When my co-founders and I first started our beef supply chain software company five years ago, we heard the same phrase over and over again:

“They’ll never use it.”

It usually came from people who knew the cattle business well. The assumption was always the same: Cattle producers wouldn’t use software, wouldn’t trust data and wouldn’t have patience for anything that felt abstract or complicated.

I’ve since come to realize that producers will use technology. But over time, I’ve also come to understand why that skepticism made sense.

For decades, the cattle business operated on a straightforward model. Most producers were trained, either directly or indirectly, to focus on one primary question: What’s the market going to be like when I sell? Revenue came in a few large checks each year, and success often felt tied to timing, weather and factors no one could fully control. In that environment, more information didn’t add value – it just drained time and resources.

When the system outgrew human memory

From a software perspective, there’s a simple truth: When a system becomes too complex to hold in your head, you need tools to help you understand it. That’s exactly what’s happened in the cattle industry.

Beef is no longer a single, uniform product. Consumer expectations have evolved. Quality, consistency and eating experience now show up in real economic terms. At the same time, grid and formula pricing have replaced much of the old cash market, tying value to outcomes rather than averages.

That shift exposed something the old system was never built to handle. Two producers can sell cattle in the same week, to the same buyer, and see dramatically different results, not because one read the market better but because their cattle performed differently.

The commodity model didn’t fail because it was wrong. It failed because it could no longer see what mattered.

Genetics changed the conversation

Nowhere is that more evident than in genetics and genomics.

What used to be a discussion about pedigree has become a data problem. Genomic predictions, carcass traits, health indicators and performance measures now interact in ways that are nearly impossible to evaluate without support. These aren’t simple inputs, and they don’t operate independently.

From a software standpoint, this kind of information quickly exceeds what human memory can manage. It doesn’t replace experience, but it demands better tools to interpret what experience is revealing.

Genetics don't complicate cattle production. They reveal complexity that was already there.

Data as context, not replacement

One of the most common misunderstandings about data is that it replaces judgment. In practice, the opposite is true.

Data becomes valuable when it helps experienced people see patterns they already suspect but can’t quantify on their own. It provides context. It confirms instincts or challenges assumptions before mistakes become expensive.

When performance data accumulates over time, decisions shift from reactive to intentional. Producers can see where cattle tend to excel, where they consistently fall short and how management choices influence outcomes. That doesn’t remove uncertainty, but it reshapes it into something manageable.

Instead of reacting to surprises, producers start recognizing patterns.

Risk has changed

For a long time, risk in the cattle business was largely tied to price and weather. Futures markets, insurance and experience helped manage most of it.

Today, risk shows up differently. Outcome risk, grid risk, compliance risk and market access all play a role. These risks don’t always announce themselves until after the fact, often in the form of missed premiums or unexpected deductions. Without data, those outcomes feel random. With data, they become understandable. Patterns emerge, weak points repeat, and strengths become clearer.

The most dangerous cattle today aren’t the cheapest ones. They’re the ones producers don’t fully understand.

Where technology actually fits

Artificial intelligence (AI) often gets framed as something that makes decisions on behalf of people. In reality, its most practical role is far more modest and far more useful.

AI excels at processing large volumes of information and translating it into something humans can understand. It can connect dots across genetics, performance and market data faster than any individual ever could. What it produces isn’t a command. It’s clarity. Used correctly, AI doesn’t replace judgment. It supports it by reducing noise and surfacing insight.

The missing piece: Capital

As production systems have become more data-driven, the financial side of agriculture hasn’t kept pace. Capital is still slow, paperwork-heavy and often disconnected from real performance in the field. For many producers, access to financing remains one of the biggest constraints, even when their cattle and management practices are strong.

This is where decentralized finance, or DeFi, begins to matter (not as a buzzword, but as infrastructure). At its core, DeFi is simply a way for people to move and use money directly with each other using secure digital records instead of traditional intermediaries. It allows capital to move faster, with fewer middlemen, and with rules that everyone can see and verify.

In practical terms, that means capital can begin to respond to real performance instead of just paperwork, delays or rigid lending structures. Ownership, production history and outcomes can be verified digitally, allowing financing to move more quickly and more fairly. It doesn’t replace traditional lenders, but it creates new pathways – especially for producers who have strong operations but limited access to capital under conventional systems.

Like data and genetics, DeFi isn’t about replacing judgment or relationships. It’s about reducing friction. It’s about making sure good decisions aren’t held back by outdated processes.

Control is the common thread

Across genetics, data, AI and finance, the underlying theme is control. Not control over markets but control over decisions.

Control comes from understanding performance, managing risk and having access to tools that reflect reality instead of assumptions. That applies whether someone is running a cow-calf operation, backgrounding cattle, feeding cattle or developing genetics. The objective isn’t sophistication for its own sake. The objective is clarity.

So will they use it?

For years, people said, “They’ll never use it.”

What they really meant was that producers wouldn’t adopt tools that didn’t respect how this business actually works. They were right about that.

What they missed is that the business itself has changed. Genetics introduced complexity. Markets introduced differentiation. Capital introduced constraints. At some point, experience alone stopped being enough. Producers didn’t suddenly become interested in technology. They became interested in staying in control of a more complicated business.

Data didn’t arrive to replace judgment. It arrived to make sure judgment still matters.