“17 gallons per query”

What we’ll cover today:

🇮🇳 India drops an 8 exaflop supercomputer and goes full sovereign AI

⚡ Sam Altman shuts down the AI water panic

🚨 Google says thin GPT wrappers are in danger

🚀 AI automations, Grok mastery, and Claude power guides you should not skip

India Is Building Sovereign AI Muscle 🇮🇳

• G42 and Cerebras are deploying an 8 exaflop AI supercomputer in India, hosted locally under Indian data laws. This is about national compute control, not just capacity expansion.

• The system will support universities, government bodies, and SMEs. It is meant to make large model training and inference accessible inside the country.

• Massive capital is flowing into Indian AI infrastructure from Adani, Reliance, OpenAI with Tata, and US cloud giants. The country is entering a full-scale compute buildout phase.

• The shift is strategic. India wants to own its models, data, and language systems instead of renting intelligence from abroad.

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Sam Altman Pushes Back on AI Energy Panic ⚡

• Sam Altman said viral claims about ChatGPT using extreme amounts of water per query are false. He argues those numbers are outdated and disconnected from current data center practices.

• He admitted that total AI energy usage is a real concern as adoption grows. His solution is faster investment in nuclear and renewable energy.

• There is no mandatory public disclosure of AI energy or water use. That leaves room for speculation and exaggerated estimates.

• Altman reframed the debate by comparing AI inference efficiency to human cognitive energy cost. His message is that AI may already be more efficient than people assume.

Google Warns LLM Wrappers Are in Danger 🚨

• Google’s startup head warned that thin LLM wrappers are no longer defensible businesses. Adding a UI on top of GPT or Gemini is not enough.

• Startups now need real differentiation through vertical focus, proprietary data, or workflow integration. Surface-level products will struggle.

• AI aggregators face margin pressure as model providers expand their own enterprise features. Middle layers risk getting squeezed out.

• The core test is simple. If OpenAI or Google can ship your feature next quarter, your moat is weak.

  Full Article →

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