South Korea has launched a two-year, KRW34 billion (US$22.22 million) project to develop domestic world model and robot foundation model technologies, aimed at reducing reliance on foreign simulation platforms for physical AI systems used in real-world environments.

Taiwan's AI infrastructure technology suppliers posted stronger sales in May, led by firms tied to artificial intelligence servers, data-center networking, optical communications, and advanced cooling. The figures suggest demand remains driven by the buildout of AI infrastructure, with higher power loads, faster interconnects, and more complex thermal systems boosting orders and product mix.

One of China's largest visual AI consumer platforms has deliberately chosen not to build its own models; a startup competing against ByteDance and Alibaba is pursuing a strategy of making its models cheaper rather than better; and Alibaba launched one of its video generation models under a pseudonymous brand before revealing its identity — what its executive described as "a very big branding moment." Those were among the more pointed observations to emerge from a panel discussion on the visual AI stack at SuperAI Singapore 2026 on Thursday.

Malaysian Prime Minister Anwar Ibrahim concluded a three-day visit to Japan, during which he met with Japanese Prime Minister Sanae Takaichi. The two leaders pledged to strengthen cooperation in critical minerals.

Anthropic is moving to take greater control of the infrastructure powering its AI models, signing more than a dozen preliminary agreements to lease US data center facilities while exploring arrangements under which Google could backstop some of its lease obligations, according to a report by The Information.

Breaking the inference barrier requires a rethink of the whole system architecture, not just faster compute. This was the key takeaway from a recent panel discussion at SuperAI Singapore, which brought chip makers and an AI model accelerator together to address how to overcome inference bottlenecks at a time when compute workloads are hitting up against physical limits.

Robotics has progressed rapidly in the past few years, but major obstacles — including data collection and trust infrastructure — remain barriers to widespread deployment. This was the takeaway from a recent panel of robotics experts at SuperAI Singapore, where they discussed the present and future of the industry.