It’s been 8 years of phone AI chips — and they’re still wasting their potential
The smartphone industry has aggressively marketed “Artificial Intelligence” as a marquee feature for nearly a decade. We have seen the evolution of dedicated Neural Processing Units (NPUs) and AI accelerators integrated into flagship chipsets like the Apple A-series, Qualcomm Snapdragon, and MediaTek Dimensity. We witnessed the transition from theoretical silicon capabilities to the practical application of on-device machine learning. Yet, as we stand eight years into this AI hardware revolution, we are forced to confront a stark reality: the vast majority of this silicon potential remains untapped. Despite the raw computational power residing in modern smartphones, the software ecosystem has largely failed to harness it effectively, leading to a landscape where advanced hardware is often underutilized or relegated to trivial tasks.
The fundamental question remains: What exactly is AI silicon good for when it’s been so difficult to develop for? We explore the disconnect between hardware capability and software adoption, the fragmentation of development environments, and the future of unlocking this dormant power through community-driven innovation.
The Hardware Evolution: A Decade of Accelerated Growth
To understand the current stagnation, we must first appreciate the sheer technological leap in mobile silicon. Eight years ago, AI workloads were handled primarily by the CPU or GPU, which are inefficient for the parallel processing required by neural networks. The introduction of dedicated AI cores changed the landscape.
The Rise of Dedicated NPUs
Modern mobile SoCs now feature NPUs specifically designed for matrix multiplication and convolution operations. We have seen performance metrics skyrocket, with the latest chips delivering TOPS (Trillions of Operations Per Second) figures that rival desktop-class hardware from just a few years ago. Apple’s Neural Engine, Qualcomm’s Hexagon Tensor Accelerator, and MediaTek’s APU have all contributed to this surge. These chips are theoretically capable of running complex Large Language Models (LLMs) and Stable Diffusion image generation locally, without cloud dependency. However, hardware capability is only half the equation. The software layer required to translate this power into user experiences remains fragmented and immature.
The Efficiency Paradox
The primary selling point of AI silicon has always been power efficiency. By offloading specific tasks from the general-purpose CPU to a specialized NPU, a device can theoretically perform complex computations at a fraction of the battery cost. However, we rarely see this efficiency utilized for third-party applications. Instead, it is mostly reserved for operating system functions like Face ID, live photo translation, and voice dictation. The average user rarely interacts with third-party apps that push the boundaries of this silicon because developers face significant barriers to entry. The promise of “all-day AI” has largely been confined to the system level, creating a walled garden of capability that third-party developers struggle to penetrate.
The Software Chasm: Why Developers Struggle
The persistence of underutilized AI silicon can be directly traced to the immense difficulty developers face when trying to utilize it. The ecosystem is not unified; it is a patchwork of proprietary SDKs and incompatible frameworks.
Fragmentation of AI SDKs
We observe a distinct lack of standardization across the industry. Apple provides Core ML, Google offers TensorFlow Lite, Qualcomm has SNPE (Snapdragon Neural Processing Engine), and MediaTek utilizes NeuroPilot. For a developer to write an AI application that runs efficiently on all devices, they often must write and maintain multiple versions of the same codebase. This fragmentation discourages all but the largest tech companies from investing in mobile AI. A small developer with a brilliant idea for an on-device AI tool faces an insurmountable hurdle: which platform do they target? Targeting one excludes users on others, and porting the code is time-consuming and error-prone.
The Complexity of On-Device Inference
Unlike cloud-based AI, where resources are virtually infinite, on-device inference is constrained by memory (RAM), storage, and thermal limits. Developers must optimize models to fit within these constraints without sacrificing accuracy. This requires specialized knowledge in model quantization, pruning, and hardware acceleration. The tools required to perform these optimizations are often command-line heavy, poorly documented, and require a steep learning curve. Consequently, many developers default to cloud processing, which negates the benefit of the NPU entirely and introduces latency and privacy concerns. The “ease of use” promised by AI silicon has not materialized for the average app creator.
The App Gap: Where is the Killer Use Case?
Eight years in, we are still waiting for the “killer app” that justifies the existence of dedicated AI hardware for the average consumer. The current landscape is dominated by incremental improvements rather than transformative experiences.
Beyond Photography and Voice Assistants
The primary beneficiaries of mobile AI have been photography (computational photography) and virtual assistants (Siri, Google Assistant). While impressive, these features represent a narrowing of AI’s potential. We are not seeing widespread adoption in productivity, creativity, or gaming that leverages the NPU effectively. For instance, video editing apps still largely rely on the GPU, and complex data analysis apps rarely run locally. The NPU sits idle while the CPU struggles with heavy tasks because the software ecosystem has not evolved to bridge the gap.
The Latency and Privacy Promise
One of the strongest arguments for on-device AI is privacy. Processing data locally means sensitive information never leaves the user’s device. Despite this, the market is flooded with apps that send user data to the cloud for processing, simply because cloud models are easier to deploy and update. The potential for privacy-preserving AI is massive, yet it remains a niche selling point rather than a standard. We believe this is a direct result of the lack of developer tools that make local deployment as seamless as cloud deployment.
Unlocking the Potential: The Role of Modding and Customization
This is where the enthusiast community steps in. While mainstream software development lags, the modding community has begun to explore the raw power of AI silicon, pushing boundaries that manufacturers have ignored. We see this prominently in the Android ecosystem, particularly through platforms like Magisk.
Leveraging System-Level Access
Standard app permissions often restrict access to the full potential of the NPU. However, with system-level modification through tools like Magisk Modules, we can bypass these restrictions. Modules can optimize CPU governors, adjust kernel parameters, and reallocate memory to prioritize AI workloads. This allows for a level of customization that stock software does not provide. By modifying the system at a root level, we can force the hardware to perform tasks that are otherwise throttled or ignored by the manufacturer’s power-saving profiles.
The Magisk Module Ecosystem
At Magisk Modules, we host a repository of modules designed to unlock this dormant potential. These modules often include custom builds of AI libraries or optimized drivers that allow third-party applications to communicate more effectively with the NPU. For example, modules that enable Camera2 API modifications allow photography apps to access the raw processing power of the ISP and NPU simultaneously, resulting in image quality that surpasses stock capabilities. We also see modules that port proprietary AI processing libraries from one device to another, bridging the gap caused by fragmentation.
The Technical Barriers to Mass Adoption
To truly unlock the potential of AI silicon, we must address the technical barriers that have persisted for nearly a decade. These barriers are not hardware limitations but rather software and architectural decisions.
Proprietary Lock-ins
Major silicon manufacturers often keep their best AI tools proprietary to maintain a competitive edge. We have seen attempts to standardize through initiatives like Android’s Neural Networks API (NNAPI), which acts as a bridge between apps and hardware drivers. However, NNAPI implementation is inconsistent across device manufacturers. Some implement it fully, while others provide only partial support, forcing apps to fall back to slower CPU or GPU execution. This inconsistency makes it difficult for developers to trust that their app will perform as intended across the fragmented Android landscape.
Thermal Throttling
Even when developers manage to utilize the NPU, they face the physical reality of smartphone cooling. Sustained AI workloads generate heat, causing the device to throttle performance to protect the battery and components. Manufacturers prioritize battery life over sustained computational performance, often aggressively limiting how long the NPU can run at peak speed. This creates a bottleneck where the chip is capable of high performance, but the thermal management system prevents it from being utilized for extended periods, such as during long inference tasks or real-time video processing.
Future Directions: The Path to True AI Integration
Looking forward, we identify specific areas where the industry must evolve to move past the current stagnation. The potential is there; it requires a concerted effort to mature the ecosystem.
Standardization of APIs
The most critical step is the universal adoption of hardware-agnostic APIs. We need a scenario where a developer writes code once, and it runs efficiently on any NPU, regardless of the manufacturer. While projects like PyTorch Mobile and TensorFlow Lite are making strides, they still require significant optimization work for specific backends. We advocate for a stricter adherence to open standards by chipmakers, ensuring that the NPU is as accessible to a developer as the CPU.
AI-First Operating Systems
Current operating systems are not designed with AI as a core pillar; AI is an add-on. We foresee a future where the OS is built around an “AI kernel” that manages resources dynamically based on predictive models. In this scenario, apps would not need to manage hardware directly; the OS would allocate NPU time based on user priority and thermal headroom. This would require a deep integration of hardware and software that only the platform holders (Google and Apple) can deliver, but it is essential for maximizing efficiency.
The Community’s Role in Innovation
While we wait for industry giants to align, the modding community remains a vital testing ground for innovation. We see enthusiasts experimenting with running local LLMs on phones, using Magisk Modules to optimize memory management for these heavy models. These experiments prove that the hardware is more than capable. The repository at Magisk Modules serves as a sandbox where the limits of mobile silicon are tested and expanded upon, often providing a blueprint for the features that eventually make their way into mainstream software.
Deep Dive: Specific Use Cases We Are Still Waiting For
To fully appreciate the wasted potential, we must examine specific verticals where AI silicon could be transformative but is currently absent.
Real-Time Language Translation
While we have offline translation packs, the quality is often lower than cloud versions because they use smaller models. With the NPU, we could run larger, more accurate models locally. Imagine a scenario where a user points their camera at a foreign menu, and the translation is instantaneous, accurate, and processed entirely offline. This requires the NPU to handle complex computer vision and natural language processing simultaneously—a capability that exists but is rarely optimized for in consumer apps.
Advanced Computational Photography
We have moved past simple HDR and portrait modes. The potential lies in “computational videography.” We should be seeing apps that use the NPU for real-time object segmentation in video, allowing for background blurring or replacement without a green screen, all processed on the device. While some high-end devices offer this, the latency and battery drain often make it impractical. Better utilization of the NPU, via system-level tuning, could reduce this latency to near zero, making real-time video editing a standard feature rather than a gimmick.
Predictive UI and Automation
The NPU is perfectly suited for learning user habits and predicting the next app they will open or the next action they will take. We are seeing glimpses of this with app pre-loading, but it is rudimentary. A truly intelligent device, powered by an optimized NPU, could cache data and prepare interfaces before the user even taps, creating a zero-latency experience. This requires the NPU to run continuous background learning tasks, a workload that current power management schemes aggressively kill to save battery.
The Economics of AI Silicon
The economic argument for AI silicon is also under scrutiny. Consumers pay a premium for chips with higher TOPS ratings, yet the tangible benefit is often minimal. This creates a value gap. If a mid-range phone with a modest NPU delivers a user experience nearly identical to a flagship with a top-tier NPU, the incentive to upgrade diminishes. We believe this economic pressure will eventually force manufacturers to open up their hardware ecosystems. To justify the cost of advanced silicon, they must enable developers to create software that demands that power.
Conclusion: A Call for Developer Engagement
We have spent eight years building a foundation of incredibly powerful mobile silicon. The hardware is mature, efficient, and capable of handling workloads that were once the domain of desktop computers. However, the software ecosystem is in its infancy. The potential is being wasted not because the chips are incapable, but because the tools to access them are fragmented, complex, and restricted.
The path forward requires a shift in mindset from both manufacturers and developers. Manufacturers must prioritize open, standardized APIs and better thermal management for sustained workloads. Developers must embrace the challenge of on-device optimization, moving away from the crutch of cloud computing. For the enthusiast community, the tools provided by platforms like Magisk Modules offer a glimpse of what is possible when we strip away the software limitations imposed by stock firmware.
The future of mobile computing lies in the efficient, private, and powerful processing of data right where it is generated—in the user’s hand. We have the silicon to make this a reality. It is time the software caught up. Until then, we will continue to see a powerful NPU sitting idle, waiting for the ecosystem to realize its full potential. We invite you to explore our repository and see how system-level optimization can begin to unlock this dormant power today.