Source: Meteorite Labs
In the past year, AI narratives have been booming in the Crypto market, with leading VCs such as a16z, Sequoia, Lightspeed, and Polychain making huge bets of tens of millions of dollars. Many high-quality teams with research backgrounds and prestigious school backgrounds have also entered Web3, moving towards decentralized AI. In the next 12 months, we will witness the gradual implementation of these high-quality projects.
In October this year, OpenAI raised another $6.6 billion, and the arms race in the AI track has reached an unprecedented height. Retail investors rarely have the opportunity to make money outside of direct investments in Nvidia and hardware, and this enthusiasm is bound to continue to spread to Crypto, especially the recent nationwide dog-catching trend driven by AI Meme. It is foreseeable that Crypto x AI, whether it is existing listed tokens or new star projects, will continue to have strong momentum.
As the decentralized AI leader Hyperbolic recently received secondary investment from Polychain and Lightspeed, we started from the 6 projects that have recently received large-scale financing from leading institutions to sort out the development of Crypto x AI infrastructure projects and look forward to how decentralized technology can safeguard the future of AI for humans.
Hyperbolic: Recently announced the completion of a $12 million Series A financing led by Variant and Polychain, with a total financing amount of more than $20 million. Well-known VCs such as Bankless Ventures, Chapter One, Lightspeed Faction, IOSG, Blockchain Builders Fund, Alumni Ventures, and Samsung Next participated in the investment.
PIN AI: Completed $10 million pre-seed round of financing, with investments from a16z CSX, Hack VC, Blockchain Builders Fund (Stanford Blockchain Accelerator) and other well-known VCs.
Vana: Completed $18 million in Series A financing and $5 million in strategic financing, with investments from well-known VCs such as Paradigm, Polychain, and Coinbase.
Sahara: Completed $43 million in Series A financing, with investments from well-known VCs such as Binance Labs, Pantera Capital, and Polychain.
Aethir: Completed $9 million in Pre-A round of financing with a valuation of $150 million in 2023, and completed node sales of approximately $120 million in 2024.
IO.NET: Completed $30 million in Series A financing, with investments from well-known VCs such as Hack VC, Delphi Digital, and Foresight Ventures.
In Capital, Marx told us that means of production, productivity, and production relations are key elements in social production. If we make an analogy, we will find that there are also such three key elements in the world of artificial intelligence.
In the AI era, computing power, data, and algorithms are the key.
In AI, data is the means of production. For example, you type and chat on your phone every day, take photos and send them to Moments. These words and pictures are data, which are like the "ingredients" of AI and the basis for AI to operate.
These data include various forms, ranging from structured numerical information to unstructured images, audio, video and text. Without data, AI algorithms cannot learn and optimize. The quality, quantity, coverage and diversity of data directly affect the performance of AI models and determine whether it can efficiently complete specific tasks.
In AI, computing power is productivity. Computing power is the underlying computing resource required to execute AI algorithms. The stronger the computing power, the faster the data can be processed and the better the effect. The strength of computing power directly determines the efficiency and capabilities of AI systems.
Powerful computing power can not only shorten the training time of the model, but also support more complex model architectures, thereby improving the intelligence level of AI. Large language models like OpenAI's ChatGPT take months to train on powerful computing clusters.
In AI, algorithms are production relations. Algorithms are the core of AI. Their design determines how data and computing power work together and is the key to transforming data into intelligent decisions. With the support of powerful computing power, algorithms can better learn patterns in data and apply them to practical problems.
In this way, data is equivalent to the fuel of AI, computing power is the engine of AI, and algorithms are the soul of AI. AI=data+computing power+algorithms. Any startup that wants to stand out in the AI track must have all three elements or show a unique leading advantage in one of them.
As AI is developing towards multimodality (models are based on multiple forms of information and can process text, images, audio, etc. at the same time), the demand for computing power and data will only grow exponentially.
The emergence of ChatGPT has not only set off a revolution in artificial intelligence, but also inadvertently pushed computing power and computing power hardware to the forefront of technology hot searches.
After the "Thousand Model War" in 2023, in 2024, as the market's understanding of AI big models continues to deepen, the global competition around big models is being divided into two paths: "capability improvement" and "scenario development".
In terms of big model capability improvement, the market's biggest expectation is the GPT-5 that OpenAI is rumored to release this year, and it is eagerly looking forward to its big model being pushed to a truly multimodal stage.
In terms of big model scenario development, AI giants are promoting big models to integrate into industry scenarios faster and generate application landing value. For example, attempts in fields such as AI Agent and AI search are constantly deepening the improvement of existing user experience by big models.
Behind these two paths, there is no doubt that higher demands are made on computing power. The improvement of large model capabilities is mainly based on training, which requires the use of huge high-performance computing power in a short period of time; large model scene applications are mainly based on reasoning, and the performance requirements for computing power are relatively low, but more emphasis is placed on stability and low latency.
As OpenAI estimated in 2018, since 2012, the computing power required for training large models has doubled every 3.5 months, and the annual computing power required has increased by up to 10 times. At the same time, as large models and applications are increasingly deployed in actual business scenarios of enterprises, the demand for inference computing power has also risen.
The problem is that the demand for high-performance GPUs is growing rapidly worldwide, but the supply has not kept up. Taking Nvidia's H100 chip as an example, it experienced a serious supply shortage in 2023, with a supply gap of more than 430,000 chips. The upcoming B100 chip, which has a 2.5x performance improvement and only a 25% increase in cost, is likely to be in short supply again. This imbalance between supply and demand will cause the cost of computing power to rise again, making it difficult for many small and medium-sized enterprises to afford the high computing costs, thus limiting their development potential in the field of AI.
Large technology companies such as OpenAI, Google, and Meta have stronger resource acquisition capabilities and have the money and resources to build their own computing infrastructure. But for AI startups, let alone those that have not yet raised funds?
Indeed, buying second-hand GPUs on platforms such as eBay and Amazon is one of the feasible methods. Although it reduces costs, there may be performance issues and long-term maintenance costs. In this era of scarce GPUs, building infrastructure will probably never be the best solution for startups.
Even if there are GPU cloud providers that can be rented on demand, the high price is still a big challenge for them. For example, the price of an Nvidia A100 is about $80 per day. If 50 are needed to run 25 days a month, the cost of computing power alone is as high as 80 x 50 x 25 = $100,000 per month.
This gives the decentralized computing power network based on DePIN an opportunity to take advantage of the situation, which can be said to be smooth sailing. As IO.NET, Aethir, and Hyperbolic have done, they transfer the computing power infrastructure costs of AI startups to the network itself. And it allows anyone in the world to connect unused GPUs at home to it, greatly reducing computing costs.
Aethir completed a $9 million Pre-A round of financing with a valuation of $150 million in September 2023, and completed the sale of Checker Node nodes worth about $120 million from March to May this year. Aethir earned $60 million in revenue by selling Checker Node in just 30 minutes, which shows the market's recognition and expectations for the project.
The core of Aethir is to build a decentralized GPU network, giving everyone the opportunity to contribute their idle GPU resources and gain benefits.It's like turning everyone's computer into a small supercomputer, so that everyone can share computing power together. The advantage of doing so is that it can greatly improve the utilization rate of GPUs and reduce resource waste. At the same time, it can also allow enterprises or individuals that need a lot of computing power to obtain the required resources at a lower cost.
Aethir has created a decentralized DePIN network, which is like a resource pool, encouraging data centers, game studios, technology companies and gamers from all over the world to connect idle GPUs to it. These GPU providers can freely connect or exit the network, so they have a higher utilization rate than idle. This enables Aethir to provide GPU resources from consumer, professional to data center levels to computing power demanders, and the price is more than 80% lower than that of Web2 cloud providers.
Aethir's DePIN architecture ensures the quality and stability of these scattered computing powers. The three core parts are:
Container is Aethir's computing unit, acting as a cloud server, responsible for executing and rendering applications. Each task is encapsulated in a separate Container, which runs the customer's tasks as a relatively isolated environment to avoid interference between tasks.
Indexer is mainly used to instantly match and schedule available computing resources according to task requirements. At the same time, the dynamic resource adjustment mechanism can dynamically allocate resources to different tasks according to the load of the entire network to achieve the best overall performance.
Checker is responsible for real-time monitoring and evaluation of the performance of the Container. It can monitor and evaluate the status of the entire network in real time and respond promptly to possible security issues. If you need to respond to security incidents such as network attacks, you can issue warnings and initiate protective measures in a timely manner after detecting abnormal behavior. Similarly, when there is a bottleneck in network performance, Checker can also issue reminders in time so that the problem can be solved in time, ensuring service quality and security.
The effective collaboration between Container, Indexer, and Checker provides customers with free customized computing power configuration, a secure, stable, and relatively inexpensive cloud service experience. For fields such as AI and games, Aethir is a good commercial-grade solution.
In general, Aethir has reshaped the allocation and use of GPU resources through DePIN, making computing power more popular and economical. It has achieved some good results in the fields of AI and games, and is constantly expanding its partners and business lines. Its future development potential is unlimited.
IO.NET completed a $30 million Series A financing in March this year, with investments from well-known VCs such as Hack VC, Delphi Digital, and Foresight Ventures.
Similar to Aethir, it builds an enterprise-level decentralized computing network to provide AI startups with lower-priced, more accessible, and more flexible computing services by gathering idle computing resources (GPU, CPU) around the world.
Unlike Aethir, IO.NET uses the Ray framework (IO-SDK) to convert thousands of GPU clusters into a whole to serve machine learning (the Ray framework is also used by OpenAI to train GPT-3). When training large models on a single device, CPU/GPU memory limitations and sequential processing workflows present huge bottlenecks. The Ray framework is used for orchestration and batch processing to achieve parallel computing tasks.
To this end, IO.NET adopts a multi-layer architecture:
User interface layer: provides users with a visual front-end interface, including a public website, a customer area, and a GPU supplier area, aiming to provide an intuitive and friendly user experience.
Security layer: ensures the integrity and security of the system, integrating mechanisms such as network protection, user authentication, and activity logging.
API layer: serves as a communication hub for websites, suppliers, and internal management, facilitating data exchange and the execution of various operations.
Backend layer: constitutes the core of the system, responsible for managing operational tasks such as clusters/GPUs, customer interactions, and automatic expansion.
Database layer: responsible for data storage and management, primary storage is responsible for structured data, and cache is used for temporary data processing.
Task layer: manages asynchronous communication and task execution to ensure efficient data processing and flow.
Infrastructure layer: It constitutes the foundation of the system, including GPU resource pool, orchestration tools and execution/ML tasks, equipped with a powerful monitoring solution.
From a technical point of view, IO.NET has launched the layered architecture of its core technology IO-SDK to solve the difficulties faced by distributed computing power, as well as reverse tunnel technology and mesh VPN architecture to solve the problems of secure connection and data privacy. It is popular in Web3 and is called the next Filecoin, with a broad prospect.
In general, the core mission of IO.NET is to build the world's largest DePIN infrastructure, concentrate all the idle GPU resources in the world, and provide support for AI and machine learning fields that require a lot of computing power.
Today, Hyperbolic once again announced the completion of a round of A financing totaling more than $12 million, led by Variant and Polychain Capital, with a total financing amount of more than $20 million. Well-known VC institutions such as Bankless Ventures, Chapter One, Lightspeed Faction, IOSG, Blockchain Builders Fund, Alumni Ventures, and Samsung Next participated in the investment. Among them, the top Silicon Valley venture capital Polychain and LightSpeed Faction doubled their investment after the seed round, which is enough to show Hyperbolic's leading position in the Web3 AI track.
Hyperbolic's core mission is to make AI available to everyone, developers can afford it, and creators can afford it.Hyperbolic aims to build an "AI Rainforest" in which developers can find the necessary resources for innovation, collaboration and development. Just like a natural rainforest, the ecosystem is interconnected, vibrant, and renewable, allowing creators to explore without limits.
In the view of the two co-founders, Jasper and Yuchen, although AI models can be open source, it is not enough without open computing resources. Currently, many large data centers control GPU resources, which discourages many people who want to use AI. Hyperbolic aims to break this situation. They have established DePIN computing power infrastructure by integrating idle computing resources around the world, making it easy for everyone to use AI.
Therefore, Hyperbolic introduced the concept of "open AI cloud", from personal computers to data centers, they can connect to Hyperbolic to provide computing power. On this basis, Hyperbolic creates a verifiable and privacy-enhancing AI layer that allows developers to build AI applications with reasoning capabilities, and the required computing power comes directly from the AI cloud.
Similar to Aethir and IO.NET, Hyperbolic's AI cloud has its own unique GPU cluster model, called the "Solar System Cluster". As we know, the solar system contains various independent planets such as Mercury and Mars. Hyperbolic's solar system cluster manages, for example, the Mercury cluster, the Mars cluster, and the Jupiter cluster. These GPU clusters are widely used and of different sizes, but they are independent of each other and are scheduled by the solar system.
Such a model ensures that the GPU cluster meets two characteristics: it is more flexible and more efficient than Aethir and IO.NET:
By adjusting the state balance, the GPU cluster will automatically expand or shrink according to demand
If a cluster is interrupted, the Solar System cluster will automatically detect and repair it
In the performance comparison experiment of the large language model (LLM), the throughput of the Hyperbolic GPU cluster was as high as 43 tokens/s, which not only exceeded the 42 tokens/s of the Together AI team of 60 people, but was also significantly higher than the 300-person team.
In the generation speed comparison experiment of the image generation model, the Hyperbolic GPU cluster also demonstrated its technical strength that should not be underestimated. Also using the SOTA open source image generation model, Hyperbolic leads with a generation speed of 17.6 images/min, which not only exceeds Together AI's 13.6 images/min, but is also much higher than IO.NET's 6.5 images/min.
These data strongly prove that Hyperbolic's GPU cluster model has extremely high efficiency, and its excellent performance makes it stand out among larger competitors. Combined with the advantage of low price, this makes Hyperbolic very suitable for complex AI applications that require high computing power to support, provide near real-time response, and ensure that AI models have higher accuracy and efficiency when handling complex tasks.
In addition, from the perspective of cryptographic innovation, we believe that Hyperbolic’s most noteworthy achievement is the development of the verification mechanism PoSP (Proof of Sampling), which uses a decentralized approach to solve one of the most difficult challenges in the field of AI - verifying whether the output comes from a specified model, thereby allowing the reasoning process to be decentralized cost-effectively.
Based on the PoSP principle, the Hyperbolic team developed a spML mechanism (sampling machine learning) for AI applications, which randomly samples transactions in the network, rewards honest people, and punishes dishonest people to achieve a lightweight verification effect, reducing the computational burden of the network, so that almost any AI startup can decentralize their AI services in a distributed and verifiable paradigm.
The specific implementation process is as follows:
1) The node calculates the function and submits the result to the orchestrator in an encrypted manner.
2) Then, the arranger decides whether to trust the result. If so, the node is rewarded for the calculation.
3) If not, the arranger will randomly select a validator in the network to challenge the node to calculate the same function. Similarly, the validator submits the result to the arranger in an encrypted manner.
4) Finally, the arranger checks whether all the results are consistent. If they are consistent, both the node and the validator will be rewarded; if they are inconsistent, an arbitration procedure will be initiated to trace the calculation process of each result. Honest people are rewarded for their accuracy, and dishonest people are punished for deceiving the system.
Nodes do not know whether the results they submit will be challenged, nor do they know which verifier the orchestrator will choose to challenge, in order to ensure the fairness of the verification. The cost of cheating far exceeds the potential benefits.
If spML is verified in the future, it will be enough to change the rules of the game for AI applications and make trustless reasoning verification a reality. In addition, Hyperbolic has the unique ability in the industry to apply the BF16 algorithm to model reasoning (comparable to peers are still at FP8), which can effectively improve the accuracy of reasoning, making Hyperbolic's decentralized reasoning service excellent in cost performance.
In addition, Hyperbolic's innovation is also reflected in its integration of AI cloud computing power supply and AI applications. The demand for decentralized computing power market itself is relatively scarce. Hyperbolic attracts developers to build AI applications by building verifiable AI infrastructure. Computing power can be directly and seamlessly integrated into AI applications without sacrificing performance and security. After expanding to a certain scale, it can be self-sufficient and achieve a balance between supply and demand.
Developers can build innovative AI applications around computing power, Web2 and Web3 on Hyperbolic, such as:
GPU Exchange, a GPU trading platform built on the GPU network (orchestration layer), commercializes "GPU resources" and freely trades them, making computing power more cost-effective.
IAO, or tokenizing AI Agents, allows contributors to earn tokens, and the income of AI Agents will be distributed to token holders.
AI-driven DAOs are DAOs that use artificial intelligence to help with governance decisions and financial management.
GPU Restaking allows users to connect their GPUs to Hyperbolic and then stake them to AI applications.
In general, Hyperbolic has built an open AI ecosystem that makes it easy for everyone to use AI. Through technological innovation, Hyperbolic is making AI more popular and accessible, making the future of AI full of interoperability and compatibility, and encouraging collaborative innovation.
Today, data is a gold mine, and personal data is being grabbed and commercialized by technology giants for free.
Data is the food of AI. Without high-quality data, even the most advanced algorithms cannot work. The quantity, quality, and diversity of data directly affect the performance of AI models.
As we mentioned earlier, the industry is looking forward to the launch of GPT-5. But it has not come out yet, probably because the amount of data is not enough. GPT-3, which is in the stage of publishing papers alone, requires 2 trillion tokens of data. GPT-5 is expected to reach 200 trillion tokens of data. In addition to the existing text data, more multimodal data is needed, which can be used for training after cleaning.
In today's public Internet data, high-quality data samples are relatively rare. A realistic situation is that large models perform very well in question-and-answer generation in any field, but they perform poorly in the face of professional field problems, and even give the illusion that the model is "talking nonsense seriously".
In order to ensure the "freshness" of the data, AI giants often reach transaction agreements with the owners of large data sources. For example, OpenAI signed a $60 million agreement with Reddit.
Recently, some social software has begun to require users to sign an agreement, requiring users to agree to authorize the use of content for the training of third-party AI models, however, users do not receive any rewards. This predatory behavior has caused public opinion to question the right to use data.
Obviously, the decentralized and traceable potential of blockchain is naturally suitable for improving the dilemma of obtaining data and resources, while providing more control and transparency for user data, and can also earn benefits by participating in the training and optimization of AI models. This new way of creating data value will greatly increase user participation and promote the overall prosperity of the ecosystem.
Web3 already has some companies targeting AI data, such as:
Data acquisition: Ocean Protocol, Vana, PIN AI, Sahara, etc.
Data processing: Public AI, Lightworks, etc.
Among them, the more interesting ones are Vana, PIN AI, and Saraha, which happened to have received large amounts of financing recently, with a luxurious lineup of investors. Both projects have gone beyond sub-fields, combining data acquisition with AI development to promote the implementation of AI applications.
Vana completed a round of $18 million in financing in December 2022 and completed a $5 million strategic financing in September this year. Well-known VCs such as Paradigm, Polychain, and Coinbase have invested.
Vana's core concept is "user-owned data, user-owned AI". In this era where data is king, Vana wants to break the monopoly of large companies on data, allowing users to control their own data and benefit from their own data.
Vana is a decentralized data network that focuses on protecting private data and enabling users' data to be used flexibly like financial assets. Vana attempts to reshape the data economy and transform users from passive data providers to active and mutually beneficial ecosystem builders.
To achieve this vision, Vana allows users to aggregate and upload data through the Data DAO, and then verify the value of the data while protecting privacy through the proof of contribution mechanism. This data can be used for AI training, and users are incentivized based on the quality of the uploaded data.
In terms of implementation, Vana's technical architecture includes five key components: data liquidity layer, data portability layer, universal connection group, non-custodial data storage, and decentralized application layer.
Data Liquidity Layer: This is the core of the Vana network, which incentivizes, aggregates and verifies valuable data through data liquidity pools (DLP). DLP is like a data version of a "liquidity pool". Each DLP is a smart contract dedicated to aggregating specific types of data assets, such as social media data such as Reddit and Twitter.
Data Portability Layer: This component gives portability to user data, ensuring that users can easily transfer and use their data between different applications and AI models.
Data Ecosystem Map: This is a map that tracks the real-time data flow of the entire ecosystem, ensuring transparency.
Non-custodial Data Storage: Vana's innovation lies in its unique data management method, which allows users to always maintain full control over their data. The user's original data will not be uploaded to the chain, but the user will choose the storage location, such as a cloud server or a personal server.
Decentralized Application Layer: Based on the data, Vana has built an open application ecosystem where developers can use the data accumulated by DLP to build various innovative applications, including AI applications, and data contributors can receive dividend rewards from these applications.
Currently, Vana has built DLPs around social media platforms such as ChatGPT, Reddit, LinkedIn, and Twitter, as well as DLPs focusing on AI and browsing data. As more DLPs join in and more innovative applications are built on the platform, Vana has the potential to become the next generation of decentralized AI and data economy infrastructure.
This reminds us of a recent news story. In order to improve the diversity of LLM, Meta is collecting data from British users of Facebook and Instagram, but it has been criticized for forcing users to choose "opt out" instead of "agree". Perhaps, it would be a better choice for Meta to build a DLP for Facebook and Instagram on Vana, which not only ensures data privacy, but also encourages more users to actively contribute data.
PIN AI completed a $10 million pre-seed round of financing in September this year. A number of well-known VCs and angel investors such as a16z CSX, Hack VC, and Blockchain Builders Fund (Stanford Blockchain Accelerator) participated in this investment.
PIN AI is an open AI network, powered by a distributed data storage network based on the DePIN architecture. Users can connect their devices to the network, provide personal data/user preferences, and receive token incentives.This move enables users to regain control and monetize their data. Developers can use the data to build useful AI Agents in it.
Its vision is to become a decentralized alternative to Apple Intelligence, and it is committed to providing user groups with applications that are useful for daily life and realizing the intentions proposed by users, such as purchasing goods online, planning travel, and planning investment behaviors.
PIN AI consists of two types of AI, personal AI assistant and external AI service.
Personal AI assistant can access user data, collect user needs, and provide corresponding data to external service AI when they need data. PIN AI is based on the DePIN distributed data storage network, which provides rich user data for the reasoning of external AI services without accessing the user's personal privacy.
With PIN AI, users will no longer need to open thousands of mobile apps to complete different tasks. When a user expresses intentions to a personal AI assistant such as "I want to buy a new dress", "what kind of takeaway should I order", or "find the best investment opportunities in this article", the AI not only understands the user's preferences, but can also perform all these tasks effectively - it will find the most relevant applications and service providers to achieve the user's intentions in the form of bidding.
Most importantly, PIN AI realizes the necessity of introducing a decentralized service that can provide more value under the current dilemma that users are accustomed to interacting directly with centralized service providers to obtain services.Personal AI assistants can legitimately obtain high-value data generated when users interact with Web2 applications in the name of users, and store and call them in a decentralized manner, so that the same data can play a greater value and benefit both data owners and callers.
Although the PIN AI mainnet has not yet been officially launched, the team has shown the product prototype to users to a small extent through Telegram to facilitate the perception of the vision.
Hi PIN Bot consists of three sections: Play, Data Connectors, and AI Agent.
Play is an AI virtual companion, supported by large models such as PIN AI-1.5b, Gemma, and Llama. This is equivalent to PIN AI's personal AI assistant.
In Data Connectors, users can connect to Google, Facebook, X, and Telegram accounts to earn points to upgrade their virtual companions. In the future, users will also be supported to connect to Amazon, Ebay, Uber and other accounts. This is equivalent to PIN AI's DePIN data network.
Your own data is used by yourself. After connecting to the data, users can make demands to the virtual companion (Coming soon), and the virtual companion will provide the user's data to the AI Agent that meets the task requirements for processing.
The official has developed some AI Agent prototypes, which are still in the testing stage. These are equivalent to PIN AI's external AI services. For example, X Insight, enter a Twitter account, and it can analyze the operation of the account. When Data Connectors support accounts on e-commerce, food delivery and other platforms, AI Agents such as Shopping and Order Food can also play a role and autonomously process orders placed by users.
In general, through the form of DePIN+AI, PIN AI has established an open AI network, allowing developers to build truly useful AI applications and make users' lives more convenient and intelligent.As more developers join, PIN AI will bring more innovative applications and truly integrate AI into daily life.
Sahara completed a $43 million Series A financing in August this year, with investments from well-known VCs such as Binance Labs, Pantera Capital, and Polychain.
Sahara AI is a multi-layer AI blockchain application platform that focuses on establishing a more fair and transparent AI development model in the AI era that can attribute the value of data and distribute profits to users, solving the pain points of privacy, security, data acquisition, and transparency in traditional AI systems.
In layman's terms, Sahara AI wants to build a decentralized AI network that allows users to control their own data and receive rewards based on the quality of the data they contribute. In this way, users are no longer passive data providers, but become ecosystem builders who can participate and share benefits.
Users can upload data to their decentralized data market, and then prove the ownership of the data ("confirmation") through a special mechanism. The data can be used to train AI, and users are rewarded based on the quality of the data.
Sahara AI includes a four-layer architecture of application, transaction, data, and execution, providing a strong foundation for the development of the AI ecosystem.
Application layer: provides tools such as secure vaults, decentralized AI data markets, no-code toolkits, and Sahara ID. These tools can ensure data privacy and promote fair compensation for users, and further simplify the process of creating and deploying AI applications.
In short, the vault uses advanced encryption technology to ensure the security of AI data; the decentralized AI data market can be used for data collection, annotation, and transformation to promote innovation and fair transactions; the no-code toolkit makes the development of AI applications easier; Sahara ID is responsible for managing user reputation and ensuring trust.
Transaction layer: Sahara blockchain uses a proof-of-stake (PoS) consensus mechanism to ensure the efficiency and stability of the network, allowing consensus to be reached even in the presence of malicious nodes. In addition, Sahara's native pre-compilation function is designed specifically for optimizing AI processing, and can perform efficient calculations directly in the blockchain environment to improve system performance.
Data layer: Manages on-chain and off-chain data. On-chain data processes untraceable operations and attribute records to ensure credibility and transparency; off-chain data processes large data sets and uses Merkle Tree and zero-knowledge proof technology to ensure data integrity and security to prevent data duplication and tampering.
Execution layer: Abstracts the operations of vaults, AI models, and AI applications, and supports various AI training, reasoning, and service paradigms.
The entire four-layer architecture not only ensures the security and scalability of the system, but also reflects Sahara AI's vision of promoting collaborative economy and AI development, aiming to completely change the application model of AI technology and bring more innovative and fair solutions to users.
With the continuous advancement of AI technology and the rise of the encryption market, we are standing on the threshold of a new era.
With the continuous emergence of AI big models and applications, the demand for computing power is also growing exponentially. However, the scarcity of computing power and the rising cost are a huge challenge for many small and medium-sized enterprises. Fortunately, decentralized solutions, especially Hyperbolic, Aethir and IO.NET, provide AI startups with new ways to obtain computing power, reduce costs and improve efficiency.
At the same time, we also see the importance of data in the development of AI. Data is not only the food of AI, but also the key to promoting the implementation of AI applications. Projects such as PIN AI and Sahara provide strong data support for the development of AI by encouraging users to participate in data collection and sharing through incentive networks.
Computing power and data are not just training links. For AI applications, from data intake to production reasoning, each link requires the use of different tools to achieve massive data processing, and this is a repetitive process.
In this world where AI and Crypto are intertwined, we have reason to believe that more innovative AI projects will be implemented in the future. These projects will not only change our work and lifestyle, but also drive the entire society towards a more intelligent and decentralized direction. With the continuous advancement of technology and the continuous maturity of the market, we look forward to the arrival of a more open, fair and efficient AI era.
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