Overview
Walleye CEO, CIO, and managing partner Will England describes how he is repositioning the hedge fund around AI—not through a symbolic executive memo, but through mandatory training, widespread tool access, internal products, incentives, and personal adoption. His conviction grew from a technical background in mathematics and quantitative trading, then accelerated after an analyst demonstrated an early AI system designed to automate parts of fundamental research. Walleye now uses language models for unstructured financial data, an internal research platform called Current, AI-assisted coding, executive communication, meeting analysis, and organizational knowledge capture. England argues that employees should treat AI as operating leverage: routine production becomes faster, allowing people to work at a higher conceptual level. However, he rejects the idea that generated output removes the need for judgment, proofreading, intellectual ownership, or clear reasoning. The firm’s longer-term ambition is a collective data layer—the “Borg”—that connects documents, communications, conversations, market data, and internal records. Throughout the interview, England frames AI adoption as a leadership responsibility: firms must prepare employees for changing expectations while protecting their competitive position. His broader historical thesis is that technological transitions reward organizations able to combine frontier experimentation with institutional resources and disciplined execution.
Sections
Memorable Quotes
Statements that capture England’s approach to AI, leadership, and personal responsibility.
- Using Chat GBT is not cheating. That's a non-applicable idea from academia.
- Not using these tools is like refusing to use the internet in 1995 because it wasn't perfect.
- Words are words are cheap particularly in a word world world of AI you can create great world words very very easily.
- A jet engine won't fly by itself. You still got to hook it up to the plane.
- I don't believe in balance. I believe in harmony across different areas of life.
Deeper Implications
Synthesis derived from the organizational practices and beliefs described in the interview.
- Walleye’s strongest advantage may be organizational learning speed rather than access to any particular model. Training, social discovery, incentives, rapid beta testing, and executive participation create a loop that can absorb successive generations of tools.
- Making AI use visible and socially acceptable is a form of workflow infrastructure. By removing embarrassment around generated drafts, leadership reduces hidden adoption and makes practices easier to share, inspect, and improve.
- The “Borg” vision implies that data capture and institutional memory could become more defensible than generic model access. Competitors can obtain similar models, but they cannot instantly reproduce years of connected, organization-specific context.
- England’s strategy combines frontier behavior with institutional discipline: tolerate failed demos and decentralized experimentation, while retaining centralized expectations, data strategy, and managerial accountability.
Practical Actions
Concrete steps leaders and knowledge workers can take from the practices discussed.
- Define baseline AI proficiency for every role involving writing, research, analysis, data processing, or communication, and make managers accountable for demonstrating it.
- Run recurring informal sessions where employees demonstrate prompts, workflows, failed experiments, and useful third-party tools.
- Audit what internal communications, documents, meetings, and numerical records can be captured and connected, subject to appropriate legal, privacy, and governance constraints.
- Require AI-assisted deliverables to retain a human owner who can explain the argument, verify the output, and defend why the recommendation makes sense.
- Identify one high-information-flow workflow where faster synthesis can materially improve decisions, then test an integrated AI product against real user behavior.
- Use short voice notes or bullet points to maintain a searchable journal across work, family, and health instead of making polished writing a prerequisite for reflection.
Key Contrasts
The central distinctions used to explain appropriate AI adoption.
- In academia, AI may violate the purpose of an assessment; in business, the objective is usually to achieve a valid result efficiently.
- Manual writing spends substantial time stitching sentences together, while AI-assisted writing can redirect that time toward concepts, context, and review.
- Machines excel at processing large, complex information structures, while human investors may retain an edge in rare, ambiguous situations with limited historical precedent.
- Startups can explore unconstrained technology frontiers, while established firms possess resources and institutional reach but often move more slowly.