Wall Street Eyes AI Software Rebound: Two High-Upside Plays in a Bear Market
While AI hardware providers have dominated the market, enterprise software has entered a localized bear market, creating a valuation gap. Wall Street analysts now identify Snowflake and MongoDB as prime AI-integrated recovery plays with projected upsides of 40% and 52%, respectively.
Key Intelligence
Key Facts
- 1Wall Street analysts have identified a 40% upside for Snowflake and a 52% upside for MongoDB as software valuations lag behind hardware.
- 2The 'Software Bear Market' is characterized by a shift in IT spending toward GPUs and infrastructure, temporarily starving software budgets.
- 3Snowflake's Cortex AI is positioned as a key catalyst for enterprise generative AI deployment within the Data Cloud.
- 4MongoDB Atlas has integrated vector search to capture the growing market for AI-native application development.
- 5The valuation gap between AI hardware and AI software has reached historic levels, prompting a 'catch-up trade' thesis.
| Metric | ||
|---|---|---|
| Projected Upside | 40% | 52% |
| Core AI Product | Cortex AI / Data Cloud | Atlas Vector Search |
| Market Position | Data Warehousing | NoSQL Database |
| Growth Driver | Data Consolidation | AI App Development |
Analysis
The divergence between artificial intelligence hardware and software has reached a critical inflection point. While semiconductor giants have seen their valuations skyrocket on the back of massive infrastructure spending, the broader software sector has languished in what many analysts are calling a 'software bear market.' This underperformance is driven by a combination of high interest rates, a shift in corporate IT budgets toward GPUs, and a 'wait-and-see' approach by enterprises regarding the ROI of generative AI applications. However, Wall Street is now signaling that the pendulum has swung too far, identifying Snowflake and MongoDB as two high-conviction plays positioned for a massive catch-up trade.
Snowflake, the cloud data warehouse leader, represents the 40% upside case. The core thesis for Snowflake rests on the fundamental reality that there is no AI without high-quality, structured data. As enterprises move beyond the experimental phase of LLM deployment, the need for a unified data layer becomes paramount. Snowflake’s recent launch of Cortex AI—a fully managed service that allows users to build AI applications directly within the Data Cloud—is expected to be a significant tailwind. Despite recent leadership changes and concerns over consumption-based revenue volatility, analysts argue that Snowflake’s massive installed base and its role as the 'single source of truth' for enterprise data make its current valuation an attractive entry point for the next leg of the AI cycle.
However, Wall Street is now signaling that the pendulum has swung too far, identifying Snowflake and MongoDB as two high-conviction plays positioned for a massive catch-up trade.
MongoDB, on the other hand, offers a more aggressive 52% upside potential according to recent consensus targets. As a leader in the NoSQL database market, MongoDB’s Atlas platform has become the preferred choice for developers building modern, AI-driven applications. The integration of vector search capabilities directly into its document model allows developers to store and query high-dimensional data—the lifeblood of generative AI—without the complexity of managing separate specialized databases. The 'software bear market' has hit MongoDB particularly hard due to its high growth multiple, but Wall Street sees this as a disconnect from the company’s underlying fundamentals. The shift toward AI-native applications is expected to accelerate MongoDB's market share gains against legacy relational database providers.
The broader implication for the market is a transition from 'AI Infrastructure' to 'AI Implementation.' For the past 18 months, the market has rewarded the 'shovels' of the AI gold rush. We are now entering the phase where the 'miners'—the software companies that enable businesses to actually extract value from AI—will begin to see revenue acceleration. Investors should monitor net revenue retention (NRR) rates and the pace of enterprise migration to cloud-native data platforms as key indicators of this recovery. While the software sector remains sensitive to macroeconomic headwinds, the valuation gap between hardware and software has rarely been this wide, suggesting that the risk-reward profile for these AI-integrated software leaders has become increasingly favorable.
Looking forward, the success of these stocks will depend on their ability to prove that AI is not just a cost-center for their customers, but a genuine productivity multiplier. If Snowflake and MongoDB can demonstrate that their AI features are driving increased consumption and higher contract values in the coming quarters, the 'software bear market' may quickly give way to a sector-wide rally. Analysts suggest that the current discount provides a rare opportunity to acquire high-quality growth assets at multiples not seen since the 2022 market correction.