As long as both companies remain stable and viable, there's probably limited upside to pouring more money into them. If they fail, and bring down the AI ecosystem with them, that is very bad news for Nvidia. So they've been there nurturing their success and providing capital to backstop their exponential growth.
You can see Nvidia stepping in throughout the ecosystem with confidence boosting investments where needed. They haven't just supported Anthropic and OpenAI.
If OpenAI and Anthropic succeed, and get their business fly-wheels fully spinning, they don't necessarily need more capital from Huang. Ultimately the goal of Nvidia is to profit from their long-term success by selling them GPUs for a long, long time. The goal isn't to keep plowing money into them forever.
These days Nvidia has more money than it knows what to do with. They could certainly push $5b+ into each company annually and never miss it. They're tracking toward an astounding $200b in operating income (maybe over the next four quarters if the music doesn't suddenly stop).
It did not raise $110 billion. According to their own SEC filings $35 billion of Amazon’s funding is contingent on “(i) OpenAI meeting specified milestones, and (ii) OpenAI directly or indirectly consummating an initial public offering or direct listing of equity securities in the United States”
Instead of pouring more money into OpenAI and Anthropic, Nvidia should invest more in expanding production capacity for the RTX 5000 series and future generations. High-end consumer GPU availability is still constrained, especially for the RTX 5090, and street prices remain elevated. Nvidia should come back to the consumer side.
Datacenter income for nVidia last quarter was something like 62B vs the gaming market of <4B. While not quite a rounding error, it feels like the gaming market is just too small for them to put more resources toward it for us consumer folks.
But so many gamers want to buy GPUs and can’t because they are sold out or won’t because they are super price inflated. Wouldn’t the gaming market be larger if the products were actually available and at their actual MSRP?
Nvidia can't sell 10x the number of GPUs they sell. As much as the supply issues are discussed, it would likely take them a long time to just double the market. They could try to become the vendor of choice for the PS6/next xbox, but that's a big strategy shift for again maybe double the market, not 10x the market.
On the other hand right now the market doesn't seem to think that the >60bn of datacenter revenue is going away or even going to slow down _growing_ any time soon. Just adding 10% more revenue there is worth more than doubling their GPU business which they likely can't do.
That is not substantiable. AI bubble is wealthy hype like a single drop of blood can be used to validate 100 different diagnostic test. Reality is parts per million fails this along with reusable medium. Wealth latches to idiocy.
Gaming and CAD market are real expectations that latch to reality. Grow the education systems and grow both. So is matrix math, such as hashing.
AI has reached a state of software issue, not hardware. And the divergence of AI hardware does not equate to CAD and Gaming math.
How many of the last ten years have had some kind of "temporary" GPU shortage? It was crypto, now it's LLMs, who knows what's next?
The only winning strategy for these guys is to exploit the market for all it's worth during shortages and carefully control production to manage the inevitable gluts.
> AI has reached a state of software issue, not hardware
Citation very much needed.
At the very least, OpenAI seems to believe more and larger datacenters is the path to better models... and they've been right about that every time so far.
Why would they do that? They launched the DGX Spark last year with multiple hardware OEMs selling flavors of the reference device (Dell, Lenovo, Asus). That contains a desktop-sized Grace Blackwell architecture GPU (GB10), and word on the street is that they're moving into laptops this year. Their market is the same market Apple is pitching the MacBook 5 Pro/Max, too: devs wanting local models. It's not currently a large market, but it's growing quickly. It makes far more sense for Nvidia to build hardware to service this market than to overly focus on their gaming lines. RTX GPUs are sell once. GB-containing consumer devices are "sell once, but then collect recurring revenue when the workloads those developers build hit production on a cloud somewhere."
If I were Nvidia, I would give more attention to consumer GPUs to hedge my bets. When (not if) this AI bubble pops, their AI customers will become worthless to them very quickly as they won't be buying more GPUs. And when that day comes, I would want to still have consumers to sell to, rather than have them all buying from AMD because I ignored them.
I should admit this is partly my personal preference.
That said, gaming has been a durable market for decades, and there’s a strong cycle where better chips enable better games, which then drives more demand for better chips.
nvidia will come back to the consumer side when the AI side stops being as profitable. Right now, it still seems like the margins for AI hardware is way higher than the same consumer product would sell for.
This leaves an opening for Intel to get in the game. Their new lines have a pretty decent value proposition for mid-tier gaming. If they focused on the higher end they would could own it. There is massive latent demand because of the NVidia situation. It’s easier to make money from than the R&D to build the next Blackwell but there is just as much demand for local/private models on the prosumer level.
Nvidia could flip a switch and start competing with former customers. They have the talent, the models, the HW, and they know how to quickly build out DCs.
That would not go well for nvidia. Why would they want to enter and compete in a market where everybody is losing money? And in so doing alienate the people that make them profitable?
They don't, not directly. That's why they've generally been winding down DGX Cloud. However, lots of folks dramatically underestimate their frontier models (Nemotron family), and they license those models (free) for embedding in MANY other, large tech companies' own products and platforms, which either directly or indirectly consume massive quantities of GPU time.
Nvidia is best known for selling huge volumes of GPUs to the hyperscalers & neoclouds, but I don't think lots of folks appreciate how many GPUs ISVs like Snowflake, Databricks, Teradata, etc consume, too, just by virtue of designing much of their internal products around CUDA & Nemotron.
Are you suggesting Nvidia doesn't have talent in the AI industry?
NVIDIA has released NVIDIA Deep Learning Super Sampling (DLSS) and a Frame Generation model, NVIDIA Super Resolution (VSR) being the most popular/well known models. (DLSS is outstanding technology, despite the sometimes misleading marketing).
Nvidia has released countless models:
Alpamayo 1 (Car navigation model)
Cosmos-Reason2 (reasoning vision language model)
Nemotron 3 (Large Language Model series)
Llama-Nemotron (Large Language Model series)
Isaac GR00T (VLA Models)
Nemotron OCR (Optical Character Recognition models)
Take a look at their HuggingFace Collections, almost 100 different collections with countless models inside each collection: https://huggingface.co/nvidia/collections
NV has a massive amount of AI talent, and a lot of them have PhDs.
Are you suggesting they're lacking on the ultra-high-end? That is: 5-10M+ in comp to sign a single researcher/IC; industry rock star territory.
Major frontier AI labs do tend to have that type of talent in abundance. I'm sure NV has the equivalent when it comes to hardware design. Surely in AI research too, but perhaps not in the same quantities.
That's not how a smart business runs and that's now how Nvidia operates.
Jensen is smart. He's gone through over 30 years of tech cycles.
Nvidia actively commoditizes the LLM models. Look at Nemotron. They've avoided making a SOTA model solely to keep the hyperscalers (aka crack addicts) coming back for more GPUs.
As soon as the bubble bursts, they can release some open weight NemoMambaDiffusiontron and keep folks buying GPUs to run the damn thing.
It still wouldn't be smart to do so, as this would fall into the common business pitfall of thinking you could easily do the next stack layer of work.
> Nvidia CEO Jensen Huang said his company’s recent investments in OpenAI and Anthropic are likely to be its last in both, saying that once they go public as anticipated later this year, the opportunity to invest closes
ok, sounds obvious
> Nvidia, for its part, isn’t offering much more on the matter
ok, so no more news from nvidia
> Still, a few other dynamics might also explain the pullback..
You can see Nvidia stepping in throughout the ecosystem with confidence boosting investments where needed. They haven't just supported Anthropic and OpenAI.
If OpenAI and Anthropic succeed, and get their business fly-wheels fully spinning, they don't necessarily need more capital from Huang. Ultimately the goal of Nvidia is to profit from their long-term success by selling them GPUs for a long, long time. The goal isn't to keep plowing money into them forever.
https://nvidianews.nvidia.com/news/nvidia-announces-financia...
It doesn't matter if the consumer market is 4T, if the AI market is 60T!
On the other hand right now the market doesn't seem to think that the >60bn of datacenter revenue is going away or even going to slow down _growing_ any time soon. Just adding 10% more revenue there is worth more than doubling their GPU business which they likely can't do.
Gaming and CAD market are real expectations that latch to reality. Grow the education systems and grow both. So is matrix math, such as hashing.
AI has reached a state of software issue, not hardware. And the divergence of AI hardware does not equate to CAD and Gaming math.
The only winning strategy for these guys is to exploit the market for all it's worth during shortages and carefully control production to manage the inevitable gluts.
Citation very much needed.
At the very least, OpenAI seems to believe more and larger datacenters is the path to better models... and they've been right about that every time so far.
Every GW of Blackwell generates more revenue than the entire gaming business does in 1 year.
Nvidia is best known for selling huge volumes of GPUs to the hyperscalers & neoclouds, but I don't think lots of folks appreciate how many GPUs ISVs like Snowflake, Databricks, Teradata, etc consume, too, just by virtue of designing much of their internal products around CUDA & Nemotron.
I don't think it's as easy as others say, though.
NVIDIA has released NVIDIA Deep Learning Super Sampling (DLSS) and a Frame Generation model, NVIDIA Super Resolution (VSR) being the most popular/well known models. (DLSS is outstanding technology, despite the sometimes misleading marketing).
Nvidia has released countless models:
Alpamayo 1 (Car navigation model) Cosmos-Reason2 (reasoning vision language model) Nemotron 3 (Large Language Model series) Llama-Nemotron (Large Language Model series) Isaac GR00T (VLA Models) Nemotron OCR (Optical Character Recognition models)
Take a look at their HuggingFace Collections, almost 100 different collections with countless models inside each collection: https://huggingface.co/nvidia/collections
nVidia has an open position for system architect, orbital station AI datacenter
Are you suggesting they're lacking on the ultra-high-end? That is: 5-10M+ in comp to sign a single researcher/IC; industry rock star territory.
Major frontier AI labs do tend to have that type of talent in abundance. I'm sure NV has the equivalent when it comes to hardware design. Surely in AI research too, but perhaps not in the same quantities.
Jensen is smart. He's gone through over 30 years of tech cycles.
Nvidia actively commoditizes the LLM models. Look at Nemotron. They've avoided making a SOTA model solely to keep the hyperscalers (aka crack addicts) coming back for more GPUs.
As soon as the bubble bursts, they can release some open weight NemoMambaDiffusiontron and keep folks buying GPUs to run the damn thing.
It still wouldn't be smart to do so, as this would fall into the common business pitfall of thinking you could easily do the next stack layer of work.
Well, classically, to capture more margin for yourself. In business school they call this Vertical Integration. Samsung did exactly this. AWS too.
Nvidia rushed some investments in both companies just before they went public and are now are just waiting to get paid.
ok, sounds obvious
> Nvidia, for its part, isn’t offering much more on the matter
ok, so no more news from nvidia
> Still, a few other dynamics might also explain the pullback..
Wait it's a pullback?
This is terrible reporting, right?