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Miserable_Study_6649@reddit

I really hope it doesn’t go that way, but I would gladly unplug from AI if I had to. It’s only accelerated my work and capabilities. But what I use it for does not directly generate revenue so I would just walk.

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Gunny2862@reddit

It's going to put an even bigger gap between big companies and small ones. (If AI turns out to be useful.)

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seanamos-1@reddit

This is the exactly where the saying, “AI won’t take your job, AI costs will”.

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ruby_fan@reddit

if you spend 5k per engineer per month no real company is going to do that.

5+ years trying to get management approve buying a handful of tools that for about $5k (and recurring 10% annual license fee) would boost the productivity of \~100 devs.

No chance, but enterprise wide AI tooling and shaming anyone who doesn't drink the koolaid by burning all their tokens so the shitty LLM can do poor code reviews that read very pretty (ooh colourful icons in the text!) and confident but are plain wrong (and losing one of the main points of reviews being that someone else is at least aware of what you're doing and why and how it works) ??

SIGN US UP NOW !!

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styroxmiekkasankari@reddit

Yeah honestly the biggest problem right now that I’m seeing is that people are generating tests (without good steering as well) and using it for reviews as well. All code is shit and garbage that could be thrown out technically speaking, but good validation of features and recognition that the RIGHT THING was built is too important to leave to a clanker.

Tests are way too important to leave to LLMs.

I usually get urges to rearchitect things along the way to make things easier to test, at which point it's the opposite of boring boilerplate.

It's also important to manually break tests from code every now and then to make sure you're testing exactly what you think you are.

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tr14l@reddit

Ai proposes arch and tests. Humans validate they are appropriate. Ai develops to those. Then the pipeline validates they were adhered to

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poralexc@reddit

Do humans verify that they work? What if your pipeline isn't actually validating anything?

With the obfuscation offered by mocks and different test harnesses, it's way too easy for agents to assert 'true == true' and for people to rubber stamp that as ok.

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tr14l@reddit

Yeah, that's why humans validate the tests. The same way a team lead would say "no, you didn't test this and this test sucks"... You need to do that now

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0xjvm@reddit

Nah but it’s ALOT easier to review tests than it is to review hundreds of lines of code across a few files.

If youre a good dev, you’ve only asked the agent to do one specific thing. You should KNOW the codebase, you should KNOW generally where the edge cases arefor this 1 case and then it’s just a matter of reviewing do those tests cover those edges.

In my experience there comes a point where the implementation details aren’t under that much review. I’m paid to ship value not produce the most optimised code. As long as those tests are in line with expectations and cover what I expect, I’m happy for LLMs to do both.

But you need to know exactly what you want in the first place, you can’t review if you have no opinion on what it should look like.

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poralexc@reddit

But you need to know exactly what you want in the first place, you can’t review if you have no opinion on what it should look like.

I agree: This is essentially the Responsibly use Guidelines I've been trying to push at work. These tools can be dangerous if you don't already kind of know the answer.

In some legacy codebases I've seen, the testing is the most complicated part. Validating layers of mocks and testcontainers requires understanding those systems as well as your actual code.

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styroxmiekkasankari@reddit

Agree. Really depends what the source should be: I think it’s probably fine for simple functions to let it do both but larger integrated pieces or policy enforcement is off limits. You can also always steer development by writing some test cases before hand.

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new2bay@reddit

If the function's that simple, why not just write both yourself?

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styroxmiekkasankari@reddit

Exactly! Unless I’m doing a hundred such functions at once I’m probably writing them myself

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binarycow@reddit

The point is, LLMs are perfectly happy with making a test an effective no-op in order to get tests passing. They're also perfectly happy with breaking the code to make tests pass.

If you write the tests and the LLM is prohibited from changing them, then that prevents that from occurring.

You might be okay with having two different sessions do their part.

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Copilot can catch obvious bugs, sure, but the part that makes me nervous is when people start treating that like real review when the tool has zero context for why the feature works that way in the first place.

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styroxmiekkasankari@reddit

Yeah not saying it’s not at all useful. Plenty of times it’s reminded me of something I forgot about or a regression after ”one last commit” type of deal.

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DualActiveBridgeLLC@reddit

Yup. My company has a really bad habit of thinking that PoCs should become products. I can see the AI psychosis rolling around where where now everyone thinks their low effort turd is now the future of the company. The worst part is that poor processes and decision making is starting to be discussed as if LLMs will fix the issue. CEO has a shitty way of deciding what customer projects to select. No worries llms will help with productivity. Terrible technical evaluations of effort, no worries our pre-sales bot will come to the rescue. It is getting very concerning.

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OHIO_PEEPS@reddit

You can use them to write tests but you need to total separate code generation from testing. The tester only gets the contract and the intent of the function written by YOU. Then you instruct it to TRY to write tests to expose bugs. And then you need another pass with a fresh context takes those same tests and writes new ones specifically at the boundaries between each layer. Then take everything you possibly can and run mutation testing till you can get to 90% killed. I dont write code anymore really. I just plan architecture and write test plans.

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Because AI is FOMO for leadership

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austinwiltshire@reddit

Because improved productivity is the present day comprimise. The long term vision for folks buying AI is firing everyone they've always been jealous of for knowing how to make computers do things.

Your better IDEs and larger screens pitch never had that as an eventual promise, and so it'd never get approved. They don't want the productivity, they want the (in their minds) incremental move to the day when they can tell everyone to fuck off and use some sort of inifinite money printer.

It doesn't make a lot of sense. But these folks were almost always in their positions of power because they just said they were entitled to be so, and there's enough enablers in society who seem to WANT bad bosses that we let them. They never were strategic geniuses.

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Hziak@reddit

I had been evaluating some new tools for my support team a while back because we don’t have a good on-call management suite. Long story short, I got turned down at the pricetag. I told the sales guy who had been pitching me the one I wanted and he said he understood, but wanted to try one thing first. He ADDED a line item that we hadn’t discussed before for an AI pre-sorting module that we absolutely didn’t need and asked me to resubmit it. Overall, the price was about 20% higher, but the purchase was approved because AI was on it in big letters. To this day, we still haven’t even enabled the module, but we’re enjoying our new on-call tools.

Moral of the story seems to be “if you’re having trouble getting approved, add a bogus AI line item to the quote and your tone deaf, ignorant, managers will eat it up.”

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darkstar3333@reddit

I mean did they say AI stood for "Artificial Intelligence" or "Actually Important".

Efficient-number-one@reddit

First world and third world are not relevant concepts anymore, just FYI.

Source: me I lived in both "worlds" and they are not that different.

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8004612286@reddit

Bro tf? A local living in Berlin has a VERY different than life than a local on Cambodia.

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L3monPi3@reddit

For sure he lived in the good part of a poor country

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eloel-@reddit

Or vice versa

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James20k@reddit

I often think this when it comes to the maths results, where AI was able to solve some maths problem. Its cool, but it cost $x trillion to get there

If you spent a trillion dollars on research - any research - we'd be able to solve pretty much research problem you want. You could pay 1000 researchers in 100 fields a decent salary for their entire lifetimes, and still be left with roughly half a trillion dollars left over

The return on investment would likely be much higher as well

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quentech@reddit

but it cost $x trillion to get there

If you spent a trillion dollars on research - any research

Building datacenters isn't research. The only way AI cost $x trillion is if you include the cost of building datacenters.

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G_Morgan@reddit

They are desperately hoping it will reach the point where it can be done without devs and then they can scale costs up and down as needed. It is why I've not remotely been worried about all this, it is a pipe dream.

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RedditNotFreeSpeech@reddit

We recently blew through one of our monthly budgets in 5 hours when they accidentally started making more calls to ai than intended.

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Megamygdala@reddit

We are told to increase our usage. Since the company is paying a lot of money for AI access, its only profitable to them if we use a lot of it. Say if 3000 developers at a F500 company used the majority of their Github or claude plan, the AI company would easily lose hundreds of thousands to millions of dollars per year.

There's even an exploit on Github copilot (although it'll be patched soon) where you can trick the model into giving you unlimited tokens and just burn Microsoft's cash

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pickledplumber@reddit

What is that trick?

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Emotional_Papaya3282@reddit

Bro the amount of money in this field is overwhelming.

I worked on a platform team early in my career and was asked to promote FinOps practices to save some cash. I told my boss I was able set up a monitor and reduce our VM costs. He barely reacted when I told him we'd be saving $2.5k a month.

KayLovesPurple@reddit

You haven't seen people bragging on LinkedIn about spending tens of thousands in tokens, to the point where they themselves admitted an actual employee would have been cheaper. And they weren't referring to an employee from a cheap country either.

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eloel-@reddit

I have not, I don't spend a lot of time on LinkedIn.

I'd be curious to see what they're doing with AI to get there though. I have some LLM or another running constantly during the work day, very often multiple, and I barely hit ~$500 a month. Are they just running 10 of them with the latest model and having them each build every context from scratch? Just spinning their wheels for no reason?

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alchebyte@reddit

yes. but they are loud about it. so implications...

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The_yulaow@reddit

they are spending more than even most others first world country salaries

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5olArchitect@reddit

You absolutely get 50% productivity increase if compared to no LLM usage. What are you comparing against? Local LLMs?

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qwertyshmerty@reddit

I have been waiting for the tipping point on AI and I think we are approaching it. It can really go 2 ways, companies stop buying into the koolaid and made up productivity numbers, and the usage will back off. Or, companies double down and lay off employees to cover the increasing costs.

Typically what happens is a FAANG (or whatever the acronym is now) makes a move that sets the precedent and everybody else follows suit. And unfortunately, Meta recently did that when they laid off 8000 to try and replace them with AI. I think the best thing we can hope for is this quickly reveals how important human engineers are, and that AI is a tool to be used but not a full replacement.

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IceMichaelStorm@reddit

I just hope Meta to fail, I anyways don’t get how they make so much money. But that’s just me

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developerknight91@reddit

Fb will fail, but Instagram, Snap and WhatsApp will endure. Honestly if the current US administration changes some of its policies the best we can hope for is for Meta to get split apart.

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If you want to hear something crazy - zoom has 7,500 employees. For a product that is the same or worse than what we had in 2020.

People are not gonna wanna hear this, but our industry is way overemployed for what we produce.

AI is just an excuse to course correct from the vastly inflated employee counts.

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developerknight91@reddit

I’ve been thinking this for years. The biggest possibly is they are using AI as “one stone to kill two birds” the FAANGs for example were heavily overinflated with employees.

When their valuations tanked they had to let go of people. And they’re hoping they can get some type of return from AI.

I don’t know if AI will ever get a return in its present state(as many have pointed out here it takes too much compute power to get decent return) but I highly doubt that even if it does tank they’ll hire back at the amount of people they have now.

I predict that what will happen is, they’ll hire back on lesser people at an inflated salary to fix the AI slop.

I do believe it can be possible to get AI to make your workflow a bit more efficient when it comes to boilerplate code - but there is no way they can replace our skillset.

Vibe coders and end users using vibe coding CAN NOT replace a seasoned Software Developer/Engineer. It’s just not happening.

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Gold_Emotion_5064@reddit

What are you basing that assumption on?

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LambdaLambo@reddit

What are you basing that assumption on?

You can also compare headcount’s pre-Covid and post-AI.

They’re betting on token cost coming down exponentially as hardware improves, models become more efficient and data centres scale

If all 3 of those things improve linearly we will see AI costs come down very fast. I’m a bit sceptical but these labs have far better insider knowledge so who am I to make any predictions

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Would be curious how this model fares for planning, design or overall project code reviews. I have been using Sonnet for dev and Opus for complex planning or architecture decision review and tradeoff analysis.

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kaeptnphlop@reddit

Don't expect Opus level with a 27B model. I've got it running in a custom built Pi Coder harness and am pleased so far though.

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OverclockingUnicorn@reddit

Imo, slowness isn't a problem, I'm happy to write out the prompts well, let it go plan overnight, return the next day, maybe do a bit more planning, then leave it going until the next day when I review the MRs. Repeat until done, just run this process a few times in parallel in a bunch of tickets/issues, then you'll have 5-10 MRs to review which is plenty

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kaeptnphlop@reddit

It’s not that slow :)

With the recent addition of multi token prediction to llama.cpp 27B runs at 300-100tk/s PP and 25-10tk/s TG. 35A3 800-300tk/s and 60-40 tk/s.

Should work well with what you’re doing.

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halfercode@reddit

I'm interested in building my own local LLM PC. Have you an experience running Ollama etc on a good GPU? I am looking at something like RTX 4090 or maybe even RTX 4090. I've tried some smaller models in CPU-only mode, but I am wondering of a chunky graphics card might end up being disappointing compared to Claude/Cursor etc.

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Sisaroth@reddit

This. I've been trying agentic coding for the first since a week ago with local qwen3.6-35B. And I made the opposite conclusion as OP.

People are selling DeepSeek v4 Pro inference (profitably, not subsidised, I'm not counting deepseek themselves here) at $1.30 / $2.60 https://openrouter.ai/deepseek/deepseek-v4-pro

Distinct_Bad_6276@reddit

Qwen 3.6 and Gemma 4 both outperform state of the art models from a year ago and I can run them on my PC at home.

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madtowneast@reddit

Sure you can run them at home, but are you getting enough throughout to support 5, 10, 100 devs? Every dev now potentially needs a laptop, and a $3-10k workstation/GPU. Plus the time spend on care and feeding of the workstation/GPU.

The baseline for full DeepSeek v4 Pro is $500k (quantized) and $800k (full model). Now add the human effort, the power cost, scaling with more users, KV caching, etc. Sure in a year things will be much better given current trends, but this stuff isn’t cheap either and even the cost/benefit of frontier models is being questioned when you have a single person burning through $1M in tokens a month without accomplishing the same as equivalent amount of people.

Note I am bullish an AI, I just think LLMs is not where the impact will come from.

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maxintos@reddit

You're thinking way too small. If there was demand there would be plenty of new companies that would focus purely on hosting and selling open model services. They could reduce costs by focusing on scale while not having to spend any money on massive engineering and research teams like the frontier model companies are doing.

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Distinct_Bad_6276@reddit

reduce costs by focusing on scale

It’s extremely hard to break into a commodity industry once your competitors have already established economies of scale, which is what the existing hyperscalers are so good at

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I would assume that you want to deploy this for more than a couple people to use. Just sticker the last 8xH200 box I saw was like $400k.

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08148694@reddit

Cost of technology has historically been coming down rapidly, it’s a fallacy to compare general cost of living (fuel, food, housing etc) to technology costs which have been historically very deflationary

Compare the capabilities of a modern GPU to the capabilities of a GPU form 5 years ago (at the same real terms cost) - the one from today is in a whole other league

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scott2449@reddit

But that hasn't been happening. Pretty much stalled after the pandemic. Compute power has been increasing but cost is scaling linearly. Even the second hand market is nuts w/ my personal computers costing the same as the day I bought them.

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Void-kun@reddit

The SSD I bought 3 years ago for $250 is now retailing for $1000. Samsung 870 EVO 4TB.

One of the silver linings of this AI Slop is that we'll probably get efficient and affordable small modular nuclear reactors in the US in my lifetime now, once that happens I don't think energy will be a bottleneck anymore. TL;DL Nuclear has really never had a proper chance to benefit from economies of scale and modern tech and now it's probably going to b/c we've exhausted all other options.

Now it might be a decade for that to hit, so in the meantime it should get interesting.

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I don't think it's nearly as bad as Enron; they were booking estimated profits for projects, that could sometimes take decades, before they had even broken ground. If those projects never came to fruition, they'd hide the losses in fake companies (see this wild chart).

Hyperscalers have already spent the money on these chips, they're just trying to stretch out how they account for the losses. Instead of forecasting $2-3 billion per year for depreciation, they're forecasting $1 billion (for instance) per year.

Enron was doing straight up accounting fraud, while what the hyperscalers are doing is more akin to financial engineering. Not great, but also not illegal.

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gravteck@reddit

Yea I was a bit demonstrative, but I am very leary of this accounting chicanery when you add the variable of the circular financing and PE involvement.

Anyone drooling over the thought of huge bargains on token prices is likely to be... disappointed.

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fsk@reddit

When in the history of modern capitalism have decreased costs been meaningfully passed to customers?

When there is actual real competition. The big AI firms don't have a monopoly. They're also competing with free open source models you can run on a desktop. Given those two factors, I don't see how the big AI firms can ever make the revenue they need to justify the money they raised from investors.

For example, look at the price of a computer. Every year, the cost decreases.

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ReachingForVega@reddit

Inference is cheap, what's not cheap is nvidia and their GPUs. China has come AI cards coming out in 2027 that should hopefully reduce the cost of training. Innovation should change the architecture.

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bigorangemachine@reddit

I honestly don't think the hardware has room to grow. All they done really is make bigger GPUs

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Congenital-Optimist@reddit

Nvidia Rubin architecture should become available later this year. It promises 2,5x lower compute per watt compared to the current Blackwell architecture. Feynman which will arrive after that promises even bigger improvement. There is plenty of improvements in hardware left.

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thecrius@reddit

And it will be cheaper because of the low demand right? Right?

And when if so, can you name me an example of an industry in which something cheaper comes along and the major actors reduce their prices?

Exactly.

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Congenital-Optimist@reddit

> And when if so, can you name me an example of an industry in which something cheaper comes along and the major actors reduce their prices?

Yes. History of computing has been a consistent trend of lower costs per compute.

But if it helps with your anxiety:
1. The hardware costs are dropping and will keep dropping. Current estimates are that in 5-6 years you can run the current SOTA models at home on your own physical hardware. So no need for subscriptions or dependencies on cloud providers.

Current open weight models are only around 9-12 months behind SOTA models. Deepseek/Gemma/Kimi/and others. If you are okay with last years performance, you can run these models by yourself, either on your own hardware or rent something from cloud. No dependencies. All within your control.

Now, the models are still improving. We haven´t hit the wall yet(but there is a decent chance that at some point the improvement stops and all the models level off at the same competence level). We might be hitting the long tail with the training right now, where 2-3% improvement in performance might need a 2-3x increased costs in training and inference. There are increasingly signs that LLM models are becoming commodified and the moat is in tooling instead. When Opus 4.8 increases token pricing by 50% you can just select Codex 5.6 in the dropdown and have similar or close enough performance that you don´t care.

While LLMs are here to stay, I wouldn´t worry too much about one single company dominating the industry. The competition and existence of open weight models makes that impossible.

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bigorangemachine@reddit

Sure but is it adding more VRAM at higher or similar speeds.

LittleLordFuckleroy1@reddit

Lmao the labs is who you’re trusting? They were betting on ASI and world domination. That was the big gamble. Turns out that was all a load of fucking bullshit, now they’re caught with their pants down.

The entire game now is them scrambling to keep hype around their products to allow for enough money to subsidize usage and hopefully get a huge swathe of the economy hopelessly coupled with their slop machines. Then they can tighten the belt and actually turn a profit.

Nah that's naive. They're betting on raising prices 100x. You don't do 100s of billions in bond offerings on "hope" that costs will go down in the future (once you already spent your bond holders money, lmfao). You do it when you know you can hook people and keep raising prices to get to 80%+ profit margins.

Valuations are expectations. Costs going down will not make the kind of money for these expectations, especially when the money's already spent.

Costs can't even go down, because land, power, water and maintenance DWARF the cost of the GPUs.

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NotMyRealNameObv@reddit

My bet is that all the major AI companies are burning through venture capital trying to get companies locked-in so they can jack up the prices.

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MiniGiantSpaceHams@reddit

Tech always gets cheaper. We have a temporary reversal in upfront hardware costs due to shortages that are caused by the sudden unplanned demand, but those will ease in time, and that only impacts the purchase cost anyways. And even with that, we're getting new hardware that is better optimized for AI loads and software optimizations that bring down the cost to run it. Those things will continue to happen, and will eventually make local AI accessible as well.

I also think we get to the point where SOTA models won't be required for a lot of tasks. You could argue we're already at that point to some extent, but it will just continue. If you're writing a CRUD app (like most are) then you can probably already get almost the same quality output from Haiku vs Opus. Or you could even switch over to a cheap Chinese model.

Similar to cloud right…. in the early days every ceo screaming to move to cloud, getting rid of on-premise DBA’s….’cos cloud can manage it all’ …then being shell shocked at the bill … buh buh we thought cloud would be cheaper…. Naah, all that happened was you moved your DBA over to your cloud provider… and while not having to deal with local infra depreciation your still paying to cover the depreciation for your cloud providers infra…. Hilarious. The cloud products are more convenient tho, one can very quickly find the true costs via dashboards etc… while still small there is a revolt happening where customers are moving off cloud to VPS with something like DokPloy for deployments.

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walterbanana@reddit

The AI companies were making 2 betts:

People will get dependent on AI tools and thus they won't have a choice on if they pay or not.
AI model will get more efficient over time.

What is odd, is that they mostly just made bigger models that are less efficient. Now investors are pulling back, because neither of this assumptions are completely true yet.

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Whitchorence@reddit

The prices are also not really broken down and I think it's very likely the case that the cost is mostly in training and not inference.

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theawesomew@reddit

I have spent an ungodly amount of time attempting to model the profitability of running large language models (using DeepSeek V4 Pro 1.6T A49B as my proxy for 'frontier models' as a class) and the modelling I have done suggests that inference — purely considering power consumption and GPU assets depreciation — might be profitable at the current pricing assuming 100% paid utilisation of GPU assets at all the time.

The biggest cost by far, and the one which has been subject to the most accounting mendacity, is the depreciation of the GPU hardware itself. Hyperscalers have extended their depreciation schedules of their GPU assets, servers, and networking hardware to 66-72 months when it has historically been 24-36 months; a number corroborated by cryptocurrency miners who are the only other 'industry' to use GPUs at such high wattage and utilisation & who are inherently reliant on their GPUs being maximally efficient for the longest period possible (cryptocurrency miners use tricks like underclocking and under-volting to extend the useful life of GPUs to 4 years but, this increases latency and TTFT in a way that would be unacceptable for most LLM providers/users)

These GPU are run at very high utilisation and very close to the their TDP (Thermal Design Power) which, if you're familiar with Black's formula, increases the rate of electromigration in the silicon itself massively reducing the MTTF (Median Time To Failure) because that quantity is inversely proportional to the current density squared [in the case of GPUs]. This is worsened by NVIDIA relentlessly releasing newer, more energy-efficient GPUs which accelerates the technological obsolensce of the GPU hardware meaning that the economic depreciation timelines are closer to 18-36 months (because competitors using Blackwell, for example, can provide more compute at lower prices than a provider using Hopper GPUs which pushes the cost of compute lower than the depreciation of the Hopper GPU itself and its electricity usage). This means that the huge capital expenditures on GPU hardware have to repeated far more frequently than most people realize which is far & away the most brutal cost for AI laboratories/neo-clouds/datacenter operators. It's more like an ongoing cost than "infrastructure" as AI laboratories continuously refer to it; trying to get you to imagine a bridge or road which lasts tens or hundreds of years.

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AndyDentPerth@reddit

I think you are possibly confusing depreciation schedules with consumption.

Actual tax depreciation occurs over 5 years with USA IRS rules.

If you burn out something in 3 years you still can’t claim it immediately.

Can you try the same experiment with DeepSeek4 Pro / Flash models?

You may need to guide them a bit, but in my experience, they've replaced frontier models for a fraction of the price.

They are also open, so I think large companies will look into internal hosting if cost becomes an issue in the long run

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bitspace@reddit

People are going to learn to not rock Opus 4.7 for document summarization

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Neeerp@reddit

My company has always been using api pricing and I personally have had weeks where I’ve personally spent nearly $2000… you underestimate how much big corporations are willing to shovel into the fire

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My $400 usage (estimated from copilot) was about 80 sessions of some work, but I do write code, and use it more like a help sometimes with finding hard to track bugs or refactoring.

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mxldevs@reddit

Someone's going to create a token burn to feature shipped metric and we're all going to have to start putting it on our resumes to prove that we aren't expensive lol

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Whitchorence@reddit

I mean, that's just as gameable as "token burn" as a measure of productivity though. All my feature tickets would get really, really tightly scoped.

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Tcamis01@reddit

I'm in the thousands. We're starting to crackdown so it will change but these things will raise your bill: - expensive models - multi tasking. Im frequently running 3 completely separate tasks at once. Like different repos / completely different subject matter. Whether I should be doing this is another story but we're a skeleton crew after massive layoffs - autonomous complex, unbounded workflows. Again probably not the best idea and falls somewhat under the below notes about poor planning. One such agent analyzes implementations across a dozen repos / layers (because no one understands the full system) and can autonomously create docs / gather context for system wide refactoring, etc. Another autonomously ports legacy SDK features to a modern SDK as they are implemented.

This has all been mostly explaratory and probably not sustainable unless token prices come down.

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Whitchorence@reddit

You can save a lot by only using the advanced models for the planning... well, sometimes you find that isn't working, but most of the time it works pretty well.

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We are encouraged to build then refine. It feels so gross.

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coworker@reddit

Don't worry that will change as token prices increase. That approach is basically brute forcing solutions which can save time when agents are doing it in the background. Once then subsidies end, engineers will be expected to subsidize with human tokens again

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ShoulderIllustrious@reddit

Same here, I use an opencode sub and I barely hit the hourly limits, like maybe 60% is that highest I've hit per hour.

I do spend a ton of time writing high level and low level design info, also add information about constraints for each feature. I like this kind of style for the little things, it really forces you have your idea on paper literally. It helps weed out the stupid ideas.

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niveknyc@reddit

I'm already hitting $2-5k a month on Claude, most of which is used for evaluating, documentation, and generating requirements docs. It's been a force multiplier so my company has no problem with that additional cost. However I agree, these AI company spent BILLIONS on this shit; very soon the cost to play with them will go up exponentially. Eshitification will strike.

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TitanTowel@reddit

Are you just letting it edit without manual approval? I'm a power user in my company but I'm only a 10th of your usage...

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krzyk@reddit

Yeah, I used 80 (out of 300) reqs and it was aroudn $400 if it would be in the new token based pricing. My code reviewer uses 300 out of 300 most of the time, and the cost per moth in tokens would be $6000 - $8000.

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allllusernamestaken@reddit

i think part of it is people are using it wrong.

Our company has copilot, and it changes from premium request into token based pricing. It will be a massacre for those that get dependent on it.

Previously one could get few hours work out of 1 premium requests (out of 300), tokens unlimited, any number of tool calls, any number of subagents (and does can get subsubagents and those can get even more).

I used it sporadically, usually around 80 premium reqs out of 300 per month (and some of them for personal things, as I couldn't find any usage in my work).

I checked how much my previous would cost in usage/token based pricing, and instead of $19 it would be ~$400, this month is a bit different but still instead of $19 it would be $90. And company is settings people at the $19, with overusage only for selected few (about 10% out of 4k).

Anthropic also converts all new companies (at least since December when we signed up) with >150 people to API usage.

It is a nice gimmick, but Juniors are cheaper and with investment they get better from LLMs quicker.

What makes LLMs really good are code reviews, they catch things that people don't notice (they give a different perspective from human reviews) - unfortunately those cost quite a bit - I wrote a code reviewer that used copilot, and in token based pricing it would use $8000 in April and ~$6000 in May (doing about 3500 pull request reviews). That is not worth it if we would pay in tokens.

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armostallion2@reddit

This hits hard, and I like that it’s both written well but also slightly 💩y at the same time. Interesting times for sure.

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public_void@reddit

This is a bad take. I am an engineering leader and if I was told to cut my budget 20% you bet your ass I’m not cutting ai tooling access, I’m cutting people. My best engineers were producing some multiple of my worst, and ai only multiplied the gap.

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AftyOfTheUK@reddit

Even at work, if you spend 5k per engineer per month no real company is going to do that.

What? I cost my company around 550k/year. Even at bill-out rates, 5k is what I cost by 11am on Wednesday. The rest of the month is free

Cool_As_Your_Dad@reddit

Exactly.

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BobJutsu@reddit

Worse. My company expects us to use agents to produce code. But also won’t pay, it’s devs responsibility to pay *personally* for their usage.

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account1233@reddit

At work, there has been an even more aggressive push by management to have all devs move to an agentic flow. When I brought up the topic of how much this will cost, I was told, "well, what's the cost of losing ground to because this is what they're going to be doing and blowing past us".

tenthousandants44@reddit

You can't replace engineers with tokens, or else it would have already happened. If you're cutting only 10-20%, those are just the dead weights who know too much to fire. Would AI actually change that? You can give it your source code but not everything gets written down, not to mention that getting things done often just means talking to the right people. Is AI going to replace those relationships, too?

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T0c2qDsd@reddit

Does what I said include me saying “I think we are in that spot” right now?

I think there’s a point where fast code generation means you can accomplish something that would have taken 5 people a month with 4 people in the same month. I think many companies in the US would pay $4k/month for that, but probably wouldn’t pay $20k/month.

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New-Inspection7034@reddit

This is exactly why I've been making my own harness and using Gemma4 with MTP. I'm able to do 90% or more now that I've add LSP support.

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aitchnyu@reddit

But the companies got a time window when people generated Rube Goldberg machines they cant understand nor update by hand.

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LambdaLambo@reddit

All depends on how you use it. Getting the right structure and architecture is still incredibly important and can be done with good engineering. And the thing is, even in the before times I would have to reread code to understand it if it’s been more than a month or 2, so I don’t care that I’m less familiar with the code now.

Having a very mature code base with very good test coverage gives a great launch point.

If you have been giving tasks to coding agents where you yourself didn’t have a clear understanding on how to do it, you are digging a hole. The people who gave coding agents a green field are going to have a hell of a bad time when usage prices accurately track operational cost.

A good term I like to use is “pouring concrete”. If you are having coding agents pour concrete, you’re going to have a really bad time eventually.

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alchebyte@reddit

yep. it's a race to get something useful out of it before the bubble bursts.

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call-the-wizards@reddit

Cost-based arguments never work because the nature of technology is that things get cheaper.

Here's a list of things some people said would never take off because of how expensive they were per usage:

Cloud hosting
Satellite internet
Mobile data
Cellphones

Things start out being out of reach for most people, then get cheap enough to afford, then become commodities that are so cheap we don't even think about them.

Most of these things (except satellite internet maybe) went from "too expensive for most companies to use" to "every company uses them" in <5 years.

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dagamer34@reddit

With the rise of “agentic workflows”, this is why the closest analogue for coding agents is healthcare: great if everyone had it, but it costs too much for what you might receive.

Which I leads me to say the next thing, because it matters how much you use, these companies do not have zero marginal cost. They are not tech companies, they are GPU rental companies with some slick software on top. When was the last time we had a company slathering itself with “tech”, was about to IPO, and it didn’t quite work out?

WeWork.

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Distinct_Bad_6276@reddit

costs too much

Qwen 3.6 and Gemma 4 beat state of the art models from a year ago and I can run them on my laptop (albeit slowly, but for background tasks who cares). In a year we’ll have local models that beat GPT 5.5 and Opus 5.7.

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dagamer34@reddit

Newer models being better than what was available a year ago is orthogonal to the class and economics of models frontier labs are serving themselves. It would only be beneficial if there was no need to keep developing new versions, and there was no fear that the competition would outclass them.

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calvintiger@reddit

> When was the last time we had a company slathering itself with “tech”, was about to IPO, and it didn’t quite work out? WeWork.

Just to make sure I have your point clear, are you saying the most recent time this happened was in November 2023? If so, doesn't that imply that every other company since then has been succeeding so far?

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dagamer34@reddit

It’s probably happened since the WeWork IPO in Sept ‘19, I am mostly referring to a quite famous example.

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Lopsided_Distance_17@reddit

Manager here, before AI, enterprises had/have no issue spending 5k per seat. $5k in tokens is an accounting error. $50k…that would be another story. And yes per month

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Dumb_Dick_Sandwich@reddit

Folks who were teens/adults from the 90s and 00s will recognize the playbook.

Cell phone providers moves from Minutes to Data around the release of the iPhone. The BlackBerry movie summer it up perfectly: “There’s only one minute in a minute”.
There are only 50,000 tokens in 50,000 tokens.

You see the lead up with a lot of tools like SpecKit that are almost *designed* to burn through as many tokens as possible

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NatoBoram@reddit

Except Canadians. Cellphone plans have always been ultra expensive for crumbs here.

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JaySocials671@reddit

USA also has a greedy telecom data problem

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Whitchorence@reddit

"Minutes" would not make any sense as a billing metric for mobiel data.

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Mundane-Charge-1900@reddit

And yet today everyone basically has "unlimited" data and minutes, or at least limits that are high enough for cheap enough, that it doesn't even matter anymore.

If you looked at what was happening at the time, you might have assumed it was a ploy by telecoms to increase revenue by shifting usage from older, cheaper tech like landline voice to more expensive, higher margin wireless voice and data. After inflation, I bet telecommunications costs for consumers are lower than ever, especially when you consider how much more convenient a smartphone is over a landline phone.

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Vinegarinmyeye@reddit

I literally just commented about this on r/shittysysadmin

Token pricing was always going to ramp up - I feel like the bean counters have finally figured it out.

I feel like a good number of the posts I see on tech professional subs are talking about reducing token use because... Money.

There was a chunk of money to be saved by giving it "Hey we'll ship absolute garbage code, get rid of the expensive engineers who know how things work, and if we have outages, leak data, whatever fuckery... We'll still be better off financially".

Maybe I'm overly optimistic, but I'm starting to notice the pendulum swing.

There's an obvious solution to AI overspend - (re) hire people who actually know what they're doing.

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themooseexperience@reddit

I'm shocked I haven't seen anyone mention how the US government is pouring money into AI at a fervency that hasn't been seen since the creation of the internet, maybe even more than that.

My theory is that it will remain artificially propped up until it becomes distributed and efficient enough to be priced more effectively.

I'm not giving my opinion on whether that's a good thing or not, but that is my prediction for what will happen in the next 5-10 years.

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Cristiano1@reddit

Yeah, I think usage-based pricing changes the psychology completely. Agents feel amazing when you stop thinking about tokens, but once every experiment feels like it’s burning money, people get way more selective fast.

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muntaxitome@reddit

Even at work, if you spend 5k per engineer per month no real company is going to do that. Those economics dont even make sense for the overpaid US engineers, where technically you maybe only need 50% productivity increase per engineer to make the cost work. You do not get that lmao. In the EU you def cant make those economics work.

Plenty of devs in EU with over 100k salary, but you seem to be confusing productivity with salary. In Netherlands average productivity per worker is around 200k. That is an average. It includes low level jobs like grocery store checkout person. Wayyy over your math.

'50% productivity increase per engineer' is absurdly high though. Majority of cases might be seeing modest increases but 50% is insane. Keep in mind that productivity is measured in revenue per hour worked. So your 50% means 50% more revenue, or halving the amount of workers or some combination of that that works out to the same math.

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Whitchorence@reddit

An engineer slopping 50% more lines of code is not a '50% productivity gain' from an economic perspective.

I mean, sure, but even before AI came along, determining exactly how economically productive each feature shipped is is pretty difficult.

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muntaxitome@reddit

On an individual feature or engineer basis it is hard to determine productivity. However, from an economic perspective productivity is revenue/hours worked. If the claim is for a company as a whole over a period that is actually a fairly trivial calculation.

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Aggressive_Amount_73@reddit

That's a very good point ppl often forget. All this nonsense about being more productive with AI is based on what? Because the only real metric that can be used is if it is translating into more revenue.

Doesn't matter if you produce way more code now. Is the product you're working on, getting more revenue because of you making more code ? In the end of the day is your company profiting more?

And this will be more and more important as these models start to be more expensive. Is the money you put in these models, bringing returns in terms of profit ? Because if in the end of the day you're spending more 5k with AI but your revenue is the same, you're losing 5k of profit. Doesn't matter how many lines of code you're doing.

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Yeah you just listed Booking, they're the prime example of golden handcuffs and top of market compensation in Amsterdam 😅

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Type-21@reddit

At this point you're just trolling. Normal devs don't even get past the interviews at the companies listed on levels

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I'm not buying this at all.

You can do plenty on a $20 Cursor subscription if you plan your architecture well and don't rely on the agent to go in circles to fix things that you should've defined in the first place.

Whitchorence@reddit

It's usage based right now for enterprise?

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waffleseggs@reddit

100%. I've already budgeted no more than $50/mo and I'm currently token-maxxing.

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Distinct_Bad_6276@reddit

But it was 50 bucks.

It's really neat the way an LLM can write an email for me, but it's far from a viable business model at the moment. As soon as the VC subsidy stops, the music will stop and it's gonna be wild.

This is one reason I have explored Ollama and self hosting. It's slower and not as good of models, but I am not going to pay hundreds and hundreds a month when this stuff finally gets priced correctly.

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rrrx3@reddit

Ed Zitron is huffing his own farts and has been consistently wrong, week over week about what he posts.

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HotDribblingDewDew@reddit

This kind of post makes me realize just how naive so many of you are lol. AI is only going to keep getting better and cheaper. In 10 years you're going to realize you posted this because you're in complete and utter denial right now. No other reason.

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Venisol@reddit (OP)

Models have stalled for like 2 years. Costs are only going up.

There is no law in the universe that forces technology to get better at a linear or exponential pace infinitely. Is your fork better than 200 years ago? Is your dishwasher better than 50 years ago? Is github better than 5 years ago? No is the answer btw.

Technology stalls. Technology can get worse, especially modern tech.

I’m trying to hurry up and build all the little things I’ve always wanted to build while it’s being subsidized. This won’t last.

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TacoTacoBheno@reddit

Devin brought down the CI/CD pipeline yesterday.

I'm sure they could figure out a way to limit builds, but for a single "feature" it would open a PR every thirty seconds, which kicks off all build!

There were 4,000 builds queued up when it died.

Agent scans for security vulnerability, 80 percent false positive. The genius principle said we should have another agent that identifies the false positives and closes them.

Wtf are we even doing?

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Mundane-Charge-1900@reddit

There's some real productivity improvements using coding agents out there, but Devin has basically been a scam since its announcement.

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iworkinprogress@reddit

$50 to build an entire app is pretty good, tbh

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Mundane-Charge-1900@reddit

I think this is what some people are missing. It's not about someone who can build said app manually if they spent the time on it. It's about someone who can't write that code themself manually today. They're not going to be able to hire someone to do it manually for $50 either. In some cases, they can prompt their way to success for the $50. This is software that wouldn't have otherwise been written.

Does that work at scale in giant mega corporations where the real revenue is? That's still an open question.

I think as time goes on, tokenization will get cheaper and faster. We'll also get new hardware meant to run these model plug-and-play style. Once that happens, usage won't matter, You can run an LLM all day and night to make whatever you want.

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Uesh@reddit

I hope that happens. But those companies that invested so much into AI will want their cut. One way or the other. So I wonder how it will play out. Also, to get hardware to run a good LLM, won't it take many many years? Unless its a company owned server for their workers.

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MrIcedCafeMocha@reddit

I’m not sure if it’s just due to inefficient use but I’ve completed months of agentic use where my total usage would cost around $150. At the highest, $300. Typically my usage is around $80-150 per month and that’s me completing 2 sprints. Not sure how everyone is racking up these $1,000+ token usages.

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SinbadBusoni@reddit

Well, it wasn’t fun while it lasted.

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donniedarko5555@reddit

I mean we're already transitioning to using local models where we can at my company.

You could easily have a Qwen model handle your cursor bug bot usage, even if you fully account for the cost of an on prem infrastructure behind it compared to the usage costs.

Especially with a rapidly growing company and massive mono-repos

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LittleLordFuckleroy1@reddit

Completely agree and I think the writing has been on the wall for a long time now. The entire game for the AI companies is to get people hooked on their drug before they run out of money and actually have to turn a profit.

AI is super useful, and it’s not going away. But it is absolutely not economically viable to be used in the way that it is today.

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tr14l@reddit

Just wait... They're going to have to do 10-100x pricing. A million tokens will probably be measured in dollars, not cents.

They're operating in deep red on compute right now for the market race. That isn't going to last much longer.

And at some point, free accounts are probably going to get either eliminated or neutered to the low tier models only with a token cap.

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circalight@reddit

Definitely going to create a moat where only the richest companies can use it.

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gjionergqwebrlkbjg@reddit

My company has been on usage-based billing for a while, the only thing they do is limit access to the most expensive models. You can get very far with Sonnet.

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-Dargs@reddit

I modified my terminal for Claude to show my usage cost for tokens. I'm spending like $1500/d, not $5000/mo, lol.

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tommyk1210@reddit

How even?

Even using opus 4.7 you’re spending about $25 per million output tokens. $1500 a day is 60 million tokens per day…

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-Dargs@reddit

Well, I'm in a staff role in a small org so there's always too much to do.

The cost of multi-multi-tasking and iterative design and development on greenfield projects. I spent a few weeks nailing down a Claude skill for implementing tickets start to finish so I've got my secondary monitor up with \~3 terminals, sometimes more, usually not less, all going at it doing simpler feature work. At the same time, I'm back-and-forth with Claude on my primary monitor building and iterating on tech specs for newer projects and exploring different approaches as there's never just one right solution for anything. After I'm satisfied with the tech spec I'll peer-review with my engineering manager, then with another team member who I pass the work off to before I begin on the next tech spec... I'll admit that there's usually only one project per 2 weeks that I'm planning. The rest of my time and tokens go towards assisting my and other team's bug/product support tickets (there's always some subtle thing that doesn't quite work right on the front-end). I'll receive a ticket, clone it, link it, reformat it in such a way that Claude/my skill is able to consume the context without much useless bloat and then send it off in another terminal.

The skill flow is quite nice. It almost never generates the wrong plan or slop code. It's backed by a series of code style and design and instructions markdowns that prevent that sort of thing. But consistently generating high quality code on the first pass comes at a token premium. I can go into more detail on the skill if someone is actually interested. But I know the subreddit I'm on, so I expect hostility, lol.

It will go local.

We used to have expensive workstations. I remember having specialized Lenovo machines for coding costing about $10k.

Now local AI is close to do proper agentic coding. Not talking about DeepSeek, it requires TB of VRAM, but more of its distillations on Qwen and similar

Get 128GB VRAM, and then you open up the path to local agentic AI.

Many companies can actually afford this. (Though their power bills will also go sky high)

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philip_laureano@reddit

I think it's going to get better because this will make the more token efficient models that can run in far fewer memory and use less tokens be the ones that people choose and pay for.

The fact that models such as Opus 4.x and ChatGPT 5.x cost so much compute is a self-own on their part. While they are both good models, their cost efficiency means they'll be phased out sooner rather than later.

And this is where the Chinese models start to beat everyone else--when those per-usage charges begin, they'll be ready with LLMs that run efficiently, even though they're not the smartest of the bunch.

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NaturalRoad2080@reddit

Something people forgets, many times the one or ones who have control of a project are very few, if you can boost their productivity by 40% it's not something you can just fix by hiring more devs.

Control I mean real control, the ones who can really see where the issues are when they happen without wasting 1 month

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ryaaan89@reddit

The aim here is to get everyone addicted and then also hold models like Mythos over your head that you _have to_ pay them for to fix security.

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haragoshi@reddit

It’s not going to be usage based. Subscriptions are great predictable streams of income. Just like gym memberships, Many paying customers don’t fully use their capacity

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WaterIll4397@reddit

No because deepseek and others get cheaper too.

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Disastrous_Poem_3781@reddit

u/Venisol

We are encouraged to use agents at work but I find them to take to long and cost to much. That's even with a detailed prompt about the work to do.

Since I have started using LLMs I still haven't found a better experience that using the studio/playground interface. You can really control the model with the temperature and system prompt.

I have developed so many feautres both at work and personally and my monthly bills have been in between 30-40 euros.

Even with all the copying and pasting between the browser and IDE at least you're still in control and review the bits of code given to you by prompting the AI.

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bob301301@reddit

i run a daily workflow that costs 1m tokens to debug our pipelines

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ricetoseeyu@reddit

You’re finding it useful and would like to continue using it if the costs were lower. Token prices are going to eventually come down with lower energy costs (SMRs, renewables), newer more efficient hardware, and more efficient models. Think of the super expensive PCs in the 80s, and then came along HP and Dell, etc.

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theycallmeJTMoney@reddit

I think you’re right about personal agent usage dropping once people feel the meter running most folks don’t want a $50 weekend experiment. Usage-based pricing changes behavior fast.

But I don’t think agents “vanish,” they narrow. They stop being toys and become tools for exactly what you described: wide, boring, cross-file work where humans are slow and error-prone.

Where I disagree a bit is the work economics. Companies don’t ask “is this worth $5k in tokens?” — they ask “did this avoid a hire, missed deadline, or outage?” For many teams the bar is lower than a clean 50% productivity gain.

Your last example is actually the best argument for agents: delegating mechanical cleanup once intent is clear. That’s the sweet spot. Small fixes → human. Large, repetitive refactors → agent.

So yeah, hype will die. But the boring, high-leverage use cases survive and those are the ones that actually matter.

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Plastic_Monitor_5786@reddit

As evidenced above they'll still be useful for writing posts.

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darkstar3333@reddit

I told finance as of June our costs are going up by 15x.

They want to go into full top to bottom agentic mode, operational experience be dammed.

I am advocating for it but I understand the cost risk of YOLO'ing your business into bankruptcy.

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the_real_seldom_seen@reddit

You lack vision. Shit it’s gonna be cheaper.

Hey guess what, computers used to be the size of a room, only companies can afford them. Now our smart phones have more compute

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joe0418@reddit

Software engineering will unfortunately evolve into maximizing value on tokens.

Sub agents with cheaper models orchestrated by a more expensive model, stuff like that.

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FriendOfEvergreens@reddit

Strongly disagree just based on my experience running cheaper models locally and on prem. They are nowhere near as good as opus but they are still miles ahead of 2024 which could definitely do simple notes apps. The harness tech does not require heavy compute, just the codegen. On my Mac I can easily run 7-11B models while still using the machine for dev work. With more advancements and better harnesses I’m pretty confident locally hosting coding LLMs is going to be doable.

But I agree with the premise of the cloud stuff, at least frontier models, will have a rug pull

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UnbeliebteMeinung@reddit

No. Cursor showed a few days ago how cheap usage based inference could be made. Also cursor customers do on demand api cost all the time and its the biggest ai ide on the market...

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I didnt use agents much, then 2 weeks ago I decided to try it. I hooked my anthropic api key to opencode and built a personal notes app with zero sync on a long weekend.

Is that supposed to ground breaking...?

For me I use the agent pretty much exclusively for "simple stuff that touches a lot of files",

...

I literally write my components with hardcoded strings...

This just comes across as a coder who lacks experience and doesn't follow best practice.

Like I've said repeatedly. The people who find LLMs the most useful are those who are the most inexperienced.

I believe that once token cost will got significantly higher we’ll see increase in small model usage. But not sure if all of it will be local. Most of agents are not that sophisticated anyway and using higher end models is a waste even now. Not to mention giving away the domain data to ai companies for training.

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Intelligent-Youth-63@reddit

I have no idea what you’re even trying to say here.

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UHM-7@reddit

He's saying that once provider's stop subsidising usage people will use LLMs less

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szank@reddit

Give it 10 years . Companies will end up spending say $500 (in 2036 dollars) per dev to rent out hardware capable or running claude 4.6 or somesuch locally. Then it will make sense.

Subscriptions are not reflecting the true cost of using the product, usage based billing is closer to the actual cost of AI, whenever investor money runs out and subscriptions increase in price, AI is dead.

It’s good for $20 but if the price for the same tier increases to $100 would you still pay it? That’s what they mean.

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Immediate_Rhubarb430@reddit

It's a pretty simple message. AI use is subsidized. Much like Netflix, they will jack up prices. At which point, the value proposal will not be attractive for customers and AI adoption will suffer as a result

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Keeyzar@reddit

Then you may not be the brightest human on earth.

built a personal notes app with zero sync on a long weekend.

... why? of all the things you could build why something that neither you nor anyone else will ever use?

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Venisol@reddit (OP)

Ive been using it for 2 weeks. Replaced my obsidian and now my notes sync across devices.

With "personal" i meant an app literally just for me. A single user, no auth. I got all my vim shortcuts hard coded in there etc. I have no intention of ever letting anyone else use it.

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chosenoneisme@reddit

For testing and learning how to use these agents. Isn't that obvious? For learning a new tech we don't directly use it on complex tech stack or anything. We try it out on simple things

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Resident-Trouble-574@reddit

It all depends on whether the hardware will scale faster than the resources usage of the agents.

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chosenoneisme@reddit

Google is doing some major upgrades. So we can expect more usage in current plan but it's still expensive

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CanIhazCooKIenOw@reddit

These hot takes are great.

Keep them coming

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coredalae@reddit

Maybe, but just as likely is that run cost will drastically fall. So it'll end up being cheap by 2032

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InterestedBalboa@reddit

I think for one person and small teams that will be true but for companies it’s just the cost of doing business, especially if human capital can be offset.

Local LLM’s will be the only way to be competitive for those trying to do their own business. All those people fishing around Reddit looking for ideas they can vibecode better get busy