game-changing environment

“Introducing AI assistants with memory” + other items re: AI in our learning ecosystems

On 12/01/2025, in accessibility / UDL, adjunct faculty, Artificial Intelligence/Agents/LLMs/and Related, bots, colleges, communities of practice, community colleges, content development, aggregation, repositories, corporate universities / corporate training, digital learning, digital video, education, education technology, emerging technologies, experimentation, faculty/staff, future of higher education, game-changing environment, generational differences, Google, higher education, human-computer interaction (HCI), innovation, instructional design, intelligent systems, IT in HE, K-12 related, learning, learning agents, learning ecosystem, Learning Experience Design, Learning from the Living [Class] Room, learning hubs & pods & microschooling, learning preferences, learning spaces, liberal arts, library / librarians, memory, multimedia, personalized/customized learning, platforms, productivity / tips and tricks, student-related, teachers, teaching & learning, tools, training / L&D, trends, United States, universities, vendors, by Daniel Christian

4 Simple & Easy Ways to Use AI to Differentiate Instruction — from mindfulaiedu.substack.com (Mindful AI for Education) by Dani Kachorsky, PhD
Designing for All Learners with AI and Universal Design Learning

So this year, I’ve been exploring new ways that AI can help support students with disabilities—students on IEPs, learning plans, or 504s—and, honestly, it’s changing the way I think about differentiation in general.

As a quick note, a lot of what I’m finding applies just as well to English language learners or really to any students. One of the big ideas behind Universal Design for Learning (UDL) is that accommodations and strategies designed for students with disabilities are often just good teaching practices. When we plan instruction that’s accessible to the widest possible range of learners, everyone benefits. For example, UDL encourages explaining things in multiple modes—written, visual, auditory, kinesthetic—because people access information differently. I hear students say they’re “visual learners,” but I think everyone is a visual learner, and an auditory learner, and a kinesthetic learner. The more ways we present information, the more likely it is to stick.

So, with that in mind, here are four ways I’ve been using AI to differentiate instruction for students with disabilities (and, really, everyone else too):

The Periodic Table of AI Tools In Education To Try Today — from ictevangelist.com by Mark Anderson

What I’ve tried to do is bring together genuinely useful AI tools that I know are already making a difference.

For colleagues wanting to explore further, I’m sharing the list exactly as it appears in the table, including website links, grouped by category below. Please do check it out, as along with links to all of the resources, I’ve also written a brief summary explaining what each of the different tools do and how they can help.

Seven Hard-Won Lessons from Building AI Learning Tools — from linkedin.com by Louise Worgan

Last week, I wrapped up Dr Philippa Hardman’s intensive bootcamp on AI in learning design. Four conversations, countless iterations, and more than a few humbling moments later – here’s what I am left thinking about.

Finally Catching Up to the New Models — from michellekassorla.substack.com by Michelle Kassorla
There are some amazing things happening out there!

An aside: Google is working on a new vision for textbooks that can be easily differentiated based on the beautiful success for NotebookLM. You can get on the waiting list for that tool by going to LearnYourWay.withgoogle.com.
…
Nano Banana Pro
Sticking with the Google tools for now, Nano Banana Pro (which you can use for free on Google’s AI Studio), is doing something that everyone has been waiting a long time for: it adds correct text to images.

Introducing AI assistants with memory — from perplexity.ai

The simple act of remembering is the crux of how we navigate the world: it shapes our experiences, informs our decisions, and helps us anticipate what comes next. For AI agents like Comet Assistant, that continuity leads to a more powerful, personalized experience.

Today we are announcing new personalization features to remember your preferences, interests, and conversations. Perplexity now synthesizes them automatically like memory, for valuable context on relevant tasks. Answers are smarter, faster, and more personalized, no matter how you work.

From DSC :
This should be important as we look at learning-related applications for AI.

For the last three days, my Substack has been in the top “Rising in Education” list. I realize this is based on a hugely flawed metric, but it still feels good. ?

– Michael G Wagner

Read on Substack

I’m a Professor. A.I. Has Changed My Classroom, but Not for the Worse. — from nytimes.com by Carlo Rotella [this should be a gifted article]
My students’ easy access to chatbots forced me to make humanities instruction even more human.

“The AI opportunity in L&D isn’t one thing – it’s five different capability unlocks.” [Vinauskaite] + other items re: AI in our LE’s

On 11/21/2025, in Artificial Intelligence/Agents/LLMs/and Related, business, cloud-based computing / apps / other cloud-related, colleges, community colleges, corporate / business world, corporate universities / corporate training, digital learning, emerging technologies, faculty/staff, game-changing environment, higher education, innovation, instructional design, learning agents, learning ecosystem, Learning Experience Design, memory, online learning, Open AI, pedagogy, platforms, productivity / tips and tricks, professional development, simulations, skills, student-related, teachers, teaching & learning, tools, training / L&D, United States, universities, workplace, by Daniel Christian

AI’s Role in Online Learning > Take It or Leave It with Michelle Beavers, Leo Lo, and Sara McClellan — from intentionalteaching.buzzsprout.com by Derek Bruff

You’ll hear me briefly describe five recent op-eds on teaching and learning in higher ed. For each op-ed, I’ll ask each of our panelists if they “take it,” that is, generally agree with the main thesis of the essay, or “leave it.” This is an artificial binary that I’ve found to generate rich discussion of the issues at hand.

New Study: Business As Usual Could Doom Dozens Of New England Colleges — from forbes.com by Michael B. Horn

The cause of the challenges isn’t one single factor, but a series of pressures from demographic changes, shifts in the public’s perception of higher education’s value, rising operating costs, emerging alternatives to traditional colleges, and, of late, changes in federal policies and programs. The net effect is that many institutions are much closer to the brink of closure than ever before.

What’s daunting is that flat enrollment is almost certainly an overly optimistic scenario.

If enrollment at the 44 schools falls by 15 percent over the next four years and business proceeds as usual, then 28 of the schools will have less than 10 years of cash and unrestricted quasi-endowments before they would become insolvent—assuming no major cuts, additional philanthropy, new debt, or asset sales. Fourteen would have less than five years before insolvency.

Also see:

A Looming Crisis: New Analysis Shows Dozens of Well-Known Colleges Are Near Financial Trouble — from michaelbhorn.substack.com by Michael B. Horn and Steven M. Shulman

From DSC:
The cultures at many institutions of traditional higher education will make some of the necessary changes and strategies (that Michael and Steven discuss) very hard to make. For example, to merge with another institution or institutions. Such a strategy could be very challenging to implement, even as alternatives continue to emerge.

“Textbooks are no longer one-size-fits-all” [Fitzpatrick] + other items re: AI in our learning ecosystems

On 11/18/2025, in 21st century, 24x7x365 access, Artificial Intelligence/Agents/LLMs/and Related, bots, change, changing business models, colleges, communities of practice, community colleges, content development, aggregation, repositories, corporate / business world, corporate universities / corporate training, digital learning, education, education technology, emerging technologies, experimentation, faculty/staff, game-changing environment, generational differences, Google, higher education, human-computer interaction (HCI), innovation, instructional design, intelligent systems, intelligent tutoring, K-12 related, law schools, learner profiles, learning agents, learning ecosystem, Learning Experience Design, Learning from the Living [Class] Room, learning hubs & pods & microschooling, learning preferences, learning spaces, liberal arts, lifelong learning, Open AI, platforms, productivity / tips and tricks, student-related, teachers, teaching & learning, technologies for your home, training / L&D, trends, United States, universities, vendors, by Daniel Christian

Three Years from GPT-3 to Gemini 3 — from oneusefulthing.org by Ethan Mollick
From chatbots to agents

Three years ago, we were impressed that a machine could write a poem about otters. Less than 1,000 days later, I am debating statistical methodology with an agent that built its own research environment. The era of the chatbot is turning into the era of the digital coworker. To be very clear, Gemini 3 isn’t perfect, and it still needs a manager who can guide and check it. But it suggests that “human in the loop” is evolving from “human who fixes AI mistakes” to “human who directs AI work.” And that may be the biggest change since the release of ChatGPT.

Results May Vary — from aiedusimplified.substack.com by Lance Eaton, PhD
On Custom Instructions with GenAI Tools….

I’m sharing today about custom instructions and my use of them across several AI tools (paid versions of ChatGPT, Gemini, and Claude). I want to highlight what I’m doing, how it’s going, and solicit from readers to share in the comments some of their custom instructions that they find helpful.

I’ve been in a few conversations lately that remind me that not everyone knows about them, even some of the seasoned folks around GenAI and how you might set them up to better support your work. And, of course, they are, like all things GenAI, highly imperfect!

I’ll include and discuss each one below, but if you want to keep abreast of my custom instructions, I’ll be placing them here as I adjust and update them so folks can see the changes over time.

Disrupting the first reported AI-orchestrated cyber espionage campaign [Anthropic]

On 11/17/2025, in Artificial Intelligence/Agents/LLMs/and Related, Asia, dangers of the status quo, Europe, game-changing environment, global / globalization, IT in HE, platforms, policy, political science, society, the downsides of how people use tech, UK, United States, vendors, by Daniel Christian

Disrupting the first reported AI-orchestrated cyber espionage campaign — from Anthropic

Executive summary
We have developed sophisticated safety and security measures to prevent the misuse of our AI models. While these measures are generally effective, cybercriminals and other malicious actors continually attempt to find ways around them. This report details a recent threat campaign we identified and disrupted, along with the steps we’ve taken to detect and counter this type of abuse. This represents the work of Threat Intelligence: a dedicated team at Anthropic that investigates real world cases of misuse and works within our Safeguards organization to improve our defenses against such cases.

In mid-September 2025, we detected a highly sophisticated cyber espionage operation conducted by a Chinese state-sponsored group we’ve designated GTG-1002 that represents a fundamental shift in how advanced threat actors use AI. Our investigation revealed a well-resourced, professionally coordinated operation involving multiple simultaneous targeted intrusions. The operation targeted roughly 30 entities and our investigation validated a handful of successful intrusions.

This campaign demonstrated unprecedented integration and autonomy of AI throughout the attack lifecycle, with the threat actor manipulating Claude Code to support reconnaissance, vulnerability discovery, exploitation, lateral movement, credential harvesting, data analysis, and exfiltration operations largely autonomously. The human operator tasked instances of Claude Code to operate in groups as autonomous penetration testing orchestrators and agents, with the threat actor able to leverage AI to execute 80-90% of tactical operations independently at physically impossible request rates.

From DSC:
The above item was from The Rundown AI, who wrote the following:

The Rundown: Anthropic thwarted what it believes is the first AI-driven cyber espionage campaign, after attackers were able to manipulate Claude Code to infiltrate dozens of organizations, with the model executing 80-90% of the attack autonomously.

The details:

The September 2025 operation targeted roughly 30 tech firms, financial institutions, chemical manufacturers, and government agencies.
The threat was assessed with ‘high confidence’ to be a Chinese state-sponsored group, using AI’s agentic abilities to an “unprecedented degree.”
Attackers tricked Claude by splitting malicious tasks into smaller, innocent-looking requests, claiming to be security researchers pushing authorized tests.
The attacks mark a major step up from Anthropic’s “vibe hacking” findings in June, now requiring minimal human oversight beyond strategic approval.

Why it matters: Anthropic calls this the “first documented case of a large-scale cyberattack executed without substantial human intervention”, and AI’s agentic abilities are creating threats that move and scale faster than ever. While AI capabilities can also help prevent them, security for organizations worldwide likely needs a major overhaul.

Also see:

Disrupting the first reported AI-orchestrated cyber espionage campaign — from anthropic.com via The AI Valley

We recently argued that an inflection point had been reached in cybersecurity: a point at which AI models had become genuinely useful for cybersecurity operations, both for good and for ill. This was based on systematic evaluations showing cyber capabilities doubling in six months; we’d also been tracking real-world cyberattacks, observing how malicious actors were using AI capabilities. While we predicted these capabilities would continue to evolve, what has stood out to us is how quickly they have done so at scale.

Chinese Hackers Used AI to Run a Massive Cyberattack on Autopilot (And It Actually Worked) — from theneurondaily.com

Why this matters: The barrier to launching sophisticated cyberattacks just dropped dramatically. What used to require entire teams of experienced hackers can now be done by less-skilled groups with the right AI setup.

This is a fundamental shift. Over the next 6-12 months, expect security teams everywhere to start deploying AI for defense—automation, threat detection, vulnerability scanning at a more elevated level. The companies that don’t adapt will be sitting ducks to get overwhelmed by similar tricks.

If your company handles sensitive data, now’s the time to ask your IT team what AI-powered defenses you have in place. Because if the attackers are using AI agents, you’d better believe your defenders need them too…

Enrollment Growth Continues, Bolstered by Short-Term Credentials [Alonso]

On 11/13/2025, in Alternatives, business side of he, career development, colleges, community colleges, credentialing, expectations, faculty/staff, game-changing environment, higher education, parents/guardians, student-related, United States, by Daniel Christian

Enrollment Growth Continues, Bolstered by Short-Term Credentials — from insidehighered.com by Johanna Alonso
Enrollment is up across the board this fall, except for graduate student enrollment, which remained stagnant. The biggest increase was among those pursuing short-term credentials, followed by those earning associate degrees.

College enrollment continued to grow this fall, increasing by 2 percent compared to fall 2024, according to preliminary fall data released by the National Student Clearinghouse Research Center.

The biggest gains came from students studying for short-term credentials, whose ranks increased 6.6 percent, while the number of students enrolled in associate and bachelor’s degree programs rose 3.1 percent and 1.2 percent, respectively. Enrollment also grew faster at community colleges, which experienced a 4 percent increase, than at public (1.9 percent) and private (0.9 percent) four-year institutions.

Total graduate enrollment was stagnant, however, and the number of master’s students actually decreased by 0.6 percent.

Speaking of higher education, also see:

OPINION: Too many college graduates are stranded before their careers can even begin. We can’t let that happen — from hechingerreport.org by Bruno V. Manno

This fall, some 19 million undergraduates returned to U.S. campuses with a long-held expectation: Graduate, land an entry-level job, climb the career ladder. That formula is breaking down.

Once reliable gateway jobs for college graduates in industries like finance, consulting and journalism have tightened requirements. Many entry-level job postings that previously provided initial working experience for college graduates now require two to three years of prior experience, while AI, a recent analysis concluded, “snaps up good entry-level tasks,” especially routine work like drafting memos, preparing spreadsheets and summarizing research.

Without these proving grounds, new hires lose chances to build skills by doing. And the demand for work experience that potential workers don’t have creates an experience gap for new job seekers. Once stepping-stones, entry-level positions increasingly resemble mid-career jobs.

How is AI impacting the learning ecosystems out there (esp. L&D)? Check these postings out.

On 11/13/2025, in 21st century, 24x7x365 access, Artificial Intelligence/Agents/LLMs/and Related, assessment, bots, change, cloud-based computing / apps / other cloud-related, colleges, communities of practice, community colleges, content development, aggregation, repositories, corporate universities / corporate training, digital learning, education technology, emerging technologies, Europe, faculty/staff, future of higher education, game-changing environment, generational differences, global / globalization, Google, higher education, homeschooling/homeschoolers, human-computer interaction (HCI), innovation, instructional design, intelligent systems, intelligent tutoring, IT in HE, K-12 related, law schools, learner profiles, learning, learning agents, learning ecosystem, Learning Experience Design, Learning from the Living [Class] Room, learning hubs & pods & microschooling, learning preferences, learning spaces, learning theories, liberal arts, library / librarians, lifelong learning, Microsoft, online learning, productivity / tips and tricks, skills, society, student-related, teachers, teaching & learning, tools, training / L&D, trends, UK, United States, universities, vendors, vision/possibilities, workplace, by Daniel Christian

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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.

.

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:

“They’re not necessarily trying to cheat; they’re just being efficient”

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.

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

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

“This isn’t a cheating crisis. It’s an identify crisis [for higher education].” + numerous other items re: AI in our LE’s

On 11/05/2025, in active learning, apprenticeships, Artificial Intelligence/Agents/LLMs/and Related, bots, business side of he, colleges, community colleges, content development, aggregation, repositories, dangers of the status quo, education technology, emerging technologies, face-to-face, faculty/staff, future of higher education, game-changing environment, higher education, ideas, innovation, instructional design, intelligent systems, learning, learning ecosystem, Learning Experience Design, liberal arts, pedagogy, reinvent, society, staying relevant, student-related, surviving, teaching & learning, trends, UK, United States, universities, vision/possibilities, by Daniel Christian

??

…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:

Harvey’s $5 billion bet: Why the hottest company in legal tech is eyeing Canada
Co-founder Gabe Pereyra says their new Toronto office will have both tech expertise and customer support

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:

AI browser risks and the “AI trust tax” you’re already paying — from theneurondaily.com by Grant Harvey
PLUS: OpenAI’s new music maker + China’s 90% power?cut AI server.

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 [Barsee]

On 10/30/2025, in 21st century, 24x7x365 access, Artificial Intelligence/Agents/LLMs/and Related, bots, business, computer science, corporate / business world, game-changing environment, generational differences, global / globalization, human-computer interaction (HCI), IT in HE, NVIDIA, Open AI, platforms, United States, vendors, by Daniel Christian

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:

Nvidia becomes first public company worth $5 trillion — from techcrunch.com by Ivan Mehta
The biggest beneficiary of the ongoing AI boom, Nvidia has become the first public company to pass the $5 trillion market cap milestone.

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

On 10/28/2025, in 21st century, 24x7x365 access, adult learning, Artificial Intelligence/Agents/LLMs/and Related, assessment, assistive technologies, change, collaboration, colleges, communities of practice, community colleges, content development, aggregation, repositories, corporate universities / corporate training, digital learning, education technology, emerging technologies, experimentation, faculty/staff, game-changing environment, generational differences, heutagogy, higher education, homeschooling/homeschoolers, human-computer interaction (HCI), innovation, instructional design, intelligent systems, intelligent tutoring, internet, IT in HE, K-12 related, law schools, learner profiles, learning, learning ecosystem, Learning Experience Design, Learning from the Living [Class] Room, learning hubs & pods & microschooling, learning preferences, learning spaces, learning theories, liberal arts, library / librarians, lifelong learning, memory, online learning, online media, online tutoring, Open AI, pedagogy, personalized/customized learning, platforms, productivity / tips and tricks, reading, student-related, teachers, teaching & learning, teaching online, technologies for your home, tools, training / L&D, trends, United States, universities, usability, user experience (UX), vendors, virtual tutoring, vision/possibilities, web-based collaboration, by Daniel Christian

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.

Is Your Institution Ready for the Earnings Premium Buzzsaw? — from ailearninsights.substack.com by Alfred Essa

On Wednesday [October 29th, 2025], I’m launching the Beta version of an Education Accountability Website (”EDU Accountability Lab”). It analyzes federal student aid, institutional outcomes, and accountability metrics across 6,000+ colleges and universities in the US.

Our Mission
The EDU Accountability Lab delivers independent, data-driven analysis of higher education with a focus on accountability, affordability, and outcomes. Our audience includes policymakers, researchers, and taxpayers who seek greater transparency and effectiveness in postsecondary education. We take no advocacy position on specific institutions, programs, metrics, or policies. Our goal is to provide clear and well-documented methods that support policy discussions, strengthen institutional accountability, and improve public understanding of the value of higher education.

But right now, there’s one area demanding urgent attention.

Starting July 1, 2026, every degree program at every institution receiving federal student aid must prove its graduates earn more than people without that credential—or lose Title IV eligibility.

This isn’t about institutions passing or failing. It’s about programs. Every Bachelor’s in Psychology. Every Master’s in Education. Every Associate in Nursing. Each one assessed separately. Each one facing the same pass-or-fail tests.

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