Nvidia Rides AI Wave to Pass Apple as Largest Company
bloomberg.com
bloomberg.com
Yes.
“Apple annual research and development expenses for 2024 were $31.37B, a 4.86% increase from 2023. Apple annual research and development expenses for 2023 were $29.915B, a 13.96% increase from 2022. Apple annual research and development expenses for 2022 were $26.251B, a 19.79% increase from 2021”
— https://www.macrotrends.net/stocks/charts/AAPL/apple/researc...
Chips, for one, don’t research themselves: https://www.apple.com/uk/newsroom/2023/03/apple-accelerates-...
Ex: https://news.ycombinator.com/item?id=41491121
https://news.ycombinator.com/item?id=41948739
Tested by community: https://news.ycombinator.com/item?id=41799324
https://news.ycombinator.com/item?id=42019694
VR was a 10 year or so project but agreed no product-market fit yet.
> They seem to have lost faith in their own ability to innovate.
But here's the truth: there is no average user. Or rather, the average user is not representative of the distribution of users. If you build for average, you build for no one. It is hard to invent things, so use the power of scale. It is literally at your fingertips if you want it. Take advantage of the fact that you have a cash cow. That means you can take risks, that you can slow down and make sure you are doing things right. You're not going to die tomorrow if you don't ship, you can take on hard problems and *really* innovate. But you have to take off the chains. Yes, powerful tools are scary, but that doesn't mean you shouldn't use them.
What does this mean? Just thinking about iPhones: As of September 2024, there are an estimated 1.382 billion active iPhone users worldwide, which is a 3.6% increase from the previous year. In the United States, there are over 150 million active iPhone users.
If you're remotely familiar with high dimensional statistics, one of the most well known facts is that the density of a normal ball lies on the shell while the uniform ball is evenly distributed. Meaning if you average samples of a normal ball, the result is not representative of the samples. The average is inside the ball, but remember, all the sampling comes from the shell! It is like drawing a straight line between two points on a basketball, the middle of that line is going to be air, not rubber. But if you do for a uniform ball, it is. That's the definition of uniform... Understanding this, we know that users preference is not determined by a single thing, and honestly, this fact becomes meaningful when we're talking like 5 dimensions...[0]. This fact isn't just true for normal balls, it is true for any distribution that is not uniform.
To try to put this is more English: there are 1.382 billion active iPhone users world wide. They come from nearly 200 countries. The average person in Silicon Valley doesn't want the same thing as the average person in Fresno California. Do you think the average person in Japan wants the same thing as the average Californian? The average American? The average Peruvian? Taste and preference vary dramatically. You aren't going to make a meal that everyone likes, but if you make a meal with no flavor, at least everyone will eat it. What I'm saying is that if you try to make something for everyone, you make something with no flavor, something without any soul. The best things in life are personal. The things you find most enjoyable are not always going to be what your partner, your best friends, your family, or even your neighbor finds most enjoyable. We may have many similarities, but our differences are the spice of life, they are what make us unique. It is what makes us individuals. We all wear different size pants, why would you think we'd all want to put the same magic square in our pockets (if we even have pockets). We can go deeper with the clothing or food analogy, but I think you get that a chef knows how to make more than one dish and a clothing designer knows you need to make more than one thing in different sizes and colors.
It would make reading the thread easier ..
For AI, I assume enterprise do care if it's Nvidia. Right now, Nvidia is in the "no one ever got fired for buying Nvidia" camp. You can buy AMD to save a few dollars, run into issues, and get fired.
And sure, for small scale and research activities Nvidia makes sense, even for the long term. But that’s not where the money is at, either.
The only way Nvidia can sustain its valuation is if it can win in the large scale production AI market long term, and that market turns out to be huge. I don’t really see how that can happen. As soon as NN architectures are stable over the economic lifetime of a chip custom ASICs will make so much more sense: 10x more performance and no Nvidia tax. It’s already happening [1].
>10x more performance and no Nvidia tax. It’s already happening
Nvidia can do the same though.
The moat isn't in inference. It's in training.
Sure, but they currently have a P/E ratio of 67… So being vastly bigger in 2035 is not necessarily enough. They have to be enormously bigger and still hugely profitable.
> Nvidia can do the same though.
Yes, but then they don’t have that moat.
> The moat isn't in inference. It's in training.
I’d say it’s even narrower than that: Nvidias AI moat is in training novel / unforeseen NN architectures. Will that be a meaningful moat in 2035?
But it all comes down to how good the GPT5 class of LLMs are for the next 2 years.
I guess what I’m saying is that it really doesn’t.
But in order to make money on shorts you not only have to be right, you also have to get the timing right. That’s a lot more difficult…
In addition to the GPUs (which they invented) that Nvidia designs and manufactures for gaming, cryptocurrency mining, and other professional applications, the company also creates chip systems for use in vehicles, robotics, and other tools.
The only reason they are this big right now is because they are selling H100s, mostly to other big tech companies.
The term "GPU" was coined by Sony in reference to the 32-bit Sony GPU (designed by Toshiba) in the PlayStation video game console, released in 1994.
The device in the PS1 has also been referred to as a "Geometry Transfer Engine"
You can see it's features and specs here: https://en.m.wikipedia.org/wiki/PlayStation_technical_specif...
Some may say that it is not a "real GPU" or certain features (like 3d) are missing to make it one.
The Nvidia claim is for the GeForce 256 released in 99.
This makes me wonder if our grandkids will be debating on what the first "real AI chip" was - would it be what we call a GPU like the H100 or will a TPU get that title?
I actually had one of these cards; https://en.wikipedia.org/wiki/S3_ViRGE
It sucked but it was technically one of the first "GPU"s.
Also let us not forget 3dfx and the Voodoo series cards.
Don't let Nvidia rewrite history please.
I don't want to rewrite history either.
It's partially telling that you write "2D/3D accelerators" which means that was a different class of thing - if they were a full GPU then you would have called them as such.
My point being - what defines what a GPU is? Apparently there were things called GTEs, accelerators, and so on. Some feature or invention crossed the line for us to label them as GPUs.
Just like over the last ~10 years we have seen GPUs losing graphical features and picking up NN/AI/LLM stuff to the point we now call these TPUs.
Will the future have confusion over the first ~AI CHIP~? Some conversation like
"Oh technically that was a GPU but it has also incorporated tensor processing so by todays standards it's an AI CHIP."
It's because that's what they were called at the time. Just because some one calls a rose a different name doesn't mean it doesn't smell the same.
What defines a GPU? it's a compute unit that process graphics, yes it is that simple. There were many cards that did this before Nvidia.
An A.I. chip is just a tensor processing unit a TPU, this not that hard to grasp, I think, in my opinion.
But you originally declared the Sony chip as the first GPU. There were many things that processed graphics before that, as you have declared. Apparently going back to the Amiga in the 70s.
It is this muddyness with retroactively declaring tech a certain kind that I'm questing here.
Development of the Amiga started in 1982, and it was launched in 1985.
The Tele-Games Video Arcade was released in 77 and renamed to the Atari 2600 in 82.
It is true that everything exists on a gradient, but for practical purposes we have to draw the line somewhere and it seems reasonable to me to draw it roughly here.
When some of Apple’s side-quests are industry giants in their own right, I think it’s fair to say that Apple are diversified.
AirPods are already usable as normal Bluetooth wireless earphones.
What I mean is, think about the addressable market comprised of the people who want to use the watch without iPhone.
I think sometimes we underestimate the number of people out there who do not have an iPhone.
Now account for the drop in companion devices (Airpods and AppleWatch work best with an iPhone, and it's pretty apparent when you aren't using one)
Then take a gander at Cloud services that no longer push themselves via the red badge on every handset, for the photos people are taking on competing platforms (Google Photos is the obvious danger, but even Dropbox through Onedrive pose risks to total revenue)
Should probably account for App Store revenue as well, considering why most Apple customers buy apps, and then how many buy Macintosh just to develop for the massive iPhone market
None of these are exclusively tied to the iPhone, but none look nearly as nice without the iPhone being a (and possibly even the) market leader
https://www.ft.com/content/e30eb646-a7ad-496e-8fa4-ff1b4445e...
If AI doesn't pan out, those H100s are not going to find a lot of sales anywhere and Nvidia could be back to a gaming GPU company.
need dataframes and pandas? cuDF. need compression/decompression? nvcomp. need vector search? cuVS. and the list goes on and on and on.
Sure, but that doesn’t mean I’m going to pay a billion extra for my next cluster – a cluster that just does matrix multiplication and exponentiation over and over again really fast.
So I’d say CUDA is clearly a moat for the (relatively tiny) GPGPU space, but not for large scale production AI.
There's a lot of other things which are very GPU parallelizable which just aren't being talked about because they're not part of the AI language model boom, but to pick a few I've seen in passing just from my (quite removed from AI) job:
- Ocean weather forecasting / modelling - Satellite imagery and remote sensing processing / pre-processing - Processing of spatial data from non-optical sensors (Lidar, sonar) - Hydrodynamic and aerodynamic turbulent flow simulation - Mechanical stress simulation
Loads of "embarrassingly parallel" stuff in the realms of industrial R&D are benefitting from the slow migration from traditional CPU-heavy compute clusters to ones with GPUs available, because even before the recent push to "decarbonise" HPC, people were seeing the increase in "work done per watt" type cost efficiency is beneficial.
Probably "relatively tiny" right now compared to the AI boom, but that stuff has been there for years and will continue to grow at a slow and steady pace, imo. Adoption of GPGPU for lots of things is probably being bolstered by the LLM bros now, to be honest.
CUDA benefits from being early to market in those areas. Mature tools, mature docs, lots of extra bolt-ons, organizational inertia "we already started this using CUDA", etc.
The silence from AMD has been deafening, though. I can't fathom why they're just ignoring the AI market.
I have NEVER even HEARD of Rocm, and neither has anyone in the GPU programming slack group I just asked.
CUDA is absolutely a moat.
At the very start they had the Apple1.
Apples share price has taken a bit of a fall this week.
https://en.m.wikipedia.org/wiki/Gartner_hype_cycle
I'm not sure how accurate it is overall, but there are companies that fit it.
What really happened: LLMs are fuelling the phishing and scam industry like never before, search engine results are shittier than ever, every other website is an automated LLM generated seo placeholder, every other image is ai generated, students are heavily using these techs for homeworks with unknown long term effects, scientists are using them to write their papers: quality--, quantity++, bots on social media increased dramatically and probably playing a huge role in social tensions. You can probably add a few dozen things in the list
Remember the Metaverse? We are talking about entities that can burn billions on speculative projects and it is just a rounding error on quarterly profits. Let alone that frequently the outlay is in-part recovered via the various "sell shovels in a gold rush" strategies.
This is what total stranglehold of a critical sector helps with. Inefficiency of capital allocation at interplanetary scale - but that's another story.
LLM's might be new but the ML/AI type of efficiency/productivity argument is decades old. The domains where it leads to "gains" are generally when the org deploying algos manages not to be held responsible for the externalities unleashed.
Companies having more money than god and spending it speculatively at least got us self driving cars
Your point 2 seems to indicate that the potential for impact is huge. People also think of positive impacts, not only negative ones, that's why they invest.
They invest because greedy mfers don't want to miss the next apple or google. They'll throw millions at the walls and see what sticks, remember juicero and theranos ?
Compare this with firms that had an actual working product (ex: Lightbulb, Internet, Windows, iPhone) and not the mimics of one (ChatGPT, Bitcoin, Metaverse, Nanotechnology, Quantum).
PS: There is just too much money in the economy now (infused from covid), so chasing speculative investments is expected.
Our health system is so screwed up it’s no wonder they can’t make anything happen. There are only two paths to really fix it, and both would cause a lot of short term issues.
I have not encountered any of the aggressively promoted use cases to be better than anything they replaced, and all the things people seem to choose to use seem of questionable long term value.
I can’t help but feel that this nonsense bubble is going to burst and a lot of this value is going to disappear.
Now you need someone semi-proficient in Python who knows enough about deployment to get a local model running. Or alternatively skills to connect to some form of secure cloud LLM like what Microsoft peddles.
For us it meant that we could cut the work from 6-12 months to a couple of weeks for the initial deployment. And from months to days for adding new document types. It also meant we need one inexpensive employee for maybe 10% or their total time, where we needed a couple of expensive full time experts before. We actually didn’t have the problem with paying the experts, the real challenge was finding them. It was almost impossible to attract and keep ML talent because they had little interest in staying with you after the initial setups, since refining, retuning and adding new document types is “boring”.
As far as selling hardware goes I agree with you. Even if they have the opportunity to sell a lot right now it must be a very risk filled future. Local models can do quite a lot on very little computation power, and it’s not like a lot of use cases like our document one need to process fast. As long as it can get all our incoming documents done by the next day, maybe even by next week, it’ll be fine.
The stuff with Python->traing->??->$$$ is what I don’t buy:
First: the “AI” stuff is “generate content with no obvious financial value to anyone”, chat bots (which no one I know actually seems to want), or “maybe better predictions”.
Second: the “person can do X with AI with less training” etc is not a value of AI, it’s just a product of improved libraries and UI for putting things together. It doesn’t mean the thing they’re doing with AI has any value outside of bandwagonning.
Third: the reason for AI start ups is just that training costs a tonne of capital - and VCs love throwing cash at bandwagons so there’s a pile of “AI” startups, all of which offering essentially the same thing below cost in the hopes that they’ll magically find a profit model.
Finally: there’s already near enough on device processing power on phones for most actual practical uses of “AI” so the need for massive gpu rigs will start to tank especially once the hype train dies off and people start asking what is actually useful in the giant AI startup buzz.
Each of these things is going to result in the valuation bubble for nvidia collapsing. Mercifully I don’t think there’s any real harm in the nvidia valuation bubble (congrats to the folk who made well on their RSUs!), but I still don’t think the valuation has significant longevity.
there isn't any competitor to the MacBook Pro and the M4.
there isn't any competitor to NVL based Blackwell racks. not even for the H100/H200.
so how do you think Huawei competes?
I’m sure both Apple and nvidia view Huawei as a serious competitor. Like, the kind of company that could, in 5 years, take a third of their market share.
Market cap is one piece of a complicated picture. On its own, you don't know too much.
Why is market cap the most important metric? It does not exist in a void. Market cap + P/E or forward P/E, P/B, P/S, etc. are all metrics of high import.
When you're evaluating your ROI on tech stocks, the #1 factor is market cap delta between when you bought the shares and now.
Profit is the number that really matters.
For a public company, it's all about return on investment, right? Market cap is the most important factor in return on investment right? Therefore, market cap is the most important measure.
Completely wrong. Hint: You are on HN. Startups obviously don't start at a large market cap.
I didn't say you should invest in companies already with a big market cap. I said market cap increasing is the ROI to investors (besides dividends, which is mostly irrelevant when investing in tech companies).
That's exactly how startups work. You invest at a cap of $50m. Its market cap increases to $1 billion. You just made 20x (provided you have liquidity).
Theoretically that's how price targets are created by analysts. So market cap is explainable through that metric.
Profit this year and no growth vs. the same profit this year and double profits next year should lead to different market caps, and it generally does.
Market cap should be equal to the expected discounted profit.
Does that mean the market cap is worth $10k? yes. is that meaningful if there are no other buyers at $100? no.
There might be no buyers. There might be no buyers willing to buy at the last sold price. There might be no sellers willing to sell at the last sold price.
Market cap would be $10,000, but if there isn’t a single person willing to buy for that price, then is it worth that much,
https://finance.yahoo.com/markets/stocks/most-active/
So yes, Nvidia's market cap is meaningful. If the reported market cap is at $3.5t and you want to sell your shares, you can easily find someone else who values Nvidia at $3.5t to buy them.
Since M4 Max allows 128GB RAM I'm expecting M4 Ultra to max out at 256GB RAM. Curious what x86's best value get consumer hardware with 256GB GPU RAM. I've noticed tinygrad offers a 192 GPU RAM setup for $40K USD [1], anything cheaper?