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Discussion (38 Comments)Read Original on HackerNews
1) We don't know how long that trend will continue, but you do know where to look for when it may end (if smaller sized models continue to compress the knowledge effectively of larger models).
2) We don't know when the appetite for higher cost models might go down and by how much if smaller models get "good enough" and price becomes far more important.
It is entirely possible that 5 years from now, there's >100x LLM inference going on - but demand for AI chips (including memory) is only 2x or less.
It is also entirely possible that at some size - LLMs pick up some emergent capability that doesn't scale well to smaller sizes - and that there's an incredible boost to demand to get that capability.
It's just very hard to predict.
The harder thing to forecast for me is if we hit a wall on increasing efficiency, either on the model weights side or silicon side, with current approaches. If we have to switch to something like burning the model weights into silicon to continue to make gains, then the current math on general purpose accelerators might be upside down.
but now I think they probably have bitten more than they can chew.
Apple already proved with their unified memory - that as long you have the capacity you can run capable models locally - thereby goes demand for inference if everyone is running some model locally.
For training - Chinese models have proved that you don't need the latest & greatest in Nvidia hardware. Same as TPUs.
only time will tell.
AMD's software story is still a lot worse than Nvidia's. But patching up vllm to run one or two models you care about on AMD hardware is a much easier proposition than using them in most other fields of AI.
Trust me guys, it's over!
I’m not predicting Nvidia will MBA themselves to death in the near future but I think there’s a tendency to overstate how profitable companies will stay. The more money Nvidia makes, the more motivated their competitors will be to get a piece of that market and the more customers will be looking for alternatives like the push into TPUs which the article discussed.
The current administration is definitely corrupt enough that you could imagine an anti-competitive deal of some sort but I don’t think there’s a way for even that to change matters because key competitors are well-connected American companies willing to play that game, too.
You can both become a company that supplies 80% of the world with your type of product, and then still have your stock go down in value.
All it takes is over evaluation by the stock market. Then a course correction from unsustained growth on growth (second order). So even if you continually replace YoY 80% of the world's hardware on a rotating business, but you don't increase market share or increase demand (aka growth)... Your business looks stagnant to the stock market, and there isn't really anything you can do about it. The best you can do is track inflation +/- 2%.
And that's why a lot of older established companies were dividend stocks. You don't expect to growth anymore, but that's not where the value is anymore... The value is in the reliable sales that will happen after infinitum because your company controls a majority share of the business... And that's ok! Unfortunately, silicon valley has created a philosophy of 'you gotta expand into new fields or your on the decline' - aka neo-monopolization
https://deepmind.google/models/gemini-robotics/
Google is mostly the party behind the whole VLA principle.
Yes, for programmers and tech companies AI is kinda boring now, but AI integration in general is still kind of uncharted territory.
There are so many small companies and individuals just getting started with AI today and I believe a large the customer base (and revenue) is still untapped. Hell, I’m discovering new use cases regularly still and the average mismanaged 30 people whatever SaaS vendor probably didn’t even get started yet.
Perhaps this has something to do with the economic dislocations and world wars between the 1870s and today?
Building a business model on the belief that “this time is different” always finds storms on the horizon.
The current setup can’t sustain a downturn, even if yes 20 years from now point B is likely to be higher than present.
That’s the danger. Those that are going to get wiped out by the AI bubble burst aren’t wrong about AI being huge long term, they just put themselves in a position to not survive the storms that happen between points A and B.
1. Circular investment/spending.
2. Too much capital in the system, so returns cannot be hit regardless because the barrier is too high. (Evidence being every capital cycle in history)
Disappointed by the lack of Tom Cruise.
ps. Being influential in Silicon Valley just means you are influential, it does not mean substantial. Leopold is still influential and gets money thrown at him at $100s of million despite having no substance.
https://semianalysis.com/
They are the marketing wing of the AI ecosystem. Their recent article on how SpaceX would drive 500B in data-center revenue was ludicrous-mode. Lets revisit this in a few years.