Earlier quoted context omitted.
I can imagine a future where new software is, mainly, written by humans while legacy software is maintained by ai. It can take months for a new developer to understand a legacy code base but an LLM with a big enough context window would be instantly productive.
The hard part of accounting systems is doing the requirements analysis (including interviewing humans who give inaccurate or incomplete answers) and writing detailed functional specifications which account for every possible edge case. No LLM can do that. Once you have the detailed specifications the coding is relatively easy. Some of that can be partially automated using AI CASE tools, but that only gives a marginal…
One of my first one afternoon (for prompt refinement and trying it out) toy GPT-3.5 “applications”—really just a prompt—was having it interview for reauirements and draft and progressively refine a requirements doc in a specified (by reference to an author in popular Agile literature, not by specific templates, so just relying on the model’s general training) format. Its pretty much what convinced me of the broad utility of the technology.
I absolutely think that with a bit of effort, GPL-4-128K, a custom RAG framework and/or the Assistants API, you could build something that handles a lot of this. I’m not really bullish near term on LLMs as full replacements, but I can see it as a big force multiplier.