chief-ai-officer-advisor

name	chief-ai-officer-advisor
description	Chief AI Officer advisory for startups: model build-vs-buy decisions (API vs fine-tune vs in-house), AI risk classification under EU AI Act + US state patchwork, AI cost economics (API-to-self-hosted breakeven), and AI team org evolution. Use when deciding whether to call an API or fine-tune, classifying AI use cases for regulatory risk, calculating when self-hosting pays off, sequencing AI hires, or when user mentions CAIO, AI strategy, model selection, foundation model, fine-tuning, EU AI Act, NIST AI RMF, AI governance, model risk, or AI economics. Strategic only — does not duplicate engineering AI/ML skills.
compatibility	opencode

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 (in force 2026) tiers:

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 AI Act / SB 21-169 — AI in consumer decisions (credit, insurance, employment, housing)
• 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 deployed in 4+ product surfaces.

See references/ai_team_org_evolution.md.

Workflows

Workflow 1: Model Selection Decision (1 hour)

Goal: Decide whether a specific use case should use API, fine-tune, or build.

# 1. Define use_case.json (volume, latency, accuracy, team size, budget)
python scripts/model_buildvsbuy_calculator.py use_case.json
# 2. Review 3-year TCO + breakeven
# 3. Cross-check with cs-cfo-advisor on budget commitment
# 4. Cross-check with cs-cto-advisor on engineering capacity (esp. for fine-tune)
# 5. Log via /cs:decide; consider /cs:freeze 60 on multi-year vendor commitment

Workflow 2: AI Risk Classification (2-4 hours)

Goal: Classify a use case under EU AI Act + US state laws, identify required controls.

# 1. Define use_case.json (decisions affected, users, geography, sector)
python scripts/ai_risk_classifier.py use_case.json
# 2. For HIGH-RISK: budget conformity assessment + registration
# 3. For LIMITED-RISK: implement transparency requirements
# 4. Cross-check with cs-general-counsel-advisor on contractual implications
# 5. Cross-check with cs-ciso-advisor on technical safeguards
# 6. Log via /cs:decide

Workflow 3: API-to-Self-Hosted Breakeven (1 day)

Goal: Decide when (and whether) to migrate from API to self-hosted inference.

# 1. Build workload.json (tokens/day, model size, latency, quality tolerance)
python scripts/ai_cost_economics.py workload.json
# 2. Run sensitivity scenarios (low/mid/high GPU rates)
# 3. Estimate migration cost (engineering time + risk)
# 4. Cross-check with cs-cfo-advisor on capex commitment
# 5. Cross-check with cs-cto-advisor on platform readiness
# 6. Log via /cs:decide; pair with /cs:freeze if signing GPU commitment

Workflow 4: AI Team Roadmap (1 week)

Goal: Sequence next 18 months of AI hires aligned to capabilities to ship.

1. List top 5 AI capabilities the product needs in 12 months
2. Map each capability to the role that ships it (see ai_team_org_evolution.md)
3. Sequence hires (one role at a time, ramp before next)
4. Cross-check with cs-chro-advisor on comp + leveling
5. Identify the centralize-vs-embed trigger

Output Standards

**Bottom Line:** [one sentence — decision and rationale]
**The Decision:** [one of: model selection | risk classification | economics | next hire]
**The Evidence:** [numbers from the tool, not adjectives]
**How to Act:** [3 concrete next steps]
**Your Decision:** [the call only the founder can make]

Adjacent Skills

• ../chief-data-officer-advisor/ — Training data rights, data product strategy (chains directly to model decisions)
• ../cto-advisor/ — Architecture capacity, scaling cliffs (esp. for self-hosted inference)
• ../ciso-advisor/ — Threat modeling for AI (prompt injection, jailbreak, training data poisoning)
• ../general-counsel-advisor/ — AI contracts (vendor liability, output ownership, training-data licensing)
• ../cfo-advisor/ — Build-vs-buy TCO math, multi-year vendor commitments
• ../chro-advisor/ — AI team hiring + comp
• ../../../engineering/rag-architect/ — Tactical RAG implementation
• ../../../engineering/agent-designer/ — Tactical agent architecture
• ../../../engineering/prompt-governance/ — Tactical prompt management
• ../../../engineering/self-eval/ — Tactical eval infrastructure
• ../../../engineering/llm-cost-optimizer/ — Tactical inference cost optimization

References

• model_buildvsbuy_strategy.md — Full decision tree + 3-year TCO components + when each path fails
• ai_risk_governance.md — EU AI Act + NIST AI RMF + US state patchwork + industry overlays + governance program
• ai_cost_economics.md — API pricing 2026 + GPU rental economics + utilization realities + migration cost
• ai_team_org_evolution.md — Stage-to-role map + role definitions (AI engineer ≠ ML engineer ≠ scientist) + anti-patterns

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Version: 1.0.0 Status: Production Ready Disclaimer: AI regulation is evolving rapidly. This skill surfaces decisions and tradeoffs as of 2026 but cannot replace qualified AI counsel for binding compliance decisions, especially under EU AI Act conformity assessments.