Decentralized MLOps, distributed hardware, and blockchain-based traceability solutions pave the way for a more decentralized and inclusive future for AI.
Written by: io.net
Compiled by Alex Liu, Foresight News
Artificial intelligence has quickly become one of the most centralized forces in the world. Developing and deploying AI requires massive resources — including large amounts of capital, advanced computing power, and highly specialized talent. Of course, only the best-funded organizations can afford to invest in cutting-edge infrastructure and attract top talent, while smaller businesses struggle to keep up.
Traditionally, MLOps (Machine Learning Operations) is controlled by large organizations that manage everything from data collection to model training and deployment internally. This closed ecosystem monopolizes talent and resources, creating significant barriers for startups and small companies.
One of the most exciting ways blockchain is challenging this centralization is by enabling decentralized, permissionless AI models. By leveraging a distributed community to secure, verify, fine-tune, and validate every stage of the LLM (Large Language Model) deployment process, we can prevent a small number of players from dominating the AI space.
io.net is taking a close look at the intersection of AI and blockchain, identifying three key areas that could reshape the landscape.
Distributed MLOps
In traditional MLOps, large tech companies have the upper hand. They have the resources to monopolize talent and run everything in-house. Decentralized MLOps, on the other hand, uses blockchain and token incentives to create a distributed network that allows for broader participation in the entire AI development lifecycle.
From data labeling to model fine-tuning, decentralized networks can scale more efficiently and fairly. The talent pool can be adjusted based on demand and complexity, making this approach particularly effective in specialized fields where talent is usually concentrated in well-funded companies.
Take CrunchDao as an example. They have built a decentralized model similar to Kaggle, where AI talents can compete to solve problems for trading companies. As specific data sets become more common, companies will increasingly rely on these talent networks to provide "people in the loop" for supervision, fine-tuning, and optimization. Another project, Codigo, is using a similar approach to build a decentralized network of crypto developers who earn tokens to train and improve cryptocurrency-specific language models.
Distributed Hardware
One of the biggest barriers to AI development today is access to cutting-edge GPUs, such as Nvidia’s A100 and H100. They are essential for training large AI models, but their cost is prohibitive for most startups. Meanwhile, companies like AWS are striking direct deals with Nvidia, further limiting access for smaller businesses.
That’s where a decentralized blockchain-based model like io.net comes in. By enabling people to monetize their idle GPUs, whether they’re in data centers, cryptocurrency mining facilities, or even gaming consoles, small companies can get the computing power they need at a fraction of the cost. It’s a permissionless, cost-effective alternative to traditional cloud providers, without the risk of censorship or high fees.
Distributed traceability
As Balaji Srinivasan said, “AI is an abundant digital product, cryptocurrency is a scarce digital asset; AI generates, cryptocurrency verifies.” As AI models increasingly rely on novel, private, and even copyrighted data, and as the threat of deep fakes grows, ensuring data provenance and appropriate licensing becomes even more important.
Copyright infringement is a serious concern when it comes to AI models trained on protected data without proper consent. This is where decentralized provenance solutions shine. Using blockchain’s transparent, decentralized ledger, we can track and verify data throughout its lifecycle, from collection to deployment, without relying on centralized authorities. This adds a layer of trust, responsibility, and respect for data rights, which is critical to the future development of AI.
in conclusion
The convergence of AI and blockchain technologies offers exciting new ways to challenge the threat of centralization in AI development. Decentralized MLOps, distributed hardware, and blockchain-based provenance solutions all play a role in creating a fairer, scalable AI ecosystem. These models allow for dynamic talent networks, leverage idle computing resources, and ensure data reliability, paving the way for a more decentralized and inclusive future for AI.