Horizontal Comparison of the Four Major AI Frameworks: A Comprehensive Analysis of Adoption Status, Strengths and Weaknesses, and Growth Potential

By: blockbeats|2024/12/30 06:15:03
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Original Article Title: A Deep Dive into Frameworks: A Sector we think Could Grow to $20b+
Original Source: Deep Value Memetics
Original Article Translation: Azuma, Odaily Planet Daily

Key Points Overview

In this report, we discuss the development landscape of several mainstream frameworks in the Crypto & AI field. We will examine the current four mainstream frameworks—Eliza (AI16Z), G.A.M.E (GAME), Rig (ARC), ZerePy (ZEREBRO), analyzing their technical differences and development potential.

Over the past week, we have analyzed and tested the above four major frameworks, and the key conclusions are summarized below.

· We believe Eliza (with a market share of about 60%, valued at approximately $900 million when the original article was written, and valued at around $1.4 billion at the time of publication) will continue to dominate the market share. Eliza's value lies in its first-mover advantage and accelerated developer adoption, with 193 contributors on GitHub, 1800 forks, and over 6000 stars proving this, making it one of the most popular repositories on GitHub.

· G.A.M.E (with a market share of about 20%, valued at approximately $300 million when the original article was written, and valued at around $257 million at the time of publication) has experienced a very smooth development up to now, and is also undergoing rapid adoption, as previously announced by the Virtuals Protocol, with over 200 projects built on G.A.M.E, daily request counts exceeding 150,000, and a weekly growth rate of over 200%. G.A.M.E will continue to benefit from the VIRTUAL's breakout and has the potential to become one of the biggest winners in the ecosystem.

· Rig (with a market share of about 15%, valued at approximately $160 million when the original article was written, and valued at around $279 million at the time of publication) has a very eye-catching and easy-to-use modular design, and is poised to dominate in the Solana ecosystem (RUST).

· Zerepy (with a market share of about 5%, valued at approximately $300 million when the original article was written, and valued at around $424 million at the time of publication) is a more niche application, specific to a fervent ZEREBRO community, and its recent collaboration with the ai16z community may result in some synergies.

In the above statistics, "Market Share" calculates a comprehensive way considering market capitalization, development track record, and the breadth of the underlying operating system's terminal market.

We believe AI frameworks will be the fastest-growing sector in this cycle, with the current market cap of approximately $17 billion likely easily growing to $200 billion. Compared to the peak valuations of Layer 1 in 2021, this number may still be relatively conservative—many individual project valuations exceeded $200 billion at that time. Although the above frameworks serve different terminal markets (chains/ecosystems), considering our view that this sector will grow overall, adopting a market cap-weighted approach may be relatively more prudent.

Four Major Frameworks

At the intersection of AI and Crypto, several frameworks have emerged to accelerate AI development, including Eliza (AI16Z), G.A.M.E (GAME), Rig (ARC), and ZerePy (ZEREBRO). From open-source community projects to performance-focused enterprise solutions, each framework caters to different needs and philosophies of agent development.

In the table below, we list the key technologies, components, and advantages of each framework.

Horizontal Comparison of the Four Major AI Frameworks: A Comprehensive Analysis of Adoption Status, Strengths and Weaknesses, and Growth Potential

This report will first focus on what these frameworks are, the programming languages they use, technical architecture, algorithms, and unique features with potential use cases. Then, we will compare each framework based on usability, scalability, adaptability, and performance, while discussing their strengths and limitations.

Eliza

Eliza is an open-source multi-agent simulation framework developed by AI16Z, aimed at creating, deploying, and managing autonomous AI agents. It is developed in TypeScript as the programming language, providing a flexible, scalable platform for building intelligent agents that can interact with humans across multiple platforms while maintaining consistent personalities and knowledge.

The core features of this framework include: supporting the simultaneous deployment and management of multiple unique AI personality multi-agent architectures; creating a diverse agent role system using a role file framework; providing long-term memory and context-aware memory management through an advanced retrieval-augmented generation (RAG) system. Additionally, the Eliza framework also offers seamless platform integrations for reliable connections with Discord, X, and other social media platforms.

Eliza excels in AI agent communication and media capabilities. In terms of communication, the framework supports integration with Discord's voice channel feature, X feature, Telegram, and direct API access for custom use cases. On the other hand, the framework's media handling capabilities have expanded to PDF document reading and analysis, link content extraction and summarization, audio transcription, video content processing, image analysis, and conversation summarization, effectively handling various media inputs and outputs.

Eliza offers flexible AI model support, enabling on-device inference using open-source models, cloud-based inference using default configurations such as OpenAI and Nous Hermes Llama 3.1 B, and integration with Claude for handling complex queries. Eliza features a modular architecture with extensive action systems, custom client support, and a comprehensive API, ensuring cross-application scalability and adaptability.

Eliza's use cases span across various domains, such as AI assistants for customer support, community management, and personal tasks; social media roles like automatic content creators and brand representatives; knowledge workers playing roles like research assistants, content analysts, and document processors; as well as interactive roles in the form of role-playing bots, educational tutors, and entertainment agents.

Eliza's architecture is built around an agent runtime that seamlessly integrates with role systems (supported by model providers), a memory manager (linked to a database), and action systems (interfacing with platform clients). Unique features of this framework include a plugin system allowing modular feature extensions, support for multi-modal interactions such as voice, text, and media, and compatibility with leading AI models like Llama, GPT-4, and Claude. With its versatility and robust design, Eliza has become a powerful tool for developing cross-domain AI applications.

G.A.M.E

G.A.M.E, developed by the Virtuals official team, stands for "The Generative Autonomous Multimodal Entities Framework." This framework aims to provide developers with an application programming interface (API) and software development kit (SDK) to experiment with AI agents. The framework offers a structured approach to managing AI agent behaviors, decisions, and learning processes.

· The key components of G.A.M.E include, firstly, the "Agent Prompting Interface," which is the developer's entry point to integrating G.A.M.E into an agent to elicit agent behaviors.

· The "Perception Subsystem" initiates a session by specifying parameters such as session ID, agent ID, user, and other relevant details. It synthesizes incoming messages into a format suitable for the "Strategic Planning Engine," serving as the sensory input mechanism for the AI agent, whether in the form of dialogue or reaction. At the core is the "Dialogue Processing Module," responsible for handling messages and responses from the agent and collaborating with the "Perception Subsystem" to effectively interpret and react to input.

· The "Strategic Planning Engine" works in conjunction with the "Dialogue Processing Module" and the "On-chain Wallet Operator" to generate responses and plans. This engine operates on two levels: as a high-level planner creating broad strategies based on context or goals; and as a low-level strategist that transforms these strategies into executable policies, further segmented into an action planner (for task specification) and a plan executor (for task execution).

· A separate but key component is the "World Context," which references the environment, world information, and game state to provide necessary context for the agent's decision-making. Additionally, the "Agent Repository" is used to store long-term attributes such as goals, reflections, experiences, and personality, collectively shaping the agent's behavior and decision-making process. The framework utilizes "Short-Term Working Memory" and a "Long-Term Memory Processor" — the short-term memory retains pertinent information about previous actions, outcomes, and current plans; in contrast, the long-term memory processor extracts key information based on criteria such as importance, recency, and relevance. This memory stores knowledge about the agent's experiences, reflections, dynamic personality, world context, and working memory to enhance decision-making and provide a foundation for learning.

· To enhance adaptability, the "Learning Module" obtains data from the "Perception Subsystem" to generate general knowledge, which is fed back into the system to optimize future interactions. Developers can provide feedback on actions, game states, and sensory data through the interface to enhance the AI agent's learning and improve its planning and decision-making capabilities.

The workflow begins with developers interacting through the agent prompt interface; the "Perception Subsystem" processes the input and forwards it to the "Dialogue Processing Module," which manages the interaction logic; then, the "Strategic Planning Engine" formulates and executes plans based on this information, utilizing high-level strategies and detailed action planning.

Data from the "World Context" and "Agent Repository" informs these processes, while the working memory tracks real-time tasks. Simultaneously, the "Long-Term Memory Processor" stores and retrieves knowledge over time. The "Learning Module" analyzes outcomes and integrates new knowledge into the system, continuously improving the agent's behavior and interactions.

Rig

Rig is an open-source framework based on Rust, designed to streamline the development of Large Language Model (LLM) applications. It offers a unified interface for interacting with multiple LLM providers (such as OpenAI and Anthropic) and supports various vector storages, including MongoDB and Neo4j. The framework's modular architecture features core components like the "Provider Abstraction Layer," "Vector Storage Integration," and "Agent System," facilitating seamless interactions with LLMs.

Rig's primary audience includes developers building AI/ML applications in Rust, while the secondary audience comprises organizations seeking to integrate multiple LLM providers and vector storages into their Rust applications. The repository is organized based on a workspace structure, containing multiple crates that achieve scalability and efficient project management. Key features of Rig include the "Provider Abstraction Layer," which standardizes APIs for LLM providers through consistent error handling; the "Vector Storage Integration" component provides an abstract interface for multiple backends and supports vector similarity search; the "Agent System" simplifies LLM interactions, supporting Retrieve-and-Generate (RAG) and tool integrations. Additionally, the embedded framework offers batch processing capabilities and type-safe embedding operations.

Rig leverages multiple technological advantages to ensure reliability and performance. Asynchronous operations utilize Rust's asynchronous runtime to efficiently handle a large number of concurrent requests; the framework's inherent error handling mechanism enhances resilience to failures from AI providers or database operations; type safety prevents errors at compile time, thus improving code maintainability; efficient serialization and deserialization processes assist in handling data in formats such as JSON, which is crucial for communication and storage in AI services; detailed logging and instrumentation further aid in debugging and monitoring applications.

The workflow in Rig begins with a client initiating a request, which flows through the "provider abstraction layer" interacting with the respective LLM model; then, the data is processed by the core layer, where agents can utilize tools or access vector storage for context; responses are generated and enhanced through complex workflows like RAG, which involve document retrieval and context understanding, before being returned to the client. The system integrates multiple LLM providers and vector storage, adapting to model availability or performance changes.

Rig's use cases are diverse, including retrieving relevant documents to provide accurate answers in question-answering systems, document search and retrieval for efficient content discovery, and chatbots or virtual assistants providing context-aware interactions for customer service or education. It also supports content generation, capable of creating text and other materials based on learned patterns, serving as a versatile tool for developers and organizations.

ZerePy

ZerePy is an open-source framework written in Python designed to deploy agents on X utilizing OpenAI or Anthropic LLM. ZerePy originates from a modular version of the Zerebro backend, allowing developers to kickstart agents with functionalities similar to the Zerebro core. While the framework provides the foundation for agent deployment, fine-tuning of models is necessary to generate creative outputs. ZerePy simplifies the development and deployment of personalized AI agents, particularly suited for content creation on social platforms, fostering an AI creative ecosystem aimed at art and decentralized applications.

Built using the Python language, the framework emphasizes agent autonomy, focusing on creative output generation, aligning with Eliza's architecture + partnerships. Its modular design supports memory system integration, facilitating agent deployment on social platforms. Key features include a command-line interface for agent management, integration with X, support for OpenAI and Anthropic LLM, and a modular connection system for enhanced functionalities.

ZerePy's use cases cover social media automation, where users can deploy AI agents for posting, replying, liking, and retweeting to increase platform engagement. Additionally, it is suitable for content creation in areas such as music, memetics, and NFTs, serving as a key tool for digital art and blockchain-based content platforms.

Horizontal Comparison

In our view, each of the above frameworks has offered a unique approach to AI development, catering to specific needs and environments. This has shifted the debate away from whether these frameworks are direct competitors to focusing on whether each framework can provide unique utility and value.

· Eliza stands out for its user-friendly interface, particularly suitable for developers familiar with JavaScript and Node.js environments. Its comprehensive documentation aids in setting up AI agents across various platforms. Despite its rich feature set, which may present a moderate learning curve, Eliza is well-suited for building agents embedded in networks, especially considering most frontend web infrastructure is TypeScript-based. The framework is renowned for its multi-agent architecture, allowing diverse AI personality agents to be deployed across platforms like Discord, X, and Telegram. Its advanced RAG system for memory management makes it particularly suitable for building AI assistants for customer support or social media applications. While it offers flexibility, strong community support, and consistent cross-platform performance, it is still in its early stages, potentially posing a learning curve for developers.

· G.A.M.E is designed specifically for game developers, offering a low-code or no-code interface through APIs, making it accessible to users with lower technical expertise in the gaming field. However, it focuses on game development and blockchain integration, with a steeper learning curve for those without relevant experience. It excels in procedural content generation and NPC behavior but is constrained by its niche focus and additional complexity when integrating with blockchain.

· Rig, utilizing the Rust language, may present a challenge to users due to the complexity of the language, posing a significant learning curve. However, for those proficient in systems programming, it can provide an intuitive interaction. Compared to TypeScript, Rust is renowned for its performance and memory safety. It features strict compile-time checks and zero-cost abstractions, essential for running complex AI algorithms. The language's efficiency and low-level control make it an ideal choice for resource-intensive AI applications. The framework adopts a modular and scalable design, offering high-performance solutions, making it well-suited for enterprise applications. Nevertheless, for developers unfamiliar with the Rust language, using Rust can introduce a steep learning curve.

· ZerePy uses the Python language, providing higher usability for creative AI tasks. For Python developers, especially those with an AI/ML background, the learning curve is lower, and due to Zerebro's popularity, strong community support is available. ZerePy excels in creative AI applications such as NFTs and positions itself as a powerful tool in the digital media and art fields. While it shines in creativity, its scope is relatively narrow compared to other frameworks.

Here is a comparison of the four frameworks in terms of scalability.

· Eliza made significant progress after the V2 version update, introducing a unified messaging pipeline and an extensible core framework, achieving efficient cross-platform management. However, without optimization, managing such cross-platform interactions may pose scalability challenges.

· G.A.M.E excels in real-time processing required for gaming, and its scalability can be managed through efficient algorithms and the potential of a blockchain distributed system. However, it may be constrained by specific game engines or blockchain network limitations.

· The Rig framework leverages Rust's performance benefits for better scalability, inherently designed for high-throughput applications, which may be particularly effective for enterprise deployments. However, achieving true scalability may require complex configurations.

· ZerePy's scalability is geared towards creative output and has support from the community. However, the framework's focus may limit its application in a broader artificial intelligence environment, and its scalability may be tested by the diversity of creative tasks rather than user volume.

In terms of applicability, Eliza leads by a large margin with its plugin system and cross-platform compatibility, followed by G.A.M.E in the gaming environment and Rig for handling complex AI tasks. ZerePy shows high adaptability in the creative field but is less suitable for broader AI applications.

In terms of performance, here are the test results for the four frameworks.

· Eliza is optimized for quick interaction on social media, but its performance may vary when handling more complex computational tasks.

· G.A.M.E focuses on high-performance real-time interaction in gaming scenarios, leveraging efficient decision-making processes and potential blockchain for decentralized AI operations.

· Rig, based on Rust, offers outstanding performance for high-performance computing tasks, suitable for enterprise applications where computational efficiency is critical.

· ZerePy's performance is geared towards creative content creation, with metrics centered around the efficiency and quality of content generation, which may not be as applicable outside the creative domain.

Combining the above strengths and weaknesses in a comprehensive analysis, Eliza provides greater flexibility and scalability. Its plugin system and role configuration make it highly adaptable and beneficial for cross-platform social AI interaction. G.A.M.E offers unique real-time interaction capabilities in gaming scenarios and provides novel AI participation through blockchain integration. Rig excels in performance and scalability, catering to enterprise-level AI tasks, with a focus on clean and modular code to ensure long-term project health. ZerePy excels in fostering creativity, leading in AI applications for digital art and supported by a vibrant community-driven development model.

In conclusion, each framework has its limitations. Eliza is still in its early stages, with potential stability issues and a steep learning curve for new developers. G.A.M.E's niche focus may limit its broader application, and blockchain integration adds complexity. Rig's learning curve is steeper due to the complexity of the Rust language, which may deter some developers. Zerepy's narrow focus on creative output may limit its application in other AI fields.

Core Comparison Items Overview

Rig (ARC)

· Language: Rust, focusing on security and performance.

· Use Case: Prioritizes efficiency and scalability, making it an ideal choice for enterprise-level AI applications.

· Community: Less community-driven, more focused on technical developers.

Eliza (AI16Z)

· Language: TypeScript, emphasizing the flexibility of Web3 and community involvement.

· Use Case: Specifically designed for social interaction, DAOs, and transactions, with a particular emphasis on multi-agent systems.

· Community: Highly community-driven, with extensive ties to GitHub.

ZerePy (ZEREBRO):

· Language: Python, which is more easily embraced by a broader AI developer community.

· Use Case: Suitable for social media automation and simpler AI agent tasks.

· Community: Relatively new, but poised for growth due to Python's popularity and support from ai16z contributors.

G.A.M.E (VIRTUAL, GMAE):

· Focus: Autonomous, adaptive AI agents that can evolve based on interactions in a virtual environment.

· Use Case: Most suitable for scenarios where agents need to learn and adapt, such as in gaming or virtual worlds.

· Community: Innovative but still establishing its position amidst competition.

Github Stars Growth Data

The above chart depicts the change in GitHub star counts since the launch of these frameworks. Generally, GitHub stars serve as an indicator of community interest, project popularity, and perceived project value.

· Eliza (Red Line): The chart demonstrates a significant and stable growth in star count for this framework, starting from a low base in July and spiking in late November, now reaching 6100 stars. This indicates a rapid surge in interest surrounding the framework, capturing developers' attention. The exponential growth suggests that Eliza has garnered significant attraction due to its features, updates, and community engagement, far surpassing other products in popularity. This signifies robust community support, indicating broader applicability or interest within the AI community.

· Rig (Blue Line): Rig stands out as the oldest among the four frameworks, with a modest yet steady increase in stars, showing a noticeable uptick in the last month. It has amassed a total of 1700 stars, still on an upward trajectory. The consistent accumulation of attention is attributed to ongoing development, updates, and a growing user base. This might reflect Rig's position as a framework still building reputation.

· ZerePy (Yellow Line): ZerePy, launched just a few days ago, has seen its star count rise to 181. It is important to note that ZerePy would require further development to enhance its visibility and adoption, and collaboration with ai16z might draw more contributors to engage with its codebase.

· G.A.M.E (Green Line): While this framework has a modest number of stars, it is worth noting that it can be directly applied to agents in Virtual ecosystems through an API, eliminating the need for a GitHub presence. Despite being made available for builders just over a month ago, over 200 projects are already using G.A.M.E for development.

Expected Upgrades to AI Frameworks

Version 2.0 of Eliza will include integration with the Coinbase Agent Toolkit. All projects using Eliza will receive support for future native TEE (Trusted Execution Environment), allowing agents to run in a secure environment. The Plugin Registry is a forthcoming feature of Eliza that will enable developers to seamlessly register and integrate plugins.

Furthermore, Eliza 2.0 will support automated anonymous cross-platform messaging. The Tokenomics whitepaper (proposal disclosed) expected to be released on January 1, 2025, will have a positive impact on the AI16Z token that underpins the Eliza framework. AI16Z plans to further enhance the utility of the framework and leverage the efforts of its key contributors to bring in top-tier talent.

The G.A.M.E framework offers no-code integration for agents, allowing both G.A.M.E and Eliza to be used within a single project, each serving a specific use case. This approach is expected to attract builders focused on business logic rather than technical complexity. Despite being publicly available for just over 30 days, the framework has made significant progress with the team's efforts to attract more contributors. It is expected that every project launched on VirtuaI will adopt G.A.M.E.

The Rig framework, driven by the ARC token, has significant potential. While the growth of the framework is in its early stages, the project contract plan driving Rig adoption has only been live for a few days. However, high-quality projects paired with ARC are expected to emerge shortly, similar to the Virtual Flywheel but focused on Solana. The Rig team is optimistic about their collaboration with Solana, positioning ARC as the Virtual for Solana. It is worth noting that the team not only incentivizes new projects launched using Rig but also incentivizes developers to enhance the Rig framework itself.

Zerepy is a newly launched framework that has garnered significant attention due to its collaboration with ai16z (Eliza framework). With contributions from Eliza actively working to improve the framework, Zerepy has gained enthusiastic support from the ZEREBRO community, creating a new opportunity for Python developers who previously lacked space in the competitive AI infrastructure arena. It is expected that this framework will play a significant role in the creative aspects of AI.

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Never Underestimate the Significance of the US Stablecoin 'Infrastructure Bill'

Original Title: "Never Underestimate the Significance of the US Stablecoin 'Genius Act'"Original Author: 0xTodd, Partner at Nothing Research


If the US stablecoin bill, the "GENIUS Act," passes smoothly this time, its significance will be tremendous. I even think it's significant enough to enter the top five in Crypto history.



Although abbreviated as the GENIUS Act, which translates directly to the Genius Act, it is actually the Guiding and Establishing National Innovation for U.S. Stablecoins, which translates to "Guiding and Establishing National Innovation for US Dollar Stablecoins."


The proposal is lengthy, with several key points summarized for everyone:


· Mandatory 1:1 Full Asset Backing: Assets include cash, demand deposits, and short-term US Treasuries. At the same time, misappropriation and rehypothecation are strictly prohibited.


· High-Frequency Disclosure: Reserve reports must be published at least monthly, introducing external audits.


· Licensing Requirement: Once the circulating market cap of the issuer's stablecoin exceeds $100 billion, it must transition into the federal regulatory system within a specified timeframe, adopting banking-grade regulation.


· Introduction of Custody: The custodian of the stablecoin and its reserve assets must be a regulated qualified financial institution.


· Clear Definition as a Payment Medium: The bill explicitly defines stablecoin as a new type of payment medium, primarily regulated by the banking regulatory system, rather than restricted by the securities or commodities regulatory system.


· Embracing Existing Stablecoins: A maximum 18-month grace period after the bill's enactment, aimed at encouraging existing stablecoin issuers (such as USDT, USDC, etc.) to promptly obtain licenses or become compliant.


After finishing the main content, let's talk about the significance of this matter with an excited heart.


Over the years, when others asked, "After working in the Crypto industry for 16 years, what application have you created?"


In the future, you can confidently tell others—Stablecoins.


First, Clearing Concerns is a Prerequisite


Some people have held opposing views. In the past, people's impression of stablecoins was that they were an opaque black box. Every few months, there would be FUD — whether Tether's assets were frozen or Circle had a significant black hole deficit.


In fact, if you think about it, Tether easily rakes in billions of dollars a year just from the interest on those underlying government bonds. Circle, slightly less, also made a $1.7 billion profit last year.


They basically made money while standing there. From a motivational standpoint, they have no malicious intentions. In fact, they are the most eager for compliance.


Now, this opaque black box will become a transparent white box.


In the past, the only complaint was that Tether's funds might have been frozen by the United States. Now, they will be directly placed into U.S. compliant custodial institutions, with high-frequency disclosures, so you can rest assured.


【No need to worry about a rug pull】 is such a huge advantage—I think especially all Crypto people understand this.


Second, Mastering the Standard is Very Important


Stablecoins were once almost on the verge of being overtaken by CBDCs. In any country, if a central bank digital currency really exists, it is highly likely not built on a blockchain, at most it is built on some internal central bank consortium chain, which to be honest, is meaningless.


When CBDCs were at their peak, that was the most dangerous time for stablecoins.


If CBDCs had become a reality back then, stablecoins today would have been relentlessly suppressed into a dark corner, and blockchain would only be able to play a minimal role.


The remaining half-dead stablecoins would even have to learn the standards of central bank digital currencies, completely relinquishing their standard-setting power.


And now, stablecoins have won (or are about to).


Instead, everyone should learn the 【Blockchain + Token】 standard.


Nowadays, many blockchains actually have no meaningful applications on top, only stablecoin transfers. For example, with Aptos, the only scenario I use Aptos for is transfers between Binance and OKX.


And now, stablecoins will be legislated, what does that mean?


That's right, blockchain will become the only standard.


In the future, every stablecoin user will be the first to learn how to use a wallet.


As an aside, I actually think Ethereum's concerted push for EIP-7702 is quite forward-thinking. While other chains are all about memes, thank you Ethereum for sticking to account abstraction.



EIP-7702 is about Account Abstraction, which can support, for example:


· Social Account Registration Wallet

· Paying GAS with Native Coin

· And more


This paves the way for future new users to heavily use stablecoins, solving the last-mile problem.


Third, Deposit Enters a New Era


Furthermore, once stablecoins receive legislative support, deposits and withdrawals will become even easier.


Let's imagine a scenario: previously, hindered by the gray nature of stablecoins, but after the bill passes, many traditional brokerages can support stablecoins themselves. The money from a US stock investor can be converted into stablecoins in minutes and instantly deposited into Coinbase. Believe it or not.



Let's imagine another scenario: if the brilliant bill smoothly passes through the House of Representatives, next, you will see:


Due to the extremely lucrative nature of this trading, existing stablecoin leaders and newly entering traditional giants will crazily start promoting their stablecoin products.


And an outsider, due to these promotions, will start using stablecoins. And then one day, after finding out that the wallet account has been created, will explore Bitcoin inside. Is mining Bitcoin difficult?


Stablecoins are a huge Trojan horse. The moment you start using stablecoins, you unwittingly step half a foot into the Crypto world.


Fourth, Conclusion


As a large reservoir for digesting US debt, although stablecoins cannot directly absorb debt, they at least provide ammunition for the US debt secondary market. These functions are quite important, and slowly, stablecoins are becoming a part of the US debt market's body. Therefore, once the US legislation is passed and experiences the benefits, there is no turning back.


And, we are also confident that stablecoins are indeed one of the great innovations in our industry. People who have used stablecoins will find it hard to return to the traditional cash-banking system.


Once the bill is passed, users can't go back. In the future, concerns are about to be resolved, standards will be mastered, and the era of large deposits seems to be on the horizon.


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$COIN Joins S&P 500, but Coinbase Isn't Celebrating

On May 13, S&P Dow Jones Indices announced that Coinbase would officially replace Discover Financial Services in the S&P 500 on May 19. While other companies like Block and MicroStrategy, closely tied to Bitcoin, were already part of the S&P 500, Coinbase became the first cryptocurrency exchange whose primary business is in the index. This also signifies that cryptocurrency is gradually moving from the fringes to the mainstream in the U.S.



On the day of the announcement, Coinbase's stock price surged by 23%, surpassing the $250 mark. However, just 3 days later, Coinbase was hit by two consecutive events: a hack where employees were bribed to steal customer data and a demand for a $20 million ransom, and an investigation by the U.S. Securities and Exchange Commission (SEC) into the authenticity of its claim of having over 100 million "verified users" in its securities filings and marketing materials. These two events acted as mini-bombs, and at the time of writing, Coinbase's stock had already dropped by over 7.3%.


Coincidentally, Discover Financial Services, being replaced by Coinbase, can also be considered the "Coinbase" of the previous payment era. Discover is a U.S.-based digital banking and payment services company headquartered in Illinois, founded in 1960. Its payment network, Discover Network, is the fourth largest payment network apart from Visa, Mastercard, and American Express.


In April, after the approval of the acquisition of Discover by the sixth-largest U.S. bank, Capital One, this well-established digital banking company of over 60 years smoothly handed over its S&P 500 "seat" to this emerging cryptocurrency "bank." This unexpected coincidence also portrayed the handover between the new and old eras in Coinbase's entry into the S&P 500, resembling a relay race scene. However, this relay baton also brought Coinbase's accumulated "external troubles and internal strife" to a tipping point.


Side Effects of ETFs


Over the past decade, cryptocurrency exchanges have been the most stable "profit machines." They play a role in providing liquidity to the entire industry and rely on trading fees to sustain their operations. However, with the comprehensive rollout of ETF products in the U.S. market, this profit model is facing unprecedented challenges. As the leader in the "American stack," with over 80% of its business coming from the U.S., Coinbase is most affected by this.



Starting from the approval of Bitcoin and Ethereum spot ETFs, traditional financial capital has significantly onboarded users and funds that originally belonged to exchanges in a more cost-effective, compliant, and transparent manner. The transaction fee revenue of cryptocurrency exchanges has started to decline, and this trend may further intensify in the coming months.


According to Coinbase's 2024 Q4 financial report, the platform's total trading revenue was $417 million, a 45% year-on-year decrease. The contribution of BTC and ETH's trading revenue dropped from 65% in the same period last year to less than 50%.


This decline is not a result of a decrease in market enthusiasm. In fact, since the approval of the Bitcoin ETF in January 2024, the inflow of BTC into the U.S. market has continued to reach new highs, with asset management giants like BlackRock and Fidelity rapidly expanding their management scale. Data shows that BlackRock's iShares Bitcoin ETF (IBIT) alone has surpassed $17 billion in assets under management. As of mid-May 2025, the cumulative net inflow of 11 major institutional Bitcoin spot ETFs on the market has exceeded $41.5 billion, with a total net asset value of $1214.69 billion, accounting for approximately 5.91% of the total Bitcoin market capitalization.


Chart showing the trend of net outflows for Grayscale among the 11 institutions


Institutional investors and some retail investors are shifting towards ETF products, partly due to compliance and tax considerations. On one hand, ETFs have much lower trading costs compared to cryptocurrency exchanges. While Coinbase's spot trading fee rate varies annually in a tiered manner but averages around 1.49%, for example, the management fee for IBIT ETF is only 0.25%, and the majority of ETF institution fees fluctuate around 0.15% to 0.25%.



In other words, the more rational users are, the more likely they are to move from exchanges to ETF products, especially for investors aiming for long-term holdings.


According to multiple sources, several institutions, including VanEck and Grayscale, have submitted applications to the SEC for a Solana (SOL) ETF, with some institutions also planning to submit an XRP ETF proposal. Once approved, this may trigger a new round of fund migration. According to a report submitted by Coinbase to the SEC, as of April, the platform's trading revenue from XRP and Solana accounted for 18% and 10%, nearly one-third of the platform's fee revenue.



However, the Bitcoin and Ethereum ETFs passed in 2024 also reduced the fees for these two tokens on Coinbase from 30% and 15% to 26% and 10%, respectively. If the SOL and XRP ETFs are approved, it will further undermine the core fee revenue of exchanges like Coinbase.


The expansion of ETF products is gradually weakening the financial intermediary status of cryptocurrency exchanges. From their original roles as matchmakers and clearers to now gradually becoming mere "on-ramps and off-ramps" for funds, exchanges are seeing their marginal value squeezed by ETFs.


Robinhood Takes a Stand, Traditional Brokerages Join the Fray


On May 12, 2025, SEC Chairman Paul S. Atkins gave a keynote speech at the Tokenization and Cryptocurrency Working Group roundtable. The theme of his speech revolved around "It is a new day at the SEC," where he indicated that the SEC would not approach enforcement and regulation the same way as before but would instead pave the way for cryptocurrency assets in the U.S. market.



With signs of cryptocurrency compliance such as the SEC's "NEW DAY" declaration, an increasing number of traditional brokerages are attempting to enter the cryptocurrency industry. One of the most representative cases is the well-known U.S. brokerage Robinhood, which began expanding its crypto business in 2018. By the time of its IPO in 2021, Robinhood's crypto business revenue accounted for over 50% of the company, with a significant boost from the Dogecoin "moonshot" promoted by Musk.


In Q1 2025 earnings report, Robinhood showcased strong growth, especially in revenue from cryptocurrency and options trading. Fueled by Trump's Memecoin, cryptocurrency-related revenue reached $250 million, nearly doubling year-over-year. Consequently, Robinhood Gold subscription users reached 3.5 million, a 90% increase from the previous year, with the rapid growth of Robinhood Gold providing the company with a stable source of income.



Meanwhile, RobinHood is actively pursuing acquisitions in the cryptocurrency space. In 2024, it announced a $2 billion acquisition of the long-standing European cryptocurrency exchange Bitstamp. Additionally, Canada's largest cryptocurrency CEX, WonderFi, which recently went public on the Toronto Stock Exchange, also announced its integration with RobinHood Crypto. After obtaining virtual asset licenses in the UK, Canada, Singapore, and other markets, RobinHood has taken a proactive approach in the compliant cryptocurrency trading market.



Furthermore, an increasing number of brokerage firms are exploring the same path. Futu Securities, Tiger Brokers, and others are also dipping their toes into cryptocurrency trading, with some having applied for or obtained the VA license from the Hong Kong SFC. Although their user bases are currently small, traditional brokerages have a natural advantage in user trust, regulatory licenses, and low fee structures. This could pose a threat to native cryptocurrency platforms in the future.



User Data Breach: Is Coinbase Still Secure?


In April 2025, security researchers discovered that some Coinbase user data was leaked on the dark web. While the platform initially responded by attributing it to a "technical misinformation," it still raised concerns among users regarding its security and privacy protection. Just two days before Dow Jones Indexes announced Coinbase's addition to the S&P 500 Index, on May 11, 2025, Coinbase received an email from an unknown threat actor claiming to have obtained customer account information and internal documents, demanding a $20 million ransom to keep the data private. Subsequent investigations confirmed the data breach.


Cybercriminals obtained the data by bribing overseas customer service agents and support staff, mainly in "non-U.S. regions such as India." These agents abused their access to Coinbase's internal customer support system and stole customer data. As early as February this year, blockchain detective ZachXBT revealed on X platform that between December 2024 and January 2025, Coinbase users lost over $65 million to social engineering scams, with the actual amount potentially higher.


Among the victims was a well-known figure, 67-year-old Ed Suman, an established artist in the art world for nearly two decades, having been involved in the creation of artworks such as Jeff Koons' "Balloon Dog" sculpture. Earlier this year, he fell victim to an impersonation scam involving fake Coinbase customer support, resulting in a loss of over $2 million in cryptocurrency. ZachXBT critiqued Coinbase for its inadequate handling of such scams, noting that other major exchanges have not faced similar issues and recommending Coinbase to enhance its security measures.


Amidst a series of ongoing social engineering incidents, although there has not been any impact on user assets at the technical level so far, it has raised concerns among many retail and institutional investors. Especially institutions holding massive assets on Coinbase. Just considering the U.S. BTC ETF institutions, as of mid-May 2025, they collectively hold nearly 840,000 BTC, and 75% of these are custodied by Coinbase. If we price BTC at $100,000, this amount reaches a staggering $63 billion, which is equivalent to the nominal GDP of two Iceland in the year 2024.


Visualization: ChatGPT, Source: Farside


In addition, Coinbase Custody also serves over 300 institutional clients, including hedge funds, family offices, pension funds, and endowments. As of the Q1 2025 financial report, Coinbase's total assets under management (including institutional and retail clients) reached $404 billion. The specific amount of institutional custodied assets was not explicitly disclosed in the latest report, but it should still be over 50% based on the Q4 2024 report.


Visualization: ChatGPT


Once this security barrier is breached, not only could the rate of user attrition far exceed expectations, but more importantly, institutional trust in it would undermine the foundation of its business. Therefore, after a hacking event, Coinbase's stock price plummeted significantly.


CEXs are All in Self-Rescue Mode


Facing a decline in spot trading fee revenue, Coinbase is also accelerating its transformation, attempting to find growth opportunities in derivatives and emerging assets. Coinbase acquired a stake in the options platform Deribit at the end of 2024 and announced the official launch of perpetual contract products in 2025. This acquisition fills in Coinbase's gap in options trading and its relatively small global market share.



Deribit has a strong presence in non-U.S. markets, especially in Asia and Europe. The acquisition has enabled Coinbase to gain a dominant position in bitcoin and ethereum options trading on Deribit, accounting for approximately 80% of the global options trading volume, with daily trading volume remaining above $2 billion.


Meanwhile, 80-90% of Deribit's customer base consists of institutional investors, with their professionalism and liquidity in the Bitcoin and Ethereum options market highly favored by institutions. Coinbase's compliance advantage, coupled with its already robust institutional ecosystem, makes it even more suitable. By using institutions as an entry point, it can face the squeeze from giants like Binance and OKX in the derivatives market.



Facing a similar dilemma is Kraken, which is attempting to replicate Binance Futures' model in non-U.S. markets. Since the derivatives market relies more on professional users, fee rates are relatively higher and stickiness is stronger, making it a significant source of revenue for exchanges. In the first half of 2025, Kraken completed the acquisition of TradeStation Crypto and a futures exchange, aiming to build a complete derivatives trading ecosystem to hedge the risk of declining spot transaction fee income.


With the surge of Memecoin in 2024, Binance, OKX, and various CEX platforms began massively listing small-market-cap, highly volatile tokens to activate active trading users. Due to the wealth effect and trading activity of Memecoins, Coinbase was also forced to join the battle, successively listing popular tokens from the Solana ecosystem such as BOOK OF MEME and Dogwifhat. Although these coins are controversial, they are frequently traded, with fee rates several times higher than mainstream coins, serving as a "blood-boosting" method for spot trading.


However, due to its status as a publicly traded company, this practice is a riskier endeavor for Coinbase. Even in the current crypto-friendly environment, the SEC is still investigating whether tokens like SOL, ADA, and SAND constitute securities.


In addition to the forced transformation strategies carried out by the aforementioned CEXs, they are also starting to lay out RWAs and the most talked-about stablecoin payment fields, such as the PYUSD launched through a collaboration between Coinbase and Paypal, Coinbase's support for the Euro stablecoin EURC by Circle that complies with EU MiCA regulatory requirements, or the USD1 launched through a collaboration between Binance and WIFL. In the increasingly crowded trading field, many CEXs have shifted their focus from just the trading market to the application field.


The golden age of transaction fees has quietly ended, and the second half of the crypto exchange platform game has silently begun.


Key Market Insights for May 16th, how much did you miss out on?

1. On-chain Flows: $111.3M inflow to Ethereum this week; $237.6M outflow from Berachain 2. Largest Price Swings: $ETHFI, $NEIRO 3. Top News: Data: Solana Network's revenue reached $7.9M on the 13th, surpassing the sum of all other L1 and L2 chains

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