China's DeepSeek developing its own AI chip, sources say DeepSeek is designing its own chip for inference, the stage of artificial intelligence computing in which a trained model generates responses for users, sources said. Chinese startup DeepSeek is developing its own artificial intelligence chip, according to three people familiar with the matter, a push that could reduce its reliance on Nvidia and Huawei chips, which it has depended on to train and run its globally popular models. The chip is designed for inference -- the stage of AI computing in which a trained model generates responses for users -- rather than for training new models, the sources said. If successful, DeepSeek's expansion into semiconductor development would mark a major strategic shift for a company widely hailed in China as the country's AI champion, potentially adding to challenges faced by Chinese tech giant Huawei. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

It is reportedly hiring engineers and speaking to manufacturers. DeepSeek, the Chinese AI company which sometimes keeps tech executives awake at night, is reportedly looking to build its own silicon. The move comes as part of a push to reduce its reliance on third-party chip providers like Huawei and NVIDIA. If the report is accurate, it could signal another entrant in the fiercely competitive Chinese AI market. The report suggests DeepSeek is already in talks with manufacturing partners and has quietly begun hiring engineers to support the effort.

Plus: Meta is pausing an AI training program that tracks workers' keystrokes. It's a bit of a schlep to get to the top of ASML's newest machine. It's about the size of a double-decker bus, weighs more than 150 tons, and costs $400 million. But if you want to make the world's most powerful chips, a lithography system like this is essential. The AI era needs ever faster chips, and ASML's machines make that possible. They pattern chip features with extreme-ultraviolet light, or EUV--radiation outside the visible spectrum, produced by shooting lasers at tiny molten drops of tin tens of thousands of times a second.

Large Language Models (LLMs) have demonstrated notable capabilities across financial tasks, including financial report summarization, earnings call transcript analysis, and asset classification. However, their real-world effectiveness in managing complex fund investment remains inadequately assessed. A fundamental limitation of existing benchmarks for evaluating LLM-driven trading strategies is their reliance on historical back-testing, inadvertently enabling LLMs to "time travel" - leveraging future information embedded in their training corpora, thus resulting in possible information leakage and overly optimistic performance estimates. To address this issue, we introduce DeepFund, a live fund benchmark tool designed to rigorously evaluate LLM in real-time market conditions. Utilizing a multi-agent architecture, DeepFund connects directly with real-time stock market data - specifically data published after each model's pretraining cutoff - to ensure fair and leakage-free evaluations. Empirical tests on nine flagship LLMs from leading global institutions across multiple investment dimensions--including ticker-level analysis, investment decision-making, portfolio management, and risk control--reveal significant practical challenges.

Plus: The Pentagon says it used Grok in strikes on Iran. For decades, physicists have hunted for weakly interacting massive particles (WIMPs), a leading candidate for dark matter. But their search has run into a new problem: neutrinos. These tiny particles from the sun and other stars can create a "neutrino fog" that drowns out any signal of dark matter. Hitting the neutrino fog does not, however, mean an end to the search. Researchers just have to shift the focus of their hunt.

Plus: SpaceX is now valued higher than Amazon. Solar geoengineering, the controversial idea that we could deliberately intervene in the climate system to counteract global warming, is moving beyond computer simulations and into the practical engineering challenges required to make it real. Researchers are now working on aircraft, materials, and other systems for solar geoengineering. But as they delve into these details, they're finding that even early deployment would require significant new infrastructure, time, and investment. Find out what happens when solar geoengineering encounters the realities of trying to cool the planet . Scientists have a word for how we sense ourselves from the inside: interoception.

Plus: Anthropic has shut down access to its top models after a US directive. That's good for our health, but bad for the planet: it already accounts for 7% of global electricity use and 3% of greenhouse-gas emissions. Feeling the heat, scientists and startups are hoping to amp up solid-state cooling. These systems move heat through conductive materials, which could cool spaces and surfaces with fewer messy side effects. The catch is whether it can match the efficiency of traditional AC. Find out how the unconventional coolers aim to dial down AC emissions .

The Download: how the World Cup ball will fly and OpenAI's "super app" Plus: OpenAI plans to turn ChatGPT into a'super app' before its IPO. Why this year's World Cup ball may not fly as far Much is new about this month's FIFA World Cup tournament. It hosts more teams than ever before. It's the first to occur in three different host countries. And, like every World Cup for over half a century, it will employ a football with a brand-new design. Through wind-tunnel experiments, researchers found that long-distance kicks with Adidas's new Trionda ball might not travel as far as they did in the past.

Plus: Illinois just passed what could become America's strongest AI safety law. Climate tech companies are going public. Solar and battery company Solv Energy went public in February, hitting a $6 billion valuation. X-energy, which builds small modular nuclear reactors, followed at $11.5 billion. Then came geothermal company Fervo Energy, reaching a market cap of about $12.4 billion. All three have been IPO success stories.

Plus: The Pope has called for governments to regulate AI. Despite the growing hysteria over AI's threat to white-collar jobs, there's still scant evidence that the technology has had a large-scale impact on the labor market. Analysis of US labor data shows that unemployment in occupations most exposed to AI is actually lower than in less-exposed jobs. There are also no signs that large numbers of workers are shifting from AI-threatened professions into supposedly safer manual-labor jobs. It's true that things aren't great in the job market--but the question is why. Here's what the data really says about AI and jobs .