We\u2019re also going to touch on\u2026<\/p>\n\n\n\n
- \n
- Disingenuous questions<\/li>\n\n\n\n
- False comparisons<\/li>\n<\/ul>\n\n\n\n
So if you\u2019ve been wondering about these issues or trying to find out how to have more productive conversations about AI, read on.<\/p>\n\n\n\n
What is AI?<\/h2>\n\n\n\n
Though AI stands for \u201cartificial intelligence,\u201d (it\u2019s sometimes also called \u201caugmented intelligence\u201d) it isn\u2019t technically an intelligence. In the same way that \u201cautopilot\u201d in cars isn\u2019t an actual autopilot, large language models aren\u2019t actually AI<\/a>.1<\/a><\/sup> At least, not yet. LLMs analyze linguistic data and make predictions based on those analyses. They \u2018recognize\u2019 patterns in speech and writing so well that they can generate their own human-sounding text. Proto-chatbots existed as far back as the 1960s (such as ELIZA), but bots as we know them today didn\u2019t appear until the late aughts. Cleverbot arrived in 2008<\/a> and began making a splash online.2<\/a><\/sup><\/p>\n\n\n\n
![\"\"](\"https:\/\/weareseven.com\/blog\/wp-content\/uploads\/2026\/08\/markusspiske-store-window-3660113_1920-1024x822.jpg\")
Image by markusspiske<\/a><\/figcaption><\/figure>\n\n\n\n Learning models were developing quickly all over the world, from England to Japan. In 2024, a news story about a computer program from a Japanese bakery started gaining traction. ZME Science reported<\/a>3<\/a><\/sup> that a decade prior, BakeryScan\u2014a neural network model4<\/a><\/sup> developed to identify pastries\u2014started making waves. The article says that a doctor in Kyoto saw the potential to use this model to identify cancer cells. A few years later a prototype appeared in hospitals.<\/p>\n\n\n\n
Eventually, this lead to generative AI models like OpenAI (ChatGPT), that could generate content that was virtually indistinguishable from a disinterested human writing to a template.<\/p>\n\n\n\n
Yes, artificial neural networks could <\/em>make life easier<\/h2>\n\n\n\n
When most people praise AI, they\u2019re thinking about its potential. These models could eliminate most of the \u201cbusy work\u201d that people\u2014especially people who can\u2019t pay for convenience\u2014have to do. Here’s one example. If you need a mechanic, a well-trained and accurate model could compile information about local auto shops, including:<\/p>\n\n\n\n
- \n
- reviews<\/li>\n\n\n\n
- years in business<\/li>\n\n\n\n
- complaints<\/li>\n\n\n\n
- average rates<\/li>\n\n\n\n
- and other important factors<\/li>\n<\/ul>\n\n\n\n
All of this, in seconds, allowing you to compare them side-by-side. That would save quite a lot of time and frustration. Or an AI tool could make grocery shopping simple. It could:<\/p>\n\n\n\n
- \n
- scan all the sales at local grocery stores<\/li>\n\n\n\n
- compare that to your weekly grocery needs<\/li>\n\n\n\n
- consider the time you have to shop<\/li>\n\n\n\n
- factor in price changes from week to week<\/li>\n<\/ul>\n\n\n\n
Then, it could generate an almost-instantaneous shopping itinerary with a route that could save you time and money.<\/p>\n\n\n\n
![\"Human](\"https:\/\/weareseven.com\/blog\/wp-content\/uploads\/2026\/08\/kiquebg-technology-4256275_1280-1024x601.jpg\")
Image by kiquebg<\/a><\/figcaption><\/figure>\n\n\n\n But, at least so far, legitimately useful AI tools like these aren\u2019t freely available to the average- or low-income person. Instead, AI is being leveraged against everyday people with applications like dynamic pricing (which can track what you buy and increase the price on items you need most). So while we acknowledge that these tools have the potential to change our existence for the better, what the average person is actually facing are more problems from AI than solutions.<\/p>\n\n\n\n
What are the supposed problems with AI?<\/h2>\n\n\n\n
What started out as a technology that offered fun interactions with chatbots and life-saving medical applications quickly cascaded into a cause for concern. We\u2019ll discuss data centers in a moment, but for now let\u2019s take an overview of the problems with AI itself. The primary concerns making headlines are the loss of jobs and the loss of skills. Let\u2019s break those down.<\/p>\n\n\n\n
Job Loss<\/h3>\n\n\n\n
In 2025 alone, AI implementation at workplaces caused 54,694 layoffs, according to this report.<\/a>5<\/a><\/sup> The Word Economic Forum predicts that by 2030, 92 million jobs will have been eliminated<\/a>.6<\/a><\/sup> This would be great news if the United States and other affected countries had guaranteed universal basic income, as it would allow many people to do meaningful, human-powered work regardless of whether or not it paid well. But in our current economic reality, those numbers are devastating.<\/p>\n\n\n\n
Skill Loss<\/h3>\n\n\n\n
Professional skill loss due to AI<\/h3>\n\n\n\n
But what if the economic devastation wasn\u2019t so severe? Would AI still present problems if no one lost their jobs and homes? Well, there are still real concerns with the human cost of relying on AI. \u201cSkill atrophy\u201d due to AI is a particular worry when it comes to doctors\u2019 abilities. In 2025, The Lancet published a study<\/a> that suggested a reliance on AI-assisted diagnostics could result in a loss of ability to correctly diagnose patients without AI. That is to say, diagnosticians could see their skills decline as they use AI assistance.7<\/a><\/sup> We\u2019ll need more rigorous and well-controlled studies to know if this is a threat, but in the meantime it is a cause for concern.<\/p>\n\n\n\n
Developmental skill loss due to AI<\/h3>\n\n\n\n
But it isn\u2019t just diagnosticians we need to keep an eye on. Psychology Today<\/em> writer Timothy Cook argues that, though offloading cognitive tasks to AI can lead to skill atrophy in adults, it will prevent those skills from ever being formed in children. He describes this as part of the AI audit problem<\/a>. That is, adults who have learned how to do these tasks and are simply looking to save time still have the capacity to examine AI\u2019s results and see if they\u2019re correct.8<\/a><\/sup> But children, who have not learned how to do the task on their own, will not have the knowledge or experience needed to perform this audit.<\/p>\n\n\n\n
AI dependence may cause adults to lose skills.
Children will never learn them.<\/p><\/blockquote><\/figure>\n\n\n\nAnd with more and more students relying on AI, we could be seeing the beginning of a loss of skill far more dramatic than the one we\u2019ve been part of for the last couple of hundred years. This isn\u2019t fear-mongering or pearl-clutching on the part of a handful of sulky, unemployed people. It\u2019s not the rant of an old man shouting at clouds. This is a real potential danger that we should be diligently monitoring and investigating.<\/p>\n\n\n\n
What\u2019s the deal with data centers?<\/h2>\n\n\n\n
Even if we somehow dealt with the loss of jobs, and it turned out that the fears about skill atrophy were overblown, there\u2019s one more major problem with AI proliferation. If we want to have a sincere and productive conversation about AI, we need to consider data centers.<\/p>\n\n\n\n
Data centers are not new. But their numbers have risen sharply with the demand for bitcoin and, especially, generative AI products. Structurally, they\u2019re similar to supercomputers; buildings filled with thousands of servers performing calculations. However, while a supercomputer usually focuses its resources on a single computation, data centers perform thousands of separate computations<\/a> simultaneously.9<\/a><\/sup> This article by Forbes <\/a>outlines the different types of data centers.10<\/a><\/sup><\/p>\n\n\n\n
The problems with data centers<\/h3>\n\n\n\n
This expansion comes at a cost. While proponents are eager to focus on the temporary construction jobs and handful of long-term jobs at the data centers themselves, residents are fighting to keep these projects out of their communities. Surges in energy costs, the co-opting of millions <\/em>of gallons daily from local water sources, and the increase in pollution that come along with data center construction are, unsurprisingly, unappealing. Now that word has gotten out about what some residents are suffering, worry and resistance are growing. This public push-back has led developers to add some sustainable features to future projects<\/a>, but with the long history of large corporations sickening and killing U.S. citizens, it\u2019s easy to see why many people are still deeply skeptical.11<\/a><\/sup><\/p>\n\n\n\n
![\"AI](\"https:\/\/weareseven.com\/blog\/wp-content\/uploads\/2026\/08\/cookieone-technology-8118374_1280-1024x575.jpg\")
Image by cookieone<\/a><\/figcaption><\/figure>\n\n\n\n Further, with the increase in energy usage due to AI applications, all that new energy has to come from somewhere. The construction of multiple nuclear power plants (as well as an unknown number of gas and coal plants) is being planned across the country. And while Meta <\/a>uses phrases like \u201cenergy leadership\u201d and \u201creliable and firm power,\u201d people become defensive when these innovations arrive in their backyards.12<\/a><\/sup><\/p>\n\n\n\n
Now we\u2019re on the same page about what AI is and what the major causes for concern are. But before we can have a productive conversation about AI, we need to address a few of the most common disingenuous questions, concerns, and talking points.<\/p>\n\n\n\n
Disingenuous questions and arguments<\/h2>\n\n\n\n
You can’t have a productive conversation about AI with these<\/h3>\n\n\n\n
Often, when someone on the internet says they\u2019re against using AI, a percentage of comments will say something like, \u201cBut AI has medical applications!\u201d Or, \u201cDo you really want people to die of cancer?\u201d These responses may be well intentioned, or they might be badly used rhetorical tools to win an argument.<\/p>\n\n\n\n
What do we mean by disingenuous? Here\u2019s an example that\u2019s been around for decades:<\/p>\n\n\n\n
\n
Bette: \u201cI don\u2019t want to eat a bunch of chemicals on my fruit.\u201d
Bob: \u201cWell actually, everything is made out of chemicals.\u201d<\/p>\n<\/blockquote>\n\n\n\nIf Bob responds to Bette, knowing full well that Bette meant artificially created and added chemicals, then Bob making a disingenuous argument. Of course, if Bob didn\u2019t know that\u2019s what Bette was referring to, that\u2019s okay. That\u2019s why it\u2019s important for all of us to ask follow-up questions instead of assuming we know what\u2019s being discussed and jumping in with an objection.<\/p>\n\n\n\n
False assumptions about AI<\/h3>\n\n\n\n
Here\u2019s an example we\u2019ve heard regarding AI:<\/p>\n\n\n\n
\n
\u201cIsn\u2019t the cost of the environmental destruction AI is causing worth the medical advancements?\u201d<\/p>\n<\/blockquote>\n\n\n\n
The problem with that question is that few people are unhappy with the existence of data centers that provide computational power to medical applications. Genuinely sustainable solutions can be found and implemented for AI usage designed to save lives. But most people are unwilling to risk poisoning their local water supply and soil just so someone else can earn money through bitcoin or so multi-billion dollar corporations can offer cheap generative AI and cloud computing. The question is comparing apples and oranges.<\/p>\n\n\n\n
![\"Typewriter](\"https:\/\/weareseven.com\/blog\/wp-content\/uploads\/2026\/08\/viarami-mockup-5288034_1280-1024x682.jpg\")
Image by Markus Winkler<\/a><\/figcaption><\/figure>\n\n\n\n Racism, classism, and ableism around AI<\/h3>\n\n\n\n
Some of these arguments go beyond being disingenuous or ignorance-based, however. Recently, we\u2019ve seen some pro-AI arguments that can only be described as racist, ableist, and classist\u2014whether or not that was the intention.<\/p>\n\n\n\n
Here\u2019s one that we\u2019ve seen several times:<\/p>\n\n\n\n
\n
\u201cBut AI is the only way disabled\/Black\/disadvantaged people can compete!\u201d<\/p>\n<\/blockquote>\n\n\n\n
While it\u2019s possible this sort of thing is said with good intentions, the racism and classism inherent in it is impossible to ignore. And yet it isn\u2019t new. In 2024, NaNoWriMo faced backlash after stating that condemning the use of generative AI had \u201cclassist and ableist undertones<\/a>.\u201d13<\/a><\/sup> Many long-time supporters of the organization seemed to find that statement itself classist and ableist, saying that it implied disabled and poor participants needed help from AI software to write. While there is a larger and more nuanced discussion around the fact that people facing socioeconomic disadvantages are, obviously, disadvantaged when it comes to paying for feedback and finding free time, many found NaNoWriMo\u2019s broad statement cloaked in politically correct language offensive and cowardly<\/a>.14<\/a><\/sup><\/p>\n\n\n\n
It isn\u2019t a lack of creativity or ability that disadvantages disabled and lower-income creators. It\u2019s a lack of access to editing and publication services. A lack of time due to working excessive hours or losing \u2018working\u2019 time to illness. And AI could help break down those barriers. But conflating the use of generative <\/em>AI to create the end product and using AI tools to gain access or reduce barriers is not helpful.<\/p>\n\n\n\n
Possible advantages for the disadvantaged?<\/h3>\n\n\n\n
How could AI level the playing field for lower-income people? Addressing that is, unfortunately, beyond the scope of this post. However, we\u2019re working on another blog that addresses the topic of AI in art and writing in greater detail. In that one, we\u2019ll look at generative AI in the context of the history of technological advancement in the arts and <\/em>as a means of eliminating gatekeeping in the literary and music publishing industries. If you\u2019re interested, subscribe to our mailing list or follow us on social media to be notified when that post is live.<\/p>\n\n\n\n