Simon Liu

The Three Managers Who Built the Operator

Liu is unusually precise about who made him. His second job, straight out of door-to-door sales, was staffing at Aerotek, where he spent roughly two and a half years recruiting under Reagan Franks while she ran the sales side. She taught him hard work, long hours, and tough feedback with no corners cut. He calls it one of the most pivotal points in his career, partly because it was his entry into the corporate world and partly because it was the first time anyone held him to a bar he could not talk his way around. "I never really had a true manager that gave me really direct feedback, held me to a high bar and never took it easy on me," he says. The work ethic and the bluntness both stuck.

At Meta, his manager was Adam Obleroski, a senior manager Liu describes without hedging as the best manager he has had in his life and the most calibrated, most dialed-in person he has worked for. Obleroski taught him recruiting end-to-end, but the more durable lessons were about the business: how to work with leadership, how to think about strategy, how to read a room. Liu ranks him among the most intellectual people he has met, inside talent and well outside it. "I'm forever grateful for him."

The third is Joanna Zhang, his leader at TikTok, based in China. She did not need to teach him sourcing or recruiting mechanics. She taught him business and stakeholder management: how to think two steps ahead, how to anticipate the exact questions a hiring manager would ask about a candidate before they asked them. The context made it demanding. Liu was hiring in the US for managers based in China, where the bar was high and the requirements were strict, and Zhang showed him how to navigate that gap without lowering either side's standards. Aerotek gave him the work ethic. Meta gave him the craft and the technical fluency. TikTok gave him the strategy. "Combination of all these managers led me to where I'm at today."

Why the Human Screen Survives AI

Liu works inside AI and is not precious about using it. Stack-ranking, scoring resumes, scoring profiles: he thinks that is fair game, and companies should do it. What he will not concede is the live conversation with a candidate.

The industry's current enthusiasm for recorded video screens and AI-led questioning strikes him as a missed opportunity rather than an efficiency. Too much of what matters is invisible on a scorecard: facial reactions, how someone reacts in the moment, how fast they move through a subject they know cold, the point where an answer stops sounding rehearsed and starts sounding true. Automated screening flattens all of it. "People don't want to talk to AI. People don't want to talk to a video of themselves," he says. His analogy is one every candidate already understands in their gut: the feeling of being rerouted between automated agents while trying to sort out a medical insurance question, reach a doctor, or resolve something with a bank. Frustrating, impersonal, and a strange thing to inflict on someone you are trying to hire.

So his advice to talent leaders is unromantic and specific. Bake AI into the top of the funnel. Then get on the phone anyway, "because you never know who you're talking to and what you learn about that person beyond the resume." Humans, he adds, want to talk to other humans, and no amount of tooling changes that.

The 10x Candidate and the 1x Candidate

The harder problem, and the one Liu is currently living inside, is how to evaluate AI-native talent without being fooled by it. Every startup now says it needs people who use AI for velocity. Liu's warning is that velocity is not intelligence, performance, or work ethic, and confusing the three is the fastest way to make a bad hire.

He sees two archetypes. There are people who came up before these tools existed, who built genuine critical thinking and problem-solving the slow way, and who have since used AI to 10x their speed on top of real fundamentals. And there are people who came up entirely inside the tools. Some of them are extraordinary: they know how to use velocity and they can still think for themselves. Others are 1x operators wearing a 10x costume. "When you ask them, how did they get to the conclusion of this answer, or how did they solve this, they're like a deer in headlights." His caution to hiring teams: do not get too excited about someone who vibe-coded something impressive in a few days until you have read the code and counted the bugs.

The mirror image matters just as much. Liu is finding senior candidates who volunteer that they still hard-code everything and do not believe in AI. He refuses to disqualify them on the spot. Ask deeper questions first, he argues, because the resistance is often just unfamiliarity or an employer that has not adopted anything yet, and the person may already be running personal projects with these tools. But he is honest about the risk. In a startup that runs on velocity, someone determined to do everything manually may struggle to keep pace with a market moving this fast. The skill talent leaders now have to build is finding the delta: distinguishing the person who uses AI to unlock their fundamentals from the person who has no fundamentals underneath.

The Leaders He Watches

The people Liu points to today are named with the same precision as his mentors. Benjamin Chung, a fellow Meta alum, was a pivotal figure in his career and remains the person he leans on for direction, now running what Liu calls one of the biggest and hottest startups around, and one of the most calibrated people he has ever met. Rich Ha at Sequoia earns the compliment Liu gives sparingly: genuine and real, in a corner of the industry where that is rare, with a wealth of knowledge that a lot of people quietly depend on. It is also, he admits, a seat he would like to occupy one day. And Brian Lee, building his own company at Swarthcraft, impressed him from the first conversation with charisma, energy, and depth.

The common thread is the same quality Liu chases in candidates and learned from every manager who shaped him. Calibration. Knowing what good actually looks like, and being willing to say so directly.