AI generated answers have created a strange new failure mode: you ask a person a question, and the person sends back machine-written text they may not have read. A short Orchid Files post captured that irritation through three small scenes: a malware-reporting problem on GitHub, a bad ChatGPT screenshot at work, and a Reddit exchange that turned out to be an AI agent.
Table of Contents
The short version
- Orchid Files argues that the worst part of AI generated answers is not the model being wrong. It is the human handoff without judgment.
- The GitHub malware example matters because security reports need context, ownership, and a clear path to action.
- The workplace example is more familiar: a coworker forwards a ChatGPT screenshot instead of answering the actual question.
- The Hacker News discussion turned into a broader argument about online trust, fake productivity, and whether human contact is getting rarer.
- For more coverage of AI and developer culture, see the IT & AI archive.
What happened
Orchid Files published “I’m tired of talking to AI” on May 22, 2026. The post is brief, but it lands because the examples are painfully ordinary.
The author says they found GitHub repositories spreading malware and asked an AI system what to do. The answer was not useful. They then opened a GitHub discussion, only to receive a reply that matched the earlier AI answer. After they called it out, the comment disappeared, and another person posted essentially the same AI-generated response.
A second example came from work. The author asked a business owner a question about a task. Instead of answering, the person sent a ChatGPT screenshot. When the author said the response did not answer the question and was wrong, another screenshot arrived almost immediately. The problem was not that ChatGPT existed. The problem was that the human in the loop seemed absent.
The last example came from Reddit. After several messages, the author realized the other side of the conversation was an AI agent. That is the line the post keeps circling: people want to talk to real people, but even real people increasingly route the conversation through AI.
Why this is worth watching
The post is useful because it moves AI fatigue away from the usual benchmark debate. The issue is not whether a model can produce a plausible answer. The issue is whether the person sending that answer understands it, agrees with it, and will stand behind it.
That distinction matters for developer teams. A generated response to a malware report, dependency question, or product requirement can sound polished while skipping the part that actually matters: who checked the facts, who owns the next step, and what context the answer depends on.
It also matters for AI product design. If a tool makes it easier to paste generated text into another person’s workflow, it should also make review and accountability harder to fake. Agent builders, support software teams, and workplace AI vendors should treat that as a product requirement, not a nice extra.
why AI generated answers feel different
AI generated answers feel different because they shift work onto the receiver. A normal bad answer can be challenged directly: the person misunderstood, missed context, or disagreed. A generated answer adds another layer. Now the receiver has to ask whether the sender read it, whether the model invented something, and whether anyone owns the claim.
That is why a screenshot can feel ruder than a short human reply. The screenshot says, in effect, “the machine said this,” while leaving the other person to do the checking. In low-stakes conversations, that is annoying. In security, hiring, customer support, or product planning, it can become expensive.
What Hacker News readers are arguing about
The Hacker News thread was large and messy, with more than 900 comments at the time it was indexed. The useful split was not pro-AI versus anti-AI. It was closer to this: some readers saw the post as evidence that people are outsourcing thought, while others argued that low-quality online content existed long before chatbots.
One recurring argument was that “thinking” may become more valuable, not less, because cheap generated text makes real judgment easier to spot. The skeptical version of that point was harsher: many workplaces already rewarded simulated work, and AI just made the simulation faster.
Another thread focused on detection. Several commenters pushed back on AI-content detector statistics, arguing that detectors produce false positives and often punish style markers rather than authorship. The more practical objection was that detection may be the wrong goal. If generated text can impersonate human communication cheaply, the social problem remains even when detection is unreliable.
There was also a builder/operator angle. Some readers were less upset about AI as a drafting tool than about unreviewed AI in business workflows. A generated note in a private draft is one thing. A generated answer sent as if it were a person’s judgment is another.
The mood was mostly weary, with a streak of gallows humor. People joked about needing to go offline, but the serious worry was trust: once every message might be machine-shaped, even real human messages start to feel suspect.
The practical read
Teams do not need a dramatic AI policy to handle this. They need a small norm: if you send an AI-assisted answer, you own it.
That means reading it before forwarding it, cutting anything you cannot verify, and adding your own judgment in plain language. If you are unsure, say what is uncertain instead of hiding behind a generated paragraph. For technical work, link to the source, issue, documentation, or log that supports the answer.
For product teams building AI assistants, the lesson is just as concrete. The best workflow is not the one that produces the most fluent text. It is the one that makes the human review step visible enough that the recipient can trust the answer.