The Challenges Of Commercializing Deep Tech

“Technology is the double-edged sword of our times. Whoever controls technology controls the world” ~Varun Sayal.

In his words, Sayal had envisioned a future where humans relied on technology to address big societal issues and solve pressing global problems. In other words, he had foreseen a future where humans embraced deep tech.

And that future is upon us. Autonomous driving vehicles, headbands that can translate thoughts into words, coffee-powered cars, cancer-detecting smart needles, fake drug detectors, spaceships that could take you to the moon and back, all fall under the label of deep tech.

These head-spinning technological wonders, if embraced, have the potential to impact the world in big ways. However, commercializing deep tech poses a lot of challenges. We’ll discuss these challenges later in this post. But first, a primer on deep tech.

What is Deep Tech?

Deep tech is that set of cutting-edge, groundbreaking technologies based on scientific discoveries, engineering, medicine, physics and mathematics.

Deep tech is not a business model. Rather, it’s an innovation in technology or science around which a business may be created. In layman’s terms, it's a scientific breakthrough which when applied, has far-reaching implications across industries and can potentially improve human lives.

The sectors where deep technology has made huge strides include biotech, clean-tech, energy, computer science, medicine, aerospace and agriculture. Robotics, 3D printing, blockchain and quantum computing are all examples of deep tech.

In many countries, deep tech is still at a nascent stage. However, the Asia Pacific region, spearheaded by China and Singapore has invested heavily in deep tech, which has seen companies such as Biolidics make unprecedented developments towards the cure for cancer.

Deep Tech Vs. General Tech: What’s the Difference?

Many tech companies, such as SentryOne, have made giant strides in improving the quality of life for data professionals by providing stellar database performance monitoring solutions. But do they fall under deep tech?

What about companies such as Uber, Facebook and Airbnb?

As innovative as these companies are, they all rely on existing technologies to survive.

Take Uber, for example, the company has disrupted the entire industry and gained millions of fans. However, it leverages the sharing economy and was established using existing technology. In other words, it hasn’t reinvented the wheel; it just arguably made it better.

Deep tech startups, on the other hand, come with novel innovations such as AI scanners that can detect fake drugs or new clean-tech solutions to combat global warming. Such companies are not built on existing technology; instead, they attempt to make it obsolete. 

Challenges of Commercializing Deep Tech

Commercializing deep tech brings advanced technological innovations to the market and makes innovative products benefit society. However, the process of commercializing deep tech is not straight forward and is marred by many challenges. In this article, we’ll discuss 4 of them.

1. Funding

Since deep tech startups require more capital than general tech startups, funding poses one of the top challenges to commercialization. 

Also, because deep technology comprises hitherto unseen mechanics and algorithms, investors feel disinclined to fund such incipient systems as they don’t have the expertise to analyze the potential value of the new technologies. 

Moreover, these startups lack standardization and third-party certification which further makes investors and well-wishers skeptical of providing financial aid.

2. Time & Scale

It requires concerted R&D to develop practical business applications and bring them from the lab to the market. For instance, it took researchers decades to develop the underlying tech behind AI and the tech is yet to reach perfection.

Another well-known example is one of Gorilla glass commonly used in smartphones. Corning first conceptualized the idea of a thin glass that could be used in a wide range of applications in 1950. After years of research and development, the glass finally showed limited use in 2005 when Apple tested it on iPhones.

3.  Marketing

In their abstract on deep tech, Pellikka & Colleagues (2012) identified marketing as a key obstacle to commercialization. The marketing challenges relate to the failure to obtain adequate market information, failure to use it properly and insufficient data on international markets.

In the business environment, commercialization may be hampered by a lack of matching business infrastructure and human talents. Since this technology is new to the market, training partners and determining the methods of distribution also pose a challenge.

4. Economic, Cultural & Industrial Challenges

Commercialization of deep tech may be faced with legal and industrial bottlenecks if the tech results in environmental pollution or it violates certain industrial or cultural rules.

For instance, biotech startups in Asia face many challenges from the government due to strict regulations, as this technology raises concerns over biosafety, food safety and ethics. Moreover, because of the varied cultures in Asia, some people may raise concerns over the use of genes derived from some animals due to religious beliefs. 

Wrapping Up

The commercialization of deep tech is indeed a process and a challenge.

However, many deep tech startups have aced it and their power is being harnessed to solve some of the world’s pressing issues. Asia, for instance, has registered over one thousand deep tech startups—with 746 startups based in China while the US takes the lead with 4,198 companies, according to a 2019 BCG report.  

What are your thoughts on the adoption of deep tech? Share your views in the comments section below.

Author’s Bio

Kevin serves as Principal Program Manager at SentryOne. He is a founder and former president of PASS and the author of popular IT books like SQL in a Nutshell. Kevin is a renowned database expert, software industry veteran, Microsoft SQL Server MVP and long-time blogger at SentryOne. As a noted leader in the SQL Server community, he blogs about Microsoft Data Platform features and best practices, SQL Server trends as well as professional development for data professionals.