Artificial intelligence startup OpenAI submitted a new trademark application for its brand name to the U.S. Patent and Trademark Office, hinting at a range of potential product lines, spanning hardware, robotics, and advanced computing.
Among the products listed in the filing are AI-powered hardware, including headphones, smartwatches, smart jewelry, augmented and virtual reality headsets, laptop and phone cases, and even remote controls. These devices are described as being designed for “AI-assisted interaction, simulation, and training.”
The application adds weight to ongoing speculation that OpenAI is venturing into hardware development. Last year, the company confirmed a collaboration with former Apple designer Jony Ive on a hardware project. OpenAI’s CEO, Sam Altman, recently told Korean publication The Elect that the company aims to develop AI-driven consumer electronics through industry partnerships.
However, Altman also tempered expectations, stating that even a prototype AI device could take years to materialize.
Notably, the trademark filing also references humanoid robots equipped with communication and learning functions. This aligns with OpenAI’s recent hiring of Caitlin Kalinowski, formerly of Meta’s AR division, to lead a new robotics team.
Reports indicate that OpenAI is experimenting with robots—possibly humanoid in form—integrating AI and custom sensors to enable human-like intelligence in real-world settings.
Further in the application, OpenAI hints at the development of custom AI chips and services designed to leverage quantum computing for enhanced AI performance. While rumors have long suggested OpenAI’s interest in proprietary chips, reports indicate the company is working with semiconductor giants Broadcom and TSMC, potentially bringing a custom chip to market by 2026.
OpenAI’s potential investment in quantum computing remains less clear. However, the company recently hired a former quantum systems architect from PsiQuantum, a move that suggests interest in the technology. Quantum computing has been touted as a game-changer for AI, offering the potential to significantly reduce computing costs and training times.
