[dns_snek]

> Fixing this "tsunami of bad code" will consist of just passing it through the better AIs that will easily just fix most of the problems.

At this point in time there's no obvious path to that reality, it's just unfounded optimism and I don't think it's particularly healthy. What happens 5, 10, or 20 years down the line when this magical solution doesn't arrive?

[naasking]

I don't know where you're getting your data that there's no obvious path, or that it's unfounded optimism. When the chatbots first came out they were unusable for code, now they're borderline good for many tasks and excellent at others, and it's only been a couple of years. Every tool has its limitations at any given time, and I think your pessimism is entirely speculative.

[dns_snek]

What we have now are LLMs that some consider to be good at tasks that are incremental, limited in scope, and require constant human oversight with many iterations.

What you want is an LLM that is exceptionally good at completely rewriting a poorly written codebase spanning tens or hundreds of thousands of lines of code, which works reliably with minimal oversight and without introducing hundreds of critical and hard to diagnose bugs.

Not realizing that these tasks are many orders of magnitudes apart in complexity is where the "unfounded optimism" comment comes from.

[krupan]

Nobody has to prove a negative, my friend

[naasking]

Anybody making a claim should be able to justify it or admit it's conjecture.

[namaria]

Goes both ways. Your extending the line in some particular way from the past couple of years isn't much more than an article of faith.

[naasking]

It's more than the past couple of years, steady improvements in machine learning stretch back decades at this point. There is no indication this is stopping or slowing down, quite the contrary. We also already know that better is possible because the human brain is still better in many ways, and it exists.

You can claim that continued progression is speculative, and some aspects are, but it's hardly "an article of faith", unlike "we've suddenly hit a surprising wall we can't surmount".

[Izkata]

> steady improvements in machine learning stretch back decades at this point

Except that's not how it's actually gone. It's more like, improvements happen in erratic jumps as new methods are discovered, then improvements slow or stall out when the limits of those methods are reached.