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Greymatter
Code Smarter, Not Harder
May 22, 2024
Greylock partner Corinne Riley discusses the challenges of developing AI coding tools that can match or surpass human engineers. Topics include improving AI capabilities for complex coding tasks, the importance of code planning and model ownership, and the debate on using GPT models versus code-specific models for code generation tools.
18:30
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Quick takeaways
- Developing AI tools for code generation offers a significant opportunity in engineering workflows with AI augmentation.
- Startups are exploring differentiation strategies in AI coding tools, focusing on code-specific models for improved code generation quality.
Deep dives
Unlocking AI Potential in Engineering Workflows
Developing AI tools for code generation and engineering workflows presents a significant opportunity as engineering tasks naturally lend themselves to AI augmentation. The abundance of existing training data, the mixture of judgment and rules-based work required in tasks, and the availability of composable modules like open source libraries contribute to the feasibility of reliable AI coding tools. Despite the growth of AI coding tools in recent times, there remains a need to address technical challenges to achieve performance on par with or surpassing human engineers.
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