What if you could map a company's competitive position like a chess game? Where piece placement = strategic strength, and board position = market reality.
I was building a dataset of AI companies — scoring them on funding, model count, risk exposure, market dominance, unique advantages, and ecosystem strength. Seven companies. Six dimensions. Straightforward spreadsheet work.
But then I realized something: these dimensions are exactly what matter in chess. Material (pieces/resources), Mobility (how many moves available), King Safety (exposure to threat), Center Control (dominance of key squares), Passed Pawns (unique advantages), Piece Coordination (ecosystem integration).
What if I mapped each company as a chess position, where their piece placement showed their actual strategic state? Not as a metaphor, but as a real analytic framework that happens to look like chess.
The result: a system that translates corporate strategy into visual, readable, and — surprisingly — transferable across industries.
7 AI companies scored across 6 strategic dimensions:
| Company | Source |
|---|---|
| OpenAI | Crunchbase, company disclosures, news |
| Anthropic | LMSYS Chatbot Arena, enterprise signals |
| Google DeepMind | Public announcements, GitHub model repos |
| Meta AI | Investor reports, open-source activity |
| Microsoft AI | Azure integration data, partnership announcements |
| Mistral | EU regulatory filings, research papers |
| xAI | Funding announcements, X/Twitter integration |
| Dimension | Definition | Source |
|---|---|---|
| Material | Total funding raised | Crunchbase, company disclosures |
| Mobility | Model portfolio + API integrations | Company websites, HuggingFace |
| King Safety | Regulatory risk + runway | EU AI Act compliance, burn rate analysis |
| Center Control | LMSYS ranking + enterprise adoption | Chatbot Arena leaderboard, case studies |
| Passed Pawns | Unique models with no direct competitor | Patent analysis, market differentiation |
| Piece Coordination | Ecosystem strength (cloud ties, tools, partners) | Integration announcements, partnership depth |
Raw Data Collection
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Normalization (logarithmic for funding, min-max for others)
↓
Weighting System A: Chess Perspective
(mobility & center control = 0.25 each — tactical focus)
↓
Weighting System B: Business Perspective
(material & coordination = 0.25/0.20 — structural focus)
↓
Position Generation
(score → piece placement on chess board)
↓
Interactive Visualization
(dual boards + radar charts + breakdown table)
| Ranking | Chess Perspective | Business Perspective |
|---|---|---|
| #1 | OpenAI (8.86) | Google DeepMind (8.75) |
| #2 | Google DeepMind (8.80) | OpenAI (8.50) |
| #3 | Anthropic (5.65) | Microsoft AI (6.99) |
Insight: OpenAI dominates tactically (models, mobility) but Google is stronger structurally (ecosystem, safety). Anthropic has the best center control (LMSYS #1) but lacks ecosystem scale. Microsoft dominates integration but lags on frontier models.
Companies where chess ≈ business (balanced position):
- Google DeepMind (+0.05 delta) — equally strong tactically and structurally
Companies where chess ≠ business (exposed position):
- Microsoft AI (-2.13 delta) — strong ecosystem, weak models
- xAI (-1.51 delta) — funded but underdeveloped across dimensions
Why chess, not just a table?
Three reasons:
-
Visualization clarity: A piece's position on the board immediately shows advancement. Advanced = high score. Back rank = underdeveloped. No need to read numbers.
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Transferability: This framework works for any competitive landscape where you can define 6 strategic dimensions and map them to pieces. Automotive (Material = scale, Mobility = model lineup), Pharma (Material = R&D budget, Passed Pawns = unique drugs), Fintech (Material = capital, King Safety = regulatory).
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Intuitive narrative: Non-technical stakeholders understand chess. CEOs, board members, marketing teams all immediately grasp "our king is exposed" (regulatory risk) or "we're losing the center" (market control).
Weighting logic:
Chess perspective weights mobility and center control heavily — in chess, a mobile piece in the center is more valuable than raw material quantity.
Business perspective weights material and ecosystem coordination — in business, capital and structural partnerships often matter more than feature count.
This isn't arbitrary. It's a deliberate choice to show how the same data yields different conclusions depending on what you prioritize.
Open chess_[Company_A]_vs_[Company_B].html in a browser to see:
- Dual chess boards (chess vs. business perspective)
- Real-time advantage bars
- Captured pieces (missing strengths)
- Dimension-by-dimension breakdown
- Strategic interpretation
OpenAI vs. Anthropic (Chess Perspective)
- OpenAI: Dominant mobility (pieces scattered across board), strong material, exposed king (safety risk)
- Anthropic: Consolidated center control, weak ecosystem, underdeveloped material
Interpretation: OpenAI is "attacking" but has exposure; Anthropic is defending the center but not expanding.
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Define your 6 dimensions — What matters in this industry? For automotive: scale, model lineup, charging network, autonomous capability, brand safety, supplier integration.
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Score 5-7 competitors — Use public data: financial reports, market research, news, patent filings.
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Normalize each dimension — Keep everything 0–10 scale for consistency.
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Map to chess pieces — Material→Rooks, Mobility→Knights, Center Control→Bishops, Coordination→Queen, Safety→King, Unique Advantage→Pawns.
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Generate positions — Run the Python script (provided in repo) to auto-generate board positions.
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Weigh and compare — Use dual perspectives (tactical vs. structural) to reveal strategy divergence.
Language: Python 3.14
Libraries: python-chess (board generation), pandas (data normalization), numpy (weighting calculations)
Visualization: Interactive HTML with CSS styling, no external charting library needed
Data Format: CSV input, HTML output
Current limitations:
- Scoring is partially qualitative (enterprise signals, unique models require judgment calls)
- Not real-time — based on snapshot data from June 2026
- Limited to 6 dimensions (could expand to 8-10 but readability decreases)
Data collected from: Crunchbase (funding), company disclosures (model counts), LMSYS Chatbot Arena (rankings), news sources (partnerships), EU regulatory filings (risk assessment), GitHub (open-source activity).
Normalization: Logarithmic for heavily-skewed dimensions (funding), min-max for bounded dimensions (scores 0–10).
Interpretation: Based on public market signals, not insider information.
Chess Framework by Filippo Leonetti Luparini • June 2026