Intuition is building a token-curated knowledge graph and a native InfoFi stack that allows people and machines to create, curate, and monetize trusted information. The platform’s strongest use case is InfoFi a market for verified claims, reputational assets, and data primitives that are discoverable, provably staked, and economically weighted for machine consumption. This post explains how InfoFi works, why it matters, how it could be adopted, and the main risks to watch.
What is Intuition
Intuition describes itself as a decentralized knowledge network and a native blockchain for InfoFi a tokenized market for information built around a token-curated knowledge graph. The project exposes mechanisms where every claim or data point can be minted, staked on, and economically weighted, enabling trusted discovery and monetization.
The core use case: InfoFi what it is, concretely
InfoFi is the convergence of three things:
1. Tokenized claims: Every meaningful fact, dataset, or assertion can be represented as an onchain asset (a claim token). Parties stake native tokens (TRUST) to express conviction and to curate the graph.
2. Economic reputation: Curators and creators build on-chain reputational capital by successfully surfacing accurate, valuable information. That reputation is transferable and programmatically useful (APIs, oracles, datasets).
3. Machine-consumable truth: Because claims are structured, cited, and economically scored, agents and models can query the graph to get prioritized, trustworthy sources unlocking higher quality LLM prompts, recommender systems, analytics, and ad targeting.
Why InfoFi is the “killer” use case
Data with economic gravity: Unlike raw data dumps, token-curated claims carry a stake backed weight. That makes ranking for both humans and ML systems defensible and attack-resistant.
Aligns incentives: Creators are paid and curators rewarded for accuracy. Bad actors must put up stake, increasing the cost of misinformation.
Composability: Onchain claim tokens can be aggregated, referenced by smart contracts, used for identity, or consumed by oracles. This makes InfoFi a primitive for many Web3 and hybrid apps.
Technical flow (simplified)
1. Create/claim: A user mints a claim token representing a factual statement, dataset, or resource (with metadata and citations).
2. Stake & curate: Other users stake TRUST to validate/contest the claim. Stakes determine the claim’s economic gravity. 7
3. Dispute/resolution: Disputes trigger governance or algorithmic resolution; correct claims accumulate stake/reputation.
4. Consume: Apps, LLMs, oracles query the graph and select high-gravity claims for use; creators/curators earn rewards for usage.
UX / product hooks for mainstream adoption
Browser extension / portal onboarding: Make creating an Identity and minting first claims < 3 minutes. Focus on simple UX for non-crypto users.
Plug-ins for LLMs & knowledge workers: Offer a plugin that lets LLMs automatically fetch top gravity claims for prompt-context, improving answer quality.
Publisher integrations: Newsrooms and academics can mint verified claims (datasets, citations) and monetize reuse.
Developer APIs: Lightweight GraphQL + SDKs to let apps consume the knowledge graph programmatically.
Tokenomics & incentives (high-level)
Staking for conviction: TRUST is used to back claims, giving them on-chain weight. Creators/curators receive fees when claims are consumed by apps. 10
Bootstrapping: Grants and early curator rewards (and partnerships) seed high-quality initial content and reputational anchors. Coin listings / presales may provide liquidity windows.
Metrics of success:
- Number of high-gravity claims indexed
- Daily active curators & staked TRUST volume
- Number of external apps (LLM plugins, newsrooms) calling the graph
- On-chain dispute rates vs. resolution time
Risks & mitigations:
Gaming & collusion: Large token holders could manipulate gravity. Mitigation: multi-sig curators, bond slashing, time, weighted reputation.
Quality vs. speed: Low-quality claims could proliferate. Mitigation: frictioned minting, community moderation incentives.
Legal / content liability: Offchain content disputes could have legal implications. Mitigation: robust TOS, opt-in for publishers, and decentralized arbitration options.
Roadmap sketch for realizing InfoFi (practical phases)
1. Alpha: Identity + minting + staking primitives + developer SDK.
2. Beta: Integrations (LLMs, portals), disputes framework, UX improvements.
3. Growth: Publisher partnerships, mainstream UX rollouts (mobile bakes), token economics tuning.
Final thoughts / call to action:
InfoFi can be the missing layer that connects human truth makers with machine consumers making information discoverable, valuable, and programmable. If you want to help, look for ways to seed high quality claims, build early plugins, or curate vertical knowledge graphs (health, law, finance).
Further reading: Intuition official profile & materials, whitepaper and documentation