The great code democratization
Posted: Tue Feb 11, 2025 5:00 am
In a recent national television commercial, between ads for coconut-scented deodorant and laundry detergent, something remarkable appeared: raw JSON code. Not hidden away in some developer conference or buried in technical documentation, but broadcast to millions of homes during prime time. This wasn't a mistake or a marketing misfire - it was Postman, a company that clearly understands something profound about our changing relationship with code.
The Original Promise
When Charles Petzold wrote that "code" is simply "a system of symbols used to convey information," he wasn't describing an arcane art form or a specialized technical skill. He was describing something fundamentally human: communication. Code, at its core, was never meant to be the exclusive domain of a technical elite. It was created because machines couldn't understand our messy, beautiful human language. We needed a bridge, a way to translate our intentions into something computers could process.
As Ken Arnold eloquently put it, "The best programs are written so that computing machines can perform the actions, but that humans can easily read the ideas." This vision of code as a readable, accessible medium for expressing ideas has been largely lost in the complexity of modern software development.
The Great Wall of Code
Somewhere along the way, we built walls around code. What started as a practical solution for human-machine communication evolved into a priesthood, with its own rituals, hierarchies, and barriers to entry. We convinced ourselves that only a select few could master the art of programming, creating artificial divisions greece whatsapp number data between "technical" and "non-technical" roles.
Brian Kernighan observed that "controlling complexity is the essence of computer programming." Ironically, in our quest to manage this complexity, we made software development more complex than it needed to be. We created specialized roles, elaborate processes, and layers of abstraction that often served to protect the priesthood rather than enhance productivity.
The AI Revolution
Enter Large Language Models. In an instant, these systems have done something remarkable: they've made machine understanding of human language possible. The original reason for code's complexity - the need to translate human intentions into precise machine instructions - is rapidly disappearing.
This isn't just about making programming easier. It's about returning code to its original purpose as a communication medium. When a Product Manager can describe their vision in plain English and have it translated into working software, we're not just improving efficiency - we're democratizing innovation itself.
The Original Promise
When Charles Petzold wrote that "code" is simply "a system of symbols used to convey information," he wasn't describing an arcane art form or a specialized technical skill. He was describing something fundamentally human: communication. Code, at its core, was never meant to be the exclusive domain of a technical elite. It was created because machines couldn't understand our messy, beautiful human language. We needed a bridge, a way to translate our intentions into something computers could process.
As Ken Arnold eloquently put it, "The best programs are written so that computing machines can perform the actions, but that humans can easily read the ideas." This vision of code as a readable, accessible medium for expressing ideas has been largely lost in the complexity of modern software development.
The Great Wall of Code
Somewhere along the way, we built walls around code. What started as a practical solution for human-machine communication evolved into a priesthood, with its own rituals, hierarchies, and barriers to entry. We convinced ourselves that only a select few could master the art of programming, creating artificial divisions greece whatsapp number data between "technical" and "non-technical" roles.
Brian Kernighan observed that "controlling complexity is the essence of computer programming." Ironically, in our quest to manage this complexity, we made software development more complex than it needed to be. We created specialized roles, elaborate processes, and layers of abstraction that often served to protect the priesthood rather than enhance productivity.
The AI Revolution
Enter Large Language Models. In an instant, these systems have done something remarkable: they've made machine understanding of human language possible. The original reason for code's complexity - the need to translate human intentions into precise machine instructions - is rapidly disappearing.
This isn't just about making programming easier. It's about returning code to its original purpose as a communication medium. When a Product Manager can describe their vision in plain English and have it translated into working software, we're not just improving efficiency - we're democratizing innovation itself.