3 6 Ke·2025-12-01 02:12Core Insights - OpenAI is facing significant challenges with its pre-training processes, particularly for the upcoming GPT-5 model, which reportedly still relies on the foundation of GPT-4o [1][3][12] - The company has not achieved substantial progress in scaling its pre-training efforts since the release of GPT-4o, leading to concerns about the performance of GPT-5 [7][12][20] - Google's TPU technology is emerging as a strong competitor, potentially undermining NVIDIA's dominance in AI hardware, which OpenAI has heavily relied upon [5][26] Pre-training Challenges - OpenAI's pre-training for GPT-5 has been described as a failure, with the internal project "Orion" being downgraded to GPT-4.5 due to unmet expectations [11][12] - The pre-training phase is critical for developing generative AI models, and OpenAI's struggles in this area have raised questions about the capabilities of GPT-5 compared to its predecessors [29][39] - Despite advancements in algorithms reducing the physical computation required for training, OpenAI's Orion project exceeded the typical training duration of 1-2 months, taking over 3 months [14][36] Performance Comparisons - The performance improvements of GPT-5 have been perceived as modest, with industry reactions indicating it is more of an enhancement of GPT-4o rather than a revolutionary upgrade [20][35] - Benchmark comparisons show that Google's Gemini 3 has outperformed GPT-5 in several areas, highlighting the competitive landscape in AI model performance [31] Strategic Shifts - OpenAI is reportedly shifting focus towards a new model, codenamed "Shallotpeat," aimed at addressing the pre-training issues encountered with previous models [46][50] - The company acknowledges the need for specialized models rather than a single "super model," reflecting a broader industry consensus on the diversification of AI applications [54][60] - OpenAI's internal discussions indicate a recognition of Google's advancements in pre-training, marking a significant shift in the competitive dynamics of the AI landscape [27][29]