The AI Builder Podcast
How Superhuman Became AI-Native with CPO Noam Lovinsky
Superhuman CPO Noam Lovinsky explains how AI-native organizations collapse roles, turn PMs into builders, redesign hiring and connect model behavior to UX.
What you’ll learn
- AI adoption becomes transformation only when workflows, ownership and operating expectations change—not when a company merely buys more tools.
- Superhuman combines bottom-up experimentation with top-down expectations, shared rituals and a deliberately small AI toolset.
- As execution becomes cheaper, functional handoffs become the constraint and roles collapse toward builders and leaders who drive adoption.
- AI-native interviews should evaluate how candidates prompt, iterate and apply judgment with models because that is now part of the work itself.
- Model-to-pixel product design treats latency, cost and orchestration as user-experience decisions, while distribution, brand, ecosystem and network effects become stronger moats.
About this episode
Most companies confuse AI tool adoption with AI-native transformation. Superhuman Chief Product Officer Noam Lovinsky argues that the real shift is operational: change how work is owned, how teams learn and how quickly an idea can move from problem to production. In this conversation with Amit Fulay, VP Product at Uber, he explains the playbook behind Superhuman's transformation.
Superhuman combines bottom-up capability with top-down expectations. Teams experiment in the flow of work, share workflows through rituals such as AI Fridays and standardize around a focused toolset; leadership then resets the definition of ownership, including the expectation that product managers can push code. As execution becomes cheaper, narrow functional handoffs become the bottleneck and roles begin collapsing toward people who build and people who drive adoption.
Lovinsky extends the same principle to hiring and product architecture. Candidates should demonstrate how they think with AI during interviews, not pretend to work without it. Product teams must design from model to pixel because latency, cost and orchestration directly shape the experience. When model access and execution are commoditized, durable advantage moves to distribution, brand, ecosystem and network effects.
