B (1:08)

Little point reality style. If, if things get out of hand, we'll be banging the gong and bringing order like it's a gavel. But I'm sure, I'm sure this will be, I, I'm sure everyone will be civil.

A (1:19)

Oh, I'm sure.

B (1:20)

Keep the name calling to a minimum. Good to have you both. Thanks so much for being here. Let's kick it off with, start off.

A (1:26)

What'S your least favorite thing about the other person? I was gonna say we should kick.

C (1:32)

It off with the original story. Like how did this all start?

B (1:34)

Yes.

A (1:35)

Give us some backstory.

B (1:36)

Yeah.

C (1:37)

You wanna give it?

D (1:38)

Yep, I'm happy to. So we were back at Founders Fund. We were starting to beef up our CRM and data science efforts. And so we were integrating some external data into our CRM, figuring out how we could filter opportunities better to each of the investment team professionals. And we were looking and we were looking at the different data, I was like, oh, it'd be really nice if we could filter this by gross margin so that all of the negative gross margin companies that come into our CRM, we could give them all the Delian because it seems like those are the types of companies that he loves to invest in. The rivalry between the low gross margin side of the house and the high gross margin side of the House was born then.

B (2:24)

Okay, and Delian justify, why do you like these businesses? Is that even a fair characterization?

C (2:35)

Fair characterization? I think my sort of one liner would be I'm not sure that gross margin is actually the right thing to focus on in a business, especially early on. What you want to be thinking about is obviously EBITDA margin in particular terminal EBITDA margin. And so when I think about the like at least founders fund ethos to investing, we think that that terminal EBITDA margin mostly is determined by ultimately how much of a monopoly your company can be in the long term. And so if you look at sort of mag 7 today, obviously there's a decent chunk of them that have some phenomenal sort of gross margins and those tend to be the ones that are a little more software oriented. If you look at the one that is at the biggest scale and has the best EBITDA margins, it's the one that is the most basically hardware oriented for for sure some of it propped up by Cuda and they're sort of software side of the house. But Nvidia is the one that is performing the best of all those. And then even if you study within those which of those companies on the hardware side have monopolies versus not you see, it's the one that with the monopoly clearly outperformed the ones that don't. So Tesla obviously in that mag 7 but a part of why they suffer much worse margins than an Apple or an Nvidia is because they actually do have competition. And so my General characterization of SaaS is people always study their original gross margin but weren't burdening in the sort of cost of sales, marketing, et cetera. And because you just have much less of a monopoly typically in SaaS that ends up totally hurting your EBITDA margin profile. So take the favorite terminal scale. Thought of as a monopoly SaaS company that EV I'm sure loves Salesforce, their market share in all things CRM is 25%. And so that's why you end up seeing yeah, gross margin profile is only burdened by cloud sort of costs, but their EBITDA margin profile that is like 40%. And so you know, the reason that I like these negative gross margin businesses is yes, they're like tougher to start. They may be more equity intensive at the beginning, but end up with way better, you know, sort of terminal margin profiles versus you know, envelopes to, you know, invest in the, you know, slot codes that might have early gross margins in revenues.

B (4:31)

But so what's the bull case for for software? What's the bull case for SaaS? What's the bull case for AI slopos?

D (4:40)

Look. So to quote the Godfather Neil Mehta himself, the laws of great businesses are the laws of great businesses. The job of a business in a.

B (4:49)

Capital society is to maximize and find.

D (4:54)

The efficiency frontier for three things. Roic, AKA return on invested capital, the amount of capital you can actually deploy and how long you can deploy that amount of capital at and above market roic. There's a lot of different framings for the paths to do this and how companies can actually do this. The one that people like in tech circles is Hamilton Hemler's seven powers. A company accumulates power in the form of scale economies, network effects, whatever power you want to take, and then uses that power to produce above market roix for as long as possible and with as much capital invested in the business as possible. There are great Adams based businesses that do this. There are terrible atom based businesses that don't do this. There are great digital businesses that do this. There's terrible digital businesses that don't do this. I mean, you want to hear about great atoms based business that does this, Listen to the acquired pod on Costco. It's certainly not like a atoms versus SaaS thing necessarily. The advantage that digital businesses have is that in this process of producing above market ROIC for a long time is that their product form factor and the way that they distribute their product lends itself more to, to the process of creating power, I'd argue, than most atoms based businesses. So if you think about like network effects, the best place to create network effects is in a digital marketplace like an Uber and Airbnb or a Doordash. And so there's a lot of these forms of power that naturally lend themselves to digital products. And the scalability of digital products tends to be a lot greater than physical products. And so you can see these, these rapid growth trajectories like we're seeing from OpenAI and Anthropic and many others.

A (6:28)

When did you guys find common ground? Was it in the E Scooter era, the sort of 15 minute delivery era? Were you ever able to kind of come together and say like, yeah, we can both agree that this is not.

B (6:43)

It, we're good, we're good. I mean, to be fair, Kleiner Perkins Founders Fund have both invested in figma, Stripe, airbnb. There is some portfolio overlap.

E (6:52)

Rippling too, right?

B (6:53)

Rippling as well.

D (6:54)

Rippling.

B (6:54)

There's a couple Modern Health I believe as well, there's a few others but yeah, to Jordy's point, where else is the common ground and where, where else is the divide or the consensus in the disagreement is you say, you know.

C (7:07)

Everyone were texting before this of like, you know, what are sort of two companies that I think, you know, both of us were enthusiastic about in sort of 2021, that actually both have trended well, but are, you know, sort of counterpoints. Our two arguments and the ones that we kind of came up with were, you know, in 2021 I was really sort of, you know, high conviction on Hadrian. In 2021 was super high conviction on rippling. Both those investments have performed quite well over the last couple of years. But look, you know, sort of wildly different in terms of profile. You know, rippling, like many other sort of SaaS companies, does end up having, you know, an initial very high gross margin, but does still have to spend a lot on sales and marketing to bring in sort of net new customers. Hadrian on the flip side, deeply sort of negative gross margin to start, but now as they've gotten to scale, they actually have like super limited sort of sales and marketing spend kind of because there's only like 10, 15 customers that matter and the moment that you're delivering for them, they just proactively start throwing revenue at you. And so I think there are times where both of our stories obviously can play out. The thing that I'd be curious to hear from Everett is to actually compare contrast. You're bringing up some of these digital businesses that end up having these network effects. I would kind of argue that 2010s negative gross margin businesses, they're like Uber doordash types I think of as more as like, you know, Adams businesses. But there was a whole set of investors in like the mid 2010s that were generally unwilling to approach both Adams based businesses that started with negative gross margin. But even some of these local marketplaces that started with negative gross margin, that they swore off of, the ubers, the doordashes, etc. You know, it's very clear that Uber doordash through, you know, lots of investment through building out these local sort of networks of both supply and demand were able to, and your drivers were able to eventually get to a point where now they actually have very attractive sort of financial profiles today. The equivalent of that is like there's all these investors that, you know, back in the 2000 and tens would have refused to invest into any company that had negative gross margin and are all now pouring cash into both the like AI application layer. Companies and those like, you know, foundation models that all have like ridiculously, I mean, I forget, I think it's girly is non stop, you know, not my favorite person in the world, but girly is non stop talking about like, you know, what is going on here. They're selling a buck for 90 cents.

A (9:15)

And so I guess so I think, I think it's an important example because you had that plenty of examples of these chained losses during that era where a restaurant was selling something below cost to a platform that was selling something below cost to a logistics provider, an individual contractor that maybe wasn't actually making money if you factored in depreciation and fuel cost of their vehicle. And that ultimately worked out right. Doordash is a massive, fantastic business based on the power of the American consumer. But when you compare that to today, where a lot of the conversation on the timeline this week has been the margin profile of this new generation of software companies that has to pay a lot for sales and marketing, but also inference. And so I think like the debate should really be, you know, continue to be around just how quickly will the cost per token fall. And I think a lot of people have a lot of confidence around that. But I think that that is the key thing that Everett's sort of like broad investment thesis right now is dependent on.

B (10:25)

Yeah.

C (10:26)

Do you think there's going to be that same path of like Uber for a while had a bunch of negative gross margin people going into it. Like, do you actually think there's that.

B (10:32)

I want to pull this post up. Everett actually posted this in January 31st of 2024. So over 18 months ago he said, I'm making a real effort to not take for granted the $3 Uber across town era of AI. And I hope you. And so I, I guess the question is, and it's funny because, because then, then a bunch of people, I thought it was a good point, I thought it was a hot take then. And I think then, you know, a bunch of people kind of parroted that take all over the timeline. Stole your whole flow, as you like to say. But, but, but, but I guess the question is like are we in some sort of different regime right now where the, the traditional gravity and like fundamentals of software investing have changed because we are out of the zero marginal cost era and does that impose risks to the strategy that you've sort of employed or like we're kind of putting you in this box. But if the fundamental structure of zero marginal cost era is going away, that presumably forces like a rewrite of your logic around investing, I would imagine. Yeah.

D (11:38)

I think that the biggest variable that's changed from the 2010s Sass era to today is that in the 2010s. And you basically made this. This point without making it. Delian, though, is that the thing that was missing from your talk track is that the competitive intensity of SaaS during the 2010s was much, much, much lower than it is today. Like, during the 2010s, there was an entire crop of companies in the 2000s, but then, especially in the 2010s, you could basically pick either a vertical segment like H Vac or car dealerships, or you could do a horizontal function like the CRM or, you know, some very niche workflow for, like, the finance team. You could build a software product around that workflow, around that vertical. And you really only had to deal with typically like two to three competitors. Like, there really wasn't that much competition relative to what there is today. And there was less, just. Just like general pricing pressure, competitive pressure, just the general pressure that you actually had a lot with with some of the digital marketplaces early on. I think there was a whole crop of investors then, and the SaaS investors then were like, well, we don't need a bunch of cash burn. And it's a really unhealthy indicator if these SaaS companies are producing a bunch of burn because they're not competing with anybody. So if they can't sell their product for good unit economics on day one, when the competitive intensity isn't very high, then they're probably not a very good business. I think the thing that's changed now is, is one, you have the change from zero marginal cost to actual meaningful marginal costs in the form of inference. And it's also just a hell of a lot more competitive than it used to be. And so you are. And by the way, there's an Immense, probably 10x more capital than there was 15 years ago to go into these companies. And so, like, every single category now has become like mini, like mini rideshare or like mini uber market, where it's like, hey, there's probably a really big pot of gold at the end of the tunnel and we need to be the ones that get first to scale. And in a lot of these categories, the ones that have gotten first to scale have gotten a lot of brand equity out of it and have gotten a pretty resounding lead. I think the. The only other piece, I would say I lost my train of thought. So.

B (13:45)

Yeah, yeah, but it's going to be basically, it's going to be like a capital fight. Now on the, on the SAS side, I wonder if, if, if the contrarian trade around hard tech is, is, is entering a similar era where it's become consensus and so we're gonna see more capital fights. And when a founder goes out and says, yeah, I'm gonna do something crazy, but I need to spend a billion dollars of capex, people are just like, yeah, this could be the next space.

A (14:09)

Sure, it made sense to have a capital war and rideshare, but now we have a capital war in like this niche agentic workflow in some industry that most people have never heard of.

B (14:18)

And then also a capital war.

A (14:19)

Here's $200 million funding for military boats.

B (14:22)

And UAS and UAP. Like all these different subsegments are going to wind up if capital wars start popping up there. That could potentially be a headwind to Delian's model. Is that roughly correct? How would you fight back against that?

C (14:36)

Look, I think it's always important to talk about specifics here, right? One of major investments in the last year is this company called Captions that basically does AI captioning of various videos on social media. When I think about handing two Stanford grads and $100 million to go try and replicate that, yeah, feels like they could go do something like that. There's clear voice recognition models, they can go pay on, ads on TikTok, et cetera and you could probably go and replicate that. And so our one liner at Founders Fund is competition is for losers. And so I think I was a loser for investing.

B (15:15)

The shower shots was fire was spicy enough and you just delivered deli. So thank you.

C (15:20)

Now if you take sort of two Stanford grads in $200 million and tell them, hey, I need you to go replicate this manufacturing facility and go start building a bunch of, you know, sort of satellites, reentry vehicles, you know, bioreactors that can actually survive the environment of space. Most, you know, sort of Stanford grads, you know, can't go, you know, ask about how to go do that.

B (15:39)

And yet, yeah, yeah, I haven't really.

C (15:42)

Faced significant competition irrespective of the fact that, you know, all things space factories are thought to be, you know, sort of the hot new thing.

B (15:48)

To be clear, we use captions here on clips. We enjoy the Captions app. We thank EV for making it possible and subsidizing our.

A (15:56)

And there is a yc, there is a Varda esque YC company. So they're coming for you.

C (16:01)

Indian Varda, I think will be a little bit less competitive than, you know, sort of Indian Captions. Also, if you're the caption CEO and you know, Founders Fund is trying to invest in your next round, please still let us do that for a helpful.

D (16:12)

Counterpoint for me, Delian, you were, you were correct that it was getting, it was getting too friendly of a debate. I did want to make sure I could pin this one on you. If you can recite the equation for a return on invested capital, I will victory to you. And I will donate $5,000 to a charity of your choice.

A (16:32)

Hopefully he's got Cluly running. Exactly.

C (16:36)

My like, you know, equivalent for Everett will be if you can explain, you know, basically why you can't create microgravity down here on Earth, I will also donate $5,000 to a charity of your choice. But I don't think you have it. You know, I may not have the basic understanding of business physics, but you.

E (16:48)

Don'T have basic understanding of physics and.

C (16:49)

One'S more important about understanding the universe around you.

B (16:51)

Okay.

A (16:52)

I mean, I'm pretty fixated on the 2035 Midas list. That's really kind of the final.

B (16:59)

The bigger.

A (16:59)

That's this.

C (17:00)

Have you been on Midas brink yet or. I forget whether or not you've made it up there.

B (17:03)

Oh, yes. Not even on the brink yet.

C (17:07)

You know, rejoins, you know, KP after.

B (17:09)

You and she beats you laughing me. It's okay. It's okay. Eventually we're going to, we're going to bring back the extra names and Kleiner Perkins. It used to be Kleiner Perkins, Caulfield Byers. It's going to be Kleiner Perkins, Randall Braswell. Eventually, once we're working on it, we're working on it, we're pitching it. Where, where should we go next?

A (17:26)

Jordy, I guess, Everett, how are you? How quickly, like how much should people be fixated on the cost per token with these frontier models over the next six months? Like, how long can. Can venture capital sort of like backstop these chained losses?

D (17:49)

Yeah, I think that the way to delineate the whole. So obviously, I think there was this kind of consensus narrative that every 12, 18 months, token costs were going down in order of magnitude. And I think that did hold for a while. I think what you've seen now is actually for frontier models that started to peter out a bit and pricing has actually started. It's still going down. It's not going down nearly as much as it. As it used to when, like when we were kind of in the, in the, like the meat of the curve of capability improvements on Frontier LLMs in terms of pricing curve. So I think that the way that you want to delineate it is like there is a certain, like what I always tell everyone is that like there hasn't been a ChatGPT query since GPT4 that like my mom hasn't been able to ask and have it answered by the model. So there's like the mom test of models where like there is a growing subset of tasks like economic or knowledge tasks that the models are tasked to do that no longer need frontier intelligence. And when you're not on the frontier, either through open source or just the, like the cheapening and distilling of, of older models, like the price still falls off a cliff. Sure, there's going to be a very, very large set of tasks that models do that are not on the frontier and those are going to continue to get dirt cheap. I actually think that at the frontier you're probably going to see continued price decreases on a per token basis, but nowhere near what you saw before, which was like this, this order of magnitude decrease on a very regular cadence. And so I think, I think for like depending on the company, it's going to depend on one, if you've actually built a company that has enough power where you have pricing power, where you can price above the kind of marginal token price from the actual model providers and then to like, how much of your inference actually needs to be at the frontier? Like how much of your inference can be an older model that's much, much cheaper versus how much do you need to do on the actual frontier? I think that's what you're seeing. Like, you know, everyone loves to talk about Cursor. And Chris Pakett over at Pace Capital had this really great kind of like mini essay, I think only like last night or a couple of nights ago. And he talked about no one knows if Cursor has power yet because, you know, coders and developers, they're very, very like they're tastemakers. They're very good at understanding the quality of the models and how much inference they're getting. And there's a lot of price sensitivity for them because they have a really good understanding of how much inference they're getting. And so no one really knows. I think no one can definitively say whether a lot of those types of companies have actual power with their users or if they're just drawn to an interface for frontier models or not. And so I think that's what everyone needs to be looking out for is those two things, like do you actually have power? Like will people give you margin above the marginal cost of tokens? And, and then two, do we even need the frontier inference for the vast majority of your product or is there a lot that you can offload to cheaper models?

C (20:29)

Yeah, I mean I guess your counter there Everett, is that a majority of what the foundation models are providing in terms of value to their end users is starting to be sort of obviated by the historical generation. Even some of the ones that are sort of open source so seem to imply that where value is accruing and where you'd expect the highest revenue growth wouldn't necessarily be at the foundation layer, but you'd see it more at the application layer since those folks can swap models out. But like in reality that's like literally just not what actually is happening. Like if you look at which companies are you know, sort of fastest on revenue growth, user growth, etc. It is the foundation model companies, it seems like a part of it is that they also have you know, sort of the most pricing power where yes, you know, your mom, you know, uses GPT4 but like she's not the one that's necessarily paying like you know, 101,000, $10,000 per month versus the true frontier capabilities on like you know, AI coding, the pro users, the one that actually do care about, you know, maybe your mom is fine with 115 IQ model and that's like fine for the rest of her life because she's just like not asking it that difficult of questions versus the people that actually are willing to pay are the ones that actually do care about the 140, 160, 180 IQ. Again maybe at some point that gets commoditized as well. But my sort of counter to you would be you've made this argument that seems to imply hey, you know, things will accrue to the application layer which if I understand your guys portfolio is largely where you guys invested. But in reality that's not what's played out. The like places that have captured the most revenue growth, the most market share have been the ones that are actually, actually pushing the true frontier, you know, of the technology forward. And so so far, at least in the last 18 months, your thesis is not playing out at all well to.

A (22:03)

Be clear, isn't it somewhat widely understood that anthropic has negative gross margins as well? So it's not like they're doing like.

C (22:13)

EV's point was that you want to Invest in these companies that have the, you know, sort of seven powers and like, you know, in the, you know, days of like Uber, you know, doordash, et cetera, that did end up using translating.

B (22:21)

It's seems like Nvidia has the most. They're basically application model labs. Maybe then the application layer, we'll see how much power develops in the application layer. But ev, we'll let you respond.

D (22:31)

Oh yeah, I was going to say that, that basically what Delian said was just wrong. Because even though it is, even though.

B (22:36)

Like if you, if you, if you.

D (22:38)

Think about, okay, like let's take like whatever OpenAI and Anthropic's recently reported revenue run rate is the majority of all of that, or at least the plurality of all of that is chatgpt. And chatgpt, even though it is served by a foundation model company, is an application. It is a consumer subscription that has an immense amount of power. It has an immense amount of branding. Like, you know, it is the only. It is like the first billion plus user consumer application that's been developed by a new company in a really long time. And so I think that like you could put whatever models you wanted through ChatGPT at this point and it would not knock it off of its perch. I think that is power. Like you could run Cloud 3 Sonnet through ChatGPT and I guarantee people like the average user wouldn't actually know the difference. And that to me is power. And just because the foundation model companies are producing apps themselves doesn't mean that it's not the application layer that is accruing the value.

C (23:29)

Okay, then my question is, you've got OpenAI with the best possible consumer application layer. You've got Anthropic that shifted over to positive gross margins and those margins are expanding. And yet Kleiner is not investing into either of those foundation model companies.

D (23:41)

Why I cannot comment on our current investment activities.

A (23:47)

Okay, switching gears.

C (23:52)

Do you like making money or do.

B (23:53)

You like, you know, can you comment on Donald Boat? Have either of you bought anything for Donald Boat? The notorious e beggar on x.com the.

C (24:04)

Everything app like my little brother, you know, you know, played the UNO reverse card and tried to get Donald Boat to buy him something smart. You know, contrarian as Bro Hovna, let's.

A (24:13)

Talk about revenue quality because I think that you guys run into this in your respective domains every single day. Just like in AI, you can have low quality revenue. Like that might be the explo explosion of like consumer prompt to app activity, you know, might not be the highest quality re revenue. Meanwhile on the hard tech side, if somebody gets like a random like sibber or like experimental gets like experimental budget from some branch of the military and it's like a fixed length contract, it's not necessarily the right strategy to slap like a 50x revenue multiple on it. So what's your view on both of those? And then I want to talk about if we should get into if accounting rules even matter at this point.

B (25:01)

Yeah, yeah, for sure. Yeah.

C (25:04)

I mean in hardware land we think about this all the time of like there's clear differences in quality of revenue. Everything from like defense program of record. You have to value that very differently than even like a $50 million SBIR. And so it has been interesting to see a bunch of investors coming into this field where I think there's a lot of pre existing 10 years of rules around software of what healthy revenue looks like. Rule 40. There's all these things that even if you're somewhat unsophisticated Infinite blog post when you look at that in the world of hardware and defense sort of investing or aerospace, there aren't infinite blog posts for people to study. And so I admit that I'm sometimes amazed when I watch people come in even for I should never sort of trash my own portfolio. But sometimes even my own portfolio companies, I watch people invest into them and I'm like wow, like you just have a deep underappreciation for just like how long this company has until gross margin flips like positive how long it's going to be until they're actually sort of ready to go scale revenue. Even if it on the back end it might be attractive, it may be years and years for them to sort of get there. And so yeah, I see huge variation on that. And then mostly what I end up sort of seeing is people just come in and like slap a 10 to. I even saw 100x rev rate multiple on this like hardware company recently and I was like holy shit.

E (26:13)

Wow.

C (26:14)

People like not having IRR for a long time.

D (26:18)

Yeah, I think so. Delian's hero and close mentor Bill Gurley had an essay a long time ago called the 10X Revenue Club. And I think it's like a good abstraction for kind of like tech revenue quality and like what makes up revenue quality. And it's things like how durable is the revenue if you sign a customer, are they going to stay for a year, 20 years, how much contribution profit is going to come off of that revenue stream over time? All the basics. I think you can take those Same building blocks and apply it to AI. I think there's several things that are worse for AI, at least relative to SaaS. For now, generally gross margins are lower, which means contribution profit coming off is lower. I actually think that depending on the category, you could have customers that are more sticky or less sticky. I know the meme is that everything's experimental run rate and none of these customers are actually sticky. I think we see something very, very different among our group of portfolio companies. I think the biggest lever that didn't exist in SaaS, that exists in AI, that could be a huge call option boon for the revenue quality of AI is the actual contract sizes as people start to eat into potential labor budgets. I know this is still kind of like inning one and inning two and it's also a little bit of a meme where everyone's like, oh, it's going to replace labor and Labor's 10 times SaaS and it hasn't really happened yet. But I think if you look at some of these coding tools and you look at something like cloud code, that is the first place where you can really actually say like no, this is replacing the labor that a developer would do and it is paid for on like a metered consumption basis. And the monetization numbers we're hearing around developers using cloud code are pretty crazy in terms of like, wow, that's like you're paying like one tenth of like a developer's full in cost to a company on an annualized basis for this product. And so I think that the like the thing to watch is like durability of revenue plus the amount of actual revenue that a customer can give you. And I think that you're going to end up the amount of gross profit that a customer can contribute over time. And I do think as some customers crack these agentic products that look and monetize More like labor, AI revenue could actually exceed the quality of SaaS revenue just because you're getting so much more gross profit per customer or like customer relationship than you would on the SaaS side. Even though there are clearly things that are worse about AI revenue at this current point in time than there are about SaaS revenue.

B (28:27)

Dillian, how do you think about the moral imperative of a venture capitalist to invest in positive sum versus zero sum markets. This idea that you're re industrializing America, you're saving the west versus moving you personally, you personally versus moving chips around the poker table, taking, taking from some legacy, you know, Web 1.0 company and putting it into an AI company. What's your thinking and argument There is. Is. Is a market beating ROIC all that you need?

C (29:03)

Yeah, I, you know, I think Peter always reminds us like, you know, our number one job is deliver returns for user LPs. And so I actually tend to not try to, you know, sort of overly moralize when like analyzing the things that I want to invest, you know, sort of invest into for sure when it comes into like policy and I'm in D.C. and I like need to, you know, sort of report to, you know, the Security Council that, you know, Bill Gurley is a, you know, sort of Chinese spy and like the investments that he's making should probably be banned from the United States. Yeah, for sure there. I have, you know, sort of moral imperatives and things that influence that may end up, you know, shifting roic. Right. So, you know, but when it comes to, you know, like, which literal investments are we making? I think of it as just like, yeah, you just have to, you know, sort of make the best possible investments irrespective of sort of moral imperatives. But in some ways I tend to think it turns out actually if you go too immoral, then that ends up affecting roic. So the last thing that I at least close on for my question for Everett is one of the upsides of Founders Fund is we're very, let's say, non centralized distributed, not many rules which ever for some reason chose to leave. And so I know nowadays everything that he says publicly, you know, probably, you know, five comms people and five compliance people that need to, you know, sort of approve it. And so my only request to you is, you know, so blink twice if somebody's, you know, got a gun behind the camera threatening to shoot you if you ever say anything that, you know, op script. That's all you got to tell us, brother. Yeah, let us know.

D (30:31)

Hey, our wonderful marketing partner Ali is behind the camera with a green and red paddle and she hasn't raised the red panel yet.

B (30:38)

So that's, that's great. Well, thank you both for that last question.

A (30:43)

Are you worried about Uncle Sam potentially having sharp elbows now that we're hearing about Intel?

B (30:48)

Yeah.

A (30:48)

The federal government taking a stake in Intel, Any concerns about him going down the stack into the early stage game competing for those seed and series A allocations?

C (30:59)

Look, if Trump Capital wants you sort of mark up some of the re industrialization companies, I'm all for it, baby.

F (31:05)

Cheap cost of capital, you're all for it.

D (31:07)

I'll say two things. I would say one, I think that the EV of like the enterprise value of founders fund probably 3x the night that Trump got elected. So I don't, I don't think Dalian would complain about that. And then as a parting gift, Dalian, you know, I think this conversation's been great and it's made me realize why you want to build factories in space because your math on earth doesn't make any sense.

B (31:30)

Well, thank you both for joining this stuff.

A (31:32)

You're both good sports. We'll have to do this again.

B (31:35)

I think it might be a draw. We'll have to have you both back soon. Thanks so much for hopping on.

A (31:39)

Great stuff.

B (31:40)

We'll see you guys later.

A (31:41)

Cheers.

B (31:41)

Let me tell you about ramp.com Time is Money save both easy use corporate cards, bill payment accounting and a whole lot more. Do accounting rules matter? Yes, they do.

A (31:50)

Yes, they do.

B (31:50)

You can, especially on rules on ramp.com all in one place.

A (31:54)

Go to ramp.com that was, that was beautiful. To two former colleagues barely holding back from saying things that they would ultimately regret. But they did. They did a good job. They.

B (32:06)

Yeah, I like the debate. We should elegant dance. I think that was a lot of fun. I think the chat enjoyed it. My favorite rude comment in here. Oh, we got Andrew Reed in the chat. Everett vs. Dalian, who can grow the most average beard. Oh, my God. Thank you for watching Andrew. Let us know when you've selected an opponent and we'll have you on the show to debate someone.

A (32:32)

Yeah, I think we need it. We need to get the Holy Trinity.

B (32:34)

Yes. Yes. If you're, if you're new to tvpn, the Holy Trinity is of course, the three venture capital firms that have done a seed deal in a now hyperscaler or now Mag 7 company. So that is Sequoia Capital, Founders Fund with Meta, originally Facebook and Kleiner Perkins, of course. And so the Holy Trinity are the three most storied venture capital firms in the valley, much like the three famous watch brands. The Holy Trinity, Vacheron Constantin, Patek Philippe and Audemars Piguet. Of course, over in Switzerland, if you enjoyed this stream and you want to make your own stream, get on restream one livestream, 30 plus destinations, multi stream and reach your audience wherever they are. If you're the backbone of your company and you're doing a launch, streaming is the way to do it.

A (33:23)

You don't have to try to poach Ben or anyone else on the team. You can just go to Restream.

B (33:27)

It's fantastic.

A (33:27)

Check it out.

B (33:28)

Anyway, going back to the Death Star, the vague post. We're one week out from GPT5. How has your GPT5 experience been? Also, Tyler, the chat wanted you to answer. Explain the formula for roic. The formula.

A (33:49)

So, yeah, I think it's. I believe it's operating income divided by book value of invested capital.

B (33:55)

Right. Oh, you got it clearly is running clearly. That's off the dome. That's off the dome. I did not just look it up. And then, and then what did. What did. What was Delian's rebuttal?

A (34:05)

He wanted why you can't achieve microgravity.

E (34:07)

Explain that.

B (34:08)

Why can't you achieve microgravity on Earth? Do you know that? You're a physicist, you studied physics. You should get this.

A (34:16)

I'll have to get back to you.

B (34:17)

Let me think about that.

A (34:18)

Let me think about that.

B (34:20)

I think it's just that. Wait, I actually, I can't really explain it. That's kind of hard. I mean, I know that it's like Earth has a gravitational field and we don't have the technology to reverse gravity. Gravity. But I can't really tell you why we don't have the technology to create an anti gravity chamber on Earth. Like, I can't walk through the physics for that. I just know that you can't do it on Earth, but mostly because we've.

A (34:39)

Been trying to get a gong in microgravity here on Earth. I think it's challenging.

B (34:43)

You can do it for like a.

A (34:44)

Very short amount of time.

C (34:45)

Right.

A (34:45)

It's like when you see. Ever see the planes?

B (34:47)

Yeah, that's not that, that's not actually microgravity. That's just falling. Right. That's just falling in a pressurized capsule. Like you're still under the. Because you are. You are literally falling down to Earth during that. It just. Your surrounding environment is pressurized and so it feels like you're floating, but in fact you're. You're really just falling closer to Earth. Like when the plane goes down, you are. You are descent. Descending. There's no, there's no machine on Earth that will effectively like levitate something and reduce the force of gravity to zero on Earth or even, or even reduce it significantly.

A (35:21)

Yeah, but it's like, that's. The whole thing is like from the observer, it's the same.

B (35:25)

Yeah, but for, for space manufacturing, for like growing a crystal, the reason that would not. You can't do it on Earth is because it's too.

A (35:31)

Like it's not long enough.

B (35:32)

No, no, no, no. I think that. I think that even if you, Even if you tried to do something in that, in that plane scenario. Like you're still subject to the force of gravity even though you're. Because you're effectively falling even though you're not. Even though you're not feeling like the relative, the like the wind speed, you still are under the force of gravity. I don't know. We'll have to figure it out. We'll have to deal on back and explain it to us anyway. What's your. What's your one week. One week review of GPT5? What's your takeaway, Jordy?

A (36:08)

I've been, I've been. It's been fine. It hasn't been that drastic. I still find myself navigating between switcher models. Using the switcher.

B (36:17)

Have you turned on the legacy models? So if you go.

A (36:22)

I haven't gone into the hidden.

B (36:24)

No. So Tyler, you have though explain.

A (36:27)

Yeah. So if you just go into settings, you can turn on. Well, so all I have in Legacy models is 4o.

B (36:31)

So. So 4o came back as a drop down. But you can go into the settings and turn on legacy models and then you can access 4.5. Right.

A (36:40)

So 4.5 still and 4.103.

B (36:43)

Oh, so you can access them all. But it's tucked behind even more Menu. I think that the Ben Thompson take was that they are, that they are not being bold enough as a consumer company and telling people, you know, the Henry Ford thing, if I asked people what they wanted, they would have said a faster horse. You know, I gave them one color of car black. I didn't ask them for input on that. Steve Jobs did the same thing famously with Apple. Made a bunch of bold product decisions and then just said consumers. I don't, I'm not taking input. You want, you want a headphone jack, Too bad. I'm taking it away. You want an extra port on your MacBook, you want. What was the thing they took away. They took away all the ports for a while. They had no ports for a while. It was just USB C ports on the edge. And so Facebook's been similar with the, with the removal of the original feed and then the, and then they moved away from a chronological feed to an algorithmic feed. And there was a lot of pushback for that. But Mark Zuckerberg channeled a mentor of his, Steve Jobs, and said, you know, I know that this is better for the long term. I know that this is better for everyone in the long term and that people will ultimately love this.

A (37:50)

And I think that's probably, I mean the main thing is it's very interesting that they. This was reported by Alex Heath and the Verge last night. Apparently he got dinner with Sam Altman, I think, and Greg as well, or some other executives. And they said last night, about an hour before the dinner started, OpenAI pushed an update to bring back the quote, unquote warmth of 4o, which is what the Reddit, the Redditors of the world, the AI is my boyfriend enthusiasts were clamoring for. So it's interesting to see how quickly they folded there, I think. Yeah, clearly they made users distraught. I also think, I imagine a lot of those users were paying the top tier subscription. And again, it's hard to read too much into Reddit or really anything you see online, but a lot of people were canceling or threatening to cancel if they didn't bring that functionality back. So anyways, Sam, I mean, I would.

B (38:51)

Expect pulling forward constant changes and iterations. Just like the YouTube algorithm is constantly changing, the X algorithm is constantly changing. These updates get pushed very incrementally. There's constantly tweaks that are happening.

A (39:05)

So Sam is quoted saying, I think we totally screwed up some things on the rollout. On the other hand, our Apex API Traffic doubled in 48 hours and is growing. We're out of GPUs. ChatGPT has been hitting a new high of users every day. A lot of users really do love the model switcher. I think we've learned a lesson about what it means to upgrade a product for hundreds of millions of people in one day.

B (39:24)

Yeah, it's always tough.

A (39:25)

He pegged the percentage of ChatGPT users who have unhealthy relationships with the product at way under 1%.

B (39:32)

I agree with that. That sounds right.

A (39:33)

But acknowledge that OpenAI employees are having a lot of meetings about the topic. Quote, there are people who actually felt like they had a relationship with Chat cbt. And again in this, in this article, this post that we read yesterday on the show that he posted eight years ago called the Merge, talking about the inevitable point that humans and machines merge. He said the merge can take a lot of forms. We could plug electrodes into our brains, or we could all just become really close friends with a chat bott. So he was aware. You know, credit to Sam for calling this one pretty much perfectly because clearly, you know, it's. It's millions and millions and millions. You know, even if it's way under 1% and this is, you know, tens of millions.

B (40:21)

Right? At least hundreds of millions of daily users. Yeah, probably like under a million. So hundreds of thousands of people. That's a lot that could have.

A (40:30)

Yeah, okay, way under 1%. So millions of people.

B (40:33)

Maybe we're not quite at a billion active users. So. And then, and then a lot of those user international in terms of like the, I mean, not that that really matters. You want to be keeping everyone healthy. But yeah, we're talking about like hundreds of thousands of people that are potentially negatively affected. So they got to drive that to, you know, 0.1% and then 0.01% and then, you know, get as close to zero as possible. There's go. There's always going to be some people, people that use. They read the newspaper and they go crazy. But the more that you can do, the better.

A (41:03)

Sam says you will definitely see some companies go make Japanese anime bots because they think they've identified something here that works. You will not see us do that. We will continue to work hard at making a useful app and we will try to let users use it the way they want, but not so much that people who have really fragile mental states get exploited accidentally.

B (41:22)

Well, as they continue to iterate on the product they have to use. Figma.com Think bigger, build faster. Figma helps design and development teams build great products together.

A (41:31)

Go make something up.

B (41:32)

We have a question from the chat for Ben. Ben Kohler producer, was recently followed by Reid Hoffman. And the question is, did Ben get any Hoffman chat? Did he slide in the DMs or did he just follow you for updates?

A (41:44)

I think just for updates.

B (41:46)

Just for updates. If you're not following Ben, you got to follow him. He posts constantly. He posts constantly during out throughout the show. When big things happen, when crazy stuff's happening on the stream. He's kind of like the premium feed. Like, you know, we put a lot of stuff on the main account. Ben's the behind the scenes guy, so go follow him. Yeah, the whole. So my, my final takeaway from the Death Star vague post is that, is that there was this viral image when in the lead up to the GPT4 launch. Is this data visualization? We can pull it up as the first slide in the deck. It was visualizing the number of parameters in the model and this went very viral multiple times. So it was GPT3 had 175 billion parameters and GPT4 had 100 trillion parameters. And so you see the small dot and then the huge circle. And a lot of people were afraid by this and it was kind of this indication of exponential takeoff. And we really did see a qualitative improvement in just Scaling up the pre training run from GPT 3 to GPT 4. But GPT 4.5 taught us that pre training scale is in fact not all you need. And the way to make a great AI product in the modern era is a mixture of techniques, experts and researchers. You need a whole host of things. Particularly with GPT5, it feels like they are old on a lot of different problems. And so to me the Death Star post represents an even bigger circle from that GPT4 circle. So the Death Star is the biggest possible circle and it's, and it's an expansion of that GPT3, GPT4, GPT5.

A (43:28)

But couldn't you read this? That GPT5 is the Earth?

B (43:32)

No, no. GPT5 is the Death Star is the perception of GPT5 as just being a bigger model is the Death Star. OpenAI blew that up. They blew up the metaphorical big circle with a model that isn't just bigger. Semianalysis called said the release is the router, the router is the release. And what that means is that the, the gain in the value delivered by this product is not just a bigger circle. It's, it's a more complex coordination. It's, it's, it's all the different X wings working together in tandem. And then the Millennium Falcon comes in and saves the day. That's the, that's the, that's, that's when you know the, the model router acts like, triggers a reasoning step and it thinks for a long time. The Death Star is the, is the end of the pre training scaling law.

A (44:26)

Well, let's get into some more coverage here from Alex Heath. Sam says you should expect OpenAI to spend trillions of dollars on data center construction in the not very distant future. He confidently told the room. We have to make these horrible trade offs. Right now we have better models and we just can't offer them because we don't have the capacity. We have other kinds of new products and services we'd love to offer. So obviously agent making that more widely available I think is what he's alluding to. He also thinks we're in an AI bubble. When bubbles happen, smart people get overexcited about a kernel of truth. If you look at most of the bubbles in history, like the tech bubble, there was a real thing. Tech was really important, but the Internet was a really big deal. People got overexcited. Are we in the phase where investors as a whole are overexcited about AI? My opinion is yes. Is AI the most important thing to happen In a very long time. My opinion is also. Yes, yes. So he obviously contributed to this excitement.

B (45:27)

In a sort of.

A (45:29)

Yeah, I would say that he, he.

B (45:31)

Didn'T invest in very many competitors. And if there's a power law here, it could play out like the social media bubble where there was an immense amount of excitement around. Facebook cracked it. It's on the way to be a trillion dollar company. And there was a belief for a while that it would be oligopolistic and Twitter and Foursquare and Pinterest and Snapchat would also be trillion dollar companies, but that didn't happen. And if you invest.

A (45:57)

I'm just saying, I'm just saying companies.

B (45:59)

At unicorn valuations, you have not seen fantastic return on invested capital as opposed to.

A (46:05)

I just think it's fair, fair to say that Sam helped get people overexcited and that in many ways he was saying, you know, with, with, with GPT6, we might be, you know, discovering novel physics and curing. What.

B (46:19)

Who's the we there? Is the we OpenAI or is the we every company that's raising in the Valley right now?

A (46:28)

It's an important distinction, speaking for OpenAI. But I think people are going to naturally.

B (46:33)

Yep, totally. No, I agree.

A (46:35)

Take that as AI as an industry, broadly in terms of its potential. You know, so people might start thinking, yeah, maybe the 20th best LLM has a good shot at curing cancer.

B (46:49)

Yeah. But the 20th best social network was not worth 1 20th of Facebook. It was worth 12000 of Facebook or 1 20,000th of Facebook. That's the nature of these power laws. But my take is that, so the release being the router and this shift towards OpenAI potentially shifting into dominating agentic commerce, having a monetizable free tier. This is actually a bull case for super intelligence. A lot of people on the timeline were like, oh, GPT5 was, was supposed to be like, you know, an order of magnitude gain. Something really qualitative like you use it and just feels different. It just 100% on all the benchmarks, whatever. It wasn't that. It felt very incremental and a lot of people were kind of, you know, we're plateauing all of that. But I think that, I think that shifting to a, shifting to a freemium model, a monetized free tier, is actually a bull case for building the trillion dollar cluster. And my thinking goes like this. So you can, you can build the first GPT2, GPT3 cluster with nonprofit donations. Like $100 million gets it done. And that advance them to that stage. But to do the GPT4 training run, they could not marshal the capital in the nonprofit space. They had to become the for profit. They had to get venture dollars in. And yeah, and so basically like the shoggoth demanded capitalism, this is the nickel. And take that artificial intelligence was sent back from the future to, to invent capitalism. Have you heard this take? It's great. And so the idea is, is you know like you could not get to GPT4, GPT5 without a for profit company with the promise of return on investment pulled in all the venture dollars. The question is to build a trillion dollar cluster. I think Masa is going to be tapped out soon. I think Masa is a card you can play once. I think that there is a limit to how much capital you can marshal in the private markets, even in the public markets. I just think it's impossible to raise a trillion dollars necessarily. And that cluster must be funded by free cash flow. It must be funded, it must be underwritten by a company that can justify a return on investment from their direct product. And so we're seeing this right now with Google and Facebook and yeah, look at investing with their free cash flow. Yeah, they're investing their free cash flow. Yeah, they're doing some, some, some debt. But I think, I think to get to the really, really big numbers, the trillion dollar cluster, it's going to have to be built on just continual free cash flow investment from a company. Tyler, what do you got?

A (49:27)

Tyler, what do you think about like situational awareness, like nationalizing labs? You think governments can.

B (49:34)

Well we're about to nationalize intel so maybe that's a path down the road. I don't know. I don't think it's on the horizon anytime soon. Mostly everybody, we're just not seeing capabilities that would threaten. It comes down.

A (49:51)

It's actually insane. So a week ago when that reporting from the Journal on Leopold, situational awareness. Everyone is just dragging him, dragging him, dragging him being why is he long intel? His fund is, his fund is probably blown up already. It's up 21% in the last five days.

B (50:09)

Wow. The gong for Leopold Aschenbrenner and situational awareness. Yeah.

A (50:19)

Anyways, there's some more, there's some more interesting stuff in here.

B (50:23)

I don't see it happening until the labs pose a threat to the US government in some way and are so dominant. I don't think we're at that phase.

A (50:33)

We're getting into the danger zone.

B (50:35)

We're in like new Google territory. It's a dominant consumer app? I don't think so.

A (50:39)

There's some more interesting reporting here from Alex. He says Sam confirmed recent reports that OpenAI is planning to fund a brain computer interface startup to rival neuralink. I think that neural interfaces are cool ideas to explore, says Sam. I would like to be able to think something and have ChatGPT respond to it. And of course, I think it was the Financial Times was reporting that Sam Altman would be a co founder of this company. Merge Does Fiji Simo joining OpenAI to run applications imply there will be other standalone apps besides ChatGPT? Sam Altman says yes, you should expect that from us. He hinted at his social media ambitions. Quote, I am interested in whether or not it is possible to build a much cooler kind of social experience with AI. He also said if Chrome is really going to sell, we should take a look at it. Alex says, well, Altman has a lot of interest. It's not clear, it's not actually clear that running OpenAI over the long run is one of them. Sam says, I'm not a naturally well suited person to be a public company CEO. He said at one point, can you imagine me on an earnings call? Alex then asked if he would be CEO in a few years. Sam says, I mean, maybe, maybe an AI is in three years. That's a long time.

B (51:57)

I love it. It's great. Some other I love Vanta Automate Compliance Manage risk Improve trust Continuously Vanta's trust management platform takes the manual work out of your security and compliance process and replaces it with continuous automation, whether you're pursuing your first framework or managing a complex program.

A (52:12)

So a few more points in here. Altman had notes on making GPT5. We had this big GPU crunch. We could go make another giant model. We could make that and a lot of people would want to use it and we would disappoint them. And so we said let's make a really smart, really useful model, but also let's try to optimize for inference costs. And I think we did a great job with that. Obviously we're clearly wanting to be more competitive with Claude in Anthropic's API business. On the AI device with Jony, I've Sam said it's going to take us a while, but I think you'll think it is very worth the wait. I think it is incredible. You don't get a new computing paradigm very often. There have been like only two in the last 50 years, so just let yourself be happy and surprised.

B (52:59)

Only two?

A (53:00)

What new computing paradigms oh yeah, yeah. He says, so just let yourself be.

B (53:04)

Happy and surprised in the last 50 years.

A (53:07)

He says in the last 50 years.

B (53:09)

PC era, mobile, cloud. I guess mobile and cloud are tied together.

A (53:13)

He's Thompson filled on the future of web and publisher. Sam says, I do think people will go to fewer websites. I think people will care more about human crafted content than ever. My directional bet would be that human created, human endorsed, human curated content all goes up in value dramatically.

B (53:28)

Let's go. Let's hear it for our livestream.

A (53:30)

Human curated, human handmade. Seriously, what AGI means? Sam says maybe the milestone that's most relevant to us is when most of our research cluster is allocated to to the AI researcher instead of the human researchers. But I don't think that's going to be so binary because I think it'll feel like people get a little more help and a little more help and a little more help. He also said if we didn't pay for training we'd be a very profitable company.

B (53:56)

That's a good question. So Tyler, have you thought more about what happened to GPT 4.5?

A (54:04)

People always say like oh it was so bad and same thing with GD5. It's like, okay, do you remember when we had Jack on and he talked about his blog? It said GPT 415 is like as good as we should expect. Yep, 5 is the same thing. I think it's a good model.

B (54:17)

Yep. If you.

A (54:19)

Wait.

B (54:20)

Okay.

A (54:20)

If you would please consult the graphs.

B (54:24)

Okay.

A (54:24)

Pull up the meter.

B (54:26)

So My question with GPT 4.5 is I understand that it's as good as we should expect. The question is just is it in the money? Because GPT2 was also on that curve and deeply unprofitable. Right. They had to pay not a ton of money to train it and it made basically no money because they didn't even sell it as an API. Remember we talked to Greg Brockman, he was like we had to pay people to use our models. Then all of a sudden the 3.5, the 3.5 DaVinci came out. Some people were using that. They might have spent, I don't know, $10 million training GPT3, 3.5. And some people paid for it. They probably made their money back. Who knows? Then GPT4, they do the big training run, the hundred trillion parameters, the big circle. And that has to be one of the most profitable training runs ever because they maybe spent $100 million, but they are making $1 billion a month inferencing it. And like the inference cost is probably gross margin positive, more or less, but 4.5. They probably sounds like they paid $1 billion to train it, something like that. A lot of money to train this big model and it's expensive and yes, it's better, but it's not better to the point where people are willing to bear the cost of inference for it. So it's kind of mothballed and you can see that in the app. It's like, yeah, but it's like, okay.

A (55:40)

It'S worth one AI researcher. I mean I think that the value.

B (55:44)

Of the like R and D, like.

A (55:45)

The knowledge that they now have training, the next model is probably worth a billion dollars.

B (55:50)

Totally. If you consider AI researchers worth billion dollars, easily 100% worth doing 100%. Not a big deal for their financials. Not a big deal. It's just interesting that we went from a paradigm of like, like the big, like the big training run was unprofitable, then it was massively profitable, then it went back to being unprofitable and the profitable research that they were doing shifted to some RL that they did on, you know, hallucination to reduce the hallucination rate. Like that RL run. Probably not a billion dollars in in training cost. I don't know. But it's clearly making the product better. People are going to use ChatGPT more, they're going to be more likely to upgrade and, and if the hallucination rate is lower, people are going to trust it to go shop for me and they're going to make a ton of money off of that. Right. Just an interesting dynamic.

A (56:37)

I don't know. Yeah, I mean, I think a lot of people were like singling out this quote, if we didn't pay for training, we'd be a very profitable company. And just, you know, obviously it's easy to poke a little bit of funny.

B (56:48)

That he's saying he's gross margin positive.

A (56:49)

No, no, yeah, no, I know, I know, but, but still, anytime you have a CEO being like, if we didn't have this cost, we'd be profitable. It's always, yeah, I mean the follow.

B (56:58)

Up on the takeaway is to stop training then.

A (57:01)

Well, yeah, exactly.

B (57:03)

So we love some net profits.

A (57:04)

You know, this is what Everett said and this is what we said, you know, in response to the GPT5 launch is that the product is now the most important thing. And what Everett said just now was that, you know, you could swap in much cheaper models, even open source models, and people would still be using the product. In the way that they are. Last quote from Sam, from Alex's coverage. He says, I don't use Google anymore. I legitimately cannot tell you the last time I did a Google search. Mogged. Yeah. So this is the interesting thing, right? When you think about the browser wars, which we went from the browser wars a month ago, being like, everybody's making their own browser to now everyone's trying to buy Chrome and it's still very much up in the air whether they'll be forced to sell it. Google's not going to sell it by choice. But it just does feel that ChatGPT with GPT or ChatGPT agent is effectively a web browser already. You're just browsing the web. And so I think that the real browser war is the fact that ChatGPT functions as Chrome plus Google search in a single page product already.

B (58:17)

Yep, yep. No, I. This is a great take. I completely agree. Wildcard Truth Social buys Chrome Truth browser. This would be the most aligned. Yes, this would be the most aligned with the current administration. Tyler, what you got for me?

A (58:33)

Okay, so yesterday it's. It's not here anymore, but if you.

B (58:36)

Went to OpenAI.com new tab page, they.

A (58:38)

Like leaked this page. Like, not on purpose. Someone just found it. But it was basically like very close to a browser style where you would type in and then it would auto fill some possible questions, then you could save links and stuff. So it very much looked like the Chrome homepage.

B (58:56)

Yeah. I wonder in the context of mobile. I mean, using generative AI to generate code in HTML has just completely pilled me on the generative UI elements. And I feel like, like I would probably be less interested, especially since, I mean, I use ChatGPT mostly on my phone, I use Chrome mostly on my computer, on my Mac. And so I wind up like, it's a very different style of working. And I could imagine that the evolution here is not the ChatGPT app likes opening iframes and Safari web views and surfacing, something that actually renders the native HTML. It's more like it scrapes all the HTML from a website into the reasoning chain. It gets all those tokens and then it kind of just like reinstantiates the UI in, like native elements and kind of cleans it up for me. And so I'm getting like a hybrid of like, ChatGPT used to just be pure text response, then it became text response. And it also has links in there now and it also has commerce.

A (60:09)

Yeah.

B (60:10)

It also has yes tables and it can. It can put in images now. And if you search for a product, it can share like little preview images with a link. And so they're hydrating like the tokens.

A (60:23)

And think about how bad, yeah, 99% of websites are.

B (60:28)

I completely agree.

A (60:29)

They're so bad having a standard, it's hard to navigate them. There's pop ups and things like that.

B (60:33)

There are people that deliberately browse the web with JavaScript turned off because it forces websites into a more usable plain text experience. And most websites have a fallback in case JavaScript's not working or blocked. And so you can wind up going to the United Airlines checkout and it'll be just like normal buttons instead of like the pages jumping around, refreshing pop ups. All that stuff. All that stuff gets turned off. And some people.

G (61:02)

Cookies.

B (61:02)

Cookies, yeah. Anyway, if you're trying to improve your website, you're managing your GitHub installation, you got to get on graphite Code review for the age of AI. Graphite Help Teams helps teams on GitHub ship higher quality software faster.

H (61:14)

Faster.

A (61:15)

Well, pull this up in the timeline, boys. We have a post here from the New York Stock Exchange, otherwise known as the New York Style Exchange. And here we are.

B (61:26)

Let's go.

A (61:27)

Nice. President Lynn Martin stuns in the TVPN Spring Summer 2025 collection.

B (61:35)

Thank you for acting as our model for this, this season's TBPN collection.

A (61:42)

We really designed it.

B (61:43)

Proud to have you as well for.

A (61:45)

Tech and finance leaders that, that are, you know, dedicating their lives to improving capital markets and maintaining American dominance globally.

B (61:54)

That was the North Star with the.

A (61:56)

With the, with the Patagonia is like, oh, this was designed for your next hike. Yeah, was designed for Everest. Well, this was designed for the trading floor on an IPO day.

B (62:05)

This was designed for the hike up to that bell.

A (62:08)

Exactly.

B (62:09)

For the Mount Everest of capital market. For that next gone New York Stock.

A (62:12)

Exchange for the gong hit that retires the next EVPN gong.

B (62:15)

Yes, exactly, exactly.

A (62:19)

We have, I think we can skip over this coverage from the Wall street journal. They said OpenAI's rocky GPT5 rollout.

B (62:25)

I just want to put this in.

A (62:25)

The true struggle to remain. Yeah. So that the art. This article which was released a couple days ago, the title is OpenAI's rocky GPT5 rollout shows struggle to remain undisputed AI leader. And it's basically coverage from a bunch.

B (62:41)

Of people complaining and it doesn't capture any of the actual underlying.

A (62:45)

Doesn't feel like they're struggling to remain the undisputed consumer AI. Leader, I think you could argue that there's certainly a much closer race in code gen. Yeah.

B (62:57)

So I, yeah, I put that in just as a reminder to talk about GPT5. The real news is the Financial Times has a, a story on Deep Seq that isn't, isn't super deep in terms of the coverage, but there are some interesting tidbits in here. So the, the article is Deep Seek's next AI model stalled by Beijing push to take up Chinese chips. We talked about this a little bit with the Nvidia H20 now available in China and what that means for, for, for Deep Seek. So Deep Seek, obviously everyone should know is the disruptive Chinese open, open source frontier reasoning model maker from High Flyer. They were in the high, they were in the high frequency trading business. Then they decided to go into foundation model training and they developed, and they developed a very, very solid open source language model very quickly and it surprised everyone. We started talking about Jevons Paradox and the idea that cheaper AI will just wind up driving more and more adoption. We've certainly seen that. And the sell off that happened in the AI trade in the public markets came rip roaring back and Nvidia rocketed to over a $4 trillion valuation after they'd sold off slightly after the deep SEQ news. So apparently they've been trying to get this R2 release out the next version of their, of their reasoning model and they're having a hard time because allegedly they're using chips from Huawei. So China, the CCP and Beijing has pushed Deepseek to switch from Nvidia to Huawei. Everyone suspected and it was, and it's.

A (64:35)

It'S not technically illegal to use Nvidia chips, but it is politically incorrect according to one person familiar with the conversations currently.

B (64:45)

Yes, and, and, and there were export controls, there were never any import controls. So if you're High Flyer or Deepseek and someone comes to you from Malaysia and says, hey, I got, I got 100,000 H100. You want to buy them a truck, you're welcome to buy those. At least you were. Now it's politically incorrect to do so. And so Huawei hasn't really gotten the job done. Lots of recent model releases have failed to live up to expectations. This is what happened with GPT 4.5 Llama 4 Behemoth. Like the models are getting more, they're getting bigger. There's more and more integration points in the training cluster as you're actually building these out. There's power management issues, there's memory issues. There's all these different things and that's why the AI researchers are making so such high salaries and the trade deals are happening. Because if that, if there's one researcher who can tell you that line of code is going to result in 20 million or more or 200 million, that's really valuable. And so this case shines a light on the, on the exact nature of the gap between Nvidia and Huawei. So when the Huawei Ascend Cloud Matrix 384 came out, everyone was kind of saying, okay, wow, like Huawei is basically caught up. It's not as efficient on a, on a dollar per flop basis. Like, it's more energy intensive, but if you're willing to spend a little bit more energy, you basically get the same capabilities. That might not be the truth. Like, like there might be actually some qualitative value to Cuda and the reliability of the drivers that, and the software on top of Nvidia and actually the underlying chips as well, such that even if you have the Three Gorges Dam, you have cheap energy, you have nuclear power. China's developing more and more energy. It's getting cheaper and cheaper. Even if you have cheap energy, if you go to set up the massive data center to do the huge training run on Huawei Cloud Matrix 384, you might still be in trouble and you might not be able to get the model out the door. It could be something else, though. We don't really know. This is all kind of just like.

A (66:46)

And it's notable tidbits. I mean, it's notable that all of these have led to. They originally wanted to launch R2 in May.

B (66:55)

Yep.

A (66:56)

And it's still delayed.

B (66:58)

Yep. Yep. And so it'll be interesting to see how Deep Seek reacts. They could potentially say, you know what, like, we are like, Huawei's just not getting the job done. We'll deal with the, we'll deal with the pushback from Beijing. We're putting it in a huge order for H20s from Nvidia. We want the best, or at least the best that's available to us, Even though the H20 of course is 4 years old at this point and severely nerfed. So we'll see. When will they get R2 out, how powerful will it be? And most importantly, what will the cost per million tokens be? Because if we get an O3 level model from Deepseek and it's 100 times cheaper, even if that doesn't displace OpenAI meaningfully, because OpenAI is operating at the application layer. It will be incredibly bullish for every wrapper company because all of their gross margins will flip positive very very quickly because they'll need to do some fine tuning. We'll need to see what Perplexity did where they made instead of Deep Seek, they made it like 1776 Sikh or something like that. They did a fine tune on it to kind of make it more American. But the most important thing was that the Deep Seq researchers figured out a bunch of interesting hacks to make just inference way way, way way cheaper. Anyway, speaking of wrapper companies, application layer companies that we love Julius, what analysis do you want to run? Chat with your data and get expert level insights in seconds. Ask Julius.

A (68:29)

Look at this view.

B (68:30)

I love it. Ask Julius to analyze your data like 2 million users have already done. Folks from Princeton BCG love Zapier.

A (68:38)

Love Julius. I wish the only thing I would change with Julius is I wish it was Rahul. Yeah or Sunwalker AI. AI. But it's always time for just like the Ford.

B (68:50)

Just like the Ford Motor Company.

A (68:51)

You know, the Sunwalker artificial intelligence company.

B (68:55)

Maybe, maybe it could it could happen so to your taxes has a take on the on the the Deep sea story because they think it's a confused narrative with no sources at Deep Sea confirming it. So to your taxes says this story is so insane dream narrative for burgers.

A (69:15)

And their I think that's an American slur.

B (69:18)

Yeah, I guess that I might well that I might well cook up my own Also based on half baked rumors, experience in an authoritarian society and just a little bit of sleuthing. As expected, the plot thickens. Xi Jinping's heavy handed central government approach is stalling development is the take that to your taxes might be debunking here. Deep Seq was a breakout hit, but patronage networks don't reform overnight. The actual Chinese national champion in AI is as we know, Huawei. They get unconditional subsidies and the nation's hopes are pinned on them. On a software side, it's also Tsinghua, the university and their brainchild Zai with glms. But Huawei does everything. In February, the party asked Ren Zhengfei to partner with major AI labs, including Deep Seq and Beat America. At AI. They approached Deep Seq, sending personnel to adopt ascend clusters for V3 inference. We've seen papers following from that and we know these clusters now work at Silicon Flow and elsewhere. They also suggested training the next generation models on Huawei, but were privately told by probably after some experiments that Ascent that the Ascend ecosystem is not yet mature or reliable enough and will go with H8 hundreds thank you very much. With the knowledge gained, they had set out to train Pangu Ultra Moe mixture of experts as a reproduction of V3R1 and may or may not have failed at that due to interconnect issues and broad lack of competence. Resorting to repackaging R1 with the intent to report to the party that Deepseek had proven uncooperative, but there's nothing special there. They can do equally well and will soon surpass sar. Now as Deep Seek is is not releasing any rumored R2, the timeline never once made sense and that's a big issue. You need to have your timeline straight. There's renewed discussion about importing Nvidia. They are trying to spin this too to their benefit, leaking to journalists that it was Deep Seek that had failed at R2. While Huawei's Noah's Ark small model lab is moving smoothly, they may know that V4 is planned to come out late enough that they still have some hope of producing a more persuasive internal result. For now, they are probably optimizing cloud matrix hardware and Cinnamon testing 910 D&920 and hiring people with LLM expertise. The above is an educated guess. The serious argument is that if you want to talk about the failure of Huawei's hardware, it's important to focus squarely on Huawei and not a fanciful and unprecedented narrative where historically independent startup is forced into changing their training stack by heavy handed politicians. And so the takeaway here is that Huawei might actually be significantly behind Nvidia and it's less about the CCP saying you know, we want I mean of course the of course the reason the CCP is saying buy Huawei is because they want to improve Huawei and give them as many advance as many advantages as possible to get to the frontier and and provide, you know, the best AI training hardware possible. But the the flip side is that Deepseek is down to use anything and train on a bunch of different stuff. And really they are they probably at least according to this, they really are just having trouble training on large Huawei clusters and so they're like let's get back in the Cuda ecosystem. Anyway, let me tell you about Profound get your brand mentioned by chat GPT reach millions of consumers who are using AI to discover new products and brands get a demo go to Profound be.

A (72:51)

Like the Mag 5. What did what did James say? He said something well he was saying, like, I can't say who's using it.

B (72:59)

But oh yeah, the Fortune 5.

A (73:00)

The Fortune 5.

B (73:01)

He's got a Fortune 5 client.

A (73:02)

Yeah.

B (73:03)

So it's like one of five companies.

A (73:04)

Leave it to you guys.

B (73:05)

20% chance you just guess it correctly. Anyway, lots of people making money on the intel story. So the story today is that the Trump administration just last week called for the resignation of Lip Bhutan, said that his ties to China were too much for an American champion like Intel. But Donald Trump has reversed course and called Tan a success. And the idea of the US government buying a stake in intel is now floating around. I don't love the idea of the people that brought us the TSA running the most advanced manufacturing process humanity has ever produced.

A (73:44)

Or the folks behind the dmv.

B (73:45)

The folks behind the DMV getting in the fab. Getting into the clean room. Might be a little bit of a stretch for me, but intel does need better shareholders. There was a few years ago, before.

A (73:57)

The chips act, long term patient shareholders.

B (74:00)

Exactly. People were talking about, oh, we need, we need an American semiconductor champion. This was during like the reindustrialization meme kicking off. Everyone was saying this like, we need American chips. And yet no one was like, I'm going to actually go build a position. And. And Intel. And so everyone was like, yeah, we need this. It was, it was a. Well, what is it? A cocktail position? It was a cocktail position, meaning something people like to talk about at cocktail parties, but they don't actually put their money where their mouth is. So you sound smart saying we need to. Yeah, we need to make intel an American champion. We need to make chips in America. But I'm not willing to put any money on the line to actually do it. And so Intel's share price has been kind of in the dumps.

A (74:38)

Well, until recently.

B (74:40)

Until recently.

A (74:41)

So Dan Gallagher in the Journal says federal support could get the trouble check chip maker over some hurdles, but risks great harm to the US tech sector and get into it. So intel definitely needs help, but the government support always comes with strings attached. And those strings in this case could ultimately trip up the Silicon Valley pioneer and the broader US chip industry. The Trump administration is discussing options with intel that would involve the federal government taking a financial statement stake in the troubled chipmaker. The idea came up during President Trump's meeting with Intel CEO Lip Bhutan on Monday, and the discussions are still in an early stage.

B (75:16)

So funny after talking to fabricated knowledge over at Semianalysis about like his main thing was like the problem with the intel board is that there's too many government type people on the board.

A (75:26)

Politicians.

B (75:27)

There's too many politicians. Too many like famous people, writers, thinkers. There's not enough like just people that understand scientists. We need like physicists on the board and like technologists. We need like an Elon type or like, you know, someone who understands the actual tech.

A (75:42)

Yeah, I mean just steel, man. It's like Trump has, you know, a multi billion dollar digital asset business. I was about to say multi billion dollar social media company.

B (75:51)

He is the founder of a tech unicorn, so it's not his first rodeo in the tech industry.

A (75:56)

That's right.

B (75:56)

So you have to give him some credit if he was able to get in there.

A (75:59)

So Dan says that marks a fast turnaround given Trump was calling for Tan to be fired just days ago. The news was encouraging for Intel's beleaguered investors who have watched the chip industry's once undisputed leader lose more than half its market cap in less than two years. The stock jumped 7% Thursday on the initial reports of the talks and gained more ground early Friday morning. But investors should still be wary. Intel's problems are such that even a big check from Uncle Sam won't fully solve them. The company has burned a total of nearly 40 billion in cash over the past three years trying to regain its manufacturing lead from TSMC. Intel has also been granted up to 8 billion so far in direct funding through the Chips act, but that hasn't been enough. Intel's most state of the art production process, called 18A, was supposed to close the gap with TSMC. But the company admitted on its own second quarter earnings call last month that 18A will most will be used mostly for its own product, meaning few outside chip designers have found the technology compelling enough to sign on as customers of Intel's contract manufacturing service. Wall street expects another 7 billion in negative free cash flow this year, according to estimates from Visible Alpha.

B (77:11)

Sorry, double duty there on the soundboard.

A (77:14)

Tan told investors in the same call that he won't commit major capital spending to Intel's next process, called 14A, without commitments from external customers.

B (77:21)

Customers Smarts couldn't get a customer for that.

A (77:23)

That was widely seen as Tan drawing a line in the sand, a line by which he would determine whether to keep intel in the business of manufacturing chips. But intel pulling out of that business would be detrimental to the government's efforts to shore up domestic chip making for national security and supply chain stability. Reasons. Doug, over at Semianalysis was talking about how the chip design business seemed like it could be a target for like PE in the sense that if you came in and kind of overhauled, Tan.

B (77:54)

Was going to come in.

A (77:54)

Yeah. If you, you know, dramatically cut cost and really focused on serving customers and of course raising prices, there's probably a good business there. But that the foundry business was critical and we don't want to risk losing that.

B (78:09)

Yeah. Hock Tan is the CEO of Broadcom. I just wanted to say have a great flight. John Exley, he says he's taking off, he's landing in one hour. And if you're trying to set up a semiconduct line, if you're trying to build or plan products, get on Linear. Linear App Great is a purpose built tool for planning and building products. Meet the system for modern software development, streamline issues, projects and product roadmaps.

A (78:34)

So I this chart chipped away showing the intel and TSMC revenue.

B (78:42)

So I'm, I'm still, I'm still sort of slid on this. We have some guests on the show today to talk about the dynamic between the US and China in the semiconductor race. I'm, I'm sort of open to the idea that we sort of solve the US based manufacturing of semiconductors through partnerships with TSMC and Samsung, even though those are not American companies. If they set up fabs here in some sort of, you know, negative conflict scenario, it's like, well, we still have the factories here, even if it's run by a company in South Korea or Japan or Taiwan. Like the factory should continue to produce for the most part because most of the team that would be building and.

A (79:24)

I think the pushback there is we don't have the talent which is key to staying on the leading edge.

B (79:29)

Yes, that's true. But I mean, yields at TSMC Arizona have been good so far and it feels like we could continue to scale up and it feels like a lot of the talent will be coming over and so there's a little bit of like, you know, you start to ramp up that supply. But it does feel like intel is particularly good at the trailing edge. But maybe that goes international, but to South America, unclear where it goes. Unclear how, at least to me, how important intel is as like a strategic company versus just like it's one of the greatest technology companies America's ever produced. It's a crown jewel. It should just be protected by because it's like good for the America brand versus like if intel disappeared tomorrow, like how bad would things be in America? Would we be able to get by with other suppliers? Because we do have AMD we do have Nvidia, and then TSMC and Samsung are not in China. That's not where we buy our chips from.

A (80:30)

Well, Dan in the journal says the government might, for instance, pressure chips designers like Nvidia, AMD, or Qualcomm to manufacture with intel, perhaps as a condition for getting export licenses for China. And that could easily go wrong if companies are forced to use Intel's factories before they can make chips with production yields that match TSMCs, it could result in inferior products and wastage by intel because so much silicon has to be thrown out to make a working chip. More broadly, if chip designers are using intel fabs, even though they aren't the most advanced or efficient, the entire US Chip industry could lose competitiveness. That would undermine the ultimate goal of government intervention in the industry, which is to maintain American technological supremacy.

B (81:10)

Yeah, the Rubicon of state intervention in chips has already crossed the administration, already signed significant leverage over intel, has already already has significant leverage over intel, thanks to government factory expansion grants that place limits on how it can restructure its chip design and manufacturing arms without government consent. But the federal government must take care not to go too far, lest it undermine the market model that made American technology.

A (81:37)

Slippery slope.

B (81:38)

It is. But at the same time, there is, there is a case to be made. And I guess, you know, like if you put the US Sovereign wealth fund under the direction of Leopold Ochsenbrenner, there is a case just for make money for the taxpayer. And this is actually the opposite of a bailout. This is what Tim Geithner got in trouble for during the. Not in trouble. He was ultimately vindicated during the financial crisis in 2008. He went and made a bunch of loans to banks on the order of like, billions and billions of dollars. And everyone was like, this is a bailout for Wall Street. This is a bailout for the banks. But he actually only invested in the, in the banks that made it through the crisis. And so those, those loans, those backstop debt instruments were paid back with interest. And so the US Taxpayer actually made money on those deals. It feels a little weird. But, but, but the same thing could happen here. Like, Intel's a 111, $110 billion company. If the US invests and is able to do things to turn it into a $300 billion company, well, like, that's an extra 3x for the US taxpayer. And it doesn't, it doesn't actually result in any, like, lost money.

A (82:49)

It's not a bail. If Trump can just get 3.3x's and string like a hundred of those together in a row will solve the whole federal debt crisis.

B (83:02)

That's a high water mark. I think he should be targeting a nice 5x fund for the first run then raise 10x more and then scale up and then start, start deploying the big, the big money. The big money. You should buy 100% of intel what you got.

A (83:16)

Tyler, this is like we should give put Jane street, you know, high frequency lawmaking.

B (83:22)

Yes. Just optimize for GDP direct. Right. Access to the legal code. Yeah. So anything they can do to just maybe one of the foundation labs. Actually instead of nationalizing the labs, we need to, you know, corporatize the government and let and do a reinforcement learning environment with a verifiable reward. The verifiable reward being the stock market. Rl for business.

A (83:46)

But the business is the government.

B (83:47)

Exactly. So what, what, what can you, what can you change in the legal code to make the stock market go up? And so you're just feeding off of that, constantly rewriting the the legal code.

A (84:00)

Well, Zoomer at zoomizoom has been going viral again. He's why AI is a house of cards. He has an entire thread breaking down these sort of chained losses that we've been talking about. And he's getting community noted quite a bit. 1 Someone added 1h 100 conserve thousands of users at once depending on batch size and model. For inference, you don't dedicate a GPU to a single user. You load the model, then stream requests from many users users in parallel. Other numbers in this post are also widely exaggerated.

B (84:35)

Yeah, this might have come from a group chat originally that was maybe not fully fact checked but you know, told a compelling story. So it's a fun with it.

A (84:45)

And Nick Carter says compelling threat, if you ignore that inference gets 10 to a thousand X cheaper every year. If you're willing to pay $200 a month for AI and VC funding and VC funding subsidizes half of that. Simply wait six months, I would say. You know, Mr. Randall earlier on the show said it's actually not all of a sudden getting 10 or 1000x cheaper, at least for Frontier models. But the point he made is that a lot of prompts could be served with older, cheaper models. And that's going to be a big focus. I mean clearly that was a focus for OpenAI with the recent launch.

B (85:20)

Yeah, I dug into this to see, you know, how would we really hit 1000x cheaper this year on the models and would the gross margin profiles of these AI Companies flip extremely quickly. Or is it more like a five year change to really optimize this stuff? I'm kind of split on it. The charts that Nick Carter shared here are pretty compelling. I just hope that the trend continues because the dynamic of reasoning models and test time inference is slightly different. Like you are just, it's less algorithmic driven, it's more just throwing raw compute at it and generating a ton of tokens. So I don't know. Tyler, do you think you're closer to 10x cheaper inference every year? Thousand x cheaper inference every year? What type of gain in cost per token do you expect over the next few years?

A (86:16)

Don't make mistakes. Do you mean in what models are you talking about?

B (86:19)

Like frontier level models? All the models.

A (86:22)

But I think frontier will probably stay similar price and then you'll just see like over time. Like now we have open source models that are easily as good as like two years ago.

B (86:31)

Yeah, right.

A (86:31)

You have like 4, 0, which is.

B (86:32)

Super cheap, doesn't mean free. Like it means free no license. But you still have to inference it on an Nvidia GPU that costs money and you have to spend electricity. That costs money. Just open sourcing a model does. Yeah. But when you open source something you.

A (86:47)

Can like distill it even further. You can like, you get some, you know, optimizations there.

B (86:53)

So, so O3 Pro, let's call that like an expensive frontier model. How cheap do you think that is next year? Do you think it's 10 times cheaper? 2 times cheaper? A thousand times cheaper? Like an equivalent model of O3 Pro. Yeah. O3 Pro heavy reasoning thinks for 10 minutes, generates tons of tokens closer to.

A (87:18)

In a single year. I'm probably closest to 2x.

B (87:22)

2X.

A (87:23)

Yeah, I wouldn't say massive gain. Well guys, I hate to interrupt but there's some breaking news. William Zhang is proud to announce that he's the reigning OpenAI McNugget champion. He ate 55 nuggets.

B (87:34)

Congratulations.

A (87:36)

Absolutely incredible.

B (87:38)

Some other breaking news. Numeral HQ sales tax and Autopilot spend less than five minutes per month on sales tax compliance. Go to Numeral HQ to get started. And we have our third guest of the stream. Since we already did the debate, we have Bill Bishop from Cynicism. How you doing?

E (87:53)

How are you? How are you? Thanks for having me.

A (87:56)

Thanks for hopping on.

B (87:57)

We really appreciate you taking the time give us. I mean I'm super familiar with your work, I think everyone should be. But give us the high level of the pushback that you saw yesterday. We enjoyed having you in the comments. And I want to know how you would frame the counterargument to what Aaron Ginn was making yesterday.

E (88:15)

Great. Well, thanks for having me. And I'm impressed that you guys respond to jerk comments. That's good.

B (88:20)

I don't see it as a jerk.

A (88:21)

Well, yeah, we don't see it. I think it's extremely fair pushback. We understand.

B (88:26)

When I have Aaron Ginn on, I see it as having Jensen Huang on the show.

E (88:30)

Okay, well, that is how I see it, too. So that's great.

B (88:34)

And I can't have. I can't have Jensen come and whiteboard out pros and cons with me. And I can't throw random jokes at Jensen all day long, but I can to Aaron. And so I enjoy having him sit there and I can throw stuff at him. And of course, he has. He has his opinions and his arguments, but it's great to have somebody that can speak the language of Nvidia. So fluid.

E (88:57)

Yeah. No, and again, I think thanks for having me. I'm psyched to see you guys on substack2, which is great. And I see your running show today. You got two great guests, Jimmy Goodrich and Leonard, who are going to be way better on this discussion. So I think that what I would say is what's been interesting to watch, and I think talking about the reversal on the H20 chip, we got our H20 chip from the video. We got to remember, right. There was a proposal teed up for the Biden folks to ban the H20. And they never did it. For whatever reason, Trump comes in, actually bans it, so it looks like he's making more hawkish. And then a couple months later, because of really effective lobbying from Nvidia and specifically from Jensen Huang at the principal level, at the, you know, to the president, to David.

A (89:44)

Yeah, I mean, it's not even. It's not even traditional lobbying because they're spending like $7 million below seven figures.

B (89:50)

On lobbying, probably 10 times that on Jensen Huang's like, flight schedule.

E (89:54)

Flights go right. No, it's brilliant. They sort of disrupt the lobbying business. Go right to the decider, go direct. And how the narrative is shifted. And so Trump, on the one hand, looked like he was tough for on China. Then he backs off. And then, of course, he gets a bunch of flak for, oh, my God, he's caving to China. He's caving to Jensen. Right. When in fact, he sort of did what Biden didn't do. And then because of this personal interaction and the Personal effort from Jensen Huang, he reversed course. And I think it has to be seen in the context of the broader, where I think there was a correct decision, the rescission of the AI diffusion rule, right, which was really, you know, David Sachs was I think a big advocate of getting rid of that rule with a Biden administration rule that was going to limit countries that could buy Nvidia GPUs. Right. The idea that the US needs to lead, the US needs to be out there competing with China, not just limiting. And the way to do that is to get people hooked on us. The US AI stack, specifically Nvidia globally x China. That makes a lot of sense. The China decision on H20 I think is flawed. The idea that, oh, we're going to keep China hooked on Nvidia by selling them H20 and then I think, you know, the Jensen Huang is lobbying to sell sort of the next somewhat nerfed chip, right, A little better, but still not near the top of the Nvidia product suite. The idea that just Nvidia is better, Cuda is better than the Chinese, the Chinese hyperscalers, the, you know, the, the, the Alibaba 10cent, ByteDance Deep Seek, they're going to want to stay with Nvidia makes sense in a world that is sort of a normal political economy, a normal competitive world. Doesn't make sense in the context of a world or market that's run by the Chinese. You know, the Communist Party of China and Xi Jinping made very clear in April at this Politburo study session that was about AI, specifically said he wants China to build its own indigenous AI stack. And so they understand very well this idea that, you know, Nvidia wants to hook their companies on Nvidia hardware, the party wants self reliance. And so this idea that we need to compete, America needs to compete by selling into China and therefore they're addicted, they won't break it. That I think is fundamentally naive and misunderstands how the party operates. I think what it does is it helps China fill the gap between where they are now in terms of lagging capabilities and lagging output or quantities in terms of the Huawei chips. It gets them to where they need to be over time, but they're going to be only intensifying their efforts to strip out Nvidia to break any reliance on the USAI tech stock. And so by selling H20 chips now were just helping China keep in the race when in fact if they didn't weren't able to get the H20s, it would probably, I think would help the US at least maintain a lead, if not start accelerating into and accelerating some amount of separation.

A (92:50)

What's your reaction to the commentary showing that the CCP is actually like actively pushing back against Deep Seek, which is, you know, the clear open source, or maybe not totally clear, but an open source is also an open source leader.

B (93:08)

But yeah. Your reaction to the news that the, that Beijing asked Chinese Foundation Labs to.

A (93:14)

Not buy Nvidia even effectively, even if it means delaying like DC cards.

E (93:20)

So there was a Financial Times report yesterday where it said they were encouraged. We'd love to know what, what the encouragement really was. But yes, they were encouraged to use the Huawei. I think it was the, the latest Ascend chips which were all based on dies that were illegally fabbed at tsmc where Huawei used a cutout company that was related to Bitmain called Soft Go to get. I think it was 2 million dies from TSMC that they can't make on their own in China. And even then they're still not where they need to be. And so I think that this goes. You have that news. You have the news since the announcement by the Trump administration that they were going to allow again allow licenses or give licenses to sell H20 to China, you've had significant pushback from some of the regulators in China. You've had talk about the chips are unsafe, maybe they have back doors, they're environmentally unfriendly, there are security risks. And so I think what you're seeing, you know, there's different hypotheses about what's going on. The pushback on the H20, it's maybe they're trying to negotiate for the better chip. I personally think it's actually more of a manifestation of parts of the system really just are like, we need to stop this reliance on American chips. We need to make sure we're focused on building our own pathway to self reliance. And that I think is related to that news that Deepseek is being encouraged to use these lesser ship even if it delays them because ultimately when they figure out how to use them. And you know, the report said Huawei has engineers on site trying to work through it. Ultimately that will help China because that will help them solve over time the various bottlenecks they're facing. But I think you can read into.

A (95:00)

It, and one way you can read into it is that the CCP doesn't believe in this sort of fast takeoff scenario, you know, like runaway AI in the next two years. Right. Which I think broadly I don't know a lot of people that still believe in that, but it's notable.

E (95:15)

No, I think that, I think that's right. I think this is more of a we are going to set the foundation for doing it in a self reliant way, even if it takes us longer. And so that's where I think what's been interesting to watch in Nvidia is how the narratives have shifted in D.C. where the Nvidia line of we have to compete, we have to compete, we have to sell them. The China, we have to addict them is basically everywhere now. There's just like there's barely any pushback and certainly in the government from what I'm understanding is there's no longer any process. Right back to the whole sort of how did Jensen lobby to get the decision made? There's no process there. No, there, no. Like all the many of the people who worked on these issues were fired and, and now it's basically like he gets to the principal, he gets a Lutnick or Sachs or the President and that's the decision. There's no like national security discussions. There's no process anymore.

B (96:05)

Yeah. It feels like interestingly, people often project like a monolithic culture upon China. But then severe division within America and it feels like there might be some division on both sides in the sense that in America there are arguments for let's export all the GPUs, keep them dependent on us versus let's hold it back and hurt their ability to scale. And then in China they might be saying the same thing, hey, we need to just buy, buy, buy and stay near the frontier. This will actually help us accelerate. And then there'll be a different argument for maybe we need to just rely on.

A (96:41)

There's also the, in many ways Deep seeking. The original Deep Seek release was, was in some ways economic warfare on, on Nvidia. Right. You saw this massive sell off immediately.

E (96:56)

Well, and there was clearly a somewhat of a coordinated hype.

A (96:59)

Yeah, I mean the App Store, the App Store chart, you know, deep seat getting all these downloads was Twitter, Twitter bots, Twitter trolls. And so, and so I think there's. You could also read into this and think Beijing doesn't think that the next deep Seq release, regardless of how much progress they make around efficiency, will have the same effect on, you know, making Nvidia sell off, you know. Yeah, massively.

B (97:22)

Do you have any, do you have any context on previous technological revolutions and the history of the US China relations? Like going back to like the cloud or mobile? Like was there Ever any similar considerations of, of don't sell iPhones. It felt like in the previous era every big tech CEO was like, I'm going to massively tam expand by getting into China. And then they got blocked. And this is kind of the opposite where Jensen's been playing that and then now he's having to pull back and the government's like, the US government's the one that's saying don't sell to China. Whereas in the past with Uber and Google and Facebook, it's been the Chinese government saying, don't come here with your technology.

E (98:02)

I think this is, this is fairly unique as far as I in my memory. Certainly with this sort of important technology, there have been certain types of things that the US government hasn't allowed to be sold into China, but not at the scale or the sort of economic importance or the frankly the market cap importance.

B (98:18)

Zooming out, how, how do you feel like US China relations are just going generally? I feel like two years ago there was a ton of saber rattling about we need to get sharp on Taiwan. Everyone needs to learn what TSMC is. We need to talk about defense technology and Taiwan invasion in, you know, six months, 12 months, it's happening, it's going to happen. And then it feels like we've been in a bit of a lull, little bit more economic, you know, economic warfare, but it feels like we might be coming out of a period of high tensions. Just give me like the general pulse check from your side.

E (98:55)

It's a great question and it's one that it's still, it's still quite unclear. I think that you see the beginning of the Trump administration, the economic tensions rose pretty high. Those have come back down to, you know, where there's now a sort of a. Tariffs are high, but there's a, it's calmer. Although the Chinese in part is calmer I think, because the Chinese pulled out their export control trump card, so to speak, around rare earths and rare earth magnets and, and really I think showed the US that they had actually a lot of leverage that, that the US didn't necessarily appreciate. And so I think you're in a bit of a lull on the US side because there are things like, for example, on the technology stuff there were a bunch of new actions around export controls, around chip related stuff. They're all tabled in part, I think because of how the Chinese were able to push back on initially the beginning of the trade war using their, their rare earths card. Generally, though, when you look at the broader you know, you look at Taiwan, you look at sort of things like the South China Sea, you know, the, this, this. You look at the other economic issues around. You know, what the US States over capacity says over capacity. The structural issues are not going away. We are, I think, as you said, we're in a bit of a lull. And the Trump administration seems to be more focused on the, the transactional bit parts of the relationship for now. But, you know, there's some people who want to talk about, oh, maybe it'll be this grand bargain, you know, Trump and she may meet this fall and they'll have some great grand bargain. You know, it, it's hard to see how that would happen and how it'd be sustainable just because of the, the real structural issues of relationship. But there's no question that the narratives have been shifted, shifting. The Chinese have been working really hard on people to people sort of.

B (100:50)

Yeah.

E (100:51)

Stuff that has, I think, pulled us back from the sort of peak of China. Hawkish. I said it, you know, my Shop China podcast last, last fall at the beginning of the year. I just, we were joking. I said, you know, I think we've hit peak China. Hawk.

A (101:04)

Right.

E (101:04)

No, seriously. Right. It's gonna, it's gonna, it's gonna, it's gonna sort of moderate, at least for the time being. I think that's what we're seeing.

B (101:10)

Is the rare earth element stuff a, an ace in, in the deck of cards or is it more like a jack or a queen? I think about, you know, we haven't even gotten to the. Obviously Taiwan invasion. Feels like more of the ace in terms of just like how much pressure that would put on the relationship, but also Apple. It feels like if China were to put pressure on Apple, that would potentially be more disruptive to the American economy just because it's such a huge company, it's so critical to American technology than, than rare earths. Or is there some other dynamic at play there?

E (101:46)

I think the Chinese, you know, Apple is one of those companies that every time there are tensions, it comes up, well, China could do something to Apple. And they have, you know, Tim Cook has been brilliant at managing President Trump and brilliant at managing Xi Jinping. And, you know, Apple. There was a great book that was written about Apple by Patrick McGee. I mean, Apple has, does a lot for the Chinese economy. They employ a lot of people directly and indirectly. The Chinese so far have not really bothered them in any direct way. The rare earths is one where they have the ability to effectively disrupt significant parts of US Industry. And European industry. And they did that. And that I think is why you see, you saw the US sort of pull back pretty quickly in the trade discussions. And you know, the way the US did it is after the first meeting in. Where was it? It was in, it was London, Geneva at the first meeting. All of a sudden the US added these new export controls on like jet engines and other things because the Chinese weren't giving the rare earth magnets that the US thought they were. Yeah, that, that is the one where the Chinese can cause pain immediately.

B (102:52)

Yeah, yeah, yeah. So with Apple, if China does anything to Apple, that's like millionaires, people unemployed in China, very disruptive to the Chinese economy. Whereas with rare earths, like you could stockpile them, you could. It's not as critical of like a labor market in China.

E (103:08)

No, it doesn't. They can't sell us. It. It, it's basically, it hurts the couple like one or two state owned companies effectively.

B (103:15)

Got it, Got it.

E (103:15)

So.

B (103:16)

So it truly is more leverage for them.

E (103:18)

But it's, but it's the card that you can only play for a certain period of time. And if the US Government and allies get serious about solving that bottleneck, it can get solved. The problem is it may be the Trump administration now is serious. This is not a, this was not an unknown issue. The Chinese threatened this in the first Trump administration. The Trump administration, then we had Covid, nothing really happened. Biden administration admired the problem, wrote some papers, had some meetings, didn't fix it. Now maybe there's the urgency to actually address it.

B (103:43)

Yep, that makes sense.

A (103:45)

Last question from my side. What is general sentiment from on the ground in China or what's your read on sentiment among business leaders today.

E (103:56)

Not being there? That's a harder question to answer. When you look at some of the data in the surveys, you know, you look at some of the multinationals, I think there is. The surveys from various foreign chambers of commerce tend to be generally pretty pessimistic, more pessimistic than been in years. When you look at some of the surveys around Chinese business confidence, it is maybe bottomed, not particularly positive. Certainly there are pockets that are positive. You talk, we talked about the deep seq moment that has had a real catalytic effect on certain tech sectors. And you certainly see in the Chinese stock market, Chinese stock market's up pretty big this year. Stock, you know, things like AI stocks are up. AI concept stocks are up big. Some of the chip stocks are up big. You know, the, the H20 news. And then the fact the Chinese maybe not want. The H20s was good for some of the domestic chip companies. So in those sectors, you know, you look at robotics, I think they're feeling quite confident because both the markets there and then they've got massive government support. So it's a mixed bag.

B (104:55)

Well, thank you so much for hopping on. We are going to jump on with Jimmy Goodrich, but we'd love to have you back. I mean, this was anytime.

A (105:02)

Also, if you're ever in the chat and you and you have a comment you want to, you want to extrapolate on, we'll just drop you, you just join the same link that you have.

B (105:09)

Really, you can join us live. So anytime you how to do it.

E (105:12)

Appreciate it.

B (105:12)

I'd love to see you.

D (105:13)

Cheers.

B (105:13)

Have a good one.

E (105:14)

Cheers, everyone. Have a good weekend. Cheers.

B (105:15)

Let me tell you about Fin AI, the number one AI agent for customer service. Number one in performance benchmarks, number one in competitive bake offs, number one in ranking on G2, Finn AI.

A (105:25)

Absolute legends.

B (105:26)

And we have Jimmy Goodrich in the restream waiting room. Let's bring him in right now and continue our conversation on chips in China. How are you?

A (105:33)

Welcome to the show.

B (105:33)

Welcome to the show. Hey, good to see you guys. Good to see you too. I'm not sure if you were. If you've been tuning in or Bill Bishop gave you a highlight, a summary of the debate. We've been debating the pros and cons of exporting H20s to China and the back and forth America has had threatening to ban it, actually banning them, then pulling back on the ban. Would love for me to tell. Would love for you to tell us how you've processed that story, where you've sat on the issue over time and where you're sitting today.

I (106:06)

Yeah, no, I caught the tail end.

A (106:08)

Of it and I think it was.

J (106:09)

A great discussion with Bill. You always got really good insights to add. I mean, clearly it's been a roller coaster. I mean, US export controls on China have typically been this sort of. The government thinks about doing things. It leaks out in Reuters or the Wall Street Journal that there might be doing an export control. China learns about it about 912 months in advance. They stockpile everything they need, then they watch the Americans sort of debate openly. You know, in our democracy, which is messy, I think they look back, all this is kind of silly and then they kind of half impose a restriction, then they undo it. I think it's just all kind of comical for Beijing.

B (106:45)

So how big do you think the H20 issue really is? The other Thing talking it up is like the most important chip. It's going to completely unlock Deepseek R3. It's going to be this amazing moment for them. On the other side, folks are saying it's a four year old, it's a four year old chip, it's heavily nerfed. Like yes, Deep Sea figured out a way to optimize around some of the limitations, but in general this is not a real threat. How are you feeling about the actual, the actual value of the capability provided by the CUDA ecosystem on top of the H20?

J (107:16)

I mean I think it's still a very valuable chip for China and for China's AI model developers for two reasons. One, in the air world, obviously you've done sort of gone into this in depth on your show. It's about training and inference and particularly for inferences where you need memory bandwidth and that's where the H20 excels. In fact, on a cost per token basis, it's probably the most competitive inference generating chip in the world because it's the same memory bandwidth of a hopper, but at a reduced price. So it's a great value chip for inference. And that's another key factor here is quantity is Nvidia can provide them in millions of units. That is something that Huawei and no indigenous producer today can do do because of the export controls, because of the complexity of advanced node chip manufacturing, China's indigenous chip manufacturing ecosystem might in the future, but does not right now have the ability to produce enough to satisfy their own domestic demand. So at least temporarily, in this sort of one to two year window, the H20 and then possibly a downgrade at Blackwell, still going to be very useful China to China. And on top of that, of course there's the CUDA advantage. And if you talk to any AI model developer in China, they want to develop their model on the Nvidia stack. They've been doing it since college days. Everybody knows how to code on cuda. It's a big pain to move to another supplier. I mean just moving to AMD for example, is difficult. So, you know, let alone a much smaller, much more nascent developed Chinese competitor. So I think it's actually going to be a big game changer for the deployment of AI, for the scaling up of Chinese AI models. And if you think about reasoning and inference, if you want to develop more capable AI agents that are going to be doing more tasks for you Autonomously, that's where H20 high memory bandwidth, good inference chips are going to come into play.

A (109:05)

Do you Think it's smart, do you think it's smart for Beijing to take maybe a more long term view here and say we're going to throttle development in the short term to really develop the industry locally?

B (109:20)

Well, I think they've got two sort.

J (109:22)

Of interest groups they're trying to take care of. On the one hand they have their AI upper stack companies, the model developers, Deepseek, Moonshot, Immi, Baidu, Tencent, they want just to be able to put out competitive models. And frankly, having spoken to many of them, they'd much rather use a better, more capable chip, irregardless of where it's from. And Nvidia certainly wins out in that right now. On the other hand, China has a self sufficiency national target that Xi Jinping set as part of the 20th party congress, call it or national technology self sufficiency. And he's talked specifically about using its secure and controllable indigenous chips. And there are, set aside Huawei, about a dozen indigenous GPU suppliers in China who want to take advantage of Nvidia not being in the market and expand their market share. And so on the one hand, Beijing is welcoming Nvidia back in. They're rolling out the red carpet when Jensen comes. They also doesn't matter why, they want an executive who's actively lobbying against tech restrictions in Washington. They want to reward that behavior. But on the other hand, they want to create a space for these indigenous GPU players. It's going to be in things like state owned contracts. China mobile telecom procurement contracts are going to go mostly to those kind of Huawei and other firms. But I expect sort of the Baidu Alibaba Tencent hyperscaler contracts are still going to be majority Nvidia, particularly if they can get the licenses. So Beijing sort of balancing both of these constituents. In fact, within China there are many who actually don't like Huawei. There was a Chinese academy of science, very senior computer scientist who's a vice minister in the Chinese government and party. And a talk of his leaked earlier this year where he was criticizing Huawei and saying Beijing should not let Huawei dominate the AI stack in China. It's not healthy. They can't have a single large monopoly that the government should support and that they should be supporting competition with inside the Chinese system. So you know, China is not a.

B (111:25)

Let'S give everything to Huawei.

J (111:27)

There's a lot of people who think they're too aggressive. They're kind of like the Apple of China. Nobody wants to really do business with them. Because they're cheap on price and very aggressive. They known as like the Long or the wolf culture. So you know, Huawei has its own enemies with inside China too.

B (111:42)

Interesting. Yeah. So let me walk through the current thinking and you can kind of push back on my reasoning chain here. So we are, we are now maybe in an era of plateauing, we're not on the cusp of super intelligence by merely scaling up, you know, a bigger large language model. And so what really matters is that inference is the actual deployment of AI. Getting AI all throughout the every crack in the economy is souping up those various SaaS systems and putting agentic workflows all over the place, increasing GDP not to 20% overnight, but maybe just bumping it from 2% one point, one point or something like that. And so giving the H20 to China allows them to do that, allows them to scale inference, distributed inference nationally into all sorts of businesses from DJI will benefit from this marginally with slightly more AI all over their organization to, you know, some small machine shop that might be using it to run their HR software more efficiently. And so although it is, it is somewhat of a more level playing field, we are still in the domain of, of just economic competition. And so it's not, it's not a major Nash, it's not perceived as a major national security risk. It's mainly an opportunity for an American company to just play by the traditional rules of free market capitalism and export their goods all over the world. Is that like roughly the modern thinking you think?

J (113:21)

I'd say I agree with you on that first point. If we want to help enable China to be competitive in AI, want to help their AI model, companies get access to the best infra chips, want to help them scale up their deployment, win in the market at home, and possibly also export their models globally, then absolutely we should be selling more H20s to China. I just don't think that's in our national interest. Of course it's in Nvidia's interest. They are agnostic to who wins in the AI race because at the end of the day, whoever wins is still going to be buying a boatload of Nvidia chips and silicon. And so whether they're Chinese, whether they're from the UAE or from the United States, you know, it's, you know, multinational company that's selling silicon is really not going to care where their chips are going to and what they're enabling from a sort of flagged country perspective. I do think though, if you look at disinformation and cyber warfare. And you look at the capability that autonomous agents are going to be able to, Even at current GPT5 or future R2 level coding capability, if you think about scaling that up with 1000, 2000 autonomous agentic AI coding capabilities that are going to be doing vulnerability scanning cyber offensive warfare, you really start to get an exponential capability increase. And so I do worry that, you know, the Chinese state with two dozen H20 capable inference data centers could use that to do more autonomous cyber activity. That's nefarious. And on the same side, disinformation. If you can have models that can reason for longer and on an agentic basis, interact with people online, shift populations opinion in places like Taiwan, that's incredibly dangerous. And we've already seen the New York Times reported about 10 days ago that state owned companies connected to the Chinese state are using deep seq, which is going to be inferenced on you guess what, the best silicon possible to do exactly that, which is disinformation campaigns against Taiwan and the United States. So actually I do think there is a national security concern here. One, there's an economic security leadership concern and then there is a enablement capability down the road that is actually going to be, I think happening relatively soon.

B (115:40)

Last question.

A (115:41)

Another concern people have had is just like giving, giving the party in Beijing broadly access to more compute and the potential applications of that in a military context, specifically drone warfare. Is that something that you worry about very much or kind of secondary?

J (115:58)

There's traditional applications of high performance computing, supercomputing, which is useful for weapons modeling, simulation. You don't need, you know, multiple large systems to do that. You might have a couple of two, a couple of boutique standalone government HPC systems. Where more of that model data is going to be useful is if you're using large distributed systems of federated drones collecting data, acting autonomously. For example, think about a world where you have your PLA signals intelligence communications battalion that's in real time collecting all the, all the battlefield communications in a Taiwan operation. Then they're transcribing that in real time into a written product that's being analyzed by autonomous agents in real time and then getting field reports into their commanders in real time telling them hey, you know, there's a, you have a squad that's hit counter fire on this beach north of Taiwan. They haven't even reported it up to their superiors. But the autonomous agenda AI system might actually be able to get that deploy a drone. If you think about just those capabilities in the future, that's where inference really matters and that's where it's going to scale up and create tons of economic opportunities for Chinese companies and E Commerce and all sorts of other areas, finance and SaaS, but also on the military side, it's really endless. If you could think about the applications as well.

A (117:21)

Great, great answer. Last question for me. What's going on with TikTok? It was, it was the talk of the timeline earlier this year. Everybody seems to have forgotten about it. Any, any updates there?

J (117:35)

You know, I don't have a whole lot. I think it's one of these things where it's pretty obvious what happened to it. You know, the president likes the tool, thought it was useful for his election. You know, they've continuously renewed the clock on that 90 day extension. Unfortunately, you know, there's no longer really an operating National Security Council inside the White House like you would have traditionally to kind of figure out and coordinate the interagency on a solution. So I think at the moment that.

A (118:02)

Feels something that you mentioned earlier. Beijing kind of laughing about how our, you know, we have our democratic system just publicly debates all these issues, creating this ability for them to, you know, make, make changes in advance. But this feels like one of those things. I mean they have to be just laughing, laughing about how we've dealt with this entire issue to date.

J (118:27)

I mean like with many of our things, I think they, they look back and just don't think we're a very serious country. I mean maybe with the exception of parts of our military, they think the US is sort of a, you know, badass that should not be messed around with. But I mean, look at us on rare earth. We can't get our act together. Export controls, we're moving back and forth whether or not we think we should actually, you know, get our act together in onshore chip manufacturing. We're, you know, interested. Intel here a little bit and then TSMC there a little bit. And the Chinese government, I think from their perspective, like look, we've got a 10 year plan, a 15 year plan, a 50, there's actually a hundred year plan and they're just sticking to it and they see us just kind of all over the place and I just don't think they take us very seriously, Unfortunately.

B (119:10)

Well, in 100 years we know the plan in America. Celebrate the 350th anniversary. You know, there's going to be a party and maybe a UFC fight.

A (119:19)

That's what we can on the White House lawn.

B (119:21)

Anyway, thank you so much.

A (119:22)

Thank you for joining, Jimmy.

B (119:23)

Very insightful Love to have you back and talk more as the stories develop. This is great.

J (119:27)

Yeah, happy to chat more.

A (119:28)

You guys always welcome.

B (119:30)

We'll talk. Cheers. In other news, a Rune post has hit the timeline. Rune says agree with Delian.

A (119:41)

Has spoken.

B (119:42)

Agree with Delian on the Maoist perspective that data centers should be turned into steel plants. I love it. And you know what else I love? Adeo Customer Relationship Magic Adio is the AI native CRM that builds scales and grows your company to the next level.

A (119:58)

If you have a free calendar this weekend, fill it up with onboarding to Adeo and spend spend 48 hours just playing around in there doing some deals.

B (120:09)

In other news, we have a. Our next guest is in the restream waiting room. We will bring in Leonard Heim second. Second time on the show. I want to talk about Mr. Beast. He says he plans to take 100 software engineers and lock them in a room with no cursor subscription with the first person to ship something that compiles taking home $1 million.

A (120:27)

Wow.

B (120:27)

This is from Vaas.

A (120:29)

Absolutely fantastic.

B (120:30)

I originally read this not as a joke. Oh, he's actually doing the PMF or Die thing because that kind of would work. And I thought it was with a cursor subscription. And then I was thinking about like, wow, he could wind up. If he covers the bills, he could wind up spending $2 million on this challenge. But Mr. Beast should get into into software.

A (120:54)

I would like to see different Mr.

B (120:56)

Beast of software.

A (120:57)

He should have a horse in the code gen race.

B (120:59)

Yeah. It was clear that PMF or die was on to something. But he required someone to make to make it their life's work.

A (121:06)

Exactly.

B (121:06)

And if someone was really. I'm going to be the Mr. Beast of Tech. I'm going to be doing crazy challenges all the time. Live streaming, experimenting with all the different formats. There's clearly something there.

A (121:15)

I think. Clearly should run it back.

B (121:16)

Cluly. Cluley is a good candidate.

A (121:18)

Cluley Hackathon.

B (121:19)

Anyway, we have Leonard back in the studio. Welcome to the TVPN ultradome, Leonard. How you doing?

A (121:25)

Hey, Happy Friday.

B (121:26)

Happy Friday.

G (121:27)

Love to talk about Mr. Beast instead of age 20s again while we're already on it.

B (121:32)

Yeah, I mean on that note, Mr. Beast, if he was going to lock a bunch of programmers in a room with cursor, how bad do you think the gross margins would be? Do you have a take on gross margins of application layer companies? We've been talking about that all week. Do you have any insight in there? Anything that is muttering through your whisper network.

G (121:57)

Unfortunately, I'm not in San Francisco, so I don't know how codas nowadays work. I'm out of the old breed. When I did software engineering, I didn't have AI, But I always notice when I use my cloud plan and I run out of like, queries, I was like, oh, damn, I need to write on my own and think on my own. Who do I query? And then I spin up my second ChatGPT subscription. So I think it would already apply to me. Being stuck without AI is quite a problem nowadays.

B (122:22)

Well, give me the current. Read your current. Take the latest and greatest on the H20 debate. Where do you stand pro export, pro banning the exports? How has anything shifted your thinking around it over the past?

A (122:39)

Do you want to nationalize Nvidia?

B (122:41)

Do you want to invest in Intel? You're trying to buy light? What do you think?

G (122:45)

Well, the last time I was listening to the president speaking about Nvidia, he was more talking about initially wants to break them up and on Nationals, Right. Because they were so big, break them.

A (122:53)

Up, but then roll them up later.

B (122:56)

We've seen this playbook like 20 times with Trump and we get shocked every single time he comes out and and says something, this is the worst thing ever. And then a week later, it's the best thing ever. And we're partnering and we're doing a deal.

A (123:07)

Every roller coaster.

G (123:09)

Well, what he said during the.

B (123:10)

What was it?

G (123:11)

It was the AI action plan launch.

B (123:12)

Right? Yeah.

G (123:13)

I was sitting in a room. He was talking about Jensen, pointing to Jensen and he was just saying, I wanted to break them up, but it's so complex and I think just basically somebody convinced them it's really hard and therefore you're not supposed to break Nvidia up.

B (123:24)

Yeah.

E (123:25)

Right.

G (123:25)

So fair enough.

B (123:26)

Nvidia doesn't have as clean of a line to break up as intel, where, you know, you could, you could gaming.

A (123:32)

We're taking you over here.

B (123:33)

Yeah, yeah, we need to see cards are going somewhere else. Yeah, I don't even know. What would you do? You'd open source Cuda or spin that into a separate company. Like, if you were even to break up Nvidia, like, what would you actually do? Do you have any idea?

G (123:46)

Yeah, I think the software ecosystem might probably be the strongest one here, but again, this just goes hand in hand with the design. Right. So again, yeah, fingers. I think it's a fairly hard one, but for what it's worth, I think the market share is only going to go down. Like there's more and more competitors. I mean the total, total evaluation will go up. Don't get me wrong. I'm like, I'm bullish on AI and Nvidia, but like all the other companies, all the other chip designers, they're just getting better.

B (124:09)

Is that, is that driven by AMD catching up or new? What does Aaron call them? They're like the, the new types of chips like Cerebras, Groq and Etched. I forget what they're called. There's, there's a new name for these crop of ASICs that are designed specifically for AI and they could potentially pose a challenge, but they're certainly not taking market share yet. It feels like it's Mostly Nvidia, then AMD, then maybe some Huawei, the hyperscalers.

G (124:38)

The TPUs, AWS with the Trainium. Google has the TPUs since forever. I mostly think about them. I think it's pretty clearly the case they have all the incentives in the world to build their own AI chips and reduce Nvidia's margins.

B (124:49)

Sure.

G (124:49)

On the startups, let's see how they're doing. Right. I think hardware is hard. Hardware startups generally fail, but if they find the right niche, you know, it's pretty hard. Nvidia builds this more general thing and if you're like a hardware startup, you want to find like a more narrow niche to be like more application specific. And if you hit the right point, right. Whatever the next big thing is in AI, they might succeed and we just see more and more of them getting there.

B (125:12)

Right.

G (125:12)

And like we see Anthropic and other companies using Google GPUs, using training. And again the debate of the shoe. Huawei is also getting better. They will also just the market share can only increase. Right.

B (125:23)

Okay, help me, help me reconcile this. Google DeepMind's been seemingly fine with TPU and not having Cuda in their back pocket. They're on the parade of Frontier. The Gemini models are great. VO3 is great. The new Genie model is great. It seems like they are not suffering or falling behind despite not having Cuda access. But then simultaneously we're hearing that Deep Seek, high flyer Alibaba, they want to train on Nvidia. They're not satisfied with Huawei. Why is Huawei behind Google's TPU business?

G (126:03)

I think that's an example I always bring up that people say it's going to be so hard to search Huawei. Google eventually succeeded, but also Google struggled. The GPUs are pretty, pretty odd. This was way before any AI hype and they actually also struggled. I'm not sure if you guys remember TensorFlow. Yeah, this was originally what they did. Right. And then later they switched to Jax and.

B (126:20)

Right.

G (126:20)

And they have the Pytorch and everything around that. So I think over time they were struggling with software. But I generally see this as a one time investment and Google is a big enough of a company that can just pay this one time investment and then you develop on top of it and eventually you will be there and you break even. You could probably do a survey if people are doing fine. Like probably people still prefer Cuda because it's a bigger ecosystem. But as you're saying, Google's doing fine. And again Huawei will struggle, it will take some time but eventually it will get there. In particular if they can use Cursor longer. Right. Who are doing it. AI helps you to build your AI ecosystem.

B (126:52)

And I guess to some degree the flip side is like TPUs have full access to TSMC ASML and Huawei is restricted all throughout the supply chain. And so the latest Huawei chips. We were just talking to Bill Bishop, he was saying that like that was from like 2 million was it dies that they got from TSMC through a shell company. And so they had this like one time batch of supply chain like ease and then they, and then from then on they were supply chain constrained again. So then they had to go back to doing everything themselves and that was a lot harder. Whereas Google just calls up TSMC and says hey, do everything that you do for Nvidia, just do it with our design which is like probably slightly different. Anything else you want to dig into?

A (127:37)

No, I think we've come in, I think we've covered.

B (127:39)

Yeah.

G (127:41)

Have you guys covered the semiconductor supply chain and how good Huawei is? Because there's this quantity thing is at the like in the middle of the debate in my opinion which I think is being missed here.

B (127:50)

Please.

G (127:51)

So we everybody compares the Nvidia H20 to the Huawei as 910C and that's the best ship they've been putting out there. And if we look at to Huawei AZ910C it's like 80% there. When H100 is so like two or three years later than Nvidia they're finally slowly getting there, approaching on the hardware specs alone. And again we can look at the specification sheet, compare them one by one, but this never tells the real story. Right. If you would do this with AMD and Nvidia, AMD is on paper on the chips as good as Nvidia though, one of them has 95% market share, the other one less than 5. Right. So looking at the spec sheets is never enough. That's where the software ecosystem come in, where we just talked about. Huawei is definitely struggling there. I think they will eventually get there. They just need to have more developers. And I think that's exactly one argument favor of letting the H20 go there. Right. The more people use the H20, less people used to Huawei. So less developers are developing this ecosystem. But where then comes in is how many chips can I produce?

B (128:49)

Right.

G (128:49)

We got one number Under Secretary class that testified. So you're supposed to tell the truth. 200,000 ASIN chips this year versus we're trying to sell, I think it's 700,000 to a million H20s this year to China. So this is, this is where then the debate struggles.

B (129:05)

Right.

G (129:05)

So if we wouldn't sell them the H20s, it's not like they have more AES and 910C standard and then more. Maybe they have more developers than, but they share a limited number of GPU resources. And that's the thing which I think needs to be debated here. And you can fall on both sides of the debate here. But like we need to understand that China is struggling and they cheated these. It's not even 2 million dice. It's 2.9 million dice. Right. Because they're struggling so much with their own production.

B (129:27)

Interesting. Yeah. So, yeah, I mean maybe 200,000 is enough for people to actually bootstrap that software ecosystem. Certainly something to keep tracking.

A (129:39)

I think people consistently overestimate China on a bunch, in a bunch of, of different areas.

B (129:45)

Yeah, yeah.

G (129:46)

I mean they can do software. I think they will eventually get there. And I think the idea that just like Huawei's ecosystem will always be terrible, it's just, look, don't get me wrong, I would hope it's true. Right. But they got good coders, they got good designers, they, they will just get better on all of these kinds of things and maybe Cuda will always be better, but like Huawei is probably at the bottom right now regarding how good the ecosystem is. And when the deep SEQ engineers, you know, they're getting to it and they're struggling.

A (130:12)

Well, we'll get better.

G (130:13)

Right? I think that's, that's for granted. Independent of the 2000 chips or million chips.

B (130:17)

Yeah, I think I still fall on the camp of probably the H20 exports do put enough pressure on Huawei to justify it, but it's tricky this is a thorny one. It's not extremely clear cut for me. Have you landed?

G (130:32)

We should do a case study on anthropic because they're the most beautiful example because they got so many different chips they're using.

B (130:37)

Yeah.

G (130:38)

Right. And how's it going for them? Right, like how, how long did it take to train on Trainium? Are they training on Trainium? Are they deploying on Trainium? And I think this would give us an insight. How many engineers are they spending there and how bad is it still is.

A (130:49)

I think one question, one question is, is, you know, is Beijing playing 4D chess by leaking out, you know, don't use the H20. Don't use the H20. So then they, then we just pile them into the country, right? I think so.

G (131:04)

Is it for D check?

B (131:05)

Yes.

G (131:05)

It's just like you create an artificial demand. You say like look guys, you better buy to buy some 910Cs. You really don't want to. So we tell them oh if it's for sensitive government use you rather use 910Cs. So we only see strong encouragements, not full on bans yet. And we've seen the same game with CPUs. Ask intel how it's going. I mean intel in general but also Intel CPU market in China. The government also started encouraging there basically can you please use homegrown CPUs.

B (131:31)

Yeah. Right.

G (131:32)

So we've seen it all over.

B (131:34)

Yeah. I mean it would be super easy for the Chinese government to import some crazy tariff level the playing field more that way like even ban the H20 importation. Like there's so many different levers that they could pull and the fact that they've stopped, they've only gone as far as like strong encouragement. We've kind of inflated that to be.

A (131:51)

Like it's political incorrectness.

B (131:54)

Yeah. Which people inflate to be like it would be insane. It's suicide to, to not to go against a recommendation from the, from the ccp. But it does feel like they could have gone a lot further very easily if they wanted to. So we'll have to see. I mean it'll show up in Nvidia's earnings, right? We'll see. Or we'll probably get data. So we'll have to have you back on then. But thank you so much for stopping by.

A (132:17)

Great to see you.

B (132:17)

Hope you have a great weekend. We'll talk to you soon.

G (132:19)

Soon.

F (132:20)

Take care.

B (132:21)

And if you get some sleep this weekend, get a date sleep apod 5 they got a 5 year warranty 30 risk free trial free returns.

A (132:28)

Free shipping code TPPN. I was going to say John Exley has landed.

B (132:33)

He's landed. Yes. Let's hear it from John Exley. Welcome to the stream. Thank you.

A (132:40)

We have, we got to pull this up. Trump and Putin are meeting right now and I have a video in the chat team if we want to pull this up. Very cool. Look at this, John. So Trump and Putin are walking and what do you see?

B (132:57)

Whoa.

A (132:59)

Little flyover.

B (133:00)

That's a show of force. Where are they? That is wild. Yeah. What a display of force. I wonder, I wonder where they're meeting. Is it.

A (133:10)

They're in Alaska.

B (133:11)

Alaska, okay. That's sort of neutral ground, I guess. It's technically America, but yeah, flying the.

A (133:17)

We got our bears there.

B (133:18)

Yeah, that's another.

A (133:19)

They should, they should have a bunch of, you know, Kodiak bears.

B (133:22)

What a fun place to meet. Alaska. Anyway, it'll be interesting to see what comes up.

A (133:27)

We should do a show.

B (133:27)

Hopefully it's a resolution of the Ukraine war. I mean, like, Trump has been, you know, talking a big game about being anti war, wanting to know more foreign wars. No, don't send all the money overseas and save that for the taxpayer. Save that for real estate deals, baby. We could be building, you know, golden skyscrapers in America with all those, with all those drones we're. We're sending. But we'll see where it goes. But hopefully a peaceful, a peaceful resolution.

A (133:53)

We will see. Well, next up we have David from.

B (133:56)

Adquick.Com out of home advertising made easy and measurable. Say goodbye to headaches of out of home advertising. Only Ad Quick combines technology, out of home expertise and data to enable efficient, seamless ad buying across the globe. And we do have our next guest here. David, welcome to the stream. How are you doing?

A (134:11)

Welcome to the show.

F (134:12)

Thank you so much.

B (134:14)

What you got for us? Jordy's warming up. He's got the mallet ready. He wants to hit the gong. It's the first one of the stream. You got some good news for us?

F (134:24)

Yeah, yeah, yeah.

B (134:27)

Web AI.

F (134:28)

We're working on some pretty interesting things. Just recently we announced our new knowledge graph mechanism which is out benchmarked all of the best models year to date.

I (134:39)

By how much?

F (134:41)

7%.

B (134:42)

7%. Let's go.

A (134:48)

There we go.

B (134:49)

We like to hit the gong for big numbers. We like to hit the gong for big fundraises. We also like to hit the gong.

A (134:54)

For improved benchmark Maxing.

F (134:57)

Yeah. No fundraising announcement today.

B (135:00)

Soon.

F (135:02)

I can't leak it today.

A (135:03)

Well, we'll be refreshing our Faraka account. But you have a fact. Talk more about the genesis of the business, why you started it and what got you guys here.

F (135:16)

Yeah, absolutely. So web AI is really focused on building models that can live on devices like the ones on your desk.

B (135:25)

Right.

F (135:26)

So the genesis, the company really started.

A (135:29)

Working like what, like on a watch or.

B (135:31)

Yeah, absolutely.

F (135:32)

Yeah, absolutely, all of it. So companies started by working in computer vision and we were working on how could we take the YOLO models if you guys are familiar with those, it was in the early 2016 era. These were the biggest models because language models weren't really mature yet and did early work there. Ended up creating our own runtime engine, so our own AI library and our own network protocol. And what this enabled is us to run state of the art AI models across devices distributed. So when you think about the future of intelligence, we really believe that civilization model is the most likely outcome for superintelligence. And what we're building is the Rails for that. So we serve and distribute models across hardware. So we're running some of the world's largest models today on things like a laptop. So when we say we out benchmark like Opus 4 or GPT 5 in knowledge retrieval, that's happening on a laptop. So it's not like it's a pretty significant breakthrough in modeling and we're doing this in lots of different industries. But what we believe is going to be a big step change in, you know, unit economics for AI as well, it's just not there in the cloud.

A (136:44)

Model seems very important because all week we've been talking about gross margins or the lack thereof in, in a bunch of these different application layer companies free.

B (136:56)

When it happens on device. Right? That's the goal. Yeah.

F (136:59)

And you can do some things that cloud players can't do. Right. So part of the way we're getting this accuracy, there's always this no free lunch, right. So why wouldn't Anthropic do what we're doing to get this huge accuracy retrieval bump? Well, it's RAM intensive. So if we're distributing across devices, we can arbitrage, right. So we can say, okay, we'll pull more RAM because we're inferring on a device. But if you're hosting this for a million users on Nvidia, you can't do that. You can't load additional RAM resource for every user. It's just not efficient. But there's real things that happen on the edge that unlock, I think technological paradigms in AI that are more meaningful like more accuracy, more context, all of that. And we're seeing more.

A (137:46)

What about privacy too?

F (137:48)

Absolutely right. So in our stack, everything's downstream only. So when we partner with a group, like we work with the Oura ring, if you know that company, we're doing the AI for them and think about like health data, like you want that to be private. So the dream there is how can we facilitate personalized models for millions of users that never leave their device?

B (138:10)

React to this post from Tae Kim, author of the Nvidia Way. He says, here's what I would do if I was the CEO of Apple. Quadruple the RAM and iPhones to 32 gigs. Have the Max model at 64 gigs. Memory is oxygen for local on device AI. More equals smarter and more powerful. Take the margin hit. Memory isn't even that expensive. What do you think?

F (138:33)

I think, I think memory. I think he's right. I think memory is fundamental in these models. I also think we need to tread lightly on this idea that, that we're retooling infrastructure and we're making all these big bets on hardware with frankly a pretty immature algorithm. Transformers are not necessarily the winning algorithm. So I think we need to be cautiously optimistic, but we need to continue to work on what's next. You retool based on all of these factors and an algorithm changes and we don't know what the long tail of hardware is going to look like. And Nvidia was really relevant because pre training and all this, but now pre training isn't really happening at the same level it used to. And I think generally more RAM is a safe decision. But also, I don't know if I would jump in and totally rewrite how we're building chips until we know that this is the architecture we want to stick with.

B (139:31)

Would you recommend someone buying a new Mac, max it out and get the most memory possible?

F (139:36)

Absolutely. Absolutely.

B (139:38)

Just. Yeah, why not? Why not? What about diffusion models? Do you think that there's a chance that they have a comeback? We saw that demo from Google where they were doing text like token generation through a diffusion model. Felt kind of like a wild card scenario. I don't know. Yeah, it's actually performing on benchmarks, but seemed like a path, a path in the tech tree that was kind of, you know, more or less forgotten, relegated to image generation, but then it kind of makes a comeback. Maybe.

F (140:04)

I think there's lots of things that have been unexplored, relatively speaking. We spent so much time on transformers, but we haven't spent equivalent amount of Energy and dollars on other architectures that we know work and we know they work at specific things, but there's typically a broader application. I think it's really interesting. I mean we're working on new architectures today with both the public sector as well as the private sector. And we're seeing a lot of breakthroughs that I think make the transformer look a little old.

B (140:35)

Interesting. How do you think about the business model here? Because you're not going to be selling hardware to an OEM in the supply chain, but you're also not an API, so you're not pricing on consumption basis. It feels like there's a world where companies are comping you to an open source thing that they have to implement. Like how? How? Like what does a great relationship with a big device manufacturer like edge computing provider look like for you?

F (141:06)

Yeah, I think, I think web AI1, we have a license, right? Because we have a proprietary tech stack. We're not a wrapper. We appreciate wrapper companies. We think they're doing cool things. But we own our stack pretty vertically. So we own our runtime, our AI library and our tooling around that. And so when we work with a partner, we typically structure a base license minimum. And when we have that license we can inject forward deployed engineers to work with these companies that honestly just don't have the AI talent quite yet and they need help. And I think that's something that people aren't talking about is these products don't necessarily solve the problem out of the box. A lot of these enterprises like Fortune 100 need help, so we do that. And additionally there's a way to take part in the success in the deployment so the usage fees we can get even though we're running on device because our network is managing that. So you could imagine web AI, you have two and a half million custom devices or maybe it's an iPhone and we're shipping across that. Our network manages all of that, so we collect fees on that.

B (142:13)

It sounds like it's somewhat case by case, but you could imagine charging like a per device license, but also like a per token license in the future.

F (142:21)

Per answer is typically how we structure it. So it could be a book, it could be a one word answer, as long as it's an output that's solving a problem. We mostly work in mission critical use cases like things like reassembling engines with multimodal AI, health diagnostics, public sector work.

B (142:43)

How quickly are you going to kill Jordy's battery if you're doing test time Inference on device he's been already complaining about the iPhone not having enough battery life, but it feels like, it feels like there was a glimmer of hope when we were just like, let's just distill the models and it'll just be like a pretty short inference chain. But even if you distill the model, if you're inferencing for 10 minutes, that feels like a lot of heat in my pocket.

F (143:10)

Well, I don't know what Jordy's using. I would assume it's a pretty nice phone. It's like an iPhone.

B (143:14)

Yeah, it's the latest iPhone. Yeah.

F (143:17)

So I mean, you mentioned quantizing, so I'm going to talk a little bit about that and what we're doing there. So we released an open source paper around a tech that we were building early that we've now expanded and it's a little, it's more sophisticated now, but the principle's still there. It's called ewq. And instead of just quantizing and tell me if I'm going way too technical here, quantizing, traditionally you have like a fixed value. So we have, let's say you have a full precision model and when you quantize something you say, okay, I'm going to quantize it to 4 bit, or I'm going to go to 16 bit. And so you're just drastically chopping the model down right from the float values that it can pass through. With ewq, what we do is we have something called device profiling. So in a web AI model hits your phone, it's running our web frame library and it profiles your hardware. And then what we do is on inference we run ewq and what EWQ does is it does real time quantization. So based on your question and the inference and what it leads to is close to 30 to 40% model reduction size in RAM while retaining accuracy. So what that means is we get bigger models inferring and instead of like just one size fits all quantization, we dynamically do that on inference and what that leads to is less energy consumption, higher accuracy, less usage on the device.

B (144:39)

Yeah. So somewhat similar to the model routing that we're seeing in ChatGPT now, what were your overall reactions to GPT5?

F (144:48)

It's just an moe router. I was kind of hoping it was a new foundational model. And when you interact with it, it's really clear that it's just a way to dynamically control price based on a question. So like you Ask a question, they route you to a different model. If it's coding, it will route you to a different model. I can see where that's valuable. I have a lot of people that are non technical that are in my life and I've watched them now switch off of GPT after the 5 release to things like Grok, which was kind of shocking to me. But I think people were used to a certain standard of response. And now the lack of transparency in picking the model you're engaging with I think created some whiplash. But I'm sure there's areas where it's amazing. I haven't really gotten to tap into everything there. Been enjoying a lot of the anthropic releases and typically probably tend to lean that way.

B (145:44)

Cool. Well, thank you so much. Congrats on all the progress and I hope you have a great weekend.

A (145:49)

Come back on again soon. Sounds like you.

F (145:51)

Yeah, absolutely.

B (145:52)

Yeah, we're excited. Yeah, yeah, absolutely.

A (145:54)

Great to meet you, David.

B (145:55)

Thanks for.

A (145:55)

Thanks for joining.

B (145:56)

Let me tell you about public.com investing for those who take it seriously. They got multi asset investing, industry leading yields. They're trusted by millions, folks. Should we go? What did Sama mean by this? If we didn't pay for training, we'd be very profitable. We talked about this. Kristen Culver says most successful coups in history. Napoleon Bonaparte's coup of 18, Brumaire October Revolution in Russia 1917, the Nazi seizure of power 1933, Egyptian coup d', Etat 1952, the Chilean coup in 1973 and the Open Door retail army at Open in 2025. Kirsten worked at Open Open Door, correct?

A (146:40)

I believe must have.

B (146:42)

And so she's having fun. It'll be interesting to see. In other news.

A (146:45)

Yeah.

B (146:45)

So the CEO stepped down this morning. Yeah, this morning. So Kerry Wheeler posted on X today, I'm stepping down as CEO of Open Door. When the board of directors asked me to take on this role at the end of 2022, the company was in crisis, the real estate market was punishing, the business needed a reset and the path forward was uncertain. My mandate was clear. Stabilize the company and do what was necessary to survive. Of course I said yes because I believed in Open Door. It wasn't easy and it wasn't about glamorous headlines. But we stopped bleeding. We restructured the business, rebuilt an exceptional leadership team, got an NPS of 80 and she says, I'm pleased the leadership team will continue to execute on the vision strategy. I'm closing this chapter with pride, clarity and gratitude.

A (147:28)

So good luck it is wild.

B (147:30)

Carry.

A (147:30)

Opendoor is up 200, 200% in the last 30 days. And they the Retail army said now we want more. They want more.

B (147:40)

New leadership are crushing it.

A (147:42)

Well, I'm excited to see where Kerry Wheeler goes next.

B (147:46)

We should watch the new Jason Carmen film.

A (147:50)

Let's do it.

B (147:52)

Coming on the stream in just a few minutes. Let's pull up the latest work from Jason Carmen.

A (147:56)

I'll be right back.

B (147:57)

Please. Dear son. Some time ago, the machines roared. The steel bent to our hands. We built for the stars, for our land, and for your future. We went fast. We went far. It made us strong. It united us. Then the sound faded. I the hunger to build drifted away. Those who knew grew tired. But now the fire returns. The steel is ready. The country needs you. New boundaries beckon. Who will you be? Oh, that. Where will you take us? The stars our calling. Our future is waiting. So will you. Answer. Sick fit. We need one of those when we're discussing hard tech. We should have gotten the seats today.

A (150:55)

Huge mess.

B (150:55)

Anyway, new video from Range View. We have the CEO Cameron Schiller in the studio in the TVPN Ultradrome. Welcome to the stream, Cameron. How you doing?

A (151:02)

What's happening?

H (151:04)

Hey, guys.

B (151:05)

Good to see you. I have so many questions. Are you. Did you act in that? Are you in that?

H (151:10)

I am not in that. That was a.

B (151:12)

You're not in the suit.

A (151:13)

What?

B (151:13)

You got it. How? I thought you financed this whole thing. I thought you made this happen and you didn't get a cameo.

A (151:19)

Gotta put yourself in.

B (151:20)

The machines got cameo. Right, the machines got cameos. And I believe that last scene takes place @range view HQ. Is that correct? Did I clock it correctly?

H (151:28)

That is correct. That takes place. Technology Demonstrator Factory. We're running two. We got a production facility down the street, which we'll see in some. Some new videos coming out. But that was at this facility which we've been at. And now we're just busting out the seam. So we've got a.

A (151:42)

We've got to move.

H (151:42)

And you'll see a bunch of content from that new one. That place is sick. They used to build space shuttle engines there.

B (151:48)

That's awesome. Yeah, the space shuttle shot was fantastic. I mean, Jason Carmen, he puts on a clinic every time he drops a video. What. What inspired it? What was the message you want to send is this just is a recruiting film. I noticed like the. The follow up post was like, come work for us. This is not like an ad that you'll be running to get customers necessarily or is it just kind of like vision film? What was, what was the thinking?

H (152:09)

Yeah, I mean, it's really a message to America. I think it's a, it's a wake up call. It's a question of who we really want to be as a country. What do we want to do?

E (152:17)

Right.

H (152:17)

I think, I mean, that was a big part of my life growing up, going back and forth to China. And you know, I saw the American dream in China when I was there, saw people from the center of the country move to the coast to work extremely hard and make a life for themselves. And when I came back to America growing up, I just didn't see that here. And, and I think we have to bring it back, I think for national security reasons, I think across the globe. So the, the real question is, you know, can, can America bring it back? Because we need to make a lot of parts very soon. And this is less so about range view. I mean, America needs a thousand range views. This is about people that are considering making a big pivot in their life to work on something that matters to the world.

B (152:58)

And when you say make a lot of parts very soon, is that specifically like defense tech and warfare? Is it, are you seeing, are you worried about great power competition? Or is it more like we won't get the next generation of 911 or the next great physical product won't be made without this happening.

H (153:21)

I think the one that I care about the most is the second one. But the first one's definitely very real. If you think about it, America did some amazing stuff. The F117, we invented stealth technology, the SR71. All that stuff happened in America. And that happened because I think of factory towns. I mean, I'm calling in from El Segundo. This is a town that literally runs on jet fuel. Like there is a refinery that's right next door that's pumping jet fuel out under this city to feed to lax, which is on the other side of the city. And you feel it in the air. There's something that happens when a community wants to be a part of something great in the world. And when we look around, everything around us has been made in China now. And with it, I, you know, with it slipping, I think a great calling to be, to be a part of something amazing has, has slipped as well. So we really need to bring that back. We're going to do that with, with parts. We're going to do that with new technologies that enable factory towns all across the country.

A (154:22)

Yeah, I mean, I think it's Super.

B (154:23)

What's going on behind you? There's like some scrolling image. What is that?

F (154:26)

That might be.

H (154:28)

I mean, we've got a lot of screens.

B (154:30)

Oh, is this a screen? It's like a TV or something. Yeah.

H (154:33)

I mean there's, there's a lot of lights and there's a lot of. This camera reacts with.

B (154:37)

Yeah, yeah. Right, right, right. Okay.

A (154:39)

Yeah. I think, I think what you're. One way to kind of summarize what you're kind of getting at in from my view is it's important for American dynamism to not be like a venture hype cycle. That's sort of of. It's not something that can be accomplished in two years since, you know, moving to El Segundo became popular and a meme and it needs to endure.

B (155:05)

Who helps with that? Was it me, you, Jason Cameron, Carmen? Yeah, we might have played a role.

A (155:12)

A little bit to do with it.

B (155:13)

But I mean, I guess the question is like you say this is like a wake up call for America. Like, are we not awake? I feel like. I feel like a lot of these. A lot of this message is broken through. Like. Like what, what's left to say? What, what, what do we say?

A (155:28)

It's broken through in the bubble.

B (155:30)

Yeah, yeah, maybe it's the bubble. Maybe it's the bubble. Yeah. Well, I mean, what is your take on, like, the re industrialized summit's huge. Like the, you know, the American dynamism summit is huge. Like, like people seem to be beating the drum. People are at the White House. They're in dc.

A (155:44)

They're a fraction of the size of the Salesforce. Dreamforce, John.

F (155:49)

That's exactly it.

B (155:50)

Yeah. Are you gonna be there? Dreamforce. Let's get you there. This man loves enterprise SaaS. He won't admit it on camera. He plays this character that likes re industrialization, but really he just wants to. He just wants to code.

H (156:03)

Yeah, it's all I want to do. That's all I want to do.

B (156:05)

John.

H (156:06)

D. No, we need to. We need to have more people. A, it's inside the bubble and B, we need to encourage the people that are working on the problems to focus on the things that matter. And that's actually making parts. We need more factories. We need more metal moving. Moving metal is the problem right now. There's a lot of people building tools for factories. There aren't that many factories.

B (156:24)

Yeah, yeah, yeah.

H (156:24)

So if you want to join, build a factory, make parts, move, you know, do real stuff in the supply chain. I'm not Talking like screwdriver factories bolting on imported components. That's the vast majority of, you know, assembly in America is like what's left. So we don't need that. We need people working on hard problems.

A (156:41)

We also need you to thousand x. We need you to 1000x range view. You said earlier we need a thousand range views. But why don't, why don't you just be copy and paste yourself?

H (156:50)

Working as hard as I can.

B (156:51)

What can you tell us about work harder. What can you tell us about the state of the art in manufacturing? I know there's additive manufacturing, subtractive manufacturing. There's CNC. We've talked to people that are 3D printing metal. Now there's casting. What, what, what are you excited about? What are you focused on and where do you think there's still pockets of opportunity?

H (157:11)

Yeah, great question. So we are working on casting and we are, we're trying to give casting at CNC moment and explain casting for those who don't.

B (157:21)

It was a.

H (157:21)

Casting is liquefying molten metal, pouring it into a mold. It's solidifying and you're getting, you know, the part that has that shape and almost everything is cast. It's, you know, even CNC shops buy castings today. Castings have, have eroded so much that machine shops are just buying cast blocks and then they waste a whole bunch of time cutting apart into, you know, into its final part. You know, a lot of chiseling. But if you really get really good at casting, you actually just cast 99% of the way and then touch the final bits up with a drill bit. So there's no one size fits all solution in manufacturing. It's one of the first things you learn. It takes like, it takes like 100 human from mining the ore out of the ground to installing the bracket on the end of the thing to actually make something happen. And there's a ton of folks, I think for people looking at technology, they're used to looking at manufacturing as just another sector. There's fintech, health tech, manufacturing tech. The truth is manufacturing represents more of America's GDP than all of tech combined. And so it's huge. And so inside of manufacturing, of all these sectors, and so many have just been not been looked at yet or not been touched. And so we're seeing resurgence. The other thing is, is, you know, you maybe shouldn't finance these things exclusively with all venture capital because the risk profile just isn't the same in the factory.

B (158:37)

Right.

H (158:37)

Like if my factory burns down, I'm going to still have, you know, a thousand pounds of super alloy. You know, maybe it's. Maybe the crate that was in caught on fire but like they're not going to move.

B (158:47)

Right.

A (158:47)

So you should have buy.

H (158:48)

It's not risky. It's not a risky bet. So you shouldn't buy that stuff for that. So I think there's a whole new level of financing that's going to come in. And you see this happening with a few of these big factory companies where you're getting really smart finance deals where you buy the technology improvements with venture capital for those returns, but the rest of the factory is financed in a different way.

B (159:05)

Yeah, that makes sense. If I were to pull a hot take out of you based on what you just said, it sounds like potentially the American manufacturing industry has over rotated towards subtractive manufacturing and needs to rebuild additive manufacturing or casting capability. Is that. Is that like roughly a reasonable take.

H (159:22)

That additive and casting are not the same. Additive is like this SPAC machine that's blown up actively as we see there are a few amazing people doing additives for the most part. Like it's missing on qualification and it's missing on real unit economics at scale, which is as a whole, that's what America is missing. Like really being able to build stuff at scale. If we had to triple the manufacturing output of the country, we'd be cooked, totally cooked. Like it would take us five years to get the factories up to do that. And all the factories that would start would be sending them money overseas because none of this equipment is made in America anymore. We lost the factory industry but we also lost the factory and machine tool industry. So all this stuff just goes overseas. So I wouldn't say that additive is it. I think casting is really important. I use traditional forms of manufacturing are really coming back and making a big play. But we should just be encouraging everyone to make a lot of parts. We need so many parts and we need to get started immediately.

A (160:14)

Parts maxing.

B (160:15)

Talk about your dad. Talk about the influence there. Jason teased it a little bit, but I haven't heard the story.

H (160:21)

Yeah, yeah. He's a big, big part of. Big part of my life. He's always encouraged me to be very, very honest and real about this world, which I think is really important adventure. And he was a maker himself. You know, his family was Pittsburgh and he was a, you know, Midwestern family values and they, you know, they. He came here to work on the B1 bone. The supersonic bomber is pretty sick. And then I ended up growing up next to where Skunk Works was, was founded so Bob Hope airport, actually the F170, all that stuff happened there. And then it went out to Palmdale and then it became a service based industry. Lots of B2B sass and entertainment happened in the area and it really changed. But he always, you know, kept me, kept me centered and he's a huge influence on my life and actually same with Jason's dad, so. So we bonded over that a lot. And they're becoming increasingly large parts of both of our lives.

A (161:15)

Can you raise like a billion and then run this ad as a Super bowl ad?

H (161:21)

You guys want to help me?

B (161:22)

Yeah. Yeah. We got to get him back on the Venture training. Cameron's always very like anti venture.

A (161:27)

But we need to run this film as a Super Bowl Range view.

B (161:31)

Come on back.

A (161:33)

Range View. Just to, just to get the capital for super bowl ad. And then, and then you can figure out a.

B (161:38)

Take 10 years of super bowl ad. Let's run it every year. This is not gonna happen overnight. Yeah, I'm in.

H (161:43)

Let's talk about it. Let's make a game plan.

B (161:45)

Fantastic. Well, thank you so much for hopping on. Congratulations.

A (161:49)

Congrats on the launch.

C (161:50)

Cheers.

B (161:51)

Wait, where can people go to apply for jobs? I know that that's important right now. Range view.com scroll down careers range view.com you heard it here. Thank you so much for having me on. Have a great weekend and I will talk to you about bezel. You want to manufacture something? Manufacture yourself a watch on getbezel.com new bezel concierge is available now to source you any watch on the planet. Seriously, any watch?

A (162:15)

Any watch. I got them all. Well, I'm very excited for this next. Well, okay, so there's. I was gonna. I thought we had our friends over at NFM Live.

B (162:26)

They will be coming on in just a few minutes, but we will be joined by Cirac or Ciroc.

A (162:31)

Cyriac.

B (162:32)

Good to meet you, Cyriak. Thank you for joining the stream. Why don't you kick us off with an introduction on yourself and the company? All right.

I (162:39)

Well, I am Cyriak from early. Early is an early cancer treatment company. And essentially what we do is we create genetic constructs that are injected into your body and they disperse everywhere in your body. They enter healthy cells randomly and if you happen to have cancer cells, they will also enter those, but only if it's cancer. These genetic constructs will switch on like a light switch. And then they turn the cancer cells into little factories that are forced to make any protein of choice. In other words, you can make something that makes the cancer visible, or you can make something that activates your immune system to attack and kill the cancer. So the whole thing is relevant because in the last 50 years, we've always tried to find some markers on cancer cells that make them detectable or druggable. Right. Billions of dollars have gone into that, and yet we still have 600,000. Yeah, we still have 600,000 people dying from cancer in the US every year and 10 million globally. So something needs to change.

B (164:02)

What's the background of the company? Is the tech transfer? Did this come out of an academic lab? What is your background?

I (164:09)

Yeah, I'm actually not a biologist. Out of 35 people, I'm, like, one of two or three people who don't have that background. I'm an engineer. I'm a serial entrepreneur. And the idea came out of Stanford university, and it was one of the world's top people in early cancer detection, who then himself, sadly passed away from cancer.

B (164:28)

Wow.

I (164:28)

Including his own son died at 16 from cancer, and his wife died two years after him. The whole family is wiped out. So I met him.

A (164:37)

Was that out of curiosity, Was that environmental exposure or.

I (164:42)

No, no, no. It's mostly genetic.

B (164:44)

Genetic.

I (164:45)

And the mother had a genetic genetic mutation that then got transferred to the sun. And what Sam gambier died from, the inventor of the whole thing, Is unclear to this point. It was cancer of unknown origin. Didn't even know where the primary tumor came from. So a very tragic story, but he was committed to flipping the tables against cancer. So I don't know if you guys have Jordi or John, Whether you have anybody in your family or in your friend's circle that has been affected by cancer.

B (165:21)

Yeah, yeah, of course.

I (165:23)

You know, it's just kind of crazy that we are always behind a step behind or two steps behind. We're always trying to find the next marker that we could hook onto. So what if we could stop looking for any marker altogether? What if instead, we could force the cancer to reveal itself and make its own therapy to kill itself?

B (165:50)

So what's the pathway to commercialization? Imagine you have to go through fda approval at some point.

A (165:56)

Yeah.

I (165:56)

We have to go through a phase 1, 2, 3 trial and then to commercialization. And we have spent the last seven years Cracking this really hard problem. You know what the biggest problem is in cancer? What's a cancer cell and what's not a cancer cell?

B (166:14)

Yeah, of course.

I (166:15)

Because, you know, different from a virus, this is your own cell that has changed. Just A little bit. And so differentiating that from a normal cell or from something that looks like cancer but is totally benign is really hard. And that's what we've spent so much time and energy on with AI. We're essentially producing AI results, liquefy them, put them into the body, into a cancer drug that then forces the cancer to produce its own therapy against itself.

B (166:50)

And what's the latest news with the company?

I (166:53)

Well, we just raised $44 million.

B (166:56)

Congratulations. Cheap work you're doing. Sounds expensive.

I (167:05)

Yeah, biotech is not cheap. So you know, I don't know how much you know about the biotech world. It is in the biggest funding crash in the last 20 years on both.

B (167:16)

The public side, the private side. I know that the government funding is certainly at an all time low, but.

I (167:22)

Across everything actually you named them. The private funding is extremely low because of two reasons. High interest rates which immediately affect a long running product like bio takes 10 to 12 years. Right. And then AI is like a vacuum cleaner for money.

B (167:41)

Makes sense.

I (167:42)

It sucks up all the money that goes to tech firms because for VC companies, many of them believe they can make a faster return by putting it into AI. Classic tech, of course, but bio and AI is a great interface that is now coming to fruition. And then the pharma companies, they are concerned about tariffs, they are concerned about China catching up to the US and they start buying ideas and drugs there. And then the government is not stepping in to flatten out the curve. And here I would actually say we really got to make a national commitment to biotech to flatten out this funding curve. Because at the end of the day, would you like to be dependent on China providing the most developed life saving drugs for cancer, for autoimmune diseases? Do we really want to depend on that? I mean it's good if they supply them, but what if they don't one day. So we should actually have a national commitment to biotech to make it to retain the world leadership that the US has had for the last 50 years.

B (168:50)

Yeah, good point. Well, thank you so much for stopping by. Have a great rest of the week, super important work and have a great weekend and congratulations. We'll talk to you soon.

I (168:59)

Thank you.

B (169:00)

Talk soon. Bye bye. We have some major guests in the restream waiting room. Let me tell you about wander first. Wander.

A (169:06)

Find your happy place. Find your happy place.

B (169:10)

Book a wander with inspiring views, hotel, great amenities, dreamy beds, top tier cleaning and 247 concierge service. It's a vacation, but better.

A (169:17)

Well joined by this is the moment you've all been waiting for. They're calling it the Brothers. Welcome to the show.

B (169:25)

You guys look fantastic. Plastic. We got headphones on.

A (169:28)

Let's go.

B (169:29)

Hear us. How you doing? Yeah, yeah. Give us some energy. Come on, come on.

A (169:36)

What time, what time is it?

B (169:37)

What time is it? Us?

A (169:39)

Yeah, it's 5:5.

E (169:40)

So 5, 5:15.

B (169:42)

In the morning. In the morning.

A (169:43)

Okay, so we're just waking up here. Here. I'm gonna help you guys. I'm gonna help you guys wake up. They're going out in Korea.

B (169:55)

Thank you. Thank you. So talk to us. How's it been going? How, how, how has it been running the show? What inspired you? Obviously you know TVPN Spider. But, but have you work, been working in media? What's the background story here?

A (170:10)

Yeah, give us, give us life stories.

H (170:14)

Yeah, so both of us coming from venture capital backgrounds, we've been based in Seoul for over like four or five years. So yeah, we love this industry. Private market, VC tech startup, this whole industrial complex.

B (170:28)

And we thought that there could be.

H (170:30)

Something we could do above just the pure investment and media could be the perfect complementary medium. So I mean, anyway, we were doing our own thing, running our own blog and writing essays since a few years ago. And this guy here, Sung Jung, he actually with his friends is running one of the biggest VC newsletters in Korea. And I've been running my own blog.

B (170:55)

Congratulations. So earlier this year we thought that.

H (170:58)

Oh, podcasts could be the most optimal medium to reach the wider audience and also to reach the global markets. And we found you guys randomly on the feed.

B (171:10)

Oh yeah, this is the shit we gotta.

H (171:16)

Yeah, so yeah, of course, you know, we, if there's anything we want to benchmark from the us it's not all in, you know, it's not the boomer.

B (171:23)

Institution, you know, like medium.

E (171:25)

So.

A (171:29)

So, so what's your, what's your guys schedule? Are you, have you, have you quit the other stuff yet? Are you going all in? Are you just putting, how much time are you putting up?

B (171:36)

Three hours a day. Do you have multiple guests? Like what, what, what have you taken from the show? What's working, what isn't? What needs to be different to succeed in Korea?

H (171:49)

So I would say so. Okay, first of all, so we're like running like three times a week. So we will ramp it up.

A (171:57)

You got to get those numbers up. You got to get those numbers up. What are people going to do on the other day? Yeah, they expect to just twiddle their thumbs.

B (172:05)

Does the Korean tech economy not function.

A (172:08)

Five days, 24, seven.

E (172:11)

Okay.

B (172:12)

But we're gonna ramp it up. I'm letting you know.

H (172:14)

Check us out in like six months. You know, we might be running like.

B (172:16)

Seven days a week.

H (172:17)

Who knows?

B (172:17)

So I love it. Yeah. Yeah. So.

H (172:19)

So anyway, yeah, you know, the, the Korean tech market is as vibrant as, you know, as the US I would say. But, you know, like, but, you know, we are just kind of like, you know, being the frontier in this. Like, you know, hold the new media. Like, just like, you know.

A (172:37)

Well, in many ways we're old media. We're old media. This is just television.

B (172:41)

Did you say that it's just tv?

A (172:43)

Is it?

B (172:44)

Talk to me about the guests. Do you have dream guests? Who's the Palmer Lucky of Korea? Who's the Elon Musk of Korea? Who do you want to get on? Who have you had on? We have a lot of venture capitalists, but then we have analysts, politicians. We've kind of gone all over the place. Where have you had success? Success Doing guest interviews or are you even doing guest interviews yet? You. I believe you are. Right.

H (173:06)

So we're in the very initial phase, you know, so we've only embodied a lot. So, you know, only a limited number of guests. But so far we have some, you know, like EMS engineers, also venture capitalists, also authors who just publish books. But, you know, we want to have. Actually we want to bring in everyone, you know, everyone vip, you know, even the president, you know, like, like, you know, like even Trump, who knows.

B (173:31)

So we want to bring you guys in our show. Yeah, we'd be happy to.

A (173:35)

Let's do it.

B (173:36)

You make us wake up at 5am I guess.

A (173:40)

We'Re ready. I mean, I mean that would, that wouldn't work time zone wise, but be more like, instead of going to bed, we'll pop on your guys show.

B (173:47)

Yeah. Are you guys live at 11am Local?

D (173:52)

Oh, it's like 6pm On LA.

B (173:55)

6Pm in LA. Okay. Yeah, we can do that.

A (173:59)

Dinner time. Our wives will be very happy. We're bailing on dinner. What's the OpenAI of Korea?

B (174:06)

Yeah. What's the hottest company? What's the one that everyone's focused on? SK Hynix is obviously like later stage, but who is ascendant? Okay. OpenAI of Korea. Okay. We got to be struggling.

H (174:21)

I would definitely pick in terms of semiconductor business. SK High and I, Samsung Semiconductor, of course. And also we got some, you know, like Hashok Korean developers at OpenAI.

A (174:33)

So you know, like, how do you say cracked engineer in Korean?

B (174:38)

Like Beachin engineer.

H (174:40)

Like, you know, like bitchin engineer is like a cracked.

B (174:43)

You know.

A (174:44)

I can hear that. Yeah, yeah.

B (174:47)

The team loves it. The team loves it. That's great. Anything else during?

A (174:51)

No, this is great. We're. We love what you guys are doing. Happy to come on the show and have fun. Have fun out there. And you guys look sharp too. Thank you for make, you know, copy and pasting the suits as well.

H (175:10)

This is an inspo from you guys. But you know, like as we want to, you know, like make our own path and only because, you know, from here on, so. So yeah, we're going to build our own brand. This is NFM live.

A (175:20)

NFM Live. Love it. Well, we support you guys.

B (175:23)

Enjoy.

A (175:24)

Thanks for coming on. Cheers.

B (175:26)

Thank you.

A (175:28)

Good stuff. Good stuff. Lads, lads. They're also pretty well positioned to cover defense tech. Korean. South Korea obviously has mandatory military service. I think most people kind of interrupt college. They kind of take a break from school, go serve, then go back. So anyways, glad that we have contact with the Korean market.

B (175:54)

Yes, yes, definitely.

A (175:56)

Last post. Close it out. From Andrew Reed.

B (175:58)

I knew you were gonna pull this one up.

A (175:59)

These shoes have gotten an obscenely high market share while accumulating zero aura.

B (176:05)

What are these?

A (176:05)

These are. I think Velas would. Wouldn't definitely came, you know, kind of a.

B (176:13)

He's just taking shots left and right.

A (176:16)

I think they're Vejas. Vejas. Vejas, Vejas.

B (176:20)

I bought a pair of these at one point.

A (176:21)

You did?

B (176:22)

Yeah. They were very much just like shoes to me.

A (176:25)

Number one question on Google, why is Vejas so popular?

B (176:29)

I mean, I wonder if the business is doing well. I wonder if they've figured out some sort of distribution, some arbitrage, maybe some. I don't know. Are they more D2C? It does seem like a newer brand and I certainly do.

A (176:42)

Founded in 2004. Okay, so headquartered in France.

B (176:47)

France. Interesting.

A (176:49)

And yeah, I don't know. I mean, I think they just kind of tapped into the common projects. Sneaker projects.

B (176:58)

What are you talking about?

A (176:59)

Well, that was just like the definitive white like sneaker. Right.

B (177:06)

That was the gap of the market. No one thought to create a white sneaker.

A (177:09)

I mean, a white leather sneaker. That was. That was not from a. No, no, no. But not sportswear. That's a key thing. Not like basketball themed street wear. Right. Something that was versatile. But yeah, I wouldn't. Wouldn't be. Wouldn't be caught dead in them.

B (177:25)

Those would be over farming you if you put them on.

A (177:27)

Yes, yes. You got to be careful not to get aura farmed by your own clothing. It happens sometimes. It happens to the best of them. But I'm happy for Veja's success. I'm happy for their success.

B (177:39)

Overnight success. 21 years. Keep it going. Anyway, that's our show. Thank you so much for listening and watching and enjoying the debate. We will see you on Monday. Leave us. Five stars on Apple Podcasts and Spotify.

A (177:50)

Can't wait.

B (177:50)

And thank you.

A (177:51)

I cannot wait. I cannot wait for Monday. I can't believe we get to do this again.

B (177:57)

And I figured out it was Friday.

A (177:58)

No, you really did think we still had. You thought today was Thursday.

B (178:00)

I thought we were a day behind. And I thought we still had more time, but.

A (178:03)

Well, have a fantastic weekend, folks. We love you.

B (178:07)

See you.

A (178:08)

Bye.