Artificial intelligence has quickly become one of the most centralizing forces in the world. Developing and deploying AI requires massive resources — think substantial capital, advanced computing power, and highly specialized talent. Naturally, only the best-funded organizations can afford to invest in cutting-edge infrastructure and attract top-tier talent, leaving smaller players struggling to keep up.
In traditional setups, MLOps (Machine Learning Operations) are controlled by large organizations that manage everything in-house, from data gathering to model training and deployment. This closed ecosystem monopolizes both talent and resources, creating major hurdles for startups and smaller companies.
One of the most exciting ways blockchain can challenge this centralization is by supporting decentralized, permissionless AI models. By leveraging distributed communities to secure, validate, finetune, and verify every stage of the LLM (Large Language Model) deployment process, we can prevent a few large players from dominating the AI space.
At io.net , we’re keeping a close eye on where AI and blockchain intersect. As the AI-driven market continues to expand, we’ve identified three key areas that could reshape the landscape.
1. Distributed MLOps
In traditional MLOps, large tech companies have the upper hand. They have the resources to monopolize talent and run everything in-house. On the other hand, decentralized MLOps use blockchain and token incentives to create distributed networks, allowing for broader participation across the AI development lifecycle.
From data labeling to model finetuning, decentralized networks can scale much more efficiently and fairly. Talent pools can adjust based on demand and complexity, which makes this approach particularly effective in specialized fields where talent is often concentrated within well-funded companies.
Take CrunchDao, for example — they’ve built a decentralized Kaggle-like model where AI talent can compete to solve problems for trading firms. As niche datasets become more common, companies will increasingly look to these talent networks to provide the “humans in the loop” for supervision, finetuning, and optimization. Another project, Codigo, is using a similar approach by building a decentralized network of crypto developers who earn tokens for training and refining crypto-specific language models.
2. Distributed Hardware
One of the biggest hurdles in AI development today is access to cutting-edge GPUs, like Nvidia’s A100s and H100s. These are essential for training large AI models, but the cost is prohibitive for most startups. Meanwhile, companies like AWS are locking in direct deals with Nvidia, further restricting access for smaller players.
This is where blockchain-based decentralized models like io.net come in. By enabling people to monetize their idle GPUs — whether they’re sitting in data centers, crypto mining facilities, or even gaming consoles — smaller companies can access 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 exorbitant fees.
3. Distributed Provenance
As Balaji Srinivasan puts it, “AI is digital abundance, crypto is digital scarcity; AI generates, crypto authenticates.” As AI models increasingly rely on novel, private, or even copyrighted data, and with the growing threat of deepfakes, ensuring data provenance and proper permissioning is becoming more crucial.
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 can shine. Using blockchain’s transparent, decentralized ledger, we can trace and verify data through its entire lifecycle — from collection to deployment — without relying on a central authority. This adds a layer of trust, accountability, and respect for data rights that’s essential for the future of AI development.
Conclusion
The convergence of AI and blockchain technology is offering exciting new ways to challenge the centralizing forces in AI development. Decentralized MLOps, distributed hardware, and blockchain-based provenance solutions all play a role in creating a more equitable, scalable AI ecosystem. These models allow for dynamic talent networks, utilize idle computing resources, and ensure data reliability, paving the way for a more decentralized and inclusive future for AI.
