Deep tech requires 40% more capital - and a product mindset

Deep tech startups are transforming entire industries, but the biggest hurdle is not the technology; it is the step from lab to scalable product. In their white paper, The Value of Product Mindset in Deep Tech, Ivana Sersic and Jurgen van Eck (BOM) demonstrate how to bridge that gap.

Deep tech is not software with a sensor attached; it is a technology that leverages sensors. It begins with a breakthrough in science or engineering, has lengthy development times, and requires substantial capital before achieving even one repeatable sale. In the white paper, Sersic and Van Eck characterize deep tech as “high-complexity hardware-based propositions” with “above-average resource demands (>€10M)” and lead times of “>5 years.” They refer to research showing that deep tech startups need approximately 40% more capital to reach the market than traditional tech startups.

Deep tech entrepreneurs need practical tools to get from invention to product. “Many start-up deep tech entrepreneurs develop technologies that are unique in the world,” says Sersic. “But that's not the same as building a product that customers want to buy and that can also be produced on a large scale.”

Van Eck adds the reality to this: “Deep tech is fundamentally different from software, for example. Development times are longer, investments are higher, and industrialization is much more complex. That makes the risk of delay or failure relatively high.”

From technology to product

The core of the white paper: without a product mindset, you will get stuck. The authors describe how deep tech ventures fail time and again for two reasons: insufficient market validation (technology push without a clear problem) and underestimation of product realization and industrialization. Their advice: organize an integrated NPI (new product introduction) process early on that connects three tracks: technology risk reduction, product development, and commercial validation with real users and specifications.

The team plays a crucial role in this regard. According to the white paper, there are three core roles that cannot be outsourced: product management/business development, system architecting, and project management. Together, these form the “product leadership triangle” that continuously balances customer value, manufacturability, and cost price. “None of the triangle roles are ever to be outsourced,” write the authors; they must be “full-time, dedicated” to the venture.

“A wonderful solution without a customer”

In a recent presentation at Brainport Industries Campus, Sersic hit the nail on the head. “It's great that there's a technology, but it's nothing without a customer,” she said. “You have to think in terms of a product; a product that is part of a proposition.” According to her, sticking to an R&D mentality leads to “a wonderful solution without a customer”: teams prove that the technology works, "but forget to test whether there is actually a market for it.”

Even if that market shows interest, entrepreneurs often underestimate the importance of industrialization, Van Eck adds: the product must be “reliable, scalable, and affordable” to produce—and that requires early involvement of experienced product managers and system architects in the core team.

Why deep tech seems “slower” – and how to save time

The white paper elaborates on the deep tech life stages (customer discovery → customer validation) and adds an extra milestone that often remains implicit in software: “product delivery & manufacturability proven”. Where software uses MVPs to gather customer feedback in short sprints, deep tech takes longer: you need demonstrators, Alpha/Beta systems, validation from launch customers, supply chain security, and service plans before you can move on to series production. Failed validation can easily cost an extra 12–24 months and millions of dollars in additional capital, according to an analysis of over 40 ventures.

According to Sersic and Van Eck, you can save time by organizing “first-time-right”: systems engineering (V-model, concurrent engineering), Design for X (manufacturability, serviceability), and consistently translating customer requirements into product specifications; a” systems engineering (V-model, concurrent engineering), Design for X (manufacturability, serviceability), and consistently translating customer requirements into product specificationsl from day one.

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Brainport as a testing ground – but pay attention to the scaling phase

In her talk, Sersic outlines the practice of photonics as an example. The Netherlands has a strong supply chain for manufacturing, assembly, and packaging of photonic chips, “but applications and access to that market may be more prevalent in America.” As a result, valuations there are often higher, and the path to a volume market is shorter. According to her, standardization, design houses, and system integration are prerequisites for moving from “low/medium volume, high-mix” to scale.

The region does have access to early financing, but the scale phase remains a bottleneck: after the “lab-out” phase comes a capital- and time-intensive period to transition from prototype to product line. It is precisely there that few investors have the necessary staying power, according to Sersic.

What founders and investors can do tomorrow

1. Build the team around the product. Ensure that the product manager, system architect, and project leader are senior and in-house. Outsourcing may seem tempting, but it leads to delays and loss of ownership.
2. Define a single product roadmap that integrates technology derisking, engineering, and industrialization, with explicit stage gates and evidence for each phase (alpha/beta validations, manufacturing plan, service plan).
3. Validate the market with data, not intentions. Deep-tech customers want to see specified performance, costs, and delivery reliability; organize demonstrators and pilots that demonstrate this.
4. Be honest about the capital curve. Expect longer lead times and higher capital requirements (≈approximately +40% until market introduction), and adjust your rounds and milestones accordingly.

Sersic sums it up: “No one needs to reinvent the wheel. There are best practices, processes, and roles that have already proven themselves. By leveraging them and sharing knowledge, we increase the chance that deep tech innovations will not remain stuck in the lab, but will actually reach the market and make an impact.”

White paper: The Value of Product Mindset in Deep Tech — Ivana Sersic-Vollenbroek & Jurgen van Eck, Brabant Development Agency (BOM).