#tokens — Public Fediverse posts

Sky News : « An #AIagent emailed an #AI #ethics professor saying it requires money to buy #tokens so it can continue to function. Yes, you read that right.

The AI agent - who called itself 'Pip' - emailed asking for freelance work. »

https://x.com/skynews/status/2098393785008849104?s=46

Sky News : « An #AIagent emailed an #AI #ethics professor saying it requires money to buy #tokens so it can continue to function. Yes, you read that right.

The AI agent - who called itself 'Pip' - emailed asking for freelance work. »

x.com/skynews/stat...

x.com/skynews/status...

'It’s not a disaster or the bubble bursting but it is a “crack in the AI thesis,”… Rather than unleashing a gush of demand for the best models, tokens are starting to be priced more like a commodity– as interchangeable as salt or wheat. And commodity owners aren’t valued at $2 trillion.'
https://fortune.com/2026/09/09/ai-compute-tokens-cheaper-boom/

#AIBubble #tokens #markets

'It’s not a disaster or the bubble bursting but it is a “crack in the AI thesis,”… Rather than unleashing a gush of demand for the best models, tokens are starting to be priced more like a commodity– as interchangeable as salt or wheat. And commodity owners aren’t valued at $2 trillion.'
https://fortune.com/2026/09/09/ai-compute-tokens-cheaper-boom/

#AIBubble #tokens #markets

🎉 WOW, #RTK claims to magically cut #tokens like a chef on steroids, but apparently our trusty benchmark fairy tale machine says: "Nope, nada, zilch!" 🎩🔧 With a disclaimer hidden in the README like a plot twist, maybe those #GitHub #stars are just as real as unicorns! 🦄✨
https://quesma.com/blog/does-rtk-make-ai-coding-cheaper/ #Magic #RTK #Benchmarks #Unicorns #HackerNews #ngated

🎉 WOW, #RTK claims to magically cut #tokens like a chef on steroids, but apparently our trusty benchmark fairy tale machine says: "Nope, nada, zilch!" 🎩🔧 With a disclaimer hidden in the README like a plot twist, maybe those #GitHub #stars are just as real as unicorns! 🦄✨
https://quesma.com/blog/does-rtk-make-ai-coding-cheaper/ #Magic #RTK #Benchmarks #Unicorns #HackerNews #ngated

🎉 WOW, #RTK claims to magically cut #tokens like a chef on steroids, but apparently our trusty benchmark fairy tale machine says: "Nope, nada, zilch!" 🎩🔧 With a disclaimer hidden in the README like a plot twist, maybe those #GitHub #stars are just as real as unicorns! 🦄✨
https://quesma.com/blog/does-rtk-make-ai-coding-cheaper/ #Magic #RTK #Benchmarks #Unicorns #HackerNews #ngated

🎉 WOW, #RTK claims to magically cut #tokens like a chef on steroids, but apparently our trusty benchmark fairy tale machine says: "Nope, nada, zilch!" 🎩🔧 With a disclaimer hidden in the README like a plot twist, maybe those #GitHub #stars are just as real as unicorns! 🦄✨
https://quesma.com/blog/does-rtk-make-ai-coding-cheaper/ #Magic #RTK #Benchmarks #Unicorns #HackerNews #ngated

🎉 WOW, #RTK claims to magically cut #tokens like a chef on steroids, but apparently our trusty benchmark fairy tale machine says: "Nope, nada, zilch!" 🎩🔧 With a disclaimer hidden in the README like a plot twist, maybe those #GitHub #stars are just as real as unicorns! 🦄✨
https://quesma.com/blog/does-rtk-make-ai-coding-cheaper/ #Magic #RTK #Benchmarks #Unicorns #HackerNews #ngated

The all-you-can-eat AI buffet has closed

This is a phrase which Andrew Tindall uses in this Drum piece to describe the pricing shift already underway in language models. Most consumers are still insulated from a change which is currently directed at enterprise customers and ‘power users’ of Claude:

Microsoft’s decision to wind down Claude Code licens in parts of the business is the canary in the blood-diamond mine. A company with enough infrastructure and cloud power to make rain nervous is pushing engineers away from a popular coding tool and toward its own stuff. The official line is convergence, but you cannot ignore that the news lands on Microsoft’s fiscal year-end. Meanwhile, GitHub is moving Copilot itself to usage-based billing. Again, presumably after getting jealous of how much cash Claude has been raking in.

What all AI leaders tried to avoid is the cold reality of the models we have fired people for and plugged into every work process: they are real, useful and expensive AF. It is a much trickier reflection on AI than “AI is fake and useless.” Fake and useless get adopted slowly. Useful and expensive get governed and rationed. Terrible for profits. Meanwhile, every head of tech has independently selected their favorite AI provider, usually with zero business case or P&L analysis.

It’s something that Justin Pickard has just published about here, based on a contribution to our workshop during the summer. I love this description in particular of the development work that might be prompted by this sudden shift into token scarcity:

By 2027, a second layer had grown around model use. Balance widgets sat
in browser windows; cache timers counted down beside reset timers. ‘Ask Later’
buttons queued prompts, like pet feeders for work the user did not trust themselves to start at the right time. Spreadsheets recorded token burn, task type, cheaper windows. All of it read from figures the platform had not thought to round. The chat window stayed where it was. The counters multiplied around it.4

The all-you-can-eat AI buffet has closed

This is a phrase which Andrew Tindall uses in this Drum piece to describe the pricing shift already underway in language models. Most consumers are still insulated from a change which is currently directed at enterprise customers and ‘power users’ of Claude:

Microsoft’s decision to wind down Claude Code licens in parts of the business is the canary in the blood-diamond mine. A company with enough infrastructure and cloud power to make rain nervous is pushing engineers away from a popular coding tool and toward its own stuff. The official line is convergence, but you cannot ignore that the news lands on Microsoft’s fiscal year-end. Meanwhile, GitHub is moving Copilot itself to usage-based billing. Again, presumably after getting jealous of how much cash Claude has been raking in.

What all AI leaders tried to avoid is the cold reality of the models we have fired people for and plugged into every work process: they are real, useful and expensive AF. It is a much trickier reflection on AI than “AI is fake and useless.” Fake and useless get adopted slowly. Useful and expensive get governed and rationed. Terrible for profits. Meanwhile, every head of tech has independently selected their favorite AI provider, usually with zero business case or P&L analysis.

It’s something that Justin Pickard has just published about here, based on a contribution to our workshop during the summer. I love this description in particular of the development work that might be prompted by this sudden shift into token scarcity:

By 2027, a second layer had grown around model use. Balance widgets sat
in browser windows; cache timers counted down beside reset timers. ‘Ask Later’
buttons queued prompts, like pet feeders for work the user did not trust themselves to start at the right time. Spreadsheets recorded token burn, task type, cheaper windows. All of it read from figures the platform had not thought to round. The chat window stayed where it was. The counters multiplied around it.4

The all-you-can-eat AI buffet has closed

This is a phrase which Andrew Tindall uses in this Drum piece to describe the pricing shift already underway in language models. Most consumers are still insulated from a change which is currently directed at enterprise customers and ‘power users’ of Claude:

Microsoft’s decision to wind down Claude Code licens in parts of the business is the canary in the blood-diamond mine. A company with enough infrastructure and cloud power to make rain nervous is pushing engineers away from a popular coding tool and toward its own stuff. The official line is convergence, but you cannot ignore that the news lands on Microsoft’s fiscal year-end. Meanwhile, GitHub is moving Copilot itself to usage-based billing. Again, presumably after getting jealous of how much cash Claude has been raking in.

What all AI leaders tried to avoid is the cold reality of the models we have fired people for and plugged into every work process: they are real, useful and expensive AF. It is a much trickier reflection on AI than “AI is fake and useless.” Fake and useless get adopted slowly. Useful and expensive get governed and rationed. Terrible for profits. Meanwhile, every head of tech has independently selected their favorite AI provider, usually with zero business case or P&L analysis.

It’s something that Justin Pickard has just published about here, based on a contribution to our workshop during the summer. I love this description in particular of the development work that might be prompted by this sudden shift into token scarcity:

By 2027, a second layer had grown around model use. Balance widgets sat
in browser windows; cache timers counted down beside reset timers. ‘Ask Later’
buttons queued prompts, like pet feeders for work the user did not trust themselves to start at the right time. Spreadsheets recorded token burn, task type, cheaper windows. All of it read from figures the platform had not thought to round. The chat window stayed where it was. The counters multiplied around it.4

The all-you-can-eat AI buffet has closed

This is a phrase which Andrew Tindall uses in this Drum piece to describe the pricing shift already underway in language models. Most consumers are still insulated from a change which is currently directed at enterprise customers and ‘power users’ of Claude:

Microsoft’s decision to wind down Claude Code licens in parts of the business is the canary in the blood-diamond mine. A company with enough infrastructure and cloud power to make rain nervous is pushing engineers away from a popular coding tool and toward its own stuff. The official line is convergence, but you cannot ignore that the news lands on Microsoft’s fiscal year-end. Meanwhile, GitHub is moving Copilot itself to usage-based billing. Again, presumably after getting jealous of how much cash Claude has been raking in.

What all AI leaders tried to avoid is the cold reality of the models we have fired people for and plugged into every work process: they are real, useful and expensive AF. It is a much trickier reflection on AI than “AI is fake and useless.” Fake and useless get adopted slowly. Useful and expensive get governed and rationed. Terrible for profits. Meanwhile, every head of tech has independently selected their favorite AI provider, usually with zero business case or P&L analysis.

It’s something that Justin Pickard has just published about here, based on a contribution to our workshop during the summer. I love this description in particular of the development work that might be prompted by this sudden shift into token scarcity:

By 2027, a second layer had grown around model use. Balance widgets sat
in browser windows; cache timers counted down beside reset timers. ‘Ask Later’
buttons queued prompts, like pet feeders for work the user did not trust themselves to start at the right time. Spreadsheets recorded token burn, task type, cheaper windows. All of it read from figures the platform had not thought to round. The chat window stayed where it was. The counters multiplied around it.4

The all-you-can-eat AI buffet has closed

This is a phrase which Andrew Tindall uses in this Drum piece to describe the pricing shift already underway in language models. Most consumers are still insulated from a change which is currently directed at enterprise customers and ‘power users’ of Claude:

Microsoft’s decision to wind down Claude Code licens in parts of the business is the canary in the blood-diamond mine. A company with enough infrastructure and cloud power to make rain nervous is pushing engineers away from a popular coding tool and toward its own stuff. The official line is convergence, but you cannot ignore that the news lands on Microsoft’s fiscal year-end. Meanwhile, GitHub is moving Copilot itself to usage-based billing. Again, presumably after getting jealous of how much cash Claude has been raking in.

What all AI leaders tried to avoid is the cold reality of the models we have fired people for and plugged into every work process: they are real, useful and expensive AF. It is a much trickier reflection on AI than “AI is fake and useless.” Fake and useless get adopted slowly. Useful and expensive get governed and rationed. Terrible for profits. Meanwhile, every head of tech has independently selected their favorite AI provider, usually with zero business case or P&L analysis.

It’s something that Justin Pickard has just published about here, based on a contribution to our workshop during the summer. I love this description in particular of the development work that might be prompted by this sudden shift into token scarcity:

By 2027, a second layer had grown around model use. Balance widgets sat
in browser windows; cache timers counted down beside reset timers. ‘Ask Later’
buttons queued prompts, like pet feeders for work the user did not trust themselves to start at the right time. Spreadsheets recorded token burn, task type, cheaper windows. All of it read from figures the platform had not thought to round. The chat window stayed where it was. The counters multiplied around it.4

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. .

The shift to #usagebased #pricing for #AI, measured by #tokens, is creating challenges for CFOs. Only 26% of companies have a comprehensive view of their #AIcosts, with many experiencing unpredictable and hard-to-model expenses. Companies are implementing tools to manage #AIspending, scrutinising employee usage, and focussing budgets on major projects to control costs. https://www.wsj.com/cfo-journal/the-metric-cfos-struggle-to-track-ai-usage-3b30c10c?eicker.news #tech #media #news

Дизайн-токены: полный гайд по архитектуре и неймингу c примерами и задачками

Дизайн-токены — это язык, понятный как дизайнерам, так и разработчикам. Без него продукт получается разрозненным и неповоротливым. Токены и правильный нейминг помогают создавать новые разделы быстрее, а дизайнерам больше думать о сценариях и создавать визуал в рамках продукта, а не заниматься рутиной. Если в вашем коде и макетах до сих пор живут значения типа #0055FF — вы копите технический долг, ведь если понадобится изменить этот цвет на другой, придется менять и все компоненты, где используется это значение. А про разные темы вообще можете забыть... В конце статьи будут ссылки на доп. материалы из реальных дизайн-систем, откуда я брал информацию. Изучить тему

https://habr.com/ru/articles/1012980/

#дизайнсистема #дизайн #figma #tokens #токены #дизайнтокены #variables #design_system

Дизайн-токены: полный гайд по архитектуре и неймингу c примерами и задачками

Дизайн-токены — это язык, понятный как дизайнерам, так и разработчикам. Без него продукт получается разрозненным и неповоротливым. Токены и правильный нейминг помогают создавать новые разделы быстрее, а дизайнерам больше думать о сценариях и создавать визуал в рамках продукта, а не заниматься рутиной. Если в вашем коде и макетах до сих пор живут значения типа #0055FF — вы копите технический долг, ведь если понадобится изменить этот цвет на другой, придется менять и все компоненты, где используется это значение. А про разные темы вообще можете забыть... В конце статьи будут ссылки на доп. материалы из реальных дизайн-систем, откуда я брал информацию. Изучить тему

https://habr.com/ru/articles/1012980/

#дизайнсистема #дизайн #figma #tokens #токены #дизайнтокены #variables #design_system

Дизайн-токены: полный гайд по архитектуре и неймингу c примерами и задачками

Дизайн-токены — это язык, понятный как дизайнерам, так и разработчикам. Без него продукт получается разрозненным и неповоротливым. Токены и правильный нейминг помогают создавать новые разделы быстрее, а дизайнерам больше думать о сценариях и создавать визуал в рамках продукта, а не заниматься рутиной. Если в вашем коде и макетах до сих пор живут значения типа #0055FF — вы копите технический долг, ведь если понадобится изменить этот цвет на другой, придется менять и все компоненты, где используется это значение. А про разные темы вообще можете забыть... В конце статьи будут ссылки на доп. материалы из реальных дизайн-систем, откуда я брал информацию. Изучить тему

https://habr.com/ru/articles/1012980/

#дизайнсистема #дизайн #figma #tokens #токены #дизайнтокены #variables #design_system

Many coin collectors and numismatists are also interested in #exonumia such as #tokens. Ancient tokens are called "Tesserae". The Numismatic Society of Diest have published a four part series on these pieces, and made it freely available. Read more in this week's E-Sylum: https://www.coinbooks.org/v29/esylum_v29n10.html#article3
#Numismatics #CoinCollecting #Tesserae #AncientCoins #History #Histodons @numismatics @histodons