From Innovation to Execution: Reality of Deep Tech Financing

November 2024

For a decade, I have worked alongside European deep-tech startups, guiding them through the challenges of raising funds and scaling up. Deeptech is not easy !! Let me assure you, it’s tough. These companies are pushing the boundaries of innovation, but turning groundbreaking ideas into real-world success is where things often become complicated. This year has been particularly brutal. We’ve witnessed some prominent companies crash, highlighting the systemic hurdles these innovators face.

Take Northvolt, for example, a Swedish gigafactory startup that recently went bankrupt. They are not alone; before them, Germany’s flying taxi dream, Lilium, and the UK’s electric van darling, Arrival, both faced significant setbacks. Even Graphcore, a British chipmaker once hailed as a rising star, was sold for less than the $700 million investors initially poured into it. These stories aren’t just headlines; they serve as cautionary tales.

So, what’s going wrong? Europe isn't lacking early-stage investment; in fact, it's quite competitive with the U.S. However, when startups require substantial capital for growth, the response is minimal. Many investors here are hesitant to invest significantly during the scaling phase. I have heard repeatedly, “If only they’d moved to the U.S. sooner.” In the U.S., late-stage capital is abundant; in Europe, it feels like a desert.

However, funding isn’t the only issue. Commercializing technology is Europe’s Achilles’ heel. Companies like Graphcore and Lilium invested heavily in research and development but overlooked the crucial question: “Does the market actually want this?” Investors are getting frustrated. They will tell you directly: too many startups treat product launches as an afterthought. They focus on great science but have shaky execution.

The reality is that blaming a lack of funding or government red tape misses the bigger picture. Deep-tech isn't like apps or SaaS. It’s messy, high-risk, and slow to develop. We’re talking years to build a working prototype, let alone scale it. And the risks? They are numerous—will the technology even work? Will customers care? Most investors aren't suited for this long marathon.

Also, are we framing the issue correctly? Are we asking the tough questions? Repeating the same strategies and expecting different results is unlikely to yield success. We need to explore questions such as: How can we expand the capital pool for deep tech? How should we finance early-stage tech development, where R&D costs are inherently high? Is traditional preferred equity the right tool? Are companies going too early to VCs ? How can we ensure these companies reach the stock market, instead of being acquired at knockdown prices by Series B?

This isn't about market intervention, but rather thinking about enhancing the economic system to foster deep-tech innovation—creating incentives for various stakeholders to participate and take the necessary risks.

In the following sections, I will share what I’ve learned from a decade in this field, drawing insights from both founders striving to save their companies and investors weighing life-changing decisions plus my two cents of opinion.

Deep Tech Investing: A Transformative Approach to Innovation

Deep tech investing is all about backing companies that develop groundbreaking technologies capable of transforming industries and solving major global challenges. Unlike conventional tech investments, deep tech ventures are rooted in scientific breakthroughs and engineering innovations, often born in research labs or universities. These companies typically have strong intellectual property portfolios and keep pushing the boundaries by patenting new technologies even after they’re established. Their work spans diverse fields like energy, food production, and healthcare, fundamentally reshaping these industries.

What makes deep tech unique is its focus on tangible engineering advancements and groundbreaking scientific discoveries. While many traditional startups find clever ways to use existing technologies, deep tech pioneers create entirely new technological frontiers. These companies are often led by scientists and researchers who transform academic insights into real-world applications, with technology as the heart of their business. This tech-first mindset means they refine their innovations before identifying commercial opportunities, setting them apart from conventional ventures.

Investing in deep tech requires patience and vision. The journey from idea to commercialization is long and complex, often exploring uncharted territory where customer needs may not yet be clear. Founders typically come from strong science and technology backgrounds, which equips them to tackle complex challenges. Their focus is on achieving large-scale breakthroughs that can address pressing issues like climate change, renewable energy, and healthcare advancements.

One of the defining features of deep tech is its foundation in revolutionary technologies rather than incremental improvements. Many deep tech startups emerge from cutting-edge scientific research conducted at universities or specialized research centers. These breakthroughs take years of dedicated R&D, often resulting in patents that provide competitive advantages. Unlike traditional startups that build on established science, deep tech companies prioritize scientific advancements first, with commercial applications coming later.

Deep tech investments are distinct from traditional venture capital investments in several key ways. First, deep tech companies often develop platform technologies with applications across multiple industries, making them highly valuable but challenging to replicate. However, these companies face long development cycles, requiring more time to find product-market fit. This means investors must be prepared for extended time horizons before seeing returns. Additionally, deep tech startups need substantial capital to move from research to market-ready products, leading to higher investment risks and challenges in securing early-stage funding.

Unlike many traditional technology startups that focus on software, deep tech frequently involves hardware development, requiring specialized equipment, facilities, and talent. Deep tech innovations often operate on a global scale, serving markets beyond national borders. Investors in this space need a solid understanding of scientific advancements and a willingness to commit significant funding over long periods. Despite the challenges, the potential rewards—both financial and societal—make deep tech investing a vital part of shaping the future.

Investing in deep tech is both challenging and rewarding. Startups in this space often face what's known as the "deep valley of death," a critical 3-6 year period where extensive technological development takes place before they can prove the viability of their solutions. This stage is particularly tough because private capital is often hard to come by, making it difficult for companies to secure the funding they need to stay afloat. Organizations like the European Innovation Council (EIC) step in to provide crucial financial support, helping deep tech ventures bridge this gap and move toward commercial success.

Bringing deep tech innovations to market is resource-intensive and costly. Proving the technology and finding practical applications require significant investment, while hiring specialized talent drives up operational costs. Scaling production, particularly for hardware-based solutions, adds further expenses. The risks are also higher, as it often takes time to validate the technology and achieve product-market fit. Early-stage funding typically comes from grants, as venture capitalists are cautious about investing in high-risk, long-term projects.

Despite these hurdles, the potential of deep tech is enormous. It can create entirely new markets and shape emerging technological trends. Many deep tech innovations lay the groundwork for future businesses and industries. Moreover, they have a transformative impact on hard-to-decarbonize sectors, such as manufacturing and energy production, through cutting-edge advancements.

What makes deep tech truly compelling is its ability to tackle major societal challenges, from combating climate change to advancing healthcare and promoting sustainable resource use. By driving large-scale breakthroughs, deep tech has the power to reshape industries and make meaningful contributions to global progress.

Mental Models to Think About Deep Tech

Braudel, Wallerstein & National Advantage

Fernand Braudel, a prominent historian associated with the Annales School, developed a nuanced understanding of economic activity by conceptualizing it as existing within three distinct layers or circles. At the base is what he termed Material Life, or the Basic Economy. This level encompasses the subsistence activities that define everyday life for the majority of the population. It includes agriculture and local trade, where people rely on self-sufficiency and engage in basic exchanges necessary for survival.

Above this foundational layer lies the Market Economy, which Braudel identified as the Intermediate Level. This sphere is characterized by commercial transactions and trade, where a burgeoning capitalist framework begins to take shape. It is comprised of merchants and entrepreneurs who engage in regulated trade, connecting local and regional markets in a web of economic interactions.

At the pinnacle of Braudel’s model sits the Capitalist Economy, the Top Level. This circle represents the realm of financial capital, market speculation, and extensive global trade networks. Here, economic activity is controlled primarily by large corporates, national government creating a landscape often marked by unregulated practices that can lead to significant wealth disparities.

Immanuel Wallerstein expanded upon Braudel’s ideas with his World-Systems Theory, which provides a broader, global perspective on economic interactions. He categorized the world into three economic zones. The Core consists of wealthy, industrialized nations that dominate trade, exert control over capital, and exploit the more vulnerable economies around them; examples include the United States and Western Europe. In contrast, the Periphery comprises poorer nations that primarily provide raw materials and labor to the core. These nations often remain dependent on core economies for finished goods, a situation evident in many African and Latin American countries.

Wallerstein also introduced the concept of the Semi-Periphery, which includes nations that have some degree of industrialization but still rely on the core for technology and capital. Countries like China and Brazil fall into this category. Through his theory, Wallerstein argued that the modern world economy functions as a single unit characterized by an unequal distribution of wealth and power, where core countries maintain their dominance through both economic and political means.

Both Fernand Braudel’s and Immanuel Wallerstein’s theories emphasize the enduring nature of economic hierarchies, illustrating how trade and capitalism evolve through structured, hierarchical relationships rather than emerging from a landscape of free-market equality. These frameworks offer valuable insights into how technological investments, particularly in deep tech, shape national economic trajectories and competitive positioning on the global stage.

Deep tech—encompassing advanced technologies such as artificial intelligence, quantum computing, and advanced materials—plays a pivotal role in reinforcing a nation's technological capabilities. These advancements not only bolster pricing power but also drive economic productivity. Investing in deep tech becomes even more critical for smaller countries that lack the advantage of vast internal markets. For these nations, survival and growth hinge on leveraging intellectual capital rather than relying on sheer market size. Nations such as Israel, Switzerland, and the Nordic countries exemplify this approach. By fostering innovation and technological leadership, they secure a competitive edge in the global economy, creating value-added industries that transcend their geographic and demographic limitations.

The innovation economy has always been tightly intertwined with the wealth creation potential of nations—a truth that has been evident since the Industrial Revolution. Braudel's and Wallerstein’s frameworks help explain why emerging powers, such as China, are making significant investments to become R&D powerhouses. China’s relentless pursuit of technological leadership is seen by the United States as a long-term threat to its economic prosperity and technological dominance. This global competition underscores how control over technological advancements becomes a strategic imperative in maintaining economic hierarchies and influence.

Understanding this conceptual framework provides emerging nations with a blueprint for reshaping their economic structures and trade. By finding a balance between fostering innovation and delivering value-added services, and raw materials these countries can navigate the complexities of the global trade and establish sustainable paths to prosperity. In a world where technological capabilities increasingly determine economic strength, deep tech investments emerge as a vital foundation for national competitiveness and resilience.

Creative Destruction and Deep Tech

Creative destruction, a term coined by economist Joseph Schumpeter, describes the process by which new innovations disrupt and ultimately replace outdated technologies, reshaping industries and economic structures in the process. Schumpeter introduced this concept in 1942, asserting that creative destruction is an essential characteristic of capitalism. It is through this cycle of innovation that economies evolve, discarding obsolete methods and embracing new, more efficient ones.

One of the most illustrative examples of creative destruction is the history of ice production. In the 16th century, ice was a luxury that only the wealthy could afford, harvested from frozen lakes and stored in ice houses. By 1818, commercial ice was available at 25 cents per pound, while the average annual income was $1,919. The commercial ice industry peaked in 1914, employing around 30,000 Americans across 2,500 companies. However, the invention of the automatic ice maker in 1950 and its widespread adoption by 1965 drastically altered the industry. This technological breakthrough led to a 75% decline in employment in the ice industry from 1914 to the present day. Despite these job losses, ice became significantly more affordable and accessible, demonstrating the broader societal benefits of creative destruction.

While creative destruction fosters innovation and long-term economic growth, it often comes with short-term challenges. The transition from old industries to new ones can be painful, displacing workers and rendering certain skills obsolete. Some individuals may struggle to adapt, facing long-term economic hardship. However, without this process, economies would stagnate, and societies would miss opportunities for increased productivity and higher living standards.

The significance of creative destruction in economic theory has been further explored by economists such as Philippe Aghion and Peter Howitt, who developed a model that encapsulates its core principles. Their model is built on three key ideas:

A fundamental contradiction lies at the heart of this growth process. On one hand, monopoly profits serve as an incentive for innovation, rewarding firms for their breakthroughs. On the other hand, firms that achieve monopoly power may attempt to stifle future innovation to protect their dominance. Managing this tension is critical to ensuring that capitalism remains dynamic and conducive to ongoing technological progress.

The Industrial Revolution in Europe around 1820 exemplifies how creative destruction can drive economic transformation. This period of rapid industrialization was fueled by institutions that encouraged cumulative innovation, protected property rights, and fostered competition among European nations. However, despite these historical successes, the modern world faces significant growth challenges. Many developing economies have struggled to converge with wealthier nations in terms of per capita income. Factors such as slow investment, stagnant productivity growth, and gaps in education have contributed to this sluggish progress.

Additionally, the phenomenon of secular stagnation poses a considerable threat to long-term economic vitality. In the United States, total factor productivity (TFP) growth initially surged due to the rise of "superstar" firms—large, highly productive companies that drove innovation. However, as these firms solidified their market positions, they began to inhibit competition and entry from smaller, emerging businesses, thereby slowing the pace of innovation.

Automation, often seen as a driver of job displacement, can also create employment opportunities by enhancing productivity, lowering export prices, and increasing overall sales. Furthermore, civil society plays a crucial role in guiding innovation, particularly in promoting environmentally sustainable technologies. Increased market competition can push firms toward greener innovations, aligning economic growth with environmental responsibility.

To maximize the benefits of creative destruction, policies such as "flexicurity"—a model combining labor market flexibility with social security—can help ease transitions for displaced workers. By fostering an adaptable workforce and a competitive economic environment, societies can harness the power of innovation while mitigating the short-term disruptions it may bring. Ultimately, creative destruction is not just an economic necessity; it is a fundamental force that drives progress, shapes industries, and determines the trajectory of global economies.

A fundamental driver of deep tech innovation is combinatorial innovation—the process of combining and recombining existing components to create novel solutions. Historically, this pattern has emerged with standardised mechanical parts in the 19th century, electrical components in the early 20th century, digital integrated circuits in the 1960s and 1970s, and software modules today. The advent of cloud computing has significantly amplified this trend, enabling startups to leverage standardised platforms, convert fixed costs into variable ones, and foster innovation on a global scale. Companies can now integrate AI, IoT, and cloud services seamlessly, accelerating the pace of technological advancement.

While innovation is often associated with creativity, some argue that the destruction aspect of creative destruction is even more critical. In the venture capital ecosystem, most startups fail, but their collective exploration of different spaces is necessary to identify breakthroughs. The principles of free market entry and exit ensure that unproductive enterprises are eliminated, reallocating capital to more promising ventures. This Darwinian process allows industries to evolve efficiently, ensuring that only the most effective innovations thrive.

Another perspective on disruption is that it is not merely about destruction but about fixing inefficiencies. Successful startups often identify and address fundamental problems within an industry. Whether it is improving user experience, reducing costs, or making a service more accessible, the best disruptors provide tangible solutions that make traditional methods obsolete. Companies like Uber, Airbnb, and Stripe exemplify this philosophy by addressing inefficiencies in transportation, hospitality, and financial services, respectively.

In recent years, Silicon Valley has ignited a significant trend toward device convergence, where multiple functionalities are seamlessly integrated into single platforms. This transformation is largely attributed to advancements in intellectual property-based (IP-based) innovation, which have significantly lowered entry barriers for new players in the tech landscape. Startups are now empowered to develop cutting-edge technologies with relatively modest capital investments, creating an environment that encourages rapid iteration and scalability.

The realm of deep tech innovations is particularly notable for its creative destruction effects, which have the power to redefine entire industries. Take, for instance, the extraordinary impact of AI-driven drug discovery. As AI-driven approaches take precedence, legacy pharmaceutical firms that resist adopting these technologies will struggle to remain competitive, giving rise to an industry dominated by AI-first companies.

Similarly, quantum computing is challenging the very foundations of classical computing and cybersecurity. Organizations such as IBM Quantum and Google's Sycamore are pushing the boundaries of computation, making impossible calculations a reality. This revolution has far-reaching implications across various fields, including material science and cryptography. Should quantum computing reach commercial viability, traditional encryption methods could become obsolete, compelling industries like cybersecurity and finance to embrace new quantum-resistant protocols. Industries that cling to classical computational models will be forced to adapt rapidly, or risk being rendered irrelevant.

In the manufacturing sector, AI-driven Computer-Aided Manufacturing (CAM) is set to disrupt traditional CNC machining processes. Companies like CloudNC and Bright Machines are automating and optimizing manufacturing workflows, significantly reducing reliance on manual programming. As AI enhances these processes, traditional manufacturing methods that rely on human expertise will find themselves inefficient in comparison. While this shift may lead to job displacement in conventional roles, it simultaneously creates new opportunities in AI-driven engineering and operations.

The landscape of food production is also undergoing a transformative shift with the rise of bioprinting and lab-grown meat. Innovators such as Upside Foods and Mosa Meat are harnessing cellular agriculture to produce meat that requires fewer resources and addresses ethical concerns associated with industrial animal farming. The widespread adoption of lab-grown alternatives could challenge the viability of traditional livestock farming, leading to a significant shift in the global food supply chain toward sustainable practices.

Finally, the energy sector is on the verge of a revolution as innovations in small modular reactors (SMRs) and nuclear fusion redefine conventional energy markets. Companies like Commonwealth Fusion Systems and Helion Energy are working to make clean, sustainable energy a feasible alternative to fossil fuels. The progression toward commercializing fusion energy, once regarded as a distant dream, now promises nearly limitless power with minimal environmental impact. If these advancements gain traction, traditional coal, oil, and large nuclear power plants may find themselves obsolete, compelling legacy energy companies to pivot or risk being left behind in an evolving landscape of decentralized energy solutions.

These technological advancements not only herald a new era of innovation but also embody the creative destruction that reshapes industries, necessitating adaptation from established players to remain relevant.

Understanding Risk, Uncertainty, and Deep Tech Investment

Uncertainty is a broad concept that encompasses anything unknown or unpredictable. In the real world, there are countless uncertainties—ranging from minor unknowns to large-scale unpredictability. However, risk is a specific subset of uncertainty. It is defined as the effect of uncertainty on objectives, meaning that risk arises only when uncertainty directly impacts what an organization is trying to achieve. Without objectives, there can be no risk. So risk is the probability of incurring a loss from a specific set of uncertainty while attaining those objectives that an organization is trying to achieve.

Because uncertainties are vast, filtering them is crucial in identifying actual risks. The key filter is whether an uncertainty affects an organization’s objectives. Only a small subset of uncertainties qualifies as risks that require active management, monitoring, and review. Furthermore, uncertainty is not limited to external events; it can also stem from a lack of knowledge or inherent variability. Different entities may perceive risks differently based on their objectives—what is a risk for one organization may be irrelevant to another.

Traditional economic models rely on probabilistic risk assessments, assuming that all uncertainties can be calculated and managed. However, Frank Knight’s seminal work distinguishes between measurable risk (where probabilities are known) and true uncertainty (where probabilities are entirely unknown and cannot be predicted). Knight argued that profit arises from true uncertainty, rather than from situations where risk can be quantified.

Leonard Savage later proposed a theory suggesting that people behave as if they know the probability distribution of future events, effectively negating Knight’s distinction. However, Daniel Ellsberg’s Paradox (1961) challenged this view, demonstrating that real-world decision-making does not always conform to a single probability model. The paradox highlighted the significance of Knightian uncertainty, which had been largely ignored in economic theory for decades.

Research has shown that when Knightian uncertainty increases, individuals and businesses often become more risk-averse. In some cases, learning does not necessarily reduce uncertainty—people may remain uncertain even as they gain new information. This reinforces the idea that entrepreneurs and investors must develop resilience and adaptability rather than solely relying on predictive models..(Saas vs Flying Taxis)

Opportunities in entrepreneurship arise from competitive imperfections in product and factor markets. Some opportunities exist independently of human action, emerging from external changes such as technological advancements or shifts in consumer behavior. These are known as discovery opportunities, and decision-making in such contexts is risky but can yield arbitrage rents—returns that arise from recognizing market inefficiencies before others do. However, once an opportunity is identified, it is easily imitated, reducing potential profits.

Entrepreneurs often exploit information asymmetries, keeping their discoveries secret and leveraging first-mover advantages to maximize gains before competitors catch up. Within large companies, these opportunities manifest as Schumpeterian rents, where returns stem from innovation and the ability to disrupt existing markets. Successful entrepreneurs have deep knowledge of their company’s products, processes, and resources, allowing them to capitalize on these rents before others do.

An alternative view suggests that opportunities are not simply discovered but actively created through entrepreneurial action. This creation view argues that markets and opportunities do not exist independently but are shaped by entrepreneurs through experimentation, iteration, and learning. Because outcomes are uncertain, decision-making in this framework is not about predicting the future but about systematically testing hypotheses through small, calculated experiments.

Navigating Knightian uncertainty in deep tech innovation demands a structured understanding of the unique challenges posed by "unknown unknowns"—risks that cannot be quantified or anticipated due to their inherent unpredictability. Deep tech ventures, rooted in scientific breakthroughs like AI, quantum computing, or biotechnology, face a convergence of uncertainties that amplify complexity. Below, we map these challenges to innovation archetypes and propose strategies to mitigate risks while leveraging opportunities.

In the realm of deep tech, a complex landscape of uncertainty looms, largely shaped by five interrelated factors that define the challenges of innovation at the forefront of science.

Technological uncertainty stands as a significant hurdle. Deep tech ventures often grapple with uncharted scientific principles, such as quantum error correction and precision gene editing. This unanticipated realm of technical roadblocks can derail projects, making it essential for innovators to adopt a modular approach to research and development. By breaking projects into smaller, testable milestones—for instance, validating a critical component of a fusion reactor—companies can mitigate risk. Collaboration with academic institutions and national laboratories also offers a strategic pathway to share expertise and diminish uncertainties.

Another prominent challenge is long development cycles. Many deep tech projects, like nuclear fusion and CRISPR therapies, may take decades to realize, which inevitably strains investor patience and exposes ventures to macroeconomic shifts. To align investor expectations with the realities of such timelines, phased funding tied to clear milestones proves effective. Pursuing non-dilutive capital sources—such as grants and government contracts—can also extend operational runways, providing the necessary breathing room in these lengthy journeys.

Regulatory hurdles pose another significant obstacle, especially in tightly regulated sectors like healthcare and aviation. Compliance requirements, such as FDA approvals for biotech innovations or drone regulations, can significantly slow progress. To navigate these complexities, engaging policymakers early through sandbox programs can create conducive environments for innovation. Furthermore, designing adaptive governance frameworks helps anticipate and manage evolving standards in these dynamic fields.

The issue of market acceptance and adoption weighs heavily as well. Convincing stakeholders to embrace radical solutions, such as lab-grown meat or advanced climate technologies, involves overcoming entrenched cultural inertia and skepticism. Crafting narrative-driven pilot projects can demonstrate the viability of these innovations. Collaboration with early adopters, such as governments focused on climate action, can foster co-development of practical use cases that inspire broader acceptance.

Lastly, the capital intensity of deep tech initiatives cannot be underestimated. The substantial upfront costs associated with research, development, and scaling—exemplified by the capital requirements for semiconductor fabrication facilities or fusion reactors—often deter traditional venture capital investment. To counteract this, leveraging hybrid funding models that combine venture capital, corporate partnerships, and public funding can alleviate financial pressures. Prioritizing asset-light scaling approaches, such as licensing intellectual property instead of constructing extensive infrastructure, further bolsters financial viability.

To navigate the unpredictable terrain of deep tech, stakeholders must blend resilience with strategic foresight. Embracing antifragility over mere optimization enables organizations to thrive amid volatility; biotech startups, for instance, can repurpose failed drug discoveries for adjacent applications, demonstrating adaptability.

Moreover, focusing on execution risk as a guiding principle is vital. Successful deep tech initiatives often hinge on teams with a robust scientific foundation and operational agility, allowing them to pivot without losing sight of core objectives, as evidenced by Moderna's transformative shift during the COVID-19 pandemic.

Structured de-risking also plays a crucial role in managing risk at both technical and market levels. Employing “killer experiments” to validate feasibility at early stages can help pinpoint viable innovations, while deploying “minimum viable miracles” (MVMs)—small-scale demonstrations of transformative potential—can effectively showcase a project’s promise.

Finally, fostering ecosystem orchestration is essential. Building coalitions among academia, government entities, and industries enables shared risks and aligned incentives. The EU’s Quantum Flagship initiative, which pools resources from thousands of researchers, exemplifies the power of collaboration in accelerating breakthroughs.

In today’s complex landscape of deep tech innovations, segmenting investors by their risk tolerance is crucial for developing effective engagement strategies. Early-stage ventures often attract government grants and impact investors who seek patient capital, while corporate venture capitalists typically target growth-stage companies poised for strategic advantages. Traditional venture capital tends to engage at the late stage when a concept’s technical feasibility is already established. By deftly navigating the uncertainties inherent in deep tech, stakeholders can foster transformative innovations that have the potential to reshape our world.

One approach to innovation is pivoting, which leverages proven technologies in untapped markets. This strategy entails repurposing established technologies to meet new demands, balancing low technological uncertainty with high market uncertainty. A prime example is Facebook, now Meta, which reimagined existing internet infrastructure into a social networking platform for college students—a demographic initially considered niche. The success of such initiatives depends on swiftly validating product-market fit through user adoption and iterative feedback. For investors and founders, key metrics include engagement rates, growth, and adaptability to evolving consumer preferences, as the main risk involves market acceptance rather than technical execution.

Another viable strategy is forecasting innovation, which focuses on refining technologies for markets that are well understood. Consider Apple's iterative iPhone upgrades: enhancements in camera quality or battery life cater to predictable consumer expectations. These innovations thrive on incremental research and development and competitive analysis, with success hinging on optimizing supply chains, pricing strategies, and brand loyalty. Investors eyeing this realm tend to prioritize stability, leveraging historical data and financial models for forecasting returns, all the while grappling with the challenge of maintaining relevance in saturated markets and warding off commoditization.

For scenarios rife with high technological uncertainty but clear market needs, projecting innovation comes into play. This involves making significant technological advancements to satisfy evident market demands. SpaceX's reusable rockets and OpenAI's ChatGPT serve as compelling examples; the market clearly craved affordable space travel and AI-driven productivity tools, yet the technical challenges were formidable. Success in this arena is contingent upon breakthroughs in research and development and scalability. Investors willing to engage here must tolerate considerable technical risk while capitalizing on a defined market appetite. The emphasis is placed on resources directed toward prototyping, testing, and patent acquisition, with critical validation points contingent upon successful pilot deployments.

Finally, backcasting innovation is characterized by high levels of both technological and market uncertainty. This approach begins with a visionary future and works backward to create both the necessary technology and market acceptance. Tesla's electric vehicle revolution exemplifies this; it required not only advancements in battery technology but also efforts to persuade consumers and regulators of the merits of electric vehicles as a sustainable alternative. The challenge here is balancing a long-term vision with short-term wins—Tesla’s luxury Roadster served as a proof of concept that validated electric vehicle feasibility prior to launching more affordable models. The risks faced by investors in this context are significant, yet the potential for outsized rewards exists if the vision is ultimately realized.

Navigating the uncertainty that permeates deep tech and venture capital calls for distinct strategic approaches. Fundraising and investing in high-uncertainty environments contrast sharply with the methodologies employed by traditional firms. Startups operate in rapidly evolving landscapes where technological disruptions can redefine entire industries almost overnight. To mitigate the inherent risks, investors often adopt strategies tailored to these challenges. They prioritize startups demonstrating incremental advancements, support teams that thrive on compounding small victories rather than relying solely on ambitious “moonshot” strategies, and focus on a startup’s capability for agile problem-solving amidst challenges.

Furthermore, investors must learn to distinguish between risk and uncertainty. They can treat unknowns as solvable problems: addressing a lack of market data through pilot programs while tackling technical risks with prototypes. Structured de-risking becomes essential, where founders focus systematically on uncertainties such as regulatory hurdles before scaling their ventures, thus aligning investor expectations with realistic timelines.

In conclusion, the spectrum of uncertainties surrounding innovation requires tailored strategies. While pivoting and backcasting innovations necessitate market creation and visionary storytelling, forecasting and projecting innovations rely on execution within established parameters. For investors, success fundamentally hinges on recognizing the various uncertainties at play and adapting strategies accordingly.

Technology Readiness Levels (TRL) and Deep Tech Investment Strategies

Technological innovation is a dynamic and often uncertain journey. Understanding how technologies evolve from concept to commercialization is critical for investors, policymakers, and entrepreneurs alike. One of the most widely recognized frameworks for evaluating this process is the Technology Readiness Levels (TRL) scale. Originally developed by NASA, the TRL framework provides a structured pathway for assessing the maturity of technological innovations. Deep tech—which encompasses fields like advanced materials, artificial intelligence, quantum computing, and biotechnology—often begins at the lower levels of this scale and requires unique strategies to overcome significant challenges along the way.

The TRL framework is divided into nine stages, each representing a step toward the full commercialization of a technology. At TRL 1, basic principles are observed, often within academic or fundamental research settings. Progressing through TRLs involves increasing levels of testing, validation, and integration until the technology reaches TRL 9, signifying full deployment in a real-world operational environment.

Deep tech innovations typically originate at the early stages of the TRL spectrum, particularly between TRL 1 and TRL 3. These levels are characterized by conceptual exploration, laboratory experiments, and preliminary proof-of-concept studies. While these initial stages are crucial for laying the groundwork for groundbreaking advancements, they are far from market-ready and require significant further development.

The “Valley of Death” (TRL 4-6)

One of the most perilous phases in the development of deep tech is the so-called “Valley of Death,” which spans TRL 4 to TRL 6. This phase represents the critical transition from laboratory validation to early prototype testing in relevant environments. Technologies at this stage often require substantial funding, technical expertise, and industry partnerships to bridge the gap between research and commercial viability.

The “Valley of Death” presents numerous challenges:

To overcome these hurdles, strategic collaborations between academia, government agencies, and private investors are essential. Government grants and innovation funds often play a critical role in de-risking investments during this phase, providing the necessary capital to advance technologies toward commercialization.

Understanding TRLs is invaluable for investors seeking to navigate the complex landscape of deep tech. By assessing a technology’s maturity level, investors can make informed decisions about where and when to allocate resources. TRLs provide a standardized framework for evaluating technological progress, enabling investors to identify promising opportunities and mitigate risks.

Investors often tailor their strategies based on TRL levels:

Navigating the TRL framework effectively requires a collaborative ecosystem involving multiple stakeholders. Governments can play a pivotal role by funding early-stage research and providing incentives for private sector participation during the “Valley of Death.” Academia serves as a breeding ground for innovative ideas, while industry partners offer the resources and market insights needed to bring technologies to fruition.

Moreover, understanding TRLs allows emerging economies to develop strategic roadmaps for fostering innovation. By targeting investments in specific stages of the TRL spectrum, countries can build robust innovation ecosystems that drive economic growth and technological leadership.

The TRL framework provides a critical lens for understanding the development and commercialization of deep tech innovations. Recognizing the challenges associated with early-stage development and the “Valley of Death” helps investors and policymakers devise strategies to support technological advancement. By fostering collaboration and aligning investments with technology readiness, stakeholders can unlock the transformative potential of deep tech and drive long-term economic and societal benefits.

How to Invest in Deep Tech: Navigating Uncertainty and Maximizing Returns

Investing in deep technology, commonly known as deep tech, requires a distinctive approach that differs significantly from traditional venture capital investments. Unlike consumer tech startups, which typically exhibit short development cycles and clear market demand, deep tech ventures are characterized by substantial scientific advancements and prolonged commercialization timelines. Fields such as artificial intelligence, quantum computing, biotechnology, and advanced materials hold transformative potential but also introduce significant levels of uncertainty.

For investors, deep tech offers extraordinary opportunities paired with substantial risks. The key to success lies in carefully assessing technological feasibility, comprehending market dynamics, managing intellectual property (IP), and adopting patient capital strategies.

The foundation of investing in deep tech begins with evaluating the capital needed for a startup to achieve financial sustainability. Unlike software-based ventures that often generate early revenue, deep tech startups demand considerable upfront investment for research, prototyping, and regulatory approvals. This entails estimating the funding necessary to hit critical technical milestones, projecting the timeline for market entry and revenue generation, and determining the long-term runway required to sustain development.

Risk evaluation in deep tech investments can vary widely, reflecting differing levels of technological maturity. Some innovations are incremental, building on existing technologies and thus easier to assess, while others may represent radical breakthroughs accompanied by high uncertainty and lengthy timelines due to absence of historical commercialization data. Timing risks must also be considered, as they are pivotal to both capital requirements and market entry success.

Many groundbreaking innovations in deep tech require decades to mature, leading investors to acknowledge timing as a crucial risk factor; being either too early or too late can drastically affect a venture's fate. Historical examples underscore this phenomenon—consider the development timelines of transistors to integrated circuits, which took over 20 years, or liquid crystal displays that took 20 to 25 years to reach maturity. Traditional venture capitalists are often hesitant to finance deep tech startups beyond the early R&D stages, resulting in funding typically sourced from government grants, research institutions, large corporations seeking long-term strategic advantages, or specialized venture firms well-versed in domain expertise.

Market forces and commercialization challenges are additional factors investors must evaluate. Deep tech innovations often depend on several complementary technologies for successful commercialization. An innovation might lie dormant for years until advancements in related fields finally unlock its potential. Take, for instance, the case of fiber-optic communication systems, which only became viable following breakthroughs in low-loss glass fibers, semiconductor lasers, and computing capabilities for managing data. Without these concurrent advancements, fiber optics may have remained commercially unviable.

Intellectual property plays a vital role in deep tech, as its protection is essential for maintaining a competitive edge, especially within software and hardware domains. However, having a robust patent portfolio does not automatically guarantee market success. Investors must scrutinize the strength and defensibility of a company’s IP while being mindful of potential patent litigation, particularly since larger corporations sometimes target startups to stifle competition. The emergence of patent trolls—entities that pursue litigation for licensing fees rather than innovation—adds another layer of complexity. As the landscape has shifted from vertically integrated tech giants to specialized vendors, competition over IP has intensified. Historically, cross-licensing agreements mitigated litigation risks, but these practices have become less common, making a sound patent strategy a critical element in investment decisions.

Strategic Investment Principles in Deep Tech: Navigating Risk and Reward

Traditional venture capital strategies, which have been fine-tuned for the rapid scalability of software solutions, often struggle in the deep tech sector. To find success in this domain, investors need to blend disciplined risk management with bold visionary thinking. The journey involves several strategic imperatives that can mean the difference between triumph and failure, as illustrated by both successes and cautionary tales from the real world.

First and foremost is the need for rational and sober judgment, striking a balance between visionary ideals and scientific rigor. Deep tech investors often find themselves walking a fine line where embracing ambitious ideas must be tempered with grounded decision-making. Take Moderna, for example. The company was at the forefront of mRNA technology long before it became a household name during the COVID-19 pandemic. Its valuation languished as many skeptics dismissed mRNA as untested. However, visionaries like Flagship Pioneering recognized its potential, investing in the robust peer-reviewed research and the bioengineering expertise of its management team. This strategic patience paid off handsomely; Moderna’s COVID vaccine racked up $18 billion in revenue in 2021, validating the broader therapeutic potential of mRNA. In stark contrast, consider the tale of Theranos, where hype overshadowed substance. The company claimed revolutionary advancements in blood testing but failed to provide peer-reviewed validation or transparent governance. Investors, entranced by the bold narrative, ignored the warning signs, leading to a catastrophic $9 billion collapse. This serves as a stark reminder: in the realm of deep tech, due diligence is imperative—real breakthroughs require evidence, not just promises.

Next is the critical importance of risk assessment and avoiding the pitfalls of overinvestment. The clean tech boom of the 2000s offers a cautionary tale of the dangers of pouring money into unproven ventures. Startups like Solyndra raised extravagant sums—$1 billion for innovative solar panels—only to crumble under the weight of flawed technology and falling silicon prices. On the flip side, selective investment strategies, such as those used by Tesla, which initially appeared to be a niche player, emphasized incremental innovation. By starting with luxury electric vehicles and gradually moving towards scalable battery solutions, Tesla transformed into a $700 billion enterprise. Today, the solid-state battery race echoes similar dynamics; while QuantumScape attracted $1.3 billion in SPAC funding amidst market excitement, critics raise concerns about unresolved engineering challenges.

Specialization and deep industry knowledge further amplify the potential for success in deep tech. The investment that Sequoia Capital made in Nvidia back in 1998 serves as a prime example. While many viewed Nvidia merely as a gaming GPU company, Sequoia’s deeper understanding recognized the architecture's significant potential in parallel computing. Conversely, funds that lack specific domain expertise and dabble indiscriminately in fields like CRISPR or quantum technology often miscalculate associated technical risks. This was evident in the disheartening collapse of several overhyped quantum software startups in 2022.

Learning from failures—a crucial aspect of the deep tech landscape—can ultimately pave the way for progress. Google’s Project Loon, which aimed to deliver internet via stratospheric balloons, was discontinued in 2021 due to cost constraints. However, the advancements made in areas like autonomous navigation and material science during this project enriched Google’s future endeavors, particularly in AI-driven disaster prediction systems. In the gene editing space, Editas Medicine faced setbacks when initial CRISPR trials yielded limited results. Instead of retreating, investors chose to reinforce efforts on delivery mechanisms, which ultimately cleared the path for Vertex’s groundbreaking CRISPR therapy for sickle-cell disease. As OpenAI’s CEO, Sam Altman, aptly notes, “In deep tech, you only need to be right once.”

Avoiding herd mentality proves essential as well; the art of cultivating contrarian conviction can safeguard against FOMO (fear of missing out) that frequently drives speculative bubbles. The frenzy surrounding AI chips from 2021 to 2022 saw a massive influx of capital—$3 billion—into startups promising “Nvidia-killer” technologies, yet many of these companies, like Graphcore, struggled as customers remained loyal to established vendors. In contrast, Groq carved a niche for itself by focusing on deterministic AI inference chips tailored for the defense and healthcare sectors, avoiding the overstated trends. Similarly, during the quantum computing rush, Rigetti Computing opted against overhyping claims of “quantum supremacy,” choosing instead to pursue practical near-term applications in hybrid quantum-classical systems, which led to valuable partnerships with major organizations like Dow and NASA.

Despite the challenges associated with funding in the deep tech space, these investments have historically outperformed traditional tech sectors, offering investors higher returns along with a unique risk-reward profile. Since 2003, deep tech investments have yielded an average internal rate of return (IRR) of 16% compared to 10% for traditional tech. The lower competition in this arena also contributes to enhanced returns, as fewer investors are willing to engage in sectors with high barriers to entry. Moreover, deep tech founders typically possess robust technical backgrounds, increasing the likelihood of success, while expansive market opportunities remain in areas like quantum computing and synthetic biology, which are still significantly underdeveloped.

The life cycle of deep tech ventures differs from traditional startups, characterized by longer research and development phases and heightened early-stage uncertainty. However, once technical challenges are surmounted, these ventures often enjoy a greater likelihood of successful commercialization. A pivotal measure of success lies in the unicorn ratio—the probability of a startup achieving a valuation of $1 billion or more—demonstrating the latent potential that exists within deep tech investments.

Addressing Long-Term Challenges in Deep Tech Investment

Deep tech innovation, encompassing revolutionary fields like quantum computing, advanced biotechnology, and next-generation semiconductors, possesses the potential to reshape global economies. However, the journey from laboratory breakthroughs to market-ready applications is laden with systemic challenges. Unlike the software sector, where startups can scale quickly and yield predictable returns, deep tech ventures require patient capital, lengthy development cycles, and a high tolerance for uncertainty. In the UK and Europe, a risk-averse financial ecosystem, fragmented funding structures, and misaligned public markets further complicate the landscape. To unlock the transformative potential of deep tech, we must consider a radical rethinking of investment frameworks, policy initiatives, and strategies for industry collaboration.

One of the most significant barriers to deep tech investment is the high cost of capital. Many family offices and institutional investors, cautious about the risk-reward dynamics of deep tech, tend to gravitate toward safer assets. Venture capital firms can diversify their risks by pooling resources; however, family offices, which often invest their own wealth, face amplified exposure. To address this imbalance, governments should implement robust tax incentives. Programs like the UK’s R&D tax credits and France’s Crédit Impôt Recherche (CIR) exemplify ways to lower entry barriers by offsetting R&D expenses. To further entice private capital, enhanced measures such as capital gains exemptions for long-term deep tech holdings or loss carryforward provisions could be introduced. Singapore’s co-investment model, which sees the government match private funding in deep tech startups, illustrates how public capital can mitigate investment risks while allowing for potential upside for investors.

Reforming public markets to reward innovation is equally critical. Europe’s dividend-centric public markets tend to disadvantage deep tech firms that prioritize reinvestment over short-term profits. Unlike their U.S. counterparts, which have seen companies like Tesla and Moderna succeed despite lengthy periods of financial loss, European exchanges often undervalue growth-stage innovators. This misalignment pushes startups, like British semiconductor firm Graphcore, to seek acquisitions or listings in the U.S., draining innovation capital from the region. To shift investor sentiment, institutional investors could follow the example of Norway’s sovereign wealth fund by allocating fixed percentages of their portfolios to deep tech. Furthermore, stock exchanges could introduce listing criteria that acknowledge and reward R&D expenditure, similar to Nasdaq’s acceptance of pre-revenue biotech firms. Without such transformative reforms, Europe risks losing its brightest innovators to markets that champion long-term vision over immediate returns.

Traditional venture capital models, optimized for the fast-paced cycles of software development, struggle to accommodate the lengthy timelines often associated with deep tech innovations. Instruments like SAFE (Simple Agreement for Future Equity) notes and standard equity rounds may fall short of meeting the funding needs of emerging technologies such as fusion energy or quantum computing. However, innovative financing mechanisms are on the rise. Staggered SAFEs could finance early milestones without imposing early pricing, while milestone-linked debt arrangements ensure that funding is tied to technological achievements, as demonstrated by startups like quantum leader Quantinuum. Additionally, R&D bonds issued by France’s Bpifrance provide a means to fund high-risk R&D with fixed returns linked to project success. Government-backed revenue sharing models, like those offered by the European Innovation Council, combine grants with equity to reduce dilution for founders. These tools are vital in bridging the “valley of death” between research and commercialization, helping startups navigate prolonged development phases.

To alleviate technology risk, public-private synergy is essential. The inherent uncertainties within deep tech, such as the scalability of novel battery chemistries, can deter private investors. Governments must step in to mitigate these risks by supporting early-stage R&D while avoiding inefficient fund allocation. South Korea’s approach serves as a valuable blueprint, incorporating national validation hubs like KAIST that grant startups access to state-of-the-art labs for accelerated prototyping. The EU’s Horizon Europe program allocates grants based on Technology Readiness Levels (TRLs), ensuring funding flows to innovations that are nearing commercialization. Furthermore, Japan’s model of IP collateralization offers startups, such as AI-driven firm Preferred Networks, the opportunity to leverage their intellectual property for liquidity. By structuring public funding to complement rather than replace private investment, governments can stimulate breakthroughs without distorting market incentives.

Lastly, even startups that secure funding often face hurdles in gaining market acceptance. Industrial giants may resist adopting unproven technologies due to fears of supply chain disruptions. Take, for example, the initial hesitation of German automakers to embrace solid-state batteries, despite their potential for electric vehicles. To successfully navigate these challenges, the formation of sector-specific consortia becomes crucial. Testbeds, like the Netherlands’ Battery Competence Cluster, enable startups to trial their innovations in real-world environments. Meanwhile, regulatory sandboxes, such as those implemented in the UK’s digital sector, provide a controlled space for new technologies to be tested and validated, fostering a culture of innovation and collaboration.

The challenges facing European deep-tech startups are significant, as they strive to transform groundbreaking scientific discoveries into viable businesses. Despite strong early-stage investment, scaling up remains a major hurdle due to the lack of substantial late-stage funding, particularly compared to the U.S.

To address these issues, the ecosystem must rethink its approach to financing and scaling deep-tech innovation. This includes expanding capital pools, exploring alternative funding mechanisms beyond traditional venture capital, and ensuring companies can reach the stock market instead of being acquired prematurely. Policymakers, investors, and industry leaders must work together to create an economic environment that supports deep-tech growth while managing the inherent risks. Despite the difficulties, deep-tech investing holds transformative potential. These companies are at the forefront of solving critical global challenges, from climate change to healthcare advancements.