Three terms, one misconception – and billions flowing incorrectly. A short history of technological misunderstandings.

“The one line we should never forget when it comes to technology transfer.” – eM. from Arise Innovations

Why the vagueness of “tech” terms is more than a semantic problem.

If you don't know what field you're playing in, you'll never find the right rules - let alone win.

— eM.

This sentence may sound like a truism at first, yet it describes one of the biggest misunderstandings in the innovation system. Although the terms High Tech, Deep Tech, and Tough Tech are increasingly used, their actual meaning is rarely clear.

Rather, they become blurred in everyday language – in presentations, in calls for funding, in pitches.

This wouldn't be a problem if these were just labels. But in reality, these terms express different technologies – and, above all, different development logics, capital requirements, and risks. And that's precisely why this confusion isn't semantic – it's systemic.

It influences where capital flows , how funding is allocated, which startups are considered “investable” and which disappear again – even though their technology might have been our greatest opportunity.

The linguistic fog surrounding “Tech”

Let's start by looking at the language that creates this fog. Why is it that terms like "deep tech" and "high tech" are often used synonymously – even though they mean completely different things?

This is partly because they serve as buzzwords – terms that attract attention without the need for precise definitions.

But it is also partly because their origins are different:

• High tech is a term that comes from the industrial context – it typically refers to advanced, mature technologies that are state-of-the-art but not necessarily disruptive.

• Deep Tech, on the other hand, was introduced to describe technologies based on scientific breakthroughs – often with long development times, great uncertainty and deep research relevance.

• Tough Tech, on the other hand, comes from the MIT environment, especially from The Engine , and describes particularly challenging deep tech projects with physical complexity, high uncertainty and extreme capital requirements – often hardware-driven, often system-relevant – and above all with impact.

The term "Tough Tech" is still largely unknown in Europe. Yet it describes precisely the technologies we need to solve our biggest problems – from energy to health to resilience.

Why the distinction is not academic

This vagueness would be unproblematic if it had no consequences. But the opposite is true. By equating "tech" with a general innovation label, the following happens:

• Capital is allocated to the wrong categories

• Funding programs formulate vague requirements that do not fit the reality of the technologies – or the other way around!

• Startups are measured by the same KPIs , even though their fundamentals are completely different

• Founding strategies are distorted because the narratives are adopted from the software world

Or in other words:

You can't mobilize trillions for technologies you don't understand.

— eM.

And we still don’t understand many technologies – structurally speaking.

Two startups, two realities

Let’s imagine two startups.

• The first is developing a platform for optimizing B2B communication. It has a clear target audience, tests with MVPs, measures user retention, and can demonstrate its PMF (product-market fit) relatively early on.

• The second is working on a new functional material for hydrogen storage. There are no clear markets, feedback cycles are long, and validation requires laboratories, equipment, infrastructure, and partners...

Both are referred to as "startups." Both are required to submit business plans, market analyses, and pitch decks. Both are required to apply for the same funding programs.

And both are intended to convince investors that they are “scalable.”

What makes sense in the first case becomes a farce in the second. Here, equal treatment becomes a mistake.

Systemic consequences

These errors in thinking are not isolated – they have systemic consequences:

• Capital flows preferentially into what seems understandable, not into what is technologically necessary

• Founders with scientific depth lose confidence in the startup system – and return to research or industry

• Funding programs fall short because they rely on standard formats and KPIs instead of real development logic

• VCs expect traction, TAM and PMF, when in reality it should still be about TRL, laboratory data and scientific readiness

The result: a system that claims to be technology-open – but in reality favors technologies that fit into traditional evaluation grids.

This confusion of terms is no accident. It is a symptom of a system that attempts to interpret technology economically without understanding it technologically.

But if we want deep and tough tech to have an impact – in climate protection, energy, health, sovereignty – then we must start speaking more clearly.

In the next chapter, we will look at what these terms really mean – and how they differ:

So, what is truly deep tech – and what isn't? The answer, not the pitch, determines fundability.

— eM.

What exactly is Tough Tech, Deep Tech, High Tech?

And why this distinction determines fundability.

Why it is important to clearly separate terms

Anyone who seriously wants to bring technology into the world – with capital, with partners, with impact – needs more than good ideas. They need a shared understanding of the rules of the game. And that starts with language.

Terms like high tech, deep tech, and tough tech are encountered today at almost every innovation conference, in funding calls, and on pitch decks. However, their meanings are rarely clearly differentiated. Much more often, they are used strategically to make projects appear more attractive – regardless of the depth of the underlying technology.

What initially appears to be a marketing problem has real consequences. Strategies fail – not because the technology is bad, but because it was misclassified from the start.

The distinction between these terms is therefore not an intellectual end in itself. It is the basis for how technology is evaluated, financed, and brought to market. And it determines whether a project is understood in depth – or remains superficial.

High Tech: The classic that is often overrated

High tech sounds like progress, cutting-edge, and the future. In reality, it often describes the exact opposite: technologies that are already well established and marketable.

That doesn't mean they're unimportant—far from it. But they play a different game: They're not based on scientific breakthroughs, but on incremental improvements. Faster. Smarter. More user-friendly.

Examples are numerous:

• the further development of e-mobility in 2024

• a new predictive feature in a SaaS application

• an even faster 5G chip

• Another AI for marketing and sales...

The market is well-known. The application is clear. Feedback is fast. The ROI is predictable. No wonder investors prefer this playing field: It's transparent and scalable.

But this is precisely the trap: High tech is often celebrated as "innovative" – but it's usually just faster growth in existing markets. That has its place. But it's not the playing field on which the most complex and potentially transformative technologies emerge.

Deep Tech: When research meets entrepreneurial thinking

Deep tech is a different universe. Here, technologies arise not from user needs, but from scientific findings . The starting point is not the app – but the laboratory. The questions are more fundamental. The uncertainties are greater. The timescales are longer.

A deep tech startup doesn't just develop a product. It enters previously uncharted territory:

• Quantum computing

• novel enzyme platforms

• Batteries that function completely differently at the molecular level than existing solutions

The problem: These technologies are difficult to capture with traditional KPIs. There's no retention rate for superconducting materials. No lifetime value for bio-based sensor technologies. No MVP for quantum sensing.

And yet that is exactly what is required of them.

Deep Tech would need a completely different way of thinking – one that is not based on product-market fit, but on technology-market fit.

The question isn't, " Is there a market for this today?" It's, " What markets could emerge from this technology? And how long will that take?"

— eM.

Tough Tech: The premier class – and the blind spot

Tough Tech begins where deep tech meets real-world complexity. These are technologies that are not only scientifically deep, but also physically, infrastructurally, and capital-intensively challenging.

These projects often fall beyond traditional scalability. They are difficult to predict. This is precisely why they are often excluded from existing financing models.

It is precisely these technologies that could have the greatest social impact:

• Superconductors at room temperature

• thermoelectric generators with atomic efficiency

• Long-distance quantum communication

Tough tech is n't just disruptive – it's existential. And it's the opposite of "investor-ready." It's demanding. It requires time, trust, and a completely new understanding of capital architecture.

And that's exactly what's currently getting lost in the system. Not because it's bad, but because it doesn't fit into PowerPoint.

criterion	High-tech	Deep Tech	Tough Tech
Technological focus	Incremental improvement of existing solutions	Scientifically driven, disruptive	Scientifically + physically complex, system-critical
Context of creation	Industry, market logic	Research, basic science	Research + infrastructure needs, long periods
Development time	Short to medium	Medium to long	Very long (5–15 years)
Capital requirements	Growth capital, clear ROI	R&D capital, often difficult to transfer	Capital architecture over 10+ years, high CapEx
ROI expectation	12–36 months	3–7 years	>10 years possible
Feedback cycles	Fast (weeks/months)	Medium (months to years)	Slow (years)
Risk type	Market and competitive risk	Validation risk	Validation + Physics + Infrastructure
Investor logic	Clearly scalable, easy to explain	Complex, requires explanation	Often labeled as “uninvestable”
Typical examples	E-mobility, SaaS with AI, predictive analytics	Quantum optics, enzyme technologies, new battery materials	Superconductors, long-distance quantum communication, novel generators
Challenge	Speed, competitive pressure	Uncertainty, lack of benchmarks	Systemic maturity, missing capital models

Why this differentiation is so crucial

When we begin to understand technologies along their depth, uncertainty and system dependency, it becomes clear: there is no one-size-fits-all solution.

Capital strategies, scaling paths, and market entry models must be aligned with the nature of the technology – not with copy-paste models from the platform economy.

The differences between high, deep, and tough tech aren't mere details. They are crucial to whether an idea survives.

In the next chapter, we'll look at what happens when we ignore this very thing. When a pitch deck has to serve as a substitute for strategy – and why deep and tough tech repeatedly fail because of this misconception.

Because the real pain begins when you measure technologies with metrics that were never intended for them.

The funding misconception – a pitch deck is not a financing strategy

Or: Why a million euros can be exactly the wrong thing.

There's a persistent narrative. It comes from the world of startups, accelerators, and investor panels, and it goes something like this:

If you have a good idea, you need a convincing pitch deck.

The pitch deck brings investors into the picture. The funding from the first round accelerates product development, delivers initial KPIs, creates traction—and opens the door to the next round.

It's classic Silicon Valley logic. It works – and works brilliantly. But only where it originated: in the context of digital products with a clear market , fast feedback loops, and scalability expressed in servers and app downloads.

But what happens when we apply this logic to technologies that follow completely different laws of nature? Technologies that were born not from user needs, but from laboratories, formulas, uncertainties, and molecular hypotheses?

Then this story becomes a dangerous misunderstanding.

When Deep Tech is evaluated with SaaS metrics

Deep tech projects rarely operate within an existing market. Often, they define that market for the first time—or challenge the basic assumptions on which existing markets are based.

But this is exactly where the problem lies:

When investors apply the same criteria as for software startups – i.e. clear TAM, rapid growth, 18-month ROI – then two completely different realities collide .

Because in the world of deep tech,

The market may not even exist yet. The return isn't three years, but ten. Validation isn't an A/B test, but rather a years-long, iterative research process.

What is considered "risk" in software—uncertainty, latency, vagueness—is a structural component of the process in deep tech. It can't be "pitched away." Nor can it be formulated away.

When deep tech startups try to fit this logic, exactly what happens so often happens:

The technology is artificially simplified , the narrative smoothed out, the potential exaggerated – just to fit into a framework written for other games.

And if that doesn't work? Then the project is considered "non-investable."

The Pitch Deck Illusion

The belief that a good pitch deck is the key to funding has become deeply ingrained – even in research-based startups. They practice, refine, and coach.

But a pitch deck is communication. It's not a concept. It's not a plan. And it's certainly not a capital strategy.

What many don’t see:

It is not the deck that determines the viability of financing, but the structure with which capital, technology, time and market interact .

Three misconceptions that slow down Deep Tech

The current funding system suffers from three deeply rooted misunderstandings.

First, there's the assumption that early capital automatically means security. For research-oriented startups, early capital can even be toxic—especially if it brings with it expectations that the team can't fulfill without compromising the technology.

Second, the belief that investors just need to be convinced. In reality, a technology-compatible capital plan is often lacking. Without it, even the most convincing deck remains a shell without substance. It appears either exaggerated or vague.

And third: the idea that grants can replace investor capital. Grants are not a stopgap measure. They are building blocks in a capital architecture that must be well-timed, strategically combined, and designed to be compatible with future developments. Otherwise, they provide short-term relief – and in the long run, they will at best lead to a nice publication.

What is really needed

The truth is: Most deep tech startups don't fail because of the technology. They fail because of faulty capital logic.

They receive funding too early—before a solid technological proof of concept is available. They are forced to meet milestones derived from a business plan, not a validation process. And they operate within a capital structure that leaves neither room for uncertainty nor offers timely backing.

What is needed instead is a different way of thinking.

A capital architecture that is designed from a technology perspective – not just from a ROI perspective. Financing paths that are not linear , but multi-level and modular. Strategic partnerships that combine expertise, infrastructure, and capital.

And a narrative that allows uncertainty to be made visible without losing trust.

The sentence everyone should remember

Funding ≠ Strategy. A million euros won't help you if you use it to scale the wrong thing.

— eM.

And now?

In the next article, we will show what a capital architecture can look like in concrete terms – and why, especially for Tough Tech, it is not an option but a condition for survival.

Because if you want to build technology that changes entire systems, you can't be guided by models that were never designed for that purpose.

💡 Ready to stop pitching and start strategizing?

→ Click here to see our Deep Tech Funding Pipeline

______________________________________________________

💜 Because Science doesn't follow Rigid Business Logic

eM. from Arise Innovations

— Your only partner for deep tech fundraising through reverse engineering

🔗 LinkedIn | 🌍 Website | ✔️ Read success stories