Ilya Sutskever recently offered a compact history of modern AI. From roughly 2012 to 2020, he argued, the field lived in an age of research. From 2020 to 2025, it entered an age of scaling. Now we are going back to research—only this time “with big computers.”
It is an appealing periodization. It also creates an immediate puzzle.
Open the release notes for almost any frontier model in 2026 and the architecture diagram looks strangely familiar. There is still a Transformer somewhere in the machine, often routed through a Mixture-of-Experts. The headline improvements are usually elsewhere: better data, longer context, stronger reinforcement learning, synthetic tasks, tool use, memory, verification, adaptive reasoning budgets, and agent orchestration.
This does not look like the arrival of a new neural species. It looks like Formula 1. The car still has four wheels and an engine. Yet enormous gains come from aerodynamics, energy recovery, tire chemistry, telemetry, software, and pit strategy. The chassis matters. The system around it increasingly decides the race.
So which era are we in: research or engineering?
Probably both. The age of research has returned, but much of that research is now expressed as industrial-scale engineering.
“Scaling did not end. It escaped.”
The Recipe That Ate the Field
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