The Season 2 finale of The Hedgineer Podcast features the return of Thomas Li, Co-founder and CEO of Daloopa, for his third appearance on the show. This episode marks a significant milestone as we transition into a new chapter for the podcast.

Before diving into the discussion, host Michael Watson announces a major shift for the upcoming season. Starting next week, The Hedgineer Podcast will move to a weekly release schedule to provide more frequent insights into the rapidly evolving world of technology, data, and AI. Joining the show as a permanent co-host is Jhanvi Virani, Hedgineer’s COO, who will help anchor our weekly updates and industry analysis.

In this finale, Michael and Thomas explore the decision to open-source Daloopa’s "investor library" of skills and agents—a move that challenges the historically closed-off nature of the financial data ecosystem. They discuss the philosophy behind treating AI agents as "text files" that can be refined by a community of sophisticated investors, effectively turning what was once proprietary alpha into the new industry beta.

The conversation delves into the technical obsession required to serve institutional clients, particularly regarding latency. Thomas explains why Daloopa prioritizes parsing unstructured press wires over waiting for structured SEC filings: in high-stakes markets, saving a few minutes of "server lag" is the difference between a successful trade and a missed opportunity.

We also cover the strategic landscape of building on frontier models. Thomas shares his experience partnering with Anthropic to build their Excel plugin and discusses whether evolving LLMs are a "wind behind the sail" or an existential risk for specialized fintech companies.

• The Open-Source Investor Library: Why Daloopa is providing its corpus of fundamental investing skills to the community and how 100+ hedge funds are already contributing back.
• Latency as a Moat: The engineering challenge of bypassing SEC server lag by parsing raw press wires to deliver verified data in seconds.
• Agents vs. Chat: Why the future of finance lies in agentic workflows (like "Scout" and "Claude Code") rather than simple prompt-and-response interfaces.
• Internal AI Adoption: How Daloopa uses AI internally—from analyzing customer feedback to helping sales teams prep for meetings—without hiring "AI Engineers," but by making everyone an AI user.

• 00:00 – Season 3 Announcement: Weekly episodes and new co-host Jhanvi Virani
• 04:15 – The decision to open-source the investor library of skills
• 11:30 – Why an "Agent" is just a text file and the power of community iteration
• 18:45 – Monetizing the "Engine": Ferrari’s philosophy applied to financial data
• 26:20 – The transition from Alpha to Beta in AI-driven research
• 35:10 – Partnering with Anthropic and the future of Excel-based agents
• 42:00 – Obsessing over seconds: Parsing press wires vs. SEC filings

About the Guest: Thomas Li is the Co-founder and CEO of Daloopa, a provider of high-fidelity data for company financials and KPIs.

About the Host: Michael Watson is the founder of Hedgineer, building data and AI platforms for institutional asset managers.

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Most asset managers treat AI as just a chatbot, failing to bridge the gap between an LLM's general reasoning and the specific, high-stakes workflows of their actual day-to-day.

In this episode of The Hedgineer Podcast, Michael Watson sits down with Jhanvi Virani, COO of Hedgineer, to discuss the practical mechanics of deploying AI within hedge funds and asset managers. Jhanvi details her experience shadowing a CIO to translate their cognitive investment process into a digital skill—a structured framework that allows Claude to synthesize fragmented data from order management systems, SharePoint research, and consensus estimates into polished, institutional-grade outputs in a one-day turnaround. We move beyond simple prompting to explore the "Agentic Loop," discussing how local schedulers and the Claude Agent SDK are enabling systems to run autonomously 24/7.

The conversation also covers the technical nuances of the Claude Ecosystem, comparing developer-centric Claude Code with user-friendly Claude Cowork. Jhanvi shares her on-the-ground findings regarding the limitations of local vs. remote execution and why building a secure, server-side environment is the ultimate bottleneck for scaling AI intelligence across a firm.

• The Skill-Based Unlock: How shadowing investment professionals allows engineers to map complex and manual research workflows into automated skills that produce consistent, high-polish one-pagers.
• Claude Code vs. Cowork: A breakdown of why developers prefer terminal-based workflows for multitasking, while non-technical users leverage Cowork for scheduled tasks and visual connector management.
• Building "AI Native" Infrastructure: The 0-to-1 process of auditing fund workflows, building custom MCP (Model Context Protocol) connectors for legacy data vendors, and establishing organizational agent management frameworks.
• The Self-Healing Feedback Loop: Using usage analytics and "meta-agents" to observe behavior, evaluate performance, and automatically suggest system improvements, creating a self-sufficient AI framework.

00:00 - Introduction and the role of skills in unlocking automation

04:15 - Evolving daily workflows with Claude Code and Cowork

08:42 - UI vs. Terminal: Optimizing screen real estate and parallel sessions

14:30 - Testing the bounds: Automating expense reports and attachment limitations

17:45 - Windows vs. Linux runtimes and the "Local Scheduler" in Cowork

22:10 - The Agentic Loop: From Claude Agent SDK to OpenClaw deployments

29:40 - CIO Shadowing: Translating a day of research into a custom AI skill

36:50 - The future of autonomous analytics and observation agents

43:15 - Deliverables for becoming AI Native: Audits, MCP servers, and data warehouses

51:00 - AI Personification: Authenticity in communication and the risk of "AI slop."

64:20 - Team expansion in Bangalore and the tech-focus of South India

Guest Bio: Jhanvi Virani is the COO of Hedgineer, where she oversees the deployment of AI infrastructure and automation for institutional asset managers. She specializes in bridging the gap between technical LLM capabilities and high-level investment workflows.

Host Bio: Michael Watson is the founder of Hedgineer and host of the podcast, focusing on the intersection of data science, AI, and hedge fund technology.

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The traditional model of purchasing financial data is structurally misaligned with the requirements of modern AI development. While hedge funds have historically navigated opaque pricing and rigid, six-figure bulk contracts, the rise of Frontier Labs and agentic workflows demands a shift toward data liquidity and consumption-based procurement.

In this episode, Michael Watson is joined by Dan Entrup (Founder of Agnowledge) and Freeman Lewin (Founder of BrickRoad) to bridge the gap between institutional data strategy and the emerging ML data marketplace. The conversation explores why the "data-centric AI" movement is forcing a reimagining of the supply pipeline, moving away from "buying data to cover your tracks" toward a world where agents autonomously discover, score, and purchase granular datasets for real-time inference.

We analyze the friction within current procurement cycles—often involving over 80 emails for a single deal—and contrast this with the "vibe coding" revolution and the Anthropic "skills" ecosystem. By treating expertise as a distributable text-based asset, firms can bypass traditional SaaS moats and build opinionated, autonomous systems that scale far beyond the capacity of human analyst teams.

• The Shift to Consumption-Based Data: Moving away from bulk annual minimums to consumption models allows firms to trial, backtest, and identify ROI within minutes rather than months, effectively creating a "spot market" for information.
• Agents as the New Data Buyers: Unlike humans, agents require high-frequency access to small data subsets for accuracy. This creates a need for automated marketplaces where data "sells itself" to machines to maintain trust in agentic outputs.
• Skills as Monetizable Data: Anthropic’s Model Context Protocol (MCP) and "skills" framework represent a shift where organizational knowledge—such as specific financial modeling styles—becomes a portable, executable asset that can be distributed via marketplaces.
• The Decline of Legacy SaaS Moats: Software companies that rely on workflow inefficiencies or "proprietary" data that is actually generally available are facing significant valuation pressure as "vibe coding" allows firms to build custom, internal alternatives like CRMs overnight.
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00:00 - Introduction to Dan Entrup and Freeman Lewin. 08:45 - The bifurcation of the data industry: Hedge funds vs. Frontier AI Labs. 15:20 - Friction in data procurement: Why it takes 80+ emails to close a deal. 23:10 - Data-centric AI: Why better data now moves the needle more than algorithmic tweaks. 32:45 - Token optimization vs. Weight fine-tuning for enterprise value. 42:15 - Building the Agentic Marketplace: Why data doesn't sell itself to humans. 54:30 - The "SaaS is Dead" debate and the transition to consumption-based revenue. 79:00 - Anthropic Skills: Structuring and distributing expert knowledge at runtime. 98:30 - Vibe coding and the future of the autonomous, multi-billion dollar "small" firm.

Dan Entrup is the Founder of Agnowledge and a veteran data strategist who previously served as Head of Data Strategy for a Fortune 500 company. He specializes in expert network curation and helping firms navigate the complexities of data commerce.

Freeman Lewin is the Founder of BrickRoad, a frontier data lab building an agentic marketplace for data procurement and liquidity. His work focuses on establishing data liquidity through on-chain transaction histories and utility scoring mechanisms.

Michael Watson is the host of The Hedgineer Podcast and founder of Hedgineer, a firm building data and AI platforms for institutional asset managers.

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