Why Judgment May Become the Most Valuable Skill of the Next Decade
July 23, 2026
Artificial intelligence, along with the AI agents and large language models riding its wake, has clearly become the dominant technology story of our moment, and yet beneath the relentless churn of headlines sits a far more interesting question that almost nobody bothers to ask, which is whether AI has genuinely rewired human behaviour or has instead simply pulled up a chair at a game that fear, greed, and the endless craving for certainty have been running for centuries. My own view falls squarely on the second answer, because behavioural psychology and behavioural economics have not been rendered quaint by the arrival of these models but have if anything grown sharper in their relevance, since AI does not manufacture fear, greed, or herd behaviour from nothing but rather amplifies them, accelerates them, and now and then exploits them with a speed and persistence that no earlier technology could ever have managed, which means human psychology still sits at the wheel while the only thing that has truly changed is the sheer velocity at which information, misinformation, and raw emotion now travel.
The Real Danger Is Treating a Tool Like an Oracle
The genuine hazard here was never the technology itself but rather the steadily growing habit of treating AI as an authority instead of a tool, and this matters because large language models answer with a kind of extraordinary confidence even in the very moments when they happen to be confidently wrong, rarely hesitating and seldom expressing the sort of uncertainty that would honestly signal ignorance, so that because their responses arrive articulate and coherent people slip almost automatically into mistaking fluency for understanding. Confidence has always been persuasive throughout human history, and AI simply delivers that confidence at industrial scale, which is exactly how the crowd ends up mistaking polished rubbish for knowledge while quietly forgetting that a convincing answer and a correct one are not remotely the same thing.
We have already watched the consequences play out in public and often in embarrassing fashion, since lawyers have submitted AI-generated legal cases that never existed and then squirmed in court when the citations turned out to be pure fiction, programmers have shipped code they never fully understood, and engineers have waved through recommendations without pausing to check the critical assumptions buried underneath them, while even medicine has begun wrestling with the same problem as clinicians experiment with AI-assisted diagnosis yet remain personally responsible for decisions the model neither comprehends nor answers for. The technology is not malicious in any of this, and if anything it is remarkably candid about what it was actually built to do, so the failures almost always trace back to the moment a human being decided to stop exercising independent judgement.
Information Was Never the Same Thing as Judgment
That distinction keeps growing in importance precisely because AI possesses something people routinely confuse with intelligence, namely information, and information has never once been interchangeable with judgement, because judgement is the thing experience teaches you about which variables genuinely matter, which ones can be safely set aside, and, most valuable of all, when the statistically probable answer is in fact the wrong one. That kind of knowing rarely arrives from reading one more book or firing off one more query, since it accumulates instead across years and sometimes decades of colliding with reality, making mistakes, and slowly learning which patterns actually survive repeated contact with the world rather than merely looking convincing on paper.
Hand an AI model fifteen variables and it will cheerfully assign probabilities to all fifteen, whereas the same variables placed in front of a seasoned investor, physician, engineer, or trial lawyer will often see twelve of them discarded before the real analysis even begins, simply because experience whispers that those twelve contribute almost nothing to the outcome, and in a great many situations the quality of the answer is effectively decided long before the model produces a single token, since it starts with the quality of the question and, even more decisively, with the judgement of the person doing the asking.
Why Seasoned Professionals May Grow More Valuable, Not Less
This is exactly why I suspect experienced professionals may end up more valuable rather than less, because for years we have all been told that AI will simply replace expertise, and yet the opposite outcome looks at least as plausible given that AI dramatically raises the productivity of people who already carry sound judgement while ruthlessly exposing the weaknesses of those who have mistaken easy access to information for real understanding. The machine amplifies whatever the user brings to it, so that a strong foundation can produce genuinely extraordinary results while a weak one simply lets AI accelerate poor thinking with unnerving efficiency, and that same dynamic is already reshaping both financial markets and social media, where algorithms optimise for engagement rather than truth so that fear breeds more fear, euphoria breeds more euphoria, and outrage breeds more outrage. AI did not invent these emotional feedback loops but merely made them faster, more personalised, and vastly more scalable than anything we had lived through before, turning every click into another data point, every pause into another signal, and every interaction into a quiet lesson teaching the system how to hold our attention for just a little longer.
In an environment like that, behavioural economics becomes more essential rather than less, because grasping incentives, cognitive biases, and crowd behaviour may ultimately prove far more useful than understanding the technology itself, given that the machine is not making independent decisions at all but learning from billions of human ones and then reflecting those behavioural patterns straight back at us, frequently with enough sophistication that we mistake the mirror for a mind of its own.
The Overlooked Irony of Abundant Answers
There is a further irony in all of this that receives remarkably little attention, and it concerns the many younger professionals who grew up in a world where an answer always sat one search away and who now step into a world where those answers surface almost instantly through AI, because convenience has become so abundant that independent reasoning itself risks turning scarce. There will always be exceptional individuals who wield these tools to think more deeply, interrogate their own assumptions, and accelerate their learning, but my worry rests with the far larger group who gradually stop asking whether an answer is even correct simply because the machine hands it over with such effortless confidence. At almost the same moment, advances in healthcare and regenerative medicine may allow experienced professionals to stay productive for far longer than earlier generations could have imagined, and if that comes to pass the competitive advantage tilts once again toward accumulated judgement, since knowledge is increasingly something anyone can reach for while experience stubbornly refuses to be downloaded.
The Amplifier Cuts Both Ways
Used well, AI has every chance of becoming one of the greatest productivity tools ever built, accelerating learning, sharpening expertise, and letting talented people crack problems that once demanded vastly more time and effort, and yet used carelessly it will accelerate ignorance with precisely the same efficiency, generating mistakes that are faster, more persuasive, and considerably harder to catch. The technology itself is therefore unlikely to be the greatest risk of the coming decade, because human judgement still holds that dubious honour, and the only real difference now is that, for the first time in history, poor judgement has been handed an extraordinarily powerful amplifier.
