John Allen - CEO & Co-Founder of Layo

Demo Night ft. Mitul Shah (Design Eng at Vercel), Cameron Collis (Design at Cursor), and Jamey Gannon

In April, Mitul Shah [https://x.com/typicalmitul] (Design Engineer at Vercel), Cameron Collis [https://cameroncollis.com] (designer at Cursor), and Jamey Gannon [https://x.com/jameygannon] walked a packed room through their actual workflows. Mitul demoed his design-to-production process, a four-stage loop he uses to ship features, illustrated through his redesign of Vercel Workflows and the Slackbot he built on top of it. Cameron took the room through a recent project at Cursor end to end, from low-fidelity Figma to the custom prototype playground his team uses to the production monorepo. Jamey, one of the most respected AI-forward creative directors working today, closed with a foundation anyone design-minded can use to get better outputs from generative tools. Mitul Shah [https://x.com/typicalmitul], Design Engineer at Vercel What we covered * Mitul’s design-to-production process: a four-stage loop he runs every project through (Context, Design, Code, Craft). * His redesign of Vercel Workflows: the new runs table, the new trace viewer, and what changed between the two-day MVP and today. * Yogurt, the Slackbot Mitul built on top of Workflows, and how using it taught him what to fix in Workflows. * Where agents stop being good enough, and where deep CSS and craft still beat anything an agent can produce. * Q&A: annotations on static images as agent instructions, trusting an agent you only see through Slack, and advice for designers moving into design engineering. Key takeaways * Coding has compressed to the point where it is no longer the bottleneck. The time has shifted to context-gathering and to the last ten percent of craft. * Dogfood the product you are designing. Mitul built Yogurt on top of Workflows, and using Yogurt taught him what to redesign in Workflows. The recursion is the feedback loop. * Craft is specific, not abstract. “How many of you guys have tried to build a sticky header on an HTML table? It’s really hard. And I promise you the agents can’t do it yet, and that’s where I’ll step in with my deep CSS knowledge.” * Annotations on a static image are a useful agent-feedback mechanism. A label on the canvas plus a skill file gets the agent to do the right thing most of the time. * “I am more bullish on design than I am on programming.” Mitul built Yogurt without looking at the code. Cameron Collis [https://cameroncollis.com], Designer at Cursor What we covered * A recent Cursor project: the self-driving PR concept inside the Review tab, where checks, BugBot, and human reviews feed into a queue an agent automatically fixes. * Why Cameron is still heavily using Figma for early-stage ideation, in black and white. * The custom prototype playground he built after the Graphite acquisition, and how the broader Cursor design team uses it. * The handoff between playground and the production monorepo, and what changes when the monorepo is heavy. * Q&A: when to ship straight to production, the translation problem from playground to monorepo, and how to stand up a similar tool inside another org. Key takeaways * Figma still wins for early-stage exploration. The agent has not yet replaced what Figma does at the start of a project. Cameron: “I find there to be a lot of friction and I cannot stay in a flow state. I’m constantly spending too much time and energy steering the agent and not enough time really thinking through an idea.” * The prototype playground is “about an 85 to 90 percent match” of the Cursor web app. Believability is what earns engagement from the team that a static Figma file cannot. * Vision work and design direction go to the playground. “I know what I want, get in, build it, get out” goes to the monorepo. * On building internal tools without org buy-in: “Just build it if you want to build it. Probably don’t get buy-in. But don’t make your other projects fall apart.” Jamey Gannon [https://x.com/jameygannon] What we covered * A foundation any design-minded person can use to get better outputs from generative AI. * Four principles, each illustrated through a single fintech art-direction brief (3D illustrations, social mockups, web). * Jamey’s Midjourney setup: profile codes, mood boards, style references, and why her actual prompts shrink to one or two words. * The diffusion-to-multimodal handoff in practice: Midjourney for aesthetic, then Nano Banana Pro and GPT Image 2 for instruction-following and paste-in-real-screenshots fidelity. * Q&A: how to build a reference library, where generative imagery belongs in product work. Key takeaways * Aim before you shoot. Starting conditions (model, references, prompt) do most of the work. When the conditions are right, prompts shrink to one or two words. * An image is worth a thousand words. The latent data inside a reference (style, mood, lighting, color) carries more information than any text prompt that tries to encode the same thing. * Nothing good arrives finished. When a one-shot fails, swap inputs, not words. The fix to a bad iPhone mockup was removing two style references, not adding more prompt instructions. * Use the right tool for the right job. “Midjourney isn’t Nano Banana isn’t Grok.” Diffusion for aesthetic, multimodal for instruction-following. * Reference libraries are the actual moat. “Just save stuff all the time. Cosmos and Pinterest, they both have Chrome plugins... Just save, save, save as much as possible.” Thanks for reading. Stay in the loop on new episodes and upcoming events by subscribing. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit doublediamondnyc.substack.com [https://doublediamondnyc.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Dylan Babbs - Co-Founder & CTO of Profound

Dylan Babbs, Co-Founder and CTO of Profound, represents a rare breed in tech: the design engineer turned founder who went from redesigning Uber’s navigation system to co-founding New York’s newest AI marketing unicorn in just 18 months. His journey from maps and data visualization to helping Fortune 500 companies stay visible in an AI-driven world reveals what happens when deep craft meets entrepreneurial ambition. At a glance… Dylan describes his philosophy of not compromising on quality even when scaling rapidly, believing that design serves as a company’s “front door” and first impression that can kickstart the entire growth flywheel. He advocates for hiring younger, passionate employees over experience alone, noting that Profound’s average age is around 26-27 and that all current department heads joined as individual contributors before growing into leadership roles. The company operated with a mandatory six-day work week for the first 14 months, trading time for the ability to maintain hiring standards and product quality. Dylan explains that AI represents a new customer that every brand must serve, fundamentally changing how companies approach digital visibility from search optimization to “Answer Engine Optimization.” He emphasizes that successful AI product design requires specialization in at least two of three areas: visual design craft, product design for business impact, and design engineering implementation. The agent builder platform demonstrates Profound’s evolution from analytics to a full “workbench” where marketers can create, orchestrate, and automate AI-powered workflows at scale. Dylan argues that inflection points like the rise of LLMs create opportunities for new companies to succeed where existing defenses no longer apply, advising founders to be mercenaries rather than missionaries when choosing what to build. From designer to founder: the path beyond traditional design roles Dylan’s transition from design engineer at Uber to co-founder reflects a broader evolution in how designers can leverage their skills. “I never really liked having to just not make things real,” he explains, describing how his informatics background combining design and engineering led him to always want to bring designs to life with code and real data. His four years at Uber, redesigning navigation systems and programmatically building 256 design system components, taught him that design principles must serve business metrics. The founding journey involved two years of failed side projects before landing on Profound, driven by what he calls the desire to “advance your own skills” and avoid looking back with regret about missing the AI inflection point. Design as company DNA and competitive advantage Profound’s investment in design from day one stems from both personal passion and strategic necessity. “If you’re a potential customer looking at Profound, the first thing you’re going to see is the website. It’s the front door,” Dylan explains. In the anxiety-filled AI space, premium design experiences build trust with enterprise customers. The investment creates a hiring advantage since top designers don’t want to work for founders who don’t take the craft seriously. Dylan frames this as exploiting a unique advantage: “We’re all designers here. If you want to actually invest in your own design capabilities, it’s going to be very hard to attract that talent if it’s not already in your DNA.” AI as customer: designing for artificial intelligence alongside humans Dylan’s most provocative thesis is that “every company on the planet just got a new customer: AI.” This reframes design from human-centered to intelligence-centered, though he argues the principles remain similar since AI models are trained on human preferences. “You’re not designing directly for the human, but you’re designing for something that represents the human,” he notes. This shift manifests in Profound’s core product: helping brands optimize how they appear in AI-generated answers across ChatGPT, Perplexity, and other platforms. The design challenge becomes creating interfaces that help marketers understand and control their representation to artificial intelligence systems. [Demo] Agent builder: from simple automations to complex marketing workflows Dylan demonstrates Profound’s agent builder, starting with a simple “poem writer” that takes an animal input and generates poetry, then scaling it through their sheets interface to process hundreds of requests simultaneously. The workflow builder resembles tools like Zapier but integrates Profound’s proprietary data about brand visibility in AI systems and fine-tuned models for marketing content. “You get the Profound special sauce in here,” he explains, showing how agents can scrape existing content, apply brand voice from knowledge bases, generate new articles, send Slack notifications for approval, and publish to WordPress automatically. The interface balances flexibility with clarity, using node-based visual programming while offering AI assistance to build workflows through natural language. Scaling quality: hiring for passion over years of experience Profound’s hiring philosophy prioritizes four intangibles over traditional experience metrics: ownership, curiosity, being a good person, and willingness to work hard. “If you’re just a boring person, you’re not going to be good at Profound,” Dylan states, describing how he asks candidates about their passions to assess genuine enthusiasm. The company deliberately hires younger employees who are “very malleable, willing to learn, willing to work a lot” and provides strong direction to normalize any experience gaps. This approach has created a team where all current department heads joined as individual contributors and grew into leadership roles within six months, proving the strategy of “getting people that get it” rather than defaulting to senior hires. The three pillars of design at Profound: craft, business impact, and implementation Dylan identifies three essential design capabilities at Profound: visual design craft, product design for business impact, and design engineering to make things real. Every designer must excel in at least two areas. He emphasizes that product design means “designing to make money, designing for the business,” drawing from his Uber experience optimizing rider pickup flows to reduce cancellations and dwelling time. “Your ability to create output as a designer is all about making money,” he argues, rejecting the pure user-centered design mindset in favor of understanding business metrics. Visual craft remains the baseline expectation, while design engineering encompasses both technical implementation and animation work that brings interfaces to life. From six-day weeks to systematic scaling without compromising standards Profound’s early growth strategy involved a mandatory six-day work week for the first 14 months, trading time for the ability to maintain high hiring standards with limited capital. “You basically have limited resources, so you just have to work a lot,” Dylan explains. As the company scaled to 155 employees, they shifted focus to systematic culture building and process creation. “Company building is all about creating systems,” he notes, describing how early decisions about quality and craft become embedded in team culture. The challenge became ensuring that employee decision-making aligns with founder values without direct oversight, achieved through careful hiring and cultural reinforcement rather than rigid processes. Finding inflection points: why timing and market shifts matter more than passion projects Dylan advocates for a “mercenary” rather than “missionary” approach to founding, choosing opportunities based on market inflection points rather than personal passion alone. “You always have to have an inflection point of something changing,” he argues, citing Profound’s timing with the ChatGPT launch as crucial for breaking through existing market defenses. He warns against the temptation to build in familiar domains like design tools, noting that “everybody’s doing that” and competition is fierce. The key insight: “You generally don’t want to swim upstream. You want to swim downstream and ride the wave.” This philosophy led him and co-founder James Cadwallader away from multiple other ideas toward the AI marketing opportunity, despite neither having deep marketing backgrounds initially. --- Thanks for reading. Stay in the loop on new episodes and upcoming events by subscribing. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit doublediamondnyc.substack.com [https://doublediamondnyc.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Jenny Wen - Design Lead at Anthropic

Jenny Wen, Design Lead at Anthropic, joined Double Diamond for a conversation about designing Claude, one of the most widely-used AI assistants in the world. From her early work on Dropbox Paper to bringing FigJam from concept to launch at Figma, Jenny has spent her career building collaboration tools that feel both powerful and deeply human. Today she’s applying that experience to designing interfaces for intelligence itself, leading design for Claude Cowork and helping shape how millions of people interact with AI. At a glance… Jenny describes designing AI products as fundamentally different from traditional software because you can’t map out all possible user flows when dealing with large language models. Where traditional products have deterministic states, AI products offer core primitives with essentially endless interaction possibilities. Her team discovered that knowledge workers are rapidly becoming more comfortable with AI-specific concepts like memory, context, and agents, leading to decisions about using technical terminology rather than abstracting it away. She demonstrates how she uses Claude extensively in her own design process, calling it especially effective at “garbage in, treasure out” analysis of large datasets from user interviews to social media feedback. The rapid advancement of AI coding capabilities has fundamentally changed how product teams operate, with single engineers now capable of shipping entire features end-to-end. As models become more capable at longer-running tasks, Anthropic has started hiding more technical details because users increasingly trust Claude enough not to need visibility into every step. Jenny emphasizes that while shipping code has become accessible to everyone through AI assistance, craft and the ability to execute well remain the most valuable designer skills. The fundamental difference between designing traditional software and AI products Jenny explains that designing AI products requires a completely different mental model than traditional software. “When you are designing now with these LLMs, you basically can’t map out all those flows. You basically just have these core primitives and these specific states that you want the model to get to, but the possibilities in which the user can actually interact with the model are just basically endless.” She compares this to her work at Figma, where despite having millions of possible design outcomes, the team still designed around specific use cases while remaining open to discovering new ones through user behavior. The challenge becomes designing the variables rather than controlling specific outcomes. Building Claude Cowork through rapid iteration and dogfooding The development of Claude Cowork reveals Anthropic’s approach to AI product development through continuous iteration and internal testing. Jenny describes their policy of putting experimental features in front of employees immediately: “Anything goes when it comes to dogfooding internally. We have the most janky stuff just running internally as long as it doesn’t disrupt people’s workflows.” The team experimented with multiple technical approaches and agent architectures before recognizing product-market fit when users started applying Claude Code to non-coding tasks. What became the “10-day ship” was actually the culmination of months of prototyping and internal feedback, with the final decision to ship happening when they saw clear user demand. Knowledge workers are rapidly adopting AI-native concepts Jenny has observed a significant shift in her target users’ AI literacy over just the past year. “Even like a year ago I was uncertain about whether we should reveal words like memory and context and agents or should we abstract them in a way where they’re sort of fluffy. But now I think people know those words.” She explains that knowledge workers at AI-adopting organizations have become much more comfortable with technical AI concepts than expected. Rather than simplifying terminology, Anthropic increasingly uses precise technical language because their users understand it. This evolution in user sophistication has influenced design decisions throughout Claude’s interface. [Demo] Using Claude Cowork to synthesize user feedback and generate design concepts Jenny demonstrates her actual design process using Claude to research and iterate on Claude itself. She shows how she can point Claude at folders of user interview transcripts or ask it to search social media for product feedback, then synthesize insights into actionable product improvements. “It’s really good at this thing that I call garbage in treasure out where you can just throw it a stack of papers essentially and then pull useful insights out of that.” She then uses Claude to generate initial wireframes and ask clarifying questions, emphasizing that she’s not having Claude make final designs but using it to overcome the blank page problem and generate starting points for further iteration in Figma. [Demo] Trust, transparency, and the evolution of showing users what AI is doing Anthropic has gradually shifted toward showing users less of the technical details as trust in Claude has grown. “When we first started, we actually showed all of this code and underlying stuff that Claude is doing, but over time we’ve actually started to obscure it more and hide it.” Jenny explains this happens because people are becoming more trusting and the models are getting better simultaneously. However, for longer-running tasks, users still want visibility into what the AI is planning to do, leading to the plan-approval interface in Cowork. The team continuously calibrates how much detail to show based on task complexity, duration, and user trust levels. [Demo] How model improvements make design decisions obsolete Jenny describes the unique challenge of designing for rapidly evolving AI capabilities. “We’ll do things where we’re like ‘we actually might not need this selector or something because you know in a month from now Claude will be able to just choose whether it wants blue or red.’” Her team regularly receives snapshots of new model capabilities and must constantly evaluate which interface elements will become unnecessary as the underlying AI improves. The key skill becomes understanding model trajectory and designing accordingly rather than building for current limitations. This requires designers to think in terms of what will be automated away versus what needs human oversight. The collapse of traditional product roles and the rise of single-engineer, single-designer features Jenny describes a fundamental shift in how product teams operate when engineers can build entire features independently using AI. “For a given sub-feature, I’m actually just working with one engineer as opposed to five to 10. We launched scheduled tasks last week and that was just me and one engineer.” This new dynamic often eliminates the need for traditional project management, freeing PMs to work on higher-leverage activities like enterprise partnerships while designers focus on polish and user experience refinement. The process becomes much more iterative and collaborative, with engineers implementing first passes that designers then refine based on user feedback. The future of human-AI interaction beyond chat While Jenny believes chat will remain important as the most flexible interaction paradigm, she expects to see more dynamic UI generation. “I think what we’re already seeing is that the models are getting better at generating UI on the fly. In moments where UI is much more helpful and faster and direct, there should be UIs.” She envisions a future where AI can conjure specialized interfaces when needed while maintaining the fallback of natural language conversation for anything not covered by the generated UI. The challenge becomes training models to understand when UI is more effective than conversation and ensuring generated interfaces follow familiar patterns. What designers should focus on learning in an AI-native world Jenny argues that shipping code has become accessible to anyone who can chat with an AI, making it less of a specialized skill. “I think getting to ship to production is not that hard anymore. If you can chat with an LLM you can ship to production now.” Instead, she emphasizes execution and craft as the most valuable capabilities: “It’s just the ability to execute and make something good. I think shipping is a skill and right now, in the way that we work, people either have it or they don’t.” She believes designers need to develop speed in getting good work into production rather than focusing on learning technical implementation details that AI can now handle. --- Thanks for reading. Stay in the loop on new episodes and upcoming events by subscribing. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit doublediamondnyc.substack.com [https://doublediamondnyc.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

John Allen - CEO & Co-Founder of Layo

Building the platform layer for AI-native apps People don’t open apps the way they used to. Increasingly, they tell an AI what they need and expect the right tool to just appear. John Allen, Co-Founder and CEO of Layo, is building the platform that makes that possible - turning existing products into AI-native experiences that live inside ChatGPT and other AI systems. From OpenAI’s app announcement in October to having paying enterprise customers in early 2026, John has moved with urgency matched to his conviction: the next operating system is a conversation. We sat down to talk about how he’s building the infrastructure underneath it. At a glance John sees ChatGPT apps as the beginning of a fundamental platform shift, comparable to the early iPhone app store but happening at internet speed. His thesis centers on the shift from screen-driven workflows to intent-driven conversations, where users express what they want naturally rather than navigate complex interfaces. Traditional web and mobile development tools can’t handle non-deterministic outputs or intent-based interaction patterns, creating a new category of infrastructure needs. Layo provides the visual builders, analytics, and configuration tools specifically designed for AI-native apps, helping enterprises ship into the ChatGPT ecosystem in days rather than months. The deeper transformation John envisions is software that adapts in real time to individual user preferences, enabled by AI platforms that maintain context across all applications. Enterprise customers consistently ask the same two questions: what should we build and when, with his answer being “the sooner the better” due to dramatically lower development costs and first-mover learning advantages. The analytics revolution enables something unprecedented in software history: full insight into user intent, measuring why people want to use your product rather than just what buttons they clicked. This leads toward a future of millions of interface variants rather than today’s A/B testing approach, where every user gets software built for exactly how they want to use it. Defining AI-native: intelligence and action as the foundation John draws a clear distinction between companies that “bolted on AI into their existing software” and those building AI-native products. “I think the real definition of AI-native are apps that actually have true intelligence as the baseline foundation. Intelligence and action, right?” He compares it to the data-native era of the 2010s, where companies competed on proprietary data. Now, data is getting commoditized and “all these apps are starting to talk to each other.” The value shifts to becoming “the system of action where people can go and do things” with global intelligence as the foundation rather than siloed data. The operating system shift: from apps you open to systems that respond John frames the current moment as comparable to previous platform transitions from desktop to web to mobile. The fundamental change is that “instead of just having the foundation of like we are the system of record, right now you’re like we’re the system of action, we’re embedded into experiences, meeting customers where they already are.” ChatGPT represents the first true AI operating system where users come to express intent. “People use software to go and do things or to get information” and AI interfaces can meet users at that moment of intent without forcing them to learn new interaction patterns for every application they use. Why the “mobile moment” parallel matters: winning through learning, not timing Drawing explicit parallels to the iPhone app store launch, John believes “the first thousand apps in the app store, they’re going to win the biggest. Not because they were the first thousand, but because they were the first to learn.” The ChatGPT app store launched December 17th, making this moment “like the cutting edge, like really day one.” The advantage comes from accumulating learning cycles about user intent patterns, optimization strategies, and interface design principles. “You never really get any product right on the first iteration” so the companies that start learning earliest will have the biggest advantage as the ecosystem matures. The infrastructure gap: why traditional dev tools fail for conversational interfaces John discovered the platform opportunity through direct experience building ChatGPT apps. After taking his team to upstate New York for a week of building, they initially tried creating a “super app” that could access everything. “We just realized like we are abstracting this stuff way too much” and “OpenAI already is the super connector.” The real insight came when they recognized that despite being technically skilled, “if we’re struggling with it then every company is going to struggle with it.” Traditional development tools assume deterministic outputs and screen-based interactions, making them poorly suited for conversational, non-deterministic AI interfaces that require entirely different architectural thinking. Enterprise speed: from months to days with 10x cost reduction The economics of AI-native development represent a dramatic shift for enterprise software teams. John explains that “most businesses once they’re established software companies they’re spending hundreds of thousands or millions of dollars on engineering” on their core products. With Layo, “they can go and actually use our product and spin up a chat GBT app in like days” with resource constraints “diminished by 10x.” This speed advantage enables rapid experimentation and learning cycles that weren’t economically feasible with traditional development approaches. The low cost of entry means companies can start learning immediately rather than waiting for perfect strategy. The analytics revolution: measuring intent for the first time in software history John identifies a fundamental measurement problem that creates unprecedented opportunity for product teams. Traditional analytics tell you “user clicked button X” but AI apps require understanding “user intended to do this. So your app was called. Why was it called?” This shift enables something entirely new: “this is probably the first time in history where you actually have full insight into why people want to use your product.” The new metrics focus on “invocation, follow-ups, success/error rates, depth, latency, and reliability rather than traditional pageviews or clicks.” Product teams can finally see the direct correlation between user intent and product performance. Customer acquisition through value demonstration: proving before asking John uses Clay as an example of how AI-native apps fundamentally change customer acquisition. When someone prompts for sales prospecting help, “Clay, if they’re well optimized, can just come up and say, ‘Great. We not only can serve you, but we actually already did the work for you. Do you like it? Great. Continue on.’” This approach proves value before asking for commitment, similar to how mortgage companies used calculators in 2016-2017. The result is warmer leads and higher conversion rates because “you’re proving to them that you’re adding value” rather than just telling them you exist. For companies like Clay, this opens access to ChatGPT’s 800 million weekly active users who may not know the product exists. From deterministic to non-deterministic: designing systems instead of flows John envisions software development fundamentally changing as outputs become unpredictable. He learned from Perplexity’s head of design that “you don’t really know the output. The only way to actually design for the output is to try to build enough outcomes, potential outcome surfaces that make sense.” This leads to a future where “that whole era of A to B testing where your designers and your developers build three different flows and spend six weeks testing which one performed better” gets replaced by “A-to-Z testing. You’re going to have a million variants constantly flowing.” The vision is “every single user will have software built exactly for how they want to use it” by 2030, enabled by AI that understands individual preferences and can adapt interfaces in real time. How product roles evolve: from screen designers to system architects The transformation from deterministic to non-deterministic software requires new types of product thinking. John sees “design engineers” emerging as tools merge: “every tool is merging into the same idea of you design and code in the same interface.” But the deeper change is conceptual: “You’re going to be the architect of outcomes and intents” rather than someone who builds specific screens or workflows. The new role requires “systematic thinkers” who can “build a million different components, a million different outputs so that the system can go and interact with your brand and your software in the way that is best suited for the user” rather than forcing everyone through the same predetermined flow. --- Thanks for reading. Stay in the loop on new episodes and upcoming events by subscribing. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit doublediamondnyc.substack.com [https://doublediamondnyc.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Carl Rivera - Chief Design Officer at Shopify

At a glance… When Carl Rivera became Shopify’s first Chief Design Officer in eight years, he inherited a team of 170+ designers across one of the world’s largest commerce platforms. But rather than optimize the existing system, Rivera chose transformation. His approach combines philosophical clarity about design’s role with hands-on building, from shipping code himself to creating internal tools that replace industry standards like Figma presentations. What makes Rivera unique is how he operates simultaneously as strategic leader and individual contributor. During our evening at the Flatiron studio, he live-demoed internal tools his team built while explaining the organizational philosophy behind eliminating job titles, restructuring teams, and requiring all designers to ship code. This is leadership through example and systematic change. Rivera discovered massive latent talent at Shopify that was operating at 70% capacity due to permission culture, which he unlocked by simply stating that design matters and giving people permission to do their best work. The company abandoned universal design processes in favor of material-driven approaches where each project gets the methodology it needs rather than forcing everything through the same workflow. Over 50% of Shopify’s designers now actively ship code to production after completing mandatory engineer onboarding, with Rivera viewing technical fear as the primary barrier holding back the design discipline. Teams have been restructured around “rovers” who move fluidly between projects and agency-style deployments that can pivot quickly to emerging opportunities rather than permanent assignments. Rivera believes taste, aesthetics, and strong points of view become the key hiring differentiators as AI democratizes baseline design skills, favoring candidates who provoke strong reactions over safe consensus picks. The apprenticeship program reflects his commitment to developing junior talent while the industry faces a K-shaped distribution where seniors accelerate with AI but juniors struggle to find opportunities. Internal tools like Artifact demonstrate his philosophy of building what you need rather than accepting limitations, combining project management with presentation capabilities that seamlessly integrate Figma designs, live prototypes, and traditional slides. Permission culture: unlocking latent talent through leadership clarity Rivera’s first major insight as CDO was recognizing that Shopify had tremendous design talent operating below capacity. “There was a ton of latent talent in the company in design specifically. There were a lot of people that were really outstanding but were producing at about 70% of their capabilities,” he observed. The root cause was permission culture where designers felt constrained from doing their best work. Rivera’s solution was direct leadership: “Just saying that design actually really matters and we’re going to become the best at it. Just saying that, my experience was that a lot of people just immediately became better designers because they felt that someone said that they were allowed to.” This experience shaped his broader philosophy about claiming leadership before having the title. The transformation demonstrates how clear communication about priorities can unlock existing talent without hiring. Material-driven process: why Shopify abandoned design frameworks Rather than implementing universal design processes, Rivera took the opposite approach based on his belief that process should follow material. “Companies generally try to come up with the process because it feels like management, and then they take all of their material and squeeze it into that process,” he explained. At Shopify, working on Shopify Payments (processing billions of dollars) requires completely different methodology than launching experimental merchant tools. Rivera’s approach demands active management to understand each project’s unique needs and adapt accordingly. This philosophy extends to team structure, where “all problems are people problems” but solutions depend on having the right people working together in the right way for that specific challenge. The approach requires more sophisticated management but produces better outcomes by matching process to problem rather than forcing standardization. Technical transformation: requiring all designers to ship code Rivera identified fear as the primary barrier preventing designers from embracing new capabilities, particularly around shipping code. “Fear combined with startup cost. You open up cursor or you open up terminal and they really don’t feel like welcoming interfaces,” he observed. His solution combined support with requirements: all designers now complete devop (engineer onboarding) and ship code to production as part of joining Shopify. The results are dramatic: over 50% of 170+ designers actively merge code, with 1,800 pull requests in six months. Rivera emphasizes the goal is overcoming initial apprehension rather than turning designers into engineers. “Once you get past it, it’s very natural and fluid and very easy.” The transformation has fundamentally changed what design teams can accomplish and how they approach problems. Rivera noted an unexpected discovery: teams often start with code rather than wireframes, flipping traditional process assumptions. Structural fluidity: rovers and the end of permanent team assignments Rivera restructured Shopify’s design organization around flexibility rather than stability, abandoning the traditional model of permanent team assignments. “I wanted to achieve an organization that was much more fluid with far fewer people attached to single defined problems,” he explained. The new structure includes “rovers” who float between projects and agency-style teams that can be deployed quickly to emerging opportunities. The Molly acquisition exemplified this philosophy, introducing a new way of working where context moves around the organization and fresh perspectives can be applied to entrenched problems. This approach allows leadership to “flex resourcing up and down depending on what is the most important problem that you’re dealing with right now” without constant reorganization overhead. The model requires different management skills but enables rapid response to changing priorities and prevents teams from becoming stuck in solution spaces. Hiring philosophy: seeking taste and spiky perspectives over consensus As AI democratizes baseline design skills, Rivera has shifted hiring criteria toward qualities that remain uniquely human. “We hire for taste. For aesthetics. For a point of view. It’s the difference between utility and affinity. Anyone can generate a good baseline, designers reach for the ceiling.” His approach favors candidates who provoke strong reactions over those who generate safe consensus. “My favorite people were the ones and fours,” he explained, referring to interview ratings where most candidates receive neutral threes. “They have something that is like they’ll bring a point of view. It will maybe be a little difficult at times, but they’ll bring something that’s a little spicy.” This philosophy reflects his belief that memorable experiences require designers who won’t settle for AI-generated baselines but push toward truly exceptional outcomes. The approach supports building a culture where strong opinions and creative risk-taking are valued over playing it safe. Quality standards: building a no-average-work culture Rivera articulated an uncompromising stance on maintaining quality standards across a large organization: “There should be no space in our company for people that can’t produce amazing work.” When challenged on this bold statement, he compared it to sports teams where having the best person in each position would be uncontroversial. His responsibility to Shopify’s mission (14% of US commerce flows through the platform) requires building “the world’s best design team.” Rivera believes this creates a self-reinforcing culture where “when you get really great people into a room, they look around and they get super excited and they work really hard and they inspire each other.” The approach prevents the drift toward mediocrity that affects many large organizations but requires active leadership to maintain standards. Rivera’s philosophy extends to the apprenticeship program, which develops junior talent while maintaining high expectations for performance and growth. Internal tooling: building what you need rather than accepting limitations Rivera demonstrated Shopify’s approach to internal tooling through Artifact, a project management and presentation platform that has replaced Figma presentations company-wide. The tool seamlessly combines project discovery with presentation capabilities, allowing fluid transitions between Figma designs, live coded prototypes, and traditional slides within a single interface. What makes Artifact special is dual functionality: practical replacement for slide decks and discovery mechanism where designers find work happening across the organization. Rivera showed diverse projects from Sidekick’s “teach” mode (where AI guides users with its own cursor) to SimJim (sending AI agents to shop and provide feedback). The tool represents his philosophy of building what teams need rather than accepting existing tool limitations. This approach extends to creating their own brand generation tools and design systems that enable the specific workflows Shopify requires. Leading before having the title: claiming ownership through action Rivera’s strongest advice for aspiring design leaders centers on taking ownership before formal authority. “Be a leader. You can claim that space and you can be a leader well before you have people report into you in an org chart.” He emphasizes that leadership emerges through caring most about outcomes and bringing conviction to conversations. “If any person cares about it, then just start to speak about it and own that space in the conversation and have the most ideas and be the most passionate about your ideas and fight for your ideas.” This philosophy shaped his own path from startup founder to CDO and reflects how direction gets set in organizations. Rather than waiting for permission or formal delegation, Rivera advocates for claiming leadership through demonstrated expertise and passion. The approach requires courage to have strong opinions but creates real influence regardless of organizational hierarchy. --- Thanks for reading. Stay in the loop on new episodes and upcoming events by subscribing. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit doublediamondnyc.substack.com [https://doublediamondnyc.substack.com?utm_medium=podcast&utm_campaign=CTA_1]