IBM announces $150Bn U.S. investment to boost quantum and AI tech
NEW YORK, UNITED STATES — Tech giant IBM announced a sweeping $150 billion investment across the United States over the next five years. The plan includes over $30 billion to advance domestic manufacturing of IBM’s flagship technologies, such as mainframes and quantum computers.
The move aligns with growing national interest in reshoring tech production and comes after President Donald Trump announced a reciprocal tariff strategy, excluding chips and key tech components from import duties to encourage domestic manufacturing.
“Technology doesn’t just build the future — it defines it,” said Arvind Krishna, IBM chairman and CEO.
“With this investment and manufacturing commitment, we are ensuring that IBM remains the epicenter of the world’s most advanced computing and AI capabilities.”
Quantum leap in U.S. manufacturing
The investment reaffirms IBM’s commitment to its New York-based manufacturing operations in Poughkeepsie, where the company produces mainframes or hardware that powers over 70% of the world’s financial transactions by value.
IBM also maintains the world’s largest fleet of quantum computer systems, all of which will continue to be designed and built in the U.S.
Quantum computing is poised to redefine industries by solving problems traditional computers can’t touch. IBM’s Quantum Network, which includes nearly 300 organizations from Fortune 500 companies to universities and national labs, now supports more than 600,000 active users.
Global reach, American roots of IBM
Founded 114 years ago, IBM is one of the largest technology employers in the U.S. and operates in over 175 countries. Its 250,000-strong workforce supports hybrid cloud infrastructure, AI development, and digital consulting services across sectors like healthcare, finance, and telecom.
In Q1 2025, IBM posted revenues of $14.54 billion. Its infrastructure segment, including mainframes, generated $2.89 billion. Earlier this month, the company launched its newest z17 AI mainframe to bolster enterprise AI workloads.
