灵魂拷问：如果AI真能造出10x工程师，那“软件洪水”在哪儿呢？

Core Insights - The article questions the effectiveness of AI in enhancing software development productivity, highlighting a discrepancy between developers' perceptions and actual performance metrics [1][6][21] - Despite widespread adoption of AI tools, there is no significant increase in the volume of new software releases, contradicting claims of enhanced productivity [8][20][21] Group 1: AI Productivity Claims - Developers believe AI tools improve their productivity by approximately 20%, but research indicates a decline in actual speed by 19% [1][6] - A personal experiment showed that using AI tools resulted in a median speed decrease of 21%, aligning with the findings of the METR report [4][21] - Prominent AI tools like GitHub Copilot and Claude Code claim to significantly boost productivity, yet these claims lack substantial evidence [6][7] Group 2: Software Release Trends - Data analysis reveals no significant increase in new software releases, suggesting that the anticipated "flood" of applications has not materialized [8][20] - The expected exponential growth in software production due to AI adoption is not reflected in the data, which shows flat trends instead [20][21] - The core premise that AI tools would lead to increased output is fundamentally flawed, as developers are not delivering more products than before [21][23] Group 3: Industry Implications - The pressure on developers to adopt AI tools is driven by a fear of missing out on competitive advantages, leading to organizational restructuring and layoffs [7][21] - Many developers feel overwhelmed by the need to master AI tools, which can lead to decreased job satisfaction and productivity [21][22] - The narrative surrounding AI's transformative impact on software development is challenged by the lack of tangible results and evidence of improved efficiency [22][23]