Continual learning

Plain English. Continual learning — also called online or lifelong learning — is the unsolved problem of a model that keeps improving from the work it does, updating its own weights as it goes, rather than being frozen the moment training ends. Today's models don't do this: they ship fixed, and any "learning" during use lives outside the weights, in agent memory and the harness. The obstacle it has to overcome is catastrophic forgetting, where new training overwrites old skills.

Why it moves money. It caps the labour-substitution thesis: an employee who never learns from experience has a ceiling, however capable on day one. A model that genuinely learned on the job would compound in value with use instead of going stale, and whoever cracks it changes agent economics outright. Until then, the workarounds — retrieval, longer context, harness memory — are themselves becoming products, and test-time training, where weights update during use, is one claimed path through.

What to watch. A demonstrated, durable on-the-job improvement without a full retraining run — and whether it survives independent replication. Treat "our agents learn from your business" as harness memory until the weights actually move.

From the signals. Prime Intellect shipped a harness built on a "Continual Harness" abstraction — self-improvement at the scaffold, not the weights. Test-time training would give each user their own model, and its own cost.