AI Doesn’t Need to Be All-Knowing. It Needs to Be Well-Trained.
Right now, we’re treating AI like it should be omniscient. That expectation is the problem. Most large AI platforms are being built as massive, do-everything systems. They’re expected to write, design, research, analyze, ideate, generate images, and somehow be exceptional at all of it. But that isn’t how expertise works. It isn’t how humans work either.
In practice, these systems behave more like interns. They can get basic work done. They can move things forward. They are helpful. But the output is rarely exceptional, and that’s not a failure. It’s a reflection of reality. Only a small number of people in any discipline ever reach true mastery. I feel this difference very clearly in my own work. When I use AI for writing, I’m generally satisfied with what comes back. Writing isn’t my deepest craft. I don’t have a hyper-trained sensitivity to every word choice, rhythm, or nuance. So the output feels strong enough to me. But when I move into visual design, everything changes. The images AI produces are almost never quite right. They’re close, but close is not the same as good. I can immediately see what’s off. The composition, the lighting, the proportions, the tone. That’s because I’ve spent my entire career training my eye. I know exactly what great looks like, and I know why something misses.
To get usable results, I often have to take AI-generated images into Photoshop, refine them myself, and feed them back into the system. I’m effectively teaching it. Showing it what I mean. Explaining the difference between acceptable and excellent. That’s a power-user move. Most people won’t do that, and they don’t need to. To them, the first output looks impressive enough. But to experts, the work reads as junior. It has the same tells a junior designer has. It’s competent, but unsophisticated. And trying to make one system do everything only guarantees that mediocrity becomes the ceiling. This is where I think the future of AI gets interesting.
Instead of massive generalist platforms, I believe we’ll see highly specialized systems built for very specific disciplines. Tools trained deeply, not broadly. Systems shaped by experts who actively mentor them, the same way you would train a junior designer or writer over years of close collaboration. Not trained on the general internet alone, but refined through high-touch feedback. Not just asking for another version, but explaining why something doesn’t work. Teaching taste. Teaching judgment. Teaching nuance. Mastery doesn’t come from volume alone. It comes from guided repetition, context, and critique. If we want AI to produce expert-level work, we need expert-level gatekeepers shaping it. The future isn’t all-knowing AI. It’s well-trained AI.