Deep tech startups always excite investors and governments alike – they usually have something cool that nobody really understands, but is really cool.
Theoretically, deep tech should possess a deep economic moat. Competitors will have a hard time coming up with an offering that can be a close substitute, which in turn means control over an uncontested market.
But control over exactly what market? And can the charming story that is sold reach its envisioned ending?
Quite often, deep tech is not as great as it is made out to be, with pitfalls in market demand assessment, scaling, and business viability.
Impressive tech, yes, but what about commercial viability?
One of the toughest challenges for deep tech is scaling to commercialize with a strong customer-product fit. A cool prototype could have a long road ahead before it can reach levels of usability that can truly value-add for customers.
Take the Segway for example. Initially brought to market in 2001, it was touted as the future of personal transport but quickly received lacklustre market response of selling just 30,000 units in 6 years. It ran into a regulatory blackhole as cities weren’t designed with Segways in mind, leaving them in an awkward grey area because they neither fit on roads nor on pedestrian paths. Also, logistical concerns such as parking, charging, and mobility on staircases or steps make the Segway more of a hassle than convenience to use. It ends up solving one problem but creating a host of others such that the cost of use outweighs the benefits.
We have also seen a number of startups with really cool products coming to us. The tech is definitely cutting edge, often performing 10 times or 100 times better than incumbent technology. The only problem: consumers do not find it this way. Yes it is much better, but there is another part of the process which is the bottleneck, rendering 10 times irrelevant – by the time the rest of the processes and products catch up, competitors have also caught up.
In other circumstances, the benefits are cool, but can’t be measured in monetary terms. Also, in order to implement, customers have to revamp their entire process, which for businesses is always a pain in the arse.
Controversial (or downright absent) business model
Moreover, the typically long R&D process of deep tech can be risky, costly, and ultimately not translatable into business applications.
AI startup DeepMind has been in the red and is a “long-term investment” by Google, having mounting losses year on year to the tune of $162mil in 2016. Despite research outcomes such as AI that plays world-class Go, the capabilities of DeepMind are still not scalable via lower marginal costs after being in business for 8 years.
Many other AI businesses have deeper issues – their algorithms can be easily bypassed, and they do not control the core of making AI work: the data. At least DeepMind is part of Google – a huge repository of data.
The technologists behind deep tech need to be savvy businesspeople. Often, there can be an intensive focus on creating the most innovative product, without enough attention being placed on the business model. Needless to say this ends up poorly in terms of business performance and monetization, and an underperforming investment choice.
Deep tech is cool with its lofty goals and futuristic promises, but it needs to be rationally analysed beyond its initial awe.
It is not an omnipotent silver bullet of innovation that can unfailing drive the economic success of a country.
On the other hand, business model innovation, while often criticised as not true innovation, can be a true driving force. Commercial success allows you to have the data and financial power to invest in real, relevant deep tech.
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Thanks for reading The Low Down, insight and inside knowledge from the team at Momentum Works. If you’d like to get in touch with us about any issues discussed on our blog, please drop us an email at hello@mworks.asia and let us know how we can help.