Comment by pmoriarty
5 hours ago
> In general. ai capacity is being used right now today.
Not according to this article[1], which says:
"I believe we are now in an inevitable overbuild situation, one with no neat, tidy Dot-Com Bubble-style exit story. Demand for NVIDIA GPUs — and those from Broadcom, AMD and other semiconductor companies — is driven by speculative capital believing that the AI industry will become magnitudes larger than it is today, largely driven by the fact that everybody believes there’s far more demand for compute capacity than actually exists. ... NVIDIA has created a remarkable illusion perpetuated by the media — that GPU sales are a direct measurement of the actual demand for AI compute, rather than a measurement of how a few companies are willing to invest in an idea two years in advance, using circular financing as a means of creating the sense that you must buy these GPUs now, or you’ll miss out on the future. ... At the very least, hyperscalers are going to be burdened with brutal depreciation charges or onerous write-offs for years to come, whether their capacity turns into revenue or not. ... I don’t see how 90%+ of NVIDIA’s sales ever end up generating a single dollar of revenue, and considering the amount of project financing-backed data center debt deals, there’s very little that exists to protect investors if AI compute demand never arrives."
That's Ed Zitron, and his calls on AI matters routinely get outperformed by that of a broken clock.
The man saw the market demand for saying "AI bubble is going to pop and AI tech is going to wither away and die", and went to meet it on the supply side - truth be damned.
So far, AI companies still keep getting bottlenecked on compute, AI utilization increases - driven by, among other things, increased price-performance of AI making it viable in more and more roles. And the demand for both AI inference and AI hardware in general shows no signs of stopping.
Ed Zitron's AI prediction track record: https://news.ycombinator.com/item?id=49526069
In 5 years from now every device. Even your oven or washing machine has somekind of local LLM running for sensor reading and smart decision models. I can already think of quite some useful use cases for those devices. So I do believe that AI becomes magnitudes bigger than today. It's a logical step in our digital journey.
Why would you need the LLM to be running on the device itself? Wouldn't it be more efficient for the intelligence to be more centralized? Maybe in one central computer per home, or (probably even more desirable for corporations) for the devices to phone home to a centralized server at the service provider in the cloud somewhere.
Oh of course thats certainly also gonna happen. But i believe there its still useful to have local dedicated AI with low latency. For example regulators that need constant adjustments. Also in cameras local AI could be extremely useful. Same for audio processing and other sensor processing. Cloud solutions wont work in those cases.
Why do you need an LLM to read a sensor? This is truly AI pilled delusion.