Nvidia Stock Crash Prediction
entropicthoughts.com
entropicthoughts.com
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.
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.
Which is absolutely the right move when your latest datacenter's power bill is literally measured in gigawatts. Power-efficient training/inference hardware simply does not look like a GPU at a hardware design level (though admittedly, it looks even less like an ordinary CPU), it's more like something that should run dog slow wrt. max design frequency but then more than make up for that with extreme throughput per watt/low energy expense per elementary operation.
The whole sector of "neuromorphic" hardware design has long shown the broad feasibility of this (and TPUs are already a partial step in that direction), so it looks like this should be an obvious response to current trends in power and cooling demands for big AI workloads.
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.
While thinking computers will replace human brains soon is rabid fanaticism this statement...
> AI will conquer the world like software or the smartphone did.
Also displays a healthy amount of fanaticism.
As far as AI conquering the world. It needs a "killer app". I don't think we'll really see that until AR glasses that happen to include AI. If it can have context about your day, take action on your behalf, and have the same battery life as a smartphone...
But yes. Cisco's value dropped when there was not same amount to spend on networking gear. Nvidia's value will drop as there is not same amount of spend on their gear.
Other impacted players in actual economic downturn could be Amazon with AWS, MS with Azure. And even more so those now betting on AI computing. At least general purpose computing can run web servers.
If it had given me the right easy to understand answer right away I would have spent 2 minutes of both MY time and ITS time. My point is if AI will improve we will need less of it, to get our questions answered. Or, perhaps AI usage goes up if it improves its answers?
I'm just wondering if there's a clear path for it to improve and on what time-table. The fact that it does not tell you when it is "unsure" of course makes things worse for users. (It is never unsure).
The data is very strongly showing the quality of AI answers is rapidly improving. If you want a good example, check out the sixty symbols video by Brady Haran, where they revisited getting AI to answer a quantum physics exam after trying the same thing 3 years ago. The improvement is IMMENSE and unavoidable.
Referencing outdated documentation or straight up hallucinating answers is still an issue. It is getting better with each model release though
Doesn't mean that crypto is not being used, of course. Plenty of people do use things like USDT, gamble on bitcoin or try to scam people with new meme coins, but this is far from what crypto enthusiasts and NFT moguls promised us in their feverish posts back in the middle of 2010s.
So imagine that AI is here to stay, but the absolutely unhinged hype train will slow down and we will settle in some kind of equilibrium of practical 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.
Eg: A chatbot assistant is much more tangible to the regular joe than blockchain technology
Their usage has declined primarily with OpenAI and Gemini tools, no one has mentioned Anthropic based models but I don't think normies know they exist honestly.
The disengagement seems to be that with enough time and real world application, the shortcomings have become more noticable and the patience they once had for incorrect or unreliable output has effectively evaporated. In cases, to the point where its starting to outweigh any gains they get.
Not all of the normies I know to be fair, but a surprising amount given the strange period of quiet inbetween "This is amazing!" and "eh, its not as good as I thought it was at first."
It's like if your taxi company bought taxis that were more fuel efficient every year.
You kind of have to.
Replacing cars every 3 years vs a couple % in efficiency is not an obvious trade off. Especially if you can do it in 5 years instead of 3.
It can make sense at a certain scale, but it’s a non trivial amount of cost and effort for potentially marginal returns.
I’m just pointing out changing it out at 5 years is likely cheaper than at 3 years.
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 scenario doesn't add up.
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.
They both refresh their equipment at the same rate.
I wish you'd said that upfront. Especially because the comment you replied to was talking about replacing at different rates.
So your version, if company A and B are refreshing at the same rate, then that means six months before B's refresh company A had the newer taxis. You implied they were charging similar amounts at that point, so company A was making bigger profits, and had been making bigger profits for a significant time. So when company B is able to cut prices 5%, company A can survive just fine. They don't need to rush into a premature upgrade that costs a ton of money, they can upgrade on their normal schedule.
TL;DR: six months ago company B was "no longer competitive" and they survived. The companies are taking turns having the best tech. It's fine.
Isn't that precisely how leasing works? Also, don't companies prefer not to own hardware for tax purposes? I've worked for several places where they leased compute equipment with upgrades coming at the end of each lease.
who cares? that's the beauty of the lease. once it's over, the old and busted gets replaced with new and shiny. what the leasing company does is up to them. it becomes one of those YP not an MP situations with deprecated equipment.
That's where the analogy breaks. There are massive efficiency gains from new process nodes, which new GPUs use. Efficiency improvements for cars are glacial, aside from "breakthroughs" like hybrid/EV cars.
It's not like the CUDA advantage is going anywhere overnight, either.
Also, if Nvidia invests in its users and in the infrastructure layouts, it gets to see upside no matter what happens.
I have not seen hard data, so this could be an oft-repeated, but false fact.
If this was anywhere close to a common failure mode, I'm pretty sure we'd know that already given how crypto mining GPUs were usually ran to the max in makeshift settings with woefully inadequate cooling and environmental control. The overwhelming anecdotal evidence from people who have bought them is that even a "worn" crypto GPU is absolutely fine.
Another commonly forgotten issue is that many electrical components are rated by hours of operation. And cheaper boards tend to have components with smaller tolerances. And that rated time is actually a graph, where hour decrease with higher temperature. There were instances of batches of cards failing due to failing MOSFETs for example.
Even in amateur setups the amount of power used is a huge factor (because of the huge draw from the cards themselves and AC units to cool the room) so minimising heat is key.
From what I remember most cards (even CPUs as well) hit peak efficiency when undervolted and hitting somewhere around 70-80% max load (this also depends on cooling setup). First thing to wear out would probably be the fan / cooler itself (repasting occasionally would of course help with this as thermal paste dries out with both time and heat)
Not sure I understand the police raid mentality - why are the police raiding amateur crypto mining setups ?
I can totally see cards used by casual amateurs being very worn / used though - especially your example of single mobo miners who were likely also using the card for gaming and other tasks.
I would imagine that anyone purposely running hardware into the ground would be running cheaper / more efficient ASICS vs expensive Nvidia GPUs since they are much easier and cheaper to replace. I would still be surprised however if most were not proritising temps and cooling
This doesn't mean much for inference, but for training, it is going to be huge.
(1) We simply don't know what the useful life is going to be because of how new the advancements of AI focused GPUs used for training and inference.
(2) Warranties and service. Most enterprise hardware has service contracts tied to purchases. I haven't seen anything publicly disclosed about what these contracts look like, but the speculation is that they are much more aggressive (3 years or less) than typical enterprise hardware contracts (Dell, HP, etc.). If it gets past those contracts the extended support contracts can typically get really pricey.
(3) Power efficiency. If new GPUs are more power efficient this could be huge savings on energy that could necessitate upgrades.
Companies can’t buy new Nvidia GPUs because their older Nvidia GPUs are obsolete. However, the old GPUs are only obsolete if companies buy the new Nvidia GPUs.
I worked at a few data centers on and off in my career. I got lots of hardware for free or on the cheap simply because the hardware was considered “EOL” after about 3 years, often when support contracts with the vendor ends.
There are a few things to consider.
Hardware that ages produce more errors, and those errors cost, one way or another.
Rack space is limited. A perfectly fine machine that consumes 2x the power for half the output cost. It’s cheaper to upgrade a perfectly fine working system simply because it performs better per watt in the same space.
Lastly. There are tax implications in buying new hardware that can often favor replacement.
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.
These things are like cars, they don't last forever and break down with usage. Yes, they can last 7 years in your home computer when you run it 1% of the time. They won't last that long in a data center where they are running 90% of the time.
Even assuming your compute demands stay fixed, its possible that a future generation of accelerator will be sufficiently more power/cooling efficient for your workload that it is a positive return on investment to upgrade, more so when you take into account you can start depreciating them again.
If your compute demands aren't fixed you have to work around limited floor space/electricity/cooling capacity/network capacity/backup generators/etc and so moving to the next generation is required to meet demand without extremely expensive (and often slow) infrastructure projects.
Two years later, H900 is released for a similar price but it performs twice as many TFlOps/Watt. Now any datacenter using H900 can offer the same performance as NeoCloud Inc at $5/month, taking all their customers.
[all costs reduced to $/minute to make a point]
Current estimates are about 1.5-2 years, which not-so-suspiciously coincides with your toy example.
For servers I've seen where the slightly used equipment is sold in bulk to a bidder and they may have a single large client buy all of it.
Then around the time the second cycle comes around it's split up in lots and a bunch ends up at places like ebay
A lot of demand out there for sure.
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.)
Rack space and power (and cooling) in the datacenter drives what hardware stays in the datacenter
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 do wonder what people would think the reasoning would be for them to increase in value this much back then, prolly would just assume crypto related still.
Now there's one thing with AR/VR that might need this kind of infrastructure though and that's basically AI driven games or Holodeck like stuff. Basically have the frames be generated rather than modeled and rendered traditionally.
Robotics is a bit of a "flying car" application that gets people to think outside the box. Right now, both Russia and Ukraine are using Nvidia hardware in drones and cruise missiles and C2 as well. The United States will join them if a peer conflict breaks out, and if push comes to shove then Europe will too. This is the kind of volatility that crazy people love to go long on.
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.
edit: 2025* not 2024
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.
It's guesswork all the way down.
Riposte: I knew you'd say that! Snap!
These days you have AI bots doing sentiment based training.
If you ask me... all these excesses are a clear sign for one thing, we need to drastically rein in the stonk markets. The markets should serve us, not the other way around.
Any claim from google that all of Gemini (including previous experiments) was trained entirely by TPUs is lies. What they are truthfully saying is that the final training run was done on all TPUs. The market shouldn’t react heavily to this, but instead should react positively to the fact that google is now finally selling TPUs externally and their fab yields are better than expected.
How far back do you go? What about experiments into architecture features that didn’t make the cut? What about pre-transformer attention?
But more generally, why are you so sure that they team that built Gemini didn’t exclusively use TPUs while they were developing it?
I think that one of the reasons that Gemini caught up so quickly is because they have so much compute at fraction of the price of everyone else.
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.
All hypothetical, of course, but to me that's the most convincing bear case I've heard for NVIDIA.
Or, you know, when LLMs don't pay off.
They continually leap frog each other and shift around customers which indicates that the current capacity is already higher than what is required for what people actually pay for.
I hear Meta is having massive VR division layoffs…who could have predicted?
Raw popularity does not guarantee sustainability. See: Vine, WeWork, MoviePass.
The companies might also downgrade the quality of the models to make it more viable to provide as an ad supported service which would again reduce utilisation.
And probably for the slightly more skilled email jobs that have infiltrated nearly all companies too.
Is that productive work? Well if people are getting paid, often a multiple of minimum wage, then it's productive-seeming enough.
Why are there still customer service reps? Shouldn’t they all be gone by now due to this amazing technology?
Ah, tumbleweed.
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".
That in itself is a goalpost shift from
> > We will run out of additional material to train on
Where it is implied "additional material" === "all data, human + synthetic"
------
There's still some headroom left in the synthetic data playground, as cited in the paper linked:
https://proceedings.mlr.press/v235/villalobos24a.html ( https://openreview.net/pdf?id=ViZcgDQjyG )
"On the other hand, training on synthetic data has shown much promise in domains where model outputs are relatively easy to verify, such as mathematics, programming, and games (Yang et al., 2023; Liu et al., 2023; Haluptzok et al., 2023)."
With the caveat that translating this success outside of these domains is hit-or-miss:
"What is less clear is whether the usefulness of synthetic data will generalize to domains where output verification is more challenging, such as natural language."
The main bottleneck for this area of the woods will be (X := how many additional domains can be made easily verifiable). So long as (the rate of X) >> (training absorption rate), the road can be extended for a while longer.
It isn’t the panacea some make it out to be, but there is obvious utility here to sell. The real argument is shifting towards the pricing.
The world is more than just text. We can never run out of pixels if we point cameras at the real world and move them around.
I work in robotics and I don’t think people talking about this stuff appreciate that text and internet pictures is just the beginning. Robotics is poised to generate and consume TONS of data from the real world, not just the internet.
Nobody talks about it, but ultimately the strongest driver for terrascale compute will be for mathematical breakthroughs in crypography (not bruteforcing keys, but bruteforcing mathematical reasoning).
Nvidia’s valuation is based on the current trend continuing and even increasing, which I consider unlikely in the long term.
People said this back when Folding@Home was dominated by Team Green years ago. Then again when GPUs sold out for the cryptocurrency boom, and now again that Nvidia is addressing the LLM demand.
Nvidia's valuation is backstopped by the fact that Russia, Ukraine, China and the United States are all tripping over themselves for the chance to deploy it operationally. If the world goes to war (which is an unfortunate likelihood) then Nvidia will be the only trillion-dollar defense empire since the DoD's Last Supper.
>> Or, you know, when LLMs don't pay off.
Heh, exactly the observation that a fanatic religious believer cannot possibly foresee. "We need more churches! More priests! Until a breakthrough in praying technique will be achieved I don't foresee less demand for religious devotion!" Nobody foresaw Nietzsche and the decline in blind faith.
But then again, like an atheist back in the day, the furious zealots would burn me at the stake if they could, for saying this. Sadly no longer possible so let them downvotes pour instead!
There is a real chance that the Japanese carry trade will close soon the BoJ seeing rates move up to 4%. This means liquidity will drain from the US markets back into Japan. On the US side there is going to be a lot of inflation between money printing, refund checks, amortization changes and a possible war footing. Who knows?
I do think China is still 3-5 years from being really competitive, but still even if they hit 40-50% of NVidia, depending on pricing and energy costs, it could still make significant inroads with legal pressure/bans, etc.
OpenCL is chronically undermaintained & undersupported, and Vulkan only covers a small subset of what CUDA does so far. Neither has the full support of the tech industry (though both are supported by Nvidia, ironically).
It feels like nobody in the industry wants to beat Nvidia badly enough, yet. Apple and AMD are trying to supplement raster hardware with inference silicon; both of them are afraid to implement a holistic compute architecture a-la CUDA. Intel is reinventing the wheel with OneAPI, Microsoft is doing the same with ONNX, Google ships generic software and withholds their bespoke hardware, and Meta is asleep at the wheel. All of them hate each other, none of them trust Khronos anymore, and the value of a CUDA replacement has ballooned to the point that greed might be their only motivator.
I've wanted a proper, industry-spanning CUDA competitor since high school. I'm beginning to realize it probably won't happen within my lifetime.
> The proposition that technological progress that increases the efficiency with which a resource is used tends to increase (rather than decrease) the rate of consumption of that resource.
See also Wikipedia: https://en.wikipedia.org/wiki/Jevons_paradox
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...
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.
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.
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.
GP was presenting fundamental analysis as an alternative to the article's method for answering the question, but then never answered the question.
This is a confusion I have around fundamental analysis. Some people appear to do it very well (Buffett?) but most of its proponents only use it to ramble about possibilities without making any forecasts speciic enough to be verifiable.
I'm curious about that gap.
What’s wrong with this logic? Any insiders willing to weigh in?
The industry badly needs to cooperate on an actual competitor to CUDA, and unfortunately they're more hostile to each other today than they were 10 years ago.
When the AI bubble bursts, it won't stop the development of AI as a technology. Or its impact on society. But it will end the era of uncritically throwing investments at anyone that works "AI" into their pitch deck. And so too will it end the era of Nvidia selling pickaxes to the miners and being able to reach soaring heights of profitability born on wings of pretty much all investment capital in the world at the moment.
If I'm understanding your prediction correctly, you're asserting that the market thinks datacenter spending will continue at this pace indefinitely, and you yourself uniquely believe that to be not true. Right? I wonder why the market (including hedge fund analysis _much_ more sophisticated than us) should be so misinformed.
Presumably the market knows that the whole earth can't be covered in datacenters, and thus has baked that into the price, no?
AKA pictures in clouds
Actually "technical analysis" (TA) has a very specific meaning in trading: TA is using past prices, volume of trading and price movements to, hopefully, give probabilities about future price moves.
https://en.wikipedia.org/wiki/Technical_analysis
But TFA doesn't do that at all: it goes in detail into one pricing model formula/method for options pricing. In the typical options pricing model all you're using is current price (of the underlying, say NVDA), strike price (of the option), expiration date, current interest rate and IV (implied volatility: influenced by recent price movements but independently of any technical analysis).
Be it Black-Scholes-Merton (european-style options), Bjerksund-Stensland (american-style options), binomial as in TFA, or other open options pricing model: none of these use technical analysis.
Here's an example (for european-style options) where one can see the parameters:
https://www.mystockoptions.com/black-scholes.cfm
You can literally compute entire options chains with these parameters.
Now it's known for a fact that many professional traders firms have their own options pricing method and shall arb when they think they find incorrectly priced options. I don't know if some use actual so forms of TA that they then mix with options pricing model or not.
> My 30k ft view is that the stock will inevitably slide as AI datacenter spending goes down.
No matter if you're right or not, I'd argue you're doing what's called fundamental analysis (but I may be wrong).
P.S: I'm not debatting the merits of TA and whether it's reading into tea leaves or not. What I'm saying is that options pricing using the binomial method cannot be called "technical analysis" for TA is something else.
This is like saying Apple stock will inevitably slide once everybody owns a smartphone.
Additionally, they mentioned that customers can cancel purchases with little to no penalty and notice [2].
This is not unique for hardware companies, but to think that all it takes is just one company to get their sales down by 12% (14b$).
To cut to the point, my guess is that nvidia is not sustainable, and at some point one or more of these big customers won’t be able to keep up with the big orders, which will cause them to miss their earnings and then it will burst. But maybe i’m wrong here.
[1] https://s201.q4cdn.com/141608511/files/doc_financials/2025/a..., page 155: > Sales to direct Customers A, B and C represented 12%, 11% and 11% of total revenue, respectively, for fiscal year 2025.
[2] same, page 116: > Because most of our sales are made on a purchase order basis, our customers can generally cancel, change, or delay product purchase commitments with little notice to us and without penalty.
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.
All the AI companies are locked in a death loop where they must spend as much money as possible otherwise everything they invested will immediately become zero. No one is going to pay for an LLM when the competitor has GAI. So it's death loop for everyone that has become involved in this race.
1. There are alternatives to nvidia: these 3 companies are probably developing their own alternative to NVIDIA, at some point they will switch to their solution or to competitors (for example: google used TPUs to train Gemini 3 [1], with no nvidia GPUs, despite being a pretty large Nvidia customer).
2. The market seems to be consolidating: for example Apple has decided to use Google Gemini for their new Siri [2]. I’m not an export (or future teller), but I think it increases the chance that other companies might follow and get off the AI race.
3. I am sure that OpenAI and related companies would want to sustain these kind of orders, but I am not sure it is possible without more and more funding, and I don’t know if even Sam himself know to estimate how many GPUs they will be able to buy from Nvidia in 2026.
[1] https://x.com/JeffDean/status/1886852442815652188
[2] https://blog.google/company-news/inside-google/company-annou...
Or US administration suddenly allows exports of top-tier to China and they get more whales on their order book.
It's all guess work, that's why their share price is high.
He's answering the question "How should options be priced?"
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.
Whether Nvidia specifically takes a big dive depends much more on whether they continue to meet growth estimates than general volatility. If they miss earnings estimates in a meaningful way the market is going to take the stock behind the shed and shoot it. If they continue to exceed estimates the stock will probably go up or at least keep its present valuation.
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.
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.
No? That’s why they have almost no competition. Hardware starting costs are astronomical
All that aside, I'm impressed it made it to the HN front page.
Presumably, inference can be done on TPUs, Nvidia chips, in Anthropic's case, new stuff like Trainium.
There would be a supply crunch but a lot of dollars will be shuffled VERY fast to ramp up production.
If something even more drastic happens. China might even attempt unification with some reasoning like protecting Taiwan from USA or other nations.
I'm not quite sure what process they run there but I believe it was an acquisition 10+ years ago, not built from the ground up by them.
Edit: their Japan fab is also a mature node so not very relevant here. And their Arizona fab is a very very small portion of their volume and with far worse margin.
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.
Either that, or the leader does have access to the best information, and they just DGAF. That condition seems to be going around too.
/s (unless???)
It is economic MAD.
Or China can wait 20-30 years and the US will no longer care about Taiwan or have the resources to have much presence in the eastern hemisphere.
I think the saber rattling over Taiwan is just to get the US to spend themselves further into oblivion in the short term. We are in the war already and the saber rattling is an incredibly effective, asymmetric financial weapon. It builds up the Chinese military kinetic capacity long term while weakening the US military kinetic capacity long term by forcing the US to prepare for something that is never going to happen.
When China takes Taiwan it will be without firing a shot. I would bet the house on that because it kind of has to be that way to win the war and not just a self destructive battle.
China is achieving its objectives brilliantly. The US is increasingly isolated and this is the process of retreating into the western hemisphere. NATO is being destroyed without firing a shot.
Maybe I’m missing something, but isn’t this just a standard American put option with a strike of $100 and expiry of Dec 31st?
The only way the stock could remain at its current price or grow (which is why you'd hold it) is if demand would just keep going up (with the same lifecycle as current GPUs) and that there would be no competition, which the latter to me us just never going to be a thing.
Investors are convinced that Nvidia can maintain its lead because they have the "software" side, I.e. CUDA, which to me is so ridiculous, as if with the kind of capital that's being deployed into these datacenters, you couldn't fit your models into other software stacks by hiring people....
assuming LLM coding agents are good, but if they aren't any good, then what is the value of the CUDA code?
For my two cents on the technical side, it is likely that any Western-origin shakiness will come from Apple and how it manages to land the Gemini deal and Apple Intelligence v2. There is an astounding amount of edge inference sitting in people’s phones and laptops that only slightly got cracked open with Apple Intelligence.
Data centre buildouts will get corrected 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. This will serve as a guiding principle for any future buildout and heavyweight inference cards that Nvidia is supplying. The 2-5 year moat top providers have with the largest models will get chomped at by the leisure/hobby/educational use cases that lightweight models capably handle. Small language and visual models are already amazing. The next crack will appear when the past gen cards (if they survive the around the clock operation) get bought up by second hand operators that can provide capable inference of even current gen models.
If past knowledge of DC operators holds (e.g. Google and its aging TPUs that still get use), the providers with the resources to buy new space for newer gens will accumulate the amount of hardware, but the providers who need to continuously shave off the financial hit that comes with using less efficient older cards.
I’m excited to see future blogs about hardware geeks buying used inference stacks and repurposing them for home use :)
is there any reason to expect that this information will ever be known outside of apple?
I don't typically buy stock to flip it right away; I have some Nvidia stock that I bought the day after ChatGPT was launched, and I bought a bit more when it was $90/share about a ~year ago. If it drops to $100, then I'll still be in the black, but even if it drops to $50, I'm not going to worry because I figure that I can just hold onto it until another upswing.
Nvidia has been around long enough and has enough market penetration in datacenters and gaming that I don't think it's going to go bust, and I figure that it will eventually appreciate again just due to inflation.
Now obviously, if it drops below from what I paid for it and then it takes inflation to catch up, then yeah, that's definitely "lost money", but that's just the risk of the stock market, especially with individual stocks. I also think that if it crashes, Nvidia might still have another surge eventually, even if it doesn't get back to its full glory.
I definitely would not buy new Nvidia stock at its current price though.
Shouldn't the same argument also apply to Intel?
LLM use age won't crash either, it might decline or taper off but it's here to stay.
My concern is better models that won't need a whole of GPU, or China comping up with their own foundry and GPUs that compete. There is also the strategy issue, can Nvidia's leadership think global enough? will they start pursuing data centers in europe, latam, asia? can they make gpus cheap enough to compete in those regions?
The way things are, lots of countries want this tech local, but they can't deny the demand either.
Europe for example might not want anything to do with American AI companies, but they still need GPUs for their own models. But can Nvidia rebrand itself as a not-so-american-but-also-american company? Like Coca Cola for example. i.e.: not just operate in europe but have an HQ in europe that has half their execs working from there, and the rest from california. Or perhaps asia is better (doubt)? either way, they can't live off of US demand forever, or ignore geopolitics.
My personal opinion, having witnessed first hand nearly 40 years of tech evolution, is that this AI revolution is different. We're at the very beginning of a true paradigm shift: the commoditization of intelligence. If that's not enough to make people think twice before betting against it, I don't know what is. And it's not just computing that is going to change. Everything is about to change, for better or worse.
Most people buy low-strike puts as insurance against catastrophic market events.
Since catastrophic crises are rare, the price of these puts is quite low. But since many people fear a crisis, the price is very inflated over the actual probabilites. Which is why there are lots of people selling those puts as a business. These guys will bite the dust in case of a major crisis, but will make a ton if the market stays afloat.
Realistically, the current US government is so obsessed with its image that it will do everything to avoid a market crash during its term. The president has been pushing for lower rates for a while, and he's likely going to succeed in removing the head of the Fed and do just that. Lowering interest rates is just another way of pumping investment.
NVidia is definitely not going below $100 in 2026.
There's a bet here on profitability and it needs to play out.
How long do investors normally wait to see if a bet on new technology is a winner? I imagine that's quite arbitrary?
The poster child for this is Tesla. Nothing fundamental justifies Tesla's valuation.
IMHO the only rational way to look at the future of AI and the companies from profit from it is to look at geopolitics.
The market seems to have decided there's going to be one winner of the AI race. I actually don't think that'll be OpenAI. I think it'll be Google or Nvidia of the companies currently in the race. But I also don't think it'll be either of them.
The magic of software is that it is infinitely reproducible. That makes it difficult to build a wall around it. Microsoft, Facebook, Apple and Google have successfully built moats around their software very successfully in spite of this. Google's big advantage in the AI race is their ability to build and manage data centers and that they'll probably end up relying on their own hardware rather than NVidia.
I think China will be the AI winner or they'll make sure there is no winner. It's simply too important to them. For me, DeepSeek was a shot across the bow that they were going to commoditize these models.
The US blocked the export of the best lithography machines AND the best chips to China. IMHO this was a mistake. Being unable to import chips meant Chinese companies had no choice but to make their own. This created a captive market for China recreating EUV technology. Chinese companies have no choice but to buy Chinese chips.
The Chinese government has the patience and infrastructure for recreating ASML's technology and it's an issue of national security. And really all it takes is hiring a few key people to recreate that technology. So Western governments and analysts who said China will take 20+ years to catch up (if they ever do) simply don't understand China or the market they're talking about.
They sound exactly like post-WW2 generals and politicians who thought the USSR would take 20+ years to copy the atomic bomb. It took 4 years. And hydrogen bombs came even quicker.
There's a story that should get more attention: China has reportedly refused a deal for NVidia's latest chips [1]. If true, why do you think they're doing that? Because they don't want to be reliant on foreign chips. They're going to make their own.
[1]: https://ca.finance.yahoo.com/news/nvidia-stock-slides-china-...
Are they already "too big to fail"? For better or worse, they are 'all in' on AI.
I do hope they crash so that I can buy as much as possible at a discount.
In general, they often get stung by the dead cat bounce, =3
This means that cash revenue will likely remain high long after the LLM hype bubble undergoes correction. The market will eventually saturate as incremental product improvements stall, and demand rolls off rather than implodes. =3
Nvidia stock crash will happen when the vendor financing bubble bursts.
They are engaged in a dangerous game of circular financing. So it is case of when, not if the chickens come home to roost.
It is simply not sustainable.
Most options are actually used to hedge large positions and are rolled over well before the "due date". YOLOing calls and puts is a Robin Hood phenomenon and the odds of "fair pricing" are heavily affected by these big players, so using that data as some sort of price discovery is flawed from the get go.
That sounds like an egregious statement. Markets don't have simple persistent arbitrage opportunities like that, do they?
> the theory of unbiased random walks assumes constant volatility throughout the year
No. I’m pretty sure it doesn’t. If you assume a brownian motion with a constant volatility as your stochastic process for computing the walk then of course vol is constant by definition, but you can use a stochastic vol process (eg Heston[1]), one with jumps or even an SVJJ process to compute the walk[2] if you want to. As long as you don’t have a drift term and the jumps are symmetrical the process will still (I think) be unbiased.
There are technical reasons why it may or may not be important to use stochastic vol, but if I recall correctly, it only really matters if you care about “forward volatility” (eg the volatility of Nvidia one year from some future point in time) which you would if pricing something that uses forward-starting options. Then the term structure of the volatility surface at a future date is important so you need a stochastic vol model. If you care about the price evolution but not the future volatility then you can validly make the simplifying assumption that jumps will cancel each other out over time and that volatility is a locally deterministic function of time and price (if not constant, which it obviously is not) and use something like a Dupire model.[3]
More significantly, implied volatility is just the market price of a particular option expressed in terms of volatility. This is convenient for traders so they can compare option prices on a like for like basis between underlyers without constantly having to adjust for differences in the underlying price, strike and time. Implied volatility is not actually the overall expected volatility of the underlying instrument. For that, you would have to fit one of the models above to market prices and calculate the expectation over all strikes and times. And that still is just the market’s opinion of the volatility, not an actual probability even if you apply the BoE adjustment thing he does in the article.
[1] https://www.homepages.ucl.ac.uk/~ucahgon/Heston.pdf
[2] “SVJ” means stochastic vol with jumps (ie discontinities) in the underlying price evolution. SVJJ means stochastic vol with jumps both in the price of the underlying and in the volatility. An example of this is the Matytsin model, which everyone just calls “SVJJ” but it’s not the only possible svjj model https://www.maplesoft.com/support/help/maple/view.aspx?path=...
[3] https://www.math.kth.se/matstat/gru/5b1575/Projects2016/Vola...
Thanks for the book recommendation. It looks like it might clear up several things I found confusing in the process of writing the blog post, but never managed to figure out.
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
Everything that can't go on forever will eventually stop. But when?
To put it another way, to price an option I need a) the current price of the underlying, b) the time until option expiry, c) the strike price of the option, and d) the collective expectation of how much the underlying's price will vary over the period between now and expiry. This last piece is "volatility", and is the only piece that can't be empirically measured; instead, through price discovery on a sufficiently liquid contract, we can reparameterize the formula to empirically derive the volatility expectation which satisfies that current price (or "implied volatility"). Due to the efficient market hypothesis, we can generally treat this as a best-effort proxy for all public information about the underlying. None of this calculation requires any measurement or analysis of the underlying's past price action, patterns, etc. The options price will necessarily include TA traders' sentiments about the underlying based on their TA (or whatever else), just as it will include fundamentals traders' sentiments (and, if you're quick and savvy enough, insiders' advance knowledge!) The price fundamentally reflects market sentiment about the future, not some projection of trends from the past.