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Introduction
This chapter discusses the concept of using auto GPT to create a product specification based on a rig study, and introduces the podcast In Depth, which provides tactical advice for founders and startup leaders.
Sprig is an AI-powered user insights platform that has raised over $88m. Today’s discussion features two key individuals in Sprig’s journey so far: Ryan Glasgow, Sprig’s CEO and founder; and Kevin Mandich, Sprig’s Head of Machine Learning. Before Sprig, Ryan was an early PM at GraphScience, Vurb, and Weeby (all of which were acquired), and Kevin was an ML Engineer at Incubit, and a Post-Doctoral Researcher at UC San Diego.
In today’s episode, we discuss:
Referenced:
Where to find Ryan Glasgow:
Where to find Kevin Mandich:
Where to find Brett Berson:
Where to find First Round Capital:
Timestamps
(02:50) Intro
(04:57) What attracted Kevin to Sprig
(05:53) Kevin's background before Sprig
(07:56) How Ryan gained conviction about Kevin
(09:55) Key technical challenges and how they solved them
(18:46) How to overcome AI skepticism
(21:47) The early difficulties of building an ML-enabled product
(25:06) Evaluating new models and knowing when to switch
(35:09) Using Chat GPT
(37:23) Product development in the pre vs. post-LLM world
(39:53) The impact of AI hype on Sprig's product development
(45:36) Balancing AI automation with user-psychology
(48:47) Do recent LLMs reduce Sprig's competitive advantage?
(51:00) The importance of "selling the vision" to customers
(54:40) How Sprig structures teams
(57:25) How Sprig upskills all team members on AI
(60:25) 3 key tips for companies trying to navigate AI
(66:05) Major limitations with LLMs right now
(70:27) The future of AI and the future of Sprig
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