Decentralized Attempts in AI Compute Networks: Secondary Market Performance of Select Web3 and AI Intersection Projects

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Decentralized attempts in AI compute networks are driving changes in the industry landscape. Representative projects such as IO.NET, Bittensor, and Akash all exhibit high volatility in the secondary market. Technological innovations continue to emerge, with distributed compute scheduling and blockchain incentive mechanisms becoming key areas of focus. Market participants closely monitor asset liquidity, token value, and network performance, but they also face real-world challenges such as performance bottlenecks and governance difficulties.

Key Takeaways

• Decentralized AI compute networks reduce user costs and improve resource utilization by sharing idle computing power.
• The combination of Web3 and AI enhances data security and transparency, promoting the development of innovative business models.
• Token price fluctuations are closely related to project fundamentals and market events, so investors need to focus on the project’s actual implementation capabilities.
• Decentralized projects face performance bottlenecks and compliance risks, requiring industry participants to seek balance between innovation and risk prevention.
• Over the next five years, the decentralized AI and Web3 intersection field is expected to experience explosive growth, with huge market opportunities.

Core Concepts and Terminology

AI Compute Network Definition

AI compute networks refer to the infrastructure that provides computing resources for artificial intelligence model training and inference. Traditional centralized AI infrastructure is usually provided by traditional service providers, and users need to pay higher fees. Decentralized attempts promote distributed scheduling of compute resources, allowing individuals or organizations to share idle computing power through the network.

• In decentralized markets, renting an NVIDIA A100 costs about $0.75 per hour, compared to $2.50 to $3.00 on traditional cloud services like AWS.
• Data providers can retain control over how their data is used.
• Incentive mechanisms encourage participants to securely share private data or compute resources.
  These advantages drive decentralized attempts in AI compute networks, improving cost efficiency and data sovereignty.

Decentralization and Web3 Foundations

There is no unified standard for the definition of decentralization in academia, but it has become an emerging concept in the blockchain field. Web3 is not just Web2 plus tokens; it emphasizes ownership, incentives, and transparency. Communities in Web3 become infrastructure rather than mere marketing channels. Transparency in product development processes and product-market fit are crucial. Documents such as DAO constitutions reflect the importance placed on decentralization commitments. Decentralized attempts continue to evolve in the Web3 ecosystem, becoming a key driver of innovation.

Types of AI and Web3 Intersections

AI and Web3 intersection projects are mainly divided into three categories:

• Blockchain technology benefits from artificial intelligence, improving data analysis and automation capabilities.
• Artificial intelligence benefits from blockchain, gaining higher data security and transparency.
• Both coexist but operate independently, each playing their roles.
  The market has seen various innovative projects. For example, MinMax AI provides users with AI-based on-chain data analysis tools, Kaito builds a large-model-based Web3 search platform, Followin integrates ChatGPT to aggregate multi-platform information, and Upshot uses AI to improve NFT pricing accuracy. These projects demonstrate diverse paths for deep integration of AI and Web3.

Decentralized Attempts and Technical Paths

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Distributed Compute Scheduling Mechanisms

The core of decentralized AI compute networks lies in efficient scheduling of distributed computing resources. Current mainstream projects adopt various technical paths to improve resource utilization and task execution efficiency.

• Decentralized Distributed Proximal Policy Optimization (DD-PPO) has become an important mechanism for high-performance compute scheduling. This method supports large-scale distributed training across multiple nodes without synchronizing parameters at every step, significantly reducing communication overhead.
• Reinforcement learning techniques are applied in scheduling systems. Schedulers optimize job wait times and overall system utilization through reinforcement learning, overcoming limitations of traditional scheduling algorithms.
• Performance validation shows that, based on real HPC job datasets, DD-PPO outperforms traditional schedulers and existing reinforcement learning scheduling algorithms in scheduling performance.
  These mechanisms promote the implementation of decentralized attempts in AI compute networks, enhancing network resilience and scalability.

Blockchain Incentives and Governance

Blockchain technology provides the incentive and governance foundation for decentralized AI compute networks.

• In decentralized platforms, projects like Numerai require data scientists to stake NMR tokens to support their models, with poor performance penalized and high accuracy rewarded.
• Bittensor adopts a competitive incentive framework, where miners receive rewards based on the value of their AI contributions. This mechanism aligns rewards with contribution quality but also introduces risks such as validator collusion and model sabotage.
• Decentralized compute resource markets mobilize unused GPU and CPU resources, with participants rewarded for contributing compute power, lowering entry barriers.
• Blockchain-driven incentive mechanisms improve participant utility and model accuracy in federated learning systems, verifying their practical effectiveness.

Blockchain incentive mechanisms not only attract more participants but also enhance overall network performance. Governance mechanisms ensure network fairness and transparency through token staking, community voting, and other methods.

Representative Project Cases

Mainstream decentralized AI compute network projects each have distinctive features in technical architecture and business models. The table below compares representative projects such as IO.NET, Golem, Bittensor, Allora, and Akash Network:

These projects achieve diverse implementations of decentralized attempts through pipelined resource scheduling, blockchain incentives, and governance mechanisms. For example, io.net leverages the Solana blockchain for efficient settlement and resource allocation, while Golem connects global compute resources through the Ethereum network. Bittensor innovatively combines AI model value exchange with blockchain incentives, driving deep integration of AI and Web3. Akash Network reduces the threshold for AI developers to access GPU resources through an open market mechanism.

These decentralized attempts not only optimize compute resource allocation but also create new business opportunities for AI developers and compute providers. As technology and markets continue to evolve, the combination of Web3 and AI will persistently drive industry innovation.

Secondary Market Performance Analysis

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Token Prices and Volatility

The token price performance of Web3 and AI intersection projects highly depends on project fundamentals, market events, and key milestones. Market data shows that token price fluctuations are closely related to project valuations.

• Token prices often experience significant fluctuations when projects release major features, partnerships, or mainnet launches.
• Projects that complete financing at reasonable valuations see smaller subsequent price declines, around 29%. High-valuation projects face larger drawdowns, approaching 88%.
• The impact of large-scale token unlocks on prices mainly depends on the proportion of unlocked tokens relative to existing supply, rather than the dollar amount unlocked.

In 2025, seven major crypto projects all experienced value declines after launch, with an average drop of 51%. Reasonably valued projects only fell by about 29%, indicating that initial valuation plays a decisive role in subsequent market performance. When evaluating AI and Web3 projects, investors need to focus on actual implementation capabilities and valuation reasonableness to avoid sharp price volatility due to market sentiment or short-term hype.

In practical research, judging these projects should not stop at token price moves alone. It also helps to view funding access, market information, and follow-up allocation within one workflow. You can first use BiyaPay’s stock information page to review related assets and market updates, then combine it with the trading entry to observe how different asset paths connect, treating it as part of research support and execution preparation.

From a positioning perspective, BiyaPay is a multi-asset wallet covering cross-border payments, investing, trading, and fund management, with relevant compliance registrations in jurisdictions including the United States and New Zealand. In the context of secondary-market analysis for Web3 and AI intersection projects, this kind of tool is more useful as a supplement for information access and asset linkage than as a substitute for your own judgment on fundamentals, liquidity, and risk.

Financing and User Growth

Leading Web3 and AI intersection projects show varying performance in user growth and financing. Some projects attract large numbers of active users through continuous innovation and community operations. The table below shows user growth data for select representative projects:

As a representative of decentralized compute markets, Akash Network sees continuous growth in active clients, reflecting strong market demand for low-cost GPU resources. Hivemapper and Helium Mobile rapidly accumulate large user bases through innovative incentive mechanisms and community-driven models. Overall, user growth rates for Web3 and AI intersection projects are closely related to product innovation capabilities, incentive mechanism design, and community activity. In terms of financing, reasonable project valuations and clear business models help attract long-term capital and reduce secondary market volatility risks.

Market Highlights and Risks

Web3 and AI intersection projects exhibit many highlights in market performance but also face significant risks. Growth drivers in the U.S. market mainly come from early blockchain applications, corporate investment, improved digital infrastructure, cryptocurrency adoption, and rapid development of decentralized applications, NFTs, and smart contracts.

Lack of regulatory clarity makes some enterprises and end-users cautious about large-scale adoption of Web3. Multiple U.S. regulatory agencies have evaluated Web3, but a unified and clear regulatory framework has not yet formed.

• Unclear regulatory frameworks lead to hesitation among enterprises and investors.
• Regulatory differences across regions increase complexity in compliance and cross-border operations.
• Over 110 countries have ambiguous or temporary regulatory attitudes toward digital assets.

Decentralized attempts bring higher resource utilization and innovation space to AI compute networks, but market participants need to be vigilant about multiple risks including regulation, compliance, and market volatility. In the future, project teams need to seek balance between technological innovation and compliance governance to promote healthy industry development.

Main Challenges of Decentralized Attempts

Performance and Resource Bottlenecks

Decentralized AI compute networks face multiple challenges in performance and resources.

• Latency issues limit the efficiency of real-time AI inference and training.
• Low-latency job scheduling becomes a core requirement for high-performance networks.
• Insufficient dynamic scaling capabilities make it difficult to handle sudden compute demands.
• Geographic latency brings complexity to cross-regional collaboration.
• Dependence on specialized hardware increases deployment difficulty.
• The popularity of inference models leads to a surge in inference workloads, with compute demand even exceeding the training phase.
• Restricted GPU access and supply make it difficult to meet the continuous growth in AI compute resource needs.

These bottlenecks directly affect the scalability and actual implementation capabilities of decentralized attempts.

Incentive and Governance Challenges

Decentralized AI and Web3 projects also face many difficulties in incentives and governance.

Different projects generally emphasize that governance structures need to co-evolve with AI agents. Transparent AI decision processes, token incentive mechanisms, built-in supervision, and dispute resolution mechanisms become important foundations for promoting collaborative economies. By tokenizing rewards for activities such as data labeling, training, and inference, project teams promote active participation from community members.

Security and Compliance Risks

Security and compliance risks remain unavoidable pain points for decentralized AI compute networks.

• Centralized data storage and management expose networks to major security threats, with hackers frequently targeting cloud servers.
• IBM 2023 data shows that the average cost of data breaches has reached $4.45 million, up 15% over three years.
• Pew Research 2022 survey shows that 79% of U.S. users are concerned about how companies use their data, with only 9% feeling they have high control over their data.
• Modern cyber attack methods are complex, and traditional security systems struggle to detect new threats. Cybersecurity Ventures estimates global ransomware losses will exceed $30 billion in 2023.
• Cisco surveys show that only 59% of organizations fully comply with GDPR, with complexity and resource shortages as main obstacles.

Compliance pressures and security risks jointly constrain the large-scale application of decentralized AI compute networks, and the industry urgently needs to seek balance between innovation and risk prevention.

Future Trends and Industry Opportunities

Technical Innovation Directions

Decentralized AI compute networks are accelerating integration of cutting-edge technologies to drive industry innovation. Zero-knowledge proofs have become a key tool for verifiable off-chain computation, allowing AI providers to submit encrypted proofs without revealing data. This mechanism enhances the network’s trust foundation. Federated learning trains AI models locally on edge devices and shares only aggregated insights, significantly enhancing data privacy protection and reducing centralized compute burdens.
The table below summarizes the applications of two key technologies:

With the widespread adoption of generative AI models, inference and real-time inference workloads continue to grow, further driving demand for efficient decentralized compute architectures.

Market Expansion Prospects

Industry forecasts indicate that the decentralized AI and Web3 intersection field will experience explosive growth over the next five years. The World Economic Forum estimates that the decentralized physical infrastructure network market will grow from the current $20 billion to $3.5 trillion by 2028, an increase of up to 6000%. By 2030, more than half of AI-driven robots are expected to run on decentralized GPU networks, with market opportunities potentially reaching $100 billion.

Market trends suggest that AI-driven smart contracts will automate complex transactions and optimize contract execution. The Web3 ecosystem will support peer-to-peer AI model markets, allowing developers to share and monetize models without centralized control. AI will also enhance user experience in Web3 gaming, DeFi, and NFT markets through personalized recommendations and automated interactions.

Decentralized compute networks bring cost advantages and flexibility to enterprises but require ongoing efforts to overcome barriers such as latency, trust, and integration.

Ecosystem Cooperation Outlook

Ecosystem cooperation has become a key driver for implementing decentralized AI compute networks. Industry alliances such as the AI Unbundled Alliance are establishing standards, improving operational capabilities, and strengthening collaboration foundations. DAO organizations like XMAQUINA promote collaborative innovation in intelligent machines through joint governance and resource sharing.

Currently, the AI and Web3 ecosystems are engaging in deep cooperation in fields such as finance, governance, insurance, robotics, and logistics. Blockchain technology provides compute and storage support for AI applications, while AI helps the Web3 ecosystem solve complex transactions and automation issues. In the future, integration of AI and Web3 is expected to achieve automation and security enhancements in scenarios such as user wallets and smart contracts, further expanding industry boundaries.

Decentralized AI compute networks promote optimization of resource allocation and innovative business models. Web3 and AI intersection projects exhibit high volatility in the secondary market, with user growth and financing capabilities becoming core competitiveness. The industry faces challenges such as performance bottlenecks, incentive mechanisms, and compliance risks. In the future, technological innovation and ecosystem cooperation will continue to drive industry evolution, with broad market opportunities. Industry participants need to focus on compliance governance and technical implementation to seize the next wave of growth dividends.

FAQ

What is a decentralized AI compute network?

A decentralized AI compute network refers to multiple independent nodes jointly providing computing resources, with users renting on demand. This model improves resource utilization, reduces costs, and enhances data sovereignty.

What are the main advantages of combining Web3 and AI?

The combination of Web3 and AI brings higher data security, transparency, and incentive mechanisms. Developers can achieve model value circulation through blockchain technology, and users gain more equitable participation opportunities.

How do representative projects ensure data privacy?

Mainstream projects adopt encryption technologies and federated learning, with data processed only locally. The network shares only model parameters, maximizing user privacy protection and reducing data breach risks.

What are the core influencing factors of token price fluctuations?

Token prices are influenced by project implementation capabilities, market sentiment, financing conditions, and token release schedules. Major feature launches or partnership announcements often trigger price fluctuations.

What are the main challenges facing the industry in the future?

The industry needs to address performance bottlenecks, incentive mechanism design, security, and compliance risks. Continuous innovation and ecosystem cooperation will become key drivers for industry development.