Four companies, four AI-native SDLCs

4 companies published their take on the AI-native software lifecycle in the last few months: Anthropic, Atlassian, Google Cloud and Shopify.

I read them side by side.

They agree on more than I expected. Writing code is no longer the bottleneck. Review is. And the quality of the context you give an agent matters more than which model you pick.

Where they split is more useful. Should the source of truth live in the repo or in a platform graph? One standard agent tool, or many? Hard blocking guardrails, or alerts that keep experiments alive?

Those are three decisions every organisation will have to make on purpose.

What surprised me most: none of the four publishes a methodology behind its numbers. Shopify's engineering head has even said publicly that its 20% productivity figure was his own rough estimate.

So I put my notes, the full comparison and a 16-point self-assessment in an open repo. The short version is a 9-page carousel.

Part 1 of 3. Next: what a software factory looks like when you build one yourself.

Which of the three decisions would be hardest where you work?

Cover of the carousel: "Four companies. Four AI-native SDLCs." with chips for Anthropic, Atlassian, Google Cloud and Shopify.

Originally a carousel on LinkedIn — see the original, or download the PDF.

The research, open for anyone to use:
📊 Scrollable visual: https://jintomjose.github.io/ai-native-sdlc-playbooks/
📚 Repo with full notes and the self-assessment: https://github.com/jintomjose/ai-native-sdlc-playbooks

Original sources:
Anthropic: https://claude.com/blog/the-ai-native-sdlc-playbook
Atlassian: https://www.atlassian.com/blog/ai-at-work/ai-sdlc-transformation-playbook
Google Cloud: https://cloud.google.com/use-cases/ai-sdlc
Shopify (Bessemer): https://www.bvp.com/atlas/inside-shopifys-ai-first-engineering-playbook

My own reading, not my employer's view.