Every time they've made smarter models we've wanted the smarter ones, and local models runnable on typical hardware are still very far behind in speed and intelligence (as neat as they are)
That is seemingly not the case. The buildout is actually slow; almost nothing of these giant projects has been completed. Nobody will say how much of anything they have actually finished. And Nvidia have made huge, huge buy-and-hold deals for GPUs that do not have data centres to go into.
Everyone is GPU poor because stuff hasn't been finished but large numbers of GPUs are spoken for, but they are GPU poor on therefore much less demand than is being built for.
Look at how tiny SpaceX's deal is with Anthropic, for example. This meaningfully turned around Anthropic's prospects — allowing them to radically lift rate limits beyond what many users needed -- but it was for just 300 megawatts. Tiny compared to the 31 gigawatts allegedly under construction by the end of last year.
So the picture is partly illusory. GPU prices and RAM prices have been pushed up by the AI firms booking them for data centres they haven't even started building yet, as well as the ones that they've only completed a tenth or an eighth of.
There will be significant oversupply. And if open weights models keep getting good and staying fuel-efficient, that picture gets worse.
I also doubt we can estimate the level of demand based on a single deal between Anthropic and SpaceX (despite which, note, Claude still stuggles at times.) Consider other signals, like Google, who we thought had an insurmountable infra advantage, also renting compute capacity from SpaceX and limiting Meta's usage (along with other clients apparently) to conserve capacity: https://www.cnbc.com/2026/06/28/google-limits-metas-use-of-i...
I am not sure Microsoft thinks there will be an oversupply either; last earnings they announced bumping up their CapEx spend, along with all the other hyperscalers.
Here's a way to estimate how much room there is for demand to grow. Various sources (linked in this comment, along with more analysis: https://news.ycombinator.com/item?id=49089296) indicate that even though a large number of people (50 - 60%) are now using AI at work, they use it for only 6% of their work hours.
That means, even if AI can only address 30% of all work, there is still 5x potential demand growth left! Note, the sources above indicate that AI is even being used in non-knowledge work industries, so the scope is already larger than we thought. This is in addition to the remaining 40 - 50% of people are still not using AI at work. Plus we know that agentic workloads consume way more tokens, so that's yet another multiplier.
But will that demand keep growing? Well, some of those same sources above mention that most executives are planning on ramping up their AI spend in coming years.
Putting all this together explains the hyperscalers' quarterly bemoaning of how strapped for compute they are and why they are spending so much to add more capacity. Given this, an oversupply seems pretty unlikely.
It's a distinction without a difference if your issue is getting hold of a GPU.
But there's a significance to it if you are trying to use demand for a GPU as a proxy for demand for AI. That is where the industry is making serious mistakes.
> I am not sure Microsoft thinks there will be an oversupply either
https://finance.yahoo.com/news/microsoft-ceo-says-overbuild-...
The rest of your comment I am not going to address because it's kind of unfalsifiable. Hypotheses about what AI might be able to do in principle aren't all that useful when talking about even medium-to-long-term demand for what LLMs and GANs can provably do now.