engineering. Have you been able to maintain that culture? Is that the main thing that you want to take forward to impulse? Like, how how has the culture of impulse changed or remained the same?
Speaker 2:What have you pulled? What can you share about how the team operates?
Speaker 3:Yeah. It's very similar. I mean, I think I think, you know, myself and my team had a huge effect, on how the culture at, SpaceX was formed, and I brought that culture here for the most part. You know, a very merit based, very ownership of the company. Every everybody gets, you know, equity ownership in the company, so we're all pulling towards a a common goal.
Speaker 3:And also, I we want it to be we want it to be interesting and fun. So, you know, like, we work hard and and see the the results of it, and it's it's you know, we're stoked. It's pretty cool.
Speaker 2:How has the team embraced or or even just dealt with the the the diffusion of AI tooling. Like we're seeing these incredible mathematical results. A lot of them don't immediately apply to, you know, specific engineering problems. AI has hallucinated a lot less, but the tolerance for hallucinations is basically zero when you're launching rockets. But how have you integrated or or paced the adoption of AI tooling at impulse?
Speaker 3:Yeah. I have a little anecdote on that. I think it was, you know, about a year ago, I was trying to do an engineering problem, actually a propulsion problem using chat.
Speaker 2:Yeah.
Speaker 3:And I think I mentioned that it it I kept telling it what I was doing wrong, I kept doing it over again. And I said, if if this was my intern, I would fire it. Now it's a lot better. Now I'm finding just in a year how much it's improved.
Speaker 2:Yeah.
Speaker 3:We're a little bit choked on what we can use here just because we we do classified programs and we have ITAR issues. So we can't just like use the latest model
Speaker 2:Yeah.
Speaker 3:Generally, but we try to to use as much as we can. I'm as as you know, a founder and CEO, a little worried about us getting left behind because we have these restraints on us where
Speaker 2:Oh, interesting.
Speaker 3:Many other tech companies don't. So Yeah. It's you know, we gotta figure out how to wade through that.
Speaker 2:Yeah. Yeah. It does seem like AWS and has done some FedRAMP stuff and is really moving on the ITAR stuff. So good luck there. I'm interested in your do you think as CEO, you're more uniquely equipped to evaluate these tools?
Speaker 2:Because maybe you're not actually deploying a propulsion problem that you're sort of sketching out with an AI system. But because you'll have a team actually go and finalize everything, but you can sort of take an idea halfway and communicate in a fuller way with your team. Is that how you're using AI these days?
Speaker 3:Yeah. I think mostly I use AI a lot just to ask questions, you know, just answer. Just I'm working on a design, and I want to know like one of the ones recently was like, what would be a good baseline seat pressure for a Vaespel seat, like a valve seat? It's just like it's it's out there, and I could go find it, but all I do is type type it in Grok, and, you know, seconds later, there it is between, you know, 3,000 to 5,000 psi. Like, there got my answer.
Speaker 3:So that's how I use it Yeah. A lot. But then, like, our guys doing coding Sure. We'll use it, you know, I think that's where probably the strongest use within the company might be.
Speaker 2:Yeah. Is there still I remember hearing all these stories about, like, NASA, everything on the space shuttle having these switches and everything built with double or triple fallbacks and risk tolerances. Is that culture still there, or when you don't have a human on board, that level of engineering is less relevant?
Speaker 3:Yeah. And this is something that we went through when we started flying humans on Falcon, you know, back at SpaceX is like, we we had to increase our level of, you know, of a backup of of fail safes. So it adds complexity, it adds cost, it adds adds it adds mass. So if you're trying to if you're trying to do to develop a low cost vehicle that you're gonna make a lot of, you might not put that many you'll figure out the things that are likely to break and to have some backup. Like, have dual dual computers or even on Helios, we have triple.
Speaker 3:So so, yep, two two agree. If one doesn't, it's out.
Speaker 7:Okay.
Speaker 3:For Miro, we have we we have two, and if one if one goes out, we can, even if both go out, we can reboot. You have time. On a lot people, you don't have time. Yeah. When when, you know, when you're in orbit, you're in a stable orbit, you can you know
Speaker 2:That's interesting.
Speaker 3:Reboot everything and turn it back on. But, so there's critical places like that that you'll have, redundancy, but, like Yeah. In many cases, you'll have one engine like we do on Helios. You might have dual igniter to light it, but you just put all your effort to make sure we get that thing lit and running because it's the only engine you got. If that engine fails, the mission's over.
Speaker 2:Yeah. That's really cool. Tyler, do you have any questions? I have one more, but
Speaker 5:No.
Speaker 2:You got it. I'm interested in in the talent wars, the hiring market. There's two effects. One is like AI is so hot, everyone wants to go work in AI. The other effect is that everyone's worried about AI taking all the jobs.
Speaker 2:They want to go and work at real things. Have you noticed anything out of the younger generation, the shape of the type of person that you're trying to hire? What does it look like to make a career at Impulse these days?
Speaker 3:Yeah. So hiring we're hiring like crazy right now, and I think we're we're be doing pretty good in this environment. There's a lot of startups around here that have really drained the talent pool. Interesting. It's a little harder to find more seasoned, people experience like at the senior level where it's it's more difficult.
Speaker 3:We're getting a lot of smart kids right out of school or people from other industries that are move that they're moving to to our exciting industry. The hardest place of course is is in in the software and coding that there's so much competition for those guys that that's always been from when I started the company, it's always been you pay a premium to those guys, you know, it's just it's just hard to get them because there's, you know, AI is like the, you know, the big moneymaker.
Speaker 2:Yeah. Yeah. Yeah. Yeah. It's crazy a time.
Speaker 2:Are you are you relocating people from the Bay Area or pulling from universities that are outside everything, all of the above, wherever you're All
Speaker 3:all over. You know, we as you know, we have an office in in Colorado. There's a lot of talent out there. Yep. We our guidance navigation and control is out there.
Speaker 3:So we got quite a quite a bit of software people. We actually put a a machine shop out there because we got some some really good machinists out there that are making precision parts for us. Yeah. You know, yeah, we're expanding to other other areas where the talent is.
Speaker 2:Yeah. That's really exciting. Well, thank you so much for coming on the show. Congratulations on the extension.
Speaker 3:Thank you. Great talking, guys.
Speaker 2:Glad to see it. Yep. I I I can't wait for more progress. Have a great rest of your week. We'll talk to you soon.
Speaker 2:You too. Goodbye. And with that, that's our show. Is there anything else that we didn't talk to? Any breaking news?
Speaker 2:We know the Fed hiked. We know that Palantir partnered with Chipotle, the two most important stories of the day, apparently. I think we've gotten through everything. If we didn't, we'll get to it tomorrow at 11AM Pacific. Get that flashbang ready.
Speaker 2:Leave us five stars on Apple Podcasts and Spotify. Sign up for our newsletter at tbpn.com, and we will see you tomorrow. Flash bang. Throwing flash bang. Boom.