Science of Reliability: Why Most Products Fail in the Boring Middle (Not Day One)

The Bathtub Curve Nobody Talks About

Engineers have known about the bathtub curve for decades. It describes failure rates over a product’s lifetime. High failures early (infant mortality), low failures in the middle (useful life), high failures late (wear-out).

The curve looks like a bathtub. High sides, low middle. Simple.

But here’s what consumer product reviews never discuss: most products you buy have been designed to fail in the boring middle. Not catastrophically. Not on day one when you’d return them. Gradually, after the warranty expires, when replacement seems reasonable.

This isn’t conspiracy. It’s economics. Engineering for true reliability is expensive. Engineering for warranty-period reliability is cheaper. The market rewards the cheaper approach because consumers can’t evaluate long-term reliability before purchase.

The result: products that work great on day one and day thirty, then start degrading on day 400, day 600, day 847.

The boring middle. Where things actually fail. Where nobody reviews.

The Review Gap

I read reviews religiously before buying anything. Specifications, comparisons, first impressions, benchmark tests. All useful for understanding day-one performance.

None useful for understanding day-847 performance.

The review industry is structurally incapable of evaluating reliability. Review cycles are measured in weeks. Products are returned after testing. Relationships with manufacturers depend on early access. Everything optimizes for first impressions.

Long-term reliability testing requires years. No review outlet has the patience, storage space, or business model to test products for years before publishing.

So we get reviews that tell us how products perform when new. We don’t get reviews that tell us how products perform when used. The gap between these is the boring middle—where you actually live with the product.

How We Evaluated

I spent two years tracking product failures in my own equipment. Not scientific in the formal sense, but systematic.

The method was straightforward: every product I owned went into a spreadsheet. Purchase date. First issue date. Issue description. Resolution. Subsequent issues. End of life date.

Over two years, I documented 127 products. Electronics, appliances, tools, furniture. Everything with a finite lifespan.

I also collected failure data from friends and family—another 84 products. Different usage patterns, different environments. More data points.

Finally, I researched reliability engineering literature. Academic papers on failure modes, mean time between failures, and designed lifespan. The theory behind what I was observing in practice.

The patterns were consistent and illuminating.

The Failure Timeline Pattern

Here’s what the data showed:

Days 1-30 (return window): 3% of products failed. These were genuine defects—dead on arrival, obvious manufacturing problems. The failures that reviews might catch.

Days 31-365 (warranty period): 7% of products failed. More subtle issues—intermittent problems, premature wear. Warranty repairs covered these.

Days 366-730 (post-warranty year one): 23% of products showed first issues. Batteries degrading. Mechanical parts loosening. Software updates breaking features. The boring middle beginning.

Days 731-1095 (years two-three): 34% of products showed significant degradation. Components wearing out. Performance declining noticeably. Repairs becoming questionable versus replacement.

The distribution is telling. More than half of all product issues occur after the warranty expires but before reasonable end-of-life. The boring middle accounts for most actual failures.

These failures don’t appear in reviews. They don’t trigger returns. They just… happen. Quietly. To everyone.

The Designed Lifespan Problem

Reliability engineers use a concept called “designed lifespan.” It’s exactly what it sounds like: how long the product is designed to last.

Designed lifespan is rarely disclosed to consumers. But you can infer it from warranty length, component choices, and repair availability.

A product with a two-year warranty and no available replacement parts has a designed lifespan of roughly two years. The manufacturer expects it to fail around then. They’ve engineered it to fail around then. Not maliciously—just economically.

Building for longer lifespan requires better components, more conservative tolerances, and designing for repair. All expensive. The market doesn’t reward these choices because consumers can’t evaluate them before purchase.

The result is a race to the bottom on reliability. Products become cheaper and shorter-lived. Consumers replace more frequently. Waste increases. Skills for evaluating and maintaining products atrophy.

We’ve automated our relationship with products: buy, use, discard, repeat. No repair. No evaluation. No understanding of why things fail or how to prevent failure.

The Component Hierarchy

Not all components fail equally. Understanding the hierarchy helps predict where products will break down.

Batteries: First to degrade. Lithium-ion batteries lose capacity with every charge cycle. After 500-800 cycles, capacity is noticeably reduced. Most consumer electronics hit this point at 18-24 months of regular use.

Mechanical moving parts: Second to fail. Hinges, buttons, fans, switches. Friction and stress accumulate. Tolerances loosen. Lubricants degrade. Timeline: 2-4 years for daily-use items.

Electrolytic capacitors: The hidden killer in electronics. These age even when not used. Heat accelerates degradation. Power supplies, monitors, and motherboards often fail here. Timeline: 3-7 years.

Software: Increasingly, products are killed by software, not hardware. Updates stop. Security vulnerabilities go unpatched. Cloud services shut down. Timeline: varies wildly, often 2-5 years.

Structural materials: Plastics become brittle. Adhesives weaken. Metals corrode. These are slow failures, often unnoticed until something snaps. Timeline: 5-10 years.

Knowing this hierarchy lets you predict where your products will fail. The battery will go before the processor. The hinge will fail before the screen. The capacitors will bulge before the case cracks.

The Automation Complacency Connection

Here’s how this connects to skill erosion: we’ve stopped understanding why things fail.

Previous generations repaired products. They learned what wore out, what broke, what could be fixed. This knowledge informed purchase decisions. A buyer who had repaired washing machines knew which components mattered.

Modern consumers don’t repair. Products are sealed, glued, proprietary. Repair requires specialized tools and knowledge. The economics favor replacement over repair.

So we don’t learn what fails. We don’t develop intuition for reliability. We just experience failure and replace.

This is automation complacency in a broad sense. We’ve automated our relationship with physical products. Buy. Use. Discard. The system handles everything except payment.

The skills we lose: diagnostic ability, repair competence, evaluation judgment. When everything is replaceable, nothing is worth understanding.

The Reliability Signals

Despite limited information, some signals predict reliability:

Warranty length: Manufacturers know when products fail. Longer warranties suggest higher confidence in durability. But watch for fine print—limited warranties often exclude common failure modes.

Component transparency: Companies that specify component brands (Panasonic capacitors, Samsung batteries) are usually confident in quality. Vague “premium components” language often hides cost-cutting.

Repair program existence: Companies with authorized repair programs expect products to last long enough to need repair. No repair program suggests designed-for-replacement.

Weight and build: Heavier products often (not always) use more robust components. Thin and light can mean compromised durability. But this is crude—good engineering can produce durable light products.

Company reputation over time: Companies with consistent reliability histories tend to maintain that reliability. Brand reputation correlates better with reliability than individual product reviews.

Price floor: Within categories, there’s usually a price below which reliability suffers dramatically. The cheapest option is rarely the most reliable. But the most expensive isn’t necessarily better than mid-range.

These signals are imperfect. But they’re more useful than day-one reviews for predicting boring-middle performance.

The True Cost Calculation

Most purchase decisions consider upfront cost. Better decisions consider total cost of ownership.

Total cost = Purchase price + Repair costs + Replacement costs + Downtime costs + Disposal costs

A $100 product that lasts one year and requires replacement costs $100/year. A $200 product that lasts four years costs $50/year. The expensive option is cheaper.

This calculation is difficult because reliability data isn’t available. We don’t know how long products will last. We guess based on limited signals.

But even rough estimates improve decisions. Ask: “If this fails in two years, what will I pay? Is that acceptable?” The question forces consideration of the boring middle.

The automation complacency here: we don’t make these calculations. We just buy the thing that reviews recommend based on day-one performance. Total cost of ownership requires thinking we’ve outsourced to impulse.

The Maintenance Illusion

Some products claim extended life through maintenance. Clean the filter. Update the firmware. Recalibrate annually.

Maintenance helps. But it doesn’t change fundamental component lifespan. A battery that’s degrading will degrade whether you optimize charging habits or not. A capacitor that’s aging will age whether you keep the device cool or not.

Maintenance creates an illusion of control. Users who maintain feel responsible for longevity. When failure occurs, they blame themselves rather than design.

This is useful for manufacturers. It shifts responsibility to users. “Did you maintain properly?” becomes the answer to any reliability complaint.

The truth: maintenance extends life marginally within designed lifespan. It doesn’t prevent designed lifespan limits. The battery will die. The question is whether maintenance buys you 18 months or 24 months.

Understanding this prevents the guilt that manufacturers exploit. Your product failed because it was designed to fail at roughly this point. Your maintenance affected the timing slightly. The outcome was determined at design.

The Software Reliability Problem

Hardware reliability follows physical laws. You can model wear, predict failure, engineer for longevity.

Software reliability follows… nothing predictable.

A software update can brick a device instantly. A cloud service shutdown can make hardware useless overnight. A security vulnerability can require replacement of perfectly functional equipment.

I have a drawer of devices killed by software. A tablet that stopped receiving updates and became a security risk. A smart home hub whose cloud service shut down. A camera that worked fine until the app was discontinued.

The hardware works. The software doesn’t. The product is useless.

This is new in reliability engineering. The physical product has infinite designed lifespan. The software has… whatever the company decides. Software obsolescence is designed obsolescence by another name.

Evaluating software reliability is nearly impossible. Companies don’t announce end-of-support dates in advance. Business models change. Acquisitions happen. The product you buy today might be abandoned tomorrow.

The skill lost here: evaluating software dependencies. Understanding which products require ongoing software support. Choosing products that work without cloud services when possible.

The Repair Revolution That Isn’t

Right-to-repair legislation is spreading. Companies are required to provide parts, tools, and documentation. This should improve reliability by enabling maintenance and repair.

In practice, the revolution is slow.

Parts are expensive. Often close to replacement cost. Tools are specialized. Documentation is technical. The economic case for repair remains weak compared to replacement.

And skills have atrophied. Who knows how to replace a capacitor? Who can diagnose why a product stopped working? Repair requires knowledge that wasn’t transmitted when products became disposable.

Right-to-repair enables repair. It doesn’t create repairers. The skills need to be rebuilt deliberately.

This connects to broader automation complacency. We have the right to repair but not the ability. The option exists but the capability doesn’t. Another freedom we can’t exercise because we didn’t maintain the underlying skills.

Generative Engine Optimization

Reliability is an interesting topic for AI-driven search. Ask an AI which laptop is most reliable, and you’ll get recommendations based on aggregated sentiment—largely from first-impression reviews.

The boring middle data barely exists in training corpora. Long-term reliability studies are rare. Consumer Reports does some. Specialized forums contain anecdotal data. But the volume is tiny compared to launch-day reviews.

AI recommendations for reliability are therefore weak. They reflect marketing success and initial quality, not long-term durability. The same bias that affects human review consumption affects AI training data.

Human judgment matters here because reliability evaluation requires experience over time. It requires noticing patterns across multiple products and years. It requires skepticism toward marketing claims.

The meta-skill is recognizing when AI can’t help. Reliability prediction is one of those domains. The data AI needs doesn’t exist in volume. Human judgment informed by limited data beats AI judgment based on the wrong data.

In an AI-mediated information landscape, knowing what AI can’t tell you is increasingly valuable. Reliability is a gap. Long-term performance is a gap. The boring middle is a gap.

Fill those gaps with human observation, not automated aggregation.

The Practical Guide

Given all this, how should you approach purchases?

Before buying:

• Ignore day-one reviews for reliability assessment
• Search specifically for “problems after one year” or “[product] failed”
• Check if repair parts are available (signals designed lifespan)
• Prefer companies with long reliability track records
• Calculate total cost of ownership, not just purchase price

After buying:

• Document purchase date and expected replacement date
• Note first symptoms of degradation
• Track whether issues appear on schedule (batteries at 18 months, etc.)
• Build personal reliability data for future decisions

For product categories:

• Pay more for frequently used items
• Accept shorter lifespans for rapidly evolving categories (phones)
• Demand longer lifespans for stable categories (appliances)
• Avoid products entirely dependent on software/cloud services

This won’t guarantee reliability. But it improves odds compared to buying based on reviews that can’t evaluate what matters.

Luna’s Reliability Assessment

My cat Luna has a sophisticated reliability evaluation system. She tests surfaces by walking on them. She tests furniture by scratching it. She tests boxes by sitting in them.

Her assessments are slow, thorough, and based on actual use—not specifications or first impressions.

She rejected a new bed that reviews praised because it didn’t pass her multi-week comfort evaluation. She accepted a cardboard box that would fail any formal assessment because it met her actual needs perfectly.

Her approach has limitations. She can’t evaluate products before acquisition. She can’t research alternatives. She operates purely on direct experience.

But her direct experience approach captures what reviews miss. The boring middle. The daily use case. The performance after novelty fades.

Humans have tools Luna lacks: research, comparison, systematic evaluation. But we’ve outsourced these tools to systems that can’t evaluate what matters. Maybe some of Luna’s direct-experience approach deserves reclaiming.

The Boring Middle Manifesto

Here’s what I’ve concluded after two years of tracking failures:

The boring middle is where products actually live or die. Day-one performance tells you almost nothing about day-847 performance. The review industry is structurally incapable of evaluating what matters most.

Reliability is a skill to develop, not information to receive. Understanding failure modes, component hierarchies, and designed lifespans requires learning that no review provides.

Total cost of ownership beats purchase price as decision metric. The cheapest option is rarely cheapest over time. The reliable option often is.

Repair skills have value even when repair isn’t economical. Understanding why things fail informs better purchasing. The knowledge transfers even if the repair doesn’t happen.

Software reliability is the new wildcard. Physical products can be engineered reliably. Software support can end arbitrarily. Minimize software dependencies for products you want to last.

The market rewards unreliability. Consumers can’t evaluate it. Competition is on first impressions. Only informed, patient buyers can reward actual durability.

Final Thoughts

Most products fail in the boring middle. Not dramatically. Not memorably. Just gradually, predictably, by design.

Reviews can’t help you here. They don’t cover the boring middle. They can’t. The business model doesn’t allow it.

Your skills can help you. Understanding failure modes. Recognizing reliability signals. Calculating true costs. These skills are yours whether reviews improve or not.

The automation complacency of modern consumption—buy, use, discard, repeat—erodes these skills. Breaking the cycle requires deliberate attention to what actually happens after the reviews end.

Pay attention to the boring middle. That’s where you actually live with products. That’s where reliability matters.

The science of reliability isn’t mysterious. It’s just unprofitable to discuss. Now you know. What you do with that knowledge is up to you.