The Science of Becoming Real: Heisenberg's Uncertainty of Venture Building in Deep Tech
- Arise Innovations
- Jul 17
- 8 min read
“The scary part is, the second you say it out loud — it changes.”
There's a moment that every scientist turned founder remembers — not because it was loud, but because everything suddenly sounded different.
It's the moment you first called your work a startup .
Not a research project.
Not a technology.
A startup.
And in that instant, something fundamentally shifted.
The experiments you once designed with elegant uncertainty now seem to demand answers. The freedom to explore gives way to the pressure to perform. What was once an open system of inquiry becomes a narrowing pipeline toward deliverables, valuation, and milestones.
You didn't change the science. You simply observe it differently. And in doing so, you changed its behavior.
Much like a quantum system, your venture existed in multiple potential states — a platform, a diagnostic tool, a licensing play, a scientific breakthrough without a market. But the moment you spoke it aloud, the wave function collapsed. The story began to solidify. And suddenly, you're not in control of the narrative — you're being pulled into it.
This is the paradox at the heart of venture building in science: the very act of declaring your intent — of looking at your work through the lens of business — alters its trajectory.
Welcome to Heisenberg's world.
The Uncertainty at the Heart of Venture Building
In quantum mechanics, Heisenberg's Uncertainty Principle tells us something profoundly counterintuitive: the more precisely you know a particle's position, the less precisely you can know its momentum — and vice versa. It's not a flaw of measurement. It's a fundamental property of reality. The very act of observing collapses one truth while blurring another.
Venture building in science follows a similar logic . At the earliest stages, when everything still feels fluid — from the technical roadmap to the market positioning — founders are faced with an impossible task: making irreversible decisions while operating in a space defined by ambiguity.
Try to lock down the technical development path, and suddenly your market use case starts to shift. Choose a customer segment or pricing model too soon, and you may force your R&D into directions it's not ready to go.
The precision of one side creates distortion in the other.
The mistake most founders make isn't uncertainty — it's pretending they don't have it. Following startup templates designed for apps and marketplaces, they rush into decisions to “show progress,” unaware that they're measuring the wrong variables. They try to know everything at once: who the buyer is, how to scale, what features matter most — all while the science is still becoming real.
But deep tech doesn't move in straight lines. It oscillates. It resists simplification. And just like in quantum systems, the job is not to eliminate uncertainty — it's to work within it.
The founders who succeed aren't the ones who wait until everything is known. They're the ones who choose a position to fix — not because it's perfect, but because forward motion requires commitment. They collapse just enough of the wave to make the next move calculable.
In this space, precision is a strategic act — and every measurement is a compromise.
Scientific Rigor Meets Venture Logic
In the lab, no credible experiment begins without a defined protocol. You set your controls. You define your variables. You isolate the conditions you want to observe — not because you crave simplicity, but because without structure, your data means nothing.
The same logic applies to venture building — especially in deep tech.
Yet here, the temptation is often the opposite: to delay every decision, to keep every option open “just in case,” to avoid commitment under the guise of flexibility. But in truth, a hypothesis without constraint isn't freedom — it's chaos.
Founders who try to navigate ten potential markets, five pricing models, and three routes to scale all at once aren't exploring. They're eroding the very conditions that allow validation to happen.
You don't need all the answers. But you do need to fix some of the parameters — your target use case, your IP path, your early buyer logic — in order to see anything meaningful emerge. Without anchoring, there is no iteration. Only noise.
The data is unforgiving:
Over 92% of science startups fail — not because of technical flaws, but because of misalignment between the logic of the invention and the logic of the business (BCG, Hello Tomorrow).
They built something extraordinary, but never gave it the scaffolding to hold up under market conditions. The science was valid. The venture wasn't.
Deep tech isn't lean. It's layered. It demands trajectories, not pivots. And it rewards founders who treat venture building like an applied experiment: one with designed constraints, embedded logic, and intentional points of measurement.
Ironically, the best founders often make commitments “too early.” Not because they're certain — but because they understand that certainty is not the prerequisite. It's the result.
The False Safety of Observation
In science, observation is never neutral. It changes the system. In venture building, it does something even more deceptive — it pretends to be neutral.
Advice piles up. Stakeholders request one more version of the roadmap. Accelerators nudge you to “validate the market” just a bit longer. Consultants suggest one more iteration before making a move.
Everyone wants clarity before commitment. But deep tech doesn't work like that. In this domain, the act of choosing is the work.
The early stages aren't slow because the tech is complex. They're slow because founders are stuck in the illusion that more observation — more analysis, more frameworks, more feedback — will eventually yield the perfect answer. It won't.
At some point, exploration becomes avoidance. The venture becomes a Schrödinger's startup — alive in theory, dead in practice, waiting for someone to open the box.
This is where the lean startup mindset begins to unravel. The MVP playbook assumes you can test quickly, iterate visibly, and get instant feedback from users.
But in science, feedback doesn't come from clicks or conversions. It comes from regulatory constraints, industrial feasibility, and the unforgiving laws of physics. You can't A/B test your way to molecule stability. You can't MVP a medtech device into clinical compliance.
And so, founders are left in a system that rewards movement — but only if that movement is visible. And that's the trap. Because the most important decisions in deep tech happen before they're externally legible. They happen when you decide, not when the world claps.
In this space, the courage isn't in launching something flashy. It's in collapsing ambiguity into a commitment. In defining the problem space before the market tells you what it wants. Knowing that sometimes, the next best move can't be tested — it just has to be made.
Reframing: Venture Building as an Applied Scientific Process
Too often, we're told that science and business live in different worlds — one governed by logic and evidence, the other by risk and instinct. But that's not just wrong. It's dangerous advice for scientists stepping into venture building.
Because the truth is: venture building is a scientific process. But only if you structure it like one.
Let's break it down.
🧩 1. Your Hypothesis = Theory of Impact
Not “what’s the product?” or “who’s the customer?” — but what is your underlying belief about how this innovation changes the world? Does it reduce friction in industrial processes? Enable new diagnostics? Alter regulatory baselines?Good founders articulate this before they articulate features.
👉 Your action: Write down the one-sentence causal belief.
"If [scientific insight] is applied to [system/environment], then [outcome] changes."
🔬 2. Your Protocol = Venture Architecture
In science, protocols define how the experiment runs. The same applies here.Will you go B2B or B2G? License or sell hardware? Build internally or co-develop?The biggest mistakes happen when founders delay these structural decisions, hoping to stay “flexible.” But clear compound value.
👉 Your action: Sketch a draft protocol:
Who's in your loop (lab, founders, IP holders)?
What's the funding sequence you're optimizing for (grants → VC → industry deal)?
What's the minimum structure needed to get signal?
📏 3. Your Measurement = Strategic KPIs
You don't measure a molecule with a stopwatch. So why measure a science startup with web traffic?The right KPIs in deep tech are lagging and layered:
Can your invention survive industrial conditions?
Are investors from your vertical responding?
Have you secured regulatory feedback or letters of interest?
👉 Your action: Choose 2-3 signal indicators based on your exit logic, not vanity metrics.
👁️ 4. Your Observation = Market Behavior
The system responds to how it's being watched. Your pitch will land differently depending on who's in the room. Investors, grant reviewers, partners — they're not giving you feedback. They're giving you data.
Don't ask what people say . Watch what they do .
Who follows up?
Who asks the hard questions?
Who goes silent when real money enters the conversation?
👉 Your action: Keep a tracking sheet. Note who leans in, who stalls, and what changes when price or IP is mentioned.
⚡ 5. Collapse = The Inflection Point
Eventually, the venture must collapse into form: a license, a deal, a grant, a Series A. That collapse doesn't come from wishful thinking — it comes from controlled input and disciplined iteration.
And just like in science, the quality of the collapse depends on how well the system was built.
👉 Your action: Map your target collapse points in advance.
What's the earliest milestone that will prove you're on the right track? Don't wait for it. Design for it.
This isn't about copying startup models. It's about reclaiming your native language — and realizing that your scientific training already gave you the tools to build a venture.
You just have to stop translating and start applying.
In science, you don't expect certainty — you build toward significance.
And in venture? The same rule holds. But don't expect this depth of approach from someone who never failed experiments in the lab or had to juggle several hypotheses into one puzzle at the same time.
“The only way to know what works is to commit to one version and test it.”
The biggest risk in deep tech isn't making the wrong move. It's staying in observation mode so long that your window closes before you ever commit.
Watching is not building. Overthinking is not strategy
Precision doesn't come from passivity — it comes from deliberate, structured collapse.
If you're still toggling between ten different narratives, you don't have a venture.
You have a quantum fog.
Pick one state.
Define it.
Test it under real conditions.
And if it breaks — good. Now you know what doesn't scale. Now you're in motion.
This is not lean startup advice dressed in a lab coat. This is venture design from the laws of nature up.Because when your raw material is scientific uncertainty, the only path forward is disciplined constraint.
And that's what we do at Arise.
We don't build your startup. We collapse its infinite states into the one that gets funded.
Ready to stop circling the problem and start structuring the solution?
—
Welcome to Arise - The Vault.
Where science is taken seriously — and the people behind it finally get the respect they deserve. This is where Maria, founder of Arise Innovations, goes beyond category definitions and restores deep tech to what it was always meant to be: rigorous, grounded, and fundable by design.
The content you're entering is shaped by personal experience inside the science and innovation landscape since 2013. What's shared here is built on observation, structured through data, and sharpened by pattern recognition across thousands of ventures. You're not just reading. You're entering a system.
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💜 Because Science doesn't follow Rigid Business Logic
eM. from Arise Innovations — Your only partner for venture building & capital acquisition for science innovation
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