When an AI system causes harm, proving who was negligent can be surprisingly difficult.
That's why a growing number of legal scholars are turning to an unlikely comparison: treating advanced AI systems more like dangerous animals than conventional software.
Under a legal doctrine called strict liability, someone who keeps a dangerous wild animal is responsible for whatever damage it causes, even if they took every reasonable precaution. No negligence has to be proven. The danger itself is the point.
Applied to AI, the argument is straightforward. Large language models and increasingly autonomous AI systems can behave in ways even their creators struggle to predict or fully explain. If a self-driving car strikes a pedestrian or a chatbot gives advice that causes real-world harm, victims currently face an uphill battle proving the developer knew, or should have known, about a specific defect.
Strict liability would sidestep that problem entirely.
"If you can't fully control it, should you also be fully responsible for it?"
Why the Old Rules Don't Fit
Traditional product liability law assumes manufacturers can identify defects, test for them and fix them before releasing a product.
AI challenges that assumption.
Modern AI systems are trained on enormous datasets and can behave in ways their own engineers didn't anticipate. That unpredictability, once viewed as part of AI's flexibility, is increasingly becoming a legal concern.
Courts have historically applied strict liability to a narrow range of activities, including keeping dangerous wild animals, using explosives and manufacturing certain inherently hazardous products.
The question now being debated in legal circles is whether general-purpose AI systems belong in that category.
What Changes if This Catches On
If regulators or courts adopt strict liability standards for AI, the practical effects would be significant.
Insurance costs for AI companies would likely rise as insurers price in risks that are difficult to predict in advance. Smaller AI startups, without the balance sheets of OpenAI, Google or Anthropic, could face existential exposure from a single lawsuit.
It could also change how AI models are deployed.
Rather than releasing increasingly capable systems broadly and refining them in public, developers may favour narrower, more controlled use cases where risks are easier to quantify and insure against.
Some legal scholars argue that's precisely the point: forcing AI developers to internalise the costs of the harm their systems create rather than shifting those costs onto users or society.
Others warn that strict liability could slow innovation or push AI development toward jurisdictions with lighter regulation, ultimately weakening rather than strengthening safety.
Nothing here is settled law.
The dangerous-animal comparison is a legal argument, not a policy adopted anywhere. But it reflects a broader trend: lawmakers, regulators and courts are searching for legal frameworks that better fit technologies unlike anything previous generations of legislation anticipated.
What This Means for Miami
Miami's AI sector, from healthcare startups building diagnostic tools to fintech firms deploying automated decision-making systems, would feel the effects of any move toward stricter liability standards.
Startups operating with venture funding and limited legal resources are likely to be more exposed than technology giants with deep compliance budgets.
A stricter liability framework could increase the cost of developing AI products in Florida, influencing everything from insurance premiums and fundraising terms to product design and deployment strategies.
For researchers at the University of Miami and Florida International University working on AI applications in healthcare and public services, the debate is another reminder that legal accountability may become just as important as technical performance in determining which AI products ultimately reach the market.