I spent 3 months testing two different extremes of content creation, and both failed The first extreme was 100% automated AI generation. I set up workflows to scrape trending topics, generate full drafts with claude, and auto-schedule them. The speed was incredible, but the content was unreadable slop. It sounded like an enthusiastic press release written by a committee Nobody read it, and it damaged my technical credibility The second extreme was 100% manual writing. I opened a blank document every morning and wrote every single word from scratch. The quality was high, but it took 2 hours per post. Within 3 weeks, I was completely burned out and stopped publishing. I only built a sustainable publishing rhythm when I adopted the 70/30 rule The rule separates the structural mechanics of writing from human editorial judgment: - 70% Structural physics (delegated to software): I let tools handle the uncreative heavy lifting. Software parses the raw transcript, extracts 3 clear takeaways, and maps them into a tested mobile reading frame. It removes the friction of staring at a blank screen - 30% Human editorial taste (reserved strictly for me): This is where the actual trust is built. I spend 5 minutes stripping out synthetic buzzwords, injecting my real dollar numbers, adding the mistake I made last Tuesday, and cutting every generic adjective Software gives you the skeleton. You supply the marrow When you try to let AI do 100% of the thinking, you get ignored. When you try to do 100% of the formatting manually, you quit. 70% structural leverage plus 30% human conviction is the only formula that survived my testing. When you draft content today, what takes up more of your stamina: building the initial outline, or polishing the final draft?

Been working with software systems for more than 10 years, mostly around backend, cloud, AI, automation, and product development. One thing I’ve learned, knowing a programming language alone usually isn’t enough. The harder part is understanding the actual business. How to automate work that’s still manual. Connect systems that don’t work well together. Build AI into real workflows. Keep the backend stable when usage grows. Deal with data scattered across different tools. These are the kinds of problems I’ve spent a lot of time working on here in Japan. But I’m curious about the US side. For people running companies in the US, what technical skills are becoming difficult to find? And what skills do you think companies will need more of over the next few years?