I hear your argument, but short of major algorithmic breakthroughs I am not convinced the global demand for GPUs will drop any time soon. Of course I could easily be wrong, but regardless I think the most predictable cause for a drop in the NVIDIA price would be that the CHIPS act/recent decisions by the CCP leads a Chinese firm to bring to market a CUDA compatible and reliable GPU at a fraction of the cost. It should be remembered that NVIDIA's /current/ value is based on their being locked out of their second largest market (China) with no investor expectation of that changing in the future. Given the current geopolitical landscape, in the hypothetical case where a Chinese firm markets such a chip we should expect that US firms would be prohibited from purchasing them, while it's less clear that Europeans or Saudis would be. Even so, if NVIDIA were not to lower their prices at all, US firms would be at a tremendous cost disadvantage while their competitors would no longer have one with respect to compute.

All hypothetical, of course, but to me that's the most convincing bear case I've heard for NVIDIA.

NVIDIA stock tanked in 2025 when people learned that Google used TPUs to train Gemini, which everyone in the community knows since at least 2021. So I think it's very likely that NVIDIA stock could crash for non-rationale reasons

edit: 2025* not 2024

I really don't understand the argument that nvidia GPUs only work for 1-3 years. I am currently using A100s and H100s every day. Those aren't exactly new anymore.

How much did you short the stock?

> My 30k ft view is that the stock will inevitably slide as AI datacenter spending goes down.

Their stock trajectory started with one boom (cryptocurrencies) and then seamlessly progressed to another (AI). You're basically looking at a decade of "number goes up". So yeah, it will probably come down eventually (or the inflation will catch up), but it's a poor argument for betting against them right now.

Meanwhile, the investors who were "wrong" anticipating a cryptocurrency revolution and who bought NVDA have not much to complain about today.

I’m sad about Grok going to them, because the market needs the competition. But ASIC inference seems to require a simpler design than training does, so it’s easier for multiple companies to enter. It seems inevitable that competition emerges. And eg a Chinese company will not be sold to Nvidia.

What’s wrong with this logic? Any insiders willing to weigh in?

I'll also point out there were insane takes a few years ago before nVidia's run up based on similar technical analysis and very limited scope fundamental analysis.

Technical analysis fails completely when there's an underlying shift that moves the line. You can't look at the past and say "nvidia is clearly overvalued at $10 because it was $3 for years earlier" when they suddenly and repeatedly 10x earnings over many quarters.

I couldn't get through to the idiots on reddit.com/r/stocks about this when there was non-stop negativity on nvidia based on technical analysis and very narrow scoped fundamental analysis. They showed a 12x gain in quarterly earnings at the time but the PE (which looks on past quarters only) was 260x due to this sudden change in earnings and pretty much all of reddit couldn't get past this.

I did well on this yet there were endless posts of "Nvidia is the easiest short ever" when it was ~$40 pre-split.

> The problem I have with this argument is that it's simply unsustainable to be spending that much every 2-3 years

Isn’t this entirely dependent on the economic value of the AI workloads? It all depends on whether AI work is more valuable than that cost. I can easily see arguments why it won’t be that valuable, but if it is, then that cost will be sustainable.

Fundamental analysis is great! But I have trouble answering concrete questions of probability with it.

How do you use fundamental analysis to assign a probability to Nvidia closing under $100 this year, and what probability do you assign to that outcome?

I'd love to hear your reasoning around specifics to get better at it.

Also there's no way Nvidia's market share isn't shrinking. Especially in inference.

I no AI fanboy at all. I think it there won’t be AGI anytime soon.

However, it’s beyond my comprehension how anyone would think that we will see a decline in demand growth for compute.

AI will conquer the world like software or the smartphone did. It’ll get implemented everywhere, more people will use it. We’re super early in the penetration so far.

“In a gold rush, sell shovels”… Well, at some point in the gold rush everyone already has their shovels and pickaxes.

Add in the fact companies seriously invested in AI (and like workloads typically reliant on GPUs) are also investing more into bespoke accelerators, and the math for nVidia looks particularly grim. Google’s TPUs set them apart from the competition, as does Apple’s NPU; it’s reasonable to assume firms like Anthropic or OpenAI are also investigating or investing into similar hardware accelerators. After all, it’s easier to lock-in customers if your models cannot run on “standard” kit like GPUs and servers, even if it’s also incredibly wasteful.

The math looks bad regardless of which way the industry goes, too. A successful AI industry has a vested interest in bespoke hardware to build better models, faster. A stalled AI industry would want custom hardware to bring down costs and reduce external reliance on competitors. A failed AI industry needs no GPUs at all, and an inference-focused industry definitely wants custom hardware, not general-purpose GPUs.

So nVidia is capitalizing on a bubble, which you could argue is the right move under such market conditions. The problem is that they’re also alienating their core customer base (smaller datacenters, HPC, gaming market) in the present, which will impact future growth. Their GPUs are scarce and overpriced relative to performance, which itself has remained a near-direct function of increased power input rather than efficiency or meaningful improvements. Their software solutions - DLSS frame-generation, ray reconstruction, etc - are locked to their cards, but competitors can and have made equivalent-performing solutions of their own with varying degrees of success. This means it’s no longer necessary to have an nVidia GPU to, say, crunch scientific workloads or render UHD game experiences, which in turn means we can utilize cheaper hardware for similar results. Rubbing salt in the wound, they’re making cards even more expensive by unbundling memory and clamping down on AIB designs. Their competition - Intel and AMD primarily - are happily enjoying the scarcity of nVidia cards and reaping the fiscal rewards, however meager they are compared to AI at present. AMD in particular is sitting pretty, powering four of the five present-gen consoles, the Steam Deck (and copycats), and the Steam Machine, not to mention outfits like Framework; if you need a smol but capable boxen on the (relative) cheap, what used to be nVidia + ARM is now just AMD (and soon, Intel, if they can stick the landing with their new iGPUs).

The business fundamentals paint a picture of cannibalizing one’s evergreen customers in favor of repeated fads (crypto and AI), and years of doing so has left those customer markets devastated and bitter at nVidia’s antics. Short of a new series of GPUs with immense performance gains at lower price and power points with availability to meet demand, my personal read is that this is merely Jenson Huang’s explosive send-off before handing the bag over to some new sap (and shareholders) once the party inevitably ends, one way or another.

> My 30k ft view is that the stock will inevitably slide as AI datacenter spending goes down. Right now Nvidia is flying high because datacenters are breaking ground everywhere but eventually that will come to an end as the supply of compute goes up.

Exactly, it is currently priced as though infinite GPUs are required indefinitely. Eventually most of the data centres and the gamers will have their GPUs, and demand will certainly decrease.

Before that, though, the data centres will likely fail to be built in full. Investors will eventually figure out that LLMs are still not profitable, no matter how many data centres you produce. People are interested in the product derivatives at a lower price than it costs to run them. The math ain't mathin'.

The longer it takes to get them all built, the more exposed they all are. Even if it turns out to be profitable, taking three years to build a data centre rather than one year is significant, as profit for these high-tech components falls off over time. And how many AI data centres do we really need?

I would go further and say that these long and complex supply chains are quite brittle. In 2019, a 13 minute power cut caused a loss of 10 weeks of memory stock [1]. Normally, the shops and warehouses act as a capacitor and can absorb small supply chain ripples. But now these components are being piped straight to data centres, they are far more sensitive to blips. What about a small issue in the silicon that means you damage large amounts of your stock trying to run it at full power through something like electromigration [2]. Or a random war...?

> The counterargument to this is that the "economic lifespan" of an Nvidia GPU is 1-3 years depending on where it's used so there's a case to be made that Nvidia will always have customers coming back for the latest and greatest chips. The problem I have with this argument is that it's simply unsustainable to be spending that much every 2-3 years and we're already seeing this as Google and others are extending their depreciation of GPU's to something like 5-7 years.

Yep. Nothing about this adds up. Existing data centres with proper infrastructure are being forced to extend use for previously uneconomical hardware because new data centres currently building infrastructure have run the price up so high. If Google really thought this new hardware was going to be so profitable, they would have bought it all up.

[1] https://blocksandfiles.com/2019/06/28/power-cut-flash-chip-p...

[2] https://www.pcworld.com/article/2415697/intels-crashing-13th...

Well, not to be too egregiously reductive… but when the M2 money supply spiked in the 2020 to 2022 timespan, a lot of new money entered the middle class. That money was then funneled back into the hands of the rich through “inflation”. That left the rich with a lot of spare capital to invest in finding the next boom. Then AI came along.

Once the money dries up, a new bubble will be invented to capture the middle class income, like NFTs and crypto before that, and commissionless stocks, etc etc

It’s not all pump-and-dump. Again, this is a pretty reductive take on market forces. I’m just saying I don’t think it’s quite as unsustainable as you might think.

I have lots of skepticism about everything involved in this, but on this particular point:

It's a bit like TSMC: you couldn't buy space on $latestGen fab because Apple had already bought it all. Many companies would have very much liked to order H200s and weren't able to, as they were all pre-sold to hyperscalers. If one of them stopped buying, it's very likely they could sell to other customers, though there might be more administrative overhead?

Now there are some interesting questions about Nvidia creating demand by investing huge amounts of money in cloud providers that will order nv hardware, but that's a different issue.

> Sure, it's possible for a big crash in Nvidia just due to volatility. But in that case, the market as a whole would likely be affected.

Other way around: if NVidia sinks, it likely takes a bunch of dependent companies with it, because the likely causes of NVidia sinking all tell us that there was indeed an AI bubble and it is popping.

Indeed, the market as a whole would be affected. But is not NVIDIA more of a software company than a hardware one? This bugs the shit out of me.

They are maintaining this astronomical growth through data centers margins from the design of their chips and all of that started from graphics related to video games.

>when the numbers come in from Apple: how large of a share in tokens used by the average consumer can be fulfilled with lightweight models and Google searches of the open internet

is there any reason to expect that this information will ever be known outside of apple?

It doesn't goto nearly zero. TSMC has a large fab in Arizona and they are continuing to expand it. They also have a fab in Washington, and in Japan. [1]

[1]https://www.tsmc.com/english/aboutTSMC/TSMC_Fabs

I agree. It's funny that this is one of the cited reason for the (relative) value suppression of tsmc, but the same factors should apply to Nvidia too.

Going to zero is one potential outcome. Equally plausible is it goes up 10% in a relatively quick battle or diplomatic outcome which ends the geopolitical uncertainty.

I think they are already hedging for Taiwan. 1. They just pseudo-acquired Groq, fully made in USA (GlobalFoundries) and with a diversified supply chain. 2. And they just announced they will be re-introducing RTX 3090 made in Korea (Samsung). 3. And they plan to produce chips in Intel's new US fabs soon.

I think the bigger problems of the AI bubble are energy and that it's gaining a terrible reputation for being the excuse for mass layoffs while suffocating the Internet with slop/brainrot content. All while depending on government funding to grow.

Idk the pro china side is getting more and more support, at this rate they’ll vote themselves into mainland

But then again what won't? Non tech stocks?

What does the US gov't do in response? Wouldn't they throw globs of money at Intel and Nvidia?

but they're expected to have 8 or 9 aircraft carriers by 2035, doesn't it make sense to wait until then?

NVIDIA has been producing Blackwell in Arizona since October. Don't be dramatic.

There would be a supply crunch but a lot of dollars will be shuffled VERY fast to ramp up production.

No because if it goes to $99.99, you don't win much. With a prediction contest it is either you win or you lose.

Not really. American put options will pay differently for 95 dollars vs 99 dollars, while this contract settles to 1 either which way.

or couldn't use a LLM to help port your CUDA code to "new framework", i.e. software is no longer a lock-in....

assuming LLM coding agents are good, but if they aren't any good, then what is the value of the CUDA code?

They implement Applicative, so by definition they do

Nvidia is the biggest link, however, I'd wager OpenAI and the likes are big enough to make a significant dent in the mammoth. So yeah, this analysis is sort of a spherical cows in a vacuum situation.

Still, it's interesting the probability is so high while ignoring real-world factors. I'd expect it to be much higher due to: - another adjacent company dipping - some earnings target not being met - china/taiwan - just the AI craze slowing down

this is gpt, right?

You have it turned upside down. The analysis is of people's beliefs. In other words, the underlying data is created from the beliefs of the people who trade it, and the analysis is taking those beliefs and applying it to a specific question.

The entire options market is built on this kind of analysis.

Them being far above the median PE ratio for the S&P 500 tells you that a future correction would be a discount and you should buy? Please walk me through your logic on this one.

This implies you think a crash would be a temporary mispricing of the stock, which will recover in value, correct?

Well put and clearly explains why "timing the market" is never a good plan.

This isn't technical analysis, this is an article on how to use the options market's price discovery mechanism to understand what the discovered price implies about the collective belief about the future price of the underlying.

how so? (i'm not too familiar)