Definitely feeling this - the subsidized subscription plans are already starting to buckle.
The beginning of scarcity in AI
161–170 of 239 posts
Re: The beginning of scarcity in AI
#162We just had a realization during a demo call the other day: The companies that are entirely AI-dependent may need to raise prices dramatically as AI prices go up. Not being dependent on LLMs for your fundamental product’s value will be a major advantage, at least in pricing.
No shit. People are just figuring this out now? This is the “Building my entire livelihood on Facebook, oh no what?” all over again. Oh no sorry I forgot, your laptops LLM can draw a potato, let me invest in you.
Seriously.
> We just had a realization during a demo call the other day
These tools have been around for years now. As they've improved, dependency on them has grown. How is any organization only just realizing this?
That's like only noticing the rising water level once it starts flooding the second floor of the house.
Re: The beginning of scarcity in AI
#163Earlier quoted context omitted.
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.
A dollar is still a useful unit as "the fraction of the economy that can be controlled by currency". It's true that printing a huge pile of it and throwing it at GPUs wouldn't instantly convert into more GPUs, but it would meaningfully represent that other things are being squeezed out to allocate more resources to GPU production even so. That such reallocation is inefficient, arguably immoral, and highly questionable in the long term versus other options wouldn't stop that from being ture.
Re: The beginning of scarcity in AI
#164Earlier quoted context omitted.
> The companies that are entirely AI-dependent may need to raise prices dramatically as AI prices go up. It's not that clear. Sure, hardware prices are going up due to the extremely tight supply, but AI models are also improving quickly to the point where a cheap mid-level model today does what the frontier model did a year ago. For the very largest models, I think the latter effect dominates quite easily.
We are processing same data for the last 2 years. Inference prices droped like 90 percent in that time (a combination of cheaper models, implicit caching, service levels, different providers and other optimizations). Quality went up. Quantity of results went up. Speed went up. Service level that we provide to our clients went up massively and justfied better deals. Headcount went down. What's not to like?
The decline of independent thoughts for one. As people become reliant on LLMs to do their thinking for them and solve all problems that they stumble upon, they become a shell of their previous self.
Sadly, this is already happening.
Re: The beginning of scarcity in AI
#165Earlier quoted context omitted.
We are processing same data for the last 2 years. Inference prices droped like 90 percent in that time (a combination of cheaper models, implicit caching, service levels, different providers and other optimizations). Quality went up. Quantity of results went up. Speed went up. Service level that we provide to our clients went up massively and justfied better deals. Headcount went down. What's not to like?
The decline of independent thoughts for one. As people become reliant on LLMs to do their thinking for them and solve all problems that they stumble upon, they become a shell of their previous self. Sadly, this is already happening.
We'll need to do faux mental work like how we do faux labor work.
Re: The beginning of scarcity in AI
#166Earlier quoted context omitted.
Seems like everybody an their mothers are using max plans these days. I wouldn't be surprised if LTV of each customer was big enough to justify spending.
Assuming there are 10 million developers and everyone is at $200 max plan, that would be $2 billion/month or $24 billion/year maximum. Note - this is just the revenue not the profit. No salaries, no compute paid for. Just plain revenue. Profit would be way less. But even that - if we take it to $24 billion/year and we take a 10x multiple, the company is barely valued at $240 billon dollar, lets be generous and make i…
Pro and Max plans are a tiny fraction of their revenue. Many businesses are spending thousands of dollars per head per month.
Re: The beginning of scarcity in AI
#167This isn't the first time they've dealt with scarcity, there's been supply chain scarcity four times since 2000. Post-dotcom boom, CDMA scarcity, HDD/flash scarcity, Pandemic scarcity.
The scarcity isn't long-term. Like all manufactured products, they'll ramp up production and flood the market with hardware, people will buy too much, market will drop. Boom and bust.
We're also still in the bubble. Eventually markets will no longer bear the lack of productivity/profit (as AI isn't really that useful) and there will be divestment and more hardware on the market as companies implode. Nobody is making 10x more from AI, they are just investing in it hoping for those profits which so far I don't think anyone has seen, other than in the companies selling the AI to other companies.
But more importantly, the models and inference keeps getting more efficient, so less hardware will do more in the future. We already have multiple models good enough for on-device small-scale work. In 5 years consumer chips and model inference will be so good you won't need a server for SOTA. When that happens, most of the billions invested in SOTA companies will disappear overnight, which'll leave a sizeable hole in the market.
Re: The beginning of scarcity in AI
#168Earlier quoted context omitted.
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.
Or simply by the fact that increasing production takes time? Any power plant takes years to build? Years, is like a lifetime for AI at this point...
Not solar. China and to a lesser extent India are pumping out huge solar farms in months.
Re: The beginning of scarcity in AI
#169To 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…
The mainframe/PC analogy is spot on. And the hardware floor keeps dropping - you can grab a mini PC with 32-64GB RAM for a few hundred bucks and run surprisingly capable quantized models locally. Something like https://terminalbytes.com/best-mini-pcs-for-home-lab-2025/ shows the kind of hardware that's now available at consumer prices. The "scarcity" framing only makes sense if you assume everyone needs frontier-tier models for everything.
Re: The beginning of scarcity in AI
#170one graph, One graph and the author is pinning an entire theory on it? Infra is always limited, even at hyper scalers. This leads to a bunch of tools dfofr caching, profiling and generally getting performance up, not to mention binpacking and all sorts of other "obvious" things.
One graph, about 100 words, AI in the title: Hacker News front page.
Not bad for a coffee break of effort.