At the 2026 Yabuli Forum Summer Annual Meeting, held from September 4-6 in Chengdu, Sichuan, under the theme "Enterprise Innovation and Cycle Crossing," Cao Qiang, Chairman of Chengdu Xinjixun Technology Co., Ltd., shared insights on the future of artificial intelligence. He noted that while the current AI landscape is dominated by an arms race in computing power and model development, the truly critical direction lies in edge-side AI that is accessible anytime, anywhere, and serves every ordinary person. Consumer-grade AI, he emphasized, is directly tied to every paying customer and everyday user.

Cao highlighted two unavoidable hurdles in developing consumer products: cost and power consumption. To achieve truly ubiquitous AI, communication and computing capabilities must be seamlessly integrated, despite being fundamentally different skill sets. How to accomplish this fusion is a key research topic for both industry practitioners and academic researchers moving forward.

The core solution for reducing the cost and power consumption of consumer AI devices lies in chip design. The industry's approach is to merge communication chips and computing chips into a single System-on-Chip (SoC) that integrates both capabilities. Such a chip would feature wide-area communication technologies like 5G and 6G, rather than being limited to short-range connections such as WiFi or Bluetooth, while also embedding AI computing power to meet the low-power, low-cost requirements of consumer AI.

Products like B Duck and Labubu are already beginning to evolve in this consumer AI direction, according to Cao. He stressed that lowering costs to make AI accessible and beneficial to the general public, while simultaneously reducing power consumption to boost product performance, will be pivotal development focuses for the next phase of AI. Currently, the industry remains largely concentrated on improving computing power and optimizing model processing, often relying on high-performance computing cards even for edge-side AI. Overcoming the twin challenges of cost and power consumption is the essential task ahead for the consumer AI sector.