Nvidia is about to pass Apple in market cap
reuters.com
reuters.com
It's not just a matter of NVDA's upside.
AAPL may have peaked, and, in light of the EU breaking up it's walled garden, may be on the way down.
It's even dubiously legal in the US ... but there it would be a war of legal fund attrition and Nvidia has deep pockets.
Still - break it in France, give it away ... US laws go poof.
It's really just the ongoing development of the software ecosystem that gives them their moat ... but it's a good technical moat because they steer future dev and others play catch up.
We've barely scratched video and volumetric video training, even if text stuff hits a data wall I think demand will stay high for quite some time due to that.
The move to on-device inference actually takes more training, you need to train it longer to make it still perform well at edge parameter counts (with lots more training, Llama 3 8B is competitive with 70B). Nvidia is already more specialized towards training and has lots of competitors in inference.
Training tends to have steps that don't just involve matmul; Jim Keller said Tesla Dojo ended up having problems from being too rigid, where GPUs have been able to adapt to new training regimes better, though Google's TPUs have seemed to not fall into that as much, maybe just iterated much faster?
"Society made me do it" is not a strategy.
Note that you need to replace search on the lucrative queries, stuff like you want to buy a product or want to know about nearby open stores, those are the queries Google focuses the most on and will be the hardest to replace them on.
But since Google is adding LLMs to search, an LM competitor might not win still.
> lucrative queries
Also the hardest for an LM.
I predict this won’t end well for anyone. Google will destroy a bunch of their own value preemptively self-disrupting and burning a ton of cash only for everyone to discover that it’s not lucrative to use an AI to tell people the distance to the moon. Viagra and insurance queries will stay valuable and be least useful for a LM to disrupt. Google will maintain their market share, at the expense of more cost per query, so they’ll purge more of the loss-leaders across their consumer offering. We’ll all discover that an LLM isn’t great for search and better integrated into Gmail and similar.
I would add that it needs to be more than just good, it also needs to be cheaper for mass adoption.
Spoiler: classic search is at least cheaper (old post, but relevant: https://www.semianalysis.com/p/the-inference-cost-of-search-...). The thing is it cannot get that much cheap without disrupting earning expectations from hyperscalers.
Nvidia has higher margins so it first need that much growth to become the world’s most profitable company.
I do agree that growth will cap out. A lot of the massive growth is due to the previous fiscal year being effectively a crash. It’s impressive that they’ve recovered but the same quarter next year will not have the benefit of a bad quarter to compare against.
P.s. - something weird happened yesterday in that the whole market was down except for nvidia - it’s like Nvidia is almost turning into a black hole, absorbing all the value and money around it, because why invest in anything else? Its market cap is now also bigger than the entire German stock market. This cannot be a good thing. Particularly because I’m quite tech-heavy and AI still isn’t making a huge impact in my life. Software yeah, but the hugely resource intensive LLMs and agents still seem like a curiosity, and I’m not sure where the profits are yet, which is like the dotcom boom all over again. I’ve no doubt that AIs will replace huge numbers of workers, but Jensen seems to envision a future of endless giant data centres running powerful AIs to do all our driving and being our assistants, but I think by the time we get there we’ll probably be looking at around the same number of data centres we have today, and the self-driving will always have to be done in-car for reasons of latency.
NVDA is also in a weird spot where their primary customers are all working to build their own thing to replace/supplement NVDA. A lot like Apple did with ARM, eventually pushing Intel completely out. As algos improve and Moores law marches forward, companies will need fewer of the 'best' GPUs and get by with their in-house versions.
[1]https://www.fool.com/investing/2024/03/14/the-scariest-nvidi...
If they ever give up that lie, we could see a contraction
Apple has the unique advantage of controlling the entire stack. Anyone who wants to compete with NV on anything AI has the disadvantage of NV's full stack - OS drivers, acceleration drivers, userland libraries, compute kernels - being extremely well developed, whereas everyone else's is a bunch of crap.
And that's not even mentioning that anyone with that sort of money is already developing their own GPUs.
It’s pretty easy to just be a perma-bear, especially when it’s a company like nvidia with a dedicated hater club. it’s not a coincidence that the company with the next-biggest hater club is… apple.
https://paulgraham.com/fh.html
The same people who were all in on $AMD-to-the-moon have been saying nvidia will pop for over 18 months at this point. People have been saying that MI200 will crash Nvidia’s party, no wait MI250X (it’s dual chip but presents like one, except it turns out that was a marketing lie in the datasheets!), no MI300X, wait MI400…
They’ve beeen saying that google and Amazon and AMD are gonna crash the party for years and years now, and we still aren’t even close to literally anyone even having a viable alternative to CUDA despite those years of internet hot-air. Apple is literally the closest and they still only have traction on inference, Nvidia’s the only game in town for training still, which is where all the money is.
Simple question, if you’re so sure: how many more years until anyone can use ROCm and not have to think about it? That’s something that only two companies offer, and both of them are in the title of this thread.
Again, reminder that the foremost contender for the third place title, is shipping AI-branded (“Ryzen AI Ultra 300”) laptop CPUs that require vendor enablement to use the neural cores… you have to not only build all the software, train it hard so that edge models are viable (oops, more money for nvidia) but then also you have to convince asus and MSI and clevo and sager to support it in their drivers on an ongoing basis. That’s the third place contender, and they don’t even have the software to run on it in the first place yet.
As much as people can’t help but trip over themselves predicting a pop any day now (for the last 18 months!) literally the third place contender is only just getting rolling, they are probably 10-15 years behind right now in terms of ecosystem. Might be able to do it in 5 if they hurry, but they also have to convince everyone else not to do their own stuff too - if everyone else runs around like a chicken with their head cut off, nvidia still wins.
Again, the only real ecosystem threats right now are apple and sony, and sony is starting from zero too (but they have a legitimate ecosystem unlike say AMD or intel). Intel/SyCL is a technical threat but it’s powerless without a social /ecosystem consensus behind it.
If it’s not a competitor taking over, that leaves a general collapse in AI, and frankly that’s just wishcasting at this point. There’s too much obvious value being delivered for it all to collapse to zero like some people are hoping. Again, not expecting - hoping.
The hater club never realizes they’re haters. That self awareness is the first thing to go. Same as with the apple haters who spent the last 4 years finding any and every reason to try and dunk on apple silicon lol.
Made no sense.
And, as they've shown. Making a car is easy, manufacturing a car at scale is hard and their competition knows how to do the hard part. And, at this point, US auto makers have shown they can build electric cars so Tesla is still over valued.
https://caredge.com/guides/electric-vehicle-market-share-and...
BYD and Xiaomi in China seem to be catching up to Tesla far quicker than the US/European auto makers are.
it looks like tesla manufactured cars just fine, there is no shortage of teslas on market. Demand on EV didn't pick up.
According to data from S3 Partners, investors betting on a decline in Nvidia's share price suffered roughly $2.9 billion in paper losses on Thursday when the stock ended the day 16% higher following the chip-maker's huge earnings beat the evening before.
https://finance.yahoo.com/news/nvidias-huge-post-earnings-st...
Everybody and their dog is long nvidia.
It's pretty obvious TSLA is massively overvalued... but will it crash this month? next year? 20 years from now? who knows.
What's that have to do with taxes (and wanting to hold 12 months+)?
The equity holder could also just sell their shares instead of hedging with puts, but that's a taxable event. Hedging with puts is not.
Think of it this way. You have nvidia shares, think the stock has ran up too quickly, want to sell, but also don't want to because of taxes. So you approach your neighbor and get him to short nvidia for you, and you agree to pay your neighbor a fixed amount for his trouble and your neighbor passes the money made/lost by shorting back to you. Now your effective share count is 0 and you don't have to pay taxes on unrealized gains.
The point is that people wrongly assume that short interest in nvidia must mean people are betting against nvidia, when it's much more likely most short interest belongs to people who are bullish.
Other possibility is something finally snapping even without that sort of trigger. But that could take rather long time still.
NVidia is getting rich following the old "in a gold rush, be the ones selling picks and shovels" aphorism, and when the gold rush ends, you still have your money, but the discounted future profits might well be much smaller than you expected.
1: I don't think it would necessarily hurt NVidia, but I do think it might hurt their suppliers. I would be very curious as to how much of the current fab boom across TSMC, Intel, United Micro, Samsung etc. is based on assumptions about AI demand continuing to grow. Those companies would be the ones I would expect to suffer if the demand doesn't materialize because those fabs are going to be ridiculously expensive if they don't keep their order books full.
Every car will get AI chips, every laptop, every server, every phone, and many home appliances. And I expect people will eagerly upgrade their electronics to get the faster AI chips in the decade to come.
At a certain point the AI chip market disappears and it's absorbed into general purpose computing.
Aaron Levie of Box.com has been outspoken about it:
There's also nothing stopping them from bringing some consumer software to the market.
And I don’t know, maybe in ten years time we will have enough AI advances that humanoid robots are useful. But AI is notoriously hard to predict, and if you believe Yann LeCun then all the autoregressive approaches the big LLM companies are using won’t get us to AI that thinks more like an embodied creature, with hierarchical reasoning, variable compute capabilities for solving hard problems, few shot learning, etc.
Also people can’t afford to spend $20k on a robot just to do their dishes. My good friend tried to make a robot that could pick up dog poop from your back yard, and he went to HAX accelerator and everything, and through extensive research he found that the addressable market for dog poop robots is just too small to fund a company. Now he makes farming robots. (I do too as it happens, on a separate project.)
The point is a humanoid robot worth the price of that robot needs to be very useful, and I don’t believe current approaches can get us there.
Maybe some uses will be found, just as current AI systems have their uses, but I say it will be a hype bubble because companies will fund raise on massive promises they will never achieve, and investment will move on to something else after 5 years or so.
Could still be robotics, but I actually think modular purpose built machines make way more sense than general purpose humanoids. What if instead of a humanoid to load the dishwasher you had a dishwasher that cleaned dishes one at a time. You load up to six table settings in to the bin and a little robotic mechanism grabs them one at a time, runs it through a little car wash setup, and stacks them on the other side. That’s what I want, not a humanoid!
[1] I’m really inspired by Yann LeCun’s recent podcast where he talks about the fundamental limitations of current popular (autoregressive) models. https://youtu.be/5t1vTLU7s40
The problem with your proposed non-humanoid dish washer robot arm is that I want the dishes to go into the cabinets, not just into the dish rack, and while the robot doesn't strictly need to be humanoid, it's better that it's mobile, and while uni-wheels like a segway is certainly an option, having cracked bipedal robots, that just seems like a better design choice.
Considering how much the industry has struggled with automation in cars, I just don’t actually think humanoids will work in the home any time soon. A fantasy humanoid that works well would be more useful than a robotic dishwashing appliance, but one of those is something I believe can actually be built in the next decade.
"This announcement is one step in our ambitious infrastructure roadmap. By the end of 2024, we’re aiming to continue to grow our infrastructure build-out that will include 350,000 NVIDIA H100 GPUs as part of a portfolio that will feature compute power equivalent to nearly 600,000 H100s."
350k NVDA GPUs, but the compute power of 600k. See here for how quickly their silicon is advancing [2].
No one is saying NVDA will go away. But the stock is priced for near perfect growth projections. NVDA's second biggest customer like Meta cutting back even just a bit will hit NVDA's bottom line. That's the stock risk.
[1] https://engineering.fb.com/2024/03/12/data-center-engineerin...
[2] https://ai.meta.com/blog/next-generation-meta-training-infer...
However, (my prediction is) AI will be able to be run on fewer resources than today. And if this optimization comes (too soon), the speculation will crash. If it comes late, the buying and selling will have already happened. And NVIDIA will reap the profits in the meantime.
NVIDIA should liquidate stock. Now is a good time.
I think apple still make a yearly phone although i can't tell the difference between the 8, 12, 14 or 16 (?)
My money is on the datacentre boys.
It wouldn't even come close to comparing to the Great Depression.
Buffett sold his stake in TSMC because of geopolitical uncertainty and the way I see it TSMC and NVIDIA are attached at the hip.
Even if the AI hype cycle dies, computing power has and continues to be the oil of the digital age.
Growth may slow, but I wouldn't go shorting this company.
What would they use this much compute for though except chasing AI/AGI? If the current ever larger transformer race doesn't lead anywhere NVIDIA will crater hard since there isn't anything else worth that much compute currently.
If AGI was to happen within 5 years it means that Nvidia is heavily underpriced as AGI would have multiples of hundreds of trillions of value.
If we knew for sure that AGI was happening in 5 years, NVIDIA is probably at least 100x under priced.
What happens if something brings a 5% reduction in productivity? It's already getting harder to find reliable information thanks to AI spam.
Like arms manufacturers wouldn't lose in value during wartime which is a destructive process for the World as a whole. But if certain weapon allows you to destroy an even opposition then productivity as a whole would decrease by 50%, but the weapon would still have massive value, probably at least 50% of the whole produce.
If there is a simplified World with 2 countries where each produces $50 million of value a year and they go to war. Either of them would be willing to pay anything they have for the weapon since alternative is to be wiped out. Even though after beating the enemy instead of $100 million produced per year, it would be temporarily $50 million.
The value of a weapon would likely be whatever any of them can dish out, so perhaps over $50 million if they have saved up enough.
There was a tech world before AI!
That way you can keep most of the gains incase of a crash.
Nvidia needs one bad quarter of growth and they see the same fate as Tesla.
Growth drives the market frenzy.
Even if no one build a human level AI, it’s hard to imagine people would stop trying.
I don't see durable moat for nvidia.
They have best stuff now (and likely for a few years), but it's design for relatively predictable workloads with known best solutions.
Everyone sees the money nvidia makes and wants a piece of it (e.g. Jim Keller and hubdreds if others). Make something with lower TCO, proper integrations with pyrorch and co and B2B will buy.
AMD made a nearly clone of CUDA API with HIP (the implementation is garbage btw). The API mostly changes prefix from "cuda" to "hip" and even has semi-automatic source code modifier tool to make the switch.
It's like any other software migration (e.g. from on-prem to cloud):
If ("money to switch library" < "savings per quarter" * quarters) { "Do the switch" } else { "Status quo" }
We'll see I guess.
You have to hand it to them, they have executed superbly, but the underlying technology is well understood. You have the hyper-scalers investing in their own silicon, and Intel/AMD are ramping up as well.
The basic problem they seem to have faced is that the hardware was over-specialized. The needs of models changed quite fast. CUDA was flexible enough to roll with it, TPUs weren't. Google went through several TPU generations in only a few years and yet don't seem to have managed to build a serious edge over NVIDIA despite being less flexible.
They also lost out because the whole TPU ecosystem is different to PyTorch which is what won out. That's a risk if you do your own hardware. It ends up with a different software stack around it and maybe people pick hw based on sw and not the other way around.
So it's not that easy.
Google does not sell TPUs to 3rd parties at all[0]. Or do you mean cloud customers prefer H100s to TPUs - if so, I'd appreciate more context, because I know Google uses TPUs internally, and gets some revenue for TPUs - I know a bunch of people who pay for Google Collab for TPU access to accelerate non-LLM training workloads.
> They also lost out because the whole TPU ecosystem is different to PyTorch which is what won out. That's a risk if you do your own hardware.
This is barely related to hardware ans mostly about Tensorflow losing the mindshare battle to Torch. Torch works fine with TPUs, as anyone who's used a Colab notebook might tell you.
0. Except their Coral SBC/accelerator which is modest and targeted at inferencing.
Existential risks such as hyped gains and regulation are starting to take a foothold.
I also wonder whether the announced stock split is contributing to the short-term price increase (since people will expect more money to flow in once the stock is a more "accessible" ~$100).
Right now, you can listen to CEOs of Nvidia's biggest clients saying things like "the current bottleneck is the bureaucracy around building nuclear powerplants to provide energy to our datacenters". The Saudi investment fund is shopping around for AI ventures to throw hundreds of billions at. Altman is suggesting he'd be able to utilise a multi-trillion dollar raise. These are big indicators that the demand for Nvidia's products will remain strong for some time to come.
The amount of money that is aimed at AI, which ultimately a large portion of will land in Nvidia's bank account, is staggering.
That should say enough about the confidence in the capacity of such giant investments in GPUs to bring revenue in the short term future. They're not selling surplus capacity (from their own products as Copilot), they're hoping to sell snake oil directly to customers.
Nvidia could buy some cloud companies to become AI-cloud vendor too, instead of just selling equipment and chips, that will make it even bigger.
Interesting. I perceive it exactly the opposite way. Apple products are hard to replace due to ecosystem advantage while everybody (Apple included!) is already working on building hardware to avoid using Nvidia's H100s.
the question is if they will succeed. Only Google so far has competitive product(TPUs).
Nvidia moat is CUDA not the hardware, how much of teh AI hype needs CUDA?
And if Nvidia can't satisfy the demand to a reasonable price companies could search for alternative ways with AMD hardware
Apple is much harder to displace.
- No wireless. Less space than a Nomad. Lame.
“Superior customer experience” is missing from this. I don’t care if the processor in the iPhone is the fastest or not, or if it has 8Gb of RAM vs some Samsung and I care even less about marketing.
That's half of the total market in many countries.