Summary

Charity argues that AI making code generation cheap and disposable doesn't signal the end of engineering discipline — it demands more of it. Drawing on Chad Fowler's Phoenix Architecture ideas, she reframes code as a 'materialized view of understanding' that is useful while current but disposable when stale, and argues that the real product of software teams has always been shared understanding and production behavior, not lines of code. 2026, she contends, is shaping up as a return to rigor: investing in observability, behavioral tests, and fast feedback loops so that the knowledge in engineers' heads can be encoded into systems AI can actually use.

Key Insight

AI making code cheap and disposable doesn't reduce the need for engineering discipline — it exposes that the hard parts of software (shared understanding, production behavior, encoded knowledge) were never about writing code in the first place, and now demand more rigorous investment than ever.

Spicy Quotes (click to share)

• 5

  What happened in 2025 was this: the economics of code production were turned upside down. Instead of being very hard, time-consuming, and expensive to generate code, it became effectively free and instant.

• 6

  Code becomes precious when it is the only place knowledge lives.

• 7

  When regeneration is easy, code stops being an asset and starts acting as a cache: a materialized view of understanding that is useful while current, disposable when stale.

• 7

  Having nondeterministic code in production is finally forcing us to do the things we should have done all along. Instrumenting with traces. Tests and evals in production. Production is not what happens after development is over, production is a stage of development.

• 5

  The knowledge in our heads is unavailable to AI until we encode it into the system, after all. The returns on those investments will be massive and nonlinear.

Tone

assertive and analytical