Bored by the Old, Obsessed by the New: How ADHD Drives AI Innovation

I should be finishing this blog post. Instead, I have 14 tabs open about a new AI framework I discovered twenty minutes ago.

This happens constantly. I start a task, encounter something new, and suddenly I’m three hours deep into exploring a tool I didn’t know existed when I woke up.

People call this “shiny object syndrome.” They say it like it’s a problem.

In most fields, it probably is. In AI? It’s how I stay ahead.

The Dopamine Hunt

Let me be clear about what ADHD novelty-seeking actually is: it’s dopamine hunting.

The ADHD brain doesn’t regulate dopamine the same way neurotypical brains do. Routine tasks don’t generate the dopamine hit that motivates action. Novel stimuli do.

So the ADHD brain is constantly seeking novelty—not because we’re undisciplined, but because that’s how our motivation system works. New things are interesting. Interesting things generate dopamine. Dopamine enables action.

This creates a brain that can’t stop exploring.

Why AI Rewards Exploration

Most fields punish constant exploration. Law, medicine, accounting—these reward depth over breadth, expertise over novelty, mastery of established practices over chasing new ones.

AI is different.

The field moves so fast that established best practices are often obsolete by the time they’re written down. The tool you learn today might be deprecated in six months. The approach you master might be replaced by something ten times better next quarter.

In this environment, constant exploration isn’t a distraction from real work—it IS real work.

When Claude Code launched, I was using it within hours. Not because I planned to—because my novelty-seeking brain couldn’t resist trying this new thing. That early adoption led to building systems that now form the core of my workflow.

When new AI architectures emerge, my brain doesn’t ask “should I learn this?” It asks “how long until I can try this?” The motivation to explore is automatic.

The First-Mover Advantage

Here’s what early adoption actually looks like:

Week 1: New tool/approach emerges. Novelty-seeker discovers it, starts experimenting.

Week 4: Early adopter has working experience, has hit the edge cases, understands the real limitations.

Month 3: Best practices start emerging. Mainstream starts paying attention.

Month 6: Mainstream is learning basics. Early adopter is teaching others.

Year 1: Tool is “established.” Early adopter has moved on to the next thing.

By the time most people learn a tool, novelty-seekers have a year of experience with it. In a field moving as fast as AI, this gap is enormous.

The same trait that makes me unable to focus on routine work makes me automatically explore the cutting edge. I don’t discipline myself to stay current—I can’t help it.

Shiny Object Syndrome as Technology Scouting

Reframe “shiny object syndrome” for a moment.

What if you had a team member whose job was to constantly scan the landscape for new tools, techniques, and approaches? Someone who spent their time trying new things, evaluating them against current problems, and reporting back on what’s worth adopting?

You’d call that person a Technology Scout. You’d pay them for exactly this behavior.

That’s what ADHD novelty-seeking does for free. My brain is running a continuous technology scouting process, sampling new approaches and flagging the promising ones.

The trick is learning to trust the process. Not every shiny object is worth pursuing. But the scanning process itself is valuable.

The Boredom Signal

ADHD boredom isn’t just “being bored.” It’s a signal that the brain isn’t getting what it needs.

When I’m bored, I can’t force focus through willpower. The attention just… leaves. Like a cat that’s decided it’s done being petted.

In most contexts, this is a problem. But in innovation work, boredom serves a purpose: it signals stagnation.

The same mechanism that makes routine work unbearable makes me allergic to stagnation. When a project stops being novel, my brain screams for change. This creates a natural pressure toward:

• Trying new approaches to old problems
• Questioning established practices
• Seeking better tools even when current tools “work”

I’m not disciplined enough to continuously improve. But I’m too bored to accept “good enough” for long.

Managing the Fire

Unmanaged novelty-seeking is destructive. Starting a hundred projects, finishing none. Knowing a little about everything, mastering nothing. Constantly distracted, never deep.

Managing novelty-seeking isn’t about suppression—that doesn’t work. It’s about channeling.

Here’s how I’ve learned to work with it:

1. Exploration Time

I schedule dedicated time for pure exploration. During this time, following shiny objects is the job. This gives my novelty-seeking brain what it needs while protecting focused work time.

Without this, my brain will steal exploration time from focused work. Better to budget it explicitly.

2. The Commitment List

Before fully pursuing a new shiny object, I check my commitment list: what have I already committed to finishing? If the list is too long, the new object goes on a “to explore later” list instead of becoming another abandoned project.

This doesn’t stop my brain from being interested. It just adds a speed bump between interest and full commitment.

3. Novelty as Reward

After completing necessary but boring work, I reward myself with exploration time. This aligns the dopamine system with productivity: finish the boring thing, earn the right to explore.

This works better than trying to do boring things through pure discipline, which doesn’t work at all.

4. Strategic Role Selection

The biggest management strategy: I’ve chosen work where novelty-seeking is actually valuable.

AI architecture requires constant learning. Staying current is literally part of the job. The novelty-seeking that would be a liability in other fields is an asset here.

I didn’t choose this field because it’s strategically optimal. I chose it because it’s the only kind of work I can actually do. The strategic optimality is a bonus.

The Progressive Disclosure Connection

In my post on progressive disclosure, I describe loading less context upfront to keep AI systems effective. The same principle applies to managing novelty-seeking.

You don’t need to know everything about a new tool immediately. Progressive disclosure for humans:

Tier 1: What is this? Is it relevant to my problems? → Quick scan, decide if it’s worth more attention

Tier 2: How does it work conceptually? What are the key tradeoffs? → Deeper exploration if Tier 1 was promising

Tier 3: How do I actually use it? What are the details? → Full investment only if Tier 2 confirmed value

This prevents every shiny object from becoming a full research project. Most new things die at Tier 1—they’re not actually relevant. Some make it to Tier 2 before I realize they’re not worth adoption. Only the truly valuable make it to Tier 3.

My brain wants to skip to Tier 3 immediately. The tiered approach adds friction that filters out most distractions while still allowing genuine exploration.

The Innovation Funnel

Novelty-seeking creates a natural innovation funnel:

Wide input: Encounter dozens of new ideas, tools, approaches Rough filtering: Most are discarded after quick evaluation Deeper exploration: Promising ones get more attention Integration: Best ones get integrated into actual work Refinement: Used ideas get refined through practice

Without the wide input, there’s nothing to filter. Without the boredom-driven exploration, the funnel doesn’t get fed.

Neurotypical brains might need to force themselves to scan for new approaches. ADHD brains do it automatically. The challenge is the filtering stages, not the input stage.

What Novelty-Seeking Isn’t

Let me be clear about what doesn’t work:

Novelty-seeking isn’t strategy: My brain explores interesting things, not necessarily important things. Judgment about importance must come from elsewhere.

Novelty-seeking isn’t completion: Starting is easy. Finishing is where ADHD brains struggle. The same drive that starts projects doesn’t finish them.

Novelty-seeking isn’t depth: Broad exploration doesn’t automatically create deep expertise. You can sample a hundred tools and master none.

Novelty-seeking isn’t reliability: The same brain that explores energetically might forget critical obligations while distracted by something new.

Novelty-seeking is one ingredient in innovation, not the whole recipe. It needs to be combined with judgment, follow-through, depth, and reliability—none of which ADHD provides automatically.

The AI Timing Luck

I’m genuinely lucky that my peak career years coincide with the AI era.

Twenty years ago, in a slower-moving field, my novelty-seeking would have been pure liability. Constantly distracted by irrelevant new things. Unable to build the deep expertise that stable fields reward.

Today, in AI, the field moves faster than anyone can track through deliberate study alone. You need exploratory instincts just to stay aware of what’s possible.

My brain didn’t change. The environment changed to reward what my brain already does.

This is part of why I’m writing this series—to help others recognize when their neurodivergent traits might align with environmental demands. The traits don’t change, but the environments we choose determine whether those traits are bugs or features.

Building for Other Novelty-Seekers

When I build systems, I assume the users include novelty-seeking brains like mine.

This means:

• Quick time-to-value (don’t bore us before we see benefit)
• Explorable interfaces (let us poke around)
• Progressive disclosure (don’t overwhelm, let us go deeper if interested)
• Interesting problems (boring solutions to boring problems won’t retain us)

Systems that require patience to learn, that hide the interesting parts behind boring setup, that punish exploration—these lose ADHD users fast.

The best onboarding is “here’s something cool, try it, here’s more if you’re interested.” That’s how my brain wants to learn. I suspect it’s how many brains want to learn.

The Exploratory Edge

In AI specifically, the exploratory edge matters more than most fields.

The tools that exist in January might be obsolete by June. The techniques that work today might be deprecated tomorrow. The best practices that everyone follows might be superceded by something nobody’s documented yet.

In this environment, the ability to constantly scan, explore, and evaluate new approaches isn’t a distraction from work—it’s a core competency.

My novelty-seeking brain doesn’t just tolerate this environment. It thrives in it. The constant change that exhausts others is the stimulation my brain craves.

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This is part 3 of the ADHD Architect series:

1. Pattern Recognition
2. Parallel Processing
3. Novelty Seeking (You are here)
4. Abstraction
5. Chaos Management

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I’m supposed to be wrapping up this post. Instead, I just saw an announcement about a new AI capability and I’m already planning how to test it.

Old me would have felt guilty about this. Current me recognizes: this is the trait that keeps me relevant in a field that moves faster than deliberate study can track.

The shiny objects aren’t the problem. They’re the radar system that keeps me aware of what’s possible.

The trick is building the judgment to know which objects are worth the chase. The exploration itself? That comes free.