# BCI Articles associés

Le Centre d'actualités HTX fournit les derniers articles et analyses approfondies sur "BCI", couvrant les tendances du marché, les mises à jour des projets, les développements technologiques et les politiques réglementaires dans l'industrie crypto.

Early Investor of Unitree Bets on a Brain-Computer Interface Company

"Wabo Technologies, a brain-computer interface (BCI) startup, has secured new funding from Vertex Ventures (under Temasek), Hongtai Fund, Chengdu Future Industry Fund, and Huifengda Capital. Vertex Ventures is known for its early bet on Unitree Robotics. Inspired by the sci-fi film "Avatar," founder Zhang Xingzhi has pursued human-machine interaction for over a decade. He established Wabo in late 2025, coinciding with China's push for BCI as a future industry. Unlike companies focused solely on medical hardware, Wabo positions itself as a model company. Its core mission is to develop a "Human Neural Intent Model" that decodes EEG, EMG, and behavioral signals to understand a user's goals, confirmations, corrections, and stop intentions before physical action, translating them into standardized control signals for machines, robots, or smart devices. The young, full-stack team, backed by academic collaborators from Tianjin University and Fudan University, quickly attracted investors. The company had previously raised a seed round from Ceyuan Capital. Investors were impressed by Wabo's systematic capabilities in data collection, signal processing, and engineering, seeing potential beyond a single hardware product toward a reusable layer of human intent interpretation. Applications span medical rehabilitation, smart homes, embodied AI, and even space exploration, with Wabo partnering with Tianjin University on pioneering "space BCI" research. Wabo plans a dual-headquarters strategy, with Shanghai focusing on high-end R&D and model development, and Chengdu handling hardware, product development, and supply chain. The BCI sector in China is heating up, with over 30 financing deals in early 2026. Zhang believes future competition will center on who can build a sustainable data-model-scenario loop. He predicts industry consolidation within three years, with only companies that master this闭环 surviving. The vision from "Avatar" is inching closer to reality."

marsbitIl y a 18 h

Early Investor of Unitree Bets on a Brain-Computer Interface Company

marsbitIl y a 18 h

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

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

AI Industry Welcomes a Cash-Rich Tether

Tether, the company behind the stablecoin USDT, reported a staggering $13 billion profit in 2024, significantly outperforming major AI companies like OpenAI and Anthropic, both of which incurred substantial losses. With only 150 employees, Tether achieved a per capita output 60 times greater than OpenAI’s. Its business model is simple: for every USDT issued, Tether holds one US dollar, primarily investing these reserves in U.S. Treasury bonds. It earns the interest income without paying any to USDT holders. In 2024, interest alone contributed $7 billion to its profits. Now, Tether is aggressively investing in AI. It loaned over $600 million to Northern Data, Europe’s largest GPU cloud provider, acquiring what is effectively an AI training base. It also released QVAC Genesis, a massive open-source AI training dataset. Furthermore, Tether invested $200 million in Blackrock Neurotech, a pioneering brain-computer interface company. Additional investments in robotics could bring its total AI-related spending to nearly $2 billion. Tether’s move into AI may be driven by both anxiety over declining Treasury yields and ambition to establish itself as a tech leader. Ironically, it promotes "decentralized AI" despite being a highly centralized company itself. While OpenAI and Anthropic struggle with profitability and continuous fundraising, Tether leverages its highly profitable stablecoin business to fund its AI ambitions risk-free, making a paradoxical case that the best business model in AI might be not to do AI at all.

marsbit01/05 09:12

AI Industry Welcomes a Cash-Rich Tether

marsbit01/05 09:12

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