How to Build AI Products in FinTech ($100B Robinhood VP Lessons)

Abhishek Fatehpuria, VP of Product at Robinhood, reveals how they built a $100B fintech empire and why most product teams fail in regulated industries. He breaks down the "swipies" framework that forces product clarity, the 60+ experiments behind their breakthrough referral program, and how to transform legal teams from blockers into enablers. ---- Transcript: https://www.news.aakashg.com/p/abhishek-fatehpuria-podcast ---- ⏰ 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 ---- 🏆 Thanks to our sponsors: 1. Kameleoon: Leading AI experimentation platform - kameleoon.com/prompt 2. Mobbin: Discover real-world design inspiration - https://mobbin.com/?via=aakash 3. AI Evals Course for PMs & Engineers: Get $1155 off with code ag-evals - https://maven.com/parlance-labs/evals?promoCode=ag-evlas 4. Amplitude: The market-leader in product analytics - https://amplitude.com/session-replay?utm_campaign=session-replay-launch-2025&utm_source=linkedin&utm_medium=organic-social&utm_content=productgrowthpodcast ---- How Robinhood Built a $100B Fintech: 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. ---- 👨‍💻 Where to find Abhishek: LinkedIn: linkedin.com/in/abhishekfatipurya Twitter: https://x.com/abhishekf96 Robinhood: https://robinhood.com/us/en/ ---- 👨‍💻 Where to find Aakash: Twitter: twitter.com/aakashg0 LinkedIn: linkedin.com/in/aagupta/ Newsletter: news.aakashg.com #Fintech #ProductManagement #Robinhood #AIProducts #Experimentation ---- 🧠 About Product Growth: The world's largest podcast focused solely on product + growth, with over 187K listeners. Hosted by Aakash Gupta, who spent 16 years in PM, rising to VP of product, this 2x/week show covers product and growth topics in depth. 🔔 Subscribe and turn on notifications to master fintech product building!

Aakash GuptahostAbhishek Fatehpuriaguest

Sep 10, 20251h 18mWatch on YouTube ↗

WHAT IT’S REALLY ABOUT

Robinhood VP on AI fintech products, experimentation, and product velocity

Robinhood Cortex is positioned as an AI investing assistant that fits existing workflows by explaining stock moves using curated, licensed data sources while avoiding direct recommendations to maintain trust and regulatory safety.
Abhishek argues successful fintech product building requires deep regulatory fluency, strong cross-functional partnership (especially legal/compliance), and patience to ship incrementally as customers and regulators build confidence.
Robinhood’s innovation DNA emphasizes delivering both customer value and a delightful, pixel-perfect experience, using “swipeys” (four-screen customer messaging) as a working-backwards artifact before building.
IPO Access is described as a retail-demand aggregation product inside the IPO selling group, with product iterations focused on clear value messaging (“Get in at the IPO price”) and emotionally resonant UX moments.
Robinhood’s product velocity is supported by GM-based org structure, keynote-driven planning, extensive experimentation (e.g., referral program iterations), and internal dogfooding with a high bar for polish.

IDEAS WORTH REMEMBERING

5 ideas

Start AI by improving an existing user workflow, not by chasing “AI features.”

Cortex began with a universal workflow moment—users see a 5% move alert and ask “why?”—so the product compresses research steps rather than inventing a new behavior.

In fintech AI, upstream data curation is a core product decision.

Robinhood emphasizes licensed, curated inputs (news providers, research reports, exchange market data, SEC filings) to reduce hallucinations and increase trustworthiness.

Sequence riskier AI capabilities only after trust and infrastructure exist.

They explicitly avoid recommendations today because advisory requires portfolio context and a much higher compliance bar, choosing “informational tool first” to build confidence.

Make legal/compliance a first-class product partner, not a blocker.

Abhishek recommends assuming good intent, selling the product vision to legal like you would to engineers/designers, and deeply understanding the rule behind each concern to find workable solutions in gray areas.

Use a crisp customer message artifact (“swipeys”) to force clarity early.

Writing the 3–4 swipe screens before building pressures teams to define value in simple language; if you can’t earn a “Get Started” in one sentence, the product isn’t ready.

WORDS WORTH SAVING

5 quotes

“We don’t want to build AI products for the sake of building AI products. We want it to fit into problems we know customers already have.”

— Abhishek Fatehpuria

“We curate almost all of the data that’s going in… and coach it to not make mistakes and to not make recommendations.”

— Abhishek Fatehpuria

“If you can’t convince a customer to hit the Get Started button in, like, one sentence, we don’t have a great product.”

— Abhishek Fatehpuria

“You shouldn’t use bad design as a way to keep people out.”

— Abhishek Fatehpuria

“The goal is the goal.”

— Abhishek Fatehpuria

Robinhood Cortex (Stock Digest, Trade Builder)AI guardrails in regulated financeCurated data, licensing, and prompting to reduce errorsTokenization: stock tokens and private-company accessIPO Access mechanics and retail allocation“Swipeys” as working-backwards product framingExperimentation culture and referral program learningsGM-based org structure and planning via product keynotesMetrics: market share, net deposits, Gold subscriptions, international customersPixel-level craft, dogfooding, and product quality bar

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