The US and Europe both need Rare Earth Minerals for their industrial bases, for the transition to green technology, and for their defence. But China controls the majority of the supply, from mining all the way through to the end-use stage. Beijing has begun to wield this strategic influence, raising questions about America and Europe's need and ability, and in this case, for the EU to find its own critical minerals. In this episode of Business Beyond, this video looks at how China conquered the market and asks if there is any way Europe can develop strategic independence.

In 2025, semiconductors are not just components in consumer devices – they are the essential building blocks for the technologies shaping America’s future. From artificial intelligence and quantum computing to advanced communications networks and defense systems, chips are at the heart of the competition for global technology leadership in the 21st century.

American engineers invented semiconductors 65 years ago, and the U.S. semiconductor industry remains the global leader, commanding just over 50% of global chip revenues. But as competitors from around the world have sought to challenge U.S. leadership, America’s share of global chip manufacturing capacity was declining sharply – from 37% in 1990 to just 10% by 2022. If this trend were to continue, the U.S. semiconductor industry would risk falling back from the forefront of further advances in manufacturing processing technology, designs and architectures, and materials critical for developing the next generation of chips that will underpin the technologies of tomorrow.

This foundational technology is the hidden force driving modern innovation – and a testament to the wonder of advanced semiconductor research, design, and manufacturing.

Semiconductors are a marvel of modern technology and the foundation of our digital world. The chips powering modern smartphones contain more than 15 Billion transistors, each smaller than a virus and capable of switching on and off billions of times/second.

Semiconductors are the heart of today’s AI data centers can contain hundreds of billions of transistors, a number so high that if you counted one transistor per second, it would take more than 6,000 years to count all the transistors on a single chip.

The Chinese tech sector has been on a roll since the arrival in January of DeepSeek, the AI startup that stunned the world with a language model that claimed to match or outperform Western rivals, at a fraction of the cost. Bloomberg's Annabelle Droulers reports on how the rapid strides in AI are poised to escalate the tech "cold war" between the US and China.

Dan Gardner is the coauthor of "How Big Things Get Done," a book that explores why so many billion-dollar projects, from nuclear power plants to Olympic Games, go wrong and how some manage to succeed. Drawing on data from over 16,000 megaprojects, Gardner and his coauthor Bent Flyvbjerg reveal the startling truth: Only 0.5% of big projects are delivered on time, on budget, and with the promised results. Business Insider interviewed Dan Gardner to learn about some of the world's most high-profile projects, like the Sydney Opera House, which soared 1,400% over budget, and the troubled California High-Speed Rail, which is expected to cost over $100 billion and hasn't moved any passengers yet. He also spotlights the rare successes, like the Empire State Building and the Hoover Dam, to show what’s possible when projects are built on smart planning, strong leadership, and modular thinking.

Rankings of the reasons why OEMs have already restored. Pinpoints factors that OEMs should prioritize more highly, geopolitical risk and the use of total cost of ownership.  Key Report findings:

** Only 26% of manufacturing workers say their company’s technology is even “somewhat advanced”, with an equal number calling it “very” or “somewhat” outdated.
** 20% of employees say they’ve seen colleagues leave due to outdated systems.
** 57% of manufacturing managers and executives cited cost as their biggest barrier to investing in IT modernization and cybersecurity.
** 51% of manufacturing employees believe U.S. factories are falling behind global competitors in technology modernization and automation.
** 75% of U.S. consumers have a preference for U.S.-made goods, one that has increased as a result of global supply-chain disruptions since the COVID era.
** 62% of consumers said other factors, such as quality and price, ultimately matter more in their purchase decisions.
** 91% of consumers said they are concerned about cybersecurity threats to U.S. manufacturers, with 30% saying they are “very” or “extremely” concerned.
** 47% of those expressing concern about cyberattacks pointed to potential threats from foreign countries such as Russia and China as a cause for anxiety.

From May 2025, Norah O'Donnell's report on why China's spies are on the rise, and what happens when one gets caught in the U.S. From June 2025, Cecilia Vega’s report on the Americans spying for Cuba in the U.S. From July 2022, Scott Pelley's interview with a former top intelligence official in the Saudi Arabian government, Saad Aljabri, who claims the kingdom's ruler plotted to kill him and has taken his children hostage. From August 2019, Anderson Cooper’s interview with Justice and FBI officials, who reveal how they caught a former CIA officer spying for the Chinese. And from March 2025, Bill Whitaker's investigation into the mysterious swarms of drones that have been spotted in the sky above the United States.

On this first episode of "Bloomberg Tech: Asia," we explore the impact of China's AI advancements on competition with the US. We hear from key players in Asia's AI revolution including Manycore, one of China's so-called "Six Dragons" and Tokyo Electron, one of the world's top chip-tool makers. China Growth Capital's Wayne Shiong and Power Dynamics' Jen Zhu Scott also weigh in on the outlook for the AI race and investments in the sector.

Recent generations of frontier language models have introduced Large Reasoning Models (LRMs) that generate detailed thinking processes before providing answers. While these models demonstrate improved performance on reasoning benchmarks, their fundamental capabilities, scaling properties, and limitations remain insufficiently understood. Current evaluations primarily focus on established mathematical and coding benchmarks, with an emphasis on final answer accuracy. However, this evaluation paradigm often suffers from data contamination and does not provide insights into the reasoning traces’ structure and quality. In this work, we systematically investigate these gaps using controllable puzzle environments that allow for precise manipulation of compositional complexity while maintaining consistent logical structures. This setup enables the analysis of not only final answers but also the internal reasoning traces, offering insights into how LRMs “think”. Through extensive experimentation across diverse puzzles, we show that frontier LRMs face a complete accuracy collapse beyond certain complexities. Moreover, they exhibit a counter-intuitive scaling limit: their reasoning effort increases with problem complexity up to a point, then declines despite having an adequate token budget. By comparing LRMs with their standard LLM counterparts under equivalent inference compute, we identify three performance regimes: (1) low-complexity tasks where standard models surprisingly outperform LRMs, (2) medium-complexity tasks where additional thinking in LRMs demonstrates advantage, and (3) high-complexity tasks where both models experience complete collapse. We found that LRMs have limitations in exact computation: they fail to use explicit algorithms and reason inconsistently across puzzles. We also investigate the reasoning traces in more depth, studying the patterns of explored solutions and analyzing the models’ computational behavior, shedding light on their strengths, limitations, and ultimately raising crucial questions about their true reasoning capabilities.