Jiaona Zhang(JZ) is the Chief Product Officer at Laurel, where the team runs its own product on itself to see exactly where AI helps and where it doesn't. Before Laurel, JZ built products at Airbnb, Dropbox, Webflow, and Linktree, and she has taught product management at Stanford for nearly a decade.
Companies are spending billions on AI tooling, but most still can't say where it returns time or revenue. Jiaona breaks down how to get that visibility, why blanket AI mandates backfire, and what it takes to re-architect a team so anyone can ship.
Her argument is simple: stop token maxing and start measuring time back.
We cover:
Why most organizations can't see where AI is actually working, and how Laurel uses time data to fix it
The token max trap that "use AI everywhere" mandates create, and how to drive efficient use instead
Why former managers make the best operators of agent fleets
How Laurel lets PMs, designers, and customer success ship features end to end
The bottom-up plus top-down playbook for re-architecting a team around AI
Why technology moats are falling away while brand and data moats endure
Laurel's bet on returning time to people instead of replacing them
(0:00) The token max trap (1:47) Why companies can't see where AI is working (5:03) What Laurel does: turning time into data (8:53) Agents as an extension of the workforce (13:43) Why former managers make the best AI users (18:23) Lean teams and shipping end to end (22:29) Enabling non-engineers to ship features (28:30) Re-architecting teams: bottom-up and top-down (32:09) Keeping your professional identity as AI shifts work (38:53) The context layer is the new race (42:06) Fundamentals plus tinkering: how to learn (48:45) Brand and data moats when tech moats fall away (54:31) Laurel's movement: returning time to people
Podden och tillhörande omslagsbild på den här sidan tillhör
Conor Bronsdon. Innehållet i podden är skapat av Conor Bronsdon och inte av,
eller tillsammans med, Poddtoppen.
