According to Fast Company, sisters Justine and Olivia Moore are central figures steering Andreessen Horowitz’s aggressive push into artificial intelligence. Their strategy involves meticulously tracking business metrics like paid conversion rates and vetting founders with backgrounds in top AI labs or specific industries. They immerse themselves in the space by attending conferences, reading research papers, and, most importantly, putting AI products through rigorous, hands-on testing beyond staged demos. Olivia’s portfolio includes companies like supply chain firm HappyRobot and creative platform Krea, while Justine was a key early investor in voice-synthesis leader ElevenLabs. The firm is reportedly seeking to raise a staggering $20 billion to further fuel these AI-focused investments, banking on the Moores’ discerning approach to find winners in a crowded field.
Beyond the Demo Reel
Here’s the thing: everyone in VC talks about “doing the work,” but the Moores’ emphasis on actually using the products is what stands out. In an era of dazzling AI-generated video clips and perfectly scripted chatbot interactions, that’s the only way to find the cracks—or the real magic. Staged demos are a founder’s highlight reel. But logging in and trying to accomplish a real task? That’s where you see the seams, the latency, the frustrating workarounds. Or, conversely, you discover an intuitive leap that no slide deck could ever capture. Olivia nailed it: that’s how you gauge the marketing hype-to-substance ratio. It’s a simple, almost obvious filter, but in the frenzy to deploy capital, how many investors are truly taking the time?
The Founder Fit Equation
Their dual focus on founder background is also telling. It’s not just about hiring someone from OpenAI or Google DeepMind. That’s one path. The other is deep, almost obsessive industry knowledge. This suggests they’re looking for founders who are solving for a real pain point, not just applying a shiny AI hammer to everything in sight. A founder who’s lived in the logistics world or the creative trenches knows what problems are worth the R&D burn. They can intuit user needs because they were the user. That combination—cutting-edge technical credibility or profound domain expertise—creates a much wider and more interesting funnel than the standard “ex-FAANG” checklist most funds run.
Why a16z’s Scale Matters Now
And then there’s the $20 billion elephant in the room. The sisters value a16z’s scale because it lets them “stick to convictions rather than chase trends.” That’s easy to say, but harder to do when you’re managing a smaller fund and feel pressure to show quick returns. With a war chest that large, they can afford to be patient. They can make contrarian bets, support founders through long R&D cycles, and literally fund the infrastructure of an AI ecosystem. But it also raises the stakes immensely. With that much capital to deploy, the pressure to pick the right horses—and the cost of missing a key one—is astronomical. Their hands-on, product-centric method seems designed to mitigate that very risk.
The Human Filter in an AI Gold Rush
So what’s the trajectory here? We’re moving from a phase of pure model innovation to one of applied, product-centric innovation. The Moores are essentially betting that the next decade of AI value will be captured by the teams that best integrate the technology, not just the ones that advance the core science. Their approach is a classic, almost old-school venture tactic—founder quality, product sense, real market need—applied to the most hyped sector in history. In a landscape drowning in noise, their process is a forced function for signal. Will it be enough to spot the winners in a market where, as they admit, the opportunities far outstrip their capacity to invest? That’s the multi-billion dollar question they’re now tasked with answering.
