Long-horizon tasks may be far harder to train

A qualitative consideration that could significantly slow AI is that next-word prediction gives very rich supervision, whereas long-horizon tasks (like being an employee over a month) provide vastly fewer effective data points; in the worst case, training costs scale linearly with the horizon over which a system must operate, making sample efficiency on economically valuable long tasks the real bottleneck.