Education should cultivate professional understanding alongside technical competence, argues SONNY IROCHE
What should a young person learn today to remain employable tomorrow? This question deserves urgent attention from parents, educators, employers and governments. Artificial intelligence is changing how knowledge is accessed, tasks are performed and value is created. Preparing young people for employment must therefore go beyond awarding certificates. We must equip them to solve problems, exercise judgement and work confidently with technologies that will continue to evolve throughout their careers.
An account of Jensen Huang’s recent question-and-answer session at NVIDIA’s GTC Berlin conference captures this challenge. Asked what he would do as a new graduate seeking work, Huang reportedly advised becoming exceptionally skilled at using AI. His emphasis was practical: experiment, understand how agents operate, discover their limitations and develop mastery through use. I regard this as a call to purposeful learning, rather than a promise that familiarity with a chatbot guarantees employment. No, it doesn’t.
Huang’s engineering background provides useful context. In his No Priors interview, he recalled designing microprocessors at AMD before moving to LSI Logic during the emergence of electronic design automation. Software was enabling engineers to design chips using computers rather than relying on manual methods. He described joining that technological transition as one of the best developments of his career. His experience illustrates the advantage of recognising a powerful new tool and learning to work with it.
Electronic design automation (EDA) concerns software used to develop and check chip designs; manufacturing concerns physically producing those chips. The distinction matters, but the broader lesson travels beyond engineering. As tools become more capable, professional value can shift towards defining problems, selecting approaches, testing results and accepting responsibility. Young people should understand the purpose of their chosen profession deeply enough to recognise how new technology can improve its contribution.
An accountant’s contribution, for example, extends beyond entering figures. It includes interpreting performance, detecting inconsistencies and explaining financial implications. A teacher does more than deliver information; the teacher recognises misunderstanding, encourages curiosity and supports development. An engineer must consider safety, cost and practical constraints. These examples suggest why education should cultivate professional understanding alongside technical competence. Knowing which button to press is useful. Knowing whether the resulting answer makes sense is indispensable.
Nevertheless, optimism must be accompanied by honesty. Automating tasks can reduce demand for particular roles, and new opportunities may require different skills or arise elsewhere. Huang’s own 2026 shareholder letter acknowledges that AI will reshape jobs, eliminate some and create others. We should therefore resist assurances that everyone will benefit automatically. Institutions must prepare for disruption through retraining, thoughtful job redesign and credible routes into new work.
For Nigeria, and other African countries, I believe the starting point is foundational education. Literacy, numeracy, scientific reasoning and clear communication remain essential. A student who cannot evaluate evidence will struggle to challenge an inaccurate AI response. A graduate who lacks basic statistical understanding may accept an impressive-looking analysis without recognising its weaknesses. AI can support learning, but educational policy should ensure that students develop the intellectual independence required to question it.
Alongside these foundations, every discipline should introduce practical AI literacy. Agriculture students could explore crop information and market analysis. Business students could examine forecasting and customer service. Engineering students could compare designs and investigate maintenance problems. These exercises should require students to document their methods, verify outputs and explain their conclusions. Assessment should reward demonstrated understanding, including the ability to identify when an AI-generated answer is unreliable.
Employability also requires evidence of application. I would encourage every student to build a portfolio addressing a real problem. A young graduate might help a small business organise inventory, develop an accessible educational resource or analyse publicly available transport data. The strongest portfolio would explain the problem, the tools used, the checks performed and the outcome achieved. It would demonstrate competence more convincingly than a collection of certificates unsupported by practical work.
We must also avoid reducing the AI economy to software development. NVIDIA describes its emerging infrastructure in terms of energy, chips, computing systems, models and applications. This wider perspective should inform our planning. Nigeria’s technical colleges and apprenticeship programmes should prepare people for electrical installation, cooling, networking, equipment maintenance and related services. Opportunities will depend on actual investment, but our preparation should recognise the physical infrastructure supporting digital intelligence.
Responsible use must be integral to that preparation. At GenAI Learning Concepts Ltd, I advocate Responsible Human-in-the-Loop oversight. Young professionals should learn to protect confidential information, recognise fabricated outputs, assess bias and understand when decisions require human approval. An employee who produces work quickly but compromises customer information creates risk. Employers need people who combine productivity with discretion, cybersecurity awareness and accountability.
Employers also have obligations. If organisations automate the routine work through which junior staff traditionally learned, they must create alternative pathways to competence. Supervised projects, simulations, mentoring and structured rotations can help. Boards should ask management how AI adoption will affect recruitment and professional development. Reducing entry-level opportunities without rebuilding the learning pathway could leave organisations short of experienced professionals in the future.
Government and educational institutions should make access a priority. Practical training requires reliable electricity, affordable connectivity, suitable devices and capable teachers. Industry partnerships can provide projects, placements and exposure to workplace expectations. Public support should be judged by demonstrated skills and employment outcomes. Training young people without improving the conditions in which they can apply their knowledge would leave much of that investment unrealised.
My message to the next generation is to develop expertise, learn AI through disciplined practice and remain curious. My message to leaders is equally direct: create the conditions in which that effort can succeed. Huang’s career offers a useful example of embracing technological change. Our responsibility is to translate that lesson into education, opportunity and responsible adoption, so that young Africans can help build the emerging economy.
Iroche is the Founder and CEO of GenAI Learning Concepts Ltd, a Nigerian finance executive and AI strategist advocating responsible AI adoption, governance and capability development across Africa.