As generative AI tools like Figma’s Make Designs evolve, experts warn that the technology risks homogenising interfaces and shifting the value from design diversity to user insight, urging a rethink in design education and strategy.
Generative AI is changing UX and UI design less by replacing designers than by altering where their value lies. The discussion intensified after Figma introduced Make Designs in June 2024, a tool that could turn a text prompt into a UI mock-up in seconds. Yet the larger story, as Kateryna Orlova, a UX/UI designer at OnePageCRM, argues, is not about whether interfaces can now be produced faster. It is about whether teams can still tell a useful product from a merely plausible one.
That question became harder to ignore when Figma temporarily disabled Make Designs after designer Andy Allen said the tool was generating layouts that resembled Apple’s Weather app. Figma later brought the feature back in September 2024 under the name First Draft, with an emphasis on less templated output and tighter use of its design systems. In its own explanation of the update, Figma said the feature is meant to give designers a fast starting point, not a finished product.
Orlova’s central concern is that widespread AI use could narrow visual and structural diversity across products. If many companies rely on the same underlying models and component libraries, she warns, they may end up with interfaces that differ mainly in branding rather than in function. That would make products harder to distinguish and could push competition away from design quality and towards price, advertising and name recognition.
Her argument is that AI is strongest when it helps teams explore options, but weakest when it is asked to diagnose the problem itself. A model can process large volumes of feedback, but it cannot discover what a team failed to observe in the first place. In practice, she says, the real work of UX still depends on watching how people behave, not just on what they say in testing. A feature that sounds simple in a user interview may still fail repeatedly in daily use, especially in business software where small frictions compound over time.
That distinction also matters for junior designers. AI can speed up early drafting and reduce repetitive work, but it can also conceal the reasoning behind a decision. If a tool returns a convincing answer immediately, younger designers may learn to select outputs without understanding why they are right, or when they will break down. Orlova believes design education will need to move away from tool fluency and towards hypothesis-building, research and judgement.
For product teams, the message is more practical than philosophical. AI can reduce the cost of first drafts, layout variations and routine production work, but it does not solve weak product thinking. The best use of the technology, Orlova says, is in the early stages: generating alternatives, structuring information and accelerating repetitive tasks. The long-term advantage, however, comes from proprietary knowledge of users, workflows and failure points. If competitors can buy the same models, the differentiator will be who understands their audience best.
Disclaimer: This content is intended for informational purposes only. Readers are advised to exercise their own judgement, conduct due diligence, or consult a qualified expert before acting on any information provided.
