OpenAI总裁透露GPT-5改了推理范式，AGI实现要靠现实反馈

Core Insights - OpenAI is transitioning from text generation to reinforcement learning as a key paradigm for developing AGI, focusing on real-world testing and feedback [1][3] - The company emphasizes the importance of computational resources as a primary bottleneck in AGI development, with the amount of computation directly influencing the speed and depth of AI research [9][11] - OpenAI aims to integrate large models into enterprise and personal workflows, packaging model capabilities into auditable service processes [13][15] Technical Paradigm Shift - The release of GPT-5 marks a significant paradigm shift in AI, being OpenAI's first hybrid model designed to bridge the gap between the GPT series and AGI [4] - OpenAI is adopting a new reasoning paradigm where models learn through supervised data and then refine their capabilities via reinforcement learning in real-world environments [8][10] Computational Capacity - Brockman identifies computational power as the main limitation in AGI development, asserting that increased computational resources can lead to improved model performance [9][11] - The current reinforcement learning approach in GPT-5, while more sample-efficient, still requires extensive computational resources for task learning [10] Model Deployment - OpenAI's goal is to embed large models into production environments, moving beyond research applications to practical implementations [13][15] - The company is developing a dual-layer "defense in depth" structure to ensure the controllability and safety of high-permission agents [15][16] Industry Opportunities - Brockman believes there are vast untapped opportunities in integrating AI into real-world applications across various industries, encouraging developers to understand industry specifics before implementing AI solutions [18][20] - The future of AI will see a high demand for computational resources, making access to and allocation of these resources a critical issue for researchers [12][20]