What is *Prediction Machines* by Ajay Agrawal about?

*Prediction Machines* reframes AI as a tool that drastically lowers the cost of prediction, enabling better decision-making under uncertainty. The authors argue that AI’s transformative power lies in its ability to enhance forecasting accuracy across industries—from healthcare diagnostics to financial risk assessment—while emphasizing the enduring role of human judgment.

Who should read *Prediction Machines*?

Business leaders, policymakers, and entrepreneurs seeking to leverage AI’s economic implications will benefit most. The book provides actionable insights for integrating AI into strategic planning, making it ideal for decision-makers navigating AI-driven disruption.

Is *Prediction Machines* worth reading?

Yes—it demystifies AI’s hype with a clear economic framework, praised by The Economist as one of the “best books to understand AI.” The updated 2022 edition addresses quantum computing’s impact, ensuring relevance for modern readers.

How does AI reduce the cost of prediction?

By automating data analysis at scale, AI minimizes the time and resources needed for accurate forecasts. This cost drop enables businesses to make frequent, high-stakes predictions (e.g., fraud detection, demand forecasting) that were previously impractical.

What is the role of human judgment in AI according to *Prediction Machines*?

Humans excel at interpreting outliers and causal relationships, while AI handles routine predictions. The authors advocate for “prediction by exception,” where machines manage standard cases and humans intervene for complex scenarios.

What are the key frameworks in *Prediction Machines*?

The book introduces: - **AI as prediction infrastructure**: Treating AI as a utility for decision support. - **The prediction-judgment trade-off**: Balancing automated forecasts with human context. - **Data economics**: Prioritizing quality over quantity in training AI models.

What are criticisms of *Prediction Machines*?

While lauded for its economic lens, some argue it undersells AI’s technical complexities and ethical challenges. Critics note its focus on prediction overlooks generative AI’s creative capabilities.

What notable quotes come from *Prediction Machines*?

- “AI’s value isn’t in replacing humans but in making predictions **cheap and abundant**.” - “Uncertainty constrains strategy; better prediction creates opportunities”

予測マシンの経済学

人工知能のシンプルな経済学

アジェイ・アグラワル、ジョシュア・ガンズ、アヴィ・ゴールドファーブ

3.86(3773 Reviews)

『予測マシンの経済学』の概要

『Prediction Machines』は、AIの経済学を解明し、予測コストの低下がいかにビジネス上の意思決定を変革するかを明らかにしています。トロント大学のエリート経済学者たちによる本書は、Thinkers50の「経営思想界のアカデミー賞」にもノミネートされており、AIが進化するにつれて、人間の「判断」の価値が下がるどころか、むしろ高まっていく理由を解き明かしています。

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第1章

When Prediction Becomes Cheap, Everything Changes

The year 2012 marked a turning point in human history that most people missed. At the ImageNet competition, a neural network suddenly achieved a dramatic breakthrough in image recognition, slashing error rates by an unprecedented margin. This moment sent shockwaves through the tech industry, triggering Google's $600 million acquisition of DeepMind (a company with minimal revenue) and eventually prompting the Chinese government to invest billions after watching AlphaGo defeat the world's best Go players. What happened? According to economists Ajay Agrawal, Joshua Gans, and Avi Goldfarb, we witnessed something profoundly transformative yet deceptively simple: prediction became cheap. "Prediction Machines" has become required reading in boardrooms across Silicon Valley and beyond. Elon Musk reportedly keeps a copy on his nightstand, while Microsoft CEO Satya Nadella distributed it to his entire executive team. The book's genius lies in stripping away the mystique surrounding artificial intelligence and reframing it through a practical economic lens that business leaders can immediately grasp. When something fundamental becomes drastically cheaper-whether it's artificial light in the 1800s or prediction today-it transforms society in ways both predictable and surprising.

第2章

The Economics of Artificial Intelligence

Economists view technological breakthroughs differently than most people-not as revolutionary innovations but as simple price reductions. When prices fall, usage increases. The commercial internet wasn't a "New Economy" as many claimed in 1995, but rather a dramatic reduction in the cost of distribution, communication, and search. Similarly, AI represents a collapse in the cost of prediction-filling in missing information using data you have to generate information you don't have. The impact of these price drops can be deceptively powerful. An improvement in prediction accuracy from 85% to 90% might seem modest, but it reduces mistakes by one-third. Going from 98% to 99.9% reduces errors twentyfold-a transformation that can completely reshape industries. Credit card fraud detection exemplifies this evolution: improving from 98% to 99.9% accuracy means the number of fraudulent transactions that slip through undetected drops by a factor of twenty. What makes prediction machines seem magical is how they're transforming activities we never previously thought of as prediction problems. Google Translate didn't improve overnight by adding more grammatical rules-it reframed translation as a prediction problem: "Given this French text, what English text would a human translator produce?" Self-driving cars don't follow pre-programmed routes; they continuously predict "what would a human driver do in this situation?" As prediction becomes cheap, we'll use it more frequently and in novel ways. But prediction is just one component of decision-making. A complete decision requires prediction plus judgment-determining the relative rewards of different outcomes. While machines excel at prediction, humans maintain the advantage in providing judgment. This creates a new division of labor where machines handle prediction while humans focus on specifying what matters.

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第3章

From Data to Intelligence

If prediction is the core of modern AI, then data is its lifeblood. Machine learning systems require three types of data: input data (what you want predictions about), training data (examples pairing inputs with known outcomes), and feedback data (information about prediction accuracy that enables improvement). Each type plays a crucial role in developing accurate and reliable AI systems. Cardiogram, an app that detects irregular heart rhythms with 97% accuracy, illustrates this perfectly. The app collects heart rate data from users' wearable devices (input data), compares patterns against training samples from 6,000 users including 200 with diagnosed conditions, and continuously improves through feedback when predictions are verified by medical professionals. Similar applications have emerged in other medical fields, like dermatology apps that analyze skin conditions and diabetes management systems that predict blood sugar fluctuations. Organizations must make strategic decisions about data acquisition's scale and scope. How many types of data should we collect? How many examples do we need for training? How frequently should we collect it? These decisions should be driven by the specific prediction problem and balanced against collection costs and privacy concerns. For instance, a facial recognition system might need millions of images across different angles, lighting conditions, and demographics to be effective, while a specialized industrial defect detection system might work well with just thousands of examples from a specific manufacturing line. While data technically shows diminishing statistical returns (the first hundred observations provide more insight than the next hundred), it often creates increasing economic returns in competitive markets. Google's advantage with rare search queries comes from having more data on such searches than competitors. While each additional search technically provides less statistical value than previous ones, having the most comprehensive dataset creates disproportionate market advantages. This phenomenon is evident in other domains too - Netflix's recommendation engine benefits from vast viewing history data, and Tesla's self-driving capabilities improve with every mile driven by their vehicle fleet. Data quality is as crucial as quantity. Clean, well-labeled data often outperforms larger quantities of noisy data. Organizations must invest in data infrastructure, including collection mechanisms, storage solutions, and preprocessing pipelines. They must also consider data governance frameworks to ensure compliance with privacy regulations like GDPR and CCPA, while maintaining the data's utility for AI applications. The timing of data collection also matters. In dynamic environments, historical data may become less relevant over time, requiring continuous updates to training datasets. For example, consumer preference prediction models need regular updates to capture changing trends, while fundamental physics models might rely more on established historical data.

第4章

The New Division of Labor

As prediction machines improve, the division of labor between humans and AI is evolving in fascinating ways. Donald Rumsfeld's framework of "known knowns, known unknowns, and unknown unknowns" helps explain when prediction machines excel or falter. With rich data on familiar scenarios ("known knowns"), machine prediction outperforms humans. This is the sweet spot for current machine intelligence, where the machine provides good predictions and we know they're reliable-as in fraud detection, medical diagnosis, and bail decisions. Humans still excel with limited data ("known unknowns"). While machines struggle with sparse information, humans can recognize faces after seeing them once or use analogy to understand new situations. Scientists are developing techniques like "one-shot learning," but currently, these scenarios require human intervention. Both humans and machines fail with truly novel events ("unknown unknowns"). As Nassim Nicholas Taleb explains in "The Black Swan," some events are fundamentally unpredictable from past data. Europeans couldn't predict black swans in Australia because they'd never seen them. Perhaps the biggest weakness of prediction machines is providing wrong answers they're confident are right ("unknown knowns"). This often stems from misunderstanding causality. When machines don't understand the decision process that generated their training data, they make critical errors. Garry Kasparov described a chess algorithm that sacrificed its queen immediately because it learned from grandmaster games that queen sacrifices preceded victories-reversing the actual causal relationship. This creates an efficient division of labor called "prediction by exception"-machines handle routine scenarios independently while humans focus only on non-routine exceptions flagged by the system. This approach allows one human to leverage a prediction machine's advantages across many routine predictions while providing critical judgment for unusual cases.

『予測マシンの経済学』をもっと深く楽しむ

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第5章

Judgment: The Human Advantage

As AI improves prediction, the value of human judgment increases. While machines predict, only humans can express the relative rewards of different actions. With better prediction come more opportunities for judgment-determining the rewards of various actions-giving us more decisions to make. Consider credit card fraud detection. For a transaction nine times likelier to be fraudulent than legitimate, the company will deny the charge unless customer satisfaction is nine times more important than the potential loss. With concrete monetary values-like $20 recovery cost on a $100 transaction-companies can calculate precise thresholds for action. While economists traditionally treat rewards as givens, determining accurate payoffs requires significant time and cognitive effort. This process involves deliberation-thinking through what you want to achieve-or experimentation-trying different approaches to learn outcomes. The more specific scenarios you evaluate, the more time-consuming judgment becomes. Navigation apps like Waze demonstrate how prediction and judgment interact. While these apps excel at predicting the fastest route, users often override suggestions based on personal objectives beyond speed-like needing fuel or avoiding stressful driving conditions. Though apps can incorporate some preferences (like Tesla's navigation accounting for charging stations), they struggle with more subjective factors. Humans maintain an advantage in understanding their multidimensional objectives, which are often idiosyncratic and subjective. As prediction machines improve, determining how to best use their predictions becomes critical. Reward function engineering-determining the rewards for various actions based on AI predictions-requires understanding both organizational needs and machine capabilities. Sometimes this means hard-coding judgment in advance (as with self-driving vehicles), but engineers must be careful that AI doesn't over-optimize one metric at the expense of broader goals.

第6章

Taming Complexity Through Prediction

Prediction machines enable robots and systems to handle vastly more complex environments by identifying more "ifs" and enabling more "thens" in decision processes. Unlike early autonomous systems like the 1980s Mailmobile that required carefully controlled environments and chemical trails, modern prediction-powered systems can adapt to varied conditions and unexpected obstacles. This evolution represents a fundamental shift from rigid, pre-programmed responses to dynamic, context-aware decision making. Better prediction identifies more situational variables-more "ifs"-allowing machines to react appropriately to complex combinations of conditions. Modern delivery robots can now assess wet surfaces, lighting conditions, human proximity, and distinguish between animals to determine appropriate actions without pre-planned paths. For example, Starship's delivery robots can navigate crowded college campuses, adjusting their speed and path based on pedestrian density, weather conditions, and surface terrain. These systems can even predict human behavior patterns, slowing down near building exits or busy intersections where people might suddenly appear. Prediction also expands our action options-the "thens"-by reducing uncertainty. Apps like Waze provide accurate travel predictions that eliminate the need for buffer strategies like arriving excessively early at airports. Rather than following rigid rules (like "leave two hours before flight"), better prediction enables contingent rules that optimize departure times based on real-time traffic and flight conditions. Modern navigation systems can even suggest alternative routes mid-journey based on emerging traffic patterns, accident reports, and weather changes, something unimaginable just a decade ago. Humans often "satisfice"-taking shortcuts rather than making perfectly rational decisions when facing complexity. Nobel Prize-winner Herbert Simon recognized this limitation in both humans and computers. We've developed workarounds like airport lounges and invasive medical procedures to manage poor prediction. These compensatory mechanisms, while useful, represent inefficient solutions to prediction problems. For instance, doctors often order multiple diagnostic tests because they can't accurately predict which one will be most informative, leading to increased healthcare costs and patient discomfort. As prediction machines improve, they enable handling more "ifs" and "thens," reducing risk and transforming decision-making by expanding options. In healthcare, AI systems can now analyze thousands of variables in patient data to predict potential complications before they occur. In manufacturing, predictive maintenance systems can forecast equipment failures weeks in advance by analyzing subtle patterns in sensor data, allowing for optimal scheduling of repairs and minimizing costly downtime. These advances are not just improving existing processes but fundamentally changing how we approach complex decision-making across industries.

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『予測マシンの経済学』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

When to Fully Automate Decisions

Full automation happens when machines handle prediction, judgment, and action-removing humans entirely from the decision process. This occurs most readily when: (1) everything except prediction is already automated, (2) speed of response is critical, or (3) waiting time for predictions is costly. Rio Tinto's iron ore mines in Australia's remote Pilbara region demonstrate successful full automation. The company deployed 73 self-driving trucks that operate continuously without breaks, saving 15% in operating costs. These trucks don't need drivers, air conditioning, or even to turn around. AI predicts hazards and coordinates movement, completing the automation process where everything except prediction was already mechanized. Automation makes most sense when there's "no time to think" (like emergency braking) or "no need to think" (when prediction leads to an obvious action). When speed is critical or judgment can be easily coded, removing humans from the decision loop becomes beneficial. Even when full automation is technically possible, legal requirements may mandate keeping humans in the loop. This reflects concerns about ethical dilemmas like the "trolley problem" in self-driving cars, where someone must program how vehicles respond to unavoidable harm scenarios. From an economic perspective, the need for human oversight increases with the potential for "externalities"-costs borne by those outside the decision-making process. Some actions remain inherently better when performed by humans rather than machines. Research shows people find jokes less funny when they believe machines recommended them, even though machines are actually better at predicting humor preferences. Similarly, artistic achievement and athletic competition derive much of their value from human experience and participation.

第8章

Strategic Implementation of AI

Large corporations must break their work flows into tasks, estimate ROI for AI implementation in each, and prioritize accordingly. While some AI tools can be dropped into existing work flows for immediate benefit, most require rethinking or "reengineering" entire processes-which explains why mainstream businesses may take time to see productivity gains from AI. The AI Canvas framework systematically separates decision elements: prediction, input, judgment, training, action, outcome, and feedback. This approach helps decompose tasks to understand a prediction machine's potential role, providing discipline and clarity in identifying each component. Atomwise exemplifies this approach with their pharmaceutical drug discovery tool that predicts binding affinity between molecules and proteins, dramatically accelerating the identification of promising drug candidates. Their AI tool processes millions of possibilities, creating ranked lists of molecules most likely to bind to specific disease proteins, while pharmaceutical companies retain the judgment role in evaluating disease targets and potential side effects based on their strategic priorities. Three critical insights emerge from decomposing tasks for AI implementation: First, task decomposition reveals where prediction machines add value, enabling cost-benefit analysis and prioritization of AI tools by ROI. Second, the AI canvas provides structure for this process, forcing clarity about data requirements, prediction objectives, judgment criteria, possible actions, and outcomes. Third, identifying the core prediction often triggers existential discussions among leadership about true organizational objectives.

『予測マシンの経済学』をもっと深く楽しむ

毎日の新しい気づきを10万件以上のレッスンから
言葉をハイライトして音声と同期
ライブラリを保存して、どの端末からでも利用

第9章

Reshaping Business Strategy Through AI

C-suite leadership must directly engage with AI strategy rather than delegating it to IT departments, as AI can fundamentally change business models when three factors align: (1) a core trade-off exists in the business model, (2) uncertainty influences this trade-off, and (3) AI reduces uncertainty enough to tip the scales toward a different strategic approach. This strategic involvement requires executives to understand both the technical capabilities and business implications of AI implementation. Consider how improved prediction could transform Amazon's business model from shopping-then-shipping to shipping-then-shopping. If Amazon's AI could accurately predict what customers want to buy, it might become more profitable to ship products before customers order them. While today's 5% prediction accuracy would make this impractical (requiring too many returns), a sufficiently accurate prediction machine could fundamentally change Amazon's strategy. This concept, known as anticipatory shipping, could revolutionize e-commerce by reducing delivery times from days to minutes, creating a significant competitive advantage. Otto, the German e-commerce company, demonstrates this perfectly-they used AI to predict with 90% accuracy what products would sell within a month, allowing them to reduce inventory by 20% while cutting returns by 2 million items. This prediction capability resolved their delivery-time dilemma without expensive inventory holdings, fundamentally changing their logistics strategy. Their system analyzes 3 billion past transactions and 200 variables including weather data, website clicks, and economic indicators to make its predictions. The success has led to Otto applying this AI approach across their entire product range, demonstrating how predictive capabilities can scale across operations. AI implementation in one area can affect other parts of the business, potentially redesigning workflows and company boundaries. For example, when AI handles routine predictions, employees can focus on higher-value activities requiring judgment and creativity. Prediction machines increase the value of complements like judgment, actions, and data-potentially changing organizational hierarchies, enabling higher-level optimization, and creating competitive advantages for those who own the actions affected by predictions. Companies like Stitch Fix demonstrate this by combining AI predictions with human stylists' judgment to provide personalized fashion recommendations, creating a hybrid service model that neither humans nor AI could achieve alone. The ripple effects of AI implementation extend to organizational structure, requiring companies to rethink traditional departmental boundaries. For instance, when AI predictions span multiple functions - from supply chain to marketing to customer service - organizations often need to create cross-functional teams and new coordination mechanisms. Companies like Ping An Insurance have restructured their entire organization around AI capabilities, creating integrated platforms that combine multiple services based on predictive insights.

第10章

Societal Implications and Trade-offs

The rise of AI presents society with three fundamental trade-offs that will shape its development and implementation. First, the productivity versus distribution dilemma: While AI will undoubtedly enhance economic productivity through automation, process optimization, and improved decision-making, it may simultaneously exacerbate income inequality. This occurs through increased competition for remaining human tasks, displacement of middle-skill workers, and disproportionate benefits flowing to highly skilled workers who can leverage AI tools. For example, AI-powered legal research tools make skilled lawyers more productive while potentially reducing demand for paralegals and junior attorneys. Second, the innovation versus competition trade-off highlights how AI's unique economics could reshape market structures. The technology exhibits strong scale economies and increasing returns - as AI systems process more data, they become more capable, creating powerful feedback loops. This dynamic, combined with the massive computational resources required for training advanced AI models, may lead to market concentration and monopolization. Companies like Google, Microsoft, and OpenAI already dominate certain AI domains, raising concerns about innovation stagnation and market power. Third, the performance versus privacy tension becomes increasingly acute as AI systems hunger for data. Modern AI algorithms demonstrate significantly improved performance with larger training datasets, especially those containing personal information. This creates an inherent conflict between maximizing AI capabilities through comprehensive data collection and protecting individual privacy rights. For instance, medical AI systems could provide better diagnoses with access to complete health records, but this raises serious privacy concerns. China's approach to AI development exemplifies these trade-offs while highlighting its three distinct advantages. First, massive government investment: China is committing unprecedented resources to AI development, with individual cities like Tianjin allocating $5 billion - exceeding entire national AI budgets like Canada's. Second, population scale provides China with an unmatched data advantage: its 1.4 billion citizens generate vast quantities of training data across diverse applications, from facial recognition to consumer behavior. Third, China's relaxed privacy protection policies give domestic companies like Face++, Baidu, and SenseTime significant advantages in developing sophisticated AI systems, particularly in areas like facial recognition and personalized services. This situation creates potential for a "race to the bottom" on privacy standards as countries compete for AI leadership. Nations may feel pressure to relax data protection regulations to remain competitive, even as their citizens express concerns about privacy. Different jurisdictions are already taking divergent approaches: while the EU emphasizes individual privacy rights through GDPR, China prioritizes data access for AI development, and the US maintains a relatively fragmented approach. These varying strategies reflect different cultural values, economic priorities, and governance models, suggesting that a unified global approach to AI development may prove elusive.

聴きながら、すべての言葉を目で追えます

『予測マシンの経済学』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11章

The Economics of Our AI Future

Prediction machines are valuable because they produce better, faster, and cheaper predictions than humans. These machines excel at pattern recognition and can process vast amounts of data to make forecasts about everything from consumer behavior to medical diagnoses to weather patterns. As prediction becomes cheaper and more accessible, human prediction skills naturally decline in value, while complementary skills like data collection, judgment, and action become increasingly valuable. For instance, while AI might predict a patient's likelihood of developing a disease, doctors' judgment in determining treatment plans becomes more critical. The AI revolution parallels trading with a fictional "Robotlandia" - economists view this as beneficial free trade that creates overall prosperity despite disrupting some jobs. Just as international trade reshapes labor markets, AI is transforming employment patterns. As prediction becomes cheaper, jobs focused on providing judgment will grow in value, particularly in reward function engineering - the complex task of defining what success looks like for AI systems. A key example is autonomous vehicle development, where engineers must carefully define what constitutes "good" driving behavior. The labor market impact is nuanced: while high-skilled prediction jobs (like radiologists and financial analysts) may decline, new opportunities emerge in judgment-focused roles. For instance, while AI might handle routine medical image analysis, healthcare workers increasingly focus on patient communication and treatment decisions. Similarly, while algorithms handle stock trading, financial professionals shift toward relationship management and complex strategy development. Current AI remains narrow in scope - AlphaGo Zero mastered Go but can't make a sandwich or adapt to board size changes. This limitation is evident across AI applications: image recognition systems excel at specific tasks but lack general understanding, and language models can generate text without truly comprehending meaning. Several well-funded companies, including DeepMind and OpenAI, are explicitly pursuing human-like intelligence, investing billions in advancing AI capabilities beyond narrow applications. The National Science and Technology Council believes general AI remains "decades" away, but the eventual move beyond prediction machines to general AI or superintelligence will fundamentally alter economic principles. This transition could reshape labor markets, productivity, and economic structures in ways current models struggle to predict. Some experts warn of potential disruption far beyond historical technological revolutions. For now, understanding AI as cheaper prediction provides a powerful framework for navigating this technological revolution. Organizations can leverage this perspective to identify where AI can add value - from improving inventory management to enhancing customer service. By focusing on economic principles rather than technological mystique, companies can make strategic decisions about AI deployment, transforming not just operations but entire business models. For example, retailers use predictive analytics to optimize supply chains, while insurance companies employ AI to improve risk assessment and pricing models.

★★★★★

『予測マシンの経済学』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。”

jayallen

『予測マシンの経済学』の主要テーマ

economic cost reduction
decision making framework
machine learning data
human judgment value
business strategy transformation

『予測マシンの経済学』の名言

Prediction became cheap.
When prices fall, usage increases.
Humans maintain the advantage in providing judgment.
Data is its lifeblood.
Clean, well-labeled data often outperforms larger quantities of noisy data.

『予測マシンの経済学』の登場人物

Ajay AgrawalAuthor and economist
Joshua GansAuthor and economist
Avi GoldfarbAuthor and economist

著者について

『予測マシンの経済学：人工知能の不都合な真実（Prediction Machines: The Simple Economics of Artificial Intelligence）』の著者であるアジェイ・アグラワル、ジョシュア・ガンズ、アヴィ・ゴールドファーブは、AIが経済に与える影響に関する第一人者であり、テクノロジー主導のビジネス戦略におけるベストセラー作家です。

トロント大学の経済学教授であり、世界最大のAIスタートアップ・インキュベーターであるクリエイティブ・デストラクション・ラボ（CDL）の創設者でもあるアグラワルは、学術的な厳密さと実世界の起業家的な洞察を融合させています。トロント大学ロットマン経営大学院の教授であるガンズと、CDLのチーフ・データサイエンティストを務めるゴールドファーブは、イノベーション経済学に関する数十年の研究成果をこの画期的な著作に注ぎ込みました。彼らの著書は、親しみやすい経済原則の枠組みを通じて、予測コストの低下がAIによる意思決定をどのように変革するかを明らかにしています。

この3人は、高く評価された続編『Power and Prediction: The Disruptive Economics of Artificial Intelligence』も共著しており、AI戦略における不可欠な論客としての地位を確立しました。アグラワルはAI政策について米国および日本政府のアドバイザーを務めており、ガンズとゴールドファーブは『ハーバード・ビジネス・レビュー』に定期的に寄稿しています。彼らの著作は15言語に翻訳され、ローレンス・H・サマーズのような業界のリーダーたちからも引用されています。『予測マシンの経済学』は経営者や政策立案者にとっての基本文献であり続けており、アグラワルのクリエイティブ・デストラクション・ラボを通じて育成されたスタートアップ企業は、280億ドル以上の株式価値を創出しています。

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483

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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

コロンビア大学卒業生がサンフランシスコで開発

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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10章

予測マシンの経済学

概要『予測マシンの経済学』の概要

1冊の本。さまざまな聴き方。

When Prediction Becomes Cheap, Everything Changes

The Economics of Artificial Intelligence

聴きながら、すべての言葉を目で追えます

From Data to Intelligence

The New Division of Labor

『予測マシンの経済学』をもっと深く楽しむ

Judgment: The Human Advantage

Taming Complexity Through Prediction

聴きながら、すべての言葉を目で追えます

When to Fully Automate Decisions

Strategic Implementation of AI

『予測マシンの経済学』をもっと深く楽しむ

Reshaping Business Strategy Through AI

Societal Implications and Trade-offs

聴きながら、すべての言葉を目で追えます

The Economics of Our AI Future

『予測マシンの経済学』を最後まで読み終えました

『予測マシンの経済学』の主要テーマ

『予測マシンの経済学』の名言

『予測マシンの経済学』の登場人物

著者について

予測マシンの経済学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 Prediction Machines by Ajay Agrawal about?

Who should read Prediction Machines?

Is Prediction Machines worth reading?

How does AI reduce the cost of prediction?

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。

When Prediction Becomes Cheap, Everything Changes

The Economics of Artificial Intelligence

聴きながら、すべての言葉を目で追えます

From Data to Intelligence

The New Division of Labor

『予測マシンの経済学』をもっと深く楽しむ

Judgment: The Human Advantage

Taming Complexity Through Prediction

聴きながら、すべての言葉を目で追えます

When to Fully Automate Decisions

Strategic Implementation of AI

『予測マシンの経済学』をもっと深く楽しむ

Reshaping Business Strategy Through AI

Societal Implications and Trade-offs

聴きながら、すべての言葉を目で追えます

The Economics of Our AI Future

『予測マシンの経済学』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

『予測マシンの経済学』の概要