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Tech News
Several US states have seen candidates win elections on promises to impose moratoriums on new data center construction, prompting AI and cloud companies to reconsider their build-out plans. Developers are reportedly exploring alternatives including overseas sites and remote desert locations to avoid local resistance.
Elon Musk has said he will give every child in America a stake in SpaceX next year, a giveaway involving roughly 72 million shares worth about $10.5 billion at current prices. Divided among the country's children, that works out to roughly $146 per child. The pledge comes amid a broader trend of large philanthropic promises from Silicon Valley's wealthiest tech figures.
A Nature briefing reports that as more AI companies prepare to go public, the number of ultra-wealthy philanthropists funding biomedical science in the US is expected to grow. These donors tend to back higher-risk, personally meaningful projects with fewer conditions than government agencies typically impose, according to the briefing.
A contributor opinion piece for Entrepreneur argues that founders outside AI are misreading their own growth as failure because they're comparing it to charts from companies like Lovable and Cursor, which hit $600 million and $4 billion annualized revenue run rates respectively in 2026. The piece attributes AI's unusual speed to frictionless sales cycles, minimal regulation and instant credit-card adoption, conditions most industries lack. It advises founders to study peers in their own category, track controllable short-term metrics, and identify specific milestones that could unlock faster growth.
Speaking to CNBC at a London media briefing, Nvidia CEO Jensen Huang said the company 'absolutely must' cooperate with Chinese AI companies to monitor risks from the technology. He said he already holds dialogues with researchers in China and urged the US and UK to pursue similar engagement.
Executives at major artificial intelligence companies are reportedly preparing contingency plans for the aftermath of a serious AI-related disaster. These preparations reportedly include strategies for shaping regulatory and policy responses once such an event occurs.
Endeavor Catalyst announced the closing of its fifth fund with $320 million in commitments, increasing its total assets under management to over $850 million. The fund aims to support founders in regions outside the Bay Area, with a focus on those who are part of Endeavor's global network of entrepreneurs. The firm plans to make 40 to 50 investments annually, targeting up to 150 companies with checks typically ranging from $1 million to $3 million.
Former FTC Chair Lina Khan argued on X that regulators don't need new legislation to rein in AI companies, pointing to decades-old consumer protection and antitrust statutes as tools to hold firms and executives accountable. She cited product liability and unfair competition laws as grounds to prosecute companies releasing unvetted or defective AI systems, or engaging in risky behavior that pressures rivals to match it.
Former FTC Chair Lina Khan argues that regulators do not need new legislation to hold AI company executives accountable, pointing to established legal precedent such as a 1934 Supreme Court ruling that barred a competitive 'race to the bottom' among companies. Her comments push back on the idea that AI's novelty exempts its leaders from existing corporate and antitrust law.
A group of mathematicians argues that while large language models have rapidly gained the ability to solve major outstanding problems, AI companies' drive to treat these solutions as benchmarks conflicts with how mathematics actually operates as a discipline. They describe this as a broader misalignment between AI industry goals and the values of the mathematical community, which relies on slow, collective processes of verification, teaching, and simplification rather than one-off problem-solving feats.
A group of 25 Fields Medal-winning mathematicians has issued a joint declaration warning that AI companies' rush to use famous unsolved math problems as benchmarks for large language models is harming the discipline. They argue that while LLMs have rapidly gained the ability to crack major open problems, this benchmark-driven approach conflicts with how mathematical knowledge has traditionally been built and validated over generations.
U.S. security agencies have publicly accused six Chinese artificial intelligence companies of systematically using American AI models to train their own systems through a technique known as distillation. The practice allows a smaller model to learn from the outputs of a larger, more advanced one, effectively transferring capabilities without the original research investment.