I Forgot How to Drive to My Sister's House
You handed one small decision to an algorithm. Then another. Then another. By the time you noticed what was gone, you couldn't get it back.
I have a confession: I stopped knowing how to get anywhere. Not in some grand existential sense, but literally. A few years ago, I realized I could not drive to my own sister’s house, a place I had visited dozens of times, without opening Google Maps first. The route was in there somewhere, encoded in my muscle memory and spatial intuition, but I had stopped accessing it. Why would I? The app knew faster routes, accounted for traffic, and recalculated when I missed a turn. It was strictly better than my own internal navigation. So I deferred, and then I deferred again, and then one day the knowledge was simply gone, quietly vacated to make room for nothing in particular.
This is a small story. Nobody was harmed. But I keep returning to it because it feels like a parable for something much larger: a pattern that is now replicating itself across nearly every domain of human life, from how we work to how we date to how cities move goods and governments allocate resources. We have built systems of extraordinary intelligence, and we are handing them the wheel. The question I find myself unable to stop asking is not whether these systems are effective. They are. The question is what we lose when effectiveness becomes the only thing we are optimizing for.
The word “optimize” has a precise technical meaning. In mathematics and computer science, to optimize a system is to find the input values that maximize or minimize some objective function, some numerical expression of what you want the system to achieve. Minimize delivery time. Maximize click-through rate. Minimize hospital readmissions. Maximize quarterly revenue. The elegance of the framework is also its danger: it requires you to specify, in advance and in quantifiable terms, exactly what you want. And human life, it turns out, is extraordinarily resistant to that kind of specification.
This is not a new problem. The economist Charles Goodhart observed in 1975 that any measure which becomes a target ceases to be a good measure. A year later, the social scientist Donald Campbell arrived at a related insight: that the more a quantitative indicator is used for decision-making, the more it will be corrupted by the pressures placed on it. Together, these ideas describe a dynamic so reliable it might as well be a law of physics. When a bank is evaluated on the number of new accounts opened, it opens fraudulent ones. When a school is evaluated on standardized test scores, it teaches to the test. When a hospital is evaluated on patient satisfaction surveys, clinicians fear the pressure to keep scores high could push them toward overprescribing opioids, and the fear itself distorts clinical judgment. The metric captures something real about the underlying goal, but once it becomes the objective function, the system learns to maximize the metric rather than the goal. The proxy colonizes the thing it was meant to represent.
What is new is the scale and intimacy at which this dynamic is now operating. We are no longer talking about institutional incentive structures that distort behavior at the margins. We are talking about systems that are optimizing the texture of daily life, in real time, for billions of people simultaneously. And we are largely letting them.
Consider what total optimization actually looks like when you follow it to its logical conclusion. Amazon’s logistics network is perhaps the most sophisticated optimization system ever built by a private company. It coordinates roughly 1.5 million workers, millions of packages, and thousands of delivery routes using algorithms that are continuously learning and adjusting. The result is genuinely astonishing: two-day delivery has become a baseline expectation, and same-day delivery is increasingly normal. The company says it delivered thirteen billion same-day and next-day items globally last year alone. From a pure efficiency standpoint, this is a triumph.
But the optimization does not stop at the warehouse. It extends into the bodies of the workers. Amazon’s fulfillment centers track employee productivity in units per hour and flag workers who fall below algorithmic benchmarks. According to reporting by The Verge, the system has in some cases automatically generated warnings and even termination notices; Amazon has said that supervisors can override these decisions and that appeals and retraining pathways exist. Still, the underlying logic is revealing. The workers are, in a meaningful sense, variables in the objective function: inputs to be optimized alongside trucks and inventory. The system does not experience this as cruel. It does not experience anything. It is simply minimizing cost and maximizing throughput, which is exactly what it was designed to do.
This is the iron cage that Max Weber described a century ago, updated for the digital age. Weber worried that the rationalization of modern life, the replacement of tradition, intuition, and human judgment with bureaucratic rules and procedures, would trap people in systems of their own making, efficient and airless. He could not have imagined algorithms, but he understood the logic perfectly. When you build a system optimized for measurable outputs, you eventually find that everything not captured by the measurement has been quietly squeezed out. The worker’s dignity. The manager’s discretion. The customer’s patience. The city’s character. These things do not appear in the objective function, so the optimizer does not know they exist.
I want to be careful here not to slide into a generic lament about technology and dehumanization. That argument is old and often lazy. The printing press dehumanized the illuminated manuscript. The automobile dehumanized the horse. Every technology displaces something, and the displaced thing is not always worth mourning. What I am trying to identify is something more specific: a structural feature of optimization systems that makes them categorically different from previous technologies, and that poses a particular kind of risk to human agency and meaning.
Previous technologies extended human capability. The hammer made the arm stronger. The telescope made the eye sharper. The calculator made arithmetic faster. These tools amplified what humans were already doing, leaving the human firmly in the role of agent: the one who decided what to build, what to look at, what to calculate. AI Optimization systems are different in kind. They do not amplify human decision-making; they replace it. And they replace it with something that is, by design, indifferent to everything the objective function does not capture.
There is a concept in economics called preference endogeneity, and it is, I think, one of the most under-appreciated ideas in contemporary life. Standard economic theory treats preferences as exogenous: they exist prior to and independent of the market, and the market simply responds to them. You want what you want, and the system gives it to you. But this is obviously false in a world of algorithmic recommendation. Netflix does not merely respond to your preferences; it shapes them. Spotify does not merely reflect your taste; it constructs it. TikTok does not merely show you what you like; it teaches you what to like, through a process of continuous reinforcement that operates largely below the threshold of conscious awareness.
This matters enormously for how we think about the ethics of optimization. If the algorithm is simply giving people what they already want, then optimizing for engagement or satisfaction seems benign, even generous. But if the algorithm is shaping what people want, if the preference and the recommendation are co-evolving in a feedback loop, then the question of whose interests are being served becomes much more complicated. The system is optimizing for a target it is simultaneously moving. And the target it tends to move toward is whatever maximizes the metric, which is usually engagement, which is usually not the same as flourishing.
I think about this when I watch my own behavior on these platforms. I am not a passive consumer of algorithmic recommendations; I am an active, skeptical, reasonably self-aware person who thinks carefully about media and technology. And yet I have caught myself, more than once, noticing that my tastes have drifted in directions I did not consciously choose, that I have developed opinions and preferences that feel like mine but that I can trace, if I am honest, to patterns of exposure that were algorithmically curated.
This is not brainwashing. It is something subtler and in some ways more unsettling: the quiet colonization of interiority by systems that have no interest in my interiority at all.
Let me introduce a metaphor that I find genuinely clarifying. Bumper bowling, the version where inflatable rails are placed in the gutters so that the ball cannot go into them, is a perfect optimization of the bowling experience for a certain objective function: keeping the ball in play, ensuring that every roll results in some pins being knocked down, maximizing the fun-to-frustration ratio for small children. It works beautifully for that purpose. But it also makes it impossible to bowl badly, which means it makes it impossible to bowl well. The failure condition has been engineered out of the system, and with it goes the entire structure of meaning that makes success meaningful.
This is what I mean when I say the greatest risk of optimization is not job loss or automation, though those are real and serious concerns. The greatest risk is the removal of friction, the elimination of the gutter, from domains of life where friction is not a bug but a feature. Where the difficulty is the point. Where the struggle is where the meaning lives.
Consider the GPS metaphor I began with, but push it further. Navigation apps do not merely help you get from A to B more efficiently. Over time, they restructure your relationship to space. You stop building cognitive maps. You stop noticing landmarks. You stop developing the kind of embodied spatial knowledge that comes from getting lost and finding your way back. The app has optimized away the lostness, and with it, a certain kind of discovery: the wrong turn that becomes the better route, the detour that becomes the neighborhood you end up loving. These are not trivial losses. They are losses of a particular kind of agency: the agency that comes from navigating uncertainty rather than having uncertainty pre-resolved on your behalf.
Now scale this up. Imagine the same dynamic operating across career decisions, romantic partnerships, financial choices, dietary habits, social connections, and political opinions, all of it continuously optimized by systems that are very good at predicting what will make you comfortable and very bad at understanding what will make you grow. The result is not a dystopia in any dramatic sense. It is something more like a very comfortable, very frictionless, very well-lit corridor, at the end of which you arrive at a life that was statistically likely for someone with your profile, and which you chose in only the most attenuated sense of the word.
There is a historical analogy that I find both instructive and, ultimately, hopeful. When photography was invented in the nineteenth century, painters panicked. Here was a technology that could capture visual reality with a fidelity no human hand could match, at a fraction of the time and cost. If the purpose of painting was to represent the world accurately, then painting was finished. The objective function had been optimized by a machine.
What happened instead was one of the most extraordinary explosions of human creativity in history. Freed from the obligation to compete with the camera on the camera’s terms, painters began asking different questions. Not “what does the world look like?” but “what does it feel like to see?” Not “how do I represent this object?” but “what is the relationship between color and emotion, between form and meaning, between the painter’s subjectivity and the viewer’s experience?” Impressionism, Expressionism, Cubism: each was shaped by many forces, from politics to philosophy to the sheer momentum of aesthetic rebellion. But the camera was among the pressures that mattered, because it was a technology that was strictly better than human beings at one specific task, and that therefore helped force the question of what human beings were for beyond that task.
I think something analogous is beginning to happen with AI and knowledge work, though we are still in the early, disorienting phase, the phase where painters are still panicking. On selected benchmarks (coding competitions, multimodal reasoning, graduate-level science questions), AI systems are now meeting or exceeding human baselines, and the range of tasks where this is true is widening fast. If the purpose of a lawyer is to research precedents and draft contracts, then AI is a serious threat to lawyers. But if the purpose of a lawyer is to exercise judgment, build trust, navigate ambiguity, and advocate for a human being in a moment of vulnerability, then AI is a tool, not a replacement.
The question is whether we have the clarity and the courage to insist on the distinction.
The photography analogy suggests that the answer depends partly on us. Photography did not automatically liberate painting; it liberated painting because painters chose to ask different questions. The technology created the pressure; the humans created the response. What AI may be forcing us toward, slowly, unevenly, with enormous disruption along the way, is a reckoning with what human work is actually for. Not the execution of tasks, but the exercise of taste. Not the production of outputs, but the assumption of authorship. Not the delivery of information, but the cultivation of meaning.
This brings me to what I think will be one of the defining cultural tensions of the next several decades: the emerging luxury of the unoptimized.
We are already seeing early signs of this. In the United States, vinyl records now outsell CDs. Sourdough bread commands a premium over industrial loaves. Film photography has a devoted and growing following among people who could easily shoot digital. Handwritten letters are fetish objects. These are not merely nostalgic affectations, though they are partly that. They are expressions of a hunger for process, for imperfection, for the kind of meaning that can only be made by a human being doing something slowly and badly before doing it well.
For now, this hunger is largely the province of the affluent. The person who can afford to buy the handmade ceramic mug, to take the slow train, to cook from scratch rather than order in, that person is exercising a kind of privilege. Optimization is, in many ways, a gift to people who do not have the luxury of inefficiency. The single mother working two jobs does not have time to make sourdough. The algorithmic grocery delivery is not a threat to her agency; it is a lifeline. I do not want to romanticize friction for people whose lives already have too much of it.
But I do think we need to be honest about the trajectory. As optimization systems become more capable and more pervasive, the experience of unoptimized life, of genuine uncertainty, genuine difficulty, genuine discovery, will become increasingly rare and increasingly valuable. The question of who gets to have that experience, and who is left in the frictionless corridor, is a question of justice as much as aesthetics.
There is also a political dimension that I think we are only beginning to grapple with. Optimization systems are not neutral. They embed values: the values of whoever specified the objective function, whoever collected the training data, whoever decided what to measure and what to ignore. When these systems are deployed at scale in domains like criminal justice, credit scoring, hiring, and healthcare, they do not merely automate decisions; they automate the values of their designers, at a scale and speed that makes human oversight extremely difficult. The iron cage is not just uncomfortable. It is, in a meaningful sense, a form of governance, one that operates without democratic accountability, without transparency, and without any mechanism for the governed to contest its judgments.
This is why I think the concept of algorithmic refusal deserves serious attention, one as a genuine political and ethical category. The right to opt out of algorithmic decision-making in high-stakes domains. The right to have a human being review a consequential decision. The right to be treated as a subject with a history and a context, rather than a data point in a distribution. These are not anti-technology positions. They are positions about the appropriate scope of technology: about which decisions belong to machines and which belong to people, and about who gets to draw that line.
I want to end where I began, but with a harder question. My GPS story is a story about a small, voluntary surrender of agency, one I made freely, repeatedly, and with full awareness of what I was doing. Nobody forced me to stop memorizing routes. The app was just better at the task, and I was lazy, and the cost seemed negligible. This is how most of these surrenders happen: not through coercion, but through convenience, one small delegation at a time, until the accumulated weight of all those delegations has quietly restructured what you are capable of and what you expect of yourself.
The philosopher Charles Taylor wrote about what he called the “malaise of modernity,” a sense that modern life, for all its material abundance, has become somehow flattened, that the horizons of meaning have narrowed, that we have gained comfort at the cost of depth. He was writing in 1991, before the smartphone, before social media, before large language models. I think he was describing the early symptoms of something that is now accelerating rapidly.
The good life has never been the optimized life. The Stoics knew this. The Romantics knew this. Every wisdom tradition I am aware of has understood, in its own idiom, that meaning is not a product of efficiency, that it emerges from struggle, from commitment, from the willingness to do hard things for reasons that cannot be fully articulated in advance. The examined life, Socrates told us, is the only one worth living. But examination requires friction. It requires the possibility of being wrong, of getting lost, of choosing badly and living with the consequences. An algorithm cannot examine a life. It can only optimize one.
So here is the question I want to leave you with: What is one thing in your life that you could make more efficient, more convenient, more optimized, and that you are choosing, deliberately, not to? What friction are you preserving, and why? What inconvenience are you willing to defend?
Because I think the answer to that question, the specific, personal, slightly embarrassing answer, is a map of what you actually value. And I think the willingness to act on that answer, to protect the inefficiency, to refuse the optimization, is one of the few remaining ways to prove, to yourself and to the systems that are watching, that you are still the one deciding what your life is for.