Goldman Sachs's interpretation of this week's market focus: "The Battle of AI-SaaS"

Goldman Sachs Group, Inc.'s technology research team pointed out that the core challenges currently faced by software stocks are centered around the new competitive landscape at the application software layer, as well as the return on capital expenditure for infrastructure companies. Wall Street News mentioned that a routine update by Anthropic this week caused a sharp decline in software stocks, despite a rebound on Friday, they are still trading at low levels. Goldman Sachs Group, Inc.'s team led by Gabriela Borges predicts that the improvement in investor sentiment will require 2-3 quarters of stable fundamental support, and the software sector will continue to face valuation pressures in the short term. Goldman Sachs Group, Inc. emphasizes the start of a new technology cycle, the large number of competitors nurtured by the financing boom of 2020-2021, and the need to redesign system architectures, all of which are driving up competition in the application software layer. Borges has outlined a checklist for investors, listing seven key indicators that may signal a stabilization of the AI-SaaS industry. This framework aims to help the market determine whether application software companies can withstand the impact of new competitors and when the substantial capital expenditures of infrastructure providers can be translated into significant returns. Two core controversies in the AI-SaaS field Goldman Sachs Group, Inc.'s technology research team pointed out two major controversies in the AI-SaaS field. The first point of contention is whether software companies will be replaced. Goldman Sachs Group, Inc. believes that new technology cycles always bring chaos. Currently, traditional SaaS giants, high-end custom software companies like Palantir (PLTR.US), and startups with exclusive datasets and products, rather than just AI-coated shells, are all competing for customers in the same battlefield. This chaos is due to three reasons. First, whenever new technology emerges, companies always want to try it themselves, giving new players a window of opportunity. Second, companies born out of hot money around 2021 may not have as strong a moat as imagined. Most importantly, in the past, software was designed for people to use, but in the future, software needs to serve both people and AI intelligences. This means that the entire software technology stack needs to be restructured, and the burden of transforming old systems for established players becomes an opportunity for new players. Therefore, the choice becomes more stringent. When investing, companies that are faster in revamping their technology to adapt to "human-machine cooperation" in the application software sector should be carefully considered. The second focus is on where the money will ultimately flow. In discussions with clients, Goldman Sachs Group, Inc. found a consensus that every software company in the future will provide AI intelligences. So, where will differentiation and excess profits come from? The key lies in the ability to "orchestrate", which means efficiently and stably integrating underlying computing power, models, enterprise data, business processes, and security rules to deliver a usable intelligence. This involves technology complexity and a deep understanding of industry knowledge across infrastructure, platform, and application layers. This makes vertical integration more important than in the cloud era. For giants like Microsoft Corporation, being able to dominate all layers becomes more advantageous. On a company level, analysts at Goldman Sachs Group, Inc. believe that, for application software companies, value will concentrate in the "orchestration layer". Transitioning from SaaS to "SaaS+AI" is a fierce race, with companies starting at different points. For infrastructure companies providing computing power, the key to victory lies in whether they can diversify their chip sources to increase profits, as well as whether they can add valuable platform services on top of basic computing power. Seven key recovery signals These changes are slow. Analysts at Goldman Sachs Group, Inc. have listed seven more specific and quarterly trackable signals, which if sequentially appear, will signal a stabilization of the industry fundamentals. The first is a quiet change in revenue structure. If this year the overall traditional software budget of companies does not increase, but the total revenue of top software companies stops falling and starts to rise, it indicates that AI orders are beginning to substantively offset or even surpass the weakness of traditional business. Secondly, the return of "self-developed" projects. When companies like ServiceNow start to complain that customers are switching from their self-built AI projects to their mature products, it is a positive signal. This indicates that packaged software is starting to establish sufficient advantages in terms of functionality, security, and compliance, and the rules of the enterprise market are beginning to take effect. The third is the willingness to increase prices. Currently, most AI features are still in the promotion stage. In the future, when vendors attempt to charge separately or increase prices for intelligent functions, and customers accept it, it proves that AI has created recognized business value. This is similar to Palantir's approach of "let customers use it first, then talk about money" a year or two ago. Next, attention should be paid to whether industry knowledge overshadows models. The market needs clearer cases to understand why, in specific scenarios, intelligences from companies like Salesforce or HubSpot with deep industry experience will outperform a general Copilot. This will determine the pricing power and market space of intelligence product among different companies. Fifth, watch out for unexpected acquisitions. If an AI platform or large model company suddenly acquires a vertical SaaS company, it should not come as a surprise. This is the most direct way for AI players to quickly acquire industry knowledge, sales channels, and "enterprise-level" credibility. Sixth is solving people problems with money. AI brings expensive competition for technical and sales talents. Investors should pay attention to how companies deal with the risks of equity dilution and employee turnover resulting from this. Whether they should be more actively repurchasing shares or decisively adjusting compensation structures to attract a new generation of talent reflects the determination of the management. Finally, it is necessary to understand the bottom line of cloud giants. For giants like Microsoft Corporation and Oracle Corporation providing AI computing power, the market needs a clearer understanding of when their new chip capacity will be online and how much of this capacity will be retained for their own business and how much can be sold to external customers. This directly affects the profitability of their cloud business. Goldman Sachs Group, Inc. ultimately points out that it is expected that it will take 2-3 quarters of stable fundamentals for investor sentiment to improve. Nevertheless, there is still a possibility that the bearish scenario will be postponed to future years. This article is from "Wall Street View", written by Bai Yilong, GMTEight editor: Chen Qiuda.