SOFTWARE DEVELOPMENT IN THE AI ERA - ARE PROGRAMMING LANGUAGES ALMOST OVER?

​For the longest time I’ve put off adding support for Google Gemini in Aurora Toolkit, because Google’s API was more complicated than others based on OpenAI’s API design, and I wanted to move on to various feature ideas I had like implementing workflows and agents. I figured eventually I’d come back around to Gemini support when I needed it, or someone else would clone the project and add it themselves.

​With all the talk about Gemini 2.5 Pro exp being “the world’s best coding model”, I tried using it to see what it can do.

Giving it access to the AuroraLLM module,​ so it could understand the structs, protocols, and how the Anthropic, OpenAI, and Ollama implementations worked, I asked Gemini to write a GoogleService implementation to match.

I don’t know whether to be thrilled or terrified, but it nailed a 99.9% working implementation in one shot, ​with the most minor of manual corrections afterward – mainly from it guessing how to use the debug logging which is defined in the AuroraCore module that I didn’t provide. So much so that after testing it with some of the practical examples in the toolkit, the commit message I wrote simply says:

​- Note: Written by Gemini 2.5 Pro experimental, with some minor edits

​While a lot of the toolkit has been written with AI as my assistant/coding partner, I drove its development, made changes, questioned ideas, refactored, and re-shared with ChatGPT to make sure it understood what I was doing and what I wanted help with. In this case, it’s mostly customization based on previous code. So, still not vibe-coding, but close enough to see the possibilities. It casts recent comments by AI industry leaders like Sam Altman and Dario Amodei that AI will be writing nearly all code by the end of this year in a different light to me.

​Knowing that the major players will need to one-up Google, this is all but certain to come true – if not the end of 2025, certainly by the end of 2026. For now, and the foreseeable future of software development, one of the best skills we can build is working efficiently with AI tools. ​

​We’re probably on the cusp of a decline of programming languages designed for humans.

I expect hardware capabilities to become much more important than software languages designed to use them. When an AI can understand all the capabilities available to it, it can effectively write its own custom language as needed based on what you ask it to do with them.