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?
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.
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.
The inability of leaders to understand Goodhart’s 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.
> Some FAANGs are doing amazing things with unlimited tokens. Others have no clue what to do with tokens.
Unlimited tokens is different from “use AI a lot or we will fire you, and we are counting token consumption as usage”. Obviously the latter is stupid and yet it was done in many places.
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 - my thesis is - that many companies will trade off havy tolen usage for (maybe a bit slower, bit less accurate) employees again.
DeepSeek is an open weights model—while it's possible hosted versions are subsidized, we know what it would cost to run on premises, and while it's expensive it's cheaper than an employee. The latest DeepSeek models are not as good as Claude, but they're not super far off either.
>"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.
“Please don't post comments saying that HN is turning into Reddit. It's a semi-noob illusion, as old as the hills.” --hn guidelines (there are links to examples in the original)
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!
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.
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 vs current cost and payoff not being there.
Except you won’t because they will threaten to fire you and force you to route all of your AI through data protection proxy to stop exfiltration by filtering and tracking prompts/response tokens.
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
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.
Waiting for tokenedging next.
Is this when you type the prompt into the text window, but don't bit enter? Make the GPU see the message "x is typing"? Lol.
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.
The inability of leaders to understand Goodhart’s 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.
> Some FAANGs are doing amazing things with unlimited tokens. Others have no clue what to do with tokens.
Unlimited tokens is different from “use AI a lot or we will fire you, and we are counting token consumption as usage”. Obviously the latter is stupid and yet it was done in many places.
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 - my thesis is - that many companies will trade off havy tolen usage for (maybe a bit slower, bit less accurate) employees again.
DeepSeek is an open weights model—while it's possible hosted versions are subsidized, we know what it would cost to run on premises, and while it's expensive it's cheaper than an employee. The latest DeepSeek models are not as good as Claude, but they're not super far off either.
This is what I’m betting on.
The financials don’t make sense now. Based on the expenditure the finances won’t ever make sense.
>"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.
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.
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.
“Please don't post comments saying that HN is turning into Reddit. It's a semi-noob illusion, as old as the hills.” --hn guidelines (there are links to examples in the original)
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 find it useful that if they cut the use altogether I will pay for it out of pocket.
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.
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.
bigCo’s don’t 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 vs current cost and payoff not being there.
[1] https://www.forbes.com/sites/janakirammsv/2026/05/17/uber-bu...
Except you won’t because they will threaten to fire you and force you to route all of your AI through data protection proxy to stop exfiltration by filtering and tracking prompts/response tokens.
It’s funny that “maxxing” 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.