The chapter explores the effectiveness of coding co-pilots based on GPT models compared to code-specific models, emphasizing the importance of training with coding data for reasoning. It discusses the possibility of large language-trained models outperforming code-specific models and raises the question of whether companies will shift towards advanced models for code generation tools.
Greylock partner Corinne Riley reads her essay "Code Smarter, Not Harder: Solving the Unknowns to Developing AI Engineers."
Building AI tools for code generation and engineering workflows is one of the most exciting and worthy undertakings by startups today. But there are still many open questions about the technical unlocks that must be solved to make coding tools that work as well as (or better than) human engineers in a production setting. Riley explores these core questions alongside an analysis of the current ecosystem of startups developing AI coding tools. You can read the essay here: https://greylock.com/greymatter/code-smarter-not-harder/
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