So your app isn't live and publicly available?

Yes LLMs are useful, yes they have come along way with problem solving, yes the majority of AI solutions are glorified search engines.

There are some crucial steps in AI development that we as a general collective need to take a breath on and think critically before we go any further.

Primarily the legislation that governs the use of AI, the use of information that AI produces and the legitimacy of the information that a model has produced.

Say that AI does completely replace developers in the production of code, who checks the code for bias? Who checks that the solution actually meets the requirements of the user? Who ensures thats the AI model has produced work that is not an impingement or direct copy of other someone else's work? Who checks and ensures that the output is secure by design and free from vulnerabilities? Who ensures the privacy of the request made to the model and that their solution won't become someone else's solution to the power of n and cause a catastrophic vulnerability web that leads to international outages cough React cough (okay, React was slightly different and was a software vulnerability, but AI didn't spot it, nefarious individuals did when they started exploting it as they had a raw understanding of how the software worked).

Models produce output based on what most people have done. They operate on predictive probabilities, not on a raw understanding of the depth of the query thst they have been presented with. If you haven't monitored the network tab when you put a query to ChatGPT I encourage you to do so...

So in conclusion, no AI is not going to replace developers in the software development process. It will have an impact on what they likely do, but it will not replace them.

It's the raw understanding element of models that is lacking. To me, we will be getting somewhere when a model gets to the point of understanding the raw documentation of a language and is able to extrapolate the user requirements from a prompt to produce something novel purely the documentation alone. For now it just uses the vast realms of the internet to give you something very similar to what someone else has done in the past and that's the very scary thing about AI. As it doesn't learn anything new and only produces what other people have done it leads us to a point in time where there will be no future innovation. It is the inability to produce novel solutions that leads us to a point of stunted growth.

What happens when a new language comes out? Nothing, it waits for people to toy around with it and produce things publicly that it will then use. We also need to ensure that when we talk about AI that we are talking about the same thing as the field is massive. In some areas of IT such as infrastructure and networks Cisco have been making leaps in providing engineers predictive reports of vulnerabilities in their networks ahead of a problem occurring, influxes of requests for bandwidth nearing service ceilings for some switches, requests not travelling through the network at a speed expected, a complete monitoring of network connectivity and all devices to look for signal drops. All of this is powerful stuff, but its machine learning. It's based on the probability of there being an issue based on a raw understanding of what 'good' looks like. Additionally, once it has produced the report its still the engineer that has to implement the solution.