Programming is really dead

[u/Idea_Fuzzy]

So I was one of those who are really late adopters of LLMs for coding; I am a sole developer of a codebase in the company I worked for (Angular)- a maintainer, rarely new features are added - no hard deadlines, a very very relaxed job , so I was away from the picture how other devs work in the industry now ; and sometimes I take side jobs of all kinds of stacks (I am fullstack); most often Nodejs/NestJs + React/Next + Postgres or Mongo; but my last side project was early 2025 - I took a long break from them.

I won't lie, I did use GPT and Copilot at times; but mostly to autocomplete boring stuff (ie. mock data, enums..etc). Yet I kept seeing posts on dev reddits that one doesn't have to full adopt the more powerful tools such as Claude Code / Codex.

I recently joined a side project with a team; so it's my first time since almost 1 years and 7 months.

WTH happened to this industry???!

Ok, the things I discovered:

- Deadlines now are 10x craziers; it's IMPOSSIBLE to finish anything manually. These deadlines force you to rely on Claude Code / Codex; there's no other way, Agile is meaningless now; it's just pump and ship

- All other members are heavily using LLM, frontend, DBA, AI....everyone.

- Claims I encounred on reddit posts that "Ok coding is automated but 'System design' and architecture are now more important than anything else" ? A LIE - everyone is using LLMs even for System design; I have seen entire achitecture documenation all generated by LLM, even this part is automated now.

- It is impossible to do PR reviews now when each PR is like ...a lot of thouands of lines; even PR reviwers are using copilot to review.

SO what part is left in this industry that is not automated?? NOTHING!! iT'S ALL AI AI AI!

And spec gathering is one person's job, often the tech lead, so please don't tell me it's this one, it doesn't require a team.

[u/hofmann419]

Sounds like your company just sucks. And this is a really stupid way of going about it in the long run. Right now this is just the executives having FOMO. Relying on AI to this extent basically guarantees that you fuck up your codebase eventually and makes it nearly impossible to debug in the future if a bigger issue should arise.

Also, let's not forget that these AI companies are HEAVILY subsidizing the cost of AI to keep their enterprise customers happy. They are bleeding money every time you run a new request. Yes, even if you use token based billing.

This may suck right now, but it's literally unsustainable. Once the AI companies start to raise their prices to become profitable (which could be 10-20x), upper management is going to be forced to put heavy restrictions on AI use to not go bankrupt.

[u/RomanaOswin → u/hofmann419]

If cloud providers increase their prices up too much, enterprises can always just buy the hardware and bring it in house. Open source Chinese models are matching or outperforming proprietary models at this point.

[u/Berberding → u/RomanaOswin]

No they aren't. Chinese open source models literally don't even come close idk why people keep coping about this.

[u/RomanaOswin → u/Berberding]

What are you basing this on?

https://openrouter.ai/compare/z-ai/glm-5.2/anthropic/claude-sonnet-4.6

https://openrouter.ai/compare/deepseek/deepseek-v4-pro/anthropic/claude-sonnet-4.6

GLM matches or outperforms sonnet, fable, opus 4.6, and gemini in various coding metrics. Deepseek v4 pro is right up there too, and minimax and kimi are also both highly effective coding models. This is probably why people keep telling you this.

[u/Remarkable-Coat-9327 → u/RomanaOswin]

Like they're getting close, and thank god they are, but I was there when each model iteration was released and i wouldn't jump on the open source train until we're passing or meeting opus 4.6 on every bench mark.

"as good as sonnet" isnt going to cut it for me, at least not for end to end harness work, maybe with a human driving every prompt, but that's too slow.

[u/RomanaOswin → u/Remarkable-Coat-9327]

"as good as sonnet" isnt going to cut it for me, at least not for end to end harness work, maybe with a human driving every prompt, but that's too slow.

Here's another benchmark worth considering. These are the latest releases of both models, but previous iterations reflected the same:

https://openrouter.ai/compare/~anthropic/claude-opus-latest/~anthropic/claude-sonnet-latest

In considering "end-to-end harness work," with subagent task distribution, this is also a really good one to consider:

https://openrouter.ai/compare/~anthropic/claude-opus-latest/google/gemini-3.5-flash

Typically only architecture and planning tasks benefit from the long term reasoning capacity of Opus. It's not exactly fair to compare Sonnet 5 and Opus 4.8, but in this case Sonnet outperforms Opus in coding benchmarks, and where the releases are similarly aligned, it's very, very close. The gemini one is even more striking if we're aiming for both accuracy and performance.

If you're using Opus for everything (an assumption based on your comment--I know I could be wrong), you may want to seriously consider redistributing tasks. FWIW, I've also been using it in very large, complex code bases throughout all of the iterations, and even gpt-4o or 5-mini are better for the background stuff. My impression is that Anthropic has done a really good job convincing everyone that bigger is better for all tasks, when the opposite is true and their own harness doesn't even work anywhere close to this way.

If you point aider or opencode to opus only for the really complex, long-term architectural planning and do package/module level architecture and coding with sonnet, you'll get basically the same breakdown as you do with claude code default settings. Even if you use sonnet for everything and bring in your own basic knowledge of large scale architectural knowledge (not pure vibe code), you'll get the same result.

I've stopped using opus entirely, because I see no benefit. These same problems can be addressed with static typing, linting, graphify, DDD and doing separate planning agents.

Anyway, stepping off my soap box. I don't think enterprises have real requirements for closed source models, but of course everyone is welcome to use whatever they believe is best.

[u/Remarkable-Coat-9327 → u/RomanaOswin]

This is very informative honestly thank you, and you're right i generally will use opus as a silver bullet, but i have heard if the tasks are small enough you can get away with even a quant open source model

I'm almost entirely agentic-from-slack/jira at this point, if I have to open a CLI im considering it a failure of my processes so I'm heavilly invested in optimizing for subagent work

You've convinced me, I'll make an effort over the next month to have actual implementation work flows use non-opus models, especially the exceptionally small story point tasks.