Coinbase + Glassnode: Charting Crypto Q2 2026

insights.glassnodeОпубліковано о 2026-05-01Востаннє оновлено о 2026-05-01

After a volatile start to the year, crypto markets have shifted into a more cautious regime. Risk appetite has cooled, sentiment has deteriorated, and macro developments now overshadow crypto-native drivers in shaping near-term price action.

Produced in collaboration with Coinbase Institutional, the latest Charting Crypto report distills the key market and on-chain trends impacting institutional crypto strategy this quarter. From investor sentiment and stablecoin liquidity to Bitcoin accumulation signals and Ethereum’s evolving market structure, the report offers a data-driven view of a market waiting for clearer direction.

Some of the many highlights in this edition:

  • Liquidity is rotating into stablecoins rather than exiting the asset class, as total crypto market cap (excluding stablecoins) fell by ~18% in Q1, while stablecoin supply increased from $308B to $318B
  • Past cycle analogues are becoming less useful for timing market inflection points as both Bitcoin and Ethereum cycles continue to diverge from historical patterns
  • Market structure shows signs of normalization, with BTC derivatives open interest recovering (particularly in perpetuals), indicating a rebuilding of risk appetite in leveraged markets
  • 82% of surveyed institutions now place the market in a bear or late-bear phase (up from 31% in December)
  • Ethereum’s ecosystem is becoming more differentiated: Capital concentrates in base-layer use cases as broader activity softens, highlighting a shift toward utility-driven demand over speculative flows.

Liquidity Remains Inside the System

Despite the broad risk-off move in Q1, liquidity dynamics tell a more nuanced story. Total crypto market capitalization (excluding stablecoins) declined by roughly 18%, yet stablecoin supply increased over the same period.

This divergence suggests that capital is not fully leaving crypto markets, but rather rotating into cash-like instruments while awaiting clearer signals. In effect, investors are de-risking without disengaging, preserving optionality for re-entry.

Bitcoin: Sentiment Reset, Supply Tightens

Bitcoin’s February drawdown was reflected in a deterioration in investor sentiment, with Net Unrealized Profit/Loss (NUPL) moving from Anxiety into Fear for the rest of Q1. While sentiment has started to show early signs of improvement in April, it remains closely tied to external developments, suggesting conviction is still fragile.

At the same time, onchain supply dynamics indicate a transfer of coins away from more reactive participants. The contraction in recently active supply, alongside a modest increase in long-term held coins, implies that shorter-term, speculative capital has been reduced.

Ethereum: Capital Concentration at the Base Layer

Ethereum data points to a divergence between activity and capital allocation. Short-term participation declined throughout the first quarter, as reflected in a sharp drop in recently active supply and a prolonged period of depressed sentiment. However, capital flows have remained concentrated on the base layer.

Stablecoin supply on Ethereum continues to expand with positive momentum, and tokenized real-world assets have reached new highs, indicating sustained demand for settlement and collateral use cases. At the same time, ETH has outperformed major L2 tokens since late 2025, suggesting that capital is consolidating at the base layer rather than rotating further out on the risk curve.

To help you navigate the current challenging crypto environment, check out all insights and data from the Glassnode x Coinbase report: download the report here.

Пов'язані матеріали

STRC Loses Peg by 11%, Can Strategy's Perpetual Motion Machine Keep Running?

The article discusses the significant and concerning depegging of MicroStrategy's (MSTR) preferred stock, STRC. Designed to trade near its $100 target par value, STRC has recently fallen sharply, reaching a low of $83.26 and closing at $88.59, representing an over 11% discount. STRC is a core component of MicroStrategy's financial strategy. As a perpetual preferred stock, it allows the company to raise capital through an "at-the-market" (ATM) issuance program without diluting common shareholders (MSTR). This capital is primarily used to purchase Bitcoin, creating a "capital flywheel": issuing STRC → raising cash → buying BTC → increasing net assets → supporting STRC's value. The flywheel's operation depends on STRC maintaining its $100 price. To enforce this, MicroStrategy employs a dynamic dividend mechanism, recently raising the rate to 11.5% and increasing payout frequency. However, this has failed to halt the depegging, indicating market concerns extend beyond yield. Analysts cite two main reasons. First, technical factors like forced liquidations from leveraged arbitrage trades may have exacerbated the sell-off. Second, and more fundamentally, is waning confidence in MicroStrategy's financial resilience. A JPMorgan report highlighted the company's limited cash relative to its ~$1.7 billion annual dividend obligation, raising liquidity concerns. While MicroStrategy counters that its massive Bitcoin holdings provide decades of coverage, this argument relies on the potential need to sell BTC—a departure from its long-standing "never sell" narrative. The company's recent sale of a small amount of Bitcoin for "testing," despite being framed as minor, has intensified these fears. The persistent depegging threatens to cripple MicroStrategy's primary funding channel. If STRC remains discounted, the company's ability to fund further Bitcoin purchases weakens. Should cash reserves dwindle while financing is constrained, the market may increasingly price in the risk of MicroStrategy becoming a forced seller of Bitcoin to meet obligations. This shift from a major marginal buyer to a potential seller could pose significant downside risk to the broader Bitcoin market.

链捕手3 хв тому

STRC Loses Peg by 11%, Can Strategy's Perpetual Motion Machine Keep Running?

链捕手3 хв тому

Behind the AI Scorecards Lies a Chinese 'Question Setter'

Behind the AI scorecards that dominate industry discussions—benchmarks like MMLU-Pro, MMMU, and MMMU-Pro—stands a Chinese-Canadian researcher: Wenhu Chen. As an assistant professor at the University of Waterloo and founder of the TIGER Lab, Chen has become a key "exam-setter" for evaluating large language and multimodal models. Chen first gained broader recognition with MMLU-Pro, a more challenging and stable update to the popular MMLU benchmark. As top models like OpenAI’s o3 began achieving near-perfect scores on the original MMLU, it became difficult to distinguish their true capabilities. MMLU-Pro introduced more complex reasoning questions, expanded answer choices, and filtered out ambiguous or simple items, effectively reintroducing differentiation among state-of-the-art models. His work on MMMU addressed the evaluation of multimodal models, requiring them to integrate visual information (like charts, diagrams, or tables) with textual knowledge across diverse academic subjects. Even the strongest models initially scored only around 56-59%, highlighting significant room for improvement in genuine multimodal reasoning. MMMU-Pro further refined this by preventing models from bypassing visual cues. Chen’s research focus has long been on complex information understanding and reasoning. His background—including a PhD at UC Santa Barbara, research at Google/DeepMind on Gemini, and now a role in Meta’s superintelligence lab—provides deep insight into model development and their potential weaknesses. His TIGER Lab also builds models (e.g., for video understanding and generation), ensuring his evaluation benchmarks are grounded in practical challenges. While AI headlines often spotlight company leaders and product launches, Chen’s work exemplifies the critical, behind-the-scenes contributions of researchers crafting the rigorous standards that define and drive progress in AI capabilities.

marsbit1 год тому

Behind the AI Scorecards Lies a Chinese 'Question Setter'

marsbit1 год тому

STRC Unpegged by 11%, Can Strategy's Perpetual Motion Machine Keep Turning?

STRC, the perpetual preferred stock of MicroStrategy, is experiencing a persistent de-pegging from its target par value of $100, with the discount recently widening to over 11%. This de-anchoring challenges the core design of STRC, which was intended as a stable, income-oriented security operating near $100. As a crucial funding engine for MicroStrategy's Bitcoin acquisition strategy, STRC's price reflects market confidence in the company's entire capital model. The company's "capital flywheel" relies on issuing STRC at or above $100 via an At-the-Market (ATM) program to raise cash for buying Bitcoin, thereby boosting company equity and theoretically supporting STRC's value. A monthly adjustable dividend mechanism was designed to maintain this peg. Despite raising the dividend to 11.5% and increasing payment frequency, the de-pegging persists. Market concerns extend beyond technical factors like leveraged arbitrage unwinding. Analysts point to MicroStrategy's limited cash reserves relative to its ~$1.7 billion annual dividend obligation for preferred shares. While the company counters that its vast Bitcoin holdings could cover decades of payments, this argument hinges on the potential need to sell Bitcoin—a shift from its longstanding "hodl" narrative. The company's recent sale of a small amount of BTC, framed as a test, amplified these liquidity and strategy concerns. If STRC remains discounted, impairing MicroStrategy's ability to raise cheap capital, fears may grow that the company could sell more Bitcoin to meet obligations. This scenario could transform MicroStrategy from a major market buyer into a potential seller, posing significant downside risk for Bitcoin. The re-pegging of STRC is thus a key indicator for the health of MicroStrategy's capital structure and its market impact.

Odaily星球日报1 год тому

STRC Unpegged by 11%, Can Strategy's Perpetual Motion Machine Keep Turning?

Odaily星球日报1 год тому

Silicon Valley's Most Sought-After New Role Has Emerged

Silicon Valley's New Most Wanted Job: The Rise of the Forward Deployment Engineer The AI industry is witnessing a significant shift. The focus has moved from developing cutting-edge models to deploying them effectively within enterprises. This has made the "Forward Deployment Engineer" (FDE) a critical and highly sought-after role at major firms like OpenAI, Anthropic, and Google. For the past three years, the industry prioritized model scientists. However, companies are now facing a harsh reality: purchasing powerful AI tools does not guarantee productivity gains or organizational change. The biggest hurdle is not the technology itself, but integrating it into complex legacy systems, workflows, and corporate cultures. This includes challenges like data silos, compliance requirements, and internal resistance. The FDE role, pioneered by Palantir Technologies, addresses this "last-mile" problem. FDEs are deployed on-site with clients for extended periods. Their job is to deeply understand the client's specific organizational structure, processes, and pain points, then tailor and implement the AI solution accordingly. They combine skills in technology, project management, and organizational change. A clear signal of this trend emerged in May 2026 when three AI giants made major moves. Anthropic launched a $1.5B joint venture for enterprise deployment. OpenAI formed an independent deployment subsidiary, DeployCo, with over $4B in commitments and acquired a deployment consultancy. Google Cloud's CEO publicly announced a large-scale recruitment drive for FDEs. This shift represents a fundamental change in the software business model: from selling tools to selling guaranteed outcomes. FDEs are the agents of this change, responsible for delivering a working system within the production environment, not just a demo. Real-world cases, such as challenges at Goldman Sachs (compliance barriers) and Target (internal cultural resistance), illustrate that the primary obstacles to AI adoption are organizational, not technical. An FDE's value lies in navigating these human and procedural complexities to facilitate a successful "AI migration." In essence, as core AI technology becomes more accessible and affordable, the true premium is shifting to the human expertise required to understand organizations and drive change—making the FDE role pivotal for the next phase of the AI revolution.

marsbit1 год тому

Silicon Valley's Most Sought-After New Role Has Emerged

marsbit1 год тому

Торгівля

Спот
Ф'ючерси
活动图片