The computing chips driving artificial intelligence demand immense amounts of electricity, posing a growing challenge for data centers worldwide. On Wednesday, Taiwan Semiconductor Manufacturing Co. (TSMC), the world’s largest chip manufacturer and primary fabricator for Nvidia, revealed a groundbreaking strategy to improve chip energy efficiency by nearly tenfold—using AI-powered software to design them.
Today’s top AI servers, such as those running Nvidia’s flagship chips, can consume up to 1,200 watts during heavy workloads. Scaled across thousands of units, that level of energy draw is equivalent to powering 1,000 U.S. homes continuously. To combat this, TSMC is turning to advanced design methods and cutting-edge software tools developed in collaboration with Cadence Design Systems and Synopsys.
A key innovation lies in packaging multiple “chiplets”—smaller computing units made with different technologies—into a single system, creating far more efficient chip architectures. However, managing the complexity of such designs requires next-generation software. AI-driven design platforms are proving faster and more effective than human engineers for certain tasks, enabling chipmakers to unlock the full potential of TSMC’s 3D integration technology.
Jim Chang, deputy director of TSMC’s 3DIC Methodology Group, emphasized that AI tools are already outperforming engineers in some areas. “This thing runs five minutes while our designer needs to work for two days,” he explained, highlighting the speed and optimization advantages AI brings to chip development.
Yet, challenges remain. Moving data between chips using traditional electrical connections is hitting physical limits. Experts like Kaushik Veeraraghavan from Meta stress that optical connections may become the future for large-scale AI data centers. These advances will be critical as the world seeks more sustainable solutions to power the accelerating demand for AI computing.
By combining AI-driven design with innovative chip architectures, TSMC and its partners aim to set new standards for efficiency, performance, and scalability in the semiconductor industry.


Toyota’s Surprise CEO Change Signals Strategic Shift Amid Global Auto Turmoil
TSMC Eyes 3nm Chip Production in Japan with $17 Billion Kumamoto Investment
Rio Tinto Shares Hit Record High After Ending Glencore Merger Talks
FDA Targets Hims & Hers Over $49 Weight-Loss Pill, Raising Legal and Safety Concerns
Global PC Makers Eye Chinese Memory Chip Suppliers Amid Ongoing Supply Crunch
Sony Q3 Profit Jumps on Gaming and Image Sensors, Full-Year Outlook Raised
Anthropic Eyes $350 Billion Valuation as AI Funding and Share Sale Accelerate
Tencent Shares Slide After WeChat Restricts YuanBao AI Promotional Links
Nintendo Shares Slide After Earnings Miss Raises Switch 2 Margin Concerns
Missouri Judge Dismisses Lawsuit Challenging Starbucks’ Diversity and Inclusion Policies
TrumpRx Website Launches to Offer Discounted Prescription Drugs for Cash-Paying Americans
SpaceX Pushes for Early Stock Index Inclusion Ahead of Potential Record-Breaking IPO
Amazon Stock Rebounds After Earnings as $200B Capex Plan Sparks AI Spending Debate
Trump Backs Nexstar–Tegna Merger Amid Shifting U.S. Media Landscape
Alphabet’s Massive AI Spending Surge Signals Confidence in Google’s Growth Engine 



