Core Viewpoint - Serve Robotics has experienced significant stock volatility, with a 23% decline last year but a 40% increase in early 2026, indicating potential recovery and growth in the autonomous delivery market [1][3]. Company Overview - Serve Robotics, spun off from Postmates in 2021, is a leading developer of autonomous last-mile logistics solutions, focusing on small delivery robots for the Uber Eats network [2][3]. - The company is building thousands of Gen 3 robots designed to operate on sidewalks, aiming to tap into a projected $450 billion market for robotic and drone delivery by 2030 [2][3]. Operational Developments - Serve has deployed robots in around 3,600 restaurants across five U.S. cities, completing over 100,000 food deliveries since 2022 [4]. - The latest robots, powered by Nvidia's Jetson Orin, have achieved Level 4 autonomy, allowing them to operate safely without human intervention [4]. Financial Performance - Serve generated $1.77 million in revenue during the first three quarters of 2025, with expectations to reach approximately $2.5 million for the full year [7][8]. - Management anticipates a tenfold revenue increase in 2026, projecting around $25 million due to the deployment of 2,000 active robots [8][13]. Cost Structure - The company reported total operating expenses of $63.7 million in the first three quarters of 2025, more than double the previous year's expenses [9]. - A significant loss of $67 million was recorded during the same period, indicating financial challenges despite revenue growth [10]. Market Valuation - Serve's stock is currently trading at a price-to-sales (P/S) ratio of 392, which is considered extremely high compared to industry peers like Nvidia and Palantir Technologies [11]. - If revenue projections are met, the forward P/S ratio could adjust to 44, suggesting a more reasonable valuation, though still not cheap [13].

Core Insights - Deutsche Bank predicts that 2026 will mark the year of large-scale deployment for Robotaxis and humanoid robots, transitioning from testing to commercialization [2][3] - The report emphasizes the emergence of a new supply chain for humanoid robots, with suppliers shifting focus to achieve mass production [3][5] Group 1: Humanoid Robot Supply Chain - The supply chain for humanoid robots is taking shape, with actuators becoming the "muscle" entry point [4] - Schaeffler aims to be a key supplier of actuators for humanoid robots, showcasing a compact integrated planetary gear actuator at CES [6] - Hyundai Mobis plans to supply actuators for Boston Dynamics' Atlas, leveraging the automotive supply chain for manufacturing [7] Group 2: Onboard Chip Landscape - Nvidia remains the dominant player in onboard processors for humanoid robots due to performance and ease of use, with various companies utilizing its Jetson Orin or Thor [8][9] - Tesla and Xpeng are developing their own inference chips, indicating a diversification in the chip landscape [9] Group 3: Physical AI Transition - A significant paradigm shift is observed from pre-programmed actions to visual-language-action (VLA), enabling robots to reason and complete tasks [11][12] - The industry debate has shifted from "simulation vs. reality" to how to efficiently close the loop between the two [14] Group 4: Commercial Viability of Humanoid Robots - The report suggests that general-purpose humanoid robots will initially be deployed in specific scenarios to prove commercial viability before entering households [18][19] - Keenon Robotics holds a 40% global market share in service robots, with plans to showcase its humanoid robot XMAN-R1 at CES 2026 [20] Group 5: Cost Reduction and Scalability - Cost reduction in humanoid robots is driven by increased volume and improved supplier negotiations, with some companies reporting costs dropping from $200,000 to $100,000 [22][24] - Mobileye's Mentee project indicates that with an annual production of 50,000 units, manufacturing costs could drop to $20,000 per unit, and potentially to $10,000 with 100,000 units [24] Group 6: Robotaxi Commercialization Momentum - Deutsche Bank believes that 2026 will see stronger commercialization momentum for Robotaxis, with Tesla planning to launch its Robotaxi in 2025 [26][27] - Waymo has provided over 10 million paid rides since its inception, with plans to expand its service to international markets [27][28] Group 7: Nvidia's Alpamayo Platform - Nvidia introduced the Alpamayo platform for autonomous driving, aiming to lower the barrier for automakers to deploy advanced capabilities [30][31] - Despite the potential advantages, concerns remain about whether Nvidia can meet real-world edge cases compared to Tesla's data collection [31][32] Group 8: Industry Innovations - Aptiv showcased an end-to-end AI-driven ADAS platform, emphasizing cross-industry applications and real-time data sharing [33] - Visteon launched a SmartCore HPC domain controller with 700 TOPS, facilitating the integration of multiple sensors into a single system [35]

Core Insights - The report from Deutsche Bank predicts that 2026 will mark a significant transition for AI in the physical world, particularly in the fields of autonomous vehicles and humanoid robots, moving from testing to scaling [1] Group 1: Humanoid Robots - The supply chain for humanoid robots is forming, with suppliers transitioning to provide integrated solutions and core components [1] - Schaeffler aims to be a key player in humanoid robotics by offering integrated planetary gear actuators, showcasing a compact unit with a torque range of 60–250 Nm [4] - Companies like NEURA and Hyundai Mobis are collaborating to leverage automotive supply chains for humanoid robot manufacturing [4] Group 2: Autonomous Vehicles - The deployment of Robotaxis is gaining momentum, with significant commercial activity expected in 2026, particularly with Tesla's planned launch [10] - Waymo has provided over 10 million paid rides and is expanding its services to international markets, indicating a shift from concept to operational data [15] - Mobileye plans to launch L4 Robotaxi services in Los Angeles this year, showcasing the industry's movement towards real-world applications [15] Group 3: Technology and Innovation - Nvidia remains the dominant player in onboard processors for humanoid robots, with companies like Boston Dynamics utilizing its technology for advanced capabilities [3] - The shift from scripted actions to visual-language-action (VLA) models allows robots to reason and adapt to new environments [3] - The competition in training methods is evolving, focusing on efficient closed-loop systems that integrate real-world data with simulations [7] Group 4: Cost Reduction and Scalability - The cost reduction formula for humanoid robots is driven by increased production volume and improved supplier negotiations [9] - Companies are targeting significant cost reductions, with projections indicating that manufacturing costs could drop from $200,000 to $50,000 as production scales [10] - Visteon is introducing modular solutions to help automakers integrate AI capabilities without overhauling existing architectures, enhancing cost competitiveness [13] Group 5: Market Dynamics - The CES 2026 event highlighted a shift in focus from feasibility to scalability and cost reduction in both autonomous vehicles and humanoid robots [14] - The industry's future will depend on tracking supply chain integration, production capacity, and unit cost curves rather than just innovative demonstrations [14]

Hey guys, it's the holiday season, the best time of the year. Mommy and dad bought all kinds of nice things for you guys. We bought you guys some toys, an avocado, a lobster, and an ice cream cone.And each one of you is something really special. Ammo. A Jetson Net. A Jetson Net.It's got 8 GB of memory. tops of computational power. It runs all kinds of models.And for Christmas, for Christmas, mommy and daddy got you a Jetson Orin. I know. It's incredible.67 trillion operations per second. 64 GB. You could us ...

Hey guys, it's the holiday season, the best time of the year. Mommy and dad bought all kinds of nice things for you guys. We bought you guys some toys an avocado a lobster and an ice cream cone.And each one of you is something really special. Ammo. A Jetson Nano. A Jetson Nano.It's got 8 GB of memory tops of computational power. It runs all kinds of models. And for Christmas Kuba, for Christmas, mommy and daddy got you a Jetson Orin.I know. It's incredible. 67 trillion operations per second.64 GB. You could ...

Group 1 - NVIDIA's newly launched Jetson Thor is considered the physical intelligence core for robots, featuring Blackwell GPU and 128 GB memory, achieving 2070 FP4 TFLOPS AI computing power, which is 7.5 times that of the previous Jetson Orin [2] - The introduction of Jetson Thor enables advanced humanoid robots to process large sensory data and large language models (LLM) in real-time, enhancing their ability to see, think, and act [2] - The humanoid robot chip market is expected to exceed $4.8 billion by 2028, driven by the adoption of this technology by companies like Agility Robotics, Boston Dynamics, and Amazon [2] Group 2 - In Q2 2025, the NAND Flash industry is projected to see a quarter-over-quarter revenue increase of over 20%, despite a slight decline in average selling prices (ASP) [4] - Major manufacturers have implemented production reduction strategies to alleviate supply-demand imbalances, resulting in significant growth in overall output [4] - The combined revenue of the top five NAND Flash manufacturers reached $14.67 billion in Q2 2025, reflecting a 22% quarter-over-quarter increase [5]

Core Viewpoint - The recent launch of NVIDIA's Jetson Thor is seen as a significant advancement in robotics, enhancing AI computing power and enabling higher capabilities in humanoid robots [1] Group 1: Stock Performance - Several US robotics stocks experienced notable gains, with Serve Robotics rising over 15%, Richtech Robotics increasing nearly 14%, and iRobot up more than 3% [1] Group 2: Technological Advancements - NVIDIA's Jetson Thor features a Blackwell GPU and 128 GB of memory, achieving 2070 FP4 TFLOPS of AI computing power, which is 7.5 times that of the previous Jetson Orin model [1] - This technological leap allows robots to process large amounts of sensory data and large language models (LLM) in real-time, enhancing their ability to see, think, and act [1]

Group 1: Industry Trends - The demand for domain controllers in the robotics industry is surging due to the increasing complexity of data and scenarios as robots become more practical [1][3] - Major companies in the robotics sector are optimistic about future sales, with predictions of annual shipment volumes potentially doubling in the coming years [2][3] - The robotics domain control market is expected to grow significantly, with estimates suggesting that embodied intelligent robots could become the next generation of mass smart terminals after smartphones and cars [2][3] Group 2: Technological Developments - NVIDIA has launched the Jetson Thor platform, which boasts a 7.5 times increase in AI computing power and a 3.5 times improvement in energy efficiency compared to its predecessor, Jetson Orin [1] - Companies are exploring high-cost, high-performance domestic solutions to compete with expensive international products like the Jetson Thor developer kit, priced at $3,499 [1][3] - The integration of multiple controllers within robots is leading to challenges in space utilization and system complexity, prompting the development of more compact and efficient solutions [4][5] Group 3: Competitive Landscape - Numerous companies, including Tianzhun Technology and Zhiwei Intelligent, are entering the robotics domain control market, intensifying competition [5][6] - Customized solutions are becoming a trend as companies adapt to the evolving needs of robot manufacturers, with a focus on modular design and standardization of input/output interfaces [6][7] - Tianzhun Technology is actively collaborating with leading robot manufacturers to develop high-performance domain control solutions based on NVIDIA's latest chips [6][7]

Core Viewpoint - NVIDIA has launched Jetson Thor, a next-generation supercomputer for robotics, significantly enhancing AI computing power and energy efficiency compared to its predecessor, Jetson Orin [1][2]. Group 1: Product Launch and Specifications - Jetson Thor offers 7.5 times the AI computing power and 3.5 times the energy efficiency of Jetson Orin, capable of running various generative AI models and specialized robotics models [1]. - The developer kit for Jetson Thor is priced at $3,499, while bulk purchases of the Jetson T5000 module are available at $2,999 each for orders over 1,000 units [1]. - Jetson Thor integrates a powerful Blackwell GPU and 128GB of memory, delivering up to 2070 FP4 TFLOPS of AI computing power, enabling real-time processing of multimodal models [7]. Group 2: Market Position and Strategy - NVIDIA positions itself as a provider of computing platforms for robotics rather than a manufacturer of robots, supporting companies in building their robotic solutions [6]. - The company has established a competitive barrier in the robotics sector through a comprehensive hardware and software ecosystem, similar to its strategy in the AI industry with CUDA technology [8]. - Major robotics companies, such as Galaxy General, are already utilizing Jetson Thor in their products, showcasing its capabilities in complex operational scenarios [8]. Group 3: Vision for Physical AI - NVIDIA's focus on "Physical AI" aims to advance robotics beyond mere perception and communication to actual execution of tasks in the real world [10]. - Jetson Thor is a critical component in NVIDIA's strategy to embed computing power directly into robots, facilitating real-time operations and reducing reliance on cloud computing [10][11]. - The integration of Jetson Thor is expected to enhance the intelligence of robots, enabling them to perform complex tasks and interact with humans more effectively [11][12]. Group 4: Future Outlook - The introduction of Jetson Thor is seen as a potential new growth curve for NVIDIA, with the robotics sector viewed as having long-term potential despite current challenges [12]. - The ability of Jetson Thor to process large amounts of sensory data and run advanced models positions it as a significant advancement towards achieving general-purpose robotics [12].

Core Viewpoint - Nvidia's upcoming Q2 FY2026 earnings report is highly anticipated as it serves as a key indicator of the AI investment climate and tech stock trends, given its status as a leading beneficiary of the AI wave and its significant market capitalization [1]. Earnings Outlook - Market expectations for Nvidia's revenue are set at $45.5 billion, reflecting a year-over-year increase of 51.5%, with Non-GAAP earnings per share projected at $0.99, up 46.2% year-over-year. The recent stock price increase is primarily driven by valuation expansion rather than significant upward revisions in earnings forecasts. Key focus will be on whether gross margins can recover this quarter, amid challenges such as slowing performance improvements per card and rising complexity and manufacturing difficulties [3]. Q1 Earnings Growth - Nvidia's Q1 earnings report shows a revenue of $44.062 billion, a substantial increase of 69.18% year-over-year, with net profit reaching $18.775 billion, up 26.17% [5]. Stock Price Volatility - Options market data indicates that Nvidia's market capitalization could experience fluctuations of approximately $260 billion following the earnings report. Analysts are closely monitoring the report to assess the reasonableness of its $4 trillion market cap. Recent ratings show 11 institutions have given Nvidia a buy rating, with target prices ranging from $190 to $206 [8]. New Product Launch - Nvidia has launched the Jetson Thor, a supercomputer for robotics based on the Blackwell architecture, which significantly enhances AI computing power by 7.5 times and energy efficiency by 3.5 times compared to its predecessor, Jetson Orin. This product is aimed at various industries, including manufacturing, logistics, and healthcare, and is priced at $3,499 for the developer kit [15][16].