Kick off your book project in 2 hours, get started with GhostAI in 2 hours, or do both! Free live workshops, on Zoom. You’ll leave with a real book project and a clear plan to keep going. Saturday, June 27, 2026.

Leanpub Header

Skip to main content
Go to Leanpub.com

The Rise of Computing Power

A Brief History

Stardust

Minimum price

$30.00

$34.00

You pay

Author earns

$

Also available for 2 book credits with a Reader Membership

PDF
EPUB
WEB
About

About

About the Book

In 1946, a machine the size of a tennis court could perform 500 calculations per second. Today, a single chip can do a billion times more. How did we get here—and why does it matter?

The Rise of Computing Power tells the story of the most transformative technology of our time. From the first electronic computers to the AI revolution, this book explores how computing power shaped our world—and who controls it.

What you'll learn:

  • How a 30-ton computer evolved into chips with billions of transistors
  • Why NVIDIA, not Intel, became the most valuable company in the world
  • How the AI boom created a global race for chips
  • The rise of China's semiconductor industry—and the geopolitics of technology
  • What's next: quantum computing, edge AI, and the post-Moore's Law era

Why this book matters:

Computing power is the new oil. It powers everything from your smartphone to military systems. Understanding its history isn't just for engineers—it's for anyone who wants to understand the modern world.

Whether you're an investor, a tech professional, or simply curious, this book will give you the context you need to make sense of the AI revolution.

Share this book

Categories

Computers and ProgrammingArtificial Intelligence

Feedback

Email the Author

Author

About the Author

Stardust

**Stardust** is a Chinese technology analyst and writer focused on the intersection of computing, artificial intelligence, and geopolitics. This book grew from a conviction that the story of computing power — from ENIAC to Tensor Processing Units — is the most consequential technological narrative of our time, yet one that is rarely told with a Chinese perspective.

The author\'s writing draws on decades of technology industry research, with a particular focus on the semiconductor supply chain, AI infrastructure economics, and the geopolitics of computation. This is not a book written from the inside of the tech industry, but from a position of observation and analysis — asking not just "what happened" but "why it matters" and "who benefits."

Contents

Table of Contents

Chapter 1: What is Computing Power?

  1. 1.0 Introduction
  2. 1.1 Defining Computing Power
  3. 1.2 Not All FLOPS Are Created Equal
  4. 1.3 Peak vs. Real-World Performance
  5. 1.4 General-Purpose vs. Specialized Computing
  6. 1.5 A Journey Through Scales: From ENIAC to Modern AI
  7. 1.6 The Five Dimensions of Computing Power
  8. 1.7 Why This Book Matters
  9. 1.8 The Economics of Compute: What AI Training Actually Costs
  10. 1.9 The Benchmarking War: MLPerf and the Limits of Performance Metrics
  11. Key Takeaways
  12. What We Learned
  13. References and Further Reading

Chapter 2: The Evolution of Computing (1940s-1990s)

  1. 2.0 Introduction
  2. 2.1 ENIAC: The Beginning of Everything
  3. 2.2 The Transistor Revolution
  4. 2.3 The Supercomputer Era
  5. 2.4 The Personal Computer Revolution
  6. 2.5 The 1990s: The Internet and the Client-Server Era
  7. 2.6 The Hidden Thread: Path Dependencies That Shaped Today
  8. Key Takeaways
  9. What We Learned
  10. References and Further Reading

Chapter 3: The Internet Age and Early Data Centers

  1. 3.0 Introduction
  2. 3.1 The Dot-Com Boom and the Data Center Explosion
  3. 3.2 The Architecture of Early Data Centers
  4. 3.3 The Birth of Colocation and Web Hosting
  5. 3.4 VMware and the Virtualization Revolution
  6. 3.5 Amazon: From Online Bookstore to Cloud Pioneer
  7. 3.6 Google and the Cluster Computing Model
  8. 3.7 Microsoft’s Late Arrival
  9. 3.8 What We Learned
  10. 3.9 The OpenStack and Cloud-Native Revolution
  11. 3.10 The Colocation Industry’s Evolution
  12. 3.11 Data Center Power Density Evolution
  13. Key Takeaways
  14. References and Further Reading

Chapter 4: NVIDIA’s Rise

  1. 4.0 Introduction
  2. 4.1 The Early Years: Surviving the Graphics Wars
  3. 4.2 The CUDA Bet
  4. 4.3 The Deep Learning Trigger
  5. 4.4 The Financial Transformation
  6. 4.5 The Road to Blackwell
  7. 4.6 Beyond Hardware: The Full-Stack Vision
  8. 4.7 What We Learned
  9. 4.8 The Mellanox Acquisition: Why Networking Became a Strategic Necessity
  10. 4.9 DGX and the Turnkey AI Solution
  11. 4.10 The Geopolitical Twist: Export Controls and the China Dilemma
  12. Key Takeaways
  13. References and Further Reading

Chapter 5: The CUDA Ecosystem

  1. 5.0 Introduction
  2. 5.1 The CUDA Software Stack: Architecture of Dominance
  3. 5.2 cuDNN: The Deep Learning Standard
  4. 5.3 TensorRT: The Inference Engine
  5. 5.4 The Developer Ecosystem: Network Effects in Practice
  6. 5.5 The Competitive Moat: Why Rivals Can’t Catch Up
  7. 5.6 The Counterpoint: OpenCL, SYCL, and the Failed Attempts at Portability
  8. 5.7 ROCm: AMD’s Open-Source Alternative—and Why It’s Struggling
  9. 5.8 The Cost of Vendor Lock-In
  10. 5.9 What We Learned
  11. Key Takeaways
  12. References and Further Reading

Chapter 6: AMD’s Comeback

  1. 6.0 Introduction
  2. 6.1 AMD’s Long History: From #2 to Nearly Dead
  3. 6.2 The Lisa Su Turnaround: Corporate Survival Class
  4. 6.3 The GPU Journey: From GCN to CDNA
  5. 6.4 The Chiplet Revolution: AMD’s Secret Weapon
  6. 6.5 Why AMD Still Lags: The Three Limitations
  7. 6.6 The MI400 Strategy and the Path Forward
  8. 6.7 What We Learned
  9. Key Takeaways
  10. References and Further Reading

Chapter 7: Intel’s Struggle

  1. 7.0 Introduction
  2. 7.1 Intel’s Missed Opportunities: A History of Near-Misses
  3. 7.2 The Silent Success: Xeon for AI Inference
  4. 7.3 The Xe Architecture: Another Almost
  5. 7.4 Gaudi: The AI Accelerator That Might Actually Work
  6. 7.5 Why Intel Struggles: The Four Root Causes
  7. 7.6 The Pat Gelsinger Pivot: IDM 2.0
  8. 7.7 Intel’s AI Future: Three Scenarios
  9. 7.8 What We Learned
  10. Key Takeaways
  11. References and Further Reading

Chapter 8: Cloud Giants’ Custom Chips

  1. 8.0 Introduction
  2. 8.1 Why Cloud Providers Built Their Own Chips
  3. 8.2 Google: The Pioneer—How TPUs Shaped AI
  4. 8.3 Amazon: The Pragmatist—Building Chips to Sell
  5. 8.4 Microsoft: The Late Arriver—and Why It Matters
  6. 8.5 Other Players: Meta, OpenAI, and the Custom Chip Tipping Point
  7. 8.6 The Competitive Dynamics: “Coopetition” with NVIDIA
  8. 8.7 What We Learned
  9. Key Takeaways
  10. References and Further Reading

Chapter 9: AWS — Amazon’s Computing Empire

  1. 9.0 Introduction: The Core Argument
  2. 9.1 The Birth of Cloud Computing: Why Amazon?
  3. 9.2 AWS Growth Story: The Flywheel in Action
  4. 9.3 AWS Architecture: The Nitro Revolution
  5. 9.4 The Custom Silicon Strategy: Building the Stack
  6. 9.5 Why AWS Dominates: The Moat and Its Weaknesses
  7. 9.6 AWS’s AI Strategy: Bedrock, SageMaker, and the GPU Gamble
  8. 9.7 The Future: Challenges and Opportunities
  9. 9.8 What We Learned
  10. Key Takeaways
  11. References and Further Reading

Chapter 10: Microsoft Azure & Google Cloud

  1. 10.0 Introduction: The Core Argument
  2. 10.1 Microsoft Azure: The Enterprise Giant Awakens
  3. 10.2 Google Cloud: The AI Specialist
  4. 10.3 The Cloud AI Arms Race
  5. 10.4 The Fourth Player: Alibaba Cloud
  6. 10.6 What We Learned
  7. Key Takeaways
  8. References and Further Reading

Chapter 11: The AI Explosion

  1. 11.0 Introduction: The Core Argument
  2. 11.1 The Deep Learning Renaissance (2012-2017)
  3. 11.2 The Transformer Revolution (2017)
  4. 11.3 The Scaling Laws (2020)
  5. 11.4 The Foundation Model Era (2020-2022)
  6. 11.5 The ChatGPT Moment (2022)
  7. 11.6 Training vs. Inference: The Economics Divide
  8. 11.7 The Open-Source AI Movement
  9. 11.8 The Regulatory Landscape
  10. 11.9 The Future: Where Is AI Compute Going?
  11. 11.10 What We Learned
  12. Key Takeaways
  13. References and Further Reading

Chapter 12: Huawei Ascend — The Sanctions and the Comeback

  1. 12.0 Introduction
  2. 12.1 The Rise of HiSilicon (2004–2019)
  3. 12.2 The Ascend Chips: China’s AI Compute Ambition
  4. 12.3 The Entity List: A Devastating Blow
  5. 12.4 The Software Breakthrough: HarmonyOS and the Kirin 9000S
  6. 12.5 Ascend Reborn: The 910B, 920, and Beyond
  7. 12.6 The Road Ahead: Constraints and Uncertainties
  8. 12.7 What We Learned
  9. Key Takeaways
  10. References and Further Reading

Chapter 13: Chinese Chip Makers — The Challengers Beneath the Export Controls

  1. 13.0 Introduction
  2. 13.1 Cambricon: The AI Chip Pioneer That Struggled to Scale
  3. 13.2 Hygon: China’s x86 Alternative
  4. 13.3 JingJia Micro: The GPU Dream
  5. 13.4 BIRENTECH: The $1 Billion Bet
  6. 13.5 MetaX: The AMD Connection
  7. 13.6 Enflame: The Anchor Customer Model
  8. 13.7 Moore Threads and Others
  9. 13.8 The Supply Chain Reality
  10. 13.9 What We Learned
  11. Key Takeaways
  12. References and Further Reading

Chapter 14: Chinese Supercomputers — The Rise, Dominance, and Strategic Silence

  1. 14.0 Introduction
  2. 14.1 The Foundations: China’s Supercomputing Origins
  3. 14.2 Tianhe Series: The Rise and the Sanctions Wake-Up
  4. 14.3 Sunway TaihuLight: The Pinnacle of Domestic Design
  5. 14.4 The Strategic Silence (2018–Present)
  6. 14.5 The Exascale Race (Without the Publicity)
  7. 14.6 The Military Connection
  8. 14.7 Supercomputing vs. AI: Two Converging Worlds
  9. 14.8 The Quantum Alternative
  10. 14.9 What We Learned
  11. Key Takeaways
  12. References and Further Reading

Chapter 15: Moore’s Law Is Dead? — The End of Silicon Scaling and What Comes Next

  1. 15.0 Introduction
  2. 15.1 The Physics of Transistor Scaling — Why Smaller Is Harder
  3. 15.2 EUV Lithography — ASML’s Monopoly and the Gatekeeper of Advanced Chips
  4. 15.3 The Economics of Scaling — Why Smaller No Longer Means Cheaper
  5. 15.4 Chiplets — The Modular Revolution
  6. 15.5 Advanced Packaging Economics — The New Critical Path
  7. 15.6 Beyond Silicon — What’s Next After Moore’s Law?
  8. Key Takeaways
  9. What We Learned
  10. References and Further Reading

Chapter 16: Quantum Computing

  1. 16.0 Introduction
  2. 16.1 Quantum Computing Basics
  3. 16.2 The Qubit Technology Landscape
  4. 16.3 Quantum Error Correction: The Heart of the Challenge
  5. 16.4 The NISQ Era and Beyond
  6. 16.5 The Cryptography Threat
  7. 16.6 China’s Quantum Program: A Detailed Assessment
  8. 16.7 The Investment Landscape
  9. 16.8 What We Learned
  10. Key Takeaways
  11. References and Further Reading

Chapter 17: The Energy Crisis

  1. 17.0 Introduction
  2. 17.1 The AI Energy Problem
  3. 17.2 Data Center Power Consumption
  4. 17.3 Cooling: The Hidden Challenge
  5. 17.4 Data Center Location Strategy
  6. 17.5 Green Computing: Renewable and Low-Carbon Solutions
  7. 17.6 Nuclear Power for AI: The New Frontier
  8. 17.7 The Policy Response
  9. 17.8 The AI Energy Debate: Estimates and Controversies
  10. 17.9 What We Learned
  11. Key Takeaways
  12. References and Further Reading

Chapter 18: Edge Computing

  1. 18.0 Introduction
  2. 18.1 What Is Edge Computing?
  3. 18.2 The Rise of the NPU
  4. 18.3 AI PCs: The New Hardware Category
  5. 18.4 Automotive Computing: The Demanding Edge
  6. 18.5 TinyML and the Internet of Intelligent Things
  7. 18.6 Hybrid Architectures: The Real Future
  8. 18.7 Challenges and Counterpoints
  9. 18.8 What We Learned
  10. Key Takeaways
  11. References and Further Reading

Chapter 19: The Computing Power Economy

  1. 19.0 Introduction
  2. 19.1 Computing Power as Economic Infrastructure
  3. 19.2 Supply Chain Concentration: The Hidden Risk
  4. 19.3 The US-CHIPS Act and Global Responses
  5. 19.4 The US-China Chip War: Geopolitical Dynamics
  6. 19.5 Cloud Market Dynamics
  7. 19.6 Computing as a Utility: The Next Frontier
  8. 19.7 The Talent and Knowledge Economy
  9. 19.8 The Macroeconomic Impact
  10. 19.9 What We Learned
  11. Key Takeaways
  12. References and Further Reading

The Leanpub 60 Day 100% Happiness Guarantee

Within 60 days of purchase you can get a 100% refund on any Leanpub purchase, in two clicks.

See full terms...

Earn $8 on a $10 Purchase, and $16 on a $20 Purchase

We pay 80% royalties on purchases of $7.99 or more, and 80% royalties minus a 50 cent flat fee on purchases between $0.99 and $7.98. You earn $8 on a $10 sale, and $16 on a $20 sale. So, if we sell 5000 non-refunded copies of your book for $20, you'll earn $80,000.

(Yes, some authors have already earned much more than that on Leanpub.)

In fact, authors have earned over $15 million writing, publishing and selling on Leanpub.

Learn more about writing on Leanpub

Free Updates. DRM Free.

If you buy a Leanpub book, you get free updates for as long as the author updates the book! Many authors use Leanpub to publish their books in-progress, while they are writing them. All readers get free updates, regardless of when they bought the book or how much they paid (including free).

Most Leanpub books are available in PDF (for computers) and EPUB (for phones, tablets and Kindle). The formats that a book includes are shown at the top right corner of this page.

Finally, Leanpub books don't have any DRM copy-protection nonsense, so you can easily read them on any supported device.

Learn more about Leanpub's ebook formats and where to read them

Write and Publish on Leanpub

You can use Leanpub to easily write, publish and sell in-progress and completed ebooks and online courses!

Leanpub is a powerful platform for serious authors, combining a simple, elegant writing and publishing workflow with a store focused on selling in-progress ebooks.

Leanpub is a magical typewriter for authors: just write in plain text, and to publish your ebook, just click a button. (Or, if you are producing your ebook your own way, you can even upload your own PDF and/or EPUB files and then publish with one click!) It really is that easy.

Learn more about writing on Leanpub