The Learning Loop and LLMs (martinfowler.com)

The piece argues that while large language models are transforming how we write software—lowering friction, bootstrapping projects, and acting as natural‑language translators across DSLs (Gradle/Maven, vmstat/iostat outputs, SVG markup, etc.)—they do not change the fundamental reality that software development is a learning process. The author reports hands‑on experience using LLMs for brainstorming, naming, and bootstrapping (build files, dependency versions, small snippets) but found much generated code was subtly incorrect or misaligned with intent, forcing rewrites. LLMs accelerate the “hello world” and early experimentation stage but can introduce a hidden maintenance cliff: apparent short‑term speedups that leave teams without the contextual knowledge needed to evolve or debug systems. Technically, the article frames learning as a three‑step loop—Observe and Understand, Experiment and Try, Recall and Apply—and links that cycle to why high‑level code reuse and low‑code/LLM shortcuts often fail beyond well‑defined libraries. For the AI/ML community the takeaway is twofold: LLMs are powerful interfaces that translate intent into many specialized languages, lowering the entry barrier; but designers of tooling, models, and workflows should avoid treating LLMs as autonomous builders. To build maintainable systems, integrate LLMs as assistants that speed setup and exploration while preserving hands‑on practices (TDD, CI, pair programming) that embed the deep, context‑dependent learning models cannot automate.