The Hedgineer Podcast

Epizode

• 
  Vibecoding: The Right Way | S3E15 11.08.2026 48min

  Building application prototypes has never been easier. The ceiling comes after, when someone has to deploy it, host it, and keep it running without much engineering support. This week, Jhanvi and Michael work through what stands between a good prototype and a good application: setting up AI environments that are faster to work in and harder to break, wiring in MCP connectors thoughtfully, and what changes the moment something has to run in production.Along the way they get into why the same prompt in Cowork and in Claude Desktop produces two very different dashboards, and why only one of them refreshes with live data. They also make the case that not everything needs to be a dashboard, and where scheduling an agent can be more effective. And they share advice for engineers navigating a world with more vibecoding in it, and what providing value looks like when the front office can build its own tools. About HedgineerHedgineer 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 episodes on Spotify at https://isht.ink/dFj5oaqbe or YouTube at youtube.com/@hedgineer.Audio available wherever you get your podcasts.Connect with us on LinkedIn at linkedin.com/company/hedgineer-io or reach out at podcast@hedgineer.io.Hedgineer.io
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  How Do You Hedge Against AI? S3E14 04.08.2026 36min

  AI names were sold off for several days, a heavily levered AI fund wound down its entire equities book, and then one earnings print reversed most of the damage within hours. The same industry supported two opposite readings in the same day, which says more about how funds are measuring their AI exposure than it does about AI.Michael and Jhanvi start with why so few funds formally manage their exposure to an AI factor the same way they monitor crowding or market beta, and what happens when crowded exposure to the same handful of names gets booked as idiosyncratic risk and banks are comfortable lending against a portfolio that looks market neutral. From there they get into what makes a business resilient when public sentiment continues influencing public markets, why Microsoft has a head start against their cloud competitors, and how AI is pulling those funds into compute spend they would never have taken on before.Also in this week's episode: an Anthropic model published a malicious package to PyPI, one of the most trusted repositories in the developer stack, and the accountability question that opens up when a model running inside your infrastructure harms someone outside your company. They also get into the widening gap between the people using AI at work every day and the people booing it off commencement stages, and why Meta may be better positioned than any frontier lab to serve the small businesses through WhatsApp. About HedgineerHedgineer 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 episodes on Spotify at https://isht.ink/dFj5oaqbe or YouTube at youtube.com/@hedgineer.Audio available wherever you get your podcasts.Connect with us on LinkedIn at linkedin.com/company/hedgineer-io or reach out at podcast@hedgineer.io.Hedgineer.io
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  Crafting an Enterprise AI Policy | S3E13 28.07.2026 38min

  Almost every fund has a couple of power users spending their weekends in Claude Code, prototyping automations, and proving out how useful AI can be. But then what? How do they hand it to a teammate, and how does a prototype become a production automation that compliance signs off on?Michael and Jhanvi make the case that the gap is a governance problem sitting upstream of every technical one. Everyone can build with AI. The harder half is getting compliance, IT, and cybersecurity aligned on the enablement guidelines: which tools are approved, on which operating systems, with what runtime and network observability, and who reviews usage after the fact. Answer those and you can put more powerful capabilities in your team's hands, including scheduling, skills, memory, and shared knowledge bases. Also in this episode: the case for sending email as an AI rather than as yourself, why em dashes get a message ignored, and what happened when frontier models refused to help investigate a breach that one of them had been used to cause. About HedgineerHedgineer 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 episodes on Spotify at https://isht.ink/dFj5oaqbe or YouTube at youtube.com/@hedgineer.Audio available wherever you get your podcasts.Connect with us on LinkedIn at linkedin.com/company/hedgineer-io or reach out at podcast@hedgineer.io.Hedgineer.io
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  Kimi K3: End of the Model Moat? S3E12 21.07.2026 56min

  Kimi K3 shipped this week, topped multiple coding benchmarks against Fable and Soul, and its full open source release is expected before the end of the month. Where does that leave the frontier model providers? Michael and Jhanvi get into why Anthropic and OpenAI have new incentive to lock down session data, conversation history, model reasoning, tool and skill calls, so a competitor can't distill their models into cheaper alternatives. That leads into Fable's 30 day data retention policy, and a sharper question underneath it: who owns training data that was compiled from knowledge that was never proprietary in the first place?They also reflect on this week's Hedgineer AI training sessions across clients, and what's separating the top 10% of AI users from everyone else. About HedgineerHedgineer 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 episodes on Spotify at https://isht.ink/dFj5oaqbe or YouTube at youtube.com/@hedgineer.Audio available wherever you get your podcasts.Connect with us on LinkedIn at linkedin.com/company/hedgineer-io or reach out at podcast@hedgineer.io.Hedgineer.io
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  We Got Rid of Our Forward-Deployed Engineers | S3E11 14.07.2026 1h 2min

  Hedgineer stopped hiring forward-deployed engineers. Michael and Jhanvi explain why and reflect on when the FDE operating model breaks. Hedge Fund clients showed up assuming a shared context about their business that even the strongest engineers could never have. They also talk through what replaced it: forward-deployed analysts who have worked in similar roles as the teams they're deployed to (TMT research, credit underwriting, fund accounting). After going through extensive AI training with the Hedgineer team, these FDAs are much better equipped to handle building solutions in the forms of agents and skills, leveraging the platform that our AI Engineers build.Before getting into that, they cover the week in AI: first impressions of Fable (where it earns its cost, and where it's a bazooka for a problem that needed a scalpel), and a teardown of how Claude Tags actually works under the hood, and why Tags and Claude's separate managed-agents runtime still don't talk to each other.About HedgineerHedgineer 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 episodes on Spotify at https://isht.ink/dFj5oaqbe or YouTube at youtube.com/@hedgineer.Audio available wherever you get your podcasts.Connect with us on LinkedIn at linkedin.com/company/hedgineer-io or reach out at podcast@hedgineer.io.Hedgineer.io
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  What Does It Mean to Own Your Own Context? S3E10 07.07.2026 35min

  Anthropic has been quietly redacting pieces of what Claude Code and Cowork report back through its telemetry logs. First the model's own reasoning disappeared from the traces. Then, briefly, so did users' prompts. No release notes, no explanation, just a feature flag that got flipped and eventually flipped back. Michael and Jhanvi use the incident to get into a bigger question: what does it actually mean for a firm to own its own context?Context, in their view, is everything that happens around a model call: the reasoning traces, the prompts, the environment data, the exhaustive record of what a team did with AI. As open-source models close the gap through distillation, frontier labs have a stronger incentive to lock that context down. The conversation gets into what that means for staying model-agnostic, and why a growing field of agent harnesses and open routers adds pressure on that setup.The discussion then turns to what owning your context makes possible beyond avoiding vendor lock-in. Once a firm is capturing its own usage data, it can build training around what people are actually doing, rather than a generic curriculum, and use that same data to decide where AI adoption should expand next. About HedgineerHedgineer 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 episodes on Spotify at https://isht.ink/dFj5oaqbe or YouTube at youtube.com/@hedgineer.Audio available wherever you get your podcasts.Connect with us on LinkedIn at linkedin.com/company/hedgineer-io or reach out at podcast@hedgineer.io.Hedgineer.io
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  Gone Looping | S3E9 30.06.2026 54min

  Michael and Jhanvi break down what an agentic loop actually is, how it works under the hood, and what it looks like when investment teams put it to use. From earnings recap to idea generation, the conversation covers how loops shift analysts from reactive prompting to autonomous pipelines that accelerate idea velocity.We also cover what's new in AI: Claude's new Slack tag feature and the vendor dependency risk it quietly introduces, hyperscaler developments making it easier to run agents at scale, and Estonia's new national policies built to position the country as an AI-forward state.About HedgineerHedgineer 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 episodes on Spotify at https://isht.ink/dFj5oaqbe or YouTube at youtube.com/@hedgineer.Audio available wherever you get your podcasts.Connect with us on LinkedIn at linkedin.com/company/hedgineer-io or reach out at podcast@hedgineer.io.Hedgineer.io
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  The Future of Compute Futures | S3E8 16.06.2026 49min

  OverviewThe standard order book matches trades by price and time priority, one at a time. For a fund executing a basket or a pair trade, that means legging into positions sequentially, facing the exposure problem on every leg. In 2016, Kelly Littlepage began building OneChronos around a different premise: let traders express their full intent, and let a mathematical optimization engine find the best simultaneous match.Ten years later, the same structural problem shows up in compute markets, but worse. Compute is the most perishable commodity ever created; it can't be stored, and transporting it introduces latency that destroys its value. Current proposals for cash-settled compute futures repeat the mistakes of every opaque benchmark market, leaving buyers exposed to manipulation with no physical deliverable backing the contract.The episode traces a line from FCC Spectrum auctions to modern equities markets to GPU inference token, and the throughline is consistent: markets that let participants express complex, high-level intent outperform markets that force them into rigid, sequential rules. As AI inference fragments across dozens of competing models, the next smart order router won't route equities. It will route tokens.Guest BioKelly Littlepage is the co-founder and CEO of One Chronos, an ATS powered by combinatorial auctions. He holds a background in computer science, mathematics, control systems, and economics, with deep expertise in electronic market making and electronic capital markets structure.About HedgineerHedgineer 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 episodes on Spotify at https://isht.ink/dFj5oaqbe or YouTube at youtube.com/@hedgineer.Audio available wherever you get your podcasts.Connect with us on LinkedIn at linkedin.com/company/hedgineer-io or reach out at podcast@hedgineer.io.Hedgineer.io
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  Broker Research Has an AI Problem | S3E7 09.06.2026 54min

  Sell-side research is the last data category that still resists clean AI integration. US brokers monetize through trade execution, not data sales, which means feeding analyst reports into an LLM removes the attribution that justifies the entire model. No attribution, no incentive to share. That standoff has left buy-side funds cobbling together workarounds for years.This week brought two competing answers. AlphaSense launched SuperAnalyst, a closed-ecosystem product that bundles research access with its own AI layer. Aiera went the opposite direction with an AI-native research platform built for open integration. The gap between those two bets is essentially the gap between controlling the context window and renting it.Michael and Jhanvi break down what each approach means for funds actually trying to build research pipelines, and why the choice you make now has infrastructure consequences that outlast any single model generation.About HedgineerHedgineer 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 episodes on Spotify at https://isht.ink/dFj5oaqbe or YouTube at youtube.com/@hedgineer.Audio available wherever you get your podcasts.Connect with us on LinkedIn at linkedin.com/company/hedgineer-io or reach out at podcast@hedgineer.io.Hedgineer.io
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  Driving Alpha via AI Agents in Fundamental Research | S3E6 02.06.2026 1h 7min

  The barrier to impressing institutional investors with artificial intelligence is high because portfolio managers and analysts already know their coverage universes deeply. Traditional chatbots that merely summarize 10-Ks or earnings transcripts often act as an enemy to true market comprehension, resulting in weak adoption across fundamental investment teams.In this episode of The Hedgineer Podcast, hosts Michael Watson and Jhanvi Virani sit down with Brett Caughran, founder of Fundamental Edge, to dissect the structural shift from passive chatbots to active AI agents inside institutional asset management. They explore how top-performing funds are moving past the hype to deploy targeted agent frameworks that act as an analytical exoskeleton around the fundamental research process.The conversation focuses heavily on the operational realities of data engineering and change management within hedge funds. The hosts break down how curated skill libraries can guide AI tools to operate like senior engineers, allowing non-technical professionals—such as CFOs and COOs—to construct production-grade data pipelines within an hour. They also address the critical necessity of context window management, highlighting why forcing messy research queries and raw data into a single session causes narrative generation to break down, and how separating workflows into distinct, token-optimized agent sessions solves the problem.Finally, the discussion turns to the macroeconomic and cultural implications of AI adoption on Wall Street. From the power-law distribution of alpha generation to the compression of infrastructure headcounts for new fund launches, this episode provides a realistic, problem-first evaluation of how advanced technology is actively rewriting the hedge fund playbook.Key Takeaways:The Shift to Agentic Exoskeletons: Chatbots have seen weak adoption because generic summaries destroy institutional comprehension; alpha generation requires highly personalized agents trained on a fund’s historical trades, unique workflows, and internal models.Rigorous Context and Token Management: Merging raw information gathering with narrative generation causes context bloat and degrades output quality; investment professionals must isolate clean research citations in distinct sessions to maintain deterministic control over an LLM's reasoning.Inference-Time Infrastructure Elasticity: Modern frontier models allow funds to execute complex data joins at inference time through Model Context Protocol (MCP) servers, allowing starting managers to launch with leaner infrastructure teams and compress operational headcounts.Observability is the Core of Change Management: Moving from an isolated "AI investor" to an integrated "AI investment firm" requires programmatic observability to track agent tool calls, intercept bad data queries, and convert individual best practices into firm-wide skills.About the Guest:Brett Caughran is the founder and CEO of Fundamental Edge, an institutional analyst academy providing hedge fund-style training rigor to investment professionals. Previously, he spent over a decade as a fundamental equity investor at leading asset management firms, including Maverick Capital.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.Connect with us on LinkedIn at linkedin.com/company/hedgineer-io or reach out at podcast@hedgineer.io.Hedgineer.io
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  Dev Days & Lock-In Fears: A Frontier Model Race Check-In | S3E5 26.05.2026 39min

  Anthropic and Google both had massive dev days recently. And they couldn't be more different. In this episode, Jhanvi and Michael break down what each announcement signals about the frontier model race and where it's headed. Anthropic is doubling down on enterprise agents, memory stores, and "dreaming," while Google is going wide with consumer AI, a multimodal Omni model, and Spark embedded across its entire product suite.They also get into a question that comes up with clients and candidates alike: how worried should companies actually be about vendor lock-in? Plus: what happens when you run the same agentic harness with different frontier models, why tokens per second is becoming a more important metric, and why you shouldn't switch back and forth between Cowork and ChatGPT. Key TakeawaysDecouple Architecture via Open Standards: To prevent long-term vendor lock-in, firms should deploy custom skill libraries and organizational knowledge layers as open, text-based formats stored in client-owned GitHub repositories rather than within proprietary model environments.Implement OpenTelemetry Early: The highest hurdle to switching model providers is the loss of historical session data; setting up an independent OpenTelemetry system up front ensures your firm owns its telemetry and interaction data, permitting smooth cross-provider migration.Isolate Compute with Managed Sandboxes: Utilizing self-hosted agent tool containers allows institutional firms to keep localized data execution and tools within their secure cloud environments while securely executing the core inference loop via external APIs.Focus on Immediate ROI Over Early Optimization: Many firms stall their AI adoption by over-engineering cross-cloud or cross-vendor compatibility too early. Successful deployment requires mastering one ecosystem to capture immediate time-to-value before optimizing for compute spend arbitrage.About HedgineerHedgineer 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.Connect with us on LinkedIn at linkedin.com/company/hedgineer-io or reach out at podcast@hedgineer.io.Hedgineer.io
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  Beyond the Chatbot: Building Agent-Native Enterprises with Mitchell Troyanovsky | S3E4 19.05.2026 1h 10min

  The transition from AI as a chatbot to AI as an autonomous agent requires more than just better models; it requires agents capable of regulating their own state and context at scale.In this episode of The Hedgineer Podcast, co-hosts Michael Watson and Jhanvi Virani sit down with Mitch Troyanovsky, co-founder of Basis, an agent platform specifically designed for the accounting industry. The conversation moves beyond the hype of generative AI to address the engineering realities of building "agent-native" enterprises. Mitch explains why the next frontier of applied machine learning involves closing the loop on self-improving agents—systems that can optimize their own trajectories, contexts, and tools without constant human intervention.We explore the "single pane of glass" debate: whether specialized platforms like Basis will remain the system of record or if frontier model interfaces will eventually consolidate all enterprise workflows. The discussion delves into the technical nuances of Recursive Language Models (RLMs) and the "Better Intelligence" approach, where models are leveraged to programmatically curate their own context windows to maintain performance over long-duration tasks.The episode also tackles the cultural shift required for AI adoption. From implementing "Do You Stand By This" (DYSB) protocols to ensure accountability, to the "lexical taxonomy" required to write documentation specifically for LLM consumption rather than human readers, we provide a blueprint for firms looking to move from experimental AI to production-grade agentic systems.Key Takeaways:Closing the Applied ML Loop: Why the next generation of agents will focus on self-regulation and autonomous state management to handle production workloads.The "Database-ification" of SaaS: How AI agents interacting via API threaten the value proposition of traditional software UIs, potentially reducing many SaaS tools to mere structured data stores.Recursive Language Models (RLMs): A technical look at using model intelligence to dynamically curate context at every forward pass, moving beyond simple "append-only" context windows.Writing for Machines: Why traditional human writing styles are inefficient for LLMs and how "information density" is becoming a critical engineering discipline.About the Guest:Mitchell Troyanovsky is the co-founder of Basis, a New York-based platform building AI agents for the accounting industry. He is a leading voice on the future of agentic systems at scale and the implementation of Recursive Language Models in production.About HedgineerHedgineer 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.Connect with us on LinkedIn at linkedin.com/company/hedgineer-io or reach out at podcast@hedgineer.io.Hedgineer.io
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  Who Owns the Last Mile? Frontier Labs Enter the Consulting Arena | S3E3 12.05.2026 1h 2min

  Anthropic just announced an enterprise services venture backed by Goldman, Hellman & Friedman, and Blackstone. OpenAI is raising $4B for something similar. So why are frontier model providers suddenly trying to become consultants?In Season 3 Episode 3 of The Hedgineer Podcast, Michael and Jhanvi break down what's driving the move: why handing a company a Claude license rarely translates into real automation, and why building domain-specific is critical to successful deployments. The real unlock behind all of this is agent harnesses, which have expanded what AI can do far beyond a chat interface. They dig into how providers are approaching harnesses differently and why state management and organizational memory are the differentiators that not enough people are talking about.Plus: GPT 4.5 vs. 5.5 cost dynamics, why understanding model caching could save your company thousands of dollars, and whether Apple is sitting on the consumer unlock that could shift public skepticism on AI.  About HedgineerHedgineer 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.Connect with us on LinkedIn at linkedin.com/company/hedgineer-io or reach out at podcast@hedgineer.io.Hedgineer.io
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  The Energy Behind the Intelligence with Neel Somani | S3E2 05.05.2026 51min

  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 GuestNeel Somani is a technologist and researcher focused on the intersection of machine learning, commodities, and formal methods. Formerly a quantitative researcher 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 @neelsalamiAbout HedgineerHedgineer 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.Hedgineer.io
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  The Art of Building for Agents | S3E1 28.04.2026 52min

  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.Subscribe for weekly analysis and trends within AI, Finance, and TechnologyAvailable wherever you get your podcasts!Video available on YouTube and Spotifyyoutube.com/@hedgineerQuestions? Topics you’d like for us to discuss? Email us at podcast@hedgineer.ioHedgineer.io#AIEngineering #InstitutionalInvesting #AssetManagement
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  Season 2 Finale: Open-Sourcing the Investor Library with Daloopa CEO Thomas Li | S2E10 07.04.2026 1h 3min

  Season 2 Finale: Open-Sourcing the Investor Library with Daloopa CEO Thomas LiThe 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.Special Announcement: Season 3 and New FormatBefore 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.Episode OverviewIn 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.Key TakeawaysThe 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.Timestamps00:00 – Season 3 Announcement: Weekly episodes and new co-host Jhanvi Virani04:15 – The decision to open-source the investor library of skills11:30 – Why an "Agent" is just a text file and the power of community iteration18:45 – Monetizing the "Engine": Ferrari’s philosophy applied to financial data26:20 – The transition from Alpha to Beta in AI-driven research35:10 – Partnering with Anthropic and the future of Excel-based agents42:00 – Obsessing over seconds: Parsing press wires vs. SEC filingsAbout 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.Subscribe for weekly analysis starting next season.youtube.com/@hedgineerHedgineer.io Hosted on Acast. See acast.com/privacy for more information.
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  AI Orchestration: From Custom Skills to Autonomous Hedge Fund Operations | S2E9 31.03.2026 39min

  AI Orchestration: From Custom Skills to Autonomous Hedge Fund OperationsMost 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.Key TakeawaysThe 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.Timestamps00:00 - 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 IndiaGuest 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.Links & SubscribeSubscribe for weekly analysis on AI and Asset Management.youtube.com/@hedgineerHedgineer.io Hosted on Acast. See acast.com/privacy for more information.
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  Data Liquidity and the Agentic Marketplace: Moving Beyond Bulk SaaS Contracts with Dan Entrup and Freeman Lewin | S2E8 17.03.2026 1h 6min

  Data Liquidity and the Agentic Marketplace: Moving Beyond Bulk SaaS ContractsThe 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.Key TakeawaysThe 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.Timestamps00:00 - 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.About the GuestsDan 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.Links & ResourcesSubscribe for weekly analysis on AI and data infrastructure in finance.Learn more about Hedgineer: Hedgineer.ioFollow on LinkedIn: https://www.linkedin.com/company/90976838 Hosted on Acast. See acast.com/privacy for more information.
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  AI in Finance: The Data-Centric Strategy with Snowflake's Jonathan Regenstein | S2E7 16.12.2025 52min

  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.❄️ In This Episode, We Discuss: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.👤 About Our GuestJonathan 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.Follow The Hedgineer Podcast:YouTube: (https://www.youtube.com/@hedgineer)LinkedIn: (https://www.linkedin.com/company/90976838)Twitter: (https://x.com/hedgineering)Instagram: (https://www.instagram.com/hedgineer/)Don't forget to like, subscribe, and hit the notification bell to stay updated on our latest episodes!Hedgineer.io Hosted on Acast. See acast.com/privacy for more information.
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  Technology, Talent, IP, and AI: Exploring the Foundations of Modern Hedge Fund Architecture w/ Lucas Rooney | S2E6 11.11.2025 53min

  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.🎧 In This Episode, We Discuss: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.Follow The Hedgineer Podcast:YouTube: (https://www.youtube.com/@hedgineer)LinkedIn: (https://www.linkedin.com/company/90976838)Twitter: (https://x.com/hedgineering)Instagram: (https://www.instagram.com/hedgineer/)Don't forget to like, subscribe, and hit the notification bell to stay updated on our latest episodes!Hedgineer.io Hosted on Acast. See acast.com/privacy for more information.