When leaving San Francisco and heading south through the Silicon Valley hubs of Menlo Park, Palo Alto, and Mountain View, the drive is chaotic bumper-to-bumper traffic flanked by billboards advertising bespoke data pipelines and turnkey compute. After clearing San Jose, the tumult of Highway 101 gives way to rolling hills and fields of strawberries and lettuces. The flashy digital billboards of Silicon Valley are replaced with hand-painted signs for roadside fruit stands and ranch supply stores. Coming along the John Steinbeck Highway into Salinas, it is impossible not to picture scenes from Grapes of Wrath, East of Eden, and (a personal favorite) In Dubious Battle.
Between the bonanzas around gold and silicon, agriculture has been the consistent throughline of California’s allure. While making up only about two percent of the state’s GDP, California grows “[nearly] half of the country’s vegetables and over three-quarters of the country’s fruits and nuts.” As one friend exaggeratedly put it, in spite of the fact that we associate California with movies, music, and technology, the Golden State is also responsible for producing the vast majority of what makes up your Sunday dinner plate.
For most of the last century, the answer to nearly every problem a grower faced came out of a tank. Chemical fertilizers helped the desired crops to grow on the same land twice per year while chemical pesticides and herbicides killed everything unwanted. That system is now failing in fields across the country. Herbicides and insecticides are losing efficacy as pests evolve resistances. Meanwhile, growers are increasingly hamstrung by diminishing yield returns when applying nitrogen fertilizers, a problem compounded by the “leaking” of around 30 percent of fertilizers into the air and groundwater. The labor situation growers face is even worse. Western Growers—one of the oldest and most influential agricultural trade organizations in the United States, which represents the fresh produce industry—has consistently warned of major labor shortages caused by “a dwindling number of available workers.”
Examining these challenges, and how a particular class of innovator is attempting to address them, is what brought me to a forty-acre lease in the Salinas Valley. Hosted by Reservoir Farms, I flew out to attend Ruggedize: a “deep tech conference for ag robotics and the physical AI stack,” designed to bring people together who are “building rugged, field-ready systems that perform in real farm environments.” I expected to hear from firms that had taken decades-old agricultural machinery and added some automated gizmos. I was sorely mistaken.
For decades, advances in agtech have been one-off: “one task, one crop, one machine,” as Reservoir founder Danny Bernstein put it. But, over the past few years, robotics, machine vision, and intelligence at the edge seem to have come far enough that siloed solutions are becoming a full stack. Machines which five years ago would have been 80 percent custom are now 80 percent off-the-shelf, stitching together parts from Nvidia, Google, Amazon, and running on open-source systems such as the Robot Operating System (ROS). Machines assembled, prototyped, and tested on farms in California are quickly moving away from being just a strawberry harvester to being modular platforms that can just as easily pick broccoli, then iceberg lettuce, then, in short order, can be retooled to work in an orchard. It appears that old promises of full stack agricultural automation are finally coming to fruition.
The Long History
My surprise would be seen as quaint by the faculty at UC Davis, especially the late Jack Hanna and Coby Lorenzen. During the New Deal era, the federal government and states passed sweeping new labor regulations around minimum wages, maximum working hours, and workers’ compensation. Since agriculture was largely exempted from these requirements at the time, farm laborers voted with their feet by seeking employment elsewhere. The economic booms brought on by World War II and the early Cold War period exacerbated this as former farm workers moved on to work in shipyards and manufacturing plants, leaving farmers without enough hands to harvest. The federal government attempted to quell the tide by importing a replacement workforce of “braceros” through the Mexican Farm Labor Agreement. In 1942, the same year the first braceros arrived in California, Hanna, an agronomist and seed breeder, started working on a different answer to the labor problem.
He started with the humble tomato. At the time, tomato picking was incredibly labor intensive as the thin skins and varying sizes made mechanized harvesting impractical. Hanna traveled around the country picking up tomato varietals which he took back to UC Davis. Around 1950, he teamed up with Lorenzen, an aeronautical engineer by training. The two spent the next twenty years co-developing tomatoes that could withstand mechanical harvesting and machines to do the harvesting. Few realized at the time how important their work would be and the two were “kind of the laughing stock around [UC Davis],” as one colleague put it.
Hanna and Lorenzen got the last laugh. Through trial and error, they managed to create both rugged tomatoes and (relatively) gentle harvesters by the early 1960s. Then Congress canceled the bracero program in 1964. Facing another labor crisis, farmers finally bought into Hanna and Lorenzen’s vision. “By 1968, there were over 1,450 machines across the state,” harvesting an estimated 95 percent of the state’s tomato crop. By that time, Hanna, Lorenzen, and others had already moved on to develop machines to harvest other crops such as lettuce, asparagus, and melons. Lorenzen’s explanation of a lettuce harvester that appeared in the New York Times in 1964 is enlightening:
The device passes over the head if it is undersized. But if the size is right, the device causes the sensor or ‘feeler’ to test the head for firmness, indicating it is ready for picking. This sensor then sends a signal to an electronic ‘memory device’ on the machine, saying ‘this one's okay.’ The ‘memory box’ stores that information until a knife, rigged under the machine, gets into position. When the knife reaches the head, the ‘memory’ says, ‘go ahead and cut.’ As the head is cut, a series of fingers padded with foam‐material clamps on the head and carries it up a conveyor belt which takes it on to an accompanying wagon. The fingers then snap back open and are ready for another head.
Ruggedization
With a few changes, Lorenzen’s description from 1964 could be copied and pasted into a press release for SAMI Robotics' lettuce harvesting machine. So what’s different?
For one thing, Hanna and Lorenzen’s machines were almost entirely mechanical. The size gate was just a physical gap that undersized heads of lettuce passed beneath. The “feeler” was a spring and a switch to determine how dense a head was. The most complex piece of the machine, the “memory box,” was a shift register: a short row of yesses and nos, one for each head that had been felt. The machine moved continuously so that, every few inches, the shift register would slide down a slot and tell the knife whether or not to cut. It was a machine that did one job very well but lacked anything we could call knowledge, memory, or real judgement.
The machines on display at Ruggedize now have all three. The new generation of agtech is integrating robotics, machine vision, big data analytics, and AI into machines that are adaptive and multifunctional. Where older machines must be driven, modern machines are self-driving. Where older machines sensed by touch, modern machines use all five senses.
The next important distinction between Hanna and Lorensen’s machines and the new age of agtech is that older machines were built for one task and one crop while modern machines are infinitely modular. Take Agtonomy, for example. Agtonomy was founded by serial entrepreneur Tim Bucher, who spent over 30 years in Silicon Valley including stints working directly for the likes of Steve Jobs, Bill Gates, and Michael Dell, and it has developed one of the most futuristic agtech platforms on the market. By working directly with original equipment manufacturers (OEMs) such as Kubota and Bobcat, Agtonomy has developed autonomous fleets of tractors that can be used to mow, weed, spray, and perform any number of other tasks. Unlike previous companies that offered growers an autonomous mower or weeder, Agtonomy offers one machine and one platform that can do it all. It’s “Farmville for real,” as Bucher puts it. “Our solutions are designed to fit naturally into existing workflows, making it easier to get more done with fewer resources while preserving the human expertise that defines great work.”
This shift helps growers by lowering the amount of capital needed to upgrade their equipment. It doesn’t take a PhD in economics to understand that, all else being equal, producers would rather buy the machine that does more. But it also helps agtech companies. One of the largest impediments to acquiring investment and scaling an agtech business is the value of the total addressable market. Startups that focus on solving one problem for growers of one particular crop have a tough time growing whereas modular solutions are attractive to many different types of producers.
The industry is replete with examples of companies that over-promise and underdeliver. One startup, Monarch Tractor, made the Forbes List by promising an autonomous, electric tractor to California growers. But Monarch “didn’t spend enough time thinking about farmers’ needs,” and the company collapsed after it failed to deliver on its promises. In a post-mortem, Monarch CEO Praveen Penmetsa admitted that the failure of Monarch had much less to do with the vision of a modular solution than with the company’s “vertically integrated approach” that focused on building a custom, full stack solution. Which brings me to the final point of difference between older agtech companies and products and the new generation.
Monarch built its own stack, raising more money than any other company in the category, and the people on the inside now recognize that as the fatal mistake. The new generation isn’t making that same error. More and more, companies like those that spoke and demoed at Ruggedize are using the standard commoditized stack: Nvidia chips, Google Cloud, standard sensors, commercial-off-the-shelf robotics, and open source middleware. Another serial entrepreneur, Paul Mikesell of Carbon Robotics, explained in his talk "Robots, Business Model, or VCs - which will kill you first?" that the economics of farming haven’t changed very much. Where things have changed is in what a small team with limited startup capital can do with commercial components, vibe coding, and tenacity.
The Robots Aren't Here Yet, But They May Be Coming
This doesn't mean the robots have arrived. The lettuce harvester Hanna and Lorenzen built was never commercialized. They solved the tomato problem and assumed that the rest of the industry would follow but it didn’t. Lettuce heads mature days apart in the same bed, which means a machine has to judge each one and leave the others standing. No amount of shift-register and memory box cleverness made that challenge pencil out against a trained crew. Over sixty years later, Western Growers reports that “2-3 percent of non-harvest is automated but 0 percent of harvest.”
Still, the money is flowing, mostly towards automating non-harvest work, where Western Growers expects 15 to 20 percent automation over the next five to seven years. One case study of the introduction of Carbon Robotics’ LaserWeeder to a 3,200-acre organic farm found that weeding labor costs dropped from $2.1 million to $1.3 million after introduction. In other words, the $1.4 million machine paid for itself in under two years, one of the first times in sixty years that the math has come out on the machine’s side without a subsidy or tax break.
Hanna and Lorenzen built a working harvester by 1960 but had to wait five years and for a politically induced labor crisis to make it commercially viable. Now the industry is quietly assembling advantageous economic conditions itself. It has built a proving ground where startups can prototype in four months rather than four years. It has discovered that a commercial-off-the-shelf tech stack is cheap and effective enough that a small team can build quickly. OEMs are putting tens of millions of dollars behind other people’s engineering while growers are pooling capital and finding creative financing to purchase the machines.
Bernstein and others are going into this with their eyes wide open. Agriculture is fundamentally different from any other industry, and builders from Silicon Valley are coming to realize that fact. As Bernstein put it:
If we compare it to defense, which we very often do, defense has two things. It has a buyer, which is of course the Department of War, which is a big buyer. It writes big checks and it writes big checks pretty early, and it also provides significant amounts of non-dilutive capital during the de-risking phase. Agriculture doesn’t have either of those things right now. It doesn’t have the one big customer, the federal customer, nor does it have a federal de-risking mechanism, or even a state-level de-risking mechanism. And so, I would say for Reservoir, in terms of our agenda, building that system-level support around agriculture is one of the big unlocks that makes this sector more investable.
Steinbeck wrote some of America’s greatest literature about this place when the primary questions centered around who picked the crops. The primary question I heard growers asking at Ruggedize was who is building the machine that picks the crops. The answer isn’t quite here yet, but it is taking shape now that parts are modular and builders move development to the farmfront. The Salinas Valley has weathered the challenges of labor crises, mechanization, displacement, and naive tech bros many times before. Having watched these cycles, this time feels different. Agtech is no longer building in a lab, demoing at a trade show, then heading back up the 101 to Silicon Valley. This time, they’ve moved onto the farm and are getting their (robots') hands dirty.
