technology adoption – Scott Loftesness

Greg Isenberg came back from San Francisco with seventeen observations. The billboards advertising either B2B inference infrastructure or vertical agent companies, the seed rounds, the forward-deployed engineers, the founders showing each other their Obsidian vaults like athletes comparing gym routines.

He noted an important thing in observation fifteen, almost as an aside.

Walking around the Mission I noticed something: the street-level businesses, the taquerias, the barbershops, the laundromats — none of them use any AI at all.

Everett Rogers formalized the technology diffusion model in 1962. He was studying hybrid seed corn in Iowa. He noticed that the farmers who adopted early weren’t just better informed — they had different social networks, different relationships to risk, different orientations toward outside knowledge. The late adopters weren’t slower. They were operating from a different set of facts about what was safe to try.

Those AI billboards in SoMa are not visible in the Mission. That’s not metaphor. That’s just geography.

What strikes me about the taqueria is not that it’s behind. It’s that the conversation happening a mile away — about MCP endpoints and agent fleets — is not legible to it. The vocabulary doesn’t exist there yet. Nobody has sat across from the woman making carnitas for twenty years and said: here is what this could do for your ordering, your scheduling, your response to a customer who asks on Yelp at 11pm whether you’re open on Monday. One day her daughter or son might.

The builder class optimizes for the builder class. You build what you understand, for people whose problems you can see. The founders in SoMa understand each other’s problems with extraordinary precision.

The woman making carnitas has different problems — thinner margins, less access to capital, relationships built over decades that don’t easily transfer to a new system. Nobody is at the Series A meeting making the case that her problems are the interesting ones.

The historian of technology David Nye wrote about the “technological sublime” — the awe Americans felt in the nineteenth century standing before a great bridge or a locomotive or the first electrified city. The feeling was real. But the sublime is a view from a particular angle. The workers who built the bridge experienced something quite different. The families displaced by the railroad’s right-of-way experienced something different still.

The question isn’t whether the technology will eventually reach her. It will. The diffusion curve is patient. It likely will surprise.

The question is whether anyone is doing the translation work. The act of standing in a specific kind of life and asking: what would this actually change here? In the actual kitchen, on the actual Tuesday.

Isenberg noted that the coworking spaces in SF are half empty but the coffee shops are packed. People want to be around people.

The taqueria is also a place where people want to be around people. It has been that for a long time.

She’ll adapt. She’s been adapting for twenty years.

But that’s a very different story than the one being told in San Francisco on those billboards.

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)

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