271–280 of 309 posts
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I would love it if AI fizzled out and nvidia had to go back to making gaming cards. Just trying to have a simple life here and play video games, and ridiculous hype after hype keeps making it expensive.
I hate to say it but there will likely come much more annying things that'll disturb your gaming experience.
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How about Apple? How is Apple training its next foundation models?
Apple is sitting this whole thing out. Bizarre.
1. Sit out and buy the tech you need from competitors.
2. Spend to the tune of ~$100B+ in infra and talent, with no guarantee that the effort will be successful.
Meta picked option 2, but Apple has always had great success with 1 (search partnership with Google, hardware partnerships with Samsung etc.) so they are applying the same philosophy to AI as well. Their core competency is building consumer devices, and they are happy to outsource everything else.
Not only has OpenAI's market share gone down significantly in the last 6mo, Nvidia has been using its newfound liquid funds to train its own family of models[1]. An alliance with OpenAI just makes less sense today than it did 6mo ago. [1] https://blogs.nvidia.com/blog/open-models-data-tools-acceler...
I think there are two things that happened 1. OpenAI bet largely on consumer. Consumers have mostly rejected AI. And in a lot of cases even hate it (can't go on TikTok or Reddit without people calling something slop, or hating on AI generated content). Anthropic on the other hand went all in on B2B and coding. That seems to be the much better market to be in. 2. Sam Altman is profoundly unlikable.
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It is starting to become clear to more and more people that Sam is a dyed in the wool True Believer in AGI. While it's obvious in hindsight that OpenAI would never have gotten anywhere if he wasn't, seeing it so starkly is really rubbing a lot of people the wrong way.
Advertising Generated Income?
https://www.davidrevoy.com/article1090/the-amphora-of-great-...
https://www.davidrevoy.com/article1092/the-amphora-of-great-...
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He is a pretty interesting case. According to the book "Empire of AI" about OpenAI, he lies constantly, even about things that are too trivial to matter. So it may be part of some compulsive behavior. And when two people want different things from him, he "resolves" the conflict by agreeing with each of them separately, and then each assumes they got what they wanted, until they talk to the other person and find out…
Saw Empire of AI in a bookshop recently but held off buying as wasn’t sure if it was going to be surface level. You’d recommend?
The second one is a bunch of SJW hand-wringing about things that are only tangentially related, like indigenous Bolivians being oppressed by Spanish Conquistadors centuries ago. That part I don't care for as much.
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Oh could I get a link to that one ?
https://www.youtube.com/watch?v=rDNyFN_eMec
I felt anxious about all the insane valuations and spending around AI lately, and I knew it couldn't last (I mean there's only so much money, land, energy, water, business value, etc). But I didn't really know when it was going to collapse, or why. But recently I've been diving into using local models, and now it's way more clear. There seems to be a specific path for the implosion of AI: - Nvidia is the most valuabl…
I've thought about this too. I do agree that open source models look good and enticing, especially from a privacy standpoint. But these solutions are always going to remain niche solutions for power users. I'm not one of them. I can't be hassled/bothered to setup that whole thing (local or cloud) to gain some privacy and end up with an inferior model and tool. Let's not forget about the cost as well! Right now I'm pa…
- 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.
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I understand what GP is saying in the sense that, yes, on an objective scale, what Sam is saying is absolutely and completely nuts... but on a relative scale he's just hyping his startup. Relative to the scale he's at, it’s no worse than the average support tool startup founder claiming they will defeat Salesforce, for example.
Exactly. Thanks for getting it, it is refreshing to encounter people who get it. Good luck with everything!
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I've thought about this too. I do agree that open source models look good and enticing, especially from a privacy standpoint. But these solutions are always going to remain niche solutions for power users. I'm not one of them. I can't be hassled/bothered to setup that whole thing (local or cloud) to gain some privacy and end up with an inferior model and tool. Let's not forget about the cost as well! Right now I'm pa…
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 a…
What about all the input data into LLMs and the conversations we're having? That must be able to produce a better next gen model, no?
> it's ridiculously easy for anyone to fine-tune training, we don't need frontier companies for that.
Not for me. It'll take me days, and then I'm pretty sure it won't be better than Gemini 3 pro for my coding needs, especially in reasoning.
> 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 wouldn't e.g. Gemini just add a triage step? And are you sure it's that much easier to get a better model for math than the big ones?
I think you underestimate the friction this causes regular users by handpicking and/or training specific models, whilst the big vendors are good enough for their needs.