LinkedIn announced in 2026 that it will not expand its AI data centers or increase its compute and storage footprint for its current fiscal year, which began in July and runs through June 2027. The professional social network is holding GPU investment steady rather than following peers in a broader infrastructure spending surge.
The company says the decision was made possible after engineers doubled the efficiency of its existing GPUs over the prior six months. LinkedIn operates its own data centers in Oregon, Texas, and Virginia — infrastructure it built out in 2022 after determining that running on Microsoft’s Azure cloud was not economically viable at its scale.
“One of the goals we’ve set is to try to basically keep our compute footprint flat or as close to flat as possible while shipping more compute-hungry things to production,” said Erran Berger, LinkedIn’s chief technology officer for engineering. “That’s a pretty bold statement to make in today’s world.”
Raghu Hiremagalur, LinkedIn’s chief technology officer for infrastructure, acknowledged the scale of the commitment. “For a company of our scale, to say a full year we’re going to do this with no incremental storage and compute is no small feat,” he said. Both executives said the constraints are intended to push engineering teams toward more creative, efficient approaches as LinkedIn continues developing generative AI features.
The move comes as companies including OpenAI, Meta, and Google continue to pour resources into data center construction, with labor and parts shortages slowing many projects. LinkedIn, which has more than 1.3 billion users, is among the largest companies to publicly address AI spending concerns by opting out of that expansion race.
Songyee Yoon, managing partner of Principal Venture Partners and a board member at server maker HP, said the approach “suggests AI is beginning to move from experimentation into production discipline.”
LinkedIn’s plan could still change, executives noted, given rapidly shifting hardware demands — though they said surging memory chip prices have already been factored into their calculations.
Source: WIRED