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I think it would be very problematic for the US to win.
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Let's say we take the most sort of fantastical scenario where if you control AI, your military is better than anyone else in this world.
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What is the game theory optimal response of China to blow up TSMC?
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If we get to a place where we have a meaningful superiority, particularly from like in terms of a military national security perspective, I think that's very dangerous for the world.
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So Ben, [music] if you can believe it, how long it's been since we last did this.
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The world was very different. No AI at the time.
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Uh we talked about aggregation theory mostly, which I'm sure we'll hit at some point today.
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I thought a fun place to begin since the world has changed so much is to hear what you think it would mean for the US to win the AI race.
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I think it would be very problematic for the US to win.
1:03
Let's say we take the most sort of fantastical scenario where if you control AI, you basically your military is better than anyone else.
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You can like somehow it fixes our manufacturing all these like things that I don't think AI is necessarily going to do because they sort of deal with the real world.
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But in this world, what is the game theory optimal response of China to blow up TSMC?
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like it and to me this is like game theory can get very sort of convoluted and complex.
1:30
To me this one actually isn't that complicated.
1:32
Uh so I there's a just a fundamental disconnect that I have with a lot of the rhetoric coming out of Silicon Valley coming out I think of one of the labs in particular where if we get to a place where we have a meaningful superiority particularly from like in terms of a military national security perspective I think that's very dangerous for the world.
1:53
But in that state, how how much does it extend beyond TSMC being blown up?
1:59
Because in that state, I would assume we figured out how to build fabs here in the US, you know, to some degree and are less reliant on that one choke point.
2:05
I think there's a little bit of magical thinking which I just invoked in terms of manufacturing and whether it be fabs whether that be actuators like all these sort of precursors like the I think the degree to which we are dependent on China is underappreciated and is not something that is going to be fixed outside of a conflict just because fixing so many of these things is going to be dramatically like dumb.
2:35
Like if your competitor is sourcing from China and you're going to start sourcing or getting things from the US, you're going to be at such a disadvantage relatively speaking that you're just not going to do it.
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So you do it when you have literally no choice.
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And that works for like very big headline items like you can browbeat Apple to move some of their iPhone manufacturing to India for example.
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But even that is a good example because Apple is not moving truly moving out of China.
3:03
like they're diversifying to an extent, but the problem it would just cost so much and it's like paying an insurance policy that if you don't have to pay it and it's astronomically expensive, you're just not going to pay it.
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It's one of those sort of hypotheses that I just have a hard time even gawking because in what the only world I see where we truly pull out and have no dependency on China such that if they want to blow up Taiwan, who cares?
3:30
has no impact on us is seems pretty fantastical to me and I think there's a bit of facing reality in this regard that is not present in these conversations.
3:43
>> So put yourself put yourself in their shoes like what do you think the motivations are?
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>> Everyone can use a good bogeyman.
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I think from the AI trade perspective nothing works better than we have to be China.
3:55
And I do think we need to be China.
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We need to be competitive.
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I despair at the extent to which over the last few years in particular so many of our responses for particular from a political perspective has been to like try to be like China.
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I think we should be going the other direction.
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Uh more openness, more innovation, less top down control, less restrictions on speech and things along those lines.
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America succeeds by being on the leading edge and by leading into that.
4:26
You said probably the US being purely dominant and AI is not the right end state for the world.
4:30
What is your ideal equilibrium for how this goes worldwide?
4:34
There's a bit where AI right now is kind of like the Taiwan situation in that it feels the current status quo actually doesn't seem so bad.
4:45
And the question is how sustainable is it?
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But maybe it's sustainable for longer than we think.
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So the the way I think about it right now is I think OpenAI and Anthropic are clearly on the frontier.
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Who knows what's happening with Google and then Grock and Meta are chasing them.
5:04
Meanwhile, the Chinese are very capable, very smart, and also definitely distilling these models to sort of stay about 6 to9 months behind.
5:12
And it feels like a pretty good equilibrium that I think is generally favorable to the US.
5:21
Now the question is how long can it stay this way right and there's lots of questions out there like can the Chinese actually pull ahead I'm still a little skeptical that you know for various reasons getting to the leading edge I think that last 6 to9 months is very difficult I think we'll see how it's
5:37
going to be instructive how meta and gro do in terms of actually actually catching up is that sort of because especially as we get to the world of AI improving itself using AI to make the AI better which I think is definitely a real thing I think you see a real acceleration
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from both uh OpenAI and Anthropic recently which and that was sort of theorized and it seems to be coming true and to the extent that's true can you actually catch up and I think the other question about this by the way is to what extent does that apply to cost to serve to marginal costs if you can apply
6:08
AI to optimizing your stack to figuring things out to analyzing all the data can is your cost to serve sort of structurally lower than anyone else this is the thing about the the open- source models the talk about them being free is bizarre to me because it's marginal costs, right? You still have to run
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You still have to run inference like GLM or Kimmy.
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Kimmy is very expensive to serve.
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The cost per answer is significantly higher.
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So the everyone referring to these as free.
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It feels like in the narrative it's in people's head that free is free.
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Now I can use AI for free.
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No, you can't use AI for free.
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You're not paying necessarily the R&D to create the AI, but you're definitely paying the inference to sort of run it.
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So right now I kind of like where we are and the push back would be oh that's right now it's not going to stay that way.
6:55
Um which I think is fair push back but I don't know if you can learn anything about the future of how this will go to be more confident in like where where the equil equilibrium will end up. What is it?
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Is it like the length of the S-curve?
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Like how far up the S-curve we are of at some point these things presumably will level out, maybe not.
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What would be the thing you'd want to know that would give you a better sense of what the future might look like?
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look like? I am concerned that with like the scare around like people freaking out about mythos and like this hugging face incident that the actual implication of that is not that we reduce these dangers but we just stop releasing stuff and we on the outside >> start to lose any sense of like where exactly what is actually the frontier and where it is And it and there becomes sort of a false sense of security
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because like right now everyone's basing their understanding of mythos on fable but how good is fable actually relative to mythos right like that sort of gap is only going to I think increase over time and so I think that that's that's a real question that that I'm not sure about this question of the AI the recursiveness and AI sort of making itself better like does that lead to sort of some sort of takeoff? And at the
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And at the end of the day, there's timing questions in lots of different ways.
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I'm worried about the timing mismatch in terms of the actual return on investment producing enough revenue to fuel investment.
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Like we're we we're working our way down the capital curve.
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Like we we started with free cash flow, then like the speed with which the tech companies blew through the debt markets is kind of incredible.
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like it took like a year and now Google's issuing equity.
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Nvidia's putting together the you know the these >> this $500 billion thing >> this $500 billion thing to tap into like pension funds and insurance floats and things like that and the what's after that?
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Where's the money come after that?
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Well, ideally we actually flip back to free cash flow funding this.
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But if there's a gap there, if we don't get there soon enough, then we could have a big blow up, right?
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But at the same time, even if we have this blowup, the AI is not going away.
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It's not going to stop improving.
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It's going to keep sort of progressing and in a way that we look back on the dot era or we look back on the railroad era or we look back on whatever bubbles through history ultimately immaterial in terms of the broad scope of humanity even if they were very devastating to lots of people.
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>> What did the railroads teach us?
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Do you think >> it's now the last bigger buildout, right?
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In terms of percent of GDP or getting >> I think we might be bigger at this point or it's like it was the biggest >> in the ballpark. Yeah. >> Yeah.
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You know, the railroads had a real duration mismatch.
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It like to build a railroad and make money off it was a decade or multiple decades long endeavor.
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and the so whereas you had to issue money to pay for it in the short term and the world ran out of money right and I think that is that's probably the the aspect I think that's why people reach for the railroads because everyone talks about are we going to have enough
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compute are we going to have enough electricity maybe the nearest term question is are we going to have enough money which is kind of a bizarre thing to think about like that's what happened in in the 1870s like we the world just ran out of money. But the the the funny
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But the the the funny thing is is the railroads kept operating and they expanded the west and they the the their contributions to GDP was astronomical.
10:41
They're still contributing to GDP.
10:42
Railroad money is what's going into Google right now from Bergkshire Hathway. Like like >> very funny.
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>> It's it's quite literal.
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>> It's literally Bergkshire Hathaway has this problem to me.
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This Nvidia deal is very much paired with the Google equity issuance which I thought was was that I mean that one was shocking what had happened. >> Why was it shocking?
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>> Because it's Google they can't raise money like why are they issuing equity right?
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Like the the why are they giving away the their you know reducing their upside if they believe so strongly in this.
11:15
But the Bergkshire the Bergkshire Hathway comparison is interesting because in to a rough approximation they make they have seized candies famously right tremendously high high margin business.
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The problem with a lot of high margin businesses is you can your your the percentage profit you can make is very high but the absolute profit you can make is reinvestment runway. >> That's right.
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Like you just you're just accumulating cash.
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And so the brilliance of the BNSF railway thing was basically they took the seas candy profits and said here's another industry whose margins are way worse but the absolute dollar amounts are so large that those way worse margins result in absolute profits that are much larger.
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like BNSF in 2025 or something, their the amount of free cash they've threw off in one year was more than Candies had thrown off its entire lifetime.
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Even though you're talking about a low margin business compared to a very high margin business and there's a I think there's an aspect from Bergkshire Hathway where if you're once your capital gets so large, you start operating in a world of like absolute numbers as opposed to percentage numbers.
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And the reason why I thought that was so interesting that story is it seems to capture where Google itself might be going.
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And so it was very symbolic for them to invest in Google.
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Google has this unbelievable high margin business of search.
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One of the most perfect beautiful business models of all time and the purest aggregator of them all.
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Like scales in every direction.
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Doesn't have to invest any money to do it.
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Everything's zero marginal cost. It's amazing.
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marginal cost. It's amazing. And meanwhile there's this AI opportunity which requires just astronomical it's just a cash incinerating cash but you can imagine if AI is intelligence and it's TAM is basically all white collar work
13:08
>> and eventually with robotics more everything potentially like why like the absolute profits available here even if the margins are lower is so much larger that will we look back and Google search was seized candies and I it feels like
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that's what's happening and and in that world even yeah you use all your free cash flow they've done that you tap the debt markets to the tune of hundreds of billions of dollars they've done that you issue equity because like the what is what does an equity issues do it
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dilutes your interest in your interest your shareholders so you have a smaller percentage of the pie Well, if you have a smaller percentage of an astronomically larger pie, at the end of the day, no one's going to be complaining. And I I just thought it was
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And I I just thought it was very symbolic.
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Bergkshire being the symbol of that equity issuance in that are they actually not just an investor in Google, but a model for Google and where they're going.
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15:31
I'm [music] curious setting aside the commercial and competitive components of this like you're describing how AI pill on the pure technology would you say you are relative to other people thinking about this space I have a view that is both super bullish and less bullish in some respects so I am not fully convinced about the generalizable argument like AI is clearly incredible at coding.
16:00
It kind of blows my mind that people were doing this a year ago, like actually like writing out code.
16:08
writing out code. It's very good at math obviously, but the obvious a you know repost is that these are sort of verifiable do domains and what is the evidence or where is the compelling evidence of being very good at verifiable domains cleanly translates to being very good at
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sort of unverifiable domains or domains that take have a very long sort of verification loops and I think that's still a little bit to be determined and it's interesting because I raised this I raised this question and there were some people at the labs that were on a panel
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and I was kind of annoyed at the answer cuz the answer took me for an AI bear and they're like oh well people thought we couldn't solve chess or we couldn't solve go and we solved those easy enough and I'm like I thought we could solve chess I thought we could solve go because they're knowable domains and you
16:58
know scale was the answer to both of those but also both of those were bounded right what what is the go-to example that's not chess that's not That's not go that is genuinely in a new space that's sort of an unknowable space where it's doing things that were not not possible. So that is sort of the I'm
17:15
So that is sort of the I'm not fully convinced sense.
17:19
However, AI trained at a rough approximation trained on all the data of the internet.
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All the data of the internet that is like that's dist distillation.
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It distilled all of human thought. >> No it didn't.
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It distilled all of the end state of human thought, the actual typing it on on Reddit.
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It doesn't have the traces, right?
17:44
It doesn't actually have the thought, the emotion or whatever that went into typing that comment or typing writing that essay.
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What if we like say neural link whatever what if the actual payoff from neural link is actually capturing the traces of of human thought that actually dramatically expands the capabilities of these models in this world.
18:06
My concerns about verifiability is like well we solve verifiability by getting more data.
18:12
My sense is that a huge number of jobs, a huge amount of economic activity does not exist in these domains that I'm not convinced that AI is good at.
18:23
Actually, there's a lot of people in the world who are kind of like sentient AIs to a certain extent.
18:27
They operate very well in verifiable domains. They're given jobs. They do them.
18:31
And that it's almost like a somewhat pessimistic view of humanity uh to a certain extent.
18:40
But I think that market is so huge and so large that if the models did not improve at all from where they are right now, the economic opportunity is actually massive.
18:49
I wrote an article a while ago, you know, there's the whole like accelerationist movement and I what I call myself was a reluctant accelerationist.
18:57
I think we need to push forward because we can't go back and the worst thing we can do is get stuck where we are.
19:03
So I'm very AI peeled in terms of its impact on the economy its sort of upside in terms of monetization.
19:13
I'm not sure about the timing.
19:16
What would be like the gradient towards it?
19:18
Like imagine law or medicine where I don't know whether or not you would consider those verifiable like law is like a code of some sort.
19:24
Medicine we have a certain state understanding of of things.
19:29
I mean, I think medicine is like by far one of the biggest opportunities. Yeah.
19:33
Like it's both one of the biggest opportunities and also one of the most challenging ones because of all the regulations and all the access.
19:39
Like if you could turn an AI turn machine learning onto all the medical records, the number of discoveries and improved treatments we could come up with in a very rapid amount of time would be unbelievable.
19:51
So that is a very optimistic view.
19:53
On the flip side, like when is that gonna happen, right?
19:56
I think the the the optimistic frame I put on humans is our capacity to create needs is sort of unlimited.
20:05
So I think we'll do a very good job of creating new opportunities and jobs sort of in the fullness of time.
20:10
The sort of more pessimistic way to put it is our ability to create red tape and muck is also fairly unlimited.
20:18
And you know how much of our economy is actually we've managed to create more and more jobs that is just sort of like make busy and make slow to a certain extent.
20:30
If I go back to the early 2010s or you know maybe the aggregation theory was stewing in your brain and then you published it in 2015.
20:36
I think it's fair to say like that theory that idea maybe you could just quickly remind people what it is defined the winners and losers of that era of technology.
20:44
I'm really curious how you're thinking about what theory or or principles will define this era of winners from like a financial perspective and market cap perspective. >> It's a good question.
20:55
I go back and forth even just on the question of of aggregation theory itself.
21:00
How much does that ex you know apply in this current? >> Yeah.
21:05
Cuz like like a push back that people have is one of the key components of aation theory is zero marginal cost and zero marginal cost uh shows in lots of ways.
21:15
The one that I sort of focused on the beginning was distribution.
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Like, and people say, "Oh, I don't have distribution.
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I have to pay Google for friends."
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Like, "Well, no, you have a website.
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Your problem isn't that you have distribution.
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Your problem is you don't have demand."
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And you're paying for demand when you're paying for ads and things on those because the aggregators control demand.
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And they control demand because in a world of abundance, the hard problem is not distribution, it's discovery.
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How do you actually find what you're interested in?
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So, the companies that solve discovery in their domain come to dominate that market.
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They get a virtuous feedback loop.
21:44
that sort of aggregation theory in in a nutshell.
21:48
And the other thing is transaction costs.
21:49
There's no transaction cost.
21:51
Google can scale to the whole world.
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And they can scale to the whole world.
21:52
Not just on the user side, but also on the monetization side.
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The vast vast vast majority of advertisers on Google or Meta never inter never talk to someone at Google or Meta.
22:02
They just go up and they buy ads.
22:03
It's all done by computers.
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And those computers from a business perspective >> cost zero dollars.
22:08
AI obviously that changes significantly like be their inference costs are real.
22:13
Uh but then again sort of how real are they?
22:18
>> They're real right now.
22:20
>> They well how real I don't know are they >> depends on the company but they're they're way more real than the those prior examples.
22:26
>> Well like if you look at gross margins or something >> for sure but but you have this incredible spread.
22:31
So you have people I think the vast majority of people who are using ad today are using it as basically a Google substitute or like a recipe maker or whatever it might be.
22:41
And my suspicion is that the cost to serve those people is extremely low and low in the basically similar to serving them a web page like I would imagine it's it's marginally higher but not not that much higher.
22:54
Then you have on the other extreme people who are actually leveraging test time scaling right.
22:58
So it used to be we just scale by making the models bigger and bigger.
23:02
Now you can scale as far as time.
23:04
How long do you think about the answer?
23:07
Well, you could think about the answer for days or weeks or months.
23:11
And that is a d that is directly marginal cost.
23:14
Like every second longer you're thinking is costing more money which speaks to like we think about AI and inference as this one question.
23:24
And that's I was sort of being a bit, you know, pushing back on you.
23:27
But actually the marginal cost question for the different user, the user using free chat GPT and the user trying to solve a a math theorem.
23:34
They're not even remotely in the same universe.
23:37
And and I think you see this challenge actually in the enterprise in a very interesting way.
23:42
So Microsoft recently, you know, they they are shifting their enterprise plan, right?
23:48
So they come out with like an E7 plan, $100 per user per month that includes some amount of usage, but then they also are charging for usage on top of that.
23:59
And this is kind of a really I think this is a kind of a fraught position for Microsoft to an extent because the positive way to think about Microsoft is they do everything you need as a business.
24:12
Every individual component might not be the best, but you get it all for one price and they all mostly work together.
24:18
And if you're, you know, particularly a small or mediumsized business or even a large enterprise, there's real value in that. That's right. It makes life easy.
24:24
The moment you start having to think about how much you're paying, it's not just that that's a new decision.
24:32
Number one, that is untethered from headcount, right?
24:38
Microsoft got the benefit is when you were hiring a new employee, you would think about the cost of that employee and baked in the cost of that employee is $100 a month or $50 a month for their their license.
24:46
It was kind of a thoughtless revenue stream for Microsoft.
24:51
Now, if you think about usage, you have to think every single month, how much do I want to spend?
24:59
And that introduces two problems.
25:02
Number one, most companies aren't set up to do this.
25:04
They make budgets like once a year.
25:06
This idea we're going to be thinking about through our budgetary aotment on like a monthly basis doesn't compute.
25:12
There's an aspect where they're used to thinking about capex decisions or one-time costs.
25:19
And there's a bit where what I'm talking about this employee like the loaded cost of employee.
25:24
It's not capex but it's kind of like capex.
25:27
It's like you make the decision up front then you don't think about it anymore if the decision is sort of already made.
25:31
But if you're thinking about usage you're doing it again.
25:34
But the the final thing is if you're every month you're looking at your Microsoft bill and how much do I use?
25:39
You start thinking about what am I paying for?
25:41
Like how good is each of these products?
25:44
Should I actually just start thinking about and spraying this out?
25:47
And I think they had to do it because that extreme of user who uses a ton of tokens and is actually leveraging AI costs way more to Microsoft than $100 a month. They can't support them.
25:58
But they want to hold on to the set cost for the vast majority of employees who can fit in that because they need to ask their customers to think a little bit for those extreme employees, but they don't want them to think too much because that sort of breaks the model in very sort of surprising ways.
26:17
surprised at all that the the recipe builder user that is very low cost to serve that there hasn't been a great business model that's emerged around them just yet like you know business Google and Facebook are sort of business perfected in this in this prior era.
26:31
They haven't seemed to figure this out at all.
26:33
I'm frustrated but not surprised.
26:37
This is obviously a market that should be supported by advertising like that.
26:40
That is why advertising is always the consumer business model.
26:43
Consumers don't want to pay there.
26:45
So there's two things to understand about consumers that Silicon Valley has to relearn about every 10 years.
26:51
Number one, consumers do not want to pay for software.
26:54
And number two, consumers do not care about being productive.
26:57
And this is like we went through this in early SAS.
27:00
Like the the canonical company for this in my mind is Dropbox.
27:06
>> So Dropbox, unbelievable product.
27:06
Like especially when it first came out in business school, I was one of the first people to use Dropbox and that was went off like crazy.
27:14
I have so much storage still like my free Dropbox cuz I gave out of my code to like so many people.
27:20
So Drew Hston makes his amazing product so easy to use, just absolutely seamless.
27:24
And I think very clear about this.
27:27
this. He wanted to build a consumer company and there's that famous story of him meeting with Steve Jobs and I think you know Apple was interested in acquiring Dropbox and they're like oh we want to build a company and Steve's you
27:41
know your feature not your feature not not a company and you know which that plain Jane just file sync Apple did make a feature as far as like sort of iCloud drive and Dropbox they grew very fast and then they had like a 2-year lull and in that 2-year year low. What they had
27:56
What they had to do was basically completely rebuild the app from the bottoms up because the people not enough consumers are going to pay for it.
28:04
They needed enterprises could see the value, they would pay, but if you want enterprise, you need permissions. You need control.
28:10
You need someone else to be able to set all these sorts of things.
28:13
And their app wasn't even created to do that at all.
28:14
So, they had to rebuild the whole thing and realize the only way we're going to make money is by selling to companies. Why do companies pay?
28:23
Because companies are paying employees.
28:26
So to the extent they can make their employees more productive, they're getting a greater return on their investment.
28:32
It's the complete inverse of a consumer.
28:34
A consumer is like, I spent all day working.
28:39
Why do I want to come home and be more productive?
28:40
I want to sit on the couch and watch reals.
28:41
And and the the and you see that with AI and you also have this overarching just skepticism of advertising.
28:49
You know, I've gotten so much traction on trajectory by being an advertising appreciator.
28:54
And I go back and read my early articles about advertising that were kind of directionally correct, but also like were not very good at all.
29:04
But I got so much traction doing it because I was the only person writing about advertising.
29:11
In a world of everyone want to have a blog in Twitter, no one want to talk about advertising.
29:14
about advertising. But even now there's in Silicon Valley there's this sort of embarrassment about the fact that the valley is in many respects monetized by advertising and particularly during the last sort of eight years there was a
29:27
Facebook's icky and like all these best engineers don't want to go work on this problem >> and so you literally had open AAI replaying the Dropbox story but at like 100x size being like no we're going to sell subscriptions to consumers and they did. They sold a lot, but they didn't
29:44
They sold a lot, but they didn't sell enough.
29:47
If you're going to be in the consumer market, you have to be doing advertising.
29:50
And now they're doing advertising now.
29:53
It's a little weird they finally pivoted to doing advertising.
29:56
At the same time, they're like, "Oh crap, we need to go for the enterprise cuz Anthropic is kicking our so quite sure what they're doing there.
30:04
They have been rolling out ad features very rapidly like things like like copy and the the connections with retailers so you know if a purchase went through so you can do all the tracking and things like that.
30:14
So I'm very interested to see how that goes.
30:16
There's a bit where had they leaned into advertising immediately as soon as Chat GPT was a hit, I think they would have a killer ad product right now.
30:28
I think that Google would be in much bigger trouble.
30:30
I think meta would be in much bigger trouble because if you have this flywheel, the thing about advertising with consumers is your ability to monetize the consumer >> goes up in because the advertisers bearing the price increase.
30:44
So there's zero elasticity issues.
30:46
If you're charging consumers a price, if you want to raise the price, like Netflix, this is their problem with with the subscription plan.
30:53
They have to be how much can they raise prices before consumers rebel and drop drop a tier or give up the service entirely, right?
31:01
Charging people money is hard.
31:04
>> Giving people things for free is easy and it's very frustrating that OpenAI did not pursue this sooner.
31:09
I know you've been spending time with, you know, some of the big money firms and sources of capital.
31:14
What is your sense of their appetite right now and how they're thinking about the future?
31:21
Because I think this year it's going to be 800 billion or something that we're going to spend in capex.
31:24
Next year it's supposed to be 1.
31:25
3 trillion I think is the current estimate.
31:27
It's going to keep, you know, keep going up from there.
31:30
We're burning through all the compute that gets installed like basically immediately.
31:32
It's such a strange circumstance that we can use the capacity right away as soon as it's online.
31:39
>> Well, that's the thing though.
31:39
So there's a few timing mismatches that are happening right now.
31:42
All the bulls on Twitter is always like we're we don't have enough comput.
31:46
We don't have enough compute.
31:47
Well, we don't have enough compute because there was insufficient investment made in 2023 and 2024, which yes, absolutely.
31:55
And by the way, if you think there's not enough compute, TSMC decreased their rate of growth in 2023 and 2024 and 2025.
32:03
So like we're our shortage of compute is going to get worse in the next few years because a fab the lead time is even greater than a data center.
32:14
So all today when we say there's not enough compute, it's not like all the money that the companies are putting in today >> manifest in comput.
32:20
No, it all manifests in compute in 2028 and 2029.
32:23
So you have like on the calls you have both Andy Jasse and Sadella are out there saying look we're just building data centers like these are the shells.
32:34
We might not use them now, maybe we'll use them in the future and we only buy GPUs when we know there's demand for them.
32:40
That is a great story to tell.
32:43
I'm not sure how much that I think is a lot of BS because the reality is is if you've built the shell that money is sitting there.
32:55
You're not going to let it just sit there.
32:57
Like if you have if you invested a fixed cost and this is the whole logic of commodity markets.
33:01
I think tech in general doesn't understand commodity markets.
33:04
markets. tech is by and large focused on if I produce a highly differentiated product and that differentiation could be like you know software it could be uh a network in terms of developers it could be a social network sort of thing where peerto-peer where I'm highly
33:19
differentiated then my ability to charge higher prices provides sort of my profit margin so the the classic example is like Apple right they have their ecosystem and they have their software and they have third party and all those sorts of things and so they can charge they have 50% margins is on their iPhone. Everyone looks at Apple as like
33:35
Everyone looks at Apple as like the ideal business model.
33:37
That's how you run a business.
33:38
But in a commodity market, the price is set by the marginal supplier.
33:43
>> Cost to serve is all that matters. >> That's right.
33:44
And so I had a good friend in Taiwan who was in shipping. Um fascinating industry.
33:50
It's kind of like the airlines too.
33:51
Another industry that I love to look at, but like you you you buy a ship and the cost of that ship is depreciation and your marginal cost is actually quite low.
34:01
It's the fuel to run the ship and the cost of the crew and like your port fees. Not that much.
34:05
What that means is you are going to run that ship >> as full as human >> basically.
34:12
No, you're going to run it no matter what.
34:13
And you're going to bring down the price of a container as low as it needs to be to cover your marginal costs.
34:19
Now, your paper losses in this situation might be very large because your accounting loss includes depreciation, but the depreciation is an accounting figment.
34:28
accounting figment. you already paid the money and so you're you're going to run that ship at whatever the market will bear and the container the beauty of the container is it is a pure commodity and so the cost of the market is going to be the marginal cost now if it gets low
34:44
enough at some point people will exit because their marginal cost actually can't like they're actually losing money on a shipment right uh not just paper money but like actual real money they will exit but then the supplies diminished So then the price the price will go back up and you get this interplay of sort of coming in and off. But then let's say the market's very
35:04
But then let's say the market's very high like it was during co it's like wow we're making so much money right now cuz there's not enough supply.
35:09
There wasn't enough supply of ships.
35:11
So containers went from like usually being like $3,000 $4,000 to 17,000 $18,000.
35:16
Like the the amount of money that these shipping companies made in a very short amount of time was insane.
35:23
And so what happens though? Well, more ships.
35:27
>> Imagine if we had more ships, right?
35:27
The problem is it takes 2 years to build a ship.
35:32
>> So by the if everyone makes this decision simultaneously, then the ship you suddenly have a lot of ships, price plummets, etc.
35:39
Where we see this is in components, in memory in particular.
35:43
Memory very famous for boom and bust cycles.
35:45
Uh people entering the market late.
35:47
But to what extent are data centers going to be memory makers where right now everyone can see we don't have enough compute.
35:57
So everyone's like we absolutely have to be investing because there's so much money to made and look at our payback period.
36:04
The problem is you're measuring your payback period in a time of scarcity.
36:07
Is that payback period going to hold in a time in a time of abundance?
36:11
Uh and and the sort of the bulls would say there's never going to be a time of abundance.
36:15
be a time of abundance. AI short time scaling we're going to be short forever which maybe we will be my concern is even if that's right we could still have an air gap >> in that there's so much money going into it right now and not enough has come online to actually make sufficient
36:31
revenues to cut to handle the situation where we run out of capital that like I again I believe in AI I think it's a real thing I think the economic impact is going to be astronomical I think all the concerns about societal impact are very real and are going to come to bear in a major way. You can believe all that
36:47
You can believe all that and still be worried about are we going to make the bridge to this actually generating the level of returns necessary to continue to fuel this sort of going forward.
37:00
Can can you zoom in on TSMC and the and the maybe the some of the component makers where fabs are involved and so far at least my understanding is that they've been quite conservative in their willingness to expand capacity, build new fabs, meet the market's demand with similar growth, which they have not done.
37:16
And if that if that just rate limits this whole thing and prevents us from getting one of these giant overbuilds.
37:22
>> Well, we can talk about a few different ones like we'll start with memory.
37:26
memory used to have tons and tons of memory makers and every time there'd be sort of a boom memory makers sort of like reenter the market um or like new countries would come in like Taiwan used to have like a memory market and but you would get these exact dynamics if there's a shortage of memory there's so much money to be made because no one you
37:46
can't bring capacity on immediately we're like it's the same as shipping it's the same as what we're seeing right now and so what would that that would do is that would spur sort of people to come in the market, you get too much capacity, prices would plunge and people would just get blown out cuz the issue is the upfront cost for these is so large. Just like buying a ship, like
38:03
Just like buying a ship, like building a fab is even more so.
38:04
And memory now, like the leading edges of memory are using things like EUV machines.
38:09
So the costs are getting into the billions of dollars for these lines.
38:13
And what happens is every time these boom bus cycles, some people would enter, more people get washed out.
38:17
You go through there's like these these famous historical moments for these memory cycles and like companies just get blown out.
38:23
One of the most interesting actually memory stories is how Samsung sort of took over memory was they saw it as an opportunity and they had studied history and they realized that actually the way to take over the market is to invest into downturns so that you're ready when the next cycle comes around which requires a ton of guts and a ton of discipline and a ton of money but they did that and basically wiped out the Japanese.
38:45
That's when the sort of the South Koreans generally took over the market in a major way.
38:49
But it got down to three.
38:52
And the problem is three, it's not a monopoly, but it's kind of an oligopoly.
38:56
And they all got a lot more discipline about let's not make the mistakes of the past.
39:02
And we're not colluding, but we all are on the same page about let's not do that.
39:07
And I think that dynamic sort of ran head on to the current moment where it just took a while for them to realize no there is a secular shift in memory demand that didn't exist for for a very long time.
39:24
And so I I think the memory solution will be solved eventually.
39:27
The other thing they the risk they run is Apple like Apple's lobbying to get Chinese memory, right?
39:34
uh and what is the number one focus of like al algorithmic changes.
39:39
How can we use less memory?
39:39
I think the memory makers probably screw themselves in the long run by creating such a massive target on their back.
39:47
I've analogized memory makers to Iran.
39:50
Like the issue with the straight of moose is it's very effective.
39:55
It's more effective if you don't use it cuz then it's always hanging out there as something you could do. Now they did it. Turns out it worked.
40:01
But like the UAE and Saudi Arabia, they're going to build pipelines.
40:07
They're going to build new ports.
40:08
They're not going to let this happen again.
40:10
It's very painful right now.
40:12
But say Iran wants to close the straight of our moves in 2035.
40:15
It's not going to have any effect because it will have been built around.
40:18
My concern for the Merry Makers is they might have done the same thing.
40:22
Like no one's going to let themselves get in this situation again as far as memory goes.
40:25
TSMC is arguably worse because there's only one.
40:29
Uh there's one company on the leading edge.
40:30
Um, obviously Intel and Samsung are trying to get there and it's the same thing like like the all markets carry risk and a lot of the question is who ends up holding the risk and what I think a lot of the tech companies didn't fully appreciate is the extent to which TSMC has offloaded risk onto the big tech companies.
40:55
And the way they've done that is the risk that TSMC is worried about is over capacity.
40:59
If we build too much, it's not just that we built too much and we have all these fixed costs that are not being fully utilized, but if we build a fab, we expect that fab to run for 30 years.
41:10
We've like baked in too much capacity into the system for years and years and years.
41:15
So, they are very biased towards being much more conservative.
41:19
And there's a little bit of a culture component to this too.
41:22
One of the most interesting TSMC stories, it's kind of analogous to that Samsung story was Morris Chang retired in like the late 2000s and new leadership took over and there was the great recession and so they pulled back their plan spending.
41:37
He comes in, fires everyone and he's like, "The iPhone just launched.
41:42
This is the biggest opportunity we've ever seen.
41:44
We need to be investing, not cutting."
41:46
And they invested through the Great Recession and through that downturn.
41:51
That's what laid the foundation for them taking over sort of leading edge semiconductors in that time.
41:57
Morschang is what a one of one like on the Mount Rushmore in my mind of the greatest sort of and most impactful tech executives of all time.
42:04
The entire fabulous model is so critical to to what tech is and what it does and also just the guts to do that right at that time particularly in you know someone who lived there a culture that doesn't necessarily tend to make those sorts of bets.
42:20
TSMC, they were pretty conservative to be totally honest.
42:23
And so what happens though? Where' the risk go?
42:27
TSMC's like, "Well, we we don't want to take the risk." Risk doesn't disappear. It just moves.
42:31
The risk is right now where you have every single big tech company realizes if we had more compute, we could be making more money.
42:45
So there's lots of foregone revenue and foregone profits.
42:48
That is the manifestation of the risk that TSMC handed off to them. Risk doesn't disappear. It just gets handed off.
42:54
And sometimes that risk doesn't manifest in losing money.
42:58
It manifests in not making money.
43:00
And there's money not being made right now because what happened was they were very excited about 5G.
43:06
They did a big like wave of like investment um in expanding their fabs in around 2020, 2021, 22.
43:12
And they're like, "Okay, we're good."
43:14
And like I said, 2024 like Chri 2022 big thing in tech in 2023.
43:21
In 2024, their growth rate went down.
43:24
In 2025, their growth rate went down. In 2026, it's up now.
43:26
It was very funny because I, you know, I was writing about this a while ago and then I think it was like one or two earnings calls ago.
43:35
Suddenly uh CCway the the CEO and chairman is talking about like the use cases for AI like the whole earnings call in a way he never had before.
43:44
This is the problem with the why the makers are scared.
43:47
Usually there's like a bull whip and they're worried about being at the end of the bull whip where the demand happens and it works its way down the chain and they're at the end and then they double down.
43:56
It's already too late and they're they're wasting all their money.
43:58
And I think the thing with AI is if it's a bull whip it's like the longest bull whip of all time.
44:02
like there's still so much to be built and it just took a while for Asia to get the message where these sort of companies are.
44:11
Uh but I think I think they've by and large gotten it but them getting the message it then takes several years for that to actually materialize.
44:21
>> Do you have a sense for like how long you think it will take given the extreme shortage of compute?
44:27
>> Well, the interesting thing is what this means for Intel and and Samsung sort of the logic business.
44:31
So, I've been writing about the problem of this dependency on TSNC for years.
44:36
Um, actually, one of my first articles in 2013 was exhorting Intel.
44:40
You I say you have to build a fab a fab business.
44:44
You you like you're not going to be this desire anymore.
44:47
You like there's a huge business in manufacturing chips.
44:49
And I thought I was late writing it then.
44:54
Their stock goes to the moon throughout the 2010s as they're riding the sort of cloud wave.
44:59
And it wasn't until like 2020 where they finally realized and we fell behind.
45:04
By the way, there's this huge opportunity.
45:06
We're totally unprepared for it.
45:08
We don't have a customer service mindset or culture organization or all the IP building blocks and all these things that TSMC has.
45:14
And they need a customer.
45:14
They need customers to help them actually build a real foundry business.
45:20
And so I would write about this problem and I'd write about like the China issue like you're dependent on on a a company that is 60 miles offshore of our greatest political you know opponent who thinks it's theirs.
45:35
So I these are big problems and that's where I came to appreciate this insurance issue for a big tech company to go to Intel and say Intel you make our chip and by the way the biggest benefactor of this is going to be you cuz you're going to learn how to work with a partner and the biggest pain is going to be us cuz we're going to have to figure out how to work with you.
45:56
We could just go to TSMC. They are awesome.
46:00
They are so great to work with that we know they're going to do a good job.
46:02
It just never made rational sense for anyone to go work with Intel.
46:06
That was their fundamental problem.
46:09
In a in a world in a unchanging world, TSMC would just win forever.
46:13
But this is where TSMC in some respects made the same mistake as the memory makers made the same mistakes as I ran.
46:18
If I can continue the analogy because they didn't invest the last few years.
46:22
The shortages are going to be so acute.
46:25
big 10 companies that we're foregoing so much revenue and so many profits because we don't have enough compute.
46:32
We will go through the pain of getting Intel of getting Intel up to speed of getting Samsung's logic up to speed.
46:39
The scarcity is what ultimately saved Intel.
46:42
Um, and I expect at some point in the near that they're going to announce like some major partner for the first time.
46:49
It's going to be a big deal.
46:51
But it was ultimately TSMC brought it on themselves.
46:54
It's the cure for high prices is high prices thing where we're going to route around them. Yep.
46:58
And like and it's just like there's all these things as like an analyst sitting on the side.
47:03
You can write these things and it it was one of those things I sort of learned like no one's going to pay insurance they don't need to pay when that insurance expected value is is negative.
47:13
The way to solve the geopolitical problem of dependence on TSMC is to come up with a compute use case that is so massive that everyone is economically incentivized to bring other people up to speed and then we get the sort of geopolitical insurance for free.
47:31
>> If you think about the let's say top 10 or 15 technology companies, which ones do you think have the most interesting setups today for their business?
47:39
>> The answer is always Amazon.
47:39
Um and the reason because what Amazon is so compelling is the extent to which they build for them.
47:46
They are their first best customer like they provide the scale to get basically anything off the ground which they then sell to other people.
47:54
AWS is the most obvious example.
47:56
AWS contrary to sort of popular thought was not spare Amazon capacity.
47:59
Actually it took a long time to get amazon. com onto AWS.
48:04
But the re what it drove was the understanding that we need to have a scalable.
48:09
We can't be having so many meetings like we need to have just compute that you can plug in purely API surface.
48:16
You don't need to talk to anyone. It's just there.
48:18
And oh by the way if we do that for our internal retail teams we could do that for anyone.
48:23
And turns out the retail is so big we have to start with everyone else.
48:27
AWS actually started serving external customers before it served internal ones.
48:30
But now it serves them all.
48:30
ones. But now it serves them all. You got other products like say the logistics right where it was the opposite like right now we're using external providers for logistics UPS and FedEx and USPS we need to build this up ourselves and now they built up themselves they're offering it to third
48:48
parties right now other people can use their use their delivery services and you see this in market after market like they're talking about things like some of their AI products that they're or their chip products right what's the beauty of the graviton or the tranium particular the early versions. The early
49:02
The early versions were terrible.
49:03
But if you're on Amazon and you're using some of their managed services, like say the Redshift database service, they don't tell you what the processor is underneath that.
49:13
You're just buying a managed service.
49:15
>> So they can put all their crappy processors underneath the services they're selling and that gives them the volume and the capacity to iterate them and get better.
49:24
And they get to the point where they can actually sell them externally.
49:27
And so because they were the first best customer for Graviton, Graviton got better because they were the first best customer for Tranium.
49:34
Tranium got better and now Trrenium is obviously, you know, running anthropic.
49:37
They're doing the same thing.
49:37
They're doing the same with AI products.
49:38
We'll see if any of them take off.
49:40
They have call center software.
49:41
Their call center or their customer experience is going through AI.
49:46
By the way, it's pretty good.
49:48
I don't I don't know if you like >> I haven't tried it.
49:50
>> Well, because I you know, moving back to to America, I've been buying lots of stuff and well, every summer I'd buy lots of stuff in a very brief amount of time.
49:57
sometime in like the last year or so, you can go on and you're clearly talking to a chatbot, but the chatbot does a great job and and it actually does take care of the problem.
50:03
So, you could see that actually starting to work in that in that regard.
50:06
But they're going to they're building up these AI services for their own business that they're going to make broadly available.
50:14
And some of them will work, some of them won't.
50:16
But this is it's such an elegant sort of approach and given they have so many investments in the real world.
50:25
Their core business feels so impervious to AI for like the model version of AI.
50:32
It will benefit from AI but their their moat feels deeper than anyone as far as their core business and their ability to just sort of generate new business lines organically is is very compelling. >> What about Apple?
50:44
They've sat this whole thing out.
50:46
It seems >> it feels like it might be a situation of better be lucky than good to a certain extent.
50:53
>> I mean, Apple has their whole has their whole ecosystem and at the end of the day, they do own access to customers.
51:00
So, they can sort of get suppliers.
51:03
This is the classic aggregator play.
51:05
If you own access to customers, suppliers come to you, not the other way around.
51:10
and and so they can get suppliers for their AI sort of as as needed.
51:14
And by the way, you know, to the extent it's true that people don't want to be productive, they just want a sort of a chatbot.
51:25
Not only can they serve them a chatbot and with, you know, finally getting a Siri that works, but you can see a future where this absolutely can work on device and they actually don't even need to pay for inference costs either because they, you know, they're using the customers electricity.
51:39
I mean, I I don't think we're quite there.
51:41
There's a reason they're using Google Cloud and and Nvidia chips, but you can certainly imagine a future where where that's the case and they're in physical goods.
51:48
Like actually making phones is is hard, right?
51:54
and having retail, having distribution for physical goods is is good.
51:58
So, they're they're more insulated.
51:59
The smartphone is so perfect.
52:01
It's small enough to fit in your pocket.
52:03
It's big enough to watch basically anything on it.
52:04
You can run your whole life on it.
52:06
All your entertainment is there.
52:07
Like when we talk about customers just want to be entertained.
52:09
The TV is now an accessory. It's all on your phone.
52:13
And you know, I don't see anyone taking over the phone.
52:17
The question is, is the phone always going to be the center or is there a bit where particularly in the home, this is where I'm very, you know, open's efforts here are very interesting, uh, where you want sort of an ambient AI where you can just talk to the AI and it tells you what you need.
52:34
Apple is the best position to provide that, but can they provide that without having leading edge models?
52:40
Can they provide that if they're so phone centric or is it like a Microsoft situation?
52:47
Microsoft didn't miss mobile.
52:50
They were very early to mobile.
52:50
The problem is their mobile was a small PC.
52:54
They assumed the PC would always be the center and their phones were going to be something that was off that Apple realized no we the f we need to reset.
53:03
The phone is not going to be accessory to the Mac.
53:04
The phone is going to be the phone.
53:06
The iPod helped them realize that and going with Windows and all that.
53:08
and going with Windows and all that. But will they fall into a Microsoft like trap like assuming the phone's so good it's always going to be the center and then let's figure out around it or is this finally the time when actually
53:20
ambient the cloud just in general AI being everywhere it can manifest through your phone it can manifest through a device can manifest on your computer is actually better and is actually disruptive to them I think it's possible I also think it's totally valid for Apple to double down on what they do. The other thing about the AI stuff is
53:43
The other thing about the AI stuff is on what basis should we expect Apple to be good at this?
53:50
Like just in like at the most crude level, AI is this probabilistic endeavor?
53:55
Apple is the king of deterministic products. like a physical product.
54:02
You ship that iPhone, you ship it once and it's got to be it's got to be good.
54:08
If it's bad, it costs you billions and billions and billions of dollars.
54:11
Apple's never had an iPhone recall, which if you think about it is actually it's amazing.
54:16
And that the sort of care and decision-m and diligence and you know fierceness in terms of your supply chain and like making hard decisions is very very different than everything that goes into like making great AI and I'm generally prefer companies to do what they're good at.
54:36
So from my perspective I'm fine with Apple not doing AI.
54:40
I I want them to keep making great devices.
54:42
of the five, let's call it five potential frontier AI winners.
54:47
So, OpenAI, Ananthropic, Gemini, let's put SpaceX AI, you know, Grock, and Meta in that pile.
54:52
Which of those firms do you think has the most interesting setup?
54:56
Open Eye and Anthropic obviously are the riskiest, but also have the biggest upside.
54:59
You know, they're just the never discount number one, the power of belief.
55:04
They think they're creating God.
55:06
think they're creating God. like like the the most impactful things in history have usually been fueled by religion and the two religious organizations in Silicon Valley are the sort of like open kind of like mainline like they go to
55:22
church every Sunday they're sort of like evangelicals is like that's anthropic like they're they're all in uh it it is core their belief that goes a long way the fact you need to make a business work for you to survive goes a very long way Google just needs like search to not die too quickly, right? Meta has the
55:39
Meta has the huge advertising business in in a world where Meta was run by anyone other than Mark Zuckerberg.
55:47
They would not be on the leading edge.
55:49
That is the one of the purest manifestations of founder sort of energy for better or for worse.
55:54
Their business is so amazing.
55:56
You see them just easily sort of doubling down on that.
55:58
Like Google, there's a bit where it it made sus.
56:04
They've been doing research in this.
56:06
It's like it makes sense why they're pursuing this meta being like actually we're going to hire a completely new team and we're going to start from scratch.
56:14
This all again is pretty insane.
56:15
So credit to Mark Zuckerberg in that regard.
56:18
Again, you could decide whether that's a good idea or not.
56:19
And then SpaceX AI, I mean the data centers in space is like that is the theory is there like do they have to own their own model though to do that?
56:35
They'd get better margins if they do.
56:35
Um then again if we actually run out whether through political opposition or power or whatever it might be if we run out of data centers on Earth like they can run whatever model they want as we're seeing with their sort of you know selling their capacity to anthropic right now.
56:52
So they're all pretty interesting.
56:53
I think um probably the case for SpaceX AI is probably the weakest because the data center and space play is so highly differentiated.
57:07
Like if that plays out, it I'm not sure to what extent they need to even have their own model.
57:11
So why are you wasting billions and billions of dollars in the meantime?
57:15
Um that's a fair question.
57:19
From a tactical perspective, I love the cursor acquisition.
57:21
like that makes so much sense for both companies and so I'm intrigued to see what they do.
57:25
Um, Meta is probably the most interesting just because you've written a lot about this recently.
57:31
>> The I think there's a very good case to make that it is actually more reckless to not be on the frontier if you're a digital company. Right?
57:41
So the the counter to the counter to Meta is actually Microsoft.
57:45
Microsoft is not on the frontier.
57:47
The reason why Microsoft has $20 billion of free cash flow last quarter, Microsoft paid a $10 billion dividend last quarter, [snorts] right?
57:54
Like there's there's some money, but their play is, okay, we're going to play all these off each other.
57:59
We're going to provide middleware.
58:00
We're going to provide the platform that enterprises will build on us and we're going to sort of inter, you know, disintermediate disintermediate the models.
58:06
And I think it's a I think it's a rational play.
58:11
It's the IBM play of the '9s.
58:11
Like there's, you know, history sort of echoes.
58:16
Everyone talks about Google, Google like following Microsoft.
58:19
Microsoft follows IBM and you can see that uh to an extent. Uh >> what did IBM do?
58:24
What's the what what's now?
58:26
>> Well, so IBM um so IBM had this dominant, you know, we talked about it in the 70s.
58:30
Uh and then you fast forward to the '9s and IBM is this very sort of distressed asset and the thought was IBM needed to break up and all these different pieces they had.
58:40
So Lou Gerson comes in, he takes it over.
58:41
And I think Gersonner's real key insight to IBM is actually everything.
58:48
We're pretty mediocre at everything.
58:50
It's kind of like what I told Microsoft before.
58:52
And that's the price of Monopoly.
58:54
Once you've been a monopoly, you kind of lose your capacity to be good because you're you didn't need to compete anymore.
59:01
And I think a lot of tech incumbent companies have this problem.
59:05
They it didn't matter what they did, they were going to rake in money.
59:09
And if you don't have the pressure, if you don't have the incentive, if you don't have the fear of death or the fear of God [laughter] as we talk about these mono companies, then you don't do your best work.
59:19
And the problem is that you once you lose that muscle, it's gone.
59:23
You're just sort of fat and flabby.
59:25
And so what Gersonner realized is actually the worst thing IBM could do would be to break it up into component pieces cuz all those component pieces are actually not very good.
59:35
our biggest asset is that we're big. It's like what? No.
59:38
What does it mean we're big? We can It's the '9s.
59:40
This internet thing is coming along.
59:43
There's all these companies that kind of know they have to figure out the internet and they don't know what to do.
59:48
They need someone who can come in, understand their business, and help them get online.
59:53
That's basically what IBM did.
59:56
So they built out and this is an echo of what's happening now huge consultant force and they put all their time into building basically it was middleware where they would go in and they put this layer between a company's old school mainframe like which all these companies had and then modern web services on the other end so they could have websites and e-commerce sites and all this sort of thing.
1:00:16
It gave IBM a 30year lease on life.
1:00:19
Yes, in theory you could go get point solutions from all these hot Silicon Valley startups but you you don't understand that.
1:00:24
you don't know how to do that. You know us. We'll come in.
1:00:27
We'll create all this middleware.
1:00:29
We'll give build this big consulting force to help you implement it and you'll get online.
1:00:32
And IBM basically brought all of corporate America online.
1:00:37
And that's what Microsoft's playbook.
1:00:40
Microsoft is um will help you figure out AI.
1:00:43
It will help you figure out in a way where you're not giving away the crown jewels to these companies.
1:00:45
We're going to build this platform, this harness, this sort of middle layer where you can we're dependable.
1:00:52
we're dependable. We're stable like you know us we we have backwards compatibility to the 80s like you can build on us and then we'll manage all the changing models and what's updating and do all those sorts of things and does that mean you'll get the absolute
1:01:07
best experience no middleware sort of saws off the sharp edges right like you you sort of get a lowest common denominator capacity but if you value in this the oldest enterprise sales motion how did Oracle go to market Oracle went to market in the 198 the 80s Larry
1:01:24
Allison with this you know another technology taken from IBM or just IBM didn't want it relational databases and they're like you don't want to be locked into IBM you want to be able to relational database you could run anywhere come come with us the the
1:01:38
reason this is a joke is cuz Oracle locks you in more than anyone right but the the the all of enterprise sales is companies whose long-term goal is to lock you in getting you on board by trying to make you scared of being locked into somebody else, right? Like
1:01:52
Like all the cloud companies are like, "Oh, portability and whatever. You can be whatever."
1:01:57
And then they're like, "Oh, just use our service that only runs in our cloud and now you're locked in."
1:02:01
Um, so that that that's Microsoft's playbook and it's a very rational playbook and I think it makes sense and that is the opposite.
1:02:10
That's why they have extra money because they're not on the frontier.
1:02:12
They are building massive data centers, but they're building data centers for inference.
1:02:15
They're not building it for training.
1:02:16
And their story about we're investing in time in response to customer demand is more believable in that regard.
1:02:21
they're not having to tell a fungeibility story where we're building big data centers for training that will be used for inference down the road maybe.
1:02:26
But go back to this notion that it's reckless to not be in the frontier as a >> digital.
1:02:30
Well, so so the the reason why that's concerning though is at the end of the day, why are we using Microsoft products again? >> Cuz we did before.
1:02:37
[laughter] >> Like to what extent does it actually make sense to have all these artifacts, all these documents, all these like email inboxes? Can't AI just do that?
1:02:50
Like there there's a real threat here where to Microsoft's software business the whole systems of record thing is it's kind of funny because one reason why systems of records are so powerful is it's so hard to move them to somewhere else because it's a very tedious repetitive job.
1:03:06
Oh AI is actually surprisingly good at that.
1:03:08
I'm not sure how good the systems of record Microsoft isn't so much systems of record.
1:03:11
They do have some like the dynamics business. It's user interface.
1:03:14
It's like where you actually interact with the computer.
1:03:16
That's the part that is like when you see codeex or when you see uh you know claude co-work or whatever it is like aimed like an arrow to the heart of what Microsoft h of what Microsoft has and in the long run all digital companies are but Microsoft is very much like there there's a re their strategy is sound it's also desperate in a existential way and also in a they might pull it off because they're desperate sort of way.
1:03:50
Meta is not threatened immediately.
1:03:53
But if this is where my bullish view of AI comes in, I think all digital companies are threatened and meta is a digital company like they they they have software.
1:04:05
Now the sort of one worry is AI takes up more and more time and that like time ultimately is is meta's currency.
1:04:13
We saw opening I tried the sora thing didn't really take off.
1:04:16
Social network is actually pretty hard also cost a lot of money like it's kind of really interesting.
1:04:20
This came up with the creator payments stuff.
1:04:22
So YouTube very famously as paid creators kind of from the beginning and that's a much bigger drag on the business than people appreciate because YouTube has marginal cost to their content.
1:04:35
Now unlike a Netflix they don't have to put pay that cost upfront.
1:04:39
They pay it after the fact.
1:04:41
So they're sharing revenue.
1:04:43
So it's a much it's a better model than a Netflix model.
1:04:47
Netflix is to pay upfront for content and then ideally make more money.
1:04:50
YouTube pays along the way.
1:04:50
But but Facebook or Meta >> pays nothing. >> They pay nothing.
1:04:55
People are like Instagram is this unbelievable product that generates all this money for which Facebook pays zero dollars for content. It's unbelievable.
1:05:04
And so it's funny because you could see a world where for YouTube AI generated content could theoretically be a positive because the inference cost to generate content could be less than what they're sharing with creators.
1:05:14
For meta AI generated content to the extent they're the ones generating it is actually a worse margin profile than what they have today.
1:05:22
What they have today is free. So they have attention.
1:05:25
Um there's a bullish world where meta is actually very well placed because in a world where we're interacting with AI all the time the desire for a human connection becomes greater and it's sort of like a Meta going back to their roots.
1:05:38
Meta one of their biggest mistakes actually Meta was always a social network company.
1:05:42
They killed Snapchat or stop Snapchat's growth by realizing Snapchat has a great product.
1:05:48
Let's layer it onto our network.
1:05:50
They they took their they brought their network to bear to kill Snapchat.
1:05:53
uh where Tik Tok the reason why Tik Tok is just a blind spot for them is Tik Tok is classified as a social network and it's not a social network at all.
1:06:01
Tik Tok is an entertainment product.
1:06:03
You it doesn't matter who you follow on Tik Tok.
1:06:05
What you see on Tik Tok is a function of what you watched and you're going to get more more of the same, right?
1:06:11
And the it's a userenerated content network.
1:06:16
And the the insight from Tik Tok was the way to get the best content to limit it to your social network is an artificial constraint.
1:06:26
We're going to give you the best content from across the whole network.
1:06:27
And the vast majority of content is going to be crap.
1:06:31
But this is like the absolute question before.
1:06:33
Like you don't think about margins, you think about absolute numbers.
1:06:36
The absolute amount of great content, even if the margin for great content is infantessimal, if we have an a ton of content, the absolute amount of great content is going to be very large.
1:06:46
And so the then Meta is like we're a social network.
1:06:49
And so Meta is serving you content from your network of people you know and Tik Tok serving you the best content from around the world.
1:06:57
That's why they took a huge chunk out of them. Meta had to shift.
1:06:59
That's what's happened with with Instagram and with reals is it's not really a social network.
1:07:06
It is a entertainment product that pulls from the entire network.
1:07:08
And social networking is like the group checked.
1:07:12
checked. it's possible in AI actually social network is important again because like we actually want humans we want to have some sort of connection to them that'll be interesting to see how that plays out then the other thing with with the models is they're so impactful on advertising biggest impact of the
1:07:25
models the biggest monetization right now is probably not anthropic openi it's it's the incremental gain that is happening for Google and meta and most of that most of most of the stuff is prel but we're getting to LMS whether it be generating advertising content like they are the bad like what do we want want verifiable domains. How do you
1:07:44
How do you verify if a generated image is good for an ad? Does the ad sell or not?
1:07:47
Like they can they actually can validate their image creation and their text creation in a way no one else can.
1:07:55
And their validation is the ad marketplace.
1:08:00
Like running a gazillion AB tests on all these different things, see what works, see what doesn't.
1:08:03
Most ads are a throwaway. It's fine.
1:08:05
Um like the vast majority of ads don't convert.
1:08:07
So they have this they have this massive advantage, this huge liquid market that is a verification machine where the verifiers are humans deciding whether they click on that ad and make a purchase or not, but they're doing it at global scale.
1:08:19
That can actually have a feedback loop to make their products better.
1:08:23
You're also going to get a world where ad matching is actually still fairly crude.
1:08:27
It's like here's the qualities of the person, here's the qualities of the ad.
1:08:30
And it's like you create an embedding like a a vector calculation uh and see what numbers match and then you sort of match an ad to the person. What do LM do?
1:08:38
LMS predict like we're going to move to this world where Meta is going to look at people and say this person probably wants to see this next and they're going to go find that thing and show it to them.
1:08:50
the potential upside in terms of just showing people better ads that are more relevant to them.
1:08:54
They only need to increase like a few percentage points for the returns to be billions and billions of dollars.
1:09:01
This alone is worth them investing in being on the leading edge in in having these amazing models.
1:09:09
I think a big problem Meta has is they don't tell this story.
1:09:11
Like it's weird, but Mark Zuckerberg has the same problem Sam Alman does. He doesn't love ads.
1:09:17
They have the best ad business in the world.
1:09:19
They have an ad business that I think is a societal positive.
1:09:20
Like you and I have set up these little content businesses that make great money, but we're content is kind of you get a ride on social media, right?
1:09:30
I grew up on Twitter, people sharing my links. It was amazing.
1:09:34
If you're selling some product, like the beauty of the internet is there is a niche out there that wants that product.
1:09:43
The question is how do you find the niche? Facebook advertising. That's what it does. It it connects.
1:09:49
It helps products find the people who didn't even know they wanted that product, but when they get it, they're so happy they got it.
1:09:57
And that is tre that's a huge societal positive.
1:10:00
You have new business from a new entrepreneur making a new product.
1:10:04
You have customers who are happy they got something that they didn't know they would get otherwise.
1:10:06
Those customers, by the way, got lots of free entertainment and they didn't have to pay for it along the way.
1:10:10
And Meta made a bunch of money for themselves and their shareholders, which is basically everyone in the world.
1:10:14
Like that this is why advertising is great.
1:10:16
And Meta's advertising in particular is awesome.
1:10:18
And I get frustrated that Meta doesn't talk about that.
1:10:23
Mark Z has never really talked about the societal benefits of advertising except in passing. >> I see. in 20 years.
1:10:28
Like he's handed it off to other people to take care of.
1:10:32
And maybe there's a bit where him not paying attention is why there is a certain like grit and grind that goes into building advertising business like and like you know all the Facebook people get frustrated or have questions about as far as data and all those sorts of things and maybe there was a bit where he didn't want to be involved in it and wipe his hands of it.
1:10:52
But you saw this like when when Apple passed ATT app tracking transparency was one of the most one of the worst antitrust violations in the history of technology like just Apple unilaterally obliterating all these business models while they're simultaneously building their own as far as advertising goes and doing like doing all this tracking. Why trust us?
1:11:13
And meanwhile they're running these advertisements.
1:11:15
So remember that advertisement of people on the bus like overhearing everyone around them what they're saying.
1:11:19
That was such a dishonest representation of how advertising works on the internet.
1:11:22
You had Tim Cook in Congress talking about companies selling data.
1:11:25
Facebook's not selling your data. That's value to them.
1:11:29
Why would they sell the D like and Meta was not prepared to respond because they I think you got this with Cheryl Sandberg back in the day.
1:11:37
She when every call would talk about advertising, how great it is and have a bunch of case studies of like people who are benefiting from advertising and these new entrepreneurs and then she left and it's kind of like that never hole never got filled and you you it feels like it's a company that's kind of like embarrassed.
1:11:52
Yeah, we make a lot of money from ads but we got glasses and uh we're doing AI.
1:11:56
It's like you have ads and ads are awesome.
1:11:58
And I think if they had made that, communicated that more consistently, they would be in a better place generally from a PR perspective.
1:12:09
They would be better place relative to Apple.
1:12:13
And I think they would have an easier time right now convincing Wall Street that let us invest.
1:12:18
The other problem is they spent cumulative hundred some billion dollars on Oculus which I dated all along.
1:12:25
Uh and so there's a bit where why like why should we let you spend money again?
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1:14:07
The one major player and company that we haven't talked about much is Jensen and Nvidia.
1:14:11
And I'm curious how you would tie this back to the notion of like not understanding commodity markets in Silicon Valley.
1:14:16
Whether or not you think compute ultimately is a commodity, I'm curious whether or not you think intelligence will ultimately be a commodity.
1:14:21
commodity. It's interesting that intelligence and compute which seem to be by far the most interesting and important topics in tech both might be commodities and less differentiated than >> well that's always the most interesting thing about the internet is free distribution >> like bandwidth is a commodity >> the the fact that I can pull out my
1:14:41
phone right now and connect to any information source in the world for free >> um free on a marginal cost basis is because it's a commodity it changed the world commodities change the world >> like the there's a aspect of differentiated products by definition have lower TAMs because you're like not there's a elasticity aspect to it. Not
1:14:57
Not everyone can afford to pay for it.
1:15:01
People's willingness to pay is going to differ.
1:15:02
Your market is going to be constrained.
1:15:03
Apple's never going to serve the whole world by having by selling a device whereas a Google can because it's free, right? That matters.
1:15:12
And commodities, you know, you're paying for a commodity, but to the extent it is available to everyone is the extent it is impactful.
1:15:19
Yeah, the internet is a commodity I would say and it changed the world.
1:15:24
So I don't think it'd be weird that intelligence ends up a commodity and changes the world.
1:15:27
>> But but still so so commodities often are not thought of as as good a businesses as these differentiated high high margin products.
1:15:34
So curious for your thoughts on yeah that Jensen and Nvidia specifically >> Nvidia's position is I think definitely unnatural.
1:15:43
It's like they've maintained all their margins. Isn't that amazing?
1:15:47
It's 2026 and everyone's coming for them and they're still charging, you know, however much money for for for a chip.
1:15:53
Um but they're actually not maintaining their margins because who is buying like this whole question of circular financing is people talk about Lucent and things like that and you know this whole deal and Nvidia's providing 25% back stop but if you actually uh ascribe
1:16:08
a value to that to Nvidia's taking equity in the Neo clouds or whatever like they guarantee they're going to buy all their compute to 2030 right and why do they do that so that the entity in question can get a lower cost of capital so they can buy more GP views etc. But
1:16:22
But implicit in that why do they get a lower cost of capital?
1:16:28
They get a lower cost of capital because Nvidia assumed risk, right? This is my point before. Risk never disappears.
1:16:35
It just sort of appears somewhere else.
1:16:37
Taking on risk has a price.
1:16:40
Like so Nvidia like now there is a world where AI takes off.
1:16:44
It never stops and everything is fine.
1:16:47
And Nvidia captured all the upside of their risk.
1:16:51
But there's also a world where say that this Neo cloud they backed up a ton of compute comes to market.
1:16:57
The hyperscalers have plenty of comput.
1:16:58
They don't have enough comput.
1:16:59
Nvidia is paying for a computer that no one wants.
1:17:02
They just lost a bunch of money.
1:17:02
So, if you think about it, there's an expected value of that investment.
1:17:06
That expected value has it's not zero. It's not 100%.
1:17:13
It's somewhere in the middle.
1:17:13
But that is a diminuation of Nvidia's profitability.
1:17:19
If you actually look at their business holistically, what that is is a price cut, >> right?
1:17:24
Like they now the price cut didn't show up in margins.
1:17:26
didn't show up in what they're offering.
1:17:27
up in what they're offering. But a lot of what Nvidia is doing is how can we maintain our margins even if the wide view sort of discounted cash flow expected value holistic view of our company people do discount cash flows
1:17:42
but are you actually considering all these pieces right the reality is is that moving stuff off the balance sheet by and large works right and so uh but they're doing all these all these deals to maintain what feels somewhat unnatural and So I would say like we have seen disc price cuts. They're just manifesting in
1:18:00
They're just manifesting in these very bizarre sort of ways.
1:18:02
Now in the long run I think the challenge is the the challenge Nvidia faces is their ultimate competitors are the hyperscalers particularly Google and Amazon.
1:18:13
So Google and Amazon aren't just building their own chips but they're also looking to sell those chips externally.
1:18:19
Google already made a deal to sell sell like 20% of their TPUs to anthropic.
1:18:22
On the last earnings call, Andy Jasse practically confirmed that they'll be selling tranium 3es or maybe tranium four or those tranium chips sort of eventually externally, which makes sense.
1:18:33
That gives them a long-term buy into these companies.
1:18:35
There's a huge amount of R&D that goes into developing chips.
1:18:38
They get more leverage on their spend.
1:18:39
It it it all makes sense.
1:18:39
And by the way, they're not selling their chips on differentiation.
1:18:43
They're selling their chips as commodities.
1:18:45
Nvidia is the one selling differentiation.
1:18:46
People aren't going to Amazon to use tranium.
1:18:51
So they're not cannibalizing like the attractiveness of their cloud by selling tranium outside.
1:18:54
So they're Nvidia's biggest problem.
1:18:57
It uh because what's the number one advantage that the hyperscalers have?
1:19:04
>> Lower cost of capital. It's a capital fight.
1:19:06
They have a lower cost of capital than the Neoclouds do.
1:19:09
The Neoclouds are they'll buy Nvidia left, right, left, right, and center.
1:19:14
And by the way, it also makes total sense that like why SpaceX like Elon's out there.
1:19:17
we will always buy Nvidia because they're the best.
1:19:20
No, you'll buy Nvidia because they're the most funible. You like Nvidia is true. It is the most funible.
1:19:25
CUDA's mode is dramatically diminished because the models don't care what they run on and that's what actually matters, what's built on top of the models, but it still matters.
1:19:33
It It's still something of a mode.
1:19:35
It's so if you're going to be if you want to play the game SpaceX is doing where we're going to build a lot and rent it out but reserve the right to pull it back, of course you're going to be on Nvidia because the easiest way to rent it out is to be on Nvidia.
1:19:46
And you saw this very early by the way.
1:19:48
You go back to 2024, 2023.
1:19:50
Nvidia starts talking about all these sovereign clouds.
1:19:52
They start talking about they tried to come out with these neo they had the neotron models, but they had all these they had this thing in 2024.
1:19:57
I remember it was the first one where was like the rockstar GTC at San Jose and like the huge coliseum and just one comes out.
1:20:04
It was a very boring GTC.
1:20:06
The old ones used to be Nvidia demonstrating like 50 gazillion things cuz they're throwing stuff at the wall.
1:20:10
They knew they had something with GPUs and they're trying to like >> find the use.
1:20:13
Once LM showed up, it's like, "Oh, we have the use case."
1:20:15
But they were coming up with all these enterprise offerings.
1:20:18
I can't remember what they were called, but they were like these modules basically that of course they were free, but they only ran on Nvidia.
1:20:24
And you could see what they were doing is they were trying to lock people in.
1:20:27
They were and and Intel is a good example here.
1:20:30
Intel got AMD cleaned them out in hyperscaler sales because the hyperscalers would put in the effort to get stuff working on AMD versus Intel.
1:20:41
There are still small differences even though they're they're they're x86 because they're buying at such scale the investment to do it is worth it to get a better chip or a lower price or whatever it might be.
1:20:51
Where Intel the part of Intel's business that never floundered was selling to government and selling to enterprises because you're like they don't have the resources of a hyperscaler.
1:21:01
They're not buying at that scale.
1:21:03
They're just going to keep buying what they had before.
1:21:05
That's why Nvidia talks about selling to sovereign clouds.
1:21:08
That's why they talk about selling to to enterprises because they want to get in these markets where they're not going to be balancing this chip versus that chip.
1:21:17
The hyperscalers have always been the threat to Nvidia for that reason just to like because they're the they're actually they're actually bigger.
1:21:23
So So you have this issue where they the hyperscalers are the threat.
1:21:26
The hyperscalers have a better cost of capital than the other companies wants to buy them.
1:21:31
That's how you get this deal this week.
1:21:33
I see this deal as a response.
1:21:35
That's why it goes with the Google deal.
1:21:36
Google can just issue equity like it's not shareholders don't love it but their their monetization capacity is at the end of the day like it's it's much higher than than than Nvidia or Nvidia's customers are.
1:21:48
I think what Nvidia is hoping for, maybe they wouldn't say this in so many words, but if we get to a world where we actually run out of power, that's probably good for Nvidia because in a world where we're totally constrained on power, >> everyone want the best.
1:22:09
>> We have to get the best efficiency, the best token efficiency.
1:22:11
And I think Nvidia is still the most token efficient.
1:22:14
Um, and so that is a good world for them.
1:22:17
I think it's been probably the biggest problem for Nvidia over the last couple years is I think the US has actually brought a lot more power online than expected.
1:22:26
They surprised me like whether it be what Elon did sort of behind the meter which has been been replicated West Texas and natural gas and but even like restarting nuclear plants like the extent to which we've >> you love how the US responds to these things. >> It's it's awesome.
1:22:41
It's actually one of the biggest like encouraging signals about the US is I was writing early on like what's going to be the long term like assume this is a bubble.
1:22:53
You want there to be a long-term payoff, right? The.
1:22:55
com we got fiber in the ground.
1:22:56
Google like and by the way Google has played this game before.
1:23:00
Google built its business by buying up dark fiber.
1:23:02
They had the killer search engine, but they so much of the the power what they do is because they bought up all this dark fiber that was basically free after the. com era.
1:23:12
Like our core internet still runs on worldcom fiber, right?
1:23:14
Like uh like the and so that was a lasting benefit.
1:23:17
The railroads BNSF is is throwing off money that's going to Google from Northern Pacific and Jay Cook selling bonds to retail investors.
1:23:28
like the the you you want a bubble that produces something that lasts.
1:23:33
And very often it's like what's going to last from from AI?
1:23:35
The GPUs don't last that long.
1:23:37
Like data centers, yeah, okay, fine.
1:23:40
But what is it going to be? It's like power. It has to be power.
1:23:44
If we have if we're in a world where this all blows up and we have way too much power, that is an amazing world to be.
1:23:50
We've always been energy constrained.
1:23:52
Energy undergurs everything.
1:23:54
What would it be like to live in a world of energy abundance?
1:23:58
Like it's it's hard to even imagine because our minds are so constrained by the fact we've actually always been in energy scarcity.
1:24:03
I think we've done an unbelievable job.
1:24:06
Like power for sure is a constraint.
1:24:10
It's going to be a constraint, but I think it has taken longer to be become a constraint than anyone expected.
1:24:17
And I wouldn't be surprised if that includes Jensen Hong.
1:24:21
Like I think he thought a power insufficient power was going to be Nvidia's moat sooner than that than that that that it happened.
1:24:31
And it turns out that the longer we have enough power, the more time Amazon has to make Tranium better, the more time Google has to to make TPUs competitive from a efficiency standpoint.
1:24:43
And if we get in a world where just a world where those margins seem very hard to sustain.
1:24:48
>> I love hearing your takes on just everything going on.
1:24:50
It's the most interesting time I've ever observed in this world that you love so much.
1:24:52
So, thank you so much for [music] your time. >> Thank you very much.
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