Source: Tencent Technology 1. Huang Renxun emphasized that generative AI is growing at an exponential rate and that businesses need to adapt and utilize this technology quickly, rather than standing by and falling behind the pace of technological development. 2. Huang Renxun believes that open and closed source AI models will coexist and that companies need to leverage their respective strengths to promote the development and application of AI technology. 3. Huang Renxun proposed that the development of AI needs to consider energy efficiency and sustainability, reducing energy consumption by optimizing the use of computing resources and promoting the inference and generation capabilities of AI models to achieve more eco-friendly intelligent solutions. 4. With the constant accumulation of data and the continuous advancement of intelligent technology, customer service will become a key area for companies to achieve intelligent transformation. 5. According to foreign media reports, at the 2024 Databricks Data + AI Summit held recently, 6. Founder and CEO Huang Renxun had a fascinating conversation with Ali Ghodsi, co-founder and CEO of Databricks. The dialogue between the two parties demonstrated the importance and development trends of artificial intelligence and data processing technology in modern enterprises, emphasizing the key role of technological innovation, data processing capabilities and energy efficiency in promoting enterprise transformation and industry development. 7. Huang Renxun looked to the future of data processing and generative AI in the conversation. He pointed out that the business data of each company is like an untapped gold mine, with tremendous value but extracting deep insight and intelligence from it has always been a daunting task. 8. Huang Renxun also talked about open source models like Llama and DBRX are driving corporate transformation into AI companies, activating a global AI movement and promoting technological development and corporate innovation. Through the collaboration between NVIDIA and Databricks, the two companies will work together to leverage their respective strengths in accelerating computing and generative AI, bringing unprecedented benefits to users. 9. The following is the transcript of the conversation: 10. Moderator: I am very excited to introduce our next guest, a man who needs no introduction, the one and only global rock star CEO - NVIDIA CEO Huang Renxun. Please come to the stage. Thank you very much for coming! I want to start with NVIDIA's remarkable performance, with a market capitalization of up to 3 trillion US dollars. Did you ever think five years ago that the world would evolve so rapidly and present such a remarkable picture today? 11. Huang Renxun: Absolutely! I expected that from the beginning. 12. Moderator: That's really amazing. Can you offer some advice to the CEOs in the audience on how to achieve their goals? 13. Huang Renxun: Whatever you decide to do, my advice is not to get involved in the development of graphics processors (GPUs). 14. Moderator: I will tell the team that we are not going to get involved in that field. We spent a lot of time today discussing the profound significance of data intelligence. Enterprises have vast amounts of proprietary data that are critical for building customized artificial intelligence models. The deep mining and application of this data are crucial to us. Have you also noticed this industry trend? Do you think we should increase our investment in this area? Have you collected any feedback and insights from the industry on this issue? 15. Huang Renxun: Every company is like a gold mine with abundant business data. If your company offers a series of services or products and customers are satisfied with them while giving valuable feedback, you have accumulated a large amount of data. These data may involve customer information, market trends, or supply chain management. Over the years, we have been collecting these data and have a huge amount of data, but until now, we have just started to extract valuable insights from them, and even higher-level intelligence. 16. Currently, we are passionate about this. We use these data in chip design, defect databases, creation of new products and services, and supply chain management. This is our first time using engineering processes based on data processing and detailed analysis, building learning models, then deploying these models, and connecting them to the Flywheel platform for data collection. 17. Our company is moving towards the world's largest companies in this way. This is, of course, due to the extensive use of artificial intelligence technology in our company, which has helped us achieve many remarkable achievements. I believe that every company is experiencing such changes, so I think we are in an extraordinary era. The starting point of this era is data, and the accumulation and effective use of data. 18. The harmonious coexistence of open source and closed source 19. Moderator: This is truly amazing and very much appreciated. At present, the debate about closed-source and open-source models is gradually heating up. Can open-source models catch up? Can they coexist? Will they eventually be dominated by a single closed-source giant? What is your view of the entire open-source ecosystem? What role does it play in the development of large language models? And how will it develop in the future?

According to foreign media reports, generative artificial intelligence has sparked one of the largest consumer frenzies in modern American history, with businesses and investors betting billions of dollars, firmly believing that this technology will reshape the global economic landscape and hold enormous profit potential. However, the question is: will this massive investment bring returns, and if so, when?

Applications such as OpenAI's chatbot ChatGPT have already attracted hundreds of millions of users, but the user base willing to pay for premium services remains limited. At the same time, the business community is still in the exploration stage, focusing on exploring the potential of generative artificial intelligence in enhancing production efficiency. Despite this, tech giants spare no expense and are injecting unprecedented amounts of funding, primarily focusing on developing cutting-edge hardware to support the research and operation of artificial intelligence models.

$Alphabet-A (GOOGL.US)$ During the latest earnings conference call, Google's CEO Sundar Pichai emphasized, "In the face of the risk of insufficient investment, we would rather take the risk of excessive investment."

Venture capitalists generally expect that in the next few years, the valuation of at least several artificial intelligence startups will soar to hundreds of billions or even trillions of dollars, although most of them are currently not profitable.

So far this year, artificial intelligence startups have raised a staggering $64.1 billion in venture capital, a figure that is approaching the historical peak set during the investment frenzy of 2021, and the proportion of venture capital in the field of artificial intelligence this year has reached its highest level in history.

Figure 2: The left figure shows the annual venture capital received by artificial intelligence startups, and the right figure shows the proportion of such investments to the total venture capital. As of 2024, about one-third of venture capital has flowed into artificial intelligence companies.

The effects of these huge investments are gradually becoming evident in various parts of the United States, where new data centers are springing up like mushrooms. Unlike traditional data centers that mainly handle data storage and non-artificial intelligence software operations, AI-optimized data centers are equipped with cutting-edge chips and are designed specifically for the development and operation of generative artificial intelligence applications.

Specifically, $Microsoft (MSFT.US)$ the number of data centers has more than doubled since the beginning of 2020, and Google is not far behind, with a growth rate of up to 80% during the same period. $Oracle (ORCL.US)$ The company is also focusing its strategic focus on the datacenter business, planning to build 100 new datacenters.

Compared to traditional datacenters, AI datacenters have higher energy consumption. This is because AI chips require uninterrupted and stable energy supply to maintain their efficient operation. Any short-term fluctuations in power supply can have adverse effects on the "training process" of AI models optimizing their performance through analysis of massive data. This risk is particularly prominent for large-scale models that require huge investment and have training costs ranging from tens of millions to hundreds of millions of dollars.

Since 2015, the amount of electricity ordered by datacenters in the United States and Canada from energy companies has increased nearly ninefold. This trend intuitively reflects the rapid growth of the power demand of datacenters driven by the development of AI.

$NVIDIA (NVDA.US)$ Has become the dominant force in the field of artificial intelligence model training and operation chips, although its GPU (graphics processing unit) initially served the video game industry, but with outstanding performance, the price of high-end GPUs has risen to tens of thousands of dollars. Technology companies dedicated to building and hosting artificial intelligence models are now competing for nvidia chip resources to meet the growing demand.

$Meta Platforms (META.US)$ Chief Executive Officer Mark Zuckerberg has publicly announced that the company's goal is to have 600,000 GPUs by the end of 2024 to support its global strategy for artificial intelligence. Similarly, Tesla's CEO and the founder of the xAi artificial intelligence startup, Elon Musk, is also planning to purchase 300,000 GPUs before next summer.

Highly skilled talents have also become scarce resources in the market. Despite recent layoffs in Silicon Valley, tech giants spare no expense, competing to recruit research scientists who can lead the exploration of artificial intelligence, many of whom previously worked in academia. Nowadays, they are among the highest-paid technical talents in the world.

Even professionals with basic knowledge of machine learning can easily obtain six-figure salary positions. It is worth noting that in July, the number of new artificial intelligence-related job openings increased by nearly 50% compared to the same period last year, contrasting sharply with the slight decline in overall technology industry recruitment during the same period, highlighting the high demand for artificial intelligence talent in the market.

Investors' patience with the massive investment in artificial intelligence in Silicon Valley is gradually wearing thin, especially with the practice of companies like Meta and Microsoft increasing their spending on artificial intelligence despite lagging revenue growth, which has already been reflected in their stock prices.

A partner at Sequoia Capital recently analyzed that in order to demonstrate a reasonable return on investment in data centers and chip fields this year, the AI ​​business needs to ultimately create annual revenue of up to $600 billion. Although most companies do not disclose the income they generate from AI, analysts estimate that the total annual revenue is at most in the billions of dollars, far from expectations.

The doubts about the prospects of artificial intelligence are reminiscent of the Internet bubble 25 years ago, when companies blindly invested in fiber optic networks in the hope of supporting overly optimistic expectations for the widespread popularity of the Internet, but the actual development fell far short of expectations.

Faced with these doubts, top executives of technology giants have called for patience. Zuckerberg admitted at the earnings conference that it will take several years for the commercialization of artificial intelligence applications to show results. Pichai also said, "There is a time curve in using fundamental technologies and transforming them into meaningful solutions."

Editor/Somer