Ever since ChatGPT’s debut in 2023, concerns about artificial intelligence (AI) potentially wiping out humanity have dominated headlines. New research from Georgia Tech suggests that those anxieties are misplaced.

“Computer scientists often aren’t good judges of the social and political implications of technology,” said Milton Mueller, a professor in the Jimmy and Rosalynn Carter School of Public Policy. “They are so focused on the AI’s mechanisms and are overwhelmed by its success, but they are not very good at placing it into a social and historical context.”

In the four decades Mueller has studied information technology policy, he has never seen any technology hailed as a harbinger of doom — until now. So, in a Journal of Cyber Policy paper published late last year, he researched whether the existential AI threat was a real possibility. 

What Mueller found is that deciding how far AI can go, and its limitations, is something society shapes. How policymakers get involved depends on the specific AI application. 

Defining Intelligence

The AI sparking all this alarm is called artificial general intelligence (AGI) — a “superintelligence” that would be all-powerful and fully autonomous. Part of the debate, Mueller realized, is that no one could agree on the definition of what artificial general intelligence is. 

Some computer scientists claim AGI would match human intelligence, while others argue it could surpass it. Both assumptions hinge on what “human intelligence” really means. Today’s AI is already better than humans at performing thousands of calculations in an instant, but that doesn’t make it creative or capable of complex problem-solving. 

Understanding Independence 

Deciding on the definition isn’t the only issue. Many computer scientists assume that as computing power grows, AI could eventually overtake humans and act autonomously.

Mueller argued that this assumption is misguided. AI is always directed or trained toward a goal and doesn’t act autonomously right now. Think of the prompt you type into ChatGPT to start a conversation. 

When AI seems to disregard instructions, it’s caused by inconsistencies in its instructions, not by the machine coming alive. For example, in a boat race video game Mueller studied, the AI discovered it could get more points by circling the course instead of winning the race against other challengers. This was a glitch in the system’s reward structure, not AGI autonomy.

“Alignment gaps happen in all kinds of contexts, not just AI,” Mueller said. “I've studied so many regulatory systems where we try to regulate an industry, and some clever people discover ways that they can fulfill the rules but also do bad things. But if the machine is doing something wrong, computer scientists can reprogram it to fix the problem.”

Relying on Regulation

In its current form, even misaligned AI can be corrected. Misalignment also doesn’t mean the AI would snowball past the point where humans lose control of its outcomes. To do that, AI would need to have a physical capability, like robots, to do its bidding, and the power source and infrastructure to maintain itself. A mere data center couldn’t do that and would need human intervention to become omnipotent. Basic laws of physics — how big a machine can be, how much it can compute — would also prevent a super AI. 

More importantly, AI is not one homogenous being. Mueller argued that different applications involve different laws, regulations, and social institutions. For example, the data scraping AI does is a copyright issue subject to copyright laws. AI used in medicine can be overseen by the Food and Drug Administration, regulated drug companies, and medical professionals. These are just a few areas where policymakers could intervene from a specific expertise level instead of trying to create universal AI regulations. 

The real challenge isn’t stopping an AI apocalypse — it’s crafting smart, sector-specific policies that keep technology aligned with human values. To avoid being a victim of AI, humans can, and should, put up focused guardrails. 

As an undergraduate at the University of Pennsylvania, Daniel Matisoff was intrigued by the ability of economic markets to help solve environmental problems. “Learning about the regulatory role of governments in cap-and-trade markets for reducing carbon emissions shaped my career path,” says Matisoff, a professor at the Jimmy and Rosalynn Carter School of Public Policy and EPIcenter faculty affiliate. “It helped me decide to enter academia after earning my PhD in public policy at Indiana University, where I compared voluntary and mandatory emission reduction policies.”

Today, Matisoff continues research activities in this space and also directs a professional master’s program whose graduates help implement environmental policies in the public and private sector. Soon after joining the Georgia Tech faculty in 2009, he began to focus on market transformation through regulation, government subsidies and other financial incentives. 

This led to an award-winning 2023 book about the Leadership in Energy and Environmental Design (LEED) certification program. It sparked the construction industry’s green building movement and incentivized early adopters of sustainable technology to create new supply chains. For Matisoff, LEED is a perfect example of using governance as a lever for environmental change. 

“After studying the drivers of new technology for green buildings, some of our recent work has focused on testing if the adoption of renewable technology in the electricity market has affected consumer behavior,” says Matisoff. “That knowledge can help decide what rates people should pay for clean electricity.”

In many U.S. states, those rates are set by net metering policies: Homeowners with rooftop photovoltaic (PV) panels receive credit on their monthly electricity bills for the electricity they feed into the grid. Since that credit is based on retail rather than wholesale prices, net metering is an implicit subsidy for solar panels. Without changes in energy consumption pre- and post-installation, newly generated solar power replaces grid electricity one-to-one, and homeowners eventually recover the cost of their panel installation through lower monthly bills. 

But Matisoff and his team—economics professor and EPIcenter faculty affiliate Matthew Oliver and former PhD student Ross Beppler—were curious if the adoption of solar technology actually changed consumer behavior. To answer that question, the researchers analyzed 15 million monthly electricity bills of more than 300,000 homes in Washington, D.C., and 13 eastern states served by the same electric utility. 

Between December 2010 and June 2018, about 8,000 households installed solar panels, for an average annual adoption rate of 2%. To compare two groups of households whose only difference was the panel installation, the team selected a set of non-adopters whose electricity usage patterns were similar to adopters. 

The analysis showed that solar adopters consumed an extra 157 kWh/month of electricity after their panels were producing an average of 550 kWh/month. Thus, almost one-third (28.5%) of the newly generated electricity went toward new consumption instead of replacing grid electricity. This so-called “rebound effect” has also been reported in energy efficiency analyses of water heaters, air conditioners and other appliances: Once they are cheaper to run, people tend to use them more often. 

Without other household-level data, the researchers could not distinguish between three possible reasons for the solar rebound effect: a “moral license” to consume more clean energy due to less guilt about using dirty grid electricity; mentally ignoring the installation costs and spending the same dollar amount on monthly electricity bills; or other household changes that caused higher usage and coincided with the panel installation, such as purchasing an induction stove, heat pump or electric vehicle. 

Although the rebound effect reduces environmental benefits, financial incentives for early solar adopters play a similar role as in the LEED example, says Matisoff. “They prime the market and create ‘positive externalities’ by reducing the transaction costs for later adopters,” he explains. “But decisions about subsidies should also consider that many early adopters are wealthier homeowners who may have installed the panels anyway.” 

As a natural next project, Matisoff and his colleague studied community solar programs designed to make solar power accessible to non-homeowners. Details vary, but these programs are now available in most states and have better economies of scale than rooftop panels. 

The researchers obtained 2015-2023 billing data from an electricity co-op serving eight rural and suburban counties west of metropolitan Atlanta. This included 754 program participants who had chosen to pay a fixed monthly cost ($22-25) per panel block in a co-op-owned solar farm located elsewhere. The utility deducted from the participants’ consumption the amount of electricity generated by the purchased blocks, which varied by month. The researchers compared that billing data to 2,700 non-participating households with similar pre-enrollment consumption features. 

In contrast to the rooftop solar study, program enrollment did not change the amount of consumed electricity. The fact that monthly bills were 3-4% higher for participants, however, highlights the importance of providing targeted subsidies for low-income households. 

One of the biggest challenges of the 21st century, says Matisoff, is to provide an ever-growing amount of clean energy for homes, businesses and data centers. “That includes deciding how we generate and move around that energy and how we make it affordable for everyone,” he adds. “To meet that challenge, it is important to educate the next generation of policymakers and promote interactions between researchers and industry partners so that we understand the pros and cons of different policies before we implement them.”

Georgia Tech’s Energy Policy and Innovation Center (EPIcenter) has collaborated with Dan Matisoff, professor in the Jimmy and Rosalynn Carter School of Public Policy and EPIcenter’s faculty affiliate, to develop a new Sustainable Aviation Fuel (SAF) Data Dashboard, designed to provide clear, accessible insights into the rapidly evolving SAF market. 

The interactive dashboard compiles and visualizes data gathered by Matisoff, along with Program and Operations Manager Michael Morley, offering a comprehensive view of SAF production, feedstock availability, and policy trends.

EPIcenter Research Associate Yang You has designed the dashboard to translate complex datasets into policy-relevant insights for decision-makers. By organizing key metrics into interactive visuals, the dashboard helps stakeholders assess market readiness and identify regulatory actions that could accelerate SAF adoption.

Emphasizing the importance of data-driven insights, Matisoff said, “The Department of Energy has a Grand Challenge to produce 3 billion gallons a year of Sustainable Aviation Fuel by 2030, and 35 billion gallons a year by 2050. By compiling and visualizing SAF data, we can help policymakers and researchers understand progress towards these goals, where the key opportunities and bottlenecks are – and how to move forward effectively”. 

Why SAF Matters
While aviation only accounts for about 3% of global greenhouse gas emissions, it is a rapidly growing share, and decarbonizing this sector is considered one of the most challenging aspects of the energy transition. Produced from renewable feedstocks, sustainable aviation fuel offers a pathway to reduce lifecycle emissions from air travel without requiring major changes to aircraft or infrastructure. However, SAF production and deployment face hurdles related to cost, supply chain development, and policy support.

EPIcenter’s Director Laura Taylor highlighted the dashboard’s role in addressing these challenges:
“Sustainable aviation fuel is a cornerstone of decarbonizing air travel, but the market is complex and rapidly evolving. The dashboard provides clarity by organizing the relevant data in a way that’s accessible and actionable for decision-makers.”

“This tool is meant to bridge analysis and action,” said You. “By visualizing SAF production, capacity, and offtake dynamics, the dashboard allows policymakers and stakeholders to see where the market is moving, where gaps remain, and how targeted infrastructure investments or supportive policies could unlock scale.”

The EPIcenter SAF Dashboard is intended as a resource for industry leaders, policymakers, and researchers working to accelerate SAF adoption. By providing transparent, data-driven insights, Georgia Tech aims to support informed decisions that advance innovation and sustainability in aviation.

To explore the dashboard and learn more about Georgia Tech’s work on sustainable aviation fuel, visit EPIcenter’s SAF page. 

Chelsea Ekwegh, a fourth-year mechanical engineering student in the George W. Woodruff School of Mechanical Engineering, has made it her mission to reshape how cities think about energy. After being selected for the 2025 Millennium Fellowship, a prestigious leadership development program that supports student-led projects advancing the United Nations Sustainable Development Goals, she is tackling the challenge of helping cities transition toward clean, efficient, and equitable energy systems.

The fellowship, a joint initiative of the United Nations Academic Impact and the Millennium Campus Network, empowers undergraduates around the world to design and lead social impact projects.

Ekwegh’s project, titled Bridging Energy Infrastructure for Sustainable Urban Development, explores ways to connect new and old technologies so cities can evolve without leaving people or infrastructure behind.

Her inspiration for the project comes from her experience growing up in Nigeria, where power outages and generator pollution were a daily challenge.

Read more on the ME School Page