How to Build AI products in FinTech | $100B Lessons from Robinhood VP PM
Product Growth Podcast
Optimizing AI Evaluation and Team Collaboration
This chapter highlights the crucial role of systematic evaluation in AI development, contrasting it with teams that use simplistic metrics. It introduces a specialized course aimed at enhancing understanding and collaboration between AI engineers, product managers, and legal partners for better product outcomes.
Today's Episode
Robinhood just crossed $100 billion in market cap. Its stock has 5.5x'd in the past year. It's one of the hottest companies in fintech.
But here's what most people don't understand: building products at Robinhood isn't just about moving fast and breaking things. It's about moving fast while navigating regulations that could shut you down.
Today I sat down with Abhishek Fatipurya, VP of Product at Robinhood, who's been there for 9 years - from intern to VP. He walked me through how they built products that democratized finance while staying compliant.
If you're building in fintech or any regulated industry, this is your playbook.
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⏰ Timestamps:
00:00 Intro
01:34 Robinhood's AI Assistant: Cortex
08:01 Advice for Products in Fintech
12:10 IPO Stories
14:37 Ads
16:31 How To Build Innovative Products
21:30 Why Most Fintech PMs Fail at Experimentation
27:15 Ads
28:54 Training the Team
30:48 Abhiskek Journey at Robinhood
39:40 Layoffs
47:02 Robinhood's Scaling Journey (2016-2025)
52:54 Should Prototypes Replace PRD's
1:05:40 Why most Fintech PMs are Failing
1:10:48 How To Build a Real Product
1:18:08 Outro
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Key Takeaways
1. Build AI products around problems customers already have rather than creating AI for AI's sake - Robinhood identified core pain points like "why did this stock move?" then built solutions that fit existing workflows instead of forcing new behaviors.
2. Write your product's "swipeys" (onboarding screens) before building anything to force clarity on value proposition. If you can't convince a customer to hit "get started" in one sentence on mobile, you don't have a great product.
3. Curate upstream data sources and focus on information rather than recommendations when building AI for regulated industries. Robinhood secures licenses with news providers while carefully prompting AI to avoid investment recommendations that trigger regulatory issues.
4. Transform legal teams into product partners by hiring domain experts who get excited about building great customer experiences within regulatory constraints. Former SEC regulators who understand both rules and product vision push for better solutions rather than adding friction.
5. Obsess over pixel-perfect details because great design shouldn't be reserved for high-net-worth customers in financial services. When the CEO spends time on animation details, it creates a competitive moat where most companies use bad design as barriers.
6. Test everything relentlessly instead of copying surface tactics - Robinhood's referral program went through 60+ iterations, evolving from $10 cash to variable stocks. Most fintechs copy "$20 for $20" without understanding the deeper insight: give users your core service, not generic rewards.
7. Democratize access by speaking to customer pain points rather than industry jargon. "Get in at the IPO price" addressed frustration of watching stocks gap up from $20 to $50 on opening day, making access emotionally resonant.
8. Unite cross-functional teams under shared business goals by switching from functional silos to business unit GMs. This eliminates "death by a thousand departments" where each function adds friction without considering holistic customer experience.
9. Think mobile-first to force clearer communication and simpler flows since mobile constraints eliminate unnecessary complexity. Even internal planning revolves around what features will be showcased in mobile-centric product keynotes.
10. Ship meaningful features consistently to create a virtuous cycle where teams stay focused and the market recognizes you as an innovation engine. This product velocity compounds into sustained performance by demonstrating consistent execution capability.
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Related Content
Podcasts:
AI Product Leadership with Julie Zhuo
AI Experimentation with Fred de Todaro
AI Product Discovery with Teresa Torres
Newsletters:
Should you invest in your referrals channel?
How to Build AI Products Right
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