TBPN - Tae Kim Sounds Off, Big Companies Are Hiring Again, NVIDIA $50B Tenant | Tae Kim, Ben Zweig, Aakash Thumaty

Episode Date: July 28, 2026

(01:17) - Big Companies Are Hiring Again (09:58) - 𝕏 Timeline Reactions (20:19) - NVIDIA $50B Tenant (23:33) - The Three Anthropic Proposals (35:00) - Tae Kim discusses negative sentiment around AI and semiconductor stocks, arguing that sensationalized headlines obscure strong underlying demand for compute, memory, and data-center infrastructure. He remains bullish on Nvidia and the broader AI trade, predicting that agentic AI, recursive self-improvement, and growing enterprise adoption will drive substantial investment and revenue growth. (01:06:32) - Kansas Town Splits Over Nuclear Reactor (01:10:58) - Ben Zweig, a labor economist and founder of workforce data company Revelio Labs, discusses how AI is reshaping hiring, employment, and workplace tasks. He explains that AI adoption generally correlates with company growth, while creative freelancing and task-based work face greater disruption, and AI-generated job applications are making it harder for employers to identify qualified candidates. (01:29:22) - Aakash Thumaty, founder and CEO of Takeoff, discusses the company’s rapid growth from near-zero to almost eight figures in revenue and its acquisition by Sierra. He explains Takeoff’s autonomous, revenue-generating AI agents, outcome-based pricing model, deep customer integrations, and how its technology is evolving into Sierra’s new Horizon product. (01:45:30) - Zuckerberg Backs AI For All TBPN is made possible by:Ramp - https://ramp.comPublic - https://public.comCisco - https://www.cisco.comConsole - https://www.console.comCrowdStrike - https://www.crowdstrike.comFigma - https://www.figma.comMongoDB - https://www.mongodb.comNYSE - https://www.nyse.comRailway - https://railway.comShopify - https://www.shopify.comCodex - http://openAI.com/codexFollow TBPN: https://TBPN.comhttps://x.com/tbpnhttps://open.spotify.com/show/2L6WMqY3GUPCGBD0dX6p00?si=674252d53acf4231https://podcasts.apple.com/us/podcast/tbpn/id1772360235https://www.youtube.com/@TBPNLive

You're watching TV. Tuesday, July 28, 2020, 26. We are live from the TVBN UltraDome. The Temple of Technology. The capital of capital. We've been having a lot of fun with Suno. Hope you have been enjoying it too. I'm sure we'll have a new one available soon.

But first, let me tell you about ramp.com. Time is money. Save both. These use corporate cards, bill pay accounting, and a whole lot more all in one place. Lace. A bunch of news today. Jordy's laughing,

laughing, laughing. All right. I think we're getting, you could bother that's enough of that. Yeah. Nothing like a couple of pints of Guinness. Codex and Guinness. Some prompt engineering,

some vibe coding going on. Well, Anthropics responded. We're going to go through that proposal, the facts and the proposal for what next, what happens next in the open model debate over whether or not that you should be banned, restricted, tested,

limited in some ways, sued. There's a whole bunch of different possible outcomes, but we'll take you all through it. And we have Tay Kim joining from first adopter at 1130. But first, we are going to talk about the hiring market because the Wall Street Journal has a very interesting report that large, the Wall Street Journal is reporting that large companies are beginning to hire again.

They have a large white pill. Yes, it is a large white pill. Has hit the front page of the journal. Yes, and I think people have been going back, and forth on this. This is a story that's just getting digested by the tech folks, like the actual AI lab leaders who had predicted crazy job losses and are now not really seeing that. They're seeing productivity boosts and different diffusion taking time in certain places and there's new capabilities, but it's not exactly a drop-in replacement for a coworker, at least in in most

scenarios. And that's what the Wall Street Journal's reporting. So let me see. set the table and then we can debate it a little bit. First, I'm going to tell you about console. Console built AI agents that automate 70% of ITHR and finance support, giving employees instant resolution for access requests and password reset. So after roughly a year of cautious hiring companies across technology, transportation, defense, and other industries now say they need more employees to work alongside AI systems. Total victory for both humans and AI. We're working together piece as possible. It's an example of Jevin's paradox. Jevins paradox. When a technology makes something more efficient, demand often rises enough. The total use and the need for people

actually increases. For roughly the past year, many companies pointed to AI while announcing layoffs. This was a huge thorn in your side. I think you hated this more than anyone else. And you were right to because it did seem like it was just PR, spin, etc. Yeah, it was a way for CEOs and management teams to save their own ass instead of saying, you know, hey, we over-hired or the business isn't doing as well as we would like. And we need to sort of basically settle down for a second and get our mojo back. Yeah. And there are-

Obviously, no one wants to say that. But I think one of my favorite posts was the, and obviously these circumstances are never great, but the new CEO of Xbox came out and just, was very honest about the situation. Yep. And I think that more of that is necessary. Yeah. Also, there's a lot of firms where they, once they get to 10,000, 20,000 employees, they might

say, look, 20,000 might be the right number, but the bottom thousand people are not performing. We would like to lay them off and then bring in a new thousand people that are better fit for the company and the current trajectory that we're on, the current skills that we need. Maybe we need more sales people. And those bottom thousand people might be top 10% at another company. Exactly. Yeah.

So the narrative appears to be shifting companies like CSX, Alphabet, ServiceNow Snap-on, and consulting giant Booz Allen Hamilton have all recently signaled plans to expand hiring, particularly in areas where employees can use AI to become more productive. We have Benzweig from Ravello Labs coming on at 1210 to talk about the difference in the AI-driven, hiring market, some very interesting data about how AI enabled firms are hiring faster than those that aren't adopting AI. But at the same time, there's a bunch of weird dynamics in the labor market where there's way more job postings than actual hirings. And so that can look like there's a fall off and it's harder to get a job, but that might just be because everyone's slopping it up

in the job postings. Everyone's like, put up a job posting for everything. Because I'd love if somebody, if some insane sales guy walks in the door, we might have a position. So, Let's keep it. Yeah, it used to be somewhat of a flex if a company was like, yeah, we put up a role and we got 2,000 applicants. Yeah. It's like, well. That's true. And then also it's a little bit of a sign of like, oh, wow, they have a hundred openings.

Like they must be like growing so fast, you know. So, but if it's just a prompt to say, oh, yeah, put up, like, look at my organizational design and and add five roles for everyone because why not, why not see who comes by? Yeah. We don't necessarily have to interview these people. So weird, weird dynamics, but we'll dig into it. So meanwhile, the latest weekly U.S. job list claims fell to one of the lowest levels in decades, underscoring the resilience of the labor market. The shift also reflects a more realistic understanding of AI's capabilities.

Sarah Franklin, CEO of H.R. Platform Latus says many companies initially assumed AI agents could replace entry-level workers, but are now recognizing that human employees remain essential, just because you have coding agents doesn't mean you're not hiring engineers. She said, adding that lattice is seeing renewed hiring among many of its customers, including for junior roles. Robert Haft, CEO, M. Keith Waddell said AI's effect on employment has been more benign than some has feared, adding that hiring demand continues to improve and market conditions are increasingly more supportive of business. And so I do think there was a little bit of like a successful sciop with the with the AI is going to be able to do everything where I do think there are some firms that we're like yeah, maybe we shouldn't hire or because like what if we get it wrong and we hire a bunch of people and then AI really does catch up and we don't need those people. That's silly. We shouldn't go through that like whipsaw effect. And so people are going back and forth on that. Bryce Roberts. Yeah, it's interesting. At least in our organization, which is unique and very niche,

and there's not that many organizations that are running a niche technology daily show. Yeah. I feel like a lot of what the value that we get out of AI would have historically been done by not super expert level freelancers, right? These sort of like upwork style tasks that you would do historically. like an idea for a funny song, right? Yeah. I've paid to get a funny song made probably a decade ago online, right? As just like a joke and now you can just go to Suno and make something like that.

Whereas, and then there's other things like, you know, make a funny website, right? I historically would maybe work with. So you're saying that I should take down the five open roles I have for Celtic punk session musicians? Not yet. Because I was going to hire five Celtic punk session musicians to constantly record dropkick Murphy's covers for us every day. Well, I'm not ready to say that you shouldn't do that. I'm actually closer than ever to hiring a full-time Celtic punk band to play music, to recreate songs. Yes.

I'm closer than ever to doing that. That was not even on the roadmap a few years ago. Yeah, I don't know. It's a good point. Yeah, there's a lot of things that you are doing that you would never do with a full-time employee. Yeah. That just sort of like fills the cracks and allows you to do more different things in your organization. But the core stuff is still like you want a person that's responsible and then you want them using AI.

I don't know. Yeah. The Wall Street Journal breaks it all down, but we went through most of that. So Bryce Roberts, he's taking the other side of this. He says, he shares a screenshot of a text message. He says, we honestly aren't hiring a ton right now, AI backfilling most roles. backfilling, is that specifically, does that specifically refer to when someone leaves the company, you backfill them with AI? So you say, oh, someone quit.

Let's see if, like, if there's Steve and Jim on two different, on one team. And Steve quits, you say, hey, Jim, can you just, instead of hiring another person, just do twice as much work with AI? Is that what this person's articulating? I mean, obviously there's some companies that are like, yeah, we're not hiring anyone. We're going for the one person, one billion dollar company. Like, I'm not going to hire anyone. I'm just going to use it. Yeah, but that's rare.

Usually, usually when your business is ripping, you're like, I can't hire great people fast enough. Yeah. And sometimes you actually, sometimes you actually don't have time to invest into various hiring processes. But yeah, I would read into this text, the company is just probably not, like, ripping. That's my, that's my takeaway. Well, Bryce Roberts says RIP New Grad's, Matthew Prince over Cloudflare takes the other side. He says, wrong strategy to stop hiring new grads. ads, they'll write strategy, hire them and insert them into legacy teams to help them better

adopt AI. And Cloudflare, of course, hired 1,000 interns. It was a crazy number. Wasn't it up there in like almost 1,000? Four digits. That's crazy. Anyway, let me tell you about the New York Stock Exchange. Want to change the world.

Raise capital at the New York Stock Exchange. Pulling a crazy rare business card. I haven't seen this. Oh, I think I know where they're going with this, but let's play the latest good work. Reel. We're just watching reels now. This is... What we got is a... Bernie Madoff.

Pretty good. Yeah. That's from the 80s, too. That's good. Yeah. Yeah, I've seen a few of these around before. Of next. Solid Sam Bankman-Fried here. That's nice. That's really nice. I like that they actually printed these. I think he made it.

For this bag. This is balsa wood? The acting is so good. Wait, John. This is a vintage Zuckerberg. This is a vintage Zuckaberg. This is a vintage 05 Zuckerberg. Let's just check the back really quick.

There it is. That is a patch from his Fruit of the Loom boxer briefs. You can tell by the smell. Is that real? What is that referring to? This is on athlete, you know, training cards. I'll put a piece of the jersey. Just leave this one.

Yeah, yeah. All right, up next. Leave it. Ooh, okay. Nice. Elizabeth Holmes. We do have two, I believe. We have two, but a triple Holmes is what every good collector has in their arsenal.

All right. Last card. One, one. One, one left. Three, two, one. Oh, my God. Oh, my God. Oh, my God.

Oh, my God. All right, turn it off. Very funny. Very funny. It is funny how the, like, business comedy canon has really something. solidified around like Elizabeth Holmes, San Banking Fried, Mark Zuckerberg. There's like a few names.

I'm surprised they didn't have an Adam Newman rookie card in there. I don't know if Adam Newman is like a big enough name relative to San Banking Fried and Elizabeth Holmes. It's just interesting like the different names that have broken out that you can do a comedy sketch that's like, you know, it goes as big as as good work does because they get

you know, I think millions and millions of views in their stuff. All right. Pull up this image from Manhattan this morning. We got sent this. We've been doing on the ground reporting. From one of our on the ground reporters in Manhattan, there's a company called Black Sheep that got 20 trucks, and they're just driving them around Google's Manhattan office. Yes.

Saying shame on you, Google, return our $80,000. We had to dig in. We got very curious. I had no idea. They make sunglasses? They make $8. sunglasses that beat $350 sunglasses in an NBC lab test.

Okay. Are you wearing Black Sheep today? I wish. I wish. So Black Sheep makes direct to factory. Okay. Factory direct prescription eyewear. Stop paying $5.00. No, no, no. This is from their own website. They're saying direct to factory optical disruptor. What do you mean? This is from their website. Wait, where are you getting this? on Black Sheep.

I'm on Blacksheep.com. It says factory direct to factory. Look, I want to send some eyewear to a factory. Direct to factory. I'll be sending it to them. Direct to factory. Yeah. So this company.

That is interesting. It is fascinating. They say direct to factory optical disruptor black sheep launches 25 truck gorilla campaign against Google and Manhattan. Okay. And then they're sort of like narrating their own. own guerrilla campaign, a fleet of 25 minimalist LED billboard trucks surrounds Google's Chelsea headquarters after the tech giant weaponized an organic search glitch to pocket nearly

$80,000 in ad spend following Black Sheep's viral NBC Today Show debut. 25 LED trucks deployed $77,000 drained in 30 hours, and then they're just continuing to market their own products. So very interesting strategy here. I think every marketer has had the experience of having a campaign go haywire. Yeah. Very fascinating to take this route. Let's see how it works for them.

If I were Google, I would say you can have your $80,000 back, but you can never advertise on Google again because I just don't know how. I don't think Google would ban them permanently for this. is ridiculous, but it, you know, they're just going to be like any other, like, as a self-serve platform. But is it a good campaign? But what actually happened? So they say, how it unfolded.

NBC Today Show segment airs. They test the retail subscription against Black Sheep's factory direct pair. National search traffic spikes. Hundreds of Americans search Black Sheep because they're seeing it on TV. The organic listing breaks. Google search engine redirected organic brand traffic to a dead end third. party 404 error page. And so with the organic route broken, users were funneled into Google's paid listings. So what is their claim? How is Google responsible for this exactly?

Sounds like user error. Because I mean, you do, you do have some control over your Google search results based on the webmaster tools. You can index certain things. And then also, if you're noticing a 404 page, you could like redirect it quickly. But again, if this is happening all very fast. They can use their error. But, I mean, it is interesting because they're probably going to get more than $77,000 worth of organic just from this. I mean, I didn't see the original campaign, and I'm seeing this because this is hilarious. But this is like, is this, they shared an AI image with tons of these like shame on you trucks.

But those are real. These are real. Yes. And are those minimalists or maximalists? Those seem maximalist to me. But maybe they're minimal. Minimalists, I guess, in the display of the, in the way they actually are leveraging the space on the truck, black and white. But truly underrated surface area for stunts and advertising.

Like, this message is sort of like squabbling with Google over this, like, sort of odd scenario. But you can imagine someone using this for something much cooler and much more positive and not like this, you know, sort of unfortunate situation for them where they're dealing with the, you know, fallout of a Google error. Well, we want to interview the truck drivers. So if you're driving a black sheep truck around Manhattan today, reach out. For sure. For sure. If you're going to show. Nick, make it happen.

Well, 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, and marketplaces. And now with AI agents. Ilya said straight shot to SSI, so they better not be gearing up to release a work agent called Francois. Francois would be a very good name for an AI agent. I like that. I do wonder what they're going to be releasing. Has the SSI is going to release? Is that complete rumor? Because all I just said they would scale their research.

So that just means they've done a bunch of research. They have some sort of architecture that they like, some sort of flywheel, and they're going to use more compute. And so that's why they're raising money. I don't think they said like, and we're going to release it. publicly.

Yeah. But everyone's thinking like probably still LLM or something different. No one really knows. Yeah, I mean, I think still broadly like generally LLB-based. Like, like next level. There are levels to vague posting when you live a vague life. It's just like your entire life is vagary. Anyway, in other news, recursive superintelligence signs a $410

to deal with Amazon. So funny. And it's in the. That crunch, it's in the header two. Of course, that is a typo. It says recursive superintelligence signs $410 million dollar compute deal with Amazon. Congratulations to recursive superintelligence. Throwing safety out the window.

That should be the tagline because there's already safe superintelligence. But we're just doing recursive super intelligence over here. But, of course, the company is doing very well. They emerged from stealth in May with six. 650 million in funding focused on building open-ended self-improving systems and potentially compute intensive approach to AI research. This multi-year deal is meant to provide flexibility as the company looks to scale up those systems.

Recurcives $410 million outlay represents the bulk of the company's fundraising to date, but on a call with TechCrunch. Hey, hey, hey, they still have a couple hundred million left over. Founder and CEO Richard Socher emphasized that he expected it to be the first of many such deals. So is this, I feel like normally when you see a, like, a compute deal signed, it's always like more complicated than just like, we're buying this expensive thing. It's usually like we're paying that. I'm like, we used to be so like anti-circular deal that now I just have come to, I've been so normalized by them that I expect them every time. And I'm like, wait, wait, this is,

there's no circularity here. I would have expected like, equity changing hands. Yeah, like Amazon's investing in you and you're buying tranium and racking it and AWS. And dude. This is just, there's no, there's no. They're new campus and they're investing and this and that and you're investing in them. Instead, it just seems like it's a pretty vanilla deal. It's like they're just buying a lot of compute from Amazon. Great. Seems like it. Well, good luck to them.

Very excited for what they're launching. Yeah. Jason, VP of startups and VC at AWS says part of the agreement is that we're going to co-develop him for a purpose built for these types of companies. So fingers crossed, but it seems like we could get some circularity. Yeah, let's hope so. Let me tell you about Railway. Railway is the all-in-one intelligent cloud provider. User favorite agent to deploy web apps and services and more,

while Railway automatically takes care of scaling, monitoring, and security. Fingers crossed. Well, here's a deal that's somewhat circular. We got Nvidia, it revealed as a tenant for a $50 billion data center that will use its chips. So they're the tenant of the data center that uses its chips. We'll talk to Take Kim about this. CEO Jensen Wong deploys balance sheet to backstop growth of AI computing market. And Vidi has signed leases worth up to $50 billion for a massive Texas data center. That's very, very big. That's very, very big for a single site. A previously undisclosed commitment

that shines a new spotlight on the chip group's growing role in financing AI. The nearly $5 trillion company is leasing the entire one gigawatt facility that developer Hut 8 is building which will house hundreds of thousands of Nvidia graphics processing units. You have to imagine that once they have these they serve something or wind up selling

them. These things change hands so many times there's a lot of different ways that this could play out ultimately. But in the can we pull up the Nvidia chart? Yeah there we go. Invidia big candle the day up 3%. 5.17.

trillion. Let's take a look at Apple. 4.99. They crossed five today. They're down a little bit since they, since they beat, breach that. But they're neck and neck. Google is sitting at four. Apple running the do nothing win strategy. Jensen doing thousands of deals.

They didn't even sign the open letter. There are three companies that still, I believe still haven't signed the open letter. Only three companies on the entire service of the earth. No, there's three major companies that are, how do I actually get that to go away? I don't know. You can keep looking at alphabet. But there are three major companies that haven't signed that Invidia open letter about banning open source and or not banning open source. And it's Amazon, Apple and Anthropic.

Anthropic put out a post yesterday very, very clear. response sort of outlining their view of open source, their stance. We should go through it. But the interesting thing that Ben Thompson was talking about today was the fact that Apple and Amazon haven't signed, and they both have like very physical elements in the world in the sense that they're not, they're sort of unslappable. Like you can't vibe code an Amazon warehouse. You can't vibe code an iPhone. There are. threats to those businesses, of course. And of course, Apple should benefit from open source and so should Amazon because they'll

be able to serve open models across AWS. But it's just potentially interesting. I think the Apple standing back is more just like, look, we're not jumping on with this crazy open letter that everyone is signing. Like we just have our own brand. We're thinking different. We're doing a whole thing. Well, and based on other Apple AI timelines, I would expect them to sign it in maybe a year or two.

Potentially. If they just signed it in 2020. It's just like robots. WWDC 2020. We're ready. We're signing the open letter. These glasses have changed you. They turn you a new beast.

Let me run through the three anthropic proposals because it's an important response. So Dario Amadeh anthropic CEO responded directly to that letter, supporting open weight models that circulated over the weekend. So to summarize his, he makes three claims just to sort of clarify things that I think are important. He says, Anthropics has never advocated for a ban on open weight models. Now, that's a blanket ban. There's obviously, like, defining what a ban is and what an open weight model, what a distilled model, what a foreign model is, these things all matter.

But he has come out and said, look, we never advocated for a total ban on open weight models. Two, he says, undergirding all of this is the U.S. must beat authoritarian governments in the AI race. He points to China, but he identifies any authoritarian government. If they get really powerful AI, they'll come over here and steamroll us, and you won't be free to do whatever you want to do in America. Three, powerful AI models may be misused to carry out cyber attacks or biological attacks. There are risks to having really, really powerful AI open source systems just running around. So he's worried about those three things, clarifying those three points.

But he makes three recommended actions. He makes three proposals. First, he says, let's continue to sanction chips. Let's not sell chips to China. He says, we should not sell powerful chips or chip making equipment to China. So this has been debated for years. Like going back to the Biden chip controls, everyone knows every different angle on this, the basics. I mean, there is a pretty good argument for chip controls, even on purely geo-economic competitive grounds, like even if you don't believe in the risk of authoritarian governments having powerful AI, even if you just think it's like, you know, fancy auto-complete, it's like, well, it's the engine of our economy.

And if you can slow down a rival economy, that's beneficial to you, right? And so, and there also seems to be basically unlimited demand for chips in America. So by restricting sales to China, that shouldn't actually hurt American chip companies all that much. But yes. Well, they just, like their argument would be we fully lose the Chinese market. Yeah. Which is the second largest computing market in the world. No, no.

Right? So I think, but you're the. counterpoint to that is you were going to lose it anyways. Yeah. And a lot of that it stems from the fact that China has been building an indigenous chip supply for decades. We've talked about going back to

a whole bunch of their, you know, state-led, state-funded chip in fab processes. They've always been a few years behind. And so maintaining that gap, all else equal, is an advantage for the United States. The second point Dario makes is

he says, we should crack down on industrial scale distillation operations. This seems totally reasonable. Companies can set their terms of service, and they have a right to maintain intellectual property with proper legal consequences for violations. Anthropics have been fighting distillation attacks, but according to them, it's not that effective. Dario proposes policy interventions to deter this behavior.

And this is where I'm still not clear on where that goes next. Like, what is the correct policy intervention? There's a, like, policy intervention is a very, very broad thing. It can mean anything from, like, a tax, a tariff, a fine, a sternly worded letter, not getting invited to a golf tournament. Like, there's so many different things that policy, like, covers these days, right? Where does this actually go? He says he doesn't want a blanket ban on open weight models, but it does seem like one possible policy intervention

would be to sort of, like, ban, restrict, or pressure open weights models. that can be reasonably shown to have been distilled. So if there's someone who's just a perfect distillation, it just gets, it just doesn't quite feel right. It's hard to quantify these things. We don't have a binary where you run some sort of algorithm, and you say, yes, this was distilled. Because you can distill half on Opus 5

and then throw in a little GPT 5.6 and then mix in some mistrawl and just be distilling from all over the place, fine-tune stuff, change the flavor, change the oral environment. There's so many different pieces of it. And Tyler, you were making a point about tinker or inkling. So the inkling model from thing machines, like it used some synthetic data that was created with, I think, Kimi K2.5.

Yes. So like does that count as like distillation? Like probably not when people usually talk about it, but like it definitely benefited from Chinese open source models. Yeah. So if you're downstream of. Yeah, I wouldn't call that industrial scale distillation. But it's sort of downstream of the industrial scale. There is like some big gray area where like how do you actually define these?

Yeah. And so defining that is going to be what that's going to be the conversation that plays out in DC, like behind the scenes on the basis of this. And that's where the actual negotiation is going to happen between, you know, the position of invidia and everyone that signed the letter versus the position of Anthropic and everyone who didn't sign the letter. They're going to sort of decide, okay, well, if you can prove this, this and this, and you can show us that your API was getting hit by these different things,

and you have a really solid report of what happened, and then the model also sort of checks these boxes quantitatively. When we do this e-vow, then maybe we will pressure it, and then what does that actually mean? You could go after the lab that committed the distillation attack with lawsuits, but that seems really difficult, given the international nature of these attacks. So we're sort of back to where we started, where you know, you're, like, what can the government do that the lab can't?

Like, the lab should be looking at every customer and saying, oh, this seems like someone who's trying to distill. They keep asking for basically what looks like a lot of training data. They're not acting like a normal user just being like, build me a website. Okay. Anyway, third, he says, all sufficiently capable models open and closed should go through mandatory safety testing. So this was recently outlined by Demis Hasabas over at Google DeepMind as well.

And it seems like the two companies are in alignment on this in particular. And it's a somewhat reasonable position, although the risk is that small companies who have safe models that aren't distilled, could get tied up in a review queue for years before they can release. Like that would be very, very annoying. If you're recursive super intelligence, for example, and you don't have a Washington, D.C. office and you're like, hey, we, you're, want to release our new model, and they're like, yeah, totally. Like, you got to go through the review process, get in line. And then it's like every, you know, every trillion dollar company is there with a ton of lobbyists being like, well, review our model first because we want to get out a week before the small startup. And that's the frustration of biotech, the FDA, anything that

goes through approval. We've talked about this with the nuclear stuff. It gets very tricky. And so you want to avoid that. And you don't want to wind up slowing down. innovation that's happening on small scales and decreasing competition. Dario does do a good job of acknowledging up front that he says it would protect USAI companies from competition, but that's never been my goal with anything that he's saying here. And so it's still worth working through what happens in a really adversarial situation. Like what if a foreign lab distills a bunch of frontier models? They're the most aggressive. They're just distilling everything. Then they jump forward a bunch capability. They get a bunch of smuggled chips. They take all the restrictions off of cyber,

all the restrictions off of bio, and then they just drop the weights on like a torrent. Or they put it up on Hugging Face and Hugging Face is like, this is really crazy. No one likes this. There's a lot of pressure to take it down. I don't know. But it's out there. Like, what does the government actually do? Like the government probably pressures or bans like hosting the weights, maybe serving the model. You maybe won't be able to run it in American data centers. You go to the neoclout and say like, hey, this thing. is actually bad. And I think people are divided on this because they see the current models not as actually dangerous, which is totally reasonable to assess that, yeah, it's not that bad.

But like, if there was a model that was like, yeah, it's actually just like the killing machine. Like, I think most people would be like, yes, I'm democratically voting to not serve that because it's just like it's an annoyance at best and like actually bad at worse. And the other big question is like how much compute do you actually need for it to be dangerous? Yeah, totally. Is having some GPUs in the back shed going to be enough? Yeah. Maybe for sufficiently advanced model, yes.

Or do you need access to a ton of racks, a ton of power? Totally. And then you do need to work with a neoclop in that case. And as soon as you're a U.S.-based company with a real data center, with a bunch of NVL-72s in there, you probably have registration and, you know, all sorts of just like business registrations, where the government can reach out to you and say, hey, we're actually really worried about this. Just like there are other things you can't host in a data center.

There's all sorts of stuff that's illegal. Even if it's intellectual property. Yeah, exactly. Yeah, that's, that's, uh, like, you can't, even just because you have a data center doesn't mean that you can, like, take an open source, you know, uh, you can't as a data. Oh, yeah, open source Marvel.

Like, they'll be like, or even, even, even, you know, a CRM company can't knowingly support like a organized, cartel that is like trafficking narcotics. Yeah. You'd have to imagine like, uh, they have to vibe for their own balances. Yeah.

So, uh, so, uh, what, what's interesting is like, what is the next step of that? So if there is a bad model, uh, and,

and everyone agrees like, okay, yeah, we got to not host this, not distribute this. Like, yeah, the weights are out there.

People are trying to like, sort of run it a little bit. Uh, but, Does it go offshore? Do we wind up in like the crypto scenario where there's like these offshore things and people are using VPNs to get access to it? Like, what level of aggression do you see from the U.S. government in that scenario? It probably should be proportionate to like the danger imposed by the model. Like if it's just a model that's like, that's like annoying or like slightly IP infringes, but like no one's really being like I'm not I'm canceling my Disney subscription because this new model will generate me Disney IP. Like that's probably not like, okay, put up a crazy fire. But if it is like the ultimate hack machine that's like stealing everyone's money from the banks, then yeah, you are going to put up the firewall and sort of be much more aggressive. So I think the response will be in reaction to whatever the power of the models are, but it'll be interesting to go back and forth. Anyway, all in all, the letter clarifies a lot about the anthropic position.

So I think it's good that it came out. But it's still worth working through the game theory of like what happens down the line. Policy interventions is all we got here. And I think it's still too generic at this point. I want to know like what policy looks like. I want to predict that. I want to understand what's actually being proposed, what people like, what people don't like. And so I think we'll learn more about this in the coming days.

Let me tell you about Figma. Agents, meet the canvas. Your AI agents can now create and modify your Figma files with design system context. We have Tate Kim in the waiting room. Let's bring him in to the TVPN Ultrodome. Tay, how you doing? Hey, guys, doing great. What's going on?

So tell me, last time you were on the show, you bottom ticked it? What's going on? I think I made the bullish call on CPU's memory and Nvidia. Invidia is up like 5, 10%, but the CPU names have still doubled even after this big drawdown and the HBM names are up 100%. So I'm hoping that, you know, it's the same thing again. I come on here and stop to go up again. Yeah, ideally we could have like an emergency reserve of take

experiences. So if the market is ever down, Strategic reserve. We call you up. You jump on. That's good. It was funny because it was literally the exact bottom. That's amazing. It went exponential after that. Okay. Take him effect.

So where are we right now with the level of FUD, the level of downward pressure on the AI trade broadly, the chips, the semi-trade, like reset for us on like where sentiment is and then we can work through the different pieces of counter examples? So I think sentiment's very negative. We kind of had this huge up, parabolic up moved the last few months, and likely a lot of retail and hedge funds piled it in. And we were seeing this unwind down. I think the first, a big part of it was Iran war getting worse.

Every time we had the first ceasefire and negotiations, stocks started taking off right after that. And then when we had the actual ceasefire, we had a follow-through. And then as soon as Trump started bombing Iran again, you know, chip stocks have kind of plummeted in the last two, three weeks. And then now we're seeing just, you know, back to the old pattern of media and the viral hot takes spreading a lot of fud. I think we saw earlier this month. I think Reuters quoted like Zuckerbergs about agentic AI.

They took it out of context. And then every media person was with a hot take that this meta was seeing bad returns. that they're going to cut in CAPEX. And then we had leaks right after that saying that it looks like matter is going to raise CAPEX. So we're seeing a lot of this hot take fud. Yesterday, I think we had a flurry of stuff that scared people, the Wall Street Journal, vendor financing article that we'll see what happens with that. We have CMXT IPO in China and everyone freaked out over that.

We have the information article on ASML. We could go through each one. And then the Kimi thing, it's obviously a big thing. Yeah, we'll definitely get there, and I want to talk about open source and Vindia's strategy there, obviously, starting with the Mark Zuckerberg news in Reuters. This was July 2nd. Meadows Zuckerberg says AI agent tech progressing slower than expected. Zuckerberg added that the company's reorganization that included major job cuts was not as clean as it could have been. Zuckerberg and other meta executives have been seeking to moderate some of the organizational changes introduced this year.

And they said that the trajectory of agenic development over the last four months hasn't really accelerated in the way we expected. The company's bets on new structure haven't come to fruition yet. And so people were sort of reading this as maybe meta is going to pull back. But then it felt like the response was extremely quick with, with Boz going on a podcast and Alex Wang sharing a whole bunch of progress across. a few different models and data points. And then a semi-analysis wrote a whole bull case for MSL talking about how they have compute. And also they have more of like the internal structural alignment to sort of properly YOLO in the AI era, if I'm boiling it down as brutally as possible. Just because with Google, there's always this debate between, oh, do you sell the TPUs or do you sell the cloud component?

Do you have it in the product? whereas Mark Zuckerberg's able to sort of like go all in on this new idea. And so maybe there's more glimmers of hope there. But what else have you been tracking downstream of meta's ambitions? Well, I mean, they've been very upfront that they're investing heavily in AI. Alexander Wang is tweeting multiple times every few weeks that they're going full force. They're going to redo open source AI models. I think he said that, the YC event over the weekend.

And it's, I mean, if you actually look at, and then Reuters came out, I think, with an article saying that they're actually going to raise CAPEX dramatically this year and next year. So all that kind of fear that that quote about its fencing AI from the town hall that kind of like spooked the market for a few days, it kind of, it was completely false. Yeah, it feels like it's a comms air because the language that's been coming out of meta has been a little bit like, AI is going to replace our employees, and it feels like it'd be much better for them to come to the market with a message of, we're going on the offensive. Like, we're a hyper-scaler. To be fair, that was the internal town hall. They didn't mean to leak it.

And readers leaked that one quote and put out the headline before they're already. Yeah, it's interesting. Did meta basically go through like an eight-year period where, like, internal town halls didn't instantly leak? I think everything leaked always. I think everything's been. I know. But there was a period where like the sort of attention of the media was way, way, way less on like what meta was doing internally relative to the 2010s.

And all that attention just went to the labs, right? Yeah, yeah. Yeah, no, that makes sense. Yeah, I guess the question is like the question that I keep coming back to is like where is their revenue ramp? Where is their AI revenue going to ramp and when? right because as super would say ads like the ads like the AI has yeah and that's always that's always that's always been my view too but when you're when you're continuing to ramp capex yeah with and saying like we're going all in on agentic and we're building a harness

and we're also going to do open source and it's like well what is the strategy sure like yeah yeah what is going to take you to yeah a billion dollars of like pure AI product revenue yep or just API revenue, and then to 5 and 10, and what's going to allow you to, like, justify the spend other than, I think the market would love if they just said, yeah, we actually need all these GPUs because we can actually be 10 times. We're already good at ads. We can be 10 times better.

And that's where we're going to get the ROI on all of this CAPEX. Well, they're definitely getting ROI on that. The market is worried about, you know, all this extra CAPX on the, they're going for the frontier AI model race again. And they had to reset. A lot of people left and now Wang hired a ton of people. And we'll see what happens. It's going to take time.

It's going to take six to 12 months before we'll see any more progress. But that first model that came out a few weeks ago was a lot better than people. It wasn't the frontier, but it was much better than what people expected. Yeah. Yeah. So how have you been processing the NVIDIA letter around open source and all the back and forth, all the people jumping on, the companies that have been staying back, how do you work through that? It's been very impressive what they've been, they basically united the entire tech industry against Anthropic in the last like three, four days.

18 trillion in market cap has signed on last time I checked across. I mean, Google took a little time. Amazon signed on eventually. Oh, they did? Yeah. He signed on yesterday. They tweeted out. I think Apple is still the holdout, which is kind of strange,

because they're the one that would most benefit from open source, open weight models being, you know, more available, I would think, but I don't know what Apple's thinking. But,

I mean, they pretty much got the whole tech industry to kind of corner anthropic in their position. Yeah. Open AI signed on. Yeah. What did you think of?

Obviously, invidia, it's afraid. Yeah, I don't know, I don't know how, if, I don't know if,

really, I don't read it as being like cornered by any means, right? Well, Jensen is on the record that, you know, he said, I think, to Bloomberg, that there was a rising sentiment that something was going to happen on the, on the regulation front. Oh. At White House or whatever. Sure. So this was kind of. Yeah.

This was last week you had at least four people in the admin say, we're not against open weights. We're against distillation. And at least I was reading into that. of some type of regulatory action around open weights and then positioning it as we're targeting. This is like yesterday. Yeah. Yeah. About, you know, pushback and restrictions.

And he's done doing it under the safety umbrella. But definitely Microsoft and Biddy are worried that what the White House or Congress is going to do something on this front. Yeah. That's why they took it. Yeah, it seems very reasonable that he would have no problem with like Gemma or, Lama or any of the open source from like American hyperscalers where if you find out that they're distilling, you just walk across the street and sue them. And also these big companies have huge, huge, I mean, they have safety teams, but also just like

huge incentives to not have a safety incident happen on their watch because you're trying to like catch up to the frontier and then all of a sudden you have a safety incident. That's going to be really bad for your overall brand. And you have a different business to protect, whether it's social networking or, you're Google search if all of a sudden the Gemma model winds up being a thorn in someone's side for a cybersecurity reason or a bio reason that would be really, really bad. But a foreign company that is just like hurling it over here can kind of just be like, you guys deal with the consequences potentially. So I think that's what Dario is worried about. What about the overall idea of like

where it feels like we're sort of replaying the deep seek moment, open source is going to reduce cost. And so that's a reason to pull back on the AI trade overall. How have you processed that? It's almost, it's almost a perfect catalog. People are worried about Kimmy. But when you actually read the technical paper and their blog posts, this is not a tiny efficient model. This is 2.8 trillion parameters. It's going to require a ton of compute to serve. I mean, we saw it the first day they put it out, but their servers got slammed. Even in the blog post, they say it's best run on a kind of a server with 64 GPUs. So big super clusters that network well,

and that's perfectly, runs great on Individia. And if you remember, during the whole DeepSeek thing about a year or so ago, the market freaked out that DeepSeek was so efficient that it will lead to a compute glut.

But DeepSeek was an example of the reasoning model that actually, it was the opposite. It created a ton of demand. And I think the same thing that's going to happen with Kimi, where when you have more capable models that come out, people find uses for them. And right now, just like last year when reasoning

models took off, agenic AI and agents are taking off right now. And the market is kind of like not realizing that because right now, just like last year when reasoning models were taking off right now, agentic AI is taking off. And the next six, nine months are going to be bigger than anyone believes. And Sam is on the record. Sam is on the record over the weekend saying at the YC thing again, like people don't, I don't know why people don't listen to you. It's on YouTube.

Yeah. That the next six months, it's going to be much more dramatically better for AI than the last two years in terms of advances in capabilities. And I heard you say RSI before. I think it's going to be RSI. People inside Open AI and definitely Anthropic, Anthropic put a blog post on this.

RSI, I think, is a lot closer than people think. And if RSI actually happens in the next three, six, nine months, that's going to soak up insane amount of commute. I mean, we have this exponential ramp for reasoning, exponential ramp forogenic, and then if RSI actually happens, and I think it sounds like both frontier labs

think it's going to happen very soon, that's going to soak up an unbelievable amount of compute as the AI models, you know, use more compute to self-develop and improve. And I think that's one thing that people are missing that both anthropic and opening eye are kind of winking that, oh, it's happening. Anytime I tweet something on RSI, all these frontier AI researchers like my tweet.

So I think that's good. What is your sort of framework around compute hoarding? Because certainly it is, it has been happening when you look at, when you look at, you know, like going back to the meta example, right? They're not selling compute yet. They're maybe curious about it or exploring some deals. they have all this compute and they're betting on their own ability to create the capability that will have enough demand to justify that. Do you just think there's so much demand overall that it just, you know, even if there's

hoarding, it just will leak out and it's okay? Well, there's so much demand overall. I mean, the SK Hinex executives said during their IPO run that their customers are asking five to six times more than they're able to serve. And they're going to double capacity over the next five years, they said. And their customers, and I'm going to assume it sounded like Jensen, are asking for five to six times more than they're able to build. So there's overwhelming demand.

You guys were at the Advanced AI A&D event. Lisa Sue raised her genetic CPU forecast. Just three months ago, it was $120 billion for 2030. Three months later, they raised. raised it to $220 billion. Yeah. Like, she doesn't do that. She doesn't do that.

You have that just on the rent? I've got that ready. I can do whatever. I mean, like, well, I just love this chart because he called it perfectly. He actually did. It's crazy. CEOs don't, don't, you know, raise their tams by, like, these multiples in a few months.

If they're not seeing insane demand coming in. Especially not public CEOs who are serious business leaders who've been running, like, non-mememe stocks for decades and are like serious people. Everyone's freaking out that this is let the dot-com bubble all over again and your finance. But what if these hyperscalor GPU cloud businesses are amazing businesses? Like Morgan Stanley says if you do inference, it's 60 to 80% profit margins, right? These are amazingly profitable businesses as long as we keep growing the next few years.

And again, just like last year, we're on this exponential run right now over the next. the next two quarters and the market isn't seeing that everyone's freaking out that oh no we're spending too much and even uh sam altman uh podcast came out today and another podcast is flogging it's out there he's out there he said that he regretted you know pulling back on the compute purchases they made a mistake by uh not putting the pedal to the metal because now things are taking off again so like i i like amazon the CEO in april if you everyone really everyone really read his annual letter, Andy Jesse wrote. He talks about how free cash flow works.

We're not betting $200 billion on a hunch. We see the demand. We know it's going to be insanely profitable and free cash flow positive in the medium to long term. So that's why you're investing $200 billion now. And in a year or two, we're going to see insane amounts of free cash flow, the thing that people are worried about right now. It takes time to build out these data centers and fabs.

And you bet now bring that in a couple years. Like, if you see free cash flow, that assumes that, like, the revenues have to catch up, and then the CAPEX can't grow more exponentially. And so that means you have to see some sort of plateauing. Maybe it's at the end of the chart. Maybe it's this 2030 range. But there is a different world of just, like, continued growth forever. And then we sort of run out of money.

The pushback I have there, that's a static view, right? If they don't grow revenue for the next three years, yes, you can't do that. But they're growing, Azure is growing 40%. Google Cloud is going 80%. Yeah. You know, Amazon's growing high double digits. So if revenue is growing 40 to 80% this year, next year, and the year after, that's more revenue you have. That's more operating cash flow you have to invest, right?

Yeah. So that's what people are missing. And if the data centers you're building now, you're spending all this now, generates unbelievable free cash flow in 12 to 18 months because, you know, this agenic AI is actually aging and re-architecting all the workflows inside companies. And you need to do the agentic AI coding agents to make your product better. Because if you don't iterate 100 different iterations for your product in R&D, if you don't do AI,

just like AT&T is doing at the Gentic advancing AI at AMD, he's talked about they're putting 100 Gen AI models into production. They're burning a trillion tokens a month and then that's growing double digit. The reason why they're doing that is because by using a genetic AI,

you're providing a better customer service, you have a better product R&D, and you're helping your companies make better products and services. And if you don't incorporate AI into your company, Verizon, your other company is going to do, it's going to incorporate AI and then disrupt you and then you lose all your revenue. So everyone's wearing the ROI.

ROI is important, but you also need return on revenue because if you don't use AI, your rival is going to use AI to beat you in the market. Yeah, yeah. I think the diffusion story is still, even though we got like sort of jitters by the token maxing thing, just the actual usage of AI across. companies is still pretty limited in terms of the amount of people that are using it, the time that those people are using it. There definitely is a San Francisco bubble of startups where everyone is using AI a lot.

But if you just walk into a normal business, a lot of people are like, yeah, I got to check that out, which is a crazy place to me. Let me give you some context here. Yeah, Ara Karazian, Jared Sleeper, and Ekfussangh, enterprise adoption, disparity remains enormous. And he cited RAS saying usage would 100X if every company adopted AI to the degree of the most advanced companies. There's a small group of companies that are. People forget in the ramp data, like adopting AI can mean like having a chat GPT pro account for someone, which is like not exactly the same as like using codex and like coding agents and stuff.

Like it's important. I think that, you know, if I have someone on my team, I want them to be able to go and do a deep research report. but that's like table stakes. The question is, like, are you actually speeding up anything that's repetitive in your job? And that diffusion is just starting to take hold. So the total market size in terms of IT and knowledge management in corporations, it's about $6 trillion, right, a year. The two main frontier AI model companies, open AI and anthropic,

I'm going to say, I think this is roughly accurate, are doing $120 billion combined in ARR. Yeah. You know, why can't that go to 200, 300, 400 billion in the next year or two? I mean, they're growing at exponential rates. Yeah. And we're taking off. And if the market is $6 trillion, right?

Why can't they grow to $200, $300, $400,000, $400 billion in next couple years? I mean, it's like just do a little logic and rational deduction. Yeah. This is definitely possible. And it's happening right now and it's accelerating. And people aren't, you know, they're just taking, you know, these. big headlines where we had this, you know, $50 billion for Financial Times, and we find out it's over 30 years. It's like on the homepage.

Yeah, yeah, yeah. I wanted to ask you about this. Invita revealed this tenant for $50 billion data center that will use its chips. Explain what is actually going on here? So the Financial Times put on their homepage today. Yeah. The Nvidia is going to backstop a lease for a data center in Texas for $50 billion. And I saw that. I was like, oh my gosh. Oh, that doesn't. sound good. It literally sounds like they're buying their own chips. It sounds like the most bad thing you could do.

Then they actually read the article like halfway down the article. It's like a 15 year lease. And it's only $50 billion if they renew the lease after 15 years. So it's like over 30 years if they renew it. Then if you think about that, you're like, wait a minute, $50 billion divide by 30s if they renew it. Oh, okay. Yeah. And Vidi is 15 year lease commitment for the.

Texas site is worth basically $20 billion, and renewal options would take the total value to $50 billion over 30 years, according to Hutt 8. Okay. What are they plans for the site? Is this they are going to have some, like, what do you expect them? So my point is this is a billion, you know, whatever, a billion or $2 billion a year, right? It's a non-story, but it's a big headline, sensitive to a headline on the homepage. Yeah, and also, it's not like you're taking a $2 billion loss every year. You are the tenant, and then you are also renting that out, so hopefully you're making profit.

It's a rounding error. It's like, you know, they're doing 320 billion run rate a year now that's going to go to 400, 500, 500 billion next year. And we're talking about something that might be a billion, you know, like this is not a story, but this is how people run with the sensationalized headlines. and people panic and freak out. I think they just wanted to say the biggest number. That's exactly the point. And we're going to see what happens with this Wall Street Journal article. Both Open AI and Vindia are not commenting so far.

We'll see. But take us through the rumor. Wait. Rumor well, it's not rumor. It's the Wall Street Journal and other people reporting that. Yeah. Invidia is in talks with OpenAI. It's a backstop, soft bank, up to 250 billion, you know, you know,

We don't know the details. And I don't want to speculate and comment, but let's actually see the details before we, I think the market had a really big negative reaction yesterday to the story. Because everyone, I mean, Jim Kramer was telling his audience, like, sell everything at the open today because AI and Data Thetters.com, you know, it was insane. It's just, let's see the actual deal and the metrics and the numbers before we panic and freak out.

Yeah. Yeah. Yeah, that makes sense. Yeah. Honestly, when you say freak out and sell everything, sell your dollars, sell your house, sell your stocks, then I'll freak out. But until then, Tay, I feel, I feel okay. I mean, I just see the fundamentals. I see the CEO of A&D expanding her TAM, you know, dramatically over the last three months. I see RSI under Horizon, like every AI researchers like, oh my God, this is going to happen. have to get there sooner. And then I see, you know, the obvious use case of a gentic AI where you have to re-architect your workflows internally. Every company has to do this. So everything is taking off. You see like, you see when the president of Korea came to San Francisco area last week. You know, they had like a day in the valley. Instantly, Enidia, CEO, Jensen Huang, broadcom CEO Hucktan, Dario,

you know, Sam Altman are there, right? You know, do a little logic deduction. Why are they there like crazy? Because they need HBM memory and they're dying to have it. So if you think about that, that means there's insane demand and HBM memory is in shortage. There's tremendous demand for it, right? Talk about the, oh, sorry, talk about the NVIDIA-Cuda mode.

It feels like a big piece of AMD's advanced AI event was maybe the Kudamote isn't as much of an issue anymore in the age of agentic AI. You can have an AI agent, write you the software that you need to use any chip, and that creates less pricing power for Nvidia. But there's another world where you're not really, like, Nvidia doesn't necessarily need a moat because everything is just growing so fast that there's still. growing, but how have you interpreted the processing of like the potential death of the Kuta moat? So AMD is on it. Kimi wrote like a couple of paragraphs in their blog post about how they created a GPU kernel, all that, so everyone, you know, get scared or whatever. It's like, we'll see what it's like in real life.

You know, this is just, you know, AMD is incentivized to say, oh, Kudo's not a problem anymore. Kuda's been a tremendous moat and I think it continues to be a moat. And the reason why is it's super reliable. All the bugs have been optimized and fixed. And that comes from hitting the software millions of times and millions of times, right? Like you don't know if you use Cloud Code or Kimmy that what they figure out using their training data is going to work in the real world, right? They could talk about one little piece that does well. Let's see how it actually works.

Yeah. But, NVIDIA's big mode is its scale, its co-design of actually working through the networking, the CPU, the GPU, and how everything works together. And the other big thing is their balance sheet and their ability to get supply commitments from, you know, I think I said this before, optical startups are like upset because NVIDIA secured all the supply for all the optical components. same thing with TSMC wafer, same thing with HBM memory. So, NVIDA is using their size and guerrilla and being able to prepay and get components that are in shortage. So they've become the dominant, you know, over the next year or two, you're going to see NVIDIA able to add tons of revenue because they were able to lock up all the supply components. That's another thing that people don't really talk about,

is their supply chain and their ability to work with partners and secure component inventory. Is there still energy fud that we would run into an energy bottleneck before we run into a chip bottleneck? So Jensen said this last week on the Bloomberg interview that there are a lot of bottlenecks, including data center shell, power, and all those things, components, energy, whatever. So all those things. It sounds really bad, right? And then right after that, he said, I think the chip industry has enough supply to double their revenue every year, basically implying Nvidia has enough supply for energy and all that stuff. No one is pricing that in.

So everyone talks about ballonics. Nvidia CEO just basically told you on Friday that they have enough supply chain and all the bottleneck stuff to double revenue every year. No one, you know, Nvidia's revenue and submits for next year are a lot lower than double. I'll tell you that. Do you think the market prices in? just how much of almost every important AI company in every category Nvidia actually owns.

Like it feels like every single, like we're constantly focused on who's going to raise CAPEX next and where is this quarter coming in. And it feels like in two or three years, people will look at Nvidia's balance sheet and be like, wait, they have what I imagine then will be, you know, could end up being a trillion dollar plus of just like, like ownership and all of these great companies, which again just goes back to the advantages of that early scale while they're, you know, while all these companies are trying to compete away Nvidia's margins and all these different things, they've been able to accumulate, again,

positions in all of these incredible companies. I mean, we saw the SSI news yesterday is a great example of that. But how do you look, how do you see it? So I think look at Jenison, history and investing in these companies in CoreWeave and see how much money they made. They just bought stake in the optical companies, momentum and coherent. Jensen is enabling the future because he sees this overwhelming title of demand, and he needs these companies to be able to build up their supply chain and to give supplies and chips to Nvidia, so they actually ramp very hard. you know, everyone's freaking out that this is vendor financing.

What if hyperscale GPU cloud is so profitable and these companies need capital to build up that supply so they can serve the GPU cloud services over the next year or two? Maybe Jensen sees that coming like he did with all these other companies like CoreWeave. And that's why he's investing in these companies to be able to expand their ability to make the components, the industry needs. So I think you're exactly right. In a year, two, three years, NVIDIA's going to have, like, all these stakes in these companies, and it's going to look like he was a good investor because he has been in the past.

I mean, the man can buy a leather jacket for like five grand and sell it for a million dollars. I don't know what else you need to see. I mean, think about Melamombeard. Did you win that? No, I got to get you a jacket. The real question is, how long until someone, distills a jacket and open sources it, you can get a dupe of a Jensen jacket for two bucks. That's what I want.

Anyway, thank you so much for coming on the show. Jordi, you got anything else? This was great. Yeah, this was great. Always a pleasure. Thanks for putting up with all of our jokes. Hopefully, this becomes the lucky charm for the markets. Yes, I agree.

I agree. The bottom is in. Great to see you, Tay. Have a good rest of the week. Goodbye. Let me tell you about CrowdStrike. Your business is AI, their business is securing it. CrowdStrike secures AI and stops breaches.

I wanted to run through on the power issue. There's an interesting article in the journal. An underground nuclear reactor is coming to this Kansas town and it's dividing locals. This is something that I had tweeted about years ago. Like, why don't we just put the nuclear reactors underground? Put solar panels on top. Best of both worlds. Optimal use of energy.

But there's a lot of fear and uncertainty. And doubt about this one. They say no one has tried operating. a commercial one a mile down. Until now, it's great that it's here. It's kind of bad that we're the guinea pig, says the residents of Parsons, Kansas. Residents of the sleepy farming outpost agree on many things. But whether to put an experimental nuclear reactor a mile deep in the granite beneath their town isn't one of them.

Elected officials and some others see a chance to create jobs and lure data centers and manufacturers to a rural patch whose economy has been flatter than the surrounding. Cornfields. Another group is effectively saying not under my backyard. It's newbie, not nimbie, because it's not under my backyard. Noomby or something like that. I put $125,000 into my house, and now a nuclear reactor is coming to town, said Gerald Johnson, an IT professional, who plan to retire in Parsons. I can't think of a worse idea. No one has tried operating a commercial nuclear reactor deep underground until now. I'm surprised, even like the Soviets in like 1950 didn't try it. I feel like they were trying everything. I'm surprised.

Yeah. But the so-called gravity reactor is the creation of Liz Mueller and her father, Richard Mueller, Emeritus, Professor of Physics at the University of California, Berkeley. It's Berkeley people again. And an inventor, they found a deep fission, a three-year-old California startup that raised $150 million in the past year, including $40 million last month through an IPO, largely to fund the work in Parsons. Parsons, with a population of 9,600 people, sits about midway between Kansas City and Tulsa, Oklahoma. Deep Fission drilled a first test hole this spring on a 100 acres. Office chairs like that had really fallen off, which tells you now might be the time to bring it back. No.

Right. I need a new office chair. I might go for one of those. The high back leather. It's good, good. Deep Fission drilled a first test hole this spring on 100 acres at a mostly overgrown industrial park, dotted with old munitions bunkers. just outside town. On a recent day,

Maurice LaFountain DeVision's senior engineering director showed off a pink-flect granite retrieved from the company's first test hole and joked that the billion-year-old rock would make a nice countertop. An empty steel container canister

sat on a cleared, drilled pad waiting to go down a second hole this year. The plan is to send another one loaded with nuclear fuel into a third hole to heat water a mile underground and generate electricity on the surface in 2027, 2028, an astonishingly short time frame by industry standards. Interesting.

Anytime you're putting a nuclear reactor in the hole, it's kind of scary, he said, in his office. It's great that it's here, but it's kind of bad that we're the guinea pigs. Very interesting. I'm surprised we haven't heard more about this company, this idea, everything that's actually being planned. There's something a little, I understand where they're coming from. There's something a little bit nerve-wracking about, like, even though you would think a mile deep. If something goes wrong, it's less of an issue. It feels like, well, people, it's harder to get to

and, like, just go and solve the problem. Deal with it. As opposed to like, oh, yeah, it's a building over there. I see people coming in and out all the time. The experts are in control. I don't know. What do you think? Are you pro nuclear underground, a mile underground? Could be the future. Could they not find maybe a place to do that that wasn't right under a town? It's not right out of town. It's outside of a town. You need some infrastructure. Yeah, yeah, yeah. I bet what they would say is, like, look, it's a 10,000 person town. We went miles away. We're on 100 acres of land. Like, we are outside of the town. But yeah, there aren't that many places that are truly uninhabited for like hundreds and hundreds of miles just because of the nature of America. There's

towns all over the place, every street. And you need roads to be able to deliver equipment and whatnot. Anyway, let me tell you about Codex. Codex is a powerful workspace for getting work done with AI agents. Whether you're writing code, analyzing data, creating content or automating business workflows. Codex helps you move projects forward from start to finish. What is this? How did we get here? Anyway, we have Ben Zweig from Rvelio Labs coming out on the show.

How are you doing, Ben? Good, good. I love that intro. That was just for you. We're testing that out for the first time. Yeah. Nice. And matches the vibe.

the vibe of the labor market. Take us through a little bit on your background, how you work, and then some of what you're tracking the labor market, and how it ties to your actual business. Yeah, for sure. So I'm a labor economist. I've been tracking labor market data for a long time and started Rebellio Labs. So Revelyelius Labs is a workforce data company.

We're collecting, curating, synthesizing all labor market-related data that's out there in the world. And, of course, you know, a big question is how is AI affecting the labor market? Of course. Though, you know, we're uniquely positioned to answer that question, and it's on everyone's mind. So we started putting out this labor market, this is kind of AI labor market tracker, which is really about answering, like, how is AI affecting the labor market today? So not really getting into the speculation of what might happen. Yeah, yeah, just today.

But really, like, what do we know? Really quickly, what is your business model? Who gets value out of this data? And then I also would love to know, how do you go about getting more accurate data? I see like, you know, obviously the census bureau, the government has access to do polling. ADP is a very logical place to get data because they run payroll so they can see the data. But what's been your strategy there? And then who's the customer?

Yeah, yeah. So I'll start with the customer. So a lot of it is hedge funds. So they're speculating of performance. So companies, yeah, nice. Thank you for your purpose. It's here for hedge funds. Yeah, they don't get a lot of love these days.

But yeah, they are speculating on the performance of a company that they have no affiliation to. Sure. To understand what's happening in the company, the workforce dynamics, HR departments for benchmarking also. So strategic workforce planning, people analytics, talent intelligence. These are all like segments of analytical HR and academic research. So they, of course, want to know what's going on. So basically we get the data not through surveys, not through payroll, but really from the internet. So, you know, LinkedIn profiles, job postings, glass to reviews, layoff notices,

immigration filings, freelance platforms, like anything and everything that is in the public domain. And that information has to be, you know, enriched and synthesized in a smart way. Like there's all sorts of sampling biases, there's lags in reporting, there's like raw text, you know, so we have to classify that to occupations, the skills, seniority levels. And, you know, importantly, work activities, which is more of a recent thing for us, but important to these. And then I imagine you can sort of back test against like the historical actuals to see that the model's working and then you can be more up to date. So we don't back test against financials because if we didn't know I mean. I mean about like if you ran your model on like what was the employment rate in 2021, you could look at the actual employment rate to sort of calibrate that your system is predicting employments correctly.

Is that roughly correct? Yes and no. I mean for some models, we can see what was retroactively revealed. So, you know, when someone changes their job, they don't necessarily update that right away. And we can see that, you know, every timestamp has, like, more information than it before. So that's, like, a solvable problem. But in terms of, you know, saying what's happening in the labor market at large, we can kind of use BLS data. So BLS is a Bureau of Labor Statistics.

We can use that data to kind of, like, proxy for it. But that's got issues itself. So I don't know if we want to use that as ground truth. So, you know, I think BLS has, you know, view on what's going on from survey. data, ADP has from payroll data, and we have from internet data, and they're all kind of independent in their own way, kind of uncorrelated errors. Okay, so I want to understand how you look at your data in the context of AI diffusion, right? So a company, an individual company or an industry might have fluctuating, like, labor data,

right? Maybe they're adding a lot of people. But then individually, if you look at those companies, maybe some companies are adopting AI quickly. Some companies in that sector aren't really adopting AI at all, or they're doing it in a very minimal way. Let's say they just have a basic, you know, chat GPT, $20 a month subscription. So like, how are you, I was talking to John, maybe, was it six months ago? I was saying, like, I really want there to be a firm that is just studying AI diffusion in specific industries and getting into the nitty-gritty, probably doing, surveys to actually understand how, because every company says they're adopting AI, but we all know that there's such a broad spectrum. And then, of course, some people are saying that just because they want to be in, feel like they're a part of the club. Yeah. Yeah, I think it's probably mostly those that want to be part of the club. But I agree.

I mean, so there's a few ways to get at adoption data. So I think adoption is the hardest part of all of this, because that's really a firm level, you know, piece of information, whereas AI exposure is like more of a person level piece of information. Sure. So I'll tell you the way, we do it in a couple ways. So one is that we had a partnership with, we still have a partnership with Ramp. So I know, a friend of the pod.

Let's go. So they can track adoption just using like AI spent. Yeah. So they can see like dollars spent on tokens, et cetera. So that's like a pretty good way to get adoption. The problem with that is that, first of all, it's like a self-selected sample, you know, ramps skews toward more like tech, which is fine.

Like, that's overcomable. The other issue is that it's anonymized. So they can't release information at the firm level. So, you know, when we collaborate with them, like we have, you know, the labor market data and they have the adoption data. So, you know, it's like complicated. You know, we have to send the data. They have to, like, run something. We have to do some matching.

So it's like a little bit, it's got some friction. The other way to do it is through, um, We use this measure, which is used in a paper, a recent paper, that measures adoption by, like, sort of hiring AI integration teams. So the thought is that, you know, if someone's, like, hiring AI integrators, you know, beyond some threshold, that they're, like, taking it seriously. Yeah. And they're embedding it into their business processes.

And by that metric, we see about 9% of firms, like, getting very serious about AI. It's a very conservative way to measure AI adoption. Yeah. but seems to be pretty good. Like it's correlated with all sorts of other things. Yeah. I sort of hate that idea as a metric, but it probably makes so much sense in larger organizations

that that is a great signal. But it just feels like completely the wrong way to go about actually changing a business. Like I feel like adoption should be so much more ground up than like, oh, we're hiring a special team to do this. But that's the way businesses work. And I think you're correct to identify that. It probably is very indicative of a change

in the stance of the business. Where's an area that AI is really good and you're seeing job loss? Because like AI is pretty good at software engineering now or generating code and, you know, the companies that are adopting it the most or hiring a lot of engineers. Whereas I've heard in LA specifically, apparently the models that do product photography, so men and women that are, that wear a bunch of clothes for like an old Navy when they're releasing a new collection. Like that work has been very impacted because that talent, they don't have a brand yet, right?

And so maybe certain companies will just say like, yeah, let's just take this shirt that we have and just generated on 20 different AI models and we're good to go, right? It just doesn't really matter that much if they're using real talent or not. And so they choose the easier, cheaper route. Yeah, I think that's a great example.

I mean, for the most part, you know, across the board, adoption is generally correlated with growth. But where we're seeing reductions, I mean, I think the creative fields are a great example. So, you know, if you need like video B-roll or just like, you know, stock images or just, you know, podcast intro music, you know, that that is like very easy to get from these kind of AI generated, you know, creative elements. Sure. Yeah. It's so fascinating because how many people, yeah, it's just quite interesting because when you, when some of these things, how many people were actually in those roles? Like, would it actually, does it show up in labor data at a large scale at all, right? People that are just doing, you know, stock photography and making their living that way or.

Yeah, and a lot of these people might have sort of sloshed around. Like, I mean, I see. I see Instagram reels from people who years ago were posting like After Effects tutorials, Premiere Pro, DaVinci Resolve, like little video editing tutorials. And now they're posting like AI enabled workflows. And instead of showing you how to deal with a green screen, the old fashioned way, they're just doing it the new way. And they're probably still doing it for clients.

And the client, like the client spec is just like, I need ads that convert. And they're just doing more of the work. then there's other stuff that's bleeding out, all sorts of different stuff. Yeah, I mean, one kind of framing I would put this in is that, you know, the, we're seeing a lot of kind of automation of things that are very task-based, things that are like really micro jobs. There aren't like full jobs at all. So we're seeing like declines in freelancing across the board. So freelancing has hit pretty hard. But that's really an environment where people transacted tasks.

They're not. Yeah. Yeah, this is why we were just talking about this earlier. The, you know, historically, like if you. you need a really specialized website, like it's not your main site, but let's say in our case, we're doing a drop. Three years ago, we would have gone. Yeah.

And maybe gone to Upwork and said, like, hey, I need a simple website made and just find somebody to do that one off. And now AI is just so good. Or like a basic logo for our first draft. That would be like 99 designs. Before you bring in like a real branding firm, you might just get a freelancer to mock something up for you. Now image generation can do that for sure.

What do you make of the computer science shifting? Because there are so many opportunities for entrepreneurs, startups are growing. There's some tech layoffs, but at the same time, it feels like just in general, if you have a computer science degree, you're probably going to be a bit better at using AI broadly. And so there's lots of opportunity. And yet the number you have here is computer science enrollment is down 28% from its 22 peak. Yeah. Yeah. I have mixed feelings on it.

First of all, it's very dramatic. Yeah. So one thing that kind of, one optimistic take is that the supply side of labor markets is actually quite responsive to changes in technology. And that wasn't obvious before. And, you know, if people can reorient themselves flexibly, that's great. That means, you know, we can be adaptive. We can have more of a dynamic economy and worry less.

So I'm encouraged by that responsiveness. I think it's an overreaction for two reasons. One is that we are not seeing declines in employment, you know, based on the firms that are adopting a lot. And that's true in engineering. It's true in tech. We're not seeing mass layoffs despite the narrative. So I think it's premature for that reason. Another reason is that I think even just a couple years ago, maybe even less, I mean, time is like elusive to me.

But I think, you know, not so long ago, you know, we thought of AI as chatbots and code assistants. And now it's more agentic tools. So it used to be such a low barrier to entry type of technology where, you know, anyone's grandma can use it. And, you know, coders, you know, engineers were really just like, you know, replacing their work at high rates. Now, you know, we're seeing, you know, complicated tools. Like, you know, agentic systems are hard to use. They kind of favor the digitally native and people who have experience. with engineering.

And even when they're easy to use, there's, there are a whole bunch of like, from a business, from an enterprise perspective, like cost tradeoffs, privacy, security, how deep is this system? Like, even just firing up a coding agent today, you're hit with prompts like, do you want this to have access to your documents folder? And that's like a question. And a lot of consumers are like, I don't know. And a lot of it businesses are like,

I don't know. So there is some sort of like capability overhang. Yeah. Yeah. And I think, you know, it's a different job than it was before. You know, like people are, you know, engineers are spending less time, you know, you know, doing the front end engineering for a website. But they're doing more of kind of that DevOps. So I think it's premature. And I think we'll, I mean, you know, I suspect we might have a shortage of engineers in the way that now we have a shortage of radiologists. Everyone was nervous that like radiologists were going to be a thing of the past. And and now there's a shortage and you know, wages are super high. It's like the final boss of AI automation. Yeah.

AI researchers like one day I'm coming for your radiologists. You imagine that it all started with like a radiologist just bullying an AI researcher and being like, what you're doing is so useless and the AI researchers like all show you radiologist. I'm going to put you out of a job and the radiologist just like, I'd like to see you try and then years and years go by. Talk to me about hires to posting ratio. It's down 38.6% since late 2022. I can imagine that there's a lot of slop posts.

We were debating this before. But how do you tease that out? What do you make of the hires to posting ratio dropping? So this is the thing that I get the most nervous about. So, you know, we're seeing some slop posts, some slop job postings. Yeah. But we're also seeing a lot of slop applications. When a job goes up, you know, you get, I don't know if you guys have posted a job

recently, but I just did last week and I got, you know, 1,000 applications in the first, like, five minutes. There are all these, like, job boards that are kind of helping people auto-apply. Yeah. Even Indeed is doing this, which I think is a bad move for the record, but they'll do what they want. It's, you know, so basically employers are getting completely. signal jammed. They're getting overrun with these applications that look strong, but they really have no way of verifying. So the utility of each job posting is going down. It's not as good of a way

to find candidates anymore. So employers are relying on networks. It's getting harder to hire. And in the economy at large, we have this kind of low hire, low fire environment where there's just not a lot of movement in the economy. And I think that is the result of, you know, AI usage in the search and match process. Yeah, you would think that, like, I've been surprised that social media has not been that overrun with slop. Like, there's definitely some slop problems here and there. But in general, the algorithmic feeds have been sort of set up to deal with this where the bad slop gets filtered out pretty quickly. Except for LinkedIn.

But yeah. Sure. But I've been, I've been surprised. that there hasn't been as much of an intermediary where you put up a job post, yeah, you get hammered with a thousand applications, but the filtering is really, really good so that you're really only looking at the top 10, maybe you dip in the top 100, but you're not at all annoyed by the bottom 900. Because I guarantee you that there are millions and millions of sloppy Instagram videos out

there that would annoy me if I saw them, but the algorithm will just never show them to me. and then maybe there's one that uses AI, but it's good, and it will show it to me because I still enjoy it. So it feels like hopefully there's people working on this. I'm sure that people are, but that feels like the next iteration to unclog this, because that seems like a major problem. Like you need the matching in the U.S. economy to be really, really strong. Yeah, I mean, there's been some regulatory challenges there, too. So a few years ago became illegal for employers to, you know, sift through candidates using AI. Wow.

And I don't know how enforced that is. Yeah. But it's a liability for employers and not a liability for candidates. So there's some asymmetry in who can use AI. That's very interesting. I had no idea. When did that? Yeah, I remember that you can't use AI to filter out candidates.

I think of it as like I understand where that came from on like bias based into models and like very preliminary. barely deep learning algorithms to sort of like look at the person's name and look at the graduation date and like try and filter for that like I'm just thinking about like did the is the resume complete slop you know like a complete like a pangram level that doesn't seem to impose like bias in the same ways that they were trying to avoid so we're in this weird like knock on effect world but that's the way these things go jordy anything else no come back on as there's Come back on as there's more data that's notable. You can tease the hedge funds a little bit.

Yeah. Give them a taste. Yeah, for sure. And congrats on the progress. Thanks so much for coming on. Yeah, great to me, Ben. We'll talk to you soon. Have a good one.

Let me tell you about MongoDB. What's the only thing faster than the AI market? Your business on MongoDB. Don't just build AI. Own the data platform that powers it. And let me also tell you about Cisco, critical infrastructure for the AI era. Unlock seamless real-time experiences and new value with CISO. Go up next. We have a kosh from Takeoff. He's the founder and CEO. He's been on the show before, but this is the first time.

Here he is. A new flag. How's it going? Give us the news. It's going well. It's great to see you guys. The news is that Sierra just bought us. We announced it last Thursday. You already completely missed. That's the first. That's the first miss. First miss. Oh, brutal. Sorry to do that to you. I got to go back to practice.

I'll work on this. No, I'm excited. I'm excited. Thank you guys. So tell us the story of the company. I mean, we got enough time for, I think, for you to tell the entire story from start to finish because all of this sort of happened pretty quickly. Where were you before you started the company? When did you start the company?

What was the growth like? Take us through the journey. Yeah, absolutely. So we started the company a little over a year ago. we started to build basically like agents that would be slightly more capable than what we were seeing today.

Sure. We fundamentally found out of the company on this hypothesis that there were two different kinds of agents. There's human and the loop agents and then there's truly autonomous agents. Human in the loop agents are the agents that we all love to talk about.

Like we're talking about quad code, codex, things that you prompt. They do things. They can do them for a very long time. It could be dozens of minutes, hours, even in some cases days. But fundamentally, you are the person that kicks them off

and evaluates their work and then like you were talking about in the previous interview, clicks, except viewing my downloads folder. Autonomous agents are not that. Autonomous agents, when you think about it from the perspective of a buyer, it should feel like you are multiplying your labor force. And when I say feel like, I mean it should be a one-to-one translation. I should feel like when I buy takeoff,

I'm buying 100,000 agents that can do what I might have a team of disparate human, software, et cetera, about doing today, but at a much more massive scale. That was like the foundational thesis of like, how do we actually build agents that can do this? We call them Long Horizon agents. We call them fully autonomous agents. And then we'd go after explicitly revenue-aligned use cases.

And the reason we went after revenue-aligned use cases is, well, there's a lot of different reasons. But the most obvious reason is, like, why are you going to buy mission-critical AI software from a kid with crazy hair? Like, the only thing that's going to get you to do that is if I can prove to you, I'm going to make you more money. And the way I get to prove to you, I'm going to make you more money is I make it very zero risk for you. I'm like, give me your lowest quality leads. If I'm talking to a lending company, give me your patients that are kind of like churn. if I'm talking to a healthcare company. And I'm like, let's see what I can do with the agents that I build for your company.

Let's see if I can recuperate that lost revenue. Let's see if I can increase your top line. And if we are all successful here at the end of the day, I'm going to be in your board deck at the end of the year because you bought a piece of software and revenue is up double digits. That was the foundational pitch thesis, the whole idea of what the company was going to be. We tried building agents in different ways.

We started actually with browser agents because we figured if we can use software, we can do what humans do. But we thought that that would actually, we realized, not thought. we realized because we get eaten up, chewed up, spit out by the market over and over again. We realize that that's like not actually thing. For like six months. For like six months. For six months.

That's fair. It's not like you were like, yeah, we were going to chew up one simple trade. For the viewers, what Jordy and John are referring to if you haven't read our literature, which I don't expect you to do, is that we entered this calendar year at effectively zero dollars in committed revenue. And by the time we got acquired by Brett and Sierra, we were at near eight figures in revenue. So what they're referring to is that. that very short and vertical kind of, no pun intended, takeoff in revenue ramp.

Let's go. And so again, like, period. More people should name their company takeoff. Great nominee. That's a lesson. That's a lesson. It's amazing. Yeah.

I mean, it's got its own SEO things. Former member of Migos, rest of the bees. We're honored to like carry the name with a positive light. That being said, like, you're only going to make money if your agents are trying to sell or like, you know, trying to be sold upon the value of adding revenue. if you actually add revenue. And so we de-risk it because, you know, I'm not Brett Taylor. I can't walk into a room, or at least I couldn't walk into a room previously,

and get someone to pay for something that's not already driving results. So we go in, we do pilots. They're not necessarily free, but they're paid on outcome. And so if I drive this outcome that we're talking about, usually directly revenue or something tied to revenue, for a lending company, loans funded, loans originated. I get, you're going to pay takeoff the way you pay a human being on commission and some sort of base units based on the amount of tokens, voice, SMS that are used.

So the customer thinks, I'm only paying when I create X thousands of dollars in margin, I'm paying hundreds of dollars of cost of goods sold. They love that tradeoff. And they're like, if it fails, it fails. And if it succeeds, we're making more money at the end of the year. That's how it keeps crazy hair walks into a room and ends up selling multi-million dollar contracts over and over and over again. Because like once it starts actually working, even what I did not expect, really, is like the compounding nature of exponential growth. I love it. And so I'll let you guys.

How deeply you're integrating or you were integrating with some of those first customers. Because there's a world where you're just like, give me the stale leads. I will go off and I will do the email. I will do the SMS. I'll do whatever happens and I'll sort of like either build those systems or maybe you'll set up your own like MailChimp account or whatever you want. And there's another version where you're like, I just want to live within your CRM, within all your tools. I will do API integrations into whatever legacy systems you have to. actually collect all the knowledge to make the correct move and actually drive revenue.

No, it's a fantastic question. And it's categorically the latter. So there was this, there's this lecture, for lack of better words, I give for every potential candidate and existing employee of takeoff, which is we are given the privilege, right, not the right, but the sheer privilege of sitting between our customer and their revenue. I could explain this in a million ways why it's so important, but the most important thing to explain is that our buyer was always the CEO or a C-suite member. It wasn't some VP of something that reports into something that reports into the CEO. When you are selling revenue, you are selling to the CEO. That's what he or she is getting greater on at the end of the year, whether they're a public company of which some of

our customers are, whether they're a massively multi-billion dollar private company. They're getting graded on revenue. And then as a function of-fired at the chief revenue officers in the audience. No, no. No, I know. I know. It's one of the things Brett pointed out. It's like it's kind of amazing that like every one of your customers, your contact, like the person who's in my iMessess, top of I messages is CEO. And so, like to answer your question, I would give this lecture that when we are referenced by our customers, they have to think of us as their best employee. When I say us, I mean myself like a Koch, Spencer, Shred, my teammates' names.

They have to think of us as their best employee. And the way we get there is we have to understand their business as well as any individual that works for them. They could be a 10,000 employee company. they should be able to ask us about anything that is even remotely related to the line of work that our agents are doing for their business and we should be able to answer it.

I'm talking about gross margins, I'm talking about conversion rates, I'm talking about time to fund, I'm talking about time between first contact to revenue generated. Literally everything. And we know we've succeeded when the CEO starts asking us questions

about their business. That's when you're in like, you know, the promised land. That's when you are literally their friend when they're texting you, 5.30 in the morning, 10 o'clock at night, and like none of your family or friends are in your top five I message anymore.

It's just your customers. CEOs. And so it's very much understanding, like, the intricacies of that business in order to build an agent that could actually do what's going to drive that company's revenue. And so we have to understand every piece of software that they're using, every single thing that somebody might do because we are trying to genuinely scale the workforce. And you can only scale the workforce if you can do it end to end. And that's like a really important thing that I think most agent companies don't get. If you're going to sell an agent into some workstream, but you're only going to take like a horizontal slice, it's virtually useless because then you have to educate everything below

and above it how to drive the end-to-end results. So if you want to actually drive the business outcomes, own the whole thing. If you want to own the whole thing, you have to be capable of owning the whole thing, which means you have to understand the business well enough to build the agent to do so. You guys had Markey,

who's been a friend and an incredibly incredible founder, who I've learned a lot from on the show, I think a month or two ago. And Markey talked about how her entire company and product is rooted in this foundational philosophy that we have to translate whatever language the company is speaking to what the agent is going to do.

Yeah. Like we think very, very similarly. Our culture is entirely predicated on that assumption that if we don't understand the business better than our customer as well as our customer, our agents aren't going to do it as well as we need them to. So what does that translation look like for you? Is it a bunch of markdown files and then your agent can interpret those? Because you could go all the way to like, we pre-trained a model just for you. And then we could be like, we fine-tuned a model for you, sort of the thinking machines model. And then you could be like, well, we're using the frontier models, but we have a custom harness for you.

you or we customize our harness for you, or we write a special integration, or it's just, or the agent just shows up and it figures it out. I love that you're giving you multiple choice, because if you didn't, that would just, like, ramble. It is the second half of answers that you just said. So, like, another foundational philosophy that we kind of built this company on is that the inference API is a commodity. That's a sound bite. You can clip me.

That'd be great. And that's a crazy thing to say, right? It's a crazy thing to say that inference API is a commodity because we think about Claude and Anthropic and Open AI at tens of billions in revenue. people can be like, that's all inference. I would disagree. I would say it's a function of the things built on top of inference. We're talking about Jatsubit, Clod, Codex, Cloud Code.

Yeah. And what we need to be able to do is you can call these, most people would call these harnesses, right? Like harnesses with a great GUI with a great command line interface. But it's functionally the thing that's delivering end-to-end value. And like coding was a great first coding and chat agents were a great first product because that was the end-to-end value. Again, it's a human-in-the-loop-type agent.

so that you're giving the value to the person that's using the product. The subsequent type of wins and enterprise AI. And when I say wins, I don't mean like hundreds and millions in revenue or even billions. I'm talking about like the next wave of tens of billions of revenue. It's going to come from the fact that your harness, which is just a fancy way of saying, agents that can do multiple things and operate across multiple different services as opposed to a single call-and-response API should be as capable as someone that is on a job listing or like something that you are hiring to drive an out-eastern. come or a result for the business.

And so it's a combination of harnesses, and specifically a takeoff what we built was what we call it as a DSL, a domain-specific language. So you should be able to educate, direct, and build the agents on takeoff using our domain-specific language that is built around the idea of we're trying to handle something end-to-end. The other kind of, like, unique thing about this is like, your agents have to be answerable to the outside world. Like, you can't just say, go do a thing.

The thing that our customers are calling APIs for is go fund this loan or an API call to go onboard this patient or go get this patient's prior authorization. That requires multiple actions by the agent that then in turn require input from the outside world. Let's use the borrower example, the loan borrower. If you're getting an API call to your agent that says go fund this loan for this borrower, this lead, you have to call that lead, contact them, help them with that initial rate quoting, understanding what options they have, whether it's a HELOC, a REFI, a home equity loan. Then you have to have a second call after whatever happened in between the first. call and the second call. You have to go reach out to third parties, the e-noteries, the underwriters, everything else involved, document collection. Then you have to have a third call saying, hey, I noticed you got stuck here because you have this weird Iowa borrower question about co-barrower

co-signing. Then you have a fourth call as getting it over the line for funded. This is something that for an agent to actually handle end-to-end, your harness is like, is transcending just like tool calls, right? It's like it's an always-on harness with a heartbeat that's answering anything that could happen by, on behalf of, or in relation to to this central entity in this example of the borrower. Tell me a little bit about post-merger integration. I could see Sierra having a product called Takeoff.

I could see Sierra just being a service or company that you work with for a bunch of different things. And I don't even know if I need different products because AI is so broad that everything sort of merges together into one product that can do multiple things. And I just flip on a switch and say, okay, I want you to handle this.

I want you to handle this. But how are you thinking about, integration. I know it's like really, really early, but I imagine that this was what you were talking to Brett about was like with a vision of what these two companies can do together. So like take us through a little bit of it. Well, I mean, that's actually great. I'm going to go on reverse order of your questions here. Like when Brett and I first chatted, we basically realized that we have a similar vision of what the world was headed towards. And what we realized was that by virtue of just, again, being a kid with crazy hair, like there's no chance that I was going to like compete and win

and customer support. There's a dozen companies, three of whites that are like. Selling yourself short. You seem, you seem, to me, I'm getting young Brett Taylor. Yeah, let's pull him a picture of Brad Taylor's hair, please, and see if you can call when he was your age. I think you got a shop, but yes, okay. The point being that, like, we had to come from a different angle, right?

We had to sell a thing that only the earlier adopters were ready for. Yeah. And, like, it's not like we, and so, like, when I say we had a similar vision of the direction the world was headed in. Sure. Like, we were further along on that timeline. Yeah. And, like, we had done this thing that I don't think most of the world realized this

possible yet. And it wasn't until we proved it was, right? That's what was really exciting to, I think, Brett and the company is like, hey, we'd love to get to where you are, but we're realizing that you're already there. And why not get there together and then scale it times a million? Sure. Right. And so that's kind of how the original conversation started.

We then kind of came to this, like, realization that, and I think what was actually really interesting for you guys to understand. for anyone who's listening and watching, is that we realized we were on to something when our first, like, three, seven-figure customers were, like, they already had customer support vendors, right? They had like a Sierra or a Deco-Dan or something else there. They were spending, on average, between a few hundred grand to maybe, like, maybe a million dollars with them. With us, they were spending at least three times more. Wow. Right? So, like, they had an AI support vendor and they also had takeoff. They were spending three times, in one case, eight times more than they were spending with their support vendor. And that makes sense because you're driving revenue and you're talking to CEO.

And if you're driving revenue, there's three kinds of software, right? There's revenue driving software. There's functional software and then there's must-have software. And if you're the first category, Google ads, Facebook ads, one dollar in equals more than $1. I will keep spending until I flat that line. And that's what we were going for. It's exactly how we want to be thought up by our customers. And so we realized, you know, again, all the things that Brett and I were talking about that we were excited about,

a lot of the same, shared ideas around where the world was headed. We're like, hey, together, this can be 1 plus 1,000 equals 1,000. And so we,

I don't know if you guys saw, probably not, because you have a lot going in your mind. We, we as in Sierra and take off the, Sierra together, launched this product called Horizon, which is this new thing.

And the reason it has to be this net new thing is because we want people to realize this is a step function jump in capability. Yeah. A step function jump in capability, which is going to drive revenue

for your business. It's not just agent and let me one second. cost savings, but it's agents that your CEO is buying. And that's really pretty exciting. Yeah. I don't know if I can swear. I'm sorry. But that's really exciting. That's awesome. Yeah, no, it makes it makes so much sense. You're great at naming. Yeah. These are all, every, every name is great. Like, these are all good. Yeah. I love them. Well, thank you so much. Jordy, anything?

It's great to meet you. I found the, I found the, the whole pitch very compelling. I was just imagining myself as a, as a CEO or enterprise buyer, just being like, I'm so, just send the contract. Send the contracts. I will say, like, I've been on a few sales calls with Brett now. And it's very flattering to hear this from Brett Taylor, right? Like, one of the best salesmen who's probably ever lived in software's history. We've had a few sales calls together.

It is magic in that route. Like, people get really freaking excited when we show them horizon. And it's like people start imagining what they're going to be doing for their business. All the awards they're going to get. The fact that in the board deck is going to be plus double to hit percentages at the end of the year. And that is very exciting for us as a company. It's very exciting. Amazing.

Thank you so like that. I can see why you guys did the deal. Great, great to hang, dude. We'll talk to you soon. Let me tell you about public investing for those who take it seriously. They've got stocks, options, bonds, crypto, treasuries, and more with great customer service. Mark Zuckerberg is in the Wall Street Journal Opinion section with a new piece. The AI future is for everyone.

He says the history of democracy and economics has proved that centralized power stifles human potential. And it's quite long. I'm going to go let you guys read it. But let's head into the comment section. Let's get a quick reaction. Let's get a quick reaction. This is the Wall Street Journal. I think it'll be pretty.

No, it looks relatively tame. It'll be tight. But yeah, making a, you know, a clear effort to position to be the overtly, there was a white space for a guy investing hundreds of billions of dollars a year in AI. that is like says hey this is going to be really great for everyone yeah and i'm going to help us get there it is it is interesting like facebook does have some monopolies but like the competition for attention is constant and there are always sources outside like they've never had a full monopoly on uh on social media even with ticot and snapchat and uh linkedin and twitch and

and YouTube and Netflix and the podcast feed and SMS and IMessage. Like there are so many other platforms for disseminating information. Like I don't know, I, it's hard to jump straight to a critique here. But the key quote that Andrew Curran pulled out was that he said, in most cases, like cybersecurity, the history of open source software has shown that giving everyone full access to powerful systems will be the best way to protect safe. and security over time. So he's firmly on the side of democratizing powerful AI,

and he is yet another one. I imagine that they signed the letter. I've lost track at this point, but you can imagine that he did. Anyway, thank you so much for tuning in. The other piece of news is that Apple is launching Apple upgrade next week, then iPhone, iPad, Mac, and Apple Watch leasing-subscription program. They said, you will own nothing and you will be happy.

We're launching our new program. You will own nothing and be happy. It's partnering with Klarna to launch in the United States at online and retail stores. It's now official leasing prices start as low as $20 or $1799 per month for iPhone, $1199 for Apple Watch, $2499 for Mac, and $1199 for iPad. So interesting. I mean, a lot of people, saying this is a direct reaction to increased prices for memory, increased prices for products.

There was a time when an iPhone was a couple hundred dollars and there were incentives to jump on a Verizon plan and you sort of amortize the cost over that. Those days are gone. Like we're in the world of like a $2,000 iPhone. It's a significant way. Same thing with our gongs. For people, honestly. You want a subscription gong? No, I'm just saying there was a time when a TBPN gong was $200. Now it's in the tens of thousands of dollars. It's actually so expensive. A friend of mine texted me and was like, where do we get the gongs? I need a gong. And I was like, I think you should start small. And this is not like you can't handle the big gong. I was more saying that like there is a joy to being on the hedonic treadmill of larger gongs. Like you don't want to jump straight to

the biggest gong. You want to start with a small gong. And work your way up. Work your way up because every gong that we've added has been so electric when we get the bigger gong. I think it's time for new one. You want an even bigger gong? Yeah, I want one that's hanging from the rafters. What a giant gong. Maybe. And also, every gong has a different flavor, different sound, you know, different amount.

You've got to warm them up, all sorts of things. We always warm up the gong. Anyways, folks, that's our show for today. Thank you so much for today. Enjoy the rest of your July 28th. Leave us five stars on Apple Podcasts on Spotify. money never sleeps you shouldn't either call me back sign for the newsletter at tbpn.com and we will see you tomorrow at 11 a.m. Pacific goodbye. Cheers.

Cheers.