In December 2023, the European Commission formally moved to block Adobe's plan to acquire Figma. The deal was valued at $20 billion — roughly 28 trillion won. Around the same time, the U.S. Department of Justice was preparing a similar antitrust case, and Adobe ultimately abandoned the acquisition altogether in early 2024. At the time, many observers saw this as Figma losing its best shot at an exit. A $20 billion acquisition offer is a rare opportunity for a software startup's founders, and when it falls apart for reasons entirely outside the company's control, the blow can hit both morale and strategic direction.
Yet Figma didn't falter after the deal collapsed. Choosing to go it alone, the company aggressively added AI features, expanded its developer-collaboration tools, and kept growing the platform. In a recent interview, CEO Dylan Field said AI has been "a tailwind, not a headwind" for the company — turning what many had flagged as its biggest threat into, by his account, a growth engine.
That comment is worth reading for Korea's solo planners and solo PMs for more than just what it says about Figma's business outlook. The shifts now underway in the design-tool market bear directly on how solo practitioners actually work.
Why AI Raised the Question of Whether Figma Is Even Necessary
Since its founding in 2012, Figma has reset the standard for design-collaboration software. Browser-based real-time collaboration was a novel concept at the time, and it quickly reshaped a market that had been dominated by Adobe XD and Sketch. Millions of designers, developers, and PMs around the world came to collaborate through Figma, and for a long stretch, few teams ran a product-development cycle without it.
That footing started to wobble after 2024. As large language models like ChatGPT, Claude, and Gemini became everyday tools, a wave of products emerged that plugged straight into code generation. Vercel's v0.dev produces working React components from natural-language input alone, while Bolt.new and Lovable can assemble an entire web app from a few lines of text. Skipping the design step entirely and going straight from prompt to code spread quickly, especially among startups and solo developers.
That trend raised a question: do you even need Figma anymore? For larger teams that share a design system and track revision history, the case for Figma still holds. But for early prototypes or MVPs, more people began arguing that a single AI coding tool was enough — and the reasons to keep paying for a Figma plan started to shrink, especially among small teams.
Field's Case for AI — and the Skepticism It Draws
Dylan Field offered a different read on this trend. His argument: the faster AI tools make code, the more weight falls on the step that defines what should be built in the first place. The quicker the code comes out, the more it matters to nail down, in advance, what the resulting screen should look like and how it should flow. Since Figma is the tool that handles that step, he argues, it stands to be used more, not less, as AI adoption grows.
Anyone who's actually used an AI coding tool will find this logic isn't entirely far-fetched. Describing a screen in natural language and actually designing and showing that screen produce results of very different precision. Vague instructions produce vague output. Field's point is that a visualized design spec can hand AI a far more precise input than a paragraph of prose.
Still, plenty of people disagree. As AI coding tools keep improving, there could come a point where natural-language descriptions alone are precise enough to produce polished results. Some tools already let you upload a single screenshot and get code back. Instead of the current flow — build the screen in Figma, then hand it to AI — the process could shift toward AI proposing a first draft of the screen, with the user then giving edit instructions.
There's also pushback on the collaboration argument. The real-time collaboration Figma touts as its core strength scales in value with team size. In settings where multiple designers work simultaneously and developers pull specs directly from design files, Figma's role is clear-cut. But for a solo planner or a two- or three-person team, that advantage is comparatively small. In other words, the "tailwind" Field describes may feel very different depending on the size and structure of the organization.
Design Sense Is Migrating Into the Planner's Job Description
The real takeaway for practitioners isn't which tool to pick. It's that as AI lowers the barrier to generating UI, visual-design instinct is shifting out of "the designer's specialty" and into territory a planner now has to handle directly.
In the old model, a planner wrote the feature spec and a designer built the screen; the planner's involvement in look and flow was limited. That's no longer the case. PMs now sketch wireframes themselves, developers review screen flow, and marketers weigh in on banner layout. As AI tools lowered the barrier to entry, the pool of people handling screens simply got wider.
There's an implication here that's easy to overlook. Which service earns a user's trust, which screen speeds up a purchase decision, which layout lowers bounce rate — none of that is a matter of feature implementation. The polish and familiarity a customer feels the instant they see a screen translates directly into business results. It's a long-standing observation in design circles that when explaining why one product sells better than another, look and feel tend to drive the purchase decision ahead of the feature list. No matter how fast an AI coding tool spits out a screen, judging whether the result fits the goal — and knowing how to direct the fix — still comes down to human judgment.
The gap between planners who have that eye and those who don't is likely to grow sharper, not narrower, as tools get faster.
Where to Start, Right Now
Start by checking how much of your work still depends on an outside designer. If you're stuck waiting days for a single screen tweak, you already have everything you need — an AI tool plus a basic design tool — to clear that bottleneck yourself. The goal doesn't have to be a "perfect" screen built in Figma's free tier or a similar tool. A more realistic starting point is building it to a level you can hand off to an AI coding tool with precision.
It also helps to set a clear rule for when to use an AI coding tool versus a design tool. AI coding tools turn an idea into a screen fast, but they're limited when it comes to fine-tuning the details of user-flow intent. Design tools take longer, but let you hand-tune layout and visual hierarchy. Lean on only one, and you either gain speed and lose precision, or the reverse.
Working with screens directly builds a vocabulary for questions like "why does this layout feel off" or "why doesn't this color combination read as trustworthy." That vocabulary is what lets you give an AI tool precise edit instructions, and it's also what lets you give a designer more concrete feedback. I'd argue this is, right now, the highest-return skill a planner can build for the lowest investment. The faster AI tools get, the more valuable the judgment that filters their output becomes.
Just as Figma charted its own course after its $20 billion buyout fell through, solo planners today stand at a similar fork in the road. You can lean on outsourced design and watch the tooling paradigm shift around you, or you can use the door AI has opened to widen the range of what you handle yourself. When Field calls AI a "tailwind," that may not be true of Figma alone. For a planner who understands screens, the wind is blowing in the same direction.
