The Etiquette of AI in Technical Collaboration: Demonstrating Human Effort

The Core Principle: Human Attention Requires Human Effort

In an era where AI can generate vast amounts of code, documentation, and debug investigations, a new professional etiquette has emerged: If you are requesting human attention, you must demonstrate human effort.

This principle addresses the growing "AI fatigue" among software engineers and technical professionals. When a teammate forwards un-digested AI output—often accompanied by disclaimers like "I didn't read this, so it might not be entirely accurate"—it signals that the content was not worth the sender's time to review, which logically implies it is not worth the recipient's time to read.

The Risks of "AI Slop" in Technical Teams

Over-reliance on raw AI output without human curation leads to several systemic issues within engineering teams:

Erosion of Trust and Review Fatigue

When team members are flooded with large, AI-generated Pull Requests (PRs) or verbose design documents that haven't been vetted, reviewers often subconsciously begin to avoid them. The effort required to weed out hallucinations and errors in a large AI-generated block of text is significantly higher than reviewing a curated human contribution.

Loss of Accountability and "Cognitive Debt"

There is a growing trend of "prompt-driven development" where the author no longer internalizes the choices made by the AI. This creates a dangerous gap in ownership:

• Lack of Maintenance: If a creator cannot explain why a specific line of code was written, they cannot effectively maintain it.
• Hidden Assumptions: AI often amplifies incorrect initial assumptions, making them sound plausible and professional, which obscures the original author's uncertainty or errors.
• Sub-agenting: Reviewers feel they have been relegated to "sub-agents" of an LLM rather than collaborators with a human peer.

The "Replaceability" Paradox

Some industry observers note that when a worker's output becomes indistinguishable from a machine's, they inadvertently signal that their role can be automated. Deep knowledge work remains valuable precisely because it requires the human effort and critical thinking that AI cannot yet replicate.

Best Practices for Integrating AI into Workflows

To leverage AI without damaging professional relationships or code quality, the following guidelines are recommended:

• Curate Before Sharing: Never forward raw AI output. Review, edit, and verify every claim or line of code before it reaches a teammate.
• Clear Labeling: Explicitly label which parts of a communication are AI-generated and which are human commentary.
• Add Human Value: Provide a synthesis or a "TL;DR" that explains why the AI output is relevant and what specific action is required from the recipient.
• Prioritize Brevity: AI is often verbose. Human effort should be demonstrated by editing AI output down to the most concise, impactful version possible.

Counterpoints and Alternative Perspectives

While the "human effort" principle is widely supported, some argue that it may be a misapplication of the labor theory of value.

"If I do something that's easy for me, and it's valuable to you, you'll still want it... Whether it was unpleasant for someone to create them isn't a factor."

From this perspective, the value of a document lies in its utility, not the effort expended to create it. Additionally, some argue that AI is simply a tool, and AI-generated output is, by extension, human output. However, the consensus among practitioners is that the lack of curation—rather than the use of the tool itself—is what constitutes the professional breach.