AI Smart Glasses: Challenges and Opportunities in the Spotlight

In the current tech landscape, AI smart glasses have emerged as a hotly contested field, capturing the attention of numerous manufacturers. Following the 2025 CES, which was densely packed with AI smart glasses, both domestic and international companies have been scrambling to launch new products in this space. However, as more players enter the fray, it has become increasingly evident that developing high-quality AI smart glasses is a formidable challenge.

Market Reality: The Fierce Hundred Glasses War

Since 2024, the hundred glasses war has been intensifying. Manufacturers from diverse industries such as power banks, education, and colored contact lenses have all made a dash for this market, driven by the desire to capitalize on the AI smart glasses trend. This has led to an oversupply of products that often fail to meet consumer expectations.

Some manufacturers have adopted a strategy of announcing products prematurely, using PPT presentations to claim现货 availability. For instance, a company held a grand launch event with over 50,000 units booked within 24 hours across all platforms. Unfortunately, this was followed by accusations of subpar shooting performance and delayed deliveries. The initial batches of products, equipped with the Unisoc W517 chip, had incomplete underlying driver development, leading to issues such as the inability to answer calls via Bluetooth, which could only be resolved through OTA updates. This case highlights the pitfalls of rushing product releases without adequate preparation.

Technical Challenges: Navigating the Chip and Algorithm Maze

Chip Selection Limitations

Currently, the Qualcomm AR1 is the only chip specifically designed for AI smart glasses. However, its high cost results in an overall BOM cost that necessitates a selling price of at least $1,500 to $2,000. Smaller manufacturers, in an effort to reduce costs, often turn to domestic chip platforms, which are only one-sixth the price of Qualcomm AR1. These chips, however, were primarily designed for smartwatches or TWS earphones, resulting in issues such as high power consumption and poor shooting performance. The Unisoc W517 chip, for example, has higher power consumption and weaker ISP capabilities compared to the Qualcomm AR1 platform, making it less suitable for AI smart glasses applications.

Image Processing Algorithm Adaptation

Achieving optimal shooting results requires not only a good ISP but also advanced image processing algorithms such as optical image stabilization, HDR, and night scene processing. Mainstream AI algorithm providers like ArcSoft and Mohu, which have extensive experience in image algorithms, primarily adapt their algorithms to traditional smartphone platforms. Many AI smart glasses manufacturers lack the necessary algorithm capabilities and must purchase algorithms from these providers, incurring additional costs.

Industry Dilemma: Bridging the AI and Product Design Gap

AI Capability Deficits

Most large AI models have limited investment in visual applications, and AI smart glasses represent a completely new domain for them. For example, the translation feature, which is highly anticipated by users, often suffers from slow response times and inaccurate translations due to the lack of specialized training for this scenario.

Product Design and Quality Control Shortcomings

AI smart glasses require significant investments in design, quality control, marketing, and sales. Many manufacturers fall short in these areas, leading to products with poor build quality, a plastic feel, and design flaws such as scratches or sharp edges. Overly aggressive marketing that raises consumer expectations, coupled with the reality of subpar product experiences, has gradually eroded consumer confidence.

Future Outlook: Prioritizing User Experience and Refining Products

To succeed in the AI smart glasses market, manufacturers must adopt a more thoughtful approach. It is crucial to avoid盲目 following trends and instead conduct thorough market research to understand the actual needs of users. This includes clearly defining the core positioning and target user base of the product. During development, a holistic optimization of both hardware and software is necessary, along with close collaboration with chip manufacturers and algorithm suppliers to enhance product performance and user experience.

In addition, manufacturers should recognize the long replacement cycle of hardware products and ensure that products are fully refined before launch. In marketing, honesty and transparency are key to building a trustworthy brand image. By prioritizing user experience and committing to continuous innovation, the AI smart glasses industry can overcome its current challenges and pave the way for sustainable growth, ultimately delivering truly valuable smart wearable devices to consumers.

在科技浪潮的推动下，AI 智能眼镜成为了 2025 年备受瞩目的领域。从 2025 年 CES 展会的盛况来看，众多厂商纷纷涌入这一赛道，试图在这片新兴市场中分得一杯羹。然而，随着百镜大战的逐步发酵，人们渐渐发现，做好一款 AI 智能眼镜并非易事。

市场现状：百镜大战，群雄逐鹿

自 2024 年起，百镜大战拉开序幕，大量厂商跨界参与其中，包括移动电源、教育、美瞳等领域的厂商。在当前的经济环境下，大家都不想错过这一风口。然而，许多厂商在产品尚未打磨成熟时便急于发布，导致市场出现了一系列问题。

一些厂商采用 PPT 发布的方式，以现货发售作为卖点，但实际交付却困难重重。例如，某厂商去年年底举办了一场盛大的发布会，24 小时内全平台预订量超过 5 万台，但随后却出现了产品交付延迟、部分订单延期发货的情况。首批交付的产品也因采用展锐 W517 芯片，部分底层驱动未开发完善，在蓝牙连接状态下无法接听电话，只能通过 OTA 方式解决。此外，还有厂商宣称是 Meta 的有力竞争对手，但实际交付的产品在续航、做工、AI 大模型体验等方面均未达到预期，用户满意度大打折扣。

技术难题：芯片与算法的双重困境

芯片选型的局限性

目前，高通 AR1 是唯一专门为 AI 智能眼镜设计研发的芯片，但其成本高昂，导致整体 BOM 成本偏高，产品售价至少在 1500-2000 元之间。中小厂商为了降低成本，多选择国产芯片平台，如展锐 W517 芯片，其售价仅为高通 AR1 的 1/6，但功耗较高，ISP 芯片较弱，拍摄效果难以调校。此外，恒玄 2800 芯片虽然功耗低、性价比高，但不成熟且调试难度大，且无集成 ISP，需外挂芯片。

图像处理算法的适配难题

拍摄效果不仅取决于 ISP，还需通过防抖、HDR、夜景处理等图像处理算法来提升。市面上的主流 AI 算法厂商如虹软和摩尔图像，其算法多适配于传统手机平台，针对 AI 智能眼镜芯片的适配较少。而许多 AI 智能眼镜厂商在图像算法方面缺乏积累，需要采购相关算法，进一步增加了成本。

行业困境：AI 能力与产品设计的短板

AI 能力的不足

大部分大模型在视觉领域的投入较少，针对 AI 智能眼镜场景更是缺乏经验。以翻译功能为例，许多 AI 大模型未针对翻译场景进行专门训练，导致翻译速度慢、结果不准确，无法满足用户期待。

产品设计与品控的欠缺

AI 智能眼镜在设计、品控、营销和销售等方面均需大量投入，进入门槛较高。一些厂商在产品设计上缺乏创新，品控不到位，导致产品做工差、塑料感强，甚至出现划痕或刺边等问题。此外，营销过度夸大也提高了用户预期，而实际体验与宣传的差距逐渐消磨了消费者的热情。

未来展望：以用户体验为核心，打磨产品

面对 AI 智能眼镜领域的种种挑战，厂商们需要重新审视自身战略。首先，应避免盲目跟风，深入研究市场和用户需求，明确产品核心定位及目标用户群体。在产品研发过程中，注重硬件与软件的协同优化，加强与芯片制造商、算法供应商的合作，提升产品性能和用户体验。

同时，厂商们还需认识到，硬件产品的升级换代周期较长，配置变更困难，因此在产品发布前必须充分打磨，确保产品品质。在营销方面，应避免过度夸大宣传，保持真实可信，树立良好的品牌形象。只有以用户体验为核心，注重产品品质和技术创新，AI 智能眼镜行业才能实现可持续发展，真正为消费者带来具有价值的智能穿戴设备。

在这个充满机遇与挑战的领域，我们期待看到更多厂商能够沉下心来，深耕技术研发和产品优化，为市场带来真正优秀的产品，推动 AI 智能眼镜行业迈向新的高度。