Neel Somani is a quant engineer turned content creator covering power markets, AI infrastructure, and crypto. In this episode of The Hedgineer Podcast, we dig into how the AI boom is reshaping energy markets, how rising compute costs are forcing companies to measure AI ROI, and whether open source models are changing the build vs. buy decision.

About the Guest

Neel Somani is a technologist and researcher focused on the intersection of machine learning, commodities, and formal methods. Formerly a quantitative developer in the power and commodities space, he has recently gained prominence for his work in mechanistic interpretability and his contributions to solving Erdős problems using large language models.

Follow Neel on X at @neelsomani, TikTok at @neelsalami, and Instagram at @neelsalami

About Hedgineer

Hedgineer is building the AI platform for institutional investing — deploying agents, skills, and data connectors directly inside hedge funds and asset managers to transform investment and operational workflows.

The Hedgineer Podcast follows CEO Michael Watson and COO Jhanvi Virani as they navigate the frontier of AI adoption in finance, sharing unfiltered perspectives from the teams, guests, and problems they work with every day.

Subscribe for weekly analysis on AI infrastructure and institutional finance.

Watch the full episode on Spotify or YouTube at youtube.com/@hedgineer.

Listen wherever you get your podcasts.

Connect with us on LinkedIn at linkedin.com/company/hedgineer-io or reach out at podcast@hedgineer.io.

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SaaS companies are pivoting: less investment in the dashboard, more in the API. Salesforce's headless MCP suite, Ramp's CLI, Linear's AI connectors — the pattern is the same. Products are being rebuilt for agents, not humans.

In this episode, Jhanvi and Michael dig into what's driving the shift and what it means for funds evaluating their stack. They also get into the architecture question that's coming up with every data vendor they talk to: how do you actually design a good MCP server? They break down the difference between open-source and closed-source skills, where intelligence belongs in the stack, and why the firms that win this next wave won't look like tech companies in the traditional sense.

About Our Hosts:

Michael Watson is the co-host of The Hedgineer Podcast, CEO of Hedgineer, and a technologist focused on deploying AI within the institutional investment space.

Jhanvi Virani is the COO of Hedgineer and co-host, specializing in scaling operations and technology platforms for hedge funds.

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Video available on YouTube and Spotify

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#AIEngineering #InstitutionalInvesting #AssetManagement

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.
• 
  

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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• Learn more about Hedgineer: Hedgineer.io
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Welcome back to The Hedgineer Podcast, where host Michael Watson dives into the world of AI, data, and technology within asset management, hedge funds, and financial services. In this episode, Michael sits down with Jonathan Regenstein, who leads AI within Financial Services at Snowflake.

This conversation explores the critical role of data and platform strategy in the successful enterprise deployment of AI, moving beyond purely technical wins to focus on commercial outcomes. Jonathan and Michael dissect the evolution of Snowflake from a powerful SQL engine to a unified platform for AI, and debate where the intelligence layer should reside for maximum effectiveness.

• The Power of Data Sharing: How Snowflake's seamless data sharing and Marketplace revolutionized the consumption of alternative data on the buy side, drastically simplifying security and licensing workflows.
• The AI Layer Debate: A deep dive into whether the AI runtime should live natively within the data platform (Snowflake) using tools like Cortex and Intelligence, or be orchestrated externally by hyperscalers or model providers.
• Beyond the Technical Win: The shift from technology-driven AI Proofs-of-Concept (POCs) to projects scoped by commercial outcomes—revenue generation or cost reduction.
• Evaluations are the Product: The crucial importance of robust evaluation frameworks (like those provided by TruEra/TruLens) for agentic workflows to avoid "chaos at scale," and how to involve business leaders—not just engineers—in defining what success looks like.
• The Semantic Layer's Role: The concept of the semantic model as a first-class citizen in Snowflake, acting as the translator between business language and data, driving accuracy in Text-to-SQL (Cortex Analyst), and building trust with non-technical users.
• The Future of BI: How AI is driving the complete rethinking of the Business Intelligence (BI) stack, moving beyond static dashboards to dynamic, generative BI that surfaces insights and visualizations on demand.

Jonathan Regenstein is a key leader in the AI for Financial Services division at Snowflake, driving the platform's strategy in machine learning and artificial intelligence for banks, asset managers, and insurance companies.

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Welcome back to The Hedgineer Podcast. In this episode, host Michael Watson sits down with crowd-favorite returning guest, Lucas Rooney.

Lucas pulls back the curtain on the "0 to 1" journey of building a new fund, from diligencing the initial idea and recruiting top-tier talent to making the critical "build vs. buy" decisions for a foundational technology stack.

But how does launching a fund today differ from just a few years ago? One answer is AI.

Michael and Lucas dive deep into how the proliferation of AI reframes the entire approach to building systems, forcing a new focus on taxonomy, data labeling, and codifying the "thought process" of an investment from day one.

The conversation shifts to one of the most critical questions facing the industry: How do incentive structures change when an individual's knowledge and intellectual property (IP) can be instantly captured and instilled into the organization's systems?. They explore how firms must re-evaluate compensation and talent, as value shifts from executing perfunctory tasks to the high-level synthesis and compounding of IP.

• The "0 to 1" process of launching a new fund.
• Key strategies for recruiting passionate technologists and investors.
• The foundational tech stack: Designing the data/ETL, analytical, trading, and risk layers from scratch.
• How AI forces better data hygiene and process documentation.
• The "IP Capture" Problem: Rethinking talent compensation when AI can learn and retain an employee's knowledge permanently.
• Why hiring is shifting from "task execution" to "IP synthesis" and "compounding".
• The "Negative Space": Why capturing the bad ideas and hypotheses you didn't run is the next frontier for evaluating skill.

Hosted by Michael Watson, The Hedgineer Podcast dives into AI technology and data in the hedge fund, asset management, and prop trading space.

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Beyond the AI Hype with Jason Strimpel

In this episode of The Hedgineer Podcast, host Michael Watson sits down with Jason Strimpel, founder of PyQuant News, long-time Pythonista, and AI enthusiast.

They dive deep into the practical and philosophical implications of artificial intelligence in both asset management and daily life. They break down how the agentic loop works with the Anthropic Agent SDK, the components of good evaluation frameworks, and even how to talk to your kids about AI

Key topics covered in this episode:

• Drugs, Sex, and AI: Jason shares his thoughts for new parents: the three things you now need to talk to your kids about are drugs, sex, and AI. Michael and Jason then discuss the difficulty of explaining the difference between humans and AI-powered avatars or toys to children.

• The "Agentic Loop": The discussion breaks down the simplicity and power of the agentic loop, identifying it as the "core substrate" of modern agentic frameworks. This framework allows a language model to loop, use tools, and determine when to exit to solve complex problems.

• "Evals are the Product": Michael and Jason iterate on the concept that a robust set of evaluations is the real product. If you can use evals to demonstrate that an agent has harnessed intelligence to solve a specific problem space, you "own that problem".

• AI vs. Python's Rise: They draw parallels between the current AI boom and the rise of Python in the early 2010s. Both technologies were initially met with skepticism for being "black box" interpreted systems, yet they unlocked massive productivity boosts.

• Capital Allocation and Moats: The conversation tackles the modern challenge of allocating capital and defending a "software moat" when new AI tools and infrastructure are being commoditized by hyperscalers at an incredible speed.

• The PyQuant News Story: Jason shares the origin story of his popular PyQuant newsletter, which started as a personal WordPress site for bookmarking research papers and grew into a major resource for the quantitative finance community.

Hosted by Michael Watson, The Hedgineer Podcast dives into AI technology and data in the hedge fund, asset management, and prop trading space.

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