By Joshua Goldberg, Co-Managing Partner 

 

Among the many discussions I continue to have about how AI adoption is changing the practice, a growing percentage has involved AI’s impact on licensing. This is true across a wide variety of technology areas, including in the agriculture industry. 

As AI becomes embedded throughout the agricultural ecosystem, traditional intellectual property licensing frameworks are being tested in new ways. Many existing agreements were drafted before widespread adoption of machine learning technologies and therefore fail to address critical questions regarding data ownership, model training, generated insights, confidentiality, and liability. Prospectively, many of these issues are not as widely considered as is warranted. As a result, agricultural companies, technology providers, and producers are increasingly confronting significant legal and commercial risks. 

The New Value Chain: Data Is the Crop

Historically, agricultural intellectual property centered on patents covering seeds, crop protection products, machinery, and biotechnology innovations. While these areas remain relevant, valuable agricultural IP increasingly includes:

  • Precision agriculture data
  • Soil and field performance information
  • Equipment telematics
  • Weather and environmental data
  • Drone and satellite imagery
  • Livestock monitoring information
  • AI models trained on agricultural datasets

For many agricultural operations, the most valuable asset may now be the data generated by years of activity. This shift is forcing companies to reconsider who owns agricultural data and how that data may be used. Many licensing agreements across the industry are silent regarding how data can be used, particularly whether this data can be used to improve AI systems being increasingly relied on across the industry. Agricultural producers may be willing to permit an AI tool to analyze their data, but not necessarily willing to allow that same data to improve products that are later sold to competitors.

Among the new issues to be accounted for are the following:

  • Ownership of AI-Generated Agricultural Insights
  • AI-Assisted Development of Seeds and Traits
  • Confidentiality Concerns
  • Liability and Risk Allocation
  • Supply Chain Implications

As agricultural AI becomes more sophisticated, contract language addressing ownership of outputs, derivative analytics, and model improvements will become increasingly important.

In another example, AI systems can help, e.g., identify desirable genetic traits, predict breeding outcomes, optimize gene-editing targets, analyze environmental performance, and accelerate development of climate-resilient crops. Who, then, owns the IP based on these discoveries? Licensing agreements should clearly allocate rights in future discoveries, patents, breeding technologies, and derivative innovations before any collaborative research begins. 

These are only some of the agricultural IP issues involving AI licensing, which is fundamentally about ownership and control of data-driven value. The central questions are no longer limited to who owns a seed variety, machine, or patent. Instead, companies must now also determine who owns the data generated in the field, who may use that data to train AI systems, who owns AI-generated insights, and who bears responsibility when those systems fail.

As precision agriculture, autonomous equipment, digital agronomy, and AI-enabled breeding continue to evolve, carefully crafted licensing agreements will become increasingly important. Agricultural organizations that proactively address data rights, training permissions, output ownership, confidentiality, and liability today will be better positioned to capture the benefits of AI while protecting the intellectual property and competitive advantages that drive modern agriculture.

I remain available to assist with your forward-looking licensing agreements.