AI tools have democratized software development, with nearly half of users who 'vibe code' coming from executive, product, operations, and sales roles. Coding is no longer an exclusive engineering function but a universal skill for problem-solving across the entire business.

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.

Groundbreaking productivity improvements from AI are often created by employees in roles like accounting or marketing, not just top engineers. This suggests that widespread, unfettered access to AI tools across an entire organization is key to unlocking value.

Contrary to expectations, professions that are typically slow to adopt new technology (medicine, law) are showing massive enthusiasm for AI. This is because it directly addresses their core need to reason with and manage large volumes of unstructured data, improving their daily work.

At OpenAI, research and engineering were first to pivot to agentic AI. Critically, less technical groups like finance and HR achieved full saturation within the next four months, suggesting a rapid internal adoption cycle for other companies to benchmark against.

AI adoption is forcing corporate legal teams to become more technical, leading to the expansion of "legal ops" roles. Companies now hire engineers directly onto their legal teams to manage systems, processes, and AI tool integrations—a significant shift from traditional legal department structures.

Contrary to its reputation for slow tech adoption, the legal industry is rapidly embracing advanced AI agents. The sheer volume of work and potential for efficiency gains are driving swift innovation, with firms even hiring lawyers specifically to help with AI product development.

Inside a company, AI adoption isn't uniform. Engineers embrace it for tools, and Sales adopts it because its ROI is easily measured. However, General & Administrative functions like Finance and Legal are slower to adopt due to data infrastructure hurdles and the models' current weakness with numerical reasoning.

Linear's data reveals that non-engineering roles, particularly PMs, show the largest increase in using agentic AI features. AI empowers them to independently perform tasks like data analysis or competitive research that they previously had to delegate, increasing their autonomy and speed.