Live data from Hacker News

Nvidia Stock Crash Prediction

entropicthoughts.com

291–300 of 385 posts

Re: Nvidia Stock Crash Prediction

#291

I'm surprised more people are not talking about the fact that the two best models in the world, Gemini 3 and Claude 4.5 Opus, were both trained on Google TPU clusters. Presumably, inference can be done on TPUs, Nvidia chips, in Anthropic's case, new stuff like Trainium.

Google is a direct competitor to many LARGE buyers of GPUs and therefore a non starter from a business perspective. In addition, many companies cannot single source due to risk considerations. Hardware is different because buying GPUs is a capital investment. You own the asset and revisit the supplier only at the next refresh cycle, not continuously as with rented compute.

Re: Nvidia Stock Crash Prediction

#292

Earlier quoted context omitted.

If the AI hasn't specifically learned about SeqTracks as part of its training it's not going to give you useful answers. AI is not a crystal ball.

The problem is it's inability to say "I don't know". As soon as you reach the limits of the models knowledge it will readily start fabricating answers.

That's nowhere near as true as it was as recently as a year ago.

Re: Nvidia Stock Crash Prediction

#293
post #2

It goes to nearly zero if China invades Taiwan, and that seems like it has at least a 10% chance of happening in the next year or two.

China invading Taiwan makes zero sense, they just flex those muscles for domestic consumption. They will probably take over Taiwan, but they'll do it how modern major powers do anything: propaganda, influence campaigns, and soft power.

Russia invading Ukraine also made zero sense, given their actual capabilities and the likely (now realized) consequences. The leader doesn't always have the best information, it turns out.

Either that, or the leader does have access to the best information, and they just DGAF. That condition seems to be going around too.

Re: Nvidia Stock Crash Prediction

#294

Earlier quoted context omitted.

You highlight the exact dilemma. Company A has taxis that are 5 percent less efficient and for the reasons you stated doesn't want to upgrade. Company B just bought new taxis, and they are undercutting company A by 5 percent while paying their drivers the same. Company A is no longer competitive.

The debt company B took on to buy those new taxis means they're no longer competitive either if they undercut by 5%. The scenario doesn't add up.

But Company A also took on debt for theirs, so that's a wash. You assume only one of them has debt to service?

Re: Nvidia Stock Crash Prediction

#295

Earlier quoted context omitted.

The debt company B took on to buy those new taxis means they're no longer competitive either if they undercut by 5%. The scenario doesn't add up.

But Company A also took on debt for theirs, so that's a wash. You assume only one of them has debt to service?

Both companies bought a set of taxis in the past. Presumably at the same time if we want this comparison to be easy to understand.

If company A still has debt from that, company B has that much debt plus more debt from buying a new set of taxis.

Refreshing your equipment more often means that you're spending more per year on equipment. If you do it too often, then even if the new equipment is better you lose money overall.

If company B wants to undercut company A, their advantage from better equipment has to overcome the cost of switching.

Re: Nvidia Stock Crash Prediction

#296
post #262

Earlier quoted context omitted.

inference requires a fraction of the power that training does. According to the Villalobos paper, the median date is 2028. At some point we won't be training bigger and bigger models every month. We will run out of additional material to train on, things will continue commodifying, and then the amount of training happening will significantly decrease unless new avenues open for new types of models. But our current LL…

> We will run out of additional material to train on This sounds a bit silly. More training will generally result in better modeling, even for a fixed amount of genuine original data. At current model sizes, it's essentially impossible to overfit to the training data so there's no reason why we should just "stop".

You'd be surprised how quickly improvement of autoregressive language models levels off with epoch count (though, admittedly, one epoch is a LOT). Diffusion language models otoh indeed keep profiting for much longer, fwiw.

Re: Nvidia Stock Crash Prediction

#297

Earlier quoted context omitted.

It also tanked to ~$90 when Trump announced tariffs on all goods for Taiwan except semiconductors. I don't know if that's non-rational, or if people can't be expected to read the second sentence of an announcement before panicking.

The market is full of people trying to anticipate how other people are going to react and exploit that by getting there first. There's a layer aimed at forecasting what that layer is going to do as well. It's guesswork all the way down.

A bunch of "Greater Fool" motivation too.

https://en.wikipedia.org/wiki/Greater_fool_theory

Re: Nvidia Stock Crash Prediction

#298
post #146

Earlier quoted context omitted.

I’ll be so happy to buy a EOL H100! But no, there’s none to be found, it is a 4 year, two generations old machine at this point and you can’t buy one used at a rate cheaper than new.

There’s plenty on eBay? But at the end of your comment you say “a rate cheaper than new” so maybe you mean you’d love to buy a discounted one. But they do seem to be available used.

> so maybe you mean you’d love to buy a discounted one

Yes. I'd expect 4 year old hardware used constantly in a datacenter to cost less than when it was new!

(And just in case you did not look carefully, most of the ebay listings are scams. The actual product pictured in those are A100 workstation GPUs.)

Re: Nvidia Stock Crash Prediction

#299

Earlier quoted context omitted.

I have still been unable to see how folks connect AI to Crypto. Crypto never connected with real use cases. There are some edge cases and people do use it but there is not a core use. AI is different and businesses are already using it a lot. Of course there is hype, it’s not doing all the things the talking heads said but it does not mean immense value is not being generated.

It's an analogy, it doesn't have to map 1:1 to AI. The point is that current situation around AI looks kind of similar to the situation and level of hype around Crypto when it was still growing: all the "ledger" startups, promises of decentralization, NFTs in video games and so on. We are somewhere around that point when it comes to AI.

No it’s an absolutely ridiculous comparison that people continue to make even though AI has well past the usefulness of crypto and at an alarming rate of speed. AI has unlocked so many projects my team would never have tackled before.
Post reply on HN