\n<\/p>\n
\n\t\tOpinions expressed by Entrepreneur contributors are their own.\t<\/p>\n
When someone talks about the true cost of AI, most of the words that pop into the head are token spend, GPU costs, model licensing, cost of having outdated infrastructure (and the associated cost of migration), training costs for people, etc. But they leave out one of the most basic and challenging aspects of AI cost.\u00a0\u00a0<\/p>\n
I run a data and AI company. I\u2019ve been telling enterprise clients for more than a decade that their AI problem is a data and governance problem, not a technology one (technology is and will always be an enabler). I still believe that. But I\u2019ve started to think our industry skipped a step.\u00a0<\/p>\n
Let me call it a thinking problem for individuals and a governed change management problem at an enterprise level. Right now, it\u2019s not about whether or not people will accept AI but more like how we ensure that the change is meaningful and that it is also beneficial to us as humans.\u00a0\u00a0<\/p>\n
Many people remember this research by MIT\u2019s Media Lab<\/a> on the cognitive debt of using an AI assistant. The findings were and still are of an alarming nature. The report basically showed that people using only ChatGPT showed the least EEG (brain activity), with brain-only participants exhibiting the strongest.\u00a0It clearly demonstrates what happens to a skill you stop exercising. Even if the skill is as simple as writing an essay. The neural pathways go quiet when you stop using them, and they don\u2019t switch back on command.\u00a0\u00a0<\/p>\n Around the same time, researchers from Microsoft and Carnegie Mellon University published a report on the impact of generative AI on critical thinking<\/a>. The report labels the shift this way: \u201cThe more confidence someone had in the AI\u2019s answer, the less critical thinking they did. The more confidence someone had in their own ability, the more they engaged. Trust in the tool and trust in your own judgment moved in opposite directions.\u201d<\/p>\n It\u2019s an individual-level cost, and it shows up in judgment long before it shows up on a token bill. But now scale this individual cost in small but impactful numbers, and lo and behold, you might have AI confidence due to low self-judgment \u2014 which is not the ideal case scenario.\u00a0\u00a0<\/p>\n Regression to the mean, but the mean is a machine.\u00a0<\/p>\n One, when the effort shifts from creation or execution to verification and AI response integration, the mean is essentially the AI baseline that has been created.\u00a0<\/p>\n Two, the artificial hive mind, where LLMs independently converge on their outputs with open-ended questions.\u00a0\u00a0<\/p>\n So, I\u2019d argue now that the mean has shifted from a natural average of human ideas to whatever the model considers the most statistically likely response. The more first AI drafts and first AI recommendations we pick, the more likely they\u2019ll nudge their own thinking a little closer to that machine average.\u00a0\u00a0<\/p>\n Do that enough times, across enough people, and the organization quietly outsources its variance, dissent and its weird, unlikely, correct-anyway ideas to a system built to predict the next most probable word.\u00a0<\/p>\n2 different ways to look at it \u2014 why the mean is a machine<\/h2>\n
Sum of people = enterprise\u00a0\u00a0<\/h2>\n