[nukem222]

I agree with all of this. I want to offer a tiny bit more hope, though:

> There have been a lot of bold promises (and genuine advances), but I don't see a world in the next 5 years where AI writes useful software by itself.

I actually think the opposite: that within five years, we will be seeing AI one-shot software, not because LLMs will experience some kind of revolution in auditing output, but because we will move the goalposts to ensure the rough spots of AI are massaged out. Is this cheating? Kind of, but any effort to do this will also ease humans accomplishing the same thing.

It's entirely possible, in other words, that LLMs will force engineers to be honest about the ease of tasks they ask developers to tackle, resulting in more easily composable software stacks.

I also believe that use of LLMs will force better naming of things. Much of the difficulty of complex projects comes from simply tracking the existence and status of all the moving parts and the wires that connect them. It wouldn't surprise me at all if LLMs struggle to manage without a clear shared ontology (that we naturally create and internalize ourselves).

[tippytippytango]

It’s fascinating how the debate is going exactly as the car debate went. People were arguing for a whole spectrum of environment modifications for self driving cars.

I’ll take the other side of that bet. The software industry won’t make things easier for LLMs. A few will try, but will get burned by the tech changing too fast to target. Seeing this, people will by and large stay focused on designing their ecosystems for humans.

[lioeters]

> we will move the goalposts to ensure the rough spots of AI are massaged out

Totally agree with this point. Software engineering will adapt to work better with LLMs. It will influence how we think about programming language design, as an interface to human readers/writers as well as for machines to "understand", generate, and refine.

There was a recent article about how LLMs will stifle innovation due to its cutoff point, where it's more productive using older or more mature frameworks and libraries whose documentation is part of the training data. I'm already seeing how this is affecting technical decisions at companies. But then again, it's similar to how such decisions are often made to maximize the labor pool, for example, choosing a more popular language due to availability of experts.

One thing I hope for is that we'll see more emphasis on thorough and precisely worded documentation. Similarly with API design and user interfaces in general, possibly leading to improvements in accessibility also.

Another aspect I think about is the recursive cycle of LLM-generated code and documentation being consumed by future LLMs, influencing what kinds of new frameworks and libraries may emerge that are particularly suited for this new kind of programming, purely AI or human/AI symbiosis.

[aprilthird2021]

> One thing I hope for is that we'll see more emphasis on thorough and precisely worded documentation.

Being on this planet long enough, I've learned this won't happen and in fact the quality of such will degrade making the AI using them degrade and we'll all have to just accept these flaws and work around them like so many myriad technical flaws in our current systems today

[lioeters]

True, that's a probable and very real risk that this recursive cycle of LLMs consuming LLM-produced content will degrade the overall quality of its "intelligence" and "creativity", maybe even the quality of literature, language, and human thought itself. By the time we collectively realize the mistake, it will be too late. Like microplastics, it will be everywhere and inside everything, and we'll just have to learn to live with it for better or worse.