I agree on this front in regards to language being a poor interface for technical tasks. NLP has some extremely useful applications, but language is inherently lossy and abstract. We use the same words to describe different things in different contexts, and it’s common to incorrectly use words in contexts we are unfamiliar with. I don’t think this is a solvable problem in a general domain. Given a more specific domain, I think it can become more accurate, but domain level understanding from the user is required to interface with the model and the scope of its utility will be dependent on how it is applied. Personally I don’t think this 1-1 interface to do a thing is an appropriate design for those situations, although perhaps IDEs will begin to use some NLP to learn when to recommend certain types of refactoring tools, sort of like linters for design choices.

I think the key aspect of this is how you capture iterative refinement. One can capture a high level intent ("Build a webpage with widgets to accomplish foo", or "Buy me tickets to the playoff game") But upon inspection there are fractally increasing levels of details that need further specification. For the webpage - say authorization requirements or visual design details just as an example. Or for the tickets ... which seats? what cost is acceptable? if the next best seat is just slightly more than the state budget is the tradeoff worth it? What about the special bundle that includes some sort of promotional item, worth it? And if it is worth it this time, does that apply next time?

For code-technical decisions, design documents, or the code itself provide a good record for the ever increasing details and scope, but we certainly haven't found the best way to capture everything. Perennial debates about code documentation, or what should go in PRs, or literate-style programing and more show we haven't figured it out. But at the same time, I definitely find myself having non-productive slack conversations, or PR threads or whatever, when my colleague (or myself) prompts: "hangout?" or similar to have call and talk through the issue. Something about the real-time conversation is able to cut-through written mis-communication very well - which then can be captured back in text for the benefits of other colleagues.

Btw I want to clarify that I don't just mean verbal. Writing is natural language as well.

However, on your point I think natural language is the gateway to all those things. If you have a spec you will need to describe it in natural language first then use it.

Visual though is I think another way we should be able to communicate with a computer in the future and it seems like we are heading in that direction.

I guess how I imagine all this working is you provide all this high level instruction to the computer through writing/speaking as well as any diagrams and it figures it all out as well as queries you for any further clarification it needs. Just like interacting with your colleagues in day to day.