When I was twelve, we got our first computer at home. A Compaq Deskpro 386, a hand-me-down from my father's company. It sat in the attic, a massive CRT monitor glowing in the dim light, humming quietly, waiting.
Within a week, I'd broken it. Deleted something I shouldn't have, probably. My father took it back to the office, and a few days later it returned, fixed, no questions asked. That's when I realized I needed to actually understand what I was doing.
I started spending afternoons at the local library. Not for school, but for the computer books. MS-DOS manuals. BASIC tutorials. Anything with code in it. I'd borrow them and renew them endlessly, always the same kid with the same stack of computer books.
One day I found a book on Pascal. It had a blue cover, I still remember. I took it home and typed in the first example program. It didn't work. I'd made a typo somewhere. I spent an hour finding it. When I finally ran the program and saw the output on that monitor with green letters, I was hooked.
A friend and I started building things together. Hangman. A DOS launcher that made our computer feel professional. Text adventures that were terrible but ours. We had no idea what we were doing. We didn't know about algorithms or data structures or any of the things I'd later learn were important.
But it felt like magic.
You type something. The computer does it. You type something else. It does that too. For a twelve-year-old, this was magic. Anything seemed possible. This was the feeling that anything could be built if you just figured out how.
Thirty years later, I'm still looking for it.
Opening old code
A while back, I opened a project from 1998. Code from when Google was just founded, when most people had never heard of the internet.
Twenty-five years. In tech, that's supposed to be ancient history. Obsolete. Unreadable.
The code was recognizable. Not perfect. I'd make different choices now. But understandable. The structure still made sense. The logic was clear. After fifteen minutes, I knew exactly what it did and why.
We assume code decays. But good code ages like a good book: the ideas remain clear, even as the world around it changes.
What survives
We're obsessed with everything new. Every tech conference is about the next big breakthrough. Every pitch deck promises disruption of the old.
But look at what actually keeps the world running.
A banking system in Central Europe processes millions of transactions every day. The core was built in Delphi in the late nineties. I've seen the code. Worked on it, too, not long ago. It's not elegant by modern standards. The architecture follows conventions nobody uses anymore. Most of the comments are in a language I don't speak.
But twenty-four hours a day, it moves money. Salaries get paid. Mortgages get processed. The economy of an entire region flows through code older than the people who might be hired to rewrite it.
Nobody writes excited blog posts about these systems. They win no awards. No startup promises to disrupt them.
But they work. Day after day, year after year. That's not a failure to modernize. That's success.
Recently I read about a startup promising to "eliminate legacy code with AI." As if old code is a disease. Here's what they don't understand: AI doesn't make old code worthless. It makes old code more valuable.
Old code is like an archaeological site. Every layer tells a story. Why does this function exist? Because a customer in 2003 needed something nobody anticipated. Why does that calculation behave differently on Tuesdays? Because a bank somewhere closes early and settlements work differently. That knowledge was always there, buried in the logic. AI finally gives us the tools to excavate it.
I've seen this before
I've always loved exploring new technology. Client/server in the nineties, when fat clients talked to databases and everyone was sure the mainframe was dead. The web, when suddenly browsers could do things only desktop software could do before. Mobile, when everyone got a computer in their pocket. Cloud, when running your own servers started to feel quaint.
Each wave was exciting. Each had real value. But I never believed the hype that any of them would replace everything that came before. I'd seen too many systems survive too many revolutions. The mainframe didn't die. Desktop software is still here. The client/server model still runs at plenty of companies. New technology builds on top of old technology, rarely replaces it.
But AI was different. From the first moment I was hooked, and instead of waiting and watching from a distance, I dove straight in.
The magic returns
Working with AI on code, old or new, gives me the same feeling I had at twelve.
Last month, I was debugging a system that's been running since 2001. The original developer retired years ago. The documentation was sparse. The code was full of mysterious business rules that nobody quite understood anymore.
In the old days, this would have meant weeks of careful reading. Tracing execution paths by hand. Building mental models of what each function did and why. It's skilled work, but slow.
Instead, I asked the AI to explain a particular calculation. Within seconds, it traced the logic, identified the business rule, and even found a related comment buried three modules away that explained the historical context.
I can ask questions about systems I didn't write. Trace logic through decades of changes. Understand decisions made by developers who left long ago. It's like having a conversation with the code itself.
The startups want to throw it all away and start fresh. But the real opportunity is the opposite: making old systems smarter, more accessible, more powerful. Not replacing the past, but finally being able to understand it.
The twelve-year-old in me recognizes this feeling. You type something. The computer does something that seems impossible. Magic.
What we inherit
"Legacy" sounds like something bad. It shouldn't. Legacy means inheritance. Something left behind. Something valuable enough to keep.
Most companies don't realize what they have. They're sitting on a gold mine of accumulated knowledge, encoded in systems that have been solving real problems for decades. The code isn't a liability. It's an asset waiting to be unlocked.
That's what excites me. Not just the technology, but the possibility of unlocking what was always there.
AI gives us back something we had lost: the ability to understand what others have built, and build on top of it. Not throwing away the past, but finally being able to read it.
The magic was never about the technology itself. It was about making something that didn't exist before.
Thirty years later, I feel it again. Different tools. Same magic.
