Can the good life be talked about in quantifiable terms? Today’s culture is increasingly concerned with metrics, argues the philosopher C. Thi Nguyen in various texts and reports that he has published over the last few years.
Cosmopolitans keep running streaks, and Duolingo streaks, while placing a great deal of respect on the number of books they’ve read in the past year, disregarding the actual level of improvement in their ability to think. What made quantitative data, raw numbers, so important for measuring human ability?
One answer lies in the seductive clarity of numbers. In an increasingly complex world, metrics offer a sense of objectivity, comparability, and control. They allow us to rank, optimize, and evaluate across contexts that would otherwise remain opaque. Governments rely on performance indicators, companies track engagement, and individuals measure productivity and self-improvement. Numbers travel well: they are portable, legible, and easy to aggregate. But as Nguyen warns, this portability comes at a cost, “Data is powerful because it’s universal. The cost is context”.
This trade-off between universality and context is not accidental, it is built into the very structure of data. For data to function at scale, it must be standardized. It must reduce the richness of lived experience into categories that can be consistently measured and compared. In doing so, it necessarily strips away nuance. Nguyen calls this process decontextualization: the transformation of situated, meaningful experiences into abstract, transferable units. Consider something as simple as grading. A teacher may provide detailed, qualitative feedback on a student’s essay, engaging with their intentions, strengths, and areas for improvement, but alongside this feedback sits a letter grade. The grade is what travels: it can be averaged, compared, and evaluated by institutions. Yet everything that made the feedback meaningful, its sensitivity to context, its responsiveness to the individual, is lost in translation.
In the cultural sphere, Nguyen recounts a conversation with AI researchers attempting to define “good art” using Netflix engagement data. The problem is obvious: engagement is not the same as artistic value. Art can challenge, unsettle, or transform us in ways that are not captured by viewing hours, but engagement is measurable, and therefore usable. “It’s very unlikely that there will ever be any such dataset” that fully captures artistic value, Nguyen notes.
Nowhere is this shift toward measurable value more visible than in the wellness and self-improvement cultures flourishing on platforms like TikTok. The platform’s recommendation system is built to optimize for engagement, watch time, likes, shares, metrics that are easily quantified and endlessly scalable. As a result, content that performs well is not necessarily what is most insightful or beneficial, but what is most engaging. This creates a subtle but powerful slippage between visibility and value. As reporting and research have shown, TikTok’s algorithm systematically amplifies content that maximizes time spent on the app, regardless of its qualitative merits. In Nguyen’s terms, engagement becomes a proxy for meaning, despite the fact that the two are only loosely connected at best.
Within the self-improvement sphere, this logic shapes not just what content is seen, but what kinds of practices are promoted. Popular formats like “5 habits to fix your life,” “30-day challenges,” “dopamine detox routines,” translate complex psychological and existential questions into measurable actions. These formats thrive because they are easily reproducible and quantifiable: they offer clear steps and visible outcomes. But in doing so, they often strip away the context that makes such practices meaningful or effective. A morning routine becomes a checklist rather than a reflection of individual needs, and mental health is a set of symptoms to count rather than an experience to interpret. Studies of TikTok mental health content show that much of it is produced by non-experts and varies widely in quality, yet gains authority through visibility and repetition rather than rigor.
Users are encouraged to track, optimize, and display their self-improvement in numerical terms. While such metrics can be motivating, research suggests they can also produce anxiety, dependency, and a narrowing of motivation. The metric itself replaces the underlying goal through perversion of emotion. A person may begin exercising to feel better, but continue primarily to avoid “breaking the streak.” In this way, Nguyen’s concept of value capture becomes more tangible. Metrics does just measure the activity, rather it reshapes its meaning.
At an industry level, these tendencies are reinforced by economic incentives. The global wellness market, now worth trillions, has found in social media an ideal infrastructure for scaling advice. But scalability demands standardization. What can be sold and shared widely must be simplified. As a result, the most successful creators and businesses are often those who can translate complex human experiences into digestible formats. The danger, as Nguyen’s framework makes clear, is not simply that such metrics are imperfect, but that they systematically exclude what cannot be easily quantified: ambivalence, context, slow change, and forms of well-being that resist visibility. In a culture increasingly mediated by platforms like TikTok, the risk is that the good life is not only measured in numbers, but instead gradually redefined by them.
The same logic applies in policymaking. Metrics such as life expectancy or economic growth are easier to quantify than qualities like happiness, community, or beauty. As a result, they tend to dominate decision making. Nguyen illustrates this with a seemingly straightforward health policy: reducing saturated fat intake. While this may improve measurable outcomes like heart disease rates, it may also erode culinary traditions and everyday pleasures, losses that are harder to quantify but no less real. What is left out of the data is not necessarily less important, it is simply less measurable.
This creates a systematic bias. Data-driven systems tend to privilege what is easily quantifiable over what is meaningful. As Nguyen puts it, “the basic methodology of data […] systematically leaves out certain kinds of information”. This is not merely a technical limitation but a philosophical one; it shapes what we come to value.
The problem deepens when metrics do not merely describe reality but begin to define it. This is value capture: the process by which our values are reshaped to fit the metrics used to measure them. Academics begin to chase citation counts rather than insight. Journalists optimize for clicks rather than truth. Individuals pursue streaks and counts rather than genuine growth. Over time, the metric ceases to be a proxy and becomes the goal itself.
This shift is subtle but profound. It is not simply that we are misled by bad metrics; rather, our understanding of what is good becomes aligned with what is measurable. In Nguyen’s words, we “outsource our values to large-scale institutions,” importing their limitations into our own evaluative frameworks. The danger is not only that we measure the wrong things, but that we forget there are things we are no longer measuring;a feedback loop is created from this. Metrics shape behavior, behavior reinforces metrics, and algorithms amplify both. Over time, the gap between what is measured and what is meaningful can widen. And because these systems often operate opaquely, their assumptions become harder to question.
But none of this should imply that data is inherently bad; Nguyen is careful to emphasize its value. Data enables large-scale coordination, accountability, and insight. It can reveal patterns that would otherwise remain hidden. It can help combat bias and corruption. The problem is not the use of data, but its overextension, the belief that it can capture the full richness of human life. The deeper challenge is cultural. It requires resisting the temptation to equate the measurable with the meaningful. It involves reclaiming spaces where value is not reduced to numbers, where art is appreciated beyond engagement, where learning is valued beyond grades, where a good life is not defined by streaks and counts.
Everything from Nguyen comes from this report: https://issues.org/wp-content/uploads/2023/12/94-101-Nguyen-The-Limits-of-Data-Winter-2024.pdf
Alexander Jejlid
- Alexander Jejlid
- Alexander Jejlid
