Social AI Adoption: Lessons from Hybrid Corn

Reflexive AI usage is now a baseline expectation at Shopify

With these words, Shopify CEO Tobi Lütke attempted to reset the behavior of his employees. I say “attempted” because history suggests that (bad news for him) this isn’t how change happens. (Good news) there are predictable, actionable patterns for how change, particularly the adoption of technical innovations, how that change happens.

In our excitement about the new new (& believe me I’m full of excitement—coding at 3AM for the first time in decades) we forget that technologies, even revolutionary ones, don't spread instantly based on technical superiority. They grow in the medium of individual & community connections.

It’s not our first time. We enter the Wayback Machine.

Hybrid Corn

Around the beginning of the 20th century, agricultural researchers started applying the sparkling new knowledge of genetics first published by Mendel in 1866 (note we’re already 40 years along). Hybridization is cool, unintuitive, & too much to go into in detail here, but the basic idea is to in-breed 4 lines of plants for several generations, then cross-breed pairs, then cross-breed those cross breeds. Voila! 15% better yields.

15%. Same farming process. Same land. Same equipment. 15%. So every farmer started using hybrid corn, right? Right?

The first hybrid seed company was founded in 1926 (60 years). Adoption clustered in Iowa at first, then spread. By 1945, 90% of the Corn Belt was planted in hybrid seed (80 years, 20 years from commercial availability). European adoption followed in the 1950s, followed by the rest of the world in the 1960s. [ed: he says, from a naively American perspective].

The Better Mousetrap vs. The Logistic Curve

Rural sociologists Bryce Ryan and Neal Gross published “The Diffusion of Hybrid Seed Corn in Two Iowa Communities” in 1943, examining the mystery of “It’s better, why don’t they?” They interviewed 259 farmers about their adoption decisions and found:

1. Adoption Categories: Different categories of adopters follow different incentives (innovators, early adopters, majority adopters, and laggards)

2. S-Shaped Diffusion Curve: Adoption followed an S-shaped curve over time, with “slow” initial uptake, followed by rapid acceleration, then leveling off

3. Information Sources: Initial awareness sources (commercial seed dealers and salesmen) and influence sources (neighboring farmers) leading to actual adoption

4. Two-Step Flow of Communication: Information flows from external sources to opinion leaders and then to followers

5. Adoption as a Process: Adoption is not a single decision but a process with multiple stages (awareness, interest, trial, evaluation, adoption)

Economics

Money is not the only driver of adoption, but money matters. Zvi Griliches published “Hybrid Corn: An Exploration in the Economics of Technological Change” in 1957. He found:

• Adoption rates varied by region based on economic profitability

• Regions with higher expected returns adopted hybrid corn more quickly

• Adoption followed a logistic growth curve

He created a model that could predict the timing and rate of adoption based on economic variables.

The Logistic Curve

The S-shaped logistic curve is another deep dive we don’t have time for here. While there are closed form expressions of the logistic curve, I think of it in terms of a tug of war between two feedback loops. The growth side is a reinforcing loop where the bigger you grow, the easier it is to grow bigger:

Left to its own devices, growth goes (from our perspective) vertical forever. We know that doesn’t happen, though. An inhibiting loop comes along & overpowers the reinforcing loop at some point. The inhibiting loop introduces friction—the larger you grow, the harder it is to grow larger:

There’s so much more to say about this, some of which I’ve said in talking about 3X: Explore/Expand/Extract. The relevant point here is that growth follows laws & it’s way more complicated than “build a better thing & then everyone will instantly use it”.

Risk Mitigation

If you’re a peddler of innovation, as I have been for lo these decades, the logistic curve seems like friction. “But my stuff is better! Why isn’t everybody using it?” Well (and this is hard for us peddlers to swallow) maybe your stuff is better and maybe it’s not. The logistic curve protects the tribe from unintended negative consequences of innovation.

Lots of people will try lots of innovations. Yours is just one. Maybe there’s a hidden gotcha in your idea. The “slow” adoption gives the tribe the chance to explore the whole space. Sometimes this results in delays in adoption versus instantly adopting the best innovation, but the information to evaluate “best” doesn’t exist at the moment that decision would have to be made.

VHS is the price you pay for avoiding disaster.

Why AI Is Adopted Slowly

Maybe it is, maybe it isn’t. You can’t tell yet. That’s the first lesson of the logistic curve—early failure & early success look identical early.

The second lesson is that adoption follows social lines, not technical analysis. Other people like you will use an innovation when they see you using it. Or vice versa.

The third lesson is that “like you” is not homogenous. There are folks incentivized by novelty [ed: raises hand], folks incentivized by finding early advantage, folks incentivized by risk reduction, & folks incentivized to maintain the status quo.

The fourth lesson (“crossing the chasm”) is that cross-pollination between those seeking early advantage & those seeking risk mitigation is rare. Reaching that early majority requires different tactics, different messages, different market structures.

The final lesson is that all this takes time. Even if time is compressed, it’s still time. Pages will need to be pulled from calendars before lots of folks program with agents.

Predicted AI Adoption Timeline

So what would I say to the Tobi Lütkes of the world?

• It’s still early: Despite the explosion of interest in generative AI over the past year, we're likely still in the early adopter phase for most AI applications. The steep part of the curve is ahead of us, not behind us.

• Social: The most effective driver of AI adoption isn't better models or improved accuracy—it's success stories from peers and respected figures in professional networks. Rather than push AI, encourage communities that will push AI.

• Time: Organizations need time to adapt workflows, retrain staff, address valid concerns, and develop the complementary skills needed to extract value from AI.

• Malicious compliance: The implied threat in the opening quote means that the downside of appearing not to use AI is a more powerful motivator than the upside of actually using it. Beware of getting what you asked for in ways that interfere with what you actually want.

Surfing the Logistic Wave

The logistic curve isn't a limitation to overcome, it's a rhythm to understand and work with.

To my frustrated “everybody in 18 months” friends: vibe coding isn't failing, it's growing. Naturally. The S-curve is the shape of success. But it won’t be 18 months. It’s both already here & it’ll take a decade.

Like the hybrid corn that transformed agriculture field by field, farm by farm, the truly transformative AI applications will spread not through marketing campaigns or technical superiority alone, but through the social fabric that connects us all, one trusted relationship at a time.

Here is the Claude chat I used as research for this piece: https://claude.ai/share/2d0e502d-c1a7-4e06-bb4f-d3e91fded2e0