LinkedIn doubled GPU efficiency and won't expand AI data centres this year

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LinkedIn will hold GPU investment and its compute and storage footprint flat rather than expanding its AI data centers

The company says it roughly doubled the efficiency of its existing GPUs in six months through accumulated improvements to utilization, model distillation, and workload allocation, not a single breakthrough

This makes it an exception to the rule under owner Microsoft's umbrella

LinkedIn has revealed it does not plan to spend aggressively on expanding its AI data centers over its next fiscal year - instead keeping GPU investment flat and holding its compute and storage footprint roughly where it is.

The stated reason is not restraint for its own sake: the company says it has found ways to get about twice as much out of its existing GPUs over the past six months and intends to spend that headroom on new features rather than new hardware.

Speaking to Wired , Erran Berger, LinkedIn's engineering CTO, framed the goal as keeping the compute footprint flat or close to it while still shipping more compute-hungry things into production - all while acknowledging this is an unusual position to take publicly right now.

Skipping the norm, both at the industry-level and the parent company

LinkedIn has been wholly owned by Microsoft since its December 2016 acquisition of the company for $26.2 billion.

Despite being part of the software giant, LinkedIn's approach seems vastly different from that of a company that recently saw its shares rally hard as it showcased AI spending finally resulting in measurable returns.

Microsoft also recently closed its own fiscal year, reporting $41 billion in capital expenditure in the most recent quarter alone. It added 31 data centers in that quarter and 88 across the year, and expects to spend more than $50 billion in the current quarter.

This draws an interesting parallel: while one part of Microsoft is deploying capital at a rate few companies in history have matched, a subsidiary with more than a billion users has decided to sit out the year, citing efficiency gains. This makes it a considerably more interesting approach than what would otherwise be a "company shows AI discipline" affair.

While parallels exist, it is prudent to point out that Microsoft's spending is overwhelmingly driven by Azure customer demand, and specifically by capacity it has contracted to supply to OpenAI, rather than by internal product workloads, while LinkedIn's compute is a rounding error against that.

Despite that, it offers an interesting alternative perspective when a large consumer platform can add generative AI features for a year without adding hardware; still, it runs counter to the prevailing assumption that AI features and capacity growth are inseparable.

What makes this possible for LinkedIn?

The answer runs back to a decision most coverage treated as an embarrassment at the time.

LinkedIn announced in 2019 that it would migrate its infrastructure onto Azure under a project codenamed Blueshift. In 2022, it quietly shelved that plan. An internal memo at the time cited Azure's own demand pressures and said LinkedIn would focus on scaling its on-premises infrastructure instead; subsequent reporting established that the migration had also run into difficulties because LinkedIn's in-house tooling did not transfer cleanly to Azure.

LinkedIn instead committed to its own data centers in Oregon, Texas, and Virginia.

LinkedIn's CTO for infrastructure, Raghu Hiremagalur, now argues that owning the full stack is precisely what makes this year's plan feasible, because the company can instrument every layer and treat efficiency as a standing investment rather than a one-off cost-cutting exercise.

“I really want to double underscore that 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, but it's taken a ton of work to get there," Hiremagalur said.

Reading what, in 2022, was a retreat as a 2026 advantage is self-serving, but it is not obviously wrong. The efficiency work itself is described as an accumulation rather than a breakthrough: better GPU utilization and workload allocation, distilling larger models into smaller ones, and rethinking how work is divided across training, inference, storage, and systems design.

Hiremagalur has also said the cost of serving each query had been climbing steadily while stored data was doubling annually, which he characterized as unsustainable. That is the more revealing framing. The efficiency push reads less like a strategic choice about the AI market and more like a company that looked at its own cost curve and decided it had to bend.

It makes it worth pointing out that LinkedIn has not really solved anything; It is that a platform of this size has publicly said out loud that its compute constraint is deliberate, at a moment when the four largest US hyperscalers have committed to something in the region of $600 billion to $700 billion of capital expenditure for the calendar year between them.

Almost every incentive in the industry currently runs toward announcing capacity rather than efficiency, making it an interesting outlier in a field dominated by daily capex announcements.

The more useful question is whether the approach holds. If it does, the argument that AI product ambition requires proportional growth in hardware gets meaningfully weaker. If it does not, this will read as an efficiency drive that met the hardware demands of a real product roadmap but failed to do so at a time when AI spending is increasingly scrutinized , even as LinkedIn itself recently allowed users to mark what they feel is 'AI slop' .