The systems-centric era

Most AI products today use the same models. But they don’t create the same value. Behind the frontier, increasing AI performance is less about choosing a model and more about building the system around it. Memory, tools, retrieval, workflows, latency, trust, interfaces, evaluation, and how all of those parts work together. This is where products become much better or much worse, and is the defining characteristic of what I’m calling the systems-centric era. We always build on what came before. First was the model-centric era. Researchers treated datasets as static and focused entirely on code; tweaking architectures and hyperparameters to beat benchmarks. It drove incredible progress. But benchmarks saturated, and we needed a new path for complex problems. That led to the data-centric era. The model became the fixed variable, and the engineering effort shifted to the data. We learned that massive quantity mattered, but quality mattered just as much. There’s still a lot of progress to…