From Prolog to Prompting: The Great Programming Paradigm Shift

How AI Changed the Way We Think About Code

There's a moment in every programmer's journey when they realize the ground has shifted beneath their feet. For me, that moment came not in a classroom or conference room, but in the radio room of a naval vessel in the middle of the ocean, staring at a printed copy of dBase IV code.

The Old School: When Code Was Poetry You Had to Memorize

Picture this: 1990s, no internet, no Stack Overflow, no GitHub Copilot whispering suggestions in your ear. Just you, a manual (if you were lucky), and pure determination. I was a radioman on a ship, facing six months of deployment, armed with nothing but a copied floppy disk of dBase IV and a dot-matrix printer.

The learning process was brutal and beautiful. Month one: staring at printed code like hieroglyphics, wondering if I was wasting my time. Month two: something clicked, and those cryptic commands became a page-turner. Month three: I didn't want the documentation to end. I was hooked.

This was programming in the age of Prolog – where logic was king, where you had to think in formal structures, where every line of code was a deliberate step in a carefully constructed argument. We built mental models of entire systems, held complex state in our heads, and debugged by pure reasoning.

Enter the AI Revolution: From Logic to "Vibing"

Fast forward to today, and watch a developer working with Claude, ChatGPT, or GitHub Copilot. The process looks almost... conversational. They're not memorizing syntax; they're describing intent. They're not building mental models of entire systems; they're collaborating with an AI that already holds those models.

"Hey Claude, create a React component that handles user authentication with TypeScript and Tailwind."

Twenty seconds later: Working code, with error handling, proper types, and modern best practices.

This isn't lazy programming – it's a fundamental shift in how we interface with computing power. We've moved from being translators (converting human logic to machine logic) to being directors (orchestrating AI capabilities toward our goals).

The Great Paradigm Shift: What We've Lost and Gained

What We've Lost

• Deep System Understanding: Modern developers might not know how memory allocation works, but they can build scalable applications.
• Debugging Intuition: When AI writes your code, debugging becomes more archaeological – you're investigating someone else's logic.
• The Struggle: That beautiful, frustrating journey from confusion to clarity that made my dBase IV experience so memorable.

What We've Gained

• Speed of Iteration: Ideas become prototypes in minutes, not months.
• Lower Barriers to Entry: My niece built her first web app using AI assistance in a weekend.
• Focus on Problems, Not Syntax: We spend more time on user experience and business logic, less time fighting semicolons.

The New Skillset: Becoming an AI Whisperer

The most successful developers today aren't necessarily the ones who've memorized every API. They're the ones who've learned to:

1. Communicate Intent Clearly: AI is only as good as your ability to describe what you want.
2. Recognize Good vs. Bad AI Output: You still need to know when the AI has hallucinated a solution.
3. Iterate Rapidly: The feedback loop between idea and implementation has never been shorter.
4. Understand Architecture: While AI can write functions, someone still needs to design the system.

From Radioman to AI Collaborator

Looking back at that young radioman printing out code examples in the middle of the Pacific, I'm struck by both the continuity and the revolution. The core joy remains the same – that magical moment when logic becomes creation, when abstract concepts become working solutions.

But the process? Completely transformed. Where I once spent months deciphering the mysteries of dBase IV, today's developers are having conversations with AI that understands not just the syntax, but the intent behind their ideas.

The shift from Prolog to prompting isn't just about tools – it's about augmenting human creativity with machine intelligence. We're not becoming lesser programmers; we're becoming different kinds of programmers. We're becoming conductors of computational orchestras, wielding AI as our instruments.

The Future: Vibing with Intelligence

The future belongs to developers who can bridge the human and artificial. Who can take the creativity, intuition, and problem-solving skills that have always defined great programmers, and combine them with AI's vast knowledge and processing power.

We're entering an era where the question isn't "Can you code?" but "Can you think computationally and communicate that thinking effectively to both humans and AI?"

That young radioman copying virus-infected games and stumbling upon dBase IV couldn't have imagined this future. But the curiosity that drove him to print out code examples and spend months deciphering them? That's exactly what today's AI-assisted developers need – just applied to a fundamentally different kind of collaboration.

The paradigm has shifted, but the magic remains. We're still turning ideas into reality, one line at a time. We just have much better conversation partners now.

What's your programming origin story? How has your development process changed with AI assistance? Share your thoughts – whether you're team "Deep System Knowledge" or team "AI Collaboration," there's room for all approaches in the future we're building.