When Elon Musk acquired Twitter, he did not simply buy a social-media company. He treated it as a material to be stripped back, examined, and rebuilt. The platform became a laboratory for a conviction he had carried across industries: that institutions become more powerful when unnecessary complexity is removed.
The new name, X, seemed to express that impulse. It was concise, abstract, and open-ended—a mark suggesting possibility, transformation, and a return to first principles. Musk was not interested merely in improving the existing object. He wanted to question why it had been designed that way at all.
His method brought together two forces. One was the engineer’s instinct to simplify systems until their essential functions became visible. The other was a libertarian distrust of inherited structures and institutional restraint. Workforce reductions, procedural upheaval, and an increasing reliance on automation were presented as the pursuit of “hardcore” efficiency.
But this was more than a corporate restructuring. It was a philosophy made operational. Musk was challenging the habits, hierarchies, and assumptions that accumulate inside large organisations. He was asking whether an institution could move faster, cost less, and perform more effectively if much of its human friction were removed.
The experiment at X did not remain confined to Silicon Valley. Its logic migrated into government through the Department of Government Efficiency, or DOGE. The proposition was simple: reduce complexity, accelerate decisions, and use technology to replace processes that appeared slow or redundant.
Yet institutions are not merely collections of processes. They are also networks of memory, judgment, trust, and responsibility. The consequences of applying startup logic to public systems became especially visible in the upheaval surrounding USAID, where cuts sent a chain reaction through humanitarian organisations working across Africa.
The language of efficiency was compelling. Move quickly by eliminating waste and doing more with less. But humanitarian work depends on qualities that are difficult to quantify: local knowledge, institutional relationships, cultural understanding, and the confidence built over years. These forms of value are often invisible until they disappear.
The continued operation of X with a dramatically reduced workforce strengthened the belief that leaner organisations were inherently better organisations. Across sectors, human capital began to be viewed less as the substance of an institution and more as a cost to be optimised— increasingly through artificial intelligence.
The media industry is confronted with this transformation. Long before generative AI became a central force, media companies had been weakened by the rise of Big Tech. Google, Meta, and other platforms absorbed an increasing share of digital advertising revenue that had once supported newsrooms, reporters, and editorial operations. The market did not simply evolve; its underlying architecture changed.
Then AI introduced a new pressure. It is not only threatening advertising models but also the way information is produced, distributed, discovered, and valued. The media industry finds itself confronting a technology capable of altering the entire chain between the creation of knowledge and the public’s access to it.
The nonprofit sector faces a similar moment. It has traditionally been associated with social conscience and public purpose, yet its funding models are now being tested by the same austere logic that has spread through technology companies. The question is no longer only how these organisations will survive. It is whether they can preserve their human purpose while operating in an economy increasingly governed by automation, scale, and measurable efficiency.
Technology alone will not resolve this tension. Organisations will need a deeper transformation—one that combines the curiosity and speed of a startup with a mature understanding of systemic consequences.
Innovation without judgment can become destruction. Efficiency without empathy can produce institutions that function beautifully in theory while failing the people they were designed to serve.
This is why technology leadership matters. The role of a technology leader is not simply to introduce new tools or oversee digital transformation. It is to see what those tools will change: how people work, where power accumulates, how trust is built, how funding moves, and which voices risk being excluded.
The most valuable leaders will act as interpreters between technology and society. They will translate technical possibility into institutional wisdom, helping organisations revise their strategies without abandoning the values that give those strategies meaning.
The question before us is therefore not whether disruption can be stopped. Those in know understand it cannot. The question is whether its energy can be directed with sufficient care—whether we can simplify without becoming indifferent, automate without becoming detached, and move quickly without losing our capacity to understand one another. This question, of surviving tech, has occupied my mind as I have watched recent developments in South Africa’s nonprofit and media sectors. Both are being reshaped by forces they did not create but must now learn to navigate. I am increasingly interested in convening conversations between technology leaders and the other parts of society affected by technological change.The goal is not to predict the future with certainty. It is to create enough shared understanding to map a way forward—one that recognizes the power of technology while preserving the diversity, judgment, and human empathy on which resilient institutions depend.