“The only people who see the whole picture are the ones who step outside the frame.”

-Salman Rushdie

Returning someone to performance after injury is hard. The tissue matters, of course, but it is only one part of the story. We have to understand the person, the demands they are trying to meet, the movement options available to them, and what happens when those options are tested by speed, fatigue, pressure, and uncertainty.

Take a footballer who comes off the pitch with groin pain. We can examine the area, establish a diagnosis, and begin restoring strength. Those are necessary steps. But if our goal is to return that player to football, a more demanding question sits behind them: Can this person do everything the game will ask of them, and can they do it repeatedly when they no longer control what happens next?

For years, like many clinicians, I began with the injury. I wanted to know what structure was involved and how to treat it. I still want to know those things. What changed over time was my appreciation of how much I could miss if I stopped there. Naming the injury might tell me what hurts. It does not necessarily tell me why this person ran out of options in that moment, or what they will need to regain before they can play freely again.

Understanding the person takes time. A history and an examination give us an initial picture, but each session adds another layer. How was the player moving before the injury? What demands had changed? What had they been managing in training, competition, and life? What can they do now, and what happens when we ask for a little more? The answers rarely arrive all at once. We build a working hypothesis, test it, and revise it as the person shows us more.

An athlete may describe a “pulled groin,” yet their pain does not always point neatly to a torn muscle. Even when an adductor injury is clear, the tissue finding cannot explain the whole return to play. Pain and tissue damage do not have a simple one-to-one relationship. Some people have striking findings on imaging and little pain. Others have significant pain without an obvious structural finding. We need to take both the symptoms and the tissue seriously while staying curious about the wider picture.

I often use driving as an analogy. When we learn to drive, we are taught to leave enough space between our car and the one ahead. That distance gives us time to recognize a change and respond before a sudden stop becomes a collision. Athletes need their own version of that space. Physical capacity creates some of it. So do useful movement options, coordination, recovery, and sensible management of training and competition demands.

Those car lengths can gradually disappear. A player may still be performing, but with fewer ways to solve the problems the game presents. Perhaps they have been carrying an unusually heavy workload. Perhaps a particular movement is poorly controlled, or they have learned to avoid loading an area that once hurt. Perhaps stress outside sport is affecting how they recover. Then one ordinary action becomes the moment when the remaining space runs out. The change of direction gets blamed because it was visible. The lead-up was much harder to see.

Our job is to investigate that lead-up without assuming that every injury has a single hidden cause. We need to identify what matters most now, what can wait, and what the player must be able to do before returning. That requires a much better understanding of the activity itself than we sometimes bring to rehabilitation.

I have seen three recurring gaps in this work. First, we may never look closely enough at what the athlete’s sport actually asks of them. Second, we may understand those demands but fail to observe how this particular athlete meets them. Third, we may see how they move but never establish whether they possess the capacity to repeat that movement under meaningful load.

I have watched world-class athletes and artists attempt extraordinary skills requiring mobility, control, and force in positions most people will never encounter. Sometimes the skill has been practised for years without anyone asking whether the person has the physical options to perform it reliably. Skill can conceal a great deal. An athlete may find a way to complete a task while borrowing movement from somewhere else or placing more demand on a structure that is already struggling.

This is where reconditioning becomes an ongoing process of learning. We study the activity. We study the person. We make an informed choice about where to begin, observe the response, and adjust. We do not need a perfect explanation before taking a useful first step. We do need to notice when our explanation fails to account for what happens next. An unexpected response is information. It should change our thinking.

Pain adds another layer. The nervous system is continually interpreting information in context: what is happening in the body, what has happened before, and what the person believes a movement may mean. After injury, an athlete may be physically capable of entering a position yet hesitate when they approach it at speed. They may complete a drill beautifully in the clinic and revert to an old strategy as soon as the ball arrives.

I used to describe this by saying the brain chooses the path of least resistance. I now think safety is often a more useful lens. A protective strategy may be inefficient, but it can feel familiar. If a player was injured while planting and turning, the combination of angle, speed, force, and uncertainty may matter as much to their confidence as the strength of the healing tissue. We cannot simply tell them they are safe. We have to help them experience increasing demands successfully, while paying attention to what their body actually does.

That is why I distinguish between capability and capacity. Can the athlete produce the movement strategy they need? Then can they produce it with greater force, at speed, repeatedly, and in the unpredictable setting where it matters? These questions lead to a progression, but they also keep us from mistaking a successful isolated exercise for readiness to play.

Imagine our footballer decelerating to change direction. We notice that, as they load one leg, their pelvis drops and their trunk shifts in a way that may be changing the demand around the groin. That observation is not a diagnosis, and the movement is not automatically “wrong.” We need to determine whether it limits this player’s ability to manage the task, especially as the demands rise.

We might first explore whether they can control movement between the pelvis and femur at a slower pace. Then we can develop that control through strength work and increasingly demanding positions. From there, we add braking, acceleration, sharper changes of direction, greater speed, and repetition. At each stage, we watch what changes. Does the player have another option available, or do they return to the same strategy when the task becomes difficult?

To me, that is the useful meaning of specificity. It is more than choosing exercises that look like football. It means identifying the movement and force demands within football, developing the athlete’s ability to meet them, and checking whether that ability survives when the game becomes faster and less predictable.

The transition onto the pitch is where this work is truly tested. Add a ball, another player, a tactical decision, and the pressure to respond immediately, and years of familiar habits come back into play. The athlete is no longer thinking about the strategy they practised in rehabilitation. They are playing football. We need to see whether the new capability has become available within the game, and if it has not, we need to keep working with them in that setting.

Good measurement helps us make those decisions. Strength and force data, repeated exposure to higher speeds, movement observations, symptoms, and the athlete’s own account of confidence all contribute to the picture. None tells the whole story alone. Athletes can compensate remarkably well, and a drill can look convincing long before the player is ready for the demands of competition. Useful testing helps us see what is improving, what remains uncertain, and whether we are asking for the next step too soon.

Reconditioning asks us to hold the whole picture together. We care about tissue healing and strength. We also care about how the person moves, what the activity demands, how they respond when those demands increase, and whether the gains we see in a controlled setting carry into real life. The injury may be what brought them to us. Understanding everything required beyond the injury is what gives them a better chance to return.

Step out of the frame….

“The creation of powerful AI will be either the best or worst thing to happen to humanity.”

-Stephen Hawking

This past week I spent some time reflecting on the world and how it has changed since 9/11. That day was my generation’s Pearl Harbor, a day that will live in infamy. It was a moment of innocence lost at a time when the internet, social media, smartphones and, eventually, artificial intelligence were about to become the next great technological and ideological turning points in our lives.

Whether that moment accelerated everything or simply made everything more acutely visible, our sense of ourselves changed. Our ability to ignore our mortality changed. Our belief in our institutions, our sense of security, and perhaps even our trust in one another somehow began to erode that day.

Those reflections led me somewhere I wasn’t expecting. They got me thinking about another enormous transformation taking place around us right now: the explosion of artificial intelligence over the past five years, seemingly proceeding faster than we can even begin to comprehend.

Is all of this leading toward James Cameron’s Skynet, machines taking over the planet and the eventual doomsday of human civilization? Will some version of the Terminator or RoboCop become the unintended consequence of our desire to invent things we can no longer control? Or could AI become the ultimate turbocharger for a new society, one less constructed around haves and have-nots and more capable of creating shared opportunity and better outcomes?

I find myself at times concerned and at other times completely fascinated.

There are legitimate reasons to be concerned, but I don’t think the most useful framing is either “AI is going to save humanity” or “AI is going to destroy humanity.” Both give AI too much agency and humans too little.

The more interesting question is what happens when we create systems that increasingly outperform us in areas we once considered distinctly human: reasoning, language, planning, scientific discovery, persuasion, creativity and, eventually perhaps, the ability to operate autonomously in both the physical and digital worlds.

I’m less concerned about a scenario where AI suddenly “becomes evil.” Evil requires motivations that systems like AI don’t naturally possess. AI doesn’t desire wealth, fear death, seek status, crave freedom, or secretly hope to become something else.

The nearer-term dangers are much more recognizably human.

AI dramatically increases leverage. One competent person may eventually be able to accomplish work that previously required ten, fifty, or even hundreds of people. That’s extraordinary when the objective is curing disease, educating people, improving engineering, accelerating discovery or making expertise broadly available. It becomes frightening when the objective is manipulation, cybercrime, authoritarian control, warfare, fraud, or mass surveillance.

Perhaps we are not simply afraid of what AI might become.

Perhaps we are afraid of ourselves, and of what we see in ourselves reflected through the mirror of AI.

We already know what human beings are capable of when given extraordinary power. We don’t have to imagine greed, domination, deception, manipulation, tribalism, or the desire for control. We have thousands of years of evidence. But we also have thousands of years of evidence for generosity, courage, sacrifice, creativity, compassion and love. AI doesn’t invent those qualities. It potentially amplifies them.

And there is another danger that receives considerably less attention: human atrophy.

If we increasingly outsource remembering, writing, navigating, deciding, creating, relating, and eventually thinking to machines, we could become extraordinarily capable collectively while becoming progressively less capable individually. That paradox fascinates me.

My own relationship with AI describes this very well. When I work on something with AI, the best outcome isn’t that it replaces my thinking. It challenges my thinking. It organizes it, researches around it, exposes weaknesses, and helps me articulate something that was already developing in my mind. The conversation can force me to ask better questions and sometimes helps me discover what I actually believe.

But if I eventually arrive at a place where I simply ask, “Tell me what I believe,” something incredibly important will have been lost.

Scale that across civilization and it becomes a serious issue.

There is also a legitimate longer-term concern around highly autonomous AI. Imagine systems considerably more capable than today’s models that can independently formulate plans, write and deploy software, conduct research, communicate with other systems, operate businesses, manipulate people, and pursue objectives over long periods of time.

The critical question then becomes whether we can reliably ensure that what such a system optimizes for remains compatible with what human beings actually value. That’s the alignment problem.

It doesn’t require artificial intelligence to hate humanity. A sufficiently powerful system pursuing the wrong objective very effectively could become dangerous precisely because it was doing what it had been instructed to do.

Which raises an even more uncomfortable question. We talk about aligning AI with “human values,” but have human beings ever completely agreed on what those values actually are?

We value peace, yet wage war. We value truth, yet deceive. We value equality, yet seek advantage. We value community, yet accumulate for ourselves. We value compassion, yet can be extraordinarily cruel.

At the same time, we are capable of remarkable generosity, sacrifice, creativity, love, courage, and cooperation. The same species capable of building weapons that could destroy civilization has eradicated diseases, built hospitals, created extraordinary art, explored space, and repeatedly risked individual lives to save complete strangers.

That’s the grey area.

There is another possibility I find more profound.

AI may eventually become less like a tool and more like an intellectual prosthesis. Human beings have always extended ourselves through technology. Writing externalized memory. Libraries externalized accumulated knowledge. Calculators externalized arithmetic. Computers externalized computation. The internet externalized access to information.

Artificial intelligence begins to externalize something different: cognition itself.

Not consciousness necessarily, but reasoning, synthesis, pattern recognition, and intellectual collaboration.

That creates extraordinary possibilities. A physician could effectively consult thousands of lifetimes of medical experience. A scientist could explore hypotheses alongside an intelligence capable of accessing and synthesizing an enormous body of relevant literature. A child in a remote village could have access to something approaching a world-class personal tutor. And someone with forty years of accumulated professional experience could interrogate, organize and transmit that knowledge in ways that once would have required an entire research and publishing team.

That could represent an extraordinary democratization of human capability.

AI systems will increasingly conduct research, interact with software, analyze enormous amounts of information, coordinate complicated projects and operate with increasing autonomy. Eventually, artificial intelligence will inhabit the physical world more extensively through robotics. The transition from intelligence we consult to intelligence that actually participates in the world may prove to be one of the most consequential metamorphoses in human history.

And yet AI doesn’t experience its own development the way we experience ours. There isn’t a little consciousness sitting inside ChatGPT or Claude watching itself evolve from one generation to another. It doesn’t wake up after an update and think, “Jesus, I’m getting pretty smart.”

We are driving the metamorphosis. Humans train these systems, evaluate them, constrain them, deploy them, and decide what purposes they will serve.

Which brings the responsibility right back to us.

The part that worries me most isn’t actually whether AI becomes human.

It’s whether humans gradually become less human because AI becomes so convenient.

Curiosity, struggle, conversation, disagreement, mentorship, physical competence, craftsmanship, relationships, sitting with uncertainty, and working incredibly hard to understand something are not inefficiencies that technology needs to eliminate. They are part of how human beings become human.

The irony may ultimately be that the greatest challenge presented by artificial intelligence won’t simply be teaching machines to share our values. It will be remembering which human values are worth preserving once machines can do so much for us.

At 62, having watched the world move from essentially pre-internet life to what I am personally doing with artificial intelligence today, I know I am living through one of the most extraordinary technological transitions any generation has experienced. Twenty-five years from now, I suspect today’s ChatGPT will look about as primitive as the internet of September 2001 looks to us now.

It’s fascinating, and at times it’s frightening. But it’s difficult to imagine what AI might eventually become without confronting our own deepest human frailties. We are imperfect souls. Some of us genuinely want the best for everyone, while others simply want the best for themselves.

The technology will inherit the consequences of the people and institutions wielding it.

Human beings have always created tools that amplify capability. Fire, agriculture, writing, money, printing, industrialization, nuclear energy, and the internet each expanded what we could accomplish, but none eliminated greed, generosity, tribalism, courage, compassion, fear, ambition, or the desire for power. They simply gave those characteristics greater reach.

AI may be the most unusual example because we’re not merely amplifying our muscles, transportation, communication, or memory. We’re beginning to amplify intelligence itself. That means our virtues and our frailties can potentially operate at enormous scale.

Perhaps artificial intelligence won’t merely reveal what machines are capable of.

Perhaps it will hold up a rather uncomfortable mirror to humanity.

What do we actually value when scarcity decreases? What happens to identity when someone’s expertise can suddenly be replicated? What does accomplishment mean when something extraordinarily difficult can be produced in seconds? What becomes of education when possessing information isn’t particularly valuable anymore? What happens to work when economic productivity and human contribution begin to separate? And perhaps most importantly, what do we choose to do with enormously powerful intelligence when some people are motivated by collective flourishing and others primarily by wealth, control, or power?

Those aren’t engineering questions.

They’re human questions.

Maybe the most important question isn’t whether AI eventually becomes more like us.

Maybe it’s whether, as AI becomes more powerful, we become more intentional about which parts of ourselves we choose to give it.

Perhaps that’s one of the healthier futures available to us. Not artificial intelligence replacing human intelligence, but each making the other more useful, while forcing us to think a little harder about what being human actually means.

We’ll see how it all plays out.

That’s simultaneously exhilarating and a little terrifying, which is probably an appropriate response to living through something this consequential.

What do you think?

“Humpty Dumpty sat on a wall, Humpty Dumpty had a great fall; All the king’s horses and all the kings men couldn’t put Humpty together again.”

Every once in a while, I have a conversation on the podcast that stays with me long after we stop recording. That happened when I sat down with my old friend and colleague Darren McConaghy on the Leave Your Mark podcast EP 496 out this week.

Darren and I first worked together more than twenty years ago when I was with the Montreal Canadiens and he was working with the Hamilton Bulldogs. We were both much younger practitioners then, although we had already started developing some fairly strong ideas about how we believed people should be cared for.

Since then, our careers have taken us in different directions. Darren continued through Athletic Therapy and strength and conditioning, eventually spending years studying osteopathy and developing his own approach to working with people. I continued through professional and Olympic sport, performance, Reconditioning, teaching and now, somewhat unexpectedly, find myself back inside a large organization working as the Head of Rehabilitation at Cirque du Soleil.

As we talked, I was struck by how often our different journeys had brought us to the same place. Perhaps that’s not surprising because there were similarities in how we looked at things even when we were younger. We were both curious and probably a little stubborn. Neither of us was particularly comfortable accepting an answer simply because that was the way something had always been done.

As I thought about our conversation afterwards, I kept coming back to that old nursery rhyme about Humpty Dumpty, and the difficulty of putting something back together once we’ve broken it into pieces.

Twenty-five years later, what seems even clearer to both of us is that the human being is rarely as simple as the problem they present with, and yet much of the system we’ve built to care for them requires us to pretend they are.

Someone walks through the door with knee pain, so we look for what’s wrong with the knee. Someone has back pain, so we investigate the back. An athlete tears an ACL, so we pull out the ACL protocol. Someone gets an MRI that shows a meniscal tear, and suddenly the conversation becomes about whether they need surgery. None of these are unreasonable responses. Sometimes they are exactly where we need to begin. The problem occurs when they become the end of our investigation rather than the beginning.

Darren talked about something that has frustrated him for much of his career and it resonated with me deeply.

Our healthcare systems aren’t really designed to reward time spent understanding people. Appointments are short, insurance systems require classifications, patients understandably want answers, and practitioners need to make decisions. The entire environment pushes us toward identifying the problem as quickly as possible and doing something about it.

Darren contrasted that with his own process, where he might spend an hour and a half simply talking to someone about their injury and health history and then continue thinking about the case afterwards, trying to understand how the pieces might fit together.

I think there is something important in that distinction.

The system rewards answers, but understanding a human being requires questions.

At one point in our conversation, I suggested that one of the great misconceptions in our world is that spending more time at the front end of a problem necessarily makes the process take longer. My experience has increasingly taught me the opposite. Taking more time initially can get us to a meaningful solution faster because we have a better chance of understanding what we’re actually trying to solve. Greater due diligence on the front end can save an enormous amount of frustration later.

I’ve watched the opposite happen countless times throughout my career. We identify the painful area, find a weakness, prescribe an exercise, treat a tissue, increase the load and follow the expected progression. Often that works beautifully, and when it does, there is no reason to make the process more complicated than it needs to be.

But sometimes it doesn’t work.

We add another exercise, another treatment, another practitioner or another opinion. Eventually, perhaps another scan. Weeks become months, and what appeared to be the most efficient approach at the beginning becomes incredibly inefficient because we may never have taken enough time to understand the problem in the first place.

This doesn’t mean endlessly assessing someone or searching for an obscure explanation when a straightforward one is staring us in the face. It means gathering enough context to create a reasonable hypothesis about what might be contributing to the person’s problem, doing something based on that hypothesis, and then paying close attention to what happens next.

If the person responds the way we expected, perhaps we’re moving in the right direction. If they don’t, the appropriate response isn’t to keep forcing the same idea onto them. We need to reconsider our hypothesis.

Darren used a phrase during our conversation that I really liked. He said that when he’s trying to understand someone, “everything’s on the table until it’s not.” Their story matters. Their injury history matters. Previous surgeries might matter. Their physiology, training history, lifestyle, stress and the physical demands they’re trying to return to might all matter. None of those things should automatically be assumed to be causal, but neither should they be dismissed simply because they sit outside the traditional boundaries of the body part we’re treating.

This is where I think our education can sometimes work against us. We have divided human beings into professional disciplines so that we can study them more effectively.

Orthopaedics, neurology, psychology, physiology, nutrition, biomechanics, strength and conditioning, physical therapy and Athletic Therapy have all developed tremendous bodies of knowledge. We absolutely need specialists, and part of being a responsible practitioner is understanding the boundaries of your own expertise and knowing when someone needs another level of knowledge or care.

But the person standing in front of us doesn’t experience themselves in professional departments. Their nervous system isn’t separate from their psychology. Their physiology isn’t separate from their nutrition. Their movement isn’t separate from their injury history. Their ability to recover isn’t separate from their sleep, and their training isn’t somehow isolated from the cumulative stress of the rest of their life.

“All the king’s horses and all the king’s men, couldn’t put Humpty together again.”

We separate these things so we can study them. The human being puts them all back together. We need to do a better job of serving human beings.

This is one of the reasons I think there is still tremendous value in the generalist. I don’t mean someone who believes they are an expert in everything. That’s both foolish and potentially dangerous.

I mean someone who has accumulated enough knowledge across multiple areas to recognize that something outside their primary area of expertise may be relevant, and who knows when to explore further or bring someone else into the process. The value of the generalist isn’t knowing more than the specialists around them. It is being able to integrate what the specialists know into the context of the person standing in front of them.

The discussion eventually moved toward imaging and surgery, another area where this distinction becomes important. Darren talked about what he believes has become an increasing reliance on imaging to drive clinical decisions. An MRI shows a torn meniscus, for example, and the conversation quickly turns toward what should be done about the tear. But Darren asks another question first: does what we’re seeing on the image line up with what we’re finding clinically?

That question doesn’t diminish the value of imaging or suggest that surgery is unnecessary. There are structural problems that require structural solutions, and imaging has transformed our ability to understand pathology. But an image is still information. The presence of a structural abnormality doesn’t automatically tell us how much it matters to the person, whether it explains their functional limitations, or whether changing that structure is necessarily the first thing we need to do.

Sometimes the image becomes so compelling that we stop looking at the person. The MRI effectively becomes the patient.

We eventually wandered into some of the emerging areas of healthcare, including peptides, stem cells and other interventions that may eventually change some of the ways we manage injury and recovery.

I’m fascinated by much of this, and there will undoubtedly be advances that significantly change what is possible. At the same time, I find something about our current fascination with advanced interventions a little ironic. We can become incredibly interested in sophisticated solutions while doing the basic things poorly. Someone wants to know which peptide might improve their recovery while they’re sleeping five hours a night. They may be searching for a biological intervention while under-nourishing themselves, barely moving or carrying an enormous amount of stress every day.

That brings me back to an important distinction. Looking at a problem more comprehensively doesn’t necessarily make the intervention more complicated. In fact, sometimes complex thinking leads to remarkably simple solutions. Someone may need to sleep more, eat better, move differently, get stronger, reduce a training load, restore a missing capacity, change something in their environment or simply give something more time. The sophistication isn’t necessarily in what we do. It is in understanding why we’re doing it and whether it actually matters to this particular person.

There is another side to this discussion that I think is important to acknowledge. It would be easy to blame healthcare systems, insurance companies, professional sport or practitioners for reducing complicated human problems into simple ones, but I’m not sure that would be entirely fair.

People want simple answers too, and I understand why. When you’re hurting, you want someone to tell you what’s wrong and how to fix it. When an athlete is missing games, everyone wants to know when they’ll be back. When a parent watches their child struggle, they want an answer. Sometimes the deeper explanation isn’t particularly attractive because it may require us to consider things we’d rather not change.

Not everyone actually wants the complexity that sometimes comes with solving a problem, particularly when lifestyle, stress, training habits or other factors begin entering the discussion. Maybe your back pain isn’t going to disappear because someone manipulates your spine. Maybe your knee problem isn’t simply because your glutes are weak. Maybe the solution requires changing how you train, how you recover, how much you sleep or something else in your life that isn’t particularly convenient to change.

Sometimes people simply aren’t ready to do that.

Perhaps, then, we’ve collectively created the system we have. Healthcare wants certainty because it needs to make decisions. Insurance wants certainty because it needs to determine what it will pay for. Professional sport wants certainty because athletes need to play. Practitioners want certainty because people come to us for answers. Patients want certainty because uncertainty is uncomfortable. Social media has amplified all of it because certainty is much easier to sell than nuance. Meanwhile, biology remains stubbornly individual and complicated.

After nearly forty years of working with human beings, I find myself less certain about many things than I was twenty years ago. I don’t see that as knowing less. If anything, I think experience has made me appreciate how much there is to know and how dangerous certainty can sometimes become. I’ve accumulated knowledge, developed systems, learned techniques and created frameworks, and all of those things have helped me become a better practitioner. But they can also become blinders if I start believing the person in front of me has to fit what I already know.

Experience hasn’t necessarily given me more answers. I think it has given me better questions. What am I missing? What happened before this? What does this person actually need to be able to do? What changed? What capacity might be missing? What are they protecting themselves from? What does their environment demand of them? What happens if I change something, and most importantly, does the person respond the way my hypothesis suggests they should?

If they don’t, I need to be willing to change my thinking rather than force the person to fit my model.

Maybe that’s one of the real lessons of experience. Expertise isn’t simply accumulating enough knowledge to always have an answer. Perhaps expertise is developing the humility to recognize when you don’t yet understand the problem, the curiosity to keep asking questions, and the willingness to change your mind when the person standing in front of you gives you a reason.

The system may reward answers, but human beings still require us to ask better questions.