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

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

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

 

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

Key Takeaways

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

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

 

 

Once globally preeminent, U.S. universities are sliding into decline — from hechingerreport.org by Jon Marcus
High costs, political attacks take a toll as fewer students come, top scholars leave

Top scholars are leaving. Fewer international students are coming. U.S. universities are slipping in international rankings. And American universities have failed to keep pace as other countries pour resources into their higher education systems.

Blaming the interruptions in funding that have already occurred, top research universities have admitted 15 percent fewer doctoral students for the coming fall, including in fields such as artificial intelligence and quantum computing, according to the Association of American Universities, or AAU. Many didn’t take any new doctoral students at all.


Colleges made deep staff cuts while adding tenure-track faculty last year — from highereddive.com by Ben Unglesbee
The latest survey of higher education employees from CUPA-HR found the biggest spike in tenure-track professors since at least 2016.

Dive Brief:

  • Full-time staff at colleges dropped 6.6% in 2025 compared to the year before, ending several years of growth, according to data released Wednesday by the College and University Professional Association for Human Resources.
  • The decline was even sharper among part-time staff, whose ranks plunged by 23.9% last year, CUPA-HR’s latest survey data showed.
  • However, faculty numbers grew overall during the year. Tenure-track faculty headcounts saw the largest spike at 7% year over year, the biggest increase for that group since at least 2016.

Pace of college closings picks up, with more projected — from hechingerreport.org by Jon Marcus
Falling enrollment, rising debt have put an estimated 1 in 4 institutions at risk

More than 440 of the nation’s private, nonprofit four-year colleges and universities are now at risk, based on enrollment trends, debt and other measures, according to projections by Huron Consulting Group. That’s a quarter of the total.

Among the most endangered: small, rural and religiously affiliated institutions.

And how to dispose of the real estate is among the least of the challenges facing shuttering campuses and the students they serve.


University of Houston slashes 40% of courses from its core curriculum — from highereddive.com by Ben Unglesbee
The Texas institution cut about 100 classes, including in women’s and LGBT studies, to comply with a state law mandating general education reviews.

Dive Brief:

  • The University of Houston has slashed about 40% of courses from the flagship’s core curriculum as it works to comply with a new state law dictating the architecture of general education requirements at Texas public colleges.
  • In the 2026-27 academic year, UH will adopt a core curriculum list with roughly 100 fewer classes after the University of Houston System’s board unanimously approved administrators’ proposal on Thursday. Among the classes dropped were courses in women’s and LGBT studies.
  • The overhaul is meant to satisfy the Texas statute known as SB 37, which mandates that public institutions’ core courses be “foundational” to postsecondary education, prepare students for the workforce and civic life, and cover “a breadth of knowledge.”

From DSC:
Re: Jeff’s posting above…I think that faculty often taught what THEY wanted to teach — versus what the market was asking for. If I were on the academic administration side of the house right now, I’d be seeking out and listening very closely to what the adjunct faculty members on campus are saying.

 

 

Using AI to create the practice opportunities online students need— from timeshighereducation.com by Kathy Miller Perkins
For disciplines that depend on interpersonal skills such as communication, leadership, negotiation and mediation, AI can provide elusive experiential learning, particularly in asynchronous environments

What if AI’s greatest educational value lies not in generating answers but in generating experiences?

The result surprised me. Students did not simply interact with the AI, they meaningfully engaged with it. Many reported that the experience felt valuable precisely because the simulated conversation partner pushed back. Unlike classmates who sometimes hesitate to challenge one another, the AI consistently maintained its position. Students had to work harder, listen more carefully and apply the communication strategies they had spent weeks learning. And, importantly, they practised.

Here’s how to show students what responsible AI use looks like — from timeshighereducation.com by Andrew Firr and Alex Fenton
Well-designed assessments can highlight generative AI’s limitations, where it can provide support and what responsible practice looks like. Andrew Firr and Alex Fenton offer strategies

We have been exploring ways to offer this in our Level 7 module for MSc engineering management students, many of whom come from the Global South. Assessment is built around a practical design problem: students examine how a real campus process operates, where delays occur, evidence they observe and how a feasible redesign might improve the system – in a 3,000-word essay.

They can use AI for planning and providing clarity, but not for generating the evidence on which the analysis rests. Invented observations, measurements, screenshots or unverifiable citations are prohibited. The assessment design reinforces this through a case study, diagram, fact-checking exercise and a short AI use statement.

Requiring an AI use statement encourages students to reflect on their processes. We ask which tool they used, how they used it, how they checked the output and which parts of the work depended on their own evidence and judgement. It reminds them that evidence must be theirs and that citations must be manually verified.


Slow math: Kids may learn more when AI makes them review mistakes — from hechingerreport.org by Jill Barshay and Kristin Fasiang
A randomized experiment involved more than 6,000 Tennessee middle school students learning fractions

In an experiment involving more than 6,000 middle schoolers in Tennessee, students learned slightly more math when an AI tutor walked them through their mistakes and then required them to demonstrate the same skill correctly three times in a row before moving on.

The winning combination wasn’t the addition of AI tutoring alone, but AI tutoring plus repetition, with the idea that students needed to stick with the skill to demonstrate some level of mastery. The students who practiced math with this AI-enhanced “mastery learning” approach scored about 3 percentage points higher than students receiving conventional computerized instruction. The advantage was small.


I Built a Team of AI Bots That Write Feedback Better than Me. Here’s How.  — from drphilippahardman.substack.com by Dr Philippa Hardman
Aka, how to make learners love AI-assisted feedback, rather than loathe it.

This wasn’t hidden from the cohort. For reasons I’ll come to below, I always tell my cohorts at the very start that an AI assistant will be helping me draft their feedback. The aim wasn’t to remove me from the process, but to find out whether AI could help me to deliver feedback at a volume and quality that I simply couldn’t maintain without AI.

Spoiler: the experiment worked — but not for the reason I expected. In this post, I’ll share exactly what I built, tell you how to build a version for your course and reveal the three conditions that decide whether learners trust AI-assisted feedback or quietly stop reading it.

TL;DR: the spec for my feedback assistant is long not because the model needs persuading, but because it’s where my judgement lives. Every rule in it is a decision AI would otherwise make by itself, with limited expertise. A spec doesn’t make the model smarter — it makes expertise and judgement legible enough for the model to follow.


Of the five stages of AI grief, some managers are stuck in denial— from timeshighereducation.com by Shadi Mohamed
Acceptance is not surrender. It is about getting beyond a policing obsession and rethinking what assessment aims to measure, says Shadi Mohamed

In that moment of institutional blindness, it struck me: universities are not merely struggling with a technological adoption curve. They are grieving the demise of the modern university’s economic and institutional logic.

To understand the scale of this threat posed by AI, consider what the internet did to journalism. Newspapers thrived by bundling together classified advertising with quick news hits, sports scores and opinion columns, using that reliable revenue to fund the expensive social necessity of investigative reporting. The rise of internet advertising did not kill journalism outright but it shattered the bundle, leaving the expensive core product without its historical financial engine.

Universities operate on a similar logic, bundling content delivery, assessment, credentialling, research and professional formation. But generative AI is now commodifying the most visible parts of that package. When AI can deliver personalised explanations instantly and generate the essays and reports we use to measure student capability, the traditional degree loses its role in the labour market as a proxy for understanding. If the bundle breaks, the economic model that funds our deeper purposes is in peril.


 

Employees from the world’s biggest AI companies want the US to be ready to slow AI development — from cnn.com by Hadas Gold

Top staffers from the biggest AI and technology companies urged the US government to slow the pace of artificial intelligence development so that safety and security measures can catch up in an open letter.

The US government should support an international effort to develop tools that can “deliberately pace the frontier of automated AI development,” according to the letter.

More than 1,000 employees from frontier AI companies signed the letter, including the chief scientist of OpenAI, one of the ChatGPT developer’s original cofounders, some of Anthropic’s cofoundersand vice presidents at Meta, Google and others.

The letter comes on the heels of major advancements and burgeoning threats from rapidly developing AI systems. OpenAI disclosed last week that two of its test models escaped a lab environment, bypassed its systems to gain access to the open internet and hacked a different company’s internal system.

 

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

 

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

 

Nikii Shaver on legal AI strategy, agentic governance, and trusted judgement — from The Geek in Review Podcast

What does legal AI value look like once speed stops serving as the headline metric? In this episode of The Geek in Review, Greg Lambert and Marlene Gebauer speak with Nikki Shaver, co-founder and CEO of  Legal Technology Jub and a member of the inaugural Financial Times Law 50. Shaver argues that law firms need to move beyond time saved toward efficacy: stronger output, stronger client outcomes, and more effective legal advice.

The conversation examines why the billable hour is far from finished yet no longer serves as the sole measure of legal value. Shaver compares hourly timekeeping to a taxi meter: useful for internal visibility, yet insufficient as the price signal for work transformed by AI. Workflow mapping, client discussions, and pricing discipline become central where an AI-enabled process compresses weeks of effort into hours.

Corporate legal departments are adopting AI at a faster pace, bringing new pressure to outside counsel. Some in-house teams see AI as a route to keep more work inside, while others see room for firms to take on work that previously sat outside budget limits. Shaver frames the strategic question around delivering more for clients, especially in practice areas where a firm holds differentiated expertise.

AI has not produced the promised empty calendar. Instead, lawyers report fuller schedules, longer documents, and a growing verification tax. Shaver flags the rise of 40-page forms, bloated redlines, and outputs that look polished yet lack sound reasoning. The episode makes a practical case for concise drafting, human review, and critical reasoning before any AI-generated material reaches a client or counterparty.

Agentic AI raises the stakes. Legal Technology Hub’s AI Agents in Law Map tracks hundreds of solutions, yet governance has not kept pace with new autonomy, connectors, and downstream system access. Shaver urges firms to establish traceability, unique identifiers, risk-based human oversight, enforceable policies, and a clear view of where data travels.

For firms aiming past baseline adoption, Shaver draws a line between routine personal use and strategic transformation. Daily use builds fluency, but competitive advantage grows from proprietary workflows, data foundations, client-facing collaboration spaces, and focused investment in the practices where a firm already excels. Her crystal-ball view is blunt: trusted judgment will become a scarce premium asset, AI-native firms will rise, and traditional firms will launch AI-native subsidiaries of their own.


Nonprofit, legal automation company design new AI tool to protect public benefits — from abajournal.com by Amanda Robert

An artificial intelligence tool from national nonprofit Frontline Justice and legal automation company Josef that aims to improve access to justice is rolling out across three states.

Frontline Q, an AI assistant that can help families navigate the complex Supplemental Nutrition Assistance Program, is now available in Arizona, Texas and Alaska. Using a combination of federal, state and local regulations and with oversight from legal aid lawyers, it offers answers to questions about the program’s eligibility and appeal rules.


CLM is a Zombie, ALSPs are In Trouble and Outside Counsel Budgets Cut in Half: Episode 54, Wordsmith.ai CEO Ross McNairn — from legallydisrupted.com by Zach Abramowitz
The understated founder of one of the hottest legal AI startups on the market makes bold predictions


Though not necessarily related to legaltech, these items caught my eye as well:

The Los Angeles Police Department (LAPD) is reportedly ending its deal with Flock Safety, a surveillance company that helps law enforcement track vehicles using thousands of its license plate cameras placed across the United States.

A senior LAPD official told news outlets, first reported by ABC7 and the Los Angeles Times, that the police department would allow its three-year contract with Flock to expire when it ends on Saturday. The department cited “serious concerns” around civil liberties and privacy. Flock’s cameras are operated by the Atlanta, Georgia-based company and not the LAPD.

To survive this nightmarish job market, candidates are now “spraying and praying,” as one career coach described it — or paying resume services to blast out thousands of CVs per day to game the system and land a gig. However, according to experts in the tech industry who spoke with SFGATE, this is only creating a vicious cycle of inefficiency that hurts both workers and companies.

 

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.

 

“Teachers ban it. Employers demand it.”

 


Also relevant/see:


The Shifting Career Ladder — from nafez.substack.com by Nafez Dakkak
AI is changing how work works and quietly removing the pathways through which young people learn to become experts.

AI is reshaping how people build skills, enter professions, and move along the career ladder and through the labour market.

In this conversation, I sit down with Matt Sigelmen founder of LightCast and now the President of Burning Glass Institute. Matt has dedicated his career to understanding the labor market and helping society improve the connections within in it.

Matt and I explore why people and opportunities are often only “a few skills apart,” why entry-level work may be losing its traditional role as the first rung of expertise, and why schools, universities, and employers now need to rethink the pathways that turn potential into mastery.

Educators need to be deeply aligned with what these changes are, and they need to shift the AI discourse from “how” questions to “what” questions. What do we need to teach? What do we need to keep in the curriculum?

 

LEGAL FUTURISMS: Consumer Legal Tech and the Soul of US Democracy

The “Dollar Legal” Thought Experiment

What if the salvation of our democratic enthusiasm doesn’t come from a political revolution, but a technological one? Enter the thought experiment of Dollar Legal.

Imagine a ubiquitous, consumer-facing legal tech platform—accessible from any smartphone—that commoditizes basic legal defense and assertion of rights. It is AI-driven legal agency for the masses, costing no more than a cup of coffee, or perhaps literally a single dollar.

Dollar Legal wouldn’t exist to litigate complex corporate mergers; it would exist to handle the agonizing friction of everyday survival. It instantly analyzes a notice to quit, drafts a legally sound response asserting warranty of habitability defenses, and files it electronically. It forces the bureaucratic machine to pause. It translates the raw, terrifying human experience of a legal threat into the cold rules and procedures that the justice system understands.

And the price, $1.00, at sufficient scale, would support the operation and maintenance of the service.

By deploying Dollar Legal, we fundamentally shift the balance of power. The democratization of legal leverage changes the calculus for bad actors who rely on the silent default judgments of the unrepresented.

 

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.

 

A Comprehensive Report on Teens, Tweens, and AI — from commonsensemedia.org

To find out what that actually looks like day-to-day, we surveyed more than a thousand 9- to 17-year-olds across the country. We asked them how they use AI, how often, and for what.

The Common Sense Media Census: AI Use by Tweens and Teens (2026) is the first in a series we’ll repeat every year to learn how this generation’s relationship with AI evolves over time.

A few things stood out:

  • Kids are using AI for many things. It’s not just a homework helper anymore. For some kids, AI has become a confidant, even though our research is clear that AI companionship is not safe for anyone under 18.
  • Guardrails are thin to nonexistent. Schools are talking about rules more than safety. Three-quarters of kids say their school has discussed what they can and cannot use AI for, but just over half have been taught how to use AI safely.
  • Just like we saw with smartphones and social media, the conversation is once again lagging behind the technology. Nearly half of kids have never had a conversation with their parents about AI safety.
 

Higher Education Can’t Wait for the Future to Arrive (Lev Gonick, Arizona State University) — from humanistxyz.substack.com by Allison Dulin Salisbury
“The biggest risk we face as a sector is assuming we can wait out AI.”

We have an opportunity right now to reorient the university around student experience—not as an aspiration, but as a necessity. I’m calling this shift TechEd, which I explore in detail in my LinkedIn series The TechEd Revolution.

AI poses a fundamental shift in how technology might empower students to own their discovery and educational journey, and to drastically reduce the friction that makes college so unappealing to so many.

To that end, we need to urgently redesign systems and opportunities around skills and competencies. That work should be far more advanced than it currently is. And one of the hardest challenges is rethinking how we operate as a workforce in academia. 

 
© 2025 | Daniel Christian