The problems and limits of centralized-AI and how decentralized-AI promises to solve those problems
Although “Big AI” or what we call Centralized AI (companies like OpenAI, Google and Microsoft) are spending record-setting amounts on developing one-size-fits-all AI tools, it’s clear that the more personalized and transparent approach promised by a lurking breed of Decentralized AI technologies will appeal to consumers’ and business’s personalized needs, whereas the centralized AI models may only serve broader day-to-day needs.
First, let’s look at the difference between Centralized and Decentralized AI products available for use.
Centralization can mean (data) isolation
Artificial Intelligence (AI) is reshaping modern life in ways both subtle and significant. AI-driven tools assist with everything from answering emails to optimizing supply chains. Models like ChatGPT are bringing human-like conversation to our devices, while AI algorithms predict weather patterns, detect fraud, and recommend products. But these developments are not without controversy. Concerns about data privacy, fairness, and power imbalances are rising alongside AI’s growing influence.
Much of this tension stems from how AI is currently structured. Most AI systems today are centralized—developed, controlled, and maintained by a few powerful organizations that collect and process enormous amounts of user data. This centralized model has delivered impressive technological advances, but it is also showing clear limitations. As AI systems become more embedded in society, critics question whether centralized control is sustainable or even desirable.
Centralized AI’s main problem lies in its reliance on data silos. Data is the fuel of AI, but much of it remains isolated in separate institutions and companies. While a hospital might possess data that could help researchers identify a new disease pattern, that information is rarely shared due to regulatory and technical challenges. Supply chains, which could benefit from seamless data-sharing across regions and industries, are similarly fragmented. This lack of data access restricts the potential of AI and slows progress in fields that could benefit from more robust collaboration.
Bias in the Machine
Bias in AI occurs when an AI system produces unfair, skewed, or inaccurate outcomes that reflect prejudiced assumptions or patterns present in the data it was trained on. This can lead to discriminatory results, such as favoring one group over another in hiring decisions or offering unequal loan approvals.
Centralized AI models were created as a ‘swiss army knife’ and are trained on large data sets that provide breadth but not depth to machine learning. Decentralized AI however promised to reduce the amount of bias by going deeper on data sets that are specific to the AI’s function.
Bias gets into AI in several ways, often unintentionally:
- Biased Training Data: AI models learn from historical data, which may already contain human biases. For example, if a hiring algorithm is trained on data from a company that historically hired mostly men, it may learn to favor male candidates.
- Sampling Issues: If the training data isn’t diverse or representative of all groups, the AI may work well for some populations but poorly for others. An AI trained only on English-language text will struggle with multilingual tasks.
- Subjective Design Choices: The goals and priorities set by developers can unintentionally embed bias. How the AI defines “success” or “accuracy” may favor certain outcomes over others.
- Reinforcement of Social Inequities: AI models sometimes reflect and amplify societal inequalities. If policing data is biased, predictive policing models might unfairly target certain communities.
Addressing bias in AI requires careful data curation, diverse datasets, and regular audits to minimize unfair patterns. It’s an ongoing challenge that underscores the importance of transparency and accountability in AI development.
Centralized AI products, are like C3PO, who knows many languages and is a jack of all trades but a master of none, whereas R2D2 is analogous to decentralized AI products that are designed with a certain purposes in mind. R2D2 can talk to mainframe computers and co-pilot an X-wing better than any other droid.
Generalization offers great data breadth, but less data depth, creating greater bias in the system
Another concern is that centralized AI models are often built as broad, generalized systems. While they may perform well in many scenarios, they can also misfire in situations requiring specific local knowledge or context. This rigidity can lead to biased outcomes and decisions that feel out of touch or even unfair. Making matters worse, centralized AI models are often described as “black boxes.” Users may interact with the AI without ever knowing how it arrived at its conclusions, creating transparency and accountability issues that erode trust.
Some believe that the solution lies in a fundamentally different approach: decentralized AI (DeAI). Unlike centralized models, DeAI focuses on distributing control and decision-making across many stakeholders. It relies on emerging technologies like blockchain and federated learning to create a more open, collaborative AI ecosystem. Proponents of this approach argue that decentralization could address many of the issues that plague centralized systems.
A (super) brief history of DeAI
The history of Decentralized AI (DeAI) traces a fascinating journey of innovation at the intersection of AI and blockchain technology. It began in the early 2010s, as researchers explored ways to combine AI with decentralized networks, culminating in significant breakthroughs around 2012. By 2013 and 2014, the focus shifted toward refining the integration of AI algorithms into blockchain frameworks, setting the stage for more advanced developments.
The late 2010s saw the rise of smart contracts, particularly in 2018, when self-executing agreements became central to DeAI. These automated systems introduced new levels of transparency and autonomy. Between 2019 and 2020, security became a primary focus. Innovations in cryptography and consensus mechanisms fortified the ecosystem, while new solutions like sharding and off-chain computation tackled scalability issues.
As DeAI continued to evolve, its applications spread across sectors such as finance and healthcare, offering groundbreaking opportunities for more secure, efficient, and adaptable AI systems. Today, DeAI is seen as a powerful tool for decentralizing innovation and expanding AI’s reach beyond centralized control.
Can DeAI provide privacy while getting to know us personally?
In theory, decentralized AI could make data more accessible without compromising privacy. For example, in healthcare, federated learning allows hospitals and research centers to collaborate on training AI models without sharing sensitive patient data. Instead of sending raw data to a central server, each institution keeps its data local while contributing to a shared model. This could accelerate breakthroughs in medical research while maintaining strict data protections.
In supply chain management, decentralized AI might provide real-time visibility across global networks, helping companies allocate resources more efficiently. In finance, it could improve fraud detection and transaction security while reducing dependence on centralized authorities. These possibilities sound promising, but they are still largely experimental.
Decentralized AI Technologies
Several key technologies are driving the development of decentralized AI. Blockchain, for example, offers a secure way to share data while ensuring transparency and traceability. Decentralized Autonomous Organizations (DAOs) take this a step further, enabling groups of people to make decisions and allocate resources collectively, using blockchain as a foundation for governance. These structures are intriguing, but they also raise new challenges around coordination, scalability, and regulation.
As Usual, No Guarantees
Despite its potential, decentralized AI is not a guaranteed solution. It brings its own set of risks and unanswered questions. While blockchain can ensure data integrity, it is notoriously energy-intensive. Federated learning sounds like a privacy-friendly alternative, but it’s not immune to bias or manipulation, especially if the data used to train the models is flawed. Even transparency—one of DeAI’s main selling points—can be a double-edged sword, exposing sensitive processes that bad actors could exploit.
Looking ahead, it’s likely that decentralized AI will become a growing part of the AI ecosystem. As awareness of its benefits spreads, more organizations may adopt these technologies. But there’s no clear roadmap for how this will unfold. Much will depend on how we balance innovation with caution, ensuring that decentralized AI isn’t just a tool for disruption but one that genuinely addresses existing problems without introducing new ones.
Ethical concerns will need to take center stage. How do we prevent decentralized systems from becoming just as concentrated in power as their centralized counterparts? How do we ensure that the data used in these systems is accurate, representative, and free from harmful bias? And who will be responsible when decentralized systems fail?
Ultimately, decentralized AI represents a new way of thinking about technology—one that challenges the status quo. Whether it will live up to its promise or create new complexities remains to be seen. What’s clear is that AI, in any form, needs careful governance, transparent processes, and thoughtful oversight to truly serve society’s best interests.
