Investment Hypotheses

Propagator Ventures
Sep 9, 2018 · 2 min read

Propagator Ventures positions itself early in the technology trajectory. We keep in close contact with our research and scientific network and seek to identify emerging technology trends on the brink of commercialization and venture capital attention.

Our hypotheses are based on the convergence of several general trends: machine & deep learning advances, engineering and computing at the nanometer level, and the commoditization of hardware and sensors. These trends often overlap and are observed in combination (e.g. Kebotix, combining new machine learning with robotics for materials discovery).

While our hypotheses remain under constant development (just like in science), we have listed several hypotheses below that describe which investment opportunities in emerging tech that we currently see:

1. Machine learning/AI

While large tech players dominate algorithm development and machine learning models, we see opportunities within more specialized domains requiring contextualized knowledge and access to data:

  • Health data is often proprietary, private and not centralized in hospitals & clinics. Further, advances in genomics, proteomics and biomics as well as new imaging techniques & sensor data offer rich datasets. Expected opportunities within decentralized data approaches, encryption and deep learning.
  • Heavy-asset industries are faced with continuous monitoring and complex collection of fragmented and decentralized data. New algorithm development has shown potential in handling such complex systems in a smarter way and to better predict critical system failure.

2. Quantum computing

Breakthroughs in quantum computing technology are rapidly progressing together with first-movers and expert teams. There is a window of opportunity for investing in such founders today before larger players or consolidation emerge.

3. Robotics

The rapid development in robotics is driven by commoditization of hardware and sensors and innovations in control algorithms, computer vision and deep learning. While AGI is still a distant goal, new highly skilled robotics systems has the potential to transform several industries in the near to intermediate term.

4. Encryption

Quantum computing may render current encryption standards obsolete within 10–15 years necessitating the need for wide deployment of quantum safe encryption algorithms. Increasing focus on privacy and ownership of data from governments and individuals will produce significant opportunities for new encryption technology.

5. New materials

The space of chemically active molecular combinations is huge (10⁶⁰) and as traditional materials discovery is guided by heuristics and expert knowledge, the process is slow. New machine learning techniques together with the ability to engineer at the nanometer scale will create opportunities for accelerated discovery of new materials through scalable business models.

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