Google, Nvidia, and OpenAI
A common explanation as to why Star Wars was such a hit, and continues to resonate nearly half a century on from its release, is that it is a nearly perfect representation of the hero’s journey. You have Luke, bored on Tatooine, called to adventure by a mysterious message born by R2-D2, that he initially refuses; a mentor in Obi-Wan Kenobi leads him across the threshold of leaving Tatooine and facing tests while finding new enemies and allies. He enters the cave — the Death Star — escapes after the ordeal of Obi-Wan’s death, and carries the battle station’s plans to the rebels while preparing for the road back to the Death Star. He trusts the force in his final test and returns transformed. And, when you zoom out to the entire original trilogy, it’s simply an expanded version of the story: this time, however, the ordeal is the entire second movie: the Empire Strikes Back.
The heros of the AI story over the last three years have been two companies: OpenAI and Nvidia. The first is a startup called, with the release of ChatGPT, to be the next great consumer tech company; the other was best known as a gaming chip company characterized by boom-and-bust cycles driven by their visionary and endlessly optimistic founder, transformed into the most essential infrastructure provider for the AI revolution. Over the last two weeks, however, both have entered the cave and are facing their greatest ordeal: the Google empire is very much striking back.
Google Strikes Back
The first Google blow was Gemini 3, which scored better than OpenAI’s state of the art model on a host of benchmarks (even if actual real-world usage was a bit more uneven). Gemini 3’s biggest advantage is its sheer size and the vast amount of compute that went into creating it; this is notable because OpenAI has had difficulty creating the next generation of models beyond the GPT-4 level of size and complexity. What has carried the company is a genuine breakthrough in reasoning that produces better results in many cases, but at the cost of time and money.
Gemini 3’s success seemed like good news for Nvidia, who I listed as a winner from the release:
This is maybe the most interesting one. Nvidia, which reports earnings later today, is on one hand a loser, because the best model in the world was not trained on their chips, proving once and for all that it is possible to be competitive without paying Nvidia’s premiums.
On the other hand, there are two reasons for Nvidia optimism. The first is that everyone needs to respond to Gemini, and they need to respond now, not at some future date when their chips are good enough. Google started its work on TPUs a decade ago; everyone else is better off sticking with Nvidia, at least if they want to catch up. Secondly, and relatedly, Gemini re-affirms that the most important factor in catching up — or moving ahead — is more compute.
This analysis, however, missed one important point: what if Google sold its TPUs as an alternative to Nvidia? That’s exactly what the search giant is doing, first with a deal with Anthropic, then a rumored deal with Meta, and third with the second wave of neoclouds, many of which started as crypto miners and are leveraging their access to power to move into AI. Suddenly it is Nvidia that is in the crosshairs, with fresh questions about their long term growth, particularly at their sky-high margins, if there were in fact a legitimate competitor to their chips. This does, needless to say, raise the pressure on OpenAI’s next pre-training, run on Nvidia’s Blackwell chips: the base model still matters, and OpenAI needs a better one, and Nvidia needs evidence one can be created on their chips.
What is interesting to consider is which company is more at risk from Google, and why? On one hand Nvidia is making tons of money, and if Blackwell is good, Vera Rubin promises to be even better; moreover, while Meta might be a natural Google partner, the other hyperscalers are not. OpenAI, meanwhile, is losing more money than ever, and is spread thinner than ever, even as the startup agrees to buy ever more compute with revenue that doesn’t yet exist. And yet, despite all that — and while still being quite bullish on Nvidia — I still like OpenAI’s chances more. Indeed, if anything my biggest concern is that I seem to like OpenAI’s chances better than OpenAI itself.
