What is *New Dark Age: Technology and the End of the Future* about?

*New Dark Age* explores how humanity’s reliance on technology exacerbates existential crises like climate change, mass surveillance, and systemic inequality. James Bridle argues that data abundance and computational systems obscure understanding, creating a “new dark age” where technological solutions often deepen problems. The book examines case studies like algorithmic bias and climate modeling failures to critique tech’s role in societal collapse.

Who should read *New Dark Age* by James Bridle?

This book is essential for technology critics, policymakers, and readers concerned about digital culture’s societal impacts. It appeals to those interested in climate change, AI ethics, and systemic critiques of surveillance capitalism. Bridle’s interdisciplinary approach connects finance, environmental science, and digital art, making it valuable for both academic and general audiences.

Is *New Dark Age* worth reading?

Yes, *New Dark Age* offers a thought-provoking analysis of technology’s unintended consequences, blending academic rigor with accessible prose. Bridle’s examples—from racist algorithms to climate data failures—provide stark insights into systemic risks. While critiquing tech’s downsides, it avoids outright pessimism, urging readers to rethink their relationship with technology.

What does James Bridle mean by a “new dark age”?

Bridle defines it as an era where technological complexity and data overload erode human comprehension. Unlike historical dark ages marked by knowledge loss, this one stems from *excessive* information that fuels misinformation, systemic biases, and environmental crises. It emphasizes the paradox of technology both illuminating and obscuring reality.

How does *New Dark Age* address climate change?

The book links computational models to climate governance failures, showing how tech’s promise of control clashes with ecological unpredictability. Bridle critiques carbon-offset algorithms and climate simulations that reduce systemic crises to quantifiable data, arguing they ignore deeper socio-political causes.

What is the “Optometrist Algorithm” in *New Dark Age*?

This Google-developed protocol combines machine learning with human intuition, exemplifying effective human-AI collaboration. Bridle contrasts it with purely automated systems, suggesting such hybrids could mitigate tech’s risks. However, he warns against overreliance on opaque algorithms.

How does Bridle critique Amazon in *New Dark Age*?

Bridle examines Amazon’s fusion of automation, worker surveillance, and neoliberal policies to maximize efficiency. He highlights how algorithms enforce grueling warehouse conditions and manipulate consumer behavior, illustrating tech’s role in entrenching labor exploitation and corporate power.

What role do conspiracy theories play in *New Dark Age*?

Bridle analyzes chemtrail conspiracies and anti-vax movements as symptoms of systemic distrust in institutions. He argues that data overload and algorithmic echo chambers fuel paranoia, reflecting a broader societal failure to address tech-driven disinformation.

新黑暗时代

技术与未来的终结

詹姆斯·布莱德尔

4.03(2709 Reviews)

《新黑暗时代》概述

在《新黑暗时代》（*New Dark Age*）中，詹姆斯·布里德尔（James Bridle）揭示了我们这个数据丰富的世界如何自相矛盾地导致了理解力的匮乏。这部发人深省的畅销书被誉为“计算机时代的奥威尔”，它提出了一个问题：我们是否正在构建一个自己无法理解的未来？马克·奥康奈尔（Mark O'Connell）称其“才华横溢，令人振奋”。

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第 1 章

The Darkening Digital Horizon: When Technology Clouds Our Understanding

In an era where our phones constantly ping with notifications and algorithms dictate what we see, James Bridle's "New Dark Age" arrives as a sobering wake-up call. This book has become something of a cult classic among tech critics and Silicon Valley insiders alike, with figures like Jaron Lanier and Shoshana Zuboff citing it as essential reading. Published in 2018, it anticipated many of the concerns about AI and algorithmic control that have since moved from fringe warnings to mainstream discourse. What makes this work particularly compelling is how Bridle, an artist and technologist himself, doesn't simply condemn technology but reveals how our relationship with it has fundamentally altered our ability to comprehend the world. As our computational systems grow more complex, our understanding paradoxically diminishes-we know more facts but comprehend less meaning. In a time when tech CEOs testify before Congress and climate change manifests in increasingly visible ways, Bridle's exploration of how technology both illuminates and obscures has never felt more relevant.

第 2 章

The Paradox of Modern Knowledge: More Information, Less Understanding

We live in a strange paradox: connected to vast repositories of knowledge yet increasingly unable to think clearly. The internet, once celebrated as an "information superhighway," now seems to darken rather than enlighten, producing not consensus but fundamentalist narratives and post-factual politics. This contradiction defines our new dark age: knowledge's value is being destroyed by its own abundance. This darkness isn't literal or hopeless, but represents both crisis and opportunity-our inability to see clearly what's before us and act meaningfully. As Virginia Woolf noted during World War I, "the future is dark, which is the best thing the future can be." This uncertainty invites us to think differently about civilization and its ceremonies. The metaphor of "the cloud" exemplifies our technological misunderstanding. Far from weightless or amorphous, it consists of physical infrastructure-phone lines, fiber optics, data centers-with enormous energy demands and jurisdictional realities. This deliberate obscurity erases agency and ownership while reinforcing existing power structures. We cannot unthink the network; we can only think through it. This isn't an argument against technology but for more thoughtful engagement with it. Technology isn't mere tool-making but metaphor-making-each tool instantiates a worldview that achieves certain effects, often unconsciously. To think anew, we must re-enchant our tools, making them less like the carpenter's hammer and more like Thor's Mjolnir-capable of taking on new symbolic meaning. Perhaps we need "cloudy thinking" that embraces unknowing rather than computational certainty. The network itself, built without single intent through countless interconnections, teaches us the inadequacy of computational thinking and the need to constantly rethink our collective responsibilities. Our great failing has been believing technology's actions are inherent and inevitable rather than co-created.

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第 3 章

When Machines Think For Us: The Hidden Dangers of Computational Thinking

Computation doesn't merely augment culture-it becomes culture itself. By operating beneath our awareness, it reshapes our relationship with knowledge, social connections, and reality. Google began by indexing human knowledge but became its arbiter; Facebook mapped social connections but became their platform, fundamentally altering societal relationships. This conditioning occurs because computational processes are both opaque and perceived as politically neutral. Computation happens inside machines, behind screens, in remote buildings-within "the cloud." Even when this opacity is penetrated by examining code and data, the aggregation of complex systems means no single person ever sees the whole picture. Faith in the machine becomes prerequisite for its use. This faith is reinforced by automation bias-our tendency to trust automated information over our own observations, even when they conflict. Studies show we value computational outputs more highly than personal experience, particularly with ambiguous observations. Confirmation bias further reshapes our awareness to align with automated information, sometimes discarding observations inconsistent with the machine's viewpoint. Aviation provides stark examples. The Korean Air Lines flight shot down in 1983 drifted hundreds of miles off course because pilots trusted their incorrectly programmed autopilot despite numerous cues something was wrong. Their final moments were spent attempting to re-engage the very autopilot that had led them astray. Even experienced pilots take drastic actions based on automated warnings despite contradictory visual evidence. In NASA simulations, 75% of crews following automated guidance shut down the wrong engine during contradictory fire warnings, while only 25% made the same mistake using paper checklists. Automation bias means technology doesn't need to malfunction to threaten lives. GPS-related incidents have become so common that Death Valley rangers call it "Death by GPS"-tourists follow digital instructions rather than their senses, driving into lakes, oceans, or deadly desert terrain. At automation bias's foundation is our tendency to engage in minimal cognitive work, especially under time pressure. Given the option to relinquish decision-making, our brains take the path of least resistance offered by automated assistants. Computation becomes a cognitive hack, offloading both decisions and responsibility to machines. As life accelerates, machines handle more cognitive tasks, reinforcing their authority regardless of consequences.

第 4 章

Climate Crisis: When the Weather Becomes Unreadable

The Svalbard Global Seed Vault represents humanity's attempt to preserve biodiversity against catastrophic loss. Located in a visa-free Arctic archipelago, this underground facility stores millions of seed samples refrigerated to minus eighteen degrees Celsius. Yet in 2016, the hottest year ever recorded, Arctic temperatures rose twenty degrees above average, causing permafrost to melt. Meltwater flooded the vault's entrance tunnel, threatening the very seeds meant to survive climate disaster. This melting represents both warning sign and metaphor-an accelerating collapse of environmental and cognitive infrastructure. The certainties of the present assume ever-crystallizing geologies of knowledge, but reality is returning to fluid, undifferentiated states. Climate change manifests in both geography and geopolitics. Syria's conflict, which displaced agricultural scientists from Aleppo, was partly triggered by an unprecedented drought linked to climate change that killed 85% of rural livestock and forced a million villagers into cities. Even if Syria stabilizes politically, it stands to lose half its agricultural capacity by 2050. In Arctic regions, melting permafrost threatens unique archaeological treasures. At Qajaa in Greenland, middens preserving 3,500 years of successive cultures contain wooden and bone artifacts preserved nowhere else on Earth. As permafrost thaws, dormant bacteria awaken, generating heat that accelerates melting in a positive feedback loop. Archaeologist Thomas McGovern laments, "We have the equivalent of the Library of Alexandria in the ground, and it's on fire." Contemporary information networks function as both economic and cognitive frameworks of society, yet they're increasingly threatened by climate change while simultaneously contributing to it. Data centers consumed about 3% of global electricity in 2015, accounting for 2% of total emissions-equivalent to the airline industry. This consumption doubles every four years. Climate change has unhinged us from linear temporality, disrupting the predictability that civilization depends upon. Meteorologist William B. Gail warns that climate disruption is undermining centuries of accumulated knowledge about weather patterns, fish migrations, plant pollination, and monsoon cycles-knowledge essential for agriculture, fisheries, and infrastructure planning. Without accurate forecasting, civilization itself falters. We may have passed "peak knowledge," with future generations knowing less about their world than we do today. Our ability to think about climate change is being degraded by climate change itself. Atmospheric CO2, which remained between 275-285 parts per million for centuries before industrialization, reached 400 ppm in 2015. At current rates, we'll reach 1,000 ppm by century's end, at which point human cognitive ability drops by 21%. The crisis of global warming is literally a crisis of thought-we are pumping into our atmosphere the very gas that clouds our minds.

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第 5 章

The Myth of Technological Progress: Moore's Law and Its Discontents

Science fiction writers call simultaneous invention "steam engine time"-when numerous writers independently produce stories about the same idea. The term refers to the steam engine's emergence at a particular historical moment despite the Romans having the technical capability to build it centuries earlier. Such inventions appear almost mystical, arising when conditions align in ways we can't fully comprehend. History shows that invention is typically simultaneous and multi-authored. Treatises on magnetism emerged independently in Greece, India, and China. Blast furnaces appeared in China and Scandinavia a millennium apart. Calculus was formulated independently by Leibniz, Newton, and others. These patterns undermine the heroic narrative of the lone genius, revealing history as networked and atemporal. Computing particularly embraces such justificatory histories, with Moore's Law being the quintessential self-fulfilling technological prophecy. Gordon Moore's 1965 observation that transistor density doubled yearly projected the exponential growth that would enable "home computers, automatic controls for automobiles, and personal portable communications equipment"-predictions that seemed to prove technology's inevitable march forward. Moore's Law began as a simple observation but transformed into a defining principle of technological progress. This "law" isn't truly a law but a projection that shaped our technological imagination. It became self-fulfilling, driving the semiconductor industry's architecture and creating the economic conditions for software's independence from hardware, leading to Silicon Valley's dominance. It also fostered a culture of computational waste-why optimize code when twice the power would arrive in 18 months? Moore's Law transformed from technical observation to economic principle to moral imperative-the promise of perpetual progress requiring no present sacrifice. As Moore himself claimed, "Moore's Law is a violation of Murphy's Law. Everything gets better and better." Despite our faith in technological progress, we're discovering its limits. The p-hacking controversy exemplifies this problem. When researchers aim for the magical p < 0.05 threshold required for publication, they can selectively manipulate data to achieve desired results. A 2015 analysis of 100,000 open access papers found evidence of p-hacking across multiple disciplines, with most reported p-values barely scraping under the 0.05 threshold. This crisis in scientific quality control connects to what scholars call "overflow"-the boundless upwelling of information that overwhelms our processing capacity. The human genome project, once history's greatest data-gathering endeavor, is now dwarfed by annual DNA sequencing output. The Large Hadron Collider generates too much data to store on-site, forcing selective preservation that may miss unexpected discoveries.

第 6 章

The Hidden Complexity of Modern Systems

Tri Alpha Energy's fusion research exemplifies how complexity challenges traditional problem-solving. Their reactor design fires plasma "smoke rings" at each other, but fine-tuning the reaction requires navigating countless interdependent variables. Simple brute-force computing won't work because there's no clear "goodness metric" for plasma, and experimental missteps could damage expensive equipment. Their solution-the Optometrist Algorithm-combines machine learning with human judgment. After each plasma shot, the algorithm suggests new settings while showing results alongside previous best attempts, letting human operators make the final choice. This hybrid approach led to unexpected discoveries when operators noticed anomalous energy spikes the algorithm wasn't programmed to detect, demonstrating how human intuition and machine exploration complement each other. The fusion researchers' approach reveals something profound: they're "attempting to optimize a hidden utility model that human experts may not be able to express explicitly." This acknowledges that some problems contain an order that exists beyond human descriptive capacity. These multidimensional spaces are mathematically real but impossible to visualize. Working with such indescribable systems forces us to confront the limits of human comprehension-a hallmark of our new dark age-while recognizing that computational tools themselves shape and constrain our thinking. As van Helden and Hankins observed, "Because instruments determine what can be done, they also determine to some extent what can be thought." The author describes a sixty-mile bicycle journey from Slough to Basildon, connecting two seemingly anonymous buildings that house critical financial infrastructure. At one end stands Equinix LD4, an unmarked warehouse that hosts the London Stock Exchange's actual processing operations. At the other end is the Euronext Data Center, the European outpost of the New York Stock Exchange. Between them runs an invisible network of microwave transmissions carrying financial data at near light-speed. The author traces how financial technology infrastructure physically manifests in unexpected places, including atop Hillingdon Hospital. Despite the hospital's crumbling facilities and staff shortages, its roof hosts microwave dishes for high-frequency trading companies-a stark juxtaposition of private wealth and public service decay. This physical arrangement symbolizes broader technological inequality, where markets have become class systems with the wealthy paying for nanosecond advantages while others remain completely unaware of the market's true nature.

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第 7 章

When Machines Learn to See: The Alien Intelligence of Neural Networks

Our attempts to understand machine learning reveal fundamental questions about knowledge and perception. When machines learn to recognize patterns, they develop ways of seeing entirely different from humans, leading to both breakthroughs and catastrophic failures. A cautionary tale describes the US Army training a neural network to detect camouflaged tanks in forests. The system performed perfectly in testing but failed miserably in the field. The revelation: the machine hadn't learned to spot tanks at all, but merely to distinguish sunny from cloudy days, as all tank photos happened to be taken on sunny mornings while empty forest photos were taken on cloudy afternoons. This likely apocryphal story illustrates a crucial insight: artificial intelligence operates fundamentally differently from human cognition and remains ultimately inscrutable to us. Modern neural networks have achieved remarkable image recognition capabilities. Google Brain's 2011 project used 16,000 processors to analyze 10 million YouTube images, spontaneously learning to recognize faces and cats without prior programming. However, these technologies quickly reveal encoded biases. Chinese researchers Wu and Zhang caused uproar by claiming AI could identify "criminal faces" from ID photos, recalling discredited 19th-century physiognomy theories. When criticized, they insisted machine learning is "neutral" with "no subjective baggages"-a claim that ignores how technology emerges from and reinforces specific cultural worldviews. Racial biases appear frequently: from Nikon cameras failing to recognize Asian faces to HP webcams not tracking Black users. Even analog photography shows this history, with Kodak only developing film that properly captured dark skin tones when furniture and chocolate companies complained about photographing their products, not when people objected. Faith in intelligent systems has been widely implemented in police and justice systems. Half of US police departments use "predictive policing" systems like PredPol, which employs "high-level mathematics, machine learning, and proven theories of crime behaviour" to forecast lawbreaking like weather patterns. These systems often build on models originally developed for other purposes-the epidemic type aftershock sequence (ETAS) model used by seismologists to study earthquake aftershocks was adapted to track crime patterns. The true challenge comes with understanding machine thought processes utterly unlike our own. Google's 2016 Translate overhaul replaced statistical language inference with neural networks, creating a multidimensional "mesh of meaning" incomprehensible to humans. As one Google engineer noted, it's impossible to "visualise thousand-dimensional vectors in three-dimensional space"-this is the unseeable realm where machine learning makes meaning. When IBM's Deep Blue defeated chess champion Garry Kasparov in 1997, its process was understandable: brute computational force analyzing 200 million board positions per second. But when AlphaGo defeated Go master Lee Sedol, something had changed. AlphaGo's second game move stunned observers-"It's not a human move. I've never seen a human play this move," said one professional, adding "So beautiful." Having trained on millions of expert moves and self-play iterations, AlphaGo developed strategies beyond human comprehension.

第 8 章

The Weaponization of Uncertainty: Conspiracy in the Digital Age

Joseph Heller's novel Catch-22 illustrates the dilemma of rational actors trapped in irrational systems. The airmen face an impossible choice: they'd be crazy to fly more dangerous missions and sane to refuse, but if they were sane they had to fly them, and if they refused they were declared sane and had to fly anyway. This exemplifies how even rational responses lead to irrational outcomes within vast, dysfunctional systems. Individuals recognize the irrationality but lose power to act in their own interest. Facing overwhelming information, we try controlling the world through narratives-inherent simplifications that cannot account for everything. Yossarian's famous line from Catch-22, "Just because you're paranoid doesn't mean they aren't after you," perfectly captures our modern surveillance dilemma. Clinical paranoia's primary symptom-believing someone is watching you-has become entirely reasonable. Our emails, texts, calls, journeys, and even breaths are now targets of automated intelligence gathering, sorting algorithms, and our own devices' sleepless gaze. Paranoia has become rational. In 2017, the International Cloud Atlas officially recognized "homogenitus"-cloud formations developing from human activity. From the stratus homogenitus fog created by urban emissions to the stratocumulus clouds generated by power plants, human impact on the atmosphere has become formally classified. Most notably, aircraft contrails-officially cirrus homogenitus-form when water vapor from jet engines freezes around fuel impurity nuclei, creating persistent ice crystal tracks that can linger for hours. The criss-crossing of contrails in our skies represents a strange global entanglement-both scientific reality and conspiracy fodder. While scientists distinguish between "normal" contrails and conspiracists' "chemtrails," both contain seeds of the same crisis: the visible manifestation of aviation's massive carbon footprint. In northern Canada, Inuit elders report the sun setting in different places, stars misaligned, and unpredictable weather patterns. Scientists dismissed their claim that "the earth had tilted on its axis" as dangerous misinformation, yet the Inuit experience aligns with scientific understanding-at high latitudes, changing snow cover and increasing atmospheric particulates dramatically alter the sun's appearance. Like the embodied knowledge of torture victims, indigenous climate knowledge remains valid even when expressed in non-scientific terms. In recent years, conspiracy theories have moved from the fringes to the mainstream. Donald Trump's political rise began with the "Birther" movement questioning Barack Obama's citizenship, which energized Republican radicalization. During his presidential campaign, Trump frequently repeated conspiracy theories from extremist sources like Alex Jones's Infowars.com. The internet's fringes had returned to the center of political power.

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第 9 章

The Algorithmic Exploitation of Attention

The chapter opens with a description of "surprise egg" videos-a genre where hands unwrap Kinder Eggs to reveal toys inside. One such video featuring Cars-branded eggs has garnered 26 million views. These videos follow a simple formula (egg, surprise, revelation) yet have spawned millions of variations with endless combinations of themes. Children become transfixed by the repetitive process, bright colors, and constant revelation, watching for hours as YouTube's recommendation algorithms serve an endless stream of similar content. Despite YouTube's official 13+ age policy, children's content dominates the platform. Channels like Ryan's Toy Review (run by a six-year-old) and Little Baby Bum (nursery rhymes) rank among YouTube's most popular, generating millions in monthly revenue. Content creators employ tactics like keyword-stuffed "word salad" titles to game the algorithm, cramming brand names and characters into unintelligible assemblages that appeal not to human viewers but to the recommendation algorithms. The Finger Family song, which debuted on YouTube in 2007, has spawned at least 17 million variations with billions of aggregated views. The simple premise makes it ideal for automation-basic software can place any character or object atop animated hands, creating endless variations. This vast, indeterminate system spans languages and cultures, with its dimensionality making it difficult to comprehend. Many videos are not only created by bots but viewed and commented on by bots, inflating numbers in an arms race Google has little incentive to address since bot activity increases ad revenue. When humans reenter the loop, the weirdness intensifies. Professional groups like Bounce Patrol find themselves performing according to algorithmic logic, acting out keyword combinations generated by machines. This is content production in the algorithmic age: humans impersonating machines to satisfy the demands of recommendation engines fed by billions of toddler clicks. The algorithmic amplification creates increasingly disturbing mutations. "Wrong Heads" videos feature character heads being mismatched while cartoon characters cry or cheer. More troubling channels feature real children in gross-out situations that many viewers consider exploitative. Imitators push boundaries further-children drinking bathroom products, cartoon characters drowning, or engaging in violent acts. This isn't merely trolling; it's a vast matrix of interactions between human desires, algorithmic rewards, and automated content generation that produces industrialized nightmare production targeting children. The crisis reflects how exploitation is encoded into our computational systems. YouTube's algorithms necessitate exploitation to sustain revenue, with humans degraded on both sides-traumatized viewers and underpaid content creators-while automated corporations profit from both. This represents a deeper cognitive crisis where the networks built to expand communication are being weaponized against us in systematic, automated ways.

第 10 章

Seeing Through the Darkness: Finding a Path Forward

In May 2013, Google's Eric Schmidt claimed that if everyone in Rwanda had smartphones during the 1994 genocide, the massacre would have been impossible as plans would have leaked and someone would have intervened. This worldview-that making something visible through technology inherently makes it better-is fundamentally flawed and dangerous. The Rwandan genocide was extensively documented through multiple channels: embassy staff, NGOs, UN personnel, intelligence agencies, and even satellite imagery. The NSA recorded nationwide radio broadcasts calling for extermination, and high-resolution satellite photos captured roadblocks, destroyed buildings, and mass graves. In all cases, surveillance proved entirely retroactive, revealing not a lack of evidence but a lack of will to act. Contrary to Schmidt's claim, smartphones often amplify rather than prevent violence. After Kenya's disputed 2007 election, cell phones became vehicles for inciting ethnic violence through circulating text messages urging people to compile lists of enemies and calling for slaughter. Studies show that across Africa, increased cell phone coverage correlates with higher levels of violence, even accounting for factors like income inequality and ethnic divisions. The phrase "data is the new oil," coined in 2006 by mathematician Clive Humby, has become a mantra for businesses and policymakers. This analogy carries profound implications: our thirst for data, like oil, follows historically imperialist and colonialist patterns tied to exploitation. Data extraction enforces computational thinking, drives societal divisions through classification, and accelerates inequality. Digital infrastructure follows colonial networks-West African data still routes through London, just as Shell continues to exploit Nigerian oil. We must see the network itself in all its complexity, not just its power. This network is our most advanced civilization-scale tool for introspection, a Borgesian infinite library that refuses to cohere. Our categories and authorities are no longer merely insufficient; they're incoherent. Despite the apparent darkness, we remain capable of thinking clearly and acting with justice. Guardianship, taking responsibility for what we've created without presuming to control the future, offers a path forward. The technologies shaping our reality aren't going away, but our understanding of them and the conscious choices we make in their design remain entirely within our capabilities. We are not powerless or limited by darkness-we only have to think, and think again, and keep thinking.

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《新黑暗时代》核心主题

computational thinking
algorithmic opacity
automation bias
technological metaphor
information overload

《新黑暗时代》经典语录

Knowledge's value is being destroyed by its own abundance.
Computation doesn't merely augment culture-it becomes culture itself.
Faith in the machine becomes prerequisite for its use.
Our great failing has been believing technology's actions are inherent and inevitable rather than co-created.

《新黑暗时代》主要人物

James BridleAuthor, artist, and technologist
Jaron LanierTech figure who cites the book as essential
Shoshana ZuboffTech critic who cites the book as essential

关于作者

James Bridle（生于 1980 年）是一位居住在希腊雅典的英国艺术家和作家，也是备受赞誉的《新黑暗时代：技术与未来的终结》（New Dark Age: Technology and the End of the Future）一书的作者。这部开创性的著作探讨了技术对人类理解和社会结构的复杂影响。

Bridle 的作品跨越非虚构写作、艺术和批判理论，审视了数字系统、环境崩溃与政治权力之间的交集。作为《连线》（WIRED）、《卫报》（The Guardian）和《大西洋月刊》（The Atlantic）的撰稿人，以及英国广播公司第四台（BBC Radio 4）《新的观看方式》（New Ways of Seeing）的主持人，他将跨学科研究与通俗易懂的分析相结合。

他的后续著作《存在的方式：动物、植物、机器：寻找行星智能》（Ways of Being: Animals, Plants, Machines: The Search for a Planetary Intelligence）通过人工智能和生态智能的视角扩展了这些主题。Bridle 的装置艺术作品曾在维多利亚和阿尔伯特博物馆（V&A）以及巴比肯艺术中心展出。他获得的荣誉包括日本媒体艺术节优秀奖和奥地利电子艺术大奖（Prix Ars Electronica）提名。

《新黑暗时代》已成为讨论技术在气候危机和算法治理中作用的关键参考资料，巩固了 Bridle 作为数字时代远见卓识评论家的声誉。

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1M+ learners

"Instead of endless scrolling, I just hit play on BeFreed. It saves me so much time."

@Moemenn

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117

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108

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254

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@djmikemoore

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"BeFreed turned my commute into learning time. 20-min podcasts are perfect for finishing books I never had time for."

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"BeFreed replaced my podcast queue. Imagine Spotify for books — that’s it. 🙌"

@Jaded_Falcon

201

"It is great for me to learn something from the book without reading it."

@OojasSalunke

"The themed book list podcasts help me connect ideas across authors—like a guided audio journey."

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483

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由哥伦比亚大学校友在旧金山创建

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1,000,000+ curious minds.

4.7

avg rating

1M+ learners

"Instead of endless scrolling, I just hit play on BeFreed. It saves me so much time."

@Moemenn

"I never knew where to start with nonfiction—BeFreed’s book lists turned into podcasts gave me a clear path."

@Chloe, Solo founder, LA

117

"Perfect balance between learning and entertainment. Finished ‘Thinking, Fast and Slow’ on my commute this week."

@Raaaaaachelw

"Crazy how much I learned while walking the dog. BeFreed = small habits → big gains."

@Matt, YC alum

108

"Reading used to feel like a chore. Now it’s just part of my lifestyle."

@Erin, Investment Banking Associate , NYC

254

"Feels effortless compared to reading. I’ve finished 6 books this month already."

@djmikemoore

"BeFreed turned my guilty doomscrolling into something that feels productive and inspiring."

@Pitiful

4.5K

"BeFreed turned my commute into learning time. 20-min podcasts are perfect for finishing books I never had time for."

@SofiaP

"BeFreed replaced my podcast queue. Imagine Spotify for books — that’s it. 🙌"

@Jaded_Falcon

201

"It is great for me to learn something from the book without reading it."

@OojasSalunke

"The themed book list podcasts help me connect ideas across authors—like a guided audio journey."

@Leo, Law Student, UPenn

483

"Makes me feel smarter every time before going to work"

@Cashflowbubu

"Instead of endless scrolling, I just hit play on BeFreed. It saves me so much time."

@Moemenn

"I never knew where to start with nonfiction—BeFreed’s book lists turned into podcasts gave me a clear path."

@Chloe, Solo founder, LA

117

"Perfect balance between learning and entertainment. Finished ‘Thinking, Fast and Slow’ on my commute this week."

@Raaaaaachelw

"Crazy how much I learned while walking the dog. BeFreed = small habits → big gains."

@Matt, YC alum

108

"Reading used to feel like a chore. Now it’s just part of my lifestyle."

@Erin, Investment Banking Associate , NYC

254

"Feels effortless compared to reading. I’ve finished 6 books this month already."

@djmikemoore

"BeFreed turned my guilty doomscrolling into something that feels productive and inspiring."

@Pitiful

4.5K

"BeFreed turned my commute into learning time. 20-min podcasts are perfect for finishing books I never had time for."

@SofiaP

"BeFreed replaced my podcast queue. Imagine Spotify for books — that’s it. 🙌"

@Jaded_Falcon

201

"It is great for me to learn something from the book without reading it."

@OojasSalunke

"The themed book list podcasts help me connect ideas across authors—like a guided audio journey."

@Leo, Law Student, UPenn

483

"Makes me feel smarter every time before going to work"

@Cashflowbubu

"Instead of endless scrolling, I just hit play on BeFreed. It saves me so much time."

@Moemenn

"I never knew where to start with nonfiction—BeFreed’s book lists turned into podcasts gave me a clear path."

@Chloe, Solo founder, LA

117

"Perfect balance between learning and entertainment. Finished ‘Thinking, Fast and Slow’ on my commute this week."

@Raaaaaachelw

"Crazy how much I learned while walking the dog. BeFreed = small habits → big gains."

@Matt, YC alum

108

"Reading used to feel like a chore. Now it’s just part of my lifestyle."

@Erin, Investment Banking Associate , NYC

254

"Feels effortless compared to reading. I’ve finished 6 books this month already."

@djmikemoore

"BeFreed turned my guilty doomscrolling into something that feels productive and inspiring."

@Pitiful

4.5K

"BeFreed turned my commute into learning time. 20-min podcasts are perfect for finishing books I never had time for."

@SofiaP

"BeFreed replaced my podcast queue. Imagine Spotify for books — that’s it. 🙌"

@Jaded_Falcon

201

"It is great for me to learn something from the book without reading it."

@OojasSalunke

"The themed book list podcasts help me connect ideas across authors—like a guided audio journey."

@Leo, Law Student, UPenn

483

"Makes me feel smarter every time before going to work"

@Cashflowbubu

查看更多网络上关于 BeFreed 的讨论

开启你的学习之旅，就是现在

第 7 章

新黑暗时代

概述《新黑暗时代》概述

一本书，多种聆听方式。

The Darkening Digital Horizon: When Technology Clouds Our Understanding

The Paradox of Modern Knowledge: More Information, Less Understanding

边听边看，每个词都清晰呈现

When Machines Think For Us: The Hidden Dangers of Computational Thinking

Climate Crisis: When the Weather Becomes Unreadable

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The Myth of Technological Progress: Moore's Law and Its Discontents

The Hidden Complexity of Modern Systems

边听边看，每个词都清晰呈现

When Machines Learn to See: The Alien Intelligence of Neural Networks

The Weaponization of Uncertainty: Conspiracy in the Digital Age

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The Algorithmic Exploitation of Attention

Seeing Through the Darkness: Finding a Path Forward

你已读完 《新黑暗时代》

《新黑暗时代》核心主题

《新黑暗时代》经典语录

《新黑暗时代》主要人物

关于作者

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关于本书的常见问题

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What does James Bridle mean by a “new dark age”?

发现更多

1,000,000+ curious minds.

1,000,000+ curious minds.

一本书，多种聆听方式。

The Darkening Digital Horizon: When Technology Clouds Our Understanding

The Paradox of Modern Knowledge: More Information, Less Understanding

边听边看，每个词都清晰呈现

When Machines Think For Us: The Hidden Dangers of Computational Thinking

Climate Crisis: When the Weather Becomes Unreadable

从 《新黑暗时代》中收获更多

The Myth of Technological Progress: Moore's Law and Its Discontents

The Hidden Complexity of Modern Systems

边听边看，每个词都清晰呈现

When Machines Learn to See: The Alien Intelligence of Neural Networks

The Weaponization of Uncertainty: Conspiracy in the Digital Age

从 《新黑暗时代》中收获更多

The Algorithmic Exploitation of Attention

Seeing Through the Darkness: Finding a Path Forward

你已读完 《新黑暗时代》

用户还喜欢

发现更多

《新黑暗时代》概述

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从《新黑暗时代》中收获更多

你已读完《新黑暗时代》

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你已读完《新黑暗时代》