Why Nvidia Leads in AI and Autonomous Vehicle Growth Opportunities
A new chapter in semiconductor history
Growth stocks in the 2017 technology shift are different from the growth stocks we saw in the 2000 technology shift—with a few exceptions, such as analog chip maker Texas Instruments (TXN) and semiconductor equipment maker Applied Materials (AMAT).
Interested in NVDA? Don't miss the next report.
Receive e-mail alerts for new research on NVDA
Analysts believe that strong stock price momentum will continue through 2017. Jefferies analyst Mark Lipacis stated that the management of semiconductor companies that participated in the 2017 Chicago Investor Summit gave a positive outlook on business trends, with the most positive outlook given for Advanced Micro Devices (AMD) and Nvidia (NVDA).
While semiconductor companies were positive, investors were cautious about inventories and the growth cycle, especially memory cycles, according to Mark Lipacis.
Growth opportunities: IoT and autonomous cars
The technology industry is moving to a data-centric economy where everything is connected to the Internet. This has created a whole new end-market of IoT (Internet of Things) and autonomous cars.
Nvidia is at the forefront of the autonomous vehicle trend, with its first level-3 autonomous car platform hitting roads by the end of 2017 inside Tesla’s (TSLA) latest models. Other than automakers, technology companies like Google (GOOG), Baidu (BIDU), and Samsung (SSNLF) are looking to develop their own autonomous cars.
Communications and data center
IoT and autonomous cars need to be connected to the Internet, creating a huge opportunity for the 5G network. Qualcomm (QCOM) and Intel (INTC) are at the forefront of this trend, with both companies testing 5G networks in some areas of the US.
IoT devices and autonomous cars could generate data that needs to be stored in data centers and cloud, giving rise to cognitive technologies such as deep learning, natural language processing, and speech-pattern recognition.
Here, Nvidia takes the lead once again as its GPUs (graphics processing units) are widely used accelerators among cloud companies. Some companies use Xilinx’s (XLNX) FPGAs (field programmable gate arrays) for deep learning. For example, Google has launched deep learning chips called TPUs (tensor processing units).
The data collected from IoT devices and autonomous cars will likely be used to create deep neural networks, which would then be used to train robots, giving rise to AI (artificial intelligence).
Anything as a service
All this will likely create a whole new market of “anything as a service,” which allows usage base consumption, stated Deloitte’s analyst Paul Sallomi in its 2017 Technology Outlook report. For instance, cloud companies like Google and Baidu offer GPU as a service and charge consumers based on their usage.