Investing in Technology in the private markets – Three Questions to Technology

Karen Wendt, SFTL President & expert in responsible, impact and sustainable investing

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Investing in Technology in the private markets – Three Questions to Technology

When we think of technological development, we often imagine a smooth exponential line upward.

In reality, technological development is mostly a series of overlapping steps. Certain technological breakthroughs occur, cause a multi-decade technological boom, and then run into a hard ceiling and stagnate until the next breakthrough emerges, says investment strategist Lyn Alden. That’s why there are often science fiction stories set at a certain date, and by the time we actually reach that date, our world isn’t as advanced as the writer expected (e.g., the 1968 book Do Androids Dream of Electric Sleep? was set in 1992, and its 1982 film adaptation, Blade Runner, was set in 2019).

The reason this is relevant is because, without the emergence of a new breakthrough, technology within a given industry often underperforms our expectations (if we assume a continuous growth model). In contrast, when there is a new breakthrough, technology within a given industry can easily outperform our expectations and change things more quickly than we expect. So how do we find the ruby in the mine? How can we know whether the technology we are interested in will underperform or outperform?

In particular, in AI, the expectations show a great variety: The takes on AI have ranged from “this is all hype and there’s nothing here” to “it’s going to kill us all in five years and take over the world”. Any exponential technology is hard to map out with a long timeline. But a useful exercise is to figure out what the right questions are, even if we don’t necessarily know all of the answers to those questions, says Lyn Alden.

The knee-jerk reaction to AI by investors is to invest in things that are needed to make AI, such as the AI software and the chips that they use. This investment space is already quite crowded, and disappointments may lie ahead except if there is a step-change. One useful question to think about is, “What products will become 10x more accessible thanks to AI?”

Another useful question could be, What are the longer-term trends worth taking seriously in AI?  For me, one question is: how can AI help pharmaceutical and biotech companies? One of the clearest applications for AI is molecule simulation.

Last but not least, we can also ask how AI can help investors create customised portfolios instead of buying what their banking agent tells them, which leads to customised wealth management at affordable costs for everyone. I just spoke to AISOT about that. If the machine is fed with the right data and you have a data-driven approach that can digest not only quantitative, structured data but also alternative data, unstructured data, and even emotional data, then we reach a point where AI and deep learning machines in investing may pave the way to a new wealth creation process for everybody, not just a couple of people that can afford a good wealth manager.

Many of these companies are startups or growth companies, and most of them are on the private market. How about becoming an investor in them and having your say in the future and about the future?

Finally, we can turn the question the other way around:

In addition to asking the question, “What will benefit from AI?” we can ask, “What will be disrupted by AI and what not? By process of elimination, this can lead us to a set of investments that, while they might not benefit greatly from AI, at least won’t be heavily disrupted by it. They will continue to provide good risk-adjusted returns when purchased at low valuations.

Our angel training will enable you to achieve exactly that.

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