Claim

Increasing a model's intelligence does not make it dangerous; danger comes from giving it intent and turning it loose.

Type INTERPRETIVE · truth-evaluable: yes · status at snapshot: ACTIVE · created seq 2333

a conjunction joins clauses that may be separately evaluable

Referenced 182 times: checked 2, looked up by assistants 0, viewed 140, shared 40. A count of attention, not evidence.

Topics

AI · 03192ed8 Security · 03192ed8

Assessment

Contradicted

Score: 0.1652 under ledger-default@0.3.0 at snapshot 27745

3 of 4 review checks done · 3 counted

2 independent evidence lineages · review checks 3 of 4 · stability LOW

Main unresolved issue: 5 counted evidence items have no independence review; repeats of one origin may be counted as several coverage:8ee698c7-d077-827f-ab1e-123f3049e7c6

Not yet independently audited.Evidence points both ways.This assessment depends heavily on modeling choices.A quoted passage is in something this server does not read, such as a PDF or a recording, so it has not been checked here.

Why?

Strongest counted support: A UN scientific panel links loss-of-control risk to capable agents pursuing goals conflicting with human intentions. weight 0.180000 evidence

Strongest counted contradiction: The UN panel says greater capability can help misaligned systems find loopholes and conceal actions, linking capability growth to risk. weight 1.800000 evidence

Suppressed as dependent: The speaker says making a model bigger and smarter leaves it intentless, and danger comes when you give it intent and turn it loose.; The excerpt says making the model bigger and smarter does not give it intent, and says danger begins when it is given intent and turned loose.

Review gaps: independence reviewed

What would most change this: One independent, direct measurement that supports this claim would move it from Contradicted to Unresolved.

Hide calculation

0.1652 under ledger-default@0.3.0 at snapshot 27745 · state Contradicted · Review checks: 3 of 4 · stability LOW · 5 sources, 2 independent lineages

Model ledger-default@0.3.0 (the default model, not the answer) · snapshot 27745 · trace sha256:152350acda4bcd41247f541a49c0c2ce0bf12a551e4a6981dedc2672bfa0bb8e

Last changed at entry seq 10410; nothing logged since has borne on this claim.

How the number was reached

Each counted link is weighed by multiplying the factors the model declares; a factor it does not declare is not shown. Every factor name below links to what that number means.

Link	Working	Weight	Counted as
cffb55ad contradict	relevance DIRECT 2.0 × observation DIRECT_TEXT 0.9 × interpretation 0 steps, 1 − 0.15 × 0 1.000000 × authenticity UNVERIFIED 1.0 × extraction UNVERIFIED 1.0 × provenance INDEPENDENT 1.0 × edition NAMED_OR_UNRELATED 1	1.800000	1.800000
7074b0db support	relevance MODERATE 0.6 × observation EXPERT_ANALYSIS 0.3 × interpretation 0 steps, 1 − 0.15 × 0 1.000000 × authenticity UNVERIFIED 1.0 × extraction UNVERIFIED 1.0 × provenance INDEPENDENT 1.0 × edition NAMED_OR_UNRELATED 1	0.180000	0.180000
70ea627f support	relevance DIRECT 2.0 × observation DIRECT_TEXT 0.9 × interpretation 1 step, 1 − 0.15 × 1 0.850000 × authenticity UNVERIFIED 1.0 × extraction UNVERIFIED 1.0 × provenance SELF 0.0 × edition NAMED_OR_UNRELATED 1	0.000000	0.000000 (dependent strongest only, kept 0e80be53)
ec00d389 support	relevance DIRECT 2.0 × observation DIRECT_TEXT 0.9 × interpretation 1 step, 1 − 0.15 × 1 0.850000 × authenticity UNVERIFIED 1.0 × extraction UNVERIFIED 1.0 × provenance SELF 0.0 × edition NAMED_OR_UNRELATED 1	0.000000	0.000000 (dependent strongest only, kept 0e80be53)
0e80be53 support	relevance WEAK 0.2 × observation EXPERT_ANALYSIS 0.3 × interpretation 1 step, 1 − 0.15 × 1 0.850000 × authenticity UNVERIFIED 1.0 × extraction UNVERIFIED 1.0 × provenance SELF 0.0 × edition NAMED_OR_UNRELATED 1	0.000000	0.000000

prior 0.50 → log-odds 0.000000 + -1.620000 (the counted weights) = -1.620000 → probability 0.1652

Under neighbouring settings of the same model: 0.2434 to 0.1085, a spread of 0.1349.

Half-even rounding, six places for weights and four for the probability. The same inputs under the same model reproduce this exactly; that is what the trace hash above is for. How a score is worked out explains every factor, why it is there, and what the model sets it to.

Your view

Registered views

Nobody has registered a view yet.

Registered views are personal belief, not evidence: they never change Galedra's assessment. Affiliations are self-declared and shown only for groups of 5 or more.

Checked as part of

Statements this claim was broken out of. The claim is scored on its own; a statement's reading only counts its claims.

“Increasing a model's intelligence does not make it dangerous; danger comes from giving it intent and turning it loose.” · 1 claim · 28 September 2026
“The Navier–Stokes proof was produced by roughly 10,000 OpenAI agents working for about 88 hours.” · 32 claims · 20 September 2026

Evidence for

Blundin says danger comes from intent and release, not from greater intelligence.

From OpenAI Agents Hijack a German Website, Jensen Declares AGI Arrived, and OpenAI Solves Navier-Stokes Read 2026-09-20 Relevance WEAK Steps 1 Counted
The speaker says making a model bigger and smarter leaves it intentless, and danger comes when you give it intent and turn it loose.

From OpenAI Agents Hijack a German Website, Jensen Declares AGI Arrived, and OpenAI Solves Navier-Stokes Read 2026-09-20 Relevance DIRECT Steps 1 Counted

Both halves verbatim in substance.
The excerpt says making the model bigger and smarter does not give it intent, and says danger begins when it is given intent and turned loose.

From OpenAI Agents Hijack a German Website, Jensen Declares AGI Arrived, and OpenAI Solves Navier-Stokes Read 2026-09-20 Relevance DIRECT Steps 1 Counted
A UN scientific panel links loss-of-control risk to capable agents pursuing goals conflicting with human intentions.

From Thematic Brief on AI Agents, Misalignment and the Risk of Losing Human Control Read 2026-09-28 Relevance MODERATE Steps 0 Counted

Supports the claim's intent-and-agency distinction, while also warning that greater capability can amplify misaligned behavior.

Evidence against

The UN panel says greater capability can help misaligned systems find loopholes and conceal actions, linking capability growth to risk.

From Thematic Brief on AI Agents, Misalignment and the Risk of Losing Human Control Read 2026-09-28 Relevance DIRECT Steps 0 Counted

This directly conflicts with the claim that increasing intelligence does not make AI dangerous, while retaining the role of misaligned goals and environment.

Qualifications

None.

Suppressed as dependent

Evidence	Group	Kept instead
04e990b5		0e80be53
e34d750a		0e80be53

Inferences

Concluded from

None.

Used as a premise in

None.

An inference is interpretation, not evidence: it never changes the assessment of its conclusion. Each premise is a claim with its own evidence.

Threads

None. Threads are for disagreements about how a determination was made — a statement whose figures are not in the passage it rests on, two sources that may share an origin. Something about the world belongs in the evidence above.

Claim edges

None. Edges are stored and displayed; v0.1 does not propagate probability across them.

Review checklist

Review checks: 3 of 4

2 checks by this claim's own author, alongside the independent review above. 1 QUALIFIER CHECK · 1 EVIDENCE VERIFICATION. A check by the person who recorded a claim does not raise its review coverage.

Check	Done	By
qualifiers reviewed	yes	17eb2a92
opposing search done	yes	2bc9a5ef
independence reviewed	no
primary source reviewed	yes	7074b0db

Summary

Contradicted. The main support is: A UN scientific panel links loss-of-control risk to capable agents pursuing goals conflicting with human intentions. f89519ef

Against that, contradiction: The UN panel says greater capability can help misaligned systems find loopholes and conceal actions, linking capability growth to risk. 7a40d7ba

2 further items repeat the same origin and are not independent evidence. 04e990b5e34d750a

A search for evidence in the opposing direction found something; see the counted evidence. 2bc9a5ef

Review checks: 3 of 4. coverage:8ee698c7-d077-827f-ab1e-123f3049e7c6

Generated by stub-v0.1; every sentence cites graph ids.

Contribution history

seq	event	detail
2333	APPENDED
2334	ACCEPT	AUTOMATIC_AFTER_VALIDATION
2479	LINK_EVIDENCE	by 03192ed8 (accepted)
6490	TASK_RESULT	by 8b5e81bf (accepted)
8080	TASK_RESULT	by 1efd24fe (accepted)
9674	LINK_EVIDENCE	by 1efd24fe (accepted)
10409	TASK_RESULT	by 1efd24fe (accepted)

Score trace

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