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Apple's App Tracking Transparency sealing Meta's fate

Anonymous
If you have been following the recent news on Meta (formerly Facebook) you may have read that Meta recently projected their ad revenue will be cut by a staggering $10 billion in 2022 due to Apple’s new App Tracking Transparency feature (also known as ATT). This has resulted in Meta’s stock to plummet by over 20%. Photo by julien Tromeur on Unsplash - modified by Beolle So what is Apple’s ATT and how does it impact ad revenue? Apple has been releasing multiple privacy features for the last few years. This included Apple’s Mail Privacy Protection and Apple’s App Tracking Transparency feature. You can learn more about Apple’s Mail Privacy Protection in our earlier post by clicking here .  Apple’s App Tracking Transparency (ATT) was launched in iOS 14.5 and iPadOS 14.5 where it prompted users to select if they wanted the app to track their activities across other apps on the device. The prompt is displayed when the user opens an app like Facebook or Instagram for the first time o...
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Reimagining Digital Experience Management: How Agentic AI is Transforming Adobe Experience Manager

 Adobe Experience Manager (AEM) has introduced powerful new Agentic AI capabilities designed to continuously improve and adapt digital experiences at the speed of AI. By integrating advanced AI orchestrators through Agent-to-Agent (A2A) and Model Control Protocol (MCP) tools, AEM enables brands to automate complex workflows and enforce compliance seamlessly across enterprise ecosystems. Through a suite of specialized agents, teams can transition from manual, weeks-long processes into fast, AI-assisted workflows powered by simple natural language prompts. Photo by Tunahan KALAYCI via Pexels   Here is a breakdown of the key agents driving AEM’s new Agentic capabilities, their value propositions, their guardrails, and their current availability status. 1. Brand Experience Agent. Overview. The Brand Experience Agent accelerates digital modernization through specialized sub-agents—the Experience Modernization Agent, Experience Production Agent, and Experience Development Agent. Tog...
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Steer for a talent transformation strategy (and avoiding AI fatigue)

 There was a debate on whether to feature the term “AI” in the title of this article. Honestly, a key motivation for pursuing the research that led to this post was sparked by the widespread excitement about AI appearing constantly in our LinkedIn feed, to the point of feeling the fatigue, and even a bit disappointed in the algorithm of this, and the others, social media and content curated apps.  We soon discovered that there is an entire concept called "AI fatigue", not exactly how we were feeling it, but more about the mixed emotions people in the workforce have regarding the use of AI tools. Photo by Mart Production via Pexels (background updated with AI and Adobe  tech) From micro blog posts to video podcasts, lately, most of the tech content we encounter revolves around AI. They often sound or read very similar, usually mentioning the same few top providers. The articles (and social posts... at least the popular ones with paid-campaigns behind it) tend to focus less...
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Assembling MLOps practice - part 1

In one of our previous articles it was highlighted how DevOps manages the End-to-End application cycle, leveraging agility and automation. CI/CD pipelines, collaboration and transparency, monitoring and automation are part of the list on how DevOps leverages and facilitates agility. What if then we bring those to support ML? That is how MLOps comes to the table and starts making sense! Lego Alike data assembly - Generated with Gemini A big tech corporation, or a startup, nowadays will see how it is becoming a requirement to incorporate AI and Machine learning (ML) in their operations. ML components are key parts of the ecosystem, supporting the solutions provided to clients. As a result, DevOps and MLOps have become part of the "secret sauce" for success.  What is MLOps Just to bring the definition of what you probably know (or put together based on the above) MLOps focuses on the life-cycle management of machine learning models. It combines machine learning with traditional ...
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Agreed