Opinion: Can AI Help Solve its Environmental Cost?

As adoption grows, energy and resource demands spike, raising the environmental cost of AI—yet the ultimate question remains whether the intelligence it generates can eventually help society solve complex problems and reduce its broader footprint.

By Andreas Hassellöf, Founder & CEO of Ombori

AI is often discussed as if it exists somewhere in the cloud. In reality, every AI model depends on physical infrastructure: data centers, advanced processors, electricity, cooling systems, networks and increasingly resilient power grids.

As AI adoption accelerates, so does demand for the infrastructure behind it. This creates an uncomfortable contradiction. AI is being developed to help organizations and societies become more efficient, while the technology itself requires significant amounts of energy and resources to build and operate.

The environmental cost is immediate. The potential benefits are more difficult to measure.

We need to distinguish between the cost of building intelligence and the value that intelligence may eventually create. That distinction is becoming increasingly important as AI moves from an emerging technology to a core layer of digital infrastructure.

The Environmental Cost of Building AI

The environmental impact of AI cannot simply be dismissed as an unavoidable cost of innovation. As demand for computing power increases, so will pressure on energy systems and the infrastructure required to support them.

The industry will need greater investment in renewable energy, solar power, nuclear energy, batteries and more resilient electricity grids. The promise of future innovation cannot be used as an excuse to ignore the impact being created today.

The challenge is that the two sides of the equation operate on different timelines. The energy consumed by a data center can be measured today. The value of a scientific breakthrough that AI might help enable years from now cannot be measured with the same precision.

We are very good at measuring the cost of the machine. We are much less good at measuring the value of the breakthrough that machine may help create.

That does not make the environmental cost of AI less important. It means that measuring the full impact of AI requires us to look beyond the energy consumed by the technology itself.

The Bigger Opportunity May Not Be Productivity

Much of the current conversation around AI focuses on productivity: writing emails, generating images, analyzing documents and automating repetitive tasks.

Those applications may be commercially valuable, but they may also represent only the most visible and immediate uses of the technology.

The larger opportunity could lie in applying AI to problems that are too complex, interconnected or time-consuming for humans to solve efficiently on their own.

Energy production, battery technology, climate modelling, materials science, transportation, food production and water management all involve vast amounts of data and multiple variables.

We may be using one of the most powerful problem-solving technologies humanity has created to make better emails. That is unlikely to be where its biggest impact lies.

There is also a less obvious connection between commercial AI and scientific discovery. The applications attracting investment and creating demand for computing infrastructure are not necessarily separate from scientific progress. The scale of everyday AI use can help create the economic engine that makes increasingly powerful models, computing capacity and research infrastructure possible.

Scientific applications do not need to represent the majority of AI usage to matter. They need access to capabilities that broad commercial demand helps make possible.

From Efficiency to Redesign

AI could eventually allow organisations to move beyond simply making existing systems more efficient.

Energy networks could optimise how power is generated and distributed. Supply chains could respond to demand before products are manufactured and transported. Agriculture could make more precise use of water and other resources. Manufacturing could identify waste before it occurs.

The opportunity may not simply be to produce more efficiently. It could be to question what needs to be produced in the first place.

This is not necessarily about building more factories to produce more things. It could be about using intelligence to question why we need to produce certain things in the first place.

The potential goes beyond individual industries. The bigger opportunity could be to rethink the way society itself operates.

If energy becomes more abundant and systems become increasingly intelligent, energy and labour may cease to be the same constraints they have historically been. The risk may not simply be that society moves too slowly. It may be that we fail to rethink systems built for an earlier technological age.

In that sense, the prize is not simply making society run faster.

It is changing its operating system.

The Alternative to AI Is Not Always Zero

Another factor often missing from discussions about AI’s environmental cost is the alternative.

The comparison is frequently framed as AI versus doing nothing. But in many cases, the alternative to AI is human labour and human activity. Millions of people may spend time and resources solving similar problems individually.

The comparison isn’t always AI versus nothing. Often, it is AI versus millions of humans spending time, energy and resources solving the same problems one by one.

The question is therefore not only whether AI will replace human brainpower. It is also what humans might be able to do when the amount of intelligence available to work on a problem is no longer limited by the number of people who can dedicate time to it.

That shift could have implications far beyond productivity.

The Breakthroughs Are Already Beginning

The argument that AI could eventually help solve complex environmental and scientific problems is no longer entirely theoretical.

In September 2025, MIT researchers introduced CRESt, an AI platform designed to combine scientific knowledge with laboratory experimentation to accelerate materials discovery, including research into energy-related problems.

Google DeepMind’s GNoME system has also demonstrated the scale at which AI can expand materials discovery, identifying millions of candidate crystal structures and hundreds of thousands of predicted stable materials with potential applications in areas including batteries and energy technologies.

More recent research has moved further toward closed-loop discovery, where AI systems help identify promising materials, guide experiments and learn from the results. Research involving McGill and Mila has explored this approach in battery cathode discovery, pointing toward a future in which scientific experimentation becomes increasingly iterative and AI-assisted.

Institutional investment is also growing. On July 22, 2026, the U.S. Department of Energy announced 278 projects under its Genesis Mission, bringing together universities, national laboratories, companies and other institutions to use AI for scientific discovery across areas including energy, materials, infrastructure and national security.

These initiatives do not prove that AI will solve humanity’s environmental problems.

They do demonstrate that the technology is beginning to move beyond generating content and automating routine tasks.

The Problems We Created

Many of the problems AI is now being asked to help solve were created by previous waves of industrial and technological development.

Climate change, resource depletion, waste, inefficient infrastructure and environmental degradation are not disconnected from the way modern societies have produced, transported and consumed goods for generations.

The question is whether this new wave of intelligence can help undo some of the damage created by earlier waves of industrialisation.

This is also why second- and third-order effects matter. A technology may consume substantial resources while being developed and still eventually enable systems that consume fewer resources at scale.

The challenge is that these effects are difficult to measure. The cost is visible now, while the potential benefit may emerge years or decades later.

That does not make the cost irrelevant. It means we need a broader way of thinking about the equation.

The Outcome Will Depend on What We Do With AI

The environmental impact of AI will depend heavily on how the technology is used.

If it primarily increases consumption, accelerates production and creates more waste, its footprint could grow significantly. But if it helps develop better energy systems, materials, batteries, industrial processes and resource management, the long-term equation could look very different.

If we can imagine using AI to help cure cancer, why wouldn’t we apply the same ambition to the environmental problems that threaten the planet?

The technology will not automatically produce a positive or negative outcome.

AI has a footprint. It does. The question is whether the intelligence we create can ultimately help us reduce the footprint of everything else.

Ultimately, the short-term environmental cost of AI is likely to increase. But its long-term impact will depend on what humanity chooses to do with the intelligence it is creating.

The technology may be asked to solve problems that humanity itself created. That does not make the environmental cost of AI acceptable by default. But it does make the question more complicated than simply measuring the energy required to build the intelligence.

I believe the long-term potential is more likely to be positive than negative. But only if we use AI to solve bigger problems, not simply to create more consumption.

AI is not environmentally free. Neither is human activity.

The real question is whether the resources invested in creating more powerful intelligence can eventually give humanity a greater ability to solve the problems it has created.

Disclaimer: The views expressed in this article are those of the author and do not necessarily reflect the views of Techitup Middle East.