Replit CEO Amjad Masad on how general models could train smaller, domain-specific models on the fly:
"There's a lot of talk of recursive self-improvement, but there's something I don't think is getting a lot of discussion, which is models training their replacements."
"You can think of it as a just-in-time compiler. As you're executing dynamic code, the interpreter realizes there's an opportunity to optimize, and it emits machine code on the fly that's a lot more optimized."
"You can imagine general models, you're doing something with Operator or Astra, some of the big models, and they realize the use case is limited, or some other agent observing realizes the use case is limited."
"General agents have all these flaws, but there's also more potential for them to be harmful, more potential for them to go off the rails."
"So the model, on the fly, trains a model that could be its replacement, but is a lot more domain specific. Therefore it's cheaper, less vulnerable to prompt injections, and less harmful for you, because it's less capable."
"It's almost like a system that's training machine learning models for specific use cases as it's monitoring the entire system."
@amasad