The last two pieces laid out a diagnosis. Stagflation compresses the demand pool while AI explodes the supply of builders. The asymmetry between those two forces is the defining condition of the current environment, and most founders are not pricing it into how they evaluate their own work.
This piece is about what to do with that diagnosis.
Because once you accept the framing, a problem becomes visible that most startup metrics are not designed to catch. Product-market fit measures whether people want what you built. It tells you nothing about whether the macro environment will let you survive building it. Two founders can have identical PMF scores1 and completely opposite outcomes over the next three years, depending entirely on where they sit inside the asymmetry machine.
So the question becomes practical… how do you actually measure that position?
Here is the framework I have been working through.
The Shift From Execution Metrics to Position Metrics
The standard builder dashboard looks something like this: lines shipped, features launched, users acquired, monthly revenue, churn rate. All of it measures execution. All of it answers the question of whether you are building and shipping effectively.
In an environment where execution was scarce, those were the right things to measure. The bottleneck was getting things built. If you could build faster and better than the next team, you had an advantage that compounded.
That bottleneck is gone. A competent developer with the current generation of tools can replicate most functionality in a fraction of the time it took two years ago. Execution speed is no longer a moat. It is table stakes.
If execution is cheap, the value is not in what you built. It is in what cannot be rebuilt.
What cannot be rebuilt is position. Distribution you own. Data that accumulates. Trust relationships that are non-portable. The customer’s cost of leaving is increasing over time rather than decreasing. These are the things that determine whether you survive the asymmetry, and almost none of the standard metrics measure them directly.
So here is a set of metrics that do.
1. The Rebuildability Ratio
The first question to ask about any project is brutally simple… how long would it take a competent developer with current tools to replicate your core functionality from scratch?
Call that number X.
Now ask a second question… how long did it take you to acquire your first hundred customers, or your first meaningful cohort of retained users? Not to build the product. To build the relationship. Call that number Y.
Your rebuildability ratio is Y divided by X.
If X is small — say, a weekend — and Y is large — say, six months of consistent distribution, content, community, or direct sales — you have a high ratio, and that is what you want. The value is not in the code. The code is just the delivery mechanism for something that took much longer to build and cannot be quickly replicated.
If X is large — you are proud of the technical complexity, the architecture, the months of engineering — and Y is small or untested, you have a low ratio, and that is the red flag. You have built something impressive that a better-funded team or a faster AI can reproduce, and you have not yet built the thing that actually protects you.
If the thing you are proudest of is how long it took to build, you are not building a business. You are building a tutorial for your future competitors.
The rebuildability ratio is not about devaluing technical work. It is about being honest about what part of your project actually creates durable value in an environment where execution has been commoditized.
2. The Scaling Efficiency Factor
The asymmetry machine punishes the middle. The founder who tries to scale by hiring runs directly into the stagflationary vise, costs that inflate upward, revenue that does not follow at the same pace, and leverage that dilutes instead of compounds.
You can measure your exposure to this trap before you walk into it.
The scaling efficiency factor is the ratio of revenue growth to headcount cost growth. How much new revenue does each dollar of new labor generate?
In a healthy scaling environment, this ratio stays high and ideally improves as you grow. In the current environment, with sticky salaries, benefits, coordination overhead, and a compressing demand pool, most businesses that hire will find this ratio deteriorating faster than they expect.
The target to aim for is a sigma that approaches infinity, meaning you can grow users, revenue, and output significantly without growing headcount proportionally. This is what AI actually enables when it is being used well. Not faster execution on the same team structure, but the same or smaller team operating at a scale that was previously impossible.
The practical question to ask before any hire… what does this person unlock that automation, tooling, or a different structural approach cannot? If the honest answer is “not much,” the hire is not a growth move. It is exposure.
In a stagflationary environment, every new seat on the org chart is a bet that revenue grows faster than costs inflate. That bet is harder to win than it looks right now.
The lean operator who stays at one or two people and scales through tooling rather than headcount has the macro working in their favor. The founder who scales through hiring has it working against them, at exactly the moment they feel most like they are succeeding.
3. The Demand Resilience Score
Not all customers are equal in the current environment. The stagflation piece laid out who gets squeezed and who does not. The builder needs to map their customer base onto that table and be honest about what they find.
Here is a simple ranking from highest to lowest risk:
Consumer and wage earner. Real income is falling. Discretionary spending is the first casualty of purchasing power compression. If your product is a nice-to-have for this customer, you are selling into a shrinking wallet. If it is essential infrastructure for their daily life, you have more resilience, but most B2C software products are not essential infrastructure.
Small business and solo founder. Medium risk, but for a specific reason. This is the closed loop described in the last piece. The solo founder you are selling to is operating inside the same asymmetry you are. Their customers are under pressure too. You are one link removed from the compression, not outside it.
Mid-market enterprise. High risk right now. Not because these businesses are struggling, but because they are in efficiency mode. The budget holder you need to convince is working inside an organization that is actively looking for reasons not to add new spend. The sale is possible, but the headwind is real.
The capital class and asset owners. Lower risk. Entities and individuals whose income is tied to assets rather than wages are not experiencing the same purchasing power compression. Products that serve this segment — wealth management, real asset infrastructure, capital allocation tooling — are selling into a different demand environment than everything else.
The efficiency mandate. The strongest position. If your product is the reason a customer can cut other costs, you are not competing against discretionary budget. You are competing against the alternative spend you are replacing. In a stagflationary environment where every organization is looking to do more with less, a product that makes that possible is structurally advantaged. This is the only customer segment where the macro is actively working in your favor.
The question to ask honestly…
When your customer pays you, are they buying a feature or buying back margin?
4. The Data Gravity Quotient
The last metric is about stickiness, but measured from the customer’s perspective rather than yours.
If a user decided to leave your product tomorrow, what percentage of the value they have built up inside it could they take with them?
If they can export a CSV, import it somewhere else, and be fully operational within an afternoon, your data gravity is near zero. You are a feature, not a platform. The switching cost is low, which means your pricing power is low, your churn risk is high, and your position in the asymmetry is weak, regardless of how good the product is.
If leaving means abandoning a proprietary data layer, trained models, historical analysis, accumulated workflows, relationships, and trust built inside your system over time, then the switching cost is structural. The longer they stay, the more expensive it becomes to leave. That is data gravity, and it is one of the few things in the current environment that genuinely cannot be replicated quickly by a competitor with better tooling.
The question is not whether your product is good. The question is whether leaving it is painful. Good products get replaced. Painful-to-leave products compound.
The practical test: describe in concrete terms what a customer loses by canceling today versus in twelve months. If the answer is the same, you have not built gravity. You have built a subscription.
The Position Map
Put the four metrics together, and three distinct positions emerge.
The noise layer. Pure execution, low rebuildability ratio, sigma that deteriorates with every hire, selling to compressed demand, and no data gravity. This is the most common position in the current formation explosion, and it is the most exposed to the asymmetry machine. These projects do not fail because they are bad. They fail because the environment is specifically designed to filter them out.
The lean ghost. High rebuildability ratio because distribution is the moat, not the code. Sigma approaching infinity because headcount stays flat while output scales. Selling to demand segments that are not in freefall. Not enough data gravity yet, but not exposed to the hiring trap either. This is a survivable position. Not glamorous, not venture-scale, but structurally sound in the current environment.
The essential layer. High rebuildability ratio because the data layer cannot be rebuilt, only accumulated. Sigma that improves with scale because the infrastructure compounds. Selling the efficiency mandate to customers whose purchasing power is intact. Data gravity that increases switching cost over time. This is where the asymmetry works in your favor rather than against you. These are the businesses that come out of the tightening cycle with stronger positions than they went in with.
The Practical Question
Most founders reading this are somewhere between the noise layer and the lean ghost, with aspirations toward the essential layer. That is fine. The position map is not a judgment. It is a navigation tool.
The questions that matter are not whether you have already achieved the right position. They are whether the choices you are making now, what to build next, whether to hire, who to sell to, how to structure your data, are moving you toward it or away from it.
In the previous environment, execution velocity was the answer to almost every strategic question. Move fast, ship more, iterate. The compound effect of speed was the advantage.
In this environment, the compound effect of position is the advantage. Every decision that increases your rebuildability ratio, improves your demand resilience, reduces your exposure to the hiring trap, or builds data gravity that did not exist before is a decision that moves you toward the side of the asymmetry where outcomes concentrate.
The founders who are going to come out of the next three years with durable businesses are not the ones who executed the fastest. They are the ones who understood early that execution is no longer the scarce resource, and built accordingly.
Be so small that inflation cannot find you, or so essential that you can pass the cost of it to your customers. Everything in the middle is where the machine does its work.
The metrics above are how you find out which one you actually are… before the market tells you the hard way.
Previous pieces in this series: Who Benefits From Stagflation? The Builder Explosion No One Is Pricing Correctly
If you enjoyed this series…
Subscribe for free to receive new posts and support my work.
consider subscribing.