tianwei-blog

Last week, I helped an e-commerce team diagnose their marketing process. They needed to produce 40 product images every week. Their designers were working until 2 AM, and the revision rate was still 60%. I asked if they had tried AI image generation. They said yes — "the text is always garbled, and the backgrounds are never right." This isn't an isolated case. For the past two years, marketing teams have viewed AI images as "impressive but impractical." Then GPT Image 2 arrived. On April 21, 2026, OpenAI released this model. Five weeks later, it topped the Artificial Analysis text-to-image leaderboard with an Elo score of 1338. But the ranking isn't the point — what matters is that, for the first time, "marketing image generation" has become viable for production workflows. This article will show you what GPT Image 2 can actually do, where it stands in the 2026 competitive landscape, and how you can start using it. 1. Core Capabilities of GPT Image...

“Why do I feel more tired after using AI?” A friend of mine was venting about this the other day: “I used to write a proposal by sitting down, thinking it through, and finishing it in half a day. Now I ask AI to help. Sure, it spits out a huge draft in 10 seconds—but then I spend the next 30 minutes checking the facts, fixing awkward sentences, and correcting all the nonsense it made up with total confidence. This isn’t artificial intelligence. It’s like hiring a clueless intern who needs constant hand-holding.” I’m guessing a lot of people can relate. For the past two years, we’ve been surrounded by nonstop AI hype. We’ve been told AI can do everything at the push of a button. But in the reality of everyday work, AI often feels less like a productivity boost and more like a flow-breaking nuisance. So what’s really going on? Are we just bad at using AI—or is AI not as smart as we’ve been told? A new paper by researchers from MIT, Yale, and Microsoft— Chaining Tasks, Redefining Work: A...