I stumbled on a post that completely reframed how I think about AI in design. Most people still treat AI tools like glorified chatbots. Type a question, get an answer, move on. But this LinkedIn creator laid out a convincing case that we’re stuck in outdated thinking, and Figma Make is the proof.
The post breaks down how AI now powers the full design lifecycle, from ideation to publishing. And honestly? It exposed a bunch of myths I’ve seen floating around the design and tech community for months. Let me walk you through the biggest ones and why they don’t hold up anymore.
Myth #1: AI in Design Is Just a Chatbot With a Pretty Interface
This is the one the original poster tackles head-on. If you think the best way to use AI is typing prompts and reading text responses, you’re missing the entire shift. Figma Make lets you prompt an idea and get back an editable, clickable prototype. Not a screenshot. Not a static mockup. A working prototype with logic, navigation, and real interactions baked in.
That’s a fundamentally different use case than chatting with a bot. You’re building, not just asking.
Myth #2: Designers Only Create Static Screens
For years, that was true. The expert points out a painful reality we’ve all lived through: wireframes looked good, mockups impressed stakeholders, but nothing actually worked until developers jumped in. That gap between “looks nice” and “functions correctly” used to eat up days of back-and-forth.
Figma Make changes the designer’s role from a screen creator to an experience builder. You’re not handing off flat files anymore. You’re delivering something people can click through, test, and give specific feedback on.
Myth #3: You Need Developers to Make a Prototype Feel Real
This one’s been true for so long that most teams don’t even question it. But the author lays out what Figma Make actually delivers:
- Text prompt turns into a clickable prototype
- Real components, not placeholder boxes
- Logic, navigation, and interactions included out of the box
- Edit by clicking or by prompting
- Connect to real data backends
- Publish in minutes
That last point is wild. Publishing in minutes means stakeholders can access the prototype immediately. Teams align faster. UX gets tested early. Feedback becomes specific instead of vague. The distance between “concept” and “something you can ship” shrinks dramatically.
Myth #4: AI Only Helps With the Creative Part
A lot of people think AI in design is limited to generating pretty visuals. The contributor breaks it down into four distinct layers where AI actually operates, and creativity is just one of them:
AI Ideation and Kickoff
- Prompt an idea
- Get an editable prototype
AI Content and Asset Enhancement
- Context-aware visuals in the web app
- Polished designs without manual tweaking
AI Productivity and Cleanup
- Intelligent asset search
- Auto layer organization
- Smart duplication
- Auto-connected user flows
AI Collaboration Intelligence
- Shared design context
- Faster component discovery
- Pattern reuse across files
- Stronger team alignment
That third and fourth layer surprised me. Auto layer organization and pattern reuse across files? That’s not flashy, but it’s the kind of thing that saves hours every single week for teams working at scale.
Myth #5: Speed Means Cutting Corners
When people hear “prototype in minutes,” their instinct is to assume quality takes a hit. The innovator behind this post actually addresses that directly with a solid list of do’s and don’ts that separate smart speed from sloppy shortcuts:
Do’s:
- ✅ Write specific, goal-driven prompts
- ✅ Test interactions early with Figma Make
- ✅ Use real components and flows
- ✅ Iterate fast before refining visuals
- ✅ Validate UX logic, not just looks
Don’ts:
- ❎ Don’t treat prototypes like static art
- ❎ Don’t skip navigation logic
- ❎ Don’t rely on vague prompts
- ❎ Don’t overpolish the first version
- ❎ Don’t confuse speed with usability
That last don’t is the key. Fast prototyping isn’t about racing to a finished product. It’s about getting to testable reality quickly so you can learn and iterate before you’ve invested too much.
Myth #6: AI Replaces Design Thinking
This is the myth I see the most, and the post’s author nails the rebuttal in one sentence: AI did not replace design thinking. AI removed the distance between thinking and building.
What used to take days of back and forth now happens in one focused session. This is a power shift.
That framing matters. The creative strategy, the user empathy, the problem definition: all of that still requires a human brain. What AI eliminates is the tedious translation layer between having an idea and seeing it come to life. You still need to think well. You just get to see the results of that thinking almost instantly.
What This Means for You
If you’re a designer, product manager, or founder who still treats prototyping as a multi-day process involving handoffs between three different roles, it’s worth rethinking that workflow. The tools have caught up to the ambition.
I think the most practical takeaway from this post is simple: start with specific prompts, validate logic before visuals, and iterate before you polish. That approach works whether you’re building a startup MVP or testing a new feature for an enterprise product.
Check out the full LinkedIn post for the complete infographic and deeper breakdown of how Figma Make fits into the AI-powered design lifecycle.