How AI is Reshaping Design Portfolio Standards: Figma’s Lead on Demanding More From Candidates
The AI revolution in design tools has created a paradox: while automation reduces repetitive tasks, it is simultaneously raising the bar for job candidates. Kris Rasmussen, Figma’s design lead, recently stated that he expects more work, not less, from job candidates in the age of AI. This insight, reported by Business Insider, signals a fundamental shift in how design skills are evaluated. For developers and designers building the next generation of interfaces, the message is clear: AI amplifies expectations for craftsmanship, product thinking, and technical depth.
This change in hiring philosophy is not limited to Figma. Across the tech industry, teams are rethinking portfolio requirements, whiteboard challenges, and take-home assignments. The core question has shifted from “Can you use this tool?” to “Can you leverage AI to produce work that genuinely stands out?” Let’s examine what this means for your career strategy and how to adapt your approach to AI-era job candidates.
What Are AI-Era Candidate Expectations?
AI-era candidate expectations refer to the heightened standards employers apply when evaluating job seekers who have access to generative AI tools. Rather than reducing the volume of work, these expectations demand higher quality, deeper systems thinking, and more polished output. Employers now assume candidates can automate routine tasks, which means manual proficiency is no longer a differentiator—it’s table stakes.
This concept applies broadly across design, frontend development, and product management. For example, a UI designer is now expected to produce multiple high-fidelity variants of a component in the same time it once took to mock up one. A developer must demonstrate not only code that runs but code that is optimized and well-documented, created with AI assistance. The bar has moved from competence to excellence.
Kris Rasmussen’s comments underscore a growing industry consensus: AI removes friction, which means the remaining work must show exceptional judgment, creativity, and attention to detail. “I expect more work, not less, from job candidates in the age of AI,” Rasmussen told Business Insider, highlighting that the availability of powerful tools does not lower standards—it increases them.
Figma’s Design Lead: Why More Work is the New Baseline
Rasmussen’s perspective comes from firsthand experience managing a world-class design team at a company that builds the tools designers rely on. He sees candidates submitting portfolios that contain work clearly accelerated by AI, but he argues that quantity alone is not sufficient. “If AI lets you do 10 things in an hour, I want to see the best 10, not just 10,” he explained.
This statement reveals a critical insight for AI-era job candidates: the bottleneck is not production speed but decision-making quality. Employers are looking for candidates who can use AI to explore a wider design space, then curate and refine the output with genuine expertise. The portfolio of the future will contain fewer, deeper case studies that demonstrate strategic reasoning, not just execution speed.
Figma itself is integrating AI features like AI-powered design suggestions and automated asset generation. Rasmussen’s comments suggest that internal hiring practices are already adapted to this reality. Candidates who rely on AI without showing evidence of critical thinking or iteration are unlikely to pass the bar. The expectation is that you use AI to do more thinking, not more clicking.
What This Means for Developers and Design Engineers
For developers who work at the intersection of code and design—often called design engineers or frontend specialists—these higher expectations translate into tangible changes in how you present your work. First, your code samples must now show evidence of architectural thinking. AI can generate boilerplate and even complex functions, but it cannot replace the structural decisions that make a codebase maintainable.
Second, your portfolio should include evidence of design system contributions, documentation, and cross-functional collaboration. Rasmussen noted that Figma values candidates who demonstrate how they improve team workflows, not just individual output. If AI automates 60% of your earlier tasks, the remaining 40% of your job involves teaching, documenting, and elevating your peers.
Third, expect more scenario-based interview questions that test how you integrate AI tools into your workflow. For example, you may be asked, “Describe a time you used an AI code assistant to solve a problem that manual coding could not address efficiently.” Your answer should demonstrate tool proficiency combined with human judgment. This is the new bar for design hiring trends in 2025.
Building a Portfolio That Exceeds Higher Expectations
To meet the demands of AI-era job candidates, your portfolio needs a structural overhaul. Start by including a “Tools and Process” section for each case study. Clearly explain which tasks were accelerated by AI (e.g., “Used AI to generate 20 variant components, then hand-finessed accessibility constraints on the top 3”). This shows you understand the tool’s limits.
Next, focus on depth over breadth. Rasmussen emphasized quality over quantity. Instead of showing 10 projects, show 4 deeply documented ones that highlight your problem-solving process, iteration history, and measurable outcomes. Include before-and-after metrics, user research insights, and code architecture diagrams. These are things AI cannot fabricate.
Finally, demonstrate your ability to critique AI output. Include a section where you explain a flawed AI suggestion and how you corrected it. This builds trust and proves you are not a passive consumer of automation. Employers like Figma are looking for candidates who treat AI as a collaborator, not a crutch. For more on this, see our guide on how to build a developer portfolio for the AI era.
The Future of Design Hiring (2025–2030)
Looking ahead, the trajectory points toward even higher specialization. By 2027, we predict that most design and frontend roles will require demonstrated proficiency with AI-augmented design tools as a baseline requirement. The candidates who will stand out will be those who can bridge the gap between design intent and production-ready code, a skill that AI amplifies but does not automate.
Rasmussen’s comments align with broader AI hiring implications across the tech industry. Companies are already investing in AI evaluation platforms that assess how candidates use tools like GitHub Copilot, Cursor, or Figma’s AI features. The hiring process itself is becoming more data-driven, with recruiters analyzing workflow efficiency as part of the evaluation.
By 2030, we expect to see a new role emerge: the “AI Design Engineer,” a hybrid position that combines visual design, component architecture, and prompt engineering. The demand for this role will come directly from the shift Rasmussen describes—companies want people who can do more with AI, but they want the “more” to be better, not just faster. For context on this trend, read our analysis of AI’s role in modern UX design teams.
💡 Pro Insight: The Skills Gap No One Is Talking About
The real story behind Rasmussen’s statement is not about workload—it’s about cognitive craftsmanship. The industry is discovering that AI removes the scaffolding of rote work, exposing the underlying structure of a designer’s or developer’s thinking. If your thinking is shallow, no amount of AI-generated polish will save you. If your thinking is deep, AI will amplify it.
Here is the uncomfortable truth: most portfolios today are already inflated by AI. Rasmussen sees it. Recruiters see it. The market is now correcting for this by demanding proof of genuine contribution. The candidates who thrive will be those who actively document their decision-making process—the dead ends, the trade-offs, the rationales. This is the new portfolio currency.
My advice is straightforward: start a “decision log” alongside your next project. Record why you chose a certain color palette, layout grid, or API structure. When AI produces a suggestion, log your critique of it. Over time, this log becomes powerful evidence of your unique value. In the age of AI, the most valuable skill is knowing what not to automate.
