In the past two years, IP professionals at every level have begun experimenting with AI tools to accelerate their work. Some of those experiments have been productive. Many have been frustrating. And a significant number have produced outputs that looked compelling until someone with real patent expertise examined them closely.

As large language models find their way into high-stakes professional workflows, the challenge is no longer whether they can produce plausible answers, but whether those answers can be trusted. The difference lies not in the intelligence of the model itself, but in the tools it can access and the control experts have over how it is used. A simple calculator analogy helps make this distinction clear.