Commentary: Singapore’s next big technology bet cannot wait till we’ve figured out AI

AI is today’s imperative. Quantum may be tomorrow’s advantage. The country must invest in both, say SGTech’s Gavin Chua and Gunasekharan Chellappan.

Office workers walk out from the OCBC Tower in the Central Business District, Oct 19, 2026. (Photo: CNA/Wallace Woon)

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SINGAPORE: Many Singapore employees and businesses are still trying to work out what to do with AI – and what AI will do to us. So when the conversation starts turning to quantum computin g and the next generation of technological capabilities, it can feel overwhelming.

In our conversations with SGTech members, AI adoption has already outpaced organisational readiness. Employees are independently experimenting with AI, but companies are struggling with structured, organisation-wide adoption, and decision uncertainty with many tools out there.

Helping businesses and workers adopt AI remains an urgent priority. The gains are already tangible, from strengthening fraud detection in financial institutions to automating tasks for small and medium-sized enterprises with little manpower to spare.

Yet, Singapore’s economic success has always depended on doing both things well at the same time: Adopting technologies with immediate, practical value, and investing in those that could shape our competitiveness a decade or more from now.

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This is the central thrust of the Economic Strategy Review (ESR), which calls on Singapore to deepen leadership in areas where we already have advantages while continuing to make bold investments in emerging fields that can drive the next phase of growth.

Singapore’s challenge is not deciding between one or the other. It is ensuring we are prepared for both.

WAITING FOR TECH TO MATURE IS NOT AN OPTION

That technologies such as quantum computing have yet to reach widespread commercial use is precisely why Singapore needs to start thinking of them now.

Our semiconductor industry , biomedical sciences sector and fintech ecosystem did not emerge overnight. Each was built through long-term investments made before commercial outcomes were certain.

Take semiconductors. In 1968, National Semiconductor established Singapore’s first semiconductor manufacturing facility, at a time when Singapore was still building its industrial base. The following year, Texas Instruments opened its first plant here.

The early focus was on building manufacturing capabilities, infrastructure and a skilled workforce, rather than waiting for a fully developed local semiconductor ecosystem to appear first. Now, the country has an ecosystem spanning much of the semiconductor value chain, and continues to attract new investments as demand grows for chips.

The same approach is needed today.

New technologies could eventually address problems beyond the reach of even the most powerful classical computers – or create new problems. Some implications already require action today and preparation cannot wait for the technology to mature.

Cybersecurity provides a clear example. Organisations that manage sensitive data with long life cycles must prepare for a future in which quantum computers may be capable of breaking some current encryption standards .

Organisations have to start transitioning to cryptography designed to withstand those future attacks. It becomes part of long-term risk management, not a distant research exercise.

NOT A CHOICE BETWEEN AI OR QUANTUM

It helps if we do not mistake AI and quantum technology as separate tracks. Their greatest value is likely to come from how they complement each other.

AI excels at recognising patterns, processing large volumes of data and generating insights. Quantum computing, if it achieves its potential, could dramatically accelerate certain forms of optimisation and simulation.

For example, global supply chains are getting more complex as tariffs and emphasis on economic security reshape trade. As that complexity grows for our maritime and logistics sector, so will the need for technology that can analyse vast operational data sets, and even solve optimisation problems like the routes goods take, the way cargo space is allocated and when ships are scheduled in port.

This puts a premium on professionals with interdisciplinary expertise and those who can bridge deep technical knowledge with industry application.

Today, much attention is rightly focused on helping workers become more confident and effective users of AI. Digital literacy, prompt engineering, data interpretation and AI governance will become valuable across almost every profession, but so is knowing how to apply it with purpose and discernment.

Software engineers should have exposure to quantum software frameworks. Cybersecurity professionals should understand post-quantum cryptography. Business leaders should understand where these technologies might create commercial value.

Skills development cannot be treated as a one-time intervention, but a continuous process that evolves alongside technology itself.

WHAT PREPARING FOR THE NEXT WAVE REQUIRES

Singapore must prepare for the next – and the next – wave of technology. But that does not mean every business needs to start investing in quantum computing while it is still figuring out AI.

If there is an ecosystem, companies will not need to build every capability on its own.

Building one requires contributions from industry, government, academia and investors to tackle different parts of the puzzles based on their strengths. Government agencies play an important role in developing national capabilities and creating platforms for experimentation. Research institutions contribute scientific expertise and breakthrough discoveries.

More resourced or innovative businesses can provide real-world use cases. Investors can help scale promising innovations. Trade associations can help bridge the gaps.

Such an ecosystem makes sense given what businesses tell us they need: Not another standalone AI tool, but clearer pathways from experimentation to implementation, trusted recommendations, practical resources and access to partners that can help them solve specific business problems.

That ecosystem must also extend beyond our shores. International collaboration can help Singapore remain connected to leading-edge developments while providing local companies with access to larger markets and talent.

The ESR's call to build global leadership in areas of strength while taking bold bets on emerging technologies reflects the strategic instinct that has served Singapore well for decades. In the next era of innovation, that means being early to build the capabilities, standards and trust that allow new technologies to be used safely and at scale.

Gavin Chua is Chairman of SGTech. Gunasekharan Chellappan is former Chairman of SGTech’s AI, Cloud and Data Chapter.

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人工智能是当今的当务之急，量子计算可能是未来的制胜法宝。新加坡科技研究院（SGTech）的蔡嘉文（Gavin Chua）和古纳塞卡兰·切拉潘（Gunasekharan Chellappan）认为，国家必须在这两方面都进行投资。

2026年10月19日，上班族们从中央商务区的华侨银行大厦走出。（图片：CNA/Wallace Woon）

这段音频由人工智能工具生成。

新加坡：许多新加坡员工和企业仍在努力探索如何利用人工智能，以及人工智能将如何影响我们。因此，当话题转向量子计算和下一代技术时，人们可能会感到不知所措。

在与SGTech成员的交流中，我们发现人工智能的普及速度已经超过了企业的准备程度。员工们正在独立尝试使用人工智能，但企业在结构化、全组织范围内推广应用方面却面临诸多挑战，面对市面上琳琅满目的工具，决策也充满不确定性。

帮助企业和员工采用人工智能仍然是当务之急。人工智能带来的益处已经显现，例如加强金融机构的欺诈检测能力，以及为人力资源匮乏的中小型企业实现任务自动化。

然而，新加坡的经济成功一直取决于同时做好这两件事：采用具有直接实用价值的技术，并投资于那些可能在未来十年或更长时间内塑造我们竞争力的技术。

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这是《经济战略评估报告》（ESR）的核心要义，该报告呼吁新加坡在已有优势的领域深化领导地位，同时继续在新兴领域进行大胆投资，以推动下一阶段的增长。

新加坡面临的挑战不是在两者之间做出选择，而是确保我们为两者都做好准备。

等待技术成熟并非可行之策。

量子计算等技术尚未得到广泛的商业应用，这恰恰是新加坡现在需要开始考虑这些技术的原因。

我们的半导体产业、生物医学科学领域和金融科技生态系统并非一朝一夕形成。它们都是在商业成果尚不明朗的情况下，通过长期投资逐步构建起来的。

以半导体行业为例。1968年，美国国家半导体公司在新加坡建立了第一家半导体制造厂，当时新加坡的工业基础尚处于起步阶段。次年，德州仪器公司也在新加坡开设了第一家工厂。

早期重点在于构建制造能力、基础设施和培养技术工人，而不是等待本土半导体生态系统完全成熟。如今，该国已拥有涵盖半导体价值链大部分环节的生态系统，并且随着芯片需求的增长，持续吸引着新的投资。

今天也需要采取同样的做法。

新技术最终或许能够解决即使是最强大的传统计算机也无法解决的问题，但也可能带来新的问题。有些影响需要我们立即采取行动，而准备工作不能等到技术成熟。

网络安全就是一个鲜明的例子。管理生命周期较长的敏感数据的组织必须为未来做好准备，因为量子计算机可能能够破解一些现有的加密标准。

各组织必须开始向能够抵御未来攻击的密码学技术转型。这已成为长期风险管理的一部分，而非遥远的研究项目。

这不是人工智能与量子力学之间的选择

我们不应将人工智能和量子技术视为两条独立的发展道路。它们最大的价值很可能来自于彼此的互补。

人工智能擅长识别模式、处理海量数据和生成洞见。量子计算如果能够充分发挥其潜力，将能显著加速某些形式的优化和模拟。

例如，随着关税和对经济安全的重视重塑贸易格局，全球供应链正变得日益复杂。随着海运和物流行业的复杂性日益增加，对能够分析海量运营数据集的技术的需求也将随之增长，这些技术甚至能够解决诸如货物运输路线、货舱空间分配以及船舶港口调度等优化问题。

这使得具备跨学科专业知识的专业人士以及能够将深厚的技术知识与行业应用相结合的人才显得尤为重要。

如今，人们理所当然地将大量精力放在帮助员工更自信、更高效地使用人工智能上。数字素养、快速工程设计、数据解读和人工智能治理在几乎所有行业都将变得至关重要，但同样重要的是，要懂得如何有目的地、有判断力地应用这些技能。

软件工程师应了解量子软件框架。网络安全专业人员应了解后量子密码学。企业领导者应了解这些技术在哪些方面可能创造商业价值。

技能发展不能被视为一次性干预，而是一个随着技术发展而不断演进的持续过程。

为迎接下一波浪潮需要做些什么？

新加坡必须为下一波乃至更远的科技浪潮做好准备。但这并不意味着每家企业都需要在人工智能仍在探索阶段就立即开始投资量子计算。

如果存在一个生态系统，企业就不需要自行构建每项能力。

构建一个这样的平台需要产业界、政府、学术界和投资者的共同努力，根据各自的优势解决不同的难题。政府机构在发展国家能力和创建实验平台方面发挥着重要作用。研究机构则贡献科学专长和突破性发现。

资源更雄厚或更具创新精神的企业可以提供实际应用案例。投资者可以帮助扩大有前景的创新规模。行业协会可以帮助弥合差距。

考虑到企业告诉我们他们的需求，这样的生态系统是有意义的：他们不需要另一个独立的 AI 工具，而是需要从实验到实施的更清晰的路径、值得信赖的建议、实用的资源以及能够帮助他们解决特定业务问题的合作伙伴。

这个生态系统也必须延伸到国界之外。国际合作可以帮助新加坡与前沿发展保持联系，同时也能让本地企业接触到更广阔的市场和人才。

ESR呼吁在自身优势领域打造全球领导地位，同时大胆投资新兴技术，这体现了新加坡数十年来行之有效的战略眼光。在下一个创新时代，这意味着要尽早建立能力、标准和信任，从而确保新技术能够安全、大规模地应用。

Gavin Chua是SGTech的董事长。Gunasekharan Chellappan是SGTech人工智能、云计算和数据分会的前任董事长。

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