Six days ago, I read a Deloitte research article about tokenomics—the economics of AI measured in tokens. Within 48 hours, I had built a comprehensive financial model of hyperscaler AI economics with 960 formulas across 10 tabs, using Claude for Excel and Microsoft Garage's Excel Agent. The model covers token pricing trajectories, edge computing cannibalization, depreciation feedback loops, stranded asset risk scenarios, and competitive dynamics.
But the 48-hour build was the culmination of months of preparation. The frameworks, the research threads, the theoretical foundations—those had been accumulating through dozens of conversations with Claude over many months. The Deloitte article was the spark. The model was the artifact. The thinking had been developing since I first encountered Jeffrey Ding's work on technology diffusion.
This article traces that arc—from research to framework to model—because I think it reveals something important about how AI changes learning, analysis, and work itself.
The timing is particularly resonant because I'm currently teaching an Intelligent Automation Immersive program at Divergence Academy, where we're applying these exact skills—Claude, modern Excel capabilities, systems thinking frameworks—to help students build their own analytical artifacts. I'm learning by teaching, teaching by building, building by learning. The recursion is the point.
The irony isn't lost on me: I used AI in the flow of work to build a model analyzing AI economics. The tool and the subject are the same. And that recursive quality—using AI to learn about AI, to model AI, to understand AI—turns out to be the most important insight of all.
The Origin: A LinkedIn Post About Excel (Months Ago)
The journey began months ago with something mundane: a LinkedIn post about Excel.
Someone had shared that Excel 365 had quietly become "Turing-complete" through the addition of LET and LAMBDA functions. These functions, introduced in 2020 and 2021, allow users to define variables and create reusable custom functions directly in formulas—capabilities that previously required VBA programming. The post also mentioned new possibilities: Jaccard similarity calculations, lexicon-based semantic analysis, and integration with generative AI models.
The phrasing caught my attention: "not your father's spreadsheet program." Most people would scroll past. But I'd been developing a framework about technology diffusion—how capability spreads through organizations—and this felt like an example of the pattern.
So I asked Claude to explain it. Not a web search. A conversation.
What came back wasn't just an explanation of LET and LAMBDA. It was an analysis connecting Excel's evolution to the very diffusion framework I'd been developing. Claude observed that the underlying algorithms—Jaccard similarity, sentiment analysis—weren't new. Data scientists had been using them for years. What was new was their accessibility at the point of work. The finance analyst, the HR manager, the operations lead could now apply computational techniques without waiting for data science team bandwidth.
"This is your GPT skill infrastructure argument in miniature," Claude wrote. "The value comes not from inventing the algorithm but from widening who can apply it."
That observation landed differently than a typical search result. It connected a technical capability to a theoretical framework. It didn't just answer my question—it extended my thinking.
The Framework: Jeffrey Ding and the Diffusion Deficit
That Excel conversation connected to research I'd been absorbing for months—Jeffrey Ding's work on technology diffusion and great power competition.
Ding, a political scientist who won the 2025 Lepgold Book Prize, upends conventional wisdom about technological advantage. His central finding: where innovations are adopted more effectively is more important than where they are first introduced. The true determinant of competitive advantage isn't innovation capacity—it's diffusion capacity. The ability to spread technology throughout an economy, not the ability to invent it.
The evidence is striking. The Soviet Union, by 1970, led the world in R&D spending as a percentage of GNP. They pioneered continuous casting steel technology in the mid-1950s—five years before Japan adopted it. By 1980, only 10.7 percent of Soviet steel used continuous casting. Japan, adopting the technology later, produced 59 percent of its steel with the technique.
General Secretary Brezhnev acknowledged the problem explicitly in 1971: "The links connected with the practical realization of scientific achievements and their adoption in mass production are the weakest." He was describing, with unusual precision for a Soviet leader, a diffusion deficit—a structural gap between the capacity to invent and the capacity to spread.