The Dawn of the AI PC Era: A New Computing Paradigm Unfolds

The AI PC, with its integrated AI processors and NPUs, ushers in a new computing paradigm, revolutionizing the user experience by performing AI functions independently of the cloud, thereby maximizing personal privacy and efficiency.

Introduction: Ushering in the AI PC Era

The AI PC represents a computer capable of performing artificial intelligence functions through its built-in processor. Specifically, it refers to personal computers equipped with a Central Processing Unit (CPU) optimized for AI functions, which goes beyond a mere upgrade of existing PC architecture. Its definition expands to include devices that can independently execute generative AI functions without relying on cloud platforms. This emergence of the AI PC is poised to bring about a tremendous transformation in how we interact with technology and accomplish tasks. The integration of new AI tools and applications, such as Microsoft's Copilot+, is creating a new computing environment by automating repetitive tasks, minimizing power consumption, and delivering immersive visual experiences.

The advent of the AI PC signifies more than just a technical improvement; it represents a paradigm shift that fundamentally alters how computing is performed and experienced. Similar to the transitions from mainframes to personal computers, and then to internet-connected PCs, the AI PC enables new types of applications and user experiences that were previously impossible or impractical due to reliance on cloud infrastructure. This shift is not merely about faster processing speeds. By integrating powerful AI capabilities directly into user devices, it enhances data privacy and responsiveness, redefining the "personal" in "personal computer." This change demands strategically important adjustments from hardware manufacturers, software developers, and end-users alike, creating new market opportunities.

This report aims to provide a comprehensive understanding of the AI PC era by analyzing its concept, core technologies, key use cases, market trends and competitive landscape, challenges, and future vision.

Defining the AI PC and Analyzing Core Technologies

Concept of AI PC and Differentiation from Traditional PCs

An AI PC is a computer equipped with an internal processor designed to perform artificial intelligence functions, specifically a CPU optimized for AI capabilities. It can be defined as a computer device with generative AI capabilities that does not require cloud platform access. The most significant difference between traditional PCs and AI PCs is the addition of a Neural Processing Unit (NPU), or neural processor, a dedicated AI processing unit. While traditional PCs can perform some AI tasks, the inclusion of an NPU in an AI PC dramatically improves operational efficiency. AI PCs offer high-speed data processing, customized user experiences, and automation features, making them particularly suitable for high-performance tasks such as data science, video editing, and game development.

This evolution signifies the rise of on-device AI and a redefinition of the NPU's role. Moving away from reliance on cloud-based AI, on-device AI, where AI tasks are processed directly within the device, is becoming a crucial trend. The NPU is the core component that enables this on-device AI. On-device processing is closely linked to personalization, offering various benefits such as reduced latency, enhanced data privacy, and decreased dependence on connectivity. For instance, Microsoft's 'Recall' feature leverages the NPU to pursue both security and convenience. Therefore, the NPU is not just an additional processing unit; it is a fundamental element enabling a new computing paradigm that deeply integrates AI into the user experience by addressing the inherent limitations of cloud-only AI models concerning data privacy, latency, and connectivity. This suggests a strategic move by manufacturers to differentiate their products through user-centric AI experiences that go beyond mere processing power.

Key Hardware Components: Roles and Interactions of CPU, GPU, and NPU

AI PCs are distinguished by their integration of not only the traditional CPU and GPU but also the NPU. Each component plays a unique and complementary role in processing AI tasks.

CPU (Central Processing Unit): Acting as the computer's brain, the CPU handles general-purpose computations. While it processes basic computations in AI tasks, it is not as efficient as a GPU or NPU. General CPUs are optimized for quickly processing complex and computationally intensive tasks, primarily leveraging large cores to optimize performance.
GPU (Graphics Processing Unit): Although primarily designed for graphics rendering, its strong parallel processing architecture makes it suitable for AI tasks. It has historically been used for building artificial neural networks and continues to offer powerful performance. However, a drawback of GPUs for AI purposes is their significantly lower power efficiency.
NPU (Neural Processing Unit): A hardware component specialized for AI computations, the NPU efficiently processes neural network operations. NPUs are crucial for improving the performance and efficiency of AI PCs by taking over a portion of the AI tasks previously handled by GPUs. They achieve higher performance with less power consumption than GPUs, effectively reducing overall energy consumption.

In an AI PC, the integrated NPU, CPU, and GPU enable local data processing without an internet connection, enhancing personal data protection. Utilizing the NPU for AI tasks allows for hours of uninterrupted work and seamless multitasking, while the CPU and GPU can be leveraged for demanding workloads. This heterogeneous computing approach optimizes generative AI end-user experiences by maximizing application performance, device thermal efficiency, and battery life.

NPU Operation and Architecture

The NPU processes information in a manner similar to the human brain. Its neural network structure for data processing and decision-making resembles how human neurons process information, making NPUs suitable for complex machine learning algorithms such as deep learning. NPUs are specifically designed to handle machine learning algorithms and are particularly efficient for tasks like image recognition and natural language processing, which require rapid processing of large multimedia datasets.

Similar to how GPUs are highly effective for processing parallel data, NPUs are specifically built for the computing required to run neural networks responsible for AI/ML processes. NPUs efficiently handle the large volumes of data and complex calculations involved in AI tasks, significantly accelerating the training and inference speeds of AI models. They particularly excel in specialized operations essential for AI processes, such as matrix multiplication and convolution.

Major semiconductor manufacturers are showcasing diverse NPU technological features:

Intel: The Intel Core Ultra series moves beyond the traditional x86 processor architecture by adding an NPU to accelerate AI computations. The NPU offloads AI computations previously handled by the CPU and GPU, achieving both computational efficiency and power consumption optimization. The Intel Core Ultra 2 (Arrow Lake) is a second-generation AI acceleration processor, offering 47 TOPS (Tera Operations Per Second) of computing performance based on the NPU 2.0 architecture, optimized for on-device AI, enhancing power efficiency, and improving AI performance by more than double compared to its predecessor.
AMD: AMD Ryzen AI supports running applications at incredible speeds through an integrated AI engine. The new architecture NPU, the XDNA 2 engine, delivers 50 TOPS of performance, with an estimated total performance, including the CPU and GPU, exceeding 100 TOPS. AMD demonstrates overwhelming power efficiency under high loads while maintaining power efficiency under low loads and idle states. It is regarded as an innovative product that inaugurates an era of high-performance laptops with excellent battery life in the Windows ecosystem.
Qualcomm: The most significant feature of Snapdragon X Elite is its AI capability, managed by the Qualcomm Hexagon NPU. The Hexagon NPU delivers 45 TOPS of AI performance, which is significantly higher than the current generation NPUs from Intel and AMD. Qualcomm's NPU is optimized for on-device AI processing, allowing complex AI tasks, such as generative AI models, to be processed locally without relying on the cloud, thereby reducing latency and improving privacy. Qualcomm's Hexagon NPU has evolved to meet rapidly changing AI requirements, particularly by adding tensor and convolution accelerators to enable efficient processing.

Memory and Storage Impacting AI PC Performance

The overall performance of an AI PC is significantly influenced not only by the CPU, GPU, and NPU but also by the capabilities of memory (RAM) and storage (SSD). These two components directly affect the amount of data that can be processed simultaneously and the speed at which data can be accessed.

Memory (RAM): RAM is directly linked to the computer's operating speed. AI and deep learning tasks involve processing vast amounts of data, so insufficient RAM capacity can slow down the computer and degrade program execution speed. In the AI era, larger capacity and faster RAM are essential.
Storage (SSD): An SSD is a device that stores data using semiconductor chips, making it much faster and more resistant to shock than traditional HDDs (Hard Disk Drives). In the AI era, SSDs, especially NVMe (Non-Volatile Memory Express) SSDs, play a crucial role in maximizing computer system performance. AI training often involves massive datasets exceeding terabytes (TB), and SSDs can load this data quickly, significantly shortening training times. Furthermore, even with excellent CPU or GPU computing performance in AI tasks, a slow data transfer can lead to I/O bottlenecks that degrade overall system performance. SSDs help reduce these bottlenecks with their fast data transfer speeds, enabling CPUs and GPUs to utilize their performance to the fullest. Since AI data is large and read/write speeds are critical, HDDs' slow speeds would make processing times excessively long, thus making SSDs essential.

The overall system design, cooling, and power delivery must also be optimized to handle continuous AI workloads, which impacts long-term performance stability.

Key Use Cases of AI PCs

AI PCs offer transformative use cases across various domains in personal and enterprise environments, including productivity, creativity, entertainment, and security.

Enhancing Productivity

AI PCs significantly boost productivity by automating routine tasks and maximizing efficiency.

Email Management: AI can classify emails, prioritize important messages, summarize key points from long email threads, and draft replies, reducing email overload.
Meeting Efficiency: Virtual assistants leverage AI to assist with scheduling, setting reminders, and responding to queries. Advanced AI assistants can provide predictive scheduling based on individual work patterns and team schedules, integrating with productivity metrics to set optimal focus times. In video conferencing, AI-powered features enhance meeting efficiency through automatic note-taking, audio distortion reduction, video quality improvement, and translated subtitles. For example, Google Meet's Gemini offers real-time translated subtitles in over 60 languages, eliminating language barriers.
Document Creation and Management Automation: AI automatically extracts and summarizes key information from documents, improving information comprehension efficiency, and handles repetitive document creation tasks, allowing employees to focus on core responsibilities.
Data Analysis and Reporting Automation: AI analyzes vast amounts of data and identifies patterns, helping businesses quickly derive crucial insights.
IT and Back-Office Automation: AI handles repetitive and time-consuming tasks, increasing operational efficiency and allowing employees to dedicate more time to creative and problem-solving activities.

Creativity and Content Creation

AI PCs amplify user creativity and streamline the content creation process.

Video Editing: AI software like CapCut provides an all-in-one solution for video editing, offering a wide range of AI features such as animated character generation, image enhancement, text-to-speech, voice cloning, and audio clarity restoration for a smooth and efficient editing experience. Non-linear AI video editors like Movavi Video Editor provide professional-grade AI video editing features to significantly enhance video quality.
Image Generation and Editing: AI software such as Midjourney and NightCafe Creator generates images based on text prompts or sketches and offers features to enhance or style existing images. Microsoft's Paint and Photos apps support AI tools for image style modification, background removal, and visual element recomposition.
Music and Voice Generation: Amper Music is utilized as AI software for music generation, and ElevenLabs for voice generation. Vosk is an offline speech recognition software that supports over 20 languages and provides accurate transcription.
Writing and Brainstorming: AI assists writers and content creators in brainstorming ideas, generating content, and co-authoring books. MindMeister, a mind mapping tool, uses AI to spark new ideas and overcome creative blocks.

Gaming and Entertainment

AI PCs revolutionize the gaming and entertainment experience.

AI Upscaling: AI video upscaling can cleanly improve low-quality videos with a single click. In gaming, it enhances the resolution of images generated by graphics cards and interpolates frames for smoother motion. AI upscaling technology is highly effective in improving the graphics of classic games, providing sharper, more detailed, and vibrant visuals through color enhancement, anti-aliasing, and resolution increase. This appeals to classic game fans and introduces new gamers to the charm of older titles.

Enhanced Security

AI PCs provide robust security features to protect user data.

Data Protection: AI tools improve data protection by classifying sensitive data, monitoring data movement, and preventing unauthorized access or leakage. AI automatically adapts to threat environments and continuously monitors threats around the clock, helping to proactively counter new cyber threats.
Endpoint Security: AI continuously monitors suspicious behavior and anomalies on endpoints such as computers, servers, and mobile devices to detect real-time security threats, thereby improving existing Endpoint Detection and Response (EDR) solutions.
Fraud Detection: AI supports financial institutions in detecting fraud by automatically analyzing transaction data for patterns indicating fraud, and it continuously improves fraud detection capabilities by responding to new threats in real-time.

Other Industrial Applications

AI PCs are being utilized across various industries, driving innovation.

Healthcare: AI is valuable in healthcare for analyzing medical images to diagnose diseases early or segment necessary areas in real-time during surgery.
Autonomous Driving: Computer vision AI is a core technology that enables autonomous vehicles to perceive roads, pedestrians, obstacles, and make decisions.
Manufacturing: AI-based predictive maintenance and defect detection in manufacturing contribute to increasing production line efficiency and reducing defect rates.

AI PC Market Trends and Competitive Landscape

Market Size and Forecast

The AI PC market is expected to experience rapid growth alongside the overall recovery of the PC market. Market research firm Canalys predicts that global AI PC shipments will reach 48 million units in 2024, accounting for 18% of total PC shipments. By 2025, AI PC shipments are expected to surpass 100 million units, reaching 40% of total PC shipments, and by 2028, they are projected to reach 205 million units, showing an impressive compound annual growth rate (CAGR) of 44% from 2024 to 2028. IDC anticipates annual AI PC shipments to approach 50 million units in 2024, while IDC Korea predicts that AI PCs will account for 25% of the domestic PC market and grow at a CAGR of 27.3% over the next five years until 2029.

This growth is closely linked to the PC replacement demand driven by the end of Windows 10 technical support in 2025. Considering that the typical PC replacement cycle is 4-5 years, the time is ripe for replacing devices purchased during the COVID-19 pandemic in 2020, accelerating the transition to AI PCs.

Key Players and Strategies

The AI PC market is characterized by fierce competition and collaboration among major chip manufacturers and PC manufacturers.

Chip Manufacturers:

Intel: Intel aims to integrate AI capabilities into all its products, from data centers to PCs and edge devices, through its "AI Everywhere" strategy. Intel predicts that 80% of all PCs will be AI PCs by 2028 and is launching its Core Ultra series with added NPUs to achieve this goal. Intel boasts high AI performance (120 TOPS) and excellent software compatibility based on the x86 architecture.
AMD: AMD is targeting the next-generation AI PC market with its Ryzen AI PRO 300 series processors. AMD plans to expand its edge AI product lineup and enhance flexibility and scalability across industries. Notably, it demonstrates overwhelming power efficiency under high loads, contributing to improved battery life for high-performance laptops.
Qualcomm: Qualcomm is introducing AI PCs designed for exceptional performance in AI applications through its Snapdragon X Elite, which features a powerful 45 TOPS-class NPU. Qualcomm focuses on optimizing for on-device AI processing, allowing complex AI tasks such as generative AI models to be processed locally without relying on the cloud, thereby reducing latency and improving privacy.

PC Manufacturers:

Samsung Electronics: Samsung is targeting the AI PC market with its Galaxy Book series. At CES 2024, Samsung showcased AI-based innovative technologies such as AI power-saving mode and SmartThings Energy integration, and also unveiled its Bespoke AI home appliance lineup with enhanced AI features like AI Food Manager and AI Mode 2.0. Samsung aims to preempt the AI PC market through an aggressive strategy of lowering new product prices compared to previous models.
LG Electronics: LG is setting a new standard for AI PCs by unveiling the 2025 'LG Gram' with 'Multi AI' functionality. The 2024 LG Gram, equipped with the latest AI CPU, includes a dedicated engine (Intel AI Boost) for AI computations without a network connection, offering double the graphics performance.
HP: HP provides its 'AI Companion' solution as a standard feature, supporting personal file summarization and analysis, GPT-4o-based knowledge search, and voice-controlled device settings. It also enhances business productivity and security through its video conferencing solution 'Poly Camera Pro' and integrated security solution 'HP Wolf Security'. HP emphasizes the importance of workstation product lines for AI users and developers alike.
Dell: Dell has introduced its 'Dell Pro' laptop lineup, featuring high-performance chips to run on-device AI functions like Copilot locally. The 'Dell Pro Max' with NVIDIA graphics cards offers even higher AI performance. Dell plans to offer a variety of customized solutions by releasing products with not only Intel but also Qualcomm and AMD processors.
Lenovo: Lenovo emphasizes enhanced security, productivity innovation, and personalized efficiency with its AI PCs equipped with Intel Core Ultra processors. Lenovo AI PCs feature intelligent threat detection, automated responses, adaptive performance, and seamless integration of advanced AI technologies. They can leverage personal and enterprise data to provide personalized responses while protecting privacy and accelerating data analysis.
Microsoft Surface: Microsoft is setting 'Copilot+ PC' as the new standard for Windows PCs, requiring NPU performance of 40 TOPS or more. Copilot+ PCs offer various AI functions such as 'Recall' to find previously viewed items, 'Live Captions' to translate 44 languages into English subtitles, and 'Windows Studio Effects' to improve video call quality. Microsoft views AI as a 'hybrid' model combining cloud and on-device AI and is designing its Surface product line with these AI features integrated and security as a fundamental basis.

Partnerships and Software Ecosystem

The growth of the AI PC market is accelerated through close partnerships between chip manufacturers and PC manufacturers. Intel highlighted its 30-year partnership with Lenovo, stating they will jointly lead the enterprise AI PC market. Microsoft has partnered with Qualcomm to integrate Qualcomm's AI engine into ARM-based Windows PCs.

The development of the software ecosystem is also essential for the widespread adoption of AI PCs. Developers can efficiently port pre-trained PyTorch or TensorFlow models to run on integrated GPUs (iGPUs) or NPUs. Developer frameworks like Intel's OpenVINO™ accelerate AI tasks such as deep learning inference, computer vision, and natural language processing, supporting Intel's CPUs, GPUs, and NPUs to achieve optimal performance for each task. OpenVINO™ improves inference performance through model optimization features, especially useful when lightweight models are needed for edge devices or CPU-only environments. Such frameworks help AI models run seamlessly on various hardware targets.

Challenges and Future Vision of AI PCs

Technical Limitations

While AI PC technology is rapidly advancing, it still faces several technical limitations.

Black Box Problem: AI systems operate based on complex algorithms, making it difficult to transparently understand their decision-making processes, leading to the "black box problem." This can reduce trustworthiness and increase the possibility of errors or biases.
Data Dependence: AI systems heavily rely on training data, so the quality and bias of the data directly affect the results. If the data contains errors or biases, the AI system may produce incorrect outcomes.
Limits of Human Intelligence: Current AI technology has not yet reached the level of human intelligence. It particularly struggles to fully implement unique human abilities such as creativity, common sense, and problem-solving skills.

Ethical Issues and Social Impact

The proliferation of AI PCs can raise various ethical and social issues beyond technical limitations.

Privacy and Security: Features that record all user activities, such as Microsoft's 'Recall,' have raised serious privacy concerns. Taking and storing screenshots could pose more severe problems than keylogging, and for Windows Home users, specifically, the lack of encryption could lead to significant exposure of personal information if hacked or stolen.
Job Displacement: Concerns are being raised that the advancement of AI and automation technologies could lead to job displacement in certain occupations. This issue can exacerbate social inequality and highlight the need for new job creation.
Digital Divide: If access to AI technology is not equally available to all individuals or nations, it could deepen existing inequalities. Differences in digital literacy can lead to polarization in income, competitiveness, and economic power.

Standardization and Compatibility Issues

For the AI PC market to grow successfully, standardization and compatibility issues must be addressed. Laptops with ARM-based CPUs may have limited software compatibility, and even within the same Windows operating system, there can be differences in the instructions that CPUs can process. Furthermore, different AI tools and models require connectivity standards for information exchange to collaborate effectively.

Power Consumption and Heat Generation

High-performance AI semiconductors generate significant heat proportional to their computational efficiency, and cooling facilities can consume nearly half of the total power. Concerns are being raised that the increasing number of AI data centers could threaten the stability of national power grids. NPUs, with their lower power consumption compared to GPUs, have the advantage of being power-efficient, which can help mitigate these issues.

Software Ecosystem and the Absence of "Killer Apps"

The development of various AI applications and software is essential for maximizing the utility of AI PCs. Simply releasing new hardware is not enough to expand the market; users need to understand the benefits of AI features and perceive clear needs and advantages for integrating them into their daily lives. In the enterprise AI PC market, the absence of "killer apps" that enhance AI PC utilization is sometimes cited as a major reason for hesitation in adoption.

Hardware Development Direction

The AI semiconductor market is projected to grow at a CAGR of 24% over the next five years, reaching $159 billion by 2028. Application areas are expected to gradually expand from data centers and smartphones to edge devices such as vehicles and PCs.

Next-generation NPU: NPUs, as dedicated processors that efficiently perform AI inference with low power, are gaining increasing attention in the era of on-device AI. On-chip memory, where memory is integrated inside the NPU chip for miniaturization and low power consumption, is being utilized, and the development of low-precision computing technology to handle massive artificial neural networks is actively underway.
PIM (Processing In Memory) Computing: PIM technology, which performs computations within memory, is being researched as a next-generation technology that enhances AI processing efficiency by minimizing data movement.
HBM (High Bandwidth Memory) and CXL (Compute Express Link): HBM is emerging as a core product for AI semiconductors, and CXL memory is expected to become a major product in the AI semiconductor memory market, following HBM.

Software Development Direction

From a software perspective, the future of AI PCs is closely linked to the advancement of on-device AI models and multimodal AI.

On-Device LLM (Large Language Model): Running generative AI inference on edge devices offers various benefits, including enhanced privacy, reliability, cost-effectiveness, and immediacy. Qualcomm demonstrated the world's first on-device execution of an LLM with over 7 billion parameters, generating multi-turn conversations by taking text and ambient sounds as input on a Windows PC equipped with Snapdragon X Elite.
Multimodal AI: Multimodal AI, which understands the world through more than just sight and hearing, processing various modalities (input forms) for input and output, offers better responses and answers. It presents new possibilities such as advancing autonomous driving technology and image description services for the visually impaired.
Cloud-Edge Collaboration Model: AI is expected to evolve into a hybrid model combining cloud and on-device AI. Large data centers and user devices can coexist, leveraging their respective strengths to achieve optimal performance and user experience. On-device AI has the advantage of operating without an internet connection and incurring no additional cost per request, while cloud AI excels in large-scale training and complex model processing. Through this division of roles, AI will act as the operating system that controls and manages all devices, with AI agents expected to be the core driving force.

Conclusion

The AI PC is more than just an upgraded computer; it is emerging as a key driver of innovation in user experience and a paradigm shift in computing through on-device AI. The integration of the NPU provides significant value by efficiently processing AI tasks locally, leading to enhanced privacy, reduced latency, and increased power efficiency. This hardware advancement is creating new use cases in various fields, including productivity enhancement, creative content creation, improved gaming experiences, and robust security features.

The global market anticipates a massive replacement demand for AI PCs, coinciding with the end of Windows 10 support. Consequently, chip manufacturers and PC manufacturers are aggressively strengthening their AI PC lineups and expanding their ecosystems through partnerships. Intel, AMD, and Qualcomm aim to lead the market with their NPU technologies, while major PC manufacturers such as Samsung, LG, HP, Dell, Lenovo, and Microsoft are fiercely competing by launching new products featuring on-device AI capabilities.

However, several challenges remain for the successful establishment of the AI PC market. Continuous discussion and effort are needed to address ethical and social issues such as the AI black box problem, data bias, privacy concerns (especially with the 'Recall' feature), and the potential for job displacement and widening digital divides due to AI. Furthermore, software compatibility issues with ARM-based systems, power consumption and heat management of high-performance AI semiconductors, and the absence of "killer apps" that provide tangible value to users remain significant hurdles for market expansion.

The future AI PC will evolve further through advancements in hardware technologies like next-generation NPUs, PIM, HBM, and CXL, as well as the development of on-device LLMs, multimodal AI, and a harmonious collaboration model between cloud and edge computing. This technological progress will enable the AI PC to become more than just a device, transforming into an intelligent companion deeply integrated into users' lives and work. The AI PC will become a necessity, not an option, creating new business opportunities across the ICT industry and accelerating the digital transformation of society as a whole.