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A “meat proxy” is a person who forwards an AI’s answer to a coworker without reading it carefully, checking it, or adding their own judgment. The problem is not using AI; it is making a colleague do the thinking the sender skipped.
What does “meat proxy” mean?
Developer Niklas Gruhn used the label in his August 3, 2026 essay, “Don’t be a meat proxy”. He describes people pasting long, verbatim Claude responses into Slack, code reviews, or group chats. If the recipient could ask the AI directly and supply their own context, an unreviewed relay adds little beyond a human handoff.
The phrase gained wider attention after Simon Willison linked Gruhn’s essay that same day, describing the behavior as blindly copying and pasting AI output to peers. Willison’s post notes that Frank Allenby had used “meat proxy” in a similar essay earlier in March 2026. Gruhn popularized the phrase in this workplace discussion, but the available accounts do not establish him as its definitive originator.
The label is about the sender’s role, not the presence of AI in the work. A person who uses an AI to draft, summarize, or brainstorm—and then reviews and adapts the result—is contributing judgment. A person who forwards the raw answer without understanding it is acting as a relay.
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Why can an unreviewed AI handoff create more work?
The recipient may have to reconstruct the question and context, identify which claims matter, check whether they are accurate, and decide what to do next. That burden follows from the handoff itself; Gruhn’s essay does not quantify its cost.
Gruhn also observes that AI output can be verbose, dense with jargon, or sound plausible while being wrong. In that situation, forwarding a large answer does not transfer useful understanding. It transfers material the recipient must first interpret and validate.
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How can you use AI at work without just forwarding its answer?
- Read the whole response. Do not assume a fluent answer is a sound one.
- Check the important parts. Verify factual claims that affect the decision, and test or inspect any technical recommendation you plan to pass along.
- Filter for the recipient’s need. Remove irrelevant detail and preserve the context they need to act.
- Add your judgment. Explain what you agree with, what remains uncertain, and why the answer matters for this particular task.
- Write the handoff in your own words. Be prepared to explain and stand behind what you send.
Gruhn’s concise prescription is: “Read it, understand it, validate it, and then write a response in your own words.” The goal is not to conceal AI assistance. It is to make sure the human sender contributes review and context rather than serving only as a forwarding channel.
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What should you check before sending AI-generated code?
In Gruhn’s example, a developer pastes a ticket into an AI coding assistant, does not inspect the generated code, and then routes reviewer feedback back to the assistant. The reviewers end up doing the implementation reasoning while the nominal author contributes little beyond transport.
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Before asking a teammate to review AI-assisted code, inspect the change and make sure you can explain it. In particular:
- Understand what each meaningful part of the diff changes and how it addresses the ticket.
- Check that the change behaves as intended, rather than relying on the assistant’s explanation alone.
- Consider the reviewer’s feedback yourself before returning to the coding assistant.
- Be able to describe the final change and answer questions about it.
AI can help produce a first draft of code; it does not take responsibility for the change away from the person submitting it. If you cannot explain what you are asking a reviewer to approve, the work is not ready for a useful handoff.
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