Figma's CEO: Job Titles Are Blending - Everyone Is a Product Builder
Designer. Engineer. Researcher. Those lines are getting blurry. Figma CEO Dylan Field says AI is pushing work across the aisle, and the people closest to the product are acting like generalists with spikes.
On "Lenny's Podcast," Field called it a "shifting and merging of roles," adding, "AI makes everyone feel the need to be more of a generalist." His point is simple: you still need depth, but cross-functional output is becoming the norm.
Why roles are blending
AI removes friction. What used to need deep code can be shipped with a prompt and a few checks. Designers can prototype with data. Engineers can mock flows that look like production. PMs can run quick research and synthesize findings without a dedicated ops queue.
Field said he's seeing designers, engineers, PMs, and researchers "dip their toe into the other roles." That toe-dip is turning into weekly work.
The data behind the shift
- 72% of Figma's respondents said AI tools are a top reason roles are expanding.
- 56% of non-designers reported doing "a great deal" of design tasks, up from 44% the year prior.
- 53% said deep knowledge is still required to do a task well, even with AI.
Translation: AI broadens access, but expertise still sets the bar for quality.
What this means for product teams
- Generalist mindset with specialist execution: everyone contributes across the stack, but keeps a core craft.
- Faster loops: fewer handoffs, tighter feedback, more ownership within pods.
- Artifacts over titles: decisions live in prototypes, specs, and experiments - not in job descriptions.
The new skill stack for "product builders"
- Prototyping fluency: wireframes, clickable flows, and basic UI polish to validate ideas quickly.
- Prompting and automation: using AI for drafts, tests, data pulls, and boilerplate code.
- Data instincts: define success metrics, run quick analyses, and spot signal from noise.
- User sense: lightweight research, clean synthesis, and sharp problem statements.
- Technical empathy: understand constraints, read code at a high level, and spec clearly.
How to structure work
- Define outcomes, not tasks: assign problems with clear metrics; let makers choose the path.
- Open up the tools: give everyone access to design files, analytics, and AI assistants.
- Short cycles: weekly demos, small bets, and fast kills on weak ideas.
- Review depth: keep a specialist review gate for durability, accessibility, and security.
Hiring and career impact
- Hire for range + spike: look for one standout craft with proven cross-functional output.
- Portfolios over resumes: prototypes, PRDs, experiments, and measurable results.
- Learning as a habit: continuous upskilling is now part of the job, not a perk.
Quarterly action plan
- Pick one flagship area to "generalize": e.g., let PMs produce first-pass flows; let engineers run concept tests.
- Standardize AI in the workflow: prompts for tickets, test plans, specs, and code comments.
- Create a shared demo ritual: every team shows working prototypes weekly.
- Add a depth check: specialists sign off on the 10% that truly needs expert rigor.
- Track two metrics: cycle time from idea to test, and % of experiments that reach a user.
Why this isn't "AI will do it for you"
The survey stat that matters most: 53% still see deep knowledge as critical. AI helps you start. It doesn't finish for you. The edge comes from taste, systems thinking, and the reps to know what good looks like.
Field's takeaway sums it up: "We're all product builders, and some of us are specialized in our particular area." Titles matter less. Shipping value matters more.
Resources
- Figma for shared design systems, prototyping, and cross-functional collaboration.
- Lenny's Podcast for conversations with operators on product, growth, and craft.
- Complete AI Training: Courses by Job to build practical AI skills mapped to product roles.
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