Source: The Era of System 2 — state of the field review, early 2026
Kahneman’s System 1 / System 2 distinction — fast, intuitive thinking versus slow, deliberate reasoning — has become one of the most useful frames for understanding what’s changing in AI right now.
System 1 AI was the first era of LLMs: you give the model a prompt, it generates the statistically most likely continuation at high speed. The model’s intelligence is entirely a function of what it learned during training. System 2 AI is the current era: models can now “think” at inference time, running search procedures, generating and evaluating candidate answers, backtracking when their reasoning leads somewhere wrong.
The mechanism that enables this is test-time compute: instead of generating a single output, the model generates multiple candidates, evaluates them using a process reward model, and returns the best candidate. The accuracy on hard reasoning tasks increases measurably as more inference compute is spent — meaning intelligence can now be “purchased” at runtime, not just during training.
The practical implications are substantial. Model capability is no longer fixed at training time — it’s a dial that can be adjusted based on the importance of the task. For a simple question, use fast System 1 mode. For a high-stakes reasoning problem, spend more compute, run more searches, verify more carefully. DeepSeek-R1 is the clearest public demonstration of this shift.