[Iv]

I remember a pretty old interview with Linus Torvalds where they are talking about object oriented programming. The interviewer asked him if he expected a similar paradigm change in the coming years and I remember being surprised by his answer: (quoting from memory) No, I don't see anything big coming. Probably the next change will be caused by AI.

Yes, differentiable code is already a new paradigm (write a function with millions of parameter, a loss function that requires more craft than people realize and train). That has a property that used to be the grail of IT project management: it is a field where, when you want to improve your code performance, you can just throw more compute at it.

And I think that the clumsy but still impressive attempts at code generation hints at the possibility that yet another AI-caused paradigm change is on the horizon: coding through prompt, adding another huge step on the abstraction ladder we have been climbing.

Forget ChatGPT coding mistakes, but down the road there is a team that will manage to propose a highly abstract yet predictable code generator fueled by language models. It will change our work totally.

[zelphirkalt]

We might get into another slump of efficiency as an outcome of this, again stopping us from making the most of the hardware and computational resources we have, due to prompts being too unspecific. Did not specify the OS your code will run on? Well, we better use this general cross OS available library here, instead of the optimized one for the actual OS the thing will run on.

The same mentality, that causes today's "everything must be a web app", will caused terrible inefficiency in AI generated (and human prompted for) code. In the end our systems might not be more performant than anything we already have, because there are dozens of useless abstraction layers inserted.

At the same time other people might complain, that the AI does not generate code, that can be run everywhere. That they have to be too specific. People might work on that, producing code generators which output even more overheady code.

At least some of that overhead will slip through the cracks into production systems, as companies wont be willing to invest into proof-reading software engineers and long prompt-generate-review-feedback cycles.

[Iv]

I think you underestimate the ease with which we will be able to refactor code.

[mattgreenrocks]

This feels like re-discovering DSLs, except the syntax is English, and the implementation is a blackbox.

[giobox]

I don't feel a comparison to DSLs works here at all. If you are just using plain human language, is a comparison to DSLs apt?

The point of DSLs are to provide a deliberately limited-scope language optimised for a specific problem or problem domain. LLMs that use general human language is like the furthest opposite of a DSL - its the broadest scope language for describing any problem, and they try to solve them all.

Also, few popular DSLs are truly blackbox in the sense chatGPT is - many of them have exposed source or even line-by-line debuggers available. There are a ton of other reasons this doesn't make sense to compare.

[umeshunni]

DSL being https://en.wikipedia.org/wiki/Domain-specific_language

[Iv]

The important aspect is not the language, but the abstraction level at which we will be.