If You Love Nature and Hate AI, Measure Everything

There is a growing group of people who love nature and deeply distrust artificial intelligence.

I understand the instinct.

AI requires data centers. Data centers require electricity, water, cooling systems, chips, metals, concrete, land, transmission infrastructure, and enormous amounts of capital. The International Energy Agency estimates that data centers consumed around 415 terawatt hours of electricity in 2024, about 1.5 percent of global electricity consumption. Its base case projects data center electricity demand to reach roughly 945 TWh by 2030.

Those numbers deserve attention.

The problem begins when AI is judged according to an environmental standard that we refuse to apply to everything else.

A person can drive a gasoline powered vehicle to a restaurant, eat a hamburger, wear clothes produced through a global manufacturing network, throw away part of the meal, pull out a smartphone made from materials mined across several continents, and post that using artificial intelligence is destroying the planet.

There is a contradiction worth examining.

AI has an environmental cost. So does almost every system supporting modern life. A useful environmental ethic has to measure all of them.

The Earth already keeps score

Climate change is physical.

The atmosphere does not respond to whether we feel virtuous about an activity. It responds to greenhouse gases.

Forests respond to land clearing. Rivers respond to contamination. Species respond to habitat destruction. Human bodies respond to pollution.

The Earth keeps a material accounting system even when human culture does not.

When global greenhouse gas emissions are divided by economic sector, the largest sources remain systems people interact with every day.

According to IPCC data summarized by the U.S. Environmental Protection Agency, electricity and heat production accounted for about 34 percent of global greenhouse gas emissions in 2019. Industry accounted for about 24 percent. Agriculture, forestry, and other land use accounted for about 22 percent. Transportation accounted for about 15 percent. Direct emissions from buildings represented about 6 percent.

AI currently exists inside that much larger physical civilization.

Its environmental consequences are real. So are the consequences of systems we have normalized.

Start with the hamburger

Food is one of the clearest examples of how familiarity hides environmental cost.

The global food system is responsible for roughly one third of human caused greenhouse gas emissions. It also places enormous pressure on freshwater, forests, biodiversity, soil, fertilizer production, transportation, refrigeration, packaging, and waste systems.

Yet most people do not experience eating lunch as participation in an enormous technological system.

It feels ordinary because it is familiar.

Consider beef.

A hamburger represents land.

It represents the animal.

It represents feed.

It represents water.

It represents machinery.

It represents fertilizer.

It represents methane.

It represents processing.

It represents refrigeration.

It represents packaging.

It represents trucking.

It represents the restaurant or grocery store.

It represents cooking.

If the food is thrown away, it represents all of those resources being consumed without accomplishing their purpose.

This does not mean people should never eat hamburgers. It means consistency matters.

If environmental impact is the standard, the standard has to follow the carbon, land, water, and material use wherever they lead.

Then look at the car

Transportation represents roughly 15 percent of global greenhouse gas emissions in the EPA summary of IPCC data, with petroleum based fuels providing approximately 95 percent of transportation energy globally.

The environmental cost of an automobile begins long before the engine turns on.

Metals have to be extracted. Steel and aluminum have to be manufactured. Rubber must become tires. Factories have to operate. Vehicles have to be shipped. Oil has to be extracted, refined, transported, and sold. Roads have to be constructed and maintained. Parking lots occupy immense amounts of land.

Communities are frequently designed around the assumption that nearly every adult will travel inside a multi thousand pound machine.

We barely notice this because the automobile has been culturally normalized for generations.

AI has not.

New systems feel more visible than old systems. Visibility is not the same thing as scale.

The same applies to almost everything around us

Look around the room you are sitting in.

Concrete. Steel. Glass. Plastic. Wood. Paint. Copper wiring. Heating. Air conditioning. Lighting. Furniture. Internet infrastructure. Electronics.

Every object arrived through a chain of extraction, manufacturing, energy, labor, transportation, and disposal.

Industry accounts for approximately 24 percent of global greenhouse gas emissions under the sector accounting used by the IPCC and EPA.

Modern civilization has a physical footprint.

AI did not create that fact.

AI is entering a civilization that already consumes enormous quantities of matter and energy.

The important question is what happens next.

AI deserves scrutiny

There is a version of the technology industry that deserves every environmental criticism aimed at it.

Build enormous computational systems. Consume unlimited energy. Generate endless disposable content. Encourage people to buy more things. Accelerate advertising. Increase consumption. Replace functioning hardware constantly. Centralize wealth. Externalize environmental costs. Call everything progress.

That would be a terrible use of artificial intelligence.

Data center demand is already increasing rapidly. The IEA reports that global data center electricity consumption reached about 415 TWh in 2024 and projects roughly 945 TWh by 2030 in its base case. AI related accelerated servers account for a major part of that growth.

This should create pressure for efficiency.

It should create pressure for cleaner power.

It should create pressure for better cooling.

It should create pressure for responsible water use.

It should create pressure for recyclable hardware and longer equipment lifespans.

It should create pressure for transparency.

AI companies should have to explain what their systems consume and what society receives in exchange.

That is a reasonable standard.

Then we should apply that standard to every other major system too.

The better question is value per resource consumed

Energy consumption alone is not a useful moral test.

Hospitals consume enormous amounts of energy.

Water treatment plants consume energy.

Schools consume energy.

Agriculture consumes energy.

Human brains consume energy.

A forest captures solar energy and converts it into an extraordinarily complex biological system.

The relevant question is what happens because that energy was used.

Did we create something valuable?

Did we prevent waste?

Did we reduce another larger source of consumption?

Did we improve people's lives?

Did we restore something?

Did we make a system more efficient?

Did we consume resources without producing meaningful value?

That is a harder conversation than simply declaring technology good or bad.

It is also a more useful one.

AI could increase consumption

AI could become the greatest persuasion engine humans have ever constructed.

Imagine every company possessing systems capable of generating unlimited personalized advertisements, manipulating attention, creating artificial desires, and optimizing the sale of products people never needed.

That future could increase resource consumption dramatically.

It deserves resistance.

There is another possibility.

AI could reduce the physical burden of civilization

Consider transportation.

What happens if better intelligence allows cities to coordinate transportation efficiently enough to reduce unnecessary vehicle trips?

Consider agriculture.

What happens if farmers can determine precisely where irrigation, fertilizer, or pesticides are required instead of applying them broadly?

Consider energy.

What happens if electrical grids become dramatically better at matching supply and demand?

Consider buildings.

What happens if heating and cooling systems continuously optimize themselves around occupancy, weather, and energy prices?

Consider manufacturing.

What happens if materials are designed more efficiently, waste is detected earlier, maintenance happens before machines fail, and supply chains stop moving unnecessary inventory around the world?

Consider government.

How many car trips, pieces of paper, offices, administrative processes, waiting rooms, and hours of human labor exist because information is poorly coordinated?

Consider healthcare. Logistics. Disaster response. Conservation. Scientific research.

The ecological equation becomes more complicated when computation replaces physical activity.

Spending one unit of energy on intelligence could potentially prevent many units of energy, material, travel, waste, or duplication elsewhere.

That possibility should be measured too.

This is ultimately a coordination problem

Many environmental failures are also failures of coordination.

We produce food while people go hungry.

We throw food away while other people cannot afford it.

Buildings sit empty while people need space.

Cars sit unused most of the day.

Equipment is purchased by thousands of organizations that could share it.

Renewable electricity is sometimes generated where transmission infrastructure cannot move it efficiently.

Scientific discoveries sit inside institutions that rarely communicate with one another.

Organizations repeatedly solve the same problems independently.

People who need one another often do not know the other exists.

Resources are wasted because information does not travel.

That matters because sustainability is partly an information and coordination problem.

Humanity often consumes additional resources because we cannot see the resources, people, capabilities, relationships, knowledge, and opportunities that already exist around us.

That is one of the problems we are building Rhiz to address.

Why Rhiz exists

Rhiz begins from a simple idea:

Better coordination can create better outcomes with fewer wasted resources.

We live inside enormous networks of people, organizations, knowledge, capabilities, relationships, and infrastructure.

Most of those networks cannot understand themselves well enough to coordinate effectively.

Someone needs something. Someone else can provide it. Neither knows the other exists.

An organization repeatedly rebuilds something another organization already solved.

A founder spends months looking for a relationship that already exists two introductions away.

A community lacks resources while valuable assets nearby sit idle.

The intelligence is distributed. The relationships are fragmented. The context is missing.

Rhiz is being built to help make those networks more legible and actionable.

Who is here?

What do they know?

What can they do?

What are they trying to accomplish?

What resources already exist?

Who trusts whom?

What opportunities are available?

What needs to happen next?

What actually happened?

What worked?

Once a system becomes more legible, better coordination becomes possible.

Coordination creates action. Action creates outcomes. Outcomes create knowledge. Knowledge can improve future coordination.

That loop is where AI becomes most interesting to me.

Intelligence should help us need less

The dominant model of technology for the last several decades has frequently been built around increasing consumption.

More clicks. More attention. More advertisements. More engagement. More products. More subscriptions. More devices. More content. More time staring at screens.

There is another model.

Use intelligence to eliminate unnecessary activity.

Find the person faster.

Find the answer faster.

Reuse what already exists.

Share resources.

Avoid redundant work.

Coordinate transportation.

Reduce waste.

Make systems understandable.

Help people spend less time administering life and more time living it.

Technology becomes most interesting when it begins disappearing into the background.

A future dominated by people staring at machines all day does not appeal to me.

A future where machines quietly remove unnecessary friction from human life does.

That could mean people spending more time with their children. More time making things. More time growing food. More time caring for their communities. More time outside. More time doing the deeply human things technology was supposedly created to enable in the first place.

People who love nature should be involved in AI

The people most skeptical of artificial intelligence may be exactly the people the field needs.

Ecologists understand limits.

Farmers understand cycles.

Indigenous knowledge systems understand relationships across generations.

Artists understand meaning.

Community organizers understand human networks.

Parents understand responsibility toward people who have not been born yet.

Engineers understand what machines can do.

None of those perspectives are sufficient alone.

We need systems that combine them.

The alternative is allowing the future of artificial intelligence to be defined entirely by companies whose primary measurement is financial growth.

Environmentalists should challenge AI. They should demand transparency, cleaner infrastructure, responsible resource use, and value worthy of what these systems consume.

They should also resist turning AI into a convenient symbol for an environmental crisis created by systems that existed long before generative AI appeared.

What happens over the next twenty years?

The future cannot be represented honestly as a single precise line. Technology adoption, efficiency, regulation, energy supply, transportation electrification, food systems, industry, and climate policy can all change direction.

The useful question is directional: what happens if the large existing sectors remain large while AI related computing grows much faster from a much smaller base?

The chart is intentionally labeled as an illustrative extension rather than an official forecast. Its purpose is to preserve scale. AI can grow very quickly while still remaining much smaller than the major sources of global greenhouse gas emissions for a substantial period. The fact that it is smaller does not remove the obligation to make it efficient. The fact that it is growing does not make larger systems disappear.

Measure everything

Measure the hamburger.

Measure the SUV.

Measure the airline ticket.

Measure the data center.

Measure the plastic bottle.

Measure the smartphone.

Measure the building.

Measure the shipment.

Measure the wasted food.

Measure the AI query.

Measure the electricity source.

Measure what each activity consumed.

Then measure what it produced.

This is an argument for environmental accounting.

AI should have to earn the resources it consumes.

So should everything else.

The next twenty years will require humanity to make enormous choices about energy, food, transportation, cities, computation, land, water, and consumption.

Artificial intelligence will increasingly participate in all of those systems.

The question is whether we use intelligence to accelerate the habits that created the crisis or to build something more efficient, coordinated, regenerative, and humane.

That decision is still ours.

Maybe the most important thing technology can eventually help humanity understand is something nature has been demonstrating for billions of years:

Nothing exists alone.

Everything participates in a system.

Everything consumes something.

Everything produces something.

Everything affects everything around it.

The future should be judged accordingly.

Measure everything. Then build better systems.