Overview

The 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 Bio

Kelly 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 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 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 [email protected].

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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 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 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 [email protected].

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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 [email protected].

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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 Takeaways

• Decouple 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 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 [email protected].

Hedgineer.io

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 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 [email protected].

Hedgineer.io

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 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 [email protected].

Hedgineer.io

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 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 @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 [email protected].

Hedgineer.io

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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#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.

Subscribe for weekly analysis starting next season.

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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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