The Usefulness Gap That Market Engineering Can't Fix: A Behavioral Economics Reading of Why Products Fail
A 41-year-old posts to Reddit: $2 million liquid, $75,000 in annual royalties, a spouse with healthcare covered, no commute, no deadlines. He plays video games most weekday mornings. His wife calls him a loser. The post goes viral, and nearly every analysis focuses on whether the wife is right.
That's the wrong question.
Nick Maggiulli at Of Dollars and Data drew a more precise reading from the same story: the man achieved financial independence and lost something more important than he had accounted for. Not purpose as an abstract concept. Usefulness. The specific, felt experience of doing things that matter to someone else.
Most commentary treats this as personal finance advice: wealth without contribution produces emptiness. That reading is true but insufficient. The behavioral mechanism underneath it has direct implications for product strategy, brand positioning, and the expanding stack of agentic AI tools now being deployed to optimize both. The FIRE man's problem is the same problem most product teams create without recognizing it. And it's the same error agentic AI is being pointed at, right now, at scale.
The Rationalization Layer Most Products Never Reach
The behavioral research gives this a sharper frame. BehavioralEconomics.com's analysis of what they call Homobiasos describes human beings not as rational agents who occasionally err, but as rationalization machines whose reasoning is primarily deployed to protect existing self-concepts. We don't evaluate products, brands, or situations objectively and then form preferences. We form preferences through identity filters, then build the rational case afterward.
The FIRE man's problem, read through this lens, is that the identity filter his wife applies doesn't have a slot for "accomplished early retiree doing meaningful leisure." It has a slot for "partner who does things that matter alongside me." He filled the financial column but left the identity column empty. The rationalization she is doing ("he's a loser") is the system running to explain a pre-existing emotional signal.
Product teams run into this constantly and interpret it incorrectly. A user churns from a tool that demonstrably performs better than its alternative. The team runs surveys. Users say the competing tool "feels better" or "fits how we work." The team redesigns the interface. Churn continues. What they're missing is that the competing tool isn't winning on capability. It's winning because it delivers the identity signal the user needs: "I am the kind of person who uses professional-grade tools." Or "I am someone who gets things done efficiently." Or, in the case of the FIRE man, "I am useful."
The brand identity dynamics that emerge around products like Harley-Davidson make this concrete. Harley buyers are not primarily optimizing for motorcycle capability. They're purchasing an identity reinforcement signal so well-calibrated to their self-concept that the Homobiasos effect works in Harley's favor: buyers rationalize the premium, the inconveniences, and the reliability complaints because abandoning the product would require revising their self-concept.
Most product teams are not building this. They're building capability.
Why Market Engineering Solves the Wrong Problem
Branding Strategy Insider's analysis of product failure is correct about the mechanism and incomplete about the cause. Their core observation is accurate: 80% of VC-backed startups fail, 40-60% of new products from mature companies fail, and the primary cited reason is "no market need." The framing of the solution, however, stays inside a functional logic. Market engineering means building the right distribution channels, the right partnership structures, the right pricing models, and the right customer success infrastructure to ensure the product reaches buyers who need it.
All of that is necessary. None of it addresses the gap that actually causes most product failures.
Products fail not because there's no market need, but because they satisfy functional need while missing the identity need. The functional need is the job to be done. The identity need is the self-concept signal the user gets from doing that job with your product instead of an alternative. Market engineering solves the distribution problem. It does not solve the signal problem.
Gap (the clothing retailer) is the illustration Branding Strategy Insider uses for their own point: "faded" jeans launched in the early 2000s with solid market research, the right channel footprint, and failed because there was "no market need." But the more interesting question is why the market research didn't surface the problem earlier. The answer is that market research asks what people want. It doesn't ask what identity signal they need their purchase to deliver. Those are different questions that generate different answers.
The faded jeans were functionally adequate. They failed to deliver the self-concept signal that Gap's target demographic needed at that specific cultural moment. A market engineering response would have focused on better distribution, better pricing, better category adjacencies. The actual problem was upstream: the product was optimized for a capability gap that didn't exist while missing a signal gap that did.
The Metric That Market Engineering Doesn't Track
Most teams track task completion, feature adoption, NPS, and retention cohorts. None of these measures whether the user experiences your product as something that makes them useful to people who matter to them.
That is a different measurement question, and it requires different instrumentation: longitudinal sentiment around self-efficacy, qualitative coding of renewal reasons, cohort analysis by user identity type rather than usage pattern. This is harder to build than a conversion dashboard. It's also the kind of measurement that would have caught the FIRE man's problem before he retired.
How Agentic AI Is About to Scale This Error
The MarketingWeek analysis of agentic AI in brand strategy frames the challenge accurately: as AI agents mediate more of the brand discovery and purchase journey, marketers face a compound problem. They need to optimize for what agents see, not just what humans see. Agent behavior is driven by behavioral signal patterns. That changes the terrain for brand positioning.
What's missing from most agentic AI discussions is the layer beneath this. Agents optimize for behavioral signals that are downstream proxies for usefulness, not usefulness itself. Conversion is a proxy. Click-through is a proxy. Re-engagement is a proxy. All of these metrics can increase while the product's actual usefulness signal to the user's identity decreases. The behavioral data will look fine. The brand is eroding.
This is the version of the FIRE man problem that plays out in product strategy: you can accumulate every financial metric of success (monthly actives, conversion rate, NPS in the 60s) while steadily evacuating the identity signal that made the product worth using. The pattern where AI tells users what they want to hear is one version of this: optimizing for positive engagement signals at the cost of genuine usefulness.
Agentic AI applied to brand strategy amplifies this because it operates at a speed and scale that makes course correction slow. A human CMO misreading the signal gets corrected by quarterly results, focus groups, and the occasional catastrophic campaign. An agentic system misreading the signal keeps optimizing in the wrong direction while every individual metric looks fine.
The observation worth flagging here: when agentic AI systems optimize brand presence for conversion and re-engagement, they are inadvertently running a usefulness-extraction operation on the brand's identity signal. They improve the financial independence metrics while eliminating the "doing useful things alongside someone who cares" metric. The FIRE man's problem, automated and scaled.
The McKinsey Distributed Ops Exception
McKinsey's analysis of transformation in distributed operations offers an accidental counter-model. Their prescription for distributed production network transformation is to put site leaders in front rather than imposing top-down process change. The reasoning McKinsey offers is operational: frontline leaders know local context and can drive adoption faster than central mandates can enforce it.
But the behavioral reason this works is usefulness. A site leader who is visibly driving a transformation effort isn't just more informed than a central mandate. They're doing something that matters, in a context where their capability is legible to colleagues who respect the domain. The identity signal is intact. The transformation succeeds not primarily because the operational logic is better (though it often is), but because the people executing it are experiencing the usefulness signal that sustains motivation.
Product teams reading McKinsey for operational lessons should notice this. The same principle applies to product adoption. Users who experience your product as something that makes them visibly useful to people who matter will adopt, retain, and evangelize in ways that users who experience it as a merely capable tool will not. Capability gets the sale. Usefulness gets the renewal, the internal champion, and the referral.
The distributed ops finding also implies something about AI-assisted workflows. Agentic systems that remove frontline judgment to gain efficiency often remove the very usefulness signal that motivates adoption. The automation looks like a win on a productivity dashboard. The behavioral consequence is a team that no longer experiences their work as meaningful.
What Usefulness-First Product Strategy Actually Requires
The practical reframe here is not about making products feel warmer or adding social features. That's the interface-layer solution to an identity problem.
Usefulness-first strategy requires a different upstream question during product design. Not "what jobs does this product do?" and not even "what does this product do better than alternatives?" The question is: when a user finishes using this product, what do they believe about themselves that they wouldn't have believed otherwise?
That question changes the analysis at every stage. It changes which features get prioritized (the ones that make the user look competent in front of others, not the ones that make the product look capable on a spec sheet). It changes which customer success interactions matter (the ones where a user tells you they solved a problem they'd been stuck on for weeks, not the ones where they score your product a 9 on NPS). It changes which metrics signal product health and which signal the slow evacuation of identity value that the financial metrics won't catch until the churn wave arrives.
For agentic AI applications specifically, this implies a constraint most teams aren't building in: optimization targets should include proxies for identity signal delivery, not just behavioral engagement. This is measurable. It requires instrumentation that goes beyond click-stream data, but it's the difference between a brand that AI agents can substitute and a brand that AI agents struggle to categorize cleanly enough to replace.
The 80% product failure rate Branding Strategy Insider cites isn't primarily a market engineering problem. It's a usefulness measurement problem. Teams can't optimize for what they're not tracking. The FIRE man tracked every financial variable that conventional advice said mattered. He had no instrument for the one variable his marriage ran on.
If you're building the measurement infrastructure to track decision quality at the product level, the research tools STI has developed for decision intelligence are a practical starting point.