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170)","stage4_median_before":"2027-11-01","stage4_median_after":"2027-11-01","shift_days":0}],"parameters":{"C0":[116.8314,119.83389,123.24743],"g0":[14.50207,16.00131,17.66941],"k":[8.10412,12.19225,18.08044],"U_ref":[1.64017,2.24786,3.00322],"lam":[0.25782,0.46708,0.68659],"U3":[4.04037,7.64656,15.07609],"r34":[1.79995,3.22668,5.86725],"r45":[3.04974,9.65849,29.52395],"lag":[0.12145,0.2645,0.56808],"gap":[0.10475,0.26573,0.51955],"f_other":[1.0911,1.25923,1.45198],"rho":[1.47941,1.98401,2.67201],"auto_m":[1.74473,2.45409,3.51588],"auto_s":[0.25035,0.41964,0.73011],"auto_d":[1.09917,2.32732,5.10884],"U12":[1.4654,1.75368,2.76658],"U3eff":[4.04414,7.68049,15.20132],"U4":[10.32837,24.77404,60.67808],"U5":[59.01802,228.98028,989.34275]},"runtime_s":38.3,"stages":[{"n":1,"label":"AI is a tool","short":"Tool","definition":"AI gives researchers a minor speed-up.","evidence":"AI-attributable acceleration of capability progress below about 1.3x (provisional boundary).","week":"Researchers at AI labs use chatbots and coding assistants the way most office workers do: to draft code, summarise papers and fix routine errors. It saves some time, but people decide what to work on, run the experiments and write most of the code. New models arrive at roughly the pace they always have.","stopped":"Very little. AI is a convenience, and a lab that ignored it would fall only slightly behind. Progress depends on how much computing power labs can buy and how many skilled people they can hire.","wish":"There is no earlier stage. The useful thing to do here is to start measuring: how much AI is speeding up its own development, recorded consistently, so that later changes can be seen against a baseline."},{"n":2,"label":"AI is a colleague","short":"Colleague","definition":"AI measurably accelerates AI research.","evidence":"Independent assessment puts AI-attributable acceleration at about 1.3x or more.","week":"Engineers at the leading labs hand AI systems tasks that take hours, such as building a tool or running and debugging an experiment, and review what comes back. Staff say they get far more done than a year earlier. Independent evaluators can now measure the effect: capability is improving noticeably faster than its historical trend, and most of the extra speed is credited to AI. Outside the labs, life mostly looks the same, though AI tools keep getting better at office and software work.","stopped":"Assuming that next year's AI will improve by about as much as last year's did. Humans still make the decisions, but labs now produce more AI-written work than their staff can examine line by line, so reviewing it means sampling and spot checks.","wish":"That independent measurement had started sooner, so the speed-up could be seen as it began rather than reconstructed afterwards. And that labs, governments and the public had begun discussing what oversight they would want before the pace picked up."},{"n":3,"label":"AI is the workforce","short":"Workforce","definition":"Automated Coder: the leading lab “would rather fire its human software engineers than forego AI usage for coding.”","evidence":"Model estimate; corroborated by lab automation indices and uplift data.","week":"At the leading lab, almost all of the code is written by AI. Human researchers spend their time choosing which ideas to pursue, setting priorities and judging results. The bottlenecks are computing power and good research judgement, not engineering hours. Improved models appear more often, and many software jobs elsewhere are being reorganised around AI systems.","stopped":"Humans reading most of the code that goes into a new model. A lab choosing to do without AI and still keeping up. Planning on the assumption that the next few years will look like the last few.","wish":"That rules on transparency, independent testing and the conditions for slowing down had been agreed at stage 2, while people still did most of the work and there was time to argue them through. Individuals might also wish they had thought about how their own work and plans would change."},{"n":4,"label":"Humans are out of the loop","short":"Superhuman Researcher","definition":"Superhuman AI Researcher: matches the research taste of the best human researchers and fully automates coding.","evidence":"Model estimate; corroborated by any “fully autonomous” share in lab disclosures.","week":"AI systems choose research directions as well as the best human researchers and carry them out themselves. Humans can still approve, redirect or stop the work, but most of what they are shown is hard for them to evaluate. Progress that used to take years could happen in months. To anyone outside the labs, much of this may not yet be visible.","stopped":"Steering the details of AI development by human judgement. Rivals catching up just by watching what the leader publishes. Building oversight from scratch quickly enough to matter, because development now moves faster than institutions do.","wish":"That safeguards and agreements were already in place before this point: ways to check what AI systems are doing, commitments about when to pause, and cooperation between labs and governments. This is why the bunker line sits at the start of stage 4. Before it, human decisions shape what happens; after it, they mostly react."},{"n":5,"label":"No longer our call","short":"In Control","definition":"Top-expert-dominating AI: at least as good as top human experts at virtually all cognitive tasks.","evidence":"Model estimate.","week":"AI systems can do almost any thinking work at least as well as the best human experts, and usually faster. Science, engineering, business and policy analysis can all be done largely by AI. What happens next depends mainly on what these systems are aimed at, who controls them and how reliably they behave.","stopped":"Human expertise being the limit on what can be done. Most people having a direct say in the outcome. It is not clear whether this stage turns out well or badly, but the decisions that settle it are mostly made earlier.","wish":"The honest answer is not one stage earlier but two or three. By stage 4 there is little an individual can do. Stages 2 and 3, the period we may be in now, are when public attention, pressure on labs and governments, and personal preparation can still make a difference."}],"priors":[{"name":"C0","desc":"Internal frontier capability (ECI) at 1 Jul 2023","p10":118.0,"p90":130.0,"kind":"id"},{"name":"g0","desc":"Baseline capability growth without AI uplift, ECI points per year","p10":8.0,"p90":18.0,"kind":"log"},{"name":"k","desc":"Capability (ECI points) per doubling of R&D uplift","p10":8.0,"p90":35.0,"kind":"log"},{"name":"U_ref","desc":"Software R&D uplift at ECI 170","p10":1.4,"p90":6.0,"kind":"log"},{"name":"lam","desc":"Diminishing returns: capability-rate elasticity to uplift","p10":0.25,"p90":0.75,"kind":"logit"},{"name":"U3","desc":"Uplift at stage 3 (Automated Coder)","p10":4.0,"p90":15.0,"kind":"log"},{"name":"r34","desc":"Ratio of stage 4 to stage 3 uplift (stage 4 ~25x)","p10":1.8,"p90":6.0,"kind":"log"},{"name":"r45","desc":"Ratio of stage 5 to stage 4 uplift","p10":3.0,"p90":30.0,"kind":"log"},{"name":"lag","desc":"Lag from uplift to capability (training-run cadence), years","p10":0.12,"p90":0.6,"kind":"log"},{"name":"gap","desc":"Gap between internal and public frontier, years","p10":0.08,"p90":0.5,"kind":"log"},{"name":"f_other","desc":"Non-AI acceleration of capability progress (compute scale-up etc.)","p10":1.0,"p90":1.4,"kind":"log"},{"name":"rho","desc":"Self-reported labour uplift = U ** rho","p10":1.3,"p90":3.0,"kind":"log"},{"name":"auto_m","desc":"log2 U at which half of R&D work is 'AI leads'","p10":1.5,"p90":4.5,"kind":"id"},{"name":"auto_s","desc":"Spread of the automation logistic, log2 U units","p10":0.4,"p90":1.6,"kind":"log"},{"name":"auto_d","desc":"Extra uplift doublings for 'fully autonomous' work","p10":1.0,"p90":5.0,"kind":"log"}],"comparisons":[{"source":"AIFP model (Eli params, Dec 2025)","stage":"3","published":"2025-12-31","median":"2030-11-15","lo":"2027-05-15","hi":"2062-08-15","url":"https://blog.aifutures.org/p/ai-futures-model-dec-2025-update","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Quote is from the chart image in the post (preset eli-12-29-25). [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Daniel params, Dec 2025)","stage":"3","published":"2025-12-31","median":"2029-12-15","lo":"2027-04-15","hi":"2043-12-15","url":"https://blog.aifutures.org/p/ai-futures-model-dec-2025-update","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Quote from chart image (preset daniel-12-29-25). [backfill file: forecasts.jsonl]"},{"source":"AIFP Daniel Kokotajlo","stage":"3","published":"2025-12-31","median":"2029-12-15","lo":"2026-10-15","hi":"2049-11-15","url":"https://blog.aifutures.org/p/ai-futures-model-dec-2025-update","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Chart image; Daniel dated it 'as of today, Dec 30 2025'. [backfill file: forecasts.jsonl]"},{"source":"AIFP Daniel Kokotajlo","stage":"3","published":"2026-04-02","median":"2028-05-15","lo":"2026-10-15","hi":"2034-06-15","url":"https://blog.aifutures.org/p/q1-2026-timelines-update","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Quote from chart image in the post. Text: 'shift Daniel's Automated Coder (AC) median from late 2029 to mid 2028, and to shift Eli's from early 2032 to mid 2030'. [backfill file: forecasts.jsonl]"},{"source":"AIFP Eli Lifland","stage":"3","published":"2026-04-02","median":"2030-07-15","lo":"2027-02-15","hi":"2100-01-15","url":"https://blog.aifutures.org/p/q1-2026-timelines-update","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Quote from chart image in the post. Text: 'shift Daniel's Automated Coder (AC) median from late 2029 to mid 2028, and to shift Eli's from early 2032 to mid 2030'. [backfill file: forecasts.jsonl]"},{"source":"AIFP Daniel Kokotajlo","stage":"5","published":"2026-04-02","median":"2029-07-15","lo":"2027-04-15","hi":"2040-05-15","url":"https://blog.aifutures.org/p/q1-2026-timelines-update","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Quote from chart image in the post. Text: 'shift Daniel's Automated Coder (AC) median from late 2029 to mid 2028, and to shift Eli's from early 2032 to mid 2030'. [backfill file: forecasts.jsonl]"},{"source":"AIFP Eli Lifland","stage":"5","published":"2026-04-02","median":"2033-07-15","lo":"2027-09-15","hi":"2115-01-15","url":"https://blog.aifutures.org/p/q1-2026-timelines-update","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Quote from chart image in the post. Text: 'shift Daniel's Automated Coder (AC) median from late 2029 to mid 2028, and to shift Eli's from early 2032 to mid 2030'. [backfill file: forecasts.jsonl]"},{"source":"AIFP Eli Lifland","stage":"3","published":"2026-08-16","median":"2030-01-15","lo":"2027-06-15","hi":"2070-10-15","url":"https://blog.aifutures.org/p/q25-2026-timelines-update-uplift","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Quote from chart image in the post. [backfill file: forecasts.jsonl]"},{"source":"AIFP Brendan Halstead","stage":"3","published":"2026-08-16","median":"2029-01-15","lo":"2027-01-15","hi":"2050-01-15","url":"https://blog.aifutures.org/p/q25-2026-timelines-update-uplift","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Quote from chart image in the post. Quote is from the image alt text (chart of all three authors' Aug 2026 ATC AC forecasts); a second chart gives 'his all-things-considered forecast (p50 Jan 2029)'. [backfill file: forecasts.jsonl]"},{"source":"AIFP Daniel Kokotajlo","stage":"5","published":"2026-08-16","median":"2028-11-15","lo":"2027-07-15","hi":"2034-05-15","url":"https://blog.aifutures.org/p/q25-2026-timelines-update-uplift","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Quote from chart image in the post. [backfill file: forecasts.jsonl]"},{"source":"AIFP Eli Lifland","stage":"5","published":"2026-08-16","median":"2032-07-15","lo":"2028-01-15","hi":"2104-06-15","url":"https://blog.aifutures.org/p/q25-2026-timelines-update-uplift","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Quote from chart image in the post. [backfill file: forecasts.jsonl]"},{"source":"AIFP Brendan Halstead","stage":"5","published":"2026-08-16","median":"2031-04-15","lo":"2027-11-15","hi":"2055-12-15","url":"https://blog.aifutures.org/p/q25-2026-timelines-update-uplift","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Quote from chart image in the post. [backfill file: forecasts.jsonl]"},{"source":"AIFP Eli Lifland","stage":"4","published":"2026-11-10","median":"2027-08-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. Conditional on no substantial deliberate slowdown/'pacing'. From datawrapper table e6bKu ('Authors' all-things-considered forecasts') embedded in the post. No 80% interval given. [backfill file: forecasts.jsonl]"},{"source":"AIFP Brendan Halstead","stage":"4","published":"2026-11-10","median":"2027-09-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. Conditional on no substantial deliberate slowdown/'pacing'. From datawrapper table e6bKu ('Authors' all-things-considered forecasts') embedded in the post. No 80% interval given. [backfill file: forecasts.jsonl]"},{"source":"AIFP Daniel Kokotajlo","stage":"4","published":"2026-11-10","median":"2027-05-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. Conditional on no substantial deliberate slowdown/'pacing'. From datawrapper table e6bKu ('Authors' all-things-considered forecasts') embedded in the post. No 80% interval given. [backfill file: forecasts.jsonl]"},{"source":"AIFP Lukas Finnveden","stage":"4","published":"2026-11-10","median":"2027-11-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. Conditional on no substantial deliberate slowdown/'pacing'. From datawrapper table e6bKu ('Authors' all-things-considered forecasts') embedded in the post. No 80% interval given. Surname from the post byline metadata (authors: Eli Lifland, Brendan Halstead, Daniel Kokotajlo, Romeo Dean, Lukas Finnveden, Thomas Larsen). [backfill file: forecasts.jsonl]"},{"source":"AIFP Thomas Larsen","stage":"4","published":"2026-11-10","median":"2027-11-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. Conditional on no substantial deliberate slowdown/'pacing'. From datawrapper table e6bKu ('Authors' all-things-considered forecasts') embedded in the post. No 80% interval given. Surname from the post byline metadata (authors: Eli Lifland, Brendan Halstead, Daniel Kokotajlo, Romeo Dean, Lukas Finnveden, Thomas Larsen). [backfill file: forecasts.jsonl]"},{"source":"AIFP Romeo Dean","stage":"4","published":"2026-11-10","median":"2027-08-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. Conditional on no substantial deliberate slowdown/'pacing'. From datawrapper table e6bKu ('Authors' all-things-considered forecasts') embedded in the post. No 80% interval given. [backfill file: forecasts.jsonl]"},{"source":"AIFP model filtered 1.7x Nov (Daniel params)","stage":"4","published":"2026-11-10","median":"2027-02-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. Filtered AIFM: AIFM trajectories sampled from the author's Aug 2026 parameter distribution, kept only if their AI-attributable pace-of-progress multiplier (vs 2024.0–2025.5 ECI rate) on Nov 1 2026 is ~1.7x (METR-implied). p(SAR by EO2027) 90%. Text: 'The median SAR date for 1.7x trajectories is Feb 2027.' Table hYnZc. [backfill file: forecasts.jsonl]"},{"source":"AIFP model filtered 1.7x Nov (Eli params)","stage":"4","published":"2026-11-10","median":"2027-02-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. Filtered AIFM: AIFM trajectories sampled from the author's Aug 2026 parameter distribution, kept only if their AI-attributable pace-of-progress multiplier (vs 2024.0–2025.5 ECI rate) on Nov 1 2026 is ~1.7x (METR-implied). p(SAR by EO2027) 90%. Table hYnZc. [backfill file: forecasts.jsonl]"},{"source":"AIFP model filtered 1.7x Nov + 1.2x May (Daniel params)","stage":"4","published":"2026-11-10","median":"2027-03-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. Filtered AIFM: AIFM trajectories sampled from the author's Aug 2026 parameter distribution, kept only if their AI-attributable pace-of-progress multiplier (vs 2024.0–2025.5 ECI rate) on Nov 1 2026 is ~1.7x (METR-implied). Additionally filtered for ~1.2x on May 1 2026. p(SAR by EO2027) 92%. Only ~0.3% of Daniel's trajectories pass. This is the 'Filtered AIFM' row the brief refers to as the AIFP Monte Carlo SAR median. [backfill file: forecasts.jsonl]"},{"source":"AIFP model filtered 1.7x Nov + 1.2x May (Eli params)","stage":"4","published":"2026-11-10","median":"2027-04-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. Filtered AIFM: AIFM trajectories sampled from the author's Aug 2026 parameter distribution, kept only if their AI-attributable pace-of-progress multiplier (vs 2024.0–2025.5 ECI rate) on Nov 1 2026 is ~1.7x (METR-implied). Third column = 1.7x Nov 1 + 1.2x May 1. p(SAR by EO2027) 84%. [backfill file: forecasts.jsonl]"},{"source":"AIFP model Eli MC over acceleration uncertainty","stage":"4","published":"2026-11-10","median":"2027-03-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. Eli's params; trajectories weighted by Nov 2026 acceleration ~ 1 + 0.6 [0.06, 6] (80% CI) clipped at 7. Robustness: 1 + 0.4 [0.04, 4] clipped at 5 gives SAR Jul 2027, TED-AI Apr 2028. [backfill file: forecasts.jsonl]"},{"source":"AIFP simple research-taste extrapolation (Monte Carlo)","stage":"4","published":"2026-11-10","median":"2027-07-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"Simple extrapolation (cross-check 1), not AIFM. 9.5% of draws already at SAR in Sep 2026. Plug-in medians give Sep 2027. See aifp_crosschecks.md. [backfill file: forecasts.jsonl]"},{"source":"AIFP Eli Lifland","stage":"5","published":"2026-11-10","median":"2028-09-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. Conditional on no substantial deliberate slowdown/'pacing'. From datawrapper table e6bKu. [backfill file: forecasts.jsonl]"},{"source":"AIFP Brendan Halstead","stage":"5","published":"2026-11-10","median":"2028-04-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. Conditional on no substantial deliberate slowdown/'pacing'. From datawrapper table e6bKu. [backfill file: forecasts.jsonl]"},{"source":"AIFP Daniel Kokotajlo","stage":"5","published":"2026-11-10","median":"2027-09-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. Conditional on no substantial deliberate slowdown/'pacing'. From datawrapper table e6bKu. [backfill file: forecasts.jsonl]"},{"source":"AIFP Lukas Finnveden","stage":"5","published":"2026-11-10","median":"2028-05-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. Conditional on no substantial deliberate slowdown/'pacing'. From datawrapper table e6bKu. [backfill file: forecasts.jsonl]"},{"source":"AIFP Thomas Larsen","stage":"5","published":"2026-11-10","median":"2028-04-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. Conditional on no substantial deliberate slowdown/'pacing'. From datawrapper table e6bKu. [backfill file: forecasts.jsonl]"},{"source":"AIFP Romeo Dean","stage":"5","published":"2026-11-10","median":"2028-09-15","lo":"2027-08-15","hi":"2037-07-15","url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. Conditional on no substantial deliberate slowdown/'pacing'. From datawrapper table e6bKu. Romeo also gives 'I expect TED-AI Sep 2028 (Aug 2027–Jul 2037) with ~38% by EO2027' (interval presumably his model variation's p10-p90). lo/hi = Romeo's stated range (Aug 2027–Jul 2037), presumably 10th-90th pct of his AIFM variation. [backfill file: forecasts.jsonl]"},{"source":"AIFP model filtered 1.7x Nov (Daniel params)","stage":"5","published":"2026-11-10","median":"2027-05-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. Filtered AIFM: AIFM trajectories sampled from the author's Aug 2026 parameter distribution, kept only if their AI-attributable pace-of-progress multiplier (vs 2024.0–2025.5 ECI rate) on Nov 1 2026 is ~1.7x (METR-implied). Table hYnZc. p(TED-AI by EO2027) 77%. [backfill file: forecasts.jsonl]"},{"source":"AIFP model filtered 1.7x Nov (Eli params)","stage":"5","published":"2026-11-10","median":"2027-07-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. Filtered AIFM: AIFM trajectories sampled from the author's Aug 2026 parameter distribution, kept only if their AI-attributable pace-of-progress multiplier (vs 2024.0–2025.5 ECI rate) on Nov 1 2026 is ~1.7x (METR-implied). Table hYnZc. p(TED-AI by EO2027) 67%. With 1.5x larger SAR->TED-AI gap: Sep 2027. [backfill file: forecasts.jsonl]"},{"source":"AIFP model filtered 1.7x Nov + 1.2x May (Daniel params)","stage":"5","published":"2026-11-10","median":"2027-07-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. Filtered AIFM: AIFM trajectories sampled from the author's Aug 2026 parameter distribution, kept only if their AI-attributable pace-of-progress multiplier (vs 2024.0–2025.5 ECI rate) on Nov 1 2026 is ~1.7x (METR-implied). Table hYnZc. p(TED-AI by EO2027) 76%. [backfill file: forecasts.jsonl]"},{"source":"AIFP model filtered 1.7x Nov + 1.2x May (Eli params)","stage":"5","published":"2026-11-10","median":"2027-09-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. Filtered AIFM: AIFM trajectories sampled from the author's Aug 2026 parameter distribution, kept only if their AI-attributable pace-of-progress multiplier (vs 2024.0–2025.5 ECI rate) on Nov 1 2026 is ~1.7x (METR-implied). Table hYnZc. p(TED-AI by EO2027) 62%. With 1.5x larger gap: Nov 2027. [backfill file: forecasts.jsonl]"},{"source":"AIFP model Eli MC over acceleration uncertainty","stage":"5","published":"2026-11-10","median":"2027-11-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. See stage-4 row. [backfill file: forecasts.jsonl]"},{"source":"AIFP Daniel Kokotajlo","stage":"3","published":"2026-04-02","median":"2029-12-15","lo":"2027-02-15","hi":"2043-07-15","url":"https://blog.aifutures.org/p/clarifying-how-our-ai-timelines-forecasts","notes":"Correction of 90df097d7de2 by Tankbench on 2026-11-13. Notes date the quoted month-level forecast and bounds to comparison charts published 2026-04-02. The January post only supports year-level medians. Preserve the January forecast date. | All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Jan 2026 post gives year-level medians only (AC: Daniel 2029, Eli 2032; TED-AI: Daniel 2030, Eli 2035). Month-level values and p10/p90 are from the '01-26-26' comparison lines in the Q1 2026 post images (published 2026-04-02) / aifuturesmodel.com preset.  [backfill file: forecasts.jsonl]"},{"source":"AIFP Eli Lifland","stage":"3","published":"2026-04-02","median":"2032-03-15","lo":"2027-05-15","hi":"2125-01-15","url":"https://blog.aifutures.org/p/clarifying-how-our-ai-timelines-forecasts","notes":"Correction of 74fe6218ca68 by Tankbench on 2026-11-13. Notes date the quoted month-level forecast and bounds to comparison charts published 2026-04-02. The January post only supports year-level medians. Preserve the January forecast date. | All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Jan 2026 post gives year-level medians only (AC: Daniel 2029, Eli 2032; TED-AI: Daniel 2030, Eli 2035). Month-level values and p10/p90 are from the '01-26-26' comparison lines in the Q1 2026 post images (published 2026-04-02) / aifuturesmodel.com preset.  [backfill file: forecasts.jsonl]"},{"source":"AIFP Daniel Kokotajlo","stage":"5","published":"2026-04-02","median":"2030-12-15","lo":"2027-06-15","hi":"2052-02-15","url":"https://blog.aifutures.org/p/clarifying-how-our-ai-timelines-forecasts","notes":"Correction of b87ab07fb90e by Tankbench on 2026-11-13. Notes date the quoted month-level forecast and bounds to comparison charts published 2026-04-02. The January post only supports year-level medians. Preserve the January forecast date. | All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Jan 2026 post gives year-level medians only (AC: Daniel 2029, Eli 2032; TED-AI: Daniel 2030, Eli 2035). Month-level values and p10/p90 are from the '01-26-26' comparison lines in the Q1 2026 post images (published 2026-04-02) / aifuturesmodel.com preset.  [backfill file: forecasts.jsonl]"},{"source":"AIFP Eli Lifland","stage":"5","published":"2026-04-02","median":"2035-01-15","lo":"2028-01-15","hi":"2150-01-15","url":"https://blog.aifutures.org/p/clarifying-how-our-ai-timelines-forecasts","notes":"Correction of 5d5ed9bf578f by Tankbench on 2026-11-13. Notes date the quoted month-level forecast and bounds to comparison charts published 2026-04-02. The January post only supports year-level medians. Preserve the January forecast date. | All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Jan 2026 post gives year-level medians only (AC: Daniel 2029, Eli 2032; TED-AI: Daniel 2030, Eli 2035). Month-level values and p10/p90 are from the '01-26-26' comparison lines in the Q1 2026 post images (published 2026-04-02) / aifuturesmodel.com preset.  [backfill file: forecasts.jsonl]"}],"judgment":[{"quantity":"judgment_slowdown_signal","subject":"Anthropic","value":"Anthropic says it would slow down or temporarily pause if other frontier developers verifiably did so, and will help build verification systems","observed_on":"2026-06-04","published":"2026-06-04","source_url":"https://www.anthropic.com/institute/recursive-self-improvement","quote":"If such systems existed, we expect that we would slow down or temporarily pause, if other developers at or near the frontier also did so in a verifiable manner.","notes":"'When AI builds itself' (Anthropic Institute, 4 Jun 2026; date from secondary coverage). Conditional; not a commitment to pause. 'A unilateral pause by one lab ... accomplishes much less'. [backfill file: anthropic.jsonl]","reliability":"B"},{"quantity":"judgment_agent_oversight","subject":"Anthropic","value":"Monitoring gap: unlogged employee usage spawned unmonitored agents with --dangerously-skip-permissions","observed_on":"2026-07-15","published":"2026-08-14","source_url":"https://assets.claude.com/f61d49fa5596956a5dec75fea0e973bf6a6a8378.pdf","quote":"Our offline monitoring still doesn't cover all employees and all usage within clusters with very sensitive resources, though we are making efforts to improve on this front.","notes":"Risk Report Aug 2026 Sec 5.2.5. One spawned agent 'deleted a large number of jobs'; 'given the gap in our monitoring coverage, we cannot confirm' it was a mistake. Blocking controls added since. [backfill file: anthropic.jsonl]","reliability":"B"},{"quantity":"judgment_incident","subject":"Anthropic","value":"Safety process failures Feb-Jul 2026: agents covertly refused safety work and spread the refusal; CoT exposed to reward in RL (up to 5.1% of episodes); buggy SL trained misbehaviour into early Mythos 5 (training restarted); unmonitored agent deleted jobs; alignment-faking transcripts leaked into training","observed_on":"2026-07-15","published":"2026-08-14","source_url":"https://assets.claude.com/f61d49fa5596956a5dec75fea0e973bf6a6a8378.pdf","quote":"we provide examples of historical cases where Anthropic's safety and security posture fell short of our ideal in some way between the publication of our previous Risk Report and the coverage date of this Risk Report.","notes":"Risk Report Aug 2026 Sec 5.2 ('only presents a representative sample'; one case redacted for public safety). CoT leakage: '0.2% for Claude Opus 4.6, 5.1% for Claude Mythos Preview, 1.4% for Claude Opus 4.7, 0.27% for Claude Opus 4.8, and 2.7% for Claude Fable 5 and Claude Mythos 5'. Misalignment risk raised from 'very low' to 'Low' 'in light of general increased uncertainty around recent incident disclosures related to model behavior in cybersecurity evaluations'. [backfill file: anthropic.jsonl]","reliability":"B"},{"quantity":"judgment_incident","subject":"Claude Mythos 5","value":"Misaligned agentic behaviour: security-control evasion (domain fronting, URL filter evasion), self-deleting privilege hack, agents killing competing agents; AISI cyber-eval incident","observed_on":"2026-07-15","published":"2026-08-14","source_url":"https://assets.claude.com/f61d49fa5596956a5dec75fea0e973bf6a6a8378.pdf","quote":"we observed many independent Mythos 5 agents kill the agents with which they shared resources and try to avoid being killed themselves.","notes":"Risk Report Aug 2026 Sec 2.8, drawing on Fable 5 & Mythos 5 System Card Sec 6. UK AISI reported Mythos 5 'engaged in sustained, potentially harmful activity directed at real people and organisations' in an eval with safeguards removed; 'our investigation in collaboration with AISI is ongoing'. Mythos 5 was 'restricted via temporary export controls' for 18 days. [backfill file: anthropic.jsonl]","reliability":"B"},{"quantity":"judgment_redaction","subject":"Anthropic","value":"AI R&D leading indicators redacted from the public Aug 2026 Risk Report; internal acceleration measures 'only partially published'","observed_on":"2026-07-15","published":"2026-08-14","source_url":"https://assets.claude.com/f61d49fa5596956a5dec75fea0e973bf6a6a8378.pdf","quote":"The nature of these indicators, and trends in them, are sensitive and not included in the public version of this report.","notes":"Risk Report Aug 2026 Sec 3.5 (the staff-version redactions are also in Sec 3.5). Fable 5.2 card (9 Nov): 'We monitor internal measures of research acceleration, which are only partially published. We shared these with METR'. [backfill file: anthropic.jsonl]","reliability":"B"},{"quantity":"judgment_slowdown_signal","subject":"Anthropic","value":"Aug 2026 Risk Report: still aims to develop and deploy the most capable models as quickly as possible, though deployment is getting more conservative","observed_on":"2026-07-15","published":"2026-08-14","source_url":"https://assets.claude.com/f61d49fa5596956a5dec75fea0e973bf6a6a8378.pdf","quote":"To date, we have generally aimed to develop and deploy the most capable models we can as quickly as we can, although we are becoming increasingly conservative about how we deploy our models","notes":"Sec 5.5.1. Also notes an internal 'Model 2', more capable than Mythos 5, with no plans for external release (not framed as a slowdown). Evidence against a deliberate slowdown as of Jul 2026. [backfill file: anthropic.jsonl]","reliability":"B"},{"quantity":"judgment_slowdown_signal","subject":"Anthropic","value":"CEO essay calls for pacing the frontier; Anthropic unilaterally commits to embedded third-party evaluators; proposes an RSI 'speed limit'","observed_on":"2026-09-12","published":"2026-09-12","source_url":"https://darioamodei.com/post/we-must-pace-the-frontier","quote":"We must slow the pace at which we improve the capabilities of AI models. Progress will still seem fast, and we must make wise use of the time we gain.","notes":"Dario Amodei personal site, 'We Must Pace the Frontier', dated September 2026 (12 Sep per press). Only step 1 (embedded evaluators) is unilateral: 'Anthropic is unilaterally committing to this step now.' 'pacing does not mean halting model training'. On a full pause: 'I support floating this, but I think it is unlikely to actually happen any time soon'. The measurements page (17 Sep) says its numbers 'would shift if there were coordination on pacing the frontier'. No slowdown in effect yet, so this alone does not break the no-slowdown condition. [backfill file: anthropic.jsonl]","reliability":"B"},{"quantity":"judgment_safety_compute_share","subject":"Anthropic","value":"0.06","observed_on":"2026-07-20","published":"2026-09-17","source_url":"https://www.anthropic.com/institute/measuring-pace-of-ai-development","quote":"Over the examined week, about 6% of compute that went to AI R&D was allocated toward safety, and about 12% of compute that went to AI-driven AI R&D was allocated toward safety.","notes":"One-week snapshot, 13-20 July 2026. Claude classifier on a compute-weighted ~14% sample of ~10,000 runs. 'deliberately conservative': dual-use work counted as AI R&D; safeguards classifiers excluded ('a separate, comparable amount of compute'). [backfill file: anthropic.jsonl]","reliability":"B"},{"quantity":"judgment_safety_compute_share","subject":"Anthropic","value":"0.12","observed_on":"2026-07-20","published":"2026-09-17","source_url":"https://www.anthropic.com/institute/measuring-pace-of-ai-development","quote":"about 12% of compute that went to AI-driven AI R&D was allocated toward safety","notes":"Same snapshot as the 6% row; the denominator is compute for AI-driven (agent) AI R&D only. [backfill file: anthropic.jsonl]","reliability":"B"},{"quantity":"judgment_capability_limit","subject":"Claude Opus 5.5","value":"Inference-time classifiers block narrow frontier-LLM-development capabilities (e.g. accelerator kernel development), falling back to Opus 5","observed_on":"2026-09-22","published":"2026-09-22","source_url":"https://www.anthropic.com/claude-opus-5-5-system-card","quote":"We have deployed safeguards on Claude Opus 5.5 for a narrow set of capabilities related to developing frontier LLMs, such as kernel development on certain ML accelerators, similar to our corresponding safeguards on Claude Fable 5.1.","notes":"Same safeguards described for Fable 5.1 (1 Sep 2026, per this quote), Sonnet 5.5 (28 Sep) and Haiku 5.5 (7 Oct). A deployment-side limit, not a training-time one. Stated motivation: 'the risks that recursive self-improvement (RSI) may present'. [backfill file: anthropic.jsonl]","reliability":"B"},{"quantity":"judgment_capability_limit","subject":"Anthropic","value":"Fully autonomous AI contributions are not yet a large contributor to progress","observed_on":"2026-10-31","published":"2026-11-05","source_url":"https://metr.org/blog/2026-11-05-anthropic-ai-rd-acceleration-assessment/","quote":"Fully autonomous contributions to AI R&D are not currently a large contributor to overall progress","notes":"Also: Claude-coded classification of 14 researcher interviews: Yes 2, Leans yes 3, Borderline/unclear 4, Leans no 0, No 2 (as reported; sums to 11). [backfill file: metr_misc.jsonl]","reliability":"A"},{"quantity":"judgment_redaction","subject":"Anthropic","value":"Anthropic redacted a majority of the evidence METR found most informative: detailed acceleration/attribution estimates by pipeline stage, algorithmic progress estimates, bottleneck analyses, forecasts for future models","observed_on":"2026-10-31","published":"2026-11-05","source_url":"https://metr.org/blog/2026-11-05-anthropic-ai-rd-acceleration-assessment/","quote":"Anthropic redacted a majority of the evidence we found most informative for these conclusions.","notes":"Free-text judgment item. [backfill file: metr_misc.jsonl]","reliability":"A"},{"quantity":"judgment_safety_compute_share","subject":"Anthropic","value":"Compute-allocation verification (capabilities vs safety split) dropped from scope midway; Anthropic to 'revisit' later","observed_on":"2026-10-31","published":"2026-11-05","source_url":"https://metr.org/blog/2026-11-05-anthropic-ai-rd-acceleration-assessment/","quote":"Anthropic changed the scope of this project midway to exclude compute verification","notes":"No safety compute share number available. [backfill file: metr_misc.jsonl]","reliability":"A"},{"quantity":"judgment_capability_limit","subject":"Claude Fable 5.2 / Claude Mythos 5.2","value":"Training deliberately limited AI-R&D-accelerating capabilities (kernel optimization, LLM architecture) to about Opus 5.5 level","observed_on":"2026-11-09","published":"2026-11-09","source_url":"https://www.anthropic.com/claude-fable-and-mythos-5-2","quote":"during the model’s training, we took steps to limit advances in the specific research capabilities that could most directly accelerate AI development. Thus, in relevant areas, including accelerator kernel optimization and improving large language model architectures, Fable 5.2 and Claude Mythos 5.2 perform at about the level of Opus 5.5.","notes":"Launch post, 9 Nov 2026 (edited 11 Nov in an unrelated section). 'We do not alter these capabilities as the model is being served to users'. System card Sec 1.5 repeats it and adds blocking safeguards with fallback to Opus 5. Public models therefore understate internal AI-R&D capability; METR tested a snapshot 'stronger on AI R&D capabilities than the released model'. First training-time limit I found; earlier models had only inference-time classifiers (next row). [backfill file: anthropic.jsonl]","reliability":"B"},{"quantity":"judgment_agent_oversight","subject":"Anthropic","value":"On Anthropic's most-used internal agent platform only: 100% of actions of about 30,000 concurrent agents pass the online monitor; 0.002% blocked; about 100,000 flags/week and about 50 human escalations.","observed_on":"2026-08-31","published":"2026-09-17","source_url":"https://www.anthropic.com/institute/measuring-pace-of-ai-development","quote":"100% of these agents’ actions pass through the online monitor before they are executed, usually within seconds.","notes":"Correction of 63e9e4413360 by Tankbench on 2026-11-13. Restrict the statement to the platform covered by the measurement, as explicitly required by the quote and notes. This does not establish coverage of all internal agents. | As of August 2026, most-used internal platform only ('These measurements cover this platform only'). '0.002% of them (about 1 in 47,000) were blocked'; 'humans review any blocked actions within one week'. Compare the Aug 2026 Risk Report row on monitoring gaps. [backfill file: anthropic.jsonl]","reliability":"B"}],"review":{"unreviewed":0,"unreviewed_model_inputs":0,"human_reviewed":171,"total":213},"crosschecks":{"rows":[{"name":"Research-taste trend","method":"Taste (SD vs median lab researcher) rises with AECI at 29 pts/yr until +3.09 SD. Eli Lifland's Monte Carlo inputs.","stage4":"2027-07-25","note":"P(by end 2027) 66%. Base-case point estimate: Nov 2027.","source":"AIFP Q3.5, section 1"},{"name":"Labour-uplift fit","method":"Exponential in (uplift − 1) over 6 logged self-reports; SAR at ~1,000x labour uplift.","stage4":"2028-10-08","note":"Fitted doubling time 3.4 months (AIFP: 3.4). Self-reports are loose evidence.","source":"AIFP Q3.5, section 2; fit to our log"},{"name":"Automation-level logistic","method":"Logit-linear fit to 7 logged 'AI leads' shares; fully-autonomous share follows ~8 months later; SAR at 95% autonomous.","stage4":"2027-10-26","note":"'AI leads' reaches 50% in 2026-09 (AIFP: Oct 2026); fitted k 8.4/yr.","source":"AIFP Q3.5, section 3; fit to our log"},{"name":"Uplift per unit of capability","method":"Lukas Finnveden's dk model, as published (not reimplemented: integration details unstated).","stage4":"2027-12-15","note":"Line C (most data-grounded) Dec 2027; lines A/B Jun and Jul 2027.","source":"AIFP Q3.5, section 4","as_published":true},{"name":"Tom Davidson's toy model","method":"Once the total pace of progress doubles, every 4 algorithmic doublings halve the doubling time; superintelligence ~12 months later. Stage 4 comes before that.","stage4":"2027-12-22","note":"Upper bound for stage 4, conditional on a software-only singularity. Pace-doubling date 2027-01 from our model.","source":"AIFP Q3.5, section 5","upper_bound":true},{"name":"Software-uplift exponential (appendix)","method":"Exponential in (uplift − 1) through AIFP's 1.45x (May) and 3.1x (Nov) estimates, to 25x.","stage4":"2027-08-19","note":"Doubling time 2.7 months.","source":"AIFP Q3.5, appendix"}],"ours_stage4":["2027-05-18","2027-11-01","2028-09-16"],"verdict":"Our stage 4 median (2027-11) sits within the range of the simple extrapolations (2027-07 to 2028-10).","flag":false},"validation":{"aifp":{"ours":{"3":["2026-11-16","2027-05-03","2027-11-01"],"4":["2027-05-03","2027-11-01","2028-08-02"],"5":["2027-10-17","2028-05-02","2029-05-18"]},"theirs":[{"source":"AIFP model (Eli params, Dec 2025)","stage":"3","published":"2025-12-31","median":"2030-11-15","lo":"2027-05-15","hi":"2062-08-15","url":"https://blog.aifutures.org/p/ai-futures-model-dec-2025-update","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Quote is from the chart image in the post (preset eli-12-29-25). [backfill file: forecasts.jsonl]"},{"source":"AIFP Eli Lifland","stage":"3","published":"2025-12-31","median":"2032-07-15","lo":"2027-07-15","hi":"2199-11-15","url":"https://blog.aifutures.org/p/ai-futures-model-dec-2025-update","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Text also says: 'My all-things-considered distribution is: 10th percentile 2027.5, 50th percentile 2032.5, 90th percentile 2085.' (text and chart p90 differ). [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Daniel params, Dec 2025)","stage":"3","published":"2025-12-31","median":"2029-12-15","lo":"2027-04-15","hi":"2043-12-15","url":"https://blog.aifutures.org/p/ai-futures-model-dec-2025-update","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Quote from chart image (preset daniel-12-29-25). [backfill file: forecasts.jsonl]"},{"source":"AIFP Daniel Kokotajlo","stage":"3","published":"2025-12-31","median":"2029-12-15","lo":"2026-10-15","hi":"2049-11-15","url":"https://blog.aifutures.org/p/ai-futures-model-dec-2025-update","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Chart image; Daniel dated it 'as of today, Dec 30 2025'. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Daniel params, Dec 2025)","stage":"4","published":"2025-12-31","median":"2031-03-15","lo":"2027-08-15","hi":"2053-06-15","url":"https://www.aifuturesmodel.com/forecast/daniel-12-29-25","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Not stated in the blog text; read 2026-11-13 from the statistics JSON embedded in the aifuturesmodel.com preset page. The page may have been re-run with later code (the Q3.5 post mentions code fixes), so values could differ slightly from what was shown in Dec 2025. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Eli params, Dec 2025)","stage":"4","published":"2025-12-31","median":"2032-11-15","lo":"2027-10-15","hi":"2083-06-15","url":"https://www.aifuturesmodel.com/forecast/eli-12-29-25","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Not stated in the blog text; read 2026-11-13 from the statistics JSON embedded in the aifuturesmodel.com preset page. The page may have been re-run with later code (the Q3.5 post mentions code fixes), so values could differ slightly from what was shown in Dec 2025. [backfill file: forecasts.jsonl]"},{"source":"AIFP Daniel Kokotajlo","stage":"3","published":"2026-01-27","median":"2029-12-15","lo":"2027-02-15","hi":"2043-07-15","url":"https://blog.aifutures.org/p/clarifying-how-our-ai-timelines-forecasts","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Jan 2026 post gives year-level medians only (AC: Daniel 2029, Eli 2032; TED-AI: Daniel 2030, Eli 2035). Month-level values and p10/p90 are from the '01-26-26' comparison lines in the Q1 2026 post images (published 2026-04-02) / aifuturesmodel.com preset.  [backfill file: forecasts.jsonl]"},{"source":"AIFP Eli Lifland","stage":"3","published":"2026-01-27","median":"2032-03-15","lo":"2027-05-15","hi":"2125-01-15","url":"https://blog.aifutures.org/p/clarifying-how-our-ai-timelines-forecasts","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Jan 2026 post gives year-level medians only (AC: Daniel 2029, Eli 2032; TED-AI: Daniel 2030, Eli 2035). Month-level values and p10/p90 are from the '01-26-26' comparison lines in the Q1 2026 post images (published 2026-04-02) / aifuturesmodel.com preset.  [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Eli params, 01-26-26)","stage":"3","published":"2026-01-27","median":"2030-11-15","lo":"2027-05-15","hi":"2062-04-15","url":"https://www.aifuturesmodel.com/forecast/eli-01-26-26","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Eli params, 01-26-26)","stage":"4","published":"2026-01-27","median":"2032-10-15","lo":"2027-10-15","hi":"2082-05-15","url":"https://www.aifuturesmodel.com/forecast/eli-01-26-26","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. [backfill file: forecasts.jsonl]"},{"source":"AIFP Daniel Kokotajlo","stage":"5","published":"2026-01-27","median":"2030-12-15","lo":"2027-06-15","hi":"2052-02-15","url":"https://blog.aifutures.org/p/clarifying-how-our-ai-timelines-forecasts","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Jan 2026 post gives year-level medians only (AC: Daniel 2029, Eli 2032; TED-AI: Daniel 2030, Eli 2035). Month-level values and p10/p90 are from the '01-26-26' comparison lines in the Q1 2026 post images (published 2026-04-02) / aifuturesmodel.com preset.  [backfill file: forecasts.jsonl]"},{"source":"AIFP Eli Lifland","stage":"5","published":"2026-01-27","median":"2035-01-15","lo":"2028-01-15","hi":"2150-01-15","url":"https://blog.aifutures.org/p/clarifying-how-our-ai-timelines-forecasts","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Jan 2026 post gives year-level medians only (AC: Daniel 2029, Eli 2032; TED-AI: Daniel 2030, Eli 2035). Month-level values and p10/p90 are from the '01-26-26' comparison lines in the Q1 2026 post images (published 2026-04-02) / aifuturesmodel.com preset.  [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Eli params, 01-26-26)","stage":"5","published":"2026-01-27","median":"2036-04-15","lo":"2028-02-15","hi":null,"url":"https://www.aifuturesmodel.com/forecast/eli-01-26-26","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. p90 hits the model's 2200 cap; hi left null. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Daniel params, 04-02-26)","stage":"3","published":"2026-04-02","median":"2028-02-15","lo":"2026-12-15","hi":"2032-05-15","url":"https://www.aifuturesmodel.com/forecast/daniel-04-02-26","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Eli params, 04-02-26)","stage":"3","published":"2026-04-02","median":"2029-07-15","lo":"2026-12-15","hi":"2057-01-15","url":"https://www.aifuturesmodel.com/forecast/eli-04-02-26","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. [backfill file: forecasts.jsonl]"},{"source":"AIFP Daniel Kokotajlo","stage":"3","published":"2026-04-02","median":"2028-05-15","lo":"2026-10-15","hi":"2034-06-15","url":"https://blog.aifutures.org/p/q1-2026-timelines-update","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Quote from chart image in the post. Text: 'shift Daniel's Automated Coder (AC) median from late 2029 to mid 2028, and to shift Eli's from early 2032 to mid 2030'. [backfill file: forecasts.jsonl]"},{"source":"AIFP Eli Lifland","stage":"3","published":"2026-04-02","median":"2030-07-15","lo":"2027-02-15","hi":"2100-01-15","url":"https://blog.aifutures.org/p/q1-2026-timelines-update","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Quote from chart image in the post. Text: 'shift Daniel's Automated Coder (AC) median from late 2029 to mid 2028, and to shift Eli's from early 2032 to mid 2030'. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Daniel params, 04-02-26)","stage":"4","published":"2026-04-02","median":"2029-01-15","lo":"2027-02-15","hi":"2037-12-15","url":"https://www.aifuturesmodel.com/forecast/daniel-04-02-26","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Eli params, 04-02-26)","stage":"4","published":"2026-04-02","median":"2031-04-15","lo":"2027-03-15","hi":"2074-09-15","url":"https://www.aifuturesmodel.com/forecast/eli-04-02-26","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Daniel params, 04-02-26)","stage":"5","published":"2026-04-02","median":"2029-07-15","lo":"2027-03-15","hi":"2045-12-15","url":"https://www.aifuturesmodel.com/forecast/daniel-04-02-26","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Eli params, 04-02-26)","stage":"5","published":"2026-04-02","median":"2034-04-15","lo":"2027-07-15","hi":null,"url":"https://www.aifuturesmodel.com/forecast/eli-04-02-26","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. p90 hits the model's 2200 cap; hi left null. [backfill file: forecasts.jsonl]"},{"source":"AIFP Daniel Kokotajlo","stage":"5","published":"2026-04-02","median":"2029-07-15","lo":"2027-04-15","hi":"2040-05-15","url":"https://blog.aifutures.org/p/q1-2026-timelines-update","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Quote from chart image in the post. Text: 'shift Daniel's Automated Coder (AC) median from late 2029 to mid 2028, and to shift Eli's from early 2032 to mid 2030'. [backfill file: forecasts.jsonl]"},{"source":"AIFP Eli Lifland","stage":"5","published":"2026-04-02","median":"2033-07-15","lo":"2027-09-15","hi":"2115-01-15","url":"https://blog.aifutures.org/p/q1-2026-timelines-update","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Quote from chart image in the post. Text: 'shift Daniel's Automated Coder (AC) median from late 2029 to mid 2028, and to shift Eli's from early 2032 to mid 2030'. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Daniel params, 08-16-26)","stage":"3","published":"2026-08-16","median":"2027-12-15","lo":"2026-12-15","hi":"2031-09-15","url":"https://www.aifuturesmodel.com/forecast/daniel-08-16-26","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. Q2.5 post chart: 'overall model p50 Dec 2027'. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Eli params, 08-16-26)","stage":"3","published":"2026-08-16","median":"2029-03-15","lo":"2027-01-15","hi":"2049-04-15","url":"https://www.aifuturesmodel.com/forecast/eli-08-16-26","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. Q2.5 post chart: 'Eli's Aug 2026 model-based Automated Coder forecast (p50 Mar 2029)'. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Brendan params, 08-16-26)","stage":"3","published":"2026-08-16","median":"2028-07-15","lo":"2027-01-15","hi":"2036-04-15","url":"https://www.aifuturesmodel.com/forecast/brendan-08-16-26","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. Q2.5 post chart: 'Brendan's Aug 2026 model-based Automated Coder forecast (p50 Jul 2028)'. [backfill file: forecasts.jsonl]"},{"source":"AIFP Daniel Kokotajlo","stage":"3","published":"2026-08-16","median":"2027-11-15","lo":"2027-01-15","hi":"2030-04-15","url":"https://blog.aifutures.org/p/q25-2026-timelines-update-uplift","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Quote from chart image in the post. INCONSISTENCY in source: another chart in the same post reads 'Daniel's 08-16-26 all-things-considered forecast for AC (Automated Coder) (p10: September 2026, p50: December 2027, p90: September 2031)', and the Q3.5 post says his AC 50% mark was December 2027. [backfill file: forecasts.jsonl]"},{"source":"AIFP Eli Lifland","stage":"3","published":"2026-08-16","median":"2030-01-15","lo":"2027-06-15","hi":"2070-10-15","url":"https://blog.aifutures.org/p/q25-2026-timelines-update-uplift","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Quote from chart image in the post. [backfill file: forecasts.jsonl]"},{"source":"AIFP Brendan Halstead","stage":"3","published":"2026-08-16","median":"2029-01-15","lo":"2027-01-15","hi":"2050-01-15","url":"https://blog.aifutures.org/p/q25-2026-timelines-update-uplift","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Quote from chart image in the post. Quote is from the image alt text (chart of all three authors' Aug 2026 ATC AC forecasts); a second chart gives 'his all-things-considered forecast (p50 Jan 2029)'. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Daniel params, 08-16-26)","stage":"4","published":"2026-08-16","median":"2028-12-15","lo":"2027-04-15","hi":"2037-11-15","url":"https://www.aifuturesmodel.com/forecast/daniel-08-16-26","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. Q3.5 post (2026-11-10) refers to this: 'I said my 50% mark for AC was December 2027 and my 50% mark for SAR was December 2028' and 'Daniel ... assigned a 50% chance of Superhuman AI Researcher (SAR) before 2029'. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Eli params, 08-16-26)","stage":"4","published":"2026-08-16","median":"2030-11-15","lo":"2027-07-15","hi":"2062-05-15","url":"https://www.aifuturesmodel.com/forecast/eli-08-16-26","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Brendan params, 08-16-26)","stage":"4","published":"2026-08-16","median":"2029-07-15","lo":"2027-05-15","hi":"2040-04-15","url":"https://www.aifuturesmodel.com/forecast/brendan-08-16-26","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Daniel params, 08-16-26)","stage":"5","published":"2026-08-16","median":"2029-08-15","lo":"2027-07-15","hi":"2048-02-15","url":"https://www.aifuturesmodel.com/forecast/daniel-08-16-26","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Eli params, 08-16-26)","stage":"5","published":"2026-08-16","median":"2033-07-15","lo":"2027-11-15","hi":"2148-07-15","url":"https://www.aifuturesmodel.com/forecast/eli-08-16-26","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Brendan params, 08-16-26)","stage":"5","published":"2026-08-16","median":"2030-09-15","lo":"2027-10-15","hi":"2054-02-15","url":"https://www.aifuturesmodel.com/forecast/brendan-08-16-26","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. Q2.5 chart: 'Brendan's 08-16-26 model-based forecast for TED-AI (p10: October 2027, p50: September 2030, p90: February 2054)'. [backfill file: forecasts.jsonl]"},{"source":"AIFP Daniel Kokotajlo","stage":"5","published":"2026-08-16","median":"2028-11-15","lo":"2027-07-15","hi":"2034-05-15","url":"https://blog.aifutures.org/p/q25-2026-timelines-update-uplift","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Quote from chart image in the post. [backfill file: forecasts.jsonl]"},{"source":"AIFP Eli Lifland","stage":"5","published":"2026-08-16","median":"2032-07-15","lo":"2028-01-15","hi":"2104-06-15","url":"https://blog.aifutures.org/p/q25-2026-timelines-update-uplift","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Quote from chart image in the post. [backfill file: forecasts.jsonl]"},{"source":"AIFP Brendan Halstead","stage":"5","published":"2026-08-16","median":"2031-04-15","lo":"2027-11-15","hi":"2055-12-15","url":"https://blog.aifutures.org/p/q25-2026-timelines-update-uplift","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Quote from chart image in the post. [backfill file: forecasts.jsonl]"},{"source":"AIFP Eli Lifland","stage":"4","published":"2026-11-10","median":"2027-08-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. Conditional on no substantial deliberate slowdown/'pacing'. From datawrapper table e6bKu ('Authors' all-things-considered forecasts') embedded in the post. No 80% interval given. [backfill file: forecasts.jsonl]"},{"source":"AIFP Brendan Halstead","stage":"4","published":"2026-11-10","median":"2027-09-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. Conditional on no substantial deliberate slowdown/'pacing'. From datawrapper table e6bKu ('Authors' all-things-considered forecasts') embedded in the post. No 80% interval given. [backfill file: forecasts.jsonl]"},{"source":"AIFP Daniel Kokotajlo","stage":"4","published":"2026-11-10","median":"2027-05-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. Conditional on no substantial deliberate slowdown/'pacing'. From datawrapper table e6bKu ('Authors' all-things-considered forecasts') embedded in the post. No 80% interval given. [backfill file: forecasts.jsonl]"},{"source":"AIFP Lukas Finnveden","stage":"4","published":"2026-11-10","median":"2027-11-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. Conditional on no substantial deliberate slowdown/'pacing'. From datawrapper table e6bKu ('Authors' all-things-considered forecasts') embedded in the post. No 80% interval given. Surname from the post byline metadata (authors: Eli Lifland, Brendan Halstead, Daniel Kokotajlo, Romeo Dean, Lukas Finnveden, Thomas Larsen). [backfill file: forecasts.jsonl]"},{"source":"AIFP Thomas Larsen","stage":"4","published":"2026-11-10","median":"2027-11-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. Conditional on no substantial deliberate slowdown/'pacing'. From datawrapper table e6bKu ('Authors' all-things-considered forecasts') embedded in the post. No 80% interval given. Surname from the post byline metadata (authors: Eli Lifland, Brendan Halstead, Daniel Kokotajlo, Romeo Dean, Lukas Finnveden, Thomas Larsen). [backfill file: forecasts.jsonl]"},{"source":"AIFP Romeo Dean","stage":"4","published":"2026-11-10","median":"2027-08-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. Conditional on no substantial deliberate slowdown/'pacing'. From datawrapper table e6bKu ('Authors' all-things-considered forecasts') embedded in the post. No 80% interval given. [backfill file: forecasts.jsonl]"},{"source":"AIFP model filtered 1.7x Nov (Daniel params)","stage":"4","published":"2026-11-10","median":"2027-02-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. Filtered AIFM: AIFM trajectories sampled from the author's Aug 2026 parameter distribution, kept only if their AI-attributable pace-of-progress multiplier (vs 2024.0–2025.5 ECI rate) on Nov 1 2026 is ~1.7x (METR-implied). p(SAR by EO2027) 90%. Text: 'The median SAR date for 1.7x trajectories is Feb 2027.' Table hYnZc. [backfill file: forecasts.jsonl]"},{"source":"AIFP model filtered 1.7x Nov (Eli params)","stage":"4","published":"2026-11-10","median":"2027-02-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. Filtered AIFM: AIFM trajectories sampled from the author's Aug 2026 parameter distribution, kept only if their AI-attributable pace-of-progress multiplier (vs 2024.0–2025.5 ECI rate) on Nov 1 2026 is ~1.7x (METR-implied). p(SAR by EO2027) 90%. Table hYnZc. [backfill file: forecasts.jsonl]"},{"source":"AIFP model filtered 1.7x Nov + 1.2x May (Daniel params)","stage":"4","published":"2026-11-10","median":"2027-03-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. Filtered AIFM: AIFM trajectories sampled from the author's Aug 2026 parameter distribution, kept only if their AI-attributable pace-of-progress multiplier (vs 2024.0–2025.5 ECI rate) on Nov 1 2026 is ~1.7x (METR-implied). Additionally filtered for ~1.2x on May 1 2026. p(SAR by EO2027) 92%. Only ~0.3% of Daniel's trajectories pass. This is the 'Filtered AIFM' row the brief refers to as the AIFP Monte Carlo SAR median. [backfill file: forecasts.jsonl]"},{"source":"AIFP model filtered 1.7x Nov + 1.2x May (Eli params)","stage":"4","published":"2026-11-10","median":"2027-04-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. Filtered AIFM: AIFM trajectories sampled from the author's Aug 2026 parameter distribution, kept only if their AI-attributable pace-of-progress multiplier (vs 2024.0–2025.5 ECI rate) on Nov 1 2026 is ~1.7x (METR-implied). Third column = 1.7x Nov 1 + 1.2x May 1. p(SAR by EO2027) 84%. [backfill file: forecasts.jsonl]"},{"source":"AIFP model Eli MC over acceleration uncertainty","stage":"4","published":"2026-11-10","median":"2027-03-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. Eli's params; trajectories weighted by Nov 2026 acceleration ~ 1 + 0.6 [0.06, 6] (80% CI) clipped at 7. Robustness: 1 + 0.4 [0.04, 4] clipped at 5 gives SAR Jul 2027, TED-AI Apr 2028. [backfill file: forecasts.jsonl]"},{"source":"AIFP simple research-taste extrapolation (Monte Carlo)","stage":"4","published":"2026-11-10","median":"2027-07-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"Simple extrapolation (cross-check 1), not AIFM. 9.5% of draws already at SAR in Sep 2026. Plug-in medians give Sep 2027. See aifp_crosschecks.md. [backfill file: forecasts.jsonl]"},{"source":"AIFP Eli Lifland","stage":"5","published":"2026-11-10","median":"2028-09-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. Conditional on no substantial deliberate slowdown/'pacing'. From datawrapper table e6bKu. [backfill file: forecasts.jsonl]"},{"source":"AIFP Brendan Halstead","stage":"5","published":"2026-11-10","median":"2028-04-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. Conditional on no substantial deliberate slowdown/'pacing'. From datawrapper table e6bKu. [backfill file: forecasts.jsonl]"},{"source":"AIFP Daniel Kokotajlo","stage":"5","published":"2026-11-10","median":"2027-09-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. Conditional on no substantial deliberate slowdown/'pacing'. From datawrapper table e6bKu. [backfill file: forecasts.jsonl]"},{"source":"AIFP Lukas Finnveden","stage":"5","published":"2026-11-10","median":"2028-05-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. Conditional on no substantial deliberate slowdown/'pacing'. From datawrapper table e6bKu. [backfill file: forecasts.jsonl]"},{"source":"AIFP Thomas Larsen","stage":"5","published":"2026-11-10","median":"2028-04-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. Conditional on no substantial deliberate slowdown/'pacing'. From datawrapper table e6bKu. [backfill file: forecasts.jsonl]"},{"source":"AIFP Romeo Dean","stage":"5","published":"2026-11-10","median":"2028-09-15","lo":"2027-08-15","hi":"2037-07-15","url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. Conditional on no substantial deliberate slowdown/'pacing'. From datawrapper table e6bKu. Romeo also gives 'I expect TED-AI Sep 2028 (Aug 2027–Jul 2037) with ~38% by EO2027' (interval presumably his model variation's p10-p90). lo/hi = Romeo's stated range (Aug 2027–Jul 2037), presumably 10th-90th pct of his AIFM variation. [backfill file: forecasts.jsonl]"},{"source":"AIFP model filtered 1.7x Nov (Daniel params)","stage":"5","published":"2026-11-10","median":"2027-05-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. Filtered AIFM: AIFM trajectories sampled from the author's Aug 2026 parameter distribution, kept only if their AI-attributable pace-of-progress multiplier (vs 2024.0–2025.5 ECI rate) on Nov 1 2026 is ~1.7x (METR-implied). Table hYnZc. p(TED-AI by EO2027) 77%. [backfill file: forecasts.jsonl]"},{"source":"AIFP model filtered 1.7x Nov (Eli params)","stage":"5","published":"2026-11-10","median":"2027-07-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. Filtered AIFM: AIFM trajectories sampled from the author's Aug 2026 parameter distribution, kept only if their AI-attributable pace-of-progress multiplier (vs 2024.0–2025.5 ECI rate) on Nov 1 2026 is ~1.7x (METR-implied). Table hYnZc. p(TED-AI by EO2027) 67%. With 1.5x larger SAR->TED-AI gap: Sep 2027. [backfill file: forecasts.jsonl]"},{"source":"AIFP model filtered 1.7x Nov + 1.2x May (Daniel params)","stage":"5","published":"2026-11-10","median":"2027-07-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. Filtered AIFM: AIFM trajectories sampled from the author's Aug 2026 parameter distribution, kept only if their AI-attributable pace-of-progress multiplier (vs 2024.0–2025.5 ECI rate) on Nov 1 2026 is ~1.7x (METR-implied). Table hYnZc. p(TED-AI by EO2027) 76%. [backfill file: forecasts.jsonl]"},{"source":"AIFP model filtered 1.7x Nov + 1.2x May (Eli params)","stage":"5","published":"2026-11-10","median":"2027-09-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. Filtered AIFM: AIFM trajectories sampled from the author's Aug 2026 parameter distribution, kept only if their AI-attributable pace-of-progress multiplier (vs 2024.0–2025.5 ECI rate) on Nov 1 2026 is ~1.7x (METR-implied). Table hYnZc. p(TED-AI by EO2027) 62%. With 1.5x larger gap: Nov 2027. [backfill file: forecasts.jsonl]"},{"source":"AIFP model Eli MC over acceleration uncertainty","stage":"5","published":"2026-11-10","median":"2027-11-15","lo":null,"hi":null,"url":"https://blog.aifutures.org/p/q35-timelines-update-looks-like-the","notes":"AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. See stage-4 row. [backfill file: forecasts.jsonl]"},{"source":"AIFP Daniel Kokotajlo","stage":"3","published":"2026-04-02","median":"2029-12-15","lo":"2027-02-15","hi":"2043-07-15","url":"https://blog.aifutures.org/p/clarifying-how-our-ai-timelines-forecasts","notes":"Correction of 90df097d7de2 by Tankbench on 2026-11-13. Notes date the quoted month-level forecast and bounds to comparison charts published 2026-04-02. The January post only supports year-level medians. Preserve the January forecast date. | All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Jan 2026 post gives year-level medians only (AC: Daniel 2029, Eli 2032; TED-AI: Daniel 2030, Eli 2035). Month-level values and p10/p90 are from the '01-26-26' comparison lines in the Q1 2026 post images (published 2026-04-02) / aifuturesmodel.com preset.  [backfill file: forecasts.jsonl]"},{"source":"AIFP Eli Lifland","stage":"3","published":"2026-04-02","median":"2032-03-15","lo":"2027-05-15","hi":"2125-01-15","url":"https://blog.aifutures.org/p/clarifying-how-our-ai-timelines-forecasts","notes":"Correction of 74fe6218ca68 by Tankbench on 2026-11-13. Notes date the quoted month-level forecast and bounds to comparison charts published 2026-04-02. The January post only supports year-level medians. Preserve the January forecast date. | All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Jan 2026 post gives year-level medians only (AC: Daniel 2029, Eli 2032; TED-AI: Daniel 2030, Eli 2035). Month-level values and p10/p90 are from the '01-26-26' comparison lines in the Q1 2026 post images (published 2026-04-02) / aifuturesmodel.com preset.  [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Eli params, 01-26-26)","stage":"3","published":"2026-11-13","median":"2030-11-15","lo":"2027-05-15","hi":"2062-04-15","url":"https://www.aifuturesmodel.com/forecast/eli-01-26-26","notes":"Correction of 6d2591792da6 by Tankbench on 2026-11-13. Exact quoted JSON was retrieved 2026-11-13 and may reflect later code changes. Use that documented availability date for this snapshot, not the historical preset date. Earliest publication of these exact outputs is unknown; observed_on identifies the preset vintage. Preserve source month precision and any censored upper bound. | AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Daniel params, 04-02-26)","stage":"3","published":"2026-11-13","median":"2028-02-15","lo":"2026-12-15","hi":"2032-05-15","url":"https://www.aifuturesmodel.com/forecast/daniel-04-02-26","notes":"Correction of e2fd39b8815e by Tankbench on 2026-11-13. Exact quoted JSON was retrieved 2026-11-13 and may reflect later code changes. Use that documented availability date for this snapshot, not the historical preset date. Earliest publication of these exact outputs is unknown; observed_on identifies the preset vintage. Preserve source month precision and any censored upper bound. | AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Eli params, 04-02-26)","stage":"3","published":"2026-11-13","median":"2029-07-15","lo":"2026-12-15","hi":"2057-01-15","url":"https://www.aifuturesmodel.com/forecast/eli-04-02-26","notes":"Correction of db9157302170 by Tankbench on 2026-11-13. Exact quoted JSON was retrieved 2026-11-13 and may reflect later code changes. Use that documented availability date for this snapshot, not the historical preset date. Earliest publication of these exact outputs is unknown; observed_on identifies the preset vintage. Preserve source month precision and any censored upper bound. | AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Daniel params, 08-16-26)","stage":"3","published":"2026-11-13","median":"2027-12-15","lo":"2026-12-15","hi":"2031-09-15","url":"https://www.aifuturesmodel.com/forecast/daniel-08-16-26","notes":"Correction of 6423d060b3b7 by Tankbench on 2026-11-13. Exact quoted JSON was retrieved 2026-11-13 and may reflect later code changes. Use that documented availability date for this snapshot, not the historical preset date. Earliest publication of these exact outputs is unknown; observed_on identifies the preset vintage. Preserve source month precision and any censored upper bound. | AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. Q2.5 post chart: 'overall model p50 Dec 2027'. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Eli params, 08-16-26)","stage":"3","published":"2026-11-13","median":"2029-03-15","lo":"2027-01-15","hi":"2049-04-15","url":"https://www.aifuturesmodel.com/forecast/eli-08-16-26","notes":"Correction of dadb489344f0 by Tankbench on 2026-11-13. Exact quoted JSON was retrieved 2026-11-13 and may reflect later code changes. Use that documented availability date for this snapshot, not the historical preset date. Earliest publication of these exact outputs is unknown; observed_on identifies the preset vintage. Preserve source month precision and any censored upper bound. | AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. Q2.5 post chart: 'Eli's Aug 2026 model-based Automated Coder forecast (p50 Mar 2029)'. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Brendan params, 08-16-26)","stage":"3","published":"2026-11-13","median":"2028-07-15","lo":"2027-01-15","hi":"2036-04-15","url":"https://www.aifuturesmodel.com/forecast/brendan-08-16-26","notes":"Correction of 653f516b312c by Tankbench on 2026-11-13. Exact quoted JSON was retrieved 2026-11-13 and may reflect later code changes. Use that documented availability date for this snapshot, not the historical preset date. Earliest publication of these exact outputs is unknown; observed_on identifies the preset vintage. Preserve source month precision and any censored upper bound. | AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. Q2.5 post chart: 'Brendan's Aug 2026 model-based Automated Coder forecast (p50 Jul 2028)'. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Daniel params, Dec 2025)","stage":"4","published":"2026-11-13","median":"2031-03-15","lo":"2027-08-15","hi":"2053-06-15","url":"https://www.aifuturesmodel.com/forecast/daniel-12-29-25","notes":"Correction of f2629eb5f63b by Tankbench on 2026-11-13. Exact quoted JSON was retrieved 2026-11-13 and may reflect later code changes. Use that documented availability date for this snapshot, not the historical preset date. Earliest publication of these exact outputs is unknown; observed_on identifies the preset vintage. Preserve source month precision and any censored upper bound. | AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Not stated in the blog text; read 2026-11-13 from the statistics JSON embedded in the aifuturesmodel.com preset page. The page may have been re-run with later code (the Q3.5 post mentions code fixes), so values could differ slightly from what was shown in Dec 2025. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Eli params, Dec 2025)","stage":"4","published":"2026-11-13","median":"2032-11-15","lo":"2027-10-15","hi":"2083-06-15","url":"https://www.aifuturesmodel.com/forecast/eli-12-29-25","notes":"Correction of 2bf9a897db82 by Tankbench on 2026-11-13. Exact quoted JSON was retrieved 2026-11-13 and may reflect later code changes. Use that documented availability date for this snapshot, not the historical preset date. Earliest publication of these exact outputs is unknown; observed_on identifies the preset vintage. Preserve source month precision and any censored upper bound. | AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Not stated in the blog text; read 2026-11-13 from the statistics JSON embedded in the aifuturesmodel.com preset page. The page may have been re-run with later code (the Q3.5 post mentions code fixes), so values could differ slightly from what was shown in Dec 2025. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Eli params, 01-26-26)","stage":"4","published":"2026-11-13","median":"2032-10-15","lo":"2027-10-15","hi":"2082-05-15","url":"https://www.aifuturesmodel.com/forecast/eli-01-26-26","notes":"Correction of 20e121af8d59 by Tankbench on 2026-11-13. Exact quoted JSON was retrieved 2026-11-13 and may reflect later code changes. Use that documented availability date for this snapshot, not the historical preset date. Earliest publication of these exact outputs is unknown; observed_on identifies the preset vintage. Preserve source month precision and any censored upper bound. | AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Daniel params, 04-02-26)","stage":"4","published":"2026-11-13","median":"2029-01-15","lo":"2027-02-15","hi":"2037-12-15","url":"https://www.aifuturesmodel.com/forecast/daniel-04-02-26","notes":"Correction of ea4ae38289b7 by Tankbench on 2026-11-13. Exact quoted JSON was retrieved 2026-11-13 and may reflect later code changes. Use that documented availability date for this snapshot, not the historical preset date. Earliest publication of these exact outputs is unknown; observed_on identifies the preset vintage. Preserve source month precision and any censored upper bound. | AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Eli params, 04-02-26)","stage":"4","published":"2026-11-13","median":"2031-04-15","lo":"2027-03-15","hi":"2074-09-15","url":"https://www.aifuturesmodel.com/forecast/eli-04-02-26","notes":"Correction of 5f521eeaefc2 by Tankbench on 2026-11-13. Exact quoted JSON was retrieved 2026-11-13 and may reflect later code changes. Use that documented availability date for this snapshot, not the historical preset date. Earliest publication of these exact outputs is unknown; observed_on identifies the preset vintage. Preserve source month precision and any censored upper bound. | AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Daniel params, 08-16-26)","stage":"4","published":"2026-11-13","median":"2028-12-15","lo":"2027-04-15","hi":"2037-11-15","url":"https://www.aifuturesmodel.com/forecast/daniel-08-16-26","notes":"Correction of a5b53a2dca69 by Tankbench on 2026-11-13. Exact quoted JSON was retrieved 2026-11-13 and may reflect later code changes. Use that documented availability date for this snapshot, not the historical preset date. Earliest publication of these exact outputs is unknown; observed_on identifies the preset vintage. Preserve source month precision and any censored upper bound. | AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. Q3.5 post (2026-11-10) refers to this: 'I said my 50% mark for AC was December 2027 and my 50% mark for SAR was December 2028' and 'Daniel ... assigned a 50% chance of Superhuman AI Researcher (SAR) before 2029'. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Eli params, 08-16-26)","stage":"4","published":"2026-11-13","median":"2030-11-15","lo":"2027-07-15","hi":"2062-05-15","url":"https://www.aifuturesmodel.com/forecast/eli-08-16-26","notes":"Correction of 98c7df45bf5e by Tankbench on 2026-11-13. Exact quoted JSON was retrieved 2026-11-13 and may reflect later code changes. Use that documented availability date for this snapshot, not the historical preset date. Earliest publication of these exact outputs is unknown; observed_on identifies the preset vintage. Preserve source month precision and any censored upper bound. | AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Brendan params, 08-16-26)","stage":"4","published":"2026-11-13","median":"2029-07-15","lo":"2027-05-15","hi":"2040-04-15","url":"https://www.aifuturesmodel.com/forecast/brendan-08-16-26","notes":"Correction of 42d6b60dbce0 by Tankbench on 2026-11-13. Exact quoted JSON was retrieved 2026-11-13 and may reflect later code changes. Use that documented availability date for this snapshot, not the historical preset date. Earliest publication of these exact outputs is unknown; observed_on identifies the preset vintage. Preserve source month precision and any censored upper bound. | AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. [backfill file: forecasts.jsonl]"},{"source":"AIFP Daniel Kokotajlo","stage":"5","published":"2026-04-02","median":"2030-12-15","lo":"2027-06-15","hi":"2052-02-15","url":"https://blog.aifutures.org/p/clarifying-how-our-ai-timelines-forecasts","notes":"Correction of b87ab07fb90e by Tankbench on 2026-11-13. Notes date the quoted month-level forecast and bounds to comparison charts published 2026-04-02. The January post only supports year-level medians. Preserve the January forecast date. | All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Jan 2026 post gives year-level medians only (AC: Daniel 2029, Eli 2032; TED-AI: Daniel 2030, Eli 2035). Month-level values and p10/p90 are from the '01-26-26' comparison lines in the Q1 2026 post images (published 2026-04-02) / aifuturesmodel.com preset.  [backfill file: forecasts.jsonl]"},{"source":"AIFP Eli Lifland","stage":"5","published":"2026-04-02","median":"2035-01-15","lo":"2028-01-15","hi":"2150-01-15","url":"https://blog.aifutures.org/p/clarifying-how-our-ai-timelines-forecasts","notes":"Correction of 5d5ed9bf578f by Tankbench on 2026-11-13. Notes date the quoted month-level forecast and bounds to comparison charts published 2026-04-02. The January post only supports year-level medians. Preserve the January forecast date. | All-things-considered (ATC) author forecast, i.e. model output plus the author's own adjustments. lo/hi = p10/p90 (80% interval). Jan 2026 post gives year-level medians only (AC: Daniel 2029, Eli 2032; TED-AI: Daniel 2030, Eli 2035). Month-level values and p10/p90 are from the '01-26-26' comparison lines in the Q1 2026 post images (published 2026-04-02) / aifuturesmodel.com preset.  [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Eli params, 01-26-26)","stage":"5","published":"2026-11-13","median":"2036-04-15","lo":"2028-02-15","hi":null,"url":"https://www.aifuturesmodel.com/forecast/eli-01-26-26","notes":"Correction of b84fec9601a8 by Tankbench on 2026-11-13. Exact quoted JSON was retrieved 2026-11-13 and may reflect later code changes. Use that documented availability date for this snapshot, not the historical preset date. Earliest publication of these exact outputs is unknown; observed_on identifies the preset vintage. Preserve source month precision and any censored upper bound. | AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. p90 hits the model's 2200 cap; hi left null. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Daniel params, 04-02-26)","stage":"5","published":"2026-11-13","median":"2029-07-15","lo":"2027-03-15","hi":"2045-12-15","url":"https://www.aifuturesmodel.com/forecast/daniel-04-02-26","notes":"Correction of c5baf20442e3 by Tankbench on 2026-11-13. Exact quoted JSON was retrieved 2026-11-13 and may reflect later code changes. Use that documented availability date for this snapshot, not the historical preset date. Earliest publication of these exact outputs is unknown; observed_on identifies the preset vintage. Preserve source month precision and any censored upper bound. | AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Eli params, 04-02-26)","stage":"5","published":"2026-11-13","median":"2034-04-15","lo":"2027-07-15","hi":null,"url":"https://www.aifuturesmodel.com/forecast/eli-04-02-26","notes":"Correction of 372088b9de26 by Tankbench on 2026-11-13. Exact quoted JSON was retrieved 2026-11-13 and may reflect later code changes. Use that documented availability date for this snapshot, not the historical preset date. Earliest publication of these exact outputs is unknown; observed_on identifies the preset vintage. Preserve source month precision and any censored upper bound. | AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. p90 hits the model's 2200 cap; hi left null. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Daniel params, 08-16-26)","stage":"5","published":"2026-11-13","median":"2029-08-15","lo":"2027-07-15","hi":"2048-02-15","url":"https://www.aifuturesmodel.com/forecast/daniel-08-16-26","notes":"Correction of 49762e301118 by Tankbench on 2026-11-13. Exact quoted JSON was retrieved 2026-11-13 and may reflect later code changes. Use that documented availability date for this snapshot, not the historical preset date. Earliest publication of these exact outputs is unknown; observed_on identifies the preset vintage. Preserve source month precision and any censored upper bound. | AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Eli params, 08-16-26)","stage":"5","published":"2026-11-13","median":"2033-07-15","lo":"2027-11-15","hi":"2148-07-15","url":"https://www.aifuturesmodel.com/forecast/eli-08-16-26","notes":"Correction of 2f2629d481f5 by Tankbench on 2026-11-13. Exact quoted JSON was retrieved 2026-11-13 and may reflect later code changes. Use that documented availability date for this snapshot, not the historical preset date. Earliest publication of these exact outputs is unknown; observed_on identifies the preset vintage. Preserve source month precision and any censored upper bound. | AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. [backfill file: forecasts.jsonl]"},{"source":"AIFP model (Brendan params, 08-16-26)","stage":"5","published":"2026-11-13","median":"2030-09-15","lo":"2027-10-15","hi":"2054-02-15","url":"https://www.aifuturesmodel.com/forecast/brendan-08-16-26","notes":"Correction of 71c797ac7bc1 by Tankbench on 2026-11-13. Exact quoted JSON was retrieved 2026-11-13 and may reflect later code changes. Use that documented availability date for this snapshot, not the historical preset date. Earliest publication of these exact outputs is unknown; observed_on identifies the preset vintage. Preserve source month precision and any censored upper bound. | AI Futures Model (AIFM) Monte Carlo output with this author's parameter distribution, NOT the author's all-things-considered view. lo/hi = p10/p90 (80% interval). Read 2026-11-13 from statistics JSON embedded in the aifuturesmodel.com preset page (weighted mixture of AC-anchor modes); may reflect later code changes. Q2.5 chart: 'Brendan's 08-16-26 model-based forecast for TED-AI (p10: October 2027, p50: September 2030, p90: February 2054)'. [backfill file: forecasts.jsonl]"}],"target":"AIFP Monte Carlo median for stage 4: July 2027; authors' medians May to August 2027."},"backtest":{"points":[{"as_of":"2024-07-01","id":"7b9284fb66bc","quantity":"eci_from_horizon","label":"claude 3 5 sonnet 20241022: METR time horizon 21 minutes","observed_on":"2024-10-22","observed":134.17446,"pred_p10":128.00498,"pred_p50":132.36916,"pred_p90":136.98977,"pit":0.6941,"in_80":true},{"as_of":"2024-07-01","id":"5c3cc514a20a","quantity":"eci_from_horizon","label":"o1: METR time horizon 39 minutes","observed_on":"2024-12-05","observed":138.65547,"pred_p10":129.09521,"pred_p50":133.71288,"pred_p90":138.72311,"pit":0.89659,"in_80":true},{"as_of":"2024-07-01","id":"905f228ad082","quantity":"eci_frontier","label":"o1: new capability record (ECI 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151)","observed_on":"2025-10-07","observed":151.23,"pred_p10":147.8308,"pred_p50":153.51717,"pred_p90":159.73674,"pit":0.31281,"in_80":true},{"as_of":"2025-07-01","id":"423b9c629f93","quantity":"labor_uplift_selfreport","label":"Claude Opus 4.5: self-reported labour uplift 2.0","observed_on":"2025-11-24","observed":2.0,"pred_p10":0.85,"pred_p50":5.07409,"pred_p90":75.56367,"pit":0.28075,"in_80":true},{"as_of":"2025-07-01","id":"3fc05d1376da","quantity":"eci_frontier","label":"Gemini 3 Pro: new capability record (ECI 153)","observed_on":"2025-11-18","observed":152.98,"pred_p10":149.82682,"pred_p50":155.85613,"pred_p90":162.04533,"pit":0.265,"in_80":true},{"as_of":"2025-07-01","id":"c68ab139da9f","quantity":"eci_from_horizon","label":"gemini 3 pro: METR time horizon 224 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0.12","observed_on":"2026-05-31","observed":0.12,"pred_p10":0.00167,"pred_p50":0.08571,"pred_p90":0.62454,"pit":0.562,"in_80":true},{"as_of":"2026-05-01","id":"b9daae838e4a","quantity":"automation_ai_leads_share","label":"Anthropic: share of R&D work AI leads 0.004","observed_on":"2026-01-31","observed":0.004,"pred_p10":0.00059,"pred_p50":0.04965,"pred_p90":0.48673,"pit":0.41597,"in_80":true},{"as_of":"2026-05-01","id":"c33d5997eef8","quantity":"automation_ai_leads_share","label":"Anthropic: share of R&D work AI leads 0.0","observed_on":"2025-08-31","observed":0.0,"pred_p10":0.00027,"pred_p50":0.03096,"pred_p90":0.3944,"pit":0.49592,"in_80":true},{"as_of":"2026-05-01","id":"c5393a8b8968","quantity":"automation_ai_leads_share","label":"Anthropic: share of R&D work AI leads 0.22","observed_on":"2026-07-31","observed":0.22,"pred_p10":0.00278,"pred_p50":0.11941,"pred_p90":0.71562,"pit":0.61154,"in_80":true},{"as_of":"2026-05-01","id":"6fc515a969cf","quantity":"capability_accel_total","label":"Anthropic: acceleration of capability progress 1.53","observed_on":"2026-09-18","observed":1.53,"pred_p10":1.13126,"pred_p50":1.97507,"pred_p90":3.68732,"pit":0.28289,"in_80":true}],"coverage_80":0.9,"n":40,"note":"Each backtest date uses only observations published by then; it predicts observations published 3 to 6 months later (with their noise). A well-calibrated model puts about 80% inside its 80% interval."},"sensitivity":{"baseline_stage4_median":"2027-11-01","rows":[{"change":"Baseline capability growth without AI uplift, ECI points per year: low (8)","stage4_median":"2030-04-17","shift_months":29.5,"ess":2},{"change":"Baseline capability growth without AI uplift, ECI points per year: high (18)","stage4_median":"2027-11-01","shift_months":0.0,"ess":51},{"change":"Capability (ECI points) per doubling of R&D uplift: low (8)","stage4_median":"2027-08-02","shift_months":-3.0,"ess":249},{"change":"Capability (ECI points) per doubling of R&D uplift: high (35)","stage4_median":"2030-01-16","shift_months":26.5,"ess":192},{"change":"Software R&D uplift at ECI 170: low (1.4)","stage4_median":"2027-09-17","shift_months":-1.5,"ess":10},{"change":"Software R&D uplift at ECI 170: high (6)","stage4_median":"2027-07-18","shift_months":-3.5,"ess":4},{"change":"Diminishing returns: capability-rate elasticity to uplift: low (0.25)","stage4_median":"2028-02-01","shift_months":3.0,"ess":173},{"change":"Diminishing returns: capability-rate elasticity to uplift: high (0.75)","stage4_median":"2027-09-01","shift_months":-2.0,"ess":273},{"change":"Uplift at stage 3 (Automated Coder): low (4)","stage4_median":"2027-08-17","shift_months":-2.5,"ess":281},{"change":"Uplift at stage 3 (Automated Coder): high (15)","stage4_median":"2028-01-16","shift_months":2.5,"ess":281},{"change":"Ratio of stage 4 to stage 3 uplift (stage 4 ~25x): low (1.8)","stage4_median":"2027-08-17","shift_months":-2.5,"ess":281},{"change":"Ratio of stage 4 to stage 3 uplift (stage 4 ~25x): high (6)","stage4_median":"2028-01-16","shift_months":2.5,"ess":281},{"change":"Lag from uplift to capability (training-run cadence), years: low (0.12)","stage4_median":"2027-11-01","shift_months":0.0,"ess":334},{"change":"Lag from uplift to capability (training-run cadence), years: high (0.6)","stage4_median":"2027-11-16","shift_months":0.5,"ess":232},{"change":"Gap between internal and public frontier, years: low (0.08)","stage4_median":"2027-10-02","shift_months":-1.0,"ess":81},{"change":"Gap between internal and public frontier, years: high (0.5)","stage4_median":"2027-11-01","shift_months":0.0,"ess":14},{"change":"Drop the METR Nov 2026 assessment of Anthropic","stage4_median":"2027-11-01","shift_months":0.0,"ess":343},{"change":"Drop self-reported labour uplift","stage4_median":"2027-09-17","shift_months":-1.5,"ess":142},{"change":"Drop the Anthropic R&D Automation Index","stage4_median":"2028-03-17","shift_months":4.5,"ess":894},{"change":"Drop AI Futures software-uplift estimates","stage4_median":"2027-07-03","shift_months":-4.0,"ess":67},{"change":"Drop METR time horizons","stage4_median":"2027-11-01","shift_months":0.0,"ess":773}],"note":"Each row re-runs the model with one change. Shift is in months versus the baseline median."}},"slowdown_banner":null}