Hard Problems: solving Adverse Selection at Stonks [Part 1]

I’m starting a new series of posts focused on really hard, intractable problems in startup-building. They are just not talked about enough, and they should be. We learn by sharing.

What is Adverse Selection? It’s a situation where in the early days of building a platform, you get poor-quality users because of structural misalignment of incentives. Some examples:

  • Early Angelist: the best VC firms and investors weren’t using AL in the early 2010s, and so the best founders didn’t want to raise there.
  • OurCrowd & EquityBee: the best investors don’t search for top deals here, so the best founders don’t list, so the best investors don’t look here, so the best founders don’t…downward spiral.
  • WeFunder & Republic: there are exceptions (such as Mercury and Customer IO), but for 7 out of 10 deals on these platforms, the startups listing here are worse-quality than your average VC deal. Funding-of-last-resort creates a downward spiral.

Stonks started off with a similar problem if we were going to let founders raise in public with a platform for demo days, how were we going to avoid these pitfalls? Why would solid startups — that don’t need the money — pitch on Stonks, vs raising via traditional methods?

This is a life-or-death problem for new funding platforms in particular. If deal quality isn’t top-tier, great investors don’t want to use your platform. And great founders in turn don’t want to use your platform either. But how do you get good investors until you have top-tier deals? Sounds like a chicken-or-the-egg problem.

A key trend this year is proactively using your cap table as a resource to solve for something hard — growth, access to customers, hiring — not just for capital.

We used our cap table to get Apex Investors — those that other founders and investors follow. If there was a hierarchy in startup ecology, like apex predators, these would be at the top. In our case, this started with A16Z, and was soon followed by many of the Top 10 syndicates on Angel list:

see full investor list at Stonks

Since these investors now owned a piece of the company, and had upside with growing the platform, our incentives were now aligned. They wanted to participate in the platform and help grow it. Incentives run the world, so its extremely important to get this right (more on this in a future post).

The Hierarchical Ecology of Startup Investing

Folks at the bottom follow those above them in this hierarchy.

Startup investing is heavily based on the quality of signals. We now had a top-of-the-pyramid signal with apex investors. Getting these investors to actively participate on our platform allowed us to attract the next tier of participants: solid, non-brand founders & startups with real traction.

I haven’t seen definitive data on this yet, but I believe that long-term 10Y+ startup returns are higher with outsiders, rather than insiders. In other words, non-brand, no-name founders with real traction often provide better IRR and DPI than FAANG-brand, big-name founders. They have lower valuations, are often more hungry, and less likely to give up.

At the very least, this category of founders would provide equal-or-higher long-term IRR to startup investors. Something that I would happily invest in myself (I’m an active angel too). I didn’t want to build something that I wouldn’t invest in myself. This passed the test.

Getting the first two categories in this hierarchy started our flywheel: Stonks went from $0 to $50 million GMV annualized in 90 days. We had great, highly-investable deals, and many investors gave us feedback that these deals were better than what they saw on YC.

It really makes me happy to hear that we have genuinely amazing deals, and we’ve (mostly) solved the adverse selection problem that plagues many funding platforms in their early days.

Next stop: $1 billion in GMV in the next 18 months 🚀🚀🚀

Shoot me your feedback, and follow Stonks on Twitter for must-read advice on startup building such as this:

https://twitter.com/Stonks_dot_com

See Part 2: How AngelList solves adverse selection here.

Stonks. Streamlabs. Peanutlabs. Building stuff people want.