AI is not “hitting a wall” in the way people think.

AI is not “hitting a wall” in the way people think. But it is approaching a structural limit that most discussions completely miss. And that’s where things get interesting. The common narrative right now is either: Both miss the real dynamic. The truth is more subtle: We’re not running out of capability. We’re running into economics, architecture, and control problems. My latest post breaks this down: AI systems are getting better at generating outputs. But the system around them is getting harder to sustain: So the real question isn’t: “Can models get smarter?” It’s: “Can we afford to run intelligence continuously at scale?” The wall isn’t intelligence. It’s persistence economics. Right now, most AI systems still behave like this: generate → respond → reset → forget But real usefulness at scale requires: Without that, you don’t get intelligence infrastructure. You get expensive autocomplete. This shift changes everything about how AI systems will evolve: This is where things like ARC-Neuron and LLMBuilder come in: not as “AI tools,” but as early attempts at building persistent AI runtime economics. AI isn’t slowing down. It’s transitioning from: “capability problem” to: “systems design problem” And most people are still arguing about the wrong layer. Full post: https://dev.to/tizwildin/ai-is-heading-toward-a-wall-and-most-people-still-dont-see-it-4f0b