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China's rare earth magnet exports to US surge 660% after breakthrough trade deal

China's rare earth magnet exports to US surge 660% after breakthrough trade deal

New York Post4 hours ago
China's exports of rare earth magnets to the United States in June soared to more than seven times their May level, marking a sharp recovery in the flow of critical minerals used in electric vehicles and wind turbines after a Sino-U.S. trade deal.
Outbound shipments to the United States from the world's largest producer of rare earth magnets surged to 353 metric tons in June, up 660% from May, data from the General Administration of Customs showed on Sunday.
That came after pacts reached in June to resolve issues around shipments of rare earth minerals and magnets to the United States. Chipmaker Nvidia (NVDA.O), plans to resume sales of its H20 AI chips to China as part of the agreement.
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3 China's exports of rare earth magnets to the United States in June soared to more than seven times their May level.
AFP via Getty Images
3 China, which provides more than 90% of the global supply of rare earth magnets, decided to add rare earth items to its export restriction list in retaliation for tariffs.
REUTERS
China, which provides more than 90% of the global supply of rare earth magnets, decided in early April to add several rare earth items to its export restriction list in retaliation for U.S. tariffs.
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The subsequent sharp fall in shipments in April and May, due to the lengthy time required to secure export licenses, had upset the global supply chain, forcing some automakers outside China to halt partial production due to a rare earths shortage.
In total, China exported 3,188 tons of rare earth permanent magnets globally last month, up 157.5% from 1,238 tons in May, although the June volume was still 38.1% lower than the corresponding month in 2024.
3 The restrictions forced some automakers outside China to halt partial production due to a shortage.
AP
Shipments of magnets are likely to recover further in July as more exporters obtained licenses in June, analysts said.
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During the first half of 2025, exports of rare earth magnets fell 18.9% on the year to 22,319 tons.
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As Trump courts a more assertive Beijing, China hawks are losing out
As Trump courts a more assertive Beijing, China hawks are losing out

Boston Globe

time17 minutes ago

  • Boston Globe

As Trump courts a more assertive Beijing, China hawks are losing out

The move was a dramatic reversal from three months ago, when President Trump banned China from accessing the H20, while also imposing triple-digit tariffs on Beijing. That set off an economically perilous trade clash, as China retaliated by clamping down on exports of minerals and magnets that are critical to American factories, including automakers and defense manufacturers. China's decision to cut off access to those materials upended the dynamic between the world's largest economies. The Trump administration, which came into office determined to bully China into changing its trade behavior with punishing tariffs, appeared to realize the perils of that approach. Now, the administration has resorted to trying to woo China instead. Officials throughout the government say the Trump administration is putting more aggressive actions against China on hold, while pushing forward with moves that the Chinese will perceive positively. That includes the reversal on the H20 chip. Advertisement The H20 decision was primarily motivated by top Trump officials who agreed with Nvidia's arguments that selling the chip would be better for American technology leadership than withholding it, people familiar with the move say. But Trump officials have also claimed that it was part of the trade talks. After telling Congress in June that there was 'no quid pro quo in terms of chips for rare earths,' Scott Bessent, the treasury secretary, reversed those comments Tuesday, saying that the H20 move was 'all part of a mosaic' of talks with China. 'They had things we wanted, we had things they wanted, and we're in a very good place,' he said. Advertisement A Chinese Ministry of Commerce official seemed to reject that Friday, saying that the United States had 'taken the initiative' to approve the H20 sales. China believes the United States should continue to remove its trade and economic restrictions, the official said. A person familiar with the talks, who spoke on the condition of anonymity because he was not authorized to speak publicly, said that the H20 chip was not specifically discussed in meetings between Chinese and US officials in Geneva and London this spring. But the reversal was part of a more recent cadence of warmer actions the United States and China have taken toward each other. For instance, Beijing agreed in recent weeks to block the export of several chemicals used to make fentanyl, an issue Trump has been concerned about. Recent events have underscored the influence that China has over the US economy. When Trump raised tariffs on Chinese exports in April, some top Trump officials thought Beijing would quickly fold, given its recent economic weakness. Instead, Beijing called Trump's bluff by restricting rare earths needed by American makers of cars, military equipment, medical devices, and electronics. As the flow of those materials stopped, Trump and other officials began receiving calls from CEOs saying their factories would soon shut down. Ford, Suzuki, and other companies shuttered factories because of the lack of supply. Advertisement Trump and his top advisers were surprised by the threat that Beijing's countermove posed, people familiar with the matter say. That brought the United States back to the negotiating table this spring to strike a fragile trade truce, which Trump officials are now wary of upsetting. That agreement dropped tariffs from a minimum 145 percent to 30 percent, with the Chinese agreeing to allow rare earths to flow as freely as before. The administration's caution when it comes to China has been amplified by Trump's desire for an invitation to Beijing later this year. The president, who has been feted on other foreign trips, wants to engage in face-to-face trade negotiations with Chinese leader Xi Jinping. Howard Lutnick, the commerce secretary, has begun recruiting chief executives for a potential delegation, setting off a competition over who will get to ride in Air Force One, according to people familiar with the plans. The Commerce Department declined to comment. The White House, the Treasury Department, and the Office of the United States Trade Representative did not respond to a request for comment. 'The government understands that forcing the world to use foreign competition would only hurt America's economic and national security,' said John Rizzo, a spokesperson for Nvidia. Opposition to China has fueled bipartisan action for the past decade. Now, Trump's more hawkish supporters are quietly watching as the president remakes the party's China strategy. Though few are willing to speak out publicly, officials in the Trump administration and in Congress have privately expressed concern that the trade war has given China an opening to finally bring US technology controls onto the negotiating table. Advertisement Christopher Padilla, a former export control official in the George W. Bush administration, said the fact that the United States was now negotiating over what were supposed to be security restrictions was 'a significant accomplishment for the Chinese.' 'They've been after this for decades, and now they've succeeded,' he said. 'I assume the Chinese are going to demand more concessions on export controls in return for whatever we want next.' This article originally appeared in

Why Machines Aren't Intelligent
Why Machines Aren't Intelligent

Forbes

time18 minutes ago

  • Forbes

Why Machines Aren't Intelligent

Abstract painting of man versus machine, cubism style artwork. Original acrylic painting on canvas. OpenAI has announced that its latest experimental reasoning LLM, referred to internally as the 'IMO gold LLM', has achieved gold‑medal level performance at the 2025 International Mathematical Olympiad (IMO). Unlike specialized systems like DeepMind's AlphaGeometry, this is a reasoning LLM, built with reinforcement learning and scaled inference, not a math-only engine. As OpenAI researcher Noam Brown put it, the model showed 'a new level of sustained creative thinking' required for multi-hour problem-solving. CEO Sam Altman said this achievement marks 'a dream… a key step toward general intelligence', and that such a model won't be generally available for months. Undoubtedly, machines are becoming exceptionally proficient at narrowly defined, high-performance cognitive tasks. This includes mathematical reasoning, formal proof construction, symbolic manipulation, code generation, and formal logic. Their capabilities also extend significantly to computer vision, complex data analysis, language processing, and strategic problem-solving, because of significant advancements in deep learning architectures (such as transformers and convolutional neural networks), the availability of vast datasets for training, substantial increases in computational power, and sophisticated algorithmic optimization techniques that enable these systems to identify intricate patterns and correlations within data at an unprecedented scale and speed. These systems can accomplish sustained multi-step reasoning, generate fluent human-like responses, and perform under expert-level constraints similar to humans. With all this, and a bit of enthusiasm, we might be tempted to think that this means machines are becoming incredibly intelligent, incredibly quickly. Yet this would be a mistake. Because being good at mathematics, formal proof construction, symbolic manipulation, code generation, formal logic, computer vision, complex data analysis, language processing, and strategic problem-solving, is neither a necessary nor a sufficient condition for 'intelligence', let alone for incredible intelligence. The fundamental distinction lies in several key characteristics that machines demonstrably lack. Machines cannot seamlessly transfer knowledge or adapt their capabilities to entirely novel, unforeseen problems or contexts without significant re-engineering or retraining. They are inherently specialized. They are proficient at tasks within their pre-defined scope and their impressive performance is confined to the specific domains and types of data on which they have been extensively trained. This contrasts sharply with the human capacity for flexible learning and adaptation across a vast and unpredictable array of situations. Machines do not possess the capacity to genuinely experience or comprehend emotions, nor can they truly interpret the nuanced mental states, intentions, or feelings of others (often referred to as "theory of mind"). Their "empathetic" or "socially aware" responses are sophisticated statistical patterns learned from vast datasets of human interaction, not a reflection of genuine subjective experience, emotional resonance, or an understanding of human affect. Machines lack self-awareness and the ability for introspection. They do not reflect on their own internal processes, motivations, or the nature of their "knowledge." Their operations are algorithmic and data-driven; they do not possess a subjective "self" that can ponder its own existence, learn from its own mistakes through conscious reflection, or develop a personal narrative. Machines do not exhibit genuine intentionality, innate curiosity, or the capacity for autonomous goal-setting driven by internal desires, values, or motivations. They operate purely based on programmed objectives and the data inputs they receive. Their "goals" are externally imposed by their human creators, rather than emerging from an internal drive or will. Machines lack the direct, lived, and felt experience that comes from having a physical body interacting with and perceiving the environment. This embodied experience is crucial for developing common sense, intuitive physics, and a deep, non-abstracted understanding of the world. While machines can interact with and navigate the physical world through sensors and actuators, their "understanding" of reality is mediated by symbolic representations and data. Machines do not demonstrate genuine conceptual leaps, the ability to invent entirely new paradigms, or to break fundamental rules in a truly meaningful and original way that transcends their training data. Generative models can only produce novel combinations of existing data, Machines often struggle with true cause-and-effect reasoning. Even though they excel at identifying correlations and patterns, correlation is not causation. They can predict "what" is likely to happen based on past data, but their understanding of "why" is limited to statistical associations rather than deep mechanistic insight. Machines cannot learn complex concepts from just a few examples. While one-shot and few-shot learning have made progress in enabling machines to recognize new patterns or categories from limited data, they cannot learn genuinely complex, abstract concepts from just a few examples, unlike humans. Machines still typically require vast datasets for effective and nuanced training. And perhaps the most profound distinction, machines do not possess subjective experience, feelings, or awareness. They are not conscious entities. Only when a machine is capable of all (are at least most of) these characteristics, even at a relatively low level, could we then reasonably claim that machines are becoming 'intelligent', without exaggeration, misuse of the term, or mere fantasy. Therefore, while machines are incredibly powerful for specific cognitive functions, their capabilities are fundamentally different from the multifaceted, adaptable, self-aware, and experientially grounded nature of what intelligence is, particularly as manifested in humans. Their proficiency is a product of advanced computational design and data processing, not an indication of a nascent form of intelligence in machines. In fact, the term "artificial general intelligence" in AI discourse emerged in part to recover the meaning of "intelligence" after it had been diluted through overuse in describing machines that are not "intelligent" to clarify what these so-called "intelligent" machines still lack in order to really be, "intelligent". We all tend to oversimplify and the field of AI is contributing to the evolution of the meaning of 'intelligence,' making the term increasingly polysemous. That's part of the charm of language. And as AI stirs both real promise and real societal anxiety, it's also worth remembering that the intelligence of machines does not exist in any meaningful sense. The rapid advances in AI signal that it is beyond time to think about the impact we want and don't want AI to have on society. In doing so, this should not only allow, but actively encourage us to consider both AI's capacities and its limitations, making every effort not to confuse 'intelligence' (i.e. in its rich, general sense) with the narrow and task-specific behaviors machines are capable of simulating or exhibiting. While some are racing for Artificial General Intelligence (AGI), the question we should now be asking is not when they think they might succeed, but whether what they believe they could make happen truly makes sense civilisationally as something we should even aim to achieve, while defining where we draw the line on algorithmic transhumanism.

AI Will Replace Recruiters and Assistants in Six Months, Says CEO Behind ChatGPT Rival
AI Will Replace Recruiters and Assistants in Six Months, Says CEO Behind ChatGPT Rival

Gizmodo

timean hour ago

  • Gizmodo

AI Will Replace Recruiters and Assistants in Six Months, Says CEO Behind ChatGPT Rival

Aravind Srinivas, the CEO of the ambitious AI startup Perplexity, has a clear and startling vision for the future of work. It begins with a simple prompt and ends with the automation of entire professional roles. 'A recruiter's work worth one week is just one prompt: sourcing and reach outs,' Srinivas stated in a recent interview with The Verge's Decoder' podcast, a prediction that serves as both a mission statement for his new AI-powered browser, Comet, and a stark warning for the modern knowledge worker. His company is at the forefront of a new technological arms race to build not just a smarter search engine, but a true AI agent. Think of it as a digital entity capable of carrying out complex, multi-step tasks from start to finish. According to Srinivas, the most natural place for this revolution to begin is the one tool every office worker already uses: the web browser. And the first jobs in its sights are those of recruiters and executive assistants. For years, the promise of AI has been to assist, not replace. But the vision Srinivas lays out is one of replacement by a vastly more capable assistant. He describes an AI agent as something that can 'carry out any workflow end to end, from instruction to actual completion of the task.' He details exactly how Comet is being designed to absorb the core functions of a recruiter. The agent can be tasked to find a list of all engineers who studied at Stanford and previously worked at Anthropic, port that list to a Google Sheet with their LinkedIn URLs, find their contact information, and then 'bulk draft personalized cold emails to each of them to reach out to for a coffee chat.' The same logic applies to the work of an executive assistant. By having secure, client-side access to a user's logged-in applications like Gmail and Google Calendar, the agent can take over the tedious back-and-forth of scheduling. 'If some people respond,' Srinivas explains, the agent can 'go and update the Google Sheets, mark the status as responded or in progress and follow up with those candidates, sync with my Google calendar, and then resolve conflicts and schedule a chat, and then push me a brief ahead of the meeting.' This is a fundamental re-imagining of productivity, where the human role shifts from performing tasks to simply defining their outcomes. While Comet cannot execute these most complex, 'long-horizon' tasks perfectly today, Srinivas is betting that the final barriers are about to fall. He is pinning his timeline on the imminent arrival of the next generation of powerful AI. 'I'm betting on progress in reasoning models to get us there,' he says, referencing upcoming models like GPT-5 or Claude 4.5. He believes these new AI brains will provide the final push needed to make seamless, end-to-end automation a reality. His timeline is aggressive and should be a wake-up call for anyone in these professions. 'I'm pretty sure six months to a year from now, it can do the entire thing,' he predicts. This suggests that the disruption isn't a far-off abstract concept but an impending reality that could reshape entire departments before the end of next year. Srinivas's ambition extends far beyond building a better browser. He envisions a future where this tool evolves into something much more integral to our digital lives. 'That's the extent to which we have an ambition to make the browser into something that feels more like an OS where these are processes that are running all the time,' he says. In this new paradigm, the browser is no longer a passive window to the internet but an active, intelligent layer that manages your work in the background. Users could 'launch a bunch of Comet assistant jobs' and then, as Srinivas puts it, spend their time on other things while the AI works. This transforms the very nature of office work from a series of active inputs to a process of delegation and oversight. What happens to the human worker when their job functions are condensed into a single prompt? Srinivas offers an optimistic view, suggesting that this newfound efficiency will free up humanity's time and attention. He believes people will spend more time on leisure and personal enrichment, that they will 'choose to spend it on entertainment more than intellectual work.' In his vision, AI does the drudgery, and we get more time to 'chill and scroll through X or whatever social media they like.' But this utopian view sidesteps the more immediate and painful economic question: What happens to the millions of people whose livelihoods are built on performing the very tasks these agents are designed to automate? While some may be elevated to the role of 'AI orchestrator,' many could face displacement. The AI agent, as described by one of its chief architects, is not merely a new feature. It is a catalyst for a profound and potentially brutal transformation of the white-collar workforce. The future of work is being written in code, and according to Srinivas, the first draft will be ready far sooner than most of us think.

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