Over the past year, AI narratives have thrived in the Crypto market, with leading VCs like a16z, Sequoia, Lightspeed, and Polychain investing tens of millions of dollars. Many high-quality teams with research backgrounds and prestigious school affiliations have also entered Web3, moving toward decentralized AI. Within the next 12 months, we will witness these high-quality projects gradually materialize.
In October this year, OpenAI raised $6.6 billion again, bringing the arms race in the AI sector to an unprecedented height. Retail investors seldom have the opportunity to profit beyond direct investments in NVIDIA and hardware, and this enthusiasm will inevitably spread to Crypto, particularly with the recent AI Meme frenzy. It is foreseeable that Crypto x AI, whether existing listed tokens or new star projects, will maintain strong momentum.
With the recent additional funding for the leading decentralized AI project Hyperbolic from Polychain and Lightspeed, we outline the development context of six projects recently receiving significant funding from top institutions, and explore how decentralized technology can safeguard humanity's future in AI.
Hyperbolic: Recently announced the completion of a $12 million Series A financing round, led by Variant and Polychain, with total funding exceeding $20 million. Notable VCs such as Bankless Ventures, Chapter One, Lightspeed Faction, IOSG, Blockchain Builders Fund, Alumni Ventures, and Samsung Next participated.
PIN AI: Completed a $10 million pre-seed round of financing, with investments from renowned VCs such as a16z CSX, Hack VC, and Blockchain Builders Fund (Stanford Blockchain Accelerator).
Vana: Completed $18 million in Series A financing and $5 million in strategic financing, with investments from notable 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: In 2023, completed $9 million in Pre-A financing at a valuation of $150 million, and in 2024 completed approximately $120 million in node sales.
IO.NET: Completed $30 million in Series A financing, with investments from well-known VCs including Hack VC, Delphi Digital, and Foresight Ventures.
The three key elements of AI: data, computing power, and algorithms.
Marx tells us in 'Capital' that the means of production, productive forces, and production relations are key elements in social production. If we draw an analogy, we find that there are also such key elements in the world of artificial intelligence.
In the AI era, computing power, data, and algorithms are key.
In AI, data is the means of production. For example, the text and images you type and share on your phone daily are data; they serve as the 'ingredients' for AI and are the foundation for AI operation.
This data includes various forms ranging from structured numerical information to unstructured images, audio, video, and text. Without data, AI algorithms cannot learn or optimize. The quality, quantity, coverage, and diversity of data directly impact the performance of AI models, determining their ability to 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 processing speed and the better the results. The strength of computing power directly determines the efficiency and capability of AI systems.
Powerful computing power can not only shorten the training time of models but also support more complex model architectures, enhancing the intelligence level of AI. Large language models like OpenAI's ChatGPT require months of training on powerful computing clusters.
In AI, algorithms are the means of production. Algorithms are the core of AI, and their design determines how data and computing power work together, being key to converting data into intelligent decisions. With powerful computing support, algorithms can better learn patterns in data and apply them to real-world problems.
Thus, data acts as fuel for AI, computing power is the engine of AI, and algorithms are the soul of AI. AI = data + computing power + algorithms. Any startup aiming to stand out in the AI race must have all three elements in place or demonstrate a unique advantage in one.
As AI develops towards multimodal capabilities (models based on various information forms that can simultaneously process text, images, audio, etc.), the demand for computing power and data will only grow exponentially.
In an era of computing power scarcity, Crypto empowers AI.
The emergence of ChatGPT has not only sparked a revolution in artificial intelligence but has inadvertently brought computing power and hardware to the forefront of tech discussions.
After the 'thousand-model war' of 2023, in 2024, as market understanding of large AI models deepens, the global competition surrounding large models is being divided into two paths: 'capability enhancement' and 'scenario development.'
In terms of enhancing large model capabilities, the market's biggest expectation is for the rumored release of OpenAI's GPT-5 this year, eagerly anticipating its large model being pushed to a truly multimodal stage.
In the development of large model scenarios, AI giants are advancing the faster integration of large models into industry scenarios to generate application value. For instance, attempts in AI Agents and AI search are all aimed at deepening the enhancement of existing user experiences through large models.
Both paths undoubtedly raise higher demands for computing power. The enhancement of large model capabilities focuses on training, requiring large amounts of high-performance computing power in a short time; while large model application scenarios focus on inference, which has relatively lower performance requirements but places greater emphasis on stability and low latency.
As OpenAI estimated in 2018, since 2012, the demand for computing power to train large models has doubled every 3.5 months, with an annual increase in required computing power of up to 10 times. At the same time, as large models and applications are increasingly deployed in actual business scenarios, the demand for inference computing power has also risen dramatically.
Here’s the problem: globally, the demand for high-performance GPUs is rapidly increasing, while supply is failing to keep up. Taking NVIDIA's H100 chip as an example, there was a severe supply shortage in 2023, with a supply gap exceeding 430,000 units. The upcoming B100 chip, which improves performance by 2.5 times with only a 25% increase in cost, might also face supply shortages. This imbalance between supply and demand could lead to a rise in computing costs again, making it difficult for many small and medium-sized enterprises to bear the high computing expenses, thus limiting their development potential in the AI field.
Large tech companies like OpenAI, Google, and Meta have stronger resource acquisition capabilities, with the money and resources to build their own computing infrastructure. But what about AI startups, especially those yet to be funded?
Indeed, purchasing second-hand GPUs on platforms like eBay and Amazon is also a feasible method. Although it reduces costs, there may be performance issues and long-term maintenance costs. In this era of GPU scarcity, building infrastructure may never be the optimal solution for startups.
Even with on-demand rental GPU cloud providers available, their high prices pose a significant challenge. For instance, a single NVIDIA A100 costs about $80 per day. If 50 units are needed to run for 25 days a month, the computing cost alone would reach $100,000/month.
This has given decentralized computing networks based on the DePIN architecture an opportunity to thrive. As seen with IO.NET, Aethir, and Hyperbolic, they shift the infrastructure costs of AI startups to the network itself. Additionally, anyone globally can connect unused GPUs from their homes, significantly reducing computing costs.
Aethir: A global GPU sharing network that makes computing power accessible to all.
Aethir completed a $9 million Pre-A round of financing in September 2023, valued at $150 million, and achieved approximately $120 million in Checker Node sales between March and May this year. Aethir generated $60 million in revenue through Checker Node sales within just 30 minutes, indicating strong market recognition and expectation for the project.
The core of Aethir is to establish a decentralized GPU network, allowing everyone the opportunity to contribute their idle GPU resources and earn rewards. This is like turning everyone's computer into a small supercomputer, where everyone shares computing power. The benefit is that it can significantly increase GPU utilization, reduce resource waste, and allow businesses or individuals that require substantial computing power to acquire the necessary resources at a lower cost.
Aethir has created a decentralized DePIN network, similar to a resource pool, incentivizing data centers, game studios, tech companies, and gamers worldwide to connect idle GPUs to the network. These GPU providers can freely connect or disconnect their GPUs from the network, leading to higher utilization than being idle. This enables Aethir to offer GPU resources from consumer-grade to professional-grade and data center-grade, while also being over 80% cheaper than Web2 cloud providers.
Aethir's DePIN architecture ensures the quality and stability of these scattered computing powers. Its three core components are:
Container is Aethir's computing unit, acting as a cloud server responsible for executing and rendering applications. Each task is encapsulated in an independent Container, running clients' tasks in a relatively isolated environment to avoid interference between tasks.
Indexer is primarily used to match and schedule available computing resources instantly based on task requirements. At the same time, the dynamic resource adjustment mechanism allocates resources to different tasks based on the overall network load, achieving optimal overall performance.
The Checker is responsible for real-time monitoring and evaluating the performance of Containers. It can monitor and assess the state of the entire network instantaneously and respond to potential security issues in a timely manner. In case of network attacks or security events, it can promptly issue warnings and initiate protective measures upon detecting abnormal behavior. Likewise, when bottlenecks in network performance occur, the Checker can also send alerts to allow for timely resolution of issues, ensuring service quality and security.
Container, Indexer, and Checker effectively collaborate to provide customers with customizable computing power configurations, offering secure, stable, and relatively low-cost cloud service experiences. For fields such as AI and gaming, Aethir is a solid commercial-grade solution.
Overall, Aethir has reshaped the allocation and use of GPU resources through the DePIN approach, making computing power more accessible and economical. It has achieved some great results in the AI and gaming sectors while continuously expanding partnerships and business lines, with tremendous development potential in the future.
IO.NET: A distributed supercomputing network that breaks through the computing power bottleneck.
IO.NET completed $30 million in 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 aims to build an enterprise-level decentralized computing network that aggregates idle computing resources (GPUs, CPUs) worldwide, providing AI startups with lower-cost, more accessible, and flexible computing power services.
Unlike Aethir, IO.NET uses the Ray framework (IO-SDK) to turn thousands of GPU clusters into a whole, serving 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 significant bottlenecks. Utilizing the Ray framework for orchestration and batch processing allows for parallelization of computing tasks.
To this end, IO.NET employs a multilayer architecture:
User Interface Layer: Provides users with a visual front-end interface, including public websites, customer areas, and GPU supplier areas, aimed at delivering an intuitive and friendly user experience.
Security Layer: Ensures system integrity and security, integrating mechanisms for network protection, user authentication, and activity logging.
API Layer: Serves as a communication hub for websites, suppliers, and internal management, facilitating data exchange and various operations.
Backend Layer: Forms the core of the system, responsible for managing clusters/GPUs, customer interactions, and automated scaling operations.
Database Layer: Responsible for data storage and management, where primary storage handles structured data and caches are used for temporary data processing.
Task Layer: Manages asynchronous communication and task execution, ensuring the efficiency of data processing and flow.
Infrastructure Layer: Forms the foundation of the system, containing the GPU resource pool, orchestration tools, and execution/ML tasks, equipped with powerful monitoring solutions.
From a technical perspective, IO.NET has introduced a layered architecture for its core technology IO-SDK, addressing the challenges of distributed computing power and implementing reverse tunneling technology and mesh VPN architecture to solve security connection and data privacy issues. As Web3 gains popularity and is referred to as the next Filecoin, its prospects are bright.
Overall, IO.NET's core mission is to build the world's largest DePIN infrastructure, concentrating all of the world's idle GPU resources to support the AI and machine learning fields that require massive computing power.
Hyperbolic: Building the 'AI rainforest' to achieve a prosperous and mutually supportive distributed AI infrastructure ecosystem.
Today, Hyperbolic announced that it has completed a total of over $12 million in Series A financing, led by Variant and Polychain Capital, with total funding exceeding $20 million. Notable VC institutions including Bankless Ventures, Chapter One, Lightspeed Faction, IOSG, Blockchain Builders Fund, Alumni Ventures, and Samsung Next participated. Notably, leading Silicon Valley venture capital firms Polychain and LightSpeed Faction increased their investments after the seed round, illustrating Hyperbolic's dominant position in the Web3 AI sector.
Hyperbolic's core mission is to make AI accessible to everyone, affordable for developers and creators. Hyperbolic aims to build an 'AI rainforest,' where developers can find the necessary resources for innovation, collaboration, and growth within its ecosystem. Like a natural rainforest, the ecosystem is interconnected, vibrant, and renewable, allowing creators to explore without limits.
According to the two co-founders Jasper and Yuchen, while AI models can be open-sourced, it's still insufficient without open computing resources. Currently, many large data centers control GPU resources, making it challenging for many who want to utilize AI. Hyperbolic aims to break this situation by integrating global idle computing resources to establish a DePIN infrastructure that enables everyone to easily use AI.
Thus, Hyperbolic introduces the concept of an 'open AI cloud,' where everything from personal computers to data centers can connect to Hyperbolic to provide computing power. On this basis, Hyperbolic creates a verifiable, privacy-preserving AI layer that allows developers to build inference-capable AI applications, with the required computing power sourced directly from the AI cloud.
Similar to Aethir and IO.NET, Hyperbolic's AI cloud features its unique GPU cluster model, referred to as the 'solar system cluster.' As we know, the solar system includes various independent planets like Mercury and Mars. Hyperbolic's solar system cluster manages multiple GPU clusters, such as the Mercury cluster, Mars cluster, and Jupiter cluster, which serve a wide range of purposes and scales but operate independently, coordinated by the solar system.
This model ensures that GPU clusters meet two characteristics: they are more flexible and maximize efficiency compared to Aethir and IO.NET.
To balance the state, the GPU cluster will automatically scale up or down based on demand.
If a cluster experiences an interruption, the solar system cluster will automatically detect and repair it.
In performance comparison experiments of large language models (LLMs), the Hyperbolic GPU cluster achieved a throughput of 43 tokens/s, surpassing the 42 tokens/s achieved by the Together AI team of 60 people and significantly exceeding the 27 tokens/s achieved by HuggingFace, which has over 300 team members.
In the image generation model speed comparison experiments, the Hyperbolic GPU cluster demonstrated its impressive technical strength. Using the SOTA open-source image generation model, Hyperbolic led with a generation speed of 17.6 images/min, surpassing Together AI's 13.6 images/min and far exceeding IO.NET's 6.5 images/min.
This data strongly demonstrates that Hyperbolic's GPU cluster model possesses high efficiency, with its outstanding performance allowing it to stand out among larger competitors. Combined with its cost-effectiveness, this makes Hyperbolic highly suitable for complex AI applications that require high computing power support, providing near-real-time responses while ensuring higher accuracy and efficiency when AI models handle complex tasks.
Additionally, from the perspective of crypto innovation, we believe Hyperbolic's most significant achievement is the development of the verification mechanism PoSP (Proof of Sampling) to address one of the toughest challenges in the AI field—verifying whether outputs come from a specified model, thereby allowing the inference process to be economically and effectively decentralized.
Based on the principle of PoSP, the Hyperbolic team developed the spML mechanism (Sampling Machine Learning) for AI applications, randomly sampling transactions in the network, rewarding honest participants and punishing dishonest ones to achieve a lightweight verification effect. This reduces the computing burden on the network, allowing almost any AI startup to complete their AI services in a distributively verifiable manner.
The specific implementation process is as follows:
1) The nodes compute the function and submit the results to the orchestrator via encrypted means.
2) The orchestrator then decides whether to trust the result; if trusted, the node receives rewards for computation.
3) If there is no trust, the orchestrator will randomly select validators from the network to challenge the nodes and compute the same function. Similarly, validators will submit results to the orchestrator via encrypted methods.
4) Finally, the orchestrator checks whether all results are consistent; if consistent, both nodes and validators receive rewards; if inconsistent, an arbitration process will be initiated to trace back the computation process of each result. Honest participants are rewarded for their accuracy, while dishonest ones are punished for deceiving the system.
Nodes do not know whether the results they submit will be challenged, nor do they know which validator the orchestrator will choose to challenge, ensuring the fairness of verification. The cost of cheating far exceeds potential gains.
If spML is validated in the future, it could fundamentally change the game for AI applications, making trustless inference verification a reality. Additionally, Hyperbolic has a unique ability in the industry to apply the BF16 algorithm in model inference (while peers are still using FP8), effectively improving inference accuracy, making Hyperbolic's decentralized inference service exceptionally cost-effective.
Moreover, Hyperbolic's innovation is reflected in its integration of AI cloud computing power supply with AI applications. The demand for a decentralized computing market is inherently low, and Hyperbolic is attracting developers to build AI applications by constructing a verifiable AI infrastructure. Computing power can be seamlessly integrated into AI applications without sacrificing performance and security, achieving self-sufficiency and balancing supply and demand as it scales.
Developers can build AI innovative applications around computing power, Web2, and Web3 on Hyperbolic, such as:
GPU Exchange, a trading platform built on the GPU network (orchestration layer), commodifies 'GPU resources' for free trade, making computing power more cost-effective.
IAO, or tokenizing AI Agents, allows contributors to earn tokens, with the income of AI Agents distributed to token holders.
AI-driven DAO, which uses artificial intelligence to assist in governance decision-making and financial management.
GPU Restaking allows users to connect GPUs to Hyperbolic and then stake them to AI applications.
Overall, Hyperbolic has built an open AI ecosystem that allows everyone to easily use AI. Through technological innovation, Hyperbolic is making AI more accessible and available, ensuring a future filled with interoperability and compatibility, and encouraging collaborative innovation.
Data returns to users, joining the AI wave.
Today, data is a gold mine; personal data is being appropriated and commercialized by tech giants without compensation.
Data is the food for AI. Without high-quality data, even the most advanced algorithms cannot perform effectively. The quantity, quality, and diversity of data directly affect the performance of AI models.
As mentioned earlier, the industry is eagerly awaiting the release of GPT-5. However, its release has been delayed, possibly due to insufficient data. For instance, the GPT-3 model, at the paper publication stage, required 20 trillion tokens of data. GPT-5 is expected to require 200 trillion tokens of data. In addition to existing textual data, more multimodal data is needed, which must be cleaned before it can be used for training.
In today's publicly available internet data, high-quality data samples are scarce. A realistic situation is that large models perform exceptionally well in generating Q&A in any field, but perform poorly when faced with specialized domain questions, even exhibiting the illusion of the model 'talking nonsense seriously.'
To ensure the 'freshness' of data, AI giants often reach trading agreements with owners of large data sources. For instance, OpenAI signed a $60 million agreement with Reddit.
Recently, some social media platforms have started requiring users to sign agreements, allowing content to be authorized for training third-party AI models. However, users receive no compensation for this. Such exploitative behavior has raised public concerns about data usage rights.
Clearly, the potential of blockchain's decentralized and traceable nature is naturally suitable for improving the challenges of data and resource acquisition while providing users with more control and transparency over their data. It also allows users to earn rewards by participating in the training and optimization of AI models. This novel approach to data value creation will significantly enhance user engagement and promote overall ecosystem prosperity.
Web3 already has some companies focused on AI data, such as:
Data Acquisition: Ocean Protocol, Vana, PIN AI, Sahara, etc.
Data Processing: Public AI, Lightworks, etc.
Interestingly, Vana, PIN AI, and Sahara have all recently secured significant funding with impressive investor lineups. Both projects have transcended subfields, integrating data acquisition with AI development to drive the implementation of AI applications.
Vana: Users control data, DAO and contribution mechanisms reshape the AI data economy.
Vana completed a round of financing of $18 million in December 2022 and secured $5 million in strategic financing in September this year. Notable VCs like Paradigm, Polychain, and Coinbase invested.
Vana's core philosophy is 'user-owned data enables user-owned AI.' In this data-driven era, Vana aims to break the monopoly that large companies have on data, allowing users to control their data and benefit from it.
Vana is a decentralized data network focused on protecting private data, allowing users' data to be utilized flexibly like financial assets. Vana seeks to reshape the landscape of the data economy, shifting users from passive data providers to active participants and co-beneficiaries in the ecosystem.
To realize this vision, Vana allows users to gather and upload data through data DAOs, then verify the data's value through contribution proof mechanisms while protecting privacy. This data can be used for AI training, and users earn incentives based on the quality of their uploaded data.
In terms of implementation, Vana's technical architecture includes five key components: Data Liquidity Layer, Data Portability Layer, Universal Connector Group, Non-custodial Data Storage, and Decentralized Application Layer.
Data Liquidity Layer: This is the core of the Vana network, incentivizing, aggregating, and verifying valuable data through liquidity pools (DLP). DLP acts like a 'liquidity pool' for data, with each DLP being a smart contract specifically designed to aggregate specific types of data assets, such as data from social media like Reddit and Twitter.
Data Portability Layer: This component gives user data portability, ensuring that users can easily transfer and utilize their data across different applications and AI models.
Data Ecosystem Map: This is a map tracking the real-time flow of data throughout the ecosystem, ensuring transparency.
Non-custodial data storage: Vana's innovation lies in its unique data management approach, allowing users to maintain complete control over their data. Users' original data does not go on-chain but can be stored at user-selected locations, such as cloud servers or personal servers.
Decentralized Application Layer: Built on the data foundation, Vana has created an open application ecosystem where developers can utilize the data accumulated by DLP to build various innovative applications, including AI applications, while data contributors earn dividends from these applications.
Currently, Vana has built DLPs around social media platforms like ChatGPT, Reddit, LinkedIn, and Twitter, as well as focusing on AI and browsing data. As more DLPs join, more innovative applications are being built on the platform, giving Vana the potential to become the next generation of decentralized AI and data economy infrastructure.
This brings to mind a recent news story where Meta was collecting data from UK users of Facebook and Instagram to improve LLM diversity but faced backlash for opting users out rather than requiring consent. Perhaps building a DLP for Facebook and Instagram on Vana, ensuring data privacy while incentivizing more users to actively contribute data, would be a better choice.
PIN AI: Decentralized AI assistant, connecting data and daily life.
PIN AI completed a $10 million pre-seed round of financing in September this year, with participation from renowned VCs and angel investors including a16z CSX, Hack VC, and Blockchain Builders Fund (Stanford Blockchain Accelerator).
PIN AI is an open AI network supported by a distributed data storage network built on the DePIN architecture, where users can connect their devices to the network, provide personal data/user preferences, and receive token incentives. This allows users to regain control and monetize their data, while developers can build useful AI Agents using the data.
Its vision is to become the decentralized alternative to Apple Intelligence, dedicated to providing useful applications for users' daily lives, fulfilling user intentions such as online shopping, planning trips, and investment behaviors.
PIN AI consists of two types of AI: personal AI assistants and external AI services.
Personal AI assistants can access user data, gather user needs, and provide the necessary data to external AI when required. The underlying structure of PIN AI is based on the DePIN distributed data storage network, providing rich user data for external AI service inference while preventing access to users' personal privacy.
With PIN AI, users will no longer need to open thousands of mobile apps to complete different tasks. When users express intentions like 'I want to buy a new outfit,' 'What kind of takeaway should I order,' or 'Find the best investment opportunity in this article,' the AI not only understands user preferences but can effectively execute all these tasks—finding the most relevant applications and service providers to fulfill user intentions through bidding.
Most importantly, PIN AI recognizes the necessity of introducing a decentralized service that provides more value in the current situation where users are accustomed to directly interacting with centralized service providers to obtain services. The personal AI assistant can legitimately access high-value data generated during user interactions with Web2 applications and store and utilize the same data in a decentralized manner, maximizing the value of the same piece of data and benefiting both data owners and users.
Although the PIN AI mainnet has not yet officially launched, the team has showcased the product prototype to users on Telegram to help them perceive the vision.
Hi PIN Bot is composed of three parts: Play, Data Connectors, and AI Agent.
Play is an AI virtual companion supported by models like PIN AI-1.5b, Gemma, and Llama. This serves as the personal AI assistant of PIN AI.
In Data Connectors, users can connect their Google, Facebook, X, and Telegram accounts to earn points and upgrade their virtual companions. In the future, it will support user connections to accounts on platforms like Amazon, eBay, Uber, etc. This effectively represents PIN AI's DePIN data network.
Use your own data, and once connected, users can express their needs to their virtual companions (coming soon), who will provide user data to suitable AI Agents to process the requests.
The official team has developed some AI Agent prototypes, which are still in the testing phase. These are essentially the external AI services of PIN AI. For example, X Insight can analyze the operational status of a Twitter account when inputting the account. Once Data Connectors support accounts from e-commerce and takeaway platforms, AI Agents like Shopping and Order Food can also function, autonomously handling user orders.
Overall, through the DePIN+AI model, PIN AI has established an open AI network that enables developers to build truly useful AI applications, making users' lives more convenient and intelligent. As more developers join, PIN AI will bring forth more innovative applications, allowing AI to truly integrate into daily life.
Sahara: Leading AI Data Rights, Privacy, and Fair Trade with a Multilayer Architecture
Sahara completed $43 million in Series A financing in August this year, with investments from renowned VCs such as Binance Labs, Pantera Capital, and Polychain.
Sahara AI is a multi-layered AI blockchain application platform focused on establishing a fairer and more transparent AI development model in the AI era, capable of assigning value to data and sharing profits with users, addressing pain points such as privacy, security, data acquisition, and transparency in traditional AI systems.
In simple terms, Sahara AI aims to build a decentralized AI network that allows users to control their own data and earn rewards based on the quality of their contributions. This means users are no longer passive data providers but become active participants and beneficiaries in the ecosystem.
Users can upload data to their decentralized data marketplace and prove ownership of the data through a special mechanism ('confirmation'). This data can be used to train AI, and users earn rewards based on data quality.
Sahara AI includes a four-layer architecture of applications, transactions, 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 ensure data privacy and promote fair compensation for users, further simplifying the creation and deployment of AI applications.
In simple terms, the vault safeguards AI data security using advanced encryption technology; the decentralized AI data marketplace can be used for data collection, labeling, and transformation, promoting innovation and fair trade; the no-code toolkit makes AI application development simpler; Sahara ID manages user reputation, ensuring trust.
Transaction Layer: Sahara blockchain ensures network efficiency and stability through a Proof of Stake (PoS) consensus mechanism, enabling consensus even in the presence of malicious nodes. Additionally, Sahara's native precompiled functions are designed specifically to optimize AI processing, allowing for efficient computation directly within the blockchain environment, enhancing system performance.
Data Layer: Manages on-chain and off-chain data. On-chain data processing tracks non-retraceable operations and attribute records, ensuring credibility and transparency; off-chain data handles large datasets and utilizes Merkle Tree and zero-knowledge proof technologies to ensure data integrity and security, preventing data duplication and tampering.
Execution Layer: Abstracts the operations of vaults, AI models, and AI applications, supporting various paradigms of AI training, inference, and services.
This entire four-layer architecture not only ensures the system's security and scalability but also reflects Sahara AI's grand vision of promoting collaborative economy and AI development, aiming to fundamentally change the application mode of AI technology and provide innovative and fair solutions for users.
Conclusion
With the continuous advancement of AI technology and the rise of the crypto market, we stand on the threshold of a new era.
With the continuous emergence of large AI models and applications, the demand for computing power is also growing exponentially. However, the scarcity of computing power and rising costs pose a significant challenge for many small and medium-sized enterprises. Fortunately, decentralized solutions, particularly Hyperbolic, Aethir, and IO.NET, provide new ways for AI startups to obtain computing power, reducing costs and improving efficiency.
Simultaneously, we have also seen the importance of data in AI development. Data is not only the food for AI but also the key to driving the implementation of AI applications. Projects like PIN AI and Sahara incentivize networks to encourage user participation in data collection and sharing, providing robust support for AI growth.
Computing power and data are not only part of the training phase; for AI applications, every step from data ingestion to production inference requires different tools to achieve massive data processing, and this is a continuously repeating process.
In this interconnected world of AI and Crypto, we have reason to believe that we will witness more innovative AI projects materialize in the future. These projects will not only change our working and living styles but will also push society towards a more intelligent and decentralized direction. With continuous technological advancements and market maturation, we look forward to the arrival of a more open, fair, and efficient AI era.
