GPUs, TPUs, & The Economics of AI Explained

I will never forget when I first met Gavin Baker. It was early days of the

Gavin Baker. It was early days of the podcast and he was one of the first people I talked to about markets outside of my area of expertise which at the time was quantitative investing about the incredible passionate experience

that he's had investing in technology across his career. I find his interest in markets, his curiosity about the world to be about as infectious as any investor that I've ever come across. He

is encyclopedic on what is going on in the world of technology today. and I've

had the good fortune to host him every year or two since that first meeting on this podcast. In this latest

this podcast. In this latest conversation, we talk about everything that interests Gavin. We talk about Nvidia, Google and its TPUs, the changing AI landscape, the changing math

and business models around AI companies.

This is a life ordeath decision that essentially everyone except Microsoft is failing it. We even discussed the crazy

failing it. We even discussed the crazy idea of data centers in space which he communicates with his usual passion and logic.

>> In every way, data centers in space from a first principles perspective are superior to data centers on earth.

Because Gavin is one of the most passionate thinkers and investors that I know, these conversations are always amongst my most favorite. I hope you enjoy this latest in a series of

discussions with Gavin Baker.

I would love to talk about how you like in the nitty-gritty process new things that come out in this whole like AI world because it's happening so constantly. I'm extremely interested in

constantly. I'm extremely interested in it and I find it very hard to keep up and I you know I have a couple blogs that I go read and friends that I call but like maybe let's take Gemini 3 as like a recent example when that comes

out. What like literally like take me

out. What like literally like take me into your office like what are you doing? How do you and your team process

doing? How do you and your team process an update like that given how often these things are happening?

>> I mean, I think the first thing is you have to use it yourself.

>> And I would just say I'm amazed at how many famous and August investors are reaching really definitive conclusions about AI. Well, no, based on the free

about AI. Well, no, based on the free tier.

>> The free tier is like you're dealing with a 10-year-old >> and you're making conclusions about the 10-year-old's capabilities as an adult.

And you could just pay and I do think actually you do need to pay for the highest tier whether it's Gemini Ultra, you know, um, Super Grock, whatever it is, you have to pay the $200 per per month ti whereas those are like a

fullyfledged 30 35y old. It's really

hard to extrapolate from an eight or a 10-year-old to the 35y old and yet a lot of people are doing that. And the second thing is there was a insider post about open AI and they said to a large degree

open AI runs on Twitter vibes >> and I just think AI happens on X and you know there have been some really

memorable moments like there was a giant fight between the PyTorch team at Meta and the Jax team at Google on X and the leaders of each lab had to step in

publicly say >> no one from my lab is allowed to say bad things about the other lab and I respect them and that is the end of that.

>> Yeah, >> the companies are all commenting on each other's posts. You know, the research

other's posts. You know, the research papers come out. You know, if on planet Earth there's 500 to a,000 people who really really understand this and are at

the cutting edge of it and a good number of them live in live in China. Um I just think you have to follow those people closely >> and I think there is incredible signal

to me. Everything in AI is just

to me. Everything in AI is just downstream >> of those people.

>> Yeah. Everything Andre Carpathy writes, you have to read it three times.

>> Yeah.

>> Minimum.

>> Yeah. He's incredible.

>> And then I would say anytime at one of those labs, the four labs that matter, you know, being uh OpenAI, Gemini, Anthropic, and XAI, which are clearly the four leading labs. Like anytime

somebody from one of those labs goes on a podcast, I just think it's so important to listen. And then for me, for me, one of the best use cases of AI

is to keep up with all of this. You

know, just like listen to a podcast and then if there are parts that I thought were interesting, just talk about it with AI. And I think it's really

with AI. And I think it's really important to like have as little friction as possible, I'll bring it up.

You know, I have it like um you know, I have I can either press this button and pull up Gro or I have this.

>> Oh, wow. I don't touch that. That just

get brings it right up.

>> Yeah, it brings it right up. What do you think of Patrick Oanaughy?

>> Oh man, Patrick Oshanaugh is one of my favorite voices in investing. His Invest

Like the Best podcast is straight fire.

Does deep dives with folks like Bill Gurly or >> Girly? Yes.

>> Girly? Yes.

>> It's so Can you believe we have this?

>> I know. It's like we have Yeah. I think

somebody said on on on X, you know, like we imbued these rocks with crazy spells and now we can summon super intelligent

genies on our phones over the air. You know,

it's crazy >> crazy.

>> So something like Gemini come 3 comes out, you know, the public interpretation was, oh, this is interesting. It seems

to say something about scaling laws and the pre-training stuff. What is your frame on like the state of prog general progress in frontier models in general?

Like what are you watching most closely?

>> Yeah. Well, I do think Gemini 3 was very important because it showed us that scaling laws for pre-training are intact. They, you know, stated that

intact. They, you know, stated that unequivocally and that's important because no one on planet Earth knows how or why scaling laws for pre-training work. It there it's actually not a law.

work. It there it's actually not a law.

It's an empirical observation and it's an empirical observation that we've measured extremely precisely and has held for a long time. But our

understanding of scaling laws for pre-training and maybe this is a little bit controversial with 20% of researchers but probably not more than that is kind of like the ancient British people's understanding of the sun are the ancient Egyptians understanding of

the sun. They can measure it so

the sun. They can measure it so precisely that the east west axis of the great pyramids are perfectly aligned with the equinoxes and so are the east axises of Stonehenge. Perfect

measurement.

>> But they had they didn't understand orbital mechanics. They had no idea how

orbital mechanics. They had no idea how or why, you know, it, you know, rose in the east, set in the west, and, you know, kind of moved across the horizon.

>> The aliens.

>> Yeah. Our god in a chariot. And so it's really important every time we get a confirmation of that.

>> Um, so Gemini 3 was very important in that way. But I'd say I think there's

that way. But I'd say I think there's been a big misunderstanding maybe in the public equity investing community or the broader more generalist community based on the scaling laws of pre-training.

There really should have been no progress in 24 and 25.

>> And the reason for that is, you know, after XAI figured out how to get um 200,000 hoppers coherent, >> you had to wait for the next generation of chips.

>> Um because you really can't get more than 200,000 hoppers coherent. And

coherent just means you could just think of it as each GPU knows what every other GPU is thinking. They kind of are sharing memory. You know, they're

sharing memory. You know, they're connected. They scale up networks and

connected. They scale up networks and scale out. and um and they have to be

scale out. and um and they have to be coherent um for during the pre-training process. And I think there's a lot of

process. And I think there's a lot of misunderstanding about Gemini 3 that I think is really important. So everything

in AI has a struggle between Google and Nvidia and Google has a TPU and Nvidia has their GPUs and each of I mean Google only has a TPU and they use a bunch of other chips for networking. You know

Nvidia has the full stack and Blackwell was delayed. Blackwell was Nvidia's next

was delayed. Blackwell was Nvidia's next generation chip and the first iteration of that was the Blackwell 200. A lot of different SKs

Blackwell 200. A lot of different SKs were cancelled and the reason for that is it was by far the most complex product transition we've ever gone through in technology. Going from Hopper

to Blackwell, first you go from air cooled to liquid cooled. Um the rack goes from weighing round numbers 1,000 lb to 3,000 lb. goes from round numbers

30 kilowatts which is 30 American homes to 130 kilowatts which is 130 American homes you know. So I I analogize it to imagine if to get a new iPhone you had

to change all the outlets in your house to you know 220 volt put in a Tesla power wall put in a generator put in solar panels that's the power you know

put in a whole home humidification system and then reinforce the floor because you know the floor can't handle this. So it was a huge product

this. So it was a huge product transition and then just the rack was so dense it was really hard for them to get get the heat out. So Blackwells have

only really started to be deployed and really scaled deployments over the last 3 or 4 months. Had reasoning not come along, there would have been no AI

progress from mid 2024 through essentially Gemini 3. there

would have been none. Everything would

have stalled and can you imagine what that would have meant to the markets like for sure we would have lived in a very different environment. So reasoning

kind of bridged this like 18month gap.

Reasoning kind of saved AI because it let AI make progress without Blackwell or the next generation of TPU which were necessary for the scaling laws for

pre-training to continue. The reason

we've had all this progress, maybe we could show like the ARC AGI slide where you had, you know, you went from 0 to 0 to 8 over four years, 0 to 8% intelligence

>> and then you went from 8% to 95% in 3 months when the first reasoning model came out from OpenAI is, you know, we have these two new scaling laws of post- training, which is just reinforcement

learning with verified rewards. Verified

is such an important concept in AI. Um,

like one of Karpathy's great things was with software, anything you can specify, you can automate. With AI, anything you can verify, you can automate. It's such

an important concept and I think an important distinction. And then test

important distinction. And then test time compute. And so all the progress

time compute. And so all the progress we've had, and we've had immense progress um, since October 24th through today was based entirely on these two

new scaling laws. And Gemini 3 was arguably the first test since Hopper came out of the scaling law for pre-training and it held. And that's

great because all these scaling laws are multiplicative. So now we're going to

multiplicative. So now we're going to apply these two new um reinforcement learning with verified rewards and test time compute um to much better base

models. Google came out with the TPU v6

models. Google came out with the TPU v6 in 2024 and the TPU v7 in 2025.

And in semiconductor time, it's like almost like imagine like Hopper is like, you know, it's like a World War II era airplane. And it was by far the best

airplane. And it was by far the best World War II era airplane. It's P-51

Mustang with the Merlin engine. And two

years later in semiconductor time, that's like, >> you know, you're an F4 Phantom. Okay.

Because Blackwell was such a complicated product and so hard to ramp, Google was training Gemini 3 on 24 and 25 era TPUs, which are like F4 Phantoms. Like

Blackwell, it's like an F-35.

>> It just took a really long time to get it going.

>> So, I think, you know, Google for sure has this temporary advantage right now.

Um, from a pre-training perspective, I think it's also important that they've been the lowest cost producer of tokens.

Okay. And this is really important because AI is the first time in my career as a tech investor that being the lowcost producer has ever mattered.

Apple is not worth trillions because they're the lowcost producer of phones.

Microsoft is not worth trillions because they're the low lowcost producer of software. Nvidia is not worth trillions

software. Nvidia is not worth trillions cuz they're the lowcost producer of AI accelerators. It's never mattered. And

accelerators. It's never mattered. And

this is really important because what Google has been doing has the lowcost producer is they have been I would say sucking the economic oxygen out of the

AI ecosystem which is an extremely rational strategy for them and for anyone who's a lowcost producer you know let's just let's make life really hard

for our competitors. Um and so what happens now I think this has pretty profound implications. One, we will see

profound implications. One, we will see the first models trained on Blackwell in early 2026.

>> Y >> I think the first Blackwell model will come from XAI. And the reason for that is just it's a according to Jensen, no one builds data centers faster than Elon. Yes, Jensen has said this on the

Elon. Yes, Jensen has said this on the record. Even once you have the

record. Even once you have the Blackwells, it it takes 6 to9 months to get them performing at the level of Hopper >> cuz the Hopper is finally tuned.

Everybody knows how to use it. The

software is perfect for it. engineers

know all its quirks. You know, everybody knows how to architect a Hopper data center at this point. And by the way, when Hopper came out, it took 6 to 12 months for it to really outperform AER,

which was generation before. So, if

you're Jensen or Nvidia, you need to get as many GPUs deployed in one data center as fast as possible in a coherent cluster so you can work out the bugs.

And so this is what XAI effectively does for Nvidia because they build the data centers the fastest. They can deploy, you know, black wells that scale the fastest and they can help work with

Nvidia to work out the bugs for everyone else. So because they're the fastest,

else. So because they're the fastest, they will they'll have the first Blackwell model. We know that scaling

Blackwell model. We know that scaling laws for pre-training are intact and this means the Blackwell models are going to be amazing. Blackwell is um I mean it's not an F35 versus an F4

Phantom, but from my perspective it is a better chip, you know, maybe it's like an F-35 versus a Raphael. And so now that we know pre-scaling holding, we know that these Blackwell models are going to be really good.

>> And you know, kind of based on the raw specs, they should probably be better.

>> Then something even more important happens.

>> So the GB200 was really really it was really hard to get a coin. Um,

the GB300 is a great chip. It is drop in compatible in every way with those GB200 racks. Now, you're not going to replace

racks. Now, you're not going to replace the GB200s. No new power walls. Yeah.

the GB200s. No new power walls. Yeah.

>> Yeah. Just any data center that can handle those. You can slot in the

handle those. You can slot in the GB300s. And now everybody's good at

GB300s. And now everybody's good at making those racks and you know how to get the heat out. You know how to cool them.

>> You're going to put those GB300s in and then the companies that use the GB300's, they are going to be the lowcost producer of tokens.

particularly if you're vertically integrated. If you're paying a margin to

integrated. If you're paying a margin to someone else to make those tokens, you're probably not going to be. I think

this has pretty profound implications because it ch I think it has to change Google's strategic calculus. If you have a decisive cost advantage and you're

Google and you have search and all these other businesses, why not run AI at a negative 30% margin?

>> It is by far the rational decision. You

take the economic oxygen out of the environment. You eventually make it hard

environment. You eventually make it hard for your competitors who need funding unlike you to raise the capital they need. And then on the other side of

need. And then on the other side of that, maybe have an extremely dominant share position. Well, all that calculus

share position. Well, all that calculus changes once Google is no longer >> the lowcost producer, which I think will be the case. The black wells are now being used for training. And then when

the that model is trained then you shift you start shifting blackwell clusters over to inference and then all these cost calculations and these dynamics change >> and I do think it's this it's very

interesting like during the strategic and economic calculations between the players. I've never seen anything like

players. I've never seen anything like it. You know everyone understands their

it. You know everyone understands their position on the board, what the prize is, you know what play their opponents are running. Um, and it's really

are running. Um, and it's really interesting to watch. So, I just think if Google changes its behavior, cuz it's going to be really painful for them as a higher cost producer to run that

negative 30% margin, it might start to impact, you know, their stock. That has

pretty profound implications for the economics of AI. And then when Reuben comes out, we'll know the gap the gap is going to expand significantly >> versus TPUs.

>> Versus TPUs and and all other AS6. Now,

I think tranium 3 is probably going to be pretty good. train for are going to be good.

>> Why is that the case? Why why won't TPU v8 V9 be every bit as good?

>> A couple of things. So one um for whatever reason um Google made more conservative design decisions.

I think part of that is so Google round numbers Google like let's say the TPU Google is so there's front end and back end of semiconductor design and then

there's you know uh dealing with Taiwan semi. You can make an ASIC in a lot of

semi. You can make an ASIC in a lot of ways. What Google does is they do mostly

ways. What Google does is they do mostly the front end for the TPU and then Broadcom does the back end and manages Taiwan mean everything. It's a crude analogy but like the front end is like the architect of a house.

>> Yep.

>> They design the house. The back end is the person who builds the house and the managing Taiwan Simmyi is like stamping out that house like LAR or you know Dr. Horton and for doing those two ladder

parts broadcoms a 50 to 55% gross margin. We don't know what on TPUs.

margin. We don't know what on TPUs.

Okay, let's say in 2027 TPU I think it sits estimates maybe somewhere around 30 billion again who knows I mean >> yeah yeah yeah but I 30 billion I think

is a reasonable estimate 50 to 55% gross margins so Google is paying Broadcom $15 billion >> okay that's a lot of money

>> and at a certain point it makes sense to bring a semiconductor program entirely in house so in other words Apple does not have an ASIC partner for their chips >> they do they do the front end themselves

the back end and they manage Taiwan semi and the reason is they don't want to pay that 50% margin so at a certain point it becomes rational to renegotiate this and just as perspective the entire opex of

Broadcom's semiconductor division is round numbers $5 billion so it would be economically rational now that Google's paying if it's 30 billion we're paying them 15 Google can go to every person

who works in Broadcom Smi double their comp >> and make an extra extra 5 billion. You

know, in 2028, let's just say it does 50 billion. Now it's 25 billion. You could

billion. Now it's 25 billion. You could

triple their comp. And by the way, you don't need them all.

>> Yeah.

>> And and of course, they're not going to do that because of competitive concerns.

>> But with TPUv8, all of this and V9, all of this is beginning to have an impact because Google is bringing in MediaTek. This is

maybe the first way you send a warning shot to Broadcom. were really not happy about >> all this money we're paying >> but they did bring MediaTek in and the Taiwanese ASIC companies have much lower gross margins so this is kind of the

first shot against the bow and then there's all this stuff people say oh but has the best certiscom has really good certis and certis is like an extremely foundational technology because it's how the chips

communicate with each other you have to serialize and do serialize but there are other good certis providers in the world a really good certis is not at a certain Maybe it's worth 10 or 15 billion a

year, but it's probably worth about worth 25 billion a year. So because of that friction, um, and I think conservative design choices on the part of Google and maybe the reason they made

those conservative design choices is because they were going to a bifurcated supply. You know, TPU is slowing down. I

supply. You know, TPU is slowing down. I

would say has kind of the GPUs are accelerating. This is the first, you

accelerating. This is the first, you know, the com the competitive response of Lisa and Jensen to everybody saying we're gonna have our own ASIC is, hey, we're just going to accelerate. We're

going to do do a GPU every year and you cannot keep up with us. And then I think what everybody is learning is like, oh wow, that's so cool. You made your own accelerator has an ASIC. Wow, what's the

nick going to be? What's the CPU going to be? You know, what's the scaleup

to be? You know, what's the scaleup switch going to be? What's the scaleup protocol? What's the scale out switch?

protocol? What's the scale out switch?

what kind of optics are you going to use? What's the software that's going to

use? What's the software that's going to make all this work together? And then

it's like, oh I made this tiny little chip and you know, like whether it's admitted or not, like you know, I'm sure the GPUs don't GPU makers don't love it when their customers make AS6

and try and compete with them >> and like whoops what what did I do? I thought this was easy. How do you know? And it also it

easy. How do you know? And it also it takes at least three generations to make a good chip like the TP TPU V1. I mean

it was an achievement and that they made it.

>> Yeah.

>> Um it was really not till TPU V3 or V4 that the TPU started to become like even vaguely competitive.

>> Is that just a classic like learning by doing thing >> 100%.

>> Yeah. And even if you've made like the first from my perspective, the best ASIC team at any semiconductor company is actually the Amazon ASIC team.

>> You know, they were the first one to make the gravitron CPU. They have this nitro. Um it was the first, it's called

nitro. Um it was the first, it's called Supernick. They've been extremely

Supernick. They've been extremely innovative, really clever. And like

Tranium and Infantry One, you know, they maybe they're a little better than the TPUV1, but only a little. Trannium 2,

you get a little better. Trium 3 it's I think the first time it's like okay and then you know I think tradeium 4 will probably be good. I will be surprised if

there are a lot of AS6 other than tranium and TPU >> and by the way and tranium and TPU will both run on customerowned tooling at

some point. We can debate when that will

some point. We can debate when that will happen but the economics of success that I just described mean it's inevitable.

Like no matter what the companies say, just the economics make it and reasoning from first principles make it absolutely inevitable.

>> If I were to zoom all the way out on this stuff, because sometimes I just it's I I find these details unbelievably interesting and it's like the grandest game that's ever been.

>> That's what I mean. It's crazy.

>> It's so crazy and so fun to follow.

Sometimes I forget to zoom out and say, "Well, well, so what?" Like, okay, so project this forward three generations past Reuben or whatever. What what is like the global human dividend of all

this crazy development? Like we keep making the loss lower on these, you know, pre uh pre pre-training scaling models like who cares? Like it's been a while since I've asked this thing something that I wasn't kind of blown

away by the answer for me personally.

What are the next couple of things that all this crazy infrastructure war allows us to unlock because they're so successful? If I were to posit like an

successful? If I were to posit like an event path, I think the Blackwell models are going to be amazing. The dramatic

reduction in per token cost enabled by the GB300 and probably more the MI450 than the MI355, you know, will lead to these models being allowed to think for

much longer, which means they're going to be able to, you know, do new things.

Like I was very impressed Gemini 3 made me a restaurant reservation.

>> It's the first time it's done something for me. And I mean, other than like go

for me. And I mean, other than like go research something and teach me stuff, >> but you know, if you can make a restaurant reservation, you're not that far from being able to make a hotel reservation and an airplane reservation

and order me an Uber and >> all of a sudden you got an assistant.

>> Yeah. And you can just imagine, everybody talks about that, but you can just imagine it's on your phone. I think

that's that's pretty near-term, but you know, it's you know, it's some big companies that are very tech forward.

you know 50% plus of customer support is already done by AI and that's a $400 billion dollar industry and then if you know what AI is great about is persuasion that's sales and customer support

>> and so of the functions of a company if you think about them them they're to make stuff sell stuff and then support the customers so right now maybe you're in late 26 you're going to be pretty

good at two of them um I do think it's going to have a big impact on media like I think robotics you know we talked about the last time are going to finally start to be real. You know, there's an explosion in kind of exciting robotic

robotic startups. I do still think that

robotic startups. I do still think that the main battle is going to be between uh Tesla's Optimus and the Chinese because, you know, it's easy to make prototypes. It's hard to massproduce

prototypes. It's hard to massproduce them. But then it goes back to that what

them. But then it goes back to that what Andre Karpathy said about AI can automate anything that can be verified.

So any function where there's a right or wrong answer, a right or wrong outcome, you can apply reinforcement learning and make the AI really good at that. Yeah.

>> What are your favorite examples of that so far or theoretically?

>> Does the model balance? They'll be

really good at making models. Does you

know do all the books globally reconcile? They'll be really good at

reconcile? They'll be really good at accounting because it you know was you know double entry bookkeeping. It has to balance. There's a verifiable you got it

balance. There's a verifiable you got it right or wrong supporter sale. Did you

make the sale or not? That's very clear.

I mean that that's that's just like AlphaGo.

>> You know, did you win or you lose? Did

the guy convert or not? Did the customer ask for an escalation during customer support or not?

>> It's like it's most important functions are important because they can be verified, >> right? So I think if all of this starts

>> right? So I think if all of this starts to happen and starts to happen in in in 26 like there'll be an ROI on Blackwell and

then all this will continue >> and then we'll have Reuben and then that'll be another big quantum of spin Reuben and the MI450 and the TPU V9 and then I do think just the most

interesting question is what are the economic returns to artificial super intelligence because all of these companies in this great game they've been in a prison prisoners dilemma.

They're terrified that if they slow down, >> they're just gone forever.

>> And their competitors don't, it's an existential risk. And you know,

existential risk. And you know, Microsoft blinked for like six weeks earlier this year.

>> Yeah.

>> And like I I think they would say they regret that.

>> Yeah.

>> But with Blackwell and for sure with Reuben, economics are going to dominate the prisoners dilemma from a decision-m and spending perspective just because

the numbers are so big. And this goes to kind of the ROI on AI question. And the

ROI on AI has empirically factually unambiguously been positive.

>> Like I just always find it strange that there's any debate about this because the largest biders on GPUs are public companies. They report something called

companies. They report something called audited quarterly financials. And you

can use those things to calculate something called a return on invested capital. And if you do that calculation,

capital. And if you do that calculation, the ROIC of the big public spenders on GPUs is higher than it was before they ramp spinning. And you could say, well,

ramp spinning. And you could say, well, part of that is, you know, opex savings.

Well, at some level that is part of what you expect the ROI to be from AI. And

then you say, well, a lot of is actually just applying GPUs, moving the big recommener systems that power the advertising and the recommendation systems from CPUs to GPUs, and you've had massive efficiency gains. And that's

why all the revenue growth at these companies has accelerated. But like, so what? the ROI has been there. Um, and it

what? the ROI has been there. Um, and it is interesting like every big internet company, >> the people who are responsible for the revenue >> are intensely annoyed at the amount of

GPUs that are being given to the researchers.

>> It's a very linear equation. If you give me more GPUs, I will drive more revenue.

Give me those GPUs, we'll have more revenue, more gross profit, and then we can spend money. So, it's this constant fight at every company. One of the factors in the prisoners dilemma is everybody has this like religious belief

that we're going to get to ASI and at the end of the day what do they all want? Almost all of them want to live

want? Almost all of them want to live forever. Okay. And they think that ASI

forever. Okay. And they think that ASI is going to help them that >> right good return.

>> That's a good return. But we don't know.

And if as humans we have pushed the boundaries of physics, biology and chemistry, the natural laws that govern the universe.

I'm very curious about your favorite sort of throw cold water on this stuff type takes that you think about sometimes. One would be like the things

sometimes. One would be like the things that would cause I'm curious what you think the things that would cause this demand for compute to change or even the trajectory of it to change. There's

there's one really obvious bare case and it is just edge AI and it's connected to the economic returns to ASI. in three

years on a bigger and bulkier phone to fit the amount of DRAM necessary, you know, and the battery won't probably last as long, you will be able to probably run a pruned down version of

something like Gemini 5 or Gro 4, Gro 4.1 or you know, Chat GPT at um I don't know 30 60 tokens per second

>> and then that's free. And this is clearly Apple's strategy. It's just

we're going to be a distributor of AI >> and we're going to make it privacy safe and run on the phone and then you can call one of the big models, you know, the the god models in the cloud whenever

you whatever you have a question. And if

that happens, if like 30 to 60 tokens at like a one whatever it is a 115 30 60 tokens a second at a 115 IQ is good enough.

I think that's >> a bare case >> other than just the scaling laws break, you know. But in terms of if we assume

you know. But in terms of if we assume scaling laws continue and we now know they're going to continue for pre-training for at least one more generation and we're very early in the two new scaling laws you know for post-

training mid-training RLVR whatever people want to call it and then test time computed inference we're so early in those and we're getting so much better at helping the models hold more

and more context in the in their minds as they do you know this test time compute and that's really powerful because you know everybody's like well you know the how's the model going to know this? Well, eventually if you can

know this? Well, eventually if you can hold enough context, you can just hold every Slack message and Outlook message and company manual in in a company in your context.

>> Yeah.

>> And then you can compute the new task >> and compare it with your knowledge of the world, what you think, what the model thinks, all this context. And you

know, it may be that like, you know, in just really really long context windows are the solution to a lot of the current limitations. Um, and that's enabled by

limitations. Um, and that's enabled by some all these cool tricks like KV cash offload and stuff. But I do think like other than scaling laws slowing down,

other than there being low economic returns to ASI, edge AI is to me by far the most plausible and scariest bare case.

>> I like to visualize like different S-curves. invested through the iPhone

S-curves. invested through the iPhone and I love to like see the visual of the iPhone models as it as it sort of went from this clunky bricky thing up to the what we have now where like each one's like a little bit, you know, obviously

we we've sort of petered out on its form factor. If you if you picture something

factor. If you if you picture something similar for the Frontier models themselves, does it feel like a like it's at a certain part of that of that natural technology paradigm progression

to you? If you're paying for Gemini

to you? If you're paying for Gemini Ultra or Super Grock and you're getting, you know, the good AI, it's hard to see differences. Like, I have to go really

differences. Like, I have to go really deep on something like, do you think PCI Express or Ethernet is a better protocol

for scale up networking and why? Show me

the scientific papers. And if you shift between models and you ask a question like that where you know it really deeply, >> know that then >> you know the answers. Yeah. then you see

differences. I do play fantasy football.

differences. I do play fantasy football.

Um, winnings are donated to charity, >> but it is like, you know, these new models are quite a bit better at helping like who should I play?

>> Yeah.

>> You know, and and they they think in much more sophisticated ways.

>> Um, and by the way, if you're if you're a historically good fantasy football player and you're having a bad season, it's why this is why because you're not using it, you know. And I think we'll

see that in more and more domains. But I

do think they are already at a level where unless you are a true expert or just have an intellect that is beyond mind

>> um it's hard to see um the progress and that's why I do think we need to shift from getting more intelligent to more useful >> unless more intelligence starts leading

to these massive scientific breakthroughs and we're curing cancer in 26 and 27. Yeah,

>> I don't know that we're going to be curing cancer, but I do think from a ROI almost an ROIS curve, we need to kind of hand off from intelligence to usefulness

>> and then usefulness will then have to hand off to scientific breakthrough, you know, just that creates whole new industries.

>> What are the building blocks of usefulness in your mind?

>> Just being able to do things consistently and reliably. And a lot of that is keeping all the context. Like

there's a lot of context if someone wants to plan a trip for me. Like you

know I've I've acquired these strange preferences. Like I follow that guy

preferences. Like I follow that guy Andrew Huberman. So I like to have an

Andrew Huberman. So I like to have an east facing balcony so I can get morning sun. You know the AI has to remember,

sun. You know the AI has to remember, you know, being on a plane with Starlink is important to me. Okay. Here are the resorts I've historically liked. Here

are the kinds of areas I've liked. Here

are the rooms that I would really like at each. That's a lot of context and to

at each. That's a lot of context and to keep all of that and kind of weight those it's a hard problem. So I think context windows are a big part of it.

You know, there's this meter task evaluation thing like >> how long it can work, >> how long it can work for. And you could think of that being related and in some

way to to context. Um although not precisely, but that just task length needs to keep expanding because you know

booking a restaurant and booking is economically useful but you know it's not that economically useful. But

booking me an entire vacation and knowing the preferences of my parents, my sister, my niece, and my nephew, and what it means that like that's a much harder problem. And that's something

harder problem. And that's something that like a human might spend three or four hours on optimizing that. And then

if you can do that, that's that's amazing. But then again, I just think it

amazing. But then again, I just think it it has to be good at sales and customer support relatively soon. I do think we're going to see an kind of an acceleration in the awesomeness of

various products just because engineers are using AI to make products better and faster.

>> We both invested in Forell, the hearing aid company, which is just absolutely remarkable. I think something I never

remarkable. I think something I never would have thought of.

>> And we're going to see I think something like that in every vertical and that's AI being used for the most core function >> Yeah. of any company which is designing

>> Yeah. of any company which is designing the product and then it will be you know there's already lots of examples of AI being used to help manufacture the product and distribute it more efficiently you know whether it's

optimizing a supply chain you know whether it's you know having a vision system watch a production line you know so I I think a lot of stuff is happening the other thing I think is really interesting in this whole ROI part is

Fortune 500 companies are always the last to adopt a new technology they're conservative they have lots of regulations lots of lawyers Startups are always the first. So let's think about the cloud which was the first which was

the last like really truly kind of transformative new technology for enterprises. Being able to you know do

enterprises. Being able to you know do have all of your um compute and the cloud and use SAS. So it's always upgraded. It's always great etc. etc.

upgraded. It's always great etc. etc. You can get it on every device. I mean

it's those were dark days before the cloud. You know

cloud. You know >> the first AWS reinvent I think it was in 2013. Every startup on planet Earth ran

2013. Every startup on planet Earth ran on the cloud.

>> Yeah. The idea that you would buy your own server and storage box and router was ridiculous. And that probably

was ridiculous. And that probably happened like even earlier that that had probably already happened before the first reinvent. And then like you know

first reinvent. And then like you know the first big Fortune 500 companies started to standardize on it like maybe 5 years later. You see that with AI I'm sure you've seen this in your startups and I think one reason VCs are more

broadly bullish on AI than public market investors is VCs see very real productivity gains. There's all these

productivity gains. There's all these charts that for a given level of revenue, a company today has significantly lower employees than a company of two years ago.

>> And the reason is AI is doing a lot of the sales, the support and helping to make the product. And I mean there is, you know, Iconic has some charts. A6Z,

by the way, David George is a good friend, great guy. You know, he has his model busters thing. So there's very clear data that this is happening. So

people who have a lens into the world of venture see this. And I do think it was very important in the third quarter, this is the first quarter where we had Fortune 500 companies outside of the

tech industry give specific quantitative examples of AIdriven uplift. So C

Robinson went up something like 20% on earnings. And should I tell people what

earnings. And should I tell people what C Robinson does?

>> Let's just say a truck goes from, you know, Chicago to Denver. And then, you know, the trucker lives in Chicago. So

it's going to go back from Denver to Chicago. There's an empty load. And CH

Chicago. There's an empty load. And CH

Robinson has all these relationships with these truckers and trucking companies and they match shippers demand with that empty load supply to make the trucking more efficient. You know,

they're a freight forwarder. You know,

there's there's there's actually lots of companies like this, but kind of they're the biggest and most dominant. So, one

of the most important things they do is they quote price and availability. So,

somebody a customer calls them up and says, "Hey, I urgently need three 18-wheelers from Chicago to Denver." You

know, in the past they said it would take them, you know, 15 to 45 minutes and they only quoted 60%

of inbound requests. With AI, they're quoting 100% and doing it in seconds.

>> And so they printed a great quarter and the stock went up 20% and it was because of AIdriven productivity that's impacting the revenue line, the cost line, everything. And so I actually

line, everything. And so I actually think that's pretty important because I was I was actually very worried about like the idea that we might have this Blackwell ROI air gap because we're spending so much money on Blackwell.

Those Blackwells are being used for training and there's no ROI on training.

Training is you're making the model. The

ROI comes from inference. So I was really worried that you know we're going to have >> you know maybe this threequarter period where the capex is unimaginably high.

>> Those black wheels are only being used for training bars staying flat eyes going up.

>> Yeah. Yeah. Exactly. And so ROIC goes down and you can see like Meta Meta they printed you know because Meta has not been able to make a frontier model. Meta

printed you know a quarter where ROIC declined and that was not good for the stock. So I was wor really worried about

stock. So I was wor really worried about this. I do think that those data points

this. I do think that those data points are important in terms of suggesting that maybe we'll be able to navigate this potential air gap and ROIC.

>> Yeah it makes me wonder about in this market I'm like everybody else. It's the

10 companies at the top that are all the market cap more than all of the attention. There's 490 other companies

attention. There's 490 other companies 500. You studied those too. Like what

500. You studied those too. Like what

what do you think about that group? Like

what what is interesting to you about the group that now nobody seems to talk about and no one really seems to care about because they don't they haven't driven returns and they're a smaller percent of the overall.

>> Well, I think that people are going to start to care if you have more and more companies print these CH Robinson like quarters that companies that have historically been really wellrun.

The reason like they have a long track record of success is they have a long you cannot succeed without using technology well and so if you have a kind of internal culture of experimentation and innovation I think

you will do well with AI >> you know so like I would bet on the best investment banks to be early you know earlier and better adopters of

AI than maybe some of the trailing banks you know just sometimes past prologue one thing that I strong opinion I you know, all these VCs are setting up these holding companies and, you know,

we're going to use AI to make traditional businesses better and, you know, they're really smart VCs and they're great track records. But that's

what private equity has been doing for 50 years.

>> You're just not going to beat private equity at their game.

>> What Vista did in the early days, right?

>> Yeah. Private equity's maybe had a little bit of a tough run. You know,

just multiples have gone up. Now,

private assets are more expensive. The

cost of financing has gone up. It's

tough to take a company public because the public valuation is 30% lower than the private valuation. So PE's had a tough run. I actually think these

tough run. I actually think these private equity firms are going to be pretty good at systematically applying AI. We haven't spent much time talking

AI. We haven't spent much time talking about meta, anthropic or open AI. And

I'd love just like your impression on everything that's going on in this infrastructure side that we talked about. These are three really important

about. These are three really important players in this in this grand battle, this grand this grand game. How does all of this development that we've discussed so far impact those players specifically do you think? The first thing let me

just say about frontier models broadly.

>> Yeah.

>> You know in in 2023 and 24 I was fond of quoting Eric Visria and Eric Fishria's statement our friend um brilliant man and Eric would always say foundation models are the fastest appreciating

assets in history.

>> And I would say he was 90% right. I

modified the statement. I said

foundation models without unique data and internet scale distribution are the fastest appreciating assets in history.

But reasoning fundamentally changed that in a really profound way. There was a loop, a flywheel to quote Jeff Bezos that it was at the heart of every great internet company and it was you made a

good product, you got users, those users using the product generated data that could be fed back into the product to make it better. And that flywheel has been spinning at Netflix, at Amazon, at

Meta, at Google, you know, for over a decade. And that's an incredibly

decade. And that's an incredibly powerful flywheel. And it's why those

powerful flywheel. And it's why those internet businesses were so tough to compete with. It's why they were

compete with. It's why they were increasing returns to scale. You

everybody talks about network effects much more and you know network effects are they were important for social networks. I I don't know to what extent

networks. I I don't know to what extent meta is a social network anymore. It's

more like a content distribution >> but they just had increasing returns to scale because of that >> flywheel. And that dynamic was not

>> flywheel. And that dynamic was not present in the pre-reasoning world of AI. You pre-trained a model, you let it

AI. You pre-trained a model, you let it out in the world, and it was what it was. And it was actually pretty hard.

was. And it was actually pretty hard.

They would do RLHF, reinforcement learning with human feedback, and you try and make the bot model better, and maybe you'd get a sense from Twitter vibes that people didn't like this, and so you tweak it, and you know, there were the little up and down arrows, but

it was actually pretty hard to feed that back into the model. with reasoning.

It's early but that flywheel has started to spin and that is really profound for these frontier labs. So one reasoning fundamentally changed the industry

dynamics of Frontier Labs. just explain

why specifically that is like what what what is going on >> because if a lot of people are asking a similar question and

they're consistently either liking or not liking the answer, then you can kind of use that like that as a verifiable reward. That's a good outcome. And then

reward. That's a good outcome. And then

you can kind of use feed those good answers back into the model. and we're

very early at this flywheel spinning >> like it's hard to do now, >> but you can see it beginning to spin.

>> So, this is important fact number one for all of those dynamics. Second,

>> I think it's really important that Meta, you know, Mark Zuckerberg at the beginning of this year in January said, you know, I anticipate, you know, I'm highly confident, I'm going to get the quote wrong, that at some point in 2025,

we're going to have the best and most performant AI. I don't know if he's in

performant AI. I don't know if he's in the top hundred. Okay,

>> so he was as wrong as it was possible to be. And I think that is a really

be. And I think that is a really important fact because it suggests that what these four companies have done is really hard to do because Meta threw a

lot of money at it and they failed.

Yamakun had to leave. They had to have the famous billion dollar for AI researchers. And by the way, Microsoft

researchers. And by the way, Microsoft also failed. They did not make such an

also failed. They did not make such an unequivocal prediction but they hire but they bought um inflection AI and you know there were a lot of comments from them that we anticipate our internal

models quickly getting better and we're going to run more and more of cop you know our AI on our internal models >> Amazon they bought a company called Adept AI >> they have their models called Nova

>> no I don't think they're in the top 20 >> so clearly it's much harder to do than people thought a year ago and there's many many reasons for that like it's actually really hard to keep a big

cluster of GPUs coherent. A lot of these companies were used to running their infrastructure to optimize for cost >> right >> instead of performance >> complexity and performance

>> complexity and keeping the GPUs running at high utilization rate in a big cluster. It's actually really hard

big cluster. It's actually really hard and there are wild variations in how well companies run GPUs.

>> Yeah. And if you're running if the most anybody because the laws of physics, you know, maybe you can get two or 30 hundred,000 black wells coherent, we'll see. But if you have 30% uptime on that

see. But if you have 30% uptime on that cluster and you're competing with somebody who has 90% uptime, >> you're not even competing. So one,

there's a huge spectrum in how well people run GPUs. Two, then I think there is, you know, these AI researchers, they like to talk about taste. I find it very funny. You know, oh why do you make so

funny. You know, oh why do you make so much money? I have very good taste. You

much money? I have very good taste. You

know what taste means is you have a good intuitive sense for the experiments to perform. And this is a this is is why

perform. And this is a this is is why you pay people a lot of money because it actually turns out that as these models get bigger, you can no longer run an experiment on a thousand GPU cluster and

replicate it on 100,000 GPUs. You need

to run that experiment on 50,000 GPUs and maybe it takes, you know, days.

>> And so there's a very high opportunity cost. And so you have to have a really

cost. And so you have to have a really good team that can make the right decisions about which experiments to run on this. And then you need to do, you

on this. And then you need to do, you know, all the reinforcement learning during post- training well and the test time compute. Well, complicated.

time compute. Well, complicated.

>> It's really hard to do. And everybody

thinks it's easy, but all those things, you know, I used to have this saying like, hey, I was a retail analyst long ago. Pick any vertical in America. If

ago. Pick any vertical in America. If

you can just run a thousand stores and have them clean, well lit, stocked with relevant goods at good prices and

staffed by friendly employees who are not stealing from you, you're going to be a $20 billion company, a $30 billion company. Like 15 companies have been

company. Like 15 companies have been able to do that. It's really hard. And

it's the same thing. Doing all of these things well is really hard.

and then reasoning with this flywheel.

This is beginning to create more separation.

>> And what's even more important, every one of those labs, XAI, Gemini, OpenAI, and Enthropic, they have a more advanced checkpoint

internally of the model. Checkpoint is

just um you're kind of continuously working on these models and then you release kind of a checkpoint and then the reason these models get fast >> the one they're using internally is for >> better and they're using that model to

train the next model >> and if you do not have >> that latest checkpoint it's >> you're behind >> you're it's getting really hard to catch up. Chinese open source is a gift from

up. Chinese open source is a gift from God to meta >> because you can use Chinese open source >> to try and that can be your checkpoint and you can use that

>> as a way to kind of bootstrap this and that's what I'm sure they're trying to do and everybody else. Um the big problem and the big a giant swing factor I think China's made a terrible mistake with this rarest thing you know I think

China because you know they have the Huawei Asin and it's a decent chip and verse something you know like you know the the deprecated hop preserving something it looks okay so they're trying to force Chinese open source to

use their Chinese chips uh their domestically designed chips. The problem

is Blackwell is going to come out now and the gap between these American frontier labs and Chinese open source is going to blow out because of Blackwell and actually DeepSeek in their most

recent technical paper v3.2 said like one of the reasons we struggle to compete with the American Frontier Labs is we don't have enough compute. That

was their very politically correct, still a little bit risky way of saying, you know, cuz China said, "We don't want the black wells, right?" And they're saying, "Guys, that might be a big mistake. That might be a big mistake."

mistake. That might be a big mistake."

And so, if you just kind of play this out, these four American labs are going to start to widen their gap versus Chinese open source, which then makes it harder for anyone else to catch up

because that gap is growing. So, you

can't use Chinese open source to bootstrap. And then geopolitically,

bootstrap. And then geopolitically, China thought they had the leverage.

They're going to realize, oh, whoopsy daisy. We do need the black wells. And

daisy. We do need the black wells. And

unfortunately, they'll probably for them um they'll probably realize that in late 26. And at that point, there's an

26. And at that point, there's an enormous effort underway. DARPA has

there's all sorts of really cool DARPA and DoD programs to incentivize really clever technological solutions for rare earths, you know, like using enzymes to refine them or there's all sorts of

really cool things happening, you know, and then, you know, there's a lot of rare earth deposits in countries that are very friendly to America that, you know, don't mind actually refining it in the, you know, traditional way. So, I

think rare earths are going to be solved way faster than anyone thinks. You know,

they're obviously not that rare. They're

just misnamed. they're rare because, you know, they're really messy to refine.

And so geopolitically, I actually think Blackwell is pretty significant. Um, and

it's going to give America a lot of leverage as this gap widens. And then in the context of all of that, going back to the dynamics between these companies, XAI will be out with the first Blackwell model and then they'll be the first ones

probably using Blackwell for inference at scale. And I think that's an

at scale. And I think that's an important moment for them. And by the way, it is funny like um you know if you go on open router you can just look they have dominant share now open router is

whatever it is it's 1% of of API tokens but it's an indication >> they process 1.35 trillion tokens Google did like eight or 900 billion this is like whatever it is last 7 days or last

month you know anthropic was at 700 billion like XAI is doing really really well and the model is fantastic I highly recommend it but you'll see XAI you know come out with this open AAI will come

want faster. OpenAI's

want faster. OpenAI's issue that they're trying to solve with Stargate is because they pay a margin to people for compute >> and maybe the people who run their compute are not the best at running

GPUs. They are a high-cost producer of

GPUs. They are a high-cost producer of tokens. Um, and I think this kind of

tokens. Um, and I think this kind of explains a lot of their >> code red recently.

>> Yeah. Well, just the 1.4 $4 trillion in spending commitments. And I think that

spending commitments. And I think that was just like, hey, they know they're going to need to raise a lot of money.

Um, particularly if Google keeps its current strategy of sucking the economic oxygen out of the room and, you know, you go from 1.4 trillion rough vibes code red like pretty fast, you know, and

the reason they have a code red is because of all these dynamics. So then

they'll come out with a model but they will not have fixed their per token cost disadvantage yet relative to both XAI and Google and almost and anthropic at that point. Anthropic is a good company.

that point. Anthropic is a good company.

You know they're burning dramatically less cash than openai and growing faster. So I think you have to give

faster. So I think you have to give anthropic a lot of credit and and a lot of that is their relationship with Google and Amazon for the TPUs and the trainiums. And so Anthropic has been able to benefit from the same dynamics

that Google has. I think is very indicative in this great game of chess.

You know, you can look at Daario Jensen maybe have taken a few there have been a few public comments, you know, that were, you know, made between them.

>> Jousting, >> a little bit of jousting. Well,

Anthropic just signed the $5 billion deal with Nvidia.

>> That is because Daario is a smart man and he understands these dynamics about Blackwell and Rubid relative to TPU. And

so Nvidia now goes from having two of the fighters, two fighters, XAI and OpenAI to three fighters. So that that helps in this

fighters. So that that helps in this Nvidia vers Google battle. And then if Meta can catch up, that's really important. And so I'm I am sure Nvidia

important. And so I'm I am sure Nvidia is doing whatever they can to help Meta, you know, whatever. Like let us you're running those GPUs this way. May maybe

we should maybe we should twist the screw this way or turn the dial that way and then it will be also if Blackwell comes back to China which it seems like it probably will happen that will also

be very good because then Chinese open source will be back. What other I'm I'm always so curious about the polls of things like one poll would be the other breakthroughs that you have your your mind on things in the data center that aren't chips that we've talked about

before as as one example. I think the most important thing that's going to happen in the world in this world in the next 3 to four years is data centers in space >> and this has really profound

implications for everyone building a power plant or a data center on planet earth. Okay. And there is a giant gold

earth. Okay. And there is a giant gold rush into this.

>> I haven't heard anything about this so please.

>> Yeah. You know it's like everybody thinks like hey AI is risky you know uh but you know what I'm going to build a data center. I'm going to build a power

data center. I'm going to build a power plant that's going to do a data center.

We will need that. But if you think about it from first principles, data centers should be in space. Okay.

What are the fundamental inputs to running a data center? There are power and there are cooling >> and then there are the chips.

>> That's like the total if you think about it from a total cost perspective.

>> Yeah. And just the the inputs to making the tokens come out of the magic machines.

>> Yeah.

So in space you can keep a satellite in the sun 24 hours a day >> and the sun is 30% more intense. You

know you can keep it in the sun just because like if the sun's here's this you know you can have the satellite you know always kind of catching >> catching the light >> catching the light. The sun is 30% more

intense and this results in six times more irradiance in outer space than the high than on planet earth. So you're

getting a lot of solar energy. Point

number one. Point number two, because you're in the sun 24 hours a day, you don't need a battery. And this is a giant percentage of the cost. So the

lowest cost energy um available in our solar system is solar energy and space.

Okay. Second, for cooling in one of these racks, a majority of the mass and the weight is cooling.

>> And the cooling in these data centers is incredibly complicated. You know, I

incredibly complicated. You know, I mean, the HVAC, the CDUs, the liquid cooling.

In space, cooling is free. You just put a radiator on the dark side of the satellite.

>> It's gold.

>> And it's as close to absolute zero as you can get.

>> So, all that goes away and that is a vast amount of cost. Okay, let's think about um how this these, you know, maybe each satellite is kind of a rack. It's

one way to think of it. Maybe some

people make bigger satellites that are three racks. Well, how are you going to

three racks. Well, how are you going to collect connect those racks? Well, it's

funny. In the data center, the racks are over a certain distance um connected with fiber optics. And that just means a laser going through a cable. The only

thing faster than a laser going through a fiber optic cable is a laser going through absolute vacuum. So, if you can link these satellites in space together

using lasers, you actually have a faster and more coherent network than in a data center on Earth. Okay, for training that's going to take a long time >> just because it's so big.

>> Yeah, just because it's so big. But for

inference, but I think even training will eventually happen. But then for inference, let's think about the user experience when I when we asked when you know when I asked Gro about you and it gave the nice answer. A radio wave

traveled from my cell phone to a cell tower. Then it hit the base station,

tower. Then it hit the base station, went into a fiber optic cable, went to some sort of metro aggregation facility in New York, probably within like, you know, 10 blocks of here. There's a small

little metro router that's routed those packets to a big XAI data center somewhere. Okay? And then the

somewhere. Okay? And then the computation was done and it came back over the same path.

If the satellites can communicate directly with the phone and Starlink has demonstrated directto cell capability, you just go boom boom. It's a much

better lowerc cost user experience. So

in every way data centers in space from a first principles perspective are superior to data centers on earth.

>> So if we could teleport that into existence, I understand that that portion. What are the frictions to that?

portion. What are the frictions to that?

H like why will that not happen? And is

it launch cost? Is it launch availability?

>> I mean, we need a lot of the space starships. Like the Starships are the

starships. Like the Starships are the only ones that can eomically make that happen.

>> We need a lot of those Starships. Um,

you know, maybe China or Russia will be able to land a rocket. Blue Origin just landed a booster. It's an entirely new and different way to think about SpaceX.

And it is interesting that you know Elon posted yesterday or said in an interview >> that Tesla, SpaceX and XAI kind of converging >> were converging and they really are. So

XAI will be the intelligence module for Optimus made by Tesla with Tesla vision has its you know perception system and then you know SpaceX will have the data

centers in space that will will you know power a lot of the AI presumably for XAI and Tesla and the Octopuses and a lot of other companies and it's just it is just

interesting the way that they're converging and each one is kind of creating competitive advantage for the other you know so it's if If you're XAI, it's really nice that you have this

built-in relationship with Optimus and now, you know, Tesla's a public company, so there's going to be like I cannot imagine the level of vetting that will go into that intercomp agreement, you

know, and then you have a big advantage with these data centers in space. Um,

and then it's also nice if you're XAI that you have two companies with a lot of customers who you can use to help build your customer support agents, your

customer sales agents with kind of built-in customers. So, they really are

built-in customers. So, they really are all kind of converging um in a neat way.

And I do think like it's going to be a big moment when that first Blackwell model comes out from XAI next year. Hm.

If I go to the other end of the spectrum and I think about something that seems to have been historically endemic to the human economic experience that uh shortages are always followed by gluts

in capital cycles. What if in this case um the shortage is compute like Mark Chen now is on the record as saying they would consume 10x as much compute if you gave it to them in like a couple weeks.

So so like there seems to still be a massive shortage of compute which is all the stuff we've talked about today. But

there also just seems to be this like iron law of history that gluts follow shortages. What do you think about like

shortages. What do you think about like that concept as it relates to this >> technology be a glut?

>> Yeah.

>> You know, and AI is fundamentally different than the software just in that every time you use AI takes compute in a way that traditional software just did

not. I mean it is true like I think

not. I mean it is true like I think every one of these companies could consume 10x more compute. Like what

would happen is just the $200 tier would get a lot better. the free tier would get like the $200 tier. Google has

started to monetize AI mode with ads >> and I think that will give everyone else permission to introduce ads into the free mode and then that is going to be an important source of ROI you know like >> seems like OpenAI is tailor made to

>> Yeah. Absolutely. All of them and

>> Yeah. Absolutely. All of them and actions like you know hey >> you know here are your three vacations would you like me to book one and then they're for sure going to collect a commission. Yeah.

commission. Yeah.

>> You know here's you know there there there's many ways you can make money. I

think we went into great detail on maybe a prior podcast about how just inventory dynamics made these inventory cycles inevitable in semis. Um, and the iron law of semis is just that customer

buffer buffer inventories have to equal lead times. And that's why you got these

lead times. And that's why you got these inventory cycles historically. We

haven't seen a true capacity cycle in semis maybe arguably since the late 90s.

And that's because Taiwan Smi has been so good at aggregating and smoothing supply.

And a big problem in the world right now is that Taiwan semi is not expanding capacity as fast as their customers want. And I think this is actually a

want. And I think this is actually a pretty big this they're they're in the process of making a mistake just because you know you do have Intel and with these fabs and they're not as good and

it's really hard to work with their PDK but now you have this guy Leapoo who's who's a really good executive um and really understands that business. I mean

by the way Patrick Elsinger I think was was also a good executive and he put Intel on the only strategy that could result in su success and I actually think it's shameful that the Intel board fired him when they did it. But Leapoo

is a good executive and now he's reaping the benefits of Patrick's strategy and Intel has all these empty fabs and eventually given the shortages we have of compute those fabs are going to be filled.

>> So I think Taiwan Sim is in the process of making a mistake but they're just so paranoid about an overbuilt. Yeah.

>> And they're so skeptical. You know

they're the guys who met with Sam Alman and laughed and said he's a podcast bro.

He has no idea what he's talking about.

You know they're terrified of an overbuild. So it may be that Taiwan

overbuild. So it may be that Taiwan Simei singlehandedly that they're cautious >> the breaks on the bubble >> is is is is the governor um and you know

and we do like I think you know I think governors are good it's good that you know it's good that power is a governor it's good that Taiwan sim is a governor if Taiwan semi opens up at the same time

when you know data centers in space relieve all power constraints but that's like I don't know five six years away that data centers in space or majority of deployed megawatt like yeah I think you get it overbuild really fast but

just we have these two really powerful natural governors >> and I think that's good you know like smoother and longer is good >> we haven't talked about the power other than alluding to it through the space thing haven't talked about power very

much power was like the most uninteresting topic because there's the de demand and nothing really changed for like a really really long time all of a sudden we're trying to figure out how to get like gigawatts here there and everywhere how do you think about are you interested in powers

>> I'm very interested I do feel lucky in a prior life I was the sector leader for the telecom and utilities team.

>> Okay, >> I I do have some base level of knowledge. So one, you know, having um

knowledge. So one, you know, having um having watts as a constraint is like really good for the most advanced compute players because if watts are the constraint, >> the price you pay for compute is

irrelevant. The TCO of your compute is

irrelevant. The TCO of your compute is absolutely irrelevant because if you could get 3x or 4x or 5x more tokens per watt, that is literally three or 4x or

5x more revenue.

And so, you know, it's just like if you're going to build a like, okay, like an advanced data center costs 50 billion. A data center with your ASIC

billion. A data center with your ASIC maybe costs 35 billion, but if that $50 billion revenue, if that $50 billion data center pumps out 25 billion of

revenue and your ASIC data center at 35 billion is only pumping out 8 billion, well, like you're, you know, you're pretty bummed. It's good for like all of

pretty bummed. It's good for like all of the most advanced technologies in the data center which is exciting to me as an investor. So as

long as power is a governor the best products are going to win irrespective of price and have crazy pricing power.

Okay, I think that's that's the first implication that's really important to me. Second, it is in the only solutions

me. Second, it is in the only solutions to this. We just can't build nuclear

to this. We just can't build nuclear fast enough in America. Like as much as we would love to build nuclear quickly, we just can't. We just can't. Yeah,

>> it's just too hard, you know. Um, NEPA,

all these rules, like it's just it's too hard. Like a a rare ant that we could

hard. Like a a rare ant that we could move and it could be in a better environment can totally delay the construction of a nuclear power plant.

You know, one ant. It's crazy actually.

Um, like humans need to come first. We

need to have a humanentric view of the world. But like the solutions are

world. But like the solutions are natural gas and solar. And the great thing is the great thing about these AI data centers is apart from the ones that you're going to do inference on, you can

locate them anywhere. So I think you were going to see and you're this is why you're seeing all this activity in Abalene, you know, because it's in the middle of a big natural gas basin and we have a lot of natural gas in America because of fracking. You I think we

we're going to have a lot of natural gas for a long time. We ramp production really fast. So I think this is going to

really fast. So I think this is going to be solved. You know, you're going to

be solved. You know, you're going to have power plants fed by gas or solar. I

think that's the solution. And you know, you're already, you know, all these turbine manufacturers were reluctant to expand capacity. Caterpillar just said,

expand capacity. Caterpillar just said, "We're going to increase capacity by 75% over the next few years." So like the system on the power side is beginning to respond. One of the reasons that I

respond. One of the reasons that I always so love talking to you is that you do every like you do as much in the top 10 companies in the world as you do looking at brand new companies with, you know, entrepreneurs that are 25 years

old trying to do something amazing. And

so you have this very broad sense of what's going on. If I think about that second category of young enterprising technologists who now are like AI, they're like kind of the first

generation of AI native entrepreneurs.

What are you seeing in that group that's notable or surprising or interesting?

>> These young CEOs, they're just so impressive in all ways and they get more polished faster. And I think the reason

polished faster. And I think the reason is is they're talking to the AI.

>> How should I deal with pitching this investor? I'm meeting with Patrick

investor? I'm meeting with Patrick Oanaughy. What What do you think the

Oanaughy. What What do you think the best ways I should pitch him are?

>> Yeah. And it works.

>> Do a deep research. And it's good. You

know, hey, I have this difficult HR situation.

>> How would you handle it?

>> That's correct.

>> And it's good at that. How would you, you know, we're struggling to sell our product. What changes would you make?

product. What changes would you make?

And it's really good at all of that today.

And so, and that goes to these, you know, VCs are seeing massive AI productivity in all their companies.

It's because their companies are full of these, you know, 23, 24 or, you know, even younger AI natives. I've been so impressed with like young investment talent

>> and it's just part of it. Like your

podcast is part of that. There's just,

you know, knowledge and very very specific knowledge has became so accessible, you know, through podcasts and the internet. Impressive young

people >> come in and they're just I feel like they're where I was as an investor like in my, you know, early 30s and they're 22 and I'm like, "Oh my god, >> like I have to run so fast to keep up."

these kids who are growing up native in AI, they are just proficient with it um in a way that I am trying really hard to become.

>> Can we talk about semi VC specifically and like what is interesting in that universe?

>> Oh, just the one thing I just think that I just think is so cool about it and so underappreciated is your average semiconductor venture founder is like 50 years old.

>> Okay. and Jensen and what's happened with Nvidia and the market cap of Nvidia has like singlehandedly ignited semiconductor venture but the way it's ignited it's ignited in an

awesome way that's like really good for actually Nvidia and Google and everyone >> is like let's just say you were the best DSP architect in the world you had made for the last 20 years every two years

because that's what you have to do semiconductors it's like every two years you have to win run a race >> and if you won the last race you start like a foot ahead

>> and over time those compound um and make each race easier to win but like maybe that person and his team maybe he's the head of networking at a big public company he's making a lot of money and

he has a good life and then because he sees these outcomes and the size of the markets in the data center he's like wow why don't I just go start my own company but the reason that's important is that you know I forget the number but I mean

there are thousands of parts in a blackwell rack and you know and there's thousands of parts in a TPU rack And in the Blackwell rack, you know, maybe Nvidia makes, I don't know, two two or 30 hundred of

those parts. And, you know, same thing

those parts. And, you know, same thing in an AMD rack. And they need all of those other parts to accelerate with them.

>> So, they couldn't go to this one-year cadence if the rest everything was not >> keeping up with them. The fact that semiconductor venture venture has come back with a vengeance, you know, Silicon

Valley stopped being Silicon Valley long ago. My little firm maybe has done more

ago. My little firm maybe has done more semiconductor deals in the last seven years than the top 10 VCs combined, you know, but that's really really important

because now you have an ecosystem of companies who can keep up and then that ecosystem of these venture companies is putting pressure on the public companies

that are also need to part of part of this if we're going to go to this annual cadence which is just so hard. Um, and

it's one reason I'm really skeptical of these AS6 that don't already have some degree of success. So, I do think that's a super super important dynamic and one that's

absolutely foundational and necessary for all of this to happen >> because not even Nvidia can do it alone.

Not AMD can't do it alone. Google can't

do it alone. You need, you know, the people who make the transceivers. You

need the people who make the wires, who make the back blades, you know, who make every who make the lasers. They all have to accelerate with you. And one thing that I think is very cool about AI as an

investor is it's just it's the first time where every level of the stack >> that I look at at least the most important competitors are public and private,

>> you know. So Nvidia they're they're very important you know private competitors you know Broadcom important private competitors Marll important private competitors you know luminum coherent all these companies um you know there's

even like a wave of innovation in memory which is really exciting to see because memory and is such a gating factor by the way something that could slow all this down and be a natural governor is if we get our first true DRAM cycle

since the late >> 90s say more what that means >> you know if like a DRAM wafer is like valued at like a 5 karat a diamond in the '90s when you had these true capacity cycles before Taiwan semi kind

of smoothed everything out and DRAM became more of an oligopoly. You know,

you would have these crazy shortages where the price would just go 10x things that are unimaginable >> relative to the last 25 years where like a giant DRAM cycle, a good DRM cycle is

the price start stops going down. An

epic cycle is maybe it goes up, you know, whatever it is 30 40 50%. But I

mean, if it starts to go up by X's instead of percentages, that's a whole different game. By the way, we should

different game. By the way, we should talk about SAS.

>> Yeah, let's talk about it. What do you think's going to happen?

>> Application SAS companies are making the exact same mistake that brick-andmortar retailers did with e-commerce.

>> So, brick and mortar retailers um you know, particularly after the um you know, the the telecom bubble crashed, you know, they looked at Amazon and they said, "Oh, it's losing money. You know,

e-commerce is going to be a low margin business." you know, how how can just,

business." you know, how how can just, you know, from first principles, how can it ever be more efficient as a business?

Right now, our customers pay to transport themselves to the store and then they pay to transport the goods home. How could it ever be more

home. How could it ever be more efficient if we're, you know, sending shipments out, you know, to individual customers, you know, and Amazon's vision, of course, well, eventually we're just going to go down a street and drop off a package at every house. And

so, they did not invest in e-commerce.

They they clearly saw customer demand for it, but they did not like the margin structure of e-commerce. That is the fundamental reason that essentially every brick brick-and-mortar retailer

was really slow to invest in e-commerce.

And now here we are and you know Amazon has higher margins in their North American retail business than a lot of retailers that are mass market retailers you know so margins can change and if

there's a fundamental transformative kind of um new new technology that customers are demanding it's always a mistake not to embrace it >> and that's exactly what the SAS

companies are doing they have their 70 80 90% gross margins and they are reluctant to accept AI gross margins you know the very nature of AI is you know software you write it once and it's

written very efficiently and then you can distribute it broadly at very low cost and that's why it was a great business AI is the exact opposite where you have to recomputee the answer every

time and so you know a good AI company might have gross margins of 40%.

Now, the crazy thing is because of those efficiency gains, they're generating cash way earlier than other people, you know, than other than SAS companies did historically, but they're generating cash earlier, not because they have high

gross margins, but because they have very few human employees. And it's just tragic to watch all of these companies like you want to have an agent, it's never going to succeed if you're not

willing to run it at a sub 35% gross margin >> because that's what the AI natives are running it at. Yeah,

>> maybe they're running it at 40. So if

you are trying to preserve an 80% gross margin structure, you are guaranteeing that you will not succeed at AI.

>> Absolute guarantee. And this is so crazy to me because one, we have an existence proof for software investors being willing to tolerate gross margin

pressure as long as gross profit dollars are okay. And it's called the cloud.

are okay. And it's called the cloud.

People don't remember but you know when Adobe converted from on premise to uh the CL you know to a SAS model not only did their margins implode their actually

revenues imploded too because you went from charging up front you know to charging over a period of years.

Microsoft, it was less dramatic, but you know, Microsoft was a tough stock in the early, you know, in the early days of the cloud transition because investors were like, "Oh my god, you're an 80%

gross margin business." And the cloud is the 50s and they're like, "Well, it's going to be gross profit dollar creative. It probably will improve those

creative. It probably will improve those margins over time." Microsoft, they bought GitHub and they use GitHub has a distribution channel for, you know, uh,

or Copilot. co-pilot for coding that's

or Copilot. co-pilot for coding that's become a giant business a giant business now for sure it runs at much lower gross margins but there are so many SAS

companies like I can't think of a single application SAS company that could not be running a successful agent strategy they have a giant advantage over these AI natives and that they have a cash

generative business >> like and I think there is room for someone to be a new kind of activist or constructive ist and just go to SAS

companies and say stop being so dumb.

>> All you have to do is say here are my AI revenues >> and here are my AI gross margins and you know it's real AI because it's low gross margins. I'm going to show you that and

margins. I'm going to show you that and here's a venture competitor over here that's losing a lot of money. So maybe

I'll actually take my gross margins to zero for a while but I have this business that the venturef funed company doesn't have. And this is just such a

doesn't have. And this is just such a like obvious playbook that you can run Salesforce, Service Now, HubSpot, GitLab Atlassian

all of them could run this. And the way that those companies could or should think about the way to use agents is just to ask the question, okay, what are the core functions we do for the customer now? Like how can we further

customer now? Like how can we further automate that with agents effectively?

Or is it some other >> 100% just like if you're in CRM? Well,

what our customers do, they talk to talk to their customers. Yeah,

>> we're customer relationship management software and we do some customer support too.

>> So, make an agent that can do that, right?

>> And sell that, >> right, >> at 10 to 20% and let that agent access all the data you have, >> right?

>> Cuz what's happening right now is another agent, >> another agent >> made by someone else is accessing your systems >> to do this job, >> pulling the data into their system,

>> and then you will eventually be turned off. And it's just crazy. And it's just

off. And it's just crazy. And it's just because, oh wow, but we want to preserve our 80% gross margins. This is a life ordeath decision. And essentially

ordeath decision. And essentially everyone except Microsoft is failing it. To quote that memo from that um Noia guy long ago, like their their platforms are burning.

>> Burning platform. Yeah.

>> Yeah. There's a really nice platform right over there and you can just hop to it and then you can put out the fire in your platform that's on fire. And now

you GOT TWO PLATFORMS AND IT'S GREAT.

You know, >> your data centers and space thing makes me wonder if there are other kind of like less discussed off-the-wall things that you're thinking about in in the

markets in general that we haven't talked about. It does feel like since

talked about. It does feel like since 2020 kicked off and you know 2022 punctured this kind of a series of rolling bubbles you know so in 2020 you

know there was a bubble in like EV startup EVs company startup EVs that were not Tesla and that's for sure a bubble and they all went down you know 99%. And there was kind of a bubble in,

99%. And there was kind of a bubble in, you know, more speculative stocks. Uh,

you know, then we had the meme stocks, you know, GameStop. And now it feels like the rolling bubble is in nuclear and quantum.

>> And these are, you know, fusion and SMR. Like it's it would be a, you know, it's it would be a transformative technology.

It's amazing. But sadly from my perspective, none of the public ways you can invest in this are really good expressions of this theme are likely to succeed or have any real fundamental

support. And same thing with quantum

support. And same thing with quantum like we I've I've been looking at quantum for 10 years. We have a really good understanding of quantum and the public quantum companies again are not the leaders. You know, from my

the leaders. You know, from my perspective, the leaders in quantum would be Google, IBM, and then the Honeywell Quantum, you know. So the

public ways you can invest in this theme which probably is exciting are not the best. So you have two really clear

best. So you have two really clear bubbles. I also think quantum supremacy

bubbles. I also think quantum supremacy is very misunderstood. People hear it and I think that mean it means that quantum computers are going to be better than classical computers at everything.

With quantum you you can do you can do some calculations that classical computers cannot do.

>> That's it. That's going to be really useful and exciting and awesome. But it

doesn't mean that quantum takes over the world. The thought that I have had, this

world. The thought that I have had, this is maybe less related to markets than just AI. I have just

been fascinated that for the last two years, whatever AI needs

>> to keep growing and advancing, it gets.

Have you ever seen public opinion change so fast in the United States on any issue has nuclear power?

>> Just happened like that.

>> Like that.

And like why did that happen like right when AI needed it to happen? Now we're

running up on boundaries of power on earth. you know, all of a sudden data

earth. you know, all of a sudden data centers in space, >> you know, just it's just a little strange to me that whenever there is something

>> a bottleneck >> that a bottleneck that might slow it down, everything accelerates, you know, like Reuben is going to be such an easy, seamless transition relative to

Blackwell and Reuben's a great chip and then you you know, you have MI, you know, AMD getting into the game with the MI450. Like it's just whatever AI needs,

MI450. Like it's just whatever AI needs, it gets.

>> You're a deep reader of sci-fi, so uh Yeah, exactly. You're making me think of

Yeah, exactly. You're making me think of of Kevin Kelly's great, uh book, What Technology Wants. He calls it the

Technology Wants. He calls it the technium, like the like the overall mass of technology that just like is supplied by humans.

>> Absolutely.

>> To grow more powerful.

>> Yes. It just wants to grow more and more powerful. And now we're going into an

powerful. And now we're going into an instate.

>> I have a selfish closing question.

Speaking of speaking of uh young people, so my kids who are 12 and 10, but especially my son who's older is developing an interest in what I do, which I think is quite natural. And I'm

going to try to start asking my friends who are the most passionate about entrepreneurship and investing why they are so passionate about it and what about it is so interesting and

life-giving to them. How would you pitch what you've done, the career you built, the this part of the world to a young person that's interested in this?

>> I do believe at some level kind of investing is the search for truth. And

if you find truth first, and you're right about it being a truth, that's how you generate alpha. And it

has to be a truth that other people don't have have not yet seen. You're

searching for hidden truths. Earliest

thing I can remember is being interested in history. You know, looking at books

in history. You know, looking at books with pictures of the Phoenetians and the Egyptians and the Greeks and the Romans and pyramids. I loved history.

and pyramids. I loved history.

>> I vividly remember like in the I think in the second grade as my dad drove me to school every day, he would we'd go we went through the whole history of World

War II in one year and I loved that. And

then that translated into a real interest in current events very early.

So, like as a pretty young person, you know, I don't know if it was eighth grade or seventh grade or ninth grade, like I was reading the New York Times and the Washington Post and I would get

so excited when the mail came because it meant that maybe there was an economist or a Newsweek or a Time or US News and I was really into current events, you know, because current events is kind of

like applied history and watching history happen and like thinking about what might happen next.

And you know, I didn't know anything about investing. My parents were both

about investing. My parents were both attorneys. Like I was anytime I won an

attorneys. Like I was anytime I won an argument, I was super rewarded.

>> Like, you know, if I could make a reasonable argument why I should stay up late, my parents would be so proud and they'd let me stay up late, but I had to beat them, you know, like I was just kind of going through life and, you know, I really love to ski and I love

rock climbing and I go to college and rock climbing is, you know, by far the most important thing in my life. I

dedicated myself to it completely. I did

all my homework at the gym. I got to the rock climbing gym like at 7 am would, you know, skip a lot of classes to stay in the gym. I'd do my homework on like a big bouldering mat.

>> Like every weekend I went and climbed somewhere with the Dartmouth Mountaineering Club. And as part of

Mountaineering Club. And as part of that, like on climbing trips, >> you know, maybe we'd play poker. The

movie came out while I was in college.

We started playing poker. I like to play chess. Um, and I was never that good at

chess. Um, and I was never that good at chess or poker. You never really dedicated myself to either. And my plan like you know after two or three years of college was I was going to leave. I

was going to work as a I I was a ski bomb at Alta in college. I I was a housekeeper. I've cleaned a lot of

housekeeper. I've cleaned a lot of toilets. Um it is it was shocking to me

toilets. Um it is it was shocking to me how people treated me and it is like permanently impacted how I treat other people. You know

people. You know >> like I want you know like you'd be cleaning somebody's room and they'd be in it and they'd be reading the same book as you and like you know you'd say oh that's a great book. you know, I'm

about where you are and a they look at you like you're a space alien, like you speak >> and then they get even more shocked. You

read, you know, so it like had a big impact on how I've like just treated everyone since then. But anyways, I was going to be a ski bum in the winters.

Um, I was going to work on a river in the in the summers and that was how I was going to support myself. And then I was going to climb in the shoulder seasons, going to try and be a wildlife photographer and write the next great American novel. I can't believe I never

American novel. I can't believe I never knew this.

>> That was my plan. This was like my plan of record. I was really lucky. My

of record. I was really lucky. My

parents very supportive of everything I wanted to do. My parents had very strict parents, so of course they're extremely permissive with me. So, you know, I'll probably end up being a strict parent.

Just the cycle continues.

>> My parents were lawyers. You know, they they they had done reasonably well. Um

they both grew up in in um I would say very economically disadvantaged circumstances. You know, like my dad

circumstances. You know, like my dad talks about like he remembers every person who bought him a beer >> just because he could not he couldn't afford a beer. You know, he worked the whole way through college. He was there

on a scholarship. You know, he had one pair of shoes all through high school.

But anyways, and so they were super on board with this plan and I'd been very lucky. They sent me to college and I

lucky. They sent me to college and I didn't have to pay pay for college. They

paid for my college education. They

said, "You know, Gavin, we think this plan of being, you know, ski bum, river rafting guide, wildlife photographer, climbing the shoulder seasons, tried to write a novel. We think it sounds like a great plan, but you know, we've never

asked you for anything. We've haven't

encouraged you to study anything. We've

supported you in everything you've wanted to do. Will you please get one professional internship, just one, and we don't care what it is."

>> The only internship I could get, this was at the end of my sophomore summer at Dartmouth, was an internship with Donaldson Lufkin Engineer. Um, DJ, my job was to every time DJ published a

research report, it was in like the private wealth management division and I worked for the guy who ran the office and my job was whenever they produced a piece of research, I would go through

and look at which of his clients owned that stock.

Then I would put the research I would mail it to the clients, you know. So

this day we wrote on General Electric.

So, I need to mail the GE report to these 30 people >> and then I need to, you know, email the Cisco report to these 20 people. And

then I started like reading the reports and I was like, "Oh my god, this is like the most interesting thing imaginable."

Investing. I kind of conceptualized it.

It's a game of skill and chance, kind of like something like poker. Um, and you know, there's obviously chance in investing. you know, like if you're an

investing. you know, like if you're an investor in a company and a meteor hits their headquarters, like that's that's bad luck, but like you own that outcome.

Um, so there is chance um that is irreducible, but there's skill, too. So

that really appealed to me. And the way you got an edge in this the greatest game of skill and chance imaginable was you had the most thorough knowledge

possible of history. And you intersected that with the most accurate understanding of current events in the world >> to form a differential opinion on what was going to happen next in this game of

skill and chance. Which stock is mispriced in the Perry Mutual system?

>> That is the stock market. And that was like day three. I went to the bookstore and I bought like the books that they had which were Peter Lynch's books. I

read those books in like two days. I'm

I'm a very fast reader. And then I read all these Warren books, books about Warren Buffett. Then I read Market

Warren Buffett. Then I read Market Wizards. Then I read Warren Buffett's

Wizards. Then I read Warren Buffett's letters to his shareholders. This is

like during my internship. Then I read Warren Buffett's letters to his shareholders again. Then I taught myself

shareholders again. Then I taught myself accounting. There's this great book, Why

accounting. There's this great book, Why Stocks Go Up and Down. Then I went back to school. I changed my majors from

to school. I changed my majors from English and history to history and economics. And I never looked back. And

economics. And I never looked back. And

it consumed like I continued to really focus on climbing. I would be in the gym and I would print out everything that the um people on the mly fool wrote.

they had these fools and and you know they they were they were early to talking about return on invested capital and in incremental ROIC is like a really important indicator and I would just

read it and I would underline it and I'd read books and then I'd read the Wall Street Journal and then eventually there was a computer terminal finally set up near the gym and I'd go to that gym and just you know read news about stocks and

it was the most important thing in my life and like I barely kept my grades up and yeah that's how I got into it man history current events skill and dance and I am a competitive person and I've

actually never been good at anything else. Okay, I got picked last for every

else. Okay, I got picked last for every sports team. Like I love to ski. I've

sports team. Like I love to ski. I've

literally spent a small fortune on private skiing lessons. I'm not that good of a skier. I like to play pingpong. All my friends could beat me.

pingpong. All my friends could beat me.

Um I tried to get really good at chess and this was before the you know when you you actually had to play the games.

It was before it was easy to do it on the phone. And my goal was to beat one

the phone. And my goal was to beat one of the people. I'm sure there's a park somewhere.

>> It's literally right there. Famous one

is right there.

>> Okay. Well, there's one in Cambridge.

And I wanted to beat one of them. Never

beat one of them. Never been good at anything. I thought I would be good at

anything. I thought I would be good at this.

>> And the idea of being good at something other than taking a test that was competitive was very appealing to me.

>> And so I think that's been a really important thing, too. And to this day, this is the only thing I've been vaguely competitive at. I'd love to be good at

competitive at. I'd love to be good at something else. I'm just not, you know.

something else. I'm just not, you know.

>> I think I'm going to start asking this question of everybody. Uh, the ongoing education of Pearson May, amazing place to close. I love talking about

to close. I love talking about everything so much.

>> This is great, man. Thank you. Thank

you. Thank you.