Larry Jin’s Journey at the Intersection of Energy and AI

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A Conversation with Larry Jin MS'15, PhD'19

In our latest alumni highlight, we’re excited to share an engaging interview between Steven Azzano and fellow alumnus Larry Jin. Their conversation dives into Larry's journey from his exposure to the Bay Area and Stanford's unique environment to his current role at C3 AI, where he bridges the fields of energy and artificial intelligence. 

Larry reflects on his Stanford experience and the diverse paths of success he witnessed, emphasizing how the rigorous training and communication skills he developed have been instrumental in his career. As he navigated the early stages of his professional life, from internships in major energy companies to his pivotal transition into the tech industry, Larry shares valuable insights into the challenges and opportunities that come with working at the intersection of traditional energy sectors and emerging technologies.

This interview not only highlights Larry's impressive accomplishments but also serves as an inspiration for current students interested in pursuing careers in energy and AI. Stay tuned for a deep dive into Larry's experiences, the challenges he’s faced, and his thoughts on the future of energy in the United States. 

From Stanford to Silicon Valley: Larry Jin’s Journey at the Intersection of Energy and AI

Stanford 

Steven Azzano: Stanford is a unique place among many excellent institutions to choose from. What brought you to the Bay Area, and what convinced you that Stanford was the right place for your goals and pursuits in life?

Larry Jin: I had my first real exposure to the Bay Area during a summer program at Berkeley, and shortly after that I attended a one-week student event at Stanford around 2011. Those in-person experiences left a strong impression. There was something about the openness, the energy, and the ambition of the place that felt different. I was drawn to that “vibe,” and decided to give it a real shot. In many ways, choosing Stanford was choosing an environment that encourages you to think bigger about what’s possible.

Steven: What is one thing that surprised you about your Stanford experience?

Larry: The thing that surprised me most was the diversity, not just in backgrounds, but in life paths. There are so many different ways people can be successful here. I was also struck by how deep and natural the startup and entrepreneurial culture is. It’s not just something people talk about, it’s embedded in everyday conversations. That was truly amazing to experience firsthand.

Steven: How do you think certain aspects of the Energy Resources Engineering program prepared you for the work you currently do - particularly, your classes, classmates, and professors?

Larry: The ERE/ESE program gave me two foundations I still rely on every day. First, technical rigor: the ability to reason about the world and represent it through mathematical and computational models. Second, communication: learning how to articulate ideas  clearly and logically. The scientific writing training, surprisingly, has been one of the most durable skills. Much of my appreciation goes to Lou (Professor Lou Durlofsky) for that, it shaped how I think and communicate to this day.

Early Career

Steven: After Stanford, you first joined Amazon. What went into your decision-making process coming out of the ERE/ESE program and what attracted you to that role?

Larry: I don’t want to over-romanticize that period. Job searching is stressful, and it certainly was for me. My original plan was to go into oil and gas. I was also very interested in business and consulting, and I even had an offer from McKinsey in their Houston office. But family played a decisive role. My wife finished her PhD and secured a position in the Bay Area, and she was very clear that she didn’t want to move back to Houston. That meant I had to look for opportunities locally, and in the Bay Area, the best options were in tech. That’s how I ended up at Amazon.

Steven: You interned at major companies like Chevron and Halliburton and worked at Amazon, but ultimately joined C3 AI, a company at the forefront of enterprise AI. What prompted that pivot from a more traditional energy path to a high-tech/AI-focused role? 

Larry: At Stanford, my research had already started to pivot toward deep learning and AI-based approaches. I was eager to understand what “state of the art” really looked like in industry, i.e., how these methods were used properly at scale. Tech companies felt like the best classroom for that. Joining C3 AI was a chance to learn, to grow, and, of course, it didn’t hurt that it paid better.

Steven: In a similar fashion to the last question, what do you see as the pros and cons of working for larger, more established corporations versus smaller, potentially more nimble groups?

Larry: For me, the difference between large companies and smaller ones is mostly about scope and ownership. In a large organization, you often contribute to a very small piece of a much bigger machine. The impact can feel indirect. In smaller companies, even if they’re less established, you tend to own a much broader scope and see the full arc of a problem. Neither is inherently better, it really depends on what motivates you as an individual.

Current Role

Steven: Tell us a bit about your current role at C3 AI and the company's direction. How long have you been in this position, and how have you grown with the company over the last several years? Where do you see yourself and your group several years down the line?

Larry: I’ve been at C3.ai for over six years, starting as a senior data scientist and growing into my current role as Director of Data Science. During that time, I built and scaled the team from zero to supporting double digit enterprise customers across multiple industries. Today, my team operates at the intersection of data science research, especially reinforcement learning and graph neural networks for operations research, machine learning modeling, mathematical optimization, and production engineering. We own the full lifecycle: from research and algorithm development, through deployment and serving, all the way to long-term customer success.

Steven: What are some core tenants you learned at Stanford that you lean on today in your day-to-day work?

Larry: The core tenets I rely on most from Stanford are the same two pillars: rigorous technical thinking, the ability to model real-world systems, and clear communication. Being able to express ideas logically and precisely, in writing and in speech, is just as important as having the right algorithm. 

Steven: The energy sector is often "heavy industry"— slow-moving, massive physical infrastructure assets. AI is "light"—fast-moving innovation and rapid iteration. What's the biggest challenge in bridging that cultural and operational gap?

Larry: I don’t think the real gap is between “heavy industry” and “light AI.” It’s more about the difference between academia and industry. When I first started, the biggest challenge was pace. What might take months in school often has to be done in days in a company. Updating your mindset to embrace that speed, and still maintain quality, is critical.

Steven: You've written about deploying optimization solutions at an enterprise scale. What's the hardest part of that job? Is it the data, the algorithms, or the human element of convincing large organizations to trust and adopt these new tools?

Larry: Data and algorithms are not easy, but they are relatively stable. The most volatile and decisive factor is always the human element. In this role, we are not only scientists, we are also educators. Understanding stakeholder needs, finding alignment, building trust, and giving credit to partners, these often determine whether a project succeeds or fails.

“ The core tenets I rely on most from Stanford are the same pillars: rigorous technical thinking, the ability to model real-world systems, and clear communication.”

Looking Forward

Steven: Energy is a heavily politicized sector in the United States. Regardless of where your affiliations are, everyone can agree the last several years have brought significant amounts of turbulence from a policy perspective. How do you think about the future of energy in the United States?

Larry: I think there will always be opportunity in this space. The surge of AI is creating enormous new demand for energy. The future belongs to systems that can adapt quickly under both policy and market uncertainty.

Steven: The ongoing AI boom has caused significant load growth projections across the country. Do you foresee the grid being able to handle the significant demand increase for electrons, and if not, what changes need to be implemented on both a physical and policy front?

Larry: I’m not an expert on grid infrastructure, but it’s hard to believe the current system can absorb this growth without major changes. We need faster interconnection, massive transmission build-out, and policies that eward flexibility, storage, demand response, dynamic pricing, rather than just static capacity. Maybe even “crazy” ideas like massive energy storage or data centers in orbit will end up being part of the solution.

Steven: As AI gets more deeply integrated into critical infrastructure like the power grid, what is a long-term ethical challenge or risk that you think about?

Larry: I haven’t fully formed a view on the long-term ethics yet, but one thought stays with me: energy could become a new kind of currency. As AI becomes embedded in critical infrastructure, transparency and fairness in how energy is allocated and controlled may become a central societal issue. 

Steven: What advice would you give a current ESE student who wants to follow a similar path at the intersection of energy and AI? What's the one skill they should be building right now?

Larry: For students who want to work at the intersection of energy and AI, I still believe the core skills are coding and modeling. The ability to translate messy physical systems into clean computational representations is what unlocks everything else.

Steven: To close, what is the most exciting or hopeful trend you see in this field that gives you optimism for the future of energy?

Larry: What gives me the most optimism is that the convergence of grid-scale sensing, computing, and optimization is making the energy system “programmable” for the first time. That fundamentally changes what’s possible.