Indepth Research

Provide in-depth research reports and independent analysis, leveraging data, technology, and economic insights to deliver a comprehensive examination of the blockchain ecosystem, project potential, and market trends.

Dialogue with Dragonfly Managing Partner Haseeb: AI Apocalypse Is Still Far Away, Smart Contracts Are Laws Born for Machines

Dragonfly Capital managing partner Haseeb Qureshi reflects on the crypto industry's evolution, the intersection of AI and crypto, and surviving market cycles. He discusses the departure of peers like Multicoin Capital’s Kyle Samani as symbolic of the industry’s maturation, noting that crypto space has shifted from rebellion to becoming part of the establishment. Haseeb argues that AI and crypto are complementary: AI agents will require cryptocurrencies for fast, predictable, cross-border transactions, with smart contracts serving as "law for machines." He cautions against overestimating the near-term impact of AGI, drawing parallels to the decades-long adoption of industrial revolution technologies. Reflecting on the recent crypto bear market, including the October 2021 flash crash, he emphasizes the importance of survival—staying at the table to participate in future cycles. His key insight: "The biggest alpha in crypto is survival. As long as you’re not knocked out, you have a seat at the feast."

marsbit02/25 08:51

Dialogue with Dragonfly Managing Partner Haseeb: AI Apocalypse Is Still Far Away, Smart Contracts Are Laws Born for Machines

marsbit02/25 08:51

AI Era's 'Scarce Assets'? Goldman Sachs: HALO—Heavy Assets, Low Obsolescence

In the AI era, market focus is shifting from scalable, light-asset business models to valuing hard-to-replicate physical assets and infrastructure, a trend Goldman Sachs terms "HALO" (Heavy Assets, Low Obsolescence). This reflects a repricing of scarcity driven by higher real interest rates, geopolitical fragmentation, supply chain restructuring, and massive AI-driven capital expenditure. HALO assets—such as power grids, pipelines, utilities, and critical industrial capacity—have high replication barriers (cost, regulation, engineering complexity) and remain economically durable across technology cycles. Meanwhile, AI is undermining the profitability and terminal value of some light-asset sectors (e.g., software, IT services) by reducing information costs and increasing competition. Notably, major tech firms are now becoming large-scale capital spenders, with projected Capex of $1.5 trillion from 2023-2026—surpassing their cumulative historical investment. Since 2025, Goldman’s heavy-asset portfolio (GSSTCAPI) has outperformed its light-asset counterpart (GSSTCAPL) by 35%, driven by valuation rerating rather than broad de-rating of light assets. Macro factors support this shift: higher rates compress valuations of long-duration growth stocks, while manufacturing and capex cycles benefit heavy-asset firms. Earnings momentum is also stronger for heavy-asset companies, with higher expected CAGR (14% vs. 10%) and improving ROE. Despite recent gains, institutional positioning remains underweight value/heavy-asset stocks, suggesting further potential for outperformance.

marsbit02/25 08:50

AI Era's 'Scarce Assets'? Goldman Sachs: HALO—Heavy Assets, Low Obsolescence

marsbit02/25 08:50

When DeFi Enters the "Exchange Era": The Ambition and Boundaries of Hyperliquid

Hyperliquid is a specialized Layer 1 blockchain designed to bring a centralized exchange-like experience to on-chain perpetual contract trading. It places order books, matching, execution, and liquidation primarily on-chain while achieving performance and usability comparable to major CEXs. Its native token, HYPE, captures value from trading fees and embodies a hybrid narrative of both exchange and blockchain utility. Key innovations include the HLP (Hyperliquid Liquidity Provider) mechanism, which incentivizes liquidity provision and supports market-making activities. However, Hyperliquid faces challenges including decentralization trade-offs, systemic risks from derivatives, potential HLP user misunderstandings, and future regulatory pressures. It represents a significant shift in DeFi towards exchange-grade usability but remains a high-risk, complex financial experiment.

比推02/25 06:18

When DeFi Enters the "Exchange Era": The Ambition and Boundaries of Hyperliquid

比推02/25 06:18

The Most Underrated Chapter in Dalio's 'Principles': On the Big Cycle, He Explained It Clearly Three Years Ago

The most underrated chapter in Ray Dalio's "Principles" explores the "Big Cycle" of investing, revealing a sobering historical pattern: 7 out of 10 major nations saw their wealth nearly wiped out at least once in the past century. Dalio argues that most investors fail to study these cycles, leading to a distorted perspective skewed by survivorship bias (e.g., focusing only on post-WWII US/UK success). The chapter details how financial wealth (stocks, bonds) expands relative to tangible wealth during cycles of debt and credit growth, eventually leading to crises where promises exceed deliverable value. This results in debt defaults, market crashes, currency devaluation, and even wealth confiscation. Historical data from 1900 shows extreme volatility: many countries experienced decades of negative real returns, market closures, and high taxes. Dalio emphasizes that investors’ biggest risks are not meeting return needs, portfolio destruction, and wealth confiscation—not just volatility. He warns that current conditions (near-zero real yields, high financial wealth vs. tangible wealth) mirror late-cycle stages where cash and bonds offer poor returns. The solution: diversify globally, understand cyclical drivers (growth, inflation, risk premiums), and avoid overexposure to any single environment or narrative.

marsbit02/25 04:59

The Most Underrated Chapter in Dalio's 'Principles': On the Big Cycle, He Explained It Clearly Three Years Ago

marsbit02/25 04:59

Wintermute Trader: BTC Stuck at $64-67k, Market Has Entered a Phase of Macro Paradigm Shift

A Wintermute OTC trader analyzes the current crypto market, noting that BTC remains range-bound between $64k-$67k with weak momentum. The market is undergoing a potential macro paradigm shift driven by three key structural forces: AI-driven repricing, deglobalization, and Federal Reserve policy inefficacy. These factors are causing a rotation away from growth assets—including crypto, which is being sold as high-beta risk-on exposure—and into value sectors like commodities, energy, and defense. AI disruption is compressing software valuations, while deglobalization and tariffs are increasing input costs and geopolitical risks. The Fed is seen as ineffective in responding to sticky inflation and slowing growth. Despite brief interest in altcoins, institutional demand remains weak, and derivatives indicate low conviction. The key question for 2026 is whether this rotation is a temporary shift or a lasting macro regime change favoring hard assets over growth. The answer remains uncertain.

marsbit02/25 04:36

Wintermute Trader: BTC Stuck at $64-67k, Market Has Entered a Phase of Macro Paradigm Shift

marsbit02/25 04:36

When 'Stability' Begins to Fluctuate: A Complete Review and Structural Analysis of the USD1 Depegging Incident

USD1, a stablecoin issued by World Liberty Financial (WLFI), experienced a brief depegging event on February 23, with its secondary market price dropping to around $0.98. WLFI attributed the incident to a "coordinated attack," claiming reserves and redemption mechanisms remained unaffected, and the price quickly recovered. The event raised questions about whether the depegging was a liquidity issue or a solvency crisis. Unlike TerraUSD's algorithmic collapse in 2022, USD1’s price dip appeared more related to temporary market liquidity stress rather than fundamental reserve failures. However, the incident highlighted that stablecoin stability relies not only on assets but also on market trust. USD1 operates under a centralized reserve model, where risks involve reserve transparency, asset liquidity, and market depth. In a low-liquidity, risk-averse market environment, even minor instability can trigger concerns about collateral devaluation, liquidations, and broader financial contraction. While the rapid price recovery suggests no systemic solvency issue, the event underscores how stablecoin depegging—even if temporary—can amplify market anxiety, reduce leverage, and increase funding costs. Trust, once questioned, takes time to restore, and credit repricing often precedes price changes in stressed markets.

marsbit02/25 04:11

When 'Stability' Begins to Fluctuate: A Complete Review and Structural Analysis of the USD1 Depegging Incident

marsbit02/25 04:11

While Everyone Is Selling Software Stocks, HSBC Says You're Wrong

Amid a severe selloff in software stocks dubbed the "SaaSpocalypse" in early 2026, HSBC’s U.S. tech research head Stephen Bersey published a contrarian report titled "Software Will Eat AI." He argues that the market’s fear—that AI agents will replace traditional enterprise software—is a misjudgment. Instead, Bersey contends that AI will be absorbed into existing software platforms, becoming an embedded capability rather than a disruptor. Key points from the report include: - AI lacks the depth to replace complex enterprise systems due to training data limitations and inability to replicate decades of proprietary business logic. - "Vibe coding" and AI-native approaches overestimate the ability to rebuild reliable, large-scale enterprise software from scratch. - High switching costs and trust in incumbent software providers create durable barriers. Bersey believes software companies with deep data moats and AI integration capabilities—such as Oracle, Microsoft, Salesforce, and ServiceNow—are well-positioned to monetize AI through task-based agents operating within software-defined boundaries. He sees 2026 as the year AI monetization scales within software, driven by inference demand, not training. HSBC recommends buying select software stocks while downgrading others like IBM and Palo Alto Networks, emphasizing that not all will benefit equally. The core thesis: software is the vehicle through which AI delivers scalable, governed enterprise value—not its replacement.

marsbit02/25 02:51

While Everyone Is Selling Software Stocks, HSBC Says You're Wrong

marsbit02/25 02:51

$2T lost in 140 days: Why this crypto market crash looks different

The crypto market has experienced a severe crash, losing $2 trillion in value over 140 days, with Bitcoin falling nearly 50% and Ethereum down 62% from their peaks. Altcoins like Solana dropped even more sharply, with some smaller tokens losing up to 90% of their value. Unlike previous cycles, this downturn appears more severe and prolonged, driven by a combination of factors including miner capitulation, weak post-halving demand, and significant forced liquidations exceeding $600 million in 24 hours. Additionally, geopolitical tensions and U.S. tariff policies have pushed investors toward safer assets like the U.S. dollar and gold. Key on-chain metrics such as negative MVRV ratios for Bitcoin and Ethereum indicate widespread losses, but historical patterns of oversold rebounds may not hold this time. The market remains under pressure, with no clear signs of immediate recovery.

ambcrypto02/24 23:02

$2T lost in 140 days: Why this crypto market crash looks different

ambcrypto02/24 23:02

More Accurate Than Polls, More Dangerous Than Imagined: Prediction Markets in the Eyes of the Fed

The Federal Reserve is exploring the use of prediction markets, particularly Kalshi, as a real-time tool for policy insights. A Fed-affiliated working paper found that Kalshi’s predictions for core CPI and unemployment are statistically comparable to—and sometimes more accurate than—Bloomberg consensus estimates. Prediction markets aggregate real-money, belief-backed trading, offering frequent updates and capturing nuanced shifts that traditional surveys miss. For instance, Kalshi priced inflation uncertainty in real time during a trade policy scare—a dynamic monthly surveys couldn’t reflect. While these markets provide valuable signals, they also carry risks. Prices reflect both expectations and risk preferences, and heavy reliance on sports betting for liquidity makes macroeconomic markets vulnerable to regulatory changes. If sports betting is restricted, liquidity could dry up, increasing manipulation risks. Moreover, if the Fed openly uses prediction markets, it could create a feedback loop where traders manipulate smaller markets like Kalshi to influence broader policy communication and traditional financial instruments. Despite these concerns, prediction markets offer a uniquely timely and distributed form of expectation aggregation—especially for events like FOMC meetings, where informed participants trade with real stakes. The Fed should require open data transparency to mitigate manipulation and carefully weigh the signal against the noise.

比推02/24 18:33

More Accurate Than Polls, More Dangerous Than Imagined: Prediction Markets in the Eyes of the Fed

比推02/24 18:33

When Dreamers Start Doing the Math: How Reality Interrupted OpenAI's $500 Billion Stargate?

OpenAI's ambitious $500 billion "Stargate" data center project has stalled due to financing challenges and internal disagreements with partners SoftBank and Oracle. After lenders rejected OpenAI's request for billions to build its own data centers—citing unproven business models and cash burn concerns—the company was forced to scale back its plans. It has now shifted from owning infrastructure to leasing capacity, signing major deals with Oracle and others. OpenAI has also slashed its 2030 computing expenditure target by 57%, from $1.4 trillion to $600 billion, and projects $280 billion in revenue by 2030. Despite having over 900 million users, only about 5% pay for ChatGPT, and competition is intensifying. Facing high costs and slower monetization, OpenAI is prioritizing financial sustainability over grand visions.

比推02/24 14:06

When Dreamers Start Doing the Math: How Reality Interrupted OpenAI's $500 Billion Stargate?

比推02/24 14:06

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