Morgan Stanley has identified Edge AI technology as a pivotal element in the evolution of real-time data processing on devices like smartphones and smart speakers. The financial institution highlighted the importance of this technology in enabling advanced features such as facial recognition, while also tackling latency issues that are critical for user experience.
Edge AI refers to artificial intelligence algorithms that are processed locally on a hardware device, rather than relying on cloud servers. This approach can significantly reduce response times and bandwidth usage, making it an attractive proposition for both consumers and businesses.
The surge in demand for Generative AI applications has been recognized as a driving force for investment opportunities, particularly during what Morgan Stanley sees as a rebounding hardware cycle. Generative AI, which includes technologies capable of creating new content like images or text based on learned data, requires robust processing capabilities that Edge AI can provide.
Several companies are poised to lead this charge, according to Morgan Stanley's analysis. Apple (NASDAQ:AAPL) has been assigned a $210 price target due to its energy-efficient silicon that is expected to enhance Edge AI applications across its range of devices. Dell (NYSE:DELL) is preparing for a wave of AI-equipped PCs and has been given an $89 price goal in anticipation of increased demand.
Other companies set to capitalize on this trend include MediaTek, with its focus on system-on-a-chip designs tailored for Edge AI, which has a targeted value of 1,000 New Taiwan dollars. Qualcomm (NASDAQ:QCOM) is enhancing Edge AI capabilities through its Snapdragon line and holds a valuation estimate of $119.
STMicroelectronics is optimizing local computing power for automotive applications among others, with a predicted worth of 48 euros. Lastly, Xiaomi (OTC:XIACF) is at the forefront of integrating Edge AI into smartphones and carries a projected price point of 15 Hong Kong dollars.
The focus on decentralized workloads to user devices at the network's edge is seen as a way to cut costs and minimize latency, extending the reach of AI into consumer and enterprise realms. This shift towards localized processing power marks a significant step in the evolution of the technology landscape, with potential benefits across multiple industries.
This article was generated with the support of AI and reviewed by an editor. For more information see our T&C.