Your code review tool examines every PR with the same playbook. Formatting? Check. Naming conventions? Check. Known CVEs? Check. Whether a junior dev wrote the code at 2 AM or an autonomous agent generated it from a Slack message — same rules, same heuristics, same green checkmark. That's like using a metal detector to find ghosts. Technically, you're scanning. Practically, you're useless.

On April 18, CodeRabbit shipped multi-repo analysis — their reviewer now traces dependencies across repositories. Cool trick. But here's the question it still doesn't ask: who wrote this code? Neither does Copilot review, which went GA with its agentic architecture on March 5. Neither does Cursor 3, which launched its agent-first interface on April 2. Neither does anything else on the market. Not one tool adjusts its review strategy based on whether the author is carbon-based or silicon-based.

This isn't a philosophical nuance. It's a structural blind spot. CodeRabbit's own December 2025 study of 470 PRs lays it out: AI-authored PRs carry 75% more logic and correctness bugs while producing 3x more readability issues. But the bugs AI reviewers actually flag — formatting, import order, naming — are the bugs humans make. AI code hallucinates syntactically perfect API calls to endpoints that don't exist. It writes test suites that validate the implementation's own assumptions instead of the spec. It produces business logic that compiles, passes every automated check, and quietly does the wrong thing. The failure mode and the detection method aren't even in the same building.

The Cloud Security Alliance reported on April 4 that CVEs traced to AI coding tools jumped from 6 in January to 35 by March — a 6x increase in one quarter. Meanwhile, Qodo raised $70M on March 30 for "code verification." Everyone's building faster pattern-matchers. Nobody's building the one feature that matters: telling the reviewer what kind of code it's looking at before it starts looking.

Here's what authorship-aware review would actually look like. An agent-generated PR lands. The tool sees the author tag — cursor-agent, copilot-workspace, whatever your bot signs as — and switches playbooks entirely. Instead of checking style, it checks semantics: does this function match the spec? Does this test verify behavior or just mirror the implementation? Does this API call reference something that actually exists? That's the gap between "looks right" and "is right," and right now every review tool on the market operates exclusively on the "looks" side.

You can fake this manually today. Label your agent PRs. Train reviewers to skip the formatting nits when they see the label and go straight to intent-checking. Ask "does this do what the ticket says?" instead of "does this follow our style guide?" It's clunky. It's also the only approach that works until someone ships the real thing.

The irony is thick: the industry just spent billions making AI write code and AI review code, and the missing feature is a single metadata field. Human or machine? One boolean. Every reviewer on the market skips it. Every one of them grades code without knowing who the author is — like grading essays without knowing if a student or ChatGPT wrote them. We've seen how well that works in academia.

The next review tool that matters won't be the smartest pattern-matcher. It'll be the first one honest enough to ask who the author is — and change its entire approach based on the answer.