4/ But Google isn’t stopping at convenience — they’re adding transparency. Photos now support C2PA Content Credentials, showing whether an image was AI-edited. No more guessing what’s real vs. AI-generated.
BIG TECH
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Environmental Impact of Large-Scale AI Models and Energy Consumption
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Google paper on Gemini: https://
services.google.com/fh/files/misc/
measuring_the_environmental_impact_of_delivering_ai_at_google_scale.pdf
… Sam Altman post: https://
blog.samaltman.com/the-gentle-sin
gularity
… Google search energy in 2008: https://
googleblog.blogspot.com/2009/01/poweri
ng-google-search.html
… Llama 3.3 power usage: -
Google DeepMind Launches Genie 3: Revolutionary Generative AI Model
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Congrats @jparkerholder
, @shlomifruchter
, & the Genie & Veo teams! If you are interested to know more, the latest episode of the @GoogleDeepMind Podcast with the brilliant @FryRsquared has just dropped, and is all about Genie 3 & its incredible potential: -
Google releases 2025 environmental impact report covering 2024
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We produce an annual report that covers Google's overall environmental impact (of which Gemini models inference is a portion). See our 2025 report (covering 2024):
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xAI Backlash Over Art Style: Cultural Perspective from Japan
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I don’t get the backlash over this. Maybe I’ve been in Japan too long, so the art style seems tame compared to stuff you see in the convenience stores here. This seems like what I would have expected from xAI. https://
x.com/elonmusk/statu
/elonmusk/status/1958512746724487602
… -
Meta Superintelligence Labs Receives Continued Investment Commitment
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We are truly only investing more and more into Meta Superintelligence Labs as a company. Any reporting to the contrary of that is clearly mistaken.
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Open Models Power 70% of AI Inference Workloads
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𝗧echnology ecosystem and install base Open models power over 70% of AI Inference workloads. NVIDIA supports 1,000+ OSS projects and 450+ open models like Llama, Gemma, and GPT-OSS, and collaborates on frameworks like @GoogleDeepMind JAX, @PyTorch
, @lmsysorg (SGLang), -
NVIDIA Blackwell GB200 NVL72 Delivers 50× AI Productivity Gains
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𝗔rchitecture and software The NVIDIA Blackwell platform + GB200 NVL72 rack system = up to 50× higher AI factory productivity. Throughput + energy and water efficiency gains + full-stack orchestration = scalable inference.
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Enterprise AI Infrastructure: Scaling Models and Inference Demands
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𝗦cale and complexity Bigger models = greater inference. From quick queries to million-token reasoning, infra demands during inference are soaring. Enterprises are building new AI factories with partners like @CoreWeave
, @Dell
, @googlecloud and more. -
NVIDIA Inference Platform: Balancing Accuracy, Latency, and Cost
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𝗠ulti-dimensional performance Optimal Inference is a trade-off: accuracy, latency, and cost. Some tasks need ultra-low latency (real-time translation), while others prioritize throughput (multi-million-token queries). The NVIDIA Inference Platform accelerates models