Lenny's Podcast: Product | Career | Growth
Lenny's Podcast: Product | Career | Growth

Why most AI products fail: Lessons from 50+ AI deployments at OpenAI, Google, and Amazon

January 11, 2026

AI Summary

5 min read

🎙️ The Voices & The Context

  • The Format: Interview-style podcast with host Lenny interviewing two expert guests on AI product building pitfalls and strategies.
  • The Key Players:
    • Ashwarya Vaganti: Early AI researcher at Alexa/Microsoft (35+ papers); teaches top-rated Maven course on AI products with Kiriti; pragmatic consultant for 50+ deployments at Amazon, OpenAI, etc.
    • Kiriti Bottom: Works on Codex at OpenAI; decade in AI/ML infra at Google, Kumo; optimistic engineer focused on practical implementation.
    • Host Chemistry: Lenny probes deeply; guests banter as married couple (revealed cutely at end), blending teaching vibes with real-world war stories.
  • The Vibe: Educational yet optimistic and fun—packed with actionable advice, hype-busting, and light moments like "pain is the new moat."

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What you'll learn

  • 1 (00:00) **🎙️ Introduction: Ashwarya Vaganti & Kiriti Bottom**
  • 2 (05:44) **State of AI Product Building in Companies**
  • 3 (07:38) **Key Differences: AI vs Traditional Products**
  • 4 (11:39) **Build Step-by-Step: Low Agency to High**
  • 5 (16:01) **Behavior Calibration & Constraints**
  • 6 (25:19) **Success Patterns in AI Teams**
  • 7 (33:15) **The Evals Debate**

+ Full timestamped outline available in the app

Show Notes

Aishwarya Naresh Reganti and Kiriti Badam have helped build and launch more than 50 enterprise AI products across companies like OpenAI, Google, Amazon, and Databricks. Based on these experiences, they’ve developed a small set of best practices for building and scaling successful AI products. The goal of this conversation is to save you and your team a lot of pain and suffering.

We discuss:

1. Two key ways AI products differ from traditional software, and why that fundamentally changes how they should be built

2. Common patterns and anti-patterns in companies that build strong AI products versus those that struggle

3. A framework they developed from real-world experience to iteratively build AI products that create a flywheel of improvement

4. Why obsessing about customer trust and reliability is an underrated driver of successful AI products

5. Why evals aren’t a cure-all, and the most common misconceptions people have about them

6. The skills that matter most for builders in the AI era

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Brought to you by:

Merge—The fastest way to ship 220+ integrations: https://merge.dev/lenny

Strella—The AI-powered customer research platform: https://strella.io/lenny

Brex—The banking solution for startups: https://www.brex.com/product/business-account?ref_code=bmk_dp_brand1H25_ln_new_fs

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Transcript: https://www.lennysnewsletter.com/p/what-openai-and-google-engineers-learned

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My biggest takeaways (for paid newsletter subscribers): https://www.lennysnewsletter.com/i/183007822/referenced

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Get 15% off Aishwarya and Kiriti’s Maven course, Building Agentic AI Applications with a Problem-First Approach, using this link: https://bit.ly/3V5XJFp

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Where to find Aishwarya Naresh Reganti:

• LinkedIn: https://www.linkedin.com/in/areganti

• GitHub: https://github.com/aishwaryanr/awe

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