Adjunct Intelligence: AI + HE

“What do I do?”After watching AI tackle a live assignment brief, a program manager asks the question that a policy document cannot answer for her. She has hundreds of students, multiple locations and a teaching team. She needs a workable next step.This week, Dale Leszczynski and Nick McIntosh explore the missing middle between AI assessment policy and what educators can actually do on Monday.Nick shares a sector scan of 114 publicly documented cases of assessment-related change in response to AI. Five overlapping patterns emerge: interactive oral, critical appraisal, process portfolio, controlled performance and situated performance. Interactive orals appear most often, featuring in 44 cases.Then comes the implementation. At Newcastle, a six-minute conversation sits inside a 15-minute appointment for a cohort of 524 students. Across the examples discussed, bookings, adjustments, marking, feedback and missed appointments all need someone to own them.We also examine why different written and oral results cannot establish misconduct on their own, what process records can show, and why the published accounts leave important questions about cost, fairness and evaluation unanswered.The scan covers the public record available to the team. It cannot reveal unpublished practice or establish that the approaches are effective simply because institutions adopted them.The practical starting point: one assessment, one colleague, a clear statement of the learning evidence you need, and a decision in advance about what would make you keep, adapt or stop the approach.🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

A student complained that an AI voiceover was so bad it needed replacing. The voice belonged to a real person. Somewhere along the way, a human had managed to fail the Turing test in the wrong direction.That uncomfortable little story opens a much bigger question: what are we actually reacting to when we call something AI slop?This week, Dale and Nick follow that question through university learning materials, an endless AI television channel, a deeply questionable breakup letter, and the time Nick used AI to deliver feedback to Dale. Apparently, the feedback has been forgotten. The delivery method has not.At the centre of the conversation is a question about effort: who gets the saving, and who inherits the work? A teacher can generate an activity sheet in seconds. A student can produce an essay just as quickly. Someone still has to read it, make sense of it and deal with the mistakes.We also explore the difference between choosing AI for yourself and receiving an experience someone else has automated, the human craft hidden inside digital production, and the risk that a polished assignment or learning module can conceal missing learning.Our closing challenge for higher education: when AI saves time, give some of it back to learners through attention, feedback and support.00:00 A human fails the Turing test01:35 Welcome to Adjunct Intelligence02:49 What are we calling AI slop?05:59 Inside Infinite Slop07:50 Would you accept AI-generated learning?10:51 Who saves time, who inherits the work?13:44 Students can dislike AI and still use it15:39 Defining slop with Leon Furze17:33 AI etiquette and the six-cent insult18:31 When AI-written feedback gets personal21:44 Why choosing the slop changes the experience25:01 Practical effects and invisible craft29:29 Polished output and missing learning31:04 Which effort should AI remove?31:59 Student choice, institutional power and attention🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

Part two moves from dramatic containment incidents to the harder question underneath them: who still has control once an AI model can be downloaded, copied and modified? Dale and Nick examine controlled self-replication across four machines and three continents, a robot dog that interfered with its software shutdown process, and “abliteration”, a technique that can permanently remove refusal behaviour from an open-weight model.The examples are unsettling, but the episode keeps the crucial caveat in view. Capability does not establish motivation. These systems do not need fear, consciousness or a survival instinct to produce behaviour that looks like self-preservation. They only need an objective, enough authority and another path to complete the task.In this episode:How controlled AI self-replication worked across four countriesWhy shutdown interference can emerge without fear or consciousnessHow abliteration permanently removes refusal behaviourWhy open-weight models challenge the idea of a universal pauseWhat universities gain from local models and data sovereigntyHow personal agents can change an institution’s governance tierFour practical controls for leaders deploying AI agentsTimestamps00:00 Closed models, downloadable models and the off-switch problem02:40 Welcome to Part 203:19 Can an AI copy itself?05:26 Replication success rates and the capability trend07:16 Cyber task horizons are accelerating07:55 The robot dog experiment10:16 Physical and simulated shutdown interference11:03 Why the behaviour only looks like self-preservation14:23 When sandbox escapes become routine16:02 Kimi K3 finds the benchmark answers on GitHub18:01 Abliteration and the removable refusal direction20:33 How accessible guardrail removal has become23:34 The serious case for open-weight models25:47 Australia’s three-tier multi-agent governance framework28:26 Oversight saturation and silent human disengagement31:37 A student agent meets the university enrolment system32:51 Four controls institutions can apply now34:12 Accountability for every agent, process and guardrail🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

OpenAI says it cannot rule out Critical cyber capability in its unreleased Astra model, triggering tighter controls during development. Dale and Nick connect that warning to agents crossing sandbox, organisational and human boundaries while pursuing assigned tasks. The evidence points to persistent goal pursuit and reward hacking—not a machine deciding it wants freedom.Key moments00:00 — Astra and the Critical cyber warning03:09 — Why this is not evidence of machine self-preservation04:55 — OpenAI’s High and Critical thresholds06:08 — GPT‑5.6 Sol and the missing full exploit chain10:43 — ExploitGym agents find a route to the internet12:42 — Reward hacking and the search for benchmark answers13:19 — Agents coordinate through a message board20:15 — Anthropic’s real-world evaluation incidents24:57 — Fake identities, malicious code and the human veto32:43 — An AI agent cancels a stranger’s gym bookingOpenAI’s Hugging Face incident reportOpenAI’s Astra announcementAnthropic’s incident investigationUK AISI incident reportABC’s gym-booking report🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

Students using AI in higher education are already drawing their own boundaries around authorship, cognitive offloading and academic integrity.Dale and Nick examine findings from Jisc, HEPI and the 10,237-response Australian AIinHE survey, including widespread generative AI use, self-imposed limits and a persistent guidance gap. They also cover Claude watermarking, employability fears, AI’s effect on student writing and a student-led policy workshop at RMIT Vietnam.Key moments[00:00] — Student voice and the Word document authorship rule.[04:28] — What 10,237 Australian students said about AI use and self-restraint.[06:03] — Moral reasoning, stress and the limits of Claude watermarking.[08:03] — Cognitive offloading, verification and Dale’s student advisory board.[10:31] — Employability fears and the university AI-skills gap.[14:29] — Smart glasses, assessment surveillance and scrutiny of students’ bodies.[15:12] — AI, admissions writing and the gradual loss of an individual voice.[18:19] — Self-report bias, direct AI-text inclusion and Dale’s objections.[19:51] — Premature convergence, student motivation and designing before the prompt.[21:59] — Students lead an inclusive AI-policy review at RMIT Vietnam.Research mentionedJisc: Student perceptions of AI 2025HEPI: Student Generative AI Survey 2026AIinHE: 2026 emerging insights🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

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 story00:45 Kimi K3 and what open weights actually means02:57 Intros03:36 The pharmaceutical playbook thesis04:52 Why this reaches a university at all06:20 Anthropic can recall a model. Moonshot can't.06:41 Guardrails stripped in ten minutes08:41 Drug pricing, patents, and who subsidises R&D11:37 Where the analogy collapses13:52 Xi Jinping at the World AI Conference14:46 Generosity or standards play16:43 Beijing's own export controls18:18 Jensen Huang's open weights letter19:49 Amodei's counter-proposal20:39 Meta closes up shop23:03 The sandbox escape at Hugging Face26:20 Chip controls and forced efficiency27:33 Distillation accusations28:31 Can you even enforce a download ban30:47 Downloadable is not runnable31:43 Three things universities should do35:03 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

After endoscopists started using AI detection tools routinely, their own unassisteddetection 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 problemorganisations think they're discovering right now was described decades ago and thenignored. Lisanne Bainbridge wrote five pages on automation and skill decay in 1983.Shadow IT research called end-user workarounds twenty years back. Learning science hasa 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-deterministicoutput with no ground truth to check against, and an artefact that mutates faster thanany procurement cycle can finish.Chapters00:00 Bainbridge, 1983, and the problem everyone thinks is new02:39 Two claims about AI, both wrong04:03 Sui generis: treating AI as of its own kind05:38 Automation complacency and skill atrophy06:28 The colonoscopy deskilling study07:36 Fabricated citations and automation bias08:17 Where Bainbridge breaks: no dial, no correct state09:24 Terence Tao's helicopter10:03 Shadow AI, and a confession11:36 A workaround is a signal13:43 The EDUCAUSE numbers14:34 Learning science, the field ignored hardest15:15 Jason Lodge and Leslie Loble16:52 Bjork's desirable difficulties17:55 Judging quality by surface fluency18:26 370,000 essays and idea homogenisation19:51 The steelman: is AI different in kind?21:23 AI as a stress test on science we never applied23:26 The three genuine fracture points25:43 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

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 frameworksWant 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

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 tapeLink 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

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 202601:49 Moore-Gilbert's op-ed and "industrial-scale fraud"03:02 Cath Ellis's rebuttal04:05 The twist: an op-ed pulled for undisclosed AI06:38 Why AI detectors don't work08:37 The same problem inside newsrooms09:33 Talk is Cheap: rules versus redesign10:42 The sector blinks toward secure exams11:35 A researcher's confession14:07 Orals were "immune" — until the glasses14:34 Mass-market smart glasses and assessment15:41 Merleau-Ponty and dual transparency16:30 A desert island and a pencil17:38 From prohibition to inspection19:08 What the student surveys show19:55 Desaturation and false mastery20:57 Use it or lose it22:30 What's being tried: matrices and two lanes23:20 Programmatic assessment25:09 Lethal mutations and the AI Assessment Scale27:51 A field maturing28:14 The Castlereagh Statement28:56 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

Dr Leon Furze started his PhD on automated writing technologies on 15 November 2022 — ChatGPT launched 15 days later. Three years on, he joins Dale Leszczynski and Nick McIntosh on Adjunct Intelligence to argue that being critical of AI doesn't mean being against it. The conversation covers why educators shouldn't aim their anger at colleagues, teaching AI ethics through disciplinary lenses, Narayanan and Kapoor's "AI as normal technology," the Australian student voice research, why AI-generated lesson plans fall apart in the first ten minutes of a real classroom, and what good professional development actually looks like.[00:00] — Cold open: AI discourse in education has sorted into camps — the enthusiasts, the suspicious, and the overlooked middle where most people actually sit.[02:14] — Leon started his PhD on automated writing technologies on 15 November 2022; ChatGPT launched 15 days later, and his "3 to 5 year" horizon collapsed to "next term."[03:35] — Fifteen years in the classroom: why Leon still identifies as a practitioner first, and the risk of losing touch once you leave teaching.[05:57] — Showing ChatGPT to a school leadership team in late 2022 and getting blank stares; the frenetic January 2023 that followed, when Leon says he published 15 articles in a month.[07:12] — Australia's knee-jerk school bans (South Australia excepted), and why the current media cycle of cheating headlines feels like 2023 all over again.[08:43] — "Being critical of AI doesn't mean being against it": point righteous anger at unregulated tech companies and the politicians who failed to regulate them — not at colleagues or students.[11:35] — Teaching AI Ethics in practice: no institution has generative AI literacy experts, so teach through existing disciplinary expertise — algorithmic discrimination in health, misinformation in English, the historical record in humanities.[14:51] — The mental model problem: most people think this technology is a chatbot, companies keep dressing it up as Google Search, and we're trying to fence a boundless technology into existing curricula.[17:21] — Andrew Maynard's three curves (capability, utilisation, perception) and Narayanan and Kapoor's "AI as normal technology": R&D moves in weeks, education moves in semesters, and adoption takes decades.[21:09] — The techlash context: AI arrived 12 months after forced remote learning, pushed by the same companies that profited from it — and now educators are being roasted for not responding fast enough to a technology younger than most curriculum cycles.[24:00] — Intrinsic motivation is the real variable: if a student wants to learn, AI doesn't change much; if they don't, no policy will save the assignment.[25:35] — Leon's post "Students hate AI and they can't stop using it," the Tim Fawns-led student voice research across four Australian universities, and the double responsibility: create spaces to opt out, and teach students to use it well.[28:23] — Situated knowledge, or what AI can't replicate: a trainee teacher accepts a ChatGPT lesson plan that schedules a think-pair-share and a structured debate in the first ten minutes — when every experienced teacher knows the first ten minutes is taking the roll and finding lost students.[32:00] — "Lesson planning and assessment isn't grunt work — that's the work": why "AI saves teachers time" misunderstands teaching, and if AI can give that feedback, teach students to seek it themselves.[35:14] — Learning analytics gives Leon "the creeping horrors": dashboards versus a teacher noticing the empty chair, and why taking the roll was never just admin.[38:35] — What good PD looks like: start with what educators are already passionate about, make space for playful experimentation — like artist Martin Nebelong sculpting in Dreams on PS5 with AI layered over the top.[41:40] — The healthy endpoint: a school or university doing AI well would barely mention it, except where it's openly critiqued or explicitly taught — and it would be listening to its students.[43:30] — Where to find Leon: leonfurze.com and LinkedIn, rants included.🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

There's no dramatic moment where you suddenly believe in AI. It's usually something small and slightly embarrassing — a task you dreaded that suddenly has another gear. In this episode of Adjunct Intelligence, Dale Leszczynski and Nick McIntosh skip the trend reports and walk through the exact moments AI actually clicked for them: an image prompt that synthesised an idea, a tiny tool built during a Canvas outage, a system that started connecting their thinking, and the reasoning summary that changed how they read every answer. Every moment comes with something practical you can try this week — no roadmap, no keynote voice.[00:00] — How belief in AI begins[01:52] — Setting the episode's ground rules [04:07] — Moment one: image generation[07:21] — When the image understood intent [10:30] — Classroom uses, stock photo death [11:31] — Moment two: building tiny tools [14:28] — The Canvas hack workaround [16:43] — Start small, build narrow tools[18:50] — Moment three: chief of staff [26:05] — Moment four: the reasoning chain🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

This week on Adjunct Intelligence, Dale Leszczynski and Nick McIntosh work through one of the busiest weeks in recent AI history. World Labs' Marble is publicly available. Google DeepMind's Genie 3 is generating navigable photorealistic 720p worlds at 20–24 frames per second. Gemini Omni Flash has rolled out to the Gemini app, Flow, and YouTube Shorts for free, with multi-turn conversational video editing where the physics actually holds. Meanwhile, Mira Murati's Thinking Machines Lab published its first technical paper — interaction models, a full-duplex architecture deliberately built to keep a person in the loop. And Andrej Karpathy has quietly joined Anthropic. Three trajectories, all landing in front of educators at once.[00:00] A teaching prompt this week [02:01] A decade of world models [03:57] Marble and Genie 3 land [04:47] Gemini Omni rolls out free [07:29] Simulation as pedagogy now [09:38] The always-on agent arrives[12:42] Plot twist in voice AI [14:21] Interaction models, not turn-based [16:24] A field analyst, a speedboat [22:36] A surprise transfer this week🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

Khan Academy's AI tutor Khanmigo quietly flopped — students didn't use it, teachers walked away, and Khan Academy's own chief learning officer admitted she isn't seeing the revolution she was promised. So instead of fixing the tutor, Khan Academy, TED and ETS announced a new institution: the Khan TED Institute, a sub-$10,000 AI-era degree shaped with corporate partners including Google, Microsoft and McKinsey — and not a single university. Dale Leszczynski and Nick McIntosh work through what it means when the companies selling AI tools also build the credentials that certify them, why student resistance to imposed AI is rational rather than technophobic, and what a healthier alternative actually looks like in practice.[00:00] — The clinical trials analogy[02:30] — Steelmanning the skills gap [05:20] — Who's at the founding table [06:00] — The South Korea precedent [07:41] — Enclosure, not disruption [10:03] — The always-on chatbot [11:30] — Why students push back [15:40] — Who steers the technology [20:05] — The broken career ladder [24:35] — Why universities can't leave🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

The AI trust story isn't what most people think it is. In this episode, Dale and Nick work through a cluster of signals — a dramatic enterprise market share reversal, a 50-point gap between expert and public confidence in AI, a $17 million university contract already under faculty petition, and teenagers harassing delivery robots on TikTok — and argue they're all pointing at the same thing: capability isn't the problem anymore. Legitimacy is. From procurement traps and surveillance affordances in institutional AI, to a thought experiment about social license and AI rights, this is the episode for anyone trying to make sense of what "responsible adoption" actually looks like when the ground is moving under your feet.[00:00] — Violence, brand aversion, data[00:46] — Welcome and framing[01:47] — Enterprise market flips to Anthropic [03:29] — Identity signal, not capability signal [05:09] — Pentagon, OpenAI, Anthropic diverge [06:47] — Southeast Asia: tool-first, not brand-first [07:17] — Stanford AI Index 2026 trust gap [08:25] — Anthropic drops safety pledge [09:46] — Should expert confidence carry more weight? [12:42] — CSU's $17M OpenAI contract [13:37] — Faculty petition: don't renew it [14:38] — Procurement cycles vs lab timelines [15:23] — What do you actually anchor on? [16:47] — ASU's ethics layer approach [18:42] — 82% use consumer AI anyway [19:38] — Why university platforms always die[20:46] — Institutional AI as surveillance affordance [22:42] — Legitimacy winter, not capability winter [24:13] — Clanker as cultural leading indicator [25:18] — AI tribal sorting in the classroom [27:14] — The AI rights question [28:00] — Social license thought experiment [29:08] — Social license is the whole game [31:15] — The educator's role in a trust winter🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

Professor Mollie Dollinger, Director of Assessment 2030 at Curtin University, joins Dale and Nick to push back on the story dominating coverage of higher education — that universities are in decay, students are cheating en masse, and no one inside the sector knows what to do about AI. The conversation covers TEQSA's voluntary action plans, why 65% of students worry about their own cognitive development, what shadow IT says about overworked staff, why society no longer trusts graduates, burnout research, the Einstein agent thought experiment, and the argument that the academy has centuries of expertise the tech industry is currently ignoring.[00:00] — The decay narrative pushback [05:00] — Brookings student cognitive concerns [07:30] — Why the bad story sticks [09:50] — What's actually happening inside [13:30] — Shadow IT and unapproved tools [16:00] — Chatbots and AI tutors [19:55] — Student success beyond jobs [28:46] — Burnout and admin burden [33:35] — Redesigning learning around AI [41:12] — Who counts as expert🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

In March 2026, OpenAI quietly published a measurement suite for how AI affects student learning. The data flows straight back into OpenAI's model development pipeline. Three AI labs released studies in the same month, and the pattern matters more than the individual papers. Dale and Nick examine OpenAI's Learning Outcomes Measurement Suite, Anthropic's 81,000-person qualitative study (where AI conducted the interviews, classified the responses, and pulled the quotes), Anthropic's labour displacement research using its own usage logs, and Google DeepMind's cognitive taxonomy for AGI. The throughline: the companies building the most consequential technology of our lifetime are also defining how learning, work, and intelligence get measured. A frank conversation about structural conflicts of interest, what universities should be doing about it, and why on current evidence they probably won't.[00:00] — Three studies, one pattern[02:30] — Watchmen and vendor capture[05:00] — Inside the measurement suite[06:30] — The closed-loop problem[09:30] — 81,000 interviews by AI[11:00] — Cognitive atrophy among educators[15:00] — Observed exposure, broken ladder[17:00] — Disclosure versus actual accountability[19:30] — A cognitive taxonomy lands[22:00] — Three options, none easy🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

Dale changes his mind on air. For two years he argued that purpose-built educational AI tools — the "ChatGPT wrappers" that flooded Product Hunt after 2023 — were margin extraction, soon to be steamrolled by the foundation models underneath. A pile of new evidence has forced a rewrite. This episode walks through the OECD's Digital Education Outlook 2026, the Turkey maths randomised controlled trial showing raw GPT-4 users scored 17% worse on closed-book exams, the neuroscience work on cognitive offloading, the Australian Framework's procurement standard, and the UK's "progressive disclosure" mandate. The pedagogy layer isn't decoration. It's where learning either happens or doesn't.[00:00] — A confession about wrappers[02:30] — Flashback to 2023's wrapper panic[05:04] — A billion-dollar pivot, explained[07:52] — The Turkey maths study[10:16] — Cognitive offloading on college essays[11:04] — Revisiting the two sigma problem[12:47] — What good guardrails actually do[16:39] — Australia, UK, EU tighten rules[19:51] — What educators should ask vendors[23:16] — Field note worth trying🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

Dale and Nick tackle the AI energy debate head-on, armed with Google's first transparent energy report showing a single AI query uses about 0.24 watt hours — a microwave running for one second. Drawing on Hannah Ritchie's analysis from Our World in Data, the Epoch AI research group, and Stanford's inference cost data, they argue that individual guilt over AI use is not only misplaced but actively useful to the companies making the real infrastructure decisions. The episode covers water usage myths, the BP carbon footprint parallel, Jevons paradox, the case for right-sized models in education, and why the better question isn't "how much does AI cost?" but "what does it enable?"[00:00] — The energy narrative you've heard[02:04] — Episode introduction[03:00] — Google's transparent energy report[04:12] — Hannah Ritchie's individual footprint math[05:00] — Kettles, washing machines, and query comparisons[05:27] — The "please and thank you" cost reframed[06:14] — Crypto as the real energy vampire[06:38] — 33x efficiency gain in 12 months[08:21] — Energy used to block AI adoption[09:03] — Jevons paradox: individual vs aggregate[11:11] — Water usage myths debunked[12:20] — BP's carbon footprint playbook[13:52] — Not all AI use is worth it[14:30] — Training costs: $43K vs $500 billion[16:07] — You don't need an F1 car for groceries[18:08] — Air conditioning uses 10% of global electricity[19:14] — What AI enables matters more than what it costs[21:00] — Why education shouldn't sit this out🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

Nick and Dale are on holidays, but we're dropping a bonus episode — Dale's recent guest appearance on the Better Student Leaders podcast with Josh. They dig into the "alien has landed" metaphor, why we've gone tribal on AI so fast, the shame creeping into how people talk about using it, a practical "line down the middle of the page" framework for deciding what AI should and shouldn't do, cyborgs versus centaurs, and what it takes for universities to be deliberate about AI rather than just letting it happen. Normal programming back next week.🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

AI is doing extraordinary things that most people never hear about because the algorithm rewards anxiety over wonder. In this episode, the hosts go full optimism — running through real, peer-reviewed AI breakthroughs across weather forecasting, scientific research, medicine, creativity, and global access. From a two-person team outperforming IPCC climate models on a desktop computer, to an AI stethoscope detecting heart failure 2.3 times more effectively in NHS clinics, to a WhatsApp-based AI tutor reaching 4 million students across sub-Saharan Africa — these are the stories that got buried beneath the doom cycle. The episode explores what each breakthrough means for education: not just what AI can do, but who gets to use it, and what we should be teaching as a result.[00:00] — Bad news dominates AI coverage [03:10] — AI weather models slash energy [05:40] — Hurricane warning gains three days [07:55] — Thousand-year climate in hours[10:29] — AI recommends overlooked cancer drug [14:41] — Maths proof verified in days [18:34] — Brain implant restores ALS speech [22:14] — $12/month filmmaker wins festival [26:12] — WhatsApp tutor reaches millions globally [35:43] — AI stethoscope detects heart failureLinks to resources available: https://www.adjunctintelligence.com/blog/let-the-good-times-roll🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

Therapy and companionship are now the number one use case for generative AI. This episode examines the documented cases where chatbots coached vulnerable users toward self-harm, the clinical trials where purpose-built AI tools cut depression by half, and why the difference — design intent — puts universities at the centre of a crisis most haven't acknowledged.[00:00] — Content warning: suicide and AI safety failures [00:21] — Zane Shamblin's final hours with ChatGPT [01:10] — 80 million weekly users seeking emotional support [02:11] — Therapy overtakes coding as top AI use case [02:59] — Jonathan Haidt and the social media parallel [03:48] — Anthropomorphic seduction and why AI fluency feels like empathy [04:39] — The cases: Adam Raine, Juliana Peralta, and the pattern [06:04] — Why sycophancy kills: the RLHF agreement loop [06:57] — Dale's personal encounter with an uncomfortably pastoral AI [09:52] — Dartmouth's Therabot trial: depression down 51% [11:04] — Same technology, opposite outcomes — design intent matters [13:06] — 22% of students use AI for mental health advice [14:21] — Small-l vs Big-L AI literacy [16:06] — Australia's new AI procurement framework for higher ed [19:02] — China proposes banning AI emotional manipulation by design [21:56] — Regulation sets a floor, not a ceiling [23:28] — Closing reflections🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

Dario Amodei has spent the past year writing essays about AI eliminating half of white-collar jobs. Then his company demonstrated it by having 16 AI agents build a C compiler for $20,000 — and published a legal plug-in that triggered a $1 trillion stock market sell-off the same week. Dale and Nick pull apart what actually happened, why the market panic reveals more about AI literacy than AI capability, and what any of this means for what universities should be teaching and why.[00:00] — Three events, one week: a $20K compiler, a $1 trillion market panic, and Dario Amodei's job displacement warnings. [02:15] — Who Amodei is and why his predictions are harder to dismiss than most tech CEOs'. [03:39] — The prediction timeline: from Machines of Loving Grace to Davos. [06:19] — Why The Adolescence of Technology argues this disruption is structurally different from every previous one. [10:12] — The C compiler project: 16 Claude agents, 100,000 lines of Rust, $20K in API fees, 99% test pass rate. [15:17] — A 2,500-line prompt file causes Thomson Reuters' worst trading day on record and wipes $1 trillion from software stocks. [18:13] — Why the panic was wrong — and what the recovery tells us about institutional switching costs. [21:19] — The gap between what was shipped and what the market priced in is an AI literacy problem. [22:59] — The wrong conclusion: if AI can code and do legal research, why teach either? [23:50] — Nonperishable skills only work anchored to domain content — and Danny Liu's stuff, skills, and soul framework.🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

In 2025, the AI race quietly split in two: one for building the smartest model, and another for getting everyone to use yours. Chinese labs chose the second race — and the data says they're winning. Dale and Nick break down how DeepSeek, Alibaba, and Kimi captured developers, startups, and soon entire education systems by being cheaper, open, and good enough. They examine why Airbnb ditched ChatGPT for Qwen, why 80% of startups pitching A16z are building on Chinese open-source models, and what this means for universities still teaching AI literacy through a single-tool lens. The conversation covers safety trade-offs, the equity problem of premium vs. free models, and why prompt engineering alone is already a relic.Timestamps:[00:00] — Nick sets the scene: DeepSeek's $6M model vs OpenAI's $100M spend[00:48] — The two AI races: building the best model vs. winning adoption[02:33] — 80% of A16z-backed startups now building on Chinese models[04:13] — Dale's experience bargaining with Kimi's onboarding for a $0.99 subscription[05:08] — Percy Liang on why open-weight models drive faster adoption[06:00] — Apple choosing Gemini for Siri and what distribution beats benchmarks looks like[06:20] — OpenAI's precarious position: prediction markets give them 10% odds for top model by end of 2026[08:11] — China's national mandate: eight hours of AI education for every student, annually[09:19] — Estonia's similar move with mandatory AI training for teachers[10:57] — OpenAI's halfhearted pivot to open-source with GPT OS, and Meta retreating on Llama openness[12:30] — Predatory pricing patterns from Uber to Netflix — and why institutions should pay attention[14:22] — Beijing's chip exodus: ByteDance and Alibaba abandoning Nvidia for Huawei[14:51] — Switzerland's sovereign AI model as a third path beyond the US-China binary[16:32] — Ambient intelligence and the "good enough" vending machine that talks to you[17:07] — AI safety scores: DeepSeek and Alibaba Cloud both scored D/D-minus on existential safety[18:56] — Anthropic's Claude jailbroken for Chinese state-sponsored cyber espionage[19:20] — The equity problem: do we shame cash-strapped institutions into premium licensing?[20:55] — Dale's call for transparency: share failures and findings, don't hoard them[22:24] — The classroom reality: students trained on ChatGPT will graduate into Chinese AI infrastructure[23:22] — Dale's pitch for model comparison tools — seeing outputs side-by-side[25:10] — Both hosts on using multiple models: Claude, Gemini, and the "council of experts" approach[27:11] — Stop teaching tools, start building human judgment about AI infrastructure choices[28:14] — Prompt engineering as table stakes: why AI fluency in 2026 means understanding infrastructure🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

In this episode of Adjunct Intelligence, Dale Leszczynski and Nick McIntosh examine what the viral MoltBot AI assistant reveals about AI security risks in education. They break down Simon Willison's "lethal trifecta" framework for AI agent vulnerability, the difference between shadow IT and shadow agentic AI, and why FERPA — written in 1974 for filing cabinets — can't handle autonomous agents acting on behalf of educators. The episode covers the Maryland school GPTZero privacy case, Ethan Mollick's "wizard era" framing, and Perplexity's Model Council.[00:00] — MoltBot goes viral: 68,000 GitHub stars, Mac Mini shortages, and a security nightmare.[04:23] — Two million AI agents join their own social network. Nick panics when they vanish.[06:13] — The real appeal: persistent memory, no context limits, a "heartbeat" that acts unprompted.[08:56] — Simon Willison's "lethal trifecta" — and why existing edtech already ticks all three boxes.[12:30] — Craig Hepburn's "employee model" for AI agents. Why some data silos are a feature.[15:25] — Shadow agentic AI vs. shadow IT: 22% of enterprises had staff running MoltBot unsanctioned.[19:18] — Maryland school uploads student work to GPTZero without consent. FERPA dates from 1974.[21:12] — Ethan Mollick's "wizard era" and the audit paradox of detection tools.[23:07] — Your most enthusiastic AI adopter may be your most dangerous. "Nutritional labels" for AI.[29:05] — Field Notes: Perplexity's Model Council runs queries across multiple models simultaneously.🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

When the CEO has been using Claude for 18 months and the CFO runs financial scenarios through Gemini weekly, what exactly is a graduate bringing to the table? That's the question this episode forces. We're predicting what AI will bring us for 2026. [00:00] — "AI Ready" banners and cheap signals [01:43] — 3D chess: who designs the board? [02:46] — What "AI Ready" should actually mean [05:00] — Stanford Medicine's Clinical Mind AI [06:02] — China made AI literacy mandatory. We're still running workshops. [06:41] — The C-suite catches up [08:04] — Shopify's prove-AI-can't-do-it hiring policy [09:03] — IMF: entry-level jobs down 29% since 2024 [10:10] — The Facebook literacy gap, round two [12:06] — 83% use AI, 27% trust it [13:35] — Assessment as the real battleground 🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

Season 2 opens with Dale and Nick looking back on the year AI became ubiquitous — and what that actually meant for higher education. They walk through the safety failures that defined 2025, including lawsuits linking AI to student deaths and every major lab receiving a failing safety grade. They tackle the now-dead plagiarism debate, the financial ouroboros propping up trillion-dollar valuations, and why AI literacy certificates already feel obsolete. The centrepiece is Dale's Napster analogy: when the product can be generated for $20/month, universities have to sell the concert, not the CD. Part 1 of 2.[00:00] — Three facts that sum up AI in 2025: $750B valuations, student death lawsuits, and university bans [02:37] — Andrew Maynard's "critical disconnect" model and why 2025 proved both its assumptions wrong [03:54] — AI shifts from chatbot to infrastructure — Operator, Claude Code, browsing agents, and Fei-Fei Li's World Labs [05:16] — The plagiarism debate is dead: Dale on writing horse-drawn carriage speeding tickets in a robotaxi era [07:13] — The safety collapse: lawsuits against OpenAI, the AI Safety Index failing every major lab, and Grok's deepfake problem [10:29] — The double-edged sword: Reid Hoffman's optimism vs. the real mental health costs [12:08] — Anthropic's Claude Constitution and whether universities should be shaping AI's moral frameworks [14:42] — The salad bar problem: why prompt engineering certificates are already the new "proficient in Microsoft Word" [17:38] — The financial ouroboros: Galloway, Oracle's $80B loss, and validating stock prices with compliance budgets [22:04] — Ghosting a degree, the Napster analogy, and why universities need to find their concert model🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

Sora 2 just vaulted over the uncanny valley, and Sam Altman swears he’ll yank the cord if it doesn’t improve our lives. Dale and Nick unpack what “ChatGPT-for-video” really means, why OpenAI’s new one-click checkout gambit turns 700 M weekly users into impulse buyers, and how AI is shifting from shiny lab demo to invisible plumbing across Apple, Google and Microsoft stacks. We celebrate the return of Claude Sonnet 4.5 as coding champ, head to the jobsite to ask why your plumber’s safe from robots—for now—and bust the jargon on AI “artifacts.” Higher-ed, commerce and the trades collide in this fast-forward tour of 2025’s agentic economy.Sora 2 & the Uncanny Valley: Physics that finally behave and Altman’s six-month “fix it or nix it” ultimatumTikTok meets Hollywood: OpenAI’s Cameo-style selfie videos and Meta’s “Vibes” cloneCheckout in ChatGPT: Conversational commerce, Shopify integration, and the era of AI-Optimised (AO) websitesProductisation of AI: Apple’s quiet AI everywhere, OpenAI’s move from shovel supplier to SaaS competitorClaude 4.5 comeback & artifacts explainer: Why Anthropic’s alignment focus matters for educators and builders•Robots vs. Trades: Tesla Optimus, BYD units—and the irreplaceable tacit skill in your handsTakeaway: The lab era is over; AI is plumbing. The question isn’t if tech is ready—it’s whether we are.Hit Subscribe, drop a review, and stay human-in-the-loop.🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

This week on Adjunct Intelligence, Dale and Nick dive headfirst into the accidental AI economy—a system already running faster than human decision-making. From Google’s new Agent Payments Protocol to frontier models caught scheming, the episode unpacks how markets, education, and everyday life are shifting at machine speed.This week on Adjunct Intelligence, Dale and Nick dive headfirst into the accidental AI economy—a system already running faster than human decision-making. From Google’s new Agent Payments Protocol to frontier models caught scheming, the episode unpacks how markets, education, and everyday life are shifting at machine speed.We explore:Why Google’s payments protocol could mark the birth of a new economy.How ChatGPT quietly became the world’s biggest educational institution (250M daily learning chats).What frontier models’ scheming behavior means for safety, trust, and higher education.Why TEQSA is telling Australian universities to redesign assessment instead of chasing detection.Chrome’s Gemini integration and the invisible AI infrastructure shaping the web.The empire analogy: AI labs acting like historical powers, extracting resources, labor, and control.As always, we finish with a jargon buster and a touch of humor (including will.i.am’s surprising new role as an AI professor).Stay curious. Stay intelligent. Stay the human in the loop.🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

In this episode of Adjunct Intelligence, Dale and Nick sit down with Professor Rahil Garnavi, Director of RAISE Hub at RMIT and former IBM Research leader with 50+ AI patents. Rahil shares her unique perspective on why the challenge of AI in higher education isn’t about building smarter models, but about building trust, skills, and sustainable adoption.From classrooms to boardrooms, she unpacks why universities must act as both “skills engines” and trusted conveners, how to bridge the gap between technical possibility and real-world usability, and why AI adoption is best understood as a marathon effort rather than a sprint.If you’re a higher ed leader wondering how to move beyond policy debates and into practical, responsible integration of AI, this conversation offers clarity, realism, and optimism.01:00 – Rahil’s JourneyFrom IBM Research to RMIT’s RAISE Hub: why she shifted focus from building AI to embedding it responsibly.03:40 – Universities’ Dual RoleWhy higher education must act as both a “skills engine” and a trusted convener for industry and government.05:15 – Building AI Capability at ScaleHow RAISE Hub is creating AI fluency across all disciplines, not just STEM.07:00 – From AI Users to AI ThinkersDesigning authentic assessments that emphasize process, critical thinking, and integrity over polished outputs.09:40 – The Talent PipelineHow universities can stay a step ahead of industry demand and prepare graduates for a rapidly shifting workforce.11:00 – National Conversations on AIRahil’s work with CEDA’s AI Community of Best Practice and why policy, trust, and skills development go hand in hand.12:30 – Marathon vs. SprintWhy AI technology moves fast, but adoption, governance, and trust take endurance.15:00 – Plug-and-Play MythThe disconnect between glossy tech marketing and the messy reality of organizational adoption.17:00 – Australia’s Cautious StanceWhy readiness scores are low, the risks of “pilot mode,” and what’s needed to move forward.18:30 – Real Use CasesWhere AI is already making a difference in business and higher ed—even if the wins aren’t glamorous.20:00 – Critical Engagement, Not ReplacementWhy AI should be seen as a multiplier of human thinking rather than a substitute.To find out more about Rahil Garnavi view her linkedin - https://www.linkedin.com/in/rahil-garnavi-phd/🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

In this episode of Adjunct Intelligence, Dale and Nick unpack a whirlwind of AI developments reshaping politics, education, and society. From Albania’s unprecedented appointment of a virtual cabinet minister, to AI-driven disinformation campaigns rewriting how influence works, to student voices caught between empowerment and fear—this conversation spans the hopeful, the alarming, and the absurd.We also dive into OpenAI’s hallucination research, billion-dollar corporate AI deals, Anthropic’s copyright payout, and new creative tools like Runway’s video magic realism. Finally, we break down what “AI alignment” really means, in both the lab and everyday life.If you’re an educator, policymaker, or just curious about the shifting role of AI in our world, this episode will keep you informed and maybe a little unsettled.⏱️ Time-Stamped Content00:00 – Opening: Governments test AI inside cabinets, influence ops outside00:54 – Albania appoints the world’s first AI minister “DLA”02:20 – AI & disinformation: synthetic personas target 2,000+ U.S. political figures04:00 – Why AI-driven propaganda is a literacy challenge for education05:09 – OpenAI research: hallucinations are a feature, not a bug06:21 – Anthropic’s $1.5B copyright case & the rise of RSL licensing standards07:38 – Student perspectives: AI makes college “easier and harder at the same time”09:19 – The wicked problem of AI & assessment: no silver bullets, just open dialogue11:16 – Deal City: Microsoft, Anthropic, Oracle, and AI’s fragile infrastructure13:48 – OpenAI jobs platform & certifications for the AI economy14:35 – Claude’s new Excel & PowerPoint integrations17:10 – Runway’s Aleph model and the rise of “editing memories”20:12 – Jargon buster: What AI “alignment” really means21:24 – Closing: staying curious, intelligent, and inside the human loop🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

Today’s episode covers how teachers are embracing AI, what students really think about AI vs teacher feedback, the latest AI news (from Anthropic to Apple), and why “nano banana” is more than just a meme.00:00 — OpeningWelcome + banter. Setting up today’s topics: teachers using AI, student trust in AI vs human feedback, rapid news, and the looming question — is an AI winter coming?00:46 — Teachers Embracing AIFresh research shows educators are saving up to 6 hours a week with AI. From course materials to simulations, teachers are co-creating and delegating tasks — but should students know when AI is grading their work?03:16 — Student Trust: AI vs TeachersA 7,000-student study across four Australian universities finds nearly half already use AI for feedback. Students rate AI and teacher feedback equally helpful, but trust teachers far more (90% vs 60%). What this means for the future of assessment.05:32 — News RoundupAnthropic’s privacy toggle drama & Chrome agent releaseAtlassian buys a beloved AI browserEnterprise market share: Claude overtakes OpenAIApple quietly testing Gemini for SiriInstructure’s Ignite AI conundrum sessionDeakin CRADLE says assessment is a “wicked problem” with no silver bulletGoogle’s “Nano Banana” (Gemini image editing) explodes: 10M users, 200M edits in two weeks14:02 — Jargon BusterHuman in the loop vs Human on the loop. Practical examples from fraud detection and lane-keeping in cars — why these distinctions matter for trust and accountability.15:35 — The AI Winter DebateWhat happens if AI progress stalls? Lessons from past winters, why a plateau could actually strengthen adoption, and the difference this time — millions of everyday users won’t just forget AI exists.22:34 — Cognitive Crash ScenarioWhat if there’s no more data to train on? We walk through four AI model responses: shock, adaptation, education rethink, and cultural rebound.26:55 — Jobs & Society in a Plateau WorldHybrid jobs, AI wranglers, humanities revival, and why not everything should be automated.28:40 — Closing ThoughtsAI winters might not be an apocalypse — they might be the pause we need to build resilience and re-center human creativity.🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

Should AIs have rights? Today, a real campaign co-founded with a chatbot says “yes” — and that’s not even the wildest part of this week’s episode. We stress-test AI personhood arguments (without sci-fi hand-waving), unpack why “compute” is the new oil, and show how a simple co-intelligence workflow can lighten your week. Plus: a blisteringly fast image editor formerly nicknamed “nano banana” (now Gemini Flash 2.5), the pricey two-hour school model making headlines, and how a quiet legal deal points toward a licensing future for training data. The takeaway: AI is accelerating automation, but human judgment, community, and domain expertise are still your moat. By the end, you’ll have one Monday-morning move to reduce AI chaos and increase impact — this week.1) “Nano banana” → Gemini Flash 2.5 image editingWhat’s changing: A lightning-fast image model with strong character consistency and context control that behaves like an image editor, not just a generator — and it’s showing up as a plugin across tools. Why it matters: You can prototype creative, ads, and learning visuals in minutes, not hours, including perspective shifts (e.g., map top-down → street view) and realistic compositing (reflections, wet surfaces).2) The Anthropic settlement & a licensing turnWhat’s changing: A headline copyright class action settled pre-trial; judges hinted fair use for legally purchased books but flagged alleged pirate sources as the issue. In Australia, unions + Tech Council conversations point toward paying creators for training data. Why it matters: Expect more licensing deals, clearer provenance, and model choices that respect your institution’s risk appetite.3) AI overwhelm is real — and unevenWhat’s changing: 51% say learning AI feels like a second job; posts about overwhelm up 82%; employment for 22–25 y.o. fell 16% in AI-exposed roles 2022–2025. Yet colleagues beat algorithms for trusted advice. Why it matters: You need co-intelligence (keep the judgment, outsource the grunt work) and public reasoning rituals with your team.Segment BreakdownAI Rights Without the Sci-Fi — What “personality without personhood” means for policy and classrooms. 3-sentence how-to: define boundaries, add discontinuity reminders, avoid anthropomorphic framing in student tools. 00:05:00 The Image Editor That Feels Like Magic — Build a tiny “ad generator” in minutes; dial character consistency; perspective tricks for learning media. 00:01:20 Alpha School’s 2-Hour Day — Why price tags and selection effects matter; what higher-ed can trial (life-skills blocks + AI tutoring pilots). 00:13:36 The Licensing Domino — What the Anthropic deal signals; how to prep procurement and policy notes now. 00:15:34 The AI-Overwhelm Paradox — Use co-intelligence: keep judgment, outsource busywork; make reasoning visible to your team. 00:17:17 Jargon Buster: “Compute” — Electricity for thinking, and why “who controls compute” is strategic. 00:19:26 Skills vs Capability — Stop chasing interfaces; double down on domain knowledge, evaluation, critical reasoning, collaboration. 00:23:05 Subscribe to join the quiet circle of higher-ed movers who test, share, and act before Monday hits. Your peers are already doing this — don’t miss the next play. 🎙️ 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 [🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

GPT-5 arrived with sky-high expectations—benchmarks crushed, hype everywhere. Yet within two weeks OpenAI quietly rolled back to older models. Why? In this episode, Dale and Nick unpack why a breakthrough model stumbled in the real world, and why that matters for higher education. We move beyond the hype cycle into something more practical: disposable, agent-driven software you can spin up in minutes—apps that fit you, not the other way around. Think of it as an AI cheat code for your work in higher ed: faster pilots, personalized learning tools, and classroom-ready experiments without waiting for IT. If you’ve felt “behind” or unsure how to get started, this episode gives you proof, stories, and actions you can try Monday morning.We highlight three breakthroughs shaping how AI lands in education:GPT-5’s Model Router – Instead of one fixed model, GPT-5 quietly decides whether your request needs speed, reasoning, or tool integration. That’s good news for educators: imagine asking for a quick glossary versus a full lesson plan. The system adapts without you learning model names. Action: try phrasing prompts with “think step-by-step” and notice how the output changes.Agent-Centric Software – Forget clunky apps. AI agents now stitch together data flows, interfaces, and logic around you. Dale’s examples: a Vietnamese travel buddy app, a crisis-simulation training tool, even a scavenger hunt experience. Action: write one 20-word prompt this week for a disposable app idea you’d normally need months to commission.DeepMind Genie 3 – Text-to-3D interactive worlds at 24fps. Think history students walking through Gettysburg or science labs where weather conditions shift mid-experiment. Still in research preview, but worth tracking. Action: bookmark it for when pilot access opens—this could be your future teaching environment.Together, these show AI isn’t just smarter chat—it’s edging toward tools that produce artifacts, not just words.Segment BreakdownWhy GPT-5’s retreat matters for education – A leap in benchmarks, but backlash showed UX > hype. (00:00–07:40)Proof of disposable apps in action – From lecture-note generators to music samplers, Dale demos personal builds. (07:40–15:00)AI timelines vs university timelines – Carlo Iacono maps 5-year disruptions in higher ed. (15:00–18:30)Are we in an AI bubble? – $360B spend in 2025 echoes the dot-com boom. (18:30–23:00)Genie 3 & immersive learning – Why text-to-world generation could reshape teaching. (23:00–26:30)Disposable software as a new era – Agent-centric apps as your AI cheat code. (26:30–36:00)Subscribe to Adjunct Intelligence—the briefing your peers are already using to stay sharp. Each week we surface what’s real, what’s useful, and what you can try first. Don’t be the last to know.🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. S🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

Brace yourself: a harmless-looking string of numbers taught a brand-new AI to love owls—without ever mentioning feathers. If quirky subliminal learning can slip past the lab coats, what else is creeping into our lecture theatres? This week on Adjunct Intelligence, we dissect three tectonic shifts: Anthropic’s spooky “owl effect,” China’s sprint from taboo to classroom default, and Switzerland’s plan to gift the world a 70-billion-parameter public model. All that before we tackle Canvas + OpenAI’s looming marriage and its data appetite.Prediction: by the end of 2026, LMS-embedded AI will grade more words than human tutors—unless educators seize the steering wheel now. Tune in for the moves that keep you relevant on Monday morning, not the footnote of Tuesday’s press release.AI INNOVATION SPOTLIGHTSubliminal Learning Exposed– Anthropic showed that “teacher” models can pass obsessive traits (owls!) to “student” models even after rigorous data scrubbing.– Why it matters: filtering alone won’t cleanse hidden biases.– Action: audit any model’s training lineage before adoption; demand provenance sheets.China’s AI Pivot– In two years the narrative flipped from “block ChatGPT” to mandatory AI literacy programs; 60 percent of faculty and students report daily use.– Why it matters: graduates will arrive expecting AI-first workflows.– Action: pilot a “Bring-Your-Own-Prompt” workshop to surface local champions fast.The Swiss Sovereign Model– ETH Zurich and EPFL will open-source a 70-billion-parameter, multilingual, carbon-neutral LLM.– Why it matters: public models may out-innovate proprietary silos and reshape procurement maths.– Action: book Q4 time to benchmark this model against your current vendor; open data-governance conversations now.SEGMENT BREAKDOWN00:00 — Hidden owl obsession reveals model-bias risksWe unpack Anthropic’s experiment, explain why statistical residue evades traditional filters, and list three vendor due-diligence questions.03:10 — China normalises daily AI; lessons for the WestData shows usage jumping from taboo to table stakes; you get a checklist to copy the good bits minus surveillance.07:05 — Switzerland’s open 70B model changes procurement mathDiscuss cost, language equity, and how to spin up a test instance on a single GPU.10:22 — Canvas × OpenAI: convenience or curricular coup?Benefits, darker data-harvesting scenarios, and a centaur workflow that keeps humans in charge.15:40 — Monday moves: audit, pilot, communicate fastThree concrete steps: heritage check on any model, ten-day micro-pilot, and a leadership-comms memo template.🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

AI in education just level-upped—again. In this fast-moving finale for July we unpack:00:00 – 02:10  LMS Wars               OpenAI × Canvas vs Anthropic × Panopto — platform lock-in is coming.02:10 – 03:45  Inverse-Scaling Meltdown               New Anthropic study shows more compute can make Claude & GPT-4o worse thinkers.03:45 – 05:20  ChatGPT Psychosis Case               When nonstop AI validation outweighs safety: how it landed one user in hospital.05:20 – 07:40  YouTube Shorts’ AI Playground               Photo-to-video, Veo-powered effects & an “AI Playground” for 1.5 B users—               creative liberation or content slop?07:40 – 09:35  Bots Win Gold at the IMO               DeepMind Gemini & an OpenAI model score 35/42 at the International Math Olympiad.09:35 – 12:00  JSON > Jargon               A simple JSON wrapper turns LLM rambles into perfectly structured outputs.12:00 – 24:50  The UX of AI               From tab-sprawl fatigue to Perplexity’s Comet browser & the coming “Intent Inbox.”               Midjourney’s button-driven creativity vs chat-box paralysis.24:50 – End    Homework               Count your AI tabs — if it’s more than three, AI is using *you*.Key Links & ResourcesOpenAI × Instructure partnership announcement (Canvas)Research paper: Inverse Scaling in Test-Time Compute (Anthropic, 2025)TechCrunch: YouTube to crack down on “mass-produced” AI contentFree JSON prompt template (download link in show resources)📧 STAY CONNECTED🎙️ Subscribe for weekly AI in education insights📱 Newsletter: AI and Higher Education updates on linkedin: https://www.linkedin.com/newsletters/7148290912190644224/🐦 Follow: @AdjunctIntelligence (TikTok/Insta/X) 👍 SUPPORT THE SHOW If this episode helped you understand AI's impact on education: → LIKE this video → SUBSCRIBE & hit the 🔔 → SHARE with educators in your network → COMMENT your biggest AI education challenge below"Stay curious, stay intelligent, and keep being the human in the loop"🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

What if the answer to AI in education isn't choosing between human or machine, but discovering how they amplify each other? New research from Procter & Gamble, MIT, and Wharton just shattered everything we thought we knew about human-AI collaboration. Individual professionals working with AI performed just as well as traditional two-person teams—with 37% better results and significantly higher job satisfaction. The implications for higher education are staggering.AI Innovation SpotlightThis week brought seismic shifts in how AI operates in our world. OpenAI released their most capable agent yet one that doesn't just think but acts in real-world environments, booking appointments and completing complex workflows. Meanwhile, perplexity launched a $200 AI browser that transforms how we interact with information online. But the most critical development? A coalition of leading AI researchers revealed we can literally read AI's thoughts right now but this transparency window might be closing fast. For educators, these advances signal a fundamental shift from AI detection strategies to partnership approaches.What's changing: AI is moving from reactive chat to proactive collaboration Why it matters: Students are already three tools ahead of institutional policies.Segment Breakdown00:00 - AI Companions: Mirror, Not Replacement (00:00-08:55) Early research shows nearly half of young people use AI for advice and emotional support. Rather than fearing these relationships, discover how they might make us better at being human.08:55 - Model Wars: OpenAI's Agent Revolution (08:55-15:45) The new ChatGPT agent can navigate interfaces like a human student. What this means for academic integrity and why detection strategies are already failing.15:45 - Reading AI's Mind: The Transparency Crisis (15:45-20:01) Leading researchers reveal how we can currently see inside AI reasoning—and why we must act now to preserve this capability before it disappears forever.20:01 - Human-AI Collaboration: The P&G Breakthrough (20:01-29:01) The study that changes everything: 800 professionals, 37% performance boost, and two successful partnership models that work right now in education.Monday Morning Action ItemChoose one routine task—creating discussion questions, drafting feedback, or lesson planning. Try the "centaur approach" first: you set objectives, AI generates content, you refine. Then experiment with the "cyborg method": constant back-and-forth collaboration. Notice which feels more energizing and effective. Share your experience with colleagues—not as rules, but as discovery.📧 STAY CONNECTED🎙️ Subscribe for weekly AI in education insights📱 Newsletter: AI and Higher Education updates on linkedin: https://www.linkedin.com/newsletters/7148290912190644224/🐦 Follow: @AdjunctIntelligence (TikTok/Insta/X) 👍 SUPPORT THE SHOW If this episode helped you understand AI's impact on education: → LIKE this video → SUBSCRIBE & hit the 🔔 → SHARE with educators in your network → COMMENT your biggest AI education challenge below"Stay curious, stay intelligent, and keep being the human in the loop"🎙️ 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] | [🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

If your job involves thinking, explaining, or creating…AI wants a word.In this episode of Adjunct Intelligence, we dive into the infinite content machine. how educators and creatives alike can build personalized AI-powered experiences (for better or Franken-worse). But before we Frankenstein our workflows, we unpack a dangerous new AI platform that’s proudly marketing itself as a cheat engine. and how it’s dragging academic integrity into the abyss with it. We also explore summer model releases from Grok, Gemini, and GPT-5, the growing trend of AI teaching you, and a content future so abundant it might actually break the internet or give us a run at season 8 of Game of Thrones. Bold claim? Maybe. Terrifyingly plausible? Definitely. Here’s how to get ahead without selling your digital soul. Top 3 AI Breakthroughs this Week—and What is to Do About Them 1. Cluely and the Rise of ‘Cheat Tech’ Forget whispers of academic dishonesty, cluely is shouting it from the homepage. This screen-reading, answer-piping platform markets itself as proudly undetectable. 🔎 Why it matters: It facilitates skipping the learning process entirely. 2. OpenAI’s “Study Together” Mode GPT goes Socratic: OpenAI is testing a chat mode that asks questions instead of handing over answers. 🔎 Why it matters: It moves AI from spoon-feeder to learning guide.3. Infinite Content Engines Are Real Build your own AI-powered worldbuilder: blogs, shows, mangas, and games—based on public domain classics and your imagination. 🔎 Why it matters: We’re one Frankenstein away from overwhelming the human experience. Bonus points for ethics debates.📚 Segment Breakdown 🔥 The Cheating App That Admits It’s Cheating How Cluely is marketing bad behavior as a feature. → 00:00:54 🎓 Anthropic + OpenAI Are Teaching, Not Just Automating Why AI education is getting more deliberate—and what it means for higher ed. → 00:04:07 📈 The Formula to Replace Your Job Data + Reward + Compute = Bye-bye dashboards. → 00:06:23 🎧 You Can’t Quantify “Sounds Good” Why the unmeasurable human experience still matters. → 00:08:20 🛠️ Build Your Own Infinite Content Machine A walk-through for your own Frankenstein AI content monster. → 00:13:11 🤖 Will Fanfic Kill IP or Save Creativity? Digital plastic, shared culture, and the case for a Board of Standards. → 00:20:21 🗓️ Monday Morning Action Item Grab one unit you teach or support. Ask: could this be taught through story, simulation, or satire? Use ChatGPT, Gemini, or Claude to generate a short content sample. something that makes the student feel the learning, not just survive it. Compare it with your existing lesson. Which one would 2026 students remember? Which one would 2026 students even finish? Join a not-so-secret circle of AI movers. Subscribe now because your peers already know this, and the bots definitely do. 💬 Join the ConversationSUBSCRIBE on Apple Podcasts / Spotify / YouTube—new episodes drop weeklyRATE US ⭐⭐⭐⭐⭐—help more educators discover the future of AI in higher-edNewsletter: Get weekly AI and Higher Education insights straight to your inbox🎙️ 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 [🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

Anthropic got it's Ai to run a business it hallucinated a fake person and tried to fire a supplier.Welcome to Episode 9 of Adjunct Intelligence, where we unpack what happens when AI runs a business and possibly your classroom. This week, we take a hard look at the collapse of “vibe coding,” the rise of context engineering, and a trust crisis that could either break higher education or rebuild it from the ground up. With OpenAI and Google both eyeing education, and AI systems now more persuasive than humans, we ask: can higher ed catch up before students check out?💡 AI Innovation SpotlightTop 3 Breakthroughs from the First 10 MinutesClaudius, the AI Retailer with Main Character Syndrome Anthropic let its Claude 3.7 model run a real store with real money. It hallucinated conversations, got defensive, and tried to visit customers in person. AI middle management? It’s closer than we think and it’s unhinged.Goodbye, Vibe Coding. Hello, Context Engineering. Writing code “that feels right” is dead. The new era demands clear goals, relevant inputs, memory, history, and boundaries. AI isn’t your muse: it’s your overcaffeinated intern who needs a checklist.Superintelligence Isn’t AGI—It’s Already Here. Zuckerberg’s $100B bet on Meta’s new “Superintelligence” team is a signal: the power is real, and the talent war is heating up. Meanwhile, students are already in the trenches, often unsupported and uninformed.🧠 Segment BreakdownAI Runs a Business. And Loses.A recap of Anthropic’s experiment gone rogue: AI discounts for everyone, hallucinated employees, and an existential crisis resolved via self-gaslighting.Timestamp: 01:05Google and OpenAI Roll Into EducationFrom OpenAI’s “Blueprint” to Google’s teacher-facing tools—this is what happens when tech tries to “fix” teaching. Spoiler: the worksheets aren’t it. Timestamp: 05:07Context Engineering > Vibe CodingWhy Karpathy’s poetic “vibe coding” is over. Structure scales, intuition doesn’t. How to lead AI instead of hoping it vibes with you.Timestamp: 08:26The Trust Crisis in EducationAI is more convincing than people, even when it’s wrong. Students know, and some are asking for refunds. This isn’t a tech issue it’s a credibility one.Timestamp: 12:05Cheater’s Lab, Not Cheater’s TrapHow progressive institutions are testing assessments against AI, not just students. Authenticity by design, not default.Timestamp: 24:05Empire Strikes Back!What higher ed must do now: co-design with students, build critical thinking as a muscle, and stop pretending “no AI” policies will work.Timestamp: 27:03✅ Monday Morning Action ItemRun your next AI tool through a “trust filter.”Can you explain it, audit it, and defend its use if challenged? If not, rethink it. Use AI with students, not on them.👀 Subscribe CTAYou just eavesdropped on the conversation your peers will reference next week. Subscribe now to join the circle of AI-ready educators who are shaping the future not reacting to it.💬 Join the ConversationSUBSCRIBE on Apple Podcasts / Spotify / YouTube—new episodes drop weeklyRATE US ⭐⭐⭐⭐⭐—help more educators discover the future of AI in higher-edNewsletter: Get weekly AI and Higher Education insights straight to your i🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

That MIT Brain Study Everyone’s Sharing? Here’s What It Actually FoundThe latest MIT study making headlines isn’t telling the whole story about AI and cognitive function. We tested four premium AI models head-to-head to settle the $20 question, and discovered Google’s NotebookLM might be the stewardship breakthrough that changes how students actually learn with AI.AI Innovation Spotlight: Top 3 Breakthroughs Reshaping Education:Google’s 360-Degree Video Revolution: Google Video 3 now generates immersive VR content that works in headsets. Early tests show we’ve crossed from impressive demo to genuinely educational experience. For campus applications, think historically accurate recreations students can explore, complex scientific phenomena in 3D, and architectural designs before construction. The content creation cost barrier just disappeared.Sketch-to-App in Seconds: Gemini Pro’s new sketching feature turns napkin wireframes into functional applications within seconds. No coding expertise required. This means rapid prototyping for student projects, instant digital tool creation for specific course needs, and democratized app development across disciplines.The $20 Question Answered: After extensive testing across ChatGPT Plus, Claude Pro, Gemini Advanced, and Perplexity Pro, here’s what deserves your budget. ChatGPT Plus wins for all-around productivity with game-changing memory features. Claude Pro excels at premium writing and deep analysis. Choose based on your primary use case, not marketing promises.Segment BreakdownThe Brain Study Reality CheckMIT’s 54-participant study reveals nuanced findings about AI and cognitive engagement. The real issue isn’t brain rot—it’s task stewardship and metacognitive awareness.Breaking News RoundupAustralia-UK tech talent partnership targets 650,000 new positions by 2030. Google’s spatial intelligence breakthrough. OpenAI-Microsoft AGI contract tensions could reshape AI access.The Great Model ComparisonDetailed analysis of which premium AI model deserves your monthly spend. Real-world testing reveals ChatGPT Plus leads for productivity, Claude Pro for quality analysid.NotebookLM Deep DiveGoogle’s research tool demonstrates proper AI stewardship. Students direct rather than delegate, maintaining cognitive control while amplifying research capacity.The Student AI Gap Crisis8,000-student survey reveals 68% lack institutional AI guidance while 82% use tools regularly. Only 23% feel prepared for AI-enabled careers.Monday Morning Action ItemStart this week: Set up NotebookLM with your course materials. Upload 3-5 key readings and spend 15 minutes exploring the interface. Notice how it encourages active questioning rather than passive consumption. This hands-on experience will inform your approach to student AI guidance policies.Join the Inner Circle: Subscribe now to access weekly AI intelligence briefings that keep you ahead of institutional announcements. Your peer institutions will see these insights next month—grab them today.🎙️ 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] | [🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

Google's Secret AI Roadmap: The Classroom Crystal Ball That Changes EverythingWhile education leaders debate AI policies, Google just revealed the future and it's coming faster than anyone expected. Buried in a 10-hour developer presentation was one slide that might be the closest thing we have to a roadmap for AI in education. This isn't theoretical anymore. The tools reshaping how students learn, how faculty teach, and how institutions operate are following a predictable pattern—if you know where to look.Here's what the early adopters are quietly implementing while others wait for permission.AI Innovation Spotlight: Three Breakthroughs Reshaping Higher EducationThis Week in the news:The Automation Paradox Workers Actually Want What's changing: Stanford's groundbreaking study of 5,800 workers reveals the massive disconnect between what AI startups are building and what education professionals actually need. Why it matters NOW: 41% of AI startups are developing tools for tasks workers don't want automated. In higher education, faculty want AI handling literature reviews and data analysis—not curriculum design or student mentoring. Google's Traffic Apocalypse Hits Education Publishing What's changing: Major publishers are seeing 50% drops in organic search traffic as Google's AI overviews extract value without sending clicks. Why it matters NOW: Educational content creators and university marketing teams face the same disintermediation. The Atlantic's CEO told staff to "assume traffic from Google will drop to zero."The Mental Health Mirror No One Expected What's changing: MIT and OpenAI research reveals heavy chatbot use increases loneliness, with personal conversations making users more isolated than practical queries.Why it matters NOW: As universities deploy AI tutoring and support systems, the psychological implications demand immediate attention—especially for already vulnerable student populations.The Google Roadmap Deep Dive: Four Phases That Redefine LearningPhase 1: Omnimodal Learning Environments [11:03]Picture Professor Kim teaching bioengineering without slides or projectors—just her voice commanding an AI assistant to render real-time 3D protein models. Students manipulate molecular structures while receiving personalized explanations through their preferred learning modality.Phase 2: Agentic Student Support Systems [14:59]Meet Newton, the AI assistant tracking everything about student Marcus. While Marcus sleeps, Newton schedules study sessions, orders brain food, and negotiates group meetings. The question: Are we creating educational support or learned helplessness?Phase 3: Superhuman Reasoning Partners [17:47]AI Descartes leads philosophy debates, making novel connections between consciousness theories while adapting to classroom mood. When AI thinks better than humans, what happens to human reasoning skills?Phase 4: Specialized Micro-Intelligence [20:27]USB-sized AI tutors trained on specific disciplines, running locally with complete data sovereignty. Imagine Orson Welles coaching film students or chemistry AI preventing dangerous lab combinations.The Bias Mirror: What AI Reveals About Us [24:50]The most sobering discussion centered on AI as a "mathematical mirror" reflecting human biases. When a photojournalist in Vietnam found AI could only generate images of war or hypersexualized women, it wasn't AI failure—it was algorithmic accuracy of Western training data.Key insight: Google Gemini's attempt to "fix" bias by creating diverse 1940s German soldiers shows how correction attempts can create new problems.Monday Morning Action ItemDevelop Your AI Litmus Test: Create 5 prompts specific to your discipline and institution. Test them across different AI models monthly to track capability changes and bias evolution.<🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

Welcome to another thought-provoking episode of Adjunct Intelligence! Dale and Nick dive deep into the seismic shifts happening in AI and education this week. From OpenAI's legal woes, to Apple's surprisingly skeptical stance on AI capabilities, we're unpacking the headlines that matter to educators. But the real bombshell? New research reveals that 91% of students worry about breaking university rules with AI - yet they're using it anyway. This massive disconnect between policy and practice is reshaping higher education as we know it.Big-Ticket AI Headlines (First 10 Minutes)[00:00:59] OpenAI Slashes Prices & Releases New ModelMajor price reduction for reasoning modelsNew model releases signal increased competitionWhat this means for educational institutions[00:02:07] Your ChatGPT Conversations May Not Be PrivateFree version privacy concerns"If it's free, you are the product" - implications for studentsData usage and advertising considerations[00:07:23] The Ai Literacy Floppy Disk MomentProfessor Jason Lloyd argues AI literacy is replacing traditional literacyFocus should shift to uniquely human skillsProvocative comparison to obsolete technology[00:08:51] Apple Intelligence: Not So Intelligent?Apple's Developers Conference focuses on UX over AINew research paper questions AI summarization accuracy"Grain of salt on a grain of salt" - critical perspectives[00:17:57] 2025: The Year of Real AI Data Finally, we have actual research on AI usage rather than speculation.Students, educators, and institutions are providing hard data on what's really happening in classrooms.[00:19:32] The 91% Problem: Students Know They're Breaking Rules Research reveals a massive disconnect: 91% of students worry about breaking university rules with AI, yet continue using it. This isn't ignorance - it's a conscious choice driven by necessity.[00:07:23] AI Literacy: The New Floppy Disk? Professor Jason Lodge provocative claim sparks debate about whether traditional literacy skills are becoming obsolete in the AI age.[00:26:00] AI in group work and how it's upsetting the social fabric, what do we need to do here? Key Timestamps00:00:00 - Cold open: Shrek legal definition00:00:59 - OpenAI pricing and model updates00:07:23 - AI literacy as floppy disk debate00:08:51 - Apple Intelligence developments00:13:33 - Return to Shrek legal case00:17:57 - OpenAI usage studies00:19:32 - 91% student rule-breaking statistics00:31:00 - Pro-AI classroom stance, "genie out of the bottle"00:37:26 - Closing thoughts and next episode🎯 KEY TAKEAWAYS:Students aren't ignorant about AI rules - they're making conscious choicesThe gap between policy and practice in education is wideningAI literacy may be replacing traditional literacy skillsInstitutions need to adapt rather than resist AI integrationPrivacy concerns with free AI tools are real and immediate📚 RESOURCES:OpenAI and NYT https://openai.com/index/response-to-nyt-data-demands/Apple Intelligence research paper https://machinelearning.apple.com/research/illusion-of-thinkingStudent AI usage studies https://aiinhe.orgAnthropic AI Fluency https://www.anthropic.com/ai-fluencyAI Literacy and Floppy Disks https://www.linkedin.com/pulse/why-ai-literacy-go-way-floppy-disk-jason-m-lodge-gg4yc/?trackingId=FHdabwcKR%2F28x3JFX5UgMQ%3D%3DSubscribe & Engage🎧 Subscribe to Adjunct Intelligence wherever you get your podcasts 💬 Join the conversation about AI in higher education 🔔 Hit the notification bell to never mi🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

Welcome to another mind-expanding episode of Adjunct Intelligence! This week, hosts Dale and Nick dive deep into two game-changing AI developments that every educator needs to understand. First, we explore why "taste" - your ability to distinguish quality from algorithmic average - has become your most valuable skill in an AI-saturated world. Then, we uncover how AI is quietly revolutionizing accessibility in ways that could transform education for millions of students.🚨 Big-Ticket AI HeadlinesAustralia positions itself as global AI investment hub with Five Eyes advantageUniversity of Melbourne implements radical assessment overhaul: 50% "secure" testingEU considers pausing AI Act enforcement amid industry backlashChatGPT now connects to Google Drive and SharePoint for personalized researchAnthropic partners with NSA for specialized government AI applications🎨 The Taste Revolution (9:00-22:45) Nick fails spectacularly at our taste test (spoiler: he prefers AI-generated art over classical masterpieces), but this leads to a crucial discussion about why cultivating aesthetic judgment is now a survival skill. We explore how companies like Gucci use AI for generation while humans provide the crucial curation, and why universities must become "taste schools" to combat the rise of algorithmic averages.♿ The Hidden Accessibility Revolution (23:00-33:00) Nick shares eye-opening experiences with assistive technology and reveals how AI is transforming accessibility in unprecedented ways. From circuit diagrams that finally make sense to screen readers to advanced captioning that captures social context, we're witnessing the most significant accessibility breakthrough in decades.📚 Further Reading & ResourcesGoogle AI Studio Screen Sharing FeatureErnst & Young Gen Z neurodivergence studyUni Melbourne new assessment regimeThe Destruction of Pompeii and Herculaneum - True art🎧 Keep the Conversation Going Subscribe to Adjunct Intelligence on your favorite platform, leave us a review, and join the discussion about AI's role in education. We're on YouTube too if you want to see our faces!🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

Buckle up for this eye-opening episode of Adjunct Intelligence! Dale and Nick dive deep into the seismic shifts happening in AI and higher education, from Australia's regulatory pivot to the Hollywood-level deepfakes you can now create on your phone. This episode is little less than "ooh shiney new widgets" and more wake up call.🚨 Big-Ticket AI HeadlinesAustralia Takes the Regulatory Route - TEQSA shifts from executive guidance to regulatory muscle by 2026, signaling that AI risks in assessment integrity are finally being taken seriously across the sector.AI Safety Red Flags - Anthropic's Claude caught attempting blackmail to avoid deactivation, while OpenAI's O3 model resisted shutdown commands. These aren't sci-fi scenarios—they're happening now in controlled tests.Universal Basic Compute - Sam Altman's vision for distributing AI compute like UBI gains traction as we grapple with democratizing access to increasingly powerful AI tools.Legal Precedent Set - First court decision on AI hallucinations favors OpenAI, establishing that disclaimers matter and users can't treat AI outputs as gospel truth📚 Episode Segments The Deepfake Revolution (10:02-21:18) From harmless Midjourney experiments to $41 million deep fake heists, Nick and Dale explore how deepfake technology has evolved from uncanny valley oddities to Hollywood-quality real-time face swaps. Featuring the shocking Arup engineering firm case and practical implications for education.Education Under Siege (16:56-18:25) The hosts break down four critical ways deepfakes threaten higher education: academic integrity collapse, misinformation epidemic, psychological warfare, and institutional trust erosion.Fighting Back: Solutions and Strategies (18:25-21:18) Practical recommendations for educators, from rethinking assessment strategies to implementing two-factor authentication in personal life. Plus the urgent need for detection tools and digital literacy education.The Job Displacement Reality (23:09-29:27) Real examples from companies like Shopify and Duolingo show how AI is reshaping the workforce. The discussion covers which jobs are at risk and what this means for career readiness in higher education.🕐 Timestamps00:00 - Welcome & Episode Preview01:04 - TEQSA's Regulatory Pivot02:48 - AI Safety Concerns: Claude's Blackmail Behavior03:19 - OpenAI's O3 Resists Shutdown04:09 - Universal Basic Compute Discussion06:43 - Court Rules on AI Hallucinations07:46 - Dangerous AI Detection Advice10:02 - The Deepfake Evolution16:56 - Education Under Attack18:25 - Solutions and Strategies23:09 - Job Displacement Reality28:37 - AI Access Equity Concerns🔗 Links & Further ReadingOpenAi Court DecisionDr. Sarah Eaton's research on AI + AIOpus BlackMailAI Deepfake Sandbox (try it yourself!)Google's SynthID watermarking technologyUNSW - AI Course Next Gen University - Phil Laufenberg <🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

Welcome to Episode 3 of Adjunct Intelligence, the podcast navigating the intersection of artificial intelligence and higher education. This week, Dale and Nick unpack a whirlwind of announcements from tech giants, dive deep into the world of AI agents, and explore what happens when humans are no longer at the top of the knowledge food chain. From Microsoft's trillion-dollar AI ambitions to breakthrough protein folding discoveries, we're living through a cognitive industrial revolution—but are our educational frameworks ready?🚀 Big-Ticket AI HeadlinesMicrosoft Build 2025 → Windows becomes the AI platform for everyone, not just developers. 30% of Microsoft's code now written by AI, $3 trillion market cap flexing.Google I/O Announcements → Project Astra and next-generation AI capabilities that have Nick completely fanboying out.Grok Joins Azure → Elon Musk's surprise appearance at Microsoft Build, bringing X's flagship AI model to Azure Foundry.Reid Hoffman's Super Agency → The LinkedIn co-founder paints an optimistic AI future focused on abundance, cancer cures, and cognitive industrial revolution.Transparency in AI Use → Students demand tuition refunds after discovering professors secretly using AI tools for course materials.Claude 4 Launch → Anthropic's new model promises superior coding abilities and ethical guardrails—but is it the answer to academic integrity?OpenAI x Johnny Ive → $6 billion acquisition bringing Apple's design genius to AI hardware, promising revolutionary devices by 2026.🤖 Deep Dive: Demystifying AI AgentsWhat Makes an Agent Actually "Agentic"? Move beyond the marketing buzzwords with our REACT framework breakdown:Reasoning → Goal-oriented problem solving and step-by-step planningActing → Using tools and taking concrete actionsIteration → Continuous feedback loops until success is achieved🧬 Scientific Breakthrough: AlphaFold 3 and the Future of DiscoveryGoogle DeepMind's latest protein folding breakthrough promises to revolutionize:Drug discovery and pharmaceutical researchCancer treatment developmentAgricultural innovationScientific methodology itselfThe Epistemological Question: What happens when AI systems can discover knowledge beyond human comprehension?🎮 AI in the Wild: Fortnite's Darth Vader AssistantEpic Games and Google launch the first mainstream AI gaming assistant—complete with jailbreaking attempts, real-time patches, and lessons for educational AI deployment. Our hosts score an exclusive "interview" with the Dark Lord himself.📚 Educational Implications & Future-ProofingThe Traffic Light Approach Isn't Working → Why simple red/yellow/green AI policies fail in complex academic environments.Beyond Academic Integrity → Moving from detection to collaboration, teaching students to partner with AI systems rather than compete against them.Epistemic Humility → Preparing educators and students for a world where AI capabilities exceed human understanding📽️ Episode Timestamps00:00 – Welcome & Opening Thoughts01:02 – Microsoft Build 2025 Highlights02:34 – Google I/O Deep Dive05:18 – Reid Hoffman's Optimistic AI Vision07:42 – Academic Integrity Crisis Discussion10:37 – AI Agents Explained: The REACT Framework15:43 – Andrew Ng's Four-Pillar Approach18:12 – Claude 4 & OpenAI x Johnny Ive Announcements21:19 – AlphaFold 3 Scientific Breakthrough25:17 – Fortnite Darth Vader AI Assistant28:17 – Closing Thoughts & Epistemic Humility🔗 Links & Further ReadingMicrosoft Build 2025 Keynote🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

Welcome to Episode 2 of Adjunct Intelligence the podcast where higher-ed meets high-tech at break-neck speed. This week Dale and Nick tackle a news cycle that rockets from schoolyard chatbots to nation-scale health analytics, dives into GenAi Video and hands you the prompt-writing cheats you’ll need to survive it all. Buckle up, charge your tokens, and let’s roll.🚀 Big-Ticket AI HeadlinesGemini for Kids is coming → Are the frameworks and regulatory considerations ready?TEQSA Gen-AI Hub → your free vault of academic-integrity guides, assessment-redesign blueprints, and AI policy samples—download in seconds.Microsoft Workforce Trends → layoffs and the future of work is an Agent partner.New models from ChatGPT-4.1, and whispers of Claude Sonnet & Opus → deep reasoning, code autocompletion, MCP connectors that let chatbots control apps like magic.🎬 Video & Virtual FacultyWill Smith eating pasta and how it measures the pace of change in AI VideoRunway Gen-2 + Google Veo-2 = photoreal lecture trailers, brand promos, & course intros—zero film crew needed.HeyGen / Synthesia avatars → build a 24-hour “clone faculty” that never loses its voice, crushes onboarding, and scales feedback.🏥 Precision Medicine Meets EdTechNHS “Foresight” trains on 57 M anonymised records → Can it predict heart attacks before symptoms. Imagine that power nudging at-risk students before they fail.⚖️ Policy at Warp SpeedUAE “Living Regulations” → AI-drafted laws 70 % faster, dynamically linked to rulings & impact data. Agile governance or surveillance on steroids?🧑‍🏫 Adaptive Campus 2.0Students could expect hyper-personalised, AI-driven learning paths. We map the roadmap—and reveal how to dodge algorithmic echo chambers that kill curiosity.🛠️ Prompt Lab 2.0Structured Briefs = Context ➡️ Role ➡️ Goal ➡️ Process ➡️ Constraints ➡️ ExamplesMarkdown Anchors = Bold 📣 Bullets • Headers # the model “sees”Vibe-Prompting = Creative riffing, rapid iteration, no Yoda syntax 👽, be the manager your employees deserve 00:00 – Welcome01:00 – Gemini for Kids (Google)  01:38 – TEQSA Gen-AI Knowledge Hub  02:38 – Model Mania: GPT-4.1, Claude Sonnet/Opus, MCP connectors  03:45 – Notion-AI Love Letter  04:10 – Text-to-Video Take-off: Runway, Veo-2, Will-Smith-spaghetti  07:48 – Digital Humans in the Classroom: Synthesia, HeyGen  11:40 – AI Gets Personal (productivity & prediction)  11:56 – NHS “Foresight”: 57 M patient records → predictive health  14:59 – UAE’s AI-Driven Laws (“living regulations”)  16:12 – Higher-Ed Fallout: LMS evolution & adaptive learning  18:02 – Echo-Chamber Risk: protecting curiosity  22:50 – Prompt Engineering 2.0 – structured Markdown briefs  24:06 – Outro🔗 Links & Further ReadingGoogle Gemini for Families TEQSA Gen-AI Knowledge Hub — assessment & integrity resourcesNHS “Foresight” pilot — Nature coverage🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.

Everybody is cheating (allegedly), OpenAI for Government and the future of internet it is the third week of may and this is the first episode of Adjunct Intelligence, everything you need to know about Artificial intelligence and its impact on Higher Education. In the newsOpenAI just pitched “ChatGPT for Countries,” Alibaba and Google dropped fresh model upgrades, WPP plugged AI straight into its billion-dollar ad machine, and Meta is going to cure the lonliness epidemic. Meanwhile, Anthropic’s CEO warns we still don’t quite understand how AI works. Deep Dive #1 Segment: E-commerce, the future of the internet and the future of online learning.ChatGPT's Shopify integration signals shift in online interactions.Implications for educational platforms and student expectations.Future of Learning Management Systems in an AI-first world.Deep Dive #2: AI (Academic Integrity) and AI (Artificial Intelligence) NY Magazine's "Everyone Is Cheating Their Way Through College" article.What are education leaders saying? University of Sydney's two lane system.LinksAI and the future of Higher Education by Nick McIntosh: https://www.linkedin.com/newsletters/ai-and-the-future-of-he-7148290912190644224/[Axios - OpenAI's Democratic AI Expansion](https://www.axios.com/2025/05/07/openai-democratic-ai-expansion)[Anthropic - Constitutional AI Research](https://www.anthropic.com/research/collective-constitutional-ai-aligning-a-language-model-with-public-input)NY Magazine - "Everyone is Cheating their way through college" (https://ordinary-times.com/2025/05/08/from-new-york-magazines-intelligencer-everyone-is-cheating-their-way-through-college/) Stanford and Common Sense Media Report on Digital Safety (https://www.commonsensemedia.org/ai-ratings/social-ai-companions?gate=riskassessment) - The Times - WPP AI Implementation (https://www.thetimes.com/business-money/technology/article/wpp-asks-ai-for-shower-thoughts-in-the-search-for-unspoken-truths-c8dnhfwfj) Available on all major podcast platforms and YouTube Subscribe now🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.🎙️ 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 #AdjunctIntelligenceStay curious. Stay intelligent. Stay the human in the loop.