NSF invests $20M in AI supercomputer despite 57% budget cut

The US National Science Foundation is financing a $20-million supercomputer at Georgia Institute of Technology despite facing a proposed 57% budget cut under the Trump administration. The supercomputer, called Nexus, will use AI to advance scientific research across multiple disciplines and represents one of the NSF’s most significant funding commitments since the administration proposed slashing its budget from $6.8 billion to $3.9 billion.

What you should know: Nexus will be one of the most powerful supercomputers dedicated to AI-driven scientific research, capable of calculating more than 400 quadrillion operations per second.

• The supercomputer is designed to find new cures for diseases, better understand the human brain, and advance quantum computing research.
• Georgia Tech will retain 10% of Nexus’s capacity, while researchers from around the US can apply to use the remaining computational power.
• The project reinforces the critical role of university research in AI innovation at a time when federal support has been significantly reduced.

The big picture: The NSF’s investment in Nexus comes as the agency faces unprecedented budget constraints that have already disrupted the academic AI research ecosystem.

• The Trump administration has paused new funding, laid off staff involved in AI research, and canceled hundreds of existing grants.
• Some of the cuts are currently tied up in court, making the NSF’s funding status uncertain.
• With a constrained budget, the NSF’s project funding decisions carry much greater weight than before.

Why this matters: Investing in a supercomputer can advance innovations across science, math, technology, and other disparate disciplines, making it a strategic choice when resources are limited.

• Projects with more focused audiences may see fewer dollars coming their way as the NSF prioritizes initiatives with broader impact.
• The funding demonstrates the agency’s commitment to maintaining critical research infrastructure despite political and budgetary pressures.
• University-based supercomputing resources are essential for training the next generation of AI researchers and maintaining America’s competitive edge in scientific computing.