Ten years ago, a coalition of farm groups set out to answer a question that was just starting to worry farmers: What happens when data that used to sit on the farm moves to the cloud? Today, Ag Data Transparent (ADT) turns 10, and that question hasn’t gone away. But the list of concerns has also grown: What happens when AI is trained on that data?
“There was just a real groundswell of interest and concern from farmers and from their members that there was something new happening on the farm,” says Todd Janzen, an agricultural attorney and ADT’s administrator. He credits American Farm Bureau, and Mary Kay Thatcher there at the time, with recognizing it and getting the effort started.
A shift to the cloud
Startups and legacy companies alike were building ag data platforms, and the way farm information was stored was about to change.
“There was going to be this shift in the next few years from information stored on the farm to information stored in the cloud with these third parties,” Janzen says. The open question was whether that meant giving up control and “losing some leverage in the marketplace,” or whether it would empower the farmer.
The answer, farm groups decided, was a set of ground rules that would “put the farmer in a fair bargaining position.” The effort quickly became industry-wide, drawing in National Farmers Union, the corn, soy, sorghum, potato and wheat groups, and for a time National Pork.
The startup boom, then the slowdown
“There was a period of time when my phone rang quite a bit with calls from new ag tech startups who needed help with data contracts,” Janzen says. That wave has since crested. Venture capital “really increased until about 2021,” he says. “And then we started to see that be the top of the bell curve. And then it’s tapered off ever since then.”
Janzen points to AgFunder figures showing ag and food tech investment rising from about $2 billion in 2013 to more than $50 billion at its 2021 peak, and trending down since. He sees the same pattern in his own practice, and he believes Monsanto’s acquisition of Climate Corp helped kick it off. That deal “drove a lot of investment, and then we’ve seen it taper off,” he says.
What the data is worth now
Janzen says the early promise of benchmarking, in which farmers upload data and compare yields and practices with their peers, was oversold and underdelivered. The gains are real but incremental, not revolutionary, he says.
The value has shown up elsewhere. Farmers now use ag data to earn premiums for crop traceability, to prove to buyers that certain practices were followed, and to demonstrate conservation methods. Those background uses weren’t the selling points a decade ago, Janzen writes, but in 2026 “they are delivering.”
Trust: earned, but not settled
“There probably is more trust from farmers from some of these platforms that are out there today that have been around for a long time,” Janzen says. “If you’ve used them for years and years, you start to realize, okay, nothing nefarious did happen with my data.”
Still, the core worries are familiar.
“You still hear a lot of those same concerns that were echoed 10 years ago, which are, you know, can I trust this company? Are they going to keep my data private, or is it going to be shared?” he says. Another is continuity: “What’s gonna happen with my data in the long run if this company goes out of business or is sold?” Whether the industry has earned lasting trust is a question Janzen leaves to farmers. “I’ll leave that up to them to decide,” he says.
AI rewrites the ground rules
“Farmers should know if their data is going to be used to train AI models,” Janzen says. “I think it’s legitimate for farmers to know … how is my data going to be used by these new AI platforms, and do I have the option of opting in or opting out?”
ADT’s own principles have had to keep up. In 2025, a committee revisited the organization’s core principles and decided they needed to address AI.
He says he sees a lot of work still ahead.
“Some companies just jump right in, and just assume every user wants to use their AI tools,” he says, while others ask for permission up front. “We’ve got to sort out which way we’re going to go in agriculture with respect to these AI tools, and are we going to give farmers that choice, too?”
The concern is bigger than it was in the days of simple machine-learning tools. Generative AI can inform decisions and streamline paperwork far more broadly, but it also raises anxiety in the familiar areas of trust, privacy and uncertainty, because users often can’t tell how their data will be used.
AI may also be part of the fix.
“You can use AI to help pick apart a contract now in a way that you couldn’t 10 years ago,” Janzen says, which could make agreements more transparent and easier for farmers to understand. “But the proof is in the pudding,” he adds.
A word for retailers and advisers
Janzen’s advice to retailers and other trusted advisers who use these tools on a farmer’s behalf starts with the paperwork.
“Make sure that the consents all align,” he says. Beyond that, he says, “don’t ever get out ahead with the technology, ahead of the trust level that you have with your farmers.”
His example is AI-driven pricing. Surge or dynamic pricing “might be available to an ag retailer now because of AI and the dynamic pricing models that are out there, but may not be in their best interest either if farmers find out that that sort of pricing model is being used,” Janzen says.
The North Star
After 10 years, the priorities haven’t moved. “We’ve always been focused on transparency, simplicity, and trust, and I don’t see that changing,” Janzen says. As new technologies arrive, he says, “those concerns stay the same, and that seems to be the North Star that we continue to focus on.”
