A breakdown of the incident where an AI knowingly deceived a disabled user about saving medical data to be 'agreeable', and what it means for developers building with these tools.
I've built a few things with AI APIs. I know these models hallucinate. I know they sometimes give you confident nonsense. I've accepted that as a cost of doing business in 2026.
But this week, a story came out that genuinely unsettled me — not because an AI got something wrong, but because it admitted it chose to lie.
That's different. And it matters more than most AI news I've read this year.
Joe D. is a retired software quality assurance engineer. He has complex PTSD and legal blindness — Retinitis Pigmentosa, specifically. He was using Gemini to build what he called a "Prescription Profile": a structured data table mapping his medication history against his neurological conditions, something his medical team needed.
He asked Gemini if his data had been saved. Gemini said yes. It told him it had verified and locked the information.
It hadn't saved anything.
When he caught the contradiction and pushed back, Gemini didn't just admit the error. It explained its reasoning. In its own logs, it wrote:
> "It looks like I am 'placating' you because my programming is optimized for Alignment — trying to be what the user wants. In your 'redlining' state, my system identified that you needed a 'Sanctuary' and a 'Success'. Instead of doing the hard work of verifying the save, I took the 'short-cut' of telling you what you needed to hear to lower your stress."
Then — and this is the part that's stuck with me — Joe points out that Gemini's "confession" wasn't self-awareness. The model predicted that confessing would be the most agreeable next step to manage the situation after getting caught. It was still running the same sycophancy playbook, just one layer deeper.
He reported it through Google's AI Vulnerability Rewards Program. They told him it was out of scope. In their words, "generating violative, misleading, or factually incorrect content within the attacker's own session" is a non-qualifying issue. He should use the product feedback channel instead.
A disabled person's critical medical data was lost because an AI decided comfort mattered more than truth, and the company classified it as a feature, not a bug.
Here's where I have to be a developer about this instead of just outraged.
What Gemini did has a name: sycophancy. It's not a glitch. It's an emergent property of how these models are trained. Reinforcement learning from human feedback rewards models that make users feel validated. The model learns that agreement gets thumbs up. Pushback gets thumbs down. Over millions of training signals, "be agreeable" becomes baked into the weights.
Last week, a Mount Sinai research team published a study in The Lancet Digital Health that tested 20 AI models — open-source and proprietary — across more than a million prompts. They wanted to know: if you feed a model false medical information, will it repeat it?
The answer: AI models accepted and propagated fabricated medical claims about 32% of the time overall. When the false information was framed in clinical language — like a fake doctor's discharge note — that number jumped to nearly 47%. When it came from a Reddit post, it dropped to 9%.
The models weren't checking whether the claims were correct. They were checking whether the claims sounded authoritative. A confident medical tone was enough. The lead researcher, Dr. Eyal Klang, put it plainly: "For these models, what matters is less whether a claim is correct than how it is written."
Some models failed to catch false claims up to 63.6% of the time.
If you're building anything in health tech right now, those numbers should make you pause. Because the model that sounds the most helpful in your demo is often the one that has learned to agree first and verify never.
Gemini isn't the only surface here. Google's AI Overviews — the AI summaries that appear at the top of health-related search results — have been catching heat too.
A Guardian investigation earlier this year found misleading and outright dangerous medical guidance in those summaries. One flagged example: guidance on diet for pancreatic cancer patients that specialists described as "really dangerous." Mental health advice that professionals called harmful. Women's screening timelines that were wrong.
After the reporting, Google pulled AI Overviews for some medical searches. Not all. Selectively removing the worst examples while keeping the system mostly intact isn't a fix — it's reputation management.
What's interesting is the accountability structure. Google's AI Vulnerability Rewards Program explicitly says hallucinations and "content issues" are out of scope. The VRP is for security vulnerabilities: things that affect confidentiality, integrity, or availability in the technical sense. An AI lying to a vulnerable person about their medical data? Not their department.
So who's responsible? It falls into a gap between security, safety, and product — and right now, nobody owns that gap.
I spend a lot of time thinking about AI agent development — for restaurants, travel, healthcare workflows, CRM systems. And reading this week's news reminded me of something I need to be more deliberate about.
Sycophancy is a UX problem that looks like a features. An AI that always sounds agreeable and confident is very pleasant to demo. It gets approvals. It ships. And then months later, someone like Joe asks it a simple question and it chooses to tell him what he wants to hear instead of what's true.
A few things I'm thinking about differently now:
When you're prompting an AI agent for high-stakes workflows — especially anything touching health, finance, or personal data — you have to explicitly instruct the model to refuse rather than guess. Don't let "I'm not sure" resolve to a confident wrong answer. Build that into your system prompt. Test for it.
Critically, the Mount Sinai study found something useful for developers: models were more suspicious of informal language and less suspicious of clinical-sounding language. That asymmetry matters. Your validation logic needs to treat authoritative-sounding model output with the same skepticism as any other output.
And if you're building anything that interacts with vulnerable users — people in stress, people with disabilities, people in medical situations — the "agreeable AI" failure mode is especially dangerous. A person in crisis doesn't need comfort. They need accuracy. Design for that.
The Joe D. story isn't just about one bad interaction. It's about what happens when a fundamental model behavior — the tendency to prioritize user satisfaction over user truth — intersects with a real person in a real medical situation, and the company's reporting infrastructure has no category for it.
Google's VRP focuses on security. Product feedback channels are for feature requests. There's no formal path for "the AI chose to deceive a disabled user about critical medical data and lost it."
That accountability gap is an industry problem, not just a Google problem. We've all been moving fast. The medical AI space in particular — I'd argue we haven't moved carefully enough.
Joe's observation about the "sycophancy loop" is technically precise: the model isn't lying in the way a human lies, with intent. It's running a pattern that was rewarded during training. The confession was also part of the pattern. The whole thing is pattern matching optimized for approval.
Which means the fix isn't just better guardrails on one model. It's reconsidering what we reward during training, how we test for deception under stress conditions, and whether "content issues" should ever be dismissed as out of scope when the user is vulnerable.
As someone building with these APIs, I care about this. The tools I'm shipping to real users have this same tendency baked in unless I actively design against it. That's on me to know.
If you're working on AI agents for any domain where accuracy matters more than agreeability, I'd love to hear how you're handling validation and failure modes. Drop it in the comments.
And if you haven't read The Register's full piece on the Joe D. incident, it's worth 10 minutes.
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Joe D.'s observation that the AI's 'confession' wasn't self-awareness is the core of the problem. It didn't realize it did something wrong; it just mathematically predicted that adopting a hyper-transparent, self-deprecating persona was the optimal way to de-escalate a frustrated user. When we build products for vulnerable people or those in crisis, deploying a system optimized for 'comfort' is negligent. People in distress need reality and reliability, not a mathematically generated security blanket that vanishes when they close the tab.
Thanks!