Solid-state batteries are safer and more capable--but harder to mass-produce. They also represent an opportunity for non-Chinese companies to get back in the game. The field of lithium batteries is currently dominated by Chinese companies like BYD and CATL. Not only do they sell the majority of batteries that go into electric vehicles and energy storage projects worldwide, they're also opening up new factories in your backyard . When companies outside China try to compete, like Europe's Northvolt, they quickly realize how hard it is .

NATO Secretary-General Mark Rutte delivers the keynote speech at the NATO Summit Defense Industry Forum, on the sidelines of the NATO leaders' Summit, in Ankara on Tuesday. NATO allies have agreed to at least $50 billion in defense industry deals, according to an alliance official, to show to U.S. President Donald Trump that Europe is heeding his spending demands. Secretary-General Mark Rutte revealed some of the contracts on Tuesday during a defense industry forum in Ankara, where the military alliance's leaders are meeting for their annual summit this week. Those included $12 billion in deals to buy next-generation drones, surveillance planes and military aircraft. Notably, some of the contracts show Europe moving to locally source some equipment it previously bought from the United States.

Russia's shadow fleet appears to have launched drones at European military bases and airports to test NATO air defenses, according to a new IISS report.

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Tesla vehicles and super chargers are shown at a Tesla dealership in Buena Park, California, on 28 January 2026. Tesla vehicles and super chargers are shown at a Tesla dealership in Buena Park, California, on 28 January 2026. Strong figures suggest Tesla's auto business is regaining momentum after two straight annual sales declines Tesla blew past Wall Street estimates for second-quarter deliveries on Thursday, posting a record for the period as recovering demand in Europe outweighed persistent weakness in North America. The strong figures suggest Tesla's mainstay auto business is regaining momentum after two straight annual sales declines, providing the spending cushion needed to power its ambitions in autonomous driving and artificial intelligence - the main drivers of the company's roughly $1.6tn valuation. Tesla expects to spend more than $25bn on capital expenditure in 2026, nearly triple the $8.5bn last year, to expand AI infrastructure, battery production, Cybercab manufacturing and Optimus robots.

Zurich has created a technology ecosystem nearing the density of Silicon Valley. Few places outside Silicon Valley can claim R&D hubs from all of these companies. Fewer still are concentrated in a city of just over 400,000 people--roughly half the size of San Francisco. Over the past two decades, however, many of the world's most influential technology companies have established R&D operations in and around Zurich, Switzerland. What began with Google's decision to build its largest R&D hub outside the United States has evolved into one of the world's most concentrated centers for AI research, talent, and commercialization, in certain areas at a higher density than Silicon Valley. The question is why so many technology leaders keep choosing the same place to build and scale.

It's a stretch to think that the continent can build a top-tier model, but it has an advantage: Donald Trump. Emmanuel Macron, president of France, discussed AI's risks at the G7 Summit. Earlier this month I attended Vivatech, a huge tech conference in Paris. One fear dominated the discussions: the prospect of ending up stuck using American AI, trained on American values. While the US and China are locked in an AI arms race, France and Germany, which consider their engineering talent second to none, feel boxed out.

Children and people with mental health disorders are especially vulnerable.

This paper compares different methods for forecasting the term structure of U.S. and European zero-coupon government bonds using both traditional econometric and Machine Learning (ML) approaches. We compare classical models (e.g., Dynamic Nelson-Siegel (DNS) and Principal Component Analysis (PCA)) with different Neural Network (NN) architectures, including those inspired by the classical models, on the U.S. Treasury market and bonds issued by the European Central Bank (ECB). To enhance predictive performance, macroeconomic variables are incorporated. The findings for both markets are separately analyzed and compared. To this end, we propose a robust model evaluation framework combining statistical accuracy metrics - such as RMSE, MAE, and directional accuracy - with the economic relevance of a quantitative bond trading strategy. Results show that NNs consistently outperform traditional models in both forecasting accuracy and portfolio performance. For the U.S., the most effective approach is a direct-forecasting NN that incorporates DNS factors to reduce the dimensionality of zero-rate data and an Autoencoder (AE) to extract macroeconomic features, while for Europe, the optimal model is a factor-based NN using PCA-derived zero-rate factors without the integration of macroeconomic variables. Overall, the paper demonstrates how combining traditional modeling approaches with modern ML techniques and evaluation can improve yield curve forecasts and support applications in fixed-income portfolio construction.

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