What AI Product Development Will Look Like in 5 Years
Rethinking Product Roles in the Agentic AI Era
As AI moves from backend utility to front-and-center product feature, how we build and manage technology is changing fast. In this episode, we sit down with Mahesh Yadav, a veteran AI product leader who has helped ship intelligent systems at Microsoft, Meta, Amazon, and Google. He's also the creator of a top-rated Maven course on agentic AI product management, where he teaches frameworks for building with language models and autonomous agents.
We explore how the rise of agentic AI is reshaping everything from evaluation metrics to team structure. Mahesh shares why traditional product lifecycles break down when you're building with intelligent systems and outlines the new responsibilities product managers and data teams must take on.
One of the biggest takeaways? Evaluation is no longer just a QA task. It's the centerpiece of AI product strategy. Mahesh introduces the Helpful, Honest, Harmless (HHH) framework and explains how to turn subjective product feedback into concrete metrics that can guide iteration. He also unpacks why precision and recall still matter, but only tell part of the story when evaluating large language models.
This episode is packed with examples, frameworks, and forward-looking insights for anyone working with AI—whether you’re a PM, data scientist, founder, or engineer.
We covered:
Why the cost of building AI products has plummeted, but the complexity of scaling them has skyrocketed
How to think about evaluation as an ongoing process, not a one-time milestone
The importance of domain-specific evaluation and why it’s the new moat in AI
How roles are shifting: product managers as builders, and data scientists as strategists
What we can learn from TikTok and Netflix about user feedback and model behavior
Why “Evaluation Scientist” might be the next big job title in AI companies
The difference between launching a product and actually sustaining intelligent behavior at scale
Key Takeaways:
Prompting is the new programming. Anyone with a clear idea and the ability to communicate can now create powerful prototypes.
Evaluation is the bottleneck. HHH and precision/recall provide a starting point, but deep domain knowledge and iteration are essential for meaningful metrics.
AI doesn’t scale like traditional software. What works for five users can completely fall apart at scale unless carefully evaluated and improved.
Product teams are evolving. PMs must be hands-on with building and evaluation. Data teams must go beyond reporting to uncover what truly matters to users.
Agentic AI acts like a smart but unpredictable intern. You’ll need to guide it with evolving rules and clear oversight.
Evaluation science is a new discipline. Just as we invented the data scientist role in response to machine learning, the AI evaluation scientist may soon become a must-have for serious AI teams.
Video Chapters
0:00 Introduction to Agentic AI
4:25 What’s Changed in the AI Product Lifecycle
7:15 Why Evaluation is the Real Challenge
14:00 TikTok vs Netflix: Lessons in Precision and Recall
19:00 The Rogue Intern Problem in Agentic Systems
25:00 Building Eval Frameworks: HHH and Domain Expertise
33:30 Scaling Evaluation with SMEs and LLMs
42:00 Roles are Changing: PMs, Engineers, and Evaluation Scientists
52:00 Dreaming of the Agentic Future