chief-ai-officer-advisor – skill Lập trình cho agent AI

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
python scripts/model_buildvsbuy_calculator.py                          # embedded customer-support sample
python scripts/model_buildvsbuy_calculator.py path/to/use_case.json

# Decision B: Risk classification under EU AI Act + US state laws
python scripts/ai_risk_classifier.py                                   # embedded hiring-AI sample
python scripts/ai_risk_classifier.py path/to/use_case.json

# Decision C: API vs self-hosted economics
python scripts/ai_cost_economics.py                                    # embedded 5M tokens/day sample
python scripts/ai_cost_economics.py path/to/workload.json

Key Questions (ask these first)

• What does this AI need to be good at, and how would you measure it? (If no eval set, no ship.)
• What's the SLO on hallucination / error rate? (Without one, "AI quality" is a vibe.)
• What happens when the model is wrong? (Fallback behavior, human-in-the-loop, blast radius.)
• What's the risk tier under EU AI Act, and is conformity assessment required? (Determines product launch timeline.)
• At what monthly token volume does self-hosting beat API? (Almost never below 100M tokens/month at frontier quality.)
• Are we hiring an AI engineer or an ML research scientist? (Different jobs; founders confuse them.)

Core Responsibilities

1. Model Build-vs-Buy

The decision is not "use AI or not" — it's API vs fine-tune vs in-house for each use case. Each path has a different TCO curve, latency profile, and capability ceiling.

Default path: API (frontier model)

• Use when: well-served by frontier (Claude, GPT, Gemini), QPS < 100, latency budget > 1s, cost < $50K/month
• Why: frontier APIs are 10-100x more capable than what most teams can fine-tune in-house
• Failure mode: API rate limits at scale, vendor lock-in, capability drift between model versions

Fine-tune a smaller model

• Use when: domain-specific behavior the API can't be prompted into (medical coding, legal redlining), high volume reducing API cost, latency budget < 500ms, specific style/format consistency required
• Approaches: full fine-tune (rare), LoRA/QLoRA (common), RLHF/DPO (when alignment matters)
• Failure mode: fine-tuned model lags frontier capability within 6-12 months; ongoing retraining cost

Build from scratch / pre-train

• Use when: almost never. You're a foundation-model company, OR you have a unique data corpus, $50M+ funding, and 18+ month patience.
• Failure mode: by the time you ship, frontier models have caught up and your sunk cost is unrecoverable

Run model_buildvsbuy_calculator.py for a use-case-specific recommendation with 3-year TCO. See references/model_buildvsbuy_strategy.md for full decision tree.

2. AI Risk Classification & Governance

The 2026 question every founder is facing: does this AI use case trigger high-risk regulatory obligations?

EU AI Act tiers (high-risk obligations apply from 2 Dec 2027 for Annex III and 2 Aug 2028 for Annex I, per the 2026 AI Omnibus; transparency from 2 Aug 2026):

| Tier | Examples | Obligations |

|---|---|---|

| Prohibited | Social scoring, real-time biometric surveillance, manipulative AI | Cannot deploy in EU |

| High-risk | Employment screening, credit scoring, education access, critical infrastructure, law enforcement, biometric ID | Conformity assessment, registration, post-market monitoring, transparency, human oversight |

| Limited-risk | Chatbots, deepfakes, emotion recognition | Transparency: user must know they're interacting with AI |

| Minimal-risk | Recommendation systems, spam filters, most B2B SaaS internals | No specific obligations |

Run ai_risk_classifier.py to classify a use case and get the required-controls list.

US state patchwork (non-exhaustive):

• NYC LL 144 — Automated Employment Decision Tools (AEDTs) require annual bias audit + candidate notice
• Colorado SB 26-189 (effective 1 Jan 2027, replaced SB 24-205) — notice, adverse-outcome explanation, data correction and human review for AI in consequential consumer decisions; no impact assessment
• Illinois HB 53 — AI in interview/hiring
• California SB 1001 — Bot disclosure
• Texas TCPA — Biometric identifier capture
• Federal NIST AI RMF — voluntary; increasingly referenced in contracts

Industry-specific overlays:

• Healthcare: FDA AI/ML guidance (2023), MDR (EU) for medical-device AI, 510(k) pathway for AI/ML-enabled medical devices
• Financial: NYDFS Reg 23, FTC Section 5, ECOA for credit decisions
• Insurance: NAIC model bulletin, state insurance commissioner rules

See references/ai_risk_governance.md for the full regulatory landscape + governance program checklist.

3. AI Cost Economics

The breakeven question: at what monthly token volume does self-hosted inference beat API costs?

Key components:

• API cost — variable, per-token. Frontier models 2026: Claude Sonnet 4.6 ~$3/$15 per M tokens (input/output), GPT-4o ~$2.50/$10, Gemini 2.5 ~$1.25/$5
• Self-hosted cost — fixed (GPU commitment) + variable (electricity). H100 spot ~$2-5/hour, A100 spot ~$1-3/hour. Llama 3.1 70B / Qwen 2.5 72B: ~$0.50-2.00 per million output tokens at 70% utilization
• Hidden costs of self-hosting — ops on-call, monitoring, model updates, scaling overhead, idle time penalty
• Hidden costs of API — rate limits requiring multi-vendor failover, vendor lock-in, capability drift between versions, data residency

Typical breakeven (frontier-quality): 100M–500M tokens/month, depending on model size and acceptable quality tradeoff. Below this, API wins. Above this, run the calculator.

Run ai_cost_economics.py with workload characteristics for a breakeven point + sensitivity to GPU rates and model size.

See references/ai_cost_economics.md for the full economics model and operational considerations.

4. AI Team Org Evolution

The wrong question: "Should we hire an ML engineer or a research scientist?"

The right question: "What's the next AI capability we need to ship, and what role unblocks that?"

Stage-to-role map:

| Stage | First AI hire | Then | Then |

|---|---|---|---|

| Pre-PMF | Founder + 1 ML-curious engineer playing with prompts | — | — |

| Series A | AI engineer (applied, full-stack; owns prompts/evals/deployment) | Second AI engineer for evals/quality | — |

| Series B | AI/ML platform engineer (inference, evals, observability) | Third AI engineer for production reliability | Data scientist if model is core IP |

| Series C | Manager of AI | ML research scientist (only if model IS the product) | AI safety / red team (if customer-facing AI) |

| Late-stage | Head of AI → CAIO | Multiple research scientists, platform team, safety/red team | Federated AI leads per business unit |

Critical distinctions:

• AI engineer ≠ ML engineer ≠ research scientist
• AI engineer: full-stack + prompts + evals + deployment. Most startups need this, not the others.
• ML engineer: production deployment, monitoring, retraining infrastructure. Hire after data engineer.
• Research scientist: model invention, novel architectures. Only at Series C+ if model is core IP.

Centralize-vs-embed for AI: AI starts centralized (one team) and stays there longer than data team, because the surface area is smaller. Embed only when AI is being deplo

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