- AI Core updates and runs AI models on the device with low latency.
- Gemini Nano runs on AICore; access via the GenAI ML Kit and AI Edge SDK.
- First major rollout on Pixel 8 Pro; builds for multiple chipsets.
- Clear advantages, but keep an eye on battery, notifications, and privacy.
Google's AI Core has crept into the tech vocabulary as the new AI core in Android that keeps smart models and experiences up-to-date right on the phone. It's a discreet but key piece of the system, already powering modern features, especially on the latest Pixels, and set to roll out to more devices in the medium term.
In this guide we compile the most reliable that has been published on this topic: from Play Store listings and APK from official documentation to real-life user experiences. We explain how Google's AICore service works, what it offers developers and users, and its advantages and limitations.
What is AI Core and why it matters
AI Core (system package) com.google.android.aicore) is a service that provides “intelligent features on Android” and provides apps with “the latest AI models.” Its presence was detected in Android 14 (an early beta already included the package), and its listing on Google Play has been shown at least in Pixel 8 and Pixel 8 Pro, with indications of wider availability in the future.
In practice, AI Core acts as a distribution and execution channel for machine learning and generative models on the device itself. According to descriptions seen in the app and in screenshots shared by the community, “AI-based functions run directly on the device with the latest models” and the phone “will update the models automatically”. The cloud image accompanying these texts suggests that the soft drink may be served from the cloud, even though the inference occurs locally.
How it works: System service and execution on the device
AI Core runs in the background as an Android service, similar in philosophy to components like Private Compute Services or Android System Intelligence. Hence, after updating to Android 14, several devices include a "stub"-type dialer ready for the service to be activated or updated when necessary.
Its mission is twofold: on the one hand, to keep AI models up to date and, on the other, to provide apps with access to the necessary computation and APIs without each developer having to carry everything. AI Core leverages the device hardware to reduce inference latency and allow many capabilities to operate offline, which also improves privacy by not sending data to the cloud for every request.
A useful comparison is Arcore: Google's augmented reality platform that manufacturers and developers use to power AR experiences. AICore aims to be that equivalent for AI on Android: a uniform layer that silently and reliably enables and updates models and capabilities, running at the system level.
Gemini Nano: Generative AI on Mobile and Access Paths
The star engine within this framework is Gemini Nano, a foundational Google model designed to run on the device. Its goal is clear: to enable rich generative experiences without network dependency, with lower execution costs, greatly reduced latency, and greater privacy guarantees by processing locally.
Gemini Nano operates integrated into the AICore service and is kept up-to-date through this same channel. Today, developer access is offered through two different paths that cover different needs and varied team profiles.
- ML Kit GenAI APIs: a high-level interface that exposes functions such as summarization, proofreading, rewriting, and image description. Ideal if you want to add capabilities. fast and proven with little integration effort.
- Google AI Edge SDK (experimental access): Designed for teams looking to explore and test on-device AI experiences with greater control. This is a useful option for prototype and experiment before a wide deployment.
This blended approach allows projects of any size to incorporate AI at a good pace: from apps that only need a pair of generative functions, to companies that want to deepen and personalize the experience on the phone itself.
Current availability and where it's headed
The initial strong update has focused on Pixel 8Pro, where it has been deployed simultaneously on stable and beta versions of Android (branches QPR1 and QPR2). At the time this information was shared, it wasn't confirmed that the "base" Pixel 8 would receive the same update at the same time, which is logical given that the Pro model boasts more AI capabilities in its software.
While the Google Play listing appears to be showing up for the Pixel 8/8 Pro for now, the language used (“provides apps with the latest AI models”) suggests a broader reach down the road. Additionally, the discovery of the package on the system and the various APK builds for various soc reinforce the idea of extended compatibility.
In parallel, the ecosystem is also moving: Samsung registered the trademarks “AI Phone” and “AI Smartphone” and is preparing an update to One UI 6.1 with deeper AI experiences on the Galaxy S24; in addition, Google integrates Gemini into FitbitAll of this fits with the industry's overall push for on-device AI, where AICore fits in as a key infrastructure piece for Android.
Versions, builds and update rate
Package listings reveal that Google is releasing platform-specific builds and that the update pace is brisk. Builds with "Android + 12" support and recent release dates have been seen, covering different platforms. hardware variants (e.g. Samsung SLSI and Qualcomm):
- 0.release.samsungslsi.aicore_20250404.03_RC07.752784090 — August 20, 2025
- 0.release.qc8650.aicore_20250404.03_RC07.752784090 — July 28, 2025
- 0.release.aicore_20250404.03_RC04.748336985 — July 21, 2025
- 0.release.prod_aicore_20250306.00_RC01.738380708 — August 2, 2025
- 0.release.qc8635.prod_aicore_20250206.00_RC11.738403691 — March 26, 2025
- 0.release.prod_aicore_20250206.00_RC11.738403691 — March 26, 2025
This detail not only proves that AI Core is updated frequently, but also confirms that Google cares about support. multichip and multioem, an essential requirement if you really want to democratize AI features on Android beyond the Pixel.
What the user gains: speed, privacy, and more features
For the end user, the biggest advantage of AICore is that many “smart” features work directly on the device, reducing latency and avoiding waiting. This is especially useful for tasks such as summarize, rewrite, or describe images from your mobile, where immediacy makes the difference.
The other great asset is the privacyBy running locally, less data leaves the phone. And when AI Core needs to update models, it will do so automatically, without the user having to chase down packages or open specific apps to stay up to date.
In line with what Google highlighted when launching Android 14 and the Pixel 8, the goal is to boast of the “fully on-device AI model” and bring that approach to more features and more manufacturers over time.
Criticisms and issues reported by users
The other side of the coin is the user reports, which serve to bring things back to reality. Some point out that the app updates and runs in the background.regardless of what they do”, consuming more battery than expected and remaining active even after deactivating or reinstalling.
Another common pattern is network management: there are complaints that AI Core “should offer the option to update with mobile data”, since in the absence of Wi-Fi the system displays the fixed notification of “waiting for Wi‑Fi connection”. This, in addition to being annoying, leaves those without Wi-Fi without the update, and with a constant notification in the bar.
There are also those who have discovered the package without having consciously “installed” it, especially on phones from manufacturers that integrate it at the system level. In some cases, Samsung users reported that “should not be forced” and that they would like to be able to choose, reflecting the common tension between system components and user control.
There have even been reviews that question the authenticity of extremely positive reviews, compared to a majority with specific complaints (battery, notifications, network). In these threads, several readers marked these reviews as helpful (e.g., 29 and 2 helpfulness votes on reviews), which shows that the discomfort is not anecdotal.
Advantages and possible disadvantages of using AI Core
When evaluating a platform, you need to balance its pros and cons. Among the advantages, the time saving for teams by not having to train models from scratch, access to modern libraries and integrated tools, and improved user experience due to latency and privacy.
Among the disadvantages, in addition to reports of battery consumption in certain situations, is the resource occupation (storage and processing) on limited devices, and the fact that there are updates and background processes that are not always transparent or configurable to the less technical user.
Finally, we must not lose sight of the dimension of privacy: The documentation that accompanies AI Core itself warns that usage data may be collected from apps that use these capabilities for service improvement purposes (and potentially for other uses, such as ad targeting, depending on applicable policies).
AI Core consolidates a common framework in Android for distributing, updating, and running AI models, supporting Google and third-party apps and accommodating a variety of chips and manufacturers.
Editor specialized in technology and internet issues with more than ten years of experience in different digital media. I have worked as an editor and content creator for e-commerce, communication, online marketing and advertising companies. I have also written on economics, finance and other sectors websites. My work is also my passion. Now, through my articles in Tecnobits, I try to explore all the news and new opportunities that the world of technology offers us every day to improve our lives.