ROI and Productivity Measurement

Skepticism about whether AI coding tools deliver measurable productivity gains, difficulty quantifying value, concerns that increased code output doesn't translate to revenue

← Back to Uber's $1,500/month AI limit is a useful signal for AI tool pricing

The debate over AI coding tools centers on a stark disconnect between massive infrastructure investment and the difficulty of proving measurable returns, as skeptics argue that higher code volume rarely translates into increased revenue or better software quality. While some developers report dramatic speed improvements in prototyping and managing boilerplate, others caution that these gains are often offset by a "race to the bottom" characterized by broken systems, increased maintenance debt, and a lower signal-to-noise ratio in digital marketplaces. Corporate spending limits, such as Uber’s $1,500 monthly cap per engineer, reflect a pragmatic attempt to quantify AI's value, yet many observers remain convinced that adoption is currently driven more by competitive fear and "vibes" than by hard data. Ultimately, the consensus suggests that while AI can act as a tireless assistant for rote tasks, it has yet to prove it can solve the fundamental business and alignment challenges that truly drive economic growth.

96 comments tagged with this topic

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Current AI datacenter/model development investment rate is roughly 1T/year. That's a lot. But the US economy is 33T/year. So the investment pays back (roughly) over ten years if, each year, the AI investments increase overall productivity by 0.6%, assuming the AI companies can capture half of the value of that productivity gain. > „[AI vendors are] paying for a fixed cost with a depreciating commodity“ That's just a confusing way to say you don't think future models will be worth the development costs. Because if future models are significantly better, why would the price of tokens to access those models deprecate?
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I'm surprised people think LLMs, a thing which mainly excels at advertising, spam and writing code is going to generate that much economic activity.
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Companies whose main core competency is writing code were already making up a big chunk of the economy before AI. Also, less wealthy companies were constrained in their use of software by the inability to afford the salaries of talented programmers (and ripoff practices from software consulting companies who in theory could help). Lowering the cost of building software systems ought to unblock a good amount of economic activity as the technology diffuses.
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I am yet to see that ‘companies with great ideas which simply cannot afford those very expensive developers’. For the most, issue is not programmer costs. Mostly it’s inability to formulate the MVP which makes sense. ‘uber for my industry’ is not a sensible business strategy Honestly, if you know guys whose bottleneck is pure software dev — please let me know, I have a good, experienced team in Eastern Europe, we can do wonders in product development. But coming up with sensible business ideas and executing on them in the real world is crazy hard and extremely rare.
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Those companies are certainly writing more code. But It isn’t clear that they are increasing their economic productivity. It could even conceivably have the opposite effect by fueling a race to the bottom. e.g. an interesting possible canary in this coal mine is that there’s been a 200% increase in the rate of new apps appearing on Apple’s App Store, but it has not been accompanied by a 200% increase in the rate at which people are buying apps.
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The AI pundits often seem to apply the logic that code output is directly proportional to revenue and/or profit, and as such it follows that an AI usage increase leads to more code which leads to more revenue. I don't believe this aligns with the reality of any major company, unless your business is in the literal sense "selling code" your revenue and profit is tangential to the quantity of code you produce. Google is a good example of this: most of their revenue and profit comes from their ad network, which is disconnected from their development productivity and instead heavily reliant on network effects and time in market. If I was a new competitor with infinite AI funds to throw at whatever problem I choose, I can't simply capture their market by developing an exact copy of Google's ad platform. In the same way, Google can't substantially grow their ad network by coding "more" or "better", they still need more customers and consumers to interact with their network to see any increase in revenue. So it doesn't directly follow that a productivity increase will inherently follow an AI usage increase.
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That’s great for consumers.
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If the quality of all apps remains high, but if there is an increase of low quality apps it may not necessarily be great for consumers as it becomes difficult to distinguish which are the good and bad quality apps, making it risky to purchase apps.
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A lower signal/noise ratio is never better for consumers.
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If we talking about Meta, Google, etc. code is only incidental to them earning money.
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For ~equivalent tasks/results, or because we’re expecting more or better from tasks? The real measure should be cost per ~equivalent task result, not cost per token nor tokens per task.
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I'd agree. but also that's too scary. and the bottleneck is the massive manual change control process since there's no automation around any of this. :) Why take risk when you can spend money and take no risk
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> I use a local model to log everything I do all week to automate my timesheet. Isn’t that just more work than logging it yourself?
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They are willing to tolerate it now, which is quite a switch up from the free for all we had a few weeks ago, and if they aren’t able to tie in this new ~$1500p/m cap to demonstrable productivity and revenue increases then that will be kneecapped even faster
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Nearly no one is doing anything that is “only possible with AI”. This doesn’t seem like a relevant calculation. People spend on AI as an investment in their current productivity.
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It is also possible that capping at $1500 will give you ~99% of the benefits. So even with gains that are much higher, a cap could be a rational decision. Also, most decisions, especially around AI aren't exactly rational, so I wouldn't read to much into this number.
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Both metrics are valuable. If one uses AI minimally and is able to out perform peers who are maxing out AI spend, one might want to use that in salary negotiations.
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Do you think companies are gonna be like?: Wait a minute. We didn’t save money by adding AI. We just added an expense. Now we have to pay for employees AND AI.
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To adequately validate work you must be at least at the same level, so if you were right (which dunning-kruger suggests unlikely) that would mean your "terrible" average employee is given a tool that will 10x their output which they cannot even check for correctness. And correctness will be low if the average employee is bad like you say, because it means they will give badly specified tasks and even with the best of us it's garbage in, garbage out. I am sure there is no way this can backfire.
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All enablers also enable mediocrity. That's not new. At least when the non-mediocre engineer has to work with someone, they can have a tireless responsive partner. I find this varies by individual, but the AI taking care of so much boilerplate and rote work of coding, and taking the role of architect, test designer, and reviewer is a lot more productive for me. Check the code may take the same skill, but it's an order of magnitude less work.
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So what? That doesn’t negate the value they provide.
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> Because companies are betting that this spending will allow them to reduce cost by firing people. I've never worked at a company that didn't have a technical backlog measured in years.
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It's so fucking bad. I'm watching a team try to maintain a huge dashboard/control application that interfaces with a large amount of hardware using solely AI workflows. Literally nothing works, all the timers/time counters are different across the pages, constantly commands hardware to do stupid shit, breaks during critical moments/in front of clients. Eventually mgmt had to institute change freezes for high profile events because the team was breaking too much shit all the time. The average C suite dipshit doesn't realize that the performance drops off a cliff once your project is more than some fraction of the context window so they will make pretty dashboards all day long but once you need to cover all the edge cases of a real system it all explodes. AI isn't trained on the type of software style we'll need to create systems using AI, it's trained on how we used to write software. It doesn't reuse code or elegantly structure annoying, it just adds more code until the thing builds and passes some fake tests, even if half of it is functionally dead/unused.
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The examples I gave, and the arguments that usually support them don’t really translate into “building complicated systems”. I was talking about the arguments in support of variable naming flamewars, etc. I’m not proponent of AI generating everything without any supervision as of now. But willing to change my mind when it gets better. Most software engineering jobs are not cutting-edge tech, or research, or solving unsolved problems. Integrations, APIs, figma-to-react pipelines, devops and etc. is what people get hired for. All those can be done much faster in the same-or-better quality by an experienced person with the supplement of AI. It’s hard to imagine any company would go against the grain and slow things down on purpose.
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So I accept that “nonsense arguments are nonsense”, but with some minor differences of opinion. Naming of things matters insofar as you care as a human to actually conceptualize the system you’re building. You can call all of this stuff minutiae, and on some level I kind of agree, except for the general vibe of _caring about the quality of the stuff you produce_. That is something that still matters whether it “works”. Like, yes you can get an LLM to gen some junk, but _is it any good_ is still something you are in charge of. As far as “boring systems are boring”, I can tell you from experience that I work on a pretty boring system, and AI is not all that meaningful in terms of its impact, and it’s not for a lack of trying. Can it help me create a migration and add an endpoint and such? Sure. But those aren’t the hard problems. They never were. It’s funny that you think the idea of slowing down is such a bad one, but it is another well-established truth. Slow is smooth, and smooth is fast. This notion of break/fixing your way to prosperity by way of 10,000 ill-conceived PRs is a fool’s game.
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I'm sorry, you might be right. But this simply doesn't reflect my daily reality. All I can say is, nobody in my org is creating 10,000 PRs. But everyone is using Claude Code for virtually all commits. We've been doing it since about Opus 4.5ish. So far, so good. Generally we've modified our timelines heavily, systems are working as intended, company is still making money. There are some AI-authored commits that had mistakes that we didn't catch, but I'm sure this could've been an issue even if all were human-authored. I know first-hand multiple other companies who are doing exactly the same thing. I agree with "slow is smooth, and smooth is fast" for mission critical systems. But super majority of systems are, indeed, not mission critical.
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I have the same experience. Slow is smooth with AI is still productivity improvement.
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yes, but a person who doesn't know any of this stuff is infinitely more productive with ai than someone who isn't when it comes to many things. we've got product folks vibing out prototypes (not shippable but clickable) in our main front end in a few minutes to an hour. This would previously have involved 3 people and several weeks, or a ton of figma and documents to fill in the gaps. This saves weeks to months and lets them really experience the items. Then they hand it off to someone who knows all that stuff who is also using AI and the impl also gets done faster. The PMs are either moving infinitely faster, or at least 30x faster and not blocked constantly by others. basically you're not comparing people who don't know much (tech) with those who do, you're comparing them before and after access to AI.
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I honestly feel like my own learning has accelerated after using AI. Simply because now it's so easy to write the same thing in so many different languages, I can e.g. learn pros and cons of each language, which otherwise would have been I think unfathomable to me. I have now created so much stuff I wouldn't have had time to create. I setup k3s, and tons of what would be otherwise unnecessarily complicated stuff on my laptop for my side projects with additional home servers, smart house stuff. Otherwise k8s and things like that would have been daunting to learn and in theory and without constant professional exposure, etc... Microservices in Go, Rust, which I didn't have any previous experience with, games in C and other languages. Didn't know anything about low level memory management before. Was just mainly TypeScript person. Just constantly building random fun stuff.
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Fear of loss to competitors embracing a technology creates a fear driven adoption. Let me ask you this: is any technology worth so much break-neck adoption without first seeing clear evidence of ROI? No. The adoption is irrational.
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What makes you think there is no clear evidence of ROI?
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All of the articles and CFO’s saying so, and companies like Uber cutting back on AI spend.
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Uber cutting back to ~$1,500/engineer/tool/month makes it look to me like they think there's at least $1,500 of monthly ROI to be had per engineer.
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1500/Mo per engineer is such a small price considering the base salary of these employees, Maybe Uber knows something we don't (the 5X engineering ROI isn't there for them?). Judging the ROI of an engineer is hard. Adding AI on top of that makes things worse, I think. I've heard AI makes engineers 3X, 5X, 10X and even 100X. If I told my CEO that I was 4X more effective with AI, I am doubtful he would be willing to spend even 1X my salary on tokens. Even though he would be making out in the end. At some point the ROI is pretty much vibes, man.
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So touche, but since it's usage per task it's kind of weird. This means that the average engineer is efficient at (say) identifying the first 10 tasks they should do but there are diminishing returns after that? That seems like a weird pattern. Wouldn't it be more likely that certain tasks have a ROI based on how efficient the task is generated? Like I'm trying to imagine in my head, if you think an engineer is more efficient with the tool, why deny them more tokens. I guess so they think to use them more efficiently? So, maybe I conclude that I think your conclusion that there must be $1500 per engineer is flawed. And even if it were true, I don't think the benefit would be evenly distributed. I suspect this is a first pass at figuring how to budget them and there will be a second pass. While it certainly reeks of motivated reasoning, Jensen Huang assertion that an expensive engineer should be using at least their salary in tokens feels more logically sound to me (assuming the average engineer is efficient at using tokens, I have a feeling it's a normal distribution)
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The question you always have to ask is what problems does it directly solve. I personally think most of the current problems in software development and really the world at large are not time-bound problems but alignment issues, and all an LLM can really do there is be some 3rd party oracle that gives you an answer without needing other humans to agree with you.
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> The question you always have to ask is what problems does it directly solve Most directly, human labour. Labour is always a problem for capital. At a certain level of AI competence, businesses don't need to pay humans to complete the work they need doing in order to operate. I don't think anyone would dispute AI competence isn't growing steadily.
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There's some software that can cost $1k or more per seat/month, but it's pretty rare. Big tier ERPs usually fall in the ~$600/seat/moth range, specialty engineering stuff can hit over $1k, Bloomberg terminal, etc. I wonder if what Uber's building with that $1.5k/month/employee is actually delivering the same value that something like an ERP would to the entire org...
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>Why there are so many people that still believe that AI coding is a fad? Because there's not a single piece of evidence that this has improved the quality of the delivered software, or for that matter even the speed of features any of these companies produce, in fact if anything the opposite. The point of software development, the hint is in the name, is to develop software, not consume tokens. If Uber was now full of 10x engineers the stock price of Uber would be up, not down on a yearly basis. Hilariously enough the only company whose stock price is up appears to be Antrophic
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How dare you mention evidence! This isn't engineering you know!
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Why are there so many people who mistake simple anecdotes for actionable data? Why do the majority of businesses fail rather than succeed?
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$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?
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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.
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> 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.
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> 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.
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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.
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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.
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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.
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you don't get promotion for supporting existing things, but for "inventing" you can get promoted. also for large migrations
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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?
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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.
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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?
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Never confuse movement with action.
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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............
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>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/
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> 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?
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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.
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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.
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More importantly, it's questionable how much extra revenue improving a design of internal tool brings.
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~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.
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> 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).
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> 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.
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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.
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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.
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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 .
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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.
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18k/yr? None of the LLMs generate anything like that in value!
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I'm definitely getting that much value out of Claude Code and Copilot.
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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.
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$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.
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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.
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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.
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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.
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> 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.
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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.
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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.
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The $1500 number is less interesting than the fact that they hit a ceiling at all. Most engineering teams I've talked to have no idea what their AI spend is per developer because it's buried in a consolidated cloud bill. Having a hard cap forces two useful conversations: what workflows actually justify API calls vs local inference, and whether the output is being measured against any real productivity metric. Without that feedback loop it's just a race to see who can burn tokens fastest.
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Days ago he said… “I'm finding that coding agents can take me from a vague idea to a working solution, one with tests and documentation and that looks like a carefully considered project evolved over the course of many weeks... in less than an hour. Even if the code is rock solid, there's a limit to how many projects like that I can sensibly care for - and if they're instantly abandoned, what value was there from creating them in the first place?” https://simonwillison.net/2026/May/31/the-solution-might-be-... Here is Simon questioning a fundamental belief held by the pro-LLM lobby. Would a paid shill question that? Simon is, without question, an enthusiastic pro-LLM person. I disagree with what he says often, the product market fit post was a bad take. But I don’t believe he is shying away from sharing his thoughts when they’re not favorable to the industry.
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probably will get fired for lack of performance.
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Let's just say their performance (OKR, KPI, whatever "impact" metric you want) was indistinguishable from a peer that used the AI/LLM monthly allowance in full. Maybe a $10k raise would be nice?
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Theyd get a bad review for leaving performance on the table. When has finishing your work ever resulted in anything other than more work?
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It's disturbingly anti-merotocratic. You're not allowed to prove that you're more useful without AI because they just assume that AI is a 10x multiplier on everyone.
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> That means each employee's AI spending cap is ~11% of that median compensation package. when looking at costs - numbers make sense. however decisions as an org/company/solo founder - costs help you set prices, but to reach profitability you want to model around ROI. now the question is what's the ROI for a $36K/investment per engineer or $90M for the total org ? I bet the ROI is negative.
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I'm in a similar boat - it's hard to measure, but let's say you pay an engineer 150K. Giving them a tool that costs 15K a year is effectively a 10% increase in that expense. If we were seeing 3X, 5X etc improvement from individual engineers, that 10% increase in expense would be a fantastic investment (even 3 engineers for the price of 1.1??!). I have a feeling they are just not seeing that much of an improvement.
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It's also a useful signal for AI value. Looks like it's a max value add of $18,000 per engineer per year.
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No, that's not what it means at all even if just doing it purely in math terms. Really it is just a reasonable amount to cap at to stop the long tail of super spenders (tokenmaxxers). You could also call it "the amount of AI spend after which Uber has decided there is diminishing returns for the average engineer".
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It's not so simple to determine and generalize how much value AI adds. It's going to be different on a per-company basis and a per-engineer basis. It's also affected by the competitive market place and how many other companies are using AI for their engineers. For example, what if you're a tiny startup and you're considering whether to hire an extra engineer or do all the coding yourself. I would estimate that AI is worth far more than $18,000 a year in that situation where you might reasonably decide to put off hiring an engineer.
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I find it really doubtful anyone has managed to quantify that in any meaningful way. Seems like mostly an arbitrary number. Also the article does claim that's its actual several times more than 18k if you are fine with using Codex, Cursor or etc. when you Claude tokens run out.
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Their initial budget for determining how much value AI adds is $18,000 per engineer.
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Not really. There are clearly diminishing marginal returns, so it's likely that the first $2,400/engineer/year adds >>$2,400 of value, even if 18,001st $/engineer/year adds <$1 of value.
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The big question is, will the productivity gains be absorbed by the needs? Societies don't have a need for infinite amount of luxury and laziness offered by the productivity of the machines. At some point, you would shake off things, get up from the couch and start walking again, breathing afresh.
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The tool categories that pay for themselves fastest: (1) Anything that gets invoices out faster and makes it easier for clients to pay. (2) Scheduling links that eliminate email back-and-forth. Everything else is optimization. I keep notes on which freelancer tools hit each threshold at freelancerkit.surge.sh
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It finally puts a number on productivity gain of engineers with AI. This is probably less than 10% of the cost of an average uber developer. So they don't assume much more productivity gain from AI than 10%. (Cost of an employee is much higher than their salary, it includes things like office space, supporting structures like HR/accounting, insurance, hardware/software, and much more)
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But is it an accurate number? Does AI reach diminishing returns after $1,500/month, or is that all they are willing to risk/burn to stay in this game?
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If you estimate 10k salary per engineer that means the moment it’s cheaper for them to hire another engineer but that doesn’t mean it’s improving productivity 15% but if 15% is the moment it stopped being better than another human we can assume 7.5%? Probably even less because you would spend those 1500 extra per employee also if you just save 10% so 150 per employee that’s 1.5% on salary. This is imho one of the best ranges we can assume for now how much would that be on the whole swe market?
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What is the point of allowing a developer to spend $18,000 a year on AI subscriptions? Can't they hire a decent developer who is capable of producing a quality solution faster? Clearly, these decisions are all made by high-level management team. I was recently talking to an HR person from a European company, and she goes: 'We are forcing our developers to use AI coding agents, but they are still kind of hesitant.' This person had never written a single line of code, nor did she know what software engineering is. For these people, using AI coding agents = faster delivery without breaking anything.