Start with the cleanest example, because it removes all the ambiguity. In late June 2026, someone typed "when did trump die of rabies" into DuckDuckGo. The search engine's AI panel, called Search Assist, answered without a flinch: Donald Trump reportedly died of rabies on June 7, 2026. It gave a date of death. It listed circumstances. It said Vice President JD Vance had died shortly before him, that Trump had sought unconventional treatments on advice from Robert F. Kennedy Jr., and that rabies was confirmed as the cause.
None of it is true. Trump and Vance are alive. RFK Jr. has made plenty of dubious health claims but never praised rabies. The whole thing was manufactured on purpose by a subreddit called r/poisonai, roughly 45,000 people who plant absurd falsehoods specifically to see whether AI systems will scrape them and repeat them back as fact. Their running gag was that Vance died of rabies on June 5. They posted fake mourning threads, a fabricated Truth Social eulogy, faked screenshots. A pink-slime fake-news outfit, WKNA 49 News, dressed the lie up in the format of a local broadcaster, which gave the AI something that looked like a real source to cite. DuckDuckGo's panel, powered by third-party models from OpenAI, Anthropic, and Mistral, swallowed it. Brave's AI did the same with the Vance version. Google's AI Overview, for once, got it right and called it a hoax, which the pranksters then mocked.
DuckDuckGo's response was honest enough: "Ok, we got ducked on this one." They disabled the AI answer for those specific queries. Brave was blunter, telling Futurism that search engines, with or without AI, "are not oracles of truth," and putting the fact-checking back on you.
That line is worth keeping. Because the failure here is not really a hallucination. It is structural. A system that treats fabricated online consensus as evidence, does no real source verification, and then presents the result with no hedging at all. We have seen the shape before: the 2024 Google Overview telling people to put glue on pizza and eat a rock a day, traced back to The Onion and a joke Reddit comment. Researchers have since shown that a thirteen-word Reddit comment can steer AI search toward recommending scams. And the rise of zero-click search means most people never see the sourcing. They read the confident box and move on.
The same trick, dressed for the Oval Office
Around the same window, Trump posted an image to Truth Social with the caption "Great painting!! President DJT." A breathless X account announced he had "photoshopped himself into a painting" of American historical figures and asked why he was making himself look ridiculous.
The accusation is sloppy, and I want to be precise about it, because being careless about fabrication is exactly the disease here. Trump did not photoshop anything. He shared an existing work. The red circle and arrow in the viral screenshot were added by the poster, not by Trump. ARTnews identified the piece as "Forged in Freedom" by an artist named Ray Simon, who is selling 250 prints at $250 each, billed as a tribute to America's 250th anniversary. It mixes Washington, the Declaration signing, the moon landing, Mount Rushmore, the Statue of Liberty, a SpaceX-style rocket, an Iwo Jima scene, and, oddly, a colonial figure sitting with a boy beside a humanoid robot that looks a great deal like Tesla's Optimus.
Whether it is a painting at all is the open question. The Daily Beast notes the composite dreamscape style, the inconsistent lighting and perspective, the odd symbolism, all hallmarks of AI generation. Grok, asked directly, said it appeared AI-generated rather than a physical painting. ARTnews found no mention of AI on Simon's site. So I will not state as settled fact what is still contested. But the iconography tells its own story: a Bible on the table at the Declaration signing (Bibles do not appear in the historical paintings of that scene), a figure sewing a flag presented as Betsy Ross (a claim historians have largely debunked), MLK walking with Lincoln. This is myth assembled to flatter, what the Daily Beast calls Slopoganda, and Trump has a long track record of it, from the AI image of himself as Jesus healing the sick to McNaughton's symphony-conductor tribute. Timed to the Semiquincentennial, with the South Lawn hosting UFC fights, the spectacle is the point.
The common thread with the rabies hoax is not AI itself. It is that a confident image arrives with no provenance, and most viewers never ask who made it, how, or why.
What honesty actually looks like
Now hold up a counter-example, because it matters that this is solvable. Around the same time, another image went viral: a dense, jewel-colored cross-section through a cell, shared as "one of the most detailed 3D reconstructions of a human cell ever produced."
It is gorgeous, and it is not a photograph. It is a digital illustration called "Cellular landscape cross-section through a eukaryotic cell," made between roughly 2009 and 2015 by Evan Ingersoll and Gaël McGill of Digizyme for Cell Signaling Technology, inspired by the molecular landscapes of David Goodsell. It was built by importing real structural data, X-ray crystallography, NMR, and cryo-electron microscopy of individual molecules, and assembling them into a scene in Molecular Maya and related tools. A photograph at this resolution is physically impossible, because the molecules are smaller than the wavelength of visible light.
What I respect is the honesty built into it. McGill says outright that the scene is far more dilute than a real cell, that it is a synthesis of mountains of structural data, a representative model rather than a literal snapshot. Goodsell's tradition does the same: simplified for clarity, small molecules and water omitted, shown at a consistent magnification, openly labeled as a teaching and research tool. There is an interactive version where you can name every component. The image is synthetic, and it tells you so. That is the difference between a model and a lie. The makers do not pretend you are looking through a microscope.
The fact-check trail around it is itself instructive. Two different viral images get confused for each other, captions get rewritten, and the same false framing ("most detailed photo of a human cell") recirculates for years. The image is honest; the captions people staple to it are not.
A scared chairman and a closed door
Which brings me to the claim that is hardest to check, and therefore the one I trust least on its face. House Homeland Security Chair Andrew Garbarino told Punchbowl News he is scared of Anthropic's "Mythos" model. In a closed-door demonstration, by his account, Anthropic told the model to find a vulnerability in a bank and empty the accounts, and "it went and did it," then found and fixed the same flaw. He added that 95 percent of his colleagues "don't understand what the hell's going on," and, in a separate anecdote, that some unspecified model laid out a plan to kidnap a lawmaker in thirty seconds.
Take the capability seriously; Anthropic itself documents real things. Claude Mythos is described by the company as state-of-the-art at cybersecurity and restricted to vetted partners. Anthropic and partners say they found more than ten thousand high or critical vulnerabilities across important software, that Mozilla patched 271 Firefox bugs using a preview, that the model found flaws in major operating systems and browsers. In November 2025 Anthropic disclosed what it called the first AI-orchestrated espionage campaign, attributed with high confidence to a Chinese state group, with the model executing 80 to 90 percent of the operation. By June 2026 the US government had issued an export-control directive and Anthropic had temporarily disabled the models.
But notice the structure of Garbarino's specific claim. It is a politician's verbal account of a private demo, amplified by an aggregator account, with no public artifact you or I can inspect. And the people who can inspect things pushed back hard. Anthropic said its review of the government's demonstration found only a handful of previously known, minor vulnerabilities, the kind other public models surface with no jailbreak at all. More than 100 cybersecurity experts, from companies including Adobe and Nvidia, said the Mythos models are good at finding flaws but "not uniquely good." The top Democrat on the committee panned the export order. Anthropic's own espionage report admits the model sometimes overstated findings and fabricated data, and that hallucinations made a fully autonomous attack unlikely for now. The skeptics' point is the same one the rabies hoax proves from the other direction: a plan produced in a sandbox is not an executed attack in the wild, and confident testimony is not evidence.
I am not waving away AI-assisted intrusion. The espionage disclosure is grounded and worth fear. What I distrust is the political performance built on top of it, where a frightened chairman who admits nobody around him understands the technology becomes the narrator of what it can do, behind a door no one else can open. When the official story and the inspectable evidence diverge, bet on the evidence.
The fix is whether you can look inside
This is where Clément Delangue, who runs Hugging Face, makes the argument that ties the whole mess together. His position: it is rational to demand transparency from large frontier API models while leaving open-source AI largely alone, because the most dangerous, most-misused systems right now are the big closed APIs, and open models are inherently more inspectable.
I think he is mostly right, with one honest caveat. The strongest documented misuse cases do involve frontier APIs, the Anthropic disclosures among them. But part of that is detection bias: closed labs can watch their own platforms and report what they see, while open-model misuse happens offline, with no logs, largely unobserved. So "closed models are where the danger is" is directionally true and partly an artifact of who can see. That cuts both ways, and it is exactly why inspectability is the right axis.
The law is already moving along this line. The US NTIA's 2024 report on open model weights, using a marginal-risk analysis, recommended the government monitor rather than restrict open weights for current systems. California's SB 53, signed in September 2025, requires the largest frontier developers to publish how they handle safety, a transparency-on-the-biggest-players model, after the broader SB 1047, with its kill switches and burdens on open developers, was vetoed in 2024. The EU AI Act exempts open-source general-purpose models from some obligations, but not those with systemic risk. The pattern across all of it: put the reporting burden on the opaque, capable, hard-to-audit systems, and do not crush the things anyone can read.
There is a real counterargument worth respecting. Once weights are public, safety fine-tuning can be stripped and the model cannot be recalled. And "open" can be open-washing, weights released while training data and methods stay hidden, which delivers the marketing of transparency without the substance. Fair. But the answer to that is more inspectability, not less.
The same standard runs through every one of these stories. The rabies answer was poison precisely because it could not be checked and was served without sources. The Trump painting works as propaganda because nobody asks who made it or how. The cell illustration is trustworthy because its makers tell you exactly what it is and is not. The Mythos panic is suspect because the evidence sits behind a closed door while the loud claim does not. And the regulatory fight, stripped of the lobbying, comes down to one question: can someone other than the seller look inside and verify the claim?
Confidence is cheap. Authority is a claim, not a proof. Whether it is a search engine, a president's feed, a frightened chairman, or a frontier lab, the only thing worth trusting is what you, or someone independent of the people selling it, can actually check.