Deep tech is a term used to describe highly sophisticated technology rooted in advanced scientific principles and engineering innovations. Also referred to as hard tech, deep tech builds on years of extensive research and development, as well as significant technical expertise, making it distinct from more mainstream or consumer-focused technology, otherwise known as shallow tech.
What Is Deep Tech?
Deep tech refers to cutting-edge technologies that build on advanced science and engineering innovations to bring disruptive new products to market.
What Is Deep Tech?
Deep tech, or deep technology, describes the work of companies developing innovations that surpass technological benchmarks and push the boundaries of current technology. It’s worth noting, however, that what qualifies as “deep tech” continually evolves over time. As scientific and technological advancements progress, the threshold for what is considered cutting-edge and revolutionary shifts.
While deep tech companies are often involved in fields like artificial intelligence, biotechnology and quantum computing, the category also includes companies operating in agriculture, aerospace, green energy, mobility and more. Some have become household names, like Moderna, Tesla and Impossible Foods. Others are bringing into reality what was science fiction just a few years ago — SpaceX’s Starlink internet satellites, for example, or Boston Dynamics’ humanoid robots.
Regardless of the industry, deep tech companies create products that tackle some of the most pressing challenges facing our species and planet today, including climate change, illness and food insecurity. Investors have noticed: Deep tech received the most venture capital funding out of any sector in 2024, and is expected to earn the most funding in 2025 behind only AI and machine learning, according to a VC Lab report.
“When we have deep tech startups in general, we know that we are making the world a better place,” Arnaud de la Tour, the CEO of deep tech accelerator Hello Tomorrow, told Built In.
How Is Deep Tech Different From Other Technology?
1. Deep Tech Requires More Research and Development
Deep tech requires large investments in research and development, as well as a much longer time horizon for ROI and an understanding that some projects may prove to be commercially — or even scientifically — unviable. Traditional tech companies, on the other hand, tend to apply established engineering techniques to address existing market needs, working with existing software frameworks in ways that are easy to commercialize.
After all, you can develop a mobile app and have it up on the app store in a few months, but building a new low-Earth-orbit satellite, for example, takes much longer, de la Tour said. “They need to go through a significant R&D phase before reaching the market and getting some revenue from paying customers.”
2. Deep Tech Requires Specific Talent and Expertise
Deep tech companies also require extremely skilled people with very specific areas of expertise — and these potential employees are often in short supply. For example, one of the companies deep tech investor WNT Ventures is funding is trying to hire electrochemists with a particular set of skills that fewer than a dozen people around the world actually possess, according to managing partner Maria Jose Alvarez.
3. Deep Tech Faces Less Market Competition
The good news is, when a deep tech company does go to market, it is pretty well insulated from any real competition, Tess Hatch, a partner at venture capital firm Bessemer Venture Partners, told Built In. “While there’s more capital that’s required and a longer feedback cycle, you really make that up in a deep and wide competitive moat — in patents, in technology and in your team.”
More traditional companies working in areas like SaaS and consumer tech don’t have this competitive advantage, because their technological advancements are much more easily replicated. For example, Netflix disrupted the entertainment industry, but it now competes with Hulu, Amazon Prime and pretty much every television network. And Meta basically invented modern-day social media, giving way to sites like Instagram, X and TikTok.
4. Deep Tech Measures Success by Reaching Milestones
Traditional tech companies measure their early-stage success or failure based on metrics like customer acquisition and churn. Meanwhile, early-stage deep tech companies evaluate their success based on technological milestones they may have hit, or the number of patents granted that strengthen their intellectual property portfolios. Depending on the industry, obtaining regulatory approvals and complying with regulations can also be a critical measure of success.
In the end, perhaps the most important metric in the deep tech industry is its ability to push humanity forward. The goal of deep tech is to create solutions that inspire further discovery and benefit society, ultimately shaping the future in ways that were previously thought to be impossible.
“For a company to be true deep tech, it must open new frontiers,” Alvarez told Built In. “I don’t think there’s another way to solve the biggest issues if it’s not with deep tech.”
Deep Tech Examples (With Use Cases)
1. Artificial Intelligence
Artificial intelligence (AI) is a branch of computer science dedicated to the development of intelligent machines capable of performing tasks that ordinarily require human cognition — allowing machines to model, or even surpass, certain capabilities of the human mind. Thanks to advancements in chip technology, as well as machine learning and deep learning capabilities, AI is transforming just about every industry.
Artificial Intelligence Use Cases
Automotive: AI is the driving force behind the perception, navigation, decision-making and control systems of all self-driving cars. By continuously processing vast amounts of data from sensors and learning from experience, AI helps these vehicles operate safely and make split-second decisions on the road without the need for human intervention.
Healthcare: AI is making the healthcare industry more efficient and accurate. For example, AI algorithms can be used to analyze medical images like X-rays, MRIs and CT scans with a high level of accuracy, helping medical professionals detect diseases at an earlier stage. It is also being used to accelerate drug discovery by analyzing massive datasets to identify potential drug candidates and predict their effectiveness, reducing the time and cost of bringing new medications to market.
Education: Alongside typical learning content, AI platforms can provide engaging activities and experiences to help students better absorb information. AI tools can also track a student’s progress to deliver feedback and personalize content, addressing the strengths and weaknesses of individual students. Generative AI tools like ChatGPT can also serve as collaborators to bounce ideas off of for writing papers and completing creative assignments.
2. Biotechnology
Biotechnology is the use of living organisms, such as cells and microorganisms, as well as biological processes, to develop new products. Breakthroughs in genomics and synthetic biology — a subset of biotech that involves altering the genetic material of organisms like plant and animal cells — are being used by many deep tech companies to extend the limits of what is possible in all kinds of areas.
Biotechnology Use Cases
Pharmaceuticals: Biotech plays a pivotal part in the pharmaceutical industry, from drug discovery to production. It can facilitate the development of more personalized medicine by analyzing a person’s genetic makeup, providing more tailored treatment plans that can increase effectiveness and minimize adverse side effects.
Food Production: Synthetic biology makes it possible to produce food in entirely new ways. It is instrumental in the production of alternative protein sources, like lab-grown meat and plant-based proteins, as well as dairy-free cheese and yogurt. These products can be solutions to much larger global challenges related to food insecurity and sustainability.
Fuel Production: Using synthetic biology, microbes can be engineered to either produce environmentally friendly fuels (also known as biofuels) or optimize the conversion of biomass into renewable energy sources. For example, yeast can be engineered to ferment plant sugars more efficiently, yielding bioethanol that can be used as renewable fuel. Or microalgae can be modified to produce hydrogen gas through photosynthesis, resulting in a clean and versatile energy source.
3. Autonomous Robots
Robotics uses a combination of science and engineering to design, construct and use mechanical robots. Using artificial intelligence, these machines can operate autonomously, and often perform tasks with greater precision, accuracy and speed than any human, making them a transformative tool across a variety of industries.
Autonomous Robot Use Cases
Space: While many forms of deep tech are essential to space exploration, robots are definitely among the most important. They can collect data, snap photos and perform experiments — both by floating through space and driving around the terrain of specific moons and planets. NASA even wants to use robots to lay the groundwork for potential space colonization, such as building structures on the moon and delivering payloads once astronauts are up there.
Healthcare: Robots have taken on a variety of roles in the healthcare industry. They are used by doctors to check in with their patients remotely, aid patients in performing certain movements post-surgery and assist in minimally invasive surgical procedures. Some researchers are even delving into microrobotics, using tiny robots to help with diagnostics and treatment.
Manufacturing and Logistics: While robots have been used to automate simple tasks in the manufacturing process (assembly, painting, etc.) for many years, recent advancements in the industry are enabling these machines to do more complicated tasks and even safely work alongside humans. Once products are made, autonomous mobile robots and drones are then used to pick, pack and deliver them to consumers. Eventually, self-driving trucks might even be used to get merchandise from A to B.
4. Quantum Computing
Quantum computers use quantum mechanics to solve problems that are too large or complex for traditional computers. They essentially upend the fundamental principles of computer science, relying on qubits instead of traditional bits to process information at a much faster rate. This industry is still very much in the early stages of development and utilization. But, once quantum computers reach “quantum advantage” — where they provide some time or cost advantage over classical computers — they will have a variety of commercial applications.
Quantum Computing Use Cases
Pharmaceuticals: Quantum computers are capable of simulating molecular interactions and analyzing vast datasets, which could be used to identify potential drug candidates and make more effective treatments. For example, traditional computers struggle with the complexity of molecular research, but quantum computing’s ability to handle protein processes can contribute to the development of more effective and personalized drugs.
Cybersecurity: Quantum computing has the potential to break existing encryption methods while also offering new cryptographic techniques for secure data transmission. This could revolutionize the cybersecurity industry by better safeguarding sensitive information and protecting communication networks from cyber attacks.
Gaming: Quantum computing promises to enhance many aspects of video games, including producing higher-quality graphics, faster load times and randomized game terrain. AI also stands to benefit from quantum computing, which could result in more life-like AI-controlled characters who demonstrate more intelligent behavior.
5. Blockchain
Blockchain is a distributed database, or ledger, that processes transactions across a computer network. Decentralized by nature, blockchains rely on each computer to help verify transactions and add and update information on the blockchain. They also use smart contracts to confirm agreements and consensus mechanisms to set verification requirements, creating an immutable ledger that all users can trust and uphold.
Blockchain Use Cases
Finance: Blockchains can be used to process digital transactions, allowing users to conduct transactions via cryptocurrencies. This approach is known as decentralized finance, where users can complete transactions within a peer-to-peer network. The ability of blockchain to conduct efficient transactions also makes it useful for banks, trading firms and other institutions.
Logistics: Blockchain provides an immutable record of data that can be updated in real time, enabling logistics companies to track their shipments throughout their supply chains. Using this information, organizations can then determine the optimal routes to improve their operations. In addition, companies can complete agreements via smart contracts for the sake of transparency and to ensure all parties are held accountable.
Digital Security: Blockchain’s ability to produce and store data that cannot be altered makes it useful for cybersecurity use cases, such as protecting patient data or preserving government data. The protocols it employs also encourage users to aid in verifying transactions and rooting out bad actors. These safeguards have culminated in decentralized identity, where users can exercise greater control over their personal information.
Deep Tech Companies
There are thousands of deep tech companies operating today, shaping the future in ways that were previously thought to be impossible. Here are just a handful of them.
OpenAI has been at the forefront of AI research and innovation for years. The company primarily focuses on building generative AI products, which leverage massive amounts of data to train a system to produce text (ChatGPT), images (DALL-E) and audio (MuseNet). Looking ahead, OpenAI claims its research will eventually lead to artificial general intelligence, a currently hypothetical form of AI allowing a machine to learn and think like a human. Achieving AGI is a significant goal in the larger AI field, and has the potential to revolutionize various industries and aspects of life.
Nature’s Fynd makes alternative meat and dairy products using Fy, a fungi-derived protein that is created by fermenting a microbe first discovered in the geothermal hot springs in Yellowstone National Park’s supervolcano. Once fermentation is complete, the biomass is harvested and processed to isolate the nutrient-packed protein, which can then be blended with other plant-based ingredients, flavorings and seasonings to create a range of food products, including dairy-free cream cheese and meatless breakfast patties. Nature’s Fynd claims its approach is more efficient and eco-friendly than traditional food production methods.
Blue Origin is an aerospace manufacturing company founded by tech mogul Jeff Bezos. It is developing reusable rockets, rocket engines and launch vehicles that are designed to significantly lower the cost of space exploration, with the stated goal of making living and traveling in outer space the norm. The company is also working on a new commercial space station called Orbital Reef, which will serve as a “mixed-use business park” and tourist destination and is on track to launch by 2030.
Valo aims to transform the drug discovery and development process using AI. Its Opal computational platform combines clinical data, tissue biology and machine learning to identify common diseases among a specific phenotype, genotype and other links, and then establishes the molecule design and clinical development. The company claims its scientists have been able to use the platform to identify previously unknown associations between genetic markers and certain diseases across various neurodegenerative, oncology and cardiovascular diseases.
Using proprietary 3D printing technology, robotics and advanced materials, ICON is changing the game of architectural manufacturing. The company has developed several 3D-printed structures, from standalone homes to entire neighborhoods. The company also partnered with NASA to construct the world’s first 3D-printed rocket launch pad, and later entered into a $57.2 million contract with the government agency to research and develop space-based construction systems in preparation for expeditions to the moon, Mars and beyond.
Xanadu specializes in photonic quantum computing, a type of quantum technology that uses particles of light (photons) to perform quantum computations. The company develops free, open-source software that allows anyone to run commands on publicly accessible, cloud-based quantum computers as part of a wider push to popularize quantum computing. It is also active in quantum research to advance the field even more. In fact, in 2022, Xanadu became the first startup of its kind to achieve quantum supremacy, meaning its quantum computers were able to outperform a supercomputer in solving a problem — it reportedly took Xanadu less than a second to solve a problem that would have taken a supercomputer 9,000 years.
Coinbase ranks among the top crypto exchanges based on trading volume, giving users access to popular currencies like Ethereum and Bitcoin. While the platform offers perks like rewards and no trading fees, it’s valued just as much for its security measures. The company uses encryption techniques, multi-approval withdrawal methods and limited data collection to protect its users against blockchain schemes and attacks.
What’s Next For Deep Tech?
Deep tech fields like AI, quantum computing and biotechnology are rapidly maturing, moving from research stages to practical market applications. Eventually, Alvarez expects these applications will become more intertwined with each other. The advancement of AI has already accelerated the progress of robotics, instilling machines with improved intelligence. Now, the brains of AI could be combined with the computing power of quantum to tackle even more complicated problems, resulting in a field known as quantum AI. “Rather than them being kind of siloed,” she said, “they will more organically interact with each other.”
In a sense, these emerging technologies will bring about a new kind of industrial revolution, substantially changing how we live and work the way coal-fired steam engines or personal computers did over the centuries. Going forward, deep tech is headed toward a future of groundbreaking advancements that will reshape industries, solve complex global challenges and offer new possibilities for the benefit of humanity.
“I imagine a future where we travel to space with the frequency in which we currently travel in an aircraft. Where a drone delivers emergency medical supplies…and maybe even a late-night pizza. Where we eat meat without hurting, harming or even interacting with a single animal — and it’s still meat,” Hatch said. “These are the tangible realities of future industries.”
Frequently Asked Questions
What do we mean by deep tech?
Deep tech refers to cutting-edge technologies that are founded on advanced science and engineering innovations, requiring years of extensive research and development, as well as significant technical expertise.
What are some types of deep tech
Deep tech includes areas like artificial intelligence, quantum computing, blockchain, robotics and biotechnology.
Deep tech vs. high tech
Deep tech involves groundbreaking innovations in science and engineering, requiring years of extensive research and development to form entirely products. High tech, on the other hand, involves cutting-edge technologies that are already part of our everyday lives. It is essentially the newest, most advanced technology on the market.
Is AI considered deep tech?
Yes — AI is considered a type of deep tech due to its ability to reshape daily life and its complexity, especially when it comes to machine learning and deep learning. More advanced forms of AI also promise to take on even greater challenges.
What is deep tech vs. shallow tech?
Deep tech refers to cutting-edge technologies that entirely reshape industries, bringing about momentous shifts. They are the products of years of research and development. Shallow tech relates more to technologies that reflect incremental changes, often based on existing technologies or products and catering more to consumers’ immediate needs.
