There’s a strange kind of wisdom hiding in dusty old books about agriculture.
When you’re caught in the middle of a technological revolution—and with AI, there’s no question that we are—it’s tempting to keep your eyes fixed on the horizon. But sometimes the most clarifying thing you can do is look back.
Tracy Alloway at Bloomberg recently pointed to something genuinely instructive from the past: Richard Critchfield’s 1990 book, Trees, Why Do You Wait? America’s Changing Rural Culture, which traced the collapse of the family farm as industrial agriculture swept through the Midwest.
The broad strokes are familiar. As machinery got more expensive and efficiency became everything, scale won. The 80-acre husband-and-wife operation got swallowed by the 2,000-acre neighbor with access to capital. It wasn’t complicated. It was just gravity.
But here’s the part that should make your ears prick up.
The Seed That Was Supposed to Save Everyone
In the late 1980s, agricultural biotechnology arrived with a very specific promise. The idea was almost elegant: if you could bake the magic directly into the seed, you wouldn’t need all that expensive machinery, all those sprawling acres, all that fertilizer. The playing field would tilt back toward the small farmer.
Critchfield quoted an Office of Technology Assessment report from 1986 that captured the mood of the moment:
“The Office of Technology Assessment in 1986 forecast that biotechnology in crops would be more quickly adopted by richer farmers… Others argue that the more that gets built into the seed itself, the more it means higher yields at lower cost… If it reduced farm income, it could work to the smaller farmer’s advantage. As it is with all new technology, it is hard to foresee the consequences.”
You can feel the cautious optimism in that language. Hard to foresee the consequences. The understatement of a century.
What Actually Happened
The biotech did raise yields. Nobody disputes that. What it didn’t do was leave the gains in the hands of the people doing the actual farming.
Thanks to intellectual property law, patent protections, and a level of corporate consolidation that would have seemed cartoonish if you’d predicted it in advance, the value flowed straight upstream. We didn’t get “more in the seed, less paid for inputs.” We got more in the seed, and vastly more paid for proprietary inputs. The tech giants of agriculture captured the surplus. The farmers got the risk.
Now Listen to How We Talk About AI
We are told AI will democratize expertise. That a one-person startup will be able to code like a ten-person engineering team. That a small business will generate world-class marketing copy. That this is, finally, the great leveler.
Sound familiar?
Alloway’s analysis lands hard precisely because it forces the uncomfortable question: who will actually capture this value? The ownership structure of AI looks eerily similar to the agricultural biotech boom—proprietary models, walled-off training data, and a handful of enormous tech companies positioned to act as tollbooths between everyone else and their own productivity gains.
She’s right to note that “the ultimate distribution of benefits isn’t determined by technology alone. Policy also plays a role.” That sentence is doing a lot of quiet work.
If the agricultural analogy holds, productivity gains from AI won’t naturally flow to the individual worker or the small business owner. Without a robust open-source ecosystem or some deliberate policy intervention, those gains will be captured by whoever controls the compute and the models.
Where the Analogy Might Break Down
Here’s where I think there’s room for genuine optimism—not naive optimism, but structurally grounded optimism.
You cannot open-source arable land. Reverse-engineering a patented biological seed is genuinely hard, legally risky, and practically difficult. Code and model weights are different. They’re infinitely replicable. The marginal cost of distribution is essentially zero.
The battle between closed, proprietary AI and open-source models is still very much live. That’s not nothing. AI is fundamentally more commoditizable than a physical farm, and the history of software suggests that open ecosystems have a real shot when the community is motivated enough to build them.
Who Owns the Harvest?
Technology can reshape daily workflows in months. Power structures take decades to budge, if they budge at all. The mistake would be assuming the former automatically changes the latter.
The question worth sitting with isn’t what can AI do—that list gets longer every week. The question is who decides how the productivity it unlocks gets distributed. That’s not an algorithm problem. It’s a political and economic one.
If we want the AI revolution to be a rising tide rather than another tractor paving over the family farm, we have to look past the technology itself. We have to decide, deliberately, who owns the harvest.
Questions to Ponder
On history and pattern recognition: The agricultural biotech optimists weren’t stupid—they were looking at the technology and making reasonable inferences. What does that tell us about the limits of predicting who benefits from a new technology by studying the technology itself?
On open source as a counterweight: The open-source AI movement (Llama, Mistral, DeepSeek) is often framed as a technical story. Should we be thinking about it primarily as a political economy story—a structural check on proprietary capture?
On the role of policy: Antitrust law, data ownership rights, compute access regulation—which levers, if any, seem realistic? And who has the incentive to pull them?
On the worker vs. the firm: If AI raises individual productivity, does the gain show up in wages, prices, profits, or somewhere else? What would need to be true for workers to actually keep a meaningful share?
On commoditization speed: Software and model weights can be replicated freely—but does speed matter? If proprietary models establish deep lock-in before open alternatives mature, does the theoretical commoditizability even help?
Inspired by Tracy Alloway’s analysis at Bloomberg and Richard Critchfield’s Trees, Why Do You Wait? (1990)