Nvidia Chief Executive Jensen Huang has ruled out any immediate possibility of selling the company’s flagship Blackwell AI chips to China, saying there are “no active discussions” to ease U.S. export restrictions.

His remarks highlight how Washington’s tightening curbs on advanced chip sales have upended one of Nvidia’s most lucrative markets, deepening the fracture between the world’s two largest economies over control of artificial intelligence and semiconductor technology.

Speaking on Friday in Tainan, Taiwan, where he was attending chipmaking partner TSMC’s sports day, Huang said there was no change in Nvidia’s position despite speculation that a diplomatic breakthrough between President Donald Trump and President Xi Jinping could result in a limited deal allowing toned-down versions of the Blackwell chips to enter China.

“Currently, we are not planning to ship anything to China,” Huang said. “It’s up to China when they would like Nvidia products to go back to serve the Chinese market. I look forward to them changing their policy.”

The Blackwell series, Nvidia’s latest and most advanced line of AI chips, powers large-scale machine learning models and data centers that drive everything from generative AI to robotics and cloud computing. But the technology has been at the center of Washington’s campaign to curb China’s access to cutting-edge semiconductors that could be repurposed for military or surveillance use.

Under a string of export control measures first introduced in 2022 and expanded in 2023, the U.S. Commerce Department banned Nvidia and other chipmakers from selling their highest-end GPUs — including the A100, H100, and now Blackwell — to Chinese firms. The restrictions have forced Nvidia to develop lower-spec alternatives like the H20 chip, which comply with export limits but fall short of the performance Chinese companies seek for large-scale AI training.

Huang acknowledged the consequences of the faceoff, saying, “Our market share in China for advanced AI chips is zero.” He said the U.S. government’s limited exemptions have done little to change the reality on the ground, as Chinese firms are now pouring resources into domestic chip development to reduce dependence on American suppliers.

The policy shift has cost Nvidia billions in potential revenue. Before the export bans, China accounted for roughly one-fifth of Nvidia’s total sales, with its GPUs powering data centers for Chinese tech giants like Alibaba, Tencent, and Baidu. But those firms are now turning to local alternatives such as Huawei’s Ascend series, which Beijing has heavily subsidized as part of its broader “self-reliance” drive in semiconductor technology.

Analysts say the outcome reflects a broader tech and trade standoff between Beijing and Washington that has transformed from a tariff dispute into a race for technological dominance. While tariffs and supply chain realignments defined the early stages of the trade war, the current conflict centers on who controls the infrastructure of the AI age — chips, data, and algorithms.

The Trump administration has positioned chip export bans as a national security imperative, arguing that cutting off access to advanced semiconductors will slow China’s military modernization and surveillance capacity. In response, Beijing has retaliated by restricting exports of critical minerals like gallium and germanium, both essential for chipmaking, and by investing heavily in its domestic semiconductor ecosystem.

Huang, who has visited Taiwan several times this year, reaffirmed Nvidia’s commitment to its manufacturing partnerships despite these tensions.

“Business is very strong,” he said. “So I came back to encourage my TSMC friends.” Nvidia relies on TSMC to produce its most advanced chips, including the Blackwell architecture, using the foundry’s cutting-edge 4-nanometer process technology.

When asked about Tesla CEO Elon Musk’s recent plan to build a semiconductor fabrication plant to support AI growth, Huang noted the formidable barriers to entry.

“Building advanced semiconductor manufacturing capabilities like TSMC does is extremely hard,” he said. “But it’s a very important technology and the demand is extremely high.”

Huang also sought to clarify earlier remarks reported by the Financial Times suggesting he had said China would win the AI race.

“That’s not what I said,” he explained. “What I said was that China has very good AI technology. They have many AI researchers.”

He added that half of the world’s AI researchers are based in China and that some of the most popular open-source AI models originate there.

“They’re moving very, very fast,” he said. “The United States has to continue to move incredibly fast; otherwise, the world is very competitive, so we have to run fast.”

IBM is cutting thousands of jobs worldwide as part of its ongoing shift toward artificial intelligence and software-driven services, yet the tech giant pledges to ramp up recruitment of young graduates skilled in emerging technologies.

The dual strategy highlights a defining paradox of the AI era: while automation is displacing traditional roles, it is also creating demand for a new generation of workers fluent in machine learning, quantum computing, and data science.

The company confirmed Tuesday that it would lay off a “low single-digit percentage” of its global workforce—potentially affecting more than 2,500 employees—as it restructures to align with what CEO Arvind Krishna called the “AI-first” transformation of business operations. IBM, which employed about 270,000 people globally at the end of 2024, said the job cuts are necessary to free up resources for its fast-growing AI and cloud divisions.

“IBM’s workforce strategy is driven by having the right people with the right skills to do the work our clients need,” an IBM spokesperson told Fortune. “We routinely review our workforce through this lens and at times rebalance accordingly.”

The layoffs, part of a sweeping corporate trend toward automation and efficiency, come as companies across industries—from Amazon and Target to Accenture—reshape operations around artificial intelligence. What sets IBM apart, however, is its simultaneous pledge to hire aggressively among new graduates, particularly those with strong foundations in AI-related skills.

“People are talking about either layoffs or freezing hiring, but I actually want to say that we are the opposite,” Krishna told CNN last week, before confirming the latest cuts. “I expect we are probably going to hire more people out of college over the next 12 months than we have in the past few years.”

Krishna, who has positioned IBM as a frontrunner in generative AI and enterprise automation, said the company’s next phase of growth depends on talent that can build, deploy, and manage AI systems for clients worldwide.

“Skills of people are really important,” he emphasized. “We need skills in AI. We need skills in quantum. We need skills that our clients feel really good about technology being deployed in their environment.”

The company’s restructuring reflects a broader transformation in global employment patterns. Entry-level and mid-tier administrative roles—long seen as stepping stones for young professionals—are being automated at record speed. A Harvard University study found that firms adopting AI have sharply reduced junior hiring, with algorithmic systems now handling tasks such as data analysis, scheduling, and customer support that were once assigned to human staff.

According to the U.S. Federal Reserve, job postings have been declining since early 2022, making it harder for graduates to find entry-level work. Yet employers continue to seek candidates who can integrate AI tools into their workflows. A joint report by Microsoft and LinkedIn found that 71% of business leaders would rather hire a less experienced applicant who understands AI systems than a veteran who doesn’t.

Against this backdrop, it is believed that IBM’s approach—replacing redundant roles while hiring workers with advanced technical fluency—signals how companies plan to navigate the transition to automation.

Alyssa Cook, a senior managing consultant at Beacon Hill Staffing, told Fortune that firms “would rather hire a candidate who has hands-on experience with the specific tools they are implementing if they have the ability and interest to train up on other skills.”

Elon Musk, Satya Nadella, and Sundar Pichai have each echoed similar views in recent months, predicting that the next phase of workforce expansion will favor “AI natives”—workers who can build, train, or adapt models to improve business efficiency.

However, experts have cautioned that technical skills alone may not guarantee job security. “We’re not just looking for people who know the tools,” said Alejandro Castellano, CEO of automation firm Caddi. “We’re looking for those who are curious, adaptable, and thoughtful about how they use AI. That mindset makes the biggest difference.”

The dual strategy underscores IBM’s effort to balance efficiency with innovation—eliminating legacy roles that can be automated, while investing in the human capital that can steer its next phase of technological expansion.

Still, the reality for many workers is less optimistic. Thousands will lose their jobs in the months ahead, even as the company celebrates new AI-driven hires.

Elixir, a decentralized finance (DeFi) liquidity provider, announced it is sunsetting its synthetic stablecoin deUSD in response to severe fallout from Stream Finance’s $93 million loss.

Stream, a DeFi yield aggregator, halted withdrawals on November 4 after an external fund manager disclosed the massive shortfall, revealing $285 million in total debt across lenders—including $68 million owed to Elixir.

This exposure triggered a cascade: deUSD’s peg to the USD collapsed to as low as $0.015, wiping out nearly all its value. Elixir has emphasized that while the token is now worthless, it remains committed to redeeming holders 1:1 in USDC.

deUSD launches as a synthetic stablecoin challenging Ethena’s USDe, backed by over-collateralized loans across DeFi protocols. Community analysis reveals recursive minting loops between Stream’s xUSD and Elixir’s deUSD, inflating TVL through circular lending (e.g., Stream mints deUSD, uses it as collateral to borrow USDC, and loops back to mint more xUSD).

FUD builds as Stream fails to provide proof-of-reserves for xUSD; users urged to withdraw from related vaults. Stream halts withdrawals after $93M loss disclosure; its staked stablecoin xUSD depegs to $0.10 amid $285M debt revelation.

Stream holds 90% of deUSD supply ($75M), borrowed to back xUSD. Stream wallets dump deUSD on Curve pools, causing price to crash from $1 to $0.40, then $0.03. Elixir processes 80% of redemptions (excluding Stream), disables mint/redeem functions, takes a holder snapshot, and announces sunset. deUSD trades at $0.026.

Claims portal launches for remaining holders; Elixir coordinates with Euler, Morpho, and Compound to liquidate Stream positions and recover funds. Elixir lent ~65% of deUSD’s backing ($68M USDC) to Stream via Morpho markets for higher yields, taking xUSD as collateral.

When xUSD depegged 77% due to Stream’s loss, deUSD’s backing “vanished,” exposing systemic risks in synthetic stablecoins. Stream controls 99% of deUSD lending positions but declined to close or repay them, freezing liquidity. Elixir disabled withdrawals to prevent Stream from liquidating deUSD holdings prematurely.

Market Panic: Thin liquidity on DEXs like Curve amplified the dump—over $30M in deUSD was sold on-chain in hours, leading to the near-total depeg. This highlights vulnerabilities in recursive leverage loops, where protocols like Stream and Elixir mutually inflate TVL (e.g., $60M growth in weeks via circular minting), but a single failure unravels the system.

Elixir’s Response and User Impact Redemptions

80% of non-Stream holders already redeemed 1:1 in USDC. A snapshot secures the rest; a claims portal live as of November 7 allows remaining deUSD/sdeUSD holders to claim full value. Elixir states: “deUSD holds no value and the stablecoin has been sunset. Please do not buy or invest in deUSD.”

Elixir is withdrawing liquid assets and collaborating with Euler, Morpho, Compound, and vault curators to unwind Stream’s positions. It claims seniority on the $68M loan and expects full honoring of obligations.

Primarily deUSD holders now worthless on secondary markets and lenders to Stream via Morpho/Euler potential losses if recoveries fall short. No direct impact on USDC holders, but it erodes trust in synthetic stables.

Implications for DeFi

This event underscores the fragility of uncollateralized synthetics and leveraged lending: interlinked exposures can propagate failures rapidly, as seen in past cascades like the October 2025 liquidation wipeouts. While Elixir prioritizes user protection, the $93M Stream loss and $128M Balancer exploit recovery signals ongoing risks—analysts warn of eroding confidence in yield-chasing protocols.