Live experiential learning: ILT as usual?
Is live experiential learning, or LEL, just a surface rebranding of traditional instructor-led training?
Absolutely not. In fact, LEL is as distant from traditional ILT as Sleep No More is from traditional theater.
Instead of sitting politely, nodding along — or nodding off — as an instructor carefully reads aloud from their slide deck, learners roam about, get their hands dirty and focus on the things that matter to them (yes, even if that means they don’t get to every topic or encounter them in the way we would have liked).
In short, LEL has the ability to shake up your learners, in a good way. And when they realize that this isn’t learning as usual, they land in a mental space that makes them more curious and receptive.
So what does this look like, really? And how does it work?
As learning and development leaders, you can create fun, engaging and challenging exercises for teams that develop these important characteristics and improve numerous markers of team efficacy. Exercises to improve team performance should be focused on four themes: negotiation, agreement, coordination and output. In this article, I’ll discuss each type of exercise briefly, then how I use a framework to create challenging and engaging exercises to improve collaborative problem-solving and performance on my teams.
Marketers have spent billions of dollars testing what works—and their insights can revolutionize microlearning. By borrowing from marketing’s best strategies, L&D professionals can create microlearning that cuts through the noise, engages learners, and drives real behavior change.
If marketing can make people remember a product, L&D can make people remember a skill.
.Get the 2025 Student Guide to Artificial Intelligence — from studentguidetoai.org This guide is made available under a Creative Commons license by Elon University and the American Association of Colleges and Universities (AAC&U). .
Agentic AI is taking these already huge strides even further. Rather than simply asking a question and receiving an answer, an AI agent can assess your current level of understanding and tailor a reply to help you learn. They can also help you come up with a timetable and personalized lesson plan to make you feel as though you have a one-on-one instructor walking you through the process. If your goal is to learn to speak a new language, for example, an agent might map out a plan starting with basic vocabulary and pronunciation exercises, then progress to simple conversations, grammar rules and finally, real-world listening and speaking practice.
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For instance, if you’re an entrepreneur looking to sharpen your leadership skills, an AI agent might suggest a mix of foundational books, insightful TED Talks and case studies on high-performing executives. If you’re aiming to master data analysis, it might point you toward hands-on coding exercises, interactive tutorials and real-world datasets to practice with.
The beauty of AI-driven learning is that it’s adaptive. As you gain proficiency, your AI coach can shift its recommendations, challenge you with new concepts and even simulate real-world scenarios to deepen your understanding.
Ironically, the very technology feared by workers can also be leveraged to help them. Rather than requiring expensive external training programs or lengthy in-person workshops, AI agents can deliver personalized, on-demand learning paths tailored to each employee’s role, skill level, and career aspirations. Given that 68% of employees find today’s workplace training to be overly “one-size-fits-all,” an AI-driven approach will not only cut costs and save time but will be more effective.
This is one reason why I don’t see AI-embedded classrooms and AI-free classrooms as opposite poles. The bone of contention, here, is not whether we can cultivate AI-free moments in the classroom, but for how long those moments are actually sustainable.
Can we sustain those AI-free moments for an hour? A class session? Longer?
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Here’s what I think will happen. As AI becomes embedded in society at large, the sustainability of imposed AI-free learning spaces will get tested. Hard. I think it’ll become more and more difficult (though maybe not impossible) to impose AI-free learning spaces on students.
However, consensual and hybrid AI-free learning spaces will continue to have a lot of value. I can imagine classes where students opt into an AI-free space. Or they’ll even create and maintain those spaces.
Duolingo’s AI Revolution — from drphilippahardman.substack.com by Dr. Philippa Hardman What 148 AI-Generated Courses Tell Us About the Future of Instructional Design & Human Learning
Last week, Duolingo announced an unprecedented expansion: 148 new language courses created using generative AI, effectively doubling their content library in just one year. This represents a seismic shift in how learning content is created — a process that previously took the company 12 years for their first 100 courses.
As CEO Luis von Ahn stated in the announcement, “This is a great example of how generative AI can directly benefit our learners… allowing us to scale at unprecedented speed and quality.”
In this week’s blog, I’ll dissect exactly how Duolingo has reimagined instructional design through AI, what this means for the learner experience, and most importantly, what it tells us about the future of our profession.
Medical education is experiencing a quiet revolution—one that’s not taking place in lecture theatres or textbooks, but with headsets and holograms. At the heart of this revolution are Mixed Reality (MR) AI Agents, a new generation of devices that combine the immersive depth of mixed reality with the flexibility of artificial intelligence. These technologies are not mere flashy gadgets; they’re revolutionising the way medical students interact with complicated content, rehearse clinical skills, and prepare for real-world situations. By combining digital simulations with the physical world, MR AI Agents are redefining what it means to learn medicine in the 21st century.
4 Reasons To Use Claude AI to Teach — from techlearning.com by Erik Ofgang Features that make Claude AI appealing to educators include a focus on privacy and conversational style.
After experimenting using Claude AI on various teaching exercises, from generating quizzes to tutoring and offering writing suggestions, I found that it’s not perfect, but I think it behaves favorably compared to other AI tools in general, with an easy-to-use interface and some unique features that make it particularly suited for use in education.
In many traditional school buildings, the design sends an unspoken message: “Sit down. Follow instructions. Stay within the lines.”
But imagine what students might believe about themselves and the world if their learning environment said instead:
“You belong here. Your ideas matter. Explore freely.”
For another item related to learning spaces, see:
Choosing the Right Technology for Today’s HyFlex Classroom — from edtechmagazine.com by Gaurav Bradoo Long-lasting adoption of the HyFlex learning modality means higher education institutions shouldn’t be afraid to invest in tools that can enhance the student experience.
The answer? Solutions built for quick and easy installation, designed to work across multiple platforms and equipped with remote update and troubleshooting features. Anything we can do to reduce the number of cords, components and required steps during installation will assist AV staff. Prioritizing user-friendly design will cut down on help tickets across the lifespan of a device, and choosing features such as remote update capability can streamline maintenance. The bottom line is that simplicity and ease of deployment are not nice-to-haves. They are essential.
I’ve visited universities where instructors are faced with control panels with laminated cheat sheets next to them. These panels are often overloaded, acting as the room system as well, and not designed with simplicity or teaching flow in mind as it relates to capturing and streaming content for HyFlex teaching.
Along the lines of learning and working/office spaces…I’d like to thank Daan van Rossum for this next article (below emphasis from DSC).Daan mentioned that:
In it, Phil introduces “Vibe Officing”—challenges the mandate-vs-flexibility debate with a third way: designing office spaces people want to return to, not because they’re told to, but because they actually enjoy being there.He also touches on how AI can enable personalized nudges to improve workplace journeys.
Global leader brings its trusted brand and powerful network to enable payments with new technologies
Launches new innovations and partnerships to drive flexibility, security and acceptance
SAN FRANCISCO–(BUSINESS WIRE)–The future of commerce is on display at the Visa Global Product Drop with powerful AI-enabled advancements allowing consumers to find and buy with AI plus the introduction of new strategic partnerships and product innovations.
Collaborates with Anthropic, IBM, Microsoft, Mistral AI, OpenAI, Perplexity, Samsung, Stripe and more
Will make shopping experiences more personal, more secure and more convenient as they become powered by AI
Introduced [on April 30th] at the Visa Global Product Drop, Visa Intelligent Commerce enables AI to find and buy. It is a groundbreaking new initiative that opens Visa’s payment network to the developers and engineers building the foundational AI agents transforming commerce.
In today’s newsletter, I’m unpacking why your next major buyers won’t be people at all. They’ll be AI agents, and your brand might already be invisible to them. We’ll dig into why traditional marketing strategies are breaking down in the age of autonomous AI shoppers, what “AI optimization” (AIO) really means, and the practical steps you can take right now to make sure your business stays visible and competitive as the new digital gatekeepers take over more digital tasks.
AI platforms and AI agents—the digital assistants that browse and actually do things powered by models like GPT-4o, Claude 3.7 Sonnet, and Gemini 2.5 Pro—are increasingly becoming the gatekeepers between your business and potential customers.
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“AI is the new front door to your business for millions of consumers.”
The 40-Point (ish) AI Agent Marketing Playbook
Here’s the longer list. I went ahead and broke these into four categories so you can more easily assign owners: Content, Structure & Design, Technical & Dev, and AI Strategy & Testing. I look forward to seeing how this space, and by extension my advice, changes in the coming months.
During a fireside chat with Meta CEO Mark Zuckerberg at Meta’s LlamaCon conference on Tuesday, Microsoft CEO Satya Nadella said that 20% to 30% of code inside the company’s repositories was “written by software” — meaning AI.
In just six months, the consumer AI landscape has been redrawn. Some products surged, others stalled, and a few unexpected players rewrote the leaderboard overnight. Deepseek rocketed from obscurity to a leading ChatGPT challenger. AI video models advanced from experimental to fairly dependable (at least for short clips!). And so-called “vibe coding” is changing who can create with AI, not just who can use it. The competition is tighter, the stakes are higher, and the winners aren’t just launching, they’re sticking.
We turned to the data to answer: Which AI apps are people actively using? What’s actually making money, beyond being popular? And which tools are moving beyond curiosity-driven dabbling to become daily staples?
This is the fourth installment of the Top 100 Gen AI Consumer Apps, our bi-annual ranking of the top 50 AI-first web products (by unique monthly visits, per Similarweb) and top 50 AI-first mobile apps (by monthly active users, per Sensor Tower). Since our last report in August 2024, 17 new companies have entered the rankings of top AI-first web products.
The AI search landscape is transforming at breakneck speed. New “Deep Research” tools from ChatGPT, Gemini and Perplexity autonomously search and gather information from dozens — even hundreds — of sites, then analyze and synthesize it to produce comprehensive reports. While a human might take days or weeks to produce these 30-page citation-backed reports, AI Deep Research reports are ready in minutes.
What’s in this post
Examples of each report type I generated for my research, so you can form your own impressions.
Tips on why & how to use Deep Research and how to craft effective queries.
Comparison of key features and strengths/limitations of the top platforms
As AI agents transition from experimental systems to production-scale applications, their growing autonomy introduces novel security challenges. In a comprehensive new report, “AI Agents Are Here. So Are the Threats,” Palo Alto Networks’ Unit 42 reveals how today’s agentic architectures—despite their innovation—are vulnerable to a wide range of attacks, most of which stem not from the frameworks themselves, but from the way agents are designed, deployed, and connected to external tools.
To evaluate the breadth of these risks, Unit 42 researchers constructed two functionally identical AI agents—one built using CrewAI and the other with AutoGen. Despite architectural differences, both systems exhibited the same vulnerabilities, confirming that the underlying issues are not framework-specific. Instead, the threats arise from misconfigurations, insecure prompt design, and insufficiently hardened tool integrations—issues that transcend implementation choices.
LLMs Can Learn Complex Math from Just One Example: Researchers from University of Washington, Microsoft, and USC Unlock the Power of 1-Shot Reinforcement Learning with Verifiable Reward — from marktechpost.com by Sana Hassan
DC: THIS could unfortunately be the ROI companies will get from large investments in #AI — reduced headcount/employees/contract workers. https://t.co/zEWlqCSWzI
Duolingo will “gradually stop using contractors to do work that AI can handle,” according to an all-hands email sent by cofounder and CEO Luis von Ahn announcing that the company will be “AI-first.” The email was posted on Duolingo’s LinkedIn account.
According to von Ahn, being “AI-first” means the company will “need to rethink much of how we work” and that “making minor tweaks to systems designed for humans won’t get us there.” As part of the shift, the company will roll out “a few constructive constraints,” including the changes to how it works with contractors, looking for AI use in hiring and in performance reviews, and that “headcount will only be given if a team cannot automate more of their work.”
Something strange, and potentially alarming, is happening to the job market for young, educated workers.
According to the New York Federal Reserve, labor conditions for recent college graduates have “deteriorated noticeably” in the past few months, and the unemployment rate now stands at an unusually high 5.8 percent. Even newly minted M.B.A.s from elite programs are struggling to find work. Meanwhile, law-school applications are surging—an ominous echo of when young people used graduate school to bunker down during the great financial crisis.
What’s going on? I see three plausible explanations, and each might be a little bit true.
The new workplace trend is not employee friendly. Artificial intelligence and automation technologies are advancing at blazing speed. A growing number of companies are using AI to streamline operations, cut costs, and boost productivity. Consequently, human workers are facing facing layoffs, replaced by AI. Like it or not, companies need to make tough decisions, including layoffs to remain competitive.
Corporations including Klarna, UPS, Duolingo, Intuit and Cisco are replacing laid-off workers with AI and automation. While these technologies enhance productivity, they raise serious concerns about future job security. For many workers, there is a big concern over whether or not their jobs will be impacted.
Key takeaway: Career navigation has remained largely unchanged for decades, relying on personal networks and static job boards. The advent of AI is changing this, offering personalised career pathways, better job matching, democratised job application support, democratised access to career advice/coaching, and tailored skill development to help you get to where you need to be.Hundreds of millions of people start new jobs every year, this transformation opens up a multi-billion dollar opportunity for innovation in the global career navigation market.
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A.4 How will AI disrupt this segment? Personalised recommendations: AI can consume a vast amount of information (skills, education, career history, even youtube history, and x/twitter feeds), standardise this data at scale, and then use data models to match candidate characteristics to relevant careers and jobs. In theory, solutions could then go layers deeper, helping you position yourself for those future roles. Currently based in Amsterdam, and working in Strategy at Uber and want to work in a Product role in the future? Here are X,Y,Z specific things YOU can do in your role today to align yourself perfectly. E.g. find opportunities to manage cross functional projects in your current remit, reach out to Joe Bloggs also at Uber in Amsterdam who did Strategy and moved to Product, etc.
No matter the school, no matter the location, when I deliver an AI workshop to a group of teachers, there are always at least a few colleagues thinking (and sometimes voicing), “Do I really need to use AI?”
Nearly three years after ChatGPT 3.5 landed in our lives and disrupted workflows in ways we’re still unpacking, most schools are swiftly catching up. Training sessions, like the ones I lead, are springing up everywhere, with principals and administrators trying to answer the same questions: Which tools should we use? How do we use them responsibly? How do we design learning in this new landscape?
But here’s what surprises me most: despite all the advances in AI technology, the questions and concerns from teachers remain strikingly consistent.
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In this article, I want to pull back the curtain on those conversations. These concerns aren’t signs of reluctance – they reflect sincere feelings. And they deserve thoughtful, honest answers.
This week, in advance of major announcements from us and other vendors, I give you a good overview of the AI Agent market, and discuss the new role of AI governance platforms, AI agent development tools, AI agent vendors, and how AI agents will actually manifest and redefine what we call an “application.”
I discuss ServiceNow, Microsoft, SAP, Workday, Paradox, Maki People, and other vendors. My goal today is to “demystify” this space and explain the market, the trends, and why and how your IT department is going to be building a lot of the agents you need. And prepare for our announcements next week!
DeepSeek has quietly launched Prover V2, an open-source model built to solve math problems using Lean 4 assistant, which ensures every step of a proof is rigorously verified.
What’s impressive about it?
Massive scale: Based on DeepSeek-V3 with 671B parameters using a mixture-of-experts (MoE) architecture, which activates only parts of the model at a time to reduce compute costs.
Theorem solving: Uses long context windows (32K+ tokens) to generate detailed, step-by-step formal proofs for a wide range of math problems — from basic algebra to advanced calculus theorems.
Research grade: Assists mathematicians in testing new theorems automatically and helps students understand formal logic by generating both Lean 4 code and readable explanations.
New benchmark: Introduces ProverBench, a new 325-question benchmark set featuring problems from recent AIME exams and curated academic sources to evaluate mathematical reasoning.
The need for deep student engagement became clear at Dartmouth Geisel School of Medicine when a potential academic-integrity issue revealed gaps in its initial approach to artificial intelligence use in the classroom, leading to significant revisions to ensure equitable learning and assessment.
From George Siemens “SAIL: Transmutation, Assessment, Robots e-newsletter on 5/2/25
All indications are that AI, even if it stops advancing, has the capacity to dramatically change knowledge work. Knowing things matters less than being able to navigate and make sense of complex environments. Put another way, sensemaking, meaningmaking, and wayfinding (with their yet to be defined subelements) will be the foundation for being knowledgeable going forward.
That will require being able to personalize learning to each individual learner so that who they are (not what our content is) forms the pedagogical entry point to learning.(DSC: And I would add WHAT THEY WANT to ACHIEVE.)LLMs are particularly good and transmutation. Want to explain AI to a farmer? A sentence or two in a system prompt achieves that. Know that a learner has ADHD? A few small prompt changes and it’s reflected in the way the LLM engages with learning. Talk like a pirate. Speak in the language of Shakespeare. Language changes. All a matter of a small meta comment send to the LLM. I’m convinced that this capability to change, transmute, information will become a central part of how LLMS and AI are adopted in education.
… Speaking of Duolingo– it took them 12 years to develop 100 courses. In the last year, they developed an additional 148. AI is an accelerant with an impact in education that is hard to overstate. “Instead of taking years to build a single course with humans the company now builds a base course and uses AI to quickly customize it for dozens of different languages.”
FutureHouse is launching our platform, bringing the first publicly available superintelligent scientific agents to scientists everywhere via a web interface and API. Try it out for free at https://platform.futurehouse.org.
We are entering a new reality—one in which AI can reason and solve problems in remarkable ways. This intelligence on tap will rewrite the rules of business and transform knowledge work as we know it. Organizations today must navigate the challenge of preparing for an AI-enhanced future, where AI agents will gain increasing levels of capability over time that humans will need to harness as they redesign their business. Human ambition, creativity, and ingenuity will continue to create new economic value and opportunity as we redefine work and workflows.
As a result, a new organizational blueprint is emerging, one that blends machine intelligence with human judgment, building systems that are AI-operated but human-led. Like the Industrial Revolution and the internet era, this transformation will take decades to reach its full promise and involve broad technological, societal, and economic change.
To help leaders understand how knowledge work will evolve, Microsoft analyzed survey data from 31,000 workers across 31 countries, LinkedIn labor market trends, and trillions of Microsoft 365 productivity signals. We also spoke with AI-native startups, academics, economists, scientists, and thought leaders to explore what work could become. The data and insights point to the emergence of an entirely new organization, a Frontier Firm that looks markedly different from those we know today. Structured around on-demand intelligence and powered by “hybrid” teams of humans + agents, these companies scale rapidly, operate with agility, and generate value faster.
Frontier Firms are already taking shape, and within the next 2–5 years we expect that every organization will be on their journey to becoming one. 82% of leaders say this is a pivotal year to rethink key aspects of strategy and operations, and 81% say they expect agents to be moderately or extensively integrated into their company’s AI strategy in the next 12–18 months. Adoption is accelerating: 24% of leaders say their companies have already deployed AI organization-wide, while just 12% remain in pilot mode.
The time to act is now. The question for every leader and employee is: how will you adapt?
Anthropic expects AI-powered virtual employees to begin roaming corporate networks in the next year, the company’s top security leader told Axios in an interview this week.
Why it matters: Managing those AI identities will require companies to reassess their cybersecurity strategies or risk exposing their networks to major security breaches.
The big picture: Virtual employees could be the next AI innovation hotbed, Jason Clinton, the company’s chief information security officer, told Axios.
4 ways community colleges can boost workforce development — from highereddive.com by Natalie Schwartz Higher education leaders at this week’s ASU+GSV Summit gave advice for how two-year institutions can boost the economic mobility of their students.
SAN DIEGO — How can community colleges deliver economic mobility to their students?
College leaders at this week’s ASU+GSV Summit, an annual education and technology conference, got a glimpse into that answer as they heard how community colleges are building support from business and industry and strengthening workforce development.
These types of initiatives may be helping to boost public perception of the value of community colleges vs. four-year institutions.
When “vibe-coding” goes wrong… or, a parable in why you shouldn’t “vibe” your entire company.
Cursor, an AI-powered coding tool that many developers love-to-hate, face-planted spectacularly yesterday when its own AI support bot went off-script and fabricated a company policy, leading to a complete user revolt.
Here’s the short version:
A bug locked Cursor users out when switching devices.
Instead of human help, Cursor’s AI support bot confidently told users this was a new policy (it wasn’t).
No human checked the replies—big mistake.
The fake news spread, and devs canceled subscriptions en masse.
A Reddit thread about it got mysteriously nuked, fueling suspicion.
The reality? Just a bug, plus a bot hallucination… doing maximum damage.
… Why it matters: This is what we’d call “vibe-companying”—blindly trusting AI with critical functions without human oversight.
Think about it like this: this was JUST a startup. If more big corporations continue to lay off entire departments, replaced by AI, these already byzantine companies will become increasingly more opaque, unaccountable systems where no one, human or AI, fully understands what’s happening or who’s responsible.
Our take?Kafka dude has it right. We need to pay attention to WHAT we’re actually automating. Because automating more bureaucracy at scale, with agents we increasingly don’t understand or don’t double check, can potentially make companies less intelligent—and harder to fix when things inevitably go wrong.
Psychological Safety and Micromanagement Those who have followed our work at Psych Safety for a while will know that we believe exploring not just what to do – the behaviours and practices that support psychological safety – but also what to avoid can be hugely valuable. Understanding the behaviours that damage psychological safety, what not to do, and even what not to say can help us build better workplaces.
There are many behaviours that damage psychological safety, and one that almost always comes up in our workshops when discussing cultures of fear is micromanagement. So we thought it was time we explored micromanagement in more detail, considering how and why it damages psychological safety and what we can do instead.
Micromanagement is a particular approach to leadership where a manager exhibits overly controlling behaviours or an excessive and inappropriate focus on minor details. They might scrutinise their team’s work closely, insist on checking work, refrain from delegating, and limit the autonomy people need to do their jobs well. It can also manifest as an authoritarian leadership style, where decision-making is centralised (back to themselves) and employees have little say in their work.
From DSC: I was fortunate to not have a manager who was a micromanager until my very last boss/supervisor of my career. But it was that particular manager who made me call it quits and leave the track. She demeaned me in front of others, and was extremely directive and controlling. She wanted constant check-ins and progress reports. And I could go on and on here.
But suffice it to say that after having worked for several decades, that kind of manager was not what I was looking for. And you wouldn’t be either. By the way…my previous boss — at the same place — and I achieved a great deal in a very short time. She taught me a lot and was a great administrator, designer, professor, mentor, and friend. But that boss was moved to a different role as upper management/leadership changed. Then the micromanagement began after I reported to a different supervisor.
Anyway, don’t be a micromanager. If you are a recent graduate or are coming up on your graduation from college, learn that lesson now. No one likes to work for a micromanager. No one. It can make your employees’ lives miserable and do damage to their mental health, their enjoyment (or lack thereof) of work, and several other things that this article mentions. Instead, respect your employees. Trust your employees. Let them do their thing. See what they might need, then help meet those needs. Then get out of their way.
In today’s fast-paced business world, continuous learning has become a vital element for both individual and organizational growth. Teams that foster a culture of learning remain adaptable, innovative, and competitive. However, simply encouraging learning isn’t enough; the way teams are structured and supported plays a huge role in achieving long-term success. In this guide, we’ll explore how to effectively organize teams for continuous learning, leveraging tools, strategies, and best practices.
So, what should you do? You really need to start trying out these AI tools. They’re getting cheaper and better, and they can genuinely help save time or make work easier—ignoring them is like ignoring smartphones ten years ago.
Just keep two big things in mind:
Making the next super-smart AI costs a crazy amount of money and uses tons of power (seriously, they’re buying nuclear plants and pushing coal again!).
Companies are still figuring out how to make AI perfectly safe and fair—cause it still makes mistakes.
So, use the tools, find what helps you, but don’t trust them completely.
We’re building this plane mid-flight, and Stanford’s report card is just another confirmation that we desperately need better safety checks before we hit major turbulence.
The speedy embrace of AI tools meant to make job hunting and hiring more efficient is causing headaches and sowing distrust in these processes, people on both sides of the equation say. While companies embrace AI recruiters and application scanning systems, many job seekers are trying to boost their odds with software that generates application materials, optimizes them for AI and applies to hundreds of jobs in minutes.
Meanwhile, recruiters and hiring managers are fielding more applicants than they can keep up with, yet contend that finding real, qualified workers amid the bots, cheaters and deepfakes is only getting tougher as candidates use AI to write their cover letters, bluff their way through interviews and even hide their identities.
“I’m pro-AI in the sense that it allows you to do things that were impossible before … but it is being misused wildly,” Freire said. The problem is “when you let it do the thinking for you, it goes from a superpower to a crutch very easily.”
powerless to fight the technology that we pioneered nostalgic for a world that moved on without us after decades of paying our dues for a payday that never came …so yeah not exactly fine.
The Gen X Career Meltdown — from nytimes.com by Steeven Kurutz (DSC: This is a gifted article for you) Just when they should be at their peak, experienced workers in creative fields find that their skills are all but obsolete.
If you entered media or image-making in the ’90s — magazine publishing, newspaper journalism, photography, graphic design, advertising, music, film, TV — there’s a good chance that you are now doing something else for work. That’s because those industries have shrunk or transformed themselves radically, shutting out those whose skills were once in high demand.
“I am having conversations every day with people whose careers are sort of over,” said Chris Wilcha, a 53-year-old film and TV director in Los Angeles.
Talk with people in their late 40s and 50s who once imagined they would be able to achieve great heights — or at least a solid career while flexing their creative muscles — and you are likely to hear about the photographer whose work dried up, the designer who can’t get hired or the magazine journalist who isn’t doing much of anything.
In the wake of the influencers comes another threat, artificial intelligence, which seems likely to replace many of the remaining Gen X copywriters, photographers and designers. By 2030, ad agencies in the United States will lose 32,000 jobs, or 7.5 percent of the industry’s work force, to the technology, according to the research firm Forrester.
From DSC: This article reminds me of how tough it is to navigate change in our lives. For me, it was often due to the fact that I was working with technologies. Being a technologist can be difficult, especially as one gets older and faces age discrimination in a variety of industries. You need to pick the right technologies and the directions that will last (for me it was email, videoconferencing, the Internet, online-based education/training, discovering/implementing instructional technologies, and becoming a futurist).
For you younger folks out there — especially students within K-16 — aim to develop a perspective and a skillset that is all about adapting to change. You will likely need to reinvent yourself and/or pick up new skills over your working years. You are most assuredly required to be a lifelong learner now. That’s why I have been pushing for school systems to be more concerned with providing more choice and control to students — so that students actually like school and enjoy learning about new things.
Why This Matters for Law and Legal Tech Firth emphasized that one of the key criteria for selecting technologies is their broader relevance — what problem do they solve? Here’s how some of these breakthroughs could impact the legal industry:
Small Language Models and Legal AI – Unlike large AI models trained on vast public datasets, small language models can be built on private, secure datasets, making them ideal for legal applications. Law firms and in-house legal teams could develop AI tools trained on their own cases and internal documents, improving efficiency while maintaining confidentiality. These models also require far less computational power, making them more practical and cost-effective.
Use of these models have lots of applications for law. They could be used on large e-discovery data sets. They could be used to access a law firm’s past efforts. They could mine clients data to provide answers to legal questions efficiently. For that matter, they could allow in house legal to answer questions from company data without engaging outside counsel on certain issues.
