Like a lot of people who work with AI now, our founder's first exposure was not ChatGPT or any of the modern tools. It was ELIZA, and he was a kid at the time.
The fascination came first. Then, fairly quickly, came the realization of how the thing actually worked, which turned out to be the more interesting part.
Taking The Toy Apart
ELIZA is the kind of program that impresses you and then disappoints you in the same afternoon. You type something, it reflects it back at you in the shape of a question, and for a few minutes it feels like something is on the other side of the screen.
Then you notice the pattern. Once you see the mechanism, the magic drains out of it, but something better replaces the magic: the desire to know how any of it works. That single moment of seeing behind the curtain is what pushed Elliott toward code instead of away from it.
LISP, COBOL, FORTRAN
That curiosity led straight into LISP, COBOL, and FORTRAN. These are not resume languages anymore, and nobody is going to be impressed at a networking event when you mention them.
They were, however, an excellent education. Languages of that era did not hide much from you, and working in them builds a habit of asking what is actually happening underneath the abstraction. That habit turned into a career built around understanding how systems really work under the hood, which is the same lens Web Experts brings to AI work today.
None Of This Was Overnight
What everyone is seeing right now did not show up out of nowhere. It is the result of decades of iteration, failure, and rediscovery, with long stretches where the whole field looked like a dead end to outsiders.
That matters because it changes how you evaluate the current moment. If you think this all appeared in late 2022, every new release feels like a miracle or a threat. If you know the longer arc, you can look at a new tool and ask the more useful question, which is what problem it actually solves.
Why The History Helps
Web Experts has been building on the web since 1999, and one thing that experience teaches is that hype cycles rhyme. The tools change, the promises get louder, and the practical question stays the same: does this do real work for a real client.
Knowing where something like ELIZA fits in the story makes it easier to stay calm about the rest. We use these tools every day, we like them, and we still want to know what is happening behind the curtain before we bet a client's project on it. That is the thread running from a kid poking at a pattern-matching chatbot to a firm shipping AI-assisted work now.