Ah, I see you’re a frood who really knows where his towel is.

It works like this:

1. Assume for the sake of argument that the deity exists.
2. Examine the horrific, implausible, and logically absurd implications of that existence.
3. Use those as evidence that the deity doesn’t actually exist.

I mean, did anyone expect anything different? The frontier providers that Copilot depends on switched to token-based billing in a desperate and ludicrous attempt to somehow turn a profit on AI, so of course Microsoft is gonna jack up their prices too.

That’s not even remotely an “atheist’s worst nightmare.” A deity exists and it’s horrifying? That’s a theist’s worst nightmare, and the basis for several arguments for atheism.

There was a lot of news about the Muttsee Dam solar project a few years ago, so it’s a real thing and a good thing. But it’s hardly current news. It’s been operational since 2022.

Very serious. Your personal amount of usage means nothing at all in this conversation. It is entirely about tokens per watt. The amount of energy the memory operations involve scale incredibly well when people are accessing the same object in memory simultaneously. Last I looked it was around a 10x difference for the same models efficiency.

Hold up. Are you talking about caching? Because if you are… yeah. That has nothing to do with the model and everything to do with the service layer around the model. The same service layers can be - and have been - implemented in tools like Lemonade Server, llama.cpp, Ollama, etc.

And I really do want to know your sources.

Mine say GPT 5.5 is probably using quite a lot more than 0.34 Wh per query (0.34 Wh is what Sam Altman claimed for the then-current version of GPT in June of 2025, but he hasn’t released numbers since then and no one has done an independent analysis). With Claude, an independent estimate from last year pegged Sonnet at 0.8 Wh for a short prompt, 2.8 Wh for a medium one, and 5.5 Wh for a long one. Current numbers are, again, almost certainly much higher. And just for fun, there’s DeepSeek (which I’ve never used and never would use), with the reasoning-tuned DeepSeek-R1 hitting a whopping 29 Wh for a complex query.

Meanwhile, small, open models are probably in the 0.07 - 0.2 range, depending on the model, the hardware it’s running on, and the nature of the query. Of course, there are much weightier open models too, with ones like Llama 3.1 405B using about 9 Wh for a medium-length prompt. On the other hand… who is going to run that on their local machine?

Look… If I’m wrong, and using local models the way I do - sparingly and infrequently - really does consume more electricity than using Claude Code, I want to know. I have no problem whatsoever with eschewing AI models entirely, since I despise all of them. But given how tight-lipped OpenAI and Anthropic are about energy consumption per average prompt, and what independent analyses have estimated, I am highly skeptical that they are acting as some sort of paragons of environmental stewardship.

You’re probably burning more energy turning it off and on again. It doesn’t really use any noticeable power sitting idle.

I am absolutely not burning more energy than a frontier model by doing things like putting my laptop to sleep or shutting down unused services when I want to conserve battery power.

Anyway, a direct comparison would be pretty difficult because your model is probably tens of billions of parameters, not over a trillion.

True.

Energy consumption per output token will probably be a bit higher for the frontier models but something that people have found is that higher quality models often need fewer tokens to achieve the same goal.

That’s actually not true. In fact it’s much the opposite. Frontier models churn through tokens at a much higher rate, because of their higher complexity and higher number of parameters. Research is still new on this, but having a frontier model analyze your code files versus a small, local model for the same task seems to be enormously wasteful. If you must use a frontier model for something, have it do that work after receiving the output from an agent using a small model to read and summarize your code.

Plus how many times do you re-prompt your local model vs Claude Fable or Opus for example to get the desired result?

…Almost never? I’m not a fan of letting AI do much of ANY of my coding, because it will inevitably bloat my codebase with garbage regardless of which model I use. So I severely restrict my model usage to simple, clearly-defined, narrow-scoped tasks that can save me a bit of time, and that’s it. With guardrails and discipline like that, I barely ever have the need to re-prompt.

Are you serious?

• Local model: Spends most of its time turned off. Only active when I want it to be active, and only for a little while. Dedicated solely to generating the small amounts of code I use it for. Does nothing else. Costs $0 per token, and electricity costs are negligible.
• Frontier model: Always on, running on millions of GPUs. Would be burning down the planet even if hardly anyone was using it. Incredibly wasteful, being used for trivial tasks and convincing people that their horrible ideas are visionary every day. Misspelling “strawberry” for the masses. Trained specifically to be addictive. Can easily cost a software developer who is addicted to AI thousands of dollars a month, with the recent price increases.

I’d love to see some data to back up the assertion that frontier models are somehow cheaper and more efficient than running a model locally.

No, there really isn’t. The frontier models are created through massive plagiarism. They’re designed to be addictive to use. They consume massive amounts of resources to feed you slop. They are inherently unethical. We’re burning the planet down to keep them running, and we don’t even have a demonstrable financial ROI to show for it.

Stop using them. If your employer makes you use them, maliciously comply by wasting tokens until the financial pain is too great for them to bear and they stop. If you yourself are addicted, switch to small, local, open-source, open-weight models you can run yourself. You won’t burn the world down running a small model on your own computer.

That can’t possibly be the actual headline.

[Checks headline]

Well I’ll be damned…

It’s becoming painfully obvious that there is no way to ethically use frontier models powered by these monstrosities. It is currently 100 F in Tuckahoe, the largest city in Henrico County… and they’re asking people to not use electricity so that these heat-and-pollution-generating slop factories can use it instead.

This is insanity.

Doesn’t it work out to something like a full kilometer of the things in order for it to work? The idea is pure madness.

Well… Okay then! This has genuinely been a pleasure.

Hey, before I say anything else, I just wanted to tell you I’ve been enjoying this conversation. It’s nice to be able to disagree with someone on something without it becoming a religious war. :)

I’d say they’re the ones most likely to get screwed on this but I just don’t see how a tech titan drawdown causes the whole world go into a great depression

I don’t think it will cause a great depression, but I do think it will cause a massive recession. If you look at the S&P 500, 35% of it is tied up in AI-related stocks. If AI crashes out, that’s a truly massive hit to the domestic economy, and there are certainly going to be ripple effects throughout the world.

Honestly, the best thing the world could do right now is something a lot of countries are already scrambling to do because of Trump: Decouple their economies from the United States.

Maybe I’m just too optimistic but if captain dickfingers giving the world a tariff hit, a global pandemic and “the worst oil crisis ever” can’t put a dent in things, I just don’t think a pullback on AI spend is going to do it (unless there’s a ton of other structural problems in the global economy that bloggers will point out how obvious it was only once it fails)

Dickfingers! Excellent nickname for the orange sack of shit. But I’ll remind you of the old saying: The market can remain irrational for longer than you can remain solvent. And right now, the market is unbelievably irrational.

The valuations and market caps of these companies are completely disconnected from their profitability (or extreme lack thereof). Among all of them, only NVIDIA seems to be making actual money, and even with them there are some indications of very esoteric math being involved, too. They’re “investing” money into the AI model providers and then having the AI model providers use that money to buy NVIDIA GPUs. Then they book those sales as profit, even though it’s the same money they just invested.

It’s not something that can continue indefinitely. Either the model providers have to show that their unit economics works - by putting actual profit into actual bank accounts - or they will eventually hit a point where no matter what the funny math on their books says, they don’t have the cold, hard cash to pay their bills.

Remember how Uber was about to go broke? Not going to lie, this literally feels like just more circlejerking about companies that are spending a lot and will go broke any day now…

Maybe. But there’s a material difference between Uber / Spotify, and the AI companies. Did you ever look at the SEC filings to go public for either of them? I actually did. They had detailed plans for how to eventually achieve profitability. Uber went public in 2019, and wasn’t profitable until 2023, but they had a solid roadmap for getting to that point, and now they’re consistently profitable.

Our ability to look at profit plans is limited at the moment. The only publicly traded AI company right now is SpaceX, and their filing is… hilarious. Grok will never be profitable. Not remotely. And their filing is full of WeWork-style insanity. They don’t have anything remotely like a roadmap to profitability. Their stock price is 100% speculation. Yet it keeps managing to tick up over $170, at least for the time being.

Speaking of WeWork, I think they’re the model SpaceX, OpenAI, and Anthropic are following. The company raised $12.8 billion in financing, and ultimately reached a valuation $47 billion, mostly from investment by SoftBank - the same bank funding a lot of AI companies now, and which owns 11% of OpenAI.

But it never made profit. It never had a roadmap for profit. It never had any means of bringing in income higher than its operating costs. WeWork declared bankruptcy a few years ago.

Reddit and Lemmy were right about WeWork. So the question now is if the AI model providers are as economically unviable as WeWork always was, or if there’s somehow a path to profitability like Uber or Spotify. SpaceX’s filing doesn’t fill me with hope on that, and the fact that both OpenAI and Anthropic are delaying their own moves to go public doesn’t fill me with hope, either. It’s not the behavior of a company with unit economics that work.

Side note: None of this is to say that Claude Code or Codex or any of their coding tools are bad. I just don’t see how they can operate them profitably. If you’ve got a good product, but the only way to get companies to adopt it is to sell it at a loss, you will eventually fold.

Sorry if I hold a grudge and you are not as dim-witted and not good with computers as the average lemmy user, it’s hard to shake hearing the same prophecies I’ve been hearing about other high spending companies for decades

That’s fine. It’s actually why I mentioned WeWork as a counter-example. Because you’re right, the group-think on both sites (Reddit and Lemmy) can blind people.

On inference alone, both companies project profitability

https://aiafterhours.substack.com/p/openai-vs-anthropic-the-121-billion

https://www.tradingkey.com/analysis/stocks/us-stocks/261756528-anthropic-openai-ipo-tradingkey

That’s true. They project it. Using non-GAAP accounting, and without letting anyone know in detail how much computing for inference costs. Their claims resemble WeWork’s claims, pre-bankruptcy. Bluntly, I don’t believe them.

And I really have to emphasize this: Focusing on the cost of computing for inference all by itself and excluding all the other costs of the business is just crazy, even if inference when looked at by itself can be theoretically profitable.

They are very clearly in the business segment, I’ve heard nothing like this for Qwen or GLM or local hosted models, in fact when self hosting was bought up the dev’s mentioned “??? you can’t self host claude ??”

No argument on Claude being in the business segment, that is absolutely true. But at my company at least (again, this is admittedly an anecdote), the skyrocketing cost of tokens has us working on implementing local models and models on the network edge. We’re also severely restricting token budgets and having devs do as much as they can by hand.

Maybe your company hasn’t reached the point where tokenmaxxing with Claude is frowned on, but the costs are enormous. And the thing is, they have to be enormous for Anthropic to have any hope of ever recouping their losses. It’s not like Claude is a loss-leader, it’s their only product.

This is like saying I work with actual system admins, they say that Windows is terrible and that it’ll be the year of the linux desktop any day now

You need to vibe check the office and devs vs the engineers

The vibe in the office (and what I’m reading) is that claude is gold

I do. I am one of the devs in the office. My boss currently loves me, because my token cost is $0 and I still get my code written. I do most of it by hand, and some of it with Qwen running on my local system (I have a workstation with a good enough GPU to run it). I have it wired up so GitHub Copilot uses it. And I’ve been teaching other developers how to do it. Once our current hardware refresh cycle is complete, our token budget is predicted to drop to almost nothing. We’ll probably still use Claude here and there, but the bulk of our work will be doable without it.

In terms of quality, Qwen 3.6 is at about the same level Claude Opus was a few months ago. I don’t see how Anthropic can compete with that over the long term.

That said I spoke to a normal person the other day and they hadn’t heard of claude at all, blew my mind

That doesn’t actually surprise me. Claude is still a niche product when it comes to general consumers. As far as the public is concerned, AI = ChatGPT (and the annoying Google AI summary).

nvidia, microsoft, google, oracle, etc are all profitable

• NVIDIA is a hugely profitable hardware company. Not an AI model provider. They’re selling the shovels for the gold rush. And their profits will tank as soon as one of the model providers fold, though pivoting back to consumer hardware is definitely an option for them.
• Microsoft is a hugely profitable business and consumer software company. Again, not an AI model provider. They have trained a couple models, but have made no profit on serving them. Their add-on “Copilot” services aren’t profitable (hence the recent enormous price hikes for GitHub Copilot, which has resulted in a bunch of companies scaling back their AI usage). All of Microsoft’s profit comes from software sales.
• Google is a hugely profitable software company as well - but again, not with AI. Their model, Gemini, isn’t remotely profitable, and has similar costs to maintain as Anthropic and OpenAI see for their models. Heck, they just did a fundraising round for the first time in years to support it, which is NOT a sign of a healthy AI business.
• Oracle is also profitable in software, but they’ve staked their company on hardware roll-outs supporting data centers for OpenAI. They’ve booked future profit from OpenAI owing them almost a trillion dollars. If OpenAI can’t pay that bill when the time comes, Oracle is completely fucked.

inference is profitable, training is not

When I don’t include the costs of doing business, my business is profitable! That’s silly. Inference might be very slightly in the green now when viewed by itself (although that’s deeply questionable; no actual GAAP accounting has shown it to be so). But since training is an ongoing expense that frontier model providers have to constantly engage in, their companies are - and will remain - very deeply in the red.

And without seeing GAAP accounting showing where all the money goes in support of inference, I am highly doubtful that it’s profitable.

training is what the majority of data centre spend is going towards

if they want to be profitable pull back the training but right now they are competing for market share

They can’t. Ever. Pulling back on training means allowing model drift. You need to understand that models are obsolete the moment they’re released. Their training data is set in stone. New version of Typescript ships? Some celebrity dies? Big election happens? The model not only doesn’t know about any of it, it can’t be updated. The best you can manage is throwing MCP and RAG at it in the hopes that the model will pay attention to it, but the point of diminishing returns on that arrives almost instantaneously. You have to train. Constantly.

feel free to look back at all the times lemmy predicted the end of spotify because it wasn’t profitable, now they turn around and cry it’s making money

Bad comparison. Spotify has already been a profitable, publicly-traded company for years.

And - this part’s important - I’m not Lemmy. The platform we’re having this conversation on has nothing to do with whether or not the AI model providers are profitable.

At work nobody is talking like this, everyone is talking about claude and it makes sense, it’s the best thing since vscode

Anecdotes aren’t data. But as long as we’re swapping anecdotes, here’s mine: I work with actual machine-learning engineers. They’re the ones who bag on Anthropic and OpenAI the most. And they use Qwen, Gemma, and a few other small, open-source, open-weight models. Have you looked at Hugging Face? Its community is huge, and growing daily. No one wants to be locked in to Claude Code or any other proprietary development tool when the service has been unstable and the pricing has becoming ridiculous in their desperate attempts to become profitable.

The cost for using Qwen tokens is $0, no matter how many tokens they use.

You say no one talks like this… Are you sure you’re listening?

Anthropic is already profitable if you take out the enormous spend they have on training, which if the bubble bursts would leave them as the number 1 ai provider, it’s also insanely in demand and has trouble keeping up with its current product, they also have several products mythos etc lined up

First off… Why in the world would you take out the enormous spend they have on training? Training is an ongoing expense, not a startup expense. If your expenses exceed your income, then you’re not making a profit.

Secondly, they had one quarter in which they reported (using non-GAAP accounting) a very slight amount of profit. That same quarter, SpaceX gave them a massive - and temporary - discount on rented compute.

We don’t have any reason to think they’re actually profitable.

I doubt it, I think it’d be closer to liberation day tariff’s or the oil crisis, it’ll go down for a bit, many articles will be written about how this is the worst thing ever then 6 months later it’ll be back up again

You’re way more optimistic than I am. If OpenAI and Anthropic crash, there are a huge number of businesses that have built themselves around their products, and those will crash, too. And I think you’re downplaying the damage the tariffs have already done.

As said all the major players in this game are super profitable major companies, that won’t change

Again, not true. OpenAI is not profitable. Anthropic is almost certainly not profitable. Grok from SpaceX is not profitable. Google is profitable, but not from Gemini. Microsoft is profitable, but not from Copilot.

No business that is built entirely on AI is profitable. Not one.

Look… No one’s arguing that the coding tools built around AI are entirely useless. They’re not (although their capabilities are way, waaaaay over-hyped). The problem is an economic one: Serving up AI models cannot be profitable. There’s just no way, especially now that we have small AI models that can be run on local workstations, and offer similar performance to the frontier models.

Qwen, running in a well-designed harness such as OpenCode, with a carefully written AGENTS.md file, is of comparable performance to at least Claude Sonnet, and possibly Claude Opus. All without the massive, ludicrous infrastructure requirements.

How is Anthropic supposed to compete with that? Sure, you can probably get something useful out of Opus faster, but at the cost of thousands of dollars. Using Qwen and similar local models is free.

No one thinks companies like Google or Microsoft are going away.

What’s going to happen is that OpenAI and Anthropic will ultimately fold because they can’t be profitable, Google and Microsoft will scale back their supremely unprofitable LLM operations, Micron and NVIDIA will plunge in value because all of a sudden their bloated prices aren’t being paid by anyone, Oracle will suffer because OpenAI can’t pay its enormous bills with them, massive data center projects will end before completion, and a whole lot of smaller businesses that embraced all this madness will collapse.

That will have devastating ripple effects throughout the economy. It’s going to be a lot larger than the dotcom bubble bursting.

Would have been? EVs are exactly that. They’re great. Ones with range can easily replace cars with internal combustion engines for most use-cases. Usually costs me about $5 a month to keep mine charged.

Fully agree on LLMs being expensive messes that aren’t very useful, though.

I mean, it seems pretty obvious at this point. The data centers aren’t getting built in time for all the hardware they’re buying up to get used, so it’s just sitting in warehouses and will be obsolete by the time any of these new data centers are built (if they ever actually are). And Jensen Huang has been waxing poetic about a future in which people don’t own computers anymore, they rent them to run AI agents on.

I’m not saying this is a sane and rational thing they’re pursuing, and it’s certainly doomed to failure because no one wants to rent computer time to run AI agents. But trying to prop up the AI bubble certainly seems to be the primary goal of all this ridiculous hardware purchasing.