Now, of course, we can train an AI system that understands all of this — a bit about how to fold a protein, how to fold the laundry, how to turn video into generated imagery. It becomes a jack of all trades, because we did not know what we were doing； we wanted to keep it open, with just one outcome-oriented alignment: keep the user using the system. But that is not the only way to use AI. There are people who know what they are doing, who want the AI only as a glorified chess clock — to transcribe, summarise, highlight differences and build widgets. For each of these, what we call small or narrow language models use literally less than a thousandth of the energy, because of the much smaller parameter size. They also do not need a data centre — this phone can run it. In fact, the local model we train runs on-device, so no data centre is required to fine-tune or to run the next conversation, if you know what you are doing.