13 ms·

Uber’s COO says it’s getting harder to justify money spent on tokenmaxxing

293 points by _____k 5mo ago

gigatexal 5mo ago
I find it useful that if they cut the use altogether I will pay for it out of pocket.
- sottol 5mo ago
  Maybe that's the plan :) But on a more serious note, do we know how much Uber spent per technical employee/month? I assume it is far more than even any of those $200 "max ai" plans. And the other question is how much the public would be willing to spend, in my estimation this is as "cheap" as it will ever get (main-stream at least).
  - KronisLV 5mo ago
    > I assume it is far more than even any of those $200 "max ai" plans. Am in a random small company, colleague spent 100 EUR a day on Sonnet through AWS Bedrock (needed to use a EU region). Paying for tokens will get you in a deep hole financially compared to any of the subscriptions, unless it's like DeepSeek or one of the other models that are priced a bit better, though that's also a tradeoff in what they can/cannot do and also where the data goes. Ended up trying out the Mistral subscription for the US stuff btw, it was fine.
  - Marciplan 5mo ago
    bigCo’s don’t get to do the $200 Max plans, they have unlimited plans but get charged like API
    - sottol 5mo ago
      Exactly. But I did find an article ([1]) and spend doesn't seem that high per engineer ($150 to $250 per eng) - at least on average, I assume the costs were skyrocketing towards the end. > Adoption climbed from 32 percent of engineers in February to 84 percent classified as agentic coding users by March. By spring, 95 percent of Uber engineers used artificial intelligence tools monthly, and roughly 70 percent of committed code originated from those tools. About 11 percent of live backend updates were written by agents with no human in the loop, according to Uber's own disclosures. > The numbers behind the spend are what make the story instructive rather than anecdotal. Monthly cost per engineer ranged from $150 to $250 on average, with power users running between $500 and $2,000. My guess is that the reason to rethink AI-spend was probably the exponential growth in cost over time, and tokenmaxxing payoff not being immediately obvious as mentioned in the article. [1] https://www.forbes.com/sites/janakirammsv/2026/05/17/uber-burns-its-2026-ai-budget-in-four-months-on-claude-code/ https://www.forbes.com/sites/janakirammsv/2026/05/17/uber-bu...
- iwontberude 5mo ago
  Except you won’t because they will threaten to fire you and force you to route all of your AI through data protection proxy to stop exfiltration by filtering and tracking prompts/response tokens.
- mattlondon 5mo ago
  Probably long term each dev gets their own GPU and runs a model locally I expect. Seems like a more sustainable approach, even if a local model is not absolute SOTA.
  - ianm218 5mo ago
    GPUs are much more efficient at parallelizing requests for LLMs so it's going to much more efficient to centrally host. Maybe big companies it would make sense to get their own though.
- throwaway613746 5mo ago
  [dead]
- dghlsakjg 5mo ago
  Would you decide its usefulness based on how high the bill is, or how many things you get done while using it? The former is the issue, and how many companies have been operating. It's like a trucking company ranking driver effectiveness by fuel used instead of by cargo moved.
  - gigatexal 5mo ago
    The former. I’m able to get more Tickets done with it than without.
    - dghlsakjg 4mo ago
      I think that’s the point being made by the CEO. Measuring token usage isn’t a good way to measure effectiveness. They should be counting output (tickets on your case) instead of input (AI spend).
  - deleted 5mo ago
    [deleted]
illithid0 5mo ago
>"He said that, based on talks with Uber's senior engineering leaders, he realized higher token usage did not translate into a proportional increase in useful consumer features." Goodhart's law strikes again at someone with enough power to be both ignorant of it and make others suffer their ignorance. You cannot simply measure productivity by tokens spent just like you can't measure it by hours spent in a chair at a desk.
- colechristensen 5mo ago
  You can measure productivity by hours spent at a desk?
  - batch12 5mo ago
    You can measure attendance by hours spent at a desk
    - devttyeu 5mo ago
      Well if you're a devshop just billing hours of mostly low impact work then hours are very much equal to productivity.
      - saghm 5mo ago
        Next time you're going to work for an hour, ping me, and I bet I can surprise you with how much less productive I am than you
  - epolanski 5mo ago
    Productivity is measured by economists in $/hour. Which is why two identical jobs with the same real life output have drastically different productivity. A nursing home in Luxembourg has 5 times the productivity of one in Romania despite the services being identical and tech-unrelated.
nekzn 5mo ago
It’s funny that “maxxing” entered the common vocabulary.
- chihuahua 5mo ago
  If you're not tokenmaxxing, you're getting tokenmogged on the AI leaderboard, and your next review ain't gonna be pretty.
- internet2000 5mo ago
  A good 80% by volume of the modern vernacular is 4chan language that got sanded down.
  - nekzn 5mo ago
    Sanding down is how we got goyslop turned into slop.
    - harvey9 5mo ago
      Slop is a word in its own right which got the goy prefix later in life.
- amirhirsch 5mo ago
  I like this too. I have been intentionally -maxxingmaxxing to get the meme out there. It's a good canary to sort out who gets the spicy takes from the pedestrians who probably still copy-paste into the ChatGPT web app like a psychopath.
7777777phil 5mo ago
As soon as tokens stop stop being subsidized, heavy agentic use will become as least as expensive than paying an (entry level) employee. When this happens many companies will trade off havy tolen usage for (maybe a bit slower, bit less accurate) employees again.
- cryo32 5mo ago
  This is what I’m betting on. The financials don’t make sense now. Based on the expenditure the finances won’t ever make sense.
- Wowfunhappy 5mo ago
  DeepSeek is an open weights model. It's possible the hosted versions are subsidized, but we know what it costs to run locally. And it's expensive, but it's also pretty clearly cheaper than an employee. Of course, the latest DeepSeek models are not as good as Claude, but they're not super far off either.
  - irishcoffee 5mo ago
    They're not far off, getting the same seamless integration as hosted models is a full time job. I think what just happened is that devops is about to explode. What will naturally follow is local hosting of all the things when people realize subscription costs for cloud-whatever are absurd. Gitlab is going to take off? This is not investment advice.
    - Wowfunhappy 5mo ago
      > What will naturally follow is local hosting of all the things when people realize subscription costs for cloud-whatever are absurd. Even acknowledging we don't know exactly what costs would look like in a world without VC money, wouldn't hosting models logically be cheaper to do at scale in a data center? When I compared to the cost of running DeepSeek locally, I meant that we can treat that cost as a price ceiling, not the floor.
      - 5mo ago
egypturnash 5mo ago
[flagged]
- epolanski 5mo ago
  Slightly ot, but I really dislike this reddit WSBization of HN. Adds nothing insightful to these discussions.
  - cwillu 5mo ago
    “Please don't post comments saying that HN is turning into Reddit. It's a semi-noob illusion, as old as the hills.” --hn guidelines (there are links to examples in the original)
  - noman-land 5mo ago
    It's unfortunately the WSBification of the entire society.
chihuahua 5mo ago
It's amazing that it took months to figure this out. "Well we thought that if engineers are told to maximize costs through AI use, to consume as much as possible of a resource that costs us money, then obviously good things will happen. Imagine my surprise when it didn't turn out that way." Imagine if engineers were ranked based on their AWS spend. People allocate VMs and fill databases with terabytes of random bits, to get to the top of the AWS leaderboard. If you don't do this, you're ranked at the bottom, and good luck at the next review cycle. Who could have expected that this is not the road to success?
- solenoid0937 5mo ago
  You say "amazing that it took months to figure this out" as if the answer to the question is obvious. But it's not. Some FAANGs are doing amazing things with unlimited tokens. Other companies have no clue what to do with tokens, they've just told their engineers to max them. It really depends on how you're using the tokens. If you're just using them for Codex and Claude Code - yeah, tokenmaxxing is incredibly dumb.
  - steveBK123 5mo ago
    > Some FAANGs are doing amazing things with unlimited tokens. Others have no clue what to do with tokens. Unlimited tokens is different from “use AI a lot or we will fire you, and we are counting token consumption as usage”. Obviously the latter is stupid and yet it was done in many places.
    - deleted 5mo ago
      [deleted]
    - SpicyLemonZest 5mo ago
      I'm not convinced it actually was done in many places, although I understand why in a bad job market people don't trust that it isn't happening in secret. Every time I've heard of a token leaderboard or such it's come with a denial that the company is using it as an employee performance metric.
cryo32 5mo ago
Waiting for tokenedging next.
- SecretDreams 5mo ago
  Is this when you type the prompt into the text window, but don't hit enter? Make the GPU see the message "x is typing"? Lol.
  - FartyMcFarter 5mo ago
    As long as there's an RPC connection established and a partially sent request, I think it would count.
- postsantum 5mo ago
  ^ Philip K. Dick's unreleased book title
Rohunyyy 5mo ago
Now we are going to get a new profession. Token Engineer! They will be experts on tokenmaxxing! The job growth that the billionaire CEOs promised us from AI is finally here!
- fsloth 5mo ago
  Well there are already offerings like githits (https://news.ycombinator.com/item?id=46105112 https://news.ycombinator.com/item?id=46105112) that sort of promise optimize bang-per-buck of inference
izanton 5mo ago
What if... we stop for a moment, and then, after thinking for a moment, we stop hammering nails with a microscope, and stop using token usage as a metric of productivity? I know it's sounds stupid, but what if
- tekno45 5mo ago
  Not very Billion Dollar Valuation of you.
  - deleted 5mo ago
    [deleted]
  - themafia 5mo ago
    Yes, but, I sleep very soundly at night.
- devin 5mo ago
  The people who have ascended to leadership positions are deeply divorced from reality. "It is difficult to get a man to understand something, when his salary depends on his not understanding it." -Upton Sinclair
  - lorecore 5mo ago
    The crazy thing is their salary does not actually benefit from riding these trends. Unless it's equally/even more clueless board level pressure with ulterior motives (i.e., lifting their other AI investments or the sector as a whole).
    - repeekad 5mo ago
      Every c suite in the country is panicking about being left behind, from their perspective it’s either token max or fade into obscurity, or at least that’s what they were sold
      - 5mo ago
FartyMcFarter 5mo ago
If any company announces that they use token consumption as an employee performance signal, for me that's close to a red flag to stay away from that company. No company with good engineering leadership should act like this is remotely a good idea.
- LaurensBER 5mo ago
  Tokens are the new "lines of code per engineer". Easy to graph, easy to "manage".
  - KellyCriterion 5mo ago
    ...and easier to bill! Back, then noboday had the idea to charge per "lines of code", but today it seems accepted to charge per words processed?
  - mig39 5mo ago
    The new TPS reports!
    - suprfnk 5mo ago
      Oh, so that was actually a Token Per Second report! Wild!
- an0malous 5mo ago
  I worked at a YC company that was doing this and left last month. I wonder where this all started from, VCs and tech execs are such a monoculture
  - ben_w 4mo ago
    The where may be the decision makers chasing social media trends. A friend sent me a link to this, this morning, about devs rather than managers, but I suspect it's the same: https://youtu.be/IW3Sbe0Hbgg https://youtu.be/IW3Sbe0Hbgg
  - 4mo ago
jhack 5mo ago
Maybe don't use the most expensive models on the planet? Maybe use AI like a tool and not this black box that grants wishes?
- dgellow 5mo ago
  Sounds like you want to be in the next round of layoffs?
- deleted 5mo ago
  [deleted]
- onlyrealcuzzo 5mo ago
  I think companies are reluctantly realizing that AI is not a magic genie in a bottle, and is instead a tool. Still very valuable. They just need to have strategies that match what the tools are capable of - not strategies that involve "rub the magic lamp and increase profits 80%". If the market is rewarding companies going after the "rub the lamp" strategy, they're going to say they're doing that to juice stock prices. Maybe the market is finally realizing blindly spending billions on LLMs with almost no strategy is not a good strategy. Who knows.
  - bandrami 4mo ago
    > Still very valuable You sure about that? Both labs and tech companies have been desperate to show ROI on LLM use and nobody can seem to
- overfeed 5mo ago
  But the executives need the fanciest models to evaluate how well they can replace the expensive labor costs.
irishcoffee 5mo ago
I just realized my company is months behind this curve. About to blow my token allocation. Before I do, anyone have requests? Sincerely.
- kibwen 5mo ago
  I hereby suggest you take the fragmentary excerpts of the infamous erotic stage play The Lusty Argonian Maid shown in The Elder Scrolls series of games and extrapolate them to 100,000 additional full-length acts.
deleted 5mo ago
[deleted]
pocksuppet 5mo ago
what the fuck is this timeline I am stuck living in
crorella 5mo ago
Tokenmaxxing makes no sense, it is akin to write extremely inefficient SQL / Spark Jobs, full of cartesian joins, ultra skewed datasets, etc, just for the sake of using as much compute / memory / IO as possible. This always happens when the metric becomes the goal, companies should nurture and foster an environment where AI is used in the most efficient way possible, first asking "do we really need an agent for this" and if so, what kind of agent is needed, what model, reasoning level, etc. They should also promote projects that aim at saving tokens, increasing cache hits, codifying the information in ways such they use as less context as possible (graphs of knowledge are pretty good for this!)
- InsideOutSanta 5mo ago
  It's toddler-level logic. "You can achieve positive outcomes by using X. Therefore, we need to use as much X as possible to maximize positive outcomes." It's like trying to win a race by setting a gas station on fire.
- SpicyLemonZest 5mo ago
  The argument in favor of "tokenmaxxing" has always been that it's creating space for employees to freely explore the broad and novel space of AI-enabled workflows. I've seen a number of use cases where I'm skeptical any value is being produced, but a number of others where some team or another has finally solved a long-standing problem of theirs with an agentic workflow that would have been hard to justify to a cost review committee. > They should also promote projects that aim at saving tokens, increasing cache hits, codifying the information in ways such they use as less context as possible (graphs of knowledge are pretty good for this!) My understanding is that most big "tokenmaxxing" companies do have teams who are working on this in the background.
  - onesociety2022 5mo ago
    +1 I find the general disdain for C-suite or senior engineering leadership on HN so silly. These people didn't get promoted or hired because of nepotism. A lot of them moved up the engineering ladder and are familiar with how software engineering works and the incentives involved. Yes, some of them are sheep and will blindly copy what is fashionable but so do a large swath of ICs. If you want incredibly fast adoption of AI within a company, the best thing you can do is to signal from the top that tokenmaxxing will be rewarded (or at least not be punished for it). 1. It forces everyone including the lazy ones who normally wouldn't invest their time in learning anything new to actually install codex/claude and learn to use them. 2. It prevents any middle manager from putting up blockers for adoption/experimentation ("this is new, I don't trust this, let's do it the old familiar way", "this might be expensive, we care about efficiency here", etc). Once the C-suite dictates tokenmaxxing is allowed, every middle manager will fall in line instantly. 3. Tokenmaxxing is not choice you have to live with the rest of your life. A year or two from now, once C-suite is satisfied with the rate of AI adoption within their org/company, they can just as easily switch the focus to efficiency. Teams will be asked to justify their token spend and start to optimize.
simonw 5mo ago
I'd be interested to know if this is about individual employee AI usage, or use of AI tokens in production features, or both - and assuming both, what the split is. I can see how Uber could burn unbelievable amounts of tokens if they start running internal features that run a bunch of prompts against every completed ride, or every customer profile, for example. Or maybe this is about employee usage, but they introduced some stupid "you get evaluated on how many tokens you used" thing a couple of months ago when that was trendy and are just beginning to notice how much that cost?
- devin 5mo ago
  IMO, it's undoubtedly both. The number of product teams who have shipped expensive-to-operate AI features is wayyyy up there, and for many of the scenarios I've seen, customers simply don't care or are unwilling to pay significant premium for access to it. At the same time I'm starting to see some direction from people in leadership that I should "use the right model for the job" and things along those lines, which is a very, very different line from what I was hearing 12 months ago. My continued prediction is that we are going to see a tweak on the SaaS model where the sweet spot moves to metered usage pricing of really fine-grained API-based access for apps which traditionally have been operated solely via the UI. Long term the trend is going to be "we'll house the data, enrich it, maintain it, provide fine-grained API access over it tailored to model usage, and you bring the model" with some services opting to give you the model interaction layer/harness. IOW I don't think SaaS is dead. Far from it. However, I do think that a lot of people are going to be looking to interact with SaaS apps via their own models with APIs that support those use cases better than a lot of those APIs do today.
  - oa335 5mo ago
    > we'll house the data, enrich it, maintain it, provide fine-grained API access over it tailored to model usage, and you bring the model isnt this just mcp servers hosted by the saas provider?
    - devin 4mo ago
      Eh, sort of. I think a lot of API surfaces are too chatty for LLM consumption, or do not provide the primitive API surfaces that compose what they currently offer today. MCP in the middle doesn't give you that for free. The data shapes are different, the surfaces are probably necessarily wider in a lot of use cases, efficient retrieval of small bits of data that feed aggregates, etc.
lorecore 5mo ago
Not all tokens are created equal. It's easy to use a ton of tokens by having agents work together in parallel. That's basically the equivalent as people spending time in meetings, hardly a productivity win. As with everything in development, results matter, how you get there doesn't (unless you're a bad manager).
JackDanMeier 5mo ago
At what point is there a difference between a burn rate and tokenmaxxing? Isn't it the same as during the dotcom bubble?
paulpauper 5mo ago
many of these leading AI companies are operating at large losses and subsidizing users with VC money. Profitability will entail having to impose greater limits and raising prices, so this will reduce to some degree the value proposition of AI compared to humans.
rcvassallo83 5mo ago
Oof leader of bubble are starting to take a step back?
mrkeen 5mo ago
I always used to wonder this about software stacks even prior to LLMs, but it seems more relevant now somehow: When will Uber (or your favourite company) be 'done'? They've been writing software for 16 years. They match drivers to passengers. More software isn't going to increase the chance that I seek them out instead of taking a bus or train. Will their software be finished in 20 years? 80?
- goldenarm 5mo ago
  Most of the codebase is custom integrations for local markets. You can systematize some of it but most of the complexity comes from there.
  - SoftTalker 5mo ago
    Can you provide an example? What is different about running Uber services in Chicago vs. Indianapolis?
    - iLoveOncall 5mo ago
      For example in Seattle you pay county fees, and then state fees, and then maybe special fees if you were picked up in the airport. I took a ride from SEATAC to my hotel in downtown Seattle and besides the ride itself, there were 5 other items on the bill, 4 of which are specific to the place I used Uber. Then I had the return trip from my hotel to SEATAC, on this one I got EIGHT items on the bill, on top of the ride fare. Some specific to Seattle itself, some specific to the road that the Uber took (a tunnel fee - which is different based on the direction you take it in), etc. So the real question is what is NOT different between two locations. Less than 15% of the bill. I also took Uber in India, where you have to share a one-time password with the driver for example, which I've never seen in any other country. In some other countries the Uber app exists but Uber drivers are actually taxis, so you're actually ordering a taxi via the app.
      - SoftTalker 5mo ago
        Ah local regulations and fees. Not so much the core service algorithms. That makes sense.
yapyap 5mo ago
wtv
bilater 5mo ago
The black bill that is coming that nobody is prepared for is that the value of a token varies greatly depending on the human. Companies will quickly find out its much better to give your top 10% engineers a lot more tokens and lay off your average engineers. The 10x engineer will become the 1000x engineer. Wrote about this and the impact of to jobs here: https://x.com/deepwhitman/status/2058324179506831372 https://x.com/deepwhitman/status/2058324179506831372
- archagon 5mo ago
  Lol, no, no one’s becoming a 1000x engineer.
- danny_codes 4mo ago
  Why stop there, you should be a millionX engineer. No, a billion! We can make up all the numbers!
InsideOutSanta 5mo ago
"He said that, based on talks with Uber's senior engineering leaders, he realized higher token usage did not translate into a proportional increase in useful consumer features." He's saying that like it's some grand epiphany and not the most self-evident, obvious thing I've heard this month. Some of the literal dumbest people on earth are in charge of these major companies.
- Barrin92 5mo ago
  >obvious thing I've heard this month not only this month, but it is the basic statement of the single most well known 50 year old book in software project management lol. At this point we need to wipe the slate clean and start over, the industry is run by illiterates.
- amluto 5mo ago
  This is also Uber we’re talking about. The company that famously developed a massively engineered ledger to track every event across the entire company, globally consistently, forever, in a single database. This definitely adds enormous value to the bottom line!
  - smoofles 4mo ago
    The fact that a company with such a ledger has trouble advocating for AI-maxxxing will make watching the "ur holding ur AI wrong bro"-reactions all the more hilarious.
aplomb1026 5mo ago
[flagged]
hmokiguess 5mo ago
Why do keep doing this? It's the same as measuring by LoC, we know it's not gonna work. Also, see Goodhart's Law[1] - https://en.wikipedia.org/wiki/Goodhart%27s_law https://en.wikipedia.org/wiki/Goodhart%27s_law
- sometimelurker 5mo ago
  hah came here to say exactly this
phendrenad2 5mo ago
AI productivity hasn't been well studied yet, but I'm betting that we'll end up with some variation on Price's Law, I.E. some small subset of workers get most of the benefit, while most just burn tokens with little to show for it. I also want to call out the false productivity opportunities AI offers. There are whole teams building their own "gas town" and not shipping features.
mustaphah 5mo ago
Feels like they are debating internally whether to cut people or AI spending. Very healthy debate. Let's hope they spare people.
rr808 5mo ago
I have Opus 4.7 at work at 15x. Burns through tokens like water. It feels like one of these new mega datacenters is just for me. I'd love to know what the bill is, but we're just encouraged to do as much AI as possible.
- bachmeier 5mo ago
  > Burns through tokens like water. Pretty sure I know what you're saying, but the visual on this one doesn't match the point you're making.
  - rr808 5mo ago
    lol yeah I'm not a poet.
    - 3eb7988a1663 5mo ago
      Just append a reactive metal. "Like water through sodium"
      - 05 5mo ago
        Or make water relativistic, xkcd What-If style https://www.youtube.com/watch?v=pfbzrrcQZjs&t=155 https://www.youtube.com/watch?v=pfbzrrcQZjs&t=155
- loeg 5mo ago
  2^30 tokens costs something like 2^10 dollars, order of magnitude, if that helps ballpark.
- tmaly 5mo ago
  [dead]
- danny_codes 4mo ago
  Does your company by any chance sell compute for LLM companies? Because if you do then that makes sense to me!
mustaphah 5mo ago
Tokenmaxxing is so dumb. You should never show your team how exactly you're measuring their performance; people will optimize for the metric, not the actual performance. Classic Goodhart’s Law: when a measure becomes a target, it ceases to be a good measure.
mchusma 5mo ago
I actually do think token maxing is good, but they should have limited it per user. I find it reallly hard to get people to max out the Claude $100 plan, let alone the $200 plan. I understand the enterprise plans are different and more expensive, which is how you get these kinds of issues. But encouraging people to try things with AI is very important, and some amount of token maxing is importsnt.
- tquinn35 5mo ago
  Who’s it important for?
  - loeg 5mo ago
    The business. Employees are hesitant to learn new tools that are very different from what they are used to, so if your business believes that AI is a productivity multiplier, it behooves it to incentivize individual employees to learn to use the tool.
    - tquinn35 5mo ago
      I think the key word is “believes”. There is no proof that AI usage improves productivity. Token maxing is essentially customers paying to try and prove a business’s unsubstantiated claim. The AI companies should be proving their claims themselves not the other way around. I do think AI has value and is useful but the idea of token maxing is ridiculous.
      - loeg 5mo ago
        Sure; I described it that way deliberately. I think you can reasonably disagree with whether or not AI improves efficiency, but regardless, you can agree that if a business believes AI does, it will logically conclude that it should incentivize employees to learn to use AI.
    - bigstrat2003 4mo ago
      > Employees are hesitant to learn new tools that are very different from what they are used to... That simply isn't true for technical employees (like software devs). They are so hungry to get stuff done that you have to hold them back from adopting new tools which they think can make them work more effectively. Tech guys will set up entire shadow IT departments just to get around corporate restrictions that are limiting their productivity. No, if software devs are not using LLMs for programming, that is proof that the tool isn't actually useful for them. It doesn't mean "they need to be forced to use it", because they didn't need to be forced to use any of the tools which came before it.
victor9000 5mo ago
Clearly they need more layoffs, and for that matter why keep anyone around? After all, AI will be writing 100% of code in 2026.
- killingtime74 5mo ago
  By 2025 we will have AGI and software developers don't be necessary. Also next year we will have self driving.
- nickvec 5mo ago
  Surprisingly, Uber hasn’t had a mass layoff since 2020. The company currently has ~34,000 FTEs, which I personally think is insanely bloated for what amounts to a taxi + food delivery app.
  - Ekaros 5mo ago
    No wonder they need to extract such a massive cut. I really have no hope we will ever get to efficient middle-men who take least they can for good of both sides beyond them.
mmastrac 5mo ago
I am certain that the max sustainable boost from AI use -- with code review and otherwise all-in -- is approximately 20% with the appropriately skilled senior engineering talent, and the token budget for any engineer should not exceed that. I do not believe that engineers who are tokenmaxxing are truely productive and I have not seen any evidence whatsoever (perhaps the opposite). I've personally found that with the right flow and codebase knowledge, that's achievable with sustainable levels of effort.
matheusmoreira 5mo ago
LLMs are great, I can understand using them in general. I can even understand chasing 100% weekly usage if you're using the gacha-like subscriptions since that's how you get the most value out of what you paid for. The way these corporations are going about it is completely insane though. They're essentially ordering their employees to set money on fire or be fired themselves. The more money you burn on tokens at insane API rates, the better an employee you are. Absolutely mind boggling.
delichon 5mo ago
There is little new under the big fusion reactor in the sky. I just read a chapter in James Glieck's "The Information" about tokenmaxxing in the telegraphy industry. There used to be a big market for code books to reduce the per-character charges for sending telegrams. Compression was cash in the pocket. The telegraph companies discouraged the practice but were forced to accept it. The telegraph code industry started with the initial commercialization of telegraphy and didn't end until the 1920s. There was a cost to it though. Codes greatly reduced redundancy, and caused large miscommunications from very small errors. As Glieck explains it, this was the opposite of the African drumming practice of adding redundancy to strengthen the relationship between the rhythm and the language that the drums mimic.
- mejutoco 5mo ago
  That is interesting but tokenmaxxing is not maximizing token usage _efficiency_. It is maximizing its usage.
  - delichon 5mo ago
    Thanks, that's so odd that I assumed it was about efficiency, which is how I treat tokens. It's hard to imagine a 19th century business man ordering his staff to send as many long winded telegrams as they can.
    - dgellow 4mo ago
      I don’t think the analogy works too well for one specific reason: you can increase your number of used tokens without ever „sending a telegram“! Run a bunch of Claude sessions, ask them to review various docs sites, create random prototypes, they just throw all of that away. Congrats, you’re a token maxxer
    - Neywiny 4mo ago
      Think of it like this: telegraphs are the hot new thing. The more you send, the more modern and relevant your company. No more Pony Express. You can either have employees sending 1-2 a day. Or, 100 per day. Wow so advanced, so modern, invest now.
avidiax 5mo ago
AI for engineering productivity seems to be widely misunderstood to be a magic button that produces the same result, but faster and more cheaply. And based on that reasoning, you should want to force employees to tokenmax, because, why wouldn't you want to get more results but faster and cheaper? A more nuanced view would be something like: * AI lets you achieve your roadmap somewhat faster, but: * You incur tech debt that's similar to if you hired a dev temporarily for the features. You don't necessarily have someone on the team that understands the new code. * Similarly, you aren't upskilling your junior team members. So you aren't getting skill/wage arbitrage as much as before. * You will complicate the product. P2 features are P2 for a reason, but AI can cause them to be included and complicate the product for lower marginal gain.
cobblr_mosaic 5mo ago
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dominotw 5mo ago
tangent: anyone have businessinsider subscription. i feel like they've really stepped up their game last few years.
alexandre_m 5mo ago
Limits are beneficial. They should be treated as a design feature, not just a stopgap. When something is abundant, people tend to waste it. I’m perfectly happy with my base subscriptions. I have Claude Code and Codex monthly subs, plus a yearly Google AI Pro account because it was a logical upgrade from the cloud storage plan I already had. I think it worked out to something like an extra $10/month for the AI features. I constantly rotate between them during the week, managing tokens carefully, cleaning sessions and contexts as soon as possible, and being intentional about usage. I honestly don’t understand the appeal of these ultra-expensive max subscriptions. It reminds me of that flying orb toy I bought for the kids a few years ago. The battery only lasted about 10 minutes, and the kids would go ape shit crazy while it worked. Then it needed a 30-minute recharge, which created a natural cooldown period. I actually considered that a good feature. I would never want the thing running nonstop.
hansmayer 5mo ago
Are you telling me, it did not make them "productive" in ways most of (us non-AI-boosters) "cannot even begin to imagine"? Who could've thought - a lot of average stuff, still ends up producing average result?
deadbabe 5mo ago
Protip: skunkworks type side projects are a great way to do tokenmaxxing when you don’t have enough work coming in, but still need to burn tokens to look productive. And because side projects are only governed by you, you can truly go nuts and let scope creep run wild. Soon enough, you’ll be one of those engineers burning six figures a month on AI and people will be in awe of your abilities, probably even elevating you to key AI evangelist positions within your company. And if you actually create something cool, you’ll be praised for your use of AI, and you can just say you built it all in a day or two instead of slacking off for months on your real work.
ath3nd 5mo ago
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dmazzoni 5mo ago
I remember at Google at around 2007 - 2009, as Google was massively expanding its data centers, there was a lot of unused capacity, especially during off-hours. Any engineer could run as many jobs as they wanted at zero priority, which means the job would be first in line to be killed if a more important task needed the resource. I did so many interesting experiments with MapReduces that would run overnight. For a while, I would even build internal services that were basically "free" because I'd just run them all at priority 0. Over time those services got less and less reliable as overall usage started to increase, so I was forced to either justify the resources or scale back - but that was a good thing. I feel like something similar would be a good model for AI token use: big tech companies ought to have their own self-hosted LLM data centers to power their own needs, then let employees use off-hours capacity to experiment. Outside of experimentation, we should be encouraging token efficiency for everyday tasks. Rather than having a certain number of tokens, engineers should be evaluated based on how much they actually get done. Using a lot of tokens to automate a process that used to require hours of human labor every week? Good use of tokens, should be encouraged. Using a lot of tokens to debug an easy frontend bug that could have been fixed by hand, and still took you 4 hours to complete? Waste of tokens, should be discouraged.
- 2dda 5mo ago
  "Using a lot of tokens to debug an easy frontend bug that could have been fixed by hand, and still took you 4 hours to complete? Waste of tokens, should be discouraged." Hahahah good luck with that! For many of us, what is happening now was super obvious. Telling a new formed crack addict (who you wanted to become addicted) to be more thoughtful about their consumption of crack... yeah not gonna work is it.
- seanmcdirmid 4mo ago
  Most AI front ends seem to be designed for interactive jobs, so they make it hard to define a job that should be done eventually with zero priority. It makes much more sense to do that with spec-driven development (have work done with the human on the loop rather in the loop), but as far as I know that just isn’t well supported by any front end yet (would be happy to be proven wrong, my experience is with Google front ends).
levhawk 5mo ago
On token consumption and efficiency... AI-champion guy in my prev company made a metric, like how many tokens are spend per line of generated code, and even put a leaderboard based on that metric, praising guys with the cheapest LOC. For me that's insanity for so many reasons...
whattheheckheck 5mo ago
The industry has to tokenmax to juice the revenue numbers. Its a big club
qwertyuiop_ 5mo ago
Not the first time supposed leaders ran into Goodhart's law.
- danny_codes 4mo ago
  This is an Uber exec. Competence is far down the list of requirements.
afinlayson 5mo ago
Replace Tokens with Gas, or water or healthcare or anything - and it's foolish. You shouldn't let the seller dictate what amount you need of something. Smart engineers are figuring out how to best use their tokens - as tokenmaxing is just as silly as gasmaxing your car.
deleted 5mo ago
[deleted]
outlore 5mo ago
Levie’s Law of AI Psychosis
spprashant 5mo ago
As with many things, users will discover a happy medium. There is scope for a lot of productivity gain here if the C-suite is willing to understand the tech and work with engineers rather than whatever Dario Amodei is selling.
Zak 5mo ago
I find it shocking that anyone ever thought tokenmaxxing was a good idea. AI maximalists like to compare the technology to electricity. Imagine if in the early days of electrification, a CEO had rewarded staff for increasing the amount of electricity they consumed rather than finding ways to use it for business impact. Institutionalizing people who showed signs of mental illness was popular in those days, and I suspect that would have been the outcome.
- bilalq 5mo ago
  The problem is that it is a good idea at the individual level. Poor management reads it as a signal of productivity.
  - Zak 4mo ago
    Regularly experimenting with AI tools as they improve and relying on them where they provide an advantage is a good idea at both individual and institutional levels. Maximizing usage for its own sake is not.
ernsheong 5mo ago
I’m genuinely curious why they don’t cap at $100/month Claude Max per employee. That would be sufficient for 80% of them.
latentframe 5mo ago
tokenmaxxing is becoming harder to justify could be a change in the labor market => when capital was free the companies optimized aggressively around retention and internal status spending but high rates + slow growth oblige firms to back toward productivity and operating leverage.
j1elo 4mo ago
They are burning money to pay for AI-assisted development. Ok. But what is the ROI of it all? Was it worth the supposed increase on efficiency? Why nobody talks about those points, which are actually the only interesting points of all this AI craze?
- mrcsharp 4mo ago
  I think it's because not many know how to measure it properly. I can output 5 useless/bad features in a day with Claude or I can output 1 useful feature per 2 day period. Which one has better impact on ROI? In this example, it might seem like it's an easy answer. But, in the real world, it is a lot more nuanced and much more difficult to measure and so not many are bothering to do it and are opting in for the simple solution of following the hype.
Copernicron 4mo ago
I don't like using AI. I don't find it particularly helpful. But my employer insists that we use it and tracks metrics so I make sure to give it pointless busywork daily. That way I show as using it even if it causes more problems than it fixes.
Simulacra 4mo ago
At what point might it be cheaper to, say, hire a human?

293 points by _____k 5mo ago

gigatexal 5mo ago

I find it useful that if they cut the use altogether I will pay for it out of pocket.

sottol 5mo ago
Maybe that's the plan :) But on a more serious note, do we know how much Uber spent per technical employee/month? I assume it is far more than even any of those $200 "max ai" plans. And the other question is how much the public would be willing to spend, in my estimation this is as "cheap" as it will ever get (main-stream at least).
- KronisLV 5mo ago
  > I assume it is far more than even any of those $200 "max ai" plans. Am in a random small company, colleague spent 100 EUR a day on Sonnet through AWS Bedrock (needed to use a EU region). Paying for tokens will get you in a deep hole financially compared to any of the subscriptions, unless it's like DeepSeek or one of the other models that are priced a bit better, though that's also a tradeoff in what they can/cannot do and also where the data goes. Ended up trying out the Mistral subscription for the US stuff btw, it was fine.
- Marciplan 5mo ago
  bigCo’s don’t get to do the $200 Max plans, they have unlimited plans but get charged like API
  - sottol 5mo ago
    Exactly. But I did find an article ([1]) and spend doesn't seem that high per engineer ($150 to $250 per eng) - at least on average, I assume the costs were skyrocketing towards the end. > Adoption climbed from 32 percent of engineers in February to 84 percent classified as agentic coding users by March. By spring, 95 percent of Uber engineers used artificial intelligence tools monthly, and roughly 70 percent of committed code originated from those tools. About 11 percent of live backend updates were written by agents with no human in the loop, according to Uber's own disclosures. > The numbers behind the spend are what make the story instructive rather than anecdotal. Monthly cost per engineer ranged from $150 to $250 on average, with power users running between $500 and $2,000. My guess is that the reason to rethink AI-spend was probably the exponential growth in cost over time, and tokenmaxxing payoff not being immediately obvious as mentioned in the article. [1] https://www.forbes.com/sites/janakirammsv/2026/05/17/uber-burns-its-2026-ai-budget-in-four-months-on-claude-code/ https://www.forbes.com/sites/janakirammsv/2026/05/17/uber-bu...
iwontberude 5mo ago
Except you won’t because they will threaten to fire you and force you to route all of your AI through data protection proxy to stop exfiltration by filtering and tracking prompts/response tokens.
mattlondon 5mo ago
Probably long term each dev gets their own GPU and runs a model locally I expect. Seems like a more sustainable approach, even if a local model is not absolute SOTA.
- ianm218 5mo ago
  GPUs are much more efficient at parallelizing requests for LLMs so it's going to much more efficient to centrally host. Maybe big companies it would make sense to get their own though.
throwaway613746 5mo ago
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dghlsakjg 5mo ago
Would you decide its usefulness based on how high the bill is, or how many things you get done while using it? The former is the issue, and how many companies have been operating. It's like a trucking company ranking driver effectiveness by fuel used instead of by cargo moved.
- gigatexal 5mo ago
  The former. I’m able to get more Tickets done with it than without.
  - dghlsakjg 4mo ago
    I think that’s the point being made by the CEO. Measuring token usage isn’t a good way to measure effectiveness. They should be counting output (tickets on your case) instead of input (AI spend).
- deleted 5mo ago
  [deleted]

illithid0 5mo ago

>"He said that, based on talks with Uber's senior engineering leaders, he realized higher token usage did not translate into a proportional increase in useful consumer features." Goodhart's law strikes again at someone with enough power to be both ignorant of it and make others suffer their ignorance. You cannot simply measure productivity by tokens spent just like you can't measure it by hours spent in a chair at a desk.

colechristensen 5mo ago
You can measure productivity by hours spent at a desk?
- batch12 5mo ago
  You can measure attendance by hours spent at a desk
  - devttyeu 5mo ago
    Well if you're a devshop just billing hours of mostly low impact work then hours are very much equal to productivity.
    - saghm 5mo ago
      Next time you're going to work for an hour, ping me, and I bet I can surprise you with how much less productive I am than you
- epolanski 5mo ago
  Productivity is measured by economists in $/hour. Which is why two identical jobs with the same real life output have drastically different productivity. A nursing home in Luxembourg has 5 times the productivity of one in Romania despite the services being identical and tech-unrelated.

nekzn 5mo ago

It’s funny that “maxxing” entered the common vocabulary.

chihuahua 5mo ago
If you're not tokenmaxxing, you're getting tokenmogged on the AI leaderboard, and your next review ain't gonna be pretty.
internet2000 5mo ago
A good 80% by volume of the modern vernacular is 4chan language that got sanded down.
- nekzn 5mo ago
  Sanding down is how we got goyslop turned into slop.
  - harvey9 5mo ago
    Slop is a word in its own right which got the goy prefix later in life.
amirhirsch 5mo ago
I like this too. I have been intentionally -maxxingmaxxing to get the meme out there. It's a good canary to sort out who gets the spicy takes from the pedestrians who probably still copy-paste into the ChatGPT web app like a psychopath.

7777777phil 5mo ago

As soon as tokens stop stop being subsidized, heavy agentic use will become as least as expensive than paying an (entry level) employee. When this happens many companies will trade off havy tolen usage for (maybe a bit slower, bit less accurate) employees again.

cryo32 5mo ago
This is what I’m betting on. The financials don’t make sense now. Based on the expenditure the finances won’t ever make sense.
Wowfunhappy 5mo ago
DeepSeek is an open weights model. It's possible the hosted versions are subsidized, but we know what it costs to run locally. And it's expensive, but it's also pretty clearly cheaper than an employee. Of course, the latest DeepSeek models are not as good as Claude, but they're not super far off either.
- irishcoffee 5mo ago
  They're not far off, getting the same seamless integration as hosted models is a full time job. I think what just happened is that devops is about to explode. What will naturally follow is local hosting of all the things when people realize subscription costs for cloud-whatever are absurd. Gitlab is going to take off? This is not investment advice.
  - Wowfunhappy 5mo ago
    > What will naturally follow is local hosting of all the things when people realize subscription costs for cloud-whatever are absurd. Even acknowledging we don't know exactly what costs would look like in a world without VC money, wouldn't hosting models logically be cheaper to do at scale in a data center? When I compared to the cost of running DeepSeek locally, I meant that we can treat that cost as a price ceiling, not the floor.
    - 5mo ago

egypturnash 5mo ago

[flagged]

epolanski 5mo ago
Slightly ot, but I really dislike this reddit WSBization of HN. Adds nothing insightful to these discussions.
- cwillu 5mo ago
  “Please don't post comments saying that HN is turning into Reddit. It's a semi-noob illusion, as old as the hills.” --hn guidelines (there are links to examples in the original)
- noman-land 5mo ago
  It's unfortunately the WSBification of the entire society.

chihuahua 5mo ago

It's amazing that it took months to figure this out. "Well we thought that if engineers are told to maximize costs through AI use, to consume as much as possible of a resource that costs us money, then obviously good things will happen. Imagine my surprise when it didn't turn out that way." Imagine if engineers were ranked based on their AWS spend. People allocate VMs and fill databases with terabytes of random bits, to get to the top of the AWS leaderboard. If you don't do this, you're ranked at the bottom, and good luck at the next review cycle. Who could have expected that this is not the road to success?

solenoid0937 5mo ago
You say "amazing that it took months to figure this out" as if the answer to the question is obvious. But it's not. Some FAANGs are doing amazing things with unlimited tokens. Other companies have no clue what to do with tokens, they've just told their engineers to max them. It really depends on how you're using the tokens. If you're just using them for Codex and Claude Code - yeah, tokenmaxxing is incredibly dumb.
- steveBK123 5mo ago
  > Some FAANGs are doing amazing things with unlimited tokens. Others have no clue what to do with tokens. Unlimited tokens is different from “use AI a lot or we will fire you, and we are counting token consumption as usage”. Obviously the latter is stupid and yet it was done in many places.
  - deleted 5mo ago
    [deleted]
  - SpicyLemonZest 5mo ago
    I'm not convinced it actually was done in many places, although I understand why in a bad job market people don't trust that it isn't happening in secret. Every time I've heard of a token leaderboard or such it's come with a denial that the company is using it as an employee performance metric.

cryo32 5mo ago

Waiting for tokenedging next.

SecretDreams 5mo ago
Is this when you type the prompt into the text window, but don't hit enter? Make the GPU see the message "x is typing"? Lol.
- FartyMcFarter 5mo ago
  As long as there's an RPC connection established and a partially sent request, I think it would count.
postsantum 5mo ago
^ Philip K. Dick's unreleased book title

Rohunyyy 5mo ago

Now we are going to get a new profession. Token Engineer! They will be experts on tokenmaxxing! The job growth that the billionaire CEOs promised us from AI is finally here!

fsloth 5mo ago
Well there are already offerings like githits (https://news.ycombinator.com/item?id=46105112 https://news.ycombinator.com/item?id=46105112) that sort of promise optimize bang-per-buck of inference

izanton 5mo ago

What if... we stop for a moment, and then, after thinking for a moment, we stop hammering nails with a microscope, and stop using token usage as a metric of productivity? I know it's sounds stupid, but what if

tekno45 5mo ago
Not very Billion Dollar Valuation of you.
- deleted 5mo ago
  [deleted]
- themafia 5mo ago
  Yes, but, I sleep very soundly at night.
devin 5mo ago
The people who have ascended to leadership positions are deeply divorced from reality. "It is difficult to get a man to understand something, when his salary depends on his not understanding it." -Upton Sinclair
- lorecore 5mo ago
  The crazy thing is their salary does not actually benefit from riding these trends. Unless it's equally/even more clueless board level pressure with ulterior motives (i.e., lifting their other AI investments or the sector as a whole).
  - repeekad 5mo ago
    Every c suite in the country is panicking about being left behind, from their perspective it’s either token max or fade into obscurity, or at least that’s what they were sold
    - 5mo ago

FartyMcFarter 5mo ago

If any company announces that they use token consumption as an employee performance signal, for me that's close to a red flag to stay away from that company. No company with good engineering leadership should act like this is remotely a good idea.

LaurensBER 5mo ago
Tokens are the new "lines of code per engineer". Easy to graph, easy to "manage".
- KellyCriterion 5mo ago
  ...and easier to bill! Back, then noboday had the idea to charge per "lines of code", but today it seems accepted to charge per words processed?
- mig39 5mo ago
  The new TPS reports!
  - suprfnk 5mo ago
    Oh, so that was actually a Token Per Second report! Wild!
an0malous 5mo ago
I worked at a YC company that was doing this and left last month. I wonder where this all started from, VCs and tech execs are such a monoculture
- ben_w 4mo ago
  The where may be the decision makers chasing social media trends. A friend sent me a link to this, this morning, about devs rather than managers, but I suspect it's the same: https://youtu.be/IW3Sbe0Hbgg https://youtu.be/IW3Sbe0Hbgg
- 4mo ago

jhack 5mo ago

Maybe don't use the most expensive models on the planet? Maybe use AI like a tool and not this black box that grants wishes?

dgellow 5mo ago
Sounds like you want to be in the next round of layoffs?
deleted 5mo ago
[deleted]
onlyrealcuzzo 5mo ago
I think companies are reluctantly realizing that AI is not a magic genie in a bottle, and is instead a tool. Still very valuable. They just need to have strategies that match what the tools are capable of - not strategies that involve "rub the magic lamp and increase profits 80%". If the market is rewarding companies going after the "rub the lamp" strategy, they're going to say they're doing that to juice stock prices. Maybe the market is finally realizing blindly spending billions on LLMs with almost no strategy is not a good strategy. Who knows.
- bandrami 4mo ago
  > Still very valuable You sure about that? Both labs and tech companies have been desperate to show ROI on LLM use and nobody can seem to
overfeed 5mo ago
But the executives need the fanciest models to evaluate how well they can replace the expensive labor costs.

irishcoffee 5mo ago

I just realized my company is months behind this curve. About to blow my token allocation. Before I do, anyone have requests? Sincerely.

kibwen 5mo ago
I hereby suggest you take the fragmentary excerpts of the infamous erotic stage play The Lusty Argonian Maid shown in The Elder Scrolls series of games and extrapolate them to 100,000 additional full-length acts.

deleted 5mo ago

[deleted]

pocksuppet 5mo ago

what the fuck is this timeline I am stuck living in

crorella 5mo ago

Tokenmaxxing makes no sense, it is akin to write extremely inefficient SQL / Spark Jobs, full of cartesian joins, ultra skewed datasets, etc, just for the sake of using as much compute / memory / IO as possible. This always happens when the metric becomes the goal, companies should nurture and foster an environment where AI is used in the most efficient way possible, first asking "do we really need an agent for this" and if so, what kind of agent is needed, what model, reasoning level, etc. They should also promote projects that aim at saving tokens, increasing cache hits, codifying the information in ways such they use as less context as possible (graphs of knowledge are pretty good for this!)

InsideOutSanta 5mo ago
It's toddler-level logic. "You can achieve positive outcomes by using X. Therefore, we need to use as much X as possible to maximize positive outcomes." It's like trying to win a race by setting a gas station on fire.
SpicyLemonZest 5mo ago
The argument in favor of "tokenmaxxing" has always been that it's creating space for employees to freely explore the broad and novel space of AI-enabled workflows. I've seen a number of use cases where I'm skeptical any value is being produced, but a number of others where some team or another has finally solved a long-standing problem of theirs with an agentic workflow that would have been hard to justify to a cost review committee. > They should also promote projects that aim at saving tokens, increasing cache hits, codifying the information in ways such they use as less context as possible (graphs of knowledge are pretty good for this!) My understanding is that most big "tokenmaxxing" companies do have teams who are working on this in the background.
- onesociety2022 5mo ago
  +1 I find the general disdain for C-suite or senior engineering leadership on HN so silly. These people didn't get promoted or hired because of nepotism. A lot of them moved up the engineering ladder and are familiar with how software engineering works and the incentives involved. Yes, some of them are sheep and will blindly copy what is fashionable but so do a large swath of ICs. If you want incredibly fast adoption of AI within a company, the best thing you can do is to signal from the top that tokenmaxxing will be rewarded (or at least not be punished for it). 1. It forces everyone including the lazy ones who normally wouldn't invest their time in learning anything new to actually install codex/claude and learn to use them. 2. It prevents any middle manager from putting up blockers for adoption/experimentation ("this is new, I don't trust this, let's do it the old familiar way", "this might be expensive, we care about efficiency here", etc). Once the C-suite dictates tokenmaxxing is allowed, every middle manager will fall in line instantly. 3. Tokenmaxxing is not choice you have to live with the rest of your life. A year or two from now, once C-suite is satisfied with the rate of AI adoption within their org/company, they can just as easily switch the focus to efficiency. Teams will be asked to justify their token spend and start to optimize.

simonw 5mo ago

I'd be interested to know if this is about individual employee AI usage, or use of AI tokens in production features, or both - and assuming both, what the split is. I can see how Uber could burn unbelievable amounts of tokens if they start running internal features that run a bunch of prompts against every completed ride, or every customer profile, for example. Or maybe this is about employee usage, but they introduced some stupid "you get evaluated on how many tokens you used" thing a couple of months ago when that was trendy and are just beginning to notice how much that cost?

devin 5mo ago
IMO, it's undoubtedly both. The number of product teams who have shipped expensive-to-operate AI features is wayyyy up there, and for many of the scenarios I've seen, customers simply don't care or are unwilling to pay significant premium for access to it. At the same time I'm starting to see some direction from people in leadership that I should "use the right model for the job" and things along those lines, which is a very, very different line from what I was hearing 12 months ago. My continued prediction is that we are going to see a tweak on the SaaS model where the sweet spot moves to metered usage pricing of really fine-grained API-based access for apps which traditionally have been operated solely via the UI. Long term the trend is going to be "we'll house the data, enrich it, maintain it, provide fine-grained API access over it tailored to model usage, and you bring the model" with some services opting to give you the model interaction layer/harness. IOW I don't think SaaS is dead. Far from it. However, I do think that a lot of people are going to be looking to interact with SaaS apps via their own models with APIs that support those use cases better than a lot of those APIs do today.
- oa335 5mo ago
  > we'll house the data, enrich it, maintain it, provide fine-grained API access over it tailored to model usage, and you bring the model isnt this just mcp servers hosted by the saas provider?
  - devin 4mo ago
    Eh, sort of. I think a lot of API surfaces are too chatty for LLM consumption, or do not provide the primitive API surfaces that compose what they currently offer today. MCP in the middle doesn't give you that for free. The data shapes are different, the surfaces are probably necessarily wider in a lot of use cases, efficient retrieval of small bits of data that feed aggregates, etc.

lorecore 5mo ago

Not all tokens are created equal. It's easy to use a ton of tokens by having agents work together in parallel. That's basically the equivalent as people spending time in meetings, hardly a productivity win. As with everything in development, results matter, how you get there doesn't (unless you're a bad manager).

JackDanMeier 5mo ago

At what point is there a difference between a burn rate and tokenmaxxing? Isn't it the same as during the dotcom bubble?

paulpauper 5mo ago

many of these leading AI companies are operating at large losses and subsidizing users with VC money. Profitability will entail having to impose greater limits and raising prices, so this will reduce to some degree the value proposition of AI compared to humans.

rcvassallo83 5mo ago

Oof leader of bubble are starting to take a step back?

mrkeen 5mo ago

I always used to wonder this about software stacks even prior to LLMs, but it seems more relevant now somehow: When will Uber (or your favourite company) be 'done'? They've been writing software for 16 years. They match drivers to passengers. More software isn't going to increase the chance that I seek them out instead of taking a bus or train. Will their software be finished in 20 years? 80?

goldenarm 5mo ago
Most of the codebase is custom integrations for local markets. You can systematize some of it but most of the complexity comes from there.
- SoftTalker 5mo ago
  Can you provide an example? What is different about running Uber services in Chicago vs. Indianapolis?
  - iLoveOncall 5mo ago
    For example in Seattle you pay county fees, and then state fees, and then maybe special fees if you were picked up in the airport. I took a ride from SEATAC to my hotel in downtown Seattle and besides the ride itself, there were 5 other items on the bill, 4 of which are specific to the place I used Uber. Then I had the return trip from my hotel to SEATAC, on this one I got EIGHT items on the bill, on top of the ride fare. Some specific to Seattle itself, some specific to the road that the Uber took (a tunnel fee - which is different based on the direction you take it in), etc. So the real question is what is NOT different between two locations. Less than 15% of the bill. I also took Uber in India, where you have to share a one-time password with the driver for example, which I've never seen in any other country. In some other countries the Uber app exists but Uber drivers are actually taxis, so you're actually ordering a taxi via the app.
    - SoftTalker 5mo ago
      Ah local regulations and fees. Not so much the core service algorithms. That makes sense.

yapyap 5mo ago

wtv

bilater 5mo ago

The black bill that is coming that nobody is prepared for is that the value of a token varies greatly depending on the human. Companies will quickly find out its much better to give your top 10% engineers a lot more tokens and lay off your average engineers. The 10x engineer will become the 1000x engineer. Wrote about this and the impact of to jobs here: https://x.com/deepwhitman/status/2058324179506831372 https://x.com/deepwhitman/status/2058324179506831372

archagon 5mo ago
Lol, no, no one’s becoming a 1000x engineer.
danny_codes 4mo ago
Why stop there, you should be a millionX engineer. No, a billion! We can make up all the numbers!

InsideOutSanta 5mo ago

"He said that, based on talks with Uber's senior engineering leaders, he realized higher token usage did not translate into a proportional increase in useful consumer features." He's saying that like it's some grand epiphany and not the most self-evident, obvious thing I've heard this month. Some of the literal dumbest people on earth are in charge of these major companies.

Barrin92 5mo ago
>obvious thing I've heard this month not only this month, but it is the basic statement of the single most well known 50 year old book in software project management lol. At this point we need to wipe the slate clean and start over, the industry is run by illiterates.
amluto 5mo ago
This is also Uber we’re talking about. The company that famously developed a massively engineered ledger to track every event across the entire company, globally consistently, forever, in a single database. This definitely adds enormous value to the bottom line!
- smoofles 4mo ago
  The fact that a company with such a ledger has trouble advocating for AI-maxxxing will make watching the "ur holding ur AI wrong bro"-reactions all the more hilarious.

aplomb1026 5mo ago

[flagged]

hmokiguess 5mo ago

Why do keep doing this? It's the same as measuring by LoC, we know it's not gonna work. Also, see Goodhart's Law[1] - https://en.wikipedia.org/wiki/Goodhart%27s_law https://en.wikipedia.org/wiki/Goodhart%27s_law

sometimelurker 5mo ago
hah came here to say exactly this

phendrenad2 5mo ago

AI productivity hasn't been well studied yet, but I'm betting that we'll end up with some variation on Price's Law, I.E. some small subset of workers get most of the benefit, while most just burn tokens with little to show for it. I also want to call out the false productivity opportunities AI offers. There are whole teams building their own "gas town" and not shipping features.

mustaphah 5mo ago

Feels like they are debating internally whether to cut people or AI spending. Very healthy debate. Let's hope they spare people.

rr808 5mo ago

I have Opus 4.7 at work at 15x. Burns through tokens like water. It feels like one of these new mega datacenters is just for me. I'd love to know what the bill is, but we're just encouraged to do as much AI as possible.

bachmeier 5mo ago
> Burns through tokens like water. Pretty sure I know what you're saying, but the visual on this one doesn't match the point you're making.
- rr808 5mo ago
  lol yeah I'm not a poet.
  - 3eb7988a1663 5mo ago
    Just append a reactive metal. "Like water through sodium"
    - 05 5mo ago
      Or make water relativistic, xkcd What-If style https://www.youtube.com/watch?v=pfbzrrcQZjs&t=155 https://www.youtube.com/watch?v=pfbzrrcQZjs&t=155
loeg 5mo ago
2^30 tokens costs something like 2^10 dollars, order of magnitude, if that helps ballpark.
tmaly 5mo ago
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danny_codes 4mo ago
Does your company by any chance sell compute for LLM companies? Because if you do then that makes sense to me!

mustaphah 5mo ago

Tokenmaxxing is so dumb. You should never show your team how exactly you're measuring their performance; people will optimize for the metric, not the actual performance. Classic Goodhart’s Law: when a measure becomes a target, it ceases to be a good measure.

mchusma 5mo ago

I actually do think token maxing is good, but they should have limited it per user. I find it reallly hard to get people to max out the Claude $100 plan, let alone the $200 plan. I understand the enterprise plans are different and more expensive, which is how you get these kinds of issues. But encouraging people to try things with AI is very important, and some amount of token maxing is importsnt.

tquinn35 5mo ago
Who’s it important for?
- loeg 5mo ago
  The business. Employees are hesitant to learn new tools that are very different from what they are used to, so if your business believes that AI is a productivity multiplier, it behooves it to incentivize individual employees to learn to use the tool.
  - tquinn35 5mo ago
    I think the key word is “believes”. There is no proof that AI usage improves productivity. Token maxing is essentially customers paying to try and prove a business’s unsubstantiated claim. The AI companies should be proving their claims themselves not the other way around. I do think AI has value and is useful but the idea of token maxing is ridiculous.
    - loeg 5mo ago
      Sure; I described it that way deliberately. I think you can reasonably disagree with whether or not AI improves efficiency, but regardless, you can agree that if a business believes AI does, it will logically conclude that it should incentivize employees to learn to use AI.
  - bigstrat2003 4mo ago
    > Employees are hesitant to learn new tools that are very different from what they are used to... That simply isn't true for technical employees (like software devs). They are so hungry to get stuff done that you have to hold them back from adopting new tools which they think can make them work more effectively. Tech guys will set up entire shadow IT departments just to get around corporate restrictions that are limiting their productivity. No, if software devs are not using LLMs for programming, that is proof that the tool isn't actually useful for them. It doesn't mean "they need to be forced to use it", because they didn't need to be forced to use any of the tools which came before it.

victor9000 5mo ago

Clearly they need more layoffs, and for that matter why keep anyone around? After all, AI will be writing 100% of code in 2026.

killingtime74 5mo ago
By 2025 we will have AGI and software developers don't be necessary. Also next year we will have self driving.
nickvec 5mo ago
Surprisingly, Uber hasn’t had a mass layoff since 2020. The company currently has ~34,000 FTEs, which I personally think is insanely bloated for what amounts to a taxi + food delivery app.
- Ekaros 5mo ago
  No wonder they need to extract such a massive cut. I really have no hope we will ever get to efficient middle-men who take least they can for good of both sides beyond them.

mmastrac 5mo ago

I am certain that the max sustainable boost from AI use -- with code review and otherwise all-in -- is approximately 20% with the appropriately skilled senior engineering talent, and the token budget for any engineer should not exceed that. I do not believe that engineers who are tokenmaxxing are truely productive and I have not seen any evidence whatsoever (perhaps the opposite). I've personally found that with the right flow and codebase knowledge, that's achievable with sustainable levels of effort.

matheusmoreira 5mo ago

LLMs are great, I can understand using them in general. I can even understand chasing 100% weekly usage if you're using the gacha-like subscriptions since that's how you get the most value out of what you paid for. The way these corporations are going about it is completely insane though. They're essentially ordering their employees to set money on fire or be fired themselves. The more money you burn on tokens at insane API rates, the better an employee you are. Absolutely mind boggling.

delichon 5mo ago

There is little new under the big fusion reactor in the sky. I just read a chapter in James Glieck's "The Information" about tokenmaxxing in the telegraphy industry. There used to be a big market for code books to reduce the per-character charges for sending telegrams. Compression was cash in the pocket. The telegraph companies discouraged the practice but were forced to accept it. The telegraph code industry started with the initial commercialization of telegraphy and didn't end until the 1920s. There was a cost to it though. Codes greatly reduced redundancy, and caused large miscommunications from very small errors. As Glieck explains it, this was the opposite of the African drumming practice of adding redundancy to strengthen the relationship between the rhythm and the language that the drums mimic.

mejutoco 5mo ago
That is interesting but tokenmaxxing is not maximizing token usage _efficiency_. It is maximizing its usage.
- delichon 5mo ago
  Thanks, that's so odd that I assumed it was about efficiency, which is how I treat tokens. It's hard to imagine a 19th century business man ordering his staff to send as many long winded telegrams as they can.
  - dgellow 4mo ago
    I don’t think the analogy works too well for one specific reason: you can increase your number of used tokens without ever „sending a telegram“! Run a bunch of Claude sessions, ask them to review various docs sites, create random prototypes, they just throw all of that away. Congrats, you’re a token maxxer
  - Neywiny 4mo ago
    Think of it like this: telegraphs are the hot new thing. The more you send, the more modern and relevant your company. No more Pony Express. You can either have employees sending 1-2 a day. Or, 100 per day. Wow so advanced, so modern, invest now.

avidiax 5mo ago

AI for engineering productivity seems to be widely misunderstood to be a magic button that produces the same result, but faster and more cheaply. And based on that reasoning, you should want to force employees to tokenmax, because, why wouldn't you want to get more results but faster and cheaper? A more nuanced view would be something like: * AI lets you achieve your roadmap somewhat faster, but: * You incur tech debt that's similar to if you hired a dev temporarily for the features. You don't necessarily have someone on the team that understands the new code. * Similarly, you aren't upskilling your junior team members. So you aren't getting skill/wage arbitrage as much as before. * You will complicate the product. P2 features are P2 for a reason, but AI can cause them to be included and complicate the product for lower marginal gain.

cobblr_mosaic 5mo ago

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dominotw 5mo ago

tangent: anyone have businessinsider subscription. i feel like they've really stepped up their game last few years.

alexandre_m 5mo ago

Limits are beneficial. They should be treated as a design feature, not just a stopgap. When something is abundant, people tend to waste it. I’m perfectly happy with my base subscriptions. I have Claude Code and Codex monthly subs, plus a yearly Google AI Pro account because it was a logical upgrade from the cloud storage plan I already had. I think it worked out to something like an extra $10/month for the AI features. I constantly rotate between them during the week, managing tokens carefully, cleaning sessions and contexts as soon as possible, and being intentional about usage. I honestly don’t understand the appeal of these ultra-expensive max subscriptions. It reminds me of that flying orb toy I bought for the kids a few years ago. The battery only lasted about 10 minutes, and the kids would go ape shit crazy while it worked. Then it needed a 30-minute recharge, which created a natural cooldown period. I actually considered that a good feature. I would never want the thing running nonstop.

hansmayer 5mo ago

Are you telling me, it did not make them "productive" in ways most of (us non-AI-boosters) "cannot even begin to imagine"? Who could've thought - a lot of average stuff, still ends up producing average result?

deadbabe 5mo ago

Protip: skunkworks type side projects are a great way to do tokenmaxxing when you don’t have enough work coming in, but still need to burn tokens to look productive. And because side projects are only governed by you, you can truly go nuts and let scope creep run wild. Soon enough, you’ll be one of those engineers burning six figures a month on AI and people will be in awe of your abilities, probably even elevating you to key AI evangelist positions within your company. And if you actually create something cool, you’ll be praised for your use of AI, and you can just say you built it all in a day or two instead of slacking off for months on your real work.

ath3nd 5mo ago

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dmazzoni 5mo ago

I remember at Google at around 2007 - 2009, as Google was massively expanding its data centers, there was a lot of unused capacity, especially during off-hours. Any engineer could run as many jobs as they wanted at zero priority, which means the job would be first in line to be killed if a more important task needed the resource. I did so many interesting experiments with MapReduces that would run overnight. For a while, I would even build internal services that were basically "free" because I'd just run them all at priority 0. Over time those services got less and less reliable as overall usage started to increase, so I was forced to either justify the resources or scale back - but that was a good thing. I feel like something similar would be a good model for AI token use: big tech companies ought to have their own self-hosted LLM data centers to power their own needs, then let employees use off-hours capacity to experiment. Outside of experimentation, we should be encouraging token efficiency for everyday tasks. Rather than having a certain number of tokens, engineers should be evaluated based on how much they actually get done. Using a lot of tokens to automate a process that used to require hours of human labor every week? Good use of tokens, should be encouraged. Using a lot of tokens to debug an easy frontend bug that could have been fixed by hand, and still took you 4 hours to complete? Waste of tokens, should be discouraged.

2dda 5mo ago
"Using a lot of tokens to debug an easy frontend bug that could have been fixed by hand, and still took you 4 hours to complete? Waste of tokens, should be discouraged." Hahahah good luck with that! For many of us, what is happening now was super obvious. Telling a new formed crack addict (who you wanted to become addicted) to be more thoughtful about their consumption of crack... yeah not gonna work is it.
seanmcdirmid 4mo ago
Most AI front ends seem to be designed for interactive jobs, so they make it hard to define a job that should be done eventually with zero priority. It makes much more sense to do that with spec-driven development (have work done with the human on the loop rather in the loop), but as far as I know that just isn’t well supported by any front end yet (would be happy to be proven wrong, my experience is with Google front ends).

levhawk 5mo ago

On token consumption and efficiency... AI-champion guy in my prev company made a metric, like how many tokens are spend per line of generated code, and even put a leaderboard based on that metric, praising guys with the cheapest LOC. For me that's insanity for so many reasons...

whattheheckheck 5mo ago

The industry has to tokenmax to juice the revenue numbers. Its a big club

qwertyuiop_ 5mo ago

Not the first time supposed leaders ran into Goodhart's law.

danny_codes 4mo ago
This is an Uber exec. Competence is far down the list of requirements.

afinlayson 5mo ago

Replace Tokens with Gas, or water or healthcare or anything - and it's foolish. You shouldn't let the seller dictate what amount you need of something. Smart engineers are figuring out how to best use their tokens - as tokenmaxing is just as silly as gasmaxing your car.

deleted 5mo ago

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outlore 5mo ago

Levie’s Law of AI Psychosis

spprashant 5mo ago

As with many things, users will discover a happy medium. There is scope for a lot of productivity gain here if the C-suite is willing to understand the tech and work with engineers rather than whatever Dario Amodei is selling.

Zak 5mo ago

I find it shocking that anyone ever thought tokenmaxxing was a good idea. AI maximalists like to compare the technology to electricity. Imagine if in the early days of electrification, a CEO had rewarded staff for increasing the amount of electricity they consumed rather than finding ways to use it for business impact. Institutionalizing people who showed signs of mental illness was popular in those days, and I suspect that would have been the outcome.

bilalq 5mo ago
The problem is that it is a good idea at the individual level. Poor management reads it as a signal of productivity.
- Zak 4mo ago
  Regularly experimenting with AI tools as they improve and relying on them where they provide an advantage is a good idea at both individual and institutional levels. Maximizing usage for its own sake is not.

ernsheong 5mo ago

I’m genuinely curious why they don’t cap at $100/month Claude Max per employee. That would be sufficient for 80% of them.

latentframe 5mo ago

tokenmaxxing is becoming harder to justify could be a change in the labor market => when capital was free the companies optimized aggressively around retention and internal status spending but high rates + slow growth oblige firms to back toward productivity and operating leverage.

j1elo 4mo ago

They are burning money to pay for AI-assisted development. Ok. But what is the ROI of it all? Was it worth the supposed increase on efficiency? Why nobody talks about those points, which are actually the only interesting points of all this AI craze?

mrcsharp 4mo ago
I think it's because not many know how to measure it properly. I can output 5 useless/bad features in a day with Claude or I can output 1 useful feature per 2 day period. Which one has better impact on ROI? In this example, it might seem like it's an easy answer. But, in the real world, it is a lot more nuanced and much more difficult to measure and so not many are bothering to do it and are opting in for the simple solution of following the hype.

Copernicron 4mo ago

I don't like using AI. I don't find it particularly helpful. But my employer insists that we use it and tracks metrics so I make sure to give it pointless busywork daily. That way I show as using it even if it causes more problems than it fixes.

Simulacra 4mo ago

At what point might it be cheaper to, say, hire a human?