Understanding the Impact of ChatGPT on American Enterprises

Social media giant Meta is facing unprecedented AI infrastructure costs, with projected AI-related spending soaring to a staggering $65 billion, potentially contributing to a total annual expenditure of $119 billion! Faced with this astronomical bill, the tech giant has decided to develop its own AI chips, and has already made significant progress. Recent reports indicate that Meta is about to begin a small-scale deployment of its custom chips, signaling a gradual move away from Nvidia and its reliance on their GPUs.
The Spanish government has recently passed a new law imposing substantial fines on companies that fail to properly label AI-generated content, aiming to combat the spread of deepfakes. Digital Transformation Minister Oscar Lopez announced that the law, inspired by the EU's AI Act, mandates strict transparency requirements for AI systems deemed high-risk. Image caption: Image generated by AI, image licensing provider Midjourney. Lopez noted...
Maxis Berhad, a Malaysian telecommunications company, and Huawei Technologies (Malaysia) Sdn Bhd have announced a strategic collaboration to enhance intelligent network operations through Artificial Intelligence (AI) and Machine Learning (ML). This collaboration aims to accelerate Maxis' digital transformation and improve user experience and operational efficiency via self-optimizing network technologies. According to Maxis, the collaboration will involve a comprehensive joint project focused on integrating AI and ML deeply into network management.
SiliconCloud's platform now officially supports batch inference for DeepSeek-R1 and V3 APIs. Users can submit requests via batch APIs to process large datasets within 24 hours, overcoming the limitations of real-time inference rates. A key highlight is the significant price reduction; DeepSeek-V3 batch inference is substantially cheaper than real-time inference.