AI informs, humans know

IN an age of abundant information, knowledge, judgment and research and development (R&D) investment matter more than ever.

With artificial intelligence (AI) generating answers in seconds, it is tempting to conclude that knowledge itself is becoming obsolete.

Why remember, analyse or deeply understand anything when information is always within reach, one may ask.

But that assumption is not just flawed—it is dangerous.

We are entering a world where answers are abundant but understanding and judgment are not. The ability to distinguish truth from error depends less on access to information and more on what we already know.

AI has accelerated this shift. With a few prompts, anyone can produce essays, analyses, codes and even strategic recommendations. The cost of generating information has collapsed.

In this abundance, lies a critical misconception: that access to information reduces the need for knowledge. In reality, it does the opposite.

 

Information is cheap

In an AI-driven world, ignorance is more dangerous than ever.

The economic value has shifted. Information is now abundant and inexpensive. Judgment—rooted in knowledge, experience and context—has become the scarce and valuable resource.

This distinction is foundational. Information is external—it can be retrieved instantly and replicated endlessly.

Knowledge, however, is internal, structured and built through experience. It allows us to interpret information, assess its validity and apply it meaningfully.

AI systems are powerful because they produce fluent and confident outputs. But they do not “know” in the human sense. They recognise patterns and generate probabilities. They can—and do—hallucinate.

This is the paradox of AI: it democratises access to information but amplifies the risks of misunderstanding for those who lack the knowledge to question it.

We are already seeing this. Students can produce polished assignments using AI yet struggle to explain underlying concepts. Junior professionals present AI-generated insights they cannot defend under scrutiny. Fluency is mistaken for understanding.

This is not a failure of technology. It is the result of prioritising efficiency—speed, cost and scale—over resilience: the ability to think independently and operate when systems fail.

The responsible use of AI requires more than prompt engineering. It requires cognitive discipline—in other words, knowledge.

 

Illusion of understanding

For young learners, the temptation to outsource thinking is immediate. Why struggle through a problem when an answer is instantly available?

Yet it is precisely that struggle—the process of grappling with uncertainty—which builds durable knowledge.

Without it, we risk raising a generation that is efficient at accessing information, but poorly equipped to understand it.

Education must therefore evolve.

It is no longer sufficient to focus on transmitting information. We must prioritise the development of judgment—teaching students not just to find answers, but to question them.

The sequence matters: first learn, then create, and only then use AI to enhance efficiency—not the other way around.

 

Individual capability to national resilience

Recent global events have exposed the fragility of an interconnected world. Supply chains can fracture. Energy markets can be weaponised. Food security can be threatened by geopolitical tensions and climate pressures.

Malaysia spends over RM70 billion annually on food imports.

While the country is self-sufficient in some categories such as poultry and certain vegetables, it produces only about 56% of its rice requirements, reports the Agriculture and Food Security Ministry. That highlights a dependence on external supply for key staples.

In such an environment, resilience depends on the ability to feed ourselves, manufacture essential goods and sustain economic activity.

These capabilities are not built on information alone. They are built on knowledge—deep, applied and continuously developed.

 

Reality of R&D investment

This is why the conversation about knowledge must be tied to how we invest.

Malaysia’s R&D expenditure stands at roughly 1% of gross domestic product (about RM18bil annually, according to the Malaysian Science, Technology and Innovation Centre, placing it behind innovation-driven economies such as Singapore and South Korea.

The difference is not just in scale – it is in intent.

In Malaysia, R&D is often treated as discretionary. In leading economies, it is a strategic instrument of national competitiveness and resilience.

When investment in research declines, the risk is not just output—it is capability. It is the erosion of our ability to generate original knowledge, train researchers and sustain long-term innovation.

A nation that underinvests in R&D is, in effect, outsourcing its future sovereignty.

 

Knowledge as a strategic asset

If information can be generated at near-zero cost, knowledge becomes the true strategic asset.

And knowledge does not emerge passively. It must be built – deliberately, consistently and often at significant cost.

R&D is the mechanism through which this happens.

It is how new knowledge is created, how expertise is developed, and how we adapt, innovate and respond under pressure.

There is no substitute for hands-on experience.

Laboratories, pilot plants and field trials are where theory meets reality—where tacit knowledge is formed and breakthroughs are achieved.

AI can assist. But relying solely on externally generated information—whether from global markets or AI systems—is to remain dependent. True capability comes from internalising knowledge.

 

Adoption to mastery

At TM R&D, this distinction is clear.

The value lies not simply in deploying existing technologies, but in understanding them deeply enough to adapt, improve and build solutions suited to local contexts.

Organisations and nations that invest in knowledge, through education, training and R&D, will be better positioned to harness AI effectively.

Those that do not risk becoming passive consumers of outputs they do not fully understand.

 

Value of Learning

The future will not reward those who can access information the fastest. It will reward those who can question, interpret and create value from it.

AI can generate answers. Only humans—grounded in knowledge and experience—can exercise judgment.

The challenge ahead is not technological. It is intellectual.

We must reaffirm the value of learning, experimentation and building knowledge from the ground up.

Because in a world where AI Knows Everything, humans must not risk Knowing Nothing.

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This article was first published in the StarBiz 8, Issue Apr 25, 2026: Fiscal constraints

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