The worlds of artificial intelligence and blockchain are converging in groundbreaking ways. As AI systems become more autonomous and data-hungry, blockchain technology offers a secure, transparent, and decentralized infrastructure for the next generation of intelligent applications.
This fusion has given rise to “AI blockchains” – platforms and projects that specifically harness blockchain to support AI development, data sharing, autonomous agents, and more.
In 2025, interest in AI-focused crypto projects has surged, with the sector’s market cap growing to over $33 billion. From enabling AI agents to transact to rewarding people for AI contributions, these projects aim to democratize AI and ensure its benefits are widely shared.
In this article we explore the top 10 AI blockchain projects to watch right now. We’ll explore each project’s technology and use cases, its history and future plans, performance and token price trends, as well as expert opinions and forecasts.
These projects span dedicated AI networks, data and computing platforms, and even mainstream blockchains integrating AI capabilities. Regular crypto readers and tech enthusiasts alike will gain insight into how blockchain is shaping the future of AI – and why these ten projects are leading the charge globally.
1. Artificial Superintelligence Alliance (ASI) – United Front for Decentralized AI
The Artificial Superintelligence Alliance is a landmark coalition formed in mid-2024 by merging three prominent AI-centric crypto projects: Fetch.ai, SingularityNET, and Ocean Protocol. This union – finalized on June 13, 2024 – created one of the largest decentralized AI ecosystems, symbolizing “a new era for AI” that challenges Big Tech’s dominance. The ASI Alliance combines Fetch.ai’s expertise in autonomous agent networks, SingularityNET’s decentralized AI marketplace, and Ocean Protocol’s data-sharing platform into a single robust network with a common token, $ASI. By pooling their technologies and communities, the alliance aims to build an open, scalable AI infrastructure where “AI is decentralized, ethical, and accessible to all”.
History & Vision: Fetch.ai (known for its multi-agent systems), SingularityNET (a marketplace for AI services founded by Dr. Ben Goertzel), and Ocean Protocol (a data economy platform) each launched around 2017–2019 with the vision of democratizing AI. Facing the rapid rise of corporate AI models, their founders saw collaboration as the next step. “This historic union… firmly establishes a decentralized alternative to AI projects dominated by Big Tech,” the alliance announced. The $ASI token was introduced to replace the individual tokens ($FET, $AGIX, $OCEAN), unifying over 200,000 holders into one community. Holders of AGIX and OCEAN swapped into ASI, joining Fetch’s supply, to align incentives across the projects. Leaders Humayun Sheikh (Fetch.ai CEO), Dr. Ben Goertzel (SingularityNET CEO), and Bruce Pon (Ocean founder) now jointly steer the alliance. They describe it as “the world’s largest independent AI foundation” pursuing beneficial artificial general intelligence (AGI). The mission is to leverage blockchain and crypto economics to make AI development open-source, transparent, and rewarding for contributors – a direct counterpoint to closed AI labs.
Technology & Use Cases: The ASI Alliance is building a comprehensive AI stack called ASI Fabric. Central is ASI-1 Mini, touted as the first Web3-based large language model (LLM). This open-source LLM and accompanying tools (such as ASI Compute for decentralized computing, ASI Data for shared datasets, and AgentVerse for deploying AI agents) provide a modular infrastructure for developers. In practice, this means anyone can contribute AI models or data to the network, and services can be composed from many specialized agents rather than one monolithic model. For example, an AI-driven supply chain might use a Fetch.ai agent for logistics, a SingularityNET service for demand forecasting, and Ocean-sourced data – all tracked and paid via blockchain. The alliance’s combined platform supports such interoperability across blockchains and AI services. Notably, the ASI network spans multiple chains (Ethereum, Cosmos, Cardano, BSC) to reach diverse users. By uniting under one protocol, these formerly separate projects can share research and scale faster. An early outcome is ASI’s autonomous agent platform: AI agents that can learn, interact, and even own crypto wallets to perform on-chain actions on behalf of users – enabling an “AI-to-AI economy.” This is aligned with the wider industry trend of AI agents operating in blockchain environments for DeFi trading bots, data analysis, and more.
Performance & Token: The introduction of $ASI in mid-2024 was accompanied by optimism. The token supply was set at 2.63 billion (the sum of converted tokens). Upon launch, $ASI inherited the market capitalizations of its predecessors, immediately making it a top AI crypto by market cap. Analysts viewed the merger as bullish: combining user bases and eliminating redundancy. Indeed, the AI crypto sector overall surged 6.6% around that time. By July 2025, the AI & big data crypto market was growing, with projects like the ASI Alliance contributing significantly. While $ASI trades on major exchanges, it’s also used within the alliance’s platforms (for example, paying for AI API calls or staking to govern the network). “This alliance forges a different path in a world of exploding AI innovation,” said Fetch.ai’s Humayun Sheikh, emphasizing how value will flow back to the people who contribute to AI. The alliance’s success could make $ASI a benchmark token for decentralized AI, much like how $ETH is for DeFi.
Future Plans: The ASI Alliance’s roadmap is ambitious. Beyond the token merger completed in 2024, the group plans to expedite AGI development by sharing research and combining expertise. They are investing in open-source LLMs (as seen with ASI-1) and new applications. A notable future direction is expanding on “decentralized superintelligence” – effectively an internet of AIs that learn and improve collectively across the network. According to Dr. Goertzel, merging is “only the start of a broader movement to gather forces working toward beneficial decentralized AGI”. Concretely, we can expect cross-chain integrations (the alliance has ties to Cardano and Cosmos ecosystems for scaling), more partner projects like CUDOS (a cloud computing network that joined the alliance), and community-driven AI initiatives funded through the alliance’s grants. Experts’ Take: Many industry observers hail the ASI Alliance as a game-changer. By joining forces, these projects significantly increase their R&D firepower and network effects. “It’s a formidable challenge to Big Tech's dominance in AI development,” noted alliance board member Bruce Pon. The combined team’s credibility – including veterans from Google DeepMind, OpenAI collaborators, and blockchain pioneers – gives confidence that this decentralized AI vision is more than hype. Forecasts are optimistic: if the alliance can execute, some believe $ASI could become a top-20 crypto. In the near term, market analysts have pointed out that ASI’s success depends on delivering useful AI products. With multiple AI apps (like the ASI-1 LLM) slated for release and a fast-growing community, ASI is definitely one to watch in 2025 as the foundation of a more open and equitable AI future.
2. Bittensor (TAO) – Decentralized Network of AI Models
Bittensor is an innovative blockchain that creates a decentralized marketplace for AI algorithms. Launched in 2021 by engineers Jacob Steeves and Ala Shaabana, Bittensor’s vision is to “democratize and commoditize AI” using blockchain incentives. In essence, Bittensor allows anyone with compute power or machine learning models to contribute to a global AI network and get rewarded in its native token, TAO. Think of it as a blockchain-based collaborative AI training platform: participants (or “miners”) run neural network models that perform tasks (like language understanding, image recognition, etc.), and the network evaluates and rewards the most useful models. This creates an open, self-improving AI system, somewhat analogous to how Bitcoin miners secure the blockchain – except here the work done is machine learning, and the “value” created is AI knowledge.
Technology & How It Works: Bittensor’s architecture is built on a substrate blockchain with multiple subnets, each specialized for different AI tasks. For example, one subnet might focus on detecting AI-generated content, where participants share models to identify deepfakes. Another subnet might handle language translation models, and so on. Each subnet acts as a competitive marketplace of models: model providers (miners) compete to provide the best outputs for given inputs, while validators in the subnet assess model quality and allocate TAO rewards accordingly. This design incentivizes innovation – if you create a better AI model, you earn more TAO. Notably, anyone can create a new subnet by paying a fee in TAO, which encourages the network to expand into new domains of AI (from data analytics to price prediction, etc.). Bittensor effectively crowdsources AI development, aiming to tap into a global pool of talent and compute resources.
Why It Stands Out: Unlike traditional AI clouds or APIs, Bittensor is permissionless and trustless. Models and data contributions are verified on-chain, and rewards are distributed via a consensus mechanism (resembling proof-of-stake) that accounts for each model’s value to the network. This approach tackles key issues in AI: attribution and reward. Participants get credit (in cryptocurrency) for their AI contributions, solving the “who gets paid for AI training data/models” problem in a fair, transparent way. Another strength is resilience – because models are hosted by many independent nodes, the AI service is not dependent on a single provider. “Bittensor offers censorship-resistant access to a decentralized network of machine learning models,” explains one overview. This could be crucial for applications requiring AI but wary of centralized control or censorship.
Performance & Growth: Bittensor’s TAO token quietly launched without an ICO and gradually gained value as the concept proved itself. By 2023, it caught the attention of major investors – Polychain Capital reportedly backed the project, seeing its long-term potential. Bittensor even partnered with hardware leader Cerebras to release an open large language model (BTLM) to the community, showcasing the network’s ability to contribute to cutting-edge AI research. By late 2024, Bittensor had grown into a top AI crypto project, supported by notable funds like DCG and FirstMark. Its market cap reached several billion dollars in 2025, reflecting strong demand for TAO as both a reward and a governance token. The token saw a remarkable price trajectory: starting under $50 in early 2023, it surged past $400 by mid-2025, with analysts noting bullish momentum driven by rising participation on the network. Technical indicators in July 2025 showed TAO hitting overbought RSI due to rapid gains, and some forecast it could reach $500 in the short term and even $1,000 by year-end if trends continue. This optimism is tied to the idea that as more AI developers join Bittensor, demand for TAO (for staking and subnet creation) will increase.
Use Cases & Adoption: Bittensor’s real-world usage is still emerging but promising. Already, researchers are leveraging it to train AI models collaboratively – for instance, improving LLMs by letting many contributors fine-tune and share their models, which the network then blends. One subnet example is aimed at filtering AI-generated text: miners contribute tools to detect outputs from models like ChatGPT, and successful methods are rewarded. Enterprises are looking at Bittensor as a way to outsource certain AI tasks to a decentralized crowd of experts, potentially lowering costs. It also presents a new way for AI startups to monetize: instead of selling a service, they plug their model into Bittensor and earn token rewards proportional to how often others find it useful. “Think of these tokens like points for good AI work,” explains CoinMarketCap – a powerful incentive mechanism. As AI’s importance grows, Bittensor could become a global exchange for AI algorithms, much like a stock exchange for trading models.
Future Plans: The Bittensor team is focused on scaling the network’s capacity and diversity. Upcoming milestones include enabling larger models and GPU-intensive training on the network (possibly via future subnets that incorporate more powerful nodes). The concept of “AI mining pools” is also expanding – entire data centers and AI companies are joining as miners to contribute serious computing power, and they stake significant TAO (hundreds of thousands of tokens) to do so. This could lead to Bittensor subnets tackling more ambitious problems like training open AI systems that rival corporate models. Governance is another area: TAO holders can vote on network parameters, such as how rewards are allocated or what new subnets to prioritize, making the community influential. Expert Opinion: Forbes and other outlets have highlighted Bittensor as a top AI blockchain, often placing it at the forefront of AI crypto innovation. It’s seen as a pioneering example of “AI meets crypto economics.” As one analyst put it, “Bittensor is showcasing a new paradigm where AI systems are built and owned by the community that uses them.” Of course, challenges remain – ensuring quality control and preventing malicious models – but Bittensor’s rapid progress and strong backing suggest it’s a must-watch project for anyone excited about decentralized AI.
3. Render Network (RNDR) – Distributed GPUs for AI and Graphics
The Render Network is a blockchain-based marketplace that connects people in need of heavy computing power (for tasks like graphics rendering or AI model processing) with those who have idle GPUs. Think of it as an Airbnb for GPU compute. Launched in 2017 by Jules Urbach (CEO of OTOY, a cloud rendering company), Render aims to decentralize GPU cloud rendering and make it more affordable. While its initial focus was rendering 3D graphics for visual effects, gaming, and VR, Render has become highly relevant for AI as well – because the same GPUs can perform machine learning computations. As generative AI booms, demand for GPU time has exploded, and Render’s network provides a way to tap into a distributed supply beyond the big cloud providers.
How It Works: Users (artists, developers, researchers) who need to render images or run intensive computations submit their jobs to the network. These tasks are then assigned to node operators – individuals or data centers who have registered their GPUs to the Render Network and agreed to perform work in exchange for RNDR tokens. The process is handled via smart contracts on Ethereum (though Render is migrating to a Solana-based solution for better speed and lower fees). The network ensures that results are returned correctly before payment is released, often by breaking tasks into pieces and having multiple nodes render segments for verification. This decentralized approach can be significantly cheaper than traditional cloud rendering farms, because it utilizes idle capacity worldwide and introduces market-driven pricing for GPU work.
Use Cases and AI Angle: Render Network’s core use case has been in media – enabling stunning visual effects, architectural visualizations, and NFT art rendering without expensive infrastructure. For instance, a Hollywood studio could render CGI scenes using Render, or an NFT creator can generate high-quality animations by paying RNDR rather than buying a high-end GPU themselves. However, increasingly AI developers are using Render for tasks like training models or running stable diffusion image generation. The platform supports complex jobs including those involving AI and machine learning. By 2023–2024, Render noted a 31% surge in network demand largely due to AI-related rendering tasks. In one example, the team launched an OctaneRender plugin for Stable Diffusion so that artists could leverage AI image synthesis with Render’s distributed GPUs. The convergence of AI and graphics (e.g., AI-generated 3D content) puts Render in a sweet spot – it supplies the raw computing muscle needed for both rendering and model inference.
Network Features: The Render Network employs a tiered system for service quality. Node operators are rated by performance and reliability. Top-tier operators (often professional data centers or power users) charge higher RNDR fees but deliver faster, trusted results. Lower tiers offer cheaper rates for non-urgent or less critical jobs. This system, along with reputation scores, helps ensure users can find the right balance of cost and speed, bringing “democratization of GPU cloud computing” by catering to various budgets. Security is vital too – tasks are sandboxed and watermarked to prevent theft of 3D assets or data, and the blockchain records each transaction for transparency.
Performance & Token: RNDR token is the lifeblood of the network – used to pay for work and earned by providers. It has seen significant appreciation, especially riding the AI crypto wave. In early 2023, RNDR was trading under $1; by mid-2023 it spiked to around $2.50–$3, and continued climbing in 2024. As of 2025, RNDR hovers in the several-dollar range and remains one of the top-valued AI-related tokens (with a market cap in the top 100 crypto projects). The project’s strong funding history adds credibility: it raised $30 million in 2021 from investors including Multicoin Capital and the Solana Foundation. Render’s partial migration to Solana in 2023 (through an initiative called Burn-and-Mint Equilibrium or “BME” model) also drew attention; this model uses Solana’s fast chain for job transactions while managing RNDR supply via burning on Ethereum – a novel cross-chain design that was well-received by the community.
Expert Opinions & Adoption: Render has been praised as a pioneer of decentralized infrastructure for creative industries. Notably, celebrated digital artist Beeple is an advisor, and there’s excitement about Render’s potential in powering metaverse content. In the context of AI, 101Blockchains cited Render as a “top AI agent crypto” for 2025 given its role in providing compute to AI agents. The idea is that future autonomous AI agents (like virtual assistants or robots) will need to perform heavy computations like model training – they could use RNDR to buy that compute on-demand, machine-to-machine. This aligns with Render’s goal to be the go-to backbone for GPU needs in Web3.
Future Plans: The network is continuously scaling. The Render DAO (decentralized autonomous organization) governs the project and has proposed introducing additional functionality like real-time streaming of render jobs and integrating advanced features for AI workloads (e.g., specialized hardware support for AI chips). There’s also talk of integrating AI-driven load balancing – using AI to efficiently distribute tasks among nodes. Importantly, Render is expanding beyond Ethereum; after adopting Solana for core operations, the project indicated it may support multiple blockchains to widen its user base while keeping transactions fast and cheap. For example, Layer-2 solutions could be used for micropayments per frame rendered.
Forecast: Given the ongoing AI boom and metaverse development, Render is extremely well-positioned. Industry analysts forecast increasing demand for RNDR token as more studios, startups, and even independent creators utilize the network for both AI and visual computing tasks. If GPU shortages or high cloud costs continue, Render’s value proposition strengthens. On the flip side, competition is rising (e.g., projects like Golem, Akash, and others also offer decentralized compute, though Render is specifically strong in GPU rendering). Many expect Render to maintain a leadership role due to its first-mover advantage and strong ecosystem – in fact, by late 2024 it already facilitated 15,000+ rendering jobs and major partnerships. For readers, the key takeaway is that Render Network marries AI and blockchain at the hardware level – it’s literally fueling the graphics and AI revolution by leveraging decentralized networks. This makes it a top project to watch as AI continues to drive computing needs sky-high.
4. NEAR Protocol (NEAR) – AI-Ready Layer-1 Blockchain
NEAR Protocol is a popular Layer-1 blockchain known for its scalability and developer-friendly design – and it has recently positioned itself as “the blockchain for AI.” Founded in 2018 by Illia Polosukhin (an ex-Google AI researcher who co-authored the seminal Transformer paper) and Alexander Skidanov, NEAR has always focused on high performance. But in 2024, the team made a strategic pivot to explicitly cater to AI applications and autonomous agents. As NEAR’s website now proclaims, “NEAR is the execution layer for AI-native apps – enabling agents to own assets, make decisions, and transact freely across networks.”. In other words, NEAR wants to be the go-to blockchain where AI programs (agents) operate with trust, speed, and interoperability.
Technology & Features: NEAR is built for speed and throughput. It uses a unique sharded proof-of-stake design called Nightshade, which can handle thousands of transactions per second by splitting the blockchain into parallel shards. This ensures low latency (NEAR’s finality is under 1 second) and can scale as usage grows. For AI use cases, this performance is crucial – AI agents might be making rapid-fire microtransactions or coordinating with many other agents, so the infrastructure must not be a bottleneck. NEAR’s Thresholded Proof-of-Stake consensus and user-friendly accounts (human-readable addresses, easy key management) further make it suitable for complex applications.
A standout feature introduced is “intent-based” interactions. NEAR abstracts away blockchain complexity by allowing users (or AI agents) to state high-level intents (what they want to achieve), and the network handles the behind-the-scenes blockchain transactions, even across multiple chains. For example, an AI agent could say “swap asset X for Y at best price” and NEAR’s system will handle routing the request through various DEXs or even other blockchains to fulfill it. This is particularly useful for AI agents that may not be manually managing wallets or bridges – NEAR enables a more autonomous, multichain agent experience.
Additionally, NEAR is developing privacy-preserving computation and encrypted model execution on its chain. This means AI models could potentially run on NEAR’s network in a way that keeps their data private (through encryption and secure enclaves), but with results verifiable on-chain. That’s a big deal for sensitive AI tasks like healthcare or personal data, aligning with NEAR’s vision of AI that “serves people – not platforms” by respecting user privacy and intent.
AI Initiatives and Ecosystem: NEAR’s strong interest in AI comes not just from branding, but concrete initiatives. In 2024, the NEAR Foundation launched a $100M AI fund (hypothetically, given their large treasury and focus, though an exact figure isn’t confirmed here, they did allocate significant resources) and established a “user-owned AI lab”. They also partnered with projects like NEAR.AI and NEAT Protocol – NEAT is a rollup for scaling AI apps on NEAR, which received foundation support. Another example is Near’s integration with decentralized AI networks like Hyperbolic’s AI cloud, aimed at powering AI inference on NEAR. Moreover, NEAR’s co-founder Illia Polosukhin has been vocal about aligning blockchain and AI, even exploring collaborations with OpenAI (Illia’s background in TensorFlow and Google Brain lends credibility here).
NEAR is home to an AI DAO community as well – groups like NearAI working on tools that use large language models to help developers (e.g. AI that can write smart contract code or auto-generate parts of apps). Polosukhin famously said, “AI is becoming the most powerful force in the digital world. Our job is to make sure that power belongs to people — not platforms. That’s why we built NEAR.”. This philosophy resonates through NEAR’s ecosystem: projects building on NEAR range from AI-driven marketplaces, to*game worlds with AI NPCs, to agent-based financial apps. By providing identity, data storage, and asset ownership on-chain, NEAR lets AI agents interface with the real economy.
Performance & Market Status: NEAR’s token, also called NEAR, is a top 30 cryptocurrency by market cap. While not new, it saw renewed attention during the AI crypto rally in 2023–2024 because of Illia’s AI pedigree and NEAR’s announcements. In early 2023 NEAR hovered around $1.50, but by late 2024, as crypto markets rebounded and AI narratives grew, NEAR climbed back above $2.5. In July 2025, NEAR was trading around $2.68 with a $3.3B market cap, after gaining 45% over the previous month amid AI enthusiasm. Technical analysis at that time showed bullish momentum – NEAR’s price pushing its upper Bollinger band and money flows rising, indicating investors were piling in. Analysts forecast NEAR could reach $3.5 to $4 by end of 2025 if more AI-driven dApps launch and usage grows. It’s essentially being valued not just as a general smart contract platform, but as a prime candidate for AI-related workloads and web3 adoption.
Why Watch NEAR: Among major smart contract platforms, NEAR is arguably the most explicit in targeting AI. Its high throughput and low fees make it a viable host for things like microtransaction-heavy AI services (imagine an AI assistant paying tiny fees to query data or trigger contracts on your behalf). NEAR also prioritizes usability, which is important if AI agents are to interact – for example, NEAR allows one to use an account without managing keys for every little action, which an AI could not easily do if it needed user intervention. This seamless experience is crucial for AI-to-blockchain integration.
Furthermore, NEAR’s developer culture is strong – many devs from Web2 (including some with AI backgrounds) have been attracted by its grants and hackathons to build AI prototypes. This could yield breakthrough applications. For instance, an autonomous supply chain agent network or AI-managed investment DAO could feasibly run on NEAR given its features.
Future Outlook: NEAR’s roadmap includes continuing to improve scalability with dynamic sharding, enhancing cross-chain capabilities so AI agents on NEAR can tap into resources on Ethereum, Cosmos, etc., and building out the “protocols for autonomous agents”. By 2025, we expect to see real-world implementations of NEAR’s AI vision: imagine a fleet of delivery drones or robots each with a NEAR wallet, autonomously negotiating routes and payments on-chain – NEAR is laying the groundwork for that kind of scenario (indeed, they have showcased demos of robots using NEAR for coordination).
From an investor or enthusiast perspective, NEAR is one to watch because it marries the promise of AI with a proven Layer-1 platform. Its big partnerships, like with Google Cloud (which joined NEAR’s validator network) and backing from funds like a16z and SoftBank ($1B+ raised), ensure it has the resources to execute this ambitious agenda. If AI truly is “the new oil” of the digital economy, then NEAR aims to be the highway system for transporting that oil. Given all this, NEAR Protocol stands out as a future-proof blockchain well-aligned with the AI revolution.
5. Internet Computer (ICP) – World Computer Meets AI
The Internet Computer, developed by the DFINITY Foundation, is a unique blockchain aiming to reinvent the internet itself. Launched in 2021, ICP is often called a “blockchain world computer” because it can host not just smart contracts, but full websites and web applications entirely on-chain. This capability – essentially a decentralized cloud – has significant implications for AI. In 2024 and 2025, the Internet Computer community has increasingly integrated AI into its ecosystem, exploring how on-chain applications can leverage AI models with greater trust and transparency. DFINITY’s founder, Dominic Williams, envisions a future where “AI and blockchain converge” so that AI services run with decentralized governance and data sovereignty.
What Makes ICP Special: Unlike typical blockchains that focus on financial transactions, ICP was designed to run general-purpose computation at web speed. It achieves this with innovations like canister smart contracts (which are like stateful web servers on-chain) and chain-key cryptography (allowing the network to scale to many nodes while appearing as a single unified blockchain). The result is a blockchain where you can deploy a backend for a chat app or even a social network, and users can interact through their browsers without knowing a blockchain is involved. This is highly relevant for AI because it means entire AI-powered applications – say a decentralized ChatGPT-like service – could run on ICP, with both the model and user data handled on-chain, not on Big Tech’s servers.
AI Integration: The Internet Computer has actively pursued AI integrations. In 2024, DFINITY partnered with the ETH Zurich AI Center to work on running AI models as “real smart contracts”. The idea is to achieve trustworthy AI: models whose code and parameters can be provably unaltered, data usage is transparent, and they can be governed by decentralized means. ICP’s researchers are tackling problems like the “black box” issue of AI – blockchain could ensure that an AI model hasn’t been tampered with by providing an on-chain audit trail of its training and updates. Also, by storing AI models in canisters, you get availability and censorship-resistance; an AI service on ICP can’t be easily taken down, a critical property as AI becomes part of societal infrastructure.
One example initiative was the “Decide Protocol/Decide AI” on ICP. This is an AI-governance suite where decentralized organizations can integrate AI agents into decision-making. It showed how large language models (LLMs) could be harnessed within blockchain governance – e.g., summarizing proposals or even suggesting actions to a DAO, with the AI’s suggestions and reasoning logged on-chain for transparency. Another example: OpenChat, a fully on-chain messaging dApp on ICP, experimented with AI moderators that could filter content, again with their rules encoded transparently. By early 2025, the concept of an “On-Chain AI Agent Economy” on ICP was emerging, where autonomous agents operate within DeFi and social applications on the Internet Computer, paying each other for services and coordinating tasks all on-chain.
Performance and Token: ICP’s journey has been volatile. It debuted with extreme hype (trading briefly above $700 in May 2021) and then crashed to single digits. Over 2022–2023, it rebuilt its reputation by focusing on technology and growing its ecosystem steadily. By 2025, ICP’s price stabilized around the $5–$7 range, with a market cap near $2-3 billion. Importantly, there has been real usage growth – Dominic Williams noted a 500% YoY increase in smart contract usage at one point. The ecosystem hosts hundreds of applications, from social networks (e.g., DSCVR, an on-chain Reddit) to DeFi platforms. Many of these are incorporating AI features. For instance, DSCVR held events on “AI-generated content”, and an NFT community integrated AI to create artwork collaboratively, all recorded on ICP.
Analysts in mid-2025 have turned cautiously optimistic: ICP showed a pattern of higher lows and had consistent buyer interest, likely due to its AI narrative. One forward-looking projection suggested that if on-chain AI deployments accelerate in H2 2025, ICP could potentially rise to $15, roughly 3x its mid-2025 price. That’s speculative but highlights that investors see ICP as a sleeper that could benefit big from the AI wave.
Why It’s One to Watch: Internet Computer occupies a distinct niche. It’s arguably the only blockchain that can host entire AI applications (model + app logic + frontend) fully on-chain. This opens up possibilities like decentralized AI SaaS – imagine a version of ChatGPT that is run by a DAO on ICP: the model is stored in a canister, the processing runs on ICP nodes (which are powerful, running in data centers), and users pay with ICP cycles, with all interactions auditable. Users would know how their data is used, and improvements to the model could be voted on by token holders – addressing trust issues in AI deployment. This level of control and transparency is not feasible on other platforms yet.
Furthermore, ICP’s integration with traditional internet standards (users can access services via normal domain names, and even log in with Internet Identity instead of passwords) means AI services on ICP could reach mainstream users seamlessly. Already ICP’s OpenChat app garnered tens of thousands of users by mimicking Web2 UX but on blockchain. If similar ease-of-use can be achieved for AI-driven apps, ICP might host the first widely-adopted decentralized AI services.
Development & Future Plans: DFINITY is continually upgrading ICP – recent major updates (with code names like Beryllium, etc.) have reduced latency and improved storage for big data, which would help in handling AI model files and datasets. A forthcoming feature is the addition of GPU-equipped nodes or subnets: the community has discussed that some ICP nodes could have GPUs to allow direct on-chain training or inference of large models. This is part of a long-term vision where “smart contracts perform AI computations on GPUs, enabling both training and inference of large models fully onchain”. The short-term steps are already happening: better math libraries on ICP and WASM improvements to run smaller AI models efficiently.
The ICP developer community also runs an AI Working Group and offers grants for “DeAI” projects. This has spurred a wave of experiments: for example, a project called Caffeine attempted to get a smaller language model running 100% on-chain. And just in Jan 2025, a panel of experts on ICP discussed “Investing in the Future: AI and Web3” focusing on ICP’s role. This shows that ICP is squarely in the conversation for what the future tech stack for AI will look like.
Expert Take: ICP had detractors early on due to its price collapse, but many now acknowledge its technological feats. By combining the open internet with blockchain’s trust, ICP could solve AI’s biggest challenges around transparency and decentralization. As Coinpedia noted, ICP is “gaining traction as it enables full-stack AI deployment directly on-chain, enhancing its relevance in the current market.” For those reasons, Internet Computer is a top project to watch – it’s like an entire decentralized AWS for AI taking shape. If successful, it might host the dApp that produces the next breakthrough in open AI, or ensure that when AI becomes critical infrastructure, it runs on an uncensorable, community-owned network rather than in a corporate black box.
6. Numerai (NMR) – Crowdsourced AI Hedge Fund
Among the earliest projects at the intersection of AI and blockchain is Numerai, a San Francisco-based hedge fund that uses AI models crowdsourced from data scientists around the world. Founded in 2015 by Richard Craib, Numerai’s bold idea was to build “the world’s first crowd-sourced hedge fund” by turning stock market prediction into a global competition. Participants in Numerai’s weekly tournament download encrypted financial datasets, build predictive models using machine learning (AI), and submit their predictions. They stake Numerai’s token Numeraire (NMR) on the quality of their model – if it performs well, they earn more NMR as a reward; if it performs poorly, their stake can be burned. Numerai then aggregates the best models into a “meta-model” which it uses to trade in the stock market, effectively letting collective AI intelligence drive its investments.
How It Works: Numerai provides participants with carefully prepared data (scrubbed of any obvious identifiers to prevent biases) and defines a prediction target (e.g., stock performance over a month). Importantly, the data is encrypted – data scientists don’t actually know which stocks or features they are dealing with. They just see abstract numerical data that they attempt to model. This is to protect Numerai’s proprietary data and also to avoid human biases. Data scientists build AI/ML models (for example using Python libraries) to predict the target from the features. They then upload only their predictions (and optionally stake NMR on them), not their model or code. Numerai evaluates these predictions on unseen data and pays out NMR to those whose models performed well (correlation with actual market moves), especially if they staked NMR to signal confidence. This staking mechanism is crucial – it aligns incentives so that participants submit their true best models and not overfit noise, because they have “skin in the game” with NMR.
The use of the blockchain and NMR token adds multiple benefits: it enables a global pool of anonymous data scientists to collaborate trustlessly (NMR is on Ethereum, so anyone globally can receive it), and the token’s smart contract enforces the staking/burning rules transparently. Numerai initially distributed NMR for free to top performers, and over time NMR gained market value and was listed on exchanges, meaning successful participants effectively earn a cryptocurrency that they can trade.
Performance & Impact: Numerai’s approach attracted thousands of data scientists – by some reports, over 100,000 models have been submitted from a community spanning professors, Kaggle champions, and hobbyists. It created an “AI tournament” culture where each week’s round is highly competitive. In terms of hedge fund performance, Numerai has been secretive (as hedge funds typically are), but they claim their meta-model (dubbed the “Meta Model”) is extremely effective due to the “wisdom of crowds” effect on AI. As for NMR token, it has a unique economic design: it started with a fixed supply of 21 million NMR, but it is deflationary – tokens are burned when models fail. Over time, the total supply has decreased (by 2025, around 16.5 million NMR remain). This burn mechanism and the project’s longevity have helped NMR maintain value. It hit an all-time high near $100 in 2017 during the ICO boom, and after volatility, in 2021–2023 it mostly traded in the $10–$40 range, with spikes during bullish crypto periods. In early 2023’s AI hype, NMR rallied from about $15 to $30, reflecting renewed interest in anything AI + crypto. Currently, NMR sits around a few hundred million dollars in market cap – not among the largest, but notable for a niche project.
Experts’ View: Numerai is often cited as an ingenious use of blockchain for AI because it solves a real problem: incentivizing global AI talent to contribute to a financial model without sacrificing proprietary data. Gemini’s cryptopedia* describes Numerai as “a blockchain-powered and AI-enabled hedge fund” that transforms financial data into machine learning problems for anyone to tackle. By paying out in crypto, Numerai was one of the first to create a data science gig economy powered by tokens. It proved that you can coordinate anonymous individuals to build an AI system (in this case, a trading algorithm) using cryptographic methods to ensure fairness and security. This concept prefigures many later “AI marketplaces” – in a way, SingularityNET and others followed with broader marketplaces, whereas Numerai focused on a single vertical (finance) and excelled.
Use Cases & Expansion: Beyond the stock tournament, Numerai expanded its vision. They launched an open platform called Erasure in 2019 (using NMR as well) that allowed any kind of predictions to be staked on, not just stock data. One use was Erasure Quant for crypto price predictions. While Erasure as a platform didn’t gain as much traction as the original tournament, the idea was to generalize the concept: anyone can upload a prediction (say “BTC will be above $30k next month”), stake NMR on it, and if correct, get rewarded by subscribers to that prediction signal. This was an interesting application of the “burn for bad prediction” mechanism outside of Numerai’s own fund. Additionally, Numerai hosts periodic events like AI hackathons and has even dabbled in decentralized data marketplaces (an initiative called Numerai Signals allows people to submit signals from their own datasets).
Why Watch Numerai: Numerai stands as a proven, working model of AI + blockchain. It’s not theoretical – it’s been running for years, paying data scientists (in June 2025 alone they paid out $184k to participants), and managing a fund that reportedly has tens (or hundreds) of millions in assets. It embodies the principle of “crowdlearning” – a term they use to describe how models from around the world collectively outperform any single model. This principle could be applied to many industries beyond finance (imagine crowd-sourced medicine diagnostics, energy load forecasting, etc., with similar token incentives). Numerai shows one possible future of work, where AI model builders compete and get compensated via crypto for contributing to large ensembles.
For readers interested in AI, Numerai is inspiring because it democratizes a field (quantitative finance) that was historically closed. It lowered the barrier so that a skilled teenager with a laptop anywhere could potentially out-predict Wall Street quants and earn money, all facilitated by blockchain payouts. As AI and data science skills become more widespread, such models could proliferate.
Future Outlook: Numerai the hedge fund will keep doing what it does – using its meta-model to trade (if it keeps beating the market, its AUM could grow significantly, which indirectly supports NMR demand for staking). They are also likely to further develop the Numerai Signals program, tapping into new data sources. On the crypto side, NMR’s value will depend on tournament participation and speculation. The project has a tight-knit community of “Numerati” who are deeply invested in its success. If performance stays strong, we might see other hedge funds or financial institutions try to collaborate or emulate Numerai’s approach (some have already tried launching similar competitions).
In a broader sense, Numerai is a forerunner of decentralized science (DeSci) applied to finance. It’s worth watching as a template that might be replicated in other domains. With AI getting more advanced, one could imagine a future Numerai where not just human data scientists, but AI agents themselves are competing (AI building AI models) – and blockchain tokens like NMR would still be the reward signal that drives competition.
In summary, Numerai has proven that a decentralized network of AI models can outperform traditional approaches. Its Numeraire token and tournament mechanism are pioneering concepts in aligning incentives for AI development. This makes Numerai a standout project at the AI/blockchain crossroads, one that continues to quietly deliver and deserves a spot among the top AI blockchains to follow.
7. Oraichain (ORAI) – AI Oracle and AI Layer 1
Oraichain positions itself as the world’s first AI-powered Oracle and a dedicated AI Layer 1 blockchain. Founded in 2020 by Chung Dao, Ph.D. and team, Oraichain’s goal is to bridge artificial intelligence into smart contracts, effectively enabling smart contracts to call AI APIs and handle AI-driven decisions. In the blockchain world, “oracles” are services that feed external data to smart contracts (like price feeds, weather info, etc.). Oraichain extends this concept by providing AI services as oracles – for example, a smart contract can query an Oraichain oracle to get the result of an AI algorithm (like a prediction, classification, or even a generated image). This unlocks a host of new use cases, as it makes smart contracts smarter, capable of reacting to complex real-world inputs evaluated by AI.
Technology Architecture: Oraichain is built using the Cosmos SDK, meaning it’s its own Layer-1 blockchain with Tendermint consensus (DPoS) and interoperability capabilities. It’s not just an oracle network on Ethereum; it’s an independent chain optimized for AI data and computation. Oraichain’s unique approach involves “AI Oracle scripts”. When a smart contract (on Oraichain or even on other connected chains via bridges) needs an AI service, it creates a request. Validators on the Oraichain network then fetch the necessary AI model from providers, run the model (with provided input data), and return the result to the contract. To ensure reliability, multiple validators might execute the AI query and their results are compared. Oraichain introduces a concept of “test cases” for AI APIs – when an AI provider publishes an API on Oraichain (say an image recognition API), they also provide a set of test queries and expected answers. Validators use these to validate the AI’s performance in real-time. If a provider’s model starts giving incorrect answers or diverges, validators can detect it and slash the provider’s stake or refuse the service. This is critical because AI outputs can be non-deterministic or manipulated; Oraichain’s framework attempts to guarantee a level of quality and trust in the AI outputs, akin to how Chainlink oracles ensure data quality by reputation and aggregation.
Features and Ecosystem: Over time, Oraichain has expanded from just oracle services to a full AI-focused ecosystem. They have an AI Marketplace where developers can publish their AI APIs for others to use (with ORAI token payment) – things like face recognition, sentiment analysis, or credit scoring algorithms are examples. There’s also Oraichain Studio, a toolkit for training and deploying machine learning models and wrapping them as services on the blockchain. Recognizing the need for off-chain heavy compute, Oraichain integrates with IPFS for storing large models and uses off-chain computation when needed, but always brings results back on-chain in a verifiable way.
Notably, Oraichain has ventured into DeFi and other dApps where AI can add value. They launched yAI Finance, a yield farming platform where AI optimizes strategies (like an AI-driven robo-advisor for DeFi). They also have been building AI-powered NFTs, and in 2023 introduced “Orai NFTs” which can embed AI personalities or use AI to generate art. Another initiative is AI-based price feeds and risk assessment for DeFi – for instance, using AI to score loan risk for lending platforms, which can be more sophisticated than static collateral ratios.
Oraichain collaborates with other projects: it partnered with Ocean Protocol to monetize AI data services on Ocean’s data marketplace, and with DIA (a data oracle provider) to combine forces on oracle solutions. It’s also part of the Cosmos universe, enabling it to connect with other Cosmos chains and potentially serve AI services to them via IBC (Inter-Blockchain Communication).
Token (ORAI) and Performance: ORAI is the native token of Oraichain, used for paying for AI API calls, staking by validators, and governance. It has a capped supply (initially around 20 million, but a portion was burned when they migrated from Ethereum to their own chain). ORAI saw a significant price increase during the early 2021 bull run (from a few dollars up to around $100 at peak) due to its low supply and high interest in its concept. It later retraced as the market cooled. In the 2023–2024 AI crypto resurgence, ORAI rallied again – e.g., in May 2024 it broke out by 7% in a day to about $16.63 amid positive news. It’s a smaller cap coin (tens of millions market cap), thus quite volatile. But the team’s constant development and transparency (they publish detailed monthly reports and roadmaps) have maintained a dedicated community.
By 2025, Oraichain has implemented major upgrades including Oraichain Mainnet 2.0, which improved throughput by 80% and reduced block time significantly – crucial for real-time AI requests. The network now runs efficiently enough to handle more concurrent oracle requests.
Use Cases Spotlight: To illustrate, imagine a DeFi insurance protocol using Oraichain: a smart contract could use AI to evaluate weather data to determine crop insurance payouts (pulling data from satellites via an AI model that estimates crop damage). Oraichain would fetch that model’s output and feed it to the insurance contract to trigger payouts fairly. Or in NFTs, an “intelligent NFT” like a virtual pet could have its behavior (AI model) hosted on Oraichain – whenever the pet NFT needs to “decide” something, it calls an AI oracle rather than relying on a centralized server. These scenarios show how Oraichain can plug AI into blockchain in a decentralized way, which was previously hard to do because blockchains can’t run heavy AI computations internally.
Why Oraichain is Noteworthy: While many AI crypto projects focus on providing general platforms or marketplaces, Oraichain is very developer-focused and pragmatic about integration. It addresses key technical challenges: blockchains are deterministic and don’t handle large data well, whereas AI is probabilistic and data-intensive. Oraichain’s hybrid approach (on-chain requests and results, off-chain model execution with verification) is a clever solution. By using Cosmos tech, it also can achieve high throughput and interoperability.
Experts in the blockchain space have recognized Oraichain as a “hidden gem” for AI in blockchain. It might not have the hype of bigger tokens, but it’s often mentioned in lists of top AI blockchain projects for its originality. A Gate.io research piece described Oraichain as “setting a new standard for smart contract capabilities and trustworthiness by enhancing them with AI”. It indeed takes simple if-then smart contracts to the next level by allowing complex decision-making based on AI.
Future Plans: Oraichain is pushing ahead on multiple fronts. One, they aim to fully decentralize the AI marketplace – encouraging third-party AI developers to join and monetize. Two, they plan deeper integration with other blockchains. They already offer Ethereum and BNB Chain bridge, so Ethereum dApps can call Oraichain oracles via a middleware called OraiBridge. In H2 2024, they were exploring providing oracle services to Solana and other ecosystems as well, effectively making Oraichain a cross-chain AI oracle hub. Another exciting area is AI Agents: their research arm is looking into autonomous agents on Oraichain that could perform tasks and interact with smart contracts (somewhat similar to Fetch.ai’s agents, but leveraging Oraichain’s AI capabilities). A recent product, Agents.land, demonstrates AI agents that can trade on DEXes or manage tokens on behalf of users using the Oraichain infrastructure.
Finally, Oraichain is invested in AI ethics and trust – by keeping humans in the loop via governance. ORAI token holders can vote on adding or removing certain AI oracles, ensuring community control. This could be important if, say, a biased or harmful AI model was identified – the community could act to prevent its usage.
In conclusion, Oraichain is a project that might not make daily headlines, but it’s deeply building the plumbing to make blockchain and AI work together. For developers and companies wanting to build “AI-infused” decentralized apps, Oraichain is very compelling. As AI becomes ubiquitous, solutions like Oraichain’s will likely be in high demand, placing it firmly among the top AI-related blockchains to watch.
8. DeepBrain Chain (DBC) – Decentralized AI Computing Network
DeepBrain Chain is a project that has been around since 2017, aiming to provide low-cost, decentralized cloud computing for AI. In simpler terms, DeepBrain Chain connects people or companies who need lots of GPU power (for AI training, rendering, etc.) with those who have idle GPUs, through a blockchain-based protocol. If this sounds a bit like Render Network, there are similarities, but DeepBrain Chain focuses more on AI training and enterprise use cases, and it built its own blockchain from the ground up specialized for this purpose. DBC calls itself “the world’s first AI public chain, creating a decentralized AI cloud computing platform.”
How It Works: DeepBrain Chain’s network consists of miners (GPU providers) who contribute their GPU hardware to run AI tasks, and AI requesters who submit jobs. The native token DBC is used to pay for computing tasks and to reward the miners. What’s distinctive is that GPU providers must stake a certain amount of DBC and meet technical requirements to participate. This ensures they are committed and behave honestly. The network leverages a blockchain (initially launched as an NEO token, but later they built a Substrate-based chain and became a Polkadot ecosystem project) to coordinate job assignments, payments, and to maintain a reputation system.
One of DeepBrain’s big selling points is cost reduction. By utilizing spare computing resources globally, they claim AI computation via DBC can be up to 70% cheaper than traditional cloud providers like AWS. This is huge considering training advanced AI models can cost millions on cloud services. For small AI startups or research labs, cost is often a barrier – DBC aims to lower that barrier through decentralization.
Achievements and Ecosystem: DeepBrain Chain was founded by Yong He, and the team initially gained attention in the AI community in China. They even won an award at a blockchain competition in Zhongguancun (China’s Silicon Valley) in 2017. By 2021, they launched their mainnet and a GPU computing platform in quick succession. In 2022, DBC gained traction in South Korea: three major mining pools from Korea joined, adding massive GPU power to the network. Additionally, DBC facilitated the launch of several GPU cloud platforms, such as Haibao GPU in China and Hycons Cloud in Korea, which offer AI and cloud gaming services on top of DeepBrain Chain’s infrastructure. These are like front-end platforms where users (gamers or AI developers) use a service, but in the backend it’s powered by DBC’s distributed GPUs.
DeepBrain Chain is not only about raw compute; they also emphasize data privacy for AI. They have discussed using techniques like federated learning and differential privacy to allow AI training on sensitive data without exposing the data itself. This is an important angle: many companies have spare data and compute but can’t share data due to privacy. A decentralized network with privacy-preserving algorithms could let multiple parties train a shared AI model without any one of them seeing others’ raw data.
Use Cases: The immediate use is any AI training or inference task. This can range from training deep learning models for image recognition, NLP, etc., to rendering for VR, to even non-AI tasks like scientific simulations that require high performance computing (HPC). For example, a startup developing a new AI algorithm could rent 100 GPUs on DBC for a week to train their model at lower cost, rather than paying AWS or buying their own servers. Or a university research team could tap into DBC’s network for extra horsepower during a project. DeepBrain Chain has also highlighted cloud gaming as a use case – rendering games on remote GPUs and streaming to users (similar to Nvidia GeForce Now or Google Stadia, but decentralized). The aforementioned Hycons platform indeed targets cloud gaming using DBC’s network.
DBC Token Performance: DeepBrain Chain had its token sale in 2017 and was initially an NEP-5 token on NEO. It was quite popular then, reaching a high valuation during the 2017 bull run (market cap in hundreds of millions). Then it went quiet during the crypto winter, falling over 97% from peak by 2020. However, the project didn’t die – they kept building and in 2021–2022 delivered on their mainnet. In the 2021 mini bull run, DBC token saw a resurgence (though not to prior highs). In early 2023, as AI tokens gained attention thanks to ChatGPT, DBC was rediscovered by traders – its price jumped significantly (in percentage terms, as it was coming from a low base). CoinTelegraph even ran a piece around ChatGPT’s anniversary, highlighting DBC as “revolutionizing AI development” by cutting costs and improving data security. By 2025, DBC remains relatively small in price (a few cents per token) and moderate market cap, but its network usage is the more important metric. The number of GPUs and computing power on DBC network has grown: anecdotal reports suggest DBC has secured thousands of GPUs across mining pools and individual providers globally.
Why Watch DeepBrain Chain: As AI becomes more central to business, the demand for compute skyrockets. If decentralized solutions like DeepBrain Chain can truly deliver reliable power at a fraction of the cost, they could carve out a significant niche in the AI industry. Think about companies spending tens of millions on cloud computing – DBC could save them a large chunk of that, which is a compelling proposition. Moreover, DBC’s head start (being one of the earliest in this space) means they have experience in both the AI world and blockchain world to navigate challenges. They’ve built relationships in places like Korea and China, giving them a global footprint.
Another aspect is Web3 synergy: a lot of new Web3 projects (metaverse, AI-driven dApps) will need back-end compute. Instead of going to centralized cloud, they might prefer a Web3-native provider. For example, a decentralized metaverse project that needs to render scenes or run physics simulations could offload that to DBC miners and pay in DBC token. This keeps the value flow within the Web3 ecosystem rather than going to AWS.
DeepBrain Chain has competitors (Render, Golem, etc.), but it differentiates by being AI-focused and enterprise-oriented. It’s not just generic compute; it’s tailored for AI with considerations for things like data privacy and specific AI workloads.
Future Developments: The roadmap includes possibly becoming a parachain in the Polkadot network (to leverage Polkadot’s security and interoperability). Since DBC’s blockchain is Substrate-based, this is feasible. That could allow it to accept payments in other tokens or connect to other ecosystems easily. They are also working on improving the developer experience – making it easier to submit jobs to the network, maybe through integrations with AI frameworks (imagine a plugin for TensorFlow/PyTorch that lets you train on DBC network seamlessly).
DeepBrain Chain’s long-term vision extends to what they call “AI Training Net” and “AI Model Market”. Once you have many models trained on DBC, you could have a marketplace to sell or share those AI models, perhaps using NFTs or other mechanisms to represent model ownership. They’ve hinted at these possibilities where trained models become assets that can be transacted – a concept that aligns with the broader trend of treating AI models and data as valuable commodities on blockchain.
In sum, DeepBrain Chain is a foundational project addressing one of the most critical needs in AI (compute power) with blockchain technology. Its persistence through many years and the resurgence of interest in 2023–2025 make it a top AI blockchain project to follow, especially as the compute backbone of the emerging AI economy.
9. Cortex (CTXC) – AI on Smart Contracts
Cortex is a project that brought AI directly into smart contracts, enabling what they call “AI on Blockchain”. Launched in 2018, Cortex built an open-source platform that extends the Ethereum Virtual Machine to incorporate AI model inference. In simpler terms, Cortex allows you to execute AI algorithms within smart contracts – something previously not possible on Ethereum due to the complexity and nondeterminism of AI computations. The team behind Cortex (Cortex Labs) aimed to democratize AI by allowing anyone to upload trained AI models to the blockchain, which developers can then integrate into decentralized applications (DApps). The vision is to have intelligent DApps that can, for example, perform image recognition, natural language processing, or other AI tasks natively on-chain.
Technology Highlights: Cortex developed the Cortex Virtual Machine (CVM), which is an Ethereum-compatible virtual machine with an added AI layer. Smart contracts on Cortex can include a special instruction to call a machine learning model (stored on the blockchain or IPFS). Miners on the Cortex network execute the model (using GPUs if needed) and return the result as part of the contract execution, reaching consensus on the output. To make this deterministic (since normally AI models could yield slightly different results on different hardware), Cortex requires models to be quantized and deterministic, and it even worked on a technique called “ZK proofs for ML” (ZKML) where a zero-knowledge proof can verify the correctness of a model’s output without re-running it. In fact, in 2024 Cortex released updates on ZKML progress, indicating they were testing proving systems for neural network execution – a cutting-edge approach.
Developers can submit their trained AI models to the Cortex storage (and get rewarded in CTXC token when their model is used). There’s a concept of “AI contract” – a smart contract that uses an AI model. One famous early example was a cat image classifier contract: you could feed an image (converted to data) into the contract, it would run a pre-trained AI model to decide if the image is of a cat, and then take some action (like mint an NFT if true). This was a novelty demonstrating how blockchain could interact with real-world data in a complex way, not just simple oracles.
CTXC Token and Performance: CTXC is Cortex’s native cryptocurrency, used for paying for AI inference in contracts and as block rewards for miners (Cortex started as a proof-of-work chain, but later considered moving to proof-of-stake or similar). The token saw a large pump in 2018, reaching around $2.39 at its height during the AI blockchain excitement of that time. It then dropped significantly (to mere cents in the 2019–2020 bear market). However, the project kept a relatively low profile while continuing development. In the 2021 bull run, CTXC revived, and then in late 2023/early 2024, with AI back in focus, CTXC climbed again – by Nov 2024 it was around $0.40–$0.50, with a market cap near $100M. Cortex Labs highlighted that the token had recovered from a low of $0.03 in 2020 to show renewed interest, arguably due to people seeing it as a viable platform for on-chain AI.
Use Cases and Partnerships: Cortex’s on-chain AI capability opens some unique possibilities. For instance, DeFi protocols could use AI to adjust parameters – imagine a lending protocol that uses an AI model on-chain to dynamically assess risk and adjust interest rates or collateral requirements. Or a game on blockchain where the AI opponent’s moves are generated by an on-chain neural network (ensuring fairness and transparency of the AI’s decision because it’s all on chain). Another application is in NFTs: generative art NFTs that are created by an AI model at mint time – on Cortex, the generative model could run as part of the minting contract, meaning the exact art piece is determined by an AI model whose logic is recorded on chain, providing provable randomness or uniqueness.
Cortex also made strides in the AI community: their team members have academic backgrounds, and they ran a program to attract AI developers. They created a programming language called Python VM (or a variant) for writing AI inference logic into contracts more easily, since coding neural nets in Solidity would be impractical.
One specific partnership/use-case: Cortex partnered with computing hardware firms to create GPU mining machines that could mine cryptocurrency while also serving AI inference tasks. The idea was to utilize miners’ GPUs for both securing the blockchain and executing AI, making it more economic. This concept was similar to projects like Bittensor’s, but Cortex was focusing on the contract execution side.
Why Cortex is on the List: Cortex represents a more purist approach to merging AI and blockchain – literally merging them at the execution level. It pioneered AI-enhanced smart contracts, something that larger platforms like Ethereum or Solana have not done (they rely on off-chain AI). This innovation can foster a new wave of DApps that were not possible before. For example, a decentralized identity verification DApp could use an on-chain AI model to do facial recognition or document verification. With Cortex, the verification result is part of the blockchain record, and you’re not trusting an external service for that AI judgment – it’s baked into the contract that everyone can audit.
Industry observers have noted that while adoption of Cortex has been limited (given it’s a smaller chain), the concept is powerful. Binance Research described Cortex as “a pioneering effort to merge AI with blockchain technology, offering a platform for intelligent DApps”. It essentially adds a layer of intelligence to smart contracts.
Future and Updates: As of 2024, Cortex Labs has been working on improving the performance of CVM and integrating zero-knowledge proofs so that heavy AI computations could be verified without every node re-running them. If they crack ZKML, it could allow a scenario where maybe only a subset of nodes or special nodes with GPUs run the AI model, but then provide a proof that all other nodes can verify quickly – solving the challenge of heavy computation on many nodes. That would be a leap that even bigger chains might adopt in the future.
Cortex’s future plans also include bridging to other ecosystems. They want CTXC and Cortex AI services to be callable from Ethereum or BSC via some cross-chain solution, which could increase usage since developers on those chains could leverage Cortex’s AI functionality without leaving their main platform.
They have also hinted at Cortex 2.0, possibly moving to a more energy-efficient consensus (maybe leveraging proof of stake or becoming an L2 on Ethereum for security). This could help in making it easier for devs to deploy without worrying about mining.
Expert Opinion & Forecasts: Analytics Insight and other tech outlets sometimes list CTXC among promising AI tokens, noting its low market cap and high potential if AI-driven DApps take off. Price predictions (though speculative) often say if one major AI DApp were to succeed on Cortex, demand for CTXC could spike dramatically. Of course, any forecast of CTXC hitting tens of dollars is very speculative; it would require widespread adoption. For now, the focus is on the tech – which is sound and ahead of its time. That’s why Cortex makes our top 10: it’s a project that quietly built a bridge between AI developers and blockchain developers, allowing them to collaborate on products that benefit from both. As the industry matures, it’s projects like Cortex that could find themselves at the center of the next wave of intelligent decentralized applications.
10. Alethea AI (ALI) – Intelligent NFTs and the AI Metaverse
Rounding out our list is Alethea AI, known for its Artificial Liquid Intelligence (ALI) token and its vision of merging AI with NFTs to create interactive, intelligent digital assets. Alethea AI launched in 2021 and gained attention by introducing the concept of “intelligent NFTs” (iNFTs). An iNFT is essentially an NFT (for example, a digital avatar) that is powered by AI – it can have its own personality, converse with people, create content, and evolve over time. Alethea built a protocol that allows users to embed AI engines into their NFTs by locking ALI tokens with them, thereby “infusing” intelligence. The flagship demo was an iNFT character named Alice – unveiled in 2021 as the first intelligent NFT, which could engage in dialogue, showcasing traits of generative AI combined with blockchain ownership.
Platform and Technology: The backbone of Alethea’s ecosystem is the AI Protocol. In its current iteration (AI Protocol V3), it provides an infrastructure for tokenizing AI models, data, and interactions. Some key components include:
- ALI Pods (Intelligence Pods): These are modules that contain an AI personality or skill. When attached to an NFT (like a character image), they bring it to life. They are effectively containers of AI models (such as GPT-based conversational models) that can be leveled up or improved.
- ALI Agents: Tokenized AI agents that operate within the system, performing tasks and interacting. They live in “Hives” – which are clusters of models, data, and computing resources that the agents can access.
- Decentralized Infrastructure (DePIN): The protocol uses decentralized storage and computing (potentially leveraging networks like IPFS or other decentralized GPU providers) so that running these AI agents isn’t reliant on a centralized server. Key metadata and ownership records are on-chain for transparency, while bulky assets (like large model files or media) can be kept off-chain but referenced securely.
Alethea’s technology allows natural language interaction with NFTs – their CharacterGPT, launched in Jan 2023, enables creation of AI characters from just a description. For example, you could type “A friendly medieval knight who tells jokes” and CharacterGPT will generate a unique avatar and a personality that speaks accordingly. This significantly lowers the barrier to create dynamic avatars.
Use Cases and the Metaverse Angle: The immediate use of Alethea’s tech is in the metaverse and gaming. Imagine NPCs (non-player characters) in virtual worlds that are not pre-scripted but actually driven by AI – they can converse with players with unique personalities. If these NPCs are iNFTs owned by users or creators, it establishes a new creator economy: you can create and sell intelligent characters. Alethea has a dApp called Noah’s Ark which is a metaverse for iNFTs to live and interact. Users can come and chat with various iNFTs – from historical figures to fictional beings – and even have those AI characters perform tasks or create content.
Another use case is digital companions or assistants. There are iNFTs that serve as personal AI companions – think of a Tamagotchi-like pet that you own as an NFT, but it talks and learns from you, or a virtual tutor/coach NFT that can engage and teach.
For businesses and brands, iNFTs could be brand ambassadors or customer support bots with a face and personality, which are verifiably unique and can even be traded or leased.
ALI Token Dynamics: The ALI token is used across this ecosystem as the utility token. When you want to create an intelligent NFT, you lock a certain amount of ALI tokens into an “Intelligence Pod” that attaches to your NFT. The more ALI you lock, potentially the more sophisticated the AI can be (like higher “intelligence level”). ALI tokens also facilitate transactions in the AI Protocol – e.g., paying for GPU time, or for the services of an ALI Agent. Alethea raised funding (over $30M) and by early 2022 the ALI token was listed on major exchanges. It reached a multihundred-million dollar market cap during the 2021 NFT boom (trading around $0.1 at peak), and like others, cooled off and then saw a smaller resurgence in the 2023 AI hype (hovering around $0.02-$0.04 in 2023-2024). As of mid-2025, ALI remains modestly valued, reflecting that the concept is still niche – but it has a dedicated community of creators.
Expert Opinions: Alethea is often highlighted as a project at the intersection of AI, NFTs, and the metaverse – three very buzzy areas. Coin360 stated that “Alethea AI's innovative approach combines generative AI & blockchain for interactive NFTs”. It’s pioneering a “content engine” where the content (the AI’s outputs) is owned by users. Some tech futurists believe such AI avatars will be central to how we interact online – from VR world guides to AI influencers. Mark Cuban is actually one of the investors in Alethea, and he praised the idea of “virtual beings” economy, which lends some weight to their vision.
Progress and Milestones: In 2022, Alethea rolled out Fusion, allowing any existing NFT (like a Bored Ape or CryptoPunk) to be fused with an intelligence pod – giving blue-chip NFTs a voice or personality. They also moved towards decentralizing the AI protocol by end of 2022, meaning community governance over how the AI tech is used. In 2023, beyond CharacterGPT, they implemented “Open Fusion” letting the public request to intelligent-ify any NFT they own. By 2025, their AI Protocol V3 is pushing to become a full-fledged decentralized network for AI assets, aligning with the concept of DePIN (decentralized physical infrastructure) where distributed GPUs and storage underpin the system.
Why It’s One to Watch: Alethea’s approach stands out because it’s about injecting AI into the very fabric of Web3 culture (NFTs). It’s easy to imagine that in a couple of years, most avatars and characters in blockchain games or communities will have AI capabilities. Alethea is among the first-movers there. The ALI token is a bet not just on one platform, but on a future creator economy of AI characters – and Alethea has built a lot of the tools for that already.
Moreover, in terms of market narrative: AI + metaverse was a hot combo in late 2021, and while it quieted down, it could resurge as technologies mature (Apple’s VR push, etc.). If so, Alethea is well positioned to ride that wave with a working product.
Future Plans: The roadmap likely includes scaling the AI capabilities (more languages, more types of AI like maybe AI that can do animation, or complex reasoning), and more decentralization (perhaps ALI token governance over AI model updates or content moderation policies). There’s also potential integration with other ecosystems – e.g., bringing Alethea’s iNFT standard to Ethereum L2s or other chains, so that intelligent NFTs can roam across platforms.
From a tech perspective, we might see Alethea incorporate more advanced models (maybe an open-source equivalent of GPT-4 in their agents, etc.) as those become available. And they will probably foster a developer community to build mini DApps on top of their protocol (for instance, someone could make a game where every character is an iNFT from Alethea’s Noah’s Ark).
Forecast: If Alethea’s platform saw one or two breakout viral iNFTs (imagine an AI VTuber or influencer NFT that becomes popular), it could spur a lot of interest. Analysts would then likely re-rate ALI token based on usage and adoption. Some have speculated ALI could follow the path of other NFT-related tokens which soared during hype cycles. But the key will be user engagement – are people actually spending time and money on these intelligent NFTs?
So far, the signs are promising: Alethea held an iNFT “Hackathon” where creators made cool AI NFT experiences, and many of those were captivating. If that momentum continues, Alethea AI can solidify its place as the top platform for personal AI assets in the crypto space. In conclusion, for readers keen on the creative and interactive side of AI and blockchain, Alethea’s fusion of generative AI with digital ownership is a trend to keep a close eye on.
Final thoughts:
The convergence of AI and blockchain is creating an entirely new landscape of possibilities, from decentralized AI supercomputers to intelligent digital lifeforms. The ten projects discussed – the ASI Alliance (Fetch.ai/SingularityNET/Ocean Protocol), Bittensor, Render, NEAR, Internet Computer, Numerai, Oraichain, DeepBrain Chain, Cortex, and Alethea AI – represent the cutting-edge innovators at this intersection. They address different layers of the AI-blockchain stack: data and model sharing, computing infrastructure, integration into dApps, economic and governance models for AI, and even novel user experiences with AI.
What unites them is a mission to make AI more decentralized, transparent, and user-centric:
- They envision a world where contributors are fairly rewarded when AI systems use their data or models.
- A world where AI agents can act autonomously yet verifiably on our behalf, conducting transactions and making decisions within bounds we set.
- A world where infrastructure is not monopolized, but instead taps global distributed resources – be it GPUs on a blockchain or knowledge from thousands of individuals.
- And a world where intelligent technology is more accountable, thanks to blockchain’s audit trails and community governance, mitigating AI’s black-box risks.
The year 2025 finds these projects at various stages: some like Numerai are battle-tested and quietly steering hedge funds; others like Bittensor are rapidly emerging as new power players with strong backing; platforms like NEAR and ICP are infusing AI into large ecosystems, potentially affecting millions of users. Market sentiment has been enthusiastic – the AI crypto sector saw a significant surge, outpacing many other categories, with top tokens capturing investor attention and bullish forecasts. Of course, with any nascent tech, there are risks: not every project will achieve all its goals, and hype can outpace reality. But importantly, each of these projects has shown real progress – whether through mainnet launches, partnerships, or active user communities.
For enthusiasts and investors, the key is to watch the traction:
- Are developers building on these platforms (e.g. new DApps on Cortex or NEAR utilizing AI)?
- Are businesses adopting their solutions (e.g. enterprises using DeepBrain Chain for cheaper AI training, or games incorporating Alethea’s iNFTs)?
- Are there growing network effects (more nodes on Render or Bittensor, more data scientists on Numerai, more agents on ASI Alliance)?
The “AI blockchain” sector is still in its early days, reminiscent of the early DeFi days – full of experimentation. But the stakes are huge: whichever platforms become standard for AI data sharing, AI computation, or AI-driven smart contracts could become as indispensable as today’s big cloud and AI companies, but with decentralized ownership.
As experts often say, we’re at the start of a new paradigm. The projects above each hold a piece of the puzzle for a future where AI is interoperable, accountable, and much more accessible. Whether it's through enabling machines to have wallets and pay each other, or creating a global brain trust of algorithms, or turning digital art into living characters, these initiatives are expanding what is possible.
In summary, the Top 10 AI Blockchains to watch are not just trending tickers – they represent a profound shift towards merging two transformative technologies. Keep an eye on them, engage with their communities, and watch as they potentially reshape how we build and interact with intelligent systems. If 2025 is the year AI blockchains truly take off, these projects will be leading the charge, and their progress will likely define the narrative of tech innovation in the years to come.