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OpenAI is adding new controls to its Sora video app. According to Sora head Bill Peebles, users can now decide where AI-generated versions of themselves can appear - for example, blocking political content or banning certain words. Users can also set style guidelines for their digital likeness. These updates come in response to criticism over abusive deepfakes on the platform. Peebles also announced that Sora will soon officially support cameos featuring copyrighted characters. Recently, CEO Sam Altman said rights holders should have "more control" and will soon receive a share of Sora's revenue.

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Venture capitalists have already invested $192.7 billion in AI startups in 2025 - a new record. For the first time, more than half of all global VC funding is flowing into AI, according to an analysis by PitchBook.

Major players like Anthropic and xAI have secured multi-billion dollar rounds, while smaller startups outside the AI sector are finding it harder to raise money. Kyle Sanford at PitchBook describes the result as a split market - it's either AI or not.

In the US, 62.7 percent of VC funding went to AI companies, compared to 53.2 percent worldwide. In total, global VC investment for 2025 stands at $366.8 billion, with $250.2 billion coming from the US.

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Cognitive scientist Melanie Mitchell is pushing back against recent New York Times columns by writer Thomas Friedman, criticizing his framing of advanced AI.

In his pieces, Friedman calls for close U.S.-China collaboration on AI regulation and warns of an approaching "superintelligence." Much of his argument leans on comments from his friend Craig Mundie, the former Microsoft executive, as well as media reports. Mitchell says these claims lack scientific evidence. According to her, examples Friedman cites - like AI "teaching itself" new languages or chatbots pursuing their own hidden agendas - can be explained by training data and have been debunked.

Mitchell describes Friedman’s outlook as "magical thinking." In her view, he ascribes AI with mysterious powers that actually stem from human data and relatively simple mechanisms. She warns that because of Friedman’s broad reach, these myths risk shaping public understanding of AI. Instead of speculative scenarios, Mitchell argues for fact-based realism and human-led regulation.

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