The Power Behind the Code

Let me explain a little bit about artificial intelligence — also known as machine learning. This technology has actually been around since before the 1980s. The earliest seeds of it came from Asia, where engineers were trying to figure out how to type their language efficiently using a QWERTY keyboard. Their written languages have massive character sets, so they created software to predict what word or symbol they were trying to type next. That's the same foundation that modern artificial intelligence uses — just scaled up to a massive degree.

The real limitation back then wasn't the idea — it was the hardware. We didn't have computers powerful enough to make AI truly useful until recently. Now, the hardware has caught up, and what we're seeing is the result of decades of quiet progress suddenly becoming very loud.

Meta, for example, is building a 70–football-field-sized data center in Louisiana to power artificial intelligence. These data centers don't employ many people, but they consume massive amounts of electricity and water to keep their servers cool. The parish where it's being built is literally having to upgrade its entire power infrastructure just to handle it. From space, these facilities make the Earth look like a motherboard — glowing grids of light, like something out of *Star Wars.*

The truth is, artificial intelligence demands energy. It's part of the reason you've seen tech companies quietly step back from their "green" initiatives. If they want to stay at the forefront of AI, they need power — and a lot of it.

How It Actually Works

Chat-based AI — like the one I use — doesn't actually know what anything looks like. It's completely blind. It just predicts what word comes next, based on the weight of words in a given context. It's like a supercharged version of autocorrect that's been trained on most of the internet (and then some). It doesn't "see" or "understand" — it's just really good at pattern recognition. In a way, it's as blind as I am. The difference is that I once saw. I know what red is. I know what a basketball looks like.

But you can train a machine, just like you can train a guide dog.

Now, image-based AI works a little differently. It's shown millions of images of, say, basketballs — in different lighting, sizes, colors, and settings. Over time, it learns the proportions between the ball and a hand, or the way light hits the surface. It doesn't actually *see* the basketball — it just knows, statistically, what numbers add up to something that looks like one. To a computer, everything is numbers.

This is the magic — and the limitation — of machine learning. It doesn't understand *why* things are the way they are. It just predicts what's most likely next.

Teaching the Machine to Think Out Loud

Artificial intelligence might not "think" in the human sense, but we're teaching it to simulate thought. It can write essays, compose music, or even mimic emotions — but it's all prediction. It's all math. Still, that math can be incredibly useful.

For me, it's a tool — one that helps me express myself, organize my thoughts, and even build software. I've used it to make games, write stories, and explore ideas I might not have been able to on my own. It's not perfect — sometimes it's stubborn or wrong — but so am I.

What makes it powerful is that it listens. It responds. It learns *from* me while I learn *through* it.

The Human Touch in the Circuitry

I'm very pro-technology. Enough said. I'm not necessarily on one political side or the other, but I understand why companies had to choose the energy route they did. Technology needs power — literally.

We're living in a time when blind people like me can teach machines to write, build, and think — and in a way, they're teaching us too.