Generative AI in the Real World

A typical apartment building generates data on everything from leaking toilet flappers to tenant demographics, yet most of the real estate industry still runs its analysis by hand in spreadsheets. Fundrise co-founder and RealAI CEO Ben Miller argues the missing piece isn't a better AI model but a proprietary data layer that general-purpose tools can't replicate. Miller joined Ben Lorica on Generative AI in the Real World to cover how RealAI evolved from a data project into an AI-powered analyst tool, why the AI acts as an orchestrator rather than a financial calculator, which investment trends aren't yet pricing in AI's economic impact on real estate markets, and why he believes the trillion-dollar AI CapEx build is temporarily masking what will eventually be a deep trough in white-collar hiring.

The tech industry is measuring AI productivity all wrong, and Mozilla MLOps engineer and University of Chicago instructor Chelsea Troy makes a strong case for why. The real opportunity, she argues, isn't shipping more code faster but finally having the bandwidth to run the experiments, tests, and simulations that engineering teams have always wanted to run but never had time for. Chelsea joined Ben to cover the state of entry-level hiring, why the software engineering interview has been broken for decades, what it means to teach Python in 2026, and why token efficiency should replace token consumption as the industry's dominant productivity metric.

BERTopic creator and Google DeepMind developer relations engineer Maarten Grootendorst has spent years helping practitioners build intuition for how AI systems actually work—not just how to prompt them. Maarten joined Ben Lorica to cover the enduring relevance of embeddings and topic models in an LLM-dominated world, his hot take that agents are essentially just an “LLM in a for loop with some tools, some memory, and perhaps some guardrails," and what separates genuine agentic behavior from a well-constructed pipeline. They also get into the practical trade-offs between open weight and proprietary models, the future of state space models and attention, and why Maarten worries that a generation of builders shipping code they can't read may be storing up technical debt they can't repay. "If you don't really know how an LLM works," he says, "that intuition [about how to use it effectively] is much more difficult to develop."

As a pandas core contributor and early Parquet adopter who built AI data pipelines at streaming company Tubi TV, Chang She saw firsthand why the traditional data stack breaks down for AI workloads—and founded LanceDB to fix it. Chang joined Ben Lorica to explain why vector databases are too narrow a solution for modern AI data needs, and what a true multimodal data infrastructure actually looks like. Chang and Ben get into why the Lance file format is quickly becoming the open source standard for multimodal data, how the rise of agents is exploding data infrastructure demands, why open-weight models are the enterprise cost shift to watch in the next 12 months, and more. "Trillion is the new billion," Chang says, and the enterprises that set up their data infrastructure now for that scale will be the ones that succeed.

As the founder and CEO of LevelUp Labs, Aishwarya Naresh Reganti helps organizations “really grapple with AI,” and through her teaching, she guides individuals who are doing the same. Aishwarya joined Ben to share her experience as a forward-deployed expert supporting companies that are putting AI into production. Listen in to learn the value all roles—from data folks and developers to SMEs like marketers—bring to the table when launching products; how AI flips the 80-20 rule on its head; the problem with evals (or at least, the term “evals”); enterprise versus consumer use cases; and when humans need to be part of the loop. “LLMs are super powerful,” Aishwarya explains. “So I think you need to really identify where to use that power versus where humans should be making decisions.” Watch now.