For a minute, forget that venture capital exists. Forget Benchmark, Sequoia, and Y Combinator. Forget Kalanick, Faulkner and Chesky. Gone. No legacy. You're trying to assemble a private market product that appeals to large institutional LP allocators, built on the promise of technology companies. Their preferences are: 1) Strong interim performance, so they can hit internal benchmarks, get bonus compensation and advance their career. 2) Protection against the visible impact of systemic drawdowns, protecting relative performance metrics even in a market correction. 3) Bragging rights, from involvement in strong brands with large media platforms and passthrough exposure to lots of hot logos. 4) A high level of service, with a fully staffed IR team to handle everything and organise spectacular AGMs with all kinds of perks. Essentially, you need to offer a scalable basket of assets that will reliably rise in value, attached to a marketing machine, with a valuation policy that aligns your marks with other managers in your network. The ideal asset for this strategy is the most obvious; the 25 year old Stanford graduate who did 2 years on Meta's product team and is now launching some B2B AI widget — and they've just gone viral with their launch video. The cost of capital for a founder that ticks so many boxes is rock bottom; coordination frictions at a minimum. They're likely to keep raising larger rounds and generating markups on sheer momentum, regardless of any idiosyncratic quality. It's also such an easy profile to identify that you can employ an army of associates and scouts to track them down, assuming they don't respond directly to your heavily advertised "call for startups". Essentially, what you have created is a synthetic "venture" asset, built on the elasticity of software companies, that prints growth metrics for allocators. It can scale as much as required, absorbing as much capital as may be shoveled into it, growing at a rate correlated with how close to the consensus you are. Of course, all of these things are correlated with worse ultimate cash returns, as they reflect attributes that are predictive of worse returns in a market with zero alpha. Surprisingly, that's ok. It's not the point. ...