The Week in AI: Gemini 3 Pro, memory, and a New small-model orchestration
A field report on fine-tuned distillation and what it changes for researchers.
There is a version of this story that is mostly hype. There is another version, the one we are interested in, that is mostly engineering.
Inside Intercom, the rollout looked less like a moonshot and more like a slow migration. A pilot, a champion, a quiet expansion, a budget line.
Teams that win with computer-use agents tend to share a habit: they write the evals before they write the prompts. Everything else follows from that.
Inside Snowflake, the rollout looked less like a moonshot and more like a slow migration. A pilot, a champion, a quiet expansion, a budget line.
The cost curve matters here. Mistral Large 3 is roughly an order of magnitude cheaper per token than the equivalent model 18 months ago, and that changes which problems are worth automating at all.
What xAI actually shipped with Gemini 3 Pro is less a single capability and more a cluster of small, compounding improvements — the kind that only show up when you put a real workflow on top.
The skeptical read is that we are watching a feature, not a platform. The optimistic read is that memory is exactly the kind of feature that becomes a platform when nobody is paying attention.
Teams that win with long-context workflows tend to share a habit: they write the evals before they write the prompts. Everything else follows from that.
None of this guarantees a clean story. Anthropic could ship a model next month that rearranges the assumptions in this piece. But the direction of travel, for now, is clear enough to plan around.