Jim VandeHei’s note to his kids: Blunt AI talk — from axios.com by CEO Jim VandeHei Axios CEO Jim VandeHei wrote this note to his wife, Autumn, and their three kids. She suggested sharing it more broadly since so many families are wrestling with how to think and talk about AI. So here it is …
Dear Family: I want to put to words what I’m hearing, seeing, thinking and writing about AI.
Simply put, I’m now certain it will upend your work and life in ways more profound than the internet or possibly electricity. This will hit in months, not years.
The changes will be fast, wide, radical, disorienting and scary. No one will avoid its reach.
I’m not trying to frighten you. And I know your opinions range from wonderment to worry. That’s natural and OK. Our species isn’t wired for change of this speed or scale.
My conversations with the CEOs and builders of these LLMs, as well as my own deep experimentation with AI, have shaken and stirred me in ways I never imagined.
All of you must figure out how to master AI for any specific job or internship you hold or take. You’d be jeopardizing your future careers by not figuring out how to use AI to amplify and improve your work. You’d be wise to replace social media scrolling with LLM testing.
What if the biggest change in education isn’t a new app… but the end of the university monopoly on credibility?
Jensen Huang has framed AI as a platform shift—an industrial revolution that turns intelligence into infrastructure. And when intelligence becomes cheap, personal, and always available, education stops being a place you go… and becomes a system that follows you. The question isn’t whether universities will disappear. The question is whether the old model—high cost, slow updates, one-size-fits-all—can survive a world where every student can have a private tutor, a lab partner, and a curriculum designer on demand.
This video explores what AI has in store for education—and why traditional universities may need to reinvent themselves fast.
In this video you’ll discover:
How AI tutors could deliver personalized learning at scale
Why credentials may shift from “degrees” to proof-of-skill portfolios
What happens when the “middle” of studying becomes automated
How universities could evolve: research hubs, networks, and high-trust credentialing
The risks: cheating, dependency, bias, and widening inequality
The 3 skills that become priceless when information is everywhere: judgment, curiosity, and responsibility
From DSC:
There appears to be another, similar video, but with a different date and length of the video. So I’m including this other recording as well here:
What if universities don’t “disappear”… but lose their monopoly on learning, credentials, and opportunity?
AI is turning education into something radically different: personal, instant, adaptive, and always available. When every student can have a 24/7 tutor, a writing coach, a coding partner, and a study plan designed specifically for them, the old model—one professor, one curriculum, one pace for everyone—starts to look outdated. And the biggest disruption isn’t the classroom. It’s the credential. Because in an AI world, proof of skill can become more valuable than a piece of paper.
This video explores the end of universities as we know them: what AI is bringing, what will break, what will survive, and what replaces the traditional path.
In this video you’ll discover:
Why AI tutoring could outperform one-size-fits-all lectures
How “degrees” may shift into skill proof: portfolios, projects, and verified competency
What happens when the “middle” of studying becomes automated
How universities may evolve: research hubs, networks, high-trust credentialing
The dark side: cheating, dependency, inequality, and biased evaluation
The new advantage: judgment, creativity, and responsibility in a world of instant answers
I’m a firm believer in conferences. This isn’t just because I have chaired the Learning Technologies Conference in London since 2000. It’s because they are invaluable in sustaining our community. So many in Learning and Development work alone or in small teams, that building and maintaining personal contacts is crucial.For a number of years, I have kept a personal list of the Learning and Development conferences running internationally. This year, I thought it would be helpful to share it.
Penelope Adams Moon suggested that instead [of] framing a workshop around “How can we integrate AI into the work of teaching?” we should ask “Given what we know about learning, how might AI be useful?” I love that reframing, and I think it connects to the students’ requests for more AI knowhow. Students have a lot of options for learning: working with their instructor, collaborating with peers, surfing YouTube for explainer videos, university-provided social annotation platforms, and, yes, using AI as a kind of tutor. I think our job (collectively) isn’t just to teach students how to use AI (as they’re requesting) but also to help them figure out when and how AI is helpful for their learning. That’s highly dependent on the student and the learning task! I wrote about this kind of metacognition on my blog.
In the same way, when I approach any kind of educational technology, I’m looking for tools that can be responsive to my pedagogical aims. The pedagogy should drive the technology use, not the other way around.
There were 15% fewer entry-level and internship job postings in 2025 than the year before, according to Handshake, a job-search platform popular with college students; meanwhile, applications per posting rose 26%.
How much AI is to blame for the fragile entry-level job market is unclear. Several research studies show AI is hitting young college-educated workers disproportionately, but broader economic forces are part of the story, too.
As Christine Y. Cruzvergara, Handshake’s chief education strategy officer, told me, AI isn’t “taking” jobs so much as employers are “choosing” to replace parts of jobs with automation rather than redesign roles around workers. “They’re replacing people instead of enabling their workforce,” she said.
Today’s graduates are stuck in an in-between moment. Many started college before AI mattered and graduated into a labor market reshaped almost overnight, where entry-level roles are disappearing faster than students can adapt.
The real story isn’t what AI can produce — it’s how it changes the decisions we make at every stage of instructional design.
After working with thousands of instructional designers on my bootcamp, I’ve learned something counterintuitive: the best teams aren’t the ones with the fanciest AI tools — they’re the ones who know when to use which mode—and when to use none at all.
Once you recognise that, you start to see instructional design differently — not as a linear process, but as a series of decision loops where AI plays distinct roles.
In this post, I show you the 3 modes of AI that actually matter in instructional design — and map them across every phase of ADDIE so you know exactly when to let AI run, and when to slow down and think.
In higher education, developing strong multiple-choice questions can be a time-intensive part of the course design process. Developing such items requires subject-matter expertise and assessment literacy, and for faculty and designers who are creating and producing online courses, it can be difficult to find the capacity to craft quality multiple-choice questions.
At the University of Michigan Center for Academic Innovation, learning experience designers are using generative artificial intelligence to streamline the multiple-choice question development process and help ameliorate this issue. In this article, I summarize one of our projects that explored effective prompting strategies to develop multiple-choice questions with ChatGPT for our open course portfolio. We examined how structured prompting can improve the quality of AI-generated assessments, producing relevant comprehension and recall items and options that include plausible distractors.
Achieving this goal enables us to develop several ungraded practice opportunities, preparing learners for their graded assessments while also freeing up more time for course instructors and designers.
Happy new year! One of the biggest L&D stories of 2025 was the rise to fame among L&D teams of AI video generator tools. As we head into 2026, platforms like Colossyan, Synthesia, HeyGen, and NotebookLM’s video creation feature are firmly embedded in most L&D tech stacks. These tools promise rapid production and multi-language output at significantly reduced costs —and they deliver on a lot of that.
But something has been playing on my mind: we rarely evaluate these tools on what matters most for learning design—whether they enable us to build instructional content that actually enables learning.
So, I spent some time over the holiday digging into this question: do the AI video tools we use most in L&D create content that supports substantive learning?
To answer it, I took two decades of learning science research and translated it into a scoring rubric. Then I scored the four most popular AI video generation platforms among L&D professionals against the rubric. .
For an AI-based tool or two — as they regard higher ed — see:
YouTube to NotebookLM: Import a Whole Playlist or Channel in One Click YouTube to NotebookLM is a remarkably useful new Chrome extension that lets you bulk-add any YouTube playlists, channels, or search results into NotebookLM. for AI-powered analysis.
… What to try
Find or create YouTube playlists on topics of interest. Then use this extension to ingest those playlists into NotebookLM. The videos are automatically indexed, and within minutes you can create reports, slides, and infographics to enhance your learning.
Summarize a playlist or channel with an audio or video overview. Or create quizzes, flash cards, data tables, or mind maps to explore a batch of YouTube videos. Or have a chat in NotebookLM with your favorite video channel. Check my recent post for some YouTube channels to try.
At CES 2026, Everything Is AI. What Matters Is How You Use It — from wired.com by Boone Ashworth Integrated chatbots and built-in machine intelligence are no longer standout features in consumer tech. If companies want to win in the AI era, they’ve got to hone the user experience.
Beyond Wearables
Right now, AI is on your face and arms—smart glasses and smart watches—but this year will see it proliferate further into products like earbuds, headphones, and smart clothing.
Health tech will see an influx of AI features too, as companies aim to use AI to monitor biometric data from wearables like rings and wristbands. Heath sensors will also continue to show up in newer places like toilets, bath mats, and brassieres.
The smart home will continue to be bolstered by machine intelligence, with more products that can listen, see, and understand what’s happening in your living space. Familiar candidates for AI-powered upgrades like smart vacuums and security cameras will be joined by surprising AI bedfellows like refrigerators and garage door openers.
After a year of bot battles, one thing stands out: There is no single best AI. The smartest way to use chatbots today is to pick different tools for different jobs — and not assume one bot can do it all.
Some enterprise platforms now support cross-agent communication and integration with ecosystems maintained by companies like Microsoft, NVIDIA, Google, and Oracle. These cross-platform data fabrics break down silos and turn isolated AI pilots into enterprise-wide services. The result is an IT backbone that not only automates but also collaborates for continuous learning, diagnostics, and system optimization in real time.
It’s difficult to think of any single company that had a bigger impact on Wall Street and the AI trade in 2025 than Nvidia (NVDA).
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Nvidia’s revenue soared in 2025, bringing in $187.1 billion, and its market capitalization continued to climb, briefly eclipsing the $5 trillion mark before settling back in the $4 trillion range.
There were plenty of major highs and deep lows throughout the year, but these 15 were among the biggest moments of Nvidia’s 2025.
How Your Learners *Actually* Learn with AI— from drphilippahardman.substack.com by Dr. Philippa Hardman What 37.5 million AI chats show us about how learners use AI at the end of 2025 — and what this means for how we design & deliver learning experiences in 2026
Last week, Microsoft released a similar analysis of a whopping 37.5 millionCopilot conversations. These conversation took place on the platform from January to September 2025, providing us with a window into if and how AI use in general — and AI use among learners specifically – has evolved in 2025.
Microsoft’s mass behavioural data gives us a detailed, global glimpse into what learners are actually doing across devices, times of day and contexts. The picture that emerges is pretty clear and largely consistent with what OpenAI’s told us back in the summer:
AI isn’t functioning primarily as an “answers machine”: the majority of us use AI as a tool to personalise and differentiate generic learning experiences and – ultimately – to augment human learning.
Let’s dive in!
Learners don’t “decide” to use AI anymore. They assume it’s there, like search, like spellcheck, like calculators. The question has shifted from “should I use this?” to “how do I use this effectively?”
So where do you start? There are many agentic tools and platforms for AI tasks on the market, and the most effective approach is to focus on practical, high-impact workflows. So here, I’ll look at some of the most compelling use cases, as well as provide an overview of the tools that can help you quickly deliver tangible wins.
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Some of the strongest opportunities in HR include:
Workforce management, administering job satisfaction surveys, monitoring and tracking performance targets, scheduling interventions, and managing staff benefits, medical leave, and holiday entitlement.
Recruitment screening, automatically generating and posting job descriptions, filtering candidates, ranking applicants against defined criteria, identifying the strongest matches, and scheduling interviews.
Employee onboarding, issuing new hires with contracts and paperwork, guiding them to onboarding and training resources, tracking compliance and completion rates, answering routine enquiries, and escalating complex cases to human HR specialists.
Training and development, identifying skills gaps, providing self-service access to upskilling and reskilling opportunities, creating personalized learning pathways aligned with roles and career goals, and tracking progress toward completion.
Compliance As Part Of The Everyday Automation Fabric
According to the Thomson Reuters Future Of Professionalsreport, most experts already expect AI to transform their work within five years, with many viewing it as a positive force. The challenge now is clear: legal and compliance leaders must understand the tools reshaping their field and prepare their teams for a very different way of working in 2026.
Addendum on 12/17/25:
Top 10 legal tech episodes in 2025 — from lawyersweekly.com.au With AI and legal technology at the forefront of the profession’s mind now more than ever, The Lawyers Weekly Show and our special series LawTech Talks delivered in-depth explorations of these game-changing topics. Now, we’re highlighting the 10 most downloaded episodes that sparked conversation across the legal tech community.
