That's because you're treating the problem as an engineer instead of an "influencer" or "10xer" or whatever. You're treating it as a problem to be solved with engineering and AI is merely a tool to do so. It is, in my experience, vanishingly rare for an engineer to have a problem that needs to be solved with multiple hours of unattended AI code generation.

I've only found one single application where it makes even the slightest amount of sense to have an AI grind away for hours on end. I'm reverse engineering a widget which contains five separate firmware images. I've dumped the binary from the widget and I set the AI to decompile and reverse engineer these interrelated firmware projects. It's a compelx task, but very well bounded. It's not complicated work, but it's a lot of work, and the end result is a C-shaped pile of text that is only informative, it never would be compilable on its own even if I did it by hand. The quality of the output is tightly bounded by the input assembly and the overall output artifact is documentation in the shape of code.

I don't have any qualms about letting an AI go ham on it unattended because the stakes are zero. But if the AI can beat the assembly into a recognizable C project, it's much easier for me to read and reason about. Easy win, I think.

I’ve watched a bunch of layman videos where they create stuff with AI, these people burning through 12 hour tasks are literally not reading the output or understanding what it’s doing. Like they’ll ask for a program, and then right after it’s been created they ask the AI how to run it. Then when there’s a bug, they ask the AI what went wrong, or scrap the entire thing and switch model/harness and try again.

Here’s an example https://m.youtube.com/watch?v=xc1296HY8Fw&ra=m

It’s completely different to a professional workflow (what you described). It’s a toy for consumers

On the topic of access control, I’m building a coding agent with no shell access, currently only supports rust though. https://github.com/Kapperchino/agent-joe

I have downgraded my Claude to the $20 one, and basically only use it for the web chat right now. For coding, I use DeepSeek @API Rates configured in Claude Code. I have spent around $4.8 for 320,000,000 tokens. I always felt like i was not using Claude plan, that i had to have the LLM working on something all the time to justify the price. Now with DeepSeek i don't think about it anymore. I don't feel bad when not using the subscription anymore, and i don't worry about limits as i just pay more. Where i really felt this was on running things in parallel as there are no hourly limits anymore!

>I think of what to do next

As everyone trying to do real work is finding, that's the actual bottleneck. If the system is keeping up with your thinking, you're doing fine. You can't "level up" your thinking by paying for more tokens. The people doing more automatic stuff are probably outpacing their own thinking, and that will bite them eventually.

Can I ask what exactly you are building? Your experience tracks for me when building a real product -- something I want other people to use. Most of my time on these projects is spent talking to my users and carefully refining my requirements and design.

For personal pet projects I can definitely see how you can blow through your token budget very quickly. If I just point my coding agent to iteratively come up with some heuristics for some NP-hard problem, it will read intermediary outputs and constantly make small changes "in the dark" until it either finds a small improvement or gives up. In a similar vein I found that you can burn many many tokens if you try to let the agent reverse engineer something where you don't have the source code. If you just give it a binary or some interface to work with and a vague task you can easily burn your entire budget with 1 prompt.

I wouldn't want anyone to use these fully vibe coded toy projects though; it is more of an exploratory curiosity for me where I learn more about some problems I'm interested in as well as gauge how good the agents are at tasks that I seem to have a much better intuition on how to approach.

I’m using $200 a month Codex working on a game for my kids for fun and curiosity since I’m a dev, I’ve played games, but I’ve never done dev for games. and have all night tasks but mostly they’re “spend time tending to and adding stuff to my 3D asset pipeline”. My RTX 5090 runs Trellis2 -> ultrashapes -> Trellis2 -> wiring up rigging and setting up animations.

But like 99% of that task is just Codex waiting for the output. So it’ll run for 12 hours but mostly it’s just setting lots of sleeps. I haven’t gotten close to running out of tokens. The $100 a month codex I hit usage limitations almost immediately, about 3 days in of working like crazy with 10 agents going at once, mostly coding an asset pipeline, I ran into my weekly limit and upgraded. So with the $200 a month plan at 4x more credits I haven’t hit any walls at all and can absolutely cook.

promote yourself to PM only and use agents for authoring, verification, tests, checking the tests

orchestrator -> parallel subagents with investigation, authoring, verification, benchmarking subagents and integration / final verification handled by parent has improved my productivity too.

I feel like from here its agent swarms against a whole spec but haven't got there yet.

Still getting plenty of bugs in the more complex scenarios, but mostly (in some projects) i never have to look at the code and treat it like a black box

I have been on $100/mo claude and it has been churning out quite good software for months now. like i estimate what would have taken me three ish years, assuming i didn't burn out from failure (i would have). i only hit limits when i double fisted claude with my main project and my side project. just the other day i noticed i had been stuck on 4.5 because i failed to update the npm package.

I'm on $100 Claude. I have a setup with bespoke local services that mitigates some high token consumption scenarios with local LAN services. I screen mcp's and hooks for cache poisoning. I run 100% on Opus with max effort, and never came close to hitting 5 hour or weekly limits before the Fable release. I am in Claude Code at least 20hrs a week.

I see people just completely wasting tokens with ridiculous setups, 100% hitting cache misses as well as dumping huge files into context all the time.

Just learn how these things work, or pay the price I guess.

I usually hit the limit when I am frustrated and I don’t want to understand what the problem is.

I am an engineer, and when I understand what’s going on, I never hit any limit.

> I don't want to give it "dangerous" access to my entire mac

I'm running Claude/Codex inside native macOS sandbox, configured with a simple script - https://github.com/sheremetyev/sandfence

always in "bypass permissions" mode - it works until task is solved, sometime 1 hour or more (which includes running tests etc)

Well, if you believe the people who sell the tokens, you should be creating loops that keep yanking the bandit’s arm.

While it's a little unstable, I've found Docker's sbx to be a great sandbox to run agents with --dangerously-skip-permissions

Same boat here. I’m able to get a lot done on CC at $100/mo and feel like I’m not being creative or productive enough somehow when I hear of people blowing past that in a day.

Patches to existing sizable codebases and reverse engineering binaries both can run a long time and use a lot of tokens without wandering off into the weeds.

I usually say run the full regression suite, all the simulator tests, install simulators and take a screenshot of every page on all applicable devices and do comprehensive fuzzing and chaos testing before I go to bed. It usually takes atleast 3-4 hours, usually longer, especially the UI/simulator tests.

>I feel like I must have plateued and don't know what to do next to level up.

Why do you need to "level up"? To have it shit out slop faster?

Just use it rationally for what you need to do.

Claude enterprise plans are 30-40x more expensive vs the consumer plans.

I used to spend $200/mth on the Max plan at a small startup. Now spending single digit thousands on Claude enterprise with the same usage levels.

Anthropic is subsidizing consumer usage, and also charging a nice margin for enterprises for zero data retention (ZDR)

> and they tell us they use $2K per month in tokens with their current employers...

perhaps they are simply trying to impress you with their mad prompting skills and like, what self-respecting engineer would be caught dead using less then $2k/month?

giving the context of your interaction with those people, it probably is the simplest answer to your rather baffling question. for the life of me the idea of using $2k/month doesn't even seem possible unless your telling it to waste credits.

Totally agree, but then a lot of the same people will be talking about all of the custom instructions/rules/skills/features etc they have set up, so that's eating up a lot of the context window before you even start

When I do use AI, it's just the pure tool itself, and the context is the exact code I'm working with (because I'm trying to see if it can help me solve a specific problem), and I understand the rest of the codebase well enough to know if it's giving me good answers or bad ones

A few things imo, 1) not prompting precisely enough (narrowing scope) means your agent will scan your entire code-base and sometimes get stuck looking at things repeatedly. 2) not checking the output is usually fine but sometimes it produces junk because it doesn't understand, and you cannot prompt your way out of it without reading the code and figuring out the problem. If you leave it on auto it will burn tokens.

Plenty of low level things can trip agents up, too. I just had one inexplicably refuse to read an error about a function needing a bool return value - trying about 10 variations of the same thing before I interrupted it. Skills probably cause issues too, it loves to for example read the source code of libraries I'm using if I give it permission. That's a rabbit hole.

To be honest I’m not doing too much. I’m just on one of the $200 plans, but always hit limits. I only use the best models and mostly use it for various software projects I always wanted to build, but didn’t have time for. I just closely monitor the usage caps and have something running on a Ralph loop most of the time, unless I get near the cap. The post here is more about how I’d start a self-funded software company, if I wasn’t already working full time.

Luckily I needed a new laptop and I bought an M1 Max secondhand from a friend quite cheaply because it was fast enough to recompile something else I am interested in.

So for me, there is no additional hardware cost; it was acquired in replacement.

I run the AI models at home on this kit because I want to; I'll use openrouter if I need to.

I accept the economics of this article are right. But I feel so incredibly sad about this outcome that we're now just to be people caretaking machines that do the job we loved that actually I am not sure that exercising this nuance is going to matter in the long term.

It turns out it is a mistake I have made in my life — now really unfixable because I am a bit too old — to believe that I will always find enough fulfilment in my work to offset the absence of personal fulfilment elsewhere; I have always enjoyed being able to help people directly by doing a thing I love and I am good at, and that has kept away the sadness of finding it difficult to build a conventional family life to enjoy.

I assumed I would always find some new way to find that enjoyment, but even the slim enjoyment from being able to explore this stuff on my own kit in my own terms will not be enough if the pendulum does not swing back towards human effort.

It is a dismal world we have made for ourselves. Lately I have found myself dreading growing too much older in it.

Also, I would anticipate at least a 5 year lifespan for a current generation card. The 3090 is still respectable simply because it has 24GB of RAM which, for years, has been the limiting factor for ML at home. If you got a 6000, sure it’s going to cost 7-8k, but the resale value is likely to be very good. Even the 3090 is 50%+ of RRP still. And if you’re not doing LLMs, it’s an interesting value proposition for “classic” CNN vision model training. You can fit enormous batch sizes on 96 GB. The biggest reason to upgrade is perf/watt has about doubled (eg 4000 pro Blackwell is half the 3090 for similar).

People tend to assume the capex is thrown away but as we’ve seen with RAM, don’t be so sure you won’t be able flip it if you need to.

And paying more for hardware costs extra!

I ran the numbers and outside of privacy it doesn't make sense. But I did it anyways. [0]

0 - https://www.williamangel.net/blog/2026/05/17/offline-llm-ene...

Actually if you have solar, it kind of is.. so prIvAt AI compute gets defacto cheaper during the day?

If you paid for solar, this is less of an issue. I also don’t worry so much about running my AC.

>> Power is not free.

There's actually an interesting thought experiment here: if it takes you a full day to build something that AI would otherwise build in a day, do you end up using more power, or less? What is the break-even point, purely from a power consumption perspective?

I'm in Florida and am already using AC, so if not "free", definitely "negligible."

work at a cafe.

> Power is not free.

its ~free if you have home solar.

My sentiments too. I'm using Qwen 3.6 35B A3B on a machine with 64Gb ram and a 24GB 5090 (an Alienware 16 Area51 I bought, serendipitously, about 15 seconds before the idiots preordered all computers for the next 3 years and ruined everything).

You can't "slop cannon" vibe code with it, but this is personal code I want to not be spaghetti, so I'm not trying to vibe code. I just want to get instant retrieval of all stack overflow and reddit posts in a chat box, and for it to be able to spare me the physical pain of actually having to type out typescript code (I am a BE dev with negative patience for all frontend) and fuck around endlessly debugging obscure docker problems (I like docker, but, no patience for it having annoying problems and endless quirks). And this model does that really well.

There are certain things you can leave running for a while. I think the distinction between vibe coding and hitl based coding routines will blur as workflows prove themselves and models become smarter and less expensive. Most of the best engineers I know have transitioned a lot more into vibe coding this year. The possibilities are much better nowadays.

Directly at DeepSeek? It was my understanding (but I didn't check) that some other AI operators were providing (some of?) DeepSeek's model for cheaper prices.

Still, that's interesting. What do you get for that price? Only coding, or also e.g. image generation?

Same for me. $20/mo is just fine and I use it to code daily.

I suspect the people that burn through tokens have several subagents and 50 skills loaded and 40 MCP tools. All those load up the context on every single turn.

YMMV but automations eat through the $100-$200 plans, which burn thousands in tokens alone.

I have hourly automations for root cause analysis on customer support issues, daily automations for eg log analysis, weekly & monthly automations for KPI tracking & actioning.

I will say, when I was building side projects that were 1) fairly well defined in scope and 2) without users/need for automations it was much easier to stay under $20/mo plan limits. Now I regularly hit weekly limits and need multiple Max plans

> What are people doing to burn through $3k in tokens?

The short answer is: they are doing slop. Most of the coding can be done quickly with a keyboard, intelisense and maybe some code generation templates.

But people became dependent on AI doing everything for them and tech bros now started to squeeze. Like a drug dealers.

I'm currently working through research and testing for an article on Ars about the Spark and what things one might do with it, and I've kind of stumbled into a two-LLM agentic setup with Qwen3.6-35B-A3B (via nvidia/Qwen3.6-35B-A3B-NVFP4) as the planning agent and the FP8 version of Qwen3-Coder-30B-A3B-Instruct (Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8) as the coding agent that the planner delegates tasks down to. I'm sticking with vLLM as the inference engine, and I've got it wired together into a 2-agent loop with Opencode.

The Qwen3.6-35B-A3B planner hums along at 50-55 tokens/s, and the Qwen3-Coder-30B-A3B-Instruct coder does 30-35. With both agents up and ready to work, RAM consumption sits at about 112 of 128GB.

It's pretty okay. I'm faffing around with having it disassemble old MS-DOS games from the 1980s, which is a task that lends itself well to the setup. It's not the fastest thing in the world, but with the planner's context window at 256k tokens and the coding agent at 128k, they chew through pretty long task lists handing things back and forth without complaint. The only real issue is that even with really tightly scoped prompts, the coding agent tends to hallucinate like it's on LSD. But the planning agent appears to be quite good at spotting the hallucinations and re-parceling work back to the coder.

It's neat. I'm going to be sad when I have to return the review unit in a couple of months.

edit - I also have been fiddling with Deepseek v4 Flash via Antirez's setup (https://github.com/antirez/ds4), and it's pretty fantastic (and fantastically easy to get running). It's pretty pokey on the Spark, though, at 14-ish tokens/sec. And unless you have a second Spark, it's going to be the only model you run at one time, as it eats alllll the rams.

Qwen 3.5 122B can fit with context at a pretty high quant (Q6). That's an excellent model.

https://www.canirun.ai/?status=tight might answer that question

Deepseek v4 flash is shockingly strong for its size and reportedly runs well on that hardware.

I wondered if there might be a no brainer "free" option on discarded hardware.

I have a GTX1080ti which i think is circa 2018, it's unused, more than paid for itself over the years, owes me nothing at this point so the hardware is free.

It runs Gemma e4b multimodal, qwen 3.5 8b or the qwen 4b embeddings models well enough (40+ t/s for the LLMs).

The machine consumes 350 watts at the wall when under load (3 watts when sleeping, 80w at idle). Electricity costs me £0.035GBP/kwh which is cheap for the UK (load shifting via house battery).

144k output tokens for around 1pence (and takes an hour to do that in theory).

It's only JUST cheaper to use than the far more capable deepseek v4 flash model despite the free hardware and ~10x cheaper than normal electricity.

Yes and no. Hardware does lock you in. Granted, I am happy with my 128gb of shared memory, but I am mildly concerned that it actually is more expensive now than when I bought mine. It does not bode well for the future; not when combined with recent WH admin moves on Anthropic and the reality that next batch of good models may require more than 128gb to run well.

edit: I am not dismissing local. I am one such user ( though I have subs too ), but one has to be clear eyed about the trade-offs.

$3k isn't getting you frontier model capability. It's barely getting you any capability if that's split into buying an entire PC rather than just GPUs.

With you here. I'm using my cheapo 16gig vram card I picked up a year or so ago, and I'm like -- yes, I percieve that you can pay for way more tokens per second that I can do at home.

But that feels like measuring productivity in lines of code. For what I'm doing, I'm not seeing the benefit in any subscription.

Sure, I can't one-prompt a whole new boring CRUD app, but oh well.

3k? Try 10

If you want frontier-level, the economically reasonable option is OpenRouter or a direct sub to frontier-of-your-choice.

The reality is that they do not offer configurations that would allow a consumer to run that much VRAM on a single setup to protect datacenter margins. Apple used to, and they stopped, those devices are going for ~$20k+ each on ebay now.

You can get very, very capable models on a 3090/4090/5090/6000 series card. But if you want 'frontier level' you are investing ~22k at a bare minimum if you go new. Used you can probably build your own server for much cheaper up-front cost but it's likely going to be 4-6x+ electricity usage.

10k will not get you anywhere near opus or sonnet. It's simply not possible for mere mortals currently.

> Can I run something comparable to Opus 4.6 locally yet?

Sadly, no. The best comparable thing you can get is about Sonnet 3.7

i spent 8k and get close to a 2-3x slower sonnet. running 2x spark deep seek v4 flash

Some benchmarks have shown Kimi K2.6 within error-bar distance of Opus 4.6, and you can run it on eight RTX6000s. Right now it's not possible to set up a machine like that from scratch for less than $100K... but right now it's also hard to put a price on autonomy.

Best you could do is connect two Mac Studio M3 Ultra 512G RAM each with Thunderbolt. Then theoretically you can run frontier Chinese models (but not Deepseek v4 Pro yet). That would be about $20k.

But - good luck finding them. Apple discontinued the model a few months ago. And more recently, even 256G model was discontinued. Big AI really really does not want people to get off their needle.

Man I’m using the $20/month sub and it works just fine for me. Granted, I have a family and house and lots of obligations so by the time I hit the limits some other task is due before I can return to coding. If I hit the limits before I have something else to do then I just code by hand or review what has been generated until I can use the agent again. Reviewing agent code is a good way to learn too, agents have shown me different approaches than what I would have done and they’re definitely worth thinking about. Also, fixing their mistakes has helped me write better prompting although being a team lead for half a decade has taught me how to specify what I want very clearly and cc gets it right most of the time haha

About interruptions, one thing AI assisted coding really helps with is coding with constant interruption. I can leave CC for half an hour and return then tell it I had to step away, catch me up, and proceed. This works well for me.

They prefer to work harder and not smarter. Forever hill climbing to nowhere.

I've never worked on a complicated codebase that started out that way until the rest of the business concerns and office politics came into effect. People may not like it, but the bureaucracy is far and away more valuable than the core functionality.

Mature codebases are years of people thinking of all the possible gotchas while solving their acute pain points. This is not fluff, but the living and breathing part of it. Without that code, it's just a machine barely doing stuff in the most obtuse ways possible that nobody wants to pay for.

I would argue that they're putting LLMs to work on that finer detail stuff, but AI is still far too dumb. No, what they're doing is playing with their skinner box.

I used Kiro in December and I burnt through 200 eur worth of tokens in a weekend. Ultimately it was money well-spent, but, I think that if you want, you can spend as much compute as you have access to. Will it be efficient use of tokens? Probably not.

From all the data, it looks like the 200usd we pay for monthly usage is subsidised… at break-even pricing … well, that 200 is starting to look like a few thousand.

Maybe one possible path(to make weaker models highly capable) is making the job of the llm as easy as possible.

I wonder if part of the solution is building/finding the right libraries, with the right documentation/language/API(one that plays well with LLM's) and maybe creating some synthetic data around them - to make it very easy for the llm.

And maybe there could be a business model around creating those libraries.

I mean, this is what I'm doing. I'm guessing my process is very different because I'm holding the hand of the project way more along the way, but even that to me probably makes for a more enjoyable.

Which is to say, I might use AI to do an outline/organizational , but I'm prompting every chunk of code "one-by-one," (e.g. at about the "function" level) which still feels lightyears ahead of what I used to do.

Deepseek v4 via deepseek themselves is significantly cheaper.

Because (1) Huawei collab and (2) vLLM etc dont implement half of the inference optimisations deepseek proposed in their paper.

Same here, deepseek v4 flash on opencode go. It's cheap, fats and good enough to follow my instructions

I'm almost certain $2800 is actually too low if you're really hitting weekly limits.

I'm on the $100/m plan and used $300 at API billing yesterday (according to ccusage)

Seems like one session is >$100 and I can get 10 full sessions per week

The $200/m plan is supposed to be 4x that in usage, so with 2 of those you could use 4*2*100*10=$8000 in just a week

Using Simon's numbers here as a bare minimum https://simonwillison.net/2026/May/27/product-market-fit/#en... you'd get 1200*4*2=$9600 a month

Maybe yours is not a large AI native workflow?

Are you saying that specs shouldn't be complex or that you shouldn't write specs at all?

SemiAnalysis pushed this to the limit and managed to get $8,000 of tokens from a $200/month Anthropic plan and $14,000 of tokens from a $200/month OpenAI plan: https://twitter.com/SemiAnalysis_/status/2064815044085318040

Even if they were making a profit, their scale and expertise will obviously give you a cheaper product than what you can build.

Similar story. I did have a pro subscription as a trial. I'm finding the free tier is as good(for my purposes) as the paid model.

With access to view usage for my org and conversations with developers, I think much of the high token usage is a result of people not knowing how to right size the model for the given task. The trend seems to be to pick the most powerful model and use it for everything. Based upon git metrics, I'm one of the top performing engineers at my org and I've yet to run into any overage or throttling on the $200/mo anthropic sub.

No, I have the same experience. Feels crazy that a GPU is too expensive and then the advice is to spend 400$+tokens on openrouter each month.