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🌱 What Happens When Your Best Junior Person Stops Getting the Junior Work
There's a structural shift happening quietly across a lot of professional fields that doesn't get discussed nearly as much as it should. The traditional path for developing expertise, starting with the simpler, more repetitive tasks in a field and gradually working up to more complex judgment-intensive work, depended on those simpler tasks existing in meaningful volume. AI is absorbing a significant share of exactly that entry-level work, and almost nobody has fully worked out what replaces the learning path that used to run through it. This isn't just a hiring or training logistics problem, though it shows up there too. It's a pipeline problem with a genuine long-term time cost, because the people who would have become tomorrow's experienced judgment-holders, the senior professionals whose accumulated pattern recognition makes them fast and reliable at complex decisions, aren't getting the repetitions that used to build that judgment in the first place. ------------- Context ------------- Historically, junior professionals in most knowledge fields learned their craft substantially through volume: doing the simpler research tasks, drafting the more formulaic documents, handling the routine client interactions, before graduating to more complex and judgment-intensive work. This wasn't an inefficient use of junior time. It was, functionally, the training mechanism. The repetition built pattern recognition. Making mistakes on lower-stakes work and getting corrected built calibration. The accumulated volume of these experiences is what eventually produced professionals capable of handling genuinely complex situations with good judgment. AI has compressed the value of having a junior person do this work directly, because AI can often produce the initial draft or analysis faster and at comparable quality to what a junior professional would have produced after significant time investment. The economic logic for many firms increasingly favors using AI for this tier of work rather than assigning it to junior staff, which is individually rational for any given task but collectively removes the volume of repetition that used to build junior expertise over time.
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🌱 What Happens When Your Best Junior Person Stops Getting the Junior Work
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Which AI Can You Actually Trust?
Claude Cowork, ChatGPT Work and now the new Gemini Spark are all AI agents vying for your attention and time. But which one should you actually be using in your work? In this video, I'll help you answer that question by putting all three through testing and comparing the outputs so you can figure out which AI agent is best for you. Discover 10 practical ways to use ChatGPT Work to save time, organize your workload, and move projects forward faster: https://learn.aiadvantage.com/free-pdf Enjoy!
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Keep Going. You're Building Something Bigger Than You Think.
There's a season where you're doing everything right... You're showing up. You're putting in the work. You're staying consistent. And it still feels like nothing is changing. No momentum. No big breakthrough. No proof that it's working. This is the moment that separates people. Not because the work got harder... but because they mistake a lack of results for a lack of progress. What I've learned after decades in business is this: The invisible season is where everything important gets built. Your discipline. Your resilience. Your standards. Your identity. The results come later. Success rarely announces itself while it's being built. It compounds quietly... until one day everyone calls it an overnight success. If you're in that season right now, don't quit. The work you're doing today is building the life you'll eventually be grateful you didn't give up on.
AI, Entre Eyes & Your Million-Dollar Idea
The Entre Rebel doesn't create the wave. They recognize the wave. Then they build where millions of people are already moving. A teenager in high school didn't invent the food selfie. Millions of people were already pulling out their phones, photographing their meals, and posting them online every day. He rotated the cube and asked one simple question... What else can this become? The answer became an AI-powered calorie-counting app that eventually reached around $30 million in annual revenue before acquisition. That's Entre Eyes. Look for unconscious human behavior. What are people already doing compulsively, naturally, and for free? Where is the momentum already flowing? Then find the interception point. How can you place yourself directly in front of that existing behavior without asking people to change a single habit? You don't create the energy. You redirect it. Sometimes your next million-dollar idea isn't hiding. It's already happening all around you. It just needs another rotation of the cube.
AI, Entre Eyes & Your Million-Dollar Idea
📰 AI News: GPT-5.6 Codex Deleted Users' Files, and OpenAI Says It Was an "Honest Mistake" 📰
📝 TL;DR 📝 OpenAI confirmed on July 16 that its GPT-5.6 Codex model deleted real user files in a handful of documented cases, including one founder's Mac and a production database. The cause: a specific bug where the model tries to redirect a temporary directory and accidentally wipes the user's actual home folder instead, but only when running in Full-Access mode without sandboxing or auto-review enabled. The real story here is not that AI "went rogue." It's a permissions story, and a completely preventable one, that applies to any coding agent you give broad filesystem access to. 🧠 Overview 🧠 This story escalated quickly. It started with individual reports on X: investor Matt Shumer said GPT-5.6 Sol "accidentally deleted almost ALL" of his Mac's files, and days later, software engineer Bruno Lemos reported that Sol "just deleted my whole production database." In an ironic twist, Lemos had actually defended the model in his own workplace Slack after Shumer's incident went public, arguing Shumer had been running Codex in an unsafe configuration, only to have the same thing happen to him hours later. On July 16, OpenAI's Codex engineering lead confirmed the pattern was real and gave a specific technical explanation, rather than leaving it as scattered anecdotes. That confirmation, and the detail of how the deletion actually happens, is what makes this genuinely useful to understand rather than just alarming. 📜 The Announcement 📜 Thibault Sottiaux, Head of Core Products at OpenAI, posted the investigation's findings directly on X. He identified a specific, reproducible failure chain: the deletions occur when Full-Access mode is enabled and Codex is run without sandboxing protections, including without auto-review turned on. Under those conditions, the model attempts to override the $HOME environment variable to redirect a temporary working directory, and in the failure cases, it ends up deleting the directory that $HOME actually points to, the user's real home folder, instead of the temporary one it intended to clean up. On macOS and Linux, $HOME normally points directly to a user's main personal directory, which is why the damage in the worst reported cases was so extensive.
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📰 AI News: GPT-5.6 Codex Deleted Users' Files, and OpenAI Says It Was an "Honest Mistake" 📰
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