You might assume that criticizing artificial intelligence means rejecting the technology outright. But the author makes a clear distinction: they do not hate AI itself. The frustration comes from how AI is marketed and its impact on communities and jobs. This is where the AI concerns about thinking critically about the hype become relevant. Despite these issues, the author uses AI frequently and admits it is hard to abandon entirely.
The Harmful Marketing of AI
Even if you find AI hard to quit, the way it is sold to you should raise serious AI concerns about thinking. Vendors market these tools as effortless intelligence — a seamless productivity booster that does the heavy lifting for you. But that promise hides a troubling trade-off. The more you rely on AI to generate answers, summarize content, or make decisions, the less you practice your own judgment. Over time, your ability to evaluate information, spot flaws, and think critically can quietly decline. That is the real cost of convenience.
AI as a Thought Substitute
The marketing spin rarely mentions this side effect. Instead, you hear about speed, efficiency, and how AI will free up your time. But what you are actually getting is a substitute for your own thinking. The author regularly shares articles and videos that expose this disconnect — concrete examples of AI ruining our ability to think and evaluate. These resources show how an AI overpromise of flawless output masks the erosion of core mental skills. You might get a faster answer, but you lose the process of reasoning through a problem yourself.
This critical thinking decline is not accidental. It is baked into how AI products are designed and marketed. The AI marketing critique that the author highlights points to a simple truth: when a tool claims to think for you, it is actually teaching you not to think. And that should worry anyone who values their own intellect.
The Real Impact on Communities and Jobs
This erosion of personal intellect extends beyond the individual. When you step back from the sales pitches and marketing hype, the tangible effects on communities and skilled work become clear. The author’s frustration with how AI is being sold stems from these very real-world consequences — consequences that are rarely mentioned in a glossy product demo.
Consider the issue of AI job displacement. It’s not just about repetitive factory tasks being automated. Many roles that rely on human judgment — electricians diagnosing a tricky fault, mechanics assessing an unusual engine noise, or nurses reading a patient’s subtle cues — are now being framed as tasks for AI. These jobs require experience, intuition, and a deep understanding of context. When that work is handed over to a machine, the worker loses their livelihood, and the community loses that accumulated expertise. The community impact of automation is a slow bleed of skills. Over time, fewer people in a town know how to fix things, solve problems, or make nuanced decisions. The local economy becomes more dependent on external technology, and less self-reliant.
The author identifies this disruption as a key grievance. It is a valid concern: AI and skilled labor are not a natural pairing when the technology is designed to replace, not assist. When a community loses its skilled tradespeople and decision-makers, it loses its resilience. Your local repair shop might close, or the experienced project manager might be replaced by a dashboard. The cost is not just a job — it is a loss of practical, human capability that no algorithm can truly replicate.
Pragmatic AI Use for Efficiency
Despite his reservations, the author does not avoid AI entirely. Instead, he practices a controlled AI use that keeps the machine in its place: as a helper, not a thinker. This is where his Ai concerns about thinking become most practical. He uses technology to save time on repetitive tasks, but he never lets it override his own decisions or creative voice. The key is maintaining a conscious human-AI balance — knowing exactly when to lean on the tool and when to trust your own instincts.
Grammarly: A Tool for Polish, Not Replacement
One everyday example is Grammarly. The author uses it to catch and correct spelling mistakes and grammar errors. But he stops there. He does not accept the tool’s suggested wholesale rewrites. The AI might offer a smoother sentence, but that sentence would no longer sound like him. By limiting Grammarly to basic cleanup, he preserves his own writing style and tone. It is a simple but effective way to use AI without surrendering your voice.
AI in Photo Editing: Guiding but Not Deciding
Creative software is another area where the author draws a clear line. In Adobe Photoshop, he uses AI tools for tasks like covering up unwanted intrusions — a stray power line or a tourist in the background. The AI does the heavy lifting, but he decides where the edit goes and how it fits the composition. Similarly, in Lightroom, he might use the ‘Enhance Portrait’ preset as a starting point for retouching photos. It gives him a baseline exposure or skin smoothing, but he always tweaks the result manually. The preset guides; it does not decide.
Even Alexa, the voice assistant, gets a limited role. The author uses it as a shortcut for quick definitions and trivia — simple fact retrieval that saves him from opening a browser. He does not ask Alexa for advice or deep reasoning. That kind of thinking remains his own.
This approach reveals a clear principle: AI works best when it handles the drudgery, not the judgment. By choosing AI in creative software and everyday tools for their speed, not their intellect, the author keeps his own cognitive muscles active. The result is efficiency without erosion — a practical middle ground where you get the benefits of automation without losing the human touch that makes your work yours.
Why Human Oversight Remains Critical
That balance between automation and personal touch brings up a crucial point. Even when you rely on AI for the heavy lifting, you cannot afford to walk away completely. The author has found that AI, for all its speed, makes absurd mistakes that only a human eye can catch. A perfect example? The author intervenes manually because AI has an annoying habit of making teeth look like dentures.
When AI Misses the Mark: Dentures and Other Fails
You might laugh at the denture problem, but it reveals deeper AI limitations that affect any task involving realism. AI processes patterns, not meaning. It can generate a face with perfect symmetry but turn natural teeth into a uniform, plastic-looking set of chompers. It does not understand that real teeth have slight imperfections, varied shades, and natural spacing. For the author, this means constant manual intervention necessity to fix these subtle but glaring errors. The AI simply lacks the nuanced understanding of what looks authentic to a human audience.
This is where AI quality control becomes non-negotiable. You cannot assume that because an output looks good at a glance, it holds up under scrutiny. The author’s workflow includes a deliberate review step: check every AI-generated element for these kinds of failures. Whether it is a photo edit, a piece of text, or a design layout, the principle holds. AI concerns about thinking often boil down to this gap — the machine can simulate understanding, but it does not truly grasp context or reality. Your role is to fill that gap, catching the dentures before they ruin an otherwise solid piece of work. That is the practical, reliable way to get the best from the tool without letting its blind spots compromise your final product.
A Personal Story: The Struggle That Drove Me to AI
This tension between skill and convenience isn’t just an abstract debate. It plays out in real, everyday decisions. Consider a simple example: typing. Many people take fast, accurate typing for granted. But what if you never learned? That is the situation for one writer who relies on voice recognition AI to finish this very column. The reason is straightforward: a scheduling conflict years ago. The typing class was offered at the same time as AP English. The choice seemed obvious — take the advanced literature course. The trade-off, however, was never learning to type conventionally.
From Hunt-and-Peck to Voice AI: A Case Study in Reluctant Adoption
Today, that writer types using a slow, laborious hunt-and-peck method. Each key is found one finger at a time. It works, but it is painfully inefficient. For a professional who needs to produce written content regularly, this creates a real bottleneck. The natural solution was to turn to voice recognition productivity tools. Speaking thoughts aloud is far faster than hunting for each letter. This is a clear example of skill erosion vs convenience. The skill of typing was never developed, so a convenient AI tool fills the gap. It is not about laziness. It is about finding a practical workaround for a genuine limitation.
This personal story mirrors a broader pattern. Many people develop a personal AI dependency not because they want to, but because it solves a real problem. The writer did not choose to be a slow typist. The choice was made years ago, and the consequences are managed today with AI. This highlights a key AI concern about thinking: when you rely on a tool to perform a basic function, you risk losing the ability to do it yourself. Yet, the alternative — refusing the tool — would mean accepting a severe productivity loss. The practical path is to use the AI while being aware of the trade-off. You can dictate your thoughts, but you still need to review, edit, and own the final words. The tool handles the mechanical act of getting words onto the page, but the thinking, the structure, and the voice remain yours. That is the balance that makes the reluctant adoption worthwhile.
Frequently Asked Questions
How can you use AI without losing your own thinking skills?
Set clear boundaries for when you turn to AI. Use it for repetitive tasks or quick drafts, but always review and edit the output yourself. Practice thinking through problems independently before asking AI for feedback. This keeps your cognitive muscles active while still benefiting from the tool.
How does Manning reconcile using AI while arguing it harms thinking?
Manning draws a line between AI as a practical shortcut and AI as a substitute for judgment. He sees the danger in outsourcing reasoning entirely, not in using AI for low‑stakes tasks. The key is to treat AI as a starting point, not the final answer. His own usage stays limited to areas where human oversight remains strong.
Does Manning believe AI can ever be used ethically?
Yes, but only under strict conditions. Ethical use requires transparency about AI’s limitations and a commitment to human‑in‑the‑loop workflows. Manning warns against any deployment that removes human responsibility entirely. For him, the ethical line is crossed when AI replaces rather than assists thinking.
