Chart pathways beyond static language models by integrating continual learning, hybrid architectures, and on-the-job adaptation.
Scaling parameter counts and datasets delivered astonishing gains, yet frontier researchers warn that static training runs eventually plateau. Models trained once struggle with evolving facts, domain shifts, and personalized tasks. Continual learning—where systems update from ongoing experience—offers a path forward, but it introduces stability-plasticity trade-offs, safety concerns, and infrastructure complexity. This lesson synthesizes viewpoints from prominent researchers exploring the next wave of AI architectures.