The Impact of Artificial Intelligence on Space Investment

Artificial Intelligence (AI) has always been a science ahead of its time. From Stanley Kubrick’s adaptation of 2001: A Space Odyssey in 1968, computers that can replace humans have been “just around the corner” for 40 years. However, now the technology has nearly caught up to the science fiction, and investors have taken a note of that.

AI was one of the largest sectors for VC investment in 2019, according to CB Insights, and the momentum continues. With respect to Space, leaders in AI and Edge Computing are even creating divisions focused exclusively on the Space industry, with a focus on geospatial. For example, AI powerhouse G42, based in the UAE, recently announced the hiring of industry superstar and former special advisor to the UAE Space Agency Talal Al-Kaissi.

2019 sees record funding to AI startups at $266B | Source: CB Insights /

Investors in this sector can also be termed “the absolute cream” of the crop. The 2019 activity was led by the likes of Intel, but major VCs like NEA, Accel and Sequoia were not far behind.

Who is investing in AI: Number of artificial intelligence investments by investor, worldwide as of May 2019 | Source:

The opportunities in Space

AI traditionally has two main investment theses, both of which are applicable to the Space sector. The first is autonomous or semi-autonomous activities such as operating machinery or equipment. This is classic robotics leveraging Artificial Intelligence and the ability to learn. Obviously in an environment as harsh as Space, these capabilities could be hugely valuable to Space missions. The second investment thesis has to do with so called “deep” or “machine” learning. This is essentially the capability to take large data sets, structure them for relevance, mine them for insights and ultimately create predictive capabilities. As I have previously written about, the Space “mega-set” that is the collection of ubiquitous, persistent hyper spectral information could be a multi-trillion dollar opportunity assuming AI and Machine Learning is properly leveraged.

In addition to G42, several platforms are emerging that are based largely on Machine Learning and AI capabilities. These include firms such as EagleView, Planet and Cape Analytics. Investors can expect a healthy number of new startups leveraging AI as well as additional consolidation to take advantage of original integration of additional data, as well as additional scale economies. It is also possible that larger data-savvy companies such as Facebook or IBM could directly enter the sector, as they see the vast opportunity that Space-based data represents. We have already seen this with Amazon’s pursuit of ground station infrastructure as well as their forthcoming Kuiper constellation for high speed Internet service.

In addition to data and analytics, AI can play an increasingly large role in Space missions themselves. It has been reported that many working on the HLS moon mission are leveraging AI capabilities in their moon-based vehicles and pitch books are now circulating for startups contemplating Avatar like capabilities for Space-based repair work completed by Earth stationed humans. In high latency environments, this becomes less practical and semi or full autonomous AI will need to be utilized. Regardless, the more autonomous robotics becomes due to AI capabilities, the more types of missions become practical.

Regardless of how disrupted Space becomes as a sector due to more advanced AI, the technical capabilities of the technology should put missions and projects in play that to date have only been contemplated in science fiction

However, at the upper right of the chart, you can see some anomalies. For example, why would private equity (primarily a US-based invention) have a higher return than emerging markets, which are typically economies that have much more systemic risk? The answer is that while you can buy emerging markets in a mutual fund, you can’t necessarily do that with private equity. Therefore, private equity is less liquid (e.g. you can’t sell it at the push of the button on the open market). This lack of liquidity means that most investors will demand a higher return in order to agree to invest.

On the horizon

If the pace of change within AI continues at its current rate, in addition to the opportunities described above, there could also be employment disruption within the broader aerospace industry itself. Even design and engineering work could become in-play due to AI, Machine Learning and automation. This could fundamentally impact the economics of the Space industry as well as impact the largely jobs-based and cost-plus politics that the Space sector has traditionally been based upon.

However, regardless of how disrupted Space becomes as a sector due to more advanced AI, the technical capabilities of the technology should put missions and projects in play that to date have only been contemplated in science fiction. This future should continue to create many new opportunities for investors.

Author Bio

Dylan Taylor is a global business leader and philanthropist. He serves as Chairman & CEO of Voyager Space Holdings, a multi-national Space holding firm that acquires and integrates leading Space exploration enterprises globally. Dylan has been cited by Harvard University, SpaceNews, BBC, CNN, Pitchbook, CNBC and others as having played a seminal role in the growth of the private Space industry.

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