GoKawiil
Tech News
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.
Nvidia's portfolio of equity stakes in tech and AI companies jumped more than tenfold over the past year, reaching $99 billion as of late July, up from roughly $7 billion a year earlier. The chip maker has committed over $40 billion to financing deals across the AI industry in 2026 alone, alongside separate pledges including up to $105 billion in conditional credit support for an OpenAI data center and a planned $12.9 billion acquisition of Hugging Face.
AI companies have started purchasing used physical books in bulk to feed data into their large language models. This practice follows a copyright settlement earlier this year in which courts determined that buying books and destroying them during the digitization process for AI training qualifies as fair use.
The Trump administration filed a court brief supporting OpenAI in its ongoing copyright dispute with publishers over AI training data. Officials argued that restricting AI firms' access to content for training purposes could pose national security risks.