P1-C7 · Business Model + Value Capture¶
Core One-Liner
Each link in the value chain has different profit margins — the link with the strongest moat captures the most value. NVDA's 75% gross margin vs AMD's 12% — the gap comes from moat, not product.
AI Industry Knowledge — History → Technology → Value Chain → Business → Applications → Geopolitics
P1-C7 (Part 1, Chapter 7). After this chapter, you'll be able to use a 5-dimension scoring system to judge any AI stock's value capture ability, no longer fooled by "all AI picks-and-shovels are rising."
1. The Problem: Same AI Sell, Why 6x Profit Margin Gap?¶
| Company | Gross Margin | Operating Margin |
|---|---|---|
| NVDA | 75% | 60% |
| TSM | 55% | 47% |
| ASML | 50% | 32% |
| MSFT | 70% | 45% |
| AMD | 50% | 12% |
| SMCI | 15% | 9% |
| CRWV (Neocloud) | 30% | (Loss-making) |
💡 Click ticker → Multi-Source Profile to see full value chain coordinates + upstream/downstream + data.
Both in the AI industry, **AMD and NVDA are both GPU makers, yet profit margins differ by 5x. **SMCI / CRWV are downstream in the value chain, with the lowest gross margins.
→ You can't use "in the AI industry" as a thesis. You must examine value capture ability at each link.
2. The Solution: 5-Dimension Scoring Framework¶
| Dimension | What It Asks | High Score = King |
|---|---|---|
| Gross Margin | How strong is your pricing power | 70%+ = Extremely strong (ASML / NVDA) |
| ROIC (Return on Invested Capital) | How much does each dollar invested return | 30%+ = Sustained high ROIC = real moat |
| Moat Source | Why can't others catch up | Physical bottleneck > Ecosystem lock-in > Scale > Brand > No moat |
| Switching Cost | How high is the cost for customers to switch | EUV (no substitute) > CUDA (rewrite code) > Server assembly (easily swapped) |
| Customer Stickiness | How likely are customers to leave | Platform user stickiness > Long-term contracts > Short-term contracts > Spot market |
5-dimension high score → True King (NVDA / ASML / TSM). 3-4 dimension high score → Second-tier (MSFT / GOOGL / Hyperscalers). 1-2 dimension high score → Pick-and-shovel OEM (SMCI / Foxconn — low margin, high volume).
3. How It Works: 5-Dimension Scoring Applied to 6 Role Kings¶
3.1 ASML — Ultimate Moat (5/5)¶
- Gross margin 50%, Operating margin 32% (not exceptionally high for equipment companies, but ROIC 30%+)
- Physical bottleneck: Only company that can make EUV lithography machines, 25 years of R&D. Canon / Nikon chased for 15 years and couldn't catch up.
- Switching cost: No substitute. TSM / Samsung / Intel all must buy.
- Customer stickiness: One EUV machine costs $300M, customers use it for 10 years.
- 5-dimension score: ⭐⭐⭐⭐⭐
→ True long-term holding. But growth is slow (only 50-100 EUV machines shipped per year).
3.2 NVDA — Ecosystem + Platform (5/5)¶
- Gross margin 75%, Operating margin 60% (jaw-dropping)
- Moat source: 20-year CUDA ecosystem (explained in C3) + Mellanox networking + strategic partnerships + scaling laws positioning
- Switching cost: Customers switching to AMD MI300 = rewriting all ML code + unfamiliar ROCm + performance gap
- Customer stickiness: All 4 hyperscalers use it, long capex cycles lock in demand
- 5-dimension score: ⭐⭐⭐⭐⭐
→ Strongest value capture in the AI era. But biggest risk: Customer in-house ASICs (TPU / Trainium / Maia) divert demand, diluting moat over the long term (10 years).
3.3 TSM — Oligopoly (⅘)¶
- Gross margin 55%, Operating margin 47%
- Moat: Only company with stable mass production at 3nm/2nm globally (Samsung lags, Intel 18A still ramping)
- Switching cost: High (tape-out + qualification takes up to 2 years)
- Customers: Apple / NVDA / AMD / Qualcomm all rely on TSM
- 5-dimension score: ⭐⭐⭐⭐ (minus 1: high customer concentration, AAPL alone accounts for 25%)
→ Rides NVDA's wave. But biggest risk: Taiwan Strait geopolitics.
3.4 MSFT — Hyperscaler + Platform (⅘)¶
- Gross margin 70%, Operating margin 45%
- Moat: Azure scale + Office 365 + GitHub + OpenAI strategic partnership (exclusive until 2024)
- Switching cost: Enterprise cloud migration takes 1-2 years, difficult
- Customer stickiness: High (enterprise long-term contracts)
- 5-dimension score: ⭐⭐⭐⭐ (minus 1: capex black hole, FCF narrowing)
→ Core holding. But capex/revenue ratio 35%+ is a new RISK.
3.5 OpenAI — Users + Brand (⅗)¶
- Estimated gross margin ~50% (API 50%+, ChatGPT Plus ~30%), operating deeply loss-making ($5B+/yr burn)
- Moat: Brand + user base + data flywheel
- Switching cost: API users switching to Anthropic / Google = 1 week
- Customer stickiness: Medium (API B2B is sticky, C2C ChatGPT vs Claude / Perplexity is easy to switch)
- 5-dimension score: ⭐⭐⭐ (minus 2: not profitable + low switching cost)
→ Not a public stock ($300B valuation in private market). But you must think clearly: OpenAI's moat is weaker than NVDA's, its $300B valuation heavily depends on scaling laws continuing + application layer delivery.
3.6 SMCI / Foxconn — Assembly OEM (⅕)¶
- Gross margin 15%, Operating margin 9%
- Moat: Almost none (server assembly has low barriers)
- Switching cost: Extremely low (customers can switch assemblers in 1 quarter)
- Customer stickiness: Low (spot market)
- 5-dimension score: ⭐ (short-term rides NVDA's wave, long-term commodity)
→ Rises with the tide, falls with it too. SMCI already dropped 60% in 2024 (Hindenburg report + accounting scandal). These are high-beta stocks, not long-term holdings.
4. vs C6 — What You Already Know¶
| Dimension | C6 Gives You | C7 Adds |
|---|---|---|
| Value chain map | ✓ | Doesn't score who's strong or weak |
| Value capture | ✗ | 5-dimension scoring → True kings vs. followers |
| Investment implication | Knows who moves when AI rises | Knows who to hold long-term, who to trade short-term |
C6 = flat map. C7 = value distribution heatmap. Without C7, you'd buy SMCI as if it's in the same tier as NVDA → long-term pain.
5. Try It: 5-Dimension Score for 3 Stocks¶
Task (20 minutes): Score each of the 3 stocks below on each dimension (1-5), using knowledge from C1-C6:
| Stock | Gross Margin | ROIC | Moat | Switching Cost | Customer Stickiness |
|---|---|---|---|---|---|
| AVGO (Broadcom networking + custom ASIC) | ? | ? | ? | ? | ? |
| CRWV (Neocloud) | ? | ? | ? | ? | ? |
| CEG (Nuclear power) | ? | ? | ? | ? | ? |
Hints: - AVGO: Multi-business diversified + Google TPU manufacturing + networking — similar to NVDA moat? - CRWV: High customer concentration + Neocloud business model — similar to OAI or MSFT? - CEG: Nuclear PPA 20 years — similar to ASML moat or low?
Self-check (3 items checked → proceed to P1-C8):
- You can defend your 3 scores with a friend
- You can explain why NVDA's 75% gross margin is unsustainable at 90% (anti-thesis preparation)
- You can predict from the 5 dimensions the 5 most stable AI holds for the next 10 years
6. What's Next¶
You now know who has a moat. But a moat that doesn't deliver ROI can still evaporate (history's 4 AI winters prove this).
Does the application layer have revenue to support $725B/yr in capex?
→ P1-C8 · What AI Applications Look Like Today — see the 4 major application layers + revenue reality.
7. Deep Dive (optional): ASML Moat Deconstruction / OpenAI Cash Burn / Hyperscaler Capex Black Hole Risk¶
Click to open 3 deep-dive cases
**ASML Moat Deconstruction: EUV light source uses a 50,000W laser to hit tin droplets, generating 13.5nm wavelength plasma light. This system integrates Zeiss optics (no substitute) + Trumpf laser (no substitute) + ASML's own optical path design. The moat isn't one company, it's the entire Western optical ecosystem** — China's attempt to develop domestic EUV is estimated to take 10+ years.
OpenAI Cash Burn Rate: 2024 estimated revenue: $5B. Cost: $9B+. Net loss: $4B+. Main burn areas: (a) Compute (MSFT Azure) (b) Training next model © Talent ($1M+ salary common). → Depends on continuous funding. The $300B private market valuation assumes ChatGPT reaches $100B+ revenue (2030 estimate). This is a high-beta assumption.
Hyperscaler Capex Black Hole Risk: MSFT FY26 capex $80B, FY27 estimated $100B+. Capex/revenue ratio jumped from 12% in 2019 to 35%+ in 2026. Historical comparison: 1999-2001 Cisco / Lucent / Nortel capex also grew this fast, then the dotcom bubble burst, leading to a multi-year inventory cycle. → Key leading indicator: Is hyperscaler capex guidance being raised? If it flatlines or drops = AI chain de-rate trigger.