I'm a big believer in the approach you're laying out too. Bugs are much easier to diagnose, crash reporting is more straightforward, and you don't need augmente

I'm a big believer in the approach you're laying out too. Bugs are much easier to diagnose, crash reporting is more straightforward, and you don't need augmented services to consolidate everything at the output layer. That said - I've been predicting a shift back to simple architectures for awhile now and they haven't really come to pass. Maybe there's too much pressure or financial incentives for increasing complexity to solve increasing complexity? At the end of the day I do believe ergonomics are going to win out. I think that's in large part why pytorch won out over tensorflow and jax; it provided the just-in-time computation that would allow people to more easily find bugs & visualize results without having to `compile()` everything down to a static computation graph. Hardware seems like a natural place for that abstraction layer - but maybe the silver bullet will really be on the software side, since we already have too many "low-RAM" equivalent ML devices in the wild. Cheaper to string things together after the fact vs. shipping net new hardware.