AI Tools for Content Creation

There’s no denying that Generative AI has boosted productivity, but at what cost? Increasingly, we’re seeing documents/comments/articles that look complete on the surface but lack depth in their thought processes. During detailed discussion, these outputs often reveal themselves as generated content with little real value. Using GenAI for a first draft is smart—starting from a blank slate is always tough. But the real magic happens when we build on that initial output, adding our own insights and expertise. The key question is: are we using these tools to enhance our thinking, or are we letting them do the thinking for us? The former drives real value; the latter creates fluff. While productivity gains are celebrated, they mean nothing if they aren’t leveraged effectively. The narrative around GenAI’s productivity boost is losing steam because, without thoughtful application, it doesn’t translate into real business impact. Idea behind getting these gains is to utilize that “saved” time for more brainstorming, better solutions, robust strategy and a happy environment. But the blatant use of technology is now creating more debt than assets. Before you adopt any tool, consider this: it can solve problems to a certain extent, but it’s up to you to add value. “Human-in-the-loop” isn’t just a method; it’s a mandate for maintaining quality and connection. If GenAI is capable of automating content creation, try tagging your next blog or article "Made with AI" and see how it resonates with your audience. It was never about generating content but connecting with people. Machines still lack that connection at face value. #ExperienceFromTheField #WrittenByHuman #EditedByAI

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

Here’s the exact AI content strategy I use to take sites from page 5 to page 1 in 2025: 1) Topical Mapping • Start with a root topic. Think “Digital Marketing,” not “How to run Facebook Ads.” • Use ChatGPT to break it into subtopics + FAQs. This is your first-pass topical map. • Validate each subtopic by checking traffic potential via tools like Ahrefs/SEMRush. • Organize content into silos (pillar + clusters). Every piece should fit somewhere. 2) AI Content Workflow (the right way) • Don’t write and publish raw AI. You’ll get nuked. • Use AI for draft generation and outline speed. • Human editor polishes for tone, accuracy, and nuance. (Or use a tool like SurferAI) • Inject real experience, stats, or original examples. That’s how you stand out. • Cap output to ~3–5 articles per day/site. Don’t trip Google’s velocity radar. 3) Entity Optimization (critical in 2025) • Think beyond keywords - identify key entities for your niche. • Use tools like SurferSEO to extract relevant entities from top pages. • Weave entities naturally into headings, body copy, image alt text, etc. • Use internal links to connect related entities and pages. • Use schema markup to help Google understand entity relationships on your site. 4) On-Page Setup for AI Content • Match search intent by checking SERPs and aligning format with top-ranking pages. • Main query in H1. Subtopics covered in H2-H3. • Answer user query as fast as possible. • Add internal links to parent and sibling pages. • Include media (images, video embeds, infographics) to lower bounce rate. • Write naturally. Google's NLP understands natural speech patterns. Explain topics as if you're talking to someone in conversation. 5) Topical Authority Building • Cover each topic fully to position your site as the best resource in that niche. • Avoid shallow posts. Go deep. Expand on how-tos, FAQs, comparisons, pros/cons. • Build out each silo based on topic size and search demand. • Revisit old posts monthly. Merge duplicates. Expand thin content. • Use internal links to connect related articles within the same silo. 6) Link Building That Complements • Don’t build links to garbage AI content. Clean it up first. • Focus on niche-relevant guest posts, citations, and digital PR. • Use branded anchors primarily. Sprinkle in partial matches where it makes sense. • Internal links do 80% of the work early on. Don’t ignore them. 7) Content Maintenance Between Core Updates • Track rankings in GSC or Ahrefs weekly. Flag drops and check affected pages. • Add new internal links when publishing fresh content. • Update old pages with new data, media, and search queries from GSC. • Remove deadweight content that doesn’t rank or convert.

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 

Researchers from Stanford, Imperial College, and the Internet Archive approached us with a question: How does public opinion about an AI-generated internet align with the data? Using Pangram v3 and the Wayback Machine, they found that by 2025, 35% of newly published websites on the open internet were AI-generated or AI-assisted. Alongside the quantitative research, the authors polled internet users on how they feel about an AI-generated internet. The verdict was bad: 83% of respondents are concerned about AI creating a stylistic monoculture, and 75% feel that an AI-dominated internet will degrade the accuracy of information available online. They're not wrong to worry. The study found that AI is already causing the range of unique viewpoints online to shrink. Semantic analysis also found that as AI content proliferates, online writing becomes artificially cheerful: the average positive sentiment score for AI-generated and AI-assisted documents was 107% higher than for human-authored websites. I'm apprehensive about the future of the internet. AI slop has the potential to erode trust and undermine the social contract. Every third website published to the internet in 2025 was written using AI, but that proportion is only going to increase, and could exceed 50% by 2027. We're proud to have contributed to this research. The study, "The Impact of AI-Generated Text on the Internet," is now available as a preprint, and you can also read about the research in WIRED. Study: https://lnkd.in/erJuUrPP WIRED: https://lnkd.in/epZiJ7eZ

Travelers ask many questions before booking, and most of them are already answered somewhere in a property’s listing. However, surfacing the right information at the right moment is far from simple, especially when listings are inconsistent or lengthy. In a recent blog, Agoda’s engineering team shares how they tackled this with a conversational AI assistant called the Property AMA Bot. Instead of hardcoding answers or relying solely on generative models, they built a retrieval-augmented system that combines relevance scoring with language generation to provide accurate, grounded responses. Here’s how it works: First, they break down each property’s content into structured “facts” using heuristics and keyword filtering. When a user asks a question, the system retrieves relevant facts using a hybrid of sparse and dense retrieval techniques. Then, these facts are passed into a fine-tuned LLM, which generates a concise answer grounded in the retrieved content. To keep answers factual and safe, they also include fallback rules, so that the bot will refrain from answering if confidence is low or the topic falls outside the known scope. This setup strikes a good balance between traditional Information Retrieval methods and generative models, making the bot both responsive and reliable. This approach is a great example of retrieval-augmented generation in practice, blending engineering pragmatism with the strengths of LLMs to improve real-world user experience. #DataScience #MachineLearning #Analytics #LLMs #ConversationalAI #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gVUT97F7

I had an “aha” moment about the future of SEO when using Microsoft 365 Copilot Search* to find a document. My search for “Q4 marketing performance vs budget” surfaced the budget presentation, an email thread about timeline changes, and a document summarizing our quarter’s campaign results. It understood I wanted the complete context behind my query, not just a keyword match. It inferred I was looking for the synthesis between marketing spend and business outcomes. This gives us a direct view into where consumer search is heading. SEO (or AEO, or GEO) strategies already focus on semantic search and user intent, but most teams still optimize individual blog posts and pages rather than building knowledge ecosystems. That approach worked when search engines were sophisticated filing systems. It falls apart when they become reasoning machines. Copilot’s system connects and interprets relationships across organizational content to understand context and deliver comprehensive answers. When Google and Bing’s consumer AI features catch up, your prospect searching “how to reduce customer acquisition costs” might discover your retention strategy content, but only if you’ve built the right conceptual bridges between those ideas. It's long been time for your content strategy to evolve beyond keywords too. Now your content strategy needs to answer, “How do our ideas connect to solve interconnected problems?” Rather than optimizing individual pieces, focus on building comprehensive topic clusters where subtopics link back to a primary expertise area. This positions you as a holistic authority when AI systems look for complete answers. If you have access to M365 Copilot Search, try searching your company’s knowledge base to see which content gets surfaced together. These connections could help reveal how AI systems understand topic relationships, which can provide insights you can apply to your external content strategy. The shift from keyword optimization to intent architecture is happening fast. *Unlike the regular Microsoft Copilot that searches the web, this is the enterprise version that works inside organizations, crawling emails, documents, and internal data. #AIMarketing #AEO #SEO #ContentStrategy

This is the third time someone has said to me: “the em dashes make it feel like AI is talking.” And the writer in me wants to address that. I have been writing 2,000-word essays almost every week for the past six years. Em dashes are not a glitch that showed up after AI. They are part of my writing rhythm. And if I am very honest, they are part of how I use “you.” So, it is hurtful to hear - remove a part of your writing identity because people now read it as AI. And I think this is becoming a bigger problem than we are willing to admit. We are starting to use tiny syntactical markers as proof of artificiality. ● An em dash. ● A semicolon. ● A certain kind of list. ● A sentence that sounds “too polished.” And in the process, we are flattening human writing to protect ourselves from uncertainty. But AI did not invent the em dash! AI learned from human writing. And even now, most people are still feeding it through “persona”, context, examples, and past materials. Which means what you are often noticing is not “AI style” in some pure form. You are noticing recycled, mimicked, patterned human style. But if we want to keep things human, we cannot start erasing the very quirks, cadences, and syntactical preferences that made human writing feel alive in the first place. We cannot say we want authentic voice — and then ask people to remove the markers of theirs. Yes, there are real conversations to be had about disclosure, overuse, lazy writing, and how AI is reshaping trust online. I am not dismissing that. I am saying: please stop using small writing markers as lazy identifiers of who is and isn’t human. Some of us have loved our em dashes for a very long time. And if we want to keep writing human, we have to stop asking humans to sound less like themselves. #nonprofits #community #aiethics

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.