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

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Jan 11, 2026

Guest

Kiriti Badam

Guest

Aishwarya Naresh Reganti

Aishwarya Naresh Reganti, an AI researcher and product builder, and Kiriti Badam, an engineer with experience at OpenAI, share insights from deploying over 50 AI products. They discuss the crucial differences between AI and traditional software, emphasizing a gradual increase in autonomy for successful products. Reliability emerges as a key blocker in enterprise adoption, while customer trust and effective monitoring methodologies are highlighted as essential. They also stress the importance of design judgment and persistent problem-solving in the evolving landscape of AI.

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00:00 / 00:00

Non-Determinism And The Agency-Control Tradeoff

AI products are fundamentally non-deterministic on both input and output sides, which breaks many traditional development assumptions.
Agent autonomy introduces an agency-control tradeoff: more autonomy requires earned trust and reliability before delegation.

00:00 / 00:00

Progress Autonomy Stepwise

Start with high human control and low agent autonomy, then increase autonomy as the system proves reliable through logged human feedback.
Use stepwise rollouts (suggestions → show answer → take actions) to gather corrective data and build trust before automating actions.

00:00 / 00:00

Reliability Is The Enterprise Bottleneck

Reliability is the top enterprise blocker to deploying AI; many enterprises avoid customer-facing AI products due to trust concerns.
This explains why current enterprise AI focuses on productivity (low-autonomy) tools rather than fully autonomous agents.

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What builders see in 2025 AI adoption

Two key AI differences from traditional software

05:08 • 7min

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Start small: increment autonomy over time

11:39 • 2min

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Customer support example: progression from routing to autonomy

13:39 • 2min

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Behavior calibration and constraining autonomy

16:02 • 4min

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Why low-autonomy iterations force problem-first thinking

19:45 • 2min

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Reliability as the enterprise blocker

21:31 • 2min

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Prompt injection and security risks

23:32 • 2min

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Success triangle: leaders, culture, technical obsession

25:43 • 8min

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The evals vs. monitoring debate

33:21 • 5min

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What 'evals' actually mean in practice

38:13 • 3min

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How Codex balances evals and customer feedback

Continuous Calibration / Continuous Development (CC/CD)

46:00 • 5min

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CC/CD applied: customer support stages and flywheel

51:19 • 6min

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When to advance stages and staying vigilant

57:17 • 4min

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Overhyped and under-hyped AI trends

01:01:24 • 3min

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Design, taste, and product judgment matter most

01:04:13 • 1min

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Product vision for 2026: proactive and multimodal agents

01:05:26 • 3min

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Skills to focus on: judgment and persistence

01:08:53 • 6min

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Final advice: obsess over customers and workflows

01:14:25 • 53sec

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Lightning round: books, shows, products, mottos

01:15:19 • 9min

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Where to follow their work and course details

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

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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/awesome-generative-ai-guide

• X: https://x.com/aish_reganti

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Where to find Kiriti Badam:

• LinkedIn: https://www.linkedin.com/in/sai-kiriti-badam

• X: https://x.com/kiritibadam

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Where to find Lenny:

• Newsletter: https://www.lennysnewsletter.com

• X: https://twitter.com/lennysan

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

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In this episode, we cover:

(00:00) Introduction to Aishwarya and Kiriti

(05:03) Challenges in AI product development

(07:36) Key differences between AI and traditional software

(13:19) Building AI products: start small and scale

(15:23) The importance of human control in AI systems

(22:38) Avoiding prompt injection and jailbreaking

(25:18) Patterns for successful AI product development

(33:20) The debate on evals and production monitoring

(41:27) Codex team’s approach to evals and customer feedback

(45:41) Continuous calibration, continuous development (CC/CD) framework

(58:07) Emerging patterns and calibration

(01:01:24) Overhyped and under-hyped AI concepts

(01:05:17) The future of AI

(01:08:41) Skills and best practices for building AI products

(01:14:04) Lightning round and final thoughts

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Referenced:

• LevelUp Labs: https://levelup-labs.ai/

• Why your AI product needs a different development lifecycle: https://www.lennysnewsletter.com/p/why-your-ai-product-needs-a-different

• Booking.com: https://www.booking.com

• Research paper on agents in production (by Matei Zaharia’s lab): https://arxiv.org/pdf/2512.04123

• Matei Zaharia’s research on Google Scholar: https://scholar.google.com/citations?user=I1EvjZsAAAAJ&hl=en

• The coming AI security crisis (and what to do about it) | Sander Schulhoff: https://www.lennysnewsletter.com/p/the-coming-ai-security-crisis

• Gajen Kandiah on LinkedIn: https://www.linkedin.com/in/gajenkandiah

• Rackspace: https://www.rackspace.com

• The AI-native startup: 5 products, 7-figure revenue, 100% AI-written code | Dan Shipper (co-founder/CEO of Every): https://www.lennysnewsletter.com/p/inside-every-dan-shipper

• Semantic Diffusion: https://martinfowler.com/bliki/SemanticDiffusion.html

• LMArena: https://lmarena.ai

• Artificial Analysis: https://artificialanalysis.ai/leaderboards/providers

• Why humans are AI’s biggest bottleneck (and what’s coming in 2026) | Alexander Embiricos (OpenAI Codex Product Lead): https://www.lennysnewsletter.com/p/why-humans-are-ais-biggest-bottleneck

• Airline held liable for its chatbot giving passenger bad advice—what this means for travellers: https://www.bbc.com/travel/article/20240222-air-canada-chatbot-misinformation-what-travellers-should-know

• Demis Hassabis on LinkedIn: https://www.linkedin.com/in/demishassabis

• We replaced our sales team with 20 AI agents—here’s what happened | Jason Lemkin (SaaStr): https://www.lennysnewsletter.com/p/we-replaced-our-sales-team-with-20-ai-agents

• Socrates’s quote: https://en.wikipedia.org/wiki/The_unexamined_life_is_not_worth_living

• Noah Smith’s newsletter: https://www.noahpinion.blog

• Silicon Valley on HBO Max: https://www.hbomax.com/shows/silicon-valley/b4583939-e39f-4b5c-822d-5b6cc186172d

• Clair Obscur: Expedition 33: https://store.steampowered.com/app/1903340/Clair_Obscur_Expedition_33/

• Wisprflow: https://wisprflow.ai

• Raycast: https://www.raycast.com

• Steve Jobs’s quote: https://www.goodreads.com/quotes/463176-you-can-t-connect-the-dots-looking-forward-you-can-only

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