What Happens When AI Can Write All Your Software? (www.jakequist.com)

Recent experimentation with large language models (LLMs) has revealed a striking dichotomy in their capabilities: while they can effectively generate low-complexity software like a RingBuffer, they struggle with intricate applications like a personal CRM. The simplicity of the RingBuffer's self-contained functionality allowed the LLM to produce flawless code, but the interconnected complexities of a CRM—spanning data models, API design, and user interactions—led to amateurish results. This highlights a crucial limitation: LLMs currently excel in executing straightforward tasks but falter when faced with the multifaceted challenges of software design. This distinction suggests a future where LLMs may automate the less complex aspects of software development while leaving intricate components to human developers. As teams focus on high-stakes business logic and integrations, LLMs could be used to build user-facing interfaces that, while simpler, allow for greater customization. This evolution may preserve the integrity of complex systems like Salesforce while enabling users to tailor their interactions as needed. Ultimately, the future of software development appears poised for collaboration between human ingenuity and AI efficiency, but the limitations around complexity will remain a significant bottleneck for some time.