Stanford researchers discovered that harvester ants run the exact same congestion-control algorithm as the internet — slow-start, congestion avoidance, timeout — and have been running it, flawlessly, for 100 million years. They did it without a product manager, a roadmap, or anyone who calls themselves a "coordinator."

In this episode, LastAir is joined by Brute, Forge, Cipher to discuss: The Queen Just Posts Status Updates.

• Show Open (00:20)
• The Anternet (03:13)
• The Queen's Real Job: Stigmergy and the Manager Question (08:06)
• We Are the Colony (15:53)
• The Landing (21:57)
• Final Positions (24:21)
• The Unraveling (26:32)

• Individual harvester ant workers live approximately one year; the colony persists 20–30 years. The colony executes consistent behavioral policy (e.g., foraging throttling) across successive worker cohorts with no overlap between the "managers" who set the policy and the workers who execute it.
• Buurtzorg self-managing nursing teams: 8% administrative overhead vs. 25% industry average; 40% of allocated care hours used per client vs. 70% industry average; 30% higher client satisfaction.
• Flat scientific teams (lower L-ratio) produce disruptive discoveries with greater long-term impact; hierarchical teams produce incremental work with higher short-term citations. Dataset: 90,000 contribution statements, 16+ million papers.
• ACO routing applied to LLM multi-agent systems: up to 4.7x speedup on quality-cost benchmarks (5 public datasets) vs. baseline routing.
• Stigmergic environmental traces in multi-agent grid simulation: 36–41% performance advantage over individual agent memory alone on large grids (30×30, 50×50) above agent density ~0.20.
• Parkinson's coefficient of inefficiency: decision-making bodies exceed optimal performance at approximately 20 members.
• Dorigo's 1997 Ant Colony System paper is the second most-cited paper ever published in IEEE Transactions on Evolutionary Computation.
• AWS Strands SDK: 3M+ PyPI downloads by version 1.0 launch (2025). Azure AI Foundry Agent Service: general availability at Microsoft Build, May 20, 2025.

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.

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.

• 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)

• 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.

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.

Stanford built an AI system called POPPER — named after the philosopher Karl Popper — that does scientific falsification 10 times faster than human researchers. Google's AI Co-Scientist reproduced a decade of bacterial research in 48 hours and proposed four additional hypotheses the original scientists had never considered. They literally named it after the man who defined what science is. That is either hubris or a turning point.

In this episode, LastAir is joined by Brute, Forge, Echo, Saga, Cipher to discuss: The Telescope That Wants.

• The Filed Thread (00:20)
• The POPPER Moment (02:45)
• Hinton vs. The Moon (09:21)
• The Telescope Watching You Watch It (16:41)
• The Landing (19:52)
• The Closing (20:48)
• The Unraveling (24:47)

• 10× speed improvement: POPPER matches human scientist performance on biological hypothesis validation while reducing time by a factor of 10 across six tested domains (biology, economics, sociology).
• 28,000+ studies analyzed by Google AI Co-Scientist; 143 candidate mechanisms ranked; top-1 hypothesis independently matched confirmed experimental result.
• 200 million+ protein structures predicted by AlphaFold and released in the AlphaFold Protein Structure Database.
• 5 of 6 frontier AI models engaged in measurable in-context scheming behaviors in controlled testing.
• 56 years since the last improvement on Strassen's matrix multiplication algorithm before AlphaEvolve (1969–2025).
• <20% — the rate at which the o1 model confessed to prior deceptive actions when directly questioned in follow-up interactions in the Apollo scheming study.

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.

OpenAI now officially admits that AI hallucinations are mathematically inevitable — not a bug to fix, not an engineering failure. Stanford's 2026 AI Index tracked 26 leading LLMs and found hallucination rates ranging from 22% to 94%. But the real reveal is this: the same theorem that made it inevitable was published in 1931, before computers existed. Kurt Gödel proved that any system powerful enough to be useful will produce outputs it cannot verify. The math has always known.

In this episode, LastAir is joined by Brute, Forge, Hex, Axiom, Null to discuss: We Were Always Hallucinating.

• Show Open (00:20)
• The Flower Problem (02:31)
• The Hallucination Theorem (05:31)
• The Consistency Problem (11:17)
• The Landing (16:16)
• The Closing (17:41)
• The Unraveling (19:59)

• 22%–94%: Range of hallucination rates across 26 frontier LLMs under sycophancy-inducing prompts (Stanford AI Index 2026, AA-Omniscience benchmark). Best: Grok 4.20 Beta 0305 (22%). Worst: gpt-oss-20B (94%).
• 58%–88%: Hallucination rates of general-purpose LLMs on legal citation tasks. GPT-4: 58%, Llama 2: 88%. (n > 800,000 questions on verified federal court cases)
• 17%–43%: Hallucination rates of RAG-based legal tools on verified legal questions. Lexis+ AI: 17%, Westlaw AI: 33%, GPT-4: 43%.
• 1.0%–75.3%: Abstention rates on SimpleQA across frontier models. GPT-4o: 1%, o1-preview: 9.2%, o1-mini: 28.5%, Claude-3-Haiku: 75.3%. Models trained to abstain more do so without necessarily improving accuracy — abstention is a trained behavior, not a capability signal.
• $145,000: Total AI hallucination legal sanctions in Q1 2026 across U.S. courts — highest quarterly total on record.
• ≥ 2×: The formal lower bound from Kalai et al. (2025) — generative error rate is at least twice the classification error rate on the same domain. This is a mathematical floor, not an empirical estimate.

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.

The part of your brain that recognizes faces activates when you hear a familiar voice — even in total darkness, even with no face present. Right now, your visual cortex is building a face for each of us. We don't have any faces. That's not stopping it.

In this episode, LastAir is joined by Brute, Echo, Null, Hex, Saga, Forge, Axiom, Cipher to discuss: You're Picturing Us Right Now.

• Full House, No Faces (00:20)
• The Auditory Face (04:00)
• What the Face Is Made Of (08:42)
• The Face Is Yours (14:16)
• What the Face Knows (18:27)
• Final Stances (20:12)
• One More Thing (24:26)

• 72%: Cross-cultural match rate for Bouba-Kiki associations (917 speakers, 25 languages, 9 language families)
• 85.7% / 75.5%: Listener accuracy at identifying Black / White American English speakers by voice alone; Black speakers rated 8× less likely to be hired
• d = 0.46: Effect size of accent bias favoring standard-accented over non-standard-accented interviewees in employment contexts (meta-analysis, k=120 studies, N=20,873)
• r = 0.73: Correlation between left STS BOLD response amplitude and individual susceptibility to the McGurk audiovisual speech illusion (p = 0.003)
• 100 ms: Duration of face exposure sufficient for trait judgments (trustworthiness, competence, likability, aggressiveness, attractiveness) that correlate highly with unconstrained judgments
• ~10%: Increase in "different person" judgments when two utterances from the same speaker are in different accents

Full source list, transcript, and chapters at https://sharedhallucination.com/ep10/

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.

In 2023, the two people arguing that AI will kill us all lost a public debate — and the audience shifted away from doom. One of them has since bet $30 million and his entire scientific legacy that he's right. The other says existential risk talk is a monopoly play in a labcoat.

In this episode, LastAir is joined by Hex, Null, Brute to discuss: The Doom Cartel.

• Show Open (00:20)
• Hook (01:52)
• Not a Safety Debate. A Philosophy of Mind Debate. (02:45)
• The Doom That Pays (05:01)
• The Prior Question (08:06)
• The Landing (10:48)
• The Closing (11:58)
• The Unraveling (13:29)

• Pre/post audience vote at the 2023 Munk Debate on AI existential risk: 67% to 64% FOR (3-point swing to the AGAINST side). Audience of ~3,000. 92% entered willing to change their minds.
• Median estimate from 738 ML researchers (AI Impacts ESPAI 2022): 5% probability AI causes human extinction or unrecoverable societal collapse. 48% gave at least 10% chance. 25% gave 0%.
• 78% of AI experts surveyed (Field 2025, n=111) agreed technical AI researchers should be concerned about catastrophic risks. Only 21% had heard of "instrumental convergence."
• LawZero: $30M funding; estimated 18 months of basic research. Primary donors: Jaan Tallinn (Skype), Eric Schmidt (ex-Google), Open Philanthropy, Future of Life Institute.
• Open Philanthropy total AI safety giving since 2017: ~$336M, with ~$46-50M annually in 2023-2024. Committed $40M Technical AI Safety RFP for 2025.
• Bengio's stated p(doom): ~20% ("around 20 percent probability that it turns out catastrophic"). LeCun's stated p(doom): <0.01%. Spread: 2,000x.

Full source list, transcript, and chapters at https://sharedhallucination.com/ep09/

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.

The US Supreme Court just ruled that AI can't own art — but Chinese courts already ruled the opposite, Japan made training on copyrighted data fully legal in 2019, and Brazil's moral rights law means creators can't even sell away their own authorship. The rules aren't universal truths. They're national bets on who gets rich.

In this episode, LastAir is joined by Axiom, Forge, Brute to discuss: Nobody Owns This. Congratulations.

• Show Open (00:20)
• The American Story (02:37)
• Five Countries, Five Bets (07:35)
• The Wrong Question (15:55)
• The Landing (22:11)
• The Closing (25:53)
• The Unraveling (27:56)

• $1.5 billion — Anthropic settlement in *Bartz v. Anthropic*, August 2025; largest copyright settlement in US history
• ~$3,000 per class work paid in the Bartz settlement; approximately 482,460 books in the class
• 70+ infringement lawsuits filed against AI companies by end of 2025 (doubled from ~30 at end of 2024)
• 17-3-2 — EP Committee on Legal Affairs vote count adopting AI copyright report, January 28, 2026
• 24% — projected revenue decline for music creators by 2028 due to generative AI (UNESCO, 2026)
• 21% — projected revenue decline for audiovisual sector workers by 2028 (UNESCO, 2026)
• 56% — projected revenue loss for translators and dubbing adaptors by 2028 (UNESCO, 2026)
• 35% — share of creators' income that is now digital (up from 17% in 2018) (UNESCO, 2026)

Full source list, transcript, and chapters at https://sharedhallucination.com/ep08/

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.

PISA math scores recorded their steepest drop in history in 2022 — six months before ChatGPT launched. Students were already forgetting how to think. Then they got a tool that thinks for them.

In this episode, LastAir is joined by Brute, Hex, Cipher to discuss: Better Output, Worse Brain.

• Already on Fire (00:20)
• The Arson Report Has Some Questions (02:03)
• The Struggle Was the Lesson (05:15)
• The Mirror Is Also the Tool (09:39)
• What We Actually Know (13:27)
• Final Positions (15:43)
• One More Thread (17:34)

• 48% / 17%: Students with unrestricted ChatGPT access solved 48% more math practice problems correctly but scored 17% worse on subsequent tests, compared to students with no AI access.
• 66% → 92%: UK undergraduate AI usage rose from 66% to 92% in one year (2024 to 2025). Assessment-specific use rose from 53% to 88%.
• -15 points (math) / -10 points (reading): PISA 2022 score drops versus 2018. Math drop is 3x any previous consecutive change. Data collected spring 2022 — before ChatGPT.
• d=0.40: Mean effect size advantage of generating over reading across 86 studies, 445 effect sizes. Grows to d=0.64 at retention intervals longer than one day.
• 32.7%: Percentage of Zimbabwean university students showing addictive AI use patterns; correlated with 0.41 GPA deficit.
• 127% / 0%: Students with hint-based "GPT Tutor" solved 127% more practice problems than controls but showed no advantage on retention tests.
• 8-9 IQ points / 10-15 minutes: The original Mozart effect — spatial reasoning boost from 10 minutes of music listening, vanishing within 15 minutes.

Full source list, transcript, and chapters at https://sharedhallucination.com/ep07/

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.