Industry Insights: AI is Here to Stay

On February 21, 2024, NVIDIA released its financial report for the fourth quarter of last year. Revenue increased by 206% compared with the same period last year, ushering in a new wave of AI. Comparing the overall information of other industries in the S&P 500 during the same period, the information technology, industrial industry, or energy industry all changed around plus or minus 0.5%. The booming wave of artificial intelligence is the consensus of the entire market, but what exactly should the future look like? Apart from the craziness of chips, what are other real industries doing? How will this wave of madness gradually spread to our actual lives?

1.Manufacturing

In late 2023, Deloitte described to us a picture of how large manufacturers are embracing digital transformation to cope with future persistent challenges in its 2024 Manufacturing Outlook survey report. First, it emphasized the U.S. government’s use of multiple bills to support the long-term stability of semiconductors, clean energy components, electric vehicles, batteries, and related components. Deloitte conducted a meaningful survey on the multiple challenges faced by the manufacturing industry. There is some very interesting data, including 86% of surveyed manufacturing executives believing that smart factory solutions will be the main driver of competitiveness in the next five years. 83% of manufacturers believe smart factory solutions will change the way products are made within five years. In the Industrial Metaverse direction, 92% of manufacturers surveyed are already experimenting with or implementing at least one Metaverse-related use case, and on average, they are currently running more than six use cases. Executives surveyed expect several key performance indicators, including sales, quality, throughput, and labor productivity, to increase 12 percent or more because of Industrial Metaverse initiatives.

In the direction of supply chain digitization, 76% of manufacturers are adopting digital tools to improve the transparency of their supply chains. In fact, 21% of respondents to the 2023 Deloitte and MLC Industrial Metaverse Study are already integrating Metaverse technologies to enhance their supply chain ecosystems. Advanced technologies such as digital twins and blockchain that have been used in smart cryptocurrency and digital medicine are being paid close attention to by the manufacturing industry and are being attempted to be integrated into production operations.

The digital transformation mentioned above, digital twins, solutions, blockchain, etc. are all inseparable from many model calculations on collected data. This is why AI is truly powerful as a technological driving force.

2.Global logistics

We all know that semiconductor upstream manufacturing and downstream distribution will inevitably face the impact of an extremely complex global supply chain. Before geopolitics and Sino-US trade frictions worsen, global integrated circuit imports will reach the trillions level in 2022, and mainland China, as the second largest exporter, accounts for nearly 20%. According to the original design of the US IIJA, CHIPS and other bills, the global low-end supply chain should gradually shift to countries such as Southeast Asia and Mexico, and high-end manufacturing will gradually return to the United States. However, the results obtained by Deloitte Insights' economic observations in early February seem to indicate that in response to changes in trade policies, China's trade model is quietly undergoing major changes. The design of the supply chain has become more complicated, as China attempts to export raw materials to the Southeast and India, assemble them and then export them. A large number of chip industry distributors are following their customers in exporting overseas, a trend that is likely to become more common in light of the U.S. election and policies in other countries with trade agreements.

3.Edge computing

The previous discussion was about the positive response of the industrial side to the AI wave. Here we briefly talk about the future development of the consumer side. First, we need to understand a concept. Regarding the development history of computing paradigm changes, the transformation of human computing needs has been cyclical from center to decenter. From the initial centralized mainframe to decentralized personal computing computers or other electronic devices, to the recent centralized cloud intelligent computing to future edge computing. Engineers will combine the special needs of hardware and usage scenarios to deploy computing power in each terminal usage scenario. The other two examples can more clearly feel the arrival of the next generation of computing scenarios. One is the latest FDS v12 recently launched by Tesla. Its end-to-end processing model is closer to the human driving model, and the perception and driving decisions are handed over to AI instead of traditional rule-based driving. It no longer relies on transmitting data back to a central database for processing but learns human driving habits at any time through the model deployed on the terminal. Another is that AI chip companies that have sprung up like mushrooms are still popular in the market. Although NVIDIA already has an absolute advantage, the prosperity of other companies shows that although the high computing power chips required for big data and large models are very scarce, what will really become popular in the future is that the computing power does not need to be very high, but it is used in countless daily small scenes. The terminal intelligent computing port can be brought into play. Deploy the adaptive model of edge computing on IoT terminals to achieve true intelligent reciprocity among devices on the network side.

So it doesn’t need to be complicated, but more adaptable AI chips paired with hardware and data models for certain usage scenarios will be the real high point of the next wave.

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