Enterprise data shows that while engineering use of AI coding tools grew 5x, adoption in other departments exploded. Legal usage grew 108x and Sales grew 41x. This indicates the most significant productivity gains are happening outside of traditional tech roles by automating bespoke business workflows.

GitHub's user base is expanding beyond professional developers. Non-technical staff in departments like legal and finance are now using tools like GitHub Copilot to build small applications and assets, effectively broadening the definition of a "developer" in the enterprise.

Modern AI coding agents allow non-technical and technical users alike to rapidly translate business problems into functional software. This shift means the primary question is no longer 'What tool can I use?' but 'Can I build a custom solution for this right now?' This dramatically shortens the cycle from idea to execution for everyone.

AI is democratizing software development by enabling non-technical subject-matter experts to build their own tools. By simply describing their ideas, they can generate fully deployed applications, shifting value from technical implementation to market and community insight.

AI tools empower employees in traditionally non-technical roles to perform complex tasks. A support agent can now use AI to diagnose a technical issue, build a new landing page, and ship code, collapsing the need for a multi-person workflow.

AI has turned coding from a scarce, specialized skill into an abundant resource. This means every team, regardless of technical background, should now be a 'software team,' using AI to produce code and build workflows without needing to understand the underlying syntax.

A new wave of AI automation is being driven by non-technical staff using agent-based platforms. These knowledge workers are building custom AI solutions for complex business processes, bypassing the need for new software purchases or dedicated engineering resources.

As AI makes building custom software cheap and easy, roles traditionally outside of product and engineering (e.g., finance, HR) will develop their own 'makers.' These individuals will prototype and build small, function-specific tools to solve their own problems, infiltrating product-style thinking throughout the entire organization.

At Block, the most surprising impact of AI hasn't been on engineers, but on non-technical staff. Teams like enterprise risk management now use AI agents to build their own software tools, compressing weeks of work into hours and bypassing the need to wait for internal engineering teams.