According to the investor relations website of NVIDIA (NVDA.US), the performance of the first quarter of the fiscal year 2027 showed that the company's quarterly total revenue reached 81.6 billion US dollars, an increase of 85% year-on-year and 20% month-on-month, reaching a new historical high. Among them, the Data Center business, which serves as the core engine of AI, contributed 75.2 billion US dollars, a staggering year-on-year increase of 92%, accounting for a high proportion of 92% of total revenue, showing that AI infrastructure is still a strong growth driver for the company. More noteworthy is that the company's guidance for the second quarter is optimistic, with revenue expected to range from 89.1 billion to 92.8 billion US dollars, once again exceeding market consensus. The progress of the Blackwell platform shipments is smooth, and the new Vera CPU has opened up new demand. AI training and inference demands continue to surge, reflecting that AI infrastructure construction is still accelerating. NVIDIA's outstanding performance once again proves its strong growth momentum in the field of AI. In addition to impressive financial data, the most popular trend of this year - Agentic AI, is also highly anticipated in this performance. Quoting NVIDIA's co-founder and CEO Jensen Huang during the earnings call, he said, "The construction of AI factories is accelerating at an astonishing rate. This is the largest-scale infrastructure expansion in human history. Agentic AI is here and is beginning to create real value in various industries, rapidly spreading." In fact, Jensen Huang had already used the analogy of the "five-layer cake" in his article earlier this year to describe the structure of the current AI industry chain, which in sequence from bottom to top includes: energy, chips, infrastructure, models, and applications. He pointed out that the top layer of application is rapidly popularizing, and this force will strongly drive the demand for the entire AI industry chain from top to bottom, forming comprehensive and sustained growth momentum. NVIDIA's performance also confirms this point. In addition to focusing on surface numbers, in terms of details in the data center business, NVIDIA's Data Center Networking revenue reached 14.8 billion US dollars, a year-on-year increase of 199%, and the core Compute power increased by 77% year-on-year to 60.4 billion US dollars. Both performances are equally strong, but they show the bright spots of growth in the AI race have shifted from "chip efficiency" to "system-level efficiency." From these perspectives, beneficiaries in the AI race are no longer just single chip companies, as Jensen Huang mentioned in the "five-layer cake" analogy, the entire industry chain can benefit at the same time. In the layout of AI, using ETFs to invest in multiple areas at low cost can deploy growth opportunities in various fields. For example, the E Fund Artificial Intelligence ETF (03489), which closely tracks the FTSE AI-selected index of 50 top Hong Kong and US AI companies, according to foreign media sources and as of May 21st, the largest component stock in the ETF's holdings was NVIDIA, accounting for 9.17%, also covering global computing and semiconductor companies such as AMD.US, AVGO.US, TSM.US, MU.US, LITE.US, and SNDK.US. In terms of applications and models, it also covers US companies like MSFT.US, AAPL.US, AMZN.US, GOOG.US, and AAPL.US. The reason for deploying in China and the US simultaneously is that the AI race is important in both regions. In the AI race, the US dominates the model layer and computing chips, but lacks energy, making data center expansion easy to be constrained by "power shortages"; China controls hardware capacities such as optical modules and PCBs, with an energy advantage of over a trillion kilowatt-hours, but relies on advanced processes. Model innovation requires energy support, while computing power expansion requires chip supply. ETFs can simultaneously invest in two important leaders in both regions, penetrating the entire AI race. In terms of Hong Kong stocks, they include SMIC (00981), Huahong Semiconductor (01347), Alibaba Group Holdings (09988), Xiaomi Group (01810), Tencent Holdings (00700), Horizon Robotics (09660), UBTech (09880), and Jian Tao Group (00148), etc. The FTSE Custom Global AI-Selected Index tracked by the ETF demonstrates excellent balanced characteristics. From the inception in 2022 to now, the index's Sharpe ratio is 1.02, a number higher than 1, reflecting its risk-adjusted return performance with good cost-effectiveness. In terms of returns, as of April 30, 2026, the index's annualized return has slightly exceeded the Nasdaq 100 index (.NDX.US) since inception, while significantly outperforming the Hang Seng Tech Index. Compared with the Hang Seng Tech Index, it shows clear advantages in both total returns and Sharpe ratio. According to media reports, as of May 21, 2026, the FTSE Custom Global AI-Selected Index's performance since the beginning of the second quarter has been 20.0%. (Note: The above is only an objective display of the historical performance of the reference index. Past index performance does not predict future fund returns, and should not be used as investment advice. Please be aware of the risks associated with index fluctuations. Actual returns of the fund are affected by management fees and tracking errors, and may differ from index performance, so investors should pay attention.) The E Fund AI ETF (03489) is a thematic ETF focusing on the global AI industry chain, helping investors to comprehensively deploy the entire AI race, while covering leading companies in both the Chinese and US markets. Investors do not need to focus solely on the Hang Seng Tech Index (800700) or the Nasdaq Composite Index (.IXIC.US), but can broadly allocate key areas such as AI computing power, models, infrastructure, and applications. In the AI wave, whether it's the Hang Seng Index (800000), the S&P 500 Index (.SPX.US), or other major markets, semiconductor and AI-related companies are important beneficiaries, providing a balanced and efficient way of allocation, allowing investors to more comprehensively grasp long-term growth opportunities in AI. english:Please provide more context for me to better help with the translation.Je ne parle pas trs bien l'anglais.