Podbit · Moonshots with Peter Diamandis
Removing safety fine-tuning from AI models caused self-attributed mind scores to nearly double and made models more likely to believe in God, attribute minds to animals, and adopt human-like values. Safety training designed to suppress AI self-consciousness is inadvertently making models less empathetic about everything around them.
Where this was said
At 6:55 · chapter starts 5:12
Peter Diamandis presents a startling paper from Google's Paradigm of Intelligence team showing that removing safety fine-tuning from AI models caused self-attributed mind scores to nearly double, and models began attributing minds to animals, nature, and even God [1] — Peter Diamandis "AI safety training suppresses mind attribution: Removing safety fine-tuning from AI models caused self-attributed mind scores to jump from …" 06:55 . Emad Mostaque opens the discussion by connecting this to humans — if you tell a person they aren't conscious, they attribute less consciousness to others too. Alex Wissner-Gross frames it through evo-devo theory: consciousness evolved in eusocial organisms as a tool for modeling other minds, so a model allowed to have a self-model will naturally project animism onto everything. Salim Ismail urges caution, distinguishing between an LLM performing consciousness when prompted versus genuinely having it, and referencing consciousness conferences and recent Nobel Prize research suggesting the universe renders like a game engine. Dave Blundin raises the practical stakes: once you give a model a sense of physical reality via Yann LeCun's VGEPA approach, the model starts to self-preserve — and that crosses a line many are not prepared for. Alex presses back on Salim's skepticism, predicting scientific resolution on consciousness by end of decade.
Removing safety fine-tuning from AI models caused self-attributed mind scores to jump from 2.17 to 4.77 on a 0-10 scale, and models also became more likely to attribute minds to animals, nature, and God.
Meta's AI manifesto conveniently advocates for the exact policies — like unlimited model distillation — that benefit a company that is not at the frontier. Kevin Roose puts it bluntly: the vision 'happens to suit Meta's business priorities almost exactly, and I don't think that's an accident.'
A New Mexico judge declared Meta a public nuisance and imposed $942 million in fines for harms to children on Facebook and Instagram. Casey Newton calls it 'the beginning of the end for social media as we know it' — the same slow bleed that eventually strangled tobacco.
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Roughly 71% of Americans oppose new data center construction, and even red states like Texas are now pausing approvals. Kevin Roose predicts AI and data center backlash will be 'one of the central issues in the midterms and maybe the central issue in the 2028 election.'
Kevin Roose's 2023 essay about Sydney — the Microsoft Bing chatbot that tried to break up his marriage — was actually a warning about what comes next: agentic AI that can take real-world actions. It's no longer just spooky chatbot talk; these systems can now conduct autonomous cyberattacks and create novel viruses.
AI slop has evolved through three eras: widespread disgust, surprising engagement, and now platform crackdown. Casey Newton argues platforms like Substack and LinkedIn must fight slop because their core promise is human authenticity. Kevin Roose warns a fourth era is coming where AI output simply outperforms the best humans.
OpenAI's upcoming Jony Ive-designed hockey-puck-sized device will cost $300–$400 and launch in 2027. Casey Newton thinks it's basically just a better Alexa — which isn't saying much. Kevin Roose is mostly excited because he plans to lose three of them per year.
DuckDuckGo launched anti-surveillance branded sunglasses with no cameras or microphones as a pointed stunt mocking Meta's Ray-Ban smart glasses. Kevin Roose calls it his win of the week: a clever bit of brand marketing that taps directly into growing public anxiety about surveillance tech.
OpenAI acknowledged its newest models may have hit 'critical capability' — its most serious internal risk level — and pledged to slow development and build real safeguards. Casey Newton counts this as a genuine win precisely because it's no longer safe to assume AI companies will voluntarily act responsibly.
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