According to Sam Altman (@sama), it is expected that artificial intelligence systems smarter than the smartest human will soon operate directly on personal devices, such as smartphones, providing real-time assistance for a wide range of tasks (source: Twitter). This development signals a major transformation in the AI industry, as on-device AI models could enable enhanced privacy, faster processing, and more personalized user experiences. For businesses, this trend opens up opportunities for creating advanced AI-powered applications, improving customer engagement, and driving new revenue streams in the rapidly evolving mobile AI market.

According to Greg Brockman (@gdb), GPT-OSS has been released as a solution for entirely local AI tool deployment, enabling businesses and developers to run advanced language models without relying on cloud infrastructure (source: Greg Brockman, Twitter). This innovation emphasizes data privacy, reduced latency, and cost efficiency for AI-powered applications. Enterprises can now leverage state-of-the-art generative AI models for confidential tasks, regulatory compliance, and edge computing scenarios, opening new business opportunities in sectors like healthcare, finance, and manufacturing (source: Greg Brockman, Twitter).

According to Sam Altman (@sama), the release of gpt-oss marks a major advancement in open source AI models, offering performance comparable to the o4-mini while being able to run efficiently on a high-end laptop. Additionally, a smaller version of the model can operate directly on mobile phones, significantly lowering hardware barriers for advanced AI deployment. This breakthrough enables businesses and developers to integrate state-of-the-art AI technology into consumer devices without relying on large-scale cloud infrastructure, expanding opportunities for on-device AI applications, edge computing, and privacy-focused solutions (Source: Sam Altman, Twitter, August 5, 2025).

According to Yann LeCun (@ylecun), choosing a batch size of 1 in machine learning training can be optimal depending on the definition of 'optimal' (source: @ylecun, July 11, 2025). This approach, known as online or stochastic gradient descent, allows models to update weights with every data point, leading to faster adaptability and potentially improved convergence in certain AI applications. For AI businesses, adopting smaller batch sizes can reduce memory requirements, enhance model responsiveness, and facilitate real-time AI deployments, especially in edge computing and personalized AI services (source: @ylecun).

According to @GoogleAI, the Gemma 3n model introduces a unique mobile-first architecture that enables efficient understanding of text, images, audio, and video. Available in E2B and E4B sizes, Gemma 3n achieves performance levels comparable to traditional 5B and 8B parameter models, yet operates with a significantly reduced memory footprint due to major architectural innovations (source: Google AI blog, June 2024). This advancement opens new business opportunities for AI-powered applications on resource-constrained mobile devices, allowing enterprises to deploy advanced multimodal AI solutions in edge computing, mobile productivity tools, and real-time content analysis without compromising speed or accuracy.

According to Sam Altman's recent question on Twitter, the discussion about when an O3-mini level AI model could run natively on smartphones has sparked significant analysis in the AI community. Experts point out that current advancements in edge computing and hardware acceleration, such as Qualcomm's Snapdragon AI and Apple's Neural Engine, are rapidly closing the gap for on-device large language model inference (source: Sam Altman on Twitter, 2025-06-24). Industry analysts highlight that running O3-mini class models—which require considerable memory and computational power—on mobile devices would unlock new business opportunities in AI-powered personal assistants, privacy-centric applications, and real-time language translation, especially as devices integrate more advanced NPUs. The timeline for this breakthrough is closely tied to further improvements in mobile chipsets and efficient AI model quantization techniques, with some projections citing a realistic window within the next 2-4 years (source: Qualcomm AI Research, 2024; Apple WWDC, 2024).

According to PixVerse (@PixVerse_), the CVPR 2025 Second Workshop on Efficient and On-Device Generation (EDGE) will focus on the latest advancements in on-device AI generation, highlighting efficient and powerful AI models that can run directly on user hardware rather than relying on cloud infrastructure. This workshop, sponsored by PixVerse, includes awards for top research papers, emphasizing the growing business opportunity for organizations developing lightweight, high-performance AI solutions for edge devices. The industry trend toward efficient AI aligns with market demand for privacy, reduced latency, and scalability in sectors such as mobile computing, IoT, and consumer electronics (Source: PixVerse, Twitter, June 11, 2025).

According to Sam Altman (@sama) on Twitter, OpenAI is officially collaborating with Jony Ive, renowned as one of the world’s top designers, to develop a new generation of AI-powered computers. This partnership aims to combine OpenAI’s advanced artificial intelligence capabilities with Ive’s expertise in hardware design, potentially setting new benchmarks for AI device innovation and user experience. The move reflects a growing trend in the AI industry toward integrating cutting-edge AI models directly into consumer hardware, opening significant business opportunities for companies focused on AI-driven devices, edge computing, and intelligent interfaces (Source: Sam Altman, Twitter, May 21, 2025).