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Today is Friday, August 15th, 2025.
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We are live from the TVPN Ultradome, the temple of technology, the fortress of finance, the capital of capital.
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We have the debate of the century, the debate of the year, a showdown between former founder fund, founders fund colleagues, >> friends turned foes, >> friends turned rivals.
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Everett Randall, he's been on the show before.
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Delian Aspir, he's also been on the show before.
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>> They haven't been mincing words, John.
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They have been throwing shots >> back and forth every TBPN appearance.
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>> They're calling the other one out. >> Yes.
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And so they will right now strategies.
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We're going to settle it today on the stream.
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The slop verse steel debate. >> Which is better?
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High margin software or capex intensive re-industrialization efforts.
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Uh we will bring in Delian and Everett into the studio. Welcome to the stream. How you guys doing? I like that background. Very good. >> Energized. >> Here we go.
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We're going to be breaking it down live here.
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>> Yeah, we're going to be breaking it down live.
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Uh >> I'll I'll every time one of you gets a point, I'll I'll put a >> we'll put a little point or if if things get out of hand, we'll be banging the gong and bringing order like it's a gavvel.
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Uh but I'm sure I'm sure this will be uh I'm sure everyone will be civil. Oh, I'm sure.
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>> Keep the name calling to a minimum.
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Uh good to have you both.
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Thanks so much for being here.
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Let's let's kick it off with off.
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What's your least favorite thing about the other person?
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>> We should kick it off with the original story, like how did this all start?
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Basically, >> give us some backtory.
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>> Yeah, >> you want to you want to give it? >> Yep. I I'm I'm happy to.
4:37
So, we were um back at Founders Fund.
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We were um starting to beef up our our like CRM and data science efforts.
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And so we were we were integrating some external data into our CRM, figuring out how we could filter opportunities better to each of the each of the investment team professionals.
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And we were looking and we were looking at the different data.
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I was like, "Ah, 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 delion because it seems like those are the types of companies that that he loves to invest in."
5:13
uh >> the the uh the the rivalry between um between you know the low gross margin side of the house and the high gross margin side of the house was born then. >> Okay.
5:24
Uh and and Delian justify wh why why do you like these businesses?
5:28
Is that is that even is that even a fair characterization?
5:34
>> Um fair characterization.
5:34
>> Um fair characterization. I think um you know my sort of oneliner would be I'm not sure that you know sort of gross margin is actually like the right thing to sort of focus on in a business especially you know sort of early on what you want to be thinking about is obviously Ebot margin in particular a terminal you know Ebidot margin and so when I think about the like at least
5:50
founders fund ethos to you know sort of investing we think that that terminal margin mostly is determined by ultimately how much of a you know monopoly your company can you be in the long term and so if you look at you know
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sort of mag 7 today obviously there's a decent chunk of them that you know have some phenomenal you know 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
6:07
If you look at the one that is at the biggest scale and has the best evidot margins, it's the one that is the most, you know, basically hardware oriented for sure.
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Some of it propped up by like CUDA and they're like, you know, sort of software side of the house.
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But like Nvidia is the one that is performing the best of all those.
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And then even if you study within those, you know, which of those um, you know, uh, companies on the hardware side have monopolies versus, you know, sort of not, you see it's the one that with the monopoly, you know, clearly outperform the ones that don't, right?
6:29
So Tesla obviously in that you know sort of mag 7 but a part of why they you know sort of suffer much worse margins than like an Apple or an Nvidia is because like they actually do have you know you know competition and so my general characterization of you know sort of SAS is people always you know sort of study their original you know sort of gross margin but weren't burdening in the you know sort of cost of sales marketing etc.
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And because you just have much less of a monopoly typically in SAS that ends up totally you know hurting your EBO you know margin profile.
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So take like the like the favorite you know terminal scale thought of as a monopoly you know sort of SAS company that you know Ev I'm sure loves Salesforce their market share and all things CRM is 25%.
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And so that's why you end up seeing like yeah gross margin profile is only burdened by like you know cloud you know sort of cost but their eB margin profile is like you know sort of 40%.
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And so, you know, the reason that I like these negative gross margin businesses, yes, they're like tougher to start.
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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, EV loves to, you know, invest in the, you know, AI slop codes that might have early gross margins and revenues, but >> So, Ev, what's the bullcase for for software?
7:34
What's the bullcase for SAS?
7:36
What's the bullcase for AI slop codes?
7:39
Look, so to to quote the godfather Neil Neil Meta himself, the laws of great businesses are the laws of great businesses.
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The job of a business in a capital society is to is to maximize and find the efficiency frontier for three things.
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Reich 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 ro.
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There's a lot of different framings for the paths to do this and how companies can actually do this.
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The one that people like in tech circles is Hamilton Hemler's seven powers.
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A company accumulates power in the form of scale economies, network effects, uh whatever whatever power you want to take and then uses that power to produce above market re as long as possible and with as much capital invested in the business as possible.
8:27
There are great Adams based businesses that do this.
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There are terrible Adam based businesses that don't do this.
8:31
There are great digital businesses that do this.
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there's great or there's terrible digital businesses that don't do this.
8:35
Um I mean you want to hear of a great Adamsbased business that does this listen to the acquired pod on Costco.
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Like it's certainly not like a Adams versus SAS thing necessarily.
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The advantage that digital businesses have is that like in this process of producing above market ro for a long time is that their product form factor and the way that they distribute their product lends itself more to the the process of creating power I'd argue than most atomsbased businesses.
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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 Door Dash.
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And so there's a lot of these forms of power that naturally lend themselves to digital products.
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And the scalability of digital products tends to be a lot um a lot greater than than physical products.
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And so you can see these these rapid growth trajectories like we're seeing from OpenAI and Anthropic and many others.
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>> When did you guys find common ground?
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Was it uh in the escooter era, the the sort of 15minute delivery era?
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Were you ever able to kind of come together and say like, "Yeah, the this is, you know, we can both agree that this is uh this is not it." >> We're good. We're good.
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I mean, uh to be fair, Kleiner Perkins, Founders Fund have both invested in Figma, Stripe, Airbnb.
9:48
There is some portfolio overlap, right? Ripling as well.
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There's a Modern Health, I believe, as well. There's a few others.
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Um but yeah well to to Jord's point where where else is the common ground and where where else is the divide >> or or or the consensus in in the disagreement? >> Yeah.
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Yeah, I was going to say you ever and I were texting before this of like, you know, what are you sort of two companies that I think um you know, both of us were enthusiastic about in you sort of 2021 that actually both have, you know, sort of trended well, but our, you know, sort of counterpoints are two arguments.
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The ones that we kind of came up with were um you know, 2021 I was really, you know, sort of, you know, high conviction on Hrien in 2021 was super, you know, high conviction on Rippling.
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Both those investments have, you know, sort of performed quite well over the last couple years, but look, you know, sort of wildly different in terms of, you know, profile.
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Um, you know, Ripling like many other, you know, sort of SAS companies does end up having, you know, an initial, you know, very high gross margin, but does still have to spend a lot on sales and marketing to bring in, you know, sort of net new customers.
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Hadrien on the flip side, deeply, you know, sort of negative gross margin to start, but now as they've gotten to scale, they actually have like super limited, you know, sort of sales and marketing spend because there's only like, you know, 10 15 customers that matter.
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And the moment that you're delivering for them, they just proactively start, you know, sort of throwing revenue, you know, at you.
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And so um you know I think there are times where um you know both of our you know sort of stories obviously you can play out.
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The thing that I'd be curious to hear from you know sort of average is to actually like compare and contrast you know you were bringing up you know some of these digital businesses you know that um end up having these you know network effects.
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know network effects. I would kind of argue that like you know the like 2010s um negative gross margin businesses like you know the like Uber Door Dash you know uh you know types um I think of as more as like you know Adams businesses but there was a whole set of investors
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in like the mid2010s 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 Door Dashes etc. Um
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Um you know it's very clear that Uber Door Dash through you know lots of investment through building out these local you know sort of networks of you know both supply and demand were able to and you know drivers were able to eventually get to a point where now they actually you know have very attractive you know sort of financial profiles.
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of financial profiles. Today the equivalent of that is like there's all these investors that you know back in the 2010s would have refused to invest into any company that had negative gross margin and are all now pouring cash into
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both the like AI application layer companies and the 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
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what is going on here they're selling a buck for 90 cents >> and so I guess >> so so I think I think it's it's an important example because you had that um uh you know plenty of examples of these chain losses during that that era where
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a restaurant was selling something below cost to a platform that was selling something below cost to a logistics provider, an individual contractor that like maybe wasn't actually making money if you factored in depreciation and fuel cost of their vehicle. And that
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And that ultimately worked out, right?
12:38
Door Dash is a is a massive fantastic business uh based on the power of the American uh consumer.
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But when you compare that to today where a lot of the conversation uh 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.
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And so I think like the debate should really be you know continue to be around just how quickly will uh the cost per token fall.
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And I think a lot of people have a lot of confidence around that.
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But I think that that is the the key thing that Everett's sort of like broad investment thesis right now is dependent on. >> Yeah.
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Ev, 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?
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Like do you actually think that >> I want to pull this post up?
13:31
Uh Everett actually posted this in Jan January 31 of 2024.
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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 are, too."
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Uh, and so I I I guess the question is, uh, and it's funny because because then then a bunch of people I I thought it was a good point.
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I thought it was a hot take then, and I think then, uh, you know, a bunch of people kind of pared that take all over the timeline.
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Um, stole your whole flow, as you'd like to say.
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Um but uh but but but I guess the question is like are we in some sort of different regime right now where uh the the traditional gravity and like uh 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 know you've sort of employed or like we're kind of putting you in this in this box.
14:24
Uh but if the if the fundamental structure of zero marginal cost era is going away that that presumably uh forces like a rewrite of your logic around investing I would imagine.
14:35
Yeah, I think that I think that the biggest variable that's changed from the 2010s SAS era to today is that in the 2010s and you you basically made this this point without making it Delian though is that the thing that that was missing from from your talk track is that the competitive intensity of SAS during the 2010s was much much much lower than it is today.
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is today. uh 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 uh you know like HVAC or car dealerships or you could do a horizontal function like the CRM or you
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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 day. Um, and there was
15:25
Um, and there was less um, just just like general pricing pressure, competitive pressure, um, just the general pressure that you actually had a lot with, um, with some of the digital marketplaces early on.
15:36
And so, so like I think there was a whole crop of investors then and like the SAS investors then were like, well, we don't need to we don't need a bunch of cash burn.
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And it's actually, it's a really unhealthy indicator if these SAS companies are producing a bunch of burn because they're not competing with anybody.
15:52
So if they can't like 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.
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I think the thing that's changed now is one you have the change from zero marginal cost to actual meaningful marginal cost in the in the form of in uh inference and it's also just a hell of a lot more competitive than it used to be.
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And so you you are have and by the way there's an immense you there's probably 10x more capital than there was 15 years ago to go into these companies.
16:17
And so like every single category now has become like mini, you know, it's like mini ride share 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.
16:29
Um, and we need to be the ones that get first to scale.
16:31
And in a lot of these categories, the ones that have gotten first to scale um have gotten a lot of brand equity out of it and have gotten um a pretty resounding lead.
16:37
I think the um the only other piece I I would say I lost my train of thought. So >> yeah. Yeah.
16:44
But it's going to basically it's going to be like a capital fight now on the on the SAS side.
16:47
Uh I I wonder if if uh if the contrarian trade around hard tech is is is entering a similar era where it's become consensus and so we're going to see more capital fights and when a founder goes out and says yeah I'm going to do something crazy but I need to spend a billion dollars of capex people are just like yeah I this could be the next base. Sure.
17:08
you it made sense to have a capital war in ride share, but now we have a capital war in like this niche agentic workflow in some industry that that most people have never heard of.
17:17
And also a capital here's $200 million for military boats and UAS and and UAP like all these different subsegments are going to wind up if if capital wars start popping up there that could potentially uh be a headwind to Delian's model.
17:31
Is that is that roughly correct?
17:33
How would you how would you uh how would you fight back against that?
17:34
Look, I think it's it's always, you know, sort of uh important to talk about, you know, sort of specifics here, right?
17:39
Um, you know, one of EV's, you know, sort of major investments in the last year is this, uh, you know, company called Captions that basically does AI captioning of, you know, various, you know, sort of videos on social media.
17:49
Um, when I think about, you know, handing, you know, sort of two Stanford grads and $100 million to go try and, you know, sort of replicate that, yeah, feels like, you know, they could, you know, go do something like that.
17:58
There's like, you know, clear, you know, voice recognition models.
18:00
They can go, you know, sort of pay on ads on Tik Tok, etc.
18:03
Um, and you could probably go and replicate that.
18:05
And so, you know, you know, our oneliner at founders respond is competition is for losers.
18:08
And so, you know, I think I was a loser for investing.
18:13
>> Um, the chat were saying this wasn't spicy enough.
18:15
And you just delivered, Deli, so thank you.
18:18
>> Um, now, you know, if you take, you know, sort of two Stanford grads and $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, re-entry vehicles, you know, bioreactors that can actually survive the environment of space.
18:32
environment of space. um most you know sort of Stanford grads you know can't go you know ask GPT how to go do that and so I haven't really faced significant competition irrespective of the fact that you know all things space factories are thought to be you know sort of the
18:46
hot new thing >> to be clear we use captions here on clips we enjoy the captions app we thank for making it possible and subsidizing our >> and there is a y there is a y a varta-esque yc company so they're coming for you Indian varta I think will be a little bit less competitive than you know Indian captions. Also, if you're
19:03
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.
19:10
Helpful counterpoint for me.
19:13
>> Delian, you were um you you were correct that it was getting it was getting too friendly of a debate.
19:16
Um I did want to make sure I could I could pin this one on you.
19:20
If you can recite the equation for return on invested capital, I will victory to you and I will donate $5,000 to a charity of your choice.
19:27
Oh, let's go >> hopefully he's got Cluey running. >> Exactly. Exactly.
19:34
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.
19:40
I will also donate $5,000 to a charity of your choice.
19:42
But I don't think you have, you know, I may not have the, you know, basic understanding of business physics, but you don't have basic understanding of physics.
19:47
And one's more important about understanding the universe around you. >> Okay.
19:51
>> I mean, I'm I'm pretty fixated on the 2035 Midas list.
19:54
That's really kind of the the final >> that's been on the brink yet or I forget whether or not you've made it up there. >> Taking shots.
20:05
>> Not even on the brink yet.
20:05
You know, Marie joins you know KP after you and she beat you. >> Laughing me. >> It's okay. It's okay.
20:10
Uh eventually we're going to we're going to bring back the extra names in Kleiner Perkins.
20:13
It used to be Kleiner Perkins Coughfield buyers.
20:17
It's going to be Kleiner Perkins Randall Brasswell.
20:19
Eventually once we're working on it we're working on it. We're pitching it.
20:23
Uh where where should we go next?
20:25
Jordy, >> I guess uh 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?
20:40
Like how how long can can uh venture capital sort of like backs stop these uh chained losses?
20:48
Yeah, I think that the the way to delineate um the the whole so so obviously like I think there was this um kind of consensus narrative that like every you know 12 to 18 months token costs were going down an order of magnitude and I think that did hold for a while.
21:00
I think what you've seen now is like actually for frontier models that's started to peter out a bit and like pricing is actually started it's still going down.
21:07
it's not going down nearly as much as it as it used to um when when like when when we were kind of in in the in the like the meat of the curve of of capability improvements on Frontier LLMs um uh in terms of of pricing curve.
21:19
So I think that the way that you want to delineate it is like there's a certain like what I always tell everyone is that like there hasn't been a chat GPT query since GPT4 that like my mom hasn't been able to ask and have it answered by the model.
21:32
Uh so there's like the mom test of models where like there's 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 um either through open source or just the like the the the cheapening and distilling of of older models like the price still falls off a cliff. Sure.
21:54
>> And 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.
22:01
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.
22:10
Uh and so I think I think for 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 the kind of marginal token um price from the actual model providers.
22:27
And then two, like how much of your inference actually needs to be at the frontier?
22:30
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 on the actual frontier.
22:35
I think that's what you're seeing like you know everyone loves to talk about cursor and Chris P over at Pace Capital had this really great um kind of like mini essay I think only like last night or a couple nights ago and he talked about like no one knows if Cursor has power yet because you know coders and developers um they're very very like they're taste makers.
22:52
are 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.
23:01
Um, 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.
23:12
And so I think that's what everyone needs to be looking out for is those two things like do you actually have power?
23:17
Like will people give you margin above the marginal cost of tokens?
23:20
And then two like do we even need the frontier uh inference for the vast majority of your product or is that is there a lot that you can offload to cheaper models?
23:28
>> Yeah, I mean I guess your counter you know sort of there ever is that um you know a majority of what the foundation models are providing in terms of you know sort of value to their end users is starting to be um you know sort of uh obiated by the like you know historical generation even some of the ones that are you know sort of open source.
23:41
that would seem to imply that where value is acrewing and where you'd expect like 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 squat models out.
23:53
But like in reality, that's like literally just not what actually is happening.
23:56
Like if you look at which companies are, you know, sort of fastest on you revenue growth, user growth, etc.
24:01
, it is the foundation model companies.
24:03
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, a hundred, a,000, $10,000 per month versus the true frontier capabilities on like, you know, AI coding, the prousers, the
24:20
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 diff difficult of questions versus the people that actually are willing to, you know, pay are the ones that actually do care about the 140, 160 60 180 IQ. Again, maybe at
24:32
Again, maybe at some point that gets, you know, should commoditize as well.
24:36
But my sort of counter to you would be you've made this argument that seems to imply, hey, you know, things will acrue to the AI 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.
24:47
The like places that have captured the most, you know, revenue growth, the most market share have been the ones that are actually pushing the true frontier, you know, of the, you know, technology forward.
24:56
And so, so far, at least in the last 18 months, your thesis is not playing out at all.
25:01
>> Well, to to be clear the isn't isn't it somewhat widely understood that Anthropic has negative gross margins as well?
25:09
So it's it's not like they're they're doing >> like EB's point was that you want to invest in these companies that have the you know seven powers and like you know in the you know days of like Uber you know Door Dash etc that did end up translating the most power then the foundation model labs maybe then the application layer we'll see how much power develops in the application layer but Ev let we'll let you respond.
25:29
you respond. Oh yeah, I was going to say that that um basically what Deian said was just wrong because even though it even though like if you if you if you think about okay like let's take like whatever open AI and anthropics recently reported revenue run rate is the majority of all of that or at least the plurality of all of that is chat GPT and
25:47
chat GBPT 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 um 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. Um, and so I think that um,
26:03
Um, and so I think that um, like you could put whatever models you wanted through chat GPT at this point and it would not knock it off of its perch. I think that is power.
26:11
Like you could you could run cloud 3 sonnet through chatgpt and I guarantee people like the average user wouldn't actually know the difference.
26:18
Um, and like that to me is power.
26:20
And just because the foundation model companies are producing apps themselves doesn't mean that it's not the application layer that it's uh that that is accuring the value. >> Okay.
26:28
Then my question is, you know, you've got, you know, opening eye with the best possible consumer application layer.
26:32
You've got Enthropic that like shifted over to positive gross margins and those margins are expanding and yet clinvesting into either of those foundation model, you know, companies. Why?
26:42
>> Uh I cannot comment on our current investment activities. >> Okay.
26:46
Uh gears can do you like making money or do you like you know all the competition?
26:56
>> Can you comment on on Donald Boat?
26:56
Have either of you bought anything for Donald Boat, the notorious ebeggger on X. com, the everything app?
27:04
>> Look, my little brother, you know, uh, you know, played the Uno reverse card and tried to get Donald Boat to buy him something.
27:08
So, >> smart, you know, contrarian nature.
27:12
>> Let's talk about uh revenue quality because I think that you guys run into this in your respective domains every single day.
27:19
Just like in AI, you can have low quality revenue like that might be the explo explosion of like consumer uh prompt to app uh activity, you know, might not be the highest quality re revenue.
27:32
Meanwhile, on the hard tech side, if somebody gets like a random like cber or like experimental gets like experimental budget from some branch of the military and it's like a a fine, you know, fixed length contract, it's not necessarily the right strategy to slap like a 50x revenue multiple on it.
27:51
So, like what's your view on on both of those?
27:54
>> Um, and then I want to talk about if we we should get into if uh if accounting rules even matter at this point. >> Yeah, for sure.
28:03
Yeah, I mean in hardware land we think about this all the time of like there's clear differences in quality of revenue.
28:07
Everything from like you know defense you know program of record you have to value that very differently than even like a $50 million you know SBIR.
28:11
like a $50 million you know 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 um pre-existing 10 years of rules around software of like what you know healthy revenue looks like rule 40 there's all these things that like you know even if you're somewhat unsophisticated infinite
28:27
blog post when you look at that in the world of like hardware and defense you know sort of investing or aerospace there aren't like 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 you know sort of trash my own portfolio but
28:40
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 to like positive how long it's going to be until they're actually you know sort of ready to you know go scale revenue even if it on the back end it might be attractive it may be years
28:56
and years for them to you know sort of get there and so u yeah I I see huge variation on that and then mostly what I end up you know 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 Wow, people like not having IR for a long time. Yeah, I think um so Delian's uh hero and
29:17
Yeah, I think um so Delian's uh 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 you know how durable is the revenue like if you sign
29:34
a customer are they going to stay for a year 20 years um you know how much contribution profit is going to come off of that revenue stream over time um all the basics and I think you can like take those same building blocks and apply it to AI I think there's several things that are worse uh for AI than at least than than relative to SAS for now. So
29:48
So generally gross like gross margins are lower which means contribution profit coming off is lower.
29:54
Um I actually think that like depending on the category you could have customers that are more sticky or less sticky.
29:59
Like I know the meme is that everything's experimental run rate and none of these customers are actually sticky.
30:04
I think we see something very very different uh among the the our group of portfolio companies.
30:08
I think that the biggest lever that didn't exist in SAS 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.
30:22
budgets. I know this is like still kind of like inning one and inning two and it's also like a little bit of a meme where everyone's like oh it's going to replace labor and labor's 10 times SAS 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
30:36
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 onetenth of like a developer's full-in cost to a company on on an annualized basis for this product. And so I think that the like
30:53
And so I think that the like the the thing to watch is like durability of revenue plus the amount of actual revenue that a customer can give you.
31:00
And I think that you're going to end up or the amount of gross profit that a customer can contribute over time.
31:04
And I do think as some customers crack these agentic products that look and monetize more like labor, um AI revenue could actually exceed the quality of SAS revenue just because you're getting so much more gross profit per customer or or like customer relationship than you would on the SAS side.
31:20
Even though there are clearly things that are worse about AI revenue at at this current point in time than there are about SAS revenue.
31:26
>> Dylan, how do you think about the uh the the moral imperative of of a venture capitalist to invest in positive sum versus zero sum markets?
31:33
This idea that you know you're re-industrializing America.
31:38
You're saving the west versus moving chips around you personally uh ver versus moving chips around the poker table.
31:46
Taking uh taking from some legacy, you know, web 1.
31:48
0 0 company and putting it into an AI company.
31:51
Uh what what's your what's your thinking and argument there?
31:55
Is is is uh a a a market beating ROIC all that you need?
32:02
Yeah, I you know I think uh Peter always reminds us like you know our number one job is deliver returns for our 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 you know sort of invest into for sure when
32:15
it comes into like policy and I'm in DC 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 a Chinese spy and like the investments that he's making should probably be banned from the United States. Yeah, for sure. There I have, Yeah, for sure.
32:26
There I have, you know, sort of moral imperatives and things that influence that may end up, you know, shifting ROIC, right?
32:31
Um, 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, you know, sort of best possible investments irrespective of, you know, sort of moral imperatives.
32:43
But in some ways, I tend to think it turns out actually if you, you know, go too immoral, um, then that ends up, you know, sort of affecting ROIC.
32:49
know, sort of affecting ROIC. So um you know maybe and the last thing that I would at least you know sort of you know close on you know for my you know sort of question you know for everit um is um you know uh one of the upsides of founders fund is you know we're very um you know sort of you know um let's say uh non-entralized distributed you know not many you know sort of rules which
33:09
you know ever for some reason you sort of 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 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, goes off Chris. Just uh that's all
33:26
Just uh that's all you got to tell us, brother.
33:29
Yeah, let it let us know.
33:31
>> Hey, our wonderful marketing partner Ally is is is behind the camera with a green and red paddle, and she hasn't the red paddle yet. So, that's great. >> Victory. >> Well, thank you both. >> Last question.
33:41
Are you worried about Uncle Sam potentially having sharp elbows now that we're hearing about >> Intel? Yeah.
33:47
the federal government taking a stake in Intel.
33:49
Any any concerns about him going down the stack into the into the early stage game competing for those seed and in series A allocations?
33:59
>> Look, if Trump Capital wants to uh, you know, sort of mark up some of the uh, you know, re-industrialization companies, I'm all for it, baby. Cheap cost of capital. >> You're all for it.
34:06
>> Yeah, I'll say I'll say two things.
34:06
I would say one, I think that the EV of like the enterprise value of Founders Fund probably 3xed the night that that Trump got elected.
34:13
So, I don't I don't think Delion would complain about that.
34:16
And then two, just as as a parting gift, Dian, um 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.
34:29
>> Well, thank you both for joining.
34:29
This is >> You're both good sports.
34:34
>> We'll have to do this again.
34:35
>> I think it might be a draw.
34:35
We'll have to have you both back soon.
34:36
Thanks so much for hopping on. >> Great stuff.
34:39
>> We'll see you guys later. >> Cheers.
34:40
>> Let me tell you about ramp. com. Time is money. Save both.
34:42
Easy to use corporate cards, bill payment, accounting, and a whole lot more.
34:45
Do accounting rules matter? Yes, they do.
34:48
And you can enforce accounting rules on ramp. com all in one place. Go to ramp. com.
34:54
>> That was that was beautiful.
34:54
Two two former colleagues barely holding back from saying things that uh they would ultimately regret, but they did. They did a good job.
35:04
They um it was >> I like the debate format.
35:07
We should definitely dance more of those.
35:08
I think that was a lot of fun.
35:09
I think the chat enjoyed it.
35:12
Uh the my my my favorite rude comment in here.
35:14
Oh, we got Andrew Reed in the chat.
35:16
Ever versus Dellion who can grow the most average beard. >> Oh my god.
35:23
>> Thank you for watching, Andrew.
35:23
Uh let us know when you've selected an opponent and and we'll have you on the show to debate someone.
35:29
Uh >> yeah, I think we need to we need to get the Holy Trinity. Uh >> yes. Yes.
35:34
If you're if you're new to TBPN, the holy trinity is of course the three venture capital firms that have done a seed deal in a now hyperscaler or now mag7 company.
35:42
So that is Sequoia Capital Founders Fund with uh Meta originally Facebook and Kleiner Perkins of course.
35:49
of course. Uh and so the holy trinity are the three most storied venture capital firms in the valley much like the three uh famous watch brands the holy trinity uh vashron constanton pek philipe and admar pig >> of course >> uh over in Switzerland um if you enjoy
36:08
this stream and you want to make your own stream get on reream one live stream 30 plus destinations multiream and reach your audience wherever they are uh if you're the backbone of your company and you're doing the launch uh streaming is the way to do it. Uh
36:20
Uh >> you don't have to try to poach Ben or anyone else on our team. Just go to reream. It's fantastic. >> Check it out.
36:27
>> Um anyway, uh going back to uh the Death Star, the vague post, we're one week out from GPT5.
36:34
Uh how have how's your GPT5 experience been?
36:39
Also, Tyler, the chat wanted you to answer uh explain the formula for ROIC. >> The formula.
36:47
Uh, so yeah, I think it's um I believe it's operating income divided by book value of invested capital, right? >> Oh, you got it.
36:54
Clearly he's running clearly. >> That's off the dome. >> That's off the dome. >> Dome.
36:59
I did not just look it up.
37:01
>> And then uh and then what did what what did what was Delian's rebuttal?
37:02
He wanted every >> Why you can't achieve >> Explain that.
37:06
Why can't you achieve microgravity on Earth? Do you know that? You're a physicist.
37:12
>> Uh >> you studied physics. You should get this.
37:15
>> I'll have to get back to you. Let me think about that.
37:16
What do you think about that?
37:19
>> I think it's just that Wait, I actually I can't really explain it.
37:22
That's That's kind of hard.
37:24
I mean, I know that it's like Earth has a gravitational field and we don't have the technology to reverse gravity, but I can't really tell you why we don't have the tech the technology to create an anti-gravity chamber on Earth.
37:33
Like, I can't walk through the physics for that.
37:35
I just know that you can't do it on Earth.
37:36
Uh but mostly because >> we've been trying to get a gong in microgravity here on Earth. Challenging.
37:42
>> You can do it for like a very short amount of time, right?
37:44
It's like when you you see ever see the planes. >> Yeah.
37:46
That's not that that's not actually microgravity.
37:48
That's just falling, right?
37:49
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.
37:58
You it just your surrounding environment is pressurized and so it feels like you're floating but in fact you're you're you're really just falling closer to earth.
38:06
Like when the plane goes down you are you are descending.
38:11
There's no there's no machine on Earth that will effect effectively like levitate something and and reduce the force of gravity to zero on Earth or or even or even reduce it significantly. >> Yeah.
38:21
But it's like that's the the whole thing is like from the observer it's the same. >> Yeah.
38:24
But uh for for space manufacturing for like growing a crystal >> the reason you can't do it on Earth is because it's too like it's not long enough. >> No no no.
38:32
I I I think that I think that even if you even if you tried to do something in that in that plane scenario uh like you're still subject to the force of gravity even though you're uh because you're effectively falling even though you're not even though you're not feeling like the relative uh the like the wind speed you still are under the force of gravity. I don't know.
38:54
We'll have to figure it out.
38:55
We'll have to have back explain it to us.
38:56
Anyway, uh what's your what what's your one week one week review of GPT5?
39:02
What's your takeaway, Jordy?
39:07
>> I've been I've been uh it's been fine.
39:09
It hasn't been that drastic.
39:09
I I still find myself navigating between different models using the switcher.
39:17
>> Have you turned on the legacy models?
39:21
>> So, if you go >> I haven't gone into the hidden >> No.
39:23
So, Tyler, you have though. Explain. >> Yeah.
39:26
So if you just go into settings, you can turn on legacy models.
39:28
>> So all I have in legacy models is 40.
39:30
>> So So 40 came back as a dropown.
39:30
Um but you can go into the settings and turn on legacy models and then you can access 45, right?
39:38
So 45 still >> and 41 03 04.
39:42
>> Oh, so you can access them all, but it's tucked behind even more menu.
39:44
tucked behind even more menu. Um I think that uh 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 uh you
40:00
know I 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 with Apple made a bunch of bold product decisions and then just said consumers I don't I I'm not taking input you want you want a headphone phone jack. Too bad. I'm taking it away. Too bad. I'm taking it away.
40:17
You want uh an an extra port on your MacBook?
40:20
You want uh what what was the thing that they took away?
40:22
They took away all the ports for a while.
40:23
They had no ports for a while.
40:25
It was just USBC ports on the edge.
40:27
Um and so and Facebook's been similar with the with the removal of the original feed and then the and then they they moved away from a chronological feed to an algorithmic feed and there was a lot of push back for that.
40:38
But Mark Zuckerberg channeled uh a mentor of his, Steve Jobs, and said, you know, I know that this is better for the long term.
40:44
I know that this is better for everyone in the long term and that people will ultimately love this.
40:49
And I think that's probably >> I mean, the main thing is it's very interesting that they uh this was reported by Alex Heath and The Verge last night.
40:57
Apparently, he got dinner with uh Sam Alman, I think, and and Greg as well or some other executives.
41:01
And they said uh last night about an hour before the dinner started, OpenAI pushed an update to bring back the quote unquote warmth of Forro, >> which is what the Reddit uh the Redditors of the world uh the AI is my boyfriend. >> Yep.
41:19
>> Uh enthusiasts uh were clamoring for.
41:23
So, it's interesting to see how quickly they folded there. >> Yep.
41:26
>> I think >> yeah, clearly they made users distraught.
41:29
I also think I imagine a lot of those users were paying >> the top tier subscription.
41:37
>> Y >> and I I you know again it's hard to read too much into Reddit or really anything you see online but a lot of people were >> cancelling or threatening to cancel if they didn't bring that functionality back.
41:48
So anyways, Sam, >> so I mean I would expect going forward like constant changes and iterations just like the YouTube algorithm is constantly changing, the X algorithm is constantly changing like these these updates get pushed very incrementally.
42:03
There's constantly tweaks that are happening.
42:05
>> So Sam is quoted saying, I think we totally screwed up some things on the roll out.
42:07
On the other hand, our API traffic doubled in 48 hours and is growing. We're out of GPUs.
42:11
Chad GBT has been hitting a new high of users every day.
42:15
A lot of users really do love the model switcher.
42:17
I think we've learned a lesson about what it means to upgrade a product for hundreds of millions of people in one day.
42:23
>> Yeah, it's always tough.
42:24
>> He pegged the percentage of Chad GPD users who have unhealthy relationships with the product at way under 1%. >> I agree with that. That sounds right.
42:32
>> But acknowledged that OpenAI employees are having a lot of meetings about the topic. Yep.
42:36
>> Quote, there are people who actually felt like they had a relationship with chatbt.
42:39
And again uh in this uh in this article this post that we read yesterday on the show that he posted eight years ago called the merge talking about the uh inevitable point that humans and machines uh merge.
42:51
Uh he said the merge can take a lot of forms.
42:57
We could plug electrodes into our brains or we could all just become really close friends with a chatbot.
43:00
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, >> right? >> Yeah. Yeah.
43:20
At least hundreds of millions of daily users. Yeah.
43:23
Probably like under a million.
43:24
So hundreds of thousands of people. That's a lot. That could have >> Okay. Way under 1%.
43:29
So millions of people >> maybe they're not quite at a billion active users.
43:36
>> So and then and then a lot of those user international in terms of like the I mean not that that really matters.
43:41
You want to be keeping everyone healthy but um yeah we're talking about like hundreds of thousands of people that are pro potentially negatively affected.
43:46
So they got to drive that to you know. 1% and then 0.
43:50
01% and then you know get as close to zero as possible.
43:53
There's there's always going to be some people that you know use they they they read the newspaper and they go crazy.
43:58
But um you know the more that you can do the better.
44:02
>> Sam says you will definitely see some companies go make Japanese anime bots because they think they've identified something here that works.
44:09
>> Y >> you will not see us do that.
44:10
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.
44:21
>> Well, as they continue to iterate on the product, they have to use figma. com.
44:25
Think bigger, build faster.
44:25
Pigma helps design and development teams build great products together. Get started. Go make something up.
44:31
>> We have a question from the chat for Ben.
44:33
Uh Ben Kohler, our producer, was recently followed by Reed Hoffman.
44:36
And the question is, did Ben get any Hoffman chat?
44:41
Did he slide in the DMs or did he just follow you for updates?
44:44
>> I think just for updates. >> Just for updates. I'll keep you updated.
44:46
>> If you're not following Ben, you got to follow him. He posts.
44:47
He posts constantly during out throughout the show when big things happen, when crazy stuff's happening on the stream.
44:53
Uh he's kind of like the the premium feed like you know we put a lot of stuff on the main account.
44:59
Ben's the behind the scenes guy.
45:01
So uh go follow him back here too. >> Yeah. The whole whole crew. >> Lads lads.
45:06
So my my final takeaway from the Death Star vague post is that um is that uh there was this viral image when in the leadup to the GPT4 launch is this data visualization.
45:18
We can pull it up as the first slide in the deck.
45:20
uh it it was visualizing the number of parameters in the model and this went very viral multiple times.
45:27
So it was GPT3 had 175 billion parameters and GPT4 had 100 trillion parameters.
45:34
And so you see the small dot and then the huge circle.
45:36
And a lot of people were afraid by this and it was kind of this indication of uh exponential takeoff.
45:41
And we really did see a qualitative improvement in just scaling up the pre-training run from GPT3 to GPT4. Um, but GPT 4.
45:49
5 taught us that pre-training scale is in fact not all you need.
45:57
And the way to make a great AI product in the modern era is a mixture of techniques, experts, and researchers.
46:05
You need a whole host of things.
46:07
Particularly with GPT5, it feels like they RLED on a lot of different problems.
46:11
Um, and so to me, the Death Star post represents an even bigger circle from that GPT4 circle.
46:17
So the the Death Star is the biggest possible circle and it's and it's it's an expansion of that GPT3, GPT4, GPT5.
46:27
>> But couldn't you read this that GPT5 is >> Earth? >> No. No.
46:31
GPT5 is the Death Star is or the perception of GPT5 as just being a bigger model is the Death Star. OpenAI blew that up.
46:39
They blew up the metaphorical big circle with a model that isn't just bigger.
46:44
Semi- analysis called said the the release is the router.
46:48
The router is the release.
46:48
And what that means is that the the gain in in the value delivered by this product is not just a bigger circle.
46:56
It's it's a more complex coordination.
46:59
It's it's it's all the different X-wings working together in tandem.
47:04
And then the Millennium Falcon comes in and saves the day.
47:09
That's the uh that's the uh that's that's when uh you know uh the the model router act like triggers a reasoning step and and it thinks for a long time.
47:20
Um it the death star is the is the end of the pre-training scaling law potentially.
47:26
>> Well, let's get into some more coverage here from Alex Heath.
47:27
Uh Sam says you should expect OpenAI to spend trillions of dollars on data center construction in the not very distant future.
47:35
He confidently told the room, "We have to make these horrible trade-offs right now.
47:41
We have better models and we just can't offer them because we don't have the capacity.
47:43
We have other kinds of new products and services we'd love to offer."
47:46
So, obviously, agent making that more widely available, I think, uh, is uh, what he's alluding to.
47:52
He also thinks we're in an AI bubble.
47:58
>> When bubbles happen, smart people get over excited about a kernel of truth.
48:00
If you look at most of the bubbles in history, like the tech bubble, there was a that there was a real thing.
48:05
Tech was really important, but the internet was a really big deal. People got over excited.
48:11
Are we in the phase where investors as a whole are over excited about AI? My opinion is yes. >> Yeah.
48:18
>> Is AI the most important thing to happen in a very long time? My opinion is also yes. Yes.
48:21
So, uh, he obviously, you know, contributed to this excitement in a >> sort of Yeah, >> I I would say that he >> he 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 there was an immense amount of excitement around Facebook cracked it.
48:40
It's on the way to be a trillion dollar company.
48:42
And there was a belief for a while that that it would be oligopolistic and Twitter and Foursquare and uh Pinterest and Snapchat would also be trillion dollar companies. But that didn't happen.
48:55
And if you invest in those companies at unicorn valuations, you have not seen fantastic return on invested capital as opposed to >> I think it's fair fair to say that Sam >> helped get people over excited and that in many ways he was saying you know with with GPT6 we might be you know >> uh discovering novel physics and curing what >> who's the Wii there?
49:19
Is the Wii open AI or is the Wii every company that's raising in the valley right now?
49:25
It's a it's important speaking for open AI, but I think people are going to naturally >> Yep. totally. No, I agree.
49:34
>> take that as as uh >> 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. >> Yeah.
49:48
But the 20th best social network was was not worth 120th of Facebook.
49:50
It was worth 12,000th of Facebook or 120,000th of Facebook.
49:56
And that's the nature of these power laws.
49:58
Um but my my take is that um so the the model the the the release being the router and this shift towards open AI potentially um uh shifting into dominating agentic commerce having a monetizable free tier.
50:17
This is actually a bullcase for super intelligence.
50:19
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 it just feels different.
50:28
use it and it just feels different. it just 100% on all the benchmarks whatever it wasn't that it felt very incremental um 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
50:41
shifting to a premium model a monetized free tier is actually a bullcase for building the trillion dollar cluster and my thinking goes like this so um you can you can build the first GPT2 GPT3 cluster with nonprofit donations like $100 million gets it done and that advanced them to that stage. But to do
51:01
But to do the GPT4 training run, they could not marshall the capital in the nonprofit space.
51:08
They had to become the for-profit.
51:09
They had to get venture dollars in.
51:10
And yeah, and so basically like the Shog demanded capitalism.
51:14
This is the Nicolan take that artificial intelligence was sent back from the future to to invent capitalism.
51:21
Have you heard this take? It's great.
51:26
And so, uh, 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.
51:34
And so, you pulled in all the venture dollars.
51:39
The question is to build a trillion dollar cluster.
51:40
I think MASA is going to be tapped out soon.
51:43
I think MASA is a card you can play once.
51:45
I think that there is a limit to to how much capital you can marshall in the the private markets, even in the public markets.
51:52
I just think it's impossible to raise a trillion dollars necessarily.
51:56
And that cluster must be must be funded by free cash flow. It must be funded.
52:00
It must be underwritten by a company that can justify a return on investment from their direct product.
52:06
And so we're seeing this right now with Google and Facebook and investing their free cash flow.
52:12
Yeah, they're investing their free cash flow.
52:14
Yeah, they're doing some some some debt.
52:15
And I think I think to get to the really really big numbers uh 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?
52:26
>> Uh what do you think about like situational awareness like nationalizing labs?
52:31
You think governments can can >> about to nationalize intel, so maybe that's a a path down the road. I don't I don't know.
52:38
I I I don't I don't think it's on the horizon anytime soon.
52:41
mostly because there we're we're just not seeing capabilities that would threaten I I it comes out >> actually insane.
52:50
So So a week ago >> when that reporting from the journal on Leopold's situational awareness >> everyone is just dragging him dragging him dragging him being like his fund is his fund is probably blown up already.
53:05
It's up 21% in the last 5 days. >> Oh my god. >> Wow.
53:08
the gong for Leopold, Ashen Brener, and situational awareness. Uh, yeah.
53:17
Uh, >> anyways, there's some more there's some more interesting stuff in here.
53:21
>> I I I I don't see it happening until the labs pose a threat to the US government in some way and and are and are so dominant.
53:29
I I I I don't think we're at that phase.
53:32
We're we're getting into the danger zone there.
53:34
>> We're in like new new Google territory.
53:36
It's a dominant consumer app.
53:36
I I don't think More interesting uh reporting here from Alex.
53:41
He says uh Sam confirmed recent reports that OpenAI is planning to fund a brain computer interface startup to rival Neurolink.
53:47
I think that neural interfaces are cool ideas to explore.
53:52
Says Sam, I would like to be able to think something and have chat GPT respond to it.
53:57
>> And of course, I think it was the Financial Times was reporting that Sam Alman would be a co-founder of of this company merge.
54:04
Um, does Fiji Simo joining OpenAI to run applications imply there will be other standalone apps besides chat GPT?
54:12
Sam Alman says, "Yes, you should expect that from us."
54:15
He hinted at his social media ambitions.
54:19
Quote, I am I am interested in whether or not it is possible to build a much cooler kind of social experience with AI.
54:24
He also said, "If Chrome is really going to sell, we should take a look at it."
54:28
Uh, Alex says, "While Altman has a lot of interest, it's not clear.
54:32
It's not actually clear that running OpenAI over the long run is one of them.
54:35
Sam says, "I'm not I'm not a naturally well suited person to be a public company CEO."
54:42
He said at one point, "Can you imagine me on an earnings call?" >> I then figure it out.
54:48
>> Alex then asked if he would be CEO in a few years.
54:51
>> Uh Sam says, "I mean, maybe maybe an AI is in three years." That's a long time. >> I love it. It's great.
54:58
>> Uh some other >> You know what else I love? Vanta.
55:00
Automate compliance, manage risk, improve trust continuously.
55:01
Advances 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.
55:11
>> So a few more points in here.
55:11
Altman had notes on making GPD5.
55:14
We had this big GPU crunch.
55:17
We could go make another giant model.
55:18
We could make that and a lot of people would want to use it and we would disappoint them.
55:22
And so we said let's make a really smart, really useful model, but also let's try to optimize for inference costs.
55:28
And I think we could did a great job with that.
55:30
>> Y >> um obviously there the um you know we're clearly wanting to be more competitive with uh Claude uh and anthropics API business >> uh on the AI device with Johnny IV.
55:41
Sam said it's going to take us a while but I think you'll think it is very worth the wait.
55:48
I think it is incredible.
55:48
You don't get a new computing paradigm very often.
55:52
There have been like only two in the last 50 years.
55:55
So just let yourself be happy and surprised. >> Only two what?
56:00
>> New computing paradigms. >> Oh yeah. Yeah.
56:02
>> Uh he says so just let yourself be happy and surprised >> in the last 50 years.
56:05
He says >> in the last 50 years >> PC era mobile cloud I guess mobile and cloud are tied together.
56:11
He's been Thompson >> P on the future of web and publishers.
56:15
Sam says I do think people will go to fewer websites.
56:17
I think people will care more about human crafted content than ever.
56:20
My directional bet would be that human created, human endorsed, human curated content all goes up in value dramatically. >> Let's go.
56:27
Let's hear it for our live stream.
56:29
Human curated, >> human handmade hand. >> Seriously.
56:33
I mean, >> what AGI means?
56:34
Sam says maybe the milestone that's most relevant to us is when most of our researcher research cluster is allocated to the AI researcher instead of the human researchers.
56:43
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.
56:51
>> He also said if we didn't pay for training, we'd be a very profitable company.
56:55
>> That's a good question.
56:55
So, uh, Tyler, have you thought more about what happened to GPT4. 5?
57:02
>> I I people It's like people always say like, "Oh, it was so bad." And same thing with GD5.
57:06
It's like, okay, do you remember when we had um Jack on and he talked about his blog? It said GPD or 4.
57:12
5 is like as good as we should expect. >> Yep.
57:15
>> Five is the same thing.
57:15
I think it's a good model. Yep. >> If you uh wait. Okay.
57:17
If you would please consult the graphs. >> Okay. Pull up the meter.
57:25
>> So, my question with GPT 4.
57:25
5 is I understand that it's as good as we should expect.
57:31
The question is just is it in the money?
57:33
because GPT2 was also on that curve and deeply unprofitable, right?
57:39
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.
57:44
Remember we talked to Greg Brockman.
57:46
He was like, "We had to pay people to use our models."
57:47
Then all of a sudden the 3. 5 uh the 3. 5 Da Vinci came out.
57:52
Some people were using that.
57:52
They might have spent I don't know $10 million training uh GPT3 3.
57:56
5 and some people paid for it.
58:00
They probably made their money back. Who knows?
58:01
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 $und00 million, but they are making a billion dollars a month inferencing it.
58:15
And like the inference cost is probably gross margin positive mo more or less, but 4.
58:18
5, they probably sounds like they paid a billion dollars to train it. Something like that.
58:26
A lot of money to train this big model. And it's expensive.
58:28
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.
58:34
So, it's kind of mothballled.
58:36
And you can see that in the app.
58:38
It's like, >> yeah, but it's like, okay, it's worth one AI researcher.
58:40
I mean, I I think that the value of the like R&D, like the knowledge that they now have training the next model is probably worth a billion dollars. >> Totally.
58:50
>> If you consider AI researchers worth billion dollars easily, >> 100% worth doing.
58:53
100% not a big deal for their financials.
58:56
for their financials. not a big it's just interesting that we went from a paradigm of like like the big the 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 um hallucination to reduce the
59:16
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 the product better people are going to use chat 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? It's just an interesting dynamic.
59:35
It's just an interesting dynamic. I don't know. >> Yeah.
59:37
I mean, I think a lot of people were 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 fun that >> saying he's gross margin positive. >> No, no, yeah. No, I know. I know.
59:48
But, but still, anytime you have a CEO being like, if we didn't have this cost, we'd be profitable.
59:53
be profitable. Uh it's it's always >> I mean the followup on the army plans to stop training then >> well well yeah exactly so we >> some net net profits >> you know this is what Everett said and this is what we said you know in in response to the GPT5 launch is that the
1:00:11
product is now the most important thing and uh what ever said just now was that you know you could you could swap in much cheaper models even open source models and people would still be using the product in the way that they Our last quote from Sam from Alex's coverage. He says, "I don't use Google
1:00:26
He says, "I don't use Google anymore.
1:00:28
I legitimately cannot tell you the last time I did a Google search." >> Moged. Yeah.
1:00:33
And this is so this is the interesting thing, right?
1:00:36
>> In in >> 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 everybody's just trying to buy Chrome.
1:00:46
And it's still very much up in the air whether they'll be forced to sell it.
1:00:50
Google's not going to sell it, you know, by choice.
1:00:55
>> And uh but it just does feel that chat GPT with with GPT or chat GPT agent is effectively a web browser already.
1:01:03
Yeah, >> you're just browsing the web. That's great.
1:01:07
>> And so uh I I think that the real browser war is the fact that chatbt functions as Chrome plus Google search in a single product already. >> Yep. Yep.
1:01:16
No, I this is a great take. I completely agree.
1:01:19
Um, wild card truth social buys crow truth browser.
1:01:25
This would be the most aligned.
1:01:28
Yes, this would be the most aligned with the current administration.
1:01:30
Tyler, what you got for me? >> Uh, okay.
1:01:32
So, yesterday it it's it's not here anymore, but if you went to open.
1:01:35
com/newab page, they like leaked this page. Like, not on purpose.
1:01:39
Someone just found it, but it was basically like very close to like a a browser style where you would type in and then it would like autofill some possible questions.
1:01:46
then you could like save uh like links and stuff.
1:01:48
So it like it very much looked like the the Chrome like homepage. >> Yeah.
1:01:55
I I I wonder in the context of mobile I mean using using generative AI to generate code in HTML has just completely pilled me on the the generative UI elements and I feel like like I I would probably be less interested especially since I I I mean I use Chat GPT mostly on my phone.
1:02:15
Um I I use Chrome mostly on my computer on my Mac and so 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 chat GPT app likes opening I frames and and Safari web views and surfacing something that actually renders the native HTML.
1:02:41
It's more like it scrapes all the HTML from a website into the the reasoning chain.
1:02:47
It gets all those tokens and then it kind of just like reinstant reinstantiates the the UI in like native elements and kind of cleans it up for me.
1:02:57
And so I'm getting like a hybrid of like chatgpt used to just be pure text response.
1:03:01
Then it became text response and it also has links in there now.
1:03:05
And it also has >> commerce. It Yeah.
1:03:07
>> commerce. It Yeah. It also has um it has 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 >> and think about how bad >> 99% of websites are
1:03:27
>> I completely agree and having a standard >> it's hard to it's hard to navigate them there's popups and things like that >> there are people that that that deliberately browse the web with JavaScript turned off because it forces websites into a more like usable plain text experience. And and most websites
1:03:41
And and most websites have a have a have a fallback in case JavaScript's not working or blocked.
1:03:47
And so you can wind up going to the United Airlines checkout and and it'll be just like normal buttons instead of like the pages jumping around refreshing popups all that stuff.
1:03:59
All that stuff gets turned off and some people like I haven't done cookies. Yeah.
1:04:01
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.
1:04:08
Graphite help teams helps teams on GitHub ship higher quality software faster.
1:04:12
Uh, >> well, pull this up uh in the timeline, boys.
1:04:17
We have a post here from the New York Stock Exchange, otherwise known >> as the New York Style Exchange. >> And here we are. >> Let's go. >> Nice.
1:04:26
President Lynn Martin stuns in the TBPN spring summer 2025. >> Fantastic. Lynn >> collection.
1:04:34
>> Thank you for acting as our model for this uh this season's uh TBPN collection where >> we really designed it proud to have you >> for >> tech and finance leaders that that are you know dedicating their lives to improving capital markets. >> Yeah.
1:04:50
>> And maintaining American dominance globally.
1:04:53
That was the north star with the with the with the collection >> Patagonia is like, "Oh, this was designed for your next hike." Yeah.
1:04:58
This was designed for Everest.
1:05:02
>> Well, this was designed for the trading floor on an IPO day.
1:05:05
>> This was designed for the hike up to that bell. >> Exactly.
1:05:08
>> For the Mount Everest of Capital Markets, New York Stock Exchange.
1:05:12
>> For the gong hit that retires the next TVPN gong. >> Yes. Exactly. Exactly.
1:05:17
>> Um, we have uh I think we can skip over this coverage from the Wall Street Journal.
1:05:21
They said OpenAI's rocky GPT5 rollout put this in the true struggle to remain. Yeah.
1:05:26
So the 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 uh >> it's basically coverage from a bunch of people complaining and it doesn't capture any of the actual underlying >> doesn't feel like they're struggling to remain the undisputed consumer AI leader.
1:05:48
I think you could argue that >> yeah, >> there's certainly a much closer race in codegen. >> Yeah.
1:05:56
So I Yeah, I I I I put that in just as a reminder to talk about uh GBT5.
1:06:01
Um the the the real news is uh the Financial Times has a uh a story on DeepSeek that isn't isn't super deep in terms of the coverage, but there are some interesting tidbits in here.
1:06:12
So, uh, the the article is, "Deepsek's next AI model stalled by Beijing push to take up Chinese chips."
1:06:20
We talked about this a little bit with the Nvidia H20, uh, now available in China and, uh, what that means for for for DeepSeek.
1:06:27
So, uh, Deepseek obviously everyone should know, is the disruptive Chinese open- source frontier reasoning model maker uh, from Highflyer.
1:06:37
They were in the high they were in the high frequency trading business.
1:06:40
Then they decided to go into foundation model training and they developed a very very solid open-source uh language model very quickly and it surprised everyone.
1:06:49
We started talking about Jevans paradox and the idea that cheaper AI will just wind up driving more and more adoption.
1:06:55
We've certainly seen that and the sell-off that happened in the AI trade in the public markets came riproing back and Nvidia rocketed to over a $4 trillion uh valuation after they'd sold off slightly after the Deep Seek news.
1:07:10
Um so uh apparently they've been trying to get this R2 release out, the next version of their re uh of their reasoning model, and they're having a hard time because allegedly they're using chips from Huawei.
1:07:23
So, uh, China, the CCP, uh, and Beijing has pushed Deepseek to switch from Nvidia to Huawei.
1:07:31
Everyone suspected >> and it was and it and it's it's not technically illegal to use Nvidia chips, but it is politically incorrect according to one person familiar with the conversations >> currently. Yes.
1:07:44
And and and there were export controls.
1:07:47
There were never any import controls.
1:07:48
So, if you're high-f flyier or deepseek and someone comes to you from Malaysia and says, "Hey, I got I got 100,000 H100s right here. You want to buy them? I fell off a truck."
1:07:57
Uh you're you're welcome to buy those. At least you were.
1:08:01
Now, it's politically incorrect to do so.
1:08:03
Um and so, >> uh Huawei uh hasn't really gotten the job done.
1:08:09
Lots of recent model releases have have failed to live up to expectations.
1:08:13
This is what happened with GPT 4. 5, Llama 4 Behemoth.
1:08:14
the models are getting more they're getting bigger.
1:08:19
There's more and more integration points in the training cluster as you're actually building these out.
1:08:23
Uh there's power management issues, there's memory issues, there's all these different things.
1:08:27
And that's why the AI researchers are making uh so such high salaries and the trade deals are happening because um 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 um that's really valuable.
1:08:43
Um, and so this case shines a light on the uh on the exact nature of the gap between Nvidia and Huawei.
1:08:47
So when the was Huawei Ascend Cloud Matrix 384 came out, um, everyone was kind of saying, "Okay, wow."
1:08:54
Like Huawei is basically caught up.
1:08:56
It's not as efficient on a on a dollar per flop basis.
1:09:01
Like it's more energy intensive, but if you're willing to spend a little bit more energy, you basically get the same capabilities.
1:09:07
Um, that might not be the truth.
1:09:10
the truth. like like there might be actually some qualitative value to CUDA and the reliability of the drivers and the software on top of Nvidia and actually the underlying chips as well such that even if you have the three gorgeous dam you have cheap energy you
1:09:25
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
1:09:41
It could be something else though. We don't really know.
1:09:42
Uh this is all kind of just likeable little tidbits.
1:09:48
>> I mean, it's notable that that all of these have led to uh they originally wanted to launch R2 in May. Yep.
1:09:55
>> And it's still delayed. >> Yep. Yep.
1:09:57
And so, uh it'll be interesting to see how Deep Seek reacts.
1:10:01
They could potentially say, you know what, like we are like Huawei's just not getting the job done.
1:10:06
We'll deal with the we'll deal with the push back from Beijing.
1:10:09
We're putting in a huge order for H20s from Nvidia.
1:10:12
We want the best or at least the best that's available to us even though the H20 of course is uh four years old at this point and severely nerfed.
1:10:20
Uh so we'll see uh when will they get R2 out, how powerful it will it be, and most importantly, what will the cost per million tokens be?
1:10:29
Because if we get an 03 level model from DeepSseek and it's a hundred times cheaper, even if that doesn't displace OpenAI meaningfully because OpenAI is operating at the application layer, um it will be incredibly bullish for every rapper company because all of their gross margins will flip positive uh very very quickly because they'll need to do some fine-tuning.
1:10:53
We'll need to see what Perplexity did where they made instead of Deep Seek, they made it like >> 1776 >> 177 1776 seek or something like that.
1:10:59
Uh they did a fine tune on it to kind of make it more American.
1:11:04
Um but uh the most important thing was that the Deep Seek researchers figured out a bunch of interesting hacks to make uh make in just inference way way way cheaper.
1:11:14
Um, anyway, speaking of uh rapper companies, application layer companies that we love, Julius, what analysis do you want to run?
1:11:23
Chat with your data and get expert level insights in seconds. Ask Julius. I love it.
1:11:29
Uh, ask Julius to analyze your data like two million users have already done.
1:11:34
Uh, folks from Princeton, BCG, >> Julius.
1:11:38
>> Um, >> I wish my only my the only thing I would change with Julius is I wish it was Rahul. >> Yeah. or Sunwalker AI.
1:11:44
AI, but it's always time for >> just like the Ford just like the Ford Motor Company, you know, >> the Sunwalker artificial intelligence company.
1:11:54
>> Maybe maybe it could it could happen.
1:11:54
Uh so Tortax has a take on uh the on the uh the Deep Seek story because uh they think it's a confused narrative with no sources at Deep Seek confirming it.
1:12:06
So uh Torax says, "This story is so insane.
1:12:13
dream narrative for burgers and their >> I think that's an American slur.
1:12:17
>> Yeah, I guess uh that I might well that I might well cook up my own uh also based on halfbaked rumors experience in an authoritarian society and just a little bit of soouththing.
1:12:27
Uh as expected the plot thickens uh Cinping's heavy-handed central government approach is stalling development is the take that um to your taxes might be debunking here.
1:12:37
Deepseek was a breakout hit but patronage networks don't reform overnight.
1:12:40
The actual Chinese national champion in AI is as we know Huawei.
1:12:45
They get unconditional subsidies and the nation's hopes are pinned on them.
1:12:48
On a software side, it's also Singwa the university and their brainchild ZAI with GLMs.
1:12:54
But Huawei does everything.
1:12:54
In February, the party asked uh Ren Jenf to partner with major AI labs including DeepSeek and beat America at AI.
1:13:02
um they approached DeepSseek sending personnel to adopt Ascend clusters for V3 inference.
1:13:11
We've seen papers following from that and we know these clusters now work at Silicon Flow and elsewhere.
1:13:16
They also suggested training the next generation models on Huawei uh but were privately told by probably after some experiments that Ascend that that the Ascend ecosystem is not yet mature or reliable enough and will go with H800's. Thank you very much.
1:13:32
With the knowledge gained, they had set out to train uh Pangu Ultrae, mixture of experts as a reproduction of V3 R1 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.
1:13:50
But there's nothing special there.
1:13:52
They can uh do equally well and uh will soon surpass SAR.
1:13:57
Now, as as Deepseek is uh is not releasing any rumored R2, the timeline never once made sense, and that's a big issue.
1:14:05
You need to have your timeline straight.
1:14:06
Uh there's renewed discussion about importing Nvidia.
1:14:10
They are trying to spin this two to their benefit, leaking to journalists that it was DeepSeek that had failed at R2.
1:14:16
While Huawei's Noah's Arc 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.
1:14:28
Uh, for now, they are probably optimizing cloud matrix hardware and CINN, testing 910D and 920 and hiring people with LLM expertise.
1:14:36
The above is an educated guess.
1:14:38
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 a historically independent startup is forced into changing their training stack by heavy-handed politicians.
1:14:52
And so the takeaway here is that um Huawei might actually be significantly behind uh Nvidia.
1:15:00
And it's less about the CCP saying, you know, we want uh I mean, of course, the of course the reason the CCP is saying uh 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.
1:15:18
But the the the flip side is that uh Deepseek is down to use anything and train on a bunch of different stuff.
1:15:26
And really they are they probably at least according to this they really are just having trouble training on large Huawei clusters.
1:15:32
And so they're like let's get back in the CUDA ecosystem.
1:15:37
Anyway, let me tell you about profound.
1:15:40
Get your brand mentioned by chat GPT.
1:15:42
Reach millions of consumers who are using AI to discover new products and brands. Get a demo. Go to profound. >> Be like the mag five.
1:15:49
What did uh what did James say? He said something.
1:15:53
Well, he was saying like I can't say who's using it, but >> Oh, yeah. The Fortune five. >> The Fortune five.
1:16:00
>> He's got a Fortune five client. >> Yeah.
1:16:02
>> So, it's like one of five companies.
1:16:03
We'll >> leave it to you guys.
1:16:04
>> 20% chance you just guessed it correctly.
1:16:06
>> Um anyway, uh lots of people making money on the Intel story.
1:16:09
So, the story today is that uh the uh the Trump administration uh just last week called for the resignation of Lip Bhutan.
1:16:18
said that his ties to China were too much for an American champion like Intel.
1:16:24
Uh but Donald Trump has reversed co and called TAN a success and the idea of the US government buying a stake in Intel is now floating around.
1:16:34
Um I don't love the idea of the people that brought us the TSA running the most advanced manufacturing process humanity has ever produced.
1:16:43
>> Or the folks behind the DMV.
1:16:43
the folks behind the DMV getting in the fab, getting into the clean room might be a little bit of a a stretch for me.
1:16:49
Um, but Intel does need better shareholders.
1:16:54
Uh, there was a few years ago before the chips act, >> long-term patient shareholders. >> Exactly.
1:16:59
People were talking about, oh, we need to we need an American semiconductor champion.
1:17:02
This was during like the re-industrialization meme kicking off.
1:17:05
Um, everyone was saying this like, we need American chips.
1:17:08
And yet no one was like, I'm going to actually go build a position in Intel.
1:17:13
And so everyone was like, "Yeah, we need this."
1:17:15
It was it was a uh what what is it? A cocktail position.
1:17:18
It was a cocktail position, meaning um something people like to talk about at cocktail parties, but they don't actually put their money where their mouth is.
1:17:25
So you sound smart saying we need to Yeah, we need to make Intel an American champion.
1:17:30
We need to make chips in America, but I'm not willing to put any money on the line to actually do it.
1:17:33
And so Intel's share price has been kind of in the dumps. >> Well, until recently. >> Until recently.
1:17:39
So Dan Gallagher in the journal says federal support could get the troubled chip chipmaker over some hurdles but risks great harm to the US tech sector. We can get into it.
1:17:47
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.
1:17:57
The Trump administration is discussing options with Intel that would involve the federal government taking a financial stake in the troubled chipmaker.
1:18:04
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.
1:18:15
>> This is so funny after talking to fabricated knowledge over at semi analysis uh 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, >> politicians, >> there's too many politicians, too many like famous people, writers, thinkers.
1:18:30
There's not enough like just nerds like scientists.
1:18:33
So, we need like physicists on the board and like technologists.
1:18:35
We need like an Elon type or like, you know, someone who understands the actual tech. Uh, >> yeah.
1:18:42
I mean, just Steelman, it's like Trump has, you know, a multi-billion dollar digital asset business.
1:18:46
He has a multi-billion dollar social media company.
1:18:51
>> Founder of a tech unicorn.
1:18:51
So, it's not his first rodeo in the tech industry. >> That's right.
1:18:56
>> So, you have to give him some credit if he was able to get in there.
1:18:57
So Dan says that marks a fast turnaround given Trump was calling for TAN to be fired just days ago.
1:19:03
The news was encouraging for Intel's belleaguered investors who have watched the chip industry's once undisputed leader lose more than half its market cap in less than two years.
1:19:12
The stock jumped 7% Thursday on the initial reports of the talks and gain more ground early Friday morning.
1:19:16
But investors should still be wary.
1:19:18
Intel's problems are such that even a big check from Uncle Sam won't fully solve them.
1:19:23
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.
1:19:33
Intel has also been granted up to 8 billion so far in direct funding through the chips act, but that hasn't been enough.
1:19:39
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 products, meaning few outside chip designers have found the technology compelling enough to sign on as customers of Intel's contract manufacturing service.
1:19:59
Wall Street expects another 7 billion in negative free cash flow >> uh on this year according to estimates from visible alpha.
1:20:09
Sorry double duty there on the soundboard.
1:20:13
>> 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. Smart.
1:20:21
>> You couldn't get a customer for that.
1:20:22
>> 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.
1:20:27
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.
1:20:37
Um Doug over at semi analysis was talking about how the uh chip design business seemed like it could be a target for like PE in the sense that if you came in and kind of overhaul >> tan was going to come in. >> Yeah.
1:20:54
if you, you know, dramatically cut cost and really focused on serving customers and and uh, 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.
1:21:08
>> Yeah, Hawkan's the CEO of Broadcom.
1:21:08
Um, I just wanted to say have a great flight, John Xley, he says he's taking off.
1:21:14
He's landing in one hour.
1:21:14
So, uh, and if you're trying to set up a semiconductor line, if you're trying to build or plan products, get on Linear, linear. app.
1:21:23
app >> greatly >> purpose-built tool for planning and building products.
1:21:26
Meet the system for modern software development.
1:21:28
Streamline issues, projects, and product roadmaps.
1:21:32
Um, so I >> this chart chipped away Intel and TSMC revenue >> is ripping.
1:21:40
So I'm I'm still I'm still sort of split on this.
1:21:43
We have some guests on the show today to talk about the dynamic between uh the US and China in the semiconductor race.
1:21:48
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.
1:22:01
If they set up FABS here in some sort of, you know, negative conflict scenario, it's like, well, we still have the the the factories here.
1:22:10
even if it's run by a company in uh 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 I think the push back there is we don't have the talent which is key to staying on the leading edge. >> Yes, that's true.
1:22:29
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 a lot of the talent will be coming over and so there's a little bit of like you know you 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 um unclear where it goes.
1:22:50
unclear how uh to at least to me how important Intel is as like a strategic company versus just like uh it's one of the greatest technology companies America's ever produced. It's a crown jewel.
1:23:02
It should just be protected because it's it's it's like good for the America brand versus like if Intel disappeared tomorrow, like how how bad would things be in America?
1:23:15
>> Like would would we be able to get by with with other suppliers? Um >> yeah.
1:23:19
So >> because we do have AMD, we do have we do have Nvidia and then TSMC and and uh and Samsung are not in China.
1:23:24
That's not where we buy our chips from.
1:23:27
So um >> well Dan in the journal says the government might for instance pressure chip designers like Nvidia, AMD or Qualcomm to manufacture with Intel perhaps as a condition for getting export licenses for China.
1:23:38
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 TSMC's.
1:23:46
It could result in inferior products and wastage by Intel because so much silicon has to be thrown out to make a working chip.
1:23:54
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.
1:24:01
That would undermine the ultimate goal of government intervention in the industry, which is to maintain American technological supremacy. >> Yeah.
1:24:09
The Rubicon of state intervention in chips has already crossed.
1:24:12
The administration already signed significant leverage over Intel uh 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.
1:24:28
But the federal government must take care not to go too far lest it undermine the market model that made American technology.
1:24:36
>> Slippery slope >> it is.
1:24:38
But at the same time there is there is a case to be made and I guess uh you know like if you put the US sovereign wealth fund under the uh direction of Leopold Ashener uh there is a case just for make money for the taxpayer and this is actually the opposite of a bailout.
1:24:54
This is what Tim Gner got in trouble for during the not in trouble he was ultimately vindicated during the financial crisis in 2008.
1:25:00
He went and made a bunch of loans to banks to the or on the order of like billions and billions of dollars and everyone was like this is a bailout for Wall Street.
1:25:10
This is a bailout for the banks.
1:25:10
But he actually only invested in the in the banks that made it through the crisis and so those those loans those backs stop debt instruments were paid back with interest and so the US taxpayer actually made money on those deals.
1:25:24
It feels a little weird, but but but the same thing could happen here.
1:25:29
Like Intel's a 11110 billion company.
1:25:34
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.
1:25:40
And it doesn't it doesn't actually uh result in any like lost money.
1:25:46
And if uh >> if Trump can just get three three X's and string like a hundred of those together in a row, we'll solve the uh the whole, you know, federal debt crisis.
1:26:01
>> That's a high water mark.
1:26:01
I think I think he should be targeting, you know, a nice 5x fund for the first run.
1:26:03
Uh then raise 10x more and then scale up and then uh you know, start start deploying the big the big money.
1:26:11
The big money you should buy 100% of Intel. What you got, Tyler?
1:26:15
This is like um we should give uh put Jane Street you know high frequency law makingaking. >> Yes.
1:26:22
>> Just optimize for GDP direct access to the legal code. Yeah.
1:26:25
Uh so any anything they can do to just maybe one of the foundation labs actually instead of nationalizing the labs.
1:26:32
We need to need to uh you know corporatize the government and let and do a re uh reinforcement learning environment with a verifiable reward.
1:26:41
The verifiable reward being the stock market >> RL for business, but the business is the government. >> Exactly.
1:26:47
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.
1:26:56
Um >> well Zoomer uh at Zooi Zoom has been going viral again.
1:27:03
He says why AI is a house of cards.
1:27:06
He has an entire thread breaking down these sort of chained uh losses that we've been talking about.
1:27:13
>> And uh he's getting community noted uh quite a bit.
1:27:17
One H uh someone added one H100 can serve thousands of users at once depending on batch size and model.
1:27:24
For inference, you don't dedicate a GPU to a single user.
1:27:26
You load the model then stream requests from many users in parallel.
1:27:29
Other numbers in this post are also wide wildly widely exaggerated.
1:27:34
>> Yeah, this might have come from a group chat.
1:27:35
uh originally that was maybe not fully fact checked but uh you know told told a compelling story so fun with it >> and Nick Carter says compelling threat if you ignore that inference gets 10 to a,000x cheaper every year if you're willing to pay $200 a month for AI and VC funding sub and VC funding subsidizes half of that simply wait six months um I would say uh you know Mr.
1:27:58
Randall earlier on the show said it's actually not all of a sudden getting 10 or 10x cheaper at least for Frontier models, but the point he made is that a lot of prompts could be served with uh older cheaper models and that's going to be a big focus.
1:28:14
I mean clearly that was a focus uh for OpenAI with the recent launch.
1:28:19
Yeah, I I I dug into this to see, you know, how would we really hit a thousandx cheaper this year on on the models and and would the 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?
1:28:37
I'm I'm kind of split on it.
1:28:39
I you know, the charts that Nick Carter shared here are pretty compelling.
1:28:43
Um, I just hope that the that the trend continues because the the dynamic of reasoning models and test time inference is slightly different like you are just it it's less algorithmic driven.
1:28:53
It's more just throwing raw compute at it and generating a ton of tokens.
1:28:57
So, I don't know Tyler what uh do you think you're closer to uh 10x cheaper inference every year, thousandx cheaper inference every year?
1:29:07
What type of gain in cost per token do you expect over the next few years?
1:29:14
>> Um, >> don't make mistakes.
1:29:16
>> Do do you mean in what what models are you talking about?
1:29:18
Like frontier level models? >> All the models.
1:29:20
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. >> Yeah. >> Right.
1:29:30
You have like 40 which is super cheap.
1:29:32
cheap. source 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 not the cost
1:29:45
>> but when you open source something you can like distill it even further you can like >> you get some you know optimizations there so so 03 pro let's call that like an expensive frontier model uh how cheap do you think that is next year. Do you think it's 10 times
1:30:01
Do you think it's 10 times cheaper, two times cheaper, a thousand times cheaper?
1:30:07
>> Um, like an equivalent model of >> Yeah.
1:30:09
03 Pro heavy reasoning, thinks for 10 minutes, generates tons of tokens.
1:30:16
>> Uh, closer to in a single year. >> 10x.
1:30:19
>> I'm probably closest to 2x. >> 2x.
1:30:22
>> Yeah, I wouldn't say massive gain.
1:30:23
>> Well, guys, I hate to interrupt, but there's some breaking news.
1:30:25
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He ate 55 nuggets in 10 minutes.
1:30:36
>> Absolutely incredible.
1:30:37
>> Some other breaking news.
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1:30:45
And we have our uh third guest of the stream since we already did the debate.
1:30:49
We have Bill Bishop from Citizen. How you doing? >> Good. How are How are you? Thanks for having me. Thanks for hopping on.
1:30:54
We really appreciate you taking the time.
1:30:58
Um, >> give us uh I mean I'm super familiar with your work.
1:31:00
I think everyone should be, but uh g give us the high level of the push back that you saw yesterday.
1:31:03
We enjoyed having you in the comments.
1:31:05
Uh uh and uh I I want to know how you would frame the counterargument to what uh Aaron Gin was making yesterday. >> Great.
1:31:14
Well, thanks for having me and I I'm impressed that you guys respond to jerk comments. That's good.
1:31:19
>> I don't see it as a jerk comment.
1:31:20
>> Well, yeah, we don't see it.
1:31:20
I think it's extremely fair push back.
1:31:22
We understand when when I have Aaron G on, I see it as having Jensen Wong on the show.
1:31:30
>> That that is that is how I see it, too. So, that's great. >> Exactly.
1:31:32
And and and I I can't have I 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.
1:31:40
Uh and so I enjoy uh having him sit there and I can throw stuff at him and of course he has he has his uh his opinions and his his arguments.
1:31:50
Um, but it's great to have somebody that can speak the language of Nvidia. So fluid. >> Yeah.
1:31:56
No, and I again I I think um, thanks for having me.
1:31:58
I'm psyched to see you guys on Substack 2, which is great.
1:32:02
Um, and uh, I see you're running a show today.
1:32:05
You got two great guests, Jimmy Goodrich and Leonard, who are going to be way better on the on sort of this discussion.
1:32:11
And so I think that um what I would say is what's been interesting to to watch and and I think talking about the the reversal on the H20 trip.
1:32:20
We got a H20 chip from Nvidia.
1:32:20
We got to remember right the there was a proposal teed up for the Biden folks to ban the H20 >> and they never did it for whatever reason.
1:32:30
Trump comes in actually bans it.
1:32:33
So it looks like he's being more hawkish and then a couple months later because of really effective lobbying from Nvidia and specifically from Jensen Hong at the principal level at the you know to the president to David.
1:32:42
I mean, it's not even it's not even traditional lobbying because they're only they're spending like a million dollar seven figures on lobby probably 10 times that on Jensen Wong's like flight schedule >> flight school, right? No, it's it's brilliant.
1:32:54
They they sort of, you know, disrupt the lobbying business, go right to the decider, >> go direct and and how the narrative is shifted and so so Trump on the one hand looked like he was tough on China, then he backs off and then of course he gets a bunch of flack for oh my god, he's caving to China, he's caving to Jensen, right?
1:33:09
right? when when in fact he he sort of did what Biden didn't do and then because of this personal interaction and the personal effort from Jensen Wong 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 recision of the AI diffusion rule right
1:33:27
which was really you know David Saxs was I think a big uh advocate of getting rid of that rule with a bid 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 out there competing with China, not just limiting. And the way to do
1:33:41
And the way to do that is to get people hooked on US, the US AI stack, specifically Nvidia, uh, globally, exchina, that makes a lot of sense.
1:33:51
The China decision on H20, I think, is flawed.
1:33:54
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 Hong is lobbying to sell sort of the next somewhat nerfed chip, right?
1:34:05
a little better but but still not near the the top of the Nvidia um product suite.
1:34:09
The idea that just Nvidia is better, CUDA is better than the Chinese the Chinese hyperscalers, the you know the the the Alibaba, Tencent, Bite Dance, uh DeepSeek, 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.
1:34:30
competitive world. doesn't make sense in the context of a world or or market that's run by the Chinese com you know the communist party of China and Xiinping made very clear in April at this polio study session that was about AI specifically said he wants China to
1:34:44
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
1:35:04
That I think is fundamentally naive and misunderstands how the party operates.
1:35:08
I think what it does is it helps China fill the gap between where they are now in terms of lagging capabilities and lagging um output or quantities in terms of the Huawei chips.
1:35:20
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 to break any reliance on the US AI tech stock.
1:35:30
And so by selling H20 chips now, we're just helping China keep in the race when in fact if they didn't weren't able to get 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.
1:35:48
What what's your reaction to uh the commentary showing that the the CCP is actually like actively pushing back against uh Deepseek which is you know the clear open-source or or maybe not totally clear but an open source is also
1:36:05
very >> an open source leader >> but yeah your your reaction to the news that the that Beijing asked LA found Chinese foundation labs to not buy Nvidia >> even effectively even if it means delaying paying like deep cards too. >> So, there was a Financial Times report
1:36:19
>> So, there was a Financial Times report yesterday where it said they were encouraged.
1:36:22
We 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 um the latest ascend chips um which were all based on dies that were illegally fabbed at TSMC where um Huawei used a cutout company that was related to Bitmain called SoftGo to get I think it was 2 million dies from TSMC that they can't make on their own in China.
1:36:45
Um, and even then they're still not where they need to be.
1:36:48
And so I think that this goes you you have that news.
1:36:51
You have the news since the announcement by the Trump administration that they were going to allow again allow licenses or or give licenses to sell H20 to China.
1:36:58
You've had significant push back from some of the regulators in China.
1:37:02
You've had talk about the chips are unsafe.
1:37:04
Maybe they have back doors.
1:37:06
They're environmentally unfriendly. There's security risks.
1:37:08
Um, and so I think what you're seeing, you know, there's different hypotheses about what's going on on the on the push back on the H20.
1:37:16
It's maybe they're trying to negotiate for the better chip.
1:37:18
Um, 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.
1:37:27
We need to make sure we're focused on building our own pathway to self-reliance.
1:37:33
And that in I think is related to that news that DeepS is being encouraged to use the lesser chip 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.
1:37:57
But I think >> you can read into it and and one way you can read into it is that the CCP doesn't believe in the sort of fast takeoff scenario, you know, like runaway AI in the next, you know, two years, right?
1:38:09
Which I think broadly I don't know a lot of people that still believe in that, but it's notable.
1:38:14
>> No, I I think that I think that's right.
1:38:16
I think this is more of a we're going to we are going to set the foundation for doing it in a self-reliant way, even if it takes us longer.
1:38:23
And so that's where I think what's been interesting to watch in Nvidia is how the narratives have shifted in DC 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.
1:38:37
There's just like there's barely any push back.
1:38:38
Um and certainly in the government from my what I'm understanding there's no longer any process, right?
1:38:43
Back to the whole sort of how did Jensen lobby to get decision made.
1:38:48
>> There's no process there.
1:38:48
No, there no like all the many of the people who worked on these issues were fired.
1:38:52
Um and and now it's basically like he gets to the principles, he gets to Lutnik or Sachs or the president and that's the decision.
1:39:00
There's no like national security discussions.
1:39:02
There's no process anymore. >> Yeah.
1:39:05
It feels like interestingly people often project uh like a monolithic culture upon China but then severe division within America.
1:39:14
And it feels like there might be some division uh 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 let's hold it back and and and hurt their ability to scale.
1:39:26
And then in China they might be saying the same thing.
1:39:30
Hey, we need to just buy by by buy and stay near the frontier.
1:39:32
This will actually help us accelerate.
1:39:34
And then there'll be a different argument for for maybe maybe we need to just >> There's also the the you know in in many ways Deepseek the original you know Deepseek release was was >> in some ways economic warfare on on Nvidia, right?
1:39:52
You saw this massive selloff immediately and >> well and there and there was clearly a somewhat of a coordinated hype. >> Yeah.
1:39:59
I mean the app store the app store chart you know DeepC getting all these downloads was completely Twitter bots Twitter trolls. Yeah.
1:40:04
And so and so I think there's you could also read into this and think uh Beijing doesn't think that the next Deep Seek 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.
1:40:21
Do you have any uh do you have any context on previous uh technological revolutions and and the history of the USChina relations like going back to uh like the cloud or mobile?
1:40:33
Like was there ever any similar considerations of of don't sell iPhones?
1:40:38
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.
1:40:44
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 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 that's saying don't come here with your technology.
1:41:00
I >> I think this is this is fairly unique as far as I in my memory.
1:41:03
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 the economic importance um or the frankly the market cap importance.
1:41:17
>> Zooming out, how how do you feel like USChina relations are just going generally?
1:41:22
I feel like two years ago there was a ton of saber rattling about we need to get sharp on Taiwan.
1:41:27
Everyone needs to learn what TSMC is.
1:41:29
needs to learn what TSMC is. uh we need to talk about defense technology and Taiwan invasion in you know six months 12 months it's happening it's gonna happen and then it feels like we've been in a bit of a lull a little bit more economic uh you know uh economic warfare but
1:41:47
>> 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 >> it's a great question and it's one that it's still um it's still quite unclear I think that you see uh the beginning of the Trump administration, the economic tensions rose pretty high. Those have
1:42:05
Those have come back down to, you know, where there's now a sort of a a tariffs are high, but there's a >> it's calmer.
1:42:13
Although the Chinese in part it's calmer, I think, because the Chinese pulled out their export control um 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.
1:42:27
Um and so I think you're in a bit of a lull on the US side because there are things like for for example on the technology stuff you know there were a bunch of new actions around um export controls around chip related stuff they're all tabled right in part I think because of how um the Chinese were able to push back on the uh initially the beginning of the of the sort of the trade war using their their rare earth's uh card.
1:42:52
their rare earth's uh card. Um generally though when you look at uh the the broader you know you look at Taiwan you look at um sort of things like the South China Sea you know the the you look at the other economic issues
1:43:07
around you know what the US over capac says over capacity the the structural issues are not going away we are I think as you said it we're in a bit of a lull and the Trump administration seems to be more focused on um the the transactional bit parts of the relationship for now. Um, but you know,
1:43:24
Um, but you know, there's some people who want to talk about, oh, maybe there'll be this grand bargain.
1:43:29
You know, Trump and she may meet this fall and they'll have some great grand bargain.
1:43:34
>> Um, you know, it it's hard to see how that would happen and how it would be sustainable just because of the the real structural issues and relationship, but there's no question that the narratives have been shifting.
1:43:44
The Chinese have been working really hard on peopleto-people sort of >> Yeah.
1:43:50
uh stuff that has I think pulled us back from the sort of peak of China hawkish.
1:43:56
I said, you know, my Sharp China podcast last last fall at the beginning of the year. I just we were joking.
1:44:00
I said, you know, I think we've hit peak China hawk, right? No, seriously. Right.
1:44:04
It's going to it's going to it's going to it's going to sort of moderate at least for the time being.
1:44:08
I think that's what we're seeing.
1:44:10
>> Is the rare earth element stuff uh a an ace in in the deck of cards or is it more like a jack or a queen?
1:44:16
I think about, you know, we haven't even gotten to the obviously Tai Taiwan invasion feels like more of the ace um in terms of just like how much pressure that would put on the relationship.
1:44:27
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.
1:44:36
It's so critical to American technology than uh than rare earths.
1:44:42
Or is there some other dynamic at play there?
1:44:44
I think the Chinese have, you know, Apple is one of those companies that every time there tensions it comes up, well, China could do something to Apple.
1:44:51
And they have, you know, Tim Cook has been brilliant at managing President Trump and brilliant at managing Xiinping.
1:44:58
And >> you know, Apple, there was a great book that was written about Apple by Patrick McGee.
1:45:03
I mean, Apple has does a lot for the Chinese economy.
1:45:06
They employ a lot of people directly and indirectly.
1:45:07
The Chinese so far have not >> really bothered them in any direct way.
1:45:13
Um the rare earth is one where they have the ability to uh effectively disrupt significant parts of US industry and European industry and they did that and and that I think is why you see you saw the US sort of pull back pretty quickly in the in the in the trade discussions and you know the way the US did it is after the first meeting in where was it?
1:45:34
It was in um it was London, Geneva at the first meeting.
1:45:40
>> All of a sudden the US added these new export controls on like jet engines and and other things because the Chinese weren't giving the rare earth magnets that the US thought they were.
1:45:46
Um that that is the one where the Chinese can cause pain immediately. >> Yeah. Yeah. Yeah.
1:45:51
So with Apple, if China does anything to Apple, that's like ma million people unemployed in China.
1:45:58
Very disruptive to the Chinese economy.
1:45:59
Whereas with rare earths, like you could stockpile them.
1:46:02
You could it's not as critical of like a labor market in China other markets if they can't sell to us it it it's basically it hurts the couple like one or two stateowned companies effectively. >> Got it. Got it.
1:46:14
So So it truly is more leverage for them.
1:46:17
>> But 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.
1:46:23
The problem is maybe the Trump administration now is serious.
1:46:28
This is not an this was not an unknown issue.
1:46:31
The Chinese threatened this in the first Trump administration.
1:46:32
The Trump administration then we had co nothing really happened.
1:46:35
B administration admired the problem, wrote some papers, had some meetings, didn't fix it.
1:46:38
Now maybe there's the urgency to actually address it.
1:46:42
>> Yep, that makes sense.
1:46:44
>> Last question from my side.
1:46:44
What is uh general sentiment from uh on the ground in China or what's your read on sentiment among business leaders today?
1:46:55
>> Um not being there, that's a harder question to answer.
1:46:57
But when you look at some of the data and the surveys, you know, you look at like some of the multinationals, I think there is uh the the the surveys from various foreign chambers of commerce tend to be generally pretty pessimistic, more pessimistic than they've been in years.
1:47:11
Um when you look at some of the surveys around Chinese business confidence, it is um maybe bottomed um not particularly um positive.
1:47:21
Certainly there are pockets that are positive.
1:47:23
that are positive. You we talk about the deepseek 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 you know things like AI stocks are up AI concept stocks are up big some of the chip stocks are
1:47:37
up big you know the the the H20 news and the and the fact that the Chinese maybe not want the H20s was good for some of the domestic uh 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. >> Well, thank you so much for hopping on.
1:47:54
>> Well, thank you so much for hopping on.
1:47:55
We are going to jump on with Jimmy Goodrich.
1:47:57
Uh but we'd love to have you back.
1:47:59
I mean, this was a long time. >> Thank you. Anytime.
1:48:01
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.
1:48:05
You just join the same link that you have.
1:48:09
>> Really, you can join us. We got it live.
1:48:10
So, anytime you do it, we'd love to see you. >> Cheers. Have a good weekend. >> Cheers, everyone. Have a good weekend. Cheers.
1:48:14
>> Let me tell you about Finn.
1:48:14
I 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.
1:48:21
AI legend, and we have Jimmy Goodrich in the reream waiting room.
1:48:27
Let's bring him in right now and continue our conversation on chips in China. How are you? >> Welcome to the show. >> Welcome to the show.
1:48:34
>> Hey, good to see you guys. >> Good to see you, too.
1:48:35
Um, I'm not sure if you if you've been tuning in or Bill Bishop gave you a uh a highlight, a summary of of the debate.
1:48:42
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.
1:48:55
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. >> Yeah.
1:49:06
No, I I caught the tail end of it, and I think it was a great discussion with Bill.
1:49:08
Uh he always got really uh good insights to add.
1:49:10
I mean clearly it's been a roller coaster.
1:49:12
I mean US export controls on China have typically been this sort of the government thinks about doing things.
1:49:19
It leaks out in Reuters or the Wall Street Journal that there might be doing an export control.
1:49:25
China learns about it about 9 12 months in advance.
1:49:27
They stockpile everything they need.
1:49:29
Then they watch the Americans sort of debate openly, you know, in our democracy which is messy.
1:49:36
>> I think they I think they look back all this is kind of silly.
1:49:38
and then they kind of half impose a restriction, then they undo it.
1:49:41
I think it's just all kind of comical for Beijing.
1:49:44
>> So, how big do you think the H20 issue really is?
1:49:46
Um, >> some people are talking it up as like the most important chip.
1:49:50
It's going to completely unlock uh Deepseek R3.
1:49:51
It's going to be this amazing moment for them.
1:49:55
On the other side, folks are saying it's a four-year-old, it's a four-year-old chip. It's heavily nerfed.
1:50:01
Like, yes, Deepseek figured out a way to optimize around some of the limitations, but in general, uh, this is not a real threat.
1:50:07
How are you feeling about the actual uh the actual value of the capability provided by the CUDA ecosystem on top of the H20?
1:50:15
>> I mean, I I think it's still a very valuable chip for China and for China's AI model developers for two reasons.
1:50:20
One, in the AI world, obviously you you've sort of gone into this in depth on your show.
1:50:24
It's about training and inference and particularly for inference is where you need memory bandwidth and that's where the H20 excels.
1:50:30
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.
1:50:40
So, it's a great value chip for for inference.
1:50:45
And that's another key factor here is quantity is Nvidia can provide them in millions of units.
1:50:50
That is something that Huawei and no indigenous producer today can do.
1:50:54
uh because of the export controls because of the you know 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.
1:51:08
So, at least temporarily in this sort of 1 to2year window, the H20 and then possibly a downgrade at Blackwheel still going to be very useful China uh to China.
1:51:17
And on top of that, of course, there's the CUDA advantage.
1:51:21
And if you talk to any AI model developer in China, they want to develop their model on the Avid Nvidia stack.
1:51:27
They've been doing it since college days.
1:51:29
Everybody knows how to code on CUDA.
1:51:31
It's a big pain uh to move to another supplier.
1:51:34
I mean, just moving to AMD, for example, is difficult.
1:51:36
to AMD, for example, is difficult. um so you know let alone a much smaller much more nent developed uh Chinese competitor so I I think it's actually going to be um a big game changer for the deployment of AI for the scaling up of Chinese AI models and if you think about uh you know reasoning and inference if you want to develop more uh
1:51:55
capable AI agents that are going to be doing more taskfree autonomously that's where H20 high memory bandwidth uh good inference chips are going to come into play >> do you think it's smart do you think it's smart for Beijing to take a a maybe a more long-term view here and say we're going to throttle uh development in the short term to really develop uh the industry locally. >> Well, I think they've got two sort of
1:52:20
>> Well, I think they've got two sort of interest groups they're trying to take care of.
1:52:23
On the one hand, they have their AI upper stack companies, the model developers, um Deepseek, Moonshot, Emmy, uh BYU, Tencent.
1:52:31
Emmy, uh BYU, Tencent. um 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 you know China has a self-sufficiency
1:52:50
national target that Cinping set as part of the 20th party congress called it or national technology self-sufficiency and he's talked specifically about using a 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 um Nvidia not being in the market and expand their market share. And so but on
1:53:14
And so but on the one hand Beijing is welcoming um Nvidia back in.
1:53:18
They're rolling out the red carpet when Jensen comes.
1:53:20
They also doesn't matter why they want an executive who's actively lobbying against tech restrictions in Washington.
1:53:28
They want to reward that behavior.
1:53:28
But on the other hand, they want to create a space for these indigenous GPU players.
1:53:35
Um, it's going to be in things like stateowned contracts, uh, China mobile, telecom procurement contracts are going to go mostly to those kind of Huawei and other firms, but I expect sort of the BU, Alibaba, Tencent, hyperscaler contracts are still going to be majority Nvidia, particularly if they can get the licenses.
1:53:50
So, Beijing sort of balancing both of these constituents.
1:53:53
In fact, within China, there are many who actually don't like Huawei.
1:53:57
Um yeah, there was a Chinese Academy of Science very senior computer scientist who's a vice minister in the Chinese government and party and a uh 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.
1:54:13
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.
1:54:20
So, you know, China is not a let's give everything to Huawei.
1:54:24
There's a lot of people who think uh they're too aggressive.
1:54:28
They're kind of like the Apple of China.
1:54:29
Nobody wants to really do business with them uh because they're cheap on price and very aggressive.
1:54:33
They known as like the long or the wolf culture.
1:54:37
>> Um so, you know, Huawei has its own enemies with inside China too. >> Interesting. Yeah.
1:54:41
So, uh let me walk through the current thinking and you can kind of push back on my reasoning chain here.
1:54:48
So, um we are we are now maybe in an era of plateauing.
1:54:52
we're not on the cusp of super intelligence by merely scaling up uh you know a bigger large language model.
1:54:59
Um 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 the various SAS systems and putting agentic workflows all over the place.
1:55:15
Uh increasing GDP not to 20% overnight but maybe just bumping it from 2% to 3% one point or something like that.
1:55:24
And so um it uh giving the H20 to China allows them to do that.
1:55:30
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.
1:55:50
Um and so although it is it is somewhat of a more level playing field, we are still in the domain of uh of just economic uh competition.
1:56:02
And so um it's not it's not a major n it's not perceived as a major national security risk.
1:56:07
It's mainly uh 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.
1:56:17
Is that like roughly the modern thinking you think?
1:56:20
>> I'd say I agree with you on that first point.
1:56:22
Um, if we um, you know, want to help enable China to be competitive in AI, want to help their AI model companies get access to the best infert chips, want to help them scale up their deployment, um, win in the market at home and possibly also export their models globally, then absolutely we should be selling, you know, more H20s to China.
1:56:41
I just don't think that's in our national interest.
1:56:42
Um, you know, of course it's it's in it's in Nvidia's interest.
1:56:46
They want, you know, 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.
1:56:57
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.
1:57:09
Um I do think though if you look at um disinformation and cyber warfare and you look at the capability that um autonomous agents are going to be able to even at current GPT5 or you know future R2 level coding capability.
1:57:26
If you think about scaling that up with, you know, a thousand 2,000 autonomous agentic AI coding capabilities are going to be doing vulnerability scanning, uh, cyber offensive warfare, you really start to get an exponential capability increase.
1:57:41
And so I do worry that, you know, the Chinese state with, you know, two dozen um, H20 capable inference data centers could use that to do more autonomous cyber activity. Um, that's nefarious.
1:57:54
And on the same side, disinformation.
1:57:54
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.
1:58:05
And we've already seen the New York Times reported about 10 days ago that stateowned companies connected to the Chinese state are using deepseek um which is going to be inferenced on you guess what the best silicon possible to do exactly that which is um disinformation campaigns against Taiwan and the United States.
1:58:22
So I actually I do think there is a national security concern here.
1:58:26
One, there's an economic security leadership concern and then there is a um you know enablement capability down the road that that is actually going to be you know I I think happening relatively soon. >> Last question.
1:58:41
>> Another uh 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.
1:58:52
Or is that something that you you worry about very much or is that kind of secondary?
1:58:57
>> There's you know there's like traditional applications of high performance computing, supercomputing which is useful for weapons modeling simulation.
1:59:04
You don't need you know uh multiple large systems to do that.
1:59:06
You might have a couple of two a couple of boutique standalone government HBC systems where where more of that model data is going to be useful is if you're using large distributed systems of federated drones uh collecting data acting autonomously.
1:59:21
For example, think about a world where you have your PLA signals intelligence communications battalion that's in real time collecting all the battlefield communications in a Taiwan operation.
1:59:33
Uh 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 um you have a squad that's hit counter fire on this beach uh north of Taiwan.
1:59:51
They haven't even reported it up to their superiors, but the autonomous agent AI system might actually be able to get that, deploy a drone.
1:59:59
If you think about just those capabilities in the future, that's where inference really matters.
2:00:02
Um, and that's where it's going to scale up and create, you know, tons of economic opportunities for Chinese companies and e-commerce and all sorts of other areas, finance and SAS, but also on the military side, it's really endless if you could think about the applications as well. >> Great, great answer. Last question for me.
2:00:23
What's going on with Tik Tok?
2:00:23
It was uh, it was the talk of the timeline earlier this year.
2:00:27
Everybody seems to have forgotten about it.
2:00:29
Uh any any updates there?
2:00:34
>> Um you know, I don't have a whole lot.
2:00:34
I think it's one of these things where it's pretty obvious what happened to it.
2:00:38
Um you know, the president likes the the tool, thought it was useful for his election.
2:00:42
You know, there they've continuously renewed the clock on that 90-day extension.
2:00:47
Um unfortunately the you know there there's no longer really an operating national security council inside the White House um like you would have traditionally to kind of figure out and coordinate the inter agency on a solution.
2:01:00
So I think at the >> feels something that you you mentioned earlier Beijing kind of laughing about how our you know we have our our democratic system just publicly debates all these issues creating this ability for them to you know uh make make changes in advance.
2:01:15
But this feels like one of those things.
2:01:16
Um, I mean, they have to be just laughing uh laughing about how we we've dealt with uh this entire issue to date.
2:01:27
>> 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.
2:01:30
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.
2:01:40
We can't get our act together.
2:01:41
Export controls, we're moving back and forth.
2:01:44
whether or not we think we should actually, you know, get our act together in onshore ship manufacturing.
2:01:49
We're, you know, interested Intel here a little bit and then TSMC there a little bit.
2:01:52
Um, you know, the Chinese government, I think from their perspective, like, look, we've got a 10-year plan, a 15-ear 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.
2:02:05
Um, and I just don't think they take us very seriously, unfortunately.
2:02:09
>> Well, in a 100red years, we know the plan in America celebrate the 350th anniversary.
2:02:14
you know, there's gonna be a party and maybe a maybe a UFC fight.
2:02:18
That's what we can find on the White House.
2:02:20
Anyway, thank you so much for joining, Jimmy. Very insightful.
2:02:22
We'd love to have you back and talk more as the stories develop. This is great.
2:02:26
>> Yeah, happy to chat more.
2:02:26
You guys are we'll talk soon.
2:02:29
In other news, a Rune post has hit the timeline.
2:02:32
Aroon says, "Agree with Delion Tsun has spoken.
2:02:39
agree with Delion on the Mauist perspective that data centers should be turned into steel plants. I love it.
2:02:48
Um, and you know what else I love?
2:02:50
Adio customer relationship magic.
2:02:53
Adio is the AI native CRM that builds, scales, and grows your company to the next level.
2:02:57
>> If you have a free calendar this weekend, fill it up with uh onboarding to Adio >> and spend spend 48 hours just playing around in there doing some deals.
2:03:07
In other news, we have a we we our next guest is in the reream waiting room.
2:03:10
We will bring in Leonard Heim, second time on the show. I want to talk about Mr. Beast.
2:03:15
He says he plans to take a hundred 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. Wow. This is from Bass. >> Absolutely fantastic.
2:03:29
>> I I I I originally read this not as a joke.
2:03:32
It's like, oh, he's actually doing the PMF or die thing because that that kind of would work.
2:03:36
Uh, and I thought it was with a cursor subscription.
2:03:38
And then I was thinking about like, wow, he could wind up spurs the bills, he could wind up spending $2 million on this challenge, but uh, Mr.
2:03:46
Beast should get into, uh, into software challenges.
2:03:53
>> I I would like to see for a different Mr. Beast of software.
2:03:56
>> He should have a horse in the codegen race. >> Yeah.
2:03:58
It was clear that uh, PMF or die was on to something, but it required someone to make to make it their life's work. Exactly.
2:04:05
And if someone was really I'm going to be the Mr. Beast of tech.
2:04:08
I'm going to be doing crazy challenges all the time, live streaming, do experimenting with all the different formats.
2:04:12
There's clearly something there.
2:04:14
>> I think clearly should run it back.
2:04:16
>> Cle is a good candidate. >> Cle hackathon.
2:04:18
>> Anyway, we have Leonard back in the studio.
2:04:20
Welcome to the TVPN Ultra Dome. Leonard, how you doing? >> Hey, happy Friday. >> Happy Friday.
2:04:26
>> Love to talk about Mr.
2:04:26
Beast instead of H20s again, you know, while we're already on it. >> Yeah.
2:04:31
Uh I mean on on that note uh the Mr.
2:04:35
Beast if he was going to lock a bunch of uh a bunch of uh programmers in a room with cursor uh how h how bad do you think the gross margins would be?
2:04:43
Do you have a take on gross margins of application layer companies?
2:04:46
We've been talking about that all week.
2:04:48
Uh do you have any insight in there?
2:04:50
Anything that is uh is muttering through your whisper network?
2:04:56
>> Unfortunately I'm not in San Francisco so I don't know how codas nowadays work and I'm I'm out of the old breed.
2:04:59
you know, when I when I did software engineering, I didn't have AI.
2:05:04
>> Um, but I always noticed when I use my cloud plan and I run out of like queries, I was like, "Oh, damn.
2:05:08
I I need to write on my own and think on my own. Who do I query?"
2:05:12
And then I spin up my second chd subscription.
2:05:14
So, I think it would already apply to me.
2:05:17
>> Um, being stuck without AI is is quite a problem nowadays.
2:05:21
>> Well, uh, give me the give me the current read, your current take, the latest and greatest on the H20 debate. Where do you stand?
2:05:27
uh pro-exports, pro banning the exports.
2:05:31
How how have has anything shifted your thinking around it over the past?
2:05:38
>> Do you want to nationalize Nvidia?
2:05:40
>> Do you want to invest in Intel?
2:05:40
You're trying to buy a lot. What are you thinking?
2:05:44
>> Well, the last time I was listening to the president speaking about Nvidia, he was more talking about initially wants to break them up in our nationals, right?
2:05:50
Because they were so big.
2:05:52
>> Break them up but then roll them up later. We've seen this.
2:05:54
We've seen this playbook like 20 times with Trump and we get shocked every single time he comes out and says something.
2:06:00
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 every roller coaster.
2:06:08
>> Well, what he said during the what was it?
2:06:10
It was the AI action plan launch, right? Yeah. I was sitting in a room.
2:06:11
He was talking about Yensen, pointing to Yansen.
2:06:14
Um, and he was just saying, "I want to break them up, but it's so complex."
2:06:18
And I think just basically somebody convinced them it's really hard and therefore you you're not supposed to break Nvidia up. Yeah. >> Right. So fair enough.
2:06:26
>> Nvidia doesn't have as clean of a line to break up as Intel where you know you could you could >> we're taking you over here. >> Yeah. Yeah.
2:06:34
>> We need a CEO cards are going somewhere else. Yeah.
2:06:36
I don't know what would you do?
2:06:39
You'd open source CUDA or spin that into a separate company?
2:06:41
Like if you were even to break up Nvidia like what would you actually do? Do you have any idea? >> Yeah.
2:06:46
I think the software ecosystem might probably be the strongest one here.
2:06:48
But again, this just goes hand in hand with the design, right?
2:06:50
So again, yeah, I think it's 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.
2:06:57
Like there's more and more competitors.
2:06:58
I mean, the total total valuation will go up. Don't get me wrong.
2:07:02
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.
2:07:08
>> Is that is that driven by AMD catching up or new uh what what does Aaron call them?
2:07:13
They're like the the the new types of chips like Cerrus Grock and uh etched.
2:07:17
I forget what they're called.
2:07:19
There's there's a new name for these crop of AS6 that are designed specifically for AI and uh and and and they they could potentially pose a challenge, but they're certainly not taking market share yet.
2:07:28
It feels like it's it's mostly uh uh Nvidia, then AMD, then maybe some Huawei Samsung there, the hyperscalers, the TPUs, >> AWS with the tranium, Google has the TPU since forever.
2:07:41
I mostly think about them, right?
2:07:42
I think it's pretty clear the case.
2:07:43
They have all the incentives in the world to build their own AI chips and reduce Nvidia's margin. >> Sure.
2:07:49
>> On the startups, let's see how they're doing, right?
2:07:50
I think >> hardware is hard.
2:07:52
Hardware startups generally fail.
2:07:54
But if they find the right niche, you know, it's pretty hard.
2:07:58
Nvidia builds this more general thing.
2:07:59
And if you're like a hardware startup, you want to find like a more narrow niche to be like more application specific.
2:08:04
And if you hit the right point, right, whatever the next big thing is in AI, they might succeed.
2:08:07
And we just see more and more of them getting there, right?
2:08:11
And like we see Enthropic and other companies using Google GPUs, using tranium and and again the debate of the show you Huawei is also getting better.
2:08:19
They will also just the market share can only increase. Right. >> Okay.
2:08:22
Help me help me reconcile this.
2:08:22
Uh Google DeepMind's been seemingly fine with TPU and not having CUDA in their back pocket.
2:08:32
They're on the parade frontier.
2:08:34
The Gemini models are great. V3 is great.
2:08:36
The new Genie model is great.
2:08:39
It seems like they are not suffering or falling behind despite not having CUDA access.
2:08:47
>> But then simultaneously we're hearing that Deep Seek, Highflyier, Alibaba, they want to train on Nvidia.
2:08:53
They're not satisfied with Huawei.
2:08:54
Why is Huawei behind Google's TPU business?
2:09:02
>> I think that's an example I always bring up and people say it's going to be so hard to switch Huawei.
2:09:05
Google eventually succeeded, but also Google struggled.
2:09:08
the TPUs are pretty pretty old.
2:09:08
This was way before an AI hype >> and they actually also struggled.
2:09:13
I'm not sure if you guys remember TensorFlow.
2:09:16
>> Yeah, >> this was originally what they did, right?
2:09:17
Then later they switched to JS and right and have the PyTorch and everything around that.
2:09:23
>> 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 onetime investment and then you develop on top of it and eventually you will be there and you break even.
2:09:33
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 they will get there in particular if they can use
2:09:46
cursor along right who are doing it AI helps you to build your AI ecosystem >> and and I guess to some degree the flip side is like uh TPUs have full access to TSMC ASML and and and Huawei is restricted all throughout the supply chain um and so and yeah and So like the the the latest Huawei chips, we were just talking to Bill Bishop. He was
2:10:06
He was saying that like that was from like 2 million uh was it dyes that they got from TSMC through a shell company.
2:10:12
And so they had this like one-time batch of supply chain like ease and then they were and then from then on they were supply chain constrained again.
2:10:21
So then they had to go back to doing everything themselves and that was a lot harder.
2:10:25
Whereas Google just calls up TSMC and says hey do everything that you do for Nvidia just do it with our design.
2:10:29
which is like yeah probably slightly different.
2:10:33
Uh anything else you want to dig into?
2:10:36
>> No, I think we've I mean I think we've covered >> Yeah, >> I think we've hit this pretty >> I think we've hit this pretty >> well actually have you guys covered the semiconductor supply chain and how good Huawei is because there's this quantity thing is that the the like in the middle of the debate in my opinion which I think is being missed here >> please.
2:10:50
So >> we everybody compares the Nvidia H20 to the Huawei AS910C and that's the best chip they've been putting out there. Yep.
2:10:57
And if we look at the Huawei A910C, it's like 80% there when H100 is.
2:11:03
>> So like two or three years later than Nvidia, they're finally slowly getting their approaching on the hardware specs alone.
2:11:08
And again, we can look at the specification sheet, compare them one by one, this but this never tells the real story, right?
2:11:13
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 five, right?
2:11:21
So looking at the spec sheets is never enough.
2:11:24
Um that's where the software ecosystem come in where we just talked about.
2:11:28
Huawei is definitely struggling there.
2:11:30
I think they will eventually get there.
2:11:31
They just need to have more developers.
2:11:33
And I think that's exactly one argument favor of letting the H20 go there. Right?
2:11:37
The more people use the H20, less people use to Huawei.
2:11:41
So less developers are developing this ecosystem.
2:11:45
>> But where then comes in is how many chips can they produce? >> Right?
2:11:48
We got one number under secretary Kesa testified.
2:11:51
So he's supposed to tell the truth.
2:11:52
200,000 AS chips this year >> versus we're trying to sell I think it's 700,000 to a million H20s this year to China.
2:12:01
So this is this is where then the debate struggles right.
2:12:03
So if we wouldn't sell them the H20s, it's not like they have more AS10C standard than more maybe they have more developers stand but they share a limited number of GPU resources and 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 two million dice it's 2.
2:12:22
9 million dice right because they're struggling so much with their own production >> interesting yeah so yeah I mean m yeah maybe 200,000's enough for people to actually bootstrap that software ecosystem.
2:12:35
Certainly something to keep tracking.
2:12:37
Uh >> yeah, I think people consistently overestimate China on a bunch in a bunch of different areas. >> Yeah. >> Yeah.
2:12:46
>> I mean they can do software.
2:12:46
I think they will eventually get there and I think the idea that just like Huawei's ecosystem will always be terrible is just look, don't get me wrong, I I would hope it's true, right?
2:12:55
But they got good coders, they got good designers, they they will just get better on all of these kinds of things.
2:13:01
Maybe CUDA will always be better, but like Huawei is probably at the bottom right now regarding how good the ecosystem is.
2:13:06
And when the Deep Seek engineers, you know, they're getting to it and they're struggling.
2:13:10
Well, well, we'll get better, right?
2:13:12
I think that's that's for granted independ, thousand chips or a million chips.
2:13:16
>> 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 it's tricky.
2:13:25
This is a this is a thorny one.
2:13:27
It's it's not it's not extremely clear-cut for me.
2:13:29
Have you you landed in do a case study on Enthropic because they're the most beautiful example because they got so many different chips they're using. >> Yeah. >> Right.
2:13:38
>> And how's it going for them, right?
2:13:38
Like how how long did it take to train on Tranium?
2:13:42
Are they training on Trrenium?
2:13:43
Are they deploying on Trrenium?
2:13:43
I think this would give us an insight.
2:13:45
How many engineers are they spending there?
2:13:46
And how bad is it still is?
2:13:51
>> Is um >> you know, is is Beijing playing 40 chess by leaking out, you know, don't use the H20, don't use the H20.
2:13:57
So then they is then we just pile them into the country, right? >> I think so. >> Is it for DS?
2:14:04
It's just like you create an artificial demand.
2:14:06
You say like, "Look guys, you better buy buy some 910C's.
2:14:09
You really don't want to."
2:14:09
So we tell them, "Oh, if it's for sensitive government use, you rarely use 910 C's."
2:14:15
So we only see strong encouragements, not full-on bans yet.
2:14:19
>> And we've seen the same game with CPUs.
2:14:21
Ask Intel how it's going.
2:14:21
I mean, Intel in general, but also Intel CPU market in China.
2:14:25
The government also started encouraging there basically, hey, can you please use homegrown CPUs? >> Yeah. >> Right.
2:14:31
So, we've seen it all over. >> Yeah.
2:14:33
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.
2:14:42
Like, there's so many different levers that they could pull.
2:14:44
And the fact that they've stopped, they've only gone as far as like strong encouragement.
2:14:48
We've kind of inflated that to be like it's >> political incorrectness. >> Yeah.
2:14:53
which people inflate to be like it would be insane.
2:14:55
It's suicide to to to not to go against a recommendation from the from the CCP, but uh it it does feel like they could have gone a lot further very easily if they wanted to.
2:15:06
So, uh we'll have to see.
2:15:08
I mean, it'll show up in Nvidia's earnings, right? We'll see.
2:15:11
Or we'll probably get data.
2:15:11
So, >> uh we'll have to have you back on then, but thank you so much for stopping by. Great to see you.
2:15:16
Hope you have a great weekend. We'll talk to you soon. Talk soon. Take care.
2:15:19
And if you're looking to get some sleep this weekend, get an eight sleep. A pod five.
2:15:23
They got a 5-year warranty, a 30 night risk free trial, free returns, free shipping. >> Code TBPN.
2:15:29
I was going to say John Xley has landed. >> He's landed. Yes.
2:15:32
Let's hear it from John Xley. Welcome to the stream.
2:15:38
>> Thank you for joining.
2:15:39
>> We have uh we got to pull this up.
2:15:39
Uh Trump and Putin are meeting right now.
2:15:44
And uh I have a video in the chat team if we want to pull this up. Very cool. Look at this, John.
2:15:50
So, >> Trump and Putin are walking and what do you see? >> Whoa.
2:15:58
That's >> a little fly over.
2:16:00
>> That's a show of force. Where are they? >> That is wild. Yeah.
2:16:03
What What a display of force.
2:16:05
Um I I I wonder I wonder where they're meeting. Is it neut?
2:16:11
Okay, that's sort of neutral ground.
2:16:11
I guess it's technically America, but yeah.
2:16:15
Fly in the >> Oh, we got our bears there.
2:16:16
Yeah, >> that's another they should they should have a bunch of, you know, Kodiak bears.
2:16:22
>> Fun place to meet uh Alaska.
2:16:22
Anyway, it'll be interesting to see what comes.
2:16:27
Hopefully, it's a resolution of the Ukraine war.
2:16:28
I mean, like Trump has been, you know, talking a big game about uh being anti-war, wanting to no more foreign wars.
2:16:34
No, don't send all the money overseas and and save that for the taxpayer.
2:16:38
Save that for real estate deals.
2:16:40
Maybe we could be building, you know, golden skyscrapers in America with all those with all those drones we're we're sending.
2:16:47
But, uh, we'll see where it goes.
2:16:48
Uh, but hopefully a peaceful a peaceful resolution. >> We will see.
2:16:52
Well, next up we have >> adqu. com.
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2:17:06
And we do have our next guest here.
2:17:08
David, welcome to the stream. How you doing? >> Welcome to the show. >> Thank you so much. >> What you got for us? Jord's warming up.
2:17:16
He's got the mallet ready.
2:17:16
He wants to hit the gong.
2:17:18
It's the first one of the stream.
2:17:20
You got some good news for us? >> Yeah. Yeah. Um uh yeah, web AI.
2:17:23
Uh we're working on some pretty interesting things.
2:17:29
Just recently, uh we announced our new knowledge graph mechanism, um which is outbarked all of the best models year to date. Um, >> by by how much? >> Uh, 7% by >> 7%. Let's go. >> There we go.
2:17:49
>> We like to hit the gong for big numbers.
2:17:50
We like to hit the gong for big fundraises.
2:17:51
We also like to hit the gong for for improvements. >> Benchmark maxing. >> Um, >> yeah.
2:17:56
No, no fundraising announcement today. Soon soon.
2:18:01
>> I can't I can't leak it today.
2:18:02
>> Well, we'll be refreshing our rocks account.
2:18:05
Uh but in fact talk talk more about the the genesis of the business why why you started it and um yeah what got you guys here. >> Yeah. Yeah. Absolutely.
2:18:16
So webi is really focused on building models that can live on devices like the ones on your desk. Right.
2:18:24
So um the genesis the company really started working >> like like on a watch or >> Yeah. Absolutely. Yeah. Absolutely. Um all of it.
2:18:33
all of it. Um yes uh so company started by working in computer vision and we were working on how could we take >> um like the YOLO models if you guys are familiar with those um uh it was in the early like 2016 era um these were like the biggest models because language models weren't really mature yet and um
2:18:52
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 oftelligence and what we're building is the rails for that. So we serve and distribute models
2:19:14
So we serve and distribute models across hardware.
2:19:16
So we're running some of the world's largest models today on things like a laptop.
2:19:20
So when we say we outbenchmark like Opus 4 or GP5 in knowledge retrieval, that's happening on a laptop.
2:19:27
a laptop. So it's not like uh it's a pretty significant breakthrough in modeling and we're doing this um in lots of different industries but uh what we believe is going to be a big step change in you know unit economics for AI as
2:19:41
well um that's just not there in the cloud model >> uh >> seems very important because all all week we've been talking about uh gross margins or the the lack thereof in uh a bunch you know a bunch of these different applications >> free when it happens on device Right. That's the goal. That's the goal. >> Yeah.
2:19:58
And you can do some things that uh cloud players can't do. Right.
2:20:00
So part of the way we're getting this accuracy like there's always like this no free lunch, right?
2:20:05
So why wouldn't you know Anthropic do what we're doing to get this huge accuracy retrieval uh bump?
2:20:12
Well, it's RAM intensive.
2:20:12
So if we're distributing across devices, uh we can arbitrage, right?
2:20:17
So we can say, okay, we'll pull more RAM because we're inferring on a device.
2:20:20
But if you're hosting this for a million users um on Nvidia um you can't do that.
2:20:25
You can't load you know additional RAM resource for every user.
2:20:30
Um uh it's just not efficient.
2:20:32
Um but there's there's there's real things that happen on the edge um that unlock I think technological paradigms in AI that are more meaningful like more accuracy, more context, all of that.
2:20:43
And we're seeing >> what about what about privacy too? >> Absolutely. Right.
2:20:48
So in our stack everything's downstream only.
2:20:50
So when we partner with a group like we work with the Aura ring if you know that company we're doing the AI for them >> and uh think about like health data like you want that to be private.
2:20:59
So the dream there is how can we facilitate personalized models for millions of users that never leave their device.
2:21:09
>> Um react to this post from Tay Kim, author of the NVIDIA way.
2:21:12
He says here's what I would do if I was the CEO of Apple.
2:21:15
Quadruple the RAM in iPhones to 32 gigs.
2:21:18
Have the Max model at 64 gigs.
2:21:21
Memory is oxygen for local ondevice AI.
2:21:25
More equals smarter and more powerful. Take the margin hit.
2:21:27
Memory isn't even that expensive. What do you think?
2:21:32
>> I think I think memory I think he's right.
2:21:34
I think memory is fundamental in these models.
2:21:37
Um, I also think we need to tread lightly on this idea that we're retooling infrastructure and we're making all these big bets on hardware with, frankly, a pretty immature algorithm.
2:21:49
>> Transformers are are not necessarily the winning algorithm.
2:21:52
So, I think we need to be, you know, cautiously optimistic.
2:21:56
Um, but we need to continue to work on what's next.
2:21:58
Like I just you retool based on all these factors and an algorithm changes.
2:22:03
Um, and we don't know what the long tale of hardware is going to look like.
2:22:09
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.
2:22:18
And um, I think generally more RAM is a safe decision.
2:22:20
But, uh, also I don't know if I would jump in and like totally rewrite how we're building chips until we know that this is the architecture we want to stick with.
2:22:29
Would you recommend someone buying a new Mac max it out and get the most memory possible? >> Yeah, absolutely. Absolutely.
2:22:37
>> Everything just Yeah, why not? Why not?
2:22:39
Um, what about diffusion models?
2:22:39
Do you think that there's a chance that they have a comeback?
2:22:42
We saw that demo from Google where they were doing uh text uh like token generation through a diffusion model felt kind of like a wildcard scenario.
2:22:50
wildcard scenario. I don't know it's actually performing on benchmarks but seemed like uh a path a path in the tech tree that was kind of you know more or less forgotten relegated to image generation but then kind of making a comeback maybe >> I think 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
2:23:10
equivalent amount of energy and dollars on other architectures that we know work >> um and we know they work at specific things but there's there's typically a broader application I think it's really interesting Um I mean we're working on
2:23:22
new architectures today um with with both like the public sector as well as the private sector and we're seeing a lot of breakthroughs that I think um make the transformer look a little old. >> Oh interesting. Uh how do you think >> Oh interesting.
2:23:34
Uh how do you think about uh the business model here?
2:23:38
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.
2:23:46
It feels like there's a world where companies are comping you to an open-source thing that they have to implement.
2:23:55
Like h how how like what does a great relationship with a big device manufacturer like edge computing provider look like for you?
2:24:05
>> Yeah, I think I think Web AI one we have a license, right?
2:24:08
Because we have a proprietary tech stack. We're not a rapper.
2:24:11
We appreciate rapper companies.
2:24:13
We think they're doing cool things.
2:24:13
Um but we own our stack pretty vertically.
2:24:17
So we own our runtime, our AI library, um, and our tooling around that.
2:24:20
And so when we work with a a partner, we we typically structure a base license minimum.
2:24:27
And when we have that when we have that license, um, we can, you know, inject forward deployed engineers to work with these companies that honestly just don't have the AI talent quite yet. Um, and they need help.
2:24:37
And I think that's something that people aren't talking about is, you know, like these products don't necessarily solve the problem out of the box.
2:24:43
A lot of these enterprises like Fortune 100 need help.
2:24:47
Um and uh so we do that and additionally there's a way to take part in the success in the deployment.
2:24:52
So the usage fees we can get um even though we're running on device.
2:24:56
So um because our network is managing that.
2:24:59
So you can imagine webi you have two and a half million custom devices or maybe it's an iPhone and we're shipping across that.
2:25:06
Our network manages all of that.
2:25:06
So we collect fees on that.
2:25:09
So, so, uh, 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 >> per answer is typically how we structure it.
2:25:22
So, it could be a book, it could be a one-word answer.
2:25:25
Um, as long as it's an output that's solving a problem.
2:25:28
Um we mostly work in mission critical use cases like things like reassembling engines with uh multimodal AI um you know health diagn diagnostics um uh public sector work. >> Yeah.
2:25:41
How quickly are you going to kill Jord's battery if you're doing test time inference on device?
2:25:45
uh he's been already complaining about the iPhone not having enough uh battery life, but uh 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 pretty short uh inference chain.
2:25:59
But if you're even if you distill the model, if you're inferencing for 10 minutes, that feels like a lot of heat in my pocket.
2:26:09
>> Well, I don't know what Jord's using, I would assume it's a pretty nice phone. Um it's like an iPhone.
2:26:13
>> Yeah, it's the latest and greatest iPhone. Yeah.
2:26:15
So I mean you mentioned quantizing.
2:26:18
So I'm going to talk a little bit about that and what we're doing there.
2:26:21
Um 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 it's more sophisticated now but the principle is still there.
2:26:30
Um it's called EWQ and um instead of just quantizing and tell me if I'm going way too technical here.
2:26:38
Quantizing traditionally you have like a fixed value.
2:26:40
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 four bit or I'm going to go to 16 bit and so you're just drastically chopping the model down right some so from the float values that
2:26:53
it can pass through um with EWQ what we do is we have something called device profiling so when a webi model hits your uh your phone um it's running our webframe library and it profiles your hardware and then what we do is on inference we run EWQ and And what EWQ does is it does real time quantization. So based on your question and the
2:27:13
So based on your question and the inference um and what it leads to is uh close to 30 to 40% model reduction size in RAM while retaining accuracy.
2:27:20
So what that means is we get bigger models inferring and instead of like this oneizefits-all quantization um we we dynamically do that on inference and what that leads to is less energy consumption, higher accuracy, less usage on the device. >> Yeah.
2:27:38
So somewhat similar to the model routing that we're seeing in chat GPT now.
2:27:42
What were your overall reaction to GPT5?
2:27:46
>> Um it's just ane router.
2:27:46
Um I I was kind of hoping it was a new foundational model.
2:27:51
Um uh and when you interact with it, it's really clear that it's just a way to dynamically control price >> um based on a question.
2:27:59
So like you ask a question, they route you to a different model.
2:28:02
Um if it's coding, it will route you to a different model.
2:28:05
Um, I can see where that's valuable.
2:28:06
Um, 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 five release to things like Grock.
2:28:17
Um, which was kind of shocking to me.
2:28:20
Um, but I think people were used to a certain standard of response and now the lack of like transparency and picking the model you're engaging with I think created some whiplash.
2:28:28
But, um, I'm sure there's areas where it's amazing.
2:28:32
I haven't really gotten to tap into everything there.
2:28:34
Um, been enjoying a lot of the anthropic releases and um, typically probably tend to lean that way. >> Cool.
2:28:43
Uh, well, thank you so much.
2:28:45
Congrats on all the progress and hope you have a great weekend.
2:28:47
We'll talk >> back on again soon. Sounds like you. >> Absolutely. >> Yeah, we're excited. We'll talk to you soon. >> Yeah, absolutely.
2:28:53
Great to meet you, David.
2:28:54
Thanks for thanks for joining.
2:28:55
>> Let me tell you about public.
2:28:55
com investing for those who take it seriously.
2:28:58
They got multi-asset investing, industryleading yields.
2:29:01
They're trusted by millions, folks. Uh, should we go soon?
2:29:06
>> Um, what did Sam mean by this?
2:29:06
If we didn't pay for training, we'd be very profitable. We talked about this.
2:29:10
Uh, Kristen Culver says, "Most successful coups in history.
2:29:14
Napoleon Bonapart's coup of 18 Bomer.
2:29:17
Uh, October Revolution in Russia 1917.
2:29:21
Uh, the Nazi seizure of power 1933. Egyptian coup d'etata. Uh, 1952.
2:29:28
the Chilean coup in 1973 and the open door retail army at open >> in 2025.
2:29:35
Uh Kristen worked at at openuh open door correct I believe >> must have >> and so she's having fun.
2:29:41
Um it'll be interesting to see in other news the CEO stepped down right this morning. Yeah, this morning.
2:29:47
Uh, so Carrie Wheeler posted on X.
2:29:49
Uh, today I'm stepping down as CEO of Open Door.
2:29:52
When the board of directors asked me to take on this role at the end of 2022, the company was in crisis.
2:29:58
The real estate market was punishing.
2:29:59
The business needed a reset and the path forward was uncertain. My mandate was clear.
2:30:04
Stabilize the company and do what was necessary to survive.
2:30:05
Of course, I said yes because I believed in open door.
2:30:09
It wasn't easy and it wasn't about glamorous headlines, but we stopped bleeding.
2:30:13
We restructured the business, rebuilt an exceptional leadership team, got an NPS of 80.
2:30:16
Uh, and she says, "I'm pleased the leadership team will continue to execute on the vision strategy.
2:30:22
I'm closing this chapter with pride, clarity, and gratitude." So, good luck. >> It is wild.
2:30:30
>> Open Door is up 200 200% in the last 30 days, and they uh the retail army said, "Nah, we want more. >> They want more. >> Are crushing it."
2:30:41
Well, I'm excited to see where Carrie Wheeler goes next.
2:30:45
>> We should um watch the new uh Jason Carman film >> killer coming on the stream in just a few minutes.
2:30:52
Let's pull up the latest work from Jason Carman. >> I'll be right back.
2:30:56
>> Please [Music] >> some time ago.
2:31:07
>> One, we have a liftoff. The machines roared.
2:31:16
The steel bent to our hands.
2:31:22
We built for the stars, for our land, and for your future. [Music] We went fast. We went far. It made us strong. It united us. Then the sound faded.
2:32:07
[Applause] The hunger to build drifted away.
2:32:27
[Music] Those who knew grew tired.
2:32:42
But now the fire returns. The steel is ready. The country needs you. New boundaries beckon. Who will you be?
2:33:20
Oh, that's where you take us slightly different last minute.
2:33:25
>> The stars, >> the sound design, >> our future is waiting.
2:33:44
>> So, will you answer that? We need one of those.
2:33:50
When we were discussing hard attack, we should have gotten the suit today. >> Huge miss.
2:33:54
>> Anyway, new video from Range View.
2:33:54
We have the CEO Cameron Schiller in the studio in the TVP Ultra Jump.
2:33:58
Welcome to the stream, Cameron. How you doing? >> What's happening?
2:34:03
>> Hey guys, good to see you.
2:34:05
>> Uh, I have so many questions.
2:34:05
Are you Did you act in that? Are you in that? >> I am not in that.
2:34:09
That was a big >> You're not in the suit. >> What?
2:34:12
>> You got to How I I thought you financed this whole thing.
2:34:15
I thought you'd made this happen and you didn't get a cameo. Got to put yourself in.
2:34:20
>> The machines got cameos, right?
2:34:22
>> The machines got cameos and I believe that last scene takes place at Range View HQ. Is that correct?
2:34:26
Did I clock it correctly? >> That is correct.
2:34:28
That takes place for technology demonstrator factory. We're running two.
2:34:31
We got a production facility down the street which we'll see in some some new videos coming out.
2:34:33
But that was at this facility which we've been at.
2:34:38
Uh and now we're just busting out the seam.
2:34:39
So we've got a we've got to move and you'll see a bunch of content from that new one. That place is sick.
2:34:44
They used to build space shuttle engines there. >> That's awesome.
2:34:47
Uh yeah, the space shuttle shot was fantastic.
2:34:49
I mean, Jason Carman, he puts on a clinic every time he drops a video.
2:34:53
Uh what what inspired it?
2:34:55
What what was the message you want to send?
2:34:56
Is this just is this a recruiting film?
2:34:58
I noticed like the the follow-up post was like, "Come work for us.
2:35:01
This is not like a an ad that you'll be running to get customers necessarily, or is it just kind of like vision film?"
2:35:07
What was what was the thinking?
2:35:08
>> Yeah, I mean, it's really a message to America.
2:35:10
I think it's a it's a wakeup call.
2:35:12
It's a question of who we really want to be as a country. What do we want to do? Right?
2:35:15
I think I mean that was a big part of my life growing up going back and forth to China.
2:35:19
And you know I saw the American dream in in China when I was there saw people from the the center of the country move to the coast to work extremely hard make a life for themsel and when I came back to America growing up I just didn't see that here and um and I think we have to bring it back.
2:35:36
I think for national security reasons I think across the globe.
2:35:38
The the real question is, you know, can can America bring it back because we need to make a lot of parts very soon.
2:35:46
And this is less so about range view.
2:35:48
I mean, America needs a thousand range views.
2:35:51
This is about people that are considering making a big pivot in their life to work on something that matters to the world.
2:35:57
>> And uh when you say make a lot of parts very soon, is that specifically like uh like defense tech and warfare?
2:36:02
Or 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 like the next the the next great physical product won't be made without the one that I care about the most is the second one.
2:36:22
Um but the first one's definitely very real. >> Yeah.
2:36:25
>> Um if you think about it, you know, America did some amazing stuff. the F-17.
2:36:29
We invented stealth technology, the SR71, uh, all that stuff happened in America.
2:36:35
And that happened because I think of factory towns.
2:36:37
I mean, I'm calling in from Alagundo.
2:36:38
This is a town that literally runs on jet fuel.
2:36:40
Like, there is a refinery that's right next door that's pumping jet fuel out, you know, under under this city to to to feed to LAX, which is on the other side of the city.
2:36:50
And you feel it in the air.
2:36:50
Like, there's something that happens when a community wants to be a part of something great in the world.
2:36:57
And when we look around, everything around us has been made in China now.
2:37:01
And with it, I you know, with it slipping, I I think a great calling to be a to be a part of something amazing has uh has slipped as well.
2:37:10
So, we we really need to bring that back.
2:37:12
We're going to do that with uh with parts.
2:37:14
We're going to do that with uh new technologies that enable uh factory towns all across the country.
2:37:21
>> Uh I mean, I think it's super >> What's going on behind you?
2:37:22
There's like some scrolling image. What is that? Uh, might be.
2:37:27
I mean, we've got a lot of screens in there. >> Oh, is this a screen?
2:37:29
It's like a TV or something.
2:37:32
>> I mean, there's there's a lot of lights and there's a lot of this camera reacts. >> People are just Yeah. Yeah. >> Right. Right. Right. Okay. Yeah.
2:37:37
I think I think what you're what one way to kind of summarize what you're kind of getting at uh in from my view is >> it's important for American dynamism to not be like a venture hype cycle.
2:37:48
that's sort of it's it's not something that can be accomplished in >> uh two years since you know moving to Elsagundo became popular >> and a meme and it needs to endure and >> who helped with that?
2:38:04
Was it me you Jason Cameron >> Carmen?
2:38:08
Yeah, we might have played a role >> a little bit to do with it.
2:38:12
>> Um but I mean I I I guess the question is like you you say this is like a you know a wakeup call for America. Like are we not awake?
2:38:18
I feel like I feel like a lot of these a lot of this message has broken through like like what what's left to say?
2:38:25
What what what do we say?
2:38:28
>> It's broken through in the bubble. >> Yeah. Yeah. Maybe it's the bubble. Maybe it's the bubble. Yeah.
2:38:31
What I mean what is your take on like the re-industrialized summit's huge like the the you know the American Dynamism Summit is huge.
2:38:38
Like like people seem to be beating the drum.
2:38:40
People are at at the White House. They're in DC.
2:38:43
>> They're a fraction of the size of Salesforce Dreamforce. That's exactly it. >> Yeah.
2:38:49
Are are you going to be there, Dreamforce? Let's get you there.
2:38:52
>> I uh >> This man This man loves Enterprise SAS.
2:38:55
He won't he won't admit it on camera.
2:38:55
He plays this character that he likes re-industrialization, but really he just wants to He just wants to code. >> Yeah. Yeah. It's all I want to do.
2:39:04
That's all I want to do, John. No.
2:39:04
Um we need to we need to have more people where it's 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.
2:39:16
We need more metal moving.
2:39:18
Moving metal is the problem right now.
2:39:19
There's a lot of people building tools for factories.
2:39:20
There aren't that many factories. >> Yeah. Yeah.
2:39:24
>> So, if you want to join, build a factory, make parts move, you know, do real stuff in the supply chain.
2:39:28
I'm not talking like screwdriver factories bolting on imported components.
2:39:31
That's the vast majority of, you know, assembly in America is like what's left. So, we don't need that.
2:39:37
We need people working on hard problems.
2:39:39
Um, >> we also need you to thousandx We need you to thousandx range view.
2:39:42
You said earlier we need a thousand range views, but why don't why don't you just be copy and paste your yourself?
2:39:49
>> I'm working as hard as I can, guys.
2:39:50
>> What is What can you tell us about what can you tell us about the state-of-the-art uh in uh in manufacturing?
2:39:56
I know you there's there's uh additive manufacturing, subtractive manufacturing, there's CNC.
2:40:02
We've talked to people that are 3D printing metal now. Uh there's casting.
2:40:05
What what what are you excited about? What are you focused on?
2:40:07
And where do you think there's still pockets of opportunity? >> Yeah, great question.
2:40:10
Um, so we are working on casting.
2:40:13
Um, and we are we are trying to give casting its CNC moment. >> Yeah.
2:40:18
And explain casting for for those who don't know.
2:40:20
>> Casting is liquefying molten metal, pouring it into a mold, it's solidifying, and you're getting the, you know, the part that that has that shape.
2:40:27
And almost everything is cast.
2:40:27
It's, you know, even CNC shops buy castings.
2:40:32
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 a part into, you know, into its final part.
2:40:41
You know, a lot of chiseling.
2:40:43
Um, 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.
2:40:48
So, um, there's no oneizefits-all solution in manufacturing.
2:40:52
It's one of the first things you learn.
2:40:53
It takes like it takes like a hundred humans from you know mining the ore out of the ground to installing the bracket on the end of the thing to actually make something happen.
2:41:02
So and there's a ton of folks I think for for people looking at technology they're used to looking at manufacturing is just another sector.
2:41:08
Um you know there's fintech health tech manufacturing tech.
2:41:11
The truth is manufacturing represents more of America's uh GDP than all of tech combined. Uh and so it's huge.
2:41:17
And so inside of manufacturing of all these sectors and so many have just been not been looked at yet um or not been touched and so we're seeing resurgence.
2:41:28
The other thing is is you know you maybe shouldn't finance these things exclusively with all venture capital because uh the risk profile just isn't the same in the factory right like if my factory burns down I'm going to still have you know thousand pounds of super alloy.
2:41:41
Uh you know maybe maybe the crate that it was in caught on fire but like they're not going to move right so you should buy it's not risky. It's not a risky bet.
2:41:48
So you shouldn't buy that stuff for that.
2:41:49
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.
2:42:04
>> Yeah, that makes sense.
2:42:04
If I were to pull a hot take out of you uh based on what you just said, it sounds like uh potentially the American manufacturing industry has overrotated towards subtractive manufacturing and needs to uh rebuild additive manufacturing or casting capability.
2:42:18
Is that is that like roughly a reasonable take that you >> additive and casting are not the same?
2:42:23
Additive is like this spack machine that's blown up actively as we see.
2:42:28
There are a few amazing people doing additive for the most part.
2:42:29
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's missing like really being able to build stuff at scale.
2:42:38
If we had to triple the manufacturing output of the country we'd be cooked totally cooked.
2:42:43
Like it would take us 5 years to get the factories up to do that and and all the factories that would start would be sending the money overseas because none of this equipment is made in America anymore.
2:42:51
Like we lost the factory industry but we also lost the factory and machine tool industry.
2:42:55
So, you know, all this stuff just goes overseas.
2:42:57
So, I wouldn't say that additive is is it.
2:42:58
I think casting is really important.
2:43:00
A lot of these traditional forms of manufacturing are really coming back and making a big play.
2:43:04
Um, but we should just be encouraging everyone to make a lot of parts.
2:43:08
Like we we need so many parts and we need to get started immediately. >> Parts maxing.
2:43:14
>> Talk about uh your dad.
2:43:14
Talk about the influence there.
2:43:16
Jason uh teased it a little bit, but I haven't heard the story. >> Yeah. Yeah.
2:43:21
He's a big big part of uh big part of my life.
2:43:23
Um he's always encouraged me to be very very honest and and real about uh about this this world which I think is really important and venture.
2:43:31
Um and he he was a maker himself.
2:43:34
Um you know his family was Pittsburgh and he was a you know mid midwestern family values.
2:43:38
Um and they you know they he came here to work on the B1 Bone um the the supersonic bomb which is pretty sick.
2:43:45
Uh and then I ended up growing up next to where skunk works was was founded.
2:43:48
So, you know, Bob Hope Airport, actually, the F1, all that stuff happened there and then it went out to Palmdale and and then it became a service-based industry.
2:43:56
Lots of B2B SAS and entertainment happened in the area and it really changed, but he always, you know, kept me kept me centered.
2:44:02
Um, and he's he's a huge influence on my life and actually same with Jason's dad.
2:44:08
So, so we bonded over that a lot and um they're becoming increasingly large parts of both of our lives.
2:44:15
Can you uh raise like a billion and then run this ad as a Super Bowl ad?
2:44:20
>> You guys want to help me? >> Yeah. Yeah.
2:44:22
We got to get him back on the venture training.
2:44:23
Cameron's always very like, "Oh, anti- venture."
2:44:25
But we >> we need to run this film as a as a Super Bowl ad. >> Back range view. Come on.
2:44:30
We >> back range view just to just to get the capital for a Super Bowl ad and then and then you can figure out a take time.
2:44:38
>> Years of Super Bowl ads. Run it every year.
2:44:40
This is not going to happen overnight. >> Yeah, I'm in. Let's talk about it.
2:44:42
Let's let's make a game plan. >> Fantastic.
2:44:45
Well, thank you so much for hopping on. Congratulations.
2:44:46
We'll talk >> on the launch.
2:44:49
>> And uh where wait, where can people go to apply for jobs?
2:44:51
I know that that's important right now. >> rangeview. com. Scroll down. Careers >> rangeview. com. You heard it here.
2:44:56
Thank you so much for having me on. Have a great weekend. We'll talk to you soon.
2:45:02
>> And I will talk to you about bezel.
2:45:02
You want to manufacture something?
2:45:06
Manufacture yourself a watch on getbbezzle. com.
2:45:10
New Bezel Concier is available now to source you any watch on the planet. Seriously, any watch. >> Any watch. I got them all. RG.
2:45:17
>> Well, I'm very excited for this next uh Well, okay.
2:45:20
So, there there's I was going to I I thought we had our friends over at NFM Live.
2:45:25
>> They will be coming on in just a few minutes, but we will be joined by Sarak or Sak >> Siraak. >> Sriak. >> Good to meet you.
2:45:33
>> Thank you for joining the stream.
2:45:33
Uh why don't you kick us off with an introduction on yourself and the company? >> All right.
2:45:38
Well, uh I am Syriak from Early.
2:45:41
Early is an early cancer treatment company.
2:45:44
And essentially what we do is we uh create genetic constructs >> that are injected into your body and they disperse everywhere in your body.
2:45:57
They enter healthy cells randomly and if you happen to have cancer cells, they will also enter those.
2:46:01
But only if it's cancer, these genetic constructs will switch on like a light switch.
2:46:11
And then they turn the cancer cells into little factories that are forced to make any protein of choice.
2:46:21
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. >> Mhm.
2:46:31
So the whole thing is 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.
2:46:46
>> Billions of dollars have gone into that.
2:46:48
>> And yet we still have 600,000. >> Yeah.
2:46:52
We still have 600,000 uh people dying from cancer in the US every year and 10 million globally.
2:46:57
So something needs to change.
2:47:00
What's the background of the company? Is this a tech transfer?
2:47:03
This come out of an academic lab?
2:47:05
Uh what is your background?
2:47:08
>> Yeah, I'm actually not a biologist.
2:47:08
I out of 35 people, I'm like one of two or three people who don't have that background. I'm an engineer.
2:47:16
>> I'm a serial entrepreneur.
2:47:16
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. >> Wow.
2:47:27
including his own son died at 16 from cancer and his wife died two years after him.
2:47:32
The whole family is wiped out. >> So I met him.
2:47:37
>> Was that >> out of curiosity?
2:47:38
Was that environmental exposure or >> No, no, no.
2:47:41
It's mostly genetic >> genetic.
2:47:44
>> Uh and the the mother had a genetic uh genetic mutation that then got transferred to the sun.
2:47:49
M >> uh and what Sam Gambia died from, the inventor of the whole thing is unclear to this point.
2:47:56
It was a cancer of unknown origin.
2:47:58
You >> didn't even know where the primary tumor came from.
2:48:04
>> So, a big a very tragic story, but he was committed to flipping the tables against cancer.
2:48:09
So I don't know if you guys have Jordi uh or John whether you have anybody in your family or in in your friends uh circle that has been affected by cancer. >> Yeah. Yeah. Of course.
2:48:22
>> You know it's just kind of crazy that we are always behind a step behind or two steps behind.
2:48:29
We're always trying to find the next marker that we could hook on to. >> Mhm.
2:48:34
>> So what if we could stop looking for any marker altogether? >> Mhm.
2:48:40
What if instead we could force the cancer to reveal itself and make its own therapy to kill itself?
2:48:50
>> So what's the pathway to commercialization?
2:48:51
Uh I imagine you have to go through FDA approvals at some point.
2:48:55
>> Yeah, we have to go through a phase one, two, three trial. >> Sure.
2:48:58
>> Uh and then to commercialization.
2:49:01
And we have spent uh the last seven years cracking this really hard problem.
2:49:08
You know what the biggest problem is in cancer?
2:49:11
>> What's a cancer cell and what's not a cancer cell? >> Yeah. Of course.
2:49:14
>> Because because you know different from a virus, this is your own cell that has changed just a little bit. >> Mhm.
2:49:22
>> And so differentiating that from a normal cell or from something that looks like cancer but is totally benign is really hard.
2:49:30
>> And that's what we've spent so much time and energy on with AI.
2:49:32
We're we're essentially producing AI results, liquefy them, put them into the body into a cancer drug that then forces the cancer to produce their own its own therapy against itself.
2:49:49
>> And what's the latest news with the company?
2:49:52
>> Well, we just raised $44 million. >> Congratulations.
2:49:57
[Applause] >> Not cheap work you're doing. Sounds expensive.
2:50:04
>> Yeah, biotech is not cheap.
2:50:04
So, you know, I don't know if how much you know about the biotech world.
2:50:08
It is in the biggest funding crash in the last 20 years >> on both the public side, the private side.
2:50:16
I know that the the government funding is certainly uh at an all-time low, but across everything >> actually you named them.
2:50:23
It the private funding Yes.
2:50:27
>> is extremely low because of two reasons.
2:50:29
high interest rates which immediately affect a long running product like bio takes 10 to 12 years right >> and then uh AI is like a vacuum cleaner for money >> that makes sense >> it sucks up all the money that goes to
2:50:43
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 and AI is a great interface that is now coming to fruition >> and then The pharma companies they are
2:51:00
concerned about tariffs they are concerned about u 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 you know here I would actually say we really got
2:51:16
to make a national commitment to biotech to flatten out this funding curve because you know 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
2:51:31
Do we really want to depend on that?
2:51:34
I mean, it's good if they supply them, but what if they don't one day?
2:51:39
So, we should actually have a national commitment to biotech to make it uh to to retain the world leadership that the US has had for the last 50 years. >> Yeah, good point.
2:51:50
Uh well, thank you so much for stopping by.
2:51:52
Have a great rest week and uh have a great weekend and congratulations. We'll talk to you soon. >> Thank you. Talk soon. Byebye. Cheers.
2:52:00
>> We have some major guests in the reream waiting room.
2:52:03
Let me tell you about Wander first. Wander.
2:52:04
Find >> your happy place. Find your happy place.
2:52:09
>> Book a wander with inspiring views, hotel, great amenities, dreamy beds, top tier cleaning, and 24/7 concier service.
2:52:15
It's a vacation but better.
2:52:15
We're joined by >> This is the moment we've all been waiting for.
2:52:20
>> They're calling it the >> brothers. Welcome to the show.
2:52:24
>> Doing you guys look fantastic. We got headphones on. >> Let's go. >> Can you hear us? How you doing? >> Yeah. Yeah. >> Give us some energy. >> Come on. Give us some energy. >> Come on.
2:52:35
What What time What time is it in the morning? What time is it for us? >> Yeah.
2:52:38
It's 5 5:15 >> in the morning. >> In the morning. >> Okay.
2:52:42
So, we're just waking up here. Here.
2:52:44
I'm going to help you guys.
2:52:44
I'm going to help you guys wake up.
2:52:46
Uh once >> for the launch of the Korea. >> Thank you. Thank you. So, uh, talk to us. How's it been going?
2:52:57
Uh, how, uh, how, uh, how has it been running the show? What inspired you?
2:53:01
Obviously, you know, TVPN inspired a little bit, but but have you been working in media?
2:53:08
What's the background story here?
2:53:10
>> Yeah, give us give us uh, life stories.
2:53:13
>> Yeah, so both of us coming from uh, venture capital backgrounds.
2:53:15
Uh, we've been based in Soul for over like four or five years.
2:53:20
Um so yeah we love this industry you know private market VC tech startup this whole ind industrial complex and we thought that it could be something you know we could do uh above just you know the pure investment and you know media could be the like perfect complimentary medium.
2:53:35
complimentary medium. So I mean you know anyway we we were doing our own thing you know like running our own like blog and you writing essays um like since a few years ago and uh this guy here Sejong uh he uh actually you know with his friends is running one of the biggest you know VC newsletters in Korea
2:53:51
and you know I've been running my own blog too congrat >> so you know earlier this year we thought that oh you know podcast could be the most optimal medium to reach you know the wider audience and also to reach the the global markets and we found you guys randomly on the feed >> and we Oh, >> yeah. This is the This is this is
2:54:10
This is the This is this is the this is the we got to >> Yeah.
2:54:15
So, um >> yeah, of course, you know, we uh if there's anything we want to benchmark from the US, it's not all in, you know, it's not the boomer, you know, like medium.
2:54:24
So, um >> you uh so, so what's your what's your guys' schedule?
2:54:29
Are you uh have have you quit the other stuff yet? Are you going all in?
2:54:33
Are you just putting how much time are you putting up?
2:54:35
>> Is it three hours a day?
2:54:35
Do you have multiple guests?
2:54:37
Um like what what what have you taken from the show? What what's working?
2:54:41
What is in what needs to be different to succeed in Korea?
2:54:47
>> Um so I would say um so okay, first of all um so we're like running like three times a week.
2:54:54
>> So we will ramp it up.
2:54:56
>> You got to get those numbers up.
2:54:56
You got to get those numbers up.
2:54:59
>> What are people going to do on the other hours? >> Yeah.
2:55:02
Do they expect to just twiddle their thumbs?
2:55:04
>> Does the Korean tech economy not function 5 days ago? >> 24/7 mustn't.
2:55:11
>> Okay, but we're going to ramp it up.
2:55:11
I'm letting you know like check us out in like 6 months.
2:55:14
You know, we might be running like seven days a week. Who knows? So, >> I see it. I love it. >> Yeah.
2:55:18
So, anyway, yeah, you know, the the Korean tech market is as vibrant as you know, like as the US, I would say.
2:55:25
But you know like um >> but you know we are just u kind of like you know being the frontier uh in this like you know hold the new media uh and like this like you know um like >> well in many ways we're old media we're old media this is just television >> say that >> it's just TV is it >> talk to me about uh the guests.
2:55:43
Do you have dream guests?
2:55:45
Who's the Palmer Lucky of of Korea?
2:55:47
Who's the Elon Musk of Korea?
2:55:50
Who do you want to get on? Who have you had on?
2:55:52
what what uh we have a lot of venture capitalists but then we have analysts, politicians, we've kind of gone all over the place.
2:55:57
Uh where have you had success uh doing guest interviews or are you even doing guest interviews yet?
2:56:03
You I believe you are right.
2:56:06
So we're in the very initial phase you know so um we've only embodied like um so you know only a limited number of guests but so far we have some you know like um EM engineers um also venture capitalists also authors who just published books but you know we want to
2:56:22
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 so >> yeah we want to bring you guys in our show >> of course yeah we'd be happy to let's do Let's do it. >> You make us wake up at 5 a.m. I guess.
2:56:35
>> You make us wake up at 5 a. m. I guess. We're ready.
2:56:41
>> I mean I mean that would that wouldn't work time zone wise, but uh be more like instead of going to bed, we'll pop on your guys' show. >> Yeah.
2:56:47
Are you guys live at 11:00 a. m. local?
2:56:51
>> Oh, it's like uh 600 p. m. in LA. >> 6 p. m. in LA. Okay. Yeah.
2:56:57
>> Yeah, we can make it happen. >> Dinner time.
2:56:58
Our wives will be very happy we're bailing on dinner to go on the on what's the open AI of Korea? >> Yeah.
2:57:06
What's the hottest company?
2:57:06
What's the one that everyone's focused on?
2:57:07
SK Highix is obviously like like later stage, but what who is Ascendant? >> Ah, okay. Open air of Korea. Okay. We got a strategic here. >> It's tough.
2:57:19
Um, I would definitely pick, you know, in terms of like, you know, semiconductor business, SKHX, Samsung semiconductor of course.
2:57:25
And also we got some you know like hasha Korean developers at OpenAI.
2:57:30
So you know like >> how do you say how do you say cracked engineer in Korean?
2:57:38
>> Like bin engine like you know like engineer is like a cracked you know like >> I can hear that. Yeah. Yeah. >> The team loves it. The teams loves it. Uh that's great. Anything else Jordy? >> No this is great.
2:57:50
Uh we're we we we love what you guys are doing um and happy to come on the show and uh help >> have fun have fun out there and you guys look sharp too.
2:58:00
Thank you for for um for make you know copy and pasting the suits as well.
2:58:09
>> This this is an input from you guys but you know like uh as as we want to you know like make our own path and own ethos you know from here on.
2:58:14
So um yeah we're going to build our own brand. Uh this is NFM live. NFM live. Love it.
2:58:21
Well, we support you guys. >> Enjoy. >> Thanks for coming on. >> We'll talk. >> Good stuff. Good stuff. >> Lads, lads.
2:58:33
>> Uh they they're also pretty well positioned to cover defense tech.
2:58:34
Korean uh South Korea obviously has mandatory military service.
2:58:40
I think most people kind of interrupt college.
2:58:42
They kind of take a break from school, go serve, then go back.
2:58:46
So, um, anyways, glad that we have, uh, contact with the Korean market. >> Yes. Yes, definitely.
2:58:53
Um, >> last post, close it out from Andrew Reed.
2:58:57
>> I knew you were going to pull this one up.
2:58:58
>> He said, "These shoes have gotten in obscenely high market share while accumulating zero aura." >> What are these?
2:59:05
>> These are, I think, veilas.
2:59:07
>> Never heard of this shoe.
2:59:08
>> Wouldn't wouldn't uh definitely came, you know, kind of a >> He's just taking shots left and right.
2:59:15
Uh, I think they're ve Veha. Vehas. Uh, Veha.
2:59:19
>> Got a pair of these at one point. You did.
2:59:21
Yeah, they were very much just like shoes to me.
2:59:24
>> Number one question on on Google. Why is Veha so popular?
2:59:28
>> I mean, I I wonder if the business is doing well.
2:59:30
I wonder if they've figured out some sort of distribution, some arbitrage, maybe some uh I don't know, are they more DTOC?
2:59:37
It does seem like a newer brand. Uh, and I certainly do. >> Founded in 2004. Okay.
2:59:41
So, >> headquartered in France. >> France. Interesting.
2:59:48
>> And uh yeah, I don't know.
2:59:48
I mean, I I think they just kind of tapped into the Common Projects sneaker project.
2:59:55
What are you talking about?
2:59:58
>> Well, that that was just like the definitive white uh like shoes, >> right? Common common.
3:00:06
>> That was the gap of the market.
3:00:06
No one thought to create a white sneaker.
3:00:07
I mean a white leather sneaker that was that was not from a No, no, no, no. But not sportsear. That's a key thing.
3:00:14
Not like basketball themed street wear, right?
3:00:18
Something that was versatile.
3:00:18
But uh but yeah, I wouldn't uh >> wouldn't be wouldn't be caught dead in them.
3:00:25
>> Those would be oring you if you put them on. >> Yes. Yes.
3:00:27
You got to be careful not to get orura farmed by your own clothing. It happens sometimes.
3:00:32
It happens to the best of them.
3:00:35
>> But I'm happy for Veha's success.
3:00:35
I'm happy for their success.
3:00:38
>> Yeah, overnight success. 21 years. Keep it going. Anyway, that's our show.
3:00:40
Thank you so much for listening and watching and enjoying the debate.
3:00:44
We will see you on Monday.
3:00:46
Leave us five stars on Apple Podcast and Spotify. >> And thank you.
3:00:50
I cannot wait for Monday.
3:00:53
>> I was I was figured it was Friday and I figured out it was Friday.
3:00:57
>> No, you really did think we still had You thought today was Thursday.
3:01:00
>> I thought we were a day behind and I thought we still had more time.
3:01:01
But >> well, have a fantastic weekend, folks. We love you. >> See you. >> Bye.