I received a lot of positive comments on my recent article about my failures investing in various Ag Startups, and many people asked for more insight into what I would do differently. I’ve thought about that, and rather than going into a ton of boring detail, I thought I would simply provide one big important litmus test I now ask myself before I invest… “do they have the right product-market fit?”
In traditional software, achieving Product-Market Fit (PMF) means finding a group of users who love your product enough to buy it, use it, and tell their friends. If the fit isn’t perfect, you can push a code update overnight, run an A/B test, and iterate. In agriculture, Product-Market Fit is an entirely different beast. Because you are dealing with deeply entrenched physical systems, razor-thin margins, and unyielding biological timelines, achieving PMF isn’t just a milestone—it is the ultimate survival filter. Below is an expanded look at why PMF in agriculture is so distinct, unforgiving, and critical to get right.
The Ag-Tech “Adoption Killer”… Meaning, operational friction is a difficult hurdle to get right. In traditional tech, software adoption is low-friction. In agriculture, however, a startup isn’t just competing against another software vendor; they are competing against a producer’s daily routine and cognitive load during the busiest weeks of their year. Producers operate on massive physical layouts under extreme time constraints dictated by weather and, often, lack of available labor. If a new technology requires a steep learning curve, takes an extra 10 minutes to calibrate in the morning, or adds a single additional step to an already grueling daily routine, the “fit” breaks down. True product-market fit in agriculture means the technology must seamlessly integrate into existing workflows. It shouldn’t require the producer to become a data scientist or a hardware technician. If it adds much friction at all, adoption will be lagging and limited.
The Micro-Yield Window (The “One Shot Per Year” Problem)… Meaning, if a traditional business software tool fails or glitches, the company might lose a day of productivity. If an agricultural input, autonomous machine, or data-driven recommendation fails, it can ruin a producer’s entire annual yield and income. Remember, Broadacre producers (corn, soybeans, wheat, cotton, rice, etc) only get about 40 opportunities (seasons) in their entire adult lifetime to plant and harvest a crop, many much less than that if they are farming with Dad and Granpa. So once they get in control, they really cannot afford to experiment with unproven tech. Since the risk of failure can be catastrophic, agricultural PMF requires an incredibly high standard of reliability and trust. Startups cannot “move fast and break things” like in traditional tech modeling. A product only achieves fit when it has been rigorously validated over multiple distinct growing seasons and cycles, which can take a lot of time and money.
The “Pragmatism Filter” (Immediate, Measurable ROI)… Producers are some of the most sophisticated, pragmatic business operators on earth. They face volatile commodity prices, unpredictable weather, and fluctuating input costs, and are price takers on both ends (both in paying for their inputs and receiving money for their crops). Because they operate on razor-thin margins, they do not buy software for “peace of mind” or “cool insights.” Meaning many startups build tools that generate beautiful maps or compile interesting data, but fail to answer the ultimate question: “How does this save me money, make things quicker or make me money this season?” This makes PMF in agriculture extremely difficult to get correct, which means your product must have a clear, immediate, and mathematically undeniable financial return. If a tool promises vague “sustainability benefits” or an ROI that takes five years to materialize, a producer facing low net farm income today simply cannot justify the spend.
The Illusion of the Monolith (Hyper-Local Fragmentation)… A massive trap for many early-stage ag tech investors and founders is assuming that “the agricultural market” is a single monolith. They look at the millions of acres globally and assume a product can scale universally. Agriculture, however, is becoming an increasingly fragmented mosaic of hyper-local ecosystems. What works perfectly for a 5,000-acre corn grower in Iowa is completely useless to a specialty almond grower in California, a cattle rancher in Texas, or a smallholder rice farmer in Southeast Asia. Achieving PMF in one geography or crop sector does not translate to product-market fit in another. Startups often bleed cash trying to scale a generic product into markets with entirely different crop biology, local regulations, and distribution structures. True PMF requires deep, localized customization. I’m actually of the belief that even though we are seeing more farm consolidation and fewer numbers of farms, fragmentation is actually increasing as consumers are demanding more specialized choices, which again makes PMF extremely tricky.
Bottom Line, in today’s world of agriculture, you can no longer simply brute-force market fit with a massive sales team or a slick marketing narrative. The agtech winners of the next decade won’t be the ones with the most advanced artificial intelligence or the most impressive laboratory science. They will be the teams that understand that Product-Market Fit in agriculture means solving a brutal, recurring, and expensive economic pain point today—built perfectly for the physical, biological, and cultural realities of the farm for tomorrow. So each time I look at a new ag startup venture, I first try to better understand the founder’s vision for Product-Market Fit and how and where they see their positioning and total addressable market (TAM). Too often, they have this wrong in their business plan, and therefore, the amount of money they will ultimately need to achieve their goals of profitability is massively underestimated. Which, as an investor, is the worst thing that can happen, as you will find yourself making capital call after capital call and being persuaded to invest in downround after downround, trying to defend your initial investment. If you make absolutely certain the Product-Market Fit is correct and accurate, you can save yourself a lot of pain and money. Just something to think about before getting yourself overly excited about the next cool story in ag… Hoping some of my failures can help someone else’s family avoid some of the pain!
