Research first.
Then build what's needed.
The tools and processes that product teams rely on were built for a different era. Design hands off to engineering. Engineering hands off to QA. The workflow is linear, and it made sense when humans owned every step.
AI changed the speed, but the process never adapted. Individual productivity went up. Organizational delivery didn't. The bottleneck shifted downstream, into review, integration, and handover.
We started LP5 to understand why, and to build what comes next. We publish our research openly, update it as the landscape evolves, and build tools that solve the problems the research identifies, not the problems we assumed existed before we looked.
How do teams move fluidly
through an AI-native process
AI is rewriting how individuals work. Designers generate code. Engineers make design decisions. Product managers build working demos. But the handoffs between these roles haven't changed, and the gap between what's designed and what ships is widening, not closing.
We're building toward a process where governance isn't a gate you pass through, but the architecture that makes fast work safe work.
Two people + AI. Researching and
working as one unit.
Rob Surpateanu
Seventeen years in product design and development, across teams at InVision, Fresha, JustEat, and bpPulse, with consulting stints at Microsoft, Deliveroo, and Reckitt Benckiser. My academic path took me from a BA in Graphic Design to a Masters at Central Saint Martins in design thinking.
Two years ago I turned my attention to early-stage AI startups, offering product design consultancy to teams building with generative tools. What started as designing AI products became something deeper: understanding how the process itself breaks down when everyone on a team has access to AI. At LP5, I authored the white paper that grounds everything we build, and I operate the AI-native process it describes firsthand.
David Lazar
Front-end developer with over five years across React, Next.js, TypeScript, and Tailwind, specialising in the areas our research keeps surfacing as critical: design systems, component architecture, and the path from design tokens to production UI. Before LP5, I built products at early-stage AI startups. My academic path, from Electrical Engineering to an MFA in Computational Arts at Goldsmiths, shapes my relationship with AI that is both technical and deeply critical.
I've exhibited at Tate Modern through the Tate × Anthropic AI residency, and published writing on the ethics of creative AI. At LP5, that combination is the point: I know how to ship components, and I know how to question the systems those components operate within.