-GM Network Team
GM Network, inspired by GM Labs, is the first consumer AIoT network, built with AltLayer and EigenLayer.
AIoT consists of AI + IoT. Over decades of internet development, IoT has permeated every aspect and constitutes today’s human life. DePIN represents the decentralized part built on IoT. For more precise expression, we believe AI + IoT better articulates the vision and objectives of GM Network, as it will subsequently be unifiedly represented as AIoT, which stands for AI + IoT/DePIN.
In 2024, with the rapid development of various AI models like ChatGPT-4o, Gemini, and Llama3, AI is increasingly influencing people’s lives. Through smartphones, wearable devices, robots, electric vehicles, and other smart devices, humans can increasingly experience the convenience of AI and communicate with AI through voice, visual, and other means. As AI becomes more widespread in the future, hardware devices are essential bridges, with IoT playing an increasingly important role, especially consumer-facing IoT which, enhanced by AI, becomes truly smarter, producing interesting products like Rabbit R1, Pin, and Oura Ring. These developments have a profound impact on human society, much like the stories in Black Mirror.
Therefore, we developed GM Network with the hope that it becomes the largest communication and incentive layer connecting AI and IoT, bridging the virtual and real worlds, and acting as a massive bridge to accelerate the data and application scenarios needed by AI, making it easier for consumers to feel the warmth and convenience of AI.
The rapid advancement of artificial intelligence (AI) is transforming the way we live, work, and interact with technology. As AI continues to evolve, its integration into everyday life becomes inevitable. From personal assistants like Siri and Alexa to advanced machine learning algorithms that power our smart devices, AI is becoming an indispensable part of our daily routines. However, the true potential of AI is unlocked when it is combined with the Internet of Things (IoT).
IoT refers to the network of physical devices, vehicles, home appliances, and other items embedded with sensors, software, and connectivity, enabling them to collect and exchange data. This interconnected ecosystem allows for seamless communication and interaction between devices, creating a smart environment that enhances efficiency and convenience.
The relationship between AI and IoT is symbiotic. IoT devices generate vast amounts of data, which AI algorithms analyze and learn from to make intelligent decisions. This data-driven approach enables AI to provide personalized and context-aware services, improving user experience and optimizing operations. For instance, smart thermostats learn user preferences and adjust temperatures accordingly, while wearable fitness trackers monitor health metrics and provide personalized fitness recommendations.
Conversely, AI enhances the functionality of IoT devices by making them more intelligent and autonomous. AI-powered IoT systems can predict maintenance needs, detect anomalies, and automate processes, reducing the need for human intervention. This level of automation is particularly valuable in industries such as manufacturing, healthcare, and transportation, where efficiency and accuracy are paramount.
As AI becomes more embedded in our lives, the need for robust IoT infrastructure grows. The integration of AI and IoT is not just a trend but a fundamental shift in how we interact with technology. This convergence is driving innovation and creating new opportunities for businesses and consumers alike. Together, AI and IoT are shaping a future where smart, connected devices enhance our quality of life and drive the digital transformation of industries.
In conclusion, the future of AI is intrinsically linked to IoT. The seamless integration of these technologies is essential for unlocking their full potential and delivering intelligent, data-driven solutions that improve our lives. As we move forward, the collaboration between AI and IoT will continue to evolve, driving new advancements and shaping the way we interact with the world around us.
Despite the rapid advancements in technology, the majority of IoT devices today remain relatively simple in their capabilities and are not fully integrated with artificial intelligence (AI). These devices, while smart, often lack the sophistication and learning capabilities that true AI can offer. On the other hand, AI itself continues to struggle with a lack of sufficient data to truly understand and predict user needs and behaviors.
For AI to reach its full potential, it needs a continuous influx of diverse and high-quality data. This data is crucial for training AI models to be more accurate and responsive to user requirements. However, the current generation of IoT devices does not generate enough meaningful data that AI can leverage effectively. This gap highlights the need for a new generation of IoT devices that are designed not just to be smart but also to be capable of generating and processing data suitable for AI applications.
To bridge this gap, more IoT devices need to be upgraded to support AI functionalities. This can be achieved through the integration of Cloud AI and Edge AI technologies. Cloud AI involves leveraging the vast computational resources available in the cloud to analyze data and derive insights. This approach allows for powerful data processing and sophisticated AI models that can be continuously updated and improved. However, it often requires reliable and fast internet connectivity, which might not always be available.
Edge AI, on the other hand, brings AI capabilities directly to the devices at the edge of the network, such as sensors, cameras, and other IoT devices. This approach enables real-time data processing and decision-making without the need for constant cloud connectivity. By embedding AI capabilities directly into the IoT devices, they can become more autonomous, efficient, and responsive. Edge AI reduces latency, enhances privacy by keeping data local, and ensures that devices can operate independently even in environments with limited connectivity.
Upgrading IoT devices to support both Cloud AI and Edge AI will allow for a more seamless and intelligent interaction between AI and IoT. This will enable IoT devices to not only collect and transmit data but also to analyze and act on this data in real-time, making them truly intelligent. For example, smart home devices could learn the habits and preferences of the users, adjusting settings autonomously to improve comfort and efficiency. Industrial IoT devices could predict maintenance needs and optimize operations to reduce downtime and costs.
In conclusion, the evolution of IoT devices from being merely smart to truly AI-driven is essential for the next wave of technological innovation. By integrating Cloud AI and Edge AI, IoT devices can generate the rich, meaningful data that AI needs to better understand and serve users. This synergy will lead to smarter, more responsive environments that enhance our daily lives and drive the digital transformation of industries.
Whether it is giants or startups, they are all launching new hardware devices. At the beginning of the year, Apple released the VisionPro, and Meta subsequently released smart glasses in collaboration with Ray-Ban. There are also examples like the Whoop smart wristband and the Oura Ring smart ring. Even Rabbit released the AI voice phone R1, and Humane launched the AI Pin. Samsung is also about to release its own Galaxy Ring. IoT will play an increasingly important role in the world of AI, providing enormous new opportunities for all entrepreneurs:
Despite the rapid advancements in artificial intelligence (AI), a significant gap remains in the availability of real-world data and practical scenarios necessary for AI to reach its full potential. This gap is particularly evident in the realm of the Internet of Things (IoT), where the integration of AI could revolutionize consumer experiences and industry operations. However, several critical issues hinder this integration.
To bridge these gaps, a closer integration between AI and IoT is essential. IoT devices can provide the personalized, real-time data that AI needs to improve its accuracy and relevance. Moreover, opening up the ecosystems of major tech companies and addressing data ownership issues can democratize access to data, fostering innovation across the industry.
For AI to become truly ubiquitous and beneficial, it must be embedded in more consumer-facing scenarios. This requires breaking the monopoly of major companies and encouraging the development of AI applications that enhance everyday experiences. Smart home devices, wearable health monitors, and intelligent personal assistants are just a few examples of how AI can be integrated into daily life, providing tangible benefits to users.
The future of AI depends on overcoming the current limitations in data and scenario availability. By leveraging the vast amounts of data generated by IoT devices and creating more diverse, consumer-facing applications, AI can realize its full potential. This will require collaborative efforts to open data ecosystems, address ownership concerns, and drive innovation beyond the confines of today’s major tech companies. Only then can AI truly transform the way we live and interact with technology.
The convergence of Web3 and IoT offers an unprecedented opportunity to revolutionize the landscape of AI. By leveraging the decentralized and secure nature of Web3, we can enhance the connectivity of IoT devices, providing a robust foundation for AI to access a wealth of real-world data and diverse scenarios. This synergy not only addresses the existing challenges faced by AI but also opens up new avenues for innovation and development.
One of the primary limitations of current AI systems is the lack of personalized and real-time data. Most AI models are trained on online and on-chain data, which, while extensive, do not fully capture the nuances of individual behaviors and environmental contexts. IoT devices, embedded in our daily lives, generate a continuous stream of data that is both personal and contextual. Integrating this data into AI systems can significantly enhance their accuracy and relevance, enabling more responsive and adaptive AI applications.
However, the challenge lies in the accessibility and utilization of this data. Large tech companies like Apple and Google often silo their data within closed ecosystems, limiting the ability of external developers to harness this information. Web3, with its decentralized architecture, offers a solution by facilitating open and secure data exchange. By enabling IoT devices to share data in a decentralized manner, Web3 ensures that valuable information is not confined to proprietary platforms but is available to drive AI innovation.
Moreover, Web3 enhances data ownership and privacy, allowing users to have control over their data and even benefit from it financially. This paradigm shift empowers individuals and fosters a more equitable data economy. Users can choose to share their IoT-generated data with AI developers in a secure and transparent manner, knowing that their privacy is safeguarded and that they are fairly compensated.
The integration of Web3 and IoT also paves the way for more diverse and consumer-facing AI applications. While leading AI companies dominate general-purpose scenarios like text processing and chatbots, there is a vast untapped potential for specialized applications that cater to specific user needs. By connecting more IoT devices through Web3, developers can create innovative AI solutions tailored to unique contexts, such as personalized health monitoring, smart home automation, and intelligent transportation systems.
For instance, a smart home equipped with various IoT devices can provide real-time data on user preferences and behaviors. This data can be utilized by AI systems to optimize energy consumption, enhance security measures, and improve overall living comfort. Similarly, wearable IoT devices can continuously monitor health metrics, offering personalized insights and recommendations powered by AI. These applications not only improve user experiences but also drive significant advancements in AI capabilities.
Furthermore, the decentralized nature of Web3 provides a level playing field for startups and smaller companies to compete with industry giants. It offers a framework where innovation is not stifled by the monopolistic control of data and resources. Entrepreneurs can leverage Web3 to develop cutting-edge AIoT solutions, bypassing traditional barriers and bringing disruptive technologies to market.
The fusion of Web3 and IoT represents a transformative opportunity to reshape the future of AI. By facilitating greater connectivity, ensuring data privacy, and democratizing access to information, this powerful combination sets the stage for unprecedented growth and innovation in the AI landscape. As we move forward, embracing this opportunity will be key to unlocking the full potential of AI and IoT, driving progress, and creating new paradigms of human-machine interaction.
In summary, Web3 offers a more secure, private, and user-centric internet, providing numerous opportunities for innovation and new economic models. Its decentralized nature empowers users, enhances transparency, and fosters a more resilient and equitable digital ecosystem.
GM Network is poised to revolutionize the intersection of AI and IoT by becoming the largest incentive and communication network connecting these two transformative technologies. As AI continues to evolve and integrate into various aspects of daily life, the need for real-time, personalized data becomes increasingly critical. IoT devices, which are already embedded in many facets of our lives, generate vast amounts of this valuable data. However, the current landscape often sees these devices and the data they produce siloed within proprietary ecosystems, limiting their potential impact.
GM Network seeks to bridge this gap by creating an open, collaborative platform where AI and IoT can seamlessly interact. By incentivizing data sharing and providing robust communication channels, GM Network enables AI systems to learn and adapt more effectively, utilizing the rich, real-time data generated by IoT devices. This approach not only enhances the functionality of AI but also drives innovation across various sectors, from smart homes and wearable health tech to autonomous vehicles and industrial automation.
Through strategic partnerships and a commitment to open data ecosystems, GM Network aspires to democratize access to AI and IoT technologies. By aligning incentives with the needs of developers, businesses, and consumers, GM Network is positioned to unlock new possibilities for intelligent, interconnected systems, paving the way for a smarter, more integrated future.
Considering that there are over 2 billion Web2 IoT devices globally and over 1 million Web3 IoT devices, building a dedicated Layer 2 around AI+IoT is extremely valuable. All IoT devices that sustainably contribute data will eventually be incentivized by GM Network and its ecosystem. Through GM’s mechanisms and gameplay, they will continuously provide real-world data for AI training, breaking down data barriers. Additionally, all incentivized IoT devices will provide real user scenarios and application opportunities for AI, allowing most consumers to experience the value of AI, enjoy better AI products, and accelerate the mass adoption of AIoT.
GM Network is composed of three layers: the Asset Layer, the Data Layer, and the User Layer. Together, these three layers form the modular, scalable, and composable foundation of the GM Network ecosystem, significantly reducing costs and barriers for developers and enabling the large-scale adoption of AIoT.
These layers work in harmony to create a comprehensive and efficient ecosystem for AIoT development and deployment.
The GM Network is the first consumer AIoT network, supported by AltLayer and EigenLayer. The GM Network has the following features:
AVS: GM Network is a Layer2 based on AVS proposed by EigenLayer and developed using AltLayer’s Restaked Rollup, utilizing the OP Stack and EigenDA to form Ethereum’s Layer2. Leveraging the power of AVS, GM Network can achieve a fully decentralized network while flexibly obtaining security and paying costs based on security needs. We firmly believe in the potential of AVS, which will not only appear in financial aspects but also in more consumer-facing application scenarios. So actually, GM Network is the first AVS consumer AIoT network.
GM ID is an Aggregated Wallet SDK Combining Passkey, Account Abstraction and DID.
GM OS is an on-chain and off-chain data protocol for accelerating the data communication between AI and IoT.
GM OS will have the following key features:
GM OS has accumulated 10 millions of on-chain data entries from QuestN and serves over 6,000 developers, covering multiple fields from DePIN, AI, Gaming, Social and DeFi. As more consumer AIoT products emerge, the data on GM OS will become increasingly accurate and valuable.
QuestN is the Best Community Growth and Marketing Platform.
Two years after its launch, QuestN has accumulated over 10M+ unique addresses with a DAU count of over 200K+ and MAU reaching 1M+. Users are spread across more than 25 countries and regions. QuestN daily engages a massive user base with numerous quests and campaigns, helping over 6,000 projects with community growth and cold starts, and delivering over 3 million USD in token rewards to users.
QuestN will continuously generate more data from the on-chain data perspective, which, together with off-chain data, will assist in AI training. It will also help more AIoT projects in building on-chain user profiles, reputation management, and behavior analysis.
GM Launchpad is a Launchpad for AIoTs and NFTs.
GM AI is the one app for all AIoTs and Agents.
AIoT=AI+IoT, IoT provides data and scenarios to AI and transforms into AIoT.
The more AIoT devices there are, the more NFTs will be minted, and the more users will join the GM Network ecosystem, continuously using devices and generating more real-world data. Therefore, the core metric for GM Network is no longer TVL (Total Value Locked) like DeFi products, but more appropriately TDVL (Total Device Value Locked) for the AIoT ecosystem. Considerations for TDVL include:
With more Layer2s and Layer3s, each chain having its own liquidity isn't the most capital-efficient or profitable. Instead, working with more abstract bridges or DEXs can still provide sufficient liquidity for the ecosystem.
Non-DeFi Layer2s aren't suitable for TVL metrics and may lead to misdirection. GM Network focuses on how many devices are connected and the value generated by these devices, making the evolution from TVL to TDVL a more rational choice.
Therefore, within the GM Network ecosystem, NFTs have many functions:
This translation outlines GM Network's innovative approach to integrating blockchain technology with real-world applications through NFTs and IoT, emphasizing a strategic pivot from traditional liquidity metrics to a more holistic valuation of networked devices and data assets.
Before the advent of AVS, the capital and security costs of all Layer2 solutions were very high, as they needed to continually synchronize with Layer 1, which is inherently expensive, making even Rollup costs relatively high (although the Cancun upgrade partially improved this).
After the emergence of EigenLayer and AltLayer, all developers can not only build DApps but also construct their own Layer2 or Layer3 at lower costs. With the security market provided by AVS, developers no longer have to worry about paying high security costs and can continue to focus on their products and user experience.
Modular combinations like AVS completely liberate innovation. Developers can build their own ecosystem around token + DApp + Appchain, offering users a complete, seamless experience while capturing value through tokens. This is why GM Network chose to build its own Layer2 based on AVS. We are very confident in the dividends and value brought by the innovations of AltLayer and EigenLayer, and it will significantly unleash the potential for innovation, accelerating the mas adoption of Ethereum.
With the power of AVS, GM Network can support all consumer AIoT products to deploy their own DApps or develop their own vertical Layer3 based on GM:
Whether DApps or L3s, they can utilize the security and scalability provided by AVS without worrying about high costs.
As more DApps and L3s are deployed, GM Network will generate more revenue, which will be automatically distributed to GM stakers, ALT stakers, AVS operators, and ETH re-stakers.
Vertical L3 developers can flexibly define their own token models and mechanisms, combining innovation and capturing significant ecosystem value.
DApps and L3s will receive support and early ecosystem incentives from GM Network, with all L3s receiving modular support from AltLayer.
[1] Satoshi Nakamoto. Bitcoin: A peer-to-peer electronic cash system.
[2] Vitalik Buterin. Ethereum white paper.
[3] EigenLayer: The Restaking Collective
https://docs.eigenlayer.xyz/assets/files/EigenLayer_WhitePaper-88c47923ca0319870c611decd6e562ad.pdf
[4] EIGEN: The Universal Intersubjective Work Token
https://docs.eigenlayer.xyz/assets/files/EIGEN_Token_Whitepaper-0df8e17b7efa052fd2a22e1ade9c6f69.pdf
[5] What is AltLayer?
[6] Cosmos Whitepaper