AI

When AI Creates Margin, Who Gets It?

Empty Beach
~ K. Quesenberry ~ Leave a comment

AI is being sold to businesses as a way to improve margin. Many employees are embracing it for the same reason. The problem is that businesses usually mean profit margin, while employees mean margin in their lives.

Same word. Different dream.

That may become one of the biggest workplace tensions of the next few years.

I learned this long before AI.

Back when I worked in the high-pressure world of advertising as a copywriter and creative director, my art director and I would sometimes leave the building and go to Starbucks. Not to waste time. To create margin.

We knew that if we stayed in the office, with people constantly checking on us, asking for things, and wanting updates, we would not have the mental room to come up with the big ideas everyone wanted from us.

That coffee shop time was not a break from productivity. It was productivity.

Some of our most creative moments were not spent typing at a computer. They were spent leaning back in a chair, getting some distance, and talking and sketching our way toward a better idea. That’s something I think many organizations still miss, especially now in the rush to adopt AI.

Empty Beach
Where this post started. Room to pause. Room to reflect. Room to dream.

When AI creates efficiency, who keeps it?

For businesses, margin means more output from the same people. Faster turnaround. Lower labor costs. Less slack in the system.

For employees, margin means less drudgery. Fewer late nights. More breathing room to think, recover, and have some life left at the end of the day.

Once AI creates efficiency, somebody decides where it goes. Back to the human being? Or right back into the machine of work?

Recently, I was able to get away. I had time to enjoy nature, spend time with family, and read something not related to work. I got caught up in the characters and story of a novel. When I came back, I felt refreshed and inspired.

And sadly, I also felt the need to justify that time by telling myself it gave me some really good ideas for work.

Somehow even breathing room can start to feel like something you have to justify.

Efficiency can quietly consume margin

If AI removes low-value tasks and gives people room for better judgment and deeper focus, that’s progress. But that margin can disappear in two ways. Management can fill the gap with more tasks, tighter deadlines, and leaner staffing. Employees can fill it themselves, because many of us have been conditioned to treat freed time as space for more work.

That may look like progress on paper, but in practice it can become just another way work expands to fill every available space. Parkinson’s Law applies to AI. When tools create margin, the instinct is to fill it.

Evidence is already mounting. A UC Berkeley study tracking AI adoption inside a real company found that even without management pressure, workers filled every hour AI freed up with more work. Deep-focus time fell and cognitive fatigue rose. There’s even a name for it: “AI brain fry.” Whatever you call it, it’s another example of a tool promising margin but quietly consuming it instead.

We’ve seen this before. Email was supposed to make communication easier. Smartphones were supposed to make work more flexible. They did both, but they also made work more constant and harder to leave behind. AI could easily follow the same path.

The real opportunity of AI isn’t just to use it to do work faster. It’s to decide what kind of margin is worth protecting.

Human margin is not waste

In my classes, we use Steven Johnson’s Where Good Ideas Come From. His study of innovators in history points to conditions that produce breakthrough ideas like liquid networks, adjacent possible, error, serendipity, and slow hunch. None of those happen easily when every minute is scheduled, measured, and filled. They need margin. Time for reflection.

I’ve seen this in my own life too. Some of my best ideas have come in places that don’t always look productive: the break room, between conference sessions, at a social hour, in the casual conversation between one thing and the next. That’s often where ideas connect.

Organizations say they want creativity, insight, and innovation. Then they build systems that leave no room for the very conditions that make those things possible.

You can’t squeeze people into breakthrough thinking.

Margin is not always waste. Sometimes it’s the condition that makes better work possible.

A better question for leaders

Some push back on this. They say pressure is the point. Constraint forces creativity. Urgency eliminates mediocrity. There’s evidence for it. Companies built on relentless intensity have produced breakthroughs that more relaxed organizations never did.

While AI is being sold as a tool to give time back, some of the very companies building AI post job descriptions glamorizing 70-plus hour weeks, or a 996 schedule. The technology that promises margin is arriving with a culture that demands you surrender it.

But that model tends to work in specific conditions: mission-driven people who opted in, often early in their careers, working on outsized problems they personally find worth the sacrifice. It also has real costs: attrition, burnout, and the quiet departure of experienced people who have other options.

Importantly, it misses what AI actually changes. A high-pressure model squeezes harder to get more. AI removes the need to squeeze people just to get routine work done.

The question isn’t whether to demand high performance. It is whether human qualities AI cannot replicate, such as judgment, creativity, and strategic thinking, flourish under constant pressure or require something different.

The companies that benefit most from AI over time may not be chasing maximum short-term output. I’d bet on the ones that use part of the gain to create better conditions for human performance: more focus, less drudgery, better decisions, more sustainable energy.

A healthier AI model could look more like defining work clearly, what done well looks like, and letting people keep some of the margin they create – for better work, and for more life.

Who gets the margin

The deeper issue isn’t that employers and employees want opposite things. Often, they both want better results and a sense that work is making a meaningful difference. Tension comes from a misunderstanding about how those outcomes are produced. Work culture often treats margin as waste to eliminate rather than the space needed to think, care, recover, and do meaningful work well.

This can lead to loss of motivation. Worker morale is meaningful. When people lose heart, productivity erodes. Eventually, the best people leave. AI didn’t create that misunderstanding.

The real negotiation happening around AI at work isn’t just about efficiency or adoption.

It’s about margin. Who captures it. Who benefits from it. Who gets the breathing room.

At the agency, I used to run during my lunch hour. It relieved stress, helped keep me healthy, and didn’t take away from family time. Anyone familiar with the creative process knows downtime matters. My subconscious mind kept working on client problems and projects. More often than not, I came back from those runs with new ideas for the work I was doing.

That doesn’t mean I never worked long hours. Big pitches and tight deadlines sometimes meant late nights, work after the kids were in bed, and Saturdays in the office. That came with the business. But there’s a difference between working hard when the work truly calls for it and treating constant overwork as proof of commitment.

After several years, my boss called me into his office. He said that my art director and I had the best work in the agency. Our work won creative awards, produced profit for our clients, and we always met deadlines while handling more clients and projects than the other teams.

Then he said, “But…” You run at lunch and go home at night.

He didn’t understand that the margin was part of what produced the results he was getting.

Shortly after that meeting, my art director and I both left for other opportunities.

The future of AI at work may not come down to the technology itself. It may come down to who gets the margin.

This post was drafted with the assistance of ChatGPT and Claude. The ideas, experiences, and opinions are my own.

Beyond the Binary: Your Narrative Brain vs. AI’s Rear-View Mirror

~ K. Quesenberry ~ Leave a comment

I’ve been forcing myself to regularly read physical books again.

Not articles. Not threads. Not AI summaries. Actual books. Cover to cover. It’s my way of reclaiming an attention span fragmented by years of algorithmic feeds designed to keep me scrolling on shallow tidbits.

If AI can consume a library of data in seconds, maybe my competitive advantage is going slower and deeper.

Two books that have been sitting on my shelf are S.I. Hayakawa’s Language in Thought and Action and Angus Fletcher’s Primal Intelligence. The first was written in 1939 and the second 2025. As I read them over several weeks, something clicked.

My brain, the neural synapses Fletcher writes about, made a connection no algorithm would have surfaced: Hayakawa’s framework for “sane” thinking during WWII and Fletcher’s research on how human brains “imagine” new paths or plans in the future.

S. I Hayakawa Language in Thought and Action and Angus Fletcher Primal Intelligence.
No AI would have picked up these two books and made a connection to imagine a new path forward.

Our Narrative Brain

This is what your Narrative Brain does. It makes imaginative leaps across disparate ideas. It asks “What if these two things connect?” A semantics book and neuroscience book written 86 years apart. No dataset, predictive analytics, or AI could have made this creative leap.

It’s a unique capability we risk losing if we don’t understand how to partner with AI correctly.

Many conversations about AI in business and marketing position it as an all or nothing proposition. AI will and should replace employees or (because of this threat) we should avoid using AI at all.

In AI lessons from 2025, I shared how I explored AI partnership versus replacement last year. But I still didn’t understand the core biological barriers and benefits.

Hayakawa and Fletcher gave me the answer. Fletcher explained the fundamental difference between how AI processes information and how our brain works. Hayakawa helped me understand the challenges in AI adoption. Both are key to staying sane (and essential) as a knowledge worker in the AI revolution.

Light Switch vs. Dimmer

Hayakawa described two ways of looking at the world. A Two-Value Orientation is like a light switch. It’s binary: people are all evil or all good. Knowledge work should be all human or all AI. When we approach business, marketing or communications this way, we ask “Should we use AI?” and expect a simple Yes or No.

A Multi-Value Orientation, however, is like a dimmer switch. It recognizes that reality exists on a scale. Instead of automatically labeling people as good or evil, we consider nuance like perspective, circumstance, and intent. Instead of asking “If” we should use AI, we ask, “To what degree and in what context is AI appropriate for each task?”

Key Insight: Two-value thinking creates conflict. Multi-value thinking creates a roadmap for collaboration.

Light Switch vs Dimmer AI Integration
Let’s consider a more nuanced approach to AI integration.

Your Biological Advantage

In his book Primal Intelligence, Angus Fletcher points out a biological truth that changes how we may view AI.

AI runs on transistors that perform Correlation. Its logic is A = B. It looks at massive datasets of the past to see what usually happens. Given A, there’s a 95% chance that B comes next.

If you ask AI for a business or marketing idea, it calculates the statistical probability of which words usually go together. It is, effectively, a high-speed rear-view mirror. It can tell you where the market has been.

Your brain, however, runs on neural synapses that perform Conjecture. Your logic is A → B. You don’t just see two things are typically related. You can imagine a potential causal link. You can look at a set of facts and ask, “What if we did the opposite?” or “Why can’t these go together?”

You can also see possible ways forward when faced with missing, incomplete, or unexpected information. Whereas AI is prone to hallucinations when faced with a lack of data.

For example, AI looks at the data and says: “90% of successful luxury brands use minimalist black-and-white logos.” That’s correlation. But a human looks at a crowded, monochrome market and asks: “What if we used neon yellow to signal a different kind of rebellion?” AI follows the trend to be safe. You break the trend to be noticed.

When correlation said people wanted better keyboards on their phones, Steve Jobs used conjecture to imagine a different story: a single piece of glass that could hold the internet. That strategy drove Apple to fill in the gaps to make that “improbable” narrative happen. AI could not have “imagined” that possibility based on previous data.

AI is a map of the past (Correlation). You are the driver of the future (Conjecture).

The Abstraction Ladder

Hayakawa also taught us about the Ladder of Abstraction. For business and marketing the top would be the “Map” with vague labels like “Customer Satisfaction.” At the bottom is the “Territory” such as the actual, concrete facts and interactions with real people.

AI is great at the top of the ladder. It can summarize the Map of “General Trends” all day. But because it lacks a physical body and lived experience (what Fletcher calls “Embodied Intelligence”), it can’t feel the Territory. Stepping into a customer’s perspective to understand their motives is a human act. AI can track a click, but it can’t feel a wince.

It is why your human empathy can’t be outsourced to AI.

Example: AI can tell you “Gen Z engagement is down 15%.” That’s a top of the ladder abstraction. You climb down to the Territory by observing and talking to Gen Z customers. By understanding their lived experience, you sense an erosion in trust or a shift in culture that doesn’t hit a data log. Territory AI can’t access without embodied experience.

A multi-value approach uses AI to handle the high-level abstractions, which frees up your human brain to climb down the ladder to the real lived experience. We use our Narrative Brain to find the specific, human story, the A → B sequence, that makes a brand feel real.

In a world where AI levels the data playing field, competitive advantage returns to the humans companies employ. Your edge is no longer who has the most data. You’ll need people who can look at a spreadsheet and still see the human story.

Instead of acting in the past you’ll begin imagining new futures and designing marketing actions to make them happen.

5 Levels of AI Integration

To help us navigate this, I created a 5-level scale of AI Integration based on multi-value orientation and our biological advantage. Not every task deserves Level 5 automation. As a professional you’ll know when to turn the dimmer switch up or down based on the human value required.

5 levels of AI integration with a multi-value orientation that leverages our brain’s primal intelligence advantage. Click image to download a PDF.

Now It’s Your Turn

If you’ve been avoiding AI, start at Level 1. This week, ask it to proofread an email you’ve already written. That’s it. You’re still the author. You’re still making all the decisions. Notice how it feels, what it catches and misses.

Then try Level 2. Or if you’re doing that try higher. Try deep research, brainstorming, outlining, drafting, feedback or variations with a reasoning model. Don’t know how? Ask AI.

The goal isn’t to become a better prompt engineer. It’s to become a better thinker.

Become someone who knows when to leverage speed and when to trust your human ability to imagine what doesn’t exist yet. Leverage AI to speed up low value tasks to free up more time for your unique human contribution.

This is why I’m back to physical books. Reading deeply is training for your Narrative Brain. It builds the stamina to stay “low on the ladder” and follow complex stories in the market, in your life and in our world. Real life is not black and white, one’s and zeros.

It ensures that when you step into a meeting, you aren’t just looking at the rear-view mirror of data. You’re the one who can internalize the customer’s perspective and imagine a future the data hasn’t seen for true innovation.

Two Books on a Shelf

Remember those two books on my shelf? No AI would have recommended I read them together. No algorithm would have surfaced their connection. But my Narrative Brain, the same you use every day in your work, made an imaginative leap that created this framework.

That’s what makes you irreplaceable: the ability to make connections that don’t exist in any dataset. Only a human can see the gray areas where the next big idea usually hides.

AI can tell you the most likely next word, but only you can imagine the most meaningful next chapter.

Moving from a two-value “Either/Or” mindset to a multi-value “Degrees-of” mindset, enables you to start imagining and start creating a better future with your narrative brain.

About This Post’s Creation

This was developed in partnership with Google Gemini 3.0 and Claude Sonnet 4.5. Both helped organize and refine. The connection of General Semantics and Narrative Science is my own. One that came from the kind of deep, sustained reading and cross-pollination of ideas that only a human narrative brain can produce.