Watch out, farmers. Computers are after your jobs, and they’re coming fast.

According to Tesla and SpaceX founder Elon Musk, artificial intelligence (AI) will beat humans at just about everything by 2030. We’re already seeing the fast-food worker at the local McDonald’s replaced by an interactive kiosk, so are farmers’ livelihoods next?

Yes. That is, if you believe some of the really terrifying research coming out of the University of Oxford. Bottom line: it’s not looking good for humans no matter their profession.

Fast-Paced Change. According to the report, machines will be superior to us in translating languages by 2024 and writing school essays by 2026.

Within 10 years, computers will be better at driving a truck than us, and by 2031, they will be better at selling goods and, thus, may put millions of retail workers out on the streets. Farmers shouldn’t be all that surprised as tractors have been driving straighter rows than they can for already more than a decade.

If you believe the computer experts that are often quizzed by university researchers, then every single human job will be automated within the next 120 years.

But don’t be too quick to turn over the keys of the farm to a silicon-based life-form just yet. Agriculture at the ground level may very well be the final frontier when it comes to industries to be conquered by artificial intelligence.

Our Industry Is Complex. Agriculture is one of the most difficult fields to contain for the purpose of statistical quantification. The real “veterans” of agriculture know exactly why agriculture does not fit nicely into the digital box of ones and zeros. 

In simple terms, agriculture is complex. Very complex. How complex, you ask? Well, Mother Nature and agriculture have already set one of the planet’s largest computing companies, numerous high-profile agricultural firms and associated industry specialists back on their heels when it comes to the field of artificial intelligence.

In 2011, IBM through its research and development headquarters in Haifa, Israel, launched what was supposed to be a groundbreaking agricultural cloud-computing project. The project had one goal—to take volumes of academic and physical data sources from an agricultural environment and turn those ones and zeros into easy answers for farmers. In other words, the Big Blue computer would outthink and outflank Joe Farmer in making critical, real-time decisions for a growing crop.

Learning Curve. It was the consensus of many of the IBM project team members that they thought it was entirely possible to “algorithm” agriculture.

Take it a step further, and algorithms could solve any problem in the world. Why shouldn’t they think that? IBM’s “learning” supercomputer system named Watson competed in the game “Jeopardy” against former game-winners and organic-based life-forms Brad Rutter and Ken Jennings. During that game, Watson wiped the floored as artificial intelligence notched its first high-profile victory over human intelligence.

In the years that followed, Watson cracked the code that led to many groundbreaking achievements in the field of medicine. Quietly behind the scenes, IBM’s agricultural computing projects were being cut back or shuttered entirely. Conclusion: Mother Nature wiped Watson’s floor and showed that even the unbelievably complex field of medicine is infinitely easier to compute than a single field within agriculture.

IBM didn’t need a supercomputer to tell it that agriculture is a tough nut to crack. The company simply should have asked a seasoned farmer. A farmer could have told the company that no crop year is exactly the same. No field or even a single square foot of dirt is exactly the same, and at the end of the day, Mother Nature always has a few curveballs up her sleeve. Agriculture at the ground level is not for the faint of heart no matter how smart you are.

Two Time Frame Obstacles. Musk’s timeline of AI dominance of agriculture by 2030 may be way off for two reasons: too many variables and too little data. Floods, heat and hail along with bugs and disease, combined with herbicide-resistant weeds and just bad timing, can turn a good crop bad with a drop of the hat. Then, there is the issue that in order for AI to work, it must be fed mountain upon mountain of data. Good data. As much as the precision ag industry would like to pat itself on the back for all of its advances, the truth is that most of the practical field-level digital data collected to this point has as many holes in it as a good slice of Swiss cheese.

Despite all of this, the insertion of artificial intelligence into agriculture is still coming. These are machines. They do not sleep. They do not get tired, and they will keep getting smarter and faster. Those who embrace AI, stay ahead of it and harness its power will likely benefit immensely. Those who don’t will likely be out on the street with the workers from McDonald’s and Macy’s.