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?
Author: Guo Xiaojing. In 2023, the stock prices of these seven companies rose by 239%, 194%, 102%, 81%, 59%, 57%, and 48%, respectively, and they were therefore named the "Magnificent Seven" in the market. During the same period, the S&P 500 index only rose 24% overall. However, on July 24, 2024, this spectacular rise seemed to come to a halt as both the Dow Jones and the S&P 500 fell by more than 2% and 3.6%, respectively, registering their biggest single-day drops since the end of 2022. The stock market's moment of terror coincided with the quarterly earnings reports of Google parent company Alphabet and Tesla. In the following days of trading, Microsoft, Meta, and Apple also released their financial reports, causing violent fluctuations in the stock market. Although the reasons for the fluctuations may be various, such as expectations of interest rate cuts, the release of employment data, the criticized impossible triangle, U.S. bond yields, and U.S. tech stocks, which have consistently maintained three highs that seem to violate financial common sense, one thing that cannot be denied is that one of the main themes affecting market sentiment is the huge AI investments made by top U.S. tech companies. The biggest driving force behind this wave of growth is the huge expectations for generative AI in the market. Irene Tunkel, Chief Equity Strategist for U.S. Stocks at BCA Research (a global economic analysis company), commented that in addition to Nvidia, the main reason why the stock prices of these seven giants performed well in 2023 was the multiple expansion of price-earnings ratios, indicating that investors have high expectations for the future profit growth of these companies. However, the most special one, Nvidia, which does have explosive business results support, has seen its market value fall by more than 22% since June, evaporating 5.2 trillion RMB, and has fallen by 20% for 17 consecutive trading days recently. In addition to negative information about the mass production of Blackwell chips, the capital market is also beginning to worry that if tech giants cannot prove that AI can bring sufficient incremental business to them, they will not be able to sustain their investment in the field of AI, and Nvidia's business will not continue to surpass expectations time and time again. Expectations are always the biggest driving force for stock price rises, far more important than past performance. Capital markets seem to be divided into two equally powerful forces: on the left side are the powerful visions of AI changing the world and continuous capital investment, while on the right side are deep skeptical doubts about the input-output ratio of AI, as well as the huge bubble that AI is creating:

$NVIDIA (NVDA.US)$and$Meta Platforms (META.US)$N/A.$Tesla (TSLA.US)$,$Amazon (AMZN.US)$and$Alphabet-C (GOOG.US)$N/A.$Microsoft (MSFT.US)$and $Apple (AAPL.US)$ In 2023, the stock prices of the Magnificent Seven rose by 239%, 194%, 102%, 81%, 59%, 57%, and 48%, respectively, and were thus named the Magnificent Seven in the market, while the S&P 500 index only rose by 24% as a whole during the same period.

Please use your Futubull account to access the feature.$S&P 500 Index (.SPX.US)$The Dow Jones fell by more than 2%,$Nasdaq Composite Index (.IXIC.US)$While the two major indexes both fell by more than 3.6%, registering their biggest single-day drops since the end of 2022. The stock market's moment of terror coincided with the quarterly earnings reports of Google parent company Alphabet and Tesla.

In the following trading days, Microsoft, Meta, and Apple also released their financial reports, causing the stock market to fluctuate violently. Although the reasons for the fluctuations may be diverse, such as the expectation of interest rate cuts, the release of employment data, and the three highs that have been criticized--the USD index, US bond yields, and US tech stocks--which have always seemed to violate financial common sense.

One of the main themes affecting market sentiment is the huge AI investments made by top U.S. tech companies, as well as the huge disagreement about whether these investments are future-oriented or simply mean the shareholders will pay the bills.

The biggest driving force behind this wave of growth is the huge expectations for generative AI in the market. Irene Tunkel, Chief Equity Strategist for U.S. Stocks at BCA Research (a global economic analysis company), commented that in addition to Nvidia, the main reason why the stock prices of these seven giants performed well in 2023 was the multiple expansion of price-earnings ratios, indicating that investors have high expectations for the future profit growth of these companies.

As the results of the financial reporting season are revealed, these overly high growth expectations begin to swing significantly.

However, the most special one, Nvidia, which does have explosive business results support, has seen its market value fall by more than 22% since June, evaporating 5.2 trillion RMB, and has fallen by 20% for 17 consecutive trading days recently. In addition to negative information about the mass production of Blackwell chips, the capital market is also beginning to worry that if tech giants cannot prove that AI can bring sufficient incremental business to them, they will not be able to sustain their investment in the field of AI, and Nvidia's business will not continue to surpass expectations time and time again.

Expectations are always the biggest driving force for stock price rises, far more important than past performance.

Capital markets seem to be divided into two equally powerful forces: on the left side are the powerful visions of AI changing the world and continuous capital investment, while on the right side are deep skeptical doubts about the input-output ratio of AI, as well as the huge bubble that AI is creating:

After delving into the AI bills of the tech giants, we realize that the future may be far more complex than we imagined.

Will the huge investments made by the tech giants in generative AI make it difficult to present short-term financial results?

Can such massive investments really bring growth? When will they pay off? If generative AI is a distant futures contract, is the bubble being blown bigger and bigger?

Why are tech giants so convinced that generative AI is the way to go?

After delving into the AI bills of the tech giants, we realize that the future may be far more complex than we imagined.

01 Tech giants have tightened their belts and invested everything in AI.

According to the just-released quarterly report, this article compiles and lists the capital expenditures of major technology companies, and extracts descriptions of investment in artificial intelligence:

Microsoft: $13.87 billion in capital expenditures for the second quarter, higher than analysts' expectations of $13.27 billion and higher than the $10.7 billion for the same period last year.

Alphabet, the parent company of Google: Capital spending in each quarter of the second half of the year will reach or exceed $12 billion, and the total spending for the whole year may exceed $49 billion, which is 84% higher than the average annual spending in the past five years. By product structure, products with operating income of 10-30 billion yuan are respectively 401/1288/60 million yuan.

Meta: Capital expenditure for this quarter was $8.47 billion, up nearly 33.4% from the same period last year; The minimum capital expenditure forecast for 2024 has been raised from $35 billion to at least $37 billion, but the maximum expenditure forecast of $40 billion remains unchanged.

Amazon: Capital expenditure in the second half of 24 will accelerate, surpassing H1's $30.5 billion, mainly for AWS infrastructure construction.

Apple: Luca Maestri, Apple's CFO, did not give a specific number for capital expenditures for this quarter during the 2024 Q2 earnings call.

When asked by analysts whether Apple's shift in focus to Artificial Intelligence and Generative Artificial Intelligence would affect the company's capital expenditure rhythm, CFO Luca Maestri said that Apple has been working to promote innovation across industries and fields for many years, and in just the past five years, Apple's expenditure on R&D in related fields has exceeded $100 billion.

Tesla: No specific data has been released for the AI field. Musk only revealed during the earnings call that capital expenditures in 2024 may reach $10 billion.Tesla announced in January 2024 that it would invest an additional $500 million to purchase about 0.01 million H100 GPUs from Nvidia. Its CEO Musk also posted on social media X that Tesla may spend $3 billion to $4 billion to buy Nvidia's chip hardware this year (2024).

From these figures, the capital investment in AI by each giant is over billions of dollars annually. At the end of 2024, it is only an estimate as to how much these giants will increase capital expenditure for Artificial Intelligence. Recently, Barclays analysts pointed out in a report that the capital expenditures on Artificial Intelligence will accumulate to $167 billion from 2023 to 2026, based on optimistic expectations of demand for AI products. However, the capital expenditure situation disclosed by these giants mentioned above is not small figures. However, the increase of cloud computing service revenue by $20 billion by 2026 is in sharp contrast to the above figures.

Only from the increase of cloud computing service revenue, it may not objectively reflect the problem. However, it can indirectly reflect that since such huge investment has been made, these giants may still not be able to answer the ROI issue for capital expenditures related to Artificial Intelligence at least by 2026.

However, this does not prevent the giants from cutting budgets in other areas or even laying off employees and continuing to invest resolutely in the field of Artificial Intelligence. They can't see the results in the short term, and as listed companies, they also face huge pressure from the capital markets. Why are giants doing this?

Google parent company Alphabet's CEO Sundar Pichai said, "Clearly, we are in the early stages of a very transformative field." He added,"For us, the risk of underinvestment is far greater than the risk of overinvestment," not to mention tech rivals Microsoft, Amazon and$Meta Platforms (META.US)$are also investing record amounts in the same field.

Meta CEO Zuckerberg expressed a stronger view, "Now, I'd rather take a risk and build capacity before I need it than wait until it's too late, because starting new inference projects takes a long time to prepare."

Amazon CFO Brian Olsavsky said, "This is a high-risk business. It's a revolutionary shift for many industries. We believe that with our existing position in the cloud computing field, we can participate in it in a very high-end way."

CEOs see the risk of huge investment, but they still firmly invest. It seems that this is not a "gamble", but a ticket for the "Noah's Ark" that must be bought.

02 What incremental value can AI bring to tech giants?

Why is that? What incremental value can generative AI bring? There is no clear new business model yet. The story told by technology giants is mainly about cloud services, advertising, autonomous driving, and on-device intelligence.

The business and payment systems of cloud services themselves are complex, and the main logic of incremental value comes from the assumption that generative AI will make more enterprises want to use generative AI, which requires a large amount of computational and storage resources. Regardless of deploying private models or using existing large models, cloud service providers cannot be bypassed, which will bring new customers and incremental value to cloud services.

Microsoft, Amazon, and Google are known as the "three clouds" of the United States. Microsoft can be said to have the upper hand among the technology giants in this wave of generative AI, investing in the startup company OpenAI that ignited the generative AI wave, and has invested $13 billion in OpenAI. Microsoft is the exclusive cloud provider for OpenAI and applies OpenAI's models to commercial customers and consumer products.

OpenAI's large model is widely recognized as the strongest closed-source model at present. However, even with a powerful combination, the incremental effect on Microsoft's cloud business is limited. According to the latest financial report, the AI service contributed 8 percentage points to Azure's revenue growth this quarter, up from 7 percentage points in the previous quarter. The growth seems to be becoming slower.

In Q2 2024, AWS revenue reached 26.281 billion US dollars, a year-on-year increase of 18.7%, slightly better than expected, but the growth of net profit slowed slightly, and the operating margin of AWS fell by 0.6% month-on-month. Although the difference from expectations is not significant, the market reaction is still negative. The slowing growth, poor performance in other businesses (such as e-commerce), and significant investments in the AI field have raised concerns among investors.

Alphabet's financial report performed well in all aspects with total revenue of 84.7 billion US dollars, a year-on-year increase of 14%, and net profit of 23.6 billion US dollars, both of which are higher than analysts' expectations. Although the cloud business is not the main source of Google's revenue, its revenue in the first quarter exceeded 10 billion US dollars for the first time, reaching 10.347 billion US dollars, a year-on-year increase of 29%.

However, the subsequent stock price performance revealed investors' conflicts. In after-hours trading, it rose by about 2% but later fell by 2.18%. As of the closing on July 25, it fell by 2.99% to USD 169.16/share. The main reason is that Alphabet's capital expenditures exceeded market expectations, nearly twice the same period last year, and such huge capital investments will continue. The incremental effect brought by AI cannot offset the panic of sustained huge capital investment.

Looking at the three major clouds in the United States, AI has indeed brought incremental revenue to cloud businesses, but the incremental effect is lower than the market's expectations.

In the advertising business, the number of daily active users is the fundamentals of the large cap. The help of AI is mainly to improve ad accuracy and creativity, so that target users can see and be willing to proactively click and open. This is the most desirable result for advertising clients, and these two points are indeed the strengths of generative AI.

Meta's advertising business achieved a 22% growth, and the daily active users of its app series (Facebook, Instagram, WhatsApp, and Messenger) reached 3.27 billion in June, a year-on-year increase of 7%. Facebook's monthly active users exceeded 3 billion for the first time, reaching 3.03 billion, a year-on-year increase of 3.4%. Zuckerberg is also full of confidence in MetaAI, believing that it is expected to become the most widely used AI assistant in the world by the end of this year.

In this wave of generative AI, Meta's Llama series model has successfully become the leader of the open source camps of large language models in the world, and is becoming more and more stable. Meta has just released the Llama 3.1 series, but according to Zuckerberg, Llama 4 requires ten times the training resources of Llama 3.

Google's advertising business also performed well, with a total revenue of $64.616 billion in the second quarter of 2024, a year-on-year increase of 11%. Among them, Google search and other ad revenue increased by 14% year-on-year; YouTube ad revenue increased by 13% year-on-year. Google's large model Gemini also ranks among the top in all models.

However, whether these growths are really brought about by AI, even the giants themselves may not be able to fully explain.

Tesla and Apple are relatively unique existences.

Tesla is a representative of radicalism. Its founder Musk has laid out the most cutting-edge fields, including autonomous driving, brain-computer interface, and space, etc., and each one requires a lot of capital investment. However, he always has the ability to persuade the world to believe in his story, obtain financing, and create a part of cash flow in these "money-burning" fields to support the continued exploration of future grand narratives.

Tesla is a huge part of the entire picture. In addition to analyzing the delivery volume like other car companies, investors are also concerned about Musk's narrative for the future. The Robotaxi and humanoid robot Optimus are Tesla's "AI stories", and FSD (full-self driving) technology is one of Tesla's core capabilities for autonomous driving, Robotaxi, and humanoid robots, and the supercomputer Dojo is the brain that supports all of these.

Tesla has been adding investment to this entire AI narrative, and Musk has posted on the social media platform X, saying that Tesla may spend $3 billion to $4 billion to purchase Nvidia chip hardware this year (2024).

Investors are more tolerant of Tesla's increased investment. According to Goldman Sachs' report, 68% of investors see AI as the main driver of Tesla's stock price in the next year, and only 33% lean toward electric vehicles.

Compared with Tesla, Apple's attitude towards generative AI seems to have been somewhat "conservative", and there has been no "sudden increase" in large capital investment. The CFO's external wording has always been "continuous investment" over the past five years. At the WWDC in June of this year, the release of Apple Intelligence made the most significant expectations focus on the new iPhone to be released in the fall of this year. Can this phone with AI (Apple Intelligence) "break through the squeezing-the-toothpaste-style innovation", create a "new paradigm of AI mobile phones", and stimulate the sales of pressure-laden smartphones?

For generative AI, the seven giants each have their own designs, which can be summarized as follows: cloud computing services, advertising, and intelligent terminals. Even the most imaginative Musk has not been able to create anything new. Generative AI is more like a stronger brain that giants have to invest in. Therefore, the biggest criticism from the outside world is that, besides writing poor articles and drawing strange pictures, I really don't experience what more generative AI can do.

Barclays' research report calls this arms race-style investment 'FOMO (fear of missing out).' But imagine if all competitors have already upgraded their business bases to 'computers,' and you are still using an 'abacus,' this may not be a matter of missing out, but direct disappearance. For example, cloud customers will directly choose cloud computing service providers that can provide generative AI capabilities.

What giants are competing to deploy is actually the infrastructure of the future, not disruptive and innovative applications facing users. This includes underlying computing infrastructure, as well as a powerful self-developed basic model. These require large capital expenditures.

03 Is the bubble of generative AI building up?

Since it is for infrastructure, there will inevitably be a long time lag between investment and return. This wave of technology stocks driven by generative AI reflects the excitement of everyone for the birth of new technology. However, no giant currently can say what the increment generative AI brings or how much increment it brings.

In addition to the competitive relationship, the seven giants have formed a network of mutual penetration in the field of generative AI. Except for Apple, all other giants are buying chips from Nvidia; Apple's first large model for external cooperation was OpenAI's large model, while Microsoft is OpenAI's biggest investor and exclusive cloud computing service provider; Apple does not publicly declare whether it uses Nvidia's computing, but explicitly stated in the external documentation of Apple Intelligence that it uses Google's TPU.

Beyond the giants, there are ambitious and even more fanatical Silicon Valley companies.

Looking at OpenAI, at the end of 2023: the annualized revenue is $1.6 billion. In June 2024: According to The Information, the revenue is estimated to reach $3.5 billion-$4.5 billion in 2024. Although it is still expected to lose about $4 billion, the scale of revenue is indeed growing at a speed of up to three times. Its valuation has also tripled. In April 2023: the valuation is about $29 billion. In February 2024: the valuation reaches $86 billion.

At this stage, capital values OpenAI's ability to capture the market more than its profitability. From this perspective, the growth of OpenAI's valuation also seems to be more rational.

According to The Information, the total amount of funds raised by generative AI companies in the second quarter reached US$12.2 billion, breaking historical records, and the number of companies that received financing also reached a new high of 55.

The hot atmosphere, skyrocketing valuations, and vague business models have led institutions including Goldman Sachs to question whether the bubble of generative AI has been inflated.

Morningstar analyst Michael Hodel said: 'I think many investors naturally think of the telecommunications bubble at the end of the 1990s and the beginning of the 21st century as a comparison object. Most of the companies involved in that expansion have since gone bankrupt. This expansion in some ways seems very similar...but the main difference is that most of the companies involved in the expansion have solid profit-making existing businesses and stable balance sheets.'

If the definition of a bubble here is 'valuation that keeps rising without fundamental profitability,'then it seems that the bubble has not yet gathered. Even giants, including Nvidia, have solid profit-making capabilities. Compared with the cash flow they create, the investment of giants is not very radical either.

However, if the macro fundamentals deteriorate and the profit expectations of giants are lowered, and capital expenditures cannot be quickly reduced. The chips, servers, and data centers purchased by previous capital investments cannot be immediately realized into profits either. The PE ratio will naturally soar, and the so-called bubble will also expand.

The definition of a bubble is dynamic, and the rise and fall factors of the stock market are very complicated. The collapse of consistent expectations or the deterioration of macro fundamentals may cause violent fluctuations in the stock market. After experiencing continuous upward trends, any event may become a catalyst for a downward trough.

The capital expenditure decisions of the CEOs of American stock market giants may consider the core competitiveness of their companies in the next ten or twenty years; Silicon Valley investors may look at the next thirty years and the probability of new giants emerging; and the ups and downs of the stock market are the common result of short-term complicated market factors and emotions. Although these three things are related, they are more independent.

However, there are several key issues that the core participants of this generative AI revolution may need to consider seriously:

• Generative AI consumes massive resources. How can it be made more efficient?

• The cost of computing power is enormous. How can it be made cheaper and cheaper?

• Is there any architecture better than Transformer, or even a new technology that can overturn the current deep learning route, so that the efficiency of artificial intelligence can be comparable to that of the human brain?

As these problems are gradually solved, we will see the arrival of the singularity of artificial intelligence, and looking back at the current investment, perhaps everything is worth it.

Editor/Somer