The Cage of Choice
How outrunning AI is actually a race to the bottom of the pay scale
I. The Illusion of Value
In the modern workplace, we have fallen into a Velocity Trap where deliberate, slow thinking is traded for “quick wins.” Under constant pressure to deliver more, faster, and cheaper, organizations willingly sacrifice quality for the sake of perceived output.
I want to challenge whether the Velocity Trap is an inevitable, rolling hamster wheel of the modern workplace or perhaps a cage built out of illusions of our own choosing.
Following Goodhart’s Law, when a measure becomes a target, it ceases to be a good measure. “Speed of delivery” has become the target itself, leaving quality as an invisible casualty. Methodologies originally meant for flexibility—such as Agile—have evolved into “velocity sprints” that prioritize closing a ticket over deep problem-solving. It gives the impression that it was designed for the professional; in reality, it is a constant loop designed for those who benefit from the advantage of our accelerated output.
At what price?
II. The Neuro-Economics of Attention
The systemic demand for speed doesn’t just lower quality; it physically degrades our ability to think. Dr. Sophie Leroy’s work on Attention Residue describes the cognitive cost of this pace. When we are forced to switch tasks or rush toward completion, our attention never fully transfers. This prevents the brain from achieving closure, leading to reduced cognitive capability and a higher margin for error.
While philosopher Byung-Chul Han states that our achievement society is driven by internal self-exploitation, we must acknowledge the external weight of the trap. We often believe this constant busyness is for our own good, but it has never been about us.
We stay in the “cage” because the system has decoupled “doing more work” from “achieving more results.”
Ironically, we often opt into this busyness to avoid the harder, more vulnerable work of deep thinking that the current system no longer knows how to measure. By keeping ourselves busy, professionals protect themselves from what’s new and unknown in order to stay in the sweet spot of the comfort zone.
III. The Era of Cheap Cognition & AI Reliance
Because our cognitive resources are depleted by constant attention residue, we turn to Cheap Cognition as a survival mechanism. Professionals increasingly rely on AI to handle the chain of micro-decisions they no longer have the mental energy to process.
However, by outsourcing judgment to machine learning, we strip ourselves of our only competitive advantage: human judgment. Quick decisions made by AI lead to mediocre, average outputs. Without human “Slow Thinking” to define what “good” looks like, AI-driven micro-decisions can completely change the direction originally set by a human.
Saying “yes” to AI is a low-cost short-term fix, but it only reinforces the trap we are trying to escape. Saying “yes” to others instead of protecting your own focus for the perception of “doing the right thing” is only putting a padlock on the exit doors of your own cage.
It takes courage and effort to say “no.” In an era of cheap cognition, the true value is to slow down, disconnect, and have the courage to say no when it’s needed. This is true resilience.
IV. Macroeconomic Consequences: The Great Decoupling
The final irony of the Velocity Trap is that the faster we go, the less we are worth. Moving at speed is no longer a human asset; AI will always win that race. As AI commoditizes execution, “human” elements—empathy, ethical judgment, and complex intuition—become the new rare resources.
Stagnant Wages vs. Algorithmic Efficiency
This is the point where the “cage” becomes visible. As Daron Acemoglu warns in Power and Progress, if AI is used only to replace tasks without creating new roles for humans, 100% of the gains flow to the owners of capital (software and infrastructure).
Analyse how “speed” as a metric has decoupled worker effort from worker compensation: if you deliver 10x more work through AI-assisted speed, you do not receive 10x the pay. Instead, the organization simply raises the baseline expectation. You aren’t running faster to get ahead; you’re running faster just to stay in the same place on the decoupling curve.
In the macro-scale, true productivity miracles (broad, sustained TFP acceleration) are still emerging and highly uncertain. AI is providing a meaningful but not yet revolutionary macro lift—more visible today through investment spending than through widespread miracles.
If there are going to be productivity gains, we must ask:
Who will be their beneficiary?
Conclusion
The modern professional is caught in a “Velocity Trap” that prizes reactivity over effectiveness. While AI has commoditized the “Fast,” it cannot replicate the “Slow”—the 24-hour human synthesis required for high-stakes, original strategy.
To remain economically relevant, professionals must stop competing on speed and start competing on depth. We are all shaping the modern workspace and are responsible for changes that ensure there is a place for humans in the value chain.
Shall we stop working “hard” to prevent making ourselves and next generations a “nice-to-have” at a workplace?
It is time to step out of the hamster wheel and back into the driver’s wheel.
The eternal challenge of speed vs quality is amplified with the use of AI. You rightly point out this challenge and the need to be even more vigilant about quality being left behind