Lenovo’s new Qira aims to solve one of modern computing’s biggest productivity drains: context switching. Framed as a personal “AI twin” that follows you from phone to PC to future wearables, Qira promises to anticipate your next step, surface the right content at the right time, and route tasks to the best compute resource—on-device or in the cloud—so you stay in the zone.
How Qira works across devices to reduce context switching
Unlike single-device assistants, Qira spans Lenovo laptops and tablets and Motorola phones at launch, with a roadmap to wearables and ambient sensors. Picture researching on a Motorola Razr during your commute, then opening a Lenovo Yoga to find the same pages, notes, and documents already queued. Qira calls this “Next Move,” an intent-aware handoff that uses your recent activity, time, and location to infer what you want to do next.
- How Qira works across devices to reduce context switching
- Hybrid AI orchestration with Copilot for smarter task routing
- Hardware and NPU readiness for on-device and cloud AI
- Expanding to wearables and ambient sensing across environments
- Privacy controls by design with transparent permissions
- Competition and ecosystem strategy across platforms and brands
- Why it matters for focus and reducing costly interruptions
The platform acts as an orchestration layer that unifies context across Android and Windows. It maintains a private, user-permissioned memory of what you’re doing and where, then synchronizes that state so you can pick up instantly on another device. The result is less rummaging through tabs and folders—and more time actually working.
Hybrid AI orchestration with Copilot for smarter task routing
Qira embraces hybrid AI. Lightweight, latency-sensitive tasks can run locally—taking advantage of modern NPUs—while heavier jobs (like long-form summarization or multimodal reasoning) can be sent to the cloud. Lenovo says Qira won’t compete with Microsoft’s Copilot; it will cooperate. Think of Qira as the conductor for your personal context, dispatching Copilot or other models when they’re the right instruments for the moment.
This approach mirrors where the industry is headed. IDC and Canalys both expect AI PCs to dominate shipments over the next few years, with on-device accelerators becoming standard. Offloading selectively reduces latency, protects sensitive data, and cuts cloud costs—without sacrificing capability when you need big-model muscle.
Hardware and NPU readiness for on-device and cloud AI
Qira won’t arrive on every Lenovo device on day one. It will first ship on select new systems with sufficient performance and memory, then expand through over-the-air updates as requirements drop and silicon improves. That timing aligns with surging NPU horsepower: early AI PCs hovered around 10–11 TOPS, recent chips push 40 TOPS, and 100 TOPS-class NPUs are on the horizon. Lenovo remains platform-agnostic, targeting modern silicon from AMD’s Ryzen AI, Intel’s next-gen mobile platforms, and Qualcomm’s Snapdragon X lineup.
Why it matters: the NPU is quickly becoming the workhorse for sustained, private, low-latency AI. Power efficiency is now as strategic as raw TOPS, because battery life determines whether users will actually keep AI features turned on.
Expanding to wearables and ambient sensing across environments
Lenovo is exploring how Qira extends beyond PCs and phones into a constellation of sensors. A Motorola pendant codenamed Project Maxwell, shown as a proof of concept, hints at always-available voice and environmental awareness. Lenovo also showcased smart glasses and is investigating ambient devices for desks and walls—systems that can register presence, context, or meetings without you lifting a finger.
The challenge is universal: packing enough compute into tiny form factors without killing battery life. Lenovo notes that rings and pins are especially constrained today, though rapid advances in low-power ML silicon could change that in a few years. Connectivity isn’t the bottleneck for now; Bluetooth and Wi‑Fi roadmaps should suffice for near-term wearables.
Privacy controls by design with transparent permissions
Qira’s value depends on sensitive context. Lenovo says data use will be transparent and permission-based, with explicit controls over what stays local and what can leave your device. That extends to bystander considerations—an area increasingly scrutinized by regulators under frameworks like GDPR and evolving state privacy laws. Expect granular toggles, clear indicators, and a bias toward on-device processing when feasible.
There’s also room for innovation in social norms. Lenovo has floated interest in concepts like a user “permission bubble” that could signal preferences to nearby sensors—limits on audio or video capture, for example—though industry standards would be needed for this to work across brands.
Competition and ecosystem strategy across platforms and brands
Qira enters a field crowded by Apple Intelligence, Google’s ecosystem tools, and Samsung’s Galaxy AI features. Lenovo’s differentiator is breadth across Windows PCs, Android devices, and future wearables, plus close alignment with Microsoft’s platform strategy. The company’s willingness to collaborate beyond its own brands could prove pivotal—cross-vendor context is what users actually want, and what few assistants truly deliver today.
Why it matters for focus and reducing costly interruptions
Context switching is expensive. Research led by UC Irvine’s Gloria Mark has shown it can take more than 20 minutes to fully refocus after an interruption. In knowledge-work settings, that adds up to hours of lost flow each week. By auto-retrieving the right materials and predicting “next moves,” Qira targets the small frictions—reopening tabs, hunting files, reconstructing where you left off—that silently erode productivity.
If Lenovo executes, Qira could be the connective tissue of daily computing: a persistent, privacy-aware memory that spans your devices, accelerates routine tasks, and keeps your attention where it belongs—on the work, not the workflow.
