- The "Loss-Model" State
- Current Market Trend: (e.g., Aggressive land-grab vs. focus on operational efficiency)
- Primary Growth Driver: (e.g., Infrastructure scaling, R&D spend, or enterprise adoption)
- The Profitability Split: Briefly distinguish between "Pure-Play Labs" (OpenAI/Anthropic) and "Ecosystem Providers" (Google/Meta/Microsoft).
-
Vendor Financial Performance Matrix
Vendor Current Status Annual Revenue
(Projected)Net P&L Strategic Moat [Vendor Name] [Profitable/Loss] $[Value] $[Value] [e.g., Vertical Integration] [Vendor Name] [Profitable/Loss] $[Value] $[Value] [e.g., Open Source Dominance] -
Unit Economics: The "Token Margin" Analysis
Ranked from Most Profitable to Least based on estimated Gross Margin per 100M Tokens.
-
[Vendor A]: $[Profit/Loss]
- Pricing Context: Current market rate per 1M tokens.
- Efficiency Note: (e.g., Custom silicon vs. rented compute)
-
[Vendor B]: $[Profit/Loss]
- Pricing Context: [Details]
- Efficiency Note: [Details]
-
[Vendor C]: $[Profit/Loss]
- Pricing Context: [Details]
- Efficiency Note: [Details]
-
-
Competitive Deep Dive
- The "Pure AI" Burn: Analyze why specialized labs are operating at a deficit (e.g., Training $100B models).
- The Ecosystem Advantage: Explain how tech giants subsidize AI costs through existing revenue streams (Ads, Cloud, Hardware).
- The "Margin Tax": Identify which vendors are paying a premium for compute versus those owning the hardware stack.
How to use this template:
- Update Quarterly: Use 10-K and 10-Q filings for public companies (Google, Meta, MSFT).
- Track Token Pricing: Monitor API pricing pages for OpenAI and Anthropic to update Section 3.
- Infrastructure News: Adjust the "Efficiency Note" based on new chip releases (e.g., Blackwell, TPU v6).