podcast

A large paint company documents four ways to process a purchase order. In reality there are 11,973 different approaches. That gap between the documented process and the real one is the subject of this episode, and it explains why AI adoption is near universal while the share of companies reporting any profit impact has been flat at 37 percent for two years.

We look at where the difficulty actually sits. The process lives in workarounds, spreadsheets and people’s heads, and is different at every site. The data is spread across roughly 900 applications of which a quarter are connected. Legacy systems were never designed to be called by a machine. And the law has started to get involved: Air Canada was held liable for what its chatbot said, Workday can be sued as an employer’s agent, Australian companies must disclose automated decisions from December 2026, and Commonwealth Bank reversed AI-attributed redundancies after call volumes went up rather than down.

We then cover what AI adds to the old problem. Language models are non-deterministic and cannot be replayed for audit. The best agents complete about 30 percent of realistic office tasks. Human oversight runs into limits Lisanne Bainbridge described in 1983, now measured in the field. And people feel faster while being measurably slower.

Finally, what works. The strongest predictor of financial return is not the model but whether the workflow was redesigned, which is the same lesson factories took forty years to learn from electrification. We walk through the method: mine the process, standardise the core, exclude the tail explicitly, run in shadow mode against a baseline, measure at the process level, and tier governance by consequence. #embeddedAI #podcast

There is an old claim that a truly random source contains all knowledge: give monkeys enough time at typewriters and Shakespeare falls out. In 2024 two Sydney mathematicians did the arithmetic and found the universe ends first. But the idea has a modern tail. We now have language models that can spot meaningful text instantly, so why not let randomness generate and an LLM extract? This episode works through why that fails, and why the failure is precise: in a random stream, the address of any text costs as many bits as the text itself. Along the way: Borges’ Library of Babel, a website that actually built it, DeepMind systems that made the generate-and-filter idea work by cheating in exactly the right way, and what your brain does with noise that an LLM cannot.

Anthropic gave thirty AI agents the same coding task. Eighteen of them named their git branch exactly the same thing.

That result opens one of the strangest findings of 2026: when teams of AI agents face the classic “hidden profile” experiment, where the shared evidence points to the wrong answer and the decisive facts are scattered across individuals, they succeed only 17 to 36 percent of the time. Human groups in the original 1985 study? 18 percent. Forty years, a completely different kind of mind, the same failure.

In this episode we dig into why. We trace the mathematics of groupthink from information cascades to Condorcet juries, then follow the trail into territory embedded engineers know well: the 1986 Knight and Leveson experiment that shattered the independence assumption in N-version software, Airbus’s dissimilar redundancy, and the Lufthansa flight where two frozen sensors outvoted the one telling the truth. Along the way, honeybees show us a working reference design: a two-milligram brain that refuses to repeat a rumor.

We close with the fixes: engineered dissenters, forced disclosure protocols, reputation infrastructure for agents, and the case for “keeping the weirdness alive” through a genuinely diverse AI ecosystem, including heterogeneous fleets of small models at the edge.

What happens when work no longer gives our lives structure, identity, or meaning? This episode explores “Purpose as a Service”—the idea that purpose could be intentionally designed and delivered in an age of mass automation.

We examine the decline of traditional anchors such as employment, religion, and community; the rise of wellness programs, professional coaching, and AI companions; and what universal basic income trials reveal about the limits of financial security. Can external services genuinely help people build fulfilling lives, or does true purpose depend on personal agency and authorship?

Consider this a blueprint for one of humanity’s biggest future challenges: learning how to live well when a job is no longer at the center of life. #embeddedAI #podcast

As foundation models move from the cloud into physical robots, a fundamental question emerges: who is accountable when an AI-controlled machine makes a decision that causes harm?

In this episode, we examine the growing collision between embodied AI, functional safety, and emerging regulation. We explore how new frameworks such as the EU AI Act and the Machinery Regulation are reshaping expectations for developers, manufacturers, and deployers of intelligent robots. From humanoid robots and autonomous mobile manipulators to AI-enabled industrial machinery, the challenge is no longer simply making robots smarter. It is making them governable.

We investigate a proposed architectural solution that is gaining traction across industry and academia: the hardware-isolated safety supervisor. By separating non-deterministic AI reasoning from deterministic safety-critical control systems, this approach aims to create clear lines of accountability while preserving the benefits of onboard intelligence.

Along the way, we examine NVIDIA’s Cosmos Reason 2 model, the EmbodiedGovBench governance framework, emerging standards efforts, and the practical realities of deploying foundation models on embedded platforms. We also ask whether traditional functional safety concepts such as SIL and ASIL can adequately address the unique challenges posed by robots whose actions are selected by large vision-language models.

The broader question is one that every roboticist, embedded engineer, and AI practitioner will soon face: when intelligence becomes local, autonomous, and physically embodied, what mechanisms ensure that accountability remains local too?

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