AI Can Be Democratized for Everyone. This Is Why We Need It  – Coincu

Artificial intelligence has made a lot of progress in a very short space of time, and we have the likes of OpenAI, Google, Microsoft, Anthropic, Cohere, DeepSeek and Elon Musk’s XAI to thank for this. Collectively, these companies have spent billions of dollars in order to develop and build powerful generative AI models that can converse with people, generate code and now even perform various tasks on our behalf. 

There’s no doubt that AI is a transformative new technology and it’s going to change the world forever, but there are significant risks in allowing these early pioneers to double-down on their domination of the industry. 

While OpenAI et al often say AI is a gift to humanity, the reality is that this technology is heavily centralized, with most of the power becoming concentrated in just a small number of corporate hands. 

Admittedly, fears of Skynet sparking into life and attempting to kill off every last human are probably not justified. But if the mass extinction of humanity is an overblown risk, there are many others that aren’t. 

The Risks of Centralized AI

1: Privacy & Governance Concerns

Today, most of the world’s AI models are hosted in centralized data centers owned by big corporations, where they harness massive amounts of personal information. This raises some big concerns around privacy and governance. The entities that control this data have all of the power with virtually no oversight, which makes it easier for them to monitor, misuse and sell this information to third-parties without the consent of users. 

2: A Single Point Of Failure

Another risk of having data housed in just a handful of centralized facilities is that it creates a single point of failure. If something goes wrong with that data center, it could have disastrous consequences, and all the more so as the world increases its reliance on AI automation. 

Should the data center that controls a critical AI system suffer an outage or fall victim to a cyberattack, it would cause immense disruption, likely taking it offline. With our increased reliance on AI systems that power real-time applications such as healthcare monitoring, financial services and autonomous cars, any downtime or even delays could be catastrophic, resulting in significant financial losses and possibly even risking human lives. 

3: The Cyber Risks

AI systems, as they accumulate more and more data, will become a major target for hackers, including both cybercriminals and state-sponsored groups like the North Korea-backed Lazarus. 

If hackers are able to breach these centralized systems, they’ll be able to access vast amounts of sensitive user data, and potentially even use their unauthorized access to manipulate the AI algorithms we come to rely on. This would have tremendous consequences for user privacy, the integrity of critical services and even national security. 

4: Energy Demands & Environmental Risks

The centralized data centers that play host to most AI services consume vast amounts of energy and generate tremendous amounts of heat, which must be handled using elaborate cooling systems that require significant amounts of resources themselves. 

Many data centers are still powered by fossil fuels, which means they produce substantial carbon emissions. And even those facilities that use nuclear or renewable energy sources put a strain on our resources, as these energy sources are in high demand. As the adoption of AI increases, the power demands are likely to spiral out of control, and that could cause climate change to accelerate. 

5: Regulatory Challenges

The regulatory landscape has not yet adjusted to accommodate AI systems, and efforts to do so are complicated by the global nature of the AI industry. Centralized data centers fall under the jurisdiction of the country they’re in, but when a company is offering its services to users in another country, this significantly complicates the challenge of applying oversight to such systems. 

As a result, a data center in the U.S. may be prohibited from serving customers in Europe due to the different data protection laws in those jurisdictions. 

6: Limited Accessibility 

Because AI data centers are so expensive to run, the large corporations that build them seek a return on investment and that inevitably means the end user has to pay to access their AI services. As a result, access to this technology is limited to those who can afford to pay for it. 

Additionally, it also causes problems in terms of innovation, as only the biggest technology companies have the finances to build these sprawling data centers filled with enormous server farms. Startups struggle to compete without access to the same computing resources. 

7: Control Over Development

As the larger players establish themselves, they’ll be firmly in the driver’s seat with regards to the direction in which AI develops and evolves over time. The likes of OpenAI and a few others will be the ones who decide which AI services get built and which do not, and which areas of research get funding at the expense of others. 

The danger is that these organizations’ decisions will be influenced by their shareholder’s interests. They’ll do what’s profitable, rather than focusing on how AI can be applied to solve the world’s most pressing problems. 

Decentralization As A Solution

The problems with centralized control over AI are numerous and significant, but fortunately there is one simple solution to them all. If the AI industry becomes decentralized and democratic, control over the technology and its resources will be in the hands of the majority, rather than a few profit-orientated companies. 

To build a decentralized AI industry, Web3 technologies such as blockchain and digital tokens are critical. They will provide the foundation of a decentralized ecosystem and network that can be retrofitted to support the development of AI models. Blockchain-based AI, by its very nature, is more open and accessible to everyone, which means more collaborative development, with participation open to everyone equally. 

The advantage of using blockchain is that we don’t need to rely on centralized servers. Instead, AI models and their data can be distributed across a global network of individually owned servers and devices. These nodes will work collaboratively to process AI training and inference, with democratic governance processes put in place for community-based decision-making. 

Making It Happen

A project called Kite AI is developing the first such blockchain, purpose-built to host AI models, their data and the applications that use them. It’s focused on solving the problem of access to AI assets, and facilitates the secure sharing of AI models, data and AI agents, creating a level playing field in terms of AI development. 

One of the most pressing challenges in AI development today is access to the necessary data. Training new AI models requires vast amounts of very specific data, but much of the information in the world is controlled by big technology corporations that are unwilling to share it. It’s made even more difficult by the fact that consumers are becoming increasingly hesitant about sharing data, due to privacy fears. 

Kite AI gets around this with its transparent data sharing environment, based on a foolproof “attribution system” that ensures anyone who creates information used by an AI model is not just acknowledged, but compensated financially. It’s called the Decentralized Data Access Engine, and it provides a way for users to securely share their data in return for a fair share of the profits generated by the AI system that uses their information. 

The system helps to protect privacy even while enabling data to be shared. It incorporates a local database that’s stored on the user’s device, so the AI models they use can process data locally instead of sending it to a faraway server. Not only can the user then restrict access to any sensitive information, but they also benefit from lower latency, meaning a better application performance. 

Besides contributing data and getting compensated, users can also provide the computation resources required by AI models. Kite AI’s network is open for anyone to participate in. So someone whose laptop is always online can contribute their idle CPU or GPU resources to the network, powering AI models in return for crypto-based payment. When they’re not using their laptop for work, its processing power will instead be utilized by someone else on the network, eliminating the need for a centralized data center controlled by a corporate entity. 

Distributed AI networks solve the problems around single points of failure, they’re more secure and regulation becomes easier through its decentralized governance process. These networks also require less power, and they’re free from control by profit-seeking entities.

If the AI industry is no longer profit-driven, then the focus will shift from thinking about how the technology can be used to generate revenue. Instead, developers will be concentrating their efforts on using AI to solve humanity’s biggest problems, making it far more beneficial to the world. 

Final Thoughts 

Decentralized AI systems promise to be much more beneficial for humanity as a whole. The big appeal of decentralization is that users remain firmly in the driving seat, with the ability to protect their data and have a say in the future of its development. With decentralized AI, corporate entities cannot keep all of the profits to themselves, and they will not be able to dictate its future evolution, or who can and cannot access the systems they build. 

To build this alternative AI reality, a lot of work needs to be done, and every one of us will need to cooperate. Decentralized AI needs greater awareness, as increased participation is the only way to provide the funds and the innovative thinking required to accelerate its progress. As more people participate, more resources will become available to the innovators that can carry us into this more equitable future.