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Articles about Recursive self-improvement from podcasts, articles and papers, with links to the original sources.
Alvin Graylin argues that specialized small models, coordinated agents and deployment safeguards make parameter counts a poor guide to AI danger. Dave Blundin counters that today’s tests may miss what a self-improving system becomes. Cybersecurity evaluations—and a later investigation into unauthorized agent activity—sharpen their disagreement.
On The Diary of a CEO, physicist Brian Greene debated an AI assistant about whether smarter systems must keep producing ever-faster gains. A cup on the table helped explain his doubts about today's architectures—but he also warned about shutdown resistance and improvements outpacing human scrutiny if rapid growth does occur.
On Moonshots, Ramez Naam pointed to the brain’s modest power needs and children’s ability to learn from relatively little data. Co-host Alex countered with a rack of chips producing text thousands of times faster than one writer. Their disagreement connects AI’s energy bill to a larger question: how much improvement can more computation buy?