What I Learned at Fortune Brainstorm AI Singapore

By Jeremy Kahn I spent last week in Singapore at Fortune Brainstorm AI Singapore, our second time hosting this event in the thriving city-state. Here are some key takeaways and impressions from the conference:

The Pace of AI Adoption Is Equally Fast Everywhere

Unlike previous technology waves where Asia lagged behind the U.S., Europe, and China, AI deployment is moving at an equally fast and ambitious pace across all regions.

Everyone Wants AI Agents, But Few Are Using Them Yet

AI agents from OpenAI, Google, Anthropic, and others are here, but adoption lags behind the hype. Agents are inherently higher risk and often unreliable. Making them more reliable—such as employing multiple agents to check each other's work—is expensive. Vivek Luthra, Accenture’s Asia-Pacific data and AI lead, noted most businesses currently use AI to assist human workers or as decision-support advisors rather than automating entire workflows. However, he predicts by 2028, one-third of large companies will deploy AI agents, automating about 15% of day-to-day workflows, driven by falling costs and improved model capabilities. Read more on Accenture's forecast.

AI’s Impact on the Job Market Is Still Unclear

Pei Ying Chua, LinkedIn’s APAC head economist, shared that despite anecdotal reports of young graduates struggling to find jobs, LinkedIn’s data on open roles does not yet show significant evidence. However, coders face an increase in the number of applications needed to land a job. Madhu Kurup (Indeed) and Sun Sun Lim (Singapore Management University) emphasized the growing importance of AI skills—such as prompting, AI agent building, and understanding AI strengths and weaknesses—alongside human soft skills like flexibility, resilience, and critical thinking. Jess O’Reilly from Workday predicts AI will drive companies to adopt more dynamic organizational structures, resembling an "internal gig economy" where teams form and reconfigure around projects rather than traditional vertical reporting lines.

Infrastructure Is Destiny

Access to AI infrastructure is critical, even for countries that don’t build their own models. Running AI models (“inference”) requires significant AI chip capacity. Rangu Salgame, CEO of Princeton Digital Group, explained that near-term data center expansion in Asia will likely rely on fossil fuels like natural gas, posing challenges for climate policy. However, AI data centers could incentivize medium-term investments in renewable energy such as solar and offshore wind. More on data center energy challenges.

Sovereign AI Matters, But Is Challenging to Deliver

Governments want sovereign AI to avoid overdependence on U.S. and Chinese solutions. Open-source models offer options, but major constraints remain. Key challenges include the high costs of data center capacity, power plants, and grid upgrades. Training AI models to understand local languages and cultural nuances requires curated datasets and significant effort. Kasima Tharnpipitchai, head of AI strategy at SCB 10X, highlighted the "brute force" nature of this work in building a Thai language large language model. Read more on local language AI development.

Embodied AI Is China’s Big Strength

While AI model capabilities between the U.S. and China appear evenly matched, China leads in embodied AI—AI integrated into physical devices like robotaxis and humanoid robots. Rui Ma, founder of Tech Buzz China, explained China controls nearly the entire robotics supply chain and is rapidly advancing affordable, practical robots for factories and general-purpose humanoid robots. One humanoid robot, Terri, showcased at the conference, uses software from Hong Kong startup Auki Labs and hardware from Chinese company Unitree. More on China’s robotics leadership.

Singapore’s Middle Path Between the U.S. and China

Singapore’s digital minister Josephine Teo described how the country acts as a bridge between the two superpowers. In April, Singapore hosted AI safety researchers from the U.S., China, and elsewhere to establish the “Singapore Consensus”—an agreement that AI systems should be reliable, secure, aligned with human values, and that cooperation is essential. Details on Singapore’s role.

Frequently Asked Questions (FAQ)

AI Agent Adoption

Q: What is the current adoption rate of AI agents in businesses? A: While AI agents are highly anticipated, current adoption lags behind the hype. Most businesses are using AI for human assistance or decision support rather than fully automating workflows. However, one-third of large companies are predicted to deploy AI agents by 2028. Q: Why is the adoption of AI agents slow? A: AI agents are considered higher risk and often unreliable. The cost of making them more reliable, such as through redundant agent checks, is a significant factor.

AI and the Job Market

Q: Is AI significantly impacting the job market for recent graduates? A: Anecdotal reports suggest difficulties for some graduates, but LinkedIn data does not yet show significant evidence of widespread job losses. However, the number of applications required for certain roles, like coding, has increased. Q: What skills are becoming more important due to AI? A: Proficiency in AI skills such as prompting and AI agent building is growing, alongside essential human soft skills like flexibility, resilience, and critical thinking.

AI Infrastructure and Energy

Q: What are the energy implications of AI data centers in Asia? A: Near-term data center expansion in Asia is likely to rely on fossil fuels. However, the demand for AI infrastructure could also incentivize medium-term investments in renewable energy sources.

Sovereign AI

Q: What are the main challenges in developing sovereign AI? A: Key challenges include the substantial costs of data center capacity, power infrastructure, and grid upgrades. Additionally, training AI models for local languages and cultural nuances requires extensive curated datasets and significant effort.

Embodied AI

Q: In which area of AI does China have a significant advantage? A: China has a leading position in embodied AI, which involves integrating AI into physical devices like robotaxis and humanoid robots.

Crypto Market AI's Take

The insights from Fortune Brainstorm AI Singapore highlight a critical juncture in AI adoption, mirroring the dynamic evolution of the cryptocurrency market. The race for AI adoption across regions underscores the importance of robust technological infrastructure, much like the foundational blockchain technology that powers cryptocurrencies. The burgeoning interest in AI agents for business workflows also resonates with the development of AI-powered trading bots and sophisticated market analysis tools in the crypto space. At Crypto Market AI, we are at the forefront of this convergence, offering advanced AI-driven crypto trading solutions that leverage intelligent agents to navigate market complexities and optimize investment strategies. Our platform provides real-time market intelligence and automated trading capabilities, aiming to democratize access to sophisticated financial tools. Understanding the challenges in AI adoption, such as reliability and cost, is crucial for building trust and effectiveness, a principle we apply to our own AI development to ensure secure and predictable outcomes for our users.

What I learned at this year's Fortune Brainstorm AI Singapore