Analyzing AngelList Data for Drone Trends in 2015


This post is part of my continued research into Data 2.0.

In today’s drone ecosystem there seems to be a few key segments that startups are focusing on:

Hardware: Drone development, whether verticalized, in different form factors, or general quadcopter/fixed-wing assembly.

Drone as a Service:  Offer a variety of services related to the usage and piloting of drones, including the on-demand drone industry. Usually the services are very simple aerial image gathering or filming, but have also expanded to other verticals such as drone marketing or emergency response.

Drone Infrastructure Tech: Those dealing with a wide range of software to manage fleets, build a drone infrastructure, or make drones smarter. This is a trend I think is very important for the future.

Drone Data Analysis: Care more about the output of the drones and focus their business model around an analytical layer on that data, instead of the gathering of the data themselves. While some still offer the data gathering capabilities, it is clear that their “special sauce” is in the analytical layer.

With this in mind, I scraped AngelList’s drone market page for all drone companies that have joined the platform since Dec 2014 (not a huge sample size, but large enough to analyze).

I then categorized each company based on this taxonomy and found a few interesting data trends:

Hardware companies were the most frequent on the list. This includes companies like Nixie Labs, which is working on a wearable drone, to Galileo, which straddles the line of hardware and Drones as a Service by creating an in-home, autonomous home security drone that can interact via SMS with its owner.

Hardware plays in the drone ecosystem continue to garner investment and founder interest despite large players like DJI and 3D Robotics existing. While form-factor differentiation could prove to be one avenue of success (a la Nixie or Ascent Aerosystems’ Sprite drone) manufacturers seem to also believe in the verticalization of drone hardware. For example, Mountain Drones has integrated specific types of sensors into their drones which are purposed for environmental monitoring.

Drone as a Service (or on-demand drone) companies were the second most frequent on the list. These include your standard on-demand delivery services like XDrone, as well as more novel approaches like VinVelli Unmanned Systems, which plans to utilize drones for crop spraying and riot control, or Cardiac Drone, which delivers Automated External Defibrillators to reach someone having a heart attack in under 2 minutes vs. the average response time of 8 minutes in the US.

Overall it will be interesting to see how this market develops, as recently startups in this space such as Dronebase and SkyCatch with their WORKMODE product have successfully raised venture capital funding, while a ton of other on-demand or DaaS companies are sprouting up every day (whether or not they all are actually operating is unclear). I only see this trend accelerating in the US as FAA regulations become clearer and permit issuances for commercial drone use increases.

In the end, this space will be a wasteland of companies that were founded by those looking to capitalize on the rise of the on-demand economy. There will be winners in the US, however greater opportunity exists in developing markets like India, where demographics point to increased purchasing and an evolving consumer, but where infrastructure is still very poor. As we’ve seen inthe online grocery delivery space, with rising real estate prices, utilizing a distribution center that sits outside major cities will be advantageous, and if you remove the biggest headache (road infrastructure) the business models get extremely efficient very quickly.

Infrastructure/smart tech drone companies were the rarest, but were the only companies to sport signal scores (a metric AngelList uses for traction of sort) of 3 or more, as PixiePath and Neurala led all companies in this metric.

This segment had multiple players who are working on the logistics/fleet management of drones, as well as data around drones in the air. In addition, the infrastructure spanned a wide range of tools including Kittyhawk, a flight logging platform that calls itself the “github for drone operators”. Other infrastructure areas include anti-drone security companies like DroneLabs (a comp to DeDrone), autonomous drone software, and even a drone racing league.

This part of the drone ecosystem is by far the most diverse. While some parts like security seem inevitable, there is a lot of noise and smoke and mirrors on the software side. Many companies are focused on smarter & autonomous flying of individual drones via obstacle avoidance or “right of way” technology, in an attempt to make drones more aware. However there is some uncertainty around how advanced and/or dynamic a large portion of this technology is. If any sort of drone infrastructure for densely populated cities will exist, the concept of reaction time and smart, autonomous decision making is paramount. A very similar necessity for the adoption of self-driving cars.

In some ways, this has led the fleet management platforms to split to two business models:

  • The first being the development of platforms playing the role of “air traffic control” to closely monitor drone activity today due to the lack of confidence in truly autonomous flight.
  • The second being focused on the assumption that autonomous technology will be both possible and most efficient come via the advancement of “aware” drone technology, leading to an algorithmic approach to the guiding and management of each drone based off of start and end coordinates.

There may be services that take advantage of the manual vs. autonomous nature of each model, however by building a company for one, you are in some way making at the very least a short-term, industry bet on the level of progress that surrounding technologies can make in short time.

This reminds me of the issue Foursquare had in its early years, where they recognized their optimal business model and roadmap, but mobile devices didn’t yet have the proper technology to enable them.

While AngelList is a small data set with a high potential for “noise”, monitoring what types of companies enter the platform can provide a proxy for where entrepreneurs see value and/or inefficiencies in emerging spaces.

If you’re interested in seeing the data behind this analysis or discussing any of it, reach out to me on twitter @mhdempsey or email me at michael (at)