Research / Watchlist checked October 2026

Research behind the fiction.

The history behind the novel, and the sources we are watching now. Documented fact, inference and speculation are kept separate.

The history behind the fiction · By Don Calvin Dhabolt

1956 → 2059: The Conjecture

The proposal

On August 31, 1955, four researchers put their names to a short proposal. John McCarthy was a young mathematician at Dartmouth College. Marvin Minsky was at Harvard, Nathaniel Rochester at IBM, and Claude Shannon at Bell Telephone Laboratories. They asked the Rockefeller Foundation for $13,500 to bring a small group of scientists to Hanover, New Hampshire, for the summer of 1956.

The second sentence of that proposal is the one that lasted:

“The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”

The field they were proposing needed a name. McCarthy gave it one: artificial intelligence.

The summer

They asked for a two-month, ten-man study. What happened was looser. According to Ray Solomonoff’s notes, the workshop ran for roughly eight weeks, from about June 18 to August 17, 1956. Only Solomonoff, Minsky and McCarthy stayed the whole time. Others came for days or weeks, among them Oliver Selfridge, Trenchard More, Arthur Samuel, Allen Newell and Herbert Simon. Newell and Simon brought the Logic Theorist, a program that proved theorems from Whitehead and Russell’s Principia Mathematica.

No single breakthrough came out of that summer. A field came out of it: a name, a community, and a confident list of problems. The proposal hoped machines could be made to “use language, form abstractions and concepts, solve kinds of problems now reserved for humans, and improve themselves.”

Seventy years

The summer became seventy years. Early optimism gave way to funding collapses in the 1970s and again in the late 1980s, the “AI winters.” Expert systems came and went. Statistical machine learning took their place, then deep learning after 2012, then the transformer in 2017 and the large language models that reached the public in 2022.

Read that 1955 list again. Machines now use language, form abstractions and handle many problems once reserved for humans, in ways the authors would recognize. The fourth item, machines that improve themselves, is now the subject of serious research and serious concern.

This month Dartmouth marks the anniversary with AI @70: What Must Remain Human?, held in Hanover on October 29–31, 2026. The Rockefeller Foundation, which funded the original summer, is supporting it again.

2059

2059: The Comfortable Cage is set 103 years after that summer. Its AI, AURA, does not rebel and does not rule. It keeps medicine, transit, power and money running so well that people stop asking whether it can think, and start asking what must remain human. That is the question Dartmouth has put at the center of its anniversary.

In the novel, the field’s centennial is marked at Dartmouth in 2056. That speech is fiction. The summer of 1956 is not.


Read the source: the original 1955 proposal, from John McCarthy’s archive at Stanford.

2059: The Comfortable Cage is an independent work of fiction. It is not affiliated with, sponsored by, or endorsed by Dartmouth College, the Rockefeller Foundation, or any organization mentioned.

Editorial rule: documented fact, inference, and speculation remain separate. A source appearing here does not mean its claims are adopted as fact beyond what the source itself establishes.

Current primary-source watchlist

Dartmouth - AI at 70: What Must Remain Human?

Official 2026 conference framing around human judgment, responsibility, and accountability in an AI-saturated world.

METR - Task-Completion Time Horizons of Frontier AI Models

Current measurements of how long and complex a task frontier AI agents can complete at defined reliability levels. The page was updated in 2026 and supersedes earlier 2025 snapshots.

Anthropic - Alignment Research

2026 work includes scalable oversight, automated alignment research, disempowerment patterns, and updated investigations of alignment failures.

Yann LeCun (NYU, AMI Labs) - JEPA World Models

A leading alternative to scaling language models: AI that learns how the physical world behaves and plans by predicting in an abstract representation space, rather than by generating words or pixels. 2026 work includes a small world model trained end-to-end from raw pixels on a single GPU, and a proof of the conditions under which such a model recovers the true structure of the world. The same proof finds those conditions can fail when training data comes from goal-directed behavior. The 2026 papers were released as arXiv preprints.

Research papers in revision

The long-form research series behind the novel is being revised and fact-checked, and will appear here in 2027. Until then, the history above and the further reading list are the best place to start.

Kindle · November 5, 2026 · paperback to follow

By 2059, artificial intelligence doesn't rule the world. It simply keeps it running.

Preorder on Kindle · $4.99