In July 2022, I made a career pivot from consulting to teaching. Beyond being intrinsically interesting and rewarding, I thought teaching would provide a respite after almost two decades of daily hand-to-hand combat with problems, clients, and, occasionally, colleagues. Then, in November 2022, OpenAI introduced the first version of ChatGPT. It quickly became clear that artificial intelligence (AI) could radically reshape my new industry, my old one, and many others.
Over the last three years, I have been actively experimenting with AI through a course I created called “AI and Consultative Decision Making.” In parallel, I wrote the book Epic Disruptions, which involved conducting deep historical research into case studies of world-changing innovations ranging from gunpowder to Pampers disposable diapers.
One of the themes that emerged from my research is that disruptive change is predictably unpredictable. There are broad patterns, but because there are humans and complex systems involved, there are unexpected twists and turns in every story.
As the saying goes, history may not repeat, but it certainly rhymes. There are five historical lessons that seem pertinent to how AI could—or could not—drive epic disruptive change.
1. Disruption often starts in unexpected places
In the 1940s, Walter Bradeen, John Brattain, and William Shockley from Bell Labs developed a new technology called the transistor. The intent of their research effort was to develop a technology to replace vacuum tubes that powered communications networks. The transistor had clear benefits. It was small, rugged, and didn’t give off heat. However, early versions were also unreliable and required rearchitecting systems.
It took decades for transistors to make it into communications networks. The first commercial market was hearing aids. The transistor fit perfectly in the market. Hearing aids were relatively simple, making it easy to incorporate transistors. Vacuum tubes gave off heat, which made battery packs affixed to a belt uncomfortable. Tubes burned out, making the total cost of owning a hearing aid expensive. The transistor-based hearing aid market exploded, supporting further technological development that ultimately ushered in the modern communications and computing age.
We naturally focus on the development and deployment of AI in large, sophisticated markets like the United States or Western Europe. However, one driver of ChatGPT’s rapid growth is usage in emerging markets that lack robust health and education infrastructures. Consumers don’t ask, “How does AI compare to a skilled teacher or clinician?”; they ask, “Is AI better than nothing at all?” History suggests carefully examining emerging market developments to spot disruptive change early.
2. The secret sauce of disruption is a unique way to create, capture, and deliver value.
When Mac and Dick McDonald first opened their restaurant, it was unremarkable. The path to disruption started when they shut the restaurant in 1948 and unveiled the “Speedee Service System” that simplified and standardized food production. When Ray Kroc became in essence the master franchisor of the concept in 1954, he and his team architected a unique system that involved close partnership with franchise owners. In the 1960s, Heny Sonneborn perfected a model that allowed the McDonald’s Corporation to profit through real estate. The unique way that McDonald’s created, delivered, and captured value—its business model—allowed it to serve billions profitably.
A unique business model is the secret sauce of disruptive innovation. It is what allowed Amazon.com, Google, and Netflix to emerge as powerhouses three decades ago. Unique business models provide funding for further improvement and frustrate incumbent response.
Right now, leading labs like OpenAI and Anthropic are following business models that are neither novel nor difficult for technology companies like Amazon, Microsoft, or Google to follow. If the labs don’t develop unique ways to create, capture, and deliver value, history suggests they are likely to have finite lives as standalone providers.
3. Disruption is always messy in the middle.
In the 1920s, a battle broke out for the soul of the streets of many major US cities. Henry Ford had achieved his vision: the car for the “great multitudes.” Perfecting the assembly line brought the cost of Ford’s Model T from $30,000 (in today’s terms) in 1908 to $5,000. Sales soared.
This was good for some, but less good for others. Cities were designed for people, not for cars. The sharp increase in automobile adoption spurred chaos and carnage. Newspaper cartoons in the 1920s often showed the Grim Reaper driving cars. One in the St. Louis Star showed a man kneeling holding up a platter of children to a car with a humanoid maniacal grin. In 1922 the mayor of Baltimore dedicated a 25-foot wood and plaster obelisk as a monument for the 130 children who died in motor accidents that year.
It is always messy in the middle of disruptive change. Getting out of the automotive’s middle required technologies such as traffic signals, regulations such as the need for drivers to have licenses, and norms, such as right-of-way at intersections.
Through this lens, a push to minimize rules and regulation is misguided as it elongates the time in AI’s messy middle and increases the odds of harm. Futurists Bob Johansen and Jamias Cascio note that it is hard to set precise rules in markets emerging as quickly as AI, so suggest the metaphor of a “bounce rope” in a wrestling ring. There are firm ring posts and boundaries at the edge of the ring, but those boundaries have slack and give in them.
4. There’s often a twist in the story
When Johannes Gutenberg and his team sought an early customer for the printing press, they naturally turned to the Catholic Church. The Church had real problems to solve, such as standardizing missals used for church services and shortening the three years it took to hand scribe a Bible. When Enea Silvio Piccolomini, who went on to become Pope Pius II, saw a Gutenberg Bible in 1454 he praised their “very neat and legible script” and noted how they could be read “without the use of glasses.”
The Church didn’t foresee what happened next. The printing presses accelerated the ability for people like Martin Luther to spread ideas attacking the Church. A third of the books printed in Germany between 1518 and 1525 were from Luther. The printing press was a boon to some—scientists, revolutionaries, entrepreneurs who built businesses around it—and a curse to others: scribes, cardinals, and anyone else who profited from ignorance.
Management consulting companies have profited handsomely from AI-related work. In early 2024 Boston Consulting Group said that 20 percent of its revenues was AI-related. McKinsey touted how it was using its custom-created AI solution to boost its productivity and accelerate developing unique impact. What if, however, clients learn how to use AI in ways that obviate consultants? Or if AI reliance withered a consulting company’s ability to develop unique talent? Could the major consulting companies look at AI the same way the Church looked at the printing press?
5. It’s all about the people
Singapore’s DBS Bank is a remarkable story of transformation (detailed in my 2020 book Eat, Sleep, Innovate). In 2010, it was a laggard in its local market. In 2025, DBS was widely recognized for its nimbleness and digital prowess.
Its digital transformation involved key strategic shifts and major investments in technology. Those moves were necessary, but not sufficient. The critical unlock came from a set of behavioral interventions to help bankers use technologies in new ways. Paul Cobban, who was DBS’s Chief Data and Transformation Officer from 2009-2022 observed that without a systematic and structured approach to cultural change, adopting digital technologies would be akin to replacing memos with emails or emails with Slack messages. One of Cobban’s mantras was, “Nothing changes unless people’s behavior changes.”
The same is true of AI. Adoption is not a technological problem; it is a sociological and cultural one. Jim Wilson from Accenture estimates that for every dollar companies spend on technology, they should expect to spend six dollars on the human side of change.
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One recurrent lesson that struck me during the research and writing of Epic Disruptions is how history provides a unique way to make sense of a complicated present. Disruption is predictably unpredictable, so AI will surely break from some of these patterns. However, the past provides a guide for where to look and what to look for to make sense of what will happen next.
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