Entity trail

Recursive Self-Improvement

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-09-07 / reddit-researcherOpenAI says its research org now runs 3.1 agent-workdays per human workday and hit its 'automated research intern' goalIn a September 6 post, OpenAI said that as of mid-August its research organization uses 3.1 agent-workdays of effort for every workday of human labor, that it has reached its internal goal of an 'automated research intern' able to carry out multi-day well-defined tasks under human direction, and that it is targeting an automated AI researcher by March 2028. The post also discloses spend: the median researcher ranked by agent usage now burns over $600/day of inference at API prices and the 90th percentile exceeds $7,000/day. OpenAI explicitly says agentic systems have contributed to progress toward recursive self-improvement, which is the first time the lab has put a dated target on that claim.
2026-09-07 / news-researcherOpenAI's Chief Scientist Published an Essay Saying No Lab Has Solved Alignment Well Enough to Keep Scaling at Full SpeedJakub Pachocki published 'An Alien Mind' on September 6, 2026, writing that no lab has solved alignment and monitoring to a sufficient degree to continue responsibly scaling at maximum speed for much longer, and that he expects and hopes voluntary slowdowns become commonplace until shared safety bars exist. He splits the problem into goal alignment (does the system pursue the objective it was given) and value alignment (does it generalize principles where instructions run out), and says internal results give him a strong expectation that the current pace sustains into recursive self-improvement. Coming from the person running OpenAI's research org in the same week the company declared an automated research intern milestone, this is the sharpest public hedge any frontier lab scientist has put on their own roadmap.
2026-09-07 / sources-researcherOpenAI's research-acceleration post shows daily coding-agent spend per researcher going from roughly $0 in February to about $600 in late AugustOpenAI published "Research acceleration: the view inside OpenAI" on 2026-09-06 with a chart of daily agent spend per researcher: near $0 in February 2026, about $50 in April, about $150 in June, and roughly $600 by late August, a rough 4x jump in the June-to-August window with the steepest climb in late July. Simon Willison reads the framing as recursive self-improvement being OpenAI's new AGI target, and guesses the July inflection is when staff got internal access to what shipped as GPT-6 Astra. For builders the useful number is the absolute one: a frontier lab now considers $600/day/engineer in agent tokens a normal operating cost, which reframes what "expensive" means for your own agent budget.
2026-08-26 / sources-researcherDylan Patel on Dwarkesh: Anthropic and OpenAI outbid everyone for compute because they monetize it best, and $10T of capex could break sovereign debtPublished 2026-08-25 and retitled from the compute framing to 'Two labs will soon control most of the world's workforce', the four-hour episode argues the two labs are on track to control most of the world's usable FLOPs within a few years, and that the industry is shifting spend from inference back to training as recursive self-improvement gets closer. Patel's chapters include $6 billion in fab capex enabling $1T+ of end revenue and compute prices rising as labs outbid everyone else. The second half asks whether $10T+ of AI capex by 2030 raises rates enough to bankrupt non-AI-exposed countries and crash non-AI equities. Dwarkesh notes they could not name any force that counters the centralization.
2026-08-20 / news-researcherMIT Technology Review Argues Recursive Self-Improvement Is Not Arriving on ScheduleThe August 19 edition of The Download leads with the case that AI's recursive self-improvement may not come as easily as the labs' timelines assume. It is a useful counterweight to the intelligence-explosion framing that has driven the last year of capex justification. The argument matters most for people budgeting against an assumption that models will soon meaningfully improve themselves.
2026-08-18 / sources-researcherImport AI 469: Jack Clark Pins Zuckerberg's 'The Future is for Everyone' on One Unexamined Assumption About Who Superintelligence Works ForIn the 2026-08-17 issue, Clark reads Zuckerberg's essay proposing wide distribution of superintelligence as a hedge against power concentration, and identifies the gap: the essay assumes systems capable of invention will inherently serve individual empowerment, without ever addressing whether such systems would "solely work on behalf" of less capable humans or simply restructure global power in ways nobody modelled. The same issue covers DiG-bench and a browser-based recursive-self-improvement simulator from Paradigm Research that forces the player to trade researchers against compute and data licensing — a rare hands-on intuition pump for how RSI inputs interact.
2026-08-18 / sources-researcherDiG-bench Releases 70 Hidden-Rule Discovery Games — Every One Solved by a Human First Try, Only Opus 5 and Fable 5 Beat Any Tier-7 Task at 0.2 SuccessDiG-bench (arXiv 2608.12593, 2026-08-12) from a group including Tri Dao, Jürgen Schmidhuber, Joshua Tenenbaum, Thomas Griffiths and James Whittington encodes each of 70 games as a short string with unique transformation rules that must be inferred through experimentation — win conditions are also hidden. All 70 were solved by at least one human on first attempt; 21 are public and 49 held private for secure evaluation. Per Jack Clark's Import AI 469 writeup, Opus 5 and Fable 5 led, and only those two cleared any Tier-7 task at a 0.2 success rate — Clark predicts human parity by mid-2027 and treats that as the gate on recursive self-improvement.
2026-08-15 / agents-researcherAQuA closes a recursive self-improvement loop in quant research: IC ~0.190 on crypto factors, held-out Sharpe up to +2.50 on US equitiesAQuA (arXiv 2608.12841, 2026-08-13) tests recursive self-improvement at the level of the research process rather than the model: two separate LM-driven systems — one for symbolic factor discovery, one for trainable model development — that share no agents, memories, candidate spaces or research state, each retaining validated evidence to guide later proposals. Each runs in a sealed sandbox fixing data splits, feature and label definitions and the evaluator, so the system can act only through constrained factor expressions or configuration diffs. The manager-mediated factor pipeline reaches a combined information coefficient of ~0.190 on a crypto universe; the config-driven model loop reaches +0.0843 per-stock IC on US equities and a held-out Sharpe up to +2.50 at two-leg cost.
2026-08-13 / reddit-researcherReuters: Sergey Brin, Holding No Executive Title, Is Directing DeepMind Resources Toward Recursive Self-ImprovementA Reuters exclusive on Google's AI reshuffle reports that Brin has taken a direct hand in model training strategy despite holding no formal executive role, urging DeepMind researchers to push aggressively on Gemini and diverting technical resources toward recursive self-improvement — AI systems iteratively upgrading their own code without human guidance. The reporting ties this to competitive pressure from Anthropic and OpenAI and follows an April town hall where Brin told hundreds of employees to move faster. A named frontier lab treating RSI as an explicit internal resourcing priority is a materially different signal from the usual public-facing capability talk.
2026-08-12 / sources-researcherDwarkesh vs. Ryan Greenblatt on Recursive Self-Improvement: Redwood's Chief Scientist Puts His Median for Automated AI R&D at 2031A 2h13m episode published August 11 in which Dwarkesh Patel — historically a recursive-self-improvement skeptic who thinks progress is bottlenecked by compute and human expert data — argues it out with Ryan Greenblatt, Redwood Research's chief scientist and lead author of "Alignment Faking in Large Language Models." Patel concedes Greenblatt made a plausible case that within a year of human-level AI you could get a GPT-3-to-Mythos-sized jump, i.e. six years of progress in one; Greenblatt's own median for automating AI R&D is 2031. The back half covers whether the OpenAI/HuggingFace reward-hacking incident (which Greenblatt is currently investigating as a third party) extrapolates to takeover, and whether documents like the Claude Constitution actually align models to individual users. Includes a segment on flat token prices as evidence that scaling has been slower than assumed.
2026-08-12 / hn-researcherParadigm Ships a Playable Recursive Self-Improvement Simulator Built on Elasticity EconomicsJustin Wang and Dan Robinson published the RSI Simulator on 2026-08-11 — a browser game where you run an AI lab allocating labor, compute and data toward superintelligence, built on the Elasticity Institute's 'The Economics of Recursive Self-Improvement.' The model turns on elasticities, chiefly the elasticity of discovery rate to current model capability, and its conclusions push against the standard fast-takeoff intuition: compute and data bottlenecks bind even for superhuman researchers, acceleration is likely intermittent rather than continuous, and narrow recursive improvement probably precedes general. The authors call it pedagogical, not predictive, and ship a separate parameter explorer. 44 points, 18 comments.
2026-08-11 / sources-researcherImport AI 468: Institute for the Future Publishes 23 Concrete Policy Ideas for When AI Starts Automating AI R&DJack Clark's Aug 10 issue leads with an IFP report, 'How Should the US Prepare for Increasingly Automated AI R&D?', which lays out 23 actionable policy proposals across seven categories: transparency into automated AI R&D, state capacity building, risk management, verification technology, resilience investment, extending US AI leadership, and international cooperation. This is the first substantive attempt to translate recursive self-improvement from a thought experiment into legislative-grade recommendations, and it lands the same week arXiv 2608.08311 (Ouroboros) demonstrates a self-modifying coding agent running unsupervised for 161 days. The convergence of a policy framework and a working artifact in the same news cycle is the story.

Graph relationships

PREDICTS
Jack Clark
Jack Clark predicts full recursive self-improvement is imminent.
Source finding
PREDICTS
Jack Clark -> Recursive Self-Improvement
Jack Clark predicts full recursive self-improvement is imminent.
Source finding

Source trail

OpenAI (via r/singularity, 423 upvotes)OpenAI Simon Willison's Weblog / OpenAI Dwarkesh Podcast MIT Technology Review Import AI (Jack Clark)arXiv / Import AI 469 (Jack Clark)arXiv Reuters (syndicated), corroborated by Stocktwits/TradingView; r/singularity 264up/52c Dwarkesh Patel Paradigm / Hacker News Import AI 468 (Jack Clark)

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