Key features of Deep Tech
Deep Tech is better thought of as a horizontal rather than a vertical—covering a number of sectors (e.g. medicine, biotech, energy, carbon capture, and material science), and technologies (e.g. machine learning, quantum, spatial computing, robotics, computer vision), and so on.
Here are some key features that encapsulate ‘Deep Tech’:
- Breakthroughs rooted in science or engineering—usually the result of extensive research and development, emerging from labs or universities
- Technology first—often only once the technology has been developed will founders identify an exact problem for it to address, rather than the other way around (‘solution in search of a problem’)
Visualisation of Jeevan's direct air capture technology
- Enabling technology—something that is a building block for other technologies, rather than an application technology that is restricted to one particular purpose
- Often blends hardware and software—not always the case, but deep tech usually involves combination of software/digital technology alongside a physical, real world element
Considerable differences stand out when comparing deep tech and digital tech. In some instances, these differences offer promise and opportunity; in other areas, a challenge to mount. Here, we break down these key differences between deep and digital tech.
The opportunities
Scope & scale
One opportunity around deep tech lies in its scope. Digital tech usually addresses existing problems with existing technology (e.g. doing X more efficiently with Y), while deep tech is focused on the creation of new markets. Potential upside, therefore, could be considered to be much greater.
Deep tech is often focused around ‘impact’—whether that’s making positive impacts on the climate, on people’s health or livelihoods, or any other improvements—meaning the scope for success is not just around capturing a huge market, but often around solving huge socio-economic or environmental problems.
Lower market risk (in exchange for higher technological risk)
Considering the lower barrier to entry, market risk is a considerable factor for digital tech. In deep tech, risk is largely focused around technology (e.g. can you make it work?); conversely, market risk is much smaller. Fundamental technologies tend to go after big, established markets with products that, if you can make it work, promise serious value capture in cost savings or revenue gains.
Moreover, deep tech tends to be shielded from market downturns: while fintech saw a funding collapse in 2023, slumping around 70%, deep tech investment remained stable (around $15BN in 2023, nearly matching 2022 levels). This speaks to the persistent market demand for technologies that can do things faster, cheaper, and better (and often greener). Lakestar investor Lukas Leitner refers to deep tech as a “hedge against momentum investing”.
Take Ki Hydrogen (FF24), focused on producing green hydrogen from waste biomass, a process that is far less costly and energy intensive than existing alternatives. They’re addressing a well-established market (hydrogen production), so if they can produce cheaper hydrogen, demand seems inevitable. Not just demand, but large scale demand: the types of customers these companies tend to deal with fall into the industrials, healthcare, or public sector categories.
Different growth dynamics—with promising financial returns
Deep tech sceptics traditionally focused on the weaker unit economics of hardware investments. But financial results of deep tech companies—the likes of NVIDIA, SpaceX, and Tesla, to name a few—demonstrate that there are attractive rewards for tackling these problems.
Growth dynamics for deep tech look quite different to digital tech. Rather than the typical ‘hockey stick’, it’s more ‘zero to huge’—growth is irregular, but the end point can be just as rewarding, if not more so. This is one common misunderstanding about deep tech: looking for ordinary financial and growth signals may miss the true value and direction of a business.
Clearer route to exit
Deep tech has a natural endpoint—selling out to larger manufacturers, or through sector-specific acquisitions—as a result of the greater ongoing cost of manufacturing and diminishing returns over time. As Creative Ventures GP James Wang says: “Deep tech tends to sell out faster, SaaS tries to take over the world.”
This means deep tech is potentially faster to exit, an attractive prospect for both founders and investors. Pitchbook data suggests that deep tech ventures are 6 - 24 months faster to exit than digital tech; and while the absolute number of exits is smaller, the percentage rate of exits is almost double that of digital tech. Why is this? One hypothesis is that deep tech’s exit value lies in their IP, which it establishes very early on, while digital tech’s value is in its traction, which takes longer to accrue.
The challenges
The distinct challenges around deep tech can be categorised into four clear categories.
Talent
The personnel and expertise required to build in deep tech creates a much higher barrier to entry. Take Peptone (FF18), a biotech company that has built an AI engine for protein drug discovery. Founder Kamil Tamiola, himself a PhD in biomolecular science, has built a team with deep expertise not just in their specific field of disordered proteins but in pharmaceuticals, AI, and biotech. Not only is finding this hyper-specific talent challenging, it requires considerable energy (and significant funding) to persuade them to join.
For this very reason, however, deep tech has considerably lower competition risk. Technologies are fundamentally harder to build, meaning that assembling the right talent and overcoming technological risk gives deep tech an inherent defensibility that digital tech doesn’t have.
Funding
For many investors, funding deep tech requires a large leap of faith. Not only is deep tech far more resource and capital intensive (requiring greater upfront investment) than digital tech, it comes with higher technical risk and longer payback periods.
This sentiment has certainly changed given the success of some of the world’s biggest deep techs—but it is still often a risk too far for your typical investor. Founders are better off engaging with seasoned deep tech investors who better understand these risks, timelines, and dynamics.
Data does suggest that this funding discrepancy narrows over time, showing that deep tech companies take the same amount of time to reach $1m in revenue (approximately 2 years) and just 11% more capital to reach $10m in revenue.
One caution is the funding ‘valley of death’: the point at which you need considerable upfront investment to start manufacturing a physical product (or with AI, training your model). There are ways to navigate this—co-development with an industrial partner or existing manufacturers, or even exploring a tech licensing model—but this still carries considerable risk.
Product development
Product development is starkly different for deep tech. Firstly, much more work is required on discovery. If you are starting with a technology, rather than a problem, this requires considerable effort from the start to narrow down your focus.
Unlike the usual ‘build, test, measure, learn’ cycles that you go through when building software, deep tech is more drawn out, with far more uncertainty. It is much more linear (represented through the Technology Readiness Levels metric), as opposed to typical cyclical testing and iteration processes.
Reaching your point of MVP is much further along for deep tech than it is for digital tech. This is because it is much harder to optimise things once a product is in production, and even harder once a prototype is out there and needs to be recalled and improved.
GTM
Once you’ve developed your product, GTM is much more complex. When you are creating a new market, you have the benefit of novelty: but there is also no existing market and no comparable competitors. You can’t target existing users if there are none.
This also reveals a bigger challenge around deep tech, that it is not built with a buyer in mind. Novel technologies are harder to articulate, especially when they have a highly technical foundation that requires translation into ‘human’ language. Deep tech is typically more than just an ‘optimisation of X’, requiring more education when selling to potential customers.
Deep tech’s potential to reshape industries and solve global challenges hinges not just on scientific breakthroughs but on the ability to commercialise them effectively. Unlike digital tech, where scaling is often fast and iterative, deep tech founders face unique hurdles—longer development cycles, capital-intensive scaling, and the need to educate markets on novel technologies.
Over this series, we’ll explore why mastering the commercialisation process is critical for deep tech success and how founders can navigate funding, go-to-market strategies, and regulatory landscapes to bring transformative innovations from lab to market.
Our next article explores one of the first steps on your journey to commercialisation—discovery.
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