# Пов'язані статті щодо Jensen Huang

Центр новин HTX надає останні статті та поглиблений аналіз на тему "Jensen Huang", що охоплює ринкові тренди, оновлення проєктів, технологічні розробки та регуляторну політику в криптоіндустрії.

Trend in US Stocks: Jensen Huang's One Sentence Triggers $47 Billion Surge; Google Raises Funds for First Time in 20 Years

U.S. markets reached record highs on June 2nd, but the real story was the intensifying AI arms race, now pivoting from chip supremacy to a scramble for capital to fund compute infrastructure. The day highlighted two stark realities: Nvidia CEO Jensen Huang's endorsement of Marvell Technology as the "next trillion-dollar company" at Computex fueled a historic 32.5% surge, adding $47 billion to its value. Conversely, Alphabet announced its first equity raise in two decades—an $80 billion plan—signaling that even its massive cash flow can't keep pace with soaring AI capital expenditures, forecast to exceed $180 billion in 2026. While the S&P 500 closed above 7,600 for the first time, led by tech and semiconductor stocks (SOXX +5.79%), sector performance was mixed. Alphabet's 4% drop dragged down communications services, illustrating market anxiety over the unsustainable cost of the AI buildout. Hewlett Packard Enterprise soared 25% on stellar earnings, proving AI's benefits extend beyond chip designers to infrastructure providers. Beneath the index highs, concerns linger over extreme concentration in a few AI stocks and geopolitical tensions. The focus now shifts to upcoming economic data, particularly Friday's nonfarm payrolls, which could challenge the market's current "ignore rates, chase AI" mentality.

marsbit52 хв тому

Trend in US Stocks: Jensen Huang's One Sentence Triggers $47 Billion Surge; Google Raises Funds for First Time in 20 Years

marsbit52 хв тому

Jensen Huang: Vera Rubin Full Mass Production, AI Agent a Key Focus, Challenging Intel to Target the Next-Generation AI PC Gateway

NVIDIA CEO Jensen Huang delivered the keynote speech at GTC Taipei 2026, announcing several major product launches and strategic directions. The company's Vera Rubin architecture is now in full-scale production, with OpenAI, Anthropic, and SpaceX among the first customers. NVIDIA highlighted AI Agent as a key future focus, introducing the Vera CPU designed for AI agents and the Vera BlueField-4 STX for secure, chip-level AI storage processing. A significant move involves challenging Intel in the PC market. NVIDIA, in collaboration with MediaTek, is developing the RTX SPARK PC chip (manufactured by TSMC) for Windows systems, set to launch this fall for laptops and desktops. This signals NVIDIA's push into the next-generation AI PC arena, aiming to provide a vertically integrated core computing platform for the entire Windows ecosystem, similar to Apple's approach. Other announcements include the new Nemotron 3 Ultra AI model and the NVIDIA DSX platform, described as a complete "playbook" for building AI factories, allowing performance simulation and validation before physical deployment. In automotive, the DRIVE Hyperion platform was positioned as a global robotaxi platform, with major Chinese automakers like BYD, Geely, Zeekr, Xiaomi, and Pony.ai already adopting or developing autonomous driving solutions based on it. The Alpamayo 2 super open inference model for robotaxis was also introduced. For robotics, NVIDIA unveiled the Isaac GR00T humanoid robot reference platform for academic research and a large open-source agent tools and skills suite for Physical AI. The company plans to collaborate with global humanoid robot manufacturers, including China's Unitree, whose H2 Plus robot served as the reference hardware for the GR00T platform demonstration.

marsbitВчора 06:14

Jensen Huang: Vera Rubin Full Mass Production, AI Agent a Key Focus, Challenging Intel to Target the Next-Generation AI PC Gateway

marsbitВчора 06:14

Jensen Huang Publicly Challenges Google and Amazon, Is the Chip Business Entirely Sustained by Anthropic?

In a candid interview, Nvidia CEO Jensen Huang challenged competitors like Google and Amazon, admitted past strategic errors, and criticized U.S. export controls on AI chips. He framed Nvidia’s role as turning “electricity in, tokens out,” emphasizing the complexity and value of AI inference. Huang dismissed rival custom chips like Google’s TPU and Amazon’s Trainium as inflexible and niche, claiming their growth relies heavily on clients like Anthropic. He also acknowledged missing early investment opportunities in OpenAI and Anthropic. On China, Huang warned that export restrictions risk pushing the country toward self-sufficiency and could cost U.S. leadership in AI. Finally, he explained Nvidia’s acquisition of Groq as a move to serve premium, low-latency token markets. Throughout, Huang emphasized ecosystem trust and Nvidia’s central role in global AI infrastructure.

marsbit04/21 07:00

Jensen Huang Publicly Challenges Google and Amazon, Is the Chip Business Entirely Sustained by Anthropic?

marsbit04/21 07:00

The TAO Subnet Team Praised by Jensen Huang Has Parted Ways with the Founder Amidst a Fallout

Nvidia CEO Jensen Huang recently praised the decentralized AI project Bittensor (TAO) during a podcast, specifically highlighting a 72-billion-parameter Llama model trained collaboratively by a subnet team called Covenant AI. This endorsement initially boosted TAO's price, but the situation deteriorated rapidly when Covenant AI's founder, Sam Dare, publicly announced the team's departure from the Bittensor network. Covenant AI accused Bittensor and its key figure, Jacob Steeves (known as Const), of centralization and abuse of power, contradicting Bittensor’s decentralized ethos. The team claimed that Const exercised unilateral control by halting subnet emissions, removing administrative rights, discarding infrastructure, and using token sales to pressure the team. They argued that Bittensor’s governance is effectively centralized under Const, despite claims of distributed control. As a result, Covenant AI decided to leave, intending to continue its work on decentralized AI training elsewhere. The exit has sparked significant concern within the Bittensor community, raising doubts about the network’s decentralization narrative, technical future, and token value. TAO’s price fell sharply following the news. Const responded vaguely on social media, suggesting the event would push Bittensor toward more decentralized, “headless” subnets, but has not addressed the specific allegations in detail. The incident has damaged Bittensor’s reputation while raising Covenant AI’s profile.

Odaily星球日报04/10 03:08

The TAO Subnet Team Praised by Jensen Huang Has Parted Ways with the Founder Amidst a Fallout

Odaily星球日报04/10 03:08

Understanding Jensen Huang's Physical AI: Why Is Crypto's Opportunity Also Hidden in the 'Nooks and Crannies'?

Jensen Huang's recent speech at Davos signals a pivotal shift in AI: the transition from the training-focused "brute force" era of AI 1.0 to the new paradigm of "Physical AI" and inference. This marks the next phase after Generative AI, focusing on real-world application and embodiment. Physical AI aims to solve the "last-mile" problem of AI: moving from digital intelligence to physical action. While LLMs have consumed vast digital data, they lack understanding of the physical world—like how to twist open a bottle cap. Physical AI requires three core capabilities: 1. Spatial Intelligence: AI must perceive and interpret 3D environments in real-time, understanding object properties, depth, and interaction dynamics. 2. Virtual Training Grounds: Systems like NVIDIA’s Omniverse enable simulation-to-real (Sim-to-Real) training, allowing robots to learn through vast virtual iterations without costly physical failures. 3. Electronic Skin and Touch Data: Sensors that capture tactile feedback—temperature, pressure, texture—are critical. This data is a new, untapped asset class. This shift opens significant opportunities for Crypto and Web3 ecosystems. DePIN networks can crowdsource hyperlocal spatial data from "every corner" of the world through token incentives. Distributed computing networks can provide edge-based rendering and inference power for low-latency physical responses. Tokenized data ownership and privacy-preserving sharing mechanisms can enable the scalable, ethical collection of sensitive tactile data. In short, Physical AI isn’t just the next chapter for Web2—it’s a catalyst for Web3 domains like DePIN, DeData, and decentralized AI.

marsbit01/23 00:35

Understanding Jensen Huang's Physical AI: Why Is Crypto's Opportunity Also Hidden in the 'Nooks and Crannies'?