🛰️ Signal Boost

Throughput isn't the goal. Learning is.

We're addicted to measuring the wrong things. Velocity points, cycle time, deployment frequency—these metrics feel scientific, objective, manageable. But they're also dangerous. When we worship throughput, we stop asking the only question that matters: are we actually getting smarter?

Kent Beck destroys this illusion in his brilliant essay "Inefficient Efficiency." He exposes the seductive trap of efficient work over effective teams. Picture this: everyone's heads-down, cranking through tickets, hitting every sprint goal. The dashboards are green. Leadership is happy. Then someone hits a wall, and the whole system crumbles because nobody was watching the periphery. Nobody was learning.

Here's the brutal truth: throughput is a lagging indicator. Learning is the leading one. The best teams don't just ship fast—they build collective intelligence, challenge sacred assumptions, and pivot gracefully when reality punches them in the face.

I've watched this play out repeatedly. Teams that optimize for raw speed without breathing room eventually suffocate. They skimp on documentation because it's "not user-facing." They avoid hard conversations because they're "not in the sprint." They punt decisions to code review when it's too expensive to change course. Collaboration becomes transactional. Quality becomes a gamble. And when things break—and they will—the damage is catastrophic.

Now contrast that with teams that invest in their own intelligence. They ask sharper questions upfront. They write specs that actually communicate intent. They pair program not because they're inefficient, but because they're building redundancy. They spend "unproductive" time in design reviews and architecture discussions. That seemingly wasteful conversation about edge cases? It prevents three weeks of emergency fixes and customer escalations.

As leaders, we're wired to optimize for visible progress. But sometimes that extra story point is worthless compared to a junior engineer finally understanding the system. That extra merge is meaningless next to catching a critical flaw before it ships. That extra deploy is hollow if your team is too burned out to innovate.

The shift is simple but profound: stop treating learning like overhead. Treat it like core infrastructure. Optimize for team intelligence, system understanding, and adaptive capacity. Throughput will follow—and when it does, it'll be sustainable, not just fast.

🔗 Lead Link

One standout article from the web that delivers signal, not noise.

Inefficient Efficiency — Kent Beck

If you’ve ever chased velocity at the expense of wisdom, this one will sting—in the best way. Kent Beck dissects why optimizing for throughput without team learning is a trap, and how slow thinking leads to fast progress.

👉 Read the full piece

🛠️ Tactical Byte

A quick tip or tactic you can try this week.

When your team’s sprint velocity goes up, ask why before you celebrate.

Was it because the team had better clarity, fewer blockers, and stronger collaboration? Or was it because they skipped design, rushed testing, and punted hard decisions down the line?

Throughput without learning is unsustainable. Treat rising velocity like a leading indicator only if it’s paired with stronger communication, better documentation, and fewer “oh no” moments in production.

Progress that’s built on understanding lasts. Everything else is just debt.

🎙️ From the Field

An insight, quote, or story from an experienced tech leader.

I know a product manager—let's call him Marcus—who learned Kent Beck's "inefficient efficiency" lesson the hard way during a major feature launch.

His team was tasked with building a comprehensive user dashboard: analytics, notifications, settings, profile management, the works. The engineering team's instinct was to architect the perfect system—build all the backend services, design the complete data models, create reusable components for every section. Classic "brew the whole pot" thinking.

Marcus pushed back. "What if we just shipped the analytics view first? Like, just that. Users can see their basic metrics and nothing else."

The engineers balked. "That's inefficient. We'd be writing throwaway code. We'd have to rebuild half of this when we add notifications. It's not scalable."

But Marcus held firm. They shipped the bare-bones analytics view in two weeks. It was embarrassingly simple—just a few charts, basic filtering, no bells and whistles. The team felt like they were shipping incomplete work.

Then the feedback started rolling in.

Turns out, users didn't care about half the analytics they'd planned to build. They wanted different metrics entirely. The filtering they'd architected was confusing. The data refresh rate was too slow. And—here's the kicker—users were way more interested in getting notifications about their data than seeing fancy visualizations.

If they'd built the "efficient" full-pot solution, they would have spent months building the wrong thing beautifully. Instead, they learned what users actually wanted in two weeks and could pivot immediately.

The next iteration focused heavily on notifications—something that would have been a minor feature in the original plan. The "inefficient" throwaway code from the first version? Most of it stayed because it solved the actual problem users had.

Marcus's team ended up shipping a product users loved in six weeks instead of building a comprehensive system users would have tolerated in four months. They made a single cup of coffee at a time, tasted it, adjusted the recipe, and ended up with something perfect for that moment.

The lesson hit me: efficiency isn't about minimizing effort—it's about minimizing the time between building something and learning whether it's right. Sometimes the most "efficient" path is the one that feels wasteful but gets you feedback faster.

💬 Open Threads

Something to chew on, debate, or share back—open questions for curious minds.

🧠 What practices in your team’s process actually teach people how the system works?

📉 Have you ever optimized for speed and later realized it slowed you down in the long run?

💬 How do you make space for learning when the pressure to deliver never lets up?