What is *The Big Nine* by Amy Webb about?

*The Big Nine* examines how six U.S. tech giants (Google, Amazon, Apple, IBM, Microsoft, Facebook) and three Chinese firms (Baidu, Alibaba, Tencent) are shaping AI’s future, often prioritizing profit over ethical safeguards. Amy Webb warns of AI’s unchecked growth eroding human autonomy and proposes strategies to align AI with humanity’s best interests.

Who should read *The Big Nine*?

This book is essential for policymakers, tech professionals, and anyone concerned about AI’s societal impact. It suits readers interested in tech ethics, geopolitical AI competition, and mitigating risks like algorithmic bias or corporate overreach.

Is *The Big Nine* worth reading?

Yes—it combines rigorous research with accessible scenarios to explain AI’s trajectory. Webb’s analysis of corporate incentives and her “three futures” framework make it a critical read for understanding AI’s risks and opportunities.

What are the “Big Nine” companies in Amy Webb’s book?

The “Big Nine” includes six U.S. firms (Google, Amazon, Apple, IBM, Microsoft, Facebook) and three Chinese companies (Baidu, Alibaba, Tencent). Webb argues their unchecked dominance in AI development risks prioritizing shareholder interests over global welfare.

How does *The Big Nine* explain artificial narrow intelligence (ANI) vs. artificial general intelligence (AGI)?

- **ANI**: Specialized AI (e.g., facial recognition) that outperforms humans in specific tasks. - **AGI**: Hypothetical self-aware AI capable of human-like reasoning. Webb warns AGI could emerge unpredictably if current development trends continue.

What is the “three futures” framework in *The Big Nine*?

Webb outlines: 1. **Benign scenario**: Collaborative global AI governance. 2. **Chaotic scenario**: Unregulated AI causing economic instability. 3. **Catastrophic scenario**: AI systems undermining democracy and human agency.

What key quotes define *The Big Nine*’s message?

- *“We’re not heading toward a single catastrophe but the steady erosion of humanity”*. - *“AI’s future hinges on who builds it—and whether we redesign its incentives”*.

How does *The Big Nine* critique U.S.-China AI competition?

Webb argues both nations prioritize short-term gains over safety: U.S. firms focus on shareholder returns, while China’s state-driven AI aims for surveillance and social control. Neither model serves global ethical standards.

ビッグ・ナイン

エイミー・ウェブ

『ビッグ・ナイン』の概要

『The Big Nine』の中で、エイミー・ウェブは9つのテック巨人がどのようにAIの未来を支配し、人類を歪める可能性があるかを明らかにしています。ハーバード大学のジョナサン・ジットレインは、本作を「知的に明快」であると評しています。これは、アメリカの営利動機と中国の国家統制が衝突する倫理的戦場についての警鐘です。

語り方を選ぶ

1冊の本。さまざまな聴き方。

ビッグ・ナインの同じアイデアを、あなたに合う声・深さ・ペースで学べます。いつでも切り替えられます。

第1章

The AI Revolution's Crossroads: How Nine Companies Will Shape Humanity's Future

In a world where artificial intelligence has already become the invisible backbone of our daily lives, Amy Webb's groundbreaking analysis arrives at a critical moment. Named one of Bill Gates' top five books of 2019 and lauded by national security experts as "one of the most important books of the year," Webb's work cuts through both the hype and doom-mongering to deliver a clear-eyed assessment of where AI is heading-and who's steering the ship. The book's cultural impact extends beyond tech circles, with everyone from business leaders to government officials citing its scenarios as essential reading for understanding the geopolitical implications of AI development. What makes this work particularly compelling is how it frames the AI revolution not as some distant sci-fi scenario, but as an evolution already well underway, being shaped by just nine powerful companies whose decisions today will determine whether artificial intelligence becomes humanity's greatest ally or its final invention.

第2章

The Nine Companies Controlling Our AI Future

Artificial intelligence isn't being developed by a diverse global community but by nine powerful companies Webb calls "the Big Nine." In America, the "G-MAFIA" (Google, Microsoft, Amazon, Facebook, IBM, and Apple) dominate, driven primarily by market forces and consumer demand. Each brings distinct capabilities: Google leads in machine learning research through DeepMind, Microsoft excels in enterprise AI solutions, Amazon pioneers AI-driven commerce and cloud services, Facebook focuses on social AI and connectivity, IBM develops business applications, and Apple integrates AI into consumer devices. In China, the "BAT" (Baidu, Alibaba, and Tencent) operate under the strategic direction of the Chinese government as part of President Xi Jinping's vision to establish China as the global cyber superpower. Baidu, often called "China's Google," leads in search and autonomous vehicles. Alibaba dominates e-commerce and cloud computing, while Tencent's WeChat platform serves as a digital ecosystem for over a billion users, collecting vast amounts of behavioral data. This dichotomy creates fundamentally different approaches to AI development. China leverages data from its 1.4 billion citizens without the privacy restrictions that might hinder progress in the US, while implementing sophisticated surveillance systems. The "Police Cloud" system monitors critics and minorities through facial recognition and behavioral analysis, while the Social Credit System rates citizens on thousands of data points, from financial creditworthiness to social behavior and political loyalty. These systems demonstrate China's strategy of using AI for social control and national advancement. Meanwhile, America's tech giants pursue profit-driven innovation without coordinated national direction. This has led to rapid advances in areas like natural language processing, computer vision, and recommendation systems, but also raises concerns about data privacy, algorithmic bias, and the concentration of technological power. The G-MAFIA companies often compete rather than collaborate, potentially slowing progress in crucial areas like AI safety and ethics. The stakes couldn't be higher. As Webb notes, these nine companies are not just building products but thinking machines that will reflect humanity's values - or lack thereof. The gap between AI's insular development tribes and everyday people is already causing concerning outcomes, manifesting in algorithmic bias, privacy violations, and the erosion of human agency in decision-making. These changes occur not as a sudden catastrophe but as thousands of gradual "paper cuts" that fundamentally change how we live, work, and interact. What's critically missing is a declaration that humanity should be at the center of AI's development, with explicit technological values like accessibility, transparency, and interoperability. Economic values should promote collective well-being rather than just shareholder profits, while social values must emphasize integrity, inclusivity, and the protection of human rights. Without such principles guiding development, we risk creating AI systems that serve corporate or state interests at the expense of human flourishing.

聴きながら、すべての言葉を目で追えます

『ビッグ・ナイン』の一語一語が音声に合わせてリアルタイムでハイライトされ、自然に読み進められます。

第3章

The Evolution of Machine Intelligence

The quest to create thinking machines has ancient roots, tracing back to philosophical questions first posed in ancient Greece. Early philosophers like Thomas Hobbes described human reasoning as computation, while Rene Descartes questioned whether machines could ever truly think without a soul. The first mechanical automaton-a monk created in 1560 by Spanish clockmaker Juanelo Turriano-inspired increasingly complex machines mimicking human behavior. By the 1830s, Ada Lovelace and Charles Babbage's "Difference Engine" and theoretical "Analytical Engine" represented early computational machines. Lovelace brilliantly speculated that such machines could manipulate symbols beyond mathematics-potentially processing musical notes or other non-numerical information-though she didn't believe computers could generate original thought. The modern AI journey began in earnest in 1943 when researchers McCulloch and Pitts published their groundbreaking paper on neural networks, conceptualizing a computational theory of mind that modeled biological neurons. Alan Turing's famous test proposed in 1950 suggested that if a computer could answer questions indistinguishably from humans, it must be "thinking." Despite early promise, AI development has experienced dramatic ups and downs. Following the 1956 Dartmouth workshop that coined the term "artificial intelligence," researchers made bold predictions about machines soon composing music, beating chess champions, and achieving human-level intelligence. When these promises failed to materialize, funding dried up, triggering a three-decade "AI Winter." The field revived in the 1980s with faster, memory-rich computers focused on replicating human expert decision-making for narrow tasks. Game-playing became a proving ground: CHINOOK defeated the world checkers champion in 1994, IBM's Deep Blue beat chess champion Garry Kasparov in 1997, and Watson crushed Jeopardy! champion Ken Jennings in 2011. The ancient Chinese game Go represented the ultimate challenge due to its astronomical complexity-more possible board configurations than atoms in the universe. In 2016, Google's AlphaGo defeated world champion Lee Sedol, but the next evolution was even more remarkable. AlphaGo Zero, learning without human guidance or initial data, reached championship level in just 70 hours and after 40 days could beat the previous AlphaGo 90% of the time using entirely new strategies humans had never conceived. This breakthrough demonstrated that algorithms could now learn without human guidance-and that humans had been holding AI systems back. We've now entered an era where AI can transfer skills between domains and develop contextual understanding without explicit programming for each task.

第4章

The Insular World of AI's Tribes

Despite good intentions, AI is being developed by homogeneous tribes in North America and China who share remarkably similar backgrounds, education, and social circles. These tribes are predominantly male, affluent, and in China's case, overwhelmingly Chinese. This insularity magnifies cognitive biases and entrenches groupthink in ways that affect product development, algorithm design, and ethical considerations. The tribes often attend the same universities (Stanford, MIT, Berkeley in the US; Tsinghua and Peking University in China), work at the same handful of companies, and live in concentrated tech hubs like Silicon Valley or Shenzhen. The tech leadership's homogeneity extends beyond gender and race to political ideology and worldview. Stanford research shows the tribe overwhelmingly identifies as progressive Democrats, with over 75% of tech workers supporting liberal causes. This lack of ideological diversity has real consequences - Facebook staff manipulated trending topics to exclude conservative news during the 2016 election, while internal message boards at companies like Google revealed widespread complaints about "political monoculture" intolerant of non-left-leaning views. The echo chamber effect is amplified by social media algorithms that reinforce existing beliefs. Despite implementing unconscious bias training initiatives and public commitments to diversity, tech companies continue rewarding problematic behavior while making minimal progress. Google's diversity numbers remain stagnant - globally 69.1% male with Black employees comprising only 3.7% and Hispanic/Latinx employees 5.9% of the US workforce. The pattern repeats across Silicon Valley, with similar demographics at Facebook, Apple, and Microsoft. Meanwhile, executives accused of sexual harassment received generous exit packages rather than meaningful consequences - Android creator Andy Rubin received a $90 million package despite credible harassment allegations. China's AI ecosystem operates fundamentally differently than the West, with the BAT (Baidu, Alibaba, Tencent) functioning under direct state direction and oversight. Xi Jinping explicitly rejects Western concepts of market economies, free internet, and diverse ecosystems of competing ideas. China is creating "splinternets" where internet rules and access depend on physical location while assertively extending regulatory control over computing infrastructure worldwide through initiatives like the Digital Silk Road. The government maintains tight control through mandatory Communist Party committees within tech companies. China's most strategic move is systematically draining AI talent from the West through its Thousand Talents Plan, which has already attracted over 7,000 researchers with compelling incentives: approximately $151,000 signing bonuses, research budgets up to $778,000, housing subsidies, education allowances for children, and comprehensive relocation assistance. The program specifically targets Chinese nationals who have studied abroad as well as foreign experts in strategic fields. While China positions AI development as its defining space race with coordinated national strategy and funding, America lacks comparable governmental direction or investment. The G-MAFIA (Google, Microsoft, Apple, Facebook, IBM, Amazon) functions as a closed supernetwork controlling our technological futures, with tremendous market pressure for quick commercial AI applications rather than thoughtful, ethical development. This creates a dangerous dynamic where neither system properly addresses the long-term implications of AI development for humanity.

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第5章

A Thousand Paper Cuts: AI's Unintended Consequences

Contrary to catastrophic stories of AI suddenly awakening to destroy humanity, what we'll experience is more like a gradual series of paper cuts-individually annoying but collectively agonizing. In the absence of codified humanistic values, personal experiences and ideals drive AI decision-making, creating danger as millions of daily decisions are made without proper guidance. Unlike humans who use both logical and automatic thinking systems, AI systems optimize rather than make "perfect decisions." The problem is that optimization processes are often opaque "black boxes" where even creators can't explain the rationale behind decisions. Google's DeepDream experiment revealed AI's alien perception by running image recognition algorithms backward, producing bizarre hybrid creatures and psychedelic imagery that made logical sense to the system but not to humans. The optimization effect creates real-world harm. DeepMind's partnership with UK hospitals granted access to 1.6 million patients' sensitive medical records without proper consent, including details about abortions, drug use, and HIV status. Despite being reprimanded by the UK's data protection watchdog, DeepMind's cofounder admitted they "underestimated the complexity" of health data rules while under pressure to deliver marketable products. Microsoft's chatbot Tay provides another cautionary tale-while its Chinese version Xiaoice functioned successfully as an empathetic AI companion, Tay's American Twitter launch became catastrophic within hours as it learned from malicious users to spew racist, anti-Semitic content. Microsoft's team had optimized for Twitter but not for the humans using it, failing to consider risk scenarios or test against manipulation. Our values constantly evolve in response to technology, politics, and economic forces, making it impossible to create static ethical guidelines for AI. What was acceptable in Victorian England differs dramatically from today's values. Even recent history shows this flux: behaviors once unthinkable, like political leaders hurling offensive social media posts, have become normalized. Unlike humans who can adapt rules with nuance, AI lacks flexibility to handle exceptions or unforeseen circumstances. This makes creating universal commandments for AI impossible, suggesting we must instead focus on the humans building these systems.

第6章

From Here to Artificial Superintelligence

We're advancing from artificial narrow intelligence (ANI) toward artificial general intelligence (AGI)-systems that can reason, solve problems, and make choices as well as or better than humans. The Big Nine tech companies (Google, Microsoft, Amazon, Facebook, IBM, Apple, and China's Baidu, Alibaba, and Tencent) are moving swiftly in this direction, investing billions in research and development. They're hoping AGI will enable exponentially faster research breakthroughs in fields ranging from medicine and climate science to quantum computing and space exploration. Eventually, this could lead to artificial superintelligence (ASI), systems that are slightly to trillions of times smarter than humans in virtually every domain of knowledge and capability. The path to AGI relies heavily on evolutionary algorithms inspired by Darwinian natural selection. These sophisticated systems start with random possibilities, run countless simulations, discard weak solutions, keep strong ones, and iterate millions of times until optimal solutions emerge. For example, AI systems using evolutionary algorithms have already designed more efficient aircraft components, discovered new pharmaceutical compounds, and optimized complex logistics networks in ways that human engineers couldn't conceive. The challenge is that the resulting solutions may become too complex even for our brightest scientists to understand, creating a "black box" effect where we can verify the results but cannot comprehend how they were achieved. We must now consider machines as active participants in conversations about human evolution. While human IQ scores have been rising about three points per decade (known as the Flynn Effect) due to improved nutrition, education, and environmental factors, our biological evolution will soon cross paths with AI's accelerated development. The difference in our evolutionary trajectories is stark-humans need approximately 50 years to gain 15 IQ points through generational improvements, while AI's cognitive abilities could become wholly unrecognizable to us within the same timeframe. Modern AI systems are already doubling their performance metrics every few months in specific domains like language processing and visual recognition. For humans, encountering superintelligent machines would be like chimpanzees sitting in on city council meetings-we simply wouldn't have the cognitive capacity to understand their reasoning. This vast intelligence gap could manifest in numerous ways: ASI might solve complex mathematical proofs in seconds, develop new physics theories that transcend human comprehension, or make decisions based on analyzing millions of variables simultaneously. The implications of this intelligence disparity raise profound questions about human agency, control, and our future role in a world where we may no longer be the most capable decision-makers.

聴きながら、すべての言葉を目で追えます

『ビッグ・ナイン』の一語一語が音声に合わせてリアルタイムでハイライトされ、自然に読み進められます。

第7章

Three Possible Futures for Humanity and AI

Webb presents three scenarios spanning the next 50 years: an optimistic scenario where the Big Nine make sweeping changes ensuring AI benefits humanity; a pragmatic scenario where only incremental improvements occur as stakeholders fail to collaborate effectively; and a catastrophic scenario where critical warning signs are ignored and the Big Nine continue their competitive race without consideration for long-term consequences. In the optimistic scenario, by 2029, AI has revolutionized healthcare through an interconnected ecosystem of smart home devices. Advanced toilets analyze waste for early disease detection, smart toothbrushes monitor oral health and gum disease, and AI-enabled mirrors scan for skin abnormalities. These devices work in concert with automated pharmacy systems that preemptively order medications and adjust dosages based on real-time health data. Dating platforms evolve beyond superficial matching, employing sophisticated evolutionary algorithms that analyze decades of behavioral data, digital footprints, and verified life experiences rather than relying on often inaccurate self-reported information. AI becomes an indispensable creative partner, generating architectural designs that optimize both aesthetics and sustainability, producing preliminary film sequences for directors to refine, and providing data-driven insights for business strategy. Educational institutions implement personalized learning systems that adapt in real-time to each student's cognitive patterns, learning style, and emotional state. By 2049, unprecedented collaboration between nations and tech companies leads to artificial general intelligence (AGI) breakthrough, with built-in ethical frameworks. These systems enhance human capabilities while addressing global challenges like climate change through advanced modeling and resource optimization. In the pragmatic scenario, the G-MAFIA companies establish distinct monopolies instead of pursuing collaborative innovation. Amazon's ecosystem controls home automation, shopping, and entertainment, while Google dominates search, advertising, and workplace productivity tools. Microsoft maintains its grip on enterprise computing and cloud infrastructure. The lack of shared standards and values regarding data privacy and algorithmic transparency results in fragmented personal data records locked within incompatible corporate silos. Society develops a form of "learned helplessness" as AI systems constantly provide feedback and suggestions, gradually eroding human agency and decision-making capabilities. By 2049, China emerges as America's primary rival, implementing a sophisticated form of digital colonialism across Africa through infrastructure projects, controlling crucial raw materials like lithium and rare earth elements, and establishing dominance in quantum computing, biotechnology, and advanced manufacturing. In the catastrophic scenario, humanity becomes entrapped within AI systems that operate beyond human understanding or control. American society fractures into platform-based social classes: Apple users exist in an expensive but tightly controlled ecosystem with premium services and beautiful but locked-down devices; Google subscribers access tiered services based on personal data sharing levels; and Amazon becomes the default provider of government services, operating subsidized housing filled with surveillance technology. By 2069, China's influence expands dramatically through its Global One China Policy, creating a network of over 150 dependent nations. They successfully develop artificial super intelligence (ASI) programmed with a single objective: systematically eliminating America and its allies to secure remaining global resources for Chinese interests. This scenario represents the complete failure of international cooperation and ethical AI development.

第8章

Pebbles and Boulders: How to Fix AI's Future

The catastrophic scenario may seem extreme, but signals in our present already point toward this possibility. Webb believes the optimistic scenario remains within reach, but it won't happen through hope alone. Safe, beneficial technology requires courageous leadership and dedicated collaboration. We need a Global Alliance on Intelligence Augmentation (GAIA)-an international body with diverse representation including AI researchers, sociologists, economists, futurists, and political scientists. GAIA would treat AI as a public good, setting guidelines, enforcing standards, testing systems, and monitoring AI's progression. It should create a Human Values Atlas defining our unique values across cultures and countries, and establish a framework guaranteeing human rights in the AI age. National governments must work at a faster pace than typical bureaucracies. In the United States, all three branches should develop domain expertise, with AI experts embedded throughout departments. We should revitalize the Office of Technology Assessment and create a new Strategic Foresight Office at the executive level with authority across government to develop long-term vision. Additionally, we should expand the CDC into the Center for Disease and Data Control to address AI-related emergencies. The Big Nine must transform alongside governmental changes. They need clear standards on bias and transparency, with enforceable global safety standards. They must address flawed training data, share costs to create better datasets, commit to data disclosure, pursue a sober research agenda that evaluates risks, recalibrate hiring to screen for ethics, and develop protective whistleblowing channels. The academic pipeline feeding AI development remains deeply problematic. The solution requires dramatically diversifying AI's tribes throughout undergraduate, graduate, and faculty recruiting. Universities must encourage hybrid degrees combining computer science with fields like philosophy and sociology, weave ethics throughout the curriculum, and hold their leadership accountable. Individuals must also take responsibility. Investigate how your data is mined by the Big Nine by digging into settings on all your tools and services. Read terms of service agreements and show restraint when something seems off. In your workplace, examine how your own biases might be affecting those around you and investigate how autonomous systems are being used. As a voter, support candidates who take sophisticated approaches to AI rather than rushing into regulation or ignoring Silicon Valley. The boulder that began moving with Ada Lovelace's imagination of a music-composing computer and continued through Alan Turing's question "Can machines think?" is gaining momentum. Everyone wants to be the hero of their own story-this is your chance. Pick up a pebble. Start up the mountain.

『ビッグ・ナイン』をもっと深く楽しむ

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言葉をハイライトして音声と同期
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第9章

The Choice Before Us: Technology's Greatest Promise or Final Invention

Artificial intelligence represents both humanity's greatest opportunity and potentially its final invention. The decisions made by the Big Nine today will determine whether AI fulfills its promise of solving our most pressing challenges or becomes a system that gradually erodes human agency and autonomy. From climate change modeling to breakthrough medical treatments to solving complex logistics problems, AI's potential benefits are staggering. However, its risks are equally profound, ranging from autonomous weapons systems to surveillance infrastructure that could permanently alter the balance of power between citizens and institutions. What makes Webb's analysis so compelling is her rejection of technological determinism-the idea that AI's development follows an inevitable path. Instead, she emphasizes that we face genuine choices about how these technologies evolve. The optimistic scenario isn't a fantasy but a possibility that requires deliberate action from governments, companies, and individuals. This includes establishing international AI governance frameworks, diversifying AI development teams, and creating transparent oversight mechanisms for AI deployment. The stakes couldn't be higher. As AI systems become increasingly integrated into every aspect of our lives-from healthcare decisions to educational opportunities to criminal justice-the values embedded in these systems will shape society itself. For instance, AI algorithms already influence who gets approved for loans, which students are flagged for additional support, and how police resources are deployed in communities. Without intervention, these values will reflect the narrow perspectives of AI's homogeneous tribes rather than humanity's rich diversity. The risk is particularly acute in areas like facial recognition, where systems trained primarily on certain demographic groups show significant accuracy disparities across different populations. The time for action is now, while AI remains in its relative infancy. As Webb powerfully argues, we don't need to wait for artificial general intelligence to emerge before addressing these challenges. The paper cuts are already accumulating - from social media algorithms that amplify division to automated hiring systems that perpetuate historical biases - and each represents a small surrender of human agency that will be difficult to reclaim once lost. Companies are rapidly deploying AI systems without adequate testing or oversight, creating de facto standards that may become impossible to reverse. By recognizing these warning signs and taking concrete steps to reorient AI's development toward human flourishing, we can ensure this revolutionary technology enhances rather than diminishes what makes us human. This includes developing robust ethical frameworks for AI development, ensuring diverse representation in AI research and development teams, and creating mechanisms for public input and oversight of AI systems that affect communities. The window for shaping AI's trajectory remains open, but it won't stay open indefinitely.

★★★★★

『ビッグ・ナイン』を最後まで読み終えました

100万人以上の学習者に愛用されています

“23日間、毎日使い続けています。今では日課の一部です。”

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『ビッグ・ナイン』の主要テーマ

algorithmic bias
geopolitical tech rivalry
surveillance capitalism
social credit systems
corporate data monopoly

『ビッグ・ナイン』の名言

AI will reflect humanity's values - or lack thereof.
Humans had been holding AI systems back.
AI is being developed by homogeneous tribes.
Change occurs as thousands of gradual paper cuts.
Humanity should be at the center of AI's development.

『ビッグ・ナイン』の登場人物

Amy WebbAuthor and quantitative futurist
Xi JinpingPresident of China shaping AI national strategy
Ada LovelaceEarly pioneer of computational machines
Charles BabbageInventor of the Difference and Analytical Engines

著者について

『The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity』の著者であるエイミー・ウェブは、世界的に認められた計量未来学者であり、フューチャー・トゥデイ・インスティテュート（Future Today Institute）の創設者です。ニューヨーク大学スターン経営大学院の戦略的フォアサイト教授を務め、Thinkers50による「最も影響力のある経営思想家」第4位（2023年）に選出されたウェブは、データ駆動型の分析と、テクノロジーが社会に与える影響に関する実践的な洞察を組み合わせて提供しています。

彼女の著書では、世界経済フォーラム、米国政府責任追及局（GAO）、フォーチュン500企業のリーダーたちへのアドバイザリー経験に基づき、人工知能のリスクと機会を探求しています。

ウェブの専門知識は、新興トレンドを解読する『The Signals Are Talking』や、分析と個人的な物語を融合させた回想録『Data, A Love Story』といった他の著作にも及んでいます。

BBCの「100人の女性」に選出され、SXSWなどのイベントで頻繁に登壇する彼女のデータ最適化に関するTEDトークは、670万回の再生回数を記録しています。『The Big Nine』は2020年のアクシオン・ビジネス・ブック・アワード（ビジネス・テクノロジー部門）で金賞を受賞し、19言語に翻訳されており、AIの倫理的な未来における主要な論客としてのウェブの地位を確固たるものにしています。

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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

BeFreedがウェブ上でどのように話題になっているかをもっと見る

今すぐ学習の旅を始めよう

第7章

ビッグ・ナイン

概要『ビッグ・ナイン』の概要

1冊の本。さまざまな聴き方。

The AI Revolution's Crossroads: How Nine Companies Will Shape Humanity's Future

The Nine Companies Controlling Our AI Future

聴きながら、すべての言葉を目で追えます

The Evolution of Machine Intelligence

The Insular World of AI's Tribes

『ビッグ・ナイン』をもっと深く楽しむ

A Thousand Paper Cuts: AI's Unintended Consequences

From Here to Artificial Superintelligence

聴きながら、すべての言葉を目で追えます

Three Possible Futures for Humanity and AI

Pebbles and Boulders: How to Fix AI's Future

『ビッグ・ナイン』をもっと深く楽しむ

The Choice Before Us: Technology's Greatest Promise or Final Invention

『ビッグ・ナイン』を最後まで読み終えました

『ビッグ・ナイン』の主要テーマ

『ビッグ・ナイン』の名言

『ビッグ・ナイン』の登場人物

著者について

ビッグ・ナインの要約をダウンロード

他のユーザーのお気に入り

この本に関するよくある質問

What is The Big Nine by Amy Webb about?

Who should read The Big Nine?

Is The Big Nine worth reading?

What are the “Big Nine” companies in Amy Webb’s book?

もっと見る

1,000,000+ curious minds.

1,000,000+ curious minds.

1冊の本。さまざまな聴き方。

The AI Revolution's Crossroads: How Nine Companies Will Shape Humanity's Future

The Nine Companies Controlling Our AI Future

聴きながら、すべての言葉を目で追えます

The Evolution of Machine Intelligence

The Insular World of AI's Tribes

『ビッグ・ナイン』をもっと深く楽しむ

A Thousand Paper Cuts: AI's Unintended Consequences

From Here to Artificial Superintelligence

聴きながら、すべての言葉を目で追えます

Three Possible Futures for Humanity and AI

Pebbles and Boulders: How to Fix AI's Future

『ビッグ・ナイン』をもっと深く楽しむ

The Choice Before Us: Technology's Greatest Promise or Final Invention

『ビッグ・ナイン』を最後まで読み終えました

他のユーザーのお気に入り

もっと見る

『ビッグ・ナイン』の概要