What Boring Technology Means and Why It Is the Greatest Luxury

The Seductive Pull of the New

Every six months, a new JavaScript framework emerges. Every quarter, someone declares that the old way of doing things is dead. Every week, a blog post explains why you should migrate your entire codebase to whatever technology shipped last Tuesday.

And every time, experienced engineers sigh quietly, close the browser tab, and continue using PostgreSQL.

This pattern repeats across our industry with the regularity of tides. The young and ambitious chase novelty. The senior and scarred chase stability. Neither group fully understands the other, yet both believe they are optimizing for success.

The term “boring technology” entered mainstream engineering discourse around 2015, when Dan McKinley published his influential essay advocating for deliberately choosing well-understood tools over exciting new ones. His central argument was simple: every technology choice carries hidden costs, and the newest technologies carry the highest costs because nobody has discovered their failure modes yet.

But boring technology is not merely about avoiding risk. It represents something deeper — a fundamental philosophy about where engineers should spend their limited cognitive resources. And in an era where automation promises to handle the tedious work for us, understanding this philosophy has become more important than ever.

My British lilac cat, who spends most of her days sleeping on precisely the same corner of my desk, seems to understand boring technology intuitively. She has optimized her entire existence around predictable patterns. Same spot. Same time. Same results. She does not experiment with new sleeping locations. She does not A/B test different cushions. She found what works and committed to it completely.

There is wisdom in that approach, even if she arrived at it through feline instinct rather than engineering experience.

The Innovation Token Economy

McKinley introduced a useful mental model: the innovation token. Every organization starts with approximately three tokens. Each time you adopt a new, unproven technology, you spend one token. Once the tokens are gone, any additional novelty becomes actively dangerous to your project’s success.

This model captures something real about how complexity accumulates in software systems. When everything is new, every problem becomes a research project. When most things are boring, the new things have room to breathe.

Consider a startup building a web application. They could choose:

• A cutting-edge frontend framework released six months ago
• A novel database designed for their specific use case
• A new deployment platform that promises magical simplicity
• An innovative authentication system based on recent research
• A experimental message queue with impressive benchmarks

Each choice sounds reasonable in isolation. Together, they create a nightmare. When something breaks — and something always breaks — debugging requires expertise in five different immature ecosystems. Documentation is sparse. Stack Overflow answers do not exist. The community is small and equally confused.

Alternatively, the same startup could choose React (or even plain HTML), PostgreSQL, a standard cloud provider, boring OAuth, and Redis. None of these choices will appear in blog posts about technological innovation. All of them will work at three in the morning when the system falls over.

The innovation token model suggests we should spend our novelty budget deliberately. Pick one area where innovation genuinely matters for your competitive advantage. Use boring technology everywhere else. This asymmetry creates sustainable systems.

graph TD
    A[New Project] --> B{Innovation Budget}
    B --> C[Token 1: Core Business Logic]
    B --> D[Token 2: Maybe One New Tool]
    B --> E[Token 3: Emergency Reserve]
    
    C --> F[Competitive Advantage Area]
    D --> G[Calculated Risk]
    E --> H[Unexpected Requirements]
    
    F --> I[Success Zone]
    G --> I
    H --> I
    
    J[Token Overrun] --> K[Technical Debt]
    K --> L[Team Burnout]
    L --> M[Project Failure]

Method: How We Evaluated Boring Technology

To understand why boring technology represents a luxury rather than a limitation, I examined the concept through several analytical lenses:

Historical analysis of technology adoption curves: I traced how technologies move from exciting to boring, and what characteristics differentiate those that successfully make this transition from those that fade into obscurity.

Cognitive load assessment: I evaluated how much mental overhead different technology choices impose on development teams, drawing on research in cognitive psychology and practical experience from multiple engineering organizations.

Risk decomposition: I broke down the various types of risk associated with technology choices — technical risk, organizational risk, market risk, and career risk — to understand which risks boring technology mitigates and which it does not address.

Case study examination: I reviewed several well-documented projects that either succeeded with boring technology or failed with exciting technology, looking for patterns that transcend individual circumstances.

Counterfactual reasoning: I considered what would have happened if specific organizations had made different technology choices, using publicly available information about their technical challenges and business outcomes.

Personal experience integration: I incorporated observations from my own career, including projects where I chose boring technology successfully and projects where I chose exciting technology regrettably.

This multi-faceted approach revealed that boring technology is not merely a defensive strategy. It is an offensive one. Organizations that consistently choose well-understood tools accumulate advantages that compound over time.

The Hidden Costs of Novelty

New technologies impose costs that their advocates rarely acknowledge. These costs fall into several categories:

Learning costs: Every engineer must climb the learning curve. With mature technologies, learning resources abound. With new technologies, learning often means reading source code and hoping for the best.

Integration costs: Mature technologies have established patterns for working with other mature technologies. New technologies require custom integration work, often involving workarounds for missing features.

Debugging costs: When PostgreSQL misbehaves, thousands of engineers have encountered similar problems. When CoolNewDB misbehaves, you might be the first person to ever see that particular failure mode.

Hiring costs: Finding engineers experienced with boring technology is straightforward. Finding engineers experienced with new technology means either hiring from a tiny pool or training extensively.

Documentation costs: Boring technology has comprehensive documentation, community tutorials, university courses, and published books. New technology has a README file that was last updated three months ago.

Stability costs: Mature technologies have stable APIs. New technologies undergo breaking changes with each release, requiring constant maintenance merely to stay current.

These costs are largely invisible at the moment of decision. The engineer evaluating CoolNewDB sees impressive benchmarks and elegant syntax. They do not see the three weeks they will spend six months later tracking down a bug that affects only their particular data pattern.

This invisibility explains why the same mistake repeats across the industry. Each engineering team believes they are making a rational choice based on available information. They are. The problem is that the available information is systematically biased toward novelty.

When Boring Becomes Luxury

Here is where the argument becomes counterintuitive: boring technology is expensive.

Not in terms of licensing fees or infrastructure costs. In terms of organizational maturity. Choosing boring technology requires the discipline to resist shiny objects. It requires enough experience to recognize that most problems have been solved before. It requires sufficient job security that engineers do not need to pad their resumes with trendy keywords.

Junior engineers often cannot afford boring technology. They need interesting projects for career advancement. They need exposure to new tools to remain marketable. They need something to talk about in interviews. Boring technology offers none of these benefits.

Organizations under pressure often cannot afford boring technology. They need to attract investors excited by technological innovation. They need to recruit engineers who want to work with cutting-edge tools. They need to generate press coverage, which requires something novel to write about.

Consultancies and agencies often cannot afford boring technology. They need to differentiate from competitors. They need to demonstrate technical sophistication to clients. They need billable hours that justify their premium rates.

The organizations that can genuinely afford boring technology are those with enough success that they no longer need to prove anything. They have attracted investment. They have recruited talent. They have established market position. Now they can optimize for execution rather than appearance.

This creates a paradox: boring technology is most available to those who need it least. Successful organizations can choose stability. Struggling organizations often cannot.

The Automation Parallel

The principles behind boring technology apply equally to automation tools. And this is where the contemporary relevance becomes acute.

Modern AI coding assistants represent the newest, shiniest technology in software development. They promise to amplify productivity, eliminate tedious work, and make every engineer more effective. These promises are not entirely false. But they follow the same pattern as every previous technological innovation.

Using AI assistants effectively requires understanding their limitations. These limitations are not yet well-documented because the technology is too new. The failure modes have not been catalogued. The debugging strategies have not been developed. The community knowledge does not exist.

Engineers who rely heavily on AI assistants today are spending innovation tokens whether they realize it or not. They are betting that the productivity gains outweigh the costs of working with immature technology. For some engineers and some tasks, this bet will pay off. For others, it will not.

The boring technology approach to AI assistants would be: use them sparingly, understand their limitations deeply before depending on them, and ensure you can accomplish your work without them when they inevitably fail.

This is not a popular position. It does not generate excitement. It does not make for compelling conference talks. But it is consistent with decades of engineering experience about how to build reliable systems.

Skill Erosion and Tool Dependency

Boring technology preserves skills. Exciting technology erodes them.

When you work with PostgreSQL for years, you develop deep intuition about relational data modeling. You learn to predict query performance before running EXPLAIN. You understand transaction isolation levels not as abstract concepts but as practical tools. This knowledge transfers across every relational database you ever encounter.

When you work with a new database every year, chasing whatever technology is currently fashionable, you never develop this depth. You remain perpetually at the surface level, competent enough to build basic applications but lacking the intuition that separates adequate engineering from excellent engineering.

The same dynamic applies to automation tools. Engineers who use AI assistants to write code from the beginning of their careers may never develop the debugging intuition that comes from manually tracing through unfamiliar codebases. They may never build the pattern recognition that comes from writing the same kind of code hundreds of times. They may never understand why certain designs work and others fail.

This is not hypothetical concern. We have seen similar patterns with GPS navigation. Drivers who learned to navigate before GPS developed spatial reasoning skills that drivers who learned with GPS often lack. The GPS-dependent drivers can reach their destinations, but they understand their cities less deeply. When the GPS fails, they are lost in ways that earlier drivers never experienced.

Boring technology keeps human skills sharp because boring technology sometimes fails in predictable ways that humans must fix. Exciting technology either works magically or fails mysteriously, providing fewer opportunities for skill development.

The Productivity Illusion

Exciting technology often creates the illusion of productivity without the substance.

Consider a team that adopts a new framework promising to reduce development time by fifty percent. Initially, development does accelerate. Simple features ship faster than ever before. The team feels productive. Management feels validated.

Then the edge cases appear. The framework handles common scenarios well but unusual scenarios poorly. The team spends increasing time working around framework limitations. They write custom code to supplement framework deficiencies. They debug framework bugs that the small maintainer team has not yet fixed.

After six months, the team has shipped roughly the same amount of functionality they would have shipped with boring technology. But their codebase is now entangled with a framework they do not fully understand. Their custom workarounds have created technical debt. Their engineers have spent time learning a tool that may not exist in five years.

The initial velocity gain was real but temporary. The long-term productivity cost was real and permanent.

Boring technology avoids this pattern by trading initial velocity for sustained velocity. PostgreSQL will not make your first week dramatically faster. It will make your fiftieth week much less painful. This tradeoff favors organizations planning to exist for fifty weeks, which should include most organizations.

Generative Engine Optimization

The rise of AI-powered search and content synthesis changes how information about technology reaches engineers. Traditional search engines ranked results based on signals like links, keywords, and user behavior. AI systems synthesize answers from multiple sources, often favoring content that appears comprehensive, confident, and recent.

This shift has implications for how boring technology is perceived and discovered.

Content about exciting technology tends to perform better in AI-summarized search results. It is more recent. It contains more superlatives. It generates more engagement. Content about boring technology is often older, more cautious, and less engagement-optimized.

Engineers relying on AI assistants for technology recommendations may therefore receive systematically biased advice. The AI has learned from a corpus that overrepresents enthusiasm for new technology and underrepresents the quiet satisfaction of engineers using PostgreSQL successfully for the fifteenth year.

This creates a meta-skill requirement: understanding how AI systems might bias your information diet toward novelty. Engineers who recognize this bias can compensate by specifically seeking out boring technology resources, consulting experienced colleagues, and treating AI recommendations with appropriate skepticism.

The ability to think critically about automation-mediated information is becoming essential. Not because AI systems are malicious, but because their training data reflects the biases of the content they learned from. And internet content is heavily biased toward the new and exciting.

Boring technology requires human judgment to appreciate. The benefits are subtle. The costs are distributed across time. The success stories are quiet. These characteristics make boring technology resistant to AI summarization and recommendation. They also make it more valuable to engineers capable of recognizing hidden value.

Organizational Dynamics

Boring technology choices often conflict with organizational incentives.

Engineers are evaluated partly on the technologies they work with. Spending two years maintaining a Java application does not enhance a resume as much as spending six months building something with the framework that launched last quarter. This creates pressure toward novelty regardless of technical merit.

Managers are evaluated partly on the initiatives they launch. A manager who proposes “let’s keep using what works” generates less visibility than a manager who proposes “let’s migrate to the new platform.” The boring choice might be correct, but it does not demonstrate leadership.

Organizations are evaluated partly on their technological sophistication. Investors want to see innovation. Recruits want to see interesting projects. Press coverage requires novelty. The boring choice might be sustainable, but it does not generate excitement.

These incentive structures explain why boring technology remains rare despite its advantages. The people making technology decisions are often rewarded for novelty and penalized for caution. They are optimizing for their careers rather than their organizations, which is entirely rational behavior given the incentives they face.

Changing this dynamic requires changing incentive structures. Organizations that genuinely want boring technology must reward engineers for stability rather than novelty. They must promote managers who maintain functioning systems rather than launch new initiatives. They must accept that their recruiting materials will be less exciting than competitors who chase trends.

Few organizations make these changes deliberately. Those that do often develop significant competitive advantages over organizations distracted by technological churn.

The Compound Interest of Stability

Boring technology creates compound benefits that exciting technology cannot match.

Consider an organization that committed to PostgreSQL fifteen years ago. Their engineers have accumulated fifteen years of PostgreSQL expertise. Their codebase has been refined over fifteen years of PostgreSQL patterns. Their operational procedures have been proven through fifteen years of PostgreSQL deployments.

A competitor that adopts a new database gains certain advantages. Perhaps better performance for specific workloads. Perhaps a more modern query language. Perhaps easier horizontal scaling.

But the competitor cannot buy fifteen years of accumulated knowledge. They cannot shortcut the process of learning failure modes. They cannot instantly develop the intuition that comes from sustained experience. The new database might be better on paper. The old database is better in the hands of engineers who truly understand it.

This compound interest effect applies across all technology choices. Organizations that resist churn accumulate expertise. Organizations that chase novelty accumulate familiarity with many tools but mastery of none.

The compound interest argument is rarely persuasive to those making technology decisions. Fifteen years is longer than most engineers stay at one company. Fifteen years is longer than most companies survive. The benefits accrue to future people solving future problems, which makes them easy to discount.

Yet the organizations that achieve lasting success often share this characteristic: they found technologies that worked and stuck with them long enough to develop genuine expertise.

The Courage of Boring Choices

Choosing boring technology requires courage.

It requires courage to admit that you do not know everything. The engineer choosing PostgreSQL acknowledges that they cannot predict all the ways a new database might fail. This humility is unfashionable in an industry that rewards confidence.

It requires courage to resist peer pressure. When everyone else is discussing the exciting new framework, admitting that you still use the old one invites condescension. Being boring is not socially rewarding.

It requires courage to accept career risk. Engineers who spend years with boring technology may find themselves less marketable than engineers with trendy resumes. The skills transfer, but the keywords do not.

It requires courage to trust proven solutions over promised solutions. The new technology might be better. Probably it is not. But you cannot know for certain until you try, and trying carries costs. Trusting the boring choice means accepting uncertainty about the road not taken.

This courage is rare because it goes unrewarded in the short term. The engineer who chose boring technology and succeeded looks the same as the engineer who chose exciting technology and succeeded, except the latter has better stories to tell. The courage only becomes visible when exciting technology fails and boring technology continues working.

Practical Guidelines

For engineers considering how to apply boring technology principles:

Inventory your current stack: List every technology you use. For each one, assess how well you understand its failure modes. Technologies you understand deeply are boring. Technologies that still surprise you are not.

Identify your innovation spending: Where have you invested your innovation tokens? Are those investments in areas that genuinely differentiate your product? Or are they in areas where boring alternatives would work equally well?

Develop expertise depth: Choose one or two core technologies and commit to mastering them completely. This depth will serve you longer than broad familiarity with many tools.

Practice solving problems without new tools: Before reaching for a new technology, ask whether existing tools could solve the problem. Often they can, with less total cost.

Build relationships with experienced engineers: People who have worked with the same technologies for decades have knowledge that cannot be found in documentation. Their pattern recognition is a valuable resource.

Accept that boring choices will sometimes be wrong: No strategy is perfect. Sometimes the new technology genuinely is better. The boring technology approach minimizes average-case costs at the expense of occasional missed opportunities.

Document your reasoning: When you choose boring technology, write down why. When the choice is questioned later, you will have a clear answer. When the choice proves correct, you will have evidence for future decisions.

The Luxury Perspective

Boring technology is a luxury because it requires resources that not everyone has:

Experience enough to recognize that most problems have been solved before. Patience enough to learn existing solutions rather than inventing new ones. Security enough to resist career pressure toward novelty. Discipline enough to maintain focus when shiny objects appear.

These resources accumulate over time. Junior engineers cannot have them yet. Struggling organizations cannot afford them. Only successful engineers and successful organizations can truly embrace boring technology.

This explains why boring technology correlates with success. Not because boring technology causes success — though it helps — but because success enables boring technology. The causal arrow points both directions. Successful organizations can afford boring choices. Boring choices make organizations more likely to succeed.

My cat, still sleeping on the same corner of my desk, has achieved a level of boring technology mastery that most engineers never reach. She has optimized her system completely. No unnecessary complexity. No experimental sleeping positions. No innovation for its own sake.

She cannot explain her philosophy. But she embodies it perfectly.

Looking Forward

The technology industry will continue generating new frameworks, new databases, new platforms, and new paradigms. The pressure toward novelty will not diminish. If anything, it will intensify as AI systems make it easier to create and market new tools.

Engineers who understand boring technology principles will have an advantage in this environment. They will evaluate new technologies more skeptically. They will adopt them more selectively. They will build systems that remain stable while others chase the latest trends.

This advantage compounds over time. As the rate of technological change increases, the ability to resist unnecessary change becomes more valuable. The engineers who master boring technology will build the systems that actually work, while others rebuild their stacks every eighteen months.

Boring technology is not about rejecting progress. It is about distinguishing genuine progress from mere novelty. PostgreSQL represents genuine progress over the file-based databases it replaced. The new database released last month might represent progress, or might represent a temporary enthusiasm that will fade.

The wisdom to tell the difference is rare. The courage to act on that wisdom is rarer. Those who develop both will build better systems, have more sustainable careers, and sleep more peacefully.

The greatest luxury in technology is not access to the newest tools. It is the freedom to ignore them.