Memory Is Not Reasoning
The next frontier is architectural, not bigger
The dominant assumption in AI is that intelligence scales with size. Pack more knowledge and more reasoning into one set of weights, train on more data, and capability follows. It worked, but it bundled two very different things into the same place. A model’s knowledge of the world and its ability to reason over that knowledge now live in the same parameters, trained together, paid for together, and impossible to pull apart.
We think that bundling is the wrong abstraction. A person does not relearn the world every time they reason through a problem. Memory and reasoning are distinct faculties, and the most interesting work in AI is starting to separate them.
Two public results point the same way. DeepMind’s Mixture of a Million Experts treats knowledge as a vast store of tiny experts, retrieving only the handful a query needs and decoupling what a model knows from what it costs to run. From the other direction, Samsung’s Tiny Recursive Model treats reasoning as a small recursive loop: 7 million parameters scoring 45% on ARC-AGI-1, beating Gemini 2.5 Pro, DeepSeek R1, and o3-mini with less than 0.01% of their parameters. One unbundles memory. The other unbundles reasoning. Neither needed scale to win.
The metric that falls out of this is not parameter count but intelligence per FLOP, capability for every unit of compute and energy spent. The author of the TRM paper calls the belief that hard problems require billion-dollar foundation models a trap, and the results are starting to agree.
We think the next frontier is architectural. The teams that separate memory from reasoning will deliver the same intelligence at a fraction of the cost, and that efficiency is exactly what physical AI and on-device inference have been waiting for. Scale was never the point. It was a proxy for the things we had not yet learned to build directly.