By Harris Hakim
Every economic boom has its defining asset.
For decades in China, it was land. Today, it is increasingly artificial intelligence (AI), computing power and digital infrastructure.
The assets may have changed, but the underlying question has not: when governments control access to resources that shape the next wave of economic growth, how can those resources be allocated fairly, transparently and competitively?
Yesterday’s Strategic Asset
Land once powered China’s transformation. Local governments controlled its supply, approved development projects and relied heavily on property investment to drive growth and revenue. Developers, in turn, depended on official discretion for approvals and infrastructure.
The model delivered extraordinary results: new cities, expanded infrastructure, and rapid urbanisation. But it also concentrated immense value around government decisions. Whenever access to a scarce resource depends on official discretion, commercial incentives naturally gravitate towards those who control it.
The downfall of an aerospace engineer-turned-senior politician, accused of exploiting property dealings for personal gain, underscores how political influence and economic opportunity became intertwined during the property boom. Whatever the legal outcome, the case highlights the risks of linking public resources too closely with private interests.
Tomorrow’s Strategic Asset
China is now entering a different phase. Property is no longer expected to drive growth. Instead, Beijing is prioritising AI, semiconductors, robotics and advanced manufacturing.
The scarce resources of this new economy are not hectares of land but megawatts of electricity, computing power, data infrastructure, engineering talent and innovation ecosystems. Companies compete not for physical plots, but for access to reliable energy, advanced networks and skilled workers.
Governments remain central players. AI infrastructure depends on planning approvals, energy supply, research funding and long‑term public investment. Local authorities compete to attract high‑value projects, just as they once competed for property developers.
The difference is that tomorrow’s strategic assets are measured in computing capacity and intellectual capital, not just land.
The Malaysian Parallel
Malaysia is positioning itself as a regional hub for data centres, AI infrastructure and semiconductor investment. Johor has emerged as one of Southeast Asia’s fastest‑growing digital infrastructure markets, while Selangor and Penang continue to attract advanced manufacturing. Federal and state governments are actively supporting these industries through incentives and planning.
This is not unusual. Governments have always shaped development during industrial transitions. The challenge lies in ensuring institutions evolve alongside strategic resources.
In the property era, fairness meant transparent land allocation. In the AI era, similar questions will arise over electricity capacity, water resources, digital infrastructure, public funding and access to talent.
Institutions as Competitive Edge
Countries seeking to build globally competitive AI industries will require close partnerships between governments, universities, investors and technology companies. Success will depend not only on attracting investment, but also on ensuring that rules governing access to strategic resources remain transparent and predictable.
China’s property boom showed how coordinated development can deliver extraordinary gains — but also how risks emerge when discretion over public assets becomes too valuable.
As Malaysia accelerates its own investments in AI and digital infrastructure, the lesson is clear: every new growth model creates new forms of strategic value, and every new strategic asset demands institutions capable of managing it with integrity.
The race to lead the AI economy will be shaped not only by algorithms and computing power, but also by the quality of governance that underpins them. That may prove to be one of the defining competitive advantages of the decades ahead.
WE