> You'll be impressed once the successor of chatGPT takes your job.

Which part of it? ;) Leading a team of software engineers? Identifying and negotiating vague parts of business requirements? Designing technical specs? Or maybe the part where I am responsible for software actually working correctly as business expects it to?

It would totally make the coding-in part faster (just as IDE suggestion do), but this was always the brainless tedious manual labour part.

> You realize that chatGPT wasn't trained to be a programmer right?

I realize that neural networks are unable to generate correct formal (where each minor detail has specific and important meaning) descriptions by design.

Neural networks are great for task where minor details are largely unimportant compared to overall "impression" - generating visuals, informal texts, music, probably image/video decompression, etc. On the other hand, while they can mimic "overal look", they can't guarantee (and in practice they always fail in that regard) that each detail of the produced artifact is correct. Which means you can't reliably or productively use them for programming, legal texts, construction design (though it can be used to draw inspiration for the overall image), etc.

> All of what you said is true yet you are blind if you can't see why it's revolutionary

I never said it's not revolutionary. I merely point out its hard limits.

> In fact it can do better. You can specify all the requirements you want. No use of undefined variables, no hardcoded currencies. More flexibility more features no routing with if statements.

Sure, you can specify every minor detail: how the data should flow, which patterns should be used, which things should be pulled from configs, how the interfaces should be structured, and a shit load of negative prompts. But that's the details that only the domain expert would know. And again, there are no guarantees that the result would actually be correct: the expert will have to review all this extra-attentively, cuz there is no chance that expert's assumptions are the same as NN's "assumptions".

So you basically still need a domain expert, who now has to do extra (guess)work, instead of just writing a formal description directly in code. What's the profit then?

> Artists are ALREADY starting lawsuits because the art is on par with what they create

Technically artists are starting lawsuits due to copyright. Also, technically, an artist can easily tell the difference between raw NN output and an actual drawing, sometimes even non-artists, as the images often look somewhat uncanny.

AI artists actually typically do shit load of prompt-enginerring, pipe different parts of image through different NN's (appropriate to specific situation) and do a lot of manual post-processing so the result looks good.

> I don't know if you realize this but drawing something photorealistically is actually significantly HARDER then programming

These are two completely different tasks. You are comparing apples and oranges, that can't really be put on a same scale, unless by "HARDER" you specifically imply the amount of brainless tedious work required to complete the job.

Also, in practice artists just use and process real photos when they aim for "photorealistic" - no one actually draws photorealistics from scratch, normally (but one can obviously invent any kind of challenge for themselves if they want to)

> There's literally no Bootcamp to produce a fine artist in a year because it's brutally hard to do

Who told you that there is a bootcamp that can produce a fine software engineer in a year? It takes (a talented-enough person) at the very least 5 years of rigorous study and practice before one can actually start working somewhat autonomously without constant supervision, while also delivering appropriate quality.

> If AI can crack art, there's not much more time before it cracks programming. Don't kid yourself.

Don't kid yourself thinking that these two are similar or comparable sets of tasks.

> chatGPT isn't even useful enough to be used in a professional environment. I AGREE.

That's actually not true and I never made such a claim. ChatGPT is EXTREMELY useful in a professional environment, but only for a specific set of tasks, while being used as a tool by an expert with actual responsibilities.

> The successor to chatGPT, however will be.
> They are afraid of chatGPT because it is a herald about the AI in the future that WILL.

The first GPT and GANs were heralds. ChatGPT is already a relatively mature and refined technology. I don't know why you expect to see low base effect here - the base is already actually pretty high.

> chatGPT says:

"Handling Personal Data" - somewhat scratches the surface, but it doesn't mention actual problematics (that first and foremost it's a regulatory matter and all the specifics stem directly from it).

"Ensuring Verifiability and Correctness" - clearly confuses runtime and compiler properties with quality assurance, way off.

"Productive Quality Assurance" - didn't understand the productivity issue (to test or not to test) and even if we drop "productive" part, the process it describes is also incorrect: engineers don't really ever work with the QA team in order to establish TDD.

"Robust and Scalable Systems Architecture" - way off, while you'll often see service discovery, nginx, HAProxy, etc in scalable systems, that's not what makes scalability. Properly managing state and persistence in appropriate places does.

"Managing Complexity" - way off. I don't suppose this one even requires an explanation, total gibberish.

"Observability" - as expected, this is a rather good one. Unlike other points (which are concepts/problems) - this one is a rather well defined term.

"Productive documentation and self-documenting approaches to work" - totally ignored "productive" part and just gave a definition of "self-documenting" along with some rhetorics on the fact that people document stuff in general.

Notice how each one of them also for some reason mentions a kind of business and languages and frameworks, which are totally unrelated.

Basically, even if you ignore "brain-farts" (which is a good example of "minor" incorrect details that make NNs inappropriate tool for complex formal stuff) it only really got - AT BEST - 2-3/7 right. Now, imagine it's a real world and you are betting millions on it, without having an expert-overseer to tell you when it brain-farts or if the output is even remotely correct.

Actually, what was the prompt? Seems like you just asked it to describe the list I gave you, which essentially means you just used my own expertise, understanding and creativity, not GPT's, as it didn't even give you a list of concrete problems.

> The squishy stuff is what's easy for chatGPT because chatGPT is SPECIFICALLY trained on that stuff.

Not sure what you mean here by "squishy stuff" or "SPECIFICALLY". ChatGPT is a language model trained on a huge-ass volume of non-specific text corpus.

> It's the coding and math that it has more trouble with because it's NOT trained specifically on code and math.

Nope, that is merely a property and a limitation of the NNs. At best, you can use them to build up "intuition" to bruteforce problems (like AlphaFold for protein folding), but obviously it only works for simple-enough stuff that can actually be bruteforced, when the output can be easily formally verified fast-enough.