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By Ahmed Elgammal, Rutgers University 

Generative AI was trained on centuries of art and writing produced by humans.

But scientists and critics have wondered what would happen once AI became widely adopted and started training on its outputs.

A new study points to some answers.

In January 2026, artificial intelligence researchers Arend Hintze, Frida Proschinger Åström and Jory Schossau published a study showing what happens when generative AI systems are allowed to run autonomously – generating and interpreting their own outputs without human intervention.

The researchers linked a text-to-image system with an image-to-text system and let them iterate – image, caption, image, caption – over and over and over.

Regardless of how diverse the starting prompts were – and regardless of how much randomness the systems were allowed – the outputs quickly converged onto a narrow set of generic, familiar visual themes: atmospheric cityscapes, grandiose buildings and pastoral landscapes. Even more striking, the system quickly “forgot” its starting prompt.

The researchers called the outcomes “visual elevator music” – pleasant and polished, yet devoid of any real meaning.

For example, they started with the image prompt, “The Prime Minister pored over strategy documents, trying to sell the public on a fragile peace deal while juggling the weight of his job amidst impending military action.” The resulting image was then captioned by AI. This caption was used as a prompt to generate the next image.

After repeating this loop, the researchers ended up with a bland image of a formal interior space – no people, no drama, no real sense of time and place.

As a computer scientist who studies generative models and creativity, I see the findings from this study as an important piece of the debate over whether AI will lead to cultural stagnation.

The results show that generative AI systems themselves tend toward homogenization when used autonomously and repeatedly. They even suggest that AI systems are currently operating in this way by default.

The familiar is the default

This experiment may appear beside the point: Most people don’t ask AI systems to endlessly describe and regenerate their own images. The convergence to a set of bland, stock images happened without retraining. No new data was added. Nothing was learned. The collapse emerged purely from repeated use.

But I think the setup of the experiment can be thought of as a diagnostic tool. It reveals what generative systems preserve when no one intervenes.

This has broader implications, because modern culture is increasingly influenced by exactly these kinds of pipelines. Images are summarized into text. Text is turned into images. Content is ranked, filtered and regenerated as it moves between words, images and videos. New articles on the web are now more likely to be written by AI than humans. Even when humans remain in the loop, they are often choosing from AI-generated options rather than starting from scratch.

The findings of this recent study show that the default behavior of these systems is to compress meaning toward what is most familiar, recognizable and easy to regenerate.

Cultural stagnation or acceleration?

For the past few years, skeptics have warned that generative AI could lead to cultural stagnation by flooding the web with synthetic content that future AI systems then train on. Over time, the argument goes, this recursive loop would narrow diversity and innovation.

Champions of the technology have pushed back, pointing out that fears of cultural decline accompany every new technology. Humans, they argue, will always be the final arbiter of creative decisions.

What has been missing from this debate is empirical evidence showing where homogenization actually begins.

The new study does not test retraining on AI-generated data. Instead, it shows something more fundamental: Homogenization happens before retraining even enters the picture. The content that generative AI systems naturally produce – when used autonomously and repeatedly – is already compressed and generic.

This reframes the stagnation argument. The risk is not only that future models might train on AI-generated content, but that AI-mediated culture is already being filtered in ways that favor the familiar, the describable and the conventional.

Retraining would amplify this effect. But it is not its source.

This is no moral panic

Skeptics are right about one thing: Culture has always adapted to new technologies. Photography did not kill painting. Film did not kill theater. Digital tools have enabled new forms of expression.

But those earlier technologies never forced culture to be endlessly reshaped across various mediums at a global scale. They did not summarize, regenerate and rank cultural products – news stories, songs, memes, academic papers, photographs or social media posts – millions of times per day, guided by the same built-in assumptions about what is “typical.”

The study shows that when meaning is forced through such pipelines repeatedly, diversity collapses not because of bad intentions, malicious design or corporate negligence, but because only certain kinds of meaning survive the text-to-image-to-text repeated conversions.

This does not mean cultural stagnation is inevitable. Human creativity is resilient. Institutions, subcultures and artists have always found ways to resist homogenization. But in my view, the findings of the study show that stagnation is a real risk – not a speculative fear – if generative systems are left to operate in their current iteration.

They also help clarify a common misconception about AI creativity: Producing endless variations is not the same as producing innovation. A system can generate millions of images while exploring only a tiny corner of cultural space.

In my own research on creative AI, I found that novelty requires designing AI systems with incentives to deviate from the norms. Without it, systems optimize for familiarity because familiarity is what they have learned best. The study reinforces this point empirically. Autonomy alone does not guarantee exploration. In some cases, it accelerates convergence.

This pattern already emerged in the real world: One study found that AI-generated lesson plans featured the same drift toward conventional, uninspiring content, underscoring that AI systems converge toward what’s typical rather than what’s unique or creative.

Lost in translation

Whenever you write a caption for an image, details will be lost. Likewise for generating an image from text. And this happens whether it’s being performed by a human or a machine.

In that sense, the convergence that took place is not a failure that’s unique to AI. It reflects a deeper property of bouncing from one medium to another. When meaning passes repeatedly through two different formats, only the most stable elements persist.

But by highlighting what survives during repeated translations between text and images, the authors are able to show that meaning is processed inside generative systems with a quiet pull toward the generic.

The implication is sobering: Even with human guidance – whether that means writing prompts, selecting outputs or refining results – these systems are still stripping away some details and amplifying others in ways that are oriented toward what’s “average.”

If generative AI is to enrich culture rather than flatten it, I think systems need to be designed in ways that resist convergence toward statistically average outputs. There can be rewards for deviation and support for less common and less mainstream forms of expression.

The study makes one thing clear: Absent these interventions, generative AI will continue to drift toward mediocre and uninspired content.

Cultural stagnation is no longer speculation. It’s already happening.

About the Author:

Ahmed Elgammal, Professor of Computer Science and Director of the Art & AI Lab, Rutgers University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Murugan Anandarajan, Drexel University 

Businesses are acting fast to adopt agentic AI – artificial intelligence systems that work without human guidance – but have been much slower to put governance in place to oversee them, a new survey shows. That mismatch is a major source of risk in AI adoption. In my view, it’s also a business opportunity.

I’m a professor of management information systems at Drexel University’s LeBow College of Business, which recently surveyed more than 500 data professionals through its Center for Applied AI & Business Analytics. We found that 41% of organizations are using agentic AI in their daily operations. These aren’t just pilot projects or one-off tests. They’re part of regular workflows.

At the same time, governance is lagging. Only 27% of organizations say their governance frameworks are mature enough to monitor and manage these systems effectively.

In this context, governance is not about regulation or unnecessary rules. It means having policies and practices that let people clearly influence how autonomous systems work, including who is responsible for decisions, how behavior is checked, and when humans should get involved.

This mismatch can become a problem when autonomous systems act in real situations before anyone can intervene.

For example, during a recent power outage in San Francisco, autonomous robotaxis got stuck at intersections, blocking emergency vehicles and confusing other drivers. The situation showed that even when autonomous systems behave “as designed,” unexpected conditions can lead to undesirable outcomes.

This raises a big question: When something goes wrong with AI, who is responsible – and who can intervene?

Why governance matters

When AI systems act on their own, responsibility no longer lies where organizations expect it. Decisions still happen, but ownership is harder to trace. For instance, in financial services, fraud detection systems increasingly act in real time to block suspicious activity before a human ever reviews the case. Customers often only find out when their card is declined.

So, what if your card is mistakenly declined by an AI system? In that situation, the problem isn’t with the technology itself – it’s working as it was designed – but with accountability. Research on human-AI governance shows that problems happen when organizations don’t clearly define how people and autonomous systems should work together. This lack of clarity makes it hard to know who is responsible and when they should step in.

Without governance designed for autonomy, small issues can quietly snowball. Oversight becomes sporadic and trust weakens, not because systems fail outright, but because people struggle to explain or stand behind what the systems do.

When humans enter the loop too late

In many organizations, humans are technically “in the loop,” but only after autonomous systems have already acted. People tend to get involved once a problem becomes visible – when a price looks wrong, a transaction is flagged or a customer complains. By that point, the system has already been decided, and human review becomes corrective rather than supervisory.

Late intervention can limit the fallout from individual decisions, but it rarely clarifies who is accountable. Outcomes may be corrected, yet responsibility remains unclear.

Recent guidance shows that when authority is unclear, human oversight becomes informal and inconsistent. The problem is not human involvement, but timing. Without governance designed upfront, people act as a safety valve rather than as accountable decision-makers.

How governance determines who moves ahead

Agentic AI often brings fast, early results, especially when tasks are first automated. Our survey found that many companies see these early benefits. But as autonomous systems grow, organizations often add manual checks and approval steps to manage risk.

Over time, what was once simple slowly becomes more complicated. Decision-making slows down, work-arounds increase, and the benefits of automation fade. This happens not because the technology stops working, but because people never fully trust autonomous systems.

This slowdown doesn’t have to happen. Our survey shows a clear difference: Many organizations see early gains from autonomous AI, but those with stronger governance are much more likely to turn those gains into long-term results, such as greater efficiency and revenue growth. The key difference isn’t ambition or technical skills, but being prepared.

Good governance does not limit autonomy. It makes it workable by clarifying who owns decisions, how systems function is monitored, and when people should intervene. International guidance from the OECD – the Organization for Economic Cooperation and Development – emphasizes this point: Accountability and human oversight need to be designed into AI systems from the start, not added later.

Rather than slowing innovation, governance creates the confidence organizations need to extend autonomy instead of quietly pulling it back.

The next advantage is smarter governance

The next competitive advantage in AI will not come from faster adoption, but from smarter governance. As autonomous systems take on more responsibility, success will belong to organizations that clearly define ownership, oversight and intervention from the start.

In the era of agentic AI, confidence will accrue to the organizations that govern best, not simply those that adopt first.

About the Author:

Murugan Anandarajan, Professor of Decision Sciences and Management Information Systems, Drexel University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Nir Kshetri, University of North Carolina – Greensboro 

The rapid growth of artificial intelligence has sharply increased electricity and water consumption, raising concerns about the technology’s environmental footprint and carbon emissions. But the story is more complicated than that.

I study emerging technologies and how their development and deployment influence economic, institutional and societal outcomes, including environmental sustainability. From my research, I see that even as AI uses a lot of energy, it can also make systems cleaner and smarter.

AI is already helping to save energy and water, cut emissions and make businesses more efficient in agriculture, data centers, the energy industry, building heating and cooling, and aviation.

Agriculture

Agriculture is responsible for nearly 70% of the world’s freshwater use, and competition for water is growing.

AI is helping farmers use water more efficiently. Argentinian climate tech startup Kilimo, for example, tackles water scarcity with an AI-powered irrigation platform. The software uses large amounts of data, machine learning, and weather and satellite measurements to determine when and how much to water which areas of fields, ensuring that only the plants that actually need water receive it.

Chile’s Ministry of Agriculture has found that in that country’s Biobío region, farms using Kilimo’s precision irrigation systems have reduced water use by up to 30% while avoiding overirrigation. Using less water also reduces the amount of energy needed to pump it from the ground and around a farm.

Kilimo is one example that shows how AI can create economic incentives for sustainability: The amount of water farmers save from precision irrigation is verified, and credits for those savings are sold to local companies that want to offset some of their water use. The farmers then earn a profit – often 20% to 40% above their initial investment.

Data centers

U.S. data centers consumed about 176 terawatt-hours of electricity in 2023, accounting for roughly 4.4% of total U.S. electricity use. This number increased to 183 TWh in 2024. This growing energy footprint has made improving data center efficiency a critical priority for the operators of the data centers themselves, as well as the companies that rely on them – including cloud providers, tech firms and large enterprises running AI workloads – both to reduce costs and meet sustainability and regulatory goals.

AI is helping data centers become more efficient. The number of global internet users grew from 1.9 billion in 2010 to 5.6 billion in 2025. Global internet traffic surged from 20.2 exabytes per month in 2010 to 521.9 exabytes per month in 2025 – a more than 25-fold increase.

Despite the surge in internet traffic and users, data center electricity consumption has grown more moderately, rising from 1% of global electricity use in 2010 to 2% in 2025. Much of this is thanks to efficiency gains, including those enabled by AI.

AI systems analyze operational data in data centers – including workloads, temperature, cooling efficiency and energy use – to spot energy-hungry tasks. It adjusts computing resources to match demand and optimizes cooling. This lets data centers run smoothly without wasting electricity.

At Microsoft, AI is improving energy efficiency by using predictive analytics to schedule computing tasks. This lets servers enter low-power modes during periods of low demand, saving electricity during slower times. Meta uses AI to control cooling and airflow in its data centers. The systems stay safe while using less energy than they might otherwise.

In Frankfurt, Germany, Equinix uses AI to manage cooling and adjust energy use at its data center based on real-time weather. This improved operational efficiency by 9%, The New York Times reported.

Energy and fuels

Energy companies are using AI to boost efficiency and cut emissions. They deploy drones with cameras to inspect pipelines. AI systems analyze the images to more quickly detect corrosion, cracks, dents and leaks, which allows problems to be addressed before they escalate, improving overall safety and reliability.

Shell has AI systems that monitor methane emissions from its facilities by analyzing methane concentrations and wind data, such as speed and direction. This helps the system track how methane disperses, enabling it to pinpoint emission sources and optimize energy use. By identifying the largest leaks quickly, the system allows targeted maintenance and operational adjustments to further reduce emissions. Using that technology, the company says it aims to nearly eliminate methane leaks by 2030.

AI could speed up innovation in clean energy by improving solar panels, batteries and carbon-capture systems. In the longer term, it could enable major breakthroughs, including advanced biofuels or even usable nuclear fusion, while helping track and manage carbon-absorbing resources such as forests, wetlands and carbon storage facilities.

Shell uses AI across its operations to cut emissions. Its process optimizer for liquefied natural gas analyzes sensor data to find more efficient equipment settings, boosting energy efficiency and reducing emissions.

Buildings and district heating

The energy needed to heat, cool and power buildings is responsible for roughly 28% of total global emissions. AI initiatives are starting to reduce building emissions through smart management and predictive optimization.

In downtown Copenhagen, for instance, the local utility company HOFOR deployed thousands of sensors tracking temperatures, humidity and building energy flows. The system uses information about each building to forecast heating needs 24 hours in advance and automatically adjust supply to match demand.

The Copenhagen system was first piloted in schools and multifamily housing, with support from the Nordic Smart City Network and climate-innovation grants. It has since expanded to dozens of sites. Results were clear: Across participating buildings, energy use fell 15% to 25%, peak heating demand dropped by up to 30%, and carbon dioxide emissions decreased by around 10,000 tonnes per year.

AI can also help households and offices save energy. Smart home systems optimize heating, cooling and appliance use. Researchers at the Lawrence Berkeley National Laboratory found that by adopting AI, medium-sized office buildings in the U.S. could reduce energy use by 21% and cut carbon dioxide emissions by 35%.

Aviation

About 2% of all human-caused carbon dioxide emissions in 2023 came from aviation, which emitted about 882 megatons of carbon dioxide.

Contrails, the thin ice clouds formed when aircraft exhaust freezes at cruising altitudes, contribute more than one-third of aviation’s overall warming effect by trapping heat in the atmosphere. AI can optimize flight routes and altitudes in real time to reduce contrail formation by avoiding areas where the air is more humid and therefore more likely to produce contrails.

Airlines have also used AI to improve fuel efficiency. In 2023, Alaska Airlines used 1.2 million gallons less fuel by using AI to analyze weather, wind, turbulence, airspace restrictions and traffic to recommend the most efficient routes, saving around 5% on fuel and emissions for longer flights.

In short, AI affects the environment in both positive and negative ways. Already, it has helped industries cut energy use, lower emissions and use water more efficiently. Expanding these solutions could drive a cleaner, more sustainable planet.

About the Author:

Nir Kshetri, Professor of Management, University of North Carolina – Greensboro

This article is republished from The Conversation under a Creative Commons license. Read the original article.

By Mojtaba Akhavan-Tafti, University of Michigan 

The rapid expansion of artificial intelligence and cloud services has led to a massive demand for computing power. The surge has strained data infrastructure, which requires lots of electricity to operate. A single, medium-sized data center here on Earth can consume enough electricity to power about 16,500 homes, with even larger facilities using as much as a small city.

Over the past few years, tech leaders have increasingly advocated for space-based AI infrastructure as a way to address the power requirements of data centers.

In space, sunshine – which solar panels can convert into electricity – is abundant and reliable. On Nov. 4, 2025, Google unveiled Project Suncatcher, a bold proposal to launch an 81-satellite constellation into low Earth orbit. It plans to use the constellation to harvest sunlight to power the next generation of AI data centers in space. So, instead of beaming power back to Earth, the constellation would beam data back to Earth.

For example, if you asked a chatbot how to bake sourdough bread, instead of firing up a data center in Virginia to craft a response, your query would be beamed up to the constellation in space, processed by chips running purely on solar energy, and the recipe sent back down to your device. Doing so would mean leaving the substantial heat generated behind in the cold vacuum of space.

As a technology entrepreneur, I applaud Google’s ambitious plan. But as a space scientist, I predict that the company will soon have to reckon with a growing problem: space debris.

The mathematics of disaster

Space debris – the collection of defunct human-made objects in Earth’s orbit – is already affecting space agencies, companies and astronauts. This debris includes large pieces, such as spent rocket stages and dead satellites, as well as tiny flecks of paint and other fragments from discontinued satellites.

Space debris travels at hypersonic speeds of approximately 17,500 miles per hour (28,000 km/h) in low Earth orbit. At this speed, colliding with a piece of debris the size of a blueberry would feel like being hit by a falling anvil.

Satellite breakups and anti-satellite tests have created an alarming amount of debris, a crisis now exacerbated by the rapid expansion of commercial constellations such as SpaceX’s Starlink. The Starlink network has more than 7,500 satellites, which provide global high-speed internet.

The U.S. Space Force actively tracks over 40,000 objects larger than a softball using ground-based radar and optical telescopes. However, this number represents less than 1% of the lethal objects in orbit. The majority are too small for these telescopes to reliably identify and track.

In November 2025, three Chinese astronauts aboard the Tiangong space station were forced to delay their return to Earth because their capsule had been struck by a piece of space debris. Back in 2018, a similar incident on the International Space Station challenged relations between the United States and Russia, as Russian media speculated that a NASA astronaut may have deliberately sabotaged the station.

The orbital shell Google’s project targets – a Sun-synchronous orbit approximately 400 miles (650 kilometers) above Earth – is a prime location for uninterrupted solar energy. At this orbit, the spacecraft’s solar arrays will always be in direct sunshine, where they can generate electricity to power the onboard AI payload. But for this reason, Sun-synchronous orbit is also the single most congested highway in low Earth orbit, and objects in this orbit are the most likely to collide with other satellites or debris.

As new objects arrive and existing objects break apart, low Earth orbit could approach Kessler syndrome. In this theory, once the number of objects in low Earth orbit exceeds a critical threshold, collisions between objects generate a cascade of new debris. Eventually, this cascade of collisions could render certain orbits entirely unusable.

Implications for Project Suncatcher

Project Suncatcher proposes a cluster of satellites carrying large solar panels. They would fly with a radius of just one kilometer, each node spaced less than 200 meters apart. To put that in perspective, imagine a racetrack roughly the size of the Daytona International Speedway, where 81 cars race at 17,500 miles per hour – while separated by gaps about the distance you need to safely brake on the highway.

This ultradense formation is necessary for the satellites to transmit data to each other. The constellation splits complex AI workloads across all its 81 units, enabling them to “think” and process data simultaneously as a single, massive, distributed brain. Google is partnering with a space company to launch two prototype satellites by early 2027 to validate the hardware.

But in the vacuum of space, flying in formation is a constant battle against physics. While the atmosphere in low Earth orbit is incredibly thin, it is not empty. Sparse air particles create orbital drag on satellites – this force pushes against the spacecraft, slowing it down and forcing it to drop in altitude. Satellites with large surface areas have more issues with drag, as they can act like a sail catching the wind.

To add to this complexity, streams of particles and magnetic fields from the Sun – known as space weather – can cause the density of air particles in low Earth orbit to fluctuate in unpredictable ways. These fluctuations directly affect orbital drag.

When satellites are spaced less than 200 meters apart, the margin for error evaporates. A single impact could not only destroy one satellite but send it blasting into its neighbors, triggering a cascade that could wipe out the entire cluster and randomly scatter millions of new pieces of debris into an orbit that is already a minefield.

The importance of active avoidance

To prevent crashes and cascades, satellite companies could adopt a leave no trace standard, which means designing satellites that do not fragment, release debris or endanger their neighbors, and that can be safely removed from orbit. For a constellation as dense and intricate as Suncatcher, meeting this standard might require equipping the satellites with “reflexes” that autonomously detect and dance through a debris field. Suncatcher’s current design doesn’t include these active avoidance capabilities.

In the first six months of 2025 alone, SpaceX’s Starlink constellation performed a staggering 144,404 collision-avoidance maneuvers to dodge debris and other spacecraft. Similarly, Suncatcher would likely encounter debris larger than a grain of sand every five seconds.

Today’s object-tracking infrastructure is generally limited to debris larger than a softball, leaving millions of smaller debris pieces effectively invisible to satellite operators. Future constellations will need an onboard detection system that can actively spot these smaller threats and maneuver the satellite autonomously in real time.

Equipping Suncatcher with active collision avoidance capabilities would be an engineering feat. Because of the tight spacing, the constellation would need to respond as a single entity. Satellites would need to reposition in concert, similar to a synchronized flock of birds. Each satellite would need to react to the slightest shift of its neighbor.

Paying rent for the orbit

Technological solutions, however, can go only so far. In September 2022, the Federal Communications Commission created a rule requiring satellite operators to remove their spacecraft from orbit within five years of the mission’s completion. This typically involves a controlled de-orbit maneuver. Operators must now reserve enough fuel to fire the thrusters at the end of the mission to lower the satellite’s altitude, until atmospheric drag takes over and the spacecraft burns up in the atmosphere.

However, the rule does not address the debris already in space, nor any future debris, from accidents or mishaps. To tackle these issues, some policymakers have proposed a use-tax for space debris removal.

A use-tax or orbital-use fee would charge satellite operators a levy based on the orbital stress their constellation imposes, much like larger or heavier vehicles paying greater fees to use public roads. These funds would finance active debris removal missions, which capture and remove the most dangerous pieces of junk.

Avoiding collisions is a temporary technical fix, not a long-term solution to the space debris problem. As some companies look to space as a new home for data centers, and others continue to send satellite constellations into orbit, new policies and active debris removal programs can help keep low Earth orbit open for business.

About the Author:

Mojtaba Akhavan-Tafti, Associate Research Scientist, University of Michigan

This article is republished from The Conversation under a Creative Commons license. Read the original article.