Grant Gochnauer

🚨 Someone just built a real-time global intelligence dashboard and open sourced it for free. It's called World Monitor. Think of it as a CNN war room meets Bloomberg Terminal for geopolitics but anyone can use it. No paid OSINT tools. No expensive subscriptions. No classified access needed. Here's what this thing tracks in real-time: → Active conflict zones with escalation scoring → 220+ military bases from 9 countries → Live military aircraft tracking (ADS-B) → Naval vessel monitoring including "dark ships" going off radar → Nuclear facilities worldwide → Undersea cables, oil pipelines, and AI datacenter clusters → Protests, sanctions, internet outages, and satellite fire detection → Prediction markets as early warning signals Here's the wildest part: It has an AI that reads 100+ news sources, classifies threats in real-time, and generates intelligence briefs automatically. Every country gets a live "Instability Index" score from 0-100 based on military activity, protests, news velocity, and structural risk. When 3+ signal types spike in the same area, military flights + protests + satellite fires, it triggers a convergence alert. This is the kind of tool governments pay millions for. It runs in your browser. One command to install. 100% Open Source. MIT License.

Everyone "knows" that as AI gets better, humans become less valuable. Except three economists just proved the exact opposite using math from 1973 and Steve Jobs. And it explains something that's been driving researchers crazy... Why did computers make inequality WORSE but ChatGPT is making it BETTER? The data is bizarre. In the 1990s, computers widened wage gaps everywhere they appeared. But study after study shows AI helping struggling workers more than experts. I spent the morning with this research paper and... the answer flips our entire mental model. Think about how you use ChatGPT. You don't just type once and walk away, right? You iterate. You refine. You spot opportunities to improve. That back-and-forth? That's the key to everything. The researchers decomposed ALL cognitive work into three parts: Implementation (doing the task) Opportunity judgment (seeing what could be better) Payoff judgment (knowing what actually matters) Here's where it gets wild... AI is really good at implementation. Like, scary good. A junior coder with Cursor can suddenly write like they have 5 years experience. But that's not the interesting part... The better AI gets at implementation, the MORE valuable your judgment becomes. It's multiplicative, not substitutive. Imagine you're a designer. AI can now execute any design in seconds. But knowing WHICH design to make? When to iterate? What the client actually needs? That's all you. The math proves something counterintuitive: as tools get more powerful, the gap between someone who can spot opportunities and someone who can't gets BIGGER. But wait - why is AI currently reducing inequality then? Because we're in phase one. Right now, AI is compensating for skill differences. The struggling workers get huge boosts. The experts? They were already good at implementation. Phase two is coming though... Once implementation is basically free (think: anyone can code, design, write), the ONLY thing that matters is judgment. Who sees the opportunity? Who knows what's valuable? And that's when inequality explodes again. The paper even calculates the exact turning point. Here's what broke my brain: better AI makes full automation LESS likely, not more. Why? Because automated systems have fixed judgment. They can't adapt. A radiologist AI might be 99% accurate, but it can't realize "wait, this patient's case is weird, I should think differently." The flexibility to adjust your judgment in real-time? That's uniquely human. And it gets MORE valuable as the tools improve. Even crazier: this changes how teams should work. The paper shows that as AI improves, control should shift from people who are good at DOING to people good at SEEING opportunities. We're already seeing this. That study about Microsoft's Kinect? Machine vision experts suddenly mattered less than generalists who could spot novel uses. You know what this reminds me of? The shift from craftsmen to designers during industrialization. The machines could make anything. The value moved to knowing WHAT to make. We're about to see the same thing with cognitive work. Next time you use ChatGPT, try this: instead of focusing on getting it to do the task perfectly, focus on recognizing opportunities to iterate. That skill - seeing what could be better - that's your moat. The researchers call it "opportunity judgment" and it's about to become the most valuable skill in the economy. Quick test: Give two people the same AI tool and the same task. The output difference? That's pure judgment. And that gap is about to get a lot wider. One finding haunts me: the paper shows task-based predictions (like "AI will replace X jobs") are missing the point entirely. They measure what people do TODAY. But the whole point is that AI changes what the job even IS. A lawyer's job won't be "writing contracts." It'll be "knowing which contract variation creates the most value in this specific situation." Completely different skill. The paper maps out exactly when to automate vs augment. The formula is complex but the intuition is simple: If judgment variance is high → augment If tasks are predictable → automate If stakes are high → definitely augment Here's my take: we're training for the wrong future. Everyone's learning to prompt better. But prompting is just implementation. The real skill is recognizing when the output could be better and knowing what "better" means for your specific context. Schools teaching "AI literacy"? They're teaching people to be better bicycles. We should be teaching people to be better riders. (That's literally where the paper's title comes from - Jobs called computers "bicycles for the mind") Last thought that changes everything: The paper proves that in high-judgment work, making AI 10x better might make humans 100x more valuable. Because you can iterate faster. Test more ideas. Explore more opportunities. Your judgment gets amplified. So the question isn't "will AI replace me?" It's "am I developing the judgment to ride increasingly powerful bicycles?" Because the bicycles are about to get VERY fast. And the gap between good riders and bad ones is about to become a chasm. What patterns are you starting to notice in your field that others are missing? That's your future edge. And it's about to matter more than ever. /end PS - If you're curious about the math, the paper actually derives the exact inequality curve. It's U-shaped. We're at the bottom of the U right now. The climb up is coming. Makes you wonder what other "obvious" things about AI we have completely backwards...