Cognitive Surrender

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Human beings have always used technology to boost their life chances. Initially, we used technology to support our physical needs, through tools and later farming. About 5,500 years ago we started writing, which was built on language and may have been one of the first technologies to support our cognitive capabilities. In more recent times we have given arithmetic to the calculator, navigation to GPS, and retrieval of information to the search engine. Each time, something was lost and something was gained, and mostly we haven’t mourned the loss. I don’t miss doing long division by hand.

AI think therefore AI am

AI is different. It reaches further into the layer of reasoning itself. It can generate the hypothesis, build the argument, critique its own plan, weigh the evidence, and decide what question to ask next. Calculators did arithmetic. AI does some of the thinking.

This can happen in several ways, and the distinction between them is meaningful:

  • Cognitive assistance. “Help me solve this problem.” The thinking is still mine; I’ve brought in a tool to sharpen it.
  • Cognitive delegation. “Work this out for me and give me the answer.” I’ve handed over the task, but I chose to, and I could take it back.
  • Cognitive dependence. “I don’t really want to think this through myself; I’ll ask AI.” The choosing has started to erode. It’s becoming the default, not the decision.
  • Cognitive surrender. “AI is better at this than I am, so I’ll defer to it.” A judgement, however implicit, that the machine’s answer is simply more trustworthy than my own thinking would be.
  • Cognitive substitution. “I can no longer do this, so will get AI to do it.” This isn’t really a choice anymore. The capacity itself has begun to disappear.

One of the stories we tell ourselves about AI is that it will make people less intelligent. The more likely story is that people gradually stop exercising certain cognitive muscles because they no longer experience any need to. Yet their output keeps rising.

Cognitive Wealth

Should that trouble us? Adam Smith might have recognised the argument. In The Wealth of Nations, he showed how specialisation and the right tools could dramatically increase productivity. A worker doing one narrow task, with the right machinery, could produce far more than a generalist.

If the AI produces a better answer, why mourn the fact that we didn’t work it out ourselves?

But Smith didn’t think rising output settled the matter. In the same book, he warned that a worker confined to a few operations had little occasion to exercise his understanding and could become “as stupid and ignorant as it is possible for a human creature to become.” Smith saw both things as true. Specialisation made us more productive, and it could also make us less capable.

There is an important difference with AI, though. Smith’s worker became narrow in one part of his working life. His broader capacity for judgement remained intact. With AI, what we’re potentially outsourcing isn’t one particular task. It’s the act of reasoning itself. Losing long division doesn’t cost me my ability to think through a difficult decision. Losing the habit of thinking difficult things through might.

Use It, Or Lose It

This is no longer only a philosophical worry. Early evidence points in this direction. A 2025 survey of 666 participants found heavier AI use correlated with lower critical thinking scores (Gerlich, 2025). An MIT Media Lab study using EEG compared essay-writers working unaided, with a search engine, or with an AI assistant, and found the AI-assisted group had the weakest neural connectivity and the lowest sense of ownership over their own work (Kosmyna et al., 2025). Both build on an older, well-established finding: that offloading mental effort onto external tools shapes what a mind stays practised at (Risko & Gilbert, 2016).

Yet, this does not mean decline is inevitable. The flaw in the historical parallel is that Smith’s factory workers were cogs in a physical system they did not control. AI is not a conveyor belt; it is a collaborative partner. The erosion of our critical thinking isn’t an inevitable consequence of the technology. It depends, at least in part, on how we choose to deploy it.

Imagine two people working the same hard problem with AI. The first treats the system as a cognitive replacement: they ask a question, accept the first 30-second response without friction, and paste the output. They have chosen cognitive substitution.

The second treats the system as a cognitive sparring partner. They use the AI to generate counterarguments to their own hypotheses, to stress-test their logic, and to explore blind spots they couldn’t see alone. They might solve the problem in a fraction of the time, but their brain has been actively engaged in an intellectual heavy-lifting session. They have chosen cognitive expansion.

AI has the profound potential to strip away the mechanical, administrative cognitive load we currently carry. By automating the grunt work, it can free up mental space for higher-order judgement, curiosity, creativity, and the fundamental human task of deciding what is actually worth doing in the first place.

The real dividing line of the AI era is not between the technology and us. It is the boundary between using AI to extend cognition versus using it instead of cognition. If we treat AI as an escape from the challenge of thinking, substitution is likely. But if we use it to push our reasoning further than we could go alone, the technology could become a powerful amplifier of human agency.

If AI makes me more capable in practice, while making me less capable without it, have I actually become more capable? The answer depends entirely on which of us is steering the machine.

Notes

  • Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), 6.
  • Kosmyna, N., Hauptmann, E., Yuan, Y. T., et al. (2025). Your brain on ChatGPT: Accumulation of cognitive debt when using an AI assistant for essay writing task. arXiv preprint 2506.08872.
  • Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688.

AI and Learning: There’s a Fraction too Little Friction

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I’ve been thinking quite a lot recently about what happens to learning when we introduce generative AI.

My concern is that if AI makes it easier to complete a task, are we also making it easier to avoid the thinking that gets us there?

That’s why I was interested in a recent paper looking at human-AI collaboration at scale.

AI, Claudius

The researchers analysed almost 250,000 real-world conversations with Claude. Rather than looking at what AI could do in a controlled experiment, they looked at what people were actually doing with it.

There’s quite a lot of learning going on.

In 67% of learning and upskilling conversations, Claude engaged in some form of teaching. This wasn’t just school or university work. Software development, health and lifestyle, and business were among the most common areas.

The most common approach was combining conceptual explanation with step-by-step guidance.

So far, so good. But teaching and learning are not the same thing. I can ask AI to explain how something works. I can also ask it to do the work for me. The two interactions might look superficially similar, but the consequences for learning are very different.

Then I read that almost half of the conversations involved some form of friction. The AI misunderstood the question, made a mistake, couldn’t do what the user wanted, or perhaps the user’s instructions weren’t clear enough.

Normally, we think of this as something technology should get better at eliminating. However, in 78.7% of these cases, users tried to recover from the problem, and when people challenged the AI’s reasoning, those attempts were successful 81.5% of the time.

This is much more interesting than the headline that AI can teach.

Resistance is not futile

Use of AI in learning often looks like this: Ask a question. Get an answer. Move on.

There is a more productive version: Ask a question. Get an answer. Notice something doesn’t make sense. Challenge it. Work through the disagreement. Correct the answer.

The second interaction looks rather like learning. There is effort, uncertainty, feedback. There is the possibility of being wrong. The learner still has to think.

Are we using AI to make learning more effective, or to make tasks more efficient?

A student who uses AI to produce a better piece of homework may well get a better piece of homework. But that doesn’t tell us whether the student has become better at the underlying task.

On the other hand, an AI system that helps a student formulate a question, tests their understanding, gives them feedback and challenges their reasoning could potentially be a very powerful learning tool.

The difference is what the human is doing.

The answer isn’t to keep AI away from learning. Quite the opposite. The potential is enormous. But we should be careful about celebrating every reduction in effort.

Effort is the point. Productive effort drives learning.

If AI removes all of it, we may discover that we have superficially made learning “easier” but also thrown the baby out with the bathwater.

The interesting question isn’t whether AI can teach: it can.

The question is whether we can use AI to make learning easier without taking the learning out of it.

AI in Education: Are We Automating Learning or Augmenting Learners?

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I was struck by a recent article in the Economist about whether AI is stopping children from learning.

The headline finding is extraordinary.

Recent research, based on 30 months of data from 26,811 Chinese secondary-school students, found that after adopting generative AI, students’ homework scores increased (on average by 18%), while the time they spent doing their homework fell (by 30%).

So far, so good. Except that their exam scores subsequently fell by 20%.

In other words, students were producing better homework, faster, but learning less.

The authors found that the problem was concentrated among students whose behaviour was consistent with outsourcing their homework to AI. Those who used AI but continued to spend roughly the same amount of time on their work experienced only small learning losses.

The Power of Productive Struggle

This made me think about productive struggle.

Learning is not simply about getting the right answer. It involves building and retrieving knowledge, making connections, trying something, getting it wrong, receiving feedback, revising your thinking and trying again.

That process requires effort and involves some friction or struggle. In the case of learning, in contrast to that of work, that effort is not a bug in the process but rather a key part of it.

This is consistent with the idea of “desirable difficulties” developed by Robert and Elizabeth Bjork: conditions that make learning feel harder or less efficient in the short term can, under the right circumstances, produce more durable learning.

The Tutoring Paradox

I keep coming back to Benjamin Bloom’s famous “2 sigma” research. Bloom found remarkably large gains from one-to-one tutoring compared with conventional classroom instruction. Shouldn’t using AI (tutors) result in similar learning gains?  

That would be the Holy Grail, but maybe we’re not there yet.  Tutoring isn’t simply a matter of giving each student the right answer. The tutor continually diagnoses where the learner is, asks questions, provides feedback, adjusts the level of challenge and keeps the learner engaged in the task. There is a human relationship involved too.

This is the question for AI in education.  How can AI provide the benefits of a tutor without removing the struggle that makes the learning happen?

Augmentation Works

Interestingly, a recent randomised experiment by Zara Contractor and Germán Reyes found that students using AI to learn an unfamiliar subject performed better on subsequent unaided knowledge tests, with the gains persisting a week later. But the strongest delayed gains came from students who used AI to augment their learning. For example, by asking it to explain concepts rather than using it to automate the production of their work. The authors found that AI users shifted time away from drafting and towards reading and searching for information.

To Be or Not To Be

The real distinction we should be looking at isn’t between “AI” and “no AI”.

It is between AI that does the thinking for you and AI that makes you think better.

If we optimise education for productivity (higher homework scores in less time) we may accidentally optimise away something much more important: the learning itself.

🎯 Seeking three executive coaching clients – pro bono

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Are you navigating a leadership transition, stepping into a new role, or facing a significant challenge as a leader?

I’d like to offer three people an executive coaching engagement consisting of three one-to-one coaching sessions.

What this involves:

🔹 Three one-to-one coaching sessions focused entirely on your goals and agenda

🔹 Sessions recorded solely to meet ICF accreditation requirements

🔹 Full confidentiality throughout

🔹 Provided at no cost

These sessions will form part of the final stage of my Professional Certificate in Executive Coaching at Henley Business School and also count towards my ICF accreditation.

You’ll receive the same coaching approach, commitment and professionalism that I bring to my paid clients. The only additional element is that the sessions will be recorded to support my qualification process.

If this sounds valuable—or someone comes to mind who would benefit—please contact me at john@executive-mentor.com.

I’d be delighted to have a short conversation to explore whether it’s a good fit.

I’d really appreciate your support, and please feel free to share this with anyone who might benefit.

Three Meters Below Sea Level (And Finally Equipped to Handle It)

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Today, after six months of lessons, I passed my swimming diploma (C). 

I can now swim a few hundred metres fully clothed (including coat and shoes), dive into the deep end and swim 15 metres underwater through a hole in a screen, and tread water in the deep end for as long as I like.

Living in the Netherlands, this feels less like a hobby and more like sensible risk management. We are surrounded by canals, crossed by rivers, and bordered by the North Sea. To add a little existential spice, my own house sits three metres below sea level. Until now, my relationship with the local geography was based largely on faith and  sturdy dikes. Now, at least, I have a fighting chance if the water ever wins. 

I still remember those first few lessons, struggling to master the breaststroke. Who invented that technique—with the arms, the frog legs, and the sequencing? It doesn’t feel like a naturally human movement. It took a surprising amount of effort to persuade my adult body to cooperate. But eventually it did. When I stopped thinking.

Looking back on the past six months, the most surprising part of the experience was how much I enjoyed learning.  In fact, I enjoyed learning to swim more than actually swimming.

The Joy of Being a Beginner

It’s funny which learning experiences stay with you. The two standout experiences in my life so far have been:

  • Learning to play the double bass as a child.
  • Learning to swim as an adult.

The two have more in common than you might think. In both cases, your ego has to leave the room. You can’t think your way through it or compensate with experience from somewhere else. You simply turn up, practise, look slightly ridiculous, improve by small increments, and one day realise you’re doing something that seemed impossible a few months earlier.

There’s something deeply satisfying about that. 

People Made the Difference

What I’ll probably remember most from these lessons are the people.

The instructors were endlessly patient, encouraging and quietly demanding in exactly the right proportions. I remember saying “I don’t think I can do it” when my instructor told me to swim through a hole in a screen after diving into the deep end.  Her reply – “don’t think, just do it” – was exactly the feedback I needed at that moment.


My fellow students were just as generous. There is a surprising amount of camaraderie that develops among people who are all trying to learn to swim as an adult. We all had our own reasons for not learning to swim earlier. Mine was a lack of talent, fear of water and physical clumsiness. (I’m great with books). I expected to feel embarrassed. Instead, I felt supported—and proud that I was doing it.

When your confidence wobbles—as it inevitably does when learning something completely new—the people around you matter enormously. They gave me the encouragement to keep going until I could do it.

So yes, at 56, I finally have my swimming diploma. I also have a newfound energy to tackle the things I’d love to do, even if I don’t naturally have the talent for them.

Anyone for tennis?

Time to Think

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There is a rhythm that takes over when you spend days in the saddle. The world narrows to the width of the road, the steady turning of the pedals, and the sound of the English countryside—rain included.

The purpose of my cycling journey across England was simple: to visit my newborn grand-niece, Florence (born on the same day as Florence Nightingale). I also wanted the journey to carry a second thread—reflection on thinking, learning, and attention. I took Nancy Kline’s Time to Think with me.

Newborn Florence. What a beautiful, pure and curious soul she is!

A mobile laboratory

Kline’s premise is simple: the quality of everything we do depends on the quality of the thinking we do first—and good thinking requires space and attention.

A bicycle tour turns out to be an unusually effective laboratory for this. No meetings. No notifications. No agenda beyond the next village. Just repetition, motion, and a mind disconnecting from noise.

Well, mostly. There was rain, there were more punctures than expected, and the traffic was occasionally overconfident in its interpretation of physics. But even that becomes part of the rhythm, eventually.

An unpaved section of National Cycle Route #1!

Places that hold thought

As I moved through England, I began to notice how often thought seems to gather in certain places.

Oxford and Stratford-upon-Avon are saturated with it. You feel it in the density of books, weight of stone, and arguments made over the centuries.

Oxford

But deep thought doesn’t only happen in institutions. Further north, at Woolsthorpe Manor, Newton’s birthplace, the story goes that much of his transformative thinking on gravity and calculus took shape not in Cambridge, but in his family’s orchard during the plague years. Whether exact or embellished, the image stays with you: revolutionary ideas generated not in a frantic hub of activity but in stillness.

That contrast stayed with me as I rode on.

Newton’s Orchard and Home

Lincoln

Standing beneath the vaulted ceilings of Lincoln Cathedral, I was struck by the scale of what people can build across generations. Stone laid upon stone, intention upon intention, each generation adding something lasting to something far larger than itself.

It made me wonder: building things that outlast us requires ultra-long-form attention that seems incompatible with today’s ‘notification economy’.

Lincoln Cathedral

The real distance

The kilometres on the bike were only the visible measure of the journey. They were scaffolding.

The real distance was mental: the slow clearing that comes from sustained movement and an open uncluttered mind.

Whether in Newton’s orchard, beneath the dreaming spires of Oxford, inside the stone vastness of Lincoln Cathedral, or in the quiet presence of Florence, the same idea kept returning in different forms:

We think best—and perhaps live best—when we step out of the noise and give ourselves time and space.

Human Literacy

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Foundational literacies

For much of my professional life, I have been committed to enabling teaching and learning, particularly the foundational literacies that help people thrive: reading and writing, numeracy, scientific and cultural literacies. These are the building blocks upon which so much else depends.

Human Literacy

As AI becomes increasingly capable—and may soon outperform us in many of these traditional literacies—another literacy is moving to the foreground: human literacy.

Human literacy: the ability to understand and regulate ourselves, relate effectively with others, and continue to learn, grow, and flourish.

If traditional literacies help us engage with knowledge, human literacy helps us engage with ourselves and one another.

Environment of Trust

Cultivating this literacy isn’t just an academic exercise, it requires intentional practice. This realisation was one of the reasons I enrolled in a nine-month coaching programme at Henley.

I expected to spend my time learning coaching theories, frameworks, tools, and techniques. What I did not fully anticipate was the importance of the environment in which that learning would take place.

One of the most striking aspects of the programme has been the sense of trust, safety, and support created among participants and faculty. Coaching requires openness, curiosity, self-awareness, and, at times, vulnerability. These qualities cannot be forced; they emerge when people feel respected and free to experiment without fear of judgement. They become more rather than less important as AI takes on a growing share of analytical and knowledge-based work.

It was remarkable how quickly accomplished professionals became willing to share uncertainties, experiment with unfamiliar approaches, and offer candid feedback. This has established a community that feels deeply collaborative rather than competitive. Credit to the faculty and team for creating this!

Personal Struggle

One of the most challenging aspects of the programme for me has been learning to step out of the driving seat. My instinct is often to steer people reach a conclusion, but effective coaching requires creating the conditions for the coachee to find their own way forward. Before joining the programme, I assumed I would be most drawn to structured, solution-focused approaches. I was surprised to find myself appreciating more humanistic and systemic perspectives. They often created a greater sense of calm and presence in the conversation, which in turn seemed to help the coachee open up and explore more freely.

It has been a useful reminder to me that some of the most valuable learning comes from the approaches we initially resist. It’s a lesson I hope to carry beyond coaching.

Learning Partnership

The connection between coaching and learning has been the most valuable insight for me so far. Good coaching is not about providing answers. It is about creating the environment in which learning can happen—cultivating trust and psychological safety, encouraging curiosity and reflection, offering challenge alongside support, and enabling continuous growth.

At its best, coaching is a learning partnership. It helps people think more clearly, discover new possibilities, and move forward with greater confidence and purpose.

I expected the course to be the project. Instead, I have found that I am the project.

Coaching and Mentoring

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Today was my last day as Chairman of Infinitas Learning — and the end of an important chapter for me in educational publishing, following my earlier years as CEO of Sanoma Learning.

Those who know me well know how passionate I am about learning, and about the role organisations like Infinitas and Sanoma play in supporting learner outcomes and helping teachers in their vitally important work.

Following its acquisition by NPM Capital as lead investor, we have doubled the size of the business, including expanding into Portugal and Poland. Working on that growth — alongside the company’s digital and, more recently, its AI transformations — has been especially rewarding. There are enormous opportunities ahead to better support both teachers and students.

Most of all, I’ve valued the people. My colleagues at Infinitas and NPM Capital have been outstanding, and I’m genuinely grateful to have been part of the journey. I wish them every success for the future.

Over the past 15 years leading and chairing organisations in edtech and learning, I’ve accumulated hard-won experience in leadership, transformation, and what it takes to grow — both as an organisation and as a person. And I’m still very much learning. What continues to fascinate me is how much of leadership ultimately comes down to learning.

In the next phase, I want to put that experience to work more directly through coaching and mentoring, alongside my ongoing board commitments in edtech, including as Chairman of Ovivio.

Where I’d most like to help:
> Executive transition coaching — supporting leaders stepping into C-suite or senior roles for the first time.
> Strategic leadership — working through the real complexity of leading organisations through change.
> Personal effectiveness — helping leaders perform at their best.

For mentoring, my focus will naturally remain close to education and edtech (while avoiding conflicts with Infinitas or Ovivio). For coaching, the methodology is different, and I am keen to work more broadly across sectors — including healthtech and business services.

If any of this resonates, or if you simply want to catch up, feel free to reach out here or directly at: johnmartin@contentconnected.com

Looking forward to what comes next.

#learning #education #edtech #coaching #mentoring

Mentorship opportunity for emerging leaders

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Over the years, I’ve enjoyed working with younger leaders as they step into bigger roles.

I’ve been fortunate to have some great people in my life who gave me space to reflect and shared perspective. I’m grateful for their support and would like to help others to make progress in their careers.

I’m interested to support one or two developing leaders as a mentor or coach – a sparring partner and sounding board. If this resonates, or you know someone who might benefit, let’s connect.

Feel free to contact me at johnmartin@contentconnected.com

Is the teacher still the ‘killer app’ in the age of AI?

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AI in education is often framed as a battle between humans and machines. Based on conversations with teachers, founders and investors over the past year, I believe the real opportunities lie in partnership, not replacement.

The OECD’s Digital Education Outlook 2026 frames AI’s role in relation to teachers across three paradigms: replacement, complementarity and augmentation. But there’s a second often overlooked dimension: institutional embedding.  Moats in education aren’t built on technology or data alone, but on alignment with pedagogical goals, curricula, regulations & governance, procurement processes and professional practice.

1. Replacement — The Productivity Play

In replacement, AI automates tasks historically done by teachers. For example, grading, summarising texts, preparing lessons, generating worksheets and providing basic feedback loops.

This is where much of today’s AI attention is focused. Tasks that were once labour-intensive can now be executed quickly using general-purpose large language models.

However,  technology that replaces discrete tasks can be easy to replicate.  Application-layer companies that don’t control workflow, data or distribution potentially become interchangeable.

2. Complementarity — Enhancing the Teacher

Complementarity is where AI does not replace teachers but meaningfully enhances their capacity. For example:

  • turning classroom data into real-time insights
  • tracking student progress against goals
  • flagging risks and opportunities
  • designing targeted interventions

Here, teachers retain judgement while AI expands insights and  sharpens execution. The result? More impactful and stickier solutions because:

  • the solution integrates with daily workflows
  • the value is tied to teacher judgement, not automation
  • switching costs rise as the technology adapts to context
  • integration with existing systems (LMS, assessment frameworks, schedules) deepens.

In Europe especially, where education systems are fragmented by language, standards and national curriculum requirements, this tailored integration is the key to durability.

3. Augmentation — Supercharging the Teacher

Augmentation involves human–AI co‑evolution: AI learns from teacher feedback over time, adapts to their pedagogical style, and augments their professional practice in ways that produce outcomes neither could achieve alone.

In theory, this is the next frontier.

But the evidence suggests caution. Recent cross‑sector analyses have found that human–AI teams often underperform the better solo performer — not because AI is weak, but because synergy is hard to design and requires:

  • structured feedback loops
  • task‑specific modelling
  • data that is pedagogically meaningful
  • long‑term usage and refinement.

These conditions are relatively rare — and do not emerge automatically from generic chatbots. Consequently, many augmentation efforts risk failing before a few succeed spectacularly.

This layer will be hard to build, slow to monetise, but potentially transformative if it materialises. The Holy Grail, but not for the faint-hearted investor.

But even the most advanced augmentation tools will fail if they don’t address a deeper challenge: institutional embedding.

The Overlooked Dimension: Institutional Embedding

If replacement, complementarity and augmentation describe how AI interacts with the teacher, the moat is arguably how deeply a solution embeds in the system.

Edtech solutions thrive where:

  • curriculum alignment exists
  • pedagogical norms reinforce its use
  • there are many rules and regulations
  • procurement frameworks are understood and effective go-to-market capabilities are developed and in place
  • teacher support boosts adoption
  • governance structures (schools, districts, ministries) endorse and fund it

Know-how about working with institutions and alignment with standards determine durability.

This is particularly true in Europe, where:

  • education is governed nationally and regionally
  • language and curriculum diversity creates product differentiation challenges
  • procurement cycles are long and complex
  • teacher autonomy is the norm.

A solution that is embedded institutionally — even if technically less advanced — will often outlive and outperform one that is technically stronger but misses the expertise around the institutions it is designed to serve.

This is where real moats are built.

The next edtech winners won’t rely on algorithms alone.  They’ll succeed by understanding that the best AI doesn’t replace teachers or even just work for them. It works with them.

Where do you see the biggest opportunities?

Looking forward >>