Longer training horizons have not slowed AI progress because modern architectures (like transformers) and techniques such as reward shaping distribute gradients effectively, making credit assignment over long episodes tractable without exponential difficulty increases.
AI progress hasn't slowed despite longer training horizons, possibly due to huge FLOP efficiency gains from fixing bugs, unreliable human intuitions about near-human intelligence, and capabilities depending on traits beyond intelligence like persistence.
Andrew Ng ranks coding agent acceleration from most to least effective: frontend development (dramatically sped up), backend (slower due to bugs and security), infrastructure (limited due to complex tradeoffs), and research (marginal help beyond coding). He uses this categorization to adjust team expectations and organization.