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Comment by enderforth | Hacker News Reader

This right here is where I feel most concerned

> If you haven’t spent at least $1,000 on tokens today per human engineer, your software factory has room for improvement

Seems to me like if this is true I'm screwed no matter if I want to "embrace" the "AI revolution" or not. No way my manager's going to approve me to blow $1000 a day on tokens, they budgeted $40,000 for our team to explore AI for the entire year.

Let alone from a personal perspective I'm screwed because I don't have $1000 a month in the budget to blow on tokens because of pesky things that also demand financial resources like a mortgage and food.

At this point it seems like damned if I do, damned if I don't. Feels bad man.

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My friend works at Shopify and they are 100% all in on AI coding. They let devs spend as much as they want on whatever tool they want. If someone ends up spending a lot of money, they ask them what is going well and please share with others. If you’re not spending they have a different talk with you.

As for me, we get Cursor seats at work, and at home I have a GPU, a cheap Chinese coding plan, and a dream.

> I have a GPU, a cheap Chinese coding plan, and a dream

Right in the feels

> If someone ends up spending a lot of money, they ask them what is going well and please share with others. If you’re not spending they have a different talk with you.

Make a "systemctl start tokenspender.service" and share it with the team?

I get $200 a month, I do wish I could get $1000 and stop worrying about trying the latest AI tools.

What results are you getting at home?

Yeah, that's one part of this that didn't sit right with me.

I don't think you need to spend anything like that amount of money to get the majority of the value they're describing here.

Edit: added a new section to my blog post about this: https://simonwillison.net/2026/Feb/7/software-factory/#wait-...

This is the part that feels right to me because agents are idiots.

I built a tool that writes (non shit) reports from unstructured data to be used internally by analysts at a trading firm.

It cost between $500 to $5000 per day per seat to run.

It could have cost a lot more but latency matters in market reports in a way it doesn't for software. I imagine they are burning $1000 per day per seat because they can't afford more.

They are idiots, but getting better. Ex: wrote an agent skill to do some read only stuff on a container filesystem. Stupid I know, it’s like a maintainer script that can make recommendations, whatever.

Another skill called skill-improver, which tries to reduce skill token usage by finding deterministic patterns in another skill that can be scripted, and writes and packages the script.

Putting them together, the container-maintenance thingy improves itself every iteration, validated with automatic testing. It works perfectly about 3/4 of the time, another half of the time it kinda works, and fails spectacularly the rest.

It’s only going to get better, and this fit within my Max plan usage while coding other stuff.

LLMs are idiots and they will never get better because they have quadratic attention and a limited context window.

If the tokens that need to attend to each other are on opposite ends of the code base the only way to do that is by reading in the whole code base and hoping for the best.

If you're very lucky you can chunk the code base in such a way that the chunks pairwise fit in your context window and you can extract the relevant tokens hierarchically.

If you're not. Well get reading monkey.

Agents, md files, etc. are bandaids to hide this fact. They work great until they don't.

You don't ask the agent to replace your pipeline by providing the data; you ask it to automate the pipeline.

I wonder if this is just a byproduct of factories being very early and very inefficient. Yegge and Huntley both acknowledge that their experiments in autonomous factories are extremely expensive and wasteful!

I would expect cost to come down over time, using approaches pioneered in the field of manufacturing.

Have these people done the math on how many engineers they can hire in other countries for USD$200k/yr? If you choose the timezone properly, they will even work overnight (your time) and have things ready in the morning for you.

USD$200k is 3 engineers in New Zealand.

https://www.levels.fyi/t/software-engineer/locations/new-zea...

> No way my manager's going to approve me to blow $1000 a day on tokens, they budgeted $40,000 for our team to explore AI for the entire year.

To be fair, I’ll bet many embracing concerning advice like that have never worked for the same company for a full year.

Same. Feels like it goes against the entire “hacker” ethos that brought me here in the first place. That sentence made me actually feel physically sick on initial read as well. Everyday now feels like a day where I have exponentially less & less interest in tech. If all of this AI that’s burning the planet is so incredible, where are the real world tangible improvements? I look around right now and everything in tech, software, internet, etc. has never looked so similar to a dumpster fire of trash.

The biggest rewards for human developers came from building addictive eyeball-getters for adverts so I don’t see how we can expect a very high bar for the results of their replacement AI factories. Real-world and tangible just seem completely out of the picture.

Maybe think about it like this: A dev is ~1k per day. If the tool gives you 3x then 2x in cost is fine.

(The current cost of 1k is "real" and ultimately, even if you tinker on your own, you're paying this in opportunity cost)

((caveats, etc))

Yes, exactly this. My biggest issue is how uncurious the approach seems. Setting a "no-look" policy seems cutting edge for two seconds, but prevents any actual learning about how and why things fail when you have all the details. They are just hamstringing their learning.

We still need to specify precisely what we want to have built. All we know from this post is what they aren't doing and that they are pissing money on LLMs. I want to know how they maintain control and specificity, share control and state between employees, handle conflicts and errors, manage design and architectural choices, etc.

All of this seems fun when hacking out a demo but how in the world does this make sense when there are any outside influences or requirements or context that needs to be considered or workflows that need to be integrated or scaling that needs to occur in a certain way or any of the number of actual concerns that software has when it isn't built in a bubble?

Isn’t that the whole point of this approach? Everything is specified just in terms of how the end user will actually use the software, at a high level. Then the LLMs basically iterate relentlessly until the software matches what the end user wants to do.

I read that as combined, up to this point in time. You have 20 engineers? If you haven't spent at least $20k up to this point, you've not explored or experienced enough of the ins and outs to know how best to optimize the use of these tools.

I didn't read that as you need to be spending $1k/day per engineer. That is an insane number.

EDIT: re-reading... it's ambiguous to me. But perhaps they mean per day, every day. This will only hasten the elimination of human developers, which I presume is the point.

May be the point is, that the one engineer replaces 10 engineers by using the dark factory which by definition doesn't need humans.

The great hope of CEOs everywhere.

And then he get replaced by a new hire when he asks for a raise.

I think corporate incentives vs personal incentives are slightly different here. As a company trying to experiment in this moment, you should be betting on token cost not being the bottleneck. If the tooling proves valuable, $1k/day per engineer is actually pretty cheap.

At home on my personal setup, I haven't even had to move past the cheapest codex/claude code subscription because it fulfills my needs ¯\_(ツ)_/¯. You can also get a lot of mileage out of the higher tiers of these subscriptions before you need to start paying the APIs directly.

How is 1k/day cheap? Even for a large company?

Takes like this are just baffling to me.

For one engineer that is ~260k a year.

I do not really agree with the below, but the logic is probably:

1) Engineering investment at companies generally pays off in multiples of what is spent on engineering time. Say you pay 10 engineers $200k / year each and the features those 10 engineers build grow yearly revenue by $10M. That’s a 4x ROI and clearly a good deal. (Of course, this only applies up to some ceiling; not every company has enough TAM to grow as big as Amazon).

2) Giving engineers near-unlimited access to token usage means they can create even more features, in a way that still produces positive ROI per token. This is the part I disagree with most. It’s complicated. You cannot just ship infinite slop and make money. It glosses over massive complexity in how software is delivered and used.

3) Therefore (so the argument goes) you should not cap tokens and should encourage engineers to use as many as possible.

Like I said, I don’t agree with this argument. But the key thing here is step 1. Engineering time is an investment to grow revenue. If you really could get positive ROI per token in revenue growth, you should buy infinite tokens until you hit the ceiling of your business.

Of course, the real world does not work like this.

Right, I understand of course that AI usage and token costs are an investment (probably even a very good one!).

But my point is moreso that saying 1k a day is cheap is ridiculous. Even for a company that expects an ROI on that investment. There’s risks involved and as you said, diminishing returns on software output.

I find AI bros view of the economics of AI usage strange. It’s reasonable to me to say you think its a good investment, but to say it’s cheap is a whole different thing.

Oh sure. We agree on all you said. I wouldn’t call it cheap either. :)

The best you can say is “high cost but positive ROI investment.” Although I don’t think that’s true beyond a certain point either, certainly not outside special cases like small startups with a lot of funding trying to build a product quickly. You can’t just spew tokens about and expect revenue to increase.

That said, I do reserve some special scorn for companies that penny-pinch on AI tooling. Any CTO or CEO who thinks a $200/month Claude Max subscription (or equivalent) for each developer is too much money to spent really needs to rethink their whole model of software ROI and costs. You’re often paying your devs >$100k yr and you won’t pay $2k / yr to make them more productive? I understand there are budget and planning cycle constraints blah blah, but… really?!

Is the time it takes for an engineer to implement PRs the bottleneck in generating revenue for a software product?

In my experience it takes humans to know what to build to generate revenue, and most of the time building that product is not spent coding at all. Coding is like the last step. Spending $1k/day in tokens only makes sense if you know exactly what to build already to generate this revenue. Otherwise you are building what exactly? Is the LLM also doing the job of the business side of the house to decide what to build?

In big companies there is always waste, it's just not possible to be super efficient when you have tens of thousands of people. It's one thing in a steady state, low-competition business where you can refine and optimize processes so everyone knows exactly what their job is, but that is generally not the environment that software companies operate in. They need to be able innovate and stay competitive, never moreso than today.

The thing with AI is that it ranges from net-negative to easily brute forcing tedious things that we never have considered wasting human time on. We can't figure out where the leverage is unless all the subject matter experts in their various organizational niches really check their assumptions and get creative about experimenting and just trying different things that may never have crossed their mind before. Obviously over time best practices will emerge and get socialized, but with the rate that AI has been improving lately, it makes a lot of sense to just give employees carte blanche to explore. Soon enough there will be more scrutiny and optimization, but that doesn't really make sense without a better understanding of what is possible.

I assumed that they are saying that you spend $1k per day and that makes the developer as productive as some multiple of the number of people you could hire for that $1k.

The math is a bit off.

One day amounts to 24 hours.

Assuming no overtime, one day translates into 3x 8 hour shifts, or 3x engineers. Suddenly, $260k a year buys 3x engineers.

Now, assuming that the dark factory stuff can actually work as conjectured, it will work 24x7, 365 days a year, it does not require annual leave, sick leave, observance of public holidays etc. So $365k (adjusted for 24x7, 365) works out to be a cheap deal.

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