I've Seen This Before: What AI Has in Common With Every Technology Wave That Came Before It

People talk about AI as if it appeared out of nowhere three years ago. It didn't.

The technology underneath it, neural networks, machine learning, and the ability to analyze massive amounts of data and find patterns, has existed for decades. The difference isn't the idea. It's the price tag. When I was at IBM early in my career, we were already experimenting with neural networks. The capability was real. The problem was the cost of processing and storing enough data to do anything meaningful required infrastructure that only the largest enterprises could afford. The technology was there. The access wasn't.

What changed recently is that those costs collapsed. What once required a server room and a team of specialists now fits on a laptop. That changes everything, but not in the way most people think.

The Pattern Always Looks the Same

Every major technology wave of the last fifty years has followed the same arc. The largest companies with the most resources get access first and build a competitive edge. They move faster than everyone else, capture market share, and by the time the technology reaches smaller businesses, the window for real advantage has started to close.

The internet is the easiest example. Amazon saw it early and built a structural advantage that most retailers couldn't overcome, no matter how hard they eventually tried. eBay saw it and created an entire category, online auctions, that hadn't existed before. Banks saw that online banking would save them a fortune in branch traffic and invested heavily before their customers even expected it.

For smaller companies, the internet eventually became table stakes. You either had an online presence or you didn't compete. The businesses that decided to wait for it to settle down found out too late that it had already settled, and not in their favor.

I Watched This Happen With CRM

Earlier in my career, I ran a marketing operation that relied heavily on direct mail. We tested different versions, tracked which ones performed, and used the results to get a little smarter with every campaign. It worked. Then came the idea of one-to-one marketing, pioneered by Don Peppers and Martha Rogers, and the vision shifted entirely.

The thinking was remarkable: if you knew enough about a person's purchasing history, you could predict their behavior and market to them as an individual rather than a broad demographic. I believed in it. A large credit card client I worked with believed in it enough to spend millions building a data warehouse designed to make it work.

It didn't work. Not because the idea was wrong. Because the technology wasn't ready, storage was too expensive. Processing was too slow. The system couldn't analyze the volume of data fast enough to be useful. It was the right vision in the wrong decade.

Today, a thousand companies have more data on your customers than that credit card client ever imagined having — and they're doing exactly what we tried to do then, at a fraction of the cost, in real time.

This Wave Is Moving Faster

Here's what's different about AI: the gap between "enterprises are using it" and "anyone with a laptop can use it" compressed from decades to years. The diffusion is faster than anything I've seen in thirty years of watching technology move through markets. That changes the math on waiting.

I had my car serviced recently at a small repair shop. Good mechanics, fair prices, and a process held together almost entirely by handwritten notes on a pad. When they called to give me a quote, the person on the phone couldn't answer my questions, so I drove down. She had to flip through pages to find my record. I eventually took the printed quote home, typed it into ChatGPT, got a clear explanation of every line item, and called back to approve it.

Within five miles of that shop, there are roughly thirty competitors. One of them, sooner or later, is going to send a digital quote with photos, the mechanic's explanation captured by voice on a phone, everything documented and delivered before the customer has to ask a single question. That shop is going to feel completely different to a customer like me. I'll go back to them. I'll tell people about them.

The shop that figures this out first isn't waiting to see how AI sorts itself out. They're the ones doing the sorting.

Don't Be the Brick-and-Mortar Retailer

The retailers that waited for e-commerce to stabilize mostly didn't survive to see it stabilize. The businesses that thrive through a technology transition are rarely the ones that moved first — but they are rarely the ones who moved last.

You don't need to overhaul everything at once. You need to find one problem in your business where the return is real and specific, get that first application working, and build from there. The competitive edge comes from starting, not from having everything figured out before you do.

That's exactly what ThriveCRM is built to help you do. If you're not sure where to start, the AI Readiness Scorecard takes two minutes and gives you a clearer picture than most businesses ever get.