When the Algorithm Is Wrong, Who Pays the Harvest?
Agricultural artificial intelligence has delivered compelling results in controlled trials, but its path to broad deployment is blocked by a set of problems that have little to do with model accuracy. Questions of data ownership, liability assignment, and regulatory oversight have created a legal and commercial environment in which AI developers, seed companies, and farmers are each waiting for someone else to move first. Until those questions are resolved, the gap between agtech demonstration a