Comment by Turfie

I think you should reconsider the idea that frontier models will be superior, for a couple reasons:

- LLMs have fixed limitations. The first one is training, the dataset you use. There's only so much information in the world and we've largely downloaded it all, so it can't get better there. Next you can do training on specific things to make it better at specific things, but that is by definition niche; and you can actually do that for free today with Google's Tensors in free Cloud products. Later people will pay for this, but the point is, it's ridiculously easy for anyone to fine-tune training, we don't need frontier companies for that. And finally, LLM improvements come by small tweaks to models that already come to open weights within a matter of months, often surpassing the frontier! All you have to do is sit on your ass for a couple months and you have a better open model. Why would anyone do this? Because once all models are extremely good (about 1 year from now) you won't need them to be better, they'll already do everything you need in 1-shot, so you can afford to sit and wait for open models. Then the only reason left to use frontier cloud is that they host a model; but other people do cloud-hosted models! Because it's a commodity! (And by the way, people like me are already pissed off at Anthropic because we're not allowed to use OAuth with 3rd party tools, which is complete bullshit. I won't use them on general principle now, they're a lock-in moat, and I don't need them) There will also be better, faster, more optimized open models, which everyone is going to use. For doing math you'll use one model, for intelligence you'll use a different model, for coding a different model, for health a different model, etc, and the reason is simple: it's faster, lower memory, and more accurate. Why do things 2x slower if you don't have to? Frontier model providers just don't provide this kind of flexibility, but the community does. Smart users will do more with less, and that means open.

On the hardware:

- Def it will continue to be investment-worthy, but be cautious. The growth simply isn't going to continue at pace, and the simple reason is we've already got enough hardware. They want more hardware so they can continue trying to "scale LLMs" the way they have with brute force. But soon the LLMs will plateau and the brute force method isn't going to net the kind of improvements that justify the cost. Demand for hardware is going to drop like a stone in 1-2 years; if they don't cease building/buying then, they risk devaluing it (supply/demand), but either way Nvidia won't be selling as much product so there goes their valuation. And RAM is eventually going to get cheaper, so even if demand goes up, spending is less. The other reason demand won't continue at pace is investors are already scared, so the taps are being tightened (I'm sure the "Megadeal" being put on-hold is the secret investment groups tightening their belts or trying to secure more favorable terms). I honestly can't say what the economic picture is going to look like, but I guarantee you Nvidia will fall from its storied heights back to normal earth, and other providers will fill the gap. I don't know who for certain, but AMD just makes sense, because they're already supported by most AI software the way Nvidia is (try to run open-source inference today, it's one of those two). Frontier and cloud providers have Tensors and other exotic hardware, which is great for them, but everyone else is gonna buy commodity chips. Watch for architectures with lower price and higher parts availability.