From Tulane to the Future of AI: Gary Butler on Engineering, Expertise, and Building What Matters

When Gary Butler arrived at Tulane University, he was a mechanical engineering student with an interest in robotics and a second defining commitment on the football field. “Those were probably the two primary things that defined my Tulane experience,” he said. “My time as a mechanical engineer and my time as a football player.”

That combination of discipline and curiosity carried him well beyond New Orleans. After graduating in 1993, Butler continued his studies in mechanical engineering at Vanderbilt before earning a Ph.D. from Cambridge. He went on to work at BBN Technologies, where he contributed to advanced research tied to DARPA before founding Camgian in 2006.

Today, Butler leads Camgian, focusing on artificial intelligence systems designed to help people make better decisions in complex environments. “We focus on decision automation,” he said. “We build AI machine learning software systems that help accelerate decisions and improve the quality of decisions, especially in contested environments.”

At a time when artificial intelligence is dominating headlines, Butler is quick to point out that not all systems are the same. “Most of what you see in the news are large language models processing very large amounts of text,” he said. “What we build are precision models based on real-world data to deliver very high precision results.”

That distinction defines how his work is applied. His systems are not designed to generate answers. They are built to interpret data and support decisions where accuracy matters most. “Being able to classify a target effectively doesn’t need a large language model,” Butler said. “It needs a system trained on the right data to deliver high precision results.”

Looking ahead, Butler sees the next phase of AI centered on more specialized and interconnected systems. “You may have a multitude of agents running within an organization,” he said. “One does one task extremely well at extremely high precision, and they’re exchanging information among each other and automating workflows.”

That shift moves AI beyond productivity into something more transformative. “Large language models are good for productivity enhancement,” he said. “But agent-based systems will drive automation.”

For students watching these changes unfold, Butler’s advice is direct. Focus on fundamentals first. “The most successful AI projects I’ve seen are the ones where you have a strong combination of subject matter expertise with the knowledge of how to apply AI,” he said.

He has also seen what happens when that balance is missing. “Where I have seen AI projects fail is when someone is adept at applying AI tools but doesn’t have the subject matter expertise,” he said. “They often don’t know how to build the models that actually apply to the problem.”

That is why he continues to emphasize the importance of a strong engineering foundation. “I don’t think the need for understanding the science, the physics, the mathematics will ever go away,” Butler said. “You need that foundational understanding to build these types of systems.”

For Butler, the future belongs to engineers who can bridge both worlds. “Learn how to be a great engineer,” he said. “Then understand how to apply AI and machine learning techniques to solve real problems.”

That perspective connects closely to what he sees happening at Tulane today. Through his involvement with the school’s advisory efforts, he has watched the School of Science and Engineering continue to grow and evolve. “I’ve been very impressed with the vision,” he said. “There are a lot of impressive people there.”

His recent appointment to the Science, Technology, and Innovation Board is a natural extension of that work, connecting his experience in AI and decision-making to broader conversations shaping the future of technology and national security.

Across his career, Butler has stayed focused on one idea: helping people make sense of complexity. From robotics as a student to building advanced AI systems today, his work reflects a consistent belief that technology is most powerful when it is applied with clarity and purpose.

As AI continues to evolve across industries, that belief feels especially relevant. The tools will continue to change. The need for strong thinking, deep knowledge, and thoughtful application will not.