At some point AI may deliver the level of net economic benefit you reference, but it's not entirely clear that we're there yet.

Right now much of the direct monetization occurs via OpenAI and Anthropic, who together have around $30B in annualized revenue. They are burning cash like crazy, though admittedly have potentially sustainable unit economics (gross margins around 40-60% before revenue share).

However, they need to spend a huge chunk of revenue on training. OpenAI spent something like $9b on training against around $13-14b in rev in 2025 (different from annualized rev) according to The Information. Anthropic's mix is supposed to be similar. Also implies a lot (maybe majority) of their compute spend is training.

If scaling laws falter, what happens to training spending? What happens to competitive degree of differentiation given Chinese open source models are a few months behind frontier? Then what happens to margins? It is very fragile.

10% capture seems highly unlikely. That level of capture is only possible for b2b high touch sales, aka "call-me" pricing.

For call-me pricing to work, you have to ensure that any sort of public sticker price is not a suitable alternative. You can not have a sticker price, make the sticker price so high essentially nobody will buy it or by finding a feature like oauth that makes the public version infeasible for businesses.

And then you also have to maintain enough of a monopoly / oligarchy to sustain that level of pricing.

I don't think either of those two conditions will apply in the future.

AI providers now have a sticker price that provides basically all functionality, almost completely eliminating the opportunity for extremely high-margin b2b. They've decided a small slice of a large pie is bigger than large piece of a smaller pie. I suspect that's true and will continue to be true in the future.

An oligarchy is difficult to sustain with more than 3 global players. Right now we seem to have 3 frontier models for coding that can and will charge more than commodity prices. However there are open source non-frontier models that you can use for inference costs only and even if those don't keep up it seems likely there will be enough non-frontier models available that their pricing will also be at the commodity level. Those cheaper models will provide significant downward pressure on frontier pricing.

That reminds me of "Chinese marketing" strategy by a lot of Western companies 30 years ago when their economy first opened up. There are billion people in China so if we can capture just 1% market share there then we'll make a fortune, right? Spoiler alert: it (mostly) didn't work.

If a different architecture to LLMs is invented (that could actually "think", that could potentially reach AGI), then perhaps it would be more efficient than LLMs. Perhaps LLMs can make themselves more efficient. They can't even remember "properly". Hallucinations cripple them for serious, professional uses. If they may hallucinate 5% of the time and you are asking mission critical queries, that's a problem.

Perhaps all of these data centers won't be needed. At least not by some of the current AI companies that won't keep up. If that happens to OpenAI, that would be quite a shock to the financial system (and GDP).

Microsoft's changes to windows have alienated some of their userbase. Copilot is poor compared to it's rivals. There's a reason they are down 20%. Linux adoption use is accelerating (still too low!).

And don't forget AI on device. When it becomes "good enough" for most tasks, data centre use will reduce.

With the talk of Nvidia backtracking and saying they won't invest 100 billion in OpenAI, Oracle in a poor financial position with the loan's for it's upcoming data centres becoming more expensive and dubious (they could fail to pay them)- the picture isn't as positive as you make it out to be. Which makes me think that you have an ulterior motive.

Yeah, this is something I am thinking a lot about. Companies won't be able to sustain this level of spending forever, and one of two things will need to happen:

1. Models become commodities and immensely cheaper to operate for inference as a result of some future innovation. This would presumably be very bad for the handful of companies who have invested that $1T and want to recoup that, but great for those of us who love cheap inference.

2. #1 doesn't happen and the model providers start begin to feel empowered to pass the true cost of training + inference down to the model consumer. We start paying thousands of dollars per month for model usage and the price gate blocks out most people from reaping the benefits of bleeding-edge AI, instead being locked into cheaper models that are just there to extract cash by selling them things.

Personally I'm leaning toward #1. Future models near as good as the absolute best will get far cheaper to train, and new techniques and specialized inference chips will make them much cheaper to use. It isn't hard for me to imagine another Deepseek moment in the not-so-distant future. Perhaps Anthropic is thinking the same thing given the rumors that they are rumored to be pushing toward an IPO as early as this year.

That why they need widen the moat; it appears not giving us access to hardware might be that moat.

They desperately need LLMs to stay rentier and hardware advances are a direct attack on their model.

Economics will be decisive force. Paying 1000USD a month for AI or buying server for 10kUSD, loading there Chinese AI model which can do 90% of what SOTA models can? Looks like a no brainer.

Yeah, they'll be free - on device and "good enough".

If you want the best, then pay.

Possibly, but that assumes continuity. New math and algorithmic breakthroughs could make much of today’s AI stack legacy, reshuffling both costs and winners.

I don't know about you, but I benefit so much from using Claude at work that I would gladly pay $1,500-$2,000 per month to keep using it.

I love how they say with a straight face that when AI takes over they will finally share all the fruits of capital with us.

To be intellectually honest about it, you have to answer a bunch of questions:

1. Awful compared to what? 2. Was there an equivalent transfer outside America? 3. What is the cause? What ratio rent-seeking/shady activity vs a consequence of natural forces (e.g. technological change)

If it’s any consolation, I’m rich yet the country is still shit. (Comparing to Europe as a previous immigrant of Western Europe.

> A massive $79T has been transferred from bottom 90% to top 1% since the 1970s

This assertion is based on comparing reality with a counterfactual where income distributions remained static from 1975 to the present. Real median personal income roughly doubled over this time period.

The use of the word "transferred" seems a little intellectually dishonest here. The use of the counterfactual seems to suggest that income distribution has no relationship with growth in total income, and total income would have been exactly the same regardless of income distribution. I see no reason to assume that to be the case.

Not to mention all the land being gobbled up to build these data centers.