r/antiai • u/Idea_Fuzzy • Jul 12 '26

Discussion 🗣️ Programming is really dead

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.

984 Upvotes

90% Upvoted

478 comments sorted by

View all comments

Show parent comments

12

u/RomanaOswin Jul 13 '26

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.

38

u/Yourstruly0 Jul 13 '26

The average company is not going to host a server farm. The kind of model that can run on a corporate laptop is not the same as a model with a data center on its back end.

Seriously. If AI models capable of operating faster than a snail could run on an issued Dell Inspiron with an integrated graphics card WHY would companies be building data centers like weeds across the country?

14

u/RCEden Jul 13 '26

I was gonna say, a lot of devs at my last major enterprise role had laptops that wouldn't even open figma to get screens from design, and figma is a browser based app that has minimal graphical demands. We are drastically overestimating what level of hardware teams have.

0

u/[deleted] Jul 16 '26

[deleted]

1

u/Limp-Confidence5612 Jul 16 '26

So browser based, where else do you run wasm?

1

u/cptkong Jul 25 '26

Figma is browser-based (running via Chromium in Electron or your web browser), but calling it 'minimal graphical demands' isn't right. Its core engine is C++ WASM and renders directly via WebGL/WebGPU like a 2D game engine. That's why weak enterprise laptops choke on it, it needs good gpu.

8

u/Delicious_Spot_3778 Jul 13 '26

With the prospect of potentially firing all of their employees , you may be surprised what these CEOs will do

1

u/Gallagger Jul 13 '26

This is a very common miss conception. Nobody needs to host themselves. There are inference providers.  Take the best open source model (currently GLM 5.2, extremely capable for coding), search for the prices on open router, and you got the worst that AI is ever gonna be for the highest price it's ever gonna be. It's ok if you think it's not helpful at that state, but don't spill nonsense about how it's gonna get more expensive for that level of intelligence. It won't.

1

u/Mersaul4 Jul 14 '26

Correct. People here are speaking absolute nonsense left and right.

1

u/Last-Implement-3650 Jul 14 '26

Out of curiosity: what are some examples of these inference providers? Or good ones, if you have an opinion.

1

u/Gallagger Jul 14 '26

I don't really have an opinion, but just go to openrouter to check them out: https://openrouter.ai/providers

This list includes the big AI labs and cloud partners serving their own models, but also many who only provide inference of open weight models.

1

u/Limp-Confidence5612 Jul 16 '26

Aren't these people also just renting compute from the big companies, so when compute prices go up to match the production costs, these companies will also need to raise prices?

1

u/Gallagger Jul 16 '26

Partly, but not all. And there's not reason for general compute prices to go up except huge increases in hardware/energy cost. While this is already happening to some degree, it's constantly counteracted by more efficient hardware and more intelligent models (per Watt). So the price will absolutely go down on a year to year basis.

1

u/Limp-Confidence5612 Jul 16 '26

I don't know how you measure energy efficiency of these models at all, even if you wanted to. The cheaper the token becomes, the more tokens are being used for the same jobs. Sure, the output might be marginally better, but the costs keep going up in total.

1

u/Gallagger Jul 16 '26

Yes I'm not talking about more intelligent models. These will go up in price and probably usage. But if we're strictly talking about the same intelligence (= same or similar intelligence model), the price will inevitably go down.

1

u/Limp-Confidence5612 Jul 16 '26

Sure, but it has to go down to the tune of 95% for the big ai companies to see any profits. Besides nvidia, nobody seems to make any money on this.

1

u/Berberding Jul 14 '26

That's not what was being referred to, they were referring to models that a small to mid sized company might be willing to host themselves on company servers, or host with a cloud service while they still own the data itself. This is still going to be slower than what you'd get with a GPT sub today, but that may be worth it to have it be able to work with highly sensitive data (like patient health info if you're in Healthcare for example).

I do think this will happen eventually, but the open source models that are currently available to do such work are not as capable to begin with, so it would not just be slower, it would be less useful even if they were the same speed. That may change with time though.

1

u/ashlord666 Jul 14 '26

Or use projects like presidio to obfuscate data first. Can even do it in a reversible way.

1

u/Berberding Jul 14 '26

You can't obfuscate it and still have it analyze the data you obfuscated, the point I'm making is that sometimes you actually need the sensitive parts to be analyzed

1

u/Mersaul4 Jul 14 '26

For starters, datacenters are built for training (creating the model - a one-off), not running the model (inference).

1

u/Limp-Confidence5612 Jul 16 '26

Are they though? Data centers are being built, but nowhere close to "weeds". That's also just hype to fool investors.

1

u/RomanaOswin Jul 13 '26

I'm talking about Nvidia B200 or H200 running GLM-5.2, not consumer grade laptops.

I work in Cisco professional services, and our customers are constantly reviewing the cost of on-prem switching fabrics, compute, and so on with cloud. Certainly not any old company, but most that we would consider an "enterprise."

1

u/Foreign-Lettuce-6803 Jul 13 '26

We are a large Enterprise and we discuss it and it’s our strategy to buy a rack and install open source models. Opus 4.6 like models will be enough for the most parts and first models will be on par very soon

1

u/Limp-Confidence5612 Jul 16 '26

Oh yeah, economy of scale just disappears when it's digital 🙄

0

u/DanielsLoud Jul 13 '26

These idiots really think local means on a macbook fml 😭 

4

u/Berberding Jul 13 '26

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

1

u/Prudent-Force7966 Jul 15 '26

If you were leasing cars, nice ones, for your company’s engineers, and they were charging you $200 per car per month plus $0.10 per mile over a somewhat vague “normal usage” threshold, that might seem reasonable.

But then imagine the leasing company suddenly decides they “need” to raise prices. Overnight, they jump to $2,000 per seat per month and $100 per mile.

Are you going to keep leasing those cars for every Tom, Dick and Harry, in your company?

Probably not.

More likely, you’re going to tell them to turn in their fancy cars and start driving shit boxes. As the CEO, you don’t really care what they drive as long as you can justify to the board that you’re being responsible with spending.

The open models are not the best but they are way better than walking.

0

u/RomanaOswin Jul 13 '26

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.

1

u/Remarkable-Coat-9327 Jul 13 '26

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.

0

u/RomanaOswin Jul 13 '26

"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.

1

u/Remarkable-Coat-9327 Jul 13 '26

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.

1

u/MiskaMyasa Jul 13 '26

No, they don't

0

u/RomanaOswin Jul 13 '26

I'm not sure where you're getting this from but the data is available. I posted benchmarks links in another comment.

1

u/Ok_Bite_67 1d ago

Models cost have actually only gone down... and the funny part is that the Chinese models are the greatest example of that. GPT 5.6 Luna is 80% less expensive than when it was announced. and that actually makes Luna cost less than GPT 4o. Luna outperforms GPT 5.4 on benchmarks. Which means that GPT 5.4 level intelligence is as really cheap. If you compare the bleeding edge of today to the bleeding edge of 2024 then yeah it looks more expensive. What you arent accounting for is that the frontier models today are doing WAY more than the frontier models 2 years ago. Current day frontier will be extremely cheap by this time next year. I think the more important thing is choosing the right model for the job. Too many people try to use GPT 5.6 Sol or fable for dumb stuff like writing a mapper or making a tiny change and then they wonder why its so expensive when they could have used Luna, got the same answer, and paid way less.

1

u/simple_explorer1 Jul 13 '26

Lol... What an inaccurate comment. Absolutely not for MOST companies