TL;DR
A software engineer reflects on using AI coding tools as both an accelerant to hands-on learning and a potential shortcut that can stunt skill growth. They propose a workflow and guardrails — rapid prototyping, deliberate teardown, iterative learning loops, and human-authored final docs — to keep AI a learning aid rather than a crutch.
What happened
The author, an avid user of AI coding tools, lays out a personal reckoning with two possible outcomes of heavy AI use: a positive path where AI accelerates experimentation and learning, and a negative one where developers coast and fail to internalize skills. They describe an existential worry that relying on AI-generated code can lead to stagnation in one’s abilities over months and years. To avoid that trap, the author offers a practical working model and a step-by-step workflow for medium-sized engineering problems. Key elements include using AI to prototype quickly, discarding messy prototype code, deliberately designing a clean solution informed by hands-on reading of source docs, committing structured skeletons and PRs, and writing final documentation and commit messages by hand. The author emphasizes learning in iterative loops and recounts debugging experiences where AI summaries missed crucial details, illustrating the need to sometimes consult originals directly.
Why it matters
- AI tools can greatly speed experimentation and reduce mechanical work, potentially improving output quality when used deliberately.
- Overreliance on AI-generated code risks skill erosion and produces maintainability problems for teams.
- Explicit workflows and guardrails can help developers extract learning from AI instead of outsourcing understanding.
- Knowing when to step out of AI summaries and read original sources remains important for accurate mental models.
Key facts
- The author is an enthusiastic, heavy user of AI coding tools but expresses concern about overreliance.
- They frame two visions: a 'glittering' one where AI enables better, faster learning, and a 'cursed' one where AI fosters lazy, inscrutable code.
- A working model recommended: use AI tooling in loops; treat AI-generated code as disposable; be opinionated about problem breakdown; make 'textbook' commits/PRs; write final docs and PR descriptions manually.
- A proposed workflow for medium-sized problems includes: rapid messy prototyping, throwing prototypes away, designing a correct structure, implementing a skeleton, and then generating final code guided by the design.
- The author stresses reviewing both AI output and original source materials (code, docs, READMEs) with human eyes when summaries conflict with observed behavior.
- They recommend committing skeletons and final implementation documents before using AI to generate production code, and producing atomic commits/PRs.
- The author notes that appropriate effort varies by context and cautions against equating more meticulousness with better outcomes in all cases.
- Two concrete debugging experiences are described where relying solely on AI summaries misled investigation and reading originals resolved contradictions.
- The post was published on 2026-01-13 and is hosted at the author’s site.
What to watch next
- Whether teams formalize similar guardrails and workflows around AI-assisted development — not confirmed in the source
- If patterns of skill erosion or maintenance debt tied to AI-generated code emerge across organizations — not confirmed in the source
- How tooling evolves to better signal when human review of original sources is necessary — not confirmed in the source
Quick glossary
- AI coding tools: Software that generates code, suggestions, or documentation using machine learning models such as large language models.
- Prototype: A quick, often imperfect implementation built to explore ideas, validate assumptions, or demonstrate functionality.
- PR (Pull Request): A request to merge code changes into a shared repository, typically used for review and collaboration.
- Observability: The practice of designing systems to expose structured outputs and signals that make their behavior easier to understand and debug.
- Experiential learning: Learning that occurs through direct engagement, practice, and reflection on doing rather than from secondhand summaries alone.
Reader FAQ
Does the author stop using AI tools altogether?
No. The author says they are 'all-in' on AI tools but urges using them as aids for learning rather than replacements for thinking.
What concrete workflow does the author recommend?
Rapid prototyping with AI, throw away prototype, design a final structure with human review, implement a skeleton, then use AI to generate code per modular commits and write final docs by hand.
Are specific AI tools or vendors named?
Not confirmed in the source.
Does the author provide examples where AI misled debugging?
Yes. They describe two debugging experiences where AI summaries missed details and consulting original docs resolved contradictions.
I use ai coding tools a lot. I love them. I’m all-in on ai tools. They unlock doors that let me do things that I cannot do with my human…
Sources
- Choosing learning over autopilot
- AI Can Revolutionize Education, but Not in the Way We Think
- #40 – AI Isn't Making Us Dumber. Autopilot Thinking Might
- Do We Still Need to Learn? Generative AI's Influence on …
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