What happens when AI passes our tests faster than we create new ones? Adam Khoja and Richard Ren helped build Humanity’s Last Exam. They return to explain what the disappearing benchmarks tell us about the future, and why smarter AI doesn’t automatically mean safer AI.
Adam Khoja and Richard Ren are research engineers at the Center for AI Safety (CAIS).
We go through the benchmarks they’ve built — Humanity’s Last Exam, the Remote Labor Index, MASK — and the safety-washing problem, where AI companies pass off raw capability gains as safety progress. Then we get to their forecasts: when AI outperforms research mathematicians, whether there will be a billion general-purpose robots by 2035, and why they put 80% odds that historians will judge we faced at least a 33% chance of catastrophe.
Watch on YouTube: https://www.youtube.com/watch?v=gBPzgJkT9e8
Timestamps
00:00:00 — Cold Open
00:00:42 — Adam Khoja and Richard Ren Return
00:04:57 — Humanity’s Last Exam
00:14:47 — AI Outrunning Its Benchmarks
00:20:24 — The Remote Labor Index
00:27:59 — AI and the Next Human Job
00:30:08 — MASK: Catching AI in a Lie
00:38:06 — Safety Washing: Capabilities Passed Off as Safety
00:44:30 — Ethical Knowledge vs. Ethical Behavior
00:48:58 — Forecasting AI from 2026 to 2060
00:53:53 — AI vs. Research Mathematicians
00:55:53 — Putting a Number on P(Doom)
01:04:30 — A Billion Robots by 2035
01:08:09 — Dyson Swarms and the Physical Singularity
01:09:56 — Risk, Precision, and Taking Action
Links
Adam Khoja's first Doom Debates appearance — https://www.youtube.com/watch?v=QqESBXuo6EI
Richard Ren's first Doom Debates appearance — https://www.youtube.com/watch?v=1glFImnyp6o
Collision — Richard Ren's Substack — https://richardren.substack.com/
"Predictions on AI (2026–2060)" — Adam and Richard's forecasts, written December 2025, with resolution status — https://richardren.substack.com/p/predictions-on-ai-20262060
Manifold Markets — where Adam built his forecasting track record — https://manifold.markets/
Center for AI Safety — https://safe.ai/
Center for AI Safety — careers — https://safe.ai/careers
Statement on AI Risk (Center for AI Safety, May 2023) — https://safe.ai/work/statement-on-ai-risk
Humanity's Last Exam — the 2,500-question closed-book exam at the frontier of human knowledge — https://agi.safe.ai/
"Humanity's Last Exam" (paper) — https://arxiv.org/abs/2501.14249
Remote Labor Index — real Upwork projects, measuring what fraction of remote work AI can actually finish — https://www.remotelabor.ai/
"Remote Labor Index: Measuring AI Automation of Remote Work" (paper) — https://arxiv.org/abs/2510.26787
MASK — the honesty benchmark: does a model contradict its own stated beliefs under pressure? — https://www.mask-benchmark.ai/
"The MASK Benchmark: Disentangling Honesty From Accuracy in AI Systems" (paper) — https://arxiv.org/abs/2503.03750
"Safetywashing: Do AI Safety Benchmarks Actually Measure Safety Progress?" — Richard Ren et al. (NeurIPS 2024) — the paper behind the safety-washing segment — https://arxiv.org/abs/2407.21792
WMDP — the weaponization benchmark for bio, cyber, and chem — https://www.wmdp.ai/
"The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning" (paper) — https://arxiv.org/abs/2403.03218
"Measuring Massive Multitask Language Understanding" (MMLU) — Dan Hendrycks et al., 2020 — https://arxiv.org/abs/2009.03300
METR, "Measuring AI Ability to Complete Long Tasks" — the time-horizon graph — https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/
FrontierMath — Epoch AI's research-level math benchmark — https://epoch.ai/frontiermath
ARC Prize — https://arcprize.org/
SWE-bench Verified — https://openai.com/index/introducing-swe-bench-verified/
AI 2027 — https://ai-2027.com/
Liron Reacts to Subbarao Kambhampati on Machine Learning Street Talk — the "stochastic parrot" episode Liron mentions — https://lironshapira.substack.com/p/liron-reacts-to-subbarao-kambhampati
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