DeAI Rising: How Decentralized Networks Are Breaking the Corporate GPU Monopoly

In 2025, mounting concerns over sustainability and the concentration of artificial intelligence power among a handful of U.S. corporations underscored the growing importance of decentralized AI.

The 2025 AI Flashpoint: A New Era of Geopolitics

The year 2025 stands as the definitive flashpoint for the global artificial intelligence “arms race.” In the United States, the scale of investment reached a fever pitch as tech titans orchestrated multibillion-dollar infrastructure plays. Notably, Microsoft and OpenAI’s $500 billion Stargate supercomputer project and Amazon’s $150 billion data center commitment signaled a move to solidify American dominance. To protect this lead, the U.S. government tightened export controls on high-end semiconductors, specifically targeting H100 and Blackwell-class chips to stifle the progress of Chinese rivals.

While Beijing has been less vocal about megadeals, the technical parity achieved by models like Deepseek—which reportedly rivals GPT-4 in efficiency—demonstrates that China has successfully pivoted toward “sovereign compute.” This strategic shift is anchored by a recent government mandate requiring domestic firms to prioritize local silicon, effectively decouples Chinese AI aspirations from Western supply chains.

Read more: Deepseek’s AI Revolution Sparks Chaos in Crypto and US and European Stock Markets

The frenzy is perhaps best illustrated by the financial markets. In 2025, AI startups raised a staggering $60 billion in the first and second quarters alone, while the major tech stocks added trillions to their combined market caps. However, this momentum is hitting a physical ceiling: energy. Estimates now suggest that AI data centers will consume up to 4% of global electricity by 2026. This has forced some companies to pivot toward nuclear energy, with Microsoft recently reopening the Three Mile Island plant to fuel its hungry clusters.

However, there are growing concerns that the AI world many envision may not be realized due to a range of factors, including inadequate energy resources to support the massive infrastructure currently being built. Training and running advanced AI models requires enormous amounts of electricity, data center capacity and cooling systems, raising questions about sustainability and whether global energy grids can keep pace with exponential demand. Some experts warn that without breakthroughs in energy efficiency or alternative power sources, the dream of ubiquitous, humanlike AI may remain out of reach.

Beyond technical and environmental challenges, others worry about the stranglehold a handful of U.S. tech giants maintain over both the industry and the narrative surrounding AI. These companies control the most powerful models, the largest datasets and the platforms through which AI is deployed, giving them disproportionate influence over how the technology evolves and who benefits from it. Critics argue that this concentration of power risks stifling competition, limiting innovation and shaping public perception in ways that serve corporate interests rather than the broader good.

These concerns have prompted U.S. politicians, including Sen. Bernie Sanders, to call for a national dialogue about AI—its trajectory, its governance and the roles different stakeholders should play. The debate is not only about technological progress but also about accountability, transparency and equity: who sets the rules, who reaps the rewards and who bears the risks.

While Sanders calls for a national dialogue to prevent corporate monopolization of intelligence, the crypto and open-source communities are building an alternative: decentralized AI (DeAI). Already, projects like Bittensor (TAO), Io.net and Near Protocol are pioneering permissionless networks that reimagine how AI infrastructure is built and governed. These initiatives are designed to break free from corporate bottlenecks and democratize access to the core resources that power machine intelligence.

Compute Crowdsourced

Instead of waiting on scarce, expensive GPUs locked behind corporate supply chains, individual hardware owners can lease their processing power directly to developers. Remarking on why this is a major concern, Andrew Sobko, co-founder at Argentum AI, argued in a recent interview that training large models requires immense GPU power. However, the supply is limited and controlled by a few vendors, creating a “walled garden” where startups and smaller players are priced out.

Like Sanders, Sobko also laments that a handful of corporations control infrastructure, access and pricing—a phenomenon he says stifles innovation and makes AI development prohibitively expensive for most organizations. However, Sobko argues that by building permissionless, distributed compute networks, individuals and organizations can contribute idle GPU power to a shared marketplace. This decentralized marketplace not only bypasses the ongoing Nvidia shortage but also unlocks latent global capacity, turning idle machines into active participants in the AI economy. Sobko’s core message is that AI’s future depends on breaking free from centralized control and embracing decentralized compute marketplaces.

Under open-source models, governance shifts from boardrooms to distributed communities. Decisions about model design, updates and usage are made collectively, ensuring transparency and reducing the risk of monopolistic control. Open-source frameworks accelerate innovation by allowing anyone to audit, contribute and build on shared foundations.

With decentralized models, users maintain cryptographic ownership of their training data, ensuring privacy and control in a world where data is often exploited without consent. Sovereign data models empower individuals to decide how their information is used, traded or rewarded, creating a more equitable ecosystem where value flows back to contributors.

The Story of DeAI in 2025

In 2025, DeAI transformed from a niche concept into a massive infrastructure alternative, fueled by the global GPU shortage and a surge in venture capital. While the broader AI sector saw over $200 billion in total funding by late 2025, the DeAI niche carved out a significant and growing share of the infrastructure and Web3 categories. DeAI startups and decentralized physical infrastructure (DePIN) projects raised approximately $12 billion to $15 billion in 2025 alone. This was driven by investors fleeing the high premiums and “walled gardens” of centralized providers like AWS and Azure.

For the first time, DeAI secured public sector funding, notably a $12 million agreement signed by Neurolov to replace traditional data centers with citizen-powered nodes.

Meanwhile, as tech giants like xAI and OpenAI raced toward clusters of 1 million H100 GPUs, decentralized networks were focused on aggregating “latent” global capacity—unused chips from mining farms, independent data centers and even high-end consumer gaming rigs. By late 2025, major decentralized networks collectively verified over 750,000 GPUs available for on-demand lease.

Read more: Experts Tout Decentralized AI Efficiency Gains as GPU Shortages and Energy Limits Loom

Networks leading the charge were Io.net, which surpassed 300,000 verified GPUs across 138 countries, specializing in high-end H100 and A100 clusters for enterprise-grade training, and Aethir, which reported over 435,000 GPU containers, focusing heavily on low-latency inference and edge computing. Neurolov reached 15,000 active nodes, demonstrating the viability of “browser-based” compute where users contribute power just by keeping a tab open.

According to one report, in 2025, decentralized networks consistently offered prices 60% to 80% lower than traditional cloud providers. While an H100 instance on AWS costs roughly $3.00 to $4.50 per hour, DeAI networks provided the same hardware for as low as $0.30 to $2.20 per hour.

During the year, a clear split in how these GPUs were utilized also emerged, with inference accounting for 70% of usage and training accounting for the remaining 30%.

The Future

As experts increasingly argue the case for decentralized AI, some critics warn that without robust ethical safeguards and clear accountability mechanisms, decentralization could quickly spiral into “the next big mistake.” Still, proponents remain confident that the benefits of decentralization—greater transparency, sovereignty over data, and reduced corporate chokeholds—far outweigh the risks.

As AI adoption accelerates, this narrative is expected to gain momentum in 2026 and beyond, shaping policy debates, investment strategies, and the very architecture of the next generation of machine intelligence.

FAQ 💡

• What’s happening in the U.S.? Tech giants like Microsoft and Amazon are pouring hundreds of billions into AI supercomputers and data centers.
• How is China responding? Beijing is pushing “sovereign compute,” mandating local silicon and models like Deepseek to rival GPT‑4.
• Why does this matter globally? AI startups raised $60B in early 2025, but energy limits loom as data centers may consume 4% of world electricity by 2026.
• What’s the alternative? Decentralized AI networks like Bittensor and Io.net offer cheaper, community‑powered compute, challenging corporate monopolies.