Original Author: Kolawole Samuel Adebayo
Original Translation: Gonka.ai
Currently, the competition for the future of artificial intelligence is focusing on a key area: computing power. Whether it is startups, research institutions, or Fortune 500 companies, everyone is scrambling for limited GPU resources. This clearly indicates that the next race in the AI field has shifted from algorithms to the acquisition of computing power. What was once a debate of ideas has now evolved into a substantial contest for computational resources.
Computing Power: The "New Power Grid" of the AI Era
If AI is likened to electricity in the new era, then computing power is the power grid that supports its operation. However, this "grid," which is crucial for innovation, is controlled by a few giants. They not only determine the allocation and pricing of GPUs but also largely decide which innovative projects can survive.
According to a Reuters report, orders for Nvidia's next-generation Blackwell GPUs have surpassed 3.6 million, with most going to major cloud service providers. For smaller startups and public institutions, the barriers to participating in this race are becoming increasingly high.
Power Dynamics: Nvidia's Invisible Empire
Nvidia currently controls about 94% of the GPU market, becoming an indispensable infrastructure behind almost all modern AI systems. More strikingly, the company recently disclosed that just two "unnamed" direct customers contributed 39% of its quarterly revenue.
This phenomenon of centralization has transcended the commercial realm and is reshaping the entire innovation ecosystem. It determines who can participate in innovation, the speed at which costs decline, and which countries will gain an advantage in the future AI economy. The global frenzy for AI chips has led to supply shortages and rising prices, further narrowing the survival space for small businesses.
Breaking the Deadlock: The Rise of Decentralized Computing Networks
In the face of this dilemma, more and more researchers and entrepreneurs are beginning to rethink the distribution of computing power. David Liberman, co-founder of the Gonka protocol, pointed out: "In an efficient market, all products tend to commoditization, driving profits down and prices to the lowest sustainable level. To achieve this for AI, we can draw inspiration from Bitcoin—not as a financial asset, but as a blueprint for building large-scale decentralized infrastructure."
This analogy is quite enlightening: "Today, Bitcoin miners collectively operate data centers with 26 gigawatts of power, surpassing the total construction of Microsoft, Google, and Amazon over decades. At the same time, advancements in Bitcoin mining hardware have reduced the cost of computing power by hundreds of thousands of times. If we can achieve the same transformation for AI computing power, it would enable everyone on Earth to truly access and afford AI."
Practical Challenges: The Paradox of Distributed Systems
However, the path to decentralization is not smooth. A study by Galaxy Research in 2025 shows that under specific workloads, decentralized networks can outperform centralized cloud services, but verification and reliability remain significant challenges.
Researchers at the nonprofit organization EPOCH AI refer to this phenomenon as the "paradox of distributed systems": the more open a system is, the more coordination it requires. Without strict verification mechanisms and incentives tied to performance, community-operated networks may fall into inefficiency or manipulation.
Governance Dilemma: Will Power Re-concentrate?
History shows that decentralized systems may gradually revert to centralization, as power tends to gravitate towards capital and production capacity. In this regard, the Liberman brothers candidly stated that even decentralized systems may inadvertently favor large participants.
"No one can unilaterally change the rules of Bitcoin or Ethereum; any changes require broad consensus," they explained. "Certain design rules do indeed give mining pools an advantage, leading to power concentration. Therefore, when building the Gonka protocol, we deliberately avoided mechanisms like delegation."
Geopolitical Dimension: Political Considerations of Computing Sovereignty
The issue of computing power has evolved into an important geopolitical topic. Liberman revealed: "In our discussions with government officials from four countries, we found that they increasingly view decentralization as the only viable path to defend their national sovereignty in the context of reliance on global AI infrastructure."
"What they are concerned about is not control itself, but the monopoly position of the US and China—this monopoly could isolate them from the prosperity brought by AI. Decentralization is the only way to ensure that their citizens equally enjoy all the benefits of AI."
Future Scenarios: Two Possible AI Worlds
When asked about future development directions, the Liberman brothers outlined two possible futures: one dominated by a few large laboratories in the US and China controlling most of the global AI computing power; the other being an open network igniting a new wave of hardware innovation, drastically reducing computing power costs and distributing it more evenly across the globe.
"In a decentralized future scenario, large cloud companies will still have a place, but they will no longer be able to charge such high premiums for access to computing power," they added.
Conclusion: The Reshaping of Innovation Rights
This competition for computing power fundamentally concerns the rights to innovation and technological inclusivity. As AI becomes the core driving force of the future, ensuring the openness and fairness of computing power networks will determine the innovation landscape and wealth distribution of the next decade. Those who can solve the problem of computing power democratization will not only achieve commercial success but also shape the trajectory of the entire artificial intelligence era.
In this competition that will determine the future, decentralized computing networks are becoming an important hope for breaking monopolies and achieving technological inclusivity. Whether it can ultimately succeed depends not only on technological innovation but also on the design of governance mechanisms and the building of consensus within the global community.
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