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Computer Sftwr-Database Rises 34 Ranks(From 56 to 22):

I. Core Investment Logic: AI-Driven "Data Platform Revaluation"

• AI as the Core Engine for TAM Expansion: Goldman Sachs clearly states that AI is the most positive driver for software TAM over the next 5–10 years. Databases/data platforms have become the "data plane" and "agent orchestration layer" for AI workloads. AI could lift software TAM CAGR from 6.7% to 9.1%.
• Cloud Migration Enters Phase Two: AI is catalyzing a data-centric cloud migration. Only ~55% of RDBMS workloads are currently in the cloud; IDC expects that to reach 80% by 2028, creating significant tailwinds for database vendors.
• Software Mapping of Storage Logic: The market is moving from "narrative diffusion" to "profit sorting." Semiconductors and storage, as "hard-constrained assets," rallied first, while databases, as the "control-layer software," are the most likely sector to take the baton from semis.

II. Market Size and Competitive Landscape

• Current TAM: The global DBMS market is ~$120 billion (Gartner).
• Growth Drivers: AI's demand for unified data, logs/memory generated by agents, OLTP/OLAP convergence.
• Competitive Landscape: Leader shares remain fragmented. Snowflake and Databricks combined ARR is ~$9 billion, less than 10% penetration of total DBMS market, implying significant upside.
• Internal Restructuring: Snowflake is evolving from a "cloud data warehouse" to an "AI data cloud" via Cortex AI and OpenFlow, covering the full data lifecycle. Databricks focuses on ML/AI workloads.
• "De-stickiness" Threat: Open table formats like Iceberg are reducing traditional storage stickiness, raising R&D and innovation pressure on all vendors.
• SaaS Giant Incursion: Salesforce (Data 360) and ServiceNow are encroaching on the data platform market through bundled data clouds.

III. Current Cycle Positioning and Crowding

• Cycle in Phase Two: The AI tech allocation sequence is "hard-constrained assets → control-layer software → high-end services/sovereign cloud." Currently, Phase 1 (storage, semis) has the strongest evidence (crowding at 99.3rd percentile), while Phase 2 (database sector) is starting to attract capital.
• Crowding Still Healthy: Goldman Sachs and J.P. Morgan data show software sector overall crowding at only the 29.8th percentile historically, and retail crowding in software at just the 5.9th percentile — still far from overheated.

IV. Key Stock Views from Institutions

• Snowflake(SNOW.US) : Goldman Sachs Buy — dual drivers from cloud migration and AI; restructured as an AI data cloud; TAM expanded 2x via Crunchy Data acquisition. Risks: Databricks' long-term advantage and Iceberg stickiness reduction.
• MongoDB, Inc. Class A(MDB.US) : Mixed market views — strong earnings and raised guidance, but AI monetization pace is in question; market is divided on how quickly AI traffic converts to ARR.
• Oracle Corporation(ORCL.US) : Goldman Sachs Buy — beneficiary of AI infrastructure; massive AI-related backlog; clear catalyst path; gross margins inflecting. Risks: customer concentration and execution risk.
• Datadog(DDOG.US) : Goldman Sachs Sell — observability data sits close to the database layer, but faces intensifying competition from AWS and CrowdStrike; budget optimization pressure in 2026.

V. Conclusion and Risk Reminders

The Computer Software-Database sector is at the inflection point where AI infrastructure logic is spilling over from hardware to software. Goldman Sachs and others view it as the core "control-layer software" of the AI era, with clear TAM expansion logic, and current crowding far below semiconductors — the structural AI shift is not yet fully priced.

Key Risks: 1) AI capex returns fall short; 2) Open standards like Iceberg suppress long-term margins; 3) High interest rates weigh on long-duration software valuations; 4) AI-driven revenue may flow more to data infrastructure than traditional databases — stock selection is critical.

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