[dogma1138]

NVIDIA has a P/E ratio of 56 that’s double that of the S&P 500 but half that of AMD and the same one as Meta.

And whether it’s overvalued or not isn’t relevant that selling a stock because the product the company produces is now even more effective is mind bogglingly stupid.

[cbg0]

> selling a stock because the product the company produces is now even more effective is mind bogglingly stupid.

No it isn't. Investors are most likely expecting there will be less demand for Nvidia's product long-term due to these alleged increased training efficiencies.

[PeterStuer]

There is afaict no inherent limit to expand on the bottom end of the market. My gut feeling is lower training costs will expand the market for hardware horizontaly far faster than any vertical scaling by a select 2 digit of mega corps could.

[locallost]

Well the price has a built in presumption that the earnings will keep growing. That's why PER is not that relevant for them, it's been over 50-70 since forever, but the stock went up 10x, which means earnings went up as well. DeepSeek might be good for their business overall, but it might mean earnings will not continue growing exponentially like they have been for the past two years. So it's time to bail.

You shouldn't underestimate the fact that a large amount of these trades are on margin. Sometimes you can't wait it out because you'll get margin called and if you can't pony up additional cash you're basically getting caught with your pants down.

Disclaimer: I am not a trader, so could be way off

[dogma1138]

Why? The compute requirements would still continue to grow the more efficient and more capable the models become.

If it’s cheaper to inference you end up using the model for more task, it it’s cheaper to train you train more than models. And if you now need only 1000’s of GPUs instead of 10’s or 100’s of thousands you’ve just unlocked a massive client base of those who can afford to invest high six to low seven figures instead of 100’s of millions or billions into to try their luck.

[locallost]

It could be, but maybe the feeling is the investments now are already massive and everyone has jumped on the AI train. If you are suddenly 10x efficient, and everyone gets 10x more efficient, there's less room to grow than before. What you're saying makes a lot of sense, but it's one thing to write it on a message board and another to use it to back up your decision that affects billions of dollars you have in your fund.

The proof is in the pudding, you're welcome to prove "everyone" wrong.

[margorczynski]

Doesn't this situation also imply to some degree that China is focused on beating the US on AI and probably they will develop a competitor to NVIDIA that will cause margins to drop significantly?

They have a lot of very smart people and the will to do it, seems like a matter of time before they succeed.

[redwood]

It might take 5 yrs to find the use cases. That's what happened with the dark fiber from the .com boom. Go look at Cisco 2001 for parallels

[InkCanon]

It's arguable how good a strategy it is to check against other P/E. During the tech bubble people would say X is cheap, because Y is trading at 100P/E instead of 200

[dogma1138]

Again that may reasonable but it’s a completely different argument. Whether there is a bubble or not and whether NVDA is overvalued is irrelevant to the subject at hand.

If it’s cheaper to train models it means far more customers that will try their luck.

If you reduce training requirement from a 100,000 GPUs to a 1000 you’ve now opened the market to 1000’s and 1000’s of potential players instead of like the 10 that can afford dumping so much money into a compute cluster.

[fragmede]

the holy grail is to not have a separate train and inference steps. when the model can be updated while it is inferencing is where we're headed. deepseek only accelerates the need for more compute, not less

[Jlagreen]

THIS is the only correct statement in all of this.

The goal for AGI and ASI MUST BE to train, inference, train, inference and so on and that all on the fly in fractions of a second from every token produced.

Now good luck calculating the compute and hard work in algorithms to get there.

Not possible? Then AGI won't ever work because how can AGI beat a human if it can't learn on the fly? Not to mention ASI lol.

[nejsjsjsbsb]

P/E alone is useless anyway. A growth company is likely not making a profit as they are reinvesting. But not a profit doesn't implies good either of course.

[nobunaga]

AMD does not have a PE double of NVIDIA. PE is high because of amortisation of an acquisition. People on hackernews talk a lot but have no idea what they talk about. You might know how to write javascript or some other language but clearly you have not read the earnings reports or financials of AMD and probably alot of the other companies you talk about. So please stop spreading nonsense.

Just for those that clearly have no idea https://old.reddit.com/r/AMD_Stock/comments/1d2okn1/when_wil...

[malinens]

You are correct but please read: https://news.ycombinator.com/newsguidelines.html#comments

[redwood]

This is hackernews, not some boilerroom pump n dump forum. Please use more professional language and take your confidence down a notch. Try to learn and add to the discussion.

You seem to believe that the more inference or training value per piece of tech the more demand there will be for that piece of tech full stop when there are multiple forces at play. As a simple example, you can think of this as a supply spike; while you can make the bet that the demand will follow there could be a lag on that demand spike due to the time it takes to find use cases with product/market fit. That could collapse prices over the near term which could in turn decrease revenue. As a reminder the stock value isn't a bet on whether "the gold trader" will sell more gold or not, it's a bet on whether the net future returns of the gold trader will occur in line with expectations, expectations that are sky high and have zero competition built in.