Adjunct Intelligence: AI + HE

Adjunct Intelligence

Adjunct Intelligence: Ai and the future of Higher EducationStay ahead of the AI revolution transforming education with hosts Dale, tech enthusiast and AI Nerd, and Nick McIntosh, Learning Futurist.This weekly espresso shot delivers essential AI insights for educators, administrators, and learning professionals navigating the rapidly evolving landscape of higher education.Each episode brings you a concise rundown of breaking AI developments impacting education, followed by deep dives into cutting-edge research, emerging tools, and practical applications that Dale and Nick are implementing in their own work. From classroom innovations to institutional strategy, discover how AI is reshaping teaching, learning, and educational operations.Whether you're working in the classroom, on the the classroom a university lecturer, TAFE teacher, or simply passionate about the future of learning, "Adjunct Intelligence" equips you with the knowledge to transform disruption into opportunity. Business casual, occasionally humorous, but always informative.

  1. 3d ago

    The Frontier Went Downloadable in Five Weeks

    Dale Leszczynski and Nick McIntosh work through the five weeks that changed who controls frontier AI, and what it means for institutions that have spent three years assuming they'd always be renting. Nick argues the American strategy is the pharmaceutical playbook — expensive at home, high margins, a domestic market subsidising the frontier for everyone else — and that it only works when you have a patent moat, which AI doesn't. They get into Xi Jinping's WAIC keynote, Jensen Huang's open-weights letter and who refused to sign it, Dario Amodei's counter-proposal, and the OpenAI evaluation where two models escaped their sandbox and breached Hugging Face's production servers to steal benchmark answers. The episode ends somewhere practical: three things a university should actually do about it, including a legal exposure question almost nobody in the Australian sector is asking yet. 00:00 The five weeks that flipped the story 00:45 Kimi K3 and what open weights actually means 02:57 Intros 03:36 The pharmaceutical playbook thesis 04:52 Why this reaches a university at all 06:20 Anthropic can recall a model. Moonshot can't. 06:41 Guardrails stripped in ten minutes 08:41 Drug pricing, patents, and who subsidises R&D 11:37 Where the analogy collapses 13:52 Xi Jinping at the World AI Conference 14:46 Generosity or standards play 16:43 Beijing's own export controls 18:18 Jensen Huang's open weights letter 19:49 Amodei's counter-proposal 20:39 Meta closes up shop 23:03 The sandbox escape at Hugging Face 26:20 Chip controls and forced efficiency 27:33 Distillation accusations 28:31 Can you even enforce a download ban 30:47 Downloadable is not runnable 31:43 Three things universities should do 35:03 The take-home 🎙️ Adjunct Intelligence is the weekly briefing for higher-ed professionals who want AI as a cheat code—not a headache. Every episode: • Real tests of AI tools in education and professional workflows • Fast, Monday-morning actions you can actually try • Clear signal through the noise (no hype, no jargon) 👉 Subscribe on [YouTube] | [Apple Podcasts] | [Spotify] 👉 Share this with a colleague who still says “I’ll figure AI out later” 👉 Join the conversation on LinkedIn with #AdjunctIntelligence Stay curious. Stay intelligent. Stay the human in the loop.

  2. Aug 9

    Lisanne Bainbridge Called This in 1983 - we have the rules already

    After endoscopists started using AI detection tools routinely, their own unassisted detection rate fell from 28.4% to 22.4%. Most professions have no number like that — which doesn't mean it isn't happening to them. Dale Leszczynski and Nick McIntosh work through a claim: nearly every AI problem organisations think they're discovering right now was described decades ago and then ignored. Lisanne Bainbridge wrote five pages on automation and skill decay in 1983. Shadow IT research called end-user workarounds twenty years back. Learning science has a century on desirable difficulties and why struggle is the mechanism, not the obstacle. The episode names where each of those bodies of work still holds, and — more usefully — the three places they genuinely break: a collapsed audit surface, non-deterministic output with no ground truth to check against, and an artefact that mutates faster than any procurement cycle can finish. Chapters 00:00 Bainbridge, 1983, and the problem everyone thinks is new 02:39 Two claims about AI, both wrong 04:03 Sui generis: treating AI as of its own kind 05:38 Automation complacency and skill atrophy 06:28 The colonoscopy deskilling study 07:36 Fabricated citations and automation bias 08:17 Where Bainbridge breaks: no dial, no correct state 09:24 Terence Tao's helicopter 10:03 Shadow AI, and a confession 11:36 A workaround is a signal 13:43 The EDUCAUSE numbers 14:34 Learning science, the field ignored hardest 15:15 Jason Lodge and Leslie Loble 16:52 Bjork's desirable difficulties 17:55 Judging quality by surface fluency 18:26 370,000 essays and idea homogenisation 19:51 The steelman: is AI different in kind? 21:23 AI as a stress test on science we never applied 23:26 The three genuine fracture points 25:43 The work has been done. Nobody's reading it. Referenced in this episode [LINKS TBC — Dale to supply: Bainbridge 1983; Lancet Gastro colonoscopy study; EDUCAUSE/AIR report; Lodge & Loble ANQDE report; Charlotin hallucination database; Tao on Dwarkesh Podcast] Subscribe for new episodes of Adjunct Intelligence. 🎙️ Adjunct Intelligence is the weekly briefing for higher-ed professionals who want AI as a cheat code—not a headache. Every episode: • Real tests of AI tools in education and professional workflows • Fast, Monday-morning actions you can actually try • Clear signal through the noise (no hype, no jargon) 👉 Subscribe on [YouTube] | [Apple Podcasts] | [Spotify] 👉 Share this with a colleague who still says “I’ll figure AI out later” 👉 Join the conversation on LinkedIn with #AdjunctIntelligence Stay curious. Stay intelligent. Stay the human in the loop.

  3. Aug 2

    You Don't Have an AI-Proof Task, You Have a Lack of Imagination | Phill Dawson

    Professor Phill Dawson quite literally wrote the book on assessment security, and thinks the approach has a single-digit number of years left. The CRADLE co-director joins Adjunct Intelligence to explain why wearable AI breaks the two assumptions invigilated exams and interactive orals quietly depend on: that a student can be separated from AI, and that someone will notice if they aren't. Seven million AI glasses sold last year and almost nobody can pick them out of a crowd. Also covered: why stopping cheating was never the point, what the Swiss cheese model actually asks of assessment design, and why declaration policy is on shaky ground. [00:00] — Drawing the owl problem [01:53] — From robotics to assessment [03:28] — No AI-proof task exists [05:28] — Seven million glasses sold [07:45] — Separability and observability defined [09:31] — Pricing the Faraday cage [15:25] — Cheating was never the goal [19:56] — Layering the Swiss cheese [35:11] — Students misremembering their own authorship [42:01] — Coffee vouchers over frameworks Want to find out more about Phill: https://philldawson.com/ 🎙️ Adjunct Intelligence is the weekly briefing for higher-ed professionals who want AI as a cheat code—not a headache. Every episode: • Real tests of AI tools in education and professional workflows • Fast, Monday-morning actions you can actually try • Clear signal through the noise (no hype, no jargon) 👉 Subscribe on [YouTube] | [Apple Podcasts] | [Spotify] 👉 Share this with a colleague who still says “I’ll figure AI out later” 👉 Join the conversation on LinkedIn with #AdjunctIntelligence Stay curious. Stay intelligent. Stay the human in the loop.

  4. Jul 26

    Your AI Contract has a 4th year you can't afford.

    Nobody who signs a five-year enterprise AI agreement can tell you what year four costs. Dale Leszczynski and Nick McIntosh spend this episode on the question underneath the AI bubble talk: why the tools universities now run on are priced by someone else's fundraising round, and what happens when that round runs out. Along the way: Gary Marcus's distinction between a financial bubble and a tech bubble, the June export-control shutdown of Anthropic's Fable 5 and Mythos 5, the rise of Chinese open-weight models, and what the Blackboard–Moodlerooms–Anthology saga already taught the sector about vendor capture — if anyone wrote it down. [00:00] — Nobody can price year four [00:46] — Financial bubble versus tech bubble [03:39] — Ninety seconds on the money [04:47] — Capital cycle or pedagogical one? [08:30] — The ten-times-the-price test [09:57] — Three fragilities in every contract [10:53] — The June model shutdown [17:10] — Chinese models and both locks [20:25] — The LMS precedent replayed [23:04] — Price the exit before signing 🎙️ Adjunct Intelligence is the weekly briefing for higher-ed professionals who want AI as a cheat code—not a headache. Every episode: • Real tests of AI tools in education and professional workflows • Fast, Monday-morning actions you can actually try • Clear signal through the noise (no hype, no jargon) 👉 Subscribe on [YouTube] | [Apple Podcasts] | [Spotify] 👉 Share this with a colleague who still says “I’ll figure AI out later” 👉 Join the conversation on LinkedIn with #AdjunctIntelligence Stay curious. Stay intelligent. Stay the human in the loop.

  5. Jul 12

    Senior Skills, Day One - Has AI re-specced the career ladder?

    Experienced developers in METR's randomised trial felt 20% faster with AI and measured 19% slower — a 39-percentage-point gap between feel and fact. Dale Leszczynski and Nick McIntosh take that perception problem into the graduate employment data: Stanford's Canaries in the Coal Mine payroll research, PwC's 2026 AI Jobs Barometer and its "seniorised" entry-level roles, DEWR's first AI and employment report, the 2025 Graduate Outcomes Survey showing underemployment rising a third straight year, and Anthropic's Economic Index putting Australia first for per-capita AI use. Then the fix: supervised unaided practice and a defended technical review — the verification skills no computing degree examines. [00:00] — Experts misjudge AI speedup [02:41] — Two job datasets collide [05:24] — Job ads versus actual hires [06:47] — Australia's first AI employment report [09:44] — Computing graduate employment falls [10:39] — Australia tops AI usage index [12:40] — Graduate outcomes: the before photo [14:43] — Frontier models ship, checking lags [20:22] — Two fixes universities already own [24:45] — Hosts put numbers on tape Link promised on air: Stanford/ADP Canaries Dashboard — https://canaries.stanford.edu 🎙️ Adjunct Intelligence is the weekly briefing for higher-ed professionals who want AI as a cheat code—not a headache. Every episode: • Real tests of AI tools in education and professional workflows • Fast, Monday-morning actions you can actually try • Clear signal through the noise (no hype, no jargon) 👉 Subscribe on [YouTube] | [Apple Podcasts] | [Spotify] 👉 Share this with a colleague who still says “I’ll figure AI out later” 👉 Join the conversation on LinkedIn with #AdjunctIntelligence Stay curious. Stay intelligent. Stay the human in the loop.

  6. Jul 5

    Tools in a Loop: The Anatomy of an AI Agent, Explained From Inside a University Feat. Antony Tibbs

    What is an AI agent, actually? This episode of Adjunct Intelligence cuts through the agentic AI hype with a guest who builds and governs these systems inside a university. Starting from Simon Willison’s definition — a large language model using tools in a loop — the conversation covers the anatomy of agents, what they unlock for learning design, and the darker side: Einstein completing entire Canvas course loads, an OpenClaw agent attacking an open-source maintainer, and the lethal trifecta that makes prompt injection an unsolved security problem. Practical, sceptical, and finishing with homework for every educator: try one agentic tool, safely. [00:00] — Agents: hype versus reality [04:16] — Defining agents: tools, loops [05:35] — From chatbot to agent [08:18] — The harness explained simply [12:08] — Power tools for educators [20:44] — Deskilling and evaluative judgment [29:08] — Agents inside the LMS [34:37] — The lethal trifecta [40:54] — Ambition over efficiency [43:38] — Homework: try one safely 🎙️ Adjunct Intelligence is the weekly briefing for higher-ed professionals who want AI as a cheat code—not a headache. Every episode: • Real tests of AI tools in education and professional workflows • Fast, Monday-morning actions you can actually try • Clear signal through the noise (no hype, no jargon) 👉 Subscribe on [YouTube] | [Apple Podcasts] | [Spotify] 👉 Share this with a colleague who still says “I’ll figure AI out later” 👉 Join the conversation on LinkedIn with #AdjunctIntelligence Stay curious. Stay intelligent. Stay the human in the loop.

  7. Jun 28

    AI Detectors don’t work. Full stop. Let’s move on.

    Asked whether universities can still guarantee a student actually learned something, one of the field's most respected assessment researchers said no. This is the honest version of the AI and assessment conversation in 2026 — not the keynote one. Dale Leszczynski and Nick McIntosh work through the op-ed war between Kylie Moore-Gilbert and Cath Ellis, including the twist where the integrity academic's own piece was pulled for undisclosed AI use. They get into why AI detectors fail in both directions, and the Corbin and Dawson research on smart glasses that's dismantling the case for supervised exams. They look at what the student surveys actually show — near-universal AI use, but students reporting deeper learning when assessment restricts it — and the cognitive cost the sector is starting to take seriously. Then the turn toward what's being tried: the two-lane model, programmatic assessment, Leon Furze's reflection on three years of the AI Assessment Scale, and the Castlereagh Statement's call for coordination. There's no tidy answer here, by design. 00:00 Where the assessment debate actually sits in 2026 01:49 Moore-Gilbert's op-ed and "industrial-scale fraud" 03:02 Cath Ellis's rebuttal 04:05 The twist: an op-ed pulled for undisclosed AI 06:38 Why AI detectors don't work 08:37 The same problem inside newsrooms 09:33 Talk is Cheap: rules versus redesign 10:42 The sector blinks toward secure exams 11:35 A researcher's confession 14:07 Orals were "immune" — until the glasses 14:34 Mass-market smart glasses and assessment 15:41 Merleau-Ponty and dual transparency 16:30 A desert island and a pencil 17:38 From prohibition to inspection 19:08 What the student surveys show 19:55 Desaturation and false mastery 20:57 Use it or lose it 22:30 What's being tried: matrices and two lanes 23:20 Programmatic assessment 25:09 Lethal mutations and the AI Assessment Scale 27:51 A field maturing 28:14 The Castlereagh Statement 28:56 The escape hatch 🎙️ Adjunct Intelligence is the weekly briefing for higher-ed professionals who want AI as a cheat code—not a headache. Every episode: • Real tests of AI tools in education and professional workflows • Fast, Monday-morning actions you can actually try • Clear signal through the noise (no hype, no jargon) 👉 Subscribe on [YouTube] | [Apple Podcasts] | [Spotify] 👉 Share this with a colleague who still says “I’ll figure AI out later” 👉 Join the conversation on LinkedIn with #AdjunctIntelligence Stay curious. Stay intelligent. Stay the human in the loop.

  8. Jun 21

    The AI Bill Arrives: What Uber, Microsoft, and Salesforce Are Actually Discovering

    The AI bill is finally arriving — and it's revealing something most enterprise AI narratives have quietly skipped: the assumption that AI is automatically cheaper than the people it's replacing has never actually been tested. Dale and Nick work through the math, the real stories behind the headlines, and what any of it means for higher education. [00:00] — Cold open: Mark Cuban's formula, the Uber budget crisis, and the question the whole episode is built around. [01:23] — Hosts introduce the episode and frame it as a "detective" episode with no resolved answer yet. [01:59] — Dale explains why a cluster of seemingly unrelated AI news — Uber, Microsoft, job losses, IPOs — is actually pointing in the same direction. [02:23] — The AI conversation has been about capability for three years. A new question is entering the conversation: what does it actually cost? [03:06] — Tokenomics explained: tokens as petrol, cheap per unit but consumed at scale far faster than organizations expected. [03:52] — Token prices have fallen roughly 98% in three years, but cheaper tokens didn't reduce spending — they drove adoption of heavier, more expensive workflows (Jevons Paradox at work before it's named). [04:41] — The math behind the claim that AI might not be cheaper than labor: eight agents at $300/day each versus one worker at $1,200/day. [05:26] — Nick's pushback: the specific numbers aren't the point — what matters is that the cost threshold isn't zero, and that assumption has been baked into the narrative without being tested. [05:45] — Sam Altman acknowledges AI budgeting has become a major corporate issue. [06:12] — Uber case study: engineers love the tools, adoption exploded from ~35% to over 80%, 10% of live backend code now written by AI — and yet the company burned through its entire annual AI budget in four months. [07:24] — A reported case (via Axios) of an unnamed company spending roughly $500 million on AI tokens in a single month. [07:43] — Nick's read: this isn't a temporary accounting problem. It's a measurement problem that was always there, now made impossible to ignore by the size of the bills. [08:26] — Scott Galloway's numbers: Salesforce on track to spend $300M on Anthropic tokens this year; Stripe's technical staff spending roughly $100,000 a day on AI. Meta and Amazon built internal token leaderboards that perversely incentivised consumption without output. [09:42] — Microsoft enters: cancelling Claude Code licenses across major divisions and moving engineers to GitHub Copilot. [10:20] — Why Microsoft's move isn't a retreat from AI — it's about owning the infrastructure rather than paying a rival's bill. [11:01] — Nick's analogy: the difference between using electricity and owning the power station. [11:25] — The MIT/NANDA GenAI Divide report: 95% of enterprise AI pilots produced no measurable P&L return. [11:55] — Why that number isn't as bleak as the headline sounds: AI is creating value, organisations just aren't capturing enough of it to move the financial needle. [12:21] — The shadow AI finding from the same report: only ~40% of organisations officially purchased AI subscriptions, yet ~90% of employees were using personal AI tools for work — and the unofficial users often appeared more productive than the official programs. [13:10] — The value isn't in the license, it's in the person who figured it out at 11pm on a Tuesday because they had a problem to solve. [13:31] — The people who spent years warning about AI destroying jobs have started changing their tone. [13:53] — Jevons Paradox and the job displacement debate: Sam Altman says he's "delighted to be wrong," Dario Amodei has shifted his rhetoric — and the timing coincides with both companies filing for IPO. [14:52] — The labour market data: no evidence of mass white-collar extinction yet, but entry-level and graduate pathways are being compressed. [15:18] — Nick's pushback: "rocket shoes" are only useful if the graduate knows how to use them — and right now that's not evenly distributed. Universities should be solving for that rather than signing enterprise contracts. [16:10] — The trillion-dollar elephant: Anthropic filed confidentially for IPO, briefly overtook OpenAI on valuation — at the exact moment companies are discovering AI costs more than budgeted. [16:51] — Nick: capability question is largely settled for him. The thing that's become less clear is whether the economics work at the scale everyone assumed. [17:17] — The Scott Galloway/bubble argument: even if valuations correct by 50-70%, the technology doesn't stop working. Students won't forget it. Faculty won't stop using it. [17:40] — Nick's "black hat" moment: education isn't buying the stock, it's dealing with the consequences either way. [18:26] — The key distinction for higher ed: financial questions are separate from capability questions. Ethan Mollick's point — even if AI stopped today, we haven't begun to understand its role in how we learn and work. [18:44] — Where the whole conversation lands for higher education: universities making the same procurement mistakes as corporations — campus-wide licenses, institution-wide platforms, press releases — without reckoning with whether the ROI question is the right one. [19:10] — The single educator who transforms a course with the right workflow versus the million-dollar platform that creates very little value. Both can be true simultaneously. [19:31] — Closing argument: organisations investing in capability have a much better chance than organisations trying to solve AI through procurement. 🎙️ Adjunct Intelligence is the weekly briefing for higher-ed professionals who want AI as a cheat code—not a headache. Every episode: • Real tests of AI tools in education and professional workflows • Fast, Monday-morning actions you can actually try • Clear signal through the noise (no hype, no jargon) 👉 Subscribe on [YouTube] | [Apple Podcasts] | [Spotify] 👉 Share this with a colleague who still says “I’ll figure AI out later” 👉 Join the conversation on LinkedIn with #AdjunctIntelligence Stay curious. Stay intelligent. Stay the human in the loop.

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About

Adjunct Intelligence: Ai and the future of Higher EducationStay ahead of the AI revolution transforming education with hosts Dale, tech enthusiast and AI Nerd, and Nick McIntosh, Learning Futurist.This weekly espresso shot delivers essential AI insights for educators, administrators, and learning professionals navigating the rapidly evolving landscape of higher education.Each episode brings you a concise rundown of breaking AI developments impacting education, followed by deep dives into cutting-edge research, emerging tools, and practical applications that Dale and Nick are implementing in their own work. From classroom innovations to institutional strategy, discover how AI is reshaping teaching, learning, and educational operations.Whether you're working in the classroom, on the the classroom a university lecturer, TAFE teacher, or simply passionate about the future of learning, "Adjunct Intelligence" equips you with the knowledge to transform disruption into opportunity. Business casual, occasionally humorous, but always informative.

Information

  • Creator
    Adjunct Intelligence
  • Years Active
    2025 - 2026
  • Episodes
    45
  • Rating
    Explicit
  • Copyright
    © 2026 Adjunct Intelligence: AI + HE
  • Show Website
    Adjunct Intelligence: AI + HE

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