According to Business Insider, 29-year-old Brian Zhan has left Silicon Valley VC firm CRV and joined Striker Venture Partners to deploy a radical investment strategy with a $165 million fund. Zhan and veteran investor Max Gazor plan to make only 10 investments total, writing seed checks as large as $30 million each for AI startups. Zhan believes venture capital is undergoing a transformation that requires firms to pounce early with massive checks, noting that seed rounds today often involve “just 22-year-old founders with a great idea.” His approach already shows results – he co-led Reflection AI’s seed round last year at a $200 million valuation, and the company just raised at an $8 billion valuation last month. Zhan previously made early bets on Skild AI, Dyna Robotics, and Periodic Labs while at CRV.
The VC playbook is being rewritten
Here’s the thing about Zhan’s strategy – it completely flips traditional venture capital wisdom. Most firms spread smaller bets across dozens of companies, hoping a few will hit. Writing $30 million checks for just 10 companies? That’s essentially making your entire fund dependent on maybe 2-3 companies succeeding. But Zhan argues that the nature of seed investing has changed dramatically. When seed rounds used to mean companies with some traction and metrics, now you’re often evaluating technical founders with little more than research papers and prototypes.
And this gets to the heart of why technical backgrounds like Zhan’s are becoming so valuable in VC. He studied computer science at Northwestern and wrote code at Facebook before moving into investing. When you’re evaluating AI startups that might have zero revenue but groundbreaking technology, you need someone who can actually understand the technical merit. MBAs looking at spreadsheets? They’re basically useless at this stage.
Why technical founders trust him
Zhan’s background gives him credibility that traditional VCs struggle to match. Misha Laskin, CEO of Reflection AI, put it perfectly: “Brian is rare in that he’s actually done the work.” Founders building at the AI frontier want investors who speak their language, not just check-writers. Zhan noticed this pattern back on Facebook’s data team – brilliant technical minds were founding companies but struggling to raise meaningful capital despite having resumes that should have made fundraising easy.
His approach to sourcing deals is equally unconventional. While many VCs spend their days in endless coffee meetings, Zhan and Gazor devote hours to reading AI research papers curated by their own AI assistant. They take very few meetings because they want to identify trends and exciting ideas first, then proactively reach out to founders they’ve already decided to back. It’s a research-first approach that makes sense when you’re dealing with rapidly evolving technology.
The Zhan investing dynasty
Interesting side note – Zhan’s older sister is Stephanie Zhan, a general partner at Sequoia Capital who also backed Reflection AI and Skild AI early. They apparently have “very similar tastes” and sometimes land on the same companies, though Brian insists they keep work separate and won’t be discussing deals around the Thanksgiving table. It’s fascinating to see how technical backgrounds are creating new investing dynasties in Silicon Valley, replacing the old networks of business school connections.
Where the big money is going
Zhan seems most excited about AI for science applications, like Periodic Labs which aims to automate scientific discovery. “Imagine if we could use AI to dramatically shorten drug discovery timelines?” he asks. That’s the kind of transformative potential that justifies $30 million seed checks. But let’s be real – this strategy carries enormous risk. Betting that big that early means you’re paying premium prices for unproven teams and technology.
The Reflection AI success story proves the model can work spectacularly – turning a $200 million valuation into $8 billion in just a year. But for every Reflection AI, how many $30 million bets will completely flame out? That’s the billion-dollar question. Zhan and Gazor are betting that their technical depth and research-focused approach will give them an edge in identifying the few companies worth these massive early bets. If they’re right, they could reshape how venture capital approaches AI investing. If they’re wrong? Well, $165 million doesn’t go very far when you’re writing $30 million checks.
