I read such articles more or less every day. This article would be 100% correct if it came out 1 year ago, 75% correct 9 months ago, 50% correct 3 months ago and it's probably 25% correct now if not less.

I totally understand where this is coming from. I too am struggling with accepting that my 30+ years of programming experience is quickly becoming obsolete. I'm losing sleep about this, it's tough.

But just go ahead and give the latest models (Opus 5.5 / Astra 6 as of today) another try. See what they are capable of and read the code which they produce. Any problem area, low level C++ or high level Typescript or Clojure or a weird combination of these..

Don't be shy, give them a big task, let them build an entire app, UI and all..

Now compare the output to Opus 4 or gpt-5 from 1 year ago - when they couldn't put together a single function without it being weird and buggy.

This is exactly my problem, not that the models are very good already, but how fast they got so good. So if coding is not solved yet, it'll get there very soon.

We also read these kinds of rebuttals almost every day. The original article made a number of substantive critiques about where AI falls short in the actual requirements for building maintainable, reliable, business-critical software. So its not enough just to point to newer models without explaining how the newer models solve these problems. Can the new models take full end-to-end ownership of a system? If not, how do they solve the problem of humans taking ownership of AI generated code?

I use the latest models every day for app development, it does still make many mistakes and poor decisions. Just today I dealt with an issue where it allowed a silent failure that would delete all of the users data without anyone noticing. The code it writes for one task is usually pretty good, but it still doesn’t always follow conventions well even if you’ve documented them. It also has no sense for long-term architecture or organization, or when to make tradeoffs for less complexity because it’s a temporary or prototype feature that needs to stay easy to change. I’ve found that if I do even 3 months of pure agentic coding with no code reviews, it’s aged 10x faster than a human coded codebase so it’s like a 30 month legacy codebase now. I know people who are obsessed with AI coding and they’re throwing away projects they’ve built over a year and starting over because it’s become too slow to make changes.

What kind of coding are you using these models for? Most of the people I know who share your perspective never go beyond the prototyping stage. I’d be curious to hear from anyone who’s been AI coding for more than six months, shipping it to real users, and isn’t looking at their code at all.

>This article would be 100% correct if it came out 1 year ago, 75% correct 9 months ago, 50% correct 3 months ago and it's probably 25% correct now if not less.

Feels like I read comment similar to this one each year since 2023.

I've been reading such arguments more or less every day for years now: dude, you need to try the latest model. Forget about last week's model, it didn't work. This week's model is the real deal.

> Don't be shy, give them a big task, let them build an entire app, UI and all..

I have found that a willingness to look like a temporary dumbass (primarily to yourself) is the largest predictor of success with pretty much everything.

What are the consequences of asking an LLM for the moon and receiving low earth orbit instead? Who cares if the proverbial rocket explodes on the pad? This is all happening entirely in a computer system completely under your control and likely at relatively low cost. No one else has to find out about your mistakes if you don't want them to.

This is where I’m at as well. A friend recently showed me what opus 5.5 is capable of and it was both awe inspiring and disturbing.

I’ve been using AI as a great pair programming partner for about a year now, but every time I prompt it to write code agentically, it just makes a pile of crap that I end up spending more time fixing. This is so very quickly becoming not the case anymore.

I think core engineering skills are never going away (I’ve been doing this for 25 years and come from a background doing C++ for video games), that you will always need to have a mental model of the code and if you’re going to call yourself “professional” you need to be able to go in and fix/build by hand. But you’re going to get left behind if you aren’t at least willing to eat some humble pie and re-evaluate your views on agentic coding every few months as these tools get better.

Grieve, I know I have, but there is joy on the other side. I used to love getting lost in flow state with Soma.fm playing and the phone unplugged, that is still there, but what that looks like is changing fast and table stakes in this industry has been “adapt or die” for as long as I’ve been in it.

You're still doing 75% of what you did before.

Now the syntax is handled for you, you have a research assistant, and someone that can really dig through the details for you.

The rest ... is still there.

> But just go ahead and give the latest models (Opus 5.5 / Astra 6 as of today) another try. See what they are capable of and read the code which they produce.

Well, for one, they're capable of draining our (or companies') wallets.

I resolved a huge merge conflict for $60 today. Opus 5.5 did a great job and spent just 1h 16min on this. I could probably run six such sessions today, if I disregarded the need to read and understand the code.

This money has to come from somewhere and my concern is that it will be from decreasing the number of people hired and/or their salaries.

At the same time I firmly believe people who had a tendency to produce tech debt will keep doing that, regardless how brilliant LLMs will become. Unscrewing this is going to cost a lot of money.

I've really tried this and it's not quite what you're saying. You still need to steer it. You still need to stop it from doing stupid things. You still need to vet the architecture and data model. You still need to be clever, it's not that clever.

I think what software development is turning into is the art of setting up the program into a shape from which the LLM can reliably and efficiently fill up the rest of it. Sometimes that might not even require writing a single line of code. But I think you still need to be as good of a software engineer as you were before.

I concur as well in the last six months we moved significantly in the it just works direction. Eight months ago, all output was either garbage or weird and buggy at best. Today the models actually solve problems and fix bugs.

but where are new shiny apps from whom who uses $RECENT sota models?

Coding is literally solved with current models. It's just a matter of inference cost at this point. Imagine if Astra 6 Max was $0.00001 per 1M ouput...

I can't think of a single example apart from perhaps the 99.9th percentile difficulty of work that wouldn't be solvable with that configuration.

Well probably you haven't done so yourself, because I use them on a fairly high-complexity system with concurrency, IoT devices, etc, and the solutions are often in need of large rework almost every time (when touching core stuff)

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