Source: Al-Wafd Newspaper
Prof. Dr. Ali Mohammed Al-Khouri
The world is currently witnessing a transformation that is reshaping the centers of influence in the economy and technology. Economic power is no longer solely based on production or capital, but rather on the possession of advanced technological tools. This transformation rests on three main pillars: the ability to operate supercomputers on which artificial intelligence models are trained; the availability of structured databases that can be used to develop these models; and the establishment of rules and standards that govern how these technologies are applied in vital fields such as medicine, finance, education, and media.
These axes have become key drivers of the global economy because they determine the pace and direction of innovation, and thus who possesses the capacity to create new economic value. However, when these capabilities are concentrated in the hands of a limited number of countries or companies, their economic and technological influence increases, and the independence of others in formulating their policies and making decisions diminishes. This is when what can be termed “digital sovereignty” emerges—a new form of influence that allows for the manipulation of individual and institutional behavior and the control of economic and social opportunities through technology.
Understanding the structure of artificial intelligence systems
To understand the nature of this transformation, it is important to consider how artificial intelligence systems themselves operate. These systems rely on an interconnected chain that begins with collecting, organizing, and interpreting data, then processing it using advanced and expensive computing devices, and finally transforming the results into services that permeate everyday life.
In each episode of this series there is an independent source of power, which may be a company that controls the flow of data, a factory that owns the electronic chips, a laboratory that develops model training protocols, or a regulatory body that sets the rules governing their use.
When the vision that links these links to the common good is absent, technology turns into a closed economy that multiplies the profits of a limited group, while leaving wide social and economic costs borne by everyone.
Dislocation of developing economies in the digital chain
In light of this reality, developing economies face a fundamental challenge of remaining on the periphery of the artificial intelligence system, merely supplying raw data and consuming services, without possessing the ability to design or build independent computing infrastructures.
The result is large markets that lack local standards and effective evaluation tools, which makes artificial intelligence treated as a ready-made product rather than a scalable system.
In contrast, advanced economies face a different challenge: the acceleration of innovation that outpaces the ability of legislation to keep pace, in addition to the accumulation of technical risks that require accurate and verifiable screening tools, not just formal oversight.
Balancing innovation and the public good
For the response to be effective, a clear objective must be defined, one that achieves a knowledge balance allowing for sustained innovation while managing its risks, and ensuring that societies benefit from the value generated by their data. This balance requires practical implementation tools whose results can be measured over three to five years, through four interconnected pathways working together in an integrated manner.
The first approach is based on the principle that computing power should be accessible to everyone, not just large corporations or a limited number of entities. This can be achieved by establishing regional computing centers managed through partnerships between governments, universities, and the private sector. These centers would allow researchers and small businesses to use their capabilities at reasonable costs, thus fostering innovation without financial barriers. Designated usage periods would support projects serving the health, education, and environment sectors, with clear rules in place to protect data privacy. Once such technological infrastructure is in place, countries can move from importing AI models to developing local models in their own languages and tailored to their specific needs.
The second path: Data as an economic asset
This approach views data as a resource with real economic value. It is assumed that it will be managed through independent funds with clear contracts that guarantee the rights of all parties, so that the entities that collect data from hospitals or schools are obligated to return a portion of the benefit to the community that provided this data, whether in the form of services, financial support, or development of health and education infrastructure.
Information about how the data is used should also be made available to the public through periodic reports that explain where the data was used and what results it achieved, so that people feel that their data is being used in a fair and transparent manner.
Third path: Transparency as a tool for accountability
The third approach focuses on implementing the principle of transparency in a measurable and trackable way. This is achieved by creating a single registry that compiles essential information about each AI system, such as the type of data used in its training, how that data was processed, and the results of tests demonstrating its accuracy and impartiality both before and after deployment. This registry helps independent bodies to thoroughly review the systems and provides governments and institutions with a clear and reliable basis for purchasing or adopting any AI system.
To ensure the credibility of this process, a network of specialized laboratories could be established to test models in sensitive areas such as medicine, finance, and employment. These laboratories would issue periodic reports, detailed for the relevant regulatory bodies, including practical observations and recommendations, and brief reports for the public.
Track Four: Building Local Capacities
The fourth track focuses on building sustainable local capacity through intensive, multi-month training programs designed to develop specialized personnel in fields such as data engineering, risk management, and technology contract management. These programs are linked to real-world government projects, such as developing digital health records, early warning systems, or educational platforms tailored to the local language and context. The success of these efforts is measured by tangible results, such as the number of completed projects, increased reliance on open-source tools, and the enhanced competence of national talent capable of developing their own technological solutions. Furthermore, it involves expanding collaboration between universities and the public and private sectors in implementing these projects, ensuring the program’s lasting impact beyond its completion.
Integration of paths in vision
These pathways are inextricably linked and cannot function in isolation. A data center without a structured data management system loses its overall benefit; a data repository without clear records and transparent testing becomes vulnerable to misuse; and training programs disconnected from real-world projects lose their impact within organizations. An effective strategic vision integrates these pathways into a single framework based on funding tied to transparency standards, balanced access to computing resources in exchange for adherence to safety testing, and investment incentives disbursed only after measurable public service indicators are met.
The true measure of the success of these policies lies in their tangible, measurable outcomes, such as reducing computing costs for researchers and innovators, ensuring that communities receive a fair share of the benefits derived from their data, providing clear reports for testing systems before deployment in critical sectors, and increasing local content in AI applications in Arabic and the languages of developing countries. When these outcomes are achieved, the economy becomes more balanced, innovation accelerates in the right direction, competition is based on the quality of solutions, and the field is opened up to new ideas and local initiatives capable of contributing to global innovation.
