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Everyone Is Invited Into the AI Pool. But Who Owns the Pool?
Everyone Is Invited Into the AI Pool. But Who Owns the Pool?
Recently, I read an article about Bill Gates and some of his concerns about the future of artificial intelligence. It got me thinking—not necessarily about whether Bill Gates is right or wrong, but about a much bigger question I've already been thinking about for some time:
What happens when a technology moves from being a convenience to becoming something we depend upon?
That question actually connects to an article I recently wrote about data centers and what I called our invisible technology infrastructure.
For decades, we've been building a massive digital infrastructure around us without really seeing it. Every time we send an email, search Google, stream a video, back up a photo, use social media or access an online service, there is physical infrastructure somewhere making that possible.
Data centers, servers, networks, electricity, cooling systems and enormous amounts of physical resources are behind the digital convenience we've come to take for granted.
We've been building this infrastructure for years.
It was just mostly invisible.
Then AI came along and started pulling back the curtain.
Suddenly we're talking about data centers, electricity consumption, water usage, computer chips and the enormous amount of infrastructure required to support artificial intelligence.
But here's the thing:
AI didn't create our dependence on digital infrastructure. It exposed it.
And I think something similar may be happening with AI itself.
At one point, a cell phone was a convenience. And eventually, something that was optional became something that was very difficult to live without.
From Convenience to Necessity
From Convenience to Necessity
Think about your smartphone.
At one point, a cell phone was a convenience. You didn't need one.
Then smartphones came along.
Eventually, having a smartphone became so common that not having one started making everyday life more difficult.
Look around.
You're asked for an email address everywhere.
Businesses communicate electronically.
Banking is increasingly digital.
Two-factor authentication often involves your phone.
Schools use online portals.
Healthcare providers use patient portals.
Tickets can live on your phone.
Employers use digital communication platforms.
Government services increasingly expect people to interact online.
Even something as simple as making a purchase can involve an app, an email receipt or a digital account.
Nobody necessarily sat down and decided:
“From now on, you need a smartphone to participate in society.”
It happened gradually.
One convenience at a time.
And eventually, something that was optional became something that was very difficult to live without.
Which brings me back to AI.
Everyone Is Invited Into the Pool
Everyone Is Invited Into the Pool
Right now, it feels like everyone has been invited into the AI pool.
You can open ChatGPT, Claude, Gemini or one of the many other AI systems and start experimenting.
You don't necessarily need to be a programmer.
You don't need a computer science degree.
You don't need to work for a technology company.
You can just jump in.
And I think that's an incredible thing.
AI is putting capabilities into the hands of ordinary people that would have been incredibly expensive or difficult to access just a few years ago.
A small business owner can use AI to help write a proposal.
A teacher can use it to develop ideas for a lesson.
A student can use it as a tutor.
Someone with an idea for a business can use AI to help research, plan and create.
And now we're moving beyond AI simply answering questions.
We're beginning to connect AI to tools, software, data and workflows so that AI can actually do things.
That's where AI agents become particularly interesting.
AI starts moving from being a tool you use to something that can potentially perform work on your behalf.
And that raises another question.
What happens when we become dependent on it?
Using a laptop in a pool looks absurd—yet willingly handing over our human capability to a system we don't control is the real absurdity.
A crowded public pool for some, a private pool for others —when AI shifts from a convenience to a necessity, the gap between the haves and have-nots won't just be about software, but about who gets a digital workforce and who gets left behind.
Who Owns the Pool?
Who Owns the Pool?
Imagine a future where AI becomes as important to running a business as email, accounting software or the Internet.
Maybe a business can't compete effectively without AI.
Maybe employees are expected to know how to work with AI.
Maybe customers expect businesses to respond instantly because AI makes that possible.
Maybe entire industries reorganize themselves around AI agents.
Again, I'm not saying this will happen exactly this way.
I'm asking what happens if it does.
Because once a technology becomes necessary to compete, access to that technology becomes much more than a technology issue.
It becomes an economic issue.
Right now, everyone can get in the pool.
There are free versions.
There are inexpensive subscriptions.
There are open-source models.
There are enterprise systems.
There are different levels of capability.
But what happens if we eventually become dependent upon AI and the most capable versions become something that only certain people or businesses can afford?
What happens if one business can afford a fleet of sophisticated AI agents and another can't?
The first business may effectively have access to an additional workforce.
That isn't simply a difference in software.
That's a difference in economic capability.
And that is something worth thinking about.
Not because I believe AI companies are going to suddenly decide to shut everyone out.
But because patterns repeat themselves.
We've seen companies gain tremendous influence when people become dependent upon their platforms and services.
We've seen technologies go from optional to essential.
We've seen businesses build their operations around technologies they don't control.
We should be willing to ask what that could look like with AI.
This Isn't Fearmongering
This Isn't Fearmongering
I want to be clear about something.
I don't think AI is something we should fear.
I also don't think we should blindly embrace it.
I'm not interested in the argument that AI is either going to save humanity or destroy it.
The reality is probably going to be much more complicated.
There are enormous opportunities.
AI can help people learn.
It can help businesses become more productive.
It can accelerate scientific research.
It can help people who previously didn't have access to certain expertise.
It can help us solve problems that are difficult, expensive or time-consuming.
Those possibilities are exciting.
But the fact that something can do enormous good doesn't mean we shouldn't think about the consequences of widespread adoption.
And the fact that something has risks doesn't mean we should reject it.
Both things can be true.
Recognizing the potential for both utopia and dystopia isn't fearmongering—it's simply acknowledging that the road we build depends entirely on the choices we make today.
We may not know exactly where this road leads, but choosing to pay attention to both the promise and the peril is how we keep from sleepwalking into the future.
We Don't Know Where This Road Ends
We Don't Know Where This Road Ends
That's probably the biggest challenge with AI.
It's moving so quickly and in so many different directions that I don't think anyone can honestly tell us exactly what the next ten years will look like.
Some predictions will come true.
Some won't.
Some technologies will disappear.
Others will become commonplace.
Things we aren't even thinking about today will probably become important.
Even many of the people building these systems don't know exactly where this road ends.
So I don't think the answer is trying to predict the future with absolute certainty.
I think the answer is learning how to recognize patterns.
Look at the past.
Look at what is happening now.
Then look forward and ask:
“If this continues, what could happen?”
That's not predicting the future.
It's preparing ourselves to think about it.
The Problem Is the Noise
The Problem Is the Noise
Unfortunately, that's becoming increasingly difficult.
AI has become one of those subjects where people often choose a side before they understand the subject.
You're either pro-AI or anti-AI.
You're either excited about it or afraid of it.
You're either a believer or a skeptic.
And then you add politics, corporate interests, social media, sensational headlines and personal biases.
The amount of noise surrounding AI is enormous.
I think we need to step outside of that noise.
Not ignore it.
Filter it.
We need to be able to put our own prejudices and assumptions aside long enough to actually examine what's happening.
If someone says AI will eliminate millions of jobs, we should ask what evidence supports that.
If someone says AI will create millions of new jobs, we should ask the same thing.
If someone says AI will revolutionize education, we should ask how.
If someone says AI will destroy education, we should ask why.
If someone says AI will make businesses more productive, we should ask who benefits.
If someone says AI will create inequality, we should ask how that could happen.
The goal shouldn't be to find the opinion we already agree with.
The goal should be to understand the issue well enough to form an informed opinion.
Cutting through the deafening noise of hype, panic, and corporate spin requires more than picking a side—it requires the clarity to look past the smoke and examine what's actually happening.
That's Where AI Literacy Comes In
That's Where AI Literacy Comes In
This is why I keep coming back to the idea of AI literacy.
AI literacy isn't simply knowing how to use ChatGPT.
It's bigger than learning how to write a good prompt.
It's understanding what AI can do, what it can't do, how it can fail, how it is trained, how information can be manipulated, who controls the technology, what infrastructure supports it, how businesses are using it and what consequences might come from widespread adoption.
It also means understanding ourselves.
Our assumptions.
Our biases.
Our fears.
Our excitement.
Our tendency to believe information that confirms what we already think.
AI literacy should give us the ability to step outside ourselves and look at the technology objectively.
Not pro-AI.
Not anti-AI.
AI aware.
This Is Where STEM and STEAM Matter
This Is Where STEM and STEAM Matter
This is also where I think STEM and STEAM education has an enormous role to play.
We shouldn't be teaching young people what to think about AI.
We should be teaching them how to think about AI.
That's fundamentally what science and engineering are about.
Ask questions.
Test ideas.
Look at evidence.
Understand systems.
Identify problems.
Consider consequences.
Change your thinking when the evidence changes!
That's the kind of thinking we need as AI continues to develop.
Students shouldn't simply learn how to use AI.
They should be asking questions about it.
Who built it?
How does it work?
What data does it use?
What happens when the data is wrong?
What happens when the system makes a mistake?
Who benefits?
Who could be harmed?
What happens when AI becomes connected to other systems?
What happens when an AI agent can actually take action?
What happens when businesses become dependent upon it?
What happens when access to the most capable systems becomes expensive?
Those aren't just computer science questions.
They're economic questions.
They're social questions.
They're ethical questions.
They're scientific questions.
They're human questions.
That's STEAM.
Teaching young people how to think instead of what to think means asking the hard questions, looking at the evidence, and having the courage to change your thinking when the evidence changes.
We don't have to choose between blind optimism and panic; we can embrace the incredible opportunities while keeping our eyes wide open to the risks, because the future is shaped by the choices we make today.
We Don't Have to Pick a Side
We Don't Have to Pick a Side
I don't think we need to know exactly where AI is going before we start preparing for it.
And I don't think we need to choose between being excited about AI and being concerned about it.
We can be both.
We can recognize the incredible opportunities while acknowledging the legitimate risks.
We can encourage innovation while asking difficult questions.
We can experiment with AI while thinking about what happens when it becomes infrastructure.
And we can recognize that the people developing AI don't necessarily know exactly where it will lead either.
That's not a reason to panic.
It's a reason to pay attention.
Because the future isn't something that's simply going to happen to us.
The choices we make today will influence what that future looks like.
And that's why I believe AI literacy is becoming so important.
Not because we need everyone to become an AI expert.
Not because everyone needs to love AI.
Not because everyone needs to fear AI.
But because AI is not going away.
We're already in the pool.
The question is whether we're going to learn how to swim—or simply assume someone else will tell us where the water is going.
And maybe the most important question we should be asking isn't just “What can AI do?”
It's:
“What kind of society are we building around it?”
Author’s Note
Author’s Note
Doug Fessler is a technology coordinator, IT consultant, and technology educator based in Pennsylvania. Through his work with organizations, schools, and community programs, he works directly with the technology infrastructure that increasingly connects our everyday lives... from networks and cloud services to emerging technologies, STEM education, and artificial intelligence.
Doug writes The Frequency to explore the intersection of technology, people, communities, and the world we are building around them. His goal isn't to tell readers what to think, but to ask better questions about where technology is taking us... and what responsibility we have for the future we are creating.
This article was written by Douglas E. Fessler. AI-assisted tools were used to structure and clarify complex concepts — a reflection, in itself, of the subject explored.
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