Generative AI in the Real World

Pete Warden has spent his career on the frontier of small, local AI, first as one of deep learning's earliest engineers (he coined the term “TinyML”) and now as founder of Useful Sensors and Moonshine AI, where he builds voice models that run entirely on-device. Pete joined Ben to make the case that local AI no longer has to be a compromise. They get into what it actually takes to run a capable model on a laptop today; why the voice interface’s bad reputation is a consequence of rough, early implementations rather than a reflection of current capabilities; and where he stands in the ongoing debate between general “end-to-end” models and the compound AI approach of chaining specialized models together. Pete also explains why he thinks browser-based inference could be an "iPhone moment" for local AI and why more and more enterprises are considering self-hosted local models over commercial options. "The shape of [LLMs] is perfect for running locally," Pete says, and local models could be a boon to enterprises worried about cost, privacy, and stability.

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.