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.
Well, classically, to capture more margin for yourself. In business school they call this Vertical Integration. Samsung did exactly this. AWS too.
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.
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
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.