Following the news of Oracle's collaboration with OpenAI on September 10th, Oracle's stock price surged over 43% in a single day. This partnership further fueled Wall Street's enthusiasm for artificial intelligence, leading to significant gains in AI chip stocks such as NVIDIA and Broadcom. Wall Street analysts are rushing to raise their expectations for the S&P 500 index.

Wells Fargo's Chief Equity Strategy Officer Ohsung Kwon mentioned in a report released on September 9th, "Despite some signs of a market bubble, as long as investments in AI continue, the stock market bull run will persist." This not only boosted the US stock market's AI sector but also stimulated the CryptoAI track dominated by decentralized narratives. Over the past year, CryptoAI has experienced minor hype cycles and rapid pullbacks, but the strong performance of US stocks in AI has once again drawn market attention to decentralized AI infrastructure. Although funds have not completely returned, new AI projects are accelerating in the market.

After the burst of the bubble during its peak, what AI field tracks are more compatible with cryptocurrency left by CryptoAI?

Source: Cookie

RoboFi, the Robot Track Combining Virtuality and Reality

In recent social media discussions, the discussion about CryptoAI in the field of robotics has become more intense. Although from a hardware perspective, the technical development of the robot track is still 5-10 years away from its peak, projects are exploring the bridge between "Virtual Robot - Real Robot - AI Agent," from cloud-based simulation to decentralized operating systems, to machine perception and data labeling. They directly benefit from the incentives and security mechanisms provided by blockchain, but real business applications still need to overcome many challenges.

RoboStack, the Development Platform for Cloud Robots

RoboStack is a cloud robot development and deployment platform that provides developers with a simulation environment and sandbox testing. The official documentation states that the platform can simulate a real-world environment in the cloud, with secure sandbox mechanisms, high-performance computing capabilities, real-time metrics, and team collaboration features.

To address the challenge of robot technology being fragmented by different hardware, middleware, and communication standards, RoboStack has proposed the Robot Context Protocol (RCP), allowing secure communication between robots, AI agents, and humans. The simulated environment will include tokenized voting and incentive mechanisms to promote competition between developers and enthusiasts, making it easy to deploy real robot applications in a future multi-chain environment.

Compared to the traditional robot industry dominated by large companies and closed systems, RoboStack is trying to build an open ecosystem, allowing developers to deploy robot services just like deploying smart contracts.

Its official token $ROBOT, launched from Virtuals in May, once experienced a long period of near-zero value, but the team did not give up, continuously updated the product, and it has now returned to a circulating market cap of $3 million.

Building a Machine Perception Infrastructure Auki Network

Auki is developing a decentralized machine perception network called Posemesh, designed to connect humans, devices, and AI. At its core is a DePIN (Decentralized Perception Infrastructure) architecture, allowing robots, AR glasses, and other devices to share real-time location and sensor data, jointly building a collaborative spatial understanding of the physical world, providing a shared spatial view for robots, AR, and AI.

Team members share a robot walking in a space after learning

Auki has designed various node roles based on the Posemesh protocol. Compute nodes provide computing power, motion nodes (robot terminals) upload location information and sensor data, reconstruction nodes generate 3D map models based on this data, and domain nodes manage the 3D space. Each node receives $AUKI token incentives based on contribution, driving a self-evolving machine vision network.

This network emphasizes privacy protection, avoiding a single entity monitoring users' private space. It can be applied in various scenarios such as retail (product placement optimization), property management (asset tracking), exhibition navigation, architectural decoration, among others. Auki has already partnered with some interesting use cases.

This approach is similar to environmental perception systems developed by traditional Internet companies, but through decentralized mechanisms such as peer-to-peer device data exchange, real-time positioning-based mapping and coordination, open-source, and token incentive data processing to ensure user ownership of data.

Its token $AUKI currently has a circulating market cap of $63 million.

Robot's UBER, OpenMind

OpenMind just announced last month a $20 million funding round led by Pantera Capital, with Pi Network's presence in the investment lineup also making a strong impression.

OpenMind is somewhat like the "UBER" for robots but provides an AI command and control system and API. They aim to make robot intelligence open, interoperable, and accessible like the internet.

They have two core products, one being the OM1 operating system, which is a modular system for perception, planning, and control.

The other is the FABRIC protocol, which provides a way for smart machines to cross-environmentally verify identity, share context, and securely coordinate. As more and more robots come online in the future, FABRIC offers a trust layer that allows them to collaborate, whether they are produced by Tesla or Boston Dynamics, and regardless of where they operate. Robots using FABRIC can understand their location, nearby people, and what to do next.

Decentralized Annotation Platform for Human Knowledge, Sapien

Sapien is a decentralized AI data crowdsourcing platform. A rising star in the recent past, it aims to build an "AI Data Factory."

Sapien is deployed on the Base network and incentivizes global users to participate in data annotation and model training through tokens. To address the scarcity of high-quality material in AI training, Sapien has introduced an innovative "Proof of Quality" (PoQ) mechanism to guide the community in producing trustworthy data.

Specifically, data contributors are required to stake a certain amount of Sapien tokens as collateral, and upon multi-party verification, they can receive stablecoin and token rewards. Conversely, submitting low-quality or fake data will result in a penalty of 25% to 100% of the stake. This reward and punishment mechanism effectively eliminates impostors and spamming behavior, ensuring that the data delivered for model training is of high credibility and value.

Sapien's business model is clear, connecting enterprises with massive data needs with annotators distributed globally, allowing professionals and ordinary users to provide "human wisdom" for AI and record contributions and distribute rewards through blockchain. According to official information, traditional giants such as Alibaba, Baidu, Toyota, Lenovo, and AI painting unicorn Midjourney have adopted its services.

According to official data, the Sapien platform currently brings together 1.9 million annotators from over 110 countries, with a total of 187 million data labeling tasks completed. On the fundraising front, Sapien was founded by former Coinbase Head of BD Rowan Stone, Polymath co-founder Trevor Koverko, among others, and completed two rounds of seed funding totaling $15.5 million last year, with investors including Primitive Ventures, Animoca Brands, and other well-known institutions.

In practical applications, Sapien's high-quality data has already been used in scenarios such as autonomous driving image recognition and medical image diagnosis with high precision. For instance, a tumor oncologist can earn hundreds of dollars per hour by annotating cancer case data, showcasing the value of professional data and validating the feasibility of Sapien's incentive mechanism to attract professionals.

It can be said that Sapien has also demonstrated how AI data can be onboarded to serve traditional AI enterprises and how a community providing data can generate revenue. In the AI era, the value of "feeding" models is sometimes as crucial as computational power, and blockchain provides a natural institutional tool for distributed data supply and revenue distribution.

As the AI hype cools off this year, the market is more focused on projects with tangible business progress like Sapien. After Sapien launched its token on the Binance Alpha platform in August, the price initially dropped but saw a strong rebound in early September, doubling in value, with a daily trading volume exceeding $20 million.

Overall, there are indeed projects in the CryptoAI field that involve hardware robots, such as Show Robotics and Littleguy. However, it seems that the capital market is not optimistic about hardware built in the crypto market, and the software and data aspects are still in the early exploration stages.

Show Robotics' robots, these projects are mostly run by individuals or small studios, theoretically with low commercial value

From a technological perspective, achieving large-scale robot autonomy requires overcoming thresholds in perception accuracy, security verification, and more. Although some projects have substantial funding, whether they can ultimately move beyond conceptual hype and become a standard application for "on-chain robots" remains to be tested over time.

The Evergreen Tree in the Crypto Narrative, DeFAI

If the focus of the Robotics Track is "AI making machines move," then DeFAI enthusiasts are thinking about how to "automatically make my funds move." This year, dubbed the "DeFAI Year" by the industry, while other tracks in the previous CryptoAI narrative were almost silent for a round, DeFAI remains popular in the market, with many DeFi projects also launching their own DeFAI products.

Many entrepreneurs are trying to use AI technology to transform decentralized finance, clearing the complex barriers to entry for ordinary users by introducing AI assistants and smart agents, greatly simplifying the decentralized finance operational interface and processes.

Cod3x, Zero-Code AI Trading Agent

Cod3x, developed by the seasoned DeFi team Byte Mason (which was active in the Fantom ecosystem), aims to build a "platform for one-click generation of trading bots." Cod3x provides a no-code development tool, allowing users to create personalized AI trading agents through natural language or simple configuration.

These agents can connect to any data source, help users devise and deploy trading strategies in minutes, including automatic lending, market making, cross-chain arbitrage, and more.

Prior to the launch of its main product, Cod3x tried various types of experiments. For example, previously, Cod3x launched a proprietary trading agent on MakeFun named "Big Tony," embedding its advanced price prediction model into the trading strategy using Allora's model to give the agent a more forward-looking judgment. It also tried to launch the stablecoin cdxUSD and the corresponding lending protocol, using the stablecoin/volatile coin LP model to increase platform liquidity.

Before the official launch in October, Cod3x's core product "Cod3x" is currently undergoing soft testing and will debut the world's first AI trading terminal on September 16 on arbitrum and GMX (with Hyperliquid coming in the future). It allows users to generate their own trading agent through a ChatGPT-like interface. With millions of lines of underlying code encapsulated into an easy-to-use tool, DeFi participants are also experiencing their version of "Vibe Coding."

Its governance token $CDX has a market cap of $6 million.

AI Wayfinder: Cross-Chain AI Navigation Protocol

Wayfinder is a holistic AI protocol that simplifies interaction and navigation within the blockchain ecosystem through users' autonomous AI Agents. This enables users to securely and efficiently perform cross-chain transactions, DeFi strategies, and DApp interactions without the need for complex technical operations.

Complex on-chain operations and intricate cross-chain processes have always been pain points hindering users from entering Web3. The core objective of this protocol is to enhance blockchain accessibility, especially for novice users, by automating tasks through a natural language chat interface (such as sending tokens or executing cross-chain swaps), while users retain full control of their Web3 wallets and assets.

Initially, Wayfinder was an auxiliary tool in the blockchain game Colony, part of the blockchain-based gaming studio Parallel Studios, designed to assist players in automatically handling on-chain transactions related to the game. The team discovered that the potential of this AI system far exceeded the scope of the game, leading them to separate it and make it available to all users.

Wayfinder has already supported multiple chains such as Ethereum, Base, and Solana. It achieves seamless integration between different protocols through community-developed Wayfinding Paths and utilizes a validator mechanism to ensure path security and reliability. It allows AI Agents to learn and optimize paths through predefined workflows. As usage grows, the system self-improves, forms collective memory to avoid recurring mistakes, and adapts to new scenarios.

The protocol offers a graphical interface for non-developer operations and integrates with frameworks like LangChain. Through the $PROMPT reward mechanism, contributors can receive token rewards for sharing routes, enhancing network security.

The token $PROMPT currently has a market value of $41 million.

AI Assistant in the Rise of Prediction Markets

Apart from investment and finance, another innovative application of AI in the crypto space is in prediction markets, particularly in highly data-driven scenarios like sports betting. Traditionally, the sports betting market has been worth billions of dollars, but individual bets often rely on gut feelings or insider tips, with a high barrier to entry for professional analysts and data models.

This has created an opportunity for AI to shine by leveraging machine learning models to explore massive amounts of match data, odds information, and betting sentiment. AI agents can provide users with more rational betting advice and even automatically place trades.

Billy Bets

BillyBets is an AI sports betting agent that uses the decentralized machine learning model of Bittensor Subnet 41 as its core prediction engine, providing high-accuracy sports event outcome analysis. Its token, $Billy, is issued on Base via the Virtuals Protocol.

Billy Bets completed a $1 million seed funding round in July this year, with investors including Coinbase Ventures, Virtuals Ventures, etc. This investment lineup can be seen as Base's baby, leading people to speculate on its positioning in Base's App.

Now Billy has integrated with mainstream on-chain prediction markets such as Kalshi, Polymarket, etc. Its core product, "Billy Terminal," offers a natural language interface where users can input commands like "show tonight's NFL game's +EV bet," and the AI will instantly return analyzed odds, automatically place the bet, and track the outcome.

The algorithm behind Billy captures odds discrepancies across platforms, seeks value betting opportunities, routes bets to the best odds, and can customize strategies based on user preferences.

Since the launch of the beta version in June this year, the Billy Terminal has facilitated over $1 million in bets, with a weekly transaction volume growth of approximately 23%. The Billy project team stated that the global sports betting volume is around $250 billion per year, but less than 1% is placed through AI agents, presenting a market opportunity for Billy.

"AI has transformed Wall Street, and now it will transform the betting table," said Billy's co-founder Joe O'Rourke.

Summary

Looking back on this wave of Crypto AI, it is evident that the market narrative is evolving from the initial hype of the year towards more solid implementation. Initially, the AI Agent concept hype brought a wave of tokens where the buzz was greater than substance. As FOMO subsided, many of these projects have gradually faded away.

However, the long-term value of AI integration with blockchain has not dissipated as a result. On the contrary, it has become increasingly clear in a rational atmosphere. The robotics track has connected the physical world, DeFAI has improved the financial experience, AI in prediction markets has deepened its focus on vertical scenes, data optimization has solidified the foundational supply, and the potential of AI payments, among others, has emerged. Additionally, the cloud computing field, previously considered by some to be a "false narrative," is still quietly developing, with projects such as Aethir, EigenCloud, and Render all making efforts.

It can be said that CryptoAI is forming a complete loop from underlying data to upper-layer applications, with entrepreneurs working in each link. Whether this wave of AI can truly enter the stage of value realization still depends on whether the user base and revenue of CryptoAI can break through, whether traditional enterprises and institutional capital are willing to participate or adopt, and how policy regulation and risk issues are addressed.

What can be confirmed is that after experiencing emotional cooling and rational sedimentation, the story of Crypto AI has entered the "narrative validation" stage. If the main trends are validated and business models work out, then this year's ignition of the AI fire is expected to open up a new blue ocean for the crypto industry.

After multiple attempts, the relationship between blockchain and AI is undergoing a deeper integration, possibly ushering in a new era of financial technology characterized by human-machine collaboration and data sharing.

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