Rising oil and gasoline prices have been the center of attention since the closure of the Strait of Hormuz. But that immediate effect tells only part of the story. Because oil and gas underpin production, transportation, and logistics, higher energy costs will gradually move through supply chains — meaning the most significant economic consequences may not appear for months. 

“The effects move slowly and appear in places people do not connect to energy,” said Tibor Besedes, professor in the School of Economics. “Oil and natural gas are part of the cost structure for an enormous range of goods.”

About 20% of global oil and liquefied natural gas flows through the waterway linking the Persian Gulf to world markets. When that flow is constrained, the impact ripples outward across industries most people never associate with an energy crisis.

“In complex supply chains, a disruption in one critical link, even if only briefly, can cascade through the system, well beyond the initial event,” says Pinar Keskinocak, chair and professor in the H. Milton Stewart School of Industrial and Systems Engineering. “As delays persist and compound, interconnected systems often take a long time to recover, rebalance, and return to normal.”

Price Pressures That Arrive Quietly

Early effects are already visible. 

Jet fuel availability is tightening, and diesel prices are rising across Asia. China has ordered refineries to stop exporting fuel, creating shortages that are increasing shipping costs for U.S. imports, from consumer electronics to pharmaceuticals.

The strait is also a key corridor for naphtha, a feedstock used to produce plastics, packaging, solvents, textiles, and pharmaceutical components. Roughly 85% of Middle Eastern polyethylene exports move through the strait. 

“Consumers won't see the effect of this quickly,” Besedes says, “but the longer the strait is closed, the higher the cost will be of all of these products naphtha is used for.”

Aluminum is equally exposed. 

“Smelters require sustained, low-cost energy,” said Chris Gaffney, a professor of the practice in the Stewart School. “The Middle East accounted for roughly 21% of U.S. unwrought aluminum imports in 2025. When energy prices spike or supply is constrained, capacity is reduced or shut down, and those decisions are difficult and slow to reverse.”

Fertilizer is one of the clearest examples of delayed inflation. Natural gas is essential for its production, and Persian Gulf states account for one-third of global urea exports and half of global sulfur exports. Urea prices at the New Orleans import hub have already climbed sharply.

“We won't see the effects quickly, but rather in six to 12 months, depending on the crop and its cycle,” Besedes says. “Without or with less fertilizer, crop yields will decrease, resulting in higher prices.”

Why Hormuz Is Different From Other Chokepoints

On top of all those factors, the strait closure presents a uniquely dangerous vulnerability. 

“Unlike a port strike or canal blockage, there is no meaningful way to reroute volume,” says Gaffney. “If it is disrupted, flow is constrained rather than redirected.” Pipeline alternatives replace only a fraction of the 20 million barrels per day that normally transit the strait.

“Choke point vulnerability arises when a large portion of flow depends on a route that is hard to substitute,” said Mathieu Dahan, associate professor in the Stewart School. “Hormuz has no scalable alternatives with sufficient capacity.” 

Alan Erera, senior associate chair in the Stewart School expanded on Dahan’s point, noting that strait disruptions raise costs across manufacturing and distribution.

“Ships are rerouted onto longer paths, which drives up fuel and labor costs, ties up vessels and containers for longer periods, and ultimately raises inventory costs for shippers because capital is locked up while goods are still in transit,” Erera said.

When Geopolitics Meets Global Supply Chains

Additionally, the strait closure raises the risk of wartime miscalculation. 

“We haven’t seen a disruption on this scale since the tanker wars of the late 1980s,” said Larry Rubin, associate professor in the Sam Nunn School of International Affairs. Gulf states' dependence on the strait constrains both regional actors and U.S. strategy, raising risks around crisis decision-making.

Rubin also points to a dimension most coverage has missed entirely. “One thing that has been overlooked by many commentators is the fact that the Iranian people have probably been hit the hardest economically,” he says. “They were already in a challenging situation. The Iranian economy won't recover quickly after the war.”

Resilience Has a Short Memory

Meanwhile, for the United States, “The Strategic Petroleum Reserve provides a buffer, and domestic energy production has improved resilience,” says Gaffney. “But the gap remains between enabling capacity and sustaining resilience. Policy can support infrastructure, but it cannot ensure private sector participants invest in resilience when cost pressures rise.”

For policymakers and industry leaders, the disruption reinforces a familiar pattern. "The supply chain remains optimized for efficiency rather than resilience, in part due to the high investment costs required to build flexibility," says Dahan. 

Gaffney added that resilience does improve after disruption, but that “it erodes over time if not actively maintained.”

Even if the strait reopens, higher costs and slow restart timelines mean the system will not snap back. Experts suggest that when headlines have moved on from this disruption, it will still be shaping prices across the economy. 

As students increasingly turn to artificial intelligence (AI) to help with coursework, some worry that their learning could be compromised. Georgia Tech researchers are working to counter this potential decline with an AI tool they hope will promote learning rather than hinder it.  

TokenSmith is a citation-supported large language model (LLM) tutor that can be hosted locally on a user’s personal computer. The tutor only provides answers based on course materials, such as the textbook or lecture slides.  

Associate Professor Joy Arulraj began the project with support from the Bill Kent Family Foundation AI in Higher Education Faculty Fellowship last year. The fellowship, led by Georgia Tech’s Center for 21st Century Universities, supports faculty projects exploring innovative and ethical uses of AI in teaching.   

Arulraj has enlisted assistant professors Kexin Rong and Steve Mussmann to help build TokenSmith. Rong recently joined the Institute for People and Technology at Georgia Tech.  

Mussmann said TokenSmith is a synergistic blend of a database system and a machine learning system. The model stores textbooks, textbook annotations by course staff, common questions and answers, a learning state of the student, and student feedback in a structured database system. However, machine learning plays a key role in the answer generation as well as adapting the system to the student, course staff guidance, and user feedback.

"What excites me most is demonstrating how data-driven ML and principled database systems design can reinforce each other — one providing adaptability and flexibility, the other providing structure and traceability — in a way that benefits students," Mussmann said.

Keeping the model local has been an important focus of the project. The team wanted to create an AI tutor that helps students learn from their class resources rather than just giving answers. With each response, TokenSmith cites the origin of the answer in the provided documents.  

“One problem with LLMs is that they can hallucinate and provide wrong answers, but in this controlled environment, we can add these guardrails to make sure it’s actually helpful in an educational setting,” Rong said.  

Rong said she feels that students often undervalue textbooks, and she hopes TokenSmith can motivate students to make better use of them.  

“Textbooks can sometimes be daunting, but maybe if we combine them with the model, students might be more willing to read a paragraph or page in the textbook, and that could help clarify something for them,” she said.  

Running the model locally is more cost-effective and helps preserve the user’s privacy. But running the new tool locally comes with technical challenges.  

One challenge with creating the model is speed. Since it is a locally based model, TokenSmith depends solely on the user’s computer memory.  Tests have also shown that the tutor currently struggles to answer more complex questions. 

“We are interested in pushing the boundaries of these local models so that they give students good answers and also run fast enough to keep students engaged,” Arulraj said.  

The Institute for People and Technology (IPaT) at Georgia Tech is proud to announce the promotion of five research faculty whose work continues to advance the institute’s mission of shaping people‑centered innovation across disciplines.

Kala Jordan has been promoted to Research Scientist II. With a background spanning biology, health informatics, and STEM education, Jordan brings a multidisciplinary approach to her work. She plays a key role in AI‑CARING, leading studies that support the development of personalized collaborative AI systems designed to improve quality of life for older adults.

Noah Posner has been promoted to Senior Research Scientist. As manager of the Interactive Product Design Lab, Posner focuses on interactive experiences grounded in physical interaction. His research spans CAD‑based prototyping, rapid fabrication, and STEAM education, and he teaches courses in physical prototyping and industrial design.

Peter Presti has been promoted to Principal Research Scientist. Over his 22‑year career at Georgia Tech, Presti has collaborated with major industry partners and federal agencies. His research spans sensor systems, biometrics, wearable computing, signal processing, embedded systems, and integrated hardware‑software prototyping.

Richard Starr has been promoted to Senior Research Scientist. Starr oversees the IPaT Secure Data Enclave, developing and managing the institute’s secure infrastructure for healthcare data. His work ensures campus‑wide compliance with HIPAA, IRB requirements, and partnership agreements.

Andrew Zhao has been promoted to Research Scientist II. Zhao, a Georgia Tech alumnus with bachelor’s and master’s degrees in Computer Science, specializes in social computing. His work examines how social media facilitates information flow and connection, particularly around mental health and elections. He supports the CANDOR Portal and AI‑CARING projects, contributing full‑stack development, data pipelines, LLM fine‑tuning, and infrastructure management.

“These promotions are wonderful and well deserved. Hearty congratulations to Andrew, Kala, Richard, Noah, and Peter!” said Michael Best, executive director of IPaT.

“These promotions are a testament to the outstanding capabilities and contributions of IPaT’s research faculty community,” added Maribeth Gandy Coleman, director of research for IPaT.

While people use search engines, chatbots, and generative artificial intelligence tools every day, most don’t know how they work. This sets unrealistic expectations for AI and leads to misuse. It also slows progress toward building new AI applications. 

Georgia Tech researchers are making AI easier to understand through their work on Transformer Explainer. The free, online tool shows non-experts how ChatGPT, Claude, and other large language models (LLMs) process language. 

Transformer Explainer is easy to use and runs on any web browser. It quickly went viral after its debut, reaching 150,000 users in its first three months. More than 563,000 people worldwide have used the tool so far.

Global interest in Transformer Explainer continues when the team presents the tool at the 2026 Conference on Human Factors in Computing Systems (CHI 2026). CHI, the world’s most prestigious conference on human-computer interaction, will take place in Barcelona, April 13-17.

“There are moments when LLMs can seem almost like a person with their own will and personality, and that misperception has real consequences. For example, there have been cases where teenagers have made poor decisions based on conversations with LLMs,” said Ph.D. student Aeree Cho.

“Understanding that an LLM is fundamentally a model that predicts the probability distribution of the next token helps users avoid taking its outputs as absolute. What you put in shapes what comes out, and that understanding helps people engage with AI more carefully and critically.”

A transformer is a neural network architecture that changes data input sequence into an output. Text, audio, and images are forms of processed data, which is why transformers are common in generative AI models. They do this by learning context and tracking mathematical relationships between sequence components.

Transformer Explainer demystifies how transformers work. The platform uses visualization and interaction to show, step by step, how text flows through a model and produces predictions.

Using this approach, Transformer Explainer impacts the AI landscape in four main ways:

• It counters hype and misconceptions surrounding AI by showing how transformers work.
• It improves AI literacy among users by removing technical barriers and lowering the entry for learning about AI.
• It expands AI education by helping instructors teach AI mechanisms without extensive setup or computing resources.
• It influences future development of AI tools and educational techniques by providing a blueprint for interpretable AI systems.

“When I first learned about transformers, I felt overwhelmed. A transformer model has many parts, each with its own complex math. Existing resources typically present all this information at once, making it difficult to see how everything fits together,” said Grace Kim, a dual B.S./M.S. computer science student. 

“By leveraging interactive visualization, we use levels of abstraction to first show the big picture of the entire model. Then users click into individual parts to reveal the underlying details and math. This way, Transformer Explainer makes learning far less intimidating.”

Many users don’t know what transformers are or how they work. The Georgia Tech team found that people often misunderstand AI. Some label AI with human-like characteristics, such as creativity. Others even describe it as working like magic.

Furthermore, barriers make it hard for students interested in transformers to start learning. Tutorials tend to be too technical and overwhelm beginners with math and code. While visualization tools exist, these often target more advanced AI experts.

Transformer Explainer overcomes these obstacles through its interactive, user-focused platform. It runs a familiar GPT model directly in any web browser, requiring no installation or special hardware. 

Users can enter their own text and watch the model predict the next word in real time. Sankey-style diagrams show how information moves through embeddings, attention heads, and transformer blocks.

The platform also lets users switch between high-level concepts and detailed math. By adjusting temperature settings, users can see how randomness affects predictions. This reveals how probabilities drive AI outputs, rather than creativity.

“Millions of people around the world interact with transformer-driven AI. We believe that it is crucial to bridge the gap between day-to-day user experience and the models' technical reality, ensuring these tools are not misinterpreted as human-like or seen as sentient,” said Ph.D. student Alex Karpekov. 

“Explaining the architecture helps users recognize that language generated by models is a product of computation, leading to a more grounded engagement with the technology.” 

Cho, Karpekov, and Kim led the development of Transformer Explainer. Ph.D. students Alex Helbling, Seongmin Lee, Ben Hoover, and alumnus Zijie (Jay) Wang assisted on the project. 

Professor Polo Chau supervised the group and their work. His lab focuses on data science, human-centered AI, and visualization for social good.

Acceptance at CHI 2026 stems from the team winning the best poster award at the 2024 IEEE Visualization Conference. This recognition from one of the top venues in visualization research highlights Transformer Explainer’s effectiveness in teaching how transformers work.

“Transformer Explainer has reached over half a million learners worldwide,” said Chau, a faculty member in the School of Computational Science and Engineering. 

“I'm thrilled to see it extend Georgia Tech's mission of expanding access to higher education, now to anyone with a web browser.”