I analyzed 180M jobs to see what jobs AI is actually replacing today — from bloomberry.com by Henley Wing Chiu; via Kim Isenberg

I analyzed nearly 180 million global job postings from January 2023 to October 2025, using data from Revealera, a provider of jobs data. While I acknowledge not all job postings result in a hire, and some are ‘ghost jobs’, since I was comparing the relative growth in job titles, this didn’t seem like a big issue to me.

I simply wanted to know which specific job titles declined or grew the most in 2025, compared to 2024. Because those were likely to be ones that AI is impacting the most.

Key Sections


Also from Kim Isenberg, see:


 

ElevenLabs just launched a voice marketplace — from elevenlabs.io; via theaivalley.com

Via the AI Valley:

Why does it matter?
AI voice cloning has already flooded the internet with unauthorized imitations, blurring legal and ethical lines. By offering a dynamic, rights-secured platform, ElevenLabs aims to legitimize the booming AI voice industry and enable transparent, collaborative commercialization of iconic IP.
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ElevenLabs just launched a voice marketplace

ElevenLabs just launched a voice marketplace


[GIFTED ARTICLE] How people really use ChatGPT, according to 47,000 conversations shared online — from by Gerrit De Vynck and Jeremy B. Merrill
What do people ask the popular chatbot? We analyzed thousands of chats to identify common topics discussed by users and patterns in ChatGPT’s responses.

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Data released by OpenAI in September from an internal study of queries sent to ChatGPT showed that most are for personal use, not work.

Emotional conversations were also common in the conversations analyzed by The Post, and users often shared highly personal details about their lives. In some chats, the AI tool could be seen adapting to match a user’s viewpoint, creating a kind of personalized echo chamber in which ChatGPT endorsed falsehoods and conspiracy theories.

Lee Rainie, director of the Imagining the Digital Future Center at Elon University, said his own research has suggested ChatGPT’s design encourages people to form emotional attachments with the chatbot. “The optimization and incentives towards intimacy are very clear,” he said. “ChatGPT is trained to further or deepen the relationship.”


Per The Rundown: OpenAI just shared its view on AI progress, predicting systems will soon become smart enough to make discoveries and calling for global coordination on safety, oversight, and resilience as the technology nears superintelligent territory.

The details:

  • OpenAI said current AI systems already outperform top humans in complex intellectual tasks and are “80% of the way to an AI researcher.”
  • The company expects AI will make small scientific discoveries by 2026 and more significant breakthroughs by 2028, as intelligence costs fall 40x per year.
  • For superintelligent AI, OAI said work with governments and safety agencies will be essential to mitigate risks like bioterrorism or runaway self-improvement.
  • It also called for safety standards among top labs, a resilience ecosystem like cybersecurity, and ongoing tracking of AI’s real impact to inform public policy.

Why it matters: While the timeline remains unclear, OAI’s message shows that the world should start bracing for superintelligent AI with coordinated safety. The company is betting that collective safeguards will be the only way to manage risk from the next era of intelligence, which may diffuse in ways humanity has never seen before.

Which linked to:

  • AI progress and recommendations — from openai.com
    AI is unlocking new knowledge and capabilities. Our responsibility is to guide that power toward broad, lasting benefit.

From DSC:
I hate to say this, but it seems like there is growing concern amongst those who have pushed very hard to release as much AI as possible — they are NOW worried. They NOW step back and see that there are many reasons to worry about how these technologies can be negatively used.

Where was this level of concern before (while they were racing ahead at 180 mph)? Surely, numerous and knowledgeable people inside those organizations warned them about the destructive/downside of these technologies. But their warnings were pretty much blown off (at least from my limited perspective). 


The state of AI in 2025: Agents, innovation, and transformation — from mckinsey.com

Key findings

  1. Most organizations are still in the experimentation or piloting phase: Nearly two-thirds of respondents say their organizations have not yet begun scaling AI across the enterprise.
  2. High curiosity in AI agents: Sixty-two percent of survey respondents say their organizations are at least experimenting with AI agents.
  3. Positive leading indicators on impact of AI: Respondents report use-case-level cost and revenue benefits, and 64 percent say that AI is enabling their innovation. However, just 39 percent report EBIT impact at the enterprise level.
  4. High performers use AI to drive growth, innovation, and cost: Eighty percent of respondents say their companies set efficiency as an objective of their AI initiatives, but the companies seeing the most value from AI often set growth or innovation as additional objectives.
  5. Redesigning workflows is a key success factor: Half of those AI high performers intend to use AI to transform their businesses, and most are redesigning workflows.
  6. Differing perspectives on employment impact: Respondents vary in their expectations of AI’s impact on the overall workforce size of their organizations in the coming year: 32 percent expect decreases, 43 percent no change, and 13 percent increases.

Marble: A Multimodal World Model — from worldlabs.ai

Spatial intelligence is the next frontier in AI, demanding powerful world models to realize its full potential. World models should reconstruct, generate, and simulate 3D worlds; and allow both humans and agents to interact with them. Spatially intelligent world models will transform a wide variety of industries over the coming years.

Two months ago we shared a preview of Marble, our World Model that creates 3D worlds from image or text prompts. Since then, Marble has been available to an early set of beta users to create 3D worlds for themselves.

Today we are making Marble, a first-in-class generative multimodal world model, generally available for anyone to use. We have also drastically expanded Marble’s capabilities, and are excited to highlight them here:

 


Gen AI Is Going Mainstream: Here’s What’s Coming Next — from joshbersin.com by Josh Bersin

I just completed nearly 60,000 miles of travel across Europe, Asia, and the Middle East meeting with hundred of companies to discuss their AI strategies. While every company’s maturity is different, one thing is clear: AI as a business tool has arrived: it’s real and the use-cases are growing.

A new survey by Wharton shows that 46% of business leaders use Gen AI daily and 80% use it weekly. And among these users, 72% are measuring ROI and 74% report a positive return. HR, by the way, is the #3 department in use cases, only slightly behind IT and Finance.

What are companies getting out of all this? Productivity. The #1 use case, by far, is what we call “stage 1” usage – individual productivity. 

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From DSC:
Josh writes: “Many of our large clients are now implementing AI-native learning systems and seeing 30-40% reduction in staff with vast improvements in workforce enablement.

While I get the appeal (and ROI) from management’s and shareholders’ perspective, this represents a growing concern for employment and people’s ability to earn a living. 

And while I highly respect Josh and his work through the years, I disagree that we’re over the problems with AI and how people are using it: 

Two years ago the NYT was trying to frighten us with stories of AI acting as a romance partner. Well those stories are over, and thanks to a $Trillion (literally) of capital investment in infrastructure, engineering, and power plants, this stuff is reasonably safe.

Those stories are just beginning…they’re not close to being over. 


“… imagine a world where there’s no separation between learning and assessment…” — from aiedusimplified.substack.com by Lance Eaton, Ph.D. and Tawnya Means
An interview with Tawnya Means

So let’s imagine a world where there’s no separation between learning and assessment: it’s ongoing. There’s always assessment, always learning, and they’re tied together. Then we can ask: what is the role of the human in that world? What is it that AI can’t do?

Imagine something like that in higher ed. There could be tutoring or skill-based work happening outside of class, and then relationship-based work happening inside of class, whether online, in person, or some hybrid mix.

The aspects of learning that don’t require relational context could be handled by AI, while the human parts remain intact. For example, I teach strategy and strategic management. I teach people how to talk with one another about the operation and function of a business. I can help students learn to be open to new ideas, recognize when someone pushes back out of fear of losing power, or draw from my own experience in leading a business and making future-oriented decisions.

But the technical parts such as the frameworks like SWOT analysis, the mechanics of comparing alternative viewpoints in a boardroom—those could be managed through simulations or reports that receive immediate feedback from AI. The relational aspects, the human mentoring, would still happen with me as their instructor.

Part 2 of their interview is here:


 




BIG unveils Suzhou Museum of Contemporary Art topped with ribbon-like roof — from dezeen.com by Christina Yao
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Also from Dezeen:

MVRDV designs giant sphere for sports arena in Tirana — from dezeen.com by Starr Charles
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Breaking News: Law Firm’s AI Pilot Lets New Lawyers Step Away from Billable Hours — from jdjournal.com

In a groundbreaking move that may redefine how law firms integrate technology training into daily practice, Ropes & Gray LLP has introduced a new pilot program allowing its first-year associates to dedicate a significant portion of their work hours to artificial intelligence (AI) learning—without the pressure of billing those hours to clients.

The initiative, called “TrAIlblazers,” marks one of the first formal attempts by a major law firm to give attorneys credit toward their billable-hour requirements for time spent exploring and developing AI skills. The firm hopes the move will both prepare young lawyers for a rapidly evolving profession and signal a new era of flexibility in how law firms evaluate performance.

 

A New AI Career Ladder — from ssir.org (Stanford Social Innovation Review) by Bruno V. Manno; via Matt Tower
The changing nature of jobs means workers need new education and training infrastructure to match.

AI has cannibalized the routine, low-risk work tasks that used to teach newcomers how to operate in complex organizations. Without those task rungs, the climb up the opportunity ladder into better employment options becomes steeper—and for many, impossible. This is not a temporary glitch. AI is reorganizing work, reshaping what knowledge and skills matter, and redefining how people are expected to acquire them.

The consequences ripple from individual career starts to the broader American promise of economic and social mobility, which includes both financial wealth and social wealth that comes from the networks and relationships we build. Yet the same technology that complicates the first job can help us reinvent how experience is earned, validated, and scaled. If we use AI to widen—not narrow—access to education, training, and proof of knowledge and skill, we can build a stronger career ladder to the middle class and beyond. A key part of doing this is a redesign of education, training, and hiring infrastructure.

What’s needed is a redesigned model that treats work as a primary venue for learning, validates capability with evidence, and helps people keep climbing after their first job. Here are ten design principles for a reinvented education and training infrastructure for the AI era.

  1. Create hybrid institutions that erase boundaries. …
  2. Make work-based learning the default, not the exception. …
  3. Create skill adjacencies to speed transitions. …
  4. Place performance-based hiring at the core. 
  5. Ongoing supports and post-placement mobility. 
  6. Portable, machine-readable credentials with proof attached. 
  7. …plus several more…
 

 

Six Transformative Technology Trends Impacting the Legal Profession — from americanbar.org

Summary

  • Law firm leaders should evaluate their legal technology and decide if they are truly helping legal work or causing a disconnect between human and AI contributions.
  • 75% of firms now rely on cloud platforms for everything from document storage to client collaboration.
  • The rise of virtual law firms and remote work is reshaping the profession’s culture. Hybrid and remote-first models, supported by cloud and collaboration tools, are growing.

Are we truly innovating, or just rearranging the furniture? That’s the question every law firm leader should be asking as the legal technology landscape shifts beneath our feet. There are many different thoughts and opinions on how the legal technology landscape will evolve in the coming years, particularly regarding the pace of generative AI-driven changes and the magnitude of these changes.

To try to answer the question posed above, we looked at six recently published technology trends reports from influential entities in the legal technology arena: the American Bar Association, Clio, Wolters Kluwer, Lexis Nexis, Thomson Reuters, and NetDocuments.

When we compared these reports, we found them to be remarkably consistent. While the level of detail on some topics varied across the reports, they identified six trends that are reshaping the very core of legal practice. These trends are summarized in the following paragraphs.

  1. Generative AI and AI-Assisted Drafting …
  2. Cloud-Based Practice Management…
  3. Cybersecurity and Data Privacy…
  4. Flat Fee and Alternative Billing Models…
  5. Legal Analytics and Data-Driven Decision Making…
  6. Virtual Law Firms and Remote Work…
 

KPMG wants junior consultants to ditch the grunt work and hand it over to teams of AI agents — from businessinsider.com by Polly Thompson

The Big Four consulting and accounting firm is training its junior consultants to manage teams of AI agents — digital assistants capable of completing tasks without human input.

“We want juniors to become managers of agents,” Niale Cleobury, KPMG’s global AI workforce lead, told Business Insider in an interview.

KPMG plans to give new consulting recruits access to a catalog of AI agents capable of creating presentation slides, analyzing data, and conducting in-depth research, Cleobury said.

The goal is for these agents to perform much of the analytical and administrative work once assigned to junior consultants, allowing them to become more involved in strategic decisions.


From DSC:
For a junior staff member to provide quality assurance in working with agents, an employee must know what they’re talking about in the first place. They must have expertise and relevant knowledge. Otherwise, how will they spot the hallucinations?

So the question is, how can businesses build such expertise in junior staff members while they are delegating things to an army of agents? This question applies to the next posting below as well. Having agents report to you is all well and good — IF you know when the agents are producing helpful/accurate information and when they got things all wrong.


This Is the Next Vital Job Skill in the AI Economy — from builtin.com by Saurabh Sharma
The future of tech work belongs to AI managers.

Summary: A fundamental shift is making knowledge workers “AI managers.” The most valuable employees will direct intelligent AI agents, which requires new competencies: delegation, quality assurance and workflow orchestration across multiple agents. Companies must bridge the training gap to enable this move from simple software use to strategic collaboration with intelligent, yet imperfect, systems.

The shift is happening subtly, but it’s happening. Workers are learning to prompt agents, navigate AI capabilities, understand failure modes and hand off complex tasks to AI. And if they haven’t started yet, they probably will: A new study from IDC and Salesforce found that 72 percent of CEOs think most employees will have an AI agent reporting to them within five years. This isn’t about using a new kind of software tool — it’s about directing intelligent systems that can reason, search, analyze and create.

Soon, the most valuable employees won’t just know how to use AI; they’ll know how to manage it. And that requires a fundamentally different skill set than anything we’ve taught in the workplace before.


AI agents failed 97% of freelance tasks; here’s why… — from theneurondaily.com by Grant Harvey

AI Agents Can’t Actually Do Your Job (Yet)—New Benchmark Reveals The Gap

DEEP DIVE: AI can make you faster at your job, but can only do 2-3% of jobs by itself.

The hype: AI agents will automate entire workflows! Replace freelancers! Handle complex tasks end-to-end!

The reality: a measly 2-3% completion rate.

See, Scale AI and CAIS just released the Remote Labor Index (paper), a benchmark where AI agents attempted real freelance tasks. The best-performing model earned just $1,810 out of $143,991 in available work, and yes, finishing only 2-3% of jobs.



 

…the above posting links to:

Higher Ed Is Sleepwalking Toward Obsolescence— And AI Won’t Be the Cause, Just the Accelerant — from substack.com by Steven Mintz
AI Has Exposed Higher Ed’s Hollow Core — The University Must Reinvent Itself or Fade

It begins with a basic reversal of mindset: Stop treating AI as a threat to be policed. Start treating it as the accelerant that finally forces us to build the education we should have created decades ago.

A serious institutional response would demand — at minimum — six structural commitments:

  • Make high-intensity human learning the norm.  …
  • Put active learning at the center, not the margins.  …
  • Replace content transmission with a focus on process.  …
  • Mainstream high-impact practices — stop hoarding them for honors students.  …
  • Redesign assessment to make learning undeniable.  …

And above all: Instructional design can no longer be a private hobby.


Teaching with AI: From Prohibition to Partnership for Critical Thinking — from facultyfocus.com by Michael Kiener, PhD, CRC

How to Integrate AI Developmentally into Your Courses

  • Lower-Level Courses: Focus on building foundational skills, which includes guided instruction on how to use AI responsibly. This moves the strategy beyond mere prohibition.
  • Mid-Level Courses: Use AI as a scaffold where faculty provide specific guidelines on when and how to use the tool, preparing students for greater independence.
  • Upper-Level/Graduate Courses: Empower students to evaluate AI’s role in their learning. This enables them to become self-regulated learners who make informed decisions about their tools.
  • Balanced Approach: Make decisions about AI use based on the content being learned and students’ developmental needs.

Now that you have a framework for how to conceptualize including AI into your courses here are a few ideas on scaffolding AI to allow students to practice using technology and develop cognitive skills.




80 per cent of young people in the UK are using AI for their schoolwork — from aipioneers.org by Graham Attwell

What was encouraging, though, is that students aren’t just passively accepting this new reality. They are actively asking for help. Almost half want their teachers to help them figure out what AI-generated content is trustworthy, and over half want clearer guidelines on when it’s appropriate to use AI in their work. This isn’t a story about students trying to cheat the system; it’s a story about a generation grappling with a powerful new technology and looking to their educators for guidance. It echoes a sentiment I heard at the recent AI Pioneers’ Conference – the issue of AI in education is fundamentally pedagogical and ethical, not just technological.


 


From DSC:
One of my sisters shared this piece with me. She is very concerned about our society’s use of technology — whether it relates to our youth’s use of social media or the relentless pressure to be first in all things AI. As she was a teacher (at the middle school level) for 37 years, I greatly appreciate her viewpoints. She keeps me grounded in some of the negatives of technology. It’s important for us to listen to each other.


 

The new legal intelligence — from jordanfurlong.substack.com by Jordan Furlong
We’ve built machines that can reason like lawyers. Artificial legal intelligence is becoming scalable, portable and accessible in ways lawyers are not. We need to think hard about the implications.

Much of the legal tech world is still talking about Clio CEO Jack Newton’s keynote at last week’s ClioCon, where he announced two major new features: the “Intelligent Legal Work Platform,” which combines legal research, drafting and workflow into a single legal workspace; and “Clio for Enterprise,” a suite of legal work offerings aimed at BigLaw.

Both these features build on Clio’s out-of-nowhere $1B acquisition of vLex (and its legally grounded LLM Vincent) back in June.

A new source of legal intelligence has entered the legal sector.

Legal intelligence, once confined uniquely to lawyers, is now available from machines. That’s going to transform the legal sector.


Where the real action is: enterprise AI’s quiet revolution in legal tech and beyond — from canadianlawyermag.com by Tim Wilbur
Harvey, Clio, and Cohere signal that organizational solutions will lead the next wave of change

The public conversation about artificial intelligence is dominated by the spectacular and the controversial: deepfake videos, AI-induced psychosis, and the privacy risks posed by consumer-facing chatbots like ChatGPT. But while these stories grab headlines, a quieter – and arguably more transformative – revolution is underway in enterprise software. In legal technology, in particular, AI is rapidly reshaping how law firms and legal departments operate and compete. This shift is just one example of how enterprise AI, not just consumer AI, is where real action is happening.

Both Harvey and Clio illustrate a crucial point: the future of legal tech is not about disruption for its own sake, but partnership and integration. Harvey’s collaborations with LexisNexis and others are about creating a cohesive experience for law firms, not rendering them obsolete. As Pereira put it, “We don’t see it so much as disruption. Law firms actually already do this… We see it as ‘how do we help you build infrastructure that supercharges this?’”

The rapid evolution in legal tech is just one example of a broader trend: the real action in AI is happening in enterprise software, not just in consumer-facing products. While ChatGPT and Google’s Gemini dominate the headlines, companies like Cohere are quietly transforming how organizations across industries leverage AI.

Also from canadianlawyermag.com, see:

The AI company’s plan to open an office in Toronto isn’t just about expanding territory – it’s a strategic push to tap into top technical talent and capture a market known for legal innovation.


Unseeable prompt injections in screenshots: more vulnerabilities in Comet and other AI browsers — from brave.com by Artem Chaikin and Shivan Kaul Sahib

Building on our previous disclosure of the Perplexity Comet vulnerability, we’ve continued our security research across the agentic browser landscape. What we’ve found confirms our initial concerns: indirect prompt injection is not an isolated issue, but a systemic challenge facing the entire category of AI-powered browsers. This post examines additional attack vectors we’ve identified and tested across different implementations.

As we’ve written before, AI-powered browsers that can take actions on your behalf are powerful yet extremely risky. If you’re signed into sensitive accounts like your bank or your email provider in your browser, simplysummarizing a Reddit postcould result in an attacker being able to steal money or your private data.

The above item was mentioned by Grant Harvey out at The Neuron in the following posting:


Robin AI’s Big Bet on Legal Tech Meets Market Reality — from lawfuel.com

Robin’s Legal Tech Backfire
Robin AI, the poster child for the “AI meets law” revolution, is learning the hard way that venture capital fairy dust doesn’t guarantee happily-ever-after. The London-based legal tech firm, once proudly waving its genAI-plus-human-experts flag, is now cutting staff after growth dreams collided with the brick wall of economic reality.

The company confirmed that redundancies are under way following a failed major funding push. Earlier promises of explosive revenue have fizzled. Despite around $50 million in venture cash over the past two years, Robin’s 2025 numbers have fallen short of investor expectations. The team that once ballooned to 200 is now shrinking.

The field is now swarming with contenders: CLM platforms stuffing genAI into every feature, corporate legal teams bypassing vendors entirely by prodding ChatGPT directly, and new entrants like Harvey and Legora guzzling capital to bulldoze into the market. Even Workday is muscling in.

Meanwhile, ALSPs and AI-powered pseudo-law firms like Crosby and Eudia are eating market share like it’s free pizza. The number of inhouse teams actually buying these tools at scale is still frustratingly small. And investors don’t have much patience for slow burns anymore.


Why Being ‘Rude’ to AI Could Win Your Next Case or Deal — from thebrainyacts.beehiiv.com by Josh Kubicki

TL;DR: AI no longer rewards politeness—new research shows direct, assertive prompts yield better, more detailed responses. Learn why this shift matters for legal precision, test real-world examples (polite vs. blunt), and set up custom instructions in OpenAI (plus tips for other models) to make your AI a concise analytical tool, not a chatty one. Actionable steps inside to upgrade your workflow immediately.



 

Nvidia becomes first $5 trillion company — from theaivallye.com by Barsee
PLUS: OpenAI IPO at $1 trillion valuation by late 2026 / early 2027

Nvidia has officially become the first company in history to cross the $5 trillion market cap, cementing its position as the undisputed leader of the AI era. Just three months ago, the chipmaker hit $4 trillion; it’s already added another trillion since.

Nvidia market cap milestones:

  • Jan 2020: $144 billion
  • May 2023: $1 trillion
  • Feb 2024: $2 trillion
  • Jun 2024: $3 trillion
  • Jul 2025: $4 trillion
  • Oct 2025: $5 trillion

The above posting linked to:

 

 

Custom AI Development: Evolving from Static AI Systems to Dynamic Learning Agents in 2025 — community.nasscom.in

This blog explores how custom AI development accelerates the evolution from static AI to dynamic learning agents and why this transformation is critical for driving innovation, efficiency, and competitive advantage.

Dynamic Learning Agents: The Next Generation
Dynamic learning agents, sometimes referred to as adaptive or agentic AI, represent a leap forward. They combine continuous learningautonomous action, and context-aware adaptability.

Custom AI development plays a crucial role here: it ensures that these agents are designed specifically for an enterprise’s unique needs rather than relying on generic, one-size-fits-all AI platforms. Tailored dynamic agents can:

  • Continuously learn from incoming data streams
  • Make autonomous, goal-directed decisions aligned with business objectives
  • Adapt behavior in real time based on context and feedback
  • Collaborate with other AI agents and human teams to solve complex challenges

The result is an AI ecosystem that evolves with the business, providing sustained competitive advantage.

Also from community.nasscom.in, see:

Building AI Agents with Multimodal Models: From Perception to Action

Perception: The Foundation of Intelligent Agents
Perception is the first step in building AI agents. It involves capturing and interpreting data from multiple modalities, including text, images, audio, and structured inputs. A multimodal AI agent relies on this comprehensive understanding to make informed decisions.

For example, in healthcare, an AI agent may process electronic health records (text), MRI scans (vision), and patient audio consultations (speech) to build a complete understanding of a patient’s condition. Similarly, in retail, AI agents can analyze purchase histories (structured data), product images (vision), and customer reviews (text) to inform recommendations and marketing strategies.

Effective perception ensures that AI agents have contextual awareness, which is essential for accurate reasoning and appropriate action.


From 70-20-10 to 90-10: a new operating system for L&D in the age of AI? — from linkedin.com by Dr. Philippa Hardman

Also from Philippa, see:



Your New ChatGPT Guide — from wondertools.substack.com by Jeremy Caplan and The PyCoach
25 AI Tips & Tricks from a guest expert

  • ChatGPT can make you more productive or dumber. An MIT study found that while AI can significantly boost productivity, it may also weaken your critical thinking. Use it as an assistant, not a substitute for your brain.
  • If you’re a student, use study mode in ChatGPT, Gemini, or Claude. When this feature is enabled, the chatbots will guide you through problems rather than just giving full answers, so you’ll be doing the critical thinking.
  • ChatGPT and other chatbots can confidently make stuff up (aka AI hallucinations). If you suspect something isn’t right, double-check its answers.
  • NotebookLM hallucinates less than most AI tools, but it requires you to upload sources (PDFs, audio, video) and won’t answer questions beyond those materials. That said, it’s great for students and anyone with materials to upload.
  • Probably the most underrated AI feature is deep research. It automates web searching for you and returns a fully cited report with minimal hallucinations in five to 30 minutes. It’s available in ChatGPT, Perplexity, and Gemini, so give it a try.

 


 

 

“OpenAI’s Atlas: the End of Online Learning—or Just the Beginning?” [Hardman] + other items re: AI in our LE’s

OpenAI’s Atlas: the End of Online Learning—or Just the Beginning? — from drphilippahardman.substack.com by Dr. Philippa Hardman

My take is this: in all of the anxiety lies a crucial and long-overdue opportunity to deliver better learning experiences. Precisely because Atlas perceives the same context in the same moment as you, it can transform learning into a process aligned with core neuro-scientific principles—including active retrieval, guided attention, adaptive feedback and context-dependent memory formation.

Perhaps in Atlas we have a browser that for the first time isn’t just a portal to information, but one which can become a co-participant in active cognitive engagement—enabling iterative practice, reflective thinking, and real-time scaffolding as you move through challenges and ideas online.

With this in mind, I put together 10 use cases for Atlas for you to try for yourself.

6. Retrieval Practice
What:
Pulling information from memory drives retention better than re-reading.
Why: Practice testing delivers medium-to-large effects (Adesope et al., 2017).
Try: Open a document with your previous notes. Ask Atlas for a mixed activity set: “Quiz me on the Krebs cycle—give me a near-miss, high-stretch MCQ, then a fill-in-the-blank, then ask me to explain it to a teen.”
Atlas uses its browser memory to generate targeted questions from your actual study materials, supporting spaced, varied retrieval.




From DSC:
A quick comment. I appreciate these ideas and approaches from Katarzyna and Rita. I do think that someone is going to want to be sure that the AI models/platforms/tools are given up-to-date information and updated instructions — i.e., any new procedures, steps to take, etc. Perhaps I’m missing the boat here, but an internal AI platform is going to need to have access to up-to-date information and instructions.


 
© 2025 | Daniel Christian