Deep Tech startups don't fail because of the technology - but because of the wrong funding strategy

The Big Misunderstanding in Deep Tech Funding – Why Business Logic alone Fails Here

Deep tech startups arise from groundbreaking scientific breakthroughs - be it in quantum physics, materials science or biotechnology. Their founders operate at the limits of what is physically possible. But this is precisely where the challenge lies: many believe that a superior technology automatically conquers the market. After all, they have solved a problem that no one could solve before.

But the market does not follow the laws of physics – it follows economic mechanisms. And that is exactly where many deep tech startups fail to succeed.

The Illusion of Technological Superiority

In science, the quality of a solution counts. In business, it is who is the first to anchor it strategically that counts. A more efficient battery chemistry or a groundbreaking biomaterial are revolutionary at the molecular level - but economic success is not achieved through technological superiority alone.

The best example is the video cassette: VHS became the standard, even though Betamax was superior.

Technology is not measured by how innovative it is, but by how well it is positioned, financed and industrialized. And this is the blind spot of many deep tech founders.

Technology, business model and exit – an inseparable system

In science, progress is linear: hypothesis, experiment, result. In the startup world, this order does not work – but many deep tech founders fall into exactly this pattern.

They develop their technology for years, then look for investors and only later ask themselves about the exit.

This order is fatal.

Technology, business model and exit must be viewed as a homogeneous system. Those who only think about monetization once the product is ready run the risk of developing a brilliant but unsellable technology.

• A company that relies on product sales when licensing models would make more sense will have massive scaling problems.
• If you don’t consider the exit, you might build an ingenious technology – but not a commercially viable company.

Why Classic Business Models Fail in Deep Tech

Investors and consultants from the classic business world evaluate companies based on sales, scalability and barriers to market entry. But this logic comes from a world in which products and services have no physical, chemical or biological boundaries.

But Deep Tech follows different rules:

• Sales are often only possible late – and that's a good thing. The value of a deep tech startup is not created by short-term sales, but by IP, regulatory approvals or industrial market access.
• Scaling is not linear. While SaaS startups can simply book more server capacity, a high-performance material cannot be produced indefinitely quickly. Thermodynamics, material availability and production bottlenecks set hard physical limits.
• Regulatory hurdles are a strategic advantage. Business economists see them as a barrier to entry. For deep tech companies, they are a line of defense: those who overcome regulatory hurdles early on secure exclusive market access that others only achieve years later.

Why traditional business models lead deep tech into a dead end

B2B SaaS – The simple world of predictable exits

SaaS has established itself as the preferred business model because it offers predictable revenues, low marginal costs and rapid scaling. Once developed, software can be sold as often as desired. Investors love SaaS because the financing structure is simple and the exit is clear: IPO or M&A by a strategic buyer.

Why this doesn’t work for deep tech:

• Physical, chemical or biological limitations prevent rapid scaling.
• Capital-intensive development: years of research, prototypes, industrial infrastructure.
• Value is not created through recurring revenue, but through IP, industrial application and regulatory approvals.

In short, anyone who evaluates deep tech with SaaS KPIs is either underestimating the potential of the technology or forcing an unsustainable business model.

Deep Tech Startups – Monetization beyond traditional revenue models

Many deep tech technologies require specialized research, expensive laboratories and industrial infrastructure. Without strategic sources of financing, bottlenecks quickly arise. Successful monetization models therefore look different:

• Industrial partnerships for co-development.
• State and European funding as targeted, earmarked financing for risk mitigation.
• Hybrid models with complementary services to generate early cash flow.

An exit in deep tech is not based on short-term revenues, but on the strategic value of the technology. Successful models:

• Asset deals: sale of IP, production capacities or exclusive technologies.
• Acquisition by corporates: Companies acquire technologies and bring them to market maturity.
• Licensing models: Technologies are licensed to several companies instead of producing them yourself.

Life Sciences – Monetization despite (or because of) regulation

No industry is more capital-intensive and more heavily regulated than the life sciences (still complaining about evil Big Pharma and expensive drugs?).

Biotech, Medtech and Pharma startups face the longest development cycles of all - it often takes more than a decade for a technology or drug to reach market maturity. The path to this is paved with clinical trials, extensive safety tests and regulatory approvals, which are not only time-consuming but also extremely costly (6-digit figures will probably be the minimum, pre-seed financing won't help here). At the same time, the barriers to market entry are so high that only a few players can even gain access to these markets. What is an obstacle for many investors can, however, become a decisive competitive advantage - if the business model is aligned correctly.

The biggest challenge for life sciences startups is financing. Traditional investors prefer business models with short development cycles, quick market launches and predictable sales - all of which are hardly possible in life sciences. The assumption that you "just have to find an investor" to finance the next few years of product development often fails in reality: very few traditional VCs are prepared to take on the high risk and long time horizons. At the same time, the fatal mistake of considering sales as the most important indicator of success is often made.

But in life sciences, a company can be highly profitable without ever selling a product – because its value does not necessarily lie in the sales generated.

A successful monetization approach in life sciences therefore does not consist of generating one's own revenue as early as possible, but rather of using strategic partnerships. Instead of fighting through the entire development phase until market readiness alone, many startups license their technology to pharmaceutical or medical technology companies at an early stage. This not only secures them financing, but also access to infrastructure, sales channels and regulatory expertise. Another proven strategy is the targeted development of a valuable IP portfolio: Patents can be licensed exclusively or non-exclusively or sold directly as part of asset deals, which releases capital early on without having to go the long way to product approval.

Even smarter is the development of hybrid platform models in which an underlying technology enables various applications - be it in drug development, diagnostics or medical technology. Instead of just developing a single product, the startup creates a scalable foundation that can be flexibly adapted to different market needs.

Anyone who approaches life sciences with the classic startup mindset will inevitably fail.

Disruptive technologies need disruptive monetization and exit strategies

Many deep tech founders try to structure their financing according to classic business models. Investors ask them: "What is your go-to-market strategy?", "When will you be profitable?", "How do you scale?" - and they try to give answers that fit into common business jargon.

But this is precisely where the challenge lies: Deep Tech is not software, not e-commerce and not a scalable process optimization tool.

Deep Tech is applied natural science – and it obeys physical, chemical or biological laws, not business wishful thinking.

- eM.

A chemist would never talk about "scaling" without taking into account the thermodynamic limitations of a system. A biotechnologist knows that a biological system cannot simply be optimized by "increasing efficiency" but is limited by highly complex interactions. Yet it is precisely these scientific principles that are regularly ignored in financing strategies.

Most investors and advisors are so caught up in their business logic that they do not understand fundamental technical realities – or worse: ignore them (!).

This is where the real paradigm shift lies: Not only the technology has to be innovative - but also its monetization. Deep tech startups cannot afford to simply imitate classic financing models. Anyone who tries to evaluate a lab-intensive materials research startup with the same metrics as a SaaS company is making a fundamental mistake. A system with long development cycles, unpredictable scaling paths and regulatory barriers cannot be financed with models based on short feedback loops, digital distribution and low marginal costs.

This is exactly where most consultants fail. Classic business economists analyze business models based on sales structures, growth rates and margins - but they have no idea about diffusion processes, economies of scale in material production or regulatory barriers to market entry. While an investor asks: "How high is your customer acquisition cost?", a natural scientist thinks in terms of reaction kinetics, process costs per unit and scaling limitations due to material availability. These languages are not just different - they are often incompatible.

Many consultants only offer isolated solutions to support financing: “I’ll get you grants”, “I’ll find you investors” – but without understanding the overall technological system, these solutions are worthless. A startup that relies exclusively on funding can later be unattractive to investors. A biotech company that relies on classic revenue models, even though the market is geared towards early IP transactions, is wasting potential.

The success of deep tech companies is not created through individual financing modules, but through a coherent strategy that considers capital requirements, technology development and exit potential as a system.

And this is exactly where the wheat is separated from the chaff: If you don't understand deep tech in all its scientific depth, you won't be able to develop a sensible financing strategy - you read it here first.

Conclusion: Anyone who does not understand this connection is wasting millions in potential - and impact.

Deep tech is financially viable - but not with the standard models developed for software or traditional industries. Anyone who tries to evaluate a physically or biologically limited technology using KPIs from the digital world will inevitably fail. Because deep tech is subject to different laws: development cycles are longer, scaling is not exponential, and the actual value is often not generated through direct sales, but through IP, regulatory market access or strategic acquisitions.

A well-thought-out business model therefore not only determines whether financing is possible, but also which sources of financing make sense and which exit is even possible. Startups that deal with their monetization strategy too late end up with a groundbreaking technology - but without a buyer who can realize it economically.

This is exactly where my expertise comes into play. While traditional consultants fail at surface metrics and deep tech founders often only focus on their technology, I combine both worlds strategically. I don't think in terms of sales projections, but in value creation models that are derived from physical, biological or regulatory conditions.

Because in the end, a crucial question arises:

If the technology is disruptive, why shouldn’t the financing and monetization strategy be just as revolutionary?

- eM.

What does this mean for you?

Deep Tech is in a league of its own – technologically, economically and strategically. Those who understand this and set the right course early on can not only create groundbreaking innovations, but also economically maximize what is technologically possible.

If you are wondering what this could look like for your startup, write us!

Your eM.