Code review has always been a bottleneck.

In the 1980s, reviewing a few hundred lines of code could take days. Engineers printed listings, gathered in conference rooms, and followed inspection checklists.

Today, AI tools can review similar changes in seconds. They flag likely bugs, call out risky patterns, and draft comments before a human reviewer even opens the pull request.

The shift isn't just in speed. It's changing when feedback happens, and what humans spend attention on.

Here's how we got from conference rooms to AI-assisted review, and what's coming next.

TLDR

• Code review evolved from formal waterfall inspections in the 1980s to lightweight peer reviews with agile, and now to AI-assisted automation.

• Waterfall-era reviews were slow, rigid, and difficult to scale, often delaying feedback until late in development.

• Agile practices made reviews faster and more collaborative, but human bottlenecks remained as teams and codebases grew.

• AI code review now analyzes changes in seconds, helping teams improve quality and consistency without slowing delivery.

The waterfall era: When code review meant conference rooms

Code reviews started as formal software inspections in the 1980s. Michael Fagan at IBM described the process as highly structured and prescriptive.

Developers printed code listings. Review teams scheduled meetings. Everyone gathered in conference rooms with checklists. They manually inspected each line, looking for errors and improvement opportunities.

The process worked for finding bugs. But it came with serious costs.

Waterfall review challenges:

• Time-consuming formal process - Reviews took days or weeks to complete

• High overhead - Coordination, scheduling, and documentation requirements slowed everything down

• Late feedback - Developers discovered issues long after writing code, when fixes cost more

• Sequential bottlenecks - Each review had to finish before the next phase could begin

• Limited adoption - Many teams skipped reviews entirely due to the burden

Industry studies from that era consistently showed low success rates and high failure risk for large waterfall projects.

Software development needed a better approach.

What changed with agile?

By the early 2000s, frustration with the waterfall's rigidity had reached a breaking point. Teams struggled to deliver working products efficiently. Bureaucratic processes overwhelmed productivity.

In 2001, 17 software practitioners met in Utah to formalize a new way of working. They created the Agile Manifesto, prioritizing individuals and interactions over processes and tools.

Code review evolved along with development methodology. Formal inspections gave way to lightweight, informal peer reviews built into daily workflows.

Key improvements:

• Informal process - No mandatory meetings or rigid checklists.

• Faster feedback - Hours or days instead of weeks.

• Team learning - Review became a knowledge-sharing platform.

• Flexibility - Teams adapted practices to their needs.

The improvement was measurable. Agile projects achieved 40-42% success rates compared to the waterfall's low success rate.

But agile didn't solve the tooling problem. Reviews still happened via email, printed diffs, or awkward custom systems.

How did pull requests change everything?

GitHub launched in 2008 with a feature that would transform code review: pull requests. This single innovation made review asynchronous, collaborative, and visual.

Pull requests let developers see exact code changes, leave inline comments, and track discussions in one place. No more emailing patches or reviewing printed diffs.

The pull request era brought:

• Visual diffs - See exactly what changed, line by line.

• Inline comments - Discuss specific code sections directly.

• Continuous integration - Automated tests run before human review.

• Distributed teams - No timezone coordination needed.

• Version control integration - Review tied directly to the git workflow.

By the mid-2010s, GitHub, GitLab, Bitbucket, and similar platforms made this style of code review standard practice. Tools like Gerrit and Phabricator offered alternatives for different workflows.

This was modern code review at its peak. Fast, collaborative, and integrated with development tools.

But human bottlenecks remained. PRs piled up. Reviews took hours or days. Context switching killed productivity as developers waited for feedback.

The next step had to address a simple problem: human reviewers couldn’t keep up.

Why did a human-only review still cause problems?

Even with pull requests, code review remained a constraint for most teams.

1. The velocity problem: PRs pile up when humans are the bottleneck. Engineers wait hours or days for feedback. Developers lose significant time refocusing after each interruption. When PRs sit in queues, developers either start new work (creating more context switches) or sit idle.

2. The consistency problem: Human reviewers have good days and bad days. Morning reviews catch different issues than afternoon reviews. Senior engineers spot different problems than juniors. Style preferences vary by person. This makes review quality unpredictable.

3. The scale problem: As teams grow and codebases expand, human review capacity doesn't scale proportionally. A 10-person team might generate 50 PRs weekly. That's 5 PRs per person to review, on top of their own development work. The math doesn't work.

The AI code review era

AI code review went mainstream in 2023-2024. What started as experimentation is now standard practice at many teams.

The numbers show explosive growth:

• 67.1% of companies now use AI for code review.

• 82% of developers use AI coding assistants daily or weekly.

• The AI code review market is projected to reach $750M, growing at 9.2% annually.

But adoption alone doesn't tell the full story. The results matter more.

What does AI code review deliver?

• Instant feedback - Reviews in seconds, not hours or days.

• Consistent analysis - Same standards every time.

• Scalable capacity - Handles unlimited PRs simultaneously.

• Pattern recognition - Remembers every past bug and team convention.

• 24/7 availability - No timezone delays.

Research on AI vs manual reviews found that AI-assisted teams complete reviews faster. The speed-quality tradeoff disappears when AI handles routine checks and humans focus on architecture and business logic.

How does AI code review actually work?

Modern AI code review tools operate differently from earlier automated tools. Traditional static analysis tools caught syntax errors. AI understands context.

The review process:

1. AI scans changed code against the full codebase context.

2. Identifies bugs, security risks, style issues, and performance problems.

3. Leaves inline comments with specific suggestions.

4. Generates a summary highlighting major concerns.

5. Developers address feedback and commit fixes.

6. AI re-reviews to verify resolution.

The entire cycle takes minutes. False positives, once a major problem, have dropped dramatically as AI models improve.

What AI excels at:

• Syntax and style violations.

• Security vulnerability scanning.

• Performance anti-patterns.

• Test coverage gaps.

• Documentation requirements.

What humans still handle better:

• Business logic validation.

• Architectural decisions.

• Edge case identification.

• Mentoring through review comments.

The best results come from combining both.

Where is AI code review headed?

AI code review is still evolving. Here's where it's headed.

• Autonomous code agents: Current AI reviews code reactively, after humans write it. The next phase involves AI agents that proactively suggest fixes, implement changes, and handle entire feature requests with minimal human direction.

• Continuous code quality: Rather than reviewing code at PR time, AI will provide real-time feedback as developers type. Think pair programming with an AI that knows your entire codebase and every bug your team has fixed.

• Self-learning systems: Today's AI improves through training on large datasets. Future systems will learn from how your team responds to suggestions, what gets accepted, what gets ignored, and what gets changed, so feedback better matches your standards over time.

• Integration depth: AI code review will integrate more deeply with the entire development lifecycle. Instead of being a step in CI/CD, it will be woven throughout: architecture design, implementation, testing, deployment, and production monitoring. The comparison of leading tools shows this is already beginning.

Why this evolution matters

Code review evolved because software development demands changed. Waterfall worked when requirements were stable, and timelines were measured in years. Agile enabled faster iteration. AI enables continuous quality at scale.

Teams that adopt AI code review don't just review faster. They build better products, ship more confidently, and spend less time fighting technical debt.

But success requires more than installing a tool. Teams need to understand what AI does well, where it falls short, and how to integrate it effectively into workflows.

cubic provides AI code review that learns from your codebase and team patterns. It catches bugs human reviewers miss, enforces standards consistently, and scales with your team without creating bottlenecks.

Start your free trial and see how AI code review eliminates delays while improving quality.