Ilya Sutskever — We're moving from the age of scaling to the age of research

Ilya Sutskever's return-to-research argument concerns finding better ways to turn computing power into useful intelligence. In the interview, he describes pretraining as a particularly dependable recipe: adding data and compute produced predictable improvements. He questions whether simply expanding that recipe can deliver the next transformation, especially as available data is finite. His central research concern is generalization, meaning whether skills acquired in one setting transfer effectively to another. This connects his criticism of strong test scores alongside uneven everyday performance to his broader position: the next advance may depend on changing how models learn, even while using very large computers.

“So it’s back to the age of research again, just with big computers.”

Ilya Sutskever

“these models somehow just generalize dramatically worse than people. It’s super obvious.”

Ilya Sutskever

“We got to the point where we are in a world where there are more companies than ideas by quite a bit.”

Ilya Sutskever

“In theory, there is no difference between theory and practice. In practice, there is.”

Ilya Sutskever

“The top-down belief is the thing that sustains you when the experiments contradict you.”

Ilya Sutskever