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Magic That Isn't Magic
“Engineering is the closest thing to magic that exists in this world,”
Elon Musk
We see so many miracles of engineering across our life, that we barely notice them anymore. Voice travels instantly across oceans through glass fibers thinner than hair. Satellites tell us our exact location anywhere on Earth. Machines smaller than a deck of cards hold the entirety of human knowledge.
Describe today’s world to someone from 300 years ago, and you’d be burned at stake for witchcraft. Describe it to someone from 200 years ago, and they’d be civilized enough to lock you up in an insane asylum for life. A grandparent in their 90’s, whose formative years might not have included electricity, running water or the radio would still find it unbelievable. And someone from 50 years ago would accuse you of plagiarizing Star Trek. But today? People have to plan extended breaks away from their phones and Internet devices, and hotels might as well not bother with bathrooms if they’re not offering free WiFi.
Over the past 3-4 years, we are seeing a different kind of magic, the era of AI. Artificial Intelligence represents a different kind of magic altogether. Type something into ChatGPT, or Claude or Grok, and it instantly spits out an answer. From completing homework assignments, to helping make travel plans or doing complex market research or even generating video on demand, today’s AI seems to know and do everything. The most unbelievable thing in all of this is that we did not have this capability 3 years ago. That is less than the length of a bachelors program in engineering :). Yet, today AI is found everywhere and touching every aspect of our lives.
Let’s rewind to November 30, 2022. A date that will go down in technology history. On that seemingly ordinary Thursday, OpenAI quietly released ChatGPT to the public. No massive marketing campaign. No celebrity endorsements. Just a simple announcement and a basic chat interface. What happened next stunned even the creators.
Within five days, a million people had signed up. Within two months, 100 million. People were having conversations with a computer that felt genuinely intelligent. They were asking it to explain quantum physics, write poetry, debug code, plan vacations, and create bedtime stories. And it was delivering—not with pre-programmed responses, but with original, contextual, surprisingly human-like answers.
The world had changed overnight. Most of us just didn’t realize how much yet.
Below illustration puts this in perspective, how long it took for ChatGPT to get a 100 million users versus other technology that we have taken for granted. History’s major technologies have followed predictable adoption patterns. The telephone needed 75 years to reach 100 million users. Television took 22 years. Mobile phones took 16 years. The World Wide Web took 7 years. Facebook took 4.5 years. ChatGPT? Two months. (That’s right, ChatGPT reached 100 million users in January 2023)
To truly grasp the unprecedented speed of ChatGPT’s adoption, let’s look at the actual rate of user acquisition. During its 75-year journey to 100 million users, the telephone added about one new user every 25 seconds—a respectable 3,653 users per day. Television picked up the pace at 12,456 users daily. The World Wide Web accelerated to 39,139 users per day. Facebook pushed it to 60,901 daily users. Then came ChatGPT, which added 1.67 million users every single day—that’s 19 users every second, 24/7, for two straight months. To put this in perspective: ChatGPT gained more users in one hour (69,444) than the telephone added in 19 days. In the time it takes you to read this sentence, ChatGPT has already added another 77 users. This isn’t just growth—it’s an explosion that makes all previous adoption curves look like flat lines. We’re not witnessing a gradual acceleration anymore; we’re seeing a vertical takeoff that breaks every model of technology adoption we’ve ever known.
The revolution goes beyond just user numbers. GitHub Copilot, an AI programming assistant, now generates almost 50% of all new code written on its platform. (Real statistic from GitHub’s 2024 reports) Marketing departments produce in hours what once took weeks. Students learn complex subjects with personalized AI tutors that never lose patience. Doctors consult AI systems that have absorbed every medical journal ever published.
Yet this “overnight success” is actually the culmination of a century-long journey that most people never saw coming.
Describe today’s world to someone from 300 years ago, and you’d be burned at stake for witchcraft. Describe it to someone from 200 years ago, and they’d be civilized enough to lock you up in an insane asylum for life. A grandparent in their 90’s, whose formative years might not have included electricity, running water or the radio would still find it unbelievable. And someone from 50 years ago would accuse you of plagiarizing Star Trek. But today? People have to plan extended breaks away from their phones and Internet devices, and hotels might as well not bother with bathrooms if they’re not offering free WiFi.
Over the past 3-4 years, we are seeing a different kind of magic, the era of AI. Artificial Intelligence represents a different kind of magic altogether. Type something into ChatGPT, or Claude or Grok, and it instantly spits out an answer. From completing homework assignments, to helping make travel plans or doing complex market research or even generating video on demand, today’s AI seems to know and do everything. The most unbelievable thing in all of this is that we did not have this capability 3 years ago. That is less than the length of a bachelors program in engineering :). Yet, today AI is found everywhere and touching every aspect of our lives.
Let’s rewind to November 30, 2022. A date that will go down in technology history. On that seemingly ordinary Thursday, OpenAI quietly released ChatGPT to the public. No massive marketing campaign. No celebrity endorsements. Just a simple announcement and a basic chat interface. What happened next stunned even the creators.
Within five days, a million people had signed up. Within two months, 100 million. People were having conversations with a computer that felt genuinely intelligent. They were asking it to explain quantum physics, write poetry, debug code, plan vacations, and create bedtime stories. And it was delivering—not with pre-programmed responses, but with original, contextual, surprisingly human-like answers.
The world had changed overnight. Most of us just didn’t realize how much yet.
Below illustration puts this in perspective, how long it took for ChatGPT to get a 100 million users versus other technology that we have taken for granted. History’s major technologies have followed predictable adoption patterns. The telephone needed 75 years to reach 100 million users. Television took 22 years. Mobile phones took 16 years. The World Wide Web took 7 years. Facebook took 4.5 years. ChatGPT? Two months. (That’s right, ChatGPT reached 100 million users in January 2023)
To truly grasp the unprecedented speed of ChatGPT’s adoption, let’s look at the actual rate of user acquisition. During its 75-year journey to 100 million users, the telephone added about one new user every 25 seconds—a respectable 3,653 users per day. Television picked up the pace at 12,456 users daily. The World Wide Web accelerated to 39,139 users per day. Facebook pushed it to 60,901 daily users. Then came ChatGPT, which added 1.67 million users every single day—that’s 19 users every second, 24/7, for two straight months. To put this in perspective: ChatGPT gained more users in one hour (69,444) than the telephone added in 19 days. In the time it takes you to read this sentence, ChatGPT has already added another 77 users. This isn’t just growth—it’s an explosion that makes all previous adoption curves look like flat lines. We’re not witnessing a gradual acceleration anymore; we’re seeing a vertical takeoff that breaks every model of technology adoption we’ve ever known.
The revolution goes beyond just user numbers. GitHub Copilot, an AI programming assistant, now generates almost 50% of all new code written on its platform. (Real statistic from GitHub’s 2024 reports) Marketing departments produce in hours what once took weeks. Students learn complex subjects with personalized AI tutors that never lose patience. Doctors consult AI systems that have absorbed every medical journal ever published.
Yet this “overnight success” is actually the culmination of a century-long journey that most people never saw coming.
The Hidden Story Behind the Headlines
Most AI coverage focuses on the current moment—the most up-to-date model, the current breakthrough, the latest controversy. It’s like watching the final scene of a movie and trying to recreate the plot.
This book tells the fuller story—the century-long journey that brought us here. Because AI didn’t emerge from a Silicon Valley garage last year. It’s the culmination of humanity’s greatest collaborative achievement.
We will meet Alan Turing, who imagined thinking machines while helping defeat the Nazis. We will discover how video game graphics cards accidentally became AI’s engine. We will also learn why a competition to identify cats in photos changed the trajectory of human history.
Each chapter stands alone—perfect for a coffee break read. No prerequisites, no required sequence. But together, they reveal how human ingenuity compounds over time, each generation building higher on the foundations others laid.
We also talk about the different sectors where AI is touching our lives. We also discuss how we can equip ourselves not only to survive but also thrive during the revolution.
This book isn’t a manual and certainly does not cover all aspects of AI. The idea is we understand the history and basic concepts well enough to use AI confidently and explain it clearly—whether to our grandmother or our board of directors.
Unlike a magician’s trick that becomes disappointing once you know the secret, AI becomes more impressive the more you understand it. When you learn that humanity literally taught sand to think—that silicon chips are processing information in ways inspired by human neurons—the achievement becomes breathtaking.
This book tells the fuller story—the century-long journey that brought us here. Because AI didn’t emerge from a Silicon Valley garage last year. It’s the culmination of humanity’s greatest collaborative achievement.
We will meet Alan Turing, who imagined thinking machines while helping defeat the Nazis. We will discover how video game graphics cards accidentally became AI’s engine. We will also learn why a competition to identify cats in photos changed the trajectory of human history.
Each chapter stands alone—perfect for a coffee break read. No prerequisites, no required sequence. But together, they reveal how human ingenuity compounds over time, each generation building higher on the foundations others laid.
We also talk about the different sectors where AI is touching our lives. We also discuss how we can equip ourselves not only to survive but also thrive during the revolution.
This book isn’t a manual and certainly does not cover all aspects of AI. The idea is we understand the history and basic concepts well enough to use AI confidently and explain it clearly—whether to our grandmother or our board of directors.
Unlike a magician’s trick that becomes disappointing once you know the secret, AI becomes more impressive the more you understand it. When you learn that humanity literally taught sand to think—that silicon chips are processing information in ways inspired by human neurons—the achievement becomes breathtaking.
So who is this book for
Three types of readers typically pick up books about AI, and most of us embody all three at different times.
The Curious Explorers, who hear about AI everywhere and want to understand what’s real versus hype. They’re not looking for a computer science degree—just clear, honest explanations of what this technology is and why it matters. They want to be informed citizens of the digital age.
The Concerned Professionals, who read headlines about AI replacing jobs and wonder about their own futures. They’re not afraid of technology but want practical insights about what’s coming and how to prepare. Should they be learning new skills? Changing careers? Or is the threat overblown?
The Ambitious Builders see opportunity in disruption. They want to understand AI well enough to harness it—whether that’s starting a business, transforming their current job, or simply staying ahead of the curve. They’re looking for actionable knowledge, not just theory.
This book speaks to all three perspectives because they’re all valid and interconnected. Curiosity leads to understanding, understanding addresses concerns, and confident knowledge enables ambition.
The Curious Explorers, who hear about AI everywhere and want to understand what’s real versus hype. They’re not looking for a computer science degree—just clear, honest explanations of what this technology is and why it matters. They want to be informed citizens of the digital age.
The Concerned Professionals, who read headlines about AI replacing jobs and wonder about their own futures. They’re not afraid of technology but want practical insights about what’s coming and how to prepare. Should they be learning new skills? Changing careers? Or is the threat overblown?
The Ambitious Builders see opportunity in disruption. They want to understand AI well enough to harness it—whether that’s starting a business, transforming their current job, or simply staying ahead of the curve. They’re looking for actionable knowledge, not just theory.
This book speaks to all three perspectives because they’re all valid and interconnected. Curiosity leads to understanding, understanding addresses concerns, and confident knowledge enables ambition.
The Revolution Has Just Begun
There is one thing I would like to highlight here, that most people miss. A lot of my friends already feel they have missed out on the AI wave. We are not at peak AI capability, not even close. We’re standing at base camp, looking at a curve that’s rising vertically to the star.
Consider the internet in 1995. People were excited about email and basic websites. Nobody imagined social media influencers, video calls becoming standard business practice, or entire economies running on platforms that didn’t exist yet. That’s exactly where AI stands today—in the “wow, email is cool” phase.
The next decade will make the last two years look primitive. AI won’t just write emails; it will negotiate contracts. It won’t just diagnose diseases; it will discover new cures. It won’t just drive cars; it will redesign how cities work.
And here’s the opportunity: anyone reading this is early to the party. Embarrassingly early. The people who understand AI today will shape tomorrow’s world.
Hope you enjoy reading this book as much as I enjoyed writing it
Consider the internet in 1995. People were excited about email and basic websites. Nobody imagined social media influencers, video calls becoming standard business practice, or entire economies running on platforms that didn’t exist yet. That’s exactly where AI stands today—in the “wow, email is cool” phase.
The next decade will make the last two years look primitive. AI won’t just write emails; it will negotiate contracts. It won’t just diagnose diseases; it will discover new cures. It won’t just drive cars; it will redesign how cities work.
And here’s the opportunity: anyone reading this is early to the party. Embarrassingly early. The people who understand AI today will shape tomorrow’s world.
Hope you enjoy reading this book as much as I enjoyed writing it
Chapter 1: When Lightning Learned to Count
The Christmas Tree That Could Think
The AI revolution we’re experiencing today is the culmination of numerous breakthroughs, each building upon the last, like layers of a technological pyramid. At the very foundation of this pyramid lies humanity’s mastery of electricity—not just as a source of light and power, but as a medium for computation itself.
February 14, 1946. Valentine’s Day. While couples across America were exchanging chocolates and celebrating victory over the Nazis, a group of exhausted engineers at the University of Pennsylvania prepared to unveil their gift to humanity—a 30-ton electronic brain that glowed like a Christmas tree having an existential crisis.
They called it ENIAC, short for Electronic Numerical Integrator and Computer. By any reasonable measure, the machine was absurd. It weighed as much as four adult elephants, contained 18,000 vacuum tubes each glowing like a desk lamp, and heated the room to a sweltering 120°F. The electricity bill alone would cost $650 per hour in today’s money—roughly the price of a decent laptop that you’d have to throw away every sixty minutes.
Yet when the engineers fired up ENIAC for its first real calculation—computing artillery trajectories for the U.S. Army—something extraordinary happened. The machine solved in 30 seconds what took human “computers” (yes, that was an actual job title), 20 hours to calculate by hand. The engineers stood there, sweating in the oppressive heat like they’d just invented the world’s most expensive sauna, watching 18,000 lights blink in orchestrated patterns. They had just taught lightning to count, and this was just the beginning.
February 14, 1946. Valentine’s Day. While couples across America were exchanging chocolates and celebrating victory over the Nazis, a group of exhausted engineers at the University of Pennsylvania prepared to unveil their gift to humanity—a 30-ton electronic brain that glowed like a Christmas tree having an existential crisis.
They called it ENIAC, short for Electronic Numerical Integrator and Computer. By any reasonable measure, the machine was absurd. It weighed as much as four adult elephants, contained 18,000 vacuum tubes each glowing like a desk lamp, and heated the room to a sweltering 120°F. The electricity bill alone would cost $650 per hour in today’s money—roughly the price of a decent laptop that you’d have to throw away every sixty minutes.
Yet when the engineers fired up ENIAC for its first real calculation—computing artillery trajectories for the U.S. Army—something extraordinary happened. The machine solved in 30 seconds what took human “computers” (yes, that was an actual job title), 20 hours to calculate by hand. The engineers stood there, sweating in the oppressive heat like they’d just invented the world’s most expensive sauna, watching 18,000 lights blink in orchestrated patterns. They had just taught lightning to count, and this was just the beginning.
The Vacuum Tube: How a Lightbulb Learned to Think
To understand how ENIAC could perform calculations, we need to understand its basic building block—the vacuum tube. Despite its grand name, a vacuum tube is surprisingly simple. It’s basically a lightbulb that went to graduate school.
Take a lightbulb, remove all the air to create a vacuum. Heat the metal filament until it glows. At this temperature, electrons literally boil off the hot metal surface like steam from a kettle—except this kettle makes math instead of tea.
Add another piece of metal inside the bulb, apply positive voltage, and watch those negatively charged electrons race toward it, kind of like Black Friday shoppers toward a discounted TV. Remove the voltage, and the flow stops instantly. Congratulations—you’ve just created an electronic switch with no moving parts, the foundation of all digital computing, the first lego that started the AI revolution.
Take a lightbulb, remove all the air to create a vacuum. Heat the metal filament until it glows. At this temperature, electrons literally boil off the hot metal surface like steam from a kettle—except this kettle makes math instead of tea.
Add another piece of metal inside the bulb, apply positive voltage, and watch those negatively charged electrons race toward it, kind of like Black Friday shoppers toward a discounted TV. Remove the voltage, and the flow stops instantly. Congratulations—you’ve just created an electronic switch with no moving parts, the foundation of all digital computing, the first lego that started the AI revolution.
The Three Superpowers That Changed Everything
So how did this “simple” invention lead to the computers of today ? The vacuum tube’s ability to control electron flow gave it three superpowers that would revolutionize technology.
Superpower #1: Amplification – Making Whispers Into Shouts
Inside a vacuum tube, engineers added a wire mesh grid between the cathode and anode. This grid acts like a bouncer at an electron nightclub—tiny voltage changes on the grid cause massive changes in the electron flow. A whisper-level signal on the grid can control a shout-level current, amplifying the original signal hundreds of times.
In 1915, AT&T used vacuum tube amplifiers to enable the first transcontinental phone call from New York to San Francisco. Without amplification, the electrical signal would fade to nothing after about 20 miles—roughly the distance your parents claimed they walked to school.
With vacuum tube repeaters boosting the signal, your voice could travel 3,000 miles – which was impressive until you realized you now had no excuse for not calling your mother.
Superpower #2: Switching – Creating Digital Decisions
The vacuum tube’s second superpower was its ability to be completely on or completely off—like your motivation on Monday mornings.
This binary state became the foundation of digital logic. By connecting tubes together, engineers could create logic gates that made decisions. Without getting into too much detail, an AND gate outputs 1 only when both inputs are 1—imagine a door that only opens when you have both your keycard AND enter the correct PIN. An OR gate outputs 1 if either input is 1—like an elevator that moves if someone presses the button inside OR outside.
These simple decisions, combined millions of times, create everything from calculators to ChatGPT.
Superpower #3: Memory – Electronic Storage
Once you set a vacuum tube to “on,” it stayed on until you deliberately switched it off—more reliable than your New Year’s resolutions. Storing this on or off for a particular tube meant we could store information. Ten tubes could store a ten-digit phone number indefinitely. Eighteen thousand tubes could store an entire program.
Before this, “memory” meant human memory or paper records. Electronic memory could be accessed in microseconds rather than the minutes needed to find that sticky note you definitely wrote but can’t find anywhere.
Superpower #1: Amplification – Making Whispers Into Shouts
Inside a vacuum tube, engineers added a wire mesh grid between the cathode and anode. This grid acts like a bouncer at an electron nightclub—tiny voltage changes on the grid cause massive changes in the electron flow. A whisper-level signal on the grid can control a shout-level current, amplifying the original signal hundreds of times.
In 1915, AT&T used vacuum tube amplifiers to enable the first transcontinental phone call from New York to San Francisco. Without amplification, the electrical signal would fade to nothing after about 20 miles—roughly the distance your parents claimed they walked to school.
With vacuum tube repeaters boosting the signal, your voice could travel 3,000 miles – which was impressive until you realized you now had no excuse for not calling your mother.
Superpower #2: Switching – Creating Digital Decisions
The vacuum tube’s second superpower was its ability to be completely on or completely off—like your motivation on Monday mornings.
This binary state became the foundation of digital logic. By connecting tubes together, engineers could create logic gates that made decisions. Without getting into too much detail, an AND gate outputs 1 only when both inputs are 1—imagine a door that only opens when you have both your keycard AND enter the correct PIN. An OR gate outputs 1 if either input is 1—like an elevator that moves if someone presses the button inside OR outside.
These simple decisions, combined millions of times, create everything from calculators to ChatGPT.
Superpower #3: Memory – Electronic Storage
Once you set a vacuum tube to “on,” it stayed on until you deliberately switched it off—more reliable than your New Year’s resolutions. Storing this on or off for a particular tube meant we could store information. Ten tubes could store a ten-digit phone number indefinitely. Eighteen thousand tubes could store an entire program.
Before this, “memory” meant human memory or paper records. Electronic memory could be accessed in microseconds rather than the minutes needed to find that sticky note you definitely wrote but can’t find anywhere.
How Vacuum Tubes Actually Compute: The Adding Machine
Let me explain how vacuum tubes perform math using a simple example —adding 5 + 3 to get 8.
First, here’s the challenge: vacuum tubes can only be in two states—completely on (electricity flowing) or completely off (no electricity). So how do you teach a bunch of on/off switches to do math?
The answer is surprisingly clever. We use a different counting system called binary, which only uses 1s and 0s instead of our normal 0 through 9. Think of it like this: instead of having ten fingers to count on, imagine you only had one finger on each hand. You could only show “finger up” (1) or “finger down” (0). That’s binary.
Let’s convert our numbers. The number 5 in binary looks like this: 0101. Here’s how we get there:
So 5 = 4 + 1, which in binary means the 4s place is “on” (1), the 2s place is “off” (0), and the 1s place is “on” (1). That gives us 0101.
Similarly, 3 = 2 + 1, which in binary is 0011 (the 2s and 1s places are both “on”).
Now imagine four vacuum tubes representing each number. For the number 5, the tubes would be: OFF-ON-OFF-ON (matching our 0101). For the number 3: OFF-OFF-ON-ON (matching 0011).
Here’s where it gets interesting. To add these numbers, the machine uses special circuits made of vacuum tubes that follow simple rules. When two “ON” signals meet (1+1), the circuit outputs “OFF” but sends an “ON” signal to the next column—just like when you add 5+5 in regular math and write down 0 but carry the 1.
The actual addition happens column by column:
Result: 1000 in binary, which equals 8 in our regular counting system.
This entire process—which took us paragraphs to explain—happened in microseconds inside ENIAC. About 40 vacuum tubes worked together to perform this single addition, each one playing its role as either a switch, a memory holder, or a part of the adding circuit.
The remarkable part? ENIAC could perform 5,000 of these additions every second. A human with a mechanical calculator might manage one addition every 10 seconds if they were fast. That means ENIAC was 50,000 times faster—the difference between walking to work and taking a rocket ship.
All this from glowing tubes that could only say “yes” or “no.” It’s like teaching a room full of light switches to do your math homework—absurd, brilliant, and the foundation of every computer that followed.
First, here’s the challenge: vacuum tubes can only be in two states—completely on (electricity flowing) or completely off (no electricity). So how do you teach a bunch of on/off switches to do math?
The answer is surprisingly clever. We use a different counting system called binary, which only uses 1s and 0s instead of our normal 0 through 9. Think of it like this: instead of having ten fingers to count on, imagine you only had one finger on each hand. You could only show “finger up” (1) or “finger down” (0). That’s binary.
Let’s convert our numbers. The number 5 in binary looks like this: 0101. Here’s how we get there:
- The rightmost digit represents 1s (like our ones column in regular math)
- The next digit represents 2s
- The next represents 4s
- The next represents 8s
So 5 = 4 + 1, which in binary means the 4s place is “on” (1), the 2s place is “off” (0), and the 1s place is “on” (1). That gives us 0101.
Similarly, 3 = 2 + 1, which in binary is 0011 (the 2s and 1s places are both “on”).
Now imagine four vacuum tubes representing each number. For the number 5, the tubes would be: OFF-ON-OFF-ON (matching our 0101). For the number 3: OFF-OFF-ON-ON (matching 0011).
Here’s where it gets interesting. To add these numbers, the machine uses special circuits made of vacuum tubes that follow simple rules. When two “ON” signals meet (1+1), the circuit outputs “OFF” but sends an “ON” signal to the next column—just like when you add 5+5 in regular math and write down 0 but carry the 1.
The actual addition happens column by column:
- Rightmost column: 1 + 1 = 0, carry 1
- Next column: 0 + 1 + (carried 1) = 0, carry 1
- Next column: 1 + 0 + (carried 1) = 0, carry 1
- Leftmost: 0 + 0 + (carried 1) = 1
Result: 1000 in binary, which equals 8 in our regular counting system.
This entire process—which took us paragraphs to explain—happened in microseconds inside ENIAC. About 40 vacuum tubes worked together to perform this single addition, each one playing its role as either a switch, a memory holder, or a part of the adding circuit.
The remarkable part? ENIAC could perform 5,000 of these additions every second. A human with a mechanical calculator might manage one addition every 10 seconds if they were fast. That means ENIAC was 50,000 times faster—the difference between walking to work and taking a rocket ship.
All this from glowing tubes that could only say “yes” or “no.” It’s like teaching a room full of light switches to do your math homework—absurd, brilliant, and the foundation of every computer that followed.
How Alan Turing's Tubes Won the War
While ENIAC was being built in Philadelphia, another group of scientists was using vacuum tubes for an even more urgent purpose—saving the world from Nazi Germany. The year was 1943, and Britain was losing the intelligence war. German communications, encrypted with machines the Nazis believed unbreakable, coordinated U-boat attacks that were starving Britain of supplies. Allied convoys were being decimated in the Atlantic. The invasion of Europe seemed impossible when the enemy could read your every move while keeping their own plans hidden in mathematical darkness.
At Bletchley Park, a Victorian mansion turned top-secret codebreaking facility 50 miles northwest of London, Alan Turing and his team faced a seemingly impossible challenge. The Germans had upgraded from their famous Enigma machine to something far more sophisticated—the Lorenz cipher, used exclusively by Hitler’s high command for their most sensitive communications. While Enigma was like a combination lock with millions of possibilities, Lorenz was exponentially more complex.
Enter Colossus, a machine that would change the course of history. Built by engineer Tommy Flowers (who spent £1,000 of his own money when the government balked at the cost), Colossus used 2,500 vacuum tubes to perform cryptographic calculations at unprecedented speed. If you’ve seen the movie “The Imitation Game” (2014), you know Alan Turing’s role.
Colossus could test 5,000 combinations per second. The glowing tubes that we discussed creating light and heat were now racing through mathematical possibilities faster than any human mind could comprehend.
The breakthrough came at a critical moment. On June 5, 1944—one day before D-Day—Colossus decoded a message from Hitler himself stating “No invasion expected at Normandy.” This intelligence confirmed that the Allies’ elaborate deception campaign had worked. Eisenhower’s invasion plans could proceed with confidence that the Germans were looking in the wrong direction. Those 2,500 vacuum tubes, glowing in a nondescript hut in the English countryside, had just guaranteed the success of the largest amphibious invasion in history.
The full impact of these decoded messages remained classified for decades. Beyond D-Day, Colossus revealed German supply routes allowing Allied bombers to devastate Nazi logistics with surgical precision. It exposed the locations of U-boat wolf packs, enabling convoys to take evasive routes and saving countless merchant vessels—and the sailors aboard them. It identified German spy networks across Europe, leading to mass arrests that crippled Nazi intelligence operations. Churchill later said that Bletchley Park’s work was “the secret weapon that won the war.”
The Germans never knew their most secure communications were being read like morning newspapers by Allied commanders. Every order from Berlin, every field report from generals, every strategic discussion between Hitler and his staff—all laid bare by vacuum tubes switching billions of times in patterns that revealed the enemy’s deepest secrets.
Even after Germany’s surrender, the secret had to be kept. The British destroyed most Colossus machines and swore everyone involved to secrecy, fearing the Soviets might learn how to defeat similar encryption. The heroes who operated these machines returned to civilian life unable to tell anyone—including their own families—what they had accomplished. The full story wasn’t declassified until the 1970s, by which time many of these pioneers had passed away without recognition.
The victory at Bletchley Park had proven that electronic computation could change the world. But it had also revealed a fundamental problem that would haunt computing for decades: these miraculous machines were insatiably hungry for electricity.
Colossus, with its 2,500 tubes, had already strained Bletchley Park’s power supply. Local residents complained about dimming lights whenever the codebreakers fired up their machines for particularly intensive decryption sessions. The British government had to install special power lines to the facility, disguised as standard utility upgrades to maintain secrecy. And this was just for breaking codes—imagine what would be needed for the broader computational dreams scientists were beginning to envision.
At Bletchley Park, a Victorian mansion turned top-secret codebreaking facility 50 miles northwest of London, Alan Turing and his team faced a seemingly impossible challenge. The Germans had upgraded from their famous Enigma machine to something far more sophisticated—the Lorenz cipher, used exclusively by Hitler’s high command for their most sensitive communications. While Enigma was like a combination lock with millions of possibilities, Lorenz was exponentially more complex.
Enter Colossus, a machine that would change the course of history. Built by engineer Tommy Flowers (who spent £1,000 of his own money when the government balked at the cost), Colossus used 2,500 vacuum tubes to perform cryptographic calculations at unprecedented speed. If you’ve seen the movie “The Imitation Game” (2014), you know Alan Turing’s role.
Colossus could test 5,000 combinations per second. The glowing tubes that we discussed creating light and heat were now racing through mathematical possibilities faster than any human mind could comprehend.
The breakthrough came at a critical moment. On June 5, 1944—one day before D-Day—Colossus decoded a message from Hitler himself stating “No invasion expected at Normandy.” This intelligence confirmed that the Allies’ elaborate deception campaign had worked. Eisenhower’s invasion plans could proceed with confidence that the Germans were looking in the wrong direction. Those 2,500 vacuum tubes, glowing in a nondescript hut in the English countryside, had just guaranteed the success of the largest amphibious invasion in history.
The full impact of these decoded messages remained classified for decades. Beyond D-Day, Colossus revealed German supply routes allowing Allied bombers to devastate Nazi logistics with surgical precision. It exposed the locations of U-boat wolf packs, enabling convoys to take evasive routes and saving countless merchant vessels—and the sailors aboard them. It identified German spy networks across Europe, leading to mass arrests that crippled Nazi intelligence operations. Churchill later said that Bletchley Park’s work was “the secret weapon that won the war.”
The Germans never knew their most secure communications were being read like morning newspapers by Allied commanders. Every order from Berlin, every field report from generals, every strategic discussion between Hitler and his staff—all laid bare by vacuum tubes switching billions of times in patterns that revealed the enemy’s deepest secrets.
Even after Germany’s surrender, the secret had to be kept. The British destroyed most Colossus machines and swore everyone involved to secrecy, fearing the Soviets might learn how to defeat similar encryption. The heroes who operated these machines returned to civilian life unable to tell anyone—including their own families—what they had accomplished. The full story wasn’t declassified until the 1970s, by which time many of these pioneers had passed away without recognition.
The victory at Bletchley Park had proven that electronic computation could change the world. But it had also revealed a fundamental problem that would haunt computing for decades: these miraculous machines were insatiably hungry for electricity.
Colossus, with its 2,500 tubes, had already strained Bletchley Park’s power supply. Local residents complained about dimming lights whenever the codebreakers fired up their machines for particularly intensive decryption sessions. The British government had to install special power lines to the facility, disguised as standard utility upgrades to maintain secrecy. And this was just for breaking codes—imagine what would be needed for the broader computational dreams scientists were beginning to envision.
The Power Problem: When Computing Hit the Wall
The full scope of this challenge became clear when ENIAC switched on in Philadelphia. If Colossus was a power-hungry prototype, ENIAC was a full-blown electrical monster. With seven times more vacuum tubes than Colossus, ENIAC alone consumed 150 kilowatts, enough to power 150 homes in 1946. When switched on, it literally dimmed lights across West Philadelphia—the world’s first computer-induced brownout.
Scientists calculated that a truly powerful computer would require 200 million vacuum tubes, consume 200 megawatts of power, and fill 50 city blocks. To put this in perspective, such a machine would need more electricity than most small countries and would create its own weather system from the heat—complete with thunderstorms in the server room.
Scientists calculated that a truly powerful computer would require 200 million vacuum tubes, consume 200 megawatts of power, and fill 50 city blocks. To put this in perspective, such a machine would need more electricity than most small countries and would create its own weather system from the heat—complete with thunderstorms in the server room.
The Shocking Truth About Modern AI's Appetite
Let’s bring this impossibility into sharp focus with something in your pocket right now. The iPhone 15 Pro contains approximately 19 billion transistors in its A17 Pro chip—each transistor doing the job of one vacuum tube but millions of times faster.
If we built your iPhone using 1946 technology, we’d need 19 billion vacuum tubes. Here’s the mind-bending reality of what that would look like:
Physical Space: This vacuum-tube iPhone would cover 380 square miles—larger than New York City. The structure would need to be 20,000 floors tall, making it visible from space. You’d need a NASA mission just to make a phone call from the top floor.
Power Consumption: It would gulp down 19 Terawatts of electricity—about 75% of humanity’s total power generation. Your monthly phone bill wouldn’t include minutes or data; it would include “percentage of global electricity grid consumed.”
Heat Generation: The thermal output would equal a medium-sized volcanic eruption running continuously. The phone would need its own weather system for cooling, complete with artificial hurricanes. Instead of a case, you’d need a containment facility with radiation warnings.
Maintenance Nightmare: With vacuum tubes failing at historical rates, you’d see 10,000 tubes dying every second. You’d need a maintenance army of 2 million people working around the clock. The replacement tubes alone would cost $40 billion annually—more than NASA’s entire budget.
Weight: Your “phone” would weigh approximately 950,000 tons—about the same as eight Nimitz-class aircraft carriers. Forget putting it in your pocket; you’d need a shipping port just to receive a text message.
And here’s the kicker: after all that absurd infrastructure, this monstrosity would still be thousands of times slower than the sleek device currently sitting in your pocket, probably with 37 unread notifications.
If we built your iPhone using 1946 technology, we’d need 19 billion vacuum tubes. Here’s the mind-bending reality of what that would look like:
Physical Space: This vacuum-tube iPhone would cover 380 square miles—larger than New York City. The structure would need to be 20,000 floors tall, making it visible from space. You’d need a NASA mission just to make a phone call from the top floor.
Power Consumption: It would gulp down 19 Terawatts of electricity—about 75% of humanity’s total power generation. Your monthly phone bill wouldn’t include minutes or data; it would include “percentage of global electricity grid consumed.”
Heat Generation: The thermal output would equal a medium-sized volcanic eruption running continuously. The phone would need its own weather system for cooling, complete with artificial hurricanes. Instead of a case, you’d need a containment facility with radiation warnings.
Maintenance Nightmare: With vacuum tubes failing at historical rates, you’d see 10,000 tubes dying every second. You’d need a maintenance army of 2 million people working around the clock. The replacement tubes alone would cost $40 billion annually—more than NASA’s entire budget.
Weight: Your “phone” would weigh approximately 950,000 tons—about the same as eight Nimitz-class aircraft carriers. Forget putting it in your pocket; you’d need a shipping port just to receive a text message.
And here’s the kicker: after all that absurd infrastructure, this monstrosity would still be thousands of times slower than the sleek device currently sitting in your pocket, probably with 37 unread notifications.
The Great Power Race: China's Shocking Lead
Modern data centers have become electricity vampires of staggering proportions. A single large data center—like those operated by Google or Microsoft—consumes about 100 megawatts continuously. That’s enough electricity to power 80,000 homes. The global data center industry now consumes more electricity than entire countries like Argentina.
China recognized this hunger early and built accordingly. In 1980, China generated just 300 TWh annually—less than Italy. By 2024, China generated 9,500 TWh, representing 32% of global production. The city of Shenzhen alone consumes more electricity than the entire country of Portugal, much of it powering the data centers that train the world’s AI models.
But here’s where the story gets interesting. The same AI revolution demanding all this power is helping solve its own energy crisis. Solar power generation has exploded from 40 gigawatts globally in 2010 to 1,600 gigawatts in 2024—a forty-fold increase. Wind power has grown from 200 gigawatts to over 1,000 gigawatts in the same period. The cost of renewable energy has plummeted 90%, making it cheaper than coal in most markets.
Tech giants are leading this transformation. Google runs its data centers on 90% carbon-free energy and aims for 100% by 2030. Microsoft has committed to being carbon negative by 2030 and recently announced plans to bring nuclear fusion power to its data centers. Amazon has become the world’s largest corporate purchaser of renewable energy, with 400 wind and solar projects globally. Even cryptocurrency miners—perhaps the most energy-intensive computers users—are flocking to renewable energy because it’s now the cheapest option.
The beautiful irony? The same computing power that demands so much electricity is optimizing power grids, predicting renewable energy output, and designing more efficient solar panels. AI systems now manage electrical grids in real-time, balancing supply and demand, storing excess renewable energy, and preventing blackouts. The monster that threatens to consume all our electricity is simultaneously teaching us how to generate it more efficiently.
From Glass to Sand
Looking back at our thought experiment, the vacuum tube version of an iPhone would be an impossible monster—covering New York City, employing millions, consuming most of Earth’s electricity.
Vacuum tubes had proven electronic computation worked, but scaling them up would turn Earth into one giant, overheated computer that still couldn’t run Crysis.
The world desperately needed switches that could turn on and off billions of times per second without the heat, size, fragility, or power consumption. We needed to shrink a glowing glass bottle the size of your thumb down to something invisible—basically, scientific magic that actually works.
The answer came from beach sand—yes, the same stuff that stays in your car for seven years after one beach trip. At Bell Telephone Laboratories, three scientists were about to transform this mundane substance into switches so small that 100 billion could fit on a chip the size of your fingernail.
But the journey from sand to silicon required multiple breakthroughs, each as remarkable as the vacuum tube itself. It’s a story that begins with germanium, a paperclip, and December 26, 1947…
China recognized this hunger early and built accordingly. In 1980, China generated just 300 TWh annually—less than Italy. By 2024, China generated 9,500 TWh, representing 32% of global production. The city of Shenzhen alone consumes more electricity than the entire country of Portugal, much of it powering the data centers that train the world’s AI models.
But here’s where the story gets interesting. The same AI revolution demanding all this power is helping solve its own energy crisis. Solar power generation has exploded from 40 gigawatts globally in 2010 to 1,600 gigawatts in 2024—a forty-fold increase. Wind power has grown from 200 gigawatts to over 1,000 gigawatts in the same period. The cost of renewable energy has plummeted 90%, making it cheaper than coal in most markets.
Tech giants are leading this transformation. Google runs its data centers on 90% carbon-free energy and aims for 100% by 2030. Microsoft has committed to being carbon negative by 2030 and recently announced plans to bring nuclear fusion power to its data centers. Amazon has become the world’s largest corporate purchaser of renewable energy, with 400 wind and solar projects globally. Even cryptocurrency miners—perhaps the most energy-intensive computers users—are flocking to renewable energy because it’s now the cheapest option.
The beautiful irony? The same computing power that demands so much electricity is optimizing power grids, predicting renewable energy output, and designing more efficient solar panels. AI systems now manage electrical grids in real-time, balancing supply and demand, storing excess renewable energy, and preventing blackouts. The monster that threatens to consume all our electricity is simultaneously teaching us how to generate it more efficiently.
From Glass to Sand
Looking back at our thought experiment, the vacuum tube version of an iPhone would be an impossible monster—covering New York City, employing millions, consuming most of Earth’s electricity.
Vacuum tubes had proven electronic computation worked, but scaling them up would turn Earth into one giant, overheated computer that still couldn’t run Crysis.
The world desperately needed switches that could turn on and off billions of times per second without the heat, size, fragility, or power consumption. We needed to shrink a glowing glass bottle the size of your thumb down to something invisible—basically, scientific magic that actually works.
The answer came from beach sand—yes, the same stuff that stays in your car for seven years after one beach trip. At Bell Telephone Laboratories, three scientists were about to transform this mundane substance into switches so small that 100 billion could fit on a chip the size of your fingernail.
But the journey from sand to silicon required multiple breakthroughs, each as remarkable as the vacuum tube itself. It’s a story that begins with germanium, a paperclip, and December 26, 1947…