Atoms and Algorithms: How Idaho's Research Institutions Are Building the AI Ecosystem of the Future

If you want to understand why Idaho is uniquely positioned in the national AI landscape, start with three institutions: Boise State University, Micron Technology, and Idaho National Laboratory. Together, they anchor a technology stack that runs from energy and semiconductors all the way to workforce development and applied research. The Atoms and Algorithms panel at the Boise Metro Chamber's 2026 Regional Leadership Conference brought leaders from all three to the same stage to talk about what that collaboration looks like in practice and what it will take to make it even stronger.
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Moderated by Jessica Hagan of KTVB, the panel featured Nancy Glenn, vice president of research and economic development at Boise State University; Scott Gatzemeier, corporate vice president for front end expansion at Micron; and Todd Combs, deputy laboratory director for science and technology and chief research officer at Idaho National Laboratory.

Idaho Has the Full AI Stack. That Is Rare.
Scott Gatzemeier offered one of the panel's most clarifying frameworks: a five-layer view of the AI infrastructure stack, and where Idaho sits within it. At the base is physical infrastructure, power, water, utilities. Above that is semiconductors, the chips and memory that make compute possible. Then come data centers, then the models themselves, and finally the applications that businesses and consumers actually use.

What makes Idaho distinctive is that it has players at every level of that stack. There are only two places in the United States where cutting edge semiconductor research is actually conducted, and one of them is Boise. Nuclear energy research at INL is not being replicated anywhere else in the country at the same scale. And at the application layer, where AI agents are increasingly being used to build other AI agents, Idaho's growing startup ecosystem is positioned to move fast.

"Idaho uniquely spans the AI stack," Gatzemeier said, "from energy and chips through infrastructure and applied AI, with Micron and INL anchoring the bottom layers and a growing Boise startup ecosystem delivering real world applications at the top."

He noted that the bottom layers of the stack, infrastructure and semiconductors, require enormous capital investment and carry a natural competitive moat. The application layer, by contrast, is far more dynamic and far more open to disruption. That is where he expects the most rapid change, and where Idaho companies have the most opportunity to build something new.

The Partnerships Are Already Happening
When Hagan asked whether a meaningful three-way collaboration between Boise State, Micron, and INL was a realistic possibility, Todd Combs had a direct answer: it is already happening. All three institutions were partners on the federal semiconductor manufacturing hub called Smart USA before it was canceled. Now they are collaborating again on the Department of Energy's Genesis mission, a large initiative to double the productivity of R&D across roughly 28 topic areas and 99 subtopics, including semiconductors, geothermal, critical minerals, and nuclear.

A key feature of Genesis is that partnership is not optional. Proposals are required to involve at least two of three parties: universities, national laboratories, and industry. In practice, Combs noted, the expectation is all three. That structure plays directly to Idaho's strengths.

Nancy Glenn described Boise State's role in that ecosystem as the connective tissue. The university has long served as the convener between industry, national labs, and research institutions, helping translate what partners need into what students and faculty are working on. The materials science program is a direct example: built in partnership with Micron, it turned out to be equally relevant to the extreme environment material needs at INL. One investment, multiple beneficiaries.

Boise State and INL formalized their broader collaboration through a memorandum of understanding that spans well beyond nuclear, covering power systems, critical minerals, geothermal, electric vehicles, energy storage, and cybersecurity. The university also recently received National Science Foundation funding through its Junction hub for entrepreneurship to build the foundation for exactly these kinds of cross-sector partnerships, and a new research traineeship AI program is integrating AI fluency across disciplines, not just in STEM.

What Makes a Partnership Actually Work
The panel spent meaningful time on a question that often gets glossed over: what does it actually take for institutions with very different cultures, regulatory environments, and incentive structures to collaborate effectively? The answers were grounded and practical.

Combs identified three prerequisites. First, a common problem to solve. Partnerships formed around a vague intent to collaborate almost always fail. Second, complementary capabilities. If the institutions are doing too much of the same thing, they compete rather than build on each other. Third, a clear answer to the question of what is in it for each organization. Every partner needs to be able to see what they gain by being at the table.

Glenn pointed to something Idaho has that cannot be manufactured: proximity and accessibility. She can call Scott Gatzemeier or Todd Combs directly. Gatzemeier and Combs both sit on the engineering dean's advisory council at Boise State. Those relationships are what allow ideas to move quickly from conversation to collaboration. Gatzemeier added that Micron has contributed significant fabrication equipment to Boise State over the years, deepening a partnership that now spans more than two decades.

On the infrastructure side, Combs and Gatzemeier described ongoing conversations about co-locating AI infrastructure, particularly as data centers, semiconductor manufacturing, and energy generation increasingly need to sit close together. Even informal coordination, staying current on where each other is with microreactors and SMRs, helps both organizations make better decisions.

AI Inside the Fab and the Lab
Gatzemeier offered a window into how deeply AI is already embedded in Micron's operations. Inside the Boise fab, an automated handling system moves thousands of lots, each containing 25 wafers, through 1,500 process steps over a nine-month production cycle. At any given moment, more than 100,000 wafers are in process. The system routing all of that is 99 percent automated, functioning something like a real-time traffic system for an entire city, except the stakes are measured in atoms. If a wafer is misprocessed, the system automatically routes it to a remediation path without human intervention.

Micron also runs multiple large language models across the organization, including Copilot, Claude, and an internal model trained on company data. Process modeling is another major application area, using AI to predict and prevent deviations at the atomic level, where a film off by a single angstrom can render a wafer unusable.

At INL, the goal is to get every member of the lab's staff using AI on a daily basis, a target set by Lab Director John Wagner. Currently around 3,000 to 3,500 staff are using tools on at least a weekly basis. The lab has deployed Claude, ChatGPT, Copilot, and supercomputer-based AI systems, and recently opened API access to software developers to enable deeper integration. Combs noted that API usage at the lab cost around $5,000 in January, grew to $10,000 to $12,000 in February, and hit $35,000 in March. A million dollars a month, he said, is not far off.

The Talent Pipeline: Fluency Without Losing the Fundamentals
The panel's closing conversation turned to people, specifically what the AI-ready graduate looks like and where the talent pipeline still has gaps. The panelists were aligned on a tension that does not have an easy resolution: students need to be fluent in AI tools to be competitive, and they also need the subject matter expertise to use those tools well. One without the other is a problem.

Gatzemeier described the gap he sees most often: engineers who have the technical depth but have not yet learned to integrate AI tools into their workflows. He also offered a practical technique for managing AI's tendency to hallucinate: ask it how it knows what it just told you. The tool will surface its sources immediately, allowing the user to trace the logic and identify where the reasoning went wrong.

Combs put it plainly when addressing university audiences: if you know these tools, you are going to be able to do genuinely exciting work as an intern. If you do not, you will end up doing the boring stuff. But he was equally clear that turning off critical thinking is a serious risk. AI is only as good as the person prompting and probing it.

Glenn outlined what Boise State is doing to respond: training programs for faculty and graduate students on integrating AI into research and coursework, a new master's degree in applied AI that spans all disciplines rather than just computer science, and a consistent emphasis on validation, knowing not just how to use a tool but how to test whether its output is actually correct.

Combs also floated a more structured vision for the future: a formal program where undergraduates spend their first two years building foundational skills at Boise State, then rotate through summer experiences at INL and Micron before deciding whether to enter the workforce or pursue graduate study. The applied experience, he argued, would make every decision that follows more informed.

The panel closed on a note of genuine optimism, grounded not in aspiration but in what is already underway. The institutions are talking, the partnerships are forming, the federal government is actively funding collaborative work, and Idaho's position in the national AI landscape is stronger than most people outside the state realize. The work now is to make sure the people side keeps pace with everything else.

Nancy Glenn, Scott Gatzemeier, and Todd Combs delivered these remarks at the Boise Metro Chamber's 2026 Regional Leadership Conference on April 20, 2026. This blog post was prepared from a partial transcript using the help of AI.

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