What is deep tech?

An endless mirror reflection with purple LEDs around the frame, to represent deep tech.
(Image credit: Getty Images)

Ask ten people "what is deep tech?" and you’ll be given ten different definitions. Also known by names including hard tech, tough tech, or frontier tech, the term deep tech is used to describe technological innovations at the very frontiers of science, which could revolutionize their field and overcome significant challenges.

A CGI representation of passkeys. A key with a biometric thumbprint on the handle is on a blue isometric grid background, surrounded by flying blocks glowing with red light and bearing the images of padlocks, keys, and shields.

(Image credit: Getty Images)

What do passkeys mean for your business?

These types of technologies require patience and large investments to take off, with one recent success story found in OpenAI and its groundbreaking tool ChatGPT. Heavy investment has been necessary to buoy OpenAI through its early years as it brought generative AI into the public eye and deep tech firms often require years of up-front investment before their products make a return.

To best understand deep tech, we’ll need to look at the other types of tech surrounding it. MIT Management Global Programs researchers consider deep tech an offshoot of innovation-driven entrepreneurship (IDEs) within companies such as IBM, Meta, and Microsoft. 

One of the biggest criteria is actually the innovation part of the business description. OpenAI represents deep tech in that it pioneered generative AI tools such as ChatGPT, whereas a previous unicorns such as Airbnb don't meet the criteria because their model was a new spin on an existing framework (in this case, bed and breakfast bookings).

What is a deep tech company?

Research firm Boston Consulting Group provided this criteria for a deep tech startup

  • The firm is part of an innovation ecosystem such as universities, angel investment collaboratives such as Y Combinator, or R&D arms of Big Tech companies such as Meta, Google, Microsoft, and Apple.

  • Long development cycles are another sign of a deep tech company. While there’s no specific number or range to quantify ‘long’ according to BCG, most deep tech companies have 10- to 15-year development cycles.

  • Firms that are problem or mission-driven often turn out to be deep tech companies. Whereas the Ubers of the world tout a unique way of approaching a market with existing technology, deep tech startups have an altruistic element.

  • Technological, scientific, and/or engineering discoveries are hallmarks of deep tech firms. 

A table with the title "Is it a deep tech company?" in which criteria such as whether a firm is problem-oriented is matched against the firms Salesforce, Magna5, and OpenAI.

(Image credit: Lisa Sparks)

Investors fight for this clarity of definition to justify investments and mitigate or predict risks. A clear definition attracts more deep-pocket investors, thus furthering the causes deep tech startups hold dear.

Is AI deep tech? Deep tech examples

Here’s a quick rundown of the most often mentioned forms of deep tech today.

Artificial intelligence

Based on what we’ve seen with the advent of generative AI, technologies seen as novel can jump to the forefront of our lives in a matter of weeks. Think back to when AI chatbots first became big in 2022. Many were amazed at what ChatGPT could do, setting off a chain reaction of fear, exorbitant investment (such as Microsoft’s $10 billion investment in OpenAI) in AI-focused startups, and tiny cottage industries around teaching, certifying, and simplifying AI. 

Machine learning

In essence, machine learning (ML) remains a cornerstone of AI development. Without it, the predictive text in search engines or language translation apps wouldn’t be at your fingertips. ML ‘learns’ in three ways: supervised, unsupervised, and reinforcement. All three share the core elements of ML which include data sets, algorithms, and refinement (what we would call training).

Quantum computing


Leverage Artificial Intelligence and Machine Learning with Zscaler Digital Experience (ZDX) whitepaper

(Image credit: Zscaler)

Find out how you can Improve end-user experiences and reduce ticket escalations


This corner of deep tech has been the problem child of the industry since its inception, offering great promise and heart-rending disappointments. Quantum computing can outperform classic computing by many orders of magnitude as it unlocks simultaneous computation by pushing the boundaries of physics.

Quantum computing has applications in biotechnology, where quantum computing can be used to track and even predict the behavior of diseases such as cancer, as well as to greatly accelerate machine learning and AI. Quantum computing systems are still some years off as prototypes continue to throw both predictable and unpredictable errors linked to quantum bits (qubits). But quantum computing could be closer to reality than many think and UK experts are already calling for a quantum technology taskforce to guard against the risks posed by this new deep tech area.


Outside of flashy, sci-fi realizations of robots, autonomous systems have practical applications such as completing tasks in areas hazardous to human life. For example, repairing electrical equipment carries a high risk of shocks and arc flashes. Cutting-edge robotics is regularly shown off at the annual Consumer Electronics Show (CES) event.


The original purpose for the blockchain, prior to cryptocurrencies, was to provide transparency and an iron-clad, unchangeable record of financial transactions thereby democratizing financial data.

At its core, blockchain serves to create a ledger, recording transactions (of any kind) and business assets. The use cases expand to both the development of contractual agreements and cybersecurity risk tracking and prevention. 

What's next for deep tech?

While we’ve listed the most well-known use cases for deep tech, the applications of the technology are endless. Other deep tech use cases include:

  • MedTech: breakthrough pharmaceutical discoveries using a combination of Generative AI and cognitive platforms; robotic surgery systems, customized drugs based on one’s DNA, and smart wearables to track and predict health outcomes.

  • Sustainability: breakthrough green energy technologies, tech to further adaptation to climate change, and carbon capture technologies.

  • Cyber security: advanced threat research and predictive analysis, secure lending between individual parties rather than financial institutions, and AI cyber security.

  • AgriTech: custom-designed proteins and production of those proteins at scale, precise and predictive agriculture to ensure the best crop selection and location, innovative fertilizers that are effective at limiting pests and are unharmful to other animals and humans.

Fashion technology and education technology also garner some investment from the deep tech investment sector. 

Some deep tech startups will go the way of OpenAI, effectively commercializing deep tech. But many deep tech companies face an uphill struggle in bringing their solutions to market, as even the most interesting innovations need a compelling use case to attract investors.

Lisa Sparks

Lisa D Sparks is an experienced editor and marketing professional with a background in journalism, content marketing, strategic development, project management, and process automation. She writes about semiconductors, data centers, and digital infrastructure for tech publications and is also the founder and editor of Digital Infrastructure News and Trends (DINT) a weekday newsletter at the intersection of tech, race, and gender.