Why Decentralized AI Could Become Blockchain’s Most Important Use Case

By: Moish Peltz and Kyle Lawrence 

For years, blockchain advocates have argued that decentralized systems could reshape everything from payments to social media. But according to Adam Sternbach, VP of Legal at Yuma Holdings, one of the most significant applications of blockchain technology may still be ahead of us in the form of decentralized artificial intelligence. 

On a recent episode of Block & Order, Sternbach joined Falcon Rappaport & Berkman attorneys Kyle Lawrence and Moish Peltz pto discuss the growing convergence between crypto infrastructure and AI systems. The conversation focused heavily on Bittensor, the blockchain network Yuma helps support, and the broader idea that decentralized coordination mechanisms could provide an alternative to an AI ecosystem increasingly dominated by a handful of centralized companies like Anthropic and OpenAI. 

As frontier AI models become more powerful and more expensive to develop, concerns around concentration of power, infrastructure control, and access to compute are becoming harder to ignore. Sternbach argues that blockchain networks may offer a way to distribute those functions across global participants rather than consolidating them inside a few large technology companies. 

Bittensor’s Approach to Decentralized AI 

Sternbach described Bittensor as “a coordination layer meets incentive mechanism for distributed work to be done to further AI infrastructure and applications.” 

In practical terms, the network uses blockchain-based incentives to encourage participants around the world to contribute computational resources, models, infrastructure, and applications. Those participants operate within “subnets,” which function as specialized ecosystems focused on particular AI tasks or use cases. Some subnets focus on decentralized model training, others on inference, while others support areas like financial tools, robotics, decentralized science, or AI-powered compliance systems. 

The model differs meaningfully from traditional AI development. Most major frontier models today are trained and operated within highly centralized environments controlled by companies with massive capital reserves and proprietary infrastructure. Bittensor instead attempts to coordinate distributed actors globally through token-based incentives and open participation. 

“You can train models in a decentralized manner,” Sternbach explained. “You can have decentralized inference — there’s different subnets and operators that are supporting that.” 

That distinction matters because compute access is quickly becoming one of the defining competitive advantages in AI development. If decentralized systems can successfully coordinate distributed infrastructure at scale, they may offer smaller developers and independent contributors a way to participate in AI markets without relying entirely on centralized providers. 

Moving Beyond Financial Use Cases 

The blockchain industry has spent much of the last decade focused on finance. Stablecoins, tokenization, trading infrastructure, and decentralized finance have dominated both investment and regulatory conversations. Sternbach suggested that decentralized AI could push blockchain into a much broader category of real-world utility. 

“There are real world applications to be built leveraging this network,” he said while discussing why he ultimately moved into the AI sector after years working in crypto infrastructure. 

The examples discussed on the podcast illustrate how quickly the category is expanding. Bittensor-related projects are already exploring decentralized science research, robotic training systems, AI vision models, and financial compliance tools. Rather than using blockchain solely as a settlement layer for financial activity, these projects use blockchain incentives to coordinate AI development itself. 

That shift could prove significant for the industry’s long-term trajectory. One of crypto’s recurring criticisms has been that many blockchain systems struggle to demonstrate utility outside speculation and trading. Decentralized AI changes that framing by tying blockchain infrastructure directly to computational services and AI functionality. 

The Governance and Liability Questions Are Just Beginning 

The legal implications of decentralized AI remain unsettled, and much of the podcast focused on the governance challenges these systems create. Sternbach repeatedly returned to questions around operational control, liability, and autonomy. 

“If I have an agent, am I liable?” he asked while discussing autonomous AI systems. 

Those questions become more complicated in decentralized environments where no single company fully controls the infrastructure, models, or applications operating on a network. 

Sternbach noted that future AI systems may eventually operate with significant independence, including the ability to monetize services, manage compute resources, or interact autonomously with other agents online. 

“The more interesting questions become kind of like, what happens when your agent is spawning agents and that agent is spawning agents,” he said. 

For regulators and courts, that creates difficult questions around accountability. Traditional legal systems generally assume there is an identifiable operator or entity responsible for a product or service. Decentralized AI systems challenge that assumption in ways that resemble earlier debates around decentralized finance and blockchain governance. 

Sternbach acknowledged regulators are unlikely to accept a world where responsibility disappears entirely simply because systems become autonomous or decentralized. That tension between decentralization and accountability is likely to shape the next generation of AI governance debates. 

Why Technical Understanding Matters 

Another recurring theme throughout the discussion was the importance of technological fluency among lawyers, regulators, and policymakers. Sternbach argued that meaningful regulation becomes difficult when policymakers cannot directly interact with or understand the systems they are trying to govern. 

“I think it’s very hard to be a lawyer in any forward technology space unless you understand the technology and use the technology,” he said. 

That observation is especially relevant for decentralized AI systems, which combine the technical complexity of blockchain infrastructure with rapidly evolving artificial intelligence. As Sternbach explained, many of the emerging questions involve nuanced distinctions around operational control, infrastructure design, and autonomous behavior that are difficult to understand purely in the abstract. Those issues are unlikely to disappear anytime soon. If anything, they will become more pressing as decentralized AI systems continue to mature and attract institutional attention. 

The Next Major Infrastructure Debate 

The conversation on Block & Order ultimately framed decentralized AI as more than just another crypto vertical. It represents a broader debate about who controls the next generation of computational infrastructure and whether AI development remains concentrated among a handful of centralized actors. 

Blockchain networks like Bittensor are attempting to offer an alternative model built around distributed participation, open coordination, and decentralized incentives. Whether that model can compete at scale remains an open question, but the legal, governance, and policy issues surrounding it are already arriving. 

As decentralized AI systems continue to evolve, businesses building in this space will need to navigate a rapidly changing regulatory and operational landscape. Falcon Rappaport & Berkman’s AI Practice Group, Artificial Intelligence and Digital Assets Practice Group, including Kyle Lawrence and  Moish Peltz, work with companies operating at the intersection of blockchain, AI, and decentralized infrastructure. If your business is exploring decentralized AI systems, tokenized incentive models, or autonomous network applications, FRB can help you evaluate the legal and regulatory considerations shaping this next phase of technological development.