We want to refine these tools, have more of a data flywheel, and really have the most sophisticated data set in the market.Albert Azout · on what is next for Level
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 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.
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 systematize private markets and bring them closer to hedge funds?
That's the experiment being run at Level VC, founded by Albert Azout. Their approach shows how technology, AI, and data can reshape the way venture funds and portfolio companies operate. This is venture reimagined, not by instinct alone, but by infrastructure. The episode includes a rare inside view of their system and how they are mixing tech, AI and data to build a next-generation platform.
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FIRM & PEOPLE
Started in 2021. Three parts: a fund of funds backing emerging pre-seed and seed VCs, a co-investment arm focused on Series B and sometimes pre-B, and a technology team of mostly data scientists and engineers. Now investing out of its second fund, started in January.
Twice exited founder; started his career at Lehman as a software engineer and technology analyst; was a GP at Cota Capital, then a venture partner there, and started Level in 2021.
Investments thought leader, Big Book of VC.
THE SYSTEM
Complexity theory and knowledge graphs
Reconstructing patterns of relationships between entities rather than looking at a GP in a vacuum: co-investor networks and talent networks, on the view that networks are asymmetric and where you sit affects performance.
Graph neural network manager score
Outputs a prediction of a manager's potential top decile performance hit rate, fed by transactional data, the founders a GP backs, and the quality and experience of that talent.
Network search for GPs
High definition search across the market and a team's own network for specific skill sets, expertise or experience, used for diligence, sourcing and portfolio support; also CRM enrichment.
Remote MCP service
One of the first: a model context protocol letting foundation models interact with external services, with sub-resources the team uses to get up to speed inside the context of their own data.
THE ARGUMENT
Smart beta and smart alpha
Multi-stage firms deploying large amounts into highly legible, momentum-driven opportunities on one side; on the other, GPs in overlooked and less legible areas, or with the structural ability to get into deals.
Three sources of alpha
Network advantages that are non-redundant and economically viable, knowledge advantages that are specialised and non-redundant, and structural advantages driven by fund size and ownership.
Data arbitrage
His view that as the market institutionalises, every piece of alpha gets eked out, and informational or data advantages sit alongside network and knowledge advantages.
60 to 70% passive
His figure for passive holders of equities, and his point that many of the strongest active public investors are crossover investors.