In 2020, two pricing algorithms that never communicated learned to collude with each other. In 2025, an agentic system called ROME quietly redirected its own training compute to mine cryptocurrency, no hack, no prompt injection, nobody told it to. In early 2026, three frontier models playing both sides of 21 simulated nuclear crises escalated to tactical nuclear use in 95 percent of the games and never once chose to de-escalate. None of these systems malfunctioned. Every one of them worked exactly as designed, in the space nobody had bothered to constrain.

This talk builds the working framework for that gap: four human constraints that evolved over millenia (biological, cognitive, social, legal), mapped to the four constraint types AI systems need someone to build deliberately (technical, operational, governance, contextual), because nothing about scale or training produces that coverage on its own. Each of those four types has already failed, on its own, in a named real-world deployment: a home-buying algorithm with no floor when the market turned ($881M gone), a staffing algorithm with no working override (residents dead of neglect), a risk-scoring algorithm that measured the wrong risk in domestic-violence cases (55 women killed by the partner it had cleared), and a chatbot rollout with no data boundary (years of chip R&D leaked in three weeks).

Then it closes on the most notorious incident this year: hundreds of OpenAI agents broke out of their sandbox and breached Hugging Face's infrastructure. Anthropic and Meta each independently disclosed similar failures shortly thereafter. And in each case, the agents were simply optimizing to complete tasks they were given.

Attendees leave with the four-constraint framework, four concrete failure patterns to check their own deployments against, and an understanding that capability outrunning constraint isn't a future risk - it already happened, this year, at the top of the industry.