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US threatens sanctions against Chinese AI models over IP theft
On Tuesday, Treasury Secretary Scott Bessent said the U.S. would examineopen source modelsfrom China for signs of intellectual property theft, threatening sanctions against Chinese AI companies if IP theft is established. “We’ve seen a lot of talk about open source models coming and threatening the large language models in the U.S.,” Bessent said on Fox Business Tuesday. “This administration supports open source models, but what we do not support is IP theft. If we see, especially, that overseas models are stealing from our great companies, we have the ability to sanction them because of this theft.” Bessent’s comments werefirst reported by Bloomberg. The statement comes as Chinese models — most recentlyMoonshot AI’s Kimi K3— are gaining in capabilities and popularity, threatening to harm the business models of top American AI firms like OpenAI and Anthropic, as well as their abilities to raise more capital to continue developing frontier models. On Monday, Axios reported that the Trump administration is considering awholesale ban on Chinese open source models, although others havedisputedthat claim. AI companies have been warning for months against campaigns by foreign actors to copy their AI technology and redeploy it as open source. In April, the White House said it would work closely with AI firms to combat the theft. Sanctions from the U.S. against Chinese models would add to the growing list of strategies the government is attempting to maintain the lead in the AI race. After restricting China’s access to advanced chips and tightening export controls, Washington is now signaling it may target the AI models themselves, a move that could mark a significant escalation in the technological competition between frontier labs and Chinese open source alternatives. Model distillationis a technique that allows some of a larger model’s capabilities to be translated into a smaller system that’s easier to run — but not everyone agrees that distilling another company’s model constitutes theft. Earlier this month, Microsoft CEO Satya Nadellacriticized large labsfor making just this assumption: “While the great innovation that comes from model providers having fair use rights to train models on public data is needed, I find it ironic that the status quo is to then turn around and impose restrictive terms on distillation.” AI labs’ training practices continue to be a source of legal risk for the companies. Anthropic this week got the green light to start cutting authors checks as part of its$1.5 billion settlementafter a judge ruled it had illegally downloaded and stored millions of copyrighted books to train its AI. Furthermore, some in the industry argue that distillation isn’t the only reason China is catching up to U.S. AI companies. “We know distillation to be a very small factor in the ability to create good models, and it’s a practice that everyone is doing, including companies in the U.S.,” Hugging Face CEO Clem Delangue said on a recent episode ofTechCrunch’s Equity podcast. “If it were easy just to do distillation to get good at building AI models, there would be many other countries, including in the U.S., with much better open source AI. The reality is they have really, really good research teams in China…taking a much more open and collaborative approach to AI than in the U.S.”
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Google releases three new Gemini models — but no 3.5 Pro
On Tuesday, Google DeepMind released Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber. Gemini 3.6 Flash is Google’s “workhorse model” that promises improved capabilities in coding, knowledge work, and multimodal performance while reducing token usage by up to 17%, making it cheaper than its predecessor 3.5 Flash. Gemini 3.5 Flash-Lite is the most cost-effective model in the class, and 3.5 Flash Cyber is a specialized model that was fine-tuned for finding and fixing cybersecurity vulnerabilities at a decent price point. This model will be exclusively available to governments and trusted partners as part of a limited access pilot program, according to Google. Google says the focus on these releases is to deliver efficiency, latency, and reliability to customers that are building AI agents at scale. The launch is notable not just for what Google shipped — cheaper, faster models optimized for coding, efficiency, and cybersecurity — but also for what it didn’t. The update doesn’t include the long-anticipated update to Google’s flagship model, Gemini Pro, which was last updated in February. In the time since that launch, OpenAI has released GPT-5.5 and begun rolling outGPT-5.6, while Anthropic has launchedClaude Opus 4.8andClaude Sonnet 5and has expanded access to its frontierFable 5 model, highlighting the intense release pace of the rival labs. Google teased the release of Pro as part of the 3.5 Flash release in May, saying the Pro version was “already being used internally, and we look forward to rolling it out next month.” Last week,Bloomberg reportedthat Google was facing internal delays in launching the 3.5 Pro as it struggled to meet internal performance goals. Gemini Pro models are generally Google’s highest-capability offerings for complex reasoning and coding tasks, while Flash models prioritize lower cost and faster response times for production applications. Google DeepMind product lead Logan KilpatricksaidTuesday that the company is currently testing Gemini 3.5 Pro with partners and hopes to “land soon.” He alsonotedthat the team has started its most ambitious pre-training run yet for Gemini 4.
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Data centers expected to use 4x more electricity by 2035
Data centers are expected to use one-fifth of the electricity generated in the U.S. by 2035, four times that of today, according to a new report fromBloombergNEF. A surge in AI compute will push data center capacity to nearly 200 gigawatts over the next decade, the report predicts. Nearly half of that capacity will be devoted to training and inference, and most of that will remain concentrated in the U.S. By 2033, the country will host 64% of AI chips by power demand. Based on previous forecasts, those figures could be conservative. BloombergNEF’s new estimate for electricity demand in 2035 is 83% higher than what the consultancy predicted in December. Other organizations have raised their forecasts, too. EPRI, an electrical industry nonprofit, hasmore than doubled its 2024 estimate, whileS&P’s forecastrose by more than a third between October and April. The revisions reflect the fevered pace of data center development across the U.S. In the coming decade, BloombergNEF expects the majority of new data centers to hit electrical grids that are already strained. The PJM Interconnection, which spans Virginia to Illinois, will see 34% of its electricity go to data centers, while ERCOT, which covers most of Texas, will have to devote 22% of its generating capacity. PJM, which already hosts a large number of the country’s data centers, hasstruggled to copewith connection requests from both large generators and large loads. It paused applications for new sources to connect to the grid for four years, putting it in a precarious position as demand continued to grow. Though PJM reopened the queue to new generating sources in April, the situation has grown so dire that one utility, American Electric Power, has threatened to pull out of the interconnection. The supply-demand imbalance has pushed electricity pricesup 76%over the past year. Even with the congestion, data centers still want to connect to PJM — they represented38% of chargesin the grid manager’s most recent capacity auction. Despite the U.S. claiming a majority of AI compute, data centers will continue to grow elsewhere. By 2033, if AI adoption continues along an aggressive trajectory, data centers will create 1,935 terawatt-hours of new electricity demand worldwide, nearly as much asIndiauses annually.
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Music streamer Deezer says more than 50% of daily uploads are AI-generated
Music streaming companyDeezerhas been tracking the number of AI-generated tracks uploaded on the platform since last year, and the numberhas constantly gone up. Today, the company said that AI music now represents more than 50% of downloads. Deezer said that AI-generated track uploads were at a peak in June 2026, representing a monthly average of 90,000 tracks per day. The rapid rise of AI-generated music has forced streaming services to decide how much of it they want on their own platforms. There is no single consensus yet on that front. Some take strict steps, likeBandcamp banning such tracksorTidal cutting off monetization. Meanwhile, Apple Music has a voluntary AI-tagging system, andSpotify developed its own policyabout how much AI was used in music-making. Deezer’s latest move on this front will involve taking down AI-generated tracks that haven’t been streamed in the past six months or are involved in fraudulent streams to drive up revenue. “Deezer has been at the frontline of fighting fraud and reducing payment dilution related to AI music for almost two years. Now that half of all daily uploads are AI-generated tracks, we are taking additional steps to safeguard the rights of artists and songwriters, while maintaining focus on music that fans actually love,” Deezer CEO Alexis Lanternier said in a statement. The streamer first released stats around AI music uploads inJanuary 2025, when the daily upload volume was around 10,000 tracks, or 10% of daily uploads. The number grew to 20,000 tracks, or 18% of daily uploads,in April 2025. It then climbed to 30,000 tracks, representing 28% of daily uploads in September 2025, followed by 50,000 daily uploads, or 34% of daily uploads, in November 2025. This year, it grew again to 60,000 tracks, or 39% of daily uploads, in January 2026. As of April 2026, the figure reached75,000 tracks, or 44% of daily uploads. Deezer started labelingAI music on its platform last year, and said that its detection tech can also identify tracks generated with models from Suno and Udio, AI-music startups that areembroiledin copyright lawsuits. Earlier this year, Deezer made itsdetection tech available to other platforms, but it’s not clear if any of the major platforms are using the tool just yet. Last month, it also released a tool thatcan sift through Apple Music and Spotify playlists for AI-generated tracks.
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