Today was less about “building a billion-dollar AI startup” and more about getting clarity on the actual problems businesses are willing to pay to solve. One thing I’m already realizing: Most businesses do not care about “AI.” They care about: - saving time - reducing repetitive work - getting more leads - responding faster - improving operations - making more money So instead of trying to create some flashy AI product immediately, I’m focusing first on identifying painful workflows that can realistically be automated. Today’s focus: - researching service-based business workflows - mapping repetitive operational tasks - exploring automation opportunities in lead management and follow-ups - studying where AI can create immediate ROI instead of “cool demos” I’m also starting to narrow down potential niches instead of trying to target everyone. One mistake I already want to avoid: Being another generic “AI agency” that offers everything to everyone. The goal is to build systems that businesses actually use consistently — not just impressive prototypes. Tomorrow I’ll start working on the first real automation concept and share the process publicly here.

Some of the clearest signals about the future of work often show up first in industries where time pressure is constant. Newsrooms are one of those environments. They live inside tight deadlines, high output demands, rapid context shifts, and constant pressure to balance speed with accuracy. That is why the current wave of AI in journalism matters far beyond media. It offers a preview of what happens when organizations try to shorten production cycles without letting quality collapse. The deeper lesson is not just that newsrooms are automating. It is that they are being forced to redesign how work moves. And that is a useful lens for every team trying to reclaim time with AI. The real opportunity is not simply to produce more, faster. It is to build workflows that reduce delay, protect verification, and keep pace from turning into chaos. ------------- Context ------------- Most teams are now dealing with some version of the same challenge. Expectations are rising faster than capacity. More content, more communication, more reporting, more responsiveness, more visible output. At the same time, attention is fragmented, review cycles are slow, and people are stretched across too many tasks. The result is a familiar kind of pressure, a constant demand to move faster without enough structural change to make that speed sustainable. Newsrooms feel this problem in an especially concentrated form. They have to gather information, verify it, shape it, edit it, publish it, and often adapt it across formats in very short windows. There is very little room for waste in that cycle. If the production model is clumsy, delay shows up immediately. If verification breaks, the consequences are immediate too. That is why AI is such a live conversation there. Not because journalism suddenly wants less rigor, but because the old production burden is too heavy for the pace now required. AI becomes appealing when it can reduce the drag around transcription, summarization, clipping, formatting, adaptation, and the repetitive assembly work that slows everything down before higher-value judgment can happen.