AI hype is everywhere in the tech landscape today, and there are many rumors and falsehoods about it's value. It feels like everybody from large corporations to aspiring startups are lunging at the opportunity to insert AI into their tools and products to capture their share of the tech angel investing budget.

Personally, I think that AI is a crucial tool, but like any tool, it has its good use cases and then use cases that it just doesn't excel at. Maybe this statement will age like milk in the future, but I'm fairly confident that AI in March 2025 is a glorified text generation tool. However, like any tool, I do realize that my own bias comes from the bad anecdotes I've seen in person and read online. I don't actually understand how AI works behind the scenes, I just see the final "products," even if they aren't necessarily applied to the correct or appropriate use cases. So, I set aside some time this spring to truly sit down with a good friend of mine and genuine learn what AI has to offer and how it works at a lower level.

But first, let's check out some final graphs showing the data + context I used to keep everyone engaged. This same context eventually is fed into the ML training loop and used to train the AI.