In the global capital markets, AI is undoubtedly a rising star in recent years. However, as AI technology development enters different stages, market volatility becomes more apparent.
In the latest research report released on the 10th, Goldman Sachs divides AI-related investments into four main stages, representing different levels from infrastructure construction to productivity enhancement.
Goldman Sachs points out that AI trading has entered the second stage and is gradually shifting from relying solely on valuation growth to being driven by corporate profitability to boost market performance.
<h2>First and Second Stages: Infrastructure Companies Taking Over from Hardware Providers</h2>
Goldman Sachs believes that the first stage of AI trading is dominated by hardware providers, with NVIDIA being the leading company in this stage.
As the market matures and investors continue to have high confidence in AI construction, infrastructure companies are taking over, reaching the second stage.
Goldman Sachs emphasizes that the focus of the second stage is on the construction of AI infrastructure, including semiconductor companies, cloud service providers, data centers, device manufacturers, and utilities companies.
These companies benefit from the increase in AI capital expenditure, with some infrastructure stocks having risen by over 27% year-to-date in 2024. Goldman Sachs highlights that with the widespread application of AI technology, the stock prices of these companies can continue to rise, with profitability gradually becoming the main driver of stock price increases, rather than solely relying on valuation expansion.
Furthermore, the valuations of second-stage stocks are higher than the average level, reflecting market optimism.
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Compared to an equal-weighted S&amp;P 500 stocks over the past 10 years, second-stage stocks are trading 0.4 standard deviations higher. In contrast, the valuations of third and fourth-stage stocks are cheaper by 0.2 and 0.4 standard deviations respectively.
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Goldman Sachs also points out that the surprise factor of AI spending in the second stage is diminishing, indicating that the returns of stocks in this stage may be more moderate. Nevertheless, increased demand may lead to mega-cap tech companies exceeding expectations in AI-related capital expenditure.
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In early 2023, demand for NVIDIA chips far exceeded analysts' expectations, with mega-cap capital expenditure becoming increasingly aggressive in the first half of 2024.
However, the magnitude of NVIDIA's sales surprises and mega-cap corporate capital expenditure surprises has been shrinking. The upcoming third-quarter earnings season will provide another litmus test for AI demand and spending.
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<h2>Third Stage: Uncertainty Remains in Monetizing AI Applications</h2>
Goldman Sachs points out that the third stage focuses on companies that are trying to generate additional revenue through AI technology, mainly software and IT service companies.
Although the valuations of these companies are lower, Goldman Sachs believes that the commercialization progress of AI applications is not as ideal as expected. While investors are optimistic about the prospects of AI applications, the actual construction and monetization still face challenges.
Through IT spending surveys, Goldman Sachs found that despite the increase in enterprise investment in AI technology in 2024, it is expected that only 3% of next year's IT budget will be used for the development and application of generative AI technology.
This means that AI-driven revenue growth is difficult to achieve in the short term, and investors still need to patiently wait for further development of AI technology.
However, the report also mentioned that platform stocks may stand out in the third stage. These platform companies, including Microsoft, MongoDB, Datadog, etc., provide the best utilization of AI infrastructure and lay the foundation for building next-generation applications.
<h2>Fourth Stage: Potential Winners in Productivity Enhancement</h2>
The fourth stage consists of companies that are expected to achieve productivity enhancement through AI. Goldman Sachs believes that these companies may see the greatest profit increase in the future due to the widespread application of AI, but currently, the comprehensive popularization of AI will still take several years.
According to Goldman Sachs' survey, only 6% of companies have used generative AI in their production processes, and there are significant differences between industries. For example, the manufacturing and technology industries have relatively extensive AI applications, while the acceptance in traditional industries is lower.
Goldman Sachs believes that only when the large-scale commercialization of AI applications in the third stage is achieved, will fourth-stage companies truly enter the investors' field of vision. This means that although fourth-stage companies have tremendous potential for growth, significant performance improvement is still difficult to see in the short term

Goldman Sachs divides AI-related investments into four main stages, believing that AI trading has now entered the second stage, where the company's profitability will gradually become the main driver of stock price increases. In the third stage, AI-driven revenue growth may be difficult to achieve in the short term, but platform stocks may stand out

- Goldman Sachs identifies four stages of AI-related investments, highlighting a shift from hardware providers to infrastructure companies.  
- The second stage focuses on AI infrastructure, with companies benefiting from increased capital expenditures, although returns may moderate as surprises in spending decrease.  
- The third and fourth stages face uncertainties in monetization and widespread adoption, indicating that significant profitability improvements may take years to materialize.

Where is overseas AI trading headed? Goldman Sachs: Profit rather than valuation is driving the market, platform companies need to focus on the future