> The vast majority of our work is already automated to the point where most non-manual workers are paid for the formulation of problems (with people), social alignment in their solutions, ownership of decision-making / risk, action under risk, and so on.
There's a lot of pretty trivial shit to automate in the economy, but I think the gist of your comment still stands. Of the trivial stuff that remains to be automated, a lot of it can be done with Zapier and low-code, or custom web services. Of what remains after that, a lot is as you (eloquently) say hugely dependent on human agency; only a small fraction of that will be solvable by LLMs.
As the CTO of a small company the only opportunities for genuinely useful application of LLMs right now are workloads that would've could've been done by NLU/NLP (extraction, synthesis, etc.). I have yet to see a task where I would trust current models to be agents of anything.
The bulk of the computer work for the “knowledge class” is data mangling and transit. Like managing a SaaS app for your sales pipeline inputting results/outcomes of leads, aggregating stuff happening in various another places, uploading lists and connecting other SaaS apps together, which all then generates other data that gets translated to excel (because SaaS BI tools are rarely good enough) and humans analyze it and communicate the data.
Even though we have a million web services there’s still tons of work getting the data in and across them all as they are all silos with niche usecases and different formats.
There’s a reason most Zapier implementations are as crazy as connected Excel sheets
There's a lot of pretty trivial shit to automate in the economy, but I think the gist of your comment still stands. Of the trivial stuff that remains to be automated, a lot of it can be done with Zapier and low-code, or custom web services. Of what remains after that, a lot is as you (eloquently) say hugely dependent on human agency; only a small fraction of that will be solvable by LLMs.
As the CTO of a small company the only opportunities for genuinely useful application of LLMs right now are workloads that would've could've been done by NLU/NLP (extraction, synthesis, etc.). I have yet to see a task where I would trust current models to be agents of anything.