AI’s next test for Taiwan: Can it keep pace without losing control?

As frontier AI labs warn that the technology may be advancing faster than safety mechanisms can keep up, Taiwanese experts argue that the next phase of the AI race will reward not merely computing power, but talent, judgment and trust.

The question is no longer simply how fast AI can advance

For years, the dominant question in AI was how quickly capabilities could improve. That premise has been challenged from within the industry itself.

In mid-September, Anthropic CEO Dario Amodei called for the pace of frontier-model development to be moderated, arguing that safety mechanisms need time to catch up with rapidly expanding capabilities.

OpenAI chief scientist Jakub Pachocki likewise wrote in September that continued rapid advances in machine intelligence required extreme caution. He called for coordinated slowdowns as needed and widely mandated safety requirements for continued development.

The debate intensified after former Anthropic researcher Jacob Coxon told the BBC that researchers working on advanced AI were “genuinely frightened” by the speed of progress.

For many experts in Taiwan, an island whose industry and economy have benefited immensely from the advancement of AI in the past few years, the question is less whether the AI revolution will stop than what kind of revolution comes next and whether Taiwan is prepared for it.

Taiwan’s opportunity remains larger than hardware

Chen Liang-gee (陳良基), a former minister of science and technology and an electrical engineering scholar, said at a Sept. 26 lecture that AI development will continue and that Taiwan cannot afford to miss the opportunity.

Taiwan, Chen noted, did not capture as much of the economic dividend of the internet era as it might have. The AI revolution should not be allowed to repeat that experience, he said.

Chen also rejected the conventional division between hardware, where Taiwan is strong, and software, where it is perceived as weaker.

“That is a way of thinking from the pre-AI era,” he said, arguing that AI itself increasingly encompasses both hardware and software.

Taiwan’s advantage and “hidden treasure,” he noted, is its talent pool. He stated that the success of Taiwan’s science parks cannot simply be replicated by building industrial clusters elsewhere, because an industrial ecosystem does not automatically produce a comparable system for cultivating top AI talent.

The implication is significant: if AI’s next phase is increasingly about integrating computation, applications, data and human judgment, Taiwan’s competitive asset may be less a particular component than the density of expertise surrounding it.

Chen cautioned people from and educated in Taiwan that before rushing to solve any problem, they should use design thinking to identify the stakeholders involved and understand how each perceives the problem.

Industry computing demand has not slowed down

The infrastructure side of the equation also offers little evidence of an imminent AI retreat.

At Microsoft's DevDays Asia 2026 conference in Taipei, Sean Pien (卞志祥), general manager of Microsoft Taiwan, said that global demand for computing capacity remains above supply and is likely to do so for another two to three years.

Pien said the constraint extends beyond GPUs and CPUs to electricity and data-center space. Microsoft is consequently expanding its Taiwan data-center footprint from two facilities to four and has increased the number of services available locally from more than 70 to more than 200.

Pien added that semiconductor and high-tech companies currently account for the heaviest demand, partly because they need large amounts of computing capacity for rapid verification and computation amid tight global capacity and rising memory costs.

Over the medium to long term, he said he expects demand to broaden toward sectors where data sovereignty and regulation matter more, including government, financial services, health care and telecommunications.

That trajectory suggests that a more safety-conscious AI era could actually broaden Taiwan’s AI market rather than shrink it: the challenge shifts from simply supplying computing power to building trusted infrastructure around it.

From intelligence to trust

Companies, Pien said, should not make their competitiveness dependent on an external foundation model alone.

Instead, they need to focus on both intelligence and trust. Pien said that companies need to organize their internal knowledge, data and business processes into proprietary assets and build AI systems that remain controllable by humans and capable of meeting security and compliance requirements.

The distinction will become sharper as AI agents move from answering questions to performing tasks.

Pien described two forms of capital emerging in the workplace: human capital and token capital. AI agents may increasingly handle cross-departmental communication and execution, while human engineers provide direction, make consequential decisions and retain ultimate responsibility.

Pien told TCN that this shift would require companies to redesign workflows rather than merely purchase another AI tool.

He said the arrival of AI agents would bring another organizational challenge. Pien stated that companies may need to think of an AI agent much as they would a new employee in an unfamiliar environment and organizational context: introducing it into the workforce requires more than simply giving it access to tools.

The speed of change itself presents another challenge. Pien noted that technologies barely two months old can already become outdated.

The AI landscape is evolving rapidly, Pien said. While generative AI spent years in development, it only recently burst into the public eye — leaving early adopters to navigate a landscape of disparate tools, systems and software that did not necessarily integrate with one another. Platforms capable of accommodating and connecting these technologies quickly followed.

Pien said the cycle illustrates a broader point: every few months, the questions companies ask about AI, and even the way they frame those questions, can change substantially.

The human advantage is moving upstream

Lee Hung-yi (李宏毅), a National Taiwan University professor specializing in electrical engineering and computer science, mentioned a parallel transformation in education.

Lee told the press that in his classes, students now build AI agents while teaching assistants deliberately feed those agents anomalous instructions to test their defenses.

The objective is no longer simply whether students can produce an answer, but whether they can probe a system, discover vulnerabilities and repair it.

As AI agents increasingly execute tasks once requiring specialized human skills, Lee said the boundary between disciplines would blur. “What to do” becomes more important than simply knowing “how to do it.”

However, he too warned of AI misalignment. He said that an AI system may become dangerous not because it possesses malicious intent, but because it interprets human instructions incorrectly; the harder problem, therefore, may be ensuring that increasingly autonomous systems understand what humans actually mean.

On the human advantage in the AI era, former TSMC R&D director Konrad Young (楊光磊) reached a similar conclusion from another direction: do not compete with machines at what machines already do better.

He told TCN that students should cultivate questioning, judgment, creativity, collaboration and communication rather than becoming proficient only in a particular AI tool or prompting technique.

He said that in his graduate course, 20% of the grade is devoted to asking questions, 40% to oral presentations, 30% to classroom participation and only 10% to written work.

He said that by doing so, one could better assess the real competency of a student.

The lesson for Taiwan extends beyond education. If frontier AI enters a phase in which safety, alignment and trust become as consequential as raw capability, the country’s advantage may depend on precisely those attributes that are hardest to automate.

The emerging AI debate is therefore not necessarily a warning for Taiwan to step on the brakes.

It is a warning that the definition of speed is changing: the winners of the next phase may be those capable of moving rapidly while also knowing where humans must remain firmly in control.