Meta Restructures: Horizon OS Becomes Independent

Meta has recently reorganized the structure of its Reality Labs division, separating Horizon OS from the original “Metaverse” business group and elevating it to an independent top-level group. For context, prior to this restructuring, the two main departments under Reality Labs were “Metaverse” and “Wearables”. The former was responsible for the Quest series of headsets, the Horizon OS used in these headsets, and metaverse applications (such as Horizon Worlds), while the latter focused on Ray-Ban/Oakley smart glasses, the development of true AR glasses, and cutting-edge wearable technologies like the Meta Neural Band.

Currently, Horizon OS has been moved out of the “Metaverse” department and established as an independent top-level group, led by Ryan Cairns. This department will continue to focus on the development of VR hardware and system platforms. At the same time, Vishal Shah, the former head of the “Metaverse” business group, has been transferred to Meta Superintelligence Labs, where he will be responsible for promoting the integration of Personal Superintelligence between Meta’s platforms and the Reality Labs ecosystem. Gabriel Aul has taken over as the new head, and the “Metaverse” department will continue to focus on developing metaverse application products such as Horizon Worlds in the future.

IT Home has noticed that Andrew Bosworth, Meta’s Chief Technology Officer, emphasized in an internal memo sent to Reality Labs that this organizational restructuring does not mean a reduction in the priority of Meta’s “metaverse projects”: “The focus of our metaverse work remains unchanged; it is still a core company-wide strategy. We have proven to the industry that our direction is correct, and competitors are trying to catch up, so we must continue to maintain our advantage.”

Bosworth also pointed out that as Meta deepens its capabilities in the fields of AI and general compute, VR is transforming from a pure gaming platform into a broader platform covering entertainment, productivity, and social interaction. External analysts believe that the independence of Horizon OS from the “Metaverse” group may be intended to strengthen the independence of the operating system ecosystem, allowing it to directly compete with Google’s newly launched Android XR system in the future. The latter has already made its debut on Samsung’s Galaxy XR headset and plans to be rolled out to more brand devices next year.

Separate leading indicators from financial outcomes

Usage, retention and pipeline often move before recognized revenue or cash. A useful dashboard combines these leading signals with financial results so management can distinguish a temporary accounting lag from a real deterioration in demand. Definitions should remain stable enough that teams do not change the metric when performance becomes inconvenient.

Document assumptions beside the forecast

Model outputs are only as reliable as their assumptions about pricing, conversion, cost and timing. Keeping those assumptions visible makes scenario analysis faster and reduces false precision.

Review the model when product behavior changes

New modalities, pricing plans or sales motions can invalidate old averages. Finance models should evolve with the product rather than forcing new behavior into outdated categories.

Measure the full contribution margin

Gross revenue can hide payment fees, AI serving cost, creator share and support burden. Contribution margin is more useful for deciding whether growth is creating economic value. Segment the calculation by product or customer type because one blended percentage can conceal weak economics in a fast-growing area.

Use scenario analysis for uncertain inputs

AI businesses face volatile usage and infrastructure cost. Base, upside and downside cases help management see which assumptions matter most and where protective actions are available.

Reconcile forecast with actual drivers monthly

Instead of only comparing total spend or revenue, explain how much variance came from volume, price, mix and unit cost. This turns the forecast into a learning system for the business.

Start with the business driver behind the metric

A finance ratio is useful only when teams understand what operational behavior moves it. Break revenue, margin or payback into user volume, conversion, pricing, usage and variable cost so a variance can be traced to a real product or sales change. This makes forecasts easier to update because assumptions can change independently instead of rebuilding one large top-down model.

Use cohort evidence before extrapolating growth

AI products often change quickly, so recent cohorts may behave differently from early adopters. Compare retention, spending and support cost by signup period or customer segment before assuming historical averages will continue.

Connect financial metrics to operational decisions

The purpose of the model is not only reporting. It should help decide pricing, hiring, infrastructure commitments and channel spend while there is still time to change course.