Here we go. Figma has just dropped their S-1, or their registration for an initial public offering (IPO).

While a lot of the risk factors are boilerplate—legalese to cover their bases—the one about AI is particularly interesting, “Competitive developments in AI and our inability to effectively respond to such developments could adversely affect our business, operating results, and financial condition.”

Developments in AI are already impacting the software industry significantly, and we expect this impact to be even greater in the future. AI has become more prevalent in the markets in which we operate and may result in significant changes in the demand for our platform, including, but not limited to, reducing the difficulty and cost for competitors to build and launch competitive products, altering how consumers and businesses interact with websites and apps and consume content in ways that may result in a reduction in the overall value of interface design, or by otherwise making aspects of our platform obsolete or decreasing the number of designers, developers, and other collaborators that utilize our platform. Any of these changes could, in turn, lead to a loss of revenue and adversely impact our business, operating results, and financial condition.

There’s a lot of uncertainty they’re highlighting:

• Could competitors use AI to build competing products?
• Could AI reduce the need for websites and apps which decreases the need for interfaces?
• Could companies reduce workforces, thus reducing the number of seats they buy?

These are all questions the greater tech industry is asking.

Darragh Burke and Alex Kern, software engineers at Figma, writing on the Figma blog:

Building code layers in Figma required us to reconcile two different models of thinking about software: design and code. Today, Figma's visual canvas is an open-ended, flexible environment that enables users to rapidly iterate on designs. Code unlocks further capabilities, but it’s more structured—it requires hierarchical organization and precise syntax. To reconcile these two models, we needed to create a hybrid approach that honored the rapid, exploratory nature of design while unlocking the full capabilities of code.

The solution turned out to be code layers, actual canvas primitives that can be manipulated just like a rectangle, and respects auto layout properties, opacity, border radius, etc.

The solution we arrived at was to implement code layers as a new canvas primitive. Code layers behave like any other layer, with complete spatial flexibility (including moving, resizing, and reparenting) and seamless layout integration (like placement in autolayout stacks). Most crucially, they can be duplicated and iterated on easily, mimicking the freeform and experimental nature of the visual canvas. This enables the creation and comparison of different versions of code side by side. Typically, making two copies of code for comparison requires creating separate git branches, but with code layers, it’s as easy as pressing ⌥ and dragging. This automatically creates a fork of the source code for rapid riffing.

In my experience, it works as advertised, though the code layer element will take a second to render when its spatial properties are edited. Makes sense though, since it's rendering code.

Nearly three weeks after it was introduced at Figma Config 2025, I finally got access to Figma Make. It is in beta and Figma made sure we all know. So I will say upfront that it’s a bit unfair to do an official review. However, many of the tools in my AI prompt-to-code shootout article are also in beta. 

Since this review is fairly visual, I made a video as well that summarizes the points in this article pretty well.

Last week, Figma held their annual user conference Config in San Francisco. Since its inception in 2020, it has become a significant UX conference that covers more than just Figma’s products and community. While I’ve not yet had the privilege of attending in person, I do try to catch the livestreams or videos afterwards.

Nearly 17 months after Adobe and Figma announced the termination of their merger talks, Figma flexed their muscle—fueld by the $1 billion breakup fee, I’m sure—by announcing four new products. They are Figma Draw, Make, Sites, and Buzz.

• Draw: It’s a new mode within Figma Design that reveals additional vector drawing features. 
• Make: This is Figma’s answer to Lovable and the other prompt-to-code generators. 
• Sites: Finally, you can design and publish websites from Figma, hosted on their infrastructure.
• Buzz: Pass off assets to clients and marketing teams and they can perform lightweight and controlled edits in Buzz. 

With these four new products, Figma is really growing up and becoming more than a two-and-half-product company, and is building their own creative suite, if you will. Thus taking a big swing at Adobe.

There has been an explosion of AI-powered prompt-to-code tools within the last year. The space began with full-on integrated development environments (IDEs) like Cursor and Windsurf. These enabled developers to use leverage AI assistants right inside their coding apps. Then came a tools like v0, Lovable, and Replit, where users could prompt screens into existence at first, and before long, entire applications.

A couple weeks ago, I decided to test out as many of these tools as I could. My aim was to find the app that would combine AI assistance, design capabilities, and the ability to use an organization’s coded design system.

While my previous essay was about the future of product design, this article will dive deep into a head-to-head between all eight apps that I tried. I recorded the screen as I did my testing, so I’ve put together a video as well, in case you didn’t want to read this.

With their annual user conference, Config, coming up in San Francisco in less than two weeks, Figma released their 2025 AI Report today.

Andrew Hogan, Insights lead:

While developers and designers alike recognize the importance of integrating AI into their workflows, and overall adoption of AI tools has increased, there’s a disconnect in sentiment around quality and efficacy between the two groups.

Developers report higher satisfaction with AI tools (82%) and feel AI improves the quality of their work (68%). Meanwhile, designers show more modest numbers—69% satisfaction rate and 54% reporting quality improvement—suggesting this group’s enthusiasm lags behind their developer counterparts.

This divide stems from how AI can support existing work and how it’s being used: 59% of developers use AI for core development responsibilities like code generation, whereas only 31% of designers use AI in core design work like asset generation. It’s also likely that AI’s ability to generate code is coming into play—68% of developers say they use prompts to generate code, and 82% say they’re satisfied with the output. Simply put, developers are more widely finding AI adoption useful in their day-to-day work, while designers are still working to determine how and if these tools best fit into their processes.

I can understand that. Code is behind the scenes. If it’s not perfect, no one will really know. But design is user-facing, so quality is more important.

Looking into the future:

Though AI’s impact on efficiency is clear, there are still questions about how to use AI to make people better at their role. This disparity between efficiency and quality is an ongoing battle for users and creators alike.

…

Looking forward, predictions about the impact of AI on work are moderate—AI’s expected impact for the coming year isn’t much higher than its expected impact last year.

In the full report, Hogan details out:

Only 27% predict AI will have a significant impact on their company goals in the next year (compared to 23% in 2024), with 15% saying it will be transformational (unchanged year-over-year).

The survey was taken in January with a panel of 2,500 users. Things in AI change in weeks. I’m surprised at the number and part of me believes that a lot of designers are hiding their heads in the sand. AI is coming. We should be agile and adapt.