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AI & Enterprise

China open-weight models close gap with U.S. closed AI, with costs one-fifth and a 4.4-month gap

Open-weight AI models are rapidly catching up with U.S. big tech’s top closed models, changing how companies deploy AI tools. Mozilla analysed the performance gap at about 4.4 months. Its report said value for money matters more than raw performance, citing Moonshot AI’s open model Kimi K3 and Anthropic’s closed model Fable 5. It said closed models still lead in expert work, intensive search and long-context tasks.

AI & Enterprise

China\'s Z.ai first-half revenue surges 400 percent as it shifts to cloud-focused model

Chinese AI company Z.ai said first-half revenue rose 400 percent from a year earlier to 953.89 million yuan, driven by cloud-based deployment and API business. The company posted a smaller overall loss of 2.07 billion yuan, while adjusted net loss widened to 1.96 billion yuan and R&D spending rose to 2.13 billion yuan. Cloud deployment revenue surged and its share of total revenue jumped, but gross margin fell as computing and inference costs weighed on profitability.

AI & Enterprise

Rethinking AI performance scaling beyond parameter count

Jie Tang, a Tsinghua University professor and founder of AI startup Z.ai, said AI model performance is not determined by parameter count alone. He said data volume, how computing resources are used, and operating conditions also matter. Tang pointed to shifts from Jared Kaplan’s 2020 scaling results to DeepMind’s 2022 Chinchilla law and argued that inference-driven lifetime costs can favor smaller models trained longer. He also described how MoE architectures change scaling trade-offs.