Help Wanted

A job posting from a large asset manager landed in my inbox last week.

[The firm] is looking for an AI Product Leader to spearhead [our] enterprise-wide AI product strategy. This critical role will own, manage, and prioritize all AI initiatives at the intersection of our business and technology, working across Operations, Finance, Investments, Asset Management, and Other teams. You will be instrumental in streamlining our technological capabilities, building AI roadmaps, driving prioritization, and managing successful rollouts of impactful AI solutions.

This is brilliant. It shows a realization that regardless of the quality of your data and technology, it’s all for naught if your people won’t use it.

I have some experience playing at the intersection noted, and if I have learned anything, it’s that you need cooperation and a common understanding if you want the adoption needed to achieve the outcomes you are shooting for. The challenge is that each side has their own north star, measures of success, and language through which they operate. And this is before you start navigating vertically from the C-suite through management down to the end users. Each of those layers has their own priorities and considerations. It gets complicated.

What makes this role compelling is its capacity to take on a broad mandate and focus 24/7 on design, communication and execution. That capacity to focus is critical. Everyone else has their day jobs—the ones they were hired for; the ones they have been doing successfully, and, highly likely, spending more than 40 hours a week at before being asked to take on this AI thing.

That focus becomes even more critical when you consider what this role actually requires: the ability to see across competing and complementary needs simultaneously, identifying the common abstractions that create leverage and do so without losing sight of the details that make or break adoption. It takes a combination of strategic thinking, tactical execution and a fluency across domains that demands both breadth and depth. Reach for the stars.

Responsibilities & Qualifications

There is a lot to the role, so there is, correspondingly, a lot laid out in these two sections. Eighteen bullet points, in fact. To their credit, they offer a coherent listing of activities and capacities needed to manifest them, providing a roadmap that runs through data science, engineering, behavioral science, and the many facets of the business itself.

A summary from the specs:

As the AI Product Leader, you will be the driving force behind shaping our technical AI roadmap in close partnership with engineering. Your mission will be to ensure the development and deployment of impactful, scalable AI solutions that accelerate productivity and innovation across [the firm]. The ideal candidate will possess a strong foundation in product management and/or engineering, with a proven ability to translate complex business needs into cutting-edge technical solutions. You’ll navigate [the firm’s] diverse business units, identifying opportunities for AI to deliver significant value and championing a user-centric, agile approach to product development.

In layman’s terms, this role will lead the AI roadmap, working hand-in-hand with engineering to turn ideas into real products. The goal is simple: build and roll out AI tools that boost productivity and spark innovation across the firm. To do that, leadership needs both product and technical know-how, plus the ability to turn messy business problems into clear outcomes. They’ll work across all of the firm’s businesses to spot where AI can add the most value and make sure the tools are built with users in mind and improved quickly through feedback.

The core elements that jump out are:

• Impactful: Perhaps this is obvious, but I still give credit for beginning with the end in mind. Understanding markets and harvesting returns have always been my main thing—technology, including AI, is a means to a business end.

• Scalable: GenAI makes prototyping and building functional proof of concepts demonstrably easier. It is easy to be seduced into thinking they can replace existing tools, or be rolled out more broadly. But at this point they are fragile, and when they break the signs can be easy to miss. Or not. Hidden within scale is the issue of security. There is a whole post to be written on this.

• Innovate: The I-word can be a cliche—we had an Innovation Day at my old shop twenty years ago. But what I read here is an understanding that like ideas, solutions need to be pursued with conviction, but held loosely. The competencies of the technology are simply evolving too fast to build otherwise.

• Translate: This may be the secret sauce. As noted, there is a rat’s nest of north stars, objectives and languages that must be navigated and reconciled. People frequently see differences when there is mostly similarity; just as often, there are stark differences masked by common misunderstandings. Being effectively multi-lingual is table stakes.

• Engineering: For all the advancements in data science, enterprise scale AI sits on a foundation of sound engineering fundamentals. Security, transparency, memory, search, agents and tools all need to be deliberately designed and integrated. Engineers do this.

• Product Management: This recognizes the softer skills that raise the likelihood that the people whose problems you are solving will participate in the building, and ultimately, use the AI solutions provided. Product oriented leadership helps overcome the barriers of adoption—inertia, fear, misunderstanding, and the lack of time.

Product Mindset

The tell that this unnamed institution “gets” the critical importance of adoption is their explicit framing of the role as Head of AI Product. Including Product in the job title emphasizes the human dimension. It’s worth noting they are also hiring someone to lead their AI Platform development, where the expectations and requirements are appropriately focused on the technical. This separation shows they understand the difference between building capabilities and driving adoption, and that both require dedicated expertise.

I spent over two decades in the front office of a large hedge fund building and innovating with technology in support of my day job. If you had asked me I would have said I was creating capabilities. The product was our returns. It wasn’t until I was readying myself to run product at a fintech that I came to appreciate that much of the process behind creating those capabilities was rooted in a particular mindset. Namely, working backwards from the outcomes wanted, identifying where we could get the most leverage, ease of use, and collaboration.

Coming at it from the direction of thinking “we can build this and you should want it” doesn’t work. I’ve had to step over too many corpses—some of my own making—of projects that failed to launch, reach escape velocity, or land where hoped. Most were well-intentioned, some were vanity projects, none were adopted.

Getting Started

Some public breadcrumbs are available, but I don’t really know the current state of play at this company. That said, the new AI Product Leader will undoubtedly inherit a complex landscape, but there’s a logical sequence to navigating it successfully.

Understand the Third Rails
In a highly regulated industry such as they’re in, the first job isn’t identifying opportunities but staying out of trouble. Age teaches you that. What simply can’t be done? What data can’t leave? What’s allowed in? Which processes require human sign-off? Where does compliance draw hard lines? What are acceptable tradeoffs? These are design parameters that will shape every solution. Better to know upfront so they can be incorporated in the components with which everything else will be built. Solving the security and regulatory requirements at the beginning will save time, effort and legal fees down the road.

Define and Prioritize Outcomes
Start with the business outcomes that matter most: faster decision-making, reduced operational risk, improved communication, whatever moves the needle for this firm. Identify the common abstractions that when addressed, offer the greatest operational and technical leverage. Then work backwards to identify which AI interventions could realistically impact those outcomes. This isn’t about what’s technically possible, per se, it’s about taking what’s possible to accelerate productivity and innovation across all business units to generate higher returns.

Provide Requirements, Resources, and Cover
Once you’ve identified the highest-leverage opportunities, the job becomes enablement. Internal stakeholders need to provide clear requirements and constraints around their particular use cases. Developer resources need to be secured (data access, compute power, but also time and attention from subject matter experts). Most importantly, provide air cover from organizational politics or when experiments don’t work out. Innovation requires permission to fail fast and learn quickly.

Experiment, Implement, Innovate
While there is experimentation and innovation taking place at every step, I’m referring here to a more organizational and sequential context. Large, successful organizations do not have the luxury of revolutionizing how they operate overnight. Better to start by learning what can and cannot be done on representative but clear use cases. At this point, you have many siloed efforts—many unknown even to management.

Next comes the implementation phase, which is where this company is entering. At this stage, you bring in common infrastructure, standardized tooling, governance frameworks, and user feedback systems needed to scale safely and predictably. Executed well, management and users will be delighted, but still able to recognize the organization they work for.

With time, the company will gain deep competencies and a clearer understanding of where AI can best add and capture value. Most importantly, it will become part of the company’s culture. Add to this the ever-growing capabilities of the technology, and there will be opportunities to reimagine how business is done. Just don’t start with that.

Execute the Work
The day-to-day comes down to signal extraction and communication. Identify what’s working (and what isn’t) from user behavior, not just user feedback. Then communicate those insights up, down, and across the organization in language each audience understands. The C-suite cares about business impact; engineers care about technical feasibility; end users care about their daily workflow. Same data, different stories.

Success in this role won’t be measured by the sophistication of their AI, but rather by how much the organization’s behavior changes for the better and the outcomes that result.

Wrapping Up

If you are reading this it’s safe to assume you already believe that we are currently in an extraordinary time. Just from when I entered the workforce, the technical gains starting with the PC have compounded to a level that is hard to comprehend. And now we have GenAI. It can feel like the growth curve is pointing straight up.

With this progression comes hyperbole. It is easy to get swept up in the emotion, or conversely, become overly skeptical. Both reactions are understandable but neither is productive, certainly in the extreme. Balance needs to be struck across risks, rewards and human nature. Part of that balance comes from recognizing the scope of the technology needed paired with an understanding of the needs and experiences of those who will be using it. This is where the worlds of engineering and product meet. The most progressive organizations recognize this duality, and are acting accordingly.

When wanting to understand what a company is actually doing, rather than just saying, I have found job postings invaluable. They show where the company is spending their money; they reveal where management sees their future, and who and what is needed to get there. I hope you have found this particular walk through revealing. Please reply and comment with your thoughts.

I’ve purposefully avoided including the name of the asset manager to avoid the distraction that comes with iconic names. I can tell you, though, it’s not Tudor. That would be too easy.

Click through for FAQs and Mind Map summaries of the key concepts from article

Custom GPT for Light Edits

Click above to access a custom GPT we spun up to help clean up our own writing. It’s designed to apply the lightest of touches. If you’re a paying ChatGPT customer, it’s free to use (we don’t get a cut). Feel free to play around with it as much as you’d like.

Disclaimer: The information contained in this newsletter is intended for educational purposes only and should not be construed as financial advice. Please consult with a qualified financial advisor before making any investment decisions. Additionally, please note that we at AInvestor may or may not have a position in any of the companies mentioned herein. This is not a recommendation to buy or sell any security. The information contained herein is presented in good faith on a best efforts basis.

Introduction

I recently came across a paper that speaks to much of what my career has been about—using computers, code, data, and ideas to generate returns: The Disappearing Edge: AI, Machine Learning, and the Future of the Discretionary Portfolio Manager by Frank J. Fabozzi, Andrew Chin, Igor Yelnik, and Jim Liew. Their practical approach and understanding of the continuum between systematic and discretionary is what caught my attention. Human judgment isn’t going away, though, but when, where, and how it’s applied will evolve. I’d be inclined to call it the changing edge rather than disappearing.

Go to paper

Abstract

The discretionary portfolio manager’s role is evolving as artificial intelligence and machine learning increasingly supplement or replace traditional investment insight. This article explores how advances in large language models and deep learning are narrowing the discretionary edge once defined by judgment and narrative skill. A new model is emerging in which the portfolio manager acts as an allocator and model steward, rather than a sole decision-maker. We examine the implications for governance, performance, and risk and argue that firms that retool talent, workflows, and oversight may be best positioned to harness the promise—and manage the limits—of AI-driven asset management.

What follows are three styles of summaries we use to highlight and profile work we believe is especially relevant to investors today. Each offers a different angle on the material. Any shortcomings or errors are ours.

One of our motivations for starting AInvestor was to create a reason to actively engage with AI in an operational setting—learning by doing. I maintain active editorial oversight of instruction, model, and platform choices, but almost everything in the summaries below was written by AI. In the context of what we’re doing, I see this as a feature, not a bug. By experiencing the highs, and yes, the lows, we can better understand both the possibilities and the limitations of this new generation of AI.

Custom GPT for Light Edits

Click above to access a custom GPT we spun up to help clean up our own writing. It’s designed to apply the lightest of touches. If you’re a paying ChatGPT customer, it’s free to use (we don’t get a cut). Feel free to play around with it as much as you’d like.

Learnings & Takeaways

1. The discretionary edge is being redefined

• Author’s words: “The edge once associated with discretionary judgment may no longer reside in the decision itself, but in how PMs frame, interpret, and adapt machine-generated insights.”

• Asset management context: Core skills like interpreting earnings calls or reading sentiment are increasingly automated. The PM’s value shifts to model calibration, governance, and contextual judgment.

• Our add: A new source of edge will come from knowing when to accept it’s better to step back and let the computer do the work, and when it is appropriate to override.

2. Discretionary vs. systematic is a spectrum

• Author’s words: “No investment strategy is entirely devoid of either discretion or structure.”

• Asset management context: LLMs allow discretionary PMs to scale breadth and systematic managers to add qualitative nuance. The line between styles is now blurred.

• Our add: This spectrum has always been so, at least in the forty years we’ve been involved. What’s changing is variability in decisions–which is the essence of discretion–which the new AI-powered tools will increasingly be made a choice, not an artifact of circumstances.

3. Hybrid PM models are often superficial

• Author’s words: “Hybrid frameworks only succeed when discretionary and quantitative teams collaborate closely, share outputs, and have mutual accountability.”

• Asset management context: Many PMs use quant tools only when they confirm existing views, ignoring conflicting signals. True integration requires cultural, governance redesign.

• Our add: We’ve been involved in projects attempting to achieve synergies between the discretionary and systematic–it is hard. In addition to the considerations covered in the paper, the requirements around data and other inputs differ across the two dimensions. There is a whole article for us to write on these differences, why and how they matter.

4. AI is eroding the last bastions of human intuition

• Author’s words: “AI models may soon rival discretionary PMs in their ability to anticipate discontinuities.”

• Asset management context: Regime shifts, once the hallmark of human intuition, are now detectable through models trained on diverse, unstructured signals.

• Our add: Regime identification and correctly anticipating change is the holy grail. We’ll take whatever help we can get.

5. Complexity creates credibility risks

• Author’s words: “Model opacity can hinder the ability to perform robust performance attribution and risk oversight.”

• Asset management context: Complex ML may outperform, but without interpretability, fiduciaries struggle to defend exposures to boards, consultants, or regulators.

• Our add: Few things are less transparent than the real reasons behind a discretionary trader’s decisions. We supported one of the best for two decades; stylized explanations are the best you should typically expect. And it’s not like they are trying to hide anything. Optimistically, AI will facilitate creating credible audit trails (we address this with Michael Mauboussin in our discussion here).

6. Institutional readiness determines success

• Author’s words: “Realizing these benefits depends on institutional readiness, not just technological capability.”

• Asset management context: Dashboards and overlays alone don’t change outcomes. Firms must retool workflows, governance, and talent development for AI to be transformative.

• Our add: Knowing what your “good” looks like determines success. Good process, good data, good IP, good governance, good incentives, and more.

Go to paper

FAQs

Q1: How is the role of the discretionary portfolio manager (PM) changing in the age of AI?

From Decision-Maker to Model Steward
The article makes clear that the PM’s traditional role—making conviction calls based on qualitative judgment—is shrinking. AI now handles tasks like parsing earnings calls, summarizing filings, and mapping sentiment across assets. That leaves the human with oversight functions: interpreting machine output, calibrating signals to liquidity and risk budgets, and deciding when to override.

Context for Asset Managers
This isn’t a minor shift. In practice, a PM’s daily work increasingly resembles editing, curating, and translating model results rather than originating every idea. Firms that cling to the “star manager intuition” model will underperform firms that redefine PMs as model stewards.

Q2: Is the distinction between discretionary and systematic strategies still valid?

A Spectrum, Not a Split
The authors argue the binary is outdated. Every “discretionary” manager now uses quantitative tools, and every “systematic” manager makes discretionary choices in model design and parameter setting.

Practical Implications
For allocators and consultants, the relevant question isn’t “Is this discretionary or systematic?” but “Where does it sit on the spectrum, and how are human and machine inputs combined?” Discretionary PMs are now using LLMs to scale breadth across universes, while systematic managers use AI to add qualitative nuance. The edge comes from the integration design, not the label.

Q3: What is the reality of the “hybrid PM” model?

Superficial Integration Is the Norm
Many firms talk up “hybrid” approaches, but the paper shows that most implementations are shallow. Quant screens or sentiment dashboards are bolted on, and PMs selectively use them when they align with their priors. This is confirmation filtering, not integration.

Why Most Firms Get Stuck
The sticking point is governance and incentives. If accountability remains tied to the discretionary PM alone, then quant inputs will always be treated as optional. True hybridization requires reassigning decision rights and creating joint accountability between discretionary and quantitative staff. Without that, the “hybrid PM” is more myth than practice.

Q4: Can AI fully replace human intuition, especially during regime shifts?

AI Closing the Gap
The article notes that regime detection—once a human edge—is being eroded. Models trained on unstructured data (policy announcements, commodity flows, social sentiment) now flag early signs of discontinuity. These signals often surface before humans recognize them.

Remaining Human Role
Still, machines are not yet fully reliable in low signal-to-noise environments or under unprecedented shocks. Human oversight matters in spotting spurious correlations, contextualizing geopolitical nuance, and deciding when to discard model output. The edge is no longer “intuition versus machine” but “intuition applied to supervising the machine.”

Q5: What risks do complex machine learning models create?

Opacity as a Fiduciary Problem
High-dimensional ML models can improve out-of-sample prediction and Sharpe ratios. But they are often black boxes. When exposures can’t be explained, fiduciaries cannot defend them to boards, consultants, or regulators.

Governance and Accountability
Opacity also clouds accountability. If a model misfires, is the fault with the PM who accepted the output or the quant team who built it? Without interpretability tools and audit trails, performance attribution and risk oversight break down. The paper argues that complexity is acceptable only if paired with transparency frameworks like feature attribution and robust documentation.

Q6: What skills must tomorrow’s PMs develop?

Model Literacy as a Core Competency
The paper emphasizes that future PMs don’t need to code neural nets but must understand how models fail. This includes recognizing overfitting, model drift, and constraint violations.

Cross-Disciplinary Integration
The winning PM profile blends market expertise with technical fluency. They must translate model outputs into investment decisions that respect liquidity, regulation, and client mandates. Firms that treat PMs as “market-only experts” risk falling behind.

Q7: What should asset management firms do to adapt?

Beyond Dashboards and Overlays
Superficial adoption—dashboards, sentiment screens, or GenAI summaries—does not change outcomes. The authors stress that genuine adaptation requires embedding models in rebalancing, signal weighting, and transaction cost management.

Organizational Redesign
That means iterative feedback loops between quants and PMs, shared attribution of performance, and protocols for model ownership and override. Talent development must include prompt engineering and fine-tuning, with PMs contributing labeled data from their judgment process. Firms that fail to retool incentives and governance will be left with symbolic AI, not competitive AI.

Go to paper

Mind Map

I. The Disappearing Edge: AI, Machine Learning, and the Future of the Discretionary Portfolio Manager

Purpose: Explore how AI and ML are reshaping discretionary portfolio management.
Key Theme: The role of the PM is shifting from decision-maker to curator, interpreter, and steward of models.

II. Discretionary vs. Systematic: The Borderline

A. Traditional Definitions

• Discretionary: Relies on human judgment, experience, qualitative/quantitative inputs.

• Systematic: Driven by models with limited human intervention.

B. Continuum, Not a Dichotomy

• All strategies contain elements of both.

• Boundaries are blurring with AI/LLM integration.

C. Practical Differences

• Systematic: backtesting, attribution, structured risk controls.

• Discretionary: adaptable but inconsistent, harder to scale.

D. Grinold Fundamental Law

• Discretionary = depth (high IC, low breadth).

• Systematic = breadth.

• AI increases breadth for discretionary, depth for systematic.

III. From Edge to Erosion

A. Historical Edge

• Human ability: tone, sentiment, subtle shifts, intuition.

B. AI’s Capabilities

• Parse transcripts, detect contradictions, infer policy directions.

• Faster, scalable, cost-effective.

C. Human Relevance

• Still strong in low signal-to-noise and regime shifts.

• Edge shifting to framing and interpreting machine insights.

IV. The Myth of the Hybrid PM

A. Concept

• Human + machine = best of both worlds.

B. Reality

• Often superficial integration.

• Confirmation filtering: use models when aligned, ignore otherwise.

C. Structural Barriers

• Legacy incentives and accountability.

D. Stronger Models

• True hybrid = redesign roles, workflows, and accountability.

• Example: “Iron-person” PM curating signals and narratives.

V. Where the Edge Used to Be and Where It Is Going

A. Past Sources

• Interpreting qualitative signals, spotting regime changes.

B. AI Encroachment

• LLMs summarizing, DL uncovering nonlinearities.

• AI starting to handle regime detection.

C. New Human Value

• Oversight, interpretation, refining assumptions, contextualizing outputs.

VI. Lessons from the Machine Learning Literature

A. Early Findings

• Nonlinear ML > linear models in prediction.

B. Practical Limitations

• Predictive accuracy ≠ investability.

• Need governance, explainability, transaction cost integration.

C. Dimensionality & Complexity

• Reduce overfitting via dimensionality reduction.

• Balance complexity with interpretability.

D. NLP & GenAI

• Extend ML into unstructured data (earnings calls, filings, news).

E. Implications for PMs

• Interpretation and application > raw generation.

• Fluency with models critical.

VII. The GenAI Shockwave

A. Unique Capabilities

• Generate text, simulate scenarios, interpret qualitative data.

B. Applications

• Draft research, translate macro, test judgments.

C. Implications

• Challenges PM narrative role.

• Enhances efficiency, assumption testing, documentation.

VIII. Redefining the Edge from Discretion

A. Past Role

• Synthesizer of info, conviction builder.

B. New Role

• Model calibration to constraints.

• Oversight, integration, validation.

C. Talent Shift

• PMs need model fluency, cross-disciplinary integration.

IX. The Virtue of Complexity? Or a Crisis of Credibility?

A. Complex Model Promise

• Nonlinear, high-dimensional patterns = performance gains.

B. Caveats

• Dependent on methodology, diminishing returns in noisy settings.

C. Governance Challenges

• Complexity = opacity.

• Problems of accountability, interpretability, client communication.

D. Balanced View

• Complexity useful if responsibly managed with explainability.

X. Adapting or Disappearing: What Firms Need to Do Now

A. Superficial vs. Deep Integration

• Dashboards/overlays vs. core process redesign.

B. Organizational Changes

• Shared attribution, iterative feedback loops.

• Talent: model literacy, prompt engineering, fine-tuning.

C. Governance

• Protocols for ownership, overrides, escalation, auditability.

D. Bottom Line

• Institutional readiness > tech capability.

• Superficial adopters risk irrelevance.

XI. Conclusion: When the Machine Becomes the PM

A. Redefined Human Role

• Editors, curators, translators of model output.

B. Firm-Level Practices

• Embed ML into core, foster cross-functional collaboration.

C. Success Factors

• Interpretability, constraint calibration, governance alignment.

D. Future of Discretion

• Traditional edge eroded.

• New edge = model supervision, validation, stewardship.

Go to paper

Notes on contributors

Frank J. Fabozzi

Frank J. Fabozzi is a Professor of Practice in Finance at Carey Business School, Johns Hopkins University, Baltimore, Maryland.

Andrew Chin

Andrew Chin is the Chief AI Officer and the previous Head of Investment Solutions and Sciences, Chief Risk Officer, and Head of Quantitative Research at AllianceBernstein LP in New York, New York.

Igor Yelnik

Igor Yelnik is the Founder, Chief Executive Officer, and Chief Investment Officer at Alphidence Capital Ltd., London, United Kingdom.

Jim Liew

Jim Liew is an Associate Professor of Practice in Finance at Carey Business School, Johns Hopkins University, Baltimore, Maryland.

References

• Bartram, Söhnke M., Jürgen Branke, and Mehrshad Motahari. 2020. Artificial Intelligence in Asset Management. CFA Institute Research Foundation.

• Blitz, David, Thijs Hoogteijling, Harald Lohre, and Patrick Messow. 2023. “How Can Machine Learning Advance Quantitative Asset Management?” The Journal of Portfolio Management 49 (9): 78–95.

• Buncic, Daniel. 2025. “Simplified: A Closer Look at the Virtue of Complexity in Return Prediction.” SSRN Working Paper

• Bybee, Alexander. 2024. “Ghost in the Machine: AI Misalignment and the Limits of Model Control.” Ethics and AI Quarterly, forthcoming

• Cartea, Álvaro, Qianjin Jin, and Yichen Shi. 2025. “The Limited Virtue of Complexity in a Noisy World.” SSRN Working Paper

• Chen, Liyun, Markus Pelger, and Juanyi Zhu. 2024. “Deep Learning in Asset Pricing.” Management Science 70 (2): 714–750.

• Chin, Andrew. 2025. “Leveling the Divide Between Discretionary and Systematic Investing: How AI Enables Breadth and Depth.” The Journal of Portfolio Management 51 (6): jpm.2025.1.730.

• Grinold, Richard C. 1989. “The Fundamental Law of Active Management.” The Journal of Portfolio Management 15 (3): 30–37.

• Gu, Shihao, Bryan Kelly, and Dacheng Xiu. 2020. “Empirical Asset Pricing via Machine Learning.” The Review of Financial Studies 33 (5): 2223–2273.

• Kelly, Bryan, Semyon Malamud, and Kien Zhou. 2024. “The Virtue of Complexity in Return Prediction.” Journal of Finance 79 (1): 459–503.

• Kirtac, Kemal, and Guido Germano. 2024. “Sentiment Trading with Large Language Models.” Finance Research Letters 62: 105227.

• Kozak, Serhiy, Stefan Nagel, and Shrihari Santosh. 2020. “Shrinking the Cross-Section.” Journal of Financial Economics 135 (2): 271–292.

• Lopez-Lira, Ariel, and Yuehua Tang. 2023. “Can ChatGPT Forecast Stock Price Movements?” SSRN Working Paper.

• Rudin, Alexander, Igor Yelnik, Juan Antolin–Diaz, Frank J. Fabozzi, and Suhail Shaikh. 2025. “From Economics to AI: Integrating Discretionary and Quantitative Approaches in Asset Management.” The Journal of Portfolio Management 51 (1): jpm.2025.1.737.

• Yelnik, Igor. 2016. “On the Style Edge: Discretionary vs. Systematic.” The Hedge Fund Journal 116.

Disclaimer: The information contained in this newsletter is intended for educational purposes only and should not be construed as financial advice. Please consult with a qualified financial advisor before making any investment decisions. Additionally, please note that we at AInvestor may or may not have a position in any of the companies mentioned herein. This is not a recommendation to buy or sell any security. The information contained herein is presented in good faith on a best efforts basis.

Introduction

It makes my world bigger.

That’s probably the best answer I can give to the question of why I’m doing AInvestor. The author of the note we are profiling here is a good example. I’m meeting new people and exploring their ideas—sometimes directly, sometimes from afar. I’ve never met or talked with Ben Lorica, but I’ve found his work to be a steady stream of signal-rich content since I started reading him.

Gradient Flow isn’t about investing per se, but it’s very much about the process of making present-day AI useful. In that sense, it has a lot to offer those of us looking to harness AI in the service of investment returns. Where I think I can play a role is in curating, translating, framing, relating, and making his insights practical for professional investors and the people who support them.

What follows are three styles of summaries we use to highlight and profile work we believe is especially relevant to investors today. Each offers a different angle on the material. Any shortcomings or errors are ours.

Go to article

One of our motivations for starting AInvestor was to create a reason to actively engage with AI in an operational setting—learning by doing. I maintain active editorial oversight of instruction, model, and platform choices, but almost everything in the summaries below was written by AI. In the context of what we’re doing, I see this as a feature, not a bug. By experiencing the highs, and yes, the lows, we can better understand both the possibilities and the limitations of this new generation of AI.

Custom GPT for Light Edits

Click above to access a custom GPT we spun up to help clean up our own writing. It’s designed to apply the lightest of touches. If you’re a paying ChatGPT customer, it’s free to use (we don’t get a cut). Feel free to play around with it as much as you’d like.

Learnings & Takeaways

1. Data is the foundation

• Author’s words: “The core issue in enterprise search is the nature of the data itself… garbage in, garbage out.”

• Asset management context: Research notes, models, policy documents, and compliance memos must have clear ownership, versions, and effective dates. Drafts and duplicates poison retrieval. Your “gold copies” are the only ones that matter.

• Our add: I would underscore the temporal element. Your data and research content will evolve over time; you don’t want to only consider the most recent but you do want to prioritize it.

2. Relevance is contextual

• Author’s words: “Relevance is deeply contextual and ambiguous.”

• Asset management context: A PM looking for “exposure” wants sector/theme/positioning, while compliance might mean a policy definition. Engines must understand user role, scope and perspective, not just keywords.

• Our add: The ability to filter for relevance is critical. [add here… your IP/view of the world… ]

3. RAG 2.0 is required

RAG colloquially refers to retrieval-augmented generation — a technique that improves LLM responses by grounding them in external data sources.

• Author’s words: “RAG is not a magic bullet… the reliable pattern is ‘RAG 2.0.’”

• Asset management context: Answers about GAAP vs. non-GAAP earnings, fund exposure, or risk limits must come from document intelligence → hybrid retrieval → reranker → model that cites sources and abstains if unclear.

• Our add: Think process, infrastructure, tools and orchestration design.

4. Curated answer engines work

• Author’s words: “The practical approach is ‘curated answer engines’ for specific domains.”

• Asset management context: Stand up narrow engines for earnings/models, risk/exposure, compliance policies, and investor relations (RFPs/DDQs). PMs and IR teams don’t trust a monolithic “Google for the firm.”

• Our add: Pulling on the orchestra metaphor, you need a deliberately curated team of players and instruments. Some parts of the AI stack reward broad capabilities, your role and task specific tools do not.

5. Service, not product

• Author’s words: “Enterprise search is a service, not a product.”

• Asset management context: Expect ongoing integration and tuning. Linking OMS, RMS, compliance, and SharePoint, for example, requires professional services. Budget for stewardship, not just licenses.

• Our add: Getting your data and IP to work effectively with AI isn’t a one-time decision or purchase. Budget and plan for the journey — you’ll be rewarded.

6. Reliability beats benchmarks

• Author’s words: “Enterprise search is never going to be turnkey… reliability comes from explainability, clear citations, and internal test sets.”

• Asset management context: Forget public LLM benchmarks. What matters is whether the system reliably points your PM to the right model tab or policy clause, every time. Or when it breaks, it breaks in a predictable manner.

• Our add: This is the AI equivalent of both sides of an accounting ledger footing out. For software developers — think unit tests. Moreover, include both relevant examples and questions that are intentionally unanswerable.

7. Agents are the future

• Author’s words: “The emerging third paradigm is the agent: ‘do this task for me.’”

• Asset management context: Beyond retrieval, agents will run exposure reports, pull comp tables from filings, or draft DDQ responses by orchestrating multiple steps across systems.

• Our add: This is where most of the investing “front office” will see AI in action and the attendant benefits. Massive efficiency gains can be plowed back into all the inefficient activities—talking, debating, thinking, long-form reading—that are the true alpha in the investing process.

8. It’s a systems problem, not an AI problem

• Author’s words: “Enterprise search is fundamentally a systems engineering and data governance challenge that happens to use AI.”

• Asset management context: Winning teams will focus less on model hype and more on curating data, encoding house rules, and governing workflows.

• Our add: Good engineering force multiplies good data science.

9. Pragmatic steps now

• Author’s words:

  • “Start with a Data Census, Not a Model Evaluation.”

  • “Ship Hybrid Retrieval with Reranking.”

  • “Stand Up One Curated Answer Engine to Pilot.”

  • “Evaluate Privately and Continuously.”

  • “Think in Workflows, Not Just Answers.”

  • “Budget for Integration and Stewardship.”

• Asset management context: Begin by cleaning research/model repositories, building one reliable compliance or earnings answer engine, and standing up private evaluation before expanding.

• Our add: Develop early on a clear picture of what good looks like. And get started.

Go to article

FAQs

Q1: Why is enterprise search still considered broken inside investment firms?

Messy Data as the Core Problem
The article argues that the real obstacle isn’t the model—it’s the data. Enterprise information often lacks clear ownership, governance, and version control. Stale, duplicative, and “shadow” documents creep into repositories, polluting the knowledge base and eroding trust. For investment firms, this translates to outdated models, draft decks, and policy versions that surface at the wrong time.

Contextual Ambiguity
Unlike the public web, the enterprise lacks universal authority signals. In practice, this leads to problems of timing and authority, among others. A query for “China exposure” might surface an analyst’s draft, a PM’s offhand remark, or the official risk report. Which one matters depends on recency and hierarchy.

Q2: What role does Retrieval-Augmented Generation (RAG) play—and why isn’t it enough?

RAG’s Dependency on Retrieval
The article stresses that retrieval-augmented generation (RAG) is “not a magic bullet but a component in a larger system.” If the wrong documents are retrieved, the generated answer is not just useless but potentially worse than a hallucination.

The Reliable Pattern: RAG 2.0
AI models are trained on data, but they are not databases. The fundamental implication is that the AI needs to be augmented with relevant and credible information. Initial efforts at this have been largely simplistic. A stronger architecture including document intelligence, a mixture of retrievers, and a reranker trained on business rules is needed. This ensures that when a PM asks about the impacts of tariffs, for example, the system points to the the latest facts, opinions and decisions within the firm.

Q3: Why are curated answer engines more effective than a single “Google for the firm”?

Domain-Specific Reliability
Employees want direct answers, not a dump of links. The article argues for narrow, curated “answer engines” with well-defined scope so users don’t have to hunt further. This makes responses more consistent, predictable, and trustworthy.

Investment Firm Application
In practice, that means separate engines for earnings/models, risk/exposure, compliance policies, and investor relations materials (Investment Memos/DDQs). PMs and IR teams won’t adopt a one-size-fits-all search box—but they will use tools that reliably serve their domain.

Q4: Why is enterprise search a service, not a product?

Fragmented IT Reality
The article highlights that data is spread across dozens of SaaS platforms, file shares, and legacy systems. A turnkey solution rarely survives contact with this complexity.

Platform Plus Services
The article stresses that search is not plug-and-play. A flexible platform must be paired with professional services—teams that handle integration with firm systems, encode business rules, curate domain-specific engines, and continuously clean, tune, and govern the data. For investment managers, this means budgeting not just for licenses but for the ongoing staffing required to keep answers reliable and trusted.

Q5: How should reliability in enterprise search be measured?

Limits of Public Benchmarks
Open-domain leaderboards are misleading because they cannot capture the private, messy, and contextual nature of enterprise data.

Internal Evaluation Suites
The article emphasizes building firm-specific test sets that reflect real questions—like, “How has the narrative on why to own this stock changed with the latest news?”—alongside unanswerables to test the model’s ability to say “I don’t know.”

Reliability is proven on your own data and use cases, with consistent, explainable answers and clear citations, not by chasing external accuracy scores.

Q6: What are the biggest pitfalls firms should avoid when building enterprise search?

Over-Reliance on Larger Models
A common mistake is believing that simply increasing context window or model size will fix retrieval issues. The article warns this raises costs and latency without addressing the trustworthiness of the output.

Monolithic Search Boxes
Broad, one-size tools are prone to unpredictability and low adoption. Domain-specific curated engines earn trust because users know what they cover and what they don’t. They reduce cognitive overload.

Q7: What pragmatic steps should asset managers take right now?

Start with a Data Census
The first step is auditing what data you have, where it lives, who owns it, and which version is authoritative. Without this foundation, no retrieval system will be reliable. Second, define what “good” looks like for your use cases.

Build in Small, Testable Increments
The article advises shipping hybrid retrieval with reranking, standing up one curated answer engine, and testing internally before scaling. For a hedge fund, that might mean piloting in the back office, compliance or covering earnings releases first, proving adoption, then expanding to other domains and use cases.

Q8: What’s next after search and chat interfaces?

The Rise of Agents
The article describes a third paradigm shift: from search boxes (“find me a document”) to chatbots with RAG (“answer my question”) to agents (“do this task for me”).

Agentic Workflows in Asset Management
For funds, this could mean agents that not only retrieve documents but also run exposure reports, extract data from websites, compare and contrast internal and external research, stress-test decisions, or monitor for events that might impact the portfolio—executing multi-step workflows with human checkpoints.

Editor’s note: The point of using agents to optimize efficiency is to maximize the time one can be “inefficient”—spending time with others or in reflective work that prepares you for the differentiated decisions which are the true source of alpha.

Go to article

Mind Map

I. Enterprise Search Reality Check

Summary: Despite advances in AI and foundation models, enterprise search remains difficult because the real obstacles are not models, but data and context .

• Disconnect between model capability (e.g., explaining quantum mechanics) and inability to answer basic enterprise questions.

• Obstacles lie in messy, unstructured, and duplicative enterprise data.

II. Foundational Rot: Data Quality Problem

A. Nature of Data

• Enterprise information lacks clear ownership, governance, and structure .

• Staleness, duplication, and “shadow documents” create ambiguity.

B. Solutions

• Appoint knowledge managers.

• Establish governance and data hygiene.

• Implement knowledge graphs to generate reliable signals.

III. Signal Problem: Ranking Fails

A. Web vs. Enterprise

• Web search thrives on authority signals (PageRank, clicks).

• Enterprise lacks clear authority; relevance is contextual.

B. Hybrid Retrieval

• Use BM25 (exact matches), dense embeddings (concepts), graph traversal (authority).

• Add an instructable reranker to encode business rules.

• Employ hard-negative mining and enterprise-tuned embeddings .

IV. Architectural Shift: Curated Answer Engines

A. From Links to Answers

• Employees expect direct answers, not link lists .

• Incorrect answers carry liability in critical functions.

B. Strategic Split

• Build curated “answer engines” for high-value domains.

• Blend internal docs, pre-written expert answers, and gated external enrichment.

V. Implementation Reality: Service, Not Product

A. Complexity of IT

• Data fragmented across many SaaS and legacy systems .

B. Deployment Model

• Platform plus services: flexible software + professional integration and tuning.

• Budget for engineering effort beyond licenses.

VI. Measurement Mandate: Prove Reliability

A. Limits of Public Benchmarks

• External leaderboards irrelevant; cannot capture enterprise context.

B. Internal Evaluation Suites

• Build gold-standard test sets from internal knowledge bases.

• Include unanswerable questions and multi-step queries.

• Prioritize explainability, citations, and predictable failure.

VII. Next Frontier: Agentic Workflows

A. Paradigm Shifts

• First: Search box (“find me a document”).

• Second: Chatbot with RAG (“answer my question”).

• Third: Agents (“do this task for me”).

B. Multi-Step Reasoning

• Agents plan and execute workflows: query, parse, cross-reference, synthesize.

• Workflows encoded as graphs (DAGs) with human-in-the-loop checkpoints.

VIII. What AI Teams Should Internalize

A. True Nature of Challenge

• Enterprise search is a systems engineering and data governance challenge, not just AI.

• Reliability > leaderboard scores.

B. Traits of Successful Teams

• Accept messy data reality.

• Build systems for predictability, trust, and auditability.

IX. Pragmatic Steps to Take Now

A. Action Items

• Start with a Data Census, not model evaluation.

• Ship hybrid retrieval with reranking.

• Stand up one curated answer engine.

• Evaluate privately and continuously.

• Think in workflows, not just answers.

• Budget for integration and stewardship

Go to article

Disclaimer: The information contained in this newsletter is intended for educational purposes only and should not be construed as financial advice. Please consult with a qualified financial advisor before making any investment decisions. Additionally, please note that we at AInvestor may or may not have a position in any of the companies mentioned herein. This is not a recommendation to buy or sell any security. The information contained herein is presented in good faith on a best efforts basis.

Introduction

Coming across this interview Will England did with Dan Shipper pushed me over the edge. I couldn’t shake the idea for AInvestor all spring, but after listening to their conversation I was finally moved to action. Dan proves to be an excellent interviewer, and in Will, he has a guest who speaks with clarity and conviction. Hopefully, we’ve done them justice. What follows are the three styles of summaries we use, each offering a different angle on the material. Any mistakes herein are ours.

Will England - Managing Partner, CEO and CIO of Walleye Capital

Will sets the strategic direction at Walleye, overseeing strategy allocation, risk, and talent. He began his career as a quantitative researcher at Man Group’s AHL division and later worked as an analyst at Valor Equity Partners. A graduate of Princeton (BSE in Operations Research and Financial Engineering, Academic All-Ivy) and Oxford (Master’s in Mathematical and Computational Finance), Will also rowed internationally for the U.S. National Team and won the Oxford-Cambridge Boat Race.

I have never met Will, but was duly impressed when I first listened to his interview with Patrick O’Shaughnessy a couple of years ago on the Invest Like The Best podcast. They cover the world of multi-strategy hedge funds and get more into Will’s backstory.

Dan Shipper - co-founder and CEO of Every

Dan is the co-founder and CEO of Every, a media and product company focused on business and AI. He runs an AI-first operation, writes the weekly Chain of Thought column, and hosts the AI & I podcast. Before Every, he founded the enterprise software startup Firefly, which he sold to Pegasystems. Today he’s best known for practical playbooks on how to use AI to think, write, and build.

He sounds like our kind of guy.

One of our motivations for starting AInvestor was to create a reason to actively engage with AI in an operational setting—learning by doing. I maintain active editorial oversight of instruction, model, and platform choices, but almost everything in the summaries below was written by AI. In the context of what we’re doing, I see this as a feature, not a bug. By experiencing the highs, and yes, the lows, we can better understand both the possibilities and the limitations of this new generation of AI.

Custom GPT for Light Edits

Click above to access a custom GPT we spun up to help clean up our own writing. It’s designed to apply the lightest of touches. If you’re a paying ChatGPT customer, it’s free to use (we don’t get a cut). Feel free to play around with it as much as you’d like.

Learnings & Takeaways

In this interview, you’ll learn:

1. Why Walleye Capital made AI usage mandatory across all departments

2. How Will England leverages large language models (LLMs) to sharpen thinking and communication

3. What Walleye’s internal AI platform “Current” does for analysts during earnings season

4. Why England sees AI adoption as a responsibility to employees and investors alike

5. How cultural rituals—like AI leaderboards and weekly meetups—accelerate adoption

6. Why he records nearly all firm communications and envisions a “Borg-like” collective data lake

7. What lessons history—railroads, barbed wire, and robber barons—offers about technological disruption

8. How incentives and intellectual honesty guide England’s decision-making framework

9. Why journaling with AI helps him track family, work, and health in harmony

10. How he frames leadership responsibility in an AI-driven world

Some takeaways:

1. AI adoption is not optional at Walleye. England mandated AI fluency across all 400 employees—whether in trading, compliance, or accounting—arguing that refusing AI is like refusing the internet in 1995.

2. Cultural design drives uptake. Walleye runs AI meetups, tool leaderboards, and incentive systems to normalize experimentation, making adoption social rather than top-down.

3. Internal tools deliver measurable edge. The “Current” platform synthesizes analyst notes, broker PDFs, and transcripts in real time—now indispensable for stock pickers during earnings.

4. AI reshapes leadership communication. England drafts memos in bullet points and uses LLMs to generate polished prose in minutes, freeing time for higher-order thinking.

5. Recording everything creates future leverage. By capturing calls, meetings, and transcripts, Walleye is building a “collective memory” that LLMs can process for insights and risk management.

6. History shows disruption accelerates. England draws parallels between AI and 19th-century railroads, barbed wire, and industrialization—technologies that rapidly obsoleted old skills.

7. First principles matter in decision-making. Incentives and intellectual honesty are England’s core anchors; he measures his own performance through journaling and tracked habits.

8. Responsibility is multi-layered. England views AI leadership as a duty: to investors (maximize returns responsibly), to employees (prepare them for change), and to his family (model adaptability).

9. AI journaling reduces friction. Capturing daily reflections across family, work, and health takes 30 seconds with AI, making the practice sustainable and insightful over time.

10. Human intuition and machine patterning complement each other. England argues that quant models and neural networks mirror intuition, and the future edge comes from combining them.

Go to interview on YouTube

FAQs

Q1: Why did Walleye Capital make AI usage mandatory?

Framing AI Like the Internet

At Walleye, AI isn’t framed as a nice-to-have experiment; it’s considered a baseline competency on par with adopting the internet in the mid-1990s. Will England’s view is that ignoring AI today is like refusing to use email or Excel when they first appeared—an untenable position for anyone who expects to remain competitive in financial markets. That conviction led him to issue a firm-wide mandate: every employee, whether in trading, research, compliance, finance, or accounting, must learn to use AI tools fluently in their day-to-day work.

Sending a Cultural Signal

England made this directive explicit in a memo where he opened by saying, “I used ChatGPT to write this email. You should be using it too and be proud of it.” The message was not only symbolic but also practical. He wanted to strip away the lingering hesitation employees felt about AI—whether it was the fear of “cheating” or anxiety about job security—and replace it with a clear cultural norm: using AI is part of the job.

Competitiveness and Responsibility

In his eyes, it would be irresponsible for leadership to allow Walleye to fall behind when competitors are already moving quickly. By making adoption mandatory and leading by example, England aimed to give the firm an organizational edge, ensuring that every team member, regardless of role or technical background, benefits from the productivity lift AI offers.

Q2: What are the top challenges Walleye has had with adoption or implementation?

Employee Fear of “Cheating”

Many employees initially felt that using ChatGPT to draft memos or emails was a form of cheating. Some even went so far as to edit or “dust up” AI-written drafts to hide the fact that they had used the tool. England confronted this stigma head-on by sending a firm-wide email that began, “I used ChatGPT to write this email. You should be using it too and be proud of it.” His intent was to normalize AI use and make it clear that adoption was not just acceptable, but encouraged as part of Walleye’s culture.

Anxiety About Job Security

There was also real concern among staff that AI might eventually replace their roles. England reframed this anxiety by emphasizing that AI doesn’t eliminate jobs, it changes them. He compared the shift to the arrival of spreadsheets: those who failed to learn Excel became obsolete, but those who did became more valuable. To reinforce this message, he mandated that every employee—whether in analysis, compliance, accounting, or another function—develop a baseline level of AI proficiency to stay competitive.

Imperfect Tools and User Frustration

Another hurdle has been the imperfection of the tools themselves. AI systems often make mistakes, and England recalled one demo where the “demo gods were against me” and everything failed in real time. Employees, frustrated by these errors, tended to give up on tools too quickly. England worked to shift the culture, encouraging staff to embrace imperfection, iterate, and keep experimenting. His message: the direction of travel is positive, even if the tools aren’t flawless today.

Discovery and Diffusion of Best Practices

England acknowledged that it’s hard for employees to discover the best AI use cases on their own. To address this, Walleye created structures that make sharing and discovery easier. Weekly AI meetups allow staff to exchange prompts and examples. Firm-wide leaderboards highlight heavy users and celebrate adoption. Incentives were also introduced so that employees who suggest tools that later roll out across the firm receive recognition. These steps transformed AI adoption from a solitary exercise into a social, collaborative effort.

Governance and Data Capture

Finally, governance around data capture remains a challenge. Walleye records nearly every call and meeting to build what England calls a “Borg”-like collective memory for the firm. While he views this as essential for long-term advantage, it raises questions about what should or shouldn’t be recorded and how to handle sensitive material. England recognizes that governance structures need to keep pace with this level of data capture, ensuring that trust and compliance aren’t compromised as the firm pushes forward.

Q3: What principles guide decision-making at the firm?

Power of Incentives

England emphasized that most human behavior in organizations can be explained through incentives. In investments, aligning incentives sheds light on why companies, management teams, or counterparties act the way they do. Inside the firm, structuring incentives ensures employees are moving in the same direction as both the firm and its investors. His litmus test is simple: “Are the incentive vectors all pointing in the same dimension?” If the answer is yes, decisions are more likely to succeed.

Intellectual Honesty

A second anchor is intellectual honesty. England has a strong distaste for fluff and empty rhetoric; the real question is whether an idea, analysis, or thesis holds up under scrutiny. Leaders at Walleye are expected to be candid about mistakes as well as successes. This kind of honesty acts as a safeguard against bias—especially relevant when using AI tools, which can produce outputs that look polished but may be misleading if not interrogated carefully.

Measurement and Feedback Loops

Another principle is the discipline of measurement. As England puts it, “you can’t manage what you can’t measure.” He applies this personally by journaling daily—now assisted by AI—and tracking his health and workouts as time series. The same philosophy applies at the firm level: track operational and investment data rigorously, revisit it over time, and use it to identify patterns and lessons that can refine decision-making.

Harmony Across Domains

Finally, England sees decision-making as spanning multiple domains. Instead of striving for strict “balance” between family, work, and health, he pursues “harmony.” Trade-offs are not judged only by their financial outcomes but also by how well they align with personal responsibilities and long-term sustainability. This integrated view ensures that decisions serve the whole, not just one isolated area of life or business.

Q4: What role does data capture play in Walleye’s AI strategy?

Creating a Collective Memory (“The Borg”)

Walleye records nearly every Zoom, phone call, and meeting with the explicit aim of building what Will England calls a “Borg”-like collective system—a firm-wide memory where nothing important is lost. The vision is to connect text data like emails, notes, and transcripts with numerical data such as market prices, accounting, and internal performance metrics, so that the firm can operate on a unified information base.

Risk Oversight and Decision Auditing

Daily risk calls are captured in full and then processed by language models. This allows the risk team, and leadership more broadly, to revisit not just what decisions were made but why they were made in context. By preserving the reasoning behind actions, the firm can spot blind spots, biases, or inconsistencies in decision-making over time, creating a feedback loop that strengthens risk oversight.

Enhancing Analyst and Portfolio Manager Productivity

Notes, broker PDFs, and earnings transcripts flow into Walleye’s internal tool, Current. This platform enables analysts and portfolio managers to instantly synthesize vast amounts of recorded information, rather than manually sorting through it. During earnings season in particular, the ability to pull together and process information in real time has become indispensable, freeing humans to focus on higher-order judgment while AI handles the heavy lifting.

Organizational Learning and Knowledge Retention

One of the less obvious but equally valuable benefits is the preservation of institutional knowledge. In most firms, when employees leave, their thought processes and reasoning walk out with them. At Walleye, recordings and transcripts capture those thought patterns, ensuring continuity for the organization and creating a training resource for new hires. Weekly meetups and demos also draw on this recorded material, turning data capture into an ongoing learning loop.

Future Vision: Full Data Lake Integration

England sees today’s recording practices as the foundation for a broader strategy. The long-term goal is to unify all data streams—structured and unstructured—into a single collective system. Once text and numerical data are connected in one place, AI can go beyond summarization to surface higher-order insights, such as linking the content of risk conversations to subsequent portfolio outcomes. In his view, this kind of predictive intelligence will define the firms that turn AI into a real operating advantage.

Q5: How does Walleye think about humans and machines working together?

Complementary Processing

At Walleye, the relationship between humans and machines isn’t viewed as a zero-sum competition but as a complementary partnership. Will England draws a parallel between human intuition and neural networks: both operate in high-dimensional spaces, processing complexity in ways that are difficult—sometimes impossible—to fully explain. Just as a seasoned investor may “feel” that a trade is right based on years of pattern recognition, a machine learning model can surface relationships across thousands of variables that no human could parse line by line. The two modes of reasoning mirror one another, and when combined, they open up possibilities that neither could achieve alone.

Extending Intuition

For England, the real opportunity lies in using machines to extend human intuition. Neural networks can highlight patterns or correlations that may feel intuitive once revealed but would have been nearly impossible for an analyst to uncover unaided. That doesn’t mean outsourcing judgment entirely. He stresses that tools are like engines—they need a plane to carry them somewhere.

Human Judgment Still Central

Humans still design the “plane”: setting objectives, framing problems, and ultimately deciding what to do with the machine’s output. In this way, AI becomes less about replacing decision-makers and more about expanding their cognitive reach, giving portfolio managers and risk teams access to insights that would otherwise stay hidden in the noise of markets and information flow.

Q6: What historical parallels inform England’s view of AI?

Railroads and Industrialization

England often looks back to the late 19th century for perspective. He points to the railroads, barbed wire, and the wave of industrialization as examples of technologies that reshaped the economy almost overnight. Railroads connected markets that had once been isolated, barbed wire transformed how land could be used and defended, and industrialization moved production from manual craft to mechanized scale. Each of these breakthroughs made entire categories of skills obsolete within a single generation. For him, AI represents the same kind of structural shift, but one that is unfolding in markets and information processing rather than physical infrastructure.

Frontier vs. Civilization

He also uses the metaphor of the American frontier to describe how technology adoption works. Startups are like cowboys—pushing into uncharted territory, experimenting, and breaking rules. Larger institutions, by contrast, act as the builders of civilization: they bring order, scale, and governance once the frontier is established. England sees AI as today’s frontier. The first movers push boundaries with speed and experimentation, while established firms like Walleye have to balance that frontier spirit with the resources and discipline needed to operate at scale.

Acceleration of Change

The most striking difference, in England’s view, is the pace. Railroads and industrial infrastructure took decades to build out. Entire lifetimes could pass with people adapting gradually to new technologies. AI is moving much faster. Shifts that once unfolded over a generation are now being compressed into a few years. For a hedge fund competing in real time, that means adaptation is no longer optional—it’s urgent. Firms that hesitate risk being left behind before they even realize the ground has shifted.

Q7: How is AI changing Will England’s personal workflow?

Bullet Points to Polished Prose

England explained that he thinks best when he writes out his thoughts. In practice, he now begins important memos or emails by jotting down bullet points—what he’s thinking, why it matters, and the context around it. He then feeds that outline into a large language model, often along with prior material he’s written on the same subject, and asks the system to draft the communication in his own voice. This workflow means he can focus on ideas while the model handles the mechanics of stitching sentences together.

Time Savings

This shift has cut hours off his process. Lengthy memos that once might have taken four or five hours can now be finished in 15 minutes. For someone in a leadership role where communication is constant—whether with employees, investors, or counterparties—that time savings adds up quickly and allows him to spend more of the day on higher-order decisions rather than wordsmithing.

Sharper Thinking

England is clear that using AI doesn’t dilute his thinking. Quite the opposite—he finds it clarifies it. By separating conceptual work from linguistic polish, he avoids getting bogged down in sentence structure or stylistic flourishes. He pointed out that much of writing is really “the tying your shoes part,” and letting a model handle that frees him to sharpen the concepts he wants to convey. The result, in his view, is clearer and more effective communication.

Go to interview on YouTube

Mind Map

I. Walleye Capital’s AI Mandate

A. Organizational Shift

• AI usage made mandatory for all 400 employees.

• Applies to all departments—investment, compliance, accounting, legal.

• Framed as equivalent to refusing the internet in 1995.

B. Leadership Role

• Will England leads by example, positioning himself as “chief AI evangelist.”

• Responsibility extends to employees, investors, and broader ecosystem.

II. Cultural Adoption Strategies

A. Social Structures

• Weekly AI meetups for sharing use cases and prompts.

• Firm-wide AI usage leaderboards to encourage competition.

• Incentives for suggesting tools adopted across the firm.

B. Overcoming Fear and Resistance

• Messaging that AI use is not “cheating.”

• Normalization of imperfect tools—public demos even when flawed.

• Emphasis on experimentation and accessibility.

III. Tools and Applications

A. Internal Platform: “Current”

• Synthesizes analyst notes, broker reports, transcripts.

• Critical during earnings periods; provides real-time analysis.

• Widely viewed as indispensable by portfolio managers.

B. Productivity Enhancements

• Large language models (LLMs) used for communication, writing memos, and emails.

• Drafting shifts from hours to minutes, freeing time for higher-level tasks.

C. Quantitative Strategies

• Longstanding use of advanced statistics and sentiment analysis.

• Integration of unstructured data with LLMs.

IV. Data and Risk Infrastructure

A. Recording and Collective Memory

• Nearly all calls and meetings recorded.

• Built toward a “Borg”-like collective data lake for firm-wide memory.

• Enables LLMs to provide retrospective insights and risk monitoring.

B. Governance and Risk Management

• Recorded data supports oversight and predictive risk processes.

• AI embedded into daily risk calls and control center activities.

V. Historical Analogies and Lessons

A. Technological Disruption

• Comparisons to railroads, barbed wire, and industrialization.

• Past innovations rapidly made old skills obsolete.

B. Frontier vs. Civilization

• Cowboy era and frontier metaphor: individual exploration vs. structured systems.

• Parallel to startups vs. institutionalized firms in technology adoption.

VI. Human and Machine Decision-Making

A. Human Nature and Patterns

• Human nature seen as timeless, shaping markets across centuries.

• Investing blends repeating patterns and human-driven intuition.

B. Intuition and Neural Networks

• Neural networks and human intuition both operate in high-dimensional spaces.

• Machines complement intuition by surfacing unexpected but intuitive patterns.

VII. Leadership Principles and Responsibility

A. Decision-Making Anchors

• Core principles: incentives and intellectual honesty.

• Journaling and tracking personal metrics reinforce self-evaluation.

B. Journaling with AI

• Daily reflections across family, work, and health categories.

• Reduced friction with AI: 30 seconds to capture and process.

C. Responsibility Layers

• To investors: maximize returns responsibly.

• To employees: prepare them for disruption and change.

• To family: model adaptability and stewardship.

Go to interview on YouTube

Disclaimer: The information contained in this newsletter is intended for educational purposes only and should not be construed as financial advice. Please consult with a qualified financial advisor before making any investment decisions. Additionally, please note that we at AInvestor may or may not have a position in any of the companies mentioned herein. This is not a recommendation to buy or sell any security. The information contained herein is presented in good faith on a best efforts basis.

Check out AI Street where we found this

Introduction

After three direct one-on-one conversations with me in the interviewer seat, this note marks our first step into the Community Wisdom part of the program. This is where we curate the best and most practical material we find on how top investors and builders are incorporating AI into their processes and investment decisions.

I’m happy to share a recent interview I came across with Mark Steed, CIO of the Arizona Public Safety Personnel Retirement System. It took place on The Institutional Edge podcast, hosted by Angelo Calvello. What struck me almost immediately was Mark’s practical, level-headed approach. The absence of hyperbole is refreshing. Hopefully, we’ve done justice to the conversation between Angelo and Mark. What follows are the three styles of summaries we use, each offering a different angle on the material. Any mistakes herein are ours.

Lastly, the value we provide, hopefully, will come not only from the content and how it is presented, but also from introducing new, quality sources to your attention. In that spirit, I encourage you to check out The Institutional Edge, hosted by Angelo, and AI Street, published by former Bloomberg reporter Matt Robinson. It was in Matt’s weekly that I found this conversation. I find his newsletter a great top-of-funnel resource.

One of our motivations for starting AInvestor was to create a reason to actively engage with AI in an operational setting—learning by doing. I maintain active editorial oversight of instruction, model, and platform choices, but almost everything in the summaries below was written by AI. In the context of what we’re doing, I see this as a feature, not a bug. By experiencing the highs, and yes, the lows, we can better understand both the possibilities and the limitations of this new generation of AI.

Custom GPT for Light Edits

Click above to access a custom GPT we spun up to help clean up our own writing. It’s designed to apply the lightest of touches. If you’re a paying ChatGPT customer, it’s free to use (we don’t get a cut). Feel free to play around with it as much as you’d like.

Learnings & Takeaways

In this interview, you’ll learn:

1. Why PSPRS focuses its AI program on two pillars: operational efficiency and decision-making.

2. How local, offline LLMs (e.g., Llama, Gemma) plus simple RPA handle secure document intake and field extraction.

3. Which unstructured-to-structured fields matter in PE diligence (partners, fund size/vintage, compliance officer, carry terms, portfolio metrics).

4. How ML improves predictions on small, non-normal, dependent datasets compared with classic linear models.

5. Where deep learning fits—pattern discovery in unstructured diligence—alongside human review.

6. What PSPRS’s governance looks like: decision logging, confidence calibration, human-in-the-loop, and parallel model vs. human runs.

7. How screening evolves toward a manager web portal once factor importance stabilizes with enough observations.

8. Why PSPRS runs on-prem first (security, cost) and what compute is required for large local models.

9. How team design (investing + data science) reduces bias and accelerates build-out.

10. Why PSPRS is skeptical of sentiment scraping as a trading edge amid flows, constraints, and misinformation.

Some takeaways:

1. Automate high-volume, low-value work to free humans for judgment. PSPRS targets doc retrieval, PDF parsing, field extraction, and first-draft memo generation so PMs spend time debating meaning, not chasing files.

2. Local LLMs solve near-term security and cost constraints. By running Llama/Gemma entirely offline and pointing models at staged repositories, PSPRS avoids sharing sensitive GP materials while proving value with quick R&D loops.

3. Structured outputs from PDFs are the backbone of the stack. Consistent extraction of partners, vintages, carry terms (with synonyms), compliance officers, and operating metrics feeds a queryable database that supports screening and attribution.

4. Use ML where statistical assumptions break. Small samples, non-normality, and dependence make linear regressions brittle; PSPRS applies ML to rank features and predict outcomes on structured data.

5. Treat deep learning as a black box—govern it accordingly. For unstructured packets, DL surfaces patterns, but PSPRS pairs it with calibration, clear decision scope, and parallel runs before granting decision authority.

6. Governance is a growth enabler, not a brake. Decision logging with explicit success criteria and confidence bands builds trust internally and with the board while de-biasing post-mortems.

7. Screening will shift to a portal with fewer, higher-signal fields. After enough observations, PSPRS will publish the handful of inputs that matter most, letting managers self-submit and enabling faster triage.

8. Right-size compute to the documents you actually read. A ~70B-parameter local model with ~96 GB RAM supports long filings and large context windows—enough for 10-Qs and data rooms without cloud exposure.

9. Blend backgrounds to reduce institutional bias. Pairing investors who learned data science with scientists learning investing keeps feature selection honest and problem framing grounded.

10. Be critical of sentiment feeds as alpha. Regulatory flows, rebalancing mechanics, and misinformation weaken any causal path from social sentiment to realized trades; PSPRS prioritizes verifiable, higher-signal data.

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FAQs

Q1: What are PSPRS’s two primary AI use cases?

Operational efficiency and decision quality. Efficiency covers automated document intake (data rooms, PDFs), structured-field extraction, and first-draft memo generation. Decision quality covers ML on structured data and deep-learning pattern discovery in unstructured diligence materials, with calibration against stated confidence.

Q2: What specific efficiency wins are in scope right now?

• Robotic access to proprietary portals (including two-factor workflows)

• Bulk download of Private Placement Memorandums (PPMs), Due Diligence Questionnaires (DDQs), spreadsheets, filings

• PDF/OCR parsing to extract fields (partners, portfolio metrics, fund size/vintage, compliance officer, carry terms)

• Normalization into a queryable database

• Auto-draft investment memos for human review

Q3: How is PSPRS handling data security?

Local-first. Large language models (LLMs) run on PSPRS machines, pointed at on-prem documents. No internet connection is required for extraction, fine-tuning, or memo drafts. Enterprise cloud will be evaluated after proof-of-concept success.

Q4: Which models and tools are being used?

Open-source LLMs such as Llama and Gemma for local use; Robotic Process Automation (RPA) scripts for retrieval; a house database for normalized fields; attribution tools (e.g., LIME/SHAP-style methods, activation maps) where interpretability helps.

Q5: What compute is required for the local LLM approach?

A high-memory workstation. For example, a ~70B-parameter model requires on the order of ~96 GB RAM to comfortably process long filings within a large context window.

Q6: What does the manager-selection workflow look like today?

Front-loaded quantitative requests to GPs, followed by model-assisted analysis of returned spreadsheets and data-room documents. Screening filters will tighten after enough observations establish which factors truly matter.

Q7: How will the external-facing data collection evolve?

A manager web portal is planned. Once factor importance stabilizes, PSPRS will publish a concise set of required fields so managers can self-submit the most predictive information up front.

Q8: How is explainability addressed?

Every material recommendation—human or model-assisted—includes success criteria and a confidence level. PSPRS tracks calibration (e.g., “80% confident” predictions should land near 80% realized accuracy). Interpretable models and attribution tools are used where practical.

Q9: What is the governance model?

• Decision logging with explicit definitions of success

• Calibration tracking across confidence bands

• Human-in-the-loop for scope and overrides

• Board education on a recurring cadence (semiannual planned)

• Parallel runs for deep-learning decisions until performance is proven

Q10: How does PSPRS handle small samples and messy data?

By design. ML methods handle nonlinearity, non-normality, and dependence better than classic linear models. For alternatives, the pipeline converts unstructured PDFs into consistent rows, then builds evidence over time via calibration and parallel testing.

Q11: What is PSPRS’s stance on market sentiment analysis?

Skeptical. Flows, regulatory constraints, and misinformation weaken the link between surface sentiment and actual trades. Priority is on verifiable, higher-signal data tied to manager quality and portfolio fundamentals.

Q12: What KPIs matter for the efficiency program?

Cycle-time reduction (from data-room access to memo), throughput (docs/fields processed per day), staff hours saved, and first-draft memo quality (edit distance vs final).

Q13: What’s on the medium-term roadmap?

A multi-agent system:

The goal is to remove low-value, high-volume work from the team and focus human time on judgment.

Q14: How are bias and overfitting mitigated?

Team design blends investment and data-science backgrounds to challenge assumptions. Feature sets are constrained to auditable, repeatable fields. Models are judged by out-of-sample calibration and tracked against explicit confidence bins.

Q15: What must an asset owner have in place to do this well?

Talent, data, compute, governance.

• Embedded data scientists working hand-in-hand with PMs

• A pipeline that turns PDFs into structured, versioned records

• Sufficient local compute for private LLM workflows

• A written governance program with calibration, HIL, and board education

Q16: How do humans and models share responsibility for decisions?

Models prepare and score. Humans decide. Deep-learning recommendations run in parallel with investment-team decisions until the evidence base is large and stable. Overrides are documented like any other decision.

Q17: What does “good enough to ship” look like for productionization?

• Stable extraction accuracy on key fields (measured against human labels)

• Demonstrated calibration at chosen confidence thresholds

• Documented decision scope and escalation paths

• Board-level fluency and sign-off on policy

Q18: What is the ultimate target state for the memo process?

End-to-end automation for first drafts: models fetch, extract, analyze, and assemble a complete memo with sources, tables, and attribution. PMs then edit for judgment, risk, and fit—without spending time on retrieval, reformatting, or basic calculations.

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Mind Map

Please share if you know of a tool capable of creating clean, professional looking mind map graphics. We lightly played around with Mermaid and that didn’t cut it.

I. Central Theme

A. One‑line Summary

• AI at PSPRS centers on two pillars: Operational Efficiency and Decision Quality, under a clear governance program.

B. Why It Matters

• Free investment staff from high‑volume, low‑value work.

• Improve screening and underwriting rigor with calibrated models.

• Build trust with stakeholders via documentation and education.

II. Operational Efficiency (RPA → Extraction → Drafts)

A. Document Ingress

• Navigate proprietary portals and 2FA; bulk‑download data‑room materials (PPMs, DDQs, spreadsheets, filings).

B. Unstructured → Structured

• Parse PDFs/OCR to extract canonical fields: partners_count; portfolio‑company metrics; fund_size; vintage_year; track‑record stats; compliance_officer; carry/“performance bonus” synonyms.

C. Normalization & Storage

• Write extracted fields to a versioned, queryable database for analysis and reporting.

D. First‑Draft Investment Memos

• LLM‑generated drafts assembled from extracted tables and standard sections; PMs review and edit.

E. Verification Loop

• Human spot checks; precision/recall tracking on key fields; error triage back into prompts/parsers.

III. Decision‑Making (Analytics)

A. Machine Learning on Structured Data

• Feature discovery and prediction when linear assumptions fail (small N, non‑normality, dependence).

B. Deep Learning on Unstructured Diligence

• Pattern surfacing across documents/spreadsheets; acknowledge black‑box aspects.

C. Interpretability Aids

• Use feature attribution (LIME/SHAP‑style) and activation‑pattern inspection where helpful.

D. Calibration & Parallel Runs

• Track realized accuracy vs. stated confidence bins (e.g., 70%, 80%).

• Keep DL recommendations in parallel with human decisions until evidence is strong.

IV. Governance & Oversight

A. Decision Logging

• Every material recommendation records success definition and confidence; benchmark PMs and models over time.

B. Human‑in‑the‑Loop (HIL)

• Define decision scope and override paths; models assist, humans decide.

C. Board Education

• Build fluency with recurring briefings (semiannual planned); align vocabulary and risk controls.

D. Rollout Discipline

• Start with verifiable tasks (labeling, math checks), then expand to predictive uses as calibration evidence builds.

V. Data & Information Architecture

A. Sources & Constraints

• GP data rooms (PPMs, DDQs), filings (10‑Q), spreadsheets—heavy PDF bias; limited sample sizes in alternatives.

B. Field Dictionary & Schema

• Standardize key entities (team, economics, track record, compliance, operating metrics) for reliable extraction and analytics.

C. Manager Web Portal (Planned)

• After sufficient observations, publish the handful of factors that matter; enable self‑submission to speed screening.

VI. Tooling & Stack

A. Local LLMs

• Run Llama/Gemma offline on PSPRS machines for security; point models at staged repositories.

B. Automation & Parsers

• RPA for retrieval/staging; PDF/OCR parsers; templated memo assembler; internal database with provenance.

C. Multi‑Agent Vision

• Ingest → Clean/Normalize → Analyze/Attribute → Draft Memo → Compliance → Humans.

VII. Compute & Deployment

A. Hardware Profile

• ~70B‑parameter local model; ~96 GB RAM for long‑context processing (suitable for long filings and multi‑doc synthesis).

B. Security Posture

• Local‑first R&D; evaluate enterprise cloud solutions after proof of concept.

VIII. Screening & Workflow

A. Current Practice

• Front‑loaded quantitative request to GPs; model‑assisted analysis of returns plus data‑room docs.

B. Evolving Filters

• Identify the 5–6 most predictive fields once observations suffice; move to tighter pre‑filters.

C. Philosophy

• 80/20 filter—accept some false negatives; focus on quality of done deals.

IX. Stances & Opinions

A. Sentiment Analysis

• Skeptical as an alpha source (causality may run trade → sentiment; misinformation and regulatory/flow confounds).

B. Explainability

• Preferred when feasible; black‑box acceptable under calibration, clear scope, and HIL.

X. Talent & Organization

A. Embedded Data Science

• Two data scientists (investor→DS; DS→investing) to counter institutional bias and align with PM workflows.

B. Stakeholders & Enablement

• Board and executives with varied AI literacy—bring them along with education and transparent metrics.

C. External Expertise

• Former PM (computer‑science focus) consulting on LLM use cases.

XI. Roadmap & Status

A. Now

• Authorization to run local LLMs; R&D on extraction and memo drafting; verification‑first approach.

B. Next

• Semiannual board education; portal design; progressive expansion of model scope; continue parallel runs.

C. Later

• Evaluate enterprise cloud; scale multi‑agent workflow.

D. End‑State Goal

• Remove low‑value, high‑volume tasks; humans focus on interpretation and judgment.

XII. Metrics & KPIs

A. Efficiency

• Cycle time (access → first draft); docs/fields processed per day; staff hours saved.

B. Quality

• Extraction precision/recall on key fields; memo edit distance (model draft → final).

C. Model Performance

• Calibration accuracy by confidence bin; stability over time.

XIII. Illustrative Anecdote (Color)

A. Worst Pitch

• Formula 1 racetrack in Monterrey; glossy pitch books requested back—illustrates disciplined triage and scarcity realities.

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Disclaimer: The information contained in this newsletter is intended for educational purposes only and should not be construed as financial advice. Please consult with a qualified financial advisor before making any investment decisions. Additionally, please note that we at AInvestor may or may not have a position in any of the companies mentioned herein. This is not a recommendation to buy or sell any security. The information contained herein is presented in good faith on a best efforts basis.