THE INTELLIGENCE HORIZON

The AI horizon, in months.

Follow the months to AGI, ASI and a conditional extinction pathway. See how the forecasts change as new evidence arrives.

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01 / SCENARIO EXPLORER

Choose an assumption set

YOUR SELECTED HORIZONS

Approximate months from today · Nov 8, 2026

01 / AGI
34approx. months remaining

Sep 2029

Central illustration

Artificial general intelligence

0–166 monthsSep 2026–Sep 2040assumption range

A system that reliably matches a competent human across most economically useful cognitive tasks, including unfamiliar multi-step work with limited supervision. Not one benchmark and not necessarily embodied intelligence.

02 / ASI
96approx. months remaining

Nov 2034

Central illustration

Artificial superintelligence

36–336 monthsNov 2029–Nov 2054assumption range

A system substantially beyond the best human experts across nearly all cognitive domains, including scientific research and strategic planning. Narrow superhuman performance does not qualify.

03 / Extinction pathway
185approx. months remaining

Apr 2042

If this pathway occurs

Conditional extinction

65–701 monthsApr 2032–Apr 2085assumption range

Human extinction substantially caused by AI. Dates are conditional illustrations of an ASI-enabled loss-of-control pathway, assuming extinction occurs. Other pathways could occur before ASI; survival indefinitely is also possible.

Reference case. Fast progress continues, but reliability, infrastructure and deployment constraints still matter.

Months remaining are rounded to the nearest whole month. Calculations retain fractional months; forecast history shows changes to one decimal place.

About these scenario dates

Illustrative scenarios—not calibrated forecasts. The dates start from editor-selected assumptions. Evidence can move each horizon independently. The final milestone assumes a catastrophic pathway occurs; humanity may survive indefinitely.

Inspect the model and its limits

A range of futures, not a deadline

Ranges are not statistical confidence intervals
202620582090
AGI
Sep 2029
ASI
Nov 2034
Extinction pathway
Apr 2042

AGI does not imply ASI. ASI does not imply extinction. These are separate, conditional transitions.

LAST COLLECTIONNov 7, 2026, 04:51 PM UTC
EVIDENCE COVERAGE92 reviewed / 240 sources
EXTINCTION PROBABILITYNot estimated
02 / THE FORECAST RECORD

Is the horizon moving?

Earlier or later, over time.
Reference case · saved predictions

Change in predicted date
MONTHS LATER ↑AGI · Sep 2029 latest
MONTHS SOONER ↓90 saved observations

Zero is the first saved forecast in this period. Down means sooner; up means later. Values are shown to the nearest tenth of a month. Passing time alone does not move the line.

View saved forecasts as a table
Reference case: actual saved runs, shown in Eastern time
SavedAGIASIConditional extinction
Nov 7, 2026, 11:51 AM ESTSep 202912.0 months soonerNov 2034No changeApr 20422.8 months later
Nov 6, 2026, 9:50 PM ESTOct 202911.3 months soonerNov 2034No changeApr 20422.8 months later
Nov 6, 2026, 1:00 PM ESTSep 202911.8 months soonerNov 2034No changeMar 20422.5 months later
Nov 5, 2026, 10:27 PM ESTSep 202912.0 months soonerNov 2034No changeFeb 20421.5 months later
Nov 5, 2026, 1:46 PM ESTSep 202911.8 months soonerNov 2034No changeFeb 20421.3 months later
Nov 4, 2026, 10:30 PM ESTOct 202911.3 months soonerNov 2034No changeFeb 20421.3 months later
Nov 4, 2026, 1:39 PM ESTOct 202911.3 months soonerNov 2034No changeFeb 20421.3 months later
Nov 3, 2026, 10:01 PM ESTOct 202911.3 months soonerNov 2034No changeFeb 20421.3 months later
Nov 3, 2026, 1:27 PM ESTOct 202911.3 months soonerNov 2034No changeFeb 20421.3 months later
Nov 2, 2026, 10:56 PM ESTOct 202911.3 months soonerNov 2034No changeFeb 20421.3 months later
Nov 2, 2026, 3:47 PM ESTSep 202911.8 months soonerNov 2034No changeFeb 20421.3 months later
Nov 1, 2026, 9:33 PM ESTSep 202912.3 months soonerNov 20340.3 months soonerFeb 20421.3 months later
Nov 1, 2026, 12:08 PM ESTSep 202912.3 months soonerNov 20340.3 months soonerFeb 20421.3 months later
Oct 31, 2026, 9:14 PM EDTAug 202912.8 months soonerNov 20340.3 months soonerFeb 20421.3 months later
Oct 31, 2026, 11:45 AM EDTSep 202912.3 months soonerNov 20340.3 months soonerFeb 20421.3 months later
Oct 30, 2026, 9:37 PM EDTSep 202912.3 months soonerNov 20340.3 months soonerFeb 20421.3 months later
Oct 30, 2026, 12:42 PM EDTAug 202912.8 months soonerNov 20340.3 months soonerFeb 20421.3 months later
Oct 29, 2026, 9:58 PM EDTAug 202913.0 months soonerNov 20340.3 months soonerFeb 20421.3 months later
Oct 29, 2026, 12:56 PM EDTAug 202913.3 months soonerNov 20340.3 months soonerFeb 20421.5 months later
Oct 28, 2026, 9:49 PM EDTAug 202913.3 months soonerNov 2034No changeFeb 20421.3 months later
Oct 28, 2026, 12:40 PM EDTJul 202913.5 months soonerNov 20340.3 months laterFeb 20421.3 months later
Oct 27, 2026, 9:25 PM EDTJul 202913.5 months soonerNov 20340.3 months laterFeb 20421.3 months later
Oct 27, 2026, 11:53 AM EDTJul 202913.5 months soonerNov 20340.3 months laterFeb 20421.3 months later
Oct 26, 2026, 9:29 PM EDTAug 202913.0 months soonerNov 20340.3 months laterFeb 20421.3 months later
Oct 26, 2026, 12:48 PM EDTAug 202912.5 months soonerNov 2034No changeFeb 20421.3 months later
Oct 25, 2026, 8:24 PM EDTSep 202912.0 months soonerNov 20340.3 months soonerFeb 20421.3 months later
Oct 25, 2026, 10:24 AM EDTSep 202911.5 months soonerNov 20340.3 months soonerFeb 20421.3 months later
Oct 24, 2026, 8:21 PM EDTOct 202911.3 months soonerOct 20340.5 months soonerFeb 20421.3 months later
Oct 24, 2026, 10:01 AM EDTOct 202911.3 months soonerOct 20340.5 months soonerFeb 20421.3 months later
Oct 23, 2026, 8:44 PM EDTSep 202911.8 months soonerOct 20340.5 months soonerFeb 20421.3 months later
Oct 23, 2026, 11:07 AM EDTNov 202910.0 months soonerOct 20340.5 months soonerFeb 20421.3 months later
Oct 22, 2026, 8:42 PM EDTNov 202910.0 months soonerOct 20340.5 months soonerFeb 20421.3 months later
Oct 22, 2026, 11:19 AM EDTNov 20299.5 months soonerOct 20340.5 months soonerFeb 20421.0 month later
Oct 21, 2026, 6:46 PM EDTDec 20298.8 months soonerOct 20340.5 months soonerFeb 20421.0 month later
Oct 21, 2026, 6:48 AM EDTDec 20299.0 months soonerOct 20340.5 months soonerFeb 20420.8 months later
Oct 20, 2026, 6:45 PM EDTDec 20299.0 months soonerOct 20340.5 months soonerFeb 20420.8 months later
Oct 20, 2026, 6:46 AM EDTDec 20299.3 months soonerOct 20340.8 months soonerJan 20420.5 months later
Oct 19, 2026, 6:44 PM EDTDec 20299.3 months soonerOct 20340.8 months soonerJan 20420.5 months later
Oct 19, 2026, 6:48 AM EDTDec 20299.3 months soonerOct 20340.8 months soonerJan 20420.5 months later
Oct 18, 2026, 6:40 PM EDTDec 20299.0 months soonerOct 20341.0 month soonerJan 20420.5 months later
Oct 18, 2026, 6:43 AM EDTDec 20299.0 months soonerOct 20341.0 month soonerJan 20420.5 months later
Oct 17, 2026, 6:39 PM EDTDec 20299.0 months soonerOct 20341.0 month soonerJan 20420.5 months later
Oct 17, 2026, 6:43 AM EDTDec 20299.0 months soonerOct 20341.3 months soonerJan 20420.5 months later
Oct 16, 2026, 6:45 PM EDTDec 20299.0 months soonerOct 20341.3 months soonerJan 20420.5 months later
Oct 16, 2026, 6:48 AM EDTDec 20299.0 months soonerOct 20341.3 months soonerJan 20420.5 months later
Oct 15, 2026, 6:44 PM EDTJan 20308.3 months soonerOct 20341.3 months soonerJan 20420.5 months later
Oct 15, 2026, 7:12 AM EDTJan 20308.3 months soonerOct 20341.3 months soonerJan 20420.5 months later
Oct 15, 2026, 6:49 AM EDTDec 20298.8 months soonerOct 20341.3 months soonerJan 20420.5 months later
Oct 14, 2026, 6:44 PM EDTFeb 20306.8 months soonerOct 20341.0 month soonerJan 20420.5 months later
Oct 14, 2026, 6:48 AM EDTFeb 20306.5 months soonerOct 20341.0 month soonerJan 20420.5 months later
Oct 13, 2026, 6:45 PM EDTFeb 20306.5 months soonerOct 20341.0 month soonerJan 20420.5 months later
Oct 13, 2026, 6:37 AM EDTMar 20306.0 months soonerOct 20341.0 month soonerJan 20420.5 months later
Oct 12, 2026, 6:32 PM EDTApr 20305.3 months soonerOct 20341.0 month soonerJan 20420.5 months later
Oct 12, 2026, 6:37 AM EDTMar 20305.8 months soonerOct 20341.0 month soonerJan 20420.5 months later
Oct 11, 2026, 6:31 PM EDTFeb 20306.5 months soonerOct 20341.0 month soonerJan 20420.5 months later
Oct 11, 2026, 6:31 AM EDTFeb 20306.5 months soonerOct 20341.0 month soonerJan 20420.5 months later
Oct 10, 2026, 6:30 PM EDTMar 20306.0 months soonerOct 20341.0 month soonerJan 20420.5 months later
Oct 10, 2026, 6:30 AM EDTFeb 20306.5 months soonerOct 20341.0 month soonerJan 20420.5 months later
Oct 9, 2026, 6:32 PM EDTFeb 20306.5 months soonerSep 20341.5 months soonerJan 20420.5 months later
Oct 9, 2026, 6:34 AM EDTFeb 20306.5 months soonerSep 20342.0 months soonerJan 20420.5 months later
Oct 8, 2026, 6:32 PM EDTMar 20305.8 months soonerSep 20342.0 months soonerJan 20420.5 months later
Oct 8, 2026, 6:35 AM EDTApr 20305.3 months soonerSep 20342.0 months soonerJan 20420.5 months later
Oct 7, 2026, 6:30 PM EDTApr 20305.0 months soonerSep 20342.0 months soonerJan 20420.5 months later
Oct 7, 2026, 6:33 AM EDTMay 20304.3 months soonerSep 20342.0 months soonerJan 20420.5 months later
Oct 6, 2026, 6:31 PM EDTMay 20304.3 months soonerSep 20342.0 months soonerJan 20420.5 months later
Oct 6, 2026, 6:32 AM EDTMay 20303.5 months soonerSep 20342.0 months soonerJan 20420.5 months later
Oct 5, 2026, 6:30 PM EDTJun 20302.8 months soonerSep 20341.8 months soonerJan 20420.5 months later
Oct 5, 2026, 6:34 AM EDTJun 20302.5 months soonerSep 20341.8 months soonerJan 20420.5 months later
Oct 4, 2026, 6:30 PM EDTJun 20302.5 months soonerSep 20341.8 months soonerJan 20420.3 months later
Oct 4, 2026, 8:46 AM EDTJun 20303.0 months soonerSep 20341.8 months soonerJan 20420.3 months later
Oct 3, 2026, 7:47 PM EDTJun 20303.0 months soonerSep 20341.8 months soonerJan 20420.3 months later
Oct 3, 2026, 10:40 AM EDTMay 20303.5 months soonerSep 20341.8 months soonerJan 20420.3 months later
Oct 3, 2026, 8:55 AM EDTJul 20301.5 months soonerOct 20340.8 months soonerJan 20420.3 months later
Oct 2, 2026, 7:29 PM EDTJul 20301.5 months soonerOct 20340.8 months soonerJan 20420.3 months later
Oct 2, 2026, 7:32 AM EDTJul 20301.8 months soonerOct 20340.8 months soonerJan 20420.3 months later
Oct 1, 2026, 7:30 PM EDTJul 20302.3 months soonerOct 20340.8 months soonerJan 20420.3 months later
Oct 1, 2026, 7:33 AM EDTJul 20302.3 months soonerOct 20340.8 months soonerJan 20420.3 months later
Sep 30, 2026, 7:30 PM EDTJun 20302.8 months soonerOct 20340.8 months soonerJan 2042No change
Sep 30, 2026, 7:31 AM EDTJul 20302.0 months soonerOct 20340.5 months soonerJan 2042No change
Sep 29, 2026, 7:29 PM EDTJul 20302.3 months soonerNov 20340.3 months soonerJan 2042No change
Sep 29, 2026, 7:31 AM EDTJul 20302.3 months soonerNov 20340.3 months soonerJan 2042No change
Sep 28, 2026, 7:30 PM EDTMay 20303.8 months soonerOct 20340.5 months soonerJan 2042No change
Sep 28, 2026, 7:34 AM EDTMay 20303.8 months soonerOct 20340.5 months soonerJan 2042No change
Sep 27, 2026, 7:28 PM EDTJun 20302.8 months soonerOct 20340.5 months soonerJan 2042No change
Sep 27, 2026, 1:29 PM EDTJul 20302.0 months soonerOct 20340.5 months soonerJan 2042No change
Sep 27, 2026, 11:58 AM EDTJul 20301.5 months soonerOct 20340.5 months soonerJan 2042No change
Sep 27, 2026, 7:29 AM EDTAug 20300.8 months soonerNov 20340.3 months soonerJan 2042No change
Sep 26, 2026, 7:28 PM EDTSep 20300.3 months soonerNov 2034No changeJan 2042No change
Sep 26, 2026, 7:29 AM EDTSep 2030No changeNov 2034No changeJan 2042No change
Sep 25, 2026, 7:28 PM EDTSep 2030No changeNov 2034No changeJan 2042No change
Up to 90 saved runs · about 45 days at two runs a dayExtinction dates remain conditional on the pathway occurring.
03 / THE EVIDENCE DESK

What’s shaping the outlook

Original sources, visible limitations.
Capability gains and protective signals both count.

240 results

Showing a mix of publishers, with the newest items first within each publisher. Search by publisher or story to narrow the results.

arXivControl

A Behavioural Framework for Predicting and Understanding Loss of Control in Frontier Artificial Intelligence Systems

Preprint proposing a behavioural framework that decomposes loss-of-control into three factors, operationalises them via measurable tasks, and reports a strong correlation (r=0.84) between its LoC score and failure rates; fine-tuning and monitoring reduced failures while preserving performance.

Limitation This is an arXiv preprint with in-paper experiments; broader independent replication and real-world adoption are not shown in the excerpt. Effects depend on uptake by builders and regulators.

Why it matters & timeline effect

The paper presents a concrete, testable framework plus empirical validation across 13 frontier models and claims targeted mitigations that lower observed failure rates. As a prospectively useful diagnostic and mitigation tool, it plausibly enables developers and regulators to detect and address loss-of-control before deployment, modestly slowing risky rollouts. Because this is a single preprint with in-paper experiments rather than broad independent adoption, medium confidence is appropriate.

Artificial general intelligence · medium confidence

Improved prospective diagnostics and targeted mitigations could encourage safer predeployment practices, plausibly producing a small delay in risky AGI deployments as issues are fixed or evaluated.

Raw proposal: +1 mo. After confidence weighting: +0.25 mo.

Artificial superintelligence · low confidence

The framework targets loss-of-control assessment and mitigation rather than core capability acceleration or research automation, so it does not reliably shift ASI timing.

Raw proposal: 0 mo. After confidence weighting: 0 mo.

Conditional extinction · medium confidence

By improving prediction and mitigation of loss-of-control before large-scale deployment, the framework could meaningfully reduce near-term extinction-pathway pressure from agent misuse, at least temporarily.

Raw proposal: +2 mo. After confidence weighting: +0.5 mo.

Negative means an earlier date; positive means later. Each horizon is assessed separately. These are proposals, not a record of the exact change applied.

Included in a bounded batch. Opposing evidence, duplicate events, movement limits and milestone order can reduce or cancel a proposal. Changing one horizon does not automatically move the others.

Overall review confidence: medium. This describes the evidence assessment, not forecast accuracy or an event probability.

“decomposes loss-of-control risk into three factors: misaligned motive, harm-enabling capability”
Source summary or abstract; the full article was not evaluated.

arXiv and the listed authors. CC0-1.0. Summary and analysis by AI Doom Clock; source text may be shortened. No publisher endorsement.

Epoch AICapability

What Huawei’s Horinger data center reveals about China’s compute gap

News report claims Huawei’s Horinger data center (Ascend chips) has estimated AI compute roughly 7% of SpaceX’s Colossus 2, highlighting a sizable national compute-capacity gap.

Limitation Single news-source claim about estimated capacity; the excerpt is a high-level figure without supporting measurement methodology or verification of ongoing utilization.

Why it matters & timeline effect

A large disparity in deployed datacenter compute reduces a country's near-term capacity to train or run the very largest models. That can slow regional ability to produce top-tier models and thus modestly delay aggregate global advancement if alternative sources aren't available.

Artificial general intelligence · medium confidence

Reduced local compute capacity plausibly slows ability to train frontier-scale models in that region, producing a small delaying effect on overall AGI progress if global supply/transfer are constrained.

Raw proposal: +1 mo. After confidence weighting: +0.25 mo.

Artificial superintelligence · low confidence

A single data-center capacity comparison does not address the research automation or capability steps required for ASI.

Raw proposal: 0 mo. After confidence weighting: 0 mo.

Conditional extinction · low confidence

No mechanism linking this compute-gap claim to civilization-scale loss of control is presented.

Raw proposal: 0 mo. After confidence weighting: 0 mo.

Negative means an earlier date; positive means later. Each horizon is assessed separately. These are proposals, not a record of the exact change applied.

Included in a bounded batch. Opposing evidence, duplicate events, movement limits and milestone order can reduce or cancel a proposal. Changing one horizon does not automatically move the others.

Overall review confidence: medium. This describes the evidence assessment, not forecast accuracy or an event probability.

“uses Ascend chips and has an estimated AI computing capacity that is just 7% that of the world’s largest”
Source summary or abstract; the full article was not evaluated.

Epoch AI · Ben Cottier. CC-BY-4.0. Summary and analysis by AI Doom Clock; source text may be shortened. No publisher endorsement.

UK science & technologyAwaiting review

Research: Artificial Intelligence sector study 2025

Newly collected source. No analytical claim or forecast effect has been assigned. Open the original to inspect the evidence.

Review status Feed excerpt too short to review

© Crown copyright; source: GOV.UK / Department for Science, Innovation and Technology. OGL-3.0. Summary and analysis by AI Doom Clock; source text may be shortened. No publisher endorsement.

International AI Safety ReportControl

Capability alone does not establish a loss-of-control forecast

The multinational synthesis separates dangerous capability, harmful behavior and enabling deployment conditions. Likelihood and timing of future loss of control remain uncertain.

Limitation This assessment is dated February 2026. It cannot certify the safety of models released afterward or supply a calibrated extinction date.

Why it matters & timeline effect

Context for the scenario assumptions, not a measured conversion into years or extinction probability.

Baseline context only. No numerical date adjustment was assigned to this source.

© Crown owned 2026. Y. Bengio et al., International AI Safety Report 2026 (DSIT 2026/001).. OGL-3.0. Summary and analysis by AI Doom Clock; source text may be shortened. No publisher endorsement.

arXivCapability

Researcher forecasts are much wider than a single countdown

A 2023 survey of 2,778 AI researchers gave an aggregate 50% date of 2047 for unaided machines outperforming people in every possible task, conditional on undisrupted science.

Limitation The survey question is stricter than this app’s AGI definition. It is 2023 opinion data, not a fresh 2026 forecast. Publication revisions do not update the survey respondents.

Why it matters & timeline effect

Context for the scenario assumptions, not a measured conversion into years or extinction probability.

Baseline context only. No numerical date adjustment was assigned to this source.

arXiv and the listed authors. CC0-1.0. Summary and analysis by AI Doom Clock; source text may be shortened. No publisher endorsement.

arXivResilience

Define the milestone before dating it

This framework separates breadth, performance and autonomy. Broadly superhuman performance is different from a model exceeding people on a narrow benchmark.

Limitation The framework is not a universal standard. Its historical model examples must not be read as September 2026 classifications.

Why it matters & timeline effect

Context for the scenario assumptions, not a measured conversion into years or extinction probability.

Baseline context only. No numerical date adjustment was assigned to this source.

arXiv and the listed authors. CC0-1.0. Summary and analysis by AI Doom Clock; source text may be shortened. No publisher endorsement.

04 / NO BLACK BOX

Uncertainty is
part of the model.

A useful warning system should show its assumptions, not hide them behind a precise-looking countdown.

Experimental scenario model · v3.0
Where do the dates come from?

The original reference dates are AGI in 2031, ASI in 2035 and conditional extinction in 2042. Fast takeoff starts at 2028, 2030 and 2033; slower progress at 2040, 2050 and 2065. These are editorial assumptions, not expert consensus, probabilities or statistical confidence intervals.

The independent model preserves the dates already reached when it is enabled, then applies separate evidence adjustments to each horizon. A change to AGI alone leaves ASI and conditional extinction unchanged. The illustrated pathway retains at least one month between milestones; a conflicting proposal is limited without moving its neighbors.

What can move the clock?

Twice-daily collection gathers allowlisted RSS feeds. An optional model reviews up to 24 excerpts per run with source quotations and limitations. Review is not independent fact-checking. Applying proposals requires automatic application to be enabled, recent dated evidence and a new event.

Each article gets a separate timing assessment for AGI, ASI and conditional extinction. High-confidence proposals carry full weight; medium-confidence proposals carry one-quarter weight; low-confidence proposals have no numerical effect. An article can affect one, several or none of the horizons. A cyber incident alone does not establish extinction timing.

Opposing proposals are combined. Each horizon is limited to two months per run and five years from its preserved starting date. Fractional-month changes can accumulate. Publisher and event limits reduce repeat counting. These are engineering choices, not statistically calibrated evidence weights.

How do the months and history chart work?

Months remaining are approximate calendar months from today, rounded to the nearest whole month on the forecast cards. Calculations keep fractional months, and history shows changes to one decimal place. The displayed precision does not imply forecast certainty. Whole-year assumptions use January of that year. They are scenario illustrations, not event deadlines. A countdown reaching zero does not establish that an event occurred.

The history chart measures how far each predicted date moved from the first saved forecast in the selected period. An unchanged forecast stays flat as time passes. Each point comes from a saved collection run; the chart does not invent earlier predictions. Switching scenarios recalculates the chart for that assumption set.

Why isn’t there a risk percentage?

No validated method converts current news into a numerical probability of human extinction. The previous version’s percentage and fixed countdown were unsupported and have been removed. A faster timeline is not the same thing as a higher probability of extinction.

What’s missing?

RSS coverage is incomplete; feeds may lag or fail. Automated analysis sees excerpts, not full articles, and can misinterpret them or miss events. arXiv papers are not necessarily peer reviewed. Developer benchmarks need independent corroboration. Unforeseen breakthroughs, plateaus, policy changes and non-ASI pathways are outside this simple model. No finite range excludes “never.”

05 / OPERATIONAL TRANSPARENCY

Behind the updates

No silent fallback to “live.”
Collection, analysis and storage have separate states.

Source health

Feeds are checked by the scheduled collector, not by visits to this page. Collection and AI review are separate steps; short excerpts may be listed without qualifying for review.

3 of 5 feeds returned items at the last collection. 8 retained articles were reviewed in the latest analysis.

arXiv · AI Source read; no eligible recent items found.
empty
arXiv · AI security Source read; no eligible recent items found.
empty
Epoch AI 9 licensed items collected; 4 have sufficient summary text. Review slots are shared across publishers.
ok
UK AI Security Institute 2 licensed items collected; 0 have sufficient summary text. Review slots are shared across publishers.
ok
UK science & technology 1 licensed items collected; 0 have sufficient summary text. Review slots are shared across publishers.
ok

Update record

Persistent storage
Connected
Last attempt
Nov 7, 2026, 04:51 PM UTC
Analysis state
updated
Last review
Nov 7, 2026, 04:51 PM UTC
Review model
gpt-5-mini

Scheduled collection: 7:17 a.m. and 7:17 p.m. Eastern, once the production scheduler is connected. Runs can be delayed. Up to 24 eligible articles are reviewed per run, rotating between publishers with a maximum of eight each. Recent AI incidents and evaluations receive reading priority, without an automatic forecast adjustment. An open page checks for a saved snapshot every five minutes.

Recent collection history

  1. updated · 0 new

    Each horizon is assessed independently. Moderate-confidence timing evidence receives reduced weight; duplicate events do not repeat. Changes are bounded and may be zero.

    Independent date changes: AGI -15.75 mo / ASI -1.75 mo / conditional extinction +3 mo
  2. updated · 1 new

    Each horizon is assessed independently. Moderate-confidence timing evidence receives reduced weight; duplicate events do not repeat. Changes are bounded and may be zero.

    Independent date changes: AGI -15 mo / ASI -1.75 mo / conditional extinction +3 mo
  3. updated · 43 new

    Each horizon is assessed independently. Moderate-confidence timing evidence receives reduced weight; duplicate events do not repeat. Changes are bounded and may be zero.

    Independent date changes: AGI -15.5 mo / ASI -1.75 mo / conditional extinction +2.75 mo
  4. updated · 0 new

    Each horizon is assessed independently. Moderate-confidence timing evidence receives reduced weight; duplicate events do not repeat. Changes are bounded and may be zero.

    Independent date changes: AGI -15.75 mo / ASI -1.75 mo / conditional extinction +1.75 mo
  5. updated · 52 new

    Each horizon is assessed independently. Moderate-confidence timing evidence receives reduced weight; duplicate events do not repeat. Changes are bounded and may be zero.

    Independent date changes: AGI -15.5 mo / ASI -1.75 mo / conditional extinction +1.5 mo