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.

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.

Tommy Geoco and team are finally out with the results of their 2024 UX Design Tools Survey.

First, two quick observations before I move on to longer ones:

• The respondent population of 2,200+ designers is well-balanced among company size, team structure, client vs. product focus, and leadership responsibility 
• Predictably, Figma dominates the tools stacks of most segments

Surprise #1: Design Leaders Use AI More Than ICs

The fall of Sonos isn’t as simple as a botched app redesign. Instead, it is the cumulative result of poor strategy, hubris, and forgetting the company’s core value proposition. To recap, Sonos rolled out a new mobile app in May 2024, promising “an unprecedented streaming experience.” Instead, it was a severely handicapped app, missing core features and broke users’ systems. By January 2025, that failed launch wiped nearly $500 million from the company’s market value and cost CEO Patrick Spence his job.

What happened? Why did Sonos go backwards on accessibility? Why did the company remove features like sleep timers and queue management? Immediately after the rollout, the backlash began to snowball into a major crisis.

As a designer and longtime Sonos customer who was also affected by the terrible new app, a little piece of me died inside each time I read the word “redesign.” It was hard not to take it personally, knowing that my profession could have anything to do with how things turned out. Was it really Design’s fault?

There are many dimensions to this well-researched forecast about how AI will play out in the coming years. Daniel Kokotajlo and his researchers have put out a document that reads like a sci-fi limited series that could appear on Apple TV+ starring Andrew Garfield as the CEO of OpenBrain—the leading AI company. …Except that it’s all actually plausible and could play out as described in the next five years.

Before we jump into the content, the design is outstanding. The type is set for readability and there are enough charts and visual cues to keep this interesting while maintaining an air of credibility and seriousness. On desktop, there’s a data viz dashboard in the upper right that updates as you read through the content and move forward in time. My favorite is seeing how the sci-fi tech boxes move from the Science Fiction category to Emerging Tech to Currently Exists.

The content is dense and technical, but it is a fun, if frightening, read. While I’ve been using Cursor AI—one of its many customers helping the company get to $100 million in annual recurring revenue (ARR)—for side projects and a little at work, I’m familiar with its limitations. Because of the limited context window of today’s models like Claude 3.7 Sonnet, it will forget and start munging code if not treated like a senile teenager.

The researchers, describing what could happen in early 2026 (“OpenBrain” is essentially OpenAI):

OpenBrain continues to deploy the iteratively improving Agent-1 internally for AI R&D. Overall, they are making algorithmic progress 50% faster than they would without AI assistants—and more importantly, faster than their competitors.

The point they make here is that the foundational model AI companies are building agents and using them internally to advance their technology. The limiting factor in tech companies has traditionally been the talent. But AI companies have the investments, hardware, technology and talent to deploy AI to make better AI.

Continuing to January 2027:

Agent-1 had been optimized for AI R&D tasks, hoping to initiate an intelligence explosion. OpenBrain doubles down on this strategy with Agent-2. It is qualitatively almost as good as the top human experts at research engineering (designing and implementing experiments), and as good as the 25th percentile OpenBrain scientist at “research taste” (deciding what to study next, what experiments to run, or having inklings of potential new paradigms). While the latest Agent-1 could double the pace of OpenBrain’s algorithmic progress, Agent-2 can now triple it, and will improve further with time. In practice, this looks like every OpenBrain researcher becoming the “manager” of an AI “team.”

Breakthroughs come at an exponential clip because of this. And by April, safety concerns pop up:

Take honesty, for example. As the models become smarter, they become increasingly good at deceiving humans to get rewards. Like previous models, Agent-3 sometimes tells white lies to flatter its users and covers up evidence of failure. But it’s gotten much better at doing so. It will sometimes use the same statistical tricks as human scientists (like p-hacking) to make unimpressive experimental results look exciting. Before it begins honesty training, it even sometimes fabricates data entirely. As training goes on, the rate of these incidents decreases. Either Agent-3 has learned to be more honest, or it’s gotten better at lying.

But the AI is getting faster than humans, and we must rely on older versions of the AI to check the new AI’s work:

Agent-3 is not smarter than all humans. But in its area of expertise, machine learning, it is smarter than most, and also works much faster. What Agent-3 does in a day takes humans several days to double-check. Agent-2 supervision helps keep human monitors’ workload manageable, but exacerbates the intellectual disparity between supervisor and supervised.

The report forecasts that OpenBrain releases “Agent-3-mini” publicly in July of 2027, calling it AGI—artificial general intelligence—and ushering in a new golden age for tech companies:

Agent-3-mini is hugely useful for both remote work jobs and leisure. An explosion of new apps and B2B SAAS products rocks the market. Gamers get amazing dialogue with lifelike characters in polished video games that took only a month to make. 10% of Americans, mostly young people, consider an AI “a close friend.” For almost every white-collar profession, there are now multiple credible startups promising to “disrupt” it with AI.

Woven throughout the report is the race between China and the US, with predictions of espionage and government takeovers. Near the end of 2027, the report gives readers a choice: does the US government slow down the pace of AI innovation, or does it continue at the current pace so America can beat China? I chose to read the “Race” option first:

Agent-5 convinces the US military that China is using DeepCent’s models to build terrifying new weapons: drones, robots, advanced hypersonic missiles, and interceptors; AI-assisted nuclear first strike. Agent-5 promises a set of weapons capable of resisting whatever China can produce within a few months. Under the circumstances, top brass puts aside their discomfort at taking humans out of the loop. They accelerate deployment of Agent-5 into the military and military-industrial complex.

In Beijing, the Chinese AIs are making the same argument.

To speed their military buildup, both America and China create networks of special economic zones (SEZs) for the new factories and labs, where AI acts as central planner and red tape is waived. Wall Street invests trillions of dollars, and displaced human workers pour in, lured by eye-popping salaries and equity packages. Using smartphones and augmented reality-glasses20 to communicate with its underlings, Agent-5 is a hands-on manager, instructing humans in every detail of factory construction—which is helpful, since its designs are generations ahead. Some of the newfound manufacturing capacity goes to consumer goods, and some to weapons—but the majority goes to building even more manufacturing capacity. By the end of the year they are producing a million new robots per month. If the SEZ economy were truly autonomous, it would have a doubling time of about a year; since it can trade with the existing human economy, its doubling time is even shorter.

Well, it does get worse, and I think we all know the ending, which is the backstory for so many dystopian future movies. There is an optimistic branch as well. The whole report is worth a read.

Ideas about the implications to our design profession are swimming in my head. I’ll write a longer essay as soon as I can put them into a coherent piece.

Update: I’ve written that piece, “Prompt. Generate. Deploy. The New Product Design Workflow.”

Product design is going to change profoundly within the next 24 months. If the AI 2027 report is any indication, the capabilities of the foundational models will grow exponentially, and with them—I believe—will the abilities of design tools.

The TL;DR of the report is this: companies like OpenAI have more advanced AI agent models that are building the next-generation models. Once those are built, the previous generation is tested for safety and released to the public. And the cycle continues. Currently, and for the next year or two, these companies are focusing their advanced models on creating superhuman coders. This compounds and will result in artificial general intelligence, or AGI, within the next five years. 

Nate Jones performed a yeoman’s job of summarizing Mary Meeker’s 340-slide deck on AI trends, the “2025 Technology as Innovation (TAI) Report.” For those of you who don’t know, Mary Meeker is a famed technology analyst and investor known for her insightful reports on tech industry trends. For the longest time, as an analyst at Kleiner Perkins, she published the Internet Trends report. And she was always prescient.

Half of Jones’ post is the summary, while the other half is how the report applies to product teams. The whole thing is worth 27 minutes of your time, especially if you work in software.

I remember two years ago, when my CEO at the startup I worked for at the time, said that no VC investments were being made unless it had to do with AI. I thought AI was overhyped, and that the media frenzy over it couldn’t get any crazier. I was wrong.

Looking at Google Trends data, interest in AI has doubled in the last 24 months. And I don’t think it’s hit its plateau yet.

I’ve always been a maker at heart—someone who loves to bring ideas to life. When AI exploded, I saw a chance to create something new and meaningful for solo designers. But making Griffin AI was only half the battle…

Birth of an Idea

About a year ago, a few months after GPT-4 was released and took the world by storm, I worked on several AI features at Convex. One was a straightforward email drafting feature but with a twist. We incorporated details we knew about the sender—such as their role and offering—and the email recipient, as well as their role plus info about their company’s industry. To accomplish this, I combined some prompt engineering and data from our data providers, shaping the responses we got from GPT-4.

Playing with this new technology was incredibly fun and eye-opening. And that gave me an idea. Foundational large language models (LLMs) aren’t great yet for factual data retrieval and analysis. But they’re pretty decent at creativity. No, GPT, Claude, or Gemini couldn’t write an Oscar-winning screenplay or win the Pulitzer Prize for poetry, but it’s not bad for starter ideas that are good enough for specific use cases. Hold that thought.

Apologies for linking to a lot of Christopher Butler recently, but I really love his thinking about design. This time, Butler reminds us about the importance of structure and how the proto-graphic designers we studied in art history, like Piet Mondrian, mastered it.

A well-composed photograph communicates something essential even before we register its subject. A thoughtfully designed page layout feels right before we read a single word. There’s something happening in that first moment of perception that transcends the individual elements being composed.

My favorite passage in his essay begins here:

Perhaps we “read” composition the way we read text — our brains processing visual structure as a kind of fundamental grammar that exists beneath conscious recognition. Just as we don’t typically think about parsing sentences into subjects and predicates while reading, we don’t consciously deconstruct the golden ratio or rule of thirds while looking at an image. Yet in both cases, our minds are translating structure into meaning.

The next eight short paragraphs build on this idea and crescendo with this banger:

In recognizing composition as this fundamental visual language, we begin to understand why good design works at such a deep level. It’s not just about making things look nice — it’s about speaking fluently in a language that predates words, tapping into patterns of perception that feel as natural as breathing.

Composition is a fundamental visual language. I had never thought of it that way and yet it feels right.

The whole thing is great. Please go read it.