Large language models (LLMs) have demonstrated three surprising capabilities in recent years: generalization—providing reasonable answers to unseen questions; multitasking—handling programming, translation, financial analysis, and legal interpretation within a single model; and reasoning—breaking down complex problems into stepwise solutions. Together, these abilities have triggered a paradigm shift in the digital world

The global AI race is no longer just about who builds the fastest chip. Increasingly, it is about who writes the rules

AI agents and their high-powered capabilities are creating a new category of cybersecurity threats among enterprises using them, from accidental data breaches to bad actors breaking into company systems

China's push for a self-sufficient AI stack is no longer theoretical — it is entering deployment. DeepSeek's upcoming V4 model, expected within weeks, signals a shift from experimentation to execution, linking software, chips, and policy into a single system

In September 2025, Nvidia CEO Jensen Huang made a rare joint livestream appearance with Intel CEO Pat Gelsinger to announce a US$5 billion equity investment in Intel. In March 2026, Nvidia followed up with a US$2 billion investment in Marvell Technology. Why Huang is investing in potential competitors so aggressively remains a question

Amid AI-driven shifts, Arm launched its AGI CPU in March 2026 to address system-level optimization lacking in highly customized data center CPUs. Partnering with Meta and supported by OpenAI, Arm seeks to offer a standardized solution that enhances ecosystem efficiency without directly competing with clients

As the global AI race accelerates, Huawei's 2025 annual report leaves little ambiguity: AI now sits at the core of its strategy. The 147-page filing references "AI" 421 times, an unusually explicit signal of strategic depth. The company is pursuing a "foundation first, expansion later" model, pairing heavy R&D with infrastructure buildout to scale its AI position

The drone industry is no longer a niche corner of the defense world — it has become a full-blown industrial race. Across the US, states are competing to attract manufacturers, research centers, and defense contractors as autonomous aerial systems move from battlefield applications toward broader commercial use. The stakes are significant: drone production corridors bring high-wage jobs, federal research dollars, and long-term anchor tenants in the form of defense primes and tech startups alike. Yet beneath the headline investments lies a more nuanced picture

A year ago, the Japanese automotive world watched Honda, the financially robust "second brother," attempt to assert dominance over its smaller sibling Nissan. The high-stakes negotiations, marked by a glaring imbalance of power, ended abruptly and bitterly

On March 30, US tech stocks fell sharply, with the Philadelphia Semiconductor Index down 4.2%. Memory stocks led the losses: Micron Technology dropped 9.88%, Western Digital fell 8.6%, while SanDisk and Seagate Technology each declined more than 6%

Generative AI is moving from concept to commercial deployment, reshaping the global technology supply chain. It is shifting from a productivity tool to a core enterprise infrastructure. At the same time, layoffs are accelerating across Silicon Valley tech firms, Wall Street institutions, semiconductor companies, and Taiwan IC design houses

The US House Committee on Foreign Affairs passed the Chip Security Act, signaling a decisive move to restrict high-performance computing exports amid rising tech tensions with China. Meanwhile, reports from within China indicate a growing consensus to halt imports of US-origin AI chips altogether

Elon Musk recently announced the launch of the "Terafab" project, aiming to expand compute production capacity to 1 TW per year, which is about 50 times the current global compute supply of around 20 GW. He plans to build advanced wafer fabs first in Austin, Texas. However, with the 1 TW scale revealed, market reactions have been relatively cautious

Google has introduced TurboQuant, a compression algorithm that reduces large language model (LLM) memory usage by at least 6x while boosting performance, targeting one of AI's most persistent bottlenecks: memory. The breakthrough lowers inference costs and expands deployment across cloud and edge environments

By late 2025, most enterprises adopting AI chose OpenAI. In 2026, that trend is reversing. Enterprise users now increasingly favor Anthropic over OpenAI, marking a shift in competitive positioning within enterprise AI