Opinion by: Jarrad Hope, co-founder of logos
As AI fast scales, people have been left in an ideological frustration with the management of this new technology. Whichever chooses to allow governments and companies to dominate how AI is trained and used to create policies that control our lives, or call for new management models developed with a foundation based on transparency, change and public good.
Network States. As technology continues to advance in the scope of digital augmentation, it is important to establish a new category of AI Development Administration dedicated to serving people, not power.
Bias problem is a data problem and a management
Generative AI is now trained in narrow data sets and managed by centralized actors, such as Xai and Openai, with limited public responsibility. Practicing a large language model in a limited data set results in language models that strengthen bias, does not fail to reflect different views and undermine equal initiatives. For example, Grok has brought backlash for giant social media because of this The extremist responses to some signals After a update.
Network states can solve this by enabling an organization that provides community management, allowing a new approach to AI training and democracy. The transfer of philosophy of philosophy to consensus, ownership, privacy and community will alleviate the negative connotations featured in the existing AI discourse. Decentralized communities within network states will identify their goals and data sets and train AI models to align with their needs.
The impact of the decentralized autonomous organization (DAO) can help to democratize AI by focusing on using blockchain technology for the benefit of society. They can collectively fund open sources of AI tools, facilitate inclusive data collection and provide continuous public supervision. This method changes management from gatekeeping to administration, ensuring that AI’s development of all humanity benefits. Shared responsibility will enable the needs of the most vulnerable populations to include and promote more stakeholder purchases for AI’s benefits.
Centralization is a threat to AI Commons
More than 60% of the leading AI development worldwide is concentrated in a United States state, California, reflecting a high centralization of influence. This imbalance is not just geography; It is political and economic. For example, Xai is Sued for use of gas turbines In Memphis, Tennessee to empower its data centers. This is a clear example of a local government who has not survived people’s call for environmental regulation. Without checks, this power can take the value from society while extinguishing the damage. This damage is aggravated by the need of AI for high energy outputs, resulting in ecological factors that affect specific communities that are not motivated.
Network states offer an alternative: Decentralized communities that have no basis of boundaries, where digital citizens are co-created by AI management frameworks. The impact of Daos embedded within these systems allows participants to suggest, vote and implement care and incentives, which become AI from a control tool to an infrastructure dedicated to Commons. The expansion of which AI is represented is to know how technology is best used for positive social impact.
Towards Transparent, Regenerative AI Management and Application
Most AI systems today operate in the algorithmic black box, which produces real-world effects without some human input or supervision. From the bias lease of algorithms to fuzzy health triage systems, people are increasingly subject to automatic decisions without telling how they are made.
Related: Network states are a day that will compete with countries-states
The network says to flip that model by allowing onchain management and transparent public records. People will see how the rules are made, participate in their formation and release if they do not agree.
The impact of Daos constitutes this vision by avoiding damage and inciting the replenishment of public goods. They invest in long-term maintenance, audible systems, creating open, transparent community development that can also invite external parties to opt in and contribute funds or other resources.
The next stage
Legacy nation-states struggle to properly fix AI due to issues such as timely digital context of lawmakers, spread policies and excessive assurance in the leadership of Legacy Tech. Network states build models from the ground up, with blockchain-roaring, decentralized coordination tools and can be programmed management. The impact of the DAO, open and public digital community -driven communities, can unlock a new AI development period. These communities can align incentives and develop participatory, representative and regenerative AI by combining decentralized blockchain and management of generative and agentic AI.
Foundations for the future of collective goodness
AI should be considered a public good, not just a efficiency tool. New management systems must be open, transparent and led by the community to achieve this, promoting wise and fair change and development planning. We can build these systems today by embracing the inclusive, technological and philosophical aspects of network states and impact on DAOs. Prioritizing investment in infrastructure that supports digital sovereignty and collective care is important for designing a future AI that benefits people, not just income.
Opinion by: Jarrad Hope, co-founder of logos.
This article is for general information purposes and is not intended to be and should not be done as legal or investment advice. The views, attitudes, and opinions expressed here are unique and do not necessarily reflect or represent the views and opinions of the cointelegraph.
