Why being right about a technology trend is not the same as capturing the value it creates. Reflections on Minitel, the telecom boom, cloud computing, big data, and AI.
In a recent article about Minitel and Station F, I wrote about how successful technologies eventually become infrastructure. After publishing it, I found myself thinking about another pattern I’ve seen over the course of my career.
I’ve been fortunate to have a front-row seat to several major technology waves. From online services and the early Internet to cloud computing, big data, and now AI, I’ve spent more than three decades watching new technologies emerge, mature, and eventually become part of everyday life. Looking back, what strikes me is that the broad technology trends were often easier to identify than the specific ways they would create value and who would ultimately capture it.
People wanted online services. Businesses wanted more connectivity. Computing resources moved into the cloud. Data became a strategic asset. AI is increasingly becoming part of products, workflows, and business processes. None of those developments feel particularly surprising in hindsight. What surprises me is how often the companies, business models, and investors that seemed best positioned at the beginning of a technology wave turned out not to be the biggest beneficiaries once that wave matured.
Early in my career, I worked for US Videotel and a related French company that brought Minitel technology to the United States. For those unfamiliar with it, Minitel was a French online service platform that allowed users to search directories, exchange messages, make reservations, conduct commerce, and access information services years before the Web became mainstream.
When people talk about Minitel today, it is often portrayed as a dead end that was eventually displaced by the Internet. Looking back, I think that misses part of the story. There was nothing fundamentally wrong with the vision. People clearly wanted online services, and many of the use cases we were pursuing eventually became part of everyday life. What I failed to appreciate at the time was how important the underlying architecture would become. While Minitel supported a surprisingly rich ecosystem of service providers, it remained tied to a particular network model, interface, and device. The Internet ultimately proved far more adaptable with its decentralized approach. The future we were imagining largely arrived, just not through the architecture many of us expected.
Later in my career, I joined Wam!Net during the telecom boom. It was a period when enormous amounts of capital were flowing into communications infrastructure, and companies such as Global Crossing were investing billions of dollars based on the belief that Internet traffic would continue to grow dramatically.
Looking back, that prediction was largely correct. Internet traffic exploded, businesses digitized, and connectivity became essential. What proved much harder to predict was the economic outcome. Infrastructure was built faster than demand could absorb it, prices fell dramatically, and many investors failed to realize the returns they expected. I don’t think the industry was wrong about where the world was heading. If anything, it may have been right about the destination and wrong about the economics required to get there.
After leaving Wam!Net, a bunch of us joined a startup called Dantis. The company’s vision would later become known as Infrastructure-as-a-Service. Today, cloud infrastructure feels so obvious that it is difficult to imagine modern software development without it. At the time, however, it was still an emerging concept, and the surrounding ecosystem was far less mature.
When the dot-com bubble burst, raising capital became extraordinarily difficult. Dantis ultimately failed to secure additional funding and did not survive long enough to see the market develop. Looking back, I still find myself wondering whether we were wrong or simply too early. The distinction is easy to debate decades later, but it was much harder to see when the company depended on getting it right. One thing that experience taught me is that a good idea at the wrong moment can look remarkably similar to a bad idea.
Years later, I found myself riding another technology wave at Zoomdata. The promise of big data was compelling. Organizations were generating unprecedented amounts of information, new data architectures were emerging, and traditional analytics platforms were struggling to keep pace. Zoomdata built innovative technology designed specifically for that environment and attracted significant investment along the way.
Again, the broader trend proved real. Data volumes exploded. Cloud data platforms became mainstream. Analytics became a strategic capability for organizations of every size. Yet despite identifying an important trend and building innovative technology, Zoomdata ultimately sold for a fraction of its previous valuation. Other companies were able to acquire technology, talent, and capabilities at a much more attractive price than earlier investors had paid.
That experience reinforced something I had started to suspect from earlier chapters of my career. Being early and being right are not the same thing as capturing the value that gets created. I’ve watched waves of technology create enormous opportunities, but the companies that first identify them are not always the ones that benefit most. Sometimes the winners arrive later. Sometimes they have a different business model. Sometimes they simply take advantage of investments someone else has already made.
As I look back across these experiences, I don’t think there was a single lesson. The details were different each time. Architecture mattered more than I appreciated. Economics mattered more than I expected. Timing proved more important than I understood. What feels consistent, however, is that the underlying technology trend was often easier to predict than the path by which value would ultimately be created and captured.
That is why discussions around AI are so interesting. The conversation feels very familiar.
The early discussion focused on capabilities. Could large language models reason? Could they write code? Could they automate work? Will AI reshape knowledge work? Will it eliminate large categories of white-collar jobs? Will it create new kinds of work that we cannot yet envision? Will it justify the enormous investments being made in models, infrastructure, and applications?
More recently, the discussion has begun to shift toward economics. Companies are trying to understand token consumption, infrastructure costs, return on investment, governance, and pricing. Product leaders are debating how to package AI capabilities. Investors are debating where durable value will reside. Organizations are beginning to separate impressive demonstrations from sustainable business outcomes.
The conversation seems to follow a familiar pattern. It starts with “Can the technology do this?” Then people begin asking what it costs and whether the economics work. Eventually, the discussion shifts to who is actually capturing the value being created. That’s the question I keep coming back to.
One reason it is so difficult is that, with the potential exception of early adopters, customers rarely buy technology for its own sake (I do own an Apple Vision Pro, so I’m probably in that camp). They buy solutions to problems. Throughout my career, I’ve watched technologies create extraordinary possibilities while the companies that captured the most value were often the ones that successfully connected those capabilities to important customer workflows. The best technology does not always win. The winner is the company that makes the technology useful, accessible, and indispensable for solving a problem customers are willing to pay for.
The experiences I’ve described don’t make me skeptical about the technology itself. If anything, they’ve made me more cautious about assuming we know how the story ends. I suspect AI will prove every bit as important as advocates believe. Where will the value ultimately accumulate? With model providers, infrastructure companies, application vendors, or enterprises that successfully embed AI into their workflows, or companies that have not yet been founded?
History suggests the answer is rarely obvious in the middle of a technology wave. Having lived through several of them, I’ve become less interested in predicting whether a technology will matter and more interested in understanding how value will be created, captured, and distributed as it matures.
In my earlier article, I argued that successful technologies eventually become infrastructure. Looking back across my career, there is another lesson hidden in that transition. Long before a technology becomes infrastructure, people have to make bets about architecture, economics, timing, and where value will ultimately accumulate. Those decisions often determine who struggles, who succeeds, and who ultimately captures the value created when the future finally arrives.
Technology is hard. Some of it is truly amazing. Anyone who has spent time building products, platforms, or infrastructure knows that. The harder challenge is figuring out where the value will ultimately land.