Workflow
10倍工程师
icon
Search documents
不用AI就被淘汰?国外工程师:“10倍生产力”太荒谬了
Hu Xiu· 2025-08-26 04:04
Group 1 - The article questions the validity of the claim that AI can lead to a tenfold increase in programming efficiency, suggesting that such assertions may be exaggerated [1][10][24] - It highlights the author's personal experience of anxiety regarding the rapid advancement of AI and its implications for software engineers [1][3][30] - The author critiques the performance of AI programming tools, stating that while they can generate template code, they often struggle with understanding larger codebases and can produce insecure code [4][5][15] Group 2 - The article argues that the notion of a "10x engineer" is often misunderstood, emphasizing that true productivity gains come from preventing unnecessary work rather than simply writing code faster [19][20][23] - It discusses the limitations of AI in software development, noting that while AI can assist in certain tasks, it does not fundamentally change the human processes involved in software engineering [12][18][24] - The author warns against the pressure to adopt AI tools hastily, advocating for a balanced approach that prioritizes quality and enjoyment in coding over mere speed [31][32][33]
“AI让你变成10x工程师?其实是一个骗局......”
3 6 Ke· 2025-08-12 09:57
Core Viewpoint - The discussion around AI's potential to increase engineer productivity by 10x or even 100x is largely exaggerated, driven by commercial interests and management pressures, rather than reflecting the real experiences of developers [1][2][3]. Group 1: AI Tools and Developer Experience - Many developers feel anxious about their skills in the face of AI advancements, fearing they may become obsolete if they do not adapt quickly [2][3]. - AI tools like Claude Code and Cursor are seen as useful for repetitive tasks but often struggle with understanding specific codebases and can introduce errors [5][6]. - The actual productivity gains from using AI tools are often overstated, with many developers finding that AI can assist but not replace the need for human oversight and expertise [9][12]. Group 2: Misconceptions about Productivity Gains - The claim of achieving 10x efficiency is misleading, as it implies that all aspects of software development, including communication and testing, would also need to improve by the same factor, which is unrealistic [8][9]. - Even if coding speed were to increase, the majority of a developer's time is spent on reading, thinking, and debugging, which AI cannot significantly accelerate [9][11]. - The notion of a "10x engineer" exists, but it is often due to their ability to avoid unnecessary work rather than a direct result of AI usage [12][14]. Group 3: The Role of Management and Industry Perception - There is a tendency for management to promote the idea of AI-driven productivity to maintain pressure on engineers, which can lead to a toxic work environment [16][21]. - Many claims about AI's capabilities come from those distanced from actual coding work, such as entrepreneurs and investors, rather than from engineers who use these tools daily [18][22]. - The narrative around AI's transformative power can create unnecessary anxiety among engineers, leading them to doubt their skills and contributions [17][22]. Group 4: Emphasis on Enjoyment and Work Satisfaction - The focus should be on finding joy in coding rather than solely on efficiency; enjoying the work can lead to better outcomes in the long run [19][20]. - Engineers are encouraged to choose methods that make them happy, as this can enhance their productivity and creativity [20][22]. - The industry should recognize that fostering a supportive environment is crucial for long-term success, rather than pushing unrealistic productivity expectations [21][22].
别焦虑!不会用AI也不会被淘汰,工程师老哥实测各类工具:10倍生产力神话太夸张了
量子位· 2025-08-10 04:11
Core Viewpoint - The article discusses the limitations of AI in software engineering, emphasizing that while AI can enhance productivity in specific tasks, it cannot replace the critical thinking and judgment required by engineers in complex projects [6][10][25]. Group 1: AI's Role in Software Engineering - AI tools can assist in writing boilerplate code and scripts quickly, but struggle with understanding the context of large codebases, leading to inefficiencies [8][9]. - Engineers must guide AI by breaking down complex tasks into smaller units to avoid logical confusion during processing [11][13]. - The myth of "10x productivity" with AI is challenged, as achieving such efficiency would require a complete overhaul of workflows, which is impractical [15][18]. Group 2: Challenges and Limitations of AI - AI-generated content often has defects and may not meet codebase standards, especially as the size of the codebase increases [19][25]. - Engineers face diminishing returns when relying on AI, as the complexity of projects grows, leading to potential productivity bottlenecks [26][22]. - The article suggests that the promotion of AI's capabilities may stem from inexperienced users or those with vested interests in AI products [28][30]. Group 3: Engineer's Perspective and Advice - Engineers should not feel pressured to adopt AI if it does not align with their preferred working style, and they should focus on their strengths [32][33]. - Leadership should avoid creating anxiety among engineers regarding AI's potential to replace them, as this can lead to decreased morale and quality of work [34]. - Trusting engineers' expertise and allowing them to use AI as a tool rather than a crutch is essential for maintaining quality in software development [34].