Original article: https://polkadot.com/blog/what-is-decentralized-ai/
By Joey Prebys
Compiled by: OneBlock+
AI is everywhere, helping us analyze dense documents in seconds, brainstorm business ideas on the fly, turn ourselves into characters in our favorite movies, and even answer questions we’re afraid to ask out loud, but as useful as AI is, it also brings serious concerns.
Today’s most popular and powerful models are controlled by a handful of tech companies, and their internal mechanisms are opaque. We don’t know the source of training data, the decision-making process, or who benefits when the model improves, and creators often don’t get recognized and compensated. Bias creeps in silently, while the tools that shape our future operate behind the scenes.
That’s why people are starting to push back, with growing concerns about surveillance, misinformation, lack of transparency, and a handful of companies controlling AI training and revenue distribution. These concerns are fueling demands for more transparent, privacy-friendly systems that are open to broad participation.
Decentralized AI (DeAI) provides a solution. These systems distribute data, computation, and governance to make AI models more accountable, transparent, and inclusive. Contributors are rewarded fairly, and the community can collectively decide how these powerful tools work. Polkadot already supports this future, providing the infrastructure to build fair decentralized AI systems that serve everyone, not just a few.
What is decentralized AI and how is it different from centralized AI?
Most AI today runs on centralized systems where a single company collects data, trains models, and controls outputs. These systems are typically not open to public input or oversight, and users have no way of knowing how the models are constructed or potential biases.
Decentralized AI is fundamentally different. Data is distributed across nodes, models are governed by the community or protocol, and the update process is transparent and open. What you get is a system built in open collaboration, with clear rules and incentives for participation, rather than a system controlled by a black box.
To make an analogy: centralized AI is like a museum run by a private foundation. You can visit the exhibits and even see your own data reflected in art, but you cannot decide how the exhibits are constructed, nor will you be recognized or compensated for your contributions. The decision-making process is opaque, and most things behind the scenes are unknown.
Decentralized AI is like an open-air art exhibition built by a global community. Artists, historians, and citizens contribute ideas, share data, and help curate the exhibition. Each contribution is traceable and transparent, and contributors are rewarded for improving the exhibition. This architecture supports stronger user protection and greater accountability - the most pressing needs in the field of AI today.
Why is decentralized AI important?
Centralized AI control brings serious problems. When a few companies own the model, they control the model's learning content, behavior, and access rights, which brings the following risks:
Concentration of power : A small number of companies control AI development, with little public oversight.
Algorithmic bias : Limited data and perspectives lead to unfair and exclusive systems.
No user control : People contribute data but have no say over how it is used, and they are not compensated.
Limited innovation : Centralized control limits model diversity and experimentation.
Decentralized AI changes the balance, opening the door to more transparent, fair, and innovative AI systems by dispersing ownership and control. Global contributors can jointly shape the model to ensure that it reflects a wider range of perspectives. Transparency also plays a key role, and many decentralized AI systems adopt open source AI principles, with code and training methods made public, which makes it easier to audit models, discover problems, and build trust.
But open source AI is not always decentralized. Models can be open source but rely on centralized infrastructure or lack privacy protection mechanisms. The core characteristics shared by both are transparency, accessibility, and community participation. Users can participate without giving up control of their data, and are more likely to contribute actively and benefit from it. Decentralization is not a panacea, but it opens the door to building AI systems that are more in line with the public interest and less influenced by private companies.
How does decentralized AI work?
Decentralized AI replaces centralized control with a distributed system, and model training, optimization, and deployment are performed in an independent node network to avoid single points of failure, improve transparency, and invite wider participation.
What are the technologies that support decentralized AI?
Federated learning : Let the AI model learn data on local devices (mobile phones, laptops, etc.), do not upload sensitive information to the central server, and only share model updates. For example, the mobile keyboard learns your typing habits and recommends more accurate automatic corrections, but does not upload the message content. It retains data privacy and distributes processing, which is in line with the goal of decentralized AI.
Distributed computing : Distributing the heavy load of training and running AI models to multiple machines in the network is equivalent to thousands of small computers sharing the work, improving speed, efficiency, scalability and resilience.
Zero-knowledge proof (ZKP) : A cryptographic tool that can verify data or operations without revealing the content, ensuring the security and trustworthiness of distributed systems.
How does blockchain support decentralized AI?
Decentralized AI systems need to coordinate tasks, protect data, and reward contributors, and blockchain provides a key foundation.
Smart contracts: Automatically execute transparent, pre-set rules such as payments or model updates without human intervention.
Oracle: Acts as a bridge between the blockchain and the outside world, providing real-world information such as weather, prices, or sensor data.
Decentralized storage: Training data and model files are stored in a decentralized manner on the network, making them more resistant to tampering, censorship, and single point failures than traditional servers.
Polkadot's unique architecture supports these systems, allowing different networks to focus on different tasks - privacy, computing, governance, etc., while maintaining interoperability. The modular design makes decentralized AI scalable, flexible, secure, and efficient. Different components are optimized for their respective functions and work together.
What are the advantages of decentralized AI?
Decentralized AI is not only a technological shift, but also a value shift. It builds a system of shared human values that embody privacy, transparency, fairness, and participation, and achieves the following advantages through decentralization:
Better privacy protection : Technologies such as federated learning, local training on devices, and zero-knowledge proof ensure data privacy.
Built-in transparency : Open systems make it easier to audit, track decisions, and identify bias.
Shared governance : The community jointly develops rules, incentives, and model evolution.
Fair economic incentives : Contributors are rewarded for providing data, computation, or model improvements.
Reduce bias : More diverse contributors bring inclusive perspectives and reduce blind spots.
Greater resilience : With no single point of failure, the system is more difficult to hack or shut down.
Polkadot supports these advantages through a modular architecture, where different networks can focus on privacy, computation, or governance while working together seamlessly to help scale decentralized AI without sacrificing security, user autonomy, or performance.
Challenges and limitations
Decentralized AI has potential, but faces challenges:
Scalability : Training large models requires a lot of computing power, and distributed coordination may slow down or complicate it.
Computing resource intensive : AI models consume a lot of resources, and distributed operations increase bandwidth and energy consumption pressure.
Regulatory uncertainty : Regulations vary from region to region, and the attribution of responsibilities in decentralized systems is complex.
Fragmentation : Lack of central supervision may lead to inconsistent standards and uneven participation.
Security and reliability : Trustless systems are still vulnerable to attacks such as data manipulation and model poisoning.
Complex user experience : Managing private keys and multiple interface operations hinder popularization.
These are real challenges, but they can be overcome. Polkadot’s modular architecture provides strong shared security, native interoperability, and allows different networks to focus on challenges while collaborating in the ecosystem, supporting responsible growth and shared risk.
Where is decentralized AI being used now?
Decentralized AI is more than just theory. Web3 projects have demonstrated in real life how distributed intelligence can drive applications, with Polkadot playing a key role. Here are five projects building decentralized AI on Polkadot:
Acurast: Confidential Computing on Everyday Devices
Acurast lets anyone turn old phones and other devices into part of a secure, decentralized cloud. You can earn rewards by contributing your unused computing power. Developers use this power to run privacy-sensitive tasks without relying on big tech company servers, creating a more private, people-centric internet.
OriginTrail: Decentralized Knowledge Graph
OriginTrail runs on a decentralized knowledge graph that connects and organizes trusted data in areas such as supply chain, education, etc. It is like a public fact base that anyone can contribute to or check, but no one company controls. This helps verify information such as the origin of a product or whether a certificate is authentic without relying on a central authority.
Phala: Privacy-preserving smart contracts
Phala is building a privacy layer for Web3. It allows developers to run smart contracts in a confidential computing environment, so even when applications use sensitive data (such as identity or health information), that data remains private. Think of it as a secure workspace for data that application creators cannot see.
PEAQ: Infrastructure for the Machine Economy
Peaq helps power decentralized physical infrastructure by enabling people and devices to earn rewards for completing real tasks. Think of it as a gig economy for machines. A robot might charge an electric car, or a sensor might report on air quality, both of which can be paid through the network, and Peaq makes it easy to coordinate and reward this machine-driven work.
Bittensor: Incentivized AI Model Training
Bittensor creates an open market where AI models compete and collaborate to provide the best output. Anyone can join the network and contribute computing power, train models, or evaluate performance. The system rewards valuable contributions through token incentives, creating an AI economy that is self-improving, censorship-resistant, and does not rely on centralized control.
Polkadot is building the future of decentralized AI
Decentralized AI is not just a technological change, but also a shift in values. It challenges the idea that intelligence should be controlled by a few companies and provides a more open and responsible alternative. These systems disperse power, protect privacy, and invite global participation in jointly shaping tools that change the world.
Blockchain makes this possible. By coordinating updates, protecting data, and rewarding contributors, it provides the foundation for AI systems that are inherently transparent. Polkadot adds a layer of modular infrastructure that enables specialized networks to excel in their respective functions while benefiting from Polkadot's native features and maintaining seamless interoperability within the broader ecosystem. This flexibility allows decentralized AI systems to continue to evolve and scale without sacrificing security, performance, or user autonomy.
From confidential computing to decentralized data management, the Polkadot ecosystem already has multiple projects putting these principles into practice, and this is just the beginning.
