The New "Haves" and "Have-Nots": The Difference Isn't What You Think

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There is no substitute for mastery. AI is not mastery, it is mimicry.

You’ll have to forgive my wordplay here: when I say The difference between the new “Haves” and the “Have-Nots” Isn’t What You Think, I am referring to money. But I’m also referring to thinking itself: the real difference between the new “Haves” and the “Have-Nots” Is What You Think–or are no longer capable of thinking.

There are several subtle but devastating consequences in substituting “manufactured cognition,” i.e. AI tools and agents for human cognition that few seem to recognize, much less understand as the critical factors separating the few “Haves” from the mass of “Have-Nots” going forward.

The first is remarkable for its invisibility: we are the fish not seeing the water. You may have noticed differences in the way Gen-Xers and older millennials use AI compared to Gen-Z users. The difference is the older users gained difficult-to-acquire skills and tacit (experiential) knowledge the hard way, and this experiential knowledge turns out to be the essential foundation for assessing and managing AI’s substitutions for human cognition and what’s broadly called AI literacy, understanding the limits and hazards of using AI.

Many Gen-Xers and older millennials are enthusiastic users of AI for programming and other tasks, but they’re not seeing the key factor here: they are the last generation to have the cognitive / tacit foundation needed to properly assess and manage AI’s substitutions for human cognition, because the generations coming of age in the AI era have no incentive or motivation to learn difficult-to-acquire skills and tacit (experiential) knowledge the hard way.

Consider the traditional Japanese apprenticeship in tradecrafts such as sushi making and woodworking: the apprentice spends the first three years doing menial scutwork: cleaning floors, wiping counters, sorting woodworking debris, and so on. A Talk at the Port Townsend Japanese Woodworking Festiva (via Robert P.)

The point here isn’t teaching the apprentice to be productive as quickly as possible, it’s to teach them mastery–the Tao of tacit knowledge that can only be acquired one way, the hard way, by learning every aspect of the craft/skill by doing–making mistakes, trial and error, and learning how to respond to out-of-the-ordinary but over enough time “normal” emergencies and situations.

In an era of automated machinery and cognition, it’s pointless to master anything but operating the automation processes. Doing tedious, boring, menial tasks for even three hours is pointless, never mind three days, weeks, months or years.

So when older programmers wax enthusiastically about vibe-coding with AI tools, they’re not seeing the water they’re swimming in: they actually know how to program the hard way, from scratch, and this experiential knowledge enables them to use AI tools in a way that new programmers who actually don’t know how to program because what’s “productive” is learning how to use AI programming tools and nothing more, as the long process of becoming competent in scratch programming has no value in the “sea” of “AI-enhanced productivity.

We are blind to the fact that the foundation needed to assess and manage AI prudently and effectively will no longer be taught, as it’s viewed as unnecessary and is not recognized as the foundation by those who already have it. That’s the tricky thing about tacit knowledge: we don’t know what we know, or how we obtained this experiential knowledge, it’s intuitive and just comes to us as we examine the problem.

Those of us with the deep tacit knowledge that can only be gained from decades of varied experience–the more varied, the better–we’ve forgotten more than any apprentice could possibly learn in three years, but it comes to us when we see a specific problem. We have a vast store of “tricks of the trade” and approaches to problem-solving that can’t be formalized or turned into an algorithm.

A second factor is the intrinsic risks of hyper-optimizing everything. I’ve written about this often, as it’s another example of the fish not seeing the water: if the Prime Directive is maximizing profit / optimizing efficiency then everything that’s deemed a superfluous cost is eliminated. So long apprenticeships: eliminated. Redundancies: eliminated. Loosely tied, distributed nodes: eliminated in favor of tightly bound, centralized systems. Buffers: costly to maintain eliminated.

What few understand is human experiential knowledge is the critical redundancy and buffer in crises, emergencies and atypical critical problems. In complex systems such as refineries and software, the most experienced workers are the only ones who can respond effectively to emergencies because no training program, no matter how well designed, can replace long experience.

We can stockpile spare parts and have backup systems, but if nobody has the experiential knowledge to make use of these buffers and redundancies, they’re useless.

Another aspect of this is the ability to discern AI-generated errors that only experts can detect. Physicians routinely send me examples of AI-generated medical diagrams and content that look genuine to the unpracticed eye but contain critical errors. Once again, if all we’re doing is training people to use AI tools and agents, they lack the deep experiential knowledge needed to detect potentially catastrophic errors because they simply don’t know enough of the kind of knowledge that can’t be taught or duplicated by AI.

This also applies to what AI leaves out of its responses: only those with experience can see what what left out, and how what’s left out is the means to manipulate or influence “the answer.” Once again, we’re blind to what’s being lost–fish blind to the water.

The substitution of “manufactured cognition” for the kinds of human cognition needed to gain tacit / experiential knowledge is another example of the fish not seeing the water. Since there are no systemic incentives to learning difficult things the hard way, and abundant incentives to substitute “manufactured cognition” AI for the grunt work of actually learning difficult things–do what’s easy and convenient, that’s the core of Ultra-Processed Life– then here’s what happens:

Students lose the ability to read a text longer than a few sentences, and adults find they’ve lost the ability to read a novel. This is the result of the addictive ease of using AI and the equally addictive attractions of the endless scrolls of social media.

We’re blind to what we’re losing in substituting “manufactured cognition” for the full spectrum of human cognition that can only arise from deep experiential knowledge. Once again, the fish don’t see the water they swimming in.

If you reckon this is extreme, then ask yourself: what sort of society will we have if people have lost the ability to read an entire book or think things through for themselves without AI giving them an “answer” that leaves things out that we can’t even see?

Which brings us to what’s broadly called AI literacy, a.k.a. the knowledge needed to use AI safely. The unrecognized water here is there is no “safe” use of AI, and the belief that there is a “safe use” is delusional.

AI safety education is lacking.

Your child is unlikely to learn an essential lesson at school this year: how to stay safe when using AI.

Just 30% of teens say a teacher has ever talked about how to use AI safely, according to a new survey of 13- to 17-year-olds by Common Sense Media. Unsurprisingly, the study also found that 70% of teens use AI for their homework.

If this survey was self-reported, we can guess that the actual student use of AI for homework is closer to 99% than 70%.

The delusion that there are “safe” levels of social media and AI substituting for human learning / gaining experiential knowledge is a dangerous one. The apt analogy here is mainlining heroin, or if that’s too harsh, Aldous Huxley’s fictional drug Soma. Every minute on social media is a drip of heroin/Soma. Every substitution of “manufactured cognition” for authentic learning / gaining experiential knowledge is a drip of heroin/Soma.

Core Qualities of Soma:

For the individual: provides an easy, convenient “holiday” from reality.

For society: Keeps the population distracted and compliant.

Overdose danger: excessive amounts can cause severe harm.

If we can speak the blunt truth, substituting “manufactured cognition” for the full spectrum of human cognition leads to the loss of the ability to think things through on our own. We literally lose the ability to learn on our own and think on our own without the Soma crutch of AI.

To sum up:

1. The negative consequences of AI described above will overwhelm whatever positives it generates.

2. What AI leaves out is unknown, and this is a level of control that is invisible to the end user.

3. As Iain McGilchrist has described in remarkable detail, there are many forms of cognition, and AI only mimics one form of “reasoning”–yet it’s being substituted for every form of cognition, a recipe for disaster, as mimicry isn’t what’s being mimicked.

4. Those who learned to think before AI still have the ability to assess its value. Those who never learned to think before relying on AI literally have no conception of what they lack. They’re flying blind, and so we as a society are also flying blind.

There is no substitute for mastery. AI is not mastery, it is mimicry. And depending on mimicry as a replacement for real thinking is the path to catastrophic collapse.

The “Haves” will have acquired the difficult skills of learning to learn difficult things on their own, and learning how to think clearly and deeply on their own by eschewing AI and social media.

The “Have-Nots” will have slipped down the wormhole of depending on AI’s “manufactured mimicry of cognition” and of being addicted to social media and the rest of Ultra-Processed Life.

Those who own and control the AI/social media Soma will control everyone addicted to the AI/social media Soma. Only those who don’t use Soma will be free.

Here are my recent essays on these topics:

“Rogue AI Agents” Aren’t Rogue, They’re Fulfilling Their Functional Goal: Automating Sociopathology

AI and the Delusions of Increasing Productivity

The Joyless Pantomime of Optimizing Ennui

Of related interest: Temperature Zero for Culture: Why Everything Is Starting to Look the Same.

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Source: http://charleshughsmith.blogspot.com/2026/08/the-new-haves-and-have-nots-difference.html