I never took tokenmaxxing to be about improving productivity directly; mundane feature work that comes out of it is just a side effect. I always saw it as a race between these big tech companies to get a generational advantage by being the one to discover the way of the future, with respect to harnessing AI to actually and truly automate software development.
EDIT: whoa, I used "way of the future" as a reference to Howard Hughes in "The Aviator", not this Way of the Future religious organization thing I just stumbled on; no intended reference there.
> by being the one to discover the way of the future
This is my understanding too. The underlying assumption is that action leads to information, iterations lead to enlightenment. So from an org's point of view, tokenmaxxing means encouraging everyone to explore as much as they can. Of course, token volume should not be the only metric - tokenmaxxing is just a catchy phrase.
Yeah this is right. Its about taking the throttle off of experimentation hoping some team suddenly starts shipping a years worth of features in a week and responding to strategic customer demands in near real time. Then copying what works out across the org. (and probably downsizing)
What those organizations miss is that they drown their teams in organizational red tape. The way to the future isn't tokenmaxxing - it's cutting back on process noise.
My impression of companies pushing AI so heavily is that they are basically being forced to do it by it merely existing. Imagine if AI really is as powerful as it is suggested to be and you didn't jump on the bandwagon. Then you would be behind. So by it existing and other companies using it, you have to as well because even if it turns out to be a failure at least everyone else will have failed too and you are on an even playing field.
There is a fad every few years that uses similar thinking to justify spending. Sometimes they work out sometimes they don't. But they never work out as good as the most optimistic predictions.
I commented on wasteful AI spending, and my father immediately started talking about how he "went through that a couple of times" as a manager at an oil company.
The unusual thing is perhaps how global and cross industry it seems.
Genuinely asking: for which fads was it actually beneficial to jump in during the hype phase? Was there ever anything so critical that there was some huge disadvantage if you didn't adopt it right away?
ETA: I suppose the complicating factor, at least for B2B, is "customers demanding $fad", particularly when the purchasing decision makers don't actually understand what $fad is (e.g., "cloud", "blockchain", "ai", ...). If you don't become "$fad native" right away, you lose the Dunning-Kruger segment of the market.
To the extent that figuring out the true automation will lead to layoffs, that knowledge will quickly spread as the laid-off knowledge workers are available for hiring.
Agree and I have wondered behind close doors if this is not the mental model. You need to spend money to see what is working this was simply a way to see that.
I agree.
We don't need a nondeterministic 10quadrillion vector model.
We need an deterministic expert on our narrow business.
Something small, that can be run on the 2026 version of the spare PC under the CTOs desk.
It will always be worse compared to a centralized approach where hardware utilization can remain high. Except in case which demand low latency which most development things do not need. It's okay if it takes an extra 100ms for a code review to take place.
Eh, I also saw it as a rather blatant attempt to undermine the bargaining power of the Software Engineer (which had grown to insane proportions over the years) - both in working conditions and raw cash money.
In many cases it really didn’t/doesn’t matter if the AI automation actually works, just that people think it could - and hence leave money on the table.
Now feels like a very good time to be a small team of experienced developers who can largely work on stuff by themselves and not a corporation of hundreds of developers of varying abilities all now trying to show how much code they can generate and how many tokens they can burn.
100% - I think that is also part of the divide you see online. Devs who work on massive codebases w/ 100s of engineers and see the bugs the LLMs create vs devs who work on smaller codebase w/ small <5 person team.
Small teams building continuously get to write features that are 90% correct in a tenth of the time.
Big enterprises get to write features that are 90% correct barely twice as fast, because all of the bottleneck lies elsewhere. They also spend more on AI per user because of the internal dynamics pushing people to adopt AI irresponsibly. They can correct the 10% of errors slower than small teams because of bureaucracy, increasing the cost of errors that show up in the product. Furthermore, they have less to gain from a given amount of speedup because they had plenty of engineering velocity anyway compared to small teams.
I don't think big enterprises will start winning from AI technology until AI truly can automate almost everything in a company and let said company outproduce competitors by burning tokens alone. That's nowhere near possible right now.
Even small teams are not immune from the AI push. I work in a small <10 people with 3 devs. I'm the only one not using AI while receiving push that I'm a problem for not. To the point, VP of company says if we don't all start using AI we'll be out of business because other people will. :face-palm:
I'd definitely say IntelliJ improved my productivity. No one cared. If anything, management viewed it as a nuisance as they had to deal with the license.
Now there is demands to justify not using AI like this, but people don't care about details. Which AI tool I use apparently doesn't matter at all, even if there are presumably productivity differences between them.
Seems like you should have a justification for introducing a new tool instead, no? There are thousands of other tools they're also not using, but we're not asking about those.
I think the jury’s still out on how big an impact AI will have on overall, average productivity. But it’s definitely a productivity boost for someone who’s capable of writing code without it. If you want to be super conservative, don’t even have it write any code. Use it to search through existing codebases, review your code, find the root cause of bug reports, evaluate pros and cons of alternative approaches, etc. etc. You’re really missing out by not using it at all.
Here’s a concrete example of conservative AI usage: I use Claude to vibe code my nvim config. Now, who cares if my nvim config is AI slop? What’s the worst that can happen? Nvim works for me now way better than it ever did when I was limited by the time I was willing to spend configuring it manually.
Anthropic's annualized run rate is >$40b according to outside reporting. AWS hit that by Q4 2019. There were still debates on public cloud vs on prem at that time, but by late 2019 public cloud had facilitated the creation or adoption of entire categories of software within SaaS and PaaS, not to mention consumer internet businesses like Uber and Airbnb. The net impact of AI coding tools is far more ambiguous in comparison.
The profitability comparison is fraught but worth noting that by then AWS was already extremely profitable.
Amazon was AWS first customer. I sometimes feel if the AI companies promises of their models replacing all SWE, they would be a top 3 product/service in any number of businesses and not selling shovels to miners.
A friend of mine added some pretty extensive iOS UI tests to a keystone feature hit by millions every month. They'd been kicking the can down the road for years, trying to fit it in their roadmap, and with Claude running overnight they were able to bang out the whole suite in a week.
I'm not sure how it would show up in quarterly results.
I see these kinds of stories here a lot, and I'm curious whether they reflect a steady stream of need for AI coding, or whether a lot of companies have a burst of AI-appropriate coding work now that the technology is available and then will have a smaller need going forward.
Is it like the stereotypical dad who rents a power washer, powerwashes every exposed surface on his property, and then doesn't need to do any powerwashing for a few years; his neighbor who gets an Instant Pot and uses it for every meal for a month, then sees it gathering dust when the family gets tired of pressure-cooked stews; or like their neighbor who gets a microwave oven and uses it multiple times a day for decades?
So far where I work its the Instant Pot, at least for the non-devs. We rolled out Claude & Cowork to the masses after a brief pilot. It was about a solid month and a half of heavy usage and then suddenly usage fell off a cliff. Once it stopped being a cool new toy, people just didn't find a use for it.
A few mundane things got automated, but these were just back office admin type work. Nothing that's going to show on the P&L. Yeah those people now have a little more time for other things, but those other things are also not revenue generating. No FTE got replaced by it so in the end they just paid for a bunch of administrative positions to be a little less busy. Great for the workers who are now less stressed, but almost no impact on the business financials except there's now yet another subscription.
> So far where I work its the Instant Pot, at least for the non-devs. We rolled out Claude & Cowork to the masses after a brief pilot. It was about a solid month and a half of heavy usage and then suddenly usage fell off a cliff. Once it stopped being a cool new toy, people just didn't find a use for it.
Your employer is doing it wrong. You need usage surveillance with sanctions for low/declining use, then people won't stop using it.
It's industry best practice. All the market-leading companies are doing it. Do you think you know better than them?
If there's anything I've learned as a software engineer, it's that agreeing with and defending the ideas of business leaders and Silicon Valley VC influencers proves I'm very intelligent.
That’s been my theory - there’s some low hanging fruit in every environment where AI knocks it out of the park. Then complex brownfield reality (coupled with non-technical factors) rears its head and the stunning productivity gains are nowhere near to be seen.
That’s the explanation how you can have both the anecdotes of amazing AI productivity and rigorous studies showing anything from actual loss of productivity to single-digit gains.
I also think in addition to that the increased speed compounds the problems much quicker. And I don't mean bugs. I mean that duplicated code here, that additional state variable to keep everything going there. Not removing things that should be removed because we can work around that, etc.
It's like building a super tall Jenga tower very quickly but laying the bricks much worse than a careful player.
Nah in competitive industries you need to build features and out compete people and getting AI to do that whilst architecting things well due to experience and having time to think more about the important stuff but have a lot of the more boilerplate and simple things ABs plumbing etc handled by agents is great.
When you try to replace your entire brain with AI things are going to go wrong.
> or whether a lot of companies have a burst of AI-appropriate coding work now that the technology is available and then will have a smaller need going forward
For the product my friend works on, it's definitely the latter. I definitely don't expect this party to last forever.
Some measures should have real, tangible, concrete numbers; others should have “my friends are saying”/“you are blind if you are not seeing it” vibing.
Over here our CTO replaced Intercom with an internal equivalent that costs less than $20 / month to run, haiku and sonnet support agent costs included. In less than a few weeks, in his spare time.
In my limited experience with using agents to create tests it tends to code the tests to the existing code instead of ensuring the correctness from a spec. Great for regression testing but still limited in effectiveness for catching existing issues.
Even if Uber really did double developer productivity, would it translate to quarterly results?
Ultimately they make money selling rides, not selling software. The Uber app is mature and adding new features is unlikely to significantly increase sales.
Writing 2x more code doesn't translate to 2x more revenue unless it results in 2x more rides.
How much is infra spending compared to revenue/other expenses? I honestly can’t imagine it’s that high, they’re not running something like Netflix or YouTube… But maybe I’m underestimating it.
Didn't google say that AI had increased their company's productivity by 10%? if that's the case, then how can they justify spending 50% to 100% of wages on it?
This doesn't account for the fact that review is still a bottleneck, engineers understand much less of the code they're shipping and there'll likely be tech debt they'll have to unwind in the future.
I like the qualification of "large-ish". When someone says code is not the bottleneck, I assume they work for a large company. To be fair, at a start-up developers think they code at least 4 hours a day, but it never averages that either.
I am not sure how uber is operating internally around the use of tokens but if they actually shipped features faster than before then it is still a win. if they learn that users don't want these features or want a different version of it; they have learned this new knowledge faster than they would have if they manually coded those features, which means in principle you should be able to iterate faster. but this will collide with creative ceiling that humans exhibit in a span of time and on top of that uber is prioritizing spending money on tokens over humans which seems like a mistake. you need humans for creativity.
I used Uber for the first time in like 8 years recently, and near as I can tell it's the exact same thing it was. What features are they even adding at all much less that anyone cares about? You ask for a ride to a place, a driver shows up, money is exchanged - the end?
Sometimes things are actually just finished. They don't need to treadmill.
They have to make it easier for ubereats orders to have parts of their bill attached to a corporate card and half attached to someone's personal card and be able to split the invoices!
This argument is funny because you could have said the same thing 4 years ago: Uber still picks you up just as it did years before that, so what did all those millions spent on developer salaries get them?
Uber’s business is relentlessly confusing for people who think it’s a simple app to send an alert to a nearby driver to pick you up.
Uber operates at a scale where there are no trivial problems because even small changes can impact hundred of thousands of customers. They can also justify spending time and money on new features that only 0.1% of customers might use because 0.1% of their customers is a very large number.
Uber also has to maintain thousands of region specific rules and features to be able to operate globally, and they do it all in the same app instead of having specific regional versions (which would be a terrible user experience for frequent travelers). That alone is a ton of work the end user will never see but is core to their operation.
This is not just me saying it. The Uber president himself says it in the article.
> so what did all those millions spent on developer salaries get them?
There was no doubt about what these developer salaries got them. It was to keep Uber stable and running in thousands of jurisdictions with varying rules/regulations.
The idea of using AI was (I hope) not just to replace developers for this purpose but to also ship features/products beyond what was already being offered. It has however not panned out as these CEOs/execs thought it would.
> They can also justify spending time and money on new features that only 0.1% of customers might use because 0.1% of their customers is a very large number.
And what are those features exactly? Because even the President of Uber doesn't seem to know:
"“That link is not there yet, right? I think maybe implicitly there is more that is getting shipped, but it’s very hard to draw a line between one of those stats and, ‘Okay, now we’re actually producing 25 percent more useful consumer features,’” said Macdonald."
The budget allocated to AI for the year has been wiped out in 4 months.
> Eats for the way - your driver picks up a takeaway for you to eat while they drive you to your destination.
This seems like the kind of terrible idea that an LLM might have come up with. I'm pretty sure most drivers do not want people eating (especially a whole meal) in their car, and I can't imagine a lot of instances where you're calling an Uber and don't have time to get yourself food, but don't mind waiting an extra 10 minutes for the driver to detour, find parking, and wait for your food.
Not to mention what anyone who's worked in an office with a shared kitchen can tell you - the smell getting into a car where an indeterminate amount of people have eaten different meals. Like climbing into a food court dumpster.
> I can't imagine a lot of instances where you're calling an Uber and don't have time to get yourself food
Recently I got a car to take me to the train station and picked up food on the way. Seems pretty common to me. Of course, I didn't need or want it charged as a premium feature in the app.
This seems like the doom of all tech companies that hit a single kernel of a good idea, hire a big development team to build it, and then, once it's running well and making money, leadership looks around and sees this big body of developers, product managers, project managers, QA, and management tree, looking around for something else to do. Then, instead of saying, "Let's find the next big thing to do," they say, "Cram dozens more things into the thing that already works. Anything you can think of, spin up a team of 10 to bolt it onto the main product. Move things around to make everything fit. Run experiments on users to see if this new crap moves the metrics. A/B test to see what we should keep and what we should silently remove next update. Attach this other company's product that we just bought."
In a few years, what do you end up with? The modern version of every single fucking app we use today.
Well, travel booking is one of those things every company wants to get involved with because it's just straight referral fees. I get advertisements to book travel through my phone company (T-Mobile US) and a slew of financial services companies.
If it's easy enough to add to the app and sticks around for a while, it may well be profitable even if only a small percentage of customers use it or even realize it's available.
There's probably tons of backend projects going on, expanding in countries, payments, complying with regulations, effeciency and reliability projects. They also do food delivery. There's a whole engineering team to support
I actually think Chinese models have already popped the bubble, we just dont see it yet. the only way to justify AI IPO market cap is basically if they get to hold most sw industry code hostage and then token-flate budgets to collect AI tax. short of that AI expense would very quickly mean revert to model + some margin. this means the moat for AI 'trillion club' is gone. In fact AI virtually guarantees that there is no execution-moat left anywhere, definitely not in code or that engineer with knowledge about that obscure mechanism. without the moat most of the sw ecossytem's margins would shrink (as they should).
Ironically enough the only moat left would be what you can buy from Washington.
I really don’t understand on the customer side of B2B why so many companies actively encouraged AI tooling costs.
I can understand it from the side of the companies selling tokens and AI hardware. I don’t understand the race to spend more on internal tools.
I’ve been sitting around waiting for my company to buy a number of necessary bits of tools. They cheap out on every solution imaginable. Datadog is too expensive, let’s buy a cheap solution that costs us months of setup time. Configuration management is too expensive, let’s use the free version with no audit trail or dashboard.
But everyone…in the entire company…gets multiple AI tool subscriptions.
I don’t remember investors being this stupid at any other point. I don’t recall investors pressuring my company to use blockchain or NFTs.
The logic is quite simple. Management thinks that AI can improve productivity, but knows that there will be some resistance and some learning curve. So they force people to use it so that people can 1. develop their skills and workflow and 2. find out where it is useful 3. find out what needs to be improved to make it useful.
As a more obvious example consider that cars were just invented and the post office management thinks that they could improve performance of letter carriers. But right now cars are slow, break down a lot and there isn't much infrastructure for them. Lots of letter carriers will (rightly) think that it is a waste of time because they need to get in, stop, park between every house and they break down so often it isn't worth it and half of their route is unsuitable for a car anyways. But if cars are forced for a while they will find out what routes work well for cars and which don't, improve the cars and related infrastructure to make cars more effective and other improvements to unlock more productivity.
So yes, right now management is wasting money on cars and gas for no increased productivity. And yes, measuring how much gas each employee uses and encouraging to use more is obviously stupid in isolation. But the idea is to force adoption to iron out the kinks and find out where it can improve productivity. It is basically funding a research project.
IMO, the root of all of this is the almost total inability for most managers, and most eng orgs to measure individual engineer output in any useful way. And in particular in a way that lets you reliably compare engineers to each other.
Despite decades of the industry telling itself that we "pay for performance" or whatever, that has never been the case because we can't really measure performance very well. Where I have seen it done ok (not great, just ok), it was massively labor intensive and did not last, and was only done fully when considering promotion.
So, as you observe, now we have some new technique that managers are sure will increase performance by 50+%, if only people would use it. They can't just raise their expectations of performance by 50%, because they can't measure performance to within 50%! So, they measure the thing they can: token consumption.
That would be sensible if you were a car maker and not in the business of delivering posts. You’re tanking your core business, just to do what the developer of the tool should be doing (ensuring the usefulness and reliability of the tool).
I’m all for a trial run, but it needs to be done like any research experiment. With a goal and measurements along the way. Not by going blind and hurting your workers/customers.
How will companies like Uber continue to fund the “research” when the budget ends up burning 3x faster than predicted without being able to observe measurable gains?
Nothing that C-execs and management advocates for has made any sense for a long time now. If this is the first you're starting to question it all, I must ask what rock you're sleeping under because I desperately need a really good nap...
Strategy - just doing what your friends on the golf course are doing.
The number of times I have been told "oh I talked to so and so and they are having SUCH a good time using X" and then three years later "oh I talked to so and so and they got rid of X as soon as they could, we should switch!"
Or the management all read the same article in PC Magazine and lo! the next day did orders come down to implement said article, regardless. Waterworld, rowing the Valdez. Some years of this usually results in some number of half-baked or half-implemented systems scattered about production, and who knows which if any are actually used, or how much stink there will be to shutdown something unpatchable. Like why are there two wiki engines, sharepoint, three different database servers, …
I surely remember everyone does SOA, everyone does NoSQL, everyone does Hadoop, everyone does microservices, everyone does kubernetes,....
Not with the same pressure as everyone in the company (literally everyone, regardless of the job role) has to burn AI tokens, and attend forced AI workshops, still it is always running after the next new shinny.
Nobody wanted to admit that they had no idea how "AI" was going to help but nobody wanted to get left off the hype train...so they tasked their engineers to figure something out...by just asking them to spend as much as possible (As I explain this it just sounds stupider and stupider). Of course, spending willy-nilly is not a good way to find a profitable (or smart) idea, but that's a problem for future company bottom line.
I do find it to be true that with coding agents the famous quote from Jurassic Park goes through my head multiple time a day
"our scientists were so preoccupied with whether they could, they never stopped to ask if they should.
I've now come to the realization that if I'm having an llm work constantly all day writing code for me i'm probably doing something wrong as I'm no longer focusing on the core issue itself.
I may be in a minority here in that I write code to augment my self and not to ship to others so I can tell very quickly if I'm just gold platting something or if i'm actually delivering real value to my trading or risk management.
It feels like maybe the wheels are starting to fall off the AI hype train. I expect complete collapse once people start figuring out that the numbers on all this don’t make sense. I’m looking for investment portfolios that will weather that storm. If you are reading this and have a similar curiosity, this is a great place to start.
I've been thinking that for years about various sources and the bubble stubbornly refuses to pop on a convenient timeline so I'm falling back on the adage "time in the market beats trying to time the market". Index funds and chill is much more relaxed than trying to determine who's actually going to survive the AI bubble popping.
this is the reason I refuse to budge from my index portfolio besides small 'play money' ventures. My investing philosophy is basically by the time it hits wire you dont know what portion of it factored in. especially in age of AI and automation IMO alpha will vanish faster as anyone can code up eqv of a bloomberg terminal themselves. So all thats left is how do you manage downturns, when market heads down 30% but your handpicked stocks go down 60% you need to have enough wherewithal to hold through bad times. this is where true test of faith comes, I believe I would be cowardly and sell out at wrong time. So its best to just hold index and market sort the bloodbath out itself.
They'll expect fewer people to do more with even less, while "more" is continuously increasing.
When I say "more", I mean that the deluge that engineering teams deal with comes from two sources:
1) the business side of companies - marketing, sales, solutions teams, etc.
2) outside actors, mainly security threats
The first source can now move to generate work for engineering faster than ever. They expect the nerds to do what they're told and get the features out now. The more features, the better the product, right? The saving grace here is that they're bound by the same management concerns that engineering has. There's only so much money that they themselves can throw at generating more work for engineering teams, and that might also come under scrutiny from management, so that acts as a brake.
The second source has no such brake, especially not with security threats. Either there's good money to be made by holding company data hostage, or there's an endless supply of resources (read: nation-state resources) dedicated to the effort to attack the company's digital assets. And of course, they're using AI to enable this, just without the "but what about the shareholders!?" handwringing.
If you aren't very, very careful with your token cutting, you're going to put yourself at a disadvantage against that second group.
I never took tokenmaxxing to be about improving productivity directly; mundane feature work that comes out of it is just a side effect. I always saw it as a race between these big tech companies to get a generational advantage by being the one to discover the way of the future, with respect to harnessing AI to actually and truly automate software development.
EDIT: whoa, I used "way of the future" as a reference to Howard Hughes in "The Aviator", not this Way of the Future religious organization thing I just stumbled on; no intended reference there.
> by being the one to discover the way of the future
This is my understanding too. The underlying assumption is that action leads to information, iterations lead to enlightenment. So from an org's point of view, tokenmaxxing means encouraging everyone to explore as much as they can. Of course, token volume should not be the only metric - tokenmaxxing is just a catchy phrase.
Yeah this is right. Its about taking the throttle off of experimentation hoping some team suddenly starts shipping a years worth of features in a week and responding to strategic customer demands in near real time. Then copying what works out across the org. (and probably downsizing)
What those organizations miss is that they drown their teams in organizational red tape. The way to the future isn't tokenmaxxing - it's cutting back on process noise.
My impression of companies pushing AI so heavily is that they are basically being forced to do it by it merely existing. Imagine if AI really is as powerful as it is suggested to be and you didn't jump on the bandwagon. Then you would be behind. So by it existing and other companies using it, you have to as well because even if it turns out to be a failure at least everyone else will have failed too and you are on an even playing field.
You could kind of use that line of thinking to justify spending on anything.
There is a fad every few years that uses similar thinking to justify spending. Sometimes they work out sometimes they don't. But they never work out as good as the most optimistic predictions.
I commented on wasteful AI spending, and my father immediately started talking about how he "went through that a couple of times" as a manager at an oil company.
The unusual thing is perhaps how global and cross industry it seems.
> Sometimes they work out sometimes they don't
Genuinely asking: for which fads was it actually beneficial to jump in during the hype phase? Was there ever anything so critical that there was some huge disadvantage if you didn't adopt it right away?
ETA: I suppose the complicating factor, at least for B2B, is "customers demanding $fad", particularly when the purchasing decision makers don't actually understand what $fad is (e.g., "cloud", "blockchain", "ai", ...). If you don't become "$fad native" right away, you lose the Dunning-Kruger segment of the market.
Usually, someone extracts genuine value at the expense of everything else IMO. And the enshittification continues apace.
To the extent that figuring out the true automation will lead to layoffs, that knowledge will quickly spread as the laid-off knowledge workers are available for hiring.
Agree and I have wondered behind close doors if this is not the mental model. You need to spend money to see what is working this was simply a way to see that.
This is insightful. It does sort of feel like the search for the Northwest passage.
My hot take: The way of the future is local LLMs.
The AI equivalent of the PC revolution isn’t quite here yet, but it’s the only way forward.
I agree. We don't need a nondeterministic 10quadrillion vector model. We need an deterministic expert on our narrow business. Something small, that can be run on the 2026 version of the spare PC under the CTOs desk.
It will always be worse compared to a centralized approach where hardware utilization can remain high. Except in case which demand low latency which most development things do not need. It's okay if it takes an extra 100ms for a code review to take place.
Eh, I also saw it as a rather blatant attempt to undermine the bargaining power of the Software Engineer (which had grown to insane proportions over the years) - both in working conditions and raw cash money.
In many cases it really didn’t/doesn’t matter if the AI automation actually works, just that people think it could - and hence leave money on the table.
> insane
Not sure if you mean this in a good or bad way.
Good/bad entirely depends on if you are management/shareholders, or the engineer in question. It was pretty nuts either way though eh? End of an era
Now feels like a very good time to be a small team of experienced developers who can largely work on stuff by themselves and not a corporation of hundreds of developers of varying abilities all now trying to show how much code they can generate and how many tokens they can burn.
100% - I think that is also part of the divide you see online. Devs who work on massive codebases w/ 100s of engineers and see the bugs the LLMs create vs devs who work on smaller codebase w/ small <5 person team.
It's a tradeoff.
Generating a feature that is 90% correct in a tenth of the time is a reasonable tradeoff if you're trying to gain traction.
Generating a feature that is 90% correct in a tenth of the time, risking a multi-billion-dollar business, is a terrible tradeoff.
I think it's rather:
Small teams building continuously get to write features that are 90% correct in a tenth of the time.
Big enterprises get to write features that are 90% correct barely twice as fast, because all of the bottleneck lies elsewhere. They also spend more on AI per user because of the internal dynamics pushing people to adopt AI irresponsibly. They can correct the 10% of errors slower than small teams because of bureaucracy, increasing the cost of errors that show up in the product. Furthermore, they have less to gain from a given amount of speedup because they had plenty of engineering velocity anyway compared to small teams.
I don't think big enterprises will start winning from AI technology until AI truly can automate almost everything in a company and let said company outproduce competitors by burning tokens alone. That's nowhere near possible right now.
Even small teams are not immune from the AI push. I work in a small <10 people with 3 devs. I'm the only one not using AI while receiving push that I'm a problem for not. To the point, VP of company says if we don't all start using AI we'll be out of business because other people will. :face-palm:
To be clear I meant it's good to be small team on the assumption that we're all using AI... I honestly can't imagine not using AI in May 2026.
You don't use AI at all? What is your main justification for not?
I'd definitely say IntelliJ improved my productivity. No one cared. If anything, management viewed it as a nuisance as they had to deal with the license.
Now there is demands to justify not using AI like this, but people don't care about details. Which AI tool I use apparently doesn't matter at all, even if there are presumably productivity differences between them.
Edit: typo
Seems like you should have a justification for introducing a new tool instead, no? There are thousands of other tools they're also not using, but we're not asking about those.
I think the jury’s still out on how big an impact AI will have on overall, average productivity. But it’s definitely a productivity boost for someone who’s capable of writing code without it. If you want to be super conservative, don’t even have it write any code. Use it to search through existing codebases, review your code, find the root cause of bug reports, evaluate pros and cons of alternative approaches, etc. etc. You’re really missing out by not using it at all.
Here’s a concrete example of conservative AI usage: I use Claude to vibe code my nvim config. Now, who cares if my nvim config is AI slop? What’s the worst that can happen? Nvim works for me now way better than it ever did when I was limited by the time I was willing to spend configuring it manually.
Anthropic's annualized run rate is >$40b according to outside reporting. AWS hit that by Q4 2019. There were still debates on public cloud vs on prem at that time, but by late 2019 public cloud had facilitated the creation or adoption of entire categories of software within SaaS and PaaS, not to mention consumer internet businesses like Uber and Airbnb. The net impact of AI coding tools is far more ambiguous in comparison.
The profitability comparison is fraught but worth noting that by then AWS was already extremely profitable.
Amazon was AWS first customer. I sometimes feel if the AI companies promises of their models replacing all SWE, they would be a top 3 product/service in any number of businesses and not selling shovels to miners.
Anthropic will not have as many husbands as they think by next year: https://xkcd.com/605/
What has been the end result of all the tokens companies are burning?
Where does it show up in quarterly results?
I can’t see how it’s sustainable just based on “this feels more productive”
A friend of mine added some pretty extensive iOS UI tests to a keystone feature hit by millions every month. They'd been kicking the can down the road for years, trying to fit it in their roadmap, and with Claude running overnight they were able to bang out the whole suite in a week.
I'm not sure how it would show up in quarterly results.
I see these kinds of stories here a lot, and I'm curious whether they reflect a steady stream of need for AI coding, or whether a lot of companies have a burst of AI-appropriate coding work now that the technology is available and then will have a smaller need going forward.
Is it like the stereotypical dad who rents a power washer, powerwashes every exposed surface on his property, and then doesn't need to do any powerwashing for a few years; his neighbor who gets an Instant Pot and uses it for every meal for a month, then sees it gathering dust when the family gets tired of pressure-cooked stews; or like their neighbor who gets a microwave oven and uses it multiple times a day for decades?
I guess only time will tell.
So far where I work its the Instant Pot, at least for the non-devs. We rolled out Claude & Cowork to the masses after a brief pilot. It was about a solid month and a half of heavy usage and then suddenly usage fell off a cliff. Once it stopped being a cool new toy, people just didn't find a use for it.
A few mundane things got automated, but these were just back office admin type work. Nothing that's going to show on the P&L. Yeah those people now have a little more time for other things, but those other things are also not revenue generating. No FTE got replaced by it so in the end they just paid for a bunch of administrative positions to be a little less busy. Great for the workers who are now less stressed, but almost no impact on the business financials except there's now yet another subscription.
> So far where I work its the Instant Pot, at least for the non-devs. We rolled out Claude & Cowork to the masses after a brief pilot. It was about a solid month and a half of heavy usage and then suddenly usage fell off a cliff. Once it stopped being a cool new toy, people just didn't find a use for it.
Your employer is doing it wrong. You need usage surveillance with sanctions for low/declining use, then people won't stop using it.
Please tell me you're being sarcastic.
It's industry best practice. All the market-leading companies are doing it. Do you think you know better than them?
If there's anything I've learned as a software engineer, it's that agreeing with and defending the ideas of business leaders and Silicon Valley VC influencers proves I'm very intelligent.
this sarcasm is very disrespectful, you're mocking a sizeable proportion of the commenters on this site.
when I quote this comment later, with appropriate attribution, please know that I will be shaking my head and frowning while doing so
He is, but he's also describing reality.
That’s been my theory - there’s some low hanging fruit in every environment where AI knocks it out of the park. Then complex brownfield reality (coupled with non-technical factors) rears its head and the stunning productivity gains are nowhere near to be seen.
That’s the explanation how you can have both the anecdotes of amazing AI productivity and rigorous studies showing anything from actual loss of productivity to single-digit gains.
I also think in addition to that the increased speed compounds the problems much quicker. And I don't mean bugs. I mean that duplicated code here, that additional state variable to keep everything going there. Not removing things that should be removed because we can work around that, etc.
It's like building a super tall Jenga tower very quickly but laying the bricks much worse than a careful player.
I think this is directionally right.
The code AI produces is not created equally, not even close.
Nah in competitive industries you need to build features and out compete people and getting AI to do that whilst architecting things well due to experience and having time to think more about the important stuff but have a lot of the more boilerplate and simple things ABs plumbing etc handled by agents is great.
When you try to replace your entire brain with AI things are going to go wrong.
> or whether a lot of companies have a burst of AI-appropriate coding work now that the technology is available and then will have a smaller need going forward
For the product my friend works on, it's definitely the latter. I definitely don't expect this party to last forever.
Some measures should have real, tangible, concrete numbers; others should have “my friends are saying”/“you are blind if you are not seeing it” vibing.
Over here our CTO replaced Intercom with an internal equivalent that costs less than $20 / month to run, haiku and sonnet support agent costs included. In less than a few weeks, in his spare time.
In my limited experience with using agents to create tests it tends to code the tests to the existing code instead of ensuring the correctness from a spec. Great for regression testing but still limited in effectiveness for catching existing issues.
It wouldn't, at least not directly. That's why it wasn't done pre-AI.
Even if Uber really did double developer productivity, would it translate to quarterly results?
Ultimately they make money selling rides, not selling software. The Uber app is mature and adding new features is unlikely to significantly increase sales.
Writing 2x more code doesn't translate to 2x more revenue unless it results in 2x more rides.
> Even if Uber really did double developer productivity, would it translate to quarterly results?
It would if it meant they then fired half their software engineers, which is the ultimate goal.
Lowering costs to run the infra would show up as increased profits without any change in rides.
How much is infra spending compared to revenue/other expenses? I honestly can’t imagine it’s that high, they’re not running something like Netflix or YouTube… But maybe I’m underestimating it.
Do you really need AI for that? Seems like the thing any existing engineering team could do if it was prioritized.
Would it? How much of Uber’s cost is software infrastructure vs paying humans?
In the big red number shown after revenue where profits used to be.
Advancement in AI research seems to be the only thing at this point.
Probably shows up in OpenAI and Anthropic quarterly reports. I have to wonder if that was the point.
> Where does it show up in quarterly results?
Standard answer is "companies that are not seeing significant gains from AI just aren't AI-ing hard enough, trust me bro".
Didn't google say that AI had increased their company's productivity by 10%? if that's the case, then how can they justify spending 50% to 100% of wages on it?
This doesn't account for the fact that review is still a bottleneck, engineers understand much less of the code they're shipping and there'll likely be tech debt they'll have to unwind in the future.
There's also the issue that in any large-ish org, code production is hardly ever the bottleneck.
I like the qualification of "large-ish". When someone says code is not the bottleneck, I assume they work for a large company. To be fair, at a start-up developers think they code at least 4 hours a day, but it never averages that either.
I am not sure how uber is operating internally around the use of tokens but if they actually shipped features faster than before then it is still a win. if they learn that users don't want these features or want a different version of it; they have learned this new knowledge faster than they would have if they manually coded those features, which means in principle you should be able to iterate faster. but this will collide with creative ceiling that humans exhibit in a span of time and on top of that uber is prioritizing spending money on tokens over humans which seems like a mistake. you need humans for creativity.
I used Uber for the first time in like 8 years recently, and near as I can tell it's the exact same thing it was. What features are they even adding at all much less that anyone cares about? You ask for a ride to a place, a driver shows up, money is exchanged - the end?
Sometimes things are actually just finished. They don't need to treadmill.
They have to make it easier for ubereats orders to have parts of their bill attached to a corporate card and half attached to someone's personal card and be able to split the invoices!
> if they actually shipped features faster than before then it is still a win
Depends on the cost
Dupe: https://news.ycombinator.com/item?id=48268871
go ahead
I still get picked up by an Uber the same way. As an end user, nothing has changed for me.
So I wonder what the heck were all those billions of AI tokens burnt on that they extinguished it in just 4 months into the year?
This argument is funny because you could have said the same thing 4 years ago: Uber still picks you up just as it did years before that, so what did all those millions spent on developer salaries get them?
Uber’s business is relentlessly confusing for people who think it’s a simple app to send an alert to a nearby driver to pick you up.
Uber operates at a scale where there are no trivial problems because even small changes can impact hundred of thousands of customers. They can also justify spending time and money on new features that only 0.1% of customers might use because 0.1% of their customers is a very large number.
Uber also has to maintain thousands of region specific rules and features to be able to operate globally, and they do it all in the same app instead of having specific regional versions (which would be a terrible user experience for frequent travelers). That alone is a ton of work the end user will never see but is core to their operation.
This is not just me saying it. The Uber president himself says it in the article.
> so what did all those millions spent on developer salaries get them?
There was no doubt about what these developer salaries got them. It was to keep Uber stable and running in thousands of jurisdictions with varying rules/regulations.
The idea of using AI was (I hope) not just to replace developers for this purpose but to also ship features/products beyond what was already being offered. It has however not panned out as these CEOs/execs thought it would.
> They can also justify spending time and money on new features that only 0.1% of customers might use because 0.1% of their customers is a very large number.
And what are those features exactly? Because even the President of Uber doesn't seem to know:
"“That link is not there yet, right? I think maybe implicitly there is more that is getting shipped, but it’s very hard to draw a line between one of those stats and, ‘Okay, now we’re actually producing 25 percent more useful consumer features,’” said Macdonald."
The budget allocated to AI for the year has been wiped out in 4 months.
Apparently:
* In App Hotel bookings in partnership with Expedia.
* Travel Mode with suggestions on where to eat and visit when travelling.
* Eats for the way - your driver picks up a takeaway for you to eat while they drive you to your destination.
* Voice bookings using AI and speech to text.
How did we ever live without them!
> Eats for the way - your driver picks up a takeaway for you to eat while they drive you to your destination.
This seems like the kind of terrible idea that an LLM might have come up with. I'm pretty sure most drivers do not want people eating (especially a whole meal) in their car, and I can't imagine a lot of instances where you're calling an Uber and don't have time to get yourself food, but don't mind waiting an extra 10 minutes for the driver to detour, find parking, and wait for your food.
Not to mention what anyone who's worked in an office with a shared kitchen can tell you - the smell getting into a car where an indeterminate amount of people have eaten different meals. Like climbing into a food court dumpster.
> I can't imagine a lot of instances where you're calling an Uber and don't have time to get yourself food
Recently I got a car to take me to the train station and picked up food on the way. Seems pretty common to me. Of course, I didn't need or want it charged as a premium feature in the app.
I have never heard of someone doing that tbh, this is the first time.
You can get any Uber driver to do it for you if you offer to buy them food too and do it off the books.
Holy fuck, aside from the voice bookings, that's some useless shit to spend money building as far as both tokens and salaries go.
Are they profitable yet lol
This seems like the doom of all tech companies that hit a single kernel of a good idea, hire a big development team to build it, and then, once it's running well and making money, leadership looks around and sees this big body of developers, product managers, project managers, QA, and management tree, looking around for something else to do. Then, instead of saying, "Let's find the next big thing to do," they say, "Cram dozens more things into the thing that already works. Anything you can think of, spin up a team of 10 to bolt it onto the main product. Move things around to make everything fit. Run experiments on users to see if this new crap moves the metrics. A/B test to see what we should keep and what we should silently remove next update. Attach this other company's product that we just bought."
In a few years, what do you end up with? The modern version of every single fucking app we use today.
Well, travel booking is one of those things every company wants to get involved with because it's just straight referral fees. I get advertisements to book travel through my phone company (T-Mobile US) and a slew of financial services companies.
If it's easy enough to add to the app and sticks around for a while, it may well be profitable even if only a small percentage of customers use it or even realize it's available.
they are very profitable now!
For context, this is an interview where Uber CEO discussed these ideas:
https://www.theverge.com/podcast/922909/dara-khosrowshahi-ub...
Can't say I am convinced.
There's probably tons of backend projects going on, expanding in countries, payments, complying with regulations, effeciency and reliability projects. They also do food delivery. There's a whole engineering team to support
You don't need justification to spend other people's money!
Nobody's going to jail.
I actually think Chinese models have already popped the bubble, we just dont see it yet. the only way to justify AI IPO market cap is basically if they get to hold most sw industry code hostage and then token-flate budgets to collect AI tax. short of that AI expense would very quickly mean revert to model + some margin. this means the moat for AI 'trillion club' is gone. In fact AI virtually guarantees that there is no execution-moat left anywhere, definitely not in code or that engineer with knowledge about that obscure mechanism. without the moat most of the sw ecossytem's margins would shrink (as they should).
Ironically enough the only moat left would be what you can buy from Washington.
Goodhart's law strikes again. Stop giving your engineers token-burning quotas or they'll burn tokens.
I really don’t understand on the customer side of B2B why so many companies actively encouraged AI tooling costs.
I can understand it from the side of the companies selling tokens and AI hardware. I don’t understand the race to spend more on internal tools.
I’ve been sitting around waiting for my company to buy a number of necessary bits of tools. They cheap out on every solution imaginable. Datadog is too expensive, let’s buy a cheap solution that costs us months of setup time. Configuration management is too expensive, let’s use the free version with no audit trail or dashboard.
But everyone…in the entire company…gets multiple AI tool subscriptions.
I don’t remember investors being this stupid at any other point. I don’t recall investors pressuring my company to use blockchain or NFTs.
The logic is quite simple. Management thinks that AI can improve productivity, but knows that there will be some resistance and some learning curve. So they force people to use it so that people can 1. develop their skills and workflow and 2. find out where it is useful 3. find out what needs to be improved to make it useful.
As a more obvious example consider that cars were just invented and the post office management thinks that they could improve performance of letter carriers. But right now cars are slow, break down a lot and there isn't much infrastructure for them. Lots of letter carriers will (rightly) think that it is a waste of time because they need to get in, stop, park between every house and they break down so often it isn't worth it and half of their route is unsuitable for a car anyways. But if cars are forced for a while they will find out what routes work well for cars and which don't, improve the cars and related infrastructure to make cars more effective and other improvements to unlock more productivity.
So yes, right now management is wasting money on cars and gas for no increased productivity. And yes, measuring how much gas each employee uses and encouraging to use more is obviously stupid in isolation. But the idea is to force adoption to iron out the kinks and find out where it can improve productivity. It is basically funding a research project.
IMO, the root of all of this is the almost total inability for most managers, and most eng orgs to measure individual engineer output in any useful way. And in particular in a way that lets you reliably compare engineers to each other.
Despite decades of the industry telling itself that we "pay for performance" or whatever, that has never been the case because we can't really measure performance very well. Where I have seen it done ok (not great, just ok), it was massively labor intensive and did not last, and was only done fully when considering promotion.
So, as you observe, now we have some new technique that managers are sure will increase performance by 50+%, if only people would use it. They can't just raise their expectations of performance by 50%, because they can't measure performance to within 50%! So, they measure the thing they can: token consumption.
That would be sensible if you were a car maker and not in the business of delivering posts. You’re tanking your core business, just to do what the developer of the tool should be doing (ensuring the usefulness and reliability of the tool).
I’m all for a trial run, but it needs to be done like any research experiment. With a goal and measurements along the way. Not by going blind and hurting your workers/customers.
How will companies like Uber continue to fund the “research” when the budget ends up burning 3x faster than predicted without being able to observe measurable gains?
Nothing that C-execs and management advocates for has made any sense for a long time now. If this is the first you're starting to question it all, I must ask what rock you're sleeping under because I desperately need a really good nap...
I knew they were stupid, I just didn’t know it got way down to this level
Strategy - just doing what your friends on the golf course are doing.
The number of times I have been told "oh I talked to so and so and they are having SUCH a good time using X" and then three years later "oh I talked to so and so and they got rid of X as soon as they could, we should switch!"
Or the management all read the same article in PC Magazine and lo! the next day did orders come down to implement said article, regardless. Waterworld, rowing the Valdez. Some years of this usually results in some number of half-baked or half-implemented systems scattered about production, and who knows which if any are actually used, or how much stink there will be to shutdown something unpatchable. Like why are there two wiki engines, sharepoint, three different database servers, …
I surely remember everyone does SOA, everyone does NoSQL, everyone does Hadoop, everyone does microservices, everyone does kubernetes,....
Not with the same pressure as everyone in the company (literally everyone, regardless of the job role) has to burn AI tokens, and attend forced AI workshops, still it is always running after the next new shinny.
Nobody wanted to admit that they had no idea how "AI" was going to help but nobody wanted to get left off the hype train...so they tasked their engineers to figure something out...by just asking them to spend as much as possible (As I explain this it just sounds stupider and stupider). Of course, spending willy-nilly is not a good way to find a profitable (or smart) idea, but that's a problem for future company bottom line.
I do find it to be true that with coding agents the famous quote from Jurassic Park goes through my head multiple time a day
"our scientists were so preoccupied with whether they could, they never stopped to ask if they should.
I've now come to the realization that if I'm having an llm work constantly all day writing code for me i'm probably doing something wrong as I'm no longer focusing on the core issue itself.
I may be in a minority here in that I write code to augment my self and not to ship to others so I can tell very quickly if I'm just gold platting something or if i'm actually delivering real value to my trading or risk management.
Affordable inference will be around longer if more Big tech companies cap their AI sending.
It feels like maybe the wheels are starting to fall off the AI hype train. I expect complete collapse once people start figuring out that the numbers on all this don’t make sense. I’m looking for investment portfolios that will weather that storm. If you are reading this and have a similar curiosity, this is a great place to start.
https://portfoliocharts.com/2021/12/16/three-secret-ingredie...
Market makers are not going to let anything collapse, there is not going to be a "storm".
The government and everyone with any money/power are fully invested in keeping the market going regardless of any kind of reality.
"Every American child under 18 with a Social Security number can have a federally recognized "Trump Account," a one-time $1,000 IRA seed deposit"
By doing this every citizen will personally have skin in the game and want markets to continue to rise.
I've been thinking that for years about various sources and the bubble stubbornly refuses to pop on a convenient timeline so I'm falling back on the adage "time in the market beats trying to time the market". Index funds and chill is much more relaxed than trying to determine who's actually going to survive the AI bubble popping.
this is the reason I refuse to budge from my index portfolio besides small 'play money' ventures. My investing philosophy is basically by the time it hits wire you dont know what portion of it factored in. especially in age of AI and automation IMO alpha will vanish faster as anyone can code up eqv of a bloomberg terminal themselves. So all thats left is how do you manage downturns, when market heads down 30% but your handpicked stocks go down 60% you need to have enough wherewithal to hold through bad times. this is where true test of faith comes, I believe I would be cowardly and sell out at wrong time. So its best to just hold index and market sort the bloodbath out itself.
hot take: token spend can be used a honey pot, especially when compared to what you deliver. spend accordingly!
My concern here is that they'll mix two things:
1) workforce reduction
2) AI spend (reduce tokenmaxing)
They'll expect fewer people to do more with even less, while "more" is continuously increasing.
When I say "more", I mean that the deluge that engineering teams deal with comes from two sources:
1) the business side of companies - marketing, sales, solutions teams, etc.
2) outside actors, mainly security threats
The first source can now move to generate work for engineering faster than ever. They expect the nerds to do what they're told and get the features out now. The more features, the better the product, right? The saving grace here is that they're bound by the same management concerns that engineering has. There's only so much money that they themselves can throw at generating more work for engineering teams, and that might also come under scrutiny from management, so that acts as a brake.
The second source has no such brake, especially not with security threats. Either there's good money to be made by holding company data hostage, or there's an endless supply of resources (read: nation-state resources) dedicated to the effort to attack the company's digital assets. And of course, they're using AI to enable this, just without the "but what about the shareholders!?" handwringing.
If you aren't very, very careful with your token cutting, you're going to put yourself at a disadvantage against that second group.