Decentralized AI 101: Everything You Need to Know

Artificial Intelligence (AI) is a rapidly emerging technology that promises to change our lives forever. However, central authorities like OpenAI (ChatGPT) and Anthropic (Claude) own many of the most powerful and widely used AI models. They dominate the AI landscape and offer the best performance, but the centralization raises interesting questions surrounding bias in models, who controls data, lackluster transparency, and vulnerability.

Decentralized AI, or DeAI, eliminates centralization from the AI landscape. It’s set to tackle issues in centralized systems by democratizing data, leveraging the blockchain for tamper-proof information storage, and distributing infrastructure across a network to remove any single points of failure. Let’s dive deeper.

What Is Decentralized AI – Explained

Decentralized AI involves the developing, storing, and operating artificial intelligence applications across a distributed network. DeAI offers many advantages compared to centralized AI systems that could benefit cloud computing, healthcare, and other prominent sectors.

Instead of being hosted on a central server, decentralized AI systems distribute all aspects of computation and decision-making across multiple devices/nodes. This approach results in data redundancy and enhanced privacy, critical for applications dealing with sensitive information or those that cannot afford to lose data.

Although not strictly required, blockchain technology is often implemented in DeAI systems to make computing verifiable. Moreover, DeAI systems levy the blockchain to facilitate secure and transparent data exchanges, which is helpful when training models and eliminating the  unnecessary data storage.

Decentralized AI aims to improve centralized systems that lack privacy, have authoritarian control over data, and can be easily manipulated by bad actors. While the impact of the technology is yet to be fully realized, it has been implemented into some projects to great success. As such, we’ll likely see a sharp uptick in applications that leverage DeAI in the coming years.

Decentralized AI vs. Centralized AI

Centralized AI systems and the data used for model training are entirely controlled by an organization. The computational power required for operation is provided by hardware concentrated in a single physical location, usually in powerful servers or a large hardware array.

Conversely, decentralized AI systems are designed to run independently without requiring the intervention of a central authority or organization. Computational resources are spread across a distributed network of participants, removing any single point of failure or control. DeAI systems typically focus on privacy, transparency, and reliability.

Aspect of Service	Centralized AI	Decentralized AI
Privacy	Data is controlled by a central authority/organization.	Users are in control of their data.
Reliability	Can go down entirely due to hardware faults or outages.	DeAI can continue operating at a reduced speed if some nodes fail.
Governance	Changes are proposed and approved unilaterally.	Network participants vote on changes, with a majority required for approval.
Scalability	Highly efficient, and upgrades are simple.	Nodes can be added or removed as needed.
Resource Acquisition	Hardware is expensive, quickly becomes outdated, and contributes to E-waste.	Leverages network participants’ unused computing power to acquire resources.

Benefits and Cons of Decentralized AI

Decentralized AI offers many benefits that centralized systems simply cannot. However, the technology also presents new challenges that result in limitations in specific applications.

Benefits

Decentralized artificial intelligence offers many advantages compared to centralized systems, which make the technology extremely valuable for sensitive applications where security is paramount.

• Data Democracy: Instead of a central authority or company having complete control over user data, DeAI systems fragment data across a network and use blockchain technology to ensure data is safe and bad actors cannot manipulate it.

• Enhanced Privacy: As data is stored and processed decentralized, no single party can access or manipulate data they don’t own, which is crucial for sensitive applications, such as the healthcare industry.

• Verifiable Computing: Due to the implementation of blockchain technology, participants in a decentralized AI network can ensure that tasks are completed efficiently and accurately, minimizing wasted computing power.

• Increased Trust: Decentralized AI is transparent and distributes decision-making across multiple nodes/devices, making it challenging to alter data. Moreover, the immutable nature of the blockchain ensures that any changes or actions are recorded indefinitely,

• No Single Point of Failure: DeAI applications use decentralized networks with many distributed hardware components. Therefore, expected events like a fire cannot disrupt the entire network, which is valuable in applications where consistent uptime is imperative.

Cons

Although DeAI seems poised to significantly impact the artificial intelligence sector and applications, it has drawbacks.

• Limits to Scalability: One of the most significant hurdles for decentralized AI is the limitation surrounding how much information can be processed by the system. As decentralized networks have more moving parts, processing takes additional resources, which can result in congestion.

• Additional Complexity: Artificial intelligence is already highly complex and challenging to understand. However, including of the blockchain and Decentralized Physical Infrastructure Networks (DePINs) raises the technological barrier even further.

• Interoperability Hurdles: As decentralized AI is an emerging technology, we’re yet to see simple ways to integrate it into existing systems and applications.

Decentralized AI and the Blockchain

Blockchain (a distributed ledger of immutable information) technology is often leveraged by decentralized AI projects to build verifiable computing systems, allocate resources across a network, and store data.

AI and blockchain technologies intersect at many points. With smart contracts, AI can handle decision-making while the blockchain verifies information. Another example is how DeAI can utilize the blockchain for federated learning, which allows multiple network nodes to contribute to model training.

As Ahmed M. Shamsan Saleh stated in a paper for ScienceDirect, the blockchain also offers profound cybersecurity benefits for decentralized AI systems. When integrated, some advantages include more efficient information sharing and the superior detection of threats.

While the blockchain and DeAI are closely intertwined, blending the two technologies can present a few challenges. For example, energy consumption usually increases, and there can be scalability issues when dealing with large quantities of data.

Top Decentralized AI Projects to Watch in 2025

Now that we’ve answered the question of decentralized AI and discussed the link between DeAI and the blockchain while sharing some pros/cons of the technology, it’s time we look at a few innovative decentralized AI projects taking leaps and bounds in the industry.

1. OORT – Cloud for Decentralized AI

OORT is a trailblazer for DeAI and is set to address industry issues like transparency, privacy, and reliability plaguing centralized AI services. OORT combines blockchain DeAI technologies to create a secure network for data processing, including computation, labeling, storage, and data crowdsourcing.

In contrast to other decentralized AI projects, which typically focus on a single segment, like computation or storage, OORT provides a full stack of DeAI infrastructure, for example OORT Storage consisting of over 60,000 nodes, OORT DataHub that gathers information across a distributed network to create data sets for AI applications and OORT Compute that pools network participants’ unused resources and distributes them to people as processing power for training and fine-tuning AI models.

OORT is committed to the advancement of DeAI technology. The company has a long-term vision that could disrupt reliance on centralized services. For example, OORT Storage offers cost savings of up to 60% compared to Amazon AWS S3.

2. Filecoin – Decentralized AI Storage Provider

Filecoin is one of the best-known DePIN projects in cryptocurrency. It utilizes the blockchain to operate a global network of storage devices. The project aims to reduce costs compared to centralized systems while improving privacy and people’s control over their data.

As information is spread across a global network, Filecoin benefits from data redundancy, making it useful for workloads where information cannot afford to be lost. The project leverages the InterPlanetary File System (IPFS) to facilitate file sharing across a Peer-to-Peer (P2P) network, eliminating a single point of failure while improving security and access to data.

Filecoin has carved out a well-defined space for itself in the storage sector by using DeAI technology for encryption, data sourcing, and storage. While it could face stiff competition in the future from more fully-featured projects, Filecoin is currently the most active and best-known decentralized storage system on the market.

3. io.net – Decentralized Computing Power for AI

io.net is a cryptocurrency project building a decentralized AI computing network for machine learning applications. The project aims to democratize access to Graphics Processing Unit (GPU) resources, reducing costs and improving the efficiency of training AI models.

The project has built a GPU DePIN network that rewards participants for providing unused GPU resources. By taking this approach, io.net saves on hardware costs, enabling it to compete on pricing. Moreover, io.net allows people to create low-latency clusters of thousands of GPUs that they can use for intensive applications.

With the complexity and the number of AI models people create increasing sharply, there is a clear need for services like io.net, which lowers the barrier to entry and removes costly hardware requirements. While io.net is gaining traction and users, its focus on a particular use case could impact revenue.

4. Render – DeAI GPU Network for Rendering and Model Training

Render Network is an established crypto project aiming to democratize access to GPU processing power using the blockchain for applications like rendering complex visual effects and training AI and machine learning models.

To ensure that access to its network of GPUs is fair and cost-effective, Render has built a substantial DePIN network that enables creators to upload and process a job while allowing participants to pledge their unused resources to earn cryptocurrency rewards.

The cost of high-end GPU hardware is rising steadily, and demand is soaring due to its use in AI applications and Computer-Generated Imagery (CGI). Applications like these require significant processing power, which can price out small creators or businesses, so projects like Render are vital to the industry’s health.

5. Bittensor – Blockchain-Based Machine Learning

Bittensor is an open-source machine-learning network that leverages the blockchain to facilitate efficient collaborative training. The project aims to advance machine learning and AI products development by incentivizing people to contribute computing resources to training advanced models. Participants can also submit their models for testing and training.

The project encourages people to contribute to model training on the Bittensor network by offering rewards paid in its native crypto token to miners who pledge resources and produce machine-learning outputs. It leverages the blockchain to achieve transparency and trust, while validators are used to ensure the quality of the data provided.

The training of machine learning models requires expensive hardware and results in ethical concerns related to privacy. However, Bittensor’s decentralized model democratizes the process, improving accessibility. Moreover, it enables people to earn from their data while awarding participants greater control, solving some issues in centralized systems.

Looking Ahead

The value of the global AI industry is expected to grow to more than $1.8 trillion by 2030. Although centralized AI systems dominate the landscape, they have attracted negative attention due to privacy, cost, and reliability issues.

Decentralized AI technology is set to shake up the industry by tackling some of the most significant issues plaguing the centralized AI space. While many of the major players in the DeAI landscape are focused on a particular segment, infrastructure projects serving multiple segments will likely become the go-to option due to their enhanced simplicity and performance.