Global Artificial Intelligence (AI) Audio and Video SoC Market to Reach US$72.8 Billion by 2030

The global market for Artificial Intelligence (AI) Audio and Video SoC estimated at US$7.9 Billion in the year 2024, is expected to reach US$72.8 Billion by 2030, growing at a CAGR of 44.9% over the analysis period 2024-2030. AI Audio SoC, one of the segments analyzed in the report, is expected to record a 39.1% CAGR and reach US$33.2 Billion by the end of the analysis period. Growth in the AI Video SoC segment is estimated at 52.2% CAGR over the analysis period.

The U.S. Market is Estimated at US$2.1 Billion While China is Forecast to Grow at 53.8% CAGR

The Artificial Intelligence (AI) Audio and Video SoC market in the U.S. is estimated at US$2.1 Billion in the year 2024. China, the world`s second largest economy, is forecast to reach a projected market size of US$17.5 Billion by the year 2030 trailing a CAGR of 53.8% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 38.4% and 41.8% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 39.5% CAGR.

Global Artificial Intelligence (AI) Audio and Video SoC Market – Key Trends & Drivers Summarized

Is AI-Driven Integration Transforming Next-Gen Audio and Video Systems?

Artificial Intelligence (AI) is revolutionizing the design and functionality of System-on-Chip (SoC) solutions used in audio and video processing, marking a major paradigm shift in how digital media systems are built and operated. Traditionally, audio and video SoCs were optimized for basic encoding, decoding, and signal transmission. However, the infusion of AI capabilities is enabling these chips to do far more—supporting intelligent scene detection, voice recognition, noise cancellation, object tracking, emotion analysis, and adaptive resolution scaling in real time. These AI-optimized SoCs are built to handle vast amounts of sensor and signal data, making them ideal for use in edge devices such as smart TVs, digital assistants, home surveillance systems, automotive infotainment units, and video conferencing platforms. The integration of deep learning accelerators within SoC architectures allows for on-chip inferencing, eliminating the need for constant cloud connectivity and ensuring faster, more secure processing. Advanced AI-enabled SoCs also support simultaneous audio-visual analytics, which improves user experiences in interactive environments like AR/VR, smart classrooms, and AI-powered healthcare diagnostics. By embedding neural network capabilities directly into the chip, developers can enable real-time responsiveness and personalization, paving the way for highly adaptive and intelligent multimedia systems. As consumer and industrial devices become more reliant on context-aware features and edge intelligence, the role of AI-enhanced SoCs is quickly moving from optional to foundational across a wide array of audio-visual applications.

How Are Shifting Consumer Expectations and Industry Use Cases Driving Demand?

The surge in demand for AI audio and video SoCs is strongly linked to evolving consumer expectations for seamless, immersive, and personalized media experiences. In the consumer electronics sector, users now expect smart devices to understand natural language, adjust audio quality dynamically, and deliver high-definition video streams with minimal latency. Smart speakers and voice assistants rely on AI SoCs for real-time language processing, beamforming, and environment-based sound adaptation, while smart TVs and streaming devices use them to upscale resolution, auto-adjust display brightness, and optimize sound according to content type and user preferences. The entertainment and gaming industries are also seeing massive demand for AI-powered chips that enable facial recognition, gesture control, and immersive soundscapes. Beyond consumer electronics, enterprise and industrial applications are emerging as major growth areas. In automotive, AI SoCs are essential for in-cabin voice control, driver monitoring, and video-based safety features. Similarly, in healthcare, AI-enabled SoCs power diagnostic imaging systems, patient monitoring tools, and telemedicine platforms that require synchronized audio-video processing with intelligent interpretation. Surveillance and security systems are leveraging these chips for facial recognition, anomaly detection, and behavioral analytics in real-time. These varied applications highlight how AI SoCs are no longer niche solutions, but mission-critical technologies reshaping user experience, security, and operational efficiency across multiple industries.

Is Technological Innovation Outpacing Traditional Chip Design Constraints?

As AI workloads become more complex and real-time responsiveness becomes a competitive necessity, chip manufacturers are rapidly evolving SoC design architectures to accommodate new processing paradigms. Traditional SoCs, which relied heavily on CPUs and DSPs, are now being augmented with dedicated AI engines, machine learning cores, and neural processing units (NPUs) that allow for parallel and low-power inference. These chips are specifically tailored to support convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models that are commonly used in voice and video recognition tasks. Recent breakthroughs in semiconductor fabrication—such as 5nm and 3nm processes—have enabled higher transistor densities, lower power consumption, and faster clock speeds, all of which enhance the efficiency of AI operations on-chip. Furthermore, SoCs are being designed with heterogeneous computing in mind, integrating CPU, GPU, NPU, and custom accelerators into a single die to optimize both flexibility and performance. The integration of AI with video codecs, audio DSPs, and connectivity modules (e.g., Wi-Fi 6, Bluetooth 5.3) is enabling multifunctional chips that support intelligent processing in compact devices. Memory bandwidth and on-chip cache optimization are also critical advancements, helping to eliminate data bottlenecks and enhance multitasking performance. Open-source AI frameworks and compiler toolchains are being increasingly supported at the hardware level, allowing for easier deployment and fine-tuning of AI models directly on SoCs. These design innovations are enabling a new class of edge AI devices that are more responsive, scalable, and power-efficient than ever before, fundamentally shifting the balance between cloud and edge computing.

What Forces Are Fueling the Global Expansion of AI Audio and Video SoCs?

The growth in the artificial intelligence (AI) audio and video SoC market is driven by several factors rooted in technology convergence, expanding use cases, changing device ecosystems, and strategic shifts in semiconductor production. One of the key drivers is the increasing demand for intelligent edge devices across consumer, industrial, and enterprise segments—pushing the need for compact chips that can perform complex processing locally without relying on cloud latency or bandwidth. The rapid proliferation of smart home devices, personal digital assistants, and smart displays is further intensifying the need for chips that combine AI with audio-video signal processing in a single, low-power package. In the automotive space, the trend toward autonomous and semi-autonomous driving is creating a strong pull for high-performance SoCs that can handle in-vehicle infotainment, driver monitoring, and speech-based navigation. Similarly, the post-pandemic normalization of video conferencing and remote work has created a sustained demand for high-quality audio-visual communication devices that rely on AI SoCs to enhance clarity, reduce background noise, and automatically frame or track speakers. Another growth factor is the increasing investment in localized AI processing capabilities in regions like North America, Europe, and Asia-Pacific, where national strategies and funding initiatives are driving domestic semiconductor production. Meanwhile, the expansion of 5G and ultra-fast connectivity is enabling richer AI workloads at the edge, supporting AI SoCs in drones, surveillance systems, and industrial robots. Finally, strategic alliances between chipmakers, cloud service providers, and AI software companies are accelerating innovation and reducing time to market, allowing AI audio and video SoCs to reach a broader spectrum of applications and devices worldwide. These diverse and high-impact forces are collectively shaping a robust and expansive future for the global AI SoC market.