AI isn't going to generate those jobs, it's going to automate them.
ALL our bullshit jobs are going away, and those people will be unemployed.
When kids stop learning to code for real, who writes GCC v38?
This whole LLM is just the next bitcoin/nft. People had a lot of video cards and wanted to find a new use for them. In my small brain it’s so obvious.
to compare that to NFT’s is pretty disingenuous. i don’t know anyone who has ever accomplished anything with an NFT. (i’m happy to be wrong about that, and i have yet to find a single example).
Trying to make them more than they are is the issue I have. Let them be great at crunching words, I’m all about that.
Pretending that OpenAI is worth billions of dollars is a joke, when I can get 90% of the value the provide for free, on my own mediocre hardware.
Maybe consider it's not all on the AI tools if they work for others but not for you.
Human-written code also needs reviews, and is also frequently broken until subjected to testing, iteration, and reviews, and so our processes are built around proper qa, and proper reviews, and then the original source does not matter much.
It's however a lot easier to force an LLM into a straighjacket of enforced linters, enforced test-suite runs, enforced sanity checks, enforced processes at a level that human developers would quit over, and so as we build out the harness around the AI code generation, we're seeing the quality of that code increase a lot faster than the quality delivered by human developers. It still doesn't beat a good senior developer, but it does often deliver code that handles tasks I could never hand to my juniors.
(In fact, the harness I'm forcing my AI generated code through was written about 95%+ by an LLM, iteratively, with its own code being forced through the verification steps with every new iteration after the first 100 lines of code or so)
You can feel free not to believe it, as I have no plans to open up my tooling anytime soon - though partly because I'm considering turning it into a service. In the meantime these tools are significantly improving the margins for my consulting, and the velocity increases steadily as every time we run into a problem we make the tooling revise its own system prompt or add additional checks to the harness it runs to avoid it next time.
A lot of it is very simple. E.g a lot of these tools can produce broken edits. They'll usually realise and fix them, but adding an edit tool that forces the code through syntax checks / linters for example saved a lot of pain. As does forcing regular test and coverage runs, not just on builds.
For one of my projects I now let this tooling edit without asking permission, and just answer yes/no to whether it can commit once it's ready. If no, I'll tell it why and review again when it thinks it's fixed things, but a majority of commit requests are now accepted on the first try.
For the same project I'm now also experimenting with asking the assistant to come up with a todo list of enhancements for it based on a high level goal, then work through it, with me just giving minor comments on the proposed list.
I'm vaguely tempted to let this assistant reload it's own modified code when tests pass and leave it to work on itself for a a while and see what comes of it. But I'd need to sandbox it first. It's already tried (and was stopped by a permissions check) to figure out how to restart itself to enable new functionality it had written, so it "understands" when it is working on itself.
But, by all means, you can choose to just treat this as fiction if it makes you feel better.
E.g. a real example: The tooling I mentioned at one point early on made the correct functional change, but it's written in Ruby and Ruby allows defining methods multiple times in the same class - the later version just overrides the former. This would of course be a compilation error in most other languages. It's a weakness of using Ruby with a careless (or mindless) developer...
But Rubocop - a linter - will catch it. So forcing all changes through Rubocop and just returning the errors to LLM made it recognise the mistake and delete the old method.
It lowers the cognitive load of the review. Instead of having to wade through and resolve a lot of cruft and make sense of unusually structured code, you can focus on the actual specific changes and subject those to more scrutiny.
And then my plan is to experiment with more semantic checks of the same style as what Rubocop uses, but less prescriptive, of the type "maybe you should pay extra attention here, and explain why this is correct/safe" etc. An example might be to trigger this for any change that involves reading a key or password field or card number whether or not there is a problem with it, and both trigger the LLM to "look twice" and indicate it as an area to pay extra attention to in a human review.
It doesn't need to be perfect, it just need to provide enough of a harness to make it easier for humans in the loop to spot the remaining issues.
So, no, I'll keep doing this because doing this is already saving me effort for my other projects.
Writing code is often easier than reading it. I suspect that coders soon will face what translators face now: fixing machine output at 2x to 3x less pay.
It's also the jobs that involve keeping people happy somehow, which may not be "productive" in the most direct sense.
One class of people that needs to be kept happy are managers. What makes managers happy is not always what is actually most productive. What makes managers happy is their perception of what's most productive, or having their ideas about how to solve some problem addressed.
This does, in fact, result in companies paying people to do nothing useful. People get paid to do things that satisfy a need that managers have perceived.
NONE of the bullshit jobs are going away, there will simply be bigger, more numerous bullshit.