Building what matters in the age of AI
The Kellogg Technology Conference is one of the largest student-led technology conferences at Kellogg, bringing together leading executives, founders, investors and alumni from across the technology industry. This year, the conference welcomed more than 250 attendees, who heard from more than 15 speakers representing companies such as NVIDIA, Neo4j, GSV Ventures and a range of high-growth startups.
Stepping into a leadership opportunity, Tommy Wong ’26 MBAi served as the 2026 conference chair. Collaborating with his peers, he helped deliver a conference experience grounded in a central question: In an age of accelerating AI, what is actually worth building? Learn more about his experience bringing the conference’s vision to life.
What drew you to lead the conference?
Wong: I came to Kellogg already deeply curious about AI, both as a technology and as a force reshaping how companies are built. I wanted to understand the broader picture across the industry: how AI is transforming enterprise software, fashion, fintech, media and the investing thesis behind all of it. The Technology Conference is one of the few places where you can get NVIDIA executives, early-stage founders and growth-stage investors in the same room on the same day, all engaging with the same questions.
This year’s theme, CTRL + ALT + CREATE: Redefining Tech in the Age of AI, captured something I think about constantly. While technological tools have changed dramatically, the underlying questions have only become sharper: What to build? And why?
I wanted to push conversations beyond surface-level AI commentary to the harder strategic questions: Where does defensibility live now? What does enterprise software look like in an agentic world? How should the next generation of leaders position themselves?
What’s one leadership lesson you learned organizing this event?
Wong: I led a team of over 20 students across operations, sponsorship, speaker outreach, content design, marketing, run-of-show and student logistics. Coordinating that many workstreams to deliver a conference for more than 250 attendees taught me quickly that no single person can hold an event of this scale together. My job wasn't to do every piece. It was to build the right team, set a clear plan and trust each lead to own their workstream.
The hardest part of leading a complex event isn't executing the plan that's on paper. It’s mapping risk points in advance, putting contingencies in place early and giving each team lead the autonomy and resources to handle them. The behind-the-scenes work no attendee ever sees is what made the visible part of the conference feel effortless, and every member of that 20-plus team deserves credit for it.
How did you shape the agenda, and what point of view did you want it to carry?
Wong: As chair, I led the direction from selecting the conference theme and defining the central thesis to curating every speaker, panel and session topic. With 18 speakers across opening and closing keynotes, four panels and breakout sessions, every slot had to earn its place. I wanted the conference to make an actual argument, not just survey what’s happening in AI.
That’s the part of chairing a conference that doesn't show up in a run-of-show document but ultimately determines whether the day is memorable or forgettable. I’m proud that more than 250 attendees walked out with a sharper point of view on where the industry is going and not just a list of company names.
What were some insights that emerged?
Wong: One theme that kept surfacing across every session, was: as foundation models become commoditized, defensibility moves elsewhere into proprietary data, industry-specific workflows and the operational complexity. And that’s hard to replicate. Speaker after speaker, across very different sectors, described the same dynamic in their own language.
Our fashion tech panel made this concrete. Yusan Lin, CEO and founder of Mirror Mirror AI; Sixuan Li, CEO and founder of BNTO; and Kate Sanner ’19 MBA, CEO and co-founder of BENI, walked through how their companies build differentiation, not through model novelty, but through depth of data and workflow specificity. That conversation was clarifying. It validated something I had felt but hadn't yet articulated: the moat isn't the model. It's the proximity to the customer and the data exhaust no one else can access.
What role did alumni and industry partners play in shaping the event?
Wong: Recruiting our 18 speakers was one of the most demanding parts of the chair role. I personally led outreach and worked closely with each of the executives at companies including NVIDIA, Neo4j and GSV Ventures to shape their session content. The calibre of those speakers is what turns this from a student event into something people fly in for. The conference works because alumni and industry partners give it their best material, not their boilerplate.
Yogesh Agrawal '00 MBA, vice president at NVIDIA’s Data Center GPU Business, kicked off the day giving an inside view of the “AI factory” concept and the potential of the company’s latest graphics processing units. It set the technical foundation for everything that followed and grounded the rest of the day in what's actually being built at the infrastructure layer rather than what's being hyped on stage somewhere else.
Partner AI Lead at GSV Ventures Claire Zau rounded out the conference discussing education and the future of work. Her point — that reskilling at scale will be the defining challenge of this era and that multimodal AI could enable entirely new models of AI-native learning — gave the audience something to take home that wasn't just about deals or products, but about people. It was a fitting bookend, pulling the conversation back to the human stakes beneath the technical discussion.
Were there insights from the day that surprised you?
Wong: What surprised me wasn't any single technical revelation — it was the convergence. Founders building in fashion tech, panelists working on agentic AI and investors looking at fintech were all describing the same underlying dynamic from completely different angles. The bottleneck has shifted. It's no longer model capability. It's deployment, integration, distribution and trust.
That convergence matters because it changes where the opportunity lives. For students in the audience, many of whom will be making career bets in the next few months, the takeaway was a useful reframe. The opportunity isn’t in chasing the frontier of model capability. It’s in being the team that knows a specific industry, customer or workflow well enough to build something durable on top of models that anyone can access.
Looking back, what impact do you think the conference had?
Wong: The most meaningful impact wasn't a single moment on stage, but the conversations that started during the conference and continued afterwards. Several attendees reached out in the weeks following with follow-up questions, introductions and collaborations they were exploring with our speakers. A few students told me the day directly shaped their internship or full-time decisions.
For an event built around the question of what’s worth building, the best indicator of success is that people walked out of the Global Hub with a sharper conviction about their own answers and started acting on it.
What advice would you give to someone on the fence about getting involved next year?
Wong: Apply, even if you're not sure you want to work in tech. This conference is one of the fastest ways to develop a real point of view on where the industry is going, and that perspective is valuable whether you're heading into investing, operating, founding or any function AI will touch over the next decade, which, increasingly, is all of them.
You also build relationships with classmates and alumni who care about the same questions and are wrestling with the same uncertainty about what to build and where to bet. The first year of an MBA can feel like drinking from a firehose, but leaning into something like this early gives you exposure most people don't get until much later in their careers.
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The views and opinions expressed in this post are those of the author, and do not necessarily reflect the position of the Kellogg School of Management or Northwestern University.