Source URL: https://www.seangoedecke.com/what-llms-cant-do/
Source: Hacker News
Title: To avoid being replaced by LLMs, do what they can’t
Feedly Summary: Comments
AI Summary and Description: Yes
**Summary:**
The text discusses the implications of advanced large language models (LLMs) on the field of software engineering, outlining strategies for engineers to adapt in light of the impending shift towards AI-driven coding capabilities. It emphasizes the need for engineers to understand AI, focus on legacy code tasks that LLMs may struggle with initially, and underscore the importance of accountability in management roles, which AI cannot replicate at present.
**Detailed Description:**
The text provides a comprehensive perspective on the evolving role of software engineers amidst the rise of advanced AI and LLM technologies. Here are the key points:
– **Immediate Adaptation Needs:**
– Software engineers should familiarize themselves with AI tools and the workings of language models to secure their relevance in the job market.
– Emphasizes acquiring status and skills that position them favorably as junior roles may be the first affected.
– **Short-Term Employment Outlook:**
– Predicts that while AI engineers are emerging, significant job replacement within the next five years is unlikely due to slow enterprise adaptation.
– Highlights the risk-averse nature of large companies in adopting new technologies.
– **Medium-Term Focus on Legacy Code:**
– Identifies legacy code as a domain likely to remain relevant: these tasks involve ill-defined problems, massive code volumes, and complex verification criteria.
– Engineers are encouraged to strengthen their expertise in maintaining and adding features to large established systems, as LLMs currently excel in well-defined, competitive programming contexts.
– **Long-Term Professional Responsibility:**
– Discusses the inherent accountability expected from engineers that AI cannot replicate, emphasizing managerial trust based on potential consequences for failures.
– Speculates that even with strong LLMs, a human engineer might still be needed to ensure accountability and translate machine output into commitments understandable by other stakeholders.
Overall, the insights offer practical guidance for professionals in software engineering and AI development, suggesting that while LLMs will change the landscape of software engineering, roles requiring deep understanding, accountability, and complex problem-solving will still require human intervention for the foreseeable future.
– **Key Implications for Professionals:**
– Emphasize continuous learning in AI technologies while refining skills in less-AI-amenable areas like legacy systems.
– Understand the importance of management accountability and how it plays a crucial role in integrating AI solutions effectively within teams and organizations.