YouTube Video

All right, folks. Um, we're going to have some uh fun in this session. Um, my

name is Apur. I'm going to be your instructor for the next uh nine weeks or so. Um, here's what we're going to go

so. Um, here's what we're going to go through today. I'm going to talk a

through today. I'm going to talk a little bit about myself. Why do I do this?

some logistics on what to expect.

Quiz, yep, I'm that guy. We're gonna

have quiz on day one.

And uh the biggest question that we've all been wrestling with, where's the money? Where's the money in AI? Some of

money? Where's the money in AI? Some of

you know me, but uh you know, my journey started in India. I uh moved to Singapore. I met a couple of Singaporean

Singapore. I met a couple of Singaporean folks here earlier. I started my career at uh Palunteer with uh Sunil and a couple other folks about 13 years ago, 14 years ago. Led a variety of

engineering teams. Uh all that to say we wrote a lot of Spark in government buildings and uh I came back to Stanford for grad school which is when I got tired of writing Spark in government buildings

and uh now I lead at Ultimter. I don't

know how many of you have heard of Altimter but Altimter is an investment firm. We focus on fairly concentrated

firm. We focus on fairly concentrated form of investing. We've got two businesses. We got a public business and

businesses. We got a public business and a private business. And then I got the biggest promotion of my life uh 6 months ago. I'm now the proud dad.

ago. I'm now the proud dad.

Um this is uh the as as people have told me the biggest investment I will make. Some

have called it the one with the most guaranteed negative IR. Um I think it's the most guaranteed positive IR. Not

financial. Uh but that's me. I live

across the street. um reach out with questions. I want to be here as a

questions. I want to be here as a resource uh to you guys and uh make the most of it. Uh we've got a great session lined up for you guys. The course is designed to be no more than 3 hours a

week. Uh and that includes class

week. Uh and that includes class readings, all the time you spend arguing with Chad GP Claude uh about whether it can do your assignment for you. So it's

about an hour of class. Uh it's about an hour or two of readings and basically the format is we we're going to do guest speakers every single class next class onwards.

Chatam house rules uh a lot of guest speakers will will share maybe overshare. So please don't record uh

overshare. So please don't record uh what they're what they're saying. Um

we'll have an actual dinner with some of them right afterwards. You guys are welcome to join um arrange the the logistics. Um grading is easy 50/50.

logistics. Um grading is easy 50/50.

Show up to class. If Ali Goatsi can show up to class, you can show up to class.

And the other half is an assignment that we'll release at the uh end of the course.

Um yeah, it's conversational. Ask

questions, be involved. The more you're involved, the more you're going to get out of it. And honestly, you know what's in it for me is I'm going to learn the most from you guys. This is the course schedule over the next nine weeks. Lots

of great speakers, one more impressive than the other. As for me, you know, honestly, putting this calendar together has been my uh my job for the last couple of couple of weeks and months. Uh

but be be present. These are all incredible leaders running incredible businesses across the stack from semis to to to to infra to energy on on the infrastructure side to models. you know,

we're going to have folks from OpenAI and Enthropic and and a bunch of applications and agents. Uh so ask all your hardest questions, save them for the speakers. Uh they're going to love

the speakers. Uh they're going to love it. Uh and we're going to assign some

it. Uh and we're going to assign some readings.

So why should you take this course? What

should you achieve in this course? Um

what is a good thing to get out of this?

You know, honestly, I I thought about this and, you know, I was just telling one of the students here of how it all began. It's, you know, I I come back to

began. It's, you know, I I come back to campus once a year and I talk about all that's happening and, you know, typically it's in the context of finding

um great people. It's I realized that this is such a big super cycle. We know

it. We we believe it at Ultimter. You

know, we positioned our entire focus around it. And I did not find a course

around it. And I did not find a course that goes deep uh in a way that I would have liked to be when I was an undergrad here or or a grad student here. And you

know I thought about in 5 years everybody was going to ask you hey did you see it coming? You were at the start of it. You were around when Chad GBD was

of it. You were around when Chad GBD was launched. You were around when you know

launched. You were around when you know the the the tectonic plates were forming and play was forming. And I think you want to be able to say yes. So half of you are going to start an AI company and

the other half are going to fund it. Um

so at least you should know where to spend the series A money that you're going to raise and uh at the very minimum you know you'll have a sense of uh what not to go or at least have mental models for hey this business that

I'm looking at or considering starting or considering funding or considering joining what are the right questions to be asking what are the laws of physics that govern this this business um at

this at this uh part of the cycle.

Uh I think it's going to be the biggest one yet and um I'm excited that you guys are uh uh uh here uh to study alongside us.

I'm going to spend some time on this slide uh because this is the this is sort of the punchline. Um how many of you have seen a version of this before?

Well, those of you who did the readings, thank you. I appreciate it. Uh we did

thank you. I appreciate it. Uh we did include a notebook LM for those who were more auditory uh inclined but uh but let's talk about this for a second. um

you know what is going on here is is is actually the probably the biggest the biggest question in generative AI right

now which is the if you listen to any of the earnings calls from the hyperscalers or or or even Nvidia and and and and

others is we are investing so much into the capex we're investing so much into building these data centers we're you know it's a five layer cake as Jensen

calls it, energy, chips, power, interconnects, memory, all that to give you a data center that you can either rent by the hour or by the token that

you can go train models on and serve those models. And then the question is,

those models. And then the question is, hey, these models that you've built, are they creating economic value?

That is basically the right hand side of this chart.

and to to to make an analog of the biggest technology revolutions that I have seen you know internet 25 years ago um mobile 20 years ago cloud probably

the most recent one 10 years ago I put up one of those charts on cloud but you know on the readings you will see the same for internet and mobile and and cloud and that's the shape of the cloud

ecosystem the cloud ecosystem looks dramatically different than the u AI ecosystem Anybody have guesses as to why that's the case or your theory on why it's so

different or reasons why this might look like we're not going to call it a pyramid. We're going to call it a

pyramid. We're going to call it a triangle inverted triangle.

Go ahead.

Is that because it's still early in the side or AI?

Yeah, definitely.

Definitely early. That's a good guess.

Yeah.

Any others? Any other thoughts?

Oh, maybe because Nvidia has like a monopoly so uh they can charge letter.

Can you ask that question again next week when we have the when we have the folks from Nvidia? But no, good. It's a

it's a great point. They do have a strangle hold, right? Um one of the one of the charts we had in the readings was the market share that Nvidia has on all

of the compute right now and um uh it's uh it's up there. Any other thoughts or hypothesis on why this is so uh

different?

Yeah, I don't know. Is it the the cloud uh stackers seem to be able to leverage the hardware to generate value perhaps and AI didn't really got you know we know how software ate the

world as Mark Andre said software ate the world because you know I could build software could build software and I could distribute it to millions of people and the marginal cost of running

that software was close to zero. these

software businesses ran at 80 some even at 90% gross margins. That is not the case with this new economic model of AI because if

we have a set of users using cursor or using um you know you hear all these stories about large scale businesses that are still not profitable at

billions of dollars of revenue scale is because of that is because the incremental user of an AI application is not free. It's not marginally free. It's

not free. It's not marginally free. It's

actually quite a bit more expensive to have AI users because turns out you've got to burn those GPUs. And I would say everything you guys said from it being

early to um Nvidia being uh dominant, we'll we'll call it to uh you know the the the physics of the problem are very different of how inference is run is certainly where we are right now. So I

think that's the case right now. I might

add another dimension to it which I spoke about in the readings was you know we analyzed what happened in internet we analyzed what happened in mobile and

cloud and how many years did it take to for these triangles to flip and you know one of the examples we take is AWS um

AWS started in the year 2004 AWS has its first customer in Netflix in 2010 again and ultimately Amazon shifted fully to AWS in 2012.

8 years from breaking ground, 8 years from the first capex investment cycle. I

don't know if any of you were around reading earnest uh earnings reports uh 20 years ago, but the big debate was, hey, is Amazon going to go bankrupt? And

that was the biggest that was the biggest question everybody had about um the buildout of AWS.

you know thankfully nobody at least yet is on the verge of bankruptcy but these are large numbers um so we'll we'll come back to this slide but I would say this is the central

theme of the course that we're going to explore we're going to have speakers um from some of the companies that are listed here to others and the central theme that we're going to pick around is

like hey in your field with the Nvidia speakers are you a dominant force how long are you going to stay to be the dominant force What are the forces that you're most worried about? Who are the

AS6 that you're most worried about? What

are the pricing compression uh uh vectors for your business to you know the folks at Enthropic and OpenAI who we're going to talk a lot about

profitability is you're serving a billion user franchise um at at OpenAI with the with the anthropic folks honestly 100% of this class is on on cloud. So we'll ask them about is this

cloud. So we'll ask them about is this group of users profitable? How do you think about profitability? um is ads going to be a bigger source of revenue than subscriptions? And then for the

than subscriptions? And then for the folks in the middle, which is the inference layer, you know, this is the most competitive part of the whole ecosystem. There's

ecosystem. There's a lot of startups that are doing really well. They're winning so far. Uh but

well. They're winning so far. Uh but

you've also got the hyperscalers who want to um have uh a dominant say in that layer. So honestly the jury is still out

layer. So honestly the jury is still out and the biggest question there is um are you a feature or or a platform? Uh a lot of new uh in uh you know uh businesses

that we are seeing on the infrastructure side that they feel like very good ideas but you if you ask yourself the question hey why is this not a part of AWS you are thinking about maybe it should be a

part of AWS. So for the speakers we're going to talk a lot about that. Any

questions before we uh jump into the quiz? Go ahead.

quiz? Go ahead.

I'm curious how you think about so on the right hand side like the the triangle being like the application layer being small like how do you think about including like incumbent platforms into that like maybe like Salesforce

maybe revenue like would you include them as part of like analyzing that pyramid and how it shifts over time?

It's a great question and um I might add you know Salesforce, Palunteer, there's a series of let's call them old economy businesses that are reinventing

themselves to have SKUs of products that are you know in the case of Salesforce um Einstein in the case of Palenter AIP in the case of you know there's a series

of these and the answer is yes they should be the answer is yes that they should be the way I solve for that in this calculation is I get the model revenue And so if you were running Salesforce,

you're probably running either one of the big uh models or running inference.

So their spend is captured in the app layer by way of the substrate. Um it's

very hard to extract that out from public disclosures, but yeah, we should.

Yeah.

is a large part of the bottom part of the right hand side pyramid basically finding capacity for future revenue which will acrew to the top which is what we're not seeing and mature quite well.

It's a great question. Um, and maybe just to rephrase the question, the question is, hey, is there a timing mismatch in the buildout of the semis layer because typically you build semis

for a 5year period or a six-year period?

Um, but the application revenue is for right now. Um it's a great question and

right now. Um it's a great question and that's what makes the um lower half of this call a triangle somewhat cyclical

and you go through phases of uh capex cycles. Uh think of it as like laying

cycles. Uh think of it as like laying down the railroads. Um that is very much the case and so there's a chart in the readings um for what happened in the mobile super cycles. Something very

similar happened. the first inning uh had inflated market caps for a lot of the u um capex heavy businesses and so

if you think about a basket of capex uh names to to call it uh steady state names you you should expect that and so I suspect we are so early that that's

happening as well I'm curious to see how you think about Google because I see that you've labeled Google as Google cloud they're on the middle layer but Google also have their

own model TPUs if they're pretty great.

How do you view position in this triangle?

Any large um conglomerate like Google deserves to be uh you know we have to call business units. So I would put the TPU business unit in semis. Um um we

include that here uh as we counted the revenue. Um their GCP unit is in the inf

revenue. Um their GCP unit is in the inf infrastructure layer and the Gemini unit unit is at the apps layer. Um the uh you know we have a chart later that we will

talk a little bit about uh Gemini is actually one of the most used uh consumer applications. It's the second

consumer applications. It's the second most used consumer application right now. And um the biggest question there

now. And um the biggest question there is how much of that is coming from the distribution advantage that Google has to it meritocratically being such a good

application. Um jury is still out but

application. Um jury is still out but we'll get into it.

Yeah.

Well, let's uh go ahead.

I just have a question about prediction.

Right now, it looks like this triangle shape and if it were to be successful, perhaps it should become inverted. Um,

but what does an unsuccessful uh new technology look like? Does it say a triangle? How would you be able to

triangle? How would you be able to predict whether this would invert or not? Yeah, I I'm not sure the

not? Yeah, I I'm not sure the maybe rephrasing your question, I um I might say it at like what is the um

stable equilibrium of this industry. Uh

I think it is pretty clear that AI is unlikely to be a fad. It is unlikely to be an unsuccessful endeavor. Um and I think about the stable equilibrium of this chart quite a bit. In fact, I got

into a little bit of a debate on Twitter with with with with somebody quite smart and who's thought a lot about this about this exact question of like what is the stable equilibrium? And you know, my

stable equilibrium? And you know, my guess is that it might stay this way for longer than I anticipated. Um, in the cloud, I I think that range is about a

decade. Um, I have a feeling this might

decade. Um, I have a feeling this might stay longer uh this way longer because of uh just how hard it is to get the um

substrate right. But there will be one

substrate right. But there will be one or two unlocks. I don't I couldn't tell you what they are but for example if one of the ASIC programs at one of the hyperscalers

be it Google's TPU or Meta's MTIA or uh you know the folks at Amazon and OpenAI and Microsoft and all the labs that we don't even know about exist um

has a break has breakout success. I

suspect that'll be the biggest repricing of that layer. Um

the other catalyst could be um you know I think about the hyperscaler capex guidance in earnings calls which by the way I recommend everybody here to listen to four times a year you'll have public company CEOs tell you their biggest

questions their biggest the things that they're thinking about and you know uh I recommend listening to those um if they stop guiding to big numbers on capex because that would imply that the

current equilibrium does not work. Uh so

that that's probably the second thing that could happen and so you see there's a lot of news about the guidance that all the hyperscalers give about their capex uh for that reason.

Go ahead. Is there also an element of training versus inference? Uh because my sense is if the only way this gives is if influence is meaningfully larger than

training and and I'm curious to hear your thoughts on when do you think that will happen because that you're saying that means multiple will stop spending

on trading because they're not seen not performing meaningfully and hence what are influenced. It's a great question

are influenced. It's a great question and it is probably the one of one of the nuggets of information that Nvidia's earnings calls have like the most sought-after nugget of what is Nvidia's

share of inference in their fleet. Um,

last I check it was about 40% or or they coded to be about 40%. Meaning that if they were selling a million GPUs uh assuming full utilization about 40% of them were used for inference and the

other 60% for training. I suspect that number will increase over time in favor of inference. But I I I wouldn't um I

of inference. But I I I wouldn't um I couldn't tell you when and how it'll happen because there's a lot of training still going on in the world and the uh shape of the training workload as you know looks very different from the shape

of the inference work workload. A

training workload is very predictable high utilization for for a short period of time. The inference workload is very

of time. The inference workload is very burst you usage typically when humans are awake until the agents take over.

Maybe then it'll be 24/7 and uh harder to predict. It goes down around

to predict. It goes down around Christmas for some reason. It goes down around Thanksgiving for some reason. But

I think that might be the case though we you know at least in this calculation we try to capture it because um it's it's a mix and uh but it's a good it's a good hypothesis.

I've seen the revenue, but where where would profitability be?

It's on slide 16. We'll come to it.

We'll come to it. I'll give you the answer. The most profitable part of the

answer. The most profitable part of the stack is the semisair by a long shot.

Nvidia's data center revenues uh earn a gross margin of about 75%. Don't quote

me on it. It's like plus minus couple percentage points from there. Um whereas

you know I estimate some of the application layer revenues to be somewhere between depending on who you ask like between zero and 30%.

And so the gap is quite wide and I think the reason to that I mean it's it's it's a it's a theory that gentleman here had is there's one player who kind of runs

the tables on the semisair and so it's very much the case and in fact if you looked at this from a profitability perspective it's even more concentrated the the the triangle is

even more concentrated.

Um I I'll I'll I'll I'll flash that in a second when we when we get to it, but it's a great question. Go ahead.

Cloud's like this year is more maturity AI, but you think that Yeah, that is definitely a big part of it is that we've gone through the investment cycle in cloud. It's

definitely an element to it.

Go ahead.

If all these intra um companies like Google, That's all their own TPUs. Um,

Nvidia is also doing it for OpenAI is also searching for some A6. Um, who like where the does of all these A6 for

startups going to sell to their own ship?

There's uh $300 billion of revenue to fight about the uh but to answer your question about half of that as as Jensen discloses on the earnings calls is from the big hyperscalers.

Uh so those are those are probably going to be your primary customers. So if you were starting a chip company today, you would have a very the shape of your customer base is a very small number of

very large orders.

Um it's a very different shape from building a consumer business or an enterprise software business. Uh and

then you might have a long tale of other enterprises though I wouldn't I wouldn't bank on it because I think they just go to the cloud providers.

Um, if you were if you were thinking about starting a chip company, it is a it is a it should be your number one consideration is like which of the five are you going to sell to first?

Last question.

I was like, well, you get a small handful of winners in each of these layers. It takes multiple years for that

layers. It takes multiple years for that to play out. Um, maybe correct me if I'm wrong, but I feel like in the past there haven't been a fully vertically integrated layer of just one.

M I guess that Google is just fully vertically integrated on the right hand side. We're wondering how that shifts

side. We're wondering how that shifts development power this what a great question. The biggest

winner on the internet super cycle was probably Google. Uh it's about three

probably Google. Uh it's about three trillion in market cap has near 99% market share in search.

I would say that that's a pretty vertically integrated player, right?

They run their own file server to search to ads on top to the user experience.

Let's see the next one's mobile. Uh the

winner of that super cycle is Apple.

what like two and a half trillion or so in market cap. You called it already.

The next one is uh let's say social.

Meta is probably the big winner in in in in social. They're not as fully

in social. They're not as fully integrated. And what is their market cap

integrated. And what is their market cap like two trillion or something right now pretty dominant but maybe they lost a trillion because they didn't fully go down to the servers. And then the cloud is fairly homog heterogeneous. We don't

have a single player that won the cloud.

You've got the three big algopies in AWS, GCP, and Azure, but they're not fully integrated. And uh you know,

fully integrated. And uh you know, Nvidia has been trying a lot. Nvidia has

been trying I don't know if you've heard of DGx Cloud, which is their cloud effort to to to to build the cloud ecosystem. Obviously, they've they've

ecosystem. Obviously, they've they've got a series of vertical apps that they're trying. So, yeah, you might you might

trying. So, yeah, you might you might you might be on to something.

Yeah, folks, I know it's a Thursday evening at 5:00. probably the last thing standing between the weekend and um your weekend. I don't want to be that person.

weekend. I don't want to be that person.

So, I'm going to jump into the part that wakes you up. I do actually have this is this is a quiz that we're going to go go through. I'm going to give you a hint

through. I'm going to give you a hint about the companies that we're going to go through. I do have a prize for the

go through. I do have a prize for the winner. This is a prize. So, you're

winner. This is a prize. So, you're

motivated and it you win points on on on two grounds. One is by being right and the

grounds. One is by being right and the other is by being fast.

The fastest way to be fast is to do fast inference and drop that thing into cloud. Please, you're welcome to do

cloud. Please, you're welcome to do that. Just give the human players 5

that. Just give the human players 5 seconds.

Let them go at it and let let them win the analog way. And if you really want to use claude, you're welcome to do it.

Just give them five seconds. All right.

So, this is question number one.

ready for the next the software engineers in the room might have a have an unfair advantage.

So, you know, I wanted to spend the next maybe 10 minutes or so um going into some of the uh uh

hypotheses that I have about what's going on and why the value is acrewing in the manner that it is. Um I think there was a question, a very good question about profitability and how it gets magnified. So, we'll jump through

gets magnified. So, we'll jump through that. But again, feel free to stop me if

that. But again, feel free to stop me if you have any questions. Um, I have a feeling we're going to have very little time left and I do want to end on time.

You guys remember this. Um,

and I I painted it slightly differently on the next chart, which is I did the same exercise that I did that I posted about two years ago. And what it looked

like 2 years ago was this thing on the left where the ecosystem was obviously a lot smaller. It was about five times

smaller. It was about five times smaller. Shockingly, the shape of it

smaller. Shockingly, the shape of it hasn't changed much. This is despite this is despite heroic growth. And you

know for if if if you look at the revenue that was added about 350 billion or so of revenue added a good like 75% of it just went straight to semis.

uh in the last two years um despite apps having grown you know more than 10x um it still hasn't made that big of a dent and so I was like okay well let's dig

deeper into this um if you started to open up each of these cells and you're like hey what what companies make up each of those parts um most of that 300 is Nvidia as you guys

know the apps is actually two companies make up about 90% of Anybody want to guess which those two are?

The infra segment is the one that has the most uh competitive intensity as we discussed. It is probably the place

discussed. It is probably the place where there's the biggest battle brewing both sideways uh but also across the stack. Uh it's also the place that has

stack. Uh it's also the place that has the highest metabolic rate in that there's a lot of companies being formed.

There's a lot of companies that are getting bought out and uh I would say it's the most uh competitive but also the most unstable of the equilibriums that we have right now.

And the and you know the question that we think about as we think about investing as you guys will think about investing your times is how much time will this chart that has moved such

little in the last 2 years what what is the amount of time it'll take to get to cloud software shape like uh like shape um is it 5 years is it 10 years is it 15

years is it never maybe it's just stays that way we do think it'll happen we think it'll happen at some point but uh it's it's it's not happening nearly fast enough.

The second thing that we've been thinking a lot about um as we think about the future of AI is you know this is this um I don't know if you guys saw this chart in the readings but consumer

AI which is the biggest call it market for AI right now outside of coding has incredibly high usage on you know at chat GPT most of it is free you know

about 95% of the users are free um and Gemini who's I don't know if you guys saw but demis u who leads Deep Mind announced that they were not planning to do ads as a subscription as as as a

revenue model. We've been thinking a lot

revenue model. We've been thinking a lot about hey how big do these businesses get? What is the monetization engine of

get? What is the monetization engine of these businesses? Do you think a

these businesses? Do you think a subscription business will be larger or ads business will be larger? And so what I did was I looked at the

largest consumer franchises outside of AI. And so you'll see that I mean you you all know these products.

There's a class of products that have gotten to three billion user scale.

These are almost near mandatory products to live your lives to to you know this is WhatsApp and Chrome which you could not live without. Then there is a class of products at the two billion one and a half to two billion user scale which are

social. These are like social products

social. These are like social products like Instagram, Tik Tok and Facebook.

They're not mandatory but they're exhibit very good network effects. If my

on one of these, I'm more likely to be there. And then you've got the third

there. And then you've got the third category of, you know, mainstream consumer products that are neither mandatory, that are neither extremely social, but I would call it like niche

products. If you're shopping, you're

products. If you're shopping, you're going to Amazon. If you're looking for music, you're going to Spotify. If

you're looking for a good debate, you're going on Twitter or cat videos. Any

guesses on where closer to which of these will Chad GPT and Gemini are right now? And the answer is on the next

now? And the answer is on the next slide, so we'll get it quickly.

Would you guess that chat GPT or or the leading AI application internal scale will be closer to a mandatory app like YouTube or WhatsApp, a social app like

Instagram or Tik Tok [clears throat] or a niche app like Spotify or Twitter?

Any guesses? If not, I'll reveal the question. Go ahead. I would say on the

question. Go ahead. I would say on the YouTube WhatsApp scale because it would be a daily utility.

Yeah.

People would just be using daily as part of their normal life.

Yeah.

You are um um well let me show you the answer and I'll we'll come back to your bicep. Any other guesses? Any different

bicep. Any other guesses? Any different

guesses? Go ahead.

It's closer to Facebook at around 900 exact.

Yeah. Yeah, that's right. You're

certainly right right now. Um here I'll show you guys the answer. Um

this is uh this is how they fair if you plot them all together.

Chat GPD has just overtaken the niche category. Gemini still has not. You're

category. Gemini still has not. You're

right that it's heading towards social.

Personally I would have loved as a as an investor at OpenAI. I would have loved for it to start heading towards the core utility. But you know one of the biggest

utility. But you know one of the biggest questions that we ask ourselves is is knowledge work work that everybody does is know is the work of you know chat GBD is not a place where you're messaging

other folks yet. It's not a place where you're getting your email inbox or your or your or your dopamine fix. It's a

place where you go and you have to do active work. You have to go ask a

active work. You have to go ask a question.

And the number of people in the world who are asking active questions of technology is not the entirety of the population that's online. You know,

there's about 8 billion people on the planet. 4 billion of them are online.

planet. 4 billion of them are online.

The rough economics of consumer applications are, you know, Alphabet has about 4 billion users. They monetize

them at about $100 a user a year. Meta

has got about three and a half billion users that monetize at about $70 a user a year.

the leading uh AI provider, Chad, GPT, has got about a billion users that are monetized at about $10 a use a year. And so the question is, how do we get the billion

up to 4 billion? I'm not sure knowledge work is the answer. I think we'd have to go beyond knowledge work.

Uh and then the second question is, hey, how do we get the $10 a user per year up from 10 to 100? And I'm not sure ads, I'm not sure subscription is the answer.

So I suspect we'll have to go into ads and I suspect the ads that chat GP will be able to serve or cloud will be able to serve will have a lot better pricing because they will understand your intent that you will be logged in. Very good

attribution, a lot more trust. Uh and I think that'll be the big other big headline this year and you heard it here first. It'll be a big deal. There's a

first. It'll be a big deal. There's a

lot of alpha in understanding the ad model really well. Once again, 10 years ago at the Facebook IPO, there was a lot of short reports on Facebook because people said, "Hey, well, these ads

worked on a on a on a computer. They're

not going to work on a phone." Why?

Because there's no space on a phone.

Shocker. We found the space on a phone.

The same thing's going on right now, which is while I'm having this conversation, it is a very personal conversation. I don't want to be shocked

conversation. I don't want to be shocked by advertisements. That's the bare

by advertisements. That's the bare debate.

I couldn't tell you what it's going to be like, but I am optimistic that we'll find it. And I think that's going to be,

find it. And I think that's going to be, you know, a big a big unlock, a big unlock for this economic model. And so,

we'll dig into that in in one of the speaker sessions later this year. Um,

I've got a bunch more slides. We are at time. Thank you.

time. Thank you.