Discover how top universities are transforming teaching methods to prepare students for a future where AI is an essential co-pilot, not a crutch. In this eye-opening episode, seasoned professors from UVA’s business and commerce schools reveal the revolutionary shift from traditional teaching to a dynamic, AI-augmented learning experience.

You’ll hear from Ryan Wright, a UVA professor working to integrate AI into learning and research, Tim Laseter, who reflects on how the case method is evolving, and Keith McCormick, who brings a practical machine-learning perspective on AI’s real impact in organizations. Together, they unpack how students should be using AI as a co-creator, why judgment and critical thinking matter more than ever, and how educators can preserve genuine learning in an AI-driven world.

You'll learn how case method classrooms are becoming interactive labs where students collaborate with AI tools, analyze real-world data, and develop judgment skills that no AI can replace.

Ryan Wright wonders how we can teach the students not just to be ‘AI janitor,’ cleaning up and refining outputs, but rather co-creator partners of AI. Tim Laseter explores how to harness AI in experiential learning and live cases, making students thrive in an uncertain future. Keith McCormick highlights the importance of understanding organization memory and how AI can augment, rather than replace, the uniquely human skills of empathy and judgment.

In this episode, we also break down concrete tactics for educators and students alike: from using AI-driven peer reviews and meta-analysis to cultivating critical thinking through iterative questioning. The conversation then tackles the delicate balance of assessment in a world where AI is ubiquitous: should exams be oral, project-based, or involve real-time interrogation of AI outputs?

Beyond individual skills, the episode underscores a profound insight: how should we shape creativity in students? Possibly, a liberal arts foundation is the best strategy for future-proofing careers in the AI era. As AI accelerates change, the ability to see beyond AI-generated answers, ask the right questions, and think critically becomes invaluable.

This episode is essential listening for educators, students, and anyone curious about shaping the next generation’s skills in an AI-powered world. Whether you're at the start of your teaching journey or a seasoned professional grappling with assessment strategies, this discussion offers a fresh perspective on how to stay ahead. Prepare to rethink what it means to learn, teach, and succeed with AI as your co-creator, not your substitute.

It’s always a pleasure talking to Luis and Joshua. This episode, we covered ground that ranged from practical career advice to the cutting edge of AI agents and time series modeling.

What We Discussed

Career Paths in AI: Luis and Joshua shared insights on navigating roles in machine learning and data science, from industry trajectories to the skills that actually matter when building real systems.

AI Agents: We explored the state of autonomous agents. What’s hype, what’s working, and why the problems are harder than they initially appear. The conversation cut through the noise to focus on what’s actually deployable.

Foundational Models for Time Series: The deep dive here was substantial. Time series modeling with large language models is a frontier area. Neither Luis nor Joshua pulled punches about the complexity. We discussed architectural choices, training considerations, and the gap between benchmark results and production performance.

Why This Matters

The intersection of general-purpose foundation models and domain-specific challenges like time series is where a lot of real innovation happens. If you’re building forecasting systems or working with temporal data at scale, the patterns we discussed apply directly.

Thanks to Luis and Joshua for diving deep. This is the work that doesn’t make it into papers. It’s what practitioners actually need.

For the latest episode of My Data Guest, I sat down with Antonio Malia, a dear friend since our Pisa days, now founder of Seltz, a startup rethinking web search from the ground up. Not for humans this time. For machines.

The core problem

Google, Bing, and every search engine we grew up with were built around a simple human loop: type a query, scan snippets, click a link, read the page. Antonio’s insight is that this entire interface breaks down for an LLM. A snippet is, in his words, a movie trailer. It hints at the answer but doesn’t give it. So the model either guesses or burns tokens and seconds fetching the full page. Multiply that by every agentic loop, and you get slow, expensive, unreliable agents.

Seltz’s bet: build the crawler, index, retrieval, and ranking stack from scratch, optimized for a completely different reward function: one where the “user” is a language model, not a person.

Why it matters: latency and trust

Two things stood out from the conversation:

* Latency compounds. If an agent can get a search result back in 100ms instead of a second, it can run ten iterations in the time a traditional engine takes for one. That’s the difference between a shallow answer and genuinely complex task completion.

* Trust is the real target. Antonio framed it sharply: AGI, to him, is the point where we trust a machine’s completed process more than we’d trust another person’s. Web search is just the first, most obvious place models need reliable, constantly-updated access to the world. No more “airplane mode” after training cutoff.

Full stack, small team, Rust

Seltz owns everything: crawler, data connectors, knowledge pipeline, ranking. Building in Rust was a pragmatic call: efficiency, memory safety, and (notably) a much larger, more excited talent pool than C++ offers today. The team, partly ex-Amazon, partly IR-research veterans, is explicitly modeled on the “flat, high-trust, senior-heavy” structure Antonio admired at places like Netflix.

On coding agents

Antonio’s take on AI coding tools was refreshingly unfussy: they’re a boost only if you already know exactly what needs to be done. Used to offload thinking, they slow you down. Used to execute a decision you’ve already made, and to be challenged when they propose something better, they’re a genuine multiplier. Design decisions stay human.

What’s next

The next six months are about making Seltz’s product the main driver of value for customers who integrate it, validating the underlying research day by day rather than chasing a big splashy release. If you want to try it yourself: sign up in the console and connect via API/MCP, or just book a call with the team.