Faster code isn’t faster delivery
Emma Bostian · 6 October 2026
Read the originalEmma Bostian, an engineering manager at Spotify, argues that AI coding tools moved the delivery bottleneck from writing code to reviewing it, and that authors must own their changes.
Emma Bostian, an engineering manager at Spotify, the music streaming company, writes in LeadDev that AI has not made software delivery faster. Pull requests now arrive faster than engineers can understand, test and approve them, and reviewers end up reconstructing the author's reasoning. She proposes five practices to protect review and the time engineers spend working together.
- Non-technical colleagues who open pull requests with AI sometimes pass each review comment back to an AI agent without understanding the problem, which repeats the cycle.
- Harvard Business Review research she cites found that each piece of low-quality AI work takes an average of one hour and 56 minutes to handle.
- She says the person who starts a change must be able to explain it, test it and review it before asking a colleague to review it.
- She recommends defining what must be true before AI-assisted code can be submitted for review, and measuring review time and rework alongside output.
A team that mandates AI use without changing its review rules may simply move work from authors to reviewers. This is an inference and is not the author's claim; the essay draws on published surveys rather than her own team's numbers.
Report how long pull requests wait for review and how many are reworked afterwards, next to the amount of code produced.
Require the author of an AI-assisted pull request to explain the change, run the relevant tests and check security concerns before requesting a review.
Derived by Working Surface from the article; more in the Playbook. Source line: Regardless of whether a pull request was written by a person or generated with AI, the person who initiated the work is responsible for the quality.
7 October 2026: once AI agents do most of the making, checking and choosing become the scarce steps, and teams protect them with author responsibility, automated checks and tools that point to risk.