Karri Saarinen, CEO of Linear, writes one of the more grounded takes on AI’s current state. Linear’s cloud agent now fixes more than 1,000 issues per month, but Karri is clear that hard problems remain hard and design tools are still challenging to use. On having a design tool operate directly on the production codebase: ​“A lot of the design work I do is not production design. I am not trying to implement the final version or test every edge case. Most design work is about making decisions, understanding the problem, and finding the fit. That process generates many variations and messy ideas.”

The expertise paradox section is the most useful: ​“AI often feels most impressive in domains where you know the least.” Expertise makes AI harder to use but also more valuable, because experts know how to steer, constrain, and evaluate the output.

“Designers have always (and will always) answer the question ​‘What’s worth making?’ ” Joel Lewenstein, Head of Design at Anthropic, argues that as the cost of software drops, the most important decisions shift from ​“can we make this?” to ​“should we?” Design, in his framing, is what narrows the possibility space fast enough to keep up with the speed of delivery. He describes Claude Design as a tool for getting ideas ​“good enough to move discussion forward,” cutting idea-to-internal-feedback time from days to hours.

“A state-of-the-art image model that can take on complex visual tasks and produce precise, immediately usable visuals, with sharper editing, richer layouts, and thinking-level intelligence.” OpenAI’s second-generation image model promises a step change in instruction following, precise object placement, dense text rendering, and cross-aspect-ratio generation.

DESIGN​.md encodes your design rules, colors, typography, and component preferences in a plain Markdown file that AI agents can parse and validate against. Google moves DESIGN​.md from a Stitch-specific feature to open-source format specification. If the format gains traction across tools, it could become the missing link between design systems and AI generation pipelines.

A useful companion to Google’s announcement above. Meng To shares 15 takeaways from actually using the format: when to Remix vs. Iterate, how to treat DESIGN​.md as ​“reusable project memory,” and why curation is part of the design process. The most actionable takeaway: ​“Start with DESIGN​.md, generate the first design, remix and expand it, create section variations, move into a builder, then assemble the full site.” Don’t miss his video tutorial on turning a DESIGN​.md into landing pages, mobile screens, and motion design.

“I’m increasingly sure that 2026 signals the end of product design as a full-fledged stand-alone function within companies.” Gokul Rajaram makes the controversial maximalist case: startups will outsource design systems to consultants, AI will generate the UI, and design headcount will shrink to 20% of its current size at larger companies.

It’s an argument that reduces design to UI mockup production, which is where most of the responses below push back. The reactions to Claude Design launch in the previous issue have already discussed confusion in terminology, the difference between design practice vs. design production, and made the case that ​“real design is the search for fit between form and context, not the generation of form itself.”

“The role of someone who figures out what needs to exist, why, how it should work, how it should be positioned, differentiated and made memorable has never been more in demand.” Josh Puckett (also the maker of Pica included below) separates the deliverable from the practice: he agrees the mockup-maker role is going away, but argues the ​“what should this be and why?” role is more in demand than ever.

“The first casualty won’t be design. It’s the deliverable.” Tommy Geoco backs it with numbers: a recent State of Prototyping survey of 1,478 design engineers finds 80.9% spending most of their week vibe coding, and 59% having shipped their own internal tool in the last six months. ​“The design function isn’t being eliminated. It’s absorbing engineering.”

Ben Blumenrose pushes back on what the whole thread is really arguing about: ​“Product designers do help with UI design systems but that’s a fraction of what they do which also includes what product to build, how it should work, how to ensure people understand how to use it, how to create a brand people love and rally around, how the system should work together, etc etc etc but yes they’ll need to do visual design of UI design systems less so I’ll concede on that.”

Kris Puckett, Design Manager at Stripe, spent months building Epilogue, a real iOS app with 14,000 lines of Swift, entirely through conversation with Claude. This essay is a specific and honest account of what the designer-building-with-AI experience actually looks like: what broke, what he learned about asking precise questions, what ​“vague frustration keeps you stuck, specific confusion gets you answers” actually looks like in practice. ​“I realized the bottleneck was never coding ability. It was articulation. The ability to describe what I wanted clearly enough that something else could build it.”

Carina B. Velasquez, a Design Systems Lead locked out of AI tools at work, spent a Friday afternoon on her personal laptop with Claude Code and the Figma MCP server. By 6pm she had built Prisme: a complete three-tier token system, four brand themes, and a bidirectional plugin (Prisme Bridge) translating between Token Studio and native Figma variable formats. Her conclusion: ​“AI is amazing, Claude Code built my design system for me, the future is here. That is not what happened. What happened is that I showed up with years of accumulated knowledge, a specific problem I understood deeply, and a clear enough mental model to describe what I wanted with precision. Claude Code was fast; impressively fast, and smart in ways that genuinely surprised me. But it was executing a vision I already had. The intelligence in that Friday afternoon was not artificial.”

Dylan Field in an interview with Peter Yang argues that AI will massively expand what’s possible in design, but the enduring edge for humans is judgment: taste, craft, and point of view, plus the ability to use AI as an iterative partner rather than a one-shot generator.

Explore the world of Figma Weave workflows in the new Community area. Figma Weave (formerly Weavy) combines multiple AI models with professional editing tools in a node-based canvas, covering image, video, animation, and VFX.

Figma opens the canvas to agents. The use_figma MCP tool lets Claude Code and Codex generate and modify designs grounded in your actual design system. The key distinction from earlier code-to-design experiments: agents work with what your team has already built, making design system quality a direct input to AI output quality. 

Figma Skills are Markdown instruction files that teach AI agents how to reliably handle specific Figma tasks — generating a design system from a codebase, building screens from existing components, and more. Community contributors are already adding new ones.

Figma and Anthropic demonstrate the Claude Code roundtrip in both directions. Write to Canvas lets Claude Code create and modify designs directly in Figma; Code to Canvas brings live browser UI back in as editable frames. Worth watching alongside the Agents on Canvas blog post and Figma Skills page for the full picture of what this workflow ecosystem looks like.

Ed Bayes leads design on Codex at OpenAI; Gui Seiz leads AI product design at Figma. Together with Aakash Gupta, they give a rare behind-the-scenes look at what the Codex-to-Figma MCP integration actually changed inside their teams. The workflow is genuinely bidirectional, code and canvas reading from each other directly. The most concrete signal that something shifted: designers are working directly in staging.

A thorough, practical guide from Christine Vallaure on what design systems need to work well with agents. Five things most systems are missing: semantic tokens, full state coverage, slot-based composition, exact Figma-to-code property matching, and token-based auto layout. Pairs well with the Brad Frost video — both cover the same design system moment from different angles.

Brad Frost and Chromatic cofounder Dominic Nguyen walk through how Storybook’s new MCP exposes design systems for agents to use reliably. The key insight: AI doesn’t invent states your components don’t have — it amplifies what’s documented. Coverage, completeness, and constraints are now load-bearing.

Christine Vallaure built a five-person AI editorial team from one text file and seven lines. Five named agents: Rachel brings reader feedback, Joan brings numbers, Caitlin flags voice problems, Miranda raises buyer concerns, and Elke runs the room as editor in chief. Technically one model shifting between personas inside a single Claude conversation, but behaviorally a team critique. The most concrete personal account I’ve read of what agentic AI actually feels like to operate as a solo creator.