Will this new company be the largest company on the Internet by 2030? [Christian]

From DSC:
The vision that I’ve been tracking for well over a decade begins with this graphic:
.


Could LearnVector be this next-generation company/platform? Perhaps. Time will tell.


Per Matt Tower via The EdSheet Vol. 38 dated 7/31/26:

  • Coursera bets $100M on its own co-founder: Andrew Ng’s new venture LearnVector lands one of the largest single checks in edtech this year — from Coursera, the company he founded 14 years ago.
    .


LearnVector.ai

 

Staffing Agencies as Apprenticeship Accelerators — from apprenticeshipsforamerica.org by Harry Leech; via Ryan Craig
Could America’s staffing industry be a catalyst for growing apprenticeship? 

Apprenticeships for America (AFA) sees the development of a mature intermediary system as crucial to scaling apprenticeships in America. There are many shapes and forms of intermediaries: spanning public sector and private, for profit and not-for-profit. One promising and relatively unexplored model is staffing agencies.

This Field Briefing reviews the landscape of opportunity for harnessing America’s $200 billion staffing industry to grow apprenticeship. It explores the fit between apprenticeship intermediary functions and capacities of staffing firms. And it offers examples of how staffing agencies todays are supporting apprenticeship activies of their customers. Ultimately, it will take policy simplification and stable funding to fully benefit from the integration of the staffing industry and apprenticeship. This report sets out how.

Download the full report here: Staffing Agencies and Apprenticeship Report

 

Digital Accessibility Lawsuits in 2026: Five Trends Companies Should Know — from blog.usablenet.com

Here are five findings companies should understand, along with practical steps for reducing risk.

1. Digital accessibility lawsuits are on pace to reach 6,000
2. Where a company sells matters more than where it is headquartered
3. E-commerce remains the primary target
…and more

 

From DSC:
Below is an item on futures thinking. Don’t blow this off. This topic — and skill/strategy — is important not only to traditional institutions of higher education, but also to businesses and organizations of all sizes. In fact, even students can practice developing a list of potential scenarios and implement their plans of action if one of those scenarios occurs.


Chris Mayer’s posting on LinkedIn.com

 

What China’s Calligraphy Lessons Can Teach Us About AI — from linkedin.com by Rebecca Winthrop

He recalled visiting a primary school classroom in China several years ago where students were practicing calligraphy—the centuries-old art of writing Chinese characters with brush and ink. While the scene appeared entirely traditional, there was something innovative happening beneath the surface. Embedded in students’ desks was an AI system capable of providing immediate feedback on their brushwork, noting misplaced strokes and suggesting corrections.

The image is striking. Students were learning in much the same way generations before them had learned—brush in hand, immersed in a deeply human and cultural practice. Yet AI was quietly enhancing the experience by providing immediate, personalized feedback that would have been difficult for any teacher to provide to her whole class.

That example captures something important for me. The most promising uses of AI may not be the applications that place technology front and center, but those that support learners to do what they have always done: connect with educators, practice with concentration, and learn and grow.


AI Can Build Your Course, but Can it Design the Learning? — from drphilippahardman.substack.com by Dr. Philippa Hardman
Aka, the current state of AI’s instructional design ability & what it means for L&D

So these models have learned design artefacts in extraordinary volume and design method barely at all. They know what e-learning looks like. They have very little access to why any of it looks that way and whether it actually works or not.

That produces a completely consistent behaviour: ask an LLM for an e-learning module and it generates the statistical centre of every e-learning module it has ever seen. What AI produces isn’t the output of a design process. It’s the output of a sampling process — the statistical centre of every e-learning module it has ever seen.

So the honest conclusion isn’t that AI can design learning; it’s that AI executes design well when someone who already knows what to ask for is doing the asking and the checking.

This finding gives us a clearer division of labour than we’ve had before and with it a clearer picture of what the AI-enhanced workflow might look like:


The Pedagogy of Trading Places: Lessons from an Unexpected Role Reversal with AI — from facultyfocus.com by Sherrie Myers Bartell, PhD

These disruptions matter. They remind us that teaching is not a static identity but a dynamic posture. They show us that our pedagogical selves are not fixed; they are responsive to context, energy, and attention. They reveal that the qualities we value most in our teaching—curiosity, metaphor, play, and expansiveness—are not automatic. They require care and cultivation.

The AI didn’t “teach” me in the traditional sense. But it did something pedagogically adjacent: it surprised me back into myself.

 

AI & Accessibility: 5 Practical Ways to Use LLMs to Support Inclusive Learning — from learningguild.com by Dr. Athena Joyce Stanley (B.A., M.A.E., Ph.D.)

The following strategies focus on how instructional designers can design for accessibility by embedding or enabling AI-supported tools within learning experiences. These approaches move beyond access to content and begin to support access to understanding, expression, and participation.

Personalized Clarification Through AI Copilots
Learners often need additional explanation, but may hesitate to ask questions in live sessions or require more individualized support.

Instructional designers can integrate AI copilots, LLM-powered conversational interfaces, into learning environments to provide on-demand clarification. These tools can act as digital coaches, helping learners understand concepts through simplified explanations and relevant examples.

This approach supports learners who benefit from additional processing time and personalized guidance.

Sample Prompt (for IDs Configuring Copilot Behavior)
You are a workplace learning coach.

Explain the following concept in a clear, simple way:

    • Use plain language
    • Provide one practical, job-relevant example
    • Avoid jargon unless necessary (and define it if used)

Then ask one follow-up question to check for understanding.


Jeff’s posting on LinkedIn.


Teaching AI Literacy with Susan Ray — from intentionalteaching.buzzsprout.com by Derek Bruff and Susan Ray

Per Derek Bruff:
In my conversation with Susan, we talk about that syllabus activity, as well as the AI transparency journals she asks her students to keep during her courses. We also talk about how her personal background prepared her to navigate the challenges and opportunities that AI has posed to her teaching, how she approaches assessing student learning in this age of AI—especially in her online asynchronous courses—and much more.


Building the AI-Native University: Student Success and Lifelong Learning — from coursera.org

In this episode, you’ll discover:

  • Why building an AI-native campus requires a data-first foundation
  • How to build an “achievement architecture” for lifelong success
  • How faculty-built AI tutors cut one program’s course attrition from 40% to 10%
  • What it means to be a lifelong learning companion

We’re Raising the First AI Generation. They’re Pushing Back. (Rebecca Winthrop, Brookings) — from humanistxyz.substack.com by Allison Dulin Salisbury
“Young people harbor real anger about AI because they’re already experiencing its consequences in their schools, relationships, job prospects, and feeds.”

As Rebecca told me:

“Kids harbor real anger. They’re pissed about climate impacts. They’re upset about job prospects. They’re outraged about AI being used to plagiarize writing and produce counterfeit artwork. Their relationships are being affected, and they’re seeing deepfakes in their feeds.”

Our conversation explores what follows from taking those concerns seriously. We discuss cognitive stunting, the case for small, purpose-built AI models over frontier models in education, why students should spend far more time in explorer mode, and how schools can cultivate agency, curiosity, and independent thinking in an age of AI.

The interview ends with one of Rebecca’s most practical recommendations: every school should have a student AI council, not as a symbolic gesture, but with real influence over the tools their schools adopt, the ways they’re used, and the data students are asked to hand over.

Every generation of technology seems to relearn the same lesson: build with people, not for them. AI should be no exception.

 

Amazon, gig companies see spike in workers on SNAP and Medicaid, study shows — from washingtonpost.com by Lauren Kaori Gurley and Rachel Lerman
Amazon workers on federal aid nearly tripled between 2020 and 2025, while ride-hailing and food delivery drivers shot up on lists compiled by the U.S. Government Accountability Office.

While the number of working adults who depend on food stamps and Medicaid has ticked up since the start of the pandemic, the number of recipients who work at Amazon and on gig economy platforms has exploded, according to a new report from the U.S. Government Accountability Office.

The number of Amazon workers relying on the federal programs for the poor nearly tripled between February 2020 and September 2025, according to the report published Wednesday. And for the first time, ride-hailing and app-based food delivery companies — Uber, Lyft, DoorDash, Grubhub and Instacart — collectively ranked among the top three employers with workers receiving aid.


From DSC:
I have long disliked what the gig economy was/is doing to workers. A handful of entrepreneurs who started these companies have done very well for themselves, while the rest of the people who are doing most of the work hardly make anything. No medical insurance, no vision or dental insurance, and no retirement contributions/benefits. Amazon works its people to the bone (I saw this firsthand with our son and his deteriorating physical health while working for them), and look what it gets them? Bezos Inc. make out great — others…not so much. 


Ride-hailing and food-delivery companies, meanwhile, shot past Walmart to collectively rank as the top employer of food stamp users and among the top three for Medicaid users, according to The Post’s analysis. 

 

Reconnecting Professional Learning — from elemenous.substack.com by Lucy Gray
Reflections from My Wednesday ISTE Panel

At ISTE this year, I facilitated a Wednesday morning panel on professional learning. The session I organized was titled, The Future of Professional Learning: Connecting Educators Across Borders. I was joined by fellow Apple Distinguished Educators Tami Brewster, Bethany LaDue Nugent, Marcus Borders, Jason Krug and global educator Julie Meltzer. Our focus: How do we move professional development away from isolated, one-time experiences and toward something more connected, meaningful, and human?

The panel was grounded in a few simple but powerful ideas: professional learning should nurture, guide, and empower. It should be relevant, contextual, reflective, and sustained. It should recognize educators as capable professionals, not as people who are broken and need to be fixed. Too often, professional development is still designed around deficit thinking. Too often, teachers’ needs are not the starting point, and sessions become dry, one-directional experiences where information is delivered at them rather than built through conversation and collaboration, with little choice, little personalization, and little connection to the realities of their classrooms. Our panel served as a call to action to re-think professional learning.

We explored six different approaches to professional learning, and while each model was distinct, a clear through line emerged: relationships matter most.

  1. Virtual Conferences at Scale — Lucy Gray — Actionable Innovations Events / GLOW
  2. Micro-Mentorship — Tami Brewster
  3. Networked, Values-Driven PD — Julie Meltzer — Institute for Humane Education
  4. AI-Personalized PD Pathways — Marcus Borders
  5. Share Stories with Voice — Bethany LaDue Nugent
  6. Speak Your Crazy — Jason Krug

  • Our Padlet – Share resources and introduce yourself
  • Our Slides – Meet our panelists and learn about our work
  • Our Google NotebookLM – This notebook contains dozens of resources related to research and best practices in educator professional learning
  • The Connected PD App – This is an app I’m building in Base 44 to help people plan great professional learning experiences

Also from Lucy Gray, see the following for some nice tips and resources:

 

The Law School Deans Driving AI Innovation in Legal Education — from natlawreview.com by Shivani Vedhere, AI & the Law Newsletter; via Colin S. Levy

Artificial intelligence is no longer a peripheral issue for legal education. It is quickly becoming one of the central questions facing law schools: how to prepare future lawyers for a profession in which AI will affect research, client counseling, litigation strategy, access to justice, and the business of law.

For decades, law schools treated legal technology as an elective or a niche interest for students already inclined toward innovation. That era is ending. Law firms are adopting AI tools at scale and even investing in developing their own tools. Clients are asking harder questions about efficiency, cost, and competence. Courts are sanctioning lawyers and litigants for AI-generated hallucinations, with the number of identified cases in the United States now exceeding 1,000. Students entering the profession will be expected to keep up with this rapidly changing landscape.

The most forward-looking law schools are responding accordingly. That transformation is being driven in large part by a group of innovative law school deans who are treating AI not as a passing trend, but as a structural change in legal education.

These initiatives signal a broader shift in legal academia where law schools are no longer merely debating whether AI belongs in the curriculum. The more pressing question is how deeply, how early, and how responsibly AI should be integrated into legal education.

 

Flipped Classrooms and Academic Achievement — from learningscientists.org by Megan Sumeracki

There are actually many, many ways to design a flipped classroom, and it has been fascinating to learn about the ways my colleague typically structures her hybrid, flipped-classroom courses. As a result, we’ve been able to engage in really interesting conversations about the best approach for this particular course, and why. As a result of some of these discussions, I came across a few recent meta-analyses related to the effects of flipped classrooms, the results of which I thought were worth sharing here (1, 2, 3).

 

 

From DSC:
I used to be able to bring up Firefly on the web and use it “free” of charge — I didn’t have to go purchase tokens or credits. (I was actually paying for the Adobe Creative Cloud Pro suite of tools…so it wasn’t really free.)

But the other day I was trying to figure out what the latest pricing is at Adobe with that suite of tools and the use of credits for AI-based features. They say Adobe Creative Cloud Pro users get 4000 credits a month. Well, I have that suite and I’m still getting prompted to purchase credits. Firefly for individuals runs from $9.99 (2,000 credits/month) to $139.91 per month (50,000 credits per month). Not inexpensive, right? Below are other items along these lines.


The Era of Affordable AI Is Over. What Comes Next? — from builtin.com by Ameya Kanitkar
AI providers are shifting to usage-based billing for their services. AI fluency is more important now than ever to make the most of your tools to avoid unnecessary spending.

Summary: The era of cheap, flat-rate AI is ending as providers shift to usage-based billing. Every prompt now carries a direct cost, turning casual use into major budget risks, as seen when Uber depleted its 2026 AI budget in four months. Leaders must now track real-time value and token efficiency.

For a brief window, companies had access to the most transformative technology in a generation at the cost of a streaming subscription. Tools like ChatGPT put AI within reach of anyone with a browser and time for experimentation, while GitHub Copilot came in at just $10 a month, with token costs remaining relatively low. In the beginning, experimentation felt cost-effective, easy and relatively low-risk. 

But that era is ending, and the bill is coming due faster than a lot of enterprise leaders anticipated. 


The Fable of AI in Education — from downes.ca by Stephen Downes
Marc Watkins, Rhetorica, Jun 17, 2026

Tokenomics will be a hot topic of discussion on university campuses because, as Marc Watkins notes in this article, there is no realistic path forward to providing all students with access to advanced AI.


From this posting on LinkedIn.com from Dr. Nick Jackson:

And now there is a third layer emerging. Institutions are waking up to a systems-level question they are likely not remotely prepared for. Who pays for AI? How are budgets managed when there are unclear token consumption pricing models? How is AI procured? Who decides what tools get used and by whom and who gets access and at what level?

.


 

From DSC:
Following are several companies that are using AI to connect people to work. That’s a significant piece of my Learning from the Living [AI-Based Class] Room vision.

These companies were listed on an article entitled,
Can AI be an effective career coach?
— from achievepartners.com and Ryan Craig


FutureFit AI
Bridge the gap between talent, training, and employment at scale

AI-powered workforce technology connecting people to careers, employers to talent, and workforce partners to tools for integrated and intelligent workforce systems.

PathPilot AI

Empowering every job seeker with personalized AI coaching. Helping organizations scale career services and improve outcomes.

Empower Students with Career-Ready Skills
Help students discover career pathways, develop essential skills, and connect with opportunities. PathPilot provides personalized guidance that scales across your entire institution.

  • AI-powered career exploration and pathway planning
  • Skills assessment aligned with NACE competencies
  • Resume builder and interview preparation tools
  • Job matching with local and national employers
  • Institutional analytics and outcome tracking
  • Integration with existing career services systems

Pathific — Design your future
The all-in-one platform that connects your strengths to programs, careers, and real salary outcomes — powered by AI.

High school, post-secondary, newcomer to Canada, or career change — Pathific meets you where you are.

Your all-in-one career compass
Quality career guidance shouldn’t depend on where you go to school, when you start your journey, or where you come from. Using the latest AI and comprehensive Canadian data, we built a platform that gives everyone clear, data-driven pathways to their future. No more one-size-fits-all advice. No more guessing. Just your strengths, connected to real data.

OpportuNext

See Where Your Skills Can Take You | Find new career path opportunities with one simple search.

OpportuNext from Signal49 Research is a free-to-use career tool created in partnership with the Future Skills Centre. Using big data, it matches a person’s skills with viable career paths — often including some you have not considered.

 

If AI Eats the Entry-Level Job, Where Do Young People Learn to Work? (Ryan Craig, Achieve Partners) — from humanistxyz.substack.com by Allison Dulin Salisbury; via Ryan Craig
“The public should not be subsidizing colleges whose students lack relevant, paid, in-field work experience.”

That is the trap at the center of this conversation: everyone wants to hire someone with three years of experience, and almost no one wants to provide those three years.

And Ryan’s policy prescription is unusually concrete: pay employers to hire and train apprentices, following the countries that have scaled apprenticeship far faster than the U.S.; require colleges receiving federal student aid to provide relevant, paid, in-field work experience; and build a market of intermediaries that can make the whole thing operational.

Ryan’s view is that higher education remains critically important. But college without meaningful work experience may become a much worse bet, especially for students who cannot afford to guess wrong.

 

The Evolving L&D Roles in 2026 Exploring who you might become next — from liftedlnd.substack.com by Lifted L&D

1. The Learning Experience Architect
This is really the evolution of the instructional designer. The difference is that the focus is no longer on building individual courses. Instead, the focus shifts towards designing capability ecosystems.

In modern learning platforms, learning is dynamic and increasingly personalised. AI engines infer skill levels, recommend resources, generate practice scenarios and adapt content based on how people engage. The role of the Learning Experience Architect is to orchestrate that environment so it genuinely supports capability development.

Across all of these emerging roles, three themes keep appearing.

The first is data fluency. …
The second is systems thinking. …
The third is human judgement.


Also relevant/see:


 
 
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