Short Overview | Part 2 featuring: Sonu Chawla, CFA

Short Overview

Sonu Chawla moves from her personal journey to high-conviction investing in action, walking through a detailed Snowflake (SNOW) case study that illustrates TimesSquare's research process, AI cycle positioning, and how the firm integrates tools like Claude into its own investment workflow.

Timestamped Sections

0:00 – 6:00 - The Snowflake Thesis — Data Infrastructure at the Center of the AI Era

Summary

Snowflake's cloud-based platform consolidates enterprise data into a single repository, enabling storage, analysis, and sharing at scale across more than 11,000 customers. TimesSquare's due diligence consistently surfaced data readiness as a top CIO spending priority in the AI era,  making Snowflake's foundational role in that infrastructure a compelling investment entry point.

Key Takeaways

• Snowflake's foundational role in helping enterprises get their data estate "AI-ready" made it a compelling investment as organizations begin deploying generative AI at scale.
• The platform's consumption-based model means revenue scales naturally as enterprises expand AI workloads — creating a durable, usage-driven growth flywheel.

6:01 – 8:09 - Where Are We in the AI Cycle — and Why Enterprise Adoption Is Still Inning One

Summary

Sonu characterizes enterprise AI deployment as firmly in the first inning. Most organizations are still consolidating and governing their data before AI workloads can scale and a significant base of legacy on-premise infrastructure from vendors like Teradata and IBM still awaits cloud migration, representing a long runway of incremental Snowflake opportunity.

Key Takeaways

• Enterprise AI deployment is effectively in the first inning, with most organizations still in the foundational stage of consolidating and governing their data estates before AI workloads can scale.
• Legacy on-premise data infrastructure represents a substantial and largely untapped migration opportunity for cloud-native platforms like Snowflake.

8:10 - 15:06 - Why Snowflake Over the Hyperscalers — Security, Data Gravity, and Everyday Usability

Summary

Snowflake differentiates from AWS, Microsoft, and Google alternatives across three dimensions: platform-managed encryption that satisfies regulated industries like major financial institutions; seamless data sharing across organizational boundaries; and superior usability for everyday business users in contrast to engineering-centric competitors like Databricks that historically required deep technical expertise to operate.

Key Takeaways

• Snowflake's managed security and encryption model makes it the preferred platform for regulated industries, including major financial institutions, where data governance is non-negotiable.
• Unlike engineering-centric competitors, Snowflake democratizes data analytics — empowering everyday business users to become their own data analysts, which directly expands consumption volume.

15:07 – 22:22 - Doubling Down — Conviction, Coco, and the Variant View During the SaaSSelloff

Summary

TimesSquare nearly doubled its Snowflake position during a sharp April 2026 software selloff, anchored by a differentiated view on Snowflake's AI coding agent, Cortex Code ("Coco"). Made generally available in February 2026 and excluded entirely from guidance, Coco compresses data migration timelines from six months to six weeks and dramatically expands query volume by enabling non-technical users to interact with data in plain language. CEO LinkedIn posts, customer case studies, and independent channel checks all confirmed real product-market fit — giving the team conviction to add while the market focused on a CRO transition and tough year-over-year comparisons.

Key Takeaways

• TimesSquare's variant view on Coco — a product with confirmed traction that was entirely excluded from guidance — created a clear asymmetric opportunity during a broad, indiscriminate software selloff.
• The team's mosaic research process, combining LinkedIn monitoring, management dialogue, and expert channel checks, surfaced conviction ahead of the market — validating TimesSquare's differentiated, analyst-driven approach.

22:23 – 35:49 - AI as an Investment Tool — How TimesSquare Uses Claude in Its Own Research Process

Summary

TimesSquare uses Claude extensively to synthesize earnings transcripts, summarize conference appearances, and manage information flow during reporting season. The firm is in early stages of building a more comprehensive internal data lake, potentially on Snowflake, to connect expert calls, earnings data, and proprietary sources, with current integrations including event transcripts, Outlook, and biotech-specific research publications.

Key Takeaways

• TimesSquare uses Claude extensively to accelerate research synthesis, manage earnings season information flow, and prepare for management meetings — compressing hours of work into minutes.
• The firm views its current AI integration as early-stage, with significant opportunity ahead to expand data connectivity and further enhance the productivity of its investment team.