When, in 2020, I read philosopher Zena Hitz’s book, Lost in Thought: The Hidden Pleasure of an Intellectual Life, one of her profound reminders resonated with me, and I boldly underlined and highlighted it so I could think upon its sentiments whenever I opened the book. My mind went back to this book this weekend when I read about IBM, and it evoked that sentiment. It is about the nature of human development. Hitz writes that:
“it is evident that our human core – our inner resources for thought, reflection, and contemplation – cannot be nurtured by mass education … online learning … it must be nurtured person to person …” and that such formation must happen “patiently.” Education, she argues, requires more than discipline or incentives; it needs “the guidance of wise elders who know what lies along certain pathways.” (Hitz, 2020)
Her insight echoes the argument I made last week in The Blindside of AI Economics (Ekeigwe, 2026): that AI economics blinds us to the slow, relational, human processes that make wisdom possible. In our rush toward efficiency and automation, we risk forgetting the fragile, irreplaceable work of forming human beings.
IBM’s recent decision is a real‑world confirmation of this. Contrary to Amazon’s inclination to reduce workforce and not hire new, Fortune reported in February 2026, that IBM is tripling its Gen Z entry‑level hiring after discovering that AI could automate tasks but could not replace the human developmental pipeline required for judgment, leadership, and long‑term organizational capability (Fore, 2026). The company found that the human learning curve cannot be automated. AI can accelerate work, but it cannot cultivate wisdom. It can process information, but it cannot develop judgment. It can replicate tasks, but it cannot replace the lived experience that gives those tasks meaning.
Hubert Dreyfus warned decades ago that “no computer can be made to match human common sense.” Shoshana Zuboff sharpened the point when she wrote that “knowledge that is abstracted from lived experience is never complete.” This is the heart of IBM’s realization. AI operates entirely on abstracted knowledge—data stripped of context, nuance, and tacit understanding. It sees patterns but not purposes, signals but not significance. And organizations that rely too heavily on abstracted knowledge lose the human depth, the human core, required for real understanding, real decision‑making, and real leadership.
This is why entry‑level roles matter. They are not merely labor inputs; they are the soil in which judgment grows. Professionals develop through exposure to ambiguity, apprenticeship under wise elders, opportunities to make and correct mistakes, and the slow accumulation of tacit knowledge. Economist Lester Thurow made the same point when he observed that “human skills only grow if one generation teaches the next” (Thurow, 1999). Skills do not emerge spontaneously. They are transmitted through apprenticeship, through the slow and often invisible work of one generation forming another. No algorithm can substitute for that intergenerational transfer.
Michael Polanyi captured this truth in a single line: “We know more than we can tell.” That tacit dimension is inaccessible to AI—and indispensable to leadership.
There is also an ethical dimension. AI systems risk committing epistemic injustice by treating system owners and junior staff as if they are not knowers of their own work. Miranda Fricker reminds us that “a hearer may wrong a speaker in her capacity as a knower.” When leaders trust algorithms more than people, they distort the informational basis of judgment. Amartya Sen’s warning is relevant here: “The informational basis of judgment matters.” AI does not reduce our responsibility to listen. It increases it.
The strategic lesson is equally clear. The future belongs to professionals who can use AI without outsourcing their judgment, interpret AI outputs without surrendering their agency, and integrate human insight with machine efficiency. Peter Senge once wrote that people do not resist change—they resist being changed. AI adoption fails when it is imposed as a replacement for human capability rather than integrated as a complement to it. IBM’s move is not a retreat from technology; it is a recognition that organizations cannot sustain themselves without cultivating human capability.
AI is powerful. But it is not omniscient, and it is not a substitute for human meaning, human development, or human judgment. The blindside of AI economics is the belief that efficiency is the same as wisdom. IBM has just reminded the world that it is not. Professionals who cultivate judgment, humility, and the courage to think beyond the algorithm will not merely survive the age of AI. They will lead it.
References
Dreyfus, H. L. (1992). What computers still can’t do: A critique of artificial reason. MIT Press.
Ekeigwe, C. (2026). The blindside of AI economics. Audit Is Trustworthy. https://auditistrustworthy.org/the-blindside-of-ai-economics/
Fore, P. (2026, February 13). IBM is tripling the number of Gen Z entry-level jobs after finding the limits of AI adoption. Fortune. https://finance.yahoo.com/news/ibm-tripling-number-gen-z-164300829.html (finance.yahoo.com in Bing)
Fricker, M. (2007). Epistemic injustice: Power and the ethics of knowing. Oxford University Press.
Hitz, Z. (2021). Lost in thought: The hidden pleasures of an intellectual life. Princeton University Press.
Polanyi, M. (1966). The tacit dimension. University of Chicago Press. Sen, A. (1999). Development as freedom. Alfred A. Knopf.
Senge, P. M. (1990). The fifth discipline: The art and practice of the learning organization. Doubleday.
Thurow, L. C. (1999). Building Wealth: The New Rules for Individuals, Companies, and Nations in a Knowledge-Based Economy. HarperBusiness.
Zuboff, S. (1988). In the age of the smart machine: The future of work and power. Basic Books.
Visionary, Audit is Trustworthy Worldwide Advocacy and Founder & Chairman, Audit Committee Institute (Non-profit). MY CORE VALUE: Godly Devotion and Contentment with Responsible Prosperity
