#168 Saurabh Sharma—Delegate IC work to AI agents, restructure hiring criteria, and build compounding advantage

Saurabh Sharma is the Chief Product Officer at You.com, where he leads product, design, and research for AI agents that power critical business workflows across search and enterprise use cases. Rising to prominence in the 2010s through work at Google, he became known for scaling applied AI, search and discovery, and trust and safety systems to hundreds of millions of users globally. He is widely regarded as an influential figure at the intersection of AI assistants, consumer products, and infrastructure for large-scale machine learning.

Previously, as Head of Search Products at OpenSea, Saurabh led a multi-product portfolio spanning search, discovery, trust and safety, and core web and mobile platforms during the 2022–2023 NFT market cycle. He became known for steering product strategy in a period when OpenSea supported millions of users and billions of dollars in NFT trading volume annually, focusing on safe discovery and high-intent search in a volatile, web3-native marketplace. His leadership aligned search quality, fraud prevention, and creator-centric experiences in an ecosystem that operated 24/7 across global markets.

His career highlights include an 11-year tenure as a Group Product Manager at Google, where he led teams of more than 12 product managers and 100 engineers building AI-powered experiences in Google Assistant, Search, Maps integrations, identity, and monetization from 2011 to 2022. At Google, he helped ship and scale products such as Google Assistant’s AI search integrations, Family Link and Google Accounts for kids, Google+, and Gmail, each serving hundreds of millions of monthly active users and operating across more than 100 countries. Earlier, as an Advisory Software Engineer at IBM from 2005 to 2010, he developed core AIX UNIX kernel infrastructure for virtual memory, including Active Memory Expansion and Large Segment Aliasing, contributing to enterprise systems that powered thousands of high-availability servers worldwide. He pairs this low-level systems background with an applied AI product lens shaped by dual BS and MS degrees in Electrical and Computer Engineering from Carnegie Mellon University.

In addition to his operating roles, Saurabh has invested in and supported early-stage voice and AI startups through Google Assistant’s strategic investment programs, including seed and Series A bets in companies such as Instreamatic, Voiceflow, and Slang Labs. As a member of the Skip Community, he collaborates with a network of current and former heads of product who collectively bring hundreds of years of leadership experience across AI, fintech, cybersecurity, e-commerce, and renewable energy, shaping best practices for how modern product organizations are structured and scaled.

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Learn how a CPO at a billion-dollar AI company is rethinking what “good” looks like for PMs — prioritizing strategic thinking over feature-building as software commoditizes.

“You gotta be laser sharp about where you can really add value versus what’s being rapidly commoditized.”

Saurabh Sharma, CPO at You.com, doesn’t deliver this line as career advice. It’s operational reality. When AI can generate user research insights in minutes and prototype features faster than most teams can write specifications, the entire foundation of product management value shifts. The skills that made someone a great PM five years ago might make them unemployable five years from now.

“Where is there a compounding advantage? Where is there a value creation that will be hard to commoditize?” he continues, and I can see him working through the implications for his own hiring decisions. “And that’s a lot of what I think about at the company. That’s a lot of what I try to help my team think about as well.”

This isn’t abstract strategy. It’s survival math. You.com processes over a billion web search API queries per month for companies including DuckDuckGo, Windsurf, and Harvey. They raised $100 million at a $1.5 billion valuation. At that scale, every hiring decision carries weight. Every capability they build internally has to justify itself against what they could buy or automate.

The question Saurabh faces daily: when AI can handle most IC work, what human skills become more valuable rather than less?

“I think what’s really changed is you gotta be laser sharp about where you can really add value versus what’s being rapidly commoditized,” he explains. “And so I think what we’ve seen at You.com is that there’s a continuous focus on where is the value really being created versus where will the value be rapidly commoditized.”

The math is brutal but clarifying. If anyone can build a basic SaaS product with AI assistance, then building basic SaaS products isn’t a differentiating capability. If anyone can synthesize user research or analyze competitor data with AI tools, then those skills command lower wages and less organizational influence.

But here’s what Saurabh has observed: some capabilities become more valuable as their supporting infrastructure gets commoditized. Strategic judgment becomes more important when you can test more strategies. Pattern recognition becomes more critical when you have more data to parse. The ability to choose which problems are worth solving becomes essential when solving problems gets easier.

“And I think it does change how you hire, in that you want people that are able to think that strategic line more so than, well, here’s this cool feature I wanna build.”

The hiring implications ripple through every product organization. The PM who excels at writing detailed PRDs and coordinating feature launches might struggle in an environment where PRD writing is automated and feature quality is determined by rapid iteration rather than upfront specification.

But the PM who can identify which customer problems create sustainable advantage, who can spot market opportunities before competitors, who can build conviction around directions that don’t yet have validation—those skills compound as the tactical work gets easier.

“Well, the cool feature—the customer might be able to replicate it themselves in a way that’s even more fit for them,” Saurabh continues. “It’s more about where is there a compounding advantage? Where is there a value creation that will be hard to commoditize?”

I push him on this. How do you interview for strategic thinking? How do you distinguish between someone who talks strategically and someone who thinks strategically? Most product candidates can articulate frameworks and principles. Fewer can demonstrate judgment under uncertainty.

“I think that taking that more strategic approach, what separates a middle manager from an executive,” he responds, drawing a connection I didn’t expect. “Nobody told me that I should spend more time with the sales team. But what I noted was, first of all, sales likes having product on road trips with them. It helps customer conversations. But the other part of it was it helps me. It helps me build my worldview. What my roadmap should be.”

The example crystallizes the difference. Strategic thinking isn’t about having better frameworks or more elegant presentations. It’s about making connections that aren’t obvious, taking actions that aren’t prescribed, developing conviction through firsthand exploration rather than secondhand analysis.

When Saurabh decided to spend more time on sales calls, he wasn’t following a playbook. He was following a hunch about where his learning edge was. That hunch—and the willingness to act on it—represents the kind of judgment that becomes more valuable as tactical execution gets automated.

But this creates new tensions in how product teams operate. When strategic judgment becomes the scarce resource, how do you structure teams to maximize it? How do you delegate the increasing scope of work that AI can handle without losing touch with the details that inform strategy?

“None of us are gonna be ICs anymore,” Saurabh says, quoting You.com CEO Richard Socher. “We are all gonna be managers in the future. Some of us will continue to manage people, but your traditional IC will now be managing a fleet of agents that’s doing a lot of work for them.”

The transition from IC to manager isn’t just about career advancement. It’s about cognitive load distribution. When AI can handle research, analysis, and initial synthesis, human intelligence gets freed up for higher-order work: choosing which questions to ask, interpreting ambiguous signals, making bets on uncertain outcomes.

But managing AI agents requires different skills than managing humans. Humans can fill in context, interpret vague instructions, escalate when they’re confused. AI agents do exactly what you ask them to do, which means the quality of your instructions determines the quality of their output.

“Many of the emails I write, I will pass through AI to help me with tone or help me think about the way I want to get to a particular objective in a given customer situation,” he explains, describing his own evolution. “That is essentially an example of offloading something that we all know how to do. I could write that perfect email to a customer to diffuse a complex situation, but it might take me an hour to really think through it and get it right. What I found is that email is now five minutes away working with AI.”

The email example is tactical, but the implications are strategic. When routine communication becomes effortless, you can maintain relationships at scale that were previously impossible. When difficult conversations can be crafted quickly, you can engage in more of them. The scope of what one person can manage expands dramatically.

This expansion creates competitive advantage for individuals and organizations that adapt quickly. But it also creates new forms of inequality. People who learn to manage AI agents effectively can take on exponentially more responsibility. People who don’t learn these skills find their scope of influence shrinking as AI-augmented colleagues outpace them.

“Some people will use the time that they get back with AI to just do more of what they already know, and that’s gonna be fine,” Saurabh observes. “But you’re gonna have other people that are able to—I sometimes think about Maslow’s hierarchy. Some people that are able to, okay, great, I got shelter and food under control. Now I can go to self-actualization.”

The Maslow reference isn’t casual. It’s how he thinks about organizational development in an AI-augmented world. Some people will use AI to get better at their current job. Others will use AI to access entirely different kinds of work. The first group maintains their position. The second group expands their influence.

But this creates new challenges for team composition. How do you balance strategic thinkers who can direct AI agents effectively with craftspeople who can execute at high quality? How do you maintain institutional knowledge when so much tactical work gets delegated to machines?

“There are exceptional middle managers that that’s what they love to do. That’s what they’re good at, and that is great,” Saurabh says when I ask about the career implications. “And then there are exceptional middle managers that graduate naturally to be exceptional executives. And that is good as well.”

The key insight: both paths remain valuable, but the skills required for each path are changing. Middle managers will increasingly manage hybrid teams of humans and AI agents. They’ll need to be excellent at coordination, quality control, and tactical execution within defined boundaries. Executives will set those boundaries, choose which problems deserve attention, and build conviction around uncertain directions.

But the boundary between these roles is becoming more porous. When AI handles routine analysis, middle managers can engage in more strategic work. When strategic insights can be tested rapidly, executives can stay closer to tactical details. The rigid hierarchies built around information scarcity start to flatten when information becomes abundant.

“Where is that compounding advantage that creates value for the customer and also creates potentially a competitive moat for us as well,” Saurabh concludes, returning to the core question.

The answer, increasingly, isn’t in what you can build. It’s in what you choose to build and why. The technical capability to create software is becoming commoditized. The judgment to create the right software at the right time for the right customers remains scarce.

Companies that hire for execution speed will compete on efficiency. Companies that hire for strategic judgment will compete on alpha. Both approaches can succeed, but they require different organizational designs and different definitions of performance.

The retailers who miss the next pickleball trend won’t be the ones with outdated technology stacks. They’ll be the ones who couldn’t distinguish between signals worth pursuing and noise worth ignoring. Who couldn’t move fast enough from insight to action. Who optimized for doing more of the same instead of doing something different.

“I think what we’ve seen at You.com is that there’s a continuous focus on where is the value really being created versus where will the value be rapidly commoditized.”

As AI makes more capabilities available to everyone, the companies that thrive will be the ones that focus obsessively on the capabilities that can’t be commoditized. Not because they’re technically difficult, but because they require the kind of human judgment that compounds over time rather than getting automated away.

The question for every product organization: are you hiring people who can do the work, or people who can choose the work? The first skill set has a shrinking shelf life. The second becomes more valuable every sprint.

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