Today I discuss Amadeus’s smaller and, in my view, more interesting comparable: Sabre Corporation (US: SABR). An important caveat up front: Sabre is highly leveraged relative to its equity cap, so if your investment process eschews debt-laden companies, this article will not appeal to you.

Sabre is the world’s #2 global distribution system (GDS), a centralized network that facilitates automated, real-time transactions between travel service providers (airlines, hotels, car rental companies) and travel agencies. It sits on top of the same agentic data flywheel as Amadeus and, in several places, is further along on agentic infrastructure. The enterprise, however, trades at a fraction of Amadeus’s valuation, as Sabre carries roughly $4.2 billion of debt against a market cap under $750 million. This is a genuine equity stub, as defined in Chapter 10 of my book, The Manual of Ideas.

Equity stubs are uncomfortable, and the bear case for Sabre is zero. However, a combination of strong cash generation, deep proprietary data, aggressive deleveraging, and active engagement by 10+% shareholder Constellation Software (Canada: CSU) tilts the risk-reward in a way I find difficult to ignore. Buffett follower Discerene Capital, with close to 10% ownership, appears to hold a similar view.

Let’s understand why the uncomfortable optics of Sabre equity may be deceiving, and why investors may capture 3-7x upside as the company executes its agentic AI strategy and delevers.

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Thesis summary:

Kitazato is Japan’s leading provider of consumables for in vitro fertilization (IVF), generating over 60% of sales internationally and posting a 56% operating margin — the sixth-highest among non-financial Japanese listings. Patrick estimates the global fertility treatment market at roughly $49 billion, with 5-8% expected growth driven by later marriage, declining male sperm counts, rising egg-freezing adoption, expanding insurance coverage, and ongoing technological improvement. Kitazato’s sales have compounded at an 8.9% CAGR over the last decade.

The company’s moat stems from the vitrification revolution it pioneered with Japan’s Kato Ladies Clinic around 2000. Vitrification flash-freezes eggs in a glass-like state using liquid nitrogen, eliminating the ice-crystal damage of slow freezing and enabling near-100% thaw survival. Industry adoption from 2005 made it the global standard of care. Roughly 65% of Kitazato’s sales tie to vitrification, where it holds 60% share in Japan, 70% in Europe, 80% in China, and 85% in India. Across Japanese product categories, share ranges from 50% to 96%, including a near-monopoly in Cryotop storage devices.

Patrick views founder Futoshi Inoue, 55, as an exceptional operator aligned with shareholders. Inoue owns 58.9% of the company, describes shareholders as co-managers, and is unhappy with post-IPO share performance. FY results due in May should beat a -7.1% operating profit guide (Patrick models +4.5%); a dividend payout lift from the current 40% toward 50-70% is plausible given ¥12 billion of net cash and minimal capex; a potential cryobank business could open a new profit pool; and US growth should accelerate under new distribution partner DeviMed. Principal risks include competition, technological disruption, margin compression, and a possible further stake sale by Inoue.

The shares recently traded at 6.9x EV/EBIT, 13.3x P/E, 2.77x P/B, and a 3.0% dividend yield, with a ¥53 billion market cap. Public peers Cooper Companies and Vitrolife trade at 17.7-23.5x EV/EBIT and 24-38x P/E despite lower margins, while recent industry M&A has taken place at 5x+ sales, implying 18x+ EBITDA. Patrick’s DCF, assuming 4-8% sales growth and 57-59% operating margins, yields a ¥1,387-¥2,238 fair value range.

Disclaimer

Asian Investing Summit 2026 was held from April 14-21, 2026. The content of this website is not an offer to sell or the solicitation of an offer to buy any security. The content is distributed for informational purposes only and should not be construed as investment advice or a recommendation to sell or buy any security or other investment, or undertake any investment strategy. There are no warranties, expressed or implied, as to the accuracy, completeness, or results obtained from any information set forth on this website. BeyondProxy’s officers, directors, employees, and/or contributing authors may have positions in and may, from time to time, make purchases or sales of the securities or other investments discussed or evaluated herein.

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Kitazato Corporation Presentation

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Before getting to the data, a few words on the format itself.

The Briefing is built as a slide deck rather than a narrative report. Every page is a single vi…

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11 Articles We Are Reading This Week

1. SpaceX - Aswath Damodaran

2. Better investor - Bogumil Baranowski

3. Hacker mindset - Henrik Karlsson

4. Painter’s painter - Scott Sumner

5. Credit reflexivity - Sam Hanson

6. Internet is real life - Erik Torenberg

7. Corporate dark arts - Andrew Walker

8. Transformer shortage - Rob L’Heureux

9. Management simulator - Rohit Krishnan

10. Interior design of software - Rex Woodbury

11. Hidden trap of compounding - James Emanuel

The Latticework 2026 website is live. Learn more and join us in November!

Letters, Presentations, Writeups…

Industry Insights: aerospace | artificial intelligence | banking | commodities | data centers | forest products | mining | quantum | semiconductors

Geographic Insights: China | Latin America | South Korea

Letters: Bretton | Bronte | Clayton | Hosking | Laughing Water | Maran | Nightview | Rowan Street | Upslope | White Brook | White Falcon

Company Insights: Amazon | Art Vivant | Copart | Danaher | Live Nation | Millrose Properties | Nintendo | Passat | VersaBank | Victory Giant | XPEL

Company Slides: Boeing | Boston Scientific | Capital One | CBRE | Danaher | D.R. Horton | Freeport | Honeywell | Lockheed | LSEG | NextEra | PG&E

5 Videos We Are Watching This Week

1. Stripe - Tony Xu on DoorDash and what drives the restaurant industry

2. Milken - Kravis and Roberts on KKR strategy and the global economy

3. Wharton - Howard Marks on value investing and AI’s grip on finance

4. Goldman Sachs - Farallon Capital’s Nicolas Giauque on the long term

5. Knowledge Project - OpenAI’s Greg Brockman on AI going parabolic

5 Podcasts We Are Listening to This Week

1. Norges - Alain Lam on Xiaomi’s evolution from startup to a tech giant

2. Odd Lots - Daniel Yergin on the global energy landscape post-Hormuz

3. Flyover Stocks - Morgan Housel on money in your 40s and far beyond

4. Excess Returns - Bloomstran on AI capex and the concentration story

5. Invest Like the Best - Dylan Patel on AI’s supply and demand puzzle

Member Updates & Events

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When I share an API key with an LLM such as Claude, the model stops guessing. It pulls from a credible source with a schema, a version, and a contractual provenance. Research gets materially better because the retrieval surface is no longer the open web but an authoritative feed.

That reframed the question for me. If I, as a niche participant, am paying for API access because it makes AI more trustworthy, then every enterprise AI deployment (e.g., credit, travel, compliance, or research agents) will eventually do the same. The companies that supply the data on the other end of those API calls could see a step-change in demand. Before LLMs, MOI had not subscribed to any APIs; now we are up to a half-dozen and growing.

I asked Claude to run a comprehensive global screen. The report came back with more than sixty names across financial, geospatial, legal, health, industrial, and consumer data categories. What follows is a curated summary of the Top 10 ideas I found most intriguing.

What makes data valuable to an agent

Five properties matter:

• Authority. Regulators, auditors, or litigants are required or expected to cite the source.

• Sensor production. The data is generated by owned infrastructure (satellites, probes, labs, payment rails, panels) and cannot be scraped.

• Network exclusivity. Two-sided platforms where buyers and sellers both stand on one surface.

• Temporal depth. Multi-decade archives that cannot be recreated by a new entrant.

• Agent-callable surface. The data is served through versioned APIs with machine-readable schemas and transparent pricing.

The durable opportunities score on at least three of the five. “We own data” on its own is not a moat. And, the revenue model has to capture per-query agent economics, not per-seat SaaS that agents will compress.

The paradox the market is handing us

In 2025–26 the market repriced data businesses downward on the fear that frontier LLMs would disintermediate them rather than elevate them. Wolters Kluwer is off ~55% YoY; Thomson Reuters ~47%; FICO ~47%; Equifax ~28%. Rightmove, Hemnet, and Scout24 have been halved. RELX sold off ~20% on the February 2026 Anthropic news. Clarivate trades at roughly 0.7× sales on a high-margin recurring-revenue base.

If the “agents need authoritative data” thesis is directionally right, then this cohort represents the cleanest contrarian setup of the cycle. Here are the Top 10 names I am going deeper on in my own research.

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The essay opens in the Grand Ballroom of the Yale Club of New York City at Latticework 2025, where Chris read a Joni Mitchell parody to a room of professional investors before walking them through the case that hyperscaler capex (on the order of $350-400 billion against less than $40 billion of corresponding revenue) is one of the largest bubbles in American history. From there, the piece moves backward to 1998, when a St. Louis patriarch who had bought GE for twelve cents a share in 1932 handed a 29-year-old Chris a family portfolio and the mandate that became Semper Augustus.

The through-line is a single word Chris has been defending for a quarter century: price. In November 2000 he wrote Price Matters and closed it with a sentence he has never really stopped arguing: the market has a way of fairly pricing stocks over long periods. Ten months earlier, in January 2000, he had written a related piece on Microsoft — predicting zero percent returns for fifteen years. Microsoft compounded earnings at 8.4% and sales at 10.1% over those fifteen years and still returned 0.6% annualized to shareholders. The company was always great. The price was the problem. When the price reset at 17× in late 2014, the same business compounded at 26.6% annualized for the next decade.

That is the argument, lived out across 26 years of client letters, letters that began at seven pages and have grown, without losing a single founding client, to 184. The essay and audiobook collect the ideas, cases, and stories — from the patriarch’s twelve-cent GE to the options-as-hidden-liability critique, from Ross Stores to the Berkshire Scorecard — into a portrait of a manager whose method is itself a kind of patience.

We are grateful to Chris for the wisdom and insights he has shared with the MOI Global community over many years.

Featured Events

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The Briefing is designed to answer a deceptively simple question. If you were sitting down before the weekly market open — as an investor rather than a trader — what would you want in front of you?

Each week, the Briefing walks through four…

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11 Articles We Are Reading This Week

1. Neglected - Michael Mauboussin

2. March madness - Chris Satterthwaite

3. Serve somebody - Phil Bak

4. American peoples - Rudyard Lynch

5. Corporate dark arts - Andrew Walker

6. Surviving price wars - Tugce Erten

7. Navigating uncertainty - Annie Duke

8. Rewriting terminal value - Todd Wenning

9. Fanaticism and competence - Scott Sumner

10. Dinner that changed everything - Robert Dee

11. Combining value and momentum - Carlos Morales

The Latticework 2026 website is live. Learn more and join us in November!

Letters, Presentations, Writeups…

Industry Insights: banking | fertility | helium | prediction | software | tech

Geographic Insights: Hong Kong | Iran | Japan | UK/Ireland

Letters: Black Bear | Distillate | GDS Investments | JEN Capital | LRZ Capital | Oakmark | Open Square | Pine (Korea) | Third Point | YorkTech

Company Insights: Anthropic | Freee | Grab | Intel | Meta | MTU | Puma

Company Slides: Alcoa | ASML | Bank of America | BlackRock | Carmax | Citigroup | Fastenal | Goldman | J.P. Morgan | Sika | TSMC | Wells Fargo

5 Videos We Are Watching This Week

1. Stripe - Mati Staniszewski of ElevenLabs on the new world of voice AI

2. Stanford - David Rubenstein of Carlyle on pursuing a life of meaning

3. Dwarkesh - Jensen Huang on Nvidia’s supply chain and why it endures

4. In Good Company - Hans Ulrich Obrist on what art teaches business

5. Knowledge Project - Mario Harik of XPO on growing a large business

5 Podcasts We Are Listening to This Week

1. TWIII - Shaun Heelan on market complacency and value case studies

2. Acquired - Ferrari: the pinnacle of luxury, scarcity, and brand strategy

3. Money Makers - Stuart Widdowson on Odyssean and UK small caps

4. Value After Hours - Chris Bloomstran on Buffett and Berkshire today

5. Behind the Balance Sheet - Michael Cembalest on markets, cycles, AI

Member Updates & Events

The following is a private section for MOI Global members. Membership is by invitation only. If you would like to become a member, be sure you are on the annual plan. It is the first step toward full membership.

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Thesis summary:

Freee is a Japanese cloud-native software platform that functions as a back-office operating system for small and medium-sized enterprises, founded in 2013 by ex-Google executive Daisuke Sasaki and CTO Ryu Yokoji. Often described as the Japanese analogue of Australia’s Xero, Freee began as an accounting tool and has expanded into HR, payroll, tax filing, expense reimbursement, electronic signatures, invoicing, sales management, and a third-party app store with integrations including Rakuten, Amazon, and Line. The platform serves roughly 500,000 businesses and, together with Money Forward, holds about 15% of the Japanese accounting software market, with legacy desktop incumbents such as Yayoi lagging behind. Michael views Freee as the true disruptor because its automated bank and credit card linkages generate entries with minimal manual input, positioning it to take share as Japanese SMEs digitalize from a base of Excel and paper.

The core of Michael’s thesis is that the market has wrongly extrapolated generative-AI commoditization risk onto enterprise SaaS. Japanese software stocks are down sharply, and Freee itself has fallen roughly 30% YTD and about 50% from its 2025 peak, with some Japanese portfolio managers reportedly barred from touching the sector. Michael argues that enterprise systems of record are structurally different from point solutions: AI remains probabilistic and unsuitable for mission-critical accounting work, distribution and trust matter more than raw code, and Freee sits on proprietary transactional data from its half-million customers with more than 1,000 banking and financial integrations that are not replicable by vibe-coded alternatives. Monthly overall churn has declined to 1.1% in 2025 (corporate churn 0.5%, lower than Money Forward’s), and learning costs, limited data migration, and a growing app store bolster the moat.

The growth algorithm has compounded revenues at a 39% CAGR since 2019, driven by a 25% CAGR in paying customers (from 160,000 to about 607,000) and a 9% CAGR in ARPU (to roughly JPY 56,700). With Japanese cloud accounting penetration at only ~25-30%, Michael sees room for penetration to triple toward Australian and US levels, supporting sustained 30% top-line growth. Management has guided to 26% revenue growth in the current fiscal year and has historically beaten its targets, recently posting close to 30%. Share-count dilution is near zero, unlike typical US SaaS peers, so organic growth accrues cleanly to minorities.

Michael believes long-term operating margins can exceed 30% and potentially reach 40%, consistent with Xero at ~55% EBITDA margins in Australia/New Zealand, Fortnox at ~50% EBIT margins in Sweden, and Intuit’s SME segment at ~50%. ARPU of roughly $30-40 per month is low for the value delivered, and modest pricing uplift would convert the existing cost base into substantial leverage, analogous to Netflix’s pricing journey a decade ago. Catalysts include Money Forward’s September 2025 price hike (driving prospects to Freee), Freee’s December 2025 launch of consolidation accounting and manual double-entry bookkeeping (closing the feature gap), and accelerated AI product rollouts led by newly appointed Chief AI Officer Ryu Yokoji, including automated receipt capture, a ChatGPT tax guidance mini-app, an AI website builder, and a business-succession matching tool. Daisuke Sasaki, founder-CEO, is described as serious, non-promotional, and fully focused on building the business.

The shares recently traded at 2.3x EV/Sales, roughly half the multiple of industry peers and meaningfully below Money Forward. Freee is around GAAP breakeven with capitalized software costs, and Japanese retail investors appear to discount the delayed profitability inherent in the SaaS model. On Michael’s numbers, EV/Sales compresses to 2.0x in FY2027, then 1.7x, 1.4x, and 1.2x, with P/E falling below 10x by 2030 as margins scale into management’s long-term 30% operating margin guidance. Using conservative margin and exit-multiple assumptions, Michael arrives at an IRR of about 25%, with no dividend — the return is entirely a function of growth continuing from the guided 26% toward the high teens over several years. Some sell-side targets (Macquarie among them) sit at roughly double the recent share price, consistent with Michael’s view that the current multiple reflects near-term AI disruption fears rather than the underlying economics of a dominant, compounding Japanese SME platform.

Disclaimer

Asian Investing Summit 2026 was held from April 14-17, 2026. The content of this website is not an offer to sell or the solicitation of an offer to buy any security. The content is distributed for informational purposes only and should not be construed as investment advice or a recommendation to sell or buy any security or other investment, or undertake any investment strategy. There are no warranties, expressed or implied, as to the accuracy, completeness, or results obtained from any information set forth on this website. BeyondProxy’s officers, directors, employees, and/or contributing authors may have positions in and may, from time to time, make purchases or sales of the securities or other investments discussed or evaluated herein.

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Michael Fritzell on Freee

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Continuous learning is at the heart of everything we do. As we look ahead to the future of the Latticework summit, we wanted to take a moment to look back and share the wealth of knowledge generated at our last gathering.

For a limited time we have removed the paywall on the Latticework 2025 sessions. Whether you want to revisit a favorite presentation or catch a session you missed, you can access the full library here:

Watch the complete Latticework 2025 replays.

We hope these sessions spark new ideas and provide valuable mental models for your investment journey.

(MOI members, scroll to the bottom for your member registration link.)

Introducing Latticework 2026

As you dive into the 2025 archives, we are thrilled to unveil the next chapter.

We have just launched our new, dedicated home for the summit.

For the first time ever, Latticework will be a two-day immersive experience. Expanding the timeframe allows us to foster deeper conversations, spark more serendipitous interactions, and provide a truly unhurried environment.

We will be gathering on November 10-11, 2026, in Chicago. Our main sessions will take place in the historic Cathedral Hall at the University Club of Chicago, with our Day 1 group dinner hosted in the beautiful White City Ballroom at the Chicago Athletic Association.

The Initial Lineup

A venue this historic requires an intellectual agenda to match. We are building what we believe is our most impactful forum of great minds yet.

Today, we are excited to announce our initial lineup of speakers:

• Chris Bloomstran, Semper Augustus Investments Group

• Pat Dorsey, Dorsey Asset Management

• Chris Kirtley, Equus Global Investors

• Saurabh Madaan, Manveen Asset Management

• Michael Melby, Gate City Capital Management

• François Rochon, Giverny Capital

• John W. Rogers, Jr., Ariel Investments

• Dave Sather, Sather Financial Group

• Will Thomson, Massif Capital

• Christopher Tsai, Tsai Capital Corporation

• W.D. (Daniel) Vasquez, W.D.Vasquez & Co.

(Please note: This is just the beginning. We will be announcing additional world-class instructors and thought leaders in the coming weeks.)

Registration is Now Open (Limited Availability)

Due to the intimate nature of the University Club and our commitment to maintaining an interactive environment, capacity is limited. We anticipate seats will fill quickly now that the website is live and the initial lineup is public.

Visit the Latticework 2026 website and secure your seat.

Thank you for your continued dedication to lifelong learning and for being an integral part of this community. Enjoy the 2025 replays, and we look forward to welcoming you to Chicago in November!

Warm regards,

John

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For those who couldn’t join us, here is a synthesis of the key insights and specific investment thesis highlights shared by our community of intelligent capital allocators. (Note: I have intentionally omitted most participant names in order to preserve their privacy.)

Life sciences: a setup still hiding in plain sight

Elliot Turner, Managing Partner and CIO of RGA Investment Advisors, opened with a multi-year research subject of his: picks-and-shovels life sciences. The argument is that the post-COVID overbuild has finally burned off, that tariffs, FDA disruption, and NIH funding strains are in the rearview mirror, and that these companies have quietly re-accelerated even as the market has refused to notice. Meanwhile, some of the most credible voices, including Jensen Huang, Demis Hassabis, Anthropic’s leadership, and even Stan Druckenmiller, are converging on life sciences as the single most compelling field for AI deployment. The gap between that chorus and the tape on these stocks is the opportunity.

Elliot’s two specific names: Sartorius (Germany: SRT, SRT3) and Bruker (US: BRKR). He frames Sartorius as one of only two pure plays on actually making biologic drugs, the other being Repligen. Within the Sartorius complex, his preferred vehicle (for investors who can live with the liquidity) is the preference shares, which currently trade at roughly a 20%+ discount to the ordinaries; historically they’ve traded close together. He also flagged a catalyst: the Sartorius family trust expires in 2028, ending a standstill with the founding family’s heirs, which could open the door to a takeover or capital actions not on the table so far.

On Bruker, the pitch is a founder-family-led business at its cheapest valuation ever (roughly 13x fully loaded earnings), with recurring revenue now at 40% of sales, up from 20%. He thinks the recent preferred issuance was unnecessary in hindsight, since the NIH resumed disbursements shortly after, but the business itself is well positioned. A large-cap-oriented member then echoed the thesis from the other end of the market-cap spectrum, favoring Danaher (US: DHR), Thermo Fisher (US: TMO), and Agilent (US: A), noting that the sell side won’t get interested until the inflection is clearly behind us.

A follow-up came on Avantor (US: AVTR). Elliot’s verdict: salvageable, but not nearly the asset management presented during the better years. Pieces of the portfolio are excellent, and the whole thing would be an attractive PE or strategic takeover target.

Payments: Adyen as a software-priced anomaly

Elliot’s largest software-adjacent position is Adyen (Netherlands: ADYEN; US: ADYEY). The bull case: 20% of market cap is cash, the company guided to 21–23% growth, got a 20% drawdown for its trouble, and trades at 22x this year’s EPS (mid-teens ex-cash), with some bottom-line leverage on top. 50% EBITDA margins, and a capex cycle rolling over. Adyen’s differentiators, as the UK-based investor who owns it as his largest position laid out, are enterprise-grade customers (Microsoft, McDonald’s, LVMH, Hugo Boss), a fully in-house stack that makes acquisitions nearly impossible, and cultural continuity despite the recent co-CEO handoff (Pieter van der Does stepped down; his co-founder remains).

The debate centered on the $5 billion of cash. Both Elliot and the UK member believe a buyback is the most sensible use of capital at this valuation, and both are implicitly betting management eventually gets there.

In response to a question on Shift4, Elliot’s work points to a lower-quality growth algorithm: processed-volume growth is high, but true organic volume on their own rails is essentially flat. It’s a “land-and-expand via acquisition” model targeting small business rather than enterprise.

Guidewire and the software-vs-AI standoff

Elliot flagged Guidewire (US: GWRE) as the rare vertical-software name that may actually benefit from AI, because it sits on a mountain of insurer data and prices on direct written premium rather than seats, so the standard “AI kills seats” argument doesn’t apply. A portfolio manager who sold the name in 2024 agreed with the moat (system, software, and service in one).

That set up the most animated debate of the call. On one side: a member who has been a student of the playbook of Constellation Software (Canada: CSU) argued that vertical market software is Constellation’s market to lose. His thesis rests on the scale of Constellation’s private-company database (essentially every VMS vendor on earth), the discipline of keeping annual relationships with every one, and Topicus’s 2025 acquisition cadence (more deals than the prior four years combined) — all of which AI only reinforces, because cheaper R&D favors incumbents who already own the workflow and the data. A second member, who has been adding to Constellation, Topicus (Canada: TOI), and Lumine Group (Canada: LMN), argued that for the typical customer, Constellation’s software is under 1% of revenue, so full in-house replacement generates rounding-error savings against the risk of breaking a mission-critical system.

The pushback: The real threat isn’t a weekend vibe-coder; it’s well-funded, industry-expert small teams with the Andreessen Horowitz playbook, AI tooling, and a plan for distribution. A16Z is literally publishing step-by-step guides on disrupting previously undisruptable verticals. And the rule that we overestimate one-year AI progress and underestimate decade progress applies with force. It’s easier to align with assets that benefit from AI adoption than debate moats that may look very different in five years.

An investor-programmer member summarized the in-between view: incumbents likely win eventually because they own the workflow and the data, but the intervening years are an arms race, and the question is how much margin and pricing gets competed away. He also nudged the group toward a more direct way to play AI: real-world operators like pest control and elevator maintenance, where productivity gains are not yet priced in.

Real assets, real estate, and Europe

I’ve been parking capital in undervalued European residential real estate, specifically Vonovia (Germany’s largest apartment owner, ~$20 billion market cap, less than one-half of NAV) and Aroundtown (less than one-third of NAV, engaged shareholder, recent buyback). Real estate is classic inflation protection that underperforms in the first leg of rates rising and catches up once pricing power gets recognized. It’s also one of the few places you don’t have to hold a view on AI.

A US-based investor added Healthpeak Properties (US: DOC) as his own life-sciences-adjacent real-estate idea: the Janus senior-housing IPO unlocked value that leaves the RemainCo (medical office buildings and lab space) trading above a 9% cap rate with a fortress balance sheet. He’s also watching Brad Jacobs’s vehicle as a roll-up in distressed home-building supply.

For European industrials more broadly, I’d point to the thesis that the US cheap-energy advantage is structural and that German energy policy has hobbled its industrial base. Plenty of US chemical names are still interesting.

A UK small-cap specialist confirmed that overseas buyers are now calling about UK-listed international businesses at London discounts, Auction Technology Group (UK: ATG) being a recent case in point. His playbook: market leaders with asset backing that have over-spent capex for three or four years and convert to cash generators in an inflationary environment. He won’t touch UK housebuilders (Barratt, Redrow, Vistry); expected rate cuts have flipped to potential hikes and build costs have exploded.

A special situation

Finally, a member I hold in high regard walked the group through a mineral-royalty owner the institutional world doesn’t bother tracking. I refer to the entity tongue-in-cheek as the next Texas Pacific Land Trust. While the member shared the name with the call participants, he stated that he is still building a position and would prefer the name not to be released.

I found yesterday’s call incredibly valuable, and I hope this summary provides you with a few actionable insights for your own capital allocation process.

Our next live call will be on June 24 at 2:00 pm ET. No formal registration is needed; you can simply click the following link at the scheduled time to join.

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It is with great sadness that we note the passing of Murray Stahl. We held immense respect for him as an investor whose original thinking made profound contributions to the investment community. We were honored to host him for a conversation about his investment philosophy in 2018 , and we are pleased to make that conversation available here.

In a fireside chat, Murray Stahl discussed finding value in an increasingly indexed market, cryptocurrency, and unconventional compounders. Bringing decades of investing experience, the former chairman of Horizon Kinetics offered a deeply contrarian perspective on the global economy.

He challenged conventional wisdom on passive investing, highlighting how indexation distorts the float and valuation of mega-cap equities. Beyond traditional markets, he dove into blockchain technology, arguing why Bitcoin might be the ultimate value investment in a world of negative real rates.

Whether dissecting hyperbolic discounting, mapping the potential of dormant assets like Texas Pacific Land Trust, or explaining leverage, his approach offered a masterclass in uncovering asymmetric opportunities. This conversation provided a rare glimpse into a mind that consistently looked where others did not.

What You’ll Learn

• The risks of indexation and its impact on float-adjusted capitalizations

• Why blockchain represents an existential threat to traditional finance

• Compounders in owner-operators, spin-offs, and dormant assets

• The mechanics of hyperbolic discounting and finding deep value

• The economic potential of water rights in legacy landholdings

• Contrarian ideas from shipbrokers to cybersecurity defense contractors

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Transcript

The following transcript has been edited for space and clarity.

Shai Dardashti: It’s a real honor to introduce the next instructor at Latticework 2018. Joining us is Murray Stahl – co-founder, chairman and CEO of Horizon Kinetics, as well as chief investment officer of the company. Murray, who brings over 30 years of investing experience, is responsible for overseeing proprietary research and portfolio management decisions across the entire firm. He is also the co-portfolio manager for a number of registered investment companies, private funds, and separate institutional accounts. Additionally, Murray is the chairman and CEO of FRMO Corp. He is also a board member of the Bermuda Stock Exchange, the Minneapolis Grain Exchange, Winland Electronics, and IL&FS Securities Services Limited.

Our discussion centers on identifying compounders in a global economy. I have in front of me a Bloomberg article from May 2018, “The 130-Year-Old Bankruptcy That Created a $5 Billion Oil Giant,” with the sub-headline “Texas Pacific Land Trust has surged almost 2,200% since 2010.” Here’s a brief excerpt: “Not everyone missed the rally. In 1995, a low-profile fund named Horizon Kinetics LLC published research on the trust titled ‘How to Buy 1 Million Acres of Fine Texas Grazing Land for $20.’ The fund, whose CEO Murray Stahl declined to comment, has been in and out of the stock since the 1970s. It bought in in a big way in 2008, before the rally, and now, as of May 2018, holds 1.8 million shares worth about $1.2 billion.”

Could you comment?

Murray Stahl: Even though I declined to comment for Bloomberg, I’ll comment to you, so you’ll now get the entire story instead of only the analytical one.

To begin with, in life, you have to be lucky. In 1984, when I was a young analyst, the New York Society of Security Analysts would hold its meetings downtown. I was going to a meeting of a gold company and, for whatever reason, I was preoccupied. I got off the elevator to go to this Society. You had to turn left, but I wasn’t paying attention and turned right, walking to the end of the corridor. I came to a door that had Texas Pacific Land Trust on it. Realizing I was in the wrong place, I walked to the other end of the corridor and attended the meeting. The gold company wasn’t all that interesting, and the entire time, I was thinking about what Texas Pacific Land Trust was. When the meeting finished, I went back and knocked on that door. I went in and asked for an annual report, which I got and read. There was a woman there; it was a one-person office. The company no longer has an office in New York; it has moved to Dallas. In any event, that was the start of it.

The point of the story is the whole idea is to pick up information other people aren’t likely to pick up. I found it fascinating: this New York Stock Exchange-listed company went bankrupt in something like 1881 and had since been doing nothing with its cash flow other than buying back shares. If you like calculus, this is the stock for you because every time the company buys a share back, the next remaining share purchase is a greater portion of the remaining denominator, which is the remaining shares outstanding.

That’s the story of how it started. Since I’m a long-term investor, I never lost sight of it.

Dardashti: You’ve written extensively about owner-operators, dormant assets, and spin-offs as fertile hunting grounds. Would you say these traits are especially pronounced in particular countries?

Stahl: I wouldn’t say so. I would say the United States has the deepest and richest stock market, so you’ll find more of them in this country. Canada and Japan have very deep and rich stock markets, and you’ll find examples of those there. Now, different nations have different industry concentrations. For obvious reasons, Canada might have more of natural resources, and you’re more likely to find a dormant asset in Canada than in Japan. Generally speaking, those things present themselves, less so now than in the past, but they do present themselves from time to time.

Dardashti: Are there any geographies outside of North America that you currently believe to be uniquely compelling landscapes for identifying compounders?

Stahl: Very few, frankly. I would say Israel is one because of the growth of technology there. In theory, I’d also say Singapore because of its great government, but you don’t have a particularly large opportunity set. Oddly enough, I’d add Japan because it has some highly intriguing owner-operator companies which can be said to go against the trend of what the major industrial companies do. The opportunities are around but less so today than in the past.

I should say one thing because I don’t want to fly under false colors: if you concentrate on dormant assets, spin-offs, or owner-operators, you understand that indexation affects everything. It is an issue people have to contend with. Let’s take the idea of an owner-operator because it’s the simplest of the three to understand. What’s an owner-operator? A company where, for good or ill, the management get to be the management because they own more equity than anyone else and essentially vote themselves in.

I’ll use Amazon, a highly successful owner-operator, as an example to illustrate the impact of indexation. We all know this company. We understand the business – it’s not that complex to understand. It’s one of the major enterprises in the world, and most people think it’s going to grow even more. I don’t know more about Amazon than the average human being – many people know far more about Amazon than I’ll ever be able to know. The only thing I will say is that to me, in my unenlightened knowledge of the company, it looks like a very expensive stock. As a matter of fact, on its formalistic number, it’s one of the more expensive stocks I’ve ever seen in my career. Nevertheless, it’s been a great stock, but I don’t presume to know more about Amazon than anyone else. More importantly, I don’t presume that because people pay what I would regard as an excessive valuation, they’re over-exuberant, euphoric, stupid, or not mindful of risk.

Amazon is in the S&P 500, where its weight is roughly 3.25%. How does the S&P decide that Amazon is going to be a 3.25% weight? The float of the company in relation to the other 499 companies in the index will determine its proportional weight. What’s the float of Amazon? You look at the shares outstanding, which, crudely speaking, are 500 million. You multiply by price, which gives you the market capitalization. You need to subtract the shares owned by management – for our purposes, let’s say Jeff Bezos owns 15%. This means 85% of the shares are presumed to be in the float, which determines the market capitalization float-adjusted and, therefore, the weight of Amazon.

The problem with that is I’m not entirely sure it is fair to say 85% of Amazon’s shares are in the float. What does float mean? Whether I owned Amazon as an active investor or didn’t, I could, in theory, buy or sell it as it’s available in the marketplace. But an index fund will only sell it in the unique circumstance when index funds have outflows, but index funds do not have outflows. Index funds only have inflows. On a typical day — of course there are days when there are outflows, but as a generalization, index funds get inflows — index funds are buying. How is it then correct to say that 85% of shares are available in the marketplace at any given day when it should be patently obvious they aren’t? That’s a big problem. As a matter of fact, if you want to call the float those shares which are not available, you could make a good case for saying the float is, in fact, shrinking.

In theory, the active managers might sell the shares, but maybe Amazon is their favorite stock. The index will buy or pay whatever price the managers are willing to sell it. The index is price-indifferent. In the world of marketplaces, you might have people who are over-exuberant, not mindful of risk, or taking things to extremes. They all have different judgmental qualities. We never have a situation where an investor will buy whatever the price is. What if the price were one million times earnings? Somebody would still come into the marketplace, go in the index, and still buy.

The point is there are so many indexes right now (I think over 7,000 in America alone) that way too many companies are swept into one index or another, and things are subject to that valuation dynamic. I don’t know when it’s going to end, but when it does, it probably won’t be a pretty picture. I have no insight into whether we are in the ninth inning or the eighth inning or going into extra innings. It’s something people need to be mindful of because it’s a risk.

Dardashti: A Barron’s publication in November 2017 has you describing Bitcoin as the ultimate value investment, which qualifies as a compounder. Could you elaborate on your current thoughts about Bitcoin and any other perspectives?

Stahl: Let’s put it this way – Bitcoin is the ultimate contrarian investment. It might be a failure, by the way, and the majority of people think so. I’m not one of them, but then I might be completely wrong, and I’m willing to acknowledge that. The only thing I would tell you is I’m willing to be exposed as being wrong. Anyway, let me give you two reasons why I think I’m right.

The first and less important reason is the situation with your normal bond investment. Let’s say I brought your typical 10-year Treasury bond yielding 2.87% or something like that. After I pay my federal taxes and adjust for the inflation rate, I clearly have a negative rate of return. I might buy a bond with a more robust coupon. Let’s say I bought 100 bonds, and they yielded 4% or even 5%. Even if I were the greatest credit analyst in the world, I’d be wrong x percent of the time. If I were wrong, say, 20% of the time and lost 20% on my investment every time I was wrong, take 400 basis points off my return. Then I have to pay some taxes. Where am I? The simple fact of the matter is pretty self-evident: in the world of fixed income – and there’s a lot of fixed income in the world – it’s a negative real rate of return. With a cryptocurrency like Bitcoin, it’s not a negative real rate of return because it’s fixed issuance. That’s its first virtue. It’s not going to replace the US dollar. It’s just going to be an alternative, or parallel, currency.

The second and more important thing has to do with the standard computer systems in finance. There are just too many attack topologies to keep the system safe, so whether the corporations of the world like it or not, they will have to move to cryptocurrency and something called blockchain. It doesn’t have to be Bitcoin, but it’s coming, and it’s coming really fast. Also, cryptocurrencies have improved a lot and are much more understanding of what they do. There are now futures available. The blockchain systems are more robust. More companies are interested in them, and one day, we are going to get effective regulation and have custody. Institutional grade custody is maybe the last pathway we need for success, and I believe we’ll get it within a year, and we’ll see cryptocurrency become a legitimate asset class. When that happens, it will be worth a lot more. Of course, I could be wrong, and time will tell.

The following are excerpts of the Q&A session with Murray Stahl:

Q: I just read a book you wrote, How They Did It, which is about great investors. You talk in there about the concept of leverage as an asset class, which is under-represented in people’s thinking. I’m curious to hear your thoughts on the usefulness of leverage.

A: Leverage as a generalization is used to excess; it is a dirty word, but there’s a concept in finance called the Miller-Modigliani capital structure and variance theorem. It says the enterprise value of a corporation is invariant to the changes in capital structure, meaning that if you’ve got a business with $1,000 of debt and $1,000 of equity, it’s worth $2,000 and would still be worth $2,000 if you had $1,500 of debt and $500 of equity, or even if you had $1,900 of debt and $100 of equity. Let’s make believe a company has $1,950 of debt and $50 of market capitalization equity. If it could pay off the debt even in relatively small increments – $20 a year, for example – if it’s still kept at $2,000 enterprise value, the equity will go from $50 to $70 and so on. As you pay down debt, you would save the interest expense, which in itself would build the cash flow.

The idea behind it was that if you could find a bunch of leveraged companies capable of handling the leverage, you could make an extraordinary rate of return. I even started the strategy to do that, and I think it did reasonably well. The only problem was I managed to talk just two people into doing it, so it never raised a lot of money.

Q: There’s leverage, and there’s leverage. Currently, you’ve got MLPs and publicly traded BDCs. All of them seem to trade well below their NAV and to be highly levered. You write this great piece every week called Bits and Pieces, and you highlight a few of these. Could you share your thoughts on discount to NAV now and if there are any particularly compelling ideas you think we should take a look at?

A: I used to call this the equity yield curve, something whose value is discounted at an extremely high rate. I read a book by George Gilder, who came up with a much better phrase, so I’m going to steal it: hyperbolic discounting. It means that whatever the then asset value is, the market discounts it. If you have an asset value of x, you’re not realizing it today; you’re going to realize it at some indeterminate point in the future. Because you don’t know when that point will come and because the value of the assets might be questionable as well, you get a discount at a higher rate.

There’s a company in Canada called Dundee Corporation, whose market capitalization is more or less C$80 million. This used to be an investment management company called Goodman Asset Management. The head of the company, who is a really astute investor, became ill. He had sold his business and thought there was going to be a lot of inflation. He put the bulk of the assets into every inflation beneficiary you can think of – gold, oil, cattle, land, all of them. You know what happened to inflation beneficiaries in the last five years – they didn’t do well. He got to a point where he couldn’t run the company anymore and turned it over to his son, who shortly thereafter got hit by a streetcar and is in really bad condition. The company got turned over to another son, who had a stroke while riding a bicycle, I think. He was also injured falling off a bicycle although I believe he’s recovered somewhat.

In any event, the stock trades at about C$1.40 (I may be off by a few pennies). The net asset value as per the company’s financial statements is 10, so that’s a hefty discount. It’s a composite of private investments and public investments, so let’s say roughly 60-odd percent of the investments are private and 30-odd percent are public. While it’s unlikely to be the case, let’s assume for the purposes of discussion that all the private investments were worthless, you’d still have a net asset value in the high threes. The public assets are worth whatever they’re trading at. You understand why these things happen now. It’s too small to be in an index, so it’s not covered that way. Unfortunately or fortunately, depending on your point of view, that’s what you need to do if your desire is to unearth value.

Staying with Canada, another example is a closed-end fund called Urbana. It has an assortment of securities in there, both private and public, say, crudely, 60% public and 40% private. It trades at maybe 37% discount to net asset value. One of the investments is a big piece of the Bombay Stock Exchange in India. I think it also owns shares of Bank of America and CBOE. Some of them are large capitalization, liquid companies. In any event, you can see the same thing – the market is valuing the private assets at almost nothing, and they are worth something.

The people who ran the predecessor company happen to own a fairly extensive amount of land in Quebec. When they started Urbana about 14 years ago or thereabouts, they took this land of no value and put it in a closed-end fund. To this day, if you look at the statement of assets, it’s not recorded as having a valuation, but some people believe there’s gold in the property. There are what I would call significant gold mining companies exploring the property, and maybe there is something there. The worst that could happen is there’s nothing. The land is probably worth something, but what if there is gold there? All of a sudden, it becomes a different investment.

You’re not going to find Urbana in any index. Unfortunately or fortunately, as the case may be, that’s where you have to go because of the rise of indexation. It means a lot of things have a reasonably robust valuation, partially because of low interest rates, partially because of the buying pressure of indexation, and partially because people know so much more. There’s so much more information and so much more efficiently pricing, but most people are now willing to go to that extreme, and there you have it.

Q: My question is regarding Bitcoin and cryptocurrency, how competitive it will be. The MIT Technology Review recently wrote an article which said “Let’s kill bitcoin.” What it referenced was the possibility of the Fed coming out with a digital currency, FedCoin, and that there’s nothing to stop Facebook from coming up with a coin, FaceCoin or individual companies issuing a plethora of additional digital coins. The question isn’t if it can be replicated, but what can give us confidence that Bitcoin would prevail in a world where you have multiple competitors?

A: To begin with, I’m not telling you Bitcoin will prevail. I don’t know if it will. By the way, the article wasn’t entitled “Let’s Kill Bitcoin”; it was “Let’s Destroy Bitcoin.” The premise was that the Federal Reserve needs to destroy cryptocurrency, which I disagree with. Neither the Fed nor any other central bank can destroy cryptocurrency, in my opinion, and I’ll tell you why.

First, I can understand why a central bank might be troubled by the idea of a cryptocurrency. The Fed, like the other central banks, reserves to itself the power of issuance and sets the price of money, which is the interest rate. The most important price in the economy is the interest rate because it determines the value of all assets and provides a backdrop for economic growth or lack thereof if central banks make the rate too high.

The second thing is that central banks, as we all know, engineer a certain amount of inflation because the public debt burden, which in most industrial economies is quite considerable, needs to be managed. This is achieved through a certain engineered inflation rate. If central banks were to cede that function to a cryptocurrency, you can understand how problematic it would be and why they wouldn’t like it.

The other side of it is that there are so many attack surfaces and attack topologies that the amount of successful hacking is unbelievable, and it’s only getting worse. You might have read about the Bank of Bangladesh getting hacked. I think it recovered about $20 million of the money stolen, but the miscreants got away with $90-odd million. That’s not the complete story, however. Yes, the Bank of Bangladesh got hacked, but what got hacked was its account at the New York Federal Reserve. They can hack the New York Fed for $90-odd million. What if they hacked it for $90-odd billion? What if they hacked a commercial bank for $90-odd billion? What would happen? Who would make that good?

You can see it’s an extremely serious problem, so the idea of a centralized counterparty versus a distributed ledger (blockchain) is dangerous. You might say, “Okay, I like the idea of blockchain, a distributed ledger. Why don’t we just have the United States government or the Federal Reserve come up with the crypto dollar, and we’ll have another blockchain?” Can’t do it. Technically, they can, but to no end. It wouldn’t do any good, because if the number of units isn’t fixed, a distributed ledger has nothing to reconcile against.

Let’s take today’s environment. Say we had a crypto dollar. Now, money is created by banks, and when they give me a loan, they’re not reaching into their vault and checking out dollars for me. They are creating money, so it’s going to be this external money creation outside the blockchain. What are you going to reconcile against? How are you going to know that your tally is accurate? The authentication problem is there. The reason gold became money is that it allowed something to be authenticated because governments, when they had control of money, would historically cheat. That’s why we went to a gold standard. Then governments promised to be good, at least in the sense that they wouldn’t illegally counterfeit. They would just legally print up whatever money they thought was appropriate for that environment.

Nevertheless, we have counterfeiting today. North Korea, for example, has something called Bureau 39. It has mastered the photoengraving process and prints up currency and other things. This is why many governments would like to eliminate cash – you can’t guarantee the authenticity of it. Now somebody, perhaps a state actor, has hacked the New York Federal Reserve, and there are plenty of other cases like that as well. You’ll have to go to fixed issuance, which means they’re not competitors.

That article, “Let’s Destroy Bitcoin,” means let’s attack the system and overwhelm it. Given the amount of knowledge you need to attack a distributed system, to figure out where all the servers are located around the world, I don’t think it’s within the power of even the most aggressive government to disrupt the way it is right now. You might be able to disrupt sections of it, but they would get back online pretty fast. There are many things one can do to defend against such an attack. It’s likely that any government is going to challenge it. As a matter of fact, the trend of regulation in America (Arizona, Georgia, Wyoming) suggests you will soon be able to pay your state taxes in cryptocurrency. The Intercontinental Exchange is moving to physical futures, and we already have futures contracts on CBOE and CME. Bank of America recently filed a patent on institutional-grade cryptocurrency custody, and on and on it goes.

It’s moving extremely rapidly. With all its imperfections and deficiencies, it is the most contrarian investment on the planet at the moment because everybody hates it.

Q: Given the work you’ve done on crypto and blockchain, are there certain industries you’re trying to avoid because you think they will get buggy-whipped or negatively impacted?

A: The two most obvious sectors endangered by the evolution of cryptocurrency are technology and finance. In the S&P, the technology sector is about 26%, and the financial sector is roughly 14%, so around 40% combined. You could clearly see if one can, via currency, bypass a trusted counterparty. Right now, when you’re buying a house, you put money in escrow and the bank is holding it. In the world of smart contracts, maybe you don’t need a bank to hold it. Maybe you don’t need title insurance. Maybe you don’t need a bank to pass your money through. Maybe you can buy or sell securities on a peer-to-peer basis. You don’t need a brokerage intermediary. If you don’t need all these institutions, they don’t need to buy all the fancy computer equipment either, and that’s the danger in technology. It’s quite a threat if it materializes, and I think it will.

Q: You sit on the board of the Bermuda Stock Exchange, right? How do you see that as a long-term threat or a long-term opportunity to a particular stock exchange?

A: Let’s put it this way – you’ll be hearing news about it in the not-too-distant future. But you see, there are only so many licenses in the world. It’s not a question of technology. In order for crypto to flourish, it needs to have a properly regulated environment. I would say Bermuda has extraordinarily good governance. The government of Bermuda is moving to create a crypto-friendly good government business environment, like the Canton of Zug in Switzerland has done, which is why they call it Crypto Valley. That gives you an idea of what the Bermuda Stock Exchange may or may not have to do with it.

Q: I was fascinated by FRMO. Is that an owner-operator company? It’s very hard to figure out what’s in there and how it’s valued, so I’d be interested in any thoughts you have on the matter.

A: To begin with, I’m the chairman of FRMO Corporation, and I own, at least at market value, about $28 million worth of stock. I got a little irrationally exuberant, but I haven’t traded it, and I still hold it. This makes me an owner, and I operate.

The idea behind creating it was that it’s a tax-inefficient structure in Horizon Kinetics. We get a lot of cash flow, and much of it goes to the government. The idea was to do our personal investing in the corporation and get the corporate rate, which now has been lowered, and it’s much more tax-efficient to do it there. A certain amount of our crypto investing is being done there. In general, anything we consider intriguing and contrarian, we do in FRMO.

There are three or four assets. We are in hard assets and in crypto. FRMO gets a certain amount of investment management revenue because it owns a piece of Horizon Kinetics. And that’s its purpose. Some years ago, we did a very small stock offering, but not because we needed the money. When you look at the financial statement, you’ll see FRMO is a cash-rich company without any debt. We just wanted to see if we could do it, so we raised a small amount of money, a few million dollars, just to see if it was possible. The whole idea is to stay liquid because I believe the indexation phenomenon will not end well, and we’ll need a liquid pool of assets one day. At that time, most clients will be loath to invest money. You might have some exceptions, but those will be the exceptions that prove the rule, so we need to have a liquid pool of investments we can count on. That’s the liquid pool, and we keep building it.

Q: I remember you had mentioned something about this idea of synthetic bonds that you create, and I think it was related to options. I’d love to hear an update on that idea.

A: The idea was that the bond coupon doesn’t give you anything. Let’s assume I found a credit-worthy bond. I get a coupon of X, and I pay my taxes. What I have left doesn’t even cover inflation. Why not take whatever is left after taxes and buy an option on something that we think has investment merit? The worst that happens is we lose a coupon. We still have the principal. The best that happens is we have an extremely high rate of return because of the inherent gearing of the option. We do that from time to time. It’s a pretty capital-intensive thing to do because you have put a lot of money up in the bond, and the principal value is continually being eroded in inflation. I wouldn’t put a heck of a lot of money into it, but we do it now and then.

Q: Following up on the FRMO question, could you comment on Winland? What is it, what are you doing?

A: Winland Electronics is a company FRMO bought an interest in. It makes information devices. If you need to know what the temperature is within a certain range in a basement or refrigerator – if you were storing drugs, for example, and the temperature is really important – you can use a monitoring device made by Winland. You would think a lot of companies make these, but they don’t because it’s not a high-margin operation. There are many interesting things you can do with the business. It’s hard to grow it because there’s limited demand for monitoring devices. A typical monitoring device doesn’t cost a lot of money, but the company does have strong cash flow and cash on the balance sheet.

We’ve been using the cash to buy credit liens. When a company goes bankrupt, instead of buying the bond, let’s say some merchant is owed a small amount of money. We would buy that lien, which ranks very highly in the bankruptcy. No institutional investor wants to deal with these things because we are talking about tiny amounts of money. I think it’s highly appropriate for Winland. Some of these credit liens measure in hundreds of thousands of dollars even though the bankruptcy might be in billions. Sometimes, for technical reasons, the bankruptcy workout funds want this thing to be eliminated for legal reasons. They might buy it at par just to get this out of there and make the legality less complicated.

We were able to buy credit liens in the Mt. Gox bankruptcy. Mt. Gox, you might recall, was the big hack some years ago. At the time, it led to a big crash in the value of Bitcoin. The company filed bankruptcy and socialized the losses, so even though not every account was hacked, the idea was to act as if all of them were. However, the bankruptcy courts are slow in the United States, and they’re even slower in Japan. As they were going through the motions, the Bitcoin that remained appreciated greatly in value. The issue at law became, at least in Japan, whether you are entitled to the market value of your account on the day of the hack or to your pro rata interest in the market value as it remains. Happily for us, the court ruled the latter.

I can’t say we knew a lot about Japanese bankruptcy law. It’s one of those things where it was valued as if that is not a conceivable outcome. By the way, another thing is you get statutory interest on the corpus in Japan, so even if they had valued the former, you would have got the corporate interest. It seemed like a reasonable investment. I can’t say we were able to buy a lot of it and create a fund to do this, but we ended up doing it in the enterprise a bit.

Q: Considering your involvement in trade claims, do you have any perspective on where we are in the cycle? Do you see an uptick in volume or a constant?

A: We don’t see any measurable uptick, at least from our perspective, but then again, we’re looking at the lowest end of the spectrum, things that are tiny. I don’t know if we’re going to see a big up from our way of looking at it. When the cycle gets bad enough, I’m sure there’ll be big trade claims. The institutionals will be there, and we won’t be in a position to compete well against them. They will know a lot more about it, and they have lawyers and attorneys. I suspect we won’t do very well. I think we’re going to stay in our niche. We’ve seen no economic data that points to a deterioration in the economy by looking at that information.

Q: I would like to pick your brain on TPL. Given the transformation of the trust in 2017 with the water business, how do you see it in five or ten years?

A: Let’s say you went to Google Earth and looked at Midland County, Texas, which is a relatively developed area of the state in terms of hydrocarbons. You can see how much water is used for fracking, the rights of way people require (easements), and filtration. Then you look at the Delaware base, where TPL is, and it’s largely empty. If you were to imagine it had the same density, how much money could be made? Even though TPL has oil royalties, let’s make believe it doesn’t. It also has real estate that I believe has some commercial value, but let’s ignore that as well. This leaves us with easements and water. It’s hard to distinguish between the two because sometimes, you’re bringing water from another property, and you have to get an easement across their property. In any event, there are 880,000 acres of land with no hydrocarbon on it but with water and good strategic positioning.

Let’s say the average acre could get $10,000 a year in easements. In easement, what’s called land damage fee, you normally get $5,000 for the right to walk across someone’s property. Let’s imagine you have two easements, $5,000 each a year, and it’s 200,000 acres. That’s a lot of money. That’s not $5,000 a year recurring; it’s a one-time $5,000 for five years, and then you get another, but then there’s the water. Large oil companies say, “We will need well over one billion barrels of water a year for fracking.” The going rate for a barrel of water is roughly $1. There are only two big land masses there – TPL Property and University of Texas – so I suspect most of the water would have to come from those two sources. Do the arithmetic – that is recurring, and it’s a big number. Besides, I’m leaving out the oil, so you can get an idea of what it could be theoretically.

Q: I’ve attended luncheons you’ve hosted in New York, and you also have a webinar periodically. Could you tell people how they might learn more and engage further?

A: You can call our office – there’s always a number provided on the website. You can also get on our mailing list. We do quarterly lunches you can attend. You don’t have to buy anything. They say there’s no such thing as a free lunch, but there is. You can listen to me for free, and there’s a dial-in or a recording if you don’t want to attend. There’s a lot of material on our website. We do have webinars from time to time, and sometimes I do engagements like this one. I think we’re reasonably accessible. I answer all questions even if they have nothing to do with investing. Not that I know that much about investing, but I don’t know that much about the other things either, so you’re not losing anything.

Q: You spoke at Best Ideas 2018. Do you have any new thoughts or comments on what you talked about?

A: To be perfectly honest with you, I don’t remember what my best ideas were, but I can tell you right now about four stocks I like. I’m leaving Dundee out because it’s so illiquid. Aside from Texas Pacific Land Trust, there is a company called Civeo, which was spun off from Oil States Petroleum several years ago. It has lodgings in natural resource areas, hotels or motels, if you like, for workers so they can be reasonably comfortable at the end of a working day. In all these remote areas, the projects with the declining natural resources, mostly oil, collapsed and so did revenue. Crudely speaking, it lost 90% of its revenue. Could it get worse? Probably not, but it’s not good. The best you could probably say is that maybe things are ever so slightly better than a bottom, and it’s a maybe. In any event, the company is cash flow-positive. There’s a certain amount of debt on the balance sheet, and I believe it can service the debt.

This is an example of what I said before about leverage. It’s not that leveraged by the Miller-Modigliani capital structure and variance theorem. There are only two possibilities: either it’s getting better or it’s not. If it gets better, it’ll get more revenue, the cost won’t go up, earnings will be a lot higher, and it will be a great stock. If it doesn’t get better or it doesn’t get materially better, gradually, it will pay down the debt and the equity will be worth more. It will be a reasonable investment, not an extraordinary one. That’s, in brief, the story here.

Then there’s a British company called Clarksons PLC, which is a ship broker. If you look on its website, there’s a really neat diagram. It has an index of rates for all kinds of shipping. In the last few years, shipping rates are roughly 67% down, maybe even more. Anyway, as a broker, it’s ad valorem pricing, like a real estate broker. In other words, you get a commission based on the value of the charter. If the charter rates rise, you’ll be getting a constant percentage of a greater amount of revenue, and the costs stay the same. I think it’s going to happen in the next couple of years because there are a lot of vessels that burn high sulfur fuel. This will become illegal at the end of 2019, and most vessels are too expensive to retrofit. It’s reasonable to suspect the supply of ships is going to contract. All things being equal, assuming the mandate is the same, it should raise the charter rates, and this should be an intriguing investment.

Last but not least, there is a company I’m always hesitant to talk about because it’s a little more expensive than I would normally buy, but I bought some at lower prices. It’s called CACI. It’s a defense contractor, but not your typical defense contractor. It’s one of the several they call beltway bandits in Washington. These companies do computer work, really top security and hush-hush work, things like intelligence gathering and cybersecurity. It has been awarded some extraordinarily large contracts relative to the size of the company. They would be good contracts if Boeing won them, and CACI is a $4 billion market cap company. If you read the DARPA annual statement, where defense dollars are going to go is into this sort of thing because an adversary interfering with your data or your ability to interfere with the adversary’s data is the difference between victory and defeat on the battlefield. Intelligence information processing is the key to the modern battlefield. There are only a handful of companies that do it, and I would regard them as the best.

Q: As a final question, any books you’ve read recently that you recommend?

A: I read George Gilder’s new book, Life After Google. I’m reading another book of his called Knowledge and Power. He makes some really intriguing points, and all his books are rich in creative thought. One of the points he makes is that you think about Google as a structure. You search for something and get an answer in fractions of a second. In order for this to happen, there’s a lot of data running along the telecommunication infrastructure, but Google doesn’t pay for it, you do. When you look up something on Google and you get an ad, it’s data-rich, meaning it has a lot of bits, a lot of information. You’re getting an ad you don’t want, and you’re paying for its service to its clients. It’s a highly unusual business circumstance.

I dare say he makes the case it’s a business circumstance that won’t endure. He doesn’t say this in the book although he does make a brief reference to it. One of the things that will happen in the future is that searches will be done through cryptocurrency. Your data is going to belong to you. Let’s say you’re interested in aircraft, and this fact might be on the blockchain. Someone can see that this human being is interested in aircraft. They won’t know it’s you. You might be a good candidate to fly in a plane, rent a private plane, perhaps buy one, or take pilot’s lessons. They will have to pay you for the right to penetrate your cyberspace. In other words, every single person on the planet is going to have a monetizable asset, their data, and sole control of it. That’s coming, and it’s a major disruption to the typical business model. There is a cryptocurrency called CAT, I believe. There are others, and it’s all in the initial stages. I don’t know which one will get it right, but one of them will.

The larger point in the books is the idea of information in general, as I see it. We live in a society where information is available to everybody. With one of these iPhones, we can get the kind of information we used to get out of the New York Public Library – it’s incredible! Therefore, the hierarchical structure of society will change in ways that are hard to imagine because historically, relatively few people were well-educated and in possession of information. Society was pyramidal, was hierarchical. A few people knew how to run tings. Everyone else, historically, was illiterate or not very knowledgeable. Now, a lot of people are incredibly knowledgeable, maybe even more knowledgeable than the policymakers themselves.

Let me conclude with an example. Say something happened with water in California (which happens all the time), and you need to do something. You consult mayors of cities and the governor of California, even the president of the United States. Whether you like him or not, we can all agree a hydrologist he is not. You’ll have to go to experts. The hierarchical structure of society will devolve in a way that to make policy, all sorts of people will be consulted and will play a major role in policymaking, which has never happened before in history. Therefore, the power at the top of a hierarchy is going to decline, and the power of the bottom of the hierarchy is going to rise, which is a good thing for democracy. We’ve never experienced that before. It’s a pretty good development for society, and I think George Gilder does too, as his ideas suggest.

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In this conversation, Shaun Heelan shares key insights forged in the high-stakes world of structured credit and translated into a rigorous, concentrated approach to equity investing. As the Chief Investment Officer at Munich-based MAAT Investment Group, Shaun brings a unique and deeply analytical perspective to modern value investing.

Shaun’s career path is anything but conventional. Starting out at Goldman Sachs during the European dot-com and telecom implosion, and later running highly profitable correlation books at another leading investment bank during the lead-up to the Great Financial Crisis, he learned early on that downside protection must always be an investor’s primary focus. Today, he applies those hard-won lessons in valuing European small and mid-cap equities. He approaches public market investing with the mindset of a private equity owner, mapping out assumptions, scrutinizing accounting realities, and actively hunting for asymmetric payoffs.

Beyond his differentiated portfolio construction methods, Shaun offers a grounded, compelling counter-narrative to the recent market euphoria surrounding artificial intelligence. Drawing on his academic background in high-performance computing, he expertly demystifies Large Language Models, describing them as “smart librarians, not scientists.” For investment professionals navigating an increasingly top-heavy market, Shaun provides a fascinating framework for understanding why AI will likely reinforce, rather than destroy, the intangible moats of established network-driven businesses.

To bridge theory and practice, the conversation dives deep into real-world applications. Shaun walks through his forensic diligence process by breaking down why his firm passed on Vistry Group (UK: VTY), a homebuilder frequently touted as the “NVR of the UK,” citing aggressive margin assumptions and hidden balance sheet liabilities. He then pivots to a compelling long thesis on Edenred (France: EDEN), demonstrating how regulatory fears and headline noise have created a deeply mispriced opportunity in an asset-light, moat-protected compounder.

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What You’ll Learn

• How crucial risk management lessons from the 2008 mortgage-backed securities crash apply to underwriting modern public equities.

• Why Value-at-Risk (VAR) models create institutional blind spots as well as opportunities for long-term, structurally advantaged investors.

• The mechanics of sizing highly concentrated positions and managing path risk in an era of surging single-stock volatility.

• Why LLMs function as “smart librarians” and the fundamental danger of conflating computational pattern-matching with true business wisdom.

• How AI is strategically poised to strengthen the barriers to entry for businesses possessing non-technological, intangible moats.

• A forensic teardown of Vistry Group, highlighting the red flags of aggressive partnership accounting and overlooked land creditor debt.

• The case for Edenred, exploring why the market is systematically mispricing its regulatory risks and underappreciating its network float.

Enjoy the conversation!

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Transcript

The following transcript has been edited for space and clarity.

John Mihaljevic: I’m joined by my co-host, Elliot Turner of RGA Investment Advisors, and our special guest, Shaun Heelan, Chief Investment Officer at MAAT Investment Group, based in Munich.

We have a lot to cover today. We’re going to talk about Shaun’s investment philosophy, his and his firm’s views on the complacency in the market and some concentrated risks. We’ll also have the AI debate, where Shaun has some great insights. Shaun, let’s start with your background and the story behind your firm. Tell us about your path in investing, some lessons you picked up along the way, and how the core team came about at MAAT.

Shaun Heelan: My path in investing has been a strange one—a long and winding road, led by curiosity, much like many people in this field. I started 24 years ago at Goldman Sachs in London. I’m Irish, born and raised there. I was recruited after completing a master’s degree in high-performance computing, which gave me some expertise and ability to speak about the current AI debate.

I went to work at Goldman as an intern in the summer of 2002. As you may recall, that was right in the middle of the collapse of the dot-com boom. What’s lesser known, especially for American audiences, is that there was a huge dot-com boom in Europe as well, particularly in the telecom sector. There was an underdeveloped high-yield credit market trying to be developed similar to what you’d seen in the US in the late seventies through early nineties. That market imploded.

I was there just as it really started to unfold in 2002. I was very fortunate to get an offer to come back and start early in December because I was finishing my thesis. Goldman had two floors in London with a bridge between them at Peterborough Court and Fleet Street. Half the trading floor had been fired—it was like a neutron bomb. That really impacted me.

The mood had changed obviously, and I think it’s informed my career ever since. I came with a natural sense of conservatism, but it created a heightened sense of risk awareness of what can go wrong in the markets and how quickly moods can turn. It also taught me one crucial lesson: the downside is the thing you need to consider first and foremost in investing. You can have prolonged periods where things look very easy, where conservatism seems like a terrible mistake. But when the correction comes or when sanity prevails, it’s better to have been conservative all along—much like the GMO message that’s been sent around for 20 years.

I also remember Buffett talking at the Sun Valley conference in 1999 and a book coming out shortly afterwards predicting that equity returns for the next decade would be in the low single digits. That had a huge influence on me. I was very lucky that as the high-yield market was imploding, Goldman in London was a very active merchant bank—something different from what you have in the US—almost like having a proprietary desk, but one that makes more structured, long-term investments. They were very famous for this.

I was put into a group that ended up doing two large rescue financings—one for Vivendi, which was a much bigger company at the time, as well as Universal Music Group and studios, and then Rhodia, a French chemical company. Because we took merchant risk, that deal ended up being probably the most profitable thing in fixed income at Goldman that year. I didn’t particularly enjoy it—I’d joined a trading desk but ended up in a merchant banking capital-raising role.

I was going to leave and head back to Ireland to potentially work at Google, which was opening up there, and I had a computer science background. But I was persuaded by friends and my father to visit the States because I’d never been to New York. I went over for training and was very fortunate. My bosses in London were quite fond of me and thought I’d done a good job. I’m a very blunt, honest person—probably too honest, if you ask my wife. I told everyone before I left that I was probably going to quit as soon as I got back. After a few weeks, they came to me and asked if I’d like to interview with some desks in New York.

I loved New York from the moment I landed. I interviewed with a few desks and was fortunate to get offers from pretty much all of them. I joined the mortgage-backed securities trading desk at Goldman. At the time, Goldman in non-agency mortgage-backed securities wasn’t a major player—maybe 12th or 13th out of 16 or 17. They were very active in the agency space, with Ginnie Mae, Fannie Mae, Freddie Mac, and CMOs, but not in the other space.

I’d seen CDS contracts in London, and they were quite active there, but the mortgage side in New York hadn’t really seen anything like that. There was an active market in total return swaps. The memory of the 1998 LTCM crash was still very present. That crash was right at the epicenter, and the long-term trades that killed them were mostly basis trades on swaps against various mortgage products.

I was part of a group that developed that market further and made ourselves more competitive. We had a cold-start disadvantage: we were cautious with our balance sheet and had a high equity proportion, but if we were competing with Barclays, Deutsche Bank, or money center banks, they’d charge their trading desk a financing cost more like what they’d get in the money markets—LIBOR minus. We had about an 80 basis point disadvantage on anything we held in inventory.

We proposed developing a new product: pay-as-you-go CDS. Something similar was happening more natively at the time, but we ended up getting a big lead and dominating that market at Goldman. I was part of the group that developed what became the standard contract for subprime and CMBS pay-as-you-go CDS. The first trade was actually bespoke—the famous basis trades done about a year before anything else. These became the standard for the ABX and CMBX indices.

The Goldman model that I helped develop with the quant group became the market standard model for those CDS contracts. As a result, I was negotiating with the dealer group as to which model should win. We won. Everyone thought I was much more senior than I was—people thought I was an MD or partner because of my deep voice and Irish accent. I started getting offers from every bank. The best one was from Countrywide in California, which was tempting, but I could see there might be problems down the road. I didn’t take that step.

I went to Merrill Lynch, where I ran their correlation business. After six months of originally trading single-name credit default swaps and mortgage-backed securities, they handed me a business they’d had for two years in which they hadn’t done a single trade. We had very low budgets. In the first year, I was asked to make five million in PnL, which sounds small now but was meaningful then. We made 45 million, and the next year we made over a billion, and every year thereafter until I left, we made over a billion with a team of around three or four people.

I’d say I won the lottery multiple times over working at Goldman and Merrill with some of the best risk takers you can find. It was an amazing training ground. I learned everything from the ground up—working from 6:30 a.m. and going past midnight.

At Merrill, we had a great distribution force and brilliant people all around. It just wasn’t as well-organized or cohesive as Goldman at the time. Because we were so profitable, I got to see a lot. If they had problems in Japan or London, often very big problems, we could figure things out quickly. We had built good analytics that could be deployed across the firm. They’d parachute me or my team in to deal with that. I got to see a lot of what happened within Merrill leading up to the financial crisis and interacted with very senior management, right up to the CEO.

I learned a lot about how people behave under extreme stress, about managing risk. When dealing with mortgage-backed securities, those principles apply to equities. I’ve always been a fan of Warren Buffett. He had that quote that when you’re looking at an equity, it’s just a bond with undefined coupons that will come for an indeterminate amount of time.

The perfect training for me was doing that in structured products—MBS, CDOs, or anything else. The beauty is you go from a spectrum of ultra-safe securities with no uncertainty around timing at the front of the capital structure—AAA rated—all the way down to things that are incredibly leveraged at the bottom. You’re playing with a variety of assumptions and get to see what the results are every month. You can compare what you predicted versus what actually came out from the surveillance reports. That was a fabulous learning experience.

I learned the principles of what makes a very good trader—how to evaluate risk, how to think about risk-taking and liquidity. I also learned how to behave in a marketplace where people are doing silly things. It’s always difficult in any industry when competitors are doing silly things not to copy that, but we found ways to manage it.

That was my background in fixed income. It’s a weird pivot, but I then went to the buy side with Brevan Howard and BlueCrest, trading mortgage-backed securities and caught the upswing of that. I finally thought the mortgage trade was over and made a crazy career move—I took a 50-to-1 pay cut and moved to Munich to learn how to value companies and invest in equities.

John: MAAT has a highly concentrated portfolio and a value-based philosophy. How do you contrast that with the concentration we’re seeing in the tech-heavy indices and passive investing? In your Q4 letter, you also referred to some cracks you’re seeing in the credit market. Tell us about your macro view.

Shaun: When it comes to what we’re doing at MAAT and the principles we apply, it relates back to what I’ve done earlier. The securities you’re investing in or the tools you’re using in building a portfolio can be quite different, but the principles of how you use them should be a solid foundation. If it’s built on granite, it will last for an incredibly long time. If it’s built on sand, it will fade away very quickly.

Anytime you confuse a trade for an investment, I think you get into trouble. A trade can be built for the short term, and the narrative doesn’t really matter. An investment has to be very different. I saw first-hand people who were trading products built for duration succumb to short-term assumptions—thinking nothing would go wrong in the short term and they’d always be able to exit. That’s kind of what you’re seeing in private credit now. The premise is: I’m going to take five or ten-year loans and put them in a fund where you can redeem the equity within a month. It’s the classic asset-liability mismatch.

What I learned on the structured products side was to really think about what assumptions you’re making. You’re always making a ton of assumptions, and it’s very dangerous if you don’t know what they are. In structured products or MBS, you know exactly what you’re dealing with—CPR or CDR recoveries—and you can do that down to whatever level of specificity you want: at the property level, MSA level, zip code level, whatever. The same principles apply when you come to investing in corporate debt or equities. The variables change, but the principles apply all the time.

Step one: Do you know what assumptions you’re making? Step two: Do you understand them well enough to make a reasonable parameterization? And step three: Are you checking to see if you’re correct? I love a quote from George Box: all models are wrong, but some are useful.

We bring that to the research side. We try to build our approach in looking at companies in a very specific way, as if we were going to own the company for 10 or 15 years. I’ve taken companies private at my prior employer and understood these companies as well as the owner-founders would. It wasn’t uncommon for some of the CEOs to come and invest with us afterwards and stay for many years. Some of our best investors came that way.

If you’re going to use a model, use it for what it’s useful for. Know the assumptions you’re making, know where you’re strong. This goes back to Munger and Buffett’s comments on knowing your circle of competence. You can expand that circle all the time, but you’ve got to do it slowly and check if you’ve actually learned. The illusion of knowledge is a very dangerous thing.

We deploy that actively when looking at a company. We do very deep research and prepare a primary report. We also prepare a model, but the model isn’t built for me—it’s built for everyone else in the investment group. The assumptions are very clearly laid out at the top, the outputs at the bottom. We treat the model as a way of sensitizing things and reflecting how we think about the business. It’s a gross oversimplification. Anyone who’s run a business knows you can try to look at it through a spreadsheet, but it’s far from that in reality.

The other thing is that we’re hyper-aware that all businesses are really just people. A better group of people with a better sense of philosophy, shared values, and good communication will get much better returns from the same assets than another group with poor incentives. We’re always trying to look at the qualitative. I’m a huge fan of Nick Sleep at Nomad Partners, and if you listen to him toward the end of his career, he was constantly talking about the qualitative aspect of investing. If you listen to Buffett and Munger over the years, they’ve said it’s 99% qualitative. For people new to investing, that sounds silly, but to me it’s obvious through experience.

The same is true for mortgage securities during the financial crisis—it’s how you thought about what could happen in the world. There’s a great quote from Jeff Gundlach about people trading subprime in early 2007, getting very excited because spreads exploded. What they failed to realize was that spreads were exploding because interest rates were falling rapidly—government rates—and that’s because a crisis was unfolding. You have to have that nuance. Be thoughtful about what parameters are happening, be thoughtful about what can happen even if it hasn’t happened before.

We try to bring that sensibility to MAAT. When we founded the firm, we founded it based on a very strict set of criteria. We wanted to make sure it wasn’t set up as a trade. We wanted something we could build for 20, 30 years and be very proud of. I’d worked at lots of different funds—I’d seen some succeed, some fail. I went around and inverted the problem, thinking about what were the common factors that made those firms fail. Almost always it was because they had not set out the rules of the game ahead of time. They did not have shared values or principles, and ultimately people were tearing each other apart. After a run of success, maybe people started saying it was all them. Or you got a situation where people felt disrespected and disengaged.

One of my pet peeves in the investment industry is that you get incredible brain power—remarkable people with great educations and work ethic—and they come into a firm and suddenly no one listens to them. They don’t have a say. They feel like they’re just furniture, and in many cases they feel they’re just there for optics.

We tried to build a firm where everyone on the investment team has full engagement. The analogy I’d make is that you can have the engine of a Ferrari, but if you’ve got the transmission of a Fiat Panda, it’s not going to work well. When we have an investment committee meeting, nobody shows up ill-prepared. Everyone’s done the work on the primary report. If there’s a difference of opinion—and there always is—we really encourage that, but it has to be parameterized in some way. It doesn’t have to be the most accurate thing, but it means that people are thinking.

When it comes to making investments, everyone has a say and everyone has thought about it deeply. Their point of view has been expressed, and it’s active engagement, not passive engagement. That’s really important to get the best out of people.

On concentration: I think it’s one of the most misunderstood things in investing. If you look at the financial crisis, the big short was in effect a very concentrated bet, but it had incredibly asymmetric payoffs. In my book, I had a 45 million maximum loss. I had call options to get the capital back. But I stood to gain 6 billion over three years, which is close to what happened ultimately. That’s a crazy thing to turn down, especially if you think the odds are massively in your favor.

Likewise, in the Euro crisis in 2011 and 2012, we were buying heavily distressed mortgage-backed securities. There was a change in regulatory capital rules for both banks and insurance companies—Basel II and Solvency II. That created a rush to the exits in about six months in 2011. Because we were well prepared—we’d studied the impact—we realized that a lot of the people holding the securities didn’t realize what the impact would be on them.

We had 100% cash for that space. We were able to deploy it and buy securities at 3 to 5 cents that had been trading at 70 to 90 cents six months beforehand. We didn’t know it would get that bad or that the opportunity would look that good, but we knew there was a decent chance of it. The opportunity cost for us not participating was only about 7 or 8 percent per year, which we could live with.

In that case, we had 100% of my portfolio at Brevan Howard concentrated in those securities, and they made 100% a year for the next few years. I don’t think concentration is good or bad. I think it’s a tool—a form of leverage. It can be very bad if you don’t know what you’re doing, if you pay the wrong price, if you’re in with bad partners, which can often happen in public equities with an unstable shareholder base or a major holder. You just have to be mindful of that.

I wouldn’t say it’s something that should be used often. It’s for when you find a situation where the downside is incredibly limited and you can demonstrate that over two to three years, and the upside is hugely asymmetric—maybe a three, four, five, tenfold or more. When you see those opportunities, by definition they’re quite rare and usually quite small. Usually something very bad has happened. You’ve got to go for the jugular.

You’ll hear that from Stanley Druckenmiller. He says he learned it from George Soros. But Buffett and Munger did the same thing. Buffett had securities that were 40, 45% of his portfolio at different points in his career. Munger had something similar. Joel Greenblatt, the same. Chris Hohn, if you look at his performance over the last 20 years, it’s incredible, and he runs a concentrated book too. In the hands of someone who knows what they’re doing and is very disciplined and good at assessing risk, it’s a very powerful thing. It’s one of the few ways to outperform the market, married to a longer-term horizon.

People push back, and it’s understandable why—there have been so many famous blowups. But what I find most surprising at the moment is the amount of concentration in the marketplace right now. If you look at the S&P or the NASDAQ, just from what’s going on in AI, you’re talking about concentration exceeding what you saw in 1999-2000.

If you look at the MSCI World and how much US indices are in there—70% plus—and then apply the concentration of technology within the S&P, which passes through that, it’s really quite overwhelming. When I say it to people, they’re often shocked to hear how much of a passive investment or tracker they’re exposed to.

Historically, anytime one given sector became extremely concentrated—industrials or the go-go companies in the late 60s, consumer staples in the Nifty Fifty in the early 70s, energy companies around 1980, technology companies around 2000, and financials around 2006-2007—anytime they got up to 25% or 30% or higher, what happened in the following 5 to 10 years tended not to be pleasant.

We deploy concentration because we think it’s a competitive edge and we think we know what we’re doing. We think it should be embraced, but with the proviso that you really understand what you own and you’ve got good discipline about assessing the downside versus the upside. It can be very powerful in the right hands and disastrous in the wrong hands. We like to think we’re the right hands for that.

Elliot TURNER: Single-stock volatility has been meaningfully higher in recent years—trending upward and then exploding since COVID. How do you think about concentration in a world where the volatility of any given stock is far greater than it had been in the past, and what does that mean for underwriting positions, the required margin of safety, and how you build a portfolio?

Shaun: I think what you described is an undeniable fact: increasing single-stock volatility. In some ways it’s positive, depending on your structure; in some ways it’s negative. I’d take it as a net positive, thinking opportunistically.

There’s a symbol in Chinese and Japanese that says “crisis” and “opportunity” together. If I go back to when I was at firms like BlueCrest, DW, and Brevan Howard—much like you’d see with multi-manager shops today—they have very strict risk limits. If you lost 6%, you were pretty much out of a job. I used to equate that to driving a Formula One car with a spike on the steering wheel. You’ll get around, but you’re going to be very careful. Those firms are amazing, but it is very limiting and frustrating, and they’re selling to an institutional audience.

Why increasing volatility is both a risk and an opportunity: if you’re in one of those seats where your risk limits are strictly defined by value at risk or stress limits, as volatility picks up—there’s typically a 12 or 24-month lookback period—it inhibits what those people can do. They have to exit the market. You’ll see lookback funds as well where if vol goes above a certain level, they have to get out.

If, on the other hand, you run a structure with investors who are like-minded and think more like business owners—who are entrepreneurial and realize that when competitors get knocked out, you can do quite well—you have a different timeline and you will not be constrained by VAR. We have deliberately gone for a niche and targeted people who understand that. We go to high-net-worth individuals or family offices who have built a fortune themselves, understand that business is unpredictable, and embrace it.

How do we get them comfortable? We try to be as transparent and honest as possible. We explain our thinking and go through it. We invite them to critique us. In our letters, when we do case studies, if you disagree with an assumption or think we missed one, or think we’re asking the wrong questions, we want to know. That tends to build comfort. They know that we are thoughtful, that we’ve done our work, and that we’re thinking in a way they would think.

The more volatility you have, the more you restrict a lot of players from competing with you. The more volatile it is, the more opportunity you’re going to see. If I go back to 2011 and look at those bonds I mentioned—bonds trading at 80 or 90 cents that traded to 5 cents. If you’re limited by a VAR model, the VAR is going to look incredibly high, and most people cannot participate. Indeed, that’s what I saw with my competitive set. They just could not get engaged. The VAR was off the charts. VAR is a very useful measure for aggregating risk across a lot of securities in a big portfolio if you know very little about the things in them. That’s what it was designed for. It originated at JP Morgan in the ‘90s, specifically for board members so they could aggregate all risk into a number.

The irony is it was very heavily criticized post-LTCM and the ‘98 blowup because it obviously didn’t capture everything. My ultimate takeaway is that for our approach, the VAR will look infinite and tell you not to invest as the price goes to zero, even if there’s value in the asset. It just doesn’t consider the value underlying it at all. The more volatile it gets, the better it is if you’re disciplined about how you enter and mindful about the upside-downside risk.

For our space—we focus on European small and midcaps—there’s a risk factor that relates to your question about volatility: path risk. As a former derivatives trader, you have to think about this all the time. The old joke goes that you can drown in a river that has an average depth of two feet but has a spot where it’s 9 or 10 feet deep.

For our companies, we tend to get the valuation right, but that’s not enough. If I look at a company today trading at 100 million euros and I’m convinced it’s worth 200 million and will get there within three to four years, but it’s got some near-term problems we’re willing to endure—we’re willing to participate in a volatility path that others won’t, and that’s our advantage with that longer-term horizon. If it first trades down to 20 million—an 80% decline—for any reason, we may still be correct, and now it’s worth 10x. But we will not get the chance to stick with it because a private equity firm, or more likely today, an industry buyer or family office will step in and buy it. If it dropped 80% and they bid a 100% premium, maybe I was right over five years, but I’ve lost 60%.

When it comes to mitigating this, that’s where correlation comes in—something I used to do as a correlation trader. When we’re building a portfolio, we are incredibly mindful of where the value will be in three to five years. We try to understand that really well and how the mechanics of the business work, the competitive dynamics. We’re really underwriting it as if we were taking it private. There are limits because it’s a public company and you can only do so much due diligence. But in addition to that, for every individual company, we’re looking at the path risk: What was it in the past? What does it look like now? What events could come that are not related to operations—it could be industry, could be another shareholder, could be anything that might cause a sudden gap down in price. If that’s likely, we have to reduce the position size.

Then there’s the correlation. Just like multi-managers, that’s how they monitor it. They overallocate capital on a nominal basis but look to lower the correlation. We do the same thing. If you look at our letters, you will see very disparate names, and the things that are likely to drive long-term performance are quite different for each. We hope that all of them work out, but it’s very unlikely that any one factor would drive them lower in unison. The exception would be something like the financial crisis where all assets fall in unison because capital is withdrawn, or a war. But we’ve even seen it in our portfolio this year—different names are performing on different bases.

Those are the elements of building a successful portfolio. It’s difficult to do well because historical correlations are not indicative of the future, especially if you’re looking at securities that have already had a big decline. We have to make a guess about that, but then we have a very different approach to portfolio management and capital allocation. That’s maybe our secret sauce. This goes back to my background as a correlation trader. One of my partners has a degree in advanced mathematics from Oxford. Another is an engineer. We’ve built our own software and analytics to think about how we parameterize these things.

We link all of our research from inputs on the operating side down to a return vector. We get expected returns across scenarios. We use a simplified version in the letter and then run that through a modified version of the Kelly criterion—a bet-sizing algorithm. Mohnish Pabrai has written about it. There’s a great book called Fortune’s Formula about that. It’s a modified version. When you’re doing it for an array of assets in a portfolio, you have to think about the covariance and correlation. If you get that right, it should give you optimal bet size. We don’t use this in a crude manner. There’s a lot of noise in what we’re doing. What we do use it for is to force-rank our choices. From first to 10 or 12 names, we feed in our return vectors and expected correlations, and it shows us what the right order of priority should be from the implied bet size. That forces us to stay honest.

One last thing: in many investment firms, you get an incredible performance from a given asset, and it becomes a marketing tool, justifiably so. But the price might have gone up three, four, or fivefold. If it doubles in price over the course of a week, unless something fundamentally has changed for the business and the cash flows it’s going to produce, the return has dropped by half. If it was 20% before, it’s 10% now. But all the behavioral biases would lead that firm to boast about that position and leave it as a big size. For us, we’re forced to address it when we run it through these criteria. It’s going to drop—if it was first, down to maybe fourth or fifth. That forces us to stay honest. We’ve built the whole investment process to keep an eye on what’s going on, making sure we’re not succumbing to biases. It’s not immunity, but it sets off different alarms.

John: You’ve described LLMs as “smart librarians, not scientists.” You distinguish between information and knowledge, and you believe AI will reinforce traditional intangible moats rather than destroy them. Tell us more about that.

Shaun: First of all, I think AI and LLMs are absolutely revolutionary. They blow my mind on a daily basis. In no way am I saying this is not a revolutionary technology or that it’s not going to change the world. I think it’s going to make the incredible compute power we already have far more accessible and democratize it. That’s wonderful, in the same way the graphical user interface did before you had a command line.

But where I get concerned, particularly as it pertains to LLMs, is the idea that these things have understanding, intuition, feelings, or empathy. That’s just not the case. This is not a new problem. Joseph Weizenbaum was a computer scientist, a professor at MIT in the 1960s. He did an experiment to show people that machines can deceive. He came up with a program called ELIZA in the mid-60s. What it did was very similar to what you see with chatbots today or the first versions of ChatGPT. He created a little algorithm that was pattern matching. It would have you give input—”Tell me about yourself” or “How do you feel?”—and it would look for keywords.

If you said “I feel sad,” its response would be “Why do you feel sad?” There was no logic—just a lookup table. If you said “dream,” it would say “What did you dream about?” He thought people would see through this very quickly. They didn’t. It became a huge sensation. In the late ‘60s and early ‘70s, people were legitimately talking about creating virtual therapists like ELIZA. Weizenbaum was horrified. He thought this was insane. A decade later, he wrote a book called Computer Power and Reason. He was saying that what you’re seeing from the computer objectively is just calculation. It presents in a manner intelligible to us, but we’re putting a human face to it. It’s just pattern matching and a scripted response.

His conclusions were roughly this: The computer can calculate, but it cannot understand. Just because the computer can do something doesn’t mean it should. He thought it was immoral to push this as a therapist when it does not understand emotion and has never experienced loss. His final point: don’t confuse cleverness with wisdom. Like Buffett’s analogy about hiring people—he always talked about the three I’s: intelligence, intellect, and integrity. If they don’t have the last one, you want to make sure they’ve got neither of the first two either, because they’ll destroy you.

The risk when you deploy these things is that you’re attributing great intelligence. People often conflate intelligence with wisdom. They’re not the same thing. Then they hand over decision-making to it. That was Weizenbaum’s big fear—that people would outsource their thinking.

By way of analogy, if you look at the financial crisis, a very long-term cause was ratings-based capital calculations. That goes back to a decision in the early ‘70s to create the NRSROs—Moody’s, S&P, and later Fitch. The idea was these agencies can assess the risk. Everyone at the time would not rely on ratings. They would do their own assessment. But by the time you got to CDOs, people just trusted the model blindly, not realizing that the incentives had been perverted.

When you look at LLMs today, on a simplified basis, they’re almost an elaborate version of predictive text in your Motorola phone from 20 years ago. They can do it to far greater depth, width, and speed. But they fail if you put them through certain tests. There’s a lot of evidence now that if you give them too long a context window or keep questioning in the same context window, the results get very bad very quickly.

There’s a great paper released late last year called “The Artificial Hive Mind,” which tested every major LLM asking open-ended questions like “What is life about?” Almost all of them gave close to verbatim responses because of the way they’re trained recursively. They’re trained to give you back the average.

When you go to the analogy of librarian versus scientist, the LLM is an incredible model for accessing and retrieving information that we already know and putting it in terms you might understand. It’s like a great tutor. But that’s based on the idea that all knowledge is known at this point and the LLM is just a way of retrieving it. That’s not how human knowledge develops over time. That’s the distinction between information and knowledge.

When we say librarian and scientist, we crib that from Demis Hassabis, the founder of DeepMind. He’s gone on record talking about this multiple times. He says it’s more like a repository of data, and it’s a brilliant tool. In the same way a Kindle can hold a thousand books and you don’t have to have physical copies in your room—that’s revolutionary. But it’s limited. Let’s not confuse it for what it’s not.

He gives what he calls the “Einstein test.” If you went back to 1904 and trained an LLM on all human knowledge up to that point, would it be able to come up with the theories of relativity? His answer is absolutely not. It’s regressing back to the norm. What it would do is give you an answer in Newtonian physics. Einstein had to do a lot of lateral things and question key assumptions. How can an LLM ever really create? As they’re structured currently, it can’t.

If you look at Pluto, for about 80 years that was a planet. Depending on when you train the LLM, Pluto either doesn’t exist, is a planet, or is no longer a planet. Which is true? I’d go back to Karl Popper—all the facts we currently have are really just things we’ve yet to falsify. With that mindset, you’re always questioning things. An LLM doesn’t think that way. It thinks it’s been trained on everything in the world and that is the truth. That’s the limit of it. It has the illusion of great wisdom and knowledge, but it’s limited to what it knows.

Humans have been fooled by things like this before. There was ELIZA back in the ‘60s. My son is an avid chess player—there’s the Mechanical Turk that fooled people for nearly a century. It’s still an incredibly powerful tool, but you have to be aware that this is probably purely a probabilistic extrapolation machine trained to provide certain answers, largely uniform. Like any tool, it’s really powerful, but you’ve got to know its strengths and weaknesses.

Elliot: In his book on AI, Ethan Pollock makes the point that the very fact that LLMs struggle with hallucinations is one of their greatest strengths and greatest weaknesses because it illustrates their power to be creative. LLMs are in fact capable of being creative, but it’s bounded—it takes a prompt and some orchestration from the human. How do you think about that, and how does AI affect the barriers to entry for companies that sell intangibles?

Shaun: On hallucinations and creativity, that’s absolutely true. The paper “The Artificial Hive Mind” made exactly this point: when you ask general open-ended questions, you get a uniform response—whether it’s Gemini, ChatGPT, Anthropic’s Claude, whatever. DeepSeek too. How do you get around this? You have to ask questions that force it to deviate from the most expected, most accepted answer, which is going to be average.

It’s a bit like when Matt Damon was on Joe Rogan talking about this for people developing scripts. I think LLMs can help with creativity by forcing you to make a lateral leap. I remember as a boy growing up, there were books by Edward de Bono about lateral thinking—trying to get away from conventional norms and think in a different manner.

If you look at what Buffett described as a great investor or great underwriter on the insurance side, it’s someone who can imagine things that have never happened and really picture them vividly. I think that’s where an LLM is incredibly powerful for investing. If you know how to direct it, if you know how to force it away from the average response, you will get very good outcomes.

One of the things we do is have the LLM question all of our assumptions, find weaknesses in the logic, try to find facts that contradict what we’ve assumed. To play the 11th man. I think it can be really helpful there. The weakness, as you said, is more on the precision side.

There was a company I had spoken to several years ago that was trying to develop a model to deal with legal contracts. In a legal contract, every clause, every “i” that’s dotted and “t” that’s crossed has to be accurate. You’re in big trouble if you get an error rate of even 50 basis points in a key clause. As a former bond trader, it’s similar. You cannot have a situation where it’s right 9 times out of 10. So anything where the error rate or severity would be really high, an LLM requiring precision is going to be a poor choice.

As it pertains to intangible moats, it’s really to do with businesses where technology might appear to be at the front, but the strength or the moat of the business is something different. An example would be Bloomberg. I’ve seen a lot of talk on X where people say “I built a new Bloomberg—I can pull this data in, I can do an earnings analysis.” That’s a very small aspect of what Bloomberg does. It’s really used for messaging, portfolio management, bond trading. You cannot live without it.

It’s the network they have. It’s their ability to take a lot of disparate data and put it in one repository that many people use, especially larger asset managers. I think they’ll be aided by AI. One of the complaints about Bloomberg in the past was it’s very difficult to figure out all the commands. It looks like something out of the ‘80s. If anything, the LLM interface will democratize it. They probably can reduce the sales force intensity. They’ve got thousands of well-trained engineers who can become a lot more efficient. I just don’t see a natural competitor coming around. They’ve got an installed base. A lot of people have built their processes and systems on it. That is the moat of that business.

Another one is Edenred. An LLM is very useful for them. They can reduce the number of programmers or make them more efficient. On the positive side, it makes a universe of customers that weren’t historically available to them—small and medium businesses. It was not cost-efficient for them before. They had an enterprise-type sales force. AI makes those customers a lot more accessible. They can automate the initial part of the funnel, get those customers in, and they benefit from the network. The marginal cost of onboarding beyond that is very low.

One more example: the UK used car market, dominated by AutoTrader. It has perhaps 90% of the share of classified ads in the UK. That’s a moat. AutoTrader is a notable case—it’s gone from its all-time high down 50%. The idea is that you can create a classified site very quickly and easily for free. The engineering barrier is no longer there. I don’t think that reflects where the moat for that business lies.

The moat is that they are a verb in that marketplace. Everyone knows AutoTrader the way you and I know Google. That’s where people go to search for vehicles. They have most of the high-value dealers on board. They’re the number one lead source. What they add in value is inventory turns. Used car sales in the UK—the actual selling of the vehicles, servicing, or financing—is a very low margin business: 7, 8, 9%, depending on the dealer.

What’s critical is that they turn inventory very quickly. If you turn it only twice a year, you’re not going to do well because you’re using the same parking spot. If you turn it 12 times, you’re fantastically profitable. What’s critical to that is the quality of the lead you get. They have 90% of the traffic and almost all of it is organic—96%. There’s 4% paid search.

People are saying they’ll create an LLM-based classified business and all the dealers will come. For sure, dealers will give their inventory to somebody who wants to come in—it’s free for them—but they’re not going to pay a premium for that. Google pretty much owned all of the search space, but they weren’t able to displace AutoTrader over 20 years.

Why? Because of tools AutoTrader has built for dealers that help inventory turns: inventory management, pricing for exit to maximize turns, pricing stock they’re going to buy at auction, financing partners. In our opinion, the barrier to entry just got bigger for someone trying to create a competitor.

Look at Heycar, a Volkswagen-sponsored business selling new and certified pre-owned vehicles. Huge support, 250 million invested in the brand—went nowhere. Never sold more than a thousand vehicles a month. Cinch and Cazoo in the UK, equivalents to Carvana in the US—collectively, these three companies spent a billion and a half plus trying to build a brand and could not compete.

The reason is: how do they get people onto their network? They’ve got a chicken-and-egg problem. They need people on the network to make it valuable to dealers so they can charge money. To get people onto the network, they have to pay for search. That’s cost per lead. What you’re seeing now with Google is the AI answer at the top, which stops people from going to rivals to AutoTrader like CarGurus. It makes the customer acquisition cost a lot higher than it was 5 or 6 years ago.

AutoTrader is so unmatched in the UK, which is a very niche market—left-hand drive, an island. Who’s going to come in to compete? Where would they get the data? There are rules in Europe that prohibit or limit the ability to scrape data. When you’re dealing with a product that turns over 10 to 12 times a year and it’s a perishable good, the data you require to price it accurately is very discrete. Who has the best data? AutoTrader.

The only way a business like that could be negatively impacted is if one LLM came to dominate—which doesn’t look to be the case—and started charging a toll for directing traffic. If Google couldn’t do it over 20 years, why would an LLM? Even if an LLM wanted to, does it make sense for them to get into a battle with the dominant data provider who has enormous market share? The cost to build a competitor just went up a lot. The advantage for that business was never technological. It wasn’t in the engineers or the user interface. It’s in everything else and the strategy of the business.

It’s similar for Edenred. It’s in their network. An LLM is not going to help you acquire merchants—go out to restaurants and sign them up. If you’ve got an existing network, AI might make sales easier or help you go to a wider network. It might make your engineering cost drop over time. For businesses where the moat is not the code but all the other aspects, we think AI probably increases the barriers. For things where it’s a simple automation tool, it’s a different story, and there I think people can get in trouble.

Elliot: I just want to lean in on some of the points you made, because I think they were fantastic and well-stated. I thought it was ironic that somebody used DoorDash as an example of where AI agents will inevitably kill it. When I followed the history of the company from when it was private, there were dozens of companies with very similar code, where the UX looked similar. They even in some cases had similar numbers of restaurants and good overlap in supply. Aggregating supply was a challenge, but others did it too. Execution matters, and the way the business runs matters. It’s literally running a service, not just software.

I tend to agree with your points. I know John’s written some really good stuff on how there are some areas where barriers to entry drop meaningfully and the pricing landscape might change. Where even just the threat of being able to code something might tilt how you approach it, or you might have lower headcounts. If a company can’t evolve from seat-based pricing to value-based or consumption-based pricing, there could be challenges.

Shaun: I think your DoorDash example was wonderful—I wish I’d used that rather than my own, Elliot. I’ve read some of John’s stuff, and I think it’s wonderful as well. I think you guys probably understand this better than I do, but I think you’re right on the money there.

John: Let’s move on to the case studies. You alluded to Edenred—that’s one of your newest portfolio additions. But let’s start with a passed opportunity: Vistry Group in the UK. It’s a good case study of your private equity-style due diligence. What made you reject the “NVR of the UK” thesis, and what were your forensic accounting concerns?

Shaun: It’s timely because they just came out with results last week, and an announcement about the departure of the chairman and CEO—Greg Fitzgerald, who’s had an amazing career at the top of that company.

Briefly, for the audience, Vistry is arguably the largest homebuilder in the UK—first or second. It’s the aggregation of three, maybe four, different homebuilders. It’s a fair analogy to NVR. NVR had an asset-light approach to building homes, which revolutionized the model in the US. It’s probably most famous because of Mohnish Pabrai, who wrote about it in the 2000s. When someone thinks of a homebuilder, you don’t think of a business that’s going to make returns like Microsoft. But if you go back to ‘93 or ‘94 when NVR emerged from bankruptcy, you made a similar return to Microsoft—about 18% per year. Quite remarkable.

We had prospective investors—about eight of them—almost in unison telling us to look at Vistry. I went into it excited and wanting to own it. As part of my research, I listened to some podcasts and heard some stuff that struck me as strange. Someone said it was a cost-plus model. When I looked at the results historically, I knew that wasn’t the case. That’s not how it worked.

When I looked at how Fitzgerald had aggregated the company—three homebuilders with a huge geographic footprint—I thought integrating these in four years was going to be a real challenge. Culturally in the UK, the difference between the North and the South is enormous.

When I went through their CMD and saw the claims about unit volumes—getting up to 30,000 homes, where 25,000 would be disappointing—and what they were claiming for margins, it seemed strange. But we investigated.

What we found was they were going to struggle on all the key variables. How we summarized it: this is a function of unit volume, margin, and average sales price. Can you parameterize them accurately?

We started with unit volumes. We looked at all the homebuilders in the UK going back 40, 50 years: What have they done? How easy is it to get to those targets? When you look at the history, it’s not easy at all. Very difficult. Nobody has done it, especially not while integrating three different businesses.

Then we asked: is there going to be huge government stimulus giving people more propensity to buy, or relaxation of very restrictive planning rules? They’d announced a roughly 35 to 39 billion program. People were very excited. But when you examined it, that program was over 10 to 12 years and it wasn’t sure when it would start. Breaking it down, you’re looking at adding maybe two or three thousand homes a year to the overall UK market. It’s not enough to move the needle for one company that needs to go from 17,000 homes per year to at least 25,000.

There’s been talk for 20 years about liberalizing planning standards in the UK. It is so difficult to do because it’s largely a municipal problem and very intensely political. People have promised it for a long time. It never gets delivered.

If you look at affordability, disposable incomes were really being squeezed. The commonest argument in favor of homebuilders—the housing shortage—does not mean people can afford to buy a house. They’re too expensive.

The final aspect particular to Vistry was their partnership housing model. They had moved to an asset-light model where they don’t have to pay for the land. They partner with local municipalities who need to build council houses or rental houses. Lower margins, maybe by four or five points, but much more rapid turnover of capital. So the return on capital would be enormous. That was the argument.

In practice, local municipalities in the UK are in dire financial straits. Many of them are doing very poorly. It was going to be difficult for them to fund this. And despite their best intentions, NIMBY tends to win out.

On the accounting side: this partnership model is one where they win a long project, anywhere from five to ten years. They don’t need the capital upfront, but they commit to a building plan and make assumptions—how much for labor, materials, timeframe. When they locked in these partnerships, yes, they had guaranteed sales, but they’d also locked in a guaranteed price. It was not cost-plus at all.

If they made a mistake on what house price inflation was going to be relative to build cost inflation, they had a problem with margins. Over three to four years, if the cost of inputs was much higher than house price inflation, margins would be much lower than expected. The margins they expected on these partnership homes were around 18% on a gross margin basis. That was the best you could do assuming your initial assumptions were correct.

All of the UK homebuilders had experienced great margins throughout the mid-2010s because home price inflation dramatically outstripped build cost inflation. That reversed with Brexit and hasn’t gotten much better since. Vistry was aiming for 75% of business to come from the partnership model. That means 75% of gross margins capped at 18%. If OpEx is 7, 8, 9%, and people were talking about 12 to 14% EBIT margins, how do you get there?

When they had the profit warning—they had three in a row over four weeks—the way the information was released was concerning. They didn’t do it upfront, which indicated they didn’t know the extent of the problem. It also indicated something rotten in the culture underneath. I don’t mean to suggest the executives knew this, and Greg Fitzgerald was an exceptional CEO. But it means integration of these mergers was not going well and the systems weren’t working properly.

Cultural problems in large, geographically dispersed organizations are very hard to reverse. We looked at things and said: if you correct for the accounting catch-up, the average margin on a net basis was in the single digits—5 to 6%. Our fear was simple: when did they win most of the new partnership business that’s going to be built in 2027, 2028, 2029? They won that in 2022, 2023. There’s a long lead time. Isn’t it very likely they bid for those projects with rose-tinted glasses?

It wasn’t that they were going to lose a ton of money or that there was fraud. It was just that the margins they were claiming seemed incredibly unlikely to us. If you mix in unit volumes—also very difficult to hit—and the margin challenges, where do you go with that?

I was open-minded to the idea that things could change. Maybe you do get revolutionary change in planning permission, but I want to see it first, not the promise of it. When we ran it through that lens, it looked pretty bad.

One last thing that applies to all the homebuilders in the UK: there are liabilities not immediately obvious. If you look on Bloomberg or CapitalIQ or FactSet, there are real liabilities you should add to the enterprise value that don’t show up on the standard form.

The two are provisions and land creditor debt. Land creditor debt is where you agree to buy the land and they give you vendor financing—you don’t pay until you start building. Provisions are exactly what you’d think. In the UK, this became a major issue after the Grenfell disaster. They found that a lot of council housing had not used the best insulation materials. Other problems were discovered.

At the time we wrote about it, in 2024, they had reported debt on the balance sheet of 541 million and cash of 320. So 180 million of net debt doesn’t seem earthshaking. But add land creditor debt—another 740 million—and provisions of 352 million. That was for a business doing adjusted EBITDA of 330 to 350.

The combination was nearly 1.1 billion, or three turns of EBITDA. All the bullish arguments said it was just a very cheap multiple, but that’s excluding things that are real liabilities. The provisions are real. Land creditor debt is real. You’re going to pay them the same as you would other debt.

Over time, provisions in 2021 were only 100 million and grew to 350. Land creditor debt went from 540 million to 740. We just never saw much cash really coming out relative to what they promised. They did some buybacks, but nothing close to what they wanted.

We were quite disappointed at the end. We’d done a lot of work. But this was a great case study for showing our investors how we think.

John: Let’s hit the highlights on one more idea: Edenred. You also presented Mayr-Melnhof Karton at MOI Global’s Best Ideas 2026 conference, which is available online on the MOI Global website and the Latticework Substack. We won’t get into that one. But give us your thesis on Edenred—a three to five-minute snapshot.

Shaun: Edenred, for those who don’t know it, is a food card and fuel card business. If you’re in the US, you may not know it. You’d be very familiar with it if you were from Brazil, France, or Germany.

These are employee perks—a way of giving extra benefits without being so inflationary. It used to be vouchers; now it’s a card. You can go to a restaurant, order food online through DoorDash, or buy groceries, and use this credit instead of cash. It’s issued at a discount. Later on, they went into the fuel card business. If you’re in Europe, you’ve seen these. It’s for truck drivers or for us driving our car—you can expense it properly.

Edenred is the biggest player, probably the most innovative. It’s a French company. The biggest companies in the space all came out of France because they originated the idea. This was done during very inflationary times as a way of not stoking inflation as much, because it’s not quite like cash compensation. A new CEO about ten years ago realized they were very dependent on regulatory markets. Why do these food cards exist? Because there was a tax deduction. If you gave this to the employee, it was not taxed as income. So you give them maybe a 10% raise without the inflationary impact.

Once these businesses got big enough, they could scale and get discounts from merchants the same way DoorDash or UberEats or Deliveroo get discounts from restaurants. So this is a business that’s done very well—very asset light, network-effect type business, fantastic growth over 10 years, very good acquisitions. People loved it in ‘21, ‘22, ‘23. It got to very high valuations—up to 18 billion. Today, it’s a market cap of 8.5 billion and an EV of 6 billion. Why?

It all goes back to what they were afraid of: these are regulated markets. In some major markets—Brazil, Italy, France—there was terrible abuse of smaller merchants. If a small operator was using Edenred’s service, they might be charged 15% on every transaction, but a large retailer might pay 1%. Think of it as a take rate. So regulators came in and capped what could be charged—4.5 to 5% in Italy, closer to 2.5% in Brazil.

If you cap that long tail of smaller merchants that pays much higher premiums and make the average 2.5%, it’s pretty catastrophic for margins. They had this horrible 18 to 24 months where Italy regulated, then Brazil did something far worse, and the French are debating it. Those are the three largest markets.

People said this company was earning ridiculous economic rent unfairly, all because of regulatory protection that’s now disappearing. When we looked at it, we inverted it and said: actually, they’ve been increasingly diversifying away from these regulated businesses. The regulatory impact in all of those countries has already happened. It’s already priced in. The horse has already bolted.

If you just value the non-regulatory-impacted businesses, it’s probably worth more than the EV today. In addition, a lot of the regulated markets are actually growing in their favor. The secret sauce that people miss is their enormous network—millions of merchants, millions of employees as end customers, and six-year contracts on average.

They have the opportunity to deploy many more services. Where 10 or 12 years ago they might have had one or two products—the fuel card or the food card—now they’ve got an average of 2.2. In some countries it’s up to 5. They can take advantage of this network and add more services at very little marginal cost.

We think next year the business is going to make somewhere between 1.35 and 1.4 billion in EBITDA. The company today is worth an EV of about 6 billion. It also generates an enormous float—about a billion and a half. There’s a lot of value there that people don’t really pay attention to.

If you take the company at face value—they think they can grow at 10% per year after this trough transition year through 2030—then you’re looking at a business with around 1.8 billion of EBITDA, very high margins. They’re actually pretty good capital allocators. It’s just hard for us to imagine a business that’s grown on average at 7% a year for five years trading at around 3 to 3.2 times EBITDA.

In a nutshell, that’s the argument. I think it’s just misunderstood, and people are focusing more on a sequence of very bad news events and really on the wrong items.

About the guest:

Shaun Heelan serves as Chief Investment Officer of MAAT Investment Group, based in Munich. Shaun brings over 20 years of investment experience across a broad range of asset classes. His career spans both the sell-side and buy-side, covering everything from highly liquid instruments to complex, illiquid assets. Known for his disciplined investment process and strong risk management, Shaun’s expertise has shaped MAAT’s philosophy and long-term approach.

The primary purpose of this podcast is to educate and inform. The views, information, or opinions expressed by hosts or guests are their own. Neither this show, nor any of its content should be construed as investment advice or as a recommendation to buy or sell any particular security. Security specific information shared on this podcast should not be relied upon as a basis for your own investment decisions -- be sure to do your own research. The podcast hosts and participants may have a position in the securities mentioned, personally, through sub accounts and/or through separate funds and may change their holdings at any time.

Featured Events

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11 Articles We Are Reading This Week

1. Letter - Jamie Dimon

2. Credit - Howard Marks

3. Big things - Ray Dalio

4. Ergodicity - Mauricio Heck

5. Junk bonds - Greg Obenshain

6. Laplace’s dictum - Massimo Fuggetta

7. Economic outlook - Philip Jefferson

8. CAPE improvement - Joachim Klement

9. Future-proof portfolio - Matt Franz & Dan Shuart

10. Fifth industrial revolution - Andre Borochovicius

11. AI adoption by the numbers - Kimberly Tan

We will unveil a new website for the Latticework 2026 summit in the coming days. As part of the launch, we will announce our first wave of confirmed speakers. A limited number of passes will also be available. Stay tuned!

Letters, Presentations, Writeups…

Industry Insights: AI | asset management | crude oil | energy | software

Letters: Alpinum | Commonwealth Private | Desert Lion | Eagle Point | Henchmen | Kingdom | Merrill | Palm Valley | Right Tail Capital | Vltava Fund

Company Insights: Amazon | Bolloré | Fiserv | Global Payments | Meta | Montana Aerospace | PayPal | Texas Pacific Land | UMG | XPEL

Company Slides: BlackBerry | Equinox Gold | Hibiscus | Neogen | PriceSmart | Roots | RPM International | Skillsoft | Stolt-Nielsen | WD-40

5 Videos We Are Watching This Week

1. Citi - Steve Schwarzman on leadership, business strategy, philanthropy

2. NPR - Jamie Dimon on economy, geopolitics, and corporate leadership

3. Stripe - Google CEO Sundar Pichai on the history and the future of AI

4. Cleo Abram - DeepMind's Demis Hassabis on AI in science, medicine

5. Drew Cohen - Chris Mayer on his investment philosophy, when to sell

5 Podcasts We Are Listening to This Week

1. Norges - Prada CEO Andrea Guerra on building iconic luxury brands

2. Odd Lots - Peterffy on professionalizing and scaling prediction market

3. Dwarkesh - Nielsen on the nature of scientific progress and discoveries

4. Acquirer’s - Cooper on global small-cap value, lessons on the markets

5. How Leaders Lead - Former Nike CEO on leadership, future of sports

Save the date: Latticework 2026 will be held at the University Club of Chicago. We will meet for two days on November 10-11, 2026. (Priority access granted to MOI Global members and paid Latticework subscribers. Stay tuned for your personal invitation.)

Member Updates & Events

The following is a private section for MOI Global members. Membership is by invitation only. If you would like to become a member, be sure you are on the annual plan. It is the first step toward full membership.

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11 Articles We Are Reading This Week

1. Copper - Will Thomson

2. Narrative - Aswath Damodaran

3. Resilience - Eric Markowitz

4. Macro brief - Mauricio Heck

5. Epic decline - Joost van Dreunen

6. Warning signs - Herb Greenberg

7. Investment fail - Harvey Sawikin

8. Information diet - David Epstein

9. End of economics - Scott Sumner

10. Hassabis’ transformation - Sebastian Mallaby

11. Jensen manifesting the future - Trung Phan

We will unveil a new website for the Latticework 2026 summit in the coming days. As part of the launch, we will announce our first wave of confirmed speakers. A limited number of passes will also be available. Stay tuned!

Letters, Presentations, Writeups…

Industry Insights: agricultural commodities | construction technology | energy delivery | geothermal | oil and gas | software | water infrastructure

Geographic Insights: China | Egypt | Japan | Middle East

Letters: Brock Milton | Hidden Rock | J.P. Morgan | McElvaine | Merrill

Company Insights: BGC | Crocs | IJM | Marex | Meta | Theravance

Company Slides: Cal-Maine Foods | Conagra | Constellation Energy | FactSet | Fortinet | Franklin Covey | Lamb Weston | Lindsay | Methanex

5 Videos We Are Watching This Week

1. Axios - Jamie Dimon on tariffs, trade, geopolitics, and modern banking

2. Norges - IEA’s Fatih Birol on global energy markets and the new crisis

3. Daily Stoic - Arthur Brooks on happiness, deeper purpose, and wisdom

4. Goldman Sachs - New Mountain’s Steve Klinsky on business building

5. Graham Bensinger - Reed Hastings on Netflix, ski resorts, and AI risk

5 Podcasts We Are Listening to This Week

1. Long View - Pat Dorsey on durable economic moats and competition

2. Squawk Pod - Warren Buffett on relaunching his charity lunch auction

3. Business Brew - Sonu Chawla on her investing process and conviction

4. Knowledge Project - Joe Liemandt on AI and the future of education

5. Intangible Economy - Kai Wu and Mauboussin on pricing intangibles

Save the date: Latticework 2026 will be held at the University Club of Chicago. We will meet for two days on November 10-11, 2026. (Priority access granted to MOI Global members and paid Latticework subscribers. Stay tuned for your personal invitation.)

Member Updates & Events

The following is a private section for MOI Global members. Membership is by invitation only. If you would like to become a member, be sure you are on the annual plan. It is the first step toward full membership.

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A new installment of The Great Rotation Playbook was published this week — Theme 3: The Industrial Renaissance.

11 Articles We Are Reading This Week

1. Lodestar - Aswath Damodaran

2. All weather - Ray Dalio

3. World models - Rohit Krishnan

4. Non-events bias - Scott Sumner

5. AI trust networks - Sakina Arsiwala

6. Disagreeing better - Annie Duke

7. Durable businesses - Todd Wenning

8. Global value spread - Brian Chingono

9. Bicycles for the mind - Rex Woodbury

10. Two paths for software - David George

11. Number of public companies - Mikhail Pevzner

Letters, Presentations, Writeups…

Industry Insights: banking | oil and gas | private credit | software | titanium

Letters: Exor | G&R | Goldman Sachs | J.P. Morgan | Merrill

Company Insights: Berkshire Hathaway | Carnival | Core Natural Resources | Ferrari | Golden Growers | Guardian Pharmacy | JD.com | Merlin Labs

Company Slides: Cemex | Core & Main | Exor | Hapag-Lloyd | KB Home | MillerKnoll | Paychex | Samsonite | Vallourec | Winnebago | Worthington

5 Videos We Are Watching This Week

1. Stripe - Dmitri Dolgov on Waymo’s quest for fully autonomous driving

2. Drew Cohen - Bill Nygren on his approach to investing and stock picks

3. Money of Mine - Django Davidson on capital cycles in mining equities

4. Whitney Tilson - Guy Spier’s ValueX presentation and audience Q&A

5. Graham Bensinger - Barry Diller on media, ambition, and his fortune

5 Podcasts We Are Listening to This Week

1. No Priors - Andrej Karpathy on making smarter AI agents and systems

2. Dwarkesh - Terence Tao on AI’s role in math and scientific discoveries

3. At the Money - Miller Value Fund's Bill Miller IV, conviction investing

4. Oaktree Insights - Howard Marks on Oaktree's 2026 conference ideas

5. Capital Allocators - GMO’s Jeremy Grantham on bubbles and the risks

Save the date: Latticework 2026 will be held at the University Club of Chicago. We will meet for two days on November 10-11, 2026. (Priority access granted to MOI Global members and paid Latticework subscribers. Stay tuned for your personal invitation.)

Member Updates & Events

The following is a private section for MOI Global members. Membership is by invitation only. If you would like to become a member, be sure you are on the annual plan. It is the first step toward full membership.

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Thesis summary:

TBC Bank Group is a holding company operating two distinct banking entities in the Caucasus and Central Asia. TBC Bank Georgia, a universal bank, generates approximately 90% of group profits and holds a 40% market share in a stable duopolistic market. The remaining 10% of profits stem from TBC Uzbekistan, a high-growth digital venture mirroring the “super-app” strategy of fintech players like Kaspi. While the Georgian operation provides a foundation of stability, the expansion into Uzbekistan offers exposure to a market with ten times the population of Georgia and low banking penetration.

The Georgian operation anchors the thesis with consistent returns, having compounded earnings while maintaining long-term ROEs above 20%. Established in 1992, the bank has transitioned from a traditional physical network to a strong digital offering. Despite being situated in a region with perceived geopolitical friction—bordering Russia and occupied territories—the currency has remained relatively flat against the British Pound over the last decade, and the economy has benefited from recent migration inflows. The high level of dollarization in the Georgian economy is being actively managed through central bank “larization” initiatives to decrease foreign exchange risk.

Jean Pierre highlights Uzbekistan as the primary growth engine, leveraging a population of nearly 40 million to deploy a fintech-enabled strategy. Through acquisitions of payment provider Payme and e-classifieds platform OLX, TBC is building an ecosystem to capture a young, digitally savvy demographic. While this segment has delivered rapid loan growth, recent regulatory interventions aimed at curbing unsecured lending and a tick-up in NPLs suggest a near-term moderation in expansion rates. Consequently, the bank is pivoting toward secured and SME lending to de-risk the Uzbek book over the coming years.

Governance and capital allocation are anchored by a management team led by a CEO who has served since 1995. The group maintains a dividend payout ratio between 35% and 40%, supplementing shareholder returns with share buybacks when excess capital is available. Although the founder’s recent legal issues and subsequent pardon present a headline risk, the operational leadership has continued to deliver efficiency improvements, driving the cost-to-income ratio down to approximately 38%.

Regarding valuation, Jean Pierre argues the market misprices the gap between the company’s fundamental performance and its share price. The stock recently traded at a P/E of roughly 6x and a trailing tangible P/B of 1.3x, despite consistent ROEs exceeding 20% and healthy capital adequacy. Jean Pierre suggests a fair multiple would be closer to 2x tangible book value. A narrowing of this valuation gap, combined with earnings growth and a ~6% dividend yield, could support a 25% CAGR, potentially doubling the share price to around £80 by 2029.

Disclaimer

Best Ideas 2026 was held from January 6-23, 2026. The content of this website is not an offer to sell or the solicitation of an offer to buy any security. The content is distributed for informational purposes only and should not be construed as investment advice or a recommendation to sell or buy any security or other investment, or undertake any investment strategy. There are no warranties, expressed or implied, as to the accuracy, completeness, or results obtained from any information set forth on this website. BeyondProxy’s officers, directors, employees, and/or contributing authors may have positions in and may, from time to time, make purchases or sales of the securities or other investments discussed or evaluated herein.

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Jean Pierre Verster on TBC Bank

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“Grassroots Macro,” as described by Bob Robotti, highlights a structural shift favoring the “boring” economy. The West, particularly North America, possesses a sustainable, multi-decade competitive advantage rooted in low-cost energy. This advantage is fueling a re-industrialization and “friend-shoring” boom, reversing decades of offshoring.

While the 2D world of software faces deflationary pressures from AI, the 3D world of industrial manufacturing and logistics is entering a renaissance. The beneficiaries are the companies that make the nuts, bolts, glass, and specialty materials that hold the physical world together. These businesses, often ignored by growth-obsessed investors, are now positioned to compound value through operational excellence, consolidation, and the return of industrial capacity to the West.

This report serves as a playbook for “Intelligent Investing 3D.” It moves beyond the macro thesis articulated in our foundational essay to identify actionable ideas within the Industrial sector, specifically targeting Chemicals, Steel, Diversified Industrials, Pulp & Paper, and Recycling. The analysis synthesizes insights from leading real-asset managers including Massif Capital, Horizon Kinetics, and Goehring & Rozencwajg, alongside data from major investment banking research divisions.

The core finding is that the “Old Economy” is not merely rebounding; it is undergoing a metamorphosis into a high-technology, capital-disciplined, and geostrategically critical ecosystem. The build-out of AI infrastructure is voraciously physical. It requires specific fluoropolymers for thermal management, grain-oriented electrical steel for voltage transformation, and sustainable fiber for packaging and fuel. Value is shifting from the digital economy to the industrial economy.

Companies mentioned inside:

Chemicals

The chemical industry stands at the intersection of energy policy and high-tech manufacturing. The investment thesis relies on two pillars: the geographic arbitrage of energy costs and the critical role of specialty chemicals in the AI hardware stack.

US Advantage vs. European Deindustrialization

A profound divergence has emerged between the North American and European chemical sectors. The US chemical industry benefits from a structural feedstock advantage: abundant, low-cost natural gas and ethane derived from the shale revolution. Conversely, the European sector faces an existential crisis driven by high energy costs and regulatory burdens.

Chemical production is energy-intensive. In Europe, natural gas prices remain significantly higher than pre-crisis levels, rendering baseload production of commodities like ammonia and petrochemicals uncompetitive.

European Decline: European chemical production contracted in 2022 and 2023, and the recovery remains elusive. Major players continue to shut down crackers and rationalize assets. The “deindustrialization” of Europe is not a temporary phenomenon but a structural unwinding of capacity. Arkema (~€52, ~€4.0B market cap) reported FY2025 revenue down 5% to €9.07B with EBITDA of €1.25B at a 13.8% margin. The company is expanding PVDF capacity at Changshu, China (20% expansion, startup 2028) and Calvert City, KY (15% expansion, startup mid-2026) for battery and semiconductor markets, but 2026 guidance calls for only “slight EBITDA growth.” Solvay (~€24, ~€2.5B market cap), the post-demerger soda ash and peroxides business, delivered €881M EBITDA on €4.26B revenue but guides to lower 2026 EBITDA of €770–850M. At ~5.2–6.6× EV/EBITDA with a 12–14% FCF yield, Solvay screens as a deep value play. Morningstar’s fair value estimate of €74 is ~3× the current stock price.

US Dominance: US producers, leveraging Henry Hub natural gas pricing, are positioned to capture global market share. Companies like Westlake Corporation (WLK, ~$111, ~$14.3B market cap) and Olin Corporation (OLN, ~$26, ~$3.0B market cap) are prime beneficiaries. They operate in energy-intensive chains (chlor-alkali, PVC) where the US cost advantage translates directly into FCF margin expansion. Both companies are currently navigating a cyclical trough: Olin’s Q4 2025 adjusted EBITDA of $67.7M badly missed guidance, with full-year EBITDA landing at ~$585–635M, well below management’s mid-cycle bridge. Westlake booked $1.39B in identified items during 2025 reflecting the shutdown of three North American chlorovinyls plants, one styrene plant, and the Pernis (Netherlands) facility. However, Westlake’s three-pillar improvement plan targets $600M of incremental earnings in 2026 through structural cost cuts, closed-facility savings, and reliability improvements.

The AI “Cooling” Thesis: Fluoropolymers

While basic chemicals play a volume game, specialty chemicals are playing a technology game. The explosion of AI data centers has created a critical bottleneck: heat. High-performance GPUs (like Nvidia’s Blackwell architecture) generate immense thermal loads that traditional air cooling cannot manage. This is driving a transition to liquid cooling, specifically two-phase immersion cooling.

The fluids required for immersion cooling are often fluorinated chemistries (fluoropolymers) due to their dielectric properties and thermal stability. Furthermore, fluoropolymers are indispensable in the semiconductor manufacturing process itself, used in piping and storage for high-purity chemicals. The production of fluoropolymers is technically difficult and heavily regulated (PFAS regulations). This creates high barriers to entry.

Key Players:

The Chemours Company (CC, ~$17.81, ~$2.7B market cap): The AI cooling thesis is materializing. Chemours’ Thermal & Specialized Solutions segment delivered a record Q4 2025, with Opteon refrigerant sales growing 56% for the full year (now representing 75% of total refrigerant revenue, up from 56%). The company has signed a JDA with 2CRSi for Opteon two-phase immersion cooling, secured Samsung qualification for data center SSDs, and sizes the liquid cooling TAM at $550M by 2026, growing to $1.5B by 2030 and $3B by 2035. However, the balance sheet is stressed at ~$4.0B+ net debt with negative tangible book value, and PFAS liability remains the tail risk (the NJ settlement alone is $875M over 25 years). 2026 guidance calls for $800–900M adjusted EBITDA, implying ~9× EV/EBITDA at midpoint.

3M (MMM, ~$146, ~$76B market cap): The litigation overhang is clearing. The $6B earplug settlement has paid out over $3.1B with zero cases remaining. The $10.3B PFAS water contamination settlement received final court approval. FY2025 adjusted EPS came in at $8.06 (+10% YoY) with $4.4B in adjusted FCF. 2026 guidance of $8.50–$8.70 adjusted EPS implies continued mid-single-digit growth. The stock trades at ~17× forward P/E and ~11× EV/EBITDA with a 5.8% FCF yield.

Arkema & Solvay: European players expanding capacity (as detailed above), though they face structural energy headwinds. Arkema trades at ~5.2–6.1× EV/EBITDA with a 6.4–6.9% dividend yield.

Chlor-Alkali: The Building Block of Infrastructure

Chlor-alkali chemistry (producing chlorine and caustic soda) is the “bread and butter” of the industrial economy. Chlorine is essential for PVC (construction, pipes) and water treatment, while caustic soda is used in pulp & paper and aluminum refining.

The sector is characterized by a “co-product imbalance.” Demand for chlorine (construction-led) and caustic soda (industrial-led) rarely moves in sync, leading to volatile pricing. However, capacity rationalization in the US (Westlake shut three chlorovinyls plants in 2025) is restoring pricing power, and caustic soda inventories entered 2026 at very low levels.

The deployment of funds from the US Infrastructure Investment and Jobs Act (IIJA) serves as a long-term demand driver for PVC (pipes) and epoxy resins (coatings), directly benefiting the chlor-alkali chain. Westlake’s Housing, Infrastructure & Pipes (HIP) segment remains resilient, with 2026 guidance of $4.4–4.6B in HIP revenue at 19–21% EBITDA margins.

Olin Corporation (OLN) and Westlake Corporation (WLK) operate in an oligopolistic US market structure. With European capacity offline due to energy costs, these US producers effectively control the Atlantic basin marginal supply. Both are currently navigating a cyclical trough: Olin at ~9–10× TTM EV/EBITDA with 4.1× leverage and a Fitch downgrade to BB+, Westlake at ~15.5× TTM EV/EBITDA but with an investment-grade balance sheet (BBB+/BBB/Baa) and $2.9B in cash. The sector faces margin compression from Chinese capacity overexpansion and export “dumping,” but this is a cyclical rather than structural headwind. Olin has filed anti-dumping petitions on epoxy resins against five countries, and the EU imposed definitive duties on similar imports in July 2025.

Smart Money Endorsement

The sector’s strategic value was validated on January 2, 2026, when Berkshire Hathaway completed its $9.7 billion all-cash acquisition of OxyChem, Occidental Petroleum’s chemical division and a top-3 US manufacturer of PVC, chlor-alkali, and chlorinated organics. The deal valued OxyChem at ~8× projected 2025 EBITDA — Berkshire’s largest acquisition since the $11.6B Alleghany deal in 2022, consistent with Buffett’s preference for productive, cash-generating commodity assets bought at attractive cycle pricing. Occidental earmarked $6.5B of proceeds for debt reduction, and legacy environmental liabilities were retained by an Occidental subsidiary.

The gap between the Berkshire transaction multiple and current public trading multiples remains significant. Olin trades at ~9–10× TTM EV/EBITDA, but management’s mid-cycle EBITDA target of $2.0B (vs. ~$585–635M in 2025) implies a forward valuation of roughly 3.5×–4.0× on normalized earnings, supported by a $1.9B remaining share repurchase authorization (though buybacks have been curtailed to $50.5M in 2025 as management prioritizes deleveraging). Westlake trades at a higher optical multiple of ~15.5× TTM EV/EBITDA due to depressed earnings but is executing a $200M structural cost reduction program and targets $600M of incremental earnings in 2026.

Investors have the opportunity to acquire similar industrial infrastructure assets in the public markets at a discount to the price paid by a disciplined capital allocator, positioning themselves to benefit from the eventual recovery in global industrial demand. The entry point is cyclically depressed, which is precisely when Buffett chose to act.

Steel

Steel is often dismissed as a commoditized, cyclical industry. This view ignores the rapid technological stratification within the sector. The opportunity lies not in generic rebar, but in Grain-Oriented Electrical Steel (GOES) and high-strength alloys required for grid modernization and defense.

The Electrical Steel Bottleneck

There is no AI without electricity, and there is no electricity without transformers. Every step-up and step-down of voltage across the grid requires a transformer, and the core of every transformer is made of GOES.

The US suffers from a chronic shortage of distribution transformers, with lead times extending significantly. This shortage is exacerbated by the demand shocks from AI data centers, EV charging networks, and renewable energy interconnects. Producing GOES is technically demanding and capital intensive. There is limited domestic capacity in the US.

Cleveland-Cliffs (CLF, ~$8.03, ~$4.5B market cap) is the sole producer of GOES in North America, a natural monopoly on a critical national security asset. The company is in distressed territory today, with ~$7.2B net debt against essentially zero full-year EBITDA ($37M adjusted in FY2025), having burned $1.0B in FCF and issued 75M new shares. However, the turnaround thesis hinges on multiple catalysts converging in 2026–2027: the GOES expansion at Butler, PA (operational late 2026–early 2027); the Weirton, WV transformer plant (online H1 2026, ~$150M investment, adding 30–40% to GOES demand); 50% Section 232 tariffs on steel imports; a strategic MOU with POSCO for a ~$700M equity investment (≥10% stake, definitive agreement targeted H1 2026); and a $500M EBITDA improvement from terminating a slab supply contract.

Recent Department of Energy efficiency standards have confirmed the continued necessity of GOES (vs. amorphous metal alternatives), solidifying CLF’s market position. Section 232 tariffs now extend to downstream electrical steel laminations, preventing circumvention and protecting domestic margins. At 0.73× P/B and 0.21× P/S, CLF is deeply distressed but carries significant optionality.

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11 Articles We Are Reading This Week

1. Hormuz - Ray Dalio

2. Ten-baggers - Django Davidson

3. Selling winners - Harvey Sawikin

4. Napkin portfolios - Bogumil Baranowski

5. Othello and Bayes - Massimo Fuggetta

6. Equity risk premium - Aswath Damodaran

7. Value premium eternal - Dan Rasmussen

8. Dividends vs. buybacks - James Emanuel

9. Vignettes from the takeoff - Rohit Krishnan

10. How optimizing kills winners - Todd Wenning

11. Using markets to evaluate policies - Scott Sumner

Letters, Presentations, Writeups…

Industry Insights: banking | electrical grid | offshore | oil and gas | software

Letters: Bumbershoot | Merrill Lynch | Stifel

Company Insights: Ashtead | Century Casinos | KVH | Pershing Sq. | SAP

Company Slides: Accenture | Couche-Tard | Darden | Dollar Tree | FedEx | General Mills | HelloFresh | Macy's | Micron | SAIC | Williams-Sonoma

5 Videos We Are Watching This Week

1. SXSW - Mohnish Pabrai on mental models, compounding, living well

2. Bloomberg - Howard Marks on importance of AI, unpredictability

3. Morgan Stanley - Jensen Huang on compute, tokens, new economy

4. Harvard Law School - Canyon Partners’ Josh Friedman on investing

5. Behind the Balance Sheet - Rory Sutherland on marketing, persuasion

5 Podcasts We Are Listening to This Week

1. All-In - Travis Kalanick and Michael Dell on AI and tech entrepreneurship

2. Acquirers - Tim Travis on private credit, the evolution of lending markets

3. How I Write - Macmillan’s Jon Yaged on how to get your book published

4. How Leaders Lead - Coach Roy Williams on a system for winning teams

5. Knowledge Project - Brookfield CEO Connor Teskey on AI infrastructure

Save the date: Latticework 2026 will be held at the University Club of Chicago. We will meet for two days on November 10-11, 2026. (Priority access granted to MOI Global members and paid Latticework subscribers. Stay tuned for your personal invitation.)

Member Updates & Events

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Thesis summary:

Gimat is an Ankara-based wholesaler-retailer operating a hybrid business model that combines a wholesale market, a modern consumer hypermarket, and a real estate-anchored ecosystem. Born from a cooperative of over 1,000 wholesalers in the early 1990s, Gimat functions similarly to a “Turkish Costco,” selling in bulk with low margins while owning its real estate assets. This structure provides a natural hedge against Turkey’s high inflation environment, protecting the company from rent escalation and supply chain disruptions. The company currently operates two locations, with the flagship store generating ~$1,200 in sales per square foot and gross margins exceeding 20%. The shareholder base remains unique, with 99% free float and thousands of small holders, largely descendants of the original cooperative members, ensuring a culture focused on continuity and working capital efficiency rather than aggressive corporate expansion.

The company’s expansion strategy is conservative and disciplined, aiming to open one new store roughly every two years. Each unit requires approximately 1 billion lira to build and reaches maturity within 18 to 24 months. The second location, opened recently, reached profitability faster than anticipated. Long-term goals include consolidating presence in Ankara before expanding to other major Turkish provinces and potentially Germany, leveraging the large Turkish diaspora. While the company does not currently charge a membership fee—a key differentiator from the Costco model—management is exploring options such as paid parking passes to introduce a membership-like revenue stream. The focus remains on sustainable growth that does not compromise their thin net margins, which historically sit near 1-2% but are bolstered by asset appreciation and high inventory turnover.

Management is led by General Manager Recai Kesimal, who holds proxies for over 25% of the wholesaler base. Kesimal’s approach is characterized by a “service” mindset, prioritizing the stability and longevity of the enterprise over short-term shareholder value creation. This alignment ensures operational discipline and aversion to excessive leverage or risky scaling. While the lack of large institutional investors and the fragmented ownership structure might typically raise governance concerns, the deep communal ties and the management’s track record of capital preservation mitigate these risks. The leadership is actively studying Costco’s operational and cultural efficiencies to further optimize their low-cost model.

Gimat recently traded at a market capitalization of approximately $156 million, which Monsoon argues is slightly below its estimated intrinsic value of $175 million. This intrinsic value calculation aggregates the earnings power of the two existing stores—generating roughly $11 million in PAT, valued at a conservative 10x multiple—and the real estate value of the “Gimat Arena” development. The latter includes projected office sales of $50 million and retained commercial property yielding $1 million in annual rent, capitalized at 6.5%. Despite trailing P/E ratios appearing inflated due to Turkish inflation accounting, the underlying asset base and cash flow generation present a “heads I win, tails I don’t lose too much” scenario, offering a free option on future growth for patient capital.

Disclaimer

Best Ideas 2026 was held from January 6-23, 2026. The content of this website is not an offer to sell or the solicitation of an offer to buy any security. The content is distributed for informational purposes only and should not be construed as investment advice or a recommendation to sell or buy any security or other investment, or undertake any investment strategy. There are no warranties, expressed or implied, as to the accuracy, completeness, or results obtained from any information set forth on this website. BeyondProxy’s officers, directors, employees, and/or contributing authors may have positions in and may, from time to time, make purchases or sales of the securities or other investments discussed or evaluated herein.

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Monsoon Pabrai on Gimat

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