Current observation

How busy AI news was this week, against the month before.

Near baseline

▼ 17 vs last week

Tracked news activity is near its recent baseline.

Exploratory news activity, not a measure of AI progress or personal anxiety. Historical readings are reconstructed retrospectively.

Last 89 dayspeak 67 · Sep 7

Aug 18dashed line = reference (50)Nov 15

Every week since Nov 5 →

Signals in this reading

last 7 days vs the 28 before

News velocity×0.3132 vs 149/wk▼11%
38
Lab activity×0.2530 vs 22/wk▲40%
77
Community attention×0.2535 vs 44/wk▼20%
30
Topic spike×0.2Products · 21 vs 13/wk▲68%
40
Employment mentionsexperimental0 vs 2/wk▼100%
6

Illustrative headlines

The formula is open — argue with it fomo.json Report (PDF)

Overall FOMO Index

How busy AI news is now, against when tracking began in Oct 2022.

High activity

3.4× the 2022–23 level

This week reads near its recent baseline, and that baseline itself runs at 3.4× the 2022–23 level.

News velocity 2.6×
Lab activity 5.3×
Community attention 3.0×

How we got here

Weekly activity since Oct 2022, with milestones for context. Milestones are an editorial selection, not inputs to the index.

1×2×3×3.4×

’23’24’25’26

2022–23 average (1×)milestone, by publisherperiod

Trailing 13 weeks vs the first tracked year; weighted geometric mean of the three counting signals, scored so that 1× = 50 and 4× = 80. Growth here includes changes in how much the sources publish, not only in AI itself.

Research notes Method, sensitivity & source coverage

Can the changing volume and attention around AI be described with a transparent, reproducible index? This study compares a week of collected news with its recent baseline. It measures activity in selected sources, not intelligence, impact, or how far anyone is falling behind.

Corpus records: 15,555
Source streams: 20
Daily reconstructions: 1,472
Data observed through: 15 Nov 2026

Sensitivity check

How much does the method matter?

Recompute this observation with equal weights, omit each signal in turn, then omit each eligible source from both current and reference windows.

These specified alternatives produce scores from 36 to 52. The range is a sensitivity diagnostic, not a confidence interval. It does not test every modeling choice.

SpecificationScore / 100

Published weights

Equal signal weights

Without news velocity

Without lab activity

Without community attention

Without topic spike

Inspect source exclusions

Without Agility Robotics46
Without Anthropic46
Without Black Forest Labs46
Without Boston Dynamics46
Without DeepMind41
Without DeepSeek45
Without Figure46
Without Google AI37
Without Hacker News52
Without Hugging Face47
Without Midjourney46
Without Mistral48
Without OpenAI52
Without Pika48
Without Qwen46
Without Runway46
Without Stability AI46
Without Suno46
Without Wayve46
Without World Labs46

The remaining corpus is re-baselined for each exclusion. Removing a source changes the population being described.

The observed corpus

A lens with visible limits.

Hacker News accounts for 79% of stored records. The corpus favors English-language, developer-facing coverage. Publisher posts, community attention and technical progress are different things.

132 of 132 records in the current window belong to eligible sources. Eligibility uses declared coverage dates; it is not proof of complete collection. Historical points use retrospectively collected data.

Inspect the underlying headlines →

Source composition · full corpus and current window
Source	All records	7 days
Hacker News	12,313	83
OpenAI	1,144	19
Hugging Face	822	11
Anthropic	283	1
DeepMind	208	6
Stability AI	115	0
Midjourney	96	0
Mistral	89	0
Boston Dynamics	73	1
Agility Robotics	71	1
Google AI	71	7
Suno	56	1
Qwen	44	0
Wayve	40	0
Black Forest Labs	31	0
Runway	25	1
Figure	24	0
World Labs	19	0
DeepSeek	16	1
Pika	15	0

Semantic annotations

Model annotations · Not human-validated

Beyond counting.
Testing semantic judgments.

Jev / System One annotates whether a headline concerns AI and whether it reports a concrete change. Each decision retains its probability distribution, model version and input fingerprint.

15,555 current corpus records have model annotations in this snapshot. 6 carry provider-consistency flags. These annotations have zero index weight.

Headline judgments cannot establish real-world impact. Human annotation, held-out evaluation and calibration checks come before any proposal to change the index.

Read the experimental protocol →

Reproduce & contribute

A study you can inspect.

Method v1.1.0 · exploratory, not independently validated. Contributions can challenge source selection, audit relevance, propose a rubric or compare alternative specifications.

Contribute to the research →

Methodology Research snapshot ↗Index JSON ↗Observation report (PDF) ↗

Dataset SHA-256 · data/index.json

a8da2c8b3535020d5b410f14a2b1f9e5973190a9a4812ea593be9ddda3d8f470

For citation, record the method version, observation date and repository commit. The snapshot includes fingerprints of data and implementation files. No DOI or peer-review status is claimed.

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