Goldman Sachs' technology research team pointed out that the core challenges currently facing software stocks are concentrated in the new competitive landscape at the application software layer and the return on capital expenditures for infrastructure companies.
Wall Street Journal mentioned that a routine update from Anthropic severely impacted software stocks this week. Although there was a rebound on Friday, they remain in a low-level fluctuation.
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Goldman Sachs' Gabriela Borges team expects that an improvement in investor sentiment requires 2-3 quarters of stable fundamental support, and the software sector will still face valuation pressure in the short term.
Goldman Sachs emphasizes that the opening of a new technology cycle, the large number of competitors nurtured by the financing boom of 2020-2021, and the need for system architecture redesign are all factors driving up the intensity of competition at the application software layer.
Borges outlined an observation checklist for investors, listing seven key indicators that may signal stability in the AI-SaaS industry. This framework aims to help the market assess whether application software companies can withstand the impact of new competitors and when the massive capital expenditures of infrastructure providers can translate into substantial returns.
Two Core Controversies in the AI-SaaS Field
Goldman Sachs' technology research team pointed out two main controversies in the AI-SaaS field.
The first controversy is whether software companies will be replaced.
Goldman Sachs believes that new technology cycles always bring chaos. Currently, traditional SaaS giants, high-end custom software vendors like Palantir (PLTR.US), and startups that genuinely possess exclusive datasets and products, rather than just wrapping AI around their offerings, are competing for customers on the same battlefield.
This chaotic battle has three layers of reasons. First, whenever new technology emerges, companies always want to try it themselves, giving new players a time window.
Second, companies that were born from hot money around 2021 may not have as deep a moat as imagined.
Most importantly, the core of software in the past was to serve humans, while future software needs to serve both humans and AI entities. This means the entire software technology stack needs to be reconstructed, and the burden of upgrading old systems for old players is precisely the opportunity for new players.
Therefore, the selection becomes stringent. When investing, one should critically look towards application software companies that are progressing faster in reconstructing their technology to adapt to &#34;human-machine collaboration.&#34;
The second focus is on where the money will ultimately flow.
Goldman Sachs found a consensus in discussions with clients that in the future, every software vendor will provide AI entities. So, where will differentiation and excess profits come from?
The core lies in the &#34;orchestration&#34; capability, which is how to efficiently and stably integrate underlying computing power, models, enterprise data, business processes, and security rules to deliver a usable intelligent entity. This spans across infrastructure, platforms, and applications, requiring both technical complexity and an understanding of industry knowledge.
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This makes the ability for vertical integration more important than in the cloud era. For giants like Microsoft, having a presence across all layers becomes more advantageous.
At the company level, Goldman Sachs analysts believe that for application software vendors, value will concentrate at the &#34;orchestration layer.&#34; The transition from SaaS to &#34;SaaS+AI&#34; is a brutal race, with each company starting from different points.
For infrastructure companies providing computing power, the key to victory lies in whether they can diversify their chip sources to enhance profits and whether they can add valuable platform services on top of basic computing power.
Seven Key Recovery Signals
The aforementioned changes are gradual. Goldman Sachs analysts have listed seven more specific, quarterly trackable signals that, if they appear in succession, will indicate that the industry's fundamentals are beginning to stabilize.
The first is a quiet change in revenue structure. If this year the overall traditional software budget for enterprises does not grow, but the total revenue of leading software companies stops declining and begins to rise, it indicates that AI orders are starting to substantively compensate for or even surpass the weakness of traditional businesses.
Secondly, the return of &#34;self-developed&#34; projects. When companies like ServiceNow start complaining that customers are shifting from self-built AI projects to their mature products, it is a positive signal. This indicates that packaged software is beginning to establish sufficient advantages in functionality, security, and compliance control, and the rules of the game in the enterprise market are taking effect.
The third is the willingness to raise prices. Currently, most AI functionalities are still in the promotion phase. In the future, when vendors attempt to charge separately for intelligent features or raise prices, and customers accept this, it will prove that AI has created recognized commercial value. This is similar to Palantir's approach one or two years ago of &#34;letting customers use it first, then discussing money.&#34;
Next, attention should be paid to whether industry knowledge is more valuable than models. The market needs clearer cases to understand why, in specific scenarios, intelligent agents from Salesforce or HubSpot, which have deep industry experience, perform better than generic Copilot. This will determine the pricing power and market space of different companies' intelligent agent products.
Fifth, watch for unexpected mergers and acquisitions. If an AI platform or large model company suddenly acquires a SaaS company in a vertical field, it should not be surprising. This is the most direct way for AI players to quickly acquire industry knowledge, sales channels, and &#34;enterprise-level&#34; credibility.
The sixth is using money to solve human problems. AI has brought about expensive competition for technical and sales talent. Investors should pay attention to how companies respond to the resulting risks of equity dilution and employee turnover. Whether it is more actively repurchasing stock or decisively adjusting compensation structures to attract a new generation of talent reflects the management's determination.
Finally, it is necessary to see through the cloud giants' bottom cards. For giants like Microsoft and Oracle that provide AI computing power, the market needs to know more clearly when their new chip production capacity will come online, how much of this capacity will be allocated to their own business, and how much can be sold to external customers. This directly affects the growth and profit margins of their cloud businesses
Goldman Sachs ultimately pointed out that it is expected to take 2-3 quarters of stable fundamentals for investor sentiment to improve. Even so, there remains the possibility that bearish scenarios could be postponed to future years

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Goldman Sachs analyzes this week's market focus, pointing out that software stocks face a new competitive landscape and issues with capital expenditure returns. Investor sentiment requires 2-3 quarters of stable fundamental support, and in the short term, the software sector will still face valuation pressure. Goldman Sachs emphasizes the intensified competition brought about by the new technology cycle, with traditional software companies needing to respond to challenges from emerging competitors. The Borges team provides an observation checklist, listing seven key indicators to assess the stability of the AI-SaaS industry

Goldman Sachs interprets this week's market focus: The "AI-SaaS" battle