# Why You Shouldn't Be an AI Output Forwarder The problem with being a 'meat proxy' for generative AI tools - **Source:** [Hacker News (news.ycombinator.com)](https://brifing.si/source/13c923cd-6d99-442b-9b8b-1181f4d8cd91) - **Published:** 2026-08-03 - **Topic:** [Data & AI](https://brifing.si/c/data-ai) - **Original article:** - **On Brifing:** https://brifing.si/s/4221c41c-886d-4760-961b-0f748959a681 ## The Meat Proxy Problem Forwarding raw AI outputs without processing is called being a 'meat proxy'. This term describes people who relay answers from tools like Claude without understanding or refining them. The phenomenon is becoming increasingly common in tech teams, especially during code reviews where developers directly copy-paste AI outputs. ## Drawbacks of AI Outputs AI-generated content tends to be overly verbose and jargon-heavy. Recipients must expend extra effort to understand these outputs. Worse, AI sometimes produces 'plausible-sounding nonsense'. One example mentions needing to look up nearly every word in a single sentence from Claude's output. ## Code Review Example In modern software development, it's possible to paste a ticket description into Claude Code and submit the resulting code without reading it. Review feedback can be processed similarly. But this blurs the lines of who actually implemented the code. The developer becomes just a proxy between reviewers and AI. ## The Value-Add Approach Instead of forwarding AI outputs directly, one should first read, understand and validate them. Then crafting a response in your own words proves you've completed these steps. This extra effort represents genuine value addition. Technical leaders especially should model this behavior. ## The Time-Saving Illusion Many think they save time by sharing AI outputs verbatim. But the time recipients spend deciphering this content is often overlooked. Processing and internalizing AI outputs before sharing actually makes the entire process more efficient in the long run. ## Responsibility and Ownership Directly sharing AI outputs amounts to dodging work responsibility. In code reviews, it becomes hard to stand behind AI-generated solutions. This negatively impacts team dynamics and product quality. Such behavior is increasingly frowned upon in professional settings. ## Communication Quality AI tools sometimes complicate communication rather than facilitating it. Especially in technical team communications, messages filled with AI jargon become harder to understand. Team members should prefer direct, human-to-human communication in plain language. ## AI Best Practices The key to productive AI use lies in treating outputs as starting points. Information from AI needs filtering, validation, and human refinement. This approach enhances communication quality while building trust in professional relationships. ## Sources covering this story - https://gruhn.me/blog/2026-08-03/ — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._