Are you willing to pay $100k a year per developer on AI?

https://www.theregister.com/2025/08/15/are_you_willing_to_pay/

by rntn • 11 months ago

64 106 11 months ago

Nope. I would consider the opposite. I would pay more for candidates not reliant on frameworks, AI, and other helpers.

taylodl2• 11 months ago

That's just being silly. We've been using frameworks for over 30 years. I remember the days before frameworks. Software was developed at a pace nobody would tolerate today. They're a productivity boost - a significant productivity boost.

AI? In the hands of a craftsman AI is just another tool that can help boost productivity. In the hands of a junior developer trying to use AI to make it appear that they're a senior developer - no. There are too many downside risks in that scenario because the junior developer doesn't have enough experience to know when the tool is providing bad results.

austin-cheney7• 11 months ago

I hear this a lot and it is mathematically incorrect.

Frameworks solve two business problems:

1. Candidate selection

2. Clearer division of labor

That’s it. Everything else is an imaginary quality from the developer. In most cases the well reasoned arguments from developers in favor of large frameworks can be performed faster without the frameworks, both in human writing speed and software execution speed. Typically these imaginary qualities taken a defensive tone from developers who have never learned to execute without the frameworks, which becomes an argument from ignorance because you know what you are arguing for but not what you are arguing against.

At any rate the result is the same that the article makes about AI: output of brittle toolchains.

reactordev2• 11 months ago

I don't think you want a web service written in assembly.

austin-cheney7• 11 months ago

I honestly don't think the user cares.

AtlasBarfed3• 11 months ago

At the stage of the AI is currently which really is a very good code generator, what you really should see is a higher valuation of people with all the fundamentals of computer science and a breadth of experience about what you can do in programming.

Contrasted with specific acronyms frameworks, languages and similar buzzwords that dominate computing recruitment.

If you have a tool that can basically take someone who's really good at programming, architecture, analysis, etc, and eliminates the barriers of domain specific knowledge, syntax idiosyncracies, library peculiarities away ..

It should mean a talented IT professional should actually be more useful across more domains. And hiring should reflect that. It's hard to tell right now because hiring is zero apparently.

For example, I haven't coded at rust. I have encoded in c++ in 20 years. Assuming of course that I am a genius, which of course I am right? Assuming that, llm should allow me to both code in a language I don't have a lot of experience in, and adapt to you or enterprises particular code base far more quickly.

Does my value go up? Probably not. Because now I compete with everyone else. That's pretty smart without domain barriers. That's a large increase in supply.

However, large amounts of IT people who don't even know the basics of computer science architecture or those types of things, Will not have any real value compared to an llm.

With the hypnosis of the executives, they do not see a difference between those two different types of professional. They see an IT budget that they want to axe.

One of the fundamental tensions in it management/labor dispute is that someone that manages and maintains a service and codes... is actually a manager. Good it professionals are providing both technical service and managerial service to a company.

Consider what computers used to do. There used to be a room full of actual human computers on calculators doing things, and of course, a manager that oversight oversaw them.

That manager was clearly considered part of the managerial class.

Then came computers and the room of people disappeared and you still had a manager managing an IT application. But without the head count, Management decided that that person wasn't a manager or a member of the manager class.

Yet they had to pay him like that.

See. I think ultimately llms are taking away some of the technical overhead on managing and Enterprise system for a company. But it still needs to be managed. And that person will still be "IT".

And if you don't pay that person reasonably well, your Enterprise will suffer.

austin-cheney7• 11 months ago

The first half of your comment mentioned the word should 5 times. This sounds like wishful thinking.

Yes, there are some fundamental skill deficits and a lot of liars out there. The historical solution to this problem is to ignore it, use some tool to flatten the bell curve, and then hope for the best. If AI is just an evolution of things already tried, then we already know what the results will look like: less accountability, less credibility, less selection risks for candidate selection, and more expensive development processes. For example frameworks allowed substantially wider developer participation at lower product quality and high costs without change to business ambitions. We should expect developers to become more of a less capable commodity than they already were.

Then when the technical debt becomes to expensive to maintain just call in consultants to tell everybody what is already commonly known.

felipeerias2• 11 months ago

To save you a click: no, the author does not provide any source for the headline.

Jedd2• 11 months ago

To save you a click: no, the author does not provide any source for the headline.

TFA kind of did, with the '20-30 dollars per person, per month, across an organisation' quote / comment - though they didn't do the math for you.

But that range of monthly spend only needs 220-400 ish people to reach the headline figure of $100k.

Whether that's good value, who can say.

malfist• 11 months ago

With a large code base and big context windows, it's easy to blow past the $20-30 allocation in an hour

CamelCaseName• 11 months ago

100K, per developer?

IshKebab• 11 months ago

I think they read this nonsense: https://news.ycombinator.com/item?id=44867312

felipeerias2• 11 months ago

Possibly, although at least that article tried to justify the $100k with some handwaving about multiple agents working in parallel with minimal human supervision.

Unfortunately, people are swallowing the headline without any critical thinking.

reactordev2• 11 months ago

Smarter financial people than me, which wouldn't take much

At least they admit they have no idea what they are talking about.

Quarrelsome• 11 months ago

for me, an admission of lack of authority is a green flag, not a red one.

htrp• 11 months ago

you're basically recreating contract dev model at this point.

jvanderbot2• 11 months ago

I'm fairly certain these companies should pivot to selling / licensing AI "s/w drivers" for commodity consumer hardware that enables all these apps to run local-first or local-only.

The token cost stopped decaying as expected, as mentioned by the original 100,000k post on HN, and the move nowadays is towards more context to keep building functionality. The cost is just going to go up for inference. These companies might be better off splitting their focus between training and tooling, and canning all the capex/opex associated with inferrence.

Forget S/W engineers for a moment ... Every white collar worker I know, especially non technical folks, use ChatGPT all the time, and believe that is AI at this point. That demand isn't going to vanish overnight.

The counter argument is usually "They'll sell data", but I'm not sure you can double the number of trillion dollar data companies without some dilution of the market, and reach a billion devices / users without nation-state level infra.

stingraycharles• 11 months ago

It’s basically the old “what Intel giveth, Microsoft taketh away” but then with NVidia and AI shops.

Models get more computationally expensive as they start doing more things, and an equilibrium will be found what people are willing to pay per token.

I do expect the quality of output to increase incrementally, not exponentially, as models start using more compute. The real problem begins when companies like NVidia can’t make serious optimizations anymore, but history has proven that this seems unlikely.

keeda• 11 months ago

What Jensen giveth, Altman taketh away.

msgodel• 11 months ago

Has it? Google has free inference for their smallest hosted model now. I'm pretty sure that's where this ends.

jvanderbot2• 11 months ago

Has what? The smallest networks are probably cheap enough they are a decent loss leader.

rootnod32• 11 months ago

The non S/W folks are currently all using it because it's free to a certain degree. There's no chance in hell they'll be paying for it. So the only other way for the AI companies to make money out of it is to add Ads to the whole shitshow, turning it into an even greater shitshow that not just dumbs down the planet but adds commercials on top of it.

Suzuran• 11 months ago

We don't even want to pay $100k a year per developer for developers.

amelius2• 11 months ago

That's because with human developers we can't get our money back if they make mistakes.

rootnod32• 11 months ago

Can you get money back with AI if they make mistakes? They are more prone to mistakes especially on larger scales in my opinion.

JCM95• 11 months ago

This is all a familiar pattern. In the early days of ride share it was an amazing advancement and very cheap, because it was highly subsidized. The quality was decent, and certainly better than taxi and car services in most cities. Tons of ride share app companies popped up.

Then reality set in. Costs were raised so they weren’t losing money anymore. Rideshare became more of a commodity and competitors got squeezed out as there wasn’t much room to compete and make money. Service quality went downhill. Uber is generally reliable but the quality has fallen off a cliff. My last car smelled bad and the rear axel sounded like was about to fall off the car. In most cities at the airport I just walk outside and get an old fashioned taxi at the rank vs dealing with all the nonsense regulations forcing one to walking to some remote corner of a parking garage for the “ride share pickup” zone.

GenAI is entering that pivot point. The products have plateaued. There’s pressure to stop the loss leaders and set prices to a more realistic level. Services are becoming commoditized. It’s not going away but we’re entering a period of rapid consolidation. GenAI will still be here in a few years and will be useful, but like rideshare the allure will wear old and we’ll look at these things like we do spell checkers today. Something everyone uses but ultimately boring commoditized tech where there’s not a lot of money to be made. A useful feature to add to actual products.

I do think there’s some good opportunity to shift to locally run small models, but that too will just become commoditized spell-checker level tech.

JimDabell4• 11 months ago

In the early days of ride share it was an amazing advancement and very cheap, because it was highly subsidized.

This is not an analogous situation.

Inference APIs aren’t subsidised, and I’m not sure the monthly plans are any more either. AI startups burn a huge amount of money on providing free service to drive growth. That’s something they can reduce at any time without raising costs for their customers at all. Not to mention the fact that the cost of providing inference is plummeting by several orders of magnitude.

Uber weren’t providing free service to huge numbers of people, so when they wanted to turn a profit they couldn’t reduce there and had to raise prices for their customers. And the fees they pay to drivers didn’t drop a thousandfold so it wasn’t getting vastly cheaper to provide service.

SalmoShalazar2• 11 months ago

What makes you think these things aren’t subsidized? It would be very impressive if Claude was making money off of their $20/month users that hit their weekly limits.

richwater• 11 months ago

Inference APIs aren’t subsidised

This made me laugh. Thanks for making my Friday a little bit better.

JCM95• 11 months ago

The unit economics of these models and APIs are really ugly. Those saying they are not losing money on inference likely are only doing so when making up funky non-GAAP accounting thinking. It’s the old “we’re making money when you ignore all the places we’re spending money” argument.

When you factor in the R&D costs required to make these models and the very limited lifespan of a model (and thus extremely high capital investment depreciation rate) the numbers are pretty nasty.

ElFitz• 11 months ago

Inference APIs aren’t subsidised

I may be wrong, but wasn’t compute part of Microsoft’s 2019 or 2023 investment deals with OpenAI?

lelanthran5• 11 months ago

inference APIs aren’t subsidised

How do you get to that conclusion? There is no inference without training, so each sale of a single inference token has a cost that includes both the inference as well as the amortised cost of training.

Spooky232• 11 months ago

Sure they are - the big companies are dumping billions in capital on it, and the small companies are getting a firehose of venture, sovereign and pe to build stuff.

The way the big AI players are playing supports the assertion that the LLM is plateuing. The differentiator between OpenAI, Gemini, Copilot, Perpexity, Grok, etc is the app and how they find novel ways to do stuff. The old GPT models that Microsoft uses are kneecapped and suck, the Copilot for Office 365 is pretty awesome because it can integrate with the Office graph and has alot of context.

disgruntledphd23• 11 months ago

AI startups burn a huge amount of money on providing free service to drive growth.

Of the pure-play companies, only OpenAI do this. Like, Anthropic are losing a bunch of money and the vast majority of their revenue comes from API usage.

So, either the training costs completely dominate the inference costs (seems unlikely but maybe) or they're just not great businesses.

I do think that OpenAI/Anthropic are probably hiring a lot of pre and post sales tech people to help customers use the products, and that's possibly something that they could cut in the future.

itsalotoffun• 11 months ago

Lots of chat about this:

Inference APIs aren’t subsidised

This is hard to pin down. There are plenty of metal companies providing hosted inference at market rates (i.e. assumed profitably if heading towards some commodity price floor). The premise that every single one of these companies is operating at a loss is unlikely. The open question is about the "off-book" training costs for the models running on these servers: are your unit economics positive when factoring training costs. And if those training costs are truly off-book, it's not a meritless argument to say the model providers are "subsidizing" the inference industry. But it's not a clear cut argument either.

Anthropic and OpenAI are their own beasts. Are their unit economics negative? Depends on the time frame you're considering. In the mid-longer run, they're staking everything on "most decidedly not negative". But what are the rest of us paying on the day OpenAI posts 50% operating margins?

JimDabell4• 11 months ago

Inference APIs aren’t subsidised

A lot of people disagreed with this point when I posted it, however Sam Altman said last week:

We're profitable on inference. If we didn't pay for training, we'd be a very profitable company.

— https://www.axios.com/2025/08/15/sam-altman-gpt5-launch-chat...

jncfhnb4• 11 months ago

Your anecdata is bad though. Rideshare is doing fine.

JCM95• 11 months ago

What city? Most cities I’ve seen the majority of entrants have either gone bust or on a path to.

code_runner• 11 months ago

I think you may be looking past the point they are making. Rideshare was better, it was cheaper, it was nice. Its no longer better, cheaper, or nicer. They're doing fine for sure.... like the AI companies will be doing fine... but once the prices go up the ROI for AI agents won't be as appealing to every company. It may raise the bar higher for new companies/products rather than lower it.

thorncorona• 11 months ago

Anecdata is fine to extrapolate from. I have ridden more than a few cars which rattle like they won’t see tomorrow.

The only thing which has gone downhill more is Airbnb.

At best a middling experience these days, and on average a poor experience.

simianwords8• 11 months ago

What does this mean? "Rideshare became more of a commodity"

energy1232• 11 months ago

I can't agree there's a plateau just a few weeks after two companies got gold medals at IOI and IMO using natural language (no Lean). Seems like progress is continuing nicely.

spreaditononon• 11 months ago

Than propaganda is working nicely on you

I am using the current models and they are still as useful as 6 or 12 months ago

The deal is still about the same: if you bother to do most of the hard part (thinking it through) the code generators can just about generate all the boilerplate

Yeah. Amazing.

mooxie• 11 months ago

In most cities at the airport I just walk outside and get an old fashioned taxi at the rank vs dealing with all the nonsense

Not the primary point of your post, but I am always evangelizing to my friends about this 'hack.' I can't believe that people are willing to walk half a mile and queue up in the rain/sun/snow to be driven by some random person who will probably make them listen to their demo tape, instead of just taking the myriad taxis that are sitting right there.

Takes probably 20-30 minutes off of my airport commute.

rswail• 11 months ago

Uber have reinvented the bus stop.

SR2Z• 11 months ago

Is it fair to say that AI is continuously subsidized? Once a datacenter is built and becomes profitable, why would anyone just scrap it? Even if the owner goes into bankruptcy, that's a huge capital asset that evidently people can't get enough of.

It's kind of undeniable at this point that at least some parts of the AI boom have been really good for society. It just took a while to realize exactly where this was useful.

ramesh312• 11 months ago

I do think there will be a great de-skilling in software as we know it currently, essentially equivalent to the shift from craftsman manufacturing to assembly line production in the physical world. There will of course always be niches for high skill experts, but the vast majority of enterprise CRUD work will be done by people with far less expertise, whose salary will be some diminished percentage based on their reliance on AI. Will the results be "better"? No, but they will be good enough, and faster and more reliably tracked/scheduled and allow capital more control. The bosses have wanted to be able to "add X dollars to a project to speed it up by Y" forever, and now they finally can.

samsonradu• 11 months ago

But will they be good enough? That’s a question that can only be answered after many years in a project’s lifecycle.

Not sure about allowing for more control, what happens when a complex/exotic issue that an AI is not able to solve arises? The bosses will have to pay a premium to whatever expert is left to address the problem.

ramesh312• 11 months ago

Not sure about allowing for more control, what happens when a complex/exotic issue that an AI is not able to solve arises?

What happens when a complex piece of machinery breaks down at the factory? You call in an expensive mechanic to fix it. You also still have engineers overseeing the day to day operations and processes to make sure that doesn't happen, but the bulk of the work is carried out by semi-skilled labor. It's not hard to imagine software going this way, as it's inevitably what capital wants.

Disposal8433• 11 months ago

the vast majority of enterprise CRUD work

Why are people always speaking about CRUD? In 30 years, I haven't done anything related to CRUD. And I'm also very confused about it.

chiffre01• 11 months ago

Commented on this the other day, I think AI is fundamentally different from ride share apps and Uber for X services. It's more likely to follow a Moore's law trajectory. Getting cheaper and better over time?

Or at least cheaper.

Larrikin• 11 months ago

It will only get cheaper if the models can run locally. Anything that is a subscription service will always get more expensive over time.

tjr• 11 months ago

Local and preferable Free. It seems odd that the majority of the software development world is gleefully becoming dependent upon proprietary tools running on someone else's machine.

https://www.gnu.org/philosophy/who-does-that-server-really-s...

celeritascelery• 11 months ago

While running locally will no doubt get cheaper over time (and hence become much more viable), cloud compute cost will also drop significantly as better hardware and more specialized models are created. We have seen this process already where the cost per million tokens has been falling rapidly.

simianwords8• 11 months ago

This is untrue simply based on the so many past instances of Gemini, OpenAI making their products cheaper. The ratelimits for GPT 5 are pretty high. The API costs have decreased by 50% over and above o3's reduction which was also massive.

This is not even considering the fact that the performance has also increased.

SalmoShalazar2• 11 months ago

It feels like a lot of the core LLM progress has plateaued or is approaching the end of the asymptote. We’re seeing a ton of great tooling being built around LLMs to increase their utility, but I wonder how much more juice we can really squeeze out of these things.

AtlasBarfed3• 11 months ago

And Moore's law will last forever!

Right?

Where as to get AI to any sort of approximation of what it's hyped up to be, may involve exponentially higher hardware costs.

So for the longest period of time, AI was sitting in about 90% accuracy. With the use of Nvidia hardware it's going to say 99 to 99.9%. I don't think it's actually 99.9%

To replace humans, I think you effectively need 99.999% and even more depending on the domain like self-driving is probably eight nines.

What's the hardware cost to get each one of those nines linear polynomial? Exponential?

abotsis• 11 months ago

I feel like people almost pay that already with pair programming.

amelius2• 11 months ago

If everybody does it, maybe there is no other choice ...

esarbe• 11 months ago

No.

sokoloff• 11 months ago

Because instead of writing code, they’re spending - wasting? - a ton of time fixing AI coding blunders. This is not a productive use of mid-level, never mind senior, programmers.

It’s amazing to think that humans have been writing blunder-free code all this time and only AI is making mistakes.

Humans coders, including good ones, make errors all the time and I don’t fully trust the code written by even my strongest team members (including myself; I’m far from the strongest programmer).

jncfhnb4• 11 months ago

Problem is juniors are now pushing code that they don’t understand and have concluded they cannot understand because the AI is smarter than them

Ancapistani• 11 months ago

That is clearly a failure on the part of the seniors on their team - not AI.

n4r9• 11 months ago

The claim is not that humans write fewer bugs than AI. The claim is that devoting senior time to fixing bugs in mass-produced AI code lowers overall quality.

ericyd• 11 months ago

This is true, but when good human developers introduce bugs, at least their code adheres to a thoughtful software design that matches expectations. My experience with AI code is its much less likely to meet that criteria.

senko• 11 months ago

One SEO content writer pulls a random number out of their behind, next thing you know it's stated as a fact by IT news sites, and I fully expect to see it parroted by mainstream media in a week.

And we blame AI for slop and hallucination...

getnormality• 11 months ago

Companies deny they're doing this, of course. Take Microsoft, for example. CEO Satya Nadella claims AI tools like GitHub Copilot now write up to 30 percent of Microsoft’s software code. Simultaneously, Microsoft has laid off over 15,000 people, nearly 7% of its workforce. Coincidence? I think not.

This is why we need the discipline of history. People's memories are nonexistent.

The world hasn't stood still during the past few years! During the pandemic, the tech companies went on a hiring binge for demand that didn't materialize. Then interest rates skyrocketed as the Fed buckled down to fight inflation. Then Elon Musk bought Twitter and laid off 80% of the staff without the service collapsing, showing twitchy tech executives that their companies could get by with less. There were also massive tax code changes, only very recently reversed, that further encouraged less headcount.

We've had so many headwinds against tech employment completely unrelated to AI. Big companies have been responding, but they don't move fast, so they likely still haven't done everything they want to do.

AI is just a convenient excuse, and it's amazing how well it's worked on so many people!

Ukv• 11 months ago

Funny. GPT-5 will confidently tell you that Willian H. Brusen is a former US president

Checking the link, it seems this mistake occurred when generating an image of the names of all the presidents, I believe using gpt-image-1 (the model which GPT-5 will call, but which predates GPT-5). Not to say inaccuracies in generated image text shouldn't also be addressed, but I feel it's relevant context when judging progress.

The verdict of some users is in. They hate GPT-5. [...] OpenAI to bring back the older, but more reliable, GPT4o model

GPT-5 is top of https://livebench.ai/#/ and top of https://lmarena.ai/leaderboard (blind user-preference ratings). Those leaderboards aren't the be-all-and-end-all, but I feel narratives formed within particular groups can be prone to confirmation bias (with different groups having different narratives and seeing the others as delusional).

and an Arizona State University study indicate that our current ways of improving LLMs have gone as far as they can go

Unclear to me that a study of a 4-layer GPT-2 on a synthetic task implies specifically now is the stopping point. I don't believe in a singularity/intelligence explosion or that we're close to replacing all human labor, but it does seem that incremental progress has continued through over a decade of people saying deep learning is hitting a wall.

Kilo Code blog observed, people have been assuming that since "the raw inference costs were coming down fast, the applications' inference costs would come down fast as well but this assumption was wrong."
[...] Of course, Sam Altman, OpenAI's CEO, can predict "The cost to use a given level of AI falls about 10x every 12 months," but I believe that just as much as I would an Old West snake oil huckster who'd guaranteed me his miracle elixir would grow my hair back.

Wouldn't necessarily trust Altman's predictions in general, but the fact that the cost of a fixed level of capability decreases dramatically over time seems fairly uncontroversial and easy to verify across providers.

That's not inconsistent with the fact that the per-token price of the current best model has stayed approximately the same, which is what the quoted blog post is referring to. I believe that's mostly just determined by how much people are willing to pay (if people are willing to pay more to be a couple months ahead of the current progress curve, scale up the model or increase context/CoT to squeeze out extra performance).

nunez• 11 months ago

from the article:

Other companies, like Microsoft and Google, are sneaking AI charges into your existing software-as-a-service (SaaS) bills

They really are. My Google Workspace bill was increased by $5/month purely because of the "value add" that AI provides.

I turned Gemini off in my tenant (like, chatted with customer support to REALLY turn it off). I have no desire to turn it back on. There is no way to remove the $5/month increase.

At this point, they are literally just stealing $60/year from me (and others) to fund their AI bullshit.

As soon as I find an alternative to Drive and Docs, I am gone. I found this unacceptable.

daft_pink• 11 months ago

This is what I believe is happening:

1. AI adoption has outpaced expectations relative to Moore's law. Silicon Valley startups typically price their services based on anticipated future costs rather than current ones. While it was clear that AI inference costs would decline substantially, the rate of adoption was faster than projections, creating a gap where usage scaled faster than costs could decrease.

2. As companies like Cursor began recruiting talent from Claude, Claude responded by raising prices, recognizing that they were now competing directly with services built by their former employees.

Helmut10001• 11 months ago

I am willing to buy AI Hardware (=TUPs/GPUs) and put it in my own rack at home, powered by my own pv plant. My limit would be (atm) about 10k, but I would expect a quality level / feature set on par with aistudio's 1 Million token context.

hoppp2• 11 months ago

No, I pray a big company gifts us with a very good local model that I can just run offline and its good enough. I know its a hard ask since releasing FOSS models will eat onto their profit margins, but eventually something should happen

Are you willing to pay $100k a year per developer on AI?

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