The competition for computing power is no longer just an internal matter for the tech industry.
On May 31, 2026, the U.S. Department of Commerce issued new export control guidance, officially closing the channel for Chinese companies to procure advanced NVIDIA chips through overseas subsidiaries in places like Malaysia. In the same month, the President of Kenya halted a $1 billion geothermal data center project involving Microsoft, stating that upon completion, it would consume one-third of the country’s electricity. President Ruto’s exact words were: “This is equivalent to shutting down half the country.” Meanwhile, Huawei announced last week that its Ascend 950PR chip has entered mass production, with projected full-year AI chip revenue to reach $12 billion. Three events, three continents, three entirely different news stories. But they point to a single emerging reality: the competition for computing power is no longer just an internal matter for the tech industry.
A new era of oligopoly is taking shape. Over the past two years, there’s been an often-overlooked reality in the AI industry: while superficially diverse, underlying resources are becoming increasingly concentrated. The current AI industry chain can be broadly divided into four layers: GPU chips, cloud computing platforms, foundational models, and application ecosystems. At each layer, control is consolidating among a few players: in the GPU sector, NVIDIA is almost the sole option; in cloud computing, AWS, Microsoft Azure, and Google Cloud dominate; at the model layer, OpenAI and Anthropic have captured the vast majority of the high-end model market.
In other words, the same set of companies is simultaneously controlling chips, cloud platforms, models, and distribution channels. Eric Posner, a law professor at the University of Chicago, calls this phenomenon the “AI Octopus,” where these companies’ tentacles reach across the entire AI industry chain. This differs from platform monopolies in the internet era – internet platforms controlled traffic, while AI platforms control intelligence itself. This “oligopolistic monopoly” brings profound systemic risks, including concentrated control and pricing dominance, infrastructure fragility, and geopolitical implications and computing power hegemony.
The deepening “AI Iron Curtain.” Facing U.S. actions on chip export controls, reactions vary greatly across countries. Saudi Arabia invested $3 billion in Musk’s xAI through its sovereign wealth fund, with one condition being the establishment of AI data centers with over 500 megawatts capacity in Saudi Arabia; the UAE is building a 5 GW AI park in Abu Dhabi. The logic of Gulf countries is straightforward: the previous era relied on selling oil, this era relies on buying computing power. The EU, through its Cloud and AI Development Act (CADA), is attempting to reclaim computing power sovereignty. The most difficult situation is for economies that lack even the basic qualifications to compete, appearing overwhelmed by the hundreds of billions of dollars in capital expenditure by tech giants.
It is against this backdrop that decentralized AI (DeAI) is gaining attention. It attempts to answer a question: besides entrusting the future to a few tech giants or a few countries, is there a third possibility? The core idea of DeAI is to coordinate independent participants through open protocols, achieving AI systems that are not controlled by a single center of power. By integrating blockchain technology, crypto-economic incentives, and cryptographic verification mechanisms, it solves the trust problem in anonymous networks, directly addressing the pain points of centralized AI.
Proponents argue that this model can reduce reliance on single suppliers, enhance system resilience, and provide participation opportunities for smaller countries and businesses. Meanwhile, institutional investors’ attitudes are shifting from curiosity to substantial investment. Venture capital firms are injecting hundreds of millions of dollars into DeAI protocols; traditional enterprises are beginning to participate in networks as validators; moreover, governments in some countries are starting to explore connecting their idle national supercomputing resources to decentralized computing power markets.
As the report “State of DeAI 2026” states, the core value proposition of DeAI is not its ability to fully surpass centralized systems in performance today, but rather its provision of an underlying architecture that resists monopoly, rejects censorship, and decentralizes power. Of course, DeAI is still a long way from becoming mainstream. But its significance may not lie in immediately challenging OpenAI, but in offering an alternative. Historical experience tells us that when an industry has only one choice, the problem is often not whether power will be abused, but when it will be abused. The existence of competition is, in itself, a form of checks and balances.
[Conflux]
The Geopolitical Computing Power Race and the Rise of DeAI: A Market Analysis
The New Reality: Computing Power as a Strategic Asset
The confluence of US chip export controls, energy constraints on data centers, and the consolidation of AI resources among a few tech giants represents a paradigm shift in how we view computing power. No longer just a commodity, advanced computing capabilities have become a strategic asset subject to geopolitical competition and monopolistic control.
The US Department of Commerce’s recent restrictions on NVIDIA chip exports to China are not merely trade policies—they represent an attempt to maintain technological hegemony. This follows a pattern where technological dominance is being weaponized in geopolitical competition. Similarly, Kenya’s rejection of Microsoft’s data center project highlights the growing tension between AI’s voracious appetite for energy and the physical limitations of infrastructure.
The “AI Octopus” and Systemic Risks
The current AI landscape is consolidating around an “AI Octopus” where a handful of companies control the entire value chain—from chip manufacturing (NVIDIA) to cloud platforms (AWS, Azure, Google Cloud) to foundational models (OpenAI, Anthropic). This concentration creates profound systemic risks:
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Monopolistic Pricing Power: As control consolidates, these entities can extract economic rents at multiple layers of the value chain.
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Infrastructure Fragility: Centralized points of failure create systemic risks that could cascade across the entire AI ecosystem.
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Geopolitical Dependencies: Nations that lack domestic AI capabilities become vulnerable to external pressures and exclusion from technological progress.
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Innovation Suppression: High barriers to entry stifle competition and limit the diversity of approaches to AI development.
This concentration differs fundamentally from previous internet monopolies because it controls “intelligence itself” rather than just distribution channels. The implications are far-reaching—concentrated AI power could influence not just markets but social and political structures.
DeAI: The Third Path Forward
Against this backdrop, decentralized AI (DeAI) emerges as a compelling alternative. DeAI attempts to solve the trust problem in anonymous networks through blockchain technology, crypto-economic incentives, and cryptographic verification mechanisms. Its core value proposition extends beyond immediate performance to provide:
- Reduced reliance on single suppliers: By distributing computing resources across a network
- Enhanced system resilience: Through architectural decentralization
- Global inclusion: Allowing smaller entities to participate in the AI economy
- Censorship resistance: Providing an alternative to controlled information flows
The geopolitical reactions to US controls further strengthen DeAI’s case. Gulf countries are investing billions to secure computing resources, while the EU attempts to reclaim sovereignty through legislation. For nations without the capital to compete directly, DeAI offers a potential pathway to participate in the AI economy without massive infrastructure investment.
Market Implications and Investment Opportunities
For crypto investors, the DeAI space represents a high-growth frontier with significant long-term potential. Several investment themes are emerging:
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Tokenized Computing Networks: Projects that create markets for computational resources, enabling efficient allocation and monetization of idle capacity. These tokens could appreciate as demand for decentralized computing grows.
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AI Model Marketplaces: Decentralized platforms for training and deploying AI models could challenge the dominance of centralized providers while creating new economic opportunities for model creators.
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Verification and Oracle Networks: The need for verifying AI outputs and training data creates opportunities for oracle networks and verification protocols that can attest to model provenance and integrity.
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Cross-Chain AI Interoperability: As multiple blockchain networks develop AI capabilities, interoperability solutions that enable models and data to move across chains will become increasingly valuable.
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Infrastructure-as-a-Service: Tokens representing ownership in decentralized AI infrastructure could capture value as enterprises adopt hybrid approaches combining centralized and decentralized systems.
Performance Considerations and Market Realities
While DeAI offers compelling architectural advantages, it currently faces significant performance limitations compared to centralized supercomputers. This creates a market reality where DeAI projects must focus initially on use cases that benefit from decentralization rather than attempting to directly compete with centralized systems in large-scale model training.
The successful DeAI projects will likely follow a hybrid approach—leveraging centralized resources for computationally intensive tasks while using decentralization for verification, governance, and specific applications requiring censorship resistance or distributed trust.
Risks and Challenges
The DeAI space faces substantial risks that investors must carefully consider:
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Technological Maturity: Current decentralized networks cannot match the performance of centralized systems for large-scale AI workloads.
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Energy Efficiency Concerns: Despite theoretical advantages, blockchain-based systems still face energy consumption scrutiny.
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Regulatory Uncertainty: Governments may view decentralized AI systems with suspicion, particularly regarding potential misuse or circumvention of controls.
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Adoption Barriers: Enterprises will be reluctant to adopt unproven technologies for critical AI workloads, creating a catch-22 for early-stage projects.
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Token Economics: Many DeAI projects struggle to demonstrate clear demand for their native tokens beyond speculation.
Conflux’s Position in the DeAI Landscape
The mention of [Conflux] at the end of the article suggests it represents an early player in the DeAI ecosystem. While specific details aren’t provided, Conflux likely focuses on providing a blockchain foundation for decentralized AI applications. Potential areas of focus include:
- Creating a scalable infrastructure for AI model training and deployment
- Developing tokenized markets for computational resources
- Enabling verification of AI processes and outputs
- Facilitating cross-border collaboration without centralized control
For investors, Conflux’s success will depend on its ability to balance technical innovation with practical applications that solve real problems in the AI landscape.
Strategic Outlook for Investors
The DeAI market is likely to evolve through several phases:
- Early Experimentation (current phase): Projects exploring various approaches with limited real-world applications.
- Niche Adoption: Specific use cases benefiting from decentralization drive initial adoption.
- Performance Improvements: Technical advancements narrow the gap with centralized systems.
- Enterprise Integration: Major players begin incorporating DeAI components into their offerings.
- Mainstream Recognition: When DeAI becomes a recognized component of the broader AI ecosystem.
For experienced crypto investors, the most promising opportunities will likely be found in projects that:
– Have genuinely innovative approaches to decentralizing AI
– Feature teams with deep expertise in both AI and blockchain
– Have clear token economics with real utility within their ecosystems
– Proactively address regulatory concerns
– Focus on practical applications that solve immediate problems
Conclusion
The competition for computing power is evolving from a technical issue to a geopolitical and economic imperative. The concentration of AI resources among a few entities creates systemic risks that demand alternative solutions. DeAI, while still in its early stages, offers a potential third path that could address concerns about monopoly, censorship, and geopolitical dependency.
For crypto investors, the DeAI space represents a high-risk, high-reward opportunity with significant long-term potential. The most successful projects will likely balance the fundamental advantages of decentralization with the practical performance requirements of AI applications, creating value through both technological innovation and market adoption.
As the article aptly states, “when an industry has only one choice, the problem is often not whether power will be abused, but when it will be abused.” The existence of competition is, in itself, a form of checks and balances—a principle that DeAI seeks to bring to the future of artificial intelligence.