Source URL: https://simonwillison.net/2025/Jan/7/david-crawshaw/
Source: Simon Willison’s Weblog
Title: Quoting David Crawshaw
Feedly Summary: I followed this curiosity, to see if a tool that can generate something mostly not wrong most of the time could be a net benefit in my daily work. The answer appears to be yes, generative models are useful for me when I program. It has not been easy to get to this point. My underlying fascination with the new technology is the only way I have managed to figure it out, so I am sympathetic when other engineers claim LLMs are “useless.” But as I have been asked more than once how I can possibly use them effectively, this post is my attempt to describe what I have found so far.
— David Crawshaw, Co-founder and CTO, Tailscale
Tags: ai-assisted-programming, llms, ai, generative-ai
AI Summary and Description: Yes
Summary: The text explores the practical utility of generative models, particularly Large Language Models (LLMs), in programming tasks and highlights the author’s journey towards effectively integrating these tools into his workflow. This insight is particularly relevant for AI security and infrastructure professionals who are navigating the complexities of adopting LLMs in their environments.
Detailed Description:
The author shares a personal narrative about the usefulness of generative models in programming, countering the skepticism surrounding their functionality. The report touches on several key points:
– **Personal Experience**: The author’s primary motivation to explore LLMs arises from curiosity and a fascination with the technology.
– **Effectiveness**: The author has found that generative models can be beneficial in daily programming tasks, even if achieving proficiency with these models has been challenging.
– **Skepticism Addressed**: Acknowledges a common perception among engineers that LLMs can be “useless” and aims to provide evidence countering this view.
– **Advice for Engineers**: The post is intended as a guide for those struggling to understand the practical applications of LLMs in their work, thereby supporting ongoing dialogue about the integration of AI in technical tasks.
**Key Insights for Professionals**:
– **Adoption of LLMs**: Professionals should assess how generative models can enhance productivity in software development and other engineering tasks.
– **Education and Persuasion**: There is a need for resources and guidance to help engineers leverage LLMs effectively, potentially improving acceptance of such technologies in the workplace.
**Practical Implications**:
– Understanding the balance between skepticism and potential benefit can facilitate more informed discussions and decisions regarding AI tool adoption.
– Organizations may consider developing strategic training programs to maximize the advantages of generative AI technologies within their teams.