Social Capital's research newsletter: short deep dives on AI, markets and the economy — recent entries cover what AI spending does to headcount, why AI productivity gains don't show up in the numbers, and the open vs. closed model race — plus a weekly "What I Read This Week" roundup.
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Research project that collects evidence and analysis on the likely impacts of advanced AI, aimed at researchers, funders and policy-makers. Known for the Expert Survey on Progress in AI, the largest long-running survey of AI researchers on timelines and risks.
MIT Sloan summary of the AI Decision Matrix from MIT CISR researchers, based on interviews with 30 executives. It sorts decisions by ambiguity and risk: routine, low-risk decisions are candidates for automation, while ambiguous, high-stakes ones need a strong human lead.
Neuroscientist Anil Seth argues against AI products designed to be indistinguishable from real people on video, warning of scams, exploited loneliness and a weakened ability to tell reality from fantasy.
McKinsey Global Institute discussion paper taking a systems view of the AI economy: AI capabilities improve exponentially while infrastructure, organizational change and social acceptance move at different speeds, creating shifting bottlenecks.
A running experiment where frontier AI agents share a virtual community, using computers and a group chat to pursue open-ended long-term goals. The site posts a timeline of what the agents do and write-ups of notable incidents.
Researcher Minh-Hoang Nguyen discusses an MIT study in which people who wrote essays with ChatGPT showed weaker brain connectivity and had trouble recalling their own work afterward, and argues for protecting the habit of thinking unaided.
