“We expect to further ramp adoption of MTIA for these use cases throughout 2025 before extending our custom silicon efforts to training workloads for ranking and recommendations next year.”

In context

“cture footprint and increasing the efficiency of our workloads. Another way we’re pursuing efficiencies is by extending the useful lives of our servers and associated networking equipment. Our expectation going forward is that we’ll be able to use both our non-AI and AI servers for a longer period of time before replacing them, which we estimate will be approximately 5.5 years. This will deliver savings in annual CapEx and resulting depreciation expense, which is already included in our guidance. Finally, we’re pursuing cost efficiencies by deploying our custom MTIA silicon in areas where we can achieve a lower cost of compute by optimizing the chip to our unique workloads. In 2024 we started deploying MTIA to our ranking and recommendation inference workloads for ads and organic content. We expect to further ramp adoption of MTIA for these use cases throughout 2025 before extending our custom silicon efforts to training workloads for ranking and 0recommendations next year. From a hiring standpoint, our focus continues to be on adding technical talent to support our strategic priorities. In the fourth quarter, nearly 90% of our year-over-year headcount growth was within the R&D function. The remaining growth was primarily in cost of revenue as we added infrastructure headcount to support our data center operations. In 2025, we expect headcount growth will continue to be primarily driven by technical roles across our priority initiatives within infrastructure, monetization, Reality Labs, generative AI, as well as regulation and compliance. We anticipate headcount growth in our business functions will remain relatively limited. To achieve our ambitions in these areas, we will need to continue executing at a rapid pace. We’re supporting this by building tools”