I Trusted AI to Write My Code… It Made Me Worse скачать в хорошем качестве

I Trusted AI to Write My Code… It Made Me Worse

DESCRIPTION: I thought AI would make me a 10x engineer. Instead… it made me worse. In this video, I break down my six-month experiment going all-in on AI coding tools—Cursor, Copilot, Claude—and why the results weren’t what I expected. On the surface, I was shipping more code than ever. More commits. More pull requests. Faster output. But underneath? Review times exploded. Bugs slipped through. Features dragged. And the data shows I’m not alone. Time stamps: 0:00 - My AI Coding Experiment 1:10 - The Productivity Paradox 3:00 - The J-Curve Trap 4:38 - What The Winners Do Differently 6:33 - The Real Lesson 7:47 - Are You Stuck in the Dip? We’re in the middle of the biggest shift in software development in decades:• 41% of all code is now AI-generated• 84% of developers are using or planning to use AI tools• Teams are completing more tasks—but PR review time is up 91%• Nearly half of AI-generated code contains security vulnerabilities So what’s actually happening? In this video, I break down: • The Productivity Paradox (why more code ≠ more value) • The hidden bottleneck AI creates in review and ownership • The J-curve effect that explains why productivity dips before it rises • What high-performing AI-native teams are doing differently • And the uncomfortable truth about why most teams feel faster… but ship slower This isn’t a “AI is bad” rant. It’s a deeper look at what breaks when you bolt AI onto old workflows—and how to avoid getting stuck in the dip. If you’re using AI coding tools (or thinking about it), this might change how you approach your entire development process. Drop a comment:Have you experienced the J-curve? Did things get worse before they got better? And if you want to see what fully AI-native software teams look like, check out the related video linked in the description.