Artificial intelligence is consuming enormous amounts of electricity in the United States. According to the International Energy Agency, AI systems and data centers used about 415 terawatt hours of power in 2024—that accounts for more than 10% of the country’s total electricity production, with demand projected to double by 2030.
Now, researchers at Tufts University’s Scheutz laboratory have developed a proof-of-concept AI system designed to be far more efficient. Their approach could reduce energy use by up to 100 times while also improving performance on tasks.
What is Neuro-Symbolic AI?
The research comes from the laboratory of Matthias Scheutz, Karol Family Applied Technology Professor. His team is developing neuro-symbolic AI, which combines traditional neural networks with symbolic reasoning. This method mirrors how people approach problems by breaking them into steps and categories.
Unlike familiar large language models such as ChatGPT and Gemini, the team focuses on AI systems used in robotics—known as visual-language-action (VLA) models. These systems extend LLM capabilities by incorporating vision and physical movement, taking in visual data from cameras and instructions from language, then translating that information into real-world actions.
Why Traditional AI Struggles
Conventional VLA systems rely heavily on data and trial-and-error learning. If a robot is asked to stack blocks into a tower, it must first analyze the scene, identify each block, and determine how to place them correctly.
This process often leads to mistakes. Shadows may confuse the system about a block’s shape, or the robot may place pieces incorrectly, causing the structure to collapse—similar to the problems seen in LLMs that can generate false or misleading outputs.
Strong Results in Puzzle Tests
The researchers tested their system using the Tower of Hanoi puzzle, a classic problem that requires careful planning.
The neuro-symbolic VLA achieved a 95% success rate, compared with just 34% for standard systems. When given a more complex version of the puzzle that it had not encountered before, the hybrid system still succeeded 78% of the time. Traditional models failed every attempt.
Training time also dropped sharply. The new system learned the task in only 34 minutes, while conventional models required more than a day and a half.
Massive Energy Savings
Energy consumption was reduced dramatically. Training the neuro-symbolic model required only 1% of the energy used by a standard VLA system. During operation, it used just 5% of the energy needed by conventional approaches.
Scheutz compared this inefficiency to everyday AI tools: “These systems are just trying to predict the next word or action in a sequence, but that can be imperfect, and they can come up with inaccurate results or hallucinations. Their energy expense is often disproportionate to the task.”
A More Sustainable Path for AI
As AI adoption accelerates across industries, demand for computing power continues to climb. Companies are building increasingly large data centers, some of which require hundreds of megawatts of electricity—that level of consumption can exceed the needs of entire small cities.
The researchers suggest that current approaches based on LLMs and VLAs may not be sustainable in the long run. Neuro-symbolic AI offers a different direction by combining learning with structured reasoning, potentially providing a more efficient and dependable foundation for future AI systems.
The work will be presented at the International Conference of Robotics and Automation in Vienna in May 2026.