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 podcast@hedgineer.io.

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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 podcast@hedgineer.io.

Hedgineer.io

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