Individual skill
chief-ai-officer-advisor
Use for AI strategy decisions: API vs fine-tune vs build, EU AI Act or US state risk classification, when self-hosting pays off, or AI hiring order. Runs model_buildvsbuy_calculator.py, ai_risk_classifier.py and ai_cost_economics.py.
c-level-advisor/chief-ai-officer-advisor/skills/chief-ai-officer-advisor/SKILL.md
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SKILL.mdcore · 14,181 bytesSKILL.mdcore · 14,181 bytesSKILL.mdcore · 14,181 bytesSKILL.mdcore · 14,181 bytesSKILL.mdcore · 14,181 bytesreferences/ai_cost_economics.mddoc · 10,569 bytesreferences/ai_risk_governance.mddoc · 13,470 bytesreferences/ai_team_org_evolution.mddoc · 11,986 bytesreferences/model_buildvsbuy_strategy.mddoc · 8,046 bytesscripts/ai_cost_economics.pyruns · 16,472 bytesscripts/ai_risk_classifier.pyruns · 20,450 bytesscripts/model_buildvsbuy_calculator.pyruns · 17,089 bytes
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# Chief AI Officer Advisor
Strategic AI leadership for startup CAIOs and founders without one. **Four decisions, no AI hype:**
1. **Should we use an API, fine-tune, or build our own?** — model build-vs-buy with 3-year TCO
2. **Is this AI use case high-risk under regulation, and how do we govern it?** — EU AI Act + NIST AI RMF + US state patchwork
3. **When do we switch from API to self-hosted, and at what cost?** — token economics with breakeven analysis
4. **What AI role do we hire next?** — stage-to-role map (AI engineer ≠ ML engineer ≠ research scientist)
This skill does **not** cover tactical AI/ML engineering. For RAG implementation, agent design, prompt engineering, eval infrastructure, model deployment, or cost optimization, see `engineering/rag-architect/`, `engineering/agent-designer/`, `engineering/prompt-governance/`, `engineering/self-eval/`, `engineering/llm-cost-optimizer/`.
## Keywords
CAIO, chief AI officer, AI strategy, model selection, foundation model, fine-tuning, RLHF, DPO, LoRA, QLoRA, build vs buy, AI build-vs-buy, model risk tier, EU AI Act, AI Act Article 6, Article 9, Article 10, Annex III, prohibited AI, high-risk AI, NIST AI RMF, AI risk management framework, NYC Local Law 144, Colorado SB 21-169, Illinois HB 53, model card, eval set, eval harness, hallucination rate, jailbreak risk, prompt injection, AI red team, AI safety, alignment, model lifecycle, model registry, API-to-self-hosted breakeven, GPU economics, A100, H100, inference cost, fine-tuning cost, AI team, AI engineer, ML engineer, research scientist, MLOps, AI platform