Could AI Ever Replace Copyeditors? Here’s What the Tech Would Require

I’ve been thinking a lot about the “Will AI replace copyeditors?” question. Not the hand-wringing version or the defensive version, but the practical, technical version: Could AI actually do what copyediting requires?

Because here’s the thing—many people asking this question don’t understand what copyediting actually demands from a technical standpoint. And most people building AI tools don’t understand it either.

So let’s talk about what would actually be required for AI to copyedit reliably. Not “pretty well” or “good enough for some projects,” but truly reliably—the way you copyedit.

What Copyediting Actually Requires

Before we get into the tech, let’s be clear about what copyediting demands:

100% consistency. When you decide to use the serial comma, you aim to use it every single time throughout the entire manuscript. When you choose “email” over “e-mail,” that choice holds for all 247 instances. You don’t accidentally switch halfway through.

Rule-based decisions. You’re not guessing whether to capitalize a title or hyphenate a compound modifier. You’re applying specific rules from CMOS or AP or whatever style manual governs the project.

Deterministic behavior. Same situation, same rule, same decision—every time. If you encounter “3 p.m.” on page 10, you handle it exactly the same way as “3 p.m.” on page 200.

Zero hallucination tolerance. You cannot make up citation details. You cannot invent a style rule. You cannot generate a “close enough” version of what the author wrote.

Now here’s the problem: Current AI technology—the large language models everyone’s talking about—can’t guarantee any of these things.

Why Large Language Models Can’t Do This

Remember from my previous post how LLMs work with numbers, not words? How they generate everything from scratch based on patterns?

That fundamental architecture makes them wrong for copyediting. Here’s why:

LLMs are probabilistic, not deterministic. They generate responses based on probability, not rules. Ask them to edit the same sentence twice, and you might get two different edits. That’s the opposite of the consistency copyediting requires.

They can’t follow rules reliably. Even if you upload an entire style guide, they’re still generating edits based on patterns, not systematically applying rules. They might apply your style guide correctly on page 5 and completely miss the same issue on page 50.

They hallucinate. It’s not a bug; it’s a feature of how they work. When you need a tool that never, ever makes up information, you can’t use a technology that’s designed to generate plausible-sounding text even when it doesn’t know the answer.

What Actually Works: The Tools We Already Have

Here’s what’s interesting: We already have technology that does the rule-based part of copyediting really well.

PerfectIt has the full Chicago Manual of Style programmed in—thousands of entries with direct lookups to the manual. It’s deterministic, rule-based, and applies CMOS systematically. It catches consistency issues, flags style violations, and cites which rule applies. (And if you use another style guide, you can build a custom style sheet to do the same thing.)

This is exactly the kind of tool copyediting needs: predictable, reliable, rule-following technology that does the same thing every time.

So if we already have tools that handle the mechanical application of style rules, what would adding LLM technology actually provide?

What LLMs Could Theoretically Add (And Why It Probably Isn’t Worth It)

The argument for adding neural networks to copyediting tools would be handling the judgment calls:

• Context understanding: Does this sentence need the serial comma for clarity, or is it clear without?
• Voice preservation: Is this technically correct change going to flatten the author’s distinctive voice?
• Ambiguity resolution: When the style guide says “use your judgment,” how do you decide?
• Knowing when to break rules: Sometimes breaking a rule serves the writing better than following it.

But these are exactly the parts of copyediting where you don’t want AI making decisions. These are the parts that require human expertise, editorial judgment, and understanding of the author’s intent.

Adding an LLM to handle judgment calls would mean:

• Accepting probabilistic decisions where you need certainty.
• Trusting pattern-matching where you need actual understanding.
• Getting different answers to the same question depending on context (or random chance).
• Having no clear explanation for why a particular decision was made.

Is that actually better than just making those calls yourself?

The Hybrid Dream (And Why It Doesn’t Make Sense)

Some tech folks talk about building “hybrid systems”—combining rule-based tools like PerfectIt with neural networks that handle the fuzzy judgment parts.

Technically, someone could build this. They would need:

• The rule-based foundation (which we have).
• A neural network trained on thousands of copyedited manuscripts with tracked changes.
• A control system that routes mechanical checks to the rule-based component and judgment calls to the neural network.
• Integration that maintains consistency across both systems.

But here’s the reality check: Why would copyeditors want this?

The rule-based part (PerfectIt) already works. It’s fast, reliable, and catches what it’s designed to catch.

The judgment-call part is where human editors shine. It’s where your expertise, understanding of context, and relationship with the author matter most.

Adding an LLM to make those judgment calls would:

• Introduce uncertainty where you currently have expertise.
• Cost significantly more—and use way more energy—to develop and run.
• Still require human oversight on every decision.
• Potentially flatten author voice in ways you’d have to fix.

You’d be replacing human judgment with AI judgment that still needs human oversight. That’s not efficiency—that’s adding steps.

What This Means for Professional Copyeditors

If you’ve been worried that AI will replace copyediting, understanding the technical reality should ease that concern.

The mechanical part of copyediting—applying rules consistently—already has good tools. Rule-based systems like PerfectIt do this better than LLMs ever will because they’re designed for exactly this kind of work.

The judgment part of copyediting—understanding context, preserving voice, knowing when to break rules—is where you add value. And that’s exactly where LLMs are weakest. They’re pattern-matchers, not judgment-makers.

Your expertise isn’t threatened by AI. It’s complemented by the tools we already have and will continue to be complemented by better versions of those same kinds of tools.

But clients and hiring managers who don’t understand how AI works are starting to believe it can do your job. That’s the actual threat—not the technology, but the misconception.

This is where your role hasn’t changed at all. You’ve always had to educate clients about what editing actually involves and why it matters. You’ve always had to advocate for your profession and articulate your value. AI just makes that communication even more crucial.

This post was published on February 23, 2026.

Whether you're experimenting with AI tools or avoiding them entirely, understanding how this technology works—and what it can and can’t do—helps you make better decisions for your business and communicate clearly with clients. Join my email list for practical updates on AI and editing.

Other Posts in My “Editors and AI” Series

• Editors and AI, Part I: What Is AI? A Primer for Editorial Professionals
• Editors and AI, Part II: AI in Editorial Software—Which Editing Tools Use AI and Which Don’t
• Editors and AI, Part III: How Generative AI Really Works—What Editors Need to Know
• Editors and AI, Part IV: Beyond “Just Say No”—A Nuanced Approach to Generative AI in Editing
• Editors and AI, Part V: Will AI Replace Human Editors?
• The Surprising History of Spell Checkers—and What It Means for AI-Anxious Editors
• Human Editors vs. AI: Why the Best Strategy Isn’t What You Think
• The AI Myth Every Editor Needs to Stop Believing

Further AI Resources for Editors

• AI Terms for Editors: A Glossary
• AI Resources for Editors