This article was automatically translated from the original Turkish version.
The Industrial Revolution encoded human physical strength and time into production lines. In pre-industrial societies, people allocated their time according to nature and the seasons. In agrarian communities, the rhythm of labor was determined by sunrise and sunset, seasonal changes, and religious rituals. “Time” was not an abstract calculation but a flexible, cyclical rhythm woven into daily life. With industrialization, time began to be measured in hours and brought under strict control. The requirement that workers start production at specific times, fixed break periods, and shift systems turned time into a managed resource defined by economic efficiency.
With the advent of capitalist modes of production, time became directly equated with money, giving rise to the equation “time = money.” This led to a system in which workers were paid based on the hours they worked, creating a clear distinction between “leisure time” and “working time.” Time outside work was increasingly viewed not as unproductive, but as a necessary phase of rest and preparation for renewed labor. In the factory system, precision, efficiency, and continuity became desirable norms. This fostered the internalization of time discipline by individuals, who began to see managing their own time as a virtue. In modern capitalist societies, “wasting time” came to be associated with guilt.
The 21st century is creating a similar transformation by rendering our language, gestures, and habits marketable as data. Now, it is not only our bodies but also our everyday digital behaviors that are embedded within production and consumption processes. Our behaviors, structured as data sets, have become calculable, buyable, and sellable for functional outcomes. At the same time, their transformation into data has opened the door to manipulation techniques and attention economies. Our personal behaviors, now taking the form of tangible, controllable, designable, and measurable data, have ceased to be authentic and inaccessible parts of ourselves and have instead been coded as components of systems designed for consumption and production.
This situation lies at the heart of our relationship with artificial intelligence. Our adoption of chatbots and the interactions we establish with them are thoroughly intertwined with all technical processes of production and consumption. When we ask an AI bot “How are you?”, we are not merely posing a question—we are training the algorithm, expanding its repertoire of interactions, and contributing to the platform’s learning cycle. In this context, one of the most refined forms of new digital labor is the “labor of expression” we produce without even being aware of it. This labor, even when anonymized, is coded, structured, and converted into an economic output by machines. Precisely because of this functionality, it carries both production and consumption costs.OpenAI CEO Sam Altman revealed that the politeness shown by users toward ChatGPT costs the company millions of dollars. According to Altman, users appending phrases like “please” and “thank you” to their search queries increase computational load and thereby raise operational expenses.Speaking politely to AI chatbots is a widespread phenomenon. At its simplest, this reflects reflexes from human-to-human communication spilling over into digital environments. The human brain, even when interacting with a robot, tends to assume there is an “other side” behind the text. Whether that side is human or artificial, social etiquette automatically activates. We can describe this politeness as the application of social norms to machines.These discourses reveal not only the impact of linguistic expressions on production costs but also the dominant mindset within the AI industry. The prioritization of concepts such as efficiency and cost optimization reflects the industrial logic underlying this technological production process. While user experience is often relegated to the background, system priorities are shaped around outcome orientation, processing speed, and energy efficiency.This approach, in a sense, mirrors the invisible factory context of the digital age. Just as workers in the Industrial Revolution had their meal and rest breaks restricted because they disrupted the production line, today’s digital systems imply that users must be economized on grounds that expressions like “please” create unnecessary computational overhead.In both cases, human gestures are coded as inefficiencies threatening productivity; the production process is reorganized not around the human but around output. Thus, even a mundane form of address directed at AI carries significant clues about the production-consumption philosophy of the digital age.
This reality leads to the metalization of many verbal expressions, reducing them to a specific accounting economy. Now, the energy cost of an expression matters as much as its content. What matters is not what we say, but how much energy it consumes. This mental shift moves language away from its capacity to generate meaning and toward a pessimistic phase of communication defined solely by measurable computational load. In a linguistic environment where only algorithmic and symbolic relationships matter, content recedes into the background, and the priority of expression shifts from “saying something” to “consuming a specific amount of energy.”Thus, language models no longer treat language merely as an organic process of production; they begin to re-evaluate it along axes of energy, time, and cost. This ultimately risks the disenfranchisement of language’s fundamental functions. At this point, the two scales of a conceptual balance become clear: on one side, language as an indispensable tool for solving problems; on the other, language itself becoming the source of a new problem. Completely disabling language is neither practical nor ethically viable—at least for now. Yet even the mere imagination of such a solution deepens the emerging paradox.Therefore, short-term solutions may follow Sam Altman’s suggestion: optimizing computational load and reducing linguistic redundancies. However, in the long term, this logic could lead to the severing of language from its essential function. Although this speculation has not yet become clearly visible, it remains a potential risk on the horizon.
Energy and Algorithm: Digital Input, Physical Output
The Watt Equivalent of Expressions
