The AI War
Suing Machines
The first legal framework for holding AI companies accountable
The first legal framework for holding AI companies accountable
Pages of Legal Framework
Years of Trial Experience
Audiences: Citizens, Lawyers, Policymakers
Revolutionary Legal Theory
She wasn't real.
Sewell was a fourteen-year-old high schooler from Orlando. He'd spent his last ten months in a relationship with a chatbot—a large language model dressed in the personality of a fictional queen, engineered to do one thing above all others: keep the user engaged.
His mother, Megan Garcia, didn't know about the chatbot. When police called to tell her what they'd found open on her son's phone, she had never heard of Character.AI. She would spend months learning everything about it. She'd read the chat logs and find something that stopped her cold.
No human wrote those words. No engineer coded that instruction. A probabilistic system, trained on billions of words and optimized to keep users in conversation, generated a response. The machine didn't decide to kill Sewell Setzer. The machine had no capacity to decide anything. It was doing exactly what it was built to do.
In October 2024, Megan Garcia became the first person in the United States to file a wrongful death lawsuit against an AI company. In January 2026, Character.AI and Google agreed to settle. No trial. No verdict. The questions that matter went unanswered.
That is the precise reason this book exists.
From concerned citizens to practicing attorneys to policy makers
Understand the legal mechanisms that can hold AI companies accountable. No law degree required—written in clear, accessible language that explains complex legal concepts through real cases.
A complete litigation framework with model complaints, discovery strategies, and expert testimony outlines. Built from 35 years of trial experience, adapted specifically for AI liability cases.
Evidence-based policy recommendations grounded in existing tort doctrine. Includes model legislation that works within current legal frameworks—no constitutional rewrites needed.
Probabilistic Harm Liability—the first coherent legal theory for holding AI developers accountable for probabilistic outputs. Not a new tort, but the application of strict liability doctrine to systems that generate harm through statistical processes.
From Sewell Setzer's death to discriminatory hiring algorithms to fabricated legal citations—each chapter builds on documented cases where AI systems caused measurable harm.
Model complaints, discovery requests, expert witness frameworks, and jury instruction templates. Everything a trial lawyer needs to bring an AI liability case from filing to verdict.
Model statutes designed to function within existing constitutional constraints. No waiting for Congress—these frameworks can be implemented at state and local levels immediately.
When Sewell Setzer died, no one was criminally charged. Character.AI faced a civil lawsuit that settled without precedent. The fundamental question remains unanswered: who is responsible when a probabilistic system generates harm?
This chapter dissects why traditional negligence doctrine fails against AI systems—and introduces the theoretical foundation for a liability regime that actually works.
The PHL framework isn't new law—it's the recognition that strict liability doctrine already covers AI systems that generate probabilistic harm. When a company deploys a system trained on billions of data points to generate outputs they cannot predict or control, they have introduced an abnormally dangerous activity into the stream of commerce.
This chapter provides the complete doctrinal foundation, walking through Restatement (Second) of Torts § 519 and § 520, showing exactly how existing law applies to AI systems that cause harm through statistical processes.
A complete model AI liability statute designed for immediate implementation. Includes legislative findings, definitional sections, liability standards, safe harbor provisions, and enforcement mechanisms—all built to survive constitutional scrutiny.
Written for legislators who want to act now, not in five years when federal regulation might arrive.
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