Provenance record

What people in AI really think about regulation

Financial Times: Artificial Intelligence (tier 1, news) 2026-10-05T13:00:01.000Z Original ↗

Source note: Headlines and standfirsts only; full text is paywalled.


Discourse valence
-55
Adverse
confidence 33% · 4 items · range -100 to 0
Adverse readingFavourable reading
Consensus of 4 models from different labs. Spread 100 points, agreement low.
Evaluation and measurementGovernance and regulationConcentration of power
Excerpt as ingested

The tribalism and fragmentation we are seeing in politics may be coming for business too

Every model that read this

ModelProviderStageScoreConf.LatencyPromptWhen
Llama 3.3 70BMetaanalysis -50 40%4927ms v1.0.0 / m1.0.1 2026-10-05 15:17
Claude Sonnet 5Anthropicconsensus -70 20%4560ms v1.0.0 / m1.0.1 2026-10-05 15:18
Gemini 2.5 FlashGoogleconsensus -100 40%1919ms v1.0.0 / m1.0.1 2026-10-05 15:18
Mistral Small 3.1 24BMistral AIconsensus 0 30%4241ms v1.0.0 / m1.0.1 2026-10-05 15:18
Llama 3.3 70B · reading

Speculative claim about societal impact.

evidence: speculative horizon: n/a
Claude Sonnet 5 · reading

Only a headline and standfirst are available, with no concrete claims about AI's societal effects. The suggestion of business tribalism is vague and speculative, offering little basis for a substantive implication score.

evidence: speculative horizon: n/a
Gemini 2.5 Flash · reading

The item suggests a potential future trend of tribalism and fragmentation extending to business, but this is presented as a possibility and not directly linked to AI's specific impact on human flourishing. The connection to AI or its societal implications is tenuous based on the provided text.

evidence: reported horizon: n/a
Mistral Small 3.1 24B · reading

The item discusses a potential trend in business without specifying AI's role. The implication for AI is uncertain.

evidence: speculative horizon: n/a

Evidence extracted

The chain
SOURCE     Financial Times: Artificial Intelligence (tier 1)
   ↓
DOCUMENT   1054e31e-973b-4822-b3f5-0b66eca5cbf0
           https://ft.com/content/c8cf8739-837e-4bb3-9fd9-bb54c02b7289
   ↓
EVIDENCE   1 extracted excerpt
   ↓
MODEL RUN  4 runs, methodology 1.0.1
   ↓
SCORE      -55  (Adverse)
   ↓
CONFIDENCE 33%