Model Hype, Workflow Automation, and AI Governance

AI Benchmarks and the Retail Edge

What matters is the gap between AI claims and real-world results. Jonathan Harris examines how AI models behave outside the lab, from conservation efforts to retail AI demand forecasting. We look at the practicalities of edge AI, the trade-offs in smart retail, and why governance and honest reporting are crucial. This week, the focus is on what works, not just what’s announced.

Agentic AI, Workflow Automation, and AI Governance

Jonathan Harris cuts through Agentic AI, Workflow Automation, AI Governance, and AI Costs in this 45-minute Turing’s Torch: Artificial Intelligence Weekly briefing. The point is not to cheer every announcement from the pavement. It is to work out what is useful, what is undercooked, and who carries the risk once the demo glow wears off. Expect plain-English context on power, money, data, labour and control, with the usual vendor fireworks left outside where they belong.

AI Agents, Ads, and Audit Trails

Jonathan Harris looks beyond the hype to examine AI's quiet shifts. This week, we assess how conversational AIs can subtly inject advertising, the challenges of turning assistants into autonomous agents, and the practical importance of benchmarks for scaling AI. It's about the plumbing, not the fireworks, and understanding the real-world implications for trust, regulation, and who ultimately controls these systems.

Model Hype, Workflow Automation, and AI Governance

Jonathan Harris cuts through Model Hype, Workflow Automation, AI Governance, and AI Costs in this 60-minute Turing’s Torch: Artificial Intelligence Weekly briefing. The point is not to cheer every announcement from the pavement. It is to work out what is useful, what is undercooked, and who carries the risk once the demo glow wears off. Expect plain-English context on power, money, data, labour and control, with the usual vendor fireworks left outside where they belong.

AI's Messy Reality: Live Coding, Physical Robots, and Data Integrity

Jonathan Harris cuts through the AI fanfare this week, examining the practicalities of building intelligent systems. We look at the surprising value of live coding sessions for knowledge transfer and the real-world challenges of physical AI, from motors to maintenance. Plus, the unglamorous but crucial role of data governance and context management in making AI useful, and the quiet shift towards voice as a default developer tool. It’s about what actually works, not just what sounds good in a press release.