Knowledge Systems

Book Review: The Infinite Alphabet: And the Laws of Knowledge

Author: César A. Hidalgo
Publisher: Allen Lane / Penguin Random House
Publication Date: 2025
Genre: Economics, Science, Complexity Theory, Business

About the Author

César A. Hidalgo is a Chilean-Spanish-American physicist, professor, and author known for pioneering work in economic complexity, data visualization, and applied artificial intelligence. His career highlights include:

MIT Collective Learning Group: Led this research group for nine years (2010-2019)
Center for Collective Learning: Founded this international research laboratory with offices at the Toulouse School of Economics and Corvinus University of Budapest
Harvard Kennedy School: Former research fellow
Datawheel: Co-founder of this award-winning company specializing in public data distribution and economic development strategy
2018 Lagrange Prize: Sole recipient, recognizing excellence and innovation in the study of complex systems
Three Webby Awards: For his work in data visualization
TED Talk: His talk on augmented democracy has been viewed over two million times

Previous Books

Why Information Grows (Basic Books/Allen Lane, 2015)
The Atlas of Economic Complexity (MIT Press, 2014, co-authored)
How Humans Judge Machines (MIT Press, 2021, co-authored)

Book Overview

The Central Question

What is knowledge, and why is it so hard to move around?

Most people intuitively believe that knowledge is simply information—something you can write down, upload to a database, or train an AI on. Hidalgo argues this understanding is fundamentally wrong. Knowledge isn't a thing you can copy and paste. It's more like a living organism that needs the right environment, the right people, and constant exercise to survive.

The Core Thesis

Knowledge shapes the fate of businesses and the growth of nations, but few of us understand the principles that govern its motion. Just as temperature and gravity follow predictable rules in physics, knowledge follows its own set of laws. These laws explain:

Why some innovation hubs thrive while others fail
Why certain technologies grow exponentially while others are forgotten
Why countries develop at different rates
Why organizations succeed or fail at adaptation

The "Infinite Alphabet" Metaphor

Hidalgo uses the metaphor of an "infinite alphabet" to describe the nature of knowledge and economic complexity. Every skill, industry, and capability represents a "letter" in this alphabet. The more letters a society possesses, the more "words" (products, services, innovations) it can create. This framework allows researchers to predict which countries and regions will grow based on the diversity and sophistication of their existing capabilities.

The Three Laws of Knowledge

Hidalgo organizes his framework around three fundamental laws that govern how knowledge behaves:

Law 1: The Law of Time (How Knowledge Grows)

This law describes the temporal dynamics of knowledge accumulation and has three key components:

Thurstone's Law: Individual Learning Curves

At the level of individuals, teams, or firms, knowledge grows following a power function—roughly like a square root. This means:

Learning is rapid at first
Growth then saturates and plateaus
Every unit has a finite "carrying capacity" for knowledge
Eventually, you must double your entire history of output just to gain one more unit of learning

Moore's Law: Societal Knowledge Growth

While individual learning is finite, society's knowledge appears infinite because of:

The constant renewal of teams and organizations
New entrants replacing incumbents on the flat part of their learning curves
Fresh perspectives bringing new approaches to problems
Market dynamics that allow for creative destruction

The Importance of Market Renewal

This law reveals why open, competitive markets matter for knowledge growth. Societies need mechanisms that allow new players to emerge when established ones reach their learning plateaus. Without renewal, economies get stuck on the flat part of learning curves.

Law 2: The Law of Space (How Knowledge Spreads)

This law addresses the geographic and social diffusion of knowledge, revealing why knowledge transfer is far more difficult than commonly assumed.

Knowledge is Embodied

Knowledge doesn't exist abstractly—it lives in:

Individual minds and muscle memory
Team dynamics and organizational cultures
Institutional practices and routines
Networks of relationships and trust

You cannot simply extract knowledge from one context and transplant it to another without the people and structures that embody it.

Intergenerational Transfer

One of the most striking findings is that knowledge diffusion mediated by migrants tends to be intergenerational. When experts move to new locations:

Their immediate impact may be limited
The real adoption of their ideas happens in the next generation
Younger people who grow up around the new knowledge internalize it more fully
This explains why knowledge transfer takes decades, not years

The Samuel Slater Example

Samuel Slater, a 21-year-old British textile worker, memorized the designs of English spinning mills and brought this knowledge to America in 1789. But the generation that truly drove American industrialization wasn't Slater's contemporaries—it was those who grew up in the environment he created.

Geographic Stickiness

Knowledge clusters in specific places because:

Tacit knowledge requires face-to-face interaction
Learning happens through apprenticeship and observation
Trust networks take time to build
Supporting infrastructure and institutions develop locally

Law 3: The Law of Value (How Knowledge Creates Economic Worth)

This law draws on Hidalgo's pioneering research in economic complexity to measure and predict the economic value of knowledge.

Economic Complexity Index

Countries and regions can be measured by their "economic complexity"—the diversity and sophistication of what they produce. This index predicts:

Future economic growth rates
Which industries a country can successfully enter
Where competitive advantages lie
What development paths are feasible

The Principle of Relatedness

New capabilities emerge most easily when they're related to existing ones. A country that makes aircraft engines can more easily move into automobile engines than into textile production. This principle explains:

Why development is path-dependent
Why "leapfrogging" is rare
Why building knowledge takes time
Why some development projects fail despite massive investment

Measuring Knowledge Potential

Hidalgo's framework allows researchers to look at what a country exports and infer the underlying knowledge base. Complex products (like machinery, electronics, and pharmaceuticals) require more sophisticated knowledge networks than simple products (like raw materials or basic textiles).

Key Case Studies and Stories

Ecuador's Failed "City of Knowledge"

One of the book's central cautionary tales involves Ecuador's ambitious attempt to build a knowledge economy from scratch. The project failed because:

Physical infrastructure alone doesn't create knowledge
You can't import expertise without the social systems that sustain it
Knowledge requires organic growth, not top-down planning
Institutions and culture matter as much as buildings and equipment

Italy's Aircraft-to-Scooter Transition

After World War II, Italian aircraft manufacturers pivoted to producing scooters (like the Vespa). This transition illustrates:

How knowledge transfers between related industries
The importance of existing capabilities in enabling new ventures
Why "relatedness" matters in economic development
How war and disruption can redirect knowledge into new channels

Samuel Slater and American Industrialization

The story of Samuel Slater demonstrates:

How individual migrants can transfer crucial knowledge
Why intergenerational effects matter more than immediate impacts
The importance of tacit knowledge that can't be written down
How a single person can catalyze an industrial revolution

China's Innovation Economy

The book examines how China built its innovation capacity through:

Strategic acquisition of foreign knowledge
Investment in education and training
Development of indigenous research capabilities
Creation of innovation hubs like Zhongguancun in Beijing

Silicon Valley's Success

Hidalgo explains why Silicon Valley became the world's premier innovation hub:

Historical accidents (like Shockley Semiconductor's location)
The "traitorous eight" who left to form Fairchild Semiconductor
Network effects among entrepreneurs and investors
Universities providing talent and research
A culture that tolerates and learns from failure

The Polaroid Paradox

The book asks why we forgot how to make Polaroid instant film, illustrating:

Knowledge decay when capabilities aren't actively used
The importance of continuous practice and transmission
How supply chains and expertise can disappear together
Why knowledge preservation requires ongoing effort

Netflix vs. Blockbuster

This case study demonstrates "architectural innovation"—the idea that seemingly small changes (like shipping DVDs directly to customers) can require completely different organizational structures. Blockbuster failed not because it didn't see the threat, but because its existing knowledge architecture couldn't adapt.

Bell Labs and the Transistor

The book traces how knowledge flowed from Bell Labs through Shockley Semiconductor to Fairchild and eventually to the entire semiconductor industry, showing:

How knowledge spreads through people, not documents
The importance of "knowledge spillovers" in innovation
Why monopolies can sometimes foster and sometimes hinder innovation
How organizational culture shapes knowledge creation

German Chemists in America

When German chemists were expelled during World War II and moved to the United States, the people who truly adopted their ideas and technologies were from the next generation—not their immediate colleagues. This reinforces the intergenerational nature of knowledge transfer.

Key Concepts and Principles

Knowledge vs. Information

Information	Knowledge
Can be codified and transmitted	Embodied in people and organizations
Static and storable	Dynamic and living
Easily copied	Difficult to transfer
Context-independent	Context-dependent
Instant transmission	Intergenerational transmission

Tacit vs. Explicit Knowledge

Explicit Knowledge: Can be written down, coded, and transmitted (recipes, formulas, procedures)
Tacit Knowledge: Learned through practice and experience, difficult to articulate (riding a bike, reading social cues, intuitive judgment)

Most valuable knowledge is tacit, which is why it's so hard to transfer.

Learning Curves and Experience Curves

Organizations improve at tasks the more they perform them. This creates:

Early advantages that compound over time
Barriers to entry for newcomers
Path dependencies that shape industry structure
Opportunities for disruption when learning curves reset

Architectural Innovation

Small changes in how components fit together can require entirely new organizational structures. This explains why incumbent firms often fail when facing seemingly minor innovations—their existing "architecture" of knowledge and organization can't adapt.

The Product Space

Hidalgo's research visualizes the global economy as a network of products, where:

Related products cluster together
Countries move through the space along paths of relatedness
Some positions offer more opportunities for growth than others
Development involves strategic navigation through this space

Knowledge Decay

Knowledge that isn't actively used decays over time through:

Retirement and death of practitioners
Organizational restructuring
Supply chain disruption
Technological obsolescence
Cultural and institutional forgetting

Implications and Applications

For Economic Development

Development is not just about capital investment
Knowledge must be grown organically, not imported wholesale
Policy should focus on building capabilities incrementally
Relatedness matters—start from what you already know
Education and training are necessary but not sufficient

For Business Strategy

Competitive advantage comes from unique knowledge combinations
Acquisitions often fail because knowledge doesn't transfer easily
Organizational culture is a knowledge system
Innovation requires balancing exploration and exploitation
Markets need room for creative destruction

For Innovation Policy

Innovation hubs emerge organically from existing capabilities
Top-down planning often fails
Universities and industry need close connections
Migration policy affects knowledge flows
Patience is required—effects are intergenerational

For Education

Learning has natural limits and plateaus
Practice and experience matter more than instruction
Knowledge is social and collaborative
Mentorship and apprenticeship are crucial
Renewal and fresh perspectives prevent stagnation

For Understanding AI and Automation

The book challenges assumptions about AI "copying" human knowledge:

AI can capture explicit knowledge but struggles with tacit knowledge
Embodied expertise requires physical practice
Social and contextual knowledge is difficult to codify
AI may accelerate some knowledge transfer but not replace human learning

Connection to Systems Thinking

Hidalgo's framework reveals knowledge dynamics as complex systems with:

Reinforcing Loops

Success in knowledge accumulation attracts more talent and resources
Innovation hubs become more attractive as they grow
Expertise breeds more expertise through spillovers and mentorship

Balancing Loops

Individual and team learning saturates over time
Markets correct through creative destruction
Resource constraints limit growth

Delays

Knowledge transfer is intergenerational, not immediate
Learning curves take years to climb
Institutional change is slow

Leverage Points

Migration policy affects knowledge flows
Education systems shape future capabilities
Market structure determines renewal rates
Culture influences learning and sharing

Emergent Properties

Innovation emerges from combinations of existing knowledge
Economic complexity arises from interactions, not planning
Clusters develop organically from individual decisions

Critical Reception and Perspectives

Strengths

Brings scientific rigor to understanding knowledge dynamics
Rich with fascinating case studies and stories
Practical implications for policy and business
Builds on decades of rigorous research
Accessible writing style for complex topics

Limitations

As one reviewer noted, the case studies may be too unique to provide simple, transferable lessons
Every situation requires dealing with specific circumstances
The framework helps understand the past but may not perfectly predict the future
Some readers may want more actionable recommendations

Comparisons to Previous Work

The book extends themes from Hidalgo's earlier Why Information Grows, which explored why economic growth occurs in only a few places. The Infinite Alphabet goes deeper into the mechanisms of knowledge creation, transfer, and decay.

Key Takeaways

Knowledge is not information: You cannot simply download expertise or write it in a manual.
Knowledge follows laws: Like physics, the dynamics of knowledge can be understood scientifically.
Learning has limits: Individuals and organizations plateau; society advances through renewal.
Transfer is intergenerational: Real knowledge adoption takes a generation, not a presentation.
Geography matters: Knowledge sticks to places because it lives in people and institutions.
Complexity predicts growth: The diversity of capabilities determines future possibilities.
Knowledge decays: Without active use and transmission, expertise disappears.
Relatedness guides development: New capabilities grow from existing ones.
Architecture matters: Organizational structure shapes what knowledge can be used.
Patience is essential: Building knowledge economies takes decades, not years.

Glossary

Economic Complexity Index (ECI): A measure of the productive knowledge embedded in a country's economy, based on the diversity and sophistication of its exports.

Experience Curve: The empirical observation that unit costs decrease as cumulative production increases.

Learning Curve: The rate at which performance improves with experience.

Product Space: A network visualization of global trade showing how products relate to each other based on required capabilities.

Tacit Knowledge: Knowledge that is difficult to articulate or codify, typically learned through practice and experience.

Thurstone's Law: The principle that individual learning follows a power function, growing quickly at first then saturating.

Moore's Law: The observation that computing power doubles approximately every two years; Hidalgo uses this as an example of societal-level knowledge growth.

Architectural Innovation: Changes in how components of a system fit together, even when the components themselves don't change dramatically.

Principle of Relatedness: The tendency for new capabilities to emerge from existing, related capabilities.

Knowledge Spillovers: The unintentional transmission of knowledge between organizations or individuals, often through proximity or social networks.

This summary is based on publicly available descriptions, reviews, and interviews about The Infinite Alphabet. For the complete arguments, evidence, and nuance, readers are encouraged to obtain the full book.