This engineering feat demonstrates how hardware constraints drive innovation in battery architecture and firmware. Rethinking cell design and power management is essential for enabling high-performance AI features in extremely constrained wearable form factors.
Smart glasses like the Ray-Ban Meta and Oakley Meta Vanguards need to pack enough energy to power features like cameras, speakers, AI workloads, and even a display. But it all has to fit into the glasses’ temple arms.
So how do you place a battery with enough power to run a pair of smart glasses all day into a form factor narrower than an adult’s pinky finger? You have to rethink how batteries are made.
In episode 86 of the Meta Tech Podcast, host Pascal Hartig sat down with Karthik and Myuran, the engineers behind Meta’s steel can battery technology, for a conversation on powering the newest and next generation of wearables.
Traditional pouch cells — the batteries in most phones and laptops– can’t cut it for devices like smart glasses because they’re difficult to reshape and shrink down. Their folds waste volume, their tolerances eat into precious millimeters of space, and at smaller sizes they can difficulty providing peak power for multitasking (for example, if someone is using the camera and asking the AI model to perform a task at the same time).
Smart glasses need a battery that can claim every micron of space – something rigid, precise, and shaped to the product rather than the other way around.
Steel-can batteries aren’t new. Power tools and watches use them. But Meta’s AI glasses needed batteries with widths as narrow as 7mm, narrower than anything that existed before. Getting there meant rethinking nearly every internal component of the battery.
Traditional steel-can cells use a wound “jelly roll” of electrode material. Meta’s engineers replaced that with die-cut stacked layers, similar to wiring small resistors in parallel. The result is dramatically lower impedance, which matters when peak power is required so that the device can avoid brownouts if a lot of power is being demanded at the same time (because someone may be making a recording while asking the AI a question at the same time).
A steel-can cell holds its shape to roughly 100 microns. On a 10mm-wide battery, that gives back real usable volume that translates directly into additional energy density and runtime.
From Gen 1 to Gen 2 the Meta Ray-Ban’s, cell capacity grew from 160 mAh to 210 mAh — roughly a 30 percent bump. Yet the product shipped with claims of double the runtime. The chemistry didn’t change. The extra gains came from system-level efficiency improvements across hardware and software such as better power management, tighter firmware control, and a form factor that allowed for a larger cell
The Oakley Meta Vanguards actually feature a battery in each temple arm, which introduced a real systems puzzle at the intersection of electrical, firmware, and mechanical engineering. The cells in each temple arm are symmetric, but the electronic loads aren’t split evenly between the two sides. That creates cross-charging risks and sequencing complexity at boot and shutdown.
Then the Meta Ray-Ban Display glasses introduced the most demanding power profile yet. Its screen draws sustained power rather than short bursts, which required designing a 248 mAh steel-can cell, the largest in Meta’s lineup.
The ultra-narrow steel-can approach we developed for our smart glasses is proving adaptable to other form factors across Meta’s hardware portfolio.
Meta is now focused on scaling and democratizing this technology across multiple vendors, ensuring we have resilient supply and can bring these batteries to the next generation of wearables.
Listen to the full episode to hear the complete story — from first sketch to global shelf — including details on cross-charging two-battery systems, software versus hardware iteration cycles, and what it’s really like to collaborate across time zones to build something the world has never seen.
You can also find the episode wherever you get your podcasts, including:
The Meta Tech Podcast is a podcast, brought to you by Meta, where we highlight the work Meta’s engineers are doing at every level – from low-level frameworks to end-user features.
Send us feedback on Instagram, Threads, or X.
And if you’re interested in learning more about career opportunities at Meta visit the Meta Careers page.
The post How Meta Engineered Ultra-Narrow Batteries for AI Glasses appeared first on Engineering at Meta.
Continue reading on the original blog to support the author
Read full articleThis article highlights the hidden complexity of scaling social features. It demonstrates how machine learning and platform-specific user behavior analysis are critical for delivering personalized experiences to billions, proving that simple UI often masks deep engineering challenges.
Scaling security updates across massive codebases is traditionally slow and error-prone. By combining secure-by-default frameworks with AI-powered codemods, Meta demonstrates how to automate large-scale security migrations, reducing developer friction and improving app safety at scale.
This research addresses the challenge of sparse signal optimization in massive-scale recommendation systems. By using hierarchical graph learning and multimodal enrichment, engineers can improve deep funnel performance and better align user intent with content in high-sparsity environments.
Storage bottlenecks are a primary cause of GPU stalls in AI workloads. Optimizing BLOB storage for low-latency retrieval is critical for maximizing expensive compute utilization and accelerating the development of frontier models.