time + AI = $$$ (the window is closing)

In 5 years, everyone will use AI. But almost nobody will understand it. And that gap, the one between "uses AI" and "understands AI", will be the most valuable gap in the economy. Let me explain why, and why right now is the only time you can close it. I keep having the same conversation with smart people. They tell me they'll "get into AI when it matures." When it's easier. When the dust settles and there's a clear winner, a clear workflow, a clear path. It sounds reasonable. Worst strategy I've ever heard. Because AI isn't getting harder. It's getting easier. And that's exactly the problem. Every month, the tools get smoother. More plug-and-play. More "just click this button." More black box. Right now, today, you can still see how it works under the hood. You can open Claude and understand why your prompt failed. You can build agents in n8n and watch every node fire. You can pull an open-source model, run it locally, and actually see what's happening between your input and the output. That window is closing. Not slowly. Fast. Think about what happened with the internet. The people who configured dial-up modems and navigated BBS boards in 1993 didn't just "understand networking better." They became the founders, the CTOs, the architects who built the companies that everyone else ended up working for. The people who understood HTTP, DNS, and FTP in 1995 weren't just "tech savvy." They were the ones who saw ecommerce, SaaS, and cloud infrastructure coming before those words even existed. They didn't have better ideas. They had better intuition. Because they'd spent years tinkering with the raw infrastructure before it got polished into products. By 2005, anyone could build a website with WordPress. But the people who'd been hand-coding HTML since 1997 weren't just making prettier sites. They were running engineering teams. They were designing systems. They were thinking at a level that "drag and drop" users couldn't reach. Not because drag-and-drop was bad. Because it hid the mechanics. And when you can't see the mechanics, you can't invent new ones. The same thing is happening with AI right now. The people tinkering with prompts, agents, and open-source models today aren't wasting time on tools that'll feel primitive in 5 years. They're building the intuition that lets them architect what comes next. There's a difference between someone who uses AI to generate a marketing email and someone who understands why their system prompt produces better output with role assignment before task specification. The first person gets a decent email. The second person builds systems that generate thousands of decent emails, then improves them, then automates the improvement. That second person isn't smarter. They just started earlier, when the tools were raw enough to teach them something. Let me be specific about what "understanding AI" actually means right now. Because most people get this wrong. It's not memorizing model names or keeping up with every release. That's trivia. Understanding AI in 2026-2027 means: Knowing why context engineering matters more than prompt engineering. A prompt is a sentence. Context engineering is designing the entire information environment your AI operates in. The people who understand this now will design the systems everyone else uses later. Understanding that LLMs are probability engines, not knowledge bases. When you know this, you stop asking "why did it make that up?" and start asking "how do I constrain the probability space?" Completely different question. Leads to completely different solutions. Being able to build a simple automation workflow. Not because you'll always build your own. But because when the enterprise tools arrive, and they will, you'll know what's actually happening behind the interface. You'll know what's possible that the tool doesn't offer. You'll know when the tool is limiting you. Recognizing the difference between a wrapper and a foundation. 90% of "AI tools" launching right now are thin interfaces on top of the same models. The people who understand this don't chase every new app. They learn the underlying model and use it directly. When the wrappers die (and most will), these people don't skip a beat. Having real opinions about tradeoffs. Speed vs. accuracy. Cost vs. quality. Open-source vs. closed. Local vs. cloud. Privacy vs. capability. These aren't abstract debates. They're daily decisions for anyone building with AI. And you only develop good judgment here through hands-on experience. Here's what worries me. The AI industry is moving toward a future where you won't need to understand any of this. On the surface, that sounds great. "AI should just work. Like electricity. You flip the switch, the light turns on, nobody needs to understand transformers and grid management." Fine. But who designs the grid? Who decides where the power goes, and how much it costs, and what gets built with it? Not the people who flip switches. The internet became "easy to