Brandon Jacoby: Taste, Decisiveness & AI Design Freedom
Great design hinges on taste—balancing innovation with patterns—supercharged by AI for decisive builders who question everything, as learned at X and in solo practice.
First-Principles Design at X Unlocks Ruthless Decisiveness
Brandon Jacoby describes his time at X (formerly Twitter) as a brain-breaking shift from conventional design processes. Unlike big companies where ideas languish in committees, X embodied Elon Musk's first-principles thinking: question every requirement, metric, and assumption instantly. In one design review, a months-tracked metric was upended on a whim because someone asked, "Why track this at all?" Decisions happened immediately—no barriers, no delays.
This environment forced Jacoby to rethink micro-decisions in interfaces. Designers often treat constraints as passive back-of-mind limits, but at X, he learned to "design without thinking," ignoring calloused habits. "There's no barriers, there's no walls," Jacoby says, emphasizing how witnessing ruthless discernment firsthand—positive or negative—redefines reality. He now views decisiveness as a critical trait in an AI era flooded with ideas, where more real estate for choices demands quick judgment.
Rid, the host, probes this intensity, noting how it aligns with successful org traits. Jacoby agrees: in zero-to-one environments, turning off preconceptions enables breakthroughs.
Onboarding Mastery: Patterns vs Reinvention
A pivotal moment came when Nikita Bier, new head of product, schooled Jacoby on onboarding during a Hawaii hotel-room call. Despite Jacoby's Cash App experience optimizing flows for 45 million users via A/B tests, Nikita's doc revealed overlooked fundamentals. At X's scale, onboarding is "precious," yet patterns dominate 90% of products for good reason—data from massive growth teams proves it.
Jacoby, who admits hating onboarding design, learned the key skill: discern when to follow proven patterns versus reinvent. Too many designers revert to the mean or over-reinvent, missing efficiency. "Know when to reinvent the wheel, know when to follow patterns," he advises, especially for juniors. This nugget carried into his post-X work: utilitarian flows thrive on borrowed intelligence, freeing creativity elsewhere.
"The biggest takeaway in my whole career for growth-related things," Jacoby calls it, highlighting how Nikita's oracle-like insights validated hyper-optimization elsewhere.
Post-X Exploration Fuels Independent AI-Powered Practice
Burnout from X led to uncertainty, but a three-month exploratory phase—helping friends' startups, incubating ideas—proved fulfilling. Overlapping with AI model explosions, it reignited inspiration. Jacoby stayed in Figma for UI tinkering ("dragging rectangles is still the best form of expression") while AI filled gaps, enabling non-technical hackers to build prototypes end-to-end.
Now in solo practice, he targets founders pushing past "good enough." Autonomy thrives with AI: agents handle logistics, email recaps, creative blocks via Figma's AI or Claude. Tools are agnostic—great design remains about judgment. "Great design has always been great design agnostic of the tools," Jacoby asserts. He builds in "cloud code" (AI-assisted coding environments) without abandoning canvas-based exploration.
This path echoes early Cash App's trailblazing: talented teams ran through walls pre-AI. Jacoby positions himself across design, product, brand, and creative direction, leveraging flexibility for high-impact work.
AI Builds Custom Tools, Amplifying Wall-Runners
AI's game-changer for Jacoby: crafting bespoke design tools. Top designers now prompt LLMs to create tailored solutions, unbound by off-the-shelf limits. Echoing John Lasseter's Pixar mantra—"the technology inspired the art, the art challenged the technology"—AI removes tooling barriers, letting vision dictate reality.
Examples abound: Rid shares Claude generating a particle effect assembling into icons, no tech knowledge needed. Jacoby nods, recounting prompting Claude for agentic tasks or WebGL renders when stuck. "Ask Claude," became his reflex, mirroring X's reality-questioning.
Yet amplification varies. AI empowers those already decisive—pre-AI "wall-runners" who partnered with engineers for inventions. They gain autonomy; the timid, who externalize blockers, stay capped. Visual design demands taste: empathy for user feelings via fundamentals like alignment, not vibe-coding slop. "Visual design requires creativity, requires taste, requires feeling," Jacoby stresses. AI supplements decisiveness, not replaces it.
Taste as the Ultimate Human Edge
Opening the conversation, Jacoby defines taste: knowing when to break rules, push boundaries, or flow with norms. AI excels at depth via prompts but lacks this zoom-in/out balance. "Not everything needs to be new," he says. Tastemakers discern battles worth fighting, creating rarity in an idea-flooded world.
This ties to indie practice: AI enables solo creators to mold products valuably, but judgment separates signal from noise.
"Key Takeaways"
- Question every requirement from first principles, as at X—instant decisions beat committee delays.
- For onboarding/growth flows, default to proven patterns (90% effective); reinvent only with clear rationale.
- Juniors: Master discerning pattern-following vs. wheel-reinvention to accelerate careers.
- Use AI to build custom design tools, prompting iteratively (e.g., Claude for effects/code) to realize visions without tech barriers.
- Amplification favors pre-AI "wall-runners": decisive, taste-driven designers who question reality.
- Stay in Figma for UI expression; AI fills gaps for autonomous prototyping.
- Taste = balancing deep prompting/innovation with knowing when to flow—AI can't replicate this yet.
- Post-burnout: Allocate 3 months for open exploration to rediscover fulfillment.
- Position solo practice for zero-to-one founders needing taste to escape "good enough."