#tokeneconomics — Public Fediverse posts

The risks of integrating AI too rapidly into higher education

I thought this was an interesting announcement from Surrey. I certainly see the appeal of being able to publicly say you’re doing this:

Rather than introducing AI as an additional skill, Surrey is undertaking a systematic redesign of every degree programme. This transformation will ensure that, as AI becomes embedded across society and the economy, Surrey students retain deep disciplinary expertise, develop critical judgement, and understand the implications of deploying AI within their field. In a course on voting behaviour, for instance, students are given a simple brief: pick an election, ask ChatGPT to explain the result, then interrogate whether AI got it right – testing its response against established theories of political behaviour, datasets from the British Election Study and published academic research. Students are enabled to identify and explain where the AI is vague or inaccurate, so that AI is not a shortcut but a prompt for deeper analysis. 

Every student – from foundation year through to postgraduate – will develop applied, discipline-specific AI capability as a core part of their studies. Surrey graduates will be equipped to shape how AI is used, challenged and governed in real-world contexts – ensuring that they remain trusted, capable and competitive in a labour market reshaped by AI.

What does “discipline-specific AI capability” mean though? My concern is that the current commercial landscape is so profoundly uncertain (given token economics etc) that a masterplan about what capacities to equip graduates with is doomed to failure. You either specify ‘capability’ in abstraction from current tools and risk a lack of practical relevance or you specify it in terms of current tools and risk being rendered out of date when those tools go through a rapid shift in terms of access, resourcing and capabilities. It’s not tenable to do nothing. But I think doing too much, all at once, risks being counter productive by locking in approaches and techniques which risk obsolence.

There is a stated commitment that AI “will be used selectively and purposefully, only where it improves educational outcomes, while ensuring that core competencies, from clinical reasoning and legal judgement to engineering design and creative practice, are preserved and strengthened”. I agree! But this is work which needs careful learning design, it needs identification of best practice, it needs willing colleagues. It’s a long-term direction of travel, rather than an outcome. I just don’t see how you do it without having an army of learning designs working 24/7 with academic teams suddenly full of time for development.

The risks of integrating AI too rapidly into higher education

I thought this was an interesting announcement from Surrey. I certainly see the appeal of being able to publicly say you’re doing this:

Rather than introducing AI as an additional skill, Surrey is undertaking a systematic redesign of every degree programme. This transformation will ensure that, as AI becomes embedded across society and the economy, Surrey students retain deep disciplinary expertise, develop critical judgement, and understand the implications of deploying AI within their field. In a course on voting behaviour, for instance, students are given a simple brief: pick an election, ask ChatGPT to explain the result, then interrogate whether AI got it right – testing its response against established theories of political behaviour, datasets from the British Election Study and published academic research. Students are enabled to identify and explain where the AI is vague or inaccurate, so that AI is not a shortcut but a prompt for deeper analysis. 

Every student – from foundation year through to postgraduate – will develop applied, discipline-specific AI capability as a core part of their studies. Surrey graduates will be equipped to shape how AI is used, challenged and governed in real-world contexts – ensuring that they remain trusted, capable and competitive in a labour market reshaped by AI.

What does “discipline-specific AI capability” mean though? My concern is that the current commercial landscape is so profoundly uncertain (given token economics etc) that a masterplan about what capacities to equip graduates with is doomed to failure. You either specify ‘capability’ in abstraction from current tools and risk a lack of practical relevance or you specify it in terms of current tools and risk being rendered out of date when those tools go through a rapid shift in terms of access, resourcing and capabilities. It’s not tenable to do nothing. But I think doing too much, all at once, risks being counter productive by locking in approaches and techniques which risk obsolence.

There is a stated commitment that AI “will be used selectively and purposefully, only where it improves educational outcomes, while ensuring that core competencies, from clinical reasoning and legal judgement to engineering design and creative practice, are preserved and strengthened”. I agree! But this is work which needs careful learning design, it needs identification of best practice, it needs willing colleagues. It’s a long-term direction of travel, rather than an outcome. I just don’t see how you do it without having an army of learning designs working 24/7 with academic teams suddenly full of time for development.

The risks of integrating AI too rapidly into higher education

I thought this was an interesting announcement from Surrey. I certainly see the appeal of being able to publicly say you’re doing this:

Rather than introducing AI as an additional skill, Surrey is undertaking a systematic redesign of every degree programme. This transformation will ensure that, as AI becomes embedded across society and the economy, Surrey students retain deep disciplinary expertise, develop critical judgement, and understand the implications of deploying AI within their field. In a course on voting behaviour, for instance, students are given a simple brief: pick an election, ask ChatGPT to explain the result, then interrogate whether AI got it right – testing its response against established theories of political behaviour, datasets from the British Election Study and published academic research. Students are enabled to identify and explain where the AI is vague or inaccurate, so that AI is not a shortcut but a prompt for deeper analysis. 

Every student – from foundation year through to postgraduate – will develop applied, discipline-specific AI capability as a core part of their studies. Surrey graduates will be equipped to shape how AI is used, challenged and governed in real-world contexts – ensuring that they remain trusted, capable and competitive in a labour market reshaped by AI.

What does “discipline-specific AI capability” mean though? My concern is that the current commercial landscape is so profoundly uncertain (given token economics etc) that a masterplan about what capacities to equip graduates with is doomed to failure. You either specify ‘capability’ in abstraction from current tools and risk a lack of practical relevance or you specify it in terms of current tools and risk being rendered out of date when those tools go through a rapid shift in terms of access, resourcing and capabilities. It’s not tenable to do nothing. But I think doing too much, all at once, risks being counter productive by locking in approaches and techniques which risk obsolence.

There is a stated commitment that AI “will be used selectively and purposefully, only where it improves educational outcomes, while ensuring that core competencies, from clinical reasoning and legal judgement to engineering design and creative practice, are preserved and strengthened”. I agree! But this is work which needs careful learning design, it needs identification of best practice, it needs willing colleagues. It’s a long-term direction of travel, rather than an outcome. I just don’t see how you do it without having an army of learning designs working 24/7 with academic teams suddenly full of time for development.

The risks of integrating AI too rapidly into higher education

I thought this was an interesting announcement from Surrey. I certainly see the appeal of being able to publicly say you’re doing this:

Rather than introducing AI as an additional skill, Surrey is undertaking a systematic redesign of every degree programme. This transformation will ensure that, as AI becomes embedded across society and the economy, Surrey students retain deep disciplinary expertise, develop critical judgement, and understand the implications of deploying AI within their field. In a course on voting behaviour, for instance, students are given a simple brief: pick an election, ask ChatGPT to explain the result, then interrogate whether AI got it right – testing its response against established theories of political behaviour, datasets from the British Election Study and published academic research. Students are enabled to identify and explain where the AI is vague or inaccurate, so that AI is not a shortcut but a prompt for deeper analysis. 

Every student – from foundation year through to postgraduate – will develop applied, discipline-specific AI capability as a core part of their studies. Surrey graduates will be equipped to shape how AI is used, challenged and governed in real-world contexts – ensuring that they remain trusted, capable and competitive in a labour market reshaped by AI.

What does “discipline-specific AI capability” mean though? My concern is that the current commercial landscape is so profoundly uncertain (given token economics etc) that a masterplan about what capacities to equip graduates with is doomed to failure. You either specify ‘capability’ in abstraction from current tools and risk a lack of practical relevance or you specify it in terms of current tools and risk being rendered out of date when those tools go through a rapid shift in terms of access, resourcing and capabilities. It’s not tenable to do nothing. But I think doing too much, all at once, risks being counter productive by locking in approaches and techniques which risk obsolence.

There is a stated commitment that AI “will be used selectively and purposefully, only where it improves educational outcomes, while ensuring that core competencies, from clinical reasoning and legal judgement to engineering design and creative practice, are preserved and strengthened”. I agree! But this is work which needs careful learning design, it needs identification of best practice, it needs willing colleagues. It’s a long-term direction of travel, rather than an outcome. I just don’t see how you do it without having an army of learning designs working 24/7 with academic teams suddenly full of time for development.

The risks of integrating AI too rapidly into higher education

I thought this was an interesting announcement from Surrey. I certainly see the appeal of being able to publicly say you’re doing this:

Rather than introducing AI as an additional skill, Surrey is undertaking a systematic redesign of every degree programme. This transformation will ensure that, as AI becomes embedded across society and the economy, Surrey students retain deep disciplinary expertise, develop critical judgement, and understand the implications of deploying AI within their field. In a course on voting behaviour, for instance, students are given a simple brief: pick an election, ask ChatGPT to explain the result, then interrogate whether AI got it right – testing its response against established theories of political behaviour, datasets from the British Election Study and published academic research. Students are enabled to identify and explain where the AI is vague or inaccurate, so that AI is not a shortcut but a prompt for deeper analysis. 

Every student – from foundation year through to postgraduate – will develop applied, discipline-specific AI capability as a core part of their studies. Surrey graduates will be equipped to shape how AI is used, challenged and governed in real-world contexts – ensuring that they remain trusted, capable and competitive in a labour market reshaped by AI.

What does “discipline-specific AI capability” mean though? My concern is that the current commercial landscape is so profoundly uncertain (given token economics etc) that a masterplan about what capacities to equip graduates with is doomed to failure. You either specify ‘capability’ in abstraction from current tools and risk a lack of practical relevance or you specify it in terms of current tools and risk being rendered out of date when those tools go through a rapid shift in terms of access, resourcing and capabilities. It’s not tenable to do nothing. But I think doing too much, all at once, risks being counter productive by locking in approaches and techniques which risk obsolence.

There is a stated commitment that AI “will be used selectively and purposefully, only where it improves educational outcomes, while ensuring that core competencies, from clinical reasoning and legal judgement to engineering design and creative practice, are preserved and strengthened”. I agree! But this is work which needs careful learning design, it needs identification of best practice, it needs willing colleagues. It’s a long-term direction of travel, rather than an outcome. I just don’t see how you do it without having an army of learning designs working 24/7 with academic teams suddenly full of time for development.

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

The token became a unit of billing in 2020. 6 years later it is a discipline, with a definition, metrics and a research field. Here's what that changes for code https://hackernoon.com/token-debt-is-the-new-technical-debt #tokeneconomics

The token became a unit of billing in 2020. 6 years later it is a discipline, with a definition, metrics and a research field. Here's what that changes for code https://hackernoon.com/token-debt-is-the-new-technical-debt #tokeneconomics

The token became a unit of billing in 2020. 6 years later it is a discipline, with a definition, metrics and a research field. Here's what that changes for code https://hackernoon.com/token-debt-is-the-new-technical-debt #tokeneconomics

The token became a unit of billing in 2020. 6 years later it is a discipline, with a definition, metrics and a research field. Here's what that changes for code https://hackernoon.com/token-debt-is-the-new-technical-debt #tokeneconomics

The token became a unit of billing in 2020. 6 years later it is a discipline, with a definition, metrics and a research field. Here's what that changes for code https://hackernoon.com/token-debt-is-the-new-technical-debt #tokeneconomics

ICYMI: Google cuts Gemini Flash prices as 3.6 uses 17% fewer output tokens: Flash-Lite hits 350 tokens per second at $0.30 per million input, while a new cyber model stays locked to governments. What changes for agent economics now? https://ppc.land/google-cuts-gemini-flash-prices-as-3-6-uses-17-fewer-output-tokens/ #Google #GeminiFlash #AI #TokenEconomics #DigitalMarketing

ICYMI: Google cuts Gemini Flash prices as 3.6 uses 17% fewer output tokens: Flash-Lite hits 350 tokens per second at $0.30 per million input, while a new cyber model stays locked to governments. What changes for agent economics now? https://ppc.land/google-cuts-gemini-flash-prices-as-3-6-uses-17-fewer-output-tokens/ #Google #GeminiFlash #AI #TokenEconomics #DigitalMarketing

ICYMI: Google cuts Gemini Flash prices as 3.6 uses 17% fewer output tokens: Flash-Lite hits 350 tokens per second at $0.30 per million input, while a new cyber model stays locked to governments. What changes for agent economics now? https://ppc.land/google-cuts-gemini-flash-prices-as-3-6-uses-17-fewer-output-tokens/ #Google #GeminiFlash #AI #TokenEconomics #DigitalMarketing

ICYMI: Google cuts Gemini Flash prices as 3.6 uses 17% fewer output tokens: Flash-Lite hits 350 tokens per second at $0.30 per million input, while a new cyber model stays locked to governments. What changes for agent economics now? https://ppc.land/google-cuts-gemini-flash-prices-as-3-6-uses-17-fewer-output-tokens/ #Google #GeminiFlash #AI #TokenEconomics #DigitalMarketing

ICYMI: Google cuts Gemini Flash prices as 3.6 uses 17% fewer output tokens: Flash-Lite hits 350 tokens per second at $0.30 per million input, while a new cyber model stays locked to governments. What changes for agent economics now? https://ppc.land/google-cuts-gemini-flash-prices-as-3-6-uses-17-fewer-output-tokens/ #Google #GeminiFlash #AI #TokenEconomics #DigitalMarketing

Google cuts Gemini Flash prices as 3.6 uses 17% fewer output tokens: Flash-Lite hits 350 tokens per second at $0.30 per million input, while a new cyber model stays locked to governments. What changes for agent economics now? https://ppc.land/google-cuts-gemini-flash-prices-as-3-6-uses-17-fewer-output-tokens/ #Google #GeminiFlash #AI #MachineLearning #TokenEconomics

Google cuts Gemini Flash prices as 3.6 uses 17% fewer output tokens: Flash-Lite hits 350 tokens per second at $0.30 per million input, while a new cyber model stays locked to governments. What changes for agent economics now? https://ppc.land/google-cuts-gemini-flash-prices-as-3-6-uses-17-fewer-output-tokens/ #Google #GeminiFlash #AI #MachineLearning #TokenEconomics

Google cuts Gemini Flash prices as 3.6 uses 17% fewer output tokens: Flash-Lite hits 350 tokens per second at $0.30 per million input, while a new cyber model stays locked to governments. What changes for agent economics now? https://ppc.land/google-cuts-gemini-flash-prices-as-3-6-uses-17-fewer-output-tokens/ #Google #GeminiFlash #AI #MachineLearning #TokenEconomics

Google cuts Gemini Flash prices as 3.6 uses 17% fewer output tokens: Flash-Lite hits 350 tokens per second at $0.30 per million input, while a new cyber model stays locked to governments. What changes for agent economics now? https://ppc.land/google-cuts-gemini-flash-prices-as-3-6-uses-17-fewer-output-tokens/ #Google #GeminiFlash #AI #MachineLearning #TokenEconomics

Google cuts Gemini Flash prices as 3.6 uses 17% fewer output tokens: Flash-Lite hits 350 tokens per second at $0.30 per million input, while a new cyber model stays locked to governments. What changes for agent economics now? https://ppc.land/google-cuts-gemini-flash-prices-as-3-6-uses-17-fewer-output-tokens/ #Google #GeminiFlash #AI #MachineLearning #TokenEconomics

GTC 2026 made something click for me: AI isn’t just software anymore — it’s infrastructure for producing tokens at scale.

Jensen Huang literally framed future data centers as “factories” whose output is tokens, with metrics like tokens/sec and tokens/watt becoming the new KPIs.

This article explores what that means economically — when compute becomes a consumable and tokens start behaving like a new kind of resource.

https://www.buysellram.com/blog/the-token-factory-how-nvidia-gtc-2026-redefined-the-economics-of-ai/

#NVIDIA #GTC2026 #AIHardware #TokenEconomics #DataCenter #ITAD #TechTrends2026 #TokenFactory #CostperToken #AIAgent #InferenceEra #technology

GTC 2026 made something click for me: AI isn’t just software anymore — it’s infrastructure for producing tokens at scale.

Jensen Huang literally framed future data centers as “factories” whose output is tokens, with metrics like tokens/sec and tokens/watt becoming the new KPIs.

This article explores what that means economically — when compute becomes a consumable and tokens start behaving like a new kind of resource.

https://www.buysellram.com/blog/the-token-factory-how-nvidia-gtc-2026-redefined-the-economics-of-ai/

#NVIDIA #GTC2026 #AIHardware #TokenEconomics #DataCenter #ITAD #TechTrends2026 #TokenFactory #CostperToken #AIAgent #InferenceEra #technology

GTC 2026 made something click for me: AI isn’t just software anymore — it’s infrastructure for producing tokens at scale.

Jensen Huang literally framed future data centers as “factories” whose output is tokens, with metrics like tokens/sec and tokens/watt becoming the new KPIs.

This article explores what that means economically — when compute becomes a consumable and tokens start behaving like a new kind of resource.

https://www.buysellram.com/blog/the-token-factory-how-nvidia-gtc-2026-redefined-the-economics-of-ai/

#NVIDIA #GTC2026 #AIHardware #TokenEconomics #DataCenter #ITAD #TechTrends2026 #TokenFactory #CostperToken #AIAgent #InferenceEra #technology

GTC 2026 made something click for me: AI isn’t just software anymore — it’s infrastructure for producing tokens at scale.

Jensen Huang literally framed future data centers as “factories” whose output is tokens, with metrics like tokens/sec and tokens/watt becoming the new KPIs.

This article explores what that means economically — when compute becomes a consumable and tokens start behaving like a new kind of resource.

https://www.buysellram.com/blog/the-token-factory-how-nvidia-gtc-2026-redefined-the-economics-of-ai/

#NVIDIA #GTC2026 #AIHardware #TokenEconomics #DataCenter #ITAD #TechTrends2026 #TokenFactory #CostperToken #AIAgent #InferenceEra #technology

GTC 2026 made something click for me: AI isn’t just software anymore — it’s infrastructure for producing tokens at scale.

Jensen Huang literally framed future data centers as “factories” whose output is tokens, with metrics like tokens/sec and tokens/watt becoming the new KPIs.

This article explores what that means economically — when compute becomes a consumable and tokens start behaving like a new kind of resource.

https://www.buysellram.com/blog/the-token-factory-how-nvidia-gtc-2026-redefined-the-economics-of-ai/

#NVIDIA #GTC2026 #AIHardware #TokenEconomics #DataCenter #ITAD #TechTrends2026 #TokenFactory #CostperToken #AIAgent #InferenceEra #technology

Jensen Huang literally framed future data centers as “factories” whose output is tokens, with metrics like tokens/sec and tokens/watt becoming the new KPIs.

This article explores what that means economically — when compute becomes a consumable and tokens start behaving like a new kind of resource.

https://www.buysellram.com/blog/the-token-factory-how-nvidia-gtc-2026-redefined-the-economics-of-ai/

#NVIDIA #GTC2026 #AIHardware #TokenEconomics #DataCenter #tech #TechTrends2026 #TokenFactory #CostperToken #AIAgent #InferenceEra

Jensen Huang literally framed future data centers as “factories” whose output is tokens, with metrics like tokens/sec and tokens/watt becoming the new KPIs.

This article explores what that means economically — when compute becomes a consumable and tokens start behaving like a new kind of resource.

https://www.buysellram.com/blog/the-token-factory-how-nvidia-gtc-2026-redefined-the-economics-of-ai/

#NVIDIA #GTC2026 #AIHardware #TokenEconomics #DataCenter #tech #TechTrends2026 #TokenFactory #CostperToken #AIAgent #InferenceEra

Jensen Huang literally framed future data centers as “factories” whose output is tokens, with metrics like tokens/sec and tokens/watt becoming the new KPIs.

This article explores what that means economically — when compute becomes a consumable and tokens start behaving like a new kind of resource.

https://www.buysellram.com/blog/the-token-factory-how-nvidia-gtc-2026-redefined-the-economics-of-ai/

#NVIDIA #GTC2026 #AIHardware #TokenEconomics #DataCenter #tech #TechTrends2026 #TokenFactory #CostperToken #AIAgent #InferenceEra

Jensen Huang literally framed future data centers as “factories” whose output is tokens, with metrics like tokens/sec and tokens/watt becoming the new KPIs.

This article explores what that means economically — when compute becomes a consumable and tokens start behaving like a new kind of resource.

https://www.buysellram.com/blog/the-token-factory-how-nvidia-gtc-2026-redefined-the-economics-of-ai/

#NVIDIA #GTC2026 #AIHardware #TokenEconomics #DataCenter #tech #TechTrends2026 #TokenFactory #CostperToken #AIAgent #InferenceEra

Jensen Huang literally framed future data centers as “factories” whose output is tokens, with metrics like tokens/sec and tokens/watt becoming the new KPIs.

This article explores what that means economically — when compute becomes a consumable and tokens start behaving like a new kind of resource.

https://www.buysellram.com/blog/the-token-factory-how-nvidia-gtc-2026-redefined-the-economics-of-ai/

#NVIDIA #GTC2026 #AIHardware #TokenEconomics #DataCenter #tech #TechTrends2026 #TokenFactory #CostperToken #AIAgent #InferenceEra

Inflation systems take value from users.

With NeuroCoin, the profit of projects goes back into NCNC pools, which makes the network stronger rather than making third parties stronger.

#NeuroCoin #TokenEconomics #CryptoEconomy #DecentralizedFinance #Blockchain #Web3

Inflation systems take value from users.

With NeuroCoin, the profit of projects goes back into NCNC pools, which makes the network stronger rather than making third parties stronger.

#NeuroCoin #TokenEconomics #CryptoEconomy #DecentralizedFinance #Blockchain #Web3

Your LLM bills aren’t rising by accident 🤯
The real leak hides in your data structure.
Meet TOON — built for tokens, not braces 🧠⚡
Read before your next prompt ships.
https://medium.com/@rogt.x1997/meet-toon-how-one-data-format-cuts-llm-token-costs-by-40-f73bcf8c6987

#GenAI #LLMs #TokenEconomics
https://medium.com/@rogt.x1997/meet-toon-how-one-data-format-cuts-llm-token-costs-by-40-f73bcf8c6987

Your LLM bills aren’t rising by accident 🤯
The real leak hides in your data structure.
Meet TOON — built for tokens, not braces 🧠⚡
Read before your next prompt ships.
https://medium.com/@rogt.x1997/meet-toon-how-one-data-format-cuts-llm-token-costs-by-40-f73bcf8c6987

#GenAI #LLMs #TokenEconomics
https://medium.com/@rogt.x1997/meet-toon-how-one-data-format-cuts-llm-token-costs-by-40-f73bcf8c6987

$1.1B TIA token release to push October’s crypto unlocks to almost $2B - Celestia tokens worth $1.1 billion will be unlocked on Oct. 31, while $3... - https://cointelegraph.com/news/tia-token-release-october-crypto-unlocks-2-billion #octobercryptoevents #digitalassetmarket #cryptocurrencynews #cryptomarkettrends #$1.1btokenrelease #tiatokenrelease #tokeneconomics #cryptounlocks #tokenunlocks

$1.1B TIA token release to push October’s crypto unlocks to almost $2B - Celestia tokens worth $1.1 billion will be unlocked on Oct. 31, while $3... - https://cointelegraph.com/news/tia-token-release-october-crypto-unlocks-2-billion #octobercryptoevents #digitalassetmarket #cryptocurrencynews #cryptomarkettrends #$1.1btokenrelease #tiatokenrelease #tokeneconomics #cryptounlocks #tokenunlocks

$1.1B TIA token release to push October’s crypto unlocks to almost $2B - Celestia tokens worth $1.1 billion will be unlocked on Oct. 31, while $3... - https://cointelegraph.com/news/tia-token-release-october-crypto-unlocks-2-billion #octobercryptoevents #digitalassetmarket #cryptocurrencynews #cryptomarkettrends #$1.1btokenrelease #tiatokenrelease #tokeneconomics #cryptounlocks #tokenunlocks

$1.1B TIA token release to push October’s crypto unlocks to almost $2B - Celestia tokens worth $1.1 billion will be unlocked on Oct. 31, while $3... - https://cointelegraph.com/news/tia-token-release-october-crypto-unlocks-2-billion #octobercryptoevents #digitalassetmarket #cryptocurrencynews #cryptomarkettrends #$1.1btokenrelease #tiatokenrelease #tokeneconomics #cryptounlocks #tokenunlocks

Por favor entra en este link https://voting.eosdt.com/ y vota haciendo click en el puesto 88. EOSVENEZUELA (número 88 en la lista), luego SUBMIT y listo. Es para apoyar a EOS Venezuela como Block Producers de EOS. Yo ya lo hice, ¿y tú?

#eos #blockchain #venezuela #tokeneconomics #token #crypto #cryptocurrencies