Intro Part II - Exaptation AI and How Everything Fits Together
What Is Exaptation AI (ExAI)?
Exaptation AI (ExAI) is the company built to turn EGS from a theory into a practice and, in time, a platform. We are building the search engine for every asset’s undiscovered functions.
Why does this need AI? Consider the arithmetic. A mid-size company holds thousands of assets and resources—equipment, data, software, patents, brands, relationships, processes, and the capabilities of its people. Each has dozens of properties, some never written down. Each could interact with many kinds of agents under many kinds of pressure. The number of candidate functions is far beyond what a workshop, a consultant, or an innovation team can enumerate, and the humans doing the enumerating carry the functional fixedness described in part I (add link here). Historically, exaptations surfaced when the right person happened to be standing in the right place, and the delay ran to a decade or more. That is not a strategy. That’s lottery.
An Exaptation Agent changes the economics of the search. In practice, the work unfolds in four moves. First, it inventories the organization’s assets and their designed functions—you cannot count new functions until you know the existing ones. Second, it applies the EGS model to generate candidate emergent functions, working outward from the resource’s properties rather than backward from a market wish list. Third, it screens, rigorously. Most proposed “new uses” are not exaptations—they are the same function sold to a new customer—and the agent is built to say so. Fourth, it converts the survivors into structured business experiments with explicit hypotheses, so operators can test cheaply before leadership commits capital.
Two principles govern the design. The agent proposes; people decide. And EGS is value-agnostic: the organization defines what counts as value—revenue, margin, mission, resilience. The search is pointed at YOUR goals, not ours.
Where are we today? The EGS practice is live. Our first service, the Latent Asset Review, applies the model to a client’s asset base and returns candidate functions with proposed experiments. The enterprise agent is in development, and we are building it in public: this blog, our Substack and LinkedIn (insert links) will publish the cases, the methods, and, in time, a public ledger of predictions so readers can judge the hit rate for themselves. A search engine for undiscovered functions is an ambitious thing to build. It is also the logical next step once you accept that the functions are already there, waiting to be found.
Putting Everything Together
Exaptation, EGS, and ExAI are three layers of one idea.
Exaptation is the phenomenon: existing things acquire new functions. It is how nature builds novelty, and whether anyone used the word or not, it is how some of the most valuable businesses of the past thirty years found their second act. Google’s search engine, Uber’s delivery business, and the humble Post-Its all began as something built for a different purpose.
EGS is the method: a theory that explains how exaptations occur and a process for searching for them deliberately, using the assets a company already owns and the practices it already runs. There is also a massive M&A and Partnerships opportunity hidden within EGS (shhh). Its contribution is to move the discovery of new functions from the accident column to the strategy column.
ExAI is the tool: a company and an AI agent that make the search systematic and scalable, so the work no longer depends on the right engineer standing beside the right magnetron.
For builders, the message is simple. Your organization’s assets were designed to do specific jobs, and they do them well. But design fixes only an asset’s first function, not its last. Somewhere in your equipment, data, software, relationships, and people are functions no one has found yet! It’s not through anyone’s negligence, but because until now there was no systematic way to search for them. The companies that found theirs did not have better assets. They had a moment of noticing. EGS turns noticing into a discipline, and ExAI gives that discipline scale.
In the coming posts we will go deeper: the six-step EGS process, detailed cases from the dissertation, how EGS relates to strategy frameworks you already use from the Resource-Based View of the firm to Hamilton Helmer’s 7 Powers, and the causes of exaptation in detail. If your organization is asset-rich and growth-hungry, this is the series to follow.
Start with one question about any asset you own: what else could this thing do?