Author: Darshan Gandhi, Founder of FutureX Labs Source: modularmedia Translation: Shan Ouba, Golden Finance
Preface
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 deeper into this ecosystem and gain a comprehensive understanding of its components. We will cover the following sections:
Crypto x AI Ecosystem 101
Drill down into subcategories
What will the future of Crypto x AI look like?
Conclusion
Crypto x AI 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 will explore today are:
calculate
AI Agent
Data availability
game
Privacy, ZKML, FHE
consumer
Coordination network
Coprocessor
Model Training
Model creator
In the following sections, we will briefly explore each category and the projects that build solutions in it. 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, more powerful future for decentralized AI.
let's start.
calculate
Decentralized computing providers offer computing resources through distributed networks rather than centralized data centers. Currently, most computing resources are controlled by hyperscale providers, which are centralized entities that rent out computing power with 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, thereby creating a market for these resources. This approach leverages underutilized computing power in PCs, servers and other devices around the world, significantly reducing costs and increasing efficiency. Decentralized networks can also enhance security and resilience against attacks or failures that could impact centralized services.
Decentralized computing providers are particularly beneficial for AI applications. AI model training and deployment require large amounts of computing power, which can be prohibitively expensive to source 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 that 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 field:
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 price and allow users to monetize idle machines. Hyperbolic serves companies, researchers, data centers, and individuals, providing high-throughput, low-latency AI inference services and scalable GPU access with pay-as-you-go packages.
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 is designed to provide cloud computing services to anyone.
It will be taken
Aethir provides secure, cost-effective, enterprise-grade GPU access worldwide. With over $400 million in computing power, Aethir is focused on 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: Providing raw GPU computing power for AI model training and inference.
Aethir Atmosphere: Supports low-latency cloud gaming.
Render Network
Render Network provides decentralized GPU rendering, aiming to provide nearly unlimited GPU computing power for 3D content creation. Founded in 2017, the company is one of the oldest players in the market, focusing on enabling creators and artists to focus on content creation without worrying about computing requirements and capabilities. It is a GPU provider in itself, 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 earn revenue through high utilization. IO.net emphasizes strong security through SOC2/HIPAA compliance and end-to-end encryption. They work internally with other computing providers such as Aethir and Render to aggregate computing services provided by these partners.
AI Agent
Decentralized AI agents are autonomous programs running within a distributed network that can perform tasks and make decisions without centralized control. These agents interact with other agents and systems to create complex multi-agent environments for collaborative task execution.
The main advantages of decentralized AI agents are their independence and collaboration capabilities, enhanced stability and scalability, and 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 useful in scenarios that require trust, security, and transparency. In financial services, they can autonomously manage and execute transactions while ensuring compliance. In supply chain management, they can track and verify the movement of goods, providing real-time insights and improving 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 agents. These agents can be used in a variety of applications such as DeFi for monetizable agents, intent networks for achieving optimal user outcomes, automated game resource collection, and DAO governance. Talus' core principles are security, speed, and enhanced developer experience, enabling the creation of secure, high-performance AI applications. This ensures that intelligent agents within Talus can be securely and transparently owned, managed, and monetized.
Guru Network
Guru Network is a 3-layer 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 partners 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 storing data securely 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. A modular system can interact with multiple blockchain ecosystems, providing a versatile foundation for decentralized AI and dApps.
Reliability is critical for AI applications that require large data sets for training and inference, even during network outages or attacks. These platforms distribute data across multiple nodes to increase transparency and trust, reducing the risk of manipulation or censorship. This reliability is especially 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 smart contracts. Its modular architecture supports ample throughput through Data Availability Sampling (DAS), which is scalable while remaining verifiable by any user.
Own DA
Built on EigenLayer, Eigen DA stores Rollup transactions until their state is finalized on the Rollup bridge. Its scalability, security, and decentralization make it an ideal choice for developers who need reliable on-demand data. The core components of Eigen DA include operators, dispersers, and retrievers, which work together to efficiently store and verify data.
0g Good
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 reasoning or fine-tuning using OPML or ZKML.
Nuff Tech (almost a derivative of 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 Final 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, thereby promoting innovation in AI dApps.
game
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 a unique, personalized experience that is 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 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:
Nim Network
Nim Network is a Dymension RollApp that focuses on the intersection of web3 gaming 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. Collaborations with platforms such as 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
Now, the game will allow players to create their own dream island and build relationships with its AI inhabitants. It will be interesting to see what they 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 fusion 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 diverse 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 game AI, which enables creators to deploy complex game AI, allowing players to earn money through games, and helping 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 the highest quality data for training AI models.
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 capability 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 while it is being processed. This is especially 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 focuses 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, thereby reducing the risks associated with data leakage and unauthorized access.
human
Giza uses 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 for creating actions in a privacy-first way
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 delivering 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 security and privacy advantages. 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 applications that handle sensitive personal information.
Some players:
Gemz
Gemz is a platform designed to enhance engagement 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 provides a variety of functions and services, including:
Users can create and deploy smart contracts, perform technical and chart analysis, and receive daily crypto market updates. The platform also offers an AI chatbot to answer blockchain and crypto-related questions.
Smart Contract Generator and Auditor
AI-driven market analysis
Artificial Intelligence Trading Assistant.
Provides daily cryptocurrency market updates
Coordination network
Coordination networks in web3 are critical to enable 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.
The strong incentive mechanisms in these networks encourage active participation and collaboration, creating an open and inclusive environment for the development of AI.
Key Projects:
Then Network
Allora Network aims to enhance the intelligence and security of applications through a modular network 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
Bitten Sensor
Bittensor is a decentralized coordination network designed to incentivize sharing and collaboration of AI models. It is open source first and ensures that all transactions and contributions are transparent and verifiable, thus 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.
Coprocessor
Decentralized coprocessors provide specialized processing power for web3 applications by offloading specific tasks to specialized 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.
A decentralized network with over 30,000 nodes.
Integration of smart contracts with large language models.
Supports advanced models such as GPT-4.
Autonomous collaboration among 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 expensive infrastructure. This opens up new opportunities for innovation and growth as more entities can participate in the AI ecosystem. Additionally, decentralized model training enhances the resilience and security of the training process as tasks are distributed across multiple nodes rather than relying on a single centralized system.
Additionally, decentralized model training allows for a more flexible and scalable process. Developers can dynamically allocate resources based on real-time demand, ensuring 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 like Gensyn, organizations can accelerate their AI development and bring innovative solutions to market faster.
Key Projects:
Revisit
Gensyn specializes in providing decentralized solutions for AI model training by creating a marketplace that efficiently allocates computing resources 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, allowing researchers and developers to obtain the required computing power without large upfront investments. The use of blockchain ensures that all transactions are secure and transparent, thereby promoting 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 is focused on developing high-level tools and frameworks for creating AI models - with a focus on driving decentralized open source. Their AI pipeline can
Runs offline on edge devices
Still customizable due to open weight
Ability to generate synthetic data for production use
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, signaling huge potential.
Collaboration between stakeholders is critical. No single solution will address all challenges, so collaboration is essential 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:
Facilitates innovation: Developers can try out new ideas without having to reinvent the entire system
Enhanced flexibility: Users can customize the solution to meet specific needs, improving adaptability to different applications.
Promote interoperability: Standardized interfaces will allow components from different projects to work together 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 great prospects. As more and more people realize the value of decentralized systems, Crypto x AI is likely to become a key development area in the next 12 months.
Conclusion
This concludes our exploration of broad categories in Crypto x AI. To summarize, the top modular AI categories are highlighted:
Computing: driven by market capitalization and immediate needs.
AI Agents: Driven by innovation and development.
Data availability: Enhanced through modular support and versatility.
Privacy: Progress through dedicated research.
Games: Advancing through novel AI-led gamification.
One of the key opportunities is to get in on the project 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 area depends on how effectively programs:
Coordination
Seamless Integration
Build a powerful incentive loop
To do this, it’s important to focus on a “modular-first” approach! In future releases, we’ll dive deeper into these categories and explore exactly how the various products work.
