Deepmind 研究人员提出“人工共生智能”作为奇点的替代方案

An essay for the Deepmind Institute challenges the familiar image of a lone superintelligence. The authors envision networks of agents, hybrid institutions, and what they call "Artificial Symbiotic Intelligence." Google-affiliated researchers Benjamin Bratton, Blaise Agüera y Arcas, and James Manyika argue that artificial general intelligence, or AGI, will emerge from a social system in which people and AI agents work together. Some of today's most capable AI systems already use frameworks that divide work among several models and coordinate them as teams . That shifts the central challenge for AI research, the authors say. Researchers must coordinate and govern a complex network of agents, people, and the systems that connect them, rather than build an isolated machine intelligence. Intelligence, in this view, is a social phenomenon, not an individual trait. The authors call their vision " Artificial Symbiotic Intelligence ," an ecosystem in which people and machines coexist over time, shape one another, and make decisions together. The idea directly challenges the notion of a "singularity" driven by a single superintelligence that continually improves itself. The argument builds on two earlier papers from the authors' circle. In the preprint "Agentic AI and the next intelligence explosion," James Evans, Benjamin Bratton, and Blaise Agüera y Arcas developed the social and institutional perspective behind the new essay. A second preprint provides empirical support. In "Reasoning Models Generate Societies of Thought," Junsol Kim, Shiyang Lai, Nino Scherrer, Blaise Agüera y Arcas, and James Evans study reasoning models such as DeepSeek-R1 and QwQ-32B. Their analysis of reasoning traces suggests that these models often produce patterns resembling internal debate, shifting perspectives, raising objections, and reconciling conflicting approaches. This behavior emerges during training rather than being explicitly programmed . When reinforcement learning rewards models only for reasoning accuracy, they develop multi-perspective, conversational behavior on their own. The Deepmind essay extends this finding from individual models to the possible design of societies made up of people and agents. The authors frame this development as a possible historical break. Urbanization, the growth of specialized professions, and falling birth rates linked to rising prosperity are shrinking human populations in industrialized countries. Meanwhile, the number of AI agent instances is growing fast. The balance between biological and synthetic thinkers at a human level could shift so quickly that, on the scale of history, it would look like a sudden leap. They compare this to the Industrial Revolution, when machines took over work once done by human muscles. A similar threshold could arrive when synthetic text, code, and administrative work exceed the combined output of biological brains. People might then serve as a slower, more abstract layer directing a distributed field of synthetic cognition. The argument rests on rethinking what an agent actually is. In the authors' usage, an AI agent is a temporary bundle of models, roles, memories, ethical orientations, tools, and skills. Users may experience it as a coherent entity with a lasting personality, but the authors describe it as an assemblage that can be taken apart and recombined, much like a collage. It may look like one thing, but it is made of many parts. A person remains a continuous self across interactions because the brain, despite its internal division of labor, forms a physically connected whole. An AI agent has no such anchor. Its ability to act is assembled anew with each request, depending on what sits in the context window and what the user asks. Treating these systems as digital twins with fixed human identities misrepresents their nature and understates what coordinated agent swarms could do, the authors argue. The same multiplication affects users too. As people direct sw