How Did AI Data Change in 2026?

Jonathan Siddharth says the data landscape completely shifted in 2026 from helping AI master tests to helping AI master real work.

In the test era, success looked like AI passing the SATs, passing the bar, or winning a gold medal in the Math Olympiad. The game was finding experts in every domain and pulling knowledge out of their heads through a dialogue with the model and an evaluation of model outputs, transferring what experts knew into the models. He used to think of that as distilling human knowledge and skills into LLMs.

Real work flips the priority. It is less about how many experts you can find and more about how close to reality you can engineer simulated RL environments where agents train. Experts still check that the environment matches reality and help set the right prompts, the right verifiers, and the right seed data. The goal is a rich enough simulation of the world that agents trained there also perform well in the real world.