Thomas Sargent, winner of the 2011 Nobel Prize in Economics and professor at New York University, said at the 2026 Inclusion · Bund Conference Opinion Forum that AI is becoming an important driving force for economic growth, but investment, decision-making, and policy formulation surrounding AI is also facing huge uncertainty. The increasing scale of capital investment does not mean that future returns are more certain. He compared 17th century Kepler to Newton. Kepler summed up the rules of planetary motion through extensive astronomical observation data, but he did not know the physical reasons behind these laws at the time; Newton further explained the basic principles behind these laws. “Today's AI is a bit like Kepler.” Sargent said that the real frontier of AI is whether it can move from the “Kepler stage” to the “Newton stage” — it can not only discover correlations, but also further understand and infer the structure behind things, achieve better generalization outside of training data, and recognize the boundaries of one's own knowledge. “We still don't know when AI will enter the 'Newton Stage'.”

Thomas Sargent, winner of the 2011 Nobel Prize in Economics and professor at New York University, said at the 2026 Inclusion · Bund Conference Opinion Forum that AI is becoming an important driving force for economic growth, but investment, decision-making, and policy formulation surrounding AI is also facing huge uncertainty. The increasing scale of capital investment does not mean that future returns are more certain. He compared 17th century Kepler to Newton. Kepler summed up the rules of planetary motion through extensive astronomical observation data, but he did not know the physical reasons behind these laws at the time; Newton further explained the basic principles behind these laws. “Today's AI is a bit like Kepler.” Sargent said that the real frontier of AI is whether it can move from the “Kepler stage” to the “Newton stage” — it can not only discover correlations, but also further understand and infer the structure behind things, achieve better generalization outside of training data, and recognize the boundaries of one's own knowledge. “We still don't know when AI will enter the 'Newton Stage'.”