A few years ago, I purchased a handful of 250GB SSDs from amazon for $17.00 each.
Last year, I purchased a few 8TB hard drives for $80.00 each.
Today, I am sad. ;-(
The beginning of scarcity in AI
181–190 of 239 posts
Re: The beginning of scarcity in AI
#182We 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.
Also: AI dependance could be explicit AI API usage by the product itself, but also anything else, like: AI assisted coding, AI used by humans in other surrounding workflows, etc.
Re: The beginning of scarcity in AI
#183Companies who could see it clearly and ignored the "AI is a bubble duh" crowd will ultimately get benefited by the GPUs they already acquired. The companies who acted cautiously will get burned.
Re: The beginning of scarcity in AI
#184We 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.
Also: AI dependance could be explicit AI API usage by the product itself, but also anything else, like: AI assisted coding, AI used by humans in other surrounding workflows, etc.
Yeah that's actually what I initially meant: just dependence on AI as a technology, not purely the usage costs. I didn't spell that out well enough.
Re: The beginning of scarcity in AI
#185The scarcity framing assumes compute is the bottleneck. For most production deployment's Ive seen, the actual bottleneck is evaluation and knowing what to trust.
You can throw cheaper models at a problem all day but, if you can't measure where the model fails on your data, You're just making mistakes faster at a lower cost.
Compute gets cheaper. Reliable evaluation doesn't.
Re: The beginning of scarcity in AI
#186Constraints can lead to innovation. Just two things that I think will get dramatically better now that companies have incentive to focus on them: * harness design * small models (both local and not) I think there is tremendous low hanging fruit in both areas still.
China already operates like this. Low cost specialized models are the name of the game. Cheaper to train, easy to deploy. The US has a problem of too much money leading to wasteful spending. If we go back to the 80s/90s, remember OS/2 vs Windows. OS/2 had more resources, more money behind it, more developers, and they built a bigger system that took more resources to run. Mac vs Lisa. Mac team had constraints, Lisa t…
On the Mac vs Lisa team, I generally agree but wasn't there a strong tension on budget vs revenue on Mac vs Apple II? And that Apple II had even more constrained budget per machine sold which led to the conflict between Mac and Apple II teams. (Apple II team: "We bring in all the revenue+profit, we offer color monitors, we serve businesses and schools at scale. Meanwhile, Steve's Mac pirate ship is a money pit that also mocks us as the boring Navy establishment when we are all one company!")
By the logic of constraints (on a unit basis), Apple II should have continued to dominate Mac sales through the early 90s but the opposite happened.
Re: The beginning of scarcity in AI
#187Earlier quoted context omitted.
If only there were some form of cheap, widely manufactured power generation technology that didn't use turbines... Are they really going to wait until 2030 to get more turbines rather than invest in solar?
I am clueless in this field, but solar seems to be unreliable and yield fraction of power required. Do you have a suggestion on something to read and learn more?
Google china solar deployments to read about the logistics end of it
Re: The beginning of scarcity in AI
#188Earlier quoted context omitted.
> 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.
Having used Claude Code extensively, catastrophic is a perfect word to describe it.
Re: The beginning of scarcity in AI
#189There is a lot of demand still coming for sure but I think I'm more optimistic. Ready to eat my hat on this but
- higher prices will result in huge demand destruction too. Currently we're burning a lot of tokens just because they're cheap, but a lot of heavy users are going to spend the time moving flows over to Haiku or onprem micro models the moment pricing becomes a topic.
- data centers do not take that long to build, probably there are bottlenecks in weird places like transformers that will cause some hicups, but nvidia's new stuff is waay more efficient and the overall pipeline of stuff coming online is massive.
- probably we will see some more optimization at the harness level still for better caching, better mix of smaller models for some use, etc etc.
These companies have so much money and they at least anthropic and openai are playing winner takes it all stakes, with competition from the smaller players too. I think they're going to be feeding us for free to win favour for quite a while still.
Let's see though.
Re: The beginning of scarcity in AI
#190This is probably even the "fun" part of the whole picture. The purely dystopia starts when investment firms just silently grow bigger and bigger data centers like cancer. There will be no press releases, no papers, no chance anyone without billions will even know the details yet alone get access. One day we realise the worlds resources (maybe not as in the paperclip maximiser, but as in memory, energy, GPUs, water, locations) are consumed by trading models and the data centres are already guarded by robot armies. While we were distracted frighting with anthropic and openAI the real war was already over. Mythos is one sign in this direction but i also met a few people who were claiming to fund fairly large research and training operations just by internal models working on financial markets. I have no way to verify those claims but this happened 3 times now and the papers/research they were working on looked pretty solid and did not seem like they were running kimi openclaw on polymarket but actual models on some significant funds. Would be really interested if anyone here has some details on this reality. I would also not be surprised if this is a thing that people in SF just claim to sound dangerous and powerful.