We treat the gap between Engineering and Product as a communication problem. It isn’t. It’s a capital allocation failure.

We pay engineers hundreds of thousands of dollars to hold high-fidelity, compounding mental models of our systems. Then we bleed margin trying to force that technical context outward — leaving stakeholders to guess incorrectly based on flattened complexity. It is a deceptively expensive place for a business to lose leverage. The post-mortem never names it. The slow bleed just makes everything else more fatal.

We try to paint over a structural failure with process. But no amount of process can hold together a structural mismatch. The problem isn’t the channel. It’s the direction.

The Inversion

So why this boundary?

One person operates at perfect efficiency. Add a second and you’ve bought headcount at the price of coordination overhead. Every role boundary after that is the same trade — more throughput, more coordination cost. Product management exists to share the load between business complexity and engineering complexity. It was a brilliant trade-off that allowed companies to scale — but we hit a local maximum and stopped. We institutionalized the boundary and never measured the impact of what crossed it.

Engineers already hold most of the context needed to make good prioritization decisions. They understand the system, the constraints, the second-order effects. They’re missing one input: what is this worth? Instead of moving the larger, more complex stack of engineering context outward, move the smaller, cleaner signal inward. Give them a dollar value — even rough napkin math based on pipeline and sales data.

The communication problem doesn’t get solved. It dissolves.

What Valuation Surfaces

Dollar values make hidden asymmetries visible. One feature touches half the sales funnel. Another is worth ten times as much but only to a fifth of it. Priority labels can’t express this. Stakeholder conviction can’t either — two people with equal passion for different features give you zero usable signal. Passion doesn’t survive the boundary any better than engineering context does. A number resolves the tie instantly because it doesn’t ask anyone to evaluate someone else’s feelings.

But a number assigned once is just a snapshot. The ground shifts slowly enough that process still feels solid — until you measure what shipped against what should have.

A prospect qualifies out. That feature’s value drops immediately. A market shifts and the whole backlog should reweight — but it doesn’t. The cost of delay changes but nobody recalculates. And the values go stale. And nobody notices. And the team keeps building. And maybe six months later you realize you shipped a feature for a prospect that you knew left, prioritized by a stakeholder who moved teams, justified by a pipeline number that was never updated, scoped by assumptions nobody remembered making, built by a team that was never told — while the work that could have actually moved the margin never shipped.

Did you feel it? That’s a capital allocation failure — visible, obvious, and the extra coats of paint did their job: they hid it.

The Closed Loop

Most engineering orgs have feedback on their prioritization decisions. Retros, sprint reviews, quarterly planning. None of them close the loop. Features ship, the team moves on, and the ROI is never measured. This is what separates it from fancy ticket labeling — not the valuation, the accountability.

Track what was predicted versus what was realized, and the variance starts to compress. Intuition gets validated by data. Data sharpens intuition. The gap between what you estimated and what is realized shrinks. Eventually, backlog prioritization is a solved problem. The destination was already charted and what’s left is arriving.