Subject: Analysis of the significant decline in web traffic to major news websites in July 2025, primarily attributed to Google's AI Overviews feature and its broader implications for the media industry. Sources: Excerpts from "News Websites' Traffic Decline: The AI Impact" (Novel Cognition and Aibrandintelligence.com research, compiling data from Press Gazette, SimilarWeb, Columbia Journalism Review, Pew Research Center, and others). Executive Summary The digital news industry is experiencing a profound crisis, dubbed a "traffic apocalypse," with a widespread and significant decline in web traffic across major U.S. news websites. In July 2025, 46 of the top 50 U.S. news sites saw year-over-year traffic declines, with some publishers losing up to 50% of their audience compared to July 2024. The primary driver of this decline is Google's AI Overviews feature, launched in May 2024, which has drastically increased "zero-click" searches and reduced organic traffic to news sites. This shift is disrupting traditional publishing business models, leading to legal challenges and a pivot towards reader revenue, while raising concerns about the future of quality journalism. Key Themes and Most Important Ideas/Facts 1. Widespread and Severe Traffic Declines Across Major News Sites - Prevalence: "46 of the top 50 news sites experienced year-over-year traffic declines" in July 2025 compared to July 2024. - Magnitude: Some publishers "losing as much as 50% of their audience." - Major Publishers Hit Hard:Forbes: -50% (63 million visits) - Daily Mail: -44% (76.8 million visits) - NBC News: -42% (74.4 million visits) - HuffPost: -42% (38.5 million visits) - Washington Post: -40% (69.4 million visits) - CNN: -34% (323 million visits) - Fox News: -25% (249 million visits) - Limited Exceptions: Only three websites saw double-digit year-over-year growth: Substack (+47%), India Times (+46%), and Newsbreak (+24%). 2. Google AI Overviews as the Primary Driver of Decline

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AI model inference is the process where a trained artificial intelligence model uses its knowledge to make predictions or draw conclusions from new, previously unseen data. It is the operational phase of AI, where the model applies what it learned during training to real-world situations.[1][4][5][7] The AI Lifecycle: Training vs. Inference The lifecycle of a machine learning model consists of two main phases: training and inference.[4][8] - Training This is the learning phase. An AI model is "trained" by processing vast amounts of labeled data to learn to recognize patterns, relationships, and features within that data. For example, a model designed to identify spam emails is fed millions of emails that are already labeled as "spam" or "not spam". This process builds the model's knowledge base, which is stored as parameters or "weights".[3][6][9][4] - Inference This is the application phase. Once trained, the model is deployed to perform its designated task on live, real-world data it has never encountered before. It uses its stored knowledge to "infer" or deduce an outcome. For example, when a new email arrives, the trained spam model analyzes its content and characteristics to predict whether it is spam. This is considered the "moment of truth" for an AI model.[6][8][9][3] Think of training as a student studying for an exam by reviewing course materials, while inference is the student taking the exam and applying that knowledge to answer new questions.[3] Examples of AI Inference AI inference is at the core of many modern technologies: - Generative AI Large language models (LLMs) like ChatGPT use inference to predict the next most likely word in a sequence, allowing them to generate coherent sentences and paragraphs.[3] - Autonomous Vehicles A self-driving car uses inference to identify a stop sign on a road it has never driven on by recognizing patterns learned from millions of images of stop signs during training.[1] - Facial Recognition A model trained on millions of facial images can infer an individual's identity in a new photo by identifying features like eye color and nose shape.[2] - Fraud Detection Banks use AI to analyze credit card transactions in real-time and infer whether a transaction is likely fraudulent based on learned patterns.[3]

I am interested in the thoughts of members here. I have seen an increase in the number of products that gives access to multiple (some say numerous) AI tools for one off pricing. As I have multiple subscriptions to AI tools this piqued my interest. I am hesitant as to how they can do this when the tools all have their own pricing models. I would appreciated any advice. The two currently are aimodelsuite and the other is aisuperbot. I apologise if this is not an appropriate question and happy to delet if necessary.

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