docs: add Borg et al. 2025 RCT on AI code maintainability (v3.27.7)

- Resource eval: arXiv:2507.00788 "Echoes of AI" (151 devs, 95% pros,
  2-phase blind RCT) — 30.7% faster median, ~55.9% habitual users,
  no significant downstream maintainability impact
- guide/learning-with-ai.md: citation + "On maintainability fear" note
- guide/ultimate-guide.md: nuance blockquote in §1.7 Trust Calibration
- machine-readable/reference.yaml: 4 new RCT/maintainability entries
- docs/resource-evaluations/: evaluation file with technical-writer audit

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

guide/ultimate-guide.md

@@ -1091,6 +1091,8 @@ Research consistently shows AI code has higher defect rates than human-written c
 
 **Key insight**: AI produces code faster but verification becomes the bottleneck. The question isn't "does it work?" but "how do I know it works?"
 
+> **Nuance on downstream maintainability**: A 2-phase blind RCT (Borg et al., 2025, n=151 professional developers) found no significant difference in the time needed for downstream developers to evolve AI-generated vs. human-generated code. The defect rates above are real — but they do not systematically translate into higher maintenance burden for the next developer. The risk is more narrowly scoped than commonly assumed. ([arXiv:2507.00788](https://arxiv.org/abs/2507.00788))
+
 ### The Verification Spectrum
 
 Not all code needs the same scrutiny. Match verification effort to risk: