Every founder faces moments where they’re not sure what to do next — such as how to go to market with AI products, when to pivot, and who to hire.
In this episode of Office Hours, YC partners Pete, Brad, Nico, and Gustaf answer real questions from founders and share stories about how great teams build conviction, learn faster, and make better decisions as they grow.
Chapters:
0:00 – Intro
0:36 – Building an AI company in a legacy industry
7:00 – Time to grow vs long term defensibility
14:31 – Spending for temporary edge vs waiting for model leap
16:10 – When should you consider pivoting if you’ve got traction?
26:07 – The power of technically challenging problems
30:42 – When to start hiring?
35:18 – When is it a good idea to open source an enterprise SaaS product?
38:16 – Outro
38:00 Is this some kind of cliff hanger tease? 🙂 Please do tell: an open source model with the option for customers to self-host sounds sweet and, granted, aligns with the desire of some customers to keep their data in-house. How are these small start-ups mentioned monetizing that?
i wish i saw this before starting my biz
Really appreciate the emphasis on feedback loops! With Rumora, I’ve been able to create those loops through real interactions in comments.
this was so relatable for my startup journey
Good to see Pete in these videos!
love how relatable this is ☺️
wished i saw this video sooner
Thank you ❤️
TL;DR
💡 Founders should deeply understand customers, iterate quickly on AI product fit, hire thoughtfully, and pivot with conviction based on real feedback.
💡 Key Points
🤖 AI startups in legacy industries succeed mostly by building focused AI software or starting new firms, with acquisition less common.
⚙ Track automation progress and maintain a strong technical team to avoid scaling manual processes.
🎯 Find empowered buyers who are motivated to adopt software early; AI SDRs work best when sales processes are proven.
📈 Learn quickly from smaller customers before tackling large enterprises with slow sales cycles.
🔄 Pivot only with conviction and energy, guided by customer feedback and experimentation.
🧩 Technical challenges often signal valuable ideas; reduce scope and engage customers early to validate.
👥 Hire when workload exceeds capacity, prioritizing trusted, opportunistic hires early on.
🛡 Open sourcing enterprise SaaS builds trust and shortens sales cycles, especially for sensitive data, but increases costs.
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