Published
- 7 min read
When Age Becomes Risk at Work
Introduction
January 5, 2026, was my first day at work in Canada.
▼Company overview
As expected, the first few days were filled with onboarding and basic training, helping me understand the company, its facilities, and the general working environment. But during that process, something else happened — something less visible, but more impactful.
As someone who had already begun to face layoffs and the so-called “mid-career crisis” back in China, I found myself unexpectedly unsettled.
A question kept repeating in my mind:
If we are all beasts of burden — oxen or horses — then what comes first:
the animal, or the mill?
In the context I was familiar with, the answer was almost implicit: the mill comes first. The animal exists to turn it. The mill is the main structure; the animal is a tool. From there, the logic follows naturally: the animal can suffer, but the mill cannot stop.
What I felt that day was almost the opposite logic:
the mill can stop, but the animal cannot be made to suffer.
Work — the act of “turning the mill” — is not an absolute necessity, but something that happens under conditions where the individual remains healthy, stable, and reasonably well.
When I saw the equipment provided, the office environment, and the benefits, my first reaction was entirely shaped by the old logic:
Is this necessary? Isn’t it wasteful? Won’t it be abused? Will anyone actually use it?
But the moment I shifted perspective — from maximizing output to maintaining human stability — all of these questions dissolved.
If the goal is not to maximize output at any cost, but to ensure that people can function comfortably and sustainably, then these arrangements are not excessive; they are required. Even if they are not fully utilized, their mere presence carries meaning. They signal that people are treated as people.
What unsettled me was not the difference in benefits, but a difference in value ordering. Everything that follows — age, capability, systems — traces back to this.
It also led me to another question:
If I were to return to the Chinese job market at age 38, without connections, relying only on my own skills and experience — in my native language environment — what kind of job would I realistically find? What would that environment look like?
The more I thought about it, the more I realized that language and ability were no longer my primary concern.
It was age.
More precisely, it was what “age” represents within a particular system.
▼A corner of the cafeteria
Age Does Not Mean Decline, but Structural Change in Capability
Some time ago, I watched a short film based on the game Sifu. In the game, the protagonist can revive after death, but each revival comes at the cost of aging. As the character ages, attack power increases, but health and defense decrease.
This mechanic mirrors the impact of age in the workplace almost perfectly. Translated into work terms:
As age increases, experience and judgment improve, and problem-solving becomes more efficient. But the ability to sustain high load decreases, the margin for error shrinks, and peak capacity declines.
The key here is that both positive and negative changes coexist. Experience is not abstract; it produces real advantages — sharper judgment, more precise output, the ability to resolve complex issues efficiently. At the same time, the human body is not a perpetual machine. Long hours, sustained pressure, and chronic overload all come with costs.
Younger workers tend to have longer “health bars,” allowing them to absorb short-term overload. Older workers tend to operate with higher precision but cannot sustain prolonged peak load. This is not a matter of attitude or morality, but a structural shift in performance over time.
The real question, then, is not whether older individuals can still work, but:
what a system defines as “working.”
Does it prioritize endurance, or output quality?
This is where structural differences begin to emerge.
Endurance vs Precision
I find it useful to divide capability into two dimensions: endurance and precision.
Endurance refers to the ability to sustain long hours, handle pressure, and absorb fluctuations.
Precision refers to experience density, judgment quality, and the ability to solve problems effectively and efficiently.
These two do not increase together. They trade off.
Young workers often rely on endurance — they push through tasks by investing time and energy. As age increases, precision improves, and work becomes more strategic and structured. But endurance inevitably declines.
This framework also clarifies job design. A role that demands extremely high endurance cannot simultaneously demand extremely high precision at all times. Such individuals rarely exist in practice.
If a job requires constant availability, long hours, and high responsiveness, while also expecting consistently high-level decision-making, the system will either fail to fill the role or sustain it by exhausting people.
To make the contrast more concrete, consider a simplified model:
In Western systems, baseline endurance requirements might be around 20, with adjustments based on role.
In Chinese systems, baseline expectations might already be 60, and rarely decrease.
The point is not precision, but structure:
One system designs thresholds that can be sustained long-term; the other sets thresholds high enough to filter people out from the beginning.
Under this structure, many familiar phenomena become explainable. Age does not suddenly reduce ability at 35; rather, the system never allows performance standards to fall back.
Static vs Dynamic Systems
The core difference is not whether age affects ability — that is universal. The difference lies in how organizations define evaluation models.
In Western systems, job requirements tend to be relatively stable. Once a threshold is met, the goal is consistent performance, not continuous escalation.
In contrast, the system I previously experienced operates differently. The threshold is not fixed; it is continuously pushed upward.
You are not evaluated on whether you meet the requirement, but on how much more you can still deliver.
This leads to a fundamental shift:
the job remains nominally the same, but the actual load keeps increasing.
At this point, the question becomes unavoidable:
Why does the system repeatedly choose to push people harder instead of optimizing itself?
Max Weber’s concept of instrumental rationality provides a useful explanation:
When efficiency becomes the sole definition of rationality,
people are systematically reduced to tools for achieving outcomes.
If higher output can be extracted by increasing load, there is no incentive to redesign the system. If younger workers are abundant, there is no incentive to design sustainable roles.
What appears as “culture” is often the outcome of structural incentives.
The Fishing Analogy
I once wondered why fishing rods are not simply made as strong as possible. The answer is simple:
Fishing is not about pulling instantly, but about exhausting the fish.
Too rigid, and the hook fails. Too strong, and feedback is lost. The optimal setup keeps the fish in a state of near exhaustion until it can no longer resist.
In workplace terms, this resembles dynamic evaluation systems.
Your capacity is tested, your limit is identified, and that limit becomes the new baseline. The system does not reduce expectations afterward, because reducing expectations reduces output.
When capacity declines with age, the system does not adjust — it replaces.
▼A small lake near the office, with a telescope for observing wildlife throughout the year
Job Switching and Peak Load
One might argue that changing jobs resets expectations.
In reality, switching jobs introduces a temporary spike in required capacity. New systems, relationships, and processes all demand additional energy.
For younger workers, this spike is manageable. For older workers, the issue is not daily performance, but whether they can absorb that temporary peak.
This explains why job mobility becomes more difficult with age — not because individuals become incapable, but because the system requires repeated peak performance events.
Conclusion
At this point, the core misunderstanding becomes clear:
Age is not inherently a problem. It becomes one when filtered through a system that does not allow standards to decline.
In a system designed for sustainability, age translates into experience and stability. In a system driven by continuous escalation, age becomes a signal for exclusion.
Foucault’s analysis of power offers a final lens:
Power operates not through isolated commands,
but through continuous monitoring, evaluation, and normalization.
Once standards are raised, the system loses the incentive to lower them.
Under such conditions, peak performance becomes the norm, and decline becomes unacceptable.
What ultimately removes people from the system is not age itself, but:
standards that are never allowed to fall back.