Catastrophic forgetting

Plain English. Catastrophic forgetting is what happens when you train a neural network on something new and it abruptly loses what it already knew. Because a model stores everything in the same shared weights, updating them for a fresh task can overwrite the patterns that encoded the old ones. It is the reason models are trained once, in bulk, rather than topped up day to day.

Why it moves money. It is a structural tax on the industry's business model. Because a deployed model cannot safely keep learning from its own use, labs must periodically retrain from scratch — an enormous capital event — instead of improving a model cheaply and incrementally. It also caps how personalised a single model can become without a fleet of expensive fine-tunes. It is the failure mode that continual learning has to defeat to unlock cheaper, always-current models.

What to watch. Any credible method that lets a model absorb new information without degrading old performance. That is the unlock that would turn training from a recurring megaproject into something closer to ongoing maintenance — and would reset the economics of keeping a model current.