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.
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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.
Enhancing Workflows with AI Co-Pilots
AI co-pilots, such as GitHub co-pilot, and AI agents are revolutionizing engineering workflows. Startups have been focusing on enhancing code generation and testing workflows, leveraging tools that offer assistance within the IDE for engineers. Despite challenges posed by established tools like GitHub co-pilot, startups are finding niches for differentiation, such as enterprise-focused approaches or specialized functionalities like code review and refactoring.
Building Code-Specific Models for Long-Term Differentiation
Some startups are investing in developing code-specific models to create long-term differentiation in the AI coding tool space. By training models specifically for coding tasks and refining them with code-specific data, startups seek to improve code generation quality. However, the debate continues on whether owning a code-specific model will outperform using existing large language models, raising questions on model pre-training requirements and the balance between model size and task performance.
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/