Figma recently launched Figma Make, a product that promises designers superpowers to prototype interactions. The marketing speaks of democratization, of lowering barriers, of making design more accessible. Yet the pricing tells a different story: Make is exclusively available to their highest "Full seat" tier. This isn't democratization. It's amplification of existing power structures.
While Make might rival tools like Vercel V0 and Loveable, it fundamentally misunderstands the moment we're in. The audience shouldn't be designers. It should be end users.
This is a textbook case of what Clayton Christensen called The Innovator's Dilemma. Figma isn't doing anything wrong by conventional business logic. They're listening to their users, responding to their needs, building features their customers request. But innovation isn't about what the voices say. It's about whose voices you choose to hear.
Figma, held captive by its existing market of design professionals, cannot see the emergent market growing from the force of AI. They're perfecting the horse-drawn carriage while the automobile is being invented in garages around the world.
Why Designers Exist
The design profession emerged from a specific historical context. Software grew complex, requiring years of specialized training to navigate tools like Photoshop or Sketch. End users lost agency over their digital environments, unable to modify or shape the tools they used daily. Developers, brilliant at building systems, struggled to empathize with users due to cognitive biases, lack of domain knowledge, and the curse of expertise.
We discovered that we are often our own worst enemies in understanding our needs. Self-authored software frequently fails because we lack the distance to see our problems clearly. So we created an entire discipline dedicated to building bridges of empathy between those who build software and those who use it.
Designers became translators, mediators, advocates. They developed methodologies to extract user needs, synthesize insights, and propose workflows that addressed jobs-to-be-done. This arrangement worked because the alternative was technically and cognitively prohibitive.
Precedents of Democratization
History offers glimpses of what happens when barriers to creation fall. IKEA didn't just sell furniture; it sold the satisfaction of building your own living space. The company recognized that given the right tools and instructions, people wanted agency over their environment.
3D printing promised similar democratization in manufacturing. Suddenly, anyone could fabricate objects without factories or professional services. The revolution was less about the technology than about the shift in agency from producer to user.
These examples reveal a pattern: when the complexity barrier drops sufficiently, people reclaim agency over their environment. They don't always produce professional-grade results, but they create solutions perfectly fitted to their specific needs.
Revisiting End-User Programming
In the democratization of software design, we attempted end-user programming, which failed for three reasons:
- The skill threshold remained high.
- Documentation was poor, written by engineers for engineers.
- Data and application ecosystems remained locked in proprietary silos.
Large language models address the first two constraints. Natural language interfaces lower skill requirements dramatically. AI can generate documentation on demand, tailored to the user's level of understanding. But the ecosystem problem requires fresh thinking.
We need an open platform where people can build, run, share, and remix applications. This platform would support new types of professionals and markets: domain specialists who create toolkits for their fields; coaches who help users shape their environments through human-AI interaction; and meta-tool builders creating tools that help others create tools.
Figma Make could have been such a platform. So could Vercel V0, Loveable, CodePen Project, GitHub Spark, Notion AI, and tldraw computer.
Rethink Job Displacement
The discourse around AI replacing human labor has become a tiresome battle cry, fixating on job displacement while missing the deeper question: which human roles deserve preservation? Not all intermediaries are equal. The travel agent booking flights disappeared when users gained direct access to airline systems. The financial advisor executing trades vanished when retail investors could trade directly. These roles existed because of technical barriers, not because they added irreplaceable value.
Designers occupy a more complex position. They do create value through empathy and synthesis, but they also exist because of artificial barriers. Recall our premise: designers emerged because software became complex, not because end users wanted to surrender their agency. If AI addresses this complexity, preserving the designer's gatekeeping role becomes an act of artificial scarcity.
Trade theory tells us that exchange benefits parties with differentiated skills. But when users can modify their environment better because of their domain knowledge, the logic of specialization breaks down. The teacher understands education better than the designer. The nurse knows clinical workflows intimately. When they can translate this knowledge directly into tools, mediation becomes friction, not value.
A teacher with decades of classroom experience can now prototype an educational app by describing pedagogical goals. A nurse can create patient tracking tools that reflect real clinical workflows. A chef can design an inventory system that thinks in terms of mise en place, not spreadsheet cells.
AI requires us to reframe the job displacement argument. What if everyone can become their own designer, crafting tools perfectly fitted to their needs without intermediaries translating their vision through layers of abstraction.
Learning To Make, Making To Learn
Figma Make represents a last gasp of the old paradigm, perfecting tools for a priesthood that may no longer be necessary.
Scaling design systems for an army of developers and optimizing the Figma-to-React conversion pipeline is the equivalent of building a faster horse. We need tools for the end users and by the end users.
Imagine, a biologist sketches a data visualization and watches it come alive. A social worker describes a case management system that actually reflects how cases unfold. A chef prototypes an inventory system that thinks in terms of mise en place, not spreadsheet cells.
The revolution will not begin in Figma's offices or on professional designers' computers, but in countless moments when domain experts realize they no longer need interpreters to make their ideas real, and in every making of their ideas, they will deepen their understanding of the domain, the tool, and the needs of themselves as makers and learners.
They are in a positive feedback loop of learning to make and making to learn. I believe systems that can help users establish and sustain this loop will bring the real future of design.