The Nebius for Startups Podcast
Brian Zhan doesn't want your business plan. In fact, if you hand him one at the seed stage, that might raise some red flags. In this episode, Anu Maheshwari, Head of Venture Ecosystem at Nebius, sits down with Brian Zhan, partner at Striker Venture Partners. They unpack what it actually means to back people, not products, at the frontier of AI. From reinforcement learning's generalization advantages, to recursive self-improvement loops in chip design, Brian makes the case that the VC consensus is missing the most important early bets by waiting for revenue. Brian has been on the boards of Reflection, Skilled, and Periodic Labs. With a personal history in robotics research dating back to 2014, he maps out where the next trillion-dollar companies will emerge, why most VCs are too focused on revenue timelines to see them, and how Striker approaches seed investing with 10% of fund concentration per bet. 0:00 - Intro teaser: Why the best founders don't need a business plan 0:27 - Why Striker invests before there's a business plan 5:04 - Is AI in a bubble? Separating hype from opportunity 7:45 - How Brian identifies world-changing founders 9:24 - Reinforcement learning and the path to superintelligence 14:05 - Robotics, world models, and the future of physical AI 17:55 - Why most VCs think too small 20:18 - What Brian looks for in a founder pitch 23:37 - The OpenAI playbook & why compute partnerships matter 26:05 - Outro What You'll Learn: -Why a detailed business plan at seed is unnecessary. -The precision behind frontier AI investing and why it's fundamentally different from SaaS. -Why reinforcement learning underpins recursive self-improvement and why most investors don't actually understand it. -How world models and simulation will accelerate robotics toward its ChatGPT moment. -Solving energy supply by unlocking insight for everything else. -How Striker evaluates founder-market fit in practice.
20 episodes
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