Re: AI heatwave

I’m trying to make sense of “The AI summer” [1].

OpenAI’s ChatGPT had a meteoric rise in popularity not because the technology works (it does, for some reasonable definition of “works”) but rather the foundation is there for viral spread because:

> a lot of this is ‘standing on the shoulders of giants’ - OpenAI didn’t have to wait for people to buy devices or for telcos to build DSL or 3G

> ChatGPT is just a website or an app, and … it could ride on all of the infrastructure we’ve built over the last 25 years. So a huge number of people went off to try it last year.

But current AI’s problem is that no one knows what to do with it:

> The problem is that most of them haven’t been back. … most people played with it once or twice, or go back only every couple of weeks

> On one hand, getting a quarter to a third of the developed world’s population to try a new product in 18 months is very hard. But on the other, most people who tried it didn’t see how it was useful.

Current AI is more R&D than basic foundational research but it is still more R than D, and it’s still far from being COTS [3] products:

> Accenture … Last summer it proudly announced that it had already done $300m of ‘generative AI’ work for clients… and that it had done 300 projects. Even an LLM can divide 300 by 300 - that’s a lot of pilots, not deployment.

> As a lot of people have now pointed out, all of that adds up to a stupefyingly large amount of capex (and a lot of other investment too) being pulled forward for a technology that’s mostly still only in the experimental budgets.

> an LLM by itself is not a product - it’s a technology that can enable a tool or a feature, and it needs to be unbundled or rebundled into new framings, UX and tools to be become useful. That takes even more time.

It took 8 years (to approx. June 2022) for cloud adoption to touch 25%. It took that long for cloud adoption expected-in-3-years to just pass 40% [2].

It took 2 more years and a pandemic (to approx. January 2024) for cloud adoption to get to about 30%. It took that long for cloud adoption expected-in-3-years to get near 50%:

> If you work in tech, cloud is old and boring and done, but it’s still only a third or so of enterprise workflows

> it took more than 20 years for 20% of US retail to move online

Gen AI and LLMs are here to stay but it’ll still take many years to decades for it to spread everywhere and displace existing technologies and labor.

[1]: https://www.ben-evans.com/benedictevans/2024/7/9/the-ai-summer

[2]: https://www.ben-evans.com/benedictevans/2023/7/2/working-with-ai

[3]: https://en.wikipedia.org/wiki/Commercial_off-the-shelf

RE: $500B AI revenue expectations gap

They say there is a $500B "gap between the revenue expectations implied by the AI infrastructure build-out, and actual revenue growth in the AI ecosystem" [1].

Part 1

Given the business that Sequoia Cap is in, it should not be surprising that they’d say things like:

> Investment incineration… a lot of people lose a lot of money during speculative technology waves. It’s hard to pick winners, but much easier to pick losers

> Winners vs. losers… there are always winners during periods of excess infrastructure building. AI is likely to be the next transformative technology wave… lt will cause harm primarily to investors.

i.e. invest right and you’d capture a huge amount of value. Invest wrong and you’d be burning your money. So do investments with us.

Part 2

What I found interesting is the point about there being a:

> $500B … gap between the revenue expectations [$600B] implied by the AI infrastructure build-out, and actual revenue growth in the AI ecosystem [$100B] … that needs to be filled for each year of CapEx at today’s levels [GPU $150B, “Data Center Facility Build and Cost to Operate” $150B (they seem to have included OpEx in their “CapEx” figure)]

This means there’s either some amazing AI killer apps that will make $500B in sales or some AI investments will get incinerated.

Investment incineration "will cause harm primarily to investors" [1] — Nvidia, the data center builders, facility operators, and power companies will all have gotten paid for the work they will do — but I wonder what are the broader implications of the $500B revenue expectations gap.

Is it — the investments, not necessarily the GPT/LLM tech — irrational exuberance?  How much of today’s Big Tech valuation is driven by it?  How sensitive is it to interest rates?  Notice this "bubble", if it is one, is not occurring during a ZIRP [3] period.

It seems AI startups aren’t the ones building AI data centers — "much of the incremental data center build-out is coming from big tech companies" [2].  So startups seem less affected by that cost.

But actually 50% of the $500B revenue expectations gap is “software margin” — that’s the margin earned by “The end user of the GPU—for example, Starbucks, X, Tesla, Github Copilot or a new startup” [2].

Which means when some of the $500B expected revenue doesn’t show up, it’ll be hitting the AI startups' margins.

Now remember the other 50% is “CapEx”: Nvidia GPU, and “Data Center Facility Build and Cost to Operate”.  And remember that Nvidia, the data center builders, facility operators, and power companies will all have gotten paid for the work they will do — because they don’t work for free or for startups' equity.  So it seems they won’t have their margins squeezed.

But doesn’t that also mean when some of the $500B expected revenue doesn’t show up, it’ll be hitting the Big Tech AI data center’s top line?

I don't know enough to know what will happen, but it seems some amount of AI Investment cooling will hit AI startups and Big Tech's AI data center buildout.  Big Tech has been and remains profitable, and their GPUs are paid for, so it'll mainly change their product priorities and revenue forecasts (and thus stock price?).  AI startups, however...

But perhaps, just in time, the Fed's interest rates will go down for unrelated reasons.

[1]: https://www.sequoiacap.com/article/ais-600b-question/
[2]: https://www.sequoiacap.com/article/follow-the-gpus-perspective/
[3]: https://en.wikipedia.org/wiki/Zero_interest-rate_policy