There’s a new sound in the workplace — not the hum of servers or the clack of keyboards, but the confident rhythm of people who’ve learned to speak productivity.
Every update hits the right notes: alignment, impact, velocity. Every dashboard gleams with motion. But if you listen closely, something’s off. The tempo’s too perfect, the tone too rehearsed, and beneath it all, the melody of actual progress has gone missing.
Productivity, once measured in outcomes, has become a performance art. Teams no longer just ship features; they broadcast momentum. We talk about deliverables the way politicians talk about promises — with the conviction of belief rather than the caution of proof.
And now, a new player has joined the orchestra: AI, the ultimate accompanist. It writes our code, drafts our decks, and polishes our prose until it all sounds certain. But certainty can be dangerous when it isn’t earned.
That’s where our story begins — with one engineer, one sprint, and one shortcut too many.
Scenario: The Shortcut That Broke the System
Situation
A mid-level engineer, fluent in prompts and full of confidence, has become the team’s new “velocity hero.”
Her commits multiply, her pull requests are instant, and the AI pair-programmer whispers perfect syntax.
Impact
Leadership hails her as proof that “AI-assisted delivery” is the future. The team quietly follows suit, adopting her shortcut habits.
‘Real’ engineers stop raising concerns out of fear of looking slow or obsolete.
Tension
One Friday afternoon, a release fails. A subtle logic loop — something a seasoned dev would spot in seconds — propagates chaos across dependencies.
No human can trace the fault, and the AI can’t explain its own code. Confidence collapses into collective confusion.
Approach
The “velocity hero” feeds more prompts, copies snippets from Stack Overflow, and posts updates about “resolving critical blockers.”
However, the optics of action replace the substance of repair. Every fix introduces new faults.
Resolution
By Monday, the system is down, the sprint’s wasted, and the “AI productivity win” becomes a post-mortem lesson.
The shortcut didn’t just fail — it multiplied failure faster. What began as efficiency ended as entropy, powered by confidence without comprehension.
What happened in that sprint wasn’t a technical failure — it was a linguistic one.
The code broke because meaning broke first. “Working software,” “velocity,” “delivery” — each had been redefined by the team’s emotional cadence rather than its engineering truth.
The language of productivity had become a language of vibes, where confidence read as competence and polish passed for progress. This is the new populism of work: emotion as evidence, performance as proof. AI didn’t invent it — it simply gave it perfect grammar.
Diagnosis
The Populist Parallel
In politics, populism converts emotion into legitimacy; in organisations, productivity theatre converts busyness into proof. Both rely on rhetorical substitution — the replacement of substance with affect. Within teams, this begins innocently: words like alignment, momentum, and impact become shorthand for progress. Over time, the shorthand becomes the goal.
Aristotle’s Rhetorical Triangle describes persuasion as a balance between ethos (credibility), pathos (emotion), and logos (reason). Populism inflates pathos until it crowds out the others. The same dynamic plays out in work culture when emotional fluency — the ability to sound aligned or sound decisive — earns more trust than clear reasoning or credible evidence.
What makes this shift dangerous is that it feels good. The meeting that ends with mutual applause satisfies our pathos circuits. The slide deck that “lands well” restores our ethos standing. The untested hypothesis quietly waiting in backlog can’t compete. In this environment, language stops describing progress and starts performing it.
And once performance becomes the measure, dissent reads as disloyalty. Just as populist rhetoric vilifies the expert who complicates the story, productivity rhetoric sidelines the colleague who asks uncomfortable questions. The crowd cheers momentum; the system forgets meaning.
Mechanism
AI as the Vibe Engine
If populism perfects the sound of conviction, AI perfects the look of competence.
Where human rhetoric once had to earn its polish through rehearsal, AI delivers it instantly. Every prompt returns prose that sounds confident, code that looks clean, and design that feels complete — even when none of it is fully understood.
The performance of understanding has become frictionless.
This is automation bias at scale: our tendency to trust machine outputs because they seem objective and fluent. In practice, AI behaves like a sociotechnical amplifier — it doesn’t just accelerate work; it accelerates belief in progress. Dashboards fill faster, prototypes appear sooner, and even the rough edges of reasoning are smoothed by synthetic eloquence.
The danger is subtle but profound. Each successful illusion teaches teams that fluency equals truth. Over time, leadership begins to mistake the presence of artefacts for the existence of insight. Real debugging — the slow, messy work of understanding cause and effect — feels increasingly like inefficiency.
AI therefore becomes the perfect vibe engine: a generator of instant coherence, a producer of ambient competence. It fulfils our collective craving for certainty while quietly atrophying the muscles that create it. The result isn’t acceleration — it’s anaesthesia.
Behavioural Fallout
The Populism of Competence
Once emotional fluency becomes the metric of success, power tilts toward the confident, not the competent.
The workplace begins to mirror populist politics: charisma rallies support, while nuance is recast as obstruction. Teams learn quickly that it’s safer to agree energetically than to question intelligently.
Amy Edmondson’s work on psychological safety describes environments where people can speak up without fear of punishment. But when productivity theatre dominates, that safety collapses. Every question risks puncturing the vibe. Every admission of uncertainty sounds like dissent. As the cost of truth-telling rises, the appetite for it evaporates.
What emerges is an internal populism — a culture where the loudest updates, the most optimistic roadmaps, and the slickest demos win. It’s a feedback loop of self-congratulation: motion is celebrated, scepticism is sidelined, and critical thinking becomes a reputational hazard. The few who still debug rather than decorate find themselves framed as negative or “not collaborative.”
In this climate, genuine expertise doesn’t just lose status — it loses oxygen. And when reassurance becomes currency, curiosity becomes debt.
Counter-Design
Clarity as Counter-Populism
If populism feeds on applause, clarity feeds on audit.
The antidote to performative productivity isn’t more technology or visibility — it’s verification. What makes the Kaizen philosophy powerful isn’t its obsession with efficiency but its reverence for incremental truth: the idea that progress is best measured in what we’ve learned, not just what we’ve launched.
In the language of productivity, Kaizen’s feedback cycle restores balance. It replaces the theatre of speed with the rhythm of reflection — plan, do, check, adjust. Each iteration demands evidence over emotion, making it harder for confidence to masquerade as competence.
Teams practising this mindset learn to celebrate correction, not charisma. Leaders model curiosity by asking, “What surprised us?” rather than “What went well?” Visibility remains, but it becomes transparency with traction: progress that can be traced, not merely performed.
Rebuilding trust at work isn’t about slowing down; it’s about showing your workings — the intellectual equivalent of open-source thinking.
In a culture addicted to certainty, the bravest act may be to admit what we don’t yet know.
Conclusion
The engineer’s sprint failure wasn’t an anomaly; it was a mirror.
AI didn’t break the code — our relationship with truth did. In chasing efficiency, we redefined it. In performing clarity, we abandoned it. And in rewarding confidence, we diluted the only trait that ever mattered: understanding.
The populist logic that governs politics also governs productivity.
We crave the feeling of progress more than its proof. We want to believe in the algorithm, the dashboard, the vibe — anything that spares us from doubt. Yet doubt is where real progress hides; it’s the debugging of thought before the debugging of code.
The Language Paradox is that the more tools we have to communicate, the less we seem to say. Words, metrics, even AI outputs have become tokens of trust rather than instruments of meaning. But trust without truth is theatre.
Perhaps the next frontier of productivity isn’t automation — it’s articulation: learning to speak precisely, to question openly, and to measure success not by how fast we sound smart, but by how deeply we understand what we’re building.
Because when work starts to sound like a rally, it’s time to stop cheering and start thinking again.
Behavioural Principles
The Confidence Complex
-
Emotion scales faster than expertise.
When confidence becomes currency, competence becomes optional. -
Fluency is not understanding.
AI can fake comprehension, but it can’t debug consequences. -
Progress without reflection is performance.
Velocity is only valuable when it moves meaning forward. -
Charisma can’t sustain clarity.
When teams reward certainty, curiosity starves. -
Trust begins where polish ends.
Imperfection is not failure—it’s evidence of real thinking. -
Clarity is counter-populism.
Replace applause with audit, and belief with understanding.
