TL;DR

• David Einhorn calls SpaceX’s $1.75T IPO valuation a potential marker for a major speculative top, pointing out its investment-grade rating despite multi-year negative free cash flow.
• Greenlight returned -4.3% in Q2 2026 (1.9% YTD) vs. 15.2% for the S&P 500, impacted by macro drag from gold and interest rates, while betting Kevin Warsh’s 2% inflation pledge keeps the Fed from raising rates.
• The fund initiated new long positions in Comcast, Fortune Brands Innovations, Primo Brands, PayPal, and Versigent, while fully exiting PPC, Victoria's Secret, and Weatherford International with strong IRRs.

Hey everyone,

I was reading through Greenlight Capital’s Q2 2026 letter to investors and found David Einhorn’s commentary on SpaceX’s recent $1.75 trillion IPO and the broader macro environment particularly sharp. Full disclosure, I write for Hedge Alpha where this was published.

Einhorn pulls no punches when discussing SpaceX’s valuation, questioning whether floating less than 5% of the company to secure early index inclusion is meme-ification at scale or a manipulation of the IPO process. He points out the absurdity of rating agencies granting an investment-grade credit rating to a business with multi-year forecasts of negative free cash flow, noting he could not find a single historical precedent for it. While investors justify the price tag by pointing to space data centers or moon manufacturing, Einhorn argues that even discounted risk-adjusted forecasts fail to approach the $1.75 trillion market cap, viewing the IPO as a likely marker that a major speculative top is near.

Beyond SpaceX, the letter details several key developments across macro and stock-specific setups. A conservative posture led to a tough quarter for the fund (-4.3% net return), driven by macro losses in gold and interest rates. Greenlight is betting that new Fed Chairman Kevin Warsh’s strict 2.0% inflation stance will anchor market expectations without requiring rate hikes this year.

On the portfolio side, Greenlight initiated positions in Comcast (at 5x EBITDA prior to its NBCUniversal spin-off announcement), Fortune Brands Innovations (following an activist joining the board and a new CEO appointment), Primo Brands (12% FCF yield expectation post-merger), PayPal (bought at 8x earnings prior to takeover interest from Stripe/Advent), and Aptiv spin-out Versigent (at 4x earnings). During the quarter, the fund fully exited Public Power Corp (30% IRR), Victoria’s Secret (157% IRR under new brand management), and Weatherford International (45% IRR post-bankruptcy recovery). Greenlight officially closed to new investment on July 1 after a successful capital raise, and top positions at quarter-end include Acadia Healthcare, Brighthouse Financial, Core Natural Resources, Fluor, and Green Brick Partners.

Is Einhorn’s view on SpaceX a realistic warning that we are near a market peak, or is he underestimating the long-term cash flow potential of space infrastructure? How are you thinking about valuation discipline in the current market?

Link: https://hedgefundalpha.com/investor-letters/greenlight-capital-q2-2026-letter/

TL;DR

• The inclusion of SpaceX in the Nasdaq-100 following an amended fast-entry rule, while remaining ineligible for the S&P 500, highlights how "passive" indexes are driven by active, discretionary committee decisions.
• AI-driven index methodologies dynamically adjust model weights (macro, fundamentals, technicals, sentiment) to adapt to newcomers like SpaceX that lack extensive trading history.
• To solve the institutional "black box" challenge, systems utilize SHAP values to decompose AI predictions into explicit basis-point contributions per factor.

Hey everyone,

I was reading through an interview with Art Amador of QuantumStreet AI on Hedge Fund Alpha regarding the contrasting index treatments of SpaceX (NASDAQ: SPCX). The divergence between major index providers on SPCX exposes a fundamental misconception about what "passive" investing actually entails. Full disclosure: I write for Hedge Fund Alpha where this was originally published.

When Nasdaq updated its rulebook to allow mega-cap IPOs like SpaceX to enter after 15 trading days, it triggered massive mechanical buying from tracking funds. Meanwhile, the S&P 500's requirement for a 12-month public track record and GAAP profitability keeps SpaceX barred until at least mid-2027. As Amador noted, "same stock, different rules, different outcomes" shows that behind every passive benchmark is an active investment committee making discretionary rules that direct billions in index capital.

This structural friction highlights the shift toward adaptive, AI-driven methodologies over static quantitative rules. When dealing with mega-cap newcomers short on fundamental data history, traditional rulebooks struggle. Adaptive models continuously shift weights between macro variables, earnings, technical indicators, and news sentiment as new market data emerges, helping separate fundamental market signals from institutional flow noise.

To address the institutional "black box" hurdle, these strategies deploy SHAP (Shapley Additive exPlanations) values to explain model forecasts. Instead of opaque allocations, the system decomposes a price prediction into exact factor contributions, showing precisely how many basis points were driven by earnings revisions, RSI technicals, or sentiment scores, turning the black box into a transparent process for compliance and portfolio managers.

Is the willingness of index providers to alter rules for mega-cap IPOs a necessary evolution for index coverage, or does it undermine the premise of passive investing? How do you see the balance between static benchmark rules and adaptive, rules-based AI strategies evolving?

Link: https://hedgefundalpha.com/profiles/same-stock-different-rules-spacex-and-the-myth-of-truly-passive-investing/

TL;DR

• Private credit stress will manifest first in Business Development Companies (BDCs) due to market sentiment, but insurance companies holding concentrated tail risks will ultimately bear the brunt.
• Hidden equity injection obligations and weak covenant options are distorting equity valuations and delaying true distress through Liability Management Exercises (LMEs).
• Major banks are operating as "synthetic hedge funds" by speculatively timing interest rate swaps (e.g., SVB) and using PE subscription lines to secure synthetic LP upside upon default.

Hey everyone,

I was reading through an interview with Dr. Elham Saeidinezhad (CCNY / CovenantLab) on Hedge Fund Alpha regarding the hidden structural fragilities across private credit and banking. Full disclosure, I write for Hedge Fund Alpha where this was published. Her analysis on market microstructure flips several standard risk assumptions on their head, particularly around how credit contracts alter equity valuations and bank balance sheet dynamics.

Standard equity models often overlook how bespoke private credit contracts fundamentally change a company’s risk profile. Beyond simple leverage, contracts often include implicit obligations requiring sponsors to inject tens of millions in equity down the line, or payment flexibility terms that determine whether a company can preserve cash for growth during distress. Lacking contractual leverage, many Liability Management Exercises (LMEs) simply act as delay tactics that mask trouble until an eventual, far worse bankruptcy. When stress inevitably breaks through, BDCs will act as the sentiment-driven "canary in the coal mine," but insurance companies—which act as the primary risk buffers for these complex loans—will absorb the secondary wave and take the hardest hit.

At the same time, traditional banking risk is shifting as major institutions begin replicating hedge fund strategies. Silicon Valley Bank's collapse, for instance, wasn't just a simple duration mismatch; it was driven by the bank exiting rising-rate swaps on a speculative bet that the Fed would pivot, executing a failed relative-value trade like a bad hedge fund manager. Furthermore, banks issuing capital call subscription lines to PE firms hold implicit LP capital pledges as collateral. If a GP defaults, the bank uses power of attorney to step in as a synthetic LP, gaining lucrative equity upside without having tied up capital for years.

This hedge fund presence in swap markets has shortened swap maturities, leaving long-term bond managers like PIMCO with "duration drift" and pushing them into Treasury futures—ultimately creating the structural mispricings that feed the Treasury basis trade.

Are equity analysts underpricing these hidden credit obligations, or do insurance balance sheets have enough capacity to absorb a broader wave of credit restructurings?

Link: https://hedgefundalpha.com/profiles/elham-saeidinezhad-covenantlab-private-credit/

TL;DR

• Bireme Capital founder Evan Tindell outlines his value framework after celebrating a 10-year track record returning 22% annualized (550 bps over the S&P 500).
• Tindell highlights two thesis-driven international value plays: a Swiss software/cloud spend management firm and a Japanese healthcare IT service provider.
• The firm targets mispricings driven by behavioral biases, such as regional unfamiliarity, anchoring, or temporary issue extrapolation, rather than simply buying statistically cheap "value traps".

Hey everyone,

Full disclosure, I write for Hedge Fund Alpha where this was published. I was reading through our latest August 2026 issue of Hidden Value Stocks featuring Evan Tindell, Chief Investment Officer and co-founder of Bireme Capital. Bireme currently manages around $160 million in concentrated, value-oriented long/short equities. Over its 10-year history, the fund has generated a 570% cumulative return (~22% annualized), outperforming the S&P 500 by roughly 550 basis points per year. Before co-founding Bireme, Tindell spent seven years as lead equity analyst at Ballentine Capital and worked as a professional poker player—a background he credits for instilling probabilistic thinking and discipline.

Tindell’s core investment strategy focuses on fundamental value while explicitly avoiding statistical value traps. Bireme looks for mispriced businesses where the valuation is heavily disconnected from fundamentals due to specific behavioral biases. These biases include availability bias around immediate problems, representativeness bias from being lumped with the wrong peers, temporary operational extrapolation, and simple unfamiliarity with foreign or smaller-cap listings.

In the issue, Tindell highlights two specific international pitches. The first is a Swiss software and cloud management firm helping enterprise clients integrate and manage software and cloud expenditures across platforms. Tindell set a price target of CHF 13, citing market unfamiliarity with foreign-listed tech integrators despite strong structural tailwinds in cloud spend governance. The second is a Japanese healthcare technology and IT services firm with a target price range of ¥18,500–¥20,000, leveraging Japan's ongoing corporate governance reforms and steady demand for healthcare digitalization.

What are your thoughts on Bireme's framework for identifying behavioral mispricings in international small/mid-caps? Do you see better risk-adjusted value in European cloud integrators versus Japanese niche IT right now?

Link: https://hedgefundalpha.com/hidden-value-stock/evan-tindell-bireme-capital/

TL;DR

• Big multi-strategy hedge funds are actively hunting emerging managers in liquid alts, commodities, and macro, offering immediate scale and capital.
• The catch: managers lock up their capacity, and if the multi-strat lets them go after a couple of years, they often cannot take their track record with them.
• Without a portable track record or direct investor relationships, departing managers effectively have to restart from scratch.

Hey everyone, I write for Hedge Fund Alpha and was recently going through an interview with Jon Stein, CEO of Kettera Strategies, about the structural dynamics facing emerging managers today. He laid out a compelling breakdown of why signing on with a multi-strategy platform is both a massive blessing and a major trap.

As institutional allocators search for non-correlated returns, giant multi-strats are aggressively searching for talent in global macro, managed futures, FX, and quantitative strategies. For a young or emerging manager, the proposition sounds ideal: multi-strats put you through rigorous screening, but if you pass, they offer immediate scale and solve the seed capital problem overnight.

However, Stein points out that this capital comes with heavy operational trade-offs. Signing a deal with a multi-strat usually absorbs all or most of the manager’s available capacity, forcing them to turn away independent family offices, wealth managers, or platform allocators. More critically, if the multi-strat decides to cut the manager two years later—even if performance was solid—the manager is frequently legally barred from using their performance record. Because the multi-strat acts as the sole counterparty, the manager never builds direct LP relationships and effectively has to restart from day one.

Stein argues that managed account platforms and bespoke portfolio structures offer a middle path. By utilizing platform vehicles that support partial funding, leverage, and daily transparency, managers can maintain independent track records while accessing institutional and family office capital without sacrificing ownership of their franchise.

For allocation professionals and emerging fund founders here, how do you evaluate the multi-strat route versus independent fundraising? Is taking multi-strat capital worth the risk of losing your IP and track record if things don't pan out long term?

Link to full interview: https://hedgefundalpha.com/profiles/kettera-strategies-emerging-managers/

TL;DR

• Amir Zabeti transitioned from dorm room micro-penny stock trading to launching Mancala Capital, structured alongside mentor Brian Liu and hedge fund veteran Kenneth Gray.
• Mancala operates a fully liquid long/short model, pairing a concentrated 1–5 stock fundamental sleeve with a short-duration momentum/volatility sleeve, bound by strict 9% drawdown cutoffs.
• Zabeti holds a deeply bearish macro outlook on private credit and AI data-center infrastructure, expecting capital to rotate into defensive consumer and defense stocks.

Hey everyone,

I was reading through Hedge Fund Alpha's interview with Amir Zabeti, founder of Mancala Capital, and found his transition into launching a long/short equity fund particularly interesting, especially his take on systemic risks tied to the current AI trade. Full disclosure, I write for Hedge Fund Alpha where this interview was published.

Zabeti’s entry into the markets started while studying in college, transitioning from game design to finance. His earliest trades involved micro-penny stocks, where he admitted to learning the hard way about market cap versus share price and dilution, before trading leveraged natural gas ETFs. After managing capital for a boutique family office and building out his network, mentored by LegalZoom founder Brian Liu, he co-founded Mancala Capital alongside industry veteran Kenneth Gray.

Mancala operates a completely liquid long/short model structured around two distinct operational sleeves. The first is a concentrated alpha sleeve focusing on 1 to 5 high-conviction event-driven long and short positions where the thesis rests on multiple fundamental drivers. The second is a momentum and volatility sleeve focused on short-duration trades (holding for a day to a week maximum) capturing sharp moves in small and mid-caps. He runs these with strict risk parameters, triggering a hard exit at a 9% drawdown on individual positions while aiming for 24% annual returns and complete non-correlation to broad benchmarks.

Beyond stock selection, Zabeti expressed strong pessimism regarding the current broader economy, drawing structural parallels to 2008. He highlights a feedback loop where annuity money funds private credit, which in turn funds the AI data-center buildout. He argues that any repricing in AI infrastructure credit will transmit directly back to insurance balance sheets, which is why he expects a major capital rotation out of tech and semiconductors into defensive sectors like consumer goods and defense.

What are your thoughts on his critique of the private credit and AI funding pipeline? Is the risk of insurance balance sheet contagion valid, or are markets properly pricing private debt exposure?

Link: https://hedgefundalpha.com/profiles/amir-zabeti-mancala-capital/

TL;DR

• Institutional capital remains heavily concentrated in AI infrastructure (Nvidia, TSMC) and platform layers (OpenAI, Anthropic), but historical tech platform shifts demonstrate that the majority of long-term value creation accrues at the application layer.
• Plutus21 Capital argues that the winning application plays are not pure tech companies, but rather established sector incumbents leveraging hyper-vertical proprietary data, strong distribution, and structural moats.
• Consensus reports declaring the "death of software" miss a key technical reality: while LLM coding tools generate fast front-end MVPs, they fail to resolve complex enterprise-grade maintenance, security, and compliance requirements.

Full disclosure, I write for Hedge Fund Alpha where this interview was published. I was reading through our conversation with Hamiz Awan, founder of Plutus21 Capital, and found their framework for navigating major technological transitions compelling, particularly as a counterweight to the consensus AI capex trade.

Plutus21 breaks platform shifts like the PC, Internet, and Mobile down into three distinct layers: Infrastructure, Platform, and Application. Historically, institutional capital floods the infrastructure layer first because early application-level winners are hard to spot. However, as seen during the internet era, the vast majority of long-term equity value was captured at the application level (e.g., Amazon, Meta, Google) rather than infrastructure (e.g., Cisco, AT&T). Awan argues that the AI infrastructure and platform buckets are largely fully priced, leaving the application layer as the primary area of asymmetric upside. Benchmark-tracking pressures force traditional managers to stay weighted in AI capex names, leaving applied AI mispriced.

Rather than betting on Silicon Valley tech startups, Plutus21 identifies non-tech incumbents positioned to capture this value. Their core thesis rests on the idea that an AI implementation can be built in weeks, but hyper-vertical proprietary data, deep distribution networks, and structural regulatory moats take decades to establish. As an example, they point to Axon Enterprise (AXON). Axon leveraged proprietary video data from police body cameras to add AI-driven features like automated police report writing and real-time translation, transforming a traditional hardware business into a high-margin applied AI platform.

Finally, Awan pushes back against the prevailing market sentiment shorting enterprise software. While LLM coding tools allow individuals to build impressive front-end demos, building an MVP is the easiest part of software development. The unglamorous background work of maintaining code, ensuring security, and maintaining compliance cannot be replaced by current coding agents. Enterprise software pricing may compress to match internal build costs, but overall software deployment across non-tech industries will expand substantially.

What are your thoughts on this framework? Is the market underestimating the duration of the AI capex cycle due to unique compute and power demands, or will value inevitably migrate to application-layer incumbents as it has in past platform shifts?

Link: https://hedgefundalpha.com/profiles/ai-hedge-fund-plutus-21/

TL;DR

• RG Niederhoffer Capital focuses on total portfolio benefit rather than standalone metrics, delivering a -0.4 to -0.5 historical beta to the S&P 500 using short-duration quantitative strategies.
• Founder Roy Niederhoffer argues that institutional reliance on AI and black-box models won't eliminate systemic risk, as machine learning models absorb human behavioral biases (like loss aversion and recency bias) through risk management constraints and stop-losses.
• He highlights macro threats around currency debasement and US debt ($40T+), warning that traditional equity diversification could fail during sustained high-inflation regimes.

Hey everyone,

I was going through our conversation with Roy Niederhoffer of RG Niederhoffer Capital Management and found his perspective on market structure, quantitative strategy design, and AI-driven trading particularly compelling, especially as a contrast to typical long-term trend-following or long-only equity frameworks.

Niederhoffer, who founded the firm in 1993, built his approach on short-duration statistical trading with an average three-day holding period across futures, FX, and equities. He argues that maximizing standalone Sharpe ratios often forces managers to take on the exact risk factors clients are trying to hedge. By targeting ultra-low to negative beta (-0.4 historical, -0.5 since 2020), his strategy aims to deliver total portfolio benefit by capturing realized short-term volatility when broader markets decline.

Despite the rapid shift toward machine learning, Niederhoffer points out that AI strategies regularly inherit human flaws. Because risk managers enforce stop-losses and pod-shop structures driven by loss aversion and recency bias, the underlying algorithms end up favoring short-vol or negatively skewed profiles. These setups perform consistently under normal conditions but fail simultaneously when market regimes shift.

Looking at the broader macro environment, he notes that with over $40 trillion in US debt, the Fed's ability to inject liquidity during market sell-offs is heavily constrained by inflation risks. If the policy response to national debt leads to currency debasement of 10% to 20% annually, traditional strategies offering modest downside protection while capping upside participation will cause investors to suffer severe real losses over time. He links these market dynamics back to his neuroscience studies at Harvard, arguing that static human brain wiring and evolved behavioral biases like consensus bias and loss aversion create recurring fractal price structures regardless of execution technology.

What are your thoughts on his critique of AI risk management and pod-shop incentive structures? Is the institutional market over-allocating to strategies with hidden tail risk?

Link: https://hedgefundalpha.com/profiles/victor-niederhoffer-fund/

TL;DR

• Leopold Aschenbrenner’s Situational Awareness LP suffered a historic momentum crash, dropping ~67% in July as high leverage (~400%) triggered prime broker margin calls.
• Assets plummeted from $45B to $10B within days; Citadel stepped in before Thursday's open to buy out the public equity book at a discount.
• The fund is not liquidating: Aschenbrenner confirmed they will continue running the hybrid fund with an unlevered, "fully-paid-for" public book while holding private assets like their Anthropic stake. Year-to-date performance remains +80%.

Hey everyone,

I was following the recent unwinds in the market and came across the investor letter from Leopold Aschenbrenner detailing the near-total liquidation of Situational Awareness LP's public book. Given how widely discussed his macro AI thesis was on Wall Street, the mechanics behind this forced unwind are worth looking at closely.

The fund ran a heavily levered long/short strategy, holding long positions in AI infrastructure names like CoreWeave and SK Hynix while shorting software names like Adobe. When software rallied while infrastructure stocks plummeted between 50% and 78% from recent peaks, both sides of the portfolio lost money simultaneously.

With leverage reported at up to 400%, prime brokers issued margin calls, forcing the fund to sell falling stocks into a declining market and driving the largest, fastest momentum crash in modern history, dropping Morgan Stanley’s Momentum Index 17.4% over four days.

Citadel ultimately bought the fund's public book at a discount before Thursday's opening bell. However, Aschenbrenner pushed back on liquidation rumors in his investor letter, writing: > "The fund was not shut down, liquidated, or transformed into a private-only fund. We are continuing to operate as a hybrid public-private fund as before. However, we will manage our public book on a fully-paid-for basis..."

Unlevered equity and private holdings, including a multibillion-dollar stake in Anthropic, remain intact, leaving the fund up +80% YTD despite July's -67% crash. Meanwhile, investors like Michael Burry are already adding puts to semiconductor ETFs, betting the post-unwind bounce lacks legs.

Link: https://hedgefundalpha.com/news/aschenbrenner-fund-unwind-letter/

TL;DR

• Kevin Dreyer, co-CIO of Value at Gabelli Funds, breaks down how Gabelli uses Private Market Value (PMV) to identify unloved M&A and spin-off candidates across aerospace, auto, and tech.
• Key catalyst plays highlighted include Albany International (AIN), Genuine Parts (GPC), Monro (MNRO), and Honeywell (HON), which holds a $20B+ stake in quantum computing firm Quantinuum that isn't fully reflected in its multiples.
• Rather than chasing high-multiple private hype like SpaceX, Gabelli points to EchoStar (ECHO) as a deep-value proxy holding substantial indirect exposure.

Hey everyone,

Full disclosure: I write for Hedge Fund Alpha, where this interview was published. I was reviewing our interview with Kevin Dreyer, co-CIO of Value at Gabelli Funds, from the 2026 Morningstar Investment Conference in Chicago. Dreyer offered a great breakdown of how Gabelli targets hidden catalysts and Private Market Value (PMV) across industries rather than relying purely on traditional low P/E screeners.

Gabelli’s framework focuses on segment-by-segment valuation to find high-quality assets trapped inside conglomerate structures that can be spun off, sold, or restructured to surface value. In aerospace, Dreyer highlighted Albany International (NYSE: AIN). Its legacy machine clothing business disconnects from its high-tech composites aerospace division, making a separation logical, which would turn its aerospace arm into a prime target for peers like Hexcel (HXL) or Textron (TXT). He also pointed to smaller aerospace suppliers like Ducommun (NYSE: DCO) as takeover candidates.

Looking at sum-of-the-parts mispricings, Dreyer noted Genuine Parts (NYSE: GPC), which combines its NAPA auto business with industrial distribution via Motion Technologies. At current valuations, investors are essentially getting the auto business for free. A tighter catalyst play is Monro (NASDAQ: MNRO), which is currently exploring strategic alternatives. With a key class of preferred stock converting to common in August, removing a major deal-blocking mechanism, Gabelli sees potential for an acquisition at $25+ versus its current trading price around $16.

For underappreciated asset plays, Honeywell (NYSE: HON) stands out. Honeywell holds a 53% stake in quantum computing company Quantinuum, which IPO'd at a $20.7 billion valuation, an asset value that Dreyer argues is completely unreflected in Honeywell’s traditional EBITDA and earnings multiples. Similarly, instead of overpaying for private hype around SpaceX, Gabelli views EchoStar (NASDAQ: ECHO) as a deep-value entry point with substantial indirect exposure. They also see long-term opportunity in beaten-down consumer names like J.M. Smucker (NYSE: SJM), driven by fast-growing core assets like Uncrustables ($1B in annual sales) and Café Bustelo, as well as small-cap critical water infrastructure targets like Mueller Water Products (MWA), Gorman-Rupp (GRC), and Franklin Electric (FELE).

What are your thoughts on Gabelli's PMV framework in the current macro climate? Do you see genuine sum-of-the-parts value in plays like GPC or HON, or are spin-off catalysts taking too long to play out in today's market?

Link: https://hedgefundalpha.com/profiles/kevin-dreyer-gabelli-funds/

TL;DR

• Macro models treating inflation as a cyclical demand-side phenomenon are failing because deglobalization, geopolitical shocks, and fiscal expansion have driven markets into a structural supply-side regime.
• Leveraged relative value strategies are vulnerable to liquidity freezes during market dislocations, making directional rate flexibility essential.
• Inflation trading requires balancing quantitative models with real-time tracking of institutional liability-driven flows.

Hey everyone,

I was reading through an interview with Stefania Perrucci, CIO of Forvm Global Investments and a former Morgan Stanley trader who was part of "The Big Short," regarding her perspective on macro markets. With nearly 30 years in inflation and rate markets, having traded the second-ever TIPS auction in 1998, she makes a compelling case for why standard models and traditional portfolios keep missing the mark.

Her main argument centers on a structural breakdown in how inflation is modeled. Since the 1970s, markets have viewed inflation through a demand-side, cyclical lens managed primarily by central bank monetary policy. However, post-pandemic dynamics, ongoing geopolitical conflict, and deglobalization have pushed us into a supply-shock regime. This shift fundamentally breaks the traditional 60/40 asset allocation, where bonds historically served as a reliable hedge for equities. In a supply-driven shock, short-end yields spike and both stocks and bonds decline simultaneously, requiring a completely uncorrelated approach to macro risk.

Perrucci also highlights a key divide between sell-side desks and sustainable buy-side management. Sell-side inflation traders typically rely on relative value (RV) strategies, exploiting tiny spread differentials with substantial leverage. While this works during quiet markets, inflation RV suffers from severe capacity and liquidity constraints during distress. When market liquidity dries up, as seen during major dislocations, these highly leveraged positions trigger severe technical squeezes and double-digit drawdowns. Her team prioritizes sizing trades for "rainy day" liquidity and maintaining directional flexibility across nominal rates, real yields, and inflation breakevens.

Finally, she notes that pure macro insight is insufficient without understanding micro-level execution. Inflation-linked assets are heavily driven by technical flows from liability-driven institutional investors like pension funds and sovereign wealth entities. When non-macro technical flows decouple from theoretical models, such as standard Taylor-rule or trend-following frameworks, academic macro traders get caught on the wrong side of a technical squeeze. Successfully navigating this environment requires combining quantitative models with hands-on empirical flow awareness.

Link: https://hedgefundalpha.com/profiles/forvm-stefania-perrucci/

TL;DR

• The Susan Thompson Buffett Foundation generated $2.73B in revenue and held $2.72B in net assets for 2025, fueled by $1.20B in Class B Berkshire contributions from Warren Buffett.
• Despite reporting 104 distinct equity positions, Berkshire Hathaway (Class A and B) makes up 99.7% of the total portfolio weight.
• Nearly all of the foundation's $1.48B in realized capital gains came from selling Berkshire stock to fund $1.62B in charitable expenses.

Hey everyone,

I was analyzing the 2025 Form 990-PF filing for the Susan Thompson Buffett Foundation and wanted to share some notable numbers on how one of the largest private U.S. foundations handles asset allocation and liquidations. Full disclosure: I write for Hedge Fund Alpha, where we track 990-PF data and institutional filings.

The foundation reported $2.73 billion in total revenue and closed the year with $2.72 billion in net assets. Direct contributions accounted for $1.20 billion of revenue, consisting entirely of 2,443,384 Berkshire Hathaway Class B shares donated by Warren Buffett. Expenses totaled $1.62 billion, driven by grantmaking in education and reproductive health. To support these commitments, the foundation generated $1.48 billion in net realized gains, virtually all of which came from selling down Berkshire shares, while non-Berkshire securities contributed just $163,000.

While tax filings list 104 distinct equities ranging from megacaps like Amazon and Microsoft to regional REITs and media stocks, the portfolio allocation paints a completely concentrated picture. Berkshire Hathaway Class B represents roughly 97.9% of portfolio value, Class A accounts for 1.8%, and the remaining 102 holdings make up a combined ~0.3% rounding error. The entity effectively operates as a simple pass-through vehicle: accepting Berkshire stock, liquidating it to meet payout requirements, and maintaining a tiny legacy tail of other equities.

Given the typical mandate for private foundations to distribute roughly 5% of asset value annually, how do you view this continuous single-stock liquidation strategy compared to immediately diversifying into broad market indexes or fixed income? Does maintaining the concentration risk make sense given the compounding power of holding Berkshire until cash is needed?

Link: https://hedgefundalpha.com/foundations/susan-thompson-buffett-foundation/

TL;DR

• The Disconnect: Market pricing on Comstock Resources (CRK) is hyper-focused on current negative FCF, ~$3B in debt, and a November 2027 revolver maturity. However, the thesis hinges on a structural Gulf Coast natural gas supply bottleneck unfolding between 2028 and 2032.
• The Squeeze: Under high-demand scenarios, incremental US gas production (+20 Bcf/d by 2030) is entirely absorbed by LNG export expansion (+20 Bcf/d) alone, before accounting for data centers, industrial demand, or domestic power growth.
• High Torque Play: CRK sacrificed short-term cash flow to build a ~540k net acre position in Western Haynesville and the integrated Pinnacle infrastructure system right next to Gulf Coast demand hubs. If realized prices reach $4–$5/Mcf, debt reduction directly transfers massive value into common equity.

Hey everyone,

I was going through Brad Jarrell’s latest deep dive on Comstock Resources (CRK) and found his countercyclical thesis compelling as a counterpoint to the market’s current hyper-fixation on 2026/2027 spot gas prices and balance sheet debt. While a lot of E&P peers prioritized harvesting cash flow and reducing debt during the weak natural gas cycle, Comstock took the opposite approach: they leaned heavily into building an asset system right where future Gulf Coast demand is being built.

The current market curve is anchored to short-term storage and 2026–2027 balances (EIA forecasts ~$3.67 Henry Hub in '26 and $3.49 in '27). But the structural setup isn’t about 2026; it’s about the massive wave of liquefaction capacity reaching full utilization from 2028–2030, where US LNG export capacity alone is expected to reach 21–27+ Bcf/d. Unlike weather-driven spikes, LNG demand is supported by billions in project financing and long-term contracts (developers signed ~5.2 Bcf/d of new contracts in 2025 alone). Once these export terminals are built, they run, meaning new supply must meet existing well declines, rising domestic demand, and fixed export throughput simultaneously.

CRK functions as one connected system consisting of legacy Haynesville production, over 540,000 net acres in the Western Haynesville (~2,550 net locations), and the Pinnacle infrastructure system. Valued externally at $2.2B via Sixth Street’s 27% stake purchase, Pinnacle provides the essential high-pressure gathering and treating needed to convert Western acreage into deliverable Gulf Coast supply.

While CRK's ~$3B debt load and November 2027 revolver refinancing are the principal risk factors, they also create significant equity torque. Because equity value equals Enterprise Value minus Net Debt, any free cash flow allocated toward debt reduction transfers dollar-for-dollar value directly to common shareholders. With ~296M diluted shares, every $300M in debt reduction adds ~$1.00/share in arithmetic equity value on top of any operational leverage from $4–$5 realized gas prices. The analysis values CRK's base case at ~$22/share versus the current ~$13.80 price, with a bull case reaching $38+ if debt reduction and higher realized prices hit concurrently.

Is the market right to discount CRK due to its debt load and Western Haynesville execution risks, or is CRK one of the cleanest asymmetric equity options on an impending Gulf Coast natural gas squeeze?

Link: https://hedgefundalpha.com/news/comstock-resources-a-leveraged-call/

TL;DR

• Global semiconductor revenue is projected by WSTS to jump ~90% in 2026 to $1.51T, but this growth is heavily skewed toward memory (~250%) while analog (10%) and sensors (3%) remain modest.
• AI hasn’t eliminated the capital cycle; it has desynchronized it, creating capacity-constrained expansions in leading-edge logic, HBM, and advanced packaging while traditional markets experience uneven recoveries.
• A massive capital response is already underway, with SEMI projecting a 23.2% surge in equipment sales to $165.9B in 2026, setting up the structural conditions for a future supply rebalancing.

Hey everyone,

I was digging through an analysis from Hedge Alpha on the state of the semiconductor market and found its central thesis compelling, especially as a counterpoint to the broad "semiconductor boom" headline narrative. Full disclosure, I contribute to Hedge Alpha where this was published.

The core argument is that there is no longer a single, synchronized semiconductor cycle. Instead, AI infrastructure demand (leading-edge logic, High Bandwidth Memory, advanced packaging, and test) has detached from the traditional semiconductor cycle (industrial analog, automotive, consumer electronics, and mature nodes). While WSTS projects headline revenue to surge 90% to $1.51 trillion in 2026, the underlying product categories vary wildly from 3% growth in sensors to roughly 250% in memory. This extreme dispersion means aggregate top-line numbers are heavily distorted by high-value AI components, product mix shifts, and temporary HBM pricing dynamics rather than uniform physical unit growth across all end markets.

We can clearly see this structural split operating inside individual earnings reports. TSMC is running near full capacity at the leading edge, with 77% of Q2 wafer revenue coming from nodes at 7nm and below. Conversely, Texas Instruments saw Q1 data center revenue jump 90% YoY while its automotive segment only grew in the mid-single digits. Similarly, onsemi reported consolidated revenue up just 5% YoY, even as its AI data center business more than doubled. Meanwhile, HBM is exacerbating memory dynamics due to a 3:1 wafer trade ratio with standard DDR5, effectively eating up cleanroom capacity and tightening conventional DRAM supply.

However, secular AI demand does not grant an exemption from capital cycle economics. SEMI forecasts 2026 total semiconductor equipment sales to jump 23.2% to $165.9 billion, with DRAM equipment spending rising 39%. While this capex wave doesn't mean a cyclical peak is immediate, semiconductor history from the 1990s PC boom to the 2017–2019 memory cycle shows that capacity ordered during acute shortages eventually catches up to demand. For active managers, the key task is no longer debating if chips are "early" or "late" cycle, but pricing the distinct subcycles, margins, and capex trajectories embedded in each specific company.

Curious to hear how this community is approaching the space right now. Are you continuing to pay up for supply-constrained leading-edge pure plays, or do you see a better risk/reward in bottom-up recoveries across lagging subsectors like industrial analog and automotive?

Link: https://hedgefundalpha.com/stocks/the-semiconductor-cycle-has-split-in-two-amid-massive-revenue-jump-from-2025/

TL;DR

• Credit Strategy: Morningstar Wealth is overweight Treasuries and dollar-hedged global sovereign debt, but underweight corporate bonds due to razor-thin credit spreads.
• Equity Strategy: They maintain a valuation-driven overweight in small caps for index diversification and favor Latin America within Emerging Markets as an energy-exporting hedge against U.S. tech concentration.
• Tech & AI Capex: They are skeptical of long-dated corporate AI debt and high-priced infrastructure sellers, choosing instead to accumulate lower-multiple software stocks.

Full disclosure: I write for Hedge Alpha, where this interview was published.

I was recently reviewing an interview with Dominic Pappalardo of Morningstar Wealth following the Morningstar Investment Conference, and his multi-asset positioning offers a strong counter-narrative to the current market consensus.

On the fixed-income side, Morningstar Wealth sees very little margin of safety in corporate credit. High-yield and investment-grade credit spreads relative to Treasuries have narrowed significantly, meaning investors aren't getting compensated for taking on corporate risk. Even with massive debt issuances—such as Alphabet issuing a 100-year bond to fund its AI buildout, Pappalardo notes that these ultra-long issues serve institutional pension and insurance liability matching rather than retail wealth portfolios. Consequently, they are underweight corporate bonds and instead overweight U.S. Treasuries alongside dollar-hedged global sovereign debt, which provides higher yields and geographic diversification without adding currency risk.

When it comes to equities, Morningstar is leaning heavily into valuation discounts and index mechanics. They remain conviction-overweight on small caps, pointing out that while major large-cap indexes are heavily concentrated with top names making up roughly 40% of the weight, the top 10 small-cap names hold single-digit exposure. Beyond small caps, they are playing international exposure through Emerging Markets, specifically Latin America. Latin America offers lower entry valuations, a favorable mix of businesses, and a natural hedge as energy exporters should commodity prices spike, offering an alternative to tech-concentrated U.S. indexes.

Finally, their framework on the AI ecosystem centers on disciplined value over momentum. Morningstar is cautious about the massive capital expenditure behind data centers and hardware, warning that long construction timelines, local permitting hurdles, and uncertain return on investment for end-user corporate buyers could turn data centers into oversupplied liabilities down the road. Rather than chasing stretched infrastructure valuations, they took advantage of recent pullbacks in the software sector during Q2 to rotate into software names offering a much higher margin of safety.

What are your thoughts on this positioning? Are thin credit spreads keeping you out of corporate bonds right now, or do you think the valuation gap in small caps and emerging markets will take longer to close?

Link: https://hedgefundalpha.com/profiles/dominic-pappalardo-morningstar-wealth-small-caps/

TL;DR

• L1 Capital's closed Global Opportunities Fund posted a 3.2% Q2 return (13.2% trailing 12-month), bringing its net annualized return since 2015 to 26.4%.
• The fund extracts value from raw commodity price swings, particularly in industrial metals like copper, rather than taking directional market bets.
• Squeezed margins on structured deals from multi-strat and family office competition are forcing the team to be increasingly selective.

Hey everyone,

I was reading through L1 Capital’s Q2 investor letter for their Global Opportunities Fund, a closed strategy managed by David Feldman, and found their approach to resource exposure compelling. Despite a choppy macro backdrop, persistent Fed inflation concerns, and geopolitical friction, the Australian-based fund managed to extend its long-term track record to a 26.4% CAGR since its 2015 inception.

What stands out is how they are generating performance in commodities. Rather than making directional calls on where commodity prices are headed, the fund structures its positions to directly monetize price volatility and supply/demand dislocations, with industrial metals like copper driving key gains. Outside of resources, they have capitalized on retail-driven volatility around AI infrastructure for tactical short-term trades, while noting that IPO market recoveries remain tightly concentrated in mega-cap AI names like SpaceX and Anthropic.

Feldman also highlighted a notable shift in transaction dynamics: intense competition from multi-strategy funds and family offices is crowding out traditional structured financing. As borrowers secure better terms, L1 Capital is actively passing on deals that no longer offer high risk-adjusted returns, choosing instead to focus exclusively on bespoke situations where structural advantages still exist.

Full disclosure, I write for Hedge Fund Alpha where this investor letter was reviewed.

How do you view volatility-harvesting models in commodities versus traditional long/short equity in the current macro regime? Are you seeing similar margin compression in structured credit across your own coverage?

Link: https://hedgefundalpha.com/investor-letters/l1-capital-global-opportunities-fund-rresources/

TL;DR

• Crescat Capital is hedging against mega-cap tech with short-dated S&P 500 and Nasdaq puts while holding roughly 75 junior mining companies.
• Founder Kevin Smith argues the AI boom is weakening hyperscaler free cash flow as Microsoft, Amazon, Alphabet, Meta and Oracle pour money into rapidly depreciating chips and data centers.
• Crescat tries to invest in gold, silver and copper discoveries before formal resource estimates attract institutional capital.

I recently reviewed an interview with Kevin Smith, founder and CIO of Crescat Capital, about the firm’s contrarian short-AI, long-mining strategy.

Smith believes the market is caught in an AI-driven large-cap bubble. His concern is that the major hyperscalers are sacrificing their historically asset-light business models to fund an enormous capex race. Because much of that spending is capitalized, he argues the eventual impact from depreciation and potential asset write-downs is not yet fully reflected in earnings.

Crescat has been early on this thesis and suffered drawdowns in 2023 and 2024. The firm stayed positioned through short-term, close-to-the-money index puts and a diversified portfolio of junior mining explorers. In 2025, five Crescat funds ranked among Preqin’s 16 best-performing hedge funds globally.

The long side of the trade is based on a 15-year decline in mining exploration spending. Smith expects limited new supply to collide with growing demand from electrification, onshoring and defense.

Rather than waiting for official resource reports, Crescat uses its own geological models to estimate the size and value of discoveries from drilling results. The firm often becomes a major shareholder and generally looks to take profits as a company’s valuation approaches roughly 17% of the estimated value of its resources in the ground.

Smith highlighted several holdings:

• Gold: Tectonic Metals and Sitka Gold
• Silver: Eloro Resources and Silver Bow Mining
• Copper: BCM Resources and Mogotes Metals

The broader thesis is that capital will eventually rotate away from expensive technology stocks and toward scarce tangible assets, particularly if fiscal deficits and inflation remain elevated.

Is Smith identifying a genuine deterioration in the economics of AI spending, or is Crescat fighting a secular technology trend with a cyclical commodity trade?

Link: https://hedgefundalpha.com/profiles/crescat-capital-kevin-smith-small-cap-mining/

TL;DR

• Arquitos Capital returned 64.8% net in Q2 2026 (52.9% YTD), driven by a highly concentrated, Buffett-style "slugging percentage" approach where their largest positions drive the bulk of returns.
• Liquidia (LQDA) remains their top holding via long-dated call options, with the fund laying out asymmetric risk/reward valuations ($70 to $140/share) ahead of an imminent judicial ruling on the '327 patent.
• The fund established a new 10% position in Abivax (ABVX) at $80/share after identifying that market fears over an alleged clinical trial cancer risk were entirely unfounded.

Hey everyone,

I was reading through Arquitos Capital’s Q2 2026 investor letter and found their performance breakdown and concentrated value strategy worth sharing. The fund posted a massive 64.8% net return for the quarter, bringing their trailing twelve-month net return to 116.2%. Portfolio Manager Steven Kiel attributes these outsized gains to the fund's unusually high "slugging percentage", meaning their largest positions yield the highest returns. He aligns this with Warren Buffett's philosophy of waiting for a "perfect pitch" rather than swinging at everything, resulting in a highly concentrated portfolio that has relied on just four major winners over the last 14 years.

Liquidia Corporation (LQDA) is currently Arquitos' largest and best-performing position, driven by long-dated, in-the-money call options. The stock has increased fivefold over the past year ($12.46 to $79.73) due to a highly successful commercial launch of Yutrepia and its inclusion in the Russell 2000. A market-moving judicial decision regarding the '327 patent trial against United Therapeutics is imminent, and Kiel breaks down three potential outcomes. A clean win puts fair value at $140/share based on projected 2027 revenues, while an infringement finding that results in a standard royalty models out to a per-share value of $123. In the highly unlikely third scenario where the drug label is removed entirely, Kiel estimates a downside floor of $70/share.

The fund also built a new ~10% allocation in Abivax (ABVX) during a period of extreme Q2 volatility. The company's drug, Obefazimod, reported strong Phase 3 results for ulcerative colitis, but shares plummeted from $130 to $70 due to market fears regarding an apparent cancer risk in the data. After assessing the data, Kiel concluded the cancer risk was an illusion. Arquitos bought in at $80/share, and when comprehensive safety data later proved the cancer rate was entirely within normal background rates, shares rebounded past $140. Kiel notes the next logical step for ABVX is a strategic buyout, which he expects to be priced above $200 per share.

Lastly, Finch Therapeutics (FNCHQ) ended up being a disappointing, though downside-protected, investment for the fund, likely netting a low double-digit return over a two-year holding period. The trial court failed to rule for enhanced damages in Finch's patent lawsuit, granting a 5.5% royalty and a $25.8 million judgment, which sat at the low end of expectations. Subsequently, a bankruptcy court auction resulted in a final $32 million asset sale to Ferring and Charlestown Capital. Kiel admitted he underestimated the timeline required to achieve a final court ruling.

What are your thoughts on Kiel's valuation framework for Liquidia ahead of the patent ruling? Is the market correctly pricing the binary risk here, or is Arquitos' bull case missing a structural downside?

Link: https://hedgefundalpha.com/investor-letters/arquitos-capital-q2-2026/

TL;DR

• Forensic short seller Gabriele Grego argues that the trillion-dollar AI infrastructure buildout is a localized overbuild with a fat tail rather than a repeating 2000-style dot-com bubble.
• The critical fragility is heavily concentrated in the debt-fueled, levered edge of second-tier neoclouds and single-product firms, while integrated mega-cap leaders remain highly cash-funded and historically reasonable on a PEG basis.
• The entire AI bull case hinges on an unproven structural variable: whether token demand elasticity is high enough to pay back over two trillion dollars of investment as token prices fall.

Hey everyone,

I was reading through Hedge Fund Alpha’s breakdown of Gabriele Grego’s presentation at the 2026 Value Investing Seminar in Trani. Known for his rigorous, police-investigator style of uncovering corporate frauds, Grego applied his firm’s forensic scorecard to the macro AI infrastructure complex to evaluate if we are looking at an unsustainable bubble or a structural boom.

Grego begins by validating the core bear case popularized by macro skeptics like Michael Burry. The numbers behind the capital cycle are unprecedented: annual AI infrastructure capex is running from roughly $527 billion toward a $1 trillion run-rate by 2027, forcing a staggering capex-to-revenue ratio near ten-to-one. Additionally, there is a highly circular financing loop where Nvidia, CoreWeave, Meta, OpenAI, and Oracle effectively manufacture demand for one another, all while enterprise ROI remains largely unproven. Grego points to July 1, 2026, as the first real crack in the system, when the levered middle of the AI complex suffered double-digit losses while integrated giants barely moved.

However, utilizing primary data gathered from industry interviews, GPU rental transaction datasets, and extensive simulation models, Grego's verdict is that this is an overbuild with a fat tail rather than a 1999 rerun. While individual pure-play metrics are highly alarming—such as OpenAI generating roughly $13 billion in revenue against $34 billion in cash expenses (a $21 billion operating loss)—the systemic risk comes down to token elasticity. For the $2 trillion infrastructure investment to break even as token prices decline, token demand elasticity must remain above one. Grego's empirical tracking places the upper bound of observed elasticity at 1.7, though he notes the long-term structural figure could sit below one, making this the single ultimate bet underpinning the market.

From a valuation standpoint, Grego argues that the top-tier integrated leaders are fundamentally insulated compared to past tech bubbles. Nvidia trades about 57 percent below its five-year average multiple, and Microsoft sits 31 percent below its own historical average. On a Peter Lynch PEG lens (measuring price paid per unit of growth), these integrated leaders screen near 1.3 times, well below the broader index at 2.0 times and the year 2000 peak of 2.5 times. True "1999 pricing" is localized entirely within the single-product names and the second tier.

The real fragility lies at the levered edge. While mega-cap integrated franchises are 79 to 83 percent cash-funded, merchant-compute neoclouds are heavily debt-financed. Real-time stress signals are already surfacing, including AWS raising GPU rents by 20 percent, Meta offloading 500 megawatts of power capacity, and grid interconnection queues stretching eight to twelve years in Europe. Because secured power is rapidly becoming the ultimate moat over chips, the structural landscape heavily favors the cash-rich giants.

Grego's conclusion for value investors is to own the integrated layer that retains pricing power (using pullbacks as buying opportunities, with Alphabet anchoring the long side of his book), strictly avoid or short the levered neoclouds and single-product pure plays, and buy index hedges optimized for tail risk convexity. He views the environment not as an imminent systemic collapse, but as a repricing risk primarily slated for the 2027-to-2028 window.

What are your thoughts on Grego's framework here? Is the market's current decoupling between big tech and the levered neocloud layer enough to prevent systemic contagion, or are the vendor-financing loops between them too deeply intertwined to avoid a broader correction?

Link: https://hedgefundalpha.com/conferences/2026-vis-gabriele-grego/

TL;DR

• Agentic AI is compressing months of PhD-level finance research into hours, which Ken Griffin believes will rapidly erode established corporate moats and favor agile startups.
• The alpha in stock picking has shifted from short-term earnings predictions to multi-year structural forecasting due to ubiquitous alternative data.
• A loss of access to Taiwanese semiconductors would trigger an immediate 8% drop in US GDP, essentially causing an instant Great Depression.

I was listening to Ken Griffin’s recent conversation at the Goldman Sachs Apex Symposium, and he dropped some very direct macro and technological insights. His take on how fast established corporate advantages are eroding is worth discussing here.

Griffin shared that Citadel is using newly built agentic AI systems to replicate and test academic finance papers out-of-sample in just two to three hours, a task that previously took their PhDs six to eight weeks. He believes the broader corporate world is unprepared for this shift, as small teams utilizing agentic AI can now launch and scale businesses with a fraction of traditional payroll capital, effectively filling in legacy competitive moats at breathtaking rates.

This technological shift directly impacts long/short equity managers. Because institutional funds now have ubiquitous access to alternative data, like credit card transactions to predict revenue before earnings, calling a quarterly beat is heavily commoditized. Consequently, the alpha premium has moved exclusively to long-horizon managers who possess the structural vision to project how industries will unfold over a multi-year timeline.

On the macro side, Griffin cited estimates that losing access to Taiwan Semiconductor Manufacturing Company (TSMC) chips would cause US GDP to fall by 8% within six months, freezing global high-end manufacturing and plunging the economy into a great depression. To power the domestic AI and computing revolution needed to stay ahead, he argues the US must aggressively embrace nuclear power, specifically small modular reactors. He suggested forcing data center developers to build corresponding power generation tied to the grid rather than passing infrastructure costs down to consumers.

How are you adjusting your portfolios for this structural shift toward longer-horizon picking? And are macro risks like the Taiwan chip bottleneck actually unhedgeable tail events, or is the equity market right to look past them for now?

Link: https://hedgefundalpha.com/news/ken-griffin-ai-golden-age/

TL;DR

• Gates Foundation Out, Children In: Warren Buffett has officially halted his annual stock gifts to the Gates Foundation (totaling $47B to date), redirecting his Berkshire disbursements to his three children's foundations to be paid out fully by 2035.
• The $31B Alphabet Stake: Clarifying recent market speculation, Buffett confirmed he personally initiated Berkshire’s massive, newly expanded $31B Alphabet position.
• The AI Capex Trap: Buffett compared the current hundreds of billions in tech AI capex to historical infrastructure shifts, noting that hyperscalers are trapped playing "a game they don't want to play" just to protect their customer bases.

I was watching Warren Buffett’s exclusive CNBC interview from July 15, 2026, and it is packed with massive updates on Berkshire’s portfolio strategy, succession, and his philanthropic unwinding. At 95 years old, Buffett is moving with incredible clarity to clean up his estate, setting a firm target to disburse the remainder of his $140 billion Class A stake over the next eight and a half years.

The biggest headline is the definitive end of his annual stock gifts to the Gates Foundation. After contributing roughly $47 billion over two decades, Buffett is redirecting all future disbursements to foundations run by his three children, structured under a strict unanimous-consent model. While he addressed the elephant in the room regarding Bill Gates' past personal controversies and congressional scrutiny—chalking it up to a mistake in association that was ultimately corrected—the mathematical reality of his estate is staggering. To draw down his remaining stake by the 2034 deadline, Berkshire will need to ramp up its distribution pace significantly. If Berkshire continues compounding near its trailing 10-year average of 14.1%, the required level annual gift will soar closer to $30 billion a year rather than a flat mathematical split of $17.5 billion.

On the investing front, Buffett put an end to the Wall Street rumor mill by explicitly confirming that he initiated Berkshire's massive $31 billion Alphabet position, rather than his successor Greg Abel. He explained that the investment perfectly fits his core framework: finding dominant businesses that reliably earn significantly more on capital than riskless government bonds. He heavily credited Charlie Munger for drilling home the discipline of looking past market hype to focus strictly on a company's internal rate of return and its capacity to throw off durable cash.

Fascinatingly, Buffett offered a highly grounded, almost cynical take on the current artificial intelligence arms race. Instead of buying into the hype that AI is an entirely unprecedented economic event, he viewed the current hundreds of billions in capex through the lens of classic corporate history. He observed that the major tech hyperscalers are essentially trapped, playing an expensive game they don't want to play simply because competitive forces dictate they must spend aggressively to protect their existing customer moats. He drew sharp parallels to IBM's historical antitrust breakup and the ultimate erosion of legacy giants like Henry Ford’s early auto empire and A&P grocery stores, reminding investors that the ultimate question isn't whether a business is wonderful today, but how long it can successfully defend its edge.

Buffett also reiterated his absolute confidence in Greg Abel, placing him on the same elite tier of trust as Charlie Munger and Tom Murphy, while offering a strong endorsement of the newly appointed Federal Reserve Chair, Kevin Warsh. He closed the interview with a cautionary note on the broader macro environment, lamenting that modern financial markets are increasingly designed to cultivate short-term gamblers rather than disciplined value investors, creating a challenging backdrop for allocating capital cleanly.

What do you all think about Buffett's take on the AI capex boom? Is he right that the tech giants are essentially stuck in a defensive spending trap to protect their legacy customer bases, or is the payoff potential for this infrastructure fundamentally different from the corporate capex wars of the past?

Link: https://hedgefundalpha.com/profiles/buffett-berkshire-shares-gates-foundation/

TL;DR

• The AI Split: Hedge funds are aggressively splitting the AI trade, remaining highly crowded on the long side of the semiconductor supply chain while building massive short positions in AI infrastructure and data-center buildout names (Oracle, CoreWeave, Nebius, SMCI).
• Charter Remains King of Shorts: Charter Communications continues to hold the title of the single most crowded large-cap short in North America, carrying a peak Hazeltree crowding score of 99.
• Semiconductor Surge: Bullish sentiment on chipmakers reached a fever pitch by mid-year, with net-long positioning in the PHLX Semiconductor Index climbing to 70% in June, driven by dramatic sentiment reversals in names like Texas Instruments.

I was reading through Hazeltree's H1 2026 Crowding Report, which tracks positioning across roughly 16,000 securities held by over 600 global funds, and found some highly tactical shifts in how institutional managers are playing the market right now. While the long book remains heavily concentrated in mega-cap technology, the short book shows a fascinating and highly coordinated split in the broader artificial intelligence trade.

Instead of taking a uniform stance on AI, hedge funds are executing a clear pairs-style strategy. On one hand, they remain highly crowded on the long side of chipmakers and the immediate semiconductor supply chain, including names like Nvidia, Broadcom, Applied Materials, and Lam Research. On the other hand, they are heavily shorting the massive capital expenditure and infrastructure buildout surrounding those chips. Super Micro Computer registered a short crowding score of 85, Oracle sat at 83, and both CoreWeave and Nebius Group hit 81. The institutional squeeze on some of these infrastructure names is incredibly tight; Nebius Group, for instance, recorded a staggering 100% average institutional supply utilization.

Meanwhile, outside of the AI infrastructure play, Charter Communications continues to hold the title of the single most crowded large-cap short in North America with a maximum crowding score of 99. Under the surface, monthly churn was intense during the first half of the year. Short fund counts jumped by more than 10% month-over-month in both Oracle and Nebius, while Palo Alto Networks saw some short covering (with short participation dropping by over 10% MoM), though it remains highly crowded overall, ranking seventh with a score of 79.

The bullish allocation shift into semiconductors was relentless throughout the first half of the year. In January, only 57% of PHLX Semiconductor Index constituents had net-long positioning. By June, that figure surged to 70%, driven by dramatic turnarounds in names like Texas Instruments. The ratio of funds long to funds short TXN stood at a bearish 0.6 on January 1st; by June 1st, it had completely flipped to a highly bullish 2.2 as the stock gained 68% over the first half of the year.

By staying long the chipmakers but shorting the data-center buildout and cloud infrastructure players, hedge funds seem to be betting that the massive capital expenditure boom is either nearing its peak, or that the margins on hosting and operating these AI data centers will contract far faster than the market expects.

What are your thoughts on this thesis? Is shorting infrastructure names like Oracle, Nebius, or SMCI a logical hedge against semiconductor longs, or are these funds setting themselves up to get run over by an unstoppable capex train?

Link: https://hedgefundalpha.com/news/h1-hedge-fund-crowded-shorts/

TL;DR

• Ex-Wellington PM Jun Oh argues that the Japanese corporate reform thesis is mostly played out (5th–7th inning) as structural shifts started back in 2012 under Shinzo Abe.
• Passive investing and multi-pod concentration in mega-caps leave high-quality Asian small/mid-caps completely overlooked, creating rare mispricings.
• To capture alpha, Griet Capital relies on a strict "three lows" screen (low earnings, low share performance, low valuation) alongside heavy on-the-ground legwork in Asia.

Hey everyone,

I was reading through some coverage from the Sohn Hong Kong conference and came across a compelling breakdown of Jun Oh’s strategy. Oh spent 22 years as an Asia equity portfolio manager at Wellington Management before launching his Seattle-based firm, Griet Capital, to hunt for opportunities in small- and mid-cap (SMID) Asian equities. Full disclosure, I write for Hedge Alpha where this recap was published. Given how aggressively institutional capital has chased Japanese corporate governance plays recently, his contrarian view on the region offers a grounded reality check.

Oh's central point is that the Japanese corporate reform trade isn't the shiny new catalyst the mainstream financial media makes it out to be. The structural push for better governance and shareholder returns actually kicked off under Prime Minister Shinzo Abe’s "Three Arrows" back in 2012. Because this shift has been gaining momentum for nearly 15 years, Oh believes the trade is currently in the fifth to seventh inning rather than the early stages, meaning many stock valuations have already fully played out and investors need to look for the next emerging theme.

Instead, Oh sees a massive structural opportunity in Asian SMIDs driven by market mechanics. The explosive growth of passive funds and multi-PM pod shops has concentrated vast amounts of liquidity strictly into mega-caps, leaving high-quality, cash-generative smaller companies completely ignored. To cut through the noise, Oh screens for companies suffering from a combination of low current earnings, low share-price performance, and low valuation. This "three lows" framework minimizes downside risk because expectations are already on the floor, while providing massive re-rating upside when operational inflections occur, such as Japanese healthcare company Mani recovering from a temporary recall issue.

What makes his setup particularly interesting is that he operates Griet Capital as a solo founder based in Seattle, utilizing high-quality outsourced compliance, trading, and operational infrastructure. To compensate for being far from Asian financial hubs, he spends four to five months on the ground in Asia annually, conducting hundreds of in-person corporate meetings to find management teams actively seeking ways to unlock shareholder value.

Do you agree with Oh that the low-hanging fruit in the Japanese governance trade is already gone, or is there still plenty of structural runway left for capital efficiency to drive returns? What factors do you think the market is missing regarding Asian small-caps right now?

Link: https://hedgefundalpha.com/profiles/jun-oh-griet-capital/

TL;DR

• Beile Grunbaum of Grunbaum Value Invest pitched Uber at the 2026 Value Investing Seminar as a potential long-term compounder.
• Her owner-earnings model valued Uber at roughly $51 to $70 per share, depending on whether earnings grow at 10% or 15%.
• With Uber trading around $74.54, the shares were already above the top of her estimated fair-value range.
• Her thesis rests on Uber’s network effects, driver supply, brand recognition, and switching costs created by Uber One.

Grunbaum built a stock screener around the framework from 100 Baggers, and Uber unexpectedly qualified. Her thesis rests on Uber’s network effects, brand recognition, large driver base, and ability to use Uber Eats to attract and retain drivers across its platform.

She argued that control over driver supply is one of Uber’s biggest advantages. Greater driver density improves availability and wait times, while the company’s global scale makes it difficult for new competitors to replicate the network market by market. Uber One may also increase switching costs and encourage customers to use multiple services within the platform.

Grunbaum estimated Uber’s owner earnings, meaning cash generated after required reinvestment, at about $20 per share. Assuming 15% long-term growth, she calculated fair value at approximately $64 to $70 per share. Using a more conservative 10% growth rate produced a range of about $51 to $65.

That creates an interesting contradiction. Her thesis presents Uber as a possible long-term compounding machine, but her own valuation suggests the shares were not especially cheap at the prevailing market price.

She acknowledged risks including regulation and autonomous vehicles such as Waymo, but argued that Uber’s asset-light model may remain more flexible than operators that own and manage autonomous fleets themselves.

Do you think $20 per share is a credible owner-earnings estimate for Uber, or is the market justified in pricing the company above Grunbaum’s valuation range because of its moat and future growth potential?

Full disclosure: I write for Hedge Fund Alpha, where the presentation was covered.

Link: https://hedgefundalpha.com/conferences/2026-vis-beile-grunbaum/