What OpenAI and Google engineers learned deploying 50+ AI products in production

Overview
This episode dives into the unique challenges and strategies for building AI products, as shared by Aishwarya Naresh Reganti and Kiriti Badam, who have collectively deployed over 50 AI products. They discuss how AI products differ from traditional software, the importance of iterative development, and the skills and mindsets necessary for success in the AI era.

Notable Quotes
- Pain is the new moat. – Kiriti Badam, on the value of persistence and learning through challenges in building AI products.
- If the unexamined life is not worth living, was the unlived life worth examining? – Aishwarya Naresh Reganti, reflecting on balancing ambition with living fully.
- AI is just a tool. Be obsessed with the problem, not the technology. – Aishwarya Naresh Reganti, on focusing on solving real customer pain points.

🧠 Key Differences Between AI and Traditional Software
- AI products are inherently non-deterministic, meaning user inputs and AI outputs are unpredictable, unlike traditional software with fixed workflows.
- The agency-control trade-off: Granting AI systems more autonomy reduces human control, requiring trust and reliability to be built over time.
- Successful AI development requires tighter collaboration between PMs, engineers, and data scientists, breaking traditional role silos.

🚀 Iterative Development and the CCCD Framework
- The Continuous Calibration, Continuous Development (CCCD) framework emphasizes starting with low-agency, high-control systems and gradually increasing autonomy as confidence grows.
- Example: In customer support, start with AI routing tickets, then move to drafting responses, and finally enable end-to-end resolution.
- Iterative development minimizes risks, builds trust, and creates a flywheel for continuous improvement.

📊 Evals and Monitoring: Misconceptions and Best Practices
- Evals (evaluation datasets) are crucial but not a cure-all. They catch known issues but fail to address emerging patterns in production.
- Combine evals with production monitoring to capture implicit user feedback (e.g., thumbs up/down, regenerating responses).
- Avoid over-reliance on evals; instead, focus on actionable feedback loops tailored to your product's context.

🏆 Patterns of Successful AI Teams
- Leadership engagement: Leaders must rebuild their intuitions and stay hands-on with AI developments. Example: A CEO dedicating daily time to learning AI.
- Empowering culture: Foster collaboration and alleviate fears of job replacement by emphasizing AI as a tool for augmentation, not substitution.
- Workflow obsession: Deeply understand workflows to identify where AI can add value, rather than forcing AI into ill-suited processes.

🔮 Future of AI Products
- Proactive agents: AI systems will anticipate user needs, offering solutions before problems arise (e.g., coding agents fixing bugs autonomously).
- Multimodal experiences: Combining text, images, and other inputs will unlock richer, more human-like interactions and tackle complex tasks like parsing messy documents.