The 2025 AI Track Landscape: Top Projects and Funding Trends

Artificial intelligence and crypto are converging fast, and 2025 is shaping up to be the year this intersection goes mainstream. Decentralized compute markets, on-chain inference, agent economies, and data provenance layers are moving from experiments to production networks, with capital concentrating around infrastructure that makes AI more open, verifiable, and permissionless. This article maps the AI track across crypto, highlights top projects, and distills funding and market structure trends investors and builders should watch.

Why AI x Crypto Matters Now

AI systems are increasingly powerful yet centralized: compute supply sits in hyperscale clouds, models are trained on opaque data, and inference is a black box. Crypto provides primitives—open markets, transparent incentives, verifiable computation, and programmable payments—that can make AI infrastructure more robust and fair. Leading research firms have outlined the complementarity between the two stacks, from decentralized resource markets to auditability for model outputs and training data. For a broader view on why crypto can make AI more trustworthy and open, see the discussion by a16z on why crypto is needed for AI and how cryptoeconomic design can align AI supply chains across compute, data, and inference. You can explore the argument in Why Crypto Is Needed for AI on a16z’s site.

Sector Map: The AI Track Across Crypto

Below is a field guide to the most investable clusters across the 2025 AI track, with representative networks and core theses.

Decentralized Compute Marketplaces (GPU and Cloud)

A persistent bottleneck in AI is compute. DePIN-style networks are aggregating underutilized GPUs and exposing them via permissionless markets:

• io.net (Solana) is building a decentralized GPU cloud focused on AI workloads and distributed training, routing jobs across a global network of nodes and paying contributors via programmable incentives. Project overview and network details are available on the io.net website.
• Akash Network offers a decentralized cloud marketplace where providers list compute and developers deploy workloads, with flexible bidding and settlement via on-chain markets. See Akash Network’s site for architecture and marketplace mechanics.
• Render Network focuses on distributed GPU rendering but increasingly services AI inference and media-generation workloads via a tokenized resource market. Their documentation and network overview are at Render Network.
• Gensyn is developing a protocol for distributed machine learning that verifies training contributions and pays workers based on cryptoeconomic proofs. Read a16z’s investment thesis and overview at Investing in Gensyn on a16z.

Thesis: The winning networks will match demand-side reliability (SLOs for latency, uptime, privacy) with supply-side composability (bring-your-own hardware), plus verifiable job execution. Expect integrations with restaking and slashing to enforce performance guarantees.

Decentralized Inference and Model Networks

Inference is the heartbeat of AI applications. Networks are emerging that reward high-quality model outputs and routing:

• Bittensor hosts a marketplace where subnets coordinate model tasks and reward nodes for useful outputs through peer-based scoring, evolving toward an ecosystem of specialized, interoperable AI services. Explore the protocol and subnet architecture at Bittensor.
• Ritual is building a trust-minimized inference and agent execution layer that connects models to blockchains, focusing on verifiable off-chain compute and composable AI pipelines for dApps. Learn more on Ritual’s website.
• Olas (Autonolas) provides tooling and incentives for autonomous services and agents that perform on- and off-chain tasks, including AI-driven automation for DeFi and governance workflows. See Olas Network’s resources for agent economics and developer docs.

Thesis: Two design patterns are gaining traction—peer-scored inference (e.g., Bittensor) and verifiable inference bridges (e.g., Ritual). The former excels at open competition; the latter at deterministic guarantees and composability for on-chain applications.

Data Provenance and Marketplaces

Models are only as good as their data. Crypto-native data layers are bringing provenance, access control, and monetization to the AI supply chain:

• Ocean Protocol enables tokenized data assets, permissioning, and marketplace tooling for training datasets, with programmable royalties and usage control baked into the protocol. Visit Ocean Protocol to understand data NFTs and market mechanics.
• Chainlink provides oracle and attestation services that help anchor off-chain facts and proofs on-chain, a foundation for trustworthy AI inputs and outputs. Explore Chainlink’s products and integrations for verifiable data flows.

Thesis: Expect growing demand for attestations and signed data streams, especially in agent-driven DeFi and AI-integrated fintech where model outputs must meet compliance and audit requirements.

Agent Economies and AI Middleware

Agent frameworks and middleware link models to wallets, protocols, and user intents:

• Fetch.ai and SingularityNET have long pursued autonomous agents and marketplaces for AI services, now collaborating across broader ecosystems with data and model exchange. Explore Fetch.ai and SingularityNET’s platforms.

Thesis: The practical bridge from traditional apps to crypto will be AI agents that can hold keys, negotiate on-chain, and automate workflows. Tooling that prioritizes safety—policy checks, intent constraints, and verifiable execution—is becoming the differentiator.

Modular Data Availability and Restaking for AI

To scale AI-integrated dApps, the base layer needs modularity and robust security guarantees:

• Celestia provides modular data availability useful for rollups and specialized execution layers, including those focused on AI workloads. See Celestia for the modular stack overview.
• EigenLayer enables restaking and Actively Validated Services (AVSs) that can secure off-chain services like oracles, storage, or inference bridges via slashing conditions. Learn more at EigenLayer.

Thesis: AI services will increasingly anchor to AVS-style security, letting protocols enforce performance via cryptoeconomic guarantees rather than trusted operators.

Funding Trends Heading Into 2025

• Concentration in infrastructure. Venture capital continues to cluster around compute markets, inference bridges, and agent middleware—areas with clear monetization (resource markets, usage fees) and defensibility (network effects, cryptoeconomic moats). For sector breakdowns and reports, check Messari Research and The Block Research.
• Token-market discovery vs. equity. Projects that can launch networks with native tokens tied to resource markets have found faster product–market fit than pure equity plays, especially in DePIN for GPU supply and inference routing. Coinbase Research regularly tracks token sector rotations and can serve as a baseline for market structure analysis.
• Grants and ecosystem financing. Ecosystem treasuries and foundations are actively subsidizing AI integrations across L2s and app-chains, including incentives for data providers, inference nodes, and agent builders. This is aligned with the broader modular stack thesis highlighted by Celestia and restaking frameworks like EigenLayer.
• Verifiability premium. Networks that can prove execution or enforce SLAs via slashing are seeing stronger institutional interest, echoing the rise of verifiable compute and zk-friendly inference. For broader context on zkML and verification trends, explore zkML resources at zkml.xyz.

What the Market Is Pricing In

• Performance and reliability. Compute networks that deliver consistent latency and uptime and can handle enterprise workloads will gain share. SLAs, attestation, and private inference options (e.g., TEEs, MPC) matter.
• Composability for developers. Tooling that plugs AI directly into smart contracts and agents with standard APIs, stable SDKs, and audit-friendly verification will win developer mindshare.
• Data rights and compliance. Provenance, licensing, and opt-in remuneration for data producers are moving from ethical nice-to-haves to regulatory requirements. AI layers that natively support attestations and usage control are better positioned.
• Open models and incentives. Token incentive design that rewards sustained contribution quality—rather than raw capacity—reduces race-to-the-bottom dynamics and helps avoid centralization.

Risk Checklist for AI x Crypto

• Sybil and collusion. Decentralized markets for compute and inference are exposed to gaming unless robust identity, reputation, and staking-slash mechanics are in place.
• Model integrity. Without attestations or reproducibility, model outputs can be spoofed. Favor networks that support verifiable inference and transparent scoring.
• Data compliance. Jurisdictions are tightening data-use rules. Ensure data marketplaces support licensing, provenance, and revocation to minimize legal risk.
• Key management. AI agents holding funds amplify operational risk. Use strict policy engines, transaction simulations, multi-sig where appropriate, and hardware wallets for custody.

Practical Portfolio and Builder Playbook

• Diversify across primitives. Exposure to compute (io.net, Akash, Render), inference (Bittensor, Ritual), and data layers (Ocean Protocol, Chainlink) hedges protocol-level risk while maintaining upside across the stack. Explore these networks directly via their official sites: io.net, Akash Network, Render Network, Bittensor, Ritual, Ocean Protocol, and Chainlink.
• Follow incentive programs. Grants and bootstrapping mechanisms can materially influence short- to mid-term returns for both token holders and builders. Track updates and sector deep dives at Messari and The Block Research.
• Prioritize verifiability. Favor protocols with transparent metrics, on-chain settlement for resource usage, and credible slashing or audit frameworks. Modular DA and AVS ecosystems like Celestia and EigenLayer are valuable complements.

A Note on Secure Custody for AI-Native Assets

As AI agents and resource-market tokens proliferate, secure self-custody becomes foundational. If you operate inference nodes, provide GPU capacity, or hold positions across multiple AI networks, a hardware wallet reduces key-exposure risk and supports operational hygiene (policy filters, offline signing, multi-chain support). OneKey is open-source, supports a wide range of EVM and non-EVM chains, and integrates well with developer workflows and multi-sig setups—making it a practical choice for teams participating in AI compute markets, agent operations, or DAO governance.

Closing Thoughts

AI is pushing crypto beyond finance into the broader digital infrastructure stack. In 2025, the winning AI-track projects will combine open resource markets with verifiable execution and composable tooling for agents and dApps. For investors, the focus is on primitives with durable network effects; for builders, it’s on shipping reliable AI services with transparent incentives and strong security. Keep an eye on decentralized compute, inference bridges, and data provenance layers—they’re not just narratives, they’re becoming critical infrastructure.

References and further reading:

• Why Crypto Is Needed for AI by a16z: a16z’s perspective on AI + crypto synergies
• Messari Research: sector reports and theses on crypto markets
• The Block Research: data-driven analysis of funding and sector rotations
• Coinbase Research: market structure and thematic reports
• io.net: decentralized GPU cloud for AI
• Akash Network: decentralized cloud marketplace
• Render Network: distributed GPU resource network
• Bittensor: decentralized AI inference marketplace
• Ritual: trust-minimized AI compute for blockchains
• Olas Network: autonomous services and agents
• Ocean Protocol: tokenized data assets and marketplaces
• Chainlink: verifiable data and oracle services
• Celestia: modular data availability
• EigenLayer: restaking and AVSs
• zkml.xyz: resources on zkML and verifiable inference