[CharlieDigital]

$1500/mo is $18,000/seat/annum.

Maybe Microsoft and Nvidia are on to something.

128 GB machines that can run local LLMs are a bargain even if priced $5-8k. Yes, tok/s is not quite there, but that's probably OK since the bottleneck really isn't the code; it's WTF did Uber build with all of that spend? How did it meaningfully impact their revenue in a positive direction?

[pqtyw]

How is tok/s not a bottleneck I? I assume most people still use ai agents interactively rather than leaving them to do their own thing during the night.

I find anything below 50 tps or so entirely unusable...

Regardless its Apples to oranges anyway, inference is quite cheap for open weight models its just that Claude and OpenAI can charge very high margins compared to e.g. DeepSeek or various provider on OpenRouter since open models are a commodity.

[brianwawok]

I startup 4 or so projects then go do other things for 4 hours. I don’t have enough energy to steer overnight, but I’m at least “semi afk” for daytime steering. So throughput is king for me, tokens per hour. Not latency or actual tokens per second.

[smallerize]

Running locally is even worse for this, because if you're running 4 jobs at once they just run at 1/4 speed. Not literally, you can make up some of the difference with batching, but you have limited resources instead of spreading your requests out on an API provider's nodes.

[sweetjuly]

Is interactive use for coding something that actually works today? With unsafe mode, even frontier hosted models are slow enough I end up just tabbing out to work on other tasks. It would need to be much faster if I am to sit and stare at it while it churns. Local models might be a lot slower but workflow-wise it doesn't change much for me.

[cyanydeez]

It's not a bottleneck if you care about the actual code.

[pqtyw]

I would expect the overwhelming majority of output tokens would not be the actual code but used for analysis, reasoning, testing and iteration. If you only use the agent for autocomplete then yes, the calculation is probably different.

[cyanydeez]

yea, and understanding that too is important. the idea you dont need to read code or analysis seems to align with the depwndcy addiction being shoved in thw pipe.

[Buttons840]

I think companies will eventually just buy a local AI server.

Using local hardware is expensive when it's running a complicated software stack that can break in 10,000 different ways.

These eventual local AI servers will just talk some protocol for AI and sit in the corner and nobody will think about them.

I guess they still might need access to various systems, so idk. Eventually I think someone will offer "AI in a box" though, running the latest open model or whatever.

[pm90]

Yep, its already quite easy to do so with tools like opencode/openrouter. Ive used some open source models and they seem … ok? Im not doing foundational math, just refactoring code, understanding existing code etc. I don’t see a future where companies blow 11% of employee compensation on a single tool; the hosted AI server + oss models will 99% win out.

[dangus]

I don’t think companies will do that. Why don’t they just buy local on-premise infrastructure even though it’s cheaper than AWS?

“AI in a box” sounds a heck of a lot like “the box” from the Silicon Valley TV show. Or the Google search appliance. Or name any other on-premise thing that is equally dinosauric.

The real finding of this article is that AI tokens are direct competitors with offshoring. $1,500/month buys you a whole employee in India.

And this is before AI companies inevitably increase pricing after the conclusion of the growth phase.

[pm90]

> I don’t think companies will do that. Why don’t they just buy local on-premise infrastructure even though it’s cheaper than AWS?

For customer facing, production software, its worth paying a cloud tax to get the reliability guarantee. For tools that are used by engineers for code development, there is no need for such bulletproof guarantees.

[dangus]

That makes very little sense. SaaS/cloud tooling is overwhelmingly popular for internal tooling.

Which category of developer tool has on-premise as the more popular option?

Cloud isn’t about “reliability,” it’s about being able to focus on your core business rather than spending all your time maintaining stuff.

[Buttons840]

Local AI servers are different because they don't have to form a single system. If one AI server goes down, just use the other one.

This is unlike customer facing systems where, if your database server goes down, you probably can't just use the other one--the whole system is down.

[dangus]

There are plenty of horizontally scaled systems where the choice is still made to use a cloud provider.

It’s really a lot more about business focus.

I don’t want to hire someone who understands how an email server works if I can pay Microsoft $10/employee/month for an email account.

[zozbot234]

I agree on the basic point, but running $1500/mo's worth of SOTA local AI is non-trivial already, and that's a figure for a single seat. That's equivalent to generating at least 20 tok/s on a 24/7 basis, in fact probably quite a bit more than that (because open-weight models are vastly cheaper than proprietary ones even when served from reputable Western providers - reaching the same spend would take around 100 tok/s or more, which is well within datacenter hardware territory).

You could probably reach the former figure on a prosumer platform but only for very special workloads. If you spend a lot of time on prefill (which is common for agentic workloads) the outlook is even worse since that's a significant constraint for any on-prem AI.

[physicsguy]

It’s non trivial now - will it get easier in 12 months though?

[dgellow]

You’re way better to run your own on premise models. Laptops are depreciating assets, do not benefit from economy of scale, have fixed specs, result in a fragmented fleet where you need to keep models up to date. Without talking about power consumption and cooling issues. I really don’t see why companies would go that direction

[bluGill]

You don't need to run on laptops, desktops plugged into mains power get more power consumption and better cooling. I want my laptop to work, but I can accept when I'm on an airplane at 32k feet I get less abilities.

[CharlieDigital]

Even if the laptop costs $5k and you upgrade it every year with the latest hardware and run local models (assuming your workload can tolerate smaller models at slower tok/s), you win.

[darkwater]

> it's WTF did Uber build with all of that spend?

You can ask the same for the median 330k salary in the US for Uber Engineering... and being a bit snarky, attending Uber engineers talks here and there at a few conferences, looks like. they love to (re)invent internal tooling/platforms. That's pretty expensive on its own.

EDIT: I'm not saying that Uber's engineers didn't add value to the company, they absolutely did and handling the scale up they had to handle is not an easy feat. But I do challenge the notion of "what features did they create with that (LLM) spending?" of GP.

[SlinkyOnStairs]

> You can ask the same for the median 330k salary in the US for Uber Engineering

People DO.

It's well known that most tech companies are ran incompetently. As you say, it's not the engineers' fault.

But most projects and hiring in these companies exists to juice promotion criteria. And that, depending on perspective, these companies are either massively overstaffed or massively underproductive.

The comparison to AI spending being wasteful holds up pretty well, these are companies that readily piss away billions in pointless spending.

[hibikir]

The massive misalignment in large companies is no secret. But neither is the fact that when someone comes to cut, they also have no idea of who is doing load bearing work that matters, and who doesn't. I look at recent cuts around my large corp, and it's clear they are made at levels that have no visibility of the ground, and are uninterested in said visibility. Obvious mistakes that are worse than what claude would have told you (yes, I asked Claude to pretend to make the budget cuts in our org y looking at the same data an exec could probably get. They were better than what happened)

I think it's a general problem, but in my rare conversations with execs nowadays, they seem rather uninterested in improving their decision making there. The actual performance of the organization does not appear to be all that relevant to them.

[FergusArgyll]

This is a very good answer but there's a flip side too.

The idea of "if you add intelligence you make more money" is contradicted by the fact companies don't just always hire more people. Wy doesn't google just hire everyone?

[CharlieDigital]

This is what all "platform engineers" have to do once things are working nicely: you have to keep inventing work.

I don't know; I'm a Ron Popeil "set it and forget it" kind of guy. Make the dumbest, simplest thing that's going to work with some clear path for scaling. Then go do valuable things instead.

[darkwater]

But most Platform Engineering teams in smaller companies (and especially non-US) add a layer on top of existing technologies. A layer that usually maps to the specific culture and idiosyncrasies of that company; a bit like the deployment flow which is usually very specifically shaped on how a company is.

But in Uber's case, they tend to reinvent lower level pieces of platform/infra.

[quantified]

Sure, but has their rate of value added increased as a result? It's a good question to ask. They added value before LLM coding, and now are more expensive than before thanks to token costs.

[throwaw12]

you don't get promotion for supporting existing things, but for "inventing" you can get promoted. also for large migrations

[ricardobayes]

128GB machines can't run anything locally that is even nearly as capable as a frontier model like Claude. We can get an idea from deepseek v4 pro being 1.6T model, requiring approx. 860GB VRAM to run.

[dkdcdev]

at their scale they could also just run a large on-premise or rented (basically still cloud, but cheaper) GPU cluster and run through that. fixed costs, even license a SOTA model’s weights if you’d like

[mrweasel]

The problem isn't really Uber, Microsoft or Nvidia, it's all the smaller none IT companies that also have developers on staff. They are screwed. $1500 per seat per month is just way to expensive, but they also can't afford to build and maintain their own on-premise solution. If Microsoft can't afford to run CoPilot for their own developer, what chance does any of their customers stand?

If the large, well founded IT companies in the world believes the current AI cost is to high, then Anthropic, OpenAI and CoPilot have no actual customer base. AI is then relegated to very profitable niche business, but that can't fund the R&D for the models.

[skybrian]

It's an extra 18k a year for developer tools when they're paying how much a year per developer? Having software developers at all isn't cheap.

Also, I don't believe you need to spend $1500 a month on a coding agent if you optimize usage at all.

[KronisLV]

In Latvia, the net salary for a Java dev is around 1729 - 4314 EUR, based on https://www.algas.lv/algu-informacija/informacijas-tehnologi... (crowd sourced data)

For the employer those employees cost between 2945 - 7736 EUR per month based on https://kalkulatori.lv/lv/algas-kalkulators (income and social taxes).

So on the lower end that's (1500 USD ~ 1300 EUR) close to half the total expenses of such a developer, on the high end here around 15-20%. That's quite significant, depends on whether their productivity also improves (if that's what the orgs care about).

And we’re not even the country with the worst pay out there, but pay the same for tokens, cause regional pricing isn’t a thing!

[skybrian]

I wonder how this plays out. Perhaps programmers in these countries will use cheaper models like Deepseek and they will be able to compete better, so offshoring continues?

[KronisLV]

> Perhaps programmers in these countries will use cheaper models like Deepseek and they will be able to compete better, so offshoring continues?

Even here, companies don't really trust Eastern providers that much, so they'd be looking for someone in the EU running DeepSeek instances, which might come with a bit of markup. Those orgs would also sometimes be weary of OpenRouter which to me seems like shooting yourself in the foot by being so picky.

That said, DeepSeek V4 Pro (with Max reasoning) is pretty okay and I'm using it instead of Opus 4.8 (my Max 100 USD subscription weekly limits ran out today) and it can do stuff passably (even better than Mistral's offering and has nice context window), but compared to the amount of work I can get done with Anthropic's models, it keeps occasionally fucking up and I have to go back and correct it, so lots of token waste. Maybe it's close to SOTA from 6-12 months ago, though, which is pretty cool on its own, though - just less confidence in its output.

It's like trying to limit the costs and therefore not gaining the maximum added value from the technology. Similarly for those trying to run stuff on-prem, we don't really have the electrical grid here for large scale inference in-country, nor is anyone exactly salivating at the idea of dropping multiple tens of thousands of EUR to build out something passable. I do host some stuff on a bunch of Nvidia L4 cards (Qwen3.6 35B A3B) and while the model has its uses, it's also a far cry from SOTA.

So I guess it depends - compared to an Anthropic subscription it kinda sucks, but then again if you have to pay for Anthropic's tokens those are robbery and then DeepSeek looks like a no-brainer alternative.

[ecshafer]

$18k a year is a non starter in most companies. Ive seen companies balk at Intellij.

[mrweasel]

That depends on where you are. $18K is the equivalent of paying around 15% more for your developer.

[ricardobayes]

In hcol locations yes, but in south of spain you can get full time talent for that figure. It's also an entry-level salary in eastern europe, with ukraine and turkey even being somewhat cheaper.

[mvdtnz]

Why are smaller non-IT companies "screwed" because they can't pay out the nose for their developers' AI usage? They're non-IT companies, developers are presumably not on their critical path, or not their bottleneck. Developers can keep on writing code the old way, or doing it with a more reasonable AI spend. I don't see how this "screws" any company.

[mrweasel]

That was badly worded on my part, my intend was to indicate that there was no way they can or will pay $1500 per month per seat.

[treis]

There's models for every price point. What was SOTA and stupid expensive to run a year ago is a cheap flash model today.

[embedding-shape]

> even license a SOTA model’s weights if you’d like

Yeah, I bet all labs releasing SOTA models are more than happy to remove the main way they make money and let you run it locally, especially if you're a big spender like Uber who seems very willing to throw money into the sea as an experiment.

[throwway120385]

That's going to stop eventually, and I think at that point we're going to see business models more like the major CAD providers.

[idiotsecant]

I don't think they'll have a choice, open weights models are not far behind. At some point it's essentially a commodity game

[dkdcdev]

they also already do this…

Anthropic and OpenAI license to the public clouds. Google reportedly licenses to Apple. licensing to Fortune 100 companies running on their own infra is an obvious next step

it is a race to the bottom and I’m not sure the labs win that race. we’ll see!

[thewebguyd]

I'm not sure the labs will win either. I wouldn't be surprised to see OpenAI & Anthropic just get acquired, either by Microsoft or Amazon and their models just become another product offering in their public cloud and and some hybrid on-prem offering like Azure Stack HCI or Azure Stack Hub (already basically a "cloud in a black box" that could become "AI in a box")

[jcgrillo]

> WTF did Uber build with all of that spend?

WTF did anyone build with all that spend? Despite all the feel-good anecdotes about how productive folks feel using ai coding tools there's a deafening silence when it comes to actual, demonstrated efficacy. How can we be this far entrenched in these workflows and still not know whether they actually do anything useful?

[awesan]

I can say at least for me at a small-ish company (~40 FTE) there has been a surge in internal productivity tools. Nothing to improve the end user product directly but a lot of tools to make processes easier and less error prone.

What would previously be janky internal dashboards or excel sheets are now actually nice to use tools. That said of course the maintenance cost of all that has yet to be discovered, and the ROI is questionable.

[jcgrillo]

Yeah this seems to be a pretty widespread story, from what I've heard as well. The thing about those janky dashboards and spreadsheets though is that somebody understood them and built them with intent to solve a particular problem. Despite the rickety appearance, they're trustworthy tools. A polished single page app might look nicer but it's harder to debug than an excel sheet, and much less transparent in its internal workings--especially if nobody actually wrote it...

[izacus]

More importantly, it's questionable how much extra revenue improving a design of internal tool brings.

[CharlieDigital]

About the same ~40 FTE team. We're doing the same thing. Smattering of internal tools, but no net gain in external revenue. Who knows which of those tools will have any value or ppl are just doing it because it's cool now to make fancy dashboards.

OK. I guess that's good, too.

[nonethewiser]

The real answer?

Software engineer quality of life.

There can be an increase in productivity without a corresponding increase in total output. The gains could be captured by software engineers doing a days work in an hour then fucking off in a variety of ways.

[pqtyw]

> doing a days work in an hour then fucking off in a variety of ways

Until companies start hiring 5x less engineers than they did before and well.. we are clearly moving towards that direction

[nonethewiser]

Quite possibly. Doubftul it will happen all at once. If you can get 8 hours of work done in 1 they'd need to ramp up demand 8x. Would be interesting to see that happen over night. Happy monday. Here, take these 30 tickets.

[MengerSponge]

But that's an inefficient use of dev salary. Y'all are gonna get ground to smooth well-compensated paste.

[slopinthebag]

Yeah I think this is probably most accurate.

[ftkftk]

~70 FTE Engineering team. We are shipping more features, especially features that previously would not have survived the cut to make it on the roadmap. Even though we are shipping more, our total amount of escaped bugs has not increased, so our escape rate has actually lowered. On top of that we are able to triage and fix escaped bugs more quickly now. And then of course there has been an uptick in internal tooling that makes the rest of the company more efficient, and we have been able to address tech debt at a higher rate than before.

I don't think this would have been possible without having solid engineering culture and processes in place before bringing in ai coding tools.

And I don't want to sugarcoat it, this hasn't been easy, requires continued discipline, and took well over a year to get good at. And we still have to continuously learn, experiment and adapt our training, tooling, and processes.

[CharlieDigital]

    > We are shipping more features

That's not really the important question; the important question: is it generating revenue.

If you increase your spend -> ship more features -> no correlated increase in revenue, that's just burning money.

If a team of 10 spends 1 extra headcount ($180k/year) and ships features with no corresponding growth in revenue, what does that mean?

There was probably a reason it was on the backlog (because it didn't really have value).

[ftkftk]

> is it generating revenue

Yes! :)

> There was probably a reason it was on the backlog (because it didn't really have value).

There are definitely things in the backlog with low value. We don't work those items, even if we could now. The additional bandwidth we have now goes to valuable features that drive revenue and retention metrics. The reason they were on the backlog were because we just didn't have the bandwidth to execute on them well and they were just somewhat less valuable than the critical path items on the roadmap.

[RugnirViking]

Imo its pretty clear that anyone who is taking the issue at least somewhat seriously knows the amount of value they provide is not non-zero. However, the problems are manifold: firstly, toolchains vary wildly, from fancy autocomplete, to engineers chatting with codebases they're unfamiliar with, to people integrating them into devops and infra, to people doing spec driven development, with a thousand philosophies inbetween. Many people suspect that those above them in the ladder are on the cusp of massive failure due to losing track of the code, and many people higher on the ladder think those below them are overly cautious. I hate to be the guy saying "oh it must be somewhere in the middle", but I will say at the very least I like being able to use it to read docs for me, and to synthesize syntax and simple scripts (give me a join that works across these tables and gives me column x, y and z - give me a python script that parses a file like this example and extracts abc data - given this api spec figure out how I can get this data from this endpoint, go)

as for building actually complex software, the art of that is not in simply chaining together such scripts. Its the art of using architecture and testing to shape uncertainty, and developing requirements (and extrapolating sensibly from incomplete requirements). I don't think llms are great at this, but they arent terrible either. A lot of the more active users in the space are doing stuff where theyve realised they need more detailed specs, which like, yeah, we knew this already - better defined problems lead to better software.

[jcgrillo]

I agree the most interesting use cases I've heard of are about increasing the rigor of software development practices, but there's definitely a lack of coherence in methodology.. I believe that some users and companies are successful in this effort, but the odd (and interesting!) thing is that so far we don't seem to know how to communicate how to do it successfully.

[danans]

> How did it meaningfully impact their revenue in a positive direction?

It probably allowed them to avoid hiring as many people to build a certain amount of software. Even if it didn't increase revenue, it could have lowered human labor costs.

> 128 GB machines that can run local LLMs are a bargain even if priced $5-8k.

Don't forget the energy costs. Searching around, advanced models use an average of 25 Wh/1000Tok.

$1500/month gets you about 150M tokens.

At the aforementioned energy/token, that's 3750kWh.

What are your local office electricity rates/tariffs? (Hint: they are going up because of AI data centers). Even if my price and energy assumptions are wrong above, you probably aren't going to get the rates that the hyperscalers do.

Even at cheap (i.e Texas) retail electricity rates, that many tokens will probably cost you hundreds per month. In most other electricity markets, probably far more.

[eclipticplane]

How much more software does Uber need?

Unless they are iteratively replacing expensive vendors and optimizing other headcount costs?

[danans]

> How much more software does Uber need? > Unless they are iteratively replacing expensive vendors and optimizing other headcount costs?

Yes and yes. Also the world changes and software has to evolve to model that change, especially at a place like Uber whose software models phenomena in the real world.

[jvanderbot]

Right - the future of LLMs is like ol' windows XP+Dell. Commercialized "things" you run locally offline, co-designed with hardware, with a known productivity suite, and large businesses building the next generation thing and suite with 18mo release cycles (ish).

[nonethewiser]

XP? I can see the argument for enterprise support but in that case the latest windows OS is going to be virtually free and I dont know if MS and Dell etc. would even support an XP machine. Might even be required for hardware. If no enterprise support wouldnt Linux make a lot more sense?

I get that if it's offline the security downside of XP doesnt matter, and I assume XP is free, but being free doesnt really seem that valuable compared to alternatives (free linux and virtually free OS if buying wholesale).

[jvanderbot]

"Windows XP+Dell" should have been in quotes. It's similar to the way enterprise productivity software was developed, packaged co-designed with hardware, and sold on an 18mo upgrade cycle assumption. It's not literally windows xp.

[nonethewiser]

Oh gotcha. Yeah that's an interesting idea.

[treis]

I don't see it. Leasing equipment and paying per seat license fees makes a lot of accounting and cash flow sense. Maybe when it gets to the point where you can run SOTA LLMs on consumer hardware. But that seems a solid decade and probably much more away.

Even then it makes more sense to rent the bigger GPU and get your answer faster.

[gedy]

There's waayyyy too much money betting on that not happening, to the point I feel there'll be regulations popping up for "safety reasons" etc to ensure the big players control this.

[thewebguyd]

3/4 of Microsoft's BUILD conference the past two days were about local AI, foundry local and Windows ML along with a big section in the keynote about running local workloads on their new hardware with Nvidia. Say what you want about Microsoft's reputation, but they are a "big player" and seem to be moving in the direction of local AI first.

[gedy]

I would love this to happen of course, just paranoid it won't.

[ungreased0675]

Your last question is really important. What did they accomplish with all that spend?

I suspect there’s some mass delusion with respect to actual accomplishments as a result of LLM use. Sure, things are moving faster, but does it matter?

[oblio]

Never confuse movement with action.

[ssivark]

Even if companies decided to move away from expensive models from the major labs, it probably much more economical to pay a cloud provider to host some open weights model which could then be amortized across all (internal) users and do inference at a substantial batch size, rather than giving everyone their own hardware -- which means the company would need to provision for peak usage and inference at batch size of one.

[sajithdilshan]

I don't think it's necessarily what Uber build, but the gained productivity. If the engineers use the AI tools the correct way, it can drastically increase the productivity and that means they can actually use the LLM as a junior or an associate engineer. $1500/mo is way cheaper for that level of productivity where as they would have had to pay far more for a human engineer.

[m3kw9]

You can't get an edge using local models, these guys may have competitors that will spend on SOTA models. They won't likely ever consider local machines even for some offloading scenarios, the complexity and costs will be even higher.

[CharlieDigital]

Consider rewiring your perspective: getting an edge doesn't really matter; the only thing that matters is will customers pay for this? Is this a useful, valuable problem to solve?

Coding faster doesn't really solve that.

Uber makes more money if people buy more rides, order more food, have some breakthrough in autonomous driving. They can save money if they can optimize some ops or spend somewhere. Is there any evidence that with the spend on AI that they achieved any of this? If they did, I'm sure we'd hear about it in some engineering blog.

[empath75]

I think probably the correct spend is something closer to 10x that if people can figure agent coordination problems out. It's not even really about capability at this point, it's about keeping track of what agents are doing.

[devttyeu]

If you believe a 128gb machine that is essentially DGX Spark in a laptop chassis can run models comparable to SOTA you either never ran open models on hard tasks, or you aren't scratching the surface of SOTA closed LLM capability in how you're using them.

[f311a]

Can you show me an example of a hard task that can't be achieved using light models? When we don't want the model to work on autopilot without reviewing the code at all. Even SOTA models will produce garbage code, if you don't guide them all the time.

Hard tasks require a lot of guidance and code reviewing, unless you are creating another throw away project where correctness, maintainability and code understanding does not matter.

[infecto]

I am wondering more and more if this becomes true as these smaller models take off. I might be old fashioned but I have yet to crack the workflows some of the hype people spout like Claude codes Boris where he and others talk about running hundreds of agents overnight.

I have still found the sweet spot for me is using LLMs but I am still in the drivers seat.

[CharlieDigital]

That's because for some of these folks, the cost of the tokens doesn't have to match the value of the output; the hype from the story is all they need.

Normal people have to produce something of value from that spend. So starting 100 agents and then waking up to something cool but useless just means you spent a few thousand dollars and created nothing of value............

[ofjcihen]

Running hundreds of agents overnight is almost certainly 99 percent waste.

[sourcecodeplz]

$1.5kpm for SOTA. 128gb you run DSV4 Flash.

[pqtyw]

What's the point of running it locally though? Inference for open models is quite cheap already. They could just selfhost, anyway. The experience of running LLMs locally will be excruciatingly bad in comparison at least for the near future.

[thelastgallon]

>WTF did Uber build with all of that spend? How did it meaningfully impact their revenue in a positive direction?

Uber (and quite a few bay area companies and startups) can afford to spend that money. There is no expectation of profit, Uber lost ~62B and growing: https://uberlosses.com/

[oblio]

As much as I love to hate on Uber, that website is from 2022. Uber has been profitable since 2023.

It's profit margin seems to have stabilized around 10%.

The real economic crime is losing at least $40bn over 10 years scaling a business that ended up having retail profit margins (i.e. low profit margins).

[analognoise]

18k/yr? None of the LLMs generate anything like that in value!

[simonw]

I'm definitely getting that much value out of Claude Code and Copilot.

[CharlieDigital]

You're a content creator; you define your revenue stream.

Uber engineers do not define their revenue stream; the product leadership team does.

$1500/mo of AI spend by engineers does not equate to revenue. They need to figure out revenue first before zeroing in on AI spend.

[Daishiman]

$18K a year is a fraction of the salary of a junior engineer.

Claude has allowed me to do refactors that would have taken weeks to instead take a couple of days. It has, objectively, increased the velocity of the engineering component of greenfield features by 40% in my org. You can put a number value on that and decide if it gives you favorable ROI.

[analognoise]

The point of a refactor is for you to think deeply about the code you are responsibility for, so you can make it better (faster, easier to work on, more tests, whatever).

You’ve gotten a result, but without the work that made you valuable, while deskilling yourself.

It’s a lose/lose situation for…I would say anyone employed as an engineer or programmer. I’m not taking responsible for AI output, the same way I won’t try to fix auto-generated code: because you just regenerate it.

The only person that wins here is the person who can pay you less because they don’t need you, they just need another “types computer guy”.

[Marsymars]

> The point of a refactor is for you to think deeply about the code you are responsibility for, so you can make it better (faster, easier to work on, more tests, whatever).

I'm pretty pessimistic on AI and don't have access to good agentic workflows, but refactors are exactly the thing where it seems to me like agents could be really strong - once I've refactored something architecturally, I might have hundreds of instances of a thing that needs to be updated in a predictable way, but is complicated enough that it's going to be faster for me to manually update hundreds of instances rather than writing a generalizable find/replace tool.

[Daishiman]

> The point of a refactor is for you to think deeply about the code you are responsibility for, so you can make it better (faster, easier to work on, more tests, whatever).

Absolutely false. Refactors (in my case) can be as simple as dropping old packages for newer packages with slightly different semantics. It can be moving legacy pages from jQuery to Vue.

> You’ve gotten a result, but without the work that made you valuable, while deskilling yourself.

I've 25 years coding, trust me, I don't lose anything by not finding out on my own that the semantics of a jQuery promise changed between major versions.

> The only person that wins here is the person who can pay you less because they don’t need you, they just need another “types computer guy”.

You have no idea of what you're talking about. There are entire classes of K8s networking issues that would have taken me a day to debug which Claude solved in minutes just because it can run 20 diagnostics commands in two minutes and deal with technical minutae that is time-consuming but ultimately irrelevant to my business goals.

[jg0r3]

$18k a year is near half of my salary as junior verging on senior developer in the conservation field. Not everyone works in FAANG.

[fg137]

In the old world, the refactor probably won't happen in the first place, but the effort would be put elsewhere. "Increased velocity of .. greenfield features" doesn't directly translate to additional revenue, and your number is very questionable in the first place.

Software engineers like to talk as if business and finance are as easy as pushing code out and refactoring. It's not and never has been.

[ofjcihen]

Can you share some examples that you would say justify that price? Not a gotcha, I’m genuinely curious where you’re seeing a return at that level.

[simonw]

I've written tens of thousands of lines of tested, working code that I would not have written otherwise, and that code is useful to me.

I effectively get to operate at the rate of a small team of engineers - I know that because I've managed small teams of engineers in the past.

[ofjcihen]

> that I would not have written otherwise

I think this is the part I struggle with. The code I write makes me money or is a way of teaching me something, both of which are reasons that I would write the code regardless.

I don’t think I have any projects in mind that I’d be willing to spend half of a car on that I also wouldn’t have written myself.

Obviously just a personal take though. I’m glad you get the usage you want out of it.

[simonw]

My "job" is building open source software for data journalism (and anyone else who needs the tools data journalists need, which is pretty much everyone else). I can build more of those tools, and better, in exchange for a fraction of the cost it would take to hire a team to help.

[dekhn]

I reached my own productivity limit on several projects (in my case, I'm building a fully automated microscope that uses realtime computer vision to solve a number of longstanding problems with microscopes). As much as I'd want to write the code for it, I hit a wall when it came to debugging some particularly tricky issues- either I couldn't do it, or the time investment was too high.

I use Gemini/ChatGPT/Claude to do that work and it unblocked the enjoyable parts of the project while taking care of the tedium.

I also find LLMs help me learn faster because they can often take a paper and turn it into working code, which I find to be a very slow process.