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.
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?
There is a complete lack of courage in the leadership of tech companies today, and top-down AI mandates are just another manifestation.
True visionaries think outside the box, but most tech executives are forcing their employees into black boxes, out of fear of not doing exactly what their competitors are doing.
We have lemmings for leaders, and that means thatâmuch like the LLMs that are being shoehorned into everythingâthere isnât room for original thinking. Everyoneâs strategy looks exactly the same.
There was an amusing post about judging developers based on token usage where some user on HN here was pushing this idea âICs donât like it but this is the best way to evaluateâ (something like that).
They have a whole management team and canât seem to find a way to judge or god forbid encourage developersâŚ
First is that despite a lot of waste, some innovation will arise from an enterprising employee finding some interesting use case. A lot of the tokenmaxxing is just waste, but out of that waste may arise a small number of genuinely powerful use cases.
Second is that many workers will be entrenched in their ways. If your executive goal is to achieve the above (find innovative ways of using AI), then you need to move everyone to use it. Most will just waste tokens, but someone may find a novel and useful way of using it that benefits the organization. It is difficult to achieve these without forcing people to act since their default is to follow the well-worn grooves.
So mandates like these are a top-down forcing function like a slime mold feeling out different paths to find resources.
Some devs in my org have fully embraced AI; some would not even use AI if not for leadership mandates and linking usage to performance reviews (I know, I think this is stupid, too). I can see why mandates could be useful since some folks definitely won't be inclined to use AI.
If one is a CxO who's looking out for one's job security, herd-like behavior is the safest option, due to the (near universal) structure of "performance"-based executive remuneration.
> and stop using token usage as a metric of productivity
I participate in some management-focused online communities. Itâs crazy how many threads there are from frustrated managers trying to get their teams to stop thinking that their token use will be used as a proxy for their performance.
I think a few dumb companies did this and then it spread across social media, triggering a mass panic from engineers afraid their companies will be doing the same thing.
Itâs getting so bad that the conversation is shifting to how to identify and coach the token-maxxers to stop wasting the teamâs budget every week.
If there are any tech CEOs out there reading, I can offer my services. I will pointlessly burn unfathomable amounts of tokens, in parallel, 24 hours a day, 7 days a week, all for you. Think big big big numbers of tokens, you know whats cooler than a trillion tokens, a quadrillion tokens.
Lets talk my bonus, I will open the bidding at $1 per token.
I feel like individually, if you sat down with literally any reasonable person on the planet they would arrive at and/or agree with the tenor here.
I'd be curious to hear from people well versed in group psychology/dynamics and/or just a lot of leadership/people experience: what leads people to this type of thinking once they get in a group setting? It just... seems endemic at this point.
Obviously nobody here is going to know what I do or don't know, but I'm just increasingly curious what I am not understanding about this type of thing. It seems so obvious, yet that makes me ever more suspect that I'm oversimplifying it, or just totally ignorant about the problem in general.
It's because the average organization has lots of people who don't care about their own productivity and won't adopt new tools or processes unless forced to. This is true of most new tech - lots of workers had to be forced into using computers - but AI also has some other bumps to cross like lots of people who tried early models and then wrote them off, not realizing how fast they'd improve. And most orgs have no infrastructure or processes for allocating individuals token budgets, and most employees have no experience of properly deploying budgets.
Roll it all together and saying "just use it dammit" has some obvious advantages:
1. It's clear.
2. It's simple.
3. It eliminates all excuses employees might come up with for not using it.
The people at the top of these companies aren't stupid. They might have miscalculated how many tokens people can actually use, but that's very hard to calculate because usage is opaque and tools/processes change on a nearly weekly basis. They will eventually build out processes, tools, social conventions and performance metrics that take into account efficiency of token usage. But this is hard! Most managers aren't really assessed on the precise productivity of their teams, for instance, because productivity is often poorly defined.
> what leads people to this type of thinking once they get in a group setting
Game theory! The downside of being brave vastly outweighs the upside. For the C-suite, there is no cost to herdlike-behavior, regardless of the outcome. However, there is a very high personal downside to being a maverick, and your board later discovers you made the wrong choice against the grain. The upside of being maverick and right is very limited.
Once a behavior has become mainstream, hopping on the bandwagon is no longer individually attributable to decision-makers, but is seen (and reported) as a macro-economic phenomenon: Nadella, Zuckerberg and Bezos didn't overhire - the American tech industry overhired.
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).
I deeply believe this but have no strong evidence. Revenue has always been a cure all remedy. This will keep model providers alive along with the very wide range of companies that are experiencing growth with them (from chips to backhoes), for a time anyway. If/when that house of cards starts going in the other direction thereâs going to be widespread pain. By analogy the nonsense of the dotcoms and that crash had a very direct impact on their suppliers (e.g. telecoms). My only advice is to let the Microsoftâs and Metaâs do the tokenmaxxing, and donât get suckered into the idea you (startup, individual, etc) should be playing that game.
Every c suite in the country is panicking about being left behind, from their perspective itâs either token max or fade into obscurity, or at least thatâs what they were sold
its a herd mentality, its a lot easier to follow the louder voices than to spend time understanding how it impacts your own particular business. Because google does this way, or apple does this way is a common argument in lot of feature/business decisions
I don't think that's accurate. I think every C suite in the country is looking to do away with labor's leverage as much as possible. I think this is a cultural thing more than anything else, C suite + investors looking to get rid of those pesky humans required to prop up their lifestyles. AI is the most credible path toward that. Short, medium or long term returns be damned, this is a reconfiguration of society and they want to shed what they consider to be baggage.
Like anything it's a mixed bag. I am certainly working with people who I think truly believe the "max out on AI usage or become irrelevant" line. There are people who will privately let you know they're just working with the current meta the best way they can, but others who are drunk on kool aid.
Trying to operate as a rational, thinking person in a lot of environments right now feels impossible. Rational thought is being treated like AI skepticism.
Please. These are the same people that force their employees to use Microsoft teams because slack is $5 an employee a month. They're not going to sit idly by while employees burn thousands a month in tokens.
It depends on which people you're referring to. The allocation toward AI budget has been so massive that I think a lot of businesses are way behind on trying to assess value for dollar for the AI-related crud they're shelling out for.
Everyone is feeling it out but the vast majority of spend has been subscription based. Some outliers may have used a massive amount of tokens but companies didn't pay for that.
That VC funded gravy train is likely coming to an end. But fortunately there are also reasonably efficient models now so that the tokenmaxxers can still make the (much cheaper) tokens go brrrr.
They get paid for saying whatever VCs want to hear and now that thing is "we have now become an AI-native company". The thing I'm still trying to understand is who is scamming whom
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.
Do you have any source for this at all? Iâve seen so many different exonerations for Metaâs layoff criteria including claims that engineers using the most AI were laid off because Meta had them build AI tools to replace themselves.
Everyone is oddly confident despite all of the conflicting explanations.
Sure, but custom integrations seem unlikely to explain the majority of Uber's technical headcount. Let's say they average a dedicated engineer for each of their 1000 largest markets/locations. Let's assume another 200 across the countless smaller markets. Let's assume 50% overhead atop this for things like infra, tools, and management. These all seem like exceedingly generous estimates to me.
They actually had 5,000 engineers in the tokenmaxxing blog post. That's a lot of engineers for the rest of Uber's business activities.
Vegas: ordering a tax "to a hotel" - hotels have different entrances, pickup / dropoff there during crazy times is hard. Uber UI for Vegas is unique / some features are designed to make it easier for driver and passanger to find each other
Airports: different regulations, different rules for pickup/dropoff. Also scammers who pretend to be in a car, walk with their phones around pick-up ares in airport and do bait-and-switch (saw that in Istanbul SAW and in Dubai Al Maktoum)
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.
Essentially every single airport in the world is custom UI and custom walking path guides and pickup instructions, and rules for where pickups/dropoffs/etc can occur can change multiple times in a day, much to everyone's enjoyment. They're almost all private property, and are so valuable that whatever they want is what they get.
And food. Most/~all? major brands get custom integrations.
Hundreds (iirc) of identity verification providers, most or all custom, and constantly weighed against cost and accuracy because it ain't cheap and it ain't good but it is far better than none (both legally and ethically).
No idea how many payment sources they accept, but it's definitely a lot more than anyone who hasn't lived on 5+ continents thinks.
And remember that this is all international. So scale is huge and law changes are constant and frequently conflicting. Darn near every useful feature is illegal somewhere, at some time, for both good and bad reasons.
---
This is not at all to say I think Uber is efficient, clearly it is not. Not by an enormous margin. But there is a legitimate need for truly absurd complexity, because the world is not consistent. You see similar things happen anywhere [thing] tightly interacts with humans.
There's an excellent HN thread that talks about this very question (that comes up on HN every now and then - what _does_ company X do that needs so many engineering resources?): https://news.ycombinator.com/item?id=25375921
TL;DR: Managing a taxi service (that's what Uber is in my mind, not whatever "ride share" means) that spans cities and states, never mind countries, is extremely complicated. To their credit, Uber manages to make it look simple to the end user, prompting such comments as "meh it's just a few screens how hard could it be", which is triumph of product engineering as far as I am concerned.
I think youâre missing how complex international operations and optimization are.
Each country has their own laws around what uber is and isnât allowed to do. This needs to be formalized in code. For example you actually call a taxi, though the uber app, and the amount you pay is per mile, not a fixed fare decided ahead of time. To add to this complexity, some cities will have their own laws. What happens if you take an uber from town a to b, where each one has different laws ? A lawyer probably has an answer but the app needs to adhere to that.
On top of that laws change all the time.
Optimization, well you can always optimize something. speed, costs, paths etc.
In a way this never ends.
I think the part we interact with as consumers is a tiny sliver of the complexity those services have to build and operate.
Werenât they trying to do their own self-driving thing?
I think this is partly a problem with companies that have had heavy investment. Uberâs value isnât based on what they are doing, it is based on the idea that they are going to render ideas like owning your own car or taking public transit obsolete (I mean thatâs an exaggeration but less of one than it ought to be).
Well there is a lot of ongoing maintenance cost. There is probably still some marginal gains possible on the matching side. There are new products to launch. So while one specific software can mostly be finished, the total software of a company is always changing.
There are always newer technologies and techniques to be implemented. Better algorithms. Larger deployments. Better reliability. There are also almost always bugs to fix. So, so many bugs.
shiny new tools but people only want to use them on the same old problems. how can we innovate the development of crud apps even more?! that was what plagued the web dev landscape for some time. Constantly seeking newer lazier means of producing the same old product. I admit it has an allure but if companies are no longer constrained by dev effort / labour then they can only ponder their own reflection as the source of their failures.
Uber is at a large enough scale that this analysis doesn't work. You and I do not care even a tiny bit about "Eats for the Way", one of their planned features this year (https://www.uber.com/us/en/newsroom/go-get-2026/) that lets Uber Black passengers specify that their car should arrive with their Starbucks coffee order. But if 0.01% of users order 1 additional ride a month because of this, that's about 200k rides a year, which may well be sufficient to justify the development costs.
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.
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!)
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.
Tokenmaxxing exists because executives think employees are resistant to change. Thats it, a way to incentivize/force every employee to experiment with a new technology. Obviously once they think everyone is utilizing AI the tokkenmaxxing stuff will end.
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.
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.
It's not hard for most people now. 6 months ago when agents first started getting big, I genuinely didn't know enough about AI tools to understand how it was possible to use so many tokens, and I don't think I would have bothered to find time to learn without a kick.
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.
I think the key word is âbelievesâ. There is no proof that AI usage improves productivity. Token maxing is essentially customers paying to try and prove a businessâs 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.
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.
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.
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?
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.
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.
"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.
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.
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.
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?
The point of this was always to explore what is possible with AI as quickly as possible. Obviously, there is going to be a lot of waste, but the 5-10% of employees who are truly thinking about it and discovering novel applications are what you are truly after. Because right now, you effectively have a giant, as of yet poorly explored space of potential uses.
Anyone who can find the actually valuable portions of the space early has a potentially huge competitive advantage. Even if the result of the experiment is the negative that AI is actually mostly not that useful, that is still extremely useful information in a time of great uncertainty regarding outcomes.
The bottom line is that this approach may be expensive, but if you have the money to burn, it's far from the worst strategy if you are trying to position yourself correctly for the future.
Whatâs the huge advantage though? Adopting workflows that give big productivity gains is relatively easy even for big corporations. Itâs only an advantage if you can keep it secret.
OTOH maybe weâre in for a future of patenting prompts.
The thing I don't get though, is that most people just don't have that much work they need to do. I can use AI to pretty easily get my work done just via the regular chat interfaces. But because of the tokenmaxxing metrics that leadership tracks, I end up just having the AI deliberate for hours on random things just so that I can boost my token numbers. I think tokenmaxxing for the end goal you described is only realistic when the engineers are truly buried under a backlog of work.
I think unfortunately it's not about what seems obvious, or even what seems more likely, but about what seems retrospectively justifiable regardless of outcome.
The incentive structure of this type of decision is 'absolutely under no circumstances existentially mess up'. Ostensibly with respect to the organisation, but in actual reality much more so with respect to the individual(s) involved in the decision.
If everyone else is doing something that kind of obviously makes no sense, and you decide to break from the crowd by instead doing what does make sense, then there's a pretty solid chance of gaining a temporary edge while reality resolves the truth. But those gains probably won't matter all that much for the organisation, or indeed your position within it. It's a solid chance of an unimportant gain.
However on the other hand, there's a tail risk that something very unexpected happens and the thing everyone's doing that makes no sense actually turns out to make sense - sometimes even for entirely unpredictable incidental reasons - and then, well, you're in trouble. Not necessarily 'you' the organisation.. they'll likely be able to catch up and it won't matter that much. But for 'you' personally, the decision maker, it's very much not good.
As a bonus, in the much more likely scenario that the thing that makes no sense turns out to indeed make no sense, you're in the same boat as everyone else, there's no relative loss, and most importantly you don't stick out as someone who did something as risky as to go against the prevailing, albeit pretty clearly nonsensical, sentiment.
So basically, game theory tells you pretty quickly to just go with the thing that makes no sense if you're optimising for some (weighted) cross of what's best for the organisation and yourself as the decision maker.
The inability of leaders to understand Goodhartâs Law is always a sight to behold. They see a number go up and pat themselves on the back for how well their employees are making it go up without ever wondering if the thing they care about is happening.
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.
In other words, people who are productive get more done when you scale up what they're already doing, and people who aren't productive will not magically become productive when you scale up what they're already doing. That's incredibly obvious, because we've seen how this plays out repeatedly in so many different ways (lines of code, commits, tickets closed, etc.), and it has nothing to do with tokens or even programming, but just how trying to manage people works.
> Some FAANGs are doing amazing things with unlimited tokens. Others have no clue what to do with tokens.
Unlimited tokens is different from âuse AI a lot or we will fire you, and we are counting token consumption as usageâ. Obviously the latter is stupid and yet it was done in many places.
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.
> But it's not. Some FAANGs are doing amazing things with unlimited tokens
Giving someone unlimited access to a resources is not the same as directing or incentivizing them to use it for the sake of using it which is what the parent comment criticized.
As for the other FAANGs, Meta and Google have (not good but still) frontier models of their own, so they are very different from a company paying API costs per token.
Show me some fang that have made nice outwards facing products through a fully embraced AI workflow?
AI is an accelerator that engineers should know and have access to, but it's not something that should have mandated usage and quotas around. It's also absolutely dangerous for young engineers and the like - it fundamentally denies you of the "learning" aspect. I'm now seeing in interviews young graduates being given AI tasks to complete and they come back with a correct solution and no concept of how it is working.
You learn and reinforce learning by DOING and reading in depth. High level summaries don't teach anything and are the kinds of things only VPs care about. So, unless the intention in the future is for everyone to be a VP using AI to do the work, we need some middle ground here and some real thought around implementation of these tools or there's going to be a generational canyon gap of knowledge between being able to "say" and being able to "do".
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 Goodhartâs Law: when a measure becomes a target, it ceases to be a good measure.
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.
>"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.
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.
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).
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.
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.
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.
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.
When you use DeepSeekâs first-party API, you are giving them your token stream. This has some training value, but it also has incredible amounts of, well, business intelligence value. When you tell AWS your secrets or your customer data, you can be fairly confident they wonât abuse that knowledge. When you give this data to, say, OpenAI, they more or less promise not to abuse it if youâre on an appropriate business plan. If you give it to DeepSeek, even incidentally as something your agent reads, I would be quite surprised if DeepSeek doesnât mine it for whatever purpose they or the government feel is appropriate.
The risk of letting your agent read .env goes far beyond the risk that the agent itself does something you donât like with the contents.
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.
> 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.
Like how server hosting at scale in a datacenter is cheaper than running your own datacenter? Despite ~every company consistently concluding that hosting their own stuff is several multiples cheaper?
No, I think local stuff using also-useful-for-other-things hardware will vastly undercut cloud hosting when the free money pipeline shuts down, and will stay that way for roughly forever. That doesn't mean cloud stuff isn't useful, clearly it is, but adding another company in the middle is rarely the solution for reducing costs.
You're assuming the price won't come down as the tech matures. That seems like a big assumption, considering how quickly open weights models are catching up to frontier models, and how little effort has been invested so far in optimizing inference costs.
It's especially a crazy assumption to make relative to the costs of employing a human. The costs of paying an entry level employee are unlikely to go down at all, and even if those costs do decline, there's a floor they can't drop below (minimum wage at the extreme end), whereas companies are free to optimize agentic costs as close to zero as possible.
So you are assuming that a cost which is extremely susceptible to optimization but which no one has yet seriously attempted to minimize will remain perpetually above a cost which is much less susceptible to optimization, is already subject to enormous efforts to minimize, and has a legally mandated floor. That seems like a bad bet.
Maybe this just counts as âlight useâ since Iâm a hobbyist programmer and I only run one coding agent session at a time, but I get about as much done as I did back when I was working while spending a lot of time browsing the Internet, etc.
Iâve spent $10-$20 a day using Claude to write code and closer to $5 a day now that I mostly use Deepseek and GLM, using API pricing (no subscriptions) since I donât use Claude Code.
This is a rounding error for a company. So I think thereâs plenty of room to use AI extensively while being more cost-conscious.
A significant caveat is that there is a pricing mismatch that makes it so first party's can subsidize quite heavily.
Agents are expensive in large part because tool calls require round trips. It's because these APIs are stateless and not streaming so you have to resend the whole context each time. This means you have roughly #tool calls x 1/2 context size cached input tokens over any given session. Most API providers overcharge you by a huge amount for cached tokens. A exception being Deepseek. Paying OpenAI $0.05 for 100k cached GPT5.5 tokens during a possibly 2 second round trip agent tool call is like paying $100/hr for what is likely to be ~10 to 20 GB of VRAM residence (holding the KV cache).
Or it got offloaded to NVME and you are paying $0.05 for that much PCIe bandwidth.
I think if local models catch up with current SOTA then that might not happen. Either way, I'm don't think the long-term for OAI, Anthropic etc. really holds up.
More straightforward to talk about the hardware directly. Full Kimi K2.6 needs an 8x H200 node to run and serve around 20 heavy users. You can rent an 8x H200 node for around $30/hr.
I'd imagine GPT-5.5 and Claude Opus 4.7 could run just fine on a 16x H200 node and serve at least 10 heavy users without the token output getting choppy.
What's funny is that this apparently wasn't something that the Uber COO seemed to think about when their company is arguably one of the most successful ever at the "subsidize to drive down costs until you capture nearly the entire market" strategy.
I have been saying the same for while. Someone always says "but Anthropic is making money on their API" or "But it's inference will get cheaper". But I don't believe it. first all the investments have to payed off at some point and second of all there are other things that cost money. I don't believe that any of them have a positive balance sheet.
I also don't think that blitz scaling will work like with Uber. The engineers are still there. We can work without the LLM tools.
If by "investments will pay off" you mean major profits, that's never going to happen as long as scaling laws hold. All revenue will just go to financing more compute, and either we hit AGI or have the greatest economic collapse in modern history.
The world will look drastically different 5 years from now; for the better or worse, so save every penny (especially if you work in tech).
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!
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.
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.
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).
> 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.
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.
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.
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.
Except you wonât 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.
âPlease don't post comments saying that HN is turning into Reddit. It's a semi-noob illusion, as old as the hills.â --hn guidelines (there are links to examples in the original)
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.
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
There is a complete lack of courage in the leadership of tech companies today, and top-down AI mandates are just another manifestation.
True visionaries think outside the box, but most tech executives are forcing their employees into black boxes, out of fear of not doing exactly what their competitors are doing.
We have lemmings for leaders, and that means thatâmuch like the LLMs that are being shoehorned into everythingâthere isnât room for original thinking. Everyoneâs strategy looks exactly the same.
There was an amusing post about judging developers based on token usage where some user on HN here was pushing this idea âICs donât like it but this is the best way to evaluateâ (something like that).
They have a whole management team and canât seem to find a way to judge or god forbid encourage developersâŚ
I'm going to offer a contrarian view here:
First is that despite a lot of waste, some innovation will arise from an enterprising employee finding some interesting use case. A lot of the tokenmaxxing is just waste, but out of that waste may arise a small number of genuinely powerful use cases.
Second is that many workers will be entrenched in their ways. If your executive goal is to achieve the above (find innovative ways of using AI), then you need to move everyone to use it. Most will just waste tokens, but someone may find a novel and useful way of using it that benefits the organization. It is difficult to achieve these without forcing people to act since their default is to follow the well-worn grooves.
So mandates like these are a top-down forcing function like a slime mold feeling out different paths to find resources.
Some devs in my org have fully embraced AI; some would not even use AI if not for leadership mandates and linking usage to performance reviews (I know, I think this is stupid, too). I can see why mandates could be useful since some folks definitely won't be inclined to use AI.
Is keeping your company private the easiest way to get around this?
> Everyoneâs strategy looks exactly the same.
If one is a CxO who's looking out for one's job security, herd-like behavior is the safest option, due to the (near universal) structure of "performance"-based executive remuneration.
Lacking not just courage, but also character. Wasting company money on buzzwords and dubious outcomes is lack of character.
> and stop using token usage as a metric of productivity
I participate in some management-focused online communities. Itâs crazy how many threads there are from frustrated managers trying to get their teams to stop thinking that their token use will be used as a proxy for their performance.
I think a few dumb companies did this and then it spread across social media, triggering a mass panic from engineers afraid their companies will be doing the same thing.
Itâs getting so bad that the conversation is shifting to how to identify and coach the token-maxxers to stop wasting the teamâs budget every week.
You're now in the last frame of the comic, getting thrown out the window.
Maybe it's time we adopt/design an economic system that isn't so easily co-opted by counterproductive prisoner's dilemmas.
What would such an economic system look like?
If there are any tech CEOs out there reading, I can offer my services. I will pointlessly burn unfathomable amounts of tokens, in parallel, 24 hours a day, 7 days a week, all for you. Think big big big numbers of tokens, you know whats cooler than a trillion tokens, a quadrillion tokens.
Lets talk my bonus, I will open the bidding at $1 per token.
Not very Billion Dollar Valuation of you.
I feel like individually, if you sat down with literally any reasonable person on the planet they would arrive at and/or agree with the tenor here.
I'd be curious to hear from people well versed in group psychology/dynamics and/or just a lot of leadership/people experience: what leads people to this type of thinking once they get in a group setting? It just... seems endemic at this point.
Obviously nobody here is going to know what I do or don't know, but I'm just increasingly curious what I am not understanding about this type of thing. It seems so obvious, yet that makes me ever more suspect that I'm oversimplifying it, or just totally ignorant about the problem in general.
It's because the average organization has lots of people who don't care about their own productivity and won't adopt new tools or processes unless forced to. This is true of most new tech - lots of workers had to be forced into using computers - but AI also has some other bumps to cross like lots of people who tried early models and then wrote them off, not realizing how fast they'd improve. And most orgs have no infrastructure or processes for allocating individuals token budgets, and most employees have no experience of properly deploying budgets.
Roll it all together and saying "just use it dammit" has some obvious advantages:
1. It's clear.
2. It's simple.
3. It eliminates all excuses employees might come up with for not using it.
The people at the top of these companies aren't stupid. They might have miscalculated how many tokens people can actually use, but that's very hard to calculate because usage is opaque and tools/processes change on a nearly weekly basis. They will eventually build out processes, tools, social conventions and performance metrics that take into account efficiency of token usage. But this is hard! Most managers aren't really assessed on the precise productivity of their teams, for instance, because productivity is often poorly defined.
> what leads people to this type of thinking once they get in a group setting
Game theory! The downside of being brave vastly outweighs the upside. For the C-suite, there is no cost to herdlike-behavior, regardless of the outcome. However, there is a very high personal downside to being a maverick, and your board later discovers you made the wrong choice against the grain. The upside of being maverick and right is very limited.
Once a behavior has become mainstream, hopping on the bandwagon is no longer individually attributable to decision-makers, but is seen (and reported) as a macro-economic phenomenon: Nadella, Zuckerberg and Bezos didn't overhire - the American tech industry overhired.
Wonât be canned for going with the herd. I think itâs that simple, even if the herd is running off a cliff.
That was a fun thought experiment while I waited for my ralph wiggum to finish running. Now thinking is over and back to the vibe
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
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).
I deeply believe this but have no strong evidence. Revenue has always been a cure all remedy. This will keep model providers alive along with the very wide range of companies that are experiencing growth with them (from chips to backhoes), for a time anyway. If/when that house of cards starts going in the other direction thereâs going to be widespread pain. By analogy the nonsense of the dotcoms and that crash had a very direct impact on their suppliers (e.g. telecoms). My only advice is to let the Microsoftâs and Metaâs do the tokenmaxxing, and donât get suckered into the idea you (startup, individual, etc) should be playing that game.
Every c suite in the country is panicking about being left behind, from their perspective itâs either token max or fade into obscurity, or at least thatâs what they were sold
its a herd mentality, its a lot easier to follow the louder voices than to spend time understanding how it impacts your own particular business. Because google does this way, or apple does this way is a common argument in lot of feature/business decisions
I don't think that's accurate. I think every C suite in the country is looking to do away with labor's leverage as much as possible. I think this is a cultural thing more than anything else, C suite + investors looking to get rid of those pesky humans required to prop up their lifestyles. AI is the most credible path toward that. Short, medium or long term returns be damned, this is a reconfiguration of society and they want to shed what they consider to be baggage.
Like anything it's a mixed bag. I am certainly working with people who I think truly believe the "max out on AI usage or become irrelevant" line. There are people who will privately let you know they're just working with the current meta the best way they can, but others who are drunk on kool aid.
Trying to operate as a rational, thinking person in a lot of environments right now feels impossible. Rational thought is being treated like AI skepticism.
Please. These are the same people that force their employees to use Microsoft teams because slack is $5 an employee a month. They're not going to sit idly by while employees burn thousands a month in tokens.
It depends on which people you're referring to. The allocation toward AI budget has been so massive that I think a lot of businesses are way behind on trying to assess value for dollar for the AI-related crud they're shelling out for.
Everyone is feeling it out but the vast majority of spend has been subscription based. Some outliers may have used a massive amount of tokens but companies didn't pay for that.
That VC funded gravy train is likely coming to an end. But fortunately there are also reasonably efficient models now so that the tokenmaxxers can still make the (much cheaper) tokens go brrrr.
They get paid for saying whatever VCs want to hear and now that thing is "we have now become an AI-native company". The thing I'm still trying to understand is who is scamming whom
Uber is publicly traded. They're not beholden to VCs any more.
Come on, donât be crazy
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.
Tokens are the new "lines of code per engineer". Easy to graph, easy to "manage".
The new TPS reports!
...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?
Meta does this. Guess what one of the criteria for their recent layoffs was.
Do you have any source for this at all? Iâve seen so many different exonerations for Metaâs layoff criteria including claims that engineers using the most AI were laid off because Meta had them build AI tools to replace themselves.
Everyone is oddly confident despite all of the conflicting explanations.
Meta tracks token consumption, but has explicitly stated that it is not a performance metric.
Indeed, they also said that previous time off for ill health wasn't a reason either.
but looking at the number of people who had taken leave, it suggests otherwise.
Anything a trash company says they're not doing is an admission of guilt.
Sure, and I have a bridge to sell you. Or alternately refer you to the inevitability of Goodhart's law.
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
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?
Most of the codebase is custom integrations for local markets. You can systematize some of it but most of the complexity comes from there.
Sure, but custom integrations seem unlikely to explain the majority of Uber's technical headcount. Let's say they average a dedicated engineer for each of their 1000 largest markets/locations. Let's assume another 200 across the countless smaller markets. Let's assume 50% overhead atop this for things like infra, tools, and management. These all seem like exceedingly generous estimates to me.
They actually had 5,000 engineers in the tokenmaxxing blog post. That's a lot of engineers for the rest of Uber's business activities.
Can you provide an example? What is different about running Uber services in Chicago vs. Indianapolis?
Vegas: ordering a tax "to a hotel" - hotels have different entrances, pickup / dropoff there during crazy times is hard. Uber UI for Vegas is unique / some features are designed to make it easier for driver and passanger to find each other
Airports: different regulations, different rules for pickup/dropoff. Also scammers who pretend to be in a car, walk with their phones around pick-up ares in airport and do bait-and-switch (saw that in Istanbul SAW and in Dubai Al Maktoum)
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.
Uber has also been public transit: https://www.theguardian.com/cities/2019/jul/16/the-innisfil-... (like actually public transit, not "lol they reinvented busses again" (though, lol, yes that too))
Essentially every single airport in the world is custom UI and custom walking path guides and pickup instructions, and rules for where pickups/dropoffs/etc can occur can change multiple times in a day, much to everyone's enjoyment. They're almost all private property, and are so valuable that whatever they want is what they get.
And food. Most/~all? major brands get custom integrations.
Hundreds (iirc) of identity verification providers, most or all custom, and constantly weighed against cost and accuracy because it ain't cheap and it ain't good but it is far better than none (both legally and ethically).
No idea how many payment sources they accept, but it's definitely a lot more than anyone who hasn't lived on 5+ continents thinks.
And remember that this is all international. So scale is huge and law changes are constant and frequently conflicting. Darn near every useful feature is illegal somewhere, at some time, for both good and bad reasons.
---
This is not at all to say I think Uber is efficient, clearly it is not. Not by an enormous margin. But there is a legitimate need for truly absurd complexity, because the world is not consistent. You see similar things happen anywhere [thing] tightly interacts with humans.
Ah local regulations and fees. Not so much the core service algorithms. That makes sense.
Use Link next time. Only $3
There's an excellent HN thread that talks about this very question (that comes up on HN every now and then - what _does_ company X do that needs so many engineering resources?): https://news.ycombinator.com/item?id=25375921
TL;DR: Managing a taxi service (that's what Uber is in my mind, not whatever "ride share" means) that spans cities and states, never mind countries, is extremely complicated. To their credit, Uber manages to make it look simple to the end user, prompting such comments as "meh it's just a few screens how hard could it be", which is triumph of product engineering as far as I am concerned.
Related: this blog from Uber talks about the problem of serving market-specific configuration data at scale: https://www.uber.com/us/en/blog/how-we-unified-configuration...
I think youâre missing how complex international operations and optimization are.
Each country has their own laws around what uber is and isnât allowed to do. This needs to be formalized in code. For example you actually call a taxi, though the uber app, and the amount you pay is per mile, not a fixed fare decided ahead of time. To add to this complexity, some cities will have their own laws. What happens if you take an uber from town a to b, where each one has different laws ? A lawyer probably has an answer but the app needs to adhere to that. On top of that laws change all the time.
Optimization, well you can always optimize something. speed, costs, paths etc. In a way this never ends.
I think the part we interact with as consumers is a tiny sliver of the complexity those services have to build and operate.
Werenât they trying to do their own self-driving thing?
I think this is partly a problem with companies that have had heavy investment. Uberâs value isnât based on what they are doing, it is based on the idea that they are going to render ideas like owning your own car or taking public transit obsolete (I mean thatâs an exaggeration but less of one than it ought to be).
They shut it down after they killed a pedestrian. (They also got sued by Waymo for illegally acquiring trade secrets, and settled.)
https://en.wikipedia.org/wiki/Death_of_Elaine_Herzberg
AFAIK they gave up on doing self-driving themselves a while ago. I'm sure they are still hoping to be able to get rid of human drivers somehow.
If they didnât have human drivers, theyâd have one less human to exploit per ride.
Well there is a lot of ongoing maintenance cost. There is probably still some marginal gains possible on the matching side. There are new products to launch. So while one specific software can mostly be finished, the total software of a company is always changing.
There are always newer technologies and techniques to be implemented. Better algorithms. Larger deployments. Better reliability. There are also almost always bugs to fix. So, so many bugs.
shiny new tools but people only want to use them on the same old problems. how can we innovate the development of crud apps even more?! that was what plagued the web dev landscape for some time. Constantly seeking newer lazier means of producing the same old product. I admit it has an allure but if companies are no longer constrained by dev effort / labour then they can only ponder their own reflection as the source of their failures.
Uber is at a large enough scale that this analysis doesn't work. You and I do not care even a tiny bit about "Eats for the Way", one of their planned features this year (https://www.uber.com/us/en/newsroom/go-get-2026/) that lets Uber Black passengers specify that their car should arrive with their Starbucks coffee order. But if 0.01% of users order 1 additional ride a month because of this, that's about 200k rides a year, which may well be sufficient to justify the development costs.
There is always a rewriting around the corner
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:
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!)
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.
Tokenmaxxing exists because executives think employees are resistant to change. Thats it, a way to incentivize/force every employee to experiment with a new technology. Obviously once they think everyone is utilizing AI the tokkenmaxxing stuff will end.
Yes. Executives think, correctly, that employees are resistant to change.
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.
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.
Man, it sure isnât hard for me to max it out.
It's not hard for most people now. 6 months ago when agents first started getting big, I genuinely didn't know enough about AI tools to understand how it was possible to use so many tokens, and I don't think I would have bothered to find time to learn without a kick.
Whoâs it important for?
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.
I think the key word is âbelievesâ. There is no proof that AI usage improves productivity. Token maxing is essentially customers paying to try and prove a businessâs 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.
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.
> 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.
lol yeah I'm not a poet.
Just append a reactive metal. "Like water through sodium"
2^30 tokens costs something like 2^10 dollars, order of magnitude, if that helps ballpark.
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?
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.
Sounds like you want to be in the next round of layoffs?
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?
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.
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
Waiting for tokenedging next.
^ Philip K. Dick's unreleased book title
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.
As long as there's an RPC connection established and a partially sent request, I think it would count.
"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.
>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.
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.
Clearly they need more layoffs, and for that matter why keep anyone around? After all, AI will be writing 100% of code in 2026.
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?
The point of this was always to explore what is possible with AI as quickly as possible. Obviously, there is going to be a lot of waste, but the 5-10% of employees who are truly thinking about it and discovering novel applications are what you are truly after. Because right now, you effectively have a giant, as of yet poorly explored space of potential uses.
Anyone who can find the actually valuable portions of the space early has a potentially huge competitive advantage. Even if the result of the experiment is the negative that AI is actually mostly not that useful, that is still extremely useful information in a time of great uncertainty regarding outcomes.
The bottom line is that this approach may be expensive, but if you have the money to burn, it's far from the worst strategy if you are trying to position yourself correctly for the future.
Whatâs the huge advantage though? Adopting workflows that give big productivity gains is relatively easy even for big corporations. Itâs only an advantage if you can keep it secret.
OTOH maybe weâre in for a future of patenting prompts.
The thing I don't get though, is that most people just don't have that much work they need to do. I can use AI to pretty easily get my work done just via the regular chat interfaces. But because of the tokenmaxxing metrics that leadership tracks, I end up just having the AI deliberate for hours on random things just so that I can boost my token numbers. I think tokenmaxxing for the end goal you described is only realistic when the engineers are truly buried under a backlog of work.
I think unfortunately it's not about what seems obvious, or even what seems more likely, but about what seems retrospectively justifiable regardless of outcome.
The incentive structure of this type of decision is 'absolutely under no circumstances existentially mess up'. Ostensibly with respect to the organisation, but in actual reality much more so with respect to the individual(s) involved in the decision.
If everyone else is doing something that kind of obviously makes no sense, and you decide to break from the crowd by instead doing what does make sense, then there's a pretty solid chance of gaining a temporary edge while reality resolves the truth. But those gains probably won't matter all that much for the organisation, or indeed your position within it. It's a solid chance of an unimportant gain.
However on the other hand, there's a tail risk that something very unexpected happens and the thing everyone's doing that makes no sense actually turns out to make sense - sometimes even for entirely unpredictable incidental reasons - and then, well, you're in trouble. Not necessarily 'you' the organisation.. they'll likely be able to catch up and it won't matter that much. But for 'you' personally, the decision maker, it's very much not good.
As a bonus, in the much more likely scenario that the thing that makes no sense turns out to indeed make no sense, you're in the same boat as everyone else, there's no relative loss, and most importantly you don't stick out as someone who did something as risky as to go against the prevailing, albeit pretty clearly nonsensical, sentiment.
So basically, game theory tells you pretty quickly to just go with the thing that makes no sense if you're optimising for some (weighted) cross of what's best for the organisation and yourself as the decision maker.
Someday maybe Goodhart's Law will be intuitive to people making decisions like this, but not any time soon I guess
> It's amazing that it took months to figure this out
We arenât there yet, so far it is just a COO questioning the investment
The inability of leaders to understand Goodhartâs Law is always a sight to behold. They see a number go up and pat themselves on the back for how well their employees are making it go up without ever wondering if the thing they care about is happening.
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.
In other words, people who are productive get more done when you scale up what they're already doing, and people who aren't productive will not magically become productive when you scale up what they're already doing. That's incredibly obvious, because we've seen how this plays out repeatedly in so many different ways (lines of code, commits, tickets closed, etc.), and it has nothing to do with tokens or even programming, but just how trying to manage people works.
> Some FAANGs are doing amazing things with unlimited tokens. Others have no clue what to do with tokens.
Unlimited tokens is different from âuse AI a lot or we will fire you, and we are counting token consumption as usageâ. Obviously the latter is stupid and yet it was done in many places.
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.
> But it's not. Some FAANGs are doing amazing things with unlimited tokens
Giving someone unlimited access to a resources is not the same as directing or incentivizing them to use it for the sake of using it which is what the parent comment criticized.
As for the other FAANGs, Meta and Google have (not good but still) frontier models of their own, so they are very different from a company paying API costs per token.
Where can I see those amazing things done by FAANGs?
> Some FAANGs are doing amazing things with unlimited tokens.
Would love to know what things!
Show me some fang that have made nice outwards facing products through a fully embraced AI workflow?
AI is an accelerator that engineers should know and have access to, but it's not something that should have mandated usage and quotas around. It's also absolutely dangerous for young engineers and the like - it fundamentally denies you of the "learning" aspect. I'm now seeing in interviews young graduates being given AI tasks to complete and they come back with a correct solution and no concept of how it is working.
You learn and reinforce learning by DOING and reading in depth. High level summaries don't teach anything and are the kinds of things only VPs care about. So, unless the intention in the future is for everyone to be a VP using AI to do the work, we need some middle ground here and some real thought around implementation of these tools or there's going to be a generational canyon gap of knowledge between being able to "say" and being able to "do".
Feels like they are debating internally whether to cut people or AI spending. Very healthy debate. Let's hope they spare people.
tangent: anyone have businessinsider subscription. i feel like they've really stepped up their game last few years.
At what point is there a difference between a burn rate and tokenmaxxing? Isn't it the same as during the dotcom bubble?
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 Goodhartâs Law: when a measure becomes a target, it ceases to be a good measure.
Oof leader of bubble are starting to take a step back?
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.
>"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.
You can measure productivity by hours spent at a desk?
You can measure attendance by hours spent at a desk
Well if you're a devshop just billing hours of mostly low impact work then hours are very much equal to productivity.
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
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.
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.
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).
I just realized my company is months behind this curve. About to blow my token allocation. Before I do, anyone have requests? Sincerely.
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.
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.
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.
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.
When you use DeepSeekâs first-party API, you are giving them your token stream. This has some training value, but it also has incredible amounts of, well, business intelligence value. When you tell AWS your secrets or your customer data, you can be fairly confident they wonât abuse that knowledge. When you give this data to, say, OpenAI, they more or less promise not to abuse it if youâre on an appropriate business plan. If you give it to DeepSeek, even incidentally as something your agent reads, I would be quite surprised if DeepSeek doesnât mine it for whatever purpose they or the government feel is appropriate.
The risk of letting your agent read .env goes far beyond the risk that the agent itself does something you donât like with the contents.
But this shouldn't be a risk if you host the model locally.
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.
> 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.
Like how server hosting at scale in a datacenter is cheaper than running your own datacenter? Despite ~every company consistently concluding that hosting their own stuff is several multiples cheaper?
No, I think local stuff using also-useful-for-other-things hardware will vastly undercut cloud hosting when the free money pipeline shuts down, and will stay that way for roughly forever. That doesn't mean cloud stuff isn't useful, clearly it is, but adding another company in the middle is rarely the solution for reducing costs.
You're assuming the price won't come down as the tech matures. That seems like a big assumption, considering how quickly open weights models are catching up to frontier models, and how little effort has been invested so far in optimizing inference costs.
It's especially a crazy assumption to make relative to the costs of employing a human. The costs of paying an entry level employee are unlikely to go down at all, and even if those costs do decline, there's a floor they can't drop below (minimum wage at the extreme end), whereas companies are free to optimize agentic costs as close to zero as possible.
So you are assuming that a cost which is extremely susceptible to optimization but which no one has yet seriously attempted to minimize will remain perpetually above a cost which is much less susceptible to optimization, is already subject to enormous efforts to minimize, and has a legally mandated floor. That seems like a bad bet.
Maybe this just counts as âlight useâ since Iâm a hobbyist programmer and I only run one coding agent session at a time, but I get about as much done as I did back when I was working while spending a lot of time browsing the Internet, etc.
Iâve spent $10-$20 a day using Claude to write code and closer to $5 a day now that I mostly use Deepseek and GLM, using API pricing (no subscriptions) since I donât use Claude Code.
This is a rounding error for a company. So I think thereâs plenty of room to use AI extensively while being more cost-conscious.
A significant caveat is that there is a pricing mismatch that makes it so first party's can subsidize quite heavily.
Agents are expensive in large part because tool calls require round trips. It's because these APIs are stateless and not streaming so you have to resend the whole context each time. This means you have roughly #tool calls x 1/2 context size cached input tokens over any given session. Most API providers overcharge you by a huge amount for cached tokens. A exception being Deepseek. Paying OpenAI $0.05 for 100k cached GPT5.5 tokens during a possibly 2 second round trip agent tool call is like paying $100/hr for what is likely to be ~10 to 20 GB of VRAM residence (holding the KV cache).
Or it got offloaded to NVME and you are paying $0.05 for that much PCIe bandwidth.
I think if local models catch up with current SOTA then that might not happen. Either way, I'm don't think the long-term for OAI, Anthropic etc. really holds up.
More straightforward to talk about the hardware directly. Full Kimi K2.6 needs an 8x H200 node to run and serve around 20 heavy users. You can rent an 8x H200 node for around $30/hr.
I'd imagine GPT-5.5 and Claude Opus 4.7 could run just fine on a 16x H200 node and serve at least 10 heavy users without the token output getting choppy.
What's funny is that this apparently wasn't something that the Uber COO seemed to think about when their company is arguably one of the most successful ever at the "subsidize to drive down costs until you capture nearly the entire market" strategy.
This is what Iâm betting on.
The financials donât make sense now. Based on the expenditure the finances wonât ever make sense.
I have been saying the same for while. Someone always says "but Anthropic is making money on their API" or "But it's inference will get cheaper". But I don't believe it. first all the investments have to payed off at some point and second of all there are other things that cost money. I don't believe that any of them have a positive balance sheet.
I also don't think that blitz scaling will work like with Uber. The engineers are still there. We can work without the LLM tools.
If by "investments will pay off" you mean major profits, that's never going to happen as long as scaling laws hold. All revenue will just go to financing more compute, and either we hit AGI or have the greatest economic collapse in modern history.
The world will look drastically different 5 years from now; for the better or worse, so save every penny (especially if you work in tech).
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!
Well there are already offerings like githits (https://news.ycombinator.com/item?id=46105112) that sort of promise optimize bang-per-buck of inference
wtv
what the fuck is this timeline I am stuck living in
Itâs funny that âmaxxingâ entered the common vocabulary.
If you're not tokenmaxxing, you're getting tokenmogged on the AI leaderboard, and your next review ain't gonna be pretty.
A good 80% by volume of the modern vernacular is 4chan language that got sanded down.
Sanding down is how we got goyslop turned into slop.
Slop is a word in its own right which got the goy prefix later in life.
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.
I find it useful that if they cut the use altogether I will pay for it out of pocket.
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.
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).
> 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.
bigCoâs donât get to do the $200 Max plans, they have unlimited plans but get charged like API
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-bu...
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.
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.
Except you wonât 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.
Uber COO says he just decided to short a bunch of AI company stock.
Slightly ot, but I really dislike this reddit WSBization of HN.
Adds nothing insightful to these discussions.
âPlease don't post comments saying that HN is turning into Reddit. It's a semi-noob illusion, as old as the hills.â --hn guidelines (there are links to examples in the original)
It's unfortunately the WSBification of the entire society.
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