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Últimas Noticias de IA
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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Cognizant Lands Centene Mega Deal Worth Over $500 Mn: Report
Cognizant’s multi-year engagement with US health insurer Centene could be worth as much as $1 billion.
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This Startup is Building the Data Layer for India’s Fuel Stations
Noida-based startup Nawgati’s AI stack helps taxi drivers track CNG pumps while also helping oil and gas companies analyse operational metrics.
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When Every Employee Can Talk to Data, Governance Becomes the Deciding Factor
As Indian banks and NBFCs swap static dashboards for conversational AI, data governance has become the industry’s central concern. Experts explain how regulated enterprises can adopt natural-language analytics without compromising compliance or data control.
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Bengaluru Beats Singapore on Startup Exits. But Liquidity Doesn't Mean Depth
Bengaluru's record startup exits signal momentum, but investors argue that funding depth, talent, and predictable deeptech exits, not headline valuations, define ecosystem maturity.
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Gritt exits stealth with $34 million for robots to build solar plants—then, everything else
One of the most important things happening on Earth today is the solar energy build-out. Around the world, companies and countries are racing to deploy solar and batteries to achieve energy independence and limit the effects of climate change. That build-out, though, is running into a labor market challenge, with a limited supply of workers to meet a growing demand for installation. Robots could be an answer, but industrial robots have historically struggled in unstructured environments, at least until now. The latest generation of AI models may have changed that equation. That’s the driving idea behindGritt, a start-up founded by two Carnegie Mellon-trained roboticists, CEO Puneet Puri and CTO Vishal Dugar. The company exited stealth Tuesday morning with a $26 million Series A round of funding led by Obvious Ventures with participation from Union Square Ventures and Active Impact Investment. That brings its total funding to $34 million, following an earlier seed round backed by First Round Capital, Climactic, Congruent Ventures, and VSC Ventures. The startup is building an intelligent system to “help civilization build infrastructure faster,” in Puri’s words. “Our thesis is that if we truly want to speed up construction,” Puri tells TechCrunch, “you need an intelligence which can work in the outdoor, chaotic environments of these construction sites, and it has to be generalizable enough that it can work in these varied environments.” Rather than building its own robots from scratch, Gritt uses off-the-shelf hardware—thus far, rented skidders and robotic arms built by companies like Kawasaki—to build platforms that are controlled by its AI models. The first job its systems handle is unloading large, glass solar panels, carrying them toward the metal frames where they need to be installed, and positioning them on the frames with sub-millimeter accuracy so workers can fasten them. “There are people who used to build rockets that went into space and had infinite budget for the smallest little part, and then there are people who know what it means to get into dirty, dull, and dangerous jobs and scale them like mad,” said Andrew Beebe, the partner at Obvious Ventures who led Gritt’s Series A round. “These guys are in the second camp, and that’s a special kind of entrepreneur that has the technical chops, the AI, and the machine vision skills to make it work.” Gritt has two systems currently deployed in the field, using the data they collect to improve their behavior. Puri says that a typical eight-person crew workers can install 800 panels a day, but the same crew working with Gritt’s systems can install 3,000 to 4,000 panels each day. Now, the company says it is contracted to help install 2.8 gigawatts of solar panels in the next 18 months, and that its customers include three of the top 10 US power construction companies. The company hopes to be operating 48 of its systems within the next six months. TechCrunch spoke to one Gritt customer who declined to be identified for competitive reasons, but who was enthusiastic about the system’s ability to improve his work. He expects it to be easier to work at remote sites where it is difficult to attract workers, and anticipates a reduction in injuries since workers won’t have to repeatedly lift 100-pound panels overhead. Gritt is competing against companies with their own panel-installing robots likeLuminous Robotics,Cosmic, and China’sTrinabot. Those companies are building their own hardware, rather than focusing on off-the-shelf vehicles and arms like Gritt, a difference that could shape who grows faster and with a leaner cost structure as demand grows. Gritt wants to add new manipulation tasks to its system so it can fasten the solar panels, drill posts, and even build the racks they sit on. Longer term, it also wants to move into other common, labor-intensive construction tasks, like tying rebar before concrete is poured over it. What’s enabled the startup to pursue this vision? Mainly, the rise of new AI models, the founders say. “Making a system for one solution was still possible to some extent five years ago, right?” Puri said, but AI is now making that work generalizable — the same underlying pipeline can be reused and improve across tasks. As an example, he noted that training the system to stack cinder blocks took weeks, while a similar demo with rebar tying took just a day using the same software. But training new tasks is just the beginning of Gritt’s vision. The founders believe the suite of sensors and intelligence its systems bring to worksites can do more than install panels; it can boost management and decision-making. For instance, they imagine their system noticing a trench is open while a storm approaches, allowing it to alert workers to cover it before rain damages components, or flagging missing inventory. “Gritt becomes now this layer of physical AI, which is doing this dextrous, labor-intensive task, plus it can help you take decisions on the site,” Puri said.
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Indian Clinicians Trust AI More Than Global Peers, But Adoption Still Lags, Reveals Report
While only 42% of Indian clinicians currently use AI at work, the Elsevier report reveals higher trust, better AI governance, and stronger digital readiness than global averages.
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Samsung Sets Up Robotics Division to Drive Humanoid Push, Says Report
This new division will report to the CEO and lead Samsung’s global robotics strategy and commercialisation efforts.
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From CCTVs to Body Scanners, Indian AI Now Powers Real-Time Threat Detection
Indian firms are now deploying screening systems, crowd-monitoring platforms, and traffic sensors at airports, metro networks, etc to make surveillance intelligent.
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