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.

2026 Crypto Industry Investor Relations and Token Transparency Status Report

The 2026 Crypto Investor Relations & Token Transparency Report by Novora analyzes over 150 protocols across major sectors. Key findings reveal a severe transparency gap: less than 1% disclose market maker terms, only 3% have dedicated IR hubs, and just 9% submitted the Blockworks Token Transparency Framework. While 91% of protocols have accessible revenue data, the communication infrastructure to present this to investors is largely absent. Additionally, 62% of tokens offer only governance rights with no active value capture mechanisms. The report concludes that while raw data is available on-chain and via third-party platforms, the critical "translation layer" for institutional adoption is missing. Protocols investing in IR infrastructure now are positioned to gain a significant advantage.

marsbit04/16 02:39

2026 Crypto Industry Investor Relations and Token Transparency Status Report

marsbit04/16 02:39

The DeepSeek You've Been Waiting For Has Long Changed

The article discusses the delayed release of DeepSeek V4, a highly anticipated AI model in China, and explores the reasons behind its slowed development. Initially a leader in the global AI race, DeepSeek has fallen behind competitors like OpenAI, Anthropic, and Google, which release major updates every few months. A key factor is DeepSeek's shift in focus due to national strategic priorities. In early 2025, the Chinese government encouraged the company to use Huawei’s Ascend processors instead of NVIDIA’s GPUs, aligning with broader efforts to achieve technological self-reliance. DeepSeek attempted to train its models on Huawei’s Ascend 910C chips but faced technical challenges, including instability and communication issues during distributed training. As a result, the company continued using NVIDIA hardware for training while only using Ascend chips for inference. In 2026, DeepSeek prioritized adapting V4 to Huawei’s new Ascend 950PR and Cambricon chips, aiming for a full migration from NVIDIA’s CUDA to Huawei’s CANN framework. This adaptation process, particularly ensuring precision alignment across hardware, consumed significant time and resources, slowing down model iteration. The delay also reflects DeepSeek’s evolving role from a purely market-driven entity to a "national mission-oriented" company. This shift has come at a cost: the model now lags behind competitors in areas like code generation and multimodal capabilities, and the company has faced talent drain, with key researchers leaving for better-paying opportunities at larger tech firms. Despite these challenges, V4’s release is seen as a potential milestone for China’s AI industry, demonstrating that advanced models can run on domestic hardware ecosystems. While it may not be a groundbreaking model in terms of performance, its success could validate China’s broader strategy for AI independence.

marsbit04/15 10:32

The DeepSeek You've Been Waiting For Has Long Changed

marsbit04/15 10:32

The Altcoin Vector #50

The Altcoin Vector #50 appears to be a subscriber-exclusive newsletter issue. The content provided indicates that the executive summary and main body of the article are behind a paywall. Access to the full analysis and insights is restricted to paid subscribers, who are prompted to log in to view the complete publication.

insights.glassnode04/15 10:21

The State of Crypto VC: Capital Quadrupled, Investors Down 93%, Where Are the Opportunities?

The state of crypto VC in 2025 is marked by a surge in total funding (up 433% to $40-50B) but a sharp decline in active investors—only 377 in Q4 2025, down from 5,500 in 2022. Deal count dropped 42%, with capital concentrated in fewer, larger rounds. Power has shifted decisively to VCs, who now favor later-stage investments, leaving early-stage opportunities with minimal competition. AI absorbed 61% of global VC funding, drawing away generalist and weak-handed investors. Even crypto-native funds like Paradigm expanded into AI and robotics. Regulatory clarity, not Bitcoin’s price, drove capital inflow. The market is bifurcated: mega-rounds (11 deals >$100M took 85% of Q4 funding) dominate, while mid-stage deals vanish. Pre-seed remains viable (23% of deals) but with higher scrutiny. Remaining investors face less competition and better deals. The focus is on infrastructure, revenue-generating, compliant sectors like Web2.5, trade, stablecoins, and payments. Speed and conviction are critical—capital moves fast to top projects. The opportunity is clear; the question is who has the courage to go all-in.

marsbit04/15 09:43

The State of Crypto VC: Capital Quadrupled, Investors Down 93%, Where Are the Opportunities?

marsbit04/15 09:43

Data: 90% of Crypto Protocols Generate Revenue, But Less Than 1% Disclose Market Maker Terms

Survey of 150 major crypto protocols reveals that while 91% generate on-chain revenue, only 8% publish investor reports, and less than 1% disclose market maker agreements. The data indicates a significant communication gap rather than a lack of available information, as third-party platforms cover 72% of protocols. DeFi protocols, particularly DEXs and lending platforms, lead in transparency, while L1 and infrastructure projects lag despite larger market capitalizations. Only 13 protocols have adopted the Token Transparency Framework since its introduction in June 2025. Additionally, 38% of protocols feature active value accumulation mechanisms, which correlate with approximately 19% higher annual returns compared to governance-only tokens. The findings highlight a structural disconnect between institutional investor expectations and current disclosure practices in the crypto industry.

marsbit04/15 06:55

Data: 90% of Crypto Protocols Generate Revenue, But Less Than 1% Disclose Market Maker Terms

marsbit04/15 06:55

YouTube Will Become the Next Neobank

The article argues that YouTube is poised to become the next major neobank, not by becoming a licensed bank, but by embedding financial services directly into its platform. Successful neobanks like SoFi, Monzo, and Nubank historically found success by targeting specific weaknesses in traditional banking, such as high fees or poor credit scoring. However, the author contends that with the commoditization of banking infrastructure through stablecoins, the key differentiator is no longer just the product but the platform itself. Platforms like YouTube, Twitch, Uber, and TikTok possess a deep, data-rich relationship with their users. They have real-time insights into creators' cash flow, growth trajectories, and earnings, enabling them to underwrite credit and offer financial products in ways traditional banks cannot. By bundling banking services like accounts, cards, and loans directly where income is generated, these companies can capture value that currently leaves their ecosystem via traditional payment rails. The piece concludes that the future of neobanking lies not in standalone apps, but as a feature integrated into the platforms that are the source of users' income.

marsbit04/15 03:57

marsbit04/15 03:57

Robinhood's Wealth Management Business Transformation Journey

Robinhood's 2025 Wealth Management Transformation: A Case Study Robinhood successfully pivoted its business model in 2025, transitioning from a platform known for speculative trading to a comprehensive wealth management service. This strategic shift was driven by launching disruptive products like a high-match-rate IRA, a high-yield cash sweep program, and full-service banking, effectively guiding its young user base toward long-term saving and investing. Key to this success was an aggressive, internet-native customer acquisition strategy. Robinhood used cash match bonuses (up to 3% for Gold members) to lower the barrier for users to transfer retirement assets (e.g., 401(k) rollovers), calculating that the high lifetime value (LTV) of these sticky assets would far exceed the customer acquisition cost (CAC). The company's revenue model evolved significantly. It reduced reliance on volatile payment for order flow (PFOF) by building a robust base of Net Interest Income (NIM) from its high-yield cash product and growing recurring revenue from its SaaS-like Robinhood Gold service, which saw subscriber count soar to 4.2 million. Robinhood built a powerful ecosystem, seamlessly connecting high-frequency trading (stocks, crypto) with low-frequency, high-value activities (retirement investing, banking, spending with its cash-back card). This created a sticky super-app experience. The strategy was underpinned by a low-cost operational structure, enabled by a self-clearing platform and automated services, leading to high revenue per employee. Robinhood's young user base (median age ~32-35) represents a structural advantage, positioning it to capture what is expected to be the largest intergenerational wealth transfer in history as these users age and accumulate more assets.

marsbit04/15 00:07

Robinhood's Wealth Management Business Transformation Journey

marsbit04/15 00:07

Why Does the Automated Market Maker (AMM) Mechanism Fail in the Prediction Market Field?

The article explores why Automated Market Makers (AMMs), particularly the x*y=k model popularized by Uniswap, fail in prediction markets despite their success in decentralized finance (DeFi). AMMs excel in DeFi by enabling permissionless liquidity provision and continuous trading for tokens that fluctuate indefinitely. However, prediction markets involve binary outcomes where assets settle to either $1 or $0 at expiration. This structural difference causes inherent flaws: liquidity providers (LPs) inevitably suffer losses as pools automatically rebalance toward the losing side upon settlement—making "impermanent loss" permanent. Additionally, AMMs distort price discovery for probability-based markets due to slippage and low liquidity. Early prediction platforms like Polymarket and Augur experimented with AMMs but abandoned them for Central Limit Order Books (CLOBs), which better handle precise probabilistic pricing and binary settlements. The article concludes that while AMMs revolutionized token swaps, their mechanics are mismatched for prediction markets, requiring alternative infrastructure that accommodates binary outcomes and mandatory settlement.

marsbit04/14 13:18

Why Does the Automated Market Maker (AMM) Mechanism Fail in the Prediction Market Field?

marsbit04/14 13:18

Franklin Templeton's Latest Research: How to Understand RWA Tokenization

Franklin Templeton's research explores the rapid growth and structural evolution of real-world asset (RWA) tokenization, which has expanded from $5 billion in 2023 to over $25 billion by early 2026. This surge is driven by clearer regulations and greater trust in blockchain technology. RWA tokenization covers assets like stocks, bonds, commodities, and real estate, distinguishing them from native cryptocurrencies. The market saw a turning point as tokenization expanded from government bonds to equities, with early movers like Robinhood, Kraken, and Ondo launching tokenized stock offerings. Traditional institutions, including DTCC, NYSE, and Nasdaq, have since announced significant tokenization initiatives, signaling a major shift in securities processing. The article identifies three tokenization models: 1. **Digital Native Tokens**: Direct ownership of the underlying asset with on-chain settlement (e.g., Franklin Templeton’s money market fund). 2. **Synthetic Asset Tokens**: Indirect economic exposure via special purpose vehicles, allowing broader DeFi utility but limited investor rights. 3. **Digital Mirror Tokens**: Tokenized receipts of off-chain assets, with legacy settlement systems and restricted transferability. Synthetic tokens are permissionless, requiring only KYT checks, while digital native and mirror tokens require full KYC/AML compliance. Each model offers distinct advantages in transparency, utility, and efficiency compared to traditional systems. Tokenization is driving convergence between crypto and traditional finance, with wallets emerging as a universal financial interface.

marsbit04/14 11:35

Franklin Templeton's Latest Research: How to Understand RWA Tokenization

marsbit04/14 11:35

IOSG: TAO is Elon Musk who invested in OpenAI, Subnet is Sam Altman

The article presents a critical analysis of Bittensor (TAO), a decentralized AI marketplace, comparing its economic model to Elon Musk's early investment in OpenAI, where funders have no guaranteed return. TAO acts as a funding layer, distributing tokens to subnet operators (akin to labs or researchers) based on their subnet token performance. However, subnets are under no obligation to return any value—such as AI models, data, or services—back to the TAO ecosystem. This creates a risk of value extraction, where developers may use TAO incentives for research and then commercialize成果 independently. The bear case highlights the lack of binding mechanisms, potential capital drain through token inflation, and competition from well-funded centralized AI giants. The optimistic view argues that continuous resource needs may naturally retain subnets within Bittensor, leveraging crypto’s ability to aggregate resources at scale, similar to Bitcoin’s success with computation. Ultimately, investing in TAO is a speculative bet on a coordination miracle—where incentives align to keep valuable AI projects within the ecosystem—rather than a traditional equity-like claim on output. The outcome distribution is highly skewed: most scenarios lead to niche use or value leakage, while a few could result in TAO becoming a foundational decentralized AI asset.

marsbit04/14 10:45

IOSG: TAO is Elon Musk who invested in OpenAI, Subnet is Sam Altman