What 30 Years of Military Operations and AI Reveal About Strategy
At the intersection of defence, artificial intelligence, systems thinking, and decision science — insights from the field and the lab
AI & Military Operations
Artificial intelligence doesn't just add capabilities to military operations — it demands entirely new organizational architectures. Staff organisations designed for human cognitive limitations become counterproductive when AI can process, synthesize, and recommend at machine speed.
- AI can perform 95% of traditional staff work — the question is what replaces the staff
- AlphaStar's lesson: AI attacks everywhere at once, making 'main effort' obsolete
- Structured AI decision processes must replace intuition, not augment it
If AI eliminates the need for a main effort, what happens to the entire doctrine of concentration of forces?
Strategic Reasoning & Decision Science
Planning is too static and sequential for a world that changes faster than plans can be updated. Assumptions are the wobbly pillars on which entire strategies rest.
- Assumptions are the weakest links — yet strategies treat them as foundations
- Sequential planning fails when the environment changes faster than the plan updates
- PowerPoint enforces dimensional poverty on inherently complex problems
If your strategy rests on assumptions you haven't stress-tested, is it a strategy or a wish?
Systems Thinking & Organizational Intelligence
Organisations are systems that cannot evaluate their own axioms — they optimise internally while becoming externally obsolete.
- Organizations cannot evaluate their own axioms — Gödel applies to institutions
- You can optimise internally while becoming externally obsolete
- Measure vulnerabilities (GlobalWeakness), not capabilities — what you can endure matters more than what you can inflict
If your organisation can't question its own foundational assumptions, who will — and will it be too late?
AI Technical Foundations
There is a continuous mathematical thread from Bayes (1763) through Markov, Monte Carlo, and Gibbs sampling to modern AI. LLMs operate in 4,096 dimensions while humans juggle 7-9 variables.
- A continuous mathematical thread runs from Bayes (1763) to modern neural networks
- LLMs reason in 4,096 dimensions — humans manage 7 to 9 variables at best
- The goal is to orchestrate AI, not compete with it
If AI processes 4,096 dimensions simultaneously and you process 7, are you supervising it — or is it tolerating you?
Future of Work & Leadership
AI democratises expertise — power is shifting from 'know how' to 'imagine what.' Creativity becomes the cardinal competence when execution is automated.
- AI democratises expertise — power shifts from 'know how' to 'imagine what'
- Creativity becomes the cardinal competence when AI handles execution
- Game theory of escalation: early AI adoption creates pressure for all players
If AI makes expertise abundant, what becomes truly scarce — and who holds power?
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