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
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).
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.