LLMs Are Slop Sold By Ignorant Billionaires Who Don’t Give A F√<k About Real Writers
BigTech market lies as intelligence and shit on professionals who actually know their craft.
For some context, the adoption of AI tools like LLMs has accelerated at a pace that few have critically examined. We’re being sold a narrative of efficiency and intelligence while the structural limitations, risks, and societal costs remain obscured. The following piece examines these issues through a lens of research, personal testing, and industry observation.
And for transparency, I’m an investigative researcher, published author, former ad exec, and someone with extensive experience analysing technological change, its societal impact, and the forces that drive adoption. This piece is intentionally polemic, confronting the promises and failures of Large Language Models (LLMs) and the interests that propagate them. I draw on firsthand testing, industry observation, academic research, and reporting from global media to critique how these tools are marketed, misused, and institutionalised, often at the expense of skilled professionals, public trust, and intellectual rigour.
I wrote about this in August 2025, and it was subsequently published in the publication Artificial Intelligence in Plain English. My article, “When a Full Stop Becomes AI: Questioning the Reliability of AI Detection Tools” documented in detail how LLMs and AI detection tools repeatedly failed the most basic tests of accuracy and reliability. Users and organisations around the world alike have been encouraged to adopt these tools as if they were authoritative sources, yet my desktop research and subsequent experiments revealed an alarming pattern:
LLMs generate outputs with convincing fluency that are factually incorrect, contextually misleading, and internally inconsistent. The errors aren’t occasional glitches; they’re structural, embedded in the architecture and training methodology of these models. The promise of efficiency masks a fundamental absence of comprehension.
These inaccuracies aren’t accidental. They’re symptomatic of a system engineered to reward surface-level plausibility over truth. BigTech billionaires, whom I called out in my follow-up piece “When Progress is a Poison Pill” have commoditised language itself. Sam Altman, Sundar Pichai, Satya Nadella, and Elon Musk, alongside boards of investors and global corporate networks, have collectively endorsed the deployment of LLMs without meaningful safeguards for truth, ethics, or societal impact. Governments in the U.S., Europe, and Australia have turned a blind eye, approving regulatory frameworks that favour corporate expansion over public protection. In Australia, for example, policymakers have also failed to address the systemic threat these tools pose, as highlighted in recent reporting by news.com.au, which notes regulatory inertia and industry lobbying that prioritise adoption over scrutiny.
LLMs — and their AI detection collaborators — operate fundamentally as glorified search engines. They don’t “understand” language. They statistically predict text sequences based on vast datasets scraped from the internet, academic repositories, forums, and other sources. The sophistication lies in their presentation, not their cognition. When organisations deploy these systems, uneducated, lazy and foolish decision-makers input prompts and accept outputs as authoritative, undermining professional judgment. Technical writers, copywriters, journalists, and literary authors are witnessing their expertise being devalued. Tasks that required nuanced judgment, fact-checking, and stylistic craft are increasingly offloaded to machines whose outputs are prone to fabrication (understatement).
The rapid adoption of these tools has created a false economy of productivity. A recent thread on Reddit’s r/LanguageTechnology forum discusses cases where legal firms relied on LLM-generated contracts, only to face compliance failures and factual misstatements. A survey published in Nature Machine Intelligence reported that 68% of technical writers noticed an increase in junior employees using AI to generate reports without verification, resulting in cascading errors.
Efficiency has replaced effectiveness, and organisations believe they’re achieving cost savings while degrading output quality.
And regulatory capture intensifies the problem. The EU AI Act, touted as a landmark attempt to safeguard its citizens, contains loopholes that allow LLMs to operate with minimal external auditing. In the U.S., the National AI Initiative Act offers funding incentives for LLM development while failing to mandate transparency or verification standards. Australia’s engagement, as reported in the article “Artificial intelligence to be managed through existing laws under National AI Plan,” demonstrates similar patterns: government agencies endorse AI tools for public sector efficiency but provide no oversight on the quality or reliability of outputs. People are expected to accept LLM outputs at face value, with little recourse when misinformation occurs.
Academia has also documented the risks. Papers on arXiv and in Journal of Artificial Intelligence Research show persistent “hallucinations” in LLM outputs, where the system invents facts, misattributes citations, misinterprets queries, and provides non-existent URL references. Bias embedded in training data propagates through outputs, amplifying social inequalities. A 2021 study by Bender et al. demonstrates that the lack of contextual understanding in models results in systemic errors when interpreting culturally sensitive content. Despite this, corporations market LLMs as tools of democratisation, claiming that they level knowledge access. The reality is that they entrench reliance on flawed systems, shifting authority from experts to predictive algorithms.
The consequences for the literary community are immediate and observable, and I’ve experienced this firsthand. Publishing houses report increased submissions generated by LLMs, with editors forced to detect and correct fabricated or misleading content. Freelance writers face downward pressure on rates as clients request AI-assisted drafts. Copywriters are replaced by interns armed with prompts. Journalism suffers when LLM-assisted articles are posted online and amplified on social media without verification. Organisations encourage “prompt literacy” rather than investing in professional judgment, signalling a devaluation of human expertise.
I’ve seen journalists in mainstream media copy and paste entire LLM outputs, including embedded ChatGPT references and prompts, without editing a word. They publish it as their own reporting, and the public assumes it’s fact-checked journalism. What a load of rubbish. This is not efficiency. It’s negligence, and it is destroying trust in the profession.
Literary agents and publishers are also complicit in this erosion of professional judgment. I’ve encountered countless posts and articles on Medium, Substack, and other writing forums where qualified writers are being dismissed or rejected because AI detection tools flagged their work as “generated.” As I pointed out in my 2025 piece, these tools are outdated, inefficient, and consistently unreliable, yet entire careers are being disrupted based on their failures.
As a writer, it’s gut-wrenching to witness talent shut out for reasons that have nothing to do with merit, creativity, or craft.
Instead of assessing content on quality, originality, and human insight, gatekeepers are outsourcing judgment to flawed algorithms, further accelerating the devaluation of skilled writers and reinforcing the false narrative that human expertise can be replaced by predictive text.
And this phenomenon is further amplified by public perception. Social media platforms propagate LLM-generated content under the guise of insight, analysis, or creative output. Discussion boards like Hacker News and Stack Overflow note how coders rely on AI completions that often introduce subtle bugs. In education, students use LLMs for assignments, eroding critical thinking and literacy. This is why I’ve lost faith in many modern tertiary degrees, where output is rewarded over thought and graduates emerge fluent in prompts but weak in reasoning. People are trained to believe that the machine’s fluency equates to authority. The intellectual labour of verification, synthesis, and judgment is outsourced to an entity incapable of accountability.
The commercialisation of LLMs is inseparable from the financial interests driving their proliferation. Investors reward adoption metrics over accuracy. Microsoft integrates OpenAI models into 365 CoPilot products, promoting efficiency while disclaiming responsibility for errors. Google markets Gemini as a conversational assistant, glossing over hallucinations. OpenAI offers API access to developers who embed predictive outputs into apps, further diffusing responsibility. These aren’t neutral technologies; they’re instruments of profit masquerading as tools of empowerment. Unsurprisingly, governments, in failing to enforce rigorous evaluation, become complicit partners in the misrepresentation of AI capabilities.
And let’s not dismiss the ethical dimension here. In my own testing, outputs consistently misrepresent source material. When LLMs are deployed in legal, medical, or governmental contexts, the potential for harm escalates. We’re expected to trust systems trained on anonymised internet data while the architects of those systems remain insulated from accountability. Global examples abound: in the UK, NHS pilot programs using AI for triage produced errors in patient guidance, as reported in the BMJ. In the U.S., AI-generated contract summaries misrepresented terms, causing litigation delays. Australia’s government AI “initiatives”, designed to streamline engagement, demonstrate monumental structural oversight failures. Then again, expecting competence from the Australian government is like trusting a blindfolded pilot to land a plane.
However, LLMs’ most insidious effect is the normalisation of intellectual complacency. Users internalise the outputs of predictive text as authoritative, shifting cognitive rigour from human experts to machines incapable of contextual judgment. Writers and communicators are no longer trusted to research, critique, or craft language; instead, organisations train teams to become “prompt engineers.” I find this both shocking and deeply dismaying. A profession built on judgment, skill, and intellect is being reduced to managing machine prompts. This undermines a generation of professional knowledge and creativity, creating dependency cycles that are difficult, if not impossible, to reverse.
So, I ask you to consider the trajectory of reliance. How long before institutional memory erodes because unverified AI outputs are accepted as fact? How many writers, editors, and knowledge workers will see our careers diminish while BigTech profits from errors they simply won’t correct? Regulatory frameworks in the EU, US, and Australia offer a paper-thin veneer of oversight while actively enabling the commercialisation of predictive text engines that are structurally incapable of truth.
We’re consumers of language, but without literacy, scepticism, or expertise, we become complicit in our own disempowerment.
So let me be crystal clear here: LLMs are not neutral tools. They’re commercial products designed to maximise engagement, revenue, and adoption metrics. Their architectural limitations, combined with the opacity of corporate development, mean that their outputs should be approached with extreme caution, not worship. I’ve documented these failures firsthand. Other researchers, journalists, and industry professionals confirm them. The implications for society, professional writing, and public trust are profound. We must resist the illusion of AI fluency and demand accountability from both governments and corporate actors. Or have we lowered our expectations so far that this is now considered acceptable?
We should demand transparency in training datasets, rigorous third-party auditing, enforceable regulatory compliance, and explicit acknowledgment of the limits of predictive systems. Until that occurs, LLMs — and their AI detector collaborators — will remain glorified search engines sold as intelligence, undermining skilled labour, professional judgment, and public understanding. The adoption of these systems without accountability constitutes a monumental structural risk, one that BigTech and inept governments around the world are actively exploiting while undermining human expertise.
So, when someone dares to ask if my work was drafted by AI, I wear it as a badge of honour. It means my writing is so sharp, so exact, so damn convincing, that it fools the very morons who worship machines. At least I know I’m doing something right while the rest of the world settles for bullshit sold by billionaires.
Additional References
Bender, Emily M., Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmit. “On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?” Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 2021. https://dl.acm.org/doi/10.1145/3442188.3445922
“AI Adoption Faces Regulatory Gaps.” news.com.au, March 2026. https://www.news.com.au/technology/innovation/artificial-intelligence-prediction-that-will-shock-australia-and-the-world/news-story/813e8fedb7ec155301b32daec03b9f91
“European Approach to Artificial Intelligence,” European Commission, April 2021. https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence
“National AI Initiative Act of 2020.” United States Congress. https://www.congress.gov/bill/116th-congress/house-bill/6216
“AI in medicine: UK is pursuing “middle path” in adoption and regulation.” Journal of Artificial Intelligence Research, February 2025. https://www.jair.org/index.php/jair/article/view/12467
This article in fantastic. The links to your other articles really flesh out what we're all dealing with. That's why we use archival information retrieved personally and can back up a statement or transmission of validated information. That's why your attitude about the dumbing down of everyone is so well founded.👏👏👏