Benjamin Bahr
Agriculture may not be the first sector investors associate with artificial intelligence because much of the market’s attention has gone to semiconductors, hyperscalers and large language models. But some of the most important long-term applications of AI may emerge from the real economy, particularly in businesses tied to real assets, which feature physical constraints and essential demand.
Few industries fit this description better than agriculture. It is an industry defined by scarcity: Labor is tight, arable land is finite, water matters, and timing is critical. These constraints pose material risks, but technologies that can help improve yields, reduce waste, lower input costs and make farming operations more efficient may meaningfully improve economic value. In that sense, agriculture is one of the clearest examples of where AI can move beyond hypothetical narratives and drive real productivity gains.
Useful Case Study
Farmers today are operating in an environment characterized by persistent volatility. Labor constraints, fluctuating commodity prices, higher financing costs and rising demands on food production have all increased the importance of maximizing efficiency. At the same time, global supply chain disruptions and geopolitical tensions have propelled food security and domestic production resilience higher as both economic and political priorities. Those pressures are accelerating adoption of smart farming technologies powered by AI, automation and data analytics.
This is not simply a story about AI-enhanced software and analytics. The industrialization of AI requires physical infrastructure, sustained research and development investment, and deep domain expertise. In agriculture, that may favor equipment manufacturers with significant engineering capabilities, large installed bases and a deep understanding of customer operations.
Never miss a story — sign up for CIO newsletters to stay up-to-date on the latest institutional investment industry news.
These competitive dynamics may resemble those seen in other industrial transitions. Companies that can invest consistently in R&D, can integrate AI capabilities into mission-critical equipment and can build long-term customer relationships may strengthen their market positions as adoption expands. In sectors such as agriculture, these advantages are often reinforced by dealer networks, servicing capabilities and installed customers actively using their existing products that can create meaningful barriers to entry for potential competitors.
For institutional investors, the broader implication is that AI’s economic impact is likely to extend well beyond technology companies, where much of the AI narrative has focused on the digital ecosystem enabling computational scale. We believe some of the more durable gains may emerge over time in sectors where AI improves tangible real-world outcomes. In our view, agriculture is a useful case study because it brings together several long-term themes likely to matter for years to come, including scarcity, food security, industrial digitization and productivity enhancement.
The farming industry has historically been an early adopter of technologies that improve efficiency and mitigate uncertainty. Over the past several decades, advances such as GPS-guided tractors, automated irrigation systems and data-driven crop management tools have steadily modernized agricultural operations. AI appears to be the next phase of that evolution, as modern agricultural equipment increasingly incorporates machine learning and sensor-driven systems capable of improving decisions in real time. Precision agriculture technologies can help reduce waste, improve yields and lower input costs, while making operations more scalable and efficient. Autonomous and semi-autonomous equipment may also help farmers manage labor shortages, particularly during narrow planting, spraying and harvesting windows.
The latest smart farming technologies are increasingly capable of collecting and analyzing large volumes of field-level data. Sensors embedded in equipment can assess soil conditions, monitor crop health and optimize the use of fertilizer, pesticides and water with a level of precision that would have been difficult to imagine only a decade ago. The result is greater efficiency, lower resource waste, better output consistency and, potentially, better economics for farmers operating in a business in which margins can be tight and the cost of mistakes catastrophic.
Finding Value
For investors, we believe the more important question is where the value accrues. In the agricultural sector, operational expertise, engineering capability and customer relationships can take years to build. The dealer networks, servicing infrastructure and long-standing customer loyalty that benefit large equipment manufacturers may become even more valuable as AI-enabled capabilities become more deeply embedded in equipment platforms.
There may also be an important service element to this story. As equipment becomes more connected, more reliant on software and more driven by data, customer relationships may become more embedded because of ongoing service, maintenance, software support and data integration. These dynamics can increase switching costs and reinforce the durability and stickiness of the installed customer base over time.
Viewed in a wider context, the AI opportunity in industrial sectors may also be part of a larger shift across global economies, as governments and corporations are increasingly prioritizing supply chain resilience, domestic production capacity and industrial modernization. In many ways, the AI investment case is as much a real assets story as it is a technology story. Capital is moving back toward physical systems, essential infrastructure and productivity-enhancing investments in the real economy.
Historically, periods of meaningful productivity growth have come from the adoption of technologies that improve how essential industries operate. AI may ultimately follow a similar path, not only through consumer applications or enterprise software, but through the modernization of foundational industries that sit closer to real assets and real economic activity.
For long-term investors, we believe this distinction matters. The next phase of AI-driven value creation may not be confined to the companies building algorithms or supplying computational infrastructure. It may increasingly accrue to the businesses using AI to solve practical operating problems in industries where scarcity is real, output is essential, and the benefits of innovation may be durable. Smart farming may prove to be one of the earliest and clearest examples of a shift that is already underway.
Research & Development Spending Growth Indexed to 100
2016
100
2017
106
2018
123
2019
125
2020
113
2021
105
2022
131
2023
150
2024
146
2025
152
Includes top three global agricultural equipment producers by market share.
Source: Bloomberg; data as of May 18, 2026.
Benjamin Bahr is a portfolio manager and senior research analyst at First Eagle Investments.
This feature is to provide general information only, does not constitute legal or tax advice, and cannot be used or substituted for legal or tax advice. Any opinions of the author do not necessarily reflect the stance of ISS STOXX or its affiliates.
Tags: Agriculture, Artificial Intelligence, farms
« With Mega-IPOs, Index Concentration Can Encourage Shift Toward Active Management
