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This is not surprising. AI in its current form is more obviously useful at generating software than it is at assisting with other kinds of knowledge work. Software can be automatically validated. Validation loops are what let these systems iterate toward acceptable solutions. If you want AI to produce a document for humans, you need to carefully review and revise the output. In that case there's little to be gained, as reviewing plausible but subtly incorrect information sentence by sentence is in some ways harder than authoring it yourself (a quote from someone who worked in like city planning). Depending on the company/industry, some of the non-tech sector workloads still involve phone calls, parcels, faxes, etc -- things AI can't really do.

Yes there are technical solutions to such problems; I'm sure there's probably a startup to make phone calls that's hooked into your company's knowledgebase, multi-LLM workflows for document review, etc. But non-tech companies aren't adopting those. Tech companies came for the software generation and stayed for the general knowledge worker productivity. Since 2023 they made the investments to procure services, change culture, and generally re-tool. Software companies are also uniquely positioned to deal with the limitations of AI in-house, like by building custom glue components to link systems, building controls to mitigate risk, etc.

So basically the tech sector leverages AI more than other sectors. At a company level, non-tech has less investment, less training, less integration of AI with business processes. At an individual level this looks like workers having fewer tools, not knowing how to use the tools they do have, not knowing even what the tools' capabilities are, and incorporating them into their work only superficially if at all.

Someone asked me for help using AI at their job (which is tech adjacent). They were trying to create a process document using ChatGPT. Their selected model was GPT Luna Light (!), and they were copying and pasting data between ChatGPT and SharePoint. There _was_ a SharePoint integration but it lacked write access. The model was not aligning the doc images. Their prompts were not specific. It was a bad experience. Their conclusion was that AI was stupid and not worth the trouble.

So even though the non-tech sector can't take advantage of AI to the same degree that the tech sector can, they're utilizing it even less than they could be.

> It’s 7:40 on a Tuesday morning in 2031, and you haven’t written a line of code yet. You probably won’t today, and that’s fine.

This sounds exactly like all my Tuesday mornings this year, except that I wasn't at work at 7:40

The full quote from that para

> We like to believe that we form our opinions by gathering evidence, weighing facts, and arriving at reasonable conclusions. Be for real. No we do not. Here’s how it actually goes: We form an emotional impression first, then we build a belief around it, and then we go looking for facts that support it. This is my theory as to why we’re so divided in this country. Beliefs built on emotion are nearly impossible to reason with.

This idea is explored in depth in The Righteous Mind: Why Good People Are Divided by Politics and Religion by Jonathan Haidt.

And related David Hume quote:

> Reason is, and ought only to be the slave of the passions

In the Aeroplane Over the Sea by Neutral Milk Hotel:

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But he would also tell you how confident he was in his guesses

All of the handful of tools in this space that I've tried have modeled an overlap/coupling between project, directory, and machine. For my workloads specifically I think these should be organized differently. Something like top-level projects that have child task/goal/sessions with each one fanning out to different agents running potentially in different worktrees, directories, and even on different machines. E.g. I have a lot of notes and other context stored locally. On a different machine I have repositories checked out and build tooling installed. Frontend and backend exist in different directories but can both be relevant to a given unit of delivery. Are there any agent orchestrators that can model things that way?

Yes, a subject matter expert who uses their domain experience to word the prompt correctly, incorporate tacit knowledge and relevant sources, ask follow-up questions, and judge the LLM-generated responses for relevance and correctness is not taking a shortcut. They're using a tool to provide a better and faster answer than they would have been able to otherwise. It's a matter of how the tool is used and maybe the optics around how the response is communicated, but relaying Claude's final answer is not inherently the wrong thing to do.

Sorry, I should’ve provided a warning. It is sad that something so distressing is just the daily reality for thousands of Americans.

The Devil in the White City stands out as one of my favorite books. And I live near the location of the prison in Philadelphia where HH Holmes was hanged, though it’s now the site of a grocery store. So I’m regularly reminded by the historical marker sign.

It's kind of antithetical to the tool's deterministic positioning, but have you considered making TERMy leverage an LLM for unseen or low-confidence queries, and then generate the config and update itself to make future similar queries deterministic?