Despite the hype, LinkedIn found that third-party AI tools for coding and design don't work out-of-the-box on their complex, legacy stack. Success requires deep customization, re-architecting internal platforms for AI reasoning, and working in "alpha mode" with vendors to adapt their tools.

The perception of AI coding assistants has shifted. They are no longer just tools for a productivity boost but are becoming a fundamental, non-negotiable part of the modern developer's workflow. This implies an eventual market penetration approaching 100%, drastically changing the market size calculation.

Historically, labor costs dwarfed software spending. As AI automates tasks, software budgets will balloon, turning into a primary corporate expense. This forces CFOs to scrutinize software ROI with the same rigor they once applied only to their workforce.

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.

Enterprises are finding immediate, high return on investment by using AI to port legacy codebases (like COBOL) to modern languages. This mundane task offers a 2x speed-up over traditional methods, unlocking significant infrastructure savings and even driving new developer hiring.

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.

The value generated by 30 million developers worldwide is estimated at $3 trillion. AI tools that augment or disrupt this work are tapping into a market equivalent to the GDP of a major economy, making it the first truly massive market for AI.

For over a decade, software development fragmented into siloed roles (PM, Design, Eng) with their own tools. AI code editors are collapsing these boundaries by creating a unified workspace where a single "maker" or a streamlined team can build, iterate, and ship, much like in the early days of computing.

The focus on AI writing code is narrow, as coding represents only 10-20% of the total software development effort. The most significant productivity gains will come from AI automating other critical, time-consuming stages like testing, security, and deployment, fundamentally reshaping the entire lifecycle.