Key Insights from Fortune Brainstorm AI Singapore
Last week’s Fortune Brainstorm AI event in Singapore provided a clear snapshot of AI’s current state and direction in Asia. It was the event’s second edition in this vibrant city-state, and several themes stood out that are particularly relevant for those working in science and research.
The Pace of AI Adoption Is Uniformly Fast
Unlike past technological waves where Asia trailed behind the U.S., Europe, and China, AI adoption is advancing at a similar speed across regions. Companies everywhere aim to deploy AI agents, yet few have fully integrated them into workflows. AI agents present higher risks compared to standard predictive or generative AI, mainly due to reliability issues and the cost of implementing multi-agent systems for checks and balances.
Currently, most businesses apply AI to assist human workers or provide decision support rather than fully automating processes. However, industry experts predict a significant shift by 2028, anticipating that one-third of large enterprises will deploy AI agents, automating approximately 15% of daily workflows. This shift will be driven by decreasing costs, improved model capabilities, and workflow redesigns that leverage AI’s unique properties.
AI’s Job Market Effects Remain Unclear
Data from LinkedIn indicates no substantial evidence of widespread job losses among recent graduates, despite anecdotal concerns. However, job seekers, particularly coders, are experiencing increased competition with more applications needed per role.
Experts stress the growing importance of combining AI skills—such as prompt engineering and building AI agents—with essential human skills like flexibility, resilience, and critical thinking. As AI transforms roles, these soft skills will become critical for career longevity.
There is also an emerging trend toward more fluid organizational structures. Companies may adopt project-based teams drawn from diverse functions, resembling an “internal gig economy.” This model requires flatter and more adaptive reporting lines to accommodate dynamic team configurations.
Infrastructure Is a Critical Factor
Access to AI infrastructure, including AI chips and data centers, is a decisive factor in AI development and deployment. Building this infrastructure demands significant energy, often powered by fossil fuels in the short term, especially natural gas in Asia.
Looking ahead, there is potential for AI data centers to accelerate renewable energy projects, such as solar and offshore wind, aligning technological progress with sustainability goals.
The Importance and Challenges of Sovereign AI
Many governments emphasize the need for sovereign AI to reduce reliance on U.S. and Chinese technologies. However, achieving this autonomy is complex and costly.
It requires investments in data center capacity and power infrastructure, plus the development of AI models tailored to local languages and cultural nuances. This localization demands extensive data curation and dedicated effort, as highlighted by those developing language-specific large language models (LLMs).
China’s Edge in Embodied AI
While AI model capabilities appear comparable between the U.S. and China, China leads in embodied AI—AI integrated into physical devices like robotaxis and humanoid robots.
China’s dominance over the robotics supply chain enables rapid production of affordable and functional robots for industrial and general-purpose use. Demonstrations of humanoid robots with software from startups illustrate the country’s progress in this field.
Singapore’s Role as a Middle Path
Singapore continues to position itself between the U.S. and China in AI development and policy. The country has facilitated cooperation on AI safety, exemplified by hosting a meeting that produced the “Singapore Consensus.” This agreement outlines shared principles for reliable, secure AI aligned with human values, reflecting a collaborative approach to AI governance.
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