Live data from Hacker News

The beginning of scarcity in AI

tomtunguz.com

61–70 of 239 posts

Re: The beginning of scarcity in AI

#61
post #23

To bang on the same damn drum: Open Weight models are 6 months to a year behind SOTA. If you were building a company a year ago based on what AI could do then, you can build a company today with models that run locally on a user's computer. Yes that may mean requiring your customers to buy Macbooks or desktops with Nvidia GPUs, but if your product actually improves productivity by any reasonable amount, that purchase…

I've seen this claimed, but I'm not sure it's been true for my use cases? I should try a more involved analysis but so far open models seem much less even in their skills. I think this makes sense if a lot of them are built based on distillations of larger models. It seems likely that with task specific fine tuning this is true?

> I've seen this claimed, but I'm not sure it's been true for my use cases?

I'd be surprised if it isn't true for your use cases. If you give GLM-5.1 and Optus 4.6 the same coding task, they will both produce code that passes all the tests. In both cases the code will be crap, as no model I've seen produces good code. GLM-5.1 is actually slightly better at following instructions exactly than Optus 4.6 (but maybe not 4.7 - as that's an area they addressed).

I've asked GLM-5.1 and Opus 4.6 to find a bug caused by a subtle race condition (the race condition leads to a number being 15172580 instead of 15172579 after about 3 months of CPU time). Both found it, in a similar amount of time. Several senior engineers had stared at the code for literally days and didn't find it.

There is no doubt the models do vary in performance at various tasks, but we are talking the difference between Ferrari vs Mercedes in F1. While the differences are undeniable, this isn't the F1. Things take a year to change there. The performance of the models from Anthropic and OpenAI literally change day by day, often not due to the model itself but because of the horsepower those companies choose to give them on the day, or them tweaking their own system prompts. You can find no end of posts here from people screaming in frustration the thing that worked yesterday doesn't work today, or suddenly they find themselves running out of tokens, or their favoured tool is blocked. It's not at all obvious the differences between the open-source models and the proprietary ones are worse than those day to day ones the proprietary companies inflict on us.

Re: The beginning of scarcity in AI

#62
post #49
post #42

Earlier quoted context omitted.

I'd be fine with a world without AI, honestly. Nobody really wins this race except the very wealthy. And I don't think it's really going to play out the way the wealthy think it will. It's more like a dog catching a car than it is a race.

> It's more like a dog catching a car than it is a race. What does this mean? I didn't understand the analogy.

"The dog that caught the car" refers to how dogs sometimes chase cars. Suppose the car stops and the dog catches up - what is it going to do? It has no plan, it has no purpose, it isn't going to bite the car, it isn't going to get anything out of catching the car. The car may even run it over. I intended it basically as "play stupid games, win stupid prizes", or "be careful what you wish for".

Re: The beginning of scarcity in AI

#65

The US is bound by energy and China is bound by compute power. The one who solves its limitation first will end this “Scarcity Era”.

US energy is constrained by the utility monopolies/oligopolies which have to extract more rents, specifically by increasing costs. Their profit is a percentage of cost, these perverse incentives + oligopolies will make it increasingly expensive to make anything (including AI) in US.

Re: The beginning of scarcity in AI

#66
post #62
post #49

Earlier quoted context omitted.

> It's more like a dog catching a car than it is a race. What does this mean? I didn't understand the analogy.

"The dog that caught the car" refers to how dogs sometimes chase cars. Suppose the car stops and the dog catches up - what is it going to do? It has no plan, it has no purpose, it isn't going to bite the car, it isn't going to get anything out of catching the car. The car may even run it over. I intended it basically as "play stupid games, win stupid prizes", or "be careful what you wish for".

My observation is that the dog sniffs all the tires, picks one tire, lifts one leg and does the deed. I don't know if its a way of marking territory or domination. We need a dogatologist to explain what it means.

Re: The beginning of scarcity in AI

#67
post #48

Earlier quoted context omitted.

China is installing something like 500 GW of wind and solar per year now. Even if they're only able to build and otherwise access chips that have half the SoTA performance per watt, they will win.

Performance per dollar may be more important than performance per watt here, though

A dollar is an entirely fictional unit and trillions of it can be manufactured at no cost, while watts are constrained by the laws of physics, photons/electrons, supply chain of electricity and all that fun stuff in the real world.

Re: The beginning of scarcity in AI

#68
post #45

There's other side to it too. Whoever running and selling their own models with inference is invested into the last dime available in the market. Those valuations are already ridiculously high be it Anthropic or OpenAI to the tune of couple of trillion dollars easily if combind. All that investment is seeking return. Correct me if I'm wrong. Developers and software companies are the only serious users because they (m…

OpenAI has an absurdly high valuation given their cash burn vs RRR.

Anthropic's is far more reasonable.

It makes no sense to lump these two companies together when talking about valuation. They have completely different financial dynamics

Re: The beginning of scarcity in AI

#69

Earlier quoted context omitted.

I've seen this claimed, but I'm not sure it's been true for my use cases? I should try a more involved analysis but so far open models seem much less even in their skills. I think this makes sense if a lot of them are built based on distillations of larger models. It seems likely that with task specific fine tuning this is true?

> I've seen this claimed, but I'm not sure it's been true for my use cases? I'd be surprised if it isn't true for your use cases. If you give GLM-5.1 and Optus 4.6 the same coding task, they will both produce code that passes all the tests. In both cases the code will be crap, as no model I've seen produces good code. GLM-5.1 is actually slightly better at following instructions exactly than Optus 4.6 (but maybe not…

> In both cases the code will be crap, as no model I've seen produces good code.

I'm wondering if you have actually used claude code because results are not so catastrophic as you describe them.

Re: The beginning of scarcity in AI

#70
post #45

There's other side to it too. Whoever running and selling their own models with inference is invested into the last dime available in the market. Those valuations are already ridiculously high be it Anthropic or OpenAI to the tune of couple of trillion dollars easily if combind. All that investment is seeking return. Correct me if I'm wrong. Developers and software companies are the only serious users because they (m…

OpenAI has an absurdly high valuation given their cash burn vs RRR. Anthropic's is far more reasonable. It makes no sense to lump these two companies together when talking about valuation. They have completely different financial dynamics

No matter how low and reasonably Anthropic is valued, don't think $200 Max plans are going to recoup the investment + some return on top because size of the software industry is not that huge and profit margins for AI inference aren't very high either.
Post reply on HN