When Aki joined Verto—a fast-scaling logistics start-up that packed itself into a single Slack workspace and an army of asynchronous threads—she expected the usual office politics. What she didn’t expect was the way the company’s most valuable work gathered like dust in the corners: the dreaded, ugly tasks everyone avoided.
There were the manual reconciliation jobs—where three mismatched spreadsheets and a broken API had to be hand-stitched every Sunday night. There were the client escalations that arrived like landmines at 3 a.m., full of reputational risk and paperwork. There were the legacy-system migrations: boring, brittle, and easy to get wrong. And because Verto measured output in automated dashboards, metric spikes showed that these unpleasant tasks, when completed well, unlocked outsized returns—customers unblocked, contracts renewed, whole automated flows unstuck.
Aki remembered the advice her first manager once muttered in a coffee queue, half joking and half gospel: “High risk, high return. Do the dirty jobs and you’ll win.” That line had the ring of gambler’s wisdom—true, but incomplete.
Aki decided to test it. She volunteered for a reconciliation nobody wanted. Her plan wasn’t just to slog through it. She brought method: a checklist from operations research (breaking the problem into smallest verifiable units), a small script to validate rows (basic automation to reduce human error), and a plan to run the whole process during off-peak hours so the systems wouldn’t time out.
She kept her face long. In meetings she sighed theatrically, confessed to being “stuck in spreadsheets again,” and moved deliberately. Her colleagues assumed she’d been saddled with the chore. Privately, Aki finished faster than anyone expected. The ticket closed with a line-item savings that, when annualized, justified hiring another person. The head of customer success put her name on a promotion list.
Word spread in the way it always does: rumors and Slack pings. Other people noticed the promotions and the bonuses attached to the “thankless” tasks. Soon, an informal rule emerged. People began to “gatekeep” the dreadful jobs—feigning reluctance, loudly complaining, deliberately working slowly—because, in Verto’s opaque reward system, visible pain signaled ownership of upside. If you looked miserable enough while doing the work, others would leave you alone. You could monopolize the high-return chores.
Aki learned fast how this behavior works in human systems. It’s a behavioral equilibrium: people respond to incentives not just by optimizing raw output but by optimizing perception. Prospect theory explains part of this—loss aversion makes people overweight the short-term pain; signaling theory explains the rest—public displays of effort become a way to claim future rents. But Aki also saw the fragility. When everyone gamed the system, the company’s culture curdled into rent-seeking: tasks were not rotated; knowledge wasn’t shared; the organization became brittle.
Then technology changed the rules. Verto adopted an AI scheduling assistant and a low-code platform that could automate parts of reconciliations. The automation was fine at first—fewer human-hours on grunt work. But it introduced another dynamic: algorithmic bias and metric-myopia. The system was optimized for speed and conformed to historical patterns, which meant it would only automate tasks that fit past templates. The truly messy, novel problems—the ones that demanded human judgment—remained. And when those popped up, they were more visible, more consequential.
Aki watched the rent-seeking strategy backfire. The colleagues who had mastered the performative reluctance found themselves trapped. Automation reduced their monopoly rents. Worse, because knowledge hadn’t been shared, when the ugly exceptions arrived the people who knew how to handle them were either burned out or had left. The company’s mean time to recovery for outages lengthened.
At a quarterly all-hands the COO presented the numbers: rapid scaling on the front end, but systemic risk from tacit knowledge pockets. The story they told was familiar across recent labor trends—“quiet quitting,” specialized burnout, labor shortages after the Great Resignation—and it had a specific root here: asymmetric information and perverse incentives.
Aki, now running a small ops pod, proposed a different playbook—one grounded in organizational design and ethical strategy rather than feints.
First: rotate the dirty jobs deliberately. Research in organizational behavior shows that job rotation reduces knowledge silos and increases resilience. Aki put in a two-week rotation schedule and paired it with documentation sprints. Every time someone did a task, they had to leave an executable checklist. That checklist became a living artifact in the team’s wiki.
Second: decouple visible suffering from reward. Instead of rewarding the theatrics, she proposed a clear credit system: credits for improvements, for automation that actually reduced work, and for mentoring. She made team bonuses transparent, tied to shared KPIs like system recovery time and customer satisfaction—not to whether you looked miserable while doing something.
Third: use smart automation to amplify people, not replace them. The ops team invested in small, explainable tools: validation scripts, unit tests for data flows, and a lightweight “runbook” generator that produced step-by-step remediation guides for on-call incidents. This leaned on the principle of anti-fragility: build systems that gain from stress by spreading knowledge and automating repeatable bits while preserving human judgment for edges.
There was resistance. Some teammates grumbled that the change removed their “competitive edge.” But within six months MTTR dropped, customer churn eased, and new engineers onboarded faster. People reported less burnout because the worst moments no longer stacked on a single person’s shoulders.
Aki learned a last lesson about ethics. The performative strategy—look gloomy, complain, work slowly—was rational at the micro level but corrosive at the macro. It felt clever until the system broke. Long-term high returns in teams require nonzero trust, transparent incentives, and a willingness to share downside as well as upside.
On a rainy Thursday, as Verto pushed a major migration live, the disaster siren came: an old endpoint started returning malformed payloads. Instead of fingers pointing, two rotated ops engineers—one who had only a month of experience, the other a senior who used to hoard reconciliations—grabbed the ticket. They followed the runbook, ran the tests, and patched the schema in half an hour. The automation caught the rest. The CEO sent a note praising the team for resilience.
Aki smiled. The high-risk, high-return truth still held—those tasks matter. But the trick was different than the old manager’s whisper. Don’t monopolize risk by performance; democratize risk by design. Teach others, automate the repeatable, reward the sharers, and make your organization antifragile. That way, when the messy, high-return problems come, the whole company is ready—not just the people pretending to be miserable.
All names of people and organizations appearing in this story are pseudonyms
EU finance ministers agree using frozen Russian assets most effective way to fund Ukraine
Comments