In the global race for artificial intelligence (AI), nations rightly chase cutting-edge technologies, big data and data centres heavy with graphics processing units (GPUs). But thought leaders including OpenAI CEO Sam Altman and institutions from the Federation of American Scientists to China’s Ministry of Education are urging investment in educator training and AI literacy for all citizens. They argue for a more human-centred AI strategy.
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Having taught AI and data analytics in China, I have seen the payoff: graduates join internet giants, leading electric-vehicle makers and the finance industry.
My case is simple: the country that best educates people to collaborate with AI will lead in productivity, innovation and competitiveness, achieving the highest level of augmented collective intelligence. This reframes the so-called AI war not as a contest of GPUs and algorithms, but as a race to build the most AI-capable human capital. Data and hardware are ammunition; the strategic weapon is AI education.
According to Norwegian Business School professor Vegard Kolbjørnsrud, six principles define how humans and AI can work together in organisations. These principles aren’t just for managers or tech executives; they form a core mindset that should be embedded in any national AI education strategy to improve productivity for professors, teachers and students.
Let’s briefly unpack each principle and how it relates to broader national competitiveness in AI education.
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The first is what he calls the addition principle. Organisational intelligence grows when human and digital actors are added effectively. We need to teach citizens to migrate from low-value to higher-level tasks with AI. A nation doesn’t need every citizen to be a machine-learning engineer, but it needs most people to understand how AI augments roles in research and development, healthcare, logistics, manufacturing, finance and creative industries. Thus, governments should democratise AI by investing in platforms that reskill everyone, fast.