This seems cognitively dissonant with the AI upheaval resulting in the industry needing fewer developers. Maybe they want cheaper developers?
If we freeze AI models at their current stage of development, then yes. If not, given the rate at which models are improving (compare Claude today to Claude 1 year ago), in 3 to 5 years software development could be fully automated.
Let's see, my grandparents were about 160cm when they were 35. My parents were about 170cm when they were 35 and I am about 180cm around 35. You are saying I can expect my grandchildren to be around 200cm at that age and their grandchildren 220cm?
Of course it is possible also that Claude will marry a chinese model (since it's asian odds are it is also smarter) bringing some diversity into the "gene" pool. Or better yet, a Russian supermodel.
If you need to be told what to do, it's a short step to your AI replacement being told what to do; money will ensure it. If your job is to discover and capture new things in an exploding possibility space, that's a career.
Schools should teach hacking, but it doesn't seem that's what this program is doing.
Basically still just a prompt jockey but knows the difference between outputs that are good and bad.
2024 me would be shocked at current me saying this.
And how you would know to do that, if you're not writing code?
It's my impression that the best way to be able to read code and distinguish good from bad is to write code. Get burned a few times and you know why certain patterns are a bad idea. You don't internalize that equally well from a powerpoint slide.
Compare this to math. Every successful mathematician has gone through the motions of proving certain basic theorems in math. Like Euclid's proof that there are infinitely many prime numbers. And then they went through related exercises. This was all learned with sweat. The statement above is akin to saying there is no point in doing any proofs by hand anymore, just read one and you will have sufficient understanding of mathematics and become a good mathematician. I don't think that's true. (And I also believe that many sw eng people would be better at their job had they groked the proof of the halting problem.)
... in Github Actions? What a great way to discourage them from entering the industry.
Quite simply, I'm more mentally drained at the end of a day working with AI than I am a normal day of coding. There is still a lot of energy and thought that needs to go into what needs to be written.
Anecdotally, I find my final mood often depends on whether I know the origin and trustworthiness of the stuff in front of me, ex:
1. [Good] I know what I wanted, I have a concept of what "correct" looks like. I use the LLM to generate an output, and I check it. Even if it's annoyingly wrong, I can fix it up.
2. [Bad] Someone else had a concept, and they put it through an LLM to generate something (Ticket, PR, review comment.) However the provenance is lost, I have to guess which parts of the thing are "real" and which are nonsense the human never actually intended or even noticed.
I think some of the latter drain comes from how reading means inferring/simulate the mind of the writer, but LLM output is wrong enough that little warning flags never stop popping up.
What value would an architect provide that a SOTA LLM can't?
But is that workflow enough in a competitive world anyway, or will there be an edge for people who do more than "outline the end-user requirements" like it's the waterfall 70s?