As AI automates more day-to-day coding, the critical skill for engineers is becoming 'systems thinking'—understanding the entire workflow and how components interact. This was once a senior-level trait but is now essential for everyone in engineering.

As AI automates narrow skills like writing code snippets, the ability to think at a system level becomes paramount. Designing how different components—including classical ML models, LLMs, and traditional software—fit together is a skill that is harder to automate and increasingly valuable.

With AI making code generation cheap, the limiting factors for development velocity are now defining what to build (product) and ensuring its quality (review). Engineers will increasingly focus on high-level systems architecture rather than typing code.

The classic, linear design process is obsolete because AI tools allow engineers to build and iterate so quickly. Designers must shift from a gatekeeping, mock-heavy process to a more fluid, collaborative role that supports rapid execution.

AI tools dramatically speed up code implementation, making engineering velocity less of a constraint. The new challenge becomes the slower, more considered process of deciding *what* to build, placing a premium on strategic design thinking and choosing when to be deliberate.

AI co-pilots have accelerated engineering velocity to the point where traditional design-led workflows are now the slowest part of product development. In response, some agile teams are flipping the process, having engineers build a functional prototype first and then creating formal Figma designs and UI polish later.

AI-driven code generation relies on design systems for instructions. A weak system leads to poor code output, making the design system a critical foundation for engineering quality and speed, not just a design team's responsibility.

To use AI effectively in design, organizations need a mature, machine-readable design system. AI tools rely on this system's well-documented components and rules to assemble experiences consistently. Without it, AI-generated designs risk being off-brand and low-quality. Investing in the design system is a key prerequisite for mitigating AI risks at scale.

In an AI-enabled workflow, designers should accept that engineers can ship features without their direct oversight. Building robust systems and automations allows for good-enough initial versions, enabling designers to focus on higher-leverage problems instead of being a bottleneck.