The surgical example is powerful because the danger is not only that watching gets mistaken for doing. It is that the watcher borrows the calm of the expert without inheriting the history that produced it.
A fluent demonstration looks smooth precisely because the hard parts have already been absorbed into the performer, the near-misses, the corrections made before they ever became visible, the tactile sense of resistance and timing and when something is starting to feel wrong. The novice sees the surface of competence, but not the accumulated error that made the surface possible.
That matters in medicine because the first real test cannot be allowed to arrive too late. If the gap between recognizing a step and producing it only becomes visible in front of a patient, the training system has already mistaken confidence for readiness. Simulation, feedback, deliberate practice, these are not just educational refinements. They are ways of making the illusion fail early, before reality has a body attached to it.
So maybe the deeper purpose of training is not simply to make competence appear. It is to make false competence collapse in time.
Great post, and it shows that the answer to learning is not always a video (as many of my clients seem to think these days). Competence comes from practice, not observation. Try fixing derailleur gears, and you'll get the idea!
Tom, fantastic article as usual. This reminds me of the Failure Mode and Effects Analysis (FMEA) risk assessments that are required as part of implementing processes, validations, process improvements, or major changes in structured environments. My personal experience with these is in the pharmaceutical manufacturing industry and operations. What "good" looks like could be a fully compliant, sterile, efficacious product that gets shipped to the customer on time and ready to be given to a patient within expiry. The actual on-the-floor manufacturing steps, testing, stability, and batch release steps are logical but numerous and prone to human error, despite the incorporation of automation and AI in various areas. Before (and during) any process step is changed or improved (which is happening all the time in manufacturing), an FMEA needs to be completed and signed off by all stakeholders. It's full focus on "failures" and evaluation of whether the current processes can identify and manage those risks quickly, safely, compliantly, and effectively. It's comprehensive and straightforward, but it takes time and careful objective review. It also gatekeeps next steps on a process improvement product. Makes sense, right?
Thank you for restacking this article and sharing this interesting addition. One thing this really highlights for me is the importance of relative context.
I spend relatively little time working in such a high-stakes environment, but it's not difficult to see how the overall approach to this would need to be ratcheted based on the potential impact of any mistakes that happen in the workplace.
There is a direct consequence of the illusion of skill that affects people at many workplaces. Watching AI generated slide decks and videos is easy and cheap to make. However, it creates illusion of skills and competence, with all the consequences: entitlement to promotions, belief people should be paid more, resentment when actually more competent people get promoted. This drives job dissatisfaction, retention challenges and workplace culture. This is why evidence informed L&D plays a crucial role in organisations who want to get ahead of generic competition, and who want to build capable and loyal workforce. We now have 2 decades of evidence showing that feelings-based workplace does not work.
The surgical example is powerful because the danger is not only that watching gets mistaken for doing. It is that the watcher borrows the calm of the expert without inheriting the history that produced it.
A fluent demonstration looks smooth precisely because the hard parts have already been absorbed into the performer, the near-misses, the corrections made before they ever became visible, the tactile sense of resistance and timing and when something is starting to feel wrong. The novice sees the surface of competence, but not the accumulated error that made the surface possible.
That matters in medicine because the first real test cannot be allowed to arrive too late. If the gap between recognizing a step and producing it only becomes visible in front of a patient, the training system has already mistaken confidence for readiness. Simulation, feedback, deliberate practice, these are not just educational refinements. They are ways of making the illusion fail early, before reality has a body attached to it.
So maybe the deeper purpose of training is not simply to make competence appear. It is to make false competence collapse in time.
Great post, and it shows that the answer to learning is not always a video (as many of my clients seem to think these days). Competence comes from practice, not observation. Try fixing derailleur gears, and you'll get the idea!
Tom, fantastic article as usual. This reminds me of the Failure Mode and Effects Analysis (FMEA) risk assessments that are required as part of implementing processes, validations, process improvements, or major changes in structured environments. My personal experience with these is in the pharmaceutical manufacturing industry and operations. What "good" looks like could be a fully compliant, sterile, efficacious product that gets shipped to the customer on time and ready to be given to a patient within expiry. The actual on-the-floor manufacturing steps, testing, stability, and batch release steps are logical but numerous and prone to human error, despite the incorporation of automation and AI in various areas. Before (and during) any process step is changed or improved (which is happening all the time in manufacturing), an FMEA needs to be completed and signed off by all stakeholders. It's full focus on "failures" and evaluation of whether the current processes can identify and manage those risks quickly, safely, compliantly, and effectively. It's comprehensive and straightforward, but it takes time and careful objective review. It also gatekeeps next steps on a process improvement product. Makes sense, right?
Thank you for restacking this article and sharing this interesting addition. One thing this really highlights for me is the importance of relative context.
I spend relatively little time working in such a high-stakes environment, but it's not difficult to see how the overall approach to this would need to be ratcheted based on the potential impact of any mistakes that happen in the workplace.
There is a direct consequence of the illusion of skill that affects people at many workplaces. Watching AI generated slide decks and videos is easy and cheap to make. However, it creates illusion of skills and competence, with all the consequences: entitlement to promotions, belief people should be paid more, resentment when actually more competent people get promoted. This drives job dissatisfaction, retention challenges and workplace culture. This is why evidence informed L&D plays a crucial role in organisations who want to get ahead of generic competition, and who want to build capable and loyal workforce. We now have 2 decades of evidence showing that feelings-based workplace does not work.