‘PANICKY’: This is the elephant in the room, says software expert

Key Concepts

• Freefall Mode: A market condition where stock prices experience rapid, sustained declines with little immediate support.
• Run Rate Revenue: An extrapolation of current revenue performance to predict annual results.
• Inference/Training (GPU Capacity): The process of using hardware (GPUs) to train AI models and execute tasks (inference); a critical infrastructure layer.
• Backlog: The total value of signed contracts that have not yet been recognized as revenue.
• Leveraged Buybacks: Using debt to repurchase company shares, often viewed negatively by investors if it signals a lack of organic growth opportunities.

1. The "Elephant in the Room": AI Revenue Cannibalization

The primary driver of current market anxiety is the unprecedented revenue growth of AI-native companies.

• Data Point: Anthropic has crossed $30 billion in run-rate revenue, up from $9 billion at the start of the year.
• The Core Conflict: OpenAI and Anthropic are projected to add more revenue this year than the entire traditional software industry combined. Investors are fearful that this revenue is being "cannibalized" from existing software budgets, leading to the current sell-off in the software sector.

2. Divergence in Software Performance

The software sector is not a monolith; performance is bifurcated based on how companies integrate with AI.

• The "Show Me" Problem: Traditional software companies (e.g., Snowflake, Salesforce) are struggling to convince investors that AI will drive immediate growth. Snowflake, despite positive commentary on AI investment returns, saw its stock drop 11% following its report, illustrating that market sentiment currently ignores management's optimistic narratives.
• Infrastructure Winners: Companies providing the "picks and shovels"—specifically GPU capacity for training and inference—are performing well. These firms are viewed as the "opposite trade" to traditional software because they are essential to the AI ecosystem rather than threatened by it.

3. Case Studies: Salesforce vs. Oracle/ServiceNow

• Salesforce: Once the "poster child" of software, the company is currently struggling. The analyst argues that investors are no longer swayed by CEO rhetoric; they require tangible evidence of growth acceleration. Salesforce’s reliance on leveraged buybacks is viewed as a potential red flag, suggesting a lack of confidence in organic growth.
• Oracle: Positioned as a "Buy," Oracle is insulated by a massive backlog exceeding half a trillion dollars. The analyst projects 25–30% growth in earnings and revenue over the next 3–5 years, driven by their specific role in cloud infrastructure and enterprise data.
• Palantir: Despite rumors of competition from Anthropic, the analyst clarifies that the two are partners. Palantir’s growth is accelerating in lockstep with AI adoption, particularly in government and complex enterprise sectors, making it a resilient player.

4. Strategic Framework for Investors

The analyst suggests a shift in investment methodology during this period of volatility:

• Selectivity is Paramount: Investors must move away from broad software exposure and focus on companies with specific, defensible moats.
• The "Old World" vs. "New World" Strategy: The analyst prefers companies tied to established CRM systems or those that provide the foundational infrastructure (GPUs/Cloud) that AI models require to function.
• Evidence-Based Valuation: The market is currently in a "show me" phase. Companies that cannot demonstrate immediate, measurable revenue acceleration from their AI products are being punished, regardless of their historical success.

5. Synthesis and Conclusion

The software sector is undergoing a painful transition as AI-native firms capture a significant portion of enterprise IT spending. The "freefall" in many software stocks is a direct result of investor skepticism regarding the near-term ROI of AI features in traditional software. The takeaway is that the market is bifurcating: companies that provide the essential infrastructure for AI (like Oracle or GPU-focused firms) are well-positioned, while traditional software firms that cannot prove their AI products are driving immediate, accelerated growth are facing significant downside pressure. Investors are advised to prioritize companies with massive, verifiable backlogs and those that serve as the backbone for AI model deployment.