A Neuroscience-Informed Field Guide for Self-Organization (Part 4 of n)
Two teams of lumberjacks work side by side in the forest. The first pair moves from tree to tree without pause, sweat pouring, determination evident in every swing. No breaks. No rest. Just relentless productivity.
The second pair cuts for a while, then pauses. They drink water. They sit. At lunch, while the first team powers through, the second takes a nap under a tree.
If you’re on a team right now, you probably know exactly which lumberjacks you are. You recognize the exhaustion. The feeling of working harder and harder while somehow falling further behind. The guilt that whispers “everyone else is working through lunch, what’s wrong with you?”
By late afternoon, something impossible has happened. The team that took breaks, that “wasted time” napping, has felled twice as many trees.
“How?” demands the exhausted first team. “We worked non-stop!”
The second team smiles. “Every time we rested, we sharpened our axes.”
The Productivity Paradox
This story has been told and retold because it captures something we know in our bones but struggle to honor in our systems: rest isn’t the opposite of productivity. It’s the foundation of it.
Yet here we are, in knowledge work environments across the world, running our people at 100+% utilization and wondering why nothing gets done.
In Post 3 of this series, we met the Five Thieves of Time and learned how the Yes Habit invites them into our systems. We identified Too Much Work in Progress as the ringleader, creating the Doom Loop where the harder we work, the less we accomplish.
But understanding what steals our time isn’t enough. We need to understand why the math itself demands we say no to overcommitment.
Because it turns out the lumberjack story isn’t just folksy wisdom. It’s mathematics.
Your Brain on Busy-ness
Before we dive into the numbers, let’s talk about why those numbers are so hard to believe.
Your brain, that remarkable prediction machine we’ve been exploring throughout this series, has a problem. It’s constructed an emotional response to “doing nothing” that feels like danger.
Remember from Post 2, Tending To Your Inner Landscape, how emotions aren’t hardwired reactions but actively constructed predictions based on context and past patterns? Your brain has learned, through years of organizational conditioning, to predict that:
- Visible busy-ness = safety and value
- Idle time = risk of being seen as unproductive
- Full calendars = importance and relevance
- Saying “I’m at capacity” = weakness or incompetence
And here’s where it gets neurochemically interesting: your brain rewards this busy-ness with dopamine hits. Every email answered, every task checked off, every meeting attended triggers a small reward. It doesn’t matter if the email was important or the meeting was valuable. Your brain gets its hit of “I did something.” (Ok, maybe not every meeting….)
This creates an addiction loop. You feel productive when you’re busy, even when that busy-ness is destroying your actual productivity. The anxiety of an empty calendar slot or an unscheduled hour feels more threatening than the exhaustion of chronic overcommitment.
Your adaptive brain is trying to help. It’s predicting that staying busy will keep you safe, valued, employed. But like the lumberjacks swinging dull axes, this prediction is catastrophically wrong for knowledge work.
Quick reflection: Notice your own response to reading this section. Did your brain just predict “but I really AM too busy to rest”? That’s the constructed emotion we’re about to challenge with math.
When Highways Become Parking Lots
There’s a mathematical principle called Kingman’s Law that explains exactly why those non-stop lumberjacks lost the race. You don’t need to understand the formula (the VUT equation is here if you’re really curious). You just need to see what happens when systems approach full capacity.
Think about traffic on a highway. At 50% of the road’s capacity, cars flow smoothly. Everyone reaches their destination quickly. At 70%, you start seeing some slowdowns as cars navigate around each other, but traffic still moves. At 85%, you’re hitting patches of congestion, but you’re still making progress.
But watch what happens as you approach 95-100% utilization. The highway doesn’t slow down linearly. It explodes into gridlock. Bumper-to-bumper crawl where nobody moves effectively. A small incident—someone taps their brakes, a car changes lanes—ripples backward, creating cascading delays that can last for hours.
The wait times don’t increase gradually. They increase exponentially.
On your team, things work exactly the same way. At 50-70% utilization, work flows smoothly. People have space to think, to collaborate, to handle the unexpected. But as you approach 100% utilization—that state most organizations demand—wait times explode. A single dependency, a question waiting for an answer, a code review that takes an extra day—these don’t cause minor delays. They cause system-wide gridlock.
Those lumberjacks working non-stop? They were operating at 100% utilization with axes that got duller with every swing. The second team maintained 80-85% utilization with sharp axes. The math isn’t intuitive, but it’s inexorable: the team with slack time outperformed the team without it.
For your team: Look at your current sprint or work cycle. Are you planning to use 100% of the estimated capacity? 110%? More? You’re not being ambitious. You’re building in mathematical certainty of delay.
The Context-Switching Tax
But wait, it gets worse. (Or better, depending on how you look at it, because understanding the problem is the first step to fixing it.)
Let’s say your team manages to avoid the utilization trap. Are you keeping WIP reasonable? Or are you still juggling multiple things because, well, that’s just how work happens, right?
Gerald Weinberg’s research on context switching reveals another mathematical reality that guts our productivity. Here’s what happens to your available capacity as you add concurrent work:
- One task: 100% of your time goes to productive work
- Two tasks: You lose 20% to context switching (80% remains productive)
- Three tasks: You lose 40% (60% remains)
- Four tasks: You lose 60% (40% remains)
- Five tasks: You lose 80% to context switching, leaving only 20% for actual productive work
Read that last one again. With five cognitively demanding things in flight simultaneously, your brain spends 80% of its energy just switching between contexts. Reloading information. Reorienting. Remembering where you were. Only 20% goes to actually creating value.
This isn’t about discipline or focus. It’s about how human cognition works. Your brain isn’t a computer that can instantly swap between creative processes. Every context switch requires rebuilding your mental model of the work. The more you switch, the more energy you burn on the switching itself.
Quick self-scan: Right now, how many things are you “working on”? Not what’s in your backlog, but what’s actually in progress? Count them. Now multiply that number by 20%. That’s roughly how much of your capacity you’re losing to context switching alone.
And here’s the kicker: this isn’t just personal. It scales to the team level. When a team has fifteen stories in progress for six people, the context-switching tax is murdering your predictability. When your organization has forty initiatives “in flight” across ten teams, you’re paying compound context-switching costs at every level.
Our Team Isn’t Drowning Because of People Problems
Here’s the emotional shift I want you to feel: Your team’s exhaustion isn’t a people problem. It’s a systems problem.
Not a failure of work ethic. Not insufficient commitment. Not a need to “do more with less.”
The mathematics of queuing theory (Kingman’s Law) and cognitive science (context switching) are proving that the way we’ve been working is structurally doomed to fail.
When you plan for 100% utilization, you’re not being efficient. You’re guaranteeing exponential delays.
When you keep starting new work instead of finishing what’s in progress, you’re not being responsive. You’re compound-charging the context-switching tax.
When you feel guilty about taking breaks or building slack time, your brain is constructing an emotional response based on outdated industrial-era predictions that don’t apply to knowledge work.
The lumberjacks who rested weren’t lazy. They were applying mathematics—even if they didn’t think of it that way. They understood intuitively what Kingman’s Law proves empirically: sustainable pace beats heroic effort every single time.
This realization should feel like relief, not defeat. Because if it’s a system problem, we can change the system. (Ok, we can try, and try again. Nudge. Influence. Move a fence.)
Sharpening Your Axes: The FINE Experiment
So what does axe-sharpening look like for a software team? A product team? Any knowledge work team?
Let me share a real example of a team that deliberately built in slack time, tracked the results, and transformed their effectiveness.
This team noticed they were constantly behind on documentation and struggling to develop new skills. The “important but not urgent” work kept getting deprioritized in favor of feature delivery. They were operating like those non-stop lumberjacks—busy constantly, but their axes (their skills, their knowledge base, their technical foundation) were getting duller.
So they designed a FINE experiment—Fast, Inexpensive, No permission needed, Easy to try:
The Setup:
- Reserve a half-day every two weeks for “deep work” time
- Wednesdays, blocked on everyone’s calendar
- Synchronous kickoff to align on goals, then asynchronous execution
- Four sessions (two months) to evaluate
What they hoped to achieve:
- Better documentation that reduces onboarding time and context-switching
- Team members developing next-level capabilities
- Increased engagement through autonomy and mastery
- Potential new features or process improvements that wouldn’t emerge otherwise
What they watched out for:
- Chat notifications disrupting deep focus (they used DND modes)
- Urgent production issues cannibalizing the time (they designated an “interception” rotation)
- Decision paralysis about what to work on (they created a “suggested projects” backlog to choose from)
The team made this experiment visible by putting the work directly in their backlog. Not hidden, not assumed, not “if we have time.” Explicit. Tracked. Protected.
The results after two months?
Documentation quality improved measurably—new team members onboarded 30% faster. Three process automation tools emerged that reduced weekly toil by 5 hours per person. And not surprisingly, their throughput increased by 15% because reduced context switching and better documentation meant less time spent figuring out what previous developers had done.
The team had sharpened their axes. And just like in the parable, they ended up cutting down more trees despite “working” fewer hours.
For your context: What’s the “important but not urgent” work your team keeps deferring? Technical debt that keeps growing? Learning a new framework? Improving your build pipeline? That’s your axe-sharpening opportunity.
The Metrics That Make It Real
Here’s where skeptical stakeholders usually push back: “That sounds nice, but we can’t afford to ‘waste’ 10% of our capacity on ‘sharpening axes’ when we’re already behind on commitments.”
This is where flow metrics become your strategic weapon.
Remember the four key metrics from Post 3, The Yes Habit and the Five Thieves of Time?
- Work in Progress (WIP): How many items are started but not finished
- Work Item Age: How long current items have been in progress
- Cycle Time: How long from start to finish for completed items
- Throughput: How many items finish per time period
When you build in slack time and reduce WIP, here’s what happens to these metrics:
WIP drops (obviously—you’re saying no to starting more). But here’s the magic:
- Work Item Age stabilizes or decreases because items aren’t sitting in queues waiting for overloaded people. Things move through the system more smoothly.
- Cycle Time drops because reduced context switching means people finish items faster, and reduced utilization means fewer exponential wait-time delays.
- Throughput increases because you’re finishing more work more predictably, even though it feels like you’re “doing less.”
It’s the lumberjack math playing out in your metrics. The team working smarter finishes more than the team working harder.
Track these metrics before and after you implement slack time experiments. The data will make your case better than any philosophical argument about sustainability or burnout prevention.
When your metrics show that throughput increased while cycle time decreased after you reduced planned utilization from 100% to 80%, suddenly “wasting capacity on axe sharpening” looks like the most strategic investment you can make.
Building Slack: Practical Starting Points
You’re convinced (I hope). The math is clear. Your own exhaustion is real. But how do you actually start saying no to overcommitment in a way that creates slack time?
Here are some experiments you can launch:
1. The Load Factor Experiment
For your next sprint or work cycle, deliberately plan to 80% of your estimated capacity instead of 100%.
If your team typically commits to 20 work items in an iteration, commit to 16. If you usually plan 10 tasks a day, plan 8. If you juggle 5 initiatives, focus on 4.
Track what happens. Did you:
- Finish everything with less stress?
- Have capacity to handle the inevitable unplanned work without derailing?
- Actually deliver more because of reduced context switching?
Most teams discover that planning to 80% results in delivering 100% more consistently than planning to 100% and delivering 70% inconsistently.
2. The “No New Work” Day
Pick one day per week (or every two weeks to start) where the team doesn’t start anything new. The only acceptable work is:
- Finishing items in progress
- Pairing if you’re not already doing so
- Swarming (across boundaries) to unblock stuck work
- Documentation or learning
This forces confrontation with WIP. It creates pressure to actually get things to “Done.” And it builds in natural slack time for improvement work.
3. The Personal Context-Switch Audit
For just one day, track every time you switch contexts. Every time you:
- Stop working on one thing to work on another
- Get pulled into a “quick question”
- Check email or Slack mid-task
- Join a meeting that interrupts focused work
Put a tick mark on a piece of paper or in a notes app. Don’t judge, just observe.
At the end of the day, count. If you’re like most knowledge workers, you’ll have 20-40 switches. Multiply by 5 minutes average reload time (being conservative), and you’ve lost 2-3 hours to context switching alone.
Share your findings with your team. Create visibility around the invisible tax you’re all paying.
4. Team Exercise: The Axe Audit
Gather your team for 30 to 50 minutes. Ask:
- What are our “axes”? (Skills, tools, processes, knowledge bases that make our work effective)
- Which axes are getting dull? (What’s degrading through neglect or outdated approaches?)
- What would “sharpening” look like? (Specific activities that would improve effectiveness)
- What’s our current sharpening frequency? (Never, rarely, sometimes, often?)
- What would it look like to build in regular sharpening time? (Daily? Weekly? Monthly?)
Don’t try to solve everything. Pick ONE dull axe and design a FINE experiment to sharpen it over the next month. Make it visible. Track the impact.
The Bridge Ahead
Understanding the mathematics of no is part of a strong foundation. But knowing the math doesn’t change your organizational culture, your stakeholder expectations, or your team’s ingrained habits.
That’s where we’re headed next.
But you don’t need to wait to start experimenting. The four practical starting points above? Pick one.
Because here’s the beautiful truth hiding in the lumberjack parable: the team that sharpened their axes didn’t need permission. They didn’t wait for organizational transformation. They just made a different choice about how to work.
They said no to the addiction of constant motion. They said yes to the mathematics of sustainable effectiveness.
You can too.
The Afternoon Count
Back in the forest, as the sun sets, the exhausted first team stares at their pile of felled trees. Half the size of their competitors’ pile, despite working twice as hard.
The second team is already heading home, axes sharp, ready for tomorrow.
“But we didn’t have time to sharpen,” protests the first team. “We had too much work to do.”
The second team pauses. “That’s exactly when you need to sharpen most.”
Your team is drowning not because you’re not working hard enough. You’re drowning because the system has devolved over time to keep your axes dull.
The mathematics proves it. The neuroscience explains it. The question is: what will you do about it?
Start sharpening. Share what you learn.
