Modeling Returns in Venture Capital: Power Law Hybrid

Before building models to sensitize the returns of different portfolio constructions we need a representation of an individual company's return. Empirically we know roughly 50% - 75% of seed stage companies die and that the hyper success case is, well, hyper-rare, maybe 1 in 200. Of course any mathematical representation of the returns likelihood of a seed stage company is steeped in uncertainty. The best we can do is use a distribution that doesn't seem too implausible. 

By many people's estimation the Power Law is the most 'usable' and 'accurate' distribution to model seed stage returns. The Power Law is also the basis of our approximation. But we create a mutant hybrid with the Log Normal because in generating and experimenting with various Power Law configurations it seems a little too harsh on the death rate (>75% die in many cases.) The Power Law also seems a little too willing to produce hyper-success cases (1 - 2 in 100.)

In sensitizing and understanding the returns of our portfolio constructions, these two 'errors' (the lower than realistic death rate and higher than realistic unicorn case) can be accepted. Indeed in any fund there must be some 'unfair advantages' that the fund manager has (superior network, proprietary deal flow, superior selection rate etc.) for he fund to exist in the first place. For Hone Capital machine learning models support our lower seed stage death rate and the AngelList network supports the higher than normal unicorn rate. 

With return (measured in multiple of original post-money valuation) the Log Normal is run with the mean at 0.3x and the standard deviation at 1.0x. The Power Law is modeled using a Power Law coefficient of 1.159. Both shown below (vertical axis cut for clarity.)

With these coefficients we can see that the 'effective' death rate is approximately 40%, another 40% return 1.0x - 3.0x heavily weighted towards the former and around 1.5% go to exit at 'unicorn' status. It is pretty clear these are just approximations which help us get a sense of the sensitivity of a portfolio construction. They are at best over-engineered and at worst wrong. 

Investment Opportunities in Artificial Intelligence

Disclaimer: Much of the following post will be proven wrong by the relentless judgement of time. Indeed, there may be something to learn from where we were wrong (in January of 2017). This post is designed as an exploration tool rather than a descriptor of fact.

There are numerous investment opportunities provided by the awakening of AI. Both in seed stage and 30 year plus public companies. This post is an attempt at structuring our current explorations and understandings of the investment opportunities afforded by the development of (current, narrow) AI. This forms the basis of our A3 Thesis.

The Three Pillars of AI

It looks like the earliest documented mention of the three pillars of AI was from Wired magazine in October 2014, these being:

·         Appropriate Algorithms

·         Data Availability

·         Computational Power

1.       Algorithms

Per Wired (and numerous academic journal articles) the algorithms that power much of the hyper AI tech of 2017 originated much earlier (the 50’s, the 80’s etc.) and have just been waiting. Geoff Hinton, now infamously sparked a resurgence in neural nets in the 2012 ImageNet competition with again now ubiquitous ‘deep learning’ approach. It seems to us that the commercial value in algorithm development may be in specialized use cases (outside traditional image recognition etc.) but it hard to imagine such opportunities today. Also much of this value lies in the prodigious minds of the human capital of the company or University; hard to monetize. It appears much of the value in AI algorithm development has been commoditized.

Opportunity: Large public companies (FANG’s); recruitment and retention of key human capital (large public tech may have the means necessary to provide utopian research environment for academic talent).

2.       Data

Incredible opportunities for early stage technology companies in data, specifically ‘data generative assets’. Any company that creates differentiated, deep, clean, useful data through sensors or incentives or any other means possible. Ginni Rometty has called data the new natural resource of our time. In fact, it is even better; companies can create their own data (effectively creating their own oil).

Opportunity: Startups: industrial sensors, new business models that incentivize people to track data, satellites for more pervasive, cleaner data, collaborative business models etc. are all attractive.  

3.       Computation Power

Given the capital intensity of developing deep learning-specialized GPUs we had originally thought the best investment opportunity here was with NVIDIA. Indeed, if you had invested at the beginning of 2016 it would have been a remarkable investment (+260% and best performed in the S&P 500). However, recently we came across Cerebras Systems, a startup run by a team who created SunMicro and sold it to AMD. Cerebras is funded by Benchmark proving that perhaps there is room for a proven team, specialized focus and deep pocketed, long term, committed investors. 

Opportunity: Market neutral NVDA/INTC; NVIDIA has been aggressive in its adoption of deep learning based AI even as early as 2014. Intel has missed the mobile revolution and risks being left behind in deep learning. Very hard to bet against Intel. More to come.