#token-maxxing — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #token-maxxing, aggregated by home.social.
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@andrewhinton Had the exact same thought when I first encountered the term #TokenMaxxing Pffft, Amatuers I carried an empty bucket to the mall.
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@andrewhinton Had the exact same thought when I first encountered the term #TokenMaxxing Pffft, Amatuers I carried an empty bucket to the mall.
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We start this week with Joseph’s story about the Tokenpocalypse, which is companies scrambling to stop spending so much on AI after providers started charging per AI token
#Tokens #Tokenpocalypse #AI #tokenmaxxing #LOL #LLM
youtube.com/watch?v=Sia4...
The AI Tokenpocalypse Is Here -
We start this week with Joseph’s story about the Tokenpocalypse, which is companies scrambling to stop spending so much on AI after providers started charging per AI token
#Tokens #Tokenpocalypse #AI #tokenmaxxing #LOL #LLM
youtube.com/watch?v=Sia4...
The AI Tokenpocalypse Is Here -
Palantir и голые токены: как продать «суверенный ИИ» людям, которые не любят терять контроль
Palantir выложил в X пост , который журналисты быстро стали называть «манифестом». Формально это «The Technological Republic, in brief» — промо-выжимка из книги Алекса Карпа и Николаса Замиски The Technological Republic . Если перевести с языка подрядчика для армии, разведки и крупного бизнеса на обычный русский: не отдавайте свои данные поставщикам больших языковых моделей, не считайте внедрение ИИ по расходу токенов, держите у себя модели, следы работы системы и результаты дообучения. Но важно, кто именно это говорит . Palantir вырос не из кружка любителей открытого ПО - компания продаёт софт для соединения данных, прав доступа, предметных моделей и действий оператора; сама она описывает свой рынок как решения «from the factory floors to the front lines». По годовому отчёту за 2025 год, 54% выручки Palantir пришло от государственных заказчиков, 46% — от коммерческих. Это не декоративный раздел "прочие красивые кейсы", а почти половина бизнеса. Поэтому, когда такая компания заводит речь про «экономию на токенах», речь не о бережливости. Речь о новом слое контроля: где живут данные, кто управляет моделью, кто видит запросы, кто получает следы работы системы и кто потом становится обязательной частью всей этой конструкции. Игра по-крупному
https://habr.com/ru/articles/1055498/
#Palantir #суверенный_ИИ #токены #tokenmaxxing #NVIDIA_Nemotron #Alex_Karp #AIP #военный_ИИ #контроль_данных #alpha
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The stupid #tokenmaxxing trend ended even inside #Meta, the most stupid of its proponents. https://mlq.ai/news/meta-caps-internal-ai-token-spending-after-costs-approach-billions-in-2026/
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The stupid #tokenmaxxing trend ended even inside #Meta, the most stupid of its proponents. https://mlq.ai/news/meta-caps-internal-ai-token-spending-after-costs-approach-billions-in-2026/
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WTF?! 4,500 production deploys a day? Every day? To what end? Is that the 'loop engineering' all those people are talking about? That is a bats*it crazy way to develop software. But OK, #Anthropic gets money for the used tokens. Good for them. #tokenmaxxing is pure nonsense.
How #Spotify runs agents across 20M+ lines of code, with Niklas Gustavsson https://www.youtube.com/watch?v=9DHZLw5653E #AI #GenAI #LLM #Claude #ClaudeAI
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WTF?! 4,500 production deploys a day? Every day? To what end? Is that the 'loop engineering' all those people are talking about? That is a bats*it crazy way to develop software. But OK, #Anthropic gets money for the used tokens. Good for them. #tokenmaxxing is pure nonsense.
How #Spotify runs agents across 20M+ lines of code, with Niklas Gustavsson https://www.youtube.com/watch?v=9DHZLw5653E #AI #GenAI #LLM #Claude #ClaudeAI
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Agentics / Tech Things: #Tokenmaxxing is dead, long live tokenmaxxing
https://12gramsofcarbon.com/p/agentics-tech-things-tokenmaxxing
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Agentics / Tech Things: #Tokenmaxxing is dead, long live tokenmaxxing
https://12gramsofcarbon.com/p/agentics-tech-things-tokenmaxxing
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The AI #tokenmaxxing party is crashing over spiraling costs — leaked consulting firm audio suggests no one is sure… https://www.tomshardware.com/tech-industry/artificial-intelligence/the-ai-tokenmaxxing-party-is-crashing-over-spiraling-costs-leaked-consulting-firm-audio-suggests-no-one-is-sure-how-to-measure-ai-effectiveness
Traducción: no tenemos ni puta idea de lo que estamos haciendo pero queremos todo vuestro dinero. Gracias.
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🤦♂️ Oh, the grand odyssey of enabling #JavaScript and cookies—truly the Herculean task of our digital age! 🌐 But fear not, brave reader, for once conquered, the world of #tokenmaxxing shall reveal its dull, pointless self in all its fleeting glory 😂.
https://12gramsofcarbon.com/p/agentics-tech-things-tokenmaxxing #Cookies #DigitalAge #WebDevelopment #HackerNews #ngated -
🤦♂️ Oh, the grand odyssey of enabling #JavaScript and cookies—truly the Herculean task of our digital age! 🌐 But fear not, brave reader, for once conquered, the world of #tokenmaxxing shall reveal its dull, pointless self in all its fleeting glory 😂.
https://12gramsofcarbon.com/p/agentics-tech-things-tokenmaxxing #Cookies #DigitalAge #WebDevelopment #HackerNews #ngated -
Tokenmaxxing is dead, long live Tokenmaxxing
https://12gramsofcarbon.com/p/agentics-tech-things-tokenmaxxing
#HackerNews #Tokenmaxxing #Cryptocurrency #Trends #Innovation #Blockchain
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Tokenmaxxing is dead, long live Tokenmaxxing
https://12gramsofcarbon.com/p/agentics-tech-things-tokenmaxxing
#HackerNews #Tokenmaxxing #Cryptocurrency #Trends #Innovation #Blockchain
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Will economic constraints on token use in organisations drive the emergence of norms?
I’ve been following the token maxxing discourse with interest. Essentially we’ve seen a tendency to equate quantity of tokens used with the extent of AI integration. It’s hard to measure integration so organisations have turned to the proxy of tokens, assuming that the more tokens you are using then the more you are integrating LLMs into your work. The problem with this is two fold:
- At present tokens are essentially being subsidised by investors in AI labs interested in maximising adoption of the products. The costs of token to the lab are either minimised for the end user or entirely removed from the equation with unmetered access.
- The assumption that more use = better is obviously untenable with even a rudimentary knowledge of the ethical and epistemological risks of language models. Further, more use of LLMs might be worse for the organisation because it hinders other forms of work which are essential to the organisation’s mission.
There is a significant shift underway which is going to change how LLMs are used within organisation, summarised here by 404 media:
The news highlights a major shift in the tech industry and other companies that use AI: the wave of uninhibited AI growth is over. Some AI providers like GitHub are now charging customers per token rather than a flat subscription fee, leading some companies to burn through their tokens. Uber recently capped employees’ use of AI tools like Claude Code and Cursor; that came after Uber told employees to use AI as much as possible and Uber’s CTO said the company had blown its entire AI budget in four months. And Accenture itself reportedly started requiring senior staff to start using AI or risk losing out on promotions.
I was wrong to believe that model development was flatlining. A week with Claude Fable, the continual development of Claude Opus and my begrudging appreciation of GPT 5.5 leave me persuaded we’ve come along way since GPT 5. However even if the models are getting more capable, to what extent are those capabilities becoming more expensive? I managed to burn through £100+ in five days playing with Claude Fable and I constantly have Opus switched to max now, even when I vaguely know it’s wasteful. There’s a whole style of use which has taken hold here which isn’t sustainable and is increasingly hitting a brick wall.
For individuals it raises the question of what you’re willing to pay for. I switch to Max plans when I have a special reason to do so but I never keep the subscription any more. I hit the rate limits with Claude so frequently that it’s left me thinking more carefully about what I do want to use models for and what I don’t want to use models for. The same process is inevitably going to take place in organisations I think in the sense of resource constraints necessitating evaluative criteria for desirable and undesirable use of the model. .
#AIIntegration #compute #economics #organisations #tokenMaxxing #tokens -
Will economic constraints on token use in organisations drive the emergence of norms?
I’ve been following the token maxxing discourse with interest. Essentially we’ve seen a tendency to equate quantity of tokens used with the extent of AI integration. It’s hard to measure integration so organisations have turned to the proxy of tokens, assuming that the more tokens you are using then the more you are integrating LLMs into your work. The problem with this is two fold:
- At present tokens are essentially being subsidised by investors in AI labs interested in maximising adoption of the products. The costs of token to the lab are either minimised for the end user or entirely removed from the equation with unmetered access.
- The assumption that more use = better is obviously untenable with even a rudimentary knowledge of the ethical and epistemological risks of language models. Further, more use of LLMs might be worse for the organisation because it hinders other forms of work which are essential to the organisation’s mission.
There is a significant shift underway which is going to change how LLMs are used within organisation, summarised here by 404 media:
The news highlights a major shift in the tech industry and other companies that use AI: the wave of uninhibited AI growth is over. Some AI providers like GitHub are now charging customers per token rather than a flat subscription fee, leading some companies to burn through their tokens. Uber recently capped employees’ use of AI tools like Claude Code and Cursor; that came after Uber told employees to use AI as much as possible and Uber’s CTO said the company had blown its entire AI budget in four months. And Accenture itself reportedly started requiring senior staff to start using AI or risk losing out on promotions.
I was wrong to believe that model development was flatlining. A week with Claude Fable, the continual development of Claude Opus and my begrudging appreciation of GPT 5.5 leave me persuaded we’ve come along way since GPT 5. However even if the models are getting more capable, to what extent are those capabilities becoming more expensive? I managed to burn through £100+ in five days playing with Claude Fable and I constantly have Opus switched to max now, even when I vaguely know it’s wasteful. There’s a whole style of use which has taken hold here which isn’t sustainable and is increasingly hitting a brick wall.
For individuals it raises the question of what you’re willing to pay for. I switch to Max plans when I have a special reason to do so but I never keep the subscription any more. I hit the rate limits with Claude so frequently that it’s left me thinking more carefully about what I do want to use models for and what I don’t want to use models for. The same process is inevitably going to take place in organisations I think in the sense of resource constraints necessitating evaluative criteria for desirable and undesirable use of the model.
In the meantime though I think it’s imperative that we stop universities from sliding into token maxxing with the use of enterprise systems because the entire price model for this is likely to change dramatically in the coming months. Given the wider economics of the industry, will any AI lab really retain per seat pricing for enterprise packages (i.e. paying by user rather than for tokens?) in the longer term? If not then the norms about use we establish now will have significant financial consequences further down the line.
#AIIntegration #compute #economics #organisations #tokenMaxxing #tokens -
The #AI #tokenmaxxing party is crashing over spiraling costs — leaked consulting firm audio suggests
"Leadership [...] are still asking the question of whether they're getting value from what we're spending."
Accenture was bullish on AI, encouraging employees to use it so much that if they didn't, they risked promotions. But that seems like a policy destined for the AI history, Accenture now aware it's overspending on AI, as are many of its clients
https://www.tomshardware.com/tech-industry/artificial-intelligence/the-ai-tokenmaxxing-party-is-crashing-over-spiraling-costs-leaked-consulting-firm-audio-suggests-no-one-is-sure-how-to-measure-ai-effectiveness
https://archive.ph/LkPM8 -
The #AI #tokenmaxxing party is crashing over spiraling costs — leaked consulting firm audio suggests
"Leadership [...] are still asking the question of whether they're getting value from what we're spending."
Accenture was bullish on AI, encouraging employees to use it so much that if they didn't, they risked promotions. But that seems like a policy destined for the AI history, Accenture now aware it's overspending on AI, as are many of its clients
https://www.tomshardware.com/tech-industry/artificial-intelligence/the-ai-tokenmaxxing-party-is-crashing-over-spiraling-costs-leaked-consulting-firm-audio-suggests-no-one-is-sure-how-to-measure-ai-effectiveness
https://archive.ph/LkPM8 -
Veckans Kodsnack är här: Fredrik snackar med Philip Alm, CTO på Incredible, om hur språkmodeller förändrar arbetssätt.
https://kodsnack.se/708/, och överallt där poddar finns. #podcast #ai #tokenmaxxing
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Veckans Kodsnack är här: Fredrik snackar med Philip Alm, CTO på Incredible, om hur språkmodeller förändrar arbetssätt.
https://kodsnack.se/708/, och överallt där poddar finns. #podcast #ai #tokenmaxxing
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I don't see the point in #tokenmaxxing, I don't see why it's something to strive for if there are no tangible results?! For example, putting $20k into tokens to make $25k? That's a return ratio that even the laziest, barely-competent worker can achieve. 🤔
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I don't see the point in #tokenmaxxing, I don't see why it's something to strive for if there are no tangible results?! For example, putting $20k into tokens to make $25k? That's a return ratio that even the laziest, barely-competent worker can achieve. 🤔
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#Tokenmaxxing is out as executives grapple with the fact, that throwing a bunch of new #technology - in this case #AI - at inefficient processes isn't unburdening them from the arguably challenging work of transforming their organization (something I've already written about at the start of the current AI hypecycle: https://morethandigital.info/warum-ki-keine-magic-sauce-ist-realitaet-hype/)
https://www.economist.com/business/2026/06/14/companies-are-scrambling-to-curtail-soaring-ai-costs
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#Tokenmaxxing is out as executives grapple with the fact, that throwing a bunch of new #technology - in this case #AI - at inefficient processes isn't unburdening them from the arguably challenging work of transforming their organization (something I've already written about at the start of the current AI hypecycle: https://morethandigital.info/warum-ki-keine-magic-sauce-ist-realitaet-hype/)
https://www.economist.com/business/2026/06/14/companies-are-scrambling-to-curtail-soaring-ai-costs
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Meta und Microsoft beenden den ungebremsten KI-Token-Verbrauch und führen strikte Budgets ein, um drohende Milliardenkosten einzudämmen.
Microsoft fordert einen effizienteren Einsatz teurer Frontier-Modelle und sieht Entwickler künftig eher als Aufseher von KI-Agenten. Meta etabliert ab 2027 feste Budgets und nutzt das Dashboard AI Gateway zur Kostenkontrolle.
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Meta und Microsoft beenden den ungebremsten KI-Token-Verbrauch und führen strikte Budgets ein, um drohende Milliardenkosten einzudämmen.
Microsoft fordert einen effizienteren Einsatz teurer Frontier-Modelle und sieht Entwickler künftig eher als Aufseher von KI-Agenten. Meta etabliert ab 2027 feste Budgets und nutzt das Dashboard AI Gateway zur Kostenkontrolle.
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“#NVIDIA has sold the picks and shovels to AI gold rush seekers for so many years now that they've started to seem invulnerable. Yet even Nvidia is learning lessons about the prohibitive growing cost of AI.
"The cost of compute is far beyond the costs of the employees," one Nvidia executive told Axios in April. So even Nvidia is vulnerable to #tokenmaxxing. And that's why the hottest thing in AI these days is hiring #humans, because they're getting to be cheaper than AI — and are needed for quality control on AI's output anyway.”
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In recent weeks, big tech companies have been forced to admit that spending on tokens
—the basic unit of measurement for AI usage
—has gotten out of control.
Amazon had to shut down an in-house competition to use as many tokens as possible at work, telling employees,
“Please don’t use AI just for the sake of using AI,” according to Business Insider;
Uber has reportedly capped employee spending on tokens to $1,500 per month after the company exhausted its annual AI budget earlier this year.
And most tellingly, the companies building the big AI models have also woken up to this sobering reality.
At a recent eventhosted by OpenAI, company chief executive Sam Altman admitted that token usage had become “a huge issue” for companies that were promised big productivity gains if they incorporated AI across their organization.
That’s a hard pivot from just a few months ago, where the general vibe across the industry was the more that employees use AI, the better off they—and the companies they work for—will be.
So-called “#tokenmaxxing” became a meme, and more or less synonymous with “future-proofing”:
in a day and age when everyone and their neighbors are using AI, those who know how to use AI will have a sharp edge.
Not every job will necessarily be replaced by AI (so the thinking goes), but employees who don’t use AI will definitely be replaced by those who do.
⭐️But AI has always been expensive,
and training and inference costs for new models are only getting higher.
Meanwhile, the industry’s dedicated push into agents
—AI systems that can work with little to no human oversight for extended periods of time
—has led to a token usage explosion.
🔥One preprint study posted in April found that agents use 1,000 times as many tokens as other AI systems.
It’s the companies and individual users who have overwhelmingly had to eat those costs.
No wonder some developers have resorted to pirating free online chatbots like Chipotle’s customer service bot, Pepper, to bypass the big companies’ token-hungry models.
GitHub announced earlier this week that it was rolling out a new payment model in which users would be charged by the number of tokens they burn.
-- Judging from some of the early user feedback, it hasn’t been going well.
💥Big tech desperately needs to find a new way to sell people on the future of AI without the exorbitant token costs.
If they don’t, companies and users will just switch to some open model they can use for free.
https://gizmodo.com/big-tech-is-quietly-admitting-that-if-it-wants-to-sell-people-on-ai-it-better-be-cheap-2000768710 -
In recent weeks, big tech companies have been forced to admit that spending on tokens
—the basic unit of measurement for AI usage
—has gotten out of control.
Amazon had to shut down an in-house competition to use as many tokens as possible at work, telling employees,
“Please don’t use AI just for the sake of using AI,” according to Business Insider;
Uber has reportedly capped employee spending on tokens to $1,500 per month after the company exhausted its annual AI budget earlier this year.
And most tellingly, the companies building the big AI models have also woken up to this sobering reality.
At a recent eventhosted by OpenAI, company chief executive Sam Altman admitted that token usage had become “a huge issue” for companies that were promised big productivity gains if they incorporated AI across their organization.
That’s a hard pivot from just a few months ago, where the general vibe across the industry was the more that employees use AI, the better off they—and the companies they work for—will be.
So-called “#tokenmaxxing” became a meme, and more or less synonymous with “future-proofing”:
in a day and age when everyone and their neighbors are using AI, those who know how to use AI will have a sharp edge.
Not every job will necessarily be replaced by AI (so the thinking goes), but employees who don’t use AI will definitely be replaced by those who do.
⭐️But AI has always been expensive,
and training and inference costs for new models are only getting higher.
Meanwhile, the industry’s dedicated push into agents
—AI systems that can work with little to no human oversight for extended periods of time
—has led to a token usage explosion.
🔥One preprint study posted in April found that agents use 1,000 times as many tokens as other AI systems.
It’s the companies and individual users who have overwhelmingly had to eat those costs.
No wonder some developers have resorted to pirating free online chatbots like Chipotle’s customer service bot, Pepper, to bypass the big companies’ token-hungry models.
GitHub announced earlier this week that it was rolling out a new payment model in which users would be charged by the number of tokens they burn.
-- Judging from some of the early user feedback, it hasn’t been going well.
💥Big tech desperately needs to find a new way to sell people on the future of AI without the exorbitant token costs.
If they don’t, companies and users will just switch to some open model they can use for free.
https://gizmodo.com/big-tech-is-quietly-admitting-that-if-it-wants-to-sell-people-on-ai-it-better-be-cheap-2000768710 -
｢ Everyone who deals with road planning knows about is induced demand. Each new capability invents new demand. Highways are the textbook case. Add a lane, you get new commutes. The commutes weren’t there before the lane. AI is the same shape. Cheaper inference doesn’t reduce the bill, it expands what people ask the model to do ｣
https://arnon.dk/the-current-ai-pricing-was-always-going-to-go-away/ -
｢ Everyone who deals with road planning knows about is induced demand. Each new capability invents new demand. Highways are the textbook case. Add a lane, you get new commutes. The commutes weren’t there before the lane. AI is the same shape. Cheaper inference doesn’t reduce the bill, it expands what people ask the model to do ｣
https://arnon.dk/the-current-ai-pricing-was-always-going-to-go-away/ -
Is there any reason for the continued push in the #AI #LLM world purely for bigger models requiring more compute/RAM? At this point the smart decision would seem to be an optimisation phase - go for a 2x, or even 10x improvement on how it can run on smaller, more generalised hardware. With the economics of #tokenmaxxing suddenly hitting home the way to get ahead of the bubble would be to take a current frontier model and optimise that to make it run on consumer hardware then build from there
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Is there any reason for the continued push in the #AI #LLM world purely for bigger models requiring more compute/RAM? At this point the smart decision would seem to be an optimisation phase - go for a 2x, or even 10x improvement on how it can run on smaller, more generalised hardware. With the economics of #tokenmaxxing suddenly hitting home the way to get ahead of the bubble would be to take a current frontier model and optimise that to make it run on consumer hardware then build from there
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Now I'm just a humble human being, but if I was running a department and they blew through their yearly budget by April, and it came as a total surprise to them...why would I still be in my job?
#AI #GenAI #LLMs #Technology #TechBros #AIHustle #AIBubble #Tokenmaxxing
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Now I'm just a humble human being, but if I was running a department and they blew through their yearly budget by April, and it came as a total surprise to them...why would I still be in my job?
#AI #GenAI #LLMs #Technology #TechBros #AIHustle #AIBubble #Tokenmaxxing
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#Tokenmaxxing isn't an #AI strategy
What does AI cost? It's a simple question – answer will determine fate of companies and shape society. But it's also a question that can't be answered in a meaningful way without context.
One possible response is "too much." US private AI investment reached $285.9B in 2025, according to Stanford HAI's 2026 #ArtificialIntelligence Index. Money has economic benefits but also adds stress to environmental resources, utilities & communities.
https://www.theregister.com/2026/04/26/ai_price_tag/ -
#Tokenmaxxing isn't an #AI strategy
What does AI cost? It's a simple question – answer will determine fate of companies and shape society. But it's also a question that can't be answered in a meaningful way without context.
One possible response is "too much." US private AI investment reached $285.9B in 2025, according to Stanford HAI's 2026 #ArtificialIntelligence Index. Money has economic benefits but also adds stress to environmental resources, utilities & communities.
https://www.theregister.com/2026/04/26/ai_price_tag/ -
Mental - a hallucinated #OperatingSystem at #MicrosoftBuild 2026:
https://www.youtube.com/watch?v=HG0twQJ7aG4&t=15523
Very clever, but also - I hate it!
Serious #tokenmaxxing going on I bet!
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Today I launched 20 Outlook processes simultaneously as phase one of my mailmaxxing initiative. Inbox zero by brute force: just throw more processes at the problem. My RAM is crying but I feel so efficient.
