How to Spot a $25 Billion Company Before It Has Revenue
The Nebius for Startups Podcast
Who’s talking
2 speakers1Transcript40 turns · 26 min
We're investing $30 million into people with no business plan. Some of the best investors I know are actually not full-time professional investors. They're just people in the know on AI. The VCs, they missed OpenAI, they missed Anthropic, they missed Midjourney, right? If we can get recursive self-improvement to work for scientific discovery, then we can solve some of the world's hardest problems. Actually, the biggest mistake that I see AI companies make is,
Welcome back to the Nebius for Startups podcast. I'm your host for today, Anu Maheshwari. I lead the venture ecosystem at Nebius. Very excited to be joined today by Brian Zan, who's a partner with Striker VC. Let's dive in. Brian, would love to start off with hearing more about your introduction and background and also sharing more about Striker VC. Striker's a newer VC firm, so understanding the history and how it was set up would be a great starting point.
Max, Matan, and myself are the early partners of Stryker. And we started this fund because we have one core belief, which is that seed stage investing is all about backing the best people in the best categories with an exciting approach. We're investing $30 million into people with no business plan. And look, we have the track record to prove it, like reflection. It's now a $30 billion company. Skill is now a $14 billion company. Periodic Labs. I think in frontier AI, you no longer have to worry so much about the business plan. Like $30 million, that's the median series B check that growth investors write. And we're writing it into just two founders with a pitch deck. And the reason that we feel confident in that is because In Frontier AI, all you need to do is identify the best categories, who are the best people with a very distinguished approach, and just back those people. We back reflection because they're the best people in reinforcement learning, the creators of AlphaGo, AlphaZero, disinterested transformers, RL at Gemini. When they say that they want to use RL to build agents before anyone else had that insight and just recruit all the best RL people. you just back them. When Deepak says the guy who made foundation models for robotics work says he's going to scale VLA models and make the foundation model for robotics happen, you just back him. And so we have that core philosophy. And in fact, I actually disagree with the VC consensus right now. You know, most VCs say, I want your business plan. I actually think that's a bad thing at the seed stage. I think that developing a good business plan, like not when you present to VCs, but like one that you actually execute on, that takes time to develop, right? When OpenAI was fundraising, they were pitching, say like, we're going to build an AI to play Dota games, right? Like that's, you don't, The truth is you don't know your business plan. The best frontier ad companies figured it out over time. And so that is just our core philosophy. Back the very best people and almost every company we've backed in our past careers is now a unicorn.
Yeah, that's such interesting framing, right? Having deep conviction early on, backing the founders and backing the core beliefs that they have of
2Episode notes
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.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.