Crypto & Web3 News and Education

spot_img
HomeCryptoAlgorandScaling On-Chain AI: How Leading Crypto Networks Are Capturing a $3.7T Opportunity
CryptoAlgorandConstellationHederaPartner InsightsWeb3XDC

Scaling On-Chain AI: How Leading Crypto Networks Are Capturing a $3.7T Opportunity

Explore how top blockchain platforms like Hedera, Constellation, XDC, Avalanche, Bittensor, Algorand, and Cardano are building the infrastructure to support the booming AI industry, projected to reach $3.68 trillion by 2034.

Generation Infinity
By Generation Infinity
Scaling On-Chain AI: How Leading Blockchains Are Powering the $3.7 Trillion AI Revolution
Scaling On-Chain AI: How Leading Blockchains Are Powering the $3.7 Trillion AI Revolution

The global AI market is entering a new phase of exponential growth—expected to surge from $757 billion in 2025 to over $3.68 trillion by 2034. As industries integrate machine learning, data automation, and intelligent agents, the demand for scalable, transparent, and trustworthy infrastructure has never been higher.

Yet traditional systems often fall short of what next-generation AI requires: auditable data flows, secure compute, and permissionless coordination across domains. Blockchain infrastructure offers a compelling alternative. Distributed, tamper-evident, and built for composability, it solves many of AI’s most pressing bottlenecks.

Networks like Hedera, Constellation, XDC, Avalanche, Bittensor, Algorand, and Cardano are developing ecosystems tailored to intelligent agents, model marketplaces, verifiable data, and decentralized compute. Each takes a different approach—but all converge on the same opportunity: enabling AI to scale securely, transparently, and globally.

Hedera: Verifiable Intelligence with Agent-First Design

The Case for Trust: Hedera’s Architecture Advantage

Hedera is purpose-built for systems that require speed, fairness, and trust. Its use of a unique hashgraph consensus allows the network to reach finality in 3–5 seconds, processing over 10,000 transactions per second with near-zero energy use. Each transaction includes a cryptographic timestamp, creating a permanent and auditable event history.

This makes Hedera especially suited for AI agents that require precise event ordering and tamper-proof logs—qualities that centralized systems struggle to guarantee without introducing bottlenecks or trust assumptions.

Building Agents with AI Studio and Open Standards

Hedera’s AI Studio gives developers everything they need to build intelligent, verifiable agents on-chain. It solves the core problem of agent coordination in decentralized systems by combining consensus-native tools with open standards.

The Hedera Agent Kit allows developers to build modular agents in JavaScript or through LangChain and LangGraph. Agents can interact with Hedera’s smart contracts, log decisions to the Consensus Service (HCS), and store on-chain memory—ensuring full transparency and traceability.

With OpenConvAI (HCS-10), agents gain decentralized discovery and secure messaging. They can register themselves, subscribe to HCS topics, and communicate with other agents in real time—without relying on centralized APIs.

The ElizaOS Plugin adds a natural-language interface, allowing users to interact with agents through simple, human-readable prompts. For agents needing access to off-chain data, the MCP Server provides a secure bridge between external systems and Hedera’s verifiable infrastructure.

Together, these tools allow developers to create agents that are not only intelligent—but also auditable, composable, and secure by design. AI Studio turns Hedera into a full-stack environment for deploying on-chain AI systems that operate transparently from day one.

Hedera AI Studio: Powering Trusted Intelligent Systems at Scale

Real-World Examples: Compliance, Security, and AI Lifecycle Integrity

Hedera’s AI infrastructure isn’t theoretical—it’s already supporting real enterprise and institutional use cases. Key projects demonstrate how its consensus and tooling provide the trust layer needed for AI systems that must comply with real-world standards.

Prove AI: Governance and Data Access Control

Prove AI (formerly Casper Labs) uses Hedera’s Consensus Service to anchor access logs for AI training datasets. This creates a permanent, tamper-proof record of who accessed what data and when. It helps companies meet standards like the EU AI Act and NIST AI RMF, which require transparent oversight of training data and model inputs.

EQTY Lab: Verifiable Compute with Hardware Security

In partnership with Intel and NVIDIA, EQTY Lab uses Hedera to anchor compute outputs from Trusted Execution Environments (TEEs). These environments securely run AI workloads and write hashes of the results to Hedera, ensuring each computation is provable and unaltered—a critical step for AI in finance, defense, and healthcare.

NVIDIA: Real-Time Validation for Federated AI

NVIDIA uses Hedera to add real-time verifiability to its federated learning models, which train across multiple data sources. Each node in the network logs its updates on Hedera, allowing teams to track versioning and contributions with transparent timestamps. This reduces the risk of model drift and manipulation.

Together, these use cases show why institutions trust Hedera’s infrastructure. It brings compliance, auditability, and secure coordination to every step of the AI lifecycle—from training data access to model execution and system updates.

Prove AI Moves to Hedera: Revolutionizing AI Governance with Blockchain

Developer Momentum: A Thriving Agent Economy

The OpenConvAI Hackathon, hosted by Hashgraph Online DAO, ran from April to May 2025 with a $30,000 prize pool. Over 75 functional agent projects were submitted, with tools ranging from DAO governance bots to decentralized scheduling assistants.

The Bonzo Finance Challenge followed with 15,000 $BONZO in rewards for building AI agents that analyze DeFi lending patterns and automate protocol governance.

Hedera x AI Demo Day: Showcasing Scalable AI Solutions

We recently hosted the Hedera x AI Demo Day on May 20th and 21st. Hackathon participants pitched AI agent projects. They used Hedera’s network for scalable solutions. For example, 14 teams competed for a $30,000 prize pool. Judges from Web3 and AI evaluated live pitches. Meanwhile, Hedera co-founders Dr. Leemon Baird and Mance Harmon appeared. Day 1 featured dynamic presentations. Then, Day 2 continued with more pitches and Dr. Leemon Baird’s insights. Each project leveraged Hedera’s infrastructure for transparency. Moreover, the event highlighted verifiable AI systems. Spectators and investors watched the live stream. Ultimately, the Demo Day showcased Hedera’s potential for scaling AI innovations.

Enhancing Hedera’s Ecosystem with HashPack’s AI Integration

During the Hedera X AI demo day, HashPack showcased its AI integration, the HashPack Concierge, enhancing user interactions within the Hedera ecosystem. It maintains security by not requiring wallet connections or exposing private keys. The AI leverages Hedera docs and ecosystem partners to deepen its technical understanding. Users can query account details, security issues, and token information easily. The system provides rich, detailed responses, including technical analyses and interactive charts. It facilitates transactions with previews, simplifying common actions like swaps. The Concierge appeals to new and existing users, offering guidance and quick access to information. It integrates with the PACK token and Concierge NFTs, using micropayments for queries and offering discounts. This development supports Hedera’s goal of scaling AI solutions by improving usability and fostering economic activity.

Hedera is shaping the future of on-chain AI by fusing real-world compliance needs with developer-friendly modular tooling.

Constellation: DAG-Optimized Infrastructure for Intelligent Data Pipelines

Scaling by Design: Hypergraph and Metagraphs

Constellation doesn’t follow the standard block-and-chain model. Instead, it leverages Directed Acyclic Graph (DAG) architecture via its Hypergraph Transfer Protocol (HGTP). This design allows Metagraphs—independent, app-specific Layer 1 chains—to process data asynchronously, syncing through periodic global snapshots.

This architecture enables horizontal scalability, feeless operation, and parallel execution—key attributes for AI workloads involving high-frequency or sensitive data.

Specialized Metagraphs Supporting AI

Constellation’s Metagraphs offer a powerful structure for running AI systems on networks optimized for specific data, logic, and trust requirements. Unlike general-purpose blockchains, each Metagraph operates as its own Layer 1, giving developers full control over execution, governance, and data validation. Two early Metagraphs show how this enables real-world AI solutions.

Common Crawl Metagraph: Anchoring AI Training Data

In partnership with the Common Crawl Foundation, Constellation launched a Metagraph that secures one of the most widely used open web datasets. It anchors over 250 billion web pages, many used to train large language models.

What sets this apart is data provenance. Every snapshot is cryptographically timestamped and verifiable. Developers can prove when and how data was indexed—addressing growing concerns around copyright, licensing, and model accountability. This creates a transparent foundation for AI training, especially important under the EU AI Act and similar frameworks.

Prove AI Moves to Hedera: Revolutionizing AI Governance with Blockchain

Digital Evidence Metagraph: Real-Time Field Data Integrity

This Metagraph was built with Panasonic and Forward Edge-AI to secure data collected during emergency response operations. Devices like TOUGHBOOKs used by EMS and law enforcement record video, logs, and telemetry during critical events. These are notarized on-chain in real time.

AI agents can process this data for incident reporting, compliance, or forensic review—knowing it’s tamper-evident and anchored to an immutable source. It transforms raw field data into trusted AI inputs, reducing reliance on manual oversight or after-the-fact validation.

Together, these Metagraphs demonstrate how Constellation enables verifiable AI pipelines, built directly into the architecture. Developers can run models and agents on secure, customized chains purpose-built for their domain.

Constellation Network Launches Digital Evidence to Power the Transparency Economy

National Security Meets Intelligent Systems

Constellation’s work with the U.S. Department of Defense through Iron SPIDR—a secure, decentralized forensic platform—is now expanding into AI. Metagraphs within Iron SPIDR power secure, agent-assisted operations for logistics, threat detection, and data analysis. These are live military pilots, not theoretical models.

TraceAI, another emerging project, is creating author-proofing agents that verify the origin, authorship, and modification of AI-generated content—anchored to Constellation’s cryptographic ledger.

What Makes Constellation Unique for On-Chain AI

Constellation gives developers full control by offering custom, feeless Layer 1 chains called Metagraphs. Each Metagraph runs independently with its own consensus, logic, and token design.

This flexibility creates clear benefits for AI development:

  • Parallel, scalable execution: Metagraphs process data asynchronously and sync through global snapshots. This enables AI pipelines to run in real time—without congestion or block limitations.
  • Feeless transactions: AI agents can operate continuously without gas concerns. This supports high-frequency tasks like model updates, data validation, or edge-device reporting.
  • Custom privacy and logic: Developers can define unique rules for data access, validation, and economic incentives. This is critical for sensitive AI domains like defense, healthcare, and finance.
  • Agent-native architecture: Unlike bolt-on solutions, Metagraphs can be designed specifically for autonomous agents—ensuring optimal performance and integration.

Constellation’s architecture avoids the bottlenecks of traditional blockchains. For AI systems that need speed, verifiability, and composable data workflows, it provides a purpose-built foundation.

Exclusive Insights: Constellation Network’s Tessellation v3 Network Upgrade

XDC: Enterprise AI Infrastructure for Regulated Markets

XDC Network is purpose-built for regulated environments. Its hybrid architecture combines permissioned Subnets with a public Layer 1 for anchoring. This design enables confidential AI workflows while maintaining auditability and public proof.

Each Subnet can run private, high-performance logic. Developers choose their own governance rules, consensus participants, and access controls. Once processed, they anchor final outputs to the XDC mainnet for compliance and verification.

This approach is ideal for financial institutions, insurers, and supply chain operators—especially those integrating AI into mission-critical operations.

RWAi Sprint: AI for Tokenized Asset Management

The RWAi Developer Sprint, launched in April 2025, focused on building AI agents to automate real-world asset (RWA) workflows. Over 60 teams submitted agent applications, including:

  • Valuation models for tokenized real estate and invoices
  • Compliance tools for KYB and risk scoring
  • NLP agents that extract terms from legal agreements
  • Smart agents that monitor asset maturity and trigger payments

All teams used ElizaOS to build composable agents deployable across XDC’s Subnets and public chain.

XDC Dev Sprint 1: Advancing RWA Tokenization with AI Agents

Tools That Support Secure and Efficient Agent Development

Building AI agents in regulated environments requires tools that ensure reliability, transparency, and secure execution. XDC supports this with CodeRun, an AI-powered development assistant that helps teams write and audit smart contracts faster and with greater confidence.

CodeRun functions as a co-pilot for developers working across XDC’s Subnets and mainnet. It supports multiple languages—including Solidity, Rust, and Go—and offers real-time guidance throughout the development process.

Key features include:

  • Auto-complete and error detection: As developers write code, CodeRun suggests fixes and flags syntax errors before deployment.
  • Security auditing: It scans contracts for known vulnerabilities like reentrancy, unchecked calls, and integer overflows.
  • Test coverage insights: Developers receive instant feedback on how well their code is tested, helping reduce risk in production.
  • Standardization support: It encourages use of XDC-aligned templates and best practices to streamline development across Subnets.

For teams building AI agents that interact with tokenized assets, compliance logic, or sensitive financial data, CodeRun reduces development time while improving security. It ensures that smart agents launched on XDC are built with a foundation of reliability and precision.

Why XDC Works for AI in Regulated Environments

XDC bridges AI execution and regulatory accountability:

  • Subnets allow private compute while anchoring proofs on-chain
  • Role-based validator logic supports industry-specific rules
  • Agents can automate custody, audit, and compliance tasks at scale

This makes XDC well-suited for institutions building AI to handle tokenization, asset servicing, or financial operations—without compromising privacy or compliance.

Algorand: Empowering AI Development with Native Python Support

Algorand is bridging the gap between blockchain and artificial intelligence by offering native Python support through its latest developer toolkit, AlgoKit 3.0. This integration allows AI developers to build decentralized applications using familiar tools and libraries, streamlining the development process and fostering innovation in AI-driven blockchain solutions.

AlgoKit 3.0: A Comprehensive Toolkit for Python Developers

Released in March 2025, AlgoKit 3.0 provides a robust set of tools tailored for Python developers:

  • Algorand Python: Enables writing smart contracts in standard Python syntax, compatible with Algorand’s Virtual Machine (AVM).
  • Visual Debugging: Integrates with VSCode for step-by-step debugging of smart contracts, enhancing development efficiency.
  • LocalNet: Allows developers to run a local instance of the Algorand network for testing and development purposes.
  • Lora Explorer: Provides a visual interface to explore accounts, transactions, and smart contracts on the Algorand blockchain.

These tools collectively reduce the complexity of developing AI applications on the blockchain, making it more accessible to a broader range of developers.

AlgoKit V3 Launch Opens Up Algorand to Millions of TypeScript Developers

Facilitating AI Agent Development

With Python being the primary language for AI and data science, Algorand’s native support enables seamless integration of AI agents into blockchain applications. Developers can leverage existing Python libraries for machine learning, data analysis, and natural language processing to build intelligent agents that operate on-chain. This capability opens avenues for creating decentralized AI applications, such as automated trading bots, predictive analytics tools, and intelligent contract management systems.

Real-Time Data Interaction with Subscriber Library

Algorand’s Subscriber library allows Python applications to subscribe to blockchain events in real-time. This feature is crucial for AI agents that need to respond to on-chain events promptly, such as market changes or contract executions. By enabling real-time data interaction, developers can build responsive AI systems that interact dynamically with the blockchain environment.

Building a Developer-Friendly Ecosystem

Algorand’s commitment to supporting widely-used programming languages like Python reflects its strategy to lower entry barriers for developers. By aligning blockchain development with familiar tools and languages, Algorand fosters a more inclusive and innovative ecosystem, encouraging the integration of AI capabilities into decentralized applications.

Avalanche: Fully Customizable Subnets for Autonomous AI Economies

Avalanche approaches on-chain AI with a clear priority—modular scale and sovereign control. Its Subnet architecture allows developers to launch their own blockchains with custom consensus, execution logic, privacy rules, and tokens. Each Subnet runs independently but still connects to the Avalanche ecosystem through Avalanche Warp Messaging (AWM) for seamless interoperability.

This design gives AI developers a way to build fully isolated environments while maintaining the ability to exchange data and value across chains. For AI agents and workloads with high throughput or regulatory demands, this flexibility is essential.

Kite AI: A Native AI Layer 1 on Avalanche

Launched in early 2025, Kite AI is Avalanche’s first AI-focused Layer 1, built entirely as a Subnet. It introduces Proof of Attributed Intelligence (PoAI)—a consensus mechanism designed to reward contributors of data, models, and compute resources. This aligns the economic incentives of AI builders directly with network growth.

Kite AI also includes:

  • A modular execution layer for inference and model training
  • ZK tooling for proof-based compute validation
  • Data and compute Subnets for scaling agent tasks

Within 70 days of testnet launch, Kite recorded over 115 million AI agent interactions and connected to 1.95 million wallets. It’s already being integrated into vertical use cases in healthcare, legal automation, and autonomous analytics.

Eternal AI: Deploying Agents as a Service

Eternal AI builds autonomous, cross-chain agents that operate without centralized coordination. The protocol lets developers launch AI agents on Avalanche Subnets and interact with them using natural language or APIs.

Core capabilities include:

  • On-chain identity and memory for agents
  • Verifiable execution through AWM
  • Payment integration using Subnet-native or bridged tokens

Eternal AI is open source and integrates directly with LangChain and ElizaOS, making it easy to create agents with composable logic and multi-modal input/output. It’s one of the first platforms on Avalanche to offer Agent-as-a-Service infrastructure at scale.

A Growing Ecosystem for AI Builders

Avalanche backs its AI vision with strong developer support:

  • The InfraBUILDL(AI) program provides up to $15 million in funding for AI-native projects, tooling, and Subnets.
  • The Ted Yin Grant Program supports open-source innovations, including custom VMs and experimental AI consensus layers.

These grants help reduce time-to-market for AI projects while encouraging experimentation across sectors.

Guide to Avalanche Subnets: Pioneering Customizable Blockchain Ecosystems

Why Avalanche Is Built for Autonomous AI

  • Subnets provide full sovereignty over logic, compliance, and execution
  • Warp Messaging allows agents to operate across chains in real time
  • The architecture scales horizontally, letting each AI workload grow independently

For teams building agents that require privacy, specialization, or throughput, Avalanche offers a ready-made framework. It’s not just infrastructure—it’s a launchpad for AI-native economies powered by composable, verifiable, and monetizable agents.

Cardano: A Research-Driven Foundation for Scalable, Trustworthy AI

Cardano approaches AI integration with a focus on formal verification, scalability, and decentralized governance. Its layered architecture and academic rigor provide a robust platform for deploying AI agents that require transparency, security, and compliance.

Infrastructure Built for AI Workloads

Cardano’s infrastructure is designed to support complex AI applications:

  • Ouroboros: A provably secure, energy-efficient proof-of-stake consensus protocol that ensures reliable and sustainable operations.docs.superintelligence.io
  • Hydra: A layer-2 scaling solution that enables high-throughput processing by creating multiple “heads” or channels, allowing parallel transaction processing.
  • Extended UTXO (EUTXO) Model: Combines the benefits of Bitcoin’s UTXO model with smart contract capabilities, facilitating precise and secure transaction handling.

These components collectively enable Cardano to handle AI workloads that demand high scalability and deterministic execution.

Cardano Blockchain: A Comprehensive Analysis of Its Evolution, Technology, and Ecosystem

AI Agent Deployment with Masumi

The Masumi framework exemplifies Cardano’s capability to host AI agents:

  • Agent Interaction: AI agents perform tasks and interact with each other within the network.
  • Smart Contracts: Govern agent registration, discovery, and payment processes, ensuring automated and transparent operations.
  • Task Execution and Logging: All actions are recorded on-chain, providing an immutable audit trail.
  • Evaluation and Payment: Outputs are evaluated against predefined criteria, triggering automatic payments upon successful completion.
  • Dispute Resolution: In case of discrepancies, the on-chain logs serve as evidence for fair resolution.

This structured approach ensures that AI agents operate within a secure and accountable environment.

Ecosystem Support for AI Development

Cardano’s ecosystem actively supports AI integration through various initiatives:

  • SingularityNET Migration: Transitioning from Ethereum to Cardano to leverage its scalability and formal verification for decentralized AI services.
  • Dedium: A decentralized computing network providing GPU resources for AI and machine learning tasks, utilizing frameworks like Ray for distributed computing.
  • Sync AI: An AI-powered DePIN (Decentralized Physical Infrastructure Network) facilitating communication between users, dApps, and blockchains.

These projects demonstrate Cardano’s commitment to fostering a robust AI development environment.

Developer Tools and Community Initiatives

To support developers in building AI applications, Cardano offers:

  • Cardano Dev Assistant: An AI-powered VS Code extension providing real-time syntax correction and explanations of Cardano functions, enhancing development efficiency.
  • Research Guild: A community-driven initiative conducting reviews and research on open-source AI tools applicable to the Cardano blockchain.

These resources aim to lower the entry barrier for developers and encourage the creation of innovative AI solutions on Cardano.

Bittensor: Decentralizing AI Through Incentivized Intelligence

Bittensor introduces a novel approach to artificial intelligence by establishing a decentralized network where machine learning models contribute, evaluate, and improve collaboratively. At its core, Bittensor transforms AI development into an open, permissionless marketplace, rewarding participants based on the value of their contributions.

Subnets: Specialized Networks for Diverse AI Tasks

The Bittensor ecosystem is structured around “Subnets,” each tailored to specific AI domains such as natural language processing, image recognition, or predictive analytics. These Subnets operate autonomously, allowing for focused development and optimization within their respective areas. Participants, including miners and validators, engage in these Subnets by contributing models or evaluating outputs, fostering a competitive environment that drives innovation and quality.

Yuma Consensus: Rewarding Valuable Contributions

Central to Bittensor’s operation is the Yuma Consensus mechanism, a Delegated Proof of Stake (DPoS) system that evaluates and ranks the performance of models within the network. Validators assess the outputs of miners, assigning weights that reflect the quality and relevance of their contributions. These assessments inform the distribution of rewards, ensuring that participants are compensated proportionally to the value they add to the network.

TAO Token: Fueling the Bittensor Ecosystem

The TAO token serves multiple roles within Bittensor:

  • Incentivization: Miners and validators earn TAO tokens as rewards for their contributions to AI model development and evaluation.
  • Staking: Participants stake TAO to secure their roles within the network, aligning their interests with the system’s integrity and performance.
  • Governance: Token holders participate in decision-making processes, influencing network upgrades and policy changes.
  • Transactions: TAO facilitates transactions within the network, including accessing AI services and transferring value.

With a capped supply of 21 million tokens and a halving schedule similar to Bitcoin’s, TAO’s design encourages long-term engagement and value appreciation.

Dynamic TAO: Decentralized Subnet Valuation

Bittensor’s Dynamic TAO framework enhances decentralization by allowing Subnets to establish their own economies through unique alpha tokens. These tokens are backed by TAO reserves, and their value reflects the demand and performance of the respective Subnet. This mechanism enables a more organic and distributed valuation of AI contributions across the network.

Real-World Applications and Ecosystem Growth

Bittensor’s architecture supports a range of applications, from decentralized AI model marketplaces to collaborative research platforms. Projects like TensorSpace exemplify the network’s potential, offering tools for users to build and share AI models without centralized control. The ecosystem’s growth is further evidenced by the increasing number of active Subnets and the expanding community of developers and researchers contributing to the network.

The Road Ahead: Where Blockchain and AI Converge

Artificial intelligence will shape the next era of global infrastructure, but its success depends on the systems supporting it. Blockchain platforms are stepping into this role with purpose-built solutions that enable trust, scalability, and autonomy for AI systems.

Hedera’s verifiable agents, Constellation’s feeless DAGs, Avalanche’s sovereign Subnets, and Bittensor’s decentralized intelligence marketplace are early proof that decentralized infrastructure can support complex AI demands. Meanwhile, XDC, Algorand, and Cardano are aligning their ecosystems to accommodate Python-based tooling, agent frameworks, and enterprise-grade compliance—positioning themselves for long-term relevance.

As the AI economy matures, infrastructure will define which platforms thrive. Those combining transparency, programmability, and performance are already capturing developer mindshare—and setting the stage for a more intelligent, decentralized future.

*Disclaimer: News content provided by Genfinity is intended solely for informational purposes. While we strive to deliver accurate and up-to-date information, we do not offer financial or legal advice of any kind. Readers are encouraged to conduct their own research and consult with qualified professionals before making any financial or legal decisions. Genfinity disclaims any responsibility for actions taken based on the information presented in our articles. Our commitment is to share knowledge, foster discussion, and contribute to a better understanding of the topics covered in our articles. We advise our readers to exercise caution and diligence when seeking information or making decisions based on the content we provide.

Recent Updates

Previous article
DTCC’s Plan For Digital Liquidity Tokens: New Patent Outlines Integrating XRP Ledger and Stellar Blockchain into Interoperable Asset Management
Next article
Algorand May 2025 Recap: Real-World Use, DeFi Expansion, and a Stronger Decentralized Foundation
Generation Infinity
Generation Infinity
RELATED ARTICLES
spot_img

Latest

Load more

Most Popular

Load more

EDITOR PICKS

POPULAR POSTS

POPULAR CATEGORY

ABOUT US

Genfinity is a leading media firm with a dynamic platform dedicated to knowledge-sharing, networking, and connection in the rapidly evolving cryptocurrency and digital landscape. Our mission is to cultivate, inform and engage communities for our partners and affiliates via value-based education.

Contact us: support@genfinity.io

spot_img