If you want to win in AI - and I mean win in the biggest, most lucrative, most shape-the-world-in-your-image kind of way - you have to do a bunch of hard things simultaneously. You need to have a model that is unquestionably one of the best on the market. You need the nearly infinite resources required to continue to improve that mode and deploy it at massive scale.
Google DeepMind CEO Demis Hassabis, whose company just released Gemini 3 to widespread acclaim, has made it clear where he stands on the issue. "The scaling of the current systems, we must push that to the maximum, because at the minimum, it will be a key component of the final AGI system," he said at the Axios' AI+ Summit in San Francisco last week. "It could be the entirety of the AGI system."
"We are not expanding a lot of square footage, per se, but we're expanding our compute," Chan said on an episode of " The a16Z Podcast" that aired November 6, when talking about their investment in Biohub, a collection of biology labs the philanthropy has backed since 2016. "The researchers, they don't want employees working for them, they don't want space, they just want GPUs," Zuckerberg added. "In a sense, that's new lab space. It's much more expensive than wet lab space," said Chan, who is a pediatrician by training.
The AI stack has become increasingly confusing and complex. We've gone from two major players (OpenAI and Anthropic) in 2023 to over 200 providers, dozens of vector databases, and a constant stream of new "AI-native" tools launching weekly. AI applications are no longer in the experimental phase. These technologies have now matured to production-ready applications that enterprises can deploy at scale.
