Key points
- AI can make output soar while human effort shrinks toward zero.
- Old yardsticks like size and headcount start to lose their meaning.
- The interesting question is where difference goes when the metrics fail.
Indulge me in a little mathematics. Brian Solis and Dave Wright recently introduced the idea of the “Infinite Company,” a provocative way of thinking about organizations built around artificial intelligence (AI). And as a bit of a math geek, I couldn’t resist taking this idea down another rabbit hole, and I began to wonder what happens if we take it a little more literally
When the Denominator Approaches Zero
Typically, we describe AI through the language of exponential change that yields more—more intelligence, greater productivity, expanding capability and capacity. But consider a simple equation where organizational leverage equals output divided by human effort. AI can increase the output while simultaneously reducing the human effort required to produce it. As that denominator gets progressively smaller and approaches zero, the ratio doesn’t simply become very large. Mathematically, it begins heading toward infinity. Conceptually a bit abstract, but stay with me.
Of course, no company will operate with literally zero human effort. People (at least for now) still provide intention, judgment, capital, responsibility, and direction, while AI itself requires enormous infrastructure and energy. So maybe the denominator never gets anywhere close to zero, and perhaps organizational leverage remains perfectly measurable. But this is a thought experiment, and it may be pushing the mathematics harder than the economics will permit or even tolerate. Still, following it toward the limit (as the denominator gets closer to zero) exposes something curious about measurement itself.
So, and much to the horror of many, let’s do some math. Imagine two concentric circles, one larger than the other. The larger circle has a greater circumference, so common sense suggests that it must contain more points along a larger surface. However, both circles actually contain infinitely many points, and they contain the same number of points. Here’s how this makes sense. Draw a line from their common center through the smaller circle and continue it outward, and every point on the smaller circle can be paired with a point on the larger one. The circles remain obviously different in size, but along this particular mathematical dimension, their infinities are equivalent. (You might want to sketch out this description to get a better sense of it.)
A couple of years ago, I explored infinity as a way of thinking about AI’s seemingly boundless possibilities. Today, it’s worth another look for a different reason. Perhaps infinity doesn’t simply expand possibility, but under certain conditions, it can also make comparison itself more difficult
Now bring this curiosity back to AI. Two companies can remain profoundly different in revenue, influence, capital, and consequence while their ratio of output to human effort moves toward the same mathematical boundary. They haven’t become equal. The problem is with the ruler we chose to compare them. At the limit, infinity destroys the metric that produced it
The Failure of Comparison
Beyond abstract mathematics, I think this is important because human judgment depends heavily on comparison. We understand much of the world through relative measures such as bigger and smaller, faster and slower, expert and novice. These scales give structure to judgment
Here’s where it gets interesting. AI may create forms of abundance that make some of those scales harder to interpret. If almost everyone has access to extraordinary computational intelligence, having “more intelligence” may tell us progressively less. If one person can summon productive capacity once associated with hundreds of employees, headcount becomes a weaker proxy for organizational power. And—the key point—if key business components like words, images, analyses, and strategies can be produced almost without limit, the sheer quantity may tell us surprisingly little about value.
Perhaps none of this means the metrics actually disappear. We can still do the “business math” and count employees, measure productivity, and calculate market value. The stranger possibility is that the numbers continue to work while the distinctions behind them become less meaningful. The ruler hasn’t broken. We may simply be measuring the wrong thing
Where Does the Difference Go?
This is where the thought experiment gets uncomfortable, at least for me. If abundance compresses some of the differences we have traditionally relied upon, difference doesn’t disappear—it moves. We then have to find it somewhere else and create a new metric for today’s infinite companies
Could it migrate? Maybe it moves toward a more human frame that can include judgment, intention, originality, trust, responsibility, or taste. And that uncertainty may be more important than choosing the next set of metrics too quickly. We have spent much of the AI era asking how much more intelligence, productivity, and capability these systems can produce. Those are natural questions because they fit comfortably on scales we already understand and are very comfortable with
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Infinity introduces a different problem. Two circles can remain unmistakably different while becoming identical under one way of counting them. AI may eventually create a similar confusion between capability and value, or between production and meaning
The numbers may continue getting larger. The more interesting moment will come when we realize that getting larger—and perhaps even abundance itself—no longer tells us what we thought it did
