AI Code Review: How Senior Engineers Spend Their Days

The New Reality of AI-Assisted Development

The engineering landscape is shifting faster than most organizations can adapt. While AI coding assistants promise to accelerate development cycles, Canva's CTO has revealed a counterintuitive truth: senior engineers are now spending substantial portions of their workdays reviewing code generated by AI technologies. This isn't a sign of failure—it's evidence of a fundamental transformation in how engineering teams operate.

As AI code review becomes the new senior engineer job, organizations face a critical question: How do we maximize the benefits of AI-assisted coding while ensuring code quality and team productivity?

The Canva Model: AI Agents Working Overnight

According to Canva's leadership, the company has embraced a workflow where AI agents generate code during off-hours, allowing senior engineers to review and refine the output during business hours. This asynchronous approach offers several advantages:

Accelerated iteration cycles: Code generation happens continuously, not just during traditional work hours
Quality gates: Senior engineers maintain control over what ships to production
Knowledge transfer: Code review becomes a teaching moment for junior developers
Reduced bottlenecks: Developers spend less time on boilerplate and more on architecture decisions

The model reflects a broader shift in how AI agents are changing engineering work. Rather than replacing engineers, these tools are reshaping their roles.

Productivity Gains and Onboarding Benefits

Canva's experience demonstrates measurable productivity improvements. The company reports that 80% of its engineers are more productive thanks to AI integration, a figure that extends beyond just code generation speed. The benefits cascade across the organization:

For Senior Engineers:

Strategic focus on architecture and design patterns
Mentorship opportunities through code review
Reduced time on routine coding tasks

For Junior Developers:

Faster onboarding through AI-assisted learning
Immediate feedback on code quality
Access to senior engineer expertise during review cycles

For Organizations:

Faster feature delivery
Improved code consistency
Better knowledge retention across teams

Integration and Tooling Considerations

The success of Canva's approach depends on thoughtful integration of AI tools into existing workflows. Organizations considering similar implementations should evaluate:

Tool compatibility: Does the AI coding assistant integrate with your existing CI/CD pipeline?
Review processes: How will code review workflows change with AI-generated code?
Team training: Do engineers understand how to effectively review AI output?
Security and compliance: Are there guardrails for sensitive code or regulated systems?

Canva's experience shows that successful AI adoption requires letting employees try different tools and find what works best for their workflows.

The Practitioner's Perspective

For engineering teams considering this transition, the key insight from Canva is that AI code review isn't a burden—it's an opportunity. Senior engineers who master the skill of reviewing AI-generated code become force multipliers for their teams. They set quality standards, catch edge cases, and ensure architectural consistency.

The shift also creates space for senior engineers to focus on higher-value work: system design, performance optimization, and strategic technical decisions that AI tools cannot yet handle effectively.

Looking Forward

As AI tools continue to evolve, the role of code review will likely become even more critical. Organizations that invest in building strong code review cultures—with clear standards, efficient processes, and engaged senior engineers—will capture the most value from AI-assisted development.

The question isn't whether AI will change engineering work. It already has. The question is whether your team is ready to lead that change.