[c048]

This is why I don't listen at all to the fearmongers that say programmers will disappear. At most, our jobs will slightly change.

There will always be people that describe a problem, and you'll always need people actually figuring out what's actually wrong.

[croes]

The problem isn’t the AI but the management that believes the PR. It doesn’t matter if AI can replace developers but if the management thinks it can.

[breakpointalpha]

That's only a problem in the short term.

Watch the company fire 50% of the engineering team then hit a brick wall at 100mph.

[ACCount36]

What makes you look at existing AI systems and then say "oh, this totally isn't capable of describing a problem or figuring out what's actually wrong"? Let alone "this wouldn't EVER be capable of that"?

[benterix]

> What makes you look at existing AI systems and then say "oh, this totally isn't capable of describing a problem or figuring out what's actually wrong"?

I wouldn't say they're completely incapable.

* They can spot (and fix) low hanging fruit instantly

* They will also "fix" things that were left out there for a reason and break things completely

* even if the code base fits entirely in their context window, as does the complete company knowledge base, including Slack conversations etc., the proposed solutions sometimes take a very strange turn, in spite of being correct 57.8% of the time.

[ACCount36]

That's about right. And this kind of performance wouldn't be concerning - if only AI performance didn't go up over time.

Today's AI systems are the worst they'll ever be. If AI is already capable of doing something, you should expect it to become more capable of it in the future.

[binary132]

why is “the worst they’ll ever be” such a popular meme with the AI inevitabilist crowd and how do we make their brains able to work again?

[insignificntape]

It's a self-evident truth. Even if today, at this very moment AI hits a hard plateau and there's nothing we can do to make AI better, ever, then this still holds true. It simply means we'll keep what we have right now. Any new model will be a step back and thus be discarded. So what we have today is the worst, and the best it will ever be. But barring that extremely unlikely scenario, like GPT-3 to GPT-4 and Claude 3 to Claude 4, we will see improvements (either incremental or abrupt) over the coming weeks/months/years. Any failed experiments will never see the light of day and the successful experiments will become Claude X or GPT X, etc.

[ACCount36]

It's popular because it's true.

By now, the main reason people expect AI progress to halt is cope. People say "AI progress is going to stop, any minute now, just you wait" because the alternative makes them very, very uncomfortable.

[benterix]

Well, to use the processor analogy, with models we reached the situations where the clocks can't do that much more. So the industry switched to multiplying cores etc. but you can actually see the slope plateauing. There are wild developments for the general public like the immediate availability of gpt-oss-120b that I'm running on my MBP right now, there is Claude Code that can work for weeks doing various stuff and being right half of the time, that's all great, but we can all see development of the SOTA models has slowed down and what we are seeing are very nice and useful incremental improvements, not great breakthroughs like we had 3-4 years ago.

(NB I'm a very rational person and based on my lifelong experience and on how many times life surprised me both negatively and positively, I'd say the chance of a great breakthrough occurring short term is 50%, but it has nothing to do or cannot be extrapolated from the current development as this can go any way actually. We already had multiple AI winters and I'm sure humanity will have dozens if not hundreds of them still.)

[disgruntledphd2]

> By now, the main reason people expect AI progress to halt is cope. People say "AI progress is going to stop, any minute now, just you wait" because the alternative makes them very, very uncomfortable.

OK, so where is the new data going to come from? Fundamentally, LLMs work by doing token prediction when some token(s) are masked. This process (which doesn't require supervision hence why it scaled) seems to be fundamental to LLM improvement. And basically all of the AI companies have slurped up all of the text (and presumably all of the videos) on the internet. Where does the next order of magnitude increase in data come from?

More fundamentally, lots of the hype is about research/novel stuff which seems to me to be very, very difficult to get from a model that's trained to produce plausible text. Like, how does one expect to see improvements in biology (for example) based on text input and output.

Remember, these models don't appear to reason much like humans, they seem to do well where the training data is sufficient (interpolation) and do badly where there isn't enough data (extrapolation).

I'd love to understand how this is all supposed to change, but haven't really seen much useful evidence (i.e. papers and experiments) on this, just AI CEOs talking their book. Happy to be corrected if I'm wrong.

[IsTom]

We're somewhere on an S-curve and you can't really determine on which part by just looking at the past progress.

[croes]

That’s not how it works. There are already cases where the fix of one problem made a previous existing capability worse.

[ACCount36]

That's exactly how it works. Every input of AI performance improves over time, and so do the outcomes.

Can you damage existing capabilities by overly specializing an AI in something? Yes. Would you expect that damage to stick around forever? No.

OpenAI damaged o3's truthfulness by frying it with too much careless RL. But Anthropic's Opus 4 proves that you can get similar task performance gains without sacrificing truthfulness. And then OpenAI comes back swinging with an algorithmic approach to train their AIs for better truthfulness specifically.

[croes]

Depends on the input. More BS training data leads to worse answers and the good sources are nearly all already used.

The next round of data is partially AI generated what leads to further deterioration

[binary132]

that must be why gpt5 can’t count the number of B’s in “blueberry”

[croes]

Turn the question around „oh, this totally is capable of describing a problem and figuring out what's actually wrong“

Even a broken clock is right two times a day.

The question is reliability.

What worked today may not work tomorrow and vice versa.

[satyrun]

At this point it is just straight denial.

Like when a relationship is obviously over. Some people enjoy the ending fleeting moments while others delude themselves that they just have to get over the hump and things will go back to normal.

I suspect a lot of the denial is from the 30 something CRUD app lottery winner. One of the smart kids all through school, graduated into a ripping CRUD app job market and then if they didn't even feel the 2022 downturn, they now see themselves as irreplaceable CRUD app genius. Something understandable since the environment has never signaled anything to the contrary until now.

[sho_hn]

My psychological reaction to what's going on is somehow pretty different.

I'm a systems/embedded/GUI dev with 25 years of C++ etc., and nearly every day I'm happy and grateful to be the last generation to get really proficient before AI tools made us all super dependant and lazy.

Don't get me wrong, I'm sure people will find other ways to remain productive and stand out from each other - just a new normal -, but I'm still glad all that mental exercise and experience can't be taken away from me.

I'm more compelled to figure out how I can contribute to making sure younger colleagues learn all the right stuff and treat their brains with self-respect than I feel any need to "save my own ass" or have any fears about the job changing.

[jononor]

You made me think of the role of mental effort/exercise. In parts of the western world, we are already experiencing a large increase in dementia/alzheimer and related. Most of it is because we are doing better with other killers like heart etc, and many cancers also. But is said that mental activity is important to stave off degenerative diseases of the brain. Could widespread AI trigger a dementia epidemic? It would be 30 years out, but still...