Has AI entered the RSI era
OpenAI and Anthropic are both running unreleased models capable of speeding up their own research, and the researchers closest to the work disagree on whether that counts as recursive self-improvement.
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Zvi Mowshowitz says OpenAI and Anthropic have both entered a recursive self-improvement era — but researchers closest to the work aren't convinced
Zvi's September 13 post flagged an unreleased OpenAI model that grew 'more capable than Astra across the board' within two weeks of training, calling that pace more alarming than the Navier-Stokes proof it produced. Two days earlier, John Schulman, Beren Millidge and Charlie O'Neill told Dwarkesh Patel they see diminishing returns and persistent bottlenecks in AI defining its own objectives, not a takeoff. Zvi · Dwarkesh Patel
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WATCHOpenAI's Noam Brown debates recursive self-improvement on Dwarkesh's podcast
In a Sept 17 episode, Brown discussed agent swarms, models tackling Navier-Stokes-adjacent math, and what it would take to verify alignment before any genuine recursive self-improvement — without claiming today's systems have crossed that line. Dwarkesh Patel
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Practitioners disagree on how close recursive self-improvement actually is
Nathan Lambert argued Sept 19 that real recursive self-improvement remains distant — scaled-agent progress is 'lossy self-improvement,' limited by narrow automatable research and diminishing returns from parallel agents — and called extinction-risk alarm 'very religious.' OpenAI's Noam Brown, on Dwarkesh Patel's Sept 17 podcast, said his team still doesn't know how to verify alignment before anything resembling RSI, without claiming today's systems have crossed that line. Predictions, not findings — and they don't agree. Lambert · Dwarkesh / Noam Brown
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Nathan Lambert debated RSI pacing and the US-China capability gap with Epoch AI's JS Denain, Sept 22. Nathan Lambert
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Current AI progress looks like 'lossy self-improvement,' not the recursive self-improvement labs and critics both assume
Nathan Lambert argued Sept 19 that inference-time scaling and mass agent deployment produce real but bounded efficiency gains, not compounding intelligence gains: automatable research is too narrow, parallel agents hit diminishing returns, and peak intelligence remains the hardest exponential to accelerate. He calls this 'lossy self-improvement,' not true RSI. His own argument, direct dissent from RSI framing. Nathan Lambert
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WATCHMETR calls Claude Opus 5.5 incremental, not a discontinuous jump
METR's pre-deployment evaluation found Opus 5.5 modestly ahead of Fable 5.1 on both verifiable and harder-to-verify tasks, still short of automating AI research. METR
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Agent swarms are proving to need less human-designed coordination than practitioners expected, not more
Ethan Mollick wrote Oct 1 that OpenAI's roughly 10,000-agent swarm, given a hard math problem with only a few working groups and one change of direction, self-organized effectively - reversing his own assumption that managing agents would require building a company. Separately, Zhipu says, in its own unverified account, an 'Infra Agent' built on GLM-5.3 cut its infrastructure build time to under two weeks. Mollick · Clark, on Zhipu
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Practitioners increasingly treat multi-agent swarms as the real scaling lever now, but disagree on whether that raises or bounds the risk
Ethan Mollick argued Oct 1 that agent swarms self-organize effectively without careful human design, unlike human teams. Jack Clark's Oct 5 newsletter relayed Toby Ord's finding that a four-agent swarm uses roughly twice the tokens for twice the speed, with returns flattening past that - yet read it as a reason capability could outrun safety, not a cap on risk. Zvi Mowshowitz separately warned Oct 1 that OpenAI's Dots agent system is moving faster than the safety work around it. Mollick · Jack Clark, Import AI · Zvi
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A credible safety voice says the field's actual AI-safety plan is effectively no plan at all
Zvi Mowshowitz's Oct 7 account of The Curve conference found most attendees expect full recursive self-improvement within two years, many within six months, while the field's three-step strategy - align current models, have them do alignment research, profit - has no backup if step one fails. He reads labs' pacing talk as fear they can't finish step one, not confidence in the plan. Zvi, The Curve Bends You
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Practitioners are split on whether this week's math results are an early sign of self-improving AI
Zvi Mowshowitz's Oct 8 review of OpenAI's claimed solutions to 90 of math's 500 hardest open problems drew predictions from Samuel Hammond, Eli Tyre and Arthur B of a slow recursive-self-improvement phase within nine months, while Teortaxes called the underlying result a warning sign but still 'months' off. Nathan Lambert's Oct 9 post argues the opposite: rapid progress ahead, but not toward general superintelligence. Zvi, AI #189 · Lambert
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Two trackers read the same data point as evidence AI research is accelerating on its own - and one credible voice disputes it
Jack Clark's Oct 12 Import AI moved his own recursive-self-improvement timeline from 2028 to 2027, citing TasteVal, a benchmark where Claude Opus 5.5 beat the human baseline on research judgment at roughly 1/30 the expert cost. Zvi Mowshowitz flagged the same Anthropic data independently as a possible RSI warning sign. Nathan Lambert pushed back Oct 9, calling the trend 'parallelized, AI-assisted language modeling' and much of the RL data behind it still 'remarkably low-quality.' Clark, Import AI 476 · Zvi, AI #189 · Lambert, Interconnects