演讲视频：https://www.youtube.com/watch?v=b6Doq2fz81U 超 15000 人的「AI 产品市集」社群！不错过每一款有价值的 AI 应用。 「所有能被验证的任务，最终都会被 AI 解决。」 「智能未来将成为一种商品，未来获取知识或进行某种推理的成本和可及性将趋近于零。」 最近，前 OpenAI 核心研究员、CoT作者 Jason Wei 在斯坦福大学 AI Club 做了一场精彩的演讲。这也是他跳槽到Meta 后少有为数的公开分享。 Jason Wei 提出了三个理解和驾驭 2025 年 AI 发展至关重要的核心思想： 验证者定律、智能的锯齿状边缘和智能商品化。 Jason 对于此前提出的 验证者定律 做了进一步补充和完善，「训练 AI 解决某个任务的容易程度，与该任务的可验证性成正比。所有既可能解决又容易验 证的任务，都将被 AI 解决。」 某种意义上来说，验证者定律决定 「 哪些点会被率先突破 」 ，智能商品化解释 「 突破后如何被规模化与降本 」 ，锯齿状边缘则强调 「 能力突破的时 间序与不均衡版图 」 。 虽然没提创业，但似乎又句句不离创业。 基于演讲视频，Fo ...

Core Viewpoint - The rapid evolution of large models is transforming their capabilities into product functionalities, making it crucial for entrepreneurs to stay informed about advancements in model technology [1][2]. Group 1: Characteristics of Tasks AI Can Solve - Tasks that AI can quickly tackle share five characteristics: objective truth, rapid verification, scalable verification, low noise, and continuous reward [2][10]. - The concept of "verification asymmetry" indicates that some tasks are much easier to verify than to solve, which is becoming a key idea in AI [3][8]. Group 2: Examples of Verification Asymmetry - Examples illustrate that verifying solutions can be significantly easier than solving the tasks themselves, such as in Sudoku or website functionality checks [4][6]. - Some tasks have verification processes that are nearly symmetrical, while others may take longer to verify than to solve, highlighting the complexity of verification [6][7]. Group 3: Importance of Verification - The "verifier's law" states that the ease of training AI to solve a task correlates with the task's verifiability, suggesting that tasks that are both solvable and easily verifiable will be addressed by AI [8][9]. - The learning potential of neural networks is maximized when tasks meet the outlined verification characteristics, leading to faster iterations and advancements in the digital realm [12]. Group 4: Case Study - AlphaEvolve - Google’s AlphaEvolve exemplifies the effective use of verification asymmetry, allowing for ruthless optimization of problems that meet the verifier's law characteristics [13]. - The focus of AlphaEvolve is on solving specific problems rather than generalizing across unseen problems, which is a departure from traditional machine learning approaches [13]. Group 5: Future Implications - Understanding verification asymmetry suggests a future where measurable tasks will be solved more efficiently, leading to a jagged edge of intelligence where AI excels in verifiable tasks [14][15].