Rethinking VC Fund-of-Funds with Data, AI, and Algorithms (with Albert Azout from Level Ventures)

For decades, venture capital’s edge came from being in the room.
The right dinners, the right syndicates, the right backchannels.

But are those edges are eroding!? 

Companies stay private longer, DPI is suppressed, and capital pools are more crowded than ever. Selection risk is up, liquidity is down, and “alpha” doesn’t look like it used to.

If yesterday’s advantage was network and knowledge arbitrage, today’s may be something different: data arbitrage.

The idea is simple but radical: what if you could reconstruct the invisible graphs behind venture—who co-invests with whom, where talent migrates, which circles spot signal first? 

What if benchmarks weren’t generic Cambridge tables, but dynamic peer sets tailored to each segment? 

What if diligence cycles compressed from weeks to days, powered by proprietary models fused with LLMs?

At that point, a fund-of-funds is no longer just a fee layer. 

It starts to look more like an operating system: allocator, co-investor, and analytics engine in one. 

A platform that doesn’t just access networks, but maps them before anyone else walks in the room.

And that raises a deeper question: when networks and judgment can be modeled, does gut feel still rule VC—or are we watching the first serious attempt to systematise private markets bringing it a lot closer to hedge funds?

That’s the bet being made by Level VC and its founder, Albert Azout.

💥 “We want to be the best quantitative investor in the private markets.”

As Albert puts it: “We’re trying to build the most sophisticated data flywheel in private markets.” 

And a lot of their tools are available to their portfolio funds and companies - that’s an instant value add.

This is venture reimagined — not by instinct alone, but by infrastructure.

Bonus feature: A rare inside view of their system and how they are mixing tech+AI+data to build a revolutionary platform.