The Repricing Equation (Pricing Series, Part 2)
The Setup
I was reading Dalio’s “How Countries Go Broke” and hit a formula he uses to explain inflation.
P = $ / Q
Price equals the dollars spent divided by the quantity of goods. More dollars chasing the same stuff, prices go up. Same dollars, more stuff, prices go down. He’s explaining economies. I’m staring at the equation thinking about a reconciliation deck I read six months ago. In Part 1 I landed on: if AI eats your highest-margin production hours, a pricing model built on those hours is already dead. I knew that was true from our own business models for margins. I didn’t have a framework for why. This formula gave me one.
My Approach + AI Role
I took the idea into AI and beat it up. I explored it from the day-to-day lens of scoping and deliverables, then zoomed out to industry-wide business models. I used the three-ways-this-is-wrong approach to play devil’s advocate. The direction didn’t change with each turn. Here’s what the equation does when you point it at a services business. AI is flooding Q. It already has. Every deliverable, every asset, every unit of production output. $700 billion in hyperscaler capex is building the infrastructure to mass-produce cheap cognitive output at scale. If your pricing rides on how many things you produce, that flood of AI efficiency isn’t helping you. It’s compressing your price per unit. But the equation has two variables. Not one.
What Actually Happened
The deck I thought of was an account’s reconciliation against first-half numbers. A monthly editorial workstream running at 64 to 81 percent of spend against a 55 percent target. And the second-half budget was set to pour more money into the exact workstreams the team had been trying to contain. The team brought process fixes. Intake forms. Feedback bibles. Revision thresholds. All good ideas. I would have assessed the same. But they were all aimed at one variable: produce the work more efficiently. Reduce the swirl. Optimize Q. And I’d been thinking the same way for months. Treating it as a discipline problem. Scope creep, revision sprawl, the usual. The reconciliation and this formula made me see it differently. The team wasn’t failing at production. The production was exactly what the client was asking for. The problem was that everything… the pricing, the scoping, the way success got measured… was organized around cost effectiveness and quantity of output. How many pieces. How many hours. How many revisions. But that production output isn’t created out of thin air. It’s shaped by another thing we made: strategy and creative plans. The strategy that informs the content. The creative direction that makes it worth producing. The thinking that happens before a single asset gets built. That’s the $ side of the equation. That’s where the value lives. When clients understand the importance of strong strategy and creative, it stabilizes both variables for production. Good strategy provides clarity on what to make and why, which gives you a rational Q (each asset serves a purpose). And it reduces the swirl of team hours because the brief is clear, which stabilizes $. Superficial strategy and creative does the opposite. Q inflates because more-is-better becomes the hedge against unclear direction. And when a senior stakeholder eventually senses something’s off (they usually do), the feedback arrives during production. Now you’re redoing work, not revising it. That’s a critical distinction. A shifting strategy masked as “revisions” is really redos. No amount of AI efficiency in production can fix that. You’re just iterating faster on the wrong stuff. So if the quality of the production equation depends on strategy and creative… shouldn’t we be investing more in strengthening that, which is what clients actually value anyway? We should probably stop giving it away.
The Real Insight
Dalio’s equation gave me a lens I wasn’t expecting. Not a business model prescription. I’m not qualified to redesign industry pricing from a book I could barely get through five pages of before getting confused. But it’s a diagnostic. A reframe. Every time you decide where to apply AI, you’re pushing on one of two variables. You’re either increasing Q… producing more, faster, lower cost per unit. Or you’re improving $… the value of the thinking, the strategy, the outcome that makes the production worth doing. Both are real. Both matter. But if you only push Q, you’re running a race where the finish line keeps moving away from you. AI will always produce more than you can. The efficiency gains are real, but they compress your price per unit every cycle. You’re not failing. You’re just feeding the wrong variable. The balance is knowing which variable you’re standing on. We had a team full of smart people optimizing Q, because that’s what we thought the client wanted. Faster production. Tighter process. More output per dollar. And the client did want that… SOWs and procurement were built around cost effectiveness of quantity. But underneath it all, we knew that wasn’t the reason they hired us. There are cheaper shops. A win on procurement spreadsheets isn’t the same as a win on real business results. They hired us for the strategic and creative thinking that made the production worth doing in the first place. And historically we were giving it away practically for free because it didn’t show up neatly on a timesheet or a line item and it led to the higher-margin executional work.
Try This If…
Next time you’re about to point AI at a workflow, pause and ask: which variable am I pushing? Am I increasing Q (more output, faster turnaround, lower unit cost)? Or am I improving $ (better strategy, sharper creative, more valuable outcome)? You need both. This isn’t a case against efficiency. But if every AI application in your shop pushes Q and nothing pushes $, you’re building a faster engine pointed at a shrinking price. You don’t need to redesign your pricing model tomorrow. You just need to know which side of the equation your effort is feeding. And maybe get the smart people in a room to start hypothesizing what the new model looks like.
Systems Lens
Months ago, in “Data Commoditizes, Perspective Compounds,” I wrote that nobody had built the commercial infrastructure for valuing perspective over data. I didn’t know how to think about it and left the question there. This is my first attempt at building a piece of it. Dalio gave me the equation. The reconciliation gave me the proof. The equation doesn’t solve the problem. It makes the problem legible. Two variables. One diagnostic question. Which side are you standing on? Something else surfaced while I was working through this. There isn’t just one equation. Strategy and creative have their own P = $/Q, and when that equation is healthy, it stabilizes production’s equation underneath it. When it’s not, production absorbs the instability. That cascade… how the equations relate to each other… is the thing I’m still thinking deeply on. It’s where Part 3 is headed.