By Artem Milinchuk, Founder & Head of Strategy at FarmTogether, a farmland investment manager

Artificial intelligence is transforming industries across the global economy. Algorithms now write software, analyze financial markets, and power autonomous systems that once required human judgment. In some sectors, companies are already pointing to AI-driven efficiency as a reason they need fewer workers.

Yet even as the world becomes increasingly digital, some things remain fundamentally physical. 

AI cannot replace food as our primary source of calories.

No algorithm can grow food without soil.

No data model can replace water, sunlight, or the biological processes that allow crops to grow.

Few industries sit more directly at the intersection of this reality than agriculture. Artificial intelligence is beginning to reshape how farms operate — improving crop monitoring, forecasting yields, and optimizing the use of water and fertilizer. But even as technology advances, food production still depends on one irreplaceable input: land.

Unlike software, farmland cannot be replicated, scaled, or copied.

Technology Has Always Advanced Agriculture

Agriculture has long evolved alongside technological innovation. The mechanization of farming in the twentieth century dramatically increased productivity. The widespread adoption of tractors reduced labor requirements, while irrigation systems expanded the amount of land that could be cultivated in arid regions. Later advances in seed genetics, crop protection chemicals, fertilizers, and farm machinery significantly increased agricultural productivity and crop yields. 

Artificial intelligence represents the next stage in that progression.

Today’s farms generate enormous amounts of data through satellite imagery, soil sensors, drones, and GPS-enabled farm equipment. Machine learning systems can analyze these datasets to detect patterns in crop health, soil conditions, pest pressure, and weather variability.

Precision agriculture tools powered by AI can help farmers optimize planting density, apply fertilizer only where needed, and monitor crop stress in near real time. According to McKinsey, digital and precision agriculture technologies could increase farm productivity while improving the efficiency of inputs such as water, fertilizer, and crop protection.

These tools allow producers to make more informed decisions about planting schedules, irrigation timing, and crop management. In practice, artificial intelligence is becoming a powerful decision-support tool that helps farmers manage increasingly complex agricultural operations with greater precision.

However, the key word is support.

Technology can improve how land is managed, but it does not eliminate the need for the land itself.

Food Production Still Relies on Physical Land

Even as agricultural technology advances, the foundations of food production remain rooted in biology and natural systems. Crop yields ultimately depend on soil quality, water availability, climate conditions, and the biological growth cycles of plants.

Those constraints matter because global food demand is projected to rise significantly in the coming decades.

The United Nations projects that the global population could reach 9.7 billion people by 2050, up from roughly 8 billion today. To meet that demand, the Food and Agriculture Organization estimates that global agricultural production may need to increase by roughly 50% by 2050 compared with 2012 levels.

At the same time, the amount of farmland available globally is not expanding meaningfully. In many countries, agricultural land is gradually being converted to urban development, infrastructure, or other uses.

In the United States, the USDA reports that total farmland has declined from roughly 900 million acres in 2017 to about 876 million acres in 2024, reflecting the gradual conversion of agricultural land to non-farm uses.

This dynamic highlights an important reality: technological innovation may improve how efficiently land is used, but it does not remove the underlying scarcity of productive farmland.

Artificial intelligence can help farmers grow more food per acre. It cannot create new acres of fertile soil.

AI Relies on Data Generated From Real Farms

Another important limitation of artificial intelligence is that it ultimately depends on data generated from real-world farming environments.

Agricultural AI models are trained on satellite imagery of actual fields, sensor measurements from real soil conditions, and historical crop performance data from farms operating in specific climates and regions.

These datasets reflect biological processes taking place in living plants and ecosystems. Algorithms can analyze the patterns, but they cannot replace the underlying systems that generate the data.

This is fundamentally different from many digital industries where artificial intelligence can replicate or simulate the core product. In agriculture, the output—food—must still be physically grown in the ground.

The result is that artificial intelligence functions primarily as an efficiency tool layered on top of existing agricultural systems.

It improves management decisions, but it does not replace the core physical inputs of farming.

AI Infrastructure Is Expanding — and Competing for Land

Artificial intelligence is driving a massive expansion of digital infrastructure.

Training and operating advanced AI models requires enormous computing capacity, which is typically housed in hyperscale data centers. These facilities consume large amounts of electricity and require substantial physical space.

According to the International Energy Agency, global data-center electricity demand could more than double by 2030, driven in part by the rapid growth of artificial intelligence computing.

To support this demand, technology companies are increasingly developing new data center campuses across the United States and other regions with access to land, power infrastructure, and cooling capacity.

Many of these projects are being proposed in rural areas where land is more available and where energy infrastructure can support the large power requirements of modern computing facilities.

In some cases, developers have explored converting agricultural land or nearby rural properties into sites for large-scale computing infrastructure as demand for power-intensive AI data centers accelerates.

This trend raises an increasingly important land-use question: how should limited rural land be allocated between digital infrastructure and food production?

Data centers power the digital economy. Farmland powers the global food system. The two uses are not interchangeable.

As demand for computing infrastructure continues to expand, decisions about how land is allocated may become increasingly consequential.

Implications for Land Use and Agricultural Investment

The rapid expansion of artificial intelligence is already reshaping the economic geography of land use. Large-scale data centers require significant physical infrastructure, reliable electricity, and access to land — factors that are increasingly drawing technology investment toward rural regions where land and power capacity are available.

As digital infrastructure expands, policymakers and local communities may increasingly face trade-offs between supporting new technology investment and preserving productive farmland.

For investors, this dynamic highlights an important structural reality: while artificial intelligence may transform many industries, it does not alter the physical constraints that govern food production.

In that sense, the growth of artificial intelligence may ultimately reinforce the long-term strategic importance of farmland. As agriculture becomes more data-driven and technologically sophisticated, the value of well-located, productive farmland may increasingly reflect both its biological output and its role within a more complex global food system.

The Physical Foundations of the Food System

Artificial intelligence can improve how land is managed. It cannot replace the land itself.

In a world increasingly shaped by digital technologies, agriculture serves as a reminder that some assets remain fundamentally rooted in the physical world. Food still comes from soil. And soil still requires land.

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