How To Move Fast Toward the Right Thing | Figma Blog
AI has made software execution dramatically faster, but it has not made judgment easier. The article argues that teams must deliberately decide what is worth building, provide agents with strong context and constraints, and preserve a distinct human point of view. Otherwise, polished prototypes can create hidden tech debt and increasingly average products. ## AI Accelerates Execution, Not Clarity - AI can quickly produce polished, production-like outputs, but apparent polish may hide weak decisions and fragile implementation. - Large language models often fill in missing requirements themselves, causing prototypes to fail under real-world constraints. - **Cognitive surrender** describes accepting AI-generated decisions without scrutiny or independent deliberation. - Teams should follow a “consideration imperative”: pause to understand the problem and define the right outcome before accepting the first plausible solution. ## Context Has to Come First - Agentic engineering shifts developers from manually writing every line of code to expressing intent clearly and directing AI. - Effective intent requires: - **Deterministic layers**, such as tests, type checks, and validation, to catch model errors consistently. - **High-signal context**, including specifications and documented components. - **Clear interfaces**, so agents understand how systems and components connect. - Tools such as Figma MCP’s Code Connect can provide agents with real production components, including props and variants, rather than forcing them to infer implementation from pixels. - Investing in design systems and documented decisions gives agents a precise vocabulary and guardrails, producing more consistent output, leaner code, and less technical debt. ## Good Can Still Be Average - AI tends to generate work that resembles common patterns in its training data, or work that is “in distribution.” - Typical AI-generated results—geometric gradient logos, familiar presentation fonts, or rounded-corner cards—are competent but interchangeable. - When teams repeatedly accept adequate results, their judgment can narrow from asking “What should this be?” to choosing “Which option is least wrong?” - As AI raises the baseline of acceptable work, products can become unremarkable unless people deliberately define what makes them distinctive. ## The Point of View Needs to Be Yours - AI can improve execution, but it cannot replace a team’s responsibility to establish intent, standards, and a meaningful perspective. - Without a clear point of view, the model’s default assumptions determine both what gets built and how it looks. - Moving quickly is valuable only when speed is paired with careful consideration, strong context, and deliberate choices. Teams should treat AI as an execution partner—not as the source of product judgment. Define the problem, encode decisions in systems and safeguards, and challenge generic outputs before shipping.
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