AI专家：对AI的质疑是对“指数级增长趋势”的“自欺欺人”

Core Argument - A leading AI researcher argues against the prevalent "AI bubble" theory, stating that skepticism towards AI's exponential growth is a serious misinterpretation of technological trends, similar to the initial underestimation of the COVID-19 pandemic [1][2] Group 1: AI Performance and Trends - AI models are doubling their ability to autonomously complete complex tasks at an exponential rate, with the latest models capable of handling over two-hour software engineering tasks [2][7] - The METR study shows a clear exponential trend in AI's ability to perform software engineering tasks, with models like Sonnet 3.7 achieving a 50% success rate for one-hour tasks seven months ago [5] - New models, including Grok 4, Opus 4.1, and GPT-5, have surpassed previous trends and can now execute tasks exceeding two hours [7] Group 2: AI's Competitiveness Across Industries - The GDPval assessment by OpenAI evaluates AI performance across 44 professions in nine industries, showing that top AI models are "astonishingly close" to human performance and even challenge industry experts [9][10] - The latest GPT-5 model has demonstrated performance that is nearly on par with human experts, indicating significant advancements in AI capabilities [10][13] Group 3: Future Projections - Based on current exponential growth data, it would be "extremely surprising" if improvements in AI suddenly halted, with predictions suggesting that by mid-2026, models will be able to work autonomously for an entire workday (8 hours) [12][15] - By the end of 2026, at least one model is expected to reach human expert performance across various industries, and by the end of 2027, models will frequently surpass experts in many tasks [15]