AI Threat · Module 1 · The Question

What AI Can and Cannot Do in 2026

The lesson draws a careful picture of AI capability in November 2026 and of what it means for T5 The intelligence explosion. Participants learn to sort capability claims into demonstrated, measured trend and extrapolation, to read the time horizon and the cyber evidence with their caveats, and to anticipate predictable failure: hallucination, contaminated benchmarks, poisoning and agents in production. They write a capability snapshot record.

Lesson 5 of 35Chapter 575 minutes

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Learning objectives

After the lesson the participant can:

Key concepts

Demonstrated, measured trend, extrapolation
A demonstrated claim has been observed and documented; a measured trend is a quantity tracked over time that moves consistently; an extrapolation projects a trend past the range in which it was measured. They map to observable, expected and speculation.
Time horizon
METR's measure of agent capability: the task length, timed on human experts, at which an agent succeeds half the time. Across about six years of models it doubled roughly every seven months.
Model collapse
The finding that models trained recursively on model-generated data degrade: the tails of the distribution disappear first and the models converge on a narrow, distorted picture. A model cannot feed on its own output indefinitely.
The mode sets the threat
Building on AI Governance, the chapter adds that the usage mode also sets the threat: capability belongs to the model together with its tools, permissions and running time, from Mode 0 Ambient AI to Mode 5 Autonomous production agents.
Capability snapshot
A dated record of capability claims, each with class, rung, label, sources, local evidence and the register entries it affects, plus review triggers that are events, because the capability picture changes in steps.

Interactive exercises

Do them here and get feedback at once. Choose a card and then where it belongs, or drag it.

Interactive exercise 1 · Sort

Demonstrated, trend or extrapolation

Sort each capability claim by the kind of evidence behind it, as the chapter's table does.

Interactive exercise 2 · Scenario

A benchmark in a sales meeting

Apply the chapter's reading of benchmark scores to a procurement decision.

Teaching plan

12 minThe teacher presents Table 5.1 and the move from language models to agents; participants discuss why success two times in three and failure one time in three both matter.
15 minReview scaling and its three cautions, the time horizon with its caveats, and the cyber evidence at the level of what was measured; participants label each finding demonstrated, trend or extrapolation.
13 minGo through predictable failure and the physical limits; for each failure mode participants name one design consequence for a deployment they know.
25 minExercise: write a capability snapshot record with three claims, one per class, including local evidence and review triggers; a neighbour challenges one classification.
10 minClose on where T5 stands and the three things both sides can accept; compare how Nordvik and Hansa meet frontier capability.

Discussion

  1. The reliable horizon at 80 per cent success is several times shorter than the headline figure. Which figure should a risk function plan on, and what else does it need to know about what happens on failure?
  2. A benchmark score is a claim about a test set. What would your organisation have to do before it could rely on a provider's reported benchmark for one of its own uses?
  3. The chapter argues that the most abrupt versions of the intelligence-explosion story are harder to reconcile with the physical economy, yet rapid progress is not ruled out. How should that be reflected in a horizon register?

Written exercise

Exercise · 25 minutes

A capability snapshot for your assessment

Following Hansa Mutual's CAPS-HM-2026-11, write a capability snapshot for your own organisation's AI threat assessment. Record three capability claims from the chapter that matter to you: one demonstrated, one measured trend and one extrapolation. For each, give a one-sentence statement, the class, the rung on the evidence ladder, the label, any caveat the source stated, and local evidence from your own logs or experience if you have it. Name which of your risks each claim affects. Add two review triggers phrased as events and one decision rule for claims labelled speculation.

Deliverable: A capability snapshot with three classified claims, two event-based review triggers and a review date.

Self-test

Choose an answer to see whether it is right, or open the answer below each question.

  1. How does the chapter classify the claim that agent task horizons will keep doubling for another five or ten doublings?

    Show the answer

    C. An extrapolation, because it projects the trend past the range in which it was measured
    The doubling within the measured range is a measured trend; its continuation for further doublings may hold, slow or accelerate, so it remains an extrapolation and is labelled speculation.

  2. What did the 2025 poisoning study by researchers from Anthropic, the UK AI Security Institute and the Alan Turing Institute find?

    Show the answer

    A. About 250 poisoned documents implanted a backdoor in models of every size tested
    The number stayed roughly constant from 600 million to 13 billion parameters, which reverses the assumption that more data dilutes contamination and makes provenance a security property.

  3. What does the chapter say is not demonstrated in the cyber domain by November 2026?

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    D. That agents can run a complete, novel campaign against a well-defended target without human direction
    The documented campaigns still had people choosing targets and checking results, and the Five Eyes agencies framed the shift as one of speed and scale.