That is very low usage, but that is a 33% savings!

Now let's put that trillions in capex into perspective. About 55% of people use it for 6% of all work hours. Approximating a bit, that's basically 3% of all of the work hours by all of the workers in all of US, and as above, AI saves a 3rd of that time, so AI is already saving 1% of all work hours in the US!

(In case you think this Math is off, the same St. Louis Fed articles above have similar numbers, and corroborates with other national data sources as well as research studies, and suggests that the impact of GenAI may already be showing up in "national level statistics" to the tune of a 1.3% bump in national labor productivity! With only this shallow level of adoption! In just 3 years! It took the computer revolution about 2 decades to show up in economics data.)

According to the BLS, employers pay $12.3 trillion for that work. So a 1% savings on that is a $123 billion. An up front ~$2T investment over 2 - 4 years that is already saving $123 billion annually in the US alone is pretty damn good actually, and will only go up as usage improves.

This is not hope, this is data, and you had already brought it with you! ;-)

> the frontier labs are priced according to the narrative that they have produced or will in the very near future produce a fully automated drop-in replacement for most knowledge workers...

Even assuming this is how the AI companies are being valued (they're not), the numbers are off.

The "value" of most knowledge workers -- based on what enterprises currently pay for them -- is $50 - 70 trillion annually. It's reasonable to assume that if AI drop-in-replaced all those knowledge workers, AI companies could credibly charge somewhere in that order of magnitude, because that's what the market is already bearing.

So if their hypothetical revenues are double-digit trillions and valuations are some multiple of that, the entire AI industry would be valued at double-digit trillions at the least.

Yet cumulatively the industry (the frontier labs + the SWAG estimate of the AI parts of all the other players) are valued at, say, ~6 - 7 trillion? Which seems like a fair approximation of how much knowledge work they can currently automate.

What do you mean? The sum of ALL US salaries is $13.4 Trillion per year. According to google $65T is the sum of ALL salaries Globally (not just knowledge workers). It's not reasonable to assume AI is a drop-in-replacement for any job yet (perhaps bottom tier customer support from oversees?).

> So if their hypothetical revenues are double-digit trillions and valuations are some multiple of that

So you're sort of premising here than more than 16% or 1/6 of all the world's jobs get replaced by AI. Hopefully you can understand that's both not the current AI capability and also would be a terrible (unprecedented?) economic shock.

And then there’s the second order effect: if all the knowledge workers get automated, who is going to buy the stuff that’s produced?

Take the Hugging Face incident. Why did it happen? Because the people whose task was to set up a testing framework took shortcuts. Why did they? Because there weren't enough people who were assigned to do the job. Why not? Because the job is too new and not enough people are qualified to do it. It's a job that simply did not exist 3 years ago. But 3 years from now, this job might very well employ tens of thousands of high skill knowledge workers.

[1] https://en.wikipedia.org/wiki/Lump_of_labour_fallacy

Unfortunately, I fear that may not be the most likely outcome. I've posted some comments on this before, but when I start thinking about how deeply everything will change once people figure out how to properly leverage AI, I see no outcome other than significant, widespread job losses.

As you indicated, at that point we will have much a bigger problem than the valuation of the AI industry. I'm not sure how it will get solved, I just know it will HAVE to be, because it would be an existential problem for everybody: people, governments, even the billionaires! Because now consider the 3rd order effects: if nobody can buy the stuff that's produced, how can billionaires get even richer? ;-)

Future supply and demand will set the price - not what is paid today. If supply by open models is vast and cheap, I can't see that the entire knowledge industry can hold the current size. It'll rather collapse to a fraction of its current value.

As long as it’s not otherwise unlawful, of course, e.g. no bypassing security measures, which is generally verboten for users and agents alike. (Websites try to leverage this aspect by throwing up bot-specific security measures.) Caveats: this was an decade+ ago, our use-cases were different, and case law has changed since then. But simplistically, anything a human can legally do, software can also do for them.

This makes sense to me. In fact it is explicitly encoded in the language of the web: browsers are identified as “User Agents” after all.

We just happen to be in an era where our User Agents are now much more autonomous than before, and this threatens a lot of moats built around human attention.

No, major cloud providers are mainly a software business. They provide unique manage cloud offerings, which provides value add over raw hardware as well as lock in. People on AWS cannot just move to GCP, let alone DO. Hence amazon can charge a substantial premium over the price of the hardware, which is why they make so much money. If all you are doing is plugging in GPUs, you don't provide that value add and are going to have very slim profits.

2. Why can’t cloud providers do the same thing with GPUs that they do with other servers? Are GPUs somehow not amenable to managed cloud offerings wrapping them?

3. Literally just having any access to GPUs (or heck, even memory) itself is the value add today. Maybe when we have supply to match the demand things will change, but that seems to be a ways away and points 1 and 2 above will still be in play anyways.

I'd say the first part is right but the second part is statistically incorrect. I would even expand from "web-apps" to just about any kind of software, especially "business software" -- very little of it was ever too difficult.

Now the following will clearly not be true for all cases, and the boundaries are very fuzzy, but generally a huge part of software development has always been the relatively straightforward translation of high-level requirements into code. Crafting the high-level requirements was typically the challenging part, but that typically was done by the more senior devs / architects, whereas the actual implementation was done by more junior / mid-level engineers. And there were typically multiple junior / mid-level devs for every senior dev, say 3:1 or more. As such, it is correct to say that the simpler aspects of software development were what kept most SWEs employed.

Now AI has completely usurped the lower-level coding work. You as an expert dev are definitely getting worth more than ever with AI, but that's because you single-handedly can now do what an entire team used to do. You may even get paid much more, but that is eventually going to be at the expense of a bunch of other people who are not required anymore.

This is why people are seeing "jobs apocalypse" written on the wall.

And since then they have grown even more powerful than they were predicted to be, which, note, also faced a lot of skepticism at the time. The Hugging Face hacks and recent steamrolling of longstanding Math problems are just two recent pieces of extraordinary evidence.

And worse, people trust this technology because it behaves like people, but it actually works in ways nobody really understands, even exhibiting deeply weird and even disturbing characteristics (https://news.ycombinator.com/item?id=49635518) -- each of those quirks is extraordinary in itself.

And now we're rushing to give it control over the real world while deploying this powerful, quasi-chaotic technology in an infinite variety of ways everywhere in this highly vulnerable society.

I don't know what the standards for "extraordinary evidence" should be, but given such extreme unpredictability and rapid change, I fear it may end up being "an actual catastrophe".

If AI makes these workers even 1% more productive, that is $500 - 700 billion value annually. At an ongoing annual $0.5T return, a $2T investment (also over the next few years, note) doesn't seem too bad!

Then consider that actual studies from all the way back in 2024, i.e. the era of spicy autocomplete, before agents landed on the scene, put the productivity boosts much higher, like 30% or more. (Interestingly, this is corroborated by survey based data from the St. Lous Fed: https://www.genaiadoptiontracker.com/) Even assuming a conservative average boost of 10%, that is $5 - 7T value annually.

Add how many ever grains of salt you want to those numbers, the investment is nowhere near as out of whack to the potential revenues as people fear. This is why everybody from Big Tech to VCs to entire nation states are desperately scrambling to get in on the action.

My point is that their math is wrong.

1. There's no way China will let any of the Western frontier labs in, so that's probably 1/3 out of those $50-70tn that they'll never touch.

2. It turns out that LLMs are more of a commodity than expected because the basic tech is basically "Attention is all you need" plus a few things everyone has access to (mixture of experts, caching, batching, etc). So yes, it's a "winner take most" market, but there will likely be a healthy base of cheap models so the "collection" (price gouging) part of the cycle (or enshittification) will be hard to execute.

3. Either hardware remains expensive, in which case every N years entire DCs have to be rebuilt and then Capex needs to flood in - think highway systems being rebuilt, but instead of every 20-30 years for highways, here we'd be talking every 5-7 years.

4. Or hardware becomes cheap in which case cheap LLM hosters are competitive and problem #2 is even worse. Or the nightmare scenario for all of these investment scenarios, local LLMs become viable for most people.

I agree LLMs are already a commodity market, definitely at the non-frontier model level, but I don't think it affects monetization prospects much. After all, server compute is a commodity and yet cloud businesses have been exploding even before AI.

And compute is exactly why it won't be a "winner takes most" market. It is clear now that compute capacity is and will likely remain the biggest moat. Looking at Claude Code is instructive; arguably it was the better product, but it kept going down so much that Codex and other competitors have gained on it. Similarly, China could have the best models, but its access to hardware is deliberately limited by geopolitics, so it's likely their threat will be manageable for a while yet.

A key part of the success of AI companies will be in securing hardware and operating that infra cost-effectively via economies of scale. Hardware will remain expensive for a long time yet because all the hyper-scalers and neo-clouds are severely crunched, and all the fabs (mostly TSMC) are already at capacity even as demand keeps exploding.

And for better or worse, most of that supply will still flow through Nvidia, despite attempts from competitors like TPUs and NPUs, for the simple reason that Nvidia has the monopoly profits to outbid everyone else on the real chokepoint, which is fab capacity.

Firstly, try to imagine stuffing as many ads as there are on a SERP into a chatbot conversation. Good way to lose users.

Secondly, Google spent decades hyper-optimizing their ad business monopoly for maximum profit, including going to extents that were recently found unlawful. People should really look into the findings of the last two Antitrust cases against Google, both of which it lost but for which it suffered only slaps on wrists. The details are eye-opening, including the bits about how Google, leveraging its prime position as the middle-man playing all sides against each other, manipulated ad auctions to make itself more money at the expense of its customers.

Even Microsoft was dinged for screwing over just their competitors, not their customers.

But now because of the paradigm shift in how people discover information, very little of the Google ad monoploy advantage transfers over. I think their AI business will keep growing, even if they don't have the best models, and it will be an exceptional source of revenue.

But it will still be no match to their ad business -- a cash cow of incomprehensible proportions -- and that is the fundamental problem they face today.