- Traditional tools like Vim and Emacs gain trust through deep customization and muscle memory, making them feel like extensions of the developer.
- AI coding agents speed up output but reduce trust; usage rises while trust falls (from 40% to 29% in a recent survey).
- New AI tools require a shift in process and culture, not just tool adoption; a broken process remains broken even with better tools.
- Code becomes cheaper to produce, but validation (code review, infrastructure cost, failure risk) becomes the new bottleneck.
- Humans must remain responsible for code; AI contributions should be flagged, and the human pushing the commit owns the outcome.
- Collaboration risks siloing—AI lets developers bypass designers and domain experts, leading to massive PRs without proper checks.
- Explicit context and prompts are essential; leaving anything to chance means it may be handled incorrectly (e.g., platform-specific builds).
- The DRY principle fights AI’s tendency to rewrite code (WET), promoting reuse of verified components.
- Knowing when *not* to use AI (e.g., using deterministic bash scripts) is a key trust-building practice.
- Building trust in AI-driven processes requires preserving human judgment, clear workflows, and continuous feedback loops.