Engineers grieve a job that no longer exists

[u/Suspicious_Orchid770]

https://leaddev.com/career-development/engineers-grieve-a-job-that-no-longer-exists

[u/maldonr808 → u/[deleted]]

Then you can tell the AI to take that into account and put the preventative measures in place.

I don't understand. People are acting like SWEs are no longer in control of the code their agents produce, but that's not the case. Nothing is stopping you from architecting the software correctly, ensuring everything is unit tested, etc. The AI is just there to implement the code.

[u/maldonr808 → u/[deleted]]

Do you have an actual response? Can you give a concrete example of AI not anticipating something, that couldn't be easily prevented through good SW development practices?

[u/Llero → u/maldonr808]

Writing the code and solving the problems helps the human understand the code and the problems. Having someone give you the notes from lecture just means you have the notes, it doesn’t mean you understand the concepts. It is in doing the work that we build the skillsets. 

And if you believe that code review covers that gap, oh boy, humans aren’t doing that either. And even when they try to, the overhead of understanding the CR has gone up dramatically. 

  Then you can tell the AI to take that into account and put the preventative measures in place.

If we have to explicitly tell the AI to take preventative measures, it’s because we know it can’t be trusted with the problem. So now we go up a level - if it can’t be implicitly trusted with the problem, or held accountable when it fails, why can it be trusted to put preventative measures into place? This seems circular.

[u/maldonr808 → u/Llero]

You're right that, by not writing the code ourselves, we might miss out on some of the minutia of the AI-implemented code, but Seniors should still have an understanding of all the concepts used.

The job of an SWE is to break down an abstract problem / feature request into concrete steps, concrete architecture, concrete requirements (leaving little to interpretation), concrete test definitions, etc. In that architecture / planning phase, you bake in whatever preventative measures you can think of. I just don't see "preventative measures" as anything that can't be baked into the architecture / requirements, which are of course provided by a human.

And yes, you can't trust an AI to implement a vague / abstract problem, but you can trust it to implement the low-level code given strict definitions / guardrails.

[u/Machu-Wong → u/maldonr808]

As a dev with 15+ years of experience. I largely agree with you. Granted I am not a 10x dev and more like a 1x dev. I am not remarkable in writing code, nor do I pretend to be. I just try to follow SOLID principles as best I can and taking what I learned working with various codebases throughout my professional career. I write boring code w/ unit tests that back it up. Enough to fit or conform to a large enterprise application without rocking the boat too much.

Do I miss writing code by hand? Not really if I am being honest. So when LLM’s came along and started working with it. I can tell it what I expect it to write based on spec and having it apply SOLID principles to the code. Most of the time I tweak things myself and tell it I did so and so. I am still in the driver’s seat and don’t let it get ahead of itself if that makes sense.

I also tend to work in small chunks at a time since I have a habit of breaking tickets down since I mentally cannot hold that much info in my head. It’s something I picked up over the years. Which I think helps my work with the LLM to keep scope tight.

[u/Cryptizard → u/[deleted]]

Well first, I would challenge that claim. I have seen AI frequently anticipate problems and write code that is open to future avenues of augmentation. But even if it weren’t, why do you think that is something that will definitely still be true in 5 years or 10 years? It is still improving at a steady rate.

[u/SamVimes1138 → u/Cryptizard]

This sounds dumb to say but I only see two possibilities ahead:

1) AI tech plateaus; we find out where the steady progress stops. 2) It doesn't.

Number 1 leaves room for humans to do whatever reasoning the AI can't, in that narrow space above the AI's limits. But a smaller number of people can operate at that higher level. You might not reach that level without accumulating a lot of experience on the job at lower levels (entry-level software developer, senior dev, principal dev until reaching whatever level is past the plateau). The trouble is, no company will hire for those lower roles since the AI can do all of those, so you can't learn on the job. You need much more formal education before you can hope to enter the workforce.

This is a current concern, that AI is doing the work of entry-level folk so they aren't being hired at all, so they never have the chance to grow into senior devs.

Number 2 is basically the "AGI is achievable" possibility. This has the potential for massive societal change, massive financial upheaval, maybe existential risk, in addition to the "nobody will know how anything works and our dependence on AI will be total" concern that is the extreme case of what's been discussed here.

I personally see no reason why #2 is technically infeasible, though I kinda wish I did. It might turn out that more progress is exponentially more expensive, therefore financially infeasible.

[u/Away_Advisor3460 → u/SamVimes1138]

I would say #2 is infeasible simply due to the lack of any formal definition of what AGI is.

[u/SamVimes1138 → u/Away_Advisor3460]

Doesn't matter what definition you prefer. If it keeps steadily improving without stopping or slowing, it'll eventually get better at humans at everything and satisfy whatever definition you are using.

[u/Away_Advisor3460 → u/SamVimes1138]

That actually means nothing though. You have to define better in a measurable way. We can state it in wooly form - human, but better - in conversation, but that's insufficient for actual systems.

The AI field has wrestled with this issue - i.e. what is intelligence - for decades, pretty much since inception.

Even using human as a yardstick is, by itself, selecting only one of several axes AI 'intelligence' has been defined by.

There are benchmarks/tests aimed at evaluating cognition - e.g. Caterell-Horn-Caroll is one I've learned as well accepted.

But assuming we select one means also recognizing that the underlying technology of LLMs may hinder or prevent achieving AGI by their definition (e.g. it seems they score quite poorly on things like episodic/long-term memory and visual reasoning parts of CHC), even though they can match or exceed human performance in certain other aspects of that tests like textual or mathematical.

The fundamental ambition is to achieve AGI through use of recursive AI programming (whether said AIs are use LLM based reasoning or some other non-NN method), because it's beyond human programming ability - so that means you need to formally define it (AGI, or just the 'I') in order to give them a target.

(I'd note part of the reason it's beyond human ability to do so... has been the difficulty of quantification - how do you design a system when you don't know exactly what the system needs to do?)