If you thought the speed of writing code was your problem, you have bigger problems - Andrew Murphy
The talk argues that focusing on increasing code writing speed via AI is optimizing the wrong metric. Drawing on the Theory of Constraints, the speaker asserts that the true bottleneck in software development is rarely the engineer's typing speed. Instead, throughput is limited by upstream processes (like discovery and requirements gathering) or downstream processes (like code review, CI/CD, and deployment). Engineers should use any 'spare capacity' gained from AI coding to improve tooling, fix tech debt, and optimize the identified process bottleneck, rather than simply writing more code.
Key takeaways
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The Bottleneck is Not Coding Speed
22:50
The throughput of a system is determined by its single constraint (Theory of Constraints). If coding is not the bottleneck, increasing code output (e.g., using AI) will only create massive Work In Progress (WIP) and worsen the problem by creating more unreviewed PRs.
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Focus on the Full Value Chain
30:50
The value of code is in its utilization, not its creation. The most critical metrics to track are cycle time (idea to production), wait time (PR review time, scoping time), and deployment frequency, rather than lines of code or commits.
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Optimize the Constraint, Not the Code
27:30
If the bottleneck is Discovery, the focus should be on improving idea validation and requirements gathering. If the bottleneck is Review, the focus should be on improving review processes, not just writing more code.
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Leverage AI for Process Improvement
35:00
Instead of using AI to write more code, use it to aggregate disparate data (e.g., usage logs, product research) during the Discovery phase, or to automate deterministic checks (like database migrations) that can be implemented in tooling.