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For most of modern management history, wasting time has been treated as a vice. This sensibility can be traced back to Frederick Taylors doctrine of scientific management, which recast work as an engineering problem and workers as components in a machine to be optimized, standardized, and controlled. In reducing human effort to measurable outputs and time-motion efficiencies, Taylorism marked the beginning of the end for seeing people as thinking agents, turning them instead into productivity units not unlike laboratory rats, rewarded or punished according to how efficiently they ran the maze. Since then, we have come a long way. The post-war rise of the knowledge worker, and later the age of talent that took shape from the 1960s onwards, marked a decisive break with the logic of the factory floor. Work was no longer merely a job to be endured, but a career to be developed. Organizations began to concern themselves with engagement, motivation, wellbeing, and worklife balance, not out of benevolence alone but because value increasingly resided in peoples minds rather than their muscles. Human capital came to mean employability, shaped by intelligence, drive, expertise, and a new, if imperfect, meritocracy that coexisted with vocational careers. The growth of the creative class reinforced this shift: machines would handle the boring, repetitive tasks, freeing humans from the assembly line to think, design, and imagine. {"blockType":"mv-promo-block","data":{"imageDesktopUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/tcp-photo-syndey-16X9.jpg","imageMobileUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/tcp-photo-syndey-1x1-2.jpg","eyebrow":"","headline":"Get more insights from Tomas Chamorro-Premuzic","dek":"Dr. Tomas Chamorro-Premuzic is a professor of organizational psychology at UCL and Columbia University, and the co-founder of DeeperSignals. He has authored 15 books and over 250 scientific articles on the psychology of talent, leadership, AI, and entrepreneurship. ","subhed":"","description":"","ctaText":"Learn More","ctaUrl":"https:\/\/drtomas.com\/intro\/","theme":{"bg":"#2b2d30","text":"#ffffff","eyebrow":"#9aa2aa","subhed":"#ffffff","buttonBg":"#3b3f46","buttonHoverBg":"#3b3f46","buttonText":"#ffffff"},"imageDesktopId":91424798,"imageMobileId":91424800,"shareable":false,"slug":""}} The latest iteration of this story is, of course, AI. What makes it different is not merely that it automates standardized and repetitive work, but that it increasingly encroaches on intellectual, creative, and cognitive tasks once thought to be distinctly human. Writing, analyzing, summarizing, designing, even ideating are now faster, cheaper, and more scalable when performed by machines. The irony is hard to miss. Just as work had evolved away from crude measures of output, we find ourselves drifting back towards a Taylorist logic, where value is once again assessed in terms of raw productivity: how much, how fast, how cheaply. Only this time, the benchmark is no longer the stopwatch but the algorithm. Worse still, the machines are not merely competing with us on these terms; they are learning from us how the work is done, refining it, and then doing it better. In the process, the very qualities that once distinguished human work risk being reduced to inputs in someone elses optimization function. This is widely framed as progress. It may turn out to be a costly misunderstanding. Engineering inefficiencies Deep thinking is inefficient by design. It is slow, cognitively demanding, and frequently unproductive in the short term. Experimentation is worse. Most experiments fail, and even the successful ones rarely succeed on schedule; plus if you know in advance whether an experiment will work, then its not truly an experiment. Intrinsic curiosity is even more unruly, leading people into intellectual detours with no obvious payoff. None of this lends itself to neat metrics or reassuring dashboards. From a narrow productivity perspective, it looks like waste. Those inefficiencies are not limited to how humans think. They also define how humans relate to one another at work. Acting human, and especially acting humane, is inefficient by design. Greeting your barista and asking how they are doing slows the line, even as the system is optimized to maximize how many lattes can be poured per hour and you are encouraged to streamline your order through an app. Asking colleagues how they are doing at the start of a meeting consumes time that could otherwise be spent racing through the agenda. Showing genuine interest in others, listening without an immediate instrumental purpose, or helping someone become better at their job often sits well outside your formal goals, your key performance indicators, or your objectives and key results. From a narrow productivity perspective, this too looks like waste. Friction in the system Efficiency, however, is indifferent to relationships. It privileges throughput over connection, output over meaning, and speed over understanding. Optimized systems have little tolerance for small talk, empathy, or curiosity because these behaviors resist standardization and cannot be cleanly measured or scaled. In a perfectly efficient organization, no one asks how anyone else is doing unless the answer can be converted into performance. Help is offered only when it aligns with incentives. Time spent listening, reflecting, or caring is treated as friction in the system. The problem is surprisingly common, namely that when organizations optimize for the system, they often end up sub-optimizing the subsystems within it. This is a familiar lesson from systems theory, but one that is easily forgotten. In the age of AI, the system increasingly appears to be designed around what machines do best, while humans are quietly downgraded to a supporting subsystem expected to adapt accordingly. We hear a great deal about augmentation, but in practice augmentation often means asking people to work in ways that better suit the technology rather than elevating the human contribution. Talent, however, will not be elevated if human output continues to be judged by the same raw, quantitative metrics that define machine performance: speed, repetition, and operational efficiency. If you are simply running faster in the same direction, you will only get lost quicker (and maybe even lose the capacity to realize that you are lost). These apparent efficiency measures reward behavior that machines naturally excel at and penalize the very qualities that distinguish human work. They focus obsessively on outut while ignoring input: the role of joy, curiosity, learning, skill development, and thoughtful deployment of expertise. In doing so, organizations risk building systems that are optimized for AI, but progressively impoverished of the human capabilities they claim to value most. Inefficiency and new value This is why efficiency so often feels dehumanizing. It removes the informal, relational, and moral dimensions of work that make organizations more than collections of tasks. Humans do not learn, trust, or collaborate best when they behave like streamlined processes. We improve through interactions that appear inefficient on paper but are foundational in practice. In this sense, the inefficiencies of acting human are not a failure of management but a feature of humanity. They are the social and psychological infrastructure that allows thinking, learning, and cooperation to occur at all, and the necessary counterweight to systems designed to optimize everything except what makes work worth doing. Incidentally, inefficiency also plays a central role in the creation of new value, both in discovering better ways of doing existing things and in discovering entirely new things to do. Many important advances in science and business did not arise from tighter optimization or marginal efficiency gains, but from allowing room for exploration, deviation from plan, and attention to unexpected outcomes. In science, this is often the product of curiosity-driven research rather than narrowly goal-directed problem solving. Alexander Flemings observation in 1928 that a mold contaminant inhibited bacterial growth on a culture plate did not, by itself, produce a usable antibiotic, but it did reveal a phenomenon that later became penicillin once developed by others. Similarly, early work that eventually led to technologies such as CRISPR gene editing emerged from basic research into bacterial immune systems, conducted without any immediate application in mind. These discoveries were not accidents in the casual sense, but they did depend on researchers having the freedom and attentiveness to notice anomalies rather than discard them as inefficiencies. The role of anomalies Business innovation shows a comparable pattern. The adhesive behind Post-it Notes was not the outcome 3M originally sought, but its unusual properties were documented rather than rejected, and only later matched to a practical use. This kind of outcome depends less on speed or optimization than on organizational tolerance for ideas that lack an immediate commercial rationale. Systems optimized exclusively for efficiency tend to filter such anomalies out before their value becomes apparent. Even in exploration and trade, progress has often followed from imperfect information and miscalculation rather than from optimal planning. European expansion into the Americas, for example, was driven in part by navigational errors and incorrect assumptions about geography. While hardly an argument in favor of error, it is a reminder that historical change frequently arises from deviations rather than from flawlessly executed plans. The broader point is not that inefficiency guarantees innovation, but that innovation is unlikely without it. Systems designed to maximize efficiency excel at refining what is already known. They are far less effective at generating what is new. Allowing space for uncertainty, exploration, and apparent waste is not an indulgence, but a necessary condition for discovering value that cannot be specified in advance. This distinction is captured neatly in the work of Dean Keith Simonton, who has argued that innovation follows a two-step process: random variation followed by rational selection. New ideas arise from error, experimentation, and departures from established rules, and only later are refined and selected for value. AI is exceptionally strong at the second step. It can evaluate options, optimize choices, and select efficiently among existing alternatives. What it cannot meaningfully do is generate the kind of genuine variation and rule breaking from which truly novel ideas emerge. That responsibility remains human. The risk in an AI-saturated environment is that organizations double down on selection while starving variation, becoming ever more efficient at refining yesterdays ideas. Reheating ideas If, in the name of efficiency, creativity itself is outsourced to AI, the result is not randomness but prefabrication: synthetic re-combinations of existing ideas, smoothed and averaged across prior human output. This often resembles creativity without delivering it, more akin to reheating ideas than inventing new ones. The food analogy is instructive. Cooking a proper meal is inefficient and time-consuming, while a frozen meal is faster and perfectly adequate. But no one serves a microwaved lasagna to an important guest and mistakes it for craft. The extra effort is the point. The same logic applies to thinking and work. Deep thinking is inefficient, but it converts familiarity into understanding. Stepping outside established processes may slow things down, but it is often how better methods are discovered. Time spent feeding curiosity rarely pays off immediately, but it expands skills, connections, and optionality. Even social inefficiencies, such as investing time in relationships that do not yield immediate returns, build trust and create opportunities that efficiency metrics fail to capture. In this sense, inefficiency is not the opposite of effectiveness but a different path to it. Systems optimized solely for speed and output may function smoothly in the short term, but they do so by eroding the very conditions that allow learning, adaptation, and originality to emerge. {"blockType":"mv-promo-block","data":{"imageDesktopUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/tcp-photo-syndey-16X9.jpg","imageMobileUrl":"https:\/\/images.fastcompany.com\/image\/upload\/f_webp,q_auto,c_fit\/wp-cms-2\/2025\/10\/tcp-photo-syndey-1x1-2.jpg","eyebrow":"","headline":"Get more insights from Tomas Chamorro-Premuzic","dek":"Dr. Tomas Chamorro-Premuzic is a professor of organizational psychology at UCL and Columbia University, and the co-founder of DeeperSignals. He has authored 15 books and over 250 scientific articles on the psychology of talent, leadership, AI, and entrepreneurship. ","subhed":"","description":"","ctaText":"Learn More","ctaUrl":"https:\/\/drtomas.com\/intro\/","theme":{"bg":"#2b2d30","text":"#ffffff","eyebrow":"#9aa2aa","subhed":"#ffffff","buttonBg":"#3b3f46","buttonHoverBg":"#3b3f46","buttonText":"#ffffff"},"imageDesktopId":91424798,"imageMobileId":91424800,"shareable":false,"slug":""}}
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E-Commerce
Think about how many emails you receive each day. Then how many of those include the phrase please find attached in the body. One X user has made a plea to retire the phrase, a relic leftover from a time when business communication relied on typewritten letters posted in envelopes, which actually included attached documents to be found. The post quickly went viral, gaining nearly 15 million views since it was posted earlier this week. While the user doesnt elaborate why exactly they personally take issue with the phrase, or what to say instead, the post had the desired effect, with many weighing in with their own takes on modern email etiquette. Some agreed that the phrase is stuffy and outdated. Please find attached adds zero information, sounds robotic, and does not respect the reader’s time, one wrote. Here’s the file does the job better than a sentence that adds zero information, another added. Its true, these days email attachments are instantly accessible, clearly marked, and dont require a physical search. While young workers have no qualms including memes, emojis, slang, and abbreviations in their emails, and despite nearly one in four employees now using AI to help write emails, please find attached has somehow slipped through the net. Others staunchly defended the use of the tried-and-tested phrase. But if I don’t type those magic words, how will Outlook know to warn me when I inevitably forget to actually attach the file? one wrote. Baby, no, another added. The people are stupid. Many of us are trapped in a terminal cycle of reaching out and circling back, with dozens of corporate buzzwords and phrases that some argue make smart people sound less intelligent. But if youre in the market for some more creative ways to signal theres a PDF attached that needs attention, the replies to the X post is a goldmine. Behold, the attachment, one X user suggested as an alternative. For a sinister edge, There are attachments in this email with us right now, another put forth, or Watch out for the attachment below. Feeling pumped about the PDF attached? Get a load of this MF attachment, is another option. Or alternatively, feeling deflated? Find attached, if you even care works here. And if youd rather the receiver doesnt open the attachment, you could simply put: Please don’t find attached, one wrote. It’ll only be more work for us both.
Category:
E-Commerce
China moved on Thursday to curb a fierce price war among automakers that has caused massive losses for the industry, after passenger car sales dropped nearly 20% in January from the year before, the fastest pace in almost two years. The State Administration for Market Regulation released guidelines for manufacturers, dealers, and parts suppliers aimed at preventing a race-to-the-bottom price war. They ban automakers from setting prices below the cost of production to squeeze out competitors or monopolize the market. Violators may face significant legal risks,” the regulator warned. The rules also target deceptive pricing strategies and price fixing between parts suppliers and auto manufacturers. Passenger car sales in China fell 19.5% in January from a year earlier, according to the China Association of Automobile Manufacturers. That was the biggest percentage drop since February 2024. The 1.4 million passenger cars sold in January compared with 2.2 million units sold in December, CAAM said. Weakening demand reflects a reluctance of cash-strapped buyers to splash out on big purchases. Sales also have suffered from a cut in tax exemptions for EV purchases, coupled with uncertainties over whether trade-in subsidies for EV purchases will continue after some regions phased them out, auto analysts said. The aggressive price war in Chinas auto sector has caused an estimated loss of 471 billion yuan ($68 billion) in output value across the whole industry in the past three years, Li Yanwei, a member of the China Automobile Dealers Association, wrote recently. Analysts expect domestic demand to dip this year. S&P has forecast sales of light vehicles, including passenger cars, in China will fall up to 3% in 2026. However, Chinese automakers are gaining ground in global markets. China’s exports of passenger cars jumped 49% year-on-year to 589,000 in January. We dont foresee a loss in momentum for the Chinese auto industry this year, said Claire Yuan, director of corporate ratings for China autos at S&P Global Ratings. Chinese automakers such as BYD the country’s largest and one that overtook Tesla as the worlds top electric vehicle maker are targeting markets in Europe and Latin America as they confront intense competition in both prices and lineups at home due to oversupply. Analysts at Citi expect Chinas car exports could jump 19% this year driven by exports of electric vehicles and plug-in hybrids. BYD is targetings around 1.3 million of overseas car sales in 2026, up from the 1.05 million last year. Other major Chinese automakers have also set ambitious sales targets with a focus on exports. Last month, Canada agreed to cut its hefty 100% tariff on China-made EV imports in a move welcomed by Chinese carmakers. China also recently reached a deal with the European Union that could allow more of its EVs to enter the European market. Earlier this week, the European Commission accepted a request by the German auto group Volkswagen to exempt import tariffs for one of its China-built EV models under the CUPRA brand as long as those vehicles are sold at or above an agreed minimum import price in a first of such exemptions. Chinas commerce ministry said Thursday that it welcomed the move and that it hopes to see more such exemptions. Chan Ho-Him, AP business writer
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E-Commerce
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