The second half of your article made me think of Gwern’s LLM daydreaming proposal (July 2025)
Despite impressive capabilities, large language models have yet to produce a genuine breakthrough. The puzzle is why.
A reason may be that they lack some fundamental aspects of human thought: they are frozen, unable to learn from experience, and they have no “default mode” for background processing, a source of spontaneous human insight.
To illustrate the issue, I describe such insights, and give an example concrete algorithm of a day-dreaming loop (DDL): a background process that continuously samples pairs of concepts from memory. A generator model explores non-obvious links between them, and a critic model filters the results for genuinely valuable ideas. These discoveries are fed back into the system’s memory, creating a compounding feedback loop where new ideas themselves become seeds for future combinations.
The cost of this process—a “daydreaming tax”—would be substantial, given the low hit rate for truly novel connections. This expense, however, may be the necessary price for innovation. It would also create a moat against model distillation, as valuable insights emerge from the combinations no one would know to ask for.
The strategic implication is counterintuitive: to make AI cheaper and faster for end users, we might first need to build systems that spend most of their compute on this “wasteful” background search. This suggests a future where expensive, daydreaming AIs are used primarily to generate proprietary training data for the next generation of efficient models, offering a path around the looming data wall.
I don’t have a sense of whether this idea is good, I just aesthetically like it.
The second half of your article made me think of Gwern’s LLM daydreaming proposal (July 2025)
I don’t have a sense of whether this idea is good, I just aesthetically like it.