A coder considers the waning days of the craft

>All these AI replacing coders and creatives is just a broad marketing campaign to put downward pressure on dev and creative salary.

I see it as the work being replaced not actually being valuable work in the first place, at least not as valuable as some of us want them to be.

Take copywriting for example, that industry got decimated basically overnight by "AI". Why? It's because the work was actually not valuable, it was work any sufficiently complicated program could do let alone a common man. Writing catchy sales catches and blurbs? It's frankly a miracle that industry lasted as long and valued as high as it did.

Or the art scene for another example; if you as an artist or illustrator can be replaced by "AI" then your art was actually only worth a dime a dozen. I say this as a blunt objective observation, not intended as any personal attack or the like.

Or for an even more brutal example: Journalism. We literally can't tell apart man-written and "AI"-written journalism, and that's because man-written journalism has been in the absolute gutter for years if not decades now. Journalism is bad enough today that "AI"-journalists could come in and do an equal if not better job of it than man-journalists.

"AI" is, like many tools of astronomical convenience that came before it, making the act of work more accurately reflect its real value. Some workers will lose, others will win, but ultimately we will all be better off because work and the perceived value of that work aligning better is a very good thing for both producers and consumers.

I see a lot of comments wondering how AI could be useful as a software engineer so I'll give my take on it:

I envision it being able to fully replace a junior engineer, and in some use-cases senior engineers as well.

In the case of junior engineers: the AI should have access to an internal knowledge base (i.e. Confluence) and the task/ticketing system (Jira), and ideally the internal chat (Slack). I would assign tickets to it, and I'd expect the AI to reply with comments asking for clarification when there is something unclear, or proposing an implementation before starting if it's not a very simple task (this could even be defined in the task, using story points for instance.

Once cleared, the AI submits a PR linked to the task - so far just like any engineer in the team would. The PR gets reviewed as usual, with suggestions/requests for changes made by the (human) team, which then get addressed by the AI. With the big difference that all this process may happen in less than 1h from ticket creation to PR review.

I wouldn't expect it to be able to implement complex features, onboard new libraries, or rearchitect the system in major ways to accommodate future features - just like I wouldn't expect that from junior team members.

It would obviously be amazing if it could incorporate previous PR comments into it's "context" for future work, so it could learn and improve.

Separately I mentioned it could also do part of the job of senior team members - in the form of PR reviews. If it has access to every previous PR review and learns from them it might be able to give insightful suggestions, and for very large codebases it could have an advantage over humans as it could find patterns or existing code that may be overlooked (i.e. "It looks like this util function you added is the same as this one that was already there", or "This code looks different to similar areas, but follows a different pattern, you might want to rewrite it this way //..."

Is GPT-4 there? Definitely not, perhaps an LLM is not even the way to achieve this, but I absolutely see this becoming an incredible asset to a tech team.

In any job if you overspecialize and don't learn the fundamentals you're at risk. A CSS specialist is like a CRT TV fixer as compared to a general programmer and an electrician, respectively. Those jobs always have numbered days.

yeah, sumproduct on google docs was the thing it kept suggesting, but not in a way that worked for the thing I wanted. I found a way around it in the end, but I feel I wasted 30 mins trying to get ChatGPT to understand what I wanted and give me something that actually worked.

I think LLMs are the wrong solution for this problem.

Why make something that produces low level code based off of existing low level code instead of building up meaningful abstractions to make development easier and ensure that low level code was written right?

Basically react and other similar abstractions for other languages did more to take "coding" out of creating applications than gpt ever will IMO.