January 15, 2025

Today, Mark Alison from Elders kindly gave my wife and me a tour of Harvard Business School, where they're running a significant global agriculture program. It was through my wife's connection that we had this opportunity to explore the beautiful campus and engage in thoughtful discussions about the future of agriculture.

Walking through HBS, something clicked. We were discussing why AI wasn't featured in this year's agriculture program, and initially, I didn't have a clear answer. But sometimes the best insights come after the conversation.

Two Levels of Organizational AI in Agriculture

1. Basic-Level AI Agents:

These represent the individual units—machinery and devices—empowered to function as connected, self-improving robots. With the integration of IoT and AI, these machines can evolve, learn from their environments, and optimize their tasks. Think of autonomous tractors, drones, and irrigation systems working smarter, together, adapting to local conditions in real time.

2. Organizational-Level AI:

At a higher level, organizational AI acts as a central intelligence system, orchestrating and optimizing large-scale agricultural operations. It brings capabilities such as:

• Precise weather forecasting for more informed decision-making.

• Personalized crop-level analysis, optimizing each field based on its unique characteristics.

• Supply chain optimization and market forecasting, helping farmers align production with demand and access the most profitable markets.

• Resource efficiency improvements, enabling more food to be grown with less input, reducing costs and environmental impact.

The Collective Intelligence of AI in Agriculture

What excites me most is the potential for collective intelligence to emerge. Each basic-level AI agent operates independently but contributes to the larger system, resulting in a dynamic, self-improving network. This mirrors the concept of organizational AI we’ve been exploring—where the synergy between individual agents and the overarching intelligence drives exponential improvements.

The combination of these levels—self-learning machines and centralized intelligence—positions agriculture as one of the most promising industries for AI-driven transformation. From increasing yields and lowering costs to empowering farmers with better tools and insights, the potential is vast.

This is just a short note to capture my evolving thoughts. I’m increasingly convinced that agriculture will not only benefit from AI but also serve as a fascinating case study for the emergence of collective intelligence in action.

Let’s keep watching this space.

Update: Just saw this relevant YouTube video shared by Australian deeptech investor Alan Cui on LinkedIn: "China's automated farming" : https://youtu.be/uKYddulc718?si=gD9w7u2cQiyGrW0z