This article goes more into the technical analysis of the stock rather than the underlying business fundamentals that would lead to a stock dump.
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.
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.
People will want more GPUs but will they be able to fund them? At what points does the venture capital and loans run out? People will not keep pouring hundreds of billions into this if the returns don't start coming.
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 suspect major algorithmic breakthroughs would accelerate the demand for GPUs instead of making it fall off, since the cost to apply LLMs would go down.
> 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.
There will always be an incentive to scale data centers. Better algorithms just mean more bang per gpu, not that “well, that’s enough now, we’ve done it”.
Doesn't even necessarily need to be CUDA compatible... there's OpenCL and Vulkan as well, and likely China will throw enough resources at the problem to bring various libraries into closer alignment to ease of use/development.
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 modern successor to OpenCL is SYCL and there's been some limited convergence with Vulkan Compute (they're still based on distinct programming models and even SPIR-V varieties under the hood, but the distance is narrowing somewhat).
Even if LLMs didn't advance at all from this point onward, there's still loads of productive work that could be optimized / fully automated by them, at no worse output quality than the low-skilled humans we're currently throwing at that work.
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 LLMs are much more compute-intensive than any other type of generative or task-specific model
Run out of training data? They’re going to put these things in humanoids (they are weirdly cheap now) and record high resolution video and other sensor data of real world tasks and train huge multimodal Vision Language Action models etc.
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.
While we may run out of human written text of value, we won't run out of symbolic sequences of tokens: we can trivially start with axioms and do random forward chaining (or random backward chaining from postulates), and then train models on 2-step, 4-step, 8-step, ... correct forward or backward chains.
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).
Yeah, another source of "unlimited data" is genetics. The human reference genome is about 6.5 GB, but these days, they're moving to pangenomes, wanting to map out not just the genome of one reference individual, but all the genetic variation in a clade. Depending on how ambitious they are about that "all", they can be humongous. And unlike say video data, this is arguably a language. We're completely swimming in unmapped, uninterpreted language data.
Inference leans heavily on GPU RAM and RAM bandwidth for the decode phase where an increasingly greater amount of time is being spent as people find better ways to leverage inference. So NVIDIA users are currently arguably going to demand a different product mix when the market shifts away from the current training-friendly products. I suspect there will be more than enough demand for inference that whatever power we release from a relative slackening of training demand will be more than made up and then some by power demand to drive a large inference market.
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.
> 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.
"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.
How much of the current usage is productive work that's worth paying for vs personal usage / spam that would just drop off after usage charges come in? I imagine flooding youtube and instagram with slop videos would reduce if users had to pay fair prices to use the models.
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.
For any "click here and type into a box" job for which you'd hire a low-skilled worker and give them an SOP to follow, you can have an LLM-ish tool do it.
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.
Exactly, the current spend on LLMs is based on extremely high expectations and the vendors operating at a loss. It’s very reasonable to assume that those expectations will not be met, and spending will slow down as well.
Nvidia’s valuation is based on the current trend continuing and even increasing, which I consider unlikely in the long term.
> Nvidia’s valuation is based on the current trend continuing
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.
China is restricting purchases of H200s. The strong likelihood is that they're doing this to promote their own domestic competitors. It may take a few years for those chips to catch up and enter full production, but it's hard to envision any "trillion dollar" Nvidia defense empire once that happens.
> short of major algorithmic breakthroughs I am not convinced the global demand for GPUs will drop any time soon
>> 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!
They aren’t yet because the big providers that paid for all of this GPU capacity aren’t profitable yet.
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.
Yeah but OpenAI is adding ads this year for the free versions, which I'm guessing is most of their users. They are probably hedging on taking a big slice of Google's advertising monopoly-pie (which is why Google is also now all-in on forcing Gemini opt-out on every product they own, they can see the writing on the wall).
Google, Amazon, and Microsoft do a lot of things that aren't profitable in themselves. There is no reason to believe a company will kill a product line just because it makes a loss. There are plenty of other reasons to keep it running.
Do you think it's odd you only listed companies with already existing revenue streams and not companies that started with and only have generative algos as their product?
Algorithmic breakthroughs (increases in efficiency) risk Jevons Paradox. More efficient processes make deploying them even more cost effective and increases demand.
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
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.
This was also on top of claims (Jan 2025) that Deepseek showed that "we don't actually need as much GPU, thus NVidia is less needed"; at least it was my impression this was one of the (now silly-seeming) reasons NVDA dropped then.
> 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.
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.
Google did not use TPUs for literally every bit of compute that led to Gemini. GCP has millions of high end Nvidia GPUs and programming for them is an order of magnitude easier, even for googlers.
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.
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.
It’s not that they don’t work. It’s how businesses handle hardware.
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.
I agree that there is hyperbole thrown around a lot here and its possible to still use some hardware for a long time or to sell it and recover some cost but my experience in planning compute at large companies is that spending money on hardware and upgrading can often result in saving money long term.
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.
Sure, but I don't think most people here are objecting to the obvious "3 years is enough for enterprise GPUs to become totally obsolete for cutting-edge workloads" point. They're just objecting to the rather bizarre notion that the hardware itself might physically break in that timeframe. Now, it would be one thing if that notion was supported by actual reliability studies drawn from that same environment - like we see for the Backblaze HDD lifecycle analyses. But instead we're just getting these weird rumors.
I agree that is a strange notion that would require some evidence and I see it in some other threads but looking at the parent comments going up it seems people are discussing economic usefulness so that is what I'm responding to.
A toy example: NeoCloud Inc builds a new datacenter full of the new H800 GPUs. It rents out a rack of them for $10/minute while paying $6/minute for electricity, interest, loan repayment, rent and staff.
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.
It's because they run 24/7 in a challenging environment. They will start dying at some point and if you aren't replacing them you will have a big problem when they all die en masse at the same time.
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.
A makeshift cryptomining rig is absolutely a "challenging environment" and most GPUs by far that went through that are just fine. The idea that the hardware might just die after 3 years' usage is bonkers.
Crypto miners undervote for efficiency GPUs and in general crypto mining is extremely light weight on GPUs compared to AI training or inference at scale
With good enough cooling they can run indefinitely!!!!! The vast majority of failures are either at the beginning due to defects or at the end due to cooling! It’s like the idea that no moving parts (except the HVAC) is somehow unreliable is coming out of thin air!
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.
Do you know how support contract lengths are determined? Seems like a path to force hardware refreshes with boilerplate failure data carried over from who knows when.
The common factoid raised in financial reports is GPUs used in model training will lose thermal insulation due to their high utilization. The GPUs ostensibly fail. I have heard anecdotal reports of GPUs used for cryptocurrency mining having similar wear patterns.
I have not seen hard data, so this could be an oft-repeated, but false fact.
It's the opposite actually - most GPU used for mining are run at a consistent temp and load which is good for long term wear. Peaky loads where the GPU goes from cold to hot and back leads to more degradation because of changes in thermal expansion. This has been known for some time now.
That is commonly repeated idea, but it doesn't take into account countless token farms which are smaller than a datacenter. Basically anything from a single MB with 8 cards to a small shed with rigs, all of which tend to disregard common engineering practices and run hardware into a ground to maximize output until next police raid or difficulty bump. Plenty of photos in the internet of crappy rigs like that, and no one guarantees which GPU comes whom where.
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.
While I'm sure there are small amateur setups done poorly that push cards to their limits this seems like a more rare and inefficient use. GPUS (even used) are expensive and running them at maximum would require large costs and time to be replacing them regularly. Not to mention the increased cost of cooling and power.
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
Miners usually don't overclock though. If anything underclocking is the best way to improve your ROI because it significantly reduces the power consumption while retaining most of the hashrate.
Exactly - more specifically undervolting. You want the minimum volts going to the card with it still performing decently.
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)
The only amatures I know doing this are trying to heat their garrage for free. so long as the heat gain is paid for they can afford to heat an otherwise unheated building.
> I have heard anecdotal reports of GPUs used for cryptocurrency mining having similar wear patterns.
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.
I can't confirm that fact - but it's important to acknowledge that consumer usage is very different from the high continuous utilization in mining and training. It is credulous that the wear on cards under such extreme usage is as high as reported considering that consumers may use their cards at peak 5% of waking hours and the wear drop off is only about 3x if it is used near 100% - that is a believable scale for endurance loss.
1-3 is too short but they aren’t making new A100s, theres 8 in a server and when one goes bad what do you do? you wont be able to renew a support contract. if you wanna diy you eventually you have to start consolidating pick and pulls. maybe the vendors will buy them back from people who want to upgrade and resell them. this is the issue we are seeing with A100s and we are trying to see what our vendor will offer for support.
Margins are typically not so razor thin that you cannot operate with technology from one generation ago. 15 vs 17 mpg is going to add up over time, but for a taxi company it's probably not a lethal situation to be in.
At least with crypto mining this was the case. Hardware from 6 months ago is useless ewaste because the new generation is more power efficient. All depends on how expensive the hardware is vs the cost of power.
And yet they aren't running planes and engines all from 2023 or beyond: See the MD-11 that crashed in Louisville: Nobody has made a new MD-11 in over 20 years. Planes move to less competitive routes, change carriers, and eventually might even stop carrying people and switch to cargo, but the plane itself doesn't get to have zero value when the new one comes out. An airline will want to replace their planes, but a new plane isn't fully amortized in a year or three: It still has value for quite a while
If a taxi company did that every year, they'd be losing a lot of money. Of course new cars and cards are cheaper to operate than old ones, but is that difference enough to offset buying a new one every one to three years?
>If a taxi company did that every year, they'd be losing a lot of money. Of course new cars and cards are cheaper to operate than old ones, but is that difference enough to offset buying a new one every one to three years?
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.
>offset buying a new one every one to three years?
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 wants to buy GPUs that were redlined for three years in a data center? Maybe there's a market for those, but most people already seem wary of lightly used GPUs from other consumers, let alone GPUs that were burning in a crypto farm or AI data center for years.
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.
The leasing company cares - the lease terms depend on the answer. That is why I can lease a car for 3 years for the same payment as a 6 year loan (more or less) - the lease company expects someone will want it. If there is no market for it after they will still lease it but the cost goes up
Depends on the price, of course. I'm wary of paying 50% of new for something run hard 3 years. Seems an NVIDIA H100 is going for $20k+ on EBay. I'm not taking that risk.
That works either because someone wants to buy old hardware for the manufacturer/lessor, or because the hardware is EOL in 3 years but it's easier to let the lessor deal with recyling / valuable parts recovery.
You can sell the old, less efficient GPUs to folks who will be running them with markedly lower duty cycles (so, less emphasis on direct operational costs), e.g. for on-prem inference or even just typical workstation/consumer use. It ends up being a win-win trade.
Building a new data center and getting power takes years to double your capacity. Swapping out out a rack that is twice as fast takes very little time in comparison.
Depends at the rate of growth of the hardware. If your data center is full and fully booked, and hardware is doubling in speed every year it's cheaper to switch it out every couple of years.
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.
> 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.
(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.
Nvidia is moving to a 1 year release life cycle for data center, and in Jensen's words once a new gen is released you lose money for being on the older hardware. It makes no longer financially sense to run it.
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.
based on my napkin math, an H200 needs to run for 4 years straight at maximum power (10.2 kW) to consume its own price of $35k worth of energy (based on 10 cents per kWh)
If power is the bottleneck, it may make business sense to rotate to a GPU that better utilizes the same power if the newer generation gives you a significant advantage.
I think the story is less about the GPUs themselves, and more about the interconnects for building massive GPU clusters. Nvidia just announced a massive switch for linking GPUs inside a rack. So the next couple of generations of GPU clusters will be capable of things that were previously impossible or impractical.
This doesn't mean much for inference, but for training, it is going to be huge.
From an accounting standpoint, it probably makes sense to have their depreciation be 3 years. But yeah, my understanding is that either they have long service lives, or the customers sell them back to the distributor so they can buy the latest and greatest. (The distributor would sell them as refurbished)
> 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.
Personally I wonder even if the LLM hype dies down we'll get a new boom in terms of AI for robotics and the "digital twin" technology Nvidia has been hyping up to train them. That's going to need GPUs for both the ML component as well as 3D visualization. Robots haven't yet had their SD 1.1 or GPT-3 moment and we're still in the early days of Pythia, GPT-J, AI Dungeon, etc. in LLM speak.
That's going to tank the stock price though as that's a much smaller market than AI, though it's not going to kill the company. Hence why I'm talking about something like robotics which has a lot of opportunity to grow and make use of all those chips and datacenters they're building.
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.
Nvidia's not your average bear, they can walk and chew bubblegum at the same time. CUDA was developed off money made from GeForce products, and now RTX products are being subsidized by the money made on CUDA compute. If an enormous demand for efficient raster compute arises, Nvidia doesn't have to pivot much further than increasing their GPU supply.
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.
That's the rub - it's clearly overvalued and will readjust... the question is when. If you can figure out when precisely then you've won the lottery, for everyone else it's a game of chicken where for "a while" money that you put into it will have a good return. Everyone would love if that lasted forever so there is a strong momentum preventing that market correction.
It was overvalued when crypto was happening too, but another boom took its place. Of course, lightening rarely strikes twice and all that, but it proves overvalued doesn’t mean the price is guaranteed to go down it seems. Predicting the future is hard.
if there was anything i was going to bet against between 2019 and now, it was nvidia... and wow it feels wild how much in the opposite direction it went.
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.
Crypto & AI can both be linked to part of a broader trend though, that we need processors capable of running compute on massive sets of data quickly. I don't think that will ever go down, whether some new tech emerges or we just continue shoveling LLMs into everything. Imagine the compute needed to allow every person on earth to run a couple million tokens through a model like Anthropic Opus every day.
Agree on looking at the company-behind-the-numbers. Though presumably you're aware of the Efficient Market Hypothesis. Shouldn't "slowed down datacenter growth" be baked into the stock price already?
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?
I saw a $100 bill on the ground. I nearly picked it up before I stopped myself. I realised that if it was a genuine currency note, the Efficient Market would have picked it up already.
> This article goes more into the technical analysis of the stock rather than the underlying business fundamentals that would lead to a stock dump.
My 30k ft view is that the stock will inevitably slide as AI
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.
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:
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.
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 large api/token providers, and large consumers are all investing in their own hardware. So, they are in an interesting position where the market is growing, and NVIDIA is taking the lion's share of enterprise, but is shrinking at the hyperscaler side (google is a good example as they shift more and more compute to TPU). So, they have a shrinking market share, but its not super visible.
> The large api/token providers, and large consumers are all investing in their own hardware.
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.
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.
At this point computation is in essence commodity. And commodities have demand cycles. If other economic factors slowdown or companies go out of business they stop using compute or start less new products that use compute. Thus it is entirely realistic to me that demand for compute might go down. Or that we are just now over provisioning compute in short or medium term.
I wonder, is the quality of AI answers going up over time or not? Last weekend I spent a lot of time with Preplexity trying to understand why my SeqTrack device didn't do what I wanted it to do and seems Perplexity had a wrong idea of how the buttons on the device are laid out, so it gave me wrong or confusing answers. I spent literally hours trying to feed it different prompts to get an answer that would solve my problem.
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?
Always worth trying a different model, especially if you’re using a free one. I wouldn’t take one data point to seriously either.
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.
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.
Both true. Perplexity knows a lot about SeqTrack, I assume it has read the UserGuide. But some things it gets wrong, seems especially things it should understand by looking at the pictures.
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).
With vision models (SOTA models like Gemini and ChatGPT can do this), you can take a picture/screenshot of the button layout, upload it, and have it work from that. Feeding it current documentation (eg a pdf of a user manual) helps too.
Referencing outdated documentation or straight up hallucinating answers is still an issue. It is getting better with each model release though
More so I meant to think of oil, copper and now silver. All follow demand for the price. All have had varying prices at different times. Compute should not really be that different.
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.
Even suggesting that computers will replace human brains brings up a moral and ethical question. If the computer is just as smart as a person, then we need to potentially consider that the computer has rights.
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...
I don’t see this as fanaticism at all. No one could predict a billion people mindlessly scrolling tiktok in 2007. This is going to happen again, only 10x. Faster and more addictive, with content generated on the fly to be so addictive, you won’t be able to look away.
What if its penetration ends up being on the same level as modern crypto? Average person doesn't seem to particularly care about meme coins or bitcoin - it is not being actively used in day to day setting, there's no signs of this status improving.
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.
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.
Anecdotally, many non-technical users or "regular joes" as it were that I know who were very enthusiastic about AI a year ago are now disengaging. With the rate really picking up the last couple of months.
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."
This seems to take for granted that China and their foundries and engineering teams will never catch up. This seems foolish. I'm working under the assumption that sometime in the next ten years some Chinese company will have a breakthrough and either meet Nvidia's level or leapfrog them. Then the market will flood with great, cheap chips.
Even though I like CUDA, I think the point is when do compute centers reach the point that they can run their workloads with other vendors, or custom accelerators.
> 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.
100% this. all of this spending is predicated on a stratospheric ROI on AI investments at the proposed investment levels. If that doesn't pan out, we'll see a lot of people left holding the cards including chip fabs, designers like Nvidia, and of course anyone that ponied up for that much compute.
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'm not an insider, but ASICs come with their own suite of issues and might be obsolete if a different architecture becomes popular. They'll have a much shorter lifespan than Nvidia hardware in all likelihood, and will probably struggle to find fab capacity that puts them on equal footing in performance. For example, look at the GPU shortage that hit crypto despite hundreds of ASIC designs existing.
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.
You can build ASICs to be a lot more energy efficient than current GPUs, especially if your power budget is heavily bound by raw compute as opposed to data movement bandwidth. The tradeoff is much higher latency for any given compute throughput, but for workloads such as training or even some kinds of "deep thinking inference" you don't care much about that.
I think the way to think about the AI bubble is that we're somewhere in 97-99 right now, heading toward the dotcom crash. The dotcom crash didn't kill the web, it kept growing in the decades that followed, influencing society more and more. But the era where tons of investments were uncritically thrown at anything to do with the web ended with a bang.
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.
Bubble or not it’s simply strange to me that people confidently put a timeline on it. To name the phases of the bubble and calling when they will collapse just seems counter intuitive to what a bubble is. Brad Gerstner was the first “influencer” I heard making these claims of a bubble time line. It just seems downright absurd.
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.
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 think the idea of fundamental analysis that you focus on return on equity and see if that valuation is appreciably more than the current price (as opposed to assigning a probability)
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.
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.
According to nvidia’s 2025 annual report [1], 34% of their sales for 2025 comes from just 3 customers.
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.
[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.
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.
Its probably not very likely that if a large buyer pulled out, NVIDIA could just sell to other customers. If a large buyer pulls out, that's a massive signal to everyone else to begin cutting costs as well. The large buyer either knows something everyone else doesn't, or knows something that everyone else has already figured out. Either way, the large buyer pulling out signals "I don't think the overall market is large enough to support this amount of compute at these prices at current interest rates" and everybody is doing the same math too.
None of those customers can afford to cancel their orders. OpenAI, Google and Meta cannot afford to get cheap on GPUs when presumably they believe GAI is around the corner. The first company to achieve GAI will win because at that point all gains will become exponential.
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.
I don't know why you are being downvoted. What you said makes sense to me but I understand I know very little about how companies think. Can someone with a differing point of view elaborate?
No idea why the downvotes, these are valid points. I still don’t fully agree with it:
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.
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.
I've been selling options for almost a decade now, including running trading algorithms, and was laughing a bit to myself because it was basically just the math in an everyday option chain. As you already know, anyone can look at the strike they are talking about, with the IV already cooked into it, on platforms like Think Or Swim or even Yahoo Finance. Some of the stuff can be pretty useful though in backtesting and exploration.
All that aside, I'm impressed it made it to the HN front page.
> 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.
It costs eight figures to create the masks (patterns) to use in the process of creating a modern chip. Just because it doesn't cost the eleven figures of the factory itself doesn't make it cheap.
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.
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]
The fab in Washington is very old (notice it's still equipped for 8 inch wafers) and so pretty irrelevant to Nvidia's business.
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 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.
It seems obvious to me this quickly escalates to a US nuclear first strike with the B2-Spirit on China manufacturing infrastructure.
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.
From my perspective, the US is foot-gunning itself into geopolitical irrelevance through the destruction of its soft power and undermining the NATO alliance. For all its many, many faults, the CCP's actions around establishing itself as the Asian regional superpower are patient, strategic and consistent over the long term. They're playing it far too smart to get into a shooting war with the US, and I'm in agreement that they'll probably end up consolidating Taiwan peacefully in a decade or so.
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.
This is the beauty of Polymarket. Then bet on it. There are so many more outcomes possible to this conflict than what you see reported in the media. Don't be so reductive.
When people do this kind of predictions, they often driven by emotional reaction. Best thing to switch actual evaluation on certain hypothesis is to make actual risks cost something.
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.
Arizona fabs don't work without TW's many sole source suppliers for fab consumables. They'll likely grind to halt after few months when stock runs out. All the dollar shuffling's not going to replace supply chain that will take (generously) years to build, if ever.
Yes, lots of other companies would be affected to a greater or lesser extent (even non-tech stocks), but specifically any company that relies on manufacturing all their product in Taiwan will be affected most of all.
I'd be curious how many of the design and verification (using computer vision) tools used at TI and Intel rely on on farms of stock GPUs thus chips still made in Taiwan. They might have in house chips just for such part of their workflows though, any insight appreciated.
The whole economy will crash. Probably won't be due to China invading Taiwan though. More likely because the president decided to delete their country's world reserve currency status (which is another word for a trade deficit).
Well, the reality is that most people don't want a bloodbath and it's increasingly looking like external support won't come, so what you gonna do... life is a very complex chess game, gotta play your pieces right.
At this rate, even if they can't get the Taiwanese population to consent, it probably makes more sense to wait anyway to see how low America can sink. The lower America goes, the better their chance for success.
Where do you see the pro china side getting more and more support? As far as I can tell it's sharply swung towards maintaining independence in the past decade or two with single digit support of unification with the mainland.
An EU type agreement will keep peace for some time. Remove all trade barriers between two countries, have a treaty preventing any side to be used militarily by third party, no attacking each other and free movement of all vessels through each other's seas. Maybe few more
Thats just buying China more time until they can get their chip manufacturing to at least a similar ballpark. Then Taiwan has no cards left to play. China can cripple TSMC depriving the west of chips while they continue onwards.
"buying China more time". China has no time-pressure to attack immediately, but all the upside right now of pretending to a stable, sensible world leader. Treaty with Taiwan will keep the ego of One China, prevent it from naval blockade by Taiwanese territories and will remove one of the major territorial issues raised against it.
I don't know about that...don't they have massive overcapacity in many of their industries as well as ~25% youth unemployment? For all the mess the US is going through at least we are seeing it out in the open. China seems to be going through their own messes right now but it is behind the great wall. Will a treaty be enough or will their leaders falter and try to push for more. Guess we will see.
I think Taiwanese elites can be bought, they say they can’t but I think that’s just part of the bargaining for a higher price. The overtures towards a costly and destructive invasion is Chinas attempt at lowering that price. As is the strategy of building up an indigenous chip manufacturing industry. The aggressive rhetoric from China has the added benefit of keeping the US on a self sabotaging aggressive posture.
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.
They're enjoying a massive demand for GPUs due to AI blowing up, at a time when there isn't much competition, yet the technology is already pleateauing, with similar offerings from AMD, not to mention proven training & inference chips from Google & AWS, plus the Chinese national strategy of prioritizing domestic chips
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....
As others have noted, the article is analysing the actual financial markets angle.
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 :)
>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?
Accuracy wise we won't know the exact numbers, but insiders and industry experts usually are able to find ballpark figures that they share with the press. The alternative is the usual find out the estimates through competitors' lost MAU numbers in apps like ChatGPT for iOS.
It's entirely possible it will crash, but I also don't think it'll go bankrupt or anything.
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.
I don't dispute that all. I'm just ok with that being the outcome; if it keeps up with inflation then it's still better than storing it in a bank, and the thing that would upset me more than "not gaining" would be "actively losing".
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.
> 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.
Worth noting that the implied volatility extracted here is largely a function of how far OTM the strike is relative to current spot, not some market-specific view on $100. If NVDA were trading at $250 today, the options chain would reprice and you'd extract similar vol for whatever strike was ~45% below. The analysis answers "what's the probability of a near-halving from here" more than "what's special about $100." Still useful for the prediction contest, but the framing makes it sound like the market is specifically opining on that price level.
Will AI remain like a simple query engine? I think not. AI is already iterative and does so many recalculations at every step and every new piece of data. Large models that know-it-all are being supplemented by more specialized models or models that have special access to private data. Why wouldn't you consult 100 models of a medical opinion or a stock market analysis. The point is, that for the next few years we will likely consume as many "tokens" as are affordably available, depending on the applications need for more computations. NVidia will develop it's other products and will develop edge devices and every type of GPUs, TPUs, etc. Markets climb a wall of worry and investors love greater fools whenever they are available.
Everyone is saying data center build outs are the main thing to look out for. But those data centers with all those gpus will need to replace those gpus right? Nvidia will come up with better, faster, more efficient gpus.
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.
There is one thing everybody forgets when making such predictions: companies don't stand still. Nvidia and every other tech business is constantly exploring new options, taking over competitors, buying startups with novel technologies etc... Nvidia is no slouch in that regard, and their recent quasi-acquisition of Groq is just one example of this. So, when attempting at making predictions, we're looking at a moving target, not systems set in stone. If the people at the helm are smart (and they are), you can expect lots of action and ups and downs - especially in the AI sphere.
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.
LLM is already an outdated term in terms of frontier models. Continued build out of ever larger data centers will drive the coming wave of robotics with varying forms of world models. People who claim LLMs won’t change the world are stuck in some sort of tunnel vision, IMO. I don’t think Nvidia is at risk of the AI industry falling flat. More like from large companies creating their own chips specific to their needs (ie Tesla). Although there aren’t many companies in the world that are THAT ambitious. I think Nvidia continues its general trend upwards.
This is a market can stay irrational problem. Modern compute infrastructure from phones, 5G, data centers, LLMs have their energy economics exactly backwards which combined with plastic waste is causing a massive global economic distortion that will correct itself bc physics doesn’t care about the white house, Vanguard, TSMC or anyone else. How long we can borrow from the future and put stress on the poor to prop up this insanely wasteful system who can say.
This kind of prediction is hard because it has a dependency on when will AI companies crash. The market would need to lose confidence in AI which should make data-centre creation stop and then impact nVidia.
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?
Predicting any stock will crash, be it from a technical analysis or from looking at fundamentals, is a fool's game. As Keynes allegedly said, the market can stay irrational longer than you can remain solvent.
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.
How much of their turnover is financed directly or indirectly by themselves, then leveraged further by their 'customers' to collaterize further investments?
Are they already "too big to fail"? For better or worse, they are 'all in' on AI.
If Nvidia stock crashed, it would likely trigger a broader sell-off in AI and semiconductor stocks, impacting the overall tech market, including my small startup :/
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.
While I am no fan of NVIDIA, they are effectively a Monopoly for CUDA GPU.
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
Sure you can predict the market. Making money off of it beyond the regular risk-adjusted return is what's hard. (And the prediction of this article is indeed based on that assumption.)
It's forward looking P/E is 24-26. That doesn't seem like a huge crash is coming. It could come down a bit but they print money. They also have potential car market and robots coming in.
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.
This is fun math to play with, but completely misses the point of how and why options are priced the way they are. Think of horse racing - when a horse is 1000 to 1 odds the odds of that horse winning are much, much lower. The odds are non-zero, but the oddsmakers are considering who the other side is and why they are buying that ticket.
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.
Are you saying options are not priced at the cost of hedging them? That implies a lot of money could be made by arbitraging between the hedge and the option.
That sounds like an egregious statement. Markets don't have simple persistent arbitrage opportunities like that, do they?
I would be wary of taking analysis like this website at face value unless you know enough about quant finance to check some of the working for yourself. Just a skim shows a few statements that are questionable at best. Eg
> 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.
[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=...
AIUI, there's nothing wrong per se with treating the "market opinion" of the volatility as a subjective probability, since that's effectively what it becomes given sensible no-arbitrage constraints. Just keep in mind that "bad" states of the world will be heavily overweighted in the resulting subjective expectation, for the risk-adjustment reasons mentioned in the OP.
Definitely. As someone once explained it to me, you always overpay for insurance and you're right (from a marginal utility/risk of ruin standpoint) to do so. That's why the vol skew is pretty much always such that crash puts are more expensive than oom calls.
Author here. Thanks for the well-reasoned and well-sourced criticism! I'm just a layman. I admit to all misunderstandings and am happy to receive links and words to search for to learn more.
It’s a cool blog post so I’m making those observations from what I hope you will see as a helpful place. It’s been a long time since I was in the industry, and the sources you’re looking at (eg Shreve) are all great. One thing you might want to check out as well is “The Volatility Surface: A Practitioner’s Guide” by Jim Gatherall. I remember really enjoying it back when I worked in Equity Exotics and it was very highly regarded by the people I really respected in the financial maths stakes.
For sure. I mean it when I say I appreciate the criticism!
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.
I’m more curious how these “future” contract will work out. Supposedly, a bunch of RAM is paid and allocated for that isn’t even made yet. If the bubble ever pops, the collateral is going to be on the order of 2007 subprime mortgage crisis
Since there's such an interdependence between nvidia and the other companies involed in AI to the point that if one fails they all fail, shouldn't the analysis focus on the weakest link in the AI circle jerk?
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
Yeah, no signs that the AI craze is slowing down, other than all the stories of it not living up to the hype and creating more jobs rather than replacing people and not doing what it says on the tin and the various security issues and that whole they can't expand for the next decade because they need more power plants thing.
I mean common sense reasoning tells me that if OpenAI has decided to turn into an ad business, the actual return expected from investing into compute isn't going to be nearly as great as advertised.
Great question! The fine print of the text on Metaculus says the outcome will be judged as if there were no stock split. The question is essentially about the valuation of the company but phrased operationally about the stock price.
It's easy to predict that a bubble will pop, but there's a variance in the timing of approximately half a human lifetime, and if you don't guess that correctly, you throw away yours.
Everything that can't go on forever will eventually stop. But when?
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.
Technical analysis is the projection of future price data through analysis of past price data (usually for the purpose of trying to create trendlines or find "patterns"). Options pricing is quite a different beast - it encodes marketwide uncertainty about the future price of the underlying, which has little to do with the past price action of the underlying, and everything to do with all known information about the actual underlying company, including fundamentals analysis, market sentiment, future expectations and risks, etc.
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.
This article goes more into the technical analysis of the stock rather than the underlying business fundamentals that would lead to a stock dump.
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.
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.
People will want more GPUs but will they be able to fund them? At what points does the venture capital and loans run out? People will not keep pouring hundreds of billions into this if the returns don't start coming.
Money will be interesting the next few years.
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?
Not that locked out: https://www.cnbc.com/2025/12/31/160-million-export-controlle...
Yeah, that's the bull case for sure. Chinese firms might not accept training setbacks even given CCP regulations that they dogfood X homegrown chip.
I suspect major algorithmic breakthroughs would accelerate the demand for GPUs instead of making it fall off, since the cost to apply LLMs would go down.
Sounds like the Jevons paradox. From https://en.wiktionary.org/wiki/Jevons_paradox :
> 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
Some changes to the algorithms and implementations will allow cheaper commodity hardware to be used.
There will always be an incentive to scale data centers. Better algorithms just mean more bang per gpu, not that “well, that’s enough now, we’ve done it”.
Doesn't even necessarily need to be CUDA compatible... there's OpenCL and Vulkan as well, and likely China will throw enough resources at the problem to bring various libraries into closer alignment to ease of use/development.
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.
> there's OpenCL and Vulkan as well
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 modern successor to OpenCL is SYCL and there's been some limited convergence with Vulkan Compute (they're still based on distinct programming models and even SPIR-V varieties under the hood, but the distance is narrowing somewhat).
Which is basically Intel for practical purposes.
Lemurian Labs is working on this https://www.lemurianlabs.com/
Ask Claude, HN tells me that it can implement the things that you ask.
> short of major algorithmic breakthroughs I am not convinced the global demand for GPUs will drop any time soon
Or, you know, when LLMs don't pay off.
Even if LLMs didn't advance at all from this point onward, there's still loads of productive work that could be optimized / fully automated by them, at no worse output quality than the low-skilled humans we're currently throwing at that work.
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 LLMs are much more compute-intensive than any other type of generative or task-specific model
Run out of training data? They’re going to put these things in humanoids (they are weirdly cheap now) and record high resolution video and other sensor data of real world tasks and train huge multimodal Vision Language Action models etc.
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.
While we may run out of human written text of value, we won't run out of symbolic sequences of tokens: we can trivially start with axioms and do random forward chaining (or random backward chaining from postulates), and then train models on 2-step, 4-step, 8-step, ... correct forward or backward chains.
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).
Yeah, another source of "unlimited data" is genetics. The human reference genome is about 6.5 GB, but these days, they're moving to pangenomes, wanting to map out not just the genome of one reference individual, but all the genetic variation in a clade. Depending on how ambitious they are about that "all", they can be humongous. And unlike say video data, this is arguably a language. We're completely swimming in unmapped, uninterpreted language data.
Can you say more?
Inference leans heavily on GPU RAM and RAM bandwidth for the decode phase where an increasingly greater amount of time is being spent as people find better ways to leverage inference. So NVIDIA users are currently arguably going to demand a different product mix when the market shifts away from the current training-friendly products. I suspect there will be more than enough demand for inference that whatever power we release from a relative slackening of training demand will be more than made up and then some by power demand to drive a large inference market.
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.
> 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.
Does this also apply to LLM training at scale? I would be a bit surprised if it does, fwiw.
Yup, as soon as data is the bottleneck and not compute, diffusion wins. Tested following the Chinchilla scaling strategy from 7M to 2.5B parameters.
https://arxiv.org/abs/2507.15857
I'm just talking about text generated by human beings. You can keep retraining with more parameters on the same corpus
https://proceedings.mlr.press/v235/villalobos24a.html
> I'm just talking about text generated by human beings.
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.
How much of the current usage is productive work that's worth paying for vs personal usage / spam that would just drop off after usage charges come in? I imagine flooding youtube and instagram with slop videos would reduce if users had to pay fair prices to use the models.
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.
For any "click here and type into a box" job for which you'd hire a low-skilled worker and give them an SOP to follow, you can have an LLM-ish tool do it.
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.
Another bozo making fun of other job classes.
Why are there still customer service reps? Shouldn’t they all be gone by now due to this amazing technology?
Ah, tumbleweed.
Who is generating videos for free?
Exactly, the current spend on LLMs is based on extremely high expectations and the vendors operating at a loss. It’s very reasonable to assume that those expectations will not be met, and spending will slow down as well.
Nvidia’s valuation is based on the current trend continuing and even increasing, which I consider unlikely in the long term.
> Nvidia’s valuation is based on the current trend continuing
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.
China is restricting purchases of H200s. The strong likelihood is that they're doing this to promote their own domestic competitors. It may take a few years for those chips to catch up and enter full production, but it's hard to envision any "trillion dollar" Nvidia defense empire once that happens.
It's very easy to envision. America needs chips, and Intel can't do most of this stuff.
Intel makes GPUs.
Intel's GPU designs make AMD look world-class by comparison. Outside of transcode applications, those Arc cards aren't putting up a fight.
...if you can't be with the one you love, love the one you're with?
Intel's GPU story all their life.
> short of major algorithmic breakthroughs I am not convinced the global demand for GPUs will drop any time soon
>> 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!
They already are paying off. The nature of LLMs means that they will require expensive, fast hardware that's a large capex.
They aren’t yet because the big providers that paid for all of this GPU capacity aren’t profitable yet.
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.
Google, Amazon, and Microsoft aren’t profitable?
I assume the reference was AI use cases are not profitable. Those companies are subsidizing and OpenAI/grok are burning money.
Yeah but OpenAI is adding ads this year for the free versions, which I'm guessing is most of their users. They are probably hedging on taking a big slice of Google's advertising monopoly-pie (which is why Google is also now all-in on forcing Gemini opt-out on every product they own, they can see the writing on the wall).
Google, Amazon, and Microsoft do a lot of things that aren't profitable in themselves. There is no reason to believe a company will kill a product line just because it makes a loss. There are plenty of other reasons to keep it running.
I didn't imply anything about what big-tech would do.
Do you think it's odd you only listed companies with already existing revenue streams and not companies that started with and only have generative algos as their product?
Aren't all Microsoft products OpenAI based? OpenAI has always been burning money.
How many business units have Google and Microsoft shut down or ceased investment for being unprofitable?
I hear Meta is having massive VR division layoffs…who could have predicted?
Raw popularity does not guarantee sustainability. See: Vine, WeWork, MoviePass.
Where? Who’s in the black?
The users.
Ehhhhhhh
Algorithmic breakthroughs (increases in efficiency) risk Jevons Paradox. More efficient processes make deploying them even more cost effective and increases demand.
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
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
Personally, I try to predict how others are going to predict that yet others will react.
You jerk
Third-derivative pun.
Riposte: I knew you'd say that! Snap!
And I just predict how you’ll predict
So we have a closed instability/volatility amplification loop. Great: Time for the straddle with finger-cross trade.
Keynesian beauty contest.
This was also on top of claims (Jan 2025) that Deepseek showed that "we don't actually need as much GPU, thus NVidia is less needed"; at least it was my impression this was one of the (now silly-seeming) reasons NVDA dropped then.
It had already recovered from the DeepSeek head fake iirc
> 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.
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.
Google did not use TPUs for literally every bit of compute that led to Gemini. GCP has millions of high end Nvidia GPUs and programming for them is an order of magnitude easier, even for googlers.
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.
> including all previous experiments
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.
Why should it not react heavily? What’s stopping this from being a start of a trend for google and even Amazon?
JAX is very easy to use. Give it a try.
They are not lies.
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.
It’s not that they don’t work. It’s how businesses handle hardware.
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.
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.
Well demand is so high currently that it's likely this cycle doesn't exist yet for fast cards.
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
Yea looking at 60 day moving average on computeprices.com H100 have actually gone UP in cost recently, at least to rent.
A lot of demand out there for sure.
Not sure why this "GPUs obsolete after 3 years" gets thrown around all the time. Sounds completely nonsensical.
Especially since AWS still have p4 instances that are 6 years old A100s. Clearly even for hyperscalers these have a useful life longer than 3 years.
I agree that there is hyperbole thrown around a lot here and its possible to still use some hardware for a long time or to sell it and recover some cost but my experience in planning compute at large companies is that spending money on hardware and upgrading can often result in saving money long term.
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.
Sure, but I don't think most people here are objecting to the obvious "3 years is enough for enterprise GPUs to become totally obsolete for cutting-edge workloads" point. They're just objecting to the rather bizarre notion that the hardware itself might physically break in that timeframe. Now, it would be one thing if that notion was supported by actual reliability studies drawn from that same environment - like we see for the Backblaze HDD lifecycle analyses. But instead we're just getting these weird rumors.
I agree that is a strange notion that would require some evidence and I see it in some other threads but looking at the parent comments going up it seems people are discussing economic usefulness so that is what I'm responding to.
A toy example: NeoCloud Inc builds a new datacenter full of the new H800 GPUs. It rents out a rack of them for $10/minute while paying $6/minute for electricity, interest, loan repayment, rent and staff.
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]
It really depends on how long `NeoCloud` takes to recoup their capital expenditure on the H800s.
Current estimates are about 1.5-2 years, which not-so-suspiciously coincides with your toy example.
It's because they run 24/7 in a challenging environment. They will start dying at some point and if you aren't replacing them you will have a big problem when they all die en masse at the same time.
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.
A makeshift cryptomining rig is absolutely a "challenging environment" and most GPUs by far that went through that are just fine. The idea that the hardware might just die after 3 years' usage is bonkers.
Crypto miners undervote for efficiency GPUs and in general crypto mining is extremely light weight on GPUs compared to AI training or inference at scale
With good enough cooling they can run indefinitely!!!!! The vast majority of failures are either at the beginning due to defects or at the end due to cooling! It’s like the idea that no moving parts (except the HVAC) is somehow unreliable is coming out of thin air!
Economically obsolete, not obsolete, I suspect this is in line with standard depreciation.
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.)
Do you know how support contract lengths are determined? Seems like a path to force hardware refreshes with boilerplate failure data carried over from who knows when.
> Rack space is limited.
Rack space and power (and cooling) in the datacenter drives what hardware stays in the datacenter
The common factoid raised in financial reports is GPUs used in model training will lose thermal insulation due to their high utilization. The GPUs ostensibly fail. I have heard anecdotal reports of GPUs used for cryptocurrency mining having similar wear patterns.
I have not seen hard data, so this could be an oft-repeated, but false fact.
It's the opposite actually - most GPU used for mining are run at a consistent temp and load which is good for long term wear. Peaky loads where the GPU goes from cold to hot and back leads to more degradation because of changes in thermal expansion. This has been known for some time now.
That is commonly repeated idea, but it doesn't take into account countless token farms which are smaller than a datacenter. Basically anything from a single MB with 8 cards to a small shed with rigs, all of which tend to disregard common engineering practices and run hardware into a ground to maximize output until next police raid or difficulty bump. Plenty of photos in the internet of crappy rigs like that, and no one guarantees which GPU comes whom where.
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.
While I'm sure there are small amateur setups done poorly that push cards to their limits this seems like a more rare and inefficient use. GPUS (even used) are expensive and running them at maximum would require large costs and time to be replacing them regularly. Not to mention the increased cost of cooling and power.
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
Let's also not forget the set of miners that either overclock or dont really care about long term in how they set up thermals
Miners usually don't overclock though. If anything underclocking is the best way to improve your ROI because it significantly reduces the power consumption while retaining most of the hashrate.
Exactly - more specifically undervolting. You want the minimum volts going to the card with it still performing decently.
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)
The only amatures I know doing this are trying to heat their garrage for free. so long as the heat gain is paid for they can afford to heat an otherwise unheated building.
Wouldn't the exact same considerations apply to AI training/inference shops, seeing as gigawatts are usually the key constraint?
Specifically, we expect a halving of lifetime per 10K increase in temperature.
Why would police raid a shed housing a compute center?
Source?
> I have heard anecdotal reports of GPUs used for cryptocurrency mining having similar wear patterns.
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.
I can't confirm that fact - but it's important to acknowledge that consumer usage is very different from the high continuous utilization in mining and training. It is credulous that the wear on cards under such extreme usage is as high as reported considering that consumers may use their cards at peak 5% of waking hours and the wear drop off is only about 3x if it is used near 100% - that is a believable scale for endurance loss.
1-3 is too short but they aren’t making new A100s, theres 8 in a server and when one goes bad what do you do? you wont be able to renew a support contract. if you wanna diy you eventually you have to start consolidating pick and pulls. maybe the vendors will buy them back from people who want to upgrade and resell them. this is the issue we are seeing with A100s and we are trying to see what our vendor will offer for support.
They're no longer energy competitive. I.e. the amount of power per compute exceeds what is available now.
It's like if your taxi company bought taxis that were more fuel efficient every year.
Margins are typically not so razor thin that you cannot operate with technology from one generation ago. 15 vs 17 mpg is going to add up over time, but for a taxi company it's probably not a lethal situation to be in.
At least with crypto mining this was the case. Hardware from 6 months ago is useless ewaste because the new generation is more power efficient. All depends on how expensive the hardware is vs the cost of power.
Tell that to the airline industry
And yet they aren't running planes and engines all from 2023 or beyond: See the MD-11 that crashed in Louisville: Nobody has made a new MD-11 in over 20 years. Planes move to less competitive routes, change carriers, and eventually might even stop carrying people and switch to cargo, but the plane itself doesn't get to have zero value when the new one comes out. An airline will want to replace their planes, but a new plane isn't fully amortized in a year or three: It still has value for quite a while
I don't think the airline industry is a great example from an IT perspective, but I agree with regard to the aircraft.
Nvidia has plenty of time and money to adjust. They're already buying out upstart competitors to their throne.
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.
If a taxi company did that every year, they'd be losing a lot of money. Of course new cars and cards are cheaper to operate than old ones, but is that difference enough to offset buying a new one every one to three years?
>If a taxi company did that every year, they'd be losing a lot of money. Of course new cars and cards are cheaper to operate than old ones, but is that difference enough to offset buying a new one every one to three years?
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.
If there was a new taxi every other year that could handle twice as many fares, they might. That’s not how taxis work but that is how chips work.
>offset buying a new one every one to three years?
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 wants to buy GPUs that were redlined for three years in a data center? Maybe there's a market for those, but most people already seem wary of lightly used GPUs from other consumers, let alone GPUs that were burning in a crypto farm or AI data center for years.
> Who wants to buy
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.
The leasing company cares - the lease terms depend on the answer. That is why I can lease a car for 3 years for the same payment as a 6 year loan (more or less) - the lease company expects someone will want it. If there is no market for it after they will still lease it but the cost goes up
Depends on the price, of course. I'm wary of paying 50% of new for something run hard 3 years. Seems an NVIDIA H100 is going for $20k+ on EBay. I'm not taking that risk.
Depending on the discount, a lot of people.
That works either because someone wants to buy old hardware for the manufacturer/lessor, or because the hardware is EOL in 3 years but it's easier to let the lessor deal with recyling / valuable parts recovery.
If your competitor refreshes their cards and you dont, they will win on margin.
You kind of have to.
Not necessarily if you count capital costs vs operating costs/margins.
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.
You can sell the old, less efficient GPUs to folks who will be running them with markedly lower duty cycles (so, less emphasis on direct operational costs), e.g. for on-prem inference or even just typical workstation/consumer use. It ends up being a win-win trade.
Then you’re dealing with a lot of labor to do the switches (and arrange sales of used equipment), plus capital float costs while you do it.
It can make sense at a certain scale, but it’s a non trivial amount of cost and effort for potentially marginal returns.
Building a new data center and getting power takes years to double your capacity. Swapping out out a rack that is twice as fast takes very little time in comparison.
Huh? What does your statements have to do with what I’m saying?
I’m just pointing out changing it out at 5 years is likely cheaper than at 3 years.
Depends at the rate of growth of the hardware. If your data center is full and fully booked, and hardware is doubling in speed every year it's cheaper to switch it out every couple of years.
So many goal posts being changed constantly?
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?
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.
You are assuming something again.
They both refresh their equipment at the same rate.
> 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.
Not saying your wrong. A few things to consider:
(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.
Nvidia is moving to a 1 year release life cycle for data center, and in Jensen's words once a new gen is released you lose money for being on the older hardware. It makes no longer financially sense to run it.
Do you not see the bad logic?
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.
That will come back to bite them in the ass if money leaves the AI race.
based on my napkin math, an H200 needs to run for 4 years straight at maximum power (10.2 kW) to consume its own price of $35k worth of energy (based on 10 cents per kWh)
If power is the bottleneck, it may make business sense to rotate to a GPU that better utilizes the same power if the newer generation gives you a significant advantage.
I think the story is less about the GPUs themselves, and more about the interconnects for building massive GPU clusters. Nvidia just announced a massive switch for linking GPUs inside a rack. So the next couple of generations of GPU clusters will be capable of things that were previously impossible or impractical.
This doesn't mean much for inference, but for training, it is going to be huge.
From an accounting standpoint, it probably makes sense to have their depreciation be 3 years. But yeah, my understanding is that either they have long service lives, or the customers sell them back to the distributor so they can buy the latest and greatest. (The distributor would sell them as refurbished)
You aren't trying to support ad-based demand like OpenAI is.
> 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.
Personally I wonder even if the LLM hype dies down we'll get a new boom in terms of AI for robotics and the "digital twin" technology Nvidia has been hyping up to train them. That's going to need GPUs for both the ML component as well as 3D visualization. Robots haven't yet had their SD 1.1 or GPT-3 moment and we're still in the early days of Pythia, GPT-J, AI Dungeon, etc. in LLM speak.
Exactly, they will pivot back to AR/VR
That's going to tank the stock price though as that's a much smaller market than AI, though it's not going to kill the company. Hence why I'm talking about something like robotics which has a lot of opportunity to grow and make use of all those chips and datacenters they're building.
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.
Nvidia's not your average bear, they can walk and chew bubblegum at the same time. CUDA was developed off money made from GeForce products, and now RTX products are being subsidized by the money made on CUDA compute. If an enormous demand for efficient raster compute arises, Nvidia doesn't have to pivot much further than increasing their GPU supply.
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.
That's the rub - it's clearly overvalued and will readjust... the question is when. If you can figure out when precisely then you've won the lottery, for everyone else it's a game of chicken where for "a while" money that you put into it will have a good return. Everyone would love if that lasted forever so there is a strong momentum preventing that market correction.
It was overvalued when crypto was happening too, but another boom took its place. Of course, lightening rarely strikes twice and all that, but it proves overvalued doesn’t mean the price is guaranteed to go down it seems. Predicting the future is hard.
As they say, the market can remain irrational far longer than you can remain solvent.
Hah! Indeed
if there was anything i was going to bet against between 2019 and now, it was nvidia... and wow it feels wild how much in the opposite direction it went.
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.
It’s not impossible they could’ve seen AI investment coming but it would’ve been very hard
Crypto & AI can both be linked to part of a broader trend though, that we need processors capable of running compute on massive sets of data quickly. I don't think that will ever go down, whether some new tech emerges or we just continue shoveling LLMs into everything. Imagine the compute needed to allow every person on earth to run a couple million tokens through a model like Anthropic Opus every day.
Agreed, single thread performance increases are dead and things are moving to massively parallel processing.
Agree on looking at the company-behind-the-numbers. Though presumably you're aware of the Efficient Market Hypothesis. Shouldn't "slowed down datacenter growth" be baked into the stock price already?
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?
I saw a $100 bill on the ground. I nearly picked it up before I stopped myself. I realised that if it was a genuine currency note, the Efficient Market would have picked it up already.
The EMH does not mean that markets are free of over-investment and asset bubbles, followed by crashes.
> This article goes more into the technical analysis of the stock rather than the underlying business fundamentals that would lead to a stock dump. My 30k ft view is that the stock will inevitably slide as AI
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.
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.
Also there's no way Nvidia's market share isn't shrinking. Especially in inference.
The large api/token providers, and large consumers are all investing in their own hardware. So, they are in an interesting position where the market is growing, and NVIDIA is taking the lion's share of enterprise, but is shrinking at the hyperscaler side (google is a good example as they shift more and more compute to TPU). So, they have a shrinking market share, but its not super visible.
> The large api/token providers, and large consumers are all investing in their own hardware.
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.
Market share can shrink but if the TAM is growing you can still grow.
But will the whole pie grow or shrink?
> My 30k ft view is that the stock will inevitably slide as AI datacenter spending goes down.
This is like saying Apple stock will inevitably slide once everybody owns a smartphone.
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.
At this point computation is in essence commodity. And commodities have demand cycles. If other economic factors slowdown or companies go out of business they stop using compute or start less new products that use compute. Thus it is entirely realistic to me that demand for compute might go down. Or that we are just now over provisioning compute in short or medium term.
I wonder, is the quality of AI answers going up over time or not? Last weekend I spent a lot of time with Preplexity trying to understand why my SeqTrack device didn't do what I wanted it to do and seems Perplexity had a wrong idea of how the buttons on the device are laid out, so it gave me wrong or confusing answers. I spent literally hours trying to feed it different prompts to get an answer that would solve my problem.
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?
Always worth trying a different model, especially if you’re using a free one. I wouldn’t take one data point to seriously either.
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.
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.
Both true. Perplexity knows a lot about SeqTrack, I assume it has read the UserGuide. But some things it gets wrong, seems especially things it should understand by looking at the pictures.
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).
That's nowhere near as true as it was as recently as a year ago.
With vision models (SOTA models like Gemini and ChatGPT can do this), you can take a picture/screenshot of the button layout, upload it, and have it work from that. Feeding it current documentation (eg a pdf of a user manual) helps too.
Referencing outdated documentation or straight up hallucinating answers is still an issue. It is getting better with each model release though
So...like Cisco during dot com bust?
More so I meant to think of oil, copper and now silver. All follow demand for the price. All have had varying prices at different times. Compute should not really be that different.
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.
> I no AI fanboy at all.
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.
Even suggesting that computers will replace human brains brings up a moral and ethical question. If the computer is just as smart as a person, then we need to potentially consider that the computer has rights.
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...
I don’t see this as fanaticism at all. No one could predict a billion people mindlessly scrolling tiktok in 2007. This is going to happen again, only 10x. Faster and more addictive, with content generated on the fly to be so addictive, you won’t be able to look away.
Vine was around then
What if its penetration ends up being on the same level as modern crypto? Average person doesn't seem to particularly care about meme coins or bitcoin - it is not being actively used in day to day setting, there's no signs of this status improving.
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.
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.
I agree with the all the startups but AI is already much more useful in everyday tasks vs crypto.
Eg: A chatbot assistant is much more tangible to the regular joe than blockchain technology
I agree that AI is much more useful than crypto ever was, but it's not as useful as AI hype valuation would like to paint it.
Anecdotally, many non-technical users or "regular joes" as it were that I know who were very enthusiastic about AI a year ago are now disengaging. With the rate really picking up the last couple of months.
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."
This seems to take for granted that China and their foundries and engineering teams will never catch up. This seems foolish. I'm working under the assumption that sometime in the next ten years some Chinese company will have a breakthrough and either meet Nvidia's level or leapfrog them. Then the market will flood with great, cheap chips.
Even though I like CUDA, I think the point is when do compute centers reach the point that they can run their workloads with other vendors, or custom accelerators.
> 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.
100% this. all of this spending is predicated on a stratospheric ROI on AI investments at the proposed investment levels. If that doesn't pan out, we'll see a lot of people left holding the cards including chip fabs, designers like Nvidia, and of course anyone that ponied up for that much compute.
Chip fabs will be fine. The demand for high end processors will remain because of the likes of Apple and AMD.
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'm not an insider, but ASICs come with their own suite of issues and might be obsolete if a different architecture becomes popular. They'll have a much shorter lifespan than Nvidia hardware in all likelihood, and will probably struggle to find fab capacity that puts them on equal footing in performance. For example, look at the GPU shortage that hit crypto despite hundreds of ASIC designs existing.
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.
You can build ASICs to be a lot more energy efficient than current GPUs, especially if your power budget is heavily bound by raw compute as opposed to data movement bandwidth. The tradeoff is much higher latency for any given compute throughput, but for workloads such as training or even some kinds of "deep thinking inference" you don't care much about that.
I think the way to think about the AI bubble is that we're somewhere in 97-99 right now, heading toward the dotcom crash. The dotcom crash didn't kill the web, it kept growing in the decades that followed, influencing society more and more. But the era where tons of investments were uncritically thrown at anything to do with the web ended with a bang.
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.
Bubble or not it’s simply strange to me that people confidently put a timeline on it. To name the phases of the bubble and calling when they will collapse just seems counter intuitive to what a bubble is. Brad Gerstner was the first “influencer” I heard making these claims of a bubble time line. It just seems downright absurd.
> technical analysis of the stock
AKA pictures in clouds
“In a gold rush, sell shovels”… Well, at some point in the gold rush everyone already has their shovels and pickaxes.
Or people start to realize that the expected gold isn't really there and so stop buying shovels
The version I heard growing up was "In a gold rush, sell eggs."
Selling jeans is the one that actually worked
It's not flat growth that's currently priced in, but continuing high growth. Which is impossible.
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.
Don't you need a model for how people will react to the fundamentals? People set the price.
Possibly? I don't know -- hence the question!
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.
I think the idea of fundamental analysis that you focus on return on equity and see if that valuation is appreciably more than the current price (as opposed to assigning a probability)
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.
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...
How much did you short the stock?
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According to nvidia’s 2025 annual report [1], 34% of their sales for 2025 comes from just 3 customers.
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.
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.
Its probably not very likely that if a large buyer pulled out, NVIDIA could just sell to other customers. If a large buyer pulls out, that's a massive signal to everyone else to begin cutting costs as well. The large buyer either knows something everyone else doesn't, or knows something that everyone else has already figured out. Either way, the large buyer pulling out signals "I don't think the overall market is large enough to support this amount of compute at these prices at current interest rates" and everybody is doing the same math too.
Or they might build another factory and fulfill all the orders they were previously unable to fulfill and increase their share even more.
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.
None of those customers can afford to cancel their orders. OpenAI, Google and Meta cannot afford to get cheap on GPUs when presumably they believe GAI is around the corner. The first company to achieve GAI will win because at that point all gains will become exponential.
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.
I don't know why you are being downvoted. What you said makes sense to me but I understand I know very little about how companies think. Can someone with a differing point of view elaborate?
No idea why the downvotes, these are valid points. I still don’t fully agree with it:
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...
He doesn't really address his own question.
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.
I've been selling options for almost a decade now, including running trading algorithms, and was laughing a bit to myself because it was basically just the math in an everyday option chain. As you already know, anyone can look at the strike they are talking about, with the IV already cooked into it, on platforms like Think Or Swim or even Yahoo Finance. Some of the stuff can be pretty useful though in backtesting and exploration.
All that aside, I'm impressed it made it to the HN front page.
> 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.
> But is not NVIDIA more of a software company than a hardware one?
No? That’s why they have almost no competition. Hardware starting costs are astronomical
But the actual manufacturing foundry is TSMC no? And they create the whole software environment based on their chips.
Apple doesn’t make hardware either, but they make a lot of money selling it.
It costs eight figures to create the masks (patterns) to use in the process of creating a modern chip. Just because it doesn't cost the eleven figures of the factory itself doesn't make it cheap.
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.
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.
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
The fab in Washington is very old (notice it's still equipped for 8 inch wafers) and so pretty irrelevant to Nvidia's business.
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.
WA fab is verrry old and makes commodity products, think like small microcontrollers etc. 160-350nm processes.
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.
If China does invade Taiwan, I feel like most people are going to have bigger problems than the Nvidia stock price.
It seems obvious to me this quickly escalates to a US nuclear first strike with the B2-Spirit on China manufacturing infrastructure.
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.
From my perspective, the US is foot-gunning itself into geopolitical irrelevance through the destruction of its soft power and undermining the NATO alliance. For all its many, many faults, the CCP's actions around establishing itself as the Asian regional superpower are patient, strategic and consistent over the long term. They're playing it far too smart to get into a shooting war with the US, and I'm in agreement that they'll probably end up consolidating Taiwan peacefully in a decade or so.
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.
There's approximately 0% chance that China will ship leading edge wafers from captured TSMC to the West.
Not true, it might be something they compromise on to restore relations
That's possible only if fabs are operational after the invasion.
This is the beauty of Polymarket. Then bet on it. There are so many more outcomes possible to this conflict than what you see reported in the media. Don't be so reductive.
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Short the stocks then, lol.
When people do this kind of predictions, they often driven by emotional reaction. Best thing to switch actual evaluation on certain hypothesis is to make actual risks cost something.
Im not recommending it, but the subject here (shorting the most valuable company on earth) is pretty degen to begin with.
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.
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.
Arizona fabs don't work without TW's many sole source suppliers for fab consumables. They'll likely grind to halt after few months when stock runs out. All the dollar shuffling's not going to replace supply chain that will take (generously) years to build, if ever.
They definitely made at least one wafer in Arizona in October.
Packaging? Assembling onto boards?
Can outsource to China. Only partial /s
But then again what won't? Non tech stocks?
Gold stocks, basic materials, MSCI world and emerging market indexes. Look at their prices and see how very smart people are positioning their money.
Isn't it included in "Non tech stocks?"?
Yes, but that’s not because of AI, it’s because of the Orange Wizard of Tarrifs.
Yes, lots of other companies would be affected to a greater or lesser extent (even non-tech stocks), but specifically any company that relies on manufacturing all their product in Taiwan will be affected most of all.
Industrial military complex and government contractors.
Don't they also depend on chips for a lot of components?
Probably a lot more from TI and Intel than Taiwan.
I'd be curious how many of the design and verification (using computer vision) tools used at TI and Intel rely on on farms of stock GPUs thus chips still made in Taiwan. They might have in house chips just for such part of their workflows though, any insight appreciated.
Jets, tanks, drones and data centers for intelligence services, even design, are full of electronics but what's the share of those not made in Taiwan?
They’re not cutting edge chips, they’re made all over, including the US, Korea, and China.
So the share is 0%? I have a hard time imagining that but OK, any source I can read on the topic please?
The whole economy will crash. Probably won't be due to China invading Taiwan though. More likely because the president decided to delete their country's world reserve currency status (which is another word for a trade deficit).
Idk the pro china side is getting more and more support, at this rate they’ll vote themselves into mainland
Well, the reality is that most people don't want a bloodbath and it's increasingly looking like external support won't come, so what you gonna do... life is a very complex chess game, gotta play your pieces right.
At this rate, even if they can't get the Taiwanese population to consent, it probably makes more sense to wait anyway to see how low America can sink. The lower America goes, the better their chance for success.
China is capable of taking long term view, beyond single election cycle. And currently USA really seems to be heading down faster and faster.
If something even more drastic happens. China might even attempt unification with some reasoning like protecting Taiwan from USA or other nations.
Where do you see the pro china side getting more and more support? As far as I can tell it's sharply swung towards maintaining independence in the past decade or two with single digit support of unification with the mainland.
https://esc.nccu.edu.tw/PageDoc/Detail?fid=7801&id=6963
https://esc.nccu.edu.tw/upload/44/doc/6963/Tondu202512.png
An EU type agreement will keep peace for some time. Remove all trade barriers between two countries, have a treaty preventing any side to be used militarily by third party, no attacking each other and free movement of all vessels through each other's seas. Maybe few more
Thats just buying China more time until they can get their chip manufacturing to at least a similar ballpark. Then Taiwan has no cards left to play. China can cripple TSMC depriving the west of chips while they continue onwards.
"buying China more time". China has no time-pressure to attack immediately, but all the upside right now of pretending to a stable, sensible world leader. Treaty with Taiwan will keep the ego of One China, prevent it from naval blockade by Taiwanese territories and will remove one of the major territorial issues raised against it.
I don't know about that...don't they have massive overcapacity in many of their industries as well as ~25% youth unemployment? For all the mess the US is going through at least we are seeing it out in the open. China seems to be going through their own messes right now but it is behind the great wall. Will a treaty be enough or will their leaders falter and try to push for more. Guess we will see.
I don't think is about China, it's more about Winnie the Pooh's legacy.
I think Taiwanese elites can be bought, they say they can’t but I think that’s just part of the bargaining for a higher price. The overtures towards a costly and destructive invasion is Chinas attempt at lowering that price. As is the strategy of building up an indigenous chip manufacturing industry. The aggressive rhetoric from China has the added benefit of keeping the US on a self sabotaging aggressive posture.
I mean that's obviously the best outcome for the Chinese government. Same thing that happens/ed to Hongkong. War is bad for everybody.
What does the US gov't do in response? Wouldn't they throw globs of money at Intel and Nvidia?
They already have.
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.
I guess I see Chinese leadership as more rational than that, but maybe you're right.
US blows up the fabs on the way out!
/s (unless???)
but they're expected to have 8 or 9 aircraft carriers by 2035, doesn't it make sense to wait until then?
It’s only about 200km across the straight. They have over a thousand fighters and a couple of hundred bombers capable of crossing that gap.
If the US is fighting with Europe and South America, China might not that many.
They're enjoying a massive demand for GPUs due to AI blowing up, at a time when there isn't much competition, yet the technology is already pleateauing, with similar offerings from AMD, not to mention proven training & inference chips from Google & AWS, plus the Chinese national strategy of prioritizing domestic chips
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....
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?
> One of the questions of the 2026 acx prediction contest is whether Nvidia’s stock price will close below $100 on any day in 2026.
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?
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.
It’s a binary option.
As others have noted, the article is analysing the actual financial markets angle.
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 :)
>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?
Accuracy wise we won't know the exact numbers, but insiders and industry experts usually are able to find ballpark figures that they share with the press. The alternative is the usual find out the estimates through competitors' lost MAU numbers in apps like ChatGPT for iOS.
It's a problem if you have to keep asking "are we in a bubble?"
It's entirely possible it will crash, but I also don't think it'll go bankrupt or anything.
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.
If it appreciates just due to inflation you didn't actually make any money. Unless your investment beats inflation, you lose value.
I don't dispute that all. I'm just ok with that being the outcome; if it keeps up with inflation then it's still better than storing it in a bank, and the thing that would upset me more than "not gaining" would be "actively losing".
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.
> 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.
Shouldn't the same argument also apply to Intel?
Intel doesn't make desirable products
Worth noting that the implied volatility extracted here is largely a function of how far OTM the strike is relative to current spot, not some market-specific view on $100. If NVDA were trading at $250 today, the options chain would reprice and you'd extract similar vol for whatever strike was ~45% below. The analysis answers "what's the probability of a near-halving from here" more than "what's special about $100." Still useful for the prediction contest, but the framing makes it sound like the market is specifically opining on that price level.
this is gpt, right?
I had a conversation (prompts) with Claude about this article because I didn't feel I could as succinctly describe my point alone.
There are grammatical mistakes and abbreviations, big tells that it's NOT ChatGPT.
Will AI remain like a simple query engine? I think not. AI is already iterative and does so many recalculations at every step and every new piece of data. Large models that know-it-all are being supplemented by more specialized models or models that have special access to private data. Why wouldn't you consult 100 models of a medical opinion or a stock market analysis. The point is, that for the next few years we will likely consume as many "tokens" as are affordably available, depending on the applications need for more computations. NVidia will develop it's other products and will develop edge devices and every type of GPUs, TPUs, etc. Markets climb a wall of worry and investors love greater fools whenever they are available.
Everyone is saying data center build outs are the main thing to look out for. But those data centers with all those gpus will need to replace those gpus right? Nvidia will come up with better, faster, more efficient gpus.
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.
There is one thing everybody forgets when making such predictions: companies don't stand still. Nvidia and every other tech business is constantly exploring new options, taking over competitors, buying startups with novel technologies etc... Nvidia is no slouch in that regard, and their recent quasi-acquisition of Groq is just one example of this. So, when attempting at making predictions, we're looking at a moving target, not systems set in stone. If the people at the helm are smart (and they are), you can expect lots of action and ups and downs - especially in the AI sphere.
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.
The option market is an insurance market.
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.
checks calendar Ah, NVIDIA earnings call is close - prepare for the inevitable doomer articles.
LLM is already an outdated term in terms of frontier models. Continued build out of ever larger data centers will drive the coming wave of robotics with varying forms of world models. People who claim LLMs won’t change the world are stuck in some sort of tunnel vision, IMO. I don’t think Nvidia is at risk of the AI industry falling flat. More like from large companies creating their own chips specific to their needs (ie Tesla). Although there aren’t many companies in the world that are THAT ambitious. I think Nvidia continues its general trend upwards.
Who said that monads don't have any application?
They implement Applicative, so by definition they do
This is more of a derivative pricing article and has nothing to do with nvidia really
This is a market can stay irrational problem. Modern compute infrastructure from phones, 5G, data centers, LLMs have their energy economics exactly backwards which combined with plastic waste is causing a massive global economic distortion that will correct itself bc physics doesn’t care about the white house, Vanguard, TSMC or anyone else. How long we can borrow from the future and put stress on the poor to prop up this insanely wasteful system who can say.
This kind of prediction is hard because it has a dependency on when will AI companies crash. The market would need to lose confidence in AI which should make data-centre creation stop and then impact nVidia.
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?
NVIDIA Vera Rubin NVL72 unveiled at CES makes any other computer look like a pocket calculator, and that's why I wouldn't want to be bearish on NVDA right now, see https://www.nvidia.com/en-us/data-center/vera-rubin-nvl72
Predicting any stock will crash, be it from a technical analysis or from looking at fundamentals, is a fool's game. As Keynes allegedly said, the market can stay irrational longer than you can remain solvent.
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-...
I'm calling it - this is a submarine article to prove that Haskell is used in the real world to solve actual problems
How much of their turnover is financed directly or indirectly by themselves, then leveraged further by their 'customers' to collaterize further investments?
Are they already "too big to fail"? For better or worse, they are 'all in' on AI.
If Nvidia stock crashed, it would likely trigger a broader sell-off in AI and semiconductor stocks, impacting the overall tech market, including my small startup :/
New competition is an issue. It wasn’t as lucrative to compete with nvidia in the past
Nvidia PE ratio: 44
I do hope they crash so that I can buy as much as possible at a discount.
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.
Every gambler thinks they can time the market, and buy the dip.
In general, they often get stung by the dead cat bounce, =3
https://en.wikipedia.org/wiki/Dead_cat_bounce
This implies you think a crash would be a temporary mispricing of the stock, which will recover in value, correct?
While I am no fan of NVIDIA, they are effectively a Monopoly for CUDA GPU.
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
The thing is, in this gold rush, Nvidia is the one selling shovels.
10% given the info we have now. Or 10% given what info are likely to come in the future that satisfies the 10% prediction? Or are these 2 the same?
I don't think it'll crash. The US Federal Government will throw money at anything right now.
That's smoke and mirrors. You can't logically predict the market. It never worked.
Sure you can predict the market. Making money off of it beyond the regular risk-adjusted return is what's hard. (And the prediction of this article is indeed based on that assumption.)
was expecting some actual reasons presented as to why this would happen. instead got some math.
The simple answer to the question:
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.
It's forward looking P/E is 24-26. That doesn't seem like a huge crash is coming. It could come down a bit but they print money. They also have potential car market and robots coming in.
The real question is what else will this cause to fall when it does happen.
People don't actually believe this type of analysis... do they?
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.
This is fun math to play with, but completely misses the point of how and why options are priced the way they are. Think of horse racing - when a horse is 1000 to 1 odds the odds of that horse winning are much, much lower. The odds are non-zero, but the oddsmakers are considering who the other side is and why they are buying that ticket.
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.
Are you saying options are not priced at the cost of hedging them? That implies a lot of money could be made by arbitraging between the hedge and the option.
That sounds like an egregious statement. Markets don't have simple persistent arbitrage opportunities like that, do they?
I would be wary of taking analysis like this website at face value unless you know enough about quant finance to check some of the working for yourself. Just a skim shows a few statements that are questionable at best. Eg
> 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...
AIUI, there's nothing wrong per se with treating the "market opinion" of the volatility as a subjective probability, since that's effectively what it becomes given sensible no-arbitrage constraints. Just keep in mind that "bad" states of the world will be heavily overweighted in the resulting subjective expectation, for the risk-adjustment reasons mentioned in the OP.
Definitely. As someone once explained it to me, you always overpay for insurance and you're right (from a marginal utility/risk of ruin standpoint) to do so. That's why the vol skew is pretty much always such that crash puts are more expensive than oom calls.
Author here. Thanks for the well-reasoned and well-sourced criticism! I'm just a layman. I admit to all misunderstandings and am happy to receive links and words to search for to learn more.
It’s a cool blog post so I’m making those observations from what I hope you will see as a helpful place. It’s been a long time since I was in the industry, and the sources you’re looking at (eg Shreve) are all great. One thing you might want to check out as well is “The Volatility Surface: A Practitioner’s Guide” by Jim Gatherall. I remember really enjoying it back when I worked in Equity Exotics and it was very highly regarded by the people I really respected in the financial maths stakes.
For sure. I mean it when I say I appreciate the criticism!
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.
https://archive.is/HXmoa
I’m more curious how these “future” contract will work out. Supposedly, a bunch of RAM is paid and allocated for that isn’t even made yet. If the bubble ever pops, the collateral is going to be on the order of 2007 subprime mortgage crisis
Since there's such an interdependence between nvidia and the other companies involed in AI to the point that if one fails they all fail, shouldn't the analysis focus on the weakest link in the AI circle jerk?
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
Yeah, no signs that the AI craze is slowing down, other than all the stories of it not living up to the hype and creating more jobs rather than replacing people and not doing what it says on the tin and the various security issues and that whole they can't expand for the next decade because they need more power plants thing.
I mean common sense reasoning tells me that if OpenAI has decided to turn into an ad business, the actual return expected from investing into compute isn't going to be nearly as great as advertised.
Click bait for teaching options analysis
"Predictably" prediction markets have opened up space in the void left by journalism for tea-leaf reading with the fig leaf of mathy jargon.
Forget the inevitable singularity and start the pseudoscience of technical analysis for stocks. Smart.
does that include the chance for a stock split?
Great question! The fine print of the text on Metaculus says the outcome will be judged as if there were no stock split. The question is essentially about the valuation of the company but phrased operationally about the stock price.
Itt: trust me, bro capitalism will fail this time
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It's easy to predict that a bubble will pop, but there's a variance in the timing of approximately half a human lifetime, and if you don't guess that correctly, you throw away yours.
Everything that can't go on forever will eventually stop. But when?
Well put and clearly explains why "timing the market" is never a good plan.
Technical analysis is amazing, it is most refined form of pseudoscience.
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.
That's what "technical analysis" means in the finance world, though... so, am I missing a joke?
Technical analysis is the projection of future price data through analysis of past price data (usually for the purpose of trying to create trendlines or find "patterns"). Options pricing is quite a different beast - it encodes marketwide uncertainty about the future price of the underlying, which has little to do with the past price action of the underlying, and everything to do with all known information about the actual underlying company, including fundamentals analysis, market sentiment, future expectations and risks, etc.
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.
how so? (i'm not too familiar)