Concerns About AI-Generated Content Quality

We’ve spent 9 months researching and testing AI-generated content's impact on Google’s algorithm. Here’s what you need to know if you’re using tools like ChatGPT to create content for your website. After extensive research and hands-on experiments (including with our own website), here’s what we’ve found: Google’s penalties on AI-generated content are real. If an article is 100% AI-written, Google can de-index it quickly—sometimes within 1-2 days of publication. Their detection of AI content, particularly from tools like ChatGPT or Claude, is sharper than ever. Even if you use human "rewriters" to make it seem more natural, half the time Google still catches it. What’s even riskier? If a significant portion of your website’s content (think 30% or more) is AI-generated, Google may penalize the entire site. While the exact percentage isn’t set in stone, it’s a gamble—the more AI content you post, the more likely Google will penalize your top-ranking keywords. We’ve seen sites lose 30-40% of their top 3 ranking keywords, while lower-ranking ones are left untouched. So, what should you do? Focus on original content with fresh ideas and perspectives. AI can be helpful for brainstorming. It cannot create, only replicate and regurgitate. Google is looking for new, valuable information, not a repeat of the same generic content. High-value content includes specific and unique insights. It should serve a net new purpose for readers that can’t be found in other content on the same topic. tl;dr Don’t rely on AI to completely write or heavily rewrite your articles. The risk of Google detecting and penalizing it is too high. Be cautious when using AI to repurpose content—it might come across as “AI-written,” which Google will quickly flag. As Google improves its ability to spot AI content, penalties for unoriginal work—whether AI-generated or not—will likely increase. 👥 AI for content writing is currently my favorite debate in the digital marketing world. What’s your take on what we’ve found? 

Common Sense Media recently released a comprehensive risk assessment of AI teacher assistants/lesson planning tools. Their findings reveal that while these tools promise increased productivity and creative support, they're also creating "invisible influencers" that could fundamentally undermine educational quality. Unlike GenAI foundation model chatbots, these tools are specifically designed for instructional planning and classroom use and are rapidly being adopted across districts. Key Concerns from their report: • "Invisible Influencers" in Student Learning: AI-generated content directly shapes what students learn through potentially biased perspectives and historical inaccuracies that teachers may miss; evidence also shows these tools suggest different approaches and responses based on student race/gender • “Outsourced Thinking" Problem: Tools make it dangerously easy to push unreviewed AI instructional content straight to classrooms, while novice teachers lack experience to spot subtle errors and biasses • High-Stakes Outputs: IEP and behavior plan generators create official-looking documents that could impact student educational trajectories even though these plans should be human-generated (and in the case of IEP goals are mandated to be human generated) • Undermining High-Quality Instructional Materials: Without proper integration, these tools fragment learning and can undermine coherent, research-backed curricula Recommendations from the report: • Experienced educator oversight required for all AI-generated educational content • Clear district policies and guidelines for AI teacher assistant implementation • Integration with existing high-quality curricula rather than replacement of established materials • Robust teacher training on identifying bias and evaluating AI outputs • Careful oversight of real-time AI feedback tools that interact directly with students We'd also recommend foundational AI literacy for teachers before they begin using GenAI teacher assistants, so that they are aware of the potential limitations. While AI teacher assistants aren't inherently problematic, they require the same careful implementation and oversight we'd expect for any tool that directly impacts student learning. The potential for enhanced productivity is real, but so are the risks to educational equity and quality. This report underscores the urgent need for GenAI EdTech tool makers to provide evidence of how their tools mitigate these issues along with evidence-based policies and professional development to help educators navigate AI tools responsibly. All of which underline how important AI Literacy is for the 2025-2026 school year. Link in the comments to check out the full report. Also check out our 5 Questions to Ask GenAI EdTech Providers resource in the comments if you are planning to implement any of these tools in your school or district. #AIinEducation #ailiteracy #Education #K12 AI for Education

💡Ever wondered what happens when AI trains on its own output?💡🤖 It's like copying a copy-the quality deteriorates, drifting further from reality. 🚩This "model collapse" is real. Studies show that AI models consuming their own content produce outputs that are less diverse and more distorted. As researchers, Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Nicolas Papernot, Ross Anderson, and Yarin Gal observed, "The model becomes poisoned with its own projection of reality" (links in comments). 🚩🚩Alarmingly, Amazon Web Services (AWS) research estimates that 57% of internet content is now AI-generated or machine-translated, flooding the web with low-quality data. This flood of low-quality content contaminates the datasets used to train AI models, creating a destructive feedback loop: AI trains on flawed data, leading to even worse outputs. 👉🏼So next time you read online content, keep in mind it might not just be AI-generated-it's possibly AI built upon AI, amplifying inaccuracies.  (*This piece was written by a human to raise awareness about this issue*) #AIEthics #DataQuality #ResponsibleAI #HumaneAI #ML #AIRisks #modelcollapse

Generative AI continues to generate excitement, but significant challenges are often overlooked. Reports from respected sources such as Harvard Business Review and Goldman Sachs highlight that current expectations may not align with reality. The technology, while promising, has limitations that need to be acknowledged and addressed. In May, Harvard Business Review discussed "AI's Trust Problem," in June, Goldman Sachs raised doubts about whether the expected $1 trillion in AI investment will deliver substantial returns. Their concern: aside from developer efficiency, there may not be enough value to justify such massive spending, especially in the near term. Jim Covello, Goldman Sachs' head of global equity research, pointed out that replacing low-wage jobs with costly technology contradicts earlier tech transitions, which focused on improving efficiency and affordability. A recent analysis from Planet Money echoes this skepticism, listing “10 reasons why AI may be overrated.” Issues like hallucinations (when AI generates false or misleading information) and declining quality in AI-generated outputs raise concerns about its readiness for widespread use. A study by The Washington Post also examined what people ask AI chatbots about, revealing unexpected trends. Along with common academic assistance, some topics raised ethical and personal concerns. 🔍 Reality check: Generative AI can be impressive but often struggles with accuracy, leading to errors or hallucinations. 💸 Investment risks: Financial experts question the value of massive investments in AI and wonder if the technology will offer enough returns in the short term. 📉 Productivity vs. quality: While AI can increase productivity, particularly in coding, research shows that the quality of AI-generated code is often subpar. 📚 Help with homework: Students turn to AI chatbots for homework help, but concerns arise when AI provides direct answers rather than guidance or learning support. ❓ Personal and sensitive queries: Many chatbot users ask about personal topics, including sex and relationships, which raises ethical questions about privacy and appropriate use. These points serve as a reminder that while generative AI is a powerful tool, it’s important to approach it with realistic expectations and a clear understanding of its current limitations. #GenerativeAI #AIEthics #AIRealityCheck #AIinEducation #TechInvestments #AIProductivity #AIChallenges #AIHomework #AIandSex #AIinConservation #AIFuture #AIHype 

When I challenge folks about using AI to generate content, I hear stuff like this: "My customers can't tell it's AI." If your customers can't tell the difference between your AI-generated and human-created content, you need to rethink your content strategy. You're pushing your teams to produce too much, too fast. You're rewarding quantity, not quality. As a result, your teams are producing unremarkable stuff. "It lets me produce at scale." Why does content need to be produced at scale? For rankings? Doesn't help. For share of voice? Getting more people to see your AI-generated garbage isn't a good thing. "It's cheaper." It's worthless, not cheaper. There's a difference. "I just use it to rank." But you're not going to rank, or if you do, it'll only be for a while. Then you'll get to experience Pandaguination, where the search engines bury you so deep no light penetrates. If you want use AI and create great content, make an AI sandwich on anthro bread: Humans develop the ideas AI helps brief and outline Humans create the content Otherwise, please, don't touch the AI.

AI Slop Is Killing YouTube—Creators, Knock It Off! The surge of AI-generated content on YouTube—dubbed “AI slop”—is quickly becoming a major concern for both creators and viewers. With algorithmically produced videos flooding the platform, the human touch that once defined YouTube is being drowned out. Authenticity and creativity are sacrificed for quantity, leading to generic, repetitive uploads that make it increasingly difficult for thoughtful, original content to stand out. This overuse threatens not only discoverability, forcing high-quality voices into obscurity, but also the creative ecosystem itself, as genuine creators become discouraged and innovation stalls. The consequences go deeper: as viewers encounter more formulaic, soulless videos, they begin to question the value and legitimacy of what they’re watching. This erodes trust between creators and audiences, undermining community loyalty and engagement. YouTube’s longstanding reputation as a source for creativity and connection is at risk. If this AI trend continues unchecked, the platform could become a wasteland of mass-produced, low-effort content, deterring aspiring creators and driving away viewers seeking meaningful entertainment. It’s critical for creators to rethink their reliance on AI, refocus on what makes their work unique, and take responsibility for nurturing the vibrant, authentic community that built YouTube’s success.

Can AI models get "Brain Rot"? New research says, Yes! A recent paper on the 'LLM Brain Rot Hypothesis' presents findings that are crucial for anyone involved in AI development. Researchers have discovered that continuous exposure to low-quality web content leads to lasting cognitive decline in large language models (LLMs). The key impacts identified include:- - 17-24% drop in reasoning tasks (ARC-Challenge). - 32% decline in long-context understanding (RULER). - Increased safety risks. - Emergence of negative personality traits (psychopathy, narcissism). What defines "junk data"? Two dimensions are significant:- - Engagement-driven content (short, viral posts). - Low semantic quality (clickbait, conspiracy theories, superficial content). The most concerning finding is that the damage is persistent. Even scaling up instruction tuning and clean data training cannot fully restore baseline capabilities, indicating deep representational drift rather than mere surface-level formatting issues. This research highlights that as we develop autonomous AI systems, data quality transcends being a mere training concern; it becomes a safety issue. We need to implement:- - Routine "cognitive health checks" for deployed models. - Careful curation during continual learning. - A better understanding of how data quality affects agent reliability. The paper emphasizes that data curation for continual pretraining is a training-time safety problem, not just a performance optimization. For those building production AI systems, this research should fundamentally alter our approach to data pipelines and model maintenance. Link to paper: https://lnkd.in/drgjvt8a #AI #MachineLearning #AgenticAI #DataQuality #AIResearch #LLM #AIEthics

“𝘛𝘩𝘦 𝘮𝘢𝘯 𝘸𝘩𝘰 𝘥𝘰𝘦𝘴𝘯’𝘵 𝘳𝘦𝘢𝘥 𝘨𝘰𝘰𝘥 𝘣𝘰𝘰𝘬𝘴 𝘩𝘢𝘴 𝘯𝘰 𝘢𝘥𝘷𝘢𝘯𝘵𝘢𝘨𝘦 𝘰𝘷𝘦𝘳 𝘵𝘩𝘦 𝘮𝘢𝘯 𝘸𝘩𝘰 𝘤𝘢𝘯’𝘵 𝘳𝘦𝘢𝘥 𝘵𝘩𝘦𝘮.” - 𝐌𝐚𝐫𝐤 𝐓𝐰𝐚𝐢𝐧 I’m concerned about the explosion of AI-generated content, even as I increasingly use AI to write. I’ve even trained a Claude agent in my voice to match my tone and style… including these signature bolds and emojis for LinkedIn! 🤖 But here’s the thing: 𝐭𝐡𝐞 𝐢𝐝𝐞𝐚𝐬 𝐬𝐭𝐢𝐥𝐥 𝐜𝐨𝐦𝐞 𝐟𝐫𝐨𝐦 𝐦𝐲 𝐦𝐢𝐧𝐝. So I continue to read. Not AI summaries of long-form content, but real depth - fiction and non-fiction that builds foundations, gets me into stories and characters, and shapes how I think. 📖 What I read has evolved over the years. I’ve shifted from platform-centric to curator-centric - still valuing high-quality journalism, but increasingly relying on curated sources like LinkedIn and Reddit upvotes to surface what matters. When I write (even AI-supported), hopefully my POV is more refined and developed. Real fact-checking from real experts - like that French professor challenging students on using AI which incorrectly writes a biography of Victor Hugo. ⚠️ Here’s my concern: when AI is only the probabilistic recombination of existing ideas, the chances of real innovation become equally probabilistic. AI-in-AI-out can lead to vanilla POVs and dangerous hallucinations. The risk of AI thought leadership? Bland sameness and costly errors. 💡 So I will continue to read, and to write, and use AI to surface information and refine my outputs - but more importantly, to refine my own understanding and thoughts in our ever-changing world. Creativity asked AI for help. AI said, ‘Sure — just give me 10,000 examples of original ideas first.’

A morning walk along East Coast, Singapore, where we talk about how copy-pasting unedited AI output is probably the most effective strategy for ensuring your work will be identified as generic, uninspired and ultimately irrelevant. Humans are incredible pattern-recognition 'machines'. That's why we can now instantly spot AI-generated content from a mile away. The em dashes for dramatic pauses, the "It's not just X, it's Y" construction, boring events turned into hyped up “one in a lifetime” stories that sound like a kid writing an essay for school, and the perfectly polished lack of any human quirks. We spent quite a bit of time telling people to use AI for their writing and…. mission accomplished, they're using it. So now, the result is a different challenge. Of course, we could talk about using detection tools (AI again!) for obvious AI content, but I think what we're talking about here is a symptom of user abdication. People are treating AI like a magic content generator instead of a writing partner, accepting whatever comes out instead of iterating, refining, and adding their own voice. Definitely not a failure of technology, but a failure of standards. Good writing still demands the same skills: knowing your audience, having something to say, and being willing to revise. The current wave of AI-generated slop really shows a failure to understand that technology does not replace the hard work of thinking, editing, and having a point of view. If you simply copy and paste single prompt AI output, you are not creating content, just noise.

The true ROI of AI will be judged not by how many prompts you run, but by how little “cleanup” downstream is needed. “Workslop”: The Hidden Productivity Killer in the AI Era and it doesn’t just waste time — it redistributes hidden cognitive debt. What is workslop? - Workslop = AI-generated content that appears polished, but lacks substance or fails to meaningfully advance the task. - It masquerades as “good work,” yet often forces recipients to decode, correct or redo it. - The problem is not just “bad work,” but bad work hidden under the façade of good design. Interesting Data Points (Refs: Workplace Insight, Axios, Slashdot and etc): 1/ ~40% of employees report having received workslop in the past month. 2/ Recipients spend on average ~1 hour 56 minutes to fix each instance. 3/ It damages trust: many rate senders as less creative, capable or reliable. When poorly generated AI content travels across teams or “downstream,” the cost shifts: recipients absorb the cleanup, reinterpretation, and rework. In effect, workslop is a tax on your colleagues’ focus and cognitive bandwidth. We tend to frame AI issues as “my draft was bad” or “it hallucinated,” but the deeper danger is systemic diffusion of low-signal content that drags everyone’s baseline upward to compensate. If your team still spends more time editing AI drafts than ideating new value — you haven’t optimized AI, you’ve institutionalized workslop. Reference: "AI-Generated “Workslop” Is Destroying Productivity", Harvard Business Review https://lnkd.in/gpRCsx4Y (Need Subscription) #workslop #generativeAI #enterpriseAI