I sat at a table in the gym at my son’s school. Around the room there was a dog groomer, a police detective, someone from the state park maintenance crew, and an archaeologist. We were there for career day. My topic was AI.
When each group of kids came over, I played them a song. I told them my son made it. He had the idea, shaped the lyrics, and used AI tools to bring it to life. It’s on Spotify and Apple Music. The instruments, the vocals, the production were all generated by AI. But the idea, the story, the words were his.
Some of the middle school kids had already used ChatGPT. A few had used it for homework. One wanted a training plan for a video game he was trying to get better at. One girl said she used it as someone to talk to.
That last one stopped me. The adults in the room were thinking about AI in terms of what it might take from these kids. That girl was using it for something none of the adults had thought to offer her.
I’ve spent more than a decade working in AI. In that time, the most important thing I’ve learned is that the most interesting question about any technology is never what it’s supposed to do. It’s what people actually do with it.
The Song
In June 2022, my son and I were in Colorado. My Tampa Bay Lightning were playing his Colorado Avalanche in the Stanley Cup Finals. We went to Game 4. The Avalanche won 7-0 on their way to winning the Cup.
It was a rout. It was also one of the best nights we’ve had.
My son has epilepsy. His memory doesn’t work the way most people’s does. Some things stick. Some things don’t. The reasons aren’t always clear, and he doesn’t always have control over which is which. But that game stuck. The score, the crowd, the improbable joy of watching your dad’s team get demolished on their way to losing the championship. That one he kept.
We still talk about it. We laugh about it. It became a core memory in a brain that doesn’t always hold onto things.
A while back he decided to make a song about it. He had the idea. He worked on the lyrics, using AI to help shape them. He used Suno to generate the music and the vocals. The result was a real song, his song, about that night in Colorado. It’s on Spotify and Apple Music.
I’ve thought a lot about what that means.
The Leveler
There’s a common criticism of AI and technology more broadly: that it’s eroding our ability to remember things. That when we outsource memory to our phones or our search engines or our AI tools, we lose the capacity to hold things ourselves. The concern is real, and there’s research to support it.
But that argument is made by people whose memory works.
For people whose brains don’t store things the same way, these tools aren’t a crutch. They’re access. They’re a leveler.
We take pictures the way most families do. But for my son, pictures do something different. He may not remember an event, but he’ll see a photo and his brain will construct a story from it, stitching together something coherent and believable from the visual evidence. The story feels real because it is real, even if the memory that produced it works differently than other people’s memories.
The pictures are doing the work his memory can’t. They’re scaffolding for an internal process that needs support.
The song is the same thing, one layer deeper.
A photograph captures a moment. The song is something he made from a moment. He wasn’t just present for that game. He processed it, shaped it into something, and now it exists outside of him as a record of how that night landed. If the memory ever fades, the song will be there. Not as documentation, but as something he built. Something that carries his version of the story.
That’s a different kind of artifact than a photograph. A photograph is evidence. A song is expression.
The tools that made it possible weren’t designed specifically for this. But he found them anyway. And made something that wouldn’t have existed without them.
The Use Case Nobody Planned For
At career day, I watched a similar thing happen in real time. The kids who came to my table weren’t thinking about productivity or enterprise deployment. They had things they wanted to make and questions they wanted to answer, and the tools were finally accessible enough to help them.
A training plan for a video game. An image of something specific. A song about a hockey game. A conversation when there was no one else to talk to.
The enterprise AI conversation focuses on scale, efficiency, and organizational impact. Those things matter. But they tend to crowd out the more personal use cases. The ones that emerge when someone with a specific need finds a general-purpose tool and redirects it toward something that actually helps them.
I’ve written before about the long tail of problems that were never going to get funded. The tasks too small and too niche to justify a project or a budget. This is the same phenomenon, but more intimate. It’s not about workflow efficiency. It’s about expression. About making something. About finding a way into something that felt out of reach.
My son couldn’t have made that song five years ago. Not because the idea wasn’t in him. It was. But because the tools that could have helped him bring it out didn’t exist in a form he could reach.
Now they do.
What This Actually Tells Us
The AI conversation tends to sort itself into two camps: those who focus on what the technology enables, and those who focus on what it threatens. Both camps tend to argue from the assumed mainstream. The average user, the average use case, the average impact.
The more interesting signal is at the edges. The girl who uses ChatGPT as someone to talk to. The kid who makes a song about a sporting event he wants to remember. The person who builds something small for a problem nobody else thought was worth solving.
These aren’t outliers to dismiss. They’re early indicators of what accessibility actually means when tools get capable enough to be redirected. Not the use case the designers intended. The use case the person needed.
I work in this space professionally. I think about AI in terms of systems, incentives, and organizational readiness. That’s the right level of analysis for the work I do.
But I also have a kid who made a song. And watching him play it for the other kids in that gym, watching their faces when I told them he made it, that told me something the systems-level analysis doesn’t.
The tools are getting accessible enough to reach people who weren’t in the original design. And some of those people are going to do things with them that nobody planned for.
That girl talking to ChatGPT. My son and his song.
Those are the use cases I’m watching.
I put together a handout for the younger kids at career day — simple AI prompts parents can try at home with their kids. No experience needed. Take it, use it, share it.