deep tech

What is deep tech?

Deep technology, or deep tech, refers to advanced technologies based on some form of substantial scientific or engineering innovation. The innovations are "deep" in that they are sophisticated, very advanced answers to complex challenges or issues. Examples of deep-tech breakthroughs include genomics, robotics, nanotechnology and clean energy initiatives coming from research labs and academia.

Deep tech is a decades-old term used, at first, to describe any form of complicated technology in general. However, in the last decade, deep tech has gained a more precise and specific meaning. In 2014, Swati Chaturvedi, CEO at investing firm Propel(x), coined the definition that is widely used today.

Deep-tech startups and companies seek to provide answers to complex problems through technologies and processes that typically require lengthy research and development (R&D) cycles led by personnel with significant technical expertise. Deep tech also typically requires substantial capital investment during the development process.

Deep tech differs from high tech, which refers more broadly to technical innovations and advancements. High-tech companies, for example, might leverage existing technology in clever ways, embrace digital transformation, or even develop apps and platforms, but they are not necessarily focused on profound scientific or engineering breakthroughs. Instead, deep tech pushes the boundaries of what is technologically possible, often upsetting the status quo through radical innovations rooted in scientific research and engineering.

Types of deep tech

Many fields are applying the innovations of deep tech. Among the most common types of deep tech are the following:

• Advanced materials. Deep tech includes engineering materials at the molecular level to create products with enhanced electrical, thermal, structural and other properties. Material science applications range from electronics to energy to medicine.
• Artificial intelligence (AI) and machine learning (ML). AI and ML are increasingly mainstream, but there remains a significant amount of deep-tech innovation still in progress -- particularly with the development of artificial general intelligence. Advances in AI capabilities continue at deep-tech companies in areas such as predictive analytics, autonomous vehicles, drug discovery and more.
• Biotechnology. Deep-tech bio startups advance precision medicine, agriculture and sustainability initiatives by leveraging advanced genetics, genomics and synthetic biology to create breakthrough therapies, materials, fuels and foods.
• Power production. Development of advanced forms of power production, including but not limited to nuclear fusion technology, is a deep-tech issue of critical importance to all life on Earth.
• Quantum computing. Deep-tech companies use quantum computing to develop algorithms, systems and computers to solve complex problems in cryptography, drug research, finance and more.
• Robotics. Robotics combines engineering and computer science to build machines that can operate autonomously. Deep-tech robotics innovators develop next-generation industrial robots, humanoid service robots, surgical bots, autonomous drones and other intelligent machines.

What's different about deep tech?

Deep tech differs from regular high tech and mainstream consumer technology in various ways.

Deep-tech startups tackle complex engineering and scientific challenges, typically without any form of existing framework, to develop transformative types of technologies. Significant upfront capital investment is essential to fund extensive -- and expensive -- R&D cycles before approaching commercial viability. That's a very different model from a typical consumer internet startup, capable of launching an app within a short period. Deep-tech companies, on the other hand, often spend years building and testing sophisticated hardware or platforms.

In addition, deep tech requires specialized technical talent, advanced scientific expertise or quite often both. The underlying innovations create valuable intellectual property that is difficult to replicate. Once the technology successfully reaches the commercialization stage, deep-tech startups, holding all the patents and an unmatched level of expertise, enjoy strong barriers against competition. In contrast, consumer internet and traditional high-tech companies compete fiercely over market share, in large part because the underlying technology is more easily commoditized and replicated.

Metrics for success also differ for deep-tech companies. For consumer startups and high tech, customer adoption and retention are key early metrics. Deep-tech startups, in contrast, track technological milestones, patents granted and regulatory approvals. Their target customers are often other enterprises or governments rather than individual consumers. While deep-tech product cycles are prolonged, the potential for societal impact is substantial.

Goals of deep tech

Deep tech intends to push the boundaries of what is technologically possible and create results that have a positive impact on humanity.

Some of the major goals of deep tech include the following:

• Push boundaries. A key intention of deep tech is to establish new benchmarks and thresholds for cutting-edge and advanced technology.
• Make scientific breakthroughs. Many deep-tech startups originate from academic research that guides promising lab discoveries with societal impact to commercial viability. In many cases, deep tech looks to turn science fiction into fact.
• Solve big problems. Deep tech aims to tackle complex and challenging issues with advanced technologies and breakthrough innovations.
• Enable change. Deep tech seeks to develop foundational technologies that drive transformational change across industries, businesses and society as a whole.
• Create new types of business applications. By advancing fields such as quantum computing and material sciences, deep tech lays the groundwork for new products, services and revenue opportunities.

Challenges for deep tech

Deep-tech startups face distinct challenges when compared with high-tech and consumer internet companies. The following are among the obstacles for deep tech:

• Long R&D cycles. Deep-tech initiatives can take many years from initial research to commercial viability, requiring stamina, patience and long-term vision from founders, staff and investors.
• Substantial capital requirements. Major upfront investments are needed for expensive R&D, as well as testing, before deep-tech developers consider the revenue potential that yields a return to investors.
• Talent acquisition. The workforce in deep tech requires advanced expertise and skills in complex fields such as quantum computing, robotics, genomics and AI. Competition is intense for this scarce talent.
• Regulatory landscape. Highly regulated industries such as aerospace, pharmaceuticals and energy require deep-tech startups to work closely with government bodies in navigating complex regulations.
• Customized production. Constructing something that has never been built before often requires creating highly customized production capabilities from scratch.
• Educating enterprise customers. Business-to-business deep-tech startups have the added challenge of educating prospective business customers on the value of emerging, unproven technologies.
• Long adoption cycles. Even after the product is commercially ready, deep-tech initiatives typically face slow adoption as users acclimate themselves to processes that differ greatly from traditional high-tech advancements.
• There is no promise of success. With deep-tech efforts, there is a nontrivial risk of a potential failure even after years of R&D and investment. Some deep tech does not culminate in a scalable product due to unforeseen scientific constraints or other technical challenges.

The future of deep tech

Deep tech is all about potential. Scientific breakthroughs that shape the future continue to drive deep tech's growth prospects.

With the widespread and continuing adoption of generative AI technologies -- following the lead of OpenAI's ChatGPT -- there is no shortage of entrepreneurs and researchers looking to further capitalize on AI's future. It's a future that points to artificial general intelligence and its advantages over generative AI.

Quantum computing is also in early development. Vendors and researchers continue to push the boundaries of physics in a bid to create useful and practical systems that can solve real-world problems.

Equally important, some deep-tech startups focus on developing clean energy systems, carbon sequestration techniques, waste management initiatives and other environmental sustainability technologies to address pressing climate change issues.

Another area likely to see massive disruption is healthcare, as deep-tech innovations in biotech and AI convergence enable more personalized medicine, sophisticated diagnostics and significantly broadened treatment options.