Solana Co-founder Anatoly Yakovenko: AI Unemployment Equals Doubling Productivity
Anatoly Yakovenko, co-founder of Solana Labs, suggested that the notion of "50% job loss" is essentially another way of expressing a doubling of productivity, meaning that less labor is required for the same output. He believes that AI will not lead to a collapse in employment, as lower costs will unleash demand and significantly expand consumption scale.
Using elderly care as an example, he pointed out that if robots turn previously extremely scarce human services into widely available products, it would replace a large-scale workforce while simultaneously creating demand that was previously unattainable. Yakovenko also cited the consumption gap, noting that the average daily consumption in the U.S. is about $70, while the global average is around $7, indicating that there is potential for demand to expand several times over.
Source: Public Information
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This definition transforms the "unemployment issue" into a "productivity issue," fitting within a typical framework of technological optimism. Throughout economic history, each wave of technological revolution has temporarily displaced labor but has ultimately absorbed it through price reductions and the creation of new demand, as seen in the industrial and internet eras.
The key variable is not production capacity, but whether "demand can be released simultaneously." The validity of Yakovenko's logic hinges on the premise that price reductions can significantly expand the consumption boundary, turning previously unaffordable services (like high-quality elderly care) into mass consumer goods, thereby creating new employment structures. However, this process relies on income distribution; if the gains from productivity improvements are concentrated mainly at the capital end, demand expansion may fall short of expectations.
Structurally, AI does not simply lead to job reductions but rather to a "rearrangement of labor types." High-repetition, standardized jobs will be compressed, while positions related to the management, design, service, and human-machine collaboration around AI systems will rise. This transition often has a time lag, resulting in short-term structural unemployment coexisting with long-term rebalancing.
A deeper issue lies in global inequality. The "global consumption uplift potential" mentioned by Yakovenko fundamentally relies on income growth and distribution improvements in developing countries. If AI primarily enhances productivity in developed economies without simultaneously improving global income structures, then the path to "tenfold demand growth" will be constrained by institutional and distributional factors, rather than by technology itself.