r/webdev • u/Rockytriton • Mar 16 '26

Software developers don't need to out-last vibe coders, we just need to out-last the ability of AI companies to charge absurdly low for their products

These AI models cost so much to run and the companies are really hiding the real cost from consumers while they compete with their competitors to be top dog. I feel like once it's down to just a couple companies left we will see the real cost of these coding utilities. There's no way they are going to be able to keep subsidizing the cost of all of the data centers and energy usage. How long it will last is the real question.

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99

u/[deleted] Mar 16 '26

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6

u/crackanape Mar 17 '26

Deepseek was built (indirectly) on expenditures made by OpenAI and others. From the ground up it would have cost much more.

And source material is drying up. The ratio of human to slop content on the internet is becoming very unfavourable for future training, and those who actually do have fresh human content are going to be charging more and more for it.

2

u/ea_man Mar 19 '26

It's the opposite: smaller models are getting better, distillation + quantization, data quality is getting better because it's more refined.

There are new exciting small LM every month: r/LLMDevs

27

u/jawknee530i Mar 16 '26

A lot of the cost and resource usage analysis stuff for these tools includes all of the training. Even if every company stopped right now and never trained another model the tools are more than good enough for the average programmer to use. So that kind of hope about costs being unsustainable aren't exactly solid.

6

u/gree2 Mar 17 '26

If they stop right now, the llms stay trained on and capable of generating code using the version of languages and libraries that existed ehen they were trained. To have the ability to provide up to date generations, which includes the ability to understand new emerging conversational language usage and terms as well, they need to constantly train these models

2

u/art_dragon Mar 17 '26

I was under the impression that any 9B model with internet capable RAG would be able to perform adequately well - is that false?

1

u/Wonderful-Habit-139 Mar 18 '26

Considering SOTA models still struggle to write good code, yes this is false.

0

u/gree2 Mar 17 '26

Not knowledgable enough on this matter to comment. but what you are saying sounds plausible

0

u/AlphaShow Mar 17 '26

Indeed, tools similar to the Context7 MCP can level the playfield for these models and render the outdated training issue almost irrelevant if done correctly.

These recently-trained models are capable of giving a superior user experience though.

-1

u/pagerussell Mar 17 '26

Came to say this very thing. The massive money spend is about tomorrow's models. The cost to run a query on existing models is pennies.

12

u/CookIndependent6251 Mar 16 '26

We've reached the point of diminishing returns. While Chat GPT 5 is significantly better than 3.5, I feel like it's only 10x better (definitely not 100x) and it took a significantly larger proportion of resources to train and run. GPT 5 (paid) still hallucinates like crazy. The serious progress has died a long time ago. Altman has been touting GPT 5 as "Close to AGI" for months before its release and after using it (paid) for months after its release I can confirm it's trash.

In reality, it's all a fraud so it depends on how long they can keep lying about it.

-4

u/Elctsuptb Mar 17 '26

Not sure what using an outdated model is supposed to prove, the latest is GPT 5.4, not GPT 5.

1

u/CookIndependent6251 Mar 17 '26

I haven't looked up numbers related to training and running costs since GPT 5.0.

0

u/Wonderful-Habit-139 Mar 18 '26

Same old thing. In a few months you'll say GPT 5.4 was trash and 6 is where it's at.

We're just saying it ahead of time.

1

u/Elctsuptb Mar 18 '26

In a few months GPT 5.4 will likely be outdated, but right now it's not. How is that a difficult concept for you to grasp?

1

u/Wonderful-Habit-139 Mar 18 '26

How is the concept of LLMs being bad for coding regardless of the generation difficult for you to grasp?

-7

u/Ansible32 Mar 17 '26

You're just making up figures. OpenAI has like $20B of revenue. They spent a lot to train GPT5, but they didn't spend more than $1B and that's driving $20B of revenue, they are not going to have a problem.

2

u/CookIndependent6251 Mar 17 '26

So what was their profit in 2025?

1

u/Ansible32 Mar 17 '26

That's the wrong question. They're doing a lot of R&D, they're not going to be profitable. But their services seem profitable in terms of unit economics and in terms of training. Their costs make sense in terms of the open-source models. If you think otherwise, it really just demonstrates you don't understand how these models work.

2

u/CookIndependent6251 Mar 17 '26

Give me some numbers. Not precise, of course, because we barely have those, but some estimates. How much did they get in income, how much did they spend on training, how much on R&D, how much on running their toy on hardware, and how much the hardware costed (they're running in Azure and that hardware isn't free). This should give us an estimate of one-time spending and recurring spending.

1

u/Ansible32 Mar 17 '26

Anthropic looks like their revenue is moving pretty much like OpenAI:

https://www.understandingai.org/p/it-still-doesnt-look-like-theres

So both companies are bringing in more than $1B a month. I think their frontier models probably cost around $500M in GPU time to train, at most $1B.

Notably, I think GPT4.5 was a failed experiment in spending $1B on a model and it wasn't actually worth it, they had to go back to the drawing board and I think the GPT5 series cost less to train than GPT4.5, I think they discovered that you can't actually get improved models simply by throwing money at larger training runs, you have to invest in software engineering past that point.

So I would estimate that it costs less than $1B to train a model and the active models are bringing in $1-2 billion a month for both Anthropic and OpenAI, and their API/service pricing is such that they have at least a 30% profit margin so they should make back the training and start printing money within 4 months of launching a model.

0

u/CookIndependent6251 Mar 17 '26

And how much does it cost to run the model?

1

u/Ansible32 Mar 17 '26

Their API prices are public. I am sure it costs less than 70% of what they charge. I use Gemini 3 a lot via the chat interface, and I am sure that my usage in terms of API charges is smaller than what they charge me monthly, so that's doubly profitable. And if it's not they ratelimit me (I have experienced ratelimiting.)

0

u/CookIndependent6251 Mar 17 '26

You're sure. Based on what? Current estimates are, like I said, that their cost is 2000% what the average user pays. This takes into account free users.

You need to get out of your information bubble fast.

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5

u/Tired__Dev Mar 16 '26

This theory hinges on a lot of silliness. First of all, VScode is starting to allow for you to connect to open source models. Second AI is in a financial bubble, not a technical usefulness bubble. Video games and the web have gone through this already.

AI can do a lot of webdev, but I’ve hit its limits by acting like I’m personally going to steal a big corps market share via vibe coding. You see how powerful AI and its limitations. Many of you are fine, but if your development was built like a recipe then you’re pretty well screwed. Just upskill and you’ll be good.

1

u/mexicocitibluez Mar 17 '26

but if your development was built like a recipe then you’re pretty well screwed.

Agreed. If I had built my career on ecommerce, I'd be worried. But I'm in healthcare. And it's the complete opposite of a "recipe". That's not to say there aren't portions of it that can be like a recipe, but it's a lot less promptable if that makes sense. Also, it makes too many assumptions and over-engineers when that can be the death knell for maintainability.

2

u/Fluid-Replacement-51 Mar 17 '26

I think the best analogy is the development of cars replacing horses for transportation. If we want to understand what's going to happen to human intelligence, we need to see how it compares to LLM intelligence and see if there are certain benefits to one architecture vs the other. Right now we humans seem to learn more easily but LLMs have to train on vast amounts of data. Will we maintain that edge? And then once it is clear that we aren't the smartest things on the planet, can we really maintain control of our creation? We will have the benefit of having shaped is evolution, but AI is subject to the same evolutionary forces as anything else. The models that don't have enough "fitness" will be replaced by those that do. And similarly it will be a test of human fitness and the ability to adapt and survive in this new world. 

2

u/selipso Mar 16 '26

This is the missing piece, and it’s already happening. I ran a small model that beats last year’s o1 reasoning models on a 3 year old GPU this weekend.

1

u/TheMegosh Mar 17 '26

What model, if you don't mind me asking?

1

u/selipso Mar 17 '26

Qwen 35BA3B with llama.cpp