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Harvard University May Have Lost $150 Million in Cryptocurrency Trading! Has Liquidated Ethereum and Significantly Reduced Bitcoin ETF Holdings

Harvard University's endowment fund, Harvard Management Company (HMC), significantly reduced its cryptocurrency holdings in Q1 2026, reportedly incurring substantial losses. According to its latest 13F filing, HMC completely sold off its position in the BlackRock Ethereum ETF (ETHA) and cut its BlackRock Bitcoin ETF (IBIT) holdings by 43%, leaving a position worth approximately $117 million. This marks a sharp decline from a peak public crypto allocation of $443 million just two quarters prior. Analysis suggests these trades resulted in estimated losses exceeding $150 million, with Bitcoin positions sold at an average loss of around 28% and Ethereum positions at roughly 35%. The moves have sparked debate on whether HMC engaged in counterproductive "buy high, sell low" behavior. The article contextualizes HMC's crypto journey, beginning with its initial disclosed investment in IBIT and gold ETF GLD in Q2 2025 as an "inflation hedge." Aggressive buying in Q3 2025 made IBIT its largest single public holding at 20% of the portfolio, coinciding with Bitcoin nearing all-time highs. Subsequent trimming began in Q4 2025, with an initial foray into ETHA. Explanations for the recent drastic cuts extend beyond market timing. Harvard faces significant financial pressure, including an annual operating deficit and a major increase in endowment tax rates. With illiquid assets like private equity dominating the portfolio, the highly liquid crypto ETFs became the most practical source for necessary portfolio rebalancing and liquidity. Furthermore, the impending retirement of HMC's CEO adds a layer of reputational risk to holding volatile assets. The article contrasts Harvard's retreat with other institutions, such as Mubadala's continued accumulation of Bitcoin ETFs and Dartmouth's expansion into staking-oriented crypto products. It concludes that HMC's actions reflect a complex interplay of fiscal needs, risk management, and institutional constraints rather than simple speculative trading, highlighting how traditional finance logic applies to crypto within large endowment portfolios.

链捕手05/18 11:44

Harvard University May Have Lost $150 Million in Cryptocurrency Trading! Has Liquidated Ethereum and Significantly Reduced Bitcoin ETF Holdings

链捕手05/18 11:44

WSJ: Unveiling the Secret Jury That Controls Disputes on Polymarket

Last month, Garrick Wilhelm lost a $567 bet on the Polymarket prediction platform about whether a ceasefire would be reached with Hezbollah. When a truce was announced, some traders argued it counted, but Wilhelm disagreed. The dispute was settled not by Polymarket, but by a decentralized group of UMA token holders who vote on such disagreements. As trading surges, resolving ambiguous outcomes is a growing challenge for prediction markets. Unlike competitors like Kalshi that decide internally, Polymarket outsources dispute resolution to UMA. Its token holders, mostly anonymous and with voting power weighted by holdings, arbitrate cases. Critics argue this system is prone to manipulation, as voters can also bet on the same markets they judge. A Wall Street Journal analysis found that over the past year, at least 60% of active UMA voters had corresponding Polymarket accounts and held positions in disputes they voted on. Voting power is also concentrated among a few large holders. Polymarket says only 0.2% of bets go to UMA and that the system disperses authority. Its founder has acknowledged flaws and promised fixes. UMA's backers deny any proven manipulation, dismissing critics as sore losers. The platform penalizes voters in the minority to incentivize "correct" outcomes. Disputes are rising, covering topics from a streamer's pregnancy announcement to Iran. This model also helps Polymarket argue it's an offshore platform outside U.S. regulation, a shift made after a 2022 settlement with the CFTC. Some losing traders have formed groups to protest, targeting entities like UMA.rocks, which aggregates votes. Its founder says traders often blame UMA for their own mistakes. A recently ousted committee member, Scout, admitted to both betting and voting but argued involved voters research more thoroughly. He highlighted the dilemma: "Either you have conflicted traders deciding, or you have uninformed outsiders voting. There is no perfect answer right now."

marsbit05/18 11:07

WSJ: Unveiling the Secret Jury That Controls Disputes on Polymarket

marsbit05/18 11:07

Bitcoin Social Euphoria Hits Yearly High Amid CLARITY Act Buzz

Bitcoin social sentiment has surged to a yearly high following the U.S. Senate Banking Committee's bipartisan approval of the CLARITY Act. Data from Santiment shows a ratio of 1.55 bullish comments for every 1 bearish comment, indicating a "FOMO zone" similar to a peak in late April. Analysts note such high optimism often signals a short-term profit-taking opportunity, contrasting with the better contrarian buy signal seen during recent "FUD zone" pessimism. Despite near-term caution, the CLARITY Act is viewed as a long-term positive, aiming to establish a clearer federal regulatory framework for digital assets by defining roles for the SEC and CFTC. The bill, supported by major crypto firms, now awaits a full Senate vote, with a critical window for passage before the August recess.

bitcoinist05/18 11:02

Bitcoin Social Euphoria Hits Yearly High Amid CLARITY Act Buzz

bitcoinist05/18 11:02

China's AI Circle Has Just Established a Pecking Order, and Capital Is Already Changing the Rules Again

The article describes how the valuation logic for major Chinese AI model companies has undergone three dramatic shifts between 2022 and 2026, driven by capital's changing priorities. The first phase (around 2022) was **technology-driven valuation**, where funding was based on model performance and benchmark scores. This logic was disrupted when DeepSeek's R1 model demonstrated that comparable capabilities could be achieved at a fraction of the cost, challenging the notion of technical superiority as an unassailable moat. The second phase shifted to **IPO window-driven valuation**. Following favorable listing conditions in Hong Kong, capital flowed to companies like Zhipu and MiniMax with the clearest path to a public listing. However, this focus on liquidity over fundamentals became apparent as their Annual Recurring Revenue (ARR) lagged far behind international peers like Anthropic. The third and current phase is **national strategy-driven valuation**. This shift was marked by the state-backed "Big Fund" leading a major investment in DeepSeek, signaling that leading domestic AI models are now viewed as strategic national assets comparable to semiconductor manufacturing. This new logic, combined with soaring US valuation benchmarks (e.g., OpenAI at $850B), propelled the combined valuation of China's top AI firms ("The Four Dragons"/"Five Strong") past 1 trillion RMB. The article presents a "pricing leap model": each shift is triggered by a key event that invalidates the old logic, leading to rapid capital reallocation under a new narrative before its flaws (particularly the gap in fundamental ARR metrics) become evident. It concludes that the next major test for these valuations will be a return to scrutinizing core business fundamentals, specifically ARR growth, suggesting a fourth pricing shift is imminent.

marsbit05/18 10:42

China's AI Circle Has Just Established a Pecking Order, and Capital Is Already Changing the Rules Again

marsbit05/18 10:42

'Stock God' Trump's 3,642 Trades Disclosed: The 'Perfect Closed Loop' of Policy and Portfolio

Summary: Donald Trump's First Quarter stock trades, totaling 3,642 transactions, have been disclosed. While the White House maintains the trades were managed by an advisor and complied with disclosure laws, they reveal a portfolio heavily aligned with his policy agenda. The trades show a rotation away from major tech stocks like Microsoft, Amazon, and Meta, and into semiconductor and AI hardware companies such as NVIDIA, AMD, Broadcom, Dell, and Intel. Notably, Trump's account purchased Dell stock before he publicly praised the company, after which its stock rose. The Dell family also pledged funds to a Trump-affiliated policy project. A critical case is Intel. The Trump administration converted $8.9 billion in CHIPS Act subsidies into a 9.9% equity stake, making the U.S. government Intel's largest shareholder. Months later, Trump's personal account also bought Intel stock. This intertwines national industrial policy with potential personal financial interest. Unlike typical insider trading concerns, this situation creates a "closed loop": policy decisions (e.g., subsidies, tariffs, crypto regulation) can boost the value of his holdings, and those holdings may, in turn, influence future policy directions. This blending of presidential power and personal portfolio, while legally disclosed, raises profound questions about conflicts of interest that current rules do not address.

marsbit05/18 10:26

'Stock God' Trump's 3,642 Trades Disclosed: The 'Perfect Closed Loop' of Policy and Portfolio

marsbit05/18 10:26

Dialogue with Figure Robotics Founder: Behind the $39 Billion Valuation Lies Ambition to Mass-Produce Millions of Units

Title: Figure's Founder on the $39B Valuation and the Ambition to Mass Produce a Million Humanoid Robots In a Sourcery podcast interview, Figure founder and CEO Brett Adcock discusses the rapid rise of his humanoid robotics company. With a valuation that surged 15x in 18 months to $39 billion, Figure aims to create general-purpose humanoid robots for work in factories and homes. Adcock states that the company's primary goal is to make robots that perform real, paid work autonomously. He shares Figure's aggressive scaling plan: producing thousands of robots this year, with an ultimate ambition to reach one million units annually. Adcock explains Figure's vertically integrated strategy, designing its own motors, sensors, and joints to control its supply chain and destiny. He details the challenges, including achieving long-term, reliable, end-to-end autonomous operation—a feat no one has yet accomplished. The biggest risk is executing this complex vision at scale, but Adcock believes the potential market is enormous, representing a significant portion of global GDP. The interview also covers his departure from OpenAI, citing that Figure's internal AI team eventually surpassed OpenAI's capabilities for robotics applications. Adcock concludes by highlighting his focus for the year: large-scale commercial deployment of robots and advancing toward a "general robot" capable of any human task, potentially seeing the first signs of AGI (Artificial General Intelligence) in the physical world at Figure.

marsbit05/18 10:26

Dialogue with Figure Robotics Founder: Behind the $39 Billion Valuation Lies Ambition to Mass-Produce Millions of Units

marsbit05/18 10:26

As the Market Prepares for the Next Expansion Phase, Ozak AI’s $7M+ Presale Is Strengthening Its Long-Term Story

The article reports that AI-powered crypto project Ozak AI has surpassed a $7 million milestone in its OZ token presale, coinciding with a market preparing for expansion. It highlights both short and long-term projections for the token, with potential ROIs of 71x at listing and up to 500x thereafter. The piece details the deployment of core technologies like the x402 Protocol and DePIN to enhance ecosystem efficiency and data security. It also notes strategic alliances, such as with Phala Network, that are strengthening the project's foundation for sustainable growth. The conclusion emphasizes Ozak AI's focus on a long-term trajectory, bolstered by presale performance, technological implementation, and key partnerships.

TheNewsCrypto05/18 10:20

As the Market Prepares for the Next Expansion Phase, Ozak AI’s $7M+ Presale Is Strengthening Its Long-Term Story

TheNewsCrypto05/18 10:20

Why Did OpenAI Decide to Make a Phone? ChatGPT Is Taking the Permissions Apple Won't Give

The article discusses OpenAI's surprising move into developing its own AI-powered smartphone, reportedly targeting a 2027 launch. Initially driven by faith that superior AI models alone would secure its dominance—evidenced by ChatGPT's viral success—OpenAI now faces a strategic pivot. Key challenges include slower-than-expected revenue growth and competition from rivals like Anthropic's Claude Code, which successfully monetized a specific, high-value user base (developers) by deeply integrating into workflows. OpenAI recognizes that for ChatGPT to evolve from a conversational tool into a true "AI Agent" that completes tasks (e.g., booking travel, managing files), it needs direct system-level permissions and a default user interface. Currently, as a service integrated into platforms like Apple's iOS and Microsoft's Windows, ChatGPT lacks the necessary access and control ("sovereignty") over hardware, data, and user interactions. Building its own device is seen as a way to give ChatGPT its "first body"—a dedicated terminal where it can operate with full autonomy, bypassing the limitations imposed by partner ecosystems. This shift underscores a broader realization: in the AI Agent era, owning the end-user device and experience is critical to capturing value and maintaining competitive advantage, even if it means directly competing with former allies like Apple.

marsbit05/18 10:19

Why Did OpenAI Decide to Make a Phone? ChatGPT Is Taking the Permissions Apple Won't Give

marsbit05/18 10:19

Perspective: Tokens on alt.fun are double-layered leverage

**Title:** Tokens on alt.fun are Double-Layered Leverage **Summary:** Tokens on alt.fun (like ALT) are not simple 5x leveraged bets on HYPE. Instead, they represent a **double layer of leverage**. The core mechanism involves HyperSwap V2 pools. After "graduation," these tokens are paired not with USDC, but with **HYPE5L**—a 5x long leverage token (LT) issued by BounceTech that tracks HYPE. Therefore, an alt.fun token's price in USDC is determined by multiplying two independent factors: 1. **AMM Exchange Rate:** The pool's ratio between the alt token and HYPE5L, driven by trading activity on alt.fun. 2. **LT Net Asset Value (NAV):** HYPE5L's value, which moves at approximately 5x the daily return of HYPE. This creates a compounding effect: * If HYPE rises 1%, HYPE5L's NAV rises ~5%. Profit-taking HYPE5L holders may then buy alt tokens, increasing demand and pushing the AMM exchange rate higher. The alt token's total gain thus exceeds 5%, potentially reaching 8-15%. * Conversely, if HYPE falls, losses are amplified beyond 5x due to combined NAV decline and AMM selling pressure. During crashes, large sell orders may fail due to non-atomic redemption paths, potentially trapping later sellers. In contrast, platforms like pump.fun pair tokens with stable assets like SOL, applying only the AMM amplifier to a 1x underlying asset. Alt.fun's use of a pre-leveraged quote asset (HYPE5L) fundamentally shifts the risk profile, creating a **second-order product with floating, often higher, effective leverage (typically 8-15x)** that is not clearly communicated in the interface. This results in amplified gains in strong trends but significantly magnified losses and unique liquidity risks during downturns.

marsbit05/18 10:16

Perspective: Tokens on alt.fun are double-layered leverage

marsbit05/18 10:16

Topping GitHub's Trending, the Essential Guide for Claude Code Users

The CLAUDE.md file, trending on GitHub, is a project-level guide for Claude Code designed to dramatically improve its accuracy and efficiency. It addresses key issues like repetitive context explanations, unauthorized code changes, and forgotten decisions across sessions. By placing this plain-text file in a project root, Claude Code reads it automatically at the start of each session. The guide includes rules to eliminate redundant explanations, enforce strict behavioral constraints (e.g., no modifications outside the requested scope without confirmation), and establish a "memory" system using companion files like MEMORY.md and ERRORS.md to log past decisions and failures. It also locks in the project's specific tech stack to prevent inappropriate tool recommendations. Highlighted are four foundational rules from Andrej Karpathy that reportedly increased coding accuracy from 65% to 94%: always ask for clarity first, implement the simplest solution, never touch unrelated code, and explicitly flag uncertainties. The article quantifies significant weekly cost savings for developers and teams by eliminating wasted time on re-explaining context, rolling back unauthorized edits, and re-evaluating previously rejected solutions. The core message is that a small, upfront investment in creating a CLAUDE.md file leads to a more predictable, controlled, and cost-effective AI programming assistant.

marsbit05/18 09:38

Topping GitHub's Trending, the Essential Guide for Claude Code Users

marsbit05/18 09:38

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