Science Innovation: The Neurodivergent Kid on the Block

Why scientific innovations are misunderstood — and what we lose when we flatten their brilliance into business buzzwords.

A scientist walks into the startup world like it’s a party they were finally invited to. They dress up their research in pretty pitch deck, starve their own doubts, pitch with hope in their voice. They think — maybe this time, the perfect investor will understand. Maybe this time, I’ll be saved... But they won’t, won't they?

Neurodivergence 101: Not a Flaw, a Framework

It often starts in childhood, though nobody calls it that.

You’re the one who asks too many questions. The one who rearranges the pieces, even after the puzzle is done. The one who needs things to make sense — not socially, but structurally. You notice when the rhythm skips a beat, when the logic breaks, when someone says “because that’s just how it is.”

That’s never been enough for you.

You learn early that most people don’t like to be interrupted by truth. They prefer comfort over clarity. And so, little by little, you soften your edges. You become fluent in their language — even though yours was made of systems, patterns, weightless leaps between faraway thoughts. You perform normal, but it costs something.

That tension — between how your brain works and how the world expects you to behave — has a name.

It’s called neurodivergence.

Neurodiversity in general refers to the natural variation in human cognitive functioning that arises from differences in neurological development and processing. Rather than classifying certain neurological conditions solely as deficits or disorders, the neurodiversity paradigm recognizes conditions such as autism spectrum condition (ASC), attention deficit hyperactivity disorder (ADHD), dyslexia, dyspraxia, and dyscalculia as part of a spectrum of normal human diversity in brain structure, function, and information processing [1, 2, 3].

Coined in the late 1990s by sociologist Judy Singer, the term emerged to challenge the medical-pathological framing of neurodevelopmental conditions, proposing instead that neurological differences are not inherently pathological but reflect alternative cognitive profiles with distinct strengths, challenges, and adaptive patterns.

From a scientific standpoint, neurodiversity is underpinned by findings in cognitive neuroscience, developmental psychology, and genetics, which demonstrate:

• Heterogeneity in brain connectivity, activation patterns, and sensory processing across populations.

• Trait clustering that may offer functional advantages in specific contexts (e.g., enhanced pattern recognition, divergent thinking, or sustained focus in niche domains).

• Population-level prevalence of 10–20%, indicating a stable presence of neurodivergent profiles in the general human genome.

Importantly, neurodiversity is a descriptive rather than diagnostic term. It does not denote a clinical condition, but a framework that affirms cognitive variation as a baseline characteristic of human populations, with both individual and systemic implications for education, work, and societal inclusion [1, 2, 3].

It doesn’t show up in the same way for everyone. For some, it’s an intense focus. For others, it’s sensitivity to sound, light, inconsistency. It can feel like being too much and not enough, both at once. But underneath the labels, the heart of it is this:

You process the world differently — and you stay with what others rush past.

That’s not a flaw. It’s the origin of almost every scientific discovery we’ve ever had.

Because science, at its core, is not built by consensus. It’s built by those who don’t let go. Who don’t move on. Who stay with the friction until it gives way to something real.

But here’s the quiet tragedy: these minds — the ones best suited to solve complexity — are often pushed to the edges of the systems they could improve.

We diagnose what we don’t understand. We exclude what we can’t categorize. We reward confidence over depth, clarity over truth.

And in the process, we flatten the very people who carry the next breakthrough inside them.

We’ve built an entire innovation landscape on borrowed language — where those who walked away from the complexity now try to define it. No wonder the space feels off. It’s not rooted. It’s not built by those who’ve lived the discomfort.

Neurodivergence isn’t a marketing trend or a workplace checkbox. It’s the architecture of real innovation. It’s what allows a person to hold conflicting truths long enough to find a new one. It’s what makes the scientific mind so rare — and so easy to misunderstand.

But if we keep treating difference like deviation, if we continue to prioritize polish over pattern recognition, we’ll keep missing the very people who could change everything.

And they won’t always wait for us to catch up.

“We don’t tolerate confusion; we pathologize it. That’s our first mistake.”

The Analogy: Science Innovation as the Neurodivergent Outlier

If science were a child, it would be the quiet one at the edge of the playground.

Not because it doesn’t want to play — but because the games don’t make sense. The rules feel arbitrary. The noise is too loud. The pace, too fast to be meaningful. It’s not that science can’t keep up. It just doesn’t see the point of running in circles.

And so it watches. Noticing things no one else sees. Making sense of what feels chaotic to everyone else. While the others race to impress, it builds invisible structures — slowly, precisely — out of what most people overlook.

Now imagine what happens when that child is forced into a startup accelerator. They hand it a lean canvas and say: “Just fill it out.” They ask for a one-minute pitch. A hook. A headline. A viral insight.

But science doesn’t pitch. It proves.

It doesn’t sell dreams. It tests them — against reality, against physics, against time.

And when you try to make it louder, faster, flashier… it breaks. Or worse, it disappears.

This is exactly what we’ve done to #deeptech.

We took something designed to unfold over years — layered, rigorous, and interdependent — and pushed it into formats built for speed. We mistook complexity for confusion, and started optimizing it out.

Real science doesn’t fit into templates made for apps. It can’t be A/B tested in a weekend. It doesn’t deliver traction on demand. And when you try to press it into startup logic, it doesn't just underperform — it goes unseen.

And so, like that neurodivergent child, science-driven startups often end up misunderstood.

They’re told they’re not “investable” — not because their logic is wrong, but because it’s too hard to explain.

They’re told to simplify — when the value is in the nuance. They’re told to move faster — when precision is the only thing that protects their integrity. They’re told to pivot — when what they need is patience.

Eventually, some give up. Some pretend. And a few, quietly, go on to build something extraordinary — if they survive long enough.

This isn’t a complaint. It’s a pattern.

And it’s costing us more than we can measure — because the most meaningful breakthroughs don’t always look like success at first. They often look like silence, or struggle, or something that doesn’t quite fit.

Until one day, they change everything.

The Data We Ignore: Who Actually Built This World?

Science didn’t swoop in and fix everything overnight. It was built — invisible piece by invisible piece — by a very small minority. Fewer than 7% of people in the world ever earn a college degree. Within that small group, those who go deeper — earning science, engineering, or mathematics degrees — are a fraction still. In the OECD, roughly 23% of bachelor’s degrees are in STEM fields. That means the people who have a shot at advancing science and technology are rare — often less than 5% of the global population.

Stop and let that sink in.

The majority of loudly claimed “deep tech” voices? They come from outside this group. Packaged as expertise, they often lack the lived experience of wrestling through equations at 2 a.m., repeating an experiment for Xth time against parameters out of their control, or mapping out variables that don’t fit neatly on a canvas.

I recently asked a question to a group of professionals I trust — all of them working in what we now call the “deep tech ecosystem.” I asked why, if they were so passionate about this field, they hadn’t pursued science themselves.

The answers came quickly, almost reflexively:

“It was too hard.”

“Too abstract.”

“I wasn’t smart enough for that.”

And that’s when it clicked.

And yet, these are the voices defining what deep tech is — and isn’t.

That contrast matters.

When those who couldn’t go deep end up shaping the narrative, the field gets curated around what’s palatable, not what’s profound. The result? Science innovation becomes noise — not because it's not valuable, but because it's not heard.

“And yet these are the people defining what deep tech is supposed to mean?”

So, Here’s How Science Works

On July 7th, 2025, for the purposes of this very article, I posted a single line on LinkedIn:

POV: AI isn’t #deeptech

No explanation. No thread. No emotional pull. Just a one-liner. A hypothesis. Within seven hours, the post received 112 impressions, two reactions, and one comment.

For context: my typical posts receive between 500–800 impressions, 15–20 reactions, and 4–6 comments within the same window. I’ve published regularly for some time now. The algorithm knows me. The network knows me. This one missed — by a lot.

And that’s exactly the point.

Because in science, results aren’t only numbers going up. No result IS a result, a very powerful one.

This is how science works — and how it differs radically from the startup theater most people mistake for strategy.

1. Experiments Always Deliver — Just Not Always What You Expected

Every action produces a result. Not always the one you hoped for, but always something you can learn from — if you’re willing to listen.

In science, this is the first principle. Whether it confirms, contradicts, or confuses, it teaches you something. Even silence, even a failed reaction, tells you what doesn’t work under these exact conditions.

The same applies to how ideas behave in systems — on platforms, in markets, in investor meetings. Every interaction gives you information. The difference is how you interpret it. Business tends to look at lack of response as rejection. Science sees it as signal.

2. One Attempt Doesn’t Prove a Pattern

A scietific rigor and integrity dictate:

If you want to know whether a phenomenon is real, you repeat the experiment. You don’t pivot. You replicate.

Most scientific protocols require at least three iterations before you can even begin to speak of a trend, let alone a conclusion. Until then, all you have is a data point — not a dead end.

This is where most business minds give up. They run a single test, don’t see traction, and change everything. But in science, this is where it begins. First datapoint = foundation. Not verdict.

3. Controlled Change: The One-Parameter Rule

Here’s another core principle of experimentation: change only one variable at a time. Everything else stays constant — or you lose your ability to learn.

If I were to repost the same hypothesis next week — “POV: AI isn’t #deeptech” — I could change the timing, but not the wording. Or the day, but not the tone. Only one thing moves. That’s how you isolate cause and identify what matters.

In business, we see the opposite. People shift everything at once — message, format, offer, platform — and then try to guess what worked. But that’s not strategy. That’s chaos. Science doesn’t guess. It tests.

4. Environmental Variables: What’s Outside Our Control

Even in the best-designed experiments, some variables remain outside your control. When I posted that one-liner, I had no influence over:

• How many people logged into LinkedIn that morning

• Whether key people were on vacation

• What the algorithm decided to prioritize that day

The platform’s behavior is, to a scientist, the equivalent of room temperature in a lab. You can’t fully control it — but it can change your outcome entirely.

In chemistry, even small shifts in humidity, air pressure, or seasonal cycles can make or break a reaction. You learn to design around those conditions — or at the very least, account for them in your interpretation.

Most startup ecosystems don’t. And that’s a problem.

5. This Is Not Just About Posts — It’s About Startups

This entire analogy holds when we zoom out from LinkedIn posts to venture building.

Science-based startups often operate in environments with high uncertainty and limited control over external conditions:

• Funding availability

• Regulatory landscapes

• Macro timing and investor sentiment

• Supply chain shifts

• Deep tech readiness across industries

Yet we judge them as if they're SaaS: one pitch, one deck, one metric. When a scientist runs an experiment and gets minimal results, they don’t cancel the project — they document, iterate, adjust. But when a startup doesn’t immediately show traction, we pull the plug.

We’ve built systems that treat science like software, then penalize it for not behaving the same. The failure isn’t in the science. It’s in how we interpret its signals.

Real science requires:

• Patience

• Controlled iteration

• The humility to accept what you don’t control

• And the discipline to test again, without bias

If we applied even a fraction of that mindset to science startups, we’d stop declaring ventures dead after one pitch or one funding call — and start building systems where real signals can emerge.

We don’t market our hypotheses. We test them.

And this — this silence — was exactly the data I needed.

If You Don’t Understand This...

you shouldn’t be advising scientists.

What Happened to Deep Tech?

Once upon a time, deep tech was a precise term. It described technologies born from scientific depth — where physics, chemistry, biology, and engineering collided with commercial intent. It referred to ventures with long research timelines, real-world constraints, and transformative potential. It was never about what could be built in a weekend. It was about what must be built, even if it took ten years.

But somewhere along the way, the term slipped. From domain-specific rigor to investor slide decks. From research-backed ambition to branded aesthetic. From a signal of seriousness to just another LinkedIn hashtag.

Now, almost anything can be called deep tech if you say it with enough confidence.

A mental health app using AI-generated affirmations? Deep tech.

A platform with a quantum-sounding name but no physics inside? Deep tech.

An AI wrapper on GPT with a new interface? Deep tech.

🤮

The word is losing its edge — and with it, the ventures that truly belong to this category are being drowned in noise.

And here's the paradox: when everything is deep tech, nothing is.

Without grounding the term in scientific logic, it becomes a placeholder for hype. And the more diluted the term becomes, the harder it is for actual innovation to be seen, understood, and funded.

Category as Capital: Define or Be Defined

In science, classification is power. It’s how we organize complexity, establish boundaries, and make phenomena observable. The same is true in venture building.

If you don’t define your category, someone else will — and you may not like where they place you.

A misdefined startup gets misjudged. A misjudged startup gets misfunded. And a misfunded startup — no matter how brilliant — often doesn’t survive.

That’s why the first act of power in this space is definition.

Not through branding. Through logic.

At Arise, we don’t chase narratives. We build them from first principles. We use scientific pattern recognition — not marketing frameworks — to diagnose business cases & fundability. We map development timelines, regulatory environments, capital requirements, and industry dynamics as systems, not slogans.

Because deep tech isn't a story you tell. It's a structure you design.

And if we get the structure right, everything else follows: the right funders, the right partners, the right pace.

We’re not here to help science startups survive in a world that wasn’t made for them. We’re here to build a world where they lead.

Where the “neurodivergent kids” — the ones with inconvenient ideas, uncomfortable precision, and undeniable depth — don’t have to play small to be seen.

They set the rules. They shape the space. They become the signal that others calibrate to.

Because they were never the problem. They were always the future.

Two Options

This isn’t about gatekeeping. It’s about grounding.

Deep tech was never supposed to be a trend. It's a terrain — and if you want to move through it, you need to learn how to walk on rock, not sand.

So here are the two options.

Option one: Learn the basics.

Not just the words — the logic, the pace, the purpose. Understand what a scientific process looks like, why it takes time, why some results look like silence. Read papers. Visit a lab. Talk to researchers. Ask better questions. You want to be in this space? Do your homework.

Or…

Option two: Step back.

Not out of shame, but out of respect.Because if you don’t understand how something works, you shouldn’t be advising it, accelerating it, or investing in it. Not because you’re a bad person — but because even with the best of intentions, you’re doing more harm than good.

A takeaway message to scienceprenuers:

Choose to work with partners who understand your technology, not just pretend to.

—

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.

_____________________

💜 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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