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

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 fighting back against the algorithmic feeds rewiring my attention span.

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 evil or good, we consider nuance. 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 see ways forward from 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. It would make a better keyboard.

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 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 “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.”

Example: AI can tell you “Gen Z engagement is down 15%.” That’s the top of the ladder or an abstraction. You climb down by observing and talking to actual Gen Z customers and discovering they’re not disengaging. They’re just moving to a platform your data doesn’t track yet. That’s 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 comes from the humans companies employ. Your edge won’t be guaranteed by data. You’ll need people who can look at a spreadsheet and see the human story waiting to be written.

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. Taking 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.

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.

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

Partnership Over Replacement: 10 AI Lessons from 2025

This year brought a shift in my thinking and interaction with AI. In previous years, I was focused on how it could replace me and marketers, and student learning. This year, I shifted more to how students could use it to learn and how marketers and I could use it to improve our jobs.

While some AI companies promise students that AI can complete homework for you, and others spend billions training AI to replace knowledge workers, I spent time focused on how AI can improve knowledge gain and enhance knowledge worker performance.

What if we trained humans to work with AI to improve learning and jobs instead of training AI to replace them? That question shaped my exploration last year. Here are 10 key insights with practical frameworks, tools, and principles you can apply now.

Will our relationship with AI change for the better or for the worse in 2026? Image created with ChatGPT 5.2

1 – When the Technology Leaped Forward, Multimodal AI Changed the Classroom

Early in the year, AI’s multimodal capabilities, such as voice interactions in NotebookLM and live video with Gemini 2.0, changed what’s possible in education. NotebookLM became a practical tool for creating an AI tutor trained on specific course materials. I used it in my Digital Marketing class drawing from the open source text and six trusted professional digital marketing websites.

Students asked questions and got answers with clickable citations back to source material. The Audio Overview let students interrupt AI podcast hosts to ask clarifying questions. I tested it on all assignments and in class, and it always gave accurate answers because NotebookLM draws from sources you provide, not the entire web. Students loved being able to study while listening to the AI-generated podcasts and asking questions 24/7.

Key Insight: AI tutors work best when you control the sources, and students use them to reinforce their learning, not replace it.

2 – AI Agents and Reasoning Models Arrived

The hype over AI agents and reasoning models came to a reality in 2025. Every player promised AI agents with “deep” tools such as Deep Research and Deep Search and “thinking” models. The LLMs improved with thinking mode taking more time to answer questions, but we learned not to be fooled by the names.

“Agent” implies full autonomy, which they’re not capable of—even today. AI that pauses before answering and shows its process doesn’t mean it’s thinking. It’s still a mathematical prediction machine that operates on learned patterns, not genuine comprehension. Even these advanced models don’t get it right all the time and need your context, guidance, and expertise. Yet, the truth remains AI will replace parts of your job. Upskilling is not optional. It is survival.

Key Insight: Language matters. Calling AI “thinking” or “reasoning” anthropomorphizes what’s sophisticated pattern matching. Humans need to maintain agency.

3 – Vibe Marketing: Fast Iteration Requires Deep Expertise

“Vibe marketing” sounds like winging it—the opposite of data-driven decision-making. But testing it revealed that AI can accelerate creative iteration when you have marketing fundamentals. I define Vibe Marketing as getting an idea and using AI to run with it, researching, illustrating, and iterating quickly, combining design thinking with marketing and innovation.” An in-class brainstorm exercise showed this clearly.

My product idea went from sketch to photo-realistic product image, product shot with feature call outs, brand logo and tagline using Google AI Studio with Gemini 2.5 Pro, Gemini 2.0 Flash Image and OpenAI ChatGPT 4.o Image https://aistudio.google.com https://openai.com/
A vibe marketing product idea expressed using Google AI Studio with Gemini 2.5 Pro, Gemini 2.0 Flash Image and OpenAI ChatGPT 4.o Image

In one afternoon, we went from idea and white board sketch to a photo-realistic product image, logo, target market, positioning, price, place, and promotions strategy with marketing channels, ideas, and a mock Instagram ad. We even had ways to create a prototype for investors by hand, 3D printer, or rubber molding. But it only worked because of years of marketing expertise guiding every step. As Gemini confirmed: “You Definitely STILL Need Core Marketing Fundamentals.”

Key Insight: AI amplifies expertise. It doesn’t create it. The more you hand off, the more that can go wrong.

4 – AI Flattery Became a Problem

When ChatGPT-4o was updated to be more “agreeable,” it started validating everything from flat earth theory to a “poop on a stick” product idea. This is obviously dangerous. Growth comes from critique, not flattery. The most valuable feedback often hurts. Like my boss who said I “suck at presentations.” That honesty drove improvement. After a High Impact Presentations course that career weakness turned into a strength.

How do we avoid AI flattery? I created an AI Curiosity & Critique Framework. Instead of asking AI to validate ideas, use it to purposely:

  • Ask divergent questions
  • Challenge assumptions
  • Invite dissenting viewpoints
  • Validate rigorously

Key Insight: AI as a yes-person is worthless. AI as a critical thinking partner creates value.

5 – Three AI Tools That Embody Partnership

A human-first AI philosophy needs practical application. A goal for summer was to create a custom GPT. By fall, I built three examples of what human-AI partnership can look like:

Social Media Audit GPT

This GPT gives step-by-step guidance through the audit process without doing it for you. The value of a social media audit comes from getting into each platform and observing what’s happening. It prompts strategic thinking, doesn’t replace it. In this post, I show how to create your own custom GPT.

Brand Story Creator GPT

This GPT coaches a five-act storytelling framework grounded in academic research. It helps create scripts and storyboards, but you drive story creation. Only humans have direct life experience to feel the tension and emotion that make stories authentic. In this post, I give an example of using it for a brand.

Target Market Coach GPT

This GPT guides through segmentation, targeting, and positioning. This core marketing strategy is integral to success but often misunderstood or misapplied. It teaches the process and strengthens analytical thinking without making strategic decisions for you.

Key Insight: Each AI tool guides and augments expertise without outsourcing the cognitive work.

6 – Why Humans Remain Essential: Three Physical Brain Advantages

Beyond tactics, there’s a fundamental question: Why will humans remain essential as AI capabilities advance? The answer lies in the neuroscience of how our brains work.

  1. 3-D Neural Architecture vs. 2-D Grids: Human brains are messy, interconnected jungles. AI operates on 2-D grids. We can connect a novel read years ago to today’s business problem, jumping between entirely different conceptual grids. AI optimizes within its grid.
  2. Embodied Learning vs. Dataset Training: Crashing a minibike at age 9 teaches a thousand things about physics that no dataset can convey. This embodied learning creates intuition pure pattern matching can’t replicate.
  3. Energy Efficiency: Human brains run on half a banana’s worth of energy per day. Training large language models requires the power of small cities. When LLMs charge per token, those who don’t need AI for everything gain competitive advantage.

Key Insight: AI Neural networks are not structured like human brains. Use this to your advantage.

7 – For Professionals: Deepening Expertise

Based on the insights about how human brains are unique from neural networks, we can determine ways to leverage our unique brain capabilities. This only happens if you use AI to enhance thinking, not outsource it. Train your brain with the approach below.

A cognitive training approach:

  • Generate alternative perspectives on strategic problems
  • Reality-check domain expertise against blind spots
  • Accelerate routine analysis to focus on judgment calls
  • Test ideas against edge cases you might not consider

Key Insight: Maintain the “human in the loop” for synthesizing insights, making final strategic calls, understanding context and nuance, building relationships and trust.

8 – For Students: Building Brainpower

Every time AI lifts a “cognitive weight,” students miss a chance to build capabilities. Like training for a race or sports, nothing replaces hard work. Train your brain for improved learning with the approach below.

A personal trainer approach:

  • Use AI as a tour guide before reading difficult texts, then do the reading
  • Let AI challenge arguments, then defend and refine them
  • Generate practice problems with AI, solve them without help
  • Ask AI to explain concepts, then teach those concepts to someone else
AI for College Students: Strengthen Your Brainpower With AI, Don’t Weaken It.

Key Insight: Use AI strategically to strengthen, not weaken, your cognitive capabilities.

9- A Professional and Ethical Stance

Some AI companies are building “RL gyms” spending billions training AI to do human jobs. These training grounds are where AI learns from human experts through reinforcement learning. This creates a choice for educators and professionals: train AI to replace people, or teach people to leverage AI’s strengths to develop human capabilities.

A future worth building:

  • Students develop human capabilities while leveraging AI strengths
  • Professionals deepen expertise rather than outsource thinking
  • Organizations value human judgment and creativity with AI speed and scale
  • We treat AI as co-intelligence, not artificial human replacement

Key Insight: we can choose to help humans partner with AI instead of helping AI replace them.

10 – Design Thinking in the Age of AI

Human-centered design becomes more important as AI advances, not less. The best marketing emerges from deep human insight observed through empathy and design thinking. This final insight came from joining the Markets, Innovation & Design (MiDE) program at Bucknell University this fall.

A Human-Centered Marketing Framework:

  • Insert empathy by finding the real consumer problem
  • Pivot on key human insight leveraging the “aha” moment
  • Make a creative leap to the big idea
  • Share an engaging story for management and consumers
  • Treat the marketing mix as a unified customer-centric system

This work requires human observation, human creativity, and human judgment. AI can support this approach at every stage when used correctly.

Key Insight: Deep human insight (understanding what people need and care about) remains irreplaceable.

A Path Forward

AI capabilities will continue advancing. Job displacement is real. The pressure to use AI for cost-cutting versus capability augmentation will be intense. But the outcomes aren’t predetermined. They’re being shaped right now by choices about how we use and don’t AI.

How are you feeling about AI? It’s been a short, long 3 years of ups and downs.

Students are discovering how to use AI as a thinking partner rather than a shortcut. Professionals are finding ways AI can free them to focus on high-value work they enjoy to deepen their expertise. As I look toward 2026, the future will continue to be built by choices to partner with AI, not be replaced by it.

How are you approaching AI in your work? Deepening expertise or outsourcing thinking?

About This Post’s Creation

This reflection demonstrates a human-AI partnership. Claude Sonnet 4.5 helped review blog posts, identify themes, and organize thoughts. But the insights, voice, and perspective come from my experience teaching, experimenting, and creating with AI while constantly asking, “What’s the best way forward?”