A Deep Dive into the Crypto x AI Ecosystem

Author: Darshan Gandhi, Founder of FutureX Labs Source: modularmedia Translation: Shan Ouba, Golden Finance

Introduction

The convergence of the cryptocurrency and AI ecosystems is developing rapidly, with many companies developing innovative solutions to address various challenges within the industry. These efforts span verticals such as data availability, coordination networks, compute providers, and model providers, essentially covering the entire AI stack.

Over the past year, the space has received strong support from key opinion leaders, builders, and innovators in the field. This support has greatly contributed to the advancement and visibility of the crypto x AI ecosystem.

In this report, we aim to delve into this ecosystem and gain a comprehensive understanding of its components. We will cover the following sections:

1. Ecosystem 101

2. Diving Deeper into Subcategories

3. What will the future of Crypto x AI look like?

4. Conclusion

Ecosystem 101

We created the above market map to give a quick overview of some of the main categories in the crypto x AI ecosystem. The main categories we’re going to explore today are:

• Computing

• AI Agents

• Data Availability

• Gambling

• Privacy, ZKML, FHE

• Consumers

• Coordination Networks

• Coprocessors

• left;">Model Training

• Model Creators

In the following sections, we will briefly explore each category and the projects that build solutions in them. We will also provide links to these projects for further exploration. The focus will be on how modularity is a key component of the entire stack, especially in terms of data availability, AI agents, and coordination networks.

Each category is a key component in creating a brighter and more powerful future for decentralized AI.

Let's get started.

Compute

Decentralized compute providers offer compute resources over a distributed network rather than in centralized data centers.Currently, most compute resources are controlled by hyperscalers, which are centralized entities that rent out compute power under the authorization of chip providers. This centralized model often results in idle computing resources, causing users to pay more than necessary.

In contrast, decentralized computing platforms allow users to rent out their idle computing power, creating a market for these resources. This approach can significantly reduce costs and improve efficiency by leveraging underutilized computing power in personal computers, servers, and other devices around the world. Decentralized networks can also enhance security and resilience against attacks or failures that can affect centralized services.

For AI applications, decentralized computing providers are particularly beneficial. AI model training and deployment require large amounts of computing power, which can be prohibitively expensive if procured from traditional centralized cloud providers. Decentralized networks such as Akash Network and Render Network provide scalable and affordable solutions to these needs, supporting a variety of computing tasks beyond AI, including scientific simulations and digital content rendering.

Decentralized computing networks are also more flexible and adaptable than traditional cloud services. They can dynamically allocate resources based on real-time demand, ensuring users get the power they need, when they need it. This flexibility, combined with cost savings and enhanced security, makes decentralized computing an attractive option for businesses and developers in the AI ​​ecosystem and beyond.

Key Players in the Space:

• Hyperbolic

  Hyperbolic unites global computing to provide accessible, affordable, and scalable GPU resources and AI services. They provide GPU access, including A100 and H100, at the lowest market prices and allow users to monetize idle machines. Serving companies, researchers, data centers, and individuals, Hyperbolic provides high-throughput, low-latency AI inference services and scalable GPU access with pay-as-you-go plans.

• Akash Network

  Akash allows users to buy and sell computing resources securely and efficiently. Its permissionless, peer-to-peer communication model focuses on data privacy and payment transparency, making it a flexible, secure, and cost-effective alternative to traditional cloud services. They claim to be nearly 5x cheaper than their web2 counterparts. Users can explore a wide range of cloud resources and real-time network pricing, earn money becoming a provider by offering hardware on the network, and deploy using the user-friendly Akash console. Akash is general purpose and designed to provide cloud computing services to anyone.

• Aethir

  Aethir provides secure, cost-effective access to enterprise-grade GPUs worldwide. With over $400 million in computing power, Aethir is focused on achieving high performance and reliability. They offer two major products:

  GPU providers can easily scale, earn significant revenue and exclusive rewards. Aethir has a strong focus on gaming and AI.

• Aethir Earth:Provides raw GPU compute power for AI model training and inference.

• Aethir Atmosphere:Enables low-latency cloud gaming.

• Render Network

  Render Network provides decentralized GPU rendering, aiming to provide nearly unlimited GPU compute power for 3D content creation. Founded in 2017, the company is one of the oldest players in the market, focused on enabling creators and artists to focus on content creation without worrying about compute requirements and capabilities. It is a GPU provider in its own right, while Akash is more community-driven.

• IO.net

  IO.net is an aggregation provider focused on global GPU resources, aiming to provide accessible, affordable and scalable computing solutions. Users can monetize idle GPUs and generate revenue through high utilization. IO.net emphasizes strong security through SOC2/HIPAA compliance and end-to-end encryption. They work internally with other compute providers such as Aethir and Render to aggregate the compute services provided by these partners.

Artificial Intelligence Agents

Decentralized AI agents are autonomous programs that run within a distributed network, and they can perform tasks and make decisions without centralized control. These agents interact with other agents and systems, creating complex multi-agent environments for collaborative task execution.

The main advantages of decentralized AI agents are their independence and collaborative capabilities, which enhance stability and scalability, and there is no single point of failure. They can run across different blockchain networks, interact with smart contracts and other decentralized applications, and provide seamlessly integrated services.

Decentralized AI agents are very useful in scenarios that require trust, security, and transparency. In the field of financial services, they can autonomously manage and execute transactions while ensuring compliance. In the field of supply chain management, they can track and verify the flow of goods, provide real-time insights and improve transparency. Organizations that leverage decentralized AI agents can build more resilient, efficient, and secure systems at scale.

Main Platforms:

• Talus Network

  Talus Network is a layer 1 blockchain that combines the security and performance of Move smart contracts to create a powerful ecosystem for AI smart agents. These agents can be used in a variety of applications such as DeFi for monetizable smart agents, intent networks for achieving optimal user outcomes, automated game resource collection, and DAO governance. Talus' core principles are security, speed, and an enhanced developer experience, enabling the creation of secure, high-performance AI applications. This ensures that smart agents within Talus can be securely and transparently owned, managed, and monetized.

• Guru Network 

  Guru Network is a layer 3 blockchain that is building a multi-chain AI computing layer that allows dApps and retail users to embed orchestrated AI agents into their daily work and earn rewards. Guru Network's Flow Orchestrator acts as an Infrastructure as a Service (IaaS) that enables AI models and processors to be published and integrated into applications. The network supports autonomous agents and compute nodes, creating a marketplace for these services. With a focus on interoperability and scalability, Guru Network aims to integrate AI-driven orchestration into both on-chain and off-chain activities.

• Myshell 

  Myshell is developing an AI consumer layer that connects users, creators, and open source AI researchers. The platform allows users to build, share, and own AI agents, enabling voice and video interactions through AI companions such as Shizuku. Using state-of-the-art generative AI models, Myshell can quickly turn ideas into AI-native applications, allowing anyone to become a creator, own their work, and be rewarded for their contributions.

Data Availability

Data availability in AI and blockchain refers to accessing and utilizing data stored in a distributed network, which is critical for decentralized applications (dApps) and AI models. The platform focuses on securely storing data and ensuring it is always available when needed, using technologies such as sharding and cryptographic proofs.

Modularity is critical to data availability (DA) because it allows components to scale independently to meet growing demand. It separates data availability from consensus and other blockchain functions, enabling specialized optimization and integration with a variety of applications. The modular system can interact with multiple blockchain ecosystems, providing a versatile foundation for decentralized AI and dApps.

For AI applications that require large datasets for training and inference, reliability is critical, even during network outages or attacks. These platforms distribute data across multiple nodes to increase transparency and trust, thereby reducing the risk of manipulation or censorship. This reliability is particularly important in industries such as finance, healthcare, and governance, where data integrity and transparency are critical.

Main Platforms:

• Celestia

  Celestia is the first modular blockchain network designed to provide a scalable and efficient data availability solution for dApps. By separating the consensus layer and the data availability layer, Celestia enables developers to deploy a customizable blockchain as easily as deploying a smart contract. Its modular architecture supports ample throughput through Data Availability Sampling (DAS), which is scalable while maintaining verifiability for any user.

• Eigen DA

  Built on EigenLayer, Eigen DA stores rollup transactions until their state is finalized on a rollup bridge. Its scalability, security, and decentralization make it ideal for developers who need reliable, on-demand data. Eigen DA's core components include operators, dispersers, and retrievers, which work together to efficiently store and verify data.

• 0g Labs

  0g Labs provides an infinitely scalable data availability and storage system to expand Web3 and enable novel on-chain use cases. Its programmable data availability infrastructure facilitates scalable and secure applications with low-latency data feeds. The 0G Storage Network provides a flexible data storage system for structured or unstructured data, supporting applications, network state offloading, and more. This flexibility enables developers to customize data pipelines, build on-chain AI applications, and perform decentralized inference or fine-tuning using OPML or ZKML.

• Nuff Tech (a spinoff of Near DA)

  Nuffle Labs has two main products:

• Near DA leverages the NEAR Protocol’s sharded architecture to provide a modular data availability layer for rollups, ensuring high throughput and low costs.

• Nuffle Fast Finality Layer (NFFL) leverages EigenLayer to provide a fast settlement layer, enabling fast information access between participating networks.

Celestia, Eigen DA, 0g Labs, and Nuffle Labs support AI in crypto by providing infrastructure for storing and retrieving large datasets that are critical to AI models. These data availability layers ensure that data for AI model training and inference is secure and accessible, facilitating innovation in AI dApps.

Gambling

By leveraging decentralized networks and AI-driven processes, Web3 games and platforms can create dynamic gaming environments that adapt and evolve based on player interactions. This approach enhances player engagement by providing unique, personalized experiences that are not possible with traditional centralized game servers.

AI algorithms analyze player behavior and preferences to tailor the gaming experience to each user: adjusting difficulty levels, suggesting in-game purchases, and generating custom content. This personalization enhances engagement by providing unique challenges and rewards based on individual preferences. Additionally, AI can create complex non-player characters (NPCs) and opponents that can learn and adapt to player strategies, resulting in a more challenging and unpredictable gaming experience.

AI optimizes the in-game economy by adjusting the supply and demand of virtual goods based on player activity. This maintains balance and fairness, ensuring a sustainable in-game economic environment.

Key Players; Main Force; Important Member:

• Nim Network

  Nim Network is a Dymension RollApp that focuses on the intersection of web3 games and AI. It leverages the Dymension modular framework, providing compatibility with the Cosmos ecosystem and EVM chain, ensuring flexibility and scalability. AI agents on Nim Network act as intermediaries between users and blockchain applications, simplifying interactions and enhancing user experience. Partnerships with platforms like Jokerace and Ocean Protocol, as well as joining the AI ​​Gaming Alliance, highlight Nim Network’s commitment to innovation and scalability in AI gaming.

• Today the Game

  Today the Game

  Today the Game will allow players to create their own dream island and build relationships with their AI inhabitants. It will be fun to see what they will build

• AI Arena

  AI Arena is an action game where AI characters learn behavioral patterns and engage in combat. Players train their AI characters, influence their strategies and watch how they perform in combat, creating an immersive blend of AI and gaming.

• Colony

  Colony is an AI-powered web3 survival simulation game featuring highly autonomous AI agents, called “avatars,” that continuously learn from the world around them. Players guide and work with these AI avatars, who possess a wide range of skills and abilities, to navigate a futuristic Earth populated by different colonies competing for survival. Colony’s AI avatars have unique personalities and worldviews, drawing individual lessons and insights from their experiences. Additionally, these avatars can autonomously transact on-chain through a dedicated wallet they control, enabling them to trade with other game avatars.

• PlayAI

  PlayAI is a modular chain designed specifically for gaming AI, which enables creators to deploy sophisticated gaming AI, empower players to earn money through gaming, and help games improve the overall user experience. PlayAI aggregates game data from the gaming community, processes it through data nodes to create model datasets, and ensures that the data used to train AI models is of the highest quality.

Privacy, ZKML, FHE

Privacy-preserving technologies such as zero-knowledge machine learning (ZKML) and fully homomorphic encryption (FHE) are critical to ensuring data privacy and security in decentralized AI applications. These technologies enable computations to be performed on encrypted data without revealing the data itself, which is particularly important for sensitive industries such as finance and healthcare.

ZKML allows AI models to be trained and deployed without exposing the underlying data. By using zero-knowledge proofs, one party can prove to another party that a statement is true without revealing any other information. This ensures that AI models respect user privacy and comply with data protection regulations. ZKML also facilitates secure multi-party computation, where multiple parties can jointly compute a function of their inputs while keeping those inputs private. This feature enables AI models to be more widely used in sensitive fields without compromising data privacy.

FHE allows arbitrary computations to be performed on encrypted data, which means that sensitive data can always remain encrypted, even during processing. This is particularly valuable for cloud computing where data security is a major concern. By using FHE, AI applications can process sensitive data without exposing it, preventing data breaches and leaks. This increases the trustworthiness of AI systems and enables them to be used in highly regulated industries, providing strong data security and privacy.

Key Projects:

• Fhenix

  Fhenix facilitates the deployment of encrypted smart contracts, ensuring that sensitive data is secure and private. The project's roadmap includes multiple phases, such as the launch of the Helium Testnet, Nitrogen Testnet v2, and the Gold mainnet.

• Inco Network

  Inco Network competes with Fhenix and is focused on building a modular, privacy-preserving machine learning ecosystem. By integrating privacy-preserving methods, Inco Network ensures the secure handling of sensitive data in machine learning applications, reducing the risks associated with data breaches and unauthorized access.

• Giza

  Giza leverages zero-knowledge proofs to ensure data is secure and private. Their goal is to simplify the process of building, managing, and hosting verifiable machine learning models, enabling developers to create trustworthy AI solutions

  They offer:

  They also recently announced the launch of their AI agent support framework.

• python-powered workflow for easy integration

• Action-based SDK to create actions in a privacy-first manner

• Modulus Labs

  Modulus Labs is focused on developing actionable AI solutions. By leveraging zero-knowledge cryptography, Modulus Labs ensures AI results are verifiable and cannot be tampered with. This feature, called Trustworthy AI, allows smart contracts to access AI outputs without compromising trust. They have various integrations with ML libraries and platforms, providing a seamless development experience for creating verifiable AI models.

• Bagel Network

  Bagel Network is committed to building a trusted and neutral peer-to-peer machine learning ecosystem. Designed for humans and artificial intelligence, Bagel Network enables a seamless, verifiable, and computable evolution from isolated networks to integrated machine learning ecosystems. The platform supports autonomous AI.

Consumer AI

The consumer AI category focuses on providing decentralized AI solutions directly to end users. Platforms focus on providing user-friendly interfaces and applications that leverage decentralized AI and blockchain technology. These platforms aim to democratize access to AI, especially in inference applications.

Decentralized consumer AI applications offer significant advantages in terms of security and privacy. Unlike centralized services that store user data on a single server, decentralized platforms distribute data across multiple nodes, reducing the risk of data leakage and unauthorized access. This is especially important for apps that handle sensitive personal information.

Some players:

• Gemz

  Gemz is a platform designed to enhance interaction and loyalty between creators and their communities. It allows creators to make and deploy custom 3D interactive tokens (essentially NFTs) that can be used to reward community members and drive fan engagement. These tokens are unique, collectible, and provide a direct connection between creators and fans. Gemz aims to ensure that interactions are secure, transparent, and verifiable, thereby fostering a deeper sense of loyalty and community among fans.

• GPT Chain

  GPT Chain offers a variety of features and services, including:

  Users can create and deploy smart contracts, perform technical and chart analysis, and receive daily crypto market updates. The platform also provides AI chatbots to answer blockchain and crypto-related questions.

• Smart Contract Generator and Auditor

• AI-driven Market Analysis

• AI Trading Assistant.

• Provide daily cryptocurrency market updates

Coordination Networks

Coordination Networks in web3 are critical to enabling seamless interaction and collaboration between data providers, compute providers, model developers, and inference providers. These networks ensure that high-quality data is readily available for training, compute resources are optimally utilized, and AI models are efficiently developed and deployed.

Strong incentive mechanisms in these networks encourage active participation and collaboration, creating an open and inclusive environment for AI development.

Key Projects:

• Allora Network

  Allora Network aims to enhance the intelligence and security of applications through modular networks of ML models. By integrating crowdsourced intelligence, federated learning, and zero-knowledge machine learning (zkML), Allora aims to create a safer, more efficient, and more collaborative AI ecosystem. Its modular architecture allows for continuous development and improvement, fostering a collaborative environment where builders can share knowledge to drive innovation. The network supports a variety of applications, including smart contracts and decentralized applications (dApps). Incentives are provided through its native token $ALLO

• Bittensor

  Bittensor is a decentralized coordination network designed to incentivize the sharing and collaboration of AI models. It is open source first and ensures that all transactions and contributions are transparent and verifiable, thereby building trust within the community and encouraging innovation. It has "subnets" - you can think of them as targeted models designed to serve specific use cases, such as data training, healthcare models, or data scraping services.

Coprocessors

Decentralized coprocessors provide specialized processing power for web3 applications by offloading specific tasks to dedicated hardware. This distributed approach enables more efficient and cost-effective processing because tasks can be distributed across a network of coprocessors rather than relying on a single centralized system.

Coprocessors are particularly beneficial for AI applications, providing high-performance computing for tasks such as model training and inference. By leveraging a trusted execution environment, decentralized coprocessors ensure the confidentiality and integrity of computations, protecting sensitive data during processing.

Key Projects

• Ritual

  Ritual is pioneering a decentralized execution layer for AI, starting with Infernet, a decentralized oracle network (DON) that allows smart contracts on any blockchain to access AI models. The next phase will introduce a sovereign chain with a custom VM optimized for AI-native operations, leveraging Celestia’s modular architecture for enhanced scalability and verifiability. Infernet allows users to access AI models both on-chain and off-chain, providing flexibility for a variety of use cases.

• Phala Network

  Phala Network is building an AI agent/coprocessor. This innovative framework helps autonomous AI agents perform tasks, manage assets, and interact with humans and other agents. At the core of Phala’s product are its five key features, which together build a powerful AI agent ecosystem.

• Decentralized network with over 30,000 nodes.

• Integration of smart contracts with large language models.

• Support for advanced models like GPT-4.

• Autonomous collaboration between AI agents.

• Trusted execution environment ensures data privacy.

Model Training

Decentralized model training platforms like Gensyn provide a distributed network for training AI models. Traditional AI model training requires a large amount of computing resources, which can be expensive and time-consuming. Gensyn addresses this challenge by leveraging distributed computing resources to reduce costs and increase the speed of training large AI models. By distributing the training process across multiple nodes, Gensyn can more efficiently utilize computing power and reduce the time required to train complex models.

A key advantage of decentralized model training is its ability to democratize access to AI capabilities. Small organizations and individual developers can leverage distributed networks to train their models without the need for expensive infrastructure. This opens up new opportunities for innovation and development as more entities can participate in the AI ​​ecosystem. In addition, decentralized model training enhances the resilience and security of the training process because tasks are distributed across multiple nodes instead of relying on a single centralized system.

In addition, decentralized model training allows for a more flexible and scalable process. Developers can dynamically allocate resources based on real-time demand, ensuring that their models get the functionality they need when they need it. This flexibility, coupled with cost savings and enhanced security, makes decentralized model training an attractive option for AI researchers and developers. By leveraging platforms such as Gensyn, organizations can accelerate their AI development and bring innovative solutions to market faster.

Key Projects:

• Gensyn

  Gensyn specializes in providing decentralized solutions for AI model training by creating a marketplace where computing resources can be efficiently allocated to train AI models. This decentralized approach not only reduces costs, but also increases the availability of computing power for AI researchers and developers. Gensyn's platform allows users to contribute idle computing resources to the network and receive rewards based on their contributions. This creates a more accessible and scalable environment for AI model training, giving researchers and developers access to the computing power they need without a large upfront investment. The use of blockchain ensures that all transactions are secure and transparent, fostering trust and collaboration within the community.

Model Creation

The Model Creator category includes platforms that facilitate the creation and deployment of AI models. Platforms such as Nous Research provide decentralized tools and frameworks for developing AI models, enabling researchers and developers to collaborate and share their work in a secure and transparent environment. This approach aims to accelerate AI innovation by facilitating a community-driven model development process.

• Nous Research

  Nous Research focuses on developing advanced tools and frameworks for creating AI models - with a focus on driving decentralized open source. Their AI pipeline can

• run offline on edge devices

• remain customizable due to open weights

• be able to generate synthetic data for production

The Future of Crypto x AI

The intersection of cryptocurrency and AI is still in its infancy, with many projects in various stages of development. The excitement is palpable and suggests great potential.

Collaboration between stakeholders is critical. No one solution will solve all challenges, so collaboration is critical to developing orchestration layers, customizable solutions, and innovation for specific use cases.

Components need to become more modular, enabling a “plug-and-play” environment. This modularity will simplify integration and encourage open source contributions. By designing components that can be easily swapped or added, developers can build complex systems more efficiently. Modularity will also:

• Foster innovation: Developers can try new ideas without having to reinvent the entire system

• Enhance flexibility: Users can tailor solutions to meet specific needs, increasing adaptability for different applications.

• Foster interoperability: Standardized interfaces will allow components from different projects to work seamlessly

Reducing dependencies between components is critical. Establishing baseline solutions that can be easily adapted to different use cases will foster innovation and accelerate the development of the entire ecosystem.

Overall, the Crypto x AI ecosystem has a promising future. As more and more people realize the value of decentralized systems, Crypto x AI is likely to be a key area of ​​development in the next 12 months.

Conclusion

This concludes our exploration of the broad categories in Crypto x AI. To summarize, the top modular AI categories are highlighted:

• Computing: led by market capitalization and immediate needs.

• Artificial Intelligence Agents: driven by innovation and development.

• Data Availability: Enhanced through modular support and versatility.

• Privacy: Advancement through dedicated research.

• Gaming: Advancement through novel AI-led gamification.

One ​​of the key opportunities is to get in on projects early and monitor the performance of the broader Crypto x AI space, influenced by catalysts like NVIDIA’s revenue numbers and support from industry leaders like Balaji and Erik Voorhees.

I believe that success in this space depends on how effectively projects:

• coordinate with each other

• integrate seamlessly

• build strong incentive loops

To do this, it’s important to focus on a “modular-first” approach! In future editions, we’ll dive deeper into these categories and explore exactly how the various products work.