The global AI race is often framed as a competition to build the most powerful model.
Every week brings news of larger foundation models, new reasoning capabilities, and billion-dollar investments in AI infrastructure. The headlines largely revolve around a handful of technology companies concentrated in Silicon Valley and, increasingly, China.
Yet for much of Asia, a different question may matter more.
Does the region need to build the next OpenAI — or could it create greater economic value by becoming the best place in the world to deploy AI at scale?
That question surfaced recently in a reflection by Val Yap, founder of Goma AI and former founder of PolicyPal, a leading fintech startup in Southeast Asia, who spent five years in Silicon Valley building and deploying AI agents with startups and SMEs and now focuses on bringing practical, business-ready AI solutions to organizations through Goma AI.
Writing about Singapore’s emerging AI strategy, Val Yap argued that the city-state’s greatest opportunity may not lie in competing directly in the frontier model race, but in becoming exceptionally good at deploying AI across businesses, government agencies, and everyday workflows.
It is a perspective worth examining because it touches on a broader challenge facing Asia’s innovation ecosystem.
The future of AI may not be determined solely by who builds the most advanced models. It may also depend on who can successfully turn those models into trusted, repeatable, real-world outcomes.
Highlights
Two Different Paths to AI Leadership
Much of the global AI industry has been shaped by Silicon Valley’s strengths.
The region possesses a unique combination of research talent, venture capital, entrepreneurial culture, and risk tolerance. It has created an environment where ambitious founders can experiment rapidly, attract funding, and scale globally.
This ecosystem is particularly effective at producing frontier innovation.
The challenge, however, is that frontier innovation and mass adoption are not the same thing.
This distinction sits at the heart of Val Yap’s argument. In her reflection on Singapore’s AI trajectory, she suggests that success in the AI era may not be determined solely by who develops the most advanced models. Instead, it may increasingly depend on who can integrate those models into everyday operations, from government services and financial institutions to healthcare systems and small businesses. For economies like Singapore, the challenge may be less about winning the race to build the next frontier model and more about ensuring that AI creates measurable value beyond research labs and technology demonstrations.
Building a powerful AI model is a technological challenge. Embedding AI into healthcare systems, financial institutions, universities, government services, and small businesses is an organisational challenge.
One requires world-class researchers.
The other requires trust, governance, infrastructure, workforce readiness, and the willingness to redesign established processes.
Many economies across Asia may find themselves better positioned for the second challenge than the first.
Singapore is perhaps one of the clearest examples.
With its highly digitalised economy, strong public institutions, and history of coordinating large-scale transformation initiatives, the country possesses characteristics that can support AI adoption even if it never produces a frontier AI laboratory on the scale of OpenAI or Anthropic.
The question is whether those characteristics become a competitive advantage in the AI era.
Why AI Adoption Is Harder Than It Looks
The technology sector often assumes that once a powerful tool exists, adoption naturally follows.
History suggests otherwise.
Many technologies spend years trapped in pilot programmes, innovation labs, and conference demonstrations before becoming embedded in everyday life.
AI faces a similar challenge.
A chatbot demonstration may impress executives. A proof-of-concept project may generate excitement. Yet neither automatically leads to meaningful productivity gains.
The real work begins after the demonstration.
Organisations must determine how AI fits into existing workflows. Employees need training. Compliance requirements need clarification. Security concerns must be addressed. Accountability structures need updating. Data systems often require extensive preparation before AI can be used effectively.
This “last-mile problem” may ultimately determine who benefits most from AI.
The winners may not necessarily be the countries producing the largest models. They may be the countries capable of integrating AI into thousands of routine decisions and processes across their economies.
That challenge plays directly into areas where many Asian economies already have experience.
From Models to Workflows
One reason the AI conversation often becomes distorted is that attention naturally gravitates toward technological breakthroughs.
A new model release is visible.
A redesigned workflow is not.
Yet economic value is often created through workflow improvements rather than technical milestones.
Consider banking.
The future impact of AI on financial services may have less to do with creating a new foundation model and more to do with streamlining compliance reviews, accelerating risk assessments, improving fraud detection, and enhancing customer service operations.
The same principle applies to healthcare.
Doctors are unlikely to be replaced by AI systems anytime soon. However, AI-assisted diagnostics, administrative automation, patient triage, and medical documentation could significantly improve efficiency across healthcare systems already facing workforce shortages.
Education presents another example.
The most meaningful AI applications may not be futuristic virtual teachers. Instead, they could involve personalised learning support, administrative automation, curriculum development assistance, and tools that allow educators to spend more time engaging directly with students.
Small and medium-sized enterprises may experience an even greater impact.
Many SMEs do not need advanced AI research capabilities. They need practical tools that reduce paperwork, improve customer engagement, automate routine tasks, and support decision-making.
In this sense, AI deployment becomes less about technology itself and more about organisational transformation.
The Case for Institutions
This is where Singapore’s approach becomes particularly interesting.
Unlike startup ecosystems that rely heavily on entrepreneurial experimentation, Singapore has historically excelled at creating trusted systems.
That institutional strength can be valuable in the AI era.
Businesses are more likely to adopt AI when clear governance frameworks exist. Consumers are more likely to engage with AI-powered services when privacy and accountability standards are understood. Public agencies are more likely to implement AI when trust mechanisms are already established.
The result is an environment where adoption can potentially occur across entire sectors rather than within isolated companies.
This does not generate the same excitement as the launch of a breakthrough model.
But it may create more durable economic value.
For a technology to transform society, it must eventually move beyond laboratories and startups. It must become part of ordinary life.
Institutions often play a critical role in that transition.
The Risk of Playing It Too Safe
Yet there is another side to the argument.
Critics of highly governed innovation ecosystems warn that excessive coordination can become a constraint.
Strong governance may create trust, but it can also slow experimentation.
Regulatory caution may reduce risk, but it can discourage ambitious innovation.
Some observers argue that countries focused primarily on deployment risk becoming dependent on technologies developed elsewhere.
This concern deserves serious consideration.
The nations and companies that build frontier models influence standards, attract elite talent, control intellectual property, and shape the future direction of the technology itself.
Without meaningful participation in frontier research, countries may find themselves adapting to decisions made elsewhere.
There is also the danger of performative adoption.
Organisations may launch AI initiatives because they feel pressure to appear innovative rather than because they have identified meaningful use cases. Employees attend training sessions, executives announce transformation plans, and dashboards are created—but little actually changes.
Successful deployment requires more than compliance.
It requires experimentation, curiosity, and a willingness to rethink how work is done.
A Different Definition of Success
The debate between frontier innovation and deployment is often presented as an either-or choice.
In reality, successful AI ecosystems will likely need elements of both.
Research remains essential.
Breakthrough technologies do not emerge without sustained investment in science, talent, and entrepreneurship.
At the same time, deployment cannot be treated as a secondary concern.
A powerful AI model that never changes how organisations operate creates limited economic value.
As AI matures, the distinction between invention and implementation may become increasingly important.
Silicon Valley’s strength will likely remain its ability to push technological boundaries.
For Singapore, and perhaps for parts of Asia more broadly, the larger opportunity may lie in proving that advanced AI can be integrated into society in ways that are practical, trusted, and scalable.
That is a different kind of ambition.
It is less visible than launching the next frontier model. It attracts fewer headlines and generates less excitement on social media.
Yet it may ultimately prove just as consequential.
The next phase of the AI race may not be won solely by the places that invent the most powerful systems. It may also be shaped by the places that successfully transform those systems into trusted, repeatable, everyday value.
And in that competition, deployment may matter as much as invention.
Editor’s note: This article was inspired by a LinkedIn and Substack essay by Val Yap, founder of Goma AI and former founder of PolicyPal. After spending five years in Silicon Valley building and deploying AI agents with startups and SMEs, she now focuses on helping organizations implement practical, business-ready AI solutions. Building on her observations about Singapore’s AI future, this feature explores the wider debate around AI innovation, adoption, governance, and implementation across Asia.
Highlights
Read the Chinese article here.
