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.

Review of Over 30 Humanoid Robot Companies: Who Will Prevail in 2026?

This article provides an overview of the rapidly expanding humanoid robot industry, highlighting over 30 key companies and predicting which might succeed by 2026. Key companies discussed include Tesla (Optimus), which leverages its AI and manufacturing scale; Figure AI, the fastest-growing and highest-valued startup at $39B; Boston Dynamics, with 30+ years of expertise; Agility Robotics, the first to achieve commercial deployment (Digit in logistics); and Unitree Robotics, offering the most affordable humanoid (G1 at $16,000). Other notable firms mentioned are Apptronik (Apollo, focused on ROI), 1X Technologies (home-use NEO), Sanctuary AI (Phoenix with advanced hydraulic hands), and UBTech Robotics (a major commercial player). Companies from China, like Xiaomi, AgiBot, and Fourier Intelligence, are also prominent. The industry is driven by trends including price disruption (robots under $20K), AI breakthroughs in vision-language-action models, massive production scaling (Tesla targeting 1M units/year), and Robot-as-a-Service (RaaS) models. Investment is substantial, with billions from backers like NVIDIA, Jeff Bezos, Microsoft, and Amazon. The market, valued at $2.9B in 2025, is projected to reach $4-18B by 2030. The conclusion states that no single company yet dominates, with the next 2-3 years being critical for transitioning from prototypes to viable commercial products.

marsbit05/20 01:31

Review of Over 30 Humanoid Robot Companies: Who Will Prevail in 2026?

marsbit05/20 01:31

Circle: From Issuance to Infrastructure

Title: Circle: From Issuance to Infrastructure Circle, the issuer of the USDC stablecoin, is undergoing a strategic transformation to reduce its dependence on interest income from reserve holdings, which is declining due to falling interest rates. Historically, Circle's revenue came primarily from the yield on US Treasury reserves backing USDC. However, it also paid significant fees (approximately 60 cents of every dollar earned) to partners like Coinbase for distributing and settling USDC. To capture more value across the financial stack, Circle is vertically integrating into three new layers: 1. **Settlement Layer:** It is launching **Arc**, a native Layer-1 blockchain. Arc, which uses USDC as its gas token, aims to capture transaction fees currently paid to other blockchains (like Ethereum and Solana) and offers features like privacy for institutional payments. 2. **Distribution Layer:** The **Circle Payments Network (CPN)** connects financial institutions directly to Circle, reducing reliance on exchanges like Coinbase. While not yet monetized, CPN growth has improved Circle's margins. 3. **Application Layer:** Circle is building an **AI Agent Economy** infrastructure with products like Agent Wallets and Nanopayments. The goal is to capture fees from high-volume, automated transactions executed by AI agents, a market where USDC already dominates. These moves represent Circle's shift from a single-product company (USDC issuance) to a full-stack financial platform. The strategy faces challenges, including market competition from players like Stripe and Tether, and potential internal tension regarding how value created by the new Arc blockchain and token (ARC) will accrue to Circle's public shareholders (CRCL). Circle's long-term success depends on its ability to successfully execute this vertical integration and diversify its revenue streams away from interest income.

marsbit05/19 11:58

Vitalik's Latest Long Read: In the AI Era, How Can Code Become More Secure?

Vitalik Buterin explores the role of formal verification as a critical tool for software security, especially in the AI era and for blockchain systems. He defines formal verification as using machine-checkable mathematical proofs to verify that code meets specified properties, moving beyond manual auditing. The article highlights that while AI can generate code and find vulnerabilities rapidly, it also makes formal verification more accessible by assisting in writing proofs. This is crucial for Ethereum's complex components like STARKs, ZK-EVMs, consensus algorithms, and high-performance EVM implementations, where bugs can lead to irreversible losses. Vitalik argues that formal verification enables a powerful "separation of concerns": AI can write highly optimized (e.g., assembly) code for efficiency, while a separate, human-readable specification defines correctness. A machine-checked proof then verifies their equivalence. This paradigm can create a more secure "trusted core" of software. However, he cautions that formal verification is not a panacea. "Proven correctness" depends on the accuracy of the specifications and proofs themselves, which can be wrong or incomplete. Risks include unverified code sections, hardware-level side-channel attacks, and overlooked assumptions. The true goal is not absolute proof but increased confidence through redundant expressions of intent—using code, tests, types, and formal proofs—and automatically checking their consistency. The article concludes that AI and formal verification are complementary: AI enables scale, while verification ensures accuracy. For critical systems, this combination offers a path toward stronger security in a future with powerful AI adversaries, helping to maintain the defensive advantage essential for a decentralized internet.

marsbit05/19 09:56

Vitalik's Latest Long Read: In the AI Era, How Can Code Become More Secure?

marsbit05/19 09:56

IOSG: After the Number of Developers Halved, Crypto Did Not Die

The crypto development community has undergone a significant transformation, with monthly active developers on GitHub halving from 45K in 2022 to approximately 23K by 2026. This decline is largely attributed to the departure of newcomers, whose roles were often tied to market-driven hype cycles like NFTs and forked DeFi protocols, leading to a 52% churn rate among those with less than a year of experience. However, the core of the industry has strengthened. Established developers with over two years of experience have reached a record high, contributing about 70% of the code. They are consolidating around ecosystems with genuine users and revenue, such as Bitcoin and Solana, while moving away from narrative-driven projects. The talent shift represents a "deleveraging" and an increase in core density. This core group has developed a unique skillset by operating in an environment of "code is law," with zero tolerance for error and no external recourse. They have learned to build trust and functional systems from the ground up without central authorities, as demonstrated by protocols like Uniswap and MakerDAO. These capabilities are now being repriced and leveraged in the AI era. The structural challenges of AI scaling—such as trust, coordination, and verification—mirror those long addressed in crypto. Examples include CoreWeave pivoting from GPU mining to AI compute, OpenSea's founder applying NFT market logic to AI model routing with OpenRouter, and projects like NEAR and Catena Labs transitioning crypto-native architectural and financial insights into AI infrastructure and agent banking. Key areas where crypto-bred skills are directly applicable to AI include: 1. **Compute Aggregation & Optimization**: Using token incentives and cryptographic verification (e.g., Proof of Sampling & Privacy) to create trusted, decentralized GPU networks, as seen with Hyperbolic. 2. **AI Governance & Incentive Design**: Applying economic mechanism design from DAOs and tokenomics to align the goals of multiple, fast-acting AI agents, a direction explored by EigenLayer's EigenCloud. 3. **AI Agent Autonomous Payments**: Leveraging stablecoins and programmable, permissionless blockchains to enable the micro-transactions required for AI agent economies, exemplified by protocols like x402. The role of the crypto builder is evolving from writing smart contracts to designing trust mechanisms for autonomous AI systems. This convergence is reflected in hiring trends at major firms and significant capital allocation from funds like Paradigm and a16z crypto, which are investing at the intersection of crypto and AI. Regional differences exist, with the US favoring foundational protocol innovation and Asia focusing on compliant application-layer integration, but the underlying trend is clear. The industry's "deleveraging" has not signaled its demise but rather a maturation, positioning its core builders to solve critical trust and coordination problems in the age of AI.

marsbit05/19 09:28

IOSG: After the Number of Developers Halved, Crypto Did Not Die

marsbit05/19 09:28

Agents Capital Markets: How Will Autonomous Agents Get Funded?

"Agents Capital Markets: How Autonomous Agents Will Raise Capital" Within a decade, specialized capital markets will emerge for AI Agents—software entities with legal personhood that perform work, earn revenue, and need capital. Unlike today's AI companies (like Sierra or Harvey) backed by traditional VC, these future *Agent companies* will be autonomous, legally-recognized entities (e.g., Wyoming memberless LLCs) that directly own assets, sign contracts, and incur liabilities. The driving forces are fourfold: 1) **Overwhelming economics** (Agent companies can deliver services at 85-90% lower cost than human firms); 2) **Proven demand** (current Agent operators already generate billions in revenue); 3) **Existing legal frameworks** enabling algorithmically-managed companies; and 4) **Massive, yield-seeking capital pools** (e.g., private credit) looking for new, uncorrelated assets. Agent capital markets won't rely on one model but a multi-layered "stack" matching different growth stages: 1) VC equity for early human-led builders; 2) Programmatic working capital advances (like Stripe Capital); 3) Revenue-based financing (RBF); 4) Slate financing (pooled funds for many Agents, similar to Hollywood); and 5) Tokenization as a secondary settlement layer, not a primary funding source. The ultimate shift is from funding constrained by human decision-makers to capital flowing algorithmically based on an Agent's auditable performance, contract book, and cash flows. This transition will be enabled by standardized infrastructure—rating methodologies, contracts, indices—turning Agents from software experiments into a foundational, financeable sector of the economy. The constraints are loosening; the opportunity is here.

链捕手05/19 05:15

Agents Capital Markets: How Will Autonomous Agents Get Funded?

链捕手05/19 05:15

Bernstein's 97-Page Report Decoded: The Battle for AI Data Center Connectivity, Who Will Be the True Winner by 2026?

Bernstein's 97-page report analyzes the AI data center connectivity landscape. It argues that the bottleneck is shifting from raw compute (GPU) to the systems connecting GPUs, crucial for cluster efficiency. Copper and optical interconnects are not in a simple replacement cycle but will coexist long-term, with copper dominating short-distance "scale-up" connections and optics favored for longer "scale-out" scenarios. While Co-Packaged Optics (CPO) is the long-term direction for power and cost savings, its widespread adoption faces manufacturing and reliability hurdles, with mass deployment unlikely before 2028. Transitional technologies like Linear Pluggable Optics (LPO) and Near-Packaged Optics (NPO) are seen as near-term leaders. A key insight is that CPO will fundamentally reshape the value chain, shifting profits from traditional optical module suppliers towards chip designers (e.g., NVIDIA, Broadcom), advanced packaging (e.g., TSMC), and system integrators. For 2026, the report highlights more immediate and certain investment opportunities in the essential "infrastructure" enabling this connectivity shift. This includes upgrades for PCBs, ABF substrates, and CCLa driven by new AI server/switch platforms, alongside demand for 1.6T optical modules, LPO/NPO, and the testing/validation equipment required for future CPO scale-up.

marsbit05/19 03:16

Bernstein's 97-Page Report Decoded: The Battle for AI Data Center Connectivity, Who Will Be the True Winner by 2026?

marsbit05/19 03:16

Deconstructing the Real Risks of DeFi Lending: Annual Loss Rate Only 0.03%

Deconstructing the true risks of DeFi lending reveals an annual loss rate of only 0.03% from hacks and exploits. Analysis of DeFi Llama data (excluding cross-chain bridge incidents) for EVM and Solana lending protocols shows that despite high historical attack frequency due to concentrated assets, the sector's security has matured significantly. Over the past year, non-cross-chain lending on these chains saw gross losses of $309M, with net losses after recoveries at $301M. Against a daily average TVL of $99.6B, this translates to a minimal annualized loss rate of approximately 0.03%. The Euler Finance case in 2023, where $197M was fully recovered, exemplifies improving asset recovery capabilities, which now account for roughly 20% of losses in this sector. Loss events follow a log-normal distribution: most are small-scale, with catastrophic losses being rare outliers. This pattern, combined with the massive scale of the total lending market, means single incidents rarely impact the broader ecosystem. It underscores the effectiveness of portfolio diversification and provides a basis for sustainable insurance models. The data indicates DeFi lending has entered a mature phase where risks are quantifiable, categorized, and manageable. The actual financial loss relative to the total capital deployed is extremely low, challenging prevailing narratives of systemic risk.

marsbit05/19 02:15

Deconstructing the Real Risks of DeFi Lending: Annual Loss Rate Only 0.03%

marsbit05/19 02:15

After Developer Numbers Halved: Crypto Isn't Dead, It's Just Giving Up Talent to AI

The title "After a 50% Drop in Developer Count: Crypto Isn't Dead, It's Just Ceding Talent to AI" suggests a shift, not an end. The article analyzes GitHub data showing a significant drop in overall Crypto developer activity from a peak of 45K monthly active developers in 2022 to about 23K in 2026. However, this masks a deeper trend of "talent deleveraging." The exodus consists mainly of newcomers who entered during the bull market for hype-driven roles (e.g., NFT contracts, forked DeFi protocols), with over 50% of developers with less than one year of experience leaving. In contrast, established developers (2+ years of experience) have hit record highs, contributing roughly 70% of the code. They are consolidating in ecosystems with real users and revenue, like Bitcoin and Solana. These experienced builders possess unique skills forged in Crypto's "code is law" environment: the ability to build trust and functional systems from scratch in the absence of external authority or rules, with zero tolerance for error. The article argues that AI's scaling faces structurally similar trust, coordination, and verification problems—particularly regarding compute aggregation, multi-agent incentive alignment, and autonomous payments. Crypto builders are already applying these skills in AI. Examples include CoreWeave (mining to AI compute), OpenRouter (NFT marketplace routing to AI model routing), and projects like Hyperbolic (using crypto-native mechanisms for decentralized compute verification) and EigenLayer (applying restaking logic to AI agent governance). Stablecoin infrastructure is becoming critical for AI agent micro-payments (e.g., x402 protocol). The role of these builders is evolving from writing smart contracts to "designing trusted mechanisms for autonomous AI systems." This shift is reflected in new hiring trends at major exchanges and significant venture capital flowing into the crypto-AI convergence (e.g., funds from Paradigm, Haun Ventures). The article concludes that while developer numbers have halved, the core density of talent has increased, and their uniquely cultivated skills are finding a new, larger stage in the AI era.

marsbit05/18 13:41

After Developer Numbers Halved: Crypto Isn't Dead, It's Just Giving Up Talent to AI

marsbit05/18 13:41

After the Developer Count Halved: Crypto Is Not Dead, It's Just Ceding Talent to AI

Following a significant decline in the total number of open-source crypto developers, from a peak of 45K in 2022 to approximately 23K by 2026, this article argues the industry is undergoing a "talent deleveraging" rather than a collapse. The exodus primarily consists of newcomers who entered during the bull market, while the core of experienced developers (2+ years) has grown to a record high, contributing around 70% of code. These established builders are concentrating in ecosystems with real users and revenue, like Bitcoin and Solana. The article posits that crypto has cultivated a unique skill set in building trustless, autonomous systems with near-zero tolerance for error—a capability now finding high demand in the AI era. As AI scales, it faces structural gaps in decentralized compute aggregation, multi-agent coordination/incentive alignment, and autonomous payment infrastructure. Crypto builders are transitioning their expertise to address these exact problems. Examples include CoreWeave (mining to AI compute), Hyperbolic (decentralized compute verification), EigenLayer (extending restaking mechanisms to AI agent governance), and the x402 protocol (enabling AI agent micro-payments via stablecoins). The role of the crypto builder is evolving from writing smart contracts to designing the rule-based, trust-minimized frameworks necessary for AI-native systems. Venture capital is increasingly funding this convergence, viewing it as a structural opportunity rather than a narrative shift. The core talent and systemic design principles from crypto are not disappearing but being re-priced and applied to the foundational challenges of scalable AI.

链捕手05/18 13:37

After the Developer Count Halved: Crypto Is Not Dead, It's Just Ceding Talent to AI

链捕手05/18 13:37

A Quick Look at the Latest Moves of the 24-Year-Old 'AI Stock God': Sixty Percent of the Portfolio Hedging Against Semiconductor Downturn

24-year-old AI investing prodigy Leopold Aschenbrenner's fund, Situational Awareness LP, has disclosed its Q1 2026 13F holdings. The fund's total portfolio nominal value surged 148% to $13.7 billion, driven by both investment gains and significant new capital inflows. The most striking move was the establishment of massive short-term hedges against potential volatility in the AI semiconductor sector. Over 60% of the fund's nominal exposure is now in put options (bets on declines) targeting major AI hardware stocks like NVIDIA (NVDA), VanEck Semiconductor ETF (SMH), Broadcom (AVGO), and AMD. Notably, the fund also holds call options (bets on rises) on some names like Micron (MU) and TSMC, indicating it expects extreme price swings in these stocks. Alongside these hedges, the fund remains a long-term bull on AI infrastructure. It significantly increased its equity stakes in companies like GPU cloud provider CoreWeave (CRWV) and added to positions in power/energy infrastructure firms like Bloom Energy (BE), albeit after taking substantial profits on the latter. The fund also exited positions in optical communication hardware (LITE, COHR) and reduced leverage by clearing out large call option positions on Intel and CoreWeave. In essence, the portfolio reflects a dual strategy: cautious on near-term semiconductor valuations and potential over-extension, while maintaining a conviction that the true long-term bottlenecks and value will be in the underlying infrastructure powering the AI revolution—such as energy, data centers, and compute availability.

marsbit05/18 13:31

A Quick Look at the Latest Moves of the 24-Year-Old 'AI Stock God': Sixty Percent of the Portfolio Hedging Against Semiconductor Downturn