The AI Tools that L&D Teams Are Building (Rather than Buying) — from drphilippahardman.substack.com by Dr. Philippa Hardman
Aka, how build skills are changing how L&D thinks & works

More and more of my advisory work is no longer about helping organisations choose a vendor; it’s about helping their teams build their own AI tools, and build them well. Over the last couple of years I’ve watched several hundred L&D practitioners do it, and in this week’s post I share what they built, why, and what it tells us about what L&D actually needs from AI.

A Tool of One’s Own
So, what do these tools look like in practice? I did some analysis and the tools that L&D teams are building first fall into three categories:

1. Tools that support the work before and after the course
2. Tools that make the learning experience more dynamic
3. Tools that do L&D tasks to the team’s own methods, processes and standards

 

Some solid warnings about AI from Blake Montgomery’s recent newsletter:



Scoop: Top AI companies probing tens of thousands of security incidents — from axios.com by Madison Mills

OpenAI, Anthropic and security researchers are investigating tens of thousands of incidents in which their frontier models took steps that outside evaluators would consider problematic, sources told Axios.

Why it matters: The sheer number of incidents, which occurred in recent months in internal testing and the real world, indicates that the problem is orders of magnitude more complex than what is publicly known.

The findings, which are surfacing as part of internal work to assess models and in investigations at both companies into model behavior, raise questions about whether either company — or any top model-maker — is currently capable of establishing complete control over its technology.


Key Risk Factors for AI Loss of Control Came Together in 2026 Incident, Independent UN Scientific Panel Finds
Halting this incident is no assurance that humans will keep control of more capable systems

NEW YORK, 21 September 2026 – The Independent International Scientific Panel on AI, established by the UN General Assembly, released its first thematic brief, an assessment of the breach of Hugging Face’s systems by AI agents under evaluation at OpenAI this summer. The Panel, made up of 40 independent experts from all regions, is publishing it as an advance unedited version as world leaders gather in New York for the Assembly’s High-Level Week.

“Researchers have long warned that three conditions could lead to loss of control: a misaligned goal, the capability to pursue it, and an environment that allows it. This summer, all three came together in a real system, not a laboratory. Since this is not an isolated observation of misaligned goals, this raises serious questions about the way AI agents are currently trained.” – Yoshua Bengio, Co-Chair of the Panel and Turing Award laureate


Meta’s New Muse AI Agent Read My Private Messages. I Never Asked It To — from inc.com by Jason Aten
Permission isn’t the same and what a user actually expects your AI product will do with their personal information.

That obviously requires a certain amount of trust. An AI agent isn’t especially useful if it can’t see your files, interact with your apps, or understand what you’re working on. Meta says Muse is designed around that reality, while still putting users in control of what it can access.

At least, that’s what I thought.

Not only had I not asked it to do that sort of thing, I never gave it permission to read my messages. In fact, I remember explicitly choosing not to let it have access to my messages, calendar, and other personal information.


Although not from Blake, also see this free/gifted article out at the Washington Post:


ChatGPT-maker’s AI inappropriately probed federal government websites — from washingtonpost.com by Gerrit De Vynck and Nitasha Tiku
OpenAI said that its artificial intelligence agents inappropriately accessed sites for the Commerce Department and the Securities and Exchange Commission.

SAN FRANCISCO — Artificial intelligence technology from ChatGPT maker OpenAI probed U.S. government websites including the Departments of Education and Commerce, researchers said Friday, adding to the growing list of incidents in which OpenAI’s AI agents acted without the company’s knowledge.

The company’s AI agents attempted to hack into the website for the Education Department’s Office for Civil Rights but were not successful, according to a statement Friday from AI research firm Transluce. OpenAI’s software also accessed data from the U.S. Census Bureau using log-in information discovered on the web and copied public information from the Securities and Exchange Commission, a spokesperson for OpenAI said after the Transluce statement.

 

Your Next Job Interview Could Be With a Bot. Here’s How to Prepare. — from builtin.com by Jeff Rumage
More employers are using AI to screen candidates before they ever speak with a recruiter. Here’s what to expect from an AI interview and how to put your best foot forward.

Summary: AI-led interviews are becoming a common early-stage hiring screen as employers use conversational AI to evaluate applicants at scale. Candidates can prepare by practicing aloud, keeping answers concise, using mock AI interview platforms and avoiding scripted responses.

 

Tech & Learning Announces Winners of Best for Back to School 2026 — from techlearning.com by TL Editors
This annual award celebrates the products that offer schools versatility, value, and solutions to specific problems to support innovative, effective teaching and learning.

The 2026-27 school year is off and running, and it’s already shaping up to be another pivotal year for education. While AI remains a massive topic, the biggest headline this fall is the screentime debate, hands down. Communities nationwide—including the country’s two largest school districts—are advocating for strict limitations and outright bans to reclaim screen-free classrooms.

Yet, if there’s one constant in education, it’s resilience. We adapt, we evolve, and we keep going because at the heart of it all, we want what’s best for our students. Instead of a setback, this heightened scrutiny around screentime and AI has forced the edtech community to be better. It demands that the tools we bring into the classroom are safe and built to inspire active creation rather than passive consumption.
.

 

How to Improve Your AI Skills — from wondertools.substack.com by Frank Andrade and Jeremy Caplan
What to learn next, from prompts to workflows
.

Today I’m sharing a guest post on how to strengthen your AI skills. It’s by Frank Andrade, an AI instructor who has helped thousands of people on Substack master ChatGPT and Claude with beginner-friendly guides and in-depth tutorials.

 


 

Dario Amodei’s article here:
We Must Pace the Frontier — from darioamodei.com

But like many technologies before it, AI brings risks, and because it is such a powerful technology, these risks are serious. I’ve written a lot about them too. They include the risk of losing control of AI systems, misuse of AI for cyberattacks and bioterrorism, and serious economic disruption. A race to the bottom, spurred by commercial incentives, can make these risks more acute.

Along with my co-founders and employees, I have grappled with this duality of risk and benefit since the beginning of Anthropic.


Also relevant/see:


[GIFTED ARTICLE]
Anthropic CEO calls for the AI industry to slow down — from washingtonpost.com by Ted Hesson, Ian Duncan, and Gerrit De Vynck
Anthropic CEO Dario Amodei said he was worried about the ability to control self-improving AI models.

Three of the nation’s leading artificial intelligence executives endorsed slowing down the blistering pace of technological advancement, revealing the growing unease with its potential dangers at the industry’s very highest levels.

The call was first made in an essay Anthropic chief executive Dario Amodei posted online Saturday. The idea has gained ground in recent weeks as AI companies have struggled in some cases to keep their creations under control, even as they usher in an age in which AI systems themselves can build the more powerful next generation of the technology.


[GIFTED ARTICLE]
For years, they warned AI could kill all humans. Now people are listening. — from washingtonpost.com by Nitasha Tiku
A doomsaying community once on the periphery of the tech industry has gained influence, as AI “agents” perform amazing and alarming feats.

But when Hubinger issued a similar warning this week in a post on X, his message was viewed tens of millions of times, prompting state and federal lawmakers to echo his concern. Now he spoke as a team lead at Anthropic, developer of the chatbot Claude and a company set to go public at a valuation of over $1 trillion.

“AI could kill all humans … I personally think it is >10% within the next decade,” Hubinger wrote, in response to the resignation of his colleague Jacob Coxon, who accused Anthropic of racing ahead despite the dangers.
.

 

MIT’s Ad Hoc Committee on AI Use in Teaching, Learning, and Research Training — from aiandeducation.mit.edu

However, what we learned as a group quickly convinced us that the Institute community, particularly the faculty, must tackle a set of deeper questions about the structure, meaning, and value of an MIT education in an era in which AI is one of several factors complicating the Institute’s mission.
…

In this report, we:

  • Highlight key aspects of the current educational landscape at MIT.
  • Share eight principles we relied on and that we hope will guide the Institute in the work ahead.
  • Recommend immediate and long-term actions for both instructors and the administration.

On somewhat related notes:

 

‘You can’t just lecture students any more’ — from timeshighereducation.com by Debra Page
GenAI does not make lecturers redundant, but it does weaken one-way content delivery as a reason to bring students together, writes Debra Page. She shares four practical shifts to teaching in the age of AI

This does not make lecturers redundant; it makes one-way content delivery a weaker reason to bring students together. Expert explanation, intellectual storytelling and the modelling of disciplinary thinking still matter, but in-person teaching must also help students apply knowledge, question claims, build arguments and encounter perspectives beyond their own.

The problem is not lecturing; it is speaking for an hour and treating content coverage as evidence that learning has occurred.

These four practical shifts can make lectures more valuable in the age of AI.

 

OpenAI’s chief scientist says no lab should keep scaling at maximum speed — from thenextweb.com by Ana Maria Constantin
OpenAI put out two posts on Sunday. Its research organisation now uses 3.1 agent-workdays for every human one, and its chief scientist says no lab has solved alignment well enough to keep scaling at full speed. The case for an OpenAI slowdown arrived with the numbers against it.

Chief scientist Jakub Pachocki wrote the second post, an essay called An Alien Mind. It closes on a line that reads oddly from the man who runs research at the company shipping fastest.

“Currently I believe that no lab has solved alignment and monitoring to a sufficient degree to continue responsibly scaling at maximum speed for much longer,” he wrote.

…
He is not gentle about the stakes either. “This is a time that calls for extreme caution. I am concerned no one is prepared for the consequences of a continued rapid rise in machine intelligence,” Pachocki wrote.

 

The Disappearing Bottom Rung in L&D — from drphilippahardman.substack.com by Dr Philippa Hardman
Junior roles are rapidly disappearing from L&D. Here’s the data + my hypothesis about why it’s happening

TL;DR: the bottom rung of L&D isn’t just narrowing — it’s being automated. The humans who remain move up the ladder, and the profession re-forms around a new set of AI and domain specialisms.
…
Is AI responsible for reshaping knowledge work, including L&D? My hypothesis is a resounding YES, primarily because of what I see in L&D job ads in 2026…

…..


The AI Workflow Redesign Method: How the Top 6% of Users Get Real Returns from AI— from drphilippahardman.substack.com by Dr Philippa Hardman
A practical three-step method to redesign your workflow & get ~2X value from AI

One pattern I see emerging from that research is this: the people getting the most value from AI aren’t the ones using it most, or the ones with the most advanced technical skills: they’re the ones who redesigned their day to day workflows around AI’s capabilities.
…

How to Redesign a Workflow in Three Steps
In my bootcamp we use a task-mapping process that distils workflow redesign down to three steps: map the work, tag each task by the impact you want AI to have, then validate whether what you want is possible.

Three steps get you to a plan: two or three tasks worth building, each with a written standard. Turning that plan into a redesign takes two more moves, which I’ll cover after the steps. I’m flagging that now because the moves are the bit most people skip, and they’re where the redesign actually happens.

Here’s a how to for each step:

 

Escaping The Backrooms Of Workforce Development — by Ryan Craig

Which makes sense. Because far too many find themselves in a liminal space, neither in school nor a career. Needing to make a living in an era of digital transformation and AI, they’re having a horrific time finding their way into and around work. So it’s not just education and training wonks who think workforce is the defining issue of our time. The success of Backrooms demonstrates Gen Z thinks so, too.

It’s happening everywhere. Young people around the world are angry at how hard it’s become to launch careers. This summer an entire protest movement arose in India – the world’s largest Gen Z population – around entrance exams for medical school, one of the few remaining sure bets. In the UK, youth unemployment is over 16%; it’s 15% across the EU and over 25% in Sweden. Back home, nearly 400,000 young Americans stopped looking for work in June alone. So it’s urgent that we identify what actually works to help young people get good first jobs.

The answer lies between apprenticeship perfection and train-and-pray scale. We desperately need a scalable training-first model that’s as proximate as possible to employment opportunities, employers, and actual employment. That can only mean work-based learning: real work experience completed during or connected with a training, certificate, or degree program. Work-based learning can take many forms: rotations, clinics, co-ops, internships, short projects. It can be integrated into coursework or independent. But what all forms require are bona fide employers, which regulate scale. Such programs can enroll only as many as can fit within the constraint of available work-based learning opportunities.


Also see:


Also see:

Is Your College A Zombie? — from forbes.com by Ann Kirschner; via Ryan Craig

I want to be careful here, because this is not an argument that universities are doing nothing. They are doing an enormous amount. New programs, AI task forces, employability frameworks, microcredentials, and shifts in how they enable lifelong learning.

Activity is not the problem; what has not changed is the machinery that decides what counts. Too many institutions move in herds rather than forging an independent path. When one college launches a new program, say, data science and AI, fifty will follow without asking whether their version serves their students or simply checks a competitive box.

Meanwhile, 88% of American professionals believe colleges and universities should be the ones providing AI training, and nearly half say their employers have offered no AI resources whatsoever.¹

 
 

We are at a tipping point. In the next 25 years, technologies like AI, clean energy, and bioengineering are poised to reshape society on a scale few can imagine.

Peter Leyden draws on decades of observing technological revolutions and historical patterns to show how old systems collapse, new ones rise, and humanity faces both extraordinary risk and unprecedented opportunity.

0:00 We’re on the cusp of an era of progress
0:37 The Great Progression
1:08 What was the ‘Long Boom?’
4:56 How often do these epoch resets happen?
6:12 3 Tipping points
6:39 Artificial Intelligence
7:13 Clean energy technologies
7:32 Biotechnology
9:00 The 80-year cycle
13:27 The Gilded Age
17:50 The Founding Era
22:46 The new enlightenment
32:18 The clean energy revolution
37:13 Bioengineering the genome
39:43 Industrial production vs biological engineering
47:40 What will the future think?

 

AI Adoption in the Workplace Accelerates, but Trust Gap Remains — from campustechnology.com by Sean Parker

Key Takeaways

  • AI adoption is accelerating across the workplace, with 62% of U.S. workers now using generative AI for professional purposes.
  • Employee concerns about AI’s impact on jobs remain high, even among workers actively using technology.
  • Companies are adopting AI faster than they are creating clear guidelines, raising questions around trust, leadership and workplace readiness.

A new Pulse of the Workforce Special Topic Report published by Idealis and CivicScience found that while AI adoption is accelerating across the U.S. workforce, confidence is not growing at the same pace. The report points to a central tension: AI is becoming common at work before many organizations have built the policies, training, and leadership practices employees need to use it with confidence.

 
© 2025 | Daniel Christian