#bottraffic — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #bottraffic, aggregated by home.social.
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Press Release: Rising #Emissions, Depleting #Water and Vanishing #Land—UN Scientists: #AI Is Threatening #NaturalResources for Billions
By 2030, AI's water use will match the needs of 1.3 billion people while its power use triples that of 650 million, UN University investigation warns
Date Published
3 Jun 2026Excerpt: "Inference, efficiency, and the rebound effect
"Public discussion has largely focused on the energy required to train massive models. Training GPT-3 was estimated to require 1.3 gigawatt-hours (GWh) of electricity, while estimates suggest GPT-4 consumed between 50 and 70 GWh. However, the report reveals this framing is outdated. Once a model is deployed, inference—the continuous running of models to answer everyday user prompts—becomes the dominant cost, accounting for 80 to 90 per cent of total #AI energy use. ChatGPT alone is estimated to process around 2.5 billion prompts per day, translating to roughly 383 GWh of electricity per year for a single product. Offsetting associated carbon emissions would require 2.6 million tree seedlings grown for 10 years, enough trees to cover a land area the size of Manhattan. The water footprint is equivalent to the minimum annual domestic water needs of roughly 500,000 people in Sub-Saharan Africa, and the land footprint is equal to over 800 football fields."
Read more:
https://unu.edu/inweh/news/environmental-cost-of-AIs-Enrgy-use-carbon-water-and-land-footprints#AIBoom #Electricity #Hyperscale #BigTech #BigData #CarbonFootprint #EnvironmentalRacism #EnvironmentalDegradation #NoisePollution #LightPollution #WaterIsLife #AIAgents #BotTraffic #GreenSpaces #Farmland #Prairies #Woodland #TechGiants #ProtectNature #NoDatacenters #EnergyConsumption #USPol #WorldPol #Datacentres
#DatacenterMoratoriums #ArtificialIntelligence -
Press Release: Rising #Emissions, Depleting #Water and Vanishing #Land—UN Scientists: #AI Is Threatening #NaturalResources for Billions
By 2030, AI's water use will match the needs of 1.3 billion people while its power use triples that of 650 million, UN University investigation warns
Date Published
3 Jun 2026Excerpt: "Inference, efficiency, and the rebound effect
"Public discussion has largely focused on the energy required to train massive models. Training GPT-3 was estimated to require 1.3 gigawatt-hours (GWh) of electricity, while estimates suggest GPT-4 consumed between 50 and 70 GWh. However, the report reveals this framing is outdated. Once a model is deployed, inference—the continuous running of models to answer everyday user prompts—becomes the dominant cost, accounting for 80 to 90 per cent of total #AI energy use. ChatGPT alone is estimated to process around 2.5 billion prompts per day, translating to roughly 383 GWh of electricity per year for a single product. Offsetting associated carbon emissions would require 2.6 million tree seedlings grown for 10 years, enough trees to cover a land area the size of Manhattan. The water footprint is equivalent to the minimum annual domestic water needs of roughly 500,000 people in Sub-Saharan Africa, and the land footprint is equal to over 800 football fields."
Read more:
https://unu.edu/inweh/news/environmental-cost-of-AIs-Enrgy-use-carbon-water-and-land-footprints#AIBoom #Electricity #Hyperscale #BigTech #BigData #CarbonFootprint #EnvironmentalRacism #EnvironmentalDegradation #NoisePollution #LightPollution #WaterIsLife #AIAgents #BotTraffic #GreenSpaces #Farmland #Prairies #Woodland #TechGiants #ProtectNature #NoDatacenters #EnergyConsumption #USPol #WorldPol #Datacentres
#DatacenterMoratoriums #ArtificialIntelligence -
Press Release: Rising #Emissions, Depleting #Water and Vanishing #Land—UN Scientists: #AI Is Threatening #NaturalResources for Billions
By 2030, AI's water use will match the needs of 1.3 billion people while its power use triples that of 650 million, UN University investigation warns
Date Published
3 Jun 2026Excerpt: "Inference, efficiency, and the rebound effect
"Public discussion has largely focused on the energy required to train massive models. Training GPT-3 was estimated to require 1.3 gigawatt-hours (GWh) of electricity, while estimates suggest GPT-4 consumed between 50 and 70 GWh. However, the report reveals this framing is outdated. Once a model is deployed, inference—the continuous running of models to answer everyday user prompts—becomes the dominant cost, accounting for 80 to 90 per cent of total #AI energy use. ChatGPT alone is estimated to process around 2.5 billion prompts per day, translating to roughly 383 GWh of electricity per year for a single product. Offsetting associated carbon emissions would require 2.6 million tree seedlings grown for 10 years, enough trees to cover a land area the size of Manhattan. The water footprint is equivalent to the minimum annual domestic water needs of roughly 500,000 people in Sub-Saharan Africa, and the land footprint is equal to over 800 football fields."
Read more:
https://unu.edu/inweh/news/environmental-cost-of-AIs-Enrgy-use-carbon-water-and-land-footprints#AIBoom #Electricity #Hyperscale #BigTech #BigData #CarbonFootprint #EnvironmentalRacism #EnvironmentalDegradation #NoisePollution #LightPollution #WaterIsLife #AIAgents #BotTraffic #GreenSpaces #Farmland #Prairies #Woodland #TechGiants #ProtectNature #NoDatacenters #EnergyConsumption #USPol #WorldPol #Datacentres
#DatacenterMoratoriums #ArtificialIntelligence -
Press Release: Rising #Emissions, Depleting #Water and Vanishing #Land—UN Scientists: #AI Is Threatening #NaturalResources for Billions
By 2030, AI's water use will match the needs of 1.3 billion people while its power use triples that of 650 million, UN University investigation warns
Date Published
3 Jun 2026Excerpt: "Inference, efficiency, and the rebound effect
"Public discussion has largely focused on the energy required to train massive models. Training GPT-3 was estimated to require 1.3 gigawatt-hours (GWh) of electricity, while estimates suggest GPT-4 consumed between 50 and 70 GWh. However, the report reveals this framing is outdated. Once a model is deployed, inference—the continuous running of models to answer everyday user prompts—becomes the dominant cost, accounting for 80 to 90 per cent of total #AI energy use. ChatGPT alone is estimated to process around 2.5 billion prompts per day, translating to roughly 383 GWh of electricity per year for a single product. Offsetting associated carbon emissions would require 2.6 million tree seedlings grown for 10 years, enough trees to cover a land area the size of Manhattan. The water footprint is equivalent to the minimum annual domestic water needs of roughly 500,000 people in Sub-Saharan Africa, and the land footprint is equal to over 800 football fields."
Read more:
https://unu.edu/inweh/news/environmental-cost-of-AIs-Enrgy-use-carbon-water-and-land-footprints#AIBoom #Electricity #Hyperscale #BigTech #BigData #CarbonFootprint #EnvironmentalRacism #EnvironmentalDegradation #NoisePollution #LightPollution #WaterIsLife #AIAgents #BotTraffic #GreenSpaces #Farmland #Prairies #Woodland #TechGiants #ProtectNature #NoDatacenters #EnergyConsumption #USPol #WorldPol #Datacentres
#DatacenterMoratoriums #ArtificialIntelligence -
Press Release: Rising #Emissions, Depleting #Water and Vanishing #Land—UN Scientists: #AI Is Threatening #NaturalResources for Billions
By 2030, AI's water use will match the needs of 1.3 billion people while its power use triples that of 650 million, UN University investigation warns
Date Published
3 Jun 2026Excerpt: "Inference, efficiency, and the rebound effect
"Public discussion has largely focused on the energy required to train massive models. Training GPT-3 was estimated to require 1.3 gigawatt-hours (GWh) of electricity, while estimates suggest GPT-4 consumed between 50 and 70 GWh. However, the report reveals this framing is outdated. Once a model is deployed, inference—the continuous running of models to answer everyday user prompts—becomes the dominant cost, accounting for 80 to 90 per cent of total #AI energy use. ChatGPT alone is estimated to process around 2.5 billion prompts per day, translating to roughly 383 GWh of electricity per year for a single product. Offsetting associated carbon emissions would require 2.6 million tree seedlings grown for 10 years, enough trees to cover a land area the size of Manhattan. The water footprint is equivalent to the minimum annual domestic water needs of roughly 500,000 people in Sub-Saharan Africa, and the land footprint is equal to over 800 football fields."
Read more:
https://unu.edu/inweh/news/environmental-cost-of-AIs-Enrgy-use-carbon-water-and-land-footprints#AIBoom #Electricity #Hyperscale #BigTech #BigData #CarbonFootprint #EnvironmentalRacism #EnvironmentalDegradation #NoisePollution #LightPollution #WaterIsLife #AIAgents #BotTraffic #GreenSpaces #Farmland #Prairies #Woodland #TechGiants #ProtectNature #NoDatacenters #EnergyConsumption #USPol #WorldPol #Datacentres
#DatacenterMoratoriums #ArtificialIntelligence -
The #EnvironmentalCost of #ArtificialIntelligence: #Carbon, #Water, and #LandFootprints
#AI’s rapid growth drives huge energy, water, and land use, raising environmental and equity challenges across its global infrastructure.
Date Published 3 Jun 2026
UNU-INWEH Report: Aczel, M., Chamanara, S., Matin, M., Farsi, A., Marwala, T., Madani, K. (2026).
"This report, Environmental Cost of Artificial Intelligence: Carbon, Water and Land Footprints, by the #UnitedNationsUniversity Institute for Water, Environment and Health (#UNU-#INWEH) on its 30th anniversary, examines one of the most underexplored consequences of AI’s rapid expansion: the environmental footprints of the energy required to power it. As artificial intelligence becomes embedded in economies, public services, research, communication, and everyday life, it depends on a growing physical infrastructure of #datacenters, advanced #chips, #CoolingSystems, #ElectricityGrids, #WaterResources, land, and #CriticalMineral supply chains. The report shows that AI is not only a digital technology, but also a material system with measurable #EnvironmentalCosts.
"The report moves beyond a carbon-only lens by quantifying the carbon, water, and land footprints associated with the electricity used to train, deploy, and operate AI systems at scale. Its central finding is that AI’s environmental costs depend not only on how much electricity is used, but also on where that electricity is generated and which energy sources power it. Every kilowatt-hour used by AI carries carbon, water, and land implications, and these footprints do not always move in the same direction: low-carbon electricity is not automatically low-water or low-land. The report also shows that AI’s footprint is shaped by both major infrastructure trends, including the rapid growth of data centers, and everyday use patterns, including model choice, output length, modality, and the growing use of text, image, and video generation.
"Importantly, the report frames AI’s environmental footprint as a governance and justice challenge, not only a technical problem. The benefits of AI often flow across borders and sectors, while the environmental burdens of data center siting, electricity demand, water withdrawals, #LandUse, MineralExtraction, and #EWaste can be concentrated in specific communities and regions. To address these risks, the report calls for a responsible AI ecosystem grounded in transparency, efficiency by design, equity and #EnvironmentalJustice, lifecycle responsibility, global cooperation, and sustainable use. By making AI’s carbon, water, and land footprints visible and comparable, the report provides a practical basis for integrating AI into energy, climate, water, and land-use planning, ensuring that innovation advances without shifting environmental costs onto vulnerable communities."
Download PDF:
https://unu.edu/inweh/collection/environmental-cost-of-AIs-Enrgy-Use-Carbon-water-and-land-footprints#AIBoom #Electricity #Hyperscale #BigTech #BigData #CarbonFootprint #EnvironmentalRacism #EnvironmentalDegradation #NoisePollution #LightPollution #WaterIsLife #AIAgents #BotTraffic #GreenSpaces #Farmland #Prairies #Woodland #TechGiants #ProtectNature #NoDatacenters #EnergyConsumption #USPol #WorldPol #Datacentres
#DatacenterMoratoriums -
The #EnvironmentalCost of #ArtificialIntelligence: #Carbon, #Water, and #LandFootprints
#AI’s rapid growth drives huge energy, water, and land use, raising environmental and equity challenges across its global infrastructure.
Date Published 3 Jun 2026
UNU-INWEH Report: Aczel, M., Chamanara, S., Matin, M., Farsi, A., Marwala, T., Madani, K. (2026).
"This report, Environmental Cost of Artificial Intelligence: Carbon, Water and Land Footprints, by the #UnitedNationsUniversity Institute for Water, Environment and Health (#UNU-#INWEH) on its 30th anniversary, examines one of the most underexplored consequences of AI’s rapid expansion: the environmental footprints of the energy required to power it. As artificial intelligence becomes embedded in economies, public services, research, communication, and everyday life, it depends on a growing physical infrastructure of #datacenters, advanced #chips, #CoolingSystems, #ElectricityGrids, #WaterResources, land, and #CriticalMineral supply chains. The report shows that AI is not only a digital technology, but also a material system with measurable #EnvironmentalCosts.
"The report moves beyond a carbon-only lens by quantifying the carbon, water, and land footprints associated with the electricity used to train, deploy, and operate AI systems at scale. Its central finding is that AI’s environmental costs depend not only on how much electricity is used, but also on where that electricity is generated and which energy sources power it. Every kilowatt-hour used by AI carries carbon, water, and land implications, and these footprints do not always move in the same direction: low-carbon electricity is not automatically low-water or low-land. The report also shows that AI’s footprint is shaped by both major infrastructure trends, including the rapid growth of data centers, and everyday use patterns, including model choice, output length, modality, and the growing use of text, image, and video generation.
"Importantly, the report frames AI’s environmental footprint as a governance and justice challenge, not only a technical problem. The benefits of AI often flow across borders and sectors, while the environmental burdens of data center siting, electricity demand, water withdrawals, #LandUse, MineralExtraction, and #EWaste can be concentrated in specific communities and regions. To address these risks, the report calls for a responsible AI ecosystem grounded in transparency, efficiency by design, equity and #EnvironmentalJustice, lifecycle responsibility, global cooperation, and sustainable use. By making AI’s carbon, water, and land footprints visible and comparable, the report provides a practical basis for integrating AI into energy, climate, water, and land-use planning, ensuring that innovation advances without shifting environmental costs onto vulnerable communities."
Download PDF:
https://unu.edu/inweh/collection/environmental-cost-of-AIs-Enrgy-Use-Carbon-water-and-land-footprints#AIBoom #Electricity #Hyperscale #BigTech #BigData #CarbonFootprint #EnvironmentalRacism #EnvironmentalDegradation #NoisePollution #LightPollution #WaterIsLife #AIAgents #BotTraffic #GreenSpaces #Farmland #Prairies #Woodland #TechGiants #ProtectNature #NoDatacenters #EnergyConsumption #USPol #WorldPol #Datacentres
#DatacenterMoratoriums -
The #EnvironmentalCost of #ArtificialIntelligence: #Carbon, #Water, and #LandFootprints
#AI’s rapid growth drives huge energy, water, and land use, raising environmental and equity challenges across its global infrastructure.
Date Published 3 Jun 2026
UNU-INWEH Report: Aczel, M., Chamanara, S., Matin, M., Farsi, A., Marwala, T., Madani, K. (2026).
"This report, Environmental Cost of Artificial Intelligence: Carbon, Water and Land Footprints, by the #UnitedNationsUniversity Institute for Water, Environment and Health (#UNU-#INWEH) on its 30th anniversary, examines one of the most underexplored consequences of AI’s rapid expansion: the environmental footprints of the energy required to power it. As artificial intelligence becomes embedded in economies, public services, research, communication, and everyday life, it depends on a growing physical infrastructure of #datacenters, advanced #chips, #CoolingSystems, #ElectricityGrids, #WaterResources, land, and #CriticalMineral supply chains. The report shows that AI is not only a digital technology, but also a material system with measurable #EnvironmentalCosts.
"The report moves beyond a carbon-only lens by quantifying the carbon, water, and land footprints associated with the electricity used to train, deploy, and operate AI systems at scale. Its central finding is that AI’s environmental costs depend not only on how much electricity is used, but also on where that electricity is generated and which energy sources power it. Every kilowatt-hour used by AI carries carbon, water, and land implications, and these footprints do not always move in the same direction: low-carbon electricity is not automatically low-water or low-land. The report also shows that AI’s footprint is shaped by both major infrastructure trends, including the rapid growth of data centers, and everyday use patterns, including model choice, output length, modality, and the growing use of text, image, and video generation.
"Importantly, the report frames AI’s environmental footprint as a governance and justice challenge, not only a technical problem. The benefits of AI often flow across borders and sectors, while the environmental burdens of data center siting, electricity demand, water withdrawals, #LandUse, MineralExtraction, and #EWaste can be concentrated in specific communities and regions. To address these risks, the report calls for a responsible AI ecosystem grounded in transparency, efficiency by design, equity and #EnvironmentalJustice, lifecycle responsibility, global cooperation, and sustainable use. By making AI’s carbon, water, and land footprints visible and comparable, the report provides a practical basis for integrating AI into energy, climate, water, and land-use planning, ensuring that innovation advances without shifting environmental costs onto vulnerable communities."
Download PDF:
https://unu.edu/inweh/collection/environmental-cost-of-AIs-Enrgy-Use-Carbon-water-and-land-footprints#AIBoom #Electricity #Hyperscale #BigTech #BigData #CarbonFootprint #EnvironmentalRacism #EnvironmentalDegradation #NoisePollution #LightPollution #WaterIsLife #AIAgents #BotTraffic #GreenSpaces #Farmland #Prairies #Woodland #TechGiants #ProtectNature #NoDatacenters #EnergyConsumption #USPol #WorldPol #Datacentres
#DatacenterMoratoriums -
The #EnvironmentalCost of #ArtificialIntelligence: #Carbon, #Water, and #LandFootprints
#AI’s rapid growth drives huge energy, water, and land use, raising environmental and equity challenges across its global infrastructure.
Date Published 3 Jun 2026
UNU-INWEH Report: Aczel, M., Chamanara, S., Matin, M., Farsi, A., Marwala, T., Madani, K. (2026).
"This report, Environmental Cost of Artificial Intelligence: Carbon, Water and Land Footprints, by the #UnitedNationsUniversity Institute for Water, Environment and Health (#UNU-#INWEH) on its 30th anniversary, examines one of the most underexplored consequences of AI’s rapid expansion: the environmental footprints of the energy required to power it. As artificial intelligence becomes embedded in economies, public services, research, communication, and everyday life, it depends on a growing physical infrastructure of #datacenters, advanced #chips, #CoolingSystems, #ElectricityGrids, #WaterResources, land, and #CriticalMineral supply chains. The report shows that AI is not only a digital technology, but also a material system with measurable #EnvironmentalCosts.
"The report moves beyond a carbon-only lens by quantifying the carbon, water, and land footprints associated with the electricity used to train, deploy, and operate AI systems at scale. Its central finding is that AI’s environmental costs depend not only on how much electricity is used, but also on where that electricity is generated and which energy sources power it. Every kilowatt-hour used by AI carries carbon, water, and land implications, and these footprints do not always move in the same direction: low-carbon electricity is not automatically low-water or low-land. The report also shows that AI’s footprint is shaped by both major infrastructure trends, including the rapid growth of data centers, and everyday use patterns, including model choice, output length, modality, and the growing use of text, image, and video generation.
"Importantly, the report frames AI’s environmental footprint as a governance and justice challenge, not only a technical problem. The benefits of AI often flow across borders and sectors, while the environmental burdens of data center siting, electricity demand, water withdrawals, #LandUse, MineralExtraction, and #EWaste can be concentrated in specific communities and regions. To address these risks, the report calls for a responsible AI ecosystem grounded in transparency, efficiency by design, equity and #EnvironmentalJustice, lifecycle responsibility, global cooperation, and sustainable use. By making AI’s carbon, water, and land footprints visible and comparable, the report provides a practical basis for integrating AI into energy, climate, water, and land-use planning, ensuring that innovation advances without shifting environmental costs onto vulnerable communities."
Download PDF:
https://unu.edu/inweh/collection/environmental-cost-of-AIs-Enrgy-Use-Carbon-water-and-land-footprints#AIBoom #Electricity #Hyperscale #BigTech #BigData #CarbonFootprint #EnvironmentalRacism #EnvironmentalDegradation #NoisePollution #LightPollution #WaterIsLife #AIAgents #BotTraffic #GreenSpaces #Farmland #Prairies #Woodland #TechGiants #ProtectNature #NoDatacenters #EnergyConsumption #USPol #WorldPol #Datacentres
#DatacenterMoratoriums -
The #EnvironmentalCost of #ArtificialIntelligence: #Carbon, #Water, and #LandFootprints
#AI’s rapid growth drives huge energy, water, and land use, raising environmental and equity challenges across its global infrastructure.
Date Published 3 Jun 2026
UNU-INWEH Report: Aczel, M., Chamanara, S., Matin, M., Farsi, A., Marwala, T., Madani, K. (2026).
"This report, Environmental Cost of Artificial Intelligence: Carbon, Water and Land Footprints, by the #UnitedNationsUniversity Institute for Water, Environment and Health (#UNU-#INWEH) on its 30th anniversary, examines one of the most underexplored consequences of AI’s rapid expansion: the environmental footprints of the energy required to power it. As artificial intelligence becomes embedded in economies, public services, research, communication, and everyday life, it depends on a growing physical infrastructure of #datacenters, advanced #chips, #CoolingSystems, #ElectricityGrids, #WaterResources, land, and #CriticalMineral supply chains. The report shows that AI is not only a digital technology, but also a material system with measurable #EnvironmentalCosts.
"The report moves beyond a carbon-only lens by quantifying the carbon, water, and land footprints associated with the electricity used to train, deploy, and operate AI systems at scale. Its central finding is that AI’s environmental costs depend not only on how much electricity is used, but also on where that electricity is generated and which energy sources power it. Every kilowatt-hour used by AI carries carbon, water, and land implications, and these footprints do not always move in the same direction: low-carbon electricity is not automatically low-water or low-land. The report also shows that AI’s footprint is shaped by both major infrastructure trends, including the rapid growth of data centers, and everyday use patterns, including model choice, output length, modality, and the growing use of text, image, and video generation.
"Importantly, the report frames AI’s environmental footprint as a governance and justice challenge, not only a technical problem. The benefits of AI often flow across borders and sectors, while the environmental burdens of data center siting, electricity demand, water withdrawals, #LandUse, MineralExtraction, and #EWaste can be concentrated in specific communities and regions. To address these risks, the report calls for a responsible AI ecosystem grounded in transparency, efficiency by design, equity and #EnvironmentalJustice, lifecycle responsibility, global cooperation, and sustainable use. By making AI’s carbon, water, and land footprints visible and comparable, the report provides a practical basis for integrating AI into energy, climate, water, and land-use planning, ensuring that innovation advances without shifting environmental costs onto vulnerable communities."
Download PDF:
https://unu.edu/inweh/collection/environmental-cost-of-AIs-Enrgy-Use-Carbon-water-and-land-footprints#AIBoom #Electricity #Hyperscale #BigTech #BigData #CarbonFootprint #EnvironmentalRacism #EnvironmentalDegradation #NoisePollution #LightPollution #WaterIsLife #AIAgents #BotTraffic #GreenSpaces #Farmland #Prairies #Woodland #TechGiants #ProtectNature #NoDatacenters #EnergyConsumption #USPol #WorldPol #Datacentres
#DatacenterMoratoriums -
#Bot #WebTraffic has overtaken human web traffic, data shows
#Cloudflare says 57.4% of requests to a selection of websites it hosts are now #AutomatedBot requests, while 42.6% are human-generated.
By Samantha Elkins
June 4, 2026, 6:27 PM EDT"Website traffic from #AI agents and #bots has eclipsed its human-generated counterpart for the first time, according to Cloudflare, an earlier-than-expected milestone that speaks to AI’s rapid advance and impact.
" 'Welp, that happened faster than I predicted,' #MatthewPrince, co-founder and CEO of Cloudflare, one of the largest internet hosting services, wrote Thursday on X.
“Thought it would be end of 2027, then early 2027,” he continued, “but agentic traffic growing so fast that bots have now passed human traffic online for the first time in the Internet’s history.”
"The rise is attributed to the continued proliferation of AI agents, largely autonomous programs that use tools that collaborate with high-level programs and data, with little human feedback.
"Cloudflare, which has a feature to display bot versus human-generated search requests, says 57.4% of requests are now initiated by bots, compared with 42.6% coming from humans.
"Humans might browse five websites before making a purchase, however, while an AI service might browse 5,000 websites."
Archived version:
https://archive.ph/Uwjne#WebBots #AIBots #AIAgents #HumanWebTraffic #BotTraffic
#BigData #BigTech is #OutOfControl ! -
#Bot #WebTraffic has overtaken human web traffic, data shows
#Cloudflare says 57.4% of requests to a selection of websites it hosts are now #AutomatedBot requests, while 42.6% are human-generated.
By Samantha Elkins
June 4, 2026, 6:27 PM EDT"Website traffic from #AI agents and #bots has eclipsed its human-generated counterpart for the first time, according to Cloudflare, an earlier-than-expected milestone that speaks to AI’s rapid advance and impact.
" 'Welp, that happened faster than I predicted,' #MatthewPrince, co-founder and CEO of Cloudflare, one of the largest internet hosting services, wrote Thursday on X.
“Thought it would be end of 2027, then early 2027,” he continued, “but agentic traffic growing so fast that bots have now passed human traffic online for the first time in the Internet’s history.”
"The rise is attributed to the continued proliferation of AI agents, largely autonomous programs that use tools that collaborate with high-level programs and data, with little human feedback.
"Cloudflare, which has a feature to display bot versus human-generated search requests, says 57.4% of requests are now initiated by bots, compared with 42.6% coming from humans.
"Humans might browse five websites before making a purchase, however, while an AI service might browse 5,000 websites."
Archived version:
https://archive.ph/Uwjne#WebBots #AIBots #AIAgents #HumanWebTraffic #BotTraffic
#BigData #BigTech is #OutOfControl ! -
Oh, look! Another #CEO playing fast and loose with "facts" 🤥. Apparently, bot traffic is bigger than Santa's bag 🎅, according to Cloudflare! Except, oops—reality check, it's not. Guess the CEO got their data from the same place they get their magic beans 🌱.
https://www.flyingpenguin.com/cloudflare-ceo-is-lying-to-you-about-the-bot-traffic-jump/ #Fails #BotTraffic #RealityCheck #Cloudflare #MagicBeans #HackerNews #ngated -
Oh, look! Another #CEO playing fast and loose with "facts" 🤥. Apparently, bot traffic is bigger than Santa's bag 🎅, according to Cloudflare! Except, oops—reality check, it's not. Guess the CEO got their data from the same place they get their magic beans 🌱.
https://www.flyingpenguin.com/cloudflare-ceo-is-lying-to-you-about-the-bot-traffic-jump/ #Fails #BotTraffic #RealityCheck #Cloudflare #MagicBeans #HackerNews #ngated -
🚀 Akamai’s latest data shows a sharp rise in AI training bots and content‑fetching crawlers since July. These bots are reshaping web traffic patterns, stressing infrastructure and raising privacy questions. How will developers and open‑source projects adapt? Dive into the numbers and what they mean for the future of machine‑learning pipelines. #AIBots #WebCrawlers #BotTraffic #MachineLearning
🔗 https://aidailypost.com/news/akamai-data-shows-ai-training-bots-contentfetching-bots-rise-since
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🚀 Akamai’s latest data shows a sharp rise in AI training bots and content‑fetching crawlers since July. These bots are reshaping web traffic patterns, stressing infrastructure and raising privacy questions. How will developers and open‑source projects adapt? Dive into the numbers and what they mean for the future of machine‑learning pipelines. #AIBots #WebCrawlers #BotTraffic #MachineLearning
🔗 https://aidailypost.com/news/akamai-data-shows-ai-training-bots-contentfetching-bots-rise-since
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Picknick an der Datenautobahn
Diese Woche wurde ich von einer ungewöhnlichen Welle an Anfragen an meinen Server überrascht. Erst dachte ich, dass ich irgendetwas falsch konfiguriert haben könnte, aber nach einem Gespräch mit dem Support von Uberspace war klar, dass mein WordPress Multisite-Setup mit dieser Seite Gefährliches Halbwissen und Um' Pudding bombardiert und somit überlastet wird. Als einfacher User eines Shared Hosting Dienstes kann man da wenig dagegen tun, außer zu versuchen herauszufinden, was genau passiert und zugucken, wie die Seite auseinandergenommen wird. Witzigerweise musste ich dabei an ein Buch denken, welches ich 2017 gelesen habe.https://niklasbarning.de/2025/12/02/picknick-an-der-datenautobahn/
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Picknick an der Datenautobahn
Diese Woche wurde ich von einer ungewöhnlichen Welle an Anfragen an meinen Server überrascht. Erst dachte ich, dass ich irgendetwas falsch konfiguriert haben könnte, aber nach einem Gespräch mit dem Support von Uberspace war klar, dass mein WordPress Multisite-Setup mit dieser Seite Gefährliches Halbwissen und Um' Pudding bombardiert und somit überlastet wird. Als einfacher User eines Shared Hosting Dienstes kann man da wenig dagegen tun, außer zu versuchen herauszufinden, was genau passiert und zugucken, wie die Seite auseinandergenommen wird. Witzigerweise musste ich dabei an ein Buch denken, welches ich 2017 gelesen habe.https://niklasbarning.de/2025/12/02/picknick-an-der-datenautobahn/
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Picknick an der Datenautobahn
Diese Woche wurde ich von einer ungewöhnlichen Welle an Anfragen an meinen Server überrascht. Erst dachte ich, dass ich irgendetwas falsch konfiguriert haben könnte, aber nach einem Gespräch mit dem Support von Uberspace war klar, dass mein WordPress Multisite-Setup mit dieser Seite Gefährliches Halbwissen und Um' Pudding bombardiert und somit überlastet wird. Als einfacher User eines Shared Hosting Dienstes kann man da wenig dagegen tun, außer zu versuchen herauszufinden, was genau passiert und zugucken, wie die Seite auseinandergenommen wird. Witzigerweise musste ich dabei an ein Buch denken, welches ich 2017 gelesen habe.https://niklasbarning.de/2025/12/02/picknick-an-der-datenautobahn/
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Picknick an der Datenautobahn
Diese Woche wurde ich von einer ungewöhnlichen Welle an Anfragen an meinen Server überrascht. Erst dachte ich, dass ich irgendetwas falsch konfiguriert haben könnte, aber nach einem Gespräch mit dem Support von Uberspace war klar, dass mein WordPress Multisite-Setup mit dieser Seite Gefährliches Halbwissen und Um' Pudding bombardiert und somit überlastet wird. Als einfacher User eines Shared Hosting Dienstes kann man da wenig dagegen tun, außer zu versuchen herauszufinden, was genau passiert und zugucken, wie die Seite auseinandergenommen wird. Witzigerweise musste ich dabei an ein Buch denken, welches ich 2017 gelesen habe.https://niklasbarning.de/2025/12/02/picknick-an-der-datenautobahn/
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How a Global Insurer Protects Customer Data & Achieves Compliance With DataDome – Source: securityboulevard.com https://ciso2ciso.com/how-a-global-insurer-protects-customer-data-achieves-compliance-with-datadome-source-securityboulevard-com/ #rssfeedpostgeneratorecho #SecurityBloggersNetwork #fakeaccountcreation #brute-forceattacks #credentialstuffing #CyberSecurityNews #SecurityBoulevard #CustomerStories #onlinefraud #bottraffic
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How a Global Insurer Protects Customer Data & Achieves Compliance With DataDome – Source: securityboulevard.com https://ciso2ciso.com/how-a-global-insurer-protects-customer-data-achieves-compliance-with-datadome-source-securityboulevard-com/ #rssfeedpostgeneratorecho #SecurityBloggersNetwork #fakeaccountcreation #brute-forceattacks #credentialstuffing #CyberSecurityNews #SecurityBoulevard #CustomerStories #onlinefraud #bottraffic
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Cloudflare finds almost 7% of Internet traffic to be malicious
https://stackdiary.com/cloudflare-finds-almost-7-of-internet-traffic-to-be-malicious/
#Cybersecurity #InternetSafety #Cloudflare #MaliciousTraffic #OnlineSecurity #DDoS #BotTraffic #APISecurity #DataProtection #WebSafety #CyberThreats #DigitalSecurity #TechNews #InternetThreats #HackerDefense #NetSecurity #CyberDefense #SecurityReport #WebProtection #InternetRisks #SecureWeb #CyberAwareness #NetworkSecurity #ThreatAnalysis #WebApplicationSecurity #CyberProtection #TechSafety #OnlineThreats
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Cloudflare finds almost 7% of Internet traffic to be malicious
https://stackdiary.com/cloudflare-finds-almost-7-of-internet-traffic-to-be-malicious/
#Cybersecurity #InternetSafety #Cloudflare #MaliciousTraffic #OnlineSecurity #DDoS #BotTraffic #APISecurity #DataProtection #WebSafety #CyberThreats #DigitalSecurity #TechNews #InternetThreats #HackerDefense #NetSecurity #CyberDefense #SecurityReport #WebProtection #InternetRisks #SecureWeb #CyberAwareness #NetworkSecurity #ThreatAnalysis #WebApplicationSecurity #CyberProtection #TechSafety #OnlineThreats
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Cloudflare finds almost 7% of Internet traffic to be malicious
https://stackdiary.com/cloudflare-finds-almost-7-of-internet-traffic-to-be-malicious/
#Cybersecurity #InternetSafety #Cloudflare #MaliciousTraffic #OnlineSecurity #DDoS #BotTraffic #APISecurity #DataProtection #WebSafety #CyberThreats #DigitalSecurity #TechNews #InternetThreats #HackerDefense #NetSecurity #CyberDefense #SecurityReport #WebProtection #InternetRisks #SecureWeb #CyberAwareness #NetworkSecurity #ThreatAnalysis #WebApplicationSecurity #CyberProtection #TechSafety #OnlineThreats
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Cloudflare finds almost 7% of Internet traffic to be malicious
https://stackdiary.com/cloudflare-finds-almost-7-of-internet-traffic-to-be-malicious/
#Cybersecurity #InternetSafety #Cloudflare #MaliciousTraffic #OnlineSecurity #DDoS #BotTraffic #APISecurity #DataProtection #WebSafety #CyberThreats #DigitalSecurity #TechNews #InternetThreats #HackerDefense #NetSecurity #CyberDefense #SecurityReport #WebProtection #InternetRisks #SecureWeb #CyberAwareness #NetworkSecurity #ThreatAnalysis #WebApplicationSecurity #CyberProtection #TechSafety #OnlineThreats
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Cloudflare finds almost 7% of Internet traffic to be malicious
https://stackdiary.com/cloudflare-finds-almost-7-of-internet-traffic-to-be-malicious/
#Cybersecurity #InternetSafety #Cloudflare #MaliciousTraffic #OnlineSecurity #DDoS #BotTraffic #APISecurity #DataProtection #WebSafety #CyberThreats #DigitalSecurity #TechNews #InternetThreats #HackerDefense #NetSecurity #CyberDefense #SecurityReport #WebProtection #InternetRisks #SecureWeb #CyberAwareness #NetworkSecurity #ThreatAnalysis #WebApplicationSecurity #CyberProtection #TechSafety #OnlineThreats