Greetings AI enthusiasts. NVIDIA is launching a major initiative to manufacture its AI chips and supercomputers entirely in the United States. In collaboration with several leading tech and manufacturing companies, this effort marks a strategic push to strengthen domestic supply chains and meet soaring demand for AI infrastructure.

In today’s email:

• NVIDIA to Produce AI Supercomputers Domestically in the U.S. for the First Time

• AI Boom Expected to Double Data Center Energy Use by 2030

• Google Maps’ New AI Feature Aims to Reduce Traffic and Improve Local Roads

LATEST NEWS

NVIDIA

🖥️ NVIDIA to Produce AI Supercomputers Domestically in the U.S. for the First Time

AI Spotlight: NVIDIA is launching a major initiative to manufacture its AI chips and supercomputers entirely in the United States. In collaboration with several leading tech and manufacturing companies, this effort marks a strategic push to strengthen domestic supply chains and meet soaring demand for AI infrastructure.

Key details:

• NVIDIA is building over a million square feet of production space in Arizona and Texas to manufacture and test Blackwell chips and AI supercomputers.

• Mass production is expected to accelerate within 12 to 15 months at new facilities operated with Foxconn in Houston and Wistron in Dallas.

• TSMC, Foxconn, and Wistron are key manufacturing partners supporting chip production and supercomputer assembly across Arizona and Texas.

• The project aims to generate up to $500 billion in U.S.-based AI infrastructure over the next four years.

This expansion positions NVIDIA as a cornerstone in the rapidly growing AI economy. By leveraging its own AI and robotics platforms, the company is modernizing manufacturing while anchoring critical tech production on U.S. soil.

INTERNATIONAL ENERGY AGENCY (IEA)

🏭️ AI Boom Expected to Double Data Center Energy Use by 2030

AI Spotlight: The International Energy Agency (IEA) has published a report forecasting that global electricity consumption by data centres will more than double by 2030, with artificial intelligence (AI) identified as a major driver of this growth. The report outlines energy usage trends, regional disparities, and infrastructure challenges linked to this expansion.

Key details:

• Projected Surge in Energy Use: Data centres are expected to increase electricity consumption from 415 TWh in 2024 to 945 TWh in 2030.

• AI’s Growing Footprint: AI-specific servers accounted for 15% of data centre energy use in 2024, though some experts believe this is underestimated.

• Regional Contributions to Energy Demand: The U.S., China, and Europe are responsible for the majority of current and future data centre energy consumption.

• Infrastructure Strain and Planning Challenges: Around 20% of planned data centres may face delays connecting to the power grid due to rising demand.

The findings highlight the urgent need to prepare for a steep rise in digital energy demand. As AI adoption accelerates, governments and industries will need to invest in smarter infrastructure, energy-efficient technologies, and long-term planning to avoid straining power systems worldwide.

GOOGLE

🌎️ Google Maps’ New AI Feature Aims to Reduce Traffic and Improve Local Roads

AI Spotlight: Google has introduced a new suite of AI-powered tools for Google Maps aimed at helping businesses and cities make more informed decisions about infrastructure, traffic, and local trends. Announced during its Cloud Next conference, these tools reflect Google’s broader effort to embed AI into real-world planning and problem-solving.

Key details:

• AI for Infrastructure Monitoring: The new Imagery Insights tool uses Street View and Vertex AI to detect and assess infrastructure elements like utility poles and signs without needing on-site inspections.

• Localized Business Intelligence: The Places Insights tool helps businesses analyze neighborhood trends and identify potential locations for expansion using aggregated data such as reviews, accessibility, and nearby amenities.

• Traffic and Road Planning: Roads Management Insights provides traffic authorities with real-time and historical traffic data to identify accident-prone areas and reduce congestion through improved forecasting and planning.

• Built-in Transparency Measures: Each tool includes confidence indicators to show users how reliable its AI-generated insights are, addressing ongoing concerns around trust in AI.

These features are designed to enhance existing workflows rather than replace human roles, aiming to improve the accuracy and efficiency of urban planning and business strategy. Some tools are already in preview, with broader availability expected in the coming months.

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