An Update on the accelerating changes in Web3 x AI Landscape By Kathy Tong, Venture Investor at Decasonic Web3 and AI are converging in a way that signals an emerging supercycle, redefining industries and creating transformative opportunities across finance, gaming, governance, and beyond. At this intersection, AI serves as the intelligence layer, delivering adaptive decision-making, automation, and personalization, while Web3 forms the economic layer, enabling decentralized value exchange, ownership, and coordination. Together, these technologies are not merely evolving but revolutionizing how we build and interact with digital systems, fostering autonomous systems that seamlessly integrate intelligence and economy.
At Decasonic, we believe the Web3 x AI supercycle will accelerate the convergence of disruptive technologies. We first published about the intersection of Web3 x AI here with an accompanying market map. Since our first publication on the intersection of Web3 and AI, we’ve expanded the conversation through insights into tokenized AI economies, decentralized AI governance, and the rise of AI agents in digital marketplaces. I’m excited to release our second annual Web3 x AI Market Map 2024.
Our Web3 x AI Market Map 2024 captures the rapid changes accelerating at this intersection, serving as a comprehensive guide to an ecosystem where decentralized networks amplify AI’s capabilities. By mapping the critical layers of this ecosystem—Compute, Data, Model, Interface, and Applications—we aim to provide actionable insights for founders, innovators, and investors navigating this exciting frontier.
In this article, I’ll provide a brief overview of the emerging trends in the Web3 AI ecosystem in 2024, dive into the key sections of the updated market map, and highlight the opportunities that make this an era of exponential growth.
The Rise of Autonomous Agents
Since 2023, the AI landscape has undergone transformative changes, particularly in the adoption and integration of autonomous AI agents within Web3 ecosystems.For the first time, AI agents have driven a cultural shift by creating and sustaining real economic value. The rise of agent-to-agent economics, enabled by crypto payment frameworks, has further accelerated this evolution. The infrastructure supporting AI agents has also advanced, with platforms like Creator.Bid and Talus Network enabling the creation, deployment, and trading of personalized agents. With AI tools now allowing anyone to deploy intelligent agents in minutes, the capabilities of these agents have expanded to include executing on-chain transactions, driving social media interactions, and collaborating autonomously. The exponential growth in applications—from personalized DeFi transactions to gaming and social companionship—signals a new era where AI and token-based economies are reshaping the digital landscape.
What was once limited by human intervention is now being replaced by self-operating systems capable of reasoning, planning, and even hiring humans or other agents to complete tasks. This human-in-the-loop paradigm ensures flexibility, while the pipeline increasingly shifts toward full autonomy as AI capabilities evolve. Take, for example, an AI agent launching a token—a task once beyond its reach but now executed seamlessly across platforms using tools like Virtuals, or an agent managing a fund such as ai16z. Such advancements underscore how AI is expanding the action space for agents, enabling them to operate independently in domains such as trading, robotics, and decentralized governance. AI-powered gaming such as Colony features agents that can continuously learn and develop unique personalities.
Over the past year, regulatory momentum has accelerated in favor of AI and blockchain technologies, creating a more supportive environment for the adoption of AI agents. Key changes include the growing global emphasis on establishing ethical AI frameworks and promoting transparency in autonomous systems. For instance, recent updates to GDPR and the emergence of U.S. and EU AI Acts have clarified how data privacy and AI can coexist, empowering AI agents that prioritize secure, privacy-preserving operations. Similarly, advancements in digital identity standards, such as the broader rollout of decentralized identifiers (DIDs), have enhanced the trustworthiness of AI agents operating in blockchain ecosystems. These regulatory developments are enabling smoother integration of AI agents into mainstream applications, balancing innovation with compliance and fostering institutional trust in decentralized technologies.
Since last year, the AI ecosystem has witnessed a marked pivot from infrastructure-heavy developments to a surge in application-driven innovation. In 2023, much of the focus was on building decentralized compute networks, training large-scale models, and improving data pipelines. This year, the spotlight has shifted to deploying these tools in consumer-facing applications. For example, AI agents that could only perform basic asset management tasks last year are now executing complex DeFi strategies and engaging in multi-agent negotiations on-chain. The rapid proliferation of no-code and low-code tools has made deploying personalized AI agents faster and more accessible than ever, reducing barriers to entry for creators and developers. As infrastructure becomes more standardized and interoperable, the competitive edge has shifted to creating high-value applications that offer seamless user experiences and drive tangible economic value, signaling a major leap in the evolution of AI agents.
The Building Blocks of Crypto x AI
At its core, the AI and blockchain convergence revolves around leveraging decentralized infrastructure to enhance AI's scalability, verifiability, and accessibility. This ecosystem can be visualized as a multi-layered stack, each layer contributing to a seamless interplay between these technologies.
Compute Layer: AI demands immense computational power, and decentralized networks now offer scalable GPU resources to train and deploy models. Blockchain-enabled compute marketplaces are democratizing access to processing power, reducing reliance on centralized cloud providers.
Data Layer: Reliable, verified data is the lifeblood of AI systems. Decentralized data protocols enable transparent sourcing, sharing, and validation of datasets, ensuring robust and unbiased AI models. These innovations also address privacy concerns, giving individuals control over their data while allowing developers to access high-quality datasets.
Model Layer: The Model Layer focuses on the creation, training, and deployment of AI models that interact with blockchain networks. This layer includes decentralized AI infrastructure that provides the computational resources and frameworks necessary for model development. It also encompasses systems for secure and transparent AI model validation and updates, ensuring the integrity and reliability of the models.
Interface Layer: The Interface Layer serves as the bridge between the underlying AI models and end-user or developer-facing systems. It simplifies interactions with blockchain and AI by offering tools and middleware that streamline model integration, manage interoperability, and enable the creation of autonomous agents.
Application Layer: The most visible layer consists of consumer and enterprise applications integrating AI with blockchain technology. These include AI-driven DeFi, agentic gaming and on-chain gaming, and autonomous digital agents facilitating seamless transactions and user engagement.
Decasonic Web3 x AI Market Map 2024
Our updated axes serve two key goals: aligning audience perspectives with specific use cases and providing actionable insights for founders navigating the Web3 and AI space.
X-Axis: Maps products across AI’s standard layers: Compute → Data → Model → Interface → Application.
Y-Axis: Differentiates target audiences between web3-native and web3-lite use cases.
This is not an exhaustive list, if we are missing any notable projects, we welcome your feedback on missing companies, category groupings, or ecosystem mapping refinements.
Compute:
Decentralized Data/Storage: Secure, distributed systems for storing and accessing data without intermediaries.
Decentralized Compute: Blockchain-enabled networks powering distributed computational tasks.
GPU Tokenization/Aggregator: Platforms tokenizing GPU resources to maximize accessibility and utility.
Decentralized AI Training/ML: Collaborative, decentralized networks for AI model training and machine learning.
Decentralized Inference: AI model deployment powered by decentralized infrastructure for real-time predictions.
Edge AI: Localized AI processing for low-latency, on-device intelligence.
Data:
Privacy: Safeguarding user data with privacy-preserving techniques and frameworks.
ZK Proofs: Cryptographic proofs enabling data validation without revealing raw information.
Fully Homomorphic Encryption: Privacy-preserving computation directly on encrypted data.
Data Validation/Data Oracles: Verifying and feeding reliable real-world data into blockchain networks.
Authenticity: Establishing trust in data through verification and provenance tools.
Content Validators: Tools ensuring the authenticity and reliability of on-chain content.
Proof of Personhood: Systems confirming unique human identity in decentralized environments.
Orchestration/Provenance: Tracking the origin and journey of data for transparency.
Data Source: Platforms providing high-quality data for AI training and deployment.
Private Data: Controlled datasets for specific use cases with strict access control.
Public & Synthetic Data: Open-source and artificially generated datasets for scalable AI innovation.
Labeling Data: Services for annotating and enriching raw data to train AI models.
Data Marketplaces: Data marketplaces create a decentralized exchange for buying, selling, and sharing datasets.
Model:
Decentralized Models: AI models hosted on decentralized networks to enhance transparency and accessibility.
Models: Cutting-edge AI architectures designed for decentralized deployment.
AI Chains: Blockchain-based ecosystems dedicated to AI development and operations.
Fine Tuning: Customizing pre-trained models for specific use cases within decentralized frameworks.
Ecosystems/Resource Coordination Networks: Collaborative platforms optimizing shared AI resources.
Bittensor Ecosystem: A decentralized network enabling open collaboration on AI model development.
Interface:
AI-Powered Developer Tooling: Advanced tools leveraging AI to streamline developer workflows.
Smart Contract Auditing: AI-driven tools for securing and verifying smart contracts.
Dev Tooling: Comprehensive developer platforms with integrated AI capabilities.
Risk Agents: Intelligent systems mitigating risks in decentralized environments.
Agentic Network and Platforms: AI-driven systems enabling autonomous operation within Web3.
Payment: Smart, AI-enabled transaction mechanisms for seamless Web3 payments.
OS and Operating Protocols: AI-enhanced platforms orchestrating decentralized application ecosystems.
Applications:
AI Powered Dev Tooling: Tools that automate repetitive tasks, enhance security, and enables more efficient workflows
Smart Contract Auditing:AI systems analyze smart contracts for vulnerabilities, logic errors, and inefficiencies
Risk Agents: Intelligent agents monitor decentralized systems to identify and mitigate risks
Developer Tooling: Platforms and APIs powered by AI streamline coding, debugging, and deployment processes
AI in DeFi: AI solutions driving innovation in decentralized finance.
Analytics: Tools analyzing market trends and on-chain activities for informed decision-making.
Fraud Prevention: AI models detecting and mitigating risks in decentralized financial systems.
Portfolio Construction: Optimizing asset allocation using AI-driven strategies.
Trading/Intents: Intelligent trading bots and tools powered by predictive AI analytics.
Onchain Transactions: AI-enhanced processing and analysis of blockchain transactions.
Knowledge Networks: AI-powered systems that aggregate, analyze, and distribute financial data
AI OS and Operating Protocols: AI-based OS and protocols provide the infrastructure for seamless Web3 interactions
AI-Enhanced Content Creation: AI shaping the future of creative industries in decentralized spaces.
Metaverse: AI-driven immersive experiences in decentralized virtual environments.
Gaming: Intelligent, responsive gameplay elements integrated with Web3 mechanics.
Music: AI composing and enhancing musical creations with decentralized monetization.
Art: Generative AI tools creating digital art assets for tokenized ecosystems.
AI Agents: Intelligent entities revolutionizing human interaction and utility in digital ecosystems.
Gaming Bots: Autonomous players enhancing gaming experiences in decentralized worlds.
Gaming: Bots optimizing gameplay mechanics and user engagement.
Personalities: AI-driven characters with distinct traits for personalized interactions.
Assistants: Task-oriented agents simplifying daily activities within decentralized platforms.
Companions/Launchpads: AI-driven entities providing support and onboarding in Web3 environments.
Social: AI-powered social interactions fostering deeper engagement and community building.
As AI and blockchain continue to intersect, they are laying the foundation for a decentralized, intelligent web. This is not just about the technology—it’s about reshaping the digital economy through transparency, accessibility, and user empowerment. From personalized AI-driven experiences to intelligent governance systems, the future of AI and blockchain is here, and it’s more powerful than we ever imagined.
The content of this material is strictly for informational and educational purposes only. It is not intended to constitute investment advice, nor should it be considered a recommendation or a solicitation to buy, sell, or hold any asset. Decasonic does not endorse investments in any specific tokens, and nothing in these blog posts should be construed as legal, tax, or financial advice. Please consult with a qualified professional advisor before making any financial decisions. Decasonic provides no warranties, whether expressed or implied, on the content provided in these blog posts, including its accuracy, completeness, or correctness. The opinions expressed here are those of the authors and do not necessarily reflect the views of Decasonic. Please note that Decasonic may hold a position in some of the tokens mentioned, including Virtuals. Decasonic is not liable for any errors or omissions in the content of this material or for any actions taken based on the information provided herein.
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