Artificial intelligence is our most powerful and expressive technology to date. AI can analyze data, recognize patterns and make decisions far more efficiently and quickly than any human ever could.
What Is Decentralized AI?
Decentralized AI leverages blockchain technology to turn black-box AI systems, whose inner workings are hidden from the public, into open networks for coordinating machine intelligence towards shared objectives. This enables decentralized, trustless applications to use AI without needing to trust a small set of trusted organizations.
Much of today’s AI exists in centralized black boxes owned by a few influential organizations. This concentration of control counters the otherwise democratizing potential of AI and hands over outsized influence on society, finance and creativity to a handful of unchecked entities.
As AI systems advance, decentralizing its development and its applications becomes even more critical. Trustless, permissionless AI can power innovation across industries.
But what about open source AI? Platforms such as Hugging Face have more than 450,000 open source AI models, including large language models, image generators and more.
While open source models are a great advancement, they are often built in silos rather than collaboratively. To effectively decentralize AI, open source developers need to coordinate to build machine learning models that can learn from each other over time. This collaborative approach across decentralized teams is key to creating AI systems that can rival centralized alternatives.
Meeting this challenge will require reimagining the established AI stack from the bottom up. In this article, we’ll explore the current landscape of centralized versus decentralized AI and propose potential avenues to help coordinate efforts to produce competitive decentralized alternatives that protect against further AI centralization.
The Promises of Decentralized AI
Decentralized AI distributes the control over humanity’s most capable technology ever, rather than concentrating its power, which mitigates the potential for overarching influence by any one entity.
With development and governance distributed across entities with diverse incentives and priorities, AI can progress in better alignment with individual needs rather than imposing homogeneous outcomes. This nurtures diverse applications rather than having a handful of prevailing models dominate the culture.
Decentralized AI also provides checks against mass surveillance and manipulation by governments or corporations. Centralized control enables advanced AI utilization against citizen interests on massive scales. But decentralized AI limits this avenue of oppression.
Overall, decentralized AI limits any one entity from imposing a single set of incentives, constraints or goals, which is necessary for such a critical tool.
How to Decentralize AI
To decentralize AI, we must rethink the fundamental layers that comprise the AI stack. This includes components like computing power, data, model training, fine-tuning and inference. Merely using open source models is not enough if other parts of the stack, such as the entities providing compute for training or inference, remain centralized.
True decentralization requires active coordination across all layers of the AI stack. After all, networks are only as decentralized as their least decentralized component.
This is where markets can provide the necessary boost. Markets are the best coordination mechanisms we have access to for organizing people. As such, decentralized AI networks can compete with their centralized counterparts by deconstructing the AI stack into basic modular functions and creating markets around them.
For example, networks like Render reward participants for sharing spare compute capacity to artists in the network who need image rendering, but don’t have the compute resources to accomplish their goals. These types of solutions coordinate people across the AI stack through financial incentives, and blockchain technology makes them possible.
However, as AI becomes a more critical part of society, we seem limited to two suboptimal options:
- Sacrifice decentralization to use cutting-edge proprietary AI, or
- Forgo access to the most powerful technology available by locking into strictly decentralized alternatives, which currently lag behind centralized models in capabilities.
To break this tradeoff, we need coordination between the disparate participants across the layers of the stack. The end goal is a collaborative substrate of artificial intelligence where decentralized infrastructure can plug in and optimally leverage AI capabilities.
To enable decentralized access and permissions, decentralization must span the full stack, from data provisioning to model training to deployment. Only then can decentralized networks preserve trustless architecture while accessing AI.
Currently, the market stack lacks comprehensive decentralization. Even when models are open source, people often rely on centralized servers for training or consumption. Providers largely centralize the function of delivering inference.
Centralized networks benefit from the low costs of coordination between participants, because most of them operate under the umbrella of a single entity: a centralized company. However, decentralized networks have the benefit of compensating for the higher costs of coordination with the higher rewards of reaping more granular market-based incentives across the compute, data, inference, and other layers of the AI stack.
The path forward requires rethinking how all components of AI can synergize through open ecosystems rather than closed verticals.
The Benefits of Decentralized Systems
This transition to decentralized AI will offer several advantages.
Models in a decentralized network continuously learn from each other, which enhances the network’s collective intelligence and enables it to self-improve over time.
Permissionless, composable access enables builders everywhere to innovate and create with AI as a foundational element.
Using zero-knowledge technology and cryptographic verification can create AI model outputs that are verifiably secure and unalterable, ensuring trust in AI's decisions.
Scalability on Demand
Decentralized AI can adapt and expand without limits, scaling with demand while maintaining high performance and robust security.
A truly decentralized AI network can protect personal data through local processing. This reduces the risk of breaches and preserves user privacy.
Decentralized AI networks leverage diverse data inputs to reduce bias, which means more balanced and fair AI-driven outcomes.
AI’s Accessible Future
Our current AI landscape holds as much potential as it does peril.
While AI has advanced exponentially in the last half decade, those advancements have largely occurred in closed, centralized systems. Decentralized AI development is the only reasonable path forward; enhanced accessibility, flexibility and expressiveness all enable complex applications not otherwise possible in walled-garden AI systems.
To achieve a decentralized future for AI, we have to coordinate across all layers of the AI stack, everywhere from data to model training and deployment. Only then will we realize the full potential of decentralized AI.