How moonshots should raise VC

A lot of people have asked me what is happening with moonshot funding lately — why, for example, we’ve had failures like Theranos and FTX crop up, and what the implications are for the venture industry. These questions are understandable: When we hear of huge amounts of money going to something that ultimately doesn't work out, people feel upset.

But I want to start out by defending the good things that venture and its risk profile have brought to us. Venture is not about never making a mistake. It’s about finding and supporting a few high-risk, high-payoff companies that make everything else worthwhile. Now, a founder should never lie about their company and VCs should do good diligence. But venture investors have to be alright with half of their bets not succeeding. That’s the exciting and beautiful part of the industry.

It’s easy to look back and point out bad investments, but betting on risky things is how you bring good technology into the world. Venture works on a power law (rather than a normal) distribution, which means that an investment in 10 companies with one extraordinary success allows for nine failures — or nine meager successes. In other words, the power law allows us to make many risky bets. On a national level, we don’t want to see our failures and revert, for example, to the funding models of the National Institutes of Health exclusively, which is notoriously risk averse and consensus driven.

Now, that doesn’t mean we don’t need better education around funding and moonshots — more education might solve some of the bigger problems we're facing. If you're working on a moonshot, and you're relatively good at pitching the problem, a lot of investors are going to be interested in your company. Few investors will take the time to diligence your startup. That means you need to approach funding intentionally, which can come with a learning curve for early founders.

It can be exciting that all of these investors are interested in your technology. Still, any early-stage moonshot founder has to segment out the different types of investors that will be helpful to them — largely to purely preserve your sanity in the long run. The more general or bigger of a fund you go to, the less incentivized its investors are to spend time keeping up with the specific categories of research your company falls into. Consider who will be thoughtful and up-to-speed on commercialization dynamics that matter most to your company — especially pre-product market fit.

If you’re a moonshot startup, start by looking for VCs who are truly interested in your idea. Especially with generalist VCs, you’ll have to figure out early on who will have a view of your space, who won’t drag out diligence until they hear from another investor that they approve of your startup.

One good sign is investors who are able to give you thoughtful feedback or ask questions about similar commercialization paths. You can even put them on the spot as you close out an initial pitch meeting, prodding gently on an area of your business that you may have been thinking most about. “Between these pricing structures, what would be most successful and why, based off of what you’ve seen?"

Make sure they answer questions like: When does your technology actually become interesting to a mass audience? What is the metric/technical achievement that makes it interesting to that greater audience? Your investor should have a sense of the business model you’re going to use. And your first or second institutional check should be able to help you think carefully through product-market fit, as well as nuances related to your own technology and the best competitors in the space.

Do they understand the last five years of what people have attempted to do here and why it has or hasn’t worked? Do they understand it on an intimate level, not just what’s out there in the press? Can they help you hire the right people, who may be more specific than usual, or do they at least have enough context to support you?

Machine learning is a good example of a space where founders will likely benefit from an investor who is tracking the research closely. There’s a lot of buzz around it and everyone wants to fund a ML company. But it’s a field you really have to spend time with to keep up to date — just in terms of funding, research, and public models. As a founder, you don't ever want to have to come into a meeting and update your investors on the latest in the industry. You want them to care about moving the space forward as much as you do from day one.

Another important thing to look for is a VC’s ability to compare parallel commercialization strategies. Even if it feels like your approach has never been tried before, realistically, there’s a business out there who’s tried something either directly adjacent to what you’re building or an earlier version of it.

I’m excited about the future of venture. The paths people take to enter the industry are becoming continuously more varied, and formerly overlooked industries are being explored as venture fundable — construction, agriculture, biotech, trucking, and manufacturing technologies. I find this one of the most exciting parts of venture — things that weren’t even considered moonshots before are now becoming possibilities.