AI x Web3 Use Cases

100 Use Cases at the Intersection of AI x Web3 -– Paul Hsu, CEO and Founder, and Abdul Al Ali, Venture Investor, Decasonic 

Decasonic is a $50M AUM venture fund operating at the intersection of AI x Web3. Our thesis centers around a five-layer stack that defines the intersection of AI x Web3:

• Infrastructure

  • Model

  • Compute

  • Data

• Applications

• Interface

Our focus at Decasonic is primarily at the Application and Interface layers. As model efficiency compounds and infrastructure costs decline, value accrual is shifting up the stack. This change reinforces our product-first investment approach, where we prioritize durable, frontier use cases rooted in real engagement. We operate as adoption-first investors, obsessively analyzing, tracking, and engaging with the core metrics of Web3 x AI adoption. We believe we’re still in the early innings of a Web3 x AI supercycle, an exponential convergence that will give rise to non-consensus, market-defining leaders.

Web3 and AI thrive at the frontier of human-agent collaboration, especially across the primitives of culture, collaboration, and co-ownership. Web3 introduces programmable, co-owned business models into the AI stack. As the open-source arc continues toward edge inference, personalized agents, and physical interfaces, tokenization unlocks new dimensions of liquidity, utilization, and composability. Web3 doesn’t just support AI, it changes the view on how AI is monetized, distributed, and governed. 

At Decasonic, we map this thesis across core categories: Consumer AI x Web3, AI x RWA, AI x DeFi, AI x DePIN, AI Infrastructure, AI x SocialFi, and AI x GameFi. Each sector represents a lens where Web3 traditional sectors intersect with AI. We’ve identified 100+ use cases across this matrix, drawn from live deal flow, evolving and identified projects, and on-chain product data.

We’re not just investors, we’re builders. We understand the depth it takes to win at the intersection of AI x Web3, and we match the speed of founders building here. Every week, we see new features, ships, labs compete for distribution, and adoption scale. We deploy our own native AI tools, integrate AI into workflows, and work alongside AI teammates across Decasonic. Below is a list of the 100+ use cases identified. 

Consumer AI x Web3

• Vibe coding dApps enabling non-developers to launch products and product tokens

• Personalized dApp recommendations based on user activity

• AI x Crypto devices, emerging opportunity in the Physical AI space

• Agents facilitate cross-border shopping by translating, pricing, and swapping tokens in-app

• AI-enhanced dApps integrate personalized UX into consumer products

• AI Agent workflows automate daily tasks like bill pay, alerts, and bridge transfers

• AI-powered Crypto EdTech platforms surface contextual education based on wallet activity and providing enhanced recommendations

• Companion AI Agents tokenized on-chain, with usage tracked based on a token-gated model 

• Swarm AI enables group agent collaboration to solve multi-agent objectives

• AI app stores and marketplaces make agent discovery consumer friendly

• Multiplier workflows support co-creation across users and agents

• Automation workflows with agents acting as underlying ‘templates’ 

• Mobile-first superapps blend AI content creation with payments and social feeds

• Wearable-synced agents recommend tokenized wellness routines

• AI avatars interact in metaverses, earning rewards based on engagement

• Loyalty programs tailor NFT rewards using behavioral AI models

• Wallets integrate AI tax tools to automate transaction summaries

• Voice agents guide consumers through dApps and on-chain transactions 

• NFT generation personalized to user taste, optimizing for rarity and value

• AI companions in the form of NFTs, storing metadata

• Conversational agents manage personal finance tasks like rebalancing and budgeting

• AI-powered support agents respond to user inquiries in real time

• Human verification protocols run AI to enforce proof-of-humanity standards

AI x Real World Assets (RWA)

• AI prices tokenized real estate based on market comps and on-chain transactions

• Loan underwriting models enable undercollateralized lending in RWA DeFi

• Provenance for tokenized art verified using AI image and metadata analysis

• AI balances production and storage for energy token protocols

• Capital allocation AI routes investor funds across tokenized T-bills or real estate

• Invoice-backed tokens scored dynamically using AI supply chain risk models

• AI adjusts collateral ratios on RWA lending protocols based on real-time signals

• Tokenized data centers and storage providers use AI to optimize capacity

• Insurance claims processed and premiums priced using climate AI predictions

• Crowdfunded property portfolios are rebalanced using AI across geos and risk

• Gold token audits monitored using anomaly detection across vault inventory

• Collectible bundles built by AI to diversify risk in tokenized luxury assets

• Carbon credits priced and tracked via AI-powered registries

• KYC verification enhanced with AI-driven fraud detection

• AI values royalty streams from music, film, and content NFTs

• Supply chain DeFi markets dynamically price invoices using predictive AI

• AI flags default risk in tokenized muni and infra bond markets

• Portfolio construction agents recommend fractionalized real estate allocations

• Yield-insured RWA strategies coordinated by AI agents

• Stablecoin agents auto-transact across RWA platforms to manage volatility

AI x DeFi

• DeFi copilots guide users across swaps, bridges, and yield opportunities

• Vaults rebalance automatically using AI liquidity and fee optimization

• Risk bots build credit scores from wallet and on-chain behavioral data

• Social sentiment AI ranks DeFi protocols using off-chain and GitHub signals

• Stablecoin peg mechanisms adjust using dynamic AI feedback loops

• AI arbitrage agents scan DEXs for pricing discrepancies across chains

• Gas prediction models optimize transaction timing and chain selection

• Personalized portfolio managers build custom on-chain strategies

• Governance summaries and impact analysis surfaced by AI

• Perp DEXs use trader behavior modeling to tune funding rates

• AI models estimate demand for synthetic assets before launch

• Aggregators route through best yield and gas paths using AI

• AMMs reconfigure pool fees dynamically using AI usage data

• Derivative risk priced live using real-time predictive modeling

• Liquidity mining shifts dynamically with AI-based user cohort retention

• AI structures token-based ETFs for broker-dealer DeFi models

• Personalized DeFi content created based on wallet behavior

• Tax bots compile and categorize trades across chains

• Lending rates optimize based on borrower AI intent prediction

• Exploit detection flagged by anomaly-aware agents

AI x DePIN

• Globally distributed GPU networks train LLMs via AI-optimized job routing

• Programmable Energy with AI forecasting for optimization 

• Robot fleets use tokenized DePIN layers for compute and navigation

• Edge inference nodes process factory and field data using AI locally

• Tokenized ownership of compute and robotics hardware coordinated by AI

• Data marketplaces sell sensor and image datasets to train vertical AI models

• Fully autonomous agents run across DePIN stack for finance and logistics

• AI manages P2P energy trading via microgrids with dynamic pricing

• Robotics-as-a-service platforms match supply to demand via AI scheduling

• Machine ID devices authenticate and transact on decentralized infra

• AR/VR spatial networks crowdsource data and serve it via AI

• Shared vehicle fleets route and transact autonomously with AI and DePIN

• Smart city sensors share traffic and climate data for AI optimization

• Telecom mesh networks balance bandwidth and demand with predictive AI

• Community networks authenticate, route, and optimize via AI agents

AI Infrastructure

• Decentralized inference nodes benchmarked and routed by AI

• Distributed compute networks

• Tokenized LLM marketplaces rank and price models on latency and quality

• Agent toolkits support one-click deployment across decentralized infra

• Agent orchestration layers support training, simulation, and reward calibration

• LLM co-creation tools enable verticalized fine-tuning on private data

• Verifiability of LLM outputs

• Verifiability of Inference Models 

• Physical AI logging devices tokenize real-world interaction data

• Edge inference networks tokenize AR, VR, or voice model compute

• Validator networks run on-chain forecasting agents for enhanced outputs 

AI x SocialFi

• Agents act as content creators across social platforms and DAOs

• Gated social networks, prioritizing human vs AI content 

• Agentic-run commerce, with AI Agents facilitating transactions on behalf of users 

• Human verifications run on facial or voice input for token access

• Launchpads for AI Agents

• AI Agent Influencers, including KOLs 

• Follower graphs optimized using AI behavioral similarity

• Reward distribution engines identify high-quality social engagement

• Prediction markets shift odds based on sentiment and prediction history

• Reputation systems update live with cross-chain and off-chain inputs

• DAOs use AI for task assignment and contributor rewards

• Meme bots auto-generate content from price feeds or events

• Agent-run DAOs manage community funds and decision-making

• Launchpads surface and distribute AI-native tokens or memecoins

• Agent commerce bots run affiliate, merch, or paid posts

• Social training agents use community feedback to evolve memetic models

AI x GameFi

• Procedurally generated quest trees adjust based on player wallet history

• AI trainers recommend builds, skills, and game economy strategies

• UGC platforms reward AI-enhanced modding and content

• Matchmaking engines match based on style, latency, and skill

• In-game avatars use AI to act like teammates or rivals

• Token rewards adjust based on user fatigue or engagement

• eSports commentary bots stream matches in real time

• Loot mechanics balance inflation using predictive AI

• Branching storylines adapt based on token interaction and NFT usage

• DAOs vote on game content drops and ecosystem rewards

• AI anti-cheat detects pattern anomalies and user spoofing

• AI NPCs deliver story arcs and character development

• Game agents replace static NPCs with dynamic personalities

Conclusion

If you're building at the intersection of Web3 and AI, if you’re pursuing any of these use cases, we want to hear from you. Reach out to us at Decasonic. Our DMs are always open. We're here to support and partner with innovators actively shaping the future of this intersection. 

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.