Vinod Khosla AI News & Updates
Venture Capitalist Vinod Khosla Proposes 10% Government Stake in Public Companies to Address AGI Economic Disruption
Vinod Khosla, founder of Khosla Ventures, proposed at TechCrunch Disrupt 2025 that the U.S. government should take a 10% stake in all public corporations to redistribute wealth as AGI transforms the economy. He argued this extreme measure is necessary to maintain social cohesion through AI-driven job displacement, predicting a "hugely deflationary economy" by 2035. Khosla acknowledged the controversial nature of the proposal but emphasized the need to share AI's abundance broadly across society.
Skynet Chance (0%): This proposal addresses economic distribution consequences of AGI rather than technical AI safety, control mechanisms, or alignment challenges that would directly impact loss of control scenarios. The focus is entirely on human socioeconomic adaptation to AI, not on preventing uncontrollable AI systems.
Skynet Date (+0 days): The proposal is a reactive economic policy framework for managing AGI's societal impact, not a technical development or capability advancement that would accelerate or decelerate the emergence of uncontrollable AI systems. It does not influence the pace of AI capability development itself.
AGI Progress (+0.02%): A prominent VC publicly discussing concrete AGI timeline predictions (2035 for massive economic transformation) and societal preparation signals growing consensus that AGI is approaching feasibility. This reflects increased confidence in the AI investment community about near-term AGI achievement, suggesting perceived progress toward that goal.
AGI Date (+0 days): Khosla's specific 2035 timeline for massive AI-driven economic deflation implies he sees AGI transformation occurring within approximately 10 years, which represents a relatively aggressive near-term timeline from a major industry figure. However, this is speculation about consequences rather than technical acceleration, so the impact on actual AGI development pace is minimal.