Generative AI in the Real World

Post-training gets your model to behave the way you want it to. As AMD VP of AI Sharon Zhou explains to Ben on this episode, the frontier labs are convinced, but the average developer is still figuring out how post-training works under the hood and why they should care. In their focused discussion, Sharon and Ben get into the process and trade-offs, techniques like supervised fine-tuning, reinforcement learning, in-context learning, and RAG, and why we still need post-training in the age of agents. (It’s how to get the agent to actually work.) Check it out.

Synthetic data has been around for a long time, decades even. But as KPMG’s Fabiana Clemente points out, “That doesn’t mean there aren’t a lot of misconceptions.” Fabiana sat down with Ben to clarify some of the current applications of synthetic data and new directions the field is taking—working with offshore teams when privacy controls just don’t allow you to share actual datasets, improving fraud detection, building simulation models of the physical world, enabling multi-agent architectures. The takeaway? Whether your data’s synthetic or from the real world, success often comes down to the processes you’ve established to build data solutions. Watch now.

SwirlAI founder Aurimas Griciūnas helps tech professionals transition into AI roles and works with organizations to create AI strategy and develop AI systems. Aurimas joins Ben to discuss the changes he’s seen over the past couple years with the rise of generative AI and where we’re headed with agents. Aurimas and Ben dive into some of the differences between ML-focused workloads and those implemented by AI engineers—particularly around LLMOps and agentic workflows—and explore some of the concerns animating agent systems and multi-agent systems. Along the way, they share some advice for keeping your talent pipeline moving and your skills sharp. Here’s a tip: Don’t dismiss junior engineers.

As the founder, editor, and lead writer of Turing Post, Ksenia Se spends her days peering into the emerging future of artificial intelligence. She joined Ben to discuss the current state of adoption: what people are actually doing right now, the big topics that got the most traction this year, and the trends to look for in 2026. Find out why Ksenia thinks the real action next year will be in areas like robotics and embodied AI, spatial intelligence, AI for science, and education.

MLOps is dead. Well, not really, but for many the job is evolving into LLMOps. In this episode, Abide AI founder and LLMOps author Abi Aryan joins Ben to discuss what LLMOps is and why it’s needed, particularly for agentic AI systems. Listen in to hear why LLMOps requires a new way of thinking about observability, why we should spend more time understanding human workflows before mimicking them with agents, how to do FinOps in the age of generative AI, and more.

In this episode, Laurence Moroney, director of AI at Arm, joins Ben Lorica to chat about the state of deep learning frameworks—and why you may be better off thinking a step higher, on the solution level. Listen in for Laurence’s thoughts about posttraining; the evolution of on-device AI (and how tools like ExecuTorch and LiteRT are helping make it possible); why culturally specific models will only grow in importance; what Hollywood can teach us about LLM privacy; and more.

In this episode, Ben Lorica and Chris Butler, director of product operations for GitHub's Synapse team, chat about the experimentation Chris is doing to incorporate generative AI into the product development process—particularly with the goal of reducing toil for cross-functional teams. It isn’t just automating busywork (although there’s some of that). He and his team have created agents that expose the right information at the right time, use feedback in meetings to develop “straw man” prototypes for the team to react to, and even offer critiques from specific perspectives (a CPO agent?). Very interesting stuff.

In this episode, Ben Lorica and Drew Breunig, a strategist at the Overture Maps Foundation, talk all things context engineering: what’s working, where things are breaking down, and what comes next. Listen in to hear why huge context windows aren’t solving the problems we hoped they might, why companies shouldn’t discount evals and testing, and why we’re doing the field a disservice by leaning into marketing and buzzwords rather than trying to leverage what current crop of LLMs are actually capable of.

Emmanuel Ameisen, an interpretability researcher who previously worked at Anthropic, shares fascinating insights into large language models. He dives into how these models resemble biological systems, revealing surprising patterns like multi-token planning and shared neurons across languages. Emmanuel discusses the mechanisms behind hallucinations and the importance of model calibration. He also explores practical applications in medicine and offers invaluable advice for developers on understanding and evaluating model behavior.

Everyone is talking about agents: single agents and, increasingly, multi-agent systems. What kind of applications will we build with agents, and how will we build with them? How will agents communicate with each other effectively? Why do we need a protocol like A2A to specify how they communicate? Join Ben Lorica as he talks with Heiko Hotz and Sokratis Kartakis about A2A and our agentic future.