Remember when your car had a 5-speed transmission and a carburetor you could actually tinker with? Yeah, neither do I—but engineers loved them. Why? Because that “inefficient” design taught them how cars actually worked. Today’s software industry is obsessed with maximum efficiency, and I’m here to argue we’re optimizing away some of the most valuable parts of our craft.

The Efficiency Cult We’ve Built

Let’s be honest: the software development world is currently gripped by what I call “efficiency mania.” The numbers are everywhere and they’re terrifying. Industry studies report that [92% of teams are dealing with inefficient developer workflows], and in large enterprises, a staggering [60% of software development is considered wasted]. These statistics trigger panic in C-suites and create an almost religious devotion to “streamlining” everything. The pressure is relentless: eliminate unnecessary meetings, automate code reviews, reduce documentation (unless it’s the “right” kind of documentation), compress planning cycles, and for goodness sake, measure everything. We’ve built entire consulting industries around trimming the fat from software development. And yes, I understand the appeal. When you’re hemorrhaging productivity, you want to stop the bleeding. But here’s where I think we’ve collectively taken a wrong turn: in our zeal to eliminate inefficiency, we’re systematically removing the very practices that build expertise, catch subtle bugs, and create sustainable software.

The Hidden Cost of Perfect Efficiency

Imagine the most efficient developer you know. They write code at lightning speed. They skip code reviews because they’re confident—and honestly, they’re usually right. They don’t waste time with “excessive” testing because they know their patterns. They document minimally because the code is “self-explanatory.” Now imagine that developer on your team five years from now. They’ve become a bottleneck. No one else understands their code. They’ve accumulated significant technical debt because optimizing for their own speed never accounted for maintainability. They’ve hit a performance ceiling because they never had to defend their architecture decisions to someone smarter about a specific domain. We’re taught that [automating repetitive tasks helps developers reserve time for growth], and that’s true—partially. But what if some of that repetition is actually the growth mechanism itself? What if the “inefficiency” of sitting in a code review for 90 minutes, defending your solution to a teammate, is exactly where you learn?

graph TD A["Perfect Efficiency"] --> B["Eliminate Code Reviews"] A --> C["Skip Documentation"] A --> D["Minimize Testing"] B --> E["Knowledge Silos"] C --> F["Maintainability Crisis"] D --> G["Silent Failures"] E --> H["Technical Debt Accumulation"] F --> H G --> H H --> I["System Collapse"]

The Underrated Value of ‘Wasteful’ Activities

Let me break down some activities that get labeled as inefficient but are actually quite valuable:

Code Reviews That Take Forever

Microsoft’s engineering team famously [automated their code review process through an internal tool called CodeFlow, reducing review time by 90%]. Fantastic result, right? Except here’s what they gained: speed. Here’s what they also got as a bonus: less friction between teams, plugged feedback loops, and better handshakes. But what did they lose? The serendipitous learning moment. You know the one—when you’re reviewing someone’s code and you think, “Oh, that’s clever, I’ve never seen that pattern before.” Or when you question an approach and the author has to articulate their thinking, which sometimes reveals assumptions that haven’t been validated. I’m not saying we should reject automation. But let’s be real: a tool that processes code review feedback is not the same as a human who understands the domain, the business context, and the team’s long-term health reviewing the code.

Documentation Nobody Reads

We’ve been told that code should be self-documenting, and up to a point, that’s true. But [teams that focus on the relationship between code and documentation see a 30% increase in code quality and a 25% decrease in time to update documentation]. That “inefficiency” of maintaining documentation serves a purpose: it forces you to think about what you’re building from a different angle. The problem isn’t documentation—it’s bad documentation written for the sake of checking boxes. But the existence of documentation, even if it feels redundant when you’re deep in the code, becomes invaluable when you’re trying to understand legacy systems, onboard new team members, or make architectural decisions.

Time Spent Thinking Instead of Coding

Here’s an unpopular opinion: some of the most important work in software development is inefficient by nature. Thinking takes time. Design thinking takes even more time. And if you’re constantly measuring “lines of code per hour” or “commits per week,” you’re optimizing for the wrong thing. The pressure to constantly produce—to show visible progress—has created a culture where sitting and thinking about a problem for an hour feels like failure. It’s not. Some of the greatest engineering decisions happen in moments that would never appear in any productivity metric.

Strategic Inefficiencies: Which Ones Actually Matter?

Not all inefficiencies are created equal. Let me be clear: I’m not advocating for abandoning all efficiency improvements. That’s silly. Instead, I’m suggesting we need a framework for distinguishing between inefficiencies that cripple delivery and inefficiencies that preserve long-term health.

The Good Inefficiencies

Thorough Code Review: Takes time. Slows down deployment. Worth it. Comprehensive Testing: [Studies on software quality] show that thorough testing—unit testing, integration testing, system testing, and performance testing—identifies and prevents issues that would cost orders of magnitude more to fix later. Architecture Design Time: Spending two weeks designing before you code beats spending two months refactoring because you painted yourself into a corner. Cross-functional Communication: Meetings can be tedious, but [inefficient workflows cause communication issues between team members, snowballing into reduced cohesion and delayed software delivery]. Sometimes talking through a problem is the most efficient path forward. Documentation: Not the excessive bureaucratic kind, but actual, useful documentation. Build it incrementally. Make it part of development, not an afterthought.

The Bad Inefficiencies

Flaky Tests: [Teams spend hours trying to diagnose intermittent test failures, leading to delayed project timelines and frustrated developers]. This is waste. Real waste. Fix it. Bureaucratic Approval Processes: The kind where a feature request passes through five layers of management for no substantive reason. Streamline this ruthlessly. Context Switching: Developers constantly switching between projects, priorities, and meetings. This is genuinely toxic. Protect developers’ focus time. Technical Debt Avoidance: The inefficiency of not setting aside time to refactor and improve existing code. This always bites back.

A Practical Framework: Implementing Strategic Inefficiency

Here’s where I actually give you actionable advice, because blog posts should do that.

Step 1: Audit Your Current State

List all the activities your team does in a typical sprint. For each one, ask three questions:

  1. Does this activity directly prevent future problems or create learning?
  2. Would removing this activity create visible damage in the next 6 months?
  3. Do people actively object to this activity, or have they just internalized that it’s “necessary”? Activities that answer “yes” to questions 1 and 2 are your strategic inefficiencies. Protect them. Activities that trigger “no, we just keep doing it” are candidates for elimination.

Step 2: Make Inefficiencies Explicit

Here’s a template for documenting strategic inefficiencies:

strategic_inefficiency:
  name: "Weekly Architecture Review"
  time_investment: "4 hours per week"
  purpose: "Catch architectural drift before it becomes systemic"
  success_criteria:
    - "New services conform to established patterns"
    - "Dependencies are explicitly mapped"
    - "Team understands why decisions were made"
  metrics_that_dont_matter:
    - "Number of PRs merged per week"
    - "Developer velocity in story points"
  failure_mode: "If eliminated: Unknown service boundaries, tightly coupled systems, repeated architectural mistakes"

Create a few of these. Share them with your team. Defend them when someone suggests removing them.

Step 3: Build Inefficiency Into Your Development Cycle

Here’s a concrete suggestion: implement a “thinking sprint” into your planning cycle. In a traditional 2-week sprint, allocate one day (roughly 12% of your time) where developers are explicitly encouraged to:

  • Refactor code that’s been bothering them
  • Document architectural decisions
  • Investigate new tools or approaches that might improve future work
  • Learn something completely unrelated to the current product
gantt title Strategic Inefficiency in Sprint Planning section Sprint 1 Feature Work: feat1, 0, 8d Code Review & Testing: review1, 0, 8d Technical Debt: debt1, 8d, 10d Thinking/Learning: think1, 10d, 11d section Sprint 2 Feature Work: feat2, 12d, 20d Code Review & Testing: review2, 12d, 20d Technical Debt: debt2, 20d, 22d Thinking/Learning: think2, 22d, 23d

Will this reduce your velocity number? Yes. Will it prevent you from hitting your quarterly goals? Probably not. Will it create better software? Absolutely.

Step 4: Defend Your Inefficiencies Against Metrics

Here’s where most teams fail: they introduce the strategic inefficiency, but then measure themselves against the old metrics, see the drop, and panic. You need to change your metrics. Instead of measuring “velocity,” measure:

  • Defect escape rate: How many bugs make it to production? Strategic inefficiencies should reduce this.
  • Rework percentage: How much time do you spend fixing things you’ve already fixed? Good testing and code review reduce this.
  • Cycle time for refactoring: When you identify technical debt, how long until you can address it? This should decrease as review efficiency improves.
  • Time to onboard new team members: How long until a new engineer can understand and contribute to your systems? Invest in inefficiencies here.
  • Architect satisfaction: Ask your architects whether the codebase is improving or degrading. This subjective measure often predicts future problems better than any metric.

The Uncomfortable Truth

The reason we love efficiency metrics is because they’re quantifiable and visible. A 90% reduction in code review time looks amazing on a presentation. Increased developer happiness from spending more time on architecture? That’s fuzzy. Harder to measure. Easier to cut. But [as we squeeze out inefficiency, we’re left with systems that are technically optimized and architecturally fragile]. We have faster builds but shakier code. We have more features but lower quality. We’re winning the sprint and losing the game.

Where This Gets Personal

I’ve worked on teams at both extremes. I’ve been on the team obsessed with efficiency—we had incredible velocity metrics. We also had the worst codebase I’ve ever seen. Every new feature took exponentially longer because the foundation was unstable. I’ve also been on teams that moved slowly, debated architecture religiously, and maintained code that was a pleasure to work with. The difference wasn’t talent. It was permission to be “inefficient.”

The Contrarian Path Forward

Here’s my actual advice: Stop optimizing for efficiency as the primary goal. Optimize for sustainability, learning, and quality. Efficiency will naturally follow once you’ve built a foundation that actually supports it. Some inefficiencies are worth paying for. They’re not bugs in your system—they’re features of a healthy engineering culture. The teams that will dominate in the next decade won’t be the ones who shaved another 5% off their build time. They’ll be the ones who realized that the time spent understanding, discussing, and improving their systems is an investment, not a cost. Now go forth and embrace some strategic inefficiency. Your future self will thank you.