At the 2026 Inclusion·Bund Summit forum titled "New Paradigms of AI-Driven Economic Growth and the Reconstruction of Social Value," Thomas Sargent, a New York University professor and 2011 Nobel laureate in economics, stated that while AI is emerging as a powerful force for economic expansion, the investments, strategic decisions, and policy frameworks surrounding it are mired in profound uncertainty. He argued that ever-increasing capital outlays do not necessarily translate into more predictable future returns.

In Sargent's view, grasping the economic shifts AI is triggering requires first acknowledging a fundamental reality: we have entered an era where we know AI matters immensely, yet we remain clueless about the specific destination toward which it will steer the economy. He drew a historical parallel to the 17th-century figures of Kepler and Newton. Kepler used massive volumes of astronomical observations to articulate the laws of planetary motion, though he was unaware of the underlying physical forces; Newton later demystified the fundamental principles governing those laws.

"Today's AI resembles Kepler," Sargent remarked. However, he sees the true frontier of AI as the transition from the 'Kepler stage' to the 'Newton stage'—a leap that involves not just identifying correlations, but also comprehending and deducing the underlying structures of phenomena, achieving superior generalization beyond the confines of training data, and recognizing the limits of one's own knowledge. "We still do not know when AI will enter the 'Newton stage'," Sargent added.

Sargent cautioned that many perceive neural networks as a form of 'model-free' learning, but in reality, every AI algorithm encapsulates a specific parameterized model and carries numerous implicit assumptions about the world. A model may excel in familiar data environments, but it is prone to failure when circumstances shift or novel situations emerge outside its training set. "We are at the very core of the unknown," he warned.

Consequently, he repeatedly emphasized the virtue of "humility." In his view, when confronting the monumental changes brought by AI, businesses, investors, and regulators alike must not rely solely on idealized models to project the future. Instead, they should acknowledge the constraints of these models, allocate room for unforeseen errors, and build decision-making frameworks flexible enough to handle a spectrum of possible outcomes.