What is deep tech? Revolutionizing industries with cutting-edge innovation

Understanding deep tech

Deep tech refers to highly advanced technologies grounded in scientific breakthroughs and sophisticated engineering. Often called hard tech, deep tech is distinct from more consumer-oriented technologies, often labeled as shallow tech. What sets deep tech apart is its foundation in extensive research, rigorous development, and specialized expertise. These technologies push the boundaries of current capabilities, driving progress in ways that more mainstream innovations don’t. The definition of deep tech evolves as new scientific discoveries and technological advancements emerge, continuously raising the bar for what is considered groundbreaking.

Deep tech companies often operate in cutting-edge fields such as artificial intelligence, biotechnology, and quantum computing. However, the sector is much broader, encompassing industries like agriculture, aerospace, renewable energy, and mobility. Some companies have become widely recognized—think of Moderna, Tesla, or Impossible Foods—while others are materializing what once seemed like futuristic ideas, such as SpaceX’s Starlink satellites or Boston Dynamics’ humanoid robots.

Regardless of their focus area, deep tech companies are united by their mission to address some of humanity's most significant challenges, including climate change, disease, and global food shortages. These ventures are not only transforming industries but also capturing the attention of investors. In the first half of 2023 alone, deep tech startups attracted $40 billion in venture capital, accounting for 20 percent of all venture capital investments by the end of that year.

As Arnaud de la Tour, CEO of the deep tech accelerator Hello Tomorrow, remarked, “When we have deep tech startups in general, we know that we are making the world a better place,” highlighting the profound impact these technologies can have on society.

How is deep tech different from other technology?

Deep tech requires extensive research and development

Deep tech is characterized by its reliance on intensive research and development (R&D). These companies often need significant investment and operate on longer timelines for return on investment (ROI). Projects may even face the risk of being commercially or scientifically unviable. In contrast, traditional tech companies usually leverage existing technologies and frameworks to address immediate market needs. For instance, developing a mobile app could take just a few months, but creating a new low-Earth-orbit satellite requires years of R&D. As Arnaud de la Tour explains, deep tech ventures undergo prolonged development phases before they can enter the market and start generating revenue.

Deep tech demands specialized talent

Deep tech ventures require highly specialized expertise, often in niche areas. These skilled professionals can be hard to find, making talent acquisition a significant challenge. Maria Jose Alvarez, managing partner at deep tech investor WNT Ventures, shared an example of a portfolio company seeking electrochemists with rare skills—fewer than a dozen people worldwide possess the necessary qualifications. This level of specialization is a core difference between deep tech and traditional tech, where talent pools are generally larger and skill sets more transferable.

Deep tech faces less market competition

Despite the longer development timelines and higher R&D costs, deep tech companies enjoy a more robust competitive edge once they reach the market. They build deep technological moats through patents, proprietary technologies, and highly specialized teams. Venture capitalist Tess Hatch points out that while traditional tech companies often face intense competition—such as Netflix vying with Hulu, Amazon Prime, and others—deep tech firms are more insulated from such rivalry. Their innovations are harder to replicate, granting them a strong market position.

Deep tech success is measured by milestones

Early-stage success in traditional tech is often gauged by metrics like customer acquisition, user growth, and churn rates. In deep tech, however, progress is measured by technological milestones and intellectual property achievements, such as patents. Securing regulatory approvals or meeting industry standards may also serve as key performance indicators. More importantly, the ultimate measure of success in deep tech is its potential to drive human advancement. According to Alvarez, deep tech ventures aim to open new frontiers and solve global challenges, making a profound impact on society and pushing the boundaries of what’s possible.

In essence, deep tech is differentiated by its focus on groundbreaking innovation, specialized expertise, and long-term societal impact—qualities that set it apart from more mainstream, rapidly-developing technologies.

Real-world applications of deep tech: Examples and use cases

Artificial Intelligence (AI) is a field of computer science focused on creating intelligent systems that can perform tasks typically requiring human cognition. These systems are designed to mimic or even surpass certain human capabilities, enabling machines to carry out complex tasks. Advances in chip technology, machine learning, and deep learning have allowed AI to revolutionize a wide range of industries.

Biotechnology

Biotechnology involves using living organisms, such as cells and microorganisms, and biological processes to create new products. Advances in genomics and synthetic biology, a branch of biotech focused on modifying the genetic material of organisms, are pushing the boundaries of innovation across many industries.

Biotechnology use cases

• Pharmaceuticals: Biotechnology plays a crucial role in drug discovery and production, enabling the development of personalized medicine. By analyzing a patient’s genetic profile, biotech can create targeted treatment plans that enhance effectiveness and minimize side effects.

• Food Production: Synthetic biology is transforming food production, making lab-grown meat, plant-based proteins, and dairy-free alternatives possible. These innovations offer solutions to global challenges like food security and sustainability.

• Fuel Production: Engineered microbes are used to create biofuels or optimize biomass conversion into renewable energy. For example, yeast can be modified to produce bioethanol, while microalgae can generate hydrogen gas through photosynthesis, offering cleaner energy options.

Artificial intelligence

AI use cases

• Automotive: AI powers the core systems of autonomous vehicles, managing perception, navigation, decision-making, and control. By continuously analyzing data from sensors and learning from real-world experience, AI enables self-driving cars to make real-time decisions and operate safely on the road, eliminating the need for human drivers.
• Healthcare: AI is improving both the accuracy and efficiency of healthcare. For instance, AI algorithms are used to interpret medical images—such as X-rays, MRIs, and CT scans—with exceptional precision, allowing for earlier and more accurate disease detection. Additionally, AI accelerates drug discovery by analyzing large datasets to identify promising drug candidates and predict their effectiveness, reducing the time and cost of bringing new treatments to market.
• Weather and climate: AI plays a critical role in weather prediction and assessing the potential impact of natural disasters. By processing data from weather sensors and satellites, machine learning models can track atmospheric changes and forecast daily weather conditions, as well as predict more severe events like hurricanes, wildfires, and extreme temperatures.

Autonomous Robots

Robotics blends science and engineering to design and build mechanical systems that can perform tasks autonomously. These robots, often enhanced by AI, are more precise, efficient, and fast than humans, making them invaluable across industries.

Autonomous robot use cases

• Space: Robots are integral to space exploration, collecting data, capturing images, and conducting experiments. NASA plans to use robots to prepare for space colonization by building structures on the moon and delivering supplies for future astronauts.

• Healthcare: Robots assist doctors in remotely monitoring patients, supporting post-surgery recovery, and aiding minimally invasive procedures. Microrobots are also being researched for diagnostic and treatment purposes.

• Manufacturing and Logistics: Robots have long been used for automating tasks in manufacturing. However, recent advancements enable them to perform more complex operations and collaborate safely with humans. Drones and autonomous robots are increasingly used for picking, packing, and delivering goods, with self-driving trucks possibly handling future logistics.

Quantum computing

Quantum computing leverages the principles of quantum mechanics to tackle problems that traditional computers cannot handle. Instead of using bits, quantum computers use qubits to process data at unprecedented speeds. Although still in its early stages, quantum computing is poised to have far-reaching applications once it achieves "quantum advantage."

Quantum computing use cases

• Pharmaceuticals: Quantum computers could simulate molecular interactions and analyze large datasets to identify new drug candidates and optimize treatments. While traditional computers handle this work now, quantum computing is expected to surpass existing methods, potentially revolutionizing the pharmaceutical industry.

• Cybersecurity: Quantum computing has the potential to break current encryption systems while offering novel cryptographic techniques for secure data transmission. This could transform the cybersecurity landscape by providing enhanced protection against cyber threats.