The United States cannot remain competitive in the age of AI and robotics without rebuilding its education system.

If the United States is truly moving into a new “Golden Age” for the American economy, it cannot do so on nostalgia and slogans. Economic eras are built on human capacity: the ability of workers to master tools, solve problems, and invent new industries. That is why education is not a cultural side issue or a partisan trophy. It is the operating system of national competitiveness. And right now, that operating system is aging.

At a recent White House cabinet meeting, Secretary of Education Linda McMahon argued that the country needs education reform. She emphasized three familiar priorities: shifting more authority to local control, expanding school-choice options, and focusing on outcomes rather than bureaucracy. Those are governance questions, and they matter. But they do not fully address the more basic problem: the U.S. public education system is still calibrated for an earlier economy.

In practice, the public-school model remains oriented toward producing a workforce for the Industrial Age. Its rhythms and incentives favor standardization, seat time, and assessment regimes that assume stability. A student learns a fixed body of knowledge, demonstrates mastery on periodic tests, and moves on. That logic made sense when workplace tasks changed slowly and when a single specialization could carry someone for decades. In an era when software can transform a profession in a year, the model no longer fits the pace of change, and it leaves too many students trained for routine rather than prepared for reinvention.

This is why we need more than a renewed push for STEM. Science, technology, engineering, and math remain essential, but they are not sufficient on their own. The next workforce will have to cultivate cognitive skills and habits that allow people to keep learning as the ground shifts beneath them. A decade ago, I described a “FACT-based” set of skillsets: Flexibility, Adaptability, Creativity, and Technology fluency. These are not vague aspirations. They are practical competencies that determine whether a graduate can navigate uncertainty, collaborate across disciplines, and stay productive as tools and expectations change.

Those skills should be embedded early in curricula, not treated as enrichment. Flexibility and adaptability are what allow a worker to pivot when a role is automated or redefined. Creativity is what enables people to ask better questions of machines, design new processes, and imagine products that do not yet exist. Technology fluency is the baseline for participating in a modern economy, including the ability to evaluate digital claims, understand basic systems, and use tools responsibly.

Now that the United States is entering the Age of Artificial Intelligence and Robotics, the case for a new Platform for Education becomes even sharper. Students will need to learn how to develop and apply next-generation AI tools, not merely consume them. They should understand what these systems can do, where they fail, how bias can enter, and why verification matters when outputs look convincing but may be wrong. They also need practical pathways into robotics, drones, sensors, and other emerging technologies that will shape logistics, manufacturing, health care, agriculture, defense, and daily life.

Higher education cannot be exempt from this rethink. Outdated curricula and siloed degree tracks increasingly produce graduates who are over-degreed yet under-prepared to deliver pragmatic results. Credentials can still signal effort and intelligence, but they can also become a substitute for demonstrated capability. In the years ahead, the premium will belong to those who can “fast-adapt” to new learning, show trainability, and shift into emerging areas without waiting for institutions to catch up.

It is telling that many business leaders have called for making computer science and AI education mandatory for every U.S. student if the country hopes to remain globally competitive. Whether one agrees with the precise prescription, the diagnosis is hard to dispute: literacy in computing and AI is becoming as fundamental as literacy in writing. As observed earlier this year, “We cannot keep teaching checkers when the world has moved forward playing chess.”

That observation points to the most important skill of all: trainability. In a labor market defined by continuous technological disruption, job security will increasingly mean one thing: the skills a person can reliably acquire, update, and apply. Trainability is the capacity and willingness to learn, unlearn, and relearn. It is not a soft add-on. It is a foundational workforce competency. Rapid technological change means constant reskilling. Cognitive agility and a learning mindset become core competencies. Employers will increasingly reward learnability over static credentials, as roles and systems continue to change.

This paradigm shift is not a thought experiment to contemplate for the next decade or two. It is an urgent national challenge. If the United States wants an economy propelled by AI and robotic innovation, public schools must align with the demands of this new era and prepare the next generation of workers accordingly. In the Age of AI and robotics, those who can learn fastest will lead, while those who resist learning risk becoming obsolete.

Finally, higher education should evolve toward multidisciplinary programs that reflect how real problems are solved: across fields, not within narrow silos. These programs should also be shorter and more modular, because the need to adapt is now tied to a faster-paced business tempo. Education must become a platform people can return to repeatedly across a career, not a one-time credentialing event at the beginning of adulthood.