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Think bigger in 3 distinct ways:

1. Lower learning curve, for new or revived skills (weaving - gather and summarize)

2. Lower incremental cost: almost always faster at choosing the next step (scouting)

3. Faster time-to-maturity: when building test suites, faster both at first-pass coverage and at gap-filling (zoo-keeping)

But the huge, gaping pitfall -- now coming down the tracks even faster -- is the same as without AI: once you build a complex system, committing to its invariants, you quickly find it difficult to change in the direction you want, because each choice triggers a tangle of incommensurable choices. Sometimes heroic re-factorings (beyond AI now) can extend life a bit, but usually the only people who can avoid abrupt stasis are those with deep experience and the insight into how to change things (incrementally). I'd guess with AI such pitfalls become more common and more difficult to escape.

So: yes, think bigger for projects with relatively definitive and complete requirements, but take care with foundational code you expect to grow.



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