Google DeepMind's research chief Ed Chi has outlined AI's next phase: a transition from fast, pattern-matching thinking to deeper, more deliberative reasoning akin to human complex problem-solving. This evolution toward reasoning-focused AI underpins the emerging era of AI agents—autonomous systems that can plan and execute multi-step tasks—and could significantly expand the range of business-critical work that AI can handle.
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Ed Chi, vice president of research at Google DeepMind, stated that AI is moving from fast pattern-recognition thinking (System 1) toward deeper reasoning capabilities (System 2), marking the next evolution in AI development as the agent era approaches.
Why it matters
This shift suggests AI systems will move beyond quick responses to tackle complex problems requiring sustained thought, which could reshape how businesses use AI for analysis, planning, and decision-making tasks that currently require human expertise.
What to watch
The transition to 'slow thinking' AI systems represents a fundamental change in how AI will approach problem-solving, though the article does not specify a timeline or concrete products implementing this shift.
Ed Chi's framing of AI's evolution draws on a well-established cognitive science distinction—the contrast between fast, intuitive System 1 thinking and slower, more analytical System 2 reasoning popularized by psychologist Daniel Kahneman. By positioning this as AI's next milestone, Chi signals that the industry believes current large language models, for all their capability at pattern recognition and text generation, lack the sustained reasoning depth needed for more complex, multi-step problem-solving. This aligns with the broader industry shift toward AI agents—systems designed to make decisions autonomously and execute longer chains of actions—which require reasoning that goes beyond immediate pattern matching. The timing of this statement reflects growing recognition within AI research that the frontier is no longer simply scaling language models, but rather enhancing their ability to think through problems methodically, much as a human would when faced with a genuinely difficult analytical or planning task.
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