Vitalik Buterin Maps AI Progress Through Three Waves And Questions Whether LLMs Can Capture All Human Capabilities
In Brief
Vitalik Buterin maps AI’s three waves of capability growth, defines AGI as civilization-sustaining intelligence, and advocates for deceleration through human-machine integration.
Ethereum co-founder Vitalik Buterin has published an essay outlining a framework for evaluating artificial intelligence capability progression through three historical waves. The first wave, the Industrial Revolution, gave machines the capacity for large-scale manufacturing and repetitive physical action.
The second, defined by calculators and computers, enabled execution of mental tasks governed by precise logical rules. The third and current wave, large language models, acquires capabilities through training on massive datasets rather than explicit programming, extending into domains such as autonomous driving.
Despite these advances, Buterin notes that a substantial range of human capabilities remains beyond machine reach. The central question is whether large language models, augmented by future improvements, will eventually encompass all remaining human abilities, or whether they will plateau like previous technologies.
He draws a parallel to the early computing era, when many assumed that machines capable of complex mathematics would quickly master simpler tasks such as visual recognition, a prediction that proved incorrect. While contemporary arguments for the comprehensive capability of large language models are stronger than in previous eras, their ultimate sufficiency remains unproven.
If large language models encounter stubborn limitations comparable to those faced by earlier technologies, Buterin describes an optimistic scenario in which human capabilities in strategic thinking, creativity, emotionally intelligent reasoning, and other domains resistant to example-based definition become the primary focus of economic activity. In this vision, widespread automation of routine tasks would generate abundance in food production, housing, and medicine, while preserving recognizable human political and economic structures.
AGI Thresholds and the Human-Machine Integration Path
Buterin defines artificial general intelligence as a system sufficiently capable that, if deployed across robotic infrastructure in a world without humans, it could independently sustain civilization. He argues that this definition captures the fundamental shift from artificial intelligence as a tool to a self-sustaining force, noting that such a development would render human dominance a matter of historical contingency rather than inherent biological superiority. He distinguishes this from artificial superintelligence, suggesting that a period of human-machine collaboration may persist even as individual machine capabilities expand.
He illustrates this concept through the history of chess, where human-machine partnerships continued to outperform machines alone for years after computers surpassed grandmasters. Buterin proposes that deep integration between humans and machines, including brain-computer interfaces and systems capable of interpreting conscious and subconscious signals, could maintain human relevance by eroding the binary distinction between biological and artificial cognition. His preferred long-term outcome includes the preservation of global pluralism, voluntary access to technological enhancement, and the maintenance of Earth as a regulated environment for those choosing traditional existence alongside expanded opportunities in space.
He acknowledges that this trajectory represents a narrow corridor fraught with risks, including the possibility of unrestrained artificial intelligence gaining decisive advantage, unilateral concentration of power by a single state or corporation, or integration technologies that erode essential human characteristics. Buterin expresses support for deceleration proposals and mechanisms that would enable development to slow, suggesting that an open-weight model ecosystem could serve as a form of economic deceleration by reducing capital expenditure concentration in frontier systems. He concludes that while favorable economic and physical conditions should not be assumed, political and incentive structures offer viable avenues for influencing the pace and direction of artificial intelligence development.
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About The Author
Alisa, a dedicated journalist at the MPost, specializes in crypto, AI, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
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Alisa, a dedicated journalist at the MPost, specializes in crypto, AI, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
