How should startups address AI intellectual property and patent strategies in 2026?

Startups should patent narrow, applied uses of AI, and default to trade secrets for everything else. A general machine learning method alone is usually not patentable under current USPTO and EPO guidance, so training data, prompts, and fine-tuning pipelines are better protected as trade secrets than as filings.

What should actually be protected, and how?

A specific applied system, for example a defined pipeline that combines a model with a particular business process, can sometimes clear the patent bar even when the underlying ML technique behind it can't. Training data, prompt design, and fine-tuning choices are usually better handled as trade secrets, covered by NDAs and internal access controls, since a patent application would require publishing exactly the detail a competitor wants. A third option worth knowing: a defensive publication, putting a method on the record publicly without filing, which doesn't create an exclusive right but does stop a competitor from patenting the same idea later and locking a startup out of its own approach.

When should outside counsel get involved?

Before a fundraising round, once investors start IP due diligence and expect a clear answer about what's actually protected. And before publishing research, open-sourcing code, or presenting technical details publicly, since disclosure can cut off patent eligibility and end trade secret protection in the same move.