In-context learning

Plain English. A model picking up a task from examples or instructions in the prompt itself — no retraining, no weight changes. Show it three worked examples and it does the fourth. The catch: the learning evaporates when the conversation ends. Nothing is retained.

Why it moves money. In-context learning is why context windows became an arms race and why "just prompt it" displaced a lot of anticipated fine-tuning spend. But its impermanence is one of the deepest constraints in the AI economy: a system that cannot accumulate experience on the job caps the labour-substitution thesis at tasks a competent stranger could do with a briefing document. The gap between in-context learning (instant, temporary) and weight updates (slow, permanent) is where a whole product layer lives — memory systems, retrieval, context engineering — and where labs are aiming next.

What to watch. Anything that makes learning persist: test-time training, continual-learning research, memory architectures. Genuine on-the-job learning would be a step-change in what AI can replace, and pricing.

From the signals. Two opposite readings of the context window as working memory. Test-time training would give each user their own model — and its own cost.

Further reading. Brown et al., Language Models are Few-Shot Learners (2020)