ACTIVATORY: Human Software's Race Against Time in the World of AI
What happens to a person when the machine generates the next possibility faster than we can decide what to do with it?
A new kind of speed has appeared in the world of AI.
A question can be answered in seconds. An idea can be turned into several variations in moments. An image, a text, an analysis, a plan, or a possible solution can come into being almost instantly.
The machine is able to produce the next thing quickly.
But the human still has to do something with what has been created.
And this is where an interesting problem begins.
What happens to human functioning when the environment changes faster than our own patterns of functioning can adapt to it?
The Human “Software”
Let’s use the word “software” as a metaphor here.
We’re not talking about a computer program, but about everything a person learns over the years:
past experiences,
learned responses,
automatic reactions,
habits,
expectations,
predictions,
decision-making routines,
and the internal rules by which we often operate without having to rethink, every single time, what we should do.
This “software” is extremely useful.
We wouldn’t want to relearn how to walk, talk, drive, open a door, or react in a familiar situation every morning.
Our past experience shortens the path to functioning.
Functioning built from the past makes the present faster.
But there’s a problem.
The present is not always like the past.
As Long as the Situation Is Familiar, the Old Functioning Works
In a familiar situation, past experience is often an advantage.
You know what to do.
You know what to expect.
You know how to react.
The functioning that belongs to the situation becomes almost automatically accessible.
But what happens when something changes?
The plan doesn’t work.
The other person reacts differently.
A problem appears in a new form.
A previously working solution no longer produces the same result.
Or you simply find yourself in a situation you’ve never been in before.
In such cases, it isn’t necessarily information that’s missing.
It may be that the old functioning no longer fits the new situation.
And this is becoming an increasingly interesting question in the age of AI.
AI Doesn’t Just Make the World Faster
AI is not simply a new tool.
It also changes how quickly we can create possibilities.
Previously, producing a single version of an idea could take a long time.
Today, ten can be created in a few minutes.
Analysing a problem can yield several possible approaches in seconds instead of hours.
Before a creative decision, we don’t necessarily face a single option, but dozens.
Generating possibilities is becoming cheaper and faster.
But this doesn’t necessarily make choosing easier.
Quite the opposite.
It may be that exactly the reverse happens.
The Next Possibility Is No Longer Missing
In the age of AI, we can easily arrive at a strange situation.
Our problem will no longer be:
“I don’t know what could be done.”
But rather:
“Too much is possible. Which one should I do something with now?”
AI may be able to generate alternatives.
But it’s up to the human to decide which of them becomes relevant in a given situation.
And even more important:
the human has to put into motion whatever they’ve chosen.
That’s why one important question for the future won’t simply be how quickly we can access information.
It will also be how quickly we can adapt to what that information changes in us and around us.
Recent human-AI research is increasingly pointing in this direction too: rather than simply examining how AI can “help” people, it looks at how the differing capabilities of humans and AI can be turned into genuine complementarity in dynamic situations.
AI Answers. The Human Has to Go Further.
Suppose you ask the AI:
“What should I do?”
You get five answers.
You ask:
“Which one is better?”
You get a comparison.
You ask:
“Give me ten more options.”
You get ten.
The system can keep generating new possibilities continuously.
But there’s a point where the screen can no longer move forward in your place.
You have to do something.
You have to send it.
You have to draw it.
You have to change it.
You have to launch it.
You have to try it.
You have to decide.
And at that moment, we return to human functioning.
The Movement Changes the Situation
The moment you do something, you’re no longer in the same place.
This seems very simple.
Yet it’s fundamental.
You write a sentence.
Now there’s a sentence.
You draw a line.
Now there’s a line.
You send an offer.
Now there’s a situation awaiting a response.
You take a step.
Now you’re in a different situation.
Action doesn’t just carry something out. It changes the environment in which the next decision is made.
That’s why the next movement never starts from exactly the same situation.
And Here Is the Problem of Human Software
Our past patterns of functioning are fast.
But they’re fast precisely because they come from past situations.
A new situation, however, doesn’t necessarily fit them.
In such cases, the system has to create something else.
Not necessarily an entirely new personality.
Not a new life philosophy.
Not a new set of rules.
Just a next working movement.
This can be surprisingly difficult.
Because a person is often not most afraid of the unknown, but of not being able to guarantee in advance the outcome of the next step.
The Problem of the Perfect Answer
In the world of AI, it’s easy to think that if we get enough information, we can find the perfect answer.
But real situations aren’t static.
The moment you choose, something changes.
The moment you act, new information appears.
The moment new information appears, new possibilities open up.
That’s why the “perfect next step” often only seems perfect until it has happened.
After that, you’re already in a new situation.
The next movement can’t be fully separated from the previous one.
The Line Doesn’t Know in Advance Where It Will Arrive
Take a pencil.
The line starts.
It doesn’t know in advance where it will arrive.
It doesn’t know its entire route.
Yet every centimetre it travels changes where it can go next.
The first line creates the situation for the second line.
The second changes the situation for the third.
The third already carries the trace of both.
Movement → trace → new state → next movement.
The line isn’t able to continue because it knows the whole picture in advance.
It’s able to continue because it can keep functioning from what has already been created.
This May Be One of the Human Questions of the AI Age
AI is getting better and better at generating possible next steps.
This is a huge advantage.
But another capability may become increasingly important:
distinguishing a possibility from an accessible possibility.
Because not every possibility becomes an actually working possibility.
Not every good idea fits the given moment.
Not every answer is usable in the given state.
And not every generated possibility turns into action.
That’s why one real question of human-AI collaboration isn’t simply:
“What can AI do?”
But rather:
“What can the human do with what AI creates?”
The Question of Activatory
Activatory doesn’t think in opposition to AI.
Nor does it claim that the human needs to be slower or “more human”.
It examines a different level.
What happens before the next move?
What state are you in?
What can you access from this state?
What becomes possible?
What happens when the first movement has already changed the situation?
And how do you create the next thing from what has already happened?
This is one of the most interesting problems of human software.
Not that it always knows the answer.
But that it’s able to function even when the situation is no longer the same.
The Race Isn’t Between Human and AI
Perhaps that’s why it’s misleading to ask:
“Who will be faster: the human or the AI?”
AI is already astonishingly fast at certain tasks.
The human, however, works with a different type of problem.
Uncertainty.
Novelty.
Environmental change.
Bodily experience.
Context.
Intention.
Consequence.
Recent research suggests that human-AI collaboration isn’t automatically better than either working alone. The real advantage depends on how tasks are divided, when AI gets involved, how appropriate the human’s trust is, and how well both sides can adapt to the situation.
This means the question for the future isn’t simply speed.
It’s the quality of functioning in an environment of variable speed.
The Next Movement
Perhaps one of the most important human capabilities of the AI age won’t be producing as many answers as possible.
AI will keep doing that faster and faster.
Perhaps what will matter is:
noticing when the situation has changed,
recognising what you can access from here,
and
creating the next working movement.
Not the perfect one.
Not the final one.
The next one.
Because every movement changes something.
And whatever comes after is born from this new situation.
The line doesn’t know in advance where it will arrive.
But it already knows where it starts.
ACTIVATORY
The next move begins here.