As farm equipment starts generating intelligence as well as output, the same machines can do the physical work while creating more value, better data and a faster return on investment.
For the past 20 years, software has been eating the world, to borrow Marc Andreessen’s famous line. For venture capital, this was close to the perfect model. Code was easy to distribute, margins were high, and the best software companies could grow exponentially without factories, inventory, servicing networks or working capital.
Hardware sat on the other side of the fence. It was slow, capital intensive, hard to iterate and usually needed manufacturing, logistics, maintenance, leasing or equipment finance. That made it a difficult fit for classic venture capital, not only in agriculture, but across most sectors.
Software transformed banking, media, advertising, retail and logistics because those industries already generated digital data. A payment, ad click, loan application or e-commerce purchase already lives inside a digital system. The data was already there. Software simply organized it, analyzed it and monetized it.
Farming was a different animal altogether. Much of the information is still analog, local and often sitting inside the farmer’s head. To make software useful, the farmer had to input data, learn another system, interpret the output and then connect it back to real decisions in the field.
A truly digital farm also required hardware. Soil sensors, cameras, drones, weather stations, storage and connectivity all had to be paid for before the software could really start working. Dashboards then had to turn that data into something useful. Even then, the payback was not always obvious.
In Brazilian agriculture, where margins are thin, interest rates are high, weather risk is constant and cash flow is seasonal, that was always a tough sell.
This is where AI comes in, increasing the value of tractors, robots and sensors by allowing them to generate intelligence while they work. Farm machinery was originally designed to replace muscle. Software tried to make those machines more efficient and connected. AI may finally make them more intelligent and profitable.
A machine that only performs one physical task has one main source of return. A machine that performs the task and produces useful intelligence at the same time can support several parts of the farm business. It can improve the operation, reduce risk, document what happened and create data that has value beyond the field.
The milking robot on my family farm is a good example. The main reason to buy it was labor shortage, as fewer people want to do repetitive dairy work anymore. But the robot does more than milk. It analyses milk quality, detects early signs of mastitis, tracks animal behavior and productivity and helps the farmer spot health issues well before they become visible.
The same logic applies in the field. A combine has traditionally been judged by its size and harvest speed, but modern machines also collect GPS, yield, moisture and performance data as they move. With AI, that information can become valuable beyond the harvest itself, helping identify field variability, detect operational problems, estimate crop quality, update inventories, improve logistics and provide evidence for lenders, insurers or buyers.
The pattern also shows up in smaller, less obvious hardware. Belgian startup SoundTalks uses AI-enabled microphones in pig barns to monitor cough patterns and other respiratory signals, detecting disease and respiratory risk weeks before symptoms are visible to the farmer.
That is an important difference from the last software wave. Traditional software worked best with structured data: numbers in rows, fields in forms and inputs that were already cleaned and organized. AI can work with messier signals: sound, images, movement, heat, vibration, color, behavior and patterns that may not be obvious to the human eye or ear. Farms are full of signals long before they become data.
Once those signals become useful intelligence, the value of the hardware changes. A microphone, camera or machine is no longer just a tool doing one job. It becomes a way to turn the physical world into decisions, documentation and risk reduction.
The investment case for venture capital may start to change as a result. Hardware is still hard because it needs manufacturing, inventory, servicing, distribution and financing. When one physical asset can create value across several workflows and several beneficiaries, the calculation looks different. The farmer gets a shorter payback period. The startup can build more than one revenue stream, and the hardware itself provides a natural competitive moat.
The broader return of interest in deep tech also matters. Investors are starting to look again at companies that combine software with complex physical systems, from robotics and industrial automation to aerospace and farm hardware. The record-breaking SpaceX IPO has pushed deep tech back into the center of the venture conversation. Farming has always been physical, and AI may finally make that physical layer more valuable.
AgTech hardware will still be difficult to build and finance, but a machine that does the job while creating useful intelligence can serve more parts of the value chain, support more business models and pay back faster. For farmers, startups and investors, these machines are still heavy, but the extra returns can help carry the load.
Thanks for reading.
KFG
Kieran Finbar Gartlan is an Irish native with over 30 years experience living and working in Brazil. He is Managing Partner at The Yield Lab Latam, a leading venture capital firm investing in Agrifood and Climate Tech startups in Latin America.
What is the best place to fundraise based on this argument of AI and services embedded around hardware?
We are seeing a lot of activity in Amsterdam.