Since joining Asymmetry Ventures, I’ve evolved my deal filter and prioritization process. Asymmetry seeks dramatically outsized return potential, even with higher risks attached than I might have taken as an angel investor. If I have a thesis for why a company could be worth $5-$10B in 7 years, the fund GP is going to ask, “OK, but could it be worth $100B in 10 years?” That’s a harder question to answer because of increased uncertainty over longer time horizons, and the fact that there are far fewer "centacorn” startups in history.
So I’ve been pondering the “contrarian and right” graph that many VCs say is the best strategy for outsized returns.
I think it’s missing something - the time dimension. Consensus opinion can apply to two different things: the product/market solution, and the timing of when that solution will scale to broad adoption. A company can generate enormous returns from a strategy that is consensus and right IF they are able to pull an expected future forward earlier than everybody else predicted. Consider, for example, Zoom, NVIDIA and Shopify - business video conferencing, GPU training of machine learning models, and SMB e-commerce have reflected consensus trends for a while, but the rate of growth arguably surprised investors by accelerating at times that were difficult to foresee.
I’d also differentiate this contrarian timing category from first mover advantage. Consider the Gartner hype cycle:
The widths of the peak and trough here differ by sector, and are hard to predict. Many first movers die after the peak, and those that remain can take a long time to climb the slope of enlightenment. For example, 3D printing companies have had a pretty wide trough (see 10 year chart for DDD below). In this case, the leaders in the space recently cracked the health care market.
Some startups have outsize growth rates because they’ve found a way to narrow the Gartner trough in a market that we all believed was going to be big one day, but most people assumed that day was much further away. This is happening in the space sector (SpaceX), fintech (Stripe, Coinbase), mRNA life sciences companies (Moderna, BioNTech), and many B2B cloud SaaS companies.
Consider also the fusion reactor space. It’s a hard sector to fund with VC money now, because the consensus view is that we’re still 20-30 years away. But if you believe a startup can bring fusion energy to the public at scale in 10 years, that’s not a contrarian bet on product/market fit, it’s a contrarian bet on timing, and that startup could produce truly unbelievable IRR returns. This is fundamentally different than backing Uber or Airbnb early, where investors who passed may have believed that due to fundamental human nature (“Who would let strangers sleep in their house?”), the model might not ever work at scale.
Consumer AR is another fascinating example. It wasn’t a contrarian bet on product when investors poured billions into Magic Leap - it was a bet that the company had made such a quantum jump in technological abilities that the AR market might leapfrog VR and become mainstream by the 20s. As we now know, things didn’t play out that way.
This pattern reminds me of the options market, where investors can decouple bets on timing from other variables, and make a fortune being contrarian (and right) on timing. The FAANG stocks were a consensus bet for the last decade. But if you were an options buyer and had more accurate predictions of theta and volatility changes than the options writers, you could make a much higher return on your investment buying and selling mispriced call options for Big Tech companies than you would just holding the underlying stock.
To bring this full circle with my earliest posts - I prefer execution risk to market risk. On average, the really hard bets that are contrarian on product/market fit are taking on a lot of market risk. However, if I see a startup that I believe can do in 5-10 years what most investors assume would take 20-30 years, then it’s valuation is going to be mispriced, and I can take execution risk in exchange for the possibility of power law returns.
This is why lately I’m looking closely at companies that have made breakthroughs in “hard science”, possess a distinct moat, and could dramatically accelerate known trends (e.g. carbon capture, automated supply chain, mRNA therapeutics, alternative energy sources, quantum computing, ML-aided drug discovery, etc.)
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