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.

Even within OpenAI, a stark performance gap exists. Engineers who avoid using agentic AI for coding are reportedly 10x less productive across metrics like code volume, commits, and business impact. This creates significant challenges for performance management and HR.

The most significant and immediate productivity leap from AI is happening in software development, with some teams reporting 10-20x faster progress. This isn't just an efficiency boost; it's forcing a fundamental re-evaluation of the structure and roles within product, engineering, and design organizations.

AI tools provide quantifiable productivity gains in technical fields. Developers using GitHub Copilot, for instance, finish tasks approximately 55% faster. Furthermore, 88% of these developers report feeling more productive, demonstrating that AI augmentation leads to significant and measurable improvements in workflow efficiency and employee satisfaction.

The Head of Engineering for Atlas estimates that north of 75% of new code is initially written by the AI assistant Codex. This indicates a profound shift where the primary engineering workflow becomes prompting, guiding, and refining AI output, rather than manually writing code from scratch.

The latest AI coding assistants facilitate a massive leap in developer productivity. The host demonstrated this by merging 44 pull requests and adding nearly 93,000 lines of code in just five days, a workload that would typically take an entire team months to complete, making the scale of the impact concrete.

Tools like OpenAI's Codex can complete hours of coding in minutes following a design phase. This creates awkward, inefficient downtime periods for the developer, fundamentally altering the daily work rhythm from a steady flow to unproductive cycles of intense work followed by waiting.

AI coding assistants won't make fundamental skills obsolete. Instead, they act as a force multiplier that separates engineers. Great engineers use AI to become exceptional by augmenting their deep understanding, while mediocre engineers who rely on it blindly will fall further behind.

Technical executives who stopped coding due to time constraints and the cognitive overhead of modern frameworks are now actively contributing to their codebases again. AI agents handle the boilerplate and syntax, allowing them to focus on logic and product features, often working asynchronously between meetings.