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Last year, various surveys, including reliable indicators, have highlighted a significant decline in reading habits over the past decades. The most striking evidence is not simply that people read less, but that their capacity for deep reading is weakening. According to OECD data, the proportion of 15-year-olds who fail to reach minimum reading proficiency has now risen to nearly one in four across advanced economies, with sharp declines in tasks requiring inference, evaluation, and integration of information across texts. In the United States, NAEP scores show that average reading performance among 13-year-olds has fallen to its lowest level in decades, reversing long-standing gains. Laboratory studies mirror these trends: experiments comparing print and screen reading consistently find that readers of digital texts score 1030% lower on comprehension and recall, particularly for longer and conceptually demanding material. {"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":""}} Eye-tracking and cognitive load research further indicates that frequent digital readers engage in more skimming, less rereading, and shallower semantic processing. Crucially, these effects are not confined to weaker readers. Even highly educated adults now report shorter attention spans for long-form text and greater mental fatigue when reading complex arguments, suggesting that the decline of reading reflects not a loss of literacy, but an erosion of the cognitive endurance and attentional discipline that deep reading uniquely develops. Not just children To make matters worse, various robust data indicators show that adults are spending less time reading, especially for pleasure. For instance: (1) A large time-use study analyzing diary data from over 236,000 Americans found that the share of adults who read for pleasure on an average day dropped from about 28% in 2003 to just 16% in 2023, a roughly 40% decline over two decades. (2) That same research showed a steady annual fall of about 3% per year in the prevalence of daily leisure reading among U.S. adults. (3) An earlier report by the World Economic Forum indicated average daily reading time in the U.S. declined from about 23 minutes per day in 2004 to around 16 minutes by 2019, even before the most recent decades drop. (4) In the U.S., fewer adults now report reading books for pleasure: about 48.5% of adults said they read at least one book in the past year in 2022, down from 54.6% in 2012. A real concern? Should this really concern us? Perhaps not. After all, reading is just one medium through which humans have ingested information and exercised their minds, including for deep thinking. For most of history, knowledge travelled orally rather than silently on the page. Ancient cultures relied on storytelling, poetry, and song to preserve and transmit complex ideas: Homers epics were memorized and performed long before they were written down; Greek philosophy unfolded through dialogue rather than textbooks; and entire moral, legal, and scientific traditions were passed across generations through ritualized speech, music, and debate. From this perspective, the book is a relatively recent cognitive technology, not an eternal prerequisite for intelligence (consider that Socrates and his fellow philosophers were concerned by the invention of writing, thinking it may atrophy memory). And today, once again, new media promise alternative routes to learning and thinking: immersive simulations, virtual and augmented reality, AI tutors, and even speculative neuro-technologies all claim to enhance understanding, creativity, or memory without requiring sustained reading at all. Perhaps these tools will indeed make us more knowledgeable or even smarter. Needless to say, not all reading is cognitively ennobling. Wading through a disposable airport romcom is unlikely to stretch the mind more than an unscripted, curious conversation with a stranger at a bar. The real question, then, is not whether reading is declining per se, but whether whatever replaces it can cultivate the same depth of attention, reflection, and intellectual effort that serious reading has historically demanded. Digital diversions To be sure, every person is different and even among those who are reading less, former reading time may be recycled or reutilized in many different ways. That said, there is a clear trend to devote more time and attention to the very technologies that have increasingly monopolized our focus over the past two decades. Time-use and media-consumption data strongly suggest that leisure reading has been displaced not by other cognitively demanding activities, but by screen-based media. In the United States, Bureau of Labor Statistics time-use surveys show that average daily reading for pleasure fell from about 23 minutes in the early 2000s to roughly 16 minutes by 2019, while time spent on digital devices and television increased steadily. Over the same period, social media use expanded rapidly: Pew Research Center reports that adult social media adoption rose from around 5% in 2005 to over 80%, with many users spending multiple hours per day on these platforms. Globally, Digital 2024 data indicate that adults now spend about 2.5 hours per day on social media and more than 6.5 hours per day consuming digital media overall, compared with a small and declining fraction of time devoted to reading books or long-form text. While time spent reading traditional text has declined, many adults are substituting other sustained listening activites that share some cognitive benefits of reading; for example, Edison Researchs Infinite Dial reports that the share of Americans ages 12 and older who listen to podcasts weekly has grown from about 11% in 2013 to over 60% in 2024, with average weekly listening around seven hours, suggesting deeper engagement than typical short-form scrolling. Audiobook consumption has also risen sharply: the Audiobook Publishers Association and APA Foundation data show that nearly 50% of American adults listened to an audiobook in the past year, with frequent listeners averaging more than 6 hours per week, offering another way to engage with complex narrative and informational content. These trends indicate that although reading declines are real, listening to long-form spoken content (whether through podcasts or audiobooks) is increasingly filling part of the gap, providing extended attention to ideas, storytelling, and analysis in ways that resemble some of readings cognitive and reflective benefits. Unique benefits And yet, cognitive and developmental psychology remind us of the distinctive benefits of traditional reading, especially when it comes to thoughtful immersion and deep processing of text. Decades of research converge on at least five lessons worth remembering. First, sustained reading strengthens attention and cognitive endurance, training the ability to concentrate for extended periods without external stimulation, a capacity that is closely linked to academic achievement and complex problem-solving. Second, reading supports deeper comprehension and critical thinking: compared with fragmented or audiovisual media, linear text promotes inferential reasoning, abstraction, and the integration of ideas across time. Third, regular reading expands vocabulary and conceptual knowledge, which in turn predicts reasoning ability (especially verbal and crystallized intelligence), learning speed, and even long-term occupational outcomes. Fourth, reading fiction in particular has been shown to enhance perspective-taking and social cognition, improving peoples ability to understand others emotions, intentions, and mental states. Finally, early and sustained exposure to reading plays a foundational role in brain development, literacy, and self-regulation, with long-lasting effects on educational attainment and cognitive resilience across the lifespan. None of this means that reading is the only route to thinking, or that newer media are inherently inferior, but it does suggest that some cognitive benefits are unusually hard to replicate without sustained engagement with text. And if you made it this far, thank you for reading this. {"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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Should I take this project? Say yes to the new job offer? Stick with this plan or walk away? Every choice we make can feel huge. And every path has its own set of risks and rewards. There are always more questions for every life-changing decision. Sometimes the pros-and-cons lists feel more like busywork than progress. You check off the boxes, stare at the lists, and still end up confused, stuck in the same mental loop. Thats why I rely on the rule of 3 framework to make tough decisions. I hope it helps you clarify your life-changing choices. How it works Whenever youre stuck, force yourself to create three paths: B, C, and D. Why not A? A is usually the default for most people. The thing youre already doing. The path of least resistance. It doesnt need your help. What you need are alternatives. Then comes the second step, and this is where most people stop thinking too soon. Now, for each path, think through: First-order effects Second-order outcomes And third-order consequences And then, and this matters, choose the path with the most meaningful but least life-changing consequences. Why the two-option path doesnt work When you only have two options, your brain keeps going back and forth. Right vs wrong. Safe vs risky. Smart vs stupid. You stop being logical. Theres a term for it: binary bias or black-and-white thinking. We do it all the time. Two choices feel better. But they are not. Theyre restrictive and create a lot of unnecessary pressure. Most decisions are not binary, and there are usually better answers waiting to be found if you do the analysis and involve the right people, Jamie Dimon, the CEO of JPMorgan Chase, says. Three options open things up. Adding a third option reduces your emotional load and improves perceived control. You feel less trapped. And more capable. For example, if you are thinking about changing jobs. This is how it usually goes. Option 1: Quit and leap.Option 2: Stay and suffer. Now try the Rule of 3. Path B: Quit and take a new role in a similar field.Path C: Stay for six months and skill up aggressively.Path D: Go part-time or freelance while testing something new. Of course, none of these options is perfect. Thats why the next stage of the process is even more important: the consequences. 1st, 2nd and 3rd order effects It simply means keep asking, and then what? First-order effects are immediate. What happens right away when you make the decision? Second-order effects come next. What does that lead to? Third-order effects are longer-term. Who do you become if this path continues? I will now apply the effects to the job-changing example. Path B: Quit and take a similar role. First-order: New environment. Relief. You may stop dreading Mondays. Second-order: You become more confident. Now, you know youre employable. You can actually change jobs. Third-order: You might stay on the same path longer than you want. Now Path C: Stay and upgrade your skills First-order: You may feel frustrated for a while. You will need a lot of discipline for this path. Second-order: You will get leverage to open your options. Third-order: You redefine yourself from stuck to building a career. You may become indispensable to your employer. The mistake most people make Most people pursue the best outcome. Thats a trap. The future is uncertain. Youre probably guessing what could work. Everyone is. Once you are done with the effects, choose the path with the least life-altering effects. The one that teaches you something. Keeps doors open. And doesnt completely make your life worse if youre wrong. Its my risk psychology approach. People regret irreversible decisions more than bad ones. We hate closing doors we didnt mean to close. Thats why picking the path that means a lot to you but wont burn bridges matters. Make better decisions with the least panic. This framework works when you are emotionally attached to the decision you are about to make. When youre stressed, your brain throws logic out of the window. The rule of 3 gets you back on the rational path. It takes you from reacting to responding to life. It helps you answer the most important question. Which future can I live with? You can use this rule anywhere. Money decisions. Relationship decisions. Creative decisions. A big purchase. Even small ones. Do I say yes to this commitment? What are the effects, and what are my options? And what path can I live with and still function? Force the three paths. Pursue the consequences in places most people ignore. Then, opt for the choice that makes life better without disrupting your entire life. Use it to pick a path with tolerable unknowns The rule of three doesnt remove uncertainty. Nothing does. Youre never picking certainty. Youre picking a path with tolerable unknowns. Good decisions come from better processes. The 3 rule takes away the emotional attachment that drains the life out of you. Most of our hard decisions become unbearable because we want a perfect choice. The one that proves we are smart and avoids regret. So you panic. Or overthink. Some people let time decide for them. Which is still a decision, by the way. I use the rule of three to pick a direction. Adjust where necessary. And keep moving. I want forward motion without self-destruction. You dont need to outsmart the future. Just stop putting so much pressure on yourself. Most choices dont need courage. They need structure. Three paths. Three consequences. It makes overthinking your options almost impossible.
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AI is no longer just a cascade of algorithms trained on massive amounts of data. It has become a physical and infrastructural phenomenon, one whose future will be determined not by breakthroughs in benchmarks, but by the hard realities of power, geography, regulation, and the very nature of intelligence. Businesses that fail to see this will be blindsided. Data centers were once the sterile backrooms of the internet: important, but invisible. Today, they are the beating heart of generative AI, the physical engines that make large language models (LLMs) possible. But what if these engines, and the models they power, are hitting limitations that cant be solved with more capital, more data centers, or more powerful chips? In 2025 and into 2026, communities around the U.S. have been pushing back against new data center construction. In Springfield, Ohio; Loudoun County, Virginia and elsewhere, local residents and officials have balked at the idea of massive facilities drawing enormous amounts of electricity, disrupting neighborhoods, and straining already stretched electrical grids. These conflicts are not isolated. They are a signal, a structural friction point in the expansion of the AI economy. At the same time, utilities are warning of a looming collision between AIs energy appetite and the cost of power infrastructure. Several states are considering higher utility rates for data-intensive operations, arguing that the massive energy consumption of AI data centers is reshaping the economics of electricity distribution, often at the expense of everyday consumers. This friction between local resistance to data centers, the energy grids physical limits, and the political pressures on utilities is more than a planning dispute. It reveals a deeper truth: AIs most serious constraint is not algorithmic ingenuity, but physical reality. When reality intrudes on the AI dream For years, the dominant narrative in technology has been that more data and bigger models equal better intelligence. The logic has been seductive: scale up the training data, scale up compute power, and intelligence will emerge. But this logic assumes that three things are true: Data can always be collected and processed at scale. Data centers can be built wherever they are needed. Language-based models can serve as proxies for understanding the world. The first assumption is faltering. The second is meeting political and physical resistance. The third, that language alone can model reality, is quietly unraveling. Large language models are trained on massive corpora of human text. But that text is not a transparent reflection of reality: It is a distillation of perceptions, biases, omissions, and misinterpretations filtered through the human use of language. Some of that is useful. Much of it is partial, anecdotal, or flat-out wrong. As these models grow, their training data becomes the lens through which they interpret the world. But that lens is inherently flawed. This matters because language is not reality: It is a representation of individual and collective narratives. A language model learns the distribution of language, not the causal structure of events, not the physics of the world, not the sensory richness of lived experience. This limitation will come home to roost as AI is pushed into domains where contextual understanding of the world, not just text patterns, is essential for performance, safety, and real-world utility. A structural crisis in the making We are approaching a strange paradox: The very success of language-based AI is leading to its structural obsolescence. As organizations invest billions in generative AI infrastructure, they are doing so on the assumption that bigger models, more parameters, and larger datasets will continue to yield better results. But that assumption is at odds with three emerging limits: Energy and location constraints: As data centers face community resistance and grid limits, the expansion of AI compute capacity will slow, especially in regions without surplus power and strong planning systems. Regulatory friction: States and countries will increasingly regulate electricity usage, data center emissions, and land use, placing new costs and hurdles on AI infrastructure. Cognitive limitations of LLMs: Models that are trained only on text are hitting a ceiling on true understanding. The next real breakthroughs in AI will require models that learn from richer, multimodal interactions from real environments, sensory data and structured causal feedback, not just text corpora. Language alone will not unlock deeper machine understanding. This is not a speculative concern. We see it in the inconsistencies of todays LLMs: confident in their errors, anchored in old data, and unable to reason about the physical or causal aspects of reality. These are not bugs: they are structural constraints. Why this matters for business strategy CEOs and leaders who continue to equate AI leadership with bigger models and more data center capacity are making a fundamental strategic error. The future of AI will not be defined by how much computing power you have, but by how well you integrate intelligence with the physical world. Industries like robotics, autonomous vehicles, medical diagnosis, climate modeling, and industrial automation demand models that can reason about causality, sense environments, and learn from experience, not just from language patterns. The winners in these domains will be those who invest in hybrid systems that combine language with perception, embodiment, and grounded interaction. Conclusion: reality bites back The narrative that AI is an infinite frontier has been convenient for investors, journalists, and technologists alike. But like all powerful narratives, it eventually encounters the hard wall of reality. Data centers are running into political and energy limits. Language-only models are showing their boundaries. And the assumption that scale solves all problems is shaking at its foundations. The next chapter of AI will not be about who builds the biggest model. It will be about who understands the world in all its physical, causal, and embodied complexity, and builds systems that are grounded in reality. Innovation in AI will increasingly be measured not by the size of the data center or the number of parameters, but by how well machines perceive, interact with, and reason about the actual world.
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