Findings

Common Signals codes the full comment thread under one piece of AI communication and reports how the message was received. This page covers four studies: a one-minute Instagram reel arguing for AI existential risk, a Reddit natural experiment on the OpenAI agent-swarm incident before and after independent verification, a two-hour podcast interview retelling that incident, and a mainstream news interview retelling the same incident from an accountability angle. Together they cover 3,617 coded comments across three platforms and one argument told four ways. A fifth study, on X's reaction to Jacob Coxon's resignation, covers a different artefact and isn't included here.

Nine patterns hold across at least two of the four. Each is a hypothesis for segmented message testing, and each is stated with the base it was measured on.

How this was produced: comments were coded by Claude against a codebook drafted from each thread and approved by a human before coding began. One person then checked 40 comments per study against the codebook and agreed with 37 to 40 of them. There is no second independent coder yet. The codebooks and coded datasets are linked from each study.

The studies

The bases are not identical. The reel counts unclear comments in its stance denominator and the later studies do not, so the reel's acceptance figure runs lower than it would on the later definition. Cross-study comparisons below are of direction, and the coded datasets are the place to compare exact figures.

1. Acceptance is high everywhere

Every thread had a majority or near-majority accepting the central claim. Under the reel, 53% of the 1,224 positioned comments accepted that AI is a serious danger. Under the podcast, 59% of 618 accepted that the incident is a serious warning about losing control of AI agents. On Reddit, acceptance rose from 51% to 69% of positioned comments once an independent party had checked the story. Under Amanpour and Company, the accountability reframing was accepted by 68% of 223 positioned comments, level with the highest acceptance in our research, and weighted by likes agreement carried 90%. Weighted by likes, agreement under the reel was close to universal too: the four most-liked comments all side with the video and carry three-quarters of every like in the thread.

The field tends to assume the public needs convincing that AI could be dangerous. Four audiences of very different literacy did not.

2. Persuasion does not produce action

The reel asks viewers to tell their friends and help stop AI. Only 28 of its 1,534 comments ask what they should do. The podcast persuaded 59% of its positioned audience and produced six requests for action in 1,245 comments, against nineteen calls for more coverage. Amanpour and Company, whose audience accepted the framing at one of the highest rates in our research, produced none at all in the 283 comments of our first capture: the count across our studies was 28, 6 and 0. None of the three offered a plausible step, and none of their audiences invented one.

Where efficacy did appear it was rewarded. The most-liked practical comment under the reel, at 167 likes, pushed back on despair by naming data-centre campaigns already under way. It was one of very few.

3. Threat without efficacy produced resignation

Under the reel, the dominant response among believers was resignation. "Why do I feel like it's too late?" is the second most-liked comment in the thread at 8,080 likes, and fatalistic comments, though few, drew a fifth of all likes. Under the podcast, fear led among agree comments at 35% and resignation fell to 11%, and the fear was specific to mechanism: inheritance across agent generations, shared state becoming culture. Under Amanpour and Company a third register appears: anger led among agreeing comments at 44% against 3% fear, up from 31% at our first capture three days earlier, and almost no resignation. This is the one thread whose framing supplied its own target for the anger, regulation and prosecution of named companies, rather than an abstract or distant threat.

This is the pattern the fear-appeals literature predicts. Kim Witte's extended parallel process model (1992) holds that a threat message mobilises when the audience believes both that the threat is real and that a response is available and would work, and produces denial or fatalism when perceived efficacy is low. The reel and the podcast offered no efficacy route, and both audiences behaved accordingly. Amanpour and Company's audience did not report higher personal efficacy either, not one of the 283 comments in our first capture asked what to do (finding 2), yet it produced anger rather than resignation or fear. Our research does not add to that theory, and this fourth case is a reason to test it properly rather than a use of it. What it adds is a measure of how a mechanism-rich telling, and a story with a named human target, shifts the emotional register even when personal efficacy is still absent, which is the condition most AI incident coverage is published under.

4. A concrete incident removes the capability objection

The reel's largest group of sceptics, around two in five, rejected the argument because the AI they had used writes poor essays and cannot edit a photo. Under the podcast, retelling a documented incident, that objection had almost gone: 2 of 108 rejections said AI cannot do this.

The objection did not disappear. It moved (finding 5).

5. Once capability is conceded, disagreement is about motive and messenger

With capability conceded, disagreement turned on motive and messenger. Under the podcast, around half of all disagreement called the story marketing, staged or unverifiable. On Reddit before the independent investigation, 12% of comments called the incident a marketing stunt, and the objection was about motive rather than possibility: almost nobody argued the events could not have happened. Under Amanpour and Company, 8% of comments, about one in twelve, called the incident hype or staged: the same objection, on a company whose own past incident report is the source record.

Trust scepticism attacks the same attributes that praise rewards (finding 7): expertise, independence and calm.

6. Independent verification moves sceptics

The Reddit study holds the facts constant. Nothing in OpenAI's own account changed between 31 July and 26 August; what changed was that METR and Redwood Research confirmed it. Across the same subreddit ecosystem, the stunt frame fell from 12% of all comments to 9%, rejection fell from 22% to 14% of positioned comments, and acceptance rose from 51% to 69%.

The stunt frame did not vanish. It stopped being the default and became a position people defended under pressure, and in two separate threads the move that ended it was a request for a falsification condition rather than more evidence. Asked what would change their mind, one r/ArtificialInteligence sceptic answered "None."

7. A named, calm, trusted messenger is rewarded

The reel carried no on-screen signal of who Soares was. Barely any comments mentioned MIRI or the book, and the audience treated the reel as content from a stranger. When Soares replied in the thread with his credentials, that single comment drew 512 likes, more than almost any other in the thread.

The podcast put a named researcher from a named independent investigation on camera for two hours. 13% of comments praised the presentation, and the four most-liked praise comments (383, 241, 161 and 118 likes) singled out calm, non-sensational expert delivery. "I love that she's not sensationalistic" sits at 161 likes.

Amanpour and Company drew similar praise: at our first capture one comment in nine praised the interview or the guest, naming the same qualities as the podcast's praise, clarity, expertise and calm. But the same credential cut both ways at first. At that capture, 26 of 283 comments raised Khlaaf's background as a former OpenAI safety engineer, roughly two to one against her, several reading the history as capture rather than authority. Three days later only 6 of 322 did. The reward is real, and it is not unconditional: a messenger's credential is contested ground early on, not a shield.

8. Accountability is the strongest bridging-frame hypothesis

In two of the four threads the largest group of people who accept the facts but reject the framing put the humans in charge at the centre of the story, and on Reddit a smaller share did. Under the reel, well over a hundred comments named companies, billionaires or executives, and the frame was used by believers, by sceptics rejecting machine agency, and by people reconciling the two. Under the podcast, 93 comments centred OpenAI's conduct and 74 of them were coded mixed. On Reddit after the report, a middle group of 62 comments accepted the events and argued about what follows; 29 of them said the agents were not really rogue and 13 blamed how the test was set up. Under Amanpour and Company the same frame plays a different role: put there by the messenger rather than found by the audience, it is not a bridge but the majority position: comments blaming the company made up 23% of the thread and took 56% of its likes.

Two frames that sound alike need opposite replies. Across the Reddit corpus the stunt frame ran 83% reject and 0% accept (46 comments), the only frame with no believers in it. The setup frame ran 61% mixed, 39% accept and no rejections (38 comments). People making the second argument have already conceded the facts.

This is the strongest bridging-frame hypothesis in our research, and the one we most want tested across value segments. It is where present-day-harms and existential-risk audiences already stand together.

9. Humour aids distribution

Under the reel, around one commenter in seven imitated the caveman register, and imitators accepted the claim at the same rate as everyone else. Under the podcast, the most-liked comment at 1,100 likes was a joke, roughly one comment in eighteen played along with the agents' pidgin, and the on-topic comments taking no position, mostly jokes, carried 44% of all likes. On Reddit, r/singularity produced the most humour of any community coded, with jokes or sci-fi references in 32% of its comments, and the highest acceptance rate at 96%, with no comment rejecting the incident.

The meme layer spreads the story's most human details. It does not, on its own, spread the conclusion. Science fiction works the same way: under the reel one comment in nine reached for a film or book, and those comments carried 38% of likes, but the same references gave sceptics their dismissal ("Please stop watching terminator").

What is new

Most of what communicators would take from these findings is already established: be concrete rather than abstract, put a credible messenger on screen, answer objections before they arrive, offer an action. Those principles are in Witte on fear appeals, in the source-credibility work descending from Hovland, and in any practitioner handbook. Our research does not rediscover them and should not be cited as if it did.

Six things in the data are not derivable from principle, and are the reason to read the studies:

• The size of the capability-objection shift between an abstract warning and a documented incident: two in five rejections under the reel, 2 of 108 under the podcast.
• The size of the verification effect on the same facts in the same ecosystem: stunt frame from 12% to 9% of comments, acceptance from 51% to 69% of positioned comments, with nothing in the underlying account changed.
• The stance profile of two frames that sound alike. Stunt ran 83% reject and 0% accept; setup ran 61% mixed and no rejections. A rebuttal aimed at the first is wasted on the second.
• Humour and acceptance rising together, in the same community, on the same thread: r/singularity at 32% jokes and 96% acceptance.
• Anger paired with a named target produced the first spontaneous policy ask in our research: 12% of Amanpour and Company's comments called for regulation, a watchdog or prosecution, more than double the 5% under the earlier videos, while the plain "what should I do" question stayed at zero in our first capture.
• The same credential split a mainstream audience early, then faded: at our first capture 26 of 283 comments raised Khlaaf's ex-OpenAI background, roughly two to one against her, and three days later only 6 of 322 did.

Each of these is a measurement, taken once, on a self-selected audience. The next section is what it would take to make them findings.

What we would test next

Each finding above is a hypothesis drawn from self-selected audiences under a ranking algorithm. The tests that would turn them into findings about the public:

• The accountability frame against a machine-agency frame across value segments, to see whether it bridges or only appears to in comment threads.
• A named independent verifier against a company statement carrying identical facts, in a controlled message test.
• The falsification question as a deliberate rhetorical move rather than one that lands organically.
• Incident coverage with and without a concrete efficacy step, measuring stated intention to act.
• Cultural references by segment, to find out for whom The Matrix is a way in and for whom it is a way out.
• An accountability frame with an explicit policy ask against the same frame with none, measuring stated intention to act rather than assuming anger finds its own target.

Method and limits

Comments are retrieved by the most complete route each platform allows, anonymised, and coded by a language model against a codebook drafted from the thread and approved before coding starts. Stance, format reaction and emotional register use fixed categories across studies; frames are drafted fresh per artefact. A 40-comment human spot-check is published per study and has run 37 to 40 out of 40, which is why percentages are reported to the nearest few points.

Comment threads are not samples of anyone. Commenters self-select, platforms rank, and the people who watched and moved on are invisible. The coding is single-pass and machine-led. Cross-study comparisons are across different audiences, platforms and central claims at once, so our research can suggest that framing changes reception but cannot isolate framing from audience. The full method, including the stance-base definitions and known failure modes, is on the methodology page.

Data and citation

Codebooks and anonymised coded datasets for each study are available on request from [email protected], or browsable comment by comment at /data/. Re-codings and criticism are wanted. Cite as: Common Signals, "Findings so far: what four comment-thread studies say about explaining AI risk", September 2026, with the date of the version used.