More workers are stuck in a frustrating cycle. Employers have assigned routine tasks to AI, but humans are still responsible for checking the AI’s work. Tech critic Cory Doctorow calls this setup the “reverse centaur.” In a recent guest column for CNET, he argues this arrangement harms workers and, surprisingly, the bosses who believe they’re cutting costs.
What Is a Reverse Centaur?
You might have heard of a “centaur” in the AI world. That’s when a human uses AI to enhance their work, like a chess grandmaster using a program to explore moves they wouldn’t see alone. The reverse centaur flips this concept: the AI does the work, and the human’s job is to catch its mistakes. Imagine a factory quality inspector, but the factory is an AI chatbot, and the products are emails, reports, and customer replies.
Doctorow argues in his CNET column that this situation creates what he calls a “special hell.” The human checker ends up responsible for errors they weren’t equipped to prevent. They can’t fully verify the AI’s output without basically redoing the work. At that point, what’s the AI for?
Why Bosses Love It (And Why It Backfires)
From a management perspective, the logic looks straightforward. AI handles the bulk of the work, while humans deal with exceptions. Costs go down, headcount shrinks, and productivity numbers appear impressive—until something goes wrong.
The real issue is accountability. When an AI system delivers an incorrect answer—a made-up legal citation, a miscalculated insurance claim, or a poor customer service response—the question of who’s accountable becomes complicated. Doctorow suggests that bosses are learning this lesson the hard way: they’ve created a system that scales up errors just as effectively as it scales up output.
There’s also a concern about skill erosion. If a customer service rep spends two years simply approving AI responses instead of crafting their own, they lose that ability. The human safety net weakens over time, even as it’s relied on more heavily.
This Is Already Happening Across Industries
This isn’t just a theory. Companies in fields like legal, finance, healthcare, and content moderation have already implemented AI-first workflows where humans review outputs instead of creating them. In some call centers, AI generates suggested responses, and agents just click “send”—or not. In legal departments, AI drafts contracts, while junior associates check for errors.
The challenge is that spotting an error in someone else’s work, especially when that someone is a confident-sounding AI, is really tough. Research on automation bias shows that people tend to trust automated systems even when they’re wrong. We often assume that if a system produces something, it’s probably accurate.
What This Means for Everyday Workers
If your job involves reviewing AI-generated content, drafts, or reports, Doctorow’s perspective is worth considering. You’re becoming the last line of defense for a system pitched to your employer as a cost-saver. This means you face real pressure to approve quickly, and if something slips through, the blame falls on you.
For job seekers, this shift alters which skills are valuable. Being able to critically evaluate AI outputs—catching hallucinations (where AI creates plausible-sounding but false information), logical gaps, or tone issues—is becoming a key workplace skill, even if it’s not yet labeled that way in job postings.
Community Reactions
“I literally spend my whole day approving or rejecting AI emails for my company. I have no idea what I actually do anymore. My job title is ‘Communications Specialist.'”
— u/PracticalMarmot88, r/antiwork
“The ‘centaur’ thing cuts both ways. I use AI and I’m more productive than I’ve ever been. But I also know what I’m doing well enough to catch its mistakes. If you don’t have that baseline expertise, you’re just a liability shield for your company.”
— YouTube commenter on a Cory Doctorow interview upload, 2025
The Bigger Picture: AI as the New Middle Manager
Doctorow’s column comes at a time when discussions about AI in the workplace are evolving. Initially, the focus was on whether AI would replace jobs. Now, the emerging question is more nuanced: what happens to the jobs that remain, as they’re reshaped around AI oversight?
A separate piece from TechCrunch this week pointed out that AI is blurring the lines between content categories. Music, video, podcasts, and text are all converging on the same platforms as AI makes creation and recommendation cheaper. The common thread is clear: AI is generating more content, while humans are taking on more sorting, checking, and approving. Whether that’s progress depends on whether those humans have the tools, time, and authority to push back when something’s wrong.
By The Numbers
| Data Point | Figure |
|---|---|
| Share of U.S. workers who say AI has changed how they do their job (Pew Research, 2024) | ~20% |
| Workers who say AI makes them more productive | ~53% of those affected |
| Workers who say they feel more stressed due to AI oversight responsibilities | ~28% of those affected |
| Industries most affected by AI output review roles | Legal, Finance, Healthcare, Customer Service |
What To Watch
- Labor policy: The issue of liability when an AI-reviewed output causes harm—a wrong medical recommendation, a flawed legal document—is heading to courts and regulators. Keep an eye on test cases in the EU, where AI liability rules are more advanced than in the U.S.
- Corporate disclosures: As more companies release AI usage policies, pay attention to whether they specify human review requirements—or quietly skip them.
- Skill frameworks: Expect major hiring platforms like LinkedIn and Indeed to start listing “AI output evaluation” as a skill category within the next 12 to 18 months, as demand rises.
- Doctorow’s broader argument: His CNET piece is part of a larger critique worth reading in full at CNET, along with the TechCrunch article on AI and entertainment consolidation for a broader context of how AI is reshaping work and media at the same time.
Maya Torres
Maya Torres is the Consumer Tech Editor at Explosion.com with 7 years covering product launches for major technology publications. She has reviewed over 300 devices across smartphones, laptops, wearables, and smart home products. Maya specializes in translating spec sheets into real-world buying advice and attends CES, MWC, and Apple keynotes as press. Her reviews focus on helping readers decide what to buy, not just what specs look good on paper.
