Hacker News

It's not too different in my opinion from the skills need to build complicated machinery like Boeing 747s despite how much Wallstreet and PHBs want to believe it's fungible. Having competent experienced engineers on the ground level watching these processes and constantly revising and adapting to everything from personnel, material, or vendor changes is so far irreplaceable.

Maybe if we get super AGI one day. Even then I suspect that from a thermodynamics perspective that might not be cost effective as you often need localized on site intelligence.

It's an interesting question but I bet humans combined with AI tooling will remain cost competitive for a long time barring leaps in say quantum compute. After all organic brains operate at the atomic level already and were honed in an extremely competitive environment for billions of years. The calories and resources required to create highly efficient massively powerful neural compute had incredibly thin resource "margins" with huge advantages for species to utilize.

You hit the nail on the head too. Coding itself is very easy for anyone halfway decent in this career — and yet there were a ton of people in CS101 and even in later courses who struggled with things like for loops. It was very hard for them to succeed in this career.

What’s hard is coming up with the algorithm/system design, making the right choices that will scale and won’t become a maintenance nightmare, etc. And yeah, after almost a decade, I have picked up enough I can at least write an outline of a solution that will work alright. But there are still so many tricky edge cases and scaling problems that make it hard to turn “alright” into “really good!”

Sure, AI can help… but it mostly helps with greenfield projects. It doesn’t know about the conversations on slack & jira from a year ago. It doesn’t know about the dozens of other systems and ways the project interacts with other parts of the business. It doesn’t know why whatever regurgitated approach won’t be a good fit for our specific use case. And elaborating all of that detail is not easy! Part of what makes you a good employee is the shit you picked up over the past several months & years that is joe ingrained in your mind when you start working on new projects.

[dead]

As software grows the problems go such that the effort to completely describe the necessary changes in plain language is often not all that much shorter than the code. Especially if you have a really really good autocomplete for the boilerplate parts of the code. So LLMs being really good at the tedious autocomplete makes LLMs have less marginal utility for the "write the whole thing" for certain types of work.

But the sneakier part of the problem is that as business rules get more complex it's usually harder to completely describe the problem in plain language than in a more formally specified language. For instance, plain-language "apply the user's promo code" doesn't capture the nasty if/else tree you might hit when you're deep in the codebase and see that there's already a bunch of restrictions on other types of promo codes and the product manager didn't think about which of those restrictions should apply to this new promo code. And at this point you're gonna need to use plain language to describe and refine the problem with the product manager - but if you instead were relying on an LLM to turn your short sentence into the right code, it might pick the wrong thing when it comes to modifying that existing code.

> ...describe the problem in plain language...

Oh, I get it. They're saying you should be able to write it in C.

[Jokes aside, I would be interested in hearing from bridge builders, aerospace engineers, or nuclear scientists. Pretty sure they're using math and not 'plain language'.]

The solution is more AI, in the browser to tell you this link is to a rubbish AI blog, AI in the mail client to tell you which email content to ignore and AI in the fridge, in the bathroom. Till each person is so tightly locked in a group bubble they have no idea how colourful the world is and how many different personalities exist that for the most bring something to the table

There's a long distance between "nuanced behavior" and what it actually does now, which is "complete 6 items of an explicit 10-item task list and then ask the user again if they want to continue".

> what makes you think they couldn't produce a larger, smarter, more expensive model?

Because they already did try making a much larger, more expensive model, it was called GPT-4.5. It failed, it wasn't actually that much smarter despite being insanely expensive, and they retired it after a few months.

Yeah. I use it for analytics/dataviz stuff (which involves a lot of python to run spark jobs, glue different APis to get some extra column of data, making png or svg pictures,, making D3 based web sites in html and JavaScript). That all works pretty well.

I also write high performance Go server code, where it works a lot less well. It doesn't follow my rules for ptr APIs or using sync mutexes or atomic operations across a code base. It (probably slightly older version than SOTA) didn't read deep call chains accurately for refactoring. It's still worth trying but if that was my sole work it would probably not be worth it.

On the other hand for personal productivity, emacs functions and config, getting a good .mcp.json, it is also very good and generates code that partakes in the exponential growth of good code. (Unlike data viz where there is a tendency to build something and then the utility declines over time).

I can confidently state that for CRUD web apps, its truly over as in those jobs are never going to command the same wages it once used to.

With the recent models its now encroaching similarly on all fronts, I think the next few iterations we'll see LLM solidify itself as a meta-compiler that will be deployed locally for more FCS type systems.

At the end of the day the hazards are still same with or without AI, you need checks and bounds, you need proper vetting of code and quality but overall it probably doesn't make sense to charge an hourly rate because an AI would drastically cut down such billing schemes.

For me "replacement" is largely a 70~80% reduction in either hourly wages, job positions or both and from the job market data I see it can get there.

You can say generate a c program that uses gcc 128 bit floats and systematically generates all quadratic roots in order of the max size of their minimal polynomial coefficients, and then sort them and calculate the distribution of the intervals between adjacent numbers, and it just does it. That's qualitatively different from the compilers I have used. Now I was careful to use properly technical words to pull in the world of numeric computation and c programming. But still saved me a lot of time. It was even able to bolt on multithreaded parallelism to speed it up using c stuff I never heard of.

Most specifically, random wrong results. Some calculators have issues with rounding, but if you understand those issues, it's consistent.

Imagine driving your car, you turn right, but today turning right slams on the brakes, and 10 people rear end you! That's current AI.

Yes in terms of raw LLM, but with some tools or a MCP the “AI” will never be wrong.

> Everyday, I see ads on YouTube with smooth-talking, real-looking AI-generated actors. Each one represents one less person that would have been paid.

Were AI-generated actors chosen over real actors, or was the alternative using some other low-cost method for an advertisement like just colorful words moving around on a screen? Or the ad not being made at all?

The existence of ads using generative AI "actors" doesn't prove that an actor wasn't paid. This is the same logical fallacy as claiming that one pirated copy of software represents a lost sale.

I agree with your sentiment. But where I struggle is: to what degree do each of those ads “represent one less person who would have been paid” versus those that represent one additional person who would not be able to afford to advertise in that medium.

Of course that line of reasoning reduces similar to other automation / minimum wage / etc discussions

YouTube has the lowest quality ads of any online platform I use by several orders of magnitude. AI being used for belly fat and erectile dysfunction ads is not exactly good for its creative reputation

There's a difference between taking one thing and putting something else in it's spot, and truly REPLACING something. Yes, some ads have AI generated actors. You know because you can tell because they're "not quite right", rather than focusing on the message of the ad. Noticing AI in ads turns more people off than on, so AI ads are treated by a lot of people as an easy "avoid this company" signal. So those AI ads are in lieu of real actors, but not actually replacing them because people don't want to watch AI actors in an ad. The ad ceases to be effective. The "replacement" failed.

> Generative AI is replacing writers, designers, actors, ... it is nothing like just a spell checker or Phtoshop.

For cheap stuff it’s true. However, nobody wants to watch or listen generated content and this will wear thin aside from the niche it’ll take hold of and permanently replace humans.

“ Everyday, I see ads on YouTube with smooth-talking, real-looking AI-generated actors. Each one represents one less person that would have been paid.”

The thing is that they would not have paid for the actor anyway. It’s that having an “actor” and special effects for your ads cost nothing, so why not?

The quality of their ads went up, the money changing hands did not change.

Anecdata: I know writers, editors, and white collar non-tech workers of all kinds who use AI daily and like it.

When GPT3.5 first landed a lifelong writer/editor saw a steep decrease in jobs. A year later the jobs changed to "can you edit this AI generated text to sound human", and now they continue to work doing normal editing for human or human-ish writing while declining the slop-correction deluge because it is terrible work.

I can't help but see the software analogy for this.

And as people get more used to the patterns of AI it’s getting called out more and more.

> Each one represents one less person that would have been paid

or equally, one more advert which (let's say rightly) wouldn't have been made.

seriously though, automation allows us to do things that would not have been possible or affordable before. some of these are good things.

Not a joke.

Proofreaders still exist, despite spell checker. Art assistants still exist, despite Photoshop. There's always more work to do, you just incorporate the new tools and bump the productivity, until it gets so commoditized it stops being a competitive advantage.

Saying AI "replaces" anyone is just a matter of rhetoric to justify lower salaries, as always.

Bad ones

> took thousands of years to accept brain as the seat of thought because “heart beat faster when excited, means heart is source of excitement”

So what you are saying is that beings without a central nervous system cannot experience "excitement"?

or perhaps the meaning of too many words has changed, and their context. When Hippocrates claimed that the brain was an organ to cool the blood, perhaps he meant that we use our thought to temper our emotions, i.e. what he said agrees with our modern understanding.

However, many people read Hippocrates and laugh at him, because they think he meant the brain was some kind of radiator.

Maybe because we stopped talking about "excitable" people as being "hot-blooded"

You're confusing ignorance with stupidity. People at the time were coming to the best conclusions they could based on the evidence they had. That isn't stupid. If humans were truly "incredibly, unimaginably stupid" we wouldn't have even gotten to the point of creating agriculture, much less splitting the atom. We didn't get here through "miracles," we got here through hard work and intelligence.

Stupid is people in 2025 believing the world is flat and germ theory is a hoax. Ignorance becomes stupidity when our species stands on the shoulders of giants but some people simply refuse to open their eyes.

There’s this saying that humans are terrible at predicting exponential growth. I believe we need another saying, those who expect exponential growth have a tough time not expecting it.

It’s not under a decade for ai to get to this stage but multiple decades of work, with algorithms finally able to take advantage of gpu hardware to massively excel.

There’s already feeling that growth has slowed, I’m not seeing the rise in performance at coding tasks that I saw over the past few years. I see no fundamental improvements that would suggest exponential growth or human level of performance.

> ... entirely on its own

ok, ok! just like you can find for much less computation power involved using a search engine, forums/websites having if not your question, something similar or a snippet [0] helping you solve your doubt... all of that free of tokens and companies profiting over what the internet have build! even FOSS generative AI can give billions USD to GPU manufacturers

[0] just a silly script that can lead a bunch of logic: https://stackoverflow.com/questions/70058132/how-do-i-make-a...

You can’t see any bottlenecks? Energy? Compute power? Model limitations? Data? Money?

So maybe the truth is somewhere in between - there is no way AI is not going to have a major societal impact - like social media.

If we don't see some serious fencing, I will not be surprised by some spectacular AI-caused failures in the next 3 years that wipe out companies.

Business typically follows a risk-based approach to things, and in this case entire industries are yolo'ing.

> I see no fundamental limitations

How about the fact that AI is only trained to complete text and literally has no "mind" within which to conceive or reason about concepts? Fundamentally, it is only trained to sound like a human.

your comment reeks of hype. no evidence whatsoever for your prediction, just an assertion that you personally don't see it not coming true

It took closer to 100 years for AI to get to this stage. Check out: https://en.wikipedia.org/wiki/History_of_artificial_intellig...

I suspect once you have studied how we actually got to where we are today, you might see why your lack of seeing any limitations may not be the flex you think it is.

When are you starting time from? AI has been a topic of research for over 70 years

We see massive initial growth followed by a slowdown constantly.

There is zero reason to think AI is some exception that will continue to exponentially improve without limit. We already seem to be at the point of diminishing returns. Sinking absurd amounts of money and resources to train models that show incremental improvements.

To get this far they have had to spend hundreds of billions and have used up the majority of the data they have access to. We are at the point of trying to train AI on generated data and hoping that it doesn’t just cause the entire thing degrade.

> I see no fundamental limitations that would prevent further improvements

How can you say this when progress has so clearly stagnated already? The past year has been nothing but marginal improvements at best, culminating in GPT-5 which can barely be considered an upgrade over 4o in terms of pure intelligence despite the significant connotation attached to the number.

>> It reeks of cope.

haha, well said, I've got to remember that one. HN is a smelly place when it comes to AI coping.

You're making a distinction that might be interesting in some esoteric sense, but that doesn't exist in the industry.

Sure it exists. Not everywhere, not for all software, but it does exist.

Please elaborate.

Share what you built and how you prompted and what you are making from it, and how many tokens you paid to rummage through.

I agree, using "chatgpt 5 in visual studio code" screams unfamiliarity with what is current.

just stop yeah?

> I can't imagine the vendor lock in of that.... You have the source, but it is in such a state that no human can maintain it?

It’s much worse than that.

What happens when the erroneous output caused by model blind spots gets fed back into the model?

Those blind spots get reinforced.

Doesn’t matter how small that error rate is (and it’s not small). The errors will compound.

Vendor lock-in won’t matter because it will simply stop working/become totally unrecoverable.

It's more "you need to learn how a tool really works in order to use it most effectively"

Talking to a chat bot like you'd talk to a human is a quick way to be disappointed. They don't work that way, despite sometimes sounding like they do.

My read was along the lines of "you're holding it wrong"

I've yet to have that happen. But then again so far I've only used it for Rust, and it's hard for the AI to maintain hallucinations with such a strict compiler.

Sure. My point is that it's not unusual for the quality of work of a junior coder to be fairly low, and that work typically needs to be double checked by someone more senior before it can be used in production. Often, an LLM will produce a higher quality output than that junior developer in which case, given that the work has to be checked by a senior coder no matter what, the LLM can replace the junior developer.

Also, not every project is highly complex or involves huge codebases and, for those that are, a junior developer might be assigned to a reasonably self contained and small module within the project (i.e. something that a LLM can do better at).

Finally, although this wasn't part of my original argument: before modern LLMs, let's say that a senior developer could produce N units of work per Y units of time. Now, with the help of a LLM, they are able to produce XN units of work per Y unit of time. I don't think it's too controversial to claim that some portion of developers are now more productive than they were before. And if developers in general are more productive, then you can expect some reduction in the number of developers that you need for a given project. So, in an indirect sense, LLMs are already capable of replacing coders.

Yeah I think you're spot on with the compatibility issues. Obviously YMMV depending on the languages and libraries in question, but yeah there's no one size fits all solution, and writing boilerplate code is the most boring thing in software development.

That's the big thing that's warmed up to programming languages with good macro systems in the past few years (and I absolutely LOVE Zig's comptime paradigm). At the same time, I've got serious questions about the programmers who dismiss macros as overkill for their needs, but then immediately pull out the LLM.