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

Quick Start

# Decision A: API vs fine-tune vs build