While AI accelerates code generation, it creates significant new chokepoints. The high volume of AI-generated code leads to "pull request fatigue," requiring more human reviewers per change. It also overwhelms automated testing systems, which must run full cycles for every minor AI-driven adjustment, offsetting initial productivity gains.

Unlike co-pilots that assist developers, Factory's “droids” are designed to be autonomous. This reframes the developer's job from writing code to mastering delegation—clearly defining tasks and success criteria for an AI agent to execute independently.

As AI coding agents generate vast amounts of code, the most tedious part of a developer's job shifts from writing code to reviewing it. This creates a new product opportunity: building tools that help developers validate and build confidence in AI-written code, making the review process less of a chore.

Proving the ROI for developer productivity tools is challenging, as studies on their impact are often inconclusive. A more defensible business model focuses on outright automation of specific tasks (e.g., auto-updating documentation in CI). This provides a clear, outcome-oriented value proposition that is easier to sell.

The most significant productivity gains come from applying AI to every stage of development, including research, planning, product marketing, and status updates. Limiting AI to just code generation misses the larger opportunity to automate the entire engineering process.

AI tools are automating code generation, reducing the time developers spend writing it. Consequently, the primary skill shifts to carefully reviewing and verifying the AI-generated code for correctness and security. This means a developer's time is now spent more on review and architecture than on implementation.

Simply deploying AI to write code faster doesn't increase end-to-end velocity. It creates a new bottleneck where human engineers are overwhelmed with reviewing a flood of AI-generated code. To truly benefit, companies must also automate verification and validation processes.

Factory frames the AI coding landscape using the Henry Ford analogy. AI assistants that simply speed up line-by-line coding are merely 'faster horses.' The true paradigm shift—the 'automobile'—is delegating entire tasks to autonomous agents, fundamentally changing the developer workflow.

AI acts as a massive force multiplier for software development. By using AI agents for coding and code review, with humans providing high-level direction and final approval, a two-person team can achieve the output of a much larger engineering organization.