Executive Summary A recent remark by Groq founder Jonathan Ross raises an important question. If models begin to improve the quality of their own learning signals, then the AI growth logic we have become familiar with may no longer follow the same path of diminishing returns. This article does not ask whether Ross’s claim should be accepted at face value. It asks whether the idea behind it is already supported by a set of meaningful
Executive Summary Meta enters 2025 at the center of a major shift in AI. The company has scaled back FAIR while recruiting new leadership with exceptionally high compensation. It has expanded its compute infrastructure at unprecedented speed, yet appears less steady as the industry moves into the era of reasoning. These actions may seem contradictory, but together they reveal a deeper transformation taking place across the AI landscape. As AI shifts from a competition of
Executive Summary DeepSeek-OCR marks a major step forward in AI research, moving beyond efficiency optimization toward a deeper understanding of how machines can remember and forget. DeepSeek introduces Contexts Optical Compression, a method that transforms long textual contexts into visual representations. This approach reduces computational cost by an order of magnitude and redefines how language models process and retain information. Technically, DeepSeek-OCR employs a hybrid design that balances high-resolution perception with low activation memory. Strategically,
Introduction to Part 2 Following our previous discussion on the cloud AI training and inference market, this article will focus on the on-premises AI chip market for training and inference. Compared to the cloud market, on-premises AI solutions offer distinct advantages in low latency and data privacy. As emerging applications such as autonomous vehicles and smart devices grow, on-premises AI training and inference are expected to be key drivers of future market expansion. This article
Executive Summary The AI chip market is undergoing significant transformations, which can be understood through two key dimensions: deployment environments (cloud vs. edge) and market segments (training vs. inference). Cloud-based training currently dominates the market and is expected to maintain strong growth in the future. Training is critical for AI model development, requiring immense computational power to process vast amounts of data, which is why it is primarily concentrated in cloud data centers. NVIDIA is