March 22, 2026 09:00 PM GMT Embodied AI | North America AI's Next Challenge: Modeling the Physical World. LLMs train on, process, and generate text in its many forms. While they have proven to be powerful tools for white-collar tasks such as coding, search, and writing, AI's broader potential may lie in the physical world, which is governed by the laws of physics - substances, thermodynamics, fluid dynamics and the behavior of light - in a constantly changing 3-dimensional space. 'World Models' are AI syste ...

Innoviz Technologies (NasdaqCM:INVZ) Update / briefing March 23, 2026 10:00 AM ET Company ParticipantsDavid Elooz - Director of Industry SolutionsOmer Keilaf - CEO and FounderOperatorHello everyone, and welcome to the Innoviz Physical AI Webinar. Before we get started, I would like to remind you that our discussion today will include forward-looking statements that are subject to risks and uncertainties relating to future events and the future financial performance of Innoviz. Actual results could differ ma ...

Core Insights - Automakers are increasingly exploring new technologies such as AI, robotics, and driverless vehicles, with companies like Tesla leading the charge [1] - Lucid Motors has made significant announcements regarding its partnership with Uber, which could enhance its market position in the EV sector [2] Company Developments - Lucid's recent investor day highlighted its strategic plans, including a new midsize platform and advancements in its driverless vehicle partnership with Uber [2] - The partnership with Uber includes a $300 million investment and aims to integrate Nuro's autonomous technology into over 20,000 Lucid Gravity SUVs for exclusive use on Uber's platform over the next six years [4] - The agreement is evolving to deploy Lucid's upcoming midsize EV platform as robotaxis, potentially doubling the program's scope to about 40,000 vehicles [6] Product Innovations - Lucid's midsize platform is designed to deliver advanced EVs at a reduced cost without sacrificing performance, with upcoming models named Cosmos, Earth, and Lunar [7] - The Lunar concept is a dedicated two-seat robotaxi designed to optimize economics throughout its lifecycle [8] Market Position and Challenges - Lucid has achieved eight consecutive quarters of record deliveries, indicating that previous production challenges are being addressed [6] - Despite the strategic expansion, Lucid's current market position may not make it an attractive buy, as Uber's role in the partnership may be more appealing to investors [9] - The evolution of the partnership is crucial for scaling production, which is essential for improving the economics of driverless vehicles [10] - Investors should focus on how Lucid improves unit economics and gross profits, especially in comparison to competitors like Rivian [11]

Core Viewpoint - Nvidia is expanding its business beyond GPUs, positioning itself as a comprehensive provider of AI infrastructure, including data center accelerators, hardware, and software solutions for physical AI applications like robotics and autonomous driving [3][4]. Group 1: Product Expansion - At the GTC conference, Nvidia introduced a variety of products, including data center accelerators, networking products, and open-source models, indicating a shift towards a broader AI infrastructure role [3]. - The introduction of the Groq 3 and Groq 3 LPX chips, which enhance the performance of Nvidia's Rubin platform, signifies a diversification in Nvidia's product offerings beyond traditional GPUs [7]. - The Groq 3 LPX chip can increase inference throughput by 35 times per megawatt when used with Rubin CPU and GPU, showcasing significant performance improvements [7]. Group 2: Chip Heterogeneity - Nvidia's strategy includes integrating LPU (Language Processing Unit) technology with GPUs to address the growing demand for faster inference speeds in large models, indicating a trend towards chip heterogeneity in AI workloads [10][11]. - Ian Buck emphasized that while GPUs will continue to dominate current AI applications, the combination of LPU and GPU will be crucial for next-generation AI workloads, particularly those involving trillion-parameter models [10]. - The industry is moving towards a heterogeneous computing environment, where different types of chips are needed for various workloads, as highlighted by AMD's collaboration with Meta to design semi-custom chips [10][11]. Group 3: Physical AI Development - Nvidia is making significant strides in physical AI, launching the Isaac simulation framework and the Cosmos model for robotics, which aims to unify synthetic world generation and physical AI reasoning [15][18]. - The company is focusing on open-source technologies to foster collaboration in the development of physical AI, as it believes that no single company can achieve this alone [15][19]. - Rev Lebaredian noted that the challenges in robotics are multifaceted, requiring advancements in hardware and software to make robots more functional and accessible [19].

要理解今天的英伟达可能比以往更不容易，但这家牵动着诸多AI领域发展的公司到底在如何勾勒AI的未来，仍值得探究。 英伟达业务扩充的信号变得明显。本届GTC大会上，英伟达发布的产品涵盖了数据中心加速器、机架、网络产品和多款开源模型。CUDA、GPU、LPU（语 言处理单元）、AI工厂、机器人、自动驾驶、开源模型等关键词在英伟达CEO黄仁勋的演讲中被频频提及。这家以GPU闻名的公司，如今将其定义为一家 包揽AI基础设施或AI工厂多个环节的厂商似乎更加合适。 即便只是在数据中心加速器这一环节，英伟达的产品类型也变得多样。Rubin平台在GPU之外，一款LPU也加入进来。原属于专用集成电路（ASIC）的LPU 与通用的GPU站在不同阵营，但英伟达拿下Groq的授权后，开启了两种芯片的联合。 而在以大型云厂商为客户的60%业务之外，看起来更为庞杂的40%业务中，英伟达也落下新子。物理AI中的自动驾驶和机器人成为两个重要抓手。为了部署 物理AI，英伟达不仅做硬件，还做自动驾驶平台和模型。 要理解今天的英伟达可能比以往更不容易，但这家牵动着诸多AI领域发展的公司到底在如何勾勒AI的未来，仍是值得探究的问题。GTC大会期间，第 ...

Core Viewpoint - The article discusses the advancements in generative video models, particularly focusing on the ProPhy framework, which aims to enhance the physical understanding and spatial alignment of video generation, moving from mere visual imitation to true physical simulation [1][8][33]. Group 1: Current State of Generative Video Models - Generative video models like Wan and NVIDIA's Cosmos can create highly realistic dynamic scenes that appear to mimic the real world [1][2]. - Despite their visual realism, these models often lack a true understanding of physical principles, leading to inconsistencies in generated videos [3][6][10]. Group 2: Limitations of Existing Models - Current models primarily rely on implicit learning and coarse global physical category labels, which do not allow for a clear understanding of different physical laws and their evolution in reality [10]. - There is a lack of fine-grained spatial alignment, meaning that models cannot accurately position physical events in the generated scenes [10]. Group 3: Introduction of ProPhy - ProPhy introduces a new progressive physical alignment framework that enables video diffusion models to achieve layered physical understanding and spatial physical alignment [8][9]. - This framework allows models to not only determine what physical phenomena to present but also where these phenomena should occur in the video [8][9]. Group 4: Mechanism of ProPhy - ProPhy employs a two-stage physical expert mechanism: the Semantic Physical Expert (SEB) for macro understanding of physical structures and the Refinement Expert Block (REB) for precise spatial alignment [13][14]. - SEB identifies potential physical phenomena from textual prompts, while REB dynamically assigns the most suitable physical expert to each spatial location [13][14]. Group 5: Experimental Results - ProPhy shows significant improvements in physical correctness and semantic adherence, with a 19.7% increase in joint metrics on the VideoPhy2 benchmark [20][22]. - In dynamic performance evaluations, ProPhy enhances the Dynamic Degree metric and overall quality scores, demonstrating its effectiveness in generating physically consistent videos [23]. Group 6: Implications and Future Directions - ProPhy represents a shift from visual similarity to adherence to physical rules, indicating a move towards a controllable physical world model [26][29]. - Future developments may include integrating continuous dynamics modeling and physical engines with generative models, potentially leading to a new AI form capable of simulating the operation of the world [34].

Group 1 - The core viewpoint of the article highlights the significant advancements in the field of artificial intelligence, particularly with the emergence of a new private agent assistant called "OpenClaw," which is setting a new standard for human-computer interaction [2] - The world model has gained unprecedented attention, with notable developments such as NVIDIA's founder Jensen Huang launching the world model platform "Cosmos" at CES 2026 and Turing Award winner Yann LeCun's startup AMI Labs completing a $1.03 billion seed round financing [2] - The ICLR 2026 conference, a premier machine learning event, will take place from April 23 to 27, 2026, in Rio de Janeiro, Brazil, receiving approximately 19,000 submissions with an acceptance rate of about 28% [2] Group 2 - To facilitate academic exchange among AI practitioners, a paper sharing event for ICLR 2026 is scheduled for April 18, 2026, in Beijing, inviting participants to register [4] - The ICLR paper sharing event will feature keynote speeches, paper presentations, roundtable discussions, and poster displays, focusing on popular topics such as agents and world models, with top experts and authors invited for academic interaction [7] - The event will take place at the Crowne Plaza Hotel in Zhongguancun, Beijing, from 09:00 to 17:30, with 200 in-person slots available [7]

Core Viewpoint - T. Rowe Price expresses a bullish outlook on Nvidia, emphasizing its leadership in robotics and physical AI, which is seen as the next major inflection point following data center AI [1][2][3]. Financial Performance - Nvidia generated $96.58 billion in free cash flow for FY2026, with $34.90 billion in Q4 alone, showcasing strong financial health and the ability to invest in next-generation platforms [6][11]. Competitive Advantage - Nvidia operates a proprietary software stack (CUDA, Isaac, Cosmos, Omniverse) that competitors would require a decade to replicate, providing a significant competitive moat [6][7]. - The company is not merely selling GPUs but also a comprehensive suite of domain-specific software, which deepens its competitive advantage [7]. Robotics and Physical AI - Nvidia's physical AI infrastructure is positioned as a critical development area, with the potential to bridge software intelligence and real-world applications [9]. - The company's IGX Thor platform for real-time physical AI extends its capabilities into industrial settings, further solidifying its market position [9]. Strategic Partnerships - Nvidia has partnered with Uber to deploy a level 4-ready mobility network, targeting 100,000 autonomous vehicles by 2027, which serves as a proof of concept for its ecosystem [10]. Market Valuation - The analyst consensus price target for Nvidia is set at $267.54, indicating significant upside potential from its current price of $183.22 [11]. - T. Rowe Price believes that if the robotics and autonomous vehicle ecosystems scale similarly to cloud AI, Nvidia's current valuation may prove conservative relative to its long-term earnings potential [11].

Group 1 - The core viewpoint of the article is that Stifel Nicolaus analyst Stephen Gengaro has maintained a $17 price target for Lucid (NASDAQ: LCID) following the March 12 Investor Day, indicating a potential 72.41% increase from the current price of $9.86 [1][2] - The bullish forecast is supported by significant updates presented during the Investor Day, including new midsize models, advancements in autonomous capabilities, and a medium- to long-term outlook for the company [2][3] - The introduction of the midsize platform is expected to expand Lucid's addressable market from approximately $40 billion to around $350 billion by 2030, although Gengaro has rated the stock as 'Hold' despite the positive outlook [3][5] Group 2 - Wall Street's consensus on Lucid stock is generally a 'Hold,' with an average 12-month price target of $12.86, reflecting a cautious approach despite the bullish sentiment from some analysts [3][5] - Morgan Stanley has issued a 'Sell' rating for Lucid, projecting a modest upside to $10, indicating differing opinions among analysts regarding the stock's potential [6] - Since the Investor Day, Lucid stock has experienced a decline of nearly 7%, and it is down 11% year-to-date, with a 54% decrease over the past 12 months [7][10]

Core Viewpoint - The article discusses the key announcements and developments presented by NVIDIA's CEO Jensen Huang at the GTC conference, highlighting the company's advancements in AI infrastructure, new chip platforms, and the potential revenue growth from AI-related products and services [3][10]. Group 1: New Chip Platforms - NVIDIA introduced the Rubin chip platform, which includes the Vera CPU, Rubin GPU, and several other components, aimed at enhancing AI and reinforcement learning capabilities [5][6]. - The Groq 3 LPU was showcased for the first time, with production set to ramp up in the second half of the year, indicating a strong focus on AI processing [6]. - The Rubin platform now consists of seven chips and five racks, designed to form an AI supercomputer that significantly boosts inference throughput and efficiency [6][8]. Group 2: Revenue Projections - Huang projected that revenue from AI chips, specifically from the Blackwell and Rubin platforms, could reach $1 trillion between 2025 and 2027, a significant increase from previous estimates [10]. - The customer base for NVIDIA has expanded to include major players like Alibaba and ByteDance, with 60% of revenue coming from large cloud service providers and 40% from diverse AI applications [10]. Group 3: Business Strategy and Ecosystem - Huang emphasized NVIDIA's commitment to collaborative design and vertical integration, positioning the company as a key player in the AI ecosystem [12]. - The company is involved in various sectors, including autonomous driving, financial services, healthcare, and telecommunications, showcasing its broad market reach [12]. Group 4: AI Impact and Innovations - Huang noted that the AI landscape has evolved dramatically over the past three years, with significant increases in computational demands and investment in AI startups [13][14]. - NVIDIA announced new partnerships in the automotive sector, including collaborations with BYD and Nissan, to develop Level 4 autonomous vehicles [14]. Group 5: New Products and Software - The GTC conference featured the introduction of several new products, including the Vera Rubin space module, which offers 25 times the AI computing power for space-based inference compared to previous models [14]. - NVIDIA also launched new software frameworks and open-source models aimed at enhancing the capabilities of intelligent robots and autonomous vehicles [15].