If that's the plan (there is no plan) then it expires at some point, because it's a spiral and such spirals always bottom out.

Of course they will - if that happens all these AI token providers won't have a use for all that hardware they bought. You'll be buying used H100s and H200s off eBay for pennies on the dollar.

Oh it gets worse than that, the money which caused all of this by OpenAI was taken from Japanese banks at cheap interest rates (by softbank for the stargate project), and the Japanese Banks are able to do it because of Japanese people/Japanese companies and also the collateral are stocks which are inflated by the value of people who invest their hard earned money into the markets

So in a way they are using real hard earned money to fund all of this, they are using your money to basically attack you behind your backs.

I once wrote an really long comment about the shaky finances of stargate, I feel like suggesting it here: https://news.ycombinator.com/item?id=47297428

Maybe you can argue that yakuza is making hard earned money but imo, they are doing illegal activities within the law and are doing something more closer to extortion.

Ironically, in a sense, what AI did in a sense is also an extortion.

One is just legal (barely, I am not even sure how or why), the other isn't. That was my intention to highlight when I said hard earned money.

That's ridiculous, "infinite money" isn't a thing. They will spend as much as they can not because they want to keep local solutions out, but because it enables them to provide cheaper services and capture more of the market. We all eventually benefit from that.

My reading of GP is that he was being sarcastic - "infinite amounts of circular fake money" is probably a reference to these circular deals going on.

If A hands B investment of $100, then B hands A $100 for purchase of hardware, A's equity in B, on paper, is $100, plus A has revenue of $100 (from B), which gives A total assets of $200.

Obviously it has to be shuffled more thoroughly, but that's the basic idea that I thought GP was referring to.

Then we can make them even bigger.

But what if it becomes "good enough", that for most intents and purposes, small models can be "good enough"

There are some people here/on r/localllama who I have seen run some small models and sometimes even run multiple of them to solve/iterate quickly and have a larger model plug into it and fix anything remaining.

This would still mean that larger/SOTA models might have some demand but I don't think that the demand would be nearly enough that people think, I mean, we all still kind of feel like there are different models which are good for different tasks and a good recommendation is to benchmark different models for your own use cases as sometimes there are some small models who can be good within your particular domain worth having within your toolset.

They say prostitution is the oldest industry of all. We know how to achieve human-level intelligence quite well. The outstanding challenge is figuring out how to produce an energy efficient human-level intelligence.

For example, if the number of AIs you can run per petaflop started to scale with the cube root of researcher-years, then even if your researcher AIs are quite fast and you can double your density in a couple years, hitting 5x will take a decade and hitting 10x will approach half a century.

Of course OpenAI wants you to think they will rule the world but if we’ve reached the plateau of LLM capabilities regardless of the amount of compute we throw at them then local models will soon be good enough.

It's simple: then we'll make our intents and purposes bigger.

Not many people would like today models comparable to what was SOTA 2 years ago.

To run models locally and have results as good as the models running in data centers we need both efficiency and to hit a wall in AI improvement.

None of those two conditions seem to become true for the near future.

The power efficiency alone is a strong enough pressure to use centralized model providers.

My 3090 running 24b or 32b models is fun, but I know I'm paying way more per token in electricity, on top of lower quality tokens.

It's fun to run them locally, but for anything actually useful it's cheaper to just pay API prices currently.

Though I'm not an expert, maybe my understanding of the memory allocation is wrong.

The problem with AI is that it's not obvious what the upper limit of capability demand might be. And until or if we get there, there will always be demand for the more capable models that run on centralized computing resources. Even if at some point I'm able to run a model on my local desktop that's equivalent to current Claude Opus, if what Anthropic is offering as a service is significantly better in a way that matters to my use case, I will still want to use the SaaS one.

Only if it's competitively priced. You wouldn't want to use the SaaS if the breakeven in investment on local instances is a matter of months.

Right now people are shelling out for Claude Code and similar because for $200/m they can consume $10k/m of tokens. If you were actually paying $10k/m, than it makes sense to splurge $20k-$30k for a local instance.