AI Can Build It. Now What? (The Future of Product Management in an AI-Native World)

The Core Argument

Guided by host Sanjay Saini, Product Owner Sanchit Kulshreshtha articulated the brutal reality of the emerging "post-efficiency era." As AI-driven execution makes building software incredibly fast and cheap, the ability to build features is no longer a competitive advantage—it's a commodity. Sanchit emphasized that product professionals who define their worth by managing backlogs and executing roadmaps will become obsolete. To survive, Product Managers must transition from taskmasters to visionaries, where their most scarce and valuable asset becomes architectural judgment, empirical market synthesis, and knowing exactly what NOT to build amidst the noise.

“When execution is abandoned to AI agents, human judgment, taste, and trust become the scarce resources. A novice PM will build all features; an expert PM will build just one.” Sanchit Kulshreshtha (42:51)

Detailed Answers

1. Could you give a brief intro about yourself and your professional background?

Sanchit Kulshreshtha started his tech journey assembling PC cabinets and eventually navigated massive technological disruptions like the 4G transformation and India's demonetization period as a frontend engineer. With deep domain exposure in fintech, logistics, and supply chain, he currently works as a Product Owner focusing on currency technology for an organization that services central banks globally.

2. What is the difference between AI as a feature and an AI-native product?

Sanchit noted that most companies today are simply bolting AI onto their legacy systems—adding a chatbot wrapper or an LLM summary function over an existing CRM. This is just "AI as a feature." An "AI-native product" relies on AI at its absolute core; if you rip the AI out of it, the entire product immediately collapses, such as tools that use image synthesis to dynamically generate entire workflows.

3. Is AI currently taking over the jobs of junior software developers?

Rather than eliminating the junior developer, AI is hyper-accelerating their capabilities. Sanchit shared how graduate engineer trainees in his organization are now operating as full-stack developers within months. Equipped with AI coding agents, a single junior engineer can write Java, handle .NET, and generate automated test cases simultaneously—transforming them from mere executors into value-driven contributors who challenge product decisions.

4. How do AI co-pilots evolve into fully autonomous AI employees?

We are actively transitioning from the "co-pilot" model—where AI requires human prompting to assist—into the "AI Employee" model. Sanchit replaced entire workflows of a shrinking 12-person team by creating specialized, autonomous AI agents (like a Marketing Analyst and a Business Analyst). These agents independently pull data, synthesize rules, generate reports, and route approvals without continuous human intervention.

5. How is the role of the Product Manager converging with Project Management?

The boundary between Product and Project is disappearing. Modern organizations demand "Quarterbacks" who possess a strategic product mindset but can also master execution, cross-functional delivery, and post-launch telemetry. With AI agents automating the boring coordination layers, PMs are held totally accountable for the ultimate business outcome rather than just handing off a specification to engineering.

6. How is AI compressing our attention span and daily workflow?

In the past, Sanchit spent the first 90 minutes of his day manually scanning emails, Jira dashboards, and Teams chats to build a to-do list. Today, an AI agent presents him with a prioritized "ready reckoner" the moment he logs in, instantly isolating the exact decisions that genuinely need his novel human judgment. AI declutters the operational noise, freeing up attention for pure strategic thinking.

7. What is product nirvana in the post-efficiency era?

Product Nirvana occurs when AI native agents seamlessly handle research, design, testing, and development. When speed, cost, and automation become mere table stakes, the ability to execute is no longer a constraint. The defining challenge in the post-efficiency era shifts dramatically: when every organization can build anything instantly, the only competitive edge is knowing what actually delivers value and what is just noise.

8. What are the top things a Product Manager should stop and start doing today?

Stop staying in a bubble of denial; AI impact isn't a future possibility, it is a current reality. Start breaking down the rigid boundaries of your job description—don't just write PRDs and wait for engineering. Begin stepping two steps upstream into market analytics and two steps downstream into QA automation and live-telemetry monitoring, integrating AI into the entire end-to-end product lifecycle.

Stop operating like an execution taskmaster and evolve into a strategic visionary. Master the architectural judgment required to lead AI-native organizations in our Artificial Intelligence training program.