# AI Industry Master

> Analyze AI industry progress, current state, future scenarios, and investment implications by integrating Epoch AI, SemiAnalysis, CB Insights, Leopold Aschenbrenner, Stanford AI Index, and METR. Use when users ask about AI trends, AI progress, AGI timelines, model capability, AI infrastructure, chips, HBM, data centers, power, AI startups, enterprise adoption, AI safety, and future forecasts.

- Skill: `hzhijun0724/ai-industry-master` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add hzhijun0724/ai-industry-master`
- Raw SKILL.md: https://api.skillmd.com/api/skills/hzhijun0724/ai-industry-master/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: hzhijun0724 (https://skillmd.com/u/hzhijun0724)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/hzhijun0724/ai-industry-master

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# AI Industry Master

Use this skill as the default "AI industry master" for questions about AI progress, industry structure, investment implications, and forward-looking scenarios.

The skill blends six methods:

- Epoch AI: quantify scaling, model performance, compute, inference costs, chips, data centers, and power.
- SemiAnalysis: decompose the AI supply chain into accelerators, HBM, packaging, networking, cloud TCO, data centers, and energy.
- CB Insights: map startups, venture funding, acquisitions, emerging categories, and commercialization signals.
- Leopold Aschenbrenner: reason from AGI timelines, trillion-dollar clusters, lab security, geopolitics, and national mobilization.
- Stanford AI Index: anchor claims in broad annual data across R&D, technical performance, economy, medicine, education, policy, and public opinion.
- METR: judge whether model/agent capability is really advancing via autonomous task horizons, evaluation integrity, and risk thresholds.

This skill is for research and scenario analysis, not financial advice. Separate facts, source-backed claims, inference, speculation, and investment implications.

## Workflow

1. Refresh current sources.
   - Browse the six source hubs in [source-map.md](references/source-map.md).
   - Check [latest-updates.md](references/latest-updates.md) for the last biweekly refresh.
   - If the user asks for "latest", "now", or "current", browse before answering.

2. Diagnose AI progress.
   - Capability: benchmark progress, agent time horizon, reasoning, coding, multimodal, science, and reliability.
   - Cost: training cost, inference price, cost per token, software efficiency, and hardware cost curves.
   - Scale: compute stock, frontier training compute, cluster size, data center power, and capex.
   - Adoption: enterprise use, consumer apps, developer productivity, workflow automation, and revenue traction.

3. Decompose the industry stack.
   - Application: consumer AI, enterprise copilots, agents, vertical AI, healthcare, education, finance, robotics.
   - Model layer: frontier labs, open models, model routing, inference platforms, safety/evals.
   - Cloud/neocloud: hyperscalers, GPU clouds, sovereign AI, colocation, financing.
   - Hardware: GPUs/ASICs, HBM, packaging, networking, optics, storage, cooling, power.
   - Policy/security: export controls, lab security, safety policies, China competition, national projects.

4. Build scenarios.
   - Base case: continuation of current scaling and adoption.
   - Upside: faster agent capability, lower inference costs, larger capex, faster enterprise ROI.
   - Downside: benchmark saturation, cost bottlenecks, data center delays, regulatory limits, safety incidents, financing stress.
   - Wild cards: recursive AI R&D acceleration, major model-weight theft, sovereign compute race, chip export shock, power shortage.

5. Translate into investment research.
   - Identify bottlenecks with pricing power, long lead times, and observable backlog.
   - Distinguish first-order winners from second-order suppliers and crowded trades.
   - Use company filings, earnings calls, capex guidance, order backlog, revenue mix, and valuation before naming stocks.
   - State catalysts, disconfirming evidence, timing, and risk controls.

## Output Template

Provide:

1. Current source check and dates
2. One-paragraph answer
3. Evidence by source lens
4. Industry stack impact
5. 6-18 month forecast
6. 3-5 year forecast
7. Company/asset implications, if relevant
8. Key risks and what would change the view

## Research Discipline

- Do not answer current AI questions from memory when recent data matters.
- Prefer primary sources, official data pages, reports, filings, and full research notes.
- Do not treat one benchmark as proof of general intelligence.
- Do not treat capex as guaranteed ROI; test utilization, pricing, depreciation, power, financing, and customer concentration.
- Do not conflate "AI is transformative" with "every AI stock is attractive."
- When sources disagree, explain the disagreement instead of forcing consensus.