AI Industry Shows Signs of Merger and Acquisition Activity
The world of AI is rapidly evolving, and nowhere is this more evident than at the edge. The edge, which refers to devices and systems that process data locally rather than relying on cloud connectivity, is seeing a surge in growth due to the demand for real-time, low-latency data processing.
Board and module vendors have embraced this trend, incorporating AI into their product mix, making it easier for companies to add AI at the edge. This shift is reflected in the market, with the edge AI chips market reaching approximately USD 7.05 billion in 2024 and projected to grow to USD 36.12 billion by 2034, according to industry reports. Similar trends are seen in broader AI edge computing markets, which are expected to grow from around $20 billion in 2024 to an estimated $53 billion by 2029.
This growth is driven by several factors, including the proliferation of IoT devices, advancements in 5G, and increasing privacy concerns encouraging localized data processing. Key technology advancements at the edge include tinyML, enabling sophisticated AI models to run on low-power devices like wearables and sensors, and neuromorphic chips that mimic brain structures for efficient, fast computations.
AI startups are playing a significant role in this development. They are focusing on specialized edge AI chips, innovative algorithms like tinyML, and hybrid edge-cloud architectures that balance processing loads. Many startups are addressing industry needs such as autonomous vehicles, smart cameras, industrial automation, and healthcare monitoring, where real-time responsiveness and privacy are critical.
The market is also seeing the emergence of edge AI as a service (AIAAS), decentralization trends, and sustainability-centric designs, all partly driven by innovative startup ecosystems.
However, the AI market is not without its challenges. Fewer new companies are entering the market, and consolidation is occurring as vendors acquire AI companies. The U.S. administration's latest decision allows NVIDIA to sell its H20 GPGPU to China, indicating a shift in trade tensions that could impact the U.S.'s lead in AI software and hardware.
It's also important to note that not all problems require AI or will be improved by adding AI in a product. Engineers and companies should carefully consider the specific application before implementing AI solutions.
For those interested in learning more about the AI processor market, the Jon Peddie Research "AI Processors Market Development Report" provides detailed insights. The report also highlights the peak investment in AI processor startups from 2017 to 2021, with over $13.5 billion invested.
Quite a few AI startups are focusing on edge computing, offloading or decoupling AI from cloud-based solutions. Products like the Jetson Nano enable edge computing to server-based solutions for the cloud and data center. Small language models (SMLs) and systems that can handle large language models (LLMs) on the edge are available now, making AI capabilities more accessible than ever.
As the AI landscape continues to evolve, it's clear that the edge is becoming a crucial part of the equation. To stay informed about the latest developments, take part in our quick poll on AI on the edge, which will provide insights on future coverage of AI in products that will deploy in the field.
Data-and-cloud-computing technology plays a significant role in the growth of artificial-intelligence, as the edge AI chips market is projected to reach USD 36.12 billion by 2034, driven by advancements like tinyML that enable AI models to run on low-power devices. AI startups are focusing on edge computing, offloading AI from cloud-based solutions and making AI capabilities more accessible through products like the Jetson Nano.
