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.

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.

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.

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.