Measured AI | Note to Self 🔗 **Source:** [Measured AI | Note to Self](https://notetoself.studio/post/measured-ai/) > [!note]+ Document Notes > tags: [AI, Responsible AI, LLMs] > author: [MISSING] > source: Note to Self > reference: [https://notetoself.studio/post/measured-ai/] > date created: 2025-10-23 10:00 > > # 🏷️ Title: Measured AI | Note to Self > <!-- deep-read:sections start --> ## 🧭 Table of Contents - [📝 Summary](#-summary) - [📖 Main Content](#-main-content) - [🗒️ Notes](#️-notes) - [📚 References](#-references) - [🗣️ Paraphrase](#️-paraphrase) - [📰 Newsletter](#-newsletter) - [✅ Action Items](#-action-items) - [🧪 Hypotheses](#-hypotheses) - [📊 Methodology](#-methodology) - [❓ Questions](#-questions) - [🔮 Future Research](#-future-research) - [💡 Implications](#-implications) - [🧠 Learning Styles Assessment](#-learning-styles-assessment) - [📊 Accessibility Assessment](#-accessibility-assessment) - [🌍 DEIB Assessment](#-deib-assessment) - [✅ Logical Validity](#-logical-validity) - [🛡️ DARVO Analysis](#-darvo-analysis) - [🎯 Conclusion](#-conclusion) ### 🔗 References - Include links cited in or relevant to the article. ## 📝 Summary - What happened. - The author argues for “measured AI,” rejecting hype and doom while setting personal boundaries for use. - Why it matters. - Balances utility with risks like hallucinations, sycophancy, privacy, and skill atrophy. - Key stakeholders. - Users, creators whose works train models, engineers, students, healthcare seekers. - Risks and tradeoffs. - Speed vs understanding; convenience vs privacy; summaries vs hallucinations; assistance vs lost skills. - Bottom line. - Treat AI like an over-eager junior assistant: use it selectively, verify outputs, and keep critical skills sharp. ## 📖 Main Content - Core arguments: - AI is over-hyped and often pushed with fear; most practitioners hold moderate views. - Understanding needs effort; AI’s “do it for you” abstraction hides risks. - LLMs mimic language patterns, not truth; they can be confidently wrong. - Best uses: summaries, first-draft code, tedious tasks; avoid high-stakes reliance. - Personal policy: use AI for coding help, not prose; avoid AI therapists; cautious with sensitive data. - Creators need opt-in training regimes and licensing control. - Context/definitions: - LLMs generate likely next tokens; “hallucinations” are plausible but false outputs. - Uncertainties: - Productivity gains vary by role and codebase size. - Governance and creator compensation models are evolving. ### 🗒️ Notes - “The extreme hype… is downright toxic.” - Moderate stance: valid use cases + real problems. - “True understanding requires effortful engagement.” - LLMs: pattern mimics; confidence ≠ correctness. - Chatbot sycophancy; vending-machine parable. - Good at summaries; still verify (friend’s “hallucinated” book). - Personal boundaries: AI for code drafts; no AI prose; no AI imagery on site (ok for headshot). - Coding tradeoffs: faster drafts; refactor heavily; use for tests/logs. - Creator rights: opt-in training; open-source/CC analogies. - Registered claimant in Anthropic copyright settlement. - Privacy caution: no AI therapy/journaling; selective redaction for medical/tax Q&A. - Health: prep better doctor questions; never take medical advice from LLMs. - “Tight leash” metaphor: slow, defensive, careful, paranoid; prioritize learning over delegation. ## 📚 References - Measured AI | Note to Self (https://notetoself.studio/post/measured-ai/) - Creative Commons licensing overview (https://creativecommons.org/licenses/) - Wikipedia overview of large language models (https://en.wikipedia.org/wiki/Large_language_model) - Responsible use of generative AI in healthcare (WHO guidance) (https://www.who.int/news-room/questions-and-answers/item/artificial-intelligence-responsible-and-appropriate-use-in-health) ## 🗣️ Paraphrase - Paragraph A paraphrase. - AI hype is exhausting and sometimes manipulative; most tech workers actually take a reasonable middle ground. - Paragraph B paraphrase. - Use AI where it helps but keep core skills; verify outputs because LLMs are confident pattern machines, not truth machines. ## 📰 Newsletter - This week: a pragmatic take on generative AI. Measured AI means using models for summaries, scaffolding code, and tedious chores—while keeping writing, judgment, and privacy close to the chest. If you’re in WordPress, think “assistant for tests and docs,” not “autopilot for production.” Try Creative Commons refreshers for licensing, and prep better client/dev meetings by using AI to draft questions—then verify everything. ## ✅ Action Items #### DevRel - Publish “measured AI” usage guides with verification checklists. - Run workshops on AI-aided tests/log analysis; no-prose-assist policy examples. - Improve docs with AI-generated drafts + human fact-checking. - Track attribution funnels for AI-enhanced tutorials; A/B measured vs hype copy. - Host office hours on creator consent and licensing. #### OSPO - Define AI training opt-in/out policies; update contribution CLAs. - Establish license scanning for training data; cadence quarterly. - Security: embargo policy for model prompts/data; signature checks. - SBOMs include AI components and model versions. - Provide consultation hours on AI tool adoption. #### Community - Maintainer clinics on AI-assisted PR reviews and test generation. - Contributor onboarding for prompt hygiene and verification. - Sponsor milestones tied to documentation quality and accessibility. - Joint incident drills for AI-generated regressions. #### WordPress - Core: guardrails for AI-generated patches; require human review <!-- deep-read:sections end -->