Unblocking AI's Continual Learning: Hints From How Humans Learn

If you've ever screamed in all-caps at an AI, then you know the difference between what it learned when it was trained, and what you can teach it by prompting. The LLMs powering today's AI don't learn on the job the way people do. They learn all at once in a big training run and once that's done, we freeze the parameters that store their skills and knowledge. So we all get the same AI with the same skills and biases, centrally trained by a frontier model company. Beyond turning everything even more same-y, there's an economic cost to this centralization: firms use AI that lacks understanding of their unique rules, culture, and quirks. Humans learn this "tacit knowledge" on the job through observation and (sometimes painful) feedback, but AI with its frozen parameters cannot. With context engineering, we can augment the prompt to help AI remember facts , but not teach it skills that last. Every time you press "new chat", AI forgets everything and resets to the state it had just after it was trained. Yes, AI can remember select facts from past conversations, but memorization is different to learning. I cover the distinction further below. It's not surprising that the domains where AI is most successful, like coding, are those suited to centralized training. Good software development skills are mostly firm-agnostic. For everything else firm-specific, AI is trained to trawl the codebase and build context from scratch for every single task. Human developers don't do this. It woul…