As someone who works on the design and construction of datacenters, I cannot stress enough how apropos this comment is. Even before the first conversation in your IDE starts, the load on national and local government resources, local utility capacity, and roadway infrastructure is enormous. We're all paying whether we're using the tools or not.

Nearly nobody cares about the load on “national and local government resources, local utility capacity, and roadway infrastructure” for any other day-to-day activity. Why should they care about the same for AI which for most people is “out there online” somewhere? Related my, crypto bros worried about electricity usage only so far as its expense went and whether they could move closer to hydro dams.

The parent comment's point is that we _should_ care because cheap frontier-model access (that many of us have quickly become hopelessly dependent on) might be temporary.

It's amazing that anyone that has seen anything in technology in the last 30 years can say, "better be careful. They might stop subsidizing this and then it's gunna get expensive!" is ridiculous. I can buy a 1Tb flash drive for $100. Please, even with every reason to amortize the hardware over the longest horizon possible are only going out 6 years. 64K should be enough for anyone right?

Yeah, I can't wait to buy some RAM for my PC! Oh, wait, the AI companies are buying up all the RAM sticks on the planet and driving up their prices to comical highs, surely these beacons of ethics and morality won't do the same with their services that are actively hemorrhaging Billions of dollars, they're providing these services to us out of the goodness of their black hearts and not any kind of monetary incentive after all!

I think the heavy investor subsidization / speculation makes this different. The high cost of early 1Tb flash drives was largely borne by buyers.

Yes, hardware has become cheaper, but services all enshittify the moment the investors start to ask for some return.

If expert devs have junior devs to assign code to, that you review and integrate, do they become “hopelessly dependent” on junior devs?

My experience of expert devs is those who are happy to have extra leverage are not slowed much by having to do it themselves.

In no cases have I seen experts become “dependent” on the junior devs.

They do quite soon after they have become managers or product owners or “architects”.

Those were probably senior only in age.

They should care because they are expensive. If we become dependent on something that is expensive, we have to maintain a certain level of economic productivity to sustain our dependence.

For AI, once these companies or shareholders start demanding profit, then users will be footing the bill. At this rate, it seems like it'll be expensive without some technological breakthrough as another user mentioned.

For other things, like roads and public utilities, we have to maintain a certain level of economic productivity to sustain those as well. Roads for example are expensive to maintain. Municipalities, states, and the federal government within the US are in lots of debt associated with roads specifically. This debt may not be a problem now, but it leaves us vulnerable to problems in the future.

> Nearly nobody cares about ...

That's an accurate and sad truth about humanity in general, isn't it? We all feel safer and saner if we avoid thinking about how things really are. It's doubly true if our hands are dirty to some extent.

At the same time, I submit that ignoring the effectiveness of very small contingents of highly motivated people is a common failure mode of humanity in general. Recall that "nearly nobody" also describes "people who are the President of the United States." Observe how that tiny rounding error of humanity is responsible for quite a bit of the way the world goes - for good or ill. Arguably, that level of effectiveness doesn't even require much intelligence.

> Why should they care about the same for AI which for most people is “out there online” somewhere?

Well, some will be smart enough to see the problem. Some portion thereof will be wise enough to see a solution. And a portion of those folks will be motivated enough to implement it. That's all that's required. Very simple even if it's not very easy or likely.

I always liken it to using Uber in ~2012. It was fun to get around major metro areas for dirt cheap. But then prices rose dramatically over the next decade+ as the company was forced to wean itself off of VC subsidies.

It’s common since year dot fir new businesses to compete on price to attract customers and gain market share. It wasn’t invented by uber

Same with Airbnb. Oh and Moviepass. Those were the days.

I watched a friend of mine minmax Moviepass so hard. They were so doomed.

Except none of those cost structures are based primarily on a resource that gets cheaper over time.. a.k.a. compute.

Computer isn’t getting cheaper, growth right now is supply constrained to memory which if you haven’t seen the news recently…

Training is getting exponentially more expensive. And inference isn’t that cheap unless you can do it locally

The energy demand doesn’t decrease.

Uber and Lyft put all the taxis out of business and now cost as much as the taxis they displaced

Ever notice that even where Uber doesn’t operate most of ride sharing alternatives work pretty much the same way? Go to South Asia, China, Middle East, or South East Asia.

Consumers pick those services because of what Uber pioneered — trust and convenience. You know exactly how much you pay, you pay everything upfront, you know you are dropped off where you need to be. There are of course exceptions, but exceptions they are.

Cost maybe the initial selling point but people stick with Uber and similar services despite higher cost, not because they don’t have other options.

> because of what Uber pioneered — trust

I really dislike the retro fitting of history. I’ve read more occurrences of serious SA by uber drivers and zero for normal taxi in the last few years

Not everywhere. Here the government fucked Uber etc. big time because it required the companies to pay for taxi licenses if I remember correctly. That is if they want to deliver a taxi service.

There's a lot in this comment that doesn't exactly fit.

First of all, there could be other solutions, such as B2B subsidizing individual user plans, or more fine grained model tiering per cost.

Also, yes you can get some access for free, but even today the higher tiers of proprietary models is around $200/mo for individual users, which might still be subsidized but is definitely not free, and is quite a chunk of money at $2400 a year!

I don't know what your setup is at the moment, but it's possible more efficient hardware and stack are available that you're not utilizing. Of course this depends on what models you're trying to run.

I think that smaller models will become a lot better, and hardware will become more optimized as well. We're starting to see this with NPUs and TPUs.

All this means running models will cost less, and maybe upgrading the power grid will also reduce cost of energy, making it more affordable.

I don't see any way that AI will go away because it "hits a wall". We have long passed the point of no return.

You are looking at it from the individual's PoV, but the OP is using the bird view from high above. It is the total amount of effort deployed today already to provide all the existing AI services, which is enormous. Data centers, electricity, planning/attention (entities focused on AI have less time to work on something else), components (Nvidia shortage, RAM shortage), etc.

This is not about finance, but about the real economy and how much of it, and/or its growth, is diverted to AI. The real economy is being reshaped, influencing a lot of other sectors independent of AI use itself. AI heavily competes with other uses for many kinds of actual real resources - without having equally much to show for it yet.

Just an example: https://www.technologyreview.com/2025/05/20/1116327/ai-energ... (it is worth skip-reading it all, the headline on its own is useless)

This is a good point but you can see the price "ceiling" by examining the prices for PCs that can effectively run local models. A DGX Spark is ~$4k (plus power) for example.

That's not nothing, but it's still not very much to pay compared to e.g. the cost of a FTE.

You're counting the cost of running the model, but what about training it? You can't count the compute and data costs at $0.

You can assume that already-published open weights models are available at $0, regardless of how much money was sunk into their original development. These models will look increasingly stale over time but most software development doesn't change quickly. If a model can generate capable and up-to-date Python, C++, Java, or Javascript code in 2025 then you can expect it to still be a useful model in 2035 (based on the observation that then-modern code in these languages from 2015 works fine today, even if styles have shifted).

>2025-2035

Depending on other people to maintain backward compatibility so that you can keep coding like it’s 2025 is its own problematic dependency.

You could certainly do it but it would be limiting. Imagine that you had a model trained on examples from before 2013 and your boss wants you to take over maintenance for a React app.

You're all referencing the strange idea in a world where there would be no open-weight coding models trained in the future. Even in a world where VC spending vanished completely, coding models are such a valuable utility that I'm sure at the very least companies/individuals would crowdsource them on a reoccurring basis, keeping them up to date.

The value of this technology has been established, it's not leaving anytime soon.

SOTA models cost hundreds of millions to train. I doubt anyone is crowdsourcing that.

And that’s assuming you already have a lot of the infrastructure in place.

I think faang and the like would probably crowdsource it given that they would—according to the hypothesis presented—would only have to do it every few years, and ostensibly are realizing improved developer productivity from them.

I don’t think the incentive to open source is there for $200 million LLM models the same way it is for frameworks like React.

And for closed source LLMs, I’ve yet to see any verifiable metrics that indicate that “productivity” increases are having any external impact—looking at new products released, new games on Steam, new startups founded etc…

Certainly not enough to justify bearing the full cost of training and infrastructure.

2013 was pre-LLM. If devs continue relying on LLMs and their training would stop (which i would find unlikely), still the tools around the LLMs will continue to evolve and new language features will get less attention and would only be used by people who don't like to use LLMs. Then it would be a race of popularity between new language (features) and using LLMs steering 'old' programming languages and APIs. Its not always the best technology that wins, often its the most popular one. You know what happened during the browser wars.

That can't come anywhere close to running the current SotA models, though.

Most things don't require SotA models.

But still, right now, you don't have to worry as even these SotA models are subsidized really so much and you can just use them for free on websites and then if you don't even want to type, go use a cheaper model or even a free model with something like opencode even to then act as a mini agent of things

Usually I just end up it being more focused in a single file which isn't really the best practise but its usually for prototyping purposes anyway so it ends up being really good

uv scripts are good for python, and I usually create golang single main.go files as well as I feel like it can be a binary, compile fast and cross compilation and still easy and simple so yeah :)

I find the cost discussion to be exceedingly more tedious. This would be a more compelling line of thinking if we didn't have highly effective open-weight models like qwen3-coder, glm 4.7 etc. which allow us to directly measure the cost of running inference with large models without confounding factors like VC money. Regardless of the cost of training, the models that exist right now are cheap and effective enough to push the conversation right back to "quibbling about the exact degree of utility LLMs provide".

>I run local LLMs infrequently, and every single prompt makes my beefy PC sounds like a jet engine taking off. It's a great reminder to not become codependent.

I would try setting the GPU to run at a lower power level. I set my GPU power level to 80% and it becomes much quieter, and only runs maybe 5% slower at most.

Also I 100% agree with the rest of your comment. We can only power the current growth we are seeing for so long.

LLMs cannot generate coherent sentences

LLMs writing prose is too robotic

LLMs output is too dependent on prompts to be interesting

LLMs take too much RAM to run effectively

LLMs take too much electricity to run locally

LLMs work locally but are a bit too slow for my taste

LLMs output mostly correct code but it isn't applicable to my codebase

LLMs make tool calls to pull in additional context

LLMs outputted code works for most developers but not my codebase <---- you are currently here

isn't this template supposed to mean that all the previous considerations are now obsolete?

I guess you could argue that the standard LLM sentence structure is too robotic but prompting mostly fixes that.

The rest is no longer true, indeed

So what'll happen to all these companies building on top of openai license. I don't hear these warnings in professional circles, only online

A competitive coding like devstral 2 runs fast enough to be very helpful: https://www.hardware-corner.net/devstral-2-hardware-requirem...

The required hardware is fits the budget for a professional developer.

Putting LLMs in the cloud allows the hardware to be utilised better and to have sharding of big models.

> One day when the well runs dry, you must be able to either pay the actual cost

What multiple of the current cost do you expect? Currently, GitHub Copilot and ChatGPT for Business cost $19/month and €29/month respectively. Even a 10×–20× increase will still be economically viable in a professional setting if the tools continue to save hours of work.

These tools (e.g. Chatgpt pro) lose money at $200/month

So expect, maybe, $1000 a month? Until your business is dependant on these LLMs. Then they can extract basically all your margin lol

At a $1,000/month price point, wouldn't the economics start favoring buying GPUs and running local LLMs? Even if they're weaker, local models can still cover enough use cases to justify the switch.

Claude max is like $100/mo, and if you’re a daily user you’re likely going to need max

Remember when Uber was cheap and showed you the surge pricing multiple?

The cost is coming down fast. You can get a $2000 desktop machine (AMD 395) that can run effectively chatGPT 3.5 levels of LLMs at over 100 tokens per second.

if you wrote this comment 70 years ago when computers were the size of rooms, it would make a lot of sense, and yet we know how history played out where everyone has a super computer in their pocket.

for some reason it feels like people are under the assumption that hardware isnt going to improve or something?

Writing my comment on this post, I kind of feel like LLM's are like similar to wordpress/drag and drop tool although its more inconsistent too perhaps not sure

I 100% share the codependent path too and had written a similar comment some 2-3 days ago but these AI companies which provide are either seriously negative/loss making subsidizing or they are barely net zero. I doubt that it will continue and so the bubble will burst I guess and prices of these will rise perhaps

We will see perhaps something like google which can feed on advertising can perhaps still provide such subsidies for a longer time but the fact of the matter is that I have no alleigance to any model as some might have and I will simply shift to the cheapest thing which can still provide me / be enough for my queries or prototypes mostly I suppose.

I am sorry, but this kind of level-headed and realistic take is completely unacceptable on hackernews, and you should be ashamed of yourself. This is not a place for rational discussion when it comes to LLMs.

LLMs are amazing and they will change everything, and then everything will be changed.