Artem Milinchuk, Founder and Head of Strategy of FarmTogether.
Artificial intelligence is reshaping industries from finance to healthcare. In some sectors, entire business models are being redefined by automation, predictive analytics and machine learning. Yet farmland sits in a unique position: It is both one of the few asset classes resistant to digital disruption and simultaneously a sector where technology—including AI—could meaningfully redefine how it is managed in the future.
For investors, this duality is worth examining. On the one hand, farmland represents a scarce, irreplaceable asset whose fundamental value cannot be coded away: U.S. farmland has been shrinking steadily since its mid-20th century peak of 1.16 billion acres, falling to 876 million acres in 2024.
On the other hand, the integration of AI and agricultural technology is beginning to change how farmland is analyzed and operated, with research from McKinsey estimating AI could deliver up to $500 billion in additional value to global agriculture by 2030.
Farmland As An AI-Resistant Asset
At its core, farmland is a biological system rooted in soil, water and sunlight. Unlike software or digital services, it cannot be replicated, automated or dematerialized. Global population growth ensures enduring demand for food, and the arable land supply is limited.
These physical characteristics underpin farmland’s long-term investment appeal. Historical data underscores this resilience, showing that farmland has produced durable returns with lower volatility than equities and commercial real estate.
In a world where AI threatens to commoditize many forms of knowledge work, farmland stands apart as an “AI-proof” anchor—a tangible, income-generating real asset not subject to replacement by algorithms.
AI’s Expanding Role Within Agriculture
At the same time, AI holds potential to reshape how farmland is operated and optimized in the years ahead. Agricultural technology companies are beginning to apply machine learning and automation across nearly every stage of the production cycle. Emerging examples include:
• Precision agriculture: AI-powered drones, imaging and sensors are enabling more efficient use of inputs such as water, fertilizer and pesticides, reducing costs and environmental impacts.
• Water and resource management: Predictive AI models are being used to optimize irrigation scheduling and resource allocation, helping farmers improve crop productivity and reduce waste.
• Crop forecasting: Machine learning models that integrate weather, soil and historical yield data are increasingly used to predict harvest outcomes, supporting farm planning and risk management. Studies show these approaches can achieve high accuracy—in some cases exceeding 90% in yield prediction.
• Labor and mechanization: Robotics guided by AI are beginning to take on labor-intensive tasks such as weeding, spraying and harvesting specialty crops, while AI-enabled weeders are using computer vision to distinguish crops from weeds in real-world field trials.
• Supply chain optimization: Predictive analytics are being applied to agricultural supply chains to improve demand forecasting, logistics and inventory management. Practical examples also show AI-driven systems streamlining cold-chain logistics and reducing waste through crop life cycle analysis.
The promise of AI in agriculture is not to replace farmland’s value, but to enhance productivity, improve sustainability and reduce risk.
The Tension Investors Should Recognize
This dual narrative—resistant asset, yet tech-enabled operations—creates an interesting tension for investors. On the defensive side, U.S. farmland has historically delivered attractive diversification benefits, with the NCREIF Farmland Index averaging annual returns of 10.15% over 33 years at a volatility of 6.82%, compared with 10.49% returns and 17.59% volatility for U.S. stocks.
These results stem from the land’s biological production, the essential demand for food calories and the physical scarcity of arable acres. As evidence, the U.S. had only 328 million acres of cropland in 2024, down from 334 million in 2023—reinforcing that cropland acreage is stable or declining and cannot expand indefinitely. Unlike digital assets, farmland acreage is finite and cannot be replaced by code.
On the offensive side, the adoption of artificial intelligence and other agricultural technologies is projected to improve farm operations. Research indicates these tools can boost yields and improve input efficiency, as well as strengthen resilience through better risk management and resource planning. These gains accrue not because the land is replaced, but because it is better managed.
For investors, the key is to recognize both dynamics: Farmland provides resilience against digital disruption while also offering exposure to potential efficiency and sustainability gains from technological adoption.
Risks And Considerations
Balanced analysis requires acknowledging risks on both fronts:
Technology adoption risk: Implementing AI tools can be capital-intensive and requires operator expertise. Not all farms are equally positioned to integrate advanced technology.
Market dynamics: If AI significantly boosts yields, supply increases could affect commodity pricing, offsetting some benefits.
Data and privacy: Agriculture remains fragmented in terms of data ownership, raising questions about how insights are captured and monetized—a challenge noted by the Food and Agriculture Organization of the United Nations.
Climate and environmental risk: While AI can improve resilience, farmland remains exposed to drought, extreme weather and shifting growing zones—risks that cannot be fully mitigated by technology.
Liquidity: As with most real assets, farmland investments are long-term.
These factors underscore the importance of disciplined evaluation of both the land and the operational strategies applied to it.
The Dual Future Of Farmland
Farmland’s relationship with AI is complex. The land itself remains immune to digital replacement—a scarce, biologically productive asset that retains its value precisely because it cannot be replicated. At the same time, farmland operations are beginning to integrate AI and other digital tools, with the potential to improve efficiency, resilience and sustainability over time.
For investors, this combination is notable. Farmland is simultaneously a defensive anchor against technological disruption and a sector where technology could redefine performance in the years ahead. That duality makes it one of the most distinctive real assets in today’s investment landscape.
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