Professor Ludovic Phalippou on the Promise and the Peril of AI for LP Decision-Making in Private Markets

Alex Harstrick on Why You Are Leaving Money on the Table by Not Implementing AI Right Now

Alex Harstrick, Managing Partner and Co-Founder of J2 Ventures, joins The Agentic Allocator to share how one of the most focused early-stage funds in the US is building AI into every aspect of how it operates. J2 invests exclusively in deep technology for the US government, and Alex brings a sharp view of what AI adoption requires from GPs and LPs alike. Alex's position is unambiguous: you are leaving money on the table by not implementing AI right now. Cybersecurity questions, accuracy concerns and open questions of all kinds remain unanswered. But waiting for all the answers, he argues, means waiting for the industry to pass you by before you have them. In this episode, Alex walks through how J2 is using AI to improve its internal operations, why the entire VC SaaS stack is ripe for disruption, and how AI is reshaping the sourcing game in ways that go far beyond automation. He also shares a pointed view on how LPs are thinking about AI adoption, why the largest and most sophisticated institutions are often the most afraid, and how AI can surface the kind of manager evaluation information that social relationships and slick pitch decks were designed to obscure. What You'll Learn: * Why Alex calls AI 'advanced computing' and what that signals about how J2 thinks about technology investment * Why the entire VC SaaS stack is overpriced, under-delivering, and vulnerable to being replaced by tools you can build yourself over a weekend * The time cost of manual CRM notes: five minutes per meeting, five to ten meetings a day, 120 hours lost per year, and what that means for deal flow * How AI changes the sourcing game not just by finding founders faster but by helping VCs actually connect with them as human beings * Why the largest and most sophisticated LPs are often the most afraid of AI, and why Alex thinks they are asking the wrong question * How AI can evaluate managers against 56,000 alternatives and surface performance information that social relationships and polished track records were designed to hide * Why AI could perpetuate backward-looking biases in manager selection and what LPs need to understand about that risk  * The retrospective analysis opportunity: tracking the nine managers you passed on and using their subsequent performance to improve your own decision-making * What the LP/GP ecosystem looks like in five to ten years: automated DDQs, blind ranking mechanisms, algorithmic portfolio company reporting, and the parts that will always need a human in the loop * Why if you do not leverage AI, someone else will, and they will out-compete you About Alex Harstrick:  Alex Harstrick is Managing Partner and Co-Founder of J2 Ventures. J2 invests exclusively in deep technology for the US government. Before founding J2, Alex had a career that spans healthcare venture capital, service as an Army intelligence officer with special operations deployments to Afghanistan and Iraq, and a role at the Defense Innovation Unit where he helped deploy nearly $2 billion into defense technology startups. That combination of operational experience, government service, and investment expertise is what shapes J2's uniquely focused approach to backing founders building at the intersection of national security and advanced computing. Episode Highlights: [01:51] Why J2 Calls It Advanced Computing, Not AI Alex believes the word AI has lost a lot of meaning. Alex explains why J2 uses the term advanced computing instead, and why an early-stage investor who only looks for AI in the broad definition will miss a lot of what is interesting   [03:12] Why the Entire VC SaaS Stack Is Ripe for Disruption VC is a niche industry served by expensive software that ratchets up pricing once it has you, without meaningfully improving. The best CRM most VCs have ever used is a shared Google Sheet. You can now build something better, bespoke for your workflows, over a weekend.   [06:02] The Real Cost of Manual Note-Taking: 120 Hours a Year Five minutes per meeting, five to ten meetings a day, 120 hours lost per year. Once you frame it that way, the urgency of fixing it becomes impossible to ignore. [09:19] LPs and the AI Question: Why the Biggest Institutions Are the Most Afraid The largest multi-asset managers are mostly asking about cybersecurity. Alex thinks that is the wrong question unless you are simultaneously asking how you are implementing AI across your workflows. [15:04] How AI Changes Sourcing: Finding Founders and Actually Connecting With Them Everyone is looking for expectational founders. What is differentiated is getting there first and walking into the room knowing the things about that person that create a real connection. AI helps with both. [21:11] How AI Evaluates Managers Against 56,000 Alternatives LPs often rely on relationship signals: impressive annual meetings, famous co-investors, high-profile connections. AI can evaluate a manager against the full universe of alternatives and surface what the polished materials were designed to obscure. [22:44] The Retrospective Analysis Opportunity When an LP meets ten managers and invests in one, the nine they passed on are almost never revisited. AI makes it possible to track those managers, compare their subsequent performance, and use that data to improve your own decision-making. [23:19] The Bias Problem: Why AI Can Perpetuate Historical Heuristics Train AI on historical manager data and it will tell you the best managers look like the ones who succeeded historically. LPs need to understand that risk explicitly and understand what to do about it.  [24:40] The Five to Ten Year View DDQs processed algorithmically, managers ranked against blind benchmarks, portfolio company reporting automated. The part that will not change: people want to meet the person they are giving up a meaningful part of their lives to work with. Episode Resources: Alex Harstrick on LinkedIn [http://linkedin.com/in/alexanderharstrick] J2 Ventures Website [https://www.j2vp.com/] Victoria Sienczewski on LinkedIn [https://www.linkedin.com/in/vsienczewski/] AuumAI Website [https://auumai.com/]

The OCIO That Keeps Raising the Bar on AI: Inside TIFF's Culture of Continuous Improvement

Brad Calder, Managing Director, Head of Equities at TIFF Investment Management, joins The Agentic Allocator to walk through what two years of AI adoption looks like inside the OCIO. Brad leads TIFF’s public equity investments, including select arbitrage and crypto strategies, and plays a central role in manager selection and portfolio construction, and has spearheaded the firm's AI implementation. TIFF is an OCIO serving endowments, foundations, and other mission-driven organizations. In this episode, Brad walks through what AI adoption looks like on the ground: the use cases that are delivering tangible ROI, the ones that are still works in progress, the infrastructure decisions that turned out to matter, and the cultural conditions that made it all possible. Brad describes one of the most ambitious projects on TIFF's roadmap: a tool for the investment committee that connects every memo the firm has ever written to structured returns and exposure data, automatically surfacing the three most comparable historical investments whenever a new decision is being discussed. For a 35-year-old organization where no current IC member has been there since inception, this tool would give the IC access to the full weight of every decision TIFF has ever made. He also makes a sharp point about what AI adoption requires that most allocators underestimate: the willingness to keep re-experimenting, because the underlying models improve fast enough that something that failed six months ago may now be entirely viable. Think of it like high school chemistry, he says: if the experiment doesn't work, you alter it and try again. What You'll Learn: * How TIFF's decade of backing machine learning oriented hedge funds gave them a head start on understanding and trusting AI before the rest of the industry caught up * Why TIFF's legacy research management system became the catalyst for their AI build-out and what the 'SaaSpocalypse' means for allocators still locked into incumbent technology providers * How TIFF is using AI across unstructured data pitch books, quarterly letters, capital call notices  * The collective intelligence tool TIFF is building for its investment committee: surfacing comparable historical investments, tied to returns data, going back to the firm's founding * Why the ROI of AI for allocators is not just efficiency and where Brad believes the real value will ultimately be felt * How TIFF combines offshore BPO with AI tools, and why the combination is complementary rather than substitutive * What an AI-enabled allocator looks like in five to ten years: instant probability estimates on prospective managers, AI-shaped due diligence agendas, and humans focused on the relationships and control rights that resist disintermediation * Why subscale endowments and foundations face a real risk of being left behind and why that is part of TIFF's mission to solve * The high school chemistry framework for AI experimentation: alter the experiment, keep trying, and never conclude something can't work based on a model that's already six months out of date About Brad Calder: Brad Calder is Managing Director, Head of Equities at TIFF Investment Management, where he leads TIFF’s public equity investments and has spearheaded its AI adoption and implementation. TIFF is an OCIO serving of endowments, foundations, and other mission-driven organizations. Brad has been at TIFF for 11 years. Before joining TIFF, he built expertise in systematic and machine learning-oriented investment strategies that has since informed the firm's approach to integrating AI across its investment and operational process. Episode Highlights: [02:12] How TIFF Got a Head Start on AI Brad traces TIFF's AI journey back a decade, to when the firm began backing machine learning oriented systematic hedge funds. That early exposure, learning how the technology worked from the inside, meant TIFF was ready to move fast when ChatGPT changed the landscape. The culture of continuous improvement that governs how TIFF evaluates external managers, Brad explains, is the same standard they've applied to their own organization. [04:39] The SaaSpocalypse and the Case for AI-Native Vendors TIFF's AI build-out was triggered, in part, by frustration with a legacy research management system that lacked the API capabilities needed to run LLM models over their database. Brad frames this as a broader market dynamic the 'SaaSpocalypse', in which allocators are waking up to the fact that incumbent SaaS providers that aren't investing in AI are creating white space for AI-native competitors to step in. [06:14] What AI Is Actually Doing for TIFF’s Investment Teams Brad walks through specific use cases: rapidly summarising incoming manager pitches against a structured template, comparing quarterly letters across managers over multiple periods to surface trends and outliers, and helping write investment memos. The shift from an analyst manually reading and cross-referencing multiple letters to an AI that can instantly identify what one manager is doing differently from the rest is, Brad argues, qualitatively different, not just faster. [07:39] Building TIFF’s Collective Intelligence Layer The most ambitious project on TIFF's roadmap is a tool for the investment committee that connects every memo the firm has ever written to structured returns and exposure data and automatically identifies the three most comparable historical investments. [11:09] Where the Real ROI Lives and Where It Doesn’t Brad is clear that AI adoption at TIFF is not primarily a headcount reduction exercise. The offshore BPO team in India has full access to TIFF's AI systems, and the combination is complementary. The true ROI, he argues, will be felt most in decision quality: the tools being built for the investment committee are designed specifically to help the team make better decisions and avoid the kind of mistakes that result in large losses.  [14:08] The Challenge of Building Confidence in AI Output Brad is candid about the adoption friction inside TIFF: even when validation processes are in place, some team members still feel the instinct to double-check, follow up, or verify. Building the confidence to trust a validated AI output takes time and deliberate cultural work. His advice: keep re-experimenting, because the models are improving fast enough that a use case that failed six months ago may be entirely viable today. [15:56] The Five-to-Ten Year Vision: Humans Focused on What AI Cannot Do Brad's picture of the AI-enabled allocator is specific: systems that can take textual data and make forecasts from it, instant probability estimates on prospective managers drawn from decades of comparable data, and AI-shaped due diligence agendas that surface questions humans wouldn't have thought to ask. Where he believes humans remain essential for longer is in the relationship and control dimension: negotiating GP terms, engaging company management, exercising the kind of judgment that equity ownership demands. [20:08] The Scale Risk for Smaller Allocators Brad closes with a note of concern for subscale endowments and foundations that lack the budget to make these investments. AI, he argues, is not a rising tide that lifts all ships. It only lifts the ones that invest in making it work. That gap is precisely why TIFF exists: to help smaller mission-driven organisations access the sam...

The CIO Who Codes: Al Hemmingsen on Weaving AI Into the Fabric of a Multi-Generational Single Family Office

Al Hemmingsen, CIO of a prominent US-based single family office, joins The Agentic Allocator to share what hands-on AI adoption looks like from inside an institutional family office. Al is the only CIO, we know of, in the LP world actively publishing open-source code on GitHub for use by fellow allocators. In this episode, Al walks through how vibe coding with Claude Code became a serious tool for unlocking legacy data and automating workflows, how he is building a culture of accountability across his team, and why the LP that gets to the best GPs first has a competitive edge that only widens over time. He also makes one of the sharpest points we have heard on the competitive stakes: some aspects of this work should be shared freely with peers, but the ability to process information faster than other LPs, build higher-quality manager meeting lists, and get to the best GPs before they fill their calendars at conferences, is a genuine edge. If you do not build it, someone else will. What You'll Learn: * How a background in SQL and Visual Basic at business school laid the foundation for a career of hobbyist coding and eventually full vibe coding with Claude Code * Why the single best measure of AI ROI at a family office is not efficiency gains but tasks that were previously too expensive or simply not feasible * How $59.32 in API credits unlocked 50-plus years of legacy transaction data and saved $10,000 a year in perpetuity, in one afternoon * How Al thinks about the boundary between enterprise systems of record and the custom scripts that live in between them * The psychological dimension of AI adoption: how to take existential fear off the table so your team can actually engage with the technology * The autopilot accountability principle: why the person who creates an AI tool is always responsible for what it does, regardless of what the machine decided * How a standing Monday meeting agenda item and a shared Microsoft environment for corporate-owned apps is building a firm-wide culture of AI experimentation * Why the investment case for AI comes down to the widest top-of-funnel possible: seeing more managers, filtering more efficiently, and getting to the best GPs faster than peers who are not augmented * Why sharing freely with peers and competing are not contradictory, and how to think about the line between the two About Al Hemmingsen: Al Hemmingsen is the CIO of a prominent US-based single family office with a history going back to the mid-20th century. He has been at the helm for the past decade, managing a multi-generational pool of capital with a quasi-perpetual mandate. Al is one of the most hands-on AI adopters in the institutional family office world and is the only CIO, that we’re aware, of actively publishing open-source code on GitHub for use by fellow allocators. Episode Highlights: [05:56] The $59.32 Moment $59.32 in API credits unlocked 50-plus years of legacy transaction data in a single afternoon. That moment set off a systematic search for every legacy problem in the organisation AI could solve. [09:18] Building in the Seams What is worth building are the tools that live between enterprise systems: workflows too specific for any software company to build a product for. [11:23] Taking Fear Off the Table The goal is to eliminate the 30% of every job that is tedious and repetitive, not the people doing it. Remove that fear first and the rest follows. [19:09] The Investment Case There are 56,000 alternative managers globally. The LP that processes information faster and gets to the best GPs have a genuine edge. [20:55] Collaboration and Competitive Advantage Al's view is that sharing tools and ideas freely with peers in the family office world makes everyone better, which is why he publishes on GitHub. But that openness has a natural boundary: the capability to see more managers, filter faster, and reach the best GPs before their schedules fill is a genuine investment edge worth building and protecting. Episode Resources: * Al Hemmingsen on LinkedIn [https://www.linkedin.com/in/alhemmingsen/] * Al Hemmingsen on GitHub [https://github.com/alhemmingsen] * Victoria Sienczewski on LinkedIn [https://www.linkedin.com/in/vsienczewski/] * AuumAI Website [https://auumai.com/] Disclaimer: The views and opinions expressed by Al Hemmingsen in this interview are his own and do not necessarily reflect the views, positions, or policies of any organization with which he is or has been affiliated.

Professor Ludovic Phalippou on the Promise and the Peril of AI for LP Decision-Making in Private Markets

Professor Ludovic Phalippou of Oxford Saïd Business School joins The Agentic Allocator to deliver a rigorous, unsparing look at what AI can and cannot do for Limited Partners in private markets. Drawing on his research paper, 'Limited Partner Versus Unlimited Technologies,' and hands-on experiments applying machine learning to real LP datasets, Professor Phalippou maps both the transformative potential and the structural risks of AI adoption in private markets.  Private markets are not data-poor - they are data-overwhelmed. A single fund investment can generate thousands of pages of PDFs and countless spreadsheets. A large LP like CalPERS monitors 200 active private equity funds and receives quarterly reports on 3,000 underlying portfolio companies. No human can absorb that. The promise of AI is that it can. But Phalippou warns that the same tools capable of unlocking hidden signals in qualitative data can just as easily be gamed, misused, or deployed in ways that amplify the industry's existing distortions. Professor Phalippou shares research showing how AI sentiment analysis of GP quarterly reports can predict future portfolio company returns with real predictive power, far beyond what the reported marks alone convey. He also lays out in precise detail how GPs could exploit AI-reliant LPs, from burying critical fee terms ever deeper in footnotes to inserting invisible prompt injections into documents. He outlines the governance framework LPs need to build before they trust any AI output with a real decision. What You'll Learn: * Why private markets have too much information, not too little, and why that is precisely where AI's power lies * How AI sentiment analysis of GP quarterly reports can predict portfolio company returns beyond what reported marks reveal * Why extracting a headline EBITDA figure with AI is a recipe for catastrophe, and what to ask for instead * How GPs could exploit AI-reliant LPs: from burying critical terms in footnotes to inserting invisible prompt injections into fund documents * The legal frontier: why AI provisions will soon appear in LP/GP confidentiality agreements * A governance blueprint for LP organisations adopting AI: who is responsible when an AI-extracted number turns out to be wrong? * Why the right question to ask AI is never 'give me the EBITDA,’ and what high-quality AI prompting for private markets actually looks like * Why LPs must avoid both naive enthusiasm and reflexive dismissal, and what a mature, risk-aware approach to AI adoption actually looks like About Professor Ludovic Phalippou: Ludovic Phalippou is a Professor of Financial Economics at Oxford’s Saïd Business School and specializes in Asset Management, with a special focus on investments in private equity funds. He is one of the world's leading academic authorities on private equity performance, fees, and transparency. His research on private markets has been widely cited by LPs, regulators, and policymakers. He sits on the investment committee of Oxford’s Queen’s College, has collaborated with institutional LPs on applied machine learning research, and authored the paper 'Limited Partner Versus Unlimited Technologies,' which examines how generative AI and machine learning are poised to reshape LP decision-making, and the risks that come with it. Episode Highlights: [00:03:21] The Real Data Problem in Private Markets Phalippou dismantles the assumption that private markets suffer from a lack of data. The problem, he argues, is the opposite: thousands of pages per fund, quarterly reports on thousands of portfolio companies, and none of it structured or comparable. AI's promise is to swallow that volume and surface what matters. [00:04:31] Sentiment Analysis as a Return Predictor Drawing on original research, Phalippou explains how running AI sentiment analysis on GP quarterly report text produces a powerful predictor of future portfolio company returns - one that adds significant information beyond the reported mark. Two companies both marked at 1.2x can have dramatically different outcomes depending on how positively or neutrally the accompanying text is written. [00:09:19] Why EBITDA Extraction Is the Dangerous Use Case The most common instinct for LPs, using AI to automatically extract and aggregate financial metrics across hundreds of portfolio companies, is also the most dangerous. EBITDA figures in private markets are loaded with definitional choices, add backs, and adjustments buried in footnotes. Asking AI for the headline number without the context around it can produce false confidence. [00:11:40] How GPs Can Game AI-Reliant LPs Once GPs know their documents are being processed by AI rather than read by humans, the incentive structure shifts. Critical fee terms and performance qualifications will be buried even deeper in footnotes. More alarmingly, Phalippou explains how invisible prompt injections, text hidden in white-on-white font, can manipulate AI summaries to generate favourable assessments. Academics have already been caught doing this with peer review systems. [00:13:29] What Good AI Prompting Looks Like for LPs Rather than asking AI for a single extracted number, Phalippou recommends asking it to surface all relevant passages in a document on a given topic, including definitions, footnotes, and any conflicting disclosures. The absence of a clear definition is itself an information signal. Over time, analysts can train AI on their own judgment: flagging specific types of add backs, identifying patterns that have historically indicated problems. [00:19:29] The Legal Frontier: AI Provisions in LP/GP Agreements Some GPs are already attempting to prohibit LPs from processing their documents with AI. Phalippou argues the logic does not hold: when an LP hired Cambridge Associates, they were paying for judgments informed by thousands of comparable documents. Giving a document to an AI does the same thing. The conversation predicts LP/GP confidentiality agreements will soon include explicit AI provisions, a structural shift in how the industry governs information. [00:21:00] The Machine-to-Machine Future Phalippou reflects on an emerging world in which GPs produce documents with AI assistance, LPs process them with AI, and the analysis in between is conducted by AI. He uses the analogy of education: when exams are written, answered, and graded by machine, what is the human layer actually doing? His answer: setting the values, defining the questions worth asking, and retaining genuine accountability for the answers. [00:23:29] A Governance Blueprint for LP Organisations Phalippou's closing framework centres on responsibility. When an AI-extracted number turns out to be wrong and a real investment decision follows from it, who is accountable? Organisations must resolve this before deploying AI, not after. He also stresses the importance of avoiding both naive adoption ('this will simplify everything') and reflexive dismissal ('it just generates noise'). The mature position is to use these tools seriously, with clear governance and a clear-eyed understanding of the risks. Episode Resources: Professor Ludovic Phalippou on LinkedIn [http://linkedin.com/in/ludovic-phalippou-5488b147/?skipRedirect=true] Oxford Saïd B... [https://www.sbs.ox.ac.uk/about-us/people/ludovic-phalippou]

From Digitally Native to AI-Driven: How Moonfare Is Rethinking Private Markets Access

Marine Eugene, Chief Commercial Officer at Moonfare, explains how one of the private markets' most recognised digital platforms is now embedding AI across its investment process, commercial operations, and client experience, without compromising on the high-touch service that sophisticated family offices and investors demand. Moonfare has built its reputation democratising access to top-tier private market funds by removing friction from the investment process. Now, with a team of 15 investment professionals monitoring over 250+ alternative funds annually and a client base spanning 23 markets, Marine explains how Moonfare is deploying AI to handle the scale of data processing, lead triage, and compliance monitoring that simply wasn’t possible manually. In this episode, Marine traces the full arc: from the early days of digitising paperwork and onboarding flows, to deploying AI for legal document extraction, Excel automation, and IC memo generation, and into the next wave - using AI to surface fund managers, track live portfolio performance, and deliver the kind of interactive, real-time data experience that family offices are now expecting as standard. What You’ll Learn: * How Moonfare’s investment team uses AI to extract key terms from 200-page fund documents in seconds, replacing manual page-by-page review * Why AI-driven lead scoring and triage is allowing Moonfare to serve 10,000-15,000 new platform registrations per year across 23 markets with a fraction of the junior sales headcount previously required * How Moonfare trained an internal AI agent on a new brand tone-of-voice book to keep all client-facing teams, from operations to investor relations, communicating consistently * Why Marine is cautious about fully automating client interactions, and where the human touch remains non-negotiable for high-net-worth and family office investors * What Moonfare’s investment team could look like in five to ten years: AI surfacing unique fund managers, tracking live portfolio performance, and eliminating the lag in quarterly reporting * The cultural playbook for AI adoption: AI working groups, team champions, and a clear message that AI is a tool to free talent, not a substitute for it * Marine’s candid advice to any CCO or CIO who hasn’t yet started: you’re already behind, get curious, learn to prompt, and be very precise about where you expect the return About Marine Eugene:  Marine Eugene is Chief Commercial Officer at Moonfare, the digital private markets platform recognised for democratising access to institutional-quality funds. Prior to Moonfare, Marine was an executive in the private aviation industry, including at NetJets and FlexJet. Over the past three years at Moonfare, she has led the firm’s commercial growth across 23 markets, with a focus on serving family offices and sophisticated institutional investors through a high-quality, digitally-native experience. Episode Highlights: [00:01:36] From Private Aviation to Private Markets: The Digital-First Shift Marine reflects on her transition from private aviation to private markets and how family office expectations have evolved, from digital onboarding as innovation, to interactive live data and portfolio-level analytics as the new baseline. [00:04:14] Inside the Investment Team: AI Meets 250 Funds Per Year With 15 investment professionals monitoring over 250 alternative funds annually, Marine explains how AI is already accelerating document review, Excel-based financial modelling, and IC memo generation, and what the shift from efficiency to value creation will look like. [00:05:50] When AI Became a Commercial Priority at Moonfare Marine traces the timeline: from deploying basic AI tools nine to twelve months ago to improve team workflows, to now fielding client expectations that AI will enhance their own user experience with more interactive, live data. [00:07:20] Lead Triage at Scale: 10,000–15,000 Registrations, 23 Markets Marine explains how AI-powered lead scoring and triage is replacing the archaic five-to-ten-person junior sales teams that previously managed lead qualification, enabling Moonfare to focus investor relations talent on the most mature, high-value opportunities. [00:10:00] The High-Net-Worth Line: Where AI Stops and Humans Begin Marine draws a sharp distinction between using AI in the back office for efficiency and using it in direct client interactions. Family offices and HNW investors don’t want to talk to a bot and the human relationship remains the firm’s commercial edge. [00:11:00] Building an AI Culture: Working Groups, Champions, and Brand Training Marine describes Moonfare’s internal AI rollout: cross-functional working groups, team champions, and a practical project using AI to enforce brand tone-of-voice consistency across every client touchpoint. [00:11:57] Human-in-the-Loop Is Non-Negotiable Marine is direct: AI is a formidable shortcut, but you cannot rely on the output without human review. The investment and commercial teams treat AI as a tool to go faster, not a replacement for judgment. [00:14:15] Compliance at 100%: AI Listening to Every Call Previously, compliance teams could only sample a fraction of recorded calls. Marine highlights how AI now enables 100% call coverage, a step-change in risk management that was simply not achievable with human resource alone. [00:15:20] The 5–10 Year Vision: AI Surfacing Unique Managers and Live Portfolio Tracking Marine paints a picture of an AI-enabled investment office that can surface niche funds, track every underlying asset in live time, and eliminate the lag of quarterly reporting, replacing tribal knowledge with something close to perfection. [00:18:00] Advice for CCOs and CIOs Who Haven’t Yet Started Marine’s message is unambiguous: if you haven’t started, you’re behind. Get curious, register for alerts on LLM prompting, be precise about the ROI you’re looking for, and don’t just buy the most talked-about tool. Episode Resources: Marine Eugene on LinkedIn [https://www.linkedin.com/in/marine-eugene/] Moonfare Website [https://www.moonfare.com] Victoria Sienczewski on LinkedIn [https://www.linkedin.com/in/vsienczewski/] AuumAI Website [https://auumai.com] If you enjoyed this conversation, make sure to subscribe, rate, and review on Apple Podcasts, Spotify, and YouTube.