[HN Gopher] Token growth indicates future AI spend per dev
___________________________________________________________________
Token growth indicates future AI spend per dev
Author : twapi
Score : 161 points
Date : 2025-08-11 17:59 UTC (5 hours ago)
(HTM) web link (blog.kilocode.ai)
(TXT) w3m dump (blog.kilocode.ai)
| senko wrote:
| tl;dr
|
| > This is driven by two developments: more parallel agents and
| more work done before human feedback is needed.
| throwanem wrote:
| "Tokenomics."
| TranquilMarmot wrote:
| I studied this in college but I think we had a different idea
| of what "toke" means
| throwanem wrote:
| Eh. The implicit claim is the same as everywhere, namely that
| that $100k/dev/year of AI opex is an enormous bargain over
| going up two orders of magnitude in capex to pay for the same
| output from a year's worth of a team. But now that Section
| 174's back and clearly set to stay for a good long while, it
| makes sense to see this line of discourse come along.
| jeanlucas wrote:
| So convenient a future AI dev will cost as much as a human
| developer, pure coincidence
| naiv wrote:
| But he works 24/7 at then maybe 20x output
| crinkly wrote:
| Like fuck that's happening. Human dev will spend entire day
| gaslighting an electronic moron rather than an an outsourced
| team.
|
| The only argument we have so far is wild extrapolation and
| faith. The burden of proof is on the proclaimer.
| nicce wrote:
| Why we can't keep the current jobs but accelerate humanity
| development by more than 20x with AI? Everyone is just
| talking about replacement, without the mention of potential.
| buzzerbetrayed wrote:
| I'm not entirely sure I understand exactly what you're
| suggesting. But I'd imagine it's because a company that
| doesn't have to pay people will out compete the company
| that does.
|
| There could be some scenario where it is advantageous to
| have humans working with AI. But if that isn't how reality
| plays out then companies won't be able to afford to pay
| people.
| hx8 wrote:
| I don't think there is market demand for 20x more software
| produced each year. I suspect AI will actively _decrease_
| demand for several major sectors of software development,
| as LLMs take over roles that were handled previously be
| independent applications.
| taftster wrote:
| Right. This is insightful. It's not so much about
| replacing developers, per se. It's about replacing
| applications that developers were previously employed to
| create/maintain.
|
| We talk about AI replacing a workforce, but your
| observation that it's more about replacing applications
| is spot on. That's definitely going to be the trend,
| especially for traditional back-office processing.
| hx8 wrote:
| I'm specifically commenting on the double whammy of
| increased software developer productivity and decreased
| demand for independent applications.
| nicce wrote:
| I think it depends on how you view it. With 20x
| productivity you can start to minimize your supply chain
| and reduce costs in the long term. No more cloud usage in
| foreign countries, since you might be able to make the
| necessary software by yourself. You can start dropping
| expensive SaaS and you make enough for your own internal
| needs. Heck, I would just increase the demand because
| there is so much potential. Consultants and third-party
| software houses will likely decrease. unless they are
| even more efficient.
|
| LLMs act as interfaces to applications which you are
| capable to build yourself and run your own hardware,
| since you are much more capable.
| LtWorf wrote:
| > and third-party software houses will likely decrease.
| unless they are even more efficient.
|
| It's going to be really fun for us people who love to
| write unicode symbols into numeric input boxes and such
| funny things.
| oblio wrote:
| That's not how this works:
|
| https://en.m.wikipedia.org/wiki/Jevons_paradox
| hx8 wrote:
| Jevon's Paradox isn't a hard rule. There's plenty of
| situations where a resource becomes cheaper and the
| overall market decreases in total value.
| dsign wrote:
| There is great potential. But if humanity can't share a
| loaf of bread with the needy, nor stop the blood irrigation
| of the cracked, dusty soil of cursed Canaan[^1], what are
| the odds that that acceleration will benefit anybody?
|
| ([^1]: They have been at it for a long while now, a few
| thousand years?)
| croes wrote:
| And is neither reliable nor liable.
| SpaceNoodled wrote:
| An LLM by itself has 0% output.
|
| An engineer shackled to an LLM has about 80% output.
| jgalt212 wrote:
| It's sort of like how high cost funds net of fees offer the
| same returns as low cost ETFs net of fees.
| oblio wrote:
| I'm not sure I understand this one.
| magicalhippo wrote:
| Similar to housing in attractive places no? Price is related to
| what people can afford, rather than what the actual house/unit
| is worth in terms of material and labor.
| maratc wrote:
| Except for "material and labor" there is an additional cost
| of land.
|
| That is already "related to what people can afford", in
| attractive places or not.
| thisisit wrote:
| This is just a ball park number. Its like the AI dev will cost
| some what less than a human developer. Enough for AI providers
| to have huge margins and allow for CTOs to say - "I replaced
| all devs and saved so much money".
| mattmanser wrote:
| And then the CTOs will learn the truth that most product
| managers are just glorified admin assistants who couldn't
| write a spec for tic-tac-toe.
|
| And that to write the business analysis that the AI can
| actually turn into working code requires senior developers.
| eli_gottlieb wrote:
| I mean, hey, rather than use AI at work, I'll just take the
| extra $100k/year and be _just that good_.
| insane_dreamer wrote:
| The full cost of an employee is a fair bit more than just their
| base salary.
| SoftTalker wrote:
| Wait until the taxes on AI come, to pay for all the
| unemployment they are creating.
| boltzmann_ wrote:
| Author just choose a nice number and give no argument to it
| mromanuk wrote:
| Probably chose $100k/yr as an example of the salary of a
| developer.
| g42gregory wrote:
| This is why it's so critical to have open source models.
|
| In a year or so, the open source models will become good enough
| (in both quality and speed) to run locally.
|
| Arguably, OpenAI OSS 120B is already good enough, in both quality
| and speed, to run on Mac Studio.
|
| Then $10k, amortized over 3 years, will be enough to run code
| LLMs 24/7.
|
| I hope that's the future.
| okdood64 wrote:
| What's performance of running OpenAI OSS 120B on a Mac Studio
| as compared to running a paid subscription frontier LLM?
| andrewmcwatters wrote:
| Chiming in here, M1 Max MacBook Pro 64GB using gpt-oss:20b
| over ollama with Visual Studio Code with GitHub Copilot is
| unusably slow compared to using Claude Sonnet 4, which
| requires (I think?) GitHub Copilot Pro.
|
| But I'm happy to pay the subscription vs buying a Mac Studio
| for now.
| Jimpulse wrote:
| Ollama's implementation for gpt-oss is poor.
| jermaustin1 wrote:
| I will answer for the 20B version on my RTX3090 for anyone
| who is interested (SUPER happy with the quality it outputs,
| as well). I've had it write a handful of HTML/CSS/JS SPAs
| already.
|
| With medium and high reasoning, I will see between 60 and 120
| tokens per second, which is outrageous compared to the LLaMa
| models I was running before (20-40tps - I'm sure I could have
| adjusted parameters somewhere in there).
| ivape wrote:
| Do we know why it's so fast barring hardware?
| mattmanser wrote:
| Because he's getting crap output. Open source locally on
| something that under-powered is vastly worse than paid
| LLMs.
|
| I'm no shill, I'm fairly skeptical about AI, but been
| doing a lot of research and playing to see what I'm
| missing.
|
| I haven't bothered running anything locally as the
| overwhelming consensus is that it's just not good enough
| yet. And that from posts and videos in the last two
| weeks.
|
| I've not seen something so positive about local LLMs
| anywhere else.
|
| It's simply just not there yet, and definitely aren't for
| a 4090.
| ivape wrote:
| I guess I meant how is a 20b param model simply faster
| than another 20b model? What techniques are they using?
| medvezhenok wrote:
| It's a MoE (mixture of experts) architecture, which means
| that there's only 3.6 billion parameters activated per
| token (but a total of 20b parameters for the model). So
| it should run at the same speed that a 3.6b model would
| run assuming that all of the parameters fit in vRAM.
|
| Generally, 20b MoE will run faster but be less smart than
| a 20b dense model. In terms of "intelligence" the rule of
| thumb is the geometric mean between the number of active
| parameters and the number of total parameters.
|
| So a 20b model with 3.6b active (like the small gpt-oss)
| should be roughly comparable in terms of output quality
| to a sqrt(3.6*20) = 8.5b parameter model, but run with
| the speed of a 3.6b model.
| jermaustin1 wrote:
| That is a bit harsh. I'm actually quite pleased with the
| code it is outputting currently.
|
| I'm not saying it is anywhere close to a paid foundation
| model, but the code it is outputting (albeit simple) has
| been generally well written and works. I do only get a
| handful of those high-thought responses before the 50k
| token window starts to delete stuff, though.
| mockingloris wrote:
| Most devs where I'm from would scrape to cough up that amount
|
| More niche use case models have to be developed for cheaper and
| energy optimized hardware.
|
| +-- Dey well
| skybrian wrote:
| This would be a business expense. Compared to hiring a
| developer for a year, it would be more reasonable.
|
| For a short-term gig, though, I don't think they would do
| that.
| asadm wrote:
| Even if they do get better. The latest closed-source
| {gemini|anthropic|openai} model will always be insanely good
| and it would be dumb to use a local one from 3 years back.
|
| Also tooling, you can use aider which is ok. But claude code
| and gemini cli will always be superior and will only work
| correctly with their respective models.
| SparkyMcUnicorn wrote:
| I use Claude Code with other models sometimes.
|
| For well defined tasks that Claude creates, I'll pass off
| execution to a locally run model (running in another Claude
| Code instance) and it works just fine. Not for every task,
| but more than you might think.
| asgraham wrote:
| I don't know about your first point: at some point the three-
| year difference may not be worth the premium, as local models
| reach "good enough."
|
| But the second point seems even less likely to be true: why
| will Claude code and Gemini cli _always_ be superior? Other
| than advantageous token prices (which the people willing to
| pay the aforementioned premium shouldn't even care about),
| what do they inherently have over third-party tooling?
| nickstinemates wrote:
| Even using Claude Code vs. something like Crush yields
| drastically different results. Same model, same prompt,
| same cost... the agent is a huge differentiator, which
| surprised me.
| asgraham wrote:
| I totally agree that the agent is essential, and that
| right now Claude Code is semi-unanimously the best agent.
| But agentic tooling is written, not trained (as far as I
| can tell--someone correct me) so it's not immediately
| obvious to me that a third-party couldn't eventually do
| it better.
|
| Maybe to answer my own question, LLM developers have one,
| potentially two advantages over third-party tooling
| developers: 1) virtually unlimited tokens, zero rate
| limiting with which to play around with tooling dev. 2)
| the opportunity to train the network on their own
| tooling.
|
| The first advantage is theoretically mitigated by insane
| VC funding, but will probably always be a problem for
| OSS.
|
| I'm probably overlooking news that the second advantage
| is where Anthropic is winning right now; I don't have
| intuition for where this advantage will change with time.
| hoppp wrote:
| I am looking forward for the AMD 395 max+ PCs to come down in
| price.
|
| The inference speed locally will be acceptable in 5-10 years
| thanks to those generation of chips and finally we can have
| good local AI apps.
| skybrian wrote:
| Open source models could be run by low-cost cloud providers,
| too. They could offer discounts for a long term contract and
| run it on dedicated hardware.
| qingcharles wrote:
| This. Your local LLM, even if shared between a pool of devs,
| is probably only going to be working 8 hours a day. Better to
| use a cloud provider, especially if you can find a way to
| ensure data security, if that is an issue for you.
| wongarsu wrote:
| Exactly. There is no shortage of providers hosting open
| source models with per-token pricing, with a variety of
| speeds and context sizes at different price points.
| Competition is strong and barriers of entry low, ensuring
| that margins stay low and prices fair.
|
| If you want complete control over your data and don't trust
| anyone's assurances that they keep it private (and why should
| you) then you have to self-host. But if all you care about is
| a good price then the free market already provides that for
| open models
| hkt wrote:
| Hetzner and Scaleway already do instances with GPUs so this
| kinda already exists
| hkt wrote:
| In fact, does anybody want to hire a server with me? I
| suspect it'll work out cheaper than Claude max etc: a
| server from hetzner starts at PS220ish:
| https://www.hetzner.com/dedicated-rootserver/matrix-gpu/
|
| It might be fun to work out how to share, too. A whole new
| breed of shell hosting.
| aydyn wrote:
| This is unrealistic hopium, and deep down you probably know it.
|
| There's no such thing as models that are "good enough". There
| are models that are better and models that are worse and OS
| models will always be worse. Businesses that use better, more
| expensive models will be more successful.
| hsuduebc2 wrote:
| I agree. It isn't in the interest of any actor including
| openai to give out their tools for free.
| seabrookmx wrote:
| Most tech hits a point of diminishing returns.
|
| I don't think we're there yet, but it's reasonable to expect
| at _some point_ your typical OS model could be 98% of the way
| to a cutting edge commercial model, and at that point your
| last sentence probably doesn't hold true.
| ch4s3 wrote:
| > Businesses that use better, more expensive models will be
| more successful.
|
| Better back of house tech can differentiate you, but startups
| history is littered with failed companies using the best
| tech, and they were often beaten by companies using a worse
| is better approach. Anyone here who has been around long
| enough has seen this play out a number of times.
| freedomben wrote:
| > _startups history is littered with failed companies using
| the best tech, and they were often beaten by companies
| using a worse is better approach._
|
| Indeed. In my idealistic youth I bought heavily into the
| "if you build it, they will come," but that turned out to
| not at all be reality. Often times the best product loses
| because of marketing, network effects, or some other reason
| that has nothing to do with the tech. I wish it weren't
| that way, but if wishes were fishes we'd all have a fry
| Fade_Dance wrote:
| There is a sweet spot, and at 100k per dev per year some
| businesses may choose lower priced options.
|
| The business itself will also massively develop in the coming
| years. For example, there will be dozens of providers for
| integrating open source models with an in-house AI framework
| that smoothly works with their stack and deployment solution.
| root_axis wrote:
| > _In a year or so, the open source models will become good
| enough (in both quality and speed) to run locally._
|
| "Good enough" for what is the question. You can already run
| them locally, the problem is that they aren't really practical
| for the use-cases we see with SOTA models, which are _just_ now
| becoming passable as semi-reliable autonomous agents. There is
| no hope of running anything like today 's SOTA models locally
| in the next decade.
| cyanydeez wrote:
| they might be passable, but there's zero chance they're
| economical atm.
| holoduke wrote:
| Problem is that it really eats all resources when using a llm
| locally. I tried it. But the whole system becomes unresponsive
| and slow. We need minimum of 1tb memory and dedicated
| processors to offload.
| cyanydeez wrote:
| Its not, capitalism isn't about efficiency; it's about lockin.
| You can't lockin open source models. If fascism under
| republicans continue, you can bet they'll be shut down due to
| child safety or whatever excuse the large corporations need to
| turn off the free efficiency.
| 6thbit wrote:
| Many of the larger enterprises (retail, manufacture, insurance,
| etc) are consistently becoming cloud-only or have reduced their
| data center foot print massively over the last 10 years.
|
| Do you think these enterprises will begin hosting their own
| models? I'm not convinced they'll join the capex race to build
| AI data centers. It would make more sense they just end up
| consuming existing services.
|
| Then there are the smaller startups that just never had their
| own data center. Are those going to start self-hosting AI
| models? And all of the related requirements to allow say a few
| hundred employees to access a local service at once? network,
| HA, upgrades, etc. Say you have multiple offices in different
| countries also, and so on.
| g42gregory wrote:
| Enterprises (depending on the sector, think semi
| manufacturing) will have no choice for two reasons:
|
| 1. Protecting their intellectual property, and
|
| 2. Unknown "safety" constraints baked in. Imagine an engineer
| unable to ran some security tests because LLM thinks it's
| "unsafe". Meanwhile, VP of Sales is on the line with the
| customer.
| nunez wrote:
| > Do you think these enterprises will begin hosting their own
| models? I'm not convinced they'll join the capex race to
| build AI data centers. It would make more sense they just end
| up consuming existing services.
|
| they already are
| physicsguy wrote:
| > manufacture
|
| They're much less strict than they were on cloud, but the
| security practices are really quite strict. I work in this
| sector and yes, they'll allow cloud, but strong data
| isolation + segregation, access controls, networking reqs,
| etc. etc. etc. are very much a thing in the industry still,
| particularly where the production process is commercially
| sensitive in itself.
| moritzwarhier wrote:
| After trying gpt-oss:20b, I'm starting to lose faith in this
| argument, but I share your hope.
|
| Also, I've never tried really huge local models and especially
| not RAG with local models.
| jvanderbot wrote:
| It's not hard to imagine a future where I license their network
| for inference on my own machine, and they can focus on
| training.
| coldtea wrote:
| > _In a year or so, the open source models will become good
| enough (in both quality and speed) to run locally._
|
| Based on what?
|
| And where? On systems < 48GB?
| habosa wrote:
| Every business building on LLMs should also have a contingency
| plan for if they needed to go to an all open-weights model
| strategy. OpenAI / Anthropic / Google have nothing stopping
| them from 100x-ing the price or limiting access or dropping old
| models or outright competing with their customers. Building
| your whole business on top of them will prove to be as foolish
| as all of the media companies that built on top of Facebook and
| got crushed later.
| OfficialTurkey wrote:
| Couldn't you also make this argument about cloud
| infrastructure from the standard hyperscaler cloud providers
| (AWS, GCP, ...)? For that matter, couldn't you make this
| argument about dependency your business has which it
| purchases from other businesses which are competing against
| each other to provide it?
| empiko wrote:
| In general, you are right, but AI as a field is pretty
| volatile still. Token producers are still pivoting and are
| generally losing money. They will have to change their
| strategy sooner or later, and there is a good chance that
| the users will not be happy about it.
| ivape wrote:
| _OpenAI / Anthropic / Google have nothing stopping them from
| 100x-ing the price_
|
| There is also nothing stopping this silly world from breaking
| out into a dispute where chips are embargoed. Then we'll have
| high API prices and hardware prices (if there's any hardware
| at all). Even for the individual it's worth having that 2-3k
| AI machine around, perhaps two.
| DiabloD3 wrote:
| Why bother mentioning this model? From what I've seen, it only
| excels at benchmarks. Qwen3 is sorta where its at right now;
| Qwen3-Coder is pretty much at "summer intern" level for coding
| tasks, and its ahead of the rest.
|
| Shame anyone is actually _paying_ for commercial inference, its
| worse than whatever you can do locally.
| typs wrote:
| This makes sense as long as people continue to value using the
| best models (which may or may not continue for lots of reasons).
|
| I'm not entirely sure that AI companies like Cursor necessarily
| miscalculated though. It's noted that the actual strategies the
| blog advertises are things used by tools like Cursor (via auto
| mode). The important thing for them is that they are able to
| successfully push users towards their auto mode and use more
| usage data to improve their routing and frontier models don't
| continue to be so much better AND so expensive that users
| continue to demand them. I wouldn't hate that bet if I were
| Cursor personally.
| sovietmudkipz wrote:
| What is everyone's favorite parallel agent stack?
|
| I've just become comfortable using GH copilot in agent mode, but
| I haven't started letting it work in an isolated way in parallel
| to me. Any advise on getting started?
| hx8 wrote:
| How many parallel agents can one developer actively keep up with?
| Right now, my number seems to be about 3-5 tasks, if I review the
| output.
|
| If we assume 5 tasks, each running $400/mo of tokens, we reach an
| annual bill of $24,000. We would have to see a 4x increase in
| token cost to reach the $100,000/yr mark. This seems possible
| with increased context sizes. Additionally, we might see
| additional context sizes lead to longer running more complicated
| tasks which would increase my number of parallel tasks.
| mockingloris wrote:
| Doesn't this segue? [We'll need a universal basic income (UBI) in
| an AI-driven world]
| https://news.ycombinator.com/item?id=44866518#44866713
|
| +-- Yarn me
| throaway920181 wrote:
| What does "Dey well" and "Yarn me" mean at the bottom of your
| comments?
| mockingloris wrote:
| They are Nigerian Pidgin English words: - Dey
| well: Be well - Yarn me: Lets talk
|
| +-- Dey well/Be well
| nmeofthestate wrote:
| Ok, don't do that.
| SoftTalker wrote:
| Please don't use signature lines in HN comments.
|
| Edit: Would have sworn that this was in the guidelines but
| I don't see it just now.
| AtNightWeCode wrote:
| Don't know about the numbers but is this not the cloud all over
| again. Promises about cheap storage and you don't maintain it
| developed into maintenance hell and storage costs steadily rising
| instead of dropping.
| masterj wrote:
| Why even stop at 100k/yr? Surely the graph is up-and-to-the-right
| forever? https://xkcd.com/605/
| jjcm wrote:
| At some point the value of remote inference becomes more
| expensive than just buying the hardware locally, even for server-
| grade components. A GB200 is ~$60-70k and will run for multiple
| years. If inference costs continue to scale, at some point it
| just makes more sense to run even the largest models locally.
|
| OSS models are only ~1 year behind SOTA proprietary, and we're
| already approaching a point where models are "good enough" for
| most usage. Where we're seeing advancements is more in tool
| calling, agentic frameworks, and thinking loops, all of which are
| independent of the base model. It's very likely that local,
| continuous thinking on an OSS model is the future.
| tempest_ wrote:
| Maybe 60-70k nominally but where can you get one that isnt in
| its entire rack configuration
| jjcm wrote:
| Fair, but even if you budget an additional $30k for a self-
| contained small-unit order, you've brought yourself to the
| equivalent proposed spend of 1 year of inference.
|
| At $100k/yr/eng inference spend, your options widen greatly
| is my point.
| whateveracct wrote:
| This is the goal. Create a reason to shave a bunch off the top of
| SWE salaries. Pay them less because you "have" to pay for AI
| tools. All so they don't have to do easy rote work - you still
| get them to do the high level stuff humans must do.
| turnsout wrote:
| Tools like Cursor rely on the gym model--plenty of people will
| pay for a tier that they don't fully utilize. The heavy users are
| subsidized by the majority who may go months without using the
| tool.
| crestfallen33 wrote:
| I'm not sure where the author gets the $100k number, but I agree
| that Cursor and Claude Code have obfuscated the true cost of
| intelligence. Tools like Cline and its forks (Roo Code, Kilo
| Code) have shown what unmitigated inference can actually deliver.
|
| The irony is that Kilo itself is playing the same game they're
| criticizing. They're burning cash on free credits (with expiry
| dates) and paid marketing to grab market share -- essentially
| subsidizing inference just like Cursor, just with VC money
| instead of subscription revenue.
|
| The author is right that the "$20 - $200" subscription model is
| broken. But Kilo's approach of giving away $100+ in credits isn't
| sustainable either. Eventually, everyone has to face the same
| reality: frontier model inference is expensive, and someone has
| to pay for it.
| fragmede wrote:
| Also frontier model training is expensive, and at some point,
| eventually, that bill also needs to get paid, by amortizing
| over inference pricing.
| cyanydeez wrote:
| oh go one more step: the reality is these models are more
| expensive than hiring an intern to do the same thing.
|
| Unless you got a trove of self starters with a lot of money,
| they arn't cost efficient.
| fercircularbuf wrote:
| It sounds like Uber
| patothon wrote:
| that's a good point, however maybe the difference is that kilo
| is not creating a situation for themselves where they either
| have to reprice or they have to throttle.
|
| I believe it's pretty clear when you use these credits that
| it's temporary (and that it's a marketing strategy), vs
| claude/cursor where they have to fit their costs into the
| subscription price and make things opaque to you
| StratusBen wrote:
| I started https://www.vantage.sh/ - a cloud cost platform that
| tracks Infra & AI spend.
|
| The $100k/dev/year figure feels like sticker shock math more than
| reality. Yes, AI bills are growing fast - but most teams I see
| are still spending substantially lower annually, and that's
| before applying even basic optimizations like prompt caching,
| model routing, or splitting work across models.
|
| The real story is the AWS playbook all over again: vendors keep
| dropping unit costs, customers keep increasing consumption faster
| than prices fall, and in the end the bills still grow. If you're
| not measuring it daily, the "marginal cost is trending down"
| narrative is meaningless - you'll still get blindsided by scale.
|
| I'm biased but the winners will be the ones who treat AI like any
| other cloud resource: ruthlessly measured, budgeted, and tuned.
| nunez wrote:
| Dude, thank you for this service. I use ec2instance.info and
| vantage.sh for Azure all of the time.
| oblio wrote:
| Ironically, except for Graviton (and that's also plateauing;
| plus it requires that you're able to use it), basically no old
| AWS service has been reduced in cost since 2019. EC2, S3, etc.
| StratusBen wrote:
| Look at the early days of AWS vs recent years. The fact that
| AWS services have been basically flat since 2019 in a high-
| inflation environment is actually pretty dang good on a
| relative basis.
| mockingloris wrote:
| @g42gregory This would mean that for the certain devs, an unfair
| advantage would be owning a decent on-prem rig running a fine
| tuned and trained model that has been optimized for specific use
| case for _the user_.
|
| A fellow HN user's post I engaged with recently talked about low
| hanging fruits.
|
| What that means for me and where I'm from is some sort of devloan
| initiative by NGOs and Government Grants, where devs have access
| to these models/hardware and repay back with some form of value.
|
| What that is, I haven't thought that far. Thoughts?
|
| +-- Dey well
| IshKebab wrote:
| > Both effects together will push costs at the top level to $100k
| a year. Spending that magnitude of money on software is not
| without precedent, chip design licenses from Cadence or Synopsys
| are already $250k a year.
|
| For how many developers? Chip design companies aren't paying
| Synopsys $250k/year _per developer_. Even when using formal tools
| which are ludicrously expensive, developers can share licenses.
|
| In any case, the reason chip design companies pay EDA vendors
| these enormous sums is because there isn't really an alternative.
| Verilator exists, but ... there's a reason commercial EDA vendors
| can basically ignore it.
|
| That isn't true for AI. Why on earth would you pay _more than a
| full time developer salary_ on AI tokens when you could just hire
| another person instead. I definitely think AI improves
| productivity but it 's like 10-20% _maybe_ , not 100%.
| cornstalks wrote:
| > _For how many developers? Chip design companies aren 't
| paying Synopsys $250k/year_ per developer. _Even when using
| formal tools which are ludicrously expensive, developers can
| share licenses._
|
| That actually probably is per developer. You might be able to
| reassign a seat to another developer, but that's still arguably
| one seat per user.
| IshKebab wrote:
| I don't think so. The company I worked for until recently had
| around 200 licenses for our main simulator - at that rate it
| would cost $50m/year, but our total run rate (including all
| salaries and EDA licenses) was only about $15m/year.
|
| They're _super_ opaque about pricing but I don 't think it's
| _that_ expensive. Apparently formal tools are _way_ more
| expensive than simulation though (which makes sense), so we
| only had a handful of those licenses.
|
| I managed to find a real price that someone posted:
|
| https://www.reddit.com/r/FPGA/comments/c8z1x9/modelsim_and_q.
| ..
|
| > Questa Prime licenses for ~$30000 USD.
|
| That sounds way more realistic, and I guess you get decent
| volume discounts if you want 200 licenses.
| yieldcrv wrote:
| I think what this model actually showed is a cyclical aspect of
| tokens as a commodity
|
| It is based on supply and demand of GPUs, the demand currently
| outstrips supply, while the 'frontier models' are also much more
| computationally efficient than last year's models in some ways -
| using far fewer computational resources to do the same thing
|
| so now that everyone wants to use frontier models in "agentic
| mode" with reasoning eating up a ton more tokens before sticking
| with a result, the demand is outpacing supply but it is possible
| it equalizes yet again, before the cycle begins anew
| zahlman wrote:
| > The difference in pay between inference and training engineers
| is because of their relative impact. You train a model with a
| handful of people while it is used by millions of people.
|
| Okay, but when did that ever create a comparable effect for any
| other kind of software dev in history?
| gedy wrote:
| Maybe this is why companies are hyping the "replacing devs"
| angle, as "wow see we're still cheaper than that engineer!" is
| going to be only viable pitch.
| woeirua wrote:
| Its not viable yet, and at current token spend rates, it's
| likely not going to be viable for several years.
| austin-cheney wrote:
| There is nothing new here and the math on this is pretty simple.
| AI greatly increases automation, but its output is not trusted.
| All research so far shows AI assisted development is a zero sum
| game regarding time and productivity because time saved by AI is
| reinvested back into more thorough code reviews than were
| otherwise required.
|
| Ultimately, this will become a people problem more than a
| financial problem. People that lack the confidence to code
| without AI will cost less to hire and dramatically more to
| employ, no differently than people reliant on large frameworks.
| All historical data indicates employers will happily eat that
| extra cost if it means candidates are easier to identify and
| select because hiring and firing remain among the most serious
| considerations for technology selection.
|
| Candidates, currently thought of 10x, that are productive without
| these helpers will continue to remain no more or less elusive
| than they are now. That means employers must choose between
| higher risks with higher selection costs for the potentially
| higher return on investment knowing that ROE is only realized if
| these high performance candidates are allowed to execute with
| high productivity. Employers will gladly eat increased expenses
| if they can qualify lower risks to candidate selection.
| jjmarr wrote:
| You're assuming it's a binary between coding with or without
| AI.
|
| In my experience, a 10x developer that can code without AI
| becomes a 100x developer because the menial tasks they'd
| delegate to less-skilled employees while setting technical
| direction can now be delegated to an AI instead.
|
| If your _only_ skill is writing boilerplate in a framework, you
| won 't be employed to do that with AI. You will not have a job
| at all and the 100xer will take your salary.
| oblio wrote:
| The thing is, the 100x can't be in all the verticals, speak
| all the languages, be a warm body required by legislation,
| etc, etc. Plus that 100x just became a 10x (x 10x) bus
| factor.
|
| This will reduce demand for devs but it's super likely that
| after a delay, demand for software development will go even
| higher.
|
| The only thing I don't know is how that demand for software
| development will look like. It could be included in DevOps
| work or IT Project Management work or whatever.
|
| I guess we'll see in a few years.
| austin-cheney wrote:
| Those are some strange guesses.
| LtWorf wrote:
| Too bad that when people actually tried to measure this,
| turned out developers were actually slower.
| zeld4 wrote:
| give me $50k raise and I need only $10k/yr.
|
| seriously, I don't see the AI outcome worth that much yet.
|
| On the current level of ai tools, the attention you need to
| manage 10+ async tasks are over limit for most human.
|
| In 10 years maybe, but $100k probably worths much less by then.
| chiffre01 wrote:
| Honestly we're in a race to the bottom right now with AI.
|
| It's only going to get cheaper to train and run these models as
| time goes on. Modes running on single consumer grade PCs today
| were almost unthinkable four years ago.
| 6thbit wrote:
| An interesting metric is when token bills per dev exceed the cost
| of hiring a new dev. But also, if paying another dev's worth in
| tokens getting you further than 2 devs without using AI will you
| still pay it?
|
| I wonder how the economics will play out, especially when you add
| in all the different geographic locations for remote devs and
| their cost.
| jjmarr wrote:
| They already do for anything not in Western Europe/North
| America.
| AstroBen wrote:
| > charge users $200 while providing at least $400 worth of
| tokens, essentially operating at -100% gross margin.
|
| Why are we assuming everyone uses the full $400? Margins aren't
| calculated based on only the heaviest users..
|
| And where are they pulling the 100k number from?
| daft_pink wrote:
| If you are throttled at $200 per month, you should probably just
| pay another $200 a month for a second subscription, because the
| value is there. That's my take from using Claude.
| dcre wrote:
| "The bet was that by the following year, the application
| inference would cost 90% less, creating a $160 gross profit (+80%
| gross margins). But this didn't happen, instead of declining the
| application inference costs actually grew!"
|
| This doesn't make any sense to me. Why would Cursor et al expect
| they could pocket the difference if inference costs went down?
| There's no stickiness to the product; they would compete down to
| zero margins regardless. If anything, higher total spend is
| _better_ for them because it 's more to skim off of.
| jvanderbot wrote:
| It's not hard to imagine a future where I license their network
| for inference on my own machine, and they can focus on training.
| oblio wrote:
| The problem with this is that the temptation to do more us too
| big. Nobody wants to be a "dumb pipe", a utility.
| thebigspacefuck wrote:
| Never heard of kilo before, pretty sure this post is just an ad
| lvl155 wrote:
| I've not heard either but now I am getting ads from them. I
| guess that was their plan.
| mwkaufma wrote:
| Title modded without merit.
| lvl155 wrote:
| What is Kilocode?
| tirumario wrote:
| open-source AI coding agent extension for VS Code
| ankit219 wrote:
| No justification for a $100k number. For $100k a year or about
| $8k a month, you will end up using 1B tokens a month (that too a
| generous blended $8 per million input/output tokens including
| caching while the number is lower than that). Per person.
|
| I think there is a case Claude did not reduce their pricing given
| that they have the best coding models out there. There recent
| fundraise had them disclose their Gross margins at 60% (and -30%
| with usage via bedrock etc). This way they can offer 2.5x more
| tokens at the same price than the vibe code companies and yet
| break even. The market movement where the assumption did not work
| out was about how we still only have claude which made vibe
| coding work and is the most tasteful when it comes to what users
| want. There are probably models better at thinking and logic,
| especially o3, but this signals the staying power of claude -
| having a lock in, it's popularity, and challenges the more
| fundamental assumption about language models being commodities.
|
| (Speculating) Many companies woudl want to move away from claude
| but cant because users love the models.
| paulhodge wrote:
| Fyi Kilocode has low credibility. They've been blasting AI
| subreddits with lots of clickbaity ads and posts, sometimes
| claiming things that are outright false.
|
| As far as spend per dev- I can't even manage to use up the limits
| on my $100 Claude plan. It gets everything done and I run out of
| things to ask it. Considering that the models will get better and
| cheaper over time, I'm personally not seeing a future where I
| will need to spend that much more than $100 a month.
___________________________________________________________________
(page generated 2025-08-11 23:01 UTC)