Rethinking the Glossary: Why Science Needs to Speak a Different Business Language

What do you call a startup that can't launch an MVP, doesn't have early adopters, and isn't chasing revenue? In the eyes of most VCs, that's a red flag. In our world, that's day one of a revolution.

Because here's the truth: when your work lives in the realm of quantum states, advanced materials, or bio-integration, the usual startup language doesn't just fail you — it misrepresents you.

We're not just renaming terms. We're rethinking what progress even means when speed isn't the currency — precision is. When the bottleneck isn't the market, but the laws of physics. When traction doesn't mean clicks or users, but scientific validation, regulatory approvals, and industrial relevance.

So if you've ever sat in a pitch, accelerator, or investor meeting thinking “This playbook wasn't written for me” — you're right. It wasn't.

This is the glossary that finally speaks your language.

Long overdue respecting the scientific complexity.

Science-driven Glossary Overview: The Core Terminology Shifts

These are not semantic tweaks. These are structural shifts in logic.

Here's the good news: We're writing a new one. And it starts with language. Because words aren't just tools. In deep tech, they're alignment devices. They define how your work is perceived, funded, and built into the world. Misuse them, and your credibility evaporates. Use them precisely, and the entire conversation changes — with investors, with partners, and even with your own team.

1. Product-Market Fit → Technology-Market Feasibility

Old Logic: "Find a market. Build what people want."

New Logic: "Invent the solution. Then rewire the market around it."

Example: Battery tech for aviation isn't solving user demand. It's navigating a regulatory-materials-engineering trilemma — safety certifications, energy density, and thermal stability — before any plane leaves the ground.

Why it matters: In deep tech, the market doesn't pre-exist. It's unlocked. The right question isn't “Will users buy this?” but “Can this solve an unsolved constraint in a regulated, industrial environment?”

One of my favorite ones (;

Visual representation of misconception between PMF and scientific reality.

2. MVP → IVP (Industrially Viable Product)

Optional Subterm: SVP – Scientifically Validated Prototype

Old Logic: "Ship early, test fast."

New Logic: "Validate rigorously, prove viability in industrial context."

Example: A microfluidics startup can't test a half-baked chip in a garage. It must pass sterilization, precision calibration, and integration with lab automation — or it's useless.

Why it matters: Prototypes in deep tech must survive not just the demo — but the factory, the clinic, the field. Anything less is a liability, not a learning.

3. Go-To-Market → Create-The-Market

Old Logic: "Enter a known space and outcompete."

New Logic: "Forge the space. Build the rails. Educate the stakeholders."

Example: An AI diagnostic tool isn't “going to market” — it's navigating FDA pathways, building insurance reimbursement logic, and training a medical adoption curve.

Why it matters: You're not launching. You're landing on uncharted terrain — and you have to build the landing pad as you descend.

4. Iterate → Scientifically Bulletproof

Old Logic: "AB test your way to success."

New Logic: "Test hypotheses under real-world conditions with scientific integrity."

Example: In medtech, iteration cycles are 18–24 months. Each "test" costs €500K, involves clinical partners, and must be statistically powered.

Why it matters: In deep tech, speed kills if it overrides rigor. Every iteration must move toward certainty, not just novelty.

5. Traction → Scientific Milestones

Old Logic: "1000 users in beta."

New Logic: "Achieved TRL 6, published results, patent granted, passed ISO standards."

Example: Your “traction” is a peer-reviewed validation of protein stability under industrial scale-up, not sign-ups. If possible, LOIs from industry players help a lot!

Why it matters: Investors can't measure deep tech by consumer metrics. They need to see scientific momentum and industrial compatibility.

EXTRA: There is another thread on investor logic, when it makes sense to get them on board and how to navigate this part. What your business advisor doesn't want you to know (because they might not understand): VCs isn't always the answer.

6. Startup → Deep Tech Venture

Old Logic: "Start lean, iterate fast, disrupt often."

New Logic: "Engineer from first principles. Build with capital strategy. Scale through industry."

Example: A company building quantum-resistant encryption is not a “startup.” It's a 20-year bet on national security infrastructure.

Why it matters: You're not playing in the sandbox. You're laying the foundation for the next era — and it requires a venture model that reflects that.

7. Growth Hacking → Capital Architecture & Ecosystem Integration

Old Logic: "Use ads, loops, and viral triggers to scale."

New Logic: "Align capital stack, form consortia, and embed into industry pipelines."

Example: A photonic chip company doesn't need Instagram ads — it needs a lead investor, a university partner, and a semiconductor foundry alliance.

Why it matters: Deep tech doesn't scale in isolation. It scales by being woven into the fabric of existing systems.

8. Fail Fast → Experiment Rigorously

Old Logic: "Speed > precision. Learn by doing."

New Logic: "Design for failure, contain it, extract maximum learning."

Example: A bio-sensing material that fails in clinical trials isn't a learning — it's a dead end that could have been avoided with preclinical rigor.

Why it matters: In deep tech, you can't afford fast failure. But you must afford precise experimentation.

9. Pivot → Scientific Recalibration

Old Logic: "Market didn't bite? Change the product."

New Logic: "Experimental results shifted? Redesign the process."

Example: You're not pivoting from B2B to B2C. You're reconfiguring your nanostructure after unexpected diffusion profiles in testing.

Why it matters: Direction changes stem from scientific discovery, not customer confusion.

10. Early Adopters → Industry Stakeholders

Old Logic: "Find users who'll try anything new."

New Logic: "Secure the buy-in of regulators, researchers, and industry anchors."

Example: A medtech company's “early adopter” isn't a doctor — it's a hospital procurement board and a regulatory advisor.

Why it matters: Without early alignment from the right stakeholders, your tech dies in limbo — not in the lab.

11. Revenue Model → Monetization Strategy

Old Logic: "Monthly subscriptions, SaaS-style."

New Logic: "IP licensing, co-development contracts, public-private consortia."

Example: Instead of selling a finished drug, a biotech startup licenses out its molecule for clinical trials with pharmaceutical giants.

Why it matters: Your path to revenue isn't linear — it's a network of monetization points across the value chain.

12. Customer Feedback → Technical Validation

Old Logic: "User says the UI needs work."

New Logic: "Lab says the material degrades after 5k thermal cycles."

Example: You don't need stars on an app store. You need ISO-validated data, mechanical stress testing, and industry trial feedback.

Why it matters: Subjective opinion is irrelevant. In deep tech, only objective validation moves the needle.

Closing the Loop: Words Build Worlds

These shifts aren't cosmetic. They're structural, essential — and in many cases, long overdue.

Because in deep tech, the wrong words don't just cause confusion. They invite misjudgment. They compress timelines that need to unfold. They frame scientific breakthroughs as commercial delays. And they lead smart people — investors, partners, even founders themselves — to the wrong conclusions.

When your technology is evaluated through a lens designed for apps and viral growth, your traction looks like stalling. Your timeline looks like a flaw. Your rigor looks like hesitation. But the problem isn't the work — it's the frame around it.

And that's what this science-driven glossary is meant to be correct. Not to simplify your science. But to give it sharper edges. To name what's actually happening beneath the surface — the years of validation, the industrial pathways, the regulatory trenches, the capital choreography.

Because in a world where the default vocabulary is built for speed, we need language that honors depth.

This isn't about sounding smarter. It's about being understood. It's about alignment. And it's about control — over how your work is perceived, funded, and embedded into the systems it was built to change.

At Arise, we help you speak the language of inevitable progress — the kind of language that doesn't need to persuade, because it already makes sense.

Your science deserves more than startup lingo. It deserves precision. It deserves authority. It deserves to be read right.

And this? This is just the beginning.

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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 venture building & capital acquisition for science innovation

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