I am in no way an AI researcher or a machine learning expert. I am, however, a full stack web developer working on the practical side of shipping code and maintaining infrastructure. From that perspective, this is where I suspect the industry is heading over the next few years.
Much of the recent progress in AI has been driven by raw scale: massive clusters, ballooning parameter counts and a race toward artificial general intelligence. But if we look past the marketing hype and venture capital rhetoric, engineering infrastructure tends to evolve in much more predictable ways. The immediate future of AI appears likely to be less about explosive leaps in intelligence and more about architectural optimisation, cost reduction and standardising how these tools communicate with our existing codebases.
Looking ahead to 2030, I think the biggest changes won't come from AI becoming dramatically smarter. They'll come from how we integrate it into the software we already build.
Token Economics: Efficiency Over Scale
The economic returns from simply scaling parameters appear to be diminishing, shifting more engineering effort toward inference efficiency. While companies may still be investing enormous sums into training larger models to squeeze out every last bit of intelligence, the immediate commercial focus is rapidly pivoting to inference optimisation.
