AGI Zeitgeist Tracker: kickoff plan

Note added 2026-09-30, later the same day: the method sections of this plan (data model, coding scheme, sampling rule, panel rule, public track) are superseded by docs/methodology-v2.md after two outside reviews. Reasons for each change are in docs/amendments.md. This file is kept as the record of where the project started.

Date: 2026-09-30 Status: plan agreed in conversation, nothing collected yet Repo: ~/Code/agi-zeitgeist (new, standalone) Related: Sentient Horizons essay to follow once the seed phase passes its check

What this is

A dated, sourced record of how expert and public opinion about artificial general intelligence (AGI, meaning AI that can do most intellectual work a person can) has moved from 2005 to the present, built so the movement can be shown in charts and described in writing. Two outputs come from one dataset:

1. A citable dataset other people can check and extend: a repo with plain CSV files, a written method, and a log of what was searched.
2. A public Sentient Horizons essay with charts, using the dataset as its evidence.

The dataset comes first. The essay is written only from what the dataset can support.

The claim we want to be able to test

my working impression: the central public question has moved in three steps. Around 2005 to 2015 it was whether AGI is possible at all. Around 2016 to 2022 it was when it will arrive. Since roughly 2023 it has become whether the current model is already capable of improving itself (recursive self-improvement, meaning an AI making the next, better AI with little human help), and even cautious thinkers now treat AGI as reachable.

This is a hypothesis, not a conclusion. The method below is designed so the data could contradict it. If the record shows the shift was smaller, earlier, or confined to one group, the essay says that.

Data model

Three tables, all plain CSV so anyone can open them.

speakers.csv (one row per person or source)

statements.csv (one row per public statement)

aggregates.csv (one row per survey result, market reading or poll)

Coding scheme

question_frame

Which question is the speaker actually answering? One value per statement.

A statement can touch several; code the one it leads with, and note others in notes. The frame distribution by year is the main evidence for the "whether, when, now" claim.

stance

How the speaker is positioned toward the prospect, independent of their timeline.

Stance and timeline are kept separate on purpose: two people can both say "2030" and mean very different things.

Coding rules

• Code what the speaker said, not what they are known for. A LeCun statement that gives a date gets a date.
• When a speaker gives both a date and a hedge, record both: the date in the timeline fields, the hedge in timeline_text.
• Secondhand reports ("Hinton reportedly believes...") get coding_confidence: low and a note, and are replaced by the primary source when found.
• Every statement needs a working link or a full citation to a book, paper or recorded talk.

Sampling rule (the defense against cherry-picking)

The failure mode is collecting the quotes that fit the story. The rule:

1. For each panel member, search year by year from their first documented statement to the present, using a fixed set of queries recorded in data/search_log.csv (speaker, year, query, where searched, what was found, date searched).
2. Collect every statement that gives a timeline or answers the possibility, capability_now or self_improvement question. Do not skip ones that cut against the pattern.
3. Where a speaker made more than ten qualifying statements in one year, keep all that give a timeline, and sample the rest at a fixed interval (every third by date), noting the sampling in the search log.
4. Statements found incidentally while searching for someone else are still recorded, flagged incidental in notes, so coverage stays auditable.
5. Panel membership is frozen before collection starts. Adding or removing someone later requires a dated amendment in docs/amendments.md saying why.

Panel inclusion rule

Core panel: a named individual with sustained public commentary on whether or when AGI arrives, with at least one documented statement on or before 2017 and at least one in 2024 or later, prominent enough that their view shaped public discussion (not just expert circles). Target 15 to 20 people, balanced across optimists, skeptics and safety-focused thinkers so the panel does not lean one way by construction.

2020s cohort: people who became central after 2020 and have no early record. Tracked separately so their arrival is itself visible in the data rather than smeared into the core series.

Candidate panel (to confirm and freeze before collection)

Core candidates, pending verification that each clears the 2017 and 2024 bar:

Ray Kurzweil, Nick Bostrom, Eliezer Yudkowsky, Robin Hanson, Geoffrey Hinton, Yann LeCun, Yoshua Bengio, Demis Hassabis, Shane Legg, Sam Altman, Gary Marcus, Andrew Ng, Stuart Russell, Rodney Brooks, Elon Musk, Ilya Sutskever, François Chollet.

Verify specifically: Chollet and Sutskever may not have a public timeline statement before 2018; if not, they move to the 2020s cohort.

2020s cohort candidates: Dario Amodei (unless a pre-2018 statement is found), Leopold Aschenbrenner, Daniel Kokotajlo, Ajeya Cotra, Jensen Huang, Mustafa Suleyman.

Note on balance: the core list above has more people who expect AGI than who doubt it. Before freezing, look for one or two more sustained skeptics with a pre-2017 record (candidates: Melanie Mitchell, Ernest Davis, Hubert Dreyfus's successors in the philosophical critique, Oren Etzioni).

Aggregate anchors (verify every figure at collection; these are from memory and may be off)

• Müller and Bostrom expert survey, run 2012 to 2013, published 2014: median 50% year for high-level machine intelligence around 2040.
• AI Impacts surveys of machine learning researchers: 2016 (median 50% year about 2061), 2022 (about 2060), 2023 (about 2047). The 2023 drop is the single most cited aggregate shift.
• Metaculus "when will the first general AI system be devised" question: community median over time, retrievable as a series.
• Manifold and other markets where a history is available.
• Epoch AI compute and capability trend reports, as context rather than opinion.
• Public opinion: Pew Research and YouGov polls on AI, Monmouth, Ipsos. Record the exact question wording, since "worried about AI" is not "expects AGI".
• Attention measures: Google Trends for "AGI", "artificial general intelligence", "singularity", "superintelligence", yearly from 2005.
• Institutional moments: the 2015 Future of Life open letter, the March 2023 pause letter, the May 2023 Center for AI Safety one-sentence statement, government statements (UK AI Safety Summit 2023, US executive order 2023). These are dated events rather than opinions, kept in a separate events.csv for chart annotations.

Seed phase (first unit of work)

Scope:

1. Repo skeleton, the three CSVs with headers, a small script that checks every row has valid codes and a source, and docs/methodology.md built from this plan.
2. All aggregate anchors above, verified and entered.
3. Five core panel members traced in full, chosen to include a constant optimist, a mind-changer, a skeptic, a critic, and a lab leader: Ray Kurzweil, Geoffrey Hinton, Yann LeCun, Gary Marcus, Sam Altman.
4. A first rough chart of question_frame by year from the seed data, to see whether the codes separate.

Pass check before scaling to the full panel: the five traces produce at least 60 statements with working sources; coding two of the five independently (I and an agent) agrees on question_frame and stance at least 80% of the time; and the codes are not all landing in one bucket. If agreement is low, the codebook gets revised and the five are recoded before anyone else is collected.

Visualizations (built after the seed, finalized after full collection)

1. Which question was being argued, by year: a stacked area chart of question_frame shares, 2005 to 2026, with institutional events marked on the time axis. This is the direct picture of the "whether, when, now" claim.
2. Where each person put the date, over time: one line per core panel member, x is the date they spoke, y is the year they predicted, so a line sloping down shows someone pulling their estimate closer. Aggregate survey medians drawn on the same axes for comparison.
3. Stance distribution by year: a small-multiples or ridge chart showing the mix of dismissive through resigned shifting.
4. Public attention versus expert timelines: Google Trends and poll series against the aggregate medians, to see which leads.

Chart conventions follow the dataviz skill when built; keep them readable on a phone since the essay will be read there.

Essay plan (Sentient Horizons, after the pass check)

Working angle: a personal record I wish I had kept, now reconstructed from the public one, told through the three questions and the people who moved between them. Lead with the strongest single picture (likely chart 1 or 2), state what the data does and does not show, name the people who changed their minds and the ones who did not, and be explicit about the panel's limits. Essay conventions: dates in month day, year form; plain words over technical ones; the dataset linked at the end as the thing to go check.

Repo layout

agi-zeitgeist/
  README.md             what this is, how to cite, how to add a row
  data/
    speakers.csv
    statements.csv
    aggregates.csv
    events.csv
    search_log.csv
  docs/
    2026-09-30-kickoff.md   this document
    methodology.md          the frozen method, written from this plan
    codebook.md             the coding scheme with worked examples
    amendments.md           dated changes to panel or codes
  scripts/
    validate.py           checks codes, dates and sources on every row
    charts.py             builds the four charts from the CSVs
  site/                   static page for the charts, later

Public track (added 2026-09-30, after the expert track was handed off)

Purpose: capture what the general public, not the named experts, was saying and assuming about AI, AGI, superintelligence (ASI) and self-improving AI, and how that moved, using only records anyone can re-pull. No personal recollection goes in the data; I ruled that out so the record stands on its own.

Every source below was probed on 2026-09-30 from a shell and returned data unless marked otherwise. Each pull is saved as a dated CSV in data/public/ with the exact query in data/public/pulls.csv (source, query, date range, retrieval date, script), so a reader can re-run it.

Layer 1: attention (how much people were looking)

Layer 2: the public forum (what ordinary enthusiasts were actually writing)

The Arctic Shift archive holds r/singularity in full from 2008-03 to the present (about 127,000 posts and 3.8 million comments, confirmed) and r/artificial likewise. Hacker News has a public search API with full text from 2007. LessWrong has a public API. From these:

• posts and comments per month per community
• monthly share of comments containing each term in a fixed vocabulary list (singularity, AGI, ASI, superintelligence, takeoff, recursive self-improvement, alignment, doom, p(doom), timelines, "feel the AGI", "AI slop"), frozen before the pull
• a coded sample: the 20 highest-scoring r/singularity posts per quarter, coded with the same question_frame and stance scheme as the experts, so the two tracks share one axis

Stated limitation: Reddit and Hacker News are young, male, technical and English-speaking. They are a proxy for the engaged public, not the public. The polls in layer 3 are the check on that.

Layer 3: default assumptions (polls with exact wording)

Pew Research "more concerned than excited about AI" series, 2021 to 2025 (37% in 2021, 52% in 2023, around half since); Monmouth 2015 versus 2023 on whether AI is a threat; YouGov repeated items on AI ending humanity and on when AI becomes smarter than people (2023 onward); Ipsos global AI monitor 2021 onward; Gallup 2024 onward. Each row carries the exact question wording and sample size, because "worried about AI" is not "expects AGI".

Layer 4: the mainstream argument (what an op-ed assumed)

Media Cloud (free account, open archive from about 2010) gives article counts over time across a fixed collection of US and UK national outlets. From the same query, a coded sample: 15 opinion pieces per year, chosen by a fixed rule (highest social-share count where available, otherwise first 15 by date), coded with question_frame and stance. This is the layer most likely to show the "relax, it can't" to "what now" turn I describe.

Layer 5: vocabulary ledger

One row per term or phrase: first appearance in Ngram, first appearance in a national outlet (with link), first month it exceeded 1% of r/singularity comments. Includes slang, because slang arriving in a national outlet is a clean marker of a concept going public.

Charts the public track adds

5. Public attention against expert timelines: Wikipedia views and poll series on one axis, survey medians on the other, with the same event annotations as chart 1.
6. Forum question-frame share by quarter beside expert question-frame share by year, to show which moved first.

Outside review before collection

The public-track method is less settled than the expert track, so before anything is pulled it goes to two or three other frontier models for critique and alternative approaches. The review prompt is docs/reviews/public-track-review-prompt.md; each reply is saved verbatim as docs/reviews/public-track-review-<model>-2026-10.md. Changes adopted from the reviews are logged in docs/amendments.md with the reason, so the method's history is visible.

Going forward

scripts/pull_public.py re-pulls layers 1 and 2 and appends a dated row, so the next five years are recorded as they happen rather than reconstructed. Run monthly from an agent session.

Open decisions for me

1. Confirm or edit the candidate panel before it is frozen (especially the skeptic balance).
2. Whether to include Elon Musk: very prominent, long record, but his statements are hard to code as considered predictions. Suggested: include, flag frontier_lab_tie: yes.
3. Whether I code one or two of the seed speakers myself for the agreement check, or whether two separate agent passes stand in for that.
4. License for the dataset (suggested: CC BY 4.0 for data, MIT for scripts).

Status log

• 2026-09-30: plan written in Cowork after agreeing scope (standalone repo, fixed panel, seed before scaling). Repo folder created at ~/Code/agi-zeitgeist/docs/. Next: Claude Agents session builds the skeleton and runs the seed phase.

Rendered from docs/2026-09-30-kickoff.md at version a7bd17a.