61–70 of 88 posts
My napkin-math approach to get a bird's eye perspective on the situation: A $1T investment needs to produce on the order of $100B in yearly earnings to be a good investment. Global GDP is about $100T. So one way for things to work out for the AI companies would be if AI raises GDP by 1% and the AI companies capture 10% of the created value.
Earlier quoted context omitted.
That is more than one month rent for most of the world. Most people are simply not going to pay this.
Well then I'm sorry but unfortunately they are going to be left behind. People who are cut out to be software developers can afford the means of production.
This is a good analyst report - lots of data. Conclusion - firms are spending ahead of sustained revenues right now, and a lot of the money is going offshore to TSMC, basically. I’m not certain of the conclusion - I think a lot depends on amortization schedules - if data centers are fully booked right now, then we don’t need very long amortization schedules at the reported 60+% margin on inference to see this capex f…
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…
Earlier quoted context omitted.
And look how it has worked out for them.
Only because the U.S. and U.K. conspired against them. The French did everything they could to keep the fire burning, by hosting people from various countries to teach them about revolution. Organizing globally against the rich parasites was hard and expensive back then. Now the only hold back, is that the rich parasites own most of the internet. But WE BUILT IT, and can take back the internet when we finally realize…
My napkin-math approach to get a bird's eye perspective on the situation: A $1T investment needs to produce on the order of $100B in yearly earnings to be a good investment. Global GDP is about $100T. So one way for things to work out for the AI companies would be if AI raises GDP by 1% and the AI companies capture 10% of the created value.
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.
My napkin-math approach to get a bird's eye perspective on the situation: A $1T investment needs to produce on the order of $100B in yearly earnings to be a good investment. Global GDP is about $100T. So one way for things to work out for the AI companies would be if AI raises GDP by 1% and the AI companies capture 10% of the created value.
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.
My napkin-math approach to get a bird's eye perspective on the situation: A $1T investment needs to produce on the order of $100B in yearly earnings to be a good investment. Global GDP is about $100T. So one way for things to work out for the AI companies would be if AI raises GDP by 1% and the AI companies capture 10% of the created value.
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 busi…
Will AI increase the rate of GDP growth by 0.5% or so over 20 years?
My napkin-math approach to get a bird's eye perspective on the situation: A $1T investment needs to produce on the order of $100B in yearly earnings to be a good investment. Global GDP is about $100T. So one way for things to work out for the AI companies would be if AI raises GDP by 1% and the AI companies capture 10% of the created value.
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.
https://youtu.be/VQKMoT-6XSg?t=4605
Now it is at 20%.
This is a good analyst report - lots of data. Conclusion - firms are spending ahead of sustained revenues right now, and a lot of the money is going offshore to TSMC, basically. I’m not certain of the conclusion - I think a lot depends on amortization schedules - if data centers are fully booked right now, then we don’t need very long amortization schedules at the reported 60+% margin on inference to see this capex f…
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…
Here's the thing - we could list technical challenges / problems all day. And still, I use way more inference than a year ago. I'd use even more, a lot more, if it were faster (latency terms). I want to buy it, the providers want to sell it to me. So, your statement "hallucinations cripple them for .. professional uses" is just incorrect. The correct statement is "despite hallucinations, professional use is skyrocketing." Openclaw has like 150,000 GitHub stars in the last month. People are using inference at all levels of society.
I propose to you that if in fact we get some sort of AGI that is 10,000x more compute efficient than transformer architectures, then datacenter investment losses will no longer matter in a material way to almost anyone in the world. So, you might be right, but you've already got that 'trade' or 'return' banked -- cheap ubiquitous AGI as you propose might happen will provide broad benefits. In those terms you're sort of doubling up on your short by not getting some upside exposure to the long.
Re: MSFT, yep, it's a contrarian position. That said, I'm interested in informed short perspective on MSFT - do you think that the loss of windows licensing revenues would offset the benefit of being the world's "safe" local AI datacenter provider? And, are you sure that there is even a reduction in windows licensing? Satya's comments in a recent interview made it sound like they see agentic usage multiplying windows licenses -- basically when you spin up a web agent, it will lease a windows license to run the browser -- and parallel agents = multiple simultaneous leases - so they are seeing more and more revenue shift to azure in this world, away from direct license for desktops. To me, it feels like this will could be an incredible new era of platform lock-in for them - the azure stack is the only way to safely run gpt5 in a nationally protected datacenter - and oh, by the way, once you're signed up, one contract gets you a full MS software license.