I’ve spent most of my professional life selling software, working at the intersection of customers, their technical teams, and my own. Much of that work has come down to a fundamental question: what does business value actually mean, and how do you translate a software implementation into measurable business outcomes?
The web was extraordinary for a reason that took even its own practitioners years to articulate clearly: it was both a distribution channel and a business-model enabler. The companies that extracted the most value from it were the ones that stopped treating it merely as a marketing channel and allowed it to reshape the business itself.
The deepest lesson of the web followed from this: product and distribution eventually become the same thing. When the two merge, distribution is no longer something added to the product. It becomes part of how the product works, grows, and compounds. That merger creates the flywheel, and every growth discipline since has been an attempt to build one.
AI entered a very different environment. It arrived on top of more than thirty years of infrastructure, data, behavior, and distribution already created by the web. The relationship runs in both directions. The web made the current AI paradigm possible: without the corpus the web generated and distributed, today’s models could not exist. AI, in turn, plugs into the web’s existing distribution infrastructure and produces growth curves at a scale and speed the web itself rarely achieved.
This is the paradigm I call Web Squared: AI does not replace the web. It compounds it. The consequence is already visible. The first industries being disrupted, expanded, and amplified by AI are largely the industries the web itself created: search, social, publishing, cloud, and software. They already possess the infrastructure, data, distribution, and economics that intelligence can immediately multiply. The web-native economy transforms first because it is the substrate AI compounds on.
There is a second distinction, and it is even more important. The web was primarily an outside-in technology. Take an existing business and put it on the web, and the first thing that changes is distribution. The retailer gets a website, the newspaper publishes online, the bank launches internet banking, while the organization, operating model, and underlying economics initially remain largely intact. It took years, arguably until the 2010s, before the web penetrated deeply enough to transform business models themselves.
AI works in the opposite direction. It is an inside-out technology. Embed intelligence inside an organization and the first-order effect is not a new channel. It is a transformation of how the organization operates: how work gets done, how decisions are made, how knowledge moves, what gets automated, what humans do, and ultimately how value is created and captured.
The sequence therefore reverses: Operating Model; Business Model; Distribution. AI changes the operating model first. That transformation reshapes the business model, which ultimately redefines distribution. This is also why enterprise AI is proving so difficult: the challenge is not simply deploying the technology. Organizational transformation is the prerequisite for the business-model transformation that follows.
The web taught us that distribution could become part of the product. AI is teaching us that intelligence becomes part of the organization itself. And in this new paradigm, transformation starts from the inside and compounds outward.
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