Nvidia’s $589B DeepSeek rout

Or just do boring sound financial planning based on investment goals and retirement age!

Nothing, and I think that was the point GP was making. Since not much of Apple's valuation is tied to AI hype they won't suffer (at least not close to as much) from the bubble bursting.

The biggest challengers are likely the hyperscalers and companies like Meta. It sort of flew under the radar when Meta released an update on their GPU plans last year and said their cluster would be as powerful as X NVDA GPUs, and not that it would have X NVDA GPUs [1].

Also, I should add that Deepseek just showed the top GPUs are not necessary to deliver big value.

[1] https://engineering.fb.com/2024/03/12/data-center-engineerin...

This announcement is one step in our ambitious infrastructure roadmap. By the end of 2024, we’re aiming to continue to grow our infrastructure build-out that will include 350,000 NVIDIA H100 GPUs as part of a portfolio that will feature compute power equivalent to nearly 600,000 H100s.

>the trading post will somehow sell fewer shovels

There is a point where there are enough shovels circulating that the demand for new shovels falters, even with zero drawback in the rush. And if so much gold was being mined that it overwhelmed the market and reduced the commodity price, the value of better shovels is reduced.

DeepSeek and friends basically reduce the commodity value of AI (and to be fair, Facebook, Microsoft et al are trying to do the same thing with their open source models, trying to chop the legs out of the upstart AI cos). If AI is worth less, there are going to be fewer mega capitalized AI ventures buying a trillion dollars worth of rapidly-depreciating GPUs in hopes at eeking out some minor advantage.

I wouldn't short nvidia stock, but at the same time there is a point where the spend of GPUs just isn't rational anymore.

>And as more efficient models proliferate it means more edge computing which means more customers with lower negotiating power than Meta and Google

Edge compute has infinitely more competition than the data center.

The part of this that doesn’t jibe with me is the fact that they also released this incredibly detailed technical report on their architecture and training strategy. The paper is well-written and has a lot of specifics. Exactly the opposite of what you would do if you had truly made an advancement of world-altering magnitude. All this says to me is that the models themselves have very little intrinsic value / are highly fungible. The true value lies in the software interfaces to the models, and the ability to make it easy to plug your data into the models.

My guess is the consumer market will ultimately be won by 2-3 players that make the best app / interface and leverage some kind of network effect, and enterprise market will just be captured by the people who have the enterprise data, I.e. MSFT, AMZN, GOOG. Depending on just how impactful AI can be for consumers, this could upend Apple if a full mobile hardware+OS redesign is able to create a step change in seamlessness of UI. That seems to me to be the biggest unknown now - how will hardware and devices adapt?

NVDA will still do quite well because as others have noted, if it’s cheaper to train, the balance will just shift toward deploying more edge devices for inference, which is necessary to realize the value built up in the bubble anyway. Some day the compute will become more fungible but the momentum behind the nvidia ecosystem is way too strong right now.