Core Concepts Governing Science Venture Systems
- Arise Innovations
- Jan 3
- 16 min read
From Misclassification to System Logic
Science ventures are consistently misclassified. They are evaluated through startup language, startup metrics, and startup expectations, even though their underlying dynamics follow a different logic. That misclassification is not just semantic. It shapes funding decisions, governance structures, timelines, and ultimately outcomes.
(Read more: Defining Science Innovation as a Distinct Venture Category, Terminology, Misclassification, and Decision Errors in Deep Tech)
Science ventures do not progress by iterating toward market fit. They evolve by resolving layers of uncertainty that are often epistemic rather than commercial. Progress is driven by validation events, dependency resolution, and irreversible knowledge creation. These dynamics cannot be understood by simply labelling them as startup, deep tech, or innovation project. They require a system-level view.
This is why system logic matters more than naming at this stage. Labels help with categorization, but systems explain behavior. They determine which constraints are binding, which signals are meaningful, and which interventions help or harm. Without a system perspective, evaluators mistake latency for failure, complexity for inefficiency, and uncertainty for lack of execution.
Moving from misclassification to system logic is therefore not a rhetorical shift. It is a methodological one. It replaces surface comparisons with structural understanding and prepares the ground for reasoning about science ventures on their own terms rather than by analogy to domains that operate under fundamentally different rules.
Complexity Is the Baseline, Not the Edge Case
Science ventures do not begin from a simplified problem that gradually becomes more complex. They start inside complexity. From the first experiment onward, they operate in high-dimensional uncertainty where multiple variables interact, influence each other, and cannot be cleanly isolated.
Unlike software or consumer ventures, uncertainty in science is not primarily about markets or execution. It is about whether the underlying phenomenon behaves as expected at all. Biological systems, chemical reactions, physical constraints, and material properties introduce dependencies that are often only partially observable. Small changes in one parameter can cascade across the system, invalidating assumptions that previously appeared stable.
This creates a landscape dominated by coupled variables and unknown unknowns. Progress depends on learning which variables matter, how they interact, and where the real constraints lie. Many early assumptions are necessarily fragile, not because of poor planning, but because the system itself has not yet revealed its structure.
Simplification strategies borrowed from startups fail in this environment because they are designed for domains where uncertainty can be reduced by decomposition. Lean experimentation, rapid iteration, and narrow hypothesis testing assume that the problem space can be broken into independent parts. In science ventures, those parts are entangled. Simplifying too early often removes the very signals needed to understand the system, leading to false confidence and misdirected effort.
Treating complexity as an exception to be managed later creates systematic error. In science ventures, complexity is the baseline condition. Effective strategy does not attempt to eliminate it prematurely. It works with it, acknowledges it, and structures decisions around the reality of interdependent, evolving knowledge rather than the illusion of early clarity.
Dependency Chains Shape Everything
In science-based ventures, the most fundamental dependencies arise long before any market traction or customer adoption enters the picture. What matters first is knowledge creation and validation because scientific ventures must uncover and prove the underlying phenomenon before they can meaningfully pursue any commercial opportunity. In deep tech ecosystems, this early focus on scientific and technical validation is repeatedly highlighted as a core differentiator from conventional startups. McKinsey identifies that deep tech innovation is driven by intensive early-stage R&D and higher technical risks that require sustained effort before later stages of market and business development can occur. (McKinsey & Company)
Because progress hinges on resolving scientific uncertainty, the checkpoints that actually matter in science ventures are validation gates rather than execution milestones. A validation gate is a point where key scientific and technical uncertainties are sufficiently resolved to enable the next phase of work. This might be demonstrating reproducible results in a lab, securing initial performance thresholds in complex systems, or acquiring evidence that a technology will scale under real conditions. These gates are structural and discrete; they unlock new degrees of freedom in the venture’s trajectory. They contrast sharply with typical startup milestones focused on product features, user adoption, or short-term growth metrics.
In practice, these scientific validation gates are deeply entangled with technical, regulatory, and infrastructural paths that cannot be separated cleanly. A breakthrough in materials science, for example, may require new manufacturing capabilities, alignment with regulatory frameworks, and access to specialized facilities before it can transition into a viable product. McKinsey’s research on deep-tech financing points to persistent structural gaps along the journey from discovery to commercial deployment, noting that ventures often face two distinct financing valleys: one between discovery and proof of concept, and another between pilot validation and scaling. (McKinsey & Company) These gaps reflect how interdependent the technical, regulatory, and infrastructural dimensions are in shaping the innovation pathway.
This interdependence leads to path lock-in as an inherent structural property of deep science ventures rather than a sign of poor planning. Early technical and scientific decisions create momentum, attract specific types of capital, and define what kinds of expertise and infrastructure a venture will need next. This momentum can make alternative approaches increasingly costly to pursue as the venture progresses. Rather than being accidental or inefficient, this phenomenon emerges because knowledge creation in complex systems is sequential and cumulative by nature.
Understand these dependency chains as the backbone of how science ventures unfold. They determine which risks must be resolved first, how resources flow, and what kinds of progress signals are meaningful. Recognizing this fundamentally different structure is key to designing evaluation frameworks, capital flows, and governance models that do not inadvertently penalize science ventures for behaving according to their own system logic.
Time Is a Structural Variable in Science Ventures
In science ventures, time is not just a schedule item you compress or stretch at will. It is a structural dimension of the system itself.
Development time as an endogenous constraint. In many science-driven fields, getting from discovery to meaningful validation simply takes years or even decades. For example, in pharmaceuticals the full development cycle from candidate nomination to launch averages about 12 years with cumulative R&D costs often exceeding €1 billion and only a tiny fraction of candidates ever reaching market approval. This long clock reflects inherent complexity, not inefficiency. (Eupati)
Deep tech ventures overall also face extended paths before commercialization. Deep tech companies that reach significant maturation often average around 9 years old and nearly $800 million in capital raised at the time they hit breakthrough stages and valuations above $1 billion. That long temporal arc highlights how deep scientific work must precede later growth phases. (BVP)
Biological, physical, and chemical irreversibility. Many scientific processes unfold on timescales that cannot be easily accelerated. In drug development, for instance, even once a therapeutic enters clinical trials it can take ~8 years just for regulatory approval due to the time needed to reliably measure effects in humans. Before that, years of lab work happen to understand biology and chemistry at a level that makes human studies possible. These stages involve irreversible steps of evidence generation that cannot be skipped because they are tied to safety, mechanism, and regulatory standards.
Why speed optimization distorts decision quality. Efforts to “compress” timelines often steer teams toward superficial checks rather than deep understanding. In science ventures, the critical events that unlock later phases are not incremental feature releases; they are knowledge transformations. Pressuring teams for fast milestones before the foundational science is solidly established frequently leads to late-stage failures, wasted capital, and rework. Investors and operators in deep tech have learned to accommodate this, building strategies around de-risking through structured validation phases rather than chasing early market signals. (McKinsey & Company)
The hidden cost of timeline pressure. By treating long timelines as inefficiencies, stakeholders misinterpret structural reality as poor execution. The consequence is misplaced interventions like premature scaling, excessive burn rate pressure, or forcing early productization. These shortcuts do not reduce real uncertainty but instead can erode optionality, reduce data quality, and increase downstream risk. Recognizing that time in science ventures reflects irreducible structural processes helps align expectations, investment horizons, and governance models with the actual logic of how scientific value is created and validated.
In sum, time in science ventures should not be managed as a metric to optimize away. It is a variable intrinsic to the system that shapes what decisions are feasible, what risks can be resolved, and what forms of value emerge.
Non-Linear Progress Is the Norm
In science ventures, progress does not unfold as a smooth, step-by-step climb. Instead, it moves through periods of long plateaus followed by abrupt transitions in capability or understanding. This pattern is one reason why the breakthrough funding diagram above shows a “valley of death”: the early and mid stages of development often produce few visible signals while foundational uncertainty is resolved, and only later do discrete leaps in readiness or performance occur that justify renewed investment. (EBAN)
Long plateaus followed by abrupt transitions. Unlike software ventures where incremental version releases produce frequent measurable progress, science ventures often sit for extended periods at relatively flat levels of observable achievement. Only once a set of complex interdependent variables is resolved do we see a sudden jump in performance or readiness. This behavior mirrors patterns of innovation adoption in technology transitions, where S-curve dynamics produce slow initial progression before accelerating dramatically once critical thresholds are crossed. (RMI)
Why progress signals are often invisible externally. Much of the work in these early plateau phases consists of invisible knowledge accumulation such as refining experimental methods, understanding boundary conditions of systems, or synthesizing new materials. Because this work does not yield external metrics that look like traction—such as users, revenue curves, or rapid prototyping—external observers can misinterpret it as stagnation even though the underlying knowledge state is evolving meaningfully. This phenomenon explains why science ventures often struggle to attract capital until a discrete validation event makes their technical credibility unmistakable.
Validation events as discrete state changes. The observable progress in science ventures tends to come in the form of validation events—points at which previously uncertain underlying assumptions are confirmed and enable a new phase of capability. Examples include demonstrating reproducible results in a relevant environment, securing regulatory acceptance of a mechanism, or proving scalability of a process. These events are structural inflection points that change a venture’s state rather than marginal improvements on a trajectory.
The illusion of stagnation in early phases. This non-linear pattern creates an illusion of stagnation during early and mid phases. Funding availability and external support often dip in these regions not because ventures are failing to make progress, but because observable outcomes lag behind the true state of knowledge and capability underneath. Only when a validation event elevates the venture into a new maturity bracket—beyond the valley of death—do momentum and support rebound. The attached figure visualizes this effect across technology readiness levels, showing why linear expectations fail to capture how science ventures actually evolve.
By recognizing non-linear progress as the norm, not the exception, we can better design evaluation systems, expectations, and capital strategies that align with how scientific value is actually generated over time.
Capital Interacts With the System, It Does Not Sit Outside It
In science ventures, capital does not simply sit waiting to be deployed. It actively shapes trajectories and stabilizes or destabilizes system behavior depending on where and when it enters. Unlike traditional consumer or software models where funding often follows adoption signals, science ventures must align capital with structural milestones in knowledge validation.
Funding as a force that reshapes trajectories.
Deep tech funding patterns reflect this. Analysis by BCG and Hello Tomorrow shows that 80% of deep tech startups cite funding as their most critical resource, signaling capital’s central role in determining whether foundational work continues or stalls. TD Shepherd Moreover, deep tech now represents about 20% of global venture capital allocation, roughly double its share a decade ago, yet this growth belies deeper dynamics: larger deal sizes often occur only after significant scientific validation has been achieved. BCG Global
Timing versus amount of capital.
Empirical research underscores that when funding arrives can influence outcomes more than how much arrives. An empirical study covering deep tech companies found that the timing of the first equity funding round has a strong non-linear effect on venture success prospects, with specific timing windows (e.g., three to eight months after launch) correlating with higher probabilities of positive outcomes under certain models. These findings reinforce that aligning capital with the venture’s knowledge state—rather than simply injecting large sums early—can materially influence success.
Premature scaling as a system-destabilizing event.
Capital that enters too early or with inappropriate expectations can destabilize rather than support progress. Studies in the broader startup ecosystem show that around 70% of high-growth technology ventures scale prematurely, meaning they expand team size, product scope, or operations before foundational conditions are established. In science ventures this problem is magnified: premature scaling driven by capital pressure often leads to burnout of cash without resolving core technical uncertainty, increasing the likelihood of later failure or pivot stress.
Why capital efficiency metrics mislead in science ventures.
Common efficiency metrics assume that value creation is smooth and tightly coupled to investment. In science ventures, that assumption breaks down because most value is realized at discrete validation events rather than continuously. Investors raising successive rounds only after critical validation milestones are passed suggests a model where capital efficiency must be evaluated relative to state changes in knowledge and readiness instead of continuous output per dollar. Otherwise, capital that looks “inefficient” early (because it is buying long, invisible R&D phases) can be penalized prematurely, even though it is buying the very uncertainties that unlock later leaps.
Taken together, these data points show that capital in science ventures is not a background condition. It is an interacting force that can either help the system traverse difficult phases or distort its dynamics when misaligned with structural needs. Effective funding strategies acknowledge this complexity rather than treating capital as simply fuel to be burned faster.
Organizational Structure Under Epistemic Uncertainty
Science ventures operate in conditions where the facts are still being created rather than simply revealed. This state of epistemic uncertainty—that is, uncertainty about the *structure of the system itself and which assumptions are true—changes how decisions have to be made and how organizations must be structured to support that process.
Decision-making when facts are still being created.
In situations of deep uncertainty, traditional models that rely on forecasts or fixed probabilities break down because the underlying system behavior is not well understood. Decision science frameworks like Decision Making under Deep Uncertainty (DMDU) emphasize exploring multiple plausible futures and designing strategies that remain robust across them rather than optimizing for a single predicted outcome. This approach acknowledges that leaders cannot wait for certainty; they must make decisions while uncertainty remains unresolved, adapting as knowledge grows. (Rand)
In science ventures, many critical decisions are in this category: which experiment to fund next, which hypothesis to drop, and when to move to scale all must be made before full clarity. Organizational structures that assume facts exist before choices are made will struggle here.
Role ambiguity as a feature, not a bug.
In environments where knowledge is evolving, rigid role definitions can inhibit progress. High-reliability organizing research shows that teams succeed not through strict hierarchies but by developing shared sense-making practices that allow specialized contributors to coordinate without fixed scripts. These patterns of action improve collective ability to interpret and respond to complex, ill-structured contingencies because they do not assume that any individual has complete information up front. This reflects a broader shift toward distributed leadership and shared cognitive load, which is necessary when no single perspective holds all the facts.
Instead of treating ambiguity in roles as dysfunction, science ventures benefit when structures support cross-functional interaction and flexible task boundaries. When scientists, product thinkers, and regulators all sit behind fixed titles and silos, the organization loses its ability to adapt as evidence evolves.
Why early managerial formalization backfires.
Formal hierarchies and rigid processes are optimized for repeatability and efficiency in known environments. In contrast, science ventures operate in a context where the proper course of action cannot be defined in advance. Research on strategic decision making under uncertainty highlights that novelty and complexity render fixed decision rules inadequate because they cannot anticipate every variable or scenario. Early formalization of management structures risks embedding assumptions into the organization before foundational knowledge has been established, which in turn constrains how the team can explore alternative hypotheses or pivot when evidence changes. (Technological Forecasting and Social Change)
In other words, premature procedural clarity creates cognitive lock-in; teams become optimized for executing a plan that may not fit the evolving reality of the science they are investigating.
Governance suited to evolving knowledge states.
Under deep uncertainty, governance that supports adaptive decision pathways and maintains optionality outperforms rigid stage-gate or waterfall models. Approaches rooted in DMDU and robust decision-making encourage organizations to build governance structures that explicitly incorporate uncertainty, scenario exploration, and iterative reassessment. Such governance is not about having all answers at the start, but about building mechanisms that allow the organization to update decisions as evidence accumulates and conditions evolve.
This perspective reframes uncertainty from a liability to be eliminated into a structural property to be navigated. Teams that accept and design for this—by prioritizing flexible authority, shared sense-making, and adaptive governance—are better equipped to manage the epistemic ambiguity at the heart of science-based ventures.
Evaluation Breaks When Systems Are Misread
When evaluators apply standard innovation frameworks to science ventures, the tools fail not because they are poorly designed, but because they are built on assumptions that don’t hold in complex, knowledge-driven systems.
Why common frameworks fail even when “adapted.”
Typical evaluation tools focus on linear inputs and outputs or short-term milestones. They were developed in contexts where progress is incremental and feedback is continuous. Deep tech and science ventures do not behave this way, so merely tweaking existing frameworks leaves the same fundamental mismatch.
Embedded assumptions about reversibility and feedback.
Most evaluation models assume that progress can be reversed and iterated rapidly. In science ventures, many critical processes—like experimental validation or regulatory acceptance—are irreversible and discontinuous. Expecting continuous feedback and adjusting based on incremental signals leads to misjudging progress as stagnation.
The gap between evaluability and real progress.
There is a real difference between what is easy to measure and what actually matters. Deep scientific work often produces invisible knowledge gains that don’t show up in conventional metrics but are essential for later breakthroughs. Standard evaluation misses these contributions and only sees movement once visible artifacts appear.
Structural blind spots in institutional decision-making.
Institutions tend to reward clarity and comparability in metrics. This leads them to privilege signals like milestone delivery, burn efficiency, or prototype demos. These signals make sense in traditional startups but are blind to the state changes that drive science venture progress. As a result, decision-making systematically undervalues critical phases where real risk is being resolved.
In short, when the system is misread, evaluation breaks down. Recognizing this helps shift focus from surface metrics to state-aligned evaluation, where progress is judged in terms of knowledge and validation rather than superficial indicators.
What System-Aware Reasoning Changes
A core advantage of thinking in systems rather than in linear stages is that it unlocks practical decisions about what to observe, invest in, and support. Rather than chasing superficial checkboxes, system-aware reasoning aligns evaluation, strategy, and policy with the actual dynamics of science ventures.
Shift from stage-based to state-based thinking.
Instead of assessing progress by stage labels (seed, series A, commercialization), evaluate ventures by state changes in knowledge and validation. This means using indicators that reflect technological readiness and scientific de-risking rather than just financing rounds or product releases. Tools like TRLs (Technology Readiness Levels) help investors understand maturity more meaningfully in science contexts because they integrate technical validation with readiness for scaling. (Equidam)
Early signals that actually matter.
In science ventures, the signals that predict future progress are not customer counts or revenue trends. Instead, meaningful early signals include:
Technical validation milestones such as reproducible experimental results and pilot demonstrations,
IP and patent strength that demonstrates novelty and defensibility,
Scientific team expertise and domain credibility, which signal capacity to navigate complex uncertainty,
Strategic partnerships with research institutions or industry players that can de-risk commercialization challenges.Investors and operators who assess these kinds of signals align better with the rhythm of technological discovery and validation rather than market traction alone. (McKinsey & Company)
Reframing risk, delay, and failure.
Under system logic, delays and apparent stagnations are not necessarily failures; they are often signs of deep uncertainty being resolved. What looks like slow progress may be building the foundation for a major leap once key validation gates are cleared. Investors and operators should build frameworks that distinguish between risk resolution phases and genuine dead ends, and design experimentation portfolios accordingly.
Implications for investors, operators, and policymakers.
Investors should adopt evaluation frameworks rooted in technical maturity and scientific validation rather than conventional milestone counts. Portfolio risk should be managed by blending capital across ventures with complementary rhythms of de-risking and by leveraging expert technical due diligence to reduce epistemic uncertainty.
Operators and founders must communicate progress in terms of state changes rather than stage progression, and build strategic narratives that connect early scientific milestones to long-term value creation.
Policymakers and ecosystem builders need to support ecosystem infrastructure and capabilities that help ventures transition through knowledge gates, such as translational research centers, shared facilities, and regulatory support pathways. They should also tailor funding instruments to reward real de-risking rather than superficial activity. (Hello Tomorrow, Walden Catalyst)
System-aware reasoning reshapes evaluation from a checklist of milestones to a map of states and transitions, improving decisions for all actors involved in science ventures and aligning incentives with the true drivers of progress.
Where This Leads Next
Recognizing science ventures as systems is only the starting point. The real work begins when this understanding is translated into tools, diagnostics, and operating logic that can be used in real decisions.
Preparing the ground for system-native tools and diagnostics.
Once progress is understood as state change rather than stage progression, evaluation can no longer rely on generic scorecards or milestone checklists. What is needed are diagnostics that map where a venture actually sits within its knowledge, validation, and dependency landscape, and what constraints are binding next. This opens the door to system-native instruments that assess readiness, risk, and optionality based on scientific and structural signals rather than surface activity.
Why better language alone is insufficient.
Correct terminology matters because it shapes perception, but language without instrumentation changes little. Even when institutions acknowledge that science ventures are different, they often continue to use the same evaluation logic underneath. Without tools that operationalize system thinking, improved language risks becoming cosmetic, aligning rhetoric while decisions remain unchanged.
Transition toward operational frameworks.
The implication is a shift from descriptive understanding to actionable structure. System-aware reasoning must be embedded into how capital is deployed, how progress is reviewed, how governance adapts, and how failure is interpreted. This requires frameworks that do not just explain why science ventures behave differently, but actively guide what to do at each point given the current state of the system.
The next step is therefore practical: how to translate system logic into concrete evaluation and decision frameworks that institutions, investors, and operators can actually use. The following article will move from concepts to mechanics, focusing on how to diagnose venture state, identify meaningful signals, and design interventions that align with how science ventures truly evolve.
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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