AI Is Merging Job Titles - "We're All Product Builders"
Designer. Engineer. Researcher. Those lines are blurring. Figma cofounder and CEO Dylan Field put it plainly: we're seeing a merging of roles across product teams, and AI is the catalyst.
On Lenny's Podcast, Field said more people are stepping outside their job title. Designers tinker with code. PMs draft flows. Researchers mock up interfaces. The shift has been building for five years and is set to accelerate.
What the data says
- 72% of respondents said AI tools are a top reason roles are expanding.
- 56% of non-designers now say they engage "a great deal" in design tasks (up from 44% the year prior).
- 53% said deep knowledge is still required to do a task well, even with AI.
Field's takeaway: "We're all product builders, and some of us are specialized in our particular area."
Why this matters for product teams
AI lowers the barrier to contribute across the stack. What once demanded heavy coding now happens with a simple "vibe-coding" or AI-assisted tool. Non-engineers ship prototypes. Non-designers run quick usability checks.
The upside: speed, tighter feedback loops, and more people contributing earlier. The risk: shallow work that ignores craft. The data backs that tension-tools help, but depth still wins.
How to adapt: a simple operating guide
- Define "product builder" behaviors: Everyone ships prototypes, writes clear problem statements, and validates with users.
- Keep craft ownership: Designers own experience quality. Engineers own reliability and security. Researchers own rigor. Shared work, clear accountability.
- Restructure rituals: Run mixed-discipline crits. Pair design-engineering spikes. Review AI-assisted work with the same bar as hand-made work.
- Set AI guardrails: What tools are approved, where data can go, what needs human review, and how you log AI-generated changes.
- Skill maps over job boxes: Map must-have skills across product sense, UX, data literacy, and code literacy. Track progression by skill, not title.
- Continuous upskilling: Pick 1-2 team-wide AI workflows each quarter (e.g., prototyping, test synthesis) and level them up.
- Hiring and leveling: Screen for generalist instincts plus one deep spike. Reward impact across the product, not just within a lane.
- Quality gates: Preserve design reviews, code reviews, and research plans. AI can draft; humans set the standard.
The modern product builder skill stack
- Product sense: Clear problem framing, constraints, and success metrics.
- Prompt fluency: Turning intent into quality prompts, iterations, and evaluations.
- Data literacy: Basic analysis, experiment design, and interpretation.
- UX fundamentals for all: Information hierarchy, accessibility basics, and usability.
- Code literacy for non-engineers: Readability, simple edits, and using AI code assistants well.
- Evaluation and QA: Test plans, edge cases, and human-in-the-loop review.
Tooling principles
- Prototype fast, validate faster: Prefer tools that move you from idea to user feedback in hours, not weeks.
- Keep artifacts reusable: Prompts, patterns, and components should compound across teams.
- Measure outcomes, not output: Track adoption, quality, and cycle time improvements-not "number of prompts."
Common pitfalls to avoid
- Shallow generalism: Cross-functional doesn't mean low standards. Invest in craft mentorship.
- Tool-first thinking: Start with the user problem and constraints. Choose the tool last.
- Compliance blind spots: Set policies for data, credentials, and model choice. Document decisions.
What to do this quarter
- Publish a one-page "product builder" charter for your team.
- Pick two workflows to upgrade with AI (e.g., design exploration and research synthesis). Define metrics and a review cadence.
- Run a weekly cross-discipline critique. Rotate lead roles.
- Create a shared prompt and component library. Review monthly.
The titles on your LinkedIn matter less than the value you ship. The orgs that win treat everyone as a product builder-and still honor deep expertise where it counts.
Resources
Figma - for teams moving faster with shared design systems and prototyping.
Lenny's Podcast - conversations with operators on what actually works.
Upskill your team
If you're building a structured plan for PMs, designers, and engineers, explore role-based AI learning paths here: Complete AI Training - Courses by Job.
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