Milton Mueller speaking at the AI Governance and Global Economic Development, an official pre-summit event of the AI Impact Summit 2026.
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.
Tess Malone, Senior Research Writer/Editor
tess.malone@gatech.edu
Neuroscience experts from across Georgia Tech have come together to form the Institute for Neuroscience, Neurotechnology, and Society (INNS), an interdisciplinary research institute launched in July. Faculty in INNS are helping to solve some of neuroscience’s most pressing problems, and many of these solutions have promising medical applications. One important aspect of studying the brain is understanding how the brain and the body work together.
Hosted by the College of Computing and IDEaS, the 2025 Generative AI Summit will showcase the strength and diversity of Gen AI research across the Georgia Tech campus as well as take feedback about challenges faced by the Tech community as they navigate the Gen AI research domain with a goal of creating an AI strategy for Georgia Tech.
7:30am - 8:30am | Registration & Breakfast
Graphic Representation of networked system: Adobe Stock
The recently awarded $20mil NSF Nexus Supercomputer grant to Georgia Tech and partner institutes promises to bring incredible computing power to the CODA building. But what makes this supercomputer different and how will it impact research in labs on campus, across disciplinary units, and across institutions?
Purpose Built for AI Discovery
Nexus is Georgia Tech’s next-generation supercomputer, replacing the HIVE. Most operational high-performance computing systems utilized for research were designed before the explosion in Machine Learning and AI. This revolution has already shown successes for scientific research and data analysis in many domains, but the compute power, complex connectivity, and data storage needs for these systems have limited their access to the academic research community. The Nexus supercomputer design process retained a robust HPC system as a base while integrating artificial intelligence, machine learning and large-scale data science analysis from the ground up.
Expert Support for Faculty and Researchers
The Institute for Data Engineering and Science (IDEaS) and the College of Computing house the Center for Artificial Intelligence in Science and Engineering (ARTISAN) group. This team has collective experience in working with national computational, cloud, commercial and institutional resources for computational activities, and decades of experience in scientific tools that aid in assisting both teaching and research faculty. Nexus is the next logical step, bringing together everything they’ve learned to build a national resource optimized for the future of AI-driven science.
Principal Research Scientist for the ARTISAN Team, Suresh Marru, highlighted the need for this new resource, “AI is a core part of the Nexus vision. Today, researchers often spend more time setting up experiments, managing data, or figuring out how to run jobs on remote clusters than doing science. With Nexus, we’re flipping that script. By embedding AI into the platform, we help automate routine tasks, suggest optimal ways to run simulations, and even assist in generating input or analyzing results. This means researchers can move faster from question to insight. Instead of wrestling with infrastructure, they can focus on discovery.”
An Accessible AI Resource for GT & US Scientific Research
90% of Nexus capacity will be made available to the national research community through the NSF Advanced Computing Systems & Services (ACSS) program. Researchers from across the country, at universities, labs, and institutions of all sizes, will have access to this next-generation AI-ready supercomputer. For Georgia Tech research faculty and staff, the new system has multiple benefits:
David Sherrill, Interim Executive Director of IDEaS notes, "Nexus brings Georgia Tech's leadership in research computing to a whole new level. It will be the first NSF Category I Supercomputer hosted on Georgia Tech's campus. The Nexus hardware and software will boost research in the foundations of AI, and applications of AI in science and engineering."