Market Mapping the Intersection of Web3 and AI
By, Danny Pantuso, Venture Investor Decasonic
The Yin and Yang of Web3 and AI
At its core, we see the intersection of AI and Web3 as a powerful and mutually reinforcing relationship. AI, with its capacity for scalable creation, operates within a realm characterized by probability, centralization, and black-boxed processes. It has ushered in unprecedented automation, data analysis, and content generation capabilities.
Conversely, Web3 embodies a decentralized paradigm—a deterministic, trustless, and transparent framework. This transformation has redefined our understanding of trust, ownership, and openness.
While there are still many unknowns in this intersection of AI and web3, we have an early thesis that this convergence tackles critical challenges for each respective technology. One hand washes the other.
Just to name a few, decentralized computing has rapidly emerged as a prominent solution to meet the growing computational shortage of AI. Blockchain allows AI agents to define objectives, create agreements, and cooperate. We anticipate that the AI Agent to AI Agent economy will be massive as these agents become ingrained in every layer of the stack. Generative AI creates an endless amount of content, while blockchain introduces digital scarcity and authenticity through on-chain provenance. Blockchain facilitates open and transparent governance of on-chain AI models, reciprocated by AI enhancing the governance of DAOs. AI augments financial decision-making, while blockchain provides a transparent, open, and immediate transaction layer.
Together, these two technologies create a pandora box of new use cases. As early-stage venture capitalists we’ve mapped this intersection and introduced two new dimensions to the traditional market map, corresponding to the concept of product-market fit (PMF).
Market Map Structure
The introduction of these axes serves a dual purpose. First, it enables us to align perspective audiences with specific use cases, providing valuable context for understanding the categories within the Web3 and AI intersection. Secondly, while the interpretation of this mapping can be subjective, our intention is to provide a valuable resource, particularly for founders operating at the PMF stage. By presenting this multifaceted view of the landscape, we aim to empower emerging startups with a clearer understanding of where they fit within the evolving Web3 and AI ecosystem.
X-Axis: This axis spans the spectrum of products, ranging from infrastructure to applications. It aligns with the standard layers of AI technology: Compute Layer → Data Layer → Model Layer → Interface Layer → Application Layer.
Y-Axis: Here, we map the target audience, differentiating between web3-native use cases and web3-lite use cases.
We've chosen to use names rather than logos for clarity and accessibility.
Of course, this is an emerging and dynamic space. If you believe we've missed any companies or have suggestions on how to group categories differently or map the ecosystems, we welcome your input.
Categories:
Decentralized Storage: data storage on a decentralized cloud.
Decentralized Inference: Compute for AI Inference that powers AI services.
Decentralized Machine Learning (aka model training): Compute to train AI models.
ZK Privacy / Fully Homomorphic Encryption: Zero Knowledge protocols can make proofs about a dataset without exposing the data, while Fully Homomorphic Encryption is a cryptographic technique that allows computations to be performed on encrypted data without the need to decrypt it, preserving privacy while enabling data processing.
Authenticity
Content validators - Utilizing blockchain to track data provenance or detect bots, AI generated content, and spoofs in the fight against malicious AI generated content.
Proof of Personhood: Privacy preserving, decentralized verification human presence or content on the internet. This will be increasingly important in a world where AI generated content and agents become increasingly indiscernible from human content.
Data Aggregators: Decentralized exchange/marketplace that enables users to create or find data sets needed to train AI models.
Data Validators: AI powered oracles that validate the off-chain data being processed, making it accessible for blockchain protocols.
Decentralized Models:
On-Chain AI: Models that live entirely on-chain. Where centralized models only produce outputs, with no insight into the inputs, fully on-chain models can be built solely off of known inputs resulting in reputable outputs.
Federated Learning: a machine learning approach where a model is trained collaboratively across multiple decentralized devices or servers while keeping the data on those devices, preserving privacy and reducing the need for centralized data aggregation.
Smart Contracts and Automated Decision Making
Smart Contract Auditing: Automated auditing of smart contracts for live-ops and test net development
DAO Governance: DAO’s that have operate with AI suggested proposals, or AI governance tooling and management platforms
Mixed Reality x Generative AI:
Interoperability: standards utilizing AI to transform NFT’s for various applications
Generative 3D: Generative AI to create 3D assets and immersive experiences
AI Agents
Consumer
Personal Assistants: executive assistants, tailored to users unique needs and preferences (ie DnD Dungeon Masters)
AI Talent / VTubers: AI streamers / influencers
Gaming Bots: AI Agents for NPC’s in game
Enterprise
Sales: AI powered sales avatars
Marketing: AI powered copywriters and company representatives
Customer support: individualized customers support agents
AI Powered Web3 Services
Developer Relationships: Companies using AI to power developer relations and quests for open source code contributors
Analytics: Raw, NLP-supported blockchain analytics
Marketing: Web3 marketing platforms, powered by AI analytics.
AI & DeFi
Yield Farming: AI optimization of token staking on DeFI projects
Pricing: Real time pricing services on open DeFI markets and on-chain assets
Portfolio Construction: Trading bots and automated investment management platforms
Fraud Detection: AI analytics to detect fraud and illegal trading on DeFi protocols
If you are among those pioneering at this intersection of Web3 x AI we encourage you to reach out to a team member to help drive this vision forward.
The content of this material is strictly for informational and educational purposes and is not meant to constitute investment advice or a recommendation or solicitation to buy or sell any asset or to make any financial decision. Nothing in these blog posts should be considered legal or tax advice. You should consult with your own professional advisor before making any financial decision. Decasonic offers no warranties on any content in the material posted in these blog posts, including that it is accurate, complete, or correct. The opinions expressed in these posts are those of the authors and do not necessarily reflect the views of Decasonic. Decasonic is not liable for any errors or omissions in the content of this newsletter or for any actions taken based on the information provided herein.
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