A Simple Way to See Where AI Will Actually Matter
Most people are asking the wrong question about AI.
They ask, “Where can we use AI?”
That sounds reasonable, but it usually sends people in the wrong direction. They start looking for tools, chatbots, copilots, agents, dashboards, prompts, and automations.
I think the better question is different:
Where are people still interacting because uncertainty has not yet been absorbed?
That is the question behind the new Interaction Stack Simulator.
The simulator is designed to help students, professionals, and curious minds see something that is usually invisible: the hidden stack of interactions underneath ordinary work.
Lecture Summary
Intro for Lecture: Before we talk about AI today, I want you to do something simpler.
I want you to notice interaction.
Most people see the obvious event.
A customer calls a restaurant.
A manager asks for a status update.
A buyer emails a supplier.
A student asks a teacher for clarification.
A patient calls a doctor’s office.
But each of those visible events usually contains a stack of hidden interactions underneath it. There are questions, checks, confirmations, trust gaps, repeated explanations, searches, approvals, corrections, and follow-ups.
That hidden stack is what we are going to study.
The simulator I’m giving you is not really an AI tool. It is a perception tool.
Its job is to help you see what most people miss.
When you open it, choose one scenario. Then open the interaction stack. As you go through it, don’t rush to solve anything. Just notice how many interactions are hiding inside something that looked simple.
Then ask yourself three questions.
What uncertainty caused this interaction?
Could a stable pattern absorb it?
What exception must still rise to attention?
That last question matters.
The goal is not to eliminate all interaction. The goal is to eliminate unnecessary interaction.
A healthy system does not suppress everything. It keeps the predictable quiet and lets the meaningful exception rise.
So as you use the simulator, I want you to look for two things:
What should disappear?
What must still return to attention?
That is the heart of the exercise.
Do not begin by asking, “Where can we use AI?”
Ask a better question:
Where are agents still interacting because uncertainty has not yet been absorbed?
That is where AI will matter.
Here is the simulator:
Spend a few minutes with it. Choose a scenario. Open the stack. Then be ready to tell us one hidden interaction you did not see at first.
Most people see the surface event.
A manager asks for a status update.
A customer calls a restaurant.
A buyer emails a supplier.
A patient calls a doctor’s office.
A student asks a teacher for clarification.
Each of these looks like one interaction. But it is not one interaction. It is usually a stack of hidden uncertainty, trust checks, searches, confirmations, interruptions, decisions, clarifications, and follow-ups.
The simulator helps you open that stack.
Why Interaction Matters
Every relationship contains uncertainty.
A buyer is uncertain whether the supplier has inventory.
A manager is uncertain whether the work is progressing.
A customer is uncertain whether the restaurant is open, what the special is, or whether there is outdoor seating.
A patient is uncertain whether symptoms require an appointment.
A student is uncertain what the assignment means.
That uncertainty creates interaction.
Someone asks.
Someone answers.
Someone checks.
Someone confirms.
Someone searches.
Someone approves.
Someone updates.
Someone waits.
We usually call this work.
But it is more than work.
It is evidence.
Every unnecessary interaction is evidence that uncertainty still exists inside the relationship.
That is why the simulator begins with a simple idea:
Interaction is the cost of unresolved uncertainty.
Once you see interaction this way, AI starts to look very different.
AI is not interesting merely because it can generate text.
AI is not interesting merely because it can answer questions.
AI is not interesting merely because it can act like an agent.
AI becomes economically important when prediction can safely remove unnecessary interaction from a relationship.
That is the deeper idea.
The Difference Between an Agent and a Synthetic Subconscious
One of the biggest confusions in AI today is the word “agent.”
An agent is anything that can act in the world.
A human can be an agent.
A company can be an agent.
A CRM system can be an agent.
An ERP system can be an agent.
A workflow engine can be an agent.
A website can be an agent.
A software system does not suddenly become agentic because generative AI was added to it. Software systems have been acting in the world for decades. They receive orders, update records, trigger workflows, notify users, check inventory, and change state.
An AI-enabled agent is an agent that uses prediction inside its action process.
That can be useful. It may make the agent faster, smarter, more flexible, or easier to use.
But it is still an agent.
It still interacts.
It still queries.
It still retrieves.
It still updates.
It still sends.
It still performs.
The synthetic subconscious is different.
A synthetic subconscious is not another agent in the relationship. It is the prediction layer of the relationship itself.
Its purpose is not to perform interactions faster.
Its purpose is to make unnecessary interactions disappear.
This distinction matters.
A faster interaction is still an interaction.
A better dashboard is still a dashboard.
A smarter chatbot is still a conversation.
A more capable agent is still an agent.
The deeper transformation happens when the relationship continues, the outcome is preserved, and the interaction no longer needs to occur.
That is absorption.
What the Simulator Does
The Interaction Stack Simulator gives you a simple exercise.
You choose a surface interaction, such as:
A weekly status meeting.
A restaurant customer phone call.
A buyer email to a supplier.
A doctor’s office appointment question.
A student asking a teacher for clarification.
Then the simulator opens the hidden interaction stack underneath that event.
For example, a “weekly status meeting” may contain many hidden interactions:
The manager wonders whether work is progressing.
The employee reconstructs work from memory.
The employee searches email for commitments.
The employee checks Slack or Teams for decisions.
The employee checks the project management tool.
The employee writes a status narrative.
The manager reads the update.
The manager asks for clarification.
The employee responds.
The team repeats part of the same update in a meeting.
The project tool gets updated manually later.
At first, that looked like one interaction: “status meeting.”
But once the stack is opened, we see a whole structure of unresolved uncertainty.
The simulator then asks you to classify the interactions.
Which ones require human attention?
Which ones are machine interactions?
Which ones are trust checks?
Which ones are duplicates?
Which ones are exceptions?
Which ones are absorbable routines?
This is where the learning happens.
The point is not to say, “AI should do everything.”
The point is to ask with discipline:
What should disappear?
What must still rise to attention?
The Goal Is Not Zero Interaction
This is important.
The goal is not to eliminate all interaction.
That would be foolish and dangerous.
The goal is to eliminate unnecessary interaction.
A healthy human subconscious does not bother you with every heartbeat, every breath, every digestive adjustment, or every balance correction. Those processes are important, but they are stable enough to remain beneath attention.
However, if your heartbeat becomes irregular, attention returns.
If breathing becomes difficult, attention returns.
If pain appears, attention returns.
The same principle applies to organizations and software systems.
Routine uncertainty should be absorbed.
Meaningful exceptions should rise.
A normal shipment can disappear beneath attention.
A shipment delay that could shut down a factory should rise.
A routine invoice match can disappear.
An invoice mismatch should rise.
A standard contract clause can disappear.
A liability change should rise.
A common restaurant question can disappear.
An allergy concern, complaint, or unusual request should rise.
The simulator teaches this distinction.
It asks not only what interaction should disappear, but also what exception must still return to attention.
That is the difference between a useful synthetic subconscious and a reckless automation.
Why This Matters for AI
The future of AI is not more agents talking to more agents forever.
That would be a noisy future.
The future is not every human prompting machines all day.
That would be a ridiculous future.
The future is better relationships with fewer unnecessary interactions.
That is where AI will matter most.
Not everywhere.
Not magically.
Not because someone installs a chatbot.
AI matters where prediction can safely absorb uncertainty that previously required interaction.
This is why the simulator does not begin by asking you to build an AI tool.
It begins by asking you to see a relationship.
Who are the agents?
What outcome are they trying to complete?
What interactions happen today?
What uncertainty causes those interactions?
Which patterns are stable?
Which interactions could disappear?
Which exceptions must remain?
Those questions are more important than the technology at first.
If you cannot see the interaction stack, you cannot see where AI belongs.
A Classroom Exercise and a Personal Lens
I built this simulator as a companion to my teaching.
In class, we will still do this exercise together. But I also wanted students and curious readers to have something they could use on their own time.
Click the link.
Choose a scenario.
Open the stack.
Classify the interactions.
Find the uncertainty.
Choose what should disappear.
Protect the exception.
Then try it again with a real relationship from your own life or work.
A recurring meeting.
A customer call.
A weekly report.
A scheduling loop.
A repeated explanation.
A dashboard check.
A “just confirming” email.
Each one is a clue.
The simulator is not really about software. It is about perception.
Once you learn to see hidden interactions, you start seeing them everywhere.
And once you see them everywhere, you can finally ask the better AI question:
Where are agents still interacting because uncertainty has not yet been absorbed?
That is where the opportunity is.
That is where productivity changes.
That is where relationships become quieter.
That is where the coming AI subconscious begins.
Try the simulator here: