Artificial intelligence is often described in terms of speed, scale, and capability, but those descriptions miss the deeper shift now underway. What is changing is not simply what machines can do, but what society expects from the people who use them. For decades, technical mastery defined leadership in innovation. The ability to understand systems, write code, and operate complex tools determined who moved ideas forward. That framework is now evolving into something more demanding.
As artificial intelligence reduces the technical barriers to entry, it increases the importance of judgment. More individuals now have access to powerful tools, but access alone does not determine outcomes. The differentiator is becoming the ability to interpret, to apply, and to guide. In this environment, leadership is less about knowing how the tool works and more about understanding what it should be used to accomplish.
The emergence of AI systems such as GPT makes this shift unmistakably clear. These systems demonstrate that content, not the tool itself, is the enduring asset. Models will evolve. Platforms will be replaced. Processing technologies will continue to advance and become more efficient. But the underlying content, the ideas, the context, and the intellectual frameworks remain the constant source of value. In this sense, content is not simply a byproduct of technology. It is the wealth that gives technology purpose and direction.
This reality carries significant implications for education, workforce development, and long-term economic competitiveness. If content is the enduring asset, then the ability to create, interpret, and apply knowledge becomes the central skill of the modern economy. That is why learning tracks in artificial intelligence, quantum computing, and cybersecurity must be introduced early and sustained continuously. These are not specialized disciplines reserved for a narrow segment of the population. They are foundational literacies for a society that will increasingly depend on digital systems for its functioning.
Abell Foundation
The responsibility is not limited to higher education or technical institutions. It extends from kindergarten through retirement. Students must be introduced to these fields early enough to build familiarity and confidence. Workers must be provided opportunities to upskill and retrain as technologies evolve. Professionals at every stage of their careers must have access to continuous learning that allows them to remain relevant in a changing landscape. A “K to retirement” framework is no longer aspirational. It is necessary.
This shift has direct implications for how we evaluate leadership at the state level. Maryland, with its proximity to federal agencies, its concentration of research institutions, and its diverse population, is positioned to play a significant role in the next phase of technological development. However, its success will not depend solely on its technical infrastructure or its ability to attract investment. It will depend on whether it can align technology with public purpose and prepare its population to engage with that technology meaningfully.
Fagan Harris enters this conversation not as a technologist, but as a systems thinker shaped by experience across government, philanthropy, and social innovation. His career has centered on managing complexity, coordinating institutions, and designing pathways for broader participation in opportunity. These are not peripheral skills in the age of artificial intelligence. They are central to it.
Artificial intelligence is not a self-directing force. It reflects the priorities and assumptions of those who deploy it. Without intentional leadership, it can reinforce existing inequalities, accelerate inefficiencies, or drift toward outcomes that serve narrow interests. With intentional leadership, it can expand access, improve decision-making, and create new forms of participation in economic and civic life. The distinction lies in how it is framed and governed.
The growing influence of AI also changes the nature of expertise. While technical knowledge remains important, it is no longer sufficient on its own. The future will place greater value on individuals who can connect disciplines, translate complexity into practical application, and ensure that technological capability is aligned with human need. This is where the emphasis on content becomes critical. Content, in this context, refers not simply to media, but to the underlying ideas, narratives, and frameworks that shape how technology is used and understood.
Maryland’s opportunity is to recognize this shift and act on it. The state can choose to focus narrowly on building technical capacity, or it can take a broader view that integrates education, workforce development, and community engagement into its AI strategy. The latter approach requires a different kind of leadership, one that is comfortable operating across sectors and attentive to the long-term social implications of technological change.
Harris’s experience with Baltimore Corps provides a relevant lens. That work was grounded in the belief that talent exists across communities but is often overlooked by traditional systems. By rethinking recruitment and career pathways, the organization demonstrated that expanding access can strengthen institutions rather than dilute them. A similar approach will be necessary in the AI era, where the question is not only who develops technology, but who is prepared to use it effectively and benefit from it.
The broader challenge for Maryland is to ensure that artificial intelligence becomes a tool for inclusion rather than division. This requires investment in education that emphasizes critical thinking alongside technical skills. It requires workforce strategies that prepare individuals for roles that do not yet exist. It requires public institutions that can adapt quickly while maintaining accountability to the communities they serve.
Leadership in this context is measured by the ability to balance innovation with responsibility. It involves making decisions that consider both immediate outcomes and long-term consequences. It also involves maintaining a clear understanding of the purpose behind technological adoption. Efficiency and advancement are important, but they are not ends in themselves. They must be connected to improvements in quality of life, access to opportunity, and public trust.
The question of whether Fagan Harris can help build an AI future for Maryland ultimately depends on how that future is defined. If it is defined narrowly in terms of technical advancement, then the focus will remain on infrastructure and expertise. If it is defined more broadly as a transformation of how society functions, then the emphasis shifts to leadership that can integrate technology into a larger vision for economic and social progress.
Maryland has the resources and institutional capacity to lead in artificial intelligence. The determining factor will be whether it can pair those assets with a coherent strategy that reflects the needs and potential of its people. That requires leadership capable of understanding both the power of technology and the responsibility that comes with it.
In the emerging AI landscape, success will not be determined solely by who builds the most advanced systems. It will be determined by who prepares people to use those systems effectively and responsibly, and by who recognizes that the enduring value of this new era will continue to rest on the strength, clarity, and purpose of the content that guides them.