You Love AI. You Use It Every Day.

You love AI.
You use it every day.

Not hypothetically. Not accidentally. Not “only if you type prompts into a chatbot.”

If you live in the modern world—if you flip a light switch, use a bank account, rely on healthcare, eat food from a supply chain, buy materials, ship packages, or use the internet—you are already participating in systems powered by artificial intelligence.

This isn’t a trend.
It’s infrastructure.

And yet, parts of the art community talk about AI as if it’s an optional moral failure—something other people use, something you can boycott, something that exists outside the systems we already depend on.

That argument doesn’t hold up.

Artificial Intelligence Is Infrastructure, Not a Preference

A lot of anti-AI rhetoric treats artificial intelligence like a single product you can refuse—like a brand you can stop buying.

But AI isn’t one thing. It’s a layer embedded across modern systems: information distribution, logistics, utilities, banking security, healthcare, manufacturing, and communications.

If you post your opinions online, what people see is filtered and ranked by automated systems designed to predict engagement.
If you search the internet, the results are ordered by systems trained to interpret relevance and intent.
If you accept online payments, your transactions are evaluated by systems designed to detect fraud.

The contradiction isn’t that artists are hypocrites.
It’s that the moral line being drawn doesn’t exist.

You can opt out of platforms.
You cannot opt out of infrastructure.

How You’re Already Using AI (In Ways You’ve Probably Never Considered)

Most people associate AI with obvious things: social media feeds, recommendation engines, chatbots.

That’s the shallow layer.

Here’s the deeper one—the part people don’t usually think about.

Electricity and power grids

Modern electrical grids rely on predictive systems to balance supply and demand, anticipate failures, integrate renewable energy, and prevent outages during extreme weather.
If you expect the power to stay on, you are already benefiting from automated forecasting and optimization.

Water and sewer systems

Municipal water systems increasingly use predictive maintenance tools to detect leaks, anticipate equipment failures, and prevent contamination.
Clean drinking water doesn’t come from guesswork.

Banking and financial security

Fraud detection, transaction monitoring, and identity verification at scale are handled by machine-driven systems.
If your bank stops a fraudulent charge before it empties your account, that isn’t a human reviewing millions of transactions—it’s automated pattern recognition.

Healthcare

Medical imaging analysis, hospital scheduling, diagnostic support, and drug research all rely on machine-assisted systems to process data faster than humans ever could.
If you expect modern healthcare to function at scale, you already trust these tools.

Manufacturing and basic materials

Glass, steel, concrete, and construction materials are now inspected using computer-vision systems that detect defects invisible to the human eye.
Your windows, buildings, and packaging are shaped by these technologies—literally.

Agriculture, food systems, and wildlife management

Crop monitoring, yield prediction, irrigation planning, livestock health tracking, and wildlife population monitoring all rely on automated data analysis.
If you eat food produced at scale, you are participating.

This isn’t “tech culture.”
This is the physical world.

The Environmental Argument Needs Context

Artificial intelligence has an environmental footprint. That’s real.

Data centers consume energy. That matters. It should be regulated, improved, and increasingly powered by renewable sources.

But environmental concern collapses into performance when it’s applied selectively.

Food loss and waste account for an estimated eight to ten percent of global greenhouse gas emissions.
Livestock supply chains account for roughly fourteen percent.
The global fashion industry contributes around four percent.
Healthcare systems contribute roughly four percent.
Global shipping and aviation add several more percentage points.

These are massive, ongoing environmental impacts—yet they’re rarely treated as moral dealbreakers in the way AI is.

Environmental concern is valid.
Selective environmental concern is not.

If someone condemns AI on environmental grounds while ignoring larger, familiar sources of damage, that isn’t climate leadership.

It’s moral signaling.

What’s Really Behind the Resistance

The discomfort around AI isn’t just ethical.
It’s psychological.

Every major technological shift has disrupted jobs:
Photography changed painting.
Digital tools changed design.
Desktop publishing changed print.
Streaming changed music and film.

This moment feels different because the change is faster, not because it’s fundamentally new.

That’s why leaders across industries—and across political ideologies—keep saying the same thing in different ways:

Jobs aren’t being replaced by AI.
They’re being replaced by people who know how to use it.

Business and technology leaders have repeatedly framed AI as a productivity layer, not a replacement for human judgment. Global economic institutions are urging reskilling and adaptation, not denial.

Refusing to engage doesn’t stop change.
It just delays adaptation.

And delay has a cost.

Where I Stand

I’m an artist. My work is mine.

I don’t use AI to make my art for me.
I use it the way I use any serious tool: to think more clearly, work more efficiently, and build systems that support my creative practice instead of draining it.

It helps me research.
It helps me plan.
It helps me write, organize, and execute.

It acts as my assistant.

The result is simple: I’m more productive, I make better decisions, and I spend more time making art and less time buried in administrative work.

That isn’t something to be ashamed of.
It’s something to be intentional about.

What AI Can Actually Do for the Art World

If the art world stopped treating this technology like a moral contaminant, here’s what could improve immediately:

• Artists spend less time on admin and more time making work

• Grant and opportunity discovery becomes accessible instead of overwhelming

• Applications and artist statements become clearer and more consistent

• Archives and collections become searchable and usable

• Provenance research becomes faster and more accurate

• Accessibility improves through better descriptions and captions

• Small studios operate professionally without hiring teams they can’t afford

• Institutions reclaim time for curatorial judgment, relationships, and storytelling

None of this replaces creativity.
It removes friction.

And friction has always been the enemy of good work.

Final Thoughts

You don’t have to like artificial intelligence.

But if you live in modern life—if you use electricity, healthcare, banking, food systems, shipping, or the internet—you already use it every day.

The conversation isn’t whether AI should exist.
It already does.

The real question is whether you’ll engage with it intentionally—or pretend you’re above it while it quietly runs everything anyway.

I hope you liked this essay.
By the way—my AI assistant, Rae, helped me write it.

And yes… that’s kind of the point.

Sources & Context

Digital Infrastructure & Usage

• International Telecommunication Union (ITU) – United Nations agency reporting approximately 5.5 billion global internet users.

• Google (Alphabet Inc.) – Public disclosures indicating over five trillion searches annually, powered by automated ranking and prediction systems.

• Meta Platforms, Inc. – Engineering documentation describing how Instagram and Facebook feeds are ranked and delivered by artificial intelligence systems.

• YouTube (Google subsidiary) – Published research on deep-learning–based recommendation systems.

Utilities, Infrastructure, and Physical Systems

• United States Department of Energy (DOE) – Use of artificial intelligence for grid resilience, outage prediction, and energy optimization.

• International Energy Agency (IEA) – Reports on machine-learning systems in global energy management.

• American Water Works Association (AWWA) – Research on predictive maintenance in water infrastructure.

• Peer-reviewed engineering journals on computer vision in glass and materials manufacturing.

Finance and Healthcare

• United States Department of the Treasury – Reports on machine-learning-based fraud detection and prevention.

• Federal Reserve System – Research on automated transaction monitoring.

• Medical and healthcare research publications on AI-assisted imaging and diagnostics.

Environment and Climate Context

• International Energy Agency (IEA) – Estimates global data center electricity use at roughly 415 terawatt-hours annually.

• United Nations Environment Programme (UNEP) – Food waste contributing approximately 8–10% of global greenhouse gas emissions.

• Food and Agriculture Organization of the United Nations (FAO) – Livestock supply chains contributing approximately 14.5% of global emissions.

• Ellen MacArthur Foundation – Fashion industry contributing roughly 4% of global emissions.

• Health Care Without Harm – Healthcare systems contributing approximately 4.4% of global net emissions.

• International Maritime Organization (IMO) – Global shipping contributing approximately 2.9% of emissions.

• Our World in Data (University of Oxford) – Aviation contributing approximately 2.5% of global carbon dioxide emissions.

Work, Productivity, and Adaptation

• National Bureau of Economic Research (NBER) – Study showing productivity increases from generative AI assistants.

• World Economic Forum (WEF) – Future of Jobs reports projecting major skill shifts by 2030.

• International Monetary Fund (IMF) – Workforce adaptation and reskilling guidance.