LH133 The Great AI Steal with Ross Stevenson
The Learning Hack podcast
Ross Stevenson is founder of Steal These Thoughts! and describes his job as AI enablement architect. Before going independent he was Head of Learning at Trainline, ran digital learning at Tesco for six years, and spent seven years in HR. If enablement sounds a lot like performance consulting, he wouldn't disagree. So is AI the thing that finally makes performance consulting happen? Also in this episode: where people really are with AI, what Ross won't hand over to a machine, and what might be left of the learning function in three year's time. How HR taught him what the business really...
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LH132 Your People Are Fine: It's The System That's Broken with Robin Hoyle
The Learning Hack podcast
A third of UK workers say their own organisation gets in the way of them doing good work. Four in ten managers would rather not be managers. So why does L&D keep building interventions that ignore the possible situational causes of poor performance? Robin Hoyle is Head of Learning Innovation at Huthwaite International and has chaired the World of Learning conference for about fifteen years. In this conversation he makes the case that training doesn't stick because the system around it is broken, and that the profession measures what is easy to measure rather than what matters. His answer?...
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LH131 AI for People People with Erica Farmer
The Learning Hack podcast
Two-thirds of HR professionals say AI is their top development priority. But only a third of organisations offer any AI training – and fewer than one in five think their leaders are equipped to lead in an AI-enabled environment. So where is the gap, and whose job is it to close it? Erica Farmer is co-founder of Quantum Rise Talent Group, host of the AI for the Average Joe podcast and author of AI for People Professionals (Kogan Page, 2026). In this conversation she argues that organisations are rolling out AI the way they rolled out every HR system before it, treating people as tech users...
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TI06 Three Predictions from SF That Came True ... And Three That Didn't.
The Learning Hack podcast
Did science fiction predict AirPods? Neuralink? The death of God? For this final episode of Season One, John Helmer and Ezri Carlebach give you what hacks call a listicle: three transcendently significant things prefigured in science fiction that definitely came true — and three that didn't. Or haven't yet. Prediction, as Niels Bohr said, is very hard. Along the way: Thirteenth-century flying machines Victorians watching live war coverage on flat-screen TV elearning before there was elearning Verne, Wells, jetpacks, flying cars, prediction markets, transporter beams, universal nudism and...
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TI06 Three Predictions from SF That Came True ... And Three That Didn't.
The Learning Hack podcast
Did science fiction predict AirPods? Neuralink? The death of God? For this final episode of Season One, John Helmer and Ezri Carlebach give you what hacks call a listicle: three transcendently significant things prefigured in science fiction that definitely came true — and three that didn't. Or haven't yet. Prediction, as Niels Bohr said, is very hard. Along the way: Thirteenth-century flying machines Victorians watching live war coverage on flat-screen TV elearning before there was elearning Verne, Wells, jetpacks, flying cars, prediction markets, transporter beams, universal nudism and...
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TI05 Frankenstein: Man Made Man
The Learning Hack podcast
How a teenage girl's waking dream birthed what is (probably) the first science fiction novel – and in the process gave the world its most enduring image of technology gone wrong. This time we're coming for the myth, the curse, the cliche that is Frankenstein. John Helmer and Ezri Carlebach climb up the tower, point the lightning conductor to the heart of the storm, and attach electrical cables to the neck-bolts of that fearful thing on the slab. Yes, it may be 200-years old but ... it's alive! They walk through the original 1818 plot — stranger and bleaker than the films — weigh the...
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TI04 Asimov 2: Foundation
The Learning Hack podcast
Can the future of an entire civilisation be calculated like the behaviour of gas molecules? In the second of two episodes on Isaac Asimov, John Helmer and Ezri Carlebach turn from his robots to his other great franchise — the Foundation saga — and the seductive idea at its heart: psychohistory, a fictional science that claims to predict the fate of galactic empires. From a Gilbert and Sullivan libretto opened at random to Apple TV's billion-dollar adaptation, this is a conversation about how one pulp idea grew into a cornerstone of science fiction and why its questions about...
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TI03 Asimov 1: The Robot Laws
The Learning Hack podcast
In 1942, a 22-year-old chemistry student and part-time writer set down three short rules for how a fictional robot ought to behave. His aim was to kill off the lazy "robot-as-Frankenstein-monster" cliché. More than eighty years later, real engineers, real ethicists and real lawmakers are still arguing about them. This is the first of two episodes on Isaac Asimov — one of the "big dogs" of science fiction whose output ran to some five hundred books. John Helmer and Ezri Carlebach take on the most enduring part of that legacy: the Three Laws of Robotics. The Laws went on to power nine linked...
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TI02 5 Foundational SF Authors You've Never Heard Of
The Learning Hack podcast
Every genre has a shadow canon — the writers who don't make the syllabus, don't sell out on Amazon, and rarely get the Netflix series. In science fiction, that shadow canon is where some of the most intellectually adventurous, politically serious and formally daring work of the twentieth century was done. Having opened the series with the big names — Wells, Verne, Poe, the Mount Rushmore of the genre — John and Ezri jump forward to the late 1960s and 1970s and turn to five authors most listeners won't know: Kate Wilhelm, Joanna Russ, John Sladek, John Brunner and Christopher Priest....
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TI01 Amazing Stories Is 100!
The Learning Hack podcast
A hundred years ago this spring, a magazine called Amazing Stories hit the newsstands and — almost by accident — gave a name and a shape to the genre we now call science fiction. Its publisher, Hugo Gernsback, was an immigrant electrical engineer, visionary and relentless self-promoter. He wanted his magazine to delight and enthrall – but also to educate. In this opening episode of The Tech Imaginarium, John and Ezri go back to 1926 to ask why this peculiar pulp magazine matters — and why its mix of techno-optimism, prophetic vision and dystopic warnings still echoes through the way we...
info_outlineWhat if the ideas that L&D has been nodding at for thirty years are finally about to become unavoidable? Bob Mosher has spent his career arguing that training and performance are not the same thing — and that building courses, however well-designed, only meets two of the five moments when people actually need to learn. The other three happen in the workflow, at the point of need. Most of the profession has agreed with him in theory. Rather fewer have changed what they do.
In this conversation, John and Bob catch up on how generative AI is changing that picture. Bob describes what AI is actually doing inside live workflow learning projects right now — including a headline figure on development time that stopped John in his tracks — and explains why the digital coach, not the course, is becoming the central deliverable. They also dig into the risks: what happens when AI accelerates the wrong thing, why L&D needs a seat at the IT table before it's too late, and what it means to be a performance architect rather than a training order-taker in an AI-assisted world.
The boulder, Bob says, has been going uphill for a long time. He thinks it's about to roll back. This episode is about what happens when it does.
TIMESTAMPS
00:00 - Start
01:55 - Intro
04:27 - The five moments of need
08:14 - How has AI changed the conversation about workflow learning?
10:44 - How does AI save time?
24:07 - Risks to workflow learning from AI
31:42 - Has AI caused a rethink in practice?
40:19 - Workflow learning impact
53:38 - End
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