Will ‘Decentralized AI’ make AI more transparent?
Although AI entered the mainstream with the launch of ChatGPT by OpenAI in 2022, its roots in academia date back decades. From the introduction of the Turing Test, designed to evaluate whether machines can mimic human behavior, to cultural depictions of AI in films like 2001: A Space Odyssey in the 1960s, AI has long been a topic of exploration and imagination.
AI's development experienced periods of stagnation over the years due to technological limitations. However, breakthroughs in Machine Learning, Deep Neural Networks, and advancements in high-performance hardware such as GPUs and TPUs have revolutionized the field. Today, AI stands as a driving force in shaping the digital era.
Challenges and Concerns of Centralized AI
AI has brought transformative changes across various industries, but its rapid development has also raised critical concerns around data ownership, privacy, and control over creative expression. Notable issues include:
- Copyright challenges: Companies developing AI face accusations of copyright infringement for using protected data to train AI models without proper authorization.
- Centralization of resources and power: Reliance on large AI models developed by a few corporations concentrates resources, data, and knowledge in the hands of these entities. This raises concerns about exploitation of creators and a lack of transparency in the operations of such organizations.
In response to these challenges, Decentralized AI has emerged as a promising alternative. By focusing on decentralizing power and resources, it aims to create systems that are more transparent, sustainable, and equitable. This approach could be a critical solution for mitigating the risks associated with centralization and addressing the negative impacts of AI development in the future.
What is ‘Decentralized AI’? Is it truly a new approach to AI that is more transparent and open?
- The concept of ‘Decentralized AI’ is an approach that distributes AI processing and data storage across multiple devices or nodes instead of relying on a single central system. This approach enhances data privacy and security by keeping data closer to its source, reducing the risk of data breaches.
- ‘Decentralized AI’ is considered a new alternative that uses Blockchain technology to make AI more transparent and open. Blockchain can help distribute data storage securely and enable AI models to be trained and accessed transparently through Smart Contracts. Using the popular concept in the AI field like Federated Learning, on the Blockchain, allows for collaborative AI model training without sharing raw data. In addition, the use of reliable security technologies like Zero-Knowledge Proofs helps maintain data privacy.
- Currently, the decentralized AI industry is growing rapidly. According to PitchBook, investors have already invested over $436 million in 2024, an increase of almost 200% compared to 2023. Furthermore, 79% of organizations also indicate that concerns about data privacy are an obstacle to AI adoption. Centralized AI models also raise significant ethical issues, with over 60% of consumers distrusting AI systems due to their non-transparent decision-making processes.
Decentralized AI and Addressing Copyright Issues
- While some centralized AI models use copyrighted data for training, leading to legal disputes over unauthorized data use, ‘Decentralized AI’ enables models that respect creators' rights and distribute benefits fairly.
- Although AI copyright law protects creative expression, it does not cover ideas or processes. The principle of “Fair use,” which allows for some uses without permission, still raises disputes about unfair use.
- There are examples of lawsuits from world-renowned authors such as George R.R. Martin that demonstrates resistance to unauthorized data use in the AI industry.
- CNBC reports that 8 publishing companies have filed lawsuits against large AI development companies for copyright infringement, falling outside the scope of “Fair use” rules. This highlights the conflict between free data usage and protecting the rights of creators. Decentralized AI presents a new alternative by providing independence and fair rewards to creators through a transparent system. However, there are projects developing decentralized AI networks aimed at creating AI systems that are not dependent on large companies or a single central authority, such as Bittensor and Ocean Protocol, which are poised to revolutionize intellectual property rights management sustainably.
Interesting Use Cases of Decentralized AI
Example Use Cases:
- Healthcare: Decentralized AI can help hospitals collaborate in researching and developing new treatments without exposing sensitive patient data
- Intelligent Manufacturing Systems: Decentralized AI enables smart factories to improve operations through local data analysis while keeping proprietary information confidential, using technologies like Blockchain and Edge Computing. Thus, decentralized AI can be an innovation that helps maintain trustworthiness and regulations across various industries.
Prominent Platforms Driving Decentralized AI
Decentralized AI uses Blockchain and distributed processing to reduce central dependency, thereby increasing security, transparency, and privacy. Example platforms include OORT, IO.NET, Akash Network, and Helium, each with specific strengths in secure data storage, distributed processing, and wireless networks for AI devices. Additionally, here are some standout platforms driving Decentralized AI:
- Vana - One of SCB 10X's partners, is a decentralized network for personal data designed for AI usage that allows users to own, control, and receive returns from AI models they participate in
- Sahara AI - A decentralized Blockchain AI platform, another partner of SCB 10X. Sahara AI focuses on developing a decentralized AI platform that enables users to have greater control and ownership of their data using Cryptographic Encryption and Blockchain technologies
- CESS Decentralized Data Infrastructure - According to Foresight News, Nicholas Zaldastani, CESS co-founder, joined industry leaders in discussions at 'CES 2025' in Las Vegas, focusing on decentralized AI innovation and Web3 technology. Zaldastani emphasized how CESS is promoting AI development by building a decentralized data infrastructure that uses Edge Computing and multi-layered decentralized network architecture. CESS provides solutions for secure and fast data storage, distribution, and model training, supporting various applications like medical AI, smart cities, and Decentralized Science (DeSci). Additionally, CESS prioritizes data privacy by offering decentralized data solutions for AI Agents.
The Future of DeAI in 2025
AI is shifting from centralized control to open protocols, which could be another major change in 2025. Current AI development is often controlled by a few large tech companies, limiting access and innovation. In 2025, we may see decentralized AI become a reality in the digital world, driving a movement to ensure fair compensation for those who contribute to AI model development. This will open up AI development opportunities for communities, creating a more collaborative open-source framework.
Decentralized AI could break down organizational walls and allow AI agents to collaborate across ecosystems. This change aligns with the core values of Web3: transparency, co-ownership, and decentralization. As a result, users will have more control over their data, and AI development will become a community-driven effort, free from monopolies by large tech companies. AI on the Blockchain will simplify complex DeFi transactions through automation and facilitate the management of multi-signature accounts. It may also impact transaction fees, making them more efficient.
Furthermore, DeAI is predicted to transition from a niche concept to the mainstream in 2025, with support from experts in the AI and Blockchain fields. Collaboration between these two technologies can create innovations with significant impacts on industries in the future.
Conclusion
Decentralized AI is not just a technological development; it may become a social necessity. As AI systems play an increasingly important role in providing critical services, the need for transparency, data integrity, and ethical practices becomes paramount. Decentralized AI, by leveraging Blockchain technology, offers a solution by creating a balance between innovation, transparency, and creators' rights in the growing age of AI.
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