According to analysis, JPMorgan Chase strategists suggest that software stocks are poised for a rebound from their historic decline, as the market has priced in unrealistic expectations for artificial intelligence (AI) to disrupt the software industry in the short term. A team of strategists led by Dubravko Lakos-Bujas indicated that, given the current "extreme price volatility," investors should increase their exposure to high-quality software companies with greater resilience to AI. They noted the possibility of capital rotating back into the sector, at least in the near term.

The team wrote in a report, "Given that market positioning has been substantially cleared, sentiment is overly pessimistic regarding AI's potential to disrupt the software industry, and fundamentals remain solid, we believe the risk-reward balance is increasingly tilting towards a rebound." US software stocks have faced sustained pressure recently due to concerns that new AI tools could impact traditional Software-as-a-Service (SaaS) business models. This sell-off has largely treated all related software companies similarly, without distinguishing between those that have established partnerships with AI firms or possess proprietary data assets. The software sector has fallen to its lowest level since the market turbulence of April last year.

Microsoft and CrowdStrike were mentioned by the JPMorgan strategists as examples of companies with AI resilience, likely to benefit from AI-driven improvements in workflow efficiency. The team stated that the high switching costs associated with enterprise software and multi-year contracts provide a buffer against short-term disruptions.

The JPMorgan team also pointed out that, while the long-term question of whether traditional software companies will be replaced by AI remains unclear, the current market pessimism about AI disruption appears to be an "overreaction" at this stage. They added that the software industry's overall fourth-quarter earnings reports have been positive, with analysts forecasting earnings growth of 16.8% for the sector by 2026.

This optimistic view echoes the assessment of a Morgan Stanley strategist team led by Michael Wilson. That team stated this week that US tech stocks still have further upside potential, and the decline in software stocks has opened an "attractive entry window." Wilson wrote in a report, "Volatility like last week's is not uncommon during major investment cycles. Nonetheless, the fundamental positives for AI-enabled companies remain, and we believe the trading value of AI adopters is still underestimated."

Last week, a team led by Wedbush analyst Dan Ives, often referred to as a prominent tech bull, similarly argued that while AI could indeed pose some pressure on traditional software business models in the short term, the market's reaction to this risk is significantly overdone. They suggested the current sell-off in software stocks already prices in an extreme assumption of "large-scale industry disruption by AI," which is not feasible in reality. Ives noted that enterprise customers are far more cautious about AI migration than the market assumes. Many companies are unwilling to expose core data to not-yet-fully-mature new platforms simply to chase AI benefits, and they are even less likely to easily abandon software infrastructure built over decades with hundreds of billions of dollars in investment. He stated, "AI is a headwind in the short term, there's no question, but the market is currently pricing software stocks as if the industry is facing an apocalypse, a judgment we see as completely detached from reality."

Wedbush emphasized that the current large enterprise software ecosystem contains trillions of data points, and emerging AI companies like OpenAI and Anthropic are unlikely to fully take over these complex systems in the short term, whether in terms of data capacity or enterprise-grade security. This suggests AI is more likely to be integrated as an "embedded tool" within existing software platforms rather than replacing them entirely. Wedbush also identified Microsoft, Palantir Technologies, CrowdStrike, Snowflake, and Salesforce as the five most compelling software stocks to hold during the current "software winter."

Last week, Nvidia CEO Jensen Huang also dismissed concerns that AI will replace software and related tools, calling such thinking "illogical." Speaking at an AI conference in San Francisco hosted by Cisco Systems, Huang stated that worries about AI diminishing the importance of software companies are misguided. He believes AI will continue to rely on existing software rather than rebuilding foundational tools from scratch. Huang said, "There's a view that the tools of the software industry are in decline and will be replaced by AI... That is the most illogical thing in the world, and time will tell. Whether you are a human or a robot, artificial or general-purpose, would you use a tool or reinvent it? The answer is obvious, you use the tool... That's why the latest breakthrough in AI is about tool use, because tools are designed explicitly to work."

From the perspective of software engineering reality and SaaS industry structure, the narrative of "AI replacing the entire enterprise software stack" is one that markets tend to extrapolate linearly. The "value density" of enterprise software lies not just in the interface and functionality, but in proprietary data, permission/audit chains, compliance and liability boundaries, system integration, SLAs and availability, change management, and organizational processes. These factors mean that even powerful large language models (LLMs) often require high-quality proprietary data, structured knowledge bases, controllable tool invocation, and traceable outputs to function in a production environment.

Examining the underlying technical logic of AI tools and the SaaS software field, the panic selling of software stocks does not equate to "software becoming unnecessary," but rather indicates a reallocation of the value chain by AI. More powerful general-purpose models and agentic workflows risk making many "point-solution functional SaaS" products obsolete through feature absorption at the model/platform layer or by being bypassed via "conversational interfaces + automated execution," thereby impacting traditional seat-based pricing and renewal logic. Consequently, the market is eager to categorize software stocks into "AI winners" and "AI losers."

Conversely, the "system of record" layers (ERP, CRM, ITSM, databases, security, compliance) dominated by leading enterprise SaaS providers often have barriers related to data sovereignty, governance, permissions, auditing, and migration costs. The more realistic outcome is that AI turns these long-established software giants into distribution channels for delivering AI capabilities, rather than replacing entire existing software infrastructures overnight.

From the Morgan Stanley strategist team's perspective, on a short-term basis, the sharp decline in the software sector has indeed triggered discussions about the sector being "technically near a阶段性 bottom," with some capital making small additions to positions. However, more capital is still waiting for concrete catalysts that demonstrate the "AI application narrative translating into actual revenue curves"—such as software companies disclosing AI-related product revenue/penetration rates, enterprise customers announcing large-scale deployments, or renewal metrics (net revenue retention, expansion rate) significantly strengthening after the introduction of AI models or agentic AI. Without such evidence, any rebound is more likely to resemble an "oversold recovery" than the start of a new trend.

This wave of heavy selling in software stocks appears to be the market's extreme way of answering a new question: To what extent will the profit pools of SaaS software vendors be reallocated by "model providers + agents"? In the short term, the answer can only be validated through two "hard metrics": (1) the speed of real-world enterprise deployment and paid adoption, and (2) the elasticity of SaaS vendors' AI-related product revenue and renewal/net retention rates. As some buy-side representatives described in a Thomson Reuters Breakingviews research report, they are waiting for "actual revenue growth data from AI-related products" or more enterprise deployment announcements as catalysts for increasing their positions.

Until then, volatility in software stocks is likely to persist. On one hand, technical factors could lead to an "oversold bounce." On the other hand, capital will continue to make structural shifts—preferring vertical software/data-asset-rich software companies with strong ties to AI training/inference systems and sticky workflows, as well as platforms that can implement AI in a "controllable, auditable, and integrable" manner. For application-layer names with weaker moats, higher homogeneity, and richer valuations, the market will continue to demand higher risk compensation. Therefore, software giants like Microsoft, MongoDB, Snowflake, Palantir, and SAP, which aggregate data assets and possess strong fundamentals, may be more likely to stage a robust recovery following the panic-induced sell-off.