# Outcomes we want to see from AI at Medium Engineering

DevFeed: [Outcomes we want to see from AI at Medium Engineering](<https://devfeed.tech/articles/outcomes-we-want-to-see-from-ai-at-medium-engineering-20325.md>)

Original publisher: [Read original article](<https://medium.engineering/outcomes-we-want-to-see-from-ai-at-medium-engineering-10891d52a19f?source=rss----2817475205d3---4>)

Author: Jacob Bennett

Published: 2026-05-26T15:33:52Z

Content type: article

Language: en

Sources: [Medium](<https://devfeed.tech/sources/medium.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Code quality](<https://devfeed.tech/topics/code-quality.md>), [Testing](<https://devfeed.tech/topics/testing.md>), [Code review](<https://devfeed.tech/topics/code-review.md>), [migration](<https://devfeed.tech/topics/migration.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [code-quality](<https://devfeed.tech/tags/code-quality.md>), [migration](<https://devfeed.tech/tags/migration.md>), [review](<https://devfeed.tech/tags/review.md>), [software-development](<https://devfeed.tech/tags/software-development.md>), [software-engineering](<https://devfeed.tech/tags/software-engineering.md>), [testing](<https://devfeed.tech/tags/testing.md>)

## AI overview

This Medium Engineering article describes the outcomes Medium wants from AI adoption: reducing repetitive work, improving test coverage and maintainability, accelerating learning across unfamiliar parts of the stack, enabling broader technical contributions, and supporting more rigorous planning and risk reduction.

## Source excerpt

This was originally written and published by Christophe Spy (Director of Engineering, Medium) as an internal Medium Eng post. Desired outcomes (what "good" looks like) 🙏🏻 These are the kinds of changes we want to see if AI is going well at Medium. Less drudgery, more leverage Engineers use AI to remove repetitive, low-leverage work (e.g. tedious refactors, boilerplate, large test suites), not just to ship more tickets. We see examples like: "We took a part of the codebase that was basically untestable and, with AI, made it testable and added meaningful coverage in days instead of months." "We cleaned up a legacy area we had been afraid to touch for years, using AI for the mechanical work." Faster learning and broader technical range Engineers use AI to ramp on new languages, frameworks, and parts of the stack much more quickly. Engineers can explore unfamiliar areas safely by asking AI to explain tradeoffs, pitfalls, risks, and how the system uses this areas of the code. We see fewer cases where work stalls or we avoid implementing things because "nobody knows this code anymore". Frontend/mobile engineers are able to make safe, reviewable backend changes with AI's help (plus a review from a backend engineer). Teams can ship more end-to-end features without long handoffs. Higher technical ambition, not just more speed Teams take on work that used to be "too expensive": Meaningful test coverage in critical areas. Pieces of M2 migration or other large refactors. Structural cost savings. Incident/action items evolve from "add an alert" to "Let's improve tests here". Engineers use AI pre-work to explore edge cases and tradeoffs (design rigor). Better planning, reasoning, and risk reduction: Engineers use AI to think through complex scenarios before building, exploring edge cases, stress-testing assumptions, and comparing multiple approaches, and better understand the pros and cons of each approach Design docs become more rigorous as AI is used to challenge thinking and l