That’s what makes a hindsight look at the series so fascinating.

They had the best player on the floor.

They had youth.

They had athleticism.

They had leads… big leads (The Ringer, 2026).

In fact, the Spurs led in all five Finals games and built double-digit advantages multiple times throughout the series. Yet the Knicks repeatedly found ways to come back and ultimately won the championship in five games.

This wasn’t something mystical. No one had the “clutch gene.” Josh Hart bricked an open fast break layup with a chance to take the lead late in that historic game 4. Jalen Brunson’s go ahead 3 in the same game? Also a brick.

So, how did they win? It was experience. But not in the way you may think.

De’Aaron Fox is an experienced veteran and yet was easily the most ill-prepared to play in this series. He made indescribably bad plays that seemed entirely disconnected from the moment, whereas the rookie Dylan Harper, was neck and neck with Wemby (no pun intended) with effectiveness on the floor.

They are proof that experience isn’t just time you spend doing something. It’s really a form of pattern recognition.

It’s seeing a situation and instinctively knowing what tends to happen next.

The Knicks had it.

The Spurs didn’t.

Which got me thinking:

What if AI could accelerate experience? Not by calling plays or replacing coaches. Not by telling Victor Wembanyama to stand in the dunker spot.

But by helping teams recognize patterns before they become lessons learned.

The Hidden Difference Between Good Teams and Championship Teams

The Spurs are young.

Extraordinarily young.

Victor Wembanyama—the undisputed leader of the team and future face of the league—is only 22.

Stephon Castle is barely old enough to rent a car.

Dylan Harper is still closer to high school than his prime. (This should strike fear in the hearts of the rest of the league, by the way).

Their Finals run wasn’t supposed to happen this quickly. The Thunder were heavily favored in the conference finals, even without their second-leading scorer Jalen Williams.

And yet they were suddenly playing on basketball’s biggest stage against a Knicks team built around battle-tested veterans who had spent years accumulating playoff scars. In fact, the Knicks didn’t to the Spurs, what the Pacers did to the Knicks in their run to the finals just last year.

That experience showed up everywhere.

When momentum shifted.

When the crowd got loud.

When every possession suddenly felt twice as important.

The Knicks seemed comfortable operating in the space where every moment could be the moment. This is because they recognized that the exact same pattern that emerged in their conference finals loss against the Pacers was emerging against the Spurs—only this time, the Knicks were the beneficiaries.

So, the Knicks weren’t seeing the future. It wasn’t some “championship DNA” that made them victorious.

They were recognizing the present.

They had seen versions of these moments before.

The Spurs looked like a team learning these patterns in real time.

Which is exactly what they were doing.

AI as a Championship Simulator

One of the most interesting applications of AI in sports isn’t prediction.

It’s simulation.

Imagine a system trained on decades of playoff basketball.

Not to predict winners.

To identify situations and patterns.

The AI notices:

“Teams with a 12-point lead entering the fourth quarter against high-pressure defenses tend to become overly isolation-heavy.”

Or:

“When this lineup begins missing consecutive corner threes, opponents increase transition opportunities by 18% over the next five possessions.”

Or:

“When Wembanyama is forced to initiate offense late in games, defensive pressure increases dramatically and ball movement decreases.”

These aren’t play calls. These are patterns that weren’t possible for Spurs head coach Mitch Johnson to learn in real-time.

Experience compressed into information.

Historically, teams gained this knowledge through years of winning and losing.

Through blown leads.

Through playoff collapses.

Through painful lessons that only reveal themselves after the fact.

The Wemby-led Spurs are now on this list.

AI offers a different possibility:

What if teams could learn from experiences they never personally played through?

The Real Value Isn’t Strategy

Most people assume AI’s value in sports is tactical.

Better rotations.

Better matchups.

Better scouting.

And those things do matter.

But I suspect the biggest opportunity is actually cognitive.

The Finals exposed how difficult decision-making becomes under pressure.

Players become tired.

Coaches miss information and hyperfocus on execution.

Crowds create noise.

Momentum alters perception.

Humans stop operating under ideal conditions.

The challenge isn’t intelligence or ability.

The challenge is judgment.

And judgment deteriorates when stress rises.

This is true in basketball. It’s true in aviation. It’s true in security operations. It’s true almost everywhere humans make high-stakes decisions.

The most interesting AI systems of the future may not be the ones that make decisions for us.

They may be the ones that help us make better decisions ourselves.

Human in the Loop

This is where I think surface-level conversations around AI often go wrong.

The goal isn’t to replace human expertise.

The goal is to augment it.

The world doesn’t need AI coaches, referees, engineers, pilots, surgeons, or architects.

We need faster access to lessons that currently take years to learn.

Because what ultimately defeated San Antonio wasn’t talent.

It’s time.

The Knicks had accumulated more playoff experience.

More late-game reps.

More failures.

More opportunities to learn what pressure feels like.

The Spurs gained some of that experience this series. And yes, that’s part of what will make them so dangerous moving forward.

But AI raises an interesting possibility:

What if experience itself becomes scalable?

What if a young coach could benefit from the collective lessons of thousands of playoff games?

What if a doctor didn’t have to cycle through multiple medications before recognizing a treatment pattern that an AI had already seen a million times?

What if a security analyst could identify an attack because an intelligent system recognized echoes of incidents that occurred years earlier on the other side of the world?

For most of human history, expertise has been constrained by a simple reality:

We can only learn from the experiences we’ve lived through.

AI may be the first technology capable of changing that and in real time.

The future of AI won’t be about replacing the humans who make our organizations, professions, and institutions possible.

It will be about helping humans recognize the moment they’re in before it slips away.

In the NBA, that might be the difference between a generational talent becoming the undisputed GOAT or ending up as little more than a trivia answer.

Elsewhere, it could be the difference between a breach and a near miss.

A successful surgery and a failed one.

Life and death.

I know that’s the new and trendy thing to say nowadays and, hey, no shade, but it’s not for me.

Now granted, ADHD can seem like a superpower on the surface. We often:

• Know things about a lot of different things

• Can hyperfocus on something that interests us for hours on end

• Notice patterns no one else does

• Think nonlinearly

• Are infinitely curious

Needless to say, watching someone with ADHD speed run through a project or learn something new so quickly can definitely give the illusion we have some sort of neurological cheat code.

But we don’t.

There’s another side to ADHD that is typically only seen by loved ones.

The stimming.

The emotional dysregulation.

The anxiety.

The burnout from those speed runs.

The inability to sit for long periods.

The mounds of laundry scattered around.

It’s not a superpower. It can really suck, especially for folks who don’t have access to medication. And it’s important to know that this picture of ADHD isn’t universal. It manifests differently for everyone.

But there are recurring themes in how we work and where we thrive. And our strengths are surprisingly aligned with modern security work.

Here are five that come to mind and how they translate to stronger security systems.

1. ADHD Minds Often Detect Anomalies Faster

Earlier, I mentioned how exceptional many people with ADHD are at noticing patterns. That also means we’re highly sensitive to novelty, inconsistency, and environmental shifts.

Sure, in everyday life this can be distracting.

In security operations, it can be incredibly valuable.

A lot of incident response is fundamentally:

“It depends.”

Those dependencies are often some pattern under some set of conditions.

ADHD brains often excel at noticing:

• inconsistencies

• irregularities

• weak signals

• contextual deviations

• unusual relationships between systems

before those signals become obvious to everyone else.

So in many ways, security work rewards people who are naturally sensitive to anomalies.

That’s essentially risk analysis and threat detection at a cognitive level.

2. Curiosity Is a Massive Advantage in Security

Security is one of the few professions where compulsive curiosity is genuinely useful.

ADHDers often struggle in environments where exploration is discouraged or tightly constrained. But security operations frequently (and sometimes necessarily) requires exactly the opposite behavior:

• digging deeper

• following rabbit holes

• asking “what if?”

• exploring edge cases

• testing assumptions

• connecting unrelated information

That instinct matters enormously in incident response and vulnerability research.

Because many major discoveries in security happen when someone refuses to stop at the obvious explanation.

Curiosity drives:

• better investigations

• deeper scoping

• stronger root cause analysis

• more creative threat modeling

• more resilient process design

And ADHD brains often operate in an unusually exploratory mode by default.

3. ADHD Can Improve Human-Centered Process Design

One of the most overlooked strengths of ADHD professionals is that we often experience workflow friction earlier and more intensely than others.

Maybe that sounds like a negative off rip.

But from a systems-design perspective, it’s incredibly valuable.

Because people who struggle with:

• ambiguous instructions

• excessive context-switching

• fragmented workflows

• notification overload

• unclear prioritization

• poorly designed interfaces

often become exceptionally good at identifying where systems break down cognitively and what gaps exist that otherwise would go unnoticed.

In other words:
ADHD professionals can become early warning systems for operational inefficiency.

If a workflow is exhausting, confusing, difficult to navigate, or mentally unsustainable for neurodivergent operators, there’s a good chance it’s creating hidden inefficiencies for everyone else too. They just don’t see or feel it yet.

This is one reason neurodiversity belongs in conversations around security UX and operational design.

People closest to cognitive friction often see system weaknesses first.

4. High-Stakes Environments Can Create Exceptional Focus

One of the paradoxes of ADHD is that attention is often interest-and-urgency-driven rather than consistently regulated.

Which means many ADHDers struggle with low-stimulation administrative work but can enter intense states of concentration during:

• incidents

• investigations

• crisis response

• time-sensitive problem solving

• complex debugging

• active threats

Security operations frequently involve exactly these conditions.

During high-severity incidents, many ADHD practitioners report:

• heightened situational awareness

• rapid information synthesis

• sustained focus

• faster contextual processing

• improved decision velocity

Again, this isn’t universal.
And it doesn’t eliminate burnout risk.

But it does suggest that certain neurocognitive styles may align unusually well with dynamic operational environments. Especially environments requiring rapid adaptation.

5. ADHD Thinking Often Produces More Flexible Security Models

Traditional process design sometimes assumes:

• linear workflows

• predictable behavior

• static attention

• perfect memory

• uninterrupted focus

Real humans don’t operate that way.

ADHDers especially don’t.

As a result, many people with ADHD naturally think in:

• systems

• contingencies

• edge cases

• interruptions

• nonlinear dependencies

• adaptive flows

That perspective can significantly improve:

• threat modeling

• process resilience

• incident escalation design

• secure UX

• automation safety

• human-in-the-loop systems

Because designing systems for realistic human behavior almost always produces more resilient systems overall.

In many ways, ADHD practitioners can help security teams ask a critically important question:

“How does this process behave under real cognitive conditions?”

Not ideal conditions.
Not uninterrupted conditions.
Human conditions.

Final Thoughts

The future of security systems will increasingly depend on cognitive diversity.

Not simply because diversity is “good.” But because different cognitive styles perceive systems differently.

And in security, perception matters.

The teams that build the most resilient systems will likely be the teams capable of integrating:

• technical expertise

• human factors

• operational psychology

• UX thinking

• cognitive diversity

• adaptive workflow design

into security itself.

ADHD doesn’t magically make someone better at security, no, but many ADHD strengths align well with the realities of modern security operations — particularly in environments that value curiosity, adaptability, systems thinking, and rapid pattern recognition.

The challenge is not whether neurodivergent people belong in security.

It’s whether security environments are willing to recognize the strengths neurodivergent practitioners already bring into them.

So embrace us.

Let our curiosity run wild.

Invite our feedback and enthusiasm.

Let us experiment, explore, and fall into rabbit holes.

We’ll come back with insights that don’t have to become lessons learned.

— Phaedra

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Why this? Why now?

The cross-section where technology stops being purely technical and starts becoming deeply human inspires me.

It’s the rare equilibrium where we explore not just whether systems work, but how people experience them. How they interpret risk. How they make decisions under pressure. How attention shifts. How trust forms. How friction quietly changes behavior. How design influences whether security feels intuitive, exhausting, invisible, empowering, or impossible.

The longer I’ve spent around security and modern technical systems, the more convinced I’ve become that some of the most important problems ahead are not purely engineering problems at all.

They are human factors problems.

Not because humans are the flaws in an otherwise perfect systems, but because humans are inseparable from the systems we grow increasingly reliant on.

Traditionally, security has focused primarily on infrastructure, code, controls, detection, and automation. Understandably so. This is how we approach problem-solving as a species. We’re the toolmakers. We see a problem, we fix the problem, then we iterate indefinitely so that we no longer have to think about said problem.

That last part is what I find myself drawn toward. The ultimate motivation for tool creation and perfection is the human impact, yet we find ourselves neglecting to ask these critical questions when we develop solutions in security:

How do humans actually interact with these systems?

What kinds of environments produce good judgment?

What kinds of workflows create fatigue?

How does interface design influence security behavior?

What happens to human cognition when AI becomes embedded into operational decision-making?

What does meaningful human oversight look like in increasingly autonomous systems?

Those questions sit at the cross-section I mentioned earlier. And they couldn’t be more urgent now.

We are entering an era where AI systems are rapidly transforming the scale and speed of human work. Security teams can now analyze more data, surface more signals, automate more workflows, and respond faster than ever before. Not to mention the enormous potential to reduce operational burden, surface insights humans would otherwise miss, and help people focus our attention where it matters most.

But, in the midst of how awe-inspiring this evolution is, we don’t want to miss the absolute necessity to think more intentionally about the relationship between humans and intelligent systems.

Not from a place of fear.

From a place of design.

Because the future of security will not simply depend on building more advanced systems and iterating our problems away. It will depend on designing systems that humans can meaningfully collaborate with.

That idea sits at the heart of Humans in the Loop.

Who’d want to read this?

This publication is a space to explore the intersection of:

• security

• AI

• cognition

• neurodiversity

• UX

• workflow design

• human-centered systems

• operational psychology

Some posts will focus directly on security workflows and design. Others may explore attention, decision-making, cognitive load, AI-assisted work, or the ways neurodivergent perspectives can reveal hidden assumptions in modern systems and make workflows better for everyone.

More than anything, though, I want this to be a space driven by curiosity.

I’m not interested in writing from the perspective that AI and modern developments are “ruining” human work in security. Mostly because it’s categorically false. I’m interested in exploring how we can shape this evolution intentionally — how we can build systems that amplify human judgment instead of overwhelming it.

Because I don’t think human-centered design and strong security are opposing ideas.

I think they are fundamentally the same idea. Remember, we make tools to solve human problems.

That’s why the most resilient systems of the future will be the ones that understand something simple but often overlooked:

Human attention is finite.

Cognition matters.

Clarity matters.

Trust matters.

Designing systems with realistic human behavior in mind will make the world more secure — not less.

I’m also deeply interested in what neurodiversity can teach us about design.

As an ADHDer, I understand first hand that neurodivergent people often experience friction in systems earlier and more intensely, which can reveal important truths about workflow design, communication, ambiguity, context switching, and cognitive overload. For me, that perspective is not niche. It is a lens for understanding human-centered systems more broadly and speaks to a philosophy I’ve come to swear by:

Products and experiences are better when we design with everyone in mind.

But this way of approaching system development requires some re-thinking, so I suspect many of the most important questions in security — and technology overall — over the next decade will revolve around the natural tension between problem-solving and the human factors that influence it.

How do we build increasingly intelligent systems without losing sight of the humans inside them?

That’s the conversation I want to have here.

As for what to expect:

I plan to publish roughly once per week, with essays ranging from operational reflections to explorations of AI, cognition, workflow design, and human-centered security. Some posts will be more research-oriented. Others will be more reflective or conceptual.

For now, all posts will remain free while I continue shaping the direction of the publication.

Eventually, I may introduce optional paid subscriptions for:

• deeper research essays

• frameworks and resources

• community discussions

• early drafts and research notes

• longer-form explorations of emerging topics

• Podcasts

But right now, my primary goal is simply to build a thoughtful space for people interested in the human side of modern systems.

A space for security practitioners, designers, engineers, researchers, and curious technologists alike.

A space grounded less in panic or hype and more in thoughtful exploration of where human-centered technology might go next.

The future’s most interesting technical questions are going to be deeply human ones. If that resonates with you, subscribe and follow along.

Let’s find out where this goes — together.