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AI Coding Reading List
Welcome to another week in the world of AI coding! Today, we’re focusing less on hype and more on practical and tactical adoption strategies and frameworks for measuring impact.
First up, Davis Keene, Senior Software Engineer at Jellyfish, shares a thoughtful blueprint for AI tool adoption, rooted in engineering enablement, real feedback loops, and a healthy dose of practicality – with real examples from the Jellyfish team. Then, Adam Ferrari dives into what success with AI coding tools really looks like – and how to measure it.
Plus reflections from Amazon developers on what they see as increasingly automated work, how Google’s Jules AI saves real time, and a look at the future for entry-level coders.
Let’s dive in.
Guiding AI Coding Tool Adoption with Intention: Best Practices for Engineering Teams
By Davis Keene, Jellyfish
Most teams exploring AI coding tools hit the same wall: early hype, slow adoption, unclear impact. At Jellyfish, we take a different path. One grounded in intention. Davis Keene, Senior Engineer at Jellyfish, shares how we approach AI coding tool adoption from the ground up:
- Start with small Copilot demos, with security involved from day one
- Build a living playbook in Confluence, plus Slack spaces for shared learning
- Create clear AI usage policies that engineers actually use
- Use DevEx surveys to track sentiment and unblock adoption
- Keep mentorship front and center. AI should accelerate learning, not replace it
Adopting AI tools isn’t just about access. It’s a series of choices that shape how your team works, collaborates, and grows. Davis walks through our full approach here.
AI Coding Impact Practical Measurement Approaches
By Adam Ferrari, Engineering Together
90%+ of orgs employ at least some AI in their development processes, but many are struggling with how to measure its impact.
The good news is that trends are quickly starting to emerge. The challenge? AI-assisted software development is transforming in real time, making it harder than ever to keep up.
In his Substack, Adam Ferrari, engineering leader and advisor, breaks down how to figure out your AI metrics strategy. Check out his recommendations here.
Also this week:
At Amazon, Some Coders Say Their Jobs Have Begun to Resemble Warehouse Work
By Noam Scheiber, The New York Times
Google’s Jules AI coding agent built a new feature I could actually ship – while I made coffee
By David Gewirtz, ZDNet
Learn to code, they said: AI is already erasing some entry-level coding jobs
By Matt Binder, Mashable
Something we missed? Find us on LinkedIn to let us know! See you next week.
About the author
Gail is Senior Content Marketing Director at Jellyfish.
