Generative AI in the Real World

AI makes it possible to generate code faster than teams can understand and support it. In this episode, Honeycomb cofounder and CTO Charity Majors joined Ben to discuss how that shift changes the work of building reliable software. Charity explained how observability helps teams connect changes with outcomes, why nondeterministic systems need “more rigor, not less,” and how traces can show the decisions agents make along the way. Over their wide-ranging conversation, she and Ben also covered automatic instrumentation with OpenTelemetry, why we should think of ourselves as “owners on the loop,”

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 de

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

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 con