Nvidia on the Mountaintop
stratechery.com
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People haven't really noticed that building a general-purpose (humanoid) robot has become 1000x more feasible now even compared to 1.5 years ago.
Check out https://www.lerf.io/ for a taste of the future
Maybe people don't realize how low the bar for mass-adoption is? We're kinda mesmerized by Boston Dynamics' Parkourbot, but the real-world is made for people of many different abilities, you don't need to do summersaults, or run, or lift a lot to be able to do useful work. A $30k robot with a lifespan of 8 years would cost $10/day. $10 is very little money for in-your-house work, being able to do basic cleanup will be worth it for a vast number of households.
> you don't need to do summersaults, or run, or lift a lot to be able to do useful work
I agree that it's a gimmick, but every time I see it I'm reminded of how far we are from having an in-home robot that can do useful work like wash the bed sheets and clean the toilet.
And bring on the snow shoveling bots. Nearly every 50+ year old in cold climates will want one. Will we have to outfit them in specialized winter gear?
Lawnmower bots are relatively cheap, so the cost for snow ones will probably go down as well over time (then again the market for them is probably much smaller and yeah, snow removal is cheaper and easier for a human to do compared to lawn mowing)
Snow removal is also lot more physically strenuous than most other yard tasks, meaning much bigger motors and batteries, almost approaching car-sized.
Robots which do this are already cheap and widely available, I see them everywhere.
Snow blower robots seem to be a thing too, not sure how useful are they. Racking leaves is probably also solvable problem.
It just seems that it might be more effective/cheap/viable to have a bunch of way more simple specialized 'robots' than a very complex/expensive general purpose one.
Also like Roombas (the brand, not robot vacuums in general) they have poor sensors and no real coverages algorithms, they just semi randomly roam around.
Battery life is also terrible, outdoor charging stations are harder to maintain etc.
All I’m saying is, there is a long way to go before they become defacto better than a person with a mower, they are a fair bit of work and fuss right now to set up and keep running.
Husqvarna supports that, but yeah it might struggle on very complex areas (I've only really have any experience with it on fairly simple lawns). It's certainly not just randomly roaming around:
https://www.youtube.com/watch?v=KLdSLJu8acg&ab_channel=Husqv...
I'm not even sure what other brands exist. They seem to completely dominate the market where I am.
Maybe this time it's different, and maybe it's not, but that's why most recent robotics predictions fail to convince the ML industry broadly.
Honestly I think there's been very dramatic improvements in robotics alongside ChatGPT but ChatGPT is easy to demo with nothing but an internet connection so it's just a lot less visible.
Computer vision, AI and inverse kinematics have all come a long ways in the past few years. That being said, it's still easier by an order of magnitude to design the box-pushing robot.
They seem cool and futuristic at a glance, but upon close inspection, they are kinda not pragmatic at all, and there are simpler, cheaper solutions that will beat it in the market.
For example, we could build a humanoid butler robot to do chores around the house, or we could build a dishwashers + roombas + washing machine. These simple appliances do 90% of the work for 10% of the headache (including the consumer side headache of performing maintenance on a humanoid machine!).
It's not the case that existing robots (dishwasher, washing machine, etc) do 90% of the work when you consider the full task in its entirety. Humanoid robots would just replace the work that humans are doing assuming they have access to the specialized robots.
I don't think you "need" either. It's just a conversation about what the market will reward.
> Humanoid robots would just replace the work that humans are doing assuming they have access to the specialized robots.
Or, there won't be any replacement and the majority of people in domestic households just keep doing that little remainder part because it's not really a big deal.
This all holds perfectly with the flying car analogy -> ground cars are pretty much fine. We could try way harder to make them flying-grade at a consumer level, or we can just keep driving road cars (or taking public transit) and be happy. That's the fundamental thing that 60s futurists missed when envisioning the future.
Are you down that there's no flying cars?
I'm pretty happy with it tbh.
edit: btw, I'd like to add that from a design perspective, humans kind of have a crappy form, which is perhaps one of the reason that rarely does one design something which ends up humanoid. E.g. something with wheels can usually move farther with less energy (or even something like tank treads if rough terrain)
a humanoid robot, i think, is a good solution for a bunch of problems.
the comparison is kind of useless and doesn't really help us.
My laundry machine is certainly better than by hand, but folding laundry and putting it away is significant.
Why? I'm not even sure how ChatGPT is that relevant for this? Sure if you want a scifi style "intelligent" robot, which is capable of basic reasoning, you can talk to it's useful.
However I don't see how does it help with it being able to pick up fragile stuff without crushing it, operate in dynamic 3d environments etc. LLM don't seem to be that useful for these kind of problems.
It is the biggest constant of NVIDIA I've witnessed over the last 2 decades. Each of these trends inevitably comes to an end, but NVIDIA internally is clearly enabling itself for the next possible trends to take advantage of.
Specific applications may vary over time, and it's NVIDIA's investment in GPGPU and CUDA that allows them to be a leader in each of these. But it's also worth noting that one of these, neural nets, have been big on NVIDIA hardware for a decade now, so it's not exactly a "trend".
You can take up both positions if you want. I've got long exposure through various ETFs. I also maintain a small hedge in the form of put options. Overall, most of my NVDA money pile says "it would be fantastic if this all works out". But, I have a small % of the pile dedicated to "Just in case...".
The only situation I don't want is one wherein things stay exactly at this level and don't move anywhere for a long time (or do so incredibly slowly).
Would you bet against volatility in the AI/GPU market over the next 12 months?
The problem with betting on volatility (via options at least) is that it's already priced into the market, which means you pay a large premium for that bet, which in turn means that things need to make big moves for it to pay out. So it's not enough to be confident that there will be some movement in either direction—it has to be a relatively large movement for it to be profitable.
So to answer your question, maybe. I don't think it's unreasonable to think that Nvidia stock won't drastically change over the next 12 months.
Disclaimer: I'm long Nvidia because I'm willing to wait and see how high this ride goes.
HFT firms and hedge funds often have data or other information that retail investors don't have access to, so that's not an apples to apples comparison.
> if you believe the fall will be as fast as the rise, put options rarely price that in.
That's not what "priced in" means. Unless you have information that others don't, there is no reason to believe that your bet is any more likely than anyone else's. So in that sense, it is priced in based on the probability (according to the broader market) of it actually happening.
The term "priced in" doesn't just apply to information - it applies to any sort of investment thesis. The common investment theses (based on a conventional understanding of public information) are usually priced in. The uncommon investment theses are not, but that doesn't mean that they are necessarily wrong (this one probably is, though).
There is certainly no reason to believe that your ideas are better than anyone else's, even if you are a sophisticated hedge fund guy, but the way hedge fund people make money is basically by having contrarian ideas, which aren't priced into securities prices, and being right. Trading that (perceived) mispricing is how a hedge fund generates uncorrelated alpha.
With that said, I just want to make clear that I don't believe in the random walk theory, and I believe it's perfectly possible to be contrarian and be correct if you have information and/or insight that most other investors don't. I just don't think that the average person on HN (myself included) fits that bill, and I wouldn't hang your life savings on a whim like this unless you're comfortable treating it as what it is: gambling. :)
God bless the short sellers, they’re made of sterner stuff than I am.
Analysts certainly misunderstand CUDA, but that doesn't change it's status as a non-negligible moat. It is a by-product of an overly hostile computer industry, spearheaded by a company that's more than happy to profit from bad blood.
The biggest issue is frankly that other stakeholders are doing their own thing, and pretending it's okay. Long-term, I think this will be Nvidia's real coup-de-grace; letting local devices do inferencing, while selling their GPUs to larger deployments and training applications. Stuff like CoreML and ROCm are nice, but don't directly threaten CUDA. If their competitors want to be rid of CUDA, then they have to gang up on them and offer a better alternative. Until then, Nvidia will have a market.
It's true what they say, nice guys really do finish last in this economy.
nevertheless that is exactly what they spent the last decade trying, with ROCm not even officially supporting the consumer-card variants of the handful of radeon pro cards that actually got support. AMD's GPGPU software support was almost entirely segmented to the CDNA cards for the HPC market until literally a month ago, and still completely ignores any sort of binary compatibility story (they really want you to distribute as source and compile in-situ, which really makes it a non-starter for end-user software or commercial distribution).
Alexnet, one of the first papers in 2012 to kickstart the current AI revolution was using Nvidia GPUs. Nvidia has been prioritizing AI research (both hardware and software) for the last decade.
This is only partially true even now, and I expect it will be less true in the future as continuous training becomes the norm. As Hericlitus said, you cannot step in the same river twice, and data drift is a real issue when modeling complex processes. It will become even more of an issue as the widespread use of models starts to impact the data streams they rely on for training: acting on a prediction can change the underlying assumptions that produced the prediction in the first place.
Can Google/Tesla/etc create an H100-like GPU in a couple of years at a fraction of the cost? And is CUDA really necessary if this much money is at stake?
[1] https://www.tomshardware.com/news/teslas-dollar300-million-a...
> in a couple of years at a fraction of the cost? And is CUDA really necessary if this much money is at stake?
I mean, that's the thing; is resisting CUDA really worth the cost when this much money is at stake?
It's not like you have to pay to license CUDA. The two big drawbacks are that it's proprietary and locks you into their ecosystem; neither of which really matter when shipping stuff at that scale. These companies could spend a few million dollars to accelerate their specific codepath, but it's frankly wasted money unless you have a specific reason to avoid Nvidia.
The eventual Nvidia-killer will probably be platform-agnostic tooling like ONNX, unless hardware manufacturers revive a sort of OpenCL-style acceleration library.
Suffice to say, there are a lot of "competing standards" a-la the XKCD : https://xkcd.com/927/
I don't think any of those will really topple CUDA, though. The best way to unseat Nvidia's dominance would be to target their two weaknesses; the closed nature of CUDA and the lock-in to Nvidia hardware. It would be difficult to overturn them both, but an open and fast CUDA alternative is really all people actually want. That's why I think libraries like ONNX have the right idea; instead of relying on chip manufacturers to not rip each other's throats out (they won't), they unify everyone's proprietary APIs. Barring some ground-up GPGPU library like OpenCL, this seems like the smartest path to me.
We get equivalent performance on non-NVIDIA chips without it and most of the stuff is abstracted away these days
We do write CUDA when needed but really only a handful of folk will actually train models as it is a pain
The real reason for the moat comes down to a number of things, like:
- The flexibility of generic GPU and ML acceleration primitives
- The availability of systems with hundreds of terabytes of GPU memory for you to scale to
- The struggle of trying to use commodity hardware for actual ML acceleration
I deploy models to freely-provisioned ARM servers, I don't think I'm a choosing beggar in the slightest. When I deploy to Nvidia hardware though, the experience is much nicer on-the-whole. This stuff is definitely possible with consumer hardware, ROCm acceleration or CoreML optimization, no doubt. It's not hard to see why Nvidia is at the mountaintop right now though, and unless the industry agrees to stop building CUDA-style moats then this is the history we're damned to repeat.
300M hires a lot of engineers. Maybe the fact that they are buying instead of building tells you how hard it is to build and how strong a moat Nvidia has.
Musk has publicly said that the true test of their own training hardware efforts is if the ML team switches to it. I.e. it’s a software, not hardware problem.
More impressive to me is that he didn’t let folk go because of the many downturns on the way
ie. Will an Intel (or indeed one of the cloud vendors) undercut their margin with a better priced GPU in a similar manner as Compaq did the IBM Mainframe?
Maybe on the data center, but in the audio world this is already happening.
VSL (a huge company in the audio/production world) added it recently to their convolution reverb product.
Like, if there's a shortage of GPUs in 2026 and Nvidia has to choose between selling limited inventory to AWS or individual gamers, it's obvious who wins (AWS and scalpers).
Stock prices are entirely based on what people think they are worth.
So yes, that makes it possible for the price to become disconnected from any reasonable prediction.
if volatility is priced at a bargain, investors doesnt care much about the stock, as he will be hedging his delta exposure anyways by means of dynamic hedging/etc
Given the kind of revenue and profit growth forecast NVDA has given, these numbers don't seem terribly far off.
Worth noting that NVDA's profits 8xed in a single year. A lot of people are basing their NVDA valuation based on the past year's revenue, which does make them look extremely overvalued. However, if NVDA has enough sales and enough of a moat to double their profits again, suddenly they'll be biting at Apple's heels profit wise with a much lower valuation.
It doesn't seem like a totally crazy valuation to me, despite the uncertainties involved.
> Stock prices are entirely based on what people think they are worth.
Are are priced on what people think they might be work in the future current financial figures don't really matter that much compared to growth.
And the market is pricing in extremely high revenue growth (and also very high margins). So far Nvidia seems to be delivering. Their forward PE went down several times and it's now "only" 30. A few more quarters like that and the current stock price might seem cheap even by traditional standards.
Obviously it's a risky bet but I don't see what's illogical about it?
The questions is whether the GPUs makes money for these companies. When the market sniffs the answer to that question, that's when the bottom falls out or not.
Is Google going to make more money from me than it already does by embedding LLMs into its search product? Am I going to be spending any meaningfully more amount of time using any of these products than I already do by the addition of AI? Because if not where does the money come from?
Google Search might not earn more money it does now by buying Nvidia devices, but might loose big to a competitor if it does not.
I mean someone will build an AI to do that right ^_^
Video and 3D are pretty much in the next year, all the components see there to dramatically shift cost structure and they need GPUs.
So over the near term, 4-5 years I suspect AI hype comes back down closer to Earth again. Sure the secular trend is all up and to the right, but the secular trend of the internet was still up and to the right in late-2000 as well.