The Gravity Well ...

Autogenerated by Google’s Notebook LM

Finally, a guy who gets AI. No bloviation, no assumption, just taking the time to see how
trees differ from weeds in the world of AI, an open ended tech forever to remain unknown.

When massive objects warp spacetime, smaller things will either get captured by the gravity well or achieve escape velocity. Google is adding enormous mass to the AI field: $93 billion in capex, custom silicon, vertical integration top to bottom, coupled with the breakthroughs from the research team at Google DeepMind.

Does that mean game over for competitors? Well only if intelligence is a monolithic mass, one dimension where the biggest model always wins. But that “God Model” fallacy, the notion that intelligence is monolithic, doesn’t sit with my intuitions, as I have written previously. Recent research from Epoch AI concurs, suggesting intelligence is more multifaceted. If that’s true, Google’s gravity well has a floor but not a ceiling. It’s tough to escape by matching their mass. You escape by achieving different densities in other dimensions.

Keep that frame in mind as we look at what Google just shipped this week.

This week Google released a new flagship LLM, Gemini 3, which includes an advanced thinking capability called DeepThink. Accompanying the launch was a remarkable new image generator called Nano Banana 3 and some developer tools.

I’ve been running Gemini 3 through my usual battery of tests in the run-up to this week’s launch. It’s a noticeable upgrade on Gemini 2.5 – sharper reasoning and a real jump in dealing with complex, theoretical questions. It’s also a concise communicator.

Compared to GPT-5, differences showed up quickly. GPT-5 tends to pile on layers of complexity; Gemini 3 gets to the point. That clarity compounds over hundreds of queries. I now default about a third of my prompts to Gemini 3. I’ve also moved a high‑stakes multi‑agent “council of elders” workflow, where several different prompts challenge and critique analyses, from GPT‑5 to Gemini 3. In that workflow GPT-5 worked noticeably better than Claude 4.5; Gemini 3 Pro is the best of the lot.

Crucially, the new model also addressed a criticism thrown at this sector. For the past few years, foundation model labs have bet on scaling laws, which state that spending more on data and compute reliably produces performance improvements. Many outside commentators claimed that scaling itself had failed. My view, last year, was that while other innovations would be welcome, scaling still had some years to go.

In the words of Google researcher, Oriol Vinyals, on the subject of pre-training scaling:

Contra the popular belief that scaling is over—the team delivered a drastic jump. The delta between [Gemini] 2.5 and 3.0 is as big as we’ve ever seen. No [scaling] walls in sight!

And as for post-training, the step where a trained model gets further refined, Vinyal’s is even more explicit:

Still a total greenfield. There’s lots of room for algorithmic progress and improvement, and 3.0 hasn’t been an exception.

What Google has shown is that scaling still works if you have the vertical stack to sustain the burn: infrastructure, data, researchers. To what extent can Anthropic or OpenAI follow that strategy?

Generated by Google NanoBanana3

And so it goes - K. Vonnegut