Mustafa Suleyman opened his MIT Technology Review essay with a crisp diagnosis of the problem: we evolved for a linear world, and that makes us catastrophically bad at perceiving exponential change. The argument flows cleanly from there.

Suleyman, who co-founded DeepMind and now runs Microsoft AI, has been inside the compute curve since 2010. By his count, the amount of training data going into frontier models has grown by a trillion times over that period—from roughly 10¹⁴ floating-point operations to numbers that require scientific notation to state without embarrassment. The skeptics who keep predicting a wall keep being wrong, he argues, not because they misunderstand the individual constraints (Moore’s Law deceleration, data exhaustion, energy limits) but because they underestimate how many parallel levers the industry is pulling simultaneously.