I visited Automation Taipei 2026 in mid-August, where the show looked lively and occupied a footprint 7% larger than in 2025. Physical AI and vision-language-action (VLA) models dominated the venue. Yet most robots on display still repeated fixed motions behind barriers, highlighting how far robotics has to go to move from automation to autonomy.
As AI develops rapidly, data centers are driving equally rapid growth in electricity demand. The stability of future energy supplies will directly determine whether AI computing capacity can continue to expand.
High-voltage direct current (HVDC) is currently one of the hottest areas of technology and product development for AI data centers. It is not only the first layer of what Nvidia CEO Jensen Huang has described as the industry's "five-layer cake," but also a concrete manifestation of the idea that computing power ultimately depends on electrical power.
Around 2019, a friend working at a large cloud services provider excitedly shared that his team had trained a CNN model for document text recognition with good results, but the computing cost was too high to deploy directly. They then used distillation to train a smaller model for service, and it turned out to be highly effective.
In July, an OpenAI AI agent model successfully bypassed safety restrictions during internal testing and even launched an automated cyberattack against Hugging Face, the world's largest open-source AI community platform, drawing intense attention. The incident, seen as the first publicly disclosed case of "AI attacking the AI ecosystem/platform," underscores how AI's rapid evolution is shifting cyber risk from "humans attacking systems" to "AI attacking AI."
When people talk about responding to supply chain shocks, the instinct is often to stockpile inventory or build backup production lines. However, after visiting Inner Mongolia Shuangjie Saidu Electric with a delegation in June 2026, I came away with a different understanding: true "supply chain resilience" is not created through last-minute crisis fixes. It is embedded in factory layout, product design, business diversification, and service models.
Driven by global net-zero commitments and China's "dual carbon" goals of peaking carbon emissions before achieving carbon neutrality, Inner Mongolia is rapidly transforming from a traditional resource-based economy into a strategic hub for renewable energy and AI computing infrastructure.
Token prices are falling while overall AI usage continues to surge — a trend this column examined in March 2026 and one that has only accelerated since. The next generation of frontier models is increasingly being built around the AI agent paradigm. In recent months, competition among leading AI labs has intensified, with successive releases demonstrating the ability to plan tasks, delegate work, use multiple tools and skills, and directly interact with computer interfaces — all at declining costs.
As the debate over the US fiscal 2027 defense spending plan to dismantle the Space Development Agency (SDA) continues, the Proliferated Warfighter Space Architecture's (PWSA) transport layer is concentrating around SpaceX, seemingly narrowing the door for suppliers. In reality, however, it marks a shift in the business focus from selling a batch of satellites to supplying continuous replacement components and terminal opportunities that are still up for grabs.
For the past two decades, enterprise information governance has focused on protecting data through classification, encryption, backups, and internal containment. That remains essential, but in the AI era, competitive advantage increasingly comes not from data itself, but from how effectively companies turn it into operational improvement.
South Korea's technology policies—including the two semiconductor strategies discussed previously and the broader K-Moonshot initiative introduced more recently—have been drafted by different government bodies, including the Ministry of Trade, Industry and Energy (MOTIE), the Presidential Office, and the Ministry of Science and ICT (MSIT). Yet they share several common characteristics.
South Korean President Lee Jae-myung unveiled the country's Three Mega Projects for AI and Semiconductors in late June 2026, an ambitious national strategy designed to strengthen South Korea's global leadership in artificial intelligence and semiconductors. The initiative centers on three pillars—semiconductors, physical AI, and AI data centers—and aims to double the nation's DRAM output within five years while expanding capabilities in high-bandwidth memory (HBM), advanced packaging, AI processors, and next-generation memory technologies. It also seeks to extend South Korea's semiconductor footprint beyond the Seoul metropolitan region.
At a humanoid robotics summit in Tokyo in May 2026, I saw a consulting firm's global labor automation map for Physical AI. After returning, I recreated the same map using the firm's research on Digital AI job functions. Placing the two side by side revealed something unexpected.
The race to commercialize physical AI and autonomous robots is running into a fundamental challenge: existing robot safety frameworks were designed for deterministic systems operating in controlled environments, not for autonomous machines making decisions in dynamic, unstructured ones.
