AI sector leaders back universal basic income as automation risk grows

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AI leaders back basic income

Highlights

AI leaders including Sam Altman, Dario Amodei, Demis Hassabis, Vinod Khosla, Elon Musk, and Geoffrey Hinton increasingly advocate universal basic income amid rising concerns about AI-driven job displacement.
OpenResearch's $60 million OpenAI-funded study from 2019 found that recipients of $1,000 monthly payments spent more on essentials, with early gains in stress reduction and food security fading over time.
Over 100 basic income pilots operate across 16 U.S. states and Washington, DC, with investors like Khosla predicting most human labor will be automated and universal basic income becoming critical for economic stability.

Tech leaders tie AI disruption to income support

As first reported by Business Insider, several leading AI executives and investors say universal basic income, or related cash-transfer models, are becoming more relevant as artificial intelligence increases productivity while threatening to displace workers.

The article highlights support from OpenAI CEO Sam Altman, Anthropic CEO Dario Amodei, Google DeepMind CEO Demis Hassabis, investor Vinod Khosla, Elon Musk, Geoffrey Hinton, and former U.S. presidential candidate Andrew Yang. Their arguments vary, but they broadly center on the idea that AI could concentrate wealth, reduce demand for human labor, and force governments to consider new ways to distribute economic gains.

Altman has for years promoted universal basic income and is also linked to research on the issue. Results published in July from a study by OpenResearch, which began in 2019 and received $60 million from OpenAI, found that recipients of $1,000 monthly payments increased spending mainly on food, rent, and transportation, while early improvements in stress and food insecurity faded over time.

The same debate is also producing alternatives to cash-based redistribution. Altman has suggested a form of "universal basic compute," in which individuals receive access to slices of AI computing resources, while Musk argues on X that federal checks are the best response to unemployment caused by AI and says future AI and robotics systems could generate enough goods and services to avoid inflationary pressure.

Policy debate expands beyond Silicon Valley

Support for basic income is growing alongside wider public discussion in the U.S., where guaranteed income pilots have already been launched more than 100 times and no-strings-attached programs operate in at least 16 states and Washington, DC. The idea gained earlier visibility through Yang's 2020 presidential campaign, which proposed $1,000 monthly payments to American adults under the "Freedom Dividend" plan.

Khosla writes in a September 2024 post on the Khosla Ventures website that AI is likely to automate most human labor and make UBI an essential safety net, with governments taking a larger role in regulation and wealth distribution. Amodei writes in an October 2024 essay that a large universal basic income may be part of the answer, but likely only one element of a broader response to the economic and social changes AI creates.

Hinton says the UK government should consider universal basic income to address AI-linked job losses, while Hassabis says at SXSW in London in July that the economy may need universal high income or another mechanism to distribute the additional productivity AI produces. Critics, however, continue to argue that such programs may discourage work, encourage wasteful spending, or strain public finances through higher taxes or budget cuts.

In our earlier article on Peter Diamandis’s take on an AI-driven “universal high income,” we explained why some tech voices believe AI, robotics, and falling production costs could enable large government payments—potentially around $3,000 a month—and even change how people think about retirement saving. We also noted the counterargument that generous income support without strong work and learning incentives could erode skills and weigh on long-term productivity, making policy design and timing critical as AI-linked job risks grow.

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