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.
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Block's CTO quantifies the impact of their internal AI agent, Goose. AI-forward engineering teams save 8-10 hours weekly, a figure he considers the absolute baseline. He notes, "this is the worst it will ever be," suggesting exponential gains are coming.
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.
High productivity isn't about using AI for everything. It's a disciplined workflow: breaking a task into sub-problems, using an LLM for high-leverage parts like scaffolding and tests, and reserving human focus for the core implementation. This avoids the sunk cost of forcing AI on unsuitable tasks.
Because AI agents operate autonomously, developers can now code collaboratively while on calls. They can brainstorm, kick off a feature build, and have it ready for production by the end of the meeting, transforming coding from a solo, heads-down activity to a social one.
Top-performing engineering teams are evolving from hands-on coding to a managerial role. Their primary job is to define tasks, kick off multiple AI agents in parallel, review plans, and approve the final output, rather than implementing the details themselves.
The role of a senior developer is evolving. They now focus on defining outcomes by writing tests that a piece of code must accomplish. The AI then generates the actual implementation, allowing small teams to build complex systems in a fraction of the traditional time.
It's infeasible for humans to manually review thousands of lines of AI-generated code. The abstraction of review is moving up the stack. Instead of checking syntax, developers will validate high-level plans, two-sentence summaries, and behavioral outcomes in a testing environment.
As AI generates more code, the core engineering task evolves from writing to reviewing. Developers will spend significantly more time evaluating AI-generated code for correctness, style, and reliability, fundamentally changing daily workflows and skill requirements.
The new Spiral app, with its complex UI and multiple features, was built almost entirely by one person. This was made possible by leveraging AI coding agents like Droid and Claude, which dramatically accelerates the development process from idea to a beautiful, functional product.
Historically, developer tools adapted to a company's codebase. The productivity gains from AI agents are so significant that the dynamic has flipped: for the first time, companies are proactively changing their code, logging, and tooling to be more 'agent-friendly,' rather than the other way around.