History of the Technological Evolution; from Manual to Digital, AI to Code Intelligence
History of the Technological Evolution
By Alvin Johnson · October 2, 2026
Technology originated first from the binary system. Numbers, alphabets, symbols, and figures used ones and zeroes to write sentences and code variations using the binary system in the beginning.
Going back to 1975, the calculator and the electric typewriter faced ridicule before they were accepted by the general public. As a student of Control Data Institute, I took a course titled Business Machine Service Technician. It taught us how to identify individual parts during operation, how to remove and replace those parts, and how to break down the devices—calculators and electric typewriters—and reassemble them for operational performance checks and approvals.
Upon the conclusion of the class, the instructor entered the classroom with a mixed look of confusion and fear on his face. He admitted his reluctance to inform the class of a new invention that had just made our learning experience obsolete. He also admitted his responsibility to be open and honest regarding the development.
IBM had just announced to him that the IBM Selectric brand no longer needed us to perform diagnostics, and that they had developed a computer that performed all service checks. The Selectric replaced the type keys with a globe that pivoted back and forth, up and down, left and right, and was digitally controlled.
He walked us into a huge room—the data center of Control Data Institute—which had an IBM Selectric connected by leads clamped to various portions of its mechanical and electrical systems with probes.
We had just advanced to performing maintenance on this Selectric typewriter. He grabbed a pair of tweezers, disconnected the spring from the escapement mechanism of the Selectric, walked over to another fully functional Selectric, began typing, and pressed enter.
The Selectric he removed the spring from stopped functioning suddenly. The instructor walked over to the printer and removed the diagnostic printout. The Performance Check detailed the fault and the outcome:
“Mainspring removed from the escapement mechanism, replace escapement mechanism mainspring.”
The students realized very soon that the manual devices were obsolete, and many retaliated against the sudden change, rebutted and disputed the announcement—to no avail.
Here We Are Again: The Age of Code Intelligence
Fifty-one years later, I recognize that same look on the faces of a new generation. It is the look my instructor wore when he walked into our classroom. Only now the machine on the bench is not a typewriter. It is the software itself—and the technician being asked to step aside is the programmer, the writer, the analyst, and the clerk.
The new generation is Code Intelligence: artificial intelligence systems that read, write, test, and repair computer code, and that increasingly diagnose problems the way that computer in the Control Data Institute data center diagnosed the Selectric. A developer describes what is broken; the system traces the fault and prints its own version of “replace escapement mechanism mainspring.”
And just as in 1975, the announcement has been met with ridicule, fear, dispute, and, slowly, acceptance. History does not repeat itself exactly, but it certainly rhymes.
The Common Thread: It All Comes Back to Ones and Zeroes
I began this article with the binary system for a reason. The digitally controlled typing globe on the Selectric, the diagnostic computer that caught the missing spring, and today’s Code Intelligence all stand on the same foundation: information broken down into ones and zeroes, then built back up into instructions.
What has changed is the level at which the machine understands. In 1975, the diagnostic computer understood electrical signals from a typewriter’s parts. Today, Code Intelligence understands language—both human language and programming language—well enough to translate between the two. Each age climbs one rung higher on the same ladder.
Side by Side: Two Ages Compared
| Aspect | The Diagnostic Age (1975) | The Code Intelligence Age (2026) |
|---|---|---|
| The skilled worker | Business machine service technician | Software developer, programmer, analyst |
| The core skill | Identifying, removing, and replacing mechanical parts by hand | Writing, reading, and debugging code line by line |
| The disruptor | A computer connected by probes that ran all service checks | AI models that write, test, and repair software |
| How the machine “sees” | Electrical leads clamped to the typewriter | Reading the entire codebase and plain-English instructions |
| The output | A printed diagnostic: what failed and what to replace | An explanation of the bug and the corrected code |
| First public reaction | Ridicule of calculators and electric typewriters | Ridicule of AI as a “toy” that makes mistakes |
| Worker reaction | Students rebutted and disputed the announcement | Professionals debate, resist, and question job security |
| Who still mattered | The person who understood the machine and could act on the diagnosis | The person who understands the problem and can judge the AI’s answer |
A Model of Every Technological Age
Having lived through more than one of these shifts, I have come to see a pattern that repeats with each new wave of technology. I offer it here as a model—seven stages that the typewriter era and the Code Intelligence era share.
Stage 1 — Mastery of the Old Way
1975: We learned every spring, gear, and linkage of the calculator and the electric typewriter. Our value came from knowing what others could not see inside the machine.
Today: Programmers spent years mastering languages, frameworks, and the art of finding a single misplaced character in thousands of lines of code.
The likeness: In both ages, expertise was measured by how deeply a person understood the inner workings of a complicated system.
Stage 2 — Ridicule
1975: People laughed at the calculator and the electric typewriter before they accepted them. Real accountants, they said, worked with pencils; real typists did not need electricity.
Today: Early AI coding tools were dismissed as clumsy and unreliable, producing code no serious engineer would trust.
The likeness: Every new tool is first judged by its worst early mistakes rather than its potential.
Stage 3 — The Announcement
1975: An instructor, visibly shaken, told his class that IBM no longer needed us to perform diagnostics.
Today: Companies announce that AI now writes a growing share of their software, and that fewer people are needed for routine coding work.
The likeness: The news usually reaches workers secondhand, through someone who is just as unsettled as they are.
Stage 4 — The Demonstration
1975: With one pair of tweezers and one missing spring, the instructor proved that the computer could find a fault faster and more precisely than any of us.
Today: A developer pastes in an error message, and within seconds the AI points to the exact broken line and writes the repair.
The likeness: Arguments do not convince people. A single undeniable demonstration does.
Stage 5 — Resistance
1975: My classmates retaliated, rebutted, and disputed the announcement—to no avail.
Today: Professionals argue that AI cannot be trusted, that it will lower quality, and that it threatens livelihoods. Some of those concerns are valid and deserve serious attention.
The likeness: Resistance is a natural human response to losing something we worked hard to earn. It rarely stops the change, but it can shape how responsibly the change is made.
Stage 6 — Adaptation
1975: The technicians who moved forward learned to read diagnostic printouts, service the computers that did the diagnosing, and eventually work on the personal computers that followed.
Today: The developers who move forward learn to direct AI, review its work, and design the larger systems that AI helps build.
The likeness: The job does not simply disappear—it moves up one level. The worker goes from doing the task to supervising the tool that does the task.
Stage 7 — Acceptance and the Next Wave
1975: Within a generation, nobody laughed at calculators. Electric and electronic typewriters gave way to word processors, and word processors gave way to computers in every home.
Today: Code Intelligence is following the same road toward becoming an ordinary, expected part of everyday work.
The likeness: What was once frightening becomes so normal that the next generation cannot imagine life without it—until the next announcement arrives.
Where the Parallel Breaks
An honest comparison must also note the differences. Three stand out to me.
Speed. The move from mechanical typewriters to personal computers took decades. Code Intelligence has moved from novelty to everyday tool in just a few years. Workers today have far less time to adjust than we did.
Reach. The Selectric diagnostic computer affected one trade: business machine repair. Code Intelligence touches nearly every trade that uses a computer—which today is almost all of them.
The nature of the machine. The 1975 diagnostic computer followed fixed rules written by engineers. It could only find the faults it was programmed to find. Today’s systems learn patterns from enormous amounts of information and can handle problems no one specifically programmed them for. That makes them more powerful—and it also means they can be confidently wrong, which is why human judgment matters more than ever.
Lessons from the Bench
- Understanding outlasts procedure. The computer could tell us the mainspring was missing, but a person still had to understand the escapement mechanism to install it. Knowing why things work is the skill that survives every transition.
- Disputing the change does not stop it. My classmates learned that in one afternoon. Energy spent resisting is better spent learning.
- Honesty matters. My instructor did not hide the news from us. Leaders today owe their people the same openness about what Code Intelligence will change.
- Every tool needs a supervisor. The diagnostic printout was only useful because someone read it and acted on it. AI output is only as good as the person checking it.
- The next age is always coming. Whoever masters today’s tool should expect to see that same look of confusion and fear again someday—and should be ready to learn once more.
Conclusion
In 1975, I watched a pair of tweezers and a single spring end one chapter of my education and begin another. The diagnostic printout did not make people unnecessary; it made a certain kind of work unnecessary and demanded that we grow into new work.
Here we are again. Code Intelligence is the new diagnostic computer, and today’s programmers are the students in that classroom. The ones and zeroes are the same; only the height of the climb has changed. Those who meet this age with curiosity rather than retaliation will find, as many of us did, that the end of one skill is the beginning of the next.
— Alvin Johnson
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