{"id":94,"date":"2026-08-17T09:00:00","date_gmt":"2026-08-17T09:00:00","guid":{"rendered":"https:\/\/thenonlinear.org\/website_1ce6c7cd\/?p=94"},"modified":"2026-08-17T09:10:53","modified_gmt":"2026-08-17T09:10:53","slug":"information-cascades-ai-adoption-convergence","status":"publish","type":"post","link":"https:\/\/thenonlinear.org\/website_1ce6c7cd\/information-cascades-ai-adoption-convergence\/","title":{"rendered":"Information Cascades: Why Your AI Adoption Is Converging on Everyone Else’s"},"content":{"rendered":"

Two restaurants sit on the same street. One is packed. The other is empty. You walk past both and your own information is ambiguous. You haven’t eaten at either, and the online reviews are mixed. So you join the queue at the full one. Not because you know it’s better, but because the crowd’s choice feels like information.<\/p>\n

The person behind you faces the same decision. They see the same packed restaurant, the same empty one. Their own research might point elsewhere, but they reason the same way you did: if all these people chose this one, it must be the better bet. By the time the tenth person walks up, the empty restaurant could be serving the best meal in the city. It doesn’t matter. The cascade is self-sustaining. Each new arrival has less incentive to evaluate independently because the weight of prior decisions has overwhelmed any individual signal. Nobody made a bad decision. Everyone was rational. And the outcome is collectively wrong.<\/p>\n

This is an information cascade, a phenomenon formalised by economists Sushil Bikhchandani, David Hirshleifer, and Ivo Welch in a 1992 paper that changed how we think about fads, fashion, and cultural change. Their insight was that cascades don’t require stupidity or conformity. They emerge from perfectly rational Bayesian reasoning. When people can observe what others do but not what they know, early decisions carry disproportionate weight. Two or three early adopters can lock an entire population into a choice that nobody would have made with full information.<\/p>\n

The AI adoption cascade<\/h2>\n

Now look at how organisations are adopting AI.<\/p>\n

Company A reads a McKinsey report about generative AI productivity gains. They see that Company B, a direct competitor, has deployed Microsoft Copilot across its workforce. Company C just announced an AI-powered customer service platform. The trade press is full of case studies. Every LinkedIn post from a peer in the industry mentions their AI initiative.<\/p>\n

Company D, watching all of this, feels the pressure. Their board asks why they haven’t moved yet. Their CIO attends the same conferences, sees the same demos, reads the same analyst reports. The decision to adopt AI is increasingly driven not by independent analysis of what AI would actually do for Company D’s specific context, but by the fact that everyone else already did. The cascade has started, and each new adoption signals safety to the next.<\/p>\n

What makes this cascade different from restaurant queueing is its speed and scale. The signals are everywhere. Vendor decks circulate through every industry. Benchmark scores on MMLU, HumanEval, and MATH are cited in board presentations from Sydney to Stockholm. The same three or four foundation models sit underneath almost every enterprise deployment: GPT-4, Claude, Gemini. When everyone trains on the same data (the public web, largely Common Crawl and Wikipedia), benchmarks against the same metrics, and consults the same models for guidance, the variety of approaches collapses.<\/p>\n

This is how monocultures form in complex adaptive systems. Not through one bad decision, but through a thousand decisions that each looked rational in isolation because everyone else was making them.<\/p>\n

When the shared model breaks<\/h2>\n

Information cascades have a property that makes them dangerous in any complex system: they can be wrong, and the wrongness compounds.<\/p>\n

In 2000, a quantitative analyst named David X. Li published a paper introducing the Gaussian copula as a way to price collateralised debt obligations. The formula was elegant and easy to implement. Within a few years, virtually every major bank, rating agency, and hedge fund was using some version of it to model the correlation of defaults across mortgage-backed securities. It became the shared model. Nobody needed to build their own because Li’s approach was the industry standard.<\/p>\n

The problem was that the Gaussian copula assumed the relationship between defaults was stable and linear. In reality, under stress, defaults correlated in ways the model couldn’t capture. When the housing market turned in 2007, every institution that had relied on the same shared assumption discovered its fragility at the same time. The model didn’t fail at one bank. It failed everywhere, simultaneously, because everyone had adopted the same one.<\/p>\n

This is the core danger of an information cascade in a complex system. A monoculture has no resilience. When the shared assumption turns out to be wrong, the entire population fails together. In a genuinely complex environment, it eventually will be wrong. The variety that lets a system absorb shocks is gone, replaced by a thousand copies of the same bet.<\/p>\n

In the context of AI adoption, the shared assumption is that the dominant models, architectures, and use cases represent the right path. Maybe they do. But the cascade structure means that most organisations aren’t actually testing that assumption. They’re outsourcing their judgment to the crowd, and the crowd is outsourcing its judgment to the same few companies and the same few benchmarks.<\/p>\n

Positive feedback and the Red Queen<\/h2>\n

Two things accelerate the cascade. First, positive feedback: each adoption makes the next one feel safer. When a Fortune 500 company announces its AI strategy, that announcement functions as a signal that reduces perceived risk for every observer. The more organisations that adopt, the stronger the signal, the faster the cascade runs. We covered this mechanism in our piece on feedback loops. Positive feedback amplifies, and in adoption cascades, it amplifies rapidly.<\/p>\n

Second, the Red Queen effect<\/a>. As more companies adopt the same AI tools, competitive pressure forces the rest to follow. But because everyone is copying the same move, nobody gains a durable advantage. They run faster and stay in the same relative position. The cascade produces motion without differentiation. The organisation that deployed AI to cut costs discovers its competitors did the same thing, so the savings competed away within a quarter.<\/p>\n

Breaking the cascade<\/h2>\n

The strategic question is not whether to adopt AI. It’s whether your adoption decision is based on independent analysis of your own context or on the accumulated weight of everyone else’s decisions. These look identical from the outside, but they are fundamentally different.<\/p>\n

Bikhchandani and his co-authors identified a property of cascades that offers a way out: they are fragile. Because cascades are built on limited information, just two or three early signals that tipped the balance, a single piece of high-quality public information can shatter them. The cascade about the empty restaurant breaks the moment a respected food critic publishes a glowing review. The information was always there. It just needed to be surfaced loudly enough to override the accumulated weight of prior decisions.<\/p>\n

For organisations adopting AI, this means investing in independent signals. What does AI actually do for your specific workflows, your data, your customer base? Not what the vendor says it does. Not what the case study claims. What happens when you run the experiment yourself, with your own metrics, in your own context? That private information is the only thing that can distinguish “everyone is doing this because it works” from “everyone is doing this because everyone is doing this.”<\/p>\n

The distinction matters because it determines whether you are building resilience or destroying it. Requisite variety<\/a>, the principle that a system’s internal diversity must match the complexity of its environment, is what lets organisations adapt. A cascade strips that variety away, quietly, one rational decision at a time. Every organisation that adopts the same model, the same benchmarks, and the same use cases is betting on the same outcome. If they’re right, they all win modestly. If they’re wrong, they all lose catastrophically.<\/p>\n

The organisations that survive the next decade of AI disruption will not be the ones who adopted fastest. They will be the ones who could tell the difference between genuine signal and social proof, who maintained enough independent judgment to spot when the crowd was right and when it was merely large. In a complex adaptive system, that distinction is the whole game. And right now, almost nobody is making it.<\/p>\n","protected":false},"excerpt":{"rendered":"

Two restaurants sit on the same street. One is packed. The other is empty. You walk past both and your own information is ambiguous. You haven’t eaten at either, and the online reviews are mixed. So you join the queue at the full one. 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