TMT Conference Day 2 Software: Incumbents' Assets Build AI Moats for Innovation Cycle Participation

To: Professional Investors Subject: Software: TMT Conference Day 2 Wrap-Up – Innovation to Ensure Participation

1. Core Thesis Leading public software platforms are leveraging their entrenched core assets—proprietary data, embedded workflows, scaled networks, and trust layers—to build durable moats and ensure participation in the AI innovation cycle. The market overestimates near-term, broad automation, while underestimating that value capture will first occur through deterministic, governable solutions integrated into systems of record and execution, conferring a structural advantage to incumbents.

2. The Market Mispricing The market likely misprices the structural advantages of incumbent software platforms in the AI era, overstating disruption risks from foundational model innovation or AI-native startups. The true 'AI moat' stems less from model novelty and more from the ability to operationalize AI into enterprise-grade solutions, which depends on control over proprietary data, workflow context, customer relationships, and complex transaction platforms—assets incumbents already possess and which are difficult to replicate.

3. Evidence Chain

• AI Moats Are Built on Existing Assets, Not Model Novelty. Defensibility is rooted in proprietary data scale, network effects, and workflow context. BILL's domain insight into ~500k SMBs and ~$1T of processed spend creates barriers in financial workflows. CCC's moat combines ~$2T of historical claims data with ~$1B/day of dynamic data flow embedded across ~100 decision points in a multi-sided ecosystem. Salesforce emphasized its 26-year accumulation of customer data and transaction context as the core asset increasing AI output determinism.
• AI Adoption Begins by Solving "Hard Problems": Determinism, Integration, Security. Contrary to broad automation expectations, executives emphasized a path focused on certainty, cross-system integration, and compliance. Tenable distinguished its strength in post-production, behind-firewall runtime visibility versus pre-production code scanning. Toast's AI product (Toast IQ) functions as an internal copilot enabling action within existing workflows, not replacing the entire system.
• AI is a Credible Driver of Operational and Financial Improvement. Benefits are translating into product acceleration and margin leverage. Snowflake's product revenue growth accelerated to 30% YoY in Q4, while it executed a ~200-person RIF in G&A driven by AI-enabled efficiencies. RingCentral expects GAAP operating margins to grow faster as SBC declines from ~20% of revenue three years ago to ~9% in FY26, targeting ~3-4% in 3-4 years. Manhattan Associates rebutted the "software gets cheaper" narrative (the "6% fallacy"), arguing AI enables faster innovation on existing platforms.
• Platform Strategy is Driving Durable, Efficient Growth. Cross-portfolio sales and integrated platforms are boosting net retention and contract values. Cloudflare's Q4 NRR improved from 111% to 120%, supported by its largest-ever contract ($130M/7 years) and "pool of funds" platform agreements (~20% of ACV). Datadog's high gross retention (high-90%s) indicates customer preference for its integrated "single pane of glass" despite debates about model providers building their own stacks. ZoomInfo accepts 1-2 points of gross margin pressure from AI action credits, betting that increased usage expands the revenue surface and gross profit dollars, with operating leverage protecting margins.

4. Key Divergences & Risks

• Uncertain Monetization Path: AI pricing and value capture models are still evolving; realization may be slower than anticipated.
• Adoption Speed: The emphasis on deep integration and solving complex, regulated workflows could lead to a slower adoption curve than optimistic market narratives, potentially causing growth story volatility.
• Competitive Dynamics: Intensifying competition from both other integrated platforms and AI-native startups targeting specific high-value use cases.

5. Valuation & Investment Implications The investment logic favors incumbent software leaders with deep proprietary data, tightly embedded workflows, strong platform network effects, and a clear roadmap for translating AI into measurable, governable outcomes. These companies possess both defensive and structural growth advantages in the AI cycle. Market fears of "disruption" should be recalibrated toward the "empowerment" and "efficiency" driving potential profit and valuation re-rating for these asset-rich platforms.

6. Appendix: Selected Operational & AI Asset Metrics