Domain knowledge as in non public aspects of the work you/ your workplace does. The AI tools are very good at whatever is public but very clueless about proprietary domains .Let's say you make CRUD apps about some confidential domain. Now the CRUD skills might be commodity but the confidential domain is even more important.
As long as there's internal documentation, which virtually every serious shop has, it can be processed and combined with AI. There are startups selling this product already. I've seen first hand some very narrowly focused domain knowledge becoming more accessible when you can ask the chatbot and the thing is right. It works.
Come to think of it, domain knowledge should be an LLMs strong suit as long as you can provide the right documentation, which is working pretty well already.
Right now the main issue I see with AI is that it doesn't do well with scaling. It's great for building demos and examples but you have to fix its code for real production work. But for how long?
In ERP software there are MLOCs without any technical documentation. And nobody would spend a dime to create one. So, the deep expert knowledge on how business processes are supposed to work (in full detail) and how they are implemented is mostly in the heads of a couple of people.
AI is most excellent at reading and understanding large codebases and, with some guidance docs, can easily reproduce accurate technical documentation. Divide and conquer.
Reading a large codebase...perhaps if it is not too large. Understanding... why a tool exists, what is the motivation for its design, what the external human systems requirements for successful utilization of the internal facing tools... especially when that knowledge exists only in the memories of a few developers and PMs... not so much.
Deep domain expertise is a long way from AI capability for effective replacement.
Again, nobody would spend a dime to create the technical documentation, even if it could be done somewhat faster with AI support. Also, in my experience AI is not so great explaining the consequences to business processes when documenting code.
Accuracy/faithfulness to the code as written isn't necessarily what you care about though, it's an understanding of the underlying problem. Just translating code doesn't actually help you do that.
No, current LLMs are already good enough to read the subtexts from documents, email, call transcripts where available. They're extremely good at identifying unwritten business practices, relationships, data flows, etc.
But everyone at the company has that private domain knowledge. The only thing you're bringing to the table that anyone in any other role doesn't offer is the commoditized skill set.
Right, and you'll not keep everything out of materials like AI
generated meeting notes for every repeat of every process so
the company doesn't really need many experts in its existing
operations.
Pre-LLMs, algorithmic knowledge was used as a proxy for skill difference at interview stage. In the workplace, you could google the implementation details and common gotchas. This was valuable knowledge.
Post-LLMs, the value of this (as differentiator) has dropped to zero. Domain knowledge (also known as business knowledge) is the obvious area to skill up on. It simply means knowledge about the area your organisation is working in. Whether it is yogurt delivery logistics, clothing manufacturing supply chain systems, etc. That's the real differentiator now. Anyone can invert a binary try in 5 minutes using an LLM. But designing a software system knowing well the domain your organisation is in is invaluable.
Right, bridging the gap of knowledge by getting closer to that of the clerical workers of the company, because pure software knowledge is no longer as valuable. That will probably make your salary closer to theirs, and that'll be a pretty big adjustment.
Can't speak to the OP, but lots of technical work (and frankly many trades are also technical) doesn't lend itself to text based documentation and teaching. Software, translation, non/fiction writing (like marketing and sales) all do. I think LLMs will take a significant part of those businesses, because I don't believe there is a Devon's Paradox for code -Tractors- Agents.
At the same time medicine, hardware design, good industrial, and specific domain knowledge (problems you solve in assembly or control loops) that are fundamentally proprietary and aren't well documented will continue to have value even when LLMs make solving the problems around them easier. Those might have increased leverage, at least for this round of LLMs. Now, maybe they succeed in World Models, but that is not today.
Really, I don't know what "kids these days" are going to do. I couldn't have predicted the influencer boom 15 years ago, but I also think there are geopolitical risks that are probably bigger than that shift, and "synergized" with the push to AI Everything, it doesn't look like a good time to be a learning/working human.
