Key Takeaways
- AI is brilliant, but not unique. Out of the box, it gives everyone access to the same intelligence; but if everyone uses the same models in the same way, then no one gains a competitive edge.
- AI fails as an expert. Underpaid, inexperienced workers train these models, and they pass on surface-level knowledge that keeps every AI output stuck at “average”.
- Out-of-the-box AI creates “fast-food knowledge.” It’s quick and convenient, but also generic, shallow, and identical across users, leaving every business sounding the same.
- Standard AI content is really just empty thinking: It may read well to the untrained eye, but it is not able to compete in the real world.
- You must use rare specialist AI’s for a true advantage, where world-leading human specialists have trained the AI specifically. Examples like AmpiFire give you an advantage through specialist processes and AI training.
It’s late 2025 and anyone who says AI isn’t impressive is lying (either to you or to themselves).
AI can pass a bar exam, win elite coding competitions, and even make for an unexpectedly decent romantic partner.
AI has turned average internet users into decent anything– writers, analysts, marketers, accountants.
And that’s exactly the problem.
If everyone has access to the same intelligence out of the box, then out-of-the-box intelligence gives you NO competitive edge.
(This is the paradox of any widespread benefit: if somehow everyone on planet earth were to magically receive something currently extremely valuable, such as one thousand pounds of gold, the inflation of the gold market would render gold essentially worthless; if you’re a Harry Potter fan, think of it as the Leprechaun Gold Problem).
But, as to all problems, there are solutions.
Bear with me (this is going to be a long one so you might want to get your favorite cuppa ready – now’s your chance).
I recently interviewed former senior AI trainers from an AI training firm that trains most of the big models.
The conversation, combined with widespread employee complaints from Reddit, gave me a worrying idea about how large language models are trained, and why they may never deliver the expert-level content businesses need to stand out.
And it also helped me understand exactly how businesses can stand out (it’s not as complicated as you probably imagine).
I like to think of it as the fast food knowledge problem;
“If everyone has access to the same intelligence out of the box, then out of the box intelligence gives you NO competitive edge.”
The Fast-Food-Knowledge Problem
The AI training firm advertised for “expert AI data trainers” to teach their models.
But their definition of expertise was surprisingly shallow: trainers needed only a bachelor’s degree in any given field to qualify as an “expert.”
The interviewed trainer, who taught the AI about accounting, admitted she had only an accounting degree, but had zero (literally) real-world experience. When asked what expertise qualified her for the role, she simply stated: “Well, my degree is in accounting, so that’s what I came in with.”
She was also assigned to train the model on business management, history, and religion—subjects where she had personal interest, but zero (again, literally) professional expertise or even credentials.
With this approach, AI models can only produce so much.
When your teachers lack deep expertise, your students—in this case, AI models—can only learn surface-level patterns, not the nuanced insights that separate mediocre content from exceptional work.
Multiply that across every business relying on those same models, and you get the modern internet in a dumb nutshell: thousands of pseudo-voices boiling down to a handful of LLMs saying the same thing in slightly different words.
And sure, they can learn patterns based on ginormous volumes on data; but again, without higher expertise to guide them through the stuff, that effort doesn’t amount to much more than pattern recognition and faulty learning (more on this later).
You get the knowledge equivalent of fast food: quick “insights” that may or may not be true (but unless you know enough about the subject already, you probably won’t be able to tell the difference), may or may not be very “healthy” or “sound”, but that will “fill” that need for a quick dose of insert-whatever-knowledge-project-you-need-done-here.
(mind you, the fast food analogy isn’t exactly new, like at all; but while it’s been generally used to describe the cheap, addictive and dumbing-down effects of AI-based learning, I’m using it to describe the very nature of AI knowledge in the first place.)
The Low-Wage Language Problem
The fast food problem is tied to another one, and they’re both defined by cost-cutting (which is ironic in an industry where year-to-date spending amounts of over $150 billion).
In my article “Is ChatGPT ‘Nigerian English’? How AI Detection Is Rigged”, I analyzed how AI models were trained by workers earning as little as $2 per hour in Africa, Asia, and South America. While some “advanced” training positions pay up to $15 per hour, this still falls far short of attracting genuine expertise.
Can you imagine a seasoned CPA, surgeon, or marketing strategist accepting $15 per hour to teach their craft?
Cost-cutting of this level directly impacts the language patterns these models learned.
AI models now favor formal, structured language because they were trained on it, leading to the overuse of words like “harness,” “foster,” and other corporate buzzwords. These words appear so frequently in AI writing that Ankita Gupta, CEO of Aktodotio, declared: “I am rejecting all content with any of these words.”
What the world is left with is AI-generated content filled with meaningless corporate fluff (vague phrases about “complex issues” and “challenging environments”) that sounds professional but says nothing.
And it’s even worse: because every company is pulling from the same dataset, they’re all serving the same boring, bland, indistinguishable-but-not-completely-crap word salad (the KFC coleslaw equivalent of internet text, if you will; imagine surviving on that alone!)
This formal, empty style comes directly from the training data: press releases, academic papers, and formal writing produced by workers in regions where this overly formal English style remains standard.
The Systemic Training Crisis
Reddit threads from current and former employees don’t paint a much rosier picture of the AI training industry.
Workers across multiple firms report:
- Inadequate trainer preparation: “Quality of training varies significantly across projects, with some materials full of errors and seemingly thrown together in haste.” Many workers describe being thrown into projects with minimal guidance.
- Poverty wages driving poor quality: Workers observe: “It’s really scary to think that AI is being trained by people who accept this treatment for $15 or less… OneForma project training chatbots for literally $4.50 an hour.” And while not everyone pays in the single figures, even at the “high end” of $15/hour, you’re not attracting people with deep, valuable expertise.
- Disposable workforce: The industry treats trainers as expendable, with someone noting: “They also made the news last year for cutting people when they got the training data they needed, but then tried to claim those workers were just ‘bad performers.'”
And lo and behold, the AI vicious cycle emerges:
Low pay attracts desperate workers → inadequate training produces poor results → companies cut corners further → AI models learn from increasingly compromised data → increasingly bad data is used by ever-cheaper, ever-tired, ever-desperate knowledge workers to learn “skills” for which they’re paid less and less.
Technical Incompetence at Scale
We’re looking at several systemic problems with AI training:
- Widespread trainer avoidance: In my interview, the senior trainer admitted that when faced with technical tasks, 60% of trainers simply didn’t show up to training sessions. They were paralyzed by tasks they viewed as “daunting,” waiting for peers to complete work first so they could quite literally copy approaches.
- Leadership lacking real understanding: Even senior trainers admitted to not understanding what they were teaching. Reddit users corroborate this: “I have seen leaders with no relevant experience mismanage projects, leading to their downfall.”
- Quantity over quality: Projects employed 50-250 trainers, so the focus was more on volume than expertise. As one Reddit commenter explained, companies compete “to offer the clients the lowest prices for trainers, which results in them treating employees like disposable trash.”
The Information Paradox & The Crisis of Truth
Modern AI systems do have one apparent advantage: they can search vast amounts of internet data and potentially identify “authoritative” sources by looking for consistency across multiple sources. However, this creates a new problem that mirrors the same issues plaguing Google’s search results.
When AI systems determine “truth” by consistency and domain authority, they learn from an echo chamber where large corporations and those who shout the loudest in the most places dominate the narrative.
The loudest voices – not the most knowledgeable – become the AI’s primary teachers.
This leaves the internet increasingly vulnerable to misinformation.
As more people place trust in AI for information, the flawed training and biased source selection compound into a literal crisis of truth (ours is fittingly called the “post-truth era”). AI doesn’t fact-check; it pattern-matches based on what its underpaid trainers taught it and what appears most frequently online.
The Useful, but Limited Reality
And here’s what many AI evangelists won’t tell you: AI excels at everyday tasks where being above average suffices.
It writes better than the average person. It handles accounting better than someone with no financial background. For routine business operations, it’s genuinely useful.
But business success doesn’t come from being average or even above average.
In content marketing, only the top few percent win. Being “good enough” means invisible. Your competitors are using the same AI tools, getting the same mediocre results. Where’s your edge?
The Business Implications
This training methodology creates a hard ceiling on AI capabilities:
For everyday tasks, AI performs adequately. It can draft emails, summarize documents, and handle routine communications because these tasks don’t require deep expertise. Use it here because it definitely saves time.
For competitive advantage, AI fails catastrophically. When you need content that outranks competitors, converts visitors, or establishes thought leadership, AI-generated content falls short. It lacks the insider knowledge, unique insights, and authoritative voice that only comes from genuine expertise—instead offering empty phrases about “stakeholders” and vague business jargon.
And here’s where it all connects back to the competitive problem:
Every business using AI gets the same mediocre output, trained by the same underpaid, undertrained workers.
And in markets where differentiation matters, this guarantees invisibility.
How to Actually Win with AI
Here’s what my experience training AI for effective content creation taught me.
The difference between AI that produces generic fluff and AI that drives real results comes down to one critical factor: expert human input.
I’ve successfully trained AI systems to create content that dominates search rankings and drives massive traffic.
The secret lies not in the AI itself, but in the niche expertise and research that experts bring to the table. Even when this input remains quick, it has an enormous impact on results.
Think of AI as an amplifier, not a generator. When you feed it genuine expertise, unique insights, and real research, it can help you produce content at scale while maintaining quality. But when you feed it nothing (or worse, feed it the generic understanding of $2-15/hour trainers who lack real-world experience) you get nothing valuable in return.
Why Human Expertise Still Wins
The interviews, Reddit testimonials, and my own experience reveal why human experts remain irreplaceable:
- Unique insights matter: While base AI models regurgitate common knowledge, expert-guided AI can surface novel connections and insights that competitors miss.
- Niche expertise becomes gold: The more specialized your knowledge, the more valuable your AI-assisted content becomes. Generic AI can’t compete with AI guided by someone who truly understands their field.
- Research makes the difference: Quick but targeted research by an expert can transform AI output from mediocre to magnificent. The AI doesn’t know what to look for—you do.
- Authentic voice breaks through: While unguided AI produces formal, empty language, expert-guided AI can maintain personality and substance that connects with real audiences.
The Bottom Line
This peek behind the curtain of AI training exposes a fundamental (post)truth: AI models reflect the conditions of their creation. When those conditions involve paying $2-15/hour for “expertise,” you get what you pay for—surface-level knowledge wrapped in corporate buzzwords.
As I warned in my AI detection article, we risk losing great content to these flawed systems.
The US Constitution would score 97.97% as “likely AI” today. Meanwhile, actual AI content floods the internet with its telltale formal emptiness.
But here’s the opportunity: While your competitors rely on vanilla AI to produce the same generic content everyone else publishes, you can combine your expertise with AI to create content that actually moves the needle.
The businesses that will win aren’t those that replace experts with AI, they’re those that empower (cliche, I know) experts with AI.
The competitive edge you seek won’t come from AI trained by workers making barely above minimum wage who don’t understand what they’re teaching. It will come from your own expertise, research, and insights, amplified by AI tools you’ve learned to wield effectively.
In the race for market dominance, expertise remains your unfair advantage. And until the AI training industry stops treating its workers as “disposable trash” and starts paying for genuine expertise, the gap between vanilla AI fluff and expert-guided content will only grow wider.
And that gap is where your opportunity lies.
Ready to turn your expertise into content that dominates?
AmpiFire combines expert training from professional marketers and content writers who regularly outcompete billion-dollar brands. Our proven workflows, training, and systems help you create expert-driven content that cuts through the AI noise and gets real results. While others compete with generic AI output, you’ll be amplifying your unique knowledge across the channels that matter most.
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