# Сопутствующие статьи по теме Robotics

Новостной центр HTX предлагает последние статьи и углубленный анализ по "Robotics", охватывающие рыночные тренды, новости проектов, развитие технологий и политику регулирования в криптоиндустрии.

Perspective: The current AI supercycle will last 15 years, but most are still buying stocks in the first FOMO stage

This article outlines a 15-year AI supercycle, segmented into four investment stages. It argues that while most investors are still focused on the first stage, smart money is already moving to the third. **Stage 1: The Foundation (2023-2025) - Priced In** The semiconductor layer (e.g., NVIDIA, AMD) is complete. While growth continues, the historic entry opportunity is over as risk/reward has compressed. **Stage 2: The Build-Out (2025-2027) - In Progress** This phase involves building the necessary physical infrastructure: power/utilities (CEG), cooling (VRT), networking (ANET), and nuclear SMRs (OKLO, SMR). Significant upside remains, but obvious names have already moved. **Stage 3: The Asymmetric Bet (2026-2028) - Positioning Window** AI moves into the physical world. Key areas include robotics/autonomy (Tesla Optimus), space/defense/drones (Rocket Lab, LUNR), and critical materials. This stage presents the best asymmetric risk/reward and is where positioning should occur now. **Stage 4: The Endgame (2028+) - Software Dominance** The mega-cap cloud platforms (Microsoft, Alphabet, Amazon, Meta), with their massive capital expenditure, will build the AI software layer and AGI infrastructure, aiming to win the entire cycle. **Core Conclusion:** The cycle is confirmed in Stage 2. Stage 3 (robotics, space, defense, nuclear SMRs) is where capital is currently rotating for maximum opportunity, while the majority of investors are expected to be 12 months behind this shift.

marsbit05/09 06:37

Perspective: The current AI supercycle will last 15 years, but most are still buying stocks in the first FOMO stage

marsbit05/09 06:37

Exploring Physical World AGI with "Visual Reasoning", ElorianAI Raises $55 Million

ElorianAI, co-founded by ex-Google AI expert Andrew Dai and former AI specialist Yinfei Yang, has raised $55 million in early funding to develop next-generation AI systems with advanced visual reasoning capabilities. While current large models excel in text-based tasks like programming and math, they perform poorly in visual reasoning—even top models like Gemini only match a 3-year-old’s ability in basic visual benchmarks. The key limitation lies in the architecture of current vision-language models (VLMs), which first convert visual inputs into text before reasoning, losing critical spatial and structural information. ElorianAI aims to build a native multimodal model that processes and reasons directly in visual space, enabling deeper understanding of physical relationships, constraints, and environments. The company plans to release a state-of-the-art visual reasoning model by 2026, with potential applications in robotics, disaster management, engineering, healthcare, and AI hardware. By using high-quality, diverse, and synthetically generated data, ElorianAI intends to create models that don’t just perceive but truly understand and reason about the physical world—bringing us closer to visual AGI.

marsbit04/23 10:10

Exploring Physical World AGI with "Visual Reasoning", ElorianAI Raises $55 Million

marsbit04/23 10:10

a16z: The Next Frontier of AI, The Triple Flywheel of Robotics, Autonomous Science, and Brain-Computer Interfaces

a16z presents a comprehensive investment thesis for the next frontier of AI: Physical AI, centered on a synergistic flywheel of robotics, autonomous science, and novel human-computer interfaces (HCIs) like brain-computers. While the current AI paradigm scales on language and code, the most disruptive future capabilities will emerge from three adjacent fields leveraging five core technical primitives: 1) learned representations of physical dynamics (via models like VLA, WAM, and native embodied models), 2) embodied action architectures (e.g., dual-system designs, diffusion-based motion generation, and RL fine-tuning like RECAP), 3) simulation and synthetic data as scaling infrastructure, 4) expanded sensory channels (touch, neural signals, silent speech, olfaction), and 5) closed-loop agent systems for long-horizon tasks. These primitives converge to power three key domains: * **Robotics:** The literal embodiment of AI, requiring all primitives for real-world physical interaction and manipulation. * **Autonomous Science:** Self-driving labs that conduct hypothesis-experiment-analysis loops, generating structured, causally-grounded data to improve physical AI models. * **Novel HCIs:** Devices (AR glasses, EMG wearables, BCIs) that expand human-AI bandwidth and act as massive data-collection networks for real-world human experience. These domains form a mutually reinforcing flywheel: Robotics enable autonomous labs, which in turn generate valuable data for robotics and materials science. New interfaces provide rich human-physical interaction data to train better robots and scientists. Together, they represent a new scaling axis for AI, moving beyond the digital realm to interact with and learn from physical reality, promising significant emergent capabilities and value.

marsbit04/18 07:05

a16z: The Next Frontier of AI, The Triple Flywheel of Robotics, Autonomous Science, and Brain-Computer Interfaces

marsbit04/18 07:05

World Models Shift from Prediction to Planning: HWM and the Challenge of Long-Horizon Control

World models have evolved from focusing on representation learning and future prediction to addressing long-horizon planning challenges. While models like V-JEPA 2 demonstrate strong predictive capabilities using large-scale video pre-training, they struggle with multi-stage control tasks due to error accumulation and exponential growth in action search space. HWM (Hierarchical World Model) introduces a two-level planning structure: a high-level planner outlines coarse subgoals over longer time horizons, while a low-level executor handles short-term actions. This decomposition reduces planning complexity and error propagation. In experiments, HWM achieved 70% success in real-world robotic tasks where flat models failed entirely. Complementary efforts include V-JEPA (focused on representation), HWM (on hierarchical planning), and WAV (World Action Verifier, on self-correction). Together, they mark a shift from pure world modeling to integrated systems capable of prediction, planning, and verification—key to deploying world models in real-world agents and long-term tasks.

marsbit04/17 10:26

World Models Shift from Prediction to Planning: HWM and the Challenge of Long-Horizon Control

marsbit04/17 10:26

Robotics' "ChatGPT Moment": AI Moves into the Physical World, Blockchain Accelerates the Arrival of the Machine Economy

The integration of AI and robotics is accelerating the shift from digital intelligence to physical AI, enabling machines to perceive, reason, and act in real-world environments. Companies like Figure, Tesla, and Skild AI are driving advancements in humanoid and specialized robots, supported by significant investments—such as Skild’s $14B and Figure’s $10B+ funding rounds. Key enablers include declining hardware costs (actuators, sensors, batteries), edge computing (e.g., NVIDIA Jetson), and AI "world models" that learn physical intuition from video data rather than programmed instructions. Despite progress, challenges remain: collecting real-world robotic training data is slow and costly, and simulation-to-reality gaps persist. Blockchain and tokenization offer solutions by incentivizing decentralized data contribution and enabling robots to function as economic agents—earning, spending, and distributing profits autonomously. By 2030, physical AI could transform industries and households, with robots operating via standardized platforms and skill-based "app stores." The convergence of affordable hardware, advanced AI, decentralized coordination, and edge compute promises a future where intelligent robots become ubiquitous assistants in daily life and high-risk applications.

marsbit04/17 01:16

Robotics' "ChatGPT Moment": AI Moves into the Physical World, Blockchain Accelerates the Arrival of the Machine Economy

marsbit04/17 01:16

a16z's 10,000-Word Article: The Next Frontier of AI Is Not in Language, But in the Physical World—The Triple Flywheel of Robotics, Autonomous Science, and Brain-Computer Interfaces

The next frontier of AI lies in the physical world, moving beyond language and code into robotics, autonomous science, and novel human-computer interfaces. These domains are powered by five core technical primitives: learned representations of dynamics, embodied action architectures, simulation and synthetic data infrastructure, expanded sensory channels, and closed-loop agent systems. Robotics applies these to real-time physical interaction, autonomous science enables AI-driven discovery through self-driving labs, and new interfaces—like AR, silent speech, and brain-computer interfaces—expand human-AI interaction bandwidth. Together, they form a mutually reinforcing flywheel: robotics enables automated science, science produces structured physical data to improve AI models, and new interfaces generate rich human-world interaction data. This convergence promises to unlock emergent capabilities as AI begins to scale in the physical domain.

marsbit04/16 06:51

a16z's 10,000-Word Article: The Next Frontier of AI Is Not in Language, But in the Physical World—The Triple Flywheel of Robotics, Autonomous Science, and Brain-Computer Interfaces

marsbit04/16 06:51

10 Charts to Understand the State of AI in 2026: US-China Gap Only 2.7%, Sharp Decline in Programmer Positions for Under-25s

The 2026 AI Index Report from Stanford HAI reveals that AI adoption is accelerating faster than PCs and the internet, with a 53% global adoption rate. However, societal systems, job markets, and measurement tools lag behind. Key findings include: - Benchmark reliability is questionable, with 42% of GSM8K math problems deemed invalid. - The U.S. and China show near-parity in model performance (2.7% gap), with the U.S. leading in compute/capital and China in research/manufacturing. - Top models (Anthropic, xAI, Google, OpenAI) show converging capabilities, shifting competition to cost and reliability. - Employment for young developers (22–25) fell nearly 20%, with McKinsey noting AI-driven reductions in services, supply chain, and engineering. - The U.S. ranks 24th in adoption (28.3%) despite leading investment ($285.9B private AI funding in 2025). - AI agent task success improved but has ~33% failure rates; physical robots struggle outside labs (12.4% home success vs. 89.4% in sim). - A stark expert-public divide exists: 73% of experts vs. 23% of the public view AI’s job impact positively. - GPT-4o’s annual water use exceeds 12M people’s needs; AI data centers consume power equivalent to New York State. The report underscores rapid AI integration amid unresolved ethical, environmental, and economic challenges.

marsbit04/15 00:18

10 Charts to Understand the State of AI in 2026: US-China Gap Only 2.7%, Sharp Decline in Programmer Positions for Under-25s

marsbit04/15 00:18

Meeting at the Pinnacle of Generalist: 30 Billion in 30 Days, What Did Qianxun AI Do Right?

Qianxun Intelligence, a Chinese embodied AI and robotics startup, completed two major funding rounds totaling 3 billion RMB within 30 days in early 2026, backed by prominent investors including Shunwei Capital (Lei Jun) and Yunfeng Capital (Jack Ma). Founded in January 2024 by a team with expertise in robotics, AI, and commercialization, the company focuses on developing general-purpose embodied AI models. Its open-source model, Spirit v1.5, surpassed competitors in performance benchmarks, demonstrating strong zero-shot generalization capabilities for complex tasks. The company follows a scaling law approach similar to large language models (LLMs), leveraging massive diverse datasets—including internet videos, wearable device data, and teleoperation data—to train its Vision-Language-Action (VLA) model. Qianxun employs a multi-source data engine, collecting over 200,000 hours of real-world interaction data, with plans to reach 1 million hours by 2026. It uses low-cost wearable devices for efficient data acquisition and emphasizes real-world deployment for continuous data feedback. The company has deployed robots like "Xiao Mo" in industrial settings (e.g., battery production lines for CATL) and commercial scenarios (e.g., as baristas in JD.com malls), using operational data to refine its models. This "commercialize while iterating" strategy supports both revenue generation and model improvement, positioning Qianxun to compete globally in embodied AI.

marsbit04/07 04:05

Meeting at the Pinnacle of Generalist: 30 Billion in 30 Days, What Did Qianxun AI Do Right?

marsbit04/07 04:05

The Year of Physical AI: A Trillion-Dollar Gamble on 'How the World Works'

The year 2026 is being positioned as the dawn of the "Physical AI" era, marked by major funding rounds and technological breakthroughs. This shift signifies AI's evolution from understanding the digital world to perceiving and acting within the physical world. Key events include Yann LeCun's AMI Labs raising $1.03 billion to develop "world models," Fei-Fei Li's World Labs securing funding, and companies like Tesla deploying humanoid robots (Optimus) in factories. This transition expands the AI model competition into a broader infrastructure battle encompassing hardware, data, simulation, and real-world integration. The core debate is between two AI paths: the established LLM (Large Language Model) approach focused on text prediction and the emerging "world model" approach, which aims to understand physical states for action-oriented tasks. Hardware, particularly dexterous robotic hands, is a critical and expensive challenge. Companies are racing to build capable robotic bodies, with Tesla, Boston Dynamics, and Figure AI making significant progress. NVIDIA is positioning itself as the essential infrastructure provider for this new era, offering a full suite of development tools and platforms. A major bottleneck is the scarcity of high-quality physical world interaction data, with companies exploring solutions through real-world data collection, synthetic data generation, and human teleoperation. Substantial investments in Q1 2026, exceeding $6.4 billion, signal strong belief in Physical AI's potential, moving beyond concept validation into infrastructure building. While challenges like the sim-to-real gap, unproven business models, and safety regulations remain, the tangible engineering progress suggests this is a genuine technological inflection point, not merely a bubble. For the global Chinese community, this shift represents a significant structural opportunity to leverage their strengths in technology, engineering, hardware manufacturing, and cross-border collaboration to become key players in building the foundational layers of the Physical AI ecosystem.

marsbit04/03 09:39

The Year of Physical AI: A Trillion-Dollar Gamble on 'How the World Works'

marsbit04/03 09:39

$300 Billion in One Quarter, AI Absorbs 80% of Global Venture Capital Funding

In Q1 2026, global venture capital funding reached a record $300 billion, with AI capturing 80% ($242 billion) of the total. Four mega-deals dominated: OpenAI ($122B), Anthropic ($30B), xAI ($20B), and Waymo ($16B), accounting for two-thirds of the quarter’s funding. The market showed extreme concentration, with fewer deals and larger average rounds, indicating a fragile breadth beneath the headline numbers. Crypto funding saw a modest recovery, reaching approximately $8.6 billion, though two-thirds of this occurred in March. Capital flowed mainly into regulated infrastructure like stablecoin payments, custody, and compliance, rather than speculative projects. Broader trends included growth in robotics, defense tech, and cybersecurity—often adjacent to AI. Geographically, the U.S. dominated with 83% of total funding, followed by China and the U.K. The venture market is increasingly fragmented: U.S.-led AI platforms attract massive late-stage capital, China’s on state-driven funding, and Europe faces a growth capital gap. Looking ahead, VC totals may remain high but concentrated. Valuation discipline is returning in secondary markets, exit windows remain volatile, and crypto must compete with AI for investor attention. The market is defined by selectivity and divergence, not broad recovery.

marsbit04/03 04:40

$300 Billion in One Quarter, AI Absorbs 80% of Global Venture Capital Funding