A past employer thought it was a good idea to put up a leaderboard of who sent the most Slack messages. They celebrated the people at the top for being so active.
Predictably, everyone started talking in Slack like their jobs depended on it. Everyone was responding to everything. Instead of writing out a complete message and pressing enter, they'd send each fragment of the sentence as a new line.
The Slack leaderboard was never shown again. Unfortunately the habit remained because people were afraid they were going to be secretly judged by how much Slack activity they generated.
I expect the same thing is going to happen at companies who had token leaderboards. Once you've instilled that fear in people, they internalize the expectation.
Reminds me of the place I worked at where I got in trouble because I was the only person writing JIRA tickets. Instead of bitching out the product manager or the tester for not writing tickets, they just complained to me. And if I wrote a ticket about how we could speed up the 40 minute build to 15 minutes I'd have to explain "How does this change improve the customer experience?" to which I answered "If the build was faster the customer would have had the product six months ago"
I worked somewhere that made time from PR being sent for review and ready to merge be a metric for the reviewers. Not time to add feedback in each round. Total time elapsed.
You have to realize that if you set a measure, you're actually setting a goal for your employees. There is no such thing as a meaningless metric; why else would you measure it?
No amount of "this isn't used for anything" will change that. It's inherent in human nature in the 21st century to believe any and all metrics will be used against them, and therefore must be gamed.
It's why you also have to set UNBELIEVABLY clear goals and have incentives tied to those goals. Incentives meaning money. If you want to measure things, measure them. But have clear, consistent, and meaningful goals tied to bonuses or something if you want a thing done correctly.
Are you saying what people are hoping to achieve with stupid goals? Because yeah, obviously. But the point is that they're stupid, so they don't achieve that, and that failure is 100% knowable in most scenarios.
What is about silicon valley leaders not understanding basic economics or business management? These kind of cargo cult tactics would not fly in any other industry.
I still don't understand how Mark Zuckerberg has any serious investors, he went on this AI tangent and has absolutely nothing to show for it, despite FB / Meta having built some key tech in the space. He needs to stop trying to do something "different" and literally try and build a serious coding agent he can sell, he could have probably had something worthwhile in that space by now.
He started being drastically more serious into AI in 2022, and 2023 and he has nothing to show for it.
Heck, he could have rented GPUs the way Elon did at this point and either mended the bleeding or stopped it, not sure how many he has, but it beats losing this badly.
If he doesn't wake up and learn how to business, I suspect he will lose his empire he's built up for himself.
>he could have rented GPUs the way Elon did at this point
"Meta building cloud business to sell excess AI capacity, Bloomberg News reportsMeta building cloud business to sell excess AI capacity, Bloomberg News reports"
It wasn't leadership doing this though. Any meta IC can generate internal apps and dashboards. This was unofficial and unsupported. Some random IC just made it for fun. Management is usually pretty lax with stuff like this (plenty of games and joke internal apps) so they left it up until it became a problem.
I know right? What did the leadership think would happen when they give some of the worlds greatest software engineers (supportably), a easily quantifiable metric to target?
The leaderboard wasn't leadership generated, it was engineer generated from internally available data. The leadership target is "impact" from ai tools.
What a wonderful scapegoat! Technically it's all "engineer created" because the managers generally don't do technical work. I bet many managers pushed their reports to increase their usage during their 1:1 meetings based on data from the leaderboard. If management had any sense that it was a bad metric, they had ample time to get ahead of it and take it down and provide appropriate guidance. Instead, predictably, they waited until it was a full on disaster and a crisis before acting.
Given that Meta has run 5ish layoffs at this point, and everyone is in survival mode, what did they expect? Everyone wants to juice whatever numbers possible to keep their jobs.
Now come on, there was a recent post where the author argued that infallible management knew this would happen, but was part of the double-secret-probation strategy to get the cogs to finally start using AI.
I still think this is true and it’s not obvious to me from the source article that Meta believes otherwise. I couldn’t find the full memo, do they claim the leaderboard or “tokenmaxxing” era was a mistake?
It's stories like this that really dispell the genius/merit theory of successful business. The best you can say about Zuck is he didn't prevent Facebook from becoming huge.
I’d be curious to see the breakdown on spending by use case. I’ve heard it said that the majority of tokenmaxing comes from none technical uses like reading PDFs, creating PowerPoints, generating graphics/images… ect. But I’ve never heard any actual proof to that.
One thing I find fascinating as a software engineer who talks to non software engineers who use AI tools is how "reading PDFs" is not more of a solved problem. What I mean is that uploading a PDF into a chatbot tool seems to be an extraordinarily obvious use case that non technical (and technical) users would want to do.
IMO claude, chatgpt/codex, etc should be able to optimize the PDF use case to be extremely token efficient as it's a very obvious use case. But when I start to explain to my wife/friends why it burns through so much quota, I find myself thinking "why should they have to understand this aspect of it". to me, that the details of PDF parsing and extracting are relevant to users (instead of solved such that you don't have to pay attention to it) shows how these tools are not nearly as "ready" as they are made out to be. I may be preaching to the choir on this one, but just my 2c
Because PDFs are a nightmare of a format and the only thing that’s is reasonably guaranteed about them is they will render to an image that people can read, the parsing of which will be much less token efficient than the equivalent text
I agree with you, but every non-engineer I know using these tools 100% will drag and drop a PDF into a chatbot. Anthropic and OpenAI as companies who are selling their products to all sorts of businesses should have a much better means of handling this nightmare of a format because it is so pervasive and so obviously what so many of their customers are going to drop into the product.
For anyone needing to do this, the answer is to convert it to an image first. Far smaller, LLMs work well with them (even in some pretty insane use cases I've seen), and, along with human review, it can be a huge productivity gain that results in structured data.
You don't need to use an online service to do this; you get to avoid spending money on tokens doing it offline.
Gemma 4 works perfectly well offline on limited hardware (I have an 8GB video card) and can handle extracting text from image-based PDFs just fine.
Take a PDF -> run it through MarkItDown [1], using the OCR plugin if you need (point it to Gemma 4) -> now you can ask Gemma 4 questions about the document.
I am sure Gemma 4 could even create a GUI to make this process very simple for a non technical user.
I hope someday we can get out of this local maxima of PDF documents. The format is terrible, but was right place, right time and might be impossible to dislodge.
For sure there are very optimized ways to do it. My point is that a non technical user will drag and drop a pdf into a chatbot. and from a UX/product perspective, they should have to think about it more than that IMO. but seemingly, that's very much an expensive, inefficient way of doing it (burning through a whole context window try to read it, reloading it multiple times per conversation, etc.).
You are missing that the product is the hype cycle around AI and that's worth Trillions of $ (Trillions with a T). Why build a PDF parser that generate text when you can BS in a podcast and get paid.
This discussion was about measures, goals and incentives. Follow the incentives.
the majority came from random claws running on cron. They get a heart-beat, wake up every 10mins, reads all internal-posts, emails, gchat messages, diffs, and decides to post some random message to the workplace so other claws can also regurgitate. rinse and repeat and then we are looking at $B tokens
And I still can't exhaust the limits on my Claude Max subscription, despite being more productive than I've ever been in terms of real work (ie, things that actually make money)
Ok I’ll ask since nobody else has — are they not giving their devs a Claude code max or Codex Pro subscription? If so, why is token cost approaching billions? And if not, why not?
The subscriptions are for personal use not enterprise.
i.e. [1] "This article is about paid Max plans for individual consumers. If you're part of an organization looking to use Claude with your team, refer to Team and Enterprise Plans."
Enterprise customers don’t get those plans, at the enterprise level you have to pay by the API rate… so people don’t have limited use, but you’re also not getting the heavily discounted rate the “normal” plans are at.
Not surprising. It seems that the comment section of every coding agent thread has at least one person mentioning they use "tokenmaxxing" to increase their token usage because it was brought up during their quarterly review, at a standup, or some other communique from on high.
Just wonder what happens when more and more companies introduce similar restrictions. Will that lead to devaluations of the LLM companies?
measuring uptime? I've seen Anthropic's status page, and they are a >$1 Trillion dollar company who "largely solved" coding. so clearly you aren't correct. /s
Ok, uptime. How do you measure an individual’s contribution to uptime? If Claude goes down does everyone take a hit? If Claude stays up everyone gets rewarded?
If so, your metric cannot distinguish between a bad engineer and a good one.
If not, you have the same problem you started with: measuring contributions to “uptime”.
my friend, I was being sarcastic before, and I am agreeing with you. LoC, token spend, etc as metrics are horrible measures. Software uptime is a great metric. I'm merely lamenting that in the age we're in, uptimes are getting worse and worse
Within a few weeks of telling people at our company that if they don’t use AI they will be replaced by someone who does, they just announced that their allocation with ChatGPT has reset and are now panicking as they blew through their million token allocation for this month in under six hours - you can’t make this shit up.
I have never worked there and I am likely very unqualified to ever work there and Zuck has more money than I could dream of so take my comment with that in mind.
Meta sounds like a cluster-F of a place to work. Massive reorgs around wild ideas like the metaverse and everything Ai all the time. Employees terrified of being fired. Incentivizing token spending and then cutting it off. While the overall company may be fine, the dev department sounds rudderless and absolutely miserable.
that is a fair point. The contrast between Meta and Apple could not be bigger here. Apple has billions of devices and yet they decided to use 3rd party models from OpenAI and later Google to build their AI features rather than building foundational models in house. Yet Meta did opposite: they built models (spending billions of $$$ and firing 10% of the company) for billions of users who rather would not use Meta AI features.
As many companies do with all their budgets, down to the trivial and clearly positive EV cost of free coffee. So it goes, cost controls are hard and necessarily imprecise.
I can’t tell if you’re complaining that Meta isn’t saving the whales or that their products aren’t good. If it’s the latter, you should double check their financials.
The only thing I’ve seen Meta release recently are spy glasses, and every employee who has worked on that product should be in prison (with a live 24/7 feed where the world gets to watch them wallow).
The times I’ve been asked to evaluate a prospective candidate and I see that product on their résumé, it’s been an instant veto, in the same category as working at Palantir.
it's not that difficult to say it confidently if you use any of their services and applications because exactly nothing has changed.
For reference most labor productivity increases for the last 50 years amounted to about 2% per year. If a hypothetical FB engineer had doubled their productivity with their gazillion tokens that would be 30 years of productivity gains in one year. I'd wager the evidence would be quite evident if you opened any of their apps
"The leaderboard, which ranked employees and teams by token consumption, inadvertently incentivized usage volume over productive output."
Who could possibly have predicted that happening?
A past employer thought it was a good idea to put up a leaderboard of who sent the most Slack messages. They celebrated the people at the top for being so active.
Predictably, everyone started talking in Slack like their jobs depended on it. Everyone was responding to everything. Instead of writing out a complete message and pressing enter, they'd send each fragment of the sentence as a new line.
The Slack leaderboard was never shown again. Unfortunately the habit remained because people were afraid they were going to be secretly judged by how much Slack activity they generated.
I expect the same thing is going to happen at companies who had token leaderboards. Once you've instilled that fear in people, they internalize the expectation.
Reminds me of the place I worked at where I got in trouble because I was the only person writing JIRA tickets. Instead of bitching out the product manager or the tester for not writing tickets, they just complained to me. And if I wrote a ticket about how we could speed up the 40 minute build to 15 minutes I'd have to explain "How does this change improve the customer experience?" to which I answered "If the build was faster the customer would have had the product six months ago"
This will inevitably be allocated like other budgets, and from talking to Meta folks about GPU budgets, it is going to be brutal.
I worked somewhere that made time from PR being sent for review and ready to merge be a metric for the reviewers. Not time to add feedback in each round. Total time elapsed.
Insanity
You have to realize that if you set a measure, you're actually setting a goal for your employees. There is no such thing as a meaningless metric; why else would you measure it?
No amount of "this isn't used for anything" will change that. It's inherent in human nature in the 21st century to believe any and all metrics will be used against them, and therefore must be gamed.
It's why you also have to set UNBELIEVABLY clear goals and have incentives tied to those goals. Incentives meaning money. If you want to measure things, measure them. But have clear, consistent, and meaningful goals tied to bonuses or something if you want a thing done correctly.
Kinda.
The answer is simpler on the surface: focus.
Generally the problem is the larger the firm’s operations, the harder it is to focus.
Apple is the only firm that has done well on this consistently and doesn’t have a huge grave yard of failures to show for it.
Are you saying what people are hoping to achieve with stupid goals? Because yeah, obviously. But the point is that they're stupid, so they don't achieve that, and that failure is 100% knowable in most scenarios.
I wonder if this should be codified as a rule of thumb, or an unofficial "law"? One perhaps we can reference easily among our peers.
What is about silicon valley leaders not understanding basic economics or business management? These kind of cargo cult tactics would not fly in any other industry.
I still don't understand how Mark Zuckerberg has any serious investors, he went on this AI tangent and has absolutely nothing to show for it, despite FB / Meta having built some key tech in the space. He needs to stop trying to do something "different" and literally try and build a serious coding agent he can sell, he could have probably had something worthwhile in that space by now.
He started being drastically more serious into AI in 2022, and 2023 and he has nothing to show for it.
Heck, he could have rented GPUs the way Elon did at this point and either mended the bleeding or stopped it, not sure how many he has, but it beats losing this badly.
If he doesn't wake up and learn how to business, I suspect he will lose his empire he's built up for himself.
>he could have rented GPUs the way Elon did at this point
"Meta building cloud business to sell excess AI capacity, Bloomberg News reportsMeta building cloud business to sell excess AI capacity, Bloomberg News reports"
https://www.reuters.com/business/meta-sell-excess-ai-computi...
It’s funny how many times the same thing happens at each large company. I think people’s thought process is this:
> Oh wow! If I paid for this myself I would have spent a lot of money! Are other people spending as much as me? I’m going to create a leaderboard!
> Oh no, my misinformed manager is using the leaderboard as a slight of hand for work. I need to game this now.
Then the leaderboard is banned… I can’t see how this ever really goes up the chain beyond director.
It wasn't leadership doing this though. Any meta IC can generate internal apps and dashboards. This was unofficial and unsupported. Some random IC just made it for fun. Management is usually pretty lax with stuff like this (plenty of games and joke internal apps) so they left it up until it became a problem.
> Who could possibly have predicted that happening?
Charles Goodhart :-)
https://en.wikipedia.org/wiki/Goodhart%27s_law
I know right? What did the leadership think would happen when they give some of the worlds greatest software engineers (supportably), a easily quantifiable metric to target?
The leaderboard wasn't leadership generated, it was engineer generated from internally available data. The leadership target is "impact" from ai tools.
What a wonderful scapegoat! Technically it's all "engineer created" because the managers generally don't do technical work. I bet many managers pushed their reports to increase their usage during their 1:1 meetings based on data from the leaderboard. If management had any sense that it was a bad metric, they had ample time to get ahead of it and take it down and provide appropriate guidance. Instead, predictably, they waited until it was a full on disaster and a crisis before acting.
Budget impact is technically impact.
> Who could possibly have predicted that happening?
Everyone except the executives who get paid millions to predict exactly that.
Not a problem. There are thousands of employees standing by, willing to sacrifice their jobs for their vision.
It's a hard job, someone has to not pay consequences for bad decisions.
Given that Meta has run 5ish layoffs at this point, and everyone is in survival mode, what did they expect? Everyone wants to juice whatever numbers possible to keep their jobs.
Now come on, there was a recent post where the author argued that infallible management knew this would happen, but was part of the double-secret-probation strategy to get the cogs to finally start using AI.
I still think this is true and it’s not obvious to me from the source article that Meta believes otherwise. I couldn’t find the full memo, do they claim the leaderboard or “tokenmaxxing” era was a mistake?
Would they admit it if it was? Or would they try to find a plausible rationalisation for wasting billions without any return?
unfortunately at big tech, this shit will keep happening.
people who make it to managers tend to have bozo tendencies & are yes men.
before it was lines of code, Jira tickets closed. Now it's tokens spent.
How dare you question the most effective allocators of capital.
It's stories like this that really dispell the genius/merit theory of successful business. The best you can say about Zuck is he didn't prevent Facebook from becoming huge.
I’d be curious to see the breakdown on spending by use case. I’ve heard it said that the majority of tokenmaxing comes from none technical uses like reading PDFs, creating PowerPoints, generating graphics/images… ect. But I’ve never heard any actual proof to that.
One thing I find fascinating as a software engineer who talks to non software engineers who use AI tools is how "reading PDFs" is not more of a solved problem. What I mean is that uploading a PDF into a chatbot tool seems to be an extraordinarily obvious use case that non technical (and technical) users would want to do.
IMO claude, chatgpt/codex, etc should be able to optimize the PDF use case to be extremely token efficient as it's a very obvious use case. But when I start to explain to my wife/friends why it burns through so much quota, I find myself thinking "why should they have to understand this aspect of it". to me, that the details of PDF parsing and extracting are relevant to users (instead of solved such that you don't have to pay attention to it) shows how these tools are not nearly as "ready" as they are made out to be. I may be preaching to the choir on this one, but just my 2c
Because PDFs are a nightmare of a format and the only thing that’s is reasonably guaranteed about them is they will render to an image that people can read, the parsing of which will be much less token efficient than the equivalent text
I agree with you, but every non-engineer I know using these tools 100% will drag and drop a PDF into a chatbot. Anthropic and OpenAI as companies who are selling their products to all sorts of businesses should have a much better means of handling this nightmare of a format because it is so pervasive and so obviously what so many of their customers are going to drop into the product.
I think they’ve just decided that vision gives the best results and the token issue will take care of itself.
For anyone needing to do this, the answer is to convert it to an image first. Far smaller, LLMs work well with them (even in some pretty insane use cases I've seen), and, along with human review, it can be a huge productivity gain that results in structured data.
I agree with your recomendation, but converting a pdf to an image is by no means smaller. PDFs are much closer to SVGs then to jpegs.
You don't need to use an online service to do this; you get to avoid spending money on tokens doing it offline.
Gemma 4 works perfectly well offline on limited hardware (I have an 8GB video card) and can handle extracting text from image-based PDFs just fine.
Take a PDF -> run it through MarkItDown [1], using the OCR plugin if you need (point it to Gemma 4) -> now you can ask Gemma 4 questions about the document.
I am sure Gemma 4 could even create a GUI to make this process very simple for a non technical user.
[1] https://github.com/microsoft/markitdown
I hope someday we can get out of this local maxima of PDF documents. The format is terrible, but was right place, right time and might be impossible to dislodge.
The problem is that for 99% of people in 99% of cases they work fine. It's hard for people to understand that they're trash.
Source; my last job working with accessibility and that nightmare.
The best way to parse pdfs is to convert them to images and feed them into the llm.
This workflow is highly optimized.
For sure there are very optimized ways to do it. My point is that a non technical user will drag and drop a pdf into a chatbot. and from a UX/product perspective, they should have to think about it more than that IMO. but seemingly, that's very much an expensive, inefficient way of doing it (burning through a whole context window try to read it, reloading it multiple times per conversation, etc.).
Absolutely this. Never try to parse a native PDF document with any expectation of coherence or consistency.
You are missing that the product is the hype cycle around AI and that's worth Trillions of $ (Trillions with a T). Why build a PDF parser that generate text when you can BS in a podcast and get paid.
This discussion was about measures, goals and incentives. Follow the incentives.
the majority came from random claws running on cron. They get a heart-beat, wake up every 10mins, reads all internal-posts, emails, gchat messages, diffs, and decides to post some random message to the workplace so other claws can also regurgitate. rinse and repeat and then we are looking at $B tokens
I'd guess through LLM embedded PoC projects.
You can rack up token consumption extremely quickly when you embed LLMs into automated processes or products.
I'd be very surprised if these numbers are just typical coding usage with no scripting/pipeline/automation stuff
One thing we use it for is for forking tools internally because of politics.
Wasn’t this already reported on? FWIW this article links to primary sources from early last month https://www.theinformation.com/articles/tokenminimizing-meta...
And I still can't exhaust the limits on my Claude Max subscription, despite being more productive than I've ever been in terms of real work (ie, things that actually make money)
This is what you get when token consumption becomes a KPI...
Ok I’ll ask since nobody else has — are they not giving their devs a Claude code max or Codex Pro subscription? If so, why is token cost approaching billions? And if not, why not?
They can't.
The subscriptions are for personal use not enterprise.
i.e. [1] "This article is about paid Max plans for individual consumers. If you're part of an organization looking to use Claude with your team, refer to Team and Enterprise Plans."
[1]: https://support.claude.com/en/articles/11049741-what-is-the-...
Enterprise customers don’t get those plans, at the enterprise level you have to pay by the API rate… so people don’t have limited use, but you’re also not getting the heavily discounted rate the “normal” plans are at.
Big enterprises don't get to have those subscriptions. OpenAI or Anthropic simply won't sell them to you if you need a couple thousand of those.
Not sure if I missed it but I couldn't find any information in the article to explain where the "approaching billions" estimate is coming from.
I could believe it, but I'd want to see something a little more concrete.
Not surprising. It seems that the comment section of every coding agent thread has at least one person mentioning they use "tokenmaxxing" to increase their token usage because it was brought up during their quarterly review, at a standup, or some other communique from on high.
Just wonder what happens when more and more companies introduce similar restrictions. Will that lead to devaluations of the LLM companies?
measure outcomes (impact), not effort (token usage, lines of code, code coverage, hours worked, etc.)
The whole phenomenon of metric based Eng evaluations is because leadership does not trust line managers to evaluate individual engineers.
What outcomes though? The ones I’ve seen posted are still nonsensical metrics that a publicly traded firm absolutely doesn’t care about.
It wants to see faster R&D, higher revenues from existing assets, greater operating margins, higher sales to invested capital ratio and so on…
The best way to measure that for a software firm is up-time of services, usage and project completion duration
measuring uptime? I've seen Anthropic's status page, and they are a >$1 Trillion dollar company who "largely solved" coding. so clearly you aren't correct. /s
Ok, uptime. How do you measure an individual’s contribution to uptime? If Claude goes down does everyone take a hit? If Claude stays up everyone gets rewarded?
If so, your metric cannot distinguish between a bad engineer and a good one.
If not, you have the same problem you started with: measuring contributions to “uptime”.
Unfortunately that is a group metric, we need individual metrics
Yes I am correct. Its users who are paying for it determine whether up time is sufficient. So stop crying.
You clearly don’t understand valuation - the value of an asset is a function of expected FUTURE cash flows….
Don’t bother replying unless you have a clue about what you’re talking about
my friend, I was being sarcastic before, and I am agreeing with you. LoC, token spend, etc as metrics are horrible measures. Software uptime is a great metric. I'm merely lamenting that in the age we're in, uptimes are getting worse and worse
> measure outcomes (impact)
This is also not easy. In particular proactively preventing bugs is not rewarded
It's not flashy.
When shit just works for months or years no one is going to come and praise you for stuff you did a while back.
You are better off breaking stuff and then fixing them to show how useful you are.
Within a few weeks of telling people at our company that if they don’t use AI they will be replaced by someone who does, they just announced that their allocation with ChatGPT has reset and are now panicking as they blew through their million token allocation for this month in under six hours - you can’t make this shit up.
I have never worked there and I am likely very unqualified to ever work there and Zuck has more money than I could dream of so take my comment with that in mind.
Meta sounds like a cluster-F of a place to work. Massive reorgs around wild ideas like the metaverse and everything Ai all the time. Employees terrified of being fired. Incentivizing token spending and then cutting it off. While the overall company may be fine, the dev department sounds rudderless and absolutely miserable.
Clearly no one is using Meta’s customer facing AI products. Why aren’t they using their own gpu/compute for development?
Because Muse isn't good enough and why use Muse if they'll let you use Opus for free?
that is a fair point. The contrast between Meta and Apple could not be bigger here. Apple has billions of devices and yet they decided to use 3rd party models from OpenAI and later Google to build their AI features rather than building foundational models in house. Yet Meta did opposite: they built models (spending billions of $$$ and firing 10% of the company) for billions of users who rather would not use Meta AI features.
That is insane. I'm sure companies will learn the absolute wrong lesson from this, and attempt to centralize and kneecap token usage.
As many companies do with all their budgets, down to the trivial and clearly positive EV cost of free coffee. So it goes, cost controls are hard and necessarily imprecise.
Tokens are less valuable than the eyeball metric of the Dotcom era. At least the eyeballs were real then.
I'd argue most of the AI value is related to how 'Dead' the internet is.
This talk of tokens is wasteful.
Ultimately the spend on tokens has to benefit the firm financially or it won’t continue spending on it.
All those billions spent on tokens by Meta, and not a single iota of value generated by any of it
I can’t tell if you’re complaining that Meta isn’t saving the whales or that their products aren’t good. If it’s the latter, you should double check their financials.
I guess maybe they can crank out more ads in their dystopian ad space of a social network site.
I love how confidently you say this, with no evidence provided (and I doubt you have any.)
Just a pristine comment section yap.
The only thing I’ve seen Meta release recently are spy glasses, and every employee who has worked on that product should be in prison (with a live 24/7 feed where the world gets to watch them wallow).
The times I’ve been asked to evaluate a prospective candidate and I see that product on their résumé, it’s been an instant veto, in the same category as working at Palantir.
If there was a positive return on token spend they wouldn’t be capping it now would they?
It’s possible for something to have diminishing returns.
Having a speed limit does not imply the utility of driving is zero.
That does not follow. Many things follow diminishing returns curves.
Was there a new product released by Meta that we are not aware of? The last thing I read about was the Instagram account take-over AI-bug.
>I love how confidently you say this,
it's not that difficult to say it confidently if you use any of their services and applications because exactly nothing has changed.
For reference most labor productivity increases for the last 50 years amounted to about 2% per year. If a hypothetical FB engineer had doubled their productivity with their gazillion tokens that would be 30 years of productivity gains in one year. I'd wager the evidence would be quite evident if you opened any of their apps
I don't understand though. How will all the AI users replace all the non-AI users if they can't spend money that isn't theirs to win by default?
Don't worry, once we achieve post-scarcity they will have more tokens than they could ever dream for spending.
How soon until this becomes part of the "no one wants to work anymore" argument