In a recent LinkedIn post, Lenny Rachitsky shares key takeaways from a conversation with Jenny Wen, the design lead for Claude at Anthropic, highlighting significant shifts in the design landscape driven by artificial intelligence. Rachitsky frames these insights as a critical update for anyone involved in product development and design in the age of rapid AI advancement.
Rachitsky relays Wen’s observation that the traditional design process, characterized by a discover-diverge-converge loop, is becoming obsolete. This change is largely due to the speed at which engineers can now leverage AI tools.
“The classic discover-diverge-converge loop that designers have relied on for years doesn’t work when engineers can spin up seven coding agents and ship a working version before a designer finishes exploring options.”
The Bifurcation of Design Work
According to Rachitsky, Wen identifies a significant split in how design work is now being executed. Design tasks are bifurcating into two primary modes: supporting the ongoing engineering execution and setting short-range strategic vision.
Supporting Execution
The first mode involves designers working closely with engineers during the build phase. This includes providing real-time feedback, polishing user interfaces, and even making adjustments directly within the code. This collaborative approach ensures that design principles are maintained as products are rapidly developed.
Setting Short-Range Vision
The second mode focuses on defining the product’s direction, but with a much shorter time horizon. Rachitsky reports that Wen suggests these strategic roadmaps are now typically scoped to three to six months, a sharp contrast to the multi-year plans of the past. This shorter focus is essential for maintaining coherence when development cycles are drastically accelerated.
Rethinking Trust and Hiring in a Fast-Paced Environment
Rachitsky also conveys Wen’s perspective on building brand trust and identifying valuable new talent in this evolving environment.
Building Trust Through Speed and Transparency
Wen’s approach, as relayed by Rachitsky, emphasizes shipping products early and iterating based on public feedback. This strategy involves labeling early releases as “research previews” to manage user expectations.
“Jenny argues that what actually degrades a brand isn’t launching something rough; it’s launching something rough and then going silent. If you ship fast, respond to feedback visibly, and keep improving, users will trust you more, not less.”
This method, according to Rachitsky, fosters greater user trust than striving for perfection before a launch, especially when coupled with transparent iteration.
The Value of ‘Cracked New Grads’
Rachitsky highlights Wen’s view on a critical, yet often overlooked, hiring profile: the “cracked new grad.” Wen suggests that early-career designers with a fresh perspective and a rapid learning capacity might be better suited for the current pace of change than experienced hires.
“Most companies are hiring senior designers with deep experience. Jenny argues that early-career people with blank slates, fast learning curves, and no attachment to legacy processes may be uniquely suited to this moment.”
These individuals, Rachitsky explains, are less likely to be bound by outdated methodologies and can adapt more readily to new tools and processes.
The Enduring Role of Chat and Evolving AI Capabilities
The post also touches upon the future of user interfaces and the developing capabilities of AI.
Chat as a Permanent Interface
Rachitsky relays Wen’s belief that chat interfaces are not a temporary trend but a permanently valuable mode of interaction due to their inherent flexibility. However, she anticipates a hybrid future.
AI’s Growing Judgment and Taste
Furthermore, Rachitsky shares Wen’s prediction that AI will increasingly develop capabilities related to “taste and judgment.” While designers may currently view these as a unique advantage, Wen suggests that accountability for AI-generated outputs will become paramount, similar to how engineers are accountable for AI-generated code.
Figma’s Continued Relevance
Despite the rise of AI and code-based development, Rachitsky notes that Wen still sees value in tools like Figma.
“Jenny says Figma remains the best tool for rapidly exploring 8 to 10 different design directions on a canvas, something that coding tools handle poorly because they’re too linear and create investment bias toward one direction.”
Figma’s strength lies in its ability to facilitate broad spatial exploration of design concepts, which Rachitsky explains is still more effective than the linear iteration often found in coding environments for initial ideation.
Rachitsky concludes by relaying Wen’s personal career shift from design director back to an individual contributor role, a move driven by a desire to gain hands-on experience during this period of rapid technological evolution and to explore the future of middle management in design.
📝 About This Content
This article is based on insights shared by Lenny Rachitsky on LinkedIn.
📅 Originally posted on March 2, 2026 | View original post on LinkedIn →