Terminology, Misclassification, and Decision Errors in Deep Tech
- Arise Innovations
- Jan 3
- 13 min read
Updated: Jan 11
Why language failures distort capital, strategy, and technological outcomes
Language Is Not Neutral Infrastructure
Language is not a decorative layer in innovation systems. It is infrastructure. The words used to describe technologies silently define how they are evaluated, funded, governed, and supported. Terminology acts as an implicit rulebook, encoding assumptions about timelines, risk, scalability, and legitimacy long before any formal decision is made.
In practice, this means that language does not merely describe reality. It shapes it. When a scientific venture is labeled a startup, it is immediately subjected to expectations derived from software economics. When a breakthrough technology is called deep tech without further qualification, it is grouped together with fundamentally different development logics. These labels determine which metrics are applied, which questions are asked, and which outcomes are considered reasonable.
The core argument of this article is therefore not semantic. Vagueness in tech terminology does not just create confusion. It produces systematic decision errors. Capital is allocated based on inappropriate benchmarks. Funding programs are designed around mismatched assumptions. Ventures are rejected or accelerated for the wrong reasons, not because of technological merit, but because they do or do not conform to a linguistic category that was never designed for them.
This is why the problem cannot be solved with better storytelling or sharper pitch decks. The issue sits deeper. It is structural. Innovation systems repeatedly misclassify science-based ventures by forcing them into language frameworks borrowed from digital startups and incremental innovation. Once that misclassification occurs, every downstream decision becomes distorted.
Misclassification, not technological weakness, is the root error. And it begins with language.
The Linguistic Fog Around “Tech”
Why This Confusion Is Systemic, Not Academic
The confusion around technology terminology persists not because definitions are unclear, but because language is treated as neutral when it is not. In innovation systems, terminology functions as a proxy for development logic. The words used to describe a technology implicitly define how risk is understood, which timelines are considered reasonable, and what kind of progress is expected at each stage.
Once a technology is named, a whole set of assumptions is activated automatically. These assumptions travel faster than any formal analysis. They determine which questions are asked in an investment committee, which milestones appear in a grant agreement, and which explanations founders feel compelled to provide. Vagueness at the language level therefore does not stay abstract. It cascades into concrete decisions.
This becomes visible across the system:
Capital allocation follows linguistic familiarity. Technologies that fit established narratives appear more fundable, while those that resist easy categorization are perceived as risky or immature, independent of their actual potential.
Funding programs translate vague categories into standardized requirements. Calls are written to be “technology-open,” yet rely on templates, timelines, and KPIs that implicitly favor certain development paths over others.
Evaluation criteria inherit assumptions from startup logic. Metrics such as traction, scalability, or market readiness are applied uniformly, even when they are structurally incompatible with scientific validation processes.
Founder behavior adapts to the language of the system. Scientific ventures reshape their narratives to match investor expectations, often simplifying or distorting the true state of the technology in order to remain legible.
What emerges is a gap between declared openness and operational reality. While institutions claim to support a wide range of technologies, their language encodes a narrow interpretation of what progress should look like. The system appears neutral on the surface, but systematically rewards what can be described within familiar terminology.
This is why the problem is not academic. It is not about refining definitions for intellectual clarity. It is about recognizing that language quietly governs behavior across the entire innovation pipeline. As long as terminology remains vague, misclassification will continue to shape outcomes long before any explicit decision is made.
Misclassification in Practice: When Equal Treatment Becomes an Error in Deep Tech
Misclassification becomes most visible when different technological realities are treated as equivalent. Nowhere is this clearer than in what can be called the “two startups” problem.
Consider two ventures that enter the innovation system under the same label.
The first develops a digital platform to optimize B2B communication. Its market is known, user behavior can be tested quickly, and feedback cycles are short. An MVP can be launched within months. Metrics such as user acquisition, retention, and conversion provide early signals of viability. Iteration is cheap, and failure is informative.
The second is developing a novel functional material for hydrogen storage. There is no established market. Validation depends on laboratory infrastructure, long experimental cycles, and external partners. Feedback takes months or years, not weeks. Progress is measured in data quality, reproducibility, and performance under controlled conditions, not in users or revenue.
Both clearly involve 'tech'. Both are called startups. Both are often required to submit the same business plans, pitch decks, market analyses, and scalability narratives.
This is where equal treatment becomes an error.
Identical requirements produce asymmetric failure because they privilege one development logic over the other. What is a reasonable expectation for a digital platform becomes a structural impossibility for a science-based venture. The absence of early traction is interpreted as weakness. Long validation cycles are misread as lack of execution. Scientific rigor is mistaken for hesitation.
Startup logic collapses under scientific uncertainty because it assumes that uncertainty can be reduced primarily through market interaction. In science-driven innovation, uncertainty is intrinsic. It is reduced through experimentation, validation, and iteration under physical and regulatory constraints. No amount of customer discovery can substitute for this process.
The deeper issue is the false assumption of universal scalability. Digital products scale through replication. Scientific technologies scale through infrastructure, manufacturing, regulation, and system integration. Treating these paths as equivalent does not create fairness. It creates a systematic bias against technologies whose value emerges slowly, but whose impact may be far greater.
Misclassification does not merely disadvantage individual ventures. It quietly reshapes the entire innovation landscape by favoring what fits existing categories, not what addresses the hardest problems.
From Terminology to Metrics: How Language Hardcodes KPIs
Terminology does not stop at description. It directly determines what gets measured. Once a technology is labeled through startup vocabulary, a predefined set of KPIs follows almost automatically. Metrics are not chosen because they fit the technology, but because they fit the language used to describe it.
This is how startup vocabulary silently imports inappropriate measurement systems into science-based innovation. Concepts such as product–market fit, MVP, and traction are not neutral tools. They are software-native constructs, designed for environments with fast feedback loops, low marginal costs, and markets that already exist. In that context, these metrics are effective. Outside of it, they become misleading.
Product–market fit assumes a discoverable market and rapid customer feedback. An MVP assumes that partial functionality can be meaningfully tested in the real world. Traction assumes early signals of adoption or revenue. None of these assumptions hold for most deep or tough tech ventures, where validation precedes market formation and failure modes are physical, not behavioral.
What actually matters at these stages is fundamentally different.
Scientific validation determines whether an underlying hypothesis holds under controlled conditions. TRL progression captures technological maturity more accurately than any market metric. Industrial and regulatory compatibility indicate whether a technology can survive real-world constraints, from manufacturing to compliance.
When premature metrics are imposed, strategy becomes distorted. Teams optimize for signals that are easy to communicate rather than for progress that is technically necessary. Technologies are simplified to fit dashboards. Narratives are stretched to satisfy expectations. Capital is deployed to accelerate visibility instead of reducing uncertainty.
The result is not faster innovation, but misaligned execution. Language hardcodes metrics, and metrics hardcode behavior. As long as science-based ventures are described in startup terms, they will continue to be measured against criteria that were never designed for them.
Capital Logic Failures Driven by Language
When science-based ventures are framed using startup vocabulary, the resulting capital logic often fails to match the actual needs of deep and tough technologies. This disconnect becomes visible in multiple patterns across the innovation system.
The Pitch Deck Illusion
The belief that a well-crafted pitch deck is the key to funding has become deeply embedded across ecosystems. Yet for science-driven ventures, a deck is a communication tool, not a financing strategy. In deep tech, achieving scientific proof of concept and progressing through Technology Readiness Levels (TRLs) are what de-risk the underlying technology for later capital, not narrative polish alone. (Equidam)
Why Funding Is Mistaken for Strategy
In the startup world, early capital often accelerates product development toward familiar milestones like user growth or revenue. For deep tech, early funding without alignment to real validation stages can be actively harmful. Long research cycles, heavy up-front investment, and extended uncertainty—sometimes 35% longer and requiring significantly more capital than traditional startups—mean that premature money can distort priorities rather than advance progress. (Euro Funding)
The Danger of Early Capital Under Wrong Expectations
Deep tech ventures face what some analysts call “valleys of death” in financing—the phase between discovery and proof of concept, and again between pilot validation and commercial deployment. Traditional venture capital, conditioned on rapid traction and short investment horizons, is often unwilling or unable to support those phases. This forces founders into narrative compromises to secure funding, often before sufficient technical proof exists. (McKinsey & Company)
Grants Versus Investors as Interchangeable Instruments
Language that treats grants and investor capital as synonymous further muddies strategy. Grants are designed to reduce scientific and technological risk. Equity capital is meant to amplify value after core risk is addressed. Conflating the two leads to inappropriate sequencing, where ventures attempt to use investor funding as a bridge over scientific uncertainty rather than as a multiplier once uncertainty has been meaningfully reduced.
The Absence of Technology-Compatible Capital Architecture
Despite deep tech’s growing share of total venture capital, traditional VC structures remain poorly matched to the dimension of capital that science-driven ventures require. (BCG Global) Deep tech often requires “patient capital” with long time horizons and staged financing models, yet the default framing of such ventures through startup language drives investors to seek short, visible milestones instead of structured developmental architectures.
Language matters because it hardcodes expectations—about risk, timeline, and return—into capital flows. When that language misframes science-driven innovation, strategies that look reasonable within a startup narrative can actually become structural errors in financing deep technology.
System-Level Consequences
The effects of misclassification and linguistic bias do not stop at individual ventures. They ripple through ecosystems, reshaping where capital flows, who stays in the game, and how public policy performs.
Capital flowing to what is easy to explain, not what is necessary
Investors and funding bodies naturally gravitate toward sectors where risk is easier to frame and rewards appear near-term. Traditional VC models prefer opportunities with clear paths to exits and familiar unit economics. In practice, this means software-like innovations often attract more capital relative to their scientific counterparts—even if high-impact technologies require deeper investment to mature. Although deep tech’s share of total venture capital has grown in recent years, it still represents only around 20% of VC funding, up from about 10% a decade ago, precisely because of the heavy capital and patience required to develop such technologies. (BCG Global)
Loss of scientifically strong founders from the venture system
Because language shapes expectations, founders with deep scientific expertise often feel pressure to conform to startup narratives that prioritize rapid commercialization over rigorous validation. This mismatch contributes to founder attrition: scientifically oriented entrepreneurs abandon venture tracks either by returning to academia or joining industry R&D teams where expectations align better with the nature of their work. Misunderstanding the unique development cycles and capital needs of deep tech ventures breeds frustration and misalignment, weakening the pool of technically rigorous founders within the innovation ecosystem. (Euro Funding)
Program inefficiency and policy underperformance
Public and private funding programs that adopt surface-level language without corresponding structural design end up underperforming relative to stated goals. When grant calls and innovation competitions use broad labels like “tech” while applying evaluation criteria optimized for digital scaling, they systematically favor less demanding projects. This misalignment contributes to inefficient allocation of capital, where dollars support ventures that are easier to articulate rather than those that advance strategic technological goals. Research on funding mechanisms shows that traditional competitive grant models can even divert scarce researcher effort into proposal writing rather than actual innovation when the system prioritizes superficial narratives over substance. (Cornell University)
Structural bias toward software-like innovation
The routinization of startup vocabulary codifies a bias toward ventures whose progress can be expressed in rapid cycles, visible user traction, and short ROI horizons. Even though deep tech ventures can deliver outsized long-term value by addressing complex societal challenges such as climate change or advanced materials, the prevailing capital logic continues to privilege growth narratives that mirror the software playbook. This structural bias makes it harder for truly transformative technologies to access the patient, staged, and ecosystem-integrated capital they require.
Together, these effects feedback into the broader innovation landscape, reinforcing a cycle where clarity of language and precision in understanding technology profoundly shape who wins and who never gets the chance to play the game in the first place.
Reframing the Vocabulary: From Startup Theater to Science Logic
To shift innovation systems away from misclassification and toward meaningful technological progress, we must replace startup language with science logic. Below is a structured comparison of the core conceptual shifts required:
What Changes When Language Becomes Precise
When innovation systems adopt language that matches the true logic of technology creation, not just the gloss of startup jargon, the entire ecosystem shifts in measurable ways. Precision in terminology does not just clarify communication. It reshapes expectations, decisions, and outcomes across founders, investors, and institutions.
Clearer expectation management between founders, investors, and institutions: Precise language reduces ambiguity and aligns mental models across stakeholders. In scientific research and technical domains, standardized terminology enhances comprehension and reduces misinterpretation among experts, which in turn supports clearer expectations about risk, timelines, and proof of concept.
Better sequencing of capital, validation, and partnerships: When language differentiates scientific progress from market signals, it becomes possible to sequence capital deployment with validation stages instead of forcing science into premature business milestones. This clarity supports capital structures adapted to technology readiness, reducing costly cycles of misdirected investment. Precision in technical vocabulary also facilitates better information exchange among experts, lowering information asymmetry that otherwise distorts investment flows. (EconStor)
Reduced narrative distortion: Imprecise or vague terms invite narrative distortion because individuals will fill gaps with assumptions or superficial interpretations. In contrast, discipline-specific and clearly defined language reduces ambiguity and enhances credibility. In scientific and technical contexts, accurate terminology ensures that complex ideas are conveyed with integrity and authority, preventing misinterpretation or overselling of capabilities. (Falcon Editing)
Increased probability of long-term technological impact: Innovation ecosystems rely on collaboration, knowledge exchange, and coordinated action. When stakeholders speak a shared, precise language grounded in technological reality, they can better understand and evaluate each other’s work. This reduces barriers to effective collaboration, supports ecosystem coherence, and enhances the likelihood that breakthrough technologies progress beyond initial discovery toward meaningful industrial and societal impact. (Springer Nature Link)
In sum, precision in language is not decoration. It is a structural tool that reduces ambiguity, aligns incentives, supports appropriate capital sequencing, and increases the likelihood that deep and tough technologies achieve their impact. As research on language use shows, communicating with contextual clarity and differentiation supports better decision-making among complex stakeholders, rather than obscuring reality with broad generalities.
Precision as a Strategic Advantage
In systems where language shapes rules, precision is itself a competitive advantage. Terminology that reflects technological reality rather than borrowed startup myths helps institutions make better decisions, allocate capital more effectively, and support ventures in ways that align with their actual dvelopment pathways.
Why institutions that fix language outperform those that do not
Ecosystems that adopt precise language grounded in science logic reduce ambiguity and information asymmetry. When stakeholders—founders, investors, policymakers, and evaluators—share a vocabulary that reflects real development risk and pathways, they coordinate better. This makes it easier to design funding instruments, support frameworks, and policies that actually fit scientific and technological complexity. Institutions that persist with vague or borrowed language unintentionally favor projects that are easier to describe over those that solve the hardest problems, slowing ecosystem progress. Effective innovation policy frameworks explicitly recognize deep tech’s structural differences, rather than treating all ventures as if they follow the same script. (McKinsey & Company)
Misclassification as the silent failure mode of deep tech ecosystems
Misclassification does not announce itself. It silently redirects capital and attention toward what is easy to explain rather than what is necessary. In Europe, for example, the mismatch between traditional VC models and deep tech realities has contributed to underfunding and stalled ventures, with founders reporting that investors often lack the domain knowledge to evaluate in-depth projects. This systemic friction slows innovation and can lead to promising technologies struggling to survive the financing “valleys of death.” (TNW | The heart of tech)
The consequences of imprecise language go beyond vocabulary. Two key questions arise next:
What happens when technologies are measured with the wrong metrics?
When systems apply metrics designed for rapid software iteration—such as short-term revenue or early user adoption—to deep technologies, they consistently reject meaningful scientific progress as failure. In the next chapter, we will explore how inappropriate metrics become invisible barriers that filter out high-impact technologies before they reach industrial relevance.
How evaluation frameworks must change once language does
Rewriting evaluation frameworks is not merely a technical exercise. It requires rethinking how progress is defined, measured, and rewarded. Once language reflects the real logic of technology development, institutions can build evaluation models that recognize scientific validation milestones, industrial compatibility, and long-term impact potential. This shift will help innovation systems support ventures that genuinely change industries and address societal challenges, rather than just those that fit into pre-existing narrative boxes.
Precision in language is not just a stylistic choice. It is a strategic tool that shapes capital flows, institutional design, and technological futures.
This article draws on the Deep Tech Playbook (2nd Edition). The playbook formalizes how scientific risk, capital sequencing, timelines, and institutional constraints interact across the venture lifecycle. It is designed for investors, policymakers, venture builders, and institutions working with science-based companies.
About the Author
Maria Ksenia Witte is a science commercialization strategist and the inventor of the 4x4-TETRA Deep Tech Matrix™, world's first RD&I-certified operating system for evaluating and building science ventures. She works with investors, institutions, and venture builders to align decision-making frameworks, capital deployment, and evaluation models with the realities of science-driven innovation.
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