The big, flashy advances in AI progress that I hear about seem to be pretty fundamental models like GPT-3 or AlphaZero, systems that are trained by throwing a big neural network at a complex problem. It seems like there are some reasons to expect this approach to work well; most fundamentally 'the bitter lesson' that scaling a good architecture beats clever specialized systems. (e.g. my understanding is that AlphaZero learns faster and performs better than previous systems that were trained on a particular game and/or given game-specific heuristics.) 

But from reading Drexler's work on AI services, it seems natural to me that the first AI systems used for complex tasks will tend to make use of existing complementary tools rather than reinventing the wheel. My sense is that this is true for e.g. current autonomous vehicle prototypes, which have specialized, "dumb" computer vision and control systems and don't make much use of general-purpose reinforcement learning systems. 

So my question is: what are the prospects for AI systems that are trained to make use of existing tools, like a GPT-3 clone that can query a calculator rather than having to figure out addition from scratch? In what cases are they promising, and who's making use of them? Should we expect to see more natural language models like this as the field evolves?