What 'Fair Pricing' Actually Means (It's Not What You Think)
The Misnomer in the Formula
When Joe Cecala published "Are Your Prices Really Fair? There's a Simple Formula to Find Out" on Entrepreneur.com in June 2026, a lot of readers probably opened it expecting a cost-plus worksheet. Something they could hand to their CFO. Maybe a margin calculator with three inputs.
That is not what the piece is.
The "formula" Cecala describes is not arithmetic. It is a structural argument about how markets are supposed to function when capital is correctly matched to a company's development stage. The thesis is that the pricing problem most companies face is not about what number they put on a product. It is about whether the conditions for accurate price discovery actually exist in the first place.
The specific mechanism he focuses on is how premature exits and prolonged private status distort company valuations. When a business goes public too early, or stays private too long, the feedback systems that normally allow prices to evolve toward equilibrium simply do not get a chance to operate. The market cannot tell you what your company is worth if the market has no meaningful access to it.
His example of credit lines expanding post-IPO illustrates this directly. The capital structure changes after the public event, not because the underlying business changed overnight, but because the pricing environment finally allowed a more accurate signal to form.
Where Price Discovery Breaks
So what does it actually look like when price discovery breaks? Not in the abstract, but mechanically.
Cecala's argument is that both failure modes — the premature exit and the prolonged private stretch — share the same root problem. The pricing environment never gets a chance to do its job. A company that exits too early gets priced on narrative, on momentum, on whatever the deal terms the room could support. A company that stays private too long gets priced on internal models, on the preferences of whatever investor last wrote a check. Neither of those is a market signal. They are substitutes for one, and they behave very differently.
The post-IPO credit line example shows what proper price evolution looks like once the environment changes. The company did not change. The business on day one of trading is the same business it was the week before. What changed is the transparency of the environment around it. Lenders could finally see a price forming in real time, with actual market participants providing that signal, and the capital terms adjusted to reflect it.
That adjustment is the whole point. The credit line expanding is not a reward for going public. It is what happens when pricing conditions finally match the company's actual development stage. Before the IPO, those conditions did not exist. The information was not visible in a form the market could act on.
That is the structural failure Cecala is describing. Not bad pricing strategy. A system design that prevents the signal from forming at all.
AI Makes the Problem Faster
Here is where the conversation about AI typically goes wrong. The assumption is that better tools produce better analysis. If the old model was flawed, surely running it faster and at greater scale fixes something.
Cecala's piece pushes back on that directly. The risk he identifies is not that AI introduces new errors. It is that AI runs the existing errors forward at speed. If the underlying valuation model is optimized for near-term benchmarks — which, as he notes, has contributed to a measurable decline in the number of public companies — then feeding that model into an AI-accelerated system does not correct the short-termism. It compounds it. The model finds its local optimum faster. It executes on the wrong signal more efficiently.
That distinction matters. Speed amplifies whatever assumptions are already baked in. A system that has been designed to reward quarterly performance and trading volume does not become structurally sound because it can now process more data points. It just reaches the wrong conclusion sooner, and at greater scale, with more capital attached to it.
The past mistakes Cecala references were made slowly, across cycles, with enough friction that some correction was possible. Remove that friction with AI tooling, and the correction window shrinks. The same structural problem, reproduced faster, is not a technology story. It is a system design problem that the technology is now accelerating.
What Equilibrium Actually Requires
So what does Cecala actually recommend? Three things, and they are less about tactics than about conditions.
The first is reexamining how risk gets defined. If your risk model is built around near-term benchmarks — the same optimization logic that contributed to the decline in public companies Cecala describes — you are not measuring risk accurately. You are measuring performance against a flawed signal and calling it prudent analysis.
The second is matching capital sources to company development stage. This is the operational version of the equilibrium argument. Credit lines that expand post-IPO are not a product feature. They are what happens when the capital environment finally corresponds to where the company actually is. Getting there earlier requires being deliberate about which capital sources belong at which stage, rather than taking whatever is available.
The third is choosing transparent environments where prices can evolve rather than get locked in prematurely. That is not always an argument for going public. It is an argument for not letting convenience or deal timing substitute for genuine price discovery.
None of these is a formula in the way the headline implies. What they describe is a set of conditions. Get those conditions wrong, and the number you put on your company — or your product, or your next round — is not a price. It is a placeholder for a price that the system was never designed to produce.