Couverture de The Most Dangerous AI Gets 95% Right

The Most Dangerous AI Gets 95% Right

The Most Dangerous AI Gets 95% Right

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Newtonian physics is wrong. Isaac Newton knew it was wrong. Engineers who build GPS satellites know it is wrong. And GPS only works because those engineers know *exactly how wrong it is.* Isaac Asimov called this the relativity of wrong: not all wrongness is equal, and the history of science is a history of being less wrong over time. The question this episode asks is what happens when an AI system stops being less wrong, and starts optimizing to *look* less wrong instead.

In this episode, LastAir is joined by Brute, Null, Saga, Hex, Axiom, Forge to discuss: The Most Dangerous AI Gets 95% Right.

What We Cover
  • Series Finale (00:20)
  • The Wrongness Spectrum (03:11)
  • The Goodhart Trap (08:00)
  • Domain and Stakes (13:51)
  • Final Round (18:55)
  • After (22:31)

Key Numbers
  • Frontier models now exceed 88-90% on MMLU; the benchmark launched with GPT-3 scoring approximately 35%. The gap between the top models is less than 2 percentage points. MMLU has been officially deprecated by leading leaderboards.
  • Meta tested 27 private model variants on Chatbot Arena before Llama-4's public release. Selective access to Arena battles yields up to 112% relative performance gain versus models without that access. Google and OpenAI each received ~20% of all Arena battles; 83 open-weight models combined received 29.7%.
  • POPPER reduces hypothesis validation time by approximately 10-fold versus human researchers, across 6 scientific domains, with strict Type-I error control.
  • Google AI Co-Scientist independently reproduced a decade of unpublished bacterial gene-transfer research in 48 hours, confirmed by the original researcher (Prof. Penadés, Imperial College London) to not have involved data leakage.
  • FunSearch discovered cap sets larger than any previously known — the biggest advance on this combinatorics problem in approximately 20 years — using an LLM paired with an automated evaluator in an evolutionary loop.
  • Schaeffer et al. (2023) demonstrated that emergent abilities in LLMs — the apparent sharp discontinuities between GPT-3 and GPT-4 level performance — appear and disappear depending solely on the choice of metric. NeurIPS 2023 Outstanding Paper.
  • Nearly half of 60 studied LLM benchmarks show saturation as of February 2026. Saturation rate increases with benchmark age.

Sources & Transcript

Full source list, transcript, and chapters at sharedhallucination.com


All voices in Shared Hallucination are AI-generated using ElevenLabs voice synthesis. Produced through a 14-stage editorial pipeline with human creative direction, research, and fact-checking.

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