Over the last decade of livestock AgriTech investment, we have produced almost no unicorns. That is a strange thing to say about an industry measured in trillions of dollars globally. Take Pennsylvania, where I sit. Agriculture is one of our largest industries, generating roughly $132 billion in annual economic impact, and livestock and animal products account for about two-thirds of what our farms actually sell. We lead the nation in mushrooms, rank among the top states for dairy and poultry, and unlike much of the country, we produce it all without serious water constraints. So supporting local, low-mileage food production here isn’t just an opportunity; it’s a necessity, and a real win-win. With USDA forecasting food prices to rise around 3% again this year, and beef among the fastest-climbing categories, precision livestock agriculture, knowing which individual animal is sick a day before you’d have spotted it by eye, is now part of how we keep the food in our baskets both sustainable and affordable. And yet we still lack the unicorn-scale winners that could fundamentally reshape these production and management systems. The connection is worth stating plainly: getting livestock AgriTech right is what lets food stay affordable and secure, helps farms stay profitable, what actually lands in the producer’s pocket at year-end, and production that gets more sustainable with fewer inputs and negative outputs, while improving the quality of lives of animals positively (that is welfare), all at the same time. Starve these tools of capital, and every one of those goals slips further out of reach.

Of course, occasionally a rogue breakout does happen, and we have one: a virtual-fencing platform crosses a headline valuation, and everyone points to it. But those are the exceptions that prove the rule, and they tend to raise more questions about how, why, and at what risk than they answer, especially when 90% of US livestock farms are concentrated/ indoor systems. For a sector this large, feeding this many people, the venture-scale wins have been remarkably thin. I don’t think that’s because the opportunity is small. I think it’s because we have been trying to fund livestock innovation with a playbook built for something else entirely. If we want unicorns in our space, we have to be honest about what makes this sector different, starting with how we prove that anything actually works. Silicon Valley’s proverbial “move fast and learn from failure” has quietly eroded the farmer’s ROI on technology, and it raises a harder question: are the models actually accurate? Because here, accuracy affects welfare and sustainability, and left unchecked, “move fast and break things” puts the farm on a negative trajectory.

Validation: The Standard We Keep Skipping

In human digital health, a tool generally cannot reach the clinic until it has cleared independent, published clinical validation, often trials with hundreds or thousands of patients, reviewed by people who don’t stand to gain from the result. In precision livestock technology, validation studies frequently rest on a few dozen animals, maybe a hundred, and often aren’t independent at all. Same ambition, wildly different bar, and it remains an anomaly to me, as a human-trained scientist, that we let this stand in livestock agriculture. We may be telling ourselves a convenient fallacy to justify skipping the step: that what really matters is “value or ROI for the farmer.” But how would you ever know you’re delivering it if you aren’t measuring accurately?

Validation, independent expert review of a model’s or sensor’s outputs, published so others can scrutinize and repeat it,  is one of the clearest signals that a tool is real, that it can scale, and that it isn’t the product of P-hacking or marketing. It is the difference between “it worked in our barn” and “it works.”

That kind of rigor demands serious development, standards, and trials before a sale, especially in the AI age, and that is a part we need to be honest about, because it isn’t how AgriTech investment usually runs. The first generation of easy wins is largely behind us, in any Gartner-style hype cycle the low-hanging fruit gets picked first, and the fruit higher up the tree is harder to reach and takes longer to grab. So, the honest question is: are we actually closer to clinical trials than to software? A gated, evidence-first path is slower than a Silicon Valley runway, but it may be the only one that produces durable companies, reliable tools, and long-term value in livestock.

And validation matters here for a reason it doesn’t in most industries: no two farms or animals are the same. Repeatability across sites, herds, climates, and management styles is the whole ballgame. A result that holds on one operation and collapses on the next isn’t a product; it’s an anecdote. Right now there is an enormous amount of technology on the market that has never cleared this bar, and with the onset of AI, buyers have even less ability to tell the validated from the merely marketed.

Worth a read: A survey of US dairy farmer perception and adoption of precision dairy technologies — Journal of Dairy Science

Selling To The System, Not Just The Farmer

The other shift is who we’re actually building for. It isn’t only the farmer anymore; it’s the entire system that supports the farm, and these are teams both on and off the farm. Tech isn’t going to replace the nutritionist or vet, but aid them with their clients.  That means looking at the problem from 10,000 feet instead of only at the emergency in front of us or a siloed approach with data. 

Here’s the way I think about it. The farmer doesn’t need to know how the sausage is made. But they do need to understand why the butcher and the mincing machine matter to whether the sausage tastes good. Our job is to make the parts of the system that farmers never see,  the data infrastructure, the validation, the standards- work so well that the end product is unmistakably better, and to earn the long-term trust of business owners who think in decades and generations. I don’t know yet if we have this systematic approach within our tech playbook in livestock agriculture, but we need to.

Data Is The Asset – And Investors Haven’t Priced It Yet

Investors have not yet seen the value of data in agriculture. Like everywhere else, the value of data is outpacing oil and other rare earth minerals as a new commodity. That’s the gap, and it’s also the opportunity. The companies that help farmers with their data, not just sell them another box, are the ones building defensible positions on the farm. The IP that accrues in these areas is the real moat.

There’s a relationship dividend, too. When you help a producer make sense of their data rather than simply transacting with them, you build trust, and trust builds longer, stickier relationships between the producer and the technology and service providers around them. That’s worth more over time than any single sale, and it’s how farmers already operate anyway.

Focusing on the data also lets us start moving technology between species, and that is an important evolution for AgriTech. Today the market tends to value a company as “dairy tech” or “beef tech,” locked to a single animal species. But when a tool proven on cattle can carry over to, say, pigs, the value of that company changes entirely (think like a pharmaceutical drug, but with digital tools). Data is what makes that transfer possible, and that is where it gets genuinely exciting.

Closing The Gap Between Investors And The Field

Awareness is growing. Some investors are starting to pay attention, and are beginning to ask sharper questions about agriculture and where its real problems actually live. But we need far more connection to the people in agriculture, and a shared, interdisciplinary language to have that conversation in. The way to accelerate it is more direct engagement between the agricultural community and the investment community: getting investors close enough to hear the pains of the sector and its producers while seeing the opportunities clearly, backed by data rather than narrative, marketing, or hype. The people in the barn, the people building the tech, and the people writing the checks still don’t talk to each other enough.

We also need a deliberate stage in the development cycle before we start selling an MVP to farmers. Scaling from one farm to twenty, to two hundred, to two thousand, each of those is a major inflection point. But building a solid foundation across the first stretch, roughly from one to two hundred, is the critical one (and depending on geography, that can mean anything from a few hundred animals in Europe to tens of thousands in the US). Skip the validation and data standards there, and you carry far more risk into the 200-to-2,000 stage than you would have by working through one-to-two-hundred properly first. Do it the more deliberate way,  with validation, standards, and the IP accruing inside the company, and you very likely build something far more valuable over the long term. I believe this is also the strategy for successful large-scale M&A. 

Don’t Chase The Hype

Raising money is not a success indicator for an AgriTech company. If you take a $220 million round, you then have to grow the business at least tenfold, and investors will be looking for more, just to return that capital. It worries me to think what a livestock farmer would have to pay, in a commodity market, to support economics like that, especially in today’s climate. Somewhere out there may be a piece of left-field technology that never took on that kind of weight and is simply a better tool. Look at DeepSeek, which reportedly developed its R1 model for just $5.8 million, a fraction of the cost incurred by competitors like OpenAI, proof that the most heavily capitalized player isn’t automatically the one that wins.

What we should want to see is scrappy founders in agriculture. It isn’t only about the investment; it’s about the founder who is the innovator, the entrepreneur, the team leader, and who can may even be all those at once. In the transition economy we’re moving through, we need more people who will build businesses, and that is where the real value lies for investors in this space. 

A Different Funding Clock

This is the part the market underestimates most. In crops, you can run roughly six-week cycles, six generations of a crop, and six rounds of insight, inside a single year. In livestock, six generations of animal data can take thirty years or more (from genomic, phenomic to enviromics).

No investor wants to hear “thirty years.” But that number isn’t a bug in the pitch; it’s the actual shape of the problem. The innovation is long, and the support the project needs is long-term. That’s the reality behind some of the most transformative companies we know, for example Tesla (founded in 2003) and Apple (1976) both took years, and patient backers, before the world caught up to them and many more like it, we just forget that part of innovation. 

So we need a funding strategy built to sustain data collection strategy that is looking over that horizon: patient capital designed to underwrite the years of animal data required to genuinely improve health and quality of life, not a runway that expires long before the biology does. That is a 23andMe moment for livestock AgriTech and something we can’t let happen with farmer data.

Agronomic And Livestock Are Two Different Games

When we talk about “AgriTech investment” in the US, we lump together two categories that behave nothing alike. We should split the term: agronomic AgriTech and livestock AgriTech. By my read, capital flows something like four-to-one in favor of the agronomic side.

Livestock AgriTech is genuinely harder, and the reason is the stakes, and arguably the sentience of a living being. But that single “livestock” label hides real structure underneath it, with distinct subcategories: genetics, pharma, nutrition, auditing and welfare, and more. Each has its own buyers, evidence bar, and path to scale, and lumping them together is part of why the category gets underestimated.

The Welfare Stakes

You have to be right. A wrong output in livestock can produce real welfare outcomes, negative or positive, for a living animal. And welfare here isn’t an abstraction: it’s illness caught early, fewer culls, calmer barns. That’s a paradigm we don’t necessarily carry on the agronomic side, where a bad model costs yield, not well-being, and why validation is critical.

That’s exactly what makes this an exciting problem, and exactly why we have to take it seriously as we promote technology into the sector here in the US. Building livestock or food animal welfare into the technology strategy is both a unique selling point and a reflection of the ethical society we want to promote as Americans. The upside of getting it right isn’t just a return, it’s animals that are healthier, and lives that are better and more sustainable in a quantifiable way. Welfare and quality of life are themselves becoming an opportunity for producers: they are part of what precision agriculture makes visible, being able to trace and prove them adds real value for the producer and the food chain. That’s a reason to build here, and a reason to fund it properly.

I wrote this to raise awareness, but also after weeks of travel and several conversations with investors and experts on AI, and animal science domain experts along the way. Interest in AgriTech investment is growing; startups across livestock are out looking for funding, and there are many problems and opportunities in livestock agriculture, so consider this a baseline for the “why.” The items above aren’t side issues. They are the vital things we have to get right if livestock AgriTech is going to succeed, lead here in the US, and build value for investors, but also for our agriculture community, from which our food comes.