Toby Ord examines whether AI systems improve as efficiently as humans when given more resources — specifically, whether 10x more tokens produces the same performance gain as 10x more time does for humans. Recent data suggests AI has scaled worse than humans historically, but newer models are closing the gap.

• Early data from METR showed humans gained ~4x score per resource doubling while AI gained less, implying AI would need 10,000x more tokens to match a human's 10x time improvement — but this used weak models without reasoning capabilities.
• UK AISI's July 2026 chart tracking how long AI can work on tasks (measured in human time-equivalent) shows all recent models scale sublinearly with tokens (γ < 1), meaning 10x tokens yields less than 10x improvement, but this ratio has improved dramatically from 10,000x to just 20x in under a year.
• The scaling relationship appears to be improving over time, with γ rising from near 0.1 to possibly around 0.67, raising the possibility that newer models could eventually match or exceed human scaling efficiency (γ = 1).
• This matters for recursive self-improvement models: if AI can eventually scale as well as or better than humans with additional resources, it changes whether an intelligence explosion becomes plausible.