📝 TL;DR A new study says heavy AI use at work can create “brain fry,” a mental fatigue that comes from constantly juggling and supervising AI tools. Instead of making work lighter, AI can expand your workload and responsibility until your brain hits a limit. 🧠 Overview A Harvard Business Review study covered by CBS News suggests some workers are experiencing a distinct mental strain from AI workflows. The pattern shows up most in people bouncing between multiple AI tools or overseeing multiple AI agents, especially early adopters who use AI all day. The result is not classic burnout, it is more like decision fatigue and cognitive overload, where you can keep iterating with AI but your brain stops feeling clear. 📜 The Announcement Researchers surveyed roughly 1,500 full time US workers and found about one in seven reported significant mental fatigue from managing AI tools at work. The study labels this pattern “brain fry,” describing it as a kind of mental hangover that can lead to more errors and poorer decisions. A key insight is that AI can “extend” your capacity, which also expands your sphere of accountability. In practice, that means people take on more tasks because AI makes it possible, until they become overwhelmed. ⚙️ How It Works • Expanded workload - AI helps you do more, which often leads to being expected to do more. • Constant context switching - Moving between multiple chatbots, copilots, and agent tools adds decision fatigue and reduces focus. • Verification burden - You still have to check AI output, which becomes its own exhausting layer of work. • Endless iteration loop - AI makes it easy to keep refining, rewriting, and rethinking, even when your brain is already depleted. • Quality drops even when speed rises - People can feel productive while their accuracy and judgment quietly decline. • Leadership effect - The research suggests better outcomes when managers are intentional about how AI is used, instead of pushing “use it everywhere” by default.

We often talk about time as if it only lives on the calendar. We count meetings, deadlines, deliverables, and hours worked. But some of the most expensive time loss in modern work never appears in a schedule at all. It lives in the background, in the mental effort required to keep unfinished work active in our head. That is where AI can become surprisingly powerful. Not just as a tool for output, but as a way to reduce the hidden time tax of carrying too much unresolved thinking at once. ------------- Where Time Leaks Before Work Even Starts ------------- A lot of people assume they need better time management when what they really need is less mental carrying. We do not just spend time doing work. We spend time remembering what needs to be done, revisiting half-formed ideas, holding open loops in memory, and trying not to lose important details before we have a chance to act on them. That overhead is real work, even if it does not look productive from the outside. Think about a normal day. We may have a proposal to finish, a follow-up email to send, a team decision we still need to make, a new idea for a process improvement, and three conversations that require thoughtful replies. Even when we are not actively working on those things, part of our attention stays attached to them. We keep mentally rehearsing, “Do not forget that point,” or “I need to circle back to that,” or “There was a better way to explain that.” That constant background processing drains energy long before the task itself is completed. This is one reason people end a day feeling busy but strangely unfinished. The issue is not always a lack of effort. It is that attention has been fragmented across too many mentally open loops. The brain becomes a storage system, a reminder system, and a drafting space all at once. That creates invisible cycle time. It slows the path from thought to action, from task to completion, and from idea to value. In that sense, the real time leak is not just workload. It is unexternalized workload. The more work we hold in our head, the more time we lose to friction, context switching, and re-entry. AI matters here because it can help us move thinking out of our head and into a form we can work with faster.

📝 TL;DR Apple is reportedly shifting its AI strategy toward wearables that can “see” the world, not just answer questions. And it will preview that direction during a March 2–4 launch week that includes a rumored low cost MacBook. 🧠 Overview Apple’s AI story is moving from “smarter Siri” to something more physical, AI that lives in devices you wear and use hands free. The key concept being teased is Visual Intelligence, using cameras and on device AI to understand what you are looking at and help you act in the moment. At the same time, Apple is preparing a burst of product launches in early March, with rumors pointing to a new entry level MacBook as one of the headline items. 📜 The Announcement Apple is expected to run a short stretch of announcements from March 2 through March 4, ending with an “Apple Experience” style event on March 4 in multiple cities. Reports suggest at least five products could be introduced across that window. The bigger strategic thread is Apple’s push into AI wearables, with Visual Intelligence positioned as the foundation for new device categories like smart glasses, a pendant style wearable, and more advanced AirPods that could include cameras. ⚙️ How It Works • Visual Intelligence - The idea is that a wearable camera plus on device AI can identify objects, read text, translate signs, and provide context without pulling out your phone. • AI wearables roadmap - Reports point to smart glasses, a pendant device, and camera enabled AirPods as the most active development tracks. • March launch week - Apple is expected to use March 2–4 to introduce multiple products and set the tone for its 2026 roadmap. • Low cost MacBook rumor - A cheaper MacBook is expected to be shown, potentially using an iPhone class chip and coming in brighter colors to hit a lower price point. • Software ties in - The wearables push likely connects to upcoming OS updates and Apple Intelligence features that make the ecosystem feel more agentic and more contextual.

I have been using ChatGPT a bit, but I want to start using AI more, especially for training (triathlon). I might use it for other things as well, like help with ideas, reviewing things I write, giving feedback on how to improve and shorten my writing, etc. Just wondering what is better Claude or Chatpgt. It seemed like Claude is upping their game, so I feel like switching over, but curious to hear your thoughts?

AI is no longer a novelty, its infrastructure. But not all AI tools are built for the same purpose. While many professionals lump them together, ChatGPT, Gemini, Claude, and Perplexity each serve distinct roles in your workflow. Understanding the differences helps you choose the right tool for the right job, and maximize productivity. 1. ChatGPT Best for: Everyday creative thinking, strategic problem-solving, and fast synthesis. What It Does Well ChatGPT is the most versatile generalist of the group. It moves fluidly between creativity and logic, making it ideal for professionals who need both imagination and structure. Ideal Use Cases - Generating ideas and campaign concepts - Writing blogs, emails, scripts, and marketing copy - Structuring content and outlining strategy - Debugging problems across domains - Simplifying complex ideas into plain language Key Strengths - Broad intelligence and flexibility - Strong at framing and organizing ideas - Large plugin/tool ecosystem - Clear, accessible explanations Key Benefit If you need an AI “thinking partner” that supports daily business operations, strategy, and creative execution, ChatGPT delivers speed and versatility. 2. Gemini Best for: AI that integrates seamlessly into a Google-powered workflow. What It Does Well Gemini shines inside the Google ecosystem. It’s designed to work natively across Gmail, Docs, Sheets, and Slides. Ideal Use Cases - Drafting emails inside Gmail - Real-time analysis in Google Sheets - Slide creation in Google Slides - Collaborative document editing - Multimedia projects Key Strengths - Deep Google integration - Real-time web access - Strong collaboration features - Works well in shared environments Key Benefit If your organization runs on Google Workspace, Gemini reduces friction by embedding AI directly into your daily tools. 3. Claude Best for: Deep reasoning, nuanced writing, and complex synthesis. What It Does Well Claude excels at handling dense, long-form material. It processes extensive context (100K+ tokens) while maintaining clarity and logical structure.