Medium source | Operator essay
Game Theory, AI, Machine Learning, and LLMs
An operator note on game theory, AI, machine learning, LLMs, incentives, and strategic behavior inside complex systems.
Thesis
AI does not remove strategy. It intensifies it. The more intelligent agents enter a system, the more leaders need to understand incentives, information asymmetry, coordination, and second-order behavior.
The signal
AI, machine learning, and LLMs are usually discussed as technology waves. The deeper shift is strategic. These systems change how organizations decide, compete, negotiate, allocate resources, and respond to uncertainty.
That is why game theory matters. It helps explain what happens when many actors adapt to each other: payers and providers, buyers and vendors, platforms and users, humans and algorithms.
The operator read
The mistake is assuming smarter models automatically create better systems. A model can optimize a local objective and still create bad system behavior if the incentives are wrong.
In healthcare, this is especially important. Automating prior authorization, lead scoring, patient outreach, or care navigation changes the game for everyone around the workflow. The design has to account for trust, fairness, incentives, and human accountability.
What founders should do
Before deploying AI into a complex workflow, map the players. Who benefits, who resists, who supplies data, who absorbs risk, and who is accountable when the model is wrong?
Then decide what the AI is allowed to optimize. If the objective is unclear, the system will optimize for whatever is easiest to measure, not necessarily what the business or patient actually needs.
Operator close
The leaders who win with AI will not just use better tools. They will understand the game those tools enter, the incentives they change, and the operating system needed to govern them.