The Homogenizing Effect of Large Language Models on Human Expression and Thought

Certain turns of phrase drive me crazy for no particular reason. AI overuses the word “shift” and it makes me bonkers. It’s like the new em dash. This paper describes a phenomena we’ve all observed recently. There is a measurable homogenization of human thought as we lean more and more on AI text. Now I see and hear AI turn of phrase everywhere. As someone who works in AI enablement these days it’s both heartening and depressing at the same time. It’s proof that AI is being adopted but I also know that group think has some pretty bad consequences beyond my personal pet peeves.

Artificial Intelligence-Induced Deskilling in Interventional Pulmonology: An International Cross-Sectional Survey on Risk Perception and Mitigation Strategies

What’s your AI free practice quota? Just like physical endurance, cognitive retention requires reps. Tech free practice is the single most effective way to keep your core skills from decaying. With humans, you have to help them build a mental model from first principles. Without it people don’t have a frame of reference to use their judgement on the output of AI models. When I see headlines about AI causing decay of critical thinking I don’t knee jerk right into a luddite response, I go back to L&D basics. If it’s important for someone to have judgement in an area, they need to practice, they need training.

The algorithmic atelier

In college I really got into linoleum cutting. Basically you make a giant stamp and paint it with different inks to press everywhere. Great fun, one work of “art”; infinite presses. Automation inevitably lowers the barrier to entry. I could suddenly put in the work for one piece of art yet have as many copies as I like. There’s a valid place for disposable utility in art and in the world. From early Photoshop tools to modern generative models, the tools may evolve, yet artistry still hinges on taste, context, and human intent. This article is some navel gazing about what is art but it’s an important question right now for people who create at work. When I want to make the cover for a book, people expect more than slop because they’re paying for it. If I AI generate art for a course employees can see direct evidence of their company spending less on them. The medium and the effort is a signal to employees about what the content means to the employer. The fact that a human is doing is a signal to the end user about the importance of the content.

Bonus!

“Here we see the real villain: not intelligence, but convenience.” Art is dealing with AI and modernity in real time. This is a review of Mission: Impossible — The Final Reckoning, and I felt it made a stronger point than “ick, AI bad”. The movie and the critique is about usefulness and why learning actual skills is worthwhile. Casual incompetence is the real villain and not the AI. If AI slop bothers us it’s in part because it’s low effort and it sends a signal of casual incompetence. Convenience can cause incompetence but it doesn’t have to if we are thoughtful about how we create, practice and teach then we can still have the upside of AI without our skills decaying or alienating the very people we’re trying to reach.

Creative Acts For Curious People

If you’re trying to lead a design effort or scoop up some tribal knowledge it’s great to put your SME’s in creative situations to get them to reveal what they know. One of my favorite exercises from the book is #36, the unpacking exercise. Fantastic book for your desk.

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I just switched jobs. Previously I was Sr Manager of L&D over at T-Mobile. Now I’m Sr Manager of AI Enablement at Tanium. To my new coworkers joining in from Tanium, welcome, here’s a recent episode explaining what this is all about. Enjoy. To all my T-Mobile friends, do keep in touch.

The swag is real, the lightning is AI enabled

The Learning Styles Challenge After 20 Years

I was having lunch with a friend and former coworker, Jimmy. He made the point that with AI, NotebookLM Gemini Notebook specifically, folks can generate a mind map and that is hilarious because it entirely defeats the point of mind maps which is to map out *your thoughts. Modality, or the delivery format of your learning, has a huge effect on what you learn and how you learn it. I’ve found that more organizations are saying just add a video because we can! It takes minutes to whip something up on Synthesia or similar products. After two decades of earnest debunking & scientific evidence the learning style idea persists among learning professionals and the general public. People overwhelmingly report that they are visual learners. So, there has been a glut of video learning. YouTube and TikTok are partly to blame for being way too good at their jobs.

Google built the ultimate education tool with Gemini Omni, then forgot to tell us

Gemini Omni is an excellent tool for creating video and well worth your time as an educator. However, pieces like this one which are breathless about the amazing transformative power of video add to the learning styles problem. Video is a passive medium and while I love it and it has a firm place in my arsenal of learning approaches people will often mistake watching a video for learning. Videos are great at conveying emotion and stories activating your episodic memory but they’re not great for procedural knowledge. I’ve encountered excellent AI explainer videos which aren’t even slop that look like they had a $40,000 budget that fail to meet their own learning objectives because it was the wrong medium to begin with. If you’re going to learn to skydive a video might help with your nerves but at some point you have to jump from a plane and your time isn’t best spent with video.

Four Things Every Learning Professional Can Take from Sports Coaching

I think sometimes in corporate learning we could be accused of oversimplifying things. There are a lot of lessons from other parts of life which could inform our daily work. This is an excellent example. I like that video can be a tool for giving feedback in addition to filming static learning objects. It’s so cheap and fast now there’s no reason why it can’t be in the ecosystem. This article beautifully distills powerful lessons from sports coaching. It highlights how feedback-rich environments and simple tools like video can revolutionize skill development beyond just content delivery. We shouldn’t be relentless about serving peoples learning styles or preferences because we already know it will not lead to better learning outcomes. Instead, as this article points out, we should be relentless about retention of skills and transfer to new environments and for that we have to have clear judgement about which modalities serve our audience and their learning goals best.

Bonus!

NSFW but maybe with some ear buds in, it can be a perfect cleanser after a long learning style discussion.

Design For How People Learn

If you work in L&D this is a must read book. This book contains actionable research to reach people and help them learn regardless of the modality. I personally enjoy the chapters on learning evaluation.

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Measurement Is a Collaborative Activity

Explain kirkpatricks levels of evaluation to a stakeholder and watch their eyes glaze over. It’s better to solve an actual problem. We need to all agree it was a problem and have some standard around what success means. That doesn’t mean it’s always as straightforward as sales numbers go up and to the right. Sometimes the problem to be solved IS amorphous like a culture problem. In order to be effective in modern L&D you need to be able to apply influence by not just appearing to be a part of the business you need to use their data, speak their language and drive towards their goals.

From education to enablement: how to engage top talent by treating L&D as a product

The end user or learner is another stakeholder. This article advocates for treating L&D like a product. If you move from creating just content to creating experiences. Classic Google focusing on the user and watching all else follow. The bigwigs may want to see a particular set of metrics go up and to the right but the key to that actually happening is with the learner. There’s real wisdom in that. There is no such thing as a learner! Same way as there is no such thing as a user. They exist as a two part relationship to the people who do training or create content or people who coach. It’s a biological process. Learner or user is shorthand for that two part relationship. Always has been.

The most important workplace skill is …

JD Dillons story about influence and enablement over courseware is a very useful lived lesson for people in L&D. His story is dated with wiki’s and people reply-all to emails but the lesson also applies to modern companies. What I like about it is the very modest and human way his story plays out. This is a lot of what we do when we think about learning. Someone asks for a course on time management. You talk to users who complain about their systems always freezing and on the other end sending tickets to IT will have a far greater impact than a course would. The real lesson to be taught could be about how to improve the system.

Bonus!

Guess which AI designed each website! This is super fun and I did better than I thought I would. When I see a new website and it falls into one of the generic AI made categories in a designers portfolio it’s a strike. If you use claude code to build a website it will be excellent but if you use it to build a website that has a point of view in its design then I know that you’re excellent.

Affiliate Link: Design is Storytelling

I found this book incredibly useful in my design, it has a ton of tools and concepts that you can use today. It’s presented in an engaging and entertaining way that will keep you flipping through, adding tools to your design toolbelt.

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Is your AI companion a colleague or rival? - Pocket Casts

This week I’m thinking about AI enablement. Unlike previous software enablement cycles AI enablement has been coming in waves as each wave of AI has new and different capabilities. I listened to this episode twice. First with an eye on what the endgame is for AI enablement. I actua…

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Happy Wednesday!

Someone asked me a question recently about how I make L&D budget decisions specifically around elearning. Platforms is a whole other conversation. High level, you have a choice to build in house, buy custom or off the shelf or rent by using content from a content library or SaaS learning company.

Build vs Buy: Custom eLearning vs Off-the…

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I’m glad to be back from my vacation. On my friend Marion’s suggestion I read Malcom Gladwell’s book Blink on the plane. It got me thinking about how we really ought to maximize human intelligence to work with AI intelligence and reap the maximum upside.

I skillfully craft candy sushi on a cruise ship.

Generative AI at Work

This study off…

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King Roger the 2nd formalized the Kingdom of Sicily and it's parliament around 1130. This was a note worthy time in learning because Roger brought together a medieval think tank of Byzantine, Jewish and Arab scholars. The crowning achievement was the most accurate…

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Data, Design Thinking and the Changing Role of the Instructional Designer | Villanova University

A reader wrote in last week. They already took the standard instructional design certificate and they were unimpressed. She wanted to know how to uplevel her craft. I wrote back that the honest answer depends on her stack and her problems, whi…

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The Complete Guide to Building a Skill for Claude

While Claude itself has a skill building skill, this comprehensive guide offers fantastic insights into the nuances of crafting effective AI skills. It’s a must-read for anyone looking to go beyond the basics and understand AI capabilities. When you’re teaching other people however I highl…

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Keeping an Eye on AI

This article looks at the social and economic consequences of AI. The whole thing is worth a read but I liked the part on the “apprenticeship problem.” In the past apprenticeships were easier to set up because you could cut out smaller parts of a job to give to junior employees till they can take on larger responsibil…

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The cost of heroism, paid in advance

This weekend is the D-Day anniversary, and I’ve been thinking about the training nobody talks about. Three weeks before D-Day, the U.S. 4th Infantry Division ran a practice landing at Slapton Sands in the UK. German E-boats found the convoy. By dawn, 749 Americans were dead. Most of them before they ev…

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What chess can teach leaders about AI enablement

This week I’m sharing some industry AI moves that I’m seeing. Google’s approach to AI enablement highlights the need for leaders to make both tactical and strategic moves simultaneously, ensuring a well-rounded integration. It’s about playing the short and long game at the same time. Very p…

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My dad and I were talking after the Forbes interview ran. He reads everything I publish now, which is its own kind of pressure. He said the interview was good, and then he said the thing that turned into this episode: a lot of people are new, do they know what this is, you should tell them.

He is usually right about this. He spent a career as a software engineer, watched a lot of smart people build things nobody could explain afterward, and developed a low tolerance for work that does not explain itself. So this one is for the new readers, and it’s his fault.

If you found Nerd Out through Forbes, the Mindtools podcast, or a forward with no context, here is what you signed up for.

The short version

I’m Alyn Kinney. Twenty-plus years in corporate learning and development, more than 15 of them running L&D teams. I designed and ran training at Google, T-Mobile, American Express, Chase, and Progressive, plus a long list of smaller shops I freelanced for. I started this newsletter in 2017 to argue with my coworkers about what was working in our field. There were seven readers. It grew by people forwarding it to other people, which is still the only growth strategy I trust. I’m not an influencer. I have a day job. Episodes stay short because they were meant to be read on the job. You can reply straight from your inbox and it comes to me.

It goes out free, most Wednesdays, unless I’m on a work trip or choosing my family. There’s a paid tier. The best stuff stays free. Paid subscribers get the back catalog and the occasional look behind the projects I’m building. People pay mostly to keep this going, and a lot of them expense it to their learning budget, which I encourage, because almost everything I write about is your job.

What this is about

Most L&D writing is hype. Here are the trends. Here is the new platform. Here is the skills taxonomy that will be obsolete before you finish the chart.

Nerd Out is not that. I read the 300-page OECD digital report so you don’t have to, then tell you the one thing in it that changes your Monday. My dad used to pay me for book reports. I blame him for this behavior. I sit through the conference keynote and count how many times the speaker says “fear,” because the tally tells you more than the slides do. I’m not a reporter. I listen and then I write to solidify my own learning.

The work sits at a strange intersection. Learning science, philosophy, and lately whatever AI did this month. I care about how humans build durable knowledge, not how a vendor’s deck says they do. I have a philosophy degree and a learning design degree and I use both, often in the same paragraph, sometimes to ruin a perfectly good buzzword.

A few hobby horses I keep coming back to.

Cognitive debt. Every time an organization outsources its thinking to a prompt, a vendor, or a tool, it buys speed today and pays later in judgment it no longer has. The bill is real. It shows up on a delay, which is exactly why nobody budgets for it. This is the topic of a longer writing project, and I write about how to see it coming.

Productive struggle. The industry sells frictionless learning as a feature. It is mostly a bug. The struggle is often where the learning lives, and designing it out on purpose is one of the more expensive mistakes we make while feeling good about ourselves. I’m not anti-AI. I teach AI adoption. But the highest use of these tools is to sharpen your own thinking, not to skip it.

L&D that solves problems instead of producing content. Too many teams measure themselves by catalog size. That’s backward, and I’ve been on the wrong side of it, which I’ll get to. The job is closer to a product manager’s than a content factory’s. Prioritize ruthlessly. Sunset what stopped working. Codesign with the people who live with what you build.

The past, mined for good ideas. We’re not the first people to care about how people learn. Microlearning showed up in the 80s. The Romans ran apprenticeships. Video-based learning was hot in the 40s. Many of today’s problems were yesterday’s problems, and sometimes they got solved.

Learners, given a damn about. I write about inclusion, accessibility, and the ethics of learning. One of my early mentors said it best: if someone can’t hear you in the back, you’re failing the business. I care because it’s who I am. You should care because it’s good business.

The part where I tell on myself

You should know this before you decide to stay.

Early in my management career I built my budget to line up with my boss’s goals. Felt smart. Felt like managing up. It took me too long to notice I had aligned my entire function to one person’s priorities instead of the business’s, and that those are not the same thing. I write about this kind of thing because I learned it the hard way and there is no reason you should have to.

High rigor, and it’s actually me. I’ll show you the front-line reporting, and I’ll tell you when I got it wrong. Both, on purpose. One without the other is either a McKinsey deck or a motivational post, and I have no interest in writing either.

I use AI to research, follow the news, explain things outside my depth, beta-read, and generate cover art. Before AI I had no cover art, so, big win. I don’t use it to write the episodes or to read the articles for me. The reading and the writing are the part I’m here for.

Who’s already here

I survey readers every few years to make sure I’m still useful. Roughly a third of you are new to corporate L&D or coming over from K-12. A third are working L&D professionals keeping up with the conversation. The last third are L&D-adjacent: consultants, founders, program managers, HR people, my dad, EdTech folks, leaders who treat development as strategy rather than a compliance line. Mostly US, with a strong contingent in India, Europe, and Australia. The relentless customer focus I picked up at Google and American Express is why I keep asking instead of guessing.

So, welcome

If any of this made you want to argue with me, good. You’re in the right place. Subscribe if you haven’t, check your junk folder for the first one and drag it somewhere it’ll survive, and come say hi on LinkedIn. I read the replies.

My dad was right that you deserved an introduction. He’s also going to read this one, so I should stop here while it’s still good.

See you Wednesday.

Google Learning Prompt Guide

Consider Star Wars: A New Hope: Luke starts with tech in the beginning, but the real magic, the mastery, comes from within. It’s a classic reminder that tools amplify, but don’t replace, our innate abilities. At the end of the movie, he dismisses the targeting computer and relies on his skills. This is Google …

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I’ve been working on this a while and the full Cognitive Debt white paper is below:

• Download a pdf copy you can drop into your LLM of choice to ask questions or for a TLDR

• Take the Cognitive Debt Quiz

• Get Cognitive Debt swag. Shout out to friend and reader, Katelyn who inspired me to put Nerd Out on a shirt so I can wear it because I’m the “Guy From Nerd Out” :-D. I made myself a couple Nerd Out inside joke products and you’re welcome to get one too.

The $20 Million Hallucination

In March 2020, our world shrank to the size of a 13-inch MacBook. I was leading the design team for the School for Googlers, and we had a $20 million problem. The high-touch-in-person-program that defined Google’s culture, “Noogler” was suddenly illegal to run.

AI edited but real. Yes, we had cake pops shaped like hats and a donut wall.

We presented leadership with a data driven choice: go for a fast elearning experience, try a virtual hybrid, or attempt to replicate the entire classroom experience on a screen. We chose speed. We moved at the speed of light toward what looked like a flawless deployment. On paper, we saved $20 million in travel and logistics. The learner satisfaction scores, which were already good, hit an unbelievable 97%. Attendance greatly improved and people were thrilled that they could complete onboarding without travel. Everyone wins.

We felt like heroes. We all got swag; I got promoted! The high touch magic of Mountain View and Google’s culture was replaced with 4k video. We were taking out a high interest loan on our employees’ ability to navigate the company. We were taking out cognitive debt.

By late 2020, we could smell the smoke even if the data didn’t show the fire. One Director told me they’d been a ‘Googler’ for months from their kitchen table, but they didn’t feel like one. The invisible connected experiences at the live orientation vanished, replaced by a view of the salt & pepper shakers at your own kitchen table. Leaders were operating in a vacuum. A few years later, when the office doors reopened, the Cognitive Debt bomb finally detonated. Senior leaders found themselves unable to lead. They were experiencing ‘organ rejection’ from their own teams. The pandemic-era goodwill had evaporated. Leaving them to learn the texture of Google’s culture while their organizations were already in revolt. Subtle cultural norms were missing. Google’s culture has been described in a lot of ways but in practice Googlers were kind and helpful because they had a thirst for problem solving. It’s one thing to hear that but another to experience volunteers from across the company to show you how high the bar is.

What is Cognitive Debt?

Cognitive debt is more malicious than just forgetting. It’s the deficit that builds when we outsource our brains to a vendor, a teammate, or a prompt. It’s the point where your people can no longer explain the logic of the systems they manage. The cost of relearning how your own company works costs more than just burning it down and starting over. AI makes the debt invisible. It’s so smooth that we mistake “AI aid” for “knowing how”. It’s a trade of fluency for speed and you don’t know you’re bankrupt till you have to make a high stakes judgement call in a crisis. This debt gets you in two primary ways:

1. Operational Weight: This is the invisible friction that makes an organization feel heavy, allergic to change, and slow to pivot. It’s the drag that makes an organization feel heavy, slow to pivot, and resistant to new ideas. It causes burnout when you force employees to drag along outdated mental models. It’s a reason why your best people leave. They’re tired of a system that fights against them.

2. The Timed Detonation: There are a lot of examples. An easy example is the compliance bomb. It’s lit when a sales rep hits their quota by ignoring the ‘boring’ training. Often this is an open secret! Then the whole thing blows up in a flurry of lawsuits and litigation. Sometimes it’s a second or third order consequence after a series of shortcuts but it can knock a business off its axis.

In an AI-enabled world, content has a marginal cost of zero, but human attention remains a finite, biological resource. If we ignore the hard limits of the human brain, we are building debt.

The danger isn’t the offloading itself. You don’t need a retail associate to memorize a thousand fluctuating SKUs. That’s a debt we can afford. The danger is that AI makes offloading so smooth we mistake output for expertise.

As the 2025 MIT research paper, ‘Your Brain on ChatGPT’ warned, we are entering an era of ‘Metacognitive Laziness.’ When we use AI as a cognitive crutch, we get a short-term productivity spike at the cost of long-term atrophy.

When you outsource the mental work that leads to judgment, fluency, and recall, you create a crisis. As I shared recently, “You could be in a board meeting, landing a huge deal with a team of lawyers. If you don’t know your stuff, if you’ve been using AI to outsource your thinking, you aren’t going to be able to call on that knowledge in your moment of need.” If something truly matters, be intentional and design to overcome laziness by making learners demonstrate what they learn, rather than just absorbing content. Good training requires friction.

Cognitive debt is manufactured at scale. Depending on the shortcuts that are taken there are a few ways you might see that:

• Slop content: This happens when we outsource creativity to the machine. We use AI to build low-quality, generic training faster than ever, leading to a flood of “AI slop” that fills inboxes but never reaches a brain. If an LLM can generate a passable version of your course in ten seconds, your training is a liability, not an asset. This content fails a brutally useful test for L&D: Would my learners pay money to take this training?

• Ego content: Ego content happens when we do things because we can, not because they’re needed. These are the high-gloss “awareness” campaigns. They look stunning in a board deck but they solve exactly zero performance gaps. The cost and time suck of the training itself becomes a business problem. It may feel like building political capital but the opportunity cost is too high. We could be fixing real problems.

• Heroism: This is the debt of inhuman expectations. We reward the ‘hero’ who stays up all night manually fixing a broken report instead of building the AI-integrated system that prevents the break. Excessive task switching adds to this debt. Workers who are asked to multi-task or take on too much work, even when it is AI aided, will have lower performance. While they and their managers feel like martyrs for shouldering the burden, they lose the ability to see the bigger picture.

• Missing Rung: We’re sawing off the bottom rung on the career ladder when we outsource experience to the machine. Junior roles that once provided the “tacit knowledge acquisition” needed for senior-level expertise are being automated. Entry level workers may never develop the “professional intuition” required to judge the quality of AI-generated work, creating a structural debt. By the time your senior leaders retire, you’ll realize the new guard has ‘institutional amnesia.’ They know how to hit ‘generate,’ but they don’t know why the company does what it does. That’s a detonation waiting to happen.

The Cognitive Debt Audit

Don’t wonder if your organization has this problem. Prove it. Answer these questions honestly to determine your learners’ cognitive debt. If the answer is, I don’t know, assume the worst but follow up in your future analysis to answer this question for your organization.

Take the quiz

See how you stack up with the research

• Category 1: The productivity hallucination 10-13 on average.

  • We are living in a productivity hallucination. Organizations are confusing volume with value. While experts see a technical speedup, the 2026 Stanford Forecast shows a 12% decline in real outcomes for high context roles. We produce more, but achieve less.

  • Even when AI generates work in seconds, verification of the work consumes up to 65% of a project’s lifecycle. If your team can’t explain the logic behind the output, they haven’t saved time. Rework is deferred to the fix it phase.

  • (Sources: Stanford AI 2026 Forecast; METR 2026)

• Category 2: Skill Loss and the Future Workforce 9-12 on average.

  • There is a direct negative correlation between constant AI reliance and critical thinking. We are sawing off the bottom rungs of the career ladder, allowing younger workers to bypass the “desirable difficulties” required to build professional intuition. By the time they reach leadership, the know-how will have atrophied.

  • This isn’t just a “junior” problem; it’s total dependency. 2026 data shows that even experienced engineers are now so reliant on these tools that they often refuse to work without them. If your team cannot explain the “why” behind the code, you’ve lost your autonomy. You are managing a black box of interdependencies you cannot diagnose or plan against. (Sources: Gerlich, 2025; METR, 2026)

• Category 3: “Fading” Company Knowledge 9-12 on average.

  • When a veteran leaves, 42% of the context required to do their job disappears with them. Without a strategy for “interaction logic,” it takes 200 hours to get a new hire back to zero. Most organizations are currently flying a “black box” of broken context. (Source: Panopto/YouGov via Stravito)

• Category 4: Focus, Energy, and Overwhelm 9-12 on average.

  • Even pre-AI modern knowledge workers were challenged, and AI adoption is making it worse. AI tools are marketed as time-savers, but they’ve created a ‘Digital Debt’ where the volume of data has outpaced human processing. We are drowning in professional grade text that nobody has the ‘bandwidth’ to actually read. The noise has been automated.

  • 68% of employees do not have uninterrupted focus time during the workday. 64% of employees struggle with having the daily time and energy to do their jobs at all. Employees who are overwhelmed are 3.5 times more likely to struggle with innovation and strategic thinking. (Source: Microsoft Global Study on Work Trend Index)

• Category 5: Building Lasting Mental Habits 9-12 on average

  • The human brain is naturally wired to conserve energy, meaning individuals naturally opt for the external answer rather than taking the route of deep thinking or manual problem-solving.

  • Because most mainstream digital interfaces are intentionally designed for seamlessness rather than maintaining human engagement (a lack of constructive friction), organizations tend to score high here simply because they lack active guardrails and training to protect independent reasoning. (Source: Research on Cognitive Conservation & Interface Design)

Performance Economist vs. Order Taker

The L&D profession is currently bifurcating.

The Order Taker is the primary agent of debt. They are the human vending machine who says “Yes” to every request for a 15-minute module or a quick video. They focus on delivery rather than discovery. In the age of agentic AI, the machine will eventually replace the Order Taker because the machine is faster and cheaper at taking orders.

The Performance Economist balances human capability (I’m not trying to coin yet another term for someone who works in corporate education, this is a metaphor). They have the courage to say, “A video won’t solve this; we need to fix the cognitive load”. They recognize that L&D must stop behaving like a content factory and start acting as a problem-solving discipline. They act like product managers, discovering problems worth solving rather than just fulfilling content requests. They understand that while we can automate the production of a course, we cannot automate the connection a learner makes with the material.

They understand that their job is to show the value created by solving business problems, remembering that, as one CLO put it, “as soon as you get into an ROI conversation, you’ve already lost.” This isn’t about finding just the right way to report on your value. It’s about spending time with learners, stakeholders and data to find the right solutions and providing undeniable solutions.

The Human Dividend

This is a manifesto for a world that values speed over depth.

AI’s best use case isn’t generating more content; it’s handling the invisible labor of research and planning so humans can return to high fidelity learning. This frees up L&D to be strategic. Let’s do the deep work. High stakes coaching, in person immersion, and the ruthless auditing of the data feeding our AI systems.

When you design ‘productive struggle’ back into your organization, you unlock the Human Dividend. This is a workforce that is agile, resilient, and capable of doing the things the machine cannot.

We need strategies that encourage independent thinking and innovation. We must design environments that prioritize independent reasoning over automated defaults. Use AI as a ‘sparring partner’ rather than a ghostwriter, and ensure that learners demonstrate mastery before we check the box. It’s time to stop building more content and start building more capability.

I’ve outlined these liabilities because we cannot solve a problem we are unwilling to audit. But this is just the beginning. We’re only just starting to imagine a better AI enabled future. Subsequent papers will focus on the approaches we can take to flourish with AI, not by outpacing the machine but by being excellent humans.

Citations

Becker, J., Rush, N., & Rein, D. (2025). Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity (arXiv:2507.09089). arXiv. https://doi.org/10.48550/arXiv.2507.09089

Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), 6. https://doi.org/10.3390/soc15010006

Kosmyna, N., Hauptmann, E., Yuan, Y. T., Situ, J., Liao, X. H., Beresnitzky, A. V., Braunstein, I., & Maes, P. (2025). Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task. arXiv. https://arxiv.org/abs/2506.08872

METR. (2026, February 24). We are changing our developer productivity experiment design. METR Blog. https://metr.org/blog/2026-02-24-uplift-update/

Microsoft. (2023). Will AI fix work? Annual Work Trend Index Report. https://www.microsoft.com/en-us/worklab/work-trend-index/will-ai-fix-work

Stack Overflow. (2025). Stack Overflow 2025 Developer Survey on AI. https://survey.stackoverflow.co/2025/

Stanford HAI. (2026). Stanford AI Experts Predict What Will Happen in 2026: The Era of Evaluation. Stanford University. https://hai.stanford.edu/news/stanford-ai-experts-predict-what-will-happen-in-2026

Stravito. (2025). Organizational memory loss: Why it matters and how to prevent it. https://www.stravito.com/resources/organizational-memory-loss-why-it-matters-and-how-to-prevent-it

Kabashkin, I. (2025). Cognitive atrophy paradox of AI–human interaction: From cognitive growth and atrophy to balance. Information, 16(11), 1009. https://doi.org/10.3390/info16111009

I tend to write themed episodes and larger themes I’m exploring over a Nerd Out season which follow what I’ve been personally nerding out about. Don’t judge! It’s my process. Anyway, my latest hobby horse since late last year has been cognitive debt. In the next couple weeks I’m doubling down on it as a crescendo of sorts.

The Current St…

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Next week something I’ve been working on is being published on Forbes. Exciting times.

Going Deeper with Empathy Maps for Instructional Design

Steve Jobs was famously opposed to organizations prioritizing process over critical thinking. Connie Malamed reminds us of the power of empathy maps in instructional design. While AI can process dat…

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When Problem Solving Followed by Instruction Works: Evidence for Productive Failure

It sounds counter-intuitive, but struggling with a problem before you’re taught the solution can dramatically improve learning outcomes. This meta-analysis shows why sometimes letting learners grapple early is key, even if it feels uncomfortable. What does…

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Beyond Reskilling: What Companies Can Do About AI Displacement

Sal Khan offers a thoughtful, and at times sobering, look at how AI will displace jobs, but also highlights crucial human-centric roles that will continue to thrive. He even proposes innovative solutions for corporate responsibility and a future-proof higher education. The ri…

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Imagine learning and development for the AI age

This piece makes a strong case for L&D to be more embedded in daily operations, acting as a strategic partner rather than a separate entity. It highlights the power of allowing space for thoughtful problem-solving, even when it means slowing down. I think it’s important here that they sugges…

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