The Inktelligence - October 3, 2025

The Three Archetypes: Which One Are You in the Age of AI?

In my last LinkedIn post, I introduced three archetypes of AI adoption.

• The Producers: Use AI to do more, faster

• The Resisters: Not using AI yet, but watching closely

• The Cultivators: Use AI to create space for curiosity

Let me be clear: this isn't about judgment. Each archetype makes sense in context. But only one builds long-term value in a world where AI keeps getting better at everything we thought made us valuable.

The Producers: Winning a Game That's About to Stop Mattering

The Producers are everywhere right now. They're the ones sharing their ChatGPT prompts, their automation workflows, their time-saving hacks. They've shaved 23 minutes off their morning routine, automated their email responses, and can generate reports in seconds.

Their metrics are impressive:

• 3x more content created

• 40% time saved on analysis

• 10 tasks automated this month

Every efficiency gain feels like winning.

Here's what I’m seeing from what Producers are doing. They are creating more spreadsheets, more slide decks, more summaries, more everything. But do they stop to think of what direction they are heading?

Let me show you the math: You save 2 hours a day on reports. You now create 3x more reports. But are you creating 3x more value?

This is what I call "productivity theater" - looking busy versus being valuable.

The Producer's self-diagnostic:

• If your AI usage can be summarized as "I use ChatGPT to..." followed by a task, you're likely a Producer

• If your resume looks like a list of things AI could do, you're replaceable

• If you measure your week by tasks completed rather than insights gained, you're playing the wrong game

Here's the uncomfortable truth: The Producers are winning a game that's about to stop mattering. When everyone has AI handling the same tasks at the same speed, what differentiates you?

The keyword for Producers is "more." But they're still playing the same game, only faster.

The Resisters: Not Wrong, Just Running Out of Time

The Resisters are in observation mode. They're waiting for clearer use cases, better tools, or proof that AI won't just create more work. Some are skeptical. Some are cautious. Some are overwhelmed.

They're all asking: "What's the catch?"

Here's what's interesting about this group: they're not necessarily wrong to wait. Some Resisters are asking exactly the right questions:

• "What problem are we actually solving?"

• "Is this making work more meaningful or just more automated?"

• "What happens to skill development if AI does everything?"

I've seen two types of Resisters in my work:

Strategic Resisters are actively learning about AI even if not using it. They're reading, observing, thinking critically. They have specific concerns and timelines for when they'll make a decision.

Fearful Avoiders are hoping the whole thing blows over or that someone else will figure it out first. They have vague unease but no concrete plan.

The Resister's self-assessment:

Three questions to determine if your resistance is strategic:

1. Can you articulate why you're waiting? (Specific concerns vs. vague unease)

2. Are you actively learning about AI even if not using it?

3. Do you have a timeline for when you'll decide?

If you answered "no" to any of these, you're not resisting strategically—you're avoiding reality.

Here's what I've observed: Some Resisters will eventually become Cultivators once they see how AI can genuinely free up space for deeper work. Others will be forced into Producer mode when their industry demands adoption. The ones who wait too long risk having the choice made for them.

The danger isn't in waiting—it's in passive waiting. Analysis paralysis while the world moves on.

The Cultivators: The Only Sustainable Path Forward

The Cultivators see something completely different. When AI handles the routine, they don't ask "what else can I fit in?" They ask "what have I been too busy to explore?"

They use the freed-up time to read about game theory, or urban design, or behavioral economics. Fields that seem to have nothing to do with their current work—until suddenly the dots connect.

I know something about this approach because I lived it. I spent part of my career at Bell Labs—a place that embodied institutional curiosity. For a century, this legendary institution has been the quiet engine behind some of the world's biggest breakthroughs.

The people there weren't just solving immediate problems; they were exploring fundamental questions that often led to breakthrough innovations nobody anticipated.

The transistor. The laser. Information theory. Unix. The cosmic microwave background radiation. Digital photography. The C programming language. These didn't come from optimizing existing processes. They came from giving brilliant people space to be curious about "useless" things.

Bell Labs didn't measure success in daily output. They measured it in whether people were asking interesting questions. They understood something profound: breakthroughs don't come from doing more of what works. They come from having the freedom to explore what might.

That experience shaped how I think about AI today. We're at a moment where individuals can create their own "Bell Labs" environment. AI can handle the routine cognitive work that used to fill our days. The question is: what will we do with that space?

Here's the thing nobody talks about: AI doesn't make you obsolete. Being replaceable does.

And you become replaceable the moment your value is measured purely by output. By tasks completed. By emails sent. By productivity metrics.

Curiosity is the only moat that matters now.

The Cultivators understand this instinctively. They're not trying to out-produce AI. They're developing the one thing AI can't replicate: the ability to ask better questions. To make unexpected connections. To bring perspectives from outside their lane.

The Cultivator's Playbook: Making This Practical

This all sounds great in theory, but what does it actually look like in practice? Let me break down the Cultivator's approach into something actionable.

The Weekly Practice

1. Protected Curiosity Time (3 hours/week minimum)

Not "learning time" where you take another course in your field. Actual curiosity time where there's no immediate payoff.

Block it on your calendar like it's a meeting with your most important client. Because it is—it's a meeting with your future self.

2. Cross-Domain Reading

One substantial article or chapter from a field completely unrelated to your work. Architecture if you're in marketing. Philosophy if you're in engineering. Anthropology if you're in finance.

Not because you'll "use" it directly. Because your brain makes connections you can't predict.

3. Connection Mapping

Keep a simple journal where you document unexpected connections. When something you read about urban design suddenly illuminates a problem you're facing in software architecture, write it down.

Over time, you'll see patterns in how your mind synthesizes across domains. This is where your unique value lives.

4. Question Collection

Maintain a running list of interesting questions you don't have time to answer. AI handles the busy work so you can answer them.

Questions like:

• "Why do some ideas spread and others die?"

• "What would this look like if we started from scratch?"

• "What are we assuming that might not be true?"

When Curiosity Creates Value

Let me share an example of what this looks like in my own journey.

My background spans engineering, materials science, telecommunications, and now AI strategy. At Bell Labs, I saw how breakthroughs happened at the intersections—a mathematician talking to a physicist talking to an computer scientist.

The labs understood something most organizations miss: innovation doesn't come from depth alone. It comes from the collision of different ways of thinking. Someone working on quantum mechanics would have lunch with someone developing new materials, and suddenly they'd see an application neither had considered.

When I work with startups and senior executives now in my consulting work, my value isn't that I can use AI better than them (they can just as well learn the tools). It's that I see patterns across industries and disciplines that create unique insights for their specific challenges.

That only works because I've cultivated breadth, not just depth.

The Skills That Actually Matter Now

Beyond the archetypes, let's talk about what "cultivating curiosity" actually builds. These are the skills that compound in value as AI gets better:

1. Context-Setting

Why AI can't replicate it (yet): AI operates on the information you give it. Understanding the unspoken context—organizational politics, cultural nuances, historical patterns—requires being embedded in the messy reality.

How to develop it: Pay attention to what's not being said. Study history. Learn what happened the last three times someone tried this.

What it looks like: You know that technically perfect solution won't work here because you understand the context that's not in any document.

2. Reframing

Why AI can't replicate it (yet): AI answers the questions you ask. Brilliant humans question whether you're asking the right question in the first place.

How to develop it: Practice asking "What if we're solving the wrong problem?" Study how different disciplines frame similar challenges.

What it looks like: Your team is trying to improve customer retention. You reframe: "What if the problem isn't that customers leave, but that we're attracting the wrong customers?"

3. Taste

Why AI can't replicate it (yet): AI can generate a thousand options. Taste is knowing which one matters. It's discernment born from exposure to excellence across domains.

How to develop it: Consume great work in many fields. Study what makes something exceptional. Develop opinions about quality.

What it looks like: You can immediately spot which of ten AI-generated strategies actually solves the core problem, because you've developed judgment about what matters.

The Stakes

In five years, being a Producer won't be impressive. Everyone will have AI doing the heavy lifting. The question isn't whether you're productive. It's whether you're interesting. Whether you can see connections others miss. Whether you've cultivated something worth paying attention to.

Bell Labs achieved what it did not by optimizing what everyone else was doing, but by creating space for people to explore what no one else was even asking about. They understood that the future belongs to those who ask different questions, not those who answer the same questions faster.

You have that same opportunity now. AI can be your vacuum tube factory, churning out the routine work. The question is: what will you do with your time?

Choose your archetype carefully. Because you're not just choosing how you work. You're choosing who you become.

I’d like to hear from you!

If you would like provide me with any feedback, suggestions, thoughts, discoveries, or just say hello, please write to me (I promise that I will read every single email): [email protected]

Till next time,

Hock